From c2e18c86e863b106af0ca8996430639c0eab6b62 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Wed, 17 Dec 2025 23:28:59 +0800
Subject: [PATCH 01/97] fix: make flash attn work with high noise diffusion
 model (#1111)

---
 stable-diffusion.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 44bd3cca..d5770215 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -537,6 +537,9 @@ public:
             if (sd_ctx_params->diffusion_flash_attn) {
                 LOG_INFO("Using flash attention in the diffusion model");
                 diffusion_model->set_flash_attn_enabled(true);
+                if (high_noise_diffusion_model) {
+                    high_noise_diffusion_model->set_flash_attn_enabled(true);
+                }
             }
 
             cond_stage_model->alloc_params_buffer();
@@ -3723,6 +3726,9 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
         init_latent = sd_ctx->sd->generate_init_latent(work_ctx, width, height, frames, true);
     }
 
+    print_ggml_tensor(init_latent, true);
+    print_ggml_tensor(concat_latent, true);
+
     // Get learned condition
     ConditionerParams condition_params;
     condition_params.clip_skip       = sd_vid_gen_params->clip_skip;

From bda7fab9f208dff4b67179a68f694b6ddec13326 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Wed, 17 Dec 2025 23:43:37 +0800
Subject: [PATCH 02/97] chore: remove unused debug code

---
 stable-diffusion.cpp | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index d5770215..e3f49aac 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -3726,9 +3726,6 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
         init_latent = sd_ctx->sd->generate_init_latent(work_ctx, width, height, frames, true);
     }
 
-    print_ggml_tensor(init_latent, true);
-    print_ggml_tensor(concat_latent, true);
-
     // Get learned condition
     ConditionerParams condition_params;
     condition_params.clip_skip       = sd_vid_gen_params->clip_skip;

From 97cf2efe45e37c00006e19e2cbdec372a5186a79 Mon Sep 17 00:00:00 2001
From: Daniele <57776841+daniandtheweb@users.noreply.github.com>
Date: Thu, 18 Dec 2025 14:02:55 +0100
Subject: [PATCH 03/97] feat: add KL Optimal scheduler (#1098)

---
 denoiser.hpp               | 39 ++++++++++++++++++++++++++++++++++++++
 examples/cli/README.md     |  2 +-
 examples/common/common.hpp |  4 ++--
 examples/server/README.md  |  4 ++--
 stable-diffusion.cpp       |  3 ++-
 stable-diffusion.h         |  1 +
 6 files changed, 47 insertions(+), 6 deletions(-)

diff --git a/denoiser.hpp b/denoiser.hpp
index 32f40278..fc5230d7 100644
--- a/denoiser.hpp
+++ b/denoiser.hpp
@@ -347,6 +347,41 @@ struct SmoothStepScheduler : SigmaScheduler {
     }
 };
 
+// Implementation adapted from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608
+struct KLOptimalScheduler : SigmaScheduler {
+    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
+        std::vector<float> sigmas;
+
+        if (n == 0) {
+            return sigmas;
+        }
+        if (n == 1) {
+            sigmas.push_back(sigma_max);
+            sigmas.push_back(0.0f);
+            return sigmas;
+        }
+
+        float alpha_min = std::atan(sigma_min);
+        float alpha_max = std::atan(sigma_max);
+
+        for (uint32_t i = 0; i < n; ++i) {
+            // t goes from 0.0 to 1.0
+            float t = static_cast<float>(i) / static_cast<float>(n-1);
+
+            // Interpolate in the angle domain
+            float angle = t * alpha_min + (1.0f - t) * alpha_max;
+
+            // Convert back to sigma
+            sigmas.push_back(std::tan(angle));
+            }
+
+        // Append the final zero to sigma
+        sigmas.push_back(0.0f);
+    
+        return sigmas;
+    }
+};
+
 struct Denoiser {
     virtual float sigma_min()                                                                = 0;
     virtual float sigma_max()                                                                = 0;
@@ -392,6 +427,10 @@ struct Denoiser {
                 LOG_INFO("get_sigmas with SmoothStep scheduler");
                 scheduler = std::make_shared<SmoothStepScheduler>();
                 break;
+            case KL_OPTIMAL_SCHEDULER:
+                LOG_INFO("get_sigmas with KL Optimal scheduler");
+                scheduler = std::make_shared<KLOptimalScheduler>();
+                break;
             case LCM_SCHEDULER:
                 LOG_INFO("get_sigmas with LCM scheduler");
                 scheduler = std::make_shared<LCMScheduler>();
diff --git a/examples/cli/README.md b/examples/cli/README.md
index 8531b2ae..da7734d3 100644
--- a/examples/cli/README.md
+++ b/examples/cli/README.md
@@ -120,7 +120,7 @@ Generation Options:
                                            tcd] (default: euler for Flux/SD3/Wan, euler_a otherwise)
   --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
                                            ddim_trailing, tcd] default: euler for Flux/SD3/Wan, euler_a otherwise
-  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, lcm],
+  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm],
                                            default: discrete
   --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
   --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index f3a56136..b81dab78 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -1409,7 +1409,7 @@ struct SDGenerationParams {
              on_high_noise_sample_method_arg},
             {"",
              "--scheduler",
-             "denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, lcm], default: discrete",
+             "denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm], default: discrete",
              on_scheduler_arg},
             {"",
              "--sigmas",
@@ -1911,4 +1911,4 @@ uint8_t* load_image_from_memory(const char* image_bytes,
                                 int expected_height  = 0,
                                 int expected_channel = 3) {
     return load_image_common(true, image_bytes, len, width, height, expected_width, expected_height, expected_channel);
-}
\ No newline at end of file
+}
diff --git a/examples/server/README.md b/examples/server/README.md
index a475856f..ae104968 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -114,11 +114,11 @@ Default Generation Options:
                                            tcd] (default: euler for Flux/SD3/Wan, euler_a otherwise)
   --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
                                            ddim_trailing, tcd] default: euler for Flux/SD3/Wan, euler_a otherwise
-  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, lcm],
+  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm],
                                            default: discrete
   --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
   --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
   --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
   -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
   --easycache                              enable EasyCache for DiT models with optional "threshold,start_percent,end_percent" (default: 0.2,0.15,0.95)
-```
\ No newline at end of file
+```
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index e3f49aac..2c77a137 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -2412,6 +2412,7 @@ const char* scheduler_to_str[] = {
     "sgm_uniform",
     "simple",
     "smoothstep",
+    "kl_optimal",
     "lcm",
 };
 
@@ -3888,4 +3889,4 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
     LOG_INFO("generate_video completed in %.2fs", (t5 - t0) * 1.0f / 1000);
 
     return result_images;
-}
\ No newline at end of file
+}
diff --git a/stable-diffusion.h b/stable-diffusion.h
index 9266ba43..110120e9 100644
--- a/stable-diffusion.h
+++ b/stable-diffusion.h
@@ -60,6 +60,7 @@ enum scheduler_t {
     SGM_UNIFORM_SCHEDULER,
     SIMPLE_SCHEDULER,
     SMOOTHSTEP_SCHEDULER,
+    KL_OPTIMAL_SCHEDULER,
     LCM_SCHEDULER,
     SCHEDULER_COUNT
 };

From 78e15bd4af2755762acf89654c79e0bfc3b2adab Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Thu, 18 Dec 2025 10:43:39 -0300
Subject: [PATCH 04/97] feat: default to LCM scheduler for LCM sampling (#1109)

* feat: default to LCM scheduler for LCM sampling

* fix bug and attempt to get default scheduler for vid_gen when none is set

---------

Co-authored-by: leejet <leejet714@gmail.com>
---
 examples/cli/main.cpp |  4 ++--
 stable-diffusion.cpp  | 17 ++++++++++++++---
 stable-diffusion.h    |  2 +-
 3 files changed, 17 insertions(+), 6 deletions(-)

diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index 42b909e4..a1b92f77 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -579,7 +579,7 @@ int main(int argc, const char* argv[]) {
         }
 
         if (gen_params.sample_params.scheduler == SCHEDULER_COUNT) {
-            gen_params.sample_params.scheduler = sd_get_default_scheduler(sd_ctx);
+            gen_params.sample_params.scheduler = sd_get_default_scheduler(sd_ctx, gen_params.sample_params.sample_method);
         }
 
         if (cli_params.mode == IMG_GEN) {
@@ -752,4 +752,4 @@ int main(int argc, const char* argv[]) {
     release_all_resources();
 
     return 0;
-}
\ No newline at end of file
+}
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 2c77a137..4485ac7a 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -2777,13 +2777,16 @@ enum sample_method_t sd_get_default_sample_method(const sd_ctx_t* sd_ctx) {
     return EULER_A_SAMPLE_METHOD;
 }
 
-enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx) {
+enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx, enum sample_method_t sample_method) {
     if (sd_ctx != nullptr && sd_ctx->sd != nullptr) {
         auto edm_v_denoiser = std::dynamic_pointer_cast<EDMVDenoiser>(sd_ctx->sd->denoiser);
         if (edm_v_denoiser) {
             return EXPONENTIAL_SCHEDULER;
         }
     }
+    if (sample_method == LCM_SAMPLE_METHOD) {
+        return LCM_SCHEDULER;
+    }
     return DISCRETE_SCHEDULER;
 }
 
@@ -3218,9 +3221,13 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
             LOG_WARN("sample_steps != custom_sigmas_count - 1, set sample_steps to %d", sample_steps);
         }
     } else {
+        scheduler_t scheduler = sd_img_gen_params->sample_params.scheduler;
+        if (scheduler == SCHEDULER_COUNT) {
+            scheduler = sd_get_default_scheduler(sd_ctx, sample_method);
+        }
         sigmas = sd_ctx->sd->denoiser->get_sigmas(sample_steps,
                                                   sd_ctx->sd->get_image_seq_len(height, width),
-                                                  sd_img_gen_params->sample_params.scheduler,
+                                                  scheduler,
                                                   sd_ctx->sd->version);
     }
 
@@ -3503,9 +3510,13 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
             }
         }
     } else {
+        scheduler_t scheduler = sd_vid_gen_params->sample_params.scheduler;
+        if (scheduler == SCHEDULER_COUNT) {
+            scheduler = sd_get_default_scheduler(sd_ctx, sample_method);
+        }
         sigmas = sd_ctx->sd->denoiser->get_sigmas(total_steps,
                                                   0,
-                                                  sd_vid_gen_params->sample_params.scheduler,
+                                                  scheduler,
                                                   sd_ctx->sd->version);
     }
 
diff --git a/stable-diffusion.h b/stable-diffusion.h
index 110120e9..adb65a1d 100644
--- a/stable-diffusion.h
+++ b/stable-diffusion.h
@@ -335,7 +335,7 @@ SD_API void sd_sample_params_init(sd_sample_params_t* sample_params);
 SD_API char* sd_sample_params_to_str(const sd_sample_params_t* sample_params);
 
 SD_API enum sample_method_t sd_get_default_sample_method(const sd_ctx_t* sd_ctx);
-SD_API enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx);
+SD_API enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx, enum sample_method_t sample_method);
 
 SD_API void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params);
 SD_API char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params);

From 8e9f3a4d9e4539d58c56cd523956106222d56713 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Thu, 18 Dec 2025 21:44:16 +0800
Subject: [PATCH 05/97] feat: add support for underline style lora of flux
 (#1103)

* feat: add support for underline style lora of flux

* add support for underline style lora of t5

* add more protected tokens
---
 name_conversion.cpp | 25 ++++++++++++++++++++++---
 1 file changed, 22 insertions(+), 3 deletions(-)

diff --git a/name_conversion.cpp b/name_conversion.cpp
index 8b521486..6a8ae72c 100644
--- a/name_conversion.cpp
+++ b/name_conversion.cpp
@@ -835,6 +835,7 @@ std::string convert_sep_to_dot(std::string name) {
         "proj_out",
         "transformer_blocks",
         "single_transformer_blocks",
+        "single_blocks",
         "diffusion_model",
         "cond_stage_model",
         "first_stage_model",
@@ -876,7 +877,18 @@ std::string convert_sep_to_dot(std::string name) {
         "ff_context",
         "norm_added_q",
         "norm_added_v",
-        "to_add_out"};
+        "to_add_out",
+        "txt_mod",
+        "img_mod",
+        "txt_mlp",
+        "img_mlp",
+        "proj_mlp",
+        "wi_0",
+        "wi_1",
+        "norm1_context",
+        "ff_context",
+        "x_embedder",
+    };
 
     // record the positions of underscores that should NOT be replaced
     std::unordered_set<size_t> protected_positions;
@@ -1020,12 +1032,14 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
             }
         }
 
-        if (sd_version_is_unet(version) || is_lycoris_underline) {
+        // LOG_DEBUG("name %s %d", name.c_str(), version);
+
+        if (sd_version_is_unet(version) || sd_version_is_flux(version) || is_lycoris_underline) {
             name = convert_sep_to_dot(name);
         }
     }
 
-    std::vector<std::pair<std::string, std::string>> prefix_map = {
+    std::unordered_map<std::string, std::string> prefix_map = {
         {"diffusion_model.", "model.diffusion_model."},
         {"unet.", "model.diffusion_model."},
         {"transformer.", "model.diffusion_model."},  // dit
@@ -1040,8 +1054,13 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
         // {"te2.text_model.encoder.layers.", "cond_stage_model.1.model.transformer.resblocks."},
         {"te2.", "cond_stage_model.1.transformer."},
         {"te1.", "cond_stage_model.transformer."},
+        {"te3.", "text_encoders.t5xxl.transformer."},
     };
 
+    if (sd_version_is_flux(version)) {
+        prefix_map["te1."] = "text_encoders.clip_l.transformer.";
+    }
+
     replace_with_prefix_map(name, prefix_map);
 
     // diffusion model

From 1f77545cf80d93a35b691b2327247e472371b6ea Mon Sep 17 00:00:00 2001
From: Weiqi Gao <gaoweiqicarl1@163.com>
Date: Fri, 19 Dec 2025 23:31:09 +0800
Subject: [PATCH 06/97] docs: document usage of tae for VRAM reduction using
 wan (#1108)

---
 docs/taesd.md | 24 +++++++++++++++++++++++-
 docs/wan.md   |  3 +++
 2 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/docs/taesd.md b/docs/taesd.md
index 5160b793..a41c64d4 100644
--- a/docs/taesd.md
+++ b/docs/taesd.md
@@ -14,4 +14,26 @@ curl -L -O https://huggingface.co/madebyollin/taesd/resolve/main/diffusion_pytor
 
 ```bash
 sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat" --taesd ../models/diffusion_pytorch_model.safetensors
-```
\ No newline at end of file
+```
+
+### Qwen-Image and wan (TAEHV)
+
+sd.cpp also supports [TAEHV](https://github.com/madebyollin/taehv) (#937), which can be used for Qwen-Image and wan.
+
+- For **Qwen-Image and wan2.1 and wan2.2-A14B**, download the wan2.1 tae [safetensors weights](https://github.com/madebyollin/taehv/blob/main/safetensors/taew2_1.safetensors)
+  
+  Or curl
+  
+  ```bash
+  curl -L -O https://github.com/madebyollin/taehv/raw/refs/heads/main/safetensors/taew2_1.safetensors
+  ```
+
+- For **wan2.2-TI2V-5B**, use the wan2.2 tae [safetensors weights](https://github.com/madebyollin/taehv/blob/main/safetensors/taew2_2.safetensors)
+  
+  Or curl
+  
+  ```bash
+  curl -L -O https://github.com/madebyollin/taehv/raw/refs/heads/main/safetensors/taew2_2.safetensors
+  ```
+
+Then simply replace the `--vae xxx.safetensors` with `--tae xxx.safetensors` in the commands. If it still out of VRAM, add `--vae-conv-direct` to your command though might be slower.
diff --git a/docs/wan.md b/docs/wan.md
index ce15ba58..6f5749c8 100644
--- a/docs/wan.md
+++ b/docs/wan.md
@@ -39,6 +39,9 @@
         - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors
     - wan_2.2_vae (for Wan2.2 TI2V 5B only)
         - safetensors: https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/blob/main/split_files/vae/wan2.2_vae.safetensors
+
+    > Wan models vae requires really much VRAM! If you do not have enough VRAM, please try tae instead, though the results may be poorer. For tae usage, please refer to [taesd](taesd.md)
+
 - Download umt5_xxl
     - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/text_encoders/umt5_xxl_fp16.safetensors
     - gguf: https://huggingface.co/city96/umt5-xxl-encoder-gguf/tree/main

From 7c88c4765ce6ef8447bc2ac5d30c73365f5b25a9 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Fri, 19 Dec 2025 12:41:52 -0300
Subject: [PATCH 07/97] chore: give feedback about cfg values smaller than 1
 (#1088)

---
 stable-diffusion.cpp | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 4485ac7a..6446c412 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -1497,6 +1497,17 @@ public:
         std::vector<int> skip_layers(guidance.slg.layers, guidance.slg.layers + guidance.slg.layer_count);
 
         float cfg_scale     = guidance.txt_cfg;
+        if (cfg_scale < 1.f) {
+            if (cfg_scale == 0.f) {
+                // Diffusers follow the convention from the original paper
+                // (https://arxiv.org/abs/2207.12598v1), so many distilled model docs
+                // recommend 0 as guidance; warn the user that it'll disable prompt folowing
+                LOG_WARN("unconditioned mode, images won't follow the prompt (use cfg-scale=1 for distilled models)");
+            } else {
+                LOG_WARN("cfg value out of expected range may produce unexpected results");
+            }
+        }
+
         float img_cfg_scale = std::isfinite(guidance.img_cfg) ? guidance.img_cfg : guidance.txt_cfg;
         float slg_scale     = guidance.slg.scale;
 

From 23fce0bd846879e6b7e710415ca7db404e6d2d83 Mon Sep 17 00:00:00 2001
From: stduhpf <stephduh@live.fr>
Date: Fri, 19 Dec 2025 17:55:57 +0100
Subject: [PATCH 08/97] feat: add support for Chroma Radiance x0 (#1091)

* Add x0 Flux pred (+prepare for others)

* Fix convert models with empty tensors

* patch_32 exp support attempt

* improve support for patch_32

* follow official pipeline

---------

Co-authored-by: leejet <leejet714@gmail.com>
---
 flux.hpp             | 33 ++++++++++++++++++++++++++++++++-
 model.cpp            |  7 +++++++
 stable-diffusion.cpp |  3 +++
 3 files changed, 42 insertions(+), 1 deletion(-)

diff --git a/flux.hpp b/flux.hpp
index 1df2874a..7ce26356 100644
--- a/flux.hpp
+++ b/flux.hpp
@@ -744,6 +744,8 @@ namespace Flux {
         int64_t nerf_mlp_ratio   = 4;
         int64_t nerf_depth       = 4;
         int64_t nerf_max_freqs   = 8;
+        bool use_x0              = false;
+        bool use_patch_size_32   = false;
     };
 
     struct FluxParams {
@@ -781,7 +783,7 @@ namespace Flux {
         Flux(FluxParams params)
             : params(params) {
             if (params.version == VERSION_CHROMA_RADIANCE) {
-                std::pair<int, int> kernel_size = {(int)params.patch_size, (int)params.patch_size};
+                std::pair<int, int> kernel_size = {16, 16};
                 std::pair<int, int> stride      = kernel_size;
 
                 blocks["img_in_patch"] = std::make_shared<Conv2d>(params.in_channels,
@@ -1044,6 +1046,15 @@ namespace Flux {
             return img;
         }
 
+        struct ggml_tensor* _apply_x0_residual(GGMLRunnerContext* ctx,
+                                               struct ggml_tensor* predicted,
+                                               struct ggml_tensor* noisy,
+                                               struct ggml_tensor* timesteps) {
+            auto x = ggml_sub(ctx->ggml_ctx, noisy, predicted);
+            x      = ggml_div(ctx->ggml_ctx, x, timesteps);
+            return x;
+        }
+
         struct ggml_tensor* forward_chroma_radiance(GGMLRunnerContext* ctx,
                                                     struct ggml_tensor* x,
                                                     struct ggml_tensor* timestep,
@@ -1068,6 +1079,13 @@ namespace Flux {
             auto img      = pad_to_patch_size(ctx->ggml_ctx, x);
             auto orig_img = img;
 
+            if (params.chroma_radiance_params.use_patch_size_32) {
+                // It's supposed to be using GGML_SCALE_MODE_NEAREST, but this seems more stable
+                // Maybe the implementation of nearest-neighbor interpolation in ggml behaves differently than the one in PyTorch?
+                // img = F.interpolate(img, size=(H//2, W//2), mode="nearest")
+                img = ggml_interpolate(ctx->ggml_ctx, img, W / 2, H / 2, C, x->ne[3], GGML_SCALE_MODE_BILINEAR);
+            }
+
             auto img_in_patch = std::dynamic_pointer_cast<Conv2d>(blocks["img_in_patch"]);
 
             img = img_in_patch->forward(ctx, img);                                                       // [N, hidden_size, H/patch_size, W/patch_size]
@@ -1104,6 +1122,10 @@ namespace Flux {
 
             out = nerf_final_layer_conv->forward(ctx, img_dct);  // [N, C, H, W]
 
+            if (params.chroma_radiance_params.use_x0) {
+                out = _apply_x0_residual(ctx, out, orig_img, timestep);
+            }
+
             return out;
         }
 
@@ -1290,6 +1312,15 @@ namespace Flux {
                     // not schnell
                     flux_params.guidance_embed = true;
                 }
+                if (tensor_name.find("__x0__") != std::string::npos) {
+                    LOG_DEBUG("using x0 prediction");
+                    flux_params.chroma_radiance_params.use_x0 = true;
+                }
+                if (tensor_name.find("__32x32__") != std::string::npos) {
+                    LOG_DEBUG("using patch size 32 prediction");
+                    flux_params.chroma_radiance_params.use_patch_size_32 = true;
+                    flux_params.patch_size                               = 32;
+                }
                 if (tensor_name.find("distilled_guidance_layer.in_proj.weight") != std::string::npos) {
                     // Chroma
                     flux_params.is_chroma = true;
diff --git a/model.cpp b/model.cpp
index 01a8c45d..561eb3cc 100644
--- a/model.cpp
+++ b/model.cpp
@@ -1737,6 +1737,13 @@ bool ModelLoader::save_to_gguf_file(const std::string& file_path, ggml_type type
         // tensor_storage.ne[0], tensor_storage.ne[1], tensor_storage.ne[2], tensor_storage.ne[3],
         // tensor->n_dims, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
 
+        if (!tensor->data) {
+            GGML_ASSERT(ggml_nelements(tensor) == 0);
+            // avoid crashing the gguf writer by setting a dummy pointer for zero-sized tensors
+            LOG_DEBUG("setting dummy pointer for zero-sized tensor %s", name.c_str());
+            tensor->data = ggml_get_mem_buffer(ggml_ctx);
+        }
+
         *dst_tensor = tensor;
 
         gguf_add_tensor(gguf_ctx, tensor);
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 6446c412..17fe3fde 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -708,6 +708,8 @@ public:
         if (stacked_id) {
             ignore_tensors.insert("pmid.unet.");
         }
+        ignore_tensors.insert("model.diffusion_model.__x0__");
+        ignore_tensors.insert("model.diffusion_model.__32x32__");
 
         if (vae_decode_only) {
             ignore_tensors.insert("first_stage_model.encoder");
@@ -842,6 +844,7 @@ public:
                     }
                 } else if (sd_version_is_flux(version)) {
                     pred_type = FLUX_FLOW_PRED;
+
                     if (flow_shift == INFINITY) {
                         flow_shift = 1.0f;  // TODO: validate
                         for (const auto& [name, tensor_storage] : tensor_storage_map) {

From 60abda56e07455f1d008b221de0f4ef4c4136741 Mon Sep 17 00:00:00 2001
From: stduhpf <stephduh@live.fr>
Date: Sun, 21 Dec 2025 08:35:38 +0100
Subject: [PATCH 09/97] feat: select vulkan device with env variable (#629)

---
 stable-diffusion.cpp | 22 +++++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 17fe3fde..74519938 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -165,7 +165,27 @@ public:
 #endif
 #ifdef SD_USE_VULKAN
         LOG_DEBUG("Using Vulkan backend");
-        for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) {
+        size_t device          = 0;
+        const int device_count = ggml_backend_vk_get_device_count();
+        if (device_count) {
+            const char* SD_VK_DEVICE = getenv("SD_VK_DEVICE");
+            if (SD_VK_DEVICE != nullptr) {
+                std::string sd_vk_device_str = SD_VK_DEVICE;
+                try {
+                    device = std::stoull(sd_vk_device_str);
+                } catch (const std::invalid_argument&) {
+                    LOG_WARN("SD_VK_DEVICE environment variable is not a valid integer (%s). Falling back to device 0.", SD_VK_DEVICE);
+                    device = 0;
+                } catch (const std::out_of_range&) {
+                    LOG_WARN("SD_VK_DEVICE environment variable value is out of range for `unsigned long long` type (%s). Falling back to device 0.", SD_VK_DEVICE);
+                    device = 0;
+                }
+                if (device >= device_count) {
+                    LOG_WARN("Cannot find targeted vulkan device (%llu). Falling back to device 0.", device);
+                    device = 0;
+                }
+            }
+            LOG_INFO("Vulkan: Using device %llu", device);
             backend = ggml_backend_vk_init(device);
         }
         if (!backend) {

From 88ec9d30b1a149cf5bdb14bf9df11d966f91bf80 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 21 Dec 2025 15:40:21 +0800
Subject: [PATCH 10/97] feat: add scale_rope support (#1121)

---
 rope.hpp | 28 ++++++++++++++++++++--------
 1 file changed, 20 insertions(+), 8 deletions(-)

diff --git a/rope.hpp b/rope.hpp
index 4abc5146..12047e3e 100644
--- a/rope.hpp
+++ b/rope.hpp
@@ -91,14 +91,23 @@ namespace Rope {
                                                                        int axes_dim_num,
                                                                        int index    = 0,
                                                                        int h_offset = 0,
-                                                                       int w_offset = 0) {
+                                                                       int w_offset = 0,
+                                                                       bool scale_rope = false) {
         int h_len = (h + (patch_size / 2)) / patch_size;
         int w_len = (w + (patch_size / 2)) / patch_size;
 
         std::vector<std::vector<float>> img_ids(h_len * w_len, std::vector<float>(axes_dim_num, 0.0));
 
-        std::vector<float> row_ids = linspace<float>(h_offset, h_len - 1 + h_offset, h_len);
-        std::vector<float> col_ids = linspace<float>(w_offset, w_len - 1 + w_offset, w_len);
+        int h_start = h_offset;
+        int w_start = w_offset;
+
+        if (scale_rope) {
+            h_start -= h_len / 2;
+            w_start -= w_len / 2;
+        }
+
+        std::vector<float> row_ids = linspace<float>(h_start, h_start + h_len - 1, h_len);
+        std::vector<float> col_ids = linspace<float>(w_start, w_start + w_len - 1, w_len);
 
         for (int i = 0; i < h_len; ++i) {
             for (int j = 0; j < w_len; ++j) {
@@ -171,7 +180,8 @@ namespace Rope {
                                                                    int axes_dim_num,
                                                                    const std::vector<ggml_tensor*>& ref_latents,
                                                                    bool increase_ref_index,
-                                                                   float ref_index_scale) {
+                                                                   float ref_index_scale,
+                                                                   bool scale_rope) {
         std::vector<std::vector<float>> ids;
         uint64_t curr_h_offset = 0;
         uint64_t curr_w_offset = 0;
@@ -185,6 +195,7 @@ namespace Rope {
                 } else {
                     h_offset = curr_h_offset;
                 }
+                scale_rope = false;
             }
 
             auto ref_ids = gen_flux_img_ids(ref->ne[1],
@@ -194,7 +205,8 @@ namespace Rope {
                                             axes_dim_num,
                                             static_cast<int>(index * ref_index_scale),
                                             h_offset,
-                                            w_offset);
+                                            w_offset,
+                                            scale_rope);
             ids          = concat_ids(ids, ref_ids, bs);
 
             if (increase_ref_index) {
@@ -222,7 +234,7 @@ namespace Rope {
 
         auto ids = concat_ids(txt_ids, img_ids, bs);
         if (ref_latents.size() > 0) {
-            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, ref_index_scale);
+            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, ref_index_scale, false);
             ids           = concat_ids(ids, refs_ids, bs);
         }
         return ids;
@@ -271,10 +283,10 @@ namespace Rope {
             }
         }
         int axes_dim_num = 3;
-        auto img_ids     = gen_flux_img_ids(h, w, patch_size, bs, axes_dim_num);
+        auto img_ids     = gen_flux_img_ids(h, w, patch_size, bs, axes_dim_num, 0, 0, 0, true);
         auto ids         = concat_ids(txt_ids_repeated, img_ids, bs);
         if (ref_latents.size() > 0) {
-            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, 1.f);
+            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, 1.f, true);
             ids           = concat_ids(ids, refs_ids, bs);
         }
         return ids;

From 50ff966445ba5b0d435238c2aead7c5f26835b09 Mon Sep 17 00:00:00 2001
From: Phylliida Dev <phylliida.dev@gmail.com>
Date: Sun, 21 Dec 2025 03:06:47 -0700
Subject: [PATCH 11/97] feat: add seamless texture generation support (#914)

* global bool

* reworked circular to global flag

* cleaner implementation of tiling support in sd cpp

* cleaned rope

* working simplified but still need wraps

* Further clean of rope

* resolve flux conflict

* switch to pad op circular only

* Set ggml to most recent

* Revert ggml temp

* Update ggml to most recent

* Revert unneded flux change

* move circular flag to the GGMLRunnerContext

* Pass through circular param in all places where conv is called

* fix of constant and minor cleanup

* Added back --circular option

* Conv2d circular in vae and various models

* Fix temporal padding for qwen image and other vaes

* Z Image circular tiling

* x and y axis seamless only

* First attempt at chroma seamless x and y

* refactor into pure x and y, almost there

* Fix crash on chroma

* Refactor into cleaner variable choices

* Removed redundant set_circular_enabled

* Sync ggml

* simplify circular parameter

* format code

* no need to perform circular pad on the clip

* simplify circular_axes setting

* unify function naming

* remove unnecessary member variables

* simplify rope

---------

Co-authored-by: Phylliida <phylliidadev@gmail.com>
Co-authored-by: leejet <leejet714@gmail.com>
---
 common.hpp                 |   2 +-
 denoiser.hpp               |   6 +-
 diffusion_model.hpp        |  29 ++++++-
 examples/common/common.hpp |  21 +++++
 flux.hpp                   |  28 ++++---
 ggml                       |   2 +-
 ggml_extend.hpp            | 119 +++++++++++++++++++++------
 lora.hpp                   |   8 ++
 mmdit.hpp                  |   2 +-
 qwen_image.hpp             |  16 ++--
 rope.hpp                   | 164 ++++++++++++++++++++++++++++++++++---
 stable-diffusion.cpp       |  26 +++++-
 stable-diffusion.h         |   2 +
 wan.hpp                    |  15 ++--
 z_image.hpp                |  14 ++--
 15 files changed, 375 insertions(+), 79 deletions(-)

diff --git a/common.hpp b/common.hpp
index 74b218ab..b17c11e3 100644
--- a/common.hpp
+++ b/common.hpp
@@ -28,7 +28,7 @@ public:
         if (vae_downsample) {
             auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
 
-            x = ggml_pad(ctx->ggml_ctx, x, 1, 1, 0, 0);
+            x = ggml_ext_pad(ctx->ggml_ctx, x, 1, 1, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
             x = conv->forward(ctx, x);
         } else {
             auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["op"]);
diff --git a/denoiser.hpp b/denoiser.hpp
index fc5230d7..7a8242e7 100644
--- a/denoiser.hpp
+++ b/denoiser.hpp
@@ -366,18 +366,18 @@ struct KLOptimalScheduler : SigmaScheduler {
 
         for (uint32_t i = 0; i < n; ++i) {
             // t goes from 0.0 to 1.0
-            float t = static_cast<float>(i) / static_cast<float>(n-1);
+            float t = static_cast<float>(i) / static_cast<float>(n - 1);
 
             // Interpolate in the angle domain
             float angle = t * alpha_min + (1.0f - t) * alpha_max;
 
             // Convert back to sigma
             sigmas.push_back(std::tan(angle));
-            }
+        }
 
         // Append the final zero to sigma
         sigmas.push_back(0.0f);
-    
+
         return sigmas;
     }
 };
diff --git a/diffusion_model.hpp b/diffusion_model.hpp
index 8c741fdc..c4e0ba1d 100644
--- a/diffusion_model.hpp
+++ b/diffusion_model.hpp
@@ -37,8 +37,9 @@ struct DiffusionModel {
     virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) = 0;
     virtual size_t get_params_buffer_size()                                             = 0;
     virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter){};
-    virtual int64_t get_adm_in_channels()             = 0;
-    virtual void set_flash_attn_enabled(bool enabled) = 0;
+    virtual int64_t get_adm_in_channels()                            = 0;
+    virtual void set_flash_attn_enabled(bool enabled)                = 0;
+    virtual void set_circular_axes(bool circular_x, bool circular_y) = 0;
 };
 
 struct UNetModel : public DiffusionModel {
@@ -87,6 +88,10 @@ struct UNetModel : public DiffusionModel {
         unet.set_flash_attention_enabled(enabled);
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        unet.set_circular_axes(circular_x, circular_y);
+    }
+
     bool compute(int n_threads,
                  DiffusionParams diffusion_params,
                  struct ggml_tensor** output     = nullptr,
@@ -148,6 +153,10 @@ struct MMDiTModel : public DiffusionModel {
         mmdit.set_flash_attention_enabled(enabled);
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        mmdit.set_circular_axes(circular_x, circular_y);
+    }
+
     bool compute(int n_threads,
                  DiffusionParams diffusion_params,
                  struct ggml_tensor** output     = nullptr,
@@ -210,6 +219,10 @@ struct FluxModel : public DiffusionModel {
         flux.set_flash_attention_enabled(enabled);
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        flux.set_circular_axes(circular_x, circular_y);
+    }
+
     bool compute(int n_threads,
                  DiffusionParams diffusion_params,
                  struct ggml_tensor** output     = nullptr,
@@ -277,6 +290,10 @@ struct WanModel : public DiffusionModel {
         wan.set_flash_attention_enabled(enabled);
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        wan.set_circular_axes(circular_x, circular_y);
+    }
+
     bool compute(int n_threads,
                  DiffusionParams diffusion_params,
                  struct ggml_tensor** output     = nullptr,
@@ -343,6 +360,10 @@ struct QwenImageModel : public DiffusionModel {
         qwen_image.set_flash_attention_enabled(enabled);
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        qwen_image.set_circular_axes(circular_x, circular_y);
+    }
+
     bool compute(int n_threads,
                  DiffusionParams diffusion_params,
                  struct ggml_tensor** output     = nullptr,
@@ -406,6 +427,10 @@ struct ZImageModel : public DiffusionModel {
         z_image.set_flash_attention_enabled(enabled);
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        z_image.set_circular_axes(circular_x, circular_y);
+    }
+
     bool compute(int n_threads,
                  DiffusionParams diffusion_params,
                  struct ggml_tensor** output     = nullptr,
diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index b81dab78..5168730a 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -449,6 +449,10 @@ struct SDContextParams {
     bool diffusion_conv_direct  = false;
     bool vae_conv_direct        = false;
 
+    bool circular   = false;
+    bool circular_x = false;
+    bool circular_y = false;
+
     bool chroma_use_dit_mask = true;
     bool chroma_use_t5_mask  = false;
     int chroma_t5_mask_pad   = 1;
@@ -605,6 +609,18 @@ struct SDContextParams {
              "--vae-conv-direct",
              "use ggml_conv2d_direct in the vae model",
              true, &vae_conv_direct},
+            {"",
+             "--circular",
+             "enable circular padding for convolutions",
+             true, &circular},
+            {"",
+             "--circularx",
+             "enable circular RoPE wrapping on x-axis (width) only",
+             true, &circular_x},
+            {"",
+             "--circulary",
+             "enable circular RoPE wrapping on y-axis (height) only",
+             true, &circular_y},
             {"",
              "--chroma-disable-dit-mask",
              "disable dit mask for chroma",
@@ -868,6 +884,9 @@ struct SDContextParams {
             << "  diffusion_flash_attn: " << (diffusion_flash_attn ? "true" : "false") << ",\n"
             << "  diffusion_conv_direct: " << (diffusion_conv_direct ? "true" : "false") << ",\n"
             << "  vae_conv_direct: " << (vae_conv_direct ? "true" : "false") << ",\n"
+            << "  circular: " << (circular ? "true" : "false") << ",\n"
+            << "  circular_x: " << (circular_x ? "true" : "false") << ",\n"
+            << "  circular_y: " << (circular_y ? "true" : "false") << ",\n"
             << "  chroma_use_dit_mask: " << (chroma_use_dit_mask ? "true" : "false") << ",\n"
             << "  chroma_use_t5_mask: " << (chroma_use_t5_mask ? "true" : "false") << ",\n"
             << "  chroma_t5_mask_pad: " << chroma_t5_mask_pad << ",\n"
@@ -928,6 +947,8 @@ struct SDContextParams {
             taesd_preview,
             diffusion_conv_direct,
             vae_conv_direct,
+            circular || circular_x,
+            circular || circular_y,
             force_sdxl_vae_conv_scale,
             chroma_use_dit_mask,
             chroma_use_t5_mask,
diff --git a/flux.hpp b/flux.hpp
index 7ce26356..bcff04cf 100644
--- a/flux.hpp
+++ b/flux.hpp
@@ -860,14 +860,14 @@ namespace Flux {
             }
         }
 
-        struct ggml_tensor* pad_to_patch_size(struct ggml_context* ctx,
+        struct ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
                                               struct ggml_tensor* x) {
             int64_t W = x->ne[0];
             int64_t H = x->ne[1];
 
             int pad_h = (params.patch_size - H % params.patch_size) % params.patch_size;
             int pad_w = (params.patch_size - W % params.patch_size) % params.patch_size;
-            x         = ggml_pad(ctx, x, pad_w, pad_h, 0, 0);  // [N, C, H + pad_h, W + pad_w]
+            x         = ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
             return x;
         }
 
@@ -893,11 +893,11 @@ namespace Flux {
             return x;
         }
 
-        struct ggml_tensor* process_img(struct ggml_context* ctx,
+        struct ggml_tensor* process_img(GGMLRunnerContext* ctx,
                                         struct ggml_tensor* x) {
             // img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
             x = pad_to_patch_size(ctx, x);
-            x = patchify(ctx, x);
+            x = patchify(ctx->ggml_ctx, x);
             return x;
         }
 
@@ -1076,7 +1076,7 @@ namespace Flux {
             int pad_h          = (patch_size - H % patch_size) % patch_size;
             int pad_w          = (patch_size - W % patch_size) % patch_size;
 
-            auto img      = pad_to_patch_size(ctx->ggml_ctx, x);
+            auto img      = pad_to_patch_size(ctx, x);
             auto orig_img = img;
 
             if (params.chroma_radiance_params.use_patch_size_32) {
@@ -1150,7 +1150,7 @@ namespace Flux {
             int pad_h          = (patch_size - H % patch_size) % patch_size;
             int pad_w          = (patch_size - W % patch_size) % patch_size;
 
-            auto img            = process_img(ctx->ggml_ctx, x);
+            auto img            = process_img(ctx, x);
             uint64_t img_tokens = img->ne[1];
 
             if (params.version == VERSION_FLUX_FILL) {
@@ -1158,8 +1158,8 @@ namespace Flux {
                 ggml_tensor* masked = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], C, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], 0);
                 ggml_tensor* mask   = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], 8 * 8, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * C);
 
-                masked = process_img(ctx->ggml_ctx, masked);
-                mask   = process_img(ctx->ggml_ctx, mask);
+                masked = process_img(ctx, masked);
+                mask   = process_img(ctx, mask);
 
                 img = ggml_concat(ctx->ggml_ctx, img, ggml_concat(ctx->ggml_ctx, masked, mask, 0), 0);
             } else if (params.version == VERSION_FLEX_2) {
@@ -1168,21 +1168,21 @@ namespace Flux {
                 ggml_tensor* mask    = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], 1, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * C);
                 ggml_tensor* control = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], C, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * (C + 1));
 
-                masked  = process_img(ctx->ggml_ctx, masked);
-                mask    = process_img(ctx->ggml_ctx, mask);
-                control = process_img(ctx->ggml_ctx, control);
+                masked  = process_img(ctx, masked);
+                mask    = process_img(ctx, mask);
+                control = process_img(ctx, control);
 
                 img = ggml_concat(ctx->ggml_ctx, img, ggml_concat(ctx->ggml_ctx, ggml_concat(ctx->ggml_ctx, masked, mask, 0), control, 0), 0);
             } else if (params.version == VERSION_FLUX_CONTROLS) {
                 GGML_ASSERT(c_concat != nullptr);
 
-                auto control = process_img(ctx->ggml_ctx, c_concat);
+                auto control = process_img(ctx, c_concat);
                 img          = ggml_concat(ctx->ggml_ctx, img, control, 0);
             }
 
             if (ref_latents.size() > 0) {
                 for (ggml_tensor* ref : ref_latents) {
-                    ref = process_img(ctx->ggml_ctx, ref);
+                    ref = process_img(ctx, ref);
                     img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
                 }
             }
@@ -1472,6 +1472,8 @@ namespace Flux {
                                             increase_ref_index,
                                             flux_params.ref_index_scale,
                                             flux_params.theta,
+                                            circular_y_enabled,
+                                            circular_x_enabled,
                                             flux_params.axes_dim);
             int pos_len = pe_vec.size() / flux_params.axes_dim_sum / 2;
             // LOG_DEBUG("pos_len %d", pos_len);
diff --git a/ggml b/ggml
index f5425c0e..3e9f2ba3 160000
--- a/ggml
+++ b/ggml
@@ -1 +1 @@
-Subproject commit f5425c0ee5e582a7d64411f06139870bff3e52e0
+Subproject commit 3e9f2ba3b934c20b26873b3c60dbf41b116978ff
diff --git a/ggml_extend.hpp b/ggml_extend.hpp
index fcaa92c9..3849562f 100644
--- a/ggml_extend.hpp
+++ b/ggml_extend.hpp
@@ -5,6 +5,7 @@
 #include <inttypes.h>
 #include <stdarg.h>
 #include <algorithm>
+#include <atomic>
 #include <cstring>
 #include <fstream>
 #include <functional>
@@ -993,6 +994,48 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_linear(struct ggml_context* ctx,
     return x;
 }
 
+__STATIC_INLINE__ struct ggml_tensor* ggml_ext_pad_ext(struct ggml_context* ctx,
+                                                       struct ggml_tensor* x,
+                                                       int lp0,
+                                                       int rp0,
+                                                       int lp1,
+                                                       int rp1,
+                                                       int lp2,
+                                                       int rp2,
+                                                       int lp3,
+                                                       int rp3,
+                                                       bool circular_x = false,
+                                                       bool circular_y = false) {
+    if (circular_x && circular_y) {
+        return ggml_pad_ext_circular(ctx, x, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
+    }
+
+    if (circular_x && (lp0 != 0 || rp0 != 0)) {
+        x   = ggml_pad_ext_circular(ctx, x, lp0, rp0, 0, 0, 0, 0, 0, 0);
+        lp0 = rp0 = 0;
+    }
+    if (circular_y && (lp1 != 0 || rp1 != 0)) {
+        x   = ggml_pad_ext_circular(ctx, x, 0, 0, lp1, rp1, 0, 0, 0, 0);
+        lp1 = rp1 = 0;
+    }
+
+    if (lp0 != 0 || rp0 != 0 || lp1 != 0 || rp1 != 0 || lp2 != 0 || rp2 != 0 || lp3 != 0 || rp3 != 0) {
+        x = ggml_pad_ext(ctx, x, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
+    }
+    return x;
+}
+
+__STATIC_INLINE__ struct ggml_tensor* ggml_ext_pad(struct ggml_context* ctx,
+                                                   struct ggml_tensor* x,
+                                                   int p0,
+                                                   int p1,
+                                                   int p2          = 0,
+                                                   int p3          = 0,
+                                                   bool circular_x = false,
+                                                   bool circular_y = false) {
+    return ggml_ext_pad_ext(ctx, x, p0, p0, p1, p1, p2, p2, p3, p3, circular_x, circular_y);
+}
+
 // w: [OC，IC, KH, KW]
 // x: [N, IC, IH, IW]
 // b: [OC,]
@@ -1001,20 +1044,29 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_conv_2d(struct ggml_context* ctx,
                                                        struct ggml_tensor* x,
                                                        struct ggml_tensor* w,
                                                        struct ggml_tensor* b,
-                                                       int s0      = 1,
-                                                       int s1      = 1,
-                                                       int p0      = 0,
-                                                       int p1      = 0,
-                                                       int d0      = 1,
-                                                       int d1      = 1,
-                                                       bool direct = false,
-                                                       float scale = 1.f) {
+                                                       int s0          = 1,
+                                                       int s1          = 1,
+                                                       int p0          = 0,
+                                                       int p1          = 0,
+                                                       int d0          = 1,
+                                                       int d1          = 1,
+                                                       bool direct     = false,
+                                                       bool circular_x = false,
+                                                       bool circular_y = false,
+                                                       float scale     = 1.f) {
     if (scale != 1.f) {
         x = ggml_scale(ctx, x, scale);
     }
     if (w->ne[2] != x->ne[2] && ggml_n_dims(w) == 2) {
         w = ggml_reshape_4d(ctx, w, 1, 1, w->ne[0], w->ne[1]);
     }
+
+    if ((p0 != 0 || p1 != 0) && (circular_x || circular_y)) {
+        x  = ggml_ext_pad(ctx, x, p0, p1, 0, 0, circular_x, circular_y);
+        p0 = 0;
+        p1 = 0;
+    }
+
     if (direct) {
         x = ggml_conv_2d_direct(ctx, w, x, s0, s1, p0, p1, d0, d1);
     } else {
@@ -1521,14 +1573,16 @@ struct WeightAdapter {
             float scale         = 1.f;
         } linear;
         struct {
-            int s0      = 1;
-            int s1      = 1;
-            int p0      = 0;
-            int p1      = 0;
-            int d0      = 1;
-            int d1      = 1;
-            bool direct = false;
-            float scale = 1.f;
+            int s0          = 1;
+            int s1          = 1;
+            int p0          = 0;
+            int p1          = 0;
+            int d0          = 1;
+            int d1          = 1;
+            bool direct     = false;
+            bool circular_x = false;
+            bool circular_y = false;
+            float scale     = 1.f;
         } conv2d;
     };
     virtual ggml_tensor* patch_weight(ggml_context* ctx, ggml_tensor* weight, const std::string& weight_name) = 0;
@@ -1546,6 +1600,8 @@ struct GGMLRunnerContext {
     ggml_context* ggml_ctx                        = nullptr;
     bool flash_attn_enabled                       = false;
     bool conv2d_direct_enabled                    = false;
+    bool circular_x_enabled                       = false;
+    bool circular_y_enabled                       = false;
     std::shared_ptr<WeightAdapter> weight_adapter = nullptr;
 };
 
@@ -1582,6 +1638,8 @@ protected:
 
     bool flash_attn_enabled    = false;
     bool conv2d_direct_enabled = false;
+    bool circular_x_enabled    = false;
+    bool circular_y_enabled    = false;
 
     void alloc_params_ctx() {
         struct ggml_init_params params;
@@ -1859,6 +1917,8 @@ public:
         runner_ctx.backend               = runtime_backend;
         runner_ctx.flash_attn_enabled    = flash_attn_enabled;
         runner_ctx.conv2d_direct_enabled = conv2d_direct_enabled;
+        runner_ctx.circular_x_enabled    = circular_x_enabled;
+        runner_ctx.circular_y_enabled    = circular_y_enabled;
         runner_ctx.weight_adapter        = weight_adapter;
         return runner_ctx;
     }
@@ -2003,6 +2063,11 @@ public:
         conv2d_direct_enabled = enabled;
     }
 
+    void set_circular_axes(bool circular_x, bool circular_y) {
+        circular_x_enabled = circular_x;
+        circular_y_enabled = circular_y;
+    }
+
     void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) {
         weight_adapter = adapter;
     }
@@ -2266,15 +2331,17 @@ public:
         }
         if (ctx->weight_adapter) {
             WeightAdapter::ForwardParams forward_params;
-            forward_params.op_type       = WeightAdapter::ForwardParams::op_type_t::OP_CONV2D;
-            forward_params.conv2d.s0     = stride.second;
-            forward_params.conv2d.s1     = stride.first;
-            forward_params.conv2d.p0     = padding.second;
-            forward_params.conv2d.p1     = padding.first;
-            forward_params.conv2d.d0     = dilation.second;
-            forward_params.conv2d.d1     = dilation.first;
-            forward_params.conv2d.direct = ctx->conv2d_direct_enabled;
-            forward_params.conv2d.scale  = scale;
+            forward_params.op_type           = WeightAdapter::ForwardParams::op_type_t::OP_CONV2D;
+            forward_params.conv2d.s0         = stride.second;
+            forward_params.conv2d.s1         = stride.first;
+            forward_params.conv2d.p0         = padding.second;
+            forward_params.conv2d.p1         = padding.first;
+            forward_params.conv2d.d0         = dilation.second;
+            forward_params.conv2d.d1         = dilation.first;
+            forward_params.conv2d.direct     = ctx->conv2d_direct_enabled;
+            forward_params.conv2d.circular_x = ctx->circular_x_enabled;
+            forward_params.conv2d.circular_y = ctx->circular_y_enabled;
+            forward_params.conv2d.scale      = scale;
             return ctx->weight_adapter->forward_with_lora(ctx->ggml_ctx, x, w, b, prefix, forward_params);
         }
         return ggml_ext_conv_2d(ctx->ggml_ctx,
@@ -2288,6 +2355,8 @@ public:
                                 dilation.second,
                                 dilation.first,
                                 ctx->conv2d_direct_enabled,
+                                ctx->circular_x_enabled,
+                                ctx->circular_y_enabled,
                                 scale);
     }
 };
diff --git a/lora.hpp b/lora.hpp
index b847f044..7d83ec5c 100644
--- a/lora.hpp
+++ b/lora.hpp
@@ -599,6 +599,8 @@ struct LoraModel : public GGMLRunner {
                                       forward_params.conv2d.d0,
                                       forward_params.conv2d.d1,
                                       forward_params.conv2d.direct,
+                                      forward_params.conv2d.circular_x,
+                                      forward_params.conv2d.circular_y,
                                       forward_params.conv2d.scale);
                 if (lora_mid) {
                     lx = ggml_ext_conv_2d(ctx,
@@ -612,6 +614,8 @@ struct LoraModel : public GGMLRunner {
                                           1,
                                           1,
                                           forward_params.conv2d.direct,
+                                          forward_params.conv2d.circular_x,
+                                          forward_params.conv2d.circular_y,
                                           forward_params.conv2d.scale);
                 }
                 lx = ggml_ext_conv_2d(ctx,
@@ -625,6 +629,8 @@ struct LoraModel : public GGMLRunner {
                                       1,
                                       1,
                                       forward_params.conv2d.direct,
+                                      forward_params.conv2d.circular_x,
+                                      forward_params.conv2d.circular_y,
                                       forward_params.conv2d.scale);
             }
 
@@ -779,6 +785,8 @@ public:
                                    forward_params.conv2d.d0,
                                    forward_params.conv2d.d1,
                                    forward_params.conv2d.direct,
+                                   forward_params.conv2d.circular_x,
+                                   forward_params.conv2d.circular_y,
                                    forward_params.conv2d.scale);
         }
         for (auto& lora_model : lora_models) {
diff --git a/mmdit.hpp b/mmdit.hpp
index 38bdc2e7..eeb74a26 100644
--- a/mmdit.hpp
+++ b/mmdit.hpp
@@ -983,4 +983,4 @@ struct MMDiTRunner : public GGMLRunner {
     }
 };
 
-#endif
\ No newline at end of file
+#endif
diff --git a/qwen_image.hpp b/qwen_image.hpp
index eeb823d5..ed1c9830 100644
--- a/qwen_image.hpp
+++ b/qwen_image.hpp
@@ -354,14 +354,14 @@ namespace Qwen {
             blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, params.patch_size * params.patch_size * params.out_channels));
         }
 
-        struct ggml_tensor* pad_to_patch_size(struct ggml_context* ctx,
+        struct ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
                                               struct ggml_tensor* x) {
             int64_t W = x->ne[0];
             int64_t H = x->ne[1];
 
             int pad_h = (params.patch_size - H % params.patch_size) % params.patch_size;
             int pad_w = (params.patch_size - W % params.patch_size) % params.patch_size;
-            x         = ggml_pad(ctx, x, pad_w, pad_h, 0, 0);  // [N, C, H + pad_h, W + pad_w]
+            x         = ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
             return x;
         }
 
@@ -387,10 +387,10 @@ namespace Qwen {
             return x;
         }
 
-        struct ggml_tensor* process_img(struct ggml_context* ctx,
+        struct ggml_tensor* process_img(GGMLRunnerContext* ctx,
                                         struct ggml_tensor* x) {
             x = pad_to_patch_size(ctx, x);
-            x = patchify(ctx, x);
+            x = patchify(ctx->ggml_ctx, x);
             return x;
         }
 
@@ -466,12 +466,12 @@ namespace Qwen {
             int64_t C = x->ne[2];
             int64_t N = x->ne[3];
 
-            auto img            = process_img(ctx->ggml_ctx, x);
+            auto img            = process_img(ctx, x);
             uint64_t img_tokens = img->ne[1];
 
             if (ref_latents.size() > 0) {
                 for (ggml_tensor* ref : ref_latents) {
-                    ref = process_img(ctx->ggml_ctx, ref);
+                    ref = process_img(ctx, ref);
                     img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
                 }
             }
@@ -565,6 +565,8 @@ namespace Qwen {
                                                   ref_latents,
                                                   increase_ref_index,
                                                   qwen_image_params.theta,
+                                                  circular_y_enabled,
+                                                  circular_x_enabled,
                                                   qwen_image_params.axes_dim);
             int pos_len = pe_vec.size() / qwen_image_params.axes_dim_sum / 2;
             // LOG_DEBUG("pos_len %d", pos_len);
@@ -684,4 +686,4 @@ namespace Qwen {
 
 }  // namespace name
 
-#endif  // __QWEN_IMAGE_HPP__
\ No newline at end of file
+#endif  // __QWEN_IMAGE_HPP__
diff --git a/rope.hpp b/rope.hpp
index 12047e3e..4e6136c1 100644
--- a/rope.hpp
+++ b/rope.hpp
@@ -1,6 +1,8 @@
 #ifndef __ROPE_HPP__
 #define __ROPE_HPP__
 
+#include <algorithm>
+#include <cmath>
 #include <vector>
 #include "ggml_extend.hpp"
 
@@ -39,7 +41,10 @@ namespace Rope {
         return flat_vec;
     }
 
-    __STATIC_INLINE__ std::vector<std::vector<float>> rope(const std::vector<float>& pos, int dim, int theta) {
+    __STATIC_INLINE__ std::vector<std::vector<float>> rope(const std::vector<float>& pos,
+                                                           int dim,
+                                                           int theta,
+                                                           const std::vector<int>& axis_wrap_dims = {}) {
         assert(dim % 2 == 0);
         int half_dim = dim / 2;
 
@@ -47,14 +52,31 @@ namespace Rope {
 
         std::vector<float> omega(half_dim);
         for (int i = 0; i < half_dim; ++i) {
-            omega[i] = 1.0 / std::pow(theta, scale[i]);
+            omega[i] = 1.0f / std::pow(theta, scale[i]);
         }
 
         int pos_size = pos.size();
         std::vector<std::vector<float>> out(pos_size, std::vector<float>(half_dim));
         for (int i = 0; i < pos_size; ++i) {
             for (int j = 0; j < half_dim; ++j) {
-                out[i][j] = pos[i] * omega[j];
+                float angle = pos[i] * omega[j];
+                if (!axis_wrap_dims.empty()) {
+                    size_t wrap_size = axis_wrap_dims.size();
+                    // mod batch size since we only store this for one item in the batch
+                    size_t wrap_idx = wrap_size > 0 ? (i % wrap_size) : 0;
+                    int wrap_dim    = axis_wrap_dims[wrap_idx];
+                    if (wrap_dim > 0) {
+                        constexpr float TWO_PI = 6.28318530717958647692f;
+                        float cycles           = omega[j] * wrap_dim / TWO_PI;
+                        // closest periodic harmonic, necessary to ensure things neatly tile
+                        // without this round, things don't tile at the boundaries and you end up
+                        // with the model knowing what is "center"
+                        float rounded = std::round(cycles);
+                        angle         = pos[i] * TWO_PI * rounded / wrap_dim;
+                    }
+                }
+
+                out[i][j] = angle;
             }
         }
 
@@ -89,9 +111,9 @@ namespace Rope {
                                                                        int patch_size,
                                                                        int bs,
                                                                        int axes_dim_num,
-                                                                       int index    = 0,
-                                                                       int h_offset = 0,
-                                                                       int w_offset = 0,
+                                                                       int index       = 0,
+                                                                       int h_offset    = 0,
+                                                                       int w_offset    = 0,
                                                                        bool scale_rope = false) {
         int h_len = (h + (patch_size / 2)) / patch_size;
         int w_len = (w + (patch_size / 2)) / patch_size;
@@ -146,7 +168,8 @@ namespace Rope {
     __STATIC_INLINE__ std::vector<float> embed_nd(const std::vector<std::vector<float>>& ids,
                                                   int bs,
                                                   int theta,
-                                                  const std::vector<int>& axes_dim) {
+                                                  const std::vector<int>& axes_dim,
+                                                  const std::vector<std::vector<int>>& wrap_dims = {}) {
         std::vector<std::vector<float>> trans_ids = transpose(ids);
         size_t pos_len                            = ids.size() / bs;
         int num_axes                              = axes_dim.size();
@@ -161,7 +184,12 @@ namespace Rope {
         std::vector<std::vector<float>> emb(bs * pos_len, std::vector<float>(emb_dim * 2 * 2, 0.0));
         int offset = 0;
         for (int i = 0; i < num_axes; ++i) {
-            std::vector<std::vector<float>> rope_emb = rope(trans_ids[i], axes_dim[i], theta);  // [bs*pos_len, axes_dim[i]/2 * 2 * 2]
+            std::vector<int> axis_wrap_dims;
+            if (!wrap_dims.empty() && i < (int)wrap_dims.size()) {
+                axis_wrap_dims = wrap_dims[i];
+            }
+            std::vector<std::vector<float>> rope_emb =
+                rope(trans_ids[i], axes_dim[i], theta, axis_wrap_dims);  // [bs*pos_len, axes_dim[i]/2 * 2 * 2]
             for (int b = 0; b < bs; ++b) {
                 for (int j = 0; j < pos_len; ++j) {
                     for (int k = 0; k < rope_emb[0].size(); ++k) {
@@ -251,6 +279,8 @@ namespace Rope {
                                                      bool increase_ref_index,
                                                      float ref_index_scale,
                                                      int theta,
+                                                     bool circular_h,
+                                                     bool circular_w,
                                                      const std::vector<int>& axes_dim) {
         std::vector<std::vector<float>> ids = gen_flux_ids(h,
                                                            w,
@@ -262,7 +292,47 @@ namespace Rope {
                                                            ref_latents,
                                                            increase_ref_index,
                                                            ref_index_scale);
-        return embed_nd(ids, bs, theta, axes_dim);
+        std::vector<std::vector<int>> wrap_dims;
+        if ((circular_h || circular_w) && bs > 0 && axes_dim.size() >= 3) {
+            int h_len = (h + (patch_size / 2)) / patch_size;
+            int w_len = (w + (patch_size / 2)) / patch_size;
+            if (h_len > 0 && w_len > 0) {
+                size_t pos_len = ids.size() / bs;
+                wrap_dims.assign(axes_dim.size(), std::vector<int>(pos_len, 0));
+                size_t cursor           = context_len;  // text first
+                const size_t img_tokens = static_cast<size_t>(h_len) * static_cast<size_t>(w_len);
+                for (size_t token_i = 0; token_i < img_tokens; ++token_i) {
+                    if (circular_h) {
+                        wrap_dims[1][cursor + token_i] = h_len;
+                    }
+                    if (circular_w) {
+                        wrap_dims[2][cursor + token_i] = w_len;
+                    }
+                }
+                cursor += img_tokens;
+                // reference latents
+                for (ggml_tensor* ref : ref_latents) {
+                    if (ref == nullptr) {
+                        continue;
+                    }
+                    int ref_h         = static_cast<int>(ref->ne[1]);
+                    int ref_w         = static_cast<int>(ref->ne[0]);
+                    int ref_h_l       = (ref_h + (patch_size / 2)) / patch_size;
+                    int ref_w_l       = (ref_w + (patch_size / 2)) / patch_size;
+                    size_t ref_tokens = static_cast<size_t>(ref_h_l) * static_cast<size_t>(ref_w_l);
+                    for (size_t token_i = 0; token_i < ref_tokens; ++token_i) {
+                        if (circular_h) {
+                            wrap_dims[1][cursor + token_i] = ref_h_l;
+                        }
+                        if (circular_w) {
+                            wrap_dims[2][cursor + token_i] = ref_w_l;
+                        }
+                    }
+                    cursor += ref_tokens;
+                }
+            }
+        }
+        return embed_nd(ids, bs, theta, axes_dim, wrap_dims);
     }
 
     __STATIC_INLINE__ std::vector<std::vector<float>> gen_qwen_image_ids(int h,
@@ -301,9 +371,57 @@ namespace Rope {
                                                            const std::vector<ggml_tensor*>& ref_latents,
                                                            bool increase_ref_index,
                                                            int theta,
+                                                           bool circular_h,
+                                                           bool circular_w,
                                                            const std::vector<int>& axes_dim) {
         std::vector<std::vector<float>> ids = gen_qwen_image_ids(h, w, patch_size, bs, context_len, ref_latents, increase_ref_index);
-        return embed_nd(ids, bs, theta, axes_dim);
+        std::vector<std::vector<int>> wrap_dims;
+        // This logic simply stores the (pad and patch_adjusted) sizes of images so we can make sure rope correctly tiles
+        if ((circular_h || circular_w) && bs > 0 && axes_dim.size() >= 3) {
+            int pad_h = (patch_size - (h % patch_size)) % patch_size;
+            int pad_w = (patch_size - (w % patch_size)) % patch_size;
+            int h_len = (h + pad_h) / patch_size;
+            int w_len = (w + pad_w) / patch_size;
+            if (h_len > 0 && w_len > 0) {
+                const size_t total_tokens = ids.size();
+                // Track per-token wrap lengths for the row/column axes so only spatial tokens become periodic.
+                wrap_dims.assign(axes_dim.size(), std::vector<int>(total_tokens / bs, 0));
+                size_t cursor           = context_len;  // ignore text tokens
+                const size_t img_tokens = static_cast<size_t>(h_len) * static_cast<size_t>(w_len);
+                for (size_t token_i = 0; token_i < img_tokens; ++token_i) {
+                    if (circular_h) {
+                        wrap_dims[1][cursor + token_i] = h_len;
+                    }
+                    if (circular_w) {
+                        wrap_dims[2][cursor + token_i] = w_len;
+                    }
+                }
+                cursor += img_tokens;
+                // For each reference image, store wrap sizes as well
+                for (ggml_tensor* ref : ref_latents) {
+                    if (ref == nullptr) {
+                        continue;
+                    }
+                    int ref_h           = static_cast<int>(ref->ne[1]);
+                    int ref_w           = static_cast<int>(ref->ne[0]);
+                    int ref_pad_h       = (patch_size - (ref_h % patch_size)) % patch_size;
+                    int ref_pad_w       = (patch_size - (ref_w % patch_size)) % patch_size;
+                    int ref_h_len       = (ref_h + ref_pad_h) / patch_size;
+                    int ref_w_len       = (ref_w + ref_pad_w) / patch_size;
+                    size_t ref_n_tokens = static_cast<size_t>(ref_h_len) * static_cast<size_t>(ref_w_len);
+                    for (size_t token_i = 0; token_i < ref_n_tokens; ++token_i) {
+                        if (circular_h) {
+                            wrap_dims[1][cursor + token_i] = ref_h_len;
+                        }
+                        if (circular_w) {
+                            wrap_dims[2][cursor + token_i] = ref_w_len;
+                        }
+                    }
+                    cursor += ref_n_tokens;
+                }
+            }
+        }
+        return embed_nd(ids, bs, theta, axes_dim, wrap_dims);
     }
 
     __STATIC_INLINE__ std::vector<std::vector<float>> gen_vid_ids(int t,
@@ -440,9 +558,33 @@ namespace Rope {
                                                         const std::vector<ggml_tensor*>& ref_latents,
                                                         bool increase_ref_index,
                                                         int theta,
+                                                        bool circular_h,
+                                                        bool circular_w,
                                                         const std::vector<int>& axes_dim) {
         std::vector<std::vector<float>> ids = gen_z_image_ids(h, w, patch_size, bs, context_len, seq_multi_of, ref_latents, increase_ref_index);
-        return embed_nd(ids, bs, theta, axes_dim);
+        std::vector<std::vector<int>> wrap_dims;
+        if ((circular_h || circular_w) && bs > 0 && axes_dim.size() >= 3) {
+            int pad_h = (patch_size - (h % patch_size)) % patch_size;
+            int pad_w = (patch_size - (w % patch_size)) % patch_size;
+            int h_len = (h + pad_h) / patch_size;
+            int w_len = (w + pad_w) / patch_size;
+            if (h_len > 0 && w_len > 0) {
+                size_t pos_len = ids.size() / bs;
+                wrap_dims.assign(axes_dim.size(), std::vector<int>(pos_len, 0));
+                size_t cursor     = context_len + bound_mod(context_len, seq_multi_of);  // skip text (and its padding)
+                size_t img_tokens = static_cast<size_t>(h_len) * static_cast<size_t>(w_len);
+                for (size_t token_i = 0; token_i < img_tokens; ++token_i) {
+                    if (circular_h) {
+                        wrap_dims[1][cursor + token_i] = h_len;
+                    }
+                    if (circular_w) {
+                        wrap_dims[2][cursor + token_i] = w_len;
+                    }
+                }
+            }
+        }
+
+        return embed_nd(ids, bs, theta, axes_dim, wrap_dims);
     }
 
     __STATIC_INLINE__ struct ggml_tensor* apply_rope(struct ggml_context* ctx,
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 74519938..24516a9b 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -405,6 +405,10 @@ public:
             vae_decode_only = false;
         }
 
+        if (sd_ctx_params->circular_x || sd_ctx_params->circular_y) {
+            LOG_INFO("Using circular padding for convolutions");
+        }
+
         bool clip_on_cpu = sd_ctx_params->keep_clip_on_cpu;
 
         {
@@ -705,6 +709,20 @@ public:
                 }
                 pmid_model->get_param_tensors(tensors, "pmid");
             }
+
+            diffusion_model->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
+            if (high_noise_diffusion_model) {
+                high_noise_diffusion_model->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
+            }
+            if (control_net) {
+                control_net->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
+            }
+            if (first_stage_model) {
+                first_stage_model->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
+            }
+            if (tae_first_stage) {
+                tae_first_stage->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
+            }
         }
 
         struct ggml_init_params params;
@@ -1519,7 +1537,7 @@ public:
         }
         std::vector<int> skip_layers(guidance.slg.layers, guidance.slg.layers + guidance.slg.layer_count);
 
-        float cfg_scale     = guidance.txt_cfg;
+        float cfg_scale = guidance.txt_cfg;
         if (cfg_scale < 1.f) {
             if (cfg_scale == 0.f) {
                 // Diffusers follow the convention from the original paper
@@ -2559,6 +2577,8 @@ void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params) {
     sd_ctx_params->keep_control_net_on_cpu = false;
     sd_ctx_params->keep_vae_on_cpu         = false;
     sd_ctx_params->diffusion_flash_attn    = false;
+    sd_ctx_params->circular_x              = false;
+    sd_ctx_params->circular_y              = false;
     sd_ctx_params->chroma_use_dit_mask     = true;
     sd_ctx_params->chroma_use_t5_mask      = false;
     sd_ctx_params->chroma_t5_mask_pad      = 1;
@@ -2598,6 +2618,8 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              "keep_control_net_on_cpu: %s\n"
              "keep_vae_on_cpu: %s\n"
              "diffusion_flash_attn: %s\n"
+             "circular_x: %s\n"
+             "circular_y: %s\n"
              "chroma_use_dit_mask: %s\n"
              "chroma_use_t5_mask: %s\n"
              "chroma_t5_mask_pad: %d\n",
@@ -2627,6 +2649,8 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              BOOL_STR(sd_ctx_params->keep_control_net_on_cpu),
              BOOL_STR(sd_ctx_params->keep_vae_on_cpu),
              BOOL_STR(sd_ctx_params->diffusion_flash_attn),
+             BOOL_STR(sd_ctx_params->circular_x),
+             BOOL_STR(sd_ctx_params->circular_y),
              BOOL_STR(sd_ctx_params->chroma_use_dit_mask),
              BOOL_STR(sd_ctx_params->chroma_use_t5_mask),
              sd_ctx_params->chroma_t5_mask_pad);
diff --git a/stable-diffusion.h b/stable-diffusion.h
index adb65a1d..30583ea1 100644
--- a/stable-diffusion.h
+++ b/stable-diffusion.h
@@ -189,6 +189,8 @@ typedef struct {
     bool tae_preview_only;
     bool diffusion_conv_direct;
     bool vae_conv_direct;
+    bool circular_x;
+    bool circular_y;
     bool force_sdxl_vae_conv_scale;
     bool chroma_use_dit_mask;
     bool chroma_use_t5_mask;
diff --git a/wan.hpp b/wan.hpp
index 75333bfe..31ecf33f 100644
--- a/wan.hpp
+++ b/wan.hpp
@@ -75,7 +75,7 @@ namespace WAN {
                 lp2 -= (int)cache_x->ne[2];
             }
 
-            x = ggml_pad_ext(ctx->ggml_ctx, x, lp0, rp0, lp1, rp1, lp2, rp2, 0, 0);
+            x = ggml_ext_pad_ext(ctx->ggml_ctx, x, lp0, rp0, lp1, rp1, lp2, rp2, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
             return ggml_ext_conv_3d(ctx->ggml_ctx, x, w, b, in_channels,
                                     std::get<2>(stride), std::get<1>(stride), std::get<0>(stride),
                                     0, 0, 0,
@@ -206,9 +206,9 @@ namespace WAN {
                 } else if (mode == "upsample3d") {
                     x = ggml_upscale(ctx->ggml_ctx, x, 2, GGML_SCALE_MODE_NEAREST);
                 } else if (mode == "downsample2d") {
-                    x = ggml_pad(ctx->ggml_ctx, x, 1, 1, 0, 0);
+                    x = ggml_ext_pad(ctx->ggml_ctx, x, 1, 1, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
                 } else if (mode == "downsample3d") {
-                    x = ggml_pad(ctx->ggml_ctx, x, 1, 1, 0, 0);
+                    x = ggml_ext_pad(ctx->ggml_ctx, x, 1, 1, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
                 }
                 x = resample_1->forward(ctx, x);
                 x = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));  // (c, t, h, w)
@@ -1826,7 +1826,7 @@ namespace WAN {
             }
         }
 
-        struct ggml_tensor* pad_to_patch_size(struct ggml_context* ctx,
+        struct ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
                                               struct ggml_tensor* x) {
             int64_t W = x->ne[0];
             int64_t H = x->ne[1];
@@ -1835,8 +1835,7 @@ namespace WAN {
             int pad_t = (std::get<0>(params.patch_size) - T % std::get<0>(params.patch_size)) % std::get<0>(params.patch_size);
             int pad_h = (std::get<1>(params.patch_size) - H % std::get<1>(params.patch_size)) % std::get<1>(params.patch_size);
             int pad_w = (std::get<2>(params.patch_size) - W % std::get<2>(params.patch_size)) % std::get<2>(params.patch_size);
-            x         = ggml_pad(ctx, x, pad_w, pad_h, pad_t, 0);  // [N*C, T + pad_t, H + pad_h, W + pad_w]
-
+            ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, pad_t, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
             return x;
         }
 
@@ -1986,14 +1985,14 @@ namespace WAN {
             int64_t T = x->ne[2];
             int64_t C = x->ne[3];
 
-            x = pad_to_patch_size(ctx->ggml_ctx, x);
+            x = pad_to_patch_size(ctx, x);
 
             int64_t t_len = ((T + (std::get<0>(params.patch_size) / 2)) / std::get<0>(params.patch_size));
             int64_t h_len = ((H + (std::get<1>(params.patch_size) / 2)) / std::get<1>(params.patch_size));
             int64_t w_len = ((W + (std::get<2>(params.patch_size) / 2)) / std::get<2>(params.patch_size));
 
             if (time_dim_concat != nullptr) {
-                time_dim_concat = pad_to_patch_size(ctx->ggml_ctx, time_dim_concat);
+                time_dim_concat = pad_to_patch_size(ctx, time_dim_concat);
                 x               = ggml_concat(ctx->ggml_ctx, x, time_dim_concat, 2);  // [N*C, (T+pad_t) + (T2+pad_t2), H + pad_h, W + pad_w]
                 t_len           = ((x->ne[2] + (std::get<0>(params.patch_size) / 2)) / std::get<0>(params.patch_size));
             }
diff --git a/z_image.hpp b/z_image.hpp
index bc554f17..af8d57e0 100644
--- a/z_image.hpp
+++ b/z_image.hpp
@@ -324,14 +324,14 @@ namespace ZImage {
             blocks["final_layer"] = std::make_shared<FinalLayer>(z_image_params.hidden_size, z_image_params.patch_size, z_image_params.out_channels);
         }
 
-        struct ggml_tensor* pad_to_patch_size(struct ggml_context* ctx,
+        struct ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
                                               struct ggml_tensor* x) {
             int64_t W = x->ne[0];
             int64_t H = x->ne[1];
 
             int pad_h = (z_image_params.patch_size - H % z_image_params.patch_size) % z_image_params.patch_size;
             int pad_w = (z_image_params.patch_size - W % z_image_params.patch_size) % z_image_params.patch_size;
-            x         = ggml_pad(ctx, x, pad_w, pad_h, 0, 0);  // [N, C, H + pad_h, W + pad_w]
+            x         = ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
             return x;
         }
 
@@ -357,10 +357,10 @@ namespace ZImage {
             return x;
         }
 
-        struct ggml_tensor* process_img(struct ggml_context* ctx,
+        struct ggml_tensor* process_img(GGMLRunnerContext* ctx,
                                         struct ggml_tensor* x) {
             x = pad_to_patch_size(ctx, x);
-            x = patchify(ctx, x);
+            x = patchify(ctx->ggml_ctx, x);
             return x;
         }
 
@@ -473,12 +473,12 @@ namespace ZImage {
             int64_t C = x->ne[2];
             int64_t N = x->ne[3];
 
-            auto img             = process_img(ctx->ggml_ctx, x);
+            auto img             = process_img(ctx, x);
             uint64_t n_img_token = img->ne[1];
 
             if (ref_latents.size() > 0) {
                 for (ggml_tensor* ref : ref_latents) {
-                    ref = process_img(ctx->ggml_ctx, ref);
+                    ref = process_img(ctx, ref);
                     img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
                 }
             }
@@ -552,6 +552,8 @@ namespace ZImage {
                                                ref_latents,
                                                increase_ref_index,
                                                z_image_params.theta,
+                                               circular_y_enabled,
+                                               circular_x_enabled,
                                                z_image_params.axes_dim);
             int pos_len = pe_vec.size() / z_image_params.axes_dim_sum / 2;
             // LOG_DEBUG("pos_len %d", pos_len);

From ca5b1969a8f56b21ae841842ced9f06c3145d3b9 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 21 Dec 2025 18:40:10 +0800
Subject: [PATCH 12/97] feat: do not convert tensor names by default in convert
 mode (#1122)

---
 examples/cli/README.md |  1 +
 examples/cli/main.cpp  |  9 ++++++++-
 model.cpp              | 11 +++++++++--
 stable-diffusion.h     |  3 ++-
 4 files changed, 20 insertions(+), 4 deletions(-)

diff --git a/examples/cli/README.md b/examples/cli/README.md
index da7734d3..d6cb6aa3 100644
--- a/examples/cli/README.md
+++ b/examples/cli/README.md
@@ -9,6 +9,7 @@ CLI Options:
   --preview-interval <int>    interval in denoising steps between consecutive updates of the image preview file (default is 1, meaning updating at
                               every step)
   --canny                     apply canny preprocessor (edge detection)
+  --convert-name              convert tensor name (for convert mode)
   -v, --verbose               print extra info
   --color                     colors the logging tags according to level
   --taesd-preview-only        prevents usage of taesd for decoding the final image. (for use with --preview tae)
diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index a1b92f77..eb6f9da2 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -32,6 +32,7 @@ struct SDCliParams {
 
     bool verbose          = false;
     bool canny_preprocess = false;
+    bool convert_name     = false;
 
     preview_t preview_method = PREVIEW_NONE;
     int preview_interval     = 1;
@@ -69,6 +70,10 @@ struct SDCliParams {
              "--canny",
              "apply canny preprocessor (edge detection)",
              true, &canny_preprocess},
+            {"",
+             "--convert-name",
+             "convert tensor name (for convert mode)",
+             true, &canny_preprocess},
             {"-v",
              "--verbose",
              "print extra info",
@@ -174,6 +179,7 @@ struct SDCliParams {
             << "  verbose: " << (verbose ? "true" : "false") << ",\n"
             << "  color: " << (color ? "true" : "false") << ",\n"
             << "  canny_preprocess: " << (canny_preprocess ? "true" : "false") << ",\n"
+            << "  convert_name: " << (convert_name ? "true" : "false") << ",\n"
             << "  preview_method: " << previews_str[preview_method] << ",\n"
             << "  preview_interval: " << preview_interval << ",\n"
             << "  preview_path: \"" << preview_path << "\",\n"
@@ -387,7 +393,8 @@ int main(int argc, const char* argv[]) {
                                ctx_params.vae_path.c_str(),
                                cli_params.output_path.c_str(),
                                ctx_params.wtype,
-                               ctx_params.tensor_type_rules.c_str());
+                               ctx_params.tensor_type_rules.c_str(),
+                               cli_params.convert_name);
         if (!success) {
             LOG_ERROR("convert '%s'/'%s' to '%s' failed",
                       ctx_params.model_path.c_str(),
diff --git a/model.cpp b/model.cpp
index 561eb3cc..b5f73442 100644
--- a/model.cpp
+++ b/model.cpp
@@ -1783,7 +1783,12 @@ int64_t ModelLoader::get_params_mem_size(ggml_backend_t backend, ggml_type type)
     return mem_size;
 }
 
-bool convert(const char* input_path, const char* vae_path, const char* output_path, sd_type_t output_type, const char* tensor_type_rules) {
+bool convert(const char* input_path,
+             const char* vae_path,
+             const char* output_path,
+             sd_type_t output_type,
+             const char* tensor_type_rules,
+             bool convert_name) {
     ModelLoader model_loader;
 
     if (!model_loader.init_from_file(input_path)) {
@@ -1797,7 +1802,9 @@ bool convert(const char* input_path, const char* vae_path, const char* output_pa
             return false;
         }
     }
-    model_loader.convert_tensors_name();
+    if (convert_name) {
+        model_loader.convert_tensors_name();
+    }
     bool success = model_loader.save_to_gguf_file(output_path, (ggml_type)output_type, tensor_type_rules);
     return success;
 }
diff --git a/stable-diffusion.h b/stable-diffusion.h
index 30583ea1..9bc1fba5 100644
--- a/stable-diffusion.h
+++ b/stable-diffusion.h
@@ -365,7 +365,8 @@ SD_API bool convert(const char* input_path,
                     const char* vae_path,
                     const char* output_path,
                     enum sd_type_t output_type,
-                    const char* tensor_type_rules);
+                    const char* tensor_type_rules,
+                    bool convert_name);
 
 SD_API bool preprocess_canny(sd_image_t image,
                              float high_threshold,

From c6937ba44ab115abf8f0ee25f391126b792016fe Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 21 Dec 2025 21:47:50 +0800
Subject: [PATCH 13/97] fix: correct the parsing of --convert-name opotion

---
 examples/cli/main.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index eb6f9da2..c5c4c9a8 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -73,7 +73,7 @@ struct SDCliParams {
             {"",
              "--convert-name",
              "convert tensor name (for convert mode)",
-             true, &canny_preprocess},
+             true, &convert_name,
             {"-v",
              "--verbose",
              "print extra info",

From 30a91138f8c94a930452ca403ae5af6981558ea5 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 21 Dec 2025 21:53:38 +0800
Subject: [PATCH 14/97] fix: add the missing }

---
 examples/cli/main.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index c5c4c9a8..708dbb10 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -73,7 +73,7 @@ struct SDCliParams {
             {"",
              "--convert-name",
              "convert tensor name (for convert mode)",
-             true, &convert_name,
+             true, &convert_name},
             {"-v",
              "--verbose",
              "print extra info",

From 298b11069fbf33f8f471ffebc4b6ab3917fdf800 Mon Sep 17 00:00:00 2001
From: rmatif <rmatif@proton.me>
Date: Mon, 22 Dec 2025 16:52:11 +0100
Subject: [PATCH 15/97] feat: add more caching methods (#1066)

---
 cache_dit.hpp              | 975 +++++++++++++++++++++++++++++++++++++
 docs/caching.md            | 126 +++++
 examples/cli/README.md     |   9 +-
 examples/cli/main.cpp      |   4 +-
 examples/common/common.hpp | 269 +++++++---
 examples/server/main.cpp   |   4 +-
 stable-diffusion.cpp       | 342 ++++++++++---
 stable-diffusion.h         |  32 +-
 ucache.hpp                 | 404 +++++++++++++++
 9 files changed, 2022 insertions(+), 143 deletions(-)
 create mode 100644 cache_dit.hpp
 create mode 100644 docs/caching.md
 create mode 100644 ucache.hpp

diff --git a/cache_dit.hpp b/cache_dit.hpp
new file mode 100644
index 00000000..f5a1f180
--- /dev/null
+++ b/cache_dit.hpp
@@ -0,0 +1,975 @@
+#ifndef __CACHE_DIT_HPP__
+#define __CACHE_DIT_HPP__
+
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "ggml_extend.hpp"
+
+struct DBCacheConfig {
+    bool enabled                        = false;
+    int Fn_compute_blocks               = 8;
+    int Bn_compute_blocks               = 0;
+    float residual_diff_threshold       = 0.08f;
+    int max_warmup_steps                = 8;
+    int max_cached_steps                = -1;
+    int max_continuous_cached_steps     = -1;
+    float max_accumulated_residual_diff = -1.0f;
+    std::vector<int> steps_computation_mask;
+    bool scm_policy_dynamic = true;
+};
+
+struct TaylorSeerConfig {
+    bool enabled            = false;
+    int n_derivatives       = 1;
+    int max_warmup_steps    = 2;
+    int skip_interval_steps = 1;
+};
+
+struct CacheDitConfig {
+    DBCacheConfig dbcache;
+    TaylorSeerConfig taylorseer;
+    int double_Fn_blocks = -1;
+    int double_Bn_blocks = -1;
+    int single_Fn_blocks = -1;
+    int single_Bn_blocks = -1;
+};
+
+struct TaylorSeerState {
+    int n_derivatives      = 1;
+    int current_step       = -1;
+    int last_computed_step = -1;
+    std::vector<std::vector<float>> dY_prev;
+    std::vector<std::vector<float>> dY_current;
+
+    void init(int n_deriv, size_t hidden_size) {
+        n_derivatives = n_deriv;
+        int order     = n_derivatives + 1;
+        dY_prev.resize(order);
+        dY_current.resize(order);
+        for (int i = 0; i < order; i++) {
+            dY_prev[i].clear();
+            dY_current[i].clear();
+        }
+        current_step       = -1;
+        last_computed_step = -1;
+    }
+
+    void reset() {
+        for (auto& v : dY_prev)
+            v.clear();
+        for (auto& v : dY_current)
+            v.clear();
+        current_step       = -1;
+        last_computed_step = -1;
+    }
+
+    bool can_approximate() const {
+        return last_computed_step >= n_derivatives && !dY_prev.empty() && !dY_prev[0].empty();
+    }
+
+    void update_derivatives(const float* Y, size_t size, int step) {
+        int order = n_derivatives + 1;
+        dY_prev   = dY_current;
+        dY_current[0].resize(size);
+        for (size_t i = 0; i < size; i++) {
+            dY_current[0][i] = Y[i];
+        }
+
+        int window = step - last_computed_step;
+        if (window <= 0)
+            window = 1;
+
+        for (int d = 0; d < n_derivatives; d++) {
+            if (!dY_prev[d].empty() && dY_prev[d].size() == size) {
+                dY_current[d + 1].resize(size);
+                for (size_t i = 0; i < size; i++) {
+                    dY_current[d + 1][i] = (dY_current[d][i] - dY_prev[d][i]) / static_cast<float>(window);
+                }
+            } else {
+                dY_current[d + 1].clear();
+            }
+        }
+
+        current_step       = step;
+        last_computed_step = step;
+    }
+
+    void approximate(float* output, size_t size, int target_step) const {
+        if (!can_approximate() || dY_prev[0].size() != size) {
+            return;
+        }
+
+        int elapsed = target_step - last_computed_step;
+        if (elapsed <= 0)
+            elapsed = 1;
+
+        std::fill(output, output + size, 0.0f);
+        float factorial = 1.0f;
+        int order       = static_cast<int>(dY_prev.size());
+
+        for (int o = 0; o < order; o++) {
+            if (dY_prev[o].empty() || dY_prev[o].size() != size)
+                continue;
+            if (o > 0)
+                factorial *= static_cast<float>(o);
+            float coeff = std::pow(static_cast<float>(elapsed), o) / factorial;
+            for (size_t i = 0; i < size; i++) {
+                output[i] += coeff * dY_prev[o][i];
+            }
+        }
+    }
+};
+
+struct BlockCacheEntry {
+    std::vector<float> residual_img;
+    std::vector<float> residual_txt;
+    std::vector<float> residual;
+    std::vector<float> prev_img;
+    std::vector<float> prev_txt;
+    std::vector<float> prev_output;
+    bool has_prev = false;
+};
+
+struct CacheDitState {
+    CacheDitConfig config;
+    bool initialized = false;
+
+    int total_double_blocks = 0;
+    int total_single_blocks = 0;
+    size_t hidden_size      = 0;
+
+    int current_step     = -1;
+    int total_steps      = 0;
+    int warmup_remaining = 0;
+    std::vector<int> cached_steps;
+    int continuous_cached_steps     = 0;
+    float accumulated_residual_diff = 0.0f;
+
+    std::vector<BlockCacheEntry> double_block_cache;
+    std::vector<BlockCacheEntry> single_block_cache;
+
+    std::vector<float> Fn_residual_img;
+    std::vector<float> Fn_residual_txt;
+    std::vector<float> prev_Fn_residual_img;
+    std::vector<float> prev_Fn_residual_txt;
+    bool has_prev_Fn_residual = false;
+
+    std::vector<float> Bn_buffer_img;
+    std::vector<float> Bn_buffer_txt;
+    std::vector<float> Bn_buffer;
+    bool has_Bn_buffer = false;
+
+    TaylorSeerState taylor_state;
+
+    bool can_cache_this_step  = false;
+    bool is_caching_this_step = false;
+
+    int total_blocks_computed = 0;
+    int total_blocks_cached   = 0;
+
+    void init(const CacheDitConfig& cfg, int num_double_blocks, int num_single_blocks, size_t h_size) {
+        config              = cfg;
+        total_double_blocks = num_double_blocks;
+        total_single_blocks = num_single_blocks;
+        hidden_size         = h_size;
+
+        initialized = cfg.dbcache.enabled || cfg.taylorseer.enabled;
+
+        if (!initialized)
+            return;
+
+        warmup_remaining = cfg.dbcache.max_warmup_steps;
+        double_block_cache.resize(total_double_blocks);
+        single_block_cache.resize(total_single_blocks);
+
+        if (cfg.taylorseer.enabled) {
+            taylor_state.init(cfg.taylorseer.n_derivatives, h_size);
+        }
+
+        reset_runtime();
+    }
+
+    void reset_runtime() {
+        current_step     = -1;
+        total_steps      = 0;
+        warmup_remaining = config.dbcache.max_warmup_steps;
+        cached_steps.clear();
+        continuous_cached_steps   = 0;
+        accumulated_residual_diff = 0.0f;
+
+        for (auto& entry : double_block_cache) {
+            entry.residual_img.clear();
+            entry.residual_txt.clear();
+            entry.prev_img.clear();
+            entry.prev_txt.clear();
+            entry.has_prev = false;
+        }
+
+        for (auto& entry : single_block_cache) {
+            entry.residual.clear();
+            entry.prev_output.clear();
+            entry.has_prev = false;
+        }
+
+        Fn_residual_img.clear();
+        Fn_residual_txt.clear();
+        prev_Fn_residual_img.clear();
+        prev_Fn_residual_txt.clear();
+        has_prev_Fn_residual = false;
+
+        Bn_buffer_img.clear();
+        Bn_buffer_txt.clear();
+        Bn_buffer.clear();
+        has_Bn_buffer = false;
+
+        taylor_state.reset();
+
+        can_cache_this_step  = false;
+        is_caching_this_step = false;
+
+        total_blocks_computed = 0;
+        total_blocks_cached   = 0;
+    }
+
+    bool enabled() const {
+        return initialized && (config.dbcache.enabled || config.taylorseer.enabled);
+    }
+
+    void begin_step(int step_index, float sigma = 0.0f) {
+        if (!enabled())
+            return;
+        if (step_index == current_step)
+            return;
+
+        current_step = step_index;
+        total_steps++;
+
+        bool in_warmup = warmup_remaining > 0;
+        if (in_warmup) {
+            warmup_remaining--;
+        }
+
+        bool scm_allows_cache = true;
+        if (!config.dbcache.steps_computation_mask.empty()) {
+            if (step_index < static_cast<int>(config.dbcache.steps_computation_mask.size())) {
+                scm_allows_cache = (config.dbcache.steps_computation_mask[step_index] == 0);
+                if (!config.dbcache.scm_policy_dynamic && scm_allows_cache) {
+                    can_cache_this_step  = true;
+                    is_caching_this_step = false;
+                    return;
+                }
+            }
+        }
+
+        bool max_cached_ok = (config.dbcache.max_cached_steps < 0) ||
+                             (static_cast<int>(cached_steps.size()) < config.dbcache.max_cached_steps);
+
+        bool max_cont_ok = (config.dbcache.max_continuous_cached_steps < 0) ||
+                           (continuous_cached_steps < config.dbcache.max_continuous_cached_steps);
+
+        bool accum_ok = (config.dbcache.max_accumulated_residual_diff < 0.0f) ||
+                        (accumulated_residual_diff < config.dbcache.max_accumulated_residual_diff);
+
+        can_cache_this_step  = !in_warmup && scm_allows_cache && max_cached_ok && max_cont_ok && accum_ok && has_prev_Fn_residual;
+        is_caching_this_step = false;
+    }
+
+    void end_step(bool was_cached) {
+        if (was_cached) {
+            cached_steps.push_back(current_step);
+            continuous_cached_steps++;
+        } else {
+            continuous_cached_steps = 0;
+        }
+    }
+
+    static float calculate_residual_diff(const float* prev, const float* curr, size_t size) {
+        if (size == 0)
+            return 0.0f;
+
+        float sum_diff = 0.0f;
+        float sum_abs  = 0.0f;
+
+        for (size_t i = 0; i < size; i++) {
+            sum_diff += std::fabs(prev[i] - curr[i]);
+            sum_abs += std::fabs(prev[i]);
+        }
+
+        return sum_diff / (sum_abs + 1e-6f);
+    }
+
+    static float calculate_residual_diff(const std::vector<float>& prev, const std::vector<float>& curr) {
+        if (prev.size() != curr.size() || prev.empty())
+            return 1.0f;
+        return calculate_residual_diff(prev.data(), curr.data(), prev.size());
+    }
+
+    int get_double_Fn_blocks() const {
+        return (config.double_Fn_blocks >= 0) ? config.double_Fn_blocks : config.dbcache.Fn_compute_blocks;
+    }
+
+    int get_double_Bn_blocks() const {
+        return (config.double_Bn_blocks >= 0) ? config.double_Bn_blocks : config.dbcache.Bn_compute_blocks;
+    }
+
+    int get_single_Fn_blocks() const {
+        return (config.single_Fn_blocks >= 0) ? config.single_Fn_blocks : config.dbcache.Fn_compute_blocks;
+    }
+
+    int get_single_Bn_blocks() const {
+        return (config.single_Bn_blocks >= 0) ? config.single_Bn_blocks : config.dbcache.Bn_compute_blocks;
+    }
+
+    bool is_Fn_double_block(int block_idx) const {
+        return block_idx < get_double_Fn_blocks();
+    }
+
+    bool is_Bn_double_block(int block_idx) const {
+        int Bn = get_double_Bn_blocks();
+        return Bn > 0 && block_idx >= (total_double_blocks - Bn);
+    }
+
+    bool is_Mn_double_block(int block_idx) const {
+        return !is_Fn_double_block(block_idx) && !is_Bn_double_block(block_idx);
+    }
+
+    bool is_Fn_single_block(int block_idx) const {
+        return block_idx < get_single_Fn_blocks();
+    }
+
+    bool is_Bn_single_block(int block_idx) const {
+        int Bn = get_single_Bn_blocks();
+        return Bn > 0 && block_idx >= (total_single_blocks - Bn);
+    }
+
+    bool is_Mn_single_block(int block_idx) const {
+        return !is_Fn_single_block(block_idx) && !is_Bn_single_block(block_idx);
+    }
+
+    void store_Fn_residual(const float* img, const float* txt, size_t img_size, size_t txt_size, const float* input_img, const float* input_txt) {
+        Fn_residual_img.resize(img_size);
+        Fn_residual_txt.resize(txt_size);
+
+        for (size_t i = 0; i < img_size; i++) {
+            Fn_residual_img[i] = img[i] - input_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            Fn_residual_txt[i] = txt[i] - input_txt[i];
+        }
+    }
+
+    bool check_cache_decision() {
+        if (!can_cache_this_step) {
+            is_caching_this_step = false;
+            return false;
+        }
+
+        if (!has_prev_Fn_residual || prev_Fn_residual_img.empty()) {
+            is_caching_this_step = false;
+            return false;
+        }
+
+        float diff_img = calculate_residual_diff(prev_Fn_residual_img, Fn_residual_img);
+        float diff_txt = calculate_residual_diff(prev_Fn_residual_txt, Fn_residual_txt);
+        float diff     = (diff_img + diff_txt) / 2.0f;
+
+        if (diff < config.dbcache.residual_diff_threshold) {
+            is_caching_this_step = true;
+            accumulated_residual_diff += diff;
+            return true;
+        }
+
+        is_caching_this_step = false;
+        return false;
+    }
+
+    void update_prev_Fn_residual() {
+        prev_Fn_residual_img = Fn_residual_img;
+        prev_Fn_residual_txt = Fn_residual_txt;
+        has_prev_Fn_residual = !prev_Fn_residual_img.empty();
+    }
+
+    void store_double_block_residual(int block_idx, const float* img, const float* txt, size_t img_size, size_t txt_size, const float* prev_img, const float* prev_txt) {
+        if (block_idx < 0 || block_idx >= static_cast<int>(double_block_cache.size()))
+            return;
+
+        BlockCacheEntry& entry = double_block_cache[block_idx];
+
+        entry.residual_img.resize(img_size);
+        entry.residual_txt.resize(txt_size);
+        for (size_t i = 0; i < img_size; i++) {
+            entry.residual_img[i] = img[i] - prev_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            entry.residual_txt[i] = txt[i] - prev_txt[i];
+        }
+
+        entry.prev_img.resize(img_size);
+        entry.prev_txt.resize(txt_size);
+        for (size_t i = 0; i < img_size; i++) {
+            entry.prev_img[i] = img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            entry.prev_txt[i] = txt[i];
+        }
+        entry.has_prev = true;
+    }
+
+    void apply_double_block_cache(int block_idx, float* img, float* txt, size_t img_size, size_t txt_size) {
+        if (block_idx < 0 || block_idx >= static_cast<int>(double_block_cache.size()))
+            return;
+
+        const BlockCacheEntry& entry = double_block_cache[block_idx];
+        if (entry.residual_img.size() != img_size || entry.residual_txt.size() != txt_size)
+            return;
+
+        for (size_t i = 0; i < img_size; i++) {
+            img[i] += entry.residual_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            txt[i] += entry.residual_txt[i];
+        }
+
+        total_blocks_cached++;
+    }
+
+    void store_single_block_residual(int block_idx, const float* output, size_t size, const float* input) {
+        if (block_idx < 0 || block_idx >= static_cast<int>(single_block_cache.size()))
+            return;
+
+        BlockCacheEntry& entry = single_block_cache[block_idx];
+
+        entry.residual.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.residual[i] = output[i] - input[i];
+        }
+
+        entry.prev_output.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.prev_output[i] = output[i];
+        }
+        entry.has_prev = true;
+    }
+
+    void apply_single_block_cache(int block_idx, float* output, size_t size) {
+        if (block_idx < 0 || block_idx >= static_cast<int>(single_block_cache.size()))
+            return;
+
+        const BlockCacheEntry& entry = single_block_cache[block_idx];
+        if (entry.residual.size() != size)
+            return;
+
+        for (size_t i = 0; i < size; i++) {
+            output[i] += entry.residual[i];
+        }
+
+        total_blocks_cached++;
+    }
+
+    void store_Bn_buffer(const float* img, const float* txt, size_t img_size, size_t txt_size, const float* Bn_start_img, const float* Bn_start_txt) {
+        Bn_buffer_img.resize(img_size);
+        Bn_buffer_txt.resize(txt_size);
+
+        for (size_t i = 0; i < img_size; i++) {
+            Bn_buffer_img[i] = img[i] - Bn_start_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            Bn_buffer_txt[i] = txt[i] - Bn_start_txt[i];
+        }
+        has_Bn_buffer = true;
+    }
+
+    void apply_Bn_buffer(float* img, float* txt, size_t img_size, size_t txt_size) {
+        if (!has_Bn_buffer)
+            return;
+        if (Bn_buffer_img.size() != img_size || Bn_buffer_txt.size() != txt_size)
+            return;
+
+        for (size_t i = 0; i < img_size; i++) {
+            img[i] += Bn_buffer_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            txt[i] += Bn_buffer_txt[i];
+        }
+    }
+
+    void taylor_update(const float* hidden_state, size_t size) {
+        if (!config.taylorseer.enabled)
+            return;
+        taylor_state.update_derivatives(hidden_state, size, current_step);
+    }
+
+    bool taylor_can_approximate() const {
+        return config.taylorseer.enabled && taylor_state.can_approximate();
+    }
+
+    void taylor_approximate(float* output, size_t size) {
+        if (!config.taylorseer.enabled)
+            return;
+        taylor_state.approximate(output, size, current_step);
+    }
+
+    bool should_use_taylor_this_step() const {
+        if (!config.taylorseer.enabled)
+            return false;
+        if (current_step < config.taylorseer.max_warmup_steps)
+            return false;
+
+        int interval = config.taylorseer.skip_interval_steps;
+        if (interval <= 0)
+            interval = 1;
+
+        return (current_step % (interval + 1)) != 0;
+    }
+
+    void log_metrics() const {
+        if (!enabled())
+            return;
+
+        int total_blocks  = total_blocks_computed + total_blocks_cached;
+        float cache_ratio = (total_blocks > 0) ? (static_cast<float>(total_blocks_cached) / total_blocks * 100.0f) : 0.0f;
+
+        float step_cache_ratio = (total_steps > 0) ? (static_cast<float>(cached_steps.size()) / total_steps * 100.0f) : 0.0f;
+
+        LOG_INFO("CacheDIT: steps_cached=%zu/%d (%.1f%%), blocks_cached=%d/%d (%.1f%%), accum_diff=%.4f",
+                 cached_steps.size(), total_steps, step_cache_ratio,
+                 total_blocks_cached, total_blocks, cache_ratio,
+                 accumulated_residual_diff);
+    }
+
+    std::string get_summary() const {
+        char buf[256];
+        snprintf(buf, sizeof(buf),
+                 "CacheDIT[thresh=%.2f]: cached %zu/%d steps, %d/%d blocks",
+                 config.dbcache.residual_diff_threshold,
+                 cached_steps.size(), total_steps,
+                 total_blocks_cached, total_blocks_computed + total_blocks_cached);
+        return std::string(buf);
+    }
+};
+
+inline std::vector<int> parse_scm_mask(const std::string& mask_str) {
+    std::vector<int> mask;
+    if (mask_str.empty())
+        return mask;
+
+    size_t pos   = 0;
+    size_t start = 0;
+    while ((pos = mask_str.find(',', start)) != std::string::npos) {
+        std::string token = mask_str.substr(start, pos - start);
+        mask.push_back(std::stoi(token));
+        start = pos + 1;
+    }
+    if (start < mask_str.length()) {
+        mask.push_back(std::stoi(mask_str.substr(start)));
+    }
+
+    return mask;
+}
+
+inline std::vector<int> generate_scm_mask(
+    const std::vector<int>& compute_bins,
+    const std::vector<int>& cache_bins,
+    int total_steps) {
+    std::vector<int> mask;
+    size_t c_idx = 0, cache_idx = 0;
+
+    while (static_cast<int>(mask.size()) < total_steps) {
+        if (c_idx < compute_bins.size()) {
+            for (int i = 0; i < compute_bins[c_idx] && static_cast<int>(mask.size()) < total_steps; i++) {
+                mask.push_back(1);
+            }
+            c_idx++;
+        }
+        if (cache_idx < cache_bins.size()) {
+            for (int i = 0; i < cache_bins[cache_idx] && static_cast<int>(mask.size()) < total_steps; i++) {
+                mask.push_back(0);
+            }
+            cache_idx++;
+        }
+        if (c_idx >= compute_bins.size() && cache_idx >= cache_bins.size())
+            break;
+    }
+
+    if (!mask.empty()) {
+        mask.back() = 1;
+    }
+
+    return mask;
+}
+
+inline std::vector<int> get_scm_preset(const std::string& preset, int total_steps) {
+    struct Preset {
+        std::vector<int> compute_bins;
+        std::vector<int> cache_bins;
+    };
+
+    Preset slow   = {{8, 3, 3, 2, 1, 1}, {1, 2, 2, 2, 3}};
+    Preset medium = {{6, 2, 2, 2, 2, 1}, {1, 3, 3, 3, 3}};
+    Preset fast   = {{6, 1, 1, 1, 1, 1}, {1, 3, 4, 5, 4}};
+    Preset ultra  = {{4, 1, 1, 1, 1}, {2, 5, 6, 7}};
+
+    Preset* p = nullptr;
+    if (preset == "slow" || preset == "s" || preset == "S")
+        p = &slow;
+    else if (preset == "medium" || preset == "m" || preset == "M")
+        p = &medium;
+    else if (preset == "fast" || preset == "f" || preset == "F")
+        p = &fast;
+    else if (preset == "ultra" || preset == "u" || preset == "U")
+        p = &ultra;
+    else
+        return {};
+
+    if (total_steps != 28 && total_steps > 0) {
+        float scale = static_cast<float>(total_steps) / 28.0f;
+        std::vector<int> scaled_compute, scaled_cache;
+
+        for (int v : p->compute_bins) {
+            scaled_compute.push_back(std::max(1, static_cast<int>(v * scale + 0.5f)));
+        }
+        for (int v : p->cache_bins) {
+            scaled_cache.push_back(std::max(1, static_cast<int>(v * scale + 0.5f)));
+        }
+
+        return generate_scm_mask(scaled_compute, scaled_cache, total_steps);
+    }
+
+    return generate_scm_mask(p->compute_bins, p->cache_bins, total_steps);
+}
+
+inline float get_preset_threshold(const std::string& preset) {
+    if (preset == "slow" || preset == "s" || preset == "S")
+        return 0.20f;
+    if (preset == "medium" || preset == "m" || preset == "M")
+        return 0.25f;
+    if (preset == "fast" || preset == "f" || preset == "F")
+        return 0.30f;
+    if (preset == "ultra" || preset == "u" || preset == "U")
+        return 0.34f;
+    return 0.08f;
+}
+
+inline int get_preset_warmup(const std::string& preset) {
+    if (preset == "slow" || preset == "s" || preset == "S")
+        return 8;
+    if (preset == "medium" || preset == "m" || preset == "M")
+        return 6;
+    if (preset == "fast" || preset == "f" || preset == "F")
+        return 6;
+    if (preset == "ultra" || preset == "u" || preset == "U")
+        return 4;
+    return 8;
+}
+
+inline int get_preset_Fn(const std::string& preset) {
+    if (preset == "slow" || preset == "s" || preset == "S")
+        return 8;
+    if (preset == "medium" || preset == "m" || preset == "M")
+        return 8;
+    if (preset == "fast" || preset == "f" || preset == "F")
+        return 6;
+    if (preset == "ultra" || preset == "u" || preset == "U")
+        return 4;
+    return 8;
+}
+
+inline int get_preset_Bn(const std::string& preset) {
+    (void)preset;
+    return 0;
+}
+
+inline void parse_dbcache_options(const std::string& opts, DBCacheConfig& cfg) {
+    if (opts.empty())
+        return;
+
+    int Fn = 8, Bn = 0, warmup = 8, max_cached = -1, max_cont = -1;
+    float thresh = 0.08f;
+
+    sscanf(opts.c_str(), "%d,%d,%f,%d,%d,%d",
+           &Fn, &Bn, &thresh, &warmup, &max_cached, &max_cont);
+
+    cfg.Fn_compute_blocks           = Fn;
+    cfg.Bn_compute_blocks           = Bn;
+    cfg.residual_diff_threshold     = thresh;
+    cfg.max_warmup_steps            = warmup;
+    cfg.max_cached_steps            = max_cached;
+    cfg.max_continuous_cached_steps = max_cont;
+}
+
+inline void parse_taylorseer_options(const std::string& opts, TaylorSeerConfig& cfg) {
+    if (opts.empty())
+        return;
+
+    int n_deriv = 1, warmup = 2, interval = 1;
+    sscanf(opts.c_str(), "%d,%d,%d", &n_deriv, &warmup, &interval);
+
+    cfg.n_derivatives       = n_deriv;
+    cfg.max_warmup_steps    = warmup;
+    cfg.skip_interval_steps = interval;
+}
+
+struct CacheDitConditionState {
+    DBCacheConfig config;
+    TaylorSeerConfig taylor_config;
+    bool initialized = false;
+
+    int current_step_index = -1;
+    bool step_active       = false;
+    bool skip_current_step = false;
+    bool initial_step      = true;
+    int warmup_remaining   = 0;
+    std::vector<int> cached_steps;
+    int continuous_cached_steps     = 0;
+    float accumulated_residual_diff = 0.0f;
+    int total_steps_skipped         = 0;
+
+    const void* anchor_condition = nullptr;
+
+    struct CacheEntry {
+        std::vector<float> diff;
+        std::vector<float> prev_input;
+        std::vector<float> prev_output;
+        bool has_prev = false;
+    };
+    std::unordered_map<const void*, CacheEntry> cache_diffs;
+
+    TaylorSeerState taylor_state;
+
+    float start_sigma = std::numeric_limits<float>::max();
+    float end_sigma   = 0.0f;
+
+    void reset_runtime() {
+        current_step_index = -1;
+        step_active        = false;
+        skip_current_step  = false;
+        initial_step       = true;
+        warmup_remaining   = config.max_warmup_steps;
+        cached_steps.clear();
+        continuous_cached_steps   = 0;
+        accumulated_residual_diff = 0.0f;
+        total_steps_skipped       = 0;
+        anchor_condition          = nullptr;
+        cache_diffs.clear();
+        taylor_state.reset();
+    }
+
+    void init(const DBCacheConfig& dbcfg, const TaylorSeerConfig& tcfg) {
+        config        = dbcfg;
+        taylor_config = tcfg;
+        initialized   = dbcfg.enabled || tcfg.enabled;
+        reset_runtime();
+
+        if (taylor_config.enabled) {
+            taylor_state.init(taylor_config.n_derivatives, 0);
+        }
+    }
+
+    void set_sigmas(const std::vector<float>& sigmas) {
+        if (!initialized || sigmas.size() < 2)
+            return;
+
+        float start_percent = 0.15f;
+        float end_percent   = 0.95f;
+
+        size_t n_steps    = sigmas.size() - 1;
+        size_t start_step = static_cast<size_t>(start_percent * n_steps);
+        size_t end_step   = static_cast<size_t>(end_percent * n_steps);
+
+        if (start_step >= n_steps)
+            start_step = n_steps - 1;
+        if (end_step >= n_steps)
+            end_step = n_steps - 1;
+
+        start_sigma = sigmas[start_step];
+        end_sigma   = sigmas[end_step];
+
+        if (start_sigma < end_sigma) {
+            std::swap(start_sigma, end_sigma);
+        }
+    }
+
+    bool enabled() const {
+        return initialized && (config.enabled || taylor_config.enabled);
+    }
+
+    void begin_step(int step_index, float sigma) {
+        if (!enabled())
+            return;
+        if (step_index == current_step_index)
+            return;
+
+        current_step_index = step_index;
+        skip_current_step  = false;
+        step_active        = false;
+
+        if (sigma > start_sigma)
+            return;
+        if (!(sigma > end_sigma))
+            return;
+
+        step_active = true;
+
+        if (warmup_remaining > 0) {
+            warmup_remaining--;
+            return;
+        }
+
+        if (!config.steps_computation_mask.empty()) {
+            if (step_index < static_cast<int>(config.steps_computation_mask.size())) {
+                if (config.steps_computation_mask[step_index] == 1) {
+                    return;
+                }
+            }
+        }
+
+        if (config.max_cached_steps >= 0 &&
+            static_cast<int>(cached_steps.size()) >= config.max_cached_steps) {
+            return;
+        }
+
+        if (config.max_continuous_cached_steps >= 0 &&
+            continuous_cached_steps >= config.max_continuous_cached_steps) {
+            return;
+        }
+    }
+
+    bool step_is_active() const {
+        return enabled() && step_active;
+    }
+
+    bool is_step_skipped() const {
+        return enabled() && step_active && skip_current_step;
+    }
+
+    bool has_cache(const void* cond) const {
+        auto it = cache_diffs.find(cond);
+        return it != cache_diffs.end() && !it->second.diff.empty();
+    }
+
+    void update_cache(const void* cond, const float* input, const float* output, size_t size) {
+        CacheEntry& entry = cache_diffs[cond];
+        entry.diff.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.diff[i] = output[i] - input[i];
+        }
+
+        entry.prev_input.resize(size);
+        entry.prev_output.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.prev_input[i]  = input[i];
+            entry.prev_output[i] = output[i];
+        }
+        entry.has_prev = true;
+    }
+
+    void apply_cache(const void* cond, const float* input, float* output, size_t size) {
+        auto it = cache_diffs.find(cond);
+        if (it == cache_diffs.end() || it->second.diff.empty())
+            return;
+        if (it->second.diff.size() != size)
+            return;
+
+        for (size_t i = 0; i < size; i++) {
+            output[i] = input[i] + it->second.diff[i];
+        }
+    }
+
+    bool before_condition(const void* cond, struct ggml_tensor* input, struct ggml_tensor* output, float sigma, int step_index) {
+        if (!enabled() || step_index < 0)
+            return false;
+
+        if (step_index != current_step_index) {
+            begin_step(step_index, sigma);
+        }
+
+        if (!step_active)
+            return false;
+
+        if (initial_step) {
+            anchor_condition = cond;
+            initial_step     = false;
+        }
+
+        bool is_anchor = (cond == anchor_condition);
+
+        if (skip_current_step) {
+            if (has_cache(cond)) {
+                apply_cache(cond, (float*)input->data, (float*)output->data,
+                            static_cast<size_t>(ggml_nelements(output)));
+                return true;
+            }
+            return false;
+        }
+
+        if (!is_anchor)
+            return false;
+
+        auto it = cache_diffs.find(cond);
+        if (it == cache_diffs.end() || !it->second.has_prev)
+            return false;
+
+        size_t ne = static_cast<size_t>(ggml_nelements(input));
+        if (it->second.prev_input.size() != ne)
+            return false;
+
+        float* input_data = (float*)input->data;
+        float diff        = CacheDitState::calculate_residual_diff(
+                   it->second.prev_input.data(), input_data, ne);
+
+        float effective_threshold = config.residual_diff_threshold;
+        if (config.Fn_compute_blocks > 0) {
+            float fn_confidence = 1.0f + 0.02f * (config.Fn_compute_blocks - 8);
+            fn_confidence       = std::max(0.5f, std::min(2.0f, fn_confidence));
+            effective_threshold *= fn_confidence;
+        }
+        if (config.Bn_compute_blocks > 0) {
+            float bn_quality = 1.0f - 0.03f * config.Bn_compute_blocks;
+            bn_quality       = std::max(0.5f, std::min(1.0f, bn_quality));
+            effective_threshold *= bn_quality;
+        }
+
+        if (diff < effective_threshold) {
+            skip_current_step = true;
+            total_steps_skipped++;
+            cached_steps.push_back(current_step_index);
+            continuous_cached_steps++;
+            accumulated_residual_diff += diff;
+            apply_cache(cond, input_data, (float*)output->data, ne);
+            return true;
+        }
+
+        continuous_cached_steps = 0;
+        return false;
+    }
+
+    void after_condition(const void* cond, struct ggml_tensor* input, struct ggml_tensor* output) {
+        if (!step_is_active())
+            return;
+
+        size_t ne = static_cast<size_t>(ggml_nelements(output));
+        update_cache(cond, (float*)input->data, (float*)output->data, ne);
+
+        if (cond == anchor_condition && taylor_config.enabled) {
+            taylor_state.update_derivatives((float*)output->data, ne, current_step_index);
+        }
+    }
+
+    void log_metrics() const {
+        if (!enabled())
+            return;
+
+        LOG_INFO("CacheDIT: steps_skipped=%d/%d (%.1f%%), accum_residual_diff=%.4f",
+                 total_steps_skipped,
+                 current_step_index + 1,
+                 (current_step_index > 0) ? (100.0f * total_steps_skipped / (current_step_index + 1)) : 0.0f,
+                 accumulated_residual_diff);
+    }
+};
+
+#endif
diff --git a/docs/caching.md b/docs/caching.md
new file mode 100644
index 00000000..7b4be3ce
--- /dev/null
+++ b/docs/caching.md
@@ -0,0 +1,126 @@
+## Caching
+
+Caching methods accelerate diffusion inference by reusing intermediate computations when changes between steps are small.
+
+### Cache Modes
+
+| Mode | Target | Description |
+|------|--------|-------------|
+| `ucache` | UNET models | Condition-level caching with error tracking |
+| `easycache` | DiT models | Condition-level cache |
+| `dbcache` | DiT models | Block-level L1 residual threshold |
+| `taylorseer` | DiT models | Taylor series approximation |
+| `cache-dit` | DiT models | Combined DBCache + TaylorSeer |
+
+### UCache (UNET Models)
+
+UCache caches the residual difference (output - input) and reuses it when input changes are below threshold.
+
+```bash
+sd-cli -m model.safetensors -p "a cat" --cache-mode ucache --cache-option "threshold=1.5"
+```
+
+#### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `threshold` | Error threshold for reuse decision | 1.0 |
+| `start` | Start caching at this percent of steps | 0.15 |
+| `end` | Stop caching at this percent of steps | 0.95 |
+| `decay` | Error decay rate (0-1) | 1.0 |
+| `relative` | Scale threshold by output norm (0/1) | 1 |
+| `reset` | Reset error after computing (0/1) | 1 |
+
+#### Reset Parameter
+
+The `reset` parameter controls error accumulation behavior:
+
+- `reset=1` (default): Resets accumulated error after each computed step. More aggressive caching, works well with most samplers.
+- `reset=0`: Keeps error accumulated. More conservative, recommended for `euler_a` sampler.
+
+### EasyCache (DiT Models)
+
+Condition-level caching for DiT models. Caches and reuses outputs when input changes are below threshold.
+
+```bash
+--cache-mode easycache --cache-option "threshold=0.3"
+```
+
+#### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `threshold` | Input change threshold for reuse | 0.2 |
+| `start` | Start caching at this percent of steps | 0.15 |
+| `end` | Stop caching at this percent of steps | 0.95 |
+
+### Cache-DIT (DiT Models)
+
+For DiT models like FLUX and QWEN, use block-level caching modes.
+
+#### DBCache
+
+Caches blocks based on L1 residual difference threshold:
+
+```bash
+--cache-mode dbcache --cache-option "threshold=0.25,warmup=4"
+```
+
+#### TaylorSeer
+
+Uses Taylor series approximation to predict block outputs:
+
+```bash
+--cache-mode taylorseer
+```
+
+#### Cache-DIT (Combined)
+
+Combines DBCache and TaylorSeer:
+
+```bash
+--cache-mode cache-dit --cache-preset fast
+```
+
+#### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `Fn` | Front blocks to always compute | 8 |
+| `Bn` | Back blocks to always compute | 0 |
+| `threshold` | L1 residual difference threshold | 0.08 |
+| `warmup` | Steps before caching starts | 8 |
+
+#### Presets
+
+Available presets: `slow`, `medium`, `fast`, `ultra` (or `s`, `m`, `f`, `u`).
+
+```bash
+--cache-mode cache-dit --cache-preset fast
+```
+
+#### SCM Options
+
+Steps Computation Mask controls which steps can be cached:
+
+```bash
+--scm-mask "1,1,1,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,1"
+```
+
+Mask values: `1` = compute, `0` = can cache.
+
+| Policy | Description |
+|--------|-------------|
+| `dynamic` | Check threshold before caching |
+| `static` | Always cache on cacheable steps |
+
+```bash
+--scm-policy dynamic
+```
+
+### Performance Tips
+
+- Start with default thresholds and adjust based on output quality
+- Lower threshold = better quality, less speedup
+- Higher threshold = more speedup, potential quality loss
+- More steps generally means more caching opportunities
diff --git a/examples/cli/README.md b/examples/cli/README.md
index d6cb6aa3..0617a463 100644
--- a/examples/cli/README.md
+++ b/examples/cli/README.md
@@ -127,5 +127,12 @@ Generation Options:
   --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
   --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
   -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
-  --easycache                              enable EasyCache for DiT models with optional "threshold,start_percent,end_percent" (default: 0.2,0.15,0.95)
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)
+  --cache-option                           named cache params (key=value format, comma-separated):
+                                           - easycache/ucache: threshold=,start=,end=,decay=,relative=,reset=
+                                           - dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=
+                                           Examples: "threshold=0.25" or "threshold=1.5,reset=0"
+  --cache-preset                           cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'
+  --scm-mask                               SCM steps mask: comma-separated 0/1 (1=compute, 0=can cache)
+  --scm-policy                             SCM policy: 'dynamic' (default) or 'static'
 ```
diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index 708dbb10..7fe3b769 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -617,7 +617,7 @@ int main(int argc, const char* argv[]) {
                     gen_params.pm_style_strength,
                 },  // pm_params
                 ctx_params.vae_tiling_params,
-                gen_params.easycache_params,
+                gen_params.cache_params,
             };
 
             results     = generate_image(sd_ctx, &img_gen_params);
@@ -642,7 +642,7 @@ int main(int argc, const char* argv[]) {
                 gen_params.seed,
                 gen_params.video_frames,
                 gen_params.vace_strength,
-                gen_params.easycache_params,
+                gen_params.cache_params,
             };
 
             results = generate_video(sd_ctx, &vid_gen_params, &num_results);
diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index 5168730a..868b06e9 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -1018,8 +1018,12 @@ struct SDGenerationParams {
 
     std::vector<float> custom_sigmas;
 
-    std::string easycache_option;
-    sd_easycache_params_t easycache_params;
+    std::string cache_mode;
+    std::string cache_option;
+    std::string cache_preset;
+    std::string scm_mask;
+    bool scm_policy_dynamic = true;
+    sd_cache_params_t cache_params{};
 
     float moe_boundary  = 0.875f;
     int video_frames    = 1;
@@ -1381,36 +1385,64 @@ struct SDGenerationParams {
             return 1;
         };
 
-        auto on_easycache_arg = [&](int argc, const char** argv, int index) {
-            const std::string default_values = "0.2,0.15,0.95";
-            auto looks_like_value            = [](const std::string& token) {
-                if (token.empty()) {
-                    return false;
-                }
-                if (token[0] != '-') {
-                    return true;
-                }
-                if (token.size() == 1) {
-                    return false;
-                }
-                unsigned char next = static_cast<unsigned char>(token[1]);
-                return std::isdigit(next) || token[1] == '.';
-            };
+        auto on_cache_mode_arg = [&](int argc, const char** argv, int index) {
+            if (++index >= argc) {
+                return -1;
+            }
+            cache_mode = argv_to_utf8(index, argv);
+            if (cache_mode != "easycache" && cache_mode != "ucache" &&
+                cache_mode != "dbcache" && cache_mode != "taylorseer" && cache_mode != "cache-dit") {
+                fprintf(stderr, "error: invalid cache mode '%s', must be 'easycache', 'ucache', 'dbcache', 'taylorseer', or 'cache-dit'\n", cache_mode.c_str());
+                return -1;
+            }
+            return 1;
+        };
 
-            std::string option_value;
-            int consumed = 0;
-            if (index + 1 < argc) {
-                std::string next_arg = argv[index + 1];
-                if (looks_like_value(next_arg)) {
-                    option_value = argv_to_utf8(index + 1, argv);
-                    consumed     = 1;
-                }
+        auto on_cache_option_arg = [&](int argc, const char** argv, int index) {
+            if (++index >= argc) {
+                return -1;
             }
-            if (option_value.empty()) {
-                option_value = default_values;
+            cache_option = argv_to_utf8(index, argv);
+            return 1;
+        };
+
+        auto on_scm_mask_arg = [&](int argc, const char** argv, int index) {
+            if (++index >= argc) {
+                return -1;
             }
-            easycache_option = option_value;
-            return consumed;
+            scm_mask = argv_to_utf8(index, argv);
+            return 1;
+        };
+
+        auto on_scm_policy_arg = [&](int argc, const char** argv, int index) {
+            if (++index >= argc) {
+                return -1;
+            }
+            std::string policy = argv_to_utf8(index, argv);
+            if (policy == "dynamic") {
+                scm_policy_dynamic = true;
+            } else if (policy == "static") {
+                scm_policy_dynamic = false;
+            } else {
+                fprintf(stderr, "error: invalid scm policy '%s', must be 'dynamic' or 'static'\n", policy.c_str());
+                return -1;
+            }
+            return 1;
+        };
+
+        auto on_cache_preset_arg = [&](int argc, const char** argv, int index) {
+            if (++index >= argc) {
+                return -1;
+            }
+            cache_preset = argv_to_utf8(index, argv);
+            if (cache_preset != "slow" && cache_preset != "s" && cache_preset != "S" &&
+                cache_preset != "medium" && cache_preset != "m" && cache_preset != "M" &&
+                cache_preset != "fast" && cache_preset != "f" && cache_preset != "F" &&
+                cache_preset != "ultra" && cache_preset != "u" && cache_preset != "U") {
+                fprintf(stderr, "error: invalid cache preset '%s', must be 'slow'/'s', 'medium'/'m', 'fast'/'f', or 'ultra'/'u'\n", cache_preset.c_str());
+                return -1;
+            }
+            return 1;
         };
 
         options.manual_options = {
@@ -1449,9 +1481,25 @@ struct SDGenerationParams {
              "reference image for Flux Kontext models (can be used multiple times)",
              on_ref_image_arg},
             {"",
-             "--easycache",
-             "enable EasyCache for DiT models with optional \"threshold,start_percent,end_percent\" (default: 0.2,0.15,0.95)",
-             on_easycache_arg},
+             "--cache-mode",
+             "caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)",
+             on_cache_mode_arg},
+            {"",
+             "--cache-option",
+             "named cache params (key=value format, comma-separated):\n                                           - easycache/ucache: threshold=,start=,end=,decay=,relative=,reset=\n                                           - dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=\n                                           Examples: \"threshold=0.25\" or \"threshold=1.5,reset=0\"",
+             on_cache_option_arg},
+            {"",
+             "--cache-preset",
+             "cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'",
+             on_cache_preset_arg},
+            {"",
+             "--scm-mask",
+             "SCM steps mask for cache-dit: comma-separated 0/1 (e.g., \"1,1,1,0,0,1,0,0,1,0\") - 1=compute, 0=can cache",
+             on_scm_mask_arg},
+            {"",
+             "--scm-policy",
+             "SCM policy: 'dynamic' (default) or 'static'",
+             on_scm_policy_arg},
 
         };
 
@@ -1494,7 +1542,10 @@ struct SDGenerationParams {
 
         load_if_exists("prompt", prompt);
         load_if_exists("negative_prompt", negative_prompt);
-        load_if_exists("easycache_option", easycache_option);
+        load_if_exists("cache_mode", cache_mode);
+        load_if_exists("cache_option", cache_option);
+        load_if_exists("cache_preset", cache_preset);
+        load_if_exists("scm_mask", scm_mask);
 
         load_if_exists("clip_skip", clip_skip);
         load_if_exists("width", width);
@@ -1634,57 +1685,118 @@ struct SDGenerationParams {
             return false;
         }
 
-        if (!easycache_option.empty()) {
-            float values[3] = {0.0f, 0.0f, 0.0f};
-            std::stringstream ss(easycache_option);
+        sd_cache_params_init(&cache_params);
+
+        auto parse_named_params = [&](const std::string& opt_str) -> bool {
+            std::stringstream ss(opt_str);
             std::string token;
-            int idx = 0;
             while (std::getline(ss, token, ',')) {
-                auto trim = [](std::string& s) {
-                    const char* whitespace = " \t\r\n";
-                    auto start             = s.find_first_not_of(whitespace);
-                    if (start == std::string::npos) {
-                        s.clear();
-                        return;
-                    }
-                    auto end = s.find_last_not_of(whitespace);
-                    s        = s.substr(start, end - start + 1);
-                };
-                trim(token);
-                if (token.empty()) {
-                    LOG_ERROR("error: invalid easycache option '%s'", easycache_option.c_str());
-                    return false;
-                }
-                if (idx >= 3) {
-                    LOG_ERROR("error: easycache expects exactly 3 comma-separated values (threshold,start,end)\n");
+                size_t eq_pos = token.find('=');
+                if (eq_pos == std::string::npos) {
+                    LOG_ERROR("error: cache option '%s' missing '=' separator", token.c_str());
                     return false;
                 }
+                std::string key = token.substr(0, eq_pos);
+                std::string val = token.substr(eq_pos + 1);
                 try {
-                    values[idx] = std::stof(token);
+                    if (key == "threshold") {
+                        if (cache_mode == "easycache" || cache_mode == "ucache") {
+                            cache_params.reuse_threshold = std::stof(val);
+                        } else {
+                            cache_params.residual_diff_threshold = std::stof(val);
+                        }
+                    } else if (key == "start") {
+                        cache_params.start_percent = std::stof(val);
+                    } else if (key == "end") {
+                        cache_params.end_percent = std::stof(val);
+                    } else if (key == "decay") {
+                        cache_params.error_decay_rate = std::stof(val);
+                    } else if (key == "relative") {
+                        cache_params.use_relative_threshold = (std::stof(val) != 0.0f);
+                    } else if (key == "reset") {
+                        cache_params.reset_error_on_compute = (std::stof(val) != 0.0f);
+                    } else if (key == "Fn" || key == "fn") {
+                        cache_params.Fn_compute_blocks = std::stoi(val);
+                    } else if (key == "Bn" || key == "bn") {
+                        cache_params.Bn_compute_blocks = std::stoi(val);
+                    } else if (key == "warmup") {
+                        cache_params.max_warmup_steps = std::stoi(val);
+                    } else {
+                        LOG_ERROR("error: unknown cache parameter '%s'", key.c_str());
+                        return false;
+                    }
                 } catch (const std::exception&) {
-                    LOG_ERROR("error: invalid easycache value '%s'", token.c_str());
+                    LOG_ERROR("error: invalid value '%s' for parameter '%s'", val.c_str(), key.c_str());
                     return false;
                 }
-                idx++;
             }
-            if (idx != 3) {
-                LOG_ERROR("error: easycache expects exactly 3 comma-separated values (threshold,start,end)\n");
-                return false;
+            return true;
+        };
+
+        if (!cache_mode.empty()) {
+            if (cache_mode == "easycache") {
+                cache_params.mode                   = SD_CACHE_EASYCACHE;
+                cache_params.reuse_threshold        = 0.2f;
+                cache_params.start_percent          = 0.15f;
+                cache_params.end_percent            = 0.95f;
+                cache_params.error_decay_rate       = 1.0f;
+                cache_params.use_relative_threshold = true;
+                cache_params.reset_error_on_compute = true;
+            } else if (cache_mode == "ucache") {
+                cache_params.mode                   = SD_CACHE_UCACHE;
+                cache_params.reuse_threshold        = 1.0f;
+                cache_params.start_percent          = 0.15f;
+                cache_params.end_percent            = 0.95f;
+                cache_params.error_decay_rate       = 1.0f;
+                cache_params.use_relative_threshold = true;
+                cache_params.reset_error_on_compute = true;
+            } else if (cache_mode == "dbcache") {
+                cache_params.mode                    = SD_CACHE_DBCACHE;
+                cache_params.Fn_compute_blocks       = 8;
+                cache_params.Bn_compute_blocks       = 0;
+                cache_params.residual_diff_threshold = 0.08f;
+                cache_params.max_warmup_steps        = 8;
+            } else if (cache_mode == "taylorseer") {
+                cache_params.mode                    = SD_CACHE_TAYLORSEER;
+                cache_params.Fn_compute_blocks       = 8;
+                cache_params.Bn_compute_blocks       = 0;
+                cache_params.residual_diff_threshold = 0.08f;
+                cache_params.max_warmup_steps        = 8;
+            } else if (cache_mode == "cache-dit") {
+                cache_params.mode                    = SD_CACHE_CACHE_DIT;
+                cache_params.Fn_compute_blocks       = 8;
+                cache_params.Bn_compute_blocks       = 0;
+                cache_params.residual_diff_threshold = 0.08f;
+                cache_params.max_warmup_steps        = 8;
             }
-            if (values[0] < 0.0f) {
-                LOG_ERROR("error: easycache threshold must be non-negative\n");
-                return false;
+
+            if (!cache_option.empty()) {
+                if (!parse_named_params(cache_option)) {
+                    return false;
+                }
             }
-            if (values[1] < 0.0f || values[1] >= 1.0f || values[2] <= 0.0f || values[2] > 1.0f || values[1] >= values[2]) {
-                LOG_ERROR("error: easycache start/end percents must satisfy 0.0 <= start < end <= 1.0\n");
-                return false;
+
+            if (cache_mode == "easycache" || cache_mode == "ucache") {
+                if (cache_params.reuse_threshold < 0.0f) {
+                    LOG_ERROR("error: cache threshold must be non-negative");
+                    return false;
+                }
+                if (cache_params.start_percent < 0.0f || cache_params.start_percent >= 1.0f ||
+                    cache_params.end_percent <= 0.0f || cache_params.end_percent > 1.0f ||
+                    cache_params.start_percent >= cache_params.end_percent) {
+                    LOG_ERROR("error: cache start/end percents must satisfy 0.0 <= start < end <= 1.0");
+                    return false;
+                }
             }
-            easycache_params.enabled         = true;
-            easycache_params.reuse_threshold = values[0];
-            easycache_params.start_percent   = values[1];
-            easycache_params.end_percent     = values[2];
-        } else {
-            easycache_params.enabled = false;
+        }
+
+        if (cache_params.mode == SD_CACHE_DBCACHE ||
+            cache_params.mode == SD_CACHE_TAYLORSEER ||
+            cache_params.mode == SD_CACHE_CACHE_DIT) {
+            if (!scm_mask.empty()) {
+                cache_params.scm_mask = scm_mask.c_str();
+            }
+            cache_params.scm_policy_dynamic = scm_policy_dynamic;
         }
 
         sample_params.guidance.slg.layers                 = skip_layers.data();
@@ -1786,12 +1898,13 @@ struct SDGenerationParams {
             << "  high_noise_skip_layers: " << vec_to_string(high_noise_skip_layers) << ",\n"
             << "  high_noise_sample_params: " << high_noise_sample_params_str << ",\n"
             << "  custom_sigmas: " << vec_to_string(custom_sigmas) << ",\n"
-            << "  easycache_option: \"" << easycache_option << "\",\n"
-            << "  easycache: "
-            << (easycache_params.enabled ? "enabled" : "disabled")
-            << " (threshold=" << easycache_params.reuse_threshold
-            << ", start=" << easycache_params.start_percent
-            << ", end=" << easycache_params.end_percent << "),\n"
+            << "  cache_mode: \"" << cache_mode << "\",\n"
+            << "  cache_option: \"" << cache_option << "\",\n"
+            << "  cache: "
+            << (cache_params.mode != SD_CACHE_DISABLED ? "enabled" : "disabled")
+            << " (threshold=" << cache_params.reuse_threshold
+            << ", start=" << cache_params.start_percent
+            << ", end=" << cache_params.end_percent << "),\n"
             << "  moe_boundary: " << moe_boundary << ",\n"
             << "  video_frames: " << video_frames << ",\n"
             << "  fps: " << fps << ",\n"
diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index 39359fbb..5c951c07 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -432,7 +432,7 @@ int main(int argc, const char** argv) {
                     gen_params.pm_style_strength,
                 },  // pm_params
                 ctx_params.vae_tiling_params,
-                gen_params.easycache_params,
+                gen_params.cache_params,
             };
 
             sd_image_t* results = nullptr;
@@ -645,7 +645,7 @@ int main(int argc, const char** argv) {
                     gen_params.pm_style_strength,
                 },  // pm_params
                 ctx_params.vae_tiling_params,
-                gen_params.easycache_params,
+                gen_params.cache_params,
             };
 
             sd_image_t* results = nullptr;
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 24516a9b..6c783e1d 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -7,6 +7,7 @@
 #include "stable-diffusion.h"
 #include "util.h"
 
+#include "cache_dit.hpp"
 #include "conditioner.hpp"
 #include "control.hpp"
 #include "denoiser.hpp"
@@ -16,6 +17,7 @@
 #include "lora.hpp"
 #include "pmid.hpp"
 #include "tae.hpp"
+#include "ucache.hpp"
 #include "vae.hpp"
 
 #include "latent-preview.h"
@@ -1525,12 +1527,12 @@ public:
                         const std::vector<float>& sigmas,
                         int start_merge_step,
                         SDCondition id_cond,
-                        std::vector<ggml_tensor*> ref_latents         = {},
-                        bool increase_ref_index                       = false,
-                        ggml_tensor* denoise_mask                     = nullptr,
-                        ggml_tensor* vace_context                     = nullptr,
-                        float vace_strength                           = 1.f,
-                        const sd_easycache_params_t* easycache_params = nullptr) {
+                        std::vector<ggml_tensor*> ref_latents = {},
+                        bool increase_ref_index               = false,
+                        ggml_tensor* denoise_mask             = nullptr,
+                        ggml_tensor* vace_context             = nullptr,
+                        float vace_strength                   = 1.f,
+                        const sd_cache_params_t* cache_params = nullptr) {
         if (shifted_timestep > 0 && !sd_version_is_sdxl(version)) {
             LOG_WARN("timestep shifting is only supported for SDXL models!");
             shifted_timestep = 0;
@@ -1558,31 +1560,40 @@ public:
         }
 
         EasyCacheState easycache_state;
+        UCacheState ucache_state;
+        CacheDitConditionState cachedit_state;
         bool easycache_enabled = false;
-        if (easycache_params != nullptr && easycache_params->enabled) {
-            bool easycache_supported = sd_version_is_dit(version);
-            if (!easycache_supported) {
-                LOG_WARN("EasyCache requested but not supported for this model type");
-            } else {
-                EasyCacheConfig easycache_config;
-                easycache_config.enabled         = true;
-                easycache_config.reuse_threshold = std::max(0.0f, easycache_params->reuse_threshold);
-                easycache_config.start_percent   = easycache_params->start_percent;
-                easycache_config.end_percent     = easycache_params->end_percent;
-                bool percent_valid               = easycache_config.start_percent >= 0.0f &&
-                                     easycache_config.start_percent < 1.0f &&
-                                     easycache_config.end_percent > 0.0f &&
-                                     easycache_config.end_percent <= 1.0f &&
-                                     easycache_config.start_percent < easycache_config.end_percent;
-                if (!percent_valid) {
-                    LOG_WARN("EasyCache disabled due to invalid percent range (start=%.3f, end=%.3f)",
-                             easycache_config.start_percent,
-                             easycache_config.end_percent);
+        bool ucache_enabled    = false;
+        bool cachedit_enabled  = false;
+
+        if (cache_params != nullptr && cache_params->mode != SD_CACHE_DISABLED) {
+            bool percent_valid = true;
+            if (cache_params->mode == SD_CACHE_EASYCACHE || cache_params->mode == SD_CACHE_UCACHE) {
+                percent_valid = cache_params->start_percent >= 0.0f &&
+                                cache_params->start_percent < 1.0f &&
+                                cache_params->end_percent > 0.0f &&
+                                cache_params->end_percent <= 1.0f &&
+                                cache_params->start_percent < cache_params->end_percent;
+            }
+
+            if (!percent_valid) {
+                LOG_WARN("Cache disabled due to invalid percent range (start=%.3f, end=%.3f)",
+                         cache_params->start_percent,
+                         cache_params->end_percent);
+            } else if (cache_params->mode == SD_CACHE_EASYCACHE) {
+                bool easycache_supported = sd_version_is_dit(version);
+                if (!easycache_supported) {
+                    LOG_WARN("EasyCache requested but not supported for this model type");
                 } else {
+                    EasyCacheConfig easycache_config;
+                    easycache_config.enabled         = true;
+                    easycache_config.reuse_threshold = std::max(0.0f, cache_params->reuse_threshold);
+                    easycache_config.start_percent   = cache_params->start_percent;
+                    easycache_config.end_percent     = cache_params->end_percent;
                     easycache_state.init(easycache_config, denoiser.get());
                     if (easycache_state.enabled()) {
                         easycache_enabled = true;
-                        LOG_INFO("EasyCache enabled - threshold: %.3f, start_percent: %.2f, end_percent: %.2f",
+                        LOG_INFO("EasyCache enabled - threshold: %.3f, start: %.2f, end: %.2f",
                                  easycache_config.reuse_threshold,
                                  easycache_config.start_percent,
                                  easycache_config.end_percent);
@@ -1590,9 +1601,84 @@ public:
                         LOG_WARN("EasyCache requested but could not be initialized for this run");
                     }
                 }
+            } else if (cache_params->mode == SD_CACHE_UCACHE) {
+                bool ucache_supported = sd_version_is_unet(version);
+                if (!ucache_supported) {
+                    LOG_WARN("UCache requested but not supported for this model type (only UNET models)");
+                } else {
+                    UCacheConfig ucache_config;
+                    ucache_config.enabled                = true;
+                    ucache_config.reuse_threshold        = std::max(0.0f, cache_params->reuse_threshold);
+                    ucache_config.start_percent          = cache_params->start_percent;
+                    ucache_config.end_percent            = cache_params->end_percent;
+                    ucache_config.error_decay_rate       = std::max(0.0f, std::min(1.0f, cache_params->error_decay_rate));
+                    ucache_config.use_relative_threshold = cache_params->use_relative_threshold;
+                    ucache_config.reset_error_on_compute = cache_params->reset_error_on_compute;
+                    ucache_state.init(ucache_config, denoiser.get());
+                    if (ucache_state.enabled()) {
+                        ucache_enabled = true;
+                        LOG_INFO("UCache enabled - threshold: %.3f, start: %.2f, end: %.2f, decay: %.2f, relative: %s, reset: %s",
+                                 ucache_config.reuse_threshold,
+                                 ucache_config.start_percent,
+                                 ucache_config.end_percent,
+                                 ucache_config.error_decay_rate,
+                                 ucache_config.use_relative_threshold ? "true" : "false",
+                                 ucache_config.reset_error_on_compute ? "true" : "false");
+                    } else {
+                        LOG_WARN("UCache requested but could not be initialized for this run");
+                    }
+                }
+            } else if (cache_params->mode == SD_CACHE_DBCACHE ||
+                       cache_params->mode == SD_CACHE_TAYLORSEER ||
+                       cache_params->mode == SD_CACHE_CACHE_DIT) {
+                bool cachedit_supported = sd_version_is_dit(version);
+                if (!cachedit_supported) {
+                    LOG_WARN("CacheDIT requested but not supported for this model type (only DiT models)");
+                } else {
+                    DBCacheConfig dbcfg;
+                    dbcfg.enabled                     = (cache_params->mode == SD_CACHE_DBCACHE ||
+                                     cache_params->mode == SD_CACHE_CACHE_DIT);
+                    dbcfg.Fn_compute_blocks           = cache_params->Fn_compute_blocks;
+                    dbcfg.Bn_compute_blocks           = cache_params->Bn_compute_blocks;
+                    dbcfg.residual_diff_threshold     = cache_params->residual_diff_threshold;
+                    dbcfg.max_warmup_steps            = cache_params->max_warmup_steps;
+                    dbcfg.max_cached_steps            = cache_params->max_cached_steps;
+                    dbcfg.max_continuous_cached_steps = cache_params->max_continuous_cached_steps;
+                    if (cache_params->scm_mask != nullptr && strlen(cache_params->scm_mask) > 0) {
+                        dbcfg.steps_computation_mask = parse_scm_mask(cache_params->scm_mask);
+                    }
+                    dbcfg.scm_policy_dynamic = cache_params->scm_policy_dynamic;
+
+                    TaylorSeerConfig tcfg;
+                    tcfg.enabled             = (cache_params->mode == SD_CACHE_TAYLORSEER ||
+                                    cache_params->mode == SD_CACHE_CACHE_DIT);
+                    tcfg.n_derivatives       = cache_params->taylorseer_n_derivatives;
+                    tcfg.skip_interval_steps = cache_params->taylorseer_skip_interval;
+
+                    cachedit_state.init(dbcfg, tcfg);
+                    if (cachedit_state.enabled()) {
+                        cachedit_enabled = true;
+                        LOG_INFO("CacheDIT enabled - mode: %s, Fn: %d, Bn: %d, threshold: %.3f, warmup: %d",
+                                 cache_params->mode == SD_CACHE_CACHE_DIT ? "DBCache+TaylorSeer" : (cache_params->mode == SD_CACHE_DBCACHE ? "DBCache" : "TaylorSeer"),
+                                 dbcfg.Fn_compute_blocks,
+                                 dbcfg.Bn_compute_blocks,
+                                 dbcfg.residual_diff_threshold,
+                                 dbcfg.max_warmup_steps);
+                    } else {
+                        LOG_WARN("CacheDIT requested but could not be initialized for this run");
+                    }
+                }
             }
         }
 
+        if (ucache_enabled) {
+            ucache_state.set_sigmas(sigmas);
+        }
+
+        if (cachedit_enabled) {
+            cachedit_state.set_sigmas(sigmas);
+        }
+
         size_t steps          = sigmas.size() - 1;
         struct ggml_tensor* x = ggml_dup_tensor(work_ctx, init_latent);
         copy_ggml_tensor(x, init_latent);
@@ -1696,6 +1782,91 @@ public:
                 return easycache_step_active && easycache_state.is_step_skipped();
             };
 
+            const bool ucache_step_active = ucache_enabled && step > 0;
+            int ucache_step_index         = ucache_step_active ? (step - 1) : -1;
+            if (ucache_step_active) {
+                ucache_state.begin_step(ucache_step_index, sigma);
+            }
+
+            auto ucache_before_condition = [&](const SDCondition* condition, struct ggml_tensor* output_tensor) -> bool {
+                if (!ucache_step_active || condition == nullptr || output_tensor == nullptr) {
+                    return false;
+                }
+                return ucache_state.before_condition(condition,
+                                                     diffusion_params.x,
+                                                     output_tensor,
+                                                     sigma,
+                                                     ucache_step_index);
+            };
+
+            auto ucache_after_condition = [&](const SDCondition* condition, struct ggml_tensor* output_tensor) {
+                if (!ucache_step_active || condition == nullptr || output_tensor == nullptr) {
+                    return;
+                }
+                ucache_state.after_condition(condition,
+                                             diffusion_params.x,
+                                             output_tensor);
+            };
+
+            auto ucache_step_is_skipped = [&]() {
+                return ucache_step_active && ucache_state.is_step_skipped();
+            };
+
+            const bool cachedit_step_active = cachedit_enabled && step > 0;
+            int cachedit_step_index         = cachedit_step_active ? (step - 1) : -1;
+            if (cachedit_step_active) {
+                cachedit_state.begin_step(cachedit_step_index, sigma);
+            }
+
+            auto cachedit_before_condition = [&](const SDCondition* condition, struct ggml_tensor* output_tensor) -> bool {
+                if (!cachedit_step_active || condition == nullptr || output_tensor == nullptr) {
+                    return false;
+                }
+                return cachedit_state.before_condition(condition,
+                                                       diffusion_params.x,
+                                                       output_tensor,
+                                                       sigma,
+                                                       cachedit_step_index);
+            };
+
+            auto cachedit_after_condition = [&](const SDCondition* condition, struct ggml_tensor* output_tensor) {
+                if (!cachedit_step_active || condition == nullptr || output_tensor == nullptr) {
+                    return;
+                }
+                cachedit_state.after_condition(condition,
+                                               diffusion_params.x,
+                                               output_tensor);
+            };
+
+            auto cachedit_step_is_skipped = [&]() {
+                return cachedit_step_active && cachedit_state.is_step_skipped();
+            };
+
+            auto cache_before_condition = [&](const SDCondition* condition, struct ggml_tensor* output_tensor) -> bool {
+                if (easycache_step_active) {
+                    return easycache_before_condition(condition, output_tensor);
+                } else if (ucache_step_active) {
+                    return ucache_before_condition(condition, output_tensor);
+                } else if (cachedit_step_active) {
+                    return cachedit_before_condition(condition, output_tensor);
+                }
+                return false;
+            };
+
+            auto cache_after_condition = [&](const SDCondition* condition, struct ggml_tensor* output_tensor) {
+                if (easycache_step_active) {
+                    easycache_after_condition(condition, output_tensor);
+                } else if (ucache_step_active) {
+                    ucache_after_condition(condition, output_tensor);
+                } else if (cachedit_step_active) {
+                    cachedit_after_condition(condition, output_tensor);
+                }
+            };
+
+            auto cache_step_is_skipped = [&]() {
+                return easycache_step_is_skipped() || ucache_step_is_skipped() || cachedit_step_is_skipped();
+            };
+
             std::vector<float> scaling = denoiser->get_scalings(sigma);
             GGML_ASSERT(scaling.size() == 3);
             float c_skip = scaling[0];
@@ -1771,7 +1942,7 @@ public:
                 active_condition          = &id_cond;
             }
 
-            bool skip_model = easycache_before_condition(active_condition, *active_output);
+            bool skip_model = cache_before_condition(active_condition, *active_output);
             if (!skip_model) {
                 if (!work_diffusion_model->compute(n_threads,
                                                    diffusion_params,
@@ -1779,10 +1950,10 @@ public:
                     LOG_ERROR("diffusion model compute failed");
                     return nullptr;
                 }
-                easycache_after_condition(active_condition, *active_output);
+                cache_after_condition(active_condition, *active_output);
             }
 
-            bool current_step_skipped = easycache_step_is_skipped();
+            bool current_step_skipped = cache_step_is_skipped();
 
             float* negative_data = nullptr;
             if (has_unconditioned) {
@@ -1794,12 +1965,12 @@ public:
                         LOG_ERROR("controlnet compute failed");
                     }
                 }
-                current_step_skipped      = easycache_step_is_skipped();
+                current_step_skipped      = cache_step_is_skipped();
                 diffusion_params.controls = controls;
                 diffusion_params.context  = uncond.c_crossattn;
                 diffusion_params.c_concat = uncond.c_concat;
                 diffusion_params.y        = uncond.c_vector;
-                bool skip_uncond          = easycache_before_condition(&uncond, out_uncond);
+                bool skip_uncond          = cache_before_condition(&uncond, out_uncond);
                 if (!skip_uncond) {
                     if (!work_diffusion_model->compute(n_threads,
                                                        diffusion_params,
@@ -1807,7 +1978,7 @@ public:
                         LOG_ERROR("diffusion model compute failed");
                         return nullptr;
                     }
-                    easycache_after_condition(&uncond, out_uncond);
+                    cache_after_condition(&uncond, out_uncond);
                 }
                 negative_data = (float*)out_uncond->data;
             }
@@ -1817,7 +1988,7 @@ public:
                 diffusion_params.context  = img_cond.c_crossattn;
                 diffusion_params.c_concat = img_cond.c_concat;
                 diffusion_params.y        = img_cond.c_vector;
-                bool skip_img_cond        = easycache_before_condition(&img_cond, out_img_cond);
+                bool skip_img_cond        = cache_before_condition(&img_cond, out_img_cond);
                 if (!skip_img_cond) {
                     if (!work_diffusion_model->compute(n_threads,
                                                        diffusion_params,
@@ -1825,7 +1996,7 @@ public:
                         LOG_ERROR("diffusion model compute failed");
                         return nullptr;
                     }
-                    easycache_after_condition(&img_cond, out_img_cond);
+                    cache_after_condition(&img_cond, out_img_cond);
                 }
                 img_cond_data = (float*)out_img_cond->data;
             }
@@ -1835,7 +2006,7 @@ public:
             float* skip_layer_data = has_skiplayer ? (float*)out_skip->data : nullptr;
             if (is_skiplayer_step) {
                 LOG_DEBUG("Skipping layers at step %d\n", step);
-                if (!easycache_step_is_skipped()) {
+                if (!cache_step_is_skipped()) {
                     // skip layer (same as conditioned)
                     diffusion_params.context     = cond.c_crossattn;
                     diffusion_params.c_concat    = cond.c_concat;
@@ -1939,6 +2110,48 @@ public:
             }
         }
 
+        if (ucache_enabled) {
+            size_t total_steps = sigmas.size() > 0 ? sigmas.size() - 1 : 0;
+            if (ucache_state.total_steps_skipped > 0 && total_steps > 0) {
+                if (ucache_state.total_steps_skipped < static_cast<int>(total_steps)) {
+                    double speedup = static_cast<double>(total_steps) /
+                                     static_cast<double>(total_steps - ucache_state.total_steps_skipped);
+                    LOG_INFO("UCache skipped %d/%zu steps (%.2fx estimated speedup)",
+                             ucache_state.total_steps_skipped,
+                             total_steps,
+                             speedup);
+                } else {
+                    LOG_INFO("UCache skipped %d/%zu steps",
+                             ucache_state.total_steps_skipped,
+                             total_steps);
+                }
+            } else if (total_steps > 0) {
+                LOG_INFO("UCache completed without skipping steps");
+            }
+        }
+
+        if (cachedit_enabled) {
+            size_t total_steps = sigmas.size() > 0 ? sigmas.size() - 1 : 0;
+            if (cachedit_state.total_steps_skipped > 0 && total_steps > 0) {
+                if (cachedit_state.total_steps_skipped < static_cast<int>(total_steps)) {
+                    double speedup = static_cast<double>(total_steps) /
+                                     static_cast<double>(total_steps - cachedit_state.total_steps_skipped);
+                    LOG_INFO("CacheDIT skipped %d/%zu steps (%.2fx estimated speedup), accum_diff: %.4f",
+                             cachedit_state.total_steps_skipped,
+                             total_steps,
+                             speedup,
+                             cachedit_state.accumulated_residual_diff);
+                } else {
+                    LOG_INFO("CacheDIT skipped %d/%zu steps, accum_diff: %.4f",
+                             cachedit_state.total_steps_skipped,
+                             total_steps,
+                             cachedit_state.accumulated_residual_diff);
+                }
+            } else if (total_steps > 0) {
+                LOG_INFO("CacheDIT completed without skipping steps");
+            }
+        }
+
         if (inverse_noise_scaling) {
             x = denoiser->inverse_noise_scaling(sigmas[sigmas.size() - 1], x);
         }
@@ -2554,12 +2767,25 @@ enum lora_apply_mode_t str_to_lora_apply_mode(const char* str) {
     return LORA_APPLY_MODE_COUNT;
 }
 
-void sd_easycache_params_init(sd_easycache_params_t* easycache_params) {
-    *easycache_params                 = {};
-    easycache_params->enabled         = false;
-    easycache_params->reuse_threshold = 0.2f;
-    easycache_params->start_percent   = 0.15f;
-    easycache_params->end_percent     = 0.95f;
+void sd_cache_params_init(sd_cache_params_t* cache_params) {
+    *cache_params                             = {};
+    cache_params->mode                        = SD_CACHE_DISABLED;
+    cache_params->reuse_threshold             = 1.0f;
+    cache_params->start_percent               = 0.15f;
+    cache_params->end_percent                 = 0.95f;
+    cache_params->error_decay_rate            = 1.0f;
+    cache_params->use_relative_threshold      = true;
+    cache_params->reset_error_on_compute      = true;
+    cache_params->Fn_compute_blocks           = 8;
+    cache_params->Bn_compute_blocks           = 0;
+    cache_params->residual_diff_threshold     = 0.08f;
+    cache_params->max_warmup_steps            = 8;
+    cache_params->max_cached_steps            = -1;
+    cache_params->max_continuous_cached_steps = -1;
+    cache_params->taylorseer_n_derivatives    = 1;
+    cache_params->taylorseer_skip_interval    = 1;
+    cache_params->scm_mask                    = nullptr;
+    cache_params->scm_policy_dynamic          = true;
 }
 
 void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params) {
@@ -2724,7 +2950,7 @@ void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params) {
     sd_img_gen_params->control_strength  = 0.9f;
     sd_img_gen_params->pm_params         = {nullptr, 0, nullptr, 20.f};
     sd_img_gen_params->vae_tiling_params = {false, 0, 0, 0.5f, 0.0f, 0.0f};
-    sd_easycache_params_init(&sd_img_gen_params->easycache);
+    sd_cache_params_init(&sd_img_gen_params->cache);
 }
 
 char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
@@ -2768,12 +2994,18 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
              sd_img_gen_params->pm_params.id_images_count,
              SAFE_STR(sd_img_gen_params->pm_params.id_embed_path),
              BOOL_STR(sd_img_gen_params->vae_tiling_params.enabled));
+    const char* cache_mode_str = "disabled";
+    if (sd_img_gen_params->cache.mode == SD_CACHE_EASYCACHE) {
+        cache_mode_str = "easycache";
+    } else if (sd_img_gen_params->cache.mode == SD_CACHE_UCACHE) {
+        cache_mode_str = "ucache";
+    }
     snprintf(buf + strlen(buf), 4096 - strlen(buf),
-             "easycache: %s (threshold=%.3f, start=%.2f, end=%.2f)\n",
-             sd_img_gen_params->easycache.enabled ? "enabled" : "disabled",
-             sd_img_gen_params->easycache.reuse_threshold,
-             sd_img_gen_params->easycache.start_percent,
-             sd_img_gen_params->easycache.end_percent);
+             "cache: %s (threshold=%.3f, start=%.2f, end=%.2f)\n",
+             cache_mode_str,
+             sd_img_gen_params->cache.reuse_threshold,
+             sd_img_gen_params->cache.start_percent,
+             sd_img_gen_params->cache.end_percent);
     free(sample_params_str);
     return buf;
 }
@@ -2790,7 +3022,7 @@ void sd_vid_gen_params_init(sd_vid_gen_params_t* sd_vid_gen_params) {
     sd_vid_gen_params->video_frames                          = 6;
     sd_vid_gen_params->moe_boundary                          = 0.875f;
     sd_vid_gen_params->vace_strength                         = 1.f;
-    sd_easycache_params_init(&sd_vid_gen_params->easycache);
+    sd_cache_params_init(&sd_vid_gen_params->cache);
 }
 
 struct sd_ctx_t {
@@ -2869,9 +3101,9 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
                                     std::vector<sd_image_t*> ref_images,
                                     std::vector<ggml_tensor*> ref_latents,
                                     bool increase_ref_index,
-                                    ggml_tensor* concat_latent                    = nullptr,
-                                    ggml_tensor* denoise_mask                     = nullptr,
-                                    const sd_easycache_params_t* easycache_params = nullptr) {
+                                    ggml_tensor* concat_latent            = nullptr,
+                                    ggml_tensor* denoise_mask             = nullptr,
+                                    const sd_cache_params_t* cache_params = nullptr) {
     if (seed < 0) {
         // Generally, when using the provided command line, the seed is always >0.
         // However, to prevent potential issues if 'stable-diffusion.cpp' is invoked as a library
@@ -3160,7 +3392,7 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
                                                      denoise_mask,
                                                      nullptr,
                                                      1.0f,
-                                                     easycache_params);
+                                                     cache_params);
         int64_t sampling_end    = ggml_time_ms();
         if (x_0 != nullptr) {
             // print_ggml_tensor(x_0);
@@ -3498,7 +3730,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
                                                         sd_img_gen_params->increase_ref_index,
                                                         concat_latent,
                                                         denoise_mask,
-                                                        &sd_img_gen_params->easycache);
+                                                        &sd_img_gen_params->cache);
 
     size_t t2 = ggml_time_ms();
 
@@ -3869,7 +4101,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
                                  denoise_mask,
                                  vace_context,
                                  sd_vid_gen_params->vace_strength,
-                                 &sd_vid_gen_params->easycache);
+                                 &sd_vid_gen_params->cache);
 
         int64_t sampling_end = ggml_time_ms();
         LOG_INFO("sampling(high noise) completed, taking %.2fs", (sampling_end - sampling_start) * 1.0f / 1000);
@@ -3906,7 +4138,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
                                           denoise_mask,
                                           vace_context,
                                           sd_vid_gen_params->vace_strength,
-                                          &sd_vid_gen_params->easycache);
+                                          &sd_vid_gen_params->cache);
 
         int64_t sampling_end = ggml_time_ms();
         LOG_INFO("sampling completed, taking %.2fs", (sampling_end - sampling_start) * 1.0f / 1000);
diff --git a/stable-diffusion.h b/stable-diffusion.h
index 9bc1fba5..53db5100 100644
--- a/stable-diffusion.h
+++ b/stable-diffusion.h
@@ -238,12 +238,34 @@ typedef struct {
     float style_strength;
 } sd_pm_params_t;  // photo maker
 
+enum sd_cache_mode_t {
+    SD_CACHE_DISABLED = 0,
+    SD_CACHE_EASYCACHE,
+    SD_CACHE_UCACHE,
+    SD_CACHE_DBCACHE,
+    SD_CACHE_TAYLORSEER,
+    SD_CACHE_CACHE_DIT,
+};
+
 typedef struct {
-    bool enabled;
+    enum sd_cache_mode_t mode;
     float reuse_threshold;
     float start_percent;
     float end_percent;
-} sd_easycache_params_t;
+    float error_decay_rate;
+    bool use_relative_threshold;
+    bool reset_error_on_compute;
+    int Fn_compute_blocks;
+    int Bn_compute_blocks;
+    float residual_diff_threshold;
+    int max_warmup_steps;
+    int max_cached_steps;
+    int max_continuous_cached_steps;
+    int taylorseer_n_derivatives;
+    int taylorseer_skip_interval;
+    const char* scm_mask;
+    bool scm_policy_dynamic;
+} sd_cache_params_t;
 
 typedef struct {
     bool is_high_noise;
@@ -273,7 +295,7 @@ typedef struct {
     float control_strength;
     sd_pm_params_t pm_params;
     sd_tiling_params_t vae_tiling_params;
-    sd_easycache_params_t easycache;
+    sd_cache_params_t cache;
 } sd_img_gen_params_t;
 
 typedef struct {
@@ -295,7 +317,7 @@ typedef struct {
     int64_t seed;
     int video_frames;
     float vace_strength;
-    sd_easycache_params_t easycache;
+    sd_cache_params_t cache;
 } sd_vid_gen_params_t;
 
 typedef struct sd_ctx_t sd_ctx_t;
@@ -325,7 +347,7 @@ SD_API enum preview_t str_to_preview(const char* str);
 SD_API const char* sd_lora_apply_mode_name(enum lora_apply_mode_t mode);
 SD_API enum lora_apply_mode_t str_to_lora_apply_mode(const char* str);
 
-SD_API void sd_easycache_params_init(sd_easycache_params_t* easycache_params);
+SD_API void sd_cache_params_init(sd_cache_params_t* cache_params);
 
 SD_API void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params);
 SD_API char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params);
diff --git a/ucache.hpp b/ucache.hpp
new file mode 100644
index 00000000..92e94a16
--- /dev/null
+++ b/ucache.hpp
@@ -0,0 +1,404 @@
+#ifndef __UCACHE_HPP__
+#define __UCACHE_HPP__
+
+#include <cmath>
+#include <limits>
+#include <unordered_map>
+#include <vector>
+
+#include "denoiser.hpp"
+#include "ggml_extend.hpp"
+
+struct UCacheConfig {
+    bool enabled                = false;
+    float reuse_threshold       = 1.0f;
+    float start_percent         = 0.15f;
+    float end_percent           = 0.95f;
+    float error_decay_rate      = 1.0f;
+    bool use_relative_threshold = true;
+    bool adaptive_threshold     = true;
+    float early_step_multiplier = 0.5f;
+    float late_step_multiplier  = 1.5f;
+    bool reset_error_on_compute = true;
+};
+
+struct UCacheCacheEntry {
+    std::vector<float> diff;
+};
+
+struct UCacheState {
+    UCacheConfig config;
+    Denoiser* denoiser                  = nullptr;
+    float start_sigma                   = std::numeric_limits<float>::max();
+    float end_sigma                     = 0.0f;
+    bool initialized                    = false;
+    bool initial_step                   = true;
+    bool skip_current_step              = false;
+    bool step_active                    = false;
+    const SDCondition* anchor_condition = nullptr;
+    std::unordered_map<const SDCondition*, UCacheCacheEntry> cache_diffs;
+    std::vector<float> prev_input;
+    std::vector<float> prev_output;
+    float output_prev_norm                = 0.0f;
+    bool has_prev_input                   = false;
+    bool has_prev_output                  = false;
+    bool has_output_prev_norm             = false;
+    bool has_relative_transformation_rate = false;
+    float relative_transformation_rate    = 0.0f;
+    float cumulative_change_rate          = 0.0f;
+    float last_input_change               = 0.0f;
+    bool has_last_input_change            = false;
+    int total_steps_skipped               = 0;
+    int current_step_index                = -1;
+    int steps_computed_since_active       = 0;
+    float accumulated_error               = 0.0f;
+    float reference_output_norm           = 0.0f;
+
+    struct BlockMetrics {
+        float sum_transformation_rate = 0.0f;
+        float sum_output_norm         = 0.0f;
+        int sample_count              = 0;
+        float min_change_rate         = std::numeric_limits<float>::max();
+        float max_change_rate         = 0.0f;
+
+        void reset() {
+            sum_transformation_rate = 0.0f;
+            sum_output_norm         = 0.0f;
+            sample_count            = 0;
+            min_change_rate         = std::numeric_limits<float>::max();
+            max_change_rate         = 0.0f;
+        }
+
+        void record(float change_rate, float output_norm) {
+            if (std::isfinite(change_rate) && change_rate > 0.0f) {
+                sum_transformation_rate += change_rate;
+                sum_output_norm += output_norm;
+                sample_count++;
+                if (change_rate < min_change_rate)
+                    min_change_rate = change_rate;
+                if (change_rate > max_change_rate)
+                    max_change_rate = change_rate;
+            }
+        }
+
+        float avg_transformation_rate() const {
+            return (sample_count > 0) ? (sum_transformation_rate / sample_count) : 0.0f;
+        }
+
+        float avg_output_norm() const {
+            return (sample_count > 0) ? (sum_output_norm / sample_count) : 0.0f;
+        }
+    };
+    BlockMetrics block_metrics;
+    int total_active_steps = 0;
+
+    void reset_runtime() {
+        initial_step      = true;
+        skip_current_step = false;
+        step_active       = false;
+        anchor_condition  = nullptr;
+        cache_diffs.clear();
+        prev_input.clear();
+        prev_output.clear();
+        output_prev_norm                 = 0.0f;
+        has_prev_input                   = false;
+        has_prev_output                  = false;
+        has_output_prev_norm             = false;
+        has_relative_transformation_rate = false;
+        relative_transformation_rate     = 0.0f;
+        cumulative_change_rate           = 0.0f;
+        last_input_change                = 0.0f;
+        has_last_input_change            = false;
+        total_steps_skipped              = 0;
+        current_step_index               = -1;
+        steps_computed_since_active      = 0;
+        accumulated_error                = 0.0f;
+        reference_output_norm            = 0.0f;
+        block_metrics.reset();
+        total_active_steps = 0;
+    }
+
+    void init(const UCacheConfig& cfg, Denoiser* d) {
+        config      = cfg;
+        denoiser    = d;
+        initialized = cfg.enabled && d != nullptr;
+        reset_runtime();
+        if (initialized) {
+            start_sigma = percent_to_sigma(config.start_percent);
+            end_sigma   = percent_to_sigma(config.end_percent);
+        }
+    }
+
+    void set_sigmas(const std::vector<float>& sigmas) {
+        if (!initialized || sigmas.size() < 2) {
+            return;
+        }
+        size_t n_steps = sigmas.size() - 1;
+
+        size_t start_step = static_cast<size_t>(config.start_percent * n_steps);
+        size_t end_step   = static_cast<size_t>(config.end_percent * n_steps);
+
+        if (start_step >= n_steps)
+            start_step = n_steps - 1;
+        if (end_step >= n_steps)
+            end_step = n_steps - 1;
+
+        start_sigma = sigmas[start_step];
+        end_sigma   = sigmas[end_step];
+
+        if (start_sigma < end_sigma) {
+            std::swap(start_sigma, end_sigma);
+        }
+    }
+
+    bool enabled() const {
+        return initialized && config.enabled;
+    }
+
+    float percent_to_sigma(float percent) const {
+        if (!denoiser) {
+            return 0.0f;
+        }
+        if (percent <= 0.0f) {
+            return std::numeric_limits<float>::max();
+        }
+        if (percent >= 1.0f) {
+            return 0.0f;
+        }
+        float t = (1.0f - percent) * (TIMESTEPS - 1);
+        return denoiser->t_to_sigma(t);
+    }
+
+    void begin_step(int step_index, float sigma) {
+        if (!enabled()) {
+            return;
+        }
+        if (step_index == current_step_index) {
+            return;
+        }
+        current_step_index    = step_index;
+        skip_current_step     = false;
+        has_last_input_change = false;
+        step_active           = false;
+
+        if (sigma > start_sigma) {
+            return;
+        }
+        if (!(sigma > end_sigma)) {
+            return;
+        }
+        step_active = true;
+        total_active_steps++;
+    }
+
+    bool step_is_active() const {
+        return enabled() && step_active;
+    }
+
+    bool is_step_skipped() const {
+        return enabled() && step_active && skip_current_step;
+    }
+
+    float get_adaptive_threshold(int estimated_total_steps = 0) const {
+        float base_threshold = config.reuse_threshold;
+
+        if (!config.adaptive_threshold) {
+            return base_threshold;
+        }
+
+        int effective_total = estimated_total_steps;
+        if (effective_total <= 0) {
+            effective_total = std::max(20, steps_computed_since_active * 2);
+        }
+
+        float progress = (effective_total > 0) ? (static_cast<float>(steps_computed_since_active) / effective_total) : 0.0f;
+
+        float multiplier = 1.0f;
+        if (progress < 0.2f) {
+            multiplier = config.early_step_multiplier;
+        } else if (progress > 0.8f) {
+            multiplier = config.late_step_multiplier;
+        }
+
+        return base_threshold * multiplier;
+    }
+
+    bool has_cache(const SDCondition* cond) const {
+        auto it = cache_diffs.find(cond);
+        return it != cache_diffs.end() && !it->second.diff.empty();
+    }
+
+    void update_cache(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+        UCacheCacheEntry& entry = cache_diffs[cond];
+        size_t ne               = static_cast<size_t>(ggml_nelements(output));
+        entry.diff.resize(ne);
+        float* out_data = (float*)output->data;
+        float* in_data  = (float*)input->data;
+
+        for (size_t i = 0; i < ne; ++i) {
+            entry.diff[i] = out_data[i] - in_data[i];
+        }
+    }
+
+    void apply_cache(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+        auto it = cache_diffs.find(cond);
+        if (it == cache_diffs.end() || it->second.diff.empty()) {
+            return;
+        }
+
+        copy_ggml_tensor(output, input);
+        float* out_data                = (float*)output->data;
+        const std::vector<float>& diff = it->second.diff;
+        for (size_t i = 0; i < diff.size(); ++i) {
+            out_data[i] += diff[i];
+        }
+    }
+
+    bool before_condition(const SDCondition* cond,
+                          ggml_tensor* input,
+                          ggml_tensor* output,
+                          float sigma,
+                          int step_index) {
+        if (!enabled() || step_index < 0) {
+            return false;
+        }
+        if (step_index != current_step_index) {
+            begin_step(step_index, sigma);
+        }
+        if (!step_active) {
+            return false;
+        }
+
+        if (initial_step) {
+            anchor_condition = cond;
+            initial_step     = false;
+        }
+
+        bool is_anchor = (cond == anchor_condition);
+
+        if (skip_current_step) {
+            if (has_cache(cond)) {
+                apply_cache(cond, input, output);
+                return true;
+            }
+            return false;
+        }
+
+        if (!is_anchor) {
+            return false;
+        }
+
+        if (!has_prev_input || !has_prev_output || !has_cache(cond)) {
+            return false;
+        }
+
+        size_t ne = static_cast<size_t>(ggml_nelements(input));
+        if (prev_input.size() != ne) {
+            return false;
+        }
+
+        float* input_data = (float*)input->data;
+        last_input_change = 0.0f;
+        for (size_t i = 0; i < ne; ++i) {
+            last_input_change += std::fabs(input_data[i] - prev_input[i]);
+        }
+        if (ne > 0) {
+            last_input_change /= static_cast<float>(ne);
+        }
+        has_last_input_change = true;
+
+        if (has_output_prev_norm && has_relative_transformation_rate &&
+            last_input_change > 0.0f && output_prev_norm > 0.0f) {
+            float approx_output_change_rate = (relative_transformation_rate * last_input_change) / output_prev_norm;
+            accumulated_error               = accumulated_error * config.error_decay_rate + approx_output_change_rate;
+
+            float effective_threshold = get_adaptive_threshold();
+            if (config.use_relative_threshold && reference_output_norm > 0.0f) {
+                effective_threshold = effective_threshold * reference_output_norm;
+            }
+
+            if (accumulated_error < effective_threshold) {
+                skip_current_step = true;
+                total_steps_skipped++;
+                apply_cache(cond, input, output);
+                return true;
+            } else if (config.reset_error_on_compute) {
+                accumulated_error = 0.0f;
+            }
+        }
+
+        return false;
+    }
+
+    void after_condition(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+        if (!step_is_active()) {
+            return;
+        }
+
+        update_cache(cond, input, output);
+
+        if (cond != anchor_condition) {
+            return;
+        }
+
+        size_t ne      = static_cast<size_t>(ggml_nelements(input));
+        float* in_data = (float*)input->data;
+        prev_input.resize(ne);
+        for (size_t i = 0; i < ne; ++i) {
+            prev_input[i] = in_data[i];
+        }
+        has_prev_input = true;
+
+        float* out_data     = (float*)output->data;
+        float output_change = 0.0f;
+        if (has_prev_output && prev_output.size() == ne) {
+            for (size_t i = 0; i < ne; ++i) {
+                output_change += std::fabs(out_data[i] - prev_output[i]);
+            }
+            if (ne > 0) {
+                output_change /= static_cast<float>(ne);
+            }
+        }
+
+        prev_output.resize(ne);
+        for (size_t i = 0; i < ne; ++i) {
+            prev_output[i] = out_data[i];
+        }
+        has_prev_output = true;
+
+        float mean_abs = 0.0f;
+        for (size_t i = 0; i < ne; ++i) {
+            mean_abs += std::fabs(out_data[i]);
+        }
+        output_prev_norm     = (ne > 0) ? (mean_abs / static_cast<float>(ne)) : 0.0f;
+        has_output_prev_norm = output_prev_norm > 0.0f;
+
+        if (reference_output_norm == 0.0f) {
+            reference_output_norm = output_prev_norm;
+        }
+
+        if (has_last_input_change && last_input_change > 0.0f && output_change > 0.0f) {
+            float rate = output_change / last_input_change;
+            if (std::isfinite(rate)) {
+                relative_transformation_rate     = rate;
+                has_relative_transformation_rate = true;
+                block_metrics.record(rate, output_prev_norm);
+            }
+        }
+
+        has_last_input_change = false;
+    }
+
+    void log_block_metrics() const {
+        if (block_metrics.sample_count > 0) {
+            LOG_INFO("UCacheBlockMetrics: samples=%d, avg_rate=%.4f, min=%.4f, max=%.4f, avg_norm=%.4f",
+                     block_metrics.sample_count,
+                     block_metrics.avg_transformation_rate(),
+                     block_metrics.min_change_rate,
+                     block_metrics.max_change_rate,
+                     block_metrics.avg_output_norm());
+        }
+    }
+};
+
+#endif  // __UCACHE_HPP__

From 98916e825686f8952126b2c54e1c4216eb418dc5 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Mon, 22 Dec 2025 23:58:28 +0800
Subject: [PATCH 16/97] docs: update README.md

---
 README.md                 |  1 +
 examples/cli/README.md    | 19 +++++++++++++------
 examples/server/README.md | 19 ++++++++++++++++---
 3 files changed, 30 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index aa29f849..3c66be93 100644
--- a/README.md
+++ b/README.md
@@ -143,6 +143,7 @@ If you want to improve performance or reduce VRAM/RAM usage, please refer to [pe
 - [Using TAESD to faster decoding](./docs/taesd.md)
 - [Docker](./docs/docker.md)
 - [Quantization and GGUF](./docs/quantization_and_gguf.md)
+- [Inference acceleration via caching](./docs/caching.md)
 
 ## Bindings
 
diff --git a/examples/cli/README.md b/examples/cli/README.md
index 0617a463..568f29d0 100644
--- a/examples/cli/README.md
+++ b/examples/cli/README.md
@@ -53,6 +53,9 @@ Context Options:
   --diffusion-fa                           use flash attention in the diffusion model
   --diffusion-conv-direct                  use ggml_conv2d_direct in the diffusion model
   --vae-conv-direct                        use ggml_conv2d_direct in the vae model
+  --circular                               enable circular padding for convolutions
+  --circularx                              enable circular RoPE wrapping on x-axis (width) only
+  --circulary                              enable circular RoPE wrapping on y-axis (height) only
   --chroma-disable-dit-mask                disable dit mask for chroma
   --chroma-enable-t5-mask                  enable t5 mask for chroma
   --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). If not specified, the default is the
@@ -94,6 +97,7 @@ Generation Options:
   --timestep-shift <int>                   shift timestep for NitroFusion models (default: 0). recommended N for NitroSD-Realism around 250 and 500 for
                                            NitroSD-Vibrant
   --upscale-repeats <int>                  Run the ESRGAN upscaler this many times (default: 1)
+  --upscale-tile-size <int>                tile size for ESRGAN upscaling (default: 128)
   --cfg-scale <float>                      unconditional guidance scale: (default: 7.0)
   --img-cfg-scale <float>                  image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)
   --guidance <float>                       distilled guidance scale for models with guidance input (default: 3.5)
@@ -121,18 +125,21 @@ Generation Options:
                                            tcd] (default: euler for Flux/SD3/Wan, euler_a otherwise)
   --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
                                            ddim_trailing, tcd] default: euler for Flux/SD3/Wan, euler_a otherwise
-  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm],
-                                           default: discrete
+  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple,
+                                           kl_optimal, lcm], default: discrete
   --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
   --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
   --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
   -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
   --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)
   --cache-option                           named cache params (key=value format, comma-separated):
-                                           - easycache/ucache: threshold=,start=,end=,decay=,relative=,reset=
-                                           - dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=
-                                           Examples: "threshold=0.25" or "threshold=1.5,reset=0"
+                                                                                      - easycache/ucache:
+                                           threshold=,start=,end=,decay=,relative=,reset=
+                                                                                      - dbcache/taylorseer/cache-dit:
+                                           Fn=,Bn=,threshold=,warmup=
+                                                                                      Examples: "threshold=0.25" or
+                                           "threshold=1.5,reset=0"
   --cache-preset                           cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'
-  --scm-mask                               SCM steps mask: comma-separated 0/1 (1=compute, 0=can cache)
+  --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g., "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
   --scm-policy                             SCM policy: 'dynamic' (default) or 'static'
 ```
diff --git a/examples/server/README.md b/examples/server/README.md
index ae104968..89d8560c 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -45,6 +45,9 @@ Context Options:
   --diffusion-fa                           use flash attention in the diffusion model
   --diffusion-conv-direct                  use ggml_conv2d_direct in the diffusion model
   --vae-conv-direct                        use ggml_conv2d_direct in the vae model
+  --circular                               enable circular padding for convolutions
+  --circularx                              enable circular RoPE wrapping on x-axis (width) only
+  --circulary                              enable circular RoPE wrapping on y-axis (height) only
   --chroma-disable-dit-mask                disable dit mask for chroma
   --chroma-enable-t5-mask                  enable t5 mask for chroma
   --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). If not specified, the default is the
@@ -114,11 +117,21 @@ Default Generation Options:
                                            tcd] (default: euler for Flux/SD3/Wan, euler_a otherwise)
   --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
                                            ddim_trailing, tcd] default: euler for Flux/SD3/Wan, euler_a otherwise
-  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm],
-                                           default: discrete
+  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple,
+                                           kl_optimal, lcm], default: discrete
   --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
   --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
   --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
   -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
-  --easycache                              enable EasyCache for DiT models with optional "threshold,start_percent,end_percent" (default: 0.2,0.15,0.95)
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)
+  --cache-option                           named cache params (key=value format, comma-separated):
+                                                                                      - easycache/ucache:
+                                           threshold=,start=,end=,decay=,relative=,reset=
+                                                                                      - dbcache/taylorseer/cache-dit:
+                                           Fn=,Bn=,threshold=,warmup=
+                                                                                      Examples: "threshold=0.25" or
+                                           "threshold=1.5,reset=0"
+  --cache-preset                           cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'
+  --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g., "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
+  --scm-policy                             SCM policy: 'dynamic' (default) or 'static'
 ```

From 3e812460cffe183150cf9bd55c44e4698fcec10e Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Tue, 23 Dec 2025 21:37:07 +0800
Subject: [PATCH 17/97] fix: correct ggml_pad_ext (#1133)

---
 ggml_extend.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml_extend.hpp b/ggml_extend.hpp
index 3849562f..76889a8b 100644
--- a/ggml_extend.hpp
+++ b/ggml_extend.hpp
@@ -1033,7 +1033,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_pad(struct ggml_context* ctx,
                                                    int p3          = 0,
                                                    bool circular_x = false,
                                                    bool circular_y = false) {
-    return ggml_ext_pad_ext(ctx, x, p0, p0, p1, p1, p2, p2, p3, p3, circular_x, circular_y);
+    return ggml_ext_pad_ext(ctx, x, 0, p0, 0, p1, 0, p2, 0, p3, circular_x, circular_y);
 }
 
 // w: [OC，IC, KH, KW]
@@ -1062,7 +1062,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_conv_2d(struct ggml_context* ctx,
     }
 
     if ((p0 != 0 || p1 != 0) && (circular_x || circular_y)) {
-        x  = ggml_ext_pad(ctx, x, p0, p1, 0, 0, circular_x, circular_y);
+        x  = ggml_ext_pad_ext(ctx, x, p0, p0, p1, p1, 0, 0, 0, 0, circular_x, circular_y);
         p0 = 0;
         p1 = 0;
     }

From 96fcb13fc097a91a876302659d5fce877514cc93 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=BC=A0=E6=98=A5=E4=B9=94?=
 <83450930+Liyulingyue@users.noreply.github.com>
Date: Wed, 24 Dec 2025 22:43:09 +0800
Subject: [PATCH 18/97] feat: add --serve-html-path option to example server
 (#1123)

---
 examples/server/README.md |  1 +
 examples/server/main.cpp  | 26 ++++++++++++++++++++++++--
 2 files changed, 25 insertions(+), 2 deletions(-)

diff --git a/examples/server/README.md b/examples/server/README.md
index 89d8560c..59141194 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -6,6 +6,7 @@ usage: ./bin/sd-server  [options]
 Svr Options:
   -l, --listen-ip <string>    server listen ip (default: 127.0.0.1)
   --listen-port <int>         server listen port (default: 1234)
+  --serve-html-path <string>  path to HTML file to serve at root (optional)
   -v, --verbose               print extra info
   --color                     colors the logging tags according to level
   -h, --help                  show this help message and exit
diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index 5c951c07..5f5333da 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -104,6 +104,7 @@ std::string iso_timestamp_now() {
 struct SDSvrParams {
     std::string listen_ip = "127.0.0.1";
     int listen_port       = 1234;
+    std::string serve_html_path;
     bool normal_exit      = false;
     bool verbose          = false;
     bool color            = false;
@@ -115,7 +116,11 @@ struct SDSvrParams {
             {"-l",
              "--listen-ip",
              "server listen ip (default: 127.0.0.1)",
-             &listen_ip}};
+             &listen_ip},
+            {"",
+             "--serve-html-path",
+             "path to HTML file to serve at root (optional)",
+             &serve_html_path}};
 
         options.int_options = {
             {"",
@@ -159,6 +164,11 @@ struct SDSvrParams {
             LOG_ERROR("error: listen_port should be in the range [0, 65535]");
             return false;
         }
+
+        if (!serve_html_path.empty() && !fs::exists(serve_html_path)) {
+            LOG_ERROR("error: serve_html_path file does not exist: %s", serve_html_path.c_str());
+            return false;
+        }
         return true;
     }
 
@@ -167,6 +177,7 @@ struct SDSvrParams {
         oss << "SDSvrParams {\n"
             << "  listen_ip: " << listen_ip << ",\n"
             << "  listen_port: \"" << listen_port << "\",\n"
+            << "  serve_html_path: \"" << serve_html_path << "\",\n"
             << "}";
         return oss.str();
     }
@@ -312,7 +323,18 @@ int main(int argc, const char** argv) {
 
     // health
     svr.Get("/", [&](const httplib::Request&, httplib::Response& res) {
-        res.set_content(R"({"ok":true,"service":"sd-cpp-http"})", "application/json");
+        if (!svr_params.serve_html_path.empty()) {
+            std::ifstream file(svr_params.serve_html_path);
+            if (file) {
+                std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
+                res.set_content(content, "text/html");
+            } else {
+                res.status = 500;
+                res.set_content("Error: Unable to read HTML file", "text/plain");
+            }
+        } else {
+            res.set_content("Stable Diffusion Server is running", "text/plain");
+        }
     });
 
     // models endpoint (minimal)

From 3e6c428c2781ff9694d1a4810abe37de4ab964e8 Mon Sep 17 00:00:00 2001
From: Weiqi Gao <gaoweiqicarl1@163.com>
Date: Wed, 24 Dec 2025 22:53:17 +0800
Subject: [PATCH 19/97] chore: use Ninja on Windows to speed up build process
 (#1120)

---
 .github/workflows/build.yml | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 7f78c354..bfe532bd 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -163,7 +163,7 @@ jobs:
           - build: "avx512"
             defines: "-DGGML_NATIVE=OFF -DGGML_AVX512=ON -DGGML_AVX=ON -DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
           - build: "cuda12"
-            defines: "-DSD_CUDA=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_CUDA_ARCHITECTURES='61;70;75;80;86;89;90;100;120'"
+            defines: "-DSD_CUDA=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_CUDA_ARCHITECTURES='61;70;75;80;86;89;90;100;120' -DCMAKE_CUDA_FLAGS='-Xcudafe \"--diag_suppress=177\" -Xcudafe \"--diag_suppress=550\"'"
           - build: 'vulkan'
             defines: "-DSD_VULKAN=ON -DSD_BUILD_SHARED_LIBS=ON"
     steps:
@@ -191,13 +191,17 @@ jobs:
           Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}"
           Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin"
 
+      - name: Activate MSVC environment
+        id: msvc_dev_cmd
+        uses: ilammy/msvc-dev-cmd@v1
+
       - name: Build
         id: cmake_build
         run: |
           mkdir build
           cd build
-          cmake .. ${{ matrix.defines }}
-          cmake --build . --config Release
+          cmake .. -DCMAKE_CXX_FLAGS='/bigobj' -G Ninja -DCMAKE_C_COMPILER=cl.exe -DCMAKE_CXX_COMPILER=cl.exe ${{ matrix.defines }}
+          cmake --build .
 
       - name: Check AVX512F support
         id: check_avx512f

From 3d5fdd7b375f3b7004117d63845efb8646e968a1 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Wed, 24 Dec 2025 22:59:23 +0800
Subject: [PATCH 20/97] feat: add support for more underline loras (#1135)

---
 name_conversion.cpp | 18 +++++++++++++++++-
 1 file changed, 17 insertions(+), 1 deletion(-)

diff --git a/name_conversion.cpp b/name_conversion.cpp
index 6a8ae72c..3ae229b6 100644
--- a/name_conversion.cpp
+++ b/name_conversion.cpp
@@ -960,6 +960,7 @@ bool is_first_stage_model_name(const std::string& name) {
 std::string convert_tensor_name(std::string name, SDVersion version) {
     bool is_lora                             = false;
     bool is_lycoris_underline                = false;
+    bool is_underline                        = false;
     std::vector<std::string> lora_prefix_vec = {
         "lora.lora.",
         "lora.lora_",
@@ -967,12 +968,27 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
         "lora.lycoris.",
         "lora.",
     };
+    std::vector<std::string> underline_lora_prefix_vec = {
+        "unet_",
+        "te_",
+        "te1_",
+        "te2_",
+        "te3_",
+        "vae_",
+    };
     for (const auto& prefix : lora_prefix_vec) {
         if (starts_with(name, prefix)) {
             is_lora = true;
             name    = name.substr(prefix.size());
             if (contains(prefix, "lycoris_")) {
                 is_lycoris_underline = true;
+            } else {
+                for (const auto& underline_lora_prefix : underline_lora_prefix_vec) {
+                    if (starts_with(name, underline_lora_prefix)) {
+                        is_underline = true;
+                        break;
+                    }
+                }
             }
             break;
         }
@@ -1034,7 +1050,7 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
 
         // LOG_DEBUG("name %s %d", name.c_str(), version);
 
-        if (sd_version_is_unet(version) || sd_version_is_flux(version) || is_lycoris_underline) {
+        if (sd_version_is_unet(version) || is_underline || is_lycoris_underline) {
             name = convert_sep_to_dot(name);
         }
     }

From a0adcfb148c66bc9480521a3beda925b161a6575 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Wed, 24 Dec 2025 23:00:08 +0800
Subject: [PATCH 21/97] feat: add support for qwen image edit 2511 (#1096)

---
 README.md                            |   4 +-
 assets/qwen/qwen_image_edit_2511.png | Bin 0 -> 460826 bytes
 diffusion_model.hpp                  |   5 +-
 docs/qwen_image_edit.md              |  15 +++-
 examples/common/common.hpp           |   8 ++
 flux.hpp                             |  13 ++--
 qwen_image.hpp                       | 111 +++++++++++++++++++++++----
 stable-diffusion.cpp                 |   3 +-
 stable-diffusion.h                   |   1 +
 9 files changed, 132 insertions(+), 28 deletions(-)
 create mode 100644 assets/qwen/qwen_image_edit_2511.png

diff --git a/README.md b/README.md
index 3c66be93..bd00761e 100644
--- a/README.md
+++ b/README.md
@@ -52,7 +52,7 @@ API and command-line option may change frequently.***
     - [Ovis-Image](./docs/ovis_image.md)
   - Image Edit Models
     - [FLUX.1-Kontext-dev](./docs/kontext.md)
-    - [Qwen Image Edit/Qwen Image Edit 2509](./docs/qwen_image_edit.md)
+    - [Qwen Image Edit series](./docs/qwen_image_edit.md)
   - Video Models
     - [Wan2.1/Wan2.2](./docs/wan.md)
   - [PhotoMaker](https://github.com/TencentARC/PhotoMaker) support.
@@ -132,7 +132,7 @@ If you want to improve performance or reduce VRAM/RAM usage, please refer to [pe
 - [FLUX.1-Kontext-dev](./docs/kontext.md)
 - [Chroma](./docs/chroma.md)
 - [🔥Qwen Image](./docs/qwen_image.md)
-- [🔥Qwen Image Edit/Qwen Image Edit 2509](./docs/qwen_image_edit.md)
+- [🔥Qwen Image Edit series](./docs/qwen_image_edit.md)
 - [🔥Wan2.1/Wan2.2](./docs/wan.md)
 - [🔥Z-Image](./docs/z_image.md)
 - [Ovis-Image](./docs/ovis_image.md)
diff --git a/assets/qwen/qwen_image_edit_2511.png b/assets/qwen/qwen_image_edit_2511.png
new file mode 100644
index 0000000000000000000000000000000000000000..18a26daca7a983ffe8ece9ecfe944f3041677930
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diff --git a/diffusion_model.hpp b/diffusion_model.hpp
index c4e0ba1d..06cbecc2 100644
--- a/diffusion_model.hpp
+++ b/diffusion_model.hpp
@@ -320,8 +320,9 @@ struct QwenImageModel : public DiffusionModel {
                    bool offload_params_to_cpu,
                    const String2TensorStorage& tensor_storage_map = {},
                    const std::string prefix                       = "model.diffusion_model",
-                   SDVersion version                              = VERSION_QWEN_IMAGE)
-        : prefix(prefix), qwen_image(backend, offload_params_to_cpu, tensor_storage_map, prefix, version) {
+                   SDVersion version                              = VERSION_QWEN_IMAGE,
+                   bool zero_cond_t                               = false)
+        : prefix(prefix), qwen_image(backend, offload_params_to_cpu, tensor_storage_map, prefix, version, zero_cond_t) {
     }
 
     std::string get_desc() override {
diff --git a/docs/qwen_image_edit.md b/docs/qwen_image_edit.md
index d376a283..4a8b0172 100644
--- a/docs/qwen_image_edit.md
+++ b/docs/qwen_image_edit.md
@@ -9,6 +9,9 @@
     - Qwen Image Edit 2509
         - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models
         - gguf: https://huggingface.co/QuantStack/Qwen-Image-Edit-2509-GGUF/tree/main
+    - Qwen Image Edit 2511
+        - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models
+        - gguf: https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF/tree/main
 - Download vae
     - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/vae
 - Download qwen_2.5_vl 7b
@@ -32,4 +35,14 @@
 .\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen-Image-Edit-2509-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf --llm_vision ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct.mmproj-Q8_0.gguf --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'Qwen Image Edit 2509'"
 ```
 
-<img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2509.png" />
\ No newline at end of file
+<img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2509.png" />
+
+### Qwen Image Edit 2511
+
+To use the new Qwen Image Edit 2511 mode, the  `--qwen-image-zero-cond-t` flag must be enabled; otherwise, image editing quality will degrade significantly.
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\qwen-image-edit-2511-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'"  --qwen-image-zero-cond-t
+```
+
+<img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2511.png" />
\ No newline at end of file
diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index 868b06e9..74560814 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -457,6 +457,8 @@ struct SDContextParams {
     bool chroma_use_t5_mask  = false;
     int chroma_t5_mask_pad   = 1;
 
+    bool qwen_image_zero_cond_t = false;
+
     prediction_t prediction           = PREDICTION_COUNT;
     lora_apply_mode_t lora_apply_mode = LORA_APPLY_AUTO;
 
@@ -625,6 +627,10 @@ struct SDContextParams {
              "--chroma-disable-dit-mask",
              "disable dit mask for chroma",
              false, &chroma_use_dit_mask},
+            {"",
+             "--qwen-image-zero-cond-t",
+             "enable zero_cond_t for qwen image",
+             true, &qwen_image_zero_cond_t},
             {"",
              "--chroma-enable-t5-mask",
              "enable t5 mask for chroma",
@@ -888,6 +894,7 @@ struct SDContextParams {
             << "  circular_x: " << (circular_x ? "true" : "false") << ",\n"
             << "  circular_y: " << (circular_y ? "true" : "false") << ",\n"
             << "  chroma_use_dit_mask: " << (chroma_use_dit_mask ? "true" : "false") << ",\n"
+            << "  qwen_image_zero_cond_t: " << (qwen_image_zero_cond_t ? "true" : "false") << ",\n"
             << "  chroma_use_t5_mask: " << (chroma_use_t5_mask ? "true" : "false") << ",\n"
             << "  chroma_t5_mask_pad: " << chroma_t5_mask_pad << ",\n"
             << "  prediction: " << sd_prediction_name(prediction) << ",\n"
@@ -953,6 +960,7 @@ struct SDContextParams {
             chroma_use_dit_mask,
             chroma_use_t5_mask,
             chroma_t5_mask_pad,
+            qwen_image_zero_cond_t,
             flow_shift,
         };
         return sd_ctx_params;
diff --git a/flux.hpp b/flux.hpp
index bcff04cf..86e2007a 100644
--- a/flux.hpp
+++ b/flux.hpp
@@ -233,14 +233,17 @@ namespace Flux {
     __STATIC_INLINE__ struct ggml_tensor* modulate(struct ggml_context* ctx,
                                                    struct ggml_tensor* x,
                                                    struct ggml_tensor* shift,
-                                                   struct ggml_tensor* scale) {
+                                                   struct ggml_tensor* scale,
+                                                   bool skip_reshape = false) {
         // x: [N, L, C]
         // scale: [N, C]
         // shift: [N, C]
-        scale = ggml_reshape_3d(ctx, scale, scale->ne[0], 1, scale->ne[1]);  // [N, 1, C]
-        shift = ggml_reshape_3d(ctx, shift, shift->ne[0], 1, shift->ne[1]);  // [N, 1, C]
-        x     = ggml_add(ctx, x, ggml_mul(ctx, x, scale));
-        x     = ggml_add(ctx, x, shift);
+        if (!skip_reshape) {
+            scale = ggml_reshape_3d(ctx, scale, scale->ne[0], 1, scale->ne[1]);  // [N, 1, C]
+            shift = ggml_reshape_3d(ctx, shift, shift->ne[0], 1, shift->ne[1]);  // [N, 1, C]
+        }
+        x = ggml_add(ctx, x, ggml_mul(ctx, x, scale));
+        x = ggml_add(ctx, x, shift);
         return x;
     }
 
diff --git a/qwen_image.hpp b/qwen_image.hpp
index ed1c9830..4b29d63d 100644
--- a/qwen_image.hpp
+++ b/qwen_image.hpp
@@ -191,11 +191,16 @@ namespace Qwen {
     };
 
     class QwenImageTransformerBlock : public GGMLBlock {
+    protected:
+        bool zero_cond_t;
+
     public:
         QwenImageTransformerBlock(int64_t dim,
                                   int64_t num_attention_heads,
                                   int64_t attention_head_dim,
-                                  float eps = 1e-6) {
+                                  float eps        = 1e-6,
+                                  bool zero_cond_t = false)
+            : zero_cond_t(zero_cond_t) {
             // img_mod.0 is nn.SiLU()
             blocks["img_mod.1"] = std::shared_ptr<GGMLBlock>(new Linear(dim, 6 * dim, true));
 
@@ -220,11 +225,37 @@ namespace Qwen {
                                                                                eps));
         }
 
+        std::vector<ggml_tensor*> get_mod_params_vec(ggml_context* ctx, ggml_tensor* mod_params, ggml_tensor* index = nullptr) {
+            // index: [N, n_img_token]
+            // mod_params: [N, hidden_size * 12]
+            if (index == nullptr) {
+                return ggml_ext_chunk(ctx, mod_params, 6, 0);
+            }
+            mod_params          = ggml_reshape_1d(ctx, mod_params, ggml_nelements(mod_params));
+            auto mod_params_vec = ggml_ext_chunk(ctx, mod_params, 12, 0);
+            index               = ggml_reshape_3d(ctx, index, 1, index->ne[0], index->ne[1]);                                      // [N, n_img_token, 1]
+            index               = ggml_repeat_4d(ctx, index, mod_params_vec[0]->ne[0], index->ne[1], index->ne[2], index->ne[3]);  // [N, n_img_token, hidden_size]
+            std::vector<ggml_tensor*> mod_results;
+            for (int i = 0; i < 6; i++) {
+                auto mod_0 = mod_params_vec[i];
+                auto mod_1 = mod_params_vec[i + 6];
+
+                // mod_result = torch.where(index == 0, mod_0, mod_1)
+                // mod_result = (1 - index)*mod_0 + index*mod_1
+                mod_0           = ggml_sub(ctx, ggml_repeat(ctx, mod_0, index), ggml_mul(ctx, index, mod_0));  // [N, n_img_token, hidden_size]
+                mod_1           = ggml_mul(ctx, index, mod_1);                                                 // [N, n_img_token, hidden_size]
+                auto mod_result = ggml_add(ctx, mod_0, mod_1);
+                mod_results.push_back(mod_result);
+            }
+            return mod_results;
+        }
+
         virtual std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
                                                               struct ggml_tensor* img,
                                                               struct ggml_tensor* txt,
                                                               struct ggml_tensor* t_emb,
-                                                              struct ggml_tensor* pe) {
+                                                              struct ggml_tensor* pe,
+                                                              struct ggml_tensor* modulate_index = nullptr) {
             // img: [N, n_img_token, hidden_size]
             // txt: [N, n_txt_token, hidden_size]
             // pe: [n_img_token + n_txt_token, d_head/2, 2, 2]
@@ -244,14 +275,18 @@ namespace Qwen {
 
             auto img_mod_params    = ggml_silu(ctx->ggml_ctx, t_emb);
             img_mod_params         = img_mod_1->forward(ctx, img_mod_params);
-            auto img_mod_param_vec = ggml_ext_chunk(ctx->ggml_ctx, img_mod_params, 6, 0);
+            auto img_mod_param_vec = get_mod_params_vec(ctx->ggml_ctx, img_mod_params, modulate_index);
+
+            if (zero_cond_t) {
+                t_emb = ggml_ext_chunk(ctx->ggml_ctx, t_emb, 2, 1)[0];
+            }
 
             auto txt_mod_params    = ggml_silu(ctx->ggml_ctx, t_emb);
             txt_mod_params         = txt_mod_1->forward(ctx, txt_mod_params);
-            auto txt_mod_param_vec = ggml_ext_chunk(ctx->ggml_ctx, txt_mod_params, 6, 0);
+            auto txt_mod_param_vec = get_mod_params_vec(ctx->ggml_ctx, txt_mod_params);
 
             auto img_normed    = img_norm1->forward(ctx, img);
-            auto img_modulated = Flux::modulate(ctx->ggml_ctx, img_normed, img_mod_param_vec[0], img_mod_param_vec[1]);
+            auto img_modulated = Flux::modulate(ctx->ggml_ctx, img_normed, img_mod_param_vec[0], img_mod_param_vec[1], modulate_index != nullptr);
             auto img_gate1     = img_mod_param_vec[2];
 
             auto txt_normed    = txt_norm1->forward(ctx, txt);
@@ -264,7 +299,7 @@ namespace Qwen {
             txt = ggml_add(ctx->ggml_ctx, txt, ggml_mul(ctx->ggml_ctx, txt_attn_output, txt_gate1));
 
             auto img_normed2    = img_norm2->forward(ctx, img);
-            auto img_modulated2 = Flux::modulate(ctx->ggml_ctx, img_normed2, img_mod_param_vec[3], img_mod_param_vec[4]);
+            auto img_modulated2 = Flux::modulate(ctx->ggml_ctx, img_normed2, img_mod_param_vec[3], img_mod_param_vec[4], modulate_index != nullptr);
             auto img_gate2      = img_mod_param_vec[5];
 
             auto txt_normed2    = txt_norm2->forward(ctx, txt);
@@ -325,6 +360,7 @@ namespace Qwen {
         float theta                 = 10000;
         std::vector<int> axes_dim   = {16, 56, 56};
         int64_t axes_dim_sum        = 128;
+        bool zero_cond_t            = false;
     };
 
     class QwenImageModel : public GGMLBlock {
@@ -346,7 +382,8 @@ namespace Qwen {
                 auto block                                        = std::shared_ptr<GGMLBlock>(new QwenImageTransformerBlock(inner_dim,
                                                                                                                              params.num_attention_heads,
                                                                                                                              params.attention_head_dim,
-                                                                                                                             1e-6f));
+                                                                                                                             1e-6f,
+                                                                                                                             params.zero_cond_t));
                 blocks["transformer_blocks." + std::to_string(i)] = block;
             }
 
@@ -421,7 +458,8 @@ namespace Qwen {
                                          struct ggml_tensor* x,
                                          struct ggml_tensor* timestep,
                                          struct ggml_tensor* context,
-                                         struct ggml_tensor* pe) {
+                                         struct ggml_tensor* pe,
+                                         struct ggml_tensor* modulate_index = nullptr) {
             auto time_text_embed = std::dynamic_pointer_cast<QwenTimestepProjEmbeddings>(blocks["time_text_embed"]);
             auto txt_norm        = std::dynamic_pointer_cast<RMSNorm>(blocks["txt_norm"]);
             auto img_in          = std::dynamic_pointer_cast<Linear>(blocks["img_in"]);
@@ -430,18 +468,26 @@ namespace Qwen {
             auto proj_out        = std::dynamic_pointer_cast<Linear>(blocks["proj_out"]);
 
             auto t_emb = time_text_embed->forward(ctx, timestep);
-            auto img   = img_in->forward(ctx, x);
-            auto txt   = txt_norm->forward(ctx, context);
-            txt        = txt_in->forward(ctx, txt);
+            if (params.zero_cond_t) {
+                auto t_emb_0 = time_text_embed->forward(ctx, ggml_ext_zeros(ctx->ggml_ctx, timestep->ne[0], timestep->ne[1], timestep->ne[2], timestep->ne[3]));
+                t_emb        = ggml_concat(ctx->ggml_ctx, t_emb, t_emb_0, 1);
+            }
+            auto img = img_in->forward(ctx, x);
+            auto txt = txt_norm->forward(ctx, context);
+            txt      = txt_in->forward(ctx, txt);
 
             for (int i = 0; i < params.num_layers; i++) {
                 auto block = std::dynamic_pointer_cast<QwenImageTransformerBlock>(blocks["transformer_blocks." + std::to_string(i)]);
 
-                auto result = block->forward(ctx, img, txt, t_emb, pe);
+                auto result = block->forward(ctx, img, txt, t_emb, pe, modulate_index);
                 img         = result.first;
                 txt         = result.second;
             }
 
+            if (params.zero_cond_t) {
+                t_emb = ggml_ext_chunk(ctx->ggml_ctx, t_emb, 2, 1)[0];
+            }
+
             img = norm_out->forward(ctx, img, t_emb);
             img = proj_out->forward(ctx, img);
 
@@ -453,7 +499,8 @@ namespace Qwen {
                                     struct ggml_tensor* timestep,
                                     struct ggml_tensor* context,
                                     struct ggml_tensor* pe,
-                                    std::vector<ggml_tensor*> ref_latents = {}) {
+                                    std::vector<ggml_tensor*> ref_latents = {},
+                                    struct ggml_tensor* modulate_index    = nullptr) {
             // Forward pass of DiT.
             // x: [N, C, H, W]
             // timestep: [N,]
@@ -479,7 +526,7 @@ namespace Qwen {
             int64_t h_len = ((H + (params.patch_size / 2)) / params.patch_size);
             int64_t w_len = ((W + (params.patch_size / 2)) / params.patch_size);
 
-            auto out = forward_orig(ctx, img, timestep, context, pe);  // [N, h_len*w_len, ph*pw*C]
+            auto out = forward_orig(ctx, img, timestep, context, pe, modulate_index);  // [N, h_len*w_len, ph*pw*C]
 
             if (out->ne[1] > img_tokens) {
                 out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, out, 0, 2, 1, 3));  // [num_tokens, N, C * patch_size * patch_size]
@@ -502,15 +549,19 @@ namespace Qwen {
         QwenImageParams qwen_image_params;
         QwenImageModel qwen_image;
         std::vector<float> pe_vec;
+        std::vector<float> modulate_index_vec;
         SDVersion version;
 
         QwenImageRunner(ggml_backend_t backend,
                         bool offload_params_to_cpu,
                         const String2TensorStorage& tensor_storage_map = {},
                         const std::string prefix                       = "",
-                        SDVersion version                              = VERSION_QWEN_IMAGE)
+                        SDVersion version                              = VERSION_QWEN_IMAGE,
+                        bool zero_cond_t                               = false)
             : GGMLRunner(backend, offload_params_to_cpu) {
-            qwen_image_params.num_layers = 0;
+            qwen_image_params.num_layers  = 0;
+            qwen_image_params.zero_cond_t = zero_cond_t;
+            LOG_DEBUG("zero_cond_t: %d", zero_cond_t);
             for (auto pair : tensor_storage_map) {
                 std::string tensor_name = pair.first;
                 if (tensor_name.find(prefix) == std::string::npos)
@@ -576,6 +627,31 @@ namespace Qwen {
             // pe->data = nullptr;
             set_backend_tensor_data(pe, pe_vec.data());
 
+            ggml_tensor* modulate_index = nullptr;
+            if (qwen_image_params.zero_cond_t) {
+                modulate_index_vec.clear();
+
+                int64_t h_len          = ((x->ne[1] + (qwen_image_params.patch_size / 2)) / qwen_image_params.patch_size);
+                int64_t w_len          = ((x->ne[0] + (qwen_image_params.patch_size / 2)) / qwen_image_params.patch_size);
+                int64_t num_img_tokens = h_len * w_len;
+
+                modulate_index_vec.insert(modulate_index_vec.end(), num_img_tokens, 0.f);
+                int64_t num_ref_img_tokens = 0;
+                for (ggml_tensor* ref : ref_latents) {
+                    int64_t h_len = ((ref->ne[1] + (qwen_image_params.patch_size / 2)) / qwen_image_params.patch_size);
+                    int64_t w_len = ((ref->ne[0] + (qwen_image_params.patch_size / 2)) / qwen_image_params.patch_size);
+
+                    num_ref_img_tokens += h_len * w_len;
+                }
+
+                if (num_ref_img_tokens > 0) {
+                    modulate_index_vec.insert(modulate_index_vec.end(), num_ref_img_tokens, 1.f);
+                }
+
+                modulate_index       = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_F32, modulate_index_vec.size());
+                set_backend_tensor_data(modulate_index, modulate_index_vec.data());
+            }
+
             auto runner_ctx = get_context();
 
             struct ggml_tensor* out = qwen_image.forward(&runner_ctx,
@@ -583,7 +659,8 @@ namespace Qwen {
                                                          timesteps,
                                                          context,
                                                          pe,
-                                                         ref_latents);
+                                                         ref_latents,
+                                                         modulate_index);
 
             ggml_build_forward_expand(gf, out);
 
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 6c783e1d..02a5ebc5 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -520,7 +520,8 @@ public:
                                                                    offload_params_to_cpu,
                                                                    tensor_storage_map,
                                                                    "model.diffusion_model",
-                                                                   version);
+                                                                   version,
+                                                                   sd_ctx_params->qwen_image_zero_cond_t);
             } else if (sd_version_is_z_image(version)) {
                 cond_stage_model = std::make_shared<LLMEmbedder>(clip_backend,
                                                                  offload_params_to_cpu,
diff --git a/stable-diffusion.h b/stable-diffusion.h
index 53db5100..3e9faf85 100644
--- a/stable-diffusion.h
+++ b/stable-diffusion.h
@@ -195,6 +195,7 @@ typedef struct {
     bool chroma_use_dit_mask;
     bool chroma_use_t5_mask;
     int chroma_t5_mask_pad;
+    bool qwen_image_zero_cond_t;
     float flow_shift;
 } sd_ctx_params_t;
 

From 860a78e24868c6fd05371905e5bddfe3a562c018 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Wed, 24 Dec 2025 23:30:12 +0800
Subject: [PATCH 22/97] fix: avoid crash when using taesd for preview only
 (#1141)

---
 examples/server/main.cpp |  6 +--
 qwen_image.hpp           |  2 +-
 stable-diffusion.cpp     | 79 ++++++++++++++++++++--------------------
 3 files changed, 43 insertions(+), 44 deletions(-)

diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index 5f5333da..c540958f 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -105,9 +105,9 @@ struct SDSvrParams {
     std::string listen_ip = "127.0.0.1";
     int listen_port       = 1234;
     std::string serve_html_path;
-    bool normal_exit      = false;
-    bool verbose          = false;
-    bool color            = false;
+    bool normal_exit = false;
+    bool verbose     = false;
+    bool color       = false;
 
     ArgOptions get_options() {
         ArgOptions options;
diff --git a/qwen_image.hpp b/qwen_image.hpp
index 4b29d63d..bbbd91bc 100644
--- a/qwen_image.hpp
+++ b/qwen_image.hpp
@@ -648,7 +648,7 @@ namespace Qwen {
                     modulate_index_vec.insert(modulate_index_vec.end(), num_ref_img_tokens, 1.f);
                 }
 
-                modulate_index       = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_F32, modulate_index_vec.size());
+                modulate_index = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_F32, modulate_index_vec.size());
                 set_backend_tensor_data(modulate_index, modulate_index_vec.data());
             }
 
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 02a5ebc5..4b1c0043 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -591,8 +591,8 @@ public:
                 vae_backend = backend;
             }
 
-            if (sd_version_is_wan(version) || sd_version_is_qwen_image(version)) {
-                if (!use_tiny_autoencoder) {
+            if (!use_tiny_autoencoder || sd_ctx_params->tae_preview_only) {
+                if (sd_version_is_wan(version) || sd_version_is_qwen_image(version)) {
                     first_stage_model = std::make_shared<WAN::WanVAERunner>(vae_backend,
                                                                             offload_params_to_cpu,
                                                                             tensor_storage_map,
@@ -601,57 +601,56 @@ public:
                                                                             version);
                     first_stage_model->alloc_params_buffer();
                     first_stage_model->get_param_tensors(tensors, "first_stage_model");
+                } else if (version == VERSION_CHROMA_RADIANCE) {
+                    first_stage_model = std::make_shared<FakeVAE>(vae_backend,
+                                                                  offload_params_to_cpu);
                 } else {
+                    first_stage_model = std::make_shared<AutoEncoderKL>(vae_backend,
+                                                                        offload_params_to_cpu,
+                                                                        tensor_storage_map,
+                                                                        "first_stage_model",
+                                                                        vae_decode_only,
+                                                                        false,
+                                                                        version);
+                    if (sd_ctx_params->vae_conv_direct) {
+                        LOG_INFO("Using Conv2d direct in the vae model");
+                        first_stage_model->set_conv2d_direct_enabled(true);
+                    }
+                    if (version == VERSION_SDXL &&
+                        (strlen(SAFE_STR(sd_ctx_params->vae_path)) == 0 || sd_ctx_params->force_sdxl_vae_conv_scale)) {
+                        float vae_conv_2d_scale = 1.f / 32.f;
+                        LOG_WARN(
+                            "No VAE specified with --vae or --force-sdxl-vae-conv-scale flag set, "
+                            "using Conv2D scale %.3f",
+                            vae_conv_2d_scale);
+                        first_stage_model->set_conv2d_scale(vae_conv_2d_scale);
+                    }
+                    first_stage_model->alloc_params_buffer();
+                    first_stage_model->get_param_tensors(tensors, "first_stage_model");
+                }
+            }
+
+            if (use_tiny_autoencoder) {
+                if (sd_version_is_wan(version) || sd_version_is_qwen_image(version)) {
                     tae_first_stage = std::make_shared<TinyVideoAutoEncoder>(vae_backend,
                                                                              offload_params_to_cpu,
                                                                              tensor_storage_map,
                                                                              "decoder",
                                                                              vae_decode_only,
                                                                              version);
-                    if (sd_ctx_params->vae_conv_direct) {
-                        LOG_INFO("Using Conv2d direct in the tae model");
-                        tae_first_stage->set_conv2d_direct_enabled(true);
-                    }
+                } else {
+                    tae_first_stage = std::make_shared<TinyImageAutoEncoder>(vae_backend,
+                                                                             offload_params_to_cpu,
+                                                                             tensor_storage_map,
+                                                                             "decoder.layers",
+                                                                             vae_decode_only,
+                                                                             version);
                 }
-            } else if (version == VERSION_CHROMA_RADIANCE) {
-                first_stage_model = std::make_shared<FakeVAE>(vae_backend,
-                                                              offload_params_to_cpu);
-            } else if (!use_tiny_autoencoder || sd_ctx_params->tae_preview_only) {
-                first_stage_model = std::make_shared<AutoEncoderKL>(vae_backend,
-                                                                    offload_params_to_cpu,
-                                                                    tensor_storage_map,
-                                                                    "first_stage_model",
-                                                                    vae_decode_only,
-                                                                    false,
-                                                                    version);
-                if (sd_ctx_params->vae_conv_direct) {
-                    LOG_INFO("Using Conv2d direct in the vae model");
-                    first_stage_model->set_conv2d_direct_enabled(true);
-                }
-                if (version == VERSION_SDXL &&
-                    (strlen(SAFE_STR(sd_ctx_params->vae_path)) == 0 || sd_ctx_params->force_sdxl_vae_conv_scale)) {
-                    float vae_conv_2d_scale = 1.f / 32.f;
-                    LOG_WARN(
-                        "No VAE specified with --vae or --force-sdxl-vae-conv-scale flag set, "
-                        "using Conv2D scale %.3f",
-                        vae_conv_2d_scale);
-                    first_stage_model->set_conv2d_scale(vae_conv_2d_scale);
-                }
-                first_stage_model->alloc_params_buffer();
-                first_stage_model->get_param_tensors(tensors, "first_stage_model");
-            } else if (use_tiny_autoencoder) {
-                tae_first_stage = std::make_shared<TinyImageAutoEncoder>(vae_backend,
-                                                                         offload_params_to_cpu,
-                                                                         tensor_storage_map,
-                                                                         "decoder.layers",
-                                                                         vae_decode_only,
-                                                                         version);
                 if (sd_ctx_params->vae_conv_direct) {
                     LOG_INFO("Using Conv2d direct in the tae model");
                     tae_first_stage->set_conv2d_direct_enabled(true);
                 }
             }
-            // first_stage_model->get_param_tensors(tensors, "first_stage_model.");
 
             if (strlen(SAFE_STR(sd_ctx_params->control_net_path)) > 0) {
                 ggml_backend_t controlnet_backend = nullptr;

From df4efe26bd3fc76d6e64d0a946ba96075ba16aeb Mon Sep 17 00:00:00 2001
From: Weiqi Gao <gaoweiqicarl1@163.com>
Date: Fri, 26 Dec 2025 22:06:13 +0800
Subject: [PATCH 23/97] feat: add png sequence output for vid_gen (#1117)

---
 examples/cli/README.md |  3 +-
 examples/cli/main.cpp  | 76 +++++++++++++++++++++++++++++++++++++-----
 2 files changed, 70 insertions(+), 9 deletions(-)

diff --git a/examples/cli/README.md b/examples/cli/README.md
index 568f29d0..c3496aa5 100644
--- a/examples/cli/README.md
+++ b/examples/cli/README.md
@@ -4,7 +4,8 @@
 usage: ./bin/sd-cli  [options]
 
 CLI Options:
-  -o, --output <string>       path to write result image to (default: ./output.png)
+  -o, --output <string>       path to write result image to. you can use printf-style %d format specifiers for image sequences (default: ./output.png) (eg. output_%03d.png)
+  --output-begin-idx <int>    starting index for output image sequence, must be non-negative (default 0 if specified %d in output path, 1 otherwise)
   --preview-path <string>     path to write preview image to (default: ./preview.png)
   --preview-interval <int>    interval in denoising steps between consecutive updates of the image preview file (default is 1, meaning updating at
                               every step)
diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index 7fe3b769..77ef1c93 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -26,9 +26,12 @@ const char* previews_str[] = {
     "vae",
 };
 
+std::regex format_specifier_regex("(?:[^%]|^)(?:%%)*(%\\d{0,3}d)");
+
 struct SDCliParams {
     SDMode mode             = IMG_GEN;
     std::string output_path = "output.png";
+    int output_begin_idx    = -1;
 
     bool verbose          = false;
     bool canny_preprocess = false;
@@ -50,7 +53,7 @@ struct SDCliParams {
         options.string_options = {
             {"-o",
              "--output",
-             "path to write result image to (default: ./output.png)",
+             "path to write result image to. you can use printf-style %d format specifiers for image sequences (default: ./output.png) (eg. output_%03d.png)",
              &output_path},
             {"",
              "--preview-path",
@@ -63,6 +66,10 @@ struct SDCliParams {
              "--preview-interval",
              "interval in denoising steps between consecutive updates of the image preview file (default is 1, meaning updating at every step)",
              &preview_interval},
+            {"",
+             "--output-begin-idx",
+             "starting index for output image sequence, must be non-negative (default 0 if specified %d in output path, 1 otherwise)",
+             &output_begin_idx},
         };
 
         options.bool_options = {
@@ -344,6 +351,25 @@ void step_callback(int step, int frame_count, sd_image_t* image, bool is_noisy,
     }
 }
 
+std::string format_frame_idx(std::string pattern, int frame_idx) {
+    std::smatch match;
+    std::string result = pattern;
+    while (std::regex_search(result, match, format_specifier_regex)) {
+        std::string specifier = match.str(1);
+        char buffer[32];
+        snprintf(buffer, sizeof(buffer), specifier.c_str(), frame_idx);
+        result.replace(match.position(1), match.length(1), buffer);
+    }
+
+    // Then replace all '%%' with '%'
+    size_t pos = 0;
+    while ((pos = result.find("%%", pos)) != std::string::npos) {
+        result.replace(pos, 2, "%");
+        pos += 1;
+    }
+    return result;
+}
+
 int main(int argc, const char* argv[]) {
     if (argc > 1 && std::string(argv[1]) == "--version") {
         std::cout << version_string() << "\n";
@@ -719,25 +745,59 @@ int main(int argc, const char* argv[]) {
         is_jpg                    = false;
     }
 
-    if (cli_params.mode == VID_GEN && num_results > 1) {
-        std::string vid_output_path = cli_params.output_path;
-        if (file_ext_lower == ".png") {
-            vid_output_path = base_path + ".avi";
+    if (std::regex_search(cli_params.output_path, format_specifier_regex)) {
+        std::string final_output_path = cli_params.output_path;
+        if (cli_params.output_begin_idx == -1) {
+            cli_params.output_begin_idx = 0;
         }
-        create_mjpg_avi_from_sd_images(vid_output_path.c_str(), results, num_results, gen_params.fps);
-        LOG_INFO("save result MJPG AVI video to '%s'\n", vid_output_path.c_str());
+        // writing image sequence, default to PNG
+        if (!is_jpg && file_ext_lower != ".png") {
+            base_path += file_ext;
+            file_ext = ".png";
+        }
+        final_output_path = base_path + file_ext;
+        for (int i = 0; i < num_results; i++) {
+            if (results[i].data == nullptr) {
+                continue;
+            }
+            std::string final_image_path = format_frame_idx(final_output_path, cli_params.output_begin_idx + i);
+            if (is_jpg) {
+                int write_ok = stbi_write_jpg(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
+                                              results[i].data, 90, get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + i).c_str());
+                LOG_INFO("save result JPEG image %d to '%s' (%s)", i, final_image_path.c_str(), write_ok == 0 ? "failure" : "success");
+            } else {
+                int write_ok = stbi_write_png(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
+                                              results[i].data, 0, get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + i).c_str());
+                LOG_INFO("save result PNG image %d to '%s' (%s)", i, final_image_path.c_str(), write_ok == 0 ? "failure" : "success");
+            }
+        }
+    } else if (cli_params.mode == VID_GEN && num_results > 1) {
+        std::string final_output_path = cli_params.output_path;
+        if (file_ext_lower != ".avi") {
+            if (!is_jpg && file_ext_lower != ".png") {
+                base_path += file_ext;
+            }
+            file_ext          = ".avi";
+            final_output_path = base_path + file_ext;
+        }
+        create_mjpg_avi_from_sd_images(final_output_path.c_str(), results, num_results, gen_params.fps);
+        LOG_INFO("save result MJPG AVI video to '%s'\n", final_output_path.c_str());
     } else {
         // appending ".png" to absent or unknown extension
         if (!is_jpg && file_ext_lower != ".png") {
             base_path += file_ext;
             file_ext = ".png";
         }
+        if (cli_params.output_begin_idx == -1) {
+            cli_params.output_begin_idx = 1;
+        }
         for (int i = 0; i < num_results; i++) {
             if (results[i].data == nullptr) {
                 continue;
             }
             int write_ok;
-            std::string final_image_path = i > 0 ? base_path + "_" + std::to_string(i + 1) + file_ext : base_path + file_ext;
+            std::string final_image_path;
+            final_image_path = i > 0 ? base_path + "_" + std::to_string(cli_params.output_begin_idx + i) + file_ext : base_path + file_ext;
             if (is_jpg) {
                 write_ok = stbi_write_jpg(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
                                           results[i].data, 90, get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + i).c_str());

From ccb6b0ac9def57e483b3d373e577e06edf031e04 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Fri, 26 Dec 2025 22:07:40 +0800
Subject: [PATCH 24/97] feat: add __index_timestep_zero__ support (#1146)

---
 qwen_image.hpp       | 7 ++++++-
 stable-diffusion.cpp | 1 +
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/qwen_image.hpp b/qwen_image.hpp
index bbbd91bc..9c095a9a 100644
--- a/qwen_image.hpp
+++ b/qwen_image.hpp
@@ -561,11 +561,13 @@ namespace Qwen {
             : GGMLRunner(backend, offload_params_to_cpu) {
             qwen_image_params.num_layers  = 0;
             qwen_image_params.zero_cond_t = zero_cond_t;
-            LOG_DEBUG("zero_cond_t: %d", zero_cond_t);
             for (auto pair : tensor_storage_map) {
                 std::string tensor_name = pair.first;
                 if (tensor_name.find(prefix) == std::string::npos)
                     continue;
+                if (tensor_name.find("__index_timestep_zero__") != std::string::npos) {
+                    qwen_image_params.zero_cond_t = true;
+                }
                 size_t pos = tensor_name.find("transformer_blocks.");
                 if (pos != std::string::npos) {
                     tensor_name = tensor_name.substr(pos);  // remove prefix
@@ -580,6 +582,9 @@ namespace Qwen {
                 }
             }
             LOG_INFO("qwen_image_params.num_layers: %ld", qwen_image_params.num_layers);
+            if (qwen_image_params.zero_cond_t) {
+                LOG_INFO("use zero_cond_t");
+            }
             qwen_image = QwenImageModel(qwen_image_params);
             qwen_image.init(params_ctx, tensor_storage_map, prefix);
         }
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 4b1c0043..58d42041 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -750,6 +750,7 @@ public:
         }
         ignore_tensors.insert("model.diffusion_model.__x0__");
         ignore_tensors.insert("model.diffusion_model.__32x32__");
+        ignore_tensors.insert("model.diffusion_model.__index_timestep_zero__");
 
         if (vae_decode_only) {
             ignore_tensors.insert("first_stage_model.encoder");

From 37c9860b795492409f3ad2edbca97929c171edb2 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sat, 27 Dec 2025 15:43:19 +0800
Subject: [PATCH 25/97] fix: handle redirected UTF-8 output correctly on
 Windows (#1147)

---
 examples/common/common.hpp | 27 +++++++++++++++++++--------
 1 file changed, 19 insertions(+), 8 deletions(-)

diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index 74560814..cdc8f181 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -95,17 +95,28 @@ static void print_utf8(FILE* stream, const char* utf8) {
                    ? GetStdHandle(STD_ERROR_HANDLE)
                    : GetStdHandle(STD_OUTPUT_HANDLE);
 
-    int wlen = MultiByteToWideChar(CP_UTF8, 0, utf8, -1, NULL, 0);
-    if (wlen <= 0)
-        return;
+    DWORD mode;
+    BOOL is_console = GetConsoleMode(h, &mode);
 
-    wchar_t* wbuf = (wchar_t*)malloc(wlen * sizeof(wchar_t));
-    MultiByteToWideChar(CP_UTF8, 0, utf8, -1, wbuf, wlen);
+    if (is_console) {
+        int wlen = MultiByteToWideChar(CP_UTF8, 0, utf8, -1, NULL, 0);
+        if (wlen <= 0)
+            return;
 
-    DWORD written;
-    WriteConsoleW(h, wbuf, wlen - 1, &written, NULL);
+        wchar_t* wbuf = (wchar_t*)malloc(wlen * sizeof(wchar_t));
+        if (!wbuf)
+            return;
 
-    free(wbuf);
+        MultiByteToWideChar(CP_UTF8, 0, utf8, -1, wbuf, wlen);
+
+        DWORD written;
+        WriteConsoleW(h, wbuf, wlen - 1, &written, NULL);
+
+        free(wbuf);
+    } else {
+        DWORD written;
+        WriteFile(h, utf8, (DWORD)strlen(utf8), &written, NULL);
+    }
 #else
     fputs(utf8, stream);
 #endif

From cc107714d7f17f7baa9976020d333fb54ce489a7 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Sat, 27 Dec 2025 04:54:18 -0300
Subject: [PATCH 26/97] fix: consistently pass 2nd-order samplers half steps as
 negatives (#1095)

---
 denoiser.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/denoiser.hpp b/denoiser.hpp
index 7a8242e7..2dc4c60e 100644
--- a/denoiser.hpp
+++ b/denoiser.hpp
@@ -869,7 +869,7 @@ static bool sample_k_diffusion(sample_method_t method,
 
             for (int i = 0; i < steps; i++) {
                 // denoise
-                ggml_tensor* denoised = model(x, sigmas[i], i + 1);
+                ggml_tensor* denoised = model(x, sigmas[i], -(i + 1));
                 if (denoised == nullptr) {
                     return false;
                 }
@@ -927,7 +927,7 @@ static bool sample_k_diffusion(sample_method_t method,
 
             for (int i = 0; i < steps; i++) {
                 // denoise
-                ggml_tensor* denoised = model(x, sigmas[i], i + 1);
+                ggml_tensor* denoised = model(x, sigmas[i], -(i + 1));
                 if (denoised == nullptr) {
                     return false;
                 }

From a2d83dd0c804dc39d7ceb2e599cfbb02488da8e9 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sat, 27 Dec 2025 16:48:15 +0800
Subject: [PATCH 27/97] refactor: move pmid condition logic into
 get_pmid_condition (#1148)

---
 stable-diffusion.cpp | 201 ++++++++++++++++++++-----------------------
 1 file changed, 92 insertions(+), 109 deletions(-)

diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 58d42041..9a97d4c7 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -129,7 +129,7 @@ public:
     bool use_tiny_autoencoder            = false;
     sd_tiling_params_t vae_tiling_params = {false, 0, 0, 0.5f, 0, 0};
     bool offload_params_to_cpu           = false;
-    bool stacked_id                      = false;
+    bool use_pmid                        = false;
 
     bool is_using_v_parameterization     = false;
     bool is_using_edm_v_parameterization = false;
@@ -701,10 +701,10 @@ public:
                 if (!model_loader.init_from_file_and_convert_name(sd_ctx_params->photo_maker_path, "pmid.")) {
                     LOG_WARN("loading stacked ID embedding from '%s' failed", sd_ctx_params->photo_maker_path);
                 } else {
-                    stacked_id = true;
+                    use_pmid = true;
                 }
             }
-            if (stacked_id) {
+            if (use_pmid) {
                 if (!pmid_model->alloc_params_buffer()) {
                     LOG_ERROR(" pmid model params buffer allocation failed");
                     return false;
@@ -745,7 +745,7 @@ public:
         if (use_tiny_autoencoder) {
             ignore_tensors.insert("first_stage_model.");
         }
-        if (stacked_id) {
+        if (use_pmid) {
             ignore_tensors.insert("pmid.unet.");
         }
         ignore_tensors.insert("model.diffusion_model.__x0__");
@@ -799,7 +799,7 @@ public:
                 control_net_params_mem_size = control_net->get_params_buffer_size();
             }
             size_t pmid_params_mem_size = 0;
-            if (stacked_id) {
+            if (use_pmid) {
                 pmid_params_mem_size = pmid_model->get_params_buffer_size();
             }
 
@@ -1211,14 +1211,89 @@ public:
         }
     }
 
-    ggml_tensor* id_encoder(ggml_context* work_ctx,
-                            ggml_tensor* init_img,
-                            ggml_tensor* prompts_embeds,
-                            ggml_tensor* id_embeds,
-                            std::vector<bool>& class_tokens_mask) {
-        ggml_tensor* res = nullptr;
-        pmid_model->compute(n_threads, init_img, prompts_embeds, id_embeds, class_tokens_mask, &res, work_ctx);
-        return res;
+    SDCondition get_pmid_conditon(ggml_context* work_ctx,
+                                  sd_pm_params_t pm_params,
+                                  ConditionerParams& condition_params) {
+        SDCondition id_cond;
+        if (use_pmid) {
+            if (!pmid_lora->applied) {
+                int64_t t0 = ggml_time_ms();
+                pmid_lora->apply(tensors, version, n_threads);
+                int64_t t1         = ggml_time_ms();
+                pmid_lora->applied = true;
+                LOG_INFO("pmid_lora apply completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
+                if (free_params_immediately) {
+                    pmid_lora->free_params_buffer();
+                }
+            }
+            // preprocess input id images
+            bool pmv2 = pmid_model->get_version() == PM_VERSION_2;
+            if (pm_params.id_images_count > 0) {
+                int clip_image_size        = 224;
+                pmid_model->style_strength = pm_params.style_strength;
+
+                auto id_image_tensor = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, clip_image_size, clip_image_size, 3, pm_params.id_images_count);
+
+                std::vector<sd_image_f32_t> processed_id_images;
+                for (int i = 0; i < pm_params.id_images_count; i++) {
+                    sd_image_f32_t id_image           = sd_image_t_to_sd_image_f32_t(pm_params.id_images[i]);
+                    sd_image_f32_t processed_id_image = clip_preprocess(id_image, clip_image_size, clip_image_size);
+                    free(id_image.data);
+                    id_image.data = nullptr;
+                    processed_id_images.push_back(processed_id_image);
+                }
+
+                ggml_ext_tensor_iter(id_image_tensor, [&](ggml_tensor* id_image_tensor, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
+                    float value = sd_image_get_f32(processed_id_images[i3], i0, i1, i2, false);
+                    ggml_ext_tensor_set_f32(id_image_tensor, value, i0, i1, i2, i3);
+                });
+
+                for (auto& image : processed_id_images) {
+                    free(image.data);
+                    image.data = nullptr;
+                }
+                processed_id_images.clear();
+
+                int64_t t0                      = ggml_time_ms();
+                condition_params.num_input_imgs = pm_params.id_images_count;
+                auto cond_tup                   = cond_stage_model->get_learned_condition_with_trigger(work_ctx,
+                                                                                                       n_threads,
+                                                                                                       condition_params);
+                id_cond                         = std::get<0>(cond_tup);
+                auto class_tokens_mask          = std::get<1>(cond_tup);
+                struct ggml_tensor* id_embeds   = nullptr;
+                if (pmv2 && pm_params.id_embed_path != nullptr) {
+                    id_embeds = load_tensor_from_file(work_ctx, pm_params.id_embed_path);
+                }
+                if (pmv2 && id_embeds == nullptr) {
+                    LOG_WARN("Provided PhotoMaker images, but NO valid ID embeds file for PM v2");
+                    LOG_WARN("Turn off PhotoMaker");
+                    use_pmid = false;
+                } else {
+                    if (pmv2 && pm_params.id_images_count != id_embeds->ne[1]) {
+                        LOG_WARN("PhotoMaker image count (%d) does NOT match ID embeds (%d). You should run face_detect.py again.", pm_params.id_images_count, id_embeds->ne[1]);
+                        LOG_WARN("Turn off PhotoMaker");
+                        use_pmid = false;
+                    } else {
+                        ggml_tensor* res = nullptr;
+                        pmid_model->compute(n_threads, id_image_tensor, id_cond.c_crossattn, id_embeds, class_tokens_mask, &res, work_ctx);
+                        id_cond.c_crossattn = res;
+                        int64_t t1          = ggml_time_ms();
+                        LOG_INFO("Photomaker ID Stacking, taking %" PRId64 " ms", t1 - t0);
+                        if (free_params_immediately) {
+                            pmid_model->free_params_buffer();
+                        }
+                        // Encode input prompt without the trigger word for delayed conditioning
+                        condition_params.text = cond_stage_model->remove_trigger_from_prompt(work_ctx, condition_params.text);
+                    }
+                }
+            } else {
+                LOG_WARN("Provided PhotoMaker model file, but NO input ID images");
+                LOG_WARN("Turn off PhotoMaker");
+                use_pmid = false;
+            }
+        }
+        return id_cond;
     }
 
     ggml_tensor* get_clip_vision_output(ggml_context* work_ctx,
@@ -3117,114 +3192,22 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
         guidance.img_cfg = guidance.txt_cfg;
     }
 
-    // for (auto v : sigmas) {
-    //     std::cout << v << " ";
-    // }
-    // std::cout << std::endl;
-
     int sample_steps = sigmas.size() - 1;
 
     int64_t t0 = ggml_time_ms();
 
-    // Photo Maker
-    std::string prompt_text_only;
-    ggml_tensor* init_img = nullptr;
-    SDCondition id_cond;
-    std::vector<bool> class_tokens_mask;
-
     ConditionerParams condition_params;
+    condition_params.text            = prompt;
     condition_params.clip_skip       = clip_skip;
     condition_params.width           = width;
     condition_params.height          = height;
     condition_params.ref_images      = ref_images;
     condition_params.adm_in_channels = sd_ctx->sd->diffusion_model->get_adm_in_channels();
 
-    if (sd_ctx->sd->stacked_id) {
-        if (!sd_ctx->sd->pmid_lora->applied) {
-            int64_t t0 = ggml_time_ms();
-            sd_ctx->sd->pmid_lora->apply(sd_ctx->sd->tensors, sd_ctx->sd->version, sd_ctx->sd->n_threads);
-            int64_t t1                     = ggml_time_ms();
-            sd_ctx->sd->pmid_lora->applied = true;
-            LOG_INFO("pmid_lora apply completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
-            if (sd_ctx->sd->free_params_immediately) {
-                sd_ctx->sd->pmid_lora->free_params_buffer();
-            }
-        }
-        // preprocess input id images
-        bool pmv2 = sd_ctx->sd->pmid_model->get_version() == PM_VERSION_2;
-        if (pm_params.id_images_count > 0) {
-            int clip_image_size                    = 224;
-            sd_ctx->sd->pmid_model->style_strength = pm_params.style_strength;
-
-            init_img = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, clip_image_size, clip_image_size, 3, pm_params.id_images_count);
-
-            std::vector<sd_image_f32_t> processed_id_images;
-            for (int i = 0; i < pm_params.id_images_count; i++) {
-                sd_image_f32_t id_image           = sd_image_t_to_sd_image_f32_t(pm_params.id_images[i]);
-                sd_image_f32_t processed_id_image = clip_preprocess(id_image, clip_image_size, clip_image_size);
-                free(id_image.data);
-                id_image.data = nullptr;
-                processed_id_images.push_back(processed_id_image);
-            }
-
-            ggml_ext_tensor_iter(init_img, [&](ggml_tensor* init_img, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
-                float value = sd_image_get_f32(processed_id_images[i3], i0, i1, i2, false);
-                ggml_ext_tensor_set_f32(init_img, value, i0, i1, i2, i3);
-            });
-
-            for (auto& image : processed_id_images) {
-                free(image.data);
-                image.data = nullptr;
-            }
-            processed_id_images.clear();
-
-            int64_t t0                      = ggml_time_ms();
-            condition_params.text           = prompt;
-            condition_params.num_input_imgs = pm_params.id_images_count;
-            auto cond_tup                   = sd_ctx->sd->cond_stage_model->get_learned_condition_with_trigger(work_ctx,
-                                                                                                               sd_ctx->sd->n_threads,
-                                                                                                               condition_params);
-            id_cond                         = std::get<0>(cond_tup);
-            class_tokens_mask               = std::get<1>(cond_tup);  //
-            struct ggml_tensor* id_embeds   = nullptr;
-            if (pmv2 && pm_params.id_embed_path != nullptr) {
-                id_embeds = load_tensor_from_file(work_ctx, pm_params.id_embed_path);
-                // print_ggml_tensor(id_embeds, true, "id_embeds:");
-            }
-            if (pmv2 && id_embeds == nullptr) {
-                LOG_WARN("Provided PhotoMaker images, but NO valid ID embeds file for PM v2");
-                LOG_WARN("Turn off PhotoMaker");
-                sd_ctx->sd->stacked_id = false;
-            } else {
-                if (pmv2 && pm_params.id_images_count != id_embeds->ne[1]) {
-                    LOG_WARN("PhotoMaker image count (%d) does NOT match ID embeds (%d). You should run face_detect.py again.", pm_params.id_images_count, id_embeds->ne[1]);
-                    LOG_WARN("Turn off PhotoMaker");
-                    sd_ctx->sd->stacked_id = false;
-                } else {
-                    id_cond.c_crossattn = sd_ctx->sd->id_encoder(work_ctx, init_img, id_cond.c_crossattn, id_embeds, class_tokens_mask);
-                    int64_t t1          = ggml_time_ms();
-                    LOG_INFO("Photomaker ID Stacking, taking %" PRId64 " ms", t1 - t0);
-                    if (sd_ctx->sd->free_params_immediately) {
-                        sd_ctx->sd->pmid_model->free_params_buffer();
-                    }
-                    // Encode input prompt without the trigger word for delayed conditioning
-                    prompt_text_only = sd_ctx->sd->cond_stage_model->remove_trigger_from_prompt(work_ctx, prompt);
-                    // printf("%s || %s \n", prompt.c_str(), prompt_text_only.c_str());
-                    prompt = prompt_text_only;  //
-                    if (sample_steps < 50) {
-                        LOG_WARN("It's recommended to use >= 50 steps for photo maker!");
-                    }
-                }
-            }
-        } else {
-            LOG_WARN("Provided PhotoMaker model file, but NO input ID images");
-            LOG_WARN("Turn off PhotoMaker");
-            sd_ctx->sd->stacked_id = false;
-        }
-    }
+    // Photo Maker
+    SDCondition id_cond = sd_ctx->sd->get_pmid_conditon(work_ctx, pm_params, condition_params);
 
     // Get learned condition
-    condition_params.text            = prompt;
     condition_params.zero_out_masked = false;
     SDCondition cond                 = sd_ctx->sd->cond_stage_model->get_learned_condition(work_ctx,
                                                                                            sd_ctx->sd->n_threads,
@@ -3364,7 +3347,7 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
         ggml_ext_im_set_randn_f32(noise, sd_ctx->sd->rng);
 
         int start_merge_step = -1;
-        if (sd_ctx->sd->stacked_id) {
+        if (sd_ctx->sd->use_pmid) {
             start_merge_step = int(sd_ctx->sd->pmid_model->style_strength / 100.f * sample_steps);
             // if (start_merge_step > 30)
             //     start_merge_step = 30;

From d0d836ae74ae5c72ef36333803df2460d7c4fc45 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Sun, 28 Dec 2025 11:38:29 -0300
Subject: [PATCH 28/97] feat: support mmap for model loading (#1059)

---
 examples/cli/README.md     |   1 +
 examples/common/common.hpp |   7 +++
 examples/server/README.md  |   1 +
 model.cpp                  |  23 ++++++--
 model.h                    |   5 +-
 stable-diffusion.cpp       |   3 +-
 stable-diffusion.h         |   1 +
 util.cpp                   | 118 +++++++++++++++++++++++++++++++++++++
 util.h                     |  23 ++++++++
 9 files changed, 175 insertions(+), 7 deletions(-)

diff --git a/examples/cli/README.md b/examples/cli/README.md
index c3496aa5..75e1703e 100644
--- a/examples/cli/README.md
+++ b/examples/cli/README.md
@@ -48,6 +48,7 @@ Context Options:
   --vae-tiling                             process vae in tiles to reduce memory usage
   --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
   --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
+  --mmap                                   whether to memory-map model
   --control-net-cpu                        keep controlnet in cpu (for low vram)
   --clip-on-cpu                            keep clip in cpu (for low vram)
   --vae-on-cpu                             keep vae in cpu (for low vram)
diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index cdc8f181..3d4963a2 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -453,6 +453,7 @@ struct SDContextParams {
     rng_type_t rng_type         = CUDA_RNG;
     rng_type_t sampler_rng_type = RNG_TYPE_COUNT;
     bool offload_params_to_cpu  = false;
+    bool enable_mmap            = false;
     bool control_net_cpu        = false;
     bool clip_on_cpu            = false;
     bool vae_on_cpu             = false;
@@ -598,6 +599,10 @@ struct SDContextParams {
              "--offload-to-cpu",
              "place the weights in RAM to save VRAM, and automatically load them into VRAM when needed",
              true, &offload_params_to_cpu},
+            {"",
+             "--mmap",
+             "whether to memory-map model",
+             true, &enable_mmap},
             {"",
              "--control-net-cpu",
              "keep controlnet in cpu (for low vram)",
@@ -895,6 +900,7 @@ struct SDContextParams {
             << "  sampler_rng_type: " << sd_rng_type_name(sampler_rng_type) << ",\n"
             << "  flow_shift: " << (std::isinf(flow_shift) ? "INF" : std::to_string(flow_shift)) << "\n"
             << "  offload_params_to_cpu: " << (offload_params_to_cpu ? "true" : "false") << ",\n"
+            << "  enable_mmap: " << (enable_mmap ? "true" : "false") << ",\n"
             << "  control_net_cpu: " << (control_net_cpu ? "true" : "false") << ",\n"
             << "  clip_on_cpu: " << (clip_on_cpu ? "true" : "false") << ",\n"
             << "  vae_on_cpu: " << (vae_on_cpu ? "true" : "false") << ",\n"
@@ -958,6 +964,7 @@ struct SDContextParams {
             prediction,
             lora_apply_mode,
             offload_params_to_cpu,
+            enable_mmap,
             clip_on_cpu,
             control_net_cpu,
             vae_on_cpu,
diff --git a/examples/server/README.md b/examples/server/README.md
index 59141194..7fb9425f 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -43,6 +43,7 @@ Context Options:
   --control-net-cpu                        keep controlnet in cpu (for low vram)
   --clip-on-cpu                            keep clip in cpu (for low vram)
   --vae-on-cpu                             keep vae in cpu (for low vram)
+  --mmap                                   whether to memory-map model
   --diffusion-fa                           use flash attention in the diffusion model
   --diffusion-conv-direct                  use ggml_conv2d_direct in the diffusion model
   --vae-conv-direct                        use ggml_conv2d_direct in the vae model
diff --git a/model.cpp b/model.cpp
index b5f73442..dcb77e81 100644
--- a/model.cpp
+++ b/model.cpp
@@ -1340,7 +1340,7 @@ std::string ModelLoader::load_umt5_tokenizer_json() {
     return json_str;
 }
 
-bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads_p) {
+bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads_p, bool enable_mmap) {
     int64_t process_time_ms = 0;
     std::atomic<int64_t> read_time_ms(0);
     std::atomic<int64_t> memcpy_time_ms(0);
@@ -1390,6 +1390,15 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
             }
         }
 
+        std::unique_ptr<MmapWrapper> mmapped;
+        if (enable_mmap && !is_zip) {
+            LOG_DEBUG("using mmap for I/O");
+            mmapped = MmapWrapper::create(file_path);
+            if (!mmapped) {
+                LOG_WARN("failed to memory-map '%s'", file_path.c_str());
+            }
+        }
+
         int n_threads = is_zip ? 1 : std::min(num_threads_to_use, (int)file_tensors.size());
         if (n_threads < 1) {
             n_threads = 1;
@@ -1411,7 +1420,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
                         failed = true;
                         return;
                     }
-                } else {
+                } else if (!mmapped) {
                     file.open(file_path, std::ios::binary);
                     if (!file.is_open()) {
                         LOG_ERROR("failed to open '%s'", file_path.c_str());
@@ -1464,6 +1473,11 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
                                 zip_entry_noallocread(zip, (void*)buf, n);
                             }
                             zip_entry_close(zip);
+                        } else if (mmapped) {
+                            if (!mmapped->copy_data(buf, n, tensor_storage.offset)) {
+                                LOG_ERROR("read tensor data failed: '%s'", file_path.c_str());
+                                failed = true;
+                            }
                         } else {
                             file.seekg(tensor_storage.offset);
                             file.read(buf, n);
@@ -1588,7 +1602,8 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
 
 bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tensors,
                                std::set<std::string> ignore_tensors,
-                               int n_threads) {
+                               int n_threads,
+                               bool enable_mmap) {
     std::set<std::string> tensor_names_in_file;
     std::mutex tensor_names_mutex;
     auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
@@ -1631,7 +1646,7 @@ bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tenso
         return true;
     };
 
-    bool success = load_tensors(on_new_tensor_cb, n_threads);
+    bool success = load_tensors(on_new_tensor_cb, n_threads, enable_mmap);
     if (!success) {
         LOG_ERROR("load tensors from file failed");
         return false;
diff --git a/model.h b/model.h
index d38aee1c..b9e50ad6 100644
--- a/model.h
+++ b/model.h
@@ -310,10 +310,11 @@ public:
     std::map<ggml_type, uint32_t> get_vae_wtype_stat();
     String2TensorStorage& get_tensor_storage_map() { return tensor_storage_map; }
     void set_wtype_override(ggml_type wtype, std::string tensor_type_rules = "");
-    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads = 0);
+    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads = 0, bool use_mmap = false);
     bool load_tensors(std::map<std::string, struct ggml_tensor*>& tensors,
                       std::set<std::string> ignore_tensors = {},
-                      int n_threads                        = 0);
+                      int n_threads                        = 0,
+                      bool use_mmap                        = false);
 
     std::vector<std::string> get_tensor_names() const {
         std::vector<std::string> names;
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 9a97d4c7..1c8c55ba 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -766,7 +766,7 @@ public:
         if (version == VERSION_SVD) {
             ignore_tensors.insert("conditioner.embedders.3");
         }
-        bool success = model_loader.load_tensors(tensors, ignore_tensors, n_threads);
+        bool success = model_loader.load_tensors(tensors, ignore_tensors, n_threads, sd_ctx_params->enable_mmap);
         if (!success) {
             LOG_ERROR("load tensors from model loader failed");
             ggml_free(ctx);
@@ -2875,6 +2875,7 @@ void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params) {
     sd_ctx_params->prediction              = PREDICTION_COUNT;
     sd_ctx_params->lora_apply_mode         = LORA_APPLY_AUTO;
     sd_ctx_params->offload_params_to_cpu   = false;
+    sd_ctx_params->enable_mmap             = false;
     sd_ctx_params->keep_clip_on_cpu        = false;
     sd_ctx_params->keep_control_net_on_cpu = false;
     sd_ctx_params->keep_vae_on_cpu         = false;
diff --git a/stable-diffusion.h b/stable-diffusion.h
index 3e9faf85..ade2cdb8 100644
--- a/stable-diffusion.h
+++ b/stable-diffusion.h
@@ -182,6 +182,7 @@ typedef struct {
     enum prediction_t prediction;
     enum lora_apply_mode_t lora_apply_mode;
     bool offload_params_to_cpu;
+    bool enable_mmap;
     bool keep_clip_on_cpu;
     bool keep_control_net_on_cpu;
     bool keep_vae_on_cpu;
diff --git a/util.cpp b/util.cpp
index 680ff804..efb5e494 100644
--- a/util.cpp
+++ b/util.cpp
@@ -95,9 +95,71 @@ bool is_directory(const std::string& path) {
     return (attributes != INVALID_FILE_ATTRIBUTES && (attributes & FILE_ATTRIBUTE_DIRECTORY));
 }
 
+class MmapWrapperImpl : public MmapWrapper {
+public:
+    MmapWrapperImpl(void* data, size_t size, HANDLE hfile, HANDLE hmapping)
+        : MmapWrapper(data, size), hfile_(hfile), hmapping_(hmapping) {}
+
+    ~MmapWrapperImpl() override {
+        UnmapViewOfFile(data_);
+        CloseHandle(hmapping_);
+        CloseHandle(hfile_);
+    }
+
+private:
+    HANDLE hfile_;
+    HANDLE hmapping_;
+};
+
+std::unique_ptr<MmapWrapper> MmapWrapper::create(const std::string& filename) {
+    void* mapped_data = nullptr;
+    size_t file_size  = 0;
+
+    HANDLE file_handle = CreateFileA(
+        filename.c_str(),
+        GENERIC_READ,
+        FILE_SHARE_READ,
+        NULL,
+        OPEN_EXISTING,
+        FILE_ATTRIBUTE_NORMAL,
+        NULL);
+
+    if (file_handle == INVALID_HANDLE_VALUE) {
+        return nullptr;
+    }
+
+    LARGE_INTEGER size;
+    if (!GetFileSizeEx(file_handle, &size)) {
+        CloseHandle(file_handle);
+        return nullptr;
+    }
+
+    file_size = static_cast<size_t>(size.QuadPart);
+
+    HANDLE mapping_handle = CreateFileMapping(file_handle, NULL, PAGE_READONLY, 0, 0, NULL);
+
+    if (mapping_handle == NULL) {
+        CloseHandle(file_handle);
+        return nullptr;
+    }
+
+    mapped_data = MapViewOfFile(mapping_handle, FILE_MAP_READ, 0, 0, file_size);
+
+    if (mapped_data == NULL) {
+        CloseHandle(mapping_handle);
+        CloseHandle(file_handle);
+        return nullptr;
+    }
+
+    return std::make_unique<MmapWrapperImpl>(mapped_data, file_size, file_handle, mapping_handle);
+}
+
 #else  // Unix
 #include <dirent.h>
+#include <fcntl.h>
+#include <sys/mman.h>
 #include <sys/stat.h>
+#include <unistd.h>
 
 bool file_exists(const std::string& filename) {
     struct stat buffer;
@@ -109,8 +171,64 @@ bool is_directory(const std::string& path) {
     return (stat(path.c_str(), &buffer) == 0 && S_ISDIR(buffer.st_mode));
 }
 
+class MmapWrapperImpl : public MmapWrapper {
+public:
+    MmapWrapperImpl(void* data, size_t size)
+        : MmapWrapper(data, size) {}
+
+    ~MmapWrapperImpl() override {
+        munmap(data_, size_);
+    }
+};
+
+std::unique_ptr<MmapWrapper> MmapWrapper::create(const std::string& filename) {
+    int file_descriptor = open(filename.c_str(), O_RDONLY);
+    if (file_descriptor == -1) {
+        return nullptr;
+    }
+
+    int mmap_flags = MAP_PRIVATE;
+
+#ifdef __linux__
+    // performance flags used by llama.cpp
+    // posix_fadvise(file_descriptor, 0, 0, POSIX_FADV_SEQUENTIAL);
+    // mmap_flags |= MAP_POPULATE;
 #endif
 
+    struct stat sb;
+    if (fstat(file_descriptor, &sb) == -1) {
+        close(file_descriptor);
+        return nullptr;
+    }
+
+    size_t file_size = sb.st_size;
+
+    void* mapped_data = mmap(NULL, file_size, PROT_READ, mmap_flags, file_descriptor, 0);
+
+    close(file_descriptor);
+
+    if (mapped_data == MAP_FAILED) {
+        return nullptr;
+    }
+
+#ifdef __linux__
+    // performance flags used by llama.cpp
+    // posix_madvise(mapped_data, file_size, POSIX_MADV_WILLNEED);
+#endif
+
+    return std::make_unique<MmapWrapperImpl>(mapped_data, file_size);
+}
+
+#endif
+
+bool MmapWrapper::copy_data(void* buf, size_t n, size_t offset) const {
+    if (offset >= size_ || n > (size_ - offset)) {
+        return false;
+    }
+    std::memcpy(buf, data() + offset, n);
+    return true;
+}
+
 // get_num_physical_cores is copy from
 // https://github.com/ggerganov/llama.cpp/blob/master/examples/common.cpp
 // LICENSE: https://github.com/ggerganov/llama.cpp/blob/master/LICENSE
diff --git a/util.h b/util.h
index dd4a0c30..7dee7bf5 100644
--- a/util.h
+++ b/util.h
@@ -2,6 +2,7 @@
 #define __UTIL_H__
 
 #include <cstdint>
+#include <memory>
 #include <string>
 #include <vector>
 
@@ -43,6 +44,28 @@ sd_image_f32_t resize_sd_image_f32_t(sd_image_f32_t image, int target_width, int
 
 sd_image_f32_t clip_preprocess(sd_image_f32_t image, int target_width, int target_height);
 
+class MmapWrapper {
+public:
+    static std::unique_ptr<MmapWrapper> create(const std::string& filename);
+
+    virtual ~MmapWrapper() = default;
+
+    MmapWrapper(const MmapWrapper&)            = delete;
+    MmapWrapper& operator=(const MmapWrapper&) = delete;
+    MmapWrapper(MmapWrapper&&)                 = delete;
+    MmapWrapper& operator=(MmapWrapper&&)      = delete;
+
+    const uint8_t* data() const { return static_cast<uint8_t*>(data_); }
+    size_t size() const { return size_; }
+    bool copy_data(void* buf, size_t n, size_t offset) const;
+
+protected:
+    MmapWrapper(void* data, size_t size)
+        : data_(data), size_(size) {}
+    void* data_  = nullptr;
+    size_t size_ = 0;
+};
+
 std::string path_join(const std::string& p1, const std::string& p2);
 std::vector<std::string> split_string(const std::string& str, char delimiter);
 void pretty_progress(int step, int steps, float time);

From 51bd9c8004964e744b85fb3be42fe972ecb2ea6e Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 28 Dec 2025 22:53:07 +0800
Subject: [PATCH 29/97] chore: reformat named cache params description into
 single line

---
 examples/cli/README.md     | 10 +++-------
 examples/common/common.hpp |  2 +-
 examples/server/README.md  | 10 +++-------
 3 files changed, 7 insertions(+), 15 deletions(-)

diff --git a/examples/cli/README.md b/examples/cli/README.md
index 75e1703e..84dd5c71 100644
--- a/examples/cli/README.md
+++ b/examples/cli/README.md
@@ -134,13 +134,9 @@ Generation Options:
   --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
   -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
   --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)
-  --cache-option                           named cache params (key=value format, comma-separated):
-                                                                                      - easycache/ucache:
-                                           threshold=,start=,end=,decay=,relative=,reset=
-                                                                                      - dbcache/taylorseer/cache-dit:
-                                           Fn=,Bn=,threshold=,warmup=
-                                                                                      Examples: "threshold=0.25" or
-                                           "threshold=1.5,reset=0"
+  --cache-option                           named cache params (key=value format, comma-separated). easycache/ucache:
+                                           threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=. Examples:
+                                           "threshold=0.25" or "threshold=1.5,reset=0"
   --cache-preset                           cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'
   --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g., "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
   --scm-policy                             SCM policy: 'dynamic' (default) or 'static'
diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index 3d4963a2..7ea95ed1 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -1512,7 +1512,7 @@ struct SDGenerationParams {
              on_cache_mode_arg},
             {"",
              "--cache-option",
-             "named cache params (key=value format, comma-separated):\n                                           - easycache/ucache: threshold=,start=,end=,decay=,relative=,reset=\n                                           - dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=\n                                           Examples: \"threshold=0.25\" or \"threshold=1.5,reset=0\"",
+             "named cache params (key=value format, comma-separated). easycache/ucache: threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=. Examples: \"threshold=0.25\" or \"threshold=1.5,reset=0\"",
              on_cache_option_arg},
             {"",
              "--cache-preset",
diff --git a/examples/server/README.md b/examples/server/README.md
index 7fb9425f..7e668157 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -126,13 +126,9 @@ Default Generation Options:
   --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
   -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
   --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)
-  --cache-option                           named cache params (key=value format, comma-separated):
-                                                                                      - easycache/ucache:
-                                           threshold=,start=,end=,decay=,relative=,reset=
-                                                                                      - dbcache/taylorseer/cache-dit:
-                                           Fn=,Bn=,threshold=,warmup=
-                                                                                      Examples: "threshold=0.25" or
-                                           "threshold=1.5,reset=0"
+  --cache-option                           named cache params (key=value format, comma-separated). easycache/ucache:
+                                           threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=. Examples:
+                                           "threshold=0.25" or "threshold=1.5,reset=0"
   --cache-preset                           cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'
   --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g., "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
   --scm-policy                             SCM policy: 'dynamic' (default) or 'static'

From 4ff2c8c74bd17c2cfffe3a01be77743fb3efba2f Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 28 Dec 2025 23:27:27 +0800
Subject: [PATCH 30/97] refactor: simplify logic for saving results (#1149)

---
 examples/cli/main.cpp | 186 +++++++++++++++++++++---------------------
 1 file changed, 91 insertions(+), 95 deletions(-)

diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index 77ef1c93..86bc1abf 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -370,6 +370,95 @@ std::string format_frame_idx(std::string pattern, int frame_idx) {
     return result;
 }
 
+bool save_results(const SDCliParams& cli_params,
+                  const SDContextParams& ctx_params,
+                  const SDGenerationParams& gen_params,
+                  sd_image_t* results,
+                  int num_results) {
+    if (results == nullptr || num_results <= 0) {
+        return false;
+    }
+
+    namespace fs      = std::filesystem;
+    fs::path out_path = cli_params.output_path;
+
+    if (!out_path.parent_path().empty()) {
+        std::error_code ec;
+        fs::create_directories(out_path.parent_path(), ec);
+        if (ec) {
+            LOG_ERROR("failed to create directory '%s': %s",
+                      out_path.parent_path().string().c_str(), ec.message().c_str());
+            return false;
+        }
+    }
+
+    fs::path base_path = out_path;
+    fs::path ext       = out_path.has_extension() ? out_path.extension() : fs::path{};
+    if (!ext.empty())
+        base_path.replace_extension();
+
+    std::string ext_lower = ext.string();
+    std::transform(ext_lower.begin(), ext_lower.end(), ext_lower.begin(), ::tolower);
+    bool is_jpg = (ext_lower == ".jpg" || ext_lower == ".jpeg" || ext_lower == ".jpe");
+
+    int output_begin_idx = cli_params.output_begin_idx;
+    if (output_begin_idx < 0) {
+        output_begin_idx = 0;
+    }
+
+    auto write_image = [&](const fs::path& path, int idx) {
+        const sd_image_t& img = results[idx];
+        if (!img.data)
+            return;
+
+        std::string params = get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + idx);
+        int ok             = 0;
+        if (is_jpg) {
+            ok = stbi_write_jpg(path.string().c_str(), img.width, img.height, img.channel, img.data, 90, params.c_str());
+        } else {
+            ok = stbi_write_png(path.string().c_str(), img.width, img.height, img.channel, img.data, 0, params.c_str());
+        }
+        LOG_INFO("save result image %d to '%s' (%s)", idx, path.string().c_str(), ok ? "success" : "failure");
+    };
+
+    if (std::regex_search(cli_params.output_path, format_specifier_regex)) {
+        if (!is_jpg && ext_lower != ".png")
+            ext = ".png";
+        fs::path pattern = base_path;
+        pattern += ext;
+
+        for (int i = 0; i < num_results; ++i) {
+            fs::path img_path = format_frame_idx(pattern.string(), output_begin_idx + i);
+            write_image(img_path, i);
+        }
+        return true;
+    }
+
+    if (cli_params.mode == VID_GEN && num_results > 1) {
+        if (ext_lower != ".avi")
+            ext = ".avi";
+        fs::path video_path = base_path;
+        video_path += ext;
+        create_mjpg_avi_from_sd_images(video_path.string().c_str(), results, num_results, gen_params.fps);
+        LOG_INFO("save result MJPG AVI video to '%s'", video_path.string().c_str());
+        return true;
+    }
+
+    if (!is_jpg && ext_lower != ".png")
+        ext = ".png";
+
+    for (int i = 0; i < num_results; ++i) {
+        fs::path img_path = base_path;
+        if (num_results > 1) {
+            img_path += "_" + std::to_string(output_begin_idx + i);
+        }
+        img_path += ext;
+        write_image(img_path, i);
+    }
+
+    return true;
+}
+
 int main(int argc, const char* argv[]) {
     if (argc > 1 && std::string(argv[1]) == "--version") {
         std::cout << version_string() << "\n";
@@ -713,101 +802,8 @@ int main(int argc, const char* argv[]) {
         }
     }
 
-    // create directory if not exists
-    {
-        const fs::path out_path = cli_params.output_path;
-        if (const fs::path out_dir = out_path.parent_path(); !out_dir.empty()) {
-            std::error_code ec;
-            fs::create_directories(out_dir, ec);  // OK if already exists
-            if (ec) {
-                LOG_ERROR("failed to create directory '%s': %s",
-                          out_dir.string().c_str(), ec.message().c_str());
-                return 1;
-            }
-        }
-    }
-
-    std::string base_path;
-    std::string file_ext;
-    std::string file_ext_lower;
-    bool is_jpg;
-    size_t last_dot_pos   = cli_params.output_path.find_last_of(".");
-    size_t last_slash_pos = std::min(cli_params.output_path.find_last_of("/"),
-                                     cli_params.output_path.find_last_of("\\"));
-    if (last_dot_pos != std::string::npos && (last_slash_pos == std::string::npos || last_dot_pos > last_slash_pos)) {  // filename has extension
-        base_path = cli_params.output_path.substr(0, last_dot_pos);
-        file_ext = file_ext_lower = cli_params.output_path.substr(last_dot_pos);
-        std::transform(file_ext.begin(), file_ext.end(), file_ext_lower.begin(), ::tolower);
-        is_jpg = (file_ext_lower == ".jpg" || file_ext_lower == ".jpeg" || file_ext_lower == ".jpe");
-    } else {
-        base_path = cli_params.output_path;
-        file_ext = file_ext_lower = "";
-        is_jpg                    = false;
-    }
-
-    if (std::regex_search(cli_params.output_path, format_specifier_regex)) {
-        std::string final_output_path = cli_params.output_path;
-        if (cli_params.output_begin_idx == -1) {
-            cli_params.output_begin_idx = 0;
-        }
-        // writing image sequence, default to PNG
-        if (!is_jpg && file_ext_lower != ".png") {
-            base_path += file_ext;
-            file_ext = ".png";
-        }
-        final_output_path = base_path + file_ext;
-        for (int i = 0; i < num_results; i++) {
-            if (results[i].data == nullptr) {
-                continue;
-            }
-            std::string final_image_path = format_frame_idx(final_output_path, cli_params.output_begin_idx + i);
-            if (is_jpg) {
-                int write_ok = stbi_write_jpg(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
-                                              results[i].data, 90, get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + i).c_str());
-                LOG_INFO("save result JPEG image %d to '%s' (%s)", i, final_image_path.c_str(), write_ok == 0 ? "failure" : "success");
-            } else {
-                int write_ok = stbi_write_png(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
-                                              results[i].data, 0, get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + i).c_str());
-                LOG_INFO("save result PNG image %d to '%s' (%s)", i, final_image_path.c_str(), write_ok == 0 ? "failure" : "success");
-            }
-        }
-    } else if (cli_params.mode == VID_GEN && num_results > 1) {
-        std::string final_output_path = cli_params.output_path;
-        if (file_ext_lower != ".avi") {
-            if (!is_jpg && file_ext_lower != ".png") {
-                base_path += file_ext;
-            }
-            file_ext          = ".avi";
-            final_output_path = base_path + file_ext;
-        }
-        create_mjpg_avi_from_sd_images(final_output_path.c_str(), results, num_results, gen_params.fps);
-        LOG_INFO("save result MJPG AVI video to '%s'\n", final_output_path.c_str());
-    } else {
-        // appending ".png" to absent or unknown extension
-        if (!is_jpg && file_ext_lower != ".png") {
-            base_path += file_ext;
-            file_ext = ".png";
-        }
-        if (cli_params.output_begin_idx == -1) {
-            cli_params.output_begin_idx = 1;
-        }
-        for (int i = 0; i < num_results; i++) {
-            if (results[i].data == nullptr) {
-                continue;
-            }
-            int write_ok;
-            std::string final_image_path;
-            final_image_path = i > 0 ? base_path + "_" + std::to_string(cli_params.output_begin_idx + i) + file_ext : base_path + file_ext;
-            if (is_jpg) {
-                write_ok = stbi_write_jpg(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
-                                          results[i].data, 90, get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + i).c_str());
-                LOG_INFO("save result JPEG image to '%s' (%s)", final_image_path.c_str(), write_ok == 0 ? "failure" : "success");
-            } else {
-                write_ok = stbi_write_png(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
-                                          results[i].data, 0, get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + i).c_str());
-                LOG_INFO("save result PNG image to '%s' (%s)", final_image_path.c_str(), write_ok == 0 ? "failure" : "success");
-            }
-        }
+    if (!save_results(cli_params, ctx_params, gen_params, results, num_results)) {
+        return 1;
     }
 
     for (int i = 0; i < num_results; i++) {

From 6eefd2d49a2a6862589328e4f2447123a3e65dee Mon Sep 17 00:00:00 2001
From: Jay4242 <96741732+Jay4242@users.noreply.github.com>
Date: Sun, 4 Jan 2026 05:57:50 -0800
Subject: [PATCH 31/97] feat: support random seed flag (#1163)

---
 examples/server/main.cpp | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index c540958f..db9f5b1a 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -202,12 +202,18 @@ void parse_args(int argc, const char** argv, SDSvrParams& svr_params, SDContextP
         exit(svr_params.normal_exit ? 0 : 1);
     }
 
+    const bool random_seed_requested = default_gen_params.seed < 0;
+
     if (!svr_params.process_and_check() ||
         !ctx_params.process_and_check(IMG_GEN) ||
         !default_gen_params.process_and_check(IMG_GEN, ctx_params.lora_model_dir)) {
         print_usage(argc, argv, options_vec);
         exit(1);
     }
+
+    if (random_seed_requested) {
+        default_gen_params.seed = -1;
+    }
 }
 
 std::string extract_and_remove_sd_cpp_extra_args(std::string& text) {

From a119a4da9a72d4c4c4a5030e61e431e201ddbedf Mon Sep 17 00:00:00 2001
From: Daniele <57776841+daniandtheweb@users.noreply.github.com>
Date: Sun, 4 Jan 2026 15:05:01 +0100
Subject: [PATCH 32/97] fix: avoid issues when sigma_min is close to 0 (#1138)

---
 denoiser.hpp | 20 ++++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/denoiser.hpp b/denoiser.hpp
index 2dc4c60e..2ed62fe9 100644
--- a/denoiser.hpp
+++ b/denoiser.hpp
@@ -276,6 +276,10 @@ struct KarrasScheduler : SigmaScheduler {
         // but does anybody ever bother to touch them?
         float rho = 7.f;
 
+        if (sigma_min <= 1e-6f) {
+            sigma_min = 1e-6f;
+        }
+
         std::vector<float> result(n + 1);
 
         float min_inv_rho = pow(sigma_min, (1.f / rho));
@@ -347,7 +351,6 @@ struct SmoothStepScheduler : SigmaScheduler {
     }
 };
 
-// Implementation adapted from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608
 struct KLOptimalScheduler : SigmaScheduler {
     std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
         std::vector<float> sigmas;
@@ -355,27 +358,28 @@ struct KLOptimalScheduler : SigmaScheduler {
         if (n == 0) {
             return sigmas;
         }
+
         if (n == 1) {
             sigmas.push_back(sigma_max);
             sigmas.push_back(0.0f);
             return sigmas;
         }
 
+        if (sigma_min <= 1e-6f) {
+            sigma_min = 1e-6f;
+        }
+
+        sigmas.reserve(n + 1);
+
         float alpha_min = std::atan(sigma_min);
         float alpha_max = std::atan(sigma_max);
 
         for (uint32_t i = 0; i < n; ++i) {
-            // t goes from 0.0 to 1.0
-            float t = static_cast<float>(i) / static_cast<float>(n - 1);
-
-            // Interpolate in the angle domain
+            float t     = static_cast<float>(i) / static_cast<float>(n - 1);
             float angle = t * alpha_min + (1.0f - t) * alpha_max;
-
-            // Convert back to sigma
             sigmas.push_back(std::tan(angle));
         }
 
-        // Append the final zero to sigma
         sigmas.push_back(0.0f);
 
         return sigmas;

From 2cef4badb8f7e5f0e39f2f5f3b7e6eaff03164f7 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 4 Jan 2026 22:26:09 +0800
Subject: [PATCH 33/97] chore: use Release build for windows-latest-cmake

---
 .github/workflows/build.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index bfe532bd..1009bf98 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -201,7 +201,7 @@ jobs:
           mkdir build
           cd build
           cmake .. -DCMAKE_CXX_FLAGS='/bigobj' -G Ninja -DCMAKE_C_COMPILER=cl.exe -DCMAKE_CXX_COMPILER=cl.exe ${{ matrix.defines }}
-          cmake --build .
+          cmake --build . --config Release
 
       - name: Check AVX512F support
         id: check_avx512f

From b90b1ee9cf84ea48b478c674dd2ec6a33fd504d6 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 4 Jan 2026 22:26:57 +0800
Subject: [PATCH 34/97] chore: eliminate compilation warnings under MSVC
 (#1170)

---
 CMakeLists.txt               |   5 ++
 cache_dit.hpp                |   2 +-
 clip.hpp                     |  20 +++----
 common.hpp                   |   8 +--
 conditioner.hpp              |  20 +++----
 denoiser.hpp                 |  43 +++++++-------
 examples/cli/avi_writer.h    |   4 +-
 examples/common/common.hpp   |   4 +-
 examples/server/main.cpp     |   6 +-
 flux.hpp                     | 112 +++++++++++++++++------------------
 ggml_extend.hpp              |  83 ++++++--------------------
 gguf_reader.hpp              |   2 +-
 latent-preview.h             |  16 ++---
 llm.hpp                      | 110 +++++++++++++++++-----------------
 mmdit.hpp                    |  22 +++----
 model.cpp                    |   8 +--
 pmid.hpp                     |   2 +-
 preprocessing.hpp            |  20 +++----
 qwen_image.hpp               |  28 ++++-----
 rng_mt19937.hpp              |   2 +-
 rope.hpp                     |  60 +++++++++----------
 stable-diffusion.cpp         |  74 +++++++++++------------
 t5.hpp                       |  24 ++++----
 thirdparty/darts.h           |  18 +++---
 thirdparty/stb_image_write.h |  14 +++--
 unet.hpp                     |  20 +++----
 util.cpp                     |  32 +++++-----
 vae.hpp                      |  34 +++++------
 wan.hpp                      |  62 +++++++++----------
 z_image.hpp                  |  26 ++++----
 30 files changed, 420 insertions(+), 461 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8ea1c47b..e731d95d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -8,6 +8,11 @@ if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
     set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
 endif()
 
+if (MSVC)
+    add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
+    add_compile_definitions(_SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING)
+endif()
+
 set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
 
diff --git a/cache_dit.hpp b/cache_dit.hpp
index f5a1f180..6fe104da 100644
--- a/cache_dit.hpp
+++ b/cache_dit.hpp
@@ -117,7 +117,7 @@ struct TaylorSeerState {
                 continue;
             if (o > 0)
                 factorial *= static_cast<float>(o);
-            float coeff = std::pow(static_cast<float>(elapsed), o) / factorial;
+            float coeff = ::powf(static_cast<float>(elapsed), static_cast<float>(o)) / factorial;
             for (size_t i = 0; i < size; i++) {
                 output[i] += coeff * dY_prev[o][i];
             }
diff --git a/clip.hpp b/clip.hpp
index 24c94f1b..7a6ebe9e 100644
--- a/clip.hpp
+++ b/clip.hpp
@@ -296,7 +296,7 @@ public:
                     size_t max_length = 0,
                     bool padding      = false) {
         if (max_length > 0 && padding) {
-            size_t n = std::ceil(tokens.size() * 1.0 / (max_length - 2));
+            size_t n = static_cast<size_t>(std::ceil(tokens.size() * 1.0 / (max_length - 2)));
             if (n == 0) {
                 n = 1;
             }
@@ -525,10 +525,10 @@ public:
 
 struct CLIPEncoder : public GGMLBlock {
 protected:
-    int64_t n_layer;
+    int n_layer;
 
 public:
-    CLIPEncoder(int64_t n_layer,
+    CLIPEncoder(int n_layer,
                 int64_t d_model,
                 int64_t n_head,
                 int64_t intermediate_size,
@@ -623,10 +623,10 @@ public:
 class CLIPVisionEmbeddings : public GGMLBlock {
 protected:
     int64_t embed_dim;
-    int64_t num_channels;
-    int64_t patch_size;
-    int64_t image_size;
-    int64_t num_patches;
+    int num_channels;
+    int patch_size;
+    int image_size;
+    int num_patches;
     int64_t num_positions;
 
     void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
@@ -641,9 +641,9 @@ protected:
 
 public:
     CLIPVisionEmbeddings(int64_t embed_dim,
-                         int64_t num_channels = 3,
-                         int64_t patch_size   = 14,
-                         int64_t image_size   = 224)
+                         int num_channels = 3,
+                         int patch_size   = 14,
+                         int image_size   = 224)
         : embed_dim(embed_dim),
           num_channels(num_channels),
           patch_size(patch_size),
diff --git a/common.hpp b/common.hpp
index b17c11e3..13ab1038 100644
--- a/common.hpp
+++ b/common.hpp
@@ -80,7 +80,7 @@ protected:
                                        std::pair<int, int> padding) {
         GGML_ASSERT(dims == 2 || dims == 3);
         if (dims == 3) {
-            return std::shared_ptr<GGMLBlock>(new Conv3dnx1x1(in_channels, out_channels, kernel_size.first, 1, padding.first));
+            return std::shared_ptr<GGMLBlock>(new Conv3d(in_channels, out_channels, {kernel_size.first, 1, 1}, {1, 1, 1}, {padding.first, 0, 0}));
         } else {
             return std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, out_channels, kernel_size, {1, 1}, padding));
         }
@@ -544,9 +544,9 @@ public:
 
 class VideoResBlock : public ResBlock {
 public:
-    VideoResBlock(int channels,
-                  int emb_channels,
-                  int out_channels,
+    VideoResBlock(int64_t channels,
+                  int64_t emb_channels,
+                  int64_t out_channels,
                   std::pair<int, int> kernel_size = {3, 3},
                   int64_t video_kernel_size       = 3,
                   int dims                        = 2)  // always 2
diff --git a/conditioner.hpp b/conditioner.hpp
index 45db314b..b6d5646a 100644
--- a/conditioner.hpp
+++ b/conditioner.hpp
@@ -303,11 +303,11 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                 int class_token = clean_input_ids[class_token_index[0]];
                 class_idx       = tokens_acc + class_token_index[0];
                 std::vector<int> clean_input_ids_tmp;
-                for (uint32_t i = 0; i < class_token_index[0]; i++)
+                for (int i = 0; i < class_token_index[0]; i++)
                     clean_input_ids_tmp.push_back(clean_input_ids[i]);
-                for (uint32_t i = 0; i < (pm_version == PM_VERSION_2 ? 2 * num_input_imgs : num_input_imgs); i++)
+                for (int i = 0; i < (pm_version == PM_VERSION_2 ? 2 * num_input_imgs : num_input_imgs); i++)
                     clean_input_ids_tmp.push_back(class_token);
-                for (uint32_t i = class_token_index[0] + 1; i < clean_input_ids.size(); i++)
+                for (int i = class_token_index[0] + 1; i < clean_input_ids.size(); i++)
                     clean_input_ids_tmp.push_back(clean_input_ids[i]);
                 clean_input_ids.clear();
                 clean_input_ids = clean_input_ids_tmp;
@@ -322,7 +322,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
 
         tokenizer.pad_tokens(tokens, weights, max_length, padding);
         int offset = pm_version == PM_VERSION_2 ? 2 * num_input_imgs : num_input_imgs;
-        for (uint32_t i = 0; i < tokens.size(); i++) {
+        for (int i = 0; i < tokens.size(); i++) {
             // if (class_idx + 1 <= i && i < class_idx + 1 + 2*num_input_imgs) // photomaker V2 has num_tokens(=2)*num_input_imgs
             if (class_idx + 1 <= i && i < class_idx + 1 + offset)  // photomaker V2 has num_tokens(=2)*num_input_imgs
                                                                    // hardcode for now
@@ -1584,7 +1584,7 @@ struct T5CLIPEmbedder : public Conditioner {
                                         chunk_hidden_states->ne[0],
                                         ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);
 
-        modify_mask_to_attend_padding(t5_attn_mask, ggml_nelements(t5_attn_mask), mask_pad);
+        modify_mask_to_attend_padding(t5_attn_mask, static_cast<int>(ggml_nelements(t5_attn_mask)), mask_pad);
 
         return {hidden_states, t5_attn_mask, nullptr};
     }
@@ -1723,8 +1723,8 @@ struct LLMEmbedder : public Conditioner {
                 double factor        = llm->params.vision.patch_size * llm->params.vision.spatial_merge_size;
                 int height           = image.height;
                 int width            = image.width;
-                int h_bar            = static_cast<int>(std::round(height / factor)) * factor;
-                int w_bar            = static_cast<int>(std::round(width / factor)) * factor;
+                int h_bar            = static_cast<int>(std::round(height / factor) * factor);
+                int w_bar            = static_cast<int>(std::round(width / factor) * factor);
 
                 if (static_cast<double>(h_bar) * w_bar > max_pixels) {
                     double beta = std::sqrt((height * width) / static_cast<double>(max_pixels));
@@ -1752,7 +1752,7 @@ struct LLMEmbedder : public Conditioner {
                 ggml_tensor* image_embed = nullptr;
                 llm->encode_image(n_threads, image_tensor, &image_embed, work_ctx);
                 image_embeds.emplace_back(image_embed_idx, image_embed);
-                image_embed_idx += 1 + image_embed->ne[1] + 6;
+                image_embed_idx += 1 + static_cast<int>(image_embed->ne[1]) + 6;
 
                 img_prompt += "Picture " + std::to_string(i + 1) + ": <|vision_start|>";  // [24669, 220, index, 25, 220, 151652]
                 int64_t num_image_tokens = image_embed->ne[1];
@@ -1799,9 +1799,9 @@ struct LLMEmbedder : public Conditioner {
 
             prompt = "[SYSTEM_PROMPT]You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object\nattribution and actions without speculation.[/SYSTEM_PROMPT][INST]";
 
-            prompt_attn_range.first = prompt.size();
+            prompt_attn_range.first = static_cast<int>(prompt.size());
             prompt += conditioner_params.text;
-            prompt_attn_range.second = prompt.size();
+            prompt_attn_range.second = static_cast<int>(prompt.size());
 
             prompt += "[/INST]";
         } else if (version == VERSION_OVIS_IMAGE) {
diff --git a/denoiser.hpp b/denoiser.hpp
index 2ed62fe9..98aef702 100644
--- a/denoiser.hpp
+++ b/denoiser.hpp
@@ -245,7 +245,7 @@ struct SGMUniformScheduler : SigmaScheduler {
         int t_max                    = TIMESTEPS - 1;
         int t_min                    = 0;
         std::vector<float> timesteps = linear_space(static_cast<float>(t_max), static_cast<float>(t_min), n + 1);
-        for (int i = 0; i < n; i++) {
+        for (uint32_t i = 0; i < n; i++) {
             result.push_back(t_to_sigma_func(timesteps[i]));
         }
         result.push_back(0.0f);
@@ -259,11 +259,11 @@ struct LCMScheduler : SigmaScheduler {
         result.reserve(n + 1);
         const int original_steps = 50;
         const int k              = TIMESTEPS / original_steps;
-        for (int i = 0; i < n; i++) {
+        for (uint32_t i = 0; i < n; i++) {
             // the rounding ensures we match the training schedule of the LCM model
             int index    = (i * original_steps) / n;
             int timestep = (original_steps - index) * k - 1;
-            result.push_back(t_to_sigma(timestep));
+            result.push_back(t_to_sigma(static_cast<float>(timestep)));
         }
         result.push_back(0.0f);
         return result;
@@ -525,8 +525,8 @@ struct CompVisVDenoiser : public CompVisDenoiser {
 };
 
 struct EDMVDenoiser : public CompVisVDenoiser {
-    float min_sigma = 0.002;
-    float max_sigma = 120.0;
+    float min_sigma = 0.002f;
+    float max_sigma = 120.0f;
 
     EDMVDenoiser(float min_sigma = 0.002, float max_sigma = 120.0)
         : min_sigma(min_sigma), max_sigma(max_sigma) {
@@ -537,7 +537,7 @@ struct EDMVDenoiser : public CompVisVDenoiser {
     }
 
     float sigma_to_t(float s) override {
-        return 0.25 * std::log(s);
+        return 0.25f * std::log(s);
     }
 
     float sigma_min() override {
@@ -569,7 +569,7 @@ struct DiscreteFlowDenoiser : public Denoiser {
 
     void set_parameters() {
         for (int i = 1; i < TIMESTEPS + 1; i++) {
-            sigmas[i - 1] = t_to_sigma(i);
+            sigmas[i - 1] = t_to_sigma(static_cast<float>(i));
         }
     }
 
@@ -612,7 +612,7 @@ struct DiscreteFlowDenoiser : public Denoiser {
 };
 
 float flux_time_shift(float mu, float sigma, float t) {
-    return std::exp(mu) / (std::exp(mu) + std::pow((1.0 / t - 1.0), sigma));
+    return ::expf(mu) / (::expf(mu) + ::powf((1.0f / t - 1.0f), sigma));
 }
 
 struct FluxFlowDenoiser : public Denoiser {
@@ -632,7 +632,7 @@ struct FluxFlowDenoiser : public Denoiser {
     void set_parameters(float shift) {
         set_shift(shift);
         for (int i = 0; i < TIMESTEPS; i++) {
-            sigmas[i] = t_to_sigma(i);
+            sigmas[i] = t_to_sigma(static_cast<float>(i));
         }
     }
 
@@ -1327,15 +1327,12 @@ static bool sample_k_diffusion(sample_method_t method,
                 // - pred_sample_direction -> "direction pointing to
                 //   x_t"
                 // - pred_prev_sample -> "x_t-1"
-                int timestep =
-                    roundf(TIMESTEPS -
-                           i * ((float)TIMESTEPS / steps)) -
-                    1;
+                int timestep = static_cast<int>(roundf(TIMESTEPS - i * ((float)TIMESTEPS / steps))) - 1;
                 // 1. get previous step value (=t-1)
-                int prev_timestep = timestep - TIMESTEPS / steps;
+                int prev_timestep = timestep - TIMESTEPS / static_cast<int>(steps);
                 // The sigma here is chosen to cause the
                 // CompVisDenoiser to produce t = timestep
-                float sigma = compvis_sigmas[timestep];
+                float sigma = static_cast<float>(compvis_sigmas[timestep]);
                 if (i == 0) {
                     // The function add_noise intializes x to
                     // Diffusers' latents * sigma (as in Diffusers'
@@ -1392,10 +1389,10 @@ static bool sample_k_diffusion(sample_method_t method,
                     }
                 }
                 // 2. compute alphas, betas
-                float alpha_prod_t = alphas_cumprod[timestep];
+                float alpha_prod_t = static_cast<float>(alphas_cumprod[timestep]);
                 // Note final_alpha_cumprod = alphas_cumprod[0] due to
                 // trailing timestep spacing
-                float alpha_prod_t_prev = prev_timestep >= 0 ? alphas_cumprod[prev_timestep] : alphas_cumprod[0];
+                float alpha_prod_t_prev = static_cast<float>(prev_timestep >= 0 ? alphas_cumprod[prev_timestep] : alphas_cumprod[0]);
                 float beta_prod_t       = 1 - alpha_prod_t;
                 // 3. compute predicted original sample from predicted
                 // noise also called "predicted x_0" of formula (12)
@@ -1442,8 +1439,8 @@ static bool sample_k_diffusion(sample_method_t method,
                         // Two step inner loop without an explicit
                         // tensor
                         float pred_sample_direction =
-                            std::sqrt(1 - alpha_prod_t_prev -
-                                      std::pow(std_dev_t, 2)) *
+                            ::sqrtf(1 - alpha_prod_t_prev -
+                                    ::powf(std_dev_t, 2)) *
                             vec_model_output[j];
                         vec_x[j] = std::sqrt(alpha_prod_t_prev) *
                                        vec_pred_original_sample[j] +
@@ -1518,7 +1515,7 @@ static bool sample_k_diffusion(sample_method_t method,
                 // Begin k-diffusion specific workaround for
                 // evaluating F_theta(x; ...) from D(x, sigma), same
                 // as in DDIM (and see there for detailed comments)
-                float sigma = compvis_sigmas[timestep];
+                float sigma = static_cast<float>(compvis_sigmas[timestep]);
                 if (i == 0) {
                     float* vec_x = (float*)x->data;
                     for (int j = 0; j < ggml_nelements(x); j++) {
@@ -1557,14 +1554,14 @@ static bool sample_k_diffusion(sample_method_t method,
                 // is different from the notation alpha_t in
                 // DPM-Solver. In fact, we have alpha_{t_n} =
                 // \sqrt{\hat{alpha_n}}, [...]"
-                float alpha_prod_t = alphas_cumprod[timestep];
+                float alpha_prod_t = static_cast<float>(alphas_cumprod[timestep]);
                 float beta_prod_t  = 1 - alpha_prod_t;
                 // Note final_alpha_cumprod = alphas_cumprod[0] since
                 // TCD is always "trailing"
-                float alpha_prod_t_prev = prev_timestep >= 0 ? alphas_cumprod[prev_timestep] : alphas_cumprod[0];
+                float alpha_prod_t_prev = static_cast<float>(prev_timestep >= 0 ? alphas_cumprod[prev_timestep] : alphas_cumprod[0]);
                 // The subscript _s are the only portion in this
                 // section (2) unique to TCD
-                float alpha_prod_s = alphas_cumprod[timestep_s];
+                float alpha_prod_s = static_cast<float>(alphas_cumprod[timestep_s]);
                 float beta_prod_s  = 1 - alpha_prod_s;
                 // 3. Compute the predicted noised sample x_s based on
                 // the model parameterization
diff --git a/examples/cli/avi_writer.h b/examples/cli/avi_writer.h
index 84b204af..53b4749c 100644
--- a/examples/cli/avi_writer.h
+++ b/examples/cli/avi_writer.h
@@ -172,9 +172,9 @@ int create_mjpg_avi_from_sd_images(const char* filename, sd_image_t* images, int
 
         // Write '00dc' chunk (video frame)
         fwrite("00dc", 4, 1, f);
-        write_u32_le(f, jpeg_data.size);
+        write_u32_le(f, (uint32_t)jpeg_data.size);
         index[i].offset = ftell(f) - 8;
-        index[i].size   = jpeg_data.size;
+        index[i].size   = (uint32_t)jpeg_data.size;
         fwrite(jpeg_data.buf, 1, jpeg_data.size, f);
 
         // Align to even byte size
diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index 7ea95ed1..c9e8eeeb 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -1386,10 +1386,10 @@ struct SDGenerationParams {
                 if (!item.empty()) {
                     try {
                         custom_sigmas.push_back(std::stof(item));
-                    } catch (const std::invalid_argument& e) {
+                    } catch (const std::invalid_argument&) {
                         LOG_ERROR("error: invalid float value '%s' in --sigmas", item.c_str());
                         return -1;
-                    } catch (const std::out_of_range& e) {
+                    } catch (const std::out_of_range&) {
                         LOG_ERROR("error: float value '%s' out of range in --sigmas", item.c_str());
                         return -1;
                     }
diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index db9f5b1a..9fa88049 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -44,7 +44,7 @@ inline bool is_base64(unsigned char c) {
 }
 
 std::vector<uint8_t> base64_decode(const std::string& encoded_string) {
-    int in_len = encoded_string.size();
+    int in_len = static_cast<int>(encoded_string.size());
     int i      = 0;
     int j      = 0;
     int in_    = 0;
@@ -617,7 +617,7 @@ int main(int argc, const char** argv) {
                 int img_h           = height;
                 uint8_t* raw_pixels = load_image_from_memory(
                     reinterpret_cast<const char*>(bytes.data()),
-                    bytes.size(),
+                    static_cast<int>(bytes.size()),
                     img_w, img_h,
                     width, height, 3);
 
@@ -635,7 +635,7 @@ int main(int argc, const char** argv) {
                 int mask_h        = height;
                 uint8_t* mask_raw = load_image_from_memory(
                     reinterpret_cast<const char*>(mask_bytes.data()),
-                    mask_bytes.size(),
+                    static_cast<int>(mask_bytes.size()),
                     mask_w, mask_h,
                     width, height, 1);
                 mask_image = {(uint32_t)mask_w, (uint32_t)mask_h, 1, mask_raw};
diff --git a/flux.hpp b/flux.hpp
index 86e2007a..5d94fc85 100644
--- a/flux.hpp
+++ b/flux.hpp
@@ -263,7 +263,7 @@ namespace Flux {
                           bool use_yak_mlp      = false,
                           bool use_mlp_silu_act = false)
             : idx(idx), prune_mod(prune_mod) {
-            int64_t mlp_hidden_dim = hidden_size * mlp_ratio;
+            int64_t mlp_hidden_dim = static_cast<int64_t>(hidden_size * mlp_ratio);
 
             if (!prune_mod && !share_modulation) {
                 blocks["img_mod"] = std::shared_ptr<GGMLBlock>(new Modulation(hidden_size, true));
@@ -442,7 +442,7 @@ namespace Flux {
             if (scale <= 0.f) {
                 scale = 1 / sqrt((float)head_dim);
             }
-            mlp_hidden_dim  = hidden_size * mlp_ratio;
+            mlp_hidden_dim  = static_cast<int64_t>(hidden_size * mlp_ratio);
             mlp_mult_factor = 1;
             if (use_yak_mlp || use_mlp_silu_act) {
                 mlp_mult_factor = 2;
@@ -744,38 +744,38 @@ namespace Flux {
 
     struct ChromaRadianceParams {
         int64_t nerf_hidden_size = 64;
-        int64_t nerf_mlp_ratio   = 4;
-        int64_t nerf_depth       = 4;
-        int64_t nerf_max_freqs   = 8;
+        int nerf_mlp_ratio       = 4;
+        int nerf_depth           = 4;
+        int nerf_max_freqs       = 8;
         bool use_x0              = false;
         bool use_patch_size_32   = false;
     };
 
     struct FluxParams {
-        SDVersion version           = VERSION_FLUX;
-        bool is_chroma              = false;
-        int64_t patch_size          = 2;
-        int64_t in_channels         = 64;
-        int64_t out_channels        = 64;
-        int64_t vec_in_dim          = 768;
-        int64_t context_in_dim      = 4096;
-        int64_t hidden_size         = 3072;
-        float mlp_ratio             = 4.0f;
-        int64_t num_heads           = 24;
-        int64_t depth               = 19;
-        int64_t depth_single_blocks = 38;
-        std::vector<int> axes_dim   = {16, 56, 56};
-        int64_t axes_dim_sum        = 128;
-        int theta                   = 10000;
-        bool qkv_bias               = true;
-        bool guidance_embed         = true;
-        int64_t in_dim              = 64;
-        bool disable_bias           = false;
-        bool share_modulation       = false;
-        bool semantic_txt_norm      = false;
-        bool use_yak_mlp            = false;
-        bool use_mlp_silu_act       = false;
-        float ref_index_scale       = 1.f;
+        SDVersion version         = VERSION_FLUX;
+        bool is_chroma            = false;
+        int patch_size            = 2;
+        int64_t in_channels       = 64;
+        int64_t out_channels      = 64;
+        int64_t vec_in_dim        = 768;
+        int64_t context_in_dim    = 4096;
+        int64_t hidden_size       = 3072;
+        float mlp_ratio           = 4.0f;
+        int num_heads             = 24;
+        int depth                 = 19;
+        int depth_single_blocks   = 38;
+        std::vector<int> axes_dim = {16, 56, 56};
+        int axes_dim_sum          = 128;
+        int theta                 = 10000;
+        bool qkv_bias             = true;
+        bool guidance_embed       = true;
+        int64_t in_dim            = 64;
+        bool disable_bias         = false;
+        bool share_modulation     = false;
+        bool semantic_txt_norm    = false;
+        bool use_yak_mlp          = false;
+        bool use_mlp_silu_act     = false;
+        float ref_index_scale     = 1.f;
         ChromaRadianceParams chroma_radiance_params;
     };
 
@@ -969,7 +969,7 @@ namespace Flux {
                 vec = approx->forward(ctx, vec);                                               // [344, N, hidden_size]
 
                 if (y != nullptr) {
-                    txt_img_mask = ggml_pad(ctx->ggml_ctx, y, img->ne[1], 0, 0, 0);
+                    txt_img_mask = ggml_pad(ctx->ggml_ctx, y, static_cast<int>(img->ne[1]), 0, 0, 0);
                 }
             } else {
                 auto time_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["time_in"]);
@@ -1072,12 +1072,12 @@ namespace Flux {
                                                     std::vector<int> skip_layers          = {}) {
             GGML_ASSERT(x->ne[3] == 1);
 
-            int64_t W          = x->ne[0];
-            int64_t H          = x->ne[1];
-            int64_t C          = x->ne[2];
-            int64_t patch_size = params.patch_size;
-            int pad_h          = (patch_size - H % patch_size) % patch_size;
-            int pad_w          = (patch_size - W % patch_size) % patch_size;
+            int64_t W      = x->ne[0];
+            int64_t H      = x->ne[1];
+            int64_t C      = x->ne[2];
+            int patch_size = params.patch_size;
+            int pad_h      = (patch_size - H % patch_size) % patch_size;
+            int pad_w      = (patch_size - W % patch_size) % patch_size;
 
             auto img      = pad_to_patch_size(ctx, x);
             auto orig_img = img;
@@ -1146,15 +1146,15 @@ namespace Flux {
                                                 std::vector<int> skip_layers          = {}) {
             GGML_ASSERT(x->ne[3] == 1);
 
-            int64_t W          = x->ne[0];
-            int64_t H          = x->ne[1];
-            int64_t C          = x->ne[2];
-            int64_t patch_size = params.patch_size;
-            int pad_h          = (patch_size - H % patch_size) % patch_size;
-            int pad_w          = (patch_size - W % patch_size) % patch_size;
+            int64_t W      = x->ne[0];
+            int64_t H      = x->ne[1];
+            int64_t C      = x->ne[2];
+            int patch_size = params.patch_size;
+            int pad_h      = (patch_size - H % patch_size) % patch_size;
+            int pad_w      = (patch_size - W % patch_size) % patch_size;
 
-            auto img            = process_img(ctx, x);
-            uint64_t img_tokens = img->ne[1];
+            auto img           = process_img(ctx, x);
+            int64_t img_tokens = img->ne[1];
 
             if (params.version == VERSION_FLUX_FILL) {
                 GGML_ASSERT(c_concat != nullptr);
@@ -1465,11 +1465,11 @@ namespace Flux {
                 txt_arange_dims = {1, 2};
             }
 
-            pe_vec      = Rope::gen_flux_pe(x->ne[1],
-                                            x->ne[0],
+            pe_vec      = Rope::gen_flux_pe(static_cast<int>(x->ne[1]),
+                                            static_cast<int>(x->ne[0]),
                                             flux_params.patch_size,
-                                            x->ne[3],
-                                            context->ne[1],
+                                            static_cast<int>(x->ne[3]),
+                                            static_cast<int>(context->ne[1]),
                                             txt_arange_dims,
                                             ref_latents,
                                             increase_ref_index,
@@ -1478,7 +1478,7 @@ namespace Flux {
                                             circular_y_enabled,
                                             circular_x_enabled,
                                             flux_params.axes_dim);
-            int pos_len = pe_vec.size() / flux_params.axes_dim_sum / 2;
+            int pos_len = static_cast<int>(pe_vec.size() / flux_params.axes_dim_sum / 2);
             // LOG_DEBUG("pos_len %d", pos_len);
             auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, flux_params.axes_dim_sum / 2, pos_len);
             // pe->data = pe_vec.data();
@@ -1487,10 +1487,10 @@ namespace Flux {
             set_backend_tensor_data(pe, pe_vec.data());
 
             if (version == VERSION_CHROMA_RADIANCE) {
-                int64_t patch_size     = flux_params.patch_size;
-                int64_t nerf_max_freqs = flux_params.chroma_radiance_params.nerf_max_freqs;
-                dct_vec                = fetch_dct_pos(patch_size, nerf_max_freqs);
-                dct                    = ggml_new_tensor_2d(compute_ctx, GGML_TYPE_F32, nerf_max_freqs * nerf_max_freqs, patch_size * patch_size);
+                int patch_size     = flux_params.patch_size;
+                int nerf_max_freqs = flux_params.chroma_radiance_params.nerf_max_freqs;
+                dct_vec            = fetch_dct_pos(patch_size, nerf_max_freqs);
+                dct                = ggml_new_tensor_2d(compute_ctx, GGML_TYPE_F32, nerf_max_freqs * nerf_max_freqs, patch_size * patch_size);
                 // dct->data = dct_vec.data();
                 // print_ggml_tensor(dct);
                 // dct->data = nullptr;
@@ -1577,12 +1577,12 @@ namespace Flux {
 
                 struct ggml_tensor* out = nullptr;
 
-                int t0 = ggml_time_ms();
+                int64_t t0 = ggml_time_ms();
                 compute(8, x, timesteps, context, nullptr, y, guidance, {}, false, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
-                LOG_DEBUG("flux test done in %dms", t1 - t0);
+                LOG_DEBUG("flux test done in %lldms", t1 - t0);
             }
         }
 
diff --git a/ggml_extend.hpp b/ggml_extend.hpp
index 76889a8b..1ff45011 100644
--- a/ggml_extend.hpp
+++ b/ggml_extend.hpp
@@ -98,10 +98,10 @@ static_assert(GGML_MAX_NAME >= 128, "GGML_MAX_NAME must be at least 128");
 __STATIC_INLINE__ struct ggml_tensor* ggml_ext_mul_n_mode(struct ggml_context* ctx, struct ggml_tensor* a, struct ggml_tensor* b, int mode = 0) {
     // reshape A
     // swap 0th and nth axis
-    a       = ggml_cont(ctx, ggml_permute(ctx, a, mode, mode != 1 ? 1 : 0, mode != 2 ? 2 : 0, mode != 3 ? 3 : 0));
-    int ne1 = a->ne[1];
-    int ne2 = a->ne[2];
-    int ne3 = a->ne[3];
+    a           = ggml_cont(ctx, ggml_permute(ctx, a, mode, mode != 1 ? 1 : 0, mode != 2 ? 2 : 0, mode != 3 ? 3 : 0));
+    int64_t ne1 = a->ne[1];
+    int64_t ne2 = a->ne[2];
+    int64_t ne3 = a->ne[3];
     // make 2D
     a = ggml_cont(ctx, ggml_reshape_2d(ctx, a, a->ne[0], (ne3 * ne2 * ne1)));
 
@@ -167,12 +167,12 @@ __STATIC_INLINE__ void ggml_ext_im_set_randn_f32(struct ggml_tensor* tensor, std
     }
 }
 
-__STATIC_INLINE__ void ggml_ext_tensor_set_f32(struct ggml_tensor* tensor, float value, int i0, int i1 = 0, int i2 = 0, int i3 = 0) {
+__STATIC_INLINE__ void ggml_ext_tensor_set_f32(struct ggml_tensor* tensor, float value, int64_t i0, int64_t i1 = 0, int64_t i2 = 0, int64_t i3 = 0) {
     GGML_ASSERT(tensor->nb[0] == sizeof(float));
     *(float*)((char*)(tensor->data) + i3 * tensor->nb[3] + i2 * tensor->nb[2] + i1 * tensor->nb[1] + i0 * tensor->nb[0]) = value;
 }
 
-__STATIC_INLINE__ float ggml_ext_tensor_get_f32(const ggml_tensor* tensor, int i0, int i1 = 0, int i2 = 0, int i3 = 0) {
+__STATIC_INLINE__ float ggml_ext_tensor_get_f32(const ggml_tensor* tensor, int64_t i0, int64_t i1 = 0, int64_t i2 = 0, int64_t i3 = 0) {
     if (tensor->buffer != nullptr) {
         float value;
         ggml_backend_tensor_get(tensor, &value, i3 * tensor->nb[3] + i2 * tensor->nb[2] + i1 * tensor->nb[1] + i0 * tensor->nb[0], sizeof(float));
@@ -182,9 +182,9 @@ __STATIC_INLINE__ float ggml_ext_tensor_get_f32(const ggml_tensor* tensor, int i
     return *(float*)((char*)(tensor->data) + i3 * tensor->nb[3] + i2 * tensor->nb[2] + i1 * tensor->nb[1] + i0 * tensor->nb[0]);
 }
 
-__STATIC_INLINE__ int ggml_ext_tensor_get_i32(const ggml_tensor* tensor, int i0, int i1 = 0, int i2 = 0, int i3 = 0) {
+__STATIC_INLINE__ int ggml_ext_tensor_get_i32(const ggml_tensor* tensor, int64_t i0, int64_t i1 = 0, int64_t i2 = 0, int64_t i3 = 0) {
     if (tensor->buffer != nullptr) {
-        float value;
+        int value;
         ggml_backend_tensor_get(tensor, &value, i3 * tensor->nb[3] + i2 * tensor->nb[2] + i1 * tensor->nb[1] + i0 * tensor->nb[0], sizeof(int));
         return value;
     }
@@ -192,12 +192,12 @@ __STATIC_INLINE__ int ggml_ext_tensor_get_i32(const ggml_tensor* tensor, int i0,
     return *(int*)((char*)(tensor->data) + i3 * tensor->nb[3] + i2 * tensor->nb[2] + i1 * tensor->nb[1] + i0 * tensor->nb[0]);
 }
 
-__STATIC_INLINE__ ggml_fp16_t ggml_ext_tensor_get_f16(const ggml_tensor* tensor, int i0, int i1 = 0, int i2 = 0, int i3 = 0) {
+__STATIC_INLINE__ ggml_fp16_t ggml_ext_tensor_get_f16(const ggml_tensor* tensor, int64_t i0, int64_t i1 = 0, int64_t i2 = 0, int64_t i3 = 0) {
     GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t));
     return *(ggml_fp16_t*)((char*)(tensor->data) + i3 * tensor->nb[3] + i2 * tensor->nb[2] + i1 * tensor->nb[1] + i0 * tensor->nb[0]);
 }
 
-__STATIC_INLINE__ float sd_image_get_f32(sd_image_t image, int iw, int ih, int ic, bool scale = true) {
+__STATIC_INLINE__ float sd_image_get_f32(sd_image_t image, int64_t iw, int64_t ih, int64_t ic, bool scale = true) {
     float value = *(image.data + ih * image.width * image.channel + iw * image.channel + ic);
     if (scale) {
         value /= 255.f;
@@ -205,7 +205,7 @@ __STATIC_INLINE__ float sd_image_get_f32(sd_image_t image, int iw, int ih, int i
     return value;
 }
 
-__STATIC_INLINE__ float sd_image_get_f32(sd_image_f32_t image, int iw, int ih, int ic, bool scale = true) {
+__STATIC_INLINE__ float sd_image_get_f32(sd_image_f32_t image, int64_t iw, int64_t ih, int64_t ic, bool scale = true) {
     float value = *(image.data + ih * image.width * image.channel + iw * image.channel + ic);
     if (scale) {
         value /= 255.f;
@@ -450,8 +450,8 @@ __STATIC_INLINE__ void ggml_ext_tensor_apply_mask(struct ggml_tensor* image_data
     int64_t width    = output->ne[0];
     int64_t height   = output->ne[1];
     int64_t channels = output->ne[2];
-    float rescale_mx = mask->ne[0] / output->ne[0];
-    float rescale_my = mask->ne[1] / output->ne[1];
+    float rescale_mx = 1.f * mask->ne[0] / output->ne[0];
+    float rescale_my = 1.f * mask->ne[1] / output->ne[1];
     GGML_ASSERT(output->type == GGML_TYPE_F32);
     for (int ix = 0; ix < width; ix++) {
         for (int iy = 0; iy < height; iy++) {
@@ -685,7 +685,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_torch_permute(struct ggml_context
 
 __STATIC_INLINE__ struct ggml_tensor* ggml_ext_slice(struct ggml_context* ctx,
                                                      struct ggml_tensor* x,
-                                                     int64_t dim,
+                                                     int dim,
                                                      int64_t start,
                                                      int64_t end) {
     GGML_ASSERT(dim >= 0 && dim < 4);
@@ -785,7 +785,7 @@ __STATIC_INLINE__ void sd_tiling_calc_tiles(int& num_tiles_dim,
                                             int small_dim,
                                             int tile_size,
                                             const float tile_overlap_factor) {
-    int tile_overlap     = (tile_size * tile_overlap_factor);
+    int tile_overlap     = static_cast<int>(tile_size * tile_overlap_factor);
     int non_tile_overlap = tile_size - tile_overlap;
 
     num_tiles_dim     = (small_dim - tile_overlap) / non_tile_overlap;
@@ -1346,7 +1346,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_attention_ext(struct ggml_context
         // LOG_DEBUG("attention_ext L_q:%d L_k:%d n_head:%d C:%d d_head:%d N:%d", L_q, L_k, n_head, C, d_head, N);
         bool can_use_flash_attn = true;
         if (can_use_flash_attn && L_k % 256 != 0) {
-            kv_pad = GGML_PAD(L_k, 256) - L_k;
+            kv_pad = GGML_PAD(L_k, 256) - static_cast<int>(L_k);
         }
 
         if (mask != nullptr) {
@@ -2361,53 +2361,6 @@ public:
     }
 };
 
-class Conv3dnx1x1 : public UnaryBlock {
-protected:
-    int64_t in_channels;
-    int64_t out_channels;
-    int64_t kernel_size;
-    int64_t stride;
-    int64_t padding;
-    int64_t dilation;
-    bool bias;
-
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map, const std::string prefix = "") override {
-        enum ggml_type wtype = GGML_TYPE_F16;
-        params["weight"]     = ggml_new_tensor_4d(ctx, wtype, 1, kernel_size, in_channels, out_channels);  // 5d => 4d
-        if (bias) {
-            enum ggml_type wtype = GGML_TYPE_F32;
-            params["bias"]       = ggml_new_tensor_1d(ctx, wtype, out_channels);
-        }
-    }
-
-public:
-    Conv3dnx1x1(int64_t in_channels,
-                int64_t out_channels,
-                int64_t kernel_size,
-                int64_t stride   = 1,
-                int64_t padding  = 0,
-                int64_t dilation = 1,
-                bool bias        = true)
-        : in_channels(in_channels),
-          out_channels(out_channels),
-          kernel_size(kernel_size),
-          stride(stride),
-          padding(padding),
-          dilation(dilation),
-          bias(bias) {}
-
-    // x: [N, IC, ID, IH*IW]
-    // result: [N, OC, OD, OH*OW]
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
-        struct ggml_tensor* w = params["weight"];
-        struct ggml_tensor* b = nullptr;
-        if (bias) {
-            b = params["bias"];
-        }
-        return ggml_ext_conv_3d_nx1x1(ctx->ggml_ctx, x, w, b, stride, padding, dilation);
-    }
-};
-
 class Conv3d : public UnaryBlock {
 protected:
     int64_t in_channels;
@@ -2523,7 +2476,7 @@ public:
 
 class GroupNorm : public GGMLBlock {
 protected:
-    int64_t num_groups;
+    int num_groups;
     int64_t num_channels;
     float eps;
     bool affine;
@@ -2540,7 +2493,7 @@ protected:
     }
 
 public:
-    GroupNorm(int64_t num_groups,
+    GroupNorm(int num_groups,
               int64_t num_channels,
               float eps   = 1e-05f,
               bool affine = true)
diff --git a/gguf_reader.hpp b/gguf_reader.hpp
index 53482662..2cc4d9d9 100644
--- a/gguf_reader.hpp
+++ b/gguf_reader.hpp
@@ -151,7 +151,7 @@ private:
         }
 
         if (n_dims > GGML_MAX_DIMS) {
-            for (int i = GGML_MAX_DIMS; i < n_dims; i++) {
+            for (uint32_t i = GGML_MAX_DIMS; i < n_dims; i++) {
                 info.shape[GGML_MAX_DIMS - 1] *= info.shape[i];  // stack to last dim;
             }
             info.shape.resize(GGML_MAX_DIMS);
diff --git a/latent-preview.h b/latent-preview.h
index 2c54c3b5..76e17415 100644
--- a/latent-preview.h
+++ b/latent-preview.h
@@ -166,12 +166,12 @@ float sd_latent_rgb_bias[3] = {-0.017478f, -0.055834f, -0.105825f};
 void preview_latent_video(uint8_t* buffer, struct ggml_tensor* latents, const float (*latent_rgb_proj)[3], const float latent_rgb_bias[3], int patch_size) {
     size_t buffer_head = 0;
 
-    uint32_t latent_width  = latents->ne[0];
-    uint32_t latent_height = latents->ne[1];
-    uint32_t dim           = latents->ne[ggml_n_dims(latents) - 1];
+    uint32_t latent_width  = static_cast<uint32_t>(latents->ne[0]);
+    uint32_t latent_height = static_cast<uint32_t>(latents->ne[1]);
+    uint32_t dim           = static_cast<uint32_t>(latents->ne[ggml_n_dims(latents) - 1]);
     uint32_t frames        = 1;
     if (ggml_n_dims(latents) == 4) {
-        frames = latents->ne[2];
+        frames = static_cast<uint32_t>(latents->ne[2]);
     }
 
     uint32_t rgb_width  = latent_width * patch_size;
@@ -179,9 +179,9 @@ void preview_latent_video(uint8_t* buffer, struct ggml_tensor* latents, const fl
 
     uint32_t unpatched_dim = dim / (patch_size * patch_size);
 
-    for (int k = 0; k < frames; k++) {
-        for (int rgb_x = 0; rgb_x < rgb_width; rgb_x++) {
-            for (int rgb_y = 0; rgb_y < rgb_height; rgb_y++) {
+    for (uint32_t k = 0; k < frames; k++) {
+        for (uint32_t rgb_x = 0; rgb_x < rgb_width; rgb_x++) {
+            for (uint32_t rgb_y = 0; rgb_y < rgb_height; rgb_y++) {
                 int latent_x = rgb_x / patch_size;
                 int latent_y = rgb_y / patch_size;
 
@@ -197,7 +197,7 @@ void preview_latent_video(uint8_t* buffer, struct ggml_tensor* latents, const fl
 
                 float r = 0, g = 0, b = 0;
                 if (latent_rgb_proj != nullptr) {
-                    for (int d = 0; d < unpatched_dim; d++) {
+                    for (uint32_t d = 0; d < unpatched_dim; d++) {
                         float value = *(float*)((char*)latents->data + latent_id + (d * patch_size * patch_size + channel_offset) * latents->nb[ggml_n_dims(latents) - 1]);
                         r += value * latent_rgb_proj[d][0];
                         g += value * latent_rgb_proj[d][1];
diff --git a/llm.hpp b/llm.hpp
index dc04c84c..67b1ea16 100644
--- a/llm.hpp
+++ b/llm.hpp
@@ -195,14 +195,14 @@ namespace LLM {
                 tokens.insert(tokens.begin(), BOS_TOKEN_ID);
             }
             if (max_length > 0 && padding) {
-                size_t n = std::ceil(tokens.size() * 1.0 / max_length);
+                size_t n = static_cast<size_t>(std::ceil(tokens.size() * 1.f / max_length));
                 if (n == 0) {
                     n = 1;
                 }
                 size_t length = max_length * n;
                 LOG_DEBUG("token length: %llu", length);
                 tokens.insert(tokens.end(), length - tokens.size(), PAD_TOKEN_ID);
-                weights.insert(weights.end(), length - weights.size(), 1.0);
+                weights.insert(weights.end(), length - weights.size(), 1.f);
             }
         }
 
@@ -377,7 +377,7 @@ namespace LLM {
 
             try {
                 vocab = nlohmann::json::parse(vocab_utf8_str);
-            } catch (const nlohmann::json::parse_error& e) {
+            } catch (const nlohmann::json::parse_error&) {
                 GGML_ABORT("invalid vocab json str");
             }
             for (const auto& [key, value] : vocab.items()) {
@@ -386,7 +386,7 @@ namespace LLM {
                 encoder[token]       = i;
                 decoder[i]           = token;
             }
-            encoder_len = vocab.size();
+            encoder_len = static_cast<int>(vocab.size());
             LOG_DEBUG("vocab size: %d", encoder_len);
 
             auto byte_unicode_pairs = bytes_to_unicode();
@@ -485,16 +485,16 @@ namespace LLM {
     };
 
     struct LLMVisionParams {
-        int64_t num_layers                  = 32;
+        int num_layers                      = 32;
         int64_t hidden_size                 = 1280;
         int64_t intermediate_size           = 3420;
-        int64_t num_heads                   = 16;
+        int num_heads                       = 16;
         int64_t in_channels                 = 3;
         int64_t out_hidden_size             = 3584;
-        int64_t temporal_patch_size         = 2;
-        int64_t patch_size                  = 14;
-        int64_t spatial_merge_size          = 2;
-        int64_t window_size                 = 112;
+        int temporal_patch_size             = 2;
+        int patch_size                      = 14;
+        int spatial_merge_size              = 2;
+        int window_size                     = 112;
         std::set<int> fullatt_block_indexes = {7, 15, 23, 31};
     };
 
@@ -503,9 +503,9 @@ namespace LLM {
         int64_t num_layers        = 28;
         int64_t hidden_size       = 3584;
         int64_t intermediate_size = 18944;
-        int64_t num_heads         = 28;
-        int64_t num_kv_heads      = 4;
-        int64_t head_dim          = 128;
+        int num_heads             = 28;
+        int num_kv_heads          = 4;
+        int head_dim              = 128;
         bool qkv_bias             = true;
         bool qk_norm              = false;
         int64_t vocab_size        = 152064;
@@ -647,15 +647,15 @@ namespace LLM {
     struct VisionAttention : public GGMLBlock {
     protected:
         bool llama_cpp_style;
-        int64_t head_dim;
-        int64_t num_heads;
+        int head_dim;
+        int num_heads;
 
     public:
         VisionAttention(bool llama_cpp_style,
                         int64_t hidden_size,
-                        int64_t num_heads)
+                        int num_heads)
             : llama_cpp_style(llama_cpp_style), num_heads(num_heads) {
-            head_dim = hidden_size / num_heads;
+            head_dim = static_cast<int>(hidden_size / num_heads);
             GGML_ASSERT(num_heads * head_dim == hidden_size);
             if (llama_cpp_style) {
                 blocks["q_proj"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, hidden_size));
@@ -709,7 +709,7 @@ namespace LLM {
         VisionBlock(bool llama_cpp_style,
                     int64_t hidden_size,
                     int64_t intermediate_size,
-                    int64_t num_heads,
+                    int num_heads,
                     float eps = 1e-6f) {
             blocks["attn"]  = std::shared_ptr<GGMLBlock>(new VisionAttention(llama_cpp_style, hidden_size, num_heads));
             blocks["mlp"]   = std::shared_ptr<GGMLBlock>(new MLP(hidden_size, intermediate_size, true));
@@ -743,22 +743,22 @@ namespace LLM {
 
     struct VisionModel : public GGMLBlock {
     protected:
-        int64_t num_layers;
-        int64_t spatial_merge_size;
+        int num_layers;
+        int spatial_merge_size;
         std::set<int> fullatt_block_indexes;
 
     public:
         VisionModel(bool llama_cpp_style,
-                    int64_t num_layers,
+                    int num_layers,
                     int64_t in_channels,
                     int64_t hidden_size,
                     int64_t out_hidden_size,
                     int64_t intermediate_size,
-                    int64_t num_heads,
-                    int64_t spatial_merge_size,
-                    int64_t patch_size,
-                    int64_t temporal_patch_size,
-                    int64_t window_size,
+                    int num_heads,
+                    int spatial_merge_size,
+                    int patch_size,
+                    int temporal_patch_size,
+                    int window_size,
                     std::set<int> fullatt_block_indexes = {7, 15, 23, 31},
                     float eps                           = 1e-6f)
             : num_layers(num_layers), fullatt_block_indexes(std::move(fullatt_block_indexes)), spatial_merge_size(spatial_merge_size) {
@@ -817,7 +817,7 @@ namespace LLM {
     struct Attention : public GGMLBlock {
     protected:
         LLMArch arch;
-        int64_t head_dim;
+        int head_dim;
         int64_t num_heads;
         int64_t num_kv_heads;
         bool qk_norm;
@@ -1227,11 +1227,11 @@ namespace LLM {
         }
 
         int64_t get_num_image_tokens(int64_t t, int64_t h, int64_t w) {
-            int grid_t     = 1;
-            int grid_h     = h / params.vision.patch_size;
-            int grid_w     = w / params.vision.patch_size;
-            int llm_grid_h = grid_h / params.vision.spatial_merge_size;
-            int llm_grid_w = grid_w / params.vision.spatial_merge_size;
+            int64_t grid_t     = 1;
+            int64_t grid_h     = h / params.vision.patch_size;
+            int64_t grid_w     = w / params.vision.patch_size;
+            int64_t llm_grid_h = grid_h / params.vision.spatial_merge_size;
+            int64_t llm_grid_w = grid_w / params.vision.spatial_merge_size;
             return grid_t * grid_h * grid_w;
         }
 
@@ -1269,8 +1269,8 @@ namespace LLM {
             GGML_ASSERT(image->ne[0] % (params.vision.patch_size * params.vision.spatial_merge_size) == 0);
 
             int grid_t                 = 1;
-            int grid_h                 = image->ne[1] / params.vision.patch_size;
-            int grid_w                 = image->ne[0] / params.vision.patch_size;
+            int grid_h                 = static_cast<int>(image->ne[1]) / params.vision.patch_size;
+            int grid_w                 = static_cast<int>(image->ne[0]) / params.vision.patch_size;
             int llm_grid_h             = grid_h / params.vision.spatial_merge_size;
             int llm_grid_w             = grid_w / params.vision.spatial_merge_size;
             int vit_merger_window_size = params.vision.window_size / params.vision.patch_size / params.vision.spatial_merge_size;
@@ -1358,14 +1358,14 @@ namespace LLM {
             set_backend_tensor_data(window_mask, window_mask_vec.data());
 
             // pe
-            int head_dim = params.vision.hidden_size / params.vision.num_heads;
+            int head_dim = static_cast<int>(params.vision.hidden_size / params.vision.num_heads);
             pe_vec       = Rope::gen_qwen2vl_pe(grid_h,
                                                 grid_w,
                                                 params.vision.spatial_merge_size,
                                                 window_inverse_index_vec,
-                                                10000.f,
+                                                10000,
                                                 {head_dim / 2, head_dim / 2});
-            int pos_len  = pe_vec.size() / head_dim / 2;
+            int pos_len  = static_cast<int>(pe_vec.size() / head_dim / 2);
             // LOG_DEBUG("pos_len %d", pos_len);
             auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, head_dim / 2, pos_len);
             // pe->data = pe_vec.data();
@@ -1485,13 +1485,13 @@ namespace LLM {
                     print_ggml_tensor(image, false, "image");
                     struct ggml_tensor* out = nullptr;
 
-                    int t0 = ggml_time_ms();
+                    int64_t t0 = ggml_time_ms();
                     model.encode_image(8, image, &out, work_ctx);
-                    int t1 = ggml_time_ms();
+                    int64_t t1 = ggml_time_ms();
 
                     print_ggml_tensor(out, false, "image_embed");
                     image_embed = out;
-                    LOG_DEBUG("llm encode_image test done in %dms", t1 - t0);
+                    LOG_DEBUG("llm encode_image test done in %lldms", t1 - t0);
                 }
 
                 std::string placeholder  = "<|image_pad|>";
@@ -1524,12 +1524,12 @@ namespace LLM {
                 auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
                 struct ggml_tensor* out = nullptr;
 
-                int t0 = ggml_time_ms();
+                int64_t t0 = ggml_time_ms();
                 model.compute(8, input_ids, image_embeds, {}, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
-                LOG_DEBUG("llm test done in %dms", t1 - t0);
+                LOG_DEBUG("llm test done in %lldms", t1 - t0);
             } else if (test_vit) {
                 // auto image = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 280, 280, 3);
                 // ggml_set_f32(image, 0.f);
@@ -1537,16 +1537,16 @@ namespace LLM {
                 print_ggml_tensor(image, false, "image");
                 struct ggml_tensor* out = nullptr;
 
-                int t0 = ggml_time_ms();
+                int64_t t0 = ggml_time_ms();
                 model.encode_image(8, image, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out, false, "out");
 
                 // auto ref_out = load_tensor_from_file(work_ctx, "qwen2vl.bin");
                 // ggml_ext_tensor_diff(ref_out, out, 0.01f);
 
-                LOG_DEBUG("llm test done in %dms", t1 - t0);
+                LOG_DEBUG("llm test done in %lldms", t1 - t0);
             } else if (test_mistral) {
                 std::pair<int, int> prompt_attn_range;
                 std::string text        = "[SYSTEM_PROMPT]You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object\nattribution and actions without speculation.[/SYSTEM_PROMPT][INST]";
@@ -1564,12 +1564,12 @@ namespace LLM {
                 auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
                 struct ggml_tensor* out = nullptr;
 
-                int t0 = ggml_time_ms();
+                int64_t t0 = ggml_time_ms();
                 model.compute(8, input_ids, {}, {10, 20, 30}, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
-                LOG_DEBUG("llm test done in %dms", t1 - t0);
+                LOG_DEBUG("llm test done in %lldms", t1 - t0);
             } else if (test_qwen3) {
                 std::pair<int, int> prompt_attn_range;
                 std::string text        = "<|im_start|>user\n";
@@ -1587,12 +1587,12 @@ namespace LLM {
                 auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
                 struct ggml_tensor* out = nullptr;
 
-                int t0 = ggml_time_ms();
+                int64_t t0 = ggml_time_ms();
                 model.compute(8, input_ids, {}, {35}, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
-                LOG_DEBUG("llm test done in %dms", t1 - t0);
+                LOG_DEBUG("llm test done in %lldms", t1 - t0);
             } else {
                 std::pair<int, int> prompt_attn_range;
                 std::string text        = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n";
@@ -1610,12 +1610,12 @@ namespace LLM {
                 auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
                 struct ggml_tensor* out = nullptr;
 
-                int t0 = ggml_time_ms();
+                int64_t t0 = ggml_time_ms();
                 model.compute(8, input_ids, {}, {}, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
-                LOG_DEBUG("llm test done in %dms", t1 - t0);
+                LOG_DEBUG("llm test done in %lldms", t1 - t0);
             }
         }
 
diff --git a/mmdit.hpp b/mmdit.hpp
index eeb74a26..cb4be7bf 100644
--- a/mmdit.hpp
+++ b/mmdit.hpp
@@ -97,12 +97,12 @@ public:
 struct TimestepEmbedder : public GGMLBlock {
     // Embeds scalar timesteps into vector representations.
 protected:
-    int64_t frequency_embedding_size;
+    int frequency_embedding_size;
 
 public:
     TimestepEmbedder(int64_t hidden_size,
-                     int64_t frequency_embedding_size = 256,
-                     int64_t out_channels             = 0)
+                     int frequency_embedding_size = 256,
+                     int64_t out_channels         = 0)
         : frequency_embedding_size(frequency_embedding_size) {
         if (out_channels <= 0) {
             out_channels = hidden_size;
@@ -167,11 +167,11 @@ public:
             blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(dim, dim));
         }
         if (qk_norm == "rms") {
-            blocks["ln_q"] = std::shared_ptr<GGMLBlock>(new RMSNorm(d_head, 1.0e-6));
-            blocks["ln_k"] = std::shared_ptr<GGMLBlock>(new RMSNorm(d_head, 1.0e-6));
+            blocks["ln_q"] = std::shared_ptr<GGMLBlock>(new RMSNorm(d_head, 1.0e-6f));
+            blocks["ln_k"] = std::shared_ptr<GGMLBlock>(new RMSNorm(d_head, 1.0e-6f));
         } else if (qk_norm == "ln") {
-            blocks["ln_q"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_head, 1.0e-6));
-            blocks["ln_k"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_head, 1.0e-6));
+            blocks["ln_q"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_head, 1.0e-6f));
+            blocks["ln_k"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_head, 1.0e-6f));
         }
     }
 
@@ -623,7 +623,7 @@ struct MMDiT : public GGMLBlock {
     // Diffusion model with a Transformer backbone.
 protected:
     int64_t input_size               = -1;
-    int64_t patch_size               = 2;
+    int patch_size                   = 2;
     int64_t in_channels              = 16;
     int64_t d_self                   = -1;  // >=0 for MMdiT-X
     int64_t depth                    = 24;
@@ -943,12 +943,12 @@ struct MMDiTRunner : public GGMLRunner {
 
             struct ggml_tensor* out = nullptr;
 
-            int t0 = ggml_time_ms();
+            int64_t t0 = ggml_time_ms();
             compute(8, x, timesteps, context, y, &out, work_ctx);
-            int t1 = ggml_time_ms();
+            int64_t t1 = ggml_time_ms();
 
             print_ggml_tensor(out);
-            LOG_DEBUG("mmdit test done in %dms", t1 - t0);
+            LOG_DEBUG("mmdit test done in %lldms", t1 - t0);
         }
     }
 
diff --git a/model.cpp b/model.cpp
index dcb77e81..a19f180d 100644
--- a/model.cpp
+++ b/model.cpp
@@ -436,7 +436,7 @@ bool ModelLoader::init_from_gguf_file(const std::string& file_path, const std::s
                 name,
                 gguf_tensor_info.type,
                 gguf_tensor_info.shape.data(),
-                gguf_tensor_info.shape.size(),
+                static_cast<int>(gguf_tensor_info.shape.size()),
                 file_index,
                 data_offset + gguf_tensor_info.offset);
 
@@ -448,7 +448,7 @@ bool ModelLoader::init_from_gguf_file(const std::string& file_path, const std::s
         return true;
     }
 
-    int n_tensors = gguf_get_n_tensors(ctx_gguf_);
+    int n_tensors = static_cast<int>(gguf_get_n_tensors(ctx_gguf_));
 
     size_t total_size  = 0;
     size_t data_offset = gguf_get_data_offset(ctx_gguf_);
@@ -1570,7 +1570,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
                 break;
             }
             size_t curr_num = total_tensors_processed + current_idx;
-            pretty_progress(curr_num, total_tensors_to_process, (ggml_time_ms() - t_start) / 1000.0f / (curr_num + 1e-6f));
+            pretty_progress(static_cast<int>(curr_num), static_cast<int>(total_tensors_to_process), (ggml_time_ms() - t_start) / 1000.0f / (curr_num + 1e-6f));
             std::this_thread::sleep_for(std::chrono::milliseconds(200));
         }
 
@@ -1583,7 +1583,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
             break;
         }
         total_tensors_processed += file_tensors.size();
-        pretty_progress(total_tensors_processed, total_tensors_to_process, (ggml_time_ms() - t_start) / 1000.0f / (total_tensors_processed + 1e-6f));
+        pretty_progress(static_cast<int>(total_tensors_processed), static_cast<int>(total_tensors_to_process), (ggml_time_ms() - t_start) / 1000.0f / (total_tensors_processed + 1e-6f));
         if (total_tensors_processed < total_tensors_to_process) {
             printf("\n");
         }
diff --git a/pmid.hpp b/pmid.hpp
index d69423ad..5b70dab6 100644
--- a/pmid.hpp
+++ b/pmid.hpp
@@ -72,7 +72,7 @@ struct PerceiverAttention : public GGMLBlock {
     int heads;     // = heads
 public:
     PerceiverAttention(int dim, int dim_h = 64, int h = 8)
-        : scale(powf(dim_h, -0.5)), dim_head(dim_h), heads(h) {
+        : scale(powf(static_cast<float>(dim_h), -0.5f)), dim_head(dim_h), heads(h) {
         int inner_dim    = dim_head * heads;
         blocks["norm1"]  = std::shared_ptr<GGMLBlock>(new LayerNorm(dim));
         blocks["norm2"]  = std::shared_ptr<GGMLBlock>(new LayerNorm(dim));
diff --git a/preprocessing.hpp b/preprocessing.hpp
index 4a1b8514..84e0ed3f 100644
--- a/preprocessing.hpp
+++ b/preprocessing.hpp
@@ -2,7 +2,7 @@
 #define __PREPROCESSING_HPP__
 
 #include "ggml_extend.hpp"
-#define M_PI_ 3.14159265358979323846
+#define M_PI_ 3.14159265358979323846f
 
 void convolve(struct ggml_tensor* input, struct ggml_tensor* output, struct ggml_tensor* kernel, int padding) {
     struct ggml_init_params params;
@@ -20,13 +20,13 @@ void convolve(struct ggml_tensor* input, struct ggml_tensor* output, struct ggml
 }
 
 void gaussian_kernel(struct ggml_tensor* kernel) {
-    int ks_mid   = kernel->ne[0] / 2;
+    int ks_mid   = static_cast<int>(kernel->ne[0] / 2);
     float sigma  = 1.4f;
     float normal = 1.f / (2.0f * M_PI_ * powf(sigma, 2.0f));
     for (int y = 0; y < kernel->ne[0]; y++) {
-        float gx = -ks_mid + y;
+        float gx = static_cast<float>(-ks_mid + y);
         for (int x = 0; x < kernel->ne[1]; x++) {
-            float gy = -ks_mid + x;
+            float gy = static_cast<float>(-ks_mid + x);
             float k_ = expf(-((gx * gx + gy * gy) / (2.0f * powf(sigma, 2.0f)))) * normal;
             ggml_ext_tensor_set_f32(kernel, k_, x, y);
         }
@@ -46,7 +46,7 @@ void grayscale(struct ggml_tensor* rgb_img, struct ggml_tensor* grayscale) {
 }
 
 void prop_hypot(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tensor* h) {
-    int n_elements = ggml_nelements(h);
+    int n_elements = static_cast<int>(ggml_nelements(h));
     float* dx      = (float*)x->data;
     float* dy      = (float*)y->data;
     float* dh      = (float*)h->data;
@@ -56,7 +56,7 @@ void prop_hypot(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tensor
 }
 
 void prop_arctan2(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tensor* h) {
-    int n_elements = ggml_nelements(h);
+    int n_elements = static_cast<int>(ggml_nelements(h));
     float* dx      = (float*)x->data;
     float* dy      = (float*)y->data;
     float* dh      = (float*)h->data;
@@ -66,7 +66,7 @@ void prop_arctan2(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tens
 }
 
 void normalize_tensor(struct ggml_tensor* g) {
-    int n_elements = ggml_nelements(g);
+    int n_elements = static_cast<int>(ggml_nelements(g));
     float* dg      = (float*)g->data;
     float max      = -INFINITY;
     for (int i = 0; i < n_elements; i++) {
@@ -118,7 +118,7 @@ void non_max_supression(struct ggml_tensor* result, struct ggml_tensor* G, struc
 }
 
 void threshold_hystersis(struct ggml_tensor* img, float high_threshold, float low_threshold, float weak, float strong) {
-    int n_elements = ggml_nelements(img);
+    int n_elements = static_cast<int>(ggml_nelements(img));
     float* imd     = (float*)img->data;
     float max      = -INFINITY;
     for (int i = 0; i < n_elements; i++) {
@@ -209,8 +209,8 @@ bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold,
     non_max_supression(image_gray, G, tetha);
     threshold_hystersis(image_gray, high_threshold, low_threshold, weak, strong);
     // to RGB channels
-    for (int iy = 0; iy < img.height; iy++) {
-        for (int ix = 0; ix < img.width; ix++) {
+    for (uint32_t iy = 0; iy < img.height; iy++) {
+        for (uint32_t ix = 0; ix < img.width; ix++) {
             float gray = ggml_ext_tensor_get_f32(image_gray, ix, iy);
             gray       = inverse ? 1.0f - gray : gray;
             ggml_ext_tensor_set_f32(image, gray, ix, iy);
diff --git a/qwen_image.hpp b/qwen_image.hpp
index 9c095a9a..ec2231b0 100644
--- a/qwen_image.hpp
+++ b/qwen_image.hpp
@@ -350,16 +350,16 @@ namespace Qwen {
     };
 
     struct QwenImageParams {
-        int64_t patch_size          = 2;
+        int patch_size              = 2;
         int64_t in_channels         = 64;
         int64_t out_channels        = 16;
-        int64_t num_layers          = 60;
+        int num_layers              = 60;
         int64_t attention_head_dim  = 128;
         int64_t num_attention_heads = 24;
         int64_t joint_attention_dim = 3584;
-        float theta                 = 10000;
+        int theta                   = 10000;
         std::vector<int> axes_dim   = {16, 56, 56};
-        int64_t axes_dim_sum        = 128;
+        int axes_dim_sum            = 128;
         bool zero_cond_t            = false;
     };
 
@@ -513,8 +513,8 @@ namespace Qwen {
             int64_t C = x->ne[2];
             int64_t N = x->ne[3];
 
-            auto img            = process_img(ctx, x);
-            uint64_t img_tokens = img->ne[1];
+            auto img           = process_img(ctx, x);
+            int64_t img_tokens = img->ne[1];
 
             if (ref_latents.size() > 0) {
                 for (ggml_tensor* ref : ref_latents) {
@@ -613,18 +613,18 @@ namespace Qwen {
                 ref_latents[i] = to_backend(ref_latents[i]);
             }
 
-            pe_vec      = Rope::gen_qwen_image_pe(x->ne[1],
-                                                  x->ne[0],
+            pe_vec      = Rope::gen_qwen_image_pe(static_cast<int>(x->ne[1]),
+                                                  static_cast<int>(x->ne[0]),
                                                   qwen_image_params.patch_size,
-                                                  x->ne[3],
-                                                  context->ne[1],
+                                                  static_cast<int>(x->ne[3]),
+                                                  static_cast<int>(context->ne[1]),
                                                   ref_latents,
                                                   increase_ref_index,
                                                   qwen_image_params.theta,
                                                   circular_y_enabled,
                                                   circular_x_enabled,
                                                   qwen_image_params.axes_dim);
-            int pos_len = pe_vec.size() / qwen_image_params.axes_dim_sum / 2;
+            int pos_len = static_cast<int>(pe_vec.size() / qwen_image_params.axes_dim_sum / 2);
             // LOG_DEBUG("pos_len %d", pos_len);
             auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, qwen_image_params.axes_dim_sum / 2, pos_len);
             // pe->data = pe_vec.data();
@@ -715,12 +715,12 @@ namespace Qwen {
 
                 struct ggml_tensor* out = nullptr;
 
-                int t0 = ggml_time_ms();
+                int64_t t0 = ggml_time_ms();
                 compute(8, x, timesteps, context, {}, false, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
-                LOG_DEBUG("qwen_image test done in %dms", t1 - t0);
+                LOG_DEBUG("qwen_image test done in %lldms", t1 - t0);
             }
         }
 
diff --git a/rng_mt19937.hpp b/rng_mt19937.hpp
index 7e619988..734554bf 100644
--- a/rng_mt19937.hpp
+++ b/rng_mt19937.hpp
@@ -90,7 +90,7 @@ class MT19937RNG : public RNG {
             float u1    = 1.0f - data[j];
             float u2    = data[j + 8];
             float r     = std::sqrt(-2.0f * std::log(u1));
-            float theta = 2.0f * 3.14159265358979323846 * u2;
+            float theta = 2.0f * 3.14159265358979323846f * u2;
             data[j]     = r * std::cos(theta) * std + mean;
             data[j + 8] = r * std::sin(theta) * std + mean;
         }
diff --git a/rope.hpp b/rope.hpp
index 4e6136c1..2d123b3c 100644
--- a/rope.hpp
+++ b/rope.hpp
@@ -22,11 +22,11 @@ namespace Rope {
     }
 
     __STATIC_INLINE__ std::vector<std::vector<float>> transpose(const std::vector<std::vector<float>>& mat) {
-        int rows = mat.size();
-        int cols = mat[0].size();
+        size_t rows = mat.size();
+        size_t cols = mat[0].size();
         std::vector<std::vector<float>> transposed(cols, std::vector<float>(rows));
-        for (int i = 0; i < rows; ++i) {
-            for (int j = 0; j < cols; ++j) {
+        for (size_t i = 0; i < rows; ++i) {
+            for (size_t j = 0; j < cols; ++j) {
                 transposed[j][i] = mat[i][j];
             }
         }
@@ -52,13 +52,13 @@ namespace Rope {
 
         std::vector<float> omega(half_dim);
         for (int i = 0; i < half_dim; ++i) {
-            omega[i] = 1.0f / std::pow(theta, scale[i]);
+            omega[i] = 1.0f / ::powf(1.f * theta, scale[i]);
         }
 
-        int pos_size = pos.size();
+        size_t pos_size = pos.size();
         std::vector<std::vector<float>> out(pos_size, std::vector<float>(half_dim));
-        for (int i = 0; i < pos_size; ++i) {
-            for (int j = 0; j < half_dim; ++j) {
+        for (size_t i = 0; i < pos_size; ++i) {
+            for (size_t j = 0; j < half_dim; ++j) {
                 float angle = pos[i] * omega[j];
                 if (!axis_wrap_dims.empty()) {
                     size_t wrap_size = axis_wrap_dims.size();
@@ -99,7 +99,7 @@ namespace Rope {
         for (int dim = 0; dim < axes_dim_num; dim++) {
             if (arange_dims.find(dim) != arange_dims.end()) {
                 for (int i = 0; i < bs * context_len; i++) {
-                    txt_ids[i][dim] = (i % context_len);
+                    txt_ids[i][dim] = 1.f * (i % context_len);
                 }
             }
         }
@@ -128,12 +128,12 @@ namespace Rope {
             w_start -= w_len / 2;
         }
 
-        std::vector<float> row_ids = linspace<float>(h_start, h_start + h_len - 1, h_len);
-        std::vector<float> col_ids = linspace<float>(w_start, w_start + w_len - 1, w_len);
+        std::vector<float> row_ids = linspace<float>(1.f * h_start, 1.f * h_start + h_len - 1, h_len);
+        std::vector<float> col_ids = linspace<float>(1.f * w_start, 1.f * w_start + w_len - 1, w_len);
 
         for (int i = 0; i < h_len; ++i) {
             for (int j = 0; j < w_len; ++j) {
-                img_ids[i * w_len + j][0] = index;
+                img_ids[i * w_len + j][0] = 1.f * index;
                 img_ids[i * w_len + j][1] = row_ids[i];
                 img_ids[i * w_len + j][2] = col_ids[j];
             }
@@ -172,7 +172,7 @@ namespace Rope {
                                                   const std::vector<std::vector<int>>& wrap_dims = {}) {
         std::vector<std::vector<float>> trans_ids = transpose(ids);
         size_t pos_len                            = ids.size() / bs;
-        int num_axes                              = axes_dim.size();
+        size_t num_axes                           = axes_dim.size();
         // for (int i = 0; i < pos_len; i++) {
         //     std::cout << trans_ids[0][i] << " " << trans_ids[1][i] << " " << trans_ids[2][i] << std::endl;
         // }
@@ -182,8 +182,8 @@ namespace Rope {
             emb_dim += d / 2;
 
         std::vector<std::vector<float>> emb(bs * pos_len, std::vector<float>(emb_dim * 2 * 2, 0.0));
-        int offset = 0;
-        for (int i = 0; i < num_axes; ++i) {
+        size_t offset = 0;
+        for (size_t i = 0; i < num_axes; ++i) {
             std::vector<int> axis_wrap_dims;
             if (!wrap_dims.empty() && i < (int)wrap_dims.size()) {
                 axis_wrap_dims = wrap_dims[i];
@@ -211,12 +211,12 @@ namespace Rope {
                                                                    float ref_index_scale,
                                                                    bool scale_rope) {
         std::vector<std::vector<float>> ids;
-        uint64_t curr_h_offset = 0;
-        uint64_t curr_w_offset = 0;
-        int index              = 1;
+        int curr_h_offset = 0;
+        int curr_w_offset = 0;
+        int index         = 1;
         for (ggml_tensor* ref : ref_latents) {
-            uint64_t h_offset = 0;
-            uint64_t w_offset = 0;
+            int h_offset = 0;
+            int w_offset = 0;
             if (!increase_ref_index) {
                 if (ref->ne[1] + curr_h_offset > ref->ne[0] + curr_w_offset) {
                     w_offset = curr_w_offset;
@@ -226,8 +226,8 @@ namespace Rope {
                 scale_rope = false;
             }
 
-            auto ref_ids = gen_flux_img_ids(ref->ne[1],
-                                            ref->ne[0],
+            auto ref_ids = gen_flux_img_ids(static_cast<int>(ref->ne[1]),
+                                            static_cast<int>(ref->ne[0]),
                                             patch_size,
                                             bs,
                                             axes_dim_num,
@@ -241,8 +241,8 @@ namespace Rope {
                 index++;
             }
 
-            curr_h_offset = std::max(curr_h_offset, ref->ne[1] + h_offset);
-            curr_w_offset = std::max(curr_w_offset, ref->ne[0] + w_offset);
+            curr_h_offset = std::max(curr_h_offset, static_cast<int>(ref->ne[1]) + h_offset);
+            curr_w_offset = std::max(curr_w_offset, static_cast<int>(ref->ne[0]) + w_offset);
         }
         return ids;
     }
@@ -345,7 +345,7 @@ namespace Rope {
         int h_len        = (h + (patch_size / 2)) / patch_size;
         int w_len        = (w + (patch_size / 2)) / patch_size;
         int txt_id_start = std::max(h_len, w_len);
-        auto txt_ids     = linspace<float>(txt_id_start, context_len + txt_id_start, context_len);
+        auto txt_ids     = linspace<float>(1.f * txt_id_start, 1.f * context_len + txt_id_start, context_len);
         std::vector<std::vector<float>> txt_ids_repeated(bs * context_len, std::vector<float>(3));
         for (int i = 0; i < bs; ++i) {
             for (int j = 0; j < txt_ids.size(); ++j) {
@@ -440,9 +440,9 @@ namespace Rope {
 
         std::vector<std::vector<float>> vid_ids(t_len * h_len * w_len, std::vector<float>(3, 0.0));
 
-        std::vector<float> t_ids = linspace<float>(t_offset, t_len - 1 + t_offset, t_len);
-        std::vector<float> h_ids = linspace<float>(h_offset, h_len - 1 + h_offset, h_len);
-        std::vector<float> w_ids = linspace<float>(w_offset, w_len - 1 + w_offset, w_len);
+        std::vector<float> t_ids = linspace<float>(1.f * t_offset, 1.f * t_len - 1 + t_offset, t_len);
+        std::vector<float> h_ids = linspace<float>(1.f * h_offset, 1.f * h_len - 1 + h_offset, h_len);
+        std::vector<float> w_ids = linspace<float>(1.f * w_offset, 1.f * w_len - 1 + w_offset, w_len);
 
         for (int i = 0; i < t_len; ++i) {
             for (int j = 0; j < h_len; ++j) {
@@ -493,8 +493,8 @@ namespace Rope {
 
                         GGML_ASSERT(i < grid_h * grid_w);
 
-                        ids[i][0] = ih + iy;
-                        ids[i][1] = iw + ix;
+                        ids[i][0] = static_cast<float>(ih + iy);
+                        ids[i][1] = static_cast<float>(iw + ix);
                         index++;
                     }
                 }
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 1c8c55ba..75689ff9 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -534,7 +534,7 @@ public:
                                                                 version);
             } else {  // SD1.x SD2.x SDXL
                 std::map<std::string, std::string> embbeding_map;
-                for (int i = 0; i < sd_ctx_params->embedding_count; i++) {
+                for (uint32_t i = 0; i < sd_ctx_params->embedding_count; i++) {
                     embbeding_map.emplace(SAFE_STR(sd_ctx_params->embeddings[i].name), SAFE_STR(sd_ctx_params->embeddings[i].path));
                 }
                 if (strstr(SAFE_STR(sd_ctx_params->photo_maker_path), "v2")) {
@@ -1191,7 +1191,7 @@ public:
 
     void apply_loras(const sd_lora_t* loras, uint32_t lora_count) {
         std::unordered_map<std::string, float> lora_f2m;
-        for (int i = 0; i < lora_count; i++) {
+        for (uint32_t i = 0; i < lora_count; i++) {
             std::string lora_id = SAFE_STR(loras[i].path);
             if (loras[i].is_high_noise) {
                 lora_id = "|high_noise|" + lora_id;
@@ -1443,12 +1443,12 @@ public:
                        void* step_callback_data,
                        bool is_noisy) {
         const uint32_t channel = 3;
-        uint32_t width         = latents->ne[0];
-        uint32_t height        = latents->ne[1];
-        uint32_t dim           = latents->ne[ggml_n_dims(latents) - 1];
+        uint32_t width         = static_cast<uint32_t>(latents->ne[0]);
+        uint32_t height        = static_cast<uint32_t>(latents->ne[1]);
+        uint32_t dim           = static_cast<uint32_t>(latents->ne[ggml_n_dims(latents) - 1]);
 
         if (preview_mode == PREVIEW_PROJ) {
-            int64_t patch_sz                       = 1;
+            int patch_sz                           = 1;
             const float(*latent_rgb_proj)[channel] = nullptr;
             float* latent_rgb_bias                 = nullptr;
 
@@ -1508,7 +1508,7 @@ public:
 
             uint32_t frames = 1;
             if (ggml_n_dims(latents) == 4) {
-                frames = latents->ne[2];
+                frames = static_cast<uint32_t>(latents->ne[2]);
             }
 
             uint32_t img_width  = width * patch_sz;
@@ -1518,7 +1518,7 @@ public:
 
             preview_latent_video(data, latents, latent_rgb_proj, latent_rgb_bias, patch_sz);
             sd_image_t* images = (sd_image_t*)malloc(frames * sizeof(sd_image_t));
-            for (int i = 0; i < frames; i++) {
+            for (uint32_t i = 0; i < frames; i++) {
                 images[i] = {img_width, img_height, channel, data + i * img_width * img_height * channel};
             }
             step_callback(step, frames, images, is_noisy, step_callback_data);
@@ -1563,22 +1563,22 @@ public:
             ggml_ext_tensor_clamp_inplace(result, 0.0f, 1.0f);
             uint32_t frames = 1;
             if (ggml_n_dims(latents) == 4) {
-                frames = result->ne[2];
+                frames = static_cast<uint32_t>(result->ne[2]);
             }
 
             sd_image_t* images = (sd_image_t*)malloc(frames * sizeof(sd_image_t));
             // print_ggml_tensor(result,true);
             for (size_t i = 0; i < frames; i++) {
-                images[i].width   = result->ne[0];
-                images[i].height  = result->ne[1];
+                images[i].width   = static_cast<uint32_t>(result->ne[0]);
+                images[i].height  = static_cast<uint32_t>(result->ne[1]);
                 images[i].channel = 3;
-                images[i].data    = ggml_tensor_to_sd_image(result, i, ggml_n_dims(latents) == 4);
+                images[i].data    = ggml_tensor_to_sd_image(result, static_cast<int>(i), ggml_n_dims(latents) == 4);
             }
 
             step_callback(step, frames, images, is_noisy, step_callback_data);
 
             ggml_ext_tensor_scale_inplace(result, 0);
-            for (int i = 0; i < frames; i++) {
+            for (uint32_t i = 0; i < frames; i++) {
                 free(images[i].data);
             }
 
@@ -1800,7 +1800,7 @@ public:
             int64_t H = x->ne[1] * get_vae_scale_factor();
             if (ggml_n_dims(x) == 4) {
                 // assuming video mode (if batch processing gets implemented this will break)
-                int T = x->ne[2];
+                int64_t T = x->ne[2];
                 if (sd_version_is_wan(version)) {
                     T = ((T - 1) * 4) + 1;
                 }
@@ -2077,7 +2077,7 @@ public:
                 img_cond_data = (float*)out_img_cond->data;
             }
 
-            int step_count         = sigmas.size();
+            int step_count         = static_cast<int>(sigmas.size());
             bool is_skiplayer_step = has_skiplayer && step > (int)(guidance.slg.layer_start * step_count) && step < (int)(guidance.slg.layer_end * step_count);
             float* skip_layer_data = has_skiplayer ? (float*)out_skip->data : nullptr;
             if (is_skiplayer_step) {
@@ -2449,11 +2449,11 @@ public:
                         int& tile_size_y,
                         float& tile_overlap,
                         const sd_tiling_params_t& params,
-                        int latent_x,
-                        int latent_y,
+                        int64_t latent_x,
+                        int64_t latent_y,
                         float encoding_factor = 1.0f) {
         tile_overlap       = std::max(std::min(params.target_overlap, 0.5f), 0.0f);
-        auto get_tile_size = [&](int requested_size, float factor, int latent_size) {
+        auto get_tile_size = [&](int requested_size, float factor, int64_t latent_size) {
             const int default_tile_size  = 32;
             const int min_tile_dimension = 4;
             int tile_size                = default_tile_size;
@@ -2462,12 +2462,12 @@ public:
             if (factor > 0.f) {
                 if (factor > 1.0)
                     factor = 1 / (factor - factor * tile_overlap + tile_overlap);
-                tile_size = std::round(latent_size * factor);
+                tile_size = static_cast<int>(std::round(latent_size * factor));
             } else if (requested_size >= min_tile_dimension) {
                 tile_size = requested_size;
             }
-            tile_size *= encoding_factor;
-            return std::max(std::min(tile_size, latent_size), min_tile_dimension);
+            tile_size = static_cast<int>(tile_size * encoding_factor);
+            return std::max(std::min(tile_size, static_cast<int>(latent_size)), min_tile_dimension);
         };
 
         tile_size_x = get_tile_size(params.tile_size_x, params.rel_size_x, latent_x);
@@ -2478,13 +2478,13 @@ public:
         int64_t t0                 = ggml_time_ms();
         ggml_tensor* result        = nullptr;
         const int vae_scale_factor = get_vae_scale_factor();
-        int W                      = x->ne[0] / vae_scale_factor;
-        int H                      = x->ne[1] / vae_scale_factor;
-        int C                      = get_latent_channel();
+        int64_t W                  = x->ne[0] / vae_scale_factor;
+        int64_t H                  = x->ne[1] / vae_scale_factor;
+        int64_t C                  = get_latent_channel();
         if (vae_tiling_params.enabled && !encode_video) {
             // TODO wan2.2 vae support?
-            int ne2;
-            int ne3;
+            int64_t ne2;
+            int64_t ne3;
             if (sd_version_is_qwen_image(version)) {
                 ne2 = 1;
                 ne3 = C * x->ne[3];
@@ -2608,7 +2608,7 @@ public:
         int64_t C                  = 3;
         ggml_tensor* result        = nullptr;
         if (decode_video) {
-            int T = x->ne[2];
+            int64_t T = x->ne[2];
             if (sd_version_is_wan(version)) {
                 T = ((T - 1) * 4) + 1;
             }
@@ -3193,7 +3193,7 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
         guidance.img_cfg = guidance.txt_cfg;
     }
 
-    int sample_steps = sigmas.size() - 1;
+    int sample_steps = static_cast<int>(sigmas.size() - 1);
 
     int64_t t0 = ggml_time_ms();
 
@@ -3203,7 +3203,7 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
     condition_params.width           = width;
     condition_params.height          = height;
     condition_params.ref_images      = ref_images;
-    condition_params.adm_in_channels = sd_ctx->sd->diffusion_model->get_adm_in_channels();
+    condition_params.adm_in_channels = static_cast<int>(sd_ctx->sd->diffusion_model->get_adm_in_channels());
 
     // Photo Maker
     SDCondition id_cond = sd_ctx->sd->get_pmid_conditon(work_ctx, pm_params, condition_params);
@@ -3799,7 +3799,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
         // timesteps ∝ sigmas for Flow models (like wan2.2 a14b)
         for (size_t i = 0; i < sigmas.size(); ++i) {
             if (sigmas[i] < sd_vid_gen_params->moe_boundary) {
-                high_noise_sample_steps = i;
+                high_noise_sample_steps = static_cast<int>(i);
                 break;
             }
         }
@@ -3977,7 +3977,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
         int64_t length = inactive->ne[2];
         if (ref_image_latent) {
             length += 1;
-            frames        = (length - 1) * 4 + 1;
+            frames        = static_cast<int>((length - 1) * 4 + 1);
             ref_image_num = 1;
         }
         vace_context = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, inactive->ne[0], inactive->ne[1], length, 96);  // [b*96, t, h/vae_scale_factor, w/vae_scale_factor]
@@ -4043,7 +4043,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
 
     int W = width / vae_scale_factor;
     int H = height / vae_scale_factor;
-    int T = init_latent->ne[2];
+    int T = static_cast<int>(init_latent->ne[2]);
     int C = sd_ctx->sd->get_latent_channel();
 
     struct ggml_tensor* final_latent;
@@ -4162,13 +4162,13 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
         ggml_free(work_ctx);
         return nullptr;
     }
-    *num_frames_out = vid->ne[2];
+    *num_frames_out = static_cast<int>(vid->ne[2]);
 
-    for (size_t i = 0; i < vid->ne[2]; i++) {
-        result_images[i].width   = vid->ne[0];
-        result_images[i].height  = vid->ne[1];
+    for (int64_t i = 0; i < vid->ne[2]; i++) {
+        result_images[i].width   = static_cast<uint32_t>(vid->ne[0]);
+        result_images[i].height  = static_cast<uint32_t>(vid->ne[1]);
         result_images[i].channel = 3;
-        result_images[i].data    = ggml_tensor_to_sd_image(vid, i, true);
+        result_images[i].data    = ggml_tensor_to_sd_image(vid, static_cast<int>(i), true);
     }
     ggml_free(work_ctx);
 
diff --git a/t5.hpp b/t5.hpp
index 4370a567..2e3e4b56 100644
--- a/t5.hpp
+++ b/t5.hpp
@@ -96,7 +96,7 @@ protected:
 
         try {
             data = nlohmann::json::parse(json_str);
-        } catch (const nlohmann::json::parse_error& e) {
+        } catch (const nlohmann::json::parse_error&) {
             status_ = INVLIAD_JSON;
             return;
         }
@@ -168,9 +168,9 @@ protected:
             kMaxTrieResultsSize);
         trie_results_size_ = 0;
         for (const auto& p : *pieces) {
-            const int num_nodes = trie_->commonPrefixSearch(
+            const size_t num_nodes = trie_->commonPrefixSearch(
                 p.first.data(), results.data(), results.size(), p.first.size());
-            trie_results_size_ = std::max(trie_results_size_, num_nodes);
+            trie_results_size_ = std::max(trie_results_size_, static_cast<int>(num_nodes));
         }
 
         if (trie_results_size_ == 0)
@@ -268,7 +268,7 @@ protected:
                 -1;  // The starting position (in utf-8) of this node. The entire best
                      // path can be constructed by backtracking along this link.
         };
-        const int size        = normalized.size();
+        const int size        = static_cast<int>(normalized.size());
         const float unk_score = min_score() - kUnkPenalty;
         // The ends are exclusive.
         std::vector<BestPathNode> best_path_ends_at(size + 1);
@@ -281,7 +281,7 @@ protected:
                 best_path_ends_at[starts_at].best_path_score;
             bool has_single_node = false;
             const int mblen =
-                std::min<int>(OneCharLen(normalized.data() + starts_at),
+                std::min<int>(static_cast<int>(OneCharLen(normalized.data() + starts_at)),
                               size - starts_at);
             while (key_pos < size) {
                 const int ret =
@@ -302,7 +302,7 @@ protected:
                         score + best_path_score_till_here;
                     if (target_node.starts_at == -1 ||
                         candidate_best_path_score > target_node.best_path_score) {
-                        target_node.best_path_score = candidate_best_path_score;
+                        target_node.best_path_score = static_cast<float>(candidate_best_path_score);
                         target_node.starts_at       = starts_at;
                         target_node.id              = ret;
                     }
@@ -394,7 +394,7 @@ public:
                     bool padding      = false) {
         if (max_length > 0 && padding) {
             size_t orig_token_num = tokens.size() - 1;
-            size_t n              = std::ceil(orig_token_num * 1.0 / (max_length - 1));
+            size_t n              = static_cast<size_t>(std::ceil(orig_token_num * 1.0 / (max_length - 1)));
             if (n == 0) {
                 n = 1;
             }
@@ -608,7 +608,7 @@ public:
             }
         }
 
-        k = ggml_scale_inplace(ctx->ggml_ctx, k, sqrt(d_head));
+        k = ggml_scale_inplace(ctx->ggml_ctx, k, ::sqrtf(static_cast<float>(d_head)));
 
         x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, mask);  // [N, n_token, d_head * n_head]
 
@@ -797,7 +797,7 @@ struct T5Runner : public GGMLRunner {
         input_ids      = to_backend(input_ids);
         attention_mask = to_backend(attention_mask);
 
-        relative_position_bucket_vec = compute_relative_position_bucket(input_ids->ne[0], input_ids->ne[0]);
+        relative_position_bucket_vec = compute_relative_position_bucket(static_cast<int>(input_ids->ne[0]), static_cast<int>(input_ids->ne[0]));
 
         // for (int i = 0; i < relative_position_bucket_vec.size(); i++) {
         //     if (i % 77 == 0) {
@@ -984,12 +984,12 @@ struct T5Embedder {
             auto attention_mask     = vector_to_ggml_tensor(work_ctx, masks);
             struct ggml_tensor* out = nullptr;
 
-            int t0 = ggml_time_ms();
+            int64_t t0 = ggml_time_ms();
             model.compute(8, input_ids, attention_mask, &out, work_ctx);
-            int t1 = ggml_time_ms();
+            int64_t t1 = ggml_time_ms();
 
             print_ggml_tensor(out);
-            LOG_DEBUG("t5 test done in %dms", t1 - t0);
+            LOG_DEBUG("t5 test done in %lldms", t1 - t0);
         }
     }
 
diff --git a/thirdparty/darts.h b/thirdparty/darts.h
index 7c25326a..bd535cd5 100644
--- a/thirdparty/darts.h
+++ b/thirdparty/darts.h
@@ -845,7 +845,7 @@ inline void BitVector::build() {
 
   num_ones_ = 0;
   for (std::size_t i = 0; i < units_.size(); ++i) {
-    ranks_[i] = num_ones_;
+    ranks_[i] = static_cast<id_type>(num_ones_);
     num_ones_ += pop_count(units_[i]);
   }
 }
@@ -1769,7 +1769,7 @@ id_type DoubleArrayBuilder::arrange_from_keyset(const Keyset<T> &keyset,
 
 inline id_type DoubleArrayBuilder::find_valid_offset(id_type id) const {
   if (extras_head_ >= units_.size()) {
-    return units_.size() | (id & LOWER_MASK);
+    return static_cast<id_type>(units_.size()) | (id & LOWER_MASK);
   }
 
   id_type unfixed_id = extras_head_;
@@ -1781,7 +1781,7 @@ inline id_type DoubleArrayBuilder::find_valid_offset(id_type id) const {
     unfixed_id = extras(unfixed_id).next();
   } while (unfixed_id != extras_head_);
 
-  return units_.size() | (id & LOWER_MASK);
+  return static_cast<id_type>(units_.size()) | (id & LOWER_MASK);
 }
 
 inline bool DoubleArrayBuilder::is_valid_offset(id_type id,
@@ -1812,7 +1812,7 @@ inline void DoubleArrayBuilder::reserve_id(id_type id) {
   if (id == extras_head_) {
     extras_head_ = extras(id).next();
     if (extras_head_ == id) {
-      extras_head_ = units_.size();
+      extras_head_ = static_cast<id_type>(units_.size());
     }
   }
   extras(extras(id).prev()).set_next(extras(id).next());
@@ -1821,8 +1821,8 @@ inline void DoubleArrayBuilder::reserve_id(id_type id) {
 }
 
 inline void DoubleArrayBuilder::expand_units() {
-  id_type src_num_units = units_.size();
-  id_type src_num_blocks = num_blocks();
+  id_type src_num_units = static_cast<id_type>(units_.size());
+  id_type src_num_blocks = static_cast<id_type>(num_blocks());
 
   id_type dest_num_units = src_num_units + BLOCK_SIZE;
   id_type dest_num_blocks = src_num_blocks + 1;
@@ -1834,7 +1834,7 @@ inline void DoubleArrayBuilder::expand_units() {
   units_.resize(dest_num_units);
 
   if (dest_num_blocks > NUM_EXTRA_BLOCKS) {
-    for (std::size_t id = src_num_units; id < dest_num_units; ++id) {
+    for (id_type id = src_num_units; id < dest_num_units; ++id) {
       extras(id).set_is_used(false);
       extras(id).set_is_fixed(false);
     }
@@ -1858,9 +1858,9 @@ inline void DoubleArrayBuilder::expand_units() {
 inline void DoubleArrayBuilder::fix_all_blocks() {
   id_type begin = 0;
   if (num_blocks() > NUM_EXTRA_BLOCKS) {
-    begin = num_blocks() - NUM_EXTRA_BLOCKS;
+    begin = static_cast<id_type>(num_blocks() - NUM_EXTRA_BLOCKS);
   }
-  id_type end = num_blocks();
+  id_type end = static_cast<id_type>(num_blocks());
 
   for (id_type block_id = begin; block_id != end; ++block_id) {
     fix_block(block_id);
diff --git a/thirdparty/stb_image_write.h b/thirdparty/stb_image_write.h
index 55118853..9128a313 100644
--- a/thirdparty/stb_image_write.h
+++ b/thirdparty/stb_image_write.h
@@ -257,6 +257,10 @@ int stbi_write_tga_with_rle = 1;
 int stbi_write_force_png_filter = -1;
 #endif
 
+#ifndef STBMIN
+#define STBMIN(a, b) ((a) < (b) ? (a) : (b))
+#endif // STBMIN
+
 static int stbi__flip_vertically_on_write = 0;
 
 STBIWDEF void stbi_flip_vertically_on_write(int flag)
@@ -1179,8 +1183,8 @@ STBIWDEF unsigned char *stbi_write_png_to_mem(const unsigned char *pixels, int s
    if (!zlib) return 0;
 
    if(parameters != NULL) {
-      param_length = strlen(parameters);
-      param_length += strlen("parameters") + 1; // For the name and the null-byte
+      param_length = (int)strlen(parameters);
+      param_length += (int)strlen("parameters") + 1; // For the name and the null-byte
    }
 
    // each tag requires 12 bytes of overhead
@@ -1526,11 +1530,11 @@ static int stbi_write_jpg_core(stbi__write_context *s, int width, int height, in
       if(parameters != NULL) {
          stbiw__putc(s, 0xFF /* comnent */ );
          stbiw__putc(s, 0xFE /* marker  */ );
-         size_t param_length = std::min(2 + strlen("parameters") + 1 + strlen(parameters) + 1, (size_t) 0xFFFF);
+         int param_length = STBMIN(2 + (int)strlen("parameters") + 1 + (int)strlen(parameters) + 1, 0xFFFF);
          stbiw__putc(s, param_length >> 8); // no need to mask, length < 65536
          stbiw__putc(s, param_length & 0xFF);
-         s->func(s->context, (void*)"parameters", strlen("parameters") + 1); // std::string is zero-terminated
-         s->func(s->context, (void*)parameters, std::min(param_length, (size_t) 65534) - 2 - strlen("parameters") - 1);
+         s->func(s->context, (void*)"parameters", (int)strlen("parameters") + 1); // std::string is zero-terminated
+         s->func(s->context, (void*)parameters, STBMIN(param_length, 65534) - 2 - (int)strlen("parameters") - 1);
          if(param_length > 65534) stbiw__putc(s, 0); // always zero-terminate for safety
          if(param_length & 1) stbiw__putc(s, 0xFF); // pad to even length
       }
diff --git a/unet.hpp b/unet.hpp
index ec7578e4..eef204d0 100644
--- a/unet.hpp
+++ b/unet.hpp
@@ -12,7 +12,7 @@
 class SpatialVideoTransformer : public SpatialTransformer {
 protected:
     int64_t time_depth;
-    int64_t max_time_embed_period;
+    int max_time_embed_period;
 
 public:
     SpatialVideoTransformer(int64_t in_channels,
@@ -21,8 +21,8 @@ public:
                             int64_t depth,
                             int64_t context_dim,
                             bool use_linear,
-                            int64_t time_depth            = 1,
-                            int64_t max_time_embed_period = 10000)
+                            int64_t time_depth        = 1,
+                            int max_time_embed_period = 10000)
         : SpatialTransformer(in_channels, n_head, d_head, depth, context_dim, use_linear),
           max_time_embed_period(max_time_embed_period) {
         // We will convert unet transformer linear to conv2d 1x1 when loading the weights, so use_linear is always False
@@ -112,9 +112,9 @@ public:
         x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 2, 0, 3));  // [N, h, w, inner_dim]
         x = ggml_reshape_3d(ctx->ggml_ctx, x, inner_dim, w * h, n);                // [N, h * w, inner_dim]
 
-        auto num_frames = ggml_arange(ctx->ggml_ctx, 0, timesteps, 1);
+        auto num_frames = ggml_arange(ctx->ggml_ctx, 0.f, static_cast<float>(timesteps), 1.f);
         // since b is 1, no need to do repeat
-        auto t_emb = ggml_ext_timestep_embedding(ctx->ggml_ctx, num_frames, in_channels, max_time_embed_period);  // [N, in_channels]
+        auto t_emb = ggml_ext_timestep_embedding(ctx->ggml_ctx, num_frames, static_cast<int>(in_channels), max_time_embed_period);  // [N, in_channels]
 
         auto emb = time_pos_embed_0->forward(ctx, t_emb);
         emb      = ggml_silu_inplace(ctx->ggml_ctx, emb);
@@ -526,7 +526,7 @@ public:
             auto cs = ggml_scale_inplace(ctx->ggml_ctx, controls[controls.size() - 1], control_strength);
             h       = ggml_add(ctx->ggml_ctx, h, cs);  // middle control
         }
-        int control_offset = controls.size() - 2;
+        int control_offset = static_cast<int>(controls.size() - 2);
 
         // output_blocks
         int output_block_idx = 0;
@@ -615,7 +615,7 @@ struct UNetModelRunner : public GGMLRunner {
         struct ggml_cgraph* gf = new_graph_custom(UNET_GRAPH_SIZE);
 
         if (num_video_frames == -1) {
-            num_video_frames = x->ne[3];
+            num_video_frames = static_cast<int>(x->ne[3]);
         }
 
         x         = to_backend(x);
@@ -700,12 +700,12 @@ struct UNetModelRunner : public GGMLRunner {
 
             struct ggml_tensor* out = nullptr;
 
-            int t0 = ggml_time_ms();
+            int64_t t0 = ggml_time_ms();
             compute(8, x, timesteps, context, nullptr, y, num_video_frames, {}, 0.f, &out, work_ctx);
-            int t1 = ggml_time_ms();
+            int64_t t1 = ggml_time_ms();
 
             print_ggml_tensor(out);
-            LOG_DEBUG("unet test done in %dms", t1 - t0);
+            LOG_DEBUG("unet test done in %lldms", t1 - t0);
         }
     }
 };
diff --git a/util.cpp b/util.cpp
index efb5e494..a94cfd98 100644
--- a/util.cpp
+++ b/util.cpp
@@ -488,7 +488,7 @@ sd_image_f32_t sd_image_t_to_sd_image_f32_t(sd_image_t image) {
     // Allocate memory for float data
     converted_image.data = (float*)malloc(image.width * image.height * image.channel * sizeof(float));
 
-    for (int i = 0; i < image.width * image.height * image.channel; i++) {
+    for (uint32_t i = 0; i < image.width * image.height * image.channel; i++) {
         // Convert uint8_t to float
         converted_image.data[i] = (float)image.data[i];
     }
@@ -520,7 +520,7 @@ sd_image_f32_t resize_sd_image_f32_t(sd_image_f32_t image, int target_width, int
             uint32_t x2 = std::min(x1 + 1, image.width - 1);
             uint32_t y2 = std::min(y1 + 1, image.height - 1);
 
-            for (int k = 0; k < image.channel; k++) {
+            for (uint32_t k = 0; k < image.channel; k++) {
                 float v1 = *(image.data + y1 * image.width * image.channel + x1 * image.channel + k);
                 float v2 = *(image.data + y1 * image.width * image.channel + x2 * image.channel + k);
                 float v3 = *(image.data + y2 * image.width * image.channel + x1 * image.channel + k);
@@ -540,9 +540,9 @@ sd_image_f32_t resize_sd_image_f32_t(sd_image_f32_t image, int target_width, int
 }
 
 void normalize_sd_image_f32_t(sd_image_f32_t image, float means[3], float stds[3]) {
-    for (int y = 0; y < image.height; y++) {
-        for (int x = 0; x < image.width; x++) {
-            for (int k = 0; k < image.channel; k++) {
+    for (uint32_t y = 0; y < image.height; y++) {
+        for (uint32_t x = 0; x < image.width; x++) {
+            for (uint32_t k = 0; k < image.channel; k++) {
                 int index         = (y * image.width + x) * image.channel + k;
                 image.data[index] = (image.data[index] - means[k]) / stds[k];
             }
@@ -551,8 +551,8 @@ void normalize_sd_image_f32_t(sd_image_f32_t image, float means[3], float stds[3
 }
 
 // Constants for means and std
-float means[3] = {0.48145466, 0.4578275, 0.40821073};
-float stds[3]  = {0.26862954, 0.26130258, 0.27577711};
+float means[3] = {0.48145466f, 0.4578275f, 0.40821073f};
+float stds[3]  = {0.26862954f, 0.26130258f, 0.27577711f};
 
 // Function to clip and preprocess sd_image_f32_t
 sd_image_f32_t clip_preprocess(sd_image_f32_t image, int target_width, int target_height) {
@@ -576,7 +576,7 @@ sd_image_f32_t clip_preprocess(sd_image_f32_t image, int target_width, int targe
             uint32_t x2 = std::min(x1 + 1, image.width - 1);
             uint32_t y2 = std::min(y1 + 1, image.height - 1);
 
-            for (int k = 0; k < image.channel; k++) {
+            for (uint32_t k = 0; k < image.channel; k++) {
                 float v1 = *(image.data + y1 * image.width * image.channel + x1 * image.channel + k);
                 float v2 = *(image.data + y1 * image.width * image.channel + x2 * image.channel + k);
                 float v3 = *(image.data + y2 * image.width * image.channel + x1 * image.channel + k);
@@ -602,11 +602,11 @@ sd_image_f32_t clip_preprocess(sd_image_f32_t image, int target_width, int targe
     result.channel = image.channel;
     result.data    = (float*)malloc(target_height * target_width * image.channel * sizeof(float));
 
-    for (int k = 0; k < image.channel; k++) {
-        for (int i = 0; i < result.height; i++) {
-            for (int j = 0; j < result.width; j++) {
-                int src_y = std::min(i + h_offset, resized_height - 1);
-                int src_x = std::min(j + w_offset, resized_width - 1);
+    for (uint32_t k = 0; k < image.channel; k++) {
+        for (uint32_t i = 0; i < result.height; i++) {
+            for (uint32_t j = 0; j < result.width; j++) {
+                int src_y = std::min(static_cast<int>(i + h_offset), resized_height - 1);
+                int src_x = std::min(static_cast<int>(j + w_offset), resized_width - 1);
                 *(result.data + i * result.width * image.channel + j * image.channel + k) =
                     fmin(fmax(*(resized_data + src_y * resized_width * image.channel + src_x * image.channel + k), 0.0f), 255.0f) / 255.0f;
             }
@@ -617,9 +617,9 @@ sd_image_f32_t clip_preprocess(sd_image_f32_t image, int target_width, int targe
     free(resized_data);
 
     // Normalize
-    for (int k = 0; k < image.channel; k++) {
-        for (int i = 0; i < result.height; i++) {
-            for (int j = 0; j < result.width; j++) {
+    for (uint32_t k = 0; k < image.channel; k++) {
+        for (uint32_t i = 0; i < result.height; i++) {
+            for (uint32_t j = 0; j < result.width; j++) {
                 // *(result.data + i * size * image.channel + j * image.channel + k) = 0.5f;
                 int offset  = i * result.width * image.channel + j * image.channel + k;
                 float value = *(result.data + offset);
diff --git a/vae.hpp b/vae.hpp
index ad5db1b5..cd055aa8 100644
--- a/vae.hpp
+++ b/vae.hpp
@@ -166,18 +166,18 @@ public:
     AE3DConv(int64_t in_channels,
              int64_t out_channels,
              std::pair<int, int> kernel_size,
-             int64_t video_kernel_size    = 3,
+             int video_kernel_size        = 3,
              std::pair<int, int> stride   = {1, 1},
              std::pair<int, int> padding  = {0, 0},
              std::pair<int, int> dilation = {1, 1},
              bool bias                    = true)
         : Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias) {
-        int64_t kernel_padding  = video_kernel_size / 2;
-        blocks["time_mix_conv"] = std::shared_ptr<GGMLBlock>(new Conv3dnx1x1(out_channels,
-                                                                             out_channels,
-                                                                             video_kernel_size,
-                                                                             1,
-                                                                             kernel_padding));
+        int kernel_padding      = video_kernel_size / 2;
+        blocks["time_mix_conv"] = std::shared_ptr<GGMLBlock>(new Conv3d(out_channels,
+                                                                        out_channels,
+                                                                        {video_kernel_size, 1, 1},
+                                                                        {1, 1, 1},
+                                                                        {kernel_padding, 0, 0}));
     }
 
     struct ggml_tensor* forward(GGMLRunnerContext* ctx,
@@ -186,7 +186,7 @@ public:
         // skip_video always False
         // x: [N, IC, IH, IW]
         // result: [N, OC, OH, OW]
-        auto time_mix_conv = std::dynamic_pointer_cast<Conv3dnx1x1>(blocks["time_mix_conv"]);
+        auto time_mix_conv = std::dynamic_pointer_cast<Conv3d>(blocks["time_mix_conv"]);
 
         x = Conv2d::forward(ctx, x);
         // timesteps = x.shape[0]
@@ -409,8 +409,8 @@ public:
           z_channels(z_channels),
           video_decoder(video_decoder),
           video_kernel_size(video_kernel_size) {
-        size_t num_resolutions = ch_mult.size();
-        int block_in           = ch * ch_mult[num_resolutions - 1];
+        int num_resolutions = static_cast<int>(ch_mult.size());
+        int block_in        = ch * ch_mult[num_resolutions - 1];
 
         blocks["conv_in"] = std::shared_ptr<GGMLBlock>(new Conv2d(z_channels, block_in, {3, 3}, {1, 1}, {1, 1}));
 
@@ -461,7 +461,7 @@ public:
         h = mid_block_2->forward(ctx, h);  // [N, block_in, h, w]
 
         // upsampling
-        size_t num_resolutions = ch_mult.size();
+        int num_resolutions = static_cast<int>(ch_mult.size());
         for (int i = num_resolutions - 1; i >= 0; i--) {
             for (int j = 0; j < num_res_blocks + 1; j++) {
                 std::string name = "up." + std::to_string(i) + ".block." + std::to_string(j);
@@ -745,12 +745,12 @@ struct AutoEncoderKL : public VAE {
             print_ggml_tensor(x);
             struct ggml_tensor* out = nullptr;
 
-            int t0 = ggml_time_ms();
+            int64_t t0 = ggml_time_ms();
             compute(8, x, false, &out, work_ctx);
-            int t1 = ggml_time_ms();
+            int64_t t1 = ggml_time_ms();
 
             print_ggml_tensor(out);
-            LOG_DEBUG("encode test done in %dms", t1 - t0);
+            LOG_DEBUG("encode test done in %lldms", t1 - t0);
         }
 
         if (false) {
@@ -763,12 +763,12 @@ struct AutoEncoderKL : public VAE {
             print_ggml_tensor(z);
             struct ggml_tensor* out = nullptr;
 
-            int t0 = ggml_time_ms();
+            int64_t t0 = ggml_time_ms();
             compute(8, z, true, &out, work_ctx);
-            int t1 = ggml_time_ms();
+            int64_t t1 = ggml_time_ms();
 
             print_ggml_tensor(out);
-            LOG_DEBUG("decode test done in %dms", t1 - t0);
+            LOG_DEBUG("decode test done in %lldms", t1 - t0);
         }
     };
 };
diff --git a/wan.hpp b/wan.hpp
index 31ecf33f..936fb6f3 100644
--- a/wan.hpp
+++ b/wan.hpp
@@ -108,7 +108,7 @@ namespace WAN {
             struct ggml_tensor* w = params["gamma"];
             w                     = ggml_reshape_1d(ctx->ggml_ctx, w, ggml_nelements(w));
             auto h                = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 3, 0, 1, 2));  // [ID, IH, IW, N*IC]
-            h                     = ggml_rms_norm(ctx->ggml_ctx, h, 1e-12);
+            h                     = ggml_rms_norm(ctx->ggml_ctx, h, 1e-12f);
             h                     = ggml_mul(ctx->ggml_ctx, h, w);
             h                     = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, h, 1, 2, 3, 0));
 
@@ -243,13 +243,13 @@ namespace WAN {
     protected:
         int64_t in_channels;
         int64_t out_channels;
-        int64_t factor_t;
-        int64_t factor_s;
-        int64_t factor;
+        int factor_t;
+        int factor_s;
+        int factor;
         int64_t group_size;
 
     public:
-        AvgDown3D(int64_t in_channels, int64_t out_channels, int64_t factor_t, int64_t factor_s = 1)
+        AvgDown3D(int64_t in_channels, int64_t out_channels, int factor_t, int factor_s = 1)
             : in_channels(in_channels), out_channels(out_channels), factor_t(factor_t), factor_s(factor_s) {
             factor = factor_t * factor_s * factor_s;
             GGML_ASSERT(in_channels * factor % out_channels == 0);
@@ -266,7 +266,7 @@ namespace WAN {
             int64_t H = x->ne[1];
             int64_t W = x->ne[0];
 
-            int64_t pad_t = (factor_t - T % factor_t) % factor_t;
+            int pad_t = (factor_t - T % factor_t) % factor_t;
 
             x = ggml_pad_ext(ctx->ggml_ctx, x, 0, 0, 0, 0, pad_t, 0, 0, 0);
             T = x->ne[2];
@@ -1071,7 +1071,7 @@ namespace WAN {
             int64_t iter_ = z->ne[2];
             auto x        = conv2->forward(ctx, z);
             struct ggml_tensor* out;
-            for (int64_t i = 0; i < iter_; i++) {
+            for (int i = 0; i < iter_; i++) {
                 _conv_idx = 0;
                 if (i == 0) {
                     auto in = ggml_ext_slice(ctx->ggml_ctx, x, 2, i, i + 1);  // [b*c, 1, h, w]
@@ -1091,7 +1091,7 @@ namespace WAN {
 
         struct ggml_tensor* decode_partial(GGMLRunnerContext* ctx,
                                            struct ggml_tensor* z,
-                                           int64_t i,
+                                           int i,
                                            int64_t b = 1) {
             // z: [b*c, t, h, w]
             GGML_ASSERT(b == 1);
@@ -1146,12 +1146,12 @@ namespace WAN {
             return gf;
         }
 
-        struct ggml_cgraph* build_graph_partial(struct ggml_tensor* z, bool decode_graph, int64_t i) {
+        struct ggml_cgraph* build_graph_partial(struct ggml_tensor* z, bool decode_graph, int i) {
             struct ggml_cgraph* gf = new_graph_custom(20480);
 
             ae.clear_cache();
 
-            for (int64_t feat_idx = 0; feat_idx < ae._feat_map.size(); feat_idx++) {
+            for (size_t feat_idx = 0; feat_idx < ae._feat_map.size(); feat_idx++) {
                 auto feat_cache        = get_cache_tensor_by_name("feat_idx:" + std::to_string(feat_idx));
                 ae._feat_map[feat_idx] = feat_cache;
             }
@@ -1162,7 +1162,7 @@ namespace WAN {
 
             struct ggml_tensor* out = decode_graph ? ae.decode_partial(&runner_ctx, z, i) : ae.encode(&runner_ctx, z);
 
-            for (int64_t feat_idx = 0; feat_idx < ae._feat_map.size(); feat_idx++) {
+            for (size_t feat_idx = 0; feat_idx < ae._feat_map.size(); feat_idx++) {
                 ggml_tensor* feat_cache = ae._feat_map[feat_idx];
                 if (feat_cache != nullptr) {
                     cache("feat_idx:" + std::to_string(feat_idx), feat_cache);
@@ -1188,7 +1188,7 @@ namespace WAN {
             } else {  // chunk 1 result is weird
                 ae.clear_cache();
                 int64_t t      = z->ne[2];
-                int64_t i      = 0;
+                int i          = 0;
                 auto get_graph = [&]() -> struct ggml_cgraph* {
                     return build_graph_partial(z, decode_graph, i);
                 };
@@ -1499,7 +1499,7 @@ namespace WAN {
 
     class WanAttentionBlock : public GGMLBlock {
     protected:
-        int dim;
+        int64_t dim;
 
         void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
             enum ggml_type wtype = get_type(prefix + "weight", tensor_storage_map, GGML_TYPE_F32);
@@ -1639,7 +1639,7 @@ namespace WAN {
 
     class Head : public GGMLBlock {
     protected:
-        int dim;
+        int64_t dim;
 
         void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
             enum ggml_type wtype = get_type(prefix + "weight", tensor_storage_map, GGML_TYPE_F32);
@@ -1685,8 +1685,8 @@ namespace WAN {
 
     class MLPProj : public GGMLBlock {
     protected:
-        int in_dim;
-        int flf_pos_embed_token_number;
+        int64_t in_dim;
+        int64_t flf_pos_embed_token_number;
 
         void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
             if (flf_pos_embed_token_number > 0) {
@@ -1739,17 +1739,17 @@ namespace WAN {
         int64_t in_dim                         = 16;
         int64_t dim                            = 2048;
         int64_t ffn_dim                        = 8192;
-        int64_t freq_dim                       = 256;
+        int freq_dim                           = 256;
         int64_t text_dim                       = 4096;
         int64_t out_dim                        = 16;
         int64_t num_heads                      = 16;
-        int64_t num_layers                     = 32;
-        int64_t vace_layers                    = 0;
+        int num_layers                         = 32;
+        int vace_layers                        = 0;
         int64_t vace_in_dim                    = 96;
         std::map<int, int> vace_layers_mapping = {};
         bool qk_norm                           = true;
         bool cross_attn_norm                   = true;
-        float eps                              = 1e-6;
+        float eps                              = 1e-6f;
         int64_t flf_pos_embed_token_number     = 0;
         int theta                              = 10000;
         // wan2.1 1.3B: 1536/12, wan2.1/2.2 14B: 5120/40, wan2.2 5B: 3074/24
@@ -2066,7 +2066,7 @@ namespace WAN {
                 if (version == VERSION_WAN2_2_TI2V) {
                     desc                 = "Wan2.2-TI2V-5B";
                     wan_params.dim       = 3072;
-                    wan_params.eps       = 1e-06;
+                    wan_params.eps       = 1e-06f;
                     wan_params.ffn_dim   = 14336;
                     wan_params.freq_dim  = 256;
                     wan_params.in_dim    = 48;
@@ -2085,7 +2085,7 @@ namespace WAN {
                         wan_params.in_dim = 16;
                     }
                     wan_params.dim       = 1536;
-                    wan_params.eps       = 1e-06;
+                    wan_params.eps       = 1e-06f;
                     wan_params.ffn_dim   = 8960;
                     wan_params.freq_dim  = 256;
                     wan_params.num_heads = 12;
@@ -2114,14 +2114,14 @@ namespace WAN {
                     }
                 }
                 wan_params.dim       = 5120;
-                wan_params.eps       = 1e-06;
+                wan_params.eps       = 1e-06f;
                 wan_params.ffn_dim   = 13824;
                 wan_params.freq_dim  = 256;
                 wan_params.num_heads = 40;
                 wan_params.out_dim   = 16;
                 wan_params.text_len  = 512;
             } else {
-                GGML_ABORT("invalid num_layers(%ld) of wan", wan_params.num_layers);
+                GGML_ABORT("invalid num_layers(%d) of wan", wan_params.num_layers);
             }
 
             LOG_INFO("%s", desc.c_str());
@@ -2156,16 +2156,16 @@ namespace WAN {
             time_dim_concat = to_backend(time_dim_concat);
             vace_context    = to_backend(vace_context);
 
-            pe_vec      = Rope::gen_wan_pe(x->ne[2],
-                                           x->ne[1],
-                                           x->ne[0],
+            pe_vec      = Rope::gen_wan_pe(static_cast<int>(x->ne[2]),
+                                           static_cast<int>(x->ne[1]),
+                                           static_cast<int>(x->ne[0]),
                                            std::get<0>(wan_params.patch_size),
                                            std::get<1>(wan_params.patch_size),
                                            std::get<2>(wan_params.patch_size),
                                            1,
                                            wan_params.theta,
                                            wan_params.axes_dim);
-            int pos_len = pe_vec.size() / wan_params.axes_dim_sum / 2;
+            int pos_len = static_cast<int>(pe_vec.size() / wan_params.axes_dim_sum / 2);
             // LOG_DEBUG("pos_len %d", pos_len);
             auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, wan_params.axes_dim_sum / 2, pos_len);
             // pe->data = pe_vec.data();
@@ -2243,12 +2243,12 @@ namespace WAN {
 
                 struct ggml_tensor* out = nullptr;
 
-                int t0 = ggml_time_ms();
+                int64_t t0 = ggml_time_ms();
                 compute(8, x, timesteps, context, nullptr, nullptr, nullptr, nullptr, 1.f, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
-                LOG_DEBUG("wan test done in %dms", t1 - t0);
+                LOG_DEBUG("wan test done in %lldms", t1 - t0);
             }
         }
 
diff --git a/z_image.hpp b/z_image.hpp
index af8d57e0..0abc7832 100644
--- a/z_image.hpp
+++ b/z_image.hpp
@@ -239,7 +239,7 @@ namespace ZImage {
     };
 
     struct ZImageParams {
-        int64_t patch_size         = 2;
+        int patch_size             = 2;
         int64_t hidden_size        = 3840;
         int64_t in_channels        = 16;
         int64_t out_channels       = 16;
@@ -249,11 +249,11 @@ namespace ZImage {
         int64_t num_heads          = 30;
         int64_t num_kv_heads       = 30;
         int64_t multiple_of        = 256;
-        float ffn_dim_multiplier   = 8.0 / 3.0f;
+        float ffn_dim_multiplier   = 8.0f / 3.0f;
         float norm_eps             = 1e-5f;
         bool qk_norm               = true;
         int64_t cap_feat_dim       = 2560;
-        float theta                = 256.f;
+        int theta                  = 256;
         std::vector<int> axes_dim  = {32, 48, 48};
         int64_t axes_dim_sum       = 128;
     };
@@ -411,13 +411,13 @@ namespace ZImage {
             auto txt = cap_embedder_1->forward(ctx, cap_embedder_0->forward(ctx, context));  // [N, n_txt_token, hidden_size]
             auto img = x_embedder->forward(ctx, x);                                          // [N, n_img_token, hidden_size]
 
-            int64_t n_txt_pad_token = Rope::bound_mod(n_txt_token, SEQ_MULTI_OF);
+            int64_t n_txt_pad_token = Rope::bound_mod(static_cast<int>(n_txt_token), SEQ_MULTI_OF);
             if (n_txt_pad_token > 0) {
                 auto txt_pad_tokens = ggml_repeat_4d(ctx->ggml_ctx, txt_pad_token, txt_pad_token->ne[0], n_txt_pad_token, N, 1);
                 txt                 = ggml_concat(ctx->ggml_ctx, txt, txt_pad_tokens, 1);  // [N, n_txt_token + n_txt_pad_token, hidden_size]
             }
 
-            int64_t n_img_pad_token = Rope::bound_mod(n_img_token, SEQ_MULTI_OF);
+            int64_t n_img_pad_token = Rope::bound_mod(static_cast<int>(n_img_token), SEQ_MULTI_OF);
             if (n_img_pad_token > 0) {
                 auto img_pad_tokens = ggml_repeat_4d(ctx->ggml_ctx, img_pad_token, img_pad_token->ne[0], n_img_pad_token, N, 1);
                 img                 = ggml_concat(ctx->ggml_ctx, img, img_pad_tokens, 1);  // [N, n_img_token + n_img_pad_token, hidden_size]
@@ -543,11 +543,11 @@ namespace ZImage {
                 ref_latents[i] = to_backend(ref_latents[i]);
             }
 
-            pe_vec      = Rope::gen_z_image_pe(x->ne[1],
-                                               x->ne[0],
+            pe_vec      = Rope::gen_z_image_pe(static_cast<int>(x->ne[1]),
+                                               static_cast<int>(x->ne[0]),
                                                z_image_params.patch_size,
-                                               x->ne[3],
-                                               context->ne[1],
+                                               static_cast<int>(x->ne[3]),
+                                               static_cast<int>(context->ne[1]),
                                                SEQ_MULTI_OF,
                                                ref_latents,
                                                increase_ref_index,
@@ -555,7 +555,7 @@ namespace ZImage {
                                                circular_y_enabled,
                                                circular_x_enabled,
                                                z_image_params.axes_dim);
-            int pos_len = pe_vec.size() / z_image_params.axes_dim_sum / 2;
+            int pos_len = static_cast<int>(pe_vec.size() / z_image_params.axes_dim_sum / 2);
             // LOG_DEBUG("pos_len %d", pos_len);
             auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, z_image_params.axes_dim_sum / 2, pos_len);
             // pe->data = pe_vec.data();
@@ -619,12 +619,12 @@ namespace ZImage {
 
                 struct ggml_tensor* out = nullptr;
 
-                int t0 = ggml_time_ms();
+                int64_t t0 = ggml_time_ms();
                 compute(8, x, timesteps, context, {}, false, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
-                LOG_DEBUG("z_image test done in %dms", t1 - t0);
+                LOG_DEBUG("z_image test done in %lldms", t1 - t0);
             }
         }
 

From 05006cd6e10777fc6acd1d8f5652a6803a677878 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Mon, 5 Jan 2026 23:29:22 +0800
Subject: [PATCH 35/97] chore: use CMAKE_BUILD_TYPE (#1175)

---
 .github/workflows/build.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 1009bf98..48a1bc02 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -146,7 +146,7 @@ jobs:
             sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip
 
   windows-latest-cmake:
-    runs-on: windows-2025
+    runs-on: windows-2022
 
     env:
       VULKAN_VERSION: 1.4.328.1
@@ -200,8 +200,8 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake .. -DCMAKE_CXX_FLAGS='/bigobj' -G Ninja -DCMAKE_C_COMPILER=cl.exe -DCMAKE_CXX_COMPILER=cl.exe ${{ matrix.defines }}
-          cmake --build . --config Release
+          cmake .. -DCMAKE_CXX_FLAGS='/bigobj' -G Ninja -DCMAKE_C_COMPILER=cl.exe -DCMAKE_CXX_COMPILER=cl.exe -DCMAKE_BUILD_TYPE=Release ${{ matrix.defines }}
+          cmake --build .
 
       - name: Check AVX512F support
         id: check_avx512f

From 496ec9421e76526734413be89a18811767af3e42 Mon Sep 17 00:00:00 2001
From: Nuno <rare-magma@posteo.eu>
Date: Mon, 5 Jan 2026 16:42:12 +0100
Subject: [PATCH 36/97] chore: add Linux Vulkan build and Docker image
 workflows (#1164)

---
 .dockerignore               |   1 +
 .github/workflows/build.yml | 121 +++++++++++++++++++++++++++++++++++-
 Dockerfile                  |   3 +-
 Dockerfile.musa             |   3 +-
 Dockerfile.sycl             |   1 +
 Dockerfile.vulkan           |  23 +++++++
 docs/docker.md              |  36 +++++++++--
 7 files changed, 179 insertions(+), 9 deletions(-)
 create mode 100644 Dockerfile.vulkan

diff --git a/.dockerignore b/.dockerignore
index 64a58a78..4627a217 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -1,4 +1,5 @@
 build*/
+docs/
 test/
 
 .cache/
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 48a1bc02..8955d5e2 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -92,6 +92,123 @@ jobs:
           path: |
             sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip
 
+  ubuntu-latest-cmake-vulkan:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v3
+        with:
+          submodules: recursive
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential libvulkan-dev glslc
+
+      - name: Build
+        id: cmake_build
+        run: |
+          mkdir build
+          cd build
+          cmake .. -DSD_BUILD_SHARED_LIBS=ON -DSD_VULKAN=ON
+          cmake --build . --config Release
+
+      - name: Get commit hash
+        id: commit
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: pr-mpt/actions-commit-hash@v2
+
+      - name: Fetch system info
+        id: system-info
+        run: |
+          echo "CPU_ARCH=`uname -m`" >> "$GITHUB_OUTPUT"
+          echo "OS_NAME=`lsb_release -s -i`" >> "$GITHUB_OUTPUT"
+          echo "OS_VERSION=`lsb_release -s -r`" >> "$GITHUB_OUTPUT"
+          echo "OS_TYPE=`uname -s`" >> "$GITHUB_OUTPUT"
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          cp ggml/LICENSE ./build/bin/ggml.txt
+          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
+          zip -j sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-vulkan.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-vulkan.zip
+          path: |
+            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-vulkan.zip
+
+  build-and-push-docker-images:
+    name: Build and push container images
+    runs-on: ubuntu-latest
+
+    permissions:
+      contents: read
+      packages: write
+      id-token: write
+      attestations: write
+      artifact-metadata: write
+
+    strategy:
+      matrix:
+        variant: [musa, sycl, vulkan]
+
+    env:
+      REGISTRY: ghcr.io
+      IMAGE_NAME: ${{ github.repository }}
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v6
+        with:
+          submodules: recursive
+
+      - name: Get commit hash
+        id: commit
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: pr-mpt/actions-commit-hash@v2
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Log in to the container registry
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      - name: Free Disk Space (Ubuntu)
+        uses: jlumbroso/free-disk-space@v1.3.1
+        with:
+          # this might remove tools that are actually needed,
+          # if set to "true" but frees about 6 GB
+          tool-cache: false
+
+      - name: Build and push Docker image
+        id: build-push
+        uses: docker/build-push-action@v6
+        with:
+          platforms: linux/amd64
+          push: true
+          file: Dockerfile.${{ matrix.variant }}
+          tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ env.BRANCH_NAME }}-${{ matrix.variant }}
+          labels: ${{ steps.meta.outputs.labels }}
+          annotations: ${{ steps.meta.outputs.annotations }}
+
   macOS-latest-cmake:
     runs-on: macos-latest
 
@@ -164,7 +281,7 @@ jobs:
             defines: "-DGGML_NATIVE=OFF -DGGML_AVX512=ON -DGGML_AVX=ON -DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
           - build: "cuda12"
             defines: "-DSD_CUDA=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_CUDA_ARCHITECTURES='61;70;75;80;86;89;90;100;120' -DCMAKE_CUDA_FLAGS='-Xcudafe \"--diag_suppress=177\" -Xcudafe \"--diag_suppress=550\"'"
-          - build: 'vulkan'
+          - build: "vulkan"
             defines: "-DSD_VULKAN=ON -DSD_BUILD_SHARED_LIBS=ON"
     steps:
       - name: Clone
@@ -371,6 +488,8 @@ jobs:
 
     needs:
       - ubuntu-latest-cmake
+      - ubuntu-latest-cmake-vulkan
+      - build-and-push-docker-images
       - macOS-latest-cmake
       - windows-latest-cmake
       - windows-latest-cmake-hip
diff --git a/Dockerfile b/Dockerfile
index da73021c..26a8f41c 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,4 +1,4 @@
-ARG UBUNTU_VERSION=22.04
+ARG UBUNTU_VERSION=24.04
 
 FROM ubuntu:$UBUNTU_VERSION AS build
 
@@ -18,5 +18,6 @@ RUN apt-get update && \
     apt-get clean
 
 COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
+COPY --from=build /sd.cpp/build/bin/sd-server /sd-server
 
 ENTRYPOINT [ "/sd-cli" ]
\ No newline at end of file
diff --git a/Dockerfile.musa b/Dockerfile.musa
index 0eac3d7f..ce7c0a90 100644
--- a/Dockerfile.musa
+++ b/Dockerfile.musa
@@ -1,5 +1,5 @@
 ARG MUSA_VERSION=rc4.2.0
-ARG UBUNTU_VERSION=22.04
+ARG UBUNTU_VERSION=24.04
 
 FROM mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}-amd64 as build
 
@@ -19,5 +19,6 @@ RUN mkdir build && cd build && \
 FROM mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64 as runtime
 
 COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
+COPY --from=build /sd.cpp/build/bin/sd-server /sd-server
 
 ENTRYPOINT [ "/sd-cli" ]
\ No newline at end of file
diff --git a/Dockerfile.sycl b/Dockerfile.sycl
index 6bcb91da..466d5517 100644
--- a/Dockerfile.sycl
+++ b/Dockerfile.sycl
@@ -15,5 +15,6 @@ RUN mkdir build && cd build && \
 FROM intel/oneapi-basekit:${SYCL_VERSION}-devel-ubuntu24.04 AS runtime
 
 COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
+COPY --from=build /sd.cpp/build/bin/sd-server /sd-server
 
 ENTRYPOINT [ "/sd-cli" ]
diff --git a/Dockerfile.vulkan b/Dockerfile.vulkan
new file mode 100644
index 00000000..5ba6cb05
--- /dev/null
+++ b/Dockerfile.vulkan
@@ -0,0 +1,23 @@
+ARG UBUNTU_VERSION=24.04
+
+FROM ubuntu:$UBUNTU_VERSION AS build
+
+RUN apt-get update && apt-get install -y --no-install-recommends build-essential git cmake libvulkan-dev glslc
+
+WORKDIR /sd.cpp
+
+COPY . .
+
+RUN cmake . -B ./build -DSD_VULKAN=ON
+RUN cmake --build ./build --config Release --parallel
+
+FROM ubuntu:$UBUNTU_VERSION AS runtime
+
+RUN apt-get update && \
+    apt-get install --yes --no-install-recommends libgomp1 libvulkan1 mesa-vulkan-drivers && \
+    apt-get clean
+
+COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
+COPY --from=build /sd.cpp/build/bin/sd-server /sd-server
+
+ENTRYPOINT [ "/sd-cli" ]
diff --git a/docs/docker.md b/docs/docker.md
index 26a5f714..660ed257 100644
--- a/docs/docker.md
+++ b/docs/docker.md
@@ -1,15 +1,39 @@
-## Docker
+# Docker
 
-### Building using Docker
+## Run CLI
+
+```shell
+docker run --rm -v /path/to/models:/models -v /path/to/output/:/output ghcr.io/leejet/stable-diffusion.cpp:master [args...]
+# For example
+# docker run --rm -v ./models:/models -v ./build:/output ghcr.io/leejet/stable-diffusion.cpp:master -m /models/sd-v1-4.ckpt -p "a lovely cat" -v -o /output/output.png
+```
+
+## Run server
+
+```shell
+docker run --rm --init -v /path/to/models:/models -v /path/to/output/:/output -p "1234:1234" --entrypoint "/sd-server" ghcr.io/leejet/stable-diffusion.cpp:master [args...]
+# For example
+# docker run --rm --init -v ./models:/models -v ./build:/output -p "1234:1234" --entrypoint "/sd-server" ghcr.io/leejet/stable-diffusion.cpp:master -m /models/sd-v1-4.ckpt -p "a lovely cat" -v -o /output/output.png
+```
+
+## Building using Docker
 
 ```shell
 docker build -t sd .
 ```
 
-### Run
+## Building variants using Docker
+
+Vulkan:
 
 ```shell
-docker run -v /path/to/models:/models -v /path/to/output/:/output sd-cli [args...]
+docker build -f Dockerfile.vulkan -t sd .
+```
+
+## Run locally built image's CLI
+
+```shell
+docker run --rm -v /path/to/models:/models -v /path/to/output/:/output sd [args...]
 # For example
-# docker run -v ./models:/models -v ./build:/output sd-cli -m /models/sd-v1-4.ckpt -p "a lovely cat" -v -o /output/output.png
-```
\ No newline at end of file
+# docker run --rm -v ./models:/models -v ./build:/output sd -m /models/sd-v1-4.ckpt -p "a lovely cat" -v -o /output/output.png
+```

From fdcacc1ebbee0513aa3f1a7b9d84f91adbe7d2b7 Mon Sep 17 00:00:00 2001
From: Nuno <rare-magma@posteo.eu>
Date: Mon, 5 Jan 2026 16:52:34 +0100
Subject: [PATCH 37/97] ci: cancel old github action runs (#1172)

* ci: cancel old github action runs

Signed-off-by: rare-magma <rare-magma@posteo.eu>

* ci: adjust concurrency to avoid canceling non-PR workflows

---------

Signed-off-by: rare-magma <rare-magma@posteo.eu>
Co-authored-by: leejet <leejet714@gmail.com>
---
 .github/workflows/build.yml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 8955d5e2..8f608591 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -38,6 +38,10 @@ on:
 env:
   BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   ubuntu-latest-cmake:
     runs-on: ubuntu-latest

From c34730d9b4d6cc966205e7e04d54540b637940e7 Mon Sep 17 00:00:00 2001
From: Nuno <rare-magma@posteo.eu>
Date: Mon, 5 Jan 2026 16:56:34 +0100
Subject: [PATCH 38/97] chore: downgrade ubuntu base image in musa container
 image (#1176)

Signed-off-by: rare-magma <rare-magma@posteo.eu>
---
 Dockerfile.musa | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Dockerfile.musa b/Dockerfile.musa
index ce7c0a90..2d95f817 100644
--- a/Dockerfile.musa
+++ b/Dockerfile.musa
@@ -1,5 +1,5 @@
 ARG MUSA_VERSION=rc4.2.0
-ARG UBUNTU_VERSION=24.04
+ARG UBUNTU_VERSION=22.04
 
 FROM mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}-amd64 as build
 

From c5602a676caff5fe5a9f3b76b2bc614faf5121a5 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Mon, 5 Jan 2026 12:58:09 -0300
Subject: [PATCH 39/97] feat: prioritize gguf and safetensors formats for
 embeddings and LoRAs (#1169)

---
 examples/common/common.hpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index c9e8eeeb..82328bcc 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -809,7 +809,7 @@ struct SDContextParams {
     }
 
     void build_embedding_map() {
-        static const std::vector<std::string> valid_ext = {".pt", ".safetensors", ".gguf"};
+        static const std::vector<std::string> valid_ext = {".gguf", ".safetensors", ".pt"};
 
         if (!fs::exists(embedding_dir) || !fs::is_directory(embedding_dir)) {
             return;
@@ -1606,7 +1606,7 @@ struct SDGenerationParams {
             return;
         }
         static const std::regex re(R"(<lora:([^:>]+):([^>]+)>)");
-        static const std::vector<std::string> valid_ext = {".pt", ".safetensors", ".gguf"};
+        static const std::vector<std::string> valid_ext = {".gguf", ".safetensors", ".pt"};
         std::smatch m;
 
         std::string tmp = prompt;

From e7e83ed4d1abe358ce666465f75cfcee8e42b4cf Mon Sep 17 00:00:00 2001
From: evanreichard <30810613+evanreichard@users.noreply.github.com>
Date: Tue, 6 Jan 2026 10:16:05 -0500
Subject: [PATCH 40/97] fix(server): use has_file for mask multipart detection
 (#1178)

---
 examples/server/main.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index 9fa88049..b15daca1 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -537,7 +537,7 @@ int main(int argc, const char** argv) {
             }
 
             std::vector<uint8_t> mask_bytes;
-            if (req.form.has_field("mask")) {
+            if (req.form.has_file("mask")) {
                 auto file = req.form.get_file("mask");
                 mask_bytes.assign(file.content.begin(), file.content.end());
             }

From 9be0b91927dfa4007d053df72dea7302990226bb Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Tue, 6 Jan 2026 23:31:03 +0800
Subject: [PATCH 41/97] docs: fix safetensors file extension notation

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index bd00761e..4e536880 100644
--- a/README.md
+++ b/README.md
@@ -70,7 +70,7 @@ API and command-line option may change frequently.***
   - SYCL
 - Supported weight formats
   - Pytorch checkpoint (`.ckpt` or `.pth`)
-  - Safetensors (`./safetensors`)
+  - Safetensors (`.safetensors`)
   - GGUF (`.gguf`)
 - Supported platforms
     - Linux

From dfe6d6c664c491395c9951ba073f8ad3bb6d6780 Mon Sep 17 00:00:00 2001
From: Flavio Bizzarri <33379291+newfla@users.noreply.github.com>
Date: Thu, 8 Jan 2026 15:52:22 +0100
Subject: [PATCH 42/97] fix: missing newline after seed in
 sd_img_gen_params_to_str (#1183)

---
 stable-diffusion.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 75689ff9..d4eed5dd 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -3046,7 +3046,7 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
              "height: %d\n"
              "sample_params: %s\n"
              "strength: %.2f\n"
-             "seed: %" PRId64
+             "seed: %" PRId64 "\n"
              "batch_count: %d\n"
              "ref_images_count: %d\n"
              "auto_resize_ref_image: %s\n"

From 27b5f1740113fbcca5e43a56e5b5aabf36914317 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Thu, 8 Jan 2026 23:03:32 +0800
Subject: [PATCH 43/97] ci: only push Docker images on master or release

---
 .github/workflows/build.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 8f608591..76b17935 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -207,7 +207,7 @@ jobs:
         uses: docker/build-push-action@v6
         with:
           platforms: linux/amd64
-          push: true
+          push: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
           file: Dockerfile.${{ matrix.variant }}
           tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ env.BRANCH_NAME }}-${{ matrix.variant }}
           labels: ${{ steps.meta.outputs.labels }}

From 0e52afc6513cc2dea9a1a017afc4a008d5acf2b0 Mon Sep 17 00:00:00 2001
From: rmatif <rmatif@proton.me>
Date: Thu, 8 Jan 2026 16:23:05 +0100
Subject: [PATCH 44/97] feat: enable vae tiling for vid gen (#1152)

* enable vae tiling for vid gen

* format code

* eliminate compilation warning

---------

Co-authored-by: leejet <leejet714@gmail.com>
---
 examples/cli/main.cpp |  1 +
 stable-diffusion.cpp  | 20 ++++++++++++++------
 stable-diffusion.h    |  1 +
 3 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index 86bc1abf..0a7da7ae 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -757,6 +757,7 @@ int main(int argc, const char* argv[]) {
                 gen_params.seed,
                 gen_params.video_frames,
                 gen_params.vace_strength,
+                ctx_params.vae_tiling_params,
                 gen_params.cache_params,
             };
 
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index d4eed5dd..60bcba4d 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -2489,10 +2489,15 @@ public:
                 ne2 = 1;
                 ne3 = C * x->ne[3];
             } else {
-                if (!use_tiny_autoencoder) {
-                    C *= 2;
+                int64_t out_channels   = C;
+                bool encode_outputs_mu = use_tiny_autoencoder ||
+                                         sd_version_is_wan(version) ||
+                                         sd_version_is_flux2(version) ||
+                                         version == VERSION_CHROMA_RADIANCE;
+                if (!encode_outputs_mu) {
+                    out_channels *= 2;
                 }
-                ne2 = C;
+                ne2 = out_channels;
                 ne3 = x->ne[3];
             }
             result = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, ne2, ne3);
@@ -2633,7 +2638,7 @@ public:
             }
             process_latent_out(x);
             // x = load_tensor_from_file(work_ctx, "wan_vae_z.bin");
-            if (vae_tiling_params.enabled && !decode_video) {
+            if (vae_tiling_params.enabled) {
                 float tile_overlap;
                 int tile_size_x, tile_size_y;
                 get_tile_sizes(tile_size_x, tile_size_y, tile_overlap, vae_tiling_params, x->ne[0], x->ne[1]);
@@ -2651,7 +2656,7 @@ public:
             first_stage_model->free_compute_buffer();
             process_vae_output_tensor(result);
         } else {
-            if (vae_tiling_params.enabled && !decode_video) {
+            if (vae_tiling_params.enabled) {
                 // split latent in 64x64 tiles and compute in several steps
                 auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
                     tae_first_stage->compute(n_threads, in, true, &out);
@@ -3046,7 +3051,8 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
              "height: %d\n"
              "sample_params: %s\n"
              "strength: %.2f\n"
-             "seed: %" PRId64 "\n"
+             "seed: %" PRId64
+             "\n"
              "batch_count: %d\n"
              "ref_images_count: %d\n"
              "auto_resize_ref_image: %s\n"
@@ -3099,6 +3105,7 @@ void sd_vid_gen_params_init(sd_vid_gen_params_t* sd_vid_gen_params) {
     sd_vid_gen_params->video_frames                          = 6;
     sd_vid_gen_params->moe_boundary                          = 0.875f;
     sd_vid_gen_params->vace_strength                         = 1.f;
+    sd_vid_gen_params->vae_tiling_params                     = {false, 0, 0, 0.5f, 0.0f, 0.0f};
     sd_cache_params_init(&sd_vid_gen_params->cache);
 }
 
@@ -3728,6 +3735,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
     if (sd_ctx == nullptr || sd_vid_gen_params == nullptr) {
         return nullptr;
     }
+    sd_ctx->sd->vae_tiling_params = sd_vid_gen_params->vae_tiling_params;
 
     std::string prompt          = SAFE_STR(sd_vid_gen_params->prompt);
     std::string negative_prompt = SAFE_STR(sd_vid_gen_params->negative_prompt);
diff --git a/stable-diffusion.h b/stable-diffusion.h
index ade2cdb8..8f040d2b 100644
--- a/stable-diffusion.h
+++ b/stable-diffusion.h
@@ -319,6 +319,7 @@ typedef struct {
     int64_t seed;
     int video_frames;
     float vace_strength;
+    sd_tiling_params_t vae_tiling_params;
     sd_cache_params_t cache;
 } sd_vid_gen_params_t;
 

From 885e62ea822e674c6837a8225d2d75f021b97a6a Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 11 Jan 2026 16:34:13 +0800
Subject: [PATCH 45/97] refactor: replace ggml_ext_attention with
 ggml_ext_attention_ext (#1185)

---
 ggml_extend.hpp | 26 +-------------------------
 vae.hpp         |  9 ++++-----
 wan.hpp         |  5 ++---
 3 files changed, 7 insertions(+), 33 deletions(-)

diff --git a/ggml_extend.hpp b/ggml_extend.hpp
index 1ff45011..6f498ffa 100644
--- a/ggml_extend.hpp
+++ b/ggml_extend.hpp
@@ -1208,35 +1208,11 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_cast_f32(ggml_context* ctx, ggml_tensor*
     } else {
         out = ggml_mul_mat(ctx, out, one);
     }
-    out                    = ggml_reshape(ctx, out, a);
+    out = ggml_reshape(ctx, out, a);
 #endif
     return out;
 }
 
-// q: [N * n_head, n_token, d_head]
-// k: [N * n_head, n_k, d_head]
-// v: [N * n_head, d_head, n_k]
-// return: [N * n_head, n_token, d_head]
-__STATIC_INLINE__ struct ggml_tensor* ggml_ext_attention(struct ggml_context* ctx,
-                                                         struct ggml_tensor* q,
-                                                         struct ggml_tensor* k,
-                                                         struct ggml_tensor* v,
-                                                         bool mask = false) {
-#if defined(SD_USE_FLASH_ATTENTION) && !defined(SD_USE_CUDA) && !defined(SD_USE_METAL) && !defined(SD_USE_VULKAN) && !defined(SD_USE_SYCL)
-    struct ggml_tensor* kqv = ggml_flash_attn(ctx, q, k, v, false);  // [N * n_head, n_token, d_head]
-#else
-    float d_head           = (float)q->ne[0];
-    struct ggml_tensor* kq = ggml_mul_mat(ctx, k, q);  // [N * n_head, n_token, n_k]
-    kq                     = ggml_scale_inplace(ctx, kq, 1.0f / sqrt(d_head));
-    if (mask) {
-        kq = ggml_diag_mask_inf_inplace(ctx, kq, 0);
-    }
-    kq                      = ggml_soft_max_inplace(ctx, kq);
-    struct ggml_tensor* kqv = ggml_mul_mat(ctx, v, kq);  // [N * n_head, n_token, d_head]
-#endif
-    return kqv;
-}
-
 // q: [N, L_q, C(n_head*d_head)] or [N*n_head, L_q, d_head]
 // k: [N, L_k, n_kv_head*d_head] or [N*n_kv_head, L_k, d_head]
 // v: [N, L_k, n_kv_head*d_head] or [N, L_k, n_kv_head, d_head]
diff --git a/vae.hpp b/vae.hpp
index cd055aa8..23250029 100644
--- a/vae.hpp
+++ b/vae.hpp
@@ -127,8 +127,6 @@ public:
             q = q_proj->forward(ctx, h_);  // [N, h * w, in_channels]
             k = k_proj->forward(ctx, h_);  // [N, h * w, in_channels]
             v = v_proj->forward(ctx, h_);  // [N, h * w, in_channels]
-
-            v = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, v, 1, 0, 2, 3));  // [N, in_channels, h * w]
         } else {
             q = q_proj->forward(ctx, h_);                                              // [N, in_channels, h, w]
             q = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, q, 1, 2, 0, 3));  // [N, h, w, in_channels]
@@ -138,11 +136,12 @@ public:
             k = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, k, 1, 2, 0, 3));  // [N, h, w, in_channels]
             k = ggml_reshape_3d(ctx->ggml_ctx, k, c, h * w, n);                        // [N, h * w, in_channels]
 
-            v = v_proj->forward(ctx, h_);                        // [N, in_channels, h, w]
-            v = ggml_reshape_3d(ctx->ggml_ctx, v, h * w, c, n);  // [N, in_channels, h * w]
+            v = v_proj->forward(ctx, h_);                                              // [N, in_channels, h, w]
+            v = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, v, 1, 2, 0, 3));  // [N, h, w, in_channels]
+            v = ggml_reshape_3d(ctx->ggml_ctx, v, c, h * w, n);                        // [N, h * w, in_channels]
         }
 
-        h_ = ggml_ext_attention(ctx->ggml_ctx, q, k, v, false);  // [N, h * w, in_channels]
+        h_ = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, false, true, false);
 
         if (use_linear) {
             h_ = proj_out->forward(ctx, h_);  // [N, h * w, in_channels]
diff --git a/wan.hpp b/wan.hpp
index 936fb6f3..3ade14bf 100644
--- a/wan.hpp
+++ b/wan.hpp
@@ -572,9 +572,8 @@ namespace WAN {
             auto v = qkv_vec[2];
             v      = ggml_reshape_3d(ctx->ggml_ctx, v, h * w, c, n);  // [t, c, h * w]
 
-            x = ggml_ext_attention(ctx->ggml_ctx, q, k, v, false);  // [t, h * w, c]
-            // v      = ggml_cont(ctx, ggml_ext_torch_permute(ctx, v, 1, 0, 2, 3));  // [t, h * w, c]
-            // x = ggml_ext_attention_ext(ctx, q, k, v, q->ne[2], nullptr, false, false, true);
+            v = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, v, 1, 0, 2, 3));                // [t, h * w, c]
+            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, false, true, false);  // [t, h * w, c]
 
             x = ggml_ext_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [t, c, h * w]
             x = ggml_reshape_4d(ctx->ggml_ctx, x, w, h, c, n);                             // [t, c, h, w]

From 271b594e749179d7ea0e417395845cea880837c9 Mon Sep 17 00:00:00 2001
From: Weiqi Gao <gaoweiqicarl1@163.com>
Date: Wed, 14 Jan 2026 00:28:55 +0800
Subject: [PATCH 46/97] sync: update ggml (#1187)

---
 ggml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml b/ggml
index 3e9f2ba3..8891ab6f 160000
--- a/ggml
+++ b/ggml
@@ -1 +1 @@
-Subproject commit 3e9f2ba3b934c20b26873b3c60dbf41b116978ff
+Subproject commit 8891ab6fc742ac1198736d3da3b73c730e42af84

From 48d3161a8dd74c6f2e30c8045c526f9b343b8231 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Tue, 13 Jan 2026 13:34:27 -0300
Subject: [PATCH 47/97] feat: add sd-server API support for steps, sampler and
 scheduler (#1173)

---
 examples/common/common.hpp | 20 ++++++++++++++++++++
 examples/server/main.cpp   |  6 ++++++
 2 files changed, 26 insertions(+)

diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index 82328bcc..d299da50 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -1594,10 +1594,30 @@ struct SDGenerationParams {
         load_if_exists("skip_layers", skip_layers);
         load_if_exists("high_noise_skip_layers", high_noise_skip_layers);
 
+        load_if_exists("steps", sample_params.sample_steps);
+        load_if_exists("high_noise_steps", high_noise_sample_params.sample_steps);
         load_if_exists("cfg_scale", sample_params.guidance.txt_cfg);
         load_if_exists("img_cfg_scale", sample_params.guidance.img_cfg);
         load_if_exists("guidance", sample_params.guidance.distilled_guidance);
 
+        auto load_sampler_if_exists = [&](const char* key, enum sample_method_t& out) {
+            if (j.contains(key) && j[key].is_string()) {
+                enum sample_method_t tmp = str_to_sample_method(j[key].get<std::string>().c_str());
+                if (tmp != SAMPLE_METHOD_COUNT) {
+                    out = tmp;
+                }
+            }
+        };
+        load_sampler_if_exists("sample_method", sample_params.sample_method);
+        load_sampler_if_exists("high_noise_sample_method", high_noise_sample_params.sample_method);
+
+        if (j.contains("scheduler") && j["scheduler"].is_string()) {
+            enum scheduler_t tmp = str_to_scheduler(j["scheduler"].get<std::string>().c_str());
+            if (tmp != SCHEDULER_COUNT) {
+                sample_params.scheduler = tmp;
+            }
+        }
+
         return true;
     }
 
diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index b15daca1..b0ac7eef 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -420,6 +420,9 @@ int main(int argc, const char** argv) {
                 return;
             }
 
+            if (gen_params.sample_params.sample_steps > 100)
+                gen_params.sample_params.sample_steps = 100;
+
             if (!gen_params.process_and_check(IMG_GEN, "")) {
                 res.status = 400;
                 res.set_content(R"({"error":"invalid params"})", "application/json");
@@ -598,6 +601,9 @@ int main(int argc, const char** argv) {
                 return;
             }
 
+            if (gen_params.sample_params.sample_steps > 100)
+                gen_params.sample_params.sample_steps = 100;
+
             if (!gen_params.process_and_check(IMG_GEN, "")) {
                 res.status = 400;
                 res.set_content(R"({"error":"invalid params"})", "application/json");

From 7010bb4dff7bd55b03d35ef9772142c21699eba9 Mon Sep 17 00:00:00 2001
From: akleine <alb.kleine@gmx.de>
Date: Tue, 13 Jan 2026 18:14:57 +0100
Subject: [PATCH 48/97] feat: support for SDXS-512 model (#1180)

* feat: add U-Net specials of SDXS

* docs: update distilled_sd.md for SDXS-512

* feat: for SDXS use AutoencoderTiny as the primary VAE

* docs: update distilled_sd.md for SDXS-512

* fix: SDXS code cleaning after review by stduhpf

* format code

* fix sdxs with --taesd-preview-only

---------

Co-authored-by: leejet <leejet714@gmail.com>
---
 docs/distilled_sd.md | 30 +++++++++++++++++++++++++++++-
 model.cpp            |  7 +++++++
 model.h              |  3 ++-
 stable-diffusion.cpp | 26 ++++++++++++++++++--------
 tae.hpp              | 11 ++++++++++-
 unet.hpp             |  5 ++++-
 6 files changed, 70 insertions(+), 12 deletions(-)

diff --git a/docs/distilled_sd.md b/docs/distilled_sd.md
index 478305f2..232c0228 100644
--- a/docs/distilled_sd.md
+++ b/docs/distilled_sd.md
@@ -83,7 +83,7 @@ python convert_diffusers_to_original_stable_diffusion.py \
 The file segmind_tiny-sd.ckpt will be generated and is now ready for use with sd.cpp. You can follow a similar process for the other models mentioned above.
 
 
-### Another available .ckpt file:
+##### Another available .ckpt file:
 
  * https://huggingface.co/ClashSAN/small-sd/resolve/main/tinySDdistilled.ckpt
 
@@ -97,3 +97,31 @@ for key, value in ckpt['state_dict'].items():
         ckpt['state_dict'][key] = value.contiguous()
 torch.save(ckpt, "tinySDdistilled_fixed.ckpt")
 ```
+
+
+### SDXS-512
+
+Another very tiny and **incredibly fast**  model is SDXS by IDKiro et al.  The authors refer to it as *"Real-Time One-Step Latent Diffusion Models with Image Conditions"*. For details read the paper: https://arxiv.org/pdf/2403.16627 . Once again the authors removed some more blocks of U-Net part and unlike other SD1 models they use an adjusted _AutoEncoderTiny_ instead of default _AutoEncoderKL_ for the VAE part.
+
+##### 1. Download the diffusers model from  Hugging Face using Python:
+
+```python
+from diffusers import StableDiffusionPipeline
+pipe = StableDiffusionPipeline.from_pretrained("IDKiro/sdxs-512-dreamshaper")
+pipe.save_pretrained(save_directory="sdxs")
+```
+##### 2. Create a safetensors file
+
+```bash
+python convert_diffusers_to_original_stable_diffusion.py \
+    --model_path  sdxs  --checkpoint_path sdxs.safetensors --half --use_safetensors
+```
+
+##### 3. Run the model as follows:
+
+```bash
+~/stable-diffusion.cpp/build/bin/sd-cli -m sdxs.safetensors -p "portrait of a lovely cat" \
+  --cfg-scale 1 --steps 1
+```
+
+Both options: ``` --cfg-scale 1 ``` and  ``` --steps 1 ``` are mandatory here.                                                 
diff --git a/model.cpp b/model.cpp
index a19f180d..e05d3146 100644
--- a/model.cpp
+++ b/model.cpp
@@ -1038,6 +1038,7 @@ SDVersion ModelLoader::get_sd_version() {
     int64_t patch_embedding_channels = 0;
     bool has_img_emb                 = false;
     bool has_middle_block_1          = false;
+    bool has_output_block_71         = false;
 
     for (auto& [name, tensor_storage] : tensor_storage_map) {
         if (!(is_xl)) {
@@ -1094,6 +1095,9 @@ SDVersion ModelLoader::get_sd_version() {
             tensor_storage.name.find("unet.mid_block.resnets.1.") != std::string::npos) {
             has_middle_block_1 = true;
         }
+        if (tensor_storage.name.find("model.diffusion_model.output_blocks.7.1") != std::string::npos) {
+            has_output_block_71 = true;
+        }
         if (tensor_storage.name == "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight" ||
             tensor_storage.name == "cond_stage_model.model.token_embedding.weight" ||
             tensor_storage.name == "text_model.embeddings.token_embedding.weight" ||
@@ -1155,6 +1159,9 @@ SDVersion ModelLoader::get_sd_version() {
             return VERSION_SD1_PIX2PIX;
         }
         if (!has_middle_block_1) {
+            if (!has_output_block_71) {
+                return VERSION_SDXS;
+            }
             return VERSION_SD1_TINY_UNET;
         }
         return VERSION_SD1;
diff --git a/model.h b/model.h
index b9e50ad6..e52766cc 100644
--- a/model.h
+++ b/model.h
@@ -28,6 +28,7 @@ enum SDVersion {
     VERSION_SD2,
     VERSION_SD2_INPAINT,
     VERSION_SD2_TINY_UNET,
+    VERSION_SDXS,
     VERSION_SDXL,
     VERSION_SDXL_INPAINT,
     VERSION_SDXL_PIX2PIX,
@@ -50,7 +51,7 @@ enum SDVersion {
 };
 
 static inline bool sd_version_is_sd1(SDVersion version) {
-    if (version == VERSION_SD1 || version == VERSION_SD1_INPAINT || version == VERSION_SD1_PIX2PIX || version == VERSION_SD1_TINY_UNET) {
+    if (version == VERSION_SD1 || version == VERSION_SD1_INPAINT || version == VERSION_SD1_PIX2PIX || version == VERSION_SD1_TINY_UNET || version == VERSION_SDXS) {
         return true;
     }
     return false;
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 60bcba4d..060b8530 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -31,6 +31,7 @@ const char* model_version_to_str[] = {
     "SD 2.x",
     "SD 2.x Inpaint",
     "SD 2.x Tiny UNet",
+    "SDXS",
     "SDXL",
     "SDXL Inpaint",
     "SDXL Instruct-Pix2Pix",
@@ -407,6 +408,11 @@ public:
             vae_decode_only = false;
         }
 
+        bool tae_preview_only = sd_ctx_params->tae_preview_only;
+        if (version == VERSION_SDXS) {
+            tae_preview_only = false;
+        }
+
         if (sd_ctx_params->circular_x || sd_ctx_params->circular_y) {
             LOG_INFO("Using circular padding for convolutions");
         }
@@ -591,7 +597,7 @@ public:
                 vae_backend = backend;
             }
 
-            if (!use_tiny_autoencoder || sd_ctx_params->tae_preview_only) {
+            if (!(use_tiny_autoencoder || version == VERSION_SDXS) || tae_preview_only) {
                 if (sd_version_is_wan(version) || sd_version_is_qwen_image(version)) {
                     first_stage_model = std::make_shared<WAN::WanVAERunner>(vae_backend,
                                                                             offload_params_to_cpu,
@@ -629,8 +635,7 @@ public:
                     first_stage_model->get_param_tensors(tensors, "first_stage_model");
                 }
             }
-
-            if (use_tiny_autoencoder) {
+            if (use_tiny_autoencoder || version == VERSION_SDXS) {
                 if (sd_version_is_wan(version) || sd_version_is_qwen_image(version)) {
                     tae_first_stage = std::make_shared<TinyVideoAutoEncoder>(vae_backend,
                                                                              offload_params_to_cpu,
@@ -645,6 +650,10 @@ public:
                                                                              "decoder.layers",
                                                                              vae_decode_only,
                                                                              version);
+                    if (version == VERSION_SDXS) {
+                        tae_first_stage->alloc_params_buffer();
+                        tae_first_stage->get_param_tensors(tensors, "first_stage_model");
+                    }
                 }
                 if (sd_ctx_params->vae_conv_direct) {
                     LOG_INFO("Using Conv2d direct in the tae model");
@@ -782,14 +791,15 @@ public:
                 unet_params_mem_size += high_noise_diffusion_model->get_params_buffer_size();
             }
             size_t vae_params_mem_size = 0;
-            if (!use_tiny_autoencoder || sd_ctx_params->tae_preview_only) {
+            if (!(use_tiny_autoencoder || version == VERSION_SDXS) || tae_preview_only) {
                 vae_params_mem_size = first_stage_model->get_params_buffer_size();
             }
-            if (use_tiny_autoencoder) {
-                if (!tae_first_stage->load_from_file(taesd_path, n_threads)) {
+            if (use_tiny_autoencoder || version == VERSION_SDXS) {
+                if (use_tiny_autoencoder && !tae_first_stage->load_from_file(taesd_path, n_threads)) {
                     return false;
                 }
-                vae_params_mem_size = tae_first_stage->get_params_buffer_size();
+                use_tiny_autoencoder = true;  // now the processing is identical for VERSION_SDXS
+                vae_params_mem_size  = tae_first_stage->get_params_buffer_size();
             }
             size_t control_net_params_mem_size = 0;
             if (control_net) {
@@ -945,7 +955,7 @@ public:
         }
 
         ggml_free(ctx);
-        use_tiny_autoencoder = use_tiny_autoencoder && !sd_ctx_params->tae_preview_only;
+        use_tiny_autoencoder = use_tiny_autoencoder && !tae_preview_only;
         return true;
     }
 
diff --git a/tae.hpp b/tae.hpp
index 5da76e69..2cfd0a19 100644
--- a/tae.hpp
+++ b/tae.hpp
@@ -505,7 +505,8 @@ struct TinyAutoEncoder : public GGMLRunner {
                          struct ggml_tensor** output,
                          struct ggml_context* output_ctx = nullptr) = 0;
 
-    virtual bool load_from_file(const std::string& file_path, int n_threads) = 0;
+    virtual bool load_from_file(const std::string& file_path, int n_threads)                                      = 0;
+    virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) = 0;
 };
 
 struct TinyImageAutoEncoder : public TinyAutoEncoder {
@@ -555,6 +556,10 @@ struct TinyImageAutoEncoder : public TinyAutoEncoder {
         return success;
     }
 
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+        taesd.get_param_tensors(tensors, prefix);
+    }
+
     struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
         struct ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
         z                       = to_backend(z);
@@ -624,6 +629,10 @@ struct TinyVideoAutoEncoder : public TinyAutoEncoder {
         return success;
     }
 
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+        taehv.get_param_tensors(tensors, prefix);
+    }
+
     struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
         struct ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
         z                       = to_backend(z);
diff --git a/unet.hpp b/unet.hpp
index eef204d0..9fe24e24 100644
--- a/unet.hpp
+++ b/unet.hpp
@@ -215,10 +215,13 @@ public:
         } else if (sd_version_is_unet_edit(version)) {
             in_channels = 8;
         }
-        if (version == VERSION_SD1_TINY_UNET || version == VERSION_SD2_TINY_UNET) {
+        if (version == VERSION_SD1_TINY_UNET || version == VERSION_SD2_TINY_UNET || version == VERSION_SDXS) {
             num_res_blocks = 1;
             channel_mult   = {1, 2, 4};
             tiny_unet      = true;
+            if (version == VERSION_SDXS) {
+                attention_resolutions = {4, 2};  // here just like SDXL
+            }
         }
 
         // dims is always 2

From fbce16e02dbdecf765b971a0398f5feac3e35b85 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Sat, 17 Jan 2026 14:14:56 -0300
Subject: [PATCH 49/97] fix: avoid undefined behavior on image mask allocation
 failure (#1198)

---
 examples/cli/main.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index 0a7da7ae..ddc28281 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -594,12 +594,12 @@ int main(int argc, const char* argv[]) {
         }
     } else {
         mask_image.data = (uint8_t*)malloc(gen_params.width * gen_params.height);
-        memset(mask_image.data, 255, gen_params.width * gen_params.height);
         if (mask_image.data == nullptr) {
             LOG_ERROR("malloc mask image failed");
             release_all_resources();
             return 1;
         }
+        memset(mask_image.data, 255, gen_params.width * gen_params.height);
     }
 
     if (gen_params.control_image_path.size() > 0) {

From 9565c7f6bd5fcff124c589147b2621244f2c4aa1 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 18 Jan 2026 01:17:33 +0800
Subject: [PATCH 50/97] add support for flux2 klein (#1193)

* add support for flux2 klein 4b

* add support for flux2 klein 8b

* use attention_mask in Flux.2 klein LLMEmbedder

* update docs
---
 README.md                            |   8 +--
 assets/flux2/flux2-klein-4b-edit.png | Bin 0 -> 522256 bytes
 assets/flux2/flux2-klein-4b.png      | Bin 0 -> 465981 bytes
 assets/flux2/flux2-klein-9b-edit.png | Bin 0 -> 523302 bytes
 assets/flux2/flux2-klein-9b.png      | Bin 0 -> 503118 bytes
 assets/flux2/flux2-klein-base-4b.png | Bin 0 -> 475177 bytes
 assets/flux2/flux2-klein-base-9b.png | Bin 0 -> 565321 bytes
 conditioner.hpp                      |  53 +++++++++++++++----
 docs/flux2.md                        |  75 ++++++++++++++++++++++++++-
 flux.hpp                             |  29 ++++++++---
 ggml_extend.hpp                      |   3 +-
 llm.hpp                              |  51 +++++++++++++-----
 model.cpp                            |  16 +++++-
 model.h                              |   3 +-
 stable-diffusion.cpp                 |   1 +
 15 files changed, 197 insertions(+), 42 deletions(-)
 create mode 100644 assets/flux2/flux2-klein-4b-edit.png
 create mode 100644 assets/flux2/flux2-klein-4b.png
 create mode 100644 assets/flux2/flux2-klein-9b-edit.png
 create mode 100644 assets/flux2/flux2-klein-9b.png
 create mode 100644 assets/flux2/flux2-klein-base-4b.png
 create mode 100644 assets/flux2/flux2-klein-base-9b.png

diff --git a/README.md b/README.md
index 4e536880..89e0b024 100644
--- a/README.md
+++ b/README.md
@@ -43,8 +43,8 @@ API and command-line option may change frequently.***
     - SDXL, [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo)
     - [Some SD1.x and SDXL distilled models](./docs/distilled_sd.md)
     - [SD3/SD3.5](./docs/sd3.md)
-    - [FlUX.1-dev/FlUX.1-schnell](./docs/flux.md)
-    - [FLUX.2-dev](./docs/flux2.md)
+    - [FLUX.1-dev/FLUX.1-schnell](./docs/flux.md)
+    - [FLUX.2-dev/FLUX.2-klein](./docs/flux2.md)
     - [Chroma](./docs/chroma.md)
     - [Chroma1-Radiance](./docs/chroma_radiance.md)
     - [Qwen Image](./docs/qwen_image.md)
@@ -127,8 +127,8 @@ If you want to improve performance or reduce VRAM/RAM usage, please refer to [pe
 
 - [SD1.x/SD2.x/SDXL](./docs/sd.md)
 - [SD3/SD3.5](./docs/sd3.md)
-- [FlUX.1-dev/FlUX.1-schnell](./docs/flux.md)
-- [FLUX.2-dev](./docs/flux2.md)
+- [FLUX.1-dev/FLUX.1-schnell](./docs/flux.md)
+- [FLUX.2-dev/FLUX.2-klein](./docs/flux2.md)
 - [FLUX.1-Kontext-dev](./docs/kontext.md)
 - [Chroma](./docs/chroma.md)
 - [🔥Qwen Image](./docs/qwen_image.md)
diff --git a/assets/flux2/flux2-klein-4b-edit.png b/assets/flux2/flux2-klein-4b-edit.png
new file mode 100644
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diff --git a/conditioner.hpp b/conditioner.hpp
index b6d5646a..a4e84aa3 100644
--- a/conditioner.hpp
+++ b/conditioner.hpp
@@ -1614,9 +1614,9 @@ struct LLMEmbedder : public Conditioner {
                 bool enable_vision                             = false)
         : version(version) {
         LLM::LLMArch arch = LLM::LLMArch::QWEN2_5_VL;
-        if (sd_version_is_flux2(version)) {
+        if (version == VERSION_FLUX2) {
             arch = LLM::LLMArch::MISTRAL_SMALL_3_2;
-        } else if (sd_version_is_z_image(version) || version == VERSION_OVIS_IMAGE) {
+        } else if (sd_version_is_z_image(version) || version == VERSION_OVIS_IMAGE || version == VERSION_FLUX2_KLEIN) {
             arch = LLM::LLMArch::QWEN3;
         }
         if (arch == LLM::LLMArch::MISTRAL_SMALL_3_2) {
@@ -1708,6 +1708,9 @@ struct LLMEmbedder : public Conditioner {
         int prompt_template_encode_start_idx = 34;
         int max_length                       = 0;
         std::set<int> out_layers;
+        std::vector<int> tokens;
+        std::vector<float> weights;
+        std::vector<float> mask;
         if (llm->enable_vision && conditioner_params.ref_images.size() > 0) {
             LOG_INFO("QwenImageEditPlusPipeline");
             prompt_template_encode_start_idx = 64;
@@ -1771,7 +1774,7 @@ struct LLMEmbedder : public Conditioner {
             prompt_attn_range.second = static_cast<int>(prompt.size());
 
             prompt += "<|im_end|>\n<|im_start|>assistant\n";
-        } else if (sd_version_is_flux2(version)) {
+        } else if (version == VERSION_FLUX2) {
             prompt_template_encode_start_idx = 0;
             out_layers                       = {10, 20, 30};
 
@@ -1793,17 +1796,28 @@ struct LLMEmbedder : public Conditioner {
             prompt_attn_range.second = static_cast<int>(prompt.size());
 
             prompt += "<|im_end|>\n<|im_start|>assistant\n";
-        } else if (sd_version_is_flux2(version)) {
+        } else if (version == VERSION_FLUX2_KLEIN) {
             prompt_template_encode_start_idx = 0;
-            out_layers                       = {10, 20, 30};
+            max_length                       = 512;
+            out_layers                       = {9, 18, 27};
 
-            prompt = "[SYSTEM_PROMPT]You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object\nattribution and actions without speculation.[/SYSTEM_PROMPT][INST]";
+            prompt = "<|im_start|>user\n";
 
             prompt_attn_range.first = static_cast<int>(prompt.size());
             prompt += conditioner_params.text;
             prompt_attn_range.second = static_cast<int>(prompt.size());
 
-            prompt += "[/INST]";
+            prompt += "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n";
+
+            auto tokens_and_weights = tokenize(prompt, prompt_attn_range, 0, false);
+            tokens                  = std::get<0>(tokens_and_weights);
+            weights                 = std::get<1>(tokens_and_weights);
+
+            mask.insert(mask.end(), tokens.size(), 1.f);
+            if (tokens.size() < max_length) {
+                mask.insert(mask.end(), max_length - tokens.size(), 0.f);
+                tokenizer->pad_tokens(tokens, weights, max_length, true);
+            }
         } else if (version == VERSION_OVIS_IMAGE) {
             prompt_template_encode_start_idx = 28;
             max_length                       = prompt_template_encode_start_idx + 256;
@@ -1827,17 +1841,34 @@ struct LLMEmbedder : public Conditioner {
             prompt += "<|im_end|>\n<|im_start|>assistant\n";
         }
 
-        auto tokens_and_weights = tokenize(prompt, prompt_attn_range, max_length, max_length > 0);
-        auto& tokens            = std::get<0>(tokens_and_weights);
-        auto& weights           = std::get<1>(tokens_and_weights);
+        if (tokens.empty()) {
+            auto tokens_and_weights = tokenize(prompt, prompt_attn_range, max_length, max_length > 0);
+            tokens                  = std::get<0>(tokens_and_weights);
+            weights                 = std::get<1>(tokens_and_weights);
+        }
 
         int64_t t0                        = ggml_time_ms();
         struct ggml_tensor* hidden_states = nullptr;  // [N, n_token, 3584]
 
         auto input_ids = vector_to_ggml_tensor_i32(work_ctx, tokens);
 
+        ggml_tensor* attention_mask = nullptr;
+        if (!mask.empty()) {
+            attention_mask = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, mask.size(), mask.size());
+            ggml_ext_tensor_iter(attention_mask, [&](ggml_tensor* attention_mask, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
+                float value = 0.f;
+                if (mask[i0] == 0.f) {
+                    value = -INFINITY;
+                } else if (i0 > i1) {
+                    value = -INFINITY;
+                }
+                ggml_ext_tensor_set_f32(attention_mask, value, i0, i1, i2, i3);
+            });
+        }
+
         llm->compute(n_threads,
                      input_ids,
+                     attention_mask,
                      image_embeds,
                      out_layers,
                      &hidden_states,
@@ -1861,7 +1892,7 @@ struct LLMEmbedder : public Conditioner {
         GGML_ASSERT(hidden_states->ne[1] > prompt_template_encode_start_idx);
 
         int64_t min_length = 0;
-        if (sd_version_is_flux2(version)) {
+        if (version == VERSION_FLUX2) {
             min_length = 512;
         }
 
diff --git a/docs/flux2.md b/docs/flux2.md
index 0c2c6d2b..1524478c 100644
--- a/docs/flux2.md
+++ b/docs/flux2.md
@@ -1,6 +1,8 @@
 # How to Use
 
-## Download weights
+## Flux.2-dev
+
+### Download weights
 
 - Download FLUX.2-dev
     - gguf: https://huggingface.co/city96/FLUX.2-dev-gguf/tree/main
@@ -9,7 +11,7 @@
 - Download Mistral-Small-3.2-24B-Instruct-2506-GGUF
     - gguf: https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF/tree/main
 
-## Examples
+### Examples
 
 ```
 .\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux2-dev-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf -r .\kontext_input.png -p "change 'flux.cpp' to 'flux2-dev.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu
@@ -17,5 +19,74 @@
 
 <img alt="flux2 example" src="../assets/flux2/example.png" />
 
+## Flux.2 klein 4B / Flux.2 klein base 4B
 
+### Download weights
 
+- Download FLUX.2-klein-4B
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-klein-4B
+    - gguf: https://huggingface.co/leejet/FLUX.2-klein-4B-GGUF/tree/main
+- Download FLUX.2-klein-base-4B
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B
+    - gguf: https://huggingface.co/leejet/FLUX.2-klein-base-4B-GGUF/tree/main
+- Download vae
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/main
+- Download Qwen3 4b
+    - safetensors: https://huggingface.co/Comfy-Org/flux2-klein-4B/tree/main/split_files/text_encoders
+    - gguf: https://huggingface.co/unsloth/Qwen3-4B-GGUF/tree/main
+
+### Examples
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu --diffusion-fa
+```
+
+<img alt="flux2-klein-4b" src="../assets/flux2/flux2-klein-4b.png" />
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_4b.safetensors -r .\kontext_input.png -p "change 'flux.cpp' to 'klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu --steps 4
+```
+
+<img alt="flux2-klein-4b-edit" src="../assets/flux2/flux2-klein-4b-edit.png" />
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-base-4b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu --diffusion-fa
+```
+
+<img alt="flux2-klein-base-4b" src="../assets/flux2/flux2-klein-base-4b.png" />
+
+## Flux.2 klein 9B / Flux.2 klein base 9B
+
+### Download weights
+
+- Download FLUX.2-klein-9B
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-klein-9B
+    - gguf: https://huggingface.co/leejet/FLUX.2-klein-9B-GGUF/tree/main
+- Download FLUX.2-klein-base-9B
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9B
+    - gguf: https://huggingface.co/leejet/FLUX.2-klein-base-9B-GGUF/tree/main
+- Download vae
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/main
+- Download Qwen3 8B
+    - safetensors: https://huggingface.co/Comfy-Org/flux2-klein-9B/tree/main/split_files/text_encoders
+    - gguf: https://huggingface.co/unsloth/Qwen3-8B-GGUF/tree/main
+
+### Examples
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu --diffusion-fa
+```
+
+<img alt="flux2-klein-9b" src="../assets/flux2/flux2-klein-9b.png" />
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_8b.safetensors -r .\kontext_input.png -p "change 'flux.cpp' to 'klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu --steps 4
+```
+
+<img alt="flux2-klein-9b-edit" src="../assets/flux2/flux2-klein-9b-edit.png" />
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-base-9b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu --diffusion-fa
+```
+
+<img alt="flux2-klein-base-9b" src="../assets/flux2/flux2-klein-base-9b.png" />
\ No newline at end of file
diff --git a/flux.hpp b/flux.hpp
index 5d94fc85..9826fade 100644
--- a/flux.hpp
+++ b/flux.hpp
@@ -1288,13 +1288,9 @@ namespace Flux {
             } else if (version == VERSION_OVIS_IMAGE) {
                 flux_params.semantic_txt_norm = true;
                 flux_params.use_yak_mlp       = true;
-                flux_params.context_in_dim    = 2048;
                 flux_params.vec_in_dim        = 0;
             } else if (sd_version_is_flux2(version)) {
-                flux_params.context_in_dim   = 15360;
                 flux_params.in_channels      = 128;
-                flux_params.hidden_size      = 6144;
-                flux_params.num_heads        = 48;
                 flux_params.patch_size       = 1;
                 flux_params.out_channels     = 128;
                 flux_params.mlp_ratio        = 3.f;
@@ -1307,12 +1303,12 @@ namespace Flux {
                 flux_params.ref_index_scale  = 10.f;
                 flux_params.use_mlp_silu_act = true;
             }
+            int64_t head_dim = 0;
             for (auto pair : tensor_storage_map) {
                 std::string tensor_name = pair.first;
                 if (!starts_with(tensor_name, prefix))
                     continue;
                 if (tensor_name.find("guidance_in.in_layer.weight") != std::string::npos) {
-                    // not schnell
                     flux_params.guidance_embed = true;
                 }
                 if (tensor_name.find("__x0__") != std::string::npos) {
@@ -1344,13 +1340,30 @@ namespace Flux {
                         flux_params.depth_single_blocks = block_depth + 1;
                     }
                 }
+                if (ends_with(tensor_name, "txt_in.weight")) {
+                    flux_params.context_in_dim = pair.second.ne[0];
+                    flux_params.hidden_size    = pair.second.ne[1];
+                }
+                if (ends_with(tensor_name, "single_blocks.0.norm.key_norm.scale")) {
+                    head_dim = pair.second.ne[0];
+                }
+                if (ends_with(tensor_name, "double_blocks.0.txt_attn.norm.key_norm.scale")) {
+                    head_dim = pair.second.ne[0];
+                }
             }
 
-            LOG_INFO("Flux blocks: %d double, %d single", flux_params.depth, flux_params.depth_single_blocks);
+            flux_params.num_heads = static_cast<int>(flux_params.hidden_size / head_dim);
+
+            LOG_INFO("flux: depth = %d, depth_single_blocks = %d, guidance_embed = %s, context_in_dim = %" PRId64
+                     ", hidden_size = %" PRId64 ", num_heads = %d",
+                     flux_params.depth,
+                     flux_params.depth_single_blocks,
+                     flux_params.guidance_embed ? "true" : "false",
+                     flux_params.context_in_dim,
+                     flux_params.hidden_size,
+                     flux_params.num_heads);
             if (flux_params.is_chroma) {
                 LOG_INFO("Using pruned modulation (Chroma)");
-            } else if (!flux_params.guidance_embed) {
-                LOG_INFO("Flux guidance is disabled (Schnell mode)");
             }
 
             flux = Flux(flux_params);
diff --git a/ggml_extend.hpp b/ggml_extend.hpp
index 6f498ffa..9d5ea316 100644
--- a/ggml_extend.hpp
+++ b/ggml_extend.hpp
@@ -1348,7 +1348,8 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_attention_ext(struct ggml_context
         v = ggml_reshape_3d(ctx, v, L_k, d_head, n_kv_head * N);   // [N * n_kv_head, d_head, L_k]
 
         auto kq = ggml_mul_mat(ctx, k, q);  // [N * n_head, L_q, L_k]
-        kq      = ggml_scale_inplace(ctx, kq, scale);
+        ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
+        kq = ggml_scale_inplace(ctx, kq, scale);
         if (mask) {
             kq = ggml_add_inplace(ctx, kq, mask);
         }
diff --git a/llm.hpp b/llm.hpp
index 67b1ea16..781774db 100644
--- a/llm.hpp
+++ b/llm.hpp
@@ -837,7 +837,8 @@ namespace LLM {
 
         struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                     struct ggml_tensor* x,
-                                    struct ggml_tensor* input_pos) {
+                                    struct ggml_tensor* input_pos,
+                                    struct ggml_tensor* attention_mask = nullptr) {
             // x: [N, n_token, hidden_size]
             int64_t n_token = x->ne[1];
             int64_t N       = x->ne[2];
@@ -880,7 +881,7 @@ namespace LLM {
             k = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, k, 0, 2, 1, 3));  // [N, num_kv_heads, n_token, head_dim]
             k = ggml_reshape_3d(ctx->ggml_ctx, k, k->ne[0], k->ne[1], k->ne[2] * k->ne[3]);      // [N*num_kv_heads, n_token, head_dim]
 
-            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, nullptr, true, true, false);  // [N, n_token, hidden_size]
+            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, attention_mask, false, true, false);  // [N, n_token, hidden_size]
 
             x = out_proj->forward(ctx, x);  // [N, n_token, hidden_size]
             return x;
@@ -898,7 +899,8 @@ namespace LLM {
 
         struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                     struct ggml_tensor* x,
-                                    struct ggml_tensor* input_pos) {
+                                    struct ggml_tensor* input_pos,
+                                    struct ggml_tensor* attention_mask = nullptr) {
             // x: [N, n_token, hidden_size]
             auto self_attn                = std::dynamic_pointer_cast<Attention>(blocks["self_attn"]);
             auto mlp                      = std::dynamic_pointer_cast<MLP>(blocks["mlp"]);
@@ -907,7 +909,7 @@ namespace LLM {
 
             auto residual = x;
             x             = input_layernorm->forward(ctx, x);
-            x             = self_attn->forward(ctx, x, input_pos);
+            x             = self_attn->forward(ctx, x, input_pos, attention_mask);
             x             = ggml_add_inplace(ctx->ggml_ctx, x, residual);
 
             residual = x;
@@ -936,6 +938,7 @@ namespace LLM {
         struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                     struct ggml_tensor* input_ids,
                                     struct ggml_tensor* input_pos,
+                                    struct ggml_tensor* attention_mask,
                                     std::vector<std::pair<int, ggml_tensor*>> image_embeds,
                                     std::set<int> out_layers) {
             // input_ids: [N, n_token]
@@ -990,7 +993,7 @@ namespace LLM {
             for (int i = 0; i < num_layers; i++) {
                 auto block = std::dynamic_pointer_cast<TransformerBlock>(blocks["layers." + std::to_string(i)]);
 
-                x = block->forward(ctx, x, input_pos);
+                x = block->forward(ctx, x, input_pos, attention_mask);
                 if (out_layers.find(i + 1) != out_layers.end()) {
                     intermediate_outputs.push_back(x);
                 }
@@ -1036,12 +1039,13 @@ namespace LLM {
         struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                     struct ggml_tensor* input_ids,
                                     struct ggml_tensor* input_pos,
+                                    struct ggml_tensor* attention_mask,
                                     std::vector<std::pair<int, ggml_tensor*>> image_embeds,
                                     std::set<int> out_layers) {
             // input_ids: [N, n_token]
             auto model = std::dynamic_pointer_cast<TextModel>(blocks["model"]);
 
-            auto x = model->forward(ctx, input_ids, input_pos, image_embeds, out_layers);
+            auto x = model->forward(ctx, input_ids, input_pos, attention_mask, image_embeds, out_layers);
             return x;
         }
 
@@ -1063,6 +1067,7 @@ namespace LLM {
         LLM model;
 
         std::vector<int> input_pos_vec;
+        std::vector<float> attention_mask_vec;
         std::vector<float> window_mask_vec;
         std::vector<int> window_index_vec;
         std::vector<int> window_inverse_index_vec;
@@ -1157,9 +1162,10 @@ namespace LLM {
         struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                     struct ggml_tensor* input_ids,
                                     struct ggml_tensor* input_pos,
+                                    struct ggml_tensor* attention_mask,
                                     std::vector<std::pair<int, ggml_tensor*>> image_embeds,
                                     std::set<int> out_layers) {
-            auto hidden_states = model.forward(ctx, input_ids, input_pos, image_embeds, out_layers);  // [N, n_token, hidden_size]
+            auto hidden_states = model.forward(ctx, input_ids, input_pos, attention_mask, image_embeds, out_layers);  // [N, n_token, hidden_size]
             return hidden_states;
         }
 
@@ -1174,6 +1180,7 @@ namespace LLM {
         }
 
         struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
+                                        struct ggml_tensor* attention_mask,
                                         std::vector<std::pair<int, ggml_tensor*>> image_embeds,
                                         std::set<int> out_layers) {
             struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
@@ -1205,9 +1212,26 @@ namespace LLM {
                                                 input_pos_vec.size());
             set_backend_tensor_data(input_pos, input_pos_vec.data());
 
+            if (attention_mask != nullptr) {
+                attention_mask = to_backend(attention_mask);
+            } else {
+                attention_mask_vec.resize(n_tokens * n_tokens);
+                for (int i0 = 0; i0 < n_tokens; i0++) {
+                    for (int i1 = 0; i1 < n_tokens; i1++) {
+                        float value = 0.f;
+                        if (i0 > i1) {
+                            value = -INFINITY;
+                        }
+                        attention_mask_vec[i1 * n_tokens + i0] = value;
+                    }
+                }
+                attention_mask = ggml_new_tensor_2d(compute_ctx, GGML_TYPE_F32, n_tokens, n_tokens);
+                set_backend_tensor_data(attention_mask, attention_mask_vec.data());
+            }
+
             auto runner_ctx = get_context();
 
-            struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, input_pos, image_embeds, out_layers);
+            struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, input_pos, attention_mask, image_embeds, out_layers);
 
             ggml_build_forward_expand(gf, hidden_states);
 
@@ -1216,12 +1240,13 @@ namespace LLM {
 
         bool compute(const int n_threads,
                      struct ggml_tensor* input_ids,
+                     struct ggml_tensor* attention_mask,
                      std::vector<std::pair<int, ggml_tensor*>> image_embeds,
                      std::set<int> out_layers,
                      ggml_tensor** output,
                      ggml_context* output_ctx = nullptr) {
             auto get_graph = [&]() -> struct ggml_cgraph* {
-                return build_graph(input_ids, image_embeds, out_layers);
+                return build_graph(input_ids, attention_mask, image_embeds, out_layers);
             };
             return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
         }
@@ -1525,7 +1550,7 @@ namespace LLM {
                 struct ggml_tensor* out = nullptr;
 
                 int64_t t0 = ggml_time_ms();
-                model.compute(8, input_ids, image_embeds, {}, &out, work_ctx);
+                model.compute(8, input_ids, nullptr, image_embeds, {}, &out, work_ctx);
                 int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
@@ -1565,7 +1590,7 @@ namespace LLM {
                 struct ggml_tensor* out = nullptr;
 
                 int64_t t0 = ggml_time_ms();
-                model.compute(8, input_ids, {}, {10, 20, 30}, &out, work_ctx);
+                model.compute(8, input_ids, nullptr, {}, {10, 20, 30}, &out, work_ctx);
                 int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
@@ -1588,7 +1613,7 @@ namespace LLM {
                 struct ggml_tensor* out = nullptr;
 
                 int64_t t0 = ggml_time_ms();
-                model.compute(8, input_ids, {}, {35}, &out, work_ctx);
+                model.compute(8, input_ids, nullptr, {}, {35}, &out, work_ctx);
                 int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
@@ -1611,7 +1636,7 @@ namespace LLM {
                 struct ggml_tensor* out = nullptr;
 
                 int64_t t0 = ggml_time_ms();
-                model.compute(8, input_ids, {}, {}, &out, work_ctx);
+                model.compute(8, input_ids, nullptr, {}, {}, &out, work_ctx);
                 int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);
diff --git a/model.cpp b/model.cpp
index e05d3146..c14f255a 100644
--- a/model.cpp
+++ b/model.cpp
@@ -1034,6 +1034,8 @@ SDVersion ModelLoader::get_sd_version() {
 
     bool is_xl                       = false;
     bool is_flux                     = false;
+    bool is_flux2                    = false;
+    bool has_single_block_47         = false;
     bool is_wan                      = false;
     int64_t patch_embedding_channels = 0;
     bool has_img_emb                 = false;
@@ -1055,7 +1057,10 @@ SDVersion ModelLoader::get_sd_version() {
                 return VERSION_QWEN_IMAGE;
             }
             if (tensor_storage.name.find("model.diffusion_model.double_stream_modulation_img.lin.weight") != std::string::npos) {
-                return VERSION_FLUX2;
+                is_flux2 = true;
+            }
+            if (tensor_storage.name.find("single_blocks.47.linear1.weight") != std::string::npos) {
+                has_single_block_47 = true;
             }
             if (tensor_storage.name.find("model.diffusion_model.double_blocks.0.img_mlp.gate_proj.weight") != std::string::npos) {
                 return VERSION_OVIS_IMAGE;
@@ -1138,7 +1143,7 @@ SDVersion ModelLoader::get_sd_version() {
         return VERSION_SDXL;
     }
 
-    if (is_flux) {
+    if (is_flux && !is_flux2) {
         if (input_block_weight.ne[0] == 384) {
             return VERSION_FLUX_FILL;
         }
@@ -1151,6 +1156,13 @@ SDVersion ModelLoader::get_sd_version() {
         return VERSION_FLUX;
     }
 
+    if (is_flux2) {
+        if (has_single_block_47) {
+            return VERSION_FLUX2;
+        }
+        return VERSION_FLUX2_KLEIN;
+    }
+
     if (token_embedding_weight.ne[0] == 768) {
         if (is_inpaint) {
             return VERSION_SD1_INPAINT;
diff --git a/model.h b/model.h
index e52766cc..3f054c46 100644
--- a/model.h
+++ b/model.h
@@ -45,6 +45,7 @@ enum SDVersion {
     VERSION_WAN2_2_TI2V,
     VERSION_QWEN_IMAGE,
     VERSION_FLUX2,
+    VERSION_FLUX2_KLEIN,
     VERSION_Z_IMAGE,
     VERSION_OVIS_IMAGE,
     VERSION_COUNT,
@@ -100,7 +101,7 @@ static inline bool sd_version_is_flux(SDVersion version) {
 }
 
 static inline bool sd_version_is_flux2(SDVersion version) {
-    if (version == VERSION_FLUX2) {
+    if (version == VERSION_FLUX2 || version == VERSION_FLUX2_KLEIN) {
         return true;
     }
     return false;
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 060b8530..2d9b6e6f 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -48,6 +48,7 @@ const char* model_version_to_str[] = {
     "Wan 2.2 TI2V",
     "Qwen Image",
     "Flux.2",
+    "Flux.2 klein",
     "Z-Image",
     "Ovis Image",
 };

From 61659ef2997906e2d1204497c17cecebd82c298c Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Sun, 18 Jan 2026 13:21:11 -0300
Subject: [PATCH 51/97] feat: add basic sdapi support to sd-server (#1197)

* feat: add basic sdapi support to sd-server

Compatible with AUTOMATIC1111 / Forge.

* fix img2img with no mask

* add more parameter validation

* eliminate MSVC warnings

---------

Co-authored-by: leejet <leejet714@gmail.com>
---
 examples/server/main.cpp | 321 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 321 insertions(+)

diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index b0ac7eef..c9a4c31b 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -732,6 +732,327 @@ int main(int argc, const char** argv) {
         }
     });
 
+    // sdapi endpoints (AUTOMATIC1111 / Forge)
+
+    auto sdapi_any2img = [&](const httplib::Request& req, httplib::Response& res, bool img2img) {
+        try {
+            if (req.body.empty()) {
+                res.status = 400;
+                res.set_content(R"({"error":"empty body"})", "application/json");
+                return;
+            }
+
+            json j = json::parse(req.body);
+
+            std::string prompt          = j.value("prompt", "");
+            std::string negative_prompt = j.value("negative_prompt", "");
+            int width                   = j.value("width", 512);
+            int height                  = j.value("height", 512);
+            int steps                   = j.value("steps", -1);
+            float cfg_scale             = j.value("cfg_scale", 7.f);
+            int64_t seed                = j.value("seed", -1);
+            int batch_size              = j.value("batch_size", 1);
+            int clip_skip               = j.value("clip_skip", -1);
+            std::string sampler_name    = j.value("sampler_name", "");
+            std::string scheduler_name  = j.value("scheduler", "");
+
+            auto bad = [&](const std::string& msg) {
+                res.status = 400;
+                res.set_content("{\"error\":\"" + msg + "\"}", "application/json");
+                return;
+            };
+
+            if (width <= 0 || height <= 0) {
+                return bad("width and height must be positive");
+            }
+
+            if (steps < 1 || steps > 150) {
+                return bad("steps must be in range [1, 150]");
+            }
+
+            if (batch_size < 1 || batch_size > 8) {
+                return bad("batch_size must be in range [1, 8]");
+            }
+
+            if (cfg_scale < 0.f) {
+                return bad("cfg_scale must be positive");
+            }
+
+            if (prompt.empty()) {
+                return bad("prompt required");
+            }
+
+            auto get_sample_method = [](std::string name) -> enum sample_method_t {
+                enum sample_method_t result = str_to_sample_method(name.c_str());
+                if (result != SAMPLE_METHOD_COUNT) return result;
+                // some applications use a hardcoded sampler list
+                std::transform(name.begin(), name.end(), name.begin(),
+                               [](unsigned char c) { return std::tolower(c); });
+                static const std::unordered_map<std::string_view, sample_method_t> hardcoded{
+                    {"euler a", EULER_A_SAMPLE_METHOD},
+                    {"k_euler_a", EULER_A_SAMPLE_METHOD},
+                    {"euler", EULER_SAMPLE_METHOD},
+                    {"k_euler", EULER_SAMPLE_METHOD},
+                    {"heun", HEUN_SAMPLE_METHOD},
+                    {"k_heun", HEUN_SAMPLE_METHOD},
+                    {"dpm2", DPM2_SAMPLE_METHOD},
+                    {"k_dpm_2", DPM2_SAMPLE_METHOD},
+                    {"lcm", LCM_SAMPLE_METHOD},
+                    {"ddim", DDIM_TRAILING_SAMPLE_METHOD},
+                    {"dpm++ 2m", DPMPP2M_SAMPLE_METHOD},
+                    {"k_dpmpp_2m", DPMPP2M_SAMPLE_METHOD}};
+                auto it            = hardcoded.find(name);
+                if (it != hardcoded.end()) return it->second;
+                return SAMPLE_METHOD_COUNT;
+            };
+
+            enum sample_method_t sample_method = get_sample_method(sampler_name);
+
+            enum scheduler_t scheduler = str_to_scheduler(scheduler_name.c_str());
+
+            // avoid excessive resource usage
+
+            SDGenerationParams gen_params         = default_gen_params;
+            gen_params.prompt                     = prompt;
+            gen_params.negative_prompt            = negative_prompt;
+            gen_params.width                      = width;
+            gen_params.height                     = height;
+            gen_params.seed                       = seed;
+            gen_params.sample_params.sample_steps = steps;
+            gen_params.batch_count                = batch_size;
+
+            if (clip_skip > 0) {
+                gen_params.clip_skip = clip_skip;
+            }
+
+            if (sample_method != SAMPLE_METHOD_COUNT) {
+                gen_params.sample_params.sample_method = sample_method;
+            }
+
+            if (scheduler != SCHEDULER_COUNT) {
+                gen_params.sample_params.scheduler = scheduler;
+            }
+
+            LOG_DEBUG("%s\n", gen_params.to_string().c_str());
+
+            sd_image_t init_image    = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
+            sd_image_t control_image = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
+            sd_image_t mask_image    = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 1, nullptr};
+            std::vector<uint8_t> mask_data;
+            std::vector<sd_image_t> pmid_images;
+            std::vector<sd_image_t> ref_images;
+
+            if (img2img) {
+                auto decode_image = [](sd_image_t& image, std::string encoded) -> bool {
+                    // remove data URI prefix if present ("data:image/png;base64,")
+                    auto comma_pos = encoded.find(',');
+                    if (comma_pos != std::string::npos) {
+                        encoded = encoded.substr(comma_pos + 1);
+                    }
+                    std::vector<uint8_t> img_data = base64_decode(encoded);
+                    if (!img_data.empty()) {
+                        int img_w         = image.width;
+                        int img_h         = image.height;
+                        uint8_t* raw_data = load_image_from_memory(
+                            (const char*)img_data.data(), (int)img_data.size(),
+                            img_w, img_h,
+                            image.width, image.height, image.channel);
+                        if (raw_data) {
+                            image = {(uint32_t)img_w, (uint32_t)img_h, image.channel, raw_data};
+                            return true;
+                        }
+                    }
+                    return false;
+                };
+
+                if (j.contains("init_images") && j["init_images"].is_array() && !j["init_images"].empty()) {
+                    std::string encoded = j["init_images"][0].get<std::string>();
+                    decode_image(init_image, encoded);
+                }
+
+                if (j.contains("mask") && j["mask"].is_string()) {
+                    std::string encoded = j["mask"].get<std::string>();
+                    decode_image(mask_image, encoded);
+                    bool inpainting_mask_invert = j.value("inpainting_mask_invert", 0) != 0;
+                    if (inpainting_mask_invert && mask_image.data != nullptr) {
+                        for (uint32_t i = 0; i < mask_image.width * mask_image.height; i++) {
+                            mask_image.data[i] = 255 - mask_image.data[i];
+                        }
+                    }
+                } else {
+                    mask_data          = std::vector<uint8_t>(width * height, 255);
+                    mask_image.width   = width;
+                    mask_image.height  = height;
+                    mask_image.channel = 1;
+                    mask_image.data    = mask_data.data();
+                }
+
+                if (j.contains("extra_images") && j["extra_images"].is_array()) {
+                    for (auto extra_image : j["extra_images"]) {
+                        std::string encoded  = extra_image.get<std::string>();
+                        sd_image_t tmp_image = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
+                        if (decode_image(tmp_image, encoded)) {
+                            ref_images.push_back(tmp_image);
+                        }
+                    }
+                }
+
+                float denoising_strength = j.value("denoising_strength", -1.f);
+                if (denoising_strength >= 0.f) {
+                    denoising_strength  = std::min(denoising_strength, 1.0f);
+                    gen_params.strength = denoising_strength;
+                }
+            }
+
+            sd_img_gen_params_t img_gen_params = {
+                gen_params.lora_vec.data(),
+                static_cast<uint32_t>(gen_params.lora_vec.size()),
+                gen_params.prompt.c_str(),
+                gen_params.negative_prompt.c_str(),
+                gen_params.clip_skip,
+                init_image,
+                ref_images.data(),
+                (int)ref_images.size(),
+                gen_params.auto_resize_ref_image,
+                gen_params.increase_ref_index,
+                mask_image,
+                gen_params.width,
+                gen_params.height,
+                gen_params.sample_params,
+                gen_params.strength,
+                gen_params.seed,
+                gen_params.batch_count,
+                control_image,
+                gen_params.control_strength,
+                {
+                    pmid_images.data(),
+                    (int)pmid_images.size(),
+                    gen_params.pm_id_embed_path.c_str(),
+                    gen_params.pm_style_strength,
+                },  // pm_params
+                ctx_params.vae_tiling_params,
+                gen_params.cache_params,
+            };
+
+            sd_image_t* results = nullptr;
+            int num_results     = 0;
+
+            {
+                std::lock_guard<std::mutex> lock(sd_ctx_mutex);
+                results     = generate_image(sd_ctx, &img_gen_params);
+                num_results = gen_params.batch_count;
+            }
+
+            json out;
+            out["images"]     = json::array();
+            out["parameters"] = j;  // TODO should return changed defaults
+            out["info"]       = "";
+
+            for (int i = 0; i < num_results; i++) {
+                if (results[i].data == nullptr) {
+                    continue;
+                }
+
+                auto image_bytes = write_image_to_vector(ImageFormat::PNG,
+                                                         results[i].data,
+                                                         results[i].width,
+                                                         results[i].height,
+                                                         results[i].channel);
+
+                if (image_bytes.empty()) {
+                    LOG_ERROR("write image to mem failed");
+                    continue;
+                }
+
+                std::string b64 = base64_encode(image_bytes);
+                out["images"].push_back(b64);
+            }
+
+            res.set_content(out.dump(), "application/json");
+            res.status = 200;
+
+            if (init_image.data) {
+                stbi_image_free(init_image.data);
+            }
+            if (mask_image.data && mask_data.empty()) {
+                stbi_image_free(mask_image.data);
+            }
+            for (auto ref_image : ref_images) {
+                stbi_image_free(ref_image.data);
+            }
+
+        } catch (const std::exception& e) {
+            res.status = 500;
+            json err;
+            err["error"]   = "server_error";
+            err["message"] = e.what();
+            res.set_content(err.dump(), "application/json");
+        }
+    };
+
+    svr.Post("/sdapi/v1/txt2img", [&](const httplib::Request& req, httplib::Response& res) {
+        sdapi_any2img(req, res, false);
+    });
+
+    svr.Post("/sdapi/v1/img2img", [&](const httplib::Request& req, httplib::Response& res) {
+        sdapi_any2img(req, res, true);
+    });
+
+    svr.Get("/sdapi/v1/samplers", [&](const httplib::Request&, httplib::Response& res) {
+        std::vector<std::string> sampler_names;
+        sampler_names.push_back("default");
+        for (int i = 0; i < SAMPLE_METHOD_COUNT; i++) {
+            sampler_names.push_back(sd_sample_method_name((sample_method_t)i));
+        }
+        json r = json::array();
+        for (auto name : sampler_names) {
+            json entry;
+            entry["name"]    = name;
+            entry["aliases"] = json::array({name});
+            entry["options"] = json::object();
+            r.push_back(entry);
+        }
+        res.set_content(r.dump(), "application/json");
+    });
+
+    svr.Get("/sdapi/v1/schedulers", [&](const httplib::Request&, httplib::Response& res) {
+        std::vector<std::string> scheduler_names;
+        scheduler_names.push_back("default");
+        for (int i = 0; i < SCHEDULER_COUNT; i++) {
+            scheduler_names.push_back(sd_scheduler_name((scheduler_t)i));
+        }
+        json r = json::array();
+        for (auto name : scheduler_names) {
+            json entry;
+            entry["name"]  = name;
+            entry["label"] = name;
+            r.push_back(entry);
+        }
+        res.set_content(r.dump(), "application/json");
+    });
+
+    svr.Get("/sdapi/v1/sd-models", [&](const httplib::Request&, httplib::Response& res) {
+        fs::path model_path = ctx_params.model_path;
+        json entry;
+        entry["title"]      = model_path.stem();
+        entry["model_name"] = model_path.stem();
+        entry["filename"]   = model_path.filename();
+        entry["hash"]       = "8888888888";
+        entry["sha256"]     = "8888888888888888888888888888888888888888888888888888888888888888";
+        entry["config"]     = nullptr;
+        json r              = json::array();
+        r.push_back(entry);
+        res.set_content(r.dump(), "application/json");
+    });
+
+    svr.Get("/sdapi/v1/options", [&](const httplib::Request&, httplib::Response& res) {
+        fs::path model_path = ctx_params.model_path;
+        json r;
+        r["samples_format"]      = "png";
+        r["sd_model_checkpoint"] = model_path.stem();
+        res.set_content(r.dump(), "application/json");
+    });
+
     LOG_INFO("listening on: %s:%d\n", svr_params.listen_ip.c_str(), svr_params.listen_port);
     svr.listen(svr_params.listen_ip, svr_params.listen_port);
 

From 2efd19978dd4164e387bf226025c9666b6ef35e2 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Mon, 19 Jan 2026 00:21:29 +0800
Subject: [PATCH 52/97] fix: use Unix timestamp for  field instead of ISO
 string (#1205)

---
 examples/server/main.cpp | 19 ++-----------------
 1 file changed, 2 insertions(+), 17 deletions(-)

diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index c9a4c31b..76199ac6 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -86,21 +86,6 @@ std::vector<uint8_t> base64_decode(const std::string& encoded_string) {
     return ret;
 }
 
-std::string iso_timestamp_now() {
-    using namespace std::chrono;
-    auto now      = system_clock::now();
-    std::time_t t = system_clock::to_time_t(now);
-    std::tm tm{};
-#ifdef _MSC_VER
-    gmtime_s(&tm, &t);
-#else
-    gmtime_r(&t, &tm);
-#endif
-    std::ostringstream oss;
-    oss << std::put_time(&tm, "%Y-%m-%dT%H:%M:%SZ");
-    return oss.str();
-}
-
 struct SDSvrParams {
     std::string listen_ip = "127.0.0.1";
     int listen_port       = 1234;
@@ -404,7 +389,7 @@ int main(int argc, const char** argv) {
             }
 
             json out;
-            out["created"]       = iso_timestamp_now();
+            out["created"]       = static_cast<long long>(std::time(nullptr));
             out["data"]          = json::array();
             out["output_format"] = output_format;
 
@@ -692,7 +677,7 @@ int main(int argc, const char** argv) {
             }
 
             json out;
-            out["created"]       = iso_timestamp_now();
+            out["created"]       = static_cast<long long>(std::time(nullptr));
             out["data"]          = json::array();
             out["output_format"] = output_format;
 

From 9293016c9da10879d34c4f80bb3fc4c115a5344e Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Mon, 19 Jan 2026 23:00:50 +0800
Subject: [PATCH 53/97] docs: update esrgan.md

---
 docs/esrgan.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/esrgan.md b/docs/esrgan.md
index 77231726..39a97605 100644
--- a/docs/esrgan.md
+++ b/docs/esrgan.md
@@ -1,6 +1,6 @@
 ## Using ESRGAN to upscale results
 
-You can use ESRGAN to upscale the generated images. At the moment, only the [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth) model is supported. Support for more models of this architecture will be added soon.
+You can use ESRGAN—such as the model [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)—to upscale the generated images and improve their overall resolution and clarity.
 
 - Specify the model path using the `--upscale-model PATH` parameter. example:
 

From 639091fbe9b4cb008a65e723274718029304fd16 Mon Sep 17 00:00:00 2001
From: akleine <alb.kleine@gmx.de>
Date: Mon, 19 Jan 2026 16:15:47 +0100
Subject: [PATCH 54/97] feat: add support for Segmind's Vega model (#1195)

---
 docs/distilled_sd.md | 16 +++++++++++++---
 model.cpp            |  7 +++++++
 model.h              |  3 ++-
 stable-diffusion.cpp |  1 +
 unet.hpp             |  7 +++++--
 5 files changed, 28 insertions(+), 6 deletions(-)

diff --git a/docs/distilled_sd.md b/docs/distilled_sd.md
index 232c0228..3174b18f 100644
--- a/docs/distilled_sd.md
+++ b/docs/distilled_sd.md
@@ -1,8 +1,8 @@
-# Running distilled models: SSD1B and SDx.x with tiny U-Nets
+# Running distilled models: SSD1B, Vega and SDx.x with tiny U-Nets
 
 ## Preface 
 
-These models feature a reduced U-Net architecture. Unlike standard SDXL models, the SSD-1B U-Net contains only one middle block and fewer attention layers in its up- and down-blocks, resulting in significantly smaller file sizes. Using these models can reduce inference time by more than 33%. For more details, refer to Segmind's paper: https://arxiv.org/abs/2401.02677v1.
+These models feature a reduced U-Net architecture. Unlike standard SDXL models, the SSD-1B and Vega U-Net contains only one middle block and fewer attention layers in its up- and down-blocks, resulting in significantly smaller file sizes. Using these models can reduce inference time by more than 33%. For more details, refer to Segmind's paper: https://arxiv.org/abs/2401.02677v1.
 Similarly, SD1.x- and SD2.x-style models with a tiny U-Net consist of only 6 U-Net blocks, leading to very small files and time savings of up to 50%. For more information, see the paper: https://arxiv.org/pdf/2305.15798.pdf.
 
 ## SSD1B
@@ -17,7 +17,17 @@ Useful LoRAs are also available:
  * https://huggingface.co/seungminh/lora-swarovski-SSD-1B/resolve/main/pytorch_lora_weights.safetensors
  * https://huggingface.co/kylielee505/mylcmlorassd/resolve/main/pytorch_lora_weights.safetensors
 
-These files can be used out-of-the-box, unlike the models described in the next section.
+## Vega
+
+Segmind's Vega model is available online here:
+
+ * https://huggingface.co/segmind/Segmind-Vega/resolve/main/segmind-vega.safetensors
+ 
+VegaRT is an example for an LCM-LoRA:
+
+ * https://huggingface.co/segmind/Segmind-VegaRT/resolve/main/pytorch_lora_weights.safetensors
+
+Both files can be used out-of-the-box, unlike the models described in next sections.
 
 
 ## SD1.x, SD2.x with tiny U-Nets
diff --git a/model.cpp b/model.cpp
index c14f255a..75914900 100644
--- a/model.cpp
+++ b/model.cpp
@@ -1040,6 +1040,7 @@ SDVersion ModelLoader::get_sd_version() {
     int64_t patch_embedding_channels = 0;
     bool has_img_emb                 = false;
     bool has_middle_block_1          = false;
+    bool has_output_block_311        = false;
     bool has_output_block_71         = false;
 
     for (auto& [name, tensor_storage] : tensor_storage_map) {
@@ -1100,6 +1101,9 @@ SDVersion ModelLoader::get_sd_version() {
             tensor_storage.name.find("unet.mid_block.resnets.1.") != std::string::npos) {
             has_middle_block_1 = true;
         }
+        if (tensor_storage.name.find("model.diffusion_model.output_blocks.3.1.transformer_blocks.1") != std::string::npos) {
+            has_output_block_311 = true;
+        }
         if (tensor_storage.name.find("model.diffusion_model.output_blocks.7.1") != std::string::npos) {
             has_output_block_71 = true;
         }
@@ -1138,6 +1142,9 @@ SDVersion ModelLoader::get_sd_version() {
             return VERSION_SDXL_PIX2PIX;
         }
         if (!has_middle_block_1) {
+            if (!has_output_block_311) {
+                return VERSION_SDXL_VEGA;
+            }
             return VERSION_SDXL_SSD1B;
         }
         return VERSION_SDXL;
diff --git a/model.h b/model.h
index 3f054c46..e16ac3a0 100644
--- a/model.h
+++ b/model.h
@@ -32,6 +32,7 @@ enum SDVersion {
     VERSION_SDXL,
     VERSION_SDXL_INPAINT,
     VERSION_SDXL_PIX2PIX,
+    VERSION_SDXL_VEGA,
     VERSION_SDXL_SSD1B,
     VERSION_SVD,
     VERSION_SD3,
@@ -66,7 +67,7 @@ static inline bool sd_version_is_sd2(SDVersion version) {
 }
 
 static inline bool sd_version_is_sdxl(SDVersion version) {
-    if (version == VERSION_SDXL || version == VERSION_SDXL_INPAINT || version == VERSION_SDXL_PIX2PIX || version == VERSION_SDXL_SSD1B) {
+    if (version == VERSION_SDXL || version == VERSION_SDXL_INPAINT || version == VERSION_SDXL_PIX2PIX || version == VERSION_SDXL_SSD1B || version == VERSION_SDXL_VEGA) {
         return true;
     }
     return false;
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 2d9b6e6f..3fc9a262 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -35,6 +35,7 @@ const char* model_version_to_str[] = {
     "SDXL",
     "SDXL Inpaint",
     "SDXL Instruct-Pix2Pix",
+    "SDXL (Vega)",
     "SDXL (SSD1B)",
     "SVD",
     "SD3.x",
diff --git a/unet.hpp b/unet.hpp
index 9fe24e24..6e15e1f4 100644
--- a/unet.hpp
+++ b/unet.hpp
@@ -201,6 +201,9 @@ public:
             num_head_channels     = 64;
             num_heads             = -1;
             use_linear_projection = true;
+            if (version == VERSION_SDXL_VEGA) {
+                transformer_depth = {1, 1, 2};
+            }
         } else if (version == VERSION_SVD) {
             in_channels           = 8;
             out_channels          = 4;
@@ -319,7 +322,7 @@ public:
         }
         if (!tiny_unet) {
             blocks["middle_block.0"] = std::shared_ptr<GGMLBlock>(get_resblock(ch, time_embed_dim, ch));
-            if (version != VERSION_SDXL_SSD1B) {
+            if (version != VERSION_SDXL_SSD1B && version != VERSION_SDXL_VEGA) {
                 blocks["middle_block.1"] = std::shared_ptr<GGMLBlock>(get_attention_layer(ch,
                                                                                           n_head,
                                                                                           d_head,
@@ -520,7 +523,7 @@ public:
         // middle_block
         if (!tiny_unet) {
             h = resblock_forward("middle_block.0", ctx, h, emb, num_video_frames);  // [N, 4*model_channels, h/8, w/8]
-            if (version != VERSION_SDXL_SSD1B) {
+            if (version != VERSION_SDXL_SSD1B && version != VERSION_SDXL_VEGA) {
                 h = attention_layer_forward("middle_block.1", ctx, h, context, num_video_frames);  // [N, 4*model_channels, h/8, w/8]
                 h = resblock_forward("middle_block.2", ctx, h, emb, num_video_frames);             // [N, 4*model_channels, h/8, w/8]
             }

From c6206fb351fe63e06525fb4eede51292d82476f4 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Mon, 19 Jan 2026 23:21:48 +0800
Subject: [PATCH 55/97] fix: set VAE conv scale for all SDXL variants

---
 stable-diffusion.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 3fc9a262..b181f994 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -624,7 +624,7 @@ public:
                         LOG_INFO("Using Conv2d direct in the vae model");
                         first_stage_model->set_conv2d_direct_enabled(true);
                     }
-                    if (version == VERSION_SDXL &&
+                    if (sd_version_is_sdxl(version) &&
                         (strlen(SAFE_STR(sd_ctx_params->vae_path)) == 0 || sd_ctx_params->force_sdxl_vae_conv_scale)) {
                         float vae_conv_2d_scale = 1.f / 32.f;
                         LOG_WARN(

From e50e1f253d229c75e1f8e3738f133daf9101ef27 Mon Sep 17 00:00:00 2001
From: Oleg Skutte <45887963+SkutteOleg@users.noreply.github.com>
Date: Mon, 19 Jan 2026 19:39:36 +0400
Subject: [PATCH 56/97] feat: add taef2 support (#1211)

---
 tae.hpp | 56 ++++++++++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 46 insertions(+), 10 deletions(-)

diff --git a/tae.hpp b/tae.hpp
index 2cfd0a19..a22db196 100644
--- a/tae.hpp
+++ b/tae.hpp
@@ -17,22 +17,43 @@ class TAEBlock : public UnaryBlock {
 protected:
     int n_in;
     int n_out;
+    bool use_midblock_gn;
 
 public:
-    TAEBlock(int n_in, int n_out)
-        : n_in(n_in), n_out(n_out) {
+    TAEBlock(int n_in, int n_out, bool use_midblock_gn = false)
+        : n_in(n_in), n_out(n_out), use_midblock_gn(use_midblock_gn) {
         blocks["conv.0"] = std::shared_ptr<GGMLBlock>(new Conv2d(n_in, n_out, {3, 3}, {1, 1}, {1, 1}));
         blocks["conv.2"] = std::shared_ptr<GGMLBlock>(new Conv2d(n_out, n_out, {3, 3}, {1, 1}, {1, 1}));
         blocks["conv.4"] = std::shared_ptr<GGMLBlock>(new Conv2d(n_out, n_out, {3, 3}, {1, 1}, {1, 1}));
         if (n_in != n_out) {
             blocks["skip"] = std::shared_ptr<GGMLBlock>(new Conv2d(n_in, n_out, {1, 1}, {1, 1}, {1, 1}, {1, 1}, false));
         }
+        if (use_midblock_gn) {
+            int n_gn         = n_in * 4;
+            blocks["pool.0"] = std::shared_ptr<GGMLBlock>(new Conv2d(n_in, n_gn, {1, 1}, {1, 1}, {0, 0}, {1, 1}, false));
+            blocks["pool.1"] = std::shared_ptr<GGMLBlock>(new GroupNorm(4, n_gn));
+            // pool.2 is ReLU, handled in forward
+            blocks["pool.3"] = std::shared_ptr<GGMLBlock>(new Conv2d(n_gn, n_in, {1, 1}, {1, 1}, {0, 0}, {1, 1}, false));
+        }
     }
 
     struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [n, n_in, h, w]
         // return: [n, n_out, h, w]
 
+        if (use_midblock_gn) {
+            auto pool_0 = std::dynamic_pointer_cast<Conv2d>(blocks["pool.0"]);
+            auto pool_1 = std::dynamic_pointer_cast<GroupNorm>(blocks["pool.1"]);
+            auto pool_3 = std::dynamic_pointer_cast<Conv2d>(blocks["pool.3"]);
+
+            auto p = pool_0->forward(ctx, x);
+            p      = pool_1->forward(ctx, p);
+            p      = ggml_relu_inplace(ctx->ggml_ctx, p);
+            p      = pool_3->forward(ctx, p);
+
+            x = ggml_add(ctx->ggml_ctx, x, p);
+        }
+
         auto conv_0 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.0"]);
         auto conv_2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.2"]);
         auto conv_4 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.4"]);
@@ -62,7 +83,7 @@ class TinyEncoder : public UnaryBlock {
     int num_blocks  = 3;
 
 public:
-    TinyEncoder(int z_channels = 4)
+    TinyEncoder(int z_channels = 4, bool use_midblock_gn = false)
         : z_channels(z_channels) {
         int index                       = 0;
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, channels, {3, 3}, {1, 1}, {1, 1}));
@@ -80,7 +101,7 @@ public:
 
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, channels, {3, 3}, {2, 2}, {1, 1}, {1, 1}, false));
         for (int i = 0; i < num_blocks; i++) {
-            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
+            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels, use_midblock_gn));
         }
 
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, z_channels, {3, 3}, {1, 1}, {1, 1}));
@@ -107,7 +128,7 @@ class TinyDecoder : public UnaryBlock {
     int num_blocks   = 3;
 
 public:
-    TinyDecoder(int z_channels = 4)
+    TinyDecoder(int z_channels = 4, bool use_midblock_gn = false)
         : z_channels(z_channels) {
         int index = 0;
 
@@ -115,7 +136,7 @@ public:
         index++;  // nn.ReLU()
 
         for (int i = 0; i < num_blocks; i++) {
-            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
+            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels, use_midblock_gn));
         }
         index++;  // nn.Upsample()
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, channels, {3, 3}, {1, 1}, {1, 1}, {1, 1}, false));
@@ -470,29 +491,44 @@ public:
 class TAESD : public GGMLBlock {
 protected:
     bool decode_only;
+    bool taef2 = false;
 
 public:
     TAESD(bool decode_only = true, SDVersion version = VERSION_SD1)
         : decode_only(decode_only) {
-        int z_channels = 4;
+        int z_channels       = 4;
+        bool use_midblock_gn = false;
+        taef2                = sd_version_is_flux2(version);
+
         if (sd_version_is_dit(version)) {
             z_channels = 16;
         }
-        blocks["decoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyDecoder(z_channels));
+        if (taef2) {
+            z_channels      = 32;
+            use_midblock_gn = true;
+        }
+        blocks["decoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyDecoder(z_channels, use_midblock_gn));
 
         if (!decode_only) {
-            blocks["encoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyEncoder(z_channels));
+            blocks["encoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyEncoder(z_channels, use_midblock_gn));
         }
     }
 
     struct ggml_tensor* decode(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
         auto decoder = std::dynamic_pointer_cast<TinyDecoder>(blocks["decoder.layers"]);
+        if (taef2) {
+            z = unpatchify(ctx->ggml_ctx, z, 2);
+        }
         return decoder->forward(ctx, z);
     }
 
     struct ggml_tensor* encode(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         auto encoder = std::dynamic_pointer_cast<TinyEncoder>(blocks["encoder.layers"]);
-        return encoder->forward(ctx, x);
+        auto z       = encoder->forward(ctx, x);
+        if (taef2) {
+            z = patchify(ctx->ggml_ctx, z, 2);
+        }
+        return z;
     }
 };
 

From b87fe13afdeb48df08ae5cd87a33b2ff98447eb3 Mon Sep 17 00:00:00 2001
From: stduhpf <stephduh@live.fr>
Date: Mon, 19 Jan 2026 16:51:26 +0100
Subject: [PATCH 57/97] feat: support new chroma radiance "x0_x32_proto"
 (#1209)

---
 flux.hpp | 28 ++++++++++++++++++++--------
 1 file changed, 20 insertions(+), 8 deletions(-)

diff --git a/flux.hpp b/flux.hpp
index 9826fade..77a65c55 100644
--- a/flux.hpp
+++ b/flux.hpp
@@ -748,7 +748,7 @@ namespace Flux {
         int nerf_depth           = 4;
         int nerf_max_freqs       = 8;
         bool use_x0              = false;
-        bool use_patch_size_32   = false;
+        bool fake_patch_size_x2  = false;
     };
 
     struct FluxParams {
@@ -786,8 +786,11 @@ namespace Flux {
         Flux(FluxParams params)
             : params(params) {
             if (params.version == VERSION_CHROMA_RADIANCE) {
-                std::pair<int, int> kernel_size = {16, 16};
-                std::pair<int, int> stride      = kernel_size;
+                std::pair<int, int> kernel_size = {params.patch_size, params.patch_size};
+                if (params.chroma_radiance_params.fake_patch_size_x2) {
+                    kernel_size = {params.patch_size / 2, params.patch_size / 2};
+                }
+                std::pair<int, int> stride = kernel_size;
 
                 blocks["img_in_patch"] = std::make_shared<Conv2d>(params.in_channels,
                                                                   params.hidden_size,
@@ -1082,7 +1085,7 @@ namespace Flux {
             auto img      = pad_to_patch_size(ctx, x);
             auto orig_img = img;
 
-            if (params.chroma_radiance_params.use_patch_size_32) {
+            if (params.chroma_radiance_params.fake_patch_size_x2) {
                 // It's supposed to be using GGML_SCALE_MODE_NEAREST, but this seems more stable
                 // Maybe the implementation of nearest-neighbor interpolation in ggml behaves differently than the one in PyTorch?
                 // img = F.interpolate(img, size=(H//2, W//2), mode="nearest")
@@ -1303,7 +1306,8 @@ namespace Flux {
                 flux_params.ref_index_scale  = 10.f;
                 flux_params.use_mlp_silu_act = true;
             }
-            int64_t head_dim = 0;
+            int64_t head_dim                   = 0;
+            int64_t actual_radiance_patch_size = -1;
             for (auto pair : tensor_storage_map) {
                 std::string tensor_name = pair.first;
                 if (!starts_with(tensor_name, prefix))
@@ -1316,9 +1320,12 @@ namespace Flux {
                     flux_params.chroma_radiance_params.use_x0 = true;
                 }
                 if (tensor_name.find("__32x32__") != std::string::npos) {
-                    LOG_DEBUG("using patch size 32 prediction");
-                    flux_params.chroma_radiance_params.use_patch_size_32 = true;
-                    flux_params.patch_size                               = 32;
+                    LOG_DEBUG("using patch size 32");
+                    flux_params.patch_size = 32;
+                }
+                if (tensor_name.find("img_in_patch.weight") != std::string::npos) {
+                    actual_radiance_patch_size = pair.second.ne[0];
+                    LOG_DEBUG("actual radiance patch size: %d", actual_radiance_patch_size);
                 }
                 if (tensor_name.find("distilled_guidance_layer.in_proj.weight") != std::string::npos) {
                     // Chroma
@@ -1351,6 +1358,11 @@ namespace Flux {
                     head_dim = pair.second.ne[0];
                 }
             }
+            if (actual_radiance_patch_size > 0 && actual_radiance_patch_size != flux_params.patch_size) {
+                GGML_ASSERT(flux_params.patch_size == 2 * actual_radiance_patch_size);
+                LOG_DEBUG("using fake x2 patch size");
+                flux_params.chroma_radiance_params.fake_patch_size_x2 = true;
+            }
 
             flux_params.num_heads = static_cast<int>(flux_params.hidden_size / head_dim);
 

From a48b4a3ade9972faf0adcad47e51c6fc03f0e46d Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Mon, 19 Jan 2026 23:56:50 +0800
Subject: [PATCH 58/97] docs: add FLUX.2-klein support to news

---
 README.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/README.md b/README.md
index 89e0b024..84d0832c 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,9 @@ API and command-line option may change frequently.***
 
 ## 🔥Important News
 
+* **2026/01/18** 🚀 stable-diffusion.cpp now supports **FLUX.2-klein**  
+  👉 Details: [PR #1193](https://github.com/leejet/stable-diffusion.cpp/pull/1193)
+
 * **2025/12/01** 🚀 stable-diffusion.cpp now supports **Z-Image**  
   👉 Details: [PR #1020](https://github.com/leejet/stable-diffusion.cpp/pull/1020)
 

From 329571131d62d64a4f49e1acbef49ae02544fdcd Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Wed, 21 Jan 2026 11:34:11 -0300
Subject: [PATCH 59/97] chore: clarify warning about missing model files
 (#1219)

---
 model.cpp | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/model.cpp b/model.cpp
index 75914900..253dd25c 100644
--- a/model.cpp
+++ b/model.cpp
@@ -376,7 +376,11 @@ bool ModelLoader::init_from_file(const std::string& file_path, const std::string
         LOG_INFO("load %s using checkpoint format", file_path.c_str());
         return init_from_ckpt_file(file_path, prefix);
     } else {
-        LOG_WARN("unknown format %s", file_path.c_str());
+        if (file_exists(file_path)) {
+            LOG_WARN("unknown format %s", file_path.c_str());
+        } else {
+            LOG_WARN("file %s not found", file_path.c_str());
+        }
         return false;
     }
 }

From 5e4579c11d0678f9765463582d024e58270faa9c Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Thu, 22 Jan 2026 23:54:41 +0800
Subject: [PATCH 60/97] feat: use image width and height when not explicitly
 set (#1206)

---
 examples/cli/main.cpp      | 112 +++++++++++++++++++------------------
 examples/common/common.hpp |  45 +++++++++++----
 2 files changed, 92 insertions(+), 65 deletions(-)

diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index ddc28281..cd23f9b5 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -245,7 +245,7 @@ std::string get_image_params(const SDCliParams& cli_params, const SDContextParam
     parameter_string += "Guidance: " + std::to_string(gen_params.sample_params.guidance.distilled_guidance) + ", ";
     parameter_string += "Eta: " + std::to_string(gen_params.sample_params.eta) + ", ";
     parameter_string += "Seed: " + std::to_string(seed) + ", ";
-    parameter_string += "Size: " + std::to_string(gen_params.width) + "x" + std::to_string(gen_params.height) + ", ";
+    parameter_string += "Size: " + std::to_string(gen_params.get_resolved_width()) + "x" + std::to_string(gen_params.get_resolved_height()) + ", ";
     parameter_string += "Model: " + sd_basename(ctx_params.model_path) + ", ";
     parameter_string += "RNG: " + std::string(sd_rng_type_name(ctx_params.rng_type)) + ", ";
     if (ctx_params.sampler_rng_type != RNG_TYPE_COUNT) {
@@ -526,10 +526,10 @@ int main(int argc, const char* argv[]) {
     }
 
     bool vae_decode_only     = true;
-    sd_image_t init_image    = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
-    sd_image_t end_image     = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
-    sd_image_t control_image = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
-    sd_image_t mask_image    = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 1, nullptr};
+    sd_image_t init_image    = {0, 0, 3, nullptr};
+    sd_image_t end_image     = {0, 0, 3, nullptr};
+    sd_image_t control_image = {0, 0, 3, nullptr};
+    sd_image_t mask_image    = {0, 0, 1, nullptr};
     std::vector<sd_image_t> ref_images;
     std::vector<sd_image_t> pmid_images;
     std::vector<sd_image_t> control_frames;
@@ -556,57 +556,79 @@ int main(int argc, const char* argv[]) {
         control_frames.clear();
     };
 
+    auto load_image_and_update_size = [&](const std::string& path,
+                                          sd_image_t& image,
+                                          bool resize_image    = true,
+                                          int expected_channel = 3) -> bool {
+        int expected_width  = 0;
+        int expected_height = 0;
+        if (resize_image && gen_params.width_and_height_are_set()) {
+            expected_width  = gen_params.width;
+            expected_height = gen_params.height;
+        }
+
+        if (!load_sd_image_from_file(&image, path.c_str(), expected_width, expected_height, expected_channel)) {
+            LOG_ERROR("load image from '%s' failed", path.c_str());
+            release_all_resources();
+            return false;
+        }
+
+        gen_params.set_width_and_height_if_unset(image.width, image.height);
+        return true;
+    };
+
     if (gen_params.init_image_path.size() > 0) {
         vae_decode_only = false;
-
-        int width       = 0;
-        int height      = 0;
-        init_image.data = load_image_from_file(gen_params.init_image_path.c_str(), width, height, gen_params.width, gen_params.height);
-        if (init_image.data == nullptr) {
-            LOG_ERROR("load image from '%s' failed", gen_params.init_image_path.c_str());
-            release_all_resources();
+        if (!load_image_and_update_size(gen_params.init_image_path, init_image)) {
             return 1;
         }
     }
 
     if (gen_params.end_image_path.size() > 0) {
         vae_decode_only = false;
-
-        int width      = 0;
-        int height     = 0;
-        end_image.data = load_image_from_file(gen_params.end_image_path.c_str(), width, height, gen_params.width, gen_params.height);
-        if (end_image.data == nullptr) {
-            LOG_ERROR("load image from '%s' failed", gen_params.end_image_path.c_str());
-            release_all_resources();
+        if (!load_image_and_update_size(gen_params.init_image_path, end_image)) {
             return 1;
         }
     }
 
+    if (gen_params.ref_image_paths.size() > 0) {
+        vae_decode_only = false;
+        for (auto& path : gen_params.ref_image_paths) {
+            sd_image_t ref_image = {0, 0, 3, nullptr};
+            if (!load_image_and_update_size(path, ref_image, false)) {
+                return 1;
+            }
+            ref_images.push_back(ref_image);
+        }
+    }
+
     if (gen_params.mask_image_path.size() > 0) {
-        int c           = 0;
-        int width       = 0;
-        int height      = 0;
-        mask_image.data = load_image_from_file(gen_params.mask_image_path.c_str(), width, height, gen_params.width, gen_params.height, 1);
-        if (mask_image.data == nullptr) {
+        if (load_sd_image_from_file(&mask_image,
+                                    gen_params.mask_image_path.c_str(),
+                                    gen_params.get_resolved_width(),
+                                    gen_params.get_resolved_height(),
+                                    1)) {
             LOG_ERROR("load image from '%s' failed", gen_params.mask_image_path.c_str());
             release_all_resources();
             return 1;
         }
     } else {
-        mask_image.data = (uint8_t*)malloc(gen_params.width * gen_params.height);
+        mask_image.data = (uint8_t*)malloc(gen_params.get_resolved_width() * gen_params.get_resolved_height());
         if (mask_image.data == nullptr) {
             LOG_ERROR("malloc mask image failed");
             release_all_resources();
             return 1;
         }
-        memset(mask_image.data, 255, gen_params.width * gen_params.height);
+        mask_image.width  = gen_params.get_resolved_width();
+        mask_image.height = gen_params.get_resolved_height();
+        memset(mask_image.data, 255, gen_params.get_resolved_width() * gen_params.get_resolved_height());
     }
 
     if (gen_params.control_image_path.size() > 0) {
-        int width          = 0;
-        int height         = 0;
-        control_image.data = load_image_from_file(gen_params.control_image_path.c_str(), width, height, gen_params.width, gen_params.height);
-        if (control_image.data == nullptr) {
+        if (load_sd_image_from_file(&control_image,
+                                    gen_params.control_image_path.c_str(),
+                                    gen_params.get_resolved_width(),
+                                    gen_params.get_resolved_height())) {
             LOG_ERROR("load image from '%s' failed", gen_params.control_image_path.c_str());
             release_all_resources();
             return 1;
@@ -621,29 +643,11 @@ int main(int argc, const char* argv[]) {
         }
     }
 
-    if (gen_params.ref_image_paths.size() > 0) {
-        vae_decode_only = false;
-        for (auto& path : gen_params.ref_image_paths) {
-            int width             = 0;
-            int height            = 0;
-            uint8_t* image_buffer = load_image_from_file(path.c_str(), width, height);
-            if (image_buffer == nullptr) {
-                LOG_ERROR("load image from '%s' failed", path.c_str());
-                release_all_resources();
-                return 1;
-            }
-            ref_images.push_back({(uint32_t)width,
-                                  (uint32_t)height,
-                                  3,
-                                  image_buffer});
-        }
-    }
-
     if (!gen_params.control_video_path.empty()) {
         if (!load_images_from_dir(gen_params.control_video_path,
                                   control_frames,
-                                  gen_params.width,
-                                  gen_params.height,
+                                  gen_params.get_resolved_width(),
+                                  gen_params.get_resolved_height(),
                                   gen_params.video_frames,
                                   cli_params.verbose)) {
             release_all_resources();
@@ -717,8 +721,8 @@ int main(int argc, const char* argv[]) {
                 gen_params.auto_resize_ref_image,
                 gen_params.increase_ref_index,
                 mask_image,
-                gen_params.width,
-                gen_params.height,
+                gen_params.get_resolved_width(),
+                gen_params.get_resolved_height(),
                 gen_params.sample_params,
                 gen_params.strength,
                 gen_params.seed,
@@ -748,8 +752,8 @@ int main(int argc, const char* argv[]) {
                 end_image,
                 control_frames.data(),
                 (int)control_frames.size(),
-                gen_params.width,
-                gen_params.height,
+                gen_params.get_resolved_width(),
+                gen_params.get_resolved_height(),
                 gen_params.sample_params,
                 gen_params.high_noise_sample_params,
                 gen_params.moe_boundary,
diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index d299da50..e7b9c20d 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -1024,8 +1024,8 @@ struct SDGenerationParams {
     std::string prompt_with_lora;  // for metadata record only
     std::string negative_prompt;
     int clip_skip   = -1;  // <= 0 represents unspecified
-    int width       = 512;
-    int height      = 512;
+    int width       = -1;
+    int height      = -1;
     int batch_count = 1;
     std::string init_image_path;
     std::string end_image_path;
@@ -1705,17 +1705,24 @@ struct SDGenerationParams {
         }
     }
 
+    bool width_and_height_are_set() const {
+        return width > 0 && height > 0;
+    }
+
+    void set_width_and_height_if_unset(int w, int h) {
+        if (!width_and_height_are_set()) {
+            LOG_INFO("set width x height to %d x %d", w, h);
+            width  = w;
+            height = h;
+        }
+    }
+
+    int get_resolved_width() const { return (width > 0) ? width : 512; }
+
+    int get_resolved_height() const { return (height > 0) ? height : 512; }
+
     bool process_and_check(SDMode mode, const std::string& lora_model_dir) {
         prompt_with_lora = prompt;
-        if (width <= 0) {
-            LOG_ERROR("error: the width must be greater than 0\n");
-            return false;
-        }
-
-        if (height <= 0) {
-            LOG_ERROR("error: the height must be greater than 0\n");
-            return false;
-        }
 
         if (sample_params.sample_steps <= 0) {
             LOG_ERROR("error: the sample_steps must be greater than 0\n");
@@ -2083,6 +2090,22 @@ uint8_t* load_image_from_file(const char* image_path,
     return load_image_common(false, image_path, 0, width, height, expected_width, expected_height, expected_channel);
 }
 
+bool load_sd_image_from_file(sd_image_t* image,
+                             const char* image_path,
+                             int expected_width   = 0,
+                             int expected_height  = 0,
+                             int expected_channel = 3) {
+    int width;
+    int height;
+    image->data = load_image_common(false, image_path, 0, width, height, expected_width, expected_height, expected_channel);
+    if (image->data == nullptr) {
+        return false;
+    }
+    image->width  = width;
+    image->height = height;
+    return true;
+}
+
 uint8_t* load_image_from_memory(const char* image_bytes,
                                 int len,
                                 int& width,

From fa61ea744d1a87fa26a63f8a86e45587bc9534d6 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Fri, 23 Jan 2026 22:13:59 +0800
Subject: [PATCH 61/97] fix: set default lora_model_dir to . (#1224)

---
 examples/common/common.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index e7b9c20d..ba1b0d8d 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -445,7 +445,7 @@ struct SDContextParams {
     std::string photo_maker_path;
     sd_type_t wtype = SD_TYPE_COUNT;
     std::string tensor_type_rules;
-    std::string lora_model_dir;
+    std::string lora_model_dir = ".";
 
     std::map<std::string, std::string> embedding_map;
     std::vector<sd_embedding_t> embedding_vec;

From 4ccce027b27337bb9af72d88aae2cfd831e739b0 Mon Sep 17 00:00:00 2001
From: Equious <76449140+Equious@users.noreply.github.com>
Date: Sun, 25 Jan 2026 07:47:52 -0700
Subject: [PATCH 62/97] fix: correct mask and control image loading in cli
 (#1229)

---
 examples/cli/main.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index cd23f9b5..0fb656dc 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -603,7 +603,7 @@ int main(int argc, const char* argv[]) {
     }
 
     if (gen_params.mask_image_path.size() > 0) {
-        if (load_sd_image_from_file(&mask_image,
+        if (!load_sd_image_from_file(&mask_image,
                                     gen_params.mask_image_path.c_str(),
                                     gen_params.get_resolved_width(),
                                     gen_params.get_resolved_height(),
@@ -625,7 +625,7 @@ int main(int argc, const char* argv[]) {
     }
 
     if (gen_params.control_image_path.size() > 0) {
-        if (load_sd_image_from_file(&control_image,
+        if (!load_sd_image_from_file(&control_image,
                                     gen_params.control_image_path.c_str(),
                                     gen_params.get_resolved_width(),
                                     gen_params.get_resolved_height())) {

From 7837232631ec3af06b9b252ae07a35a8c1eed22f Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 25 Jan 2026 22:50:10 +0800
Subject: [PATCH 63/97] perf: make dit faster (#1228)

---
 clip.hpp        |  4 +--
 common.hpp      |  8 ++---
 esrgan.hpp      |  4 +--
 flux.hpp        | 93 +++++++++++++++++++------------------------------
 ggml_extend.hpp | 90 ++++++++++++++++++++++++++++++++---------------
 llm.hpp         |  2 +-
 lora.hpp        | 10 +++---
 mmdit.hpp       | 75 +++++++++++++++++----------------------
 pmid.hpp        |  6 ++--
 qwen_image.hpp  | 15 ++++----
 t5.hpp          |  4 +--
 tae.hpp         | 13 +++----
 unet.hpp        |  4 +--
 vae.hpp         |  4 +--
 wan.hpp         | 15 ++++----
 z_image.hpp     | 40 ++++++++++++++++-----
 16 files changed, 202 insertions(+), 185 deletions(-)

diff --git a/clip.hpp b/clip.hpp
index 7a6ebe9e..4eec0241 100644
--- a/clip.hpp
+++ b/clip.hpp
@@ -479,9 +479,9 @@ public:
 
         x = fc1->forward(ctx, x);
         if (use_gelu) {
-            x = ggml_gelu_inplace(ctx->ggml_ctx, x);
+            x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
         } else {
-            x = ggml_gelu_quick_inplace(ctx->ggml_ctx, x);
+            x = ggml_ext_gelu_quick(ctx->ggml_ctx, x, true);
         }
         x = fc2->forward(ctx, x);
         return x;
diff --git a/common.hpp b/common.hpp
index 13ab1038..7183eb82 100644
--- a/common.hpp
+++ b/common.hpp
@@ -200,7 +200,7 @@ public:
 
         gate = ggml_cont(ctx->ggml_ctx, gate);
 
-        gate = ggml_gelu_inplace(ctx->ggml_ctx, gate);
+        gate = ggml_ext_gelu(ctx->ggml_ctx, gate, true);
 
         x = ggml_mul(ctx->ggml_ctx, x, gate);  // [ne3, ne2, ne1, dim_out]
 
@@ -220,7 +220,7 @@ public:
         auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
 
         x = proj->forward(ctx, x);
-        x = ggml_gelu_inplace(ctx->ggml_ctx, x);
+        x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
         return x;
     }
 };
@@ -536,8 +536,8 @@ public:
         // image_only_indicator is always tensor([0.])
         float alpha = get_alpha();
         auto x      = ggml_add(ctx->ggml_ctx,
-                               ggml_scale(ctx->ggml_ctx, x_spatial, alpha),
-                               ggml_scale(ctx->ggml_ctx, x_temporal, 1.0f - alpha));
+                               ggml_ext_scale(ctx->ggml_ctx, x_spatial, alpha),
+                               ggml_ext_scale(ctx->ggml_ctx, x_temporal, 1.0f - alpha));
         return x;
     }
 };
diff --git a/esrgan.hpp b/esrgan.hpp
index 961e84f8..f740c2bc 100644
--- a/esrgan.hpp
+++ b/esrgan.hpp
@@ -51,7 +51,7 @@ public:
         x_cat      = ggml_concat(ctx->ggml_ctx, x_cat, x4, 2);
         auto x5    = conv5->forward(ctx, x_cat);
 
-        x5 = ggml_add(ctx->ggml_ctx, ggml_scale(ctx->ggml_ctx, x5, 0.2f), x);
+        x5 = ggml_add(ctx->ggml_ctx, ggml_ext_scale(ctx->ggml_ctx, x5, 0.2f), x);
         return x5;
     }
 };
@@ -76,7 +76,7 @@ public:
         out      = rdb2->forward(ctx, out);
         out      = rdb3->forward(ctx, out);
 
-        out = ggml_add(ctx->ggml_ctx, ggml_scale(ctx->ggml_ctx, out, 0.2f), x);
+        out = ggml_add(ctx->ggml_ctx, ggml_ext_scale(ctx->ggml_ctx, out, 0.2f), x);
         return out;
     }
 };
diff --git a/flux.hpp b/flux.hpp
index 77a65c55..ff8c1899 100644
--- a/flux.hpp
+++ b/flux.hpp
@@ -103,7 +103,7 @@ namespace Flux {
             auto norm     = std::dynamic_pointer_cast<QKNorm>(blocks["norm"]);
 
             auto qkv         = qkv_proj->forward(ctx, x);
-            auto qkv_vec     = split_qkv(ctx->ggml_ctx, qkv);
+            auto qkv_vec     = ggml_ext_chunk(ctx->ggml_ctx, qkv, 3, 0, true);
             int64_t head_dim = qkv_vec[0]->ne[0] / num_heads;
             auto q           = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[0], head_dim, num_heads, qkv_vec[0]->ne[1], qkv_vec[0]->ne[2]);
             auto k           = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[1], head_dim, num_heads, qkv_vec[1]->ne[1], qkv_vec[1]->ne[2]);
@@ -153,7 +153,7 @@ namespace Flux {
             if (use_mlp_silu_act) {
                 x = ggml_ext_silu_act(ctx->ggml_ctx, x);
             } else {
-                x = ggml_gelu_inplace(ctx->ggml_ctx, x);
+                x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
             }
             x = mlp_2->forward(ctx, x);
             return x;
@@ -376,26 +376,23 @@ namespace Flux {
             auto k = ggml_concat(ctx->ggml_ctx, txt_k, img_k, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
             auto v = ggml_concat(ctx->ggml_ctx, txt_v, img_v, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
 
-            auto attn         = Rope::attention(ctx, q, k, v, pe, mask);                                  // [N, n_txt_token + n_img_token, n_head*d_head]
-            attn              = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, attn, 0, 2, 1, 3));  // [n_txt_token + n_img_token, N, hidden_size]
+            auto attn         = Rope::attention(ctx, q, k, v, pe, mask);  // [N, n_txt_token + n_img_token, n_head*d_head]
             auto txt_attn_out = ggml_view_3d(ctx->ggml_ctx,
                                              attn,
                                              attn->ne[0],
-                                             attn->ne[1],
                                              txt->ne[1],
+                                             attn->ne[2],
                                              attn->nb[1],
                                              attn->nb[2],
-                                             0);                                                                  // [n_txt_token, N, hidden_size]
-            txt_attn_out      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, txt_attn_out, 0, 2, 1, 3));  // [N, n_txt_token, hidden_size]
+                                             0);  // [N, n_txt_token, hidden_size]
             auto img_attn_out = ggml_view_3d(ctx->ggml_ctx,
                                              attn,
                                              attn->ne[0],
-                                             attn->ne[1],
                                              img->ne[1],
+                                             attn->ne[2],
                                              attn->nb[1],
                                              attn->nb[2],
-                                             attn->nb[2] * txt->ne[1]);                                           // [n_img_token, N, hidden_size]
-            img_attn_out      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, img_attn_out, 0, 2, 1, 3));  // [N, n_img_token, hidden_size]
+                                             txt->ne[1] * attn->nb[1]);  // [N, n_img_token, hidden_size]
 
             // calculate the img bloks
             img = ggml_add(ctx->ggml_ctx, img, ggml_mul(ctx->ggml_ctx, img_attn->post_attention(ctx, img_attn_out), img_mod1.gate));
@@ -492,43 +489,29 @@ namespace Flux {
             }
 
             auto x_mod   = Flux::modulate(ctx->ggml_ctx, pre_norm->forward(ctx, x), mod.shift, mod.scale);
-            auto qkv_mlp = linear1->forward(ctx, x_mod);                                                // [N, n_token, hidden_size * 3 + mlp_hidden_dim]
-            qkv_mlp      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, qkv_mlp, 2, 0, 1, 3));  // [hidden_size * 3 + mlp_hidden_dim, N, n_token]
+            auto qkv_mlp = linear1->forward(ctx, x_mod);  // [N, n_token, hidden_size * 3 + mlp_hidden_dim*mlp_mult_factor]
 
-            auto qkv = ggml_view_3d(ctx->ggml_ctx,
-                                    qkv_mlp,
-                                    qkv_mlp->ne[0],
-                                    qkv_mlp->ne[1],
-                                    hidden_size * 3,
-                                    qkv_mlp->nb[1],
-                                    qkv_mlp->nb[2],
-                                    0);                                                         // [hidden_size * 3 , N, n_token]
-            qkv      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, qkv, 1, 2, 0, 3));  // [N, n_token, hidden_size * 3]
-            auto mlp = ggml_view_3d(ctx->ggml_ctx,
-                                    qkv_mlp,
-                                    qkv_mlp->ne[0],
-                                    qkv_mlp->ne[1],
-                                    mlp_hidden_dim * mlp_mult_factor,
-                                    qkv_mlp->nb[1],
-                                    qkv_mlp->nb[2],
-                                    qkv_mlp->nb[2] * hidden_size * 3);                          // [mlp_hidden_dim*mlp_mult_factor , N, n_token]
-            mlp      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, mlp, 1, 2, 0, 3));  // [N, n_token, mlp_hidden_dim*mlp_mult_factor]
+            auto q = ggml_view_3d(ctx->ggml_ctx, qkv_mlp, hidden_size, qkv_mlp->ne[1], qkv_mlp->ne[2], qkv_mlp->nb[1], qkv_mlp->nb[2], 0);
+            auto k = ggml_view_3d(ctx->ggml_ctx, qkv_mlp, hidden_size, qkv_mlp->ne[1], qkv_mlp->ne[2], qkv_mlp->nb[1], qkv_mlp->nb[2], hidden_size * qkv_mlp->nb[0]);
+            auto v = ggml_view_3d(ctx->ggml_ctx, qkv_mlp, hidden_size, qkv_mlp->ne[1], qkv_mlp->ne[2], qkv_mlp->nb[1], qkv_mlp->nb[2], hidden_size * 2 * qkv_mlp->nb[0]);
 
-            auto qkv_vec     = split_qkv(ctx->ggml_ctx, qkv);  // q,k,v: [N, n_token, hidden_size]
             int64_t head_dim = hidden_size / num_heads;
-            auto q           = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[0], head_dim, num_heads, qkv_vec[0]->ne[1], qkv_vec[0]->ne[2]);  // [N, n_token, n_head, d_head]
-            auto k           = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[1], head_dim, num_heads, qkv_vec[1]->ne[1], qkv_vec[1]->ne[2]);  // [N, n_token, n_head, d_head]
-            auto v           = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[2], head_dim, num_heads, qkv_vec[2]->ne[1], qkv_vec[2]->ne[2]);  // [N, n_token, n_head, d_head]
-            q                = norm->query_norm(ctx, q);
-            k                = norm->key_norm(ctx, k);
-            auto attn        = Rope::attention(ctx, q, k, v, pe, mask);  // [N, n_token, hidden_size]
 
+            q = ggml_reshape_4d(ctx->ggml_ctx, ggml_cont(ctx->ggml_ctx, q), head_dim, num_heads, q->ne[1], q->ne[2]);  // [N, n_token, n_head, d_head]
+            k = ggml_reshape_4d(ctx->ggml_ctx, ggml_cont(ctx->ggml_ctx, k), head_dim, num_heads, k->ne[1], k->ne[2]);  // [N, n_token, n_head, d_head]
+            v = ggml_reshape_4d(ctx->ggml_ctx, ggml_cont(ctx->ggml_ctx, v), head_dim, num_heads, v->ne[1], v->ne[2]);  // [N, n_token, n_head, d_head]
+
+            q         = norm->query_norm(ctx, q);
+            k         = norm->key_norm(ctx, k);
+            auto attn = Rope::attention(ctx, q, k, v, pe, mask);  // [N, n_token, hidden_size]
+
+            auto mlp = ggml_view_3d(ctx->ggml_ctx, qkv_mlp, mlp_hidden_dim * mlp_mult_factor, qkv_mlp->ne[1], qkv_mlp->ne[2], qkv_mlp->nb[1], qkv_mlp->nb[2], hidden_size * 3 * qkv_mlp->nb[0]);
             if (use_yak_mlp) {
                 mlp = ggml_ext_silu_act(ctx->ggml_ctx, mlp, false);
             } else if (use_mlp_silu_act) {
                 mlp = ggml_ext_silu_act(ctx->ggml_ctx, mlp);
             } else {
-                mlp = ggml_gelu_inplace(ctx->ggml_ctx, mlp);
+                mlp = ggml_ext_gelu(ctx->ggml_ctx, mlp, true);
             }
             auto attn_mlp = ggml_concat(ctx->ggml_ctx, attn, mlp, 0);  // [N, n_token, hidden_size + mlp_hidden_dim]
             auto output   = linear2->forward(ctx, attn_mlp);           // [N, n_token, hidden_size]
@@ -580,13 +563,10 @@ namespace Flux {
             } else {
                 auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);
 
-                auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));         // [N, 2 * hidden_size]
-                m      = ggml_reshape_3d(ctx->ggml_ctx, m, c->ne[0], 2, c->ne[1]);              // [N, 2, hidden_size]
-                m      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, m, 0, 2, 1, 3));  // [2, N, hidden_size]
-
-                int64_t offset = m->nb[1] * m->ne[1];
-                shift          = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
-                scale          = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
+                auto m     = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));  // [N, 2 * hidden_size]
+                auto m_vec = ggml_ext_chunk(ctx->ggml_ctx, m, 2, 0);
+                shift      = m_vec[0];  // [N, hidden_size]
+                scale      = m_vec[1];  // [N, hidden_size]
             }
 
             x = Flux::modulate(ctx->ggml_ctx, norm_final->forward(ctx, x), shift, scale);
@@ -1034,16 +1014,14 @@ namespace Flux {
                 txt_img = block->forward(ctx, txt_img, vec, pe, txt_img_mask, ss_mods);
             }
 
-            txt_img = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, txt_img, 0, 2, 1, 3));  // [n_txt_token + n_img_token, N, hidden_size]
-            img     = ggml_view_3d(ctx->ggml_ctx,
-                                   txt_img,
-                                   txt_img->ne[0],
-                                   txt_img->ne[1],
-                                   img->ne[1],
-                                   txt_img->nb[1],
-                                   txt_img->nb[2],
-                                   txt_img->nb[2] * txt->ne[1]);                               // [n_img_token, N, hidden_size]
-            img     = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, img, 0, 2, 1, 3));  // [N, n_img_token, hidden_size]
+            img = ggml_view_3d(ctx->ggml_ctx,
+                               txt_img,
+                               txt_img->ne[0],
+                               img->ne[1],
+                               txt_img->ne[2],
+                               txt_img->nb[1],
+                               txt_img->nb[2],
+                               txt->ne[1] * txt_img->nb[1]);  // [N, n_img_token, hidden_size]
 
             if (final_layer) {
                 img = final_layer->forward(ctx, img, vec);  // (N, T, patch_size ** 2 * out_channels)
@@ -1196,9 +1174,8 @@ namespace Flux {
             auto out = forward_orig(ctx, img, context, timestep, y, guidance, pe, mod_index_arange, skip_layers);  // [N, num_tokens, C * patch_size * patch_size]
 
             if (out->ne[1] > img_tokens) {
-                out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, out, 0, 2, 1, 3));  // [num_tokens, N, C * patch_size * patch_size]
-                out = ggml_view_3d(ctx->ggml_ctx, out, out->ne[0], out->ne[1], img_tokens, out->nb[1], out->nb[2], 0);
-                out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, out, 0, 2, 1, 3));  // [N, h*w, C * patch_size * patch_size]
+                out = ggml_view_3d(ctx->ggml_ctx, out, out->ne[0], img_tokens, out->ne[2], out->nb[1], out->nb[2], 0);
+                out = ggml_cont(ctx->ggml_ctx, out);
             }
 
             // rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)
diff --git a/ggml_extend.hpp b/ggml_extend.hpp
index 9d5ea316..fedab380 100644
--- a/ggml_extend.hpp
+++ b/ggml_extend.hpp
@@ -687,7 +687,8 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_slice(struct ggml_context* ctx,
                                                      struct ggml_tensor* x,
                                                      int dim,
                                                      int64_t start,
-                                                     int64_t end) {
+                                                     int64_t end,
+                                                     bool cont = true) {
     GGML_ASSERT(dim >= 0 && dim < 4);
     if (x->ne[dim] == 1) {
         return x;
@@ -702,27 +703,15 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_slice(struct ggml_context* ctx,
     GGML_ASSERT(start >= 0 && start < x->ne[dim]);
     GGML_ASSERT(end > start && end <= x->ne[dim]);
 
-    int perm[4] = {0, 1, 2, 3};
-    for (int i = dim; i < 3; ++i)
-        perm[i] = perm[i + 1];
-    perm[3] = dim;
+    int64_t slice_size  = end - start;
+    int64_t slice_ne[4] = {x->ne[0], x->ne[1], x->ne[2], x->ne[3]};
+    slice_ne[dim]       = slice_size;
 
-    int inv_perm[4];
-    for (int i = 0; i < 4; ++i)
-        inv_perm[perm[i]] = i;
+    x = ggml_view_4d(ctx, x,
+                     slice_ne[0], slice_ne[1], slice_ne[2], slice_ne[3],
+                     x->nb[1], x->nb[2], x->nb[3], start * x->nb[dim]);
 
-    if (dim != 3) {
-        x = ggml_ext_torch_permute(ctx, x, perm[0], perm[1], perm[2], perm[3]);
-        x = ggml_cont(ctx, x);
-    }
-
-    x = ggml_view_4d(
-        ctx, x,
-        x->ne[0], x->ne[1], x->ne[2], end - start,
-        x->nb[1], x->nb[2], x->nb[3], x->nb[3] * start);
-
-    if (dim != 3) {
-        x = ggml_ext_torch_permute(ctx, x, inv_perm[0], inv_perm[1], inv_perm[2], inv_perm[3]);
+    if (cont) {
         x = ggml_cont(ctx, x);
     }
 
@@ -960,6 +949,49 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_group_norm_32(struct ggml_context
     return ggml_group_norm(ctx, a, 32, eps);
 }
 
+__STATIC_INLINE__ struct ggml_tensor* ggml_ext_scale(struct ggml_context* ctx,
+                                                     struct ggml_tensor* x,
+                                                     float factor,
+                                                     bool inplace = false) {
+    if (!ggml_is_contiguous(x)) {
+        x = ggml_cont(ctx, x);
+    }
+    if (inplace) {
+        x = ggml_scale_inplace(ctx, x, factor);
+    } else {
+        x = ggml_scale(ctx, x, factor);
+    }
+    return x;
+}
+
+__STATIC_INLINE__ struct ggml_tensor* ggml_ext_gelu(struct ggml_context* ctx,
+                                                    struct ggml_tensor* x,
+                                                    bool inplace = false) {
+    if (!ggml_is_contiguous(x)) {
+        x = ggml_cont(ctx, x);
+    }
+    if (inplace) {
+        x = ggml_gelu_inplace(ctx, x);
+    } else {
+        x = ggml_gelu(ctx, x);
+    }
+    return x;
+}
+
+__STATIC_INLINE__ struct ggml_tensor* ggml_ext_gelu_quick(struct ggml_context* ctx,
+                                                          struct ggml_tensor* x,
+                                                          bool inplace = false) {
+    if (!ggml_is_contiguous(x)) {
+        x = ggml_cont(ctx, x);
+    }
+    if (inplace) {
+        x = ggml_gelu_quick_inplace(ctx, x);
+    } else {
+        x = ggml_gelu_quick(ctx, x);
+    }
+    return x;
+}
+
 __STATIC_INLINE__ struct ggml_tensor* ggml_ext_linear(struct ggml_context* ctx,
                                                       struct ggml_tensor* x,
                                                       struct ggml_tensor* w,
@@ -967,7 +999,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_linear(struct ggml_context* ctx,
                                                       bool force_prec_f32 = false,
                                                       float scale         = 1.f) {
     if (scale != 1.f) {
-        x = ggml_scale(ctx, x, scale);
+        x = ggml_ext_scale(ctx, x, scale);
     }
     if (x->ne[2] * x->ne[3] > 1024) {
         // workaround: avoid ggml cuda error
@@ -986,7 +1018,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_linear(struct ggml_context* ctx,
         }
     }
     if (scale != 1.f) {
-        x = ggml_scale(ctx, x, 1.f / scale);
+        x = ggml_ext_scale(ctx, x, 1.f / scale);
     }
     if (b != nullptr) {
         x = ggml_add_inplace(ctx, x, b);
@@ -1055,7 +1087,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_conv_2d(struct ggml_context* ctx,
                                                        bool circular_y = false,
                                                        float scale     = 1.f) {
     if (scale != 1.f) {
-        x = ggml_scale(ctx, x, scale);
+        x = ggml_ext_scale(ctx, x, scale);
     }
     if (w->ne[2] != x->ne[2] && ggml_n_dims(w) == 2) {
         w = ggml_reshape_4d(ctx, w, 1, 1, w->ne[0], w->ne[1]);
@@ -1073,7 +1105,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_conv_2d(struct ggml_context* ctx,
         x = ggml_conv_2d(ctx, w, x, s0, s1, p0, p1, d0, d1);
     }
     if (scale != 1.f) {
-        x = ggml_scale(ctx, x, 1.f / scale);
+        x = ggml_ext_scale(ctx, x, 1.f / scale);
     }
     if (b != nullptr) {
         b = ggml_reshape_4d(ctx, b, 1, 1, b->ne[0], 1);
@@ -1171,7 +1203,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_full(struct ggml_context* ctx,
                                                     int64_t ne2,
                                                     int64_t ne3) {
     auto one = ggml_get_tensor(ctx, "ggml_runner_build_in_tensor:one");
-    auto t   = ggml_scale(ctx, one, value);                 // [1,]
+    auto t   = ggml_ext_scale(ctx, one, value);             // [1,]
     t        = ggml_repeat_4d(ctx, t, ne0, ne1, ne2, ne3);  // [ne0, ne1, ne2, ne3]
     return t;
 }
@@ -1271,7 +1303,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_attention_ext(struct ggml_context
             k_in = ggml_pad(ctx, k_in, 0, kv_pad, 0, 0);
         }
         if (kv_scale != 1.0f) {
-            k_in = ggml_scale(ctx, k_in, kv_scale);
+            k_in = ggml_ext_scale(ctx, k_in, kv_scale);
         }
         k_in = ggml_cast(ctx, k_in, GGML_TYPE_F16);
 
@@ -1281,7 +1313,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_attention_ext(struct ggml_context
             v_in = ggml_pad(ctx, v_in, 0, kv_pad, 0, 0);
         }
         if (kv_scale != 1.0f) {
-            v_in = ggml_scale(ctx, v_in, kv_scale);
+            v_in = ggml_ext_scale(ctx, v_in, kv_scale);
         }
         v_in = ggml_cast(ctx, v_in, GGML_TYPE_F16);
 
@@ -1313,7 +1345,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_attention_ext(struct ggml_context
         auto out = ggml_flash_attn_ext(ctx, q_in, k_in, v_in, mask_in, scale / kv_scale, 0, 0);
         ggml_flash_attn_ext_set_prec(out, GGML_PREC_F32);
         if (kv_scale != 1.0f) {
-            out = ggml_scale(ctx, out, 1.0f / kv_scale);
+            out = ggml_ext_scale(ctx, out, 1.0f / kv_scale);
         }
         return out;
     };
@@ -1523,7 +1555,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_timestep_embedding(
     int dim,
     int max_period    = 10000,
     float time_factor = 1.0f) {
-    timesteps = ggml_scale(ctx, timesteps, time_factor);
+    timesteps = ggml_ext_scale(ctx, timesteps, time_factor);
     return ggml_timestep_embedding(ctx, timesteps, dim, max_period);
 }
 
diff --git a/llm.hpp b/llm.hpp
index 781774db..7feb8d3c 100644
--- a/llm.hpp
+++ b/llm.hpp
@@ -638,7 +638,7 @@ namespace LLM {
             x = ln_q->forward(ctx, x);
             x = ggml_reshape_2d(ctx->ggml_ctx, x, hidden_size, ggml_nelements(x) / hidden_size);
             x = mlp_0->forward(ctx, x);
-            x = ggml_gelu(ctx->ggml_ctx, x);
+            x = ggml_ext_gelu(ctx->ggml_ctx, x);
             x = mlp_2->forward(ctx, x);
             return x;
         }
diff --git a/lora.hpp b/lora.hpp
index 7d83ec5c..e5d9906f 100644
--- a/lora.hpp
+++ b/lora.hpp
@@ -195,7 +195,7 @@ struct LoraModel : public GGMLRunner {
             scale_value *= multiplier;
 
             auto curr_updown = ggml_ext_merge_lora(ctx, lora_down, lora_up, lora_mid);
-            curr_updown      = ggml_scale_inplace(ctx, curr_updown, scale_value);
+            curr_updown      = ggml_ext_scale(ctx, curr_updown, scale_value, true);
 
             if (updown == nullptr) {
                 updown = curr_updown;
@@ -235,7 +235,7 @@ struct LoraModel : public GGMLRunner {
             float scale_value = 1.0f;
             scale_value *= multiplier;
 
-            curr_updown = ggml_scale_inplace(ctx, curr_updown, scale_value);
+            curr_updown = ggml_ext_scale(ctx, curr_updown, scale_value, true);
 
             if (updown == nullptr) {
                 updown = curr_updown;
@@ -340,7 +340,7 @@ struct LoraModel : public GGMLRunner {
             struct ggml_tensor* updown_1 = ggml_ext_merge_lora(ctx, hada_1_down, hada_1_up, hada_1_mid);
             struct ggml_tensor* updown_2 = ggml_ext_merge_lora(ctx, hada_2_down, hada_2_up, hada_2_mid);
             auto curr_updown             = ggml_mul_inplace(ctx, updown_1, updown_2);
-            curr_updown                  = ggml_scale_inplace(ctx, curr_updown, scale_value);
+            curr_updown                  = ggml_ext_scale(ctx, curr_updown, scale_value, true);
             if (updown == nullptr) {
                 updown = curr_updown;
             } else {
@@ -456,7 +456,7 @@ struct LoraModel : public GGMLRunner {
             scale_value *= multiplier;
 
             auto curr_updown = ggml_ext_kronecker(ctx, lokr_w1, lokr_w2);
-            curr_updown      = ggml_scale_inplace(ctx, curr_updown, scale_value);
+            curr_updown      = ggml_ext_scale(ctx, curr_updown, scale_value, true);
 
             if (updown == nullptr) {
                 updown = curr_updown;
@@ -634,7 +634,7 @@ struct LoraModel : public GGMLRunner {
                                       forward_params.conv2d.scale);
             }
 
-            auto curr_out_diff = ggml_scale_inplace(ctx, lx, scale_value);
+            auto curr_out_diff = ggml_ext_scale(ctx, lx, scale_value, true);
 
             if (out_diff == nullptr) {
                 out_diff = curr_out_diff;
diff --git a/mmdit.hpp b/mmdit.hpp
index cb4be7bf..086b444d 100644
--- a/mmdit.hpp
+++ b/mmdit.hpp
@@ -33,7 +33,7 @@ public:
         auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
 
         x = fc1->forward(ctx, x);
-        x = ggml_gelu_inplace(ctx->ggml_ctx, x);
+        x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
         x = fc2->forward(ctx, x);
         return x;
     }
@@ -284,23 +284,19 @@ public:
         auto attn2              = std::dynamic_pointer_cast<SelfAttention>(blocks["attn2"]);
         auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);
 
-        int64_t n_mods = 9;
-        auto m         = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));         // [N, n_mods * hidden_size]
-        m              = ggml_reshape_3d(ctx->ggml_ctx, m, c->ne[0], n_mods, c->ne[1]);         // [N, n_mods, hidden_size]
-        m              = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, m, 0, 2, 1, 3));  // [n_mods, N, hidden_size]
+        int n_mods = 9;
+        auto m     = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));  // [N, n_mods * hidden_size]
+        auto m_vec = ggml_ext_chunk(ctx->ggml_ctx, m, n_mods, 0);
 
-        int64_t offset = m->nb[1] * m->ne[1];
-        auto shift_msa = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
-        auto scale_msa = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
-        auto gate_msa  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 2);  // [N, hidden_size]
-
-        auto shift_mlp = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 3);  // [N, hidden_size]
-        auto scale_mlp = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 4);  // [N, hidden_size]
-        auto gate_mlp  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 5);  // [N, hidden_size]
-
-        auto shift_msa2 = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 6);  // [N, hidden_size]
-        auto scale_msa2 = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 7);  // [N, hidden_size]
-        auto gate_msa2  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 8);  // [N, hidden_size]
+        auto shift_msa  = m_vec[0];  // [N, hidden_size]
+        auto scale_msa  = m_vec[1];  // [N, hidden_size]
+        auto gate_msa   = m_vec[2];  // [N, hidden_size]
+        auto shift_mlp  = m_vec[3];  // [N, hidden_size]
+        auto scale_mlp  = m_vec[4];  // [N, hidden_size]
+        auto gate_mlp   = m_vec[5];  // [N, hidden_size]
+        auto shift_msa2 = m_vec[6];  // [N, hidden_size]
+        auto scale_msa2 = m_vec[7];  // [N, hidden_size]
+        auto gate_msa2  = m_vec[8];  // [N, hidden_size]
 
         auto x_norm = norm1->forward(ctx, x);
 
@@ -322,22 +318,20 @@ public:
         auto attn               = std::dynamic_pointer_cast<SelfAttention>(blocks["attn"]);
         auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);
 
-        int64_t n_mods = 6;
+        int n_mods = 6;
         if (pre_only) {
             n_mods = 2;
         }
-        auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));         // [N, n_mods * hidden_size]
-        m      = ggml_reshape_3d(ctx->ggml_ctx, m, c->ne[0], n_mods, c->ne[1]);         // [N, n_mods, hidden_size]
-        m      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, m, 0, 2, 1, 3));  // [n_mods, N, hidden_size]
+        auto m     = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));  // [N, n_mods * hidden_size]
+        auto m_vec = ggml_ext_chunk(ctx->ggml_ctx, m, n_mods, 0);
 
-        int64_t offset = m->nb[1] * m->ne[1];
-        auto shift_msa = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
-        auto scale_msa = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
+        auto shift_msa = m_vec[0];  // [N, hidden_size]
+        auto scale_msa = m_vec[1];  // [N, hidden_size]
         if (!pre_only) {
-            auto gate_msa  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 2);  // [N, hidden_size]
-            auto shift_mlp = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 3);  // [N, hidden_size]
-            auto scale_mlp = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 4);  // [N, hidden_size]
-            auto gate_mlp  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 5);  // [N, hidden_size]
+            auto gate_msa  = m_vec[2];  // [N, hidden_size]
+            auto shift_mlp = m_vec[3];  // [N, hidden_size]
+            auto scale_mlp = m_vec[4];  // [N, hidden_size]
+            auto gate_mlp  = m_vec[5];  // [N, hidden_size]
 
             auto attn_in = modulate(ctx->ggml_ctx, norm1->forward(ctx, x), shift_msa, scale_msa);
 
@@ -500,26 +494,24 @@ block_mixing(GGMLRunnerContext* ctx,
         qkv.push_back(ggml_concat(ctx->ggml_ctx, context_qkv[i], x_qkv[i], 1));
     }
 
-    auto attn         = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], x_block->num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_context + n_token, hidden_size]
-    attn              = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, attn, 0, 2, 1, 3));                                                                          // [n_context + n_token, N, hidden_size]
+    auto attn = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], x_block->num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_context + n_token, hidden_size]
+
     auto context_attn = ggml_view_3d(ctx->ggml_ctx,
                                      attn,
                                      attn->ne[0],
-                                     attn->ne[1],
                                      context->ne[1],
+                                     attn->ne[2],
                                      attn->nb[1],
                                      attn->nb[2],
-                                     0);                                                                  // [n_context, N, hidden_size]
-    context_attn      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, context_attn, 0, 2, 1, 3));  // [N, n_context, hidden_size]
+                                     0);  // [N, n_context, hidden_size]
     auto x_attn       = ggml_view_3d(ctx->ggml_ctx,
                                      attn,
                                      attn->ne[0],
-                                     attn->ne[1],
                                      x->ne[1],
+                                     attn->ne[2],
                                      attn->nb[1],
                                      attn->nb[2],
-                                     attn->nb[2] * context->ne[1]);                                 // [n_token, N, hidden_size]
-    x_attn            = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x_attn, 0, 2, 1, 3));  // [N, n_token, hidden_size]
+                                     context->ne[1] * attn->nb[1]);  // [N, n_token, hidden_size]
 
     if (!context_block->pre_only) {
         context = context_block->post_attention(ctx,
@@ -604,13 +596,10 @@ public:
         auto linear             = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
         auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);
 
-        auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));         // [N, 2 * hidden_size]
-        m      = ggml_reshape_3d(ctx->ggml_ctx, m, c->ne[0], 2, c->ne[1]);              // [N, 2, hidden_size]
-        m      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, m, 0, 2, 1, 3));  // [2, N, hidden_size]
-
-        int64_t offset = m->nb[1] * m->ne[1];
-        auto shift     = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
-        auto scale     = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
+        auto m     = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));  // [N, 2 * hidden_size]
+        auto m_vec = ggml_ext_chunk(ctx->ggml_ctx, m, 2, 0);
+        auto shift = m_vec[0];  // [N, hidden_size]
+        auto scale = m_vec[1];  // [N, hidden_size]
 
         x = modulate(ctx->ggml_ctx, norm_final->forward(ctx, x), shift, scale);
         x = linear->forward(ctx, x);
diff --git a/pmid.hpp b/pmid.hpp
index 5b70dab6..8ce78d3a 100644
--- a/pmid.hpp
+++ b/pmid.hpp
@@ -33,7 +33,7 @@ public:
         x = layer_norm->forward(ctx, x);
         // x = ggml_add(ctx, ggml_mul_mat(ctx, fc1_w, x),  fc1_b);
         x = fc1->forward(ctx, x);
-        x = ggml_gelu_inplace(ctx->ggml_ctx, x);
+        x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
         x = fc2->forward(ctx, x);
         // x = ggml_add(ctx, ggml_mul_mat(ctx, fc2_w, x),  fc2_b);
         if (use_residue)
@@ -129,8 +129,8 @@ public:
         k             = reshape_tensor(ctx->ggml_ctx, k, heads);
         v             = reshape_tensor(ctx->ggml_ctx, v, heads);
         scale         = 1.f / sqrt(sqrt((float)dim_head));
-        k             = ggml_scale_inplace(ctx->ggml_ctx, k, scale);
-        q             = ggml_scale_inplace(ctx->ggml_ctx, q, scale);
+        k             = ggml_ext_scale(ctx->ggml_ctx, k, scale, true);
+        q             = ggml_ext_scale(ctx->ggml_ctx, q, scale, true);
         // auto weight = ggml_mul_mat(ctx, q, k);
         auto weight = ggml_mul_mat(ctx->ggml_ctx, k, q);  // NOTE order of mul is opposite to pytorch
 
diff --git a/qwen_image.hpp b/qwen_image.hpp
index ec2231b0..dfa53978 100644
--- a/qwen_image.hpp
+++ b/qwen_image.hpp
@@ -162,26 +162,25 @@ namespace Qwen {
             auto k = ggml_concat(ctx->ggml_ctx, txt_k, img_k, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
             auto v = ggml_concat(ctx->ggml_ctx, txt_v, img_v, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
 
-            auto attn         = Rope::attention(ctx, q, k, v, pe, mask, (1.0f / 128.f));                  // [N, n_txt_token + n_img_token, n_head*d_head]
-            attn              = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, attn, 0, 2, 1, 3));  // [n_txt_token + n_img_token, N, hidden_size]
+            auto attn         = Rope::attention(ctx, q, k, v, pe, mask, (1.0f / 128.f));  // [N, n_txt_token + n_img_token, n_head*d_head]
             auto txt_attn_out = ggml_view_3d(ctx->ggml_ctx,
                                              attn,
                                              attn->ne[0],
-                                             attn->ne[1],
                                              txt->ne[1],
+                                             attn->ne[2],
                                              attn->nb[1],
                                              attn->nb[2],
-                                             0);                                                                  // [n_txt_token, N, hidden_size]
-            txt_attn_out      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, txt_attn_out, 0, 2, 1, 3));  // [N, n_txt_token, hidden_size]
+                                             0);  // [N, n_txt_token, n_head*d_head]
             auto img_attn_out = ggml_view_3d(ctx->ggml_ctx,
                                              attn,
                                              attn->ne[0],
-                                             attn->ne[1],
                                              img->ne[1],
+                                             attn->ne[2],
                                              attn->nb[1],
                                              attn->nb[2],
-                                             attn->nb[2] * txt->ne[1]);                                           // [n_img_token, N, hidden_size]
-            img_attn_out      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, img_attn_out, 0, 2, 1, 3));  // [N, n_img_token, hidden_size]
+                                             txt->ne[1] * attn->nb[1]);  // [N, n_img_token, n_head*d_head]
+            img_attn_out      = ggml_cont(ctx->ggml_ctx, img_attn_out);
+            txt_attn_out      = ggml_cont(ctx->ggml_ctx, txt_attn_out);
 
             img_attn_out = to_out_0->forward(ctx, img_attn_out);
             txt_attn_out = to_add_out->forward(ctx, txt_attn_out);
diff --git a/t5.hpp b/t5.hpp
index 2e3e4b56..fdac3475 100644
--- a/t5.hpp
+++ b/t5.hpp
@@ -515,7 +515,7 @@ public:
         auto wi_1 = std::dynamic_pointer_cast<Linear>(blocks["wi_1"]);
         auto wo   = std::dynamic_pointer_cast<Linear>(blocks["wo"]);
 
-        auto hidden_gelu   = ggml_gelu_inplace(ctx->ggml_ctx, wi_0->forward(ctx, x));
+        auto hidden_gelu   = ggml_ext_gelu(ctx->ggml_ctx, wi_0->forward(ctx, x), true);
         auto hidden_linear = wi_1->forward(ctx, x);
         x                  = ggml_mul_inplace(ctx->ggml_ctx, hidden_gelu, hidden_linear);
         x                  = wo->forward(ctx, x);
@@ -608,7 +608,7 @@ public:
             }
         }
 
-        k = ggml_scale_inplace(ctx->ggml_ctx, k, ::sqrtf(static_cast<float>(d_head)));
+        k = ggml_ext_scale(ctx->ggml_ctx, k, ::sqrtf(static_cast<float>(d_head)), true);
 
         x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, mask);  // [N, n_token, d_head * n_head]
 
diff --git a/tae.hpp b/tae.hpp
index a22db196..83152578 100644
--- a/tae.hpp
+++ b/tae.hpp
@@ -161,9 +161,9 @@ public:
         // z: [n, z_channels, h, w]
         // return: [n, out_channels, h*8, w*8]
 
-        auto h = ggml_scale(ctx->ggml_ctx, z, 1.0f / 3.0f);
+        auto h = ggml_ext_scale(ctx->ggml_ctx, z, 1.0f / 3.0f);
         h      = ggml_tanh_inplace(ctx->ggml_ctx, h);
-        h      = ggml_scale(ctx->ggml_ctx, h, 3.0f);
+        h      = ggml_ext_scale(ctx->ggml_ctx, h, 3.0f);
 
         for (int i = 0; i < num_blocks * 3 + 10; i++) {
             if (blocks.find(std::to_string(i)) == blocks.end()) {
@@ -400,10 +400,11 @@ public:
         auto first_conv = std::dynamic_pointer_cast<Conv2d>(blocks["1"]);
 
         // Clamp()
-        auto h = ggml_scale_inplace(ctx->ggml_ctx,
-                                    ggml_tanh_inplace(ctx->ggml_ctx,
-                                                      ggml_scale(ctx->ggml_ctx, z, 1.0f / 3.0f)),
-                                    3.0f);
+        auto h = ggml_ext_scale(ctx->ggml_ctx,
+                                ggml_tanh_inplace(ctx->ggml_ctx,
+                                                  ggml_ext_scale(ctx->ggml_ctx, z, 1.0f / 3.0f)),
+                                3.0f,
+                                true);
 
         h         = first_conv->forward(ctx, h);
         h         = ggml_relu_inplace(ctx->ggml_ctx, h);
diff --git a/unet.hpp b/unet.hpp
index 6e15e1f4..2dd79e0e 100644
--- a/unet.hpp
+++ b/unet.hpp
@@ -529,7 +529,7 @@ public:
             }
         }
         if (controls.size() > 0) {
-            auto cs = ggml_scale_inplace(ctx->ggml_ctx, controls[controls.size() - 1], control_strength);
+            auto cs = ggml_ext_scale(ctx->ggml_ctx, controls[controls.size() - 1], control_strength, true);
             h       = ggml_add(ctx->ggml_ctx, h, cs);  // middle control
         }
         int control_offset = static_cast<int>(controls.size() - 2);
@@ -542,7 +542,7 @@ public:
                 hs.pop_back();
 
                 if (controls.size() > 0) {
-                    auto cs = ggml_scale_inplace(ctx->ggml_ctx, controls[control_offset], control_strength);
+                    auto cs = ggml_ext_scale(ctx->ggml_ctx, controls[control_offset], control_strength, true);
                     h_skip  = ggml_add(ctx->ggml_ctx, h_skip, cs);  // control net condition
                     control_offset--;
                 }
diff --git a/vae.hpp b/vae.hpp
index 23250029..fdddc8ae 100644
--- a/vae.hpp
+++ b/vae.hpp
@@ -253,8 +253,8 @@ public:
 
         float alpha = get_alpha();
         x           = ggml_add(ctx->ggml_ctx,
-                               ggml_scale(ctx->ggml_ctx, x, alpha),
-                               ggml_scale(ctx->ggml_ctx, x_mix, 1.0f - alpha));
+                               ggml_ext_scale(ctx->ggml_ctx, x, alpha),
+                               ggml_ext_scale(ctx->ggml_ctx, x_mix, 1.0f - alpha));
 
         x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
         x = ggml_reshape_4d(ctx->ggml_ctx, x, W, H, C, T * B);                     // b t c (h w) -> (b t) c h w
diff --git a/wan.hpp b/wan.hpp
index 3ade14bf..c56e1f92 100644
--- a/wan.hpp
+++ b/wan.hpp
@@ -1442,11 +1442,8 @@ namespace WAN {
             int64_t dim             = x->ne[0];
             int64_t context_txt_len = context->ne[1] - context_img_len;
 
-            context          = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, context, 0, 2, 1, 3));  // [context_img_len + context_txt_len, N, dim]
-            auto context_img = ggml_view_3d(ctx->ggml_ctx, context, dim, N, context_img_len, context->nb[1], context->nb[2], 0);
-            auto context_txt = ggml_view_3d(ctx->ggml_ctx, context, dim, N, context_txt_len, context->nb[1], context->nb[2], context_img_len * context->nb[2]);
-            context_img      = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, context_img, 0, 2, 1, 3));  // [N, context_img_len, dim]
-            context_txt      = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, context_txt, 0, 2, 1, 3));  // [N, context_txt_len, dim]
+            auto context_img = ggml_view_3d(ctx->ggml_ctx, context, dim, context_img_len, N, context->nb[1], context->nb[2], 0);                                 // [N, context_img_len, dim]
+            auto context_txt = ggml_view_3d(ctx->ggml_ctx, context, dim, context_txt_len, N, context->nb[1], context->nb[2], context_img_len * context->nb[1]);  // [N, context_txt_len, dim]
 
             auto q = q_proj->forward(ctx, x);
             q      = norm_q->forward(ctx, q);
@@ -1576,7 +1573,7 @@ namespace WAN {
             y = modulate_add(ctx->ggml_ctx, y, es[3]);
 
             y = ffn_0->forward(ctx, y);
-            y = ggml_gelu_inplace(ctx->ggml_ctx, y);
+            y = ggml_ext_gelu(ctx->ggml_ctx, y, true);
             y = ffn_2->forward(ctx, y);
 
             x = ggml_add(ctx->ggml_ctx, x, modulate_mul(ctx->ggml_ctx, y, es[5]));
@@ -1723,7 +1720,7 @@ namespace WAN {
 
             auto x = proj_0->forward(ctx, image_embeds);
             x      = proj_1->forward(ctx, x);
-            x      = ggml_gelu_inplace(ctx->ggml_ctx, x);
+            x      = ggml_ext_gelu(ctx->ggml_ctx, x, true);
             x      = proj_3->forward(ctx, x);
             x      = proj_4->forward(ctx, x);
 
@@ -1910,7 +1907,7 @@ namespace WAN {
             e0      = ggml_reshape_4d(ctx->ggml_ctx, e0, e0->ne[0] / 6, 6, e0->ne[1], e0->ne[2]);  //  [N, 6, dim] or [N, T, 6, dim]
 
             context = text_embedding_0->forward(ctx, context);
-            context = ggml_gelu(ctx->ggml_ctx, context);
+            context = ggml_ext_gelu(ctx->ggml_ctx, context);
             context = text_embedding_2->forward(ctx, context);  // [N, context_txt_len, dim]
 
             int64_t context_img_len = 0;
@@ -1949,7 +1946,7 @@ namespace WAN {
                     auto result = vace_block->forward(ctx, c, x_orig, e0, pe, context, context_img_len);
                     auto c_skip = result.first;
                     c           = result.second;
-                    c_skip      = ggml_scale(ctx->ggml_ctx, c_skip, vace_strength);
+                    c_skip      = ggml_ext_scale(ctx->ggml_ctx, c_skip, vace_strength);
                     x           = ggml_add(ctx->ggml_ctx, x, c_skip);
                 }
             }
diff --git a/z_image.hpp b/z_image.hpp
index 0abc7832..cee23833 100644
--- a/z_image.hpp
+++ b/z_image.hpp
@@ -54,15 +54,37 @@ namespace ZImage {
 
             auto qkv = qkv_proj->forward(ctx, x);                                                                            // [N, n_token, (num_heads + num_kv_heads*2)*head_dim]
             qkv      = ggml_reshape_4d(ctx->ggml_ctx, qkv, head_dim, num_heads + num_kv_heads * 2, qkv->ne[1], qkv->ne[2]);  // [N, n_token, num_heads + num_kv_heads*2, head_dim]
-            qkv      = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, qkv, 0, 2, 3, 1));                     // [num_heads + num_kv_heads*2, N, n_token, head_dim]
 
-            auto q = ggml_view_4d(ctx->ggml_ctx, qkv, qkv->ne[0], qkv->ne[1], qkv->ne[2], num_heads, qkv->nb[1], qkv->nb[2], qkv->nb[3], 0);                                           // [num_heads, N, n_token, head_dim]
-            auto k = ggml_view_4d(ctx->ggml_ctx, qkv, qkv->ne[0], qkv->ne[1], qkv->ne[2], num_kv_heads, qkv->nb[1], qkv->nb[2], qkv->nb[3], qkv->nb[3] * num_heads);                   // [num_kv_heads, N, n_token, head_dim]
-            auto v = ggml_view_4d(ctx->ggml_ctx, qkv, qkv->ne[0], qkv->ne[1], qkv->ne[2], num_kv_heads, qkv->nb[1], qkv->nb[2], qkv->nb[3], qkv->nb[3] * (num_heads + num_kv_heads));  // [num_kv_heads, N, n_token, head_dim]
-
-            q = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, q, 0, 3, 1, 2));  // [N, n_token, num_heads, head_dim]
-            k = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, k, 0, 3, 1, 2));  // [N, n_token, num_kv_heads, head_dim]
-            v = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, v, 0, 3, 1, 2));  // [N, n_token, num_kv_heads, head_dim]
+            auto q = ggml_view_4d(ctx->ggml_ctx,
+                                  qkv,
+                                  qkv->ne[0],
+                                  num_heads,
+                                  qkv->ne[2],
+                                  qkv->ne[3],
+                                  qkv->nb[1],
+                                  qkv->nb[2],
+                                  qkv->nb[3],
+                                  0);  // [N, n_token, num_heads, head_dim]
+            auto k = ggml_view_4d(ctx->ggml_ctx,
+                                  qkv,
+                                  qkv->ne[0],
+                                  num_kv_heads,
+                                  qkv->ne[2],
+                                  qkv->ne[3],
+                                  qkv->nb[1],
+                                  qkv->nb[2],
+                                  qkv->nb[3],
+                                  num_heads * qkv->nb[1]);  // [N, n_token, num_kv_heads, head_dim]
+            auto v = ggml_view_4d(ctx->ggml_ctx,
+                                  qkv,
+                                  qkv->ne[0],
+                                  num_kv_heads,
+                                  qkv->ne[2],
+                                  qkv->ne[3],
+                                  qkv->nb[1],
+                                  qkv->nb[2],
+                                  qkv->nb[3],
+                                  (num_heads + num_kv_heads) * qkv->nb[1]);  // [N, n_token, num_kv_heads, head_dim]
 
             if (qk_norm) {
                 auto q_norm = std::dynamic_pointer_cast<RMSNorm>(blocks["q_norm"]);
@@ -495,7 +517,7 @@ namespace ZImage {
             out = ggml_ext_slice(ctx->ggml_ctx, out, 1, 0, H);  // [N, C, H, W + pad_w]
             out = ggml_ext_slice(ctx->ggml_ctx, out, 0, 0, W);  // [N, C, H, W]
 
-            out = ggml_scale(ctx->ggml_ctx, out, -1.f);
+            out = ggml_ext_scale(ctx->ggml_ctx, out, -1.f);
 
             return out;
         }

From 43e829f21966abb96b08c712bccee872dc820914 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Mon, 26 Jan 2026 00:33:34 +0800
Subject: [PATCH 64/97] refactor: unify the processing of attention mask
 (#1230)

---
 clip.hpp              | 43 +++++++++++++++++++++++++++++++------------
 common.hpp            |  2 +-
 examples/cli/main.cpp | 14 +++++++-------
 ggml_extend.hpp       |  8 ++------
 llm.hpp               |  2 +-
 mmdit.hpp             | 14 +++++++-------
 rope.hpp              |  2 +-
 vae.hpp               |  2 +-
 wan.hpp               | 10 +++++-----
 9 files changed, 56 insertions(+), 41 deletions(-)

diff --git a/clip.hpp b/clip.hpp
index 4eec0241..3fc65665 100644
--- a/clip.hpp
+++ b/clip.hpp
@@ -510,7 +510,7 @@ public:
         blocks["mlp"] = std::shared_ptr<GGMLBlock>(new CLIPMLP(d_model, intermediate_size));
     }
 
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, bool mask = true) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, struct ggml_tensor* mask = nullptr) {
         // x: [N, n_token, d_model]
         auto self_attn   = std::dynamic_pointer_cast<MultiheadAttention>(blocks["self_attn"]);
         auto layer_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm1"]);
@@ -542,8 +542,8 @@ public:
 
     struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
-                                int clip_skip = -1,
-                                bool mask     = true) {
+                                struct ggml_tensor* mask = nullptr,
+                                int clip_skip            = -1) {
         // x: [N, n_token, d_model]
         int layer_idx = n_layer - 1;
         // LOG_DEBUG("clip_skip %d", clip_skip);
@@ -741,16 +741,17 @@ public:
     struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* input_ids,
                                 struct ggml_tensor* tkn_embeddings,
-                                size_t max_token_idx = 0,
-                                bool return_pooled   = false,
-                                int clip_skip        = -1) {
+                                struct ggml_tensor* mask = nullptr,
+                                size_t max_token_idx     = 0,
+                                bool return_pooled       = false,
+                                int clip_skip            = -1) {
         // input_ids: [N, n_token]
         auto embeddings       = std::dynamic_pointer_cast<CLIPEmbeddings>(blocks["embeddings"]);
         auto encoder          = std::dynamic_pointer_cast<CLIPEncoder>(blocks["encoder"]);
         auto final_layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["final_layer_norm"]);
 
         auto x = embeddings->forward(ctx, input_ids, tkn_embeddings);  // [N, n_token, hidden_size]
-        x      = encoder->forward(ctx, x, return_pooled ? -1 : clip_skip, true);
+        x      = encoder->forward(ctx, x, mask, return_pooled ? -1 : clip_skip);
         if (return_pooled || with_final_ln) {
             x = final_layer_norm->forward(ctx, x);
         }
@@ -814,10 +815,11 @@ public:
 
         auto x = embeddings->forward(ctx, pixel_values);  // [N, num_positions, embed_dim]
         x      = pre_layernorm->forward(ctx, x);
-        x      = encoder->forward(ctx, x, clip_skip, false);
-        // print_ggml_tensor(x, true, "ClipVisionModel x: ");
+        x      = encoder->forward(ctx, x, nullptr, clip_skip);
+
         auto last_hidden_state = x;
-        x                      = post_layernorm->forward(ctx, x);  // [N, n_token, hidden_size]
+
+        x = post_layernorm->forward(ctx, x);  // [N, n_token, hidden_size]
 
         GGML_ASSERT(x->ne[3] == 1);
         if (return_pooled) {
@@ -905,6 +907,8 @@ public:
 struct CLIPTextModelRunner : public GGMLRunner {
     CLIPTextModel model;
 
+    std::vector<float> attention_mask_vec;
+
     CLIPTextModelRunner(ggml_backend_t backend,
                         bool offload_params_to_cpu,
                         const String2TensorStorage& tensor_storage_map,
@@ -938,6 +942,7 @@ struct CLIPTextModelRunner : public GGMLRunner {
     struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* input_ids,
                                 struct ggml_tensor* embeddings,
+                                struct ggml_tensor* mask,
                                 size_t max_token_idx = 0,
                                 bool return_pooled   = false,
                                 int clip_skip        = -1) {
@@ -948,7 +953,7 @@ struct CLIPTextModelRunner : public GGMLRunner {
             input_ids = ggml_reshape_2d(ctx->ggml_ctx, input_ids, model.n_token, input_ids->ne[0] / model.n_token);
         }
 
-        return model.forward(ctx, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
+        return model.forward(ctx, input_ids, embeddings, mask, max_token_idx, return_pooled, clip_skip);
     }
 
     struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
@@ -975,9 +980,23 @@ struct CLIPTextModelRunner : public GGMLRunner {
             embeddings = ggml_concat(compute_ctx, token_embed_weight, custom_embeddings, 1);
         }
 
+        int n_tokens = static_cast<int>(input_ids->ne[0]);
+        attention_mask_vec.resize(n_tokens * n_tokens);
+        for (int i0 = 0; i0 < n_tokens; i0++) {
+            for (int i1 = 0; i1 < n_tokens; i1++) {
+                float value = 0.f;
+                if (i0 > i1) {
+                    value = -INFINITY;
+                }
+                attention_mask_vec[i1 * n_tokens + i0] = value;
+            }
+        }
+        auto attention_mask = ggml_new_tensor_2d(compute_ctx, GGML_TYPE_F32, n_tokens, n_tokens);
+        set_backend_tensor_data(attention_mask, attention_mask_vec.data());
+
         auto runner_ctx = get_context();
 
-        struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
+        struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, embeddings, attention_mask, max_token_idx, return_pooled, clip_skip);
 
         ggml_build_forward_expand(gf, hidden_states);
 
diff --git a/common.hpp b/common.hpp
index 7183eb82..d9c823df 100644
--- a/common.hpp
+++ b/common.hpp
@@ -317,7 +317,7 @@ public:
         auto k = to_k->forward(ctx, context);  // [N, n_context, inner_dim]
         auto v = to_v->forward(ctx, context);  // [N, n_context, inner_dim]
 
-        x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, inner_dim]
+        x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, inner_dim]
 
         x = to_out_0->forward(ctx, x);  // [N, n_token, query_dim]
         return x;
diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index 0fb656dc..ab58ab5f 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -604,10 +604,10 @@ int main(int argc, const char* argv[]) {
 
     if (gen_params.mask_image_path.size() > 0) {
         if (!load_sd_image_from_file(&mask_image,
-                                    gen_params.mask_image_path.c_str(),
-                                    gen_params.get_resolved_width(),
-                                    gen_params.get_resolved_height(),
-                                    1)) {
+                                     gen_params.mask_image_path.c_str(),
+                                     gen_params.get_resolved_width(),
+                                     gen_params.get_resolved_height(),
+                                     1)) {
             LOG_ERROR("load image from '%s' failed", gen_params.mask_image_path.c_str());
             release_all_resources();
             return 1;
@@ -626,9 +626,9 @@ int main(int argc, const char* argv[]) {
 
     if (gen_params.control_image_path.size() > 0) {
         if (!load_sd_image_from_file(&control_image,
-                                    gen_params.control_image_path.c_str(),
-                                    gen_params.get_resolved_width(),
-                                    gen_params.get_resolved_height())) {
+                                     gen_params.control_image_path.c_str(),
+                                     gen_params.get_resolved_width(),
+                                     gen_params.get_resolved_height())) {
             LOG_ERROR("load image from '%s' failed", gen_params.control_image_path.c_str());
             release_all_resources();
             return 1;
diff --git a/ggml_extend.hpp b/ggml_extend.hpp
index fedab380..7dac0373 100644
--- a/ggml_extend.hpp
+++ b/ggml_extend.hpp
@@ -1257,7 +1257,6 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_attention_ext(struct ggml_context
                                                              struct ggml_tensor* v,
                                                              int64_t n_head,
                                                              struct ggml_tensor* mask = nullptr,
-                                                             bool diag_mask_inf       = false,
                                                              bool skip_reshape        = false,
                                                              bool flash_attn          = false,
                                                              float kv_scale           = 1.0f) {  // avoid overflow
@@ -1385,9 +1384,6 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_attention_ext(struct ggml_context
         if (mask) {
             kq = ggml_add_inplace(ctx, kq, mask);
         }
-        if (diag_mask_inf) {
-            kq = ggml_diag_mask_inf_inplace(ctx, kq, 0);
-        }
         kq = ggml_soft_max_inplace(ctx, kq);
 
         kqv = ggml_mul_mat(ctx, v, kq);  // [N * n_head, L_q, d_head]
@@ -2604,7 +2600,7 @@ public:
     // x: [N, n_token, embed_dim]
     struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
-                                bool mask = false) {
+                                struct ggml_tensor* mask = nullptr) {
         auto out_proj = std::dynamic_pointer_cast<Linear>(blocks[out_proj_name]);
 
         ggml_tensor* q;
@@ -2627,7 +2623,7 @@ public:
             v = v_proj->forward(ctx, x);
         }
 
-        x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, nullptr, mask);  // [N, n_token, embed_dim]
+        x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, mask);  // [N, n_token, embed_dim]
 
         x = out_proj->forward(ctx, x);  // [N, n_token, embed_dim]
         return x;
diff --git a/llm.hpp b/llm.hpp
index 7feb8d3c..31555751 100644
--- a/llm.hpp
+++ b/llm.hpp
@@ -881,7 +881,7 @@ namespace LLM {
             k = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, k, 0, 2, 1, 3));  // [N, num_kv_heads, n_token, head_dim]
             k = ggml_reshape_3d(ctx->ggml_ctx, k, k->ne[0], k->ne[1], k->ne[2] * k->ne[3]);      // [N*num_kv_heads, n_token, head_dim]
 
-            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, attention_mask, false, true, false);  // [N, n_token, hidden_size]
+            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, attention_mask, true, false);  // [N, n_token, hidden_size]
 
             x = out_proj->forward(ctx, x);  // [N, n_token, hidden_size]
             return x;
diff --git a/mmdit.hpp b/mmdit.hpp
index 086b444d..726f60c2 100644
--- a/mmdit.hpp
+++ b/mmdit.hpp
@@ -211,8 +211,8 @@ public:
     struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x) {
         auto qkv = pre_attention(ctx, x);
-        x        = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
-        x        = post_attention(ctx, x);                                                                                                                  // [N, n_token, dim]
+        x        = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
+        x        = post_attention(ctx, x);                                                                                                           // [N, n_token, dim]
         return x;
     }
 };
@@ -433,8 +433,8 @@ public:
             auto qkv2          = std::get<1>(qkv_intermediates);
             auto intermediates = std::get<2>(qkv_intermediates);
 
-            auto attn_out  = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, false, ctx->flash_attn_enabled);     // [N, n_token, dim]
-            auto attn2_out = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv2[0], qkv2[1], qkv2[2], num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
+            auto attn_out  = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, ctx->flash_attn_enabled);     // [N, n_token, dim]
+            auto attn2_out = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv2[0], qkv2[1], qkv2[2], num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
             x              = post_attention_x(ctx,
                                               attn_out,
                                               attn2_out,
@@ -450,7 +450,7 @@ public:
             auto qkv               = qkv_intermediates.first;
             auto intermediates     = qkv_intermediates.second;
 
-            auto attn_out = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
+            auto attn_out = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
             x             = post_attention(ctx,
                                            attn_out,
                                            intermediates[0],
@@ -494,7 +494,7 @@ block_mixing(GGMLRunnerContext* ctx,
         qkv.push_back(ggml_concat(ctx->ggml_ctx, context_qkv[i], x_qkv[i], 1));
     }
 
-    auto attn = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], x_block->num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_context + n_token, hidden_size]
+    auto attn = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], x_block->num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_context + n_token, hidden_size]
 
     auto context_attn = ggml_view_3d(ctx->ggml_ctx,
                                      attn,
@@ -526,7 +526,7 @@ block_mixing(GGMLRunnerContext* ctx,
     }
 
     if (x_block->self_attn) {
-        auto attn2 = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, x_qkv2[0], x_qkv2[1], x_qkv2[2], x_block->num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, hidden_size]
+        auto attn2 = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, x_qkv2[0], x_qkv2[1], x_qkv2[2], x_block->num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, hidden_size]
 
         x = x_block->post_attention_x(ctx,
                                       x_attn,
diff --git a/rope.hpp b/rope.hpp
index 2d123b3c..45e88c83 100644
--- a/rope.hpp
+++ b/rope.hpp
@@ -642,7 +642,7 @@ namespace Rope {
         q = apply_rope(ctx->ggml_ctx, q, pe, rope_interleaved);  // [N*n_head, L, d_head]
         k = apply_rope(ctx->ggml_ctx, k, pe, rope_interleaved);  // [N*n_head, L, d_head]
 
-        auto x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, v->ne[1], mask, false, true, ctx->flash_attn_enabled, kv_scale);  // [N, L, n_head*d_head]
+        auto x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, v->ne[1], mask, true, ctx->flash_attn_enabled, kv_scale);  // [N, L, n_head*d_head]
         return x;
     }
 };  // namespace Rope
diff --git a/vae.hpp b/vae.hpp
index fdddc8ae..01b99e89 100644
--- a/vae.hpp
+++ b/vae.hpp
@@ -141,7 +141,7 @@ public:
             v = ggml_reshape_3d(ctx->ggml_ctx, v, c, h * w, n);                        // [N, h * w, in_channels]
         }
 
-        h_ = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, false, true, false);
+        h_ = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, true, false);
 
         if (use_linear) {
             h_ = proj_out->forward(ctx, h_);  // [N, h * w, in_channels]
diff --git a/wan.hpp b/wan.hpp
index c56e1f92..81959efc 100644
--- a/wan.hpp
+++ b/wan.hpp
@@ -572,8 +572,8 @@ namespace WAN {
             auto v = qkv_vec[2];
             v      = ggml_reshape_3d(ctx->ggml_ctx, v, h * w, c, n);  // [t, c, h * w]
 
-            v = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, v, 1, 0, 2, 3));                // [t, h * w, c]
-            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, false, true, false);  // [t, h * w, c]
+            v = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, v, 1, 0, 2, 3));         // [t, h * w, c]
+            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, true, false);  // [t, h * w, c]
 
             x = ggml_ext_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [t, c, h * w]
             x = ggml_reshape_4d(ctx->ggml_ctx, x, w, h, c, n);                             // [t, c, h, w]
@@ -1393,7 +1393,7 @@ namespace WAN {
             k      = norm_k->forward(ctx, k);
             auto v = v_proj->forward(ctx, context);  // [N, n_context, dim]
 
-            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
+            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
 
             x = o_proj->forward(ctx, x);  // [N, n_token, dim]
             return x;
@@ -1455,8 +1455,8 @@ namespace WAN {
             k_img      = norm_k_img->forward(ctx, k_img);
             auto v_img = v_img_proj->forward(ctx, context_img);  // [N, context_img_len, dim]
 
-            auto img_x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k_img, v_img, num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
-            x          = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, nullptr, false, false, ctx->flash_attn_enabled);          // [N, n_token, dim]
+            auto img_x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k_img, v_img, num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
+            x          = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, nullptr, false, ctx->flash_attn_enabled);          // [N, n_token, dim]
 
             x = ggml_add(ctx->ggml_ctx, x, img_x);
 

From e411520407663e1ddf8ff2e5ed4ff3a116fbbc97 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Wed, 28 Jan 2026 21:47:36 +0800
Subject: [PATCH 65/97] docs: add z-image-base example

---
 assets/z_image/base_bf16.png | Bin 0 -> 890814 bytes
 docs/z_image.md              |  13 +++++++++++++
 2 files changed, 13 insertions(+)
 create mode 100644 assets/z_image/base_bf16.png

diff --git a/assets/z_image/base_bf16.png b/assets/z_image/base_bf16.png
new file mode 100644
index 0000000000000000000000000000000000000000..f2b918c0a410af9cda2b719c0a7643494f8c0173
GIT binary patch
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HcmV?d00001

diff --git a/docs/z_image.md b/docs/z_image.md
index 122f1f20..2ea66f9b 100644
--- a/docs/z_image.md
+++ b/docs/z_image.md
@@ -7,6 +7,9 @@ You can run Z-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — or ev
 - Download Z-Image-Turbo
     - safetensors: https://huggingface.co/Comfy-Org/z_image_turbo/tree/main/split_files/diffusion_models
     - gguf: https://huggingface.co/leejet/Z-Image-Turbo-GGUF/tree/main
+- Download Z-Image
+    - safetensors: https://huggingface.co/Comfy-Org/z_image/tree/main/split_files/diffusion_models
+    - gguf: https://huggingface.co/unsloth/Z-Image-GGUF/tree/main
 - Download vae
     - safetensors: https://huggingface.co/black-forest-labs/FLUX.1-schnell/tree/main
 - Download Qwen3 4b
@@ -15,12 +18,22 @@ You can run Z-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — or ev
 
 ## Examples
 
+### Z-Image-Turbo
+
 ```
 .\bin\Release\sd-cli.exe --diffusion-model  z_image_turbo-Q3_K.gguf --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\Qwen3-4B-Instruct-2507-Q4_K_M.gguf -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 1.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512
 ```
 
 <img width="256" alt="z-image example" src="../assets/z_image/q3_K.png" />
 
+### Z-Image-Base
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\z_image_bf16.safetensors --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_4b.safetensors -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512
+```
+
+<img width="256" alt="z-image example" src="../assets/z_image/base_bf16.png" />
+
 ## Comparison of Different Quantization Types
 
 | bf16 | q8_0 | q6_K | q5_0 | q4_K | q4_0 | q3_K | q2_K|

From 3959109281dd8c6e8b9b5e2be51412599792dc1f Mon Sep 17 00:00:00 2001
From: stduhpf <stephduh@live.fr>
Date: Sun, 1 Feb 2026 13:00:16 +0100
Subject: [PATCH 66/97] fix: improve LoCon support with other naming
 conventions (#1239)

---
 name_conversion.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/name_conversion.cpp b/name_conversion.cpp
index 3ae229b6..d3e863b8 100644
--- a/name_conversion.cpp
+++ b/name_conversion.cpp
@@ -842,6 +842,7 @@ std::string convert_sep_to_dot(std::string name) {
         "conv_in",
         "conv_out",
         "lora_down",
+        "lora_mid",
         "lora_up",
         "diff_b",
         "hada_w1_a",
@@ -997,10 +998,13 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
     if (is_lora) {
         std::map<std::string, std::string> lora_suffix_map = {
             {".lora_down.weight", ".weight.lora_down"},
+            {".lora_mid.weight", ".weight.lora_mid"},
             {".lora_up.weight", ".weight.lora_up"},
             {".lora.down.weight", ".weight.lora_down"},
+            {".lora.mid.weight", ".weight.lora_mid"},
             {".lora.up.weight", ".weight.lora_up"},
             {"_lora.down.weight", ".weight.lora_down"},
+            {"_lora.mid.weight", ".weight.lora_mid"},
             {"_lora.up.weight", ".weight.lora_up"},
             {".lora_A.weight", ".weight.lora_down"},
             {".lora_B.weight", ".weight.lora_up"},

From e63daba33dbd502c3ad201c903827a945ce4fd46 Mon Sep 17 00:00:00 2001
From: rmatif <rmatif@proton.me>
Date: Sun, 1 Feb 2026 13:05:27 +0100
Subject: [PATCH 67/97] feat: add res_multistep, res_2s sampler and bong
 tangent scheduler (#1234)

---
 denoiser.hpp               | 305 +++++++++++++++++++++++++++++++++++++
 examples/cli/README.md     |  14 +-
 examples/common/common.hpp |   6 +-
 examples/server/README.md  |  14 +-
 examples/server/main.cpp   |   6 +-
 stable-diffusion.cpp       |   5 +
 stable-diffusion.h         |   3 +
 7 files changed, 335 insertions(+), 18 deletions(-)

diff --git a/denoiser.hpp b/denoiser.hpp
index 98aef702..7e99b84a 100644
--- a/denoiser.hpp
+++ b/denoiser.hpp
@@ -1,6 +1,8 @@
 #ifndef __DENOISER_HPP__
 #define __DENOISER_HPP__
 
+#include <cmath>
+
 #include "ggml_extend.hpp"
 #include "gits_noise.inl"
 
@@ -351,6 +353,95 @@ struct SmoothStepScheduler : SigmaScheduler {
     }
 };
 
+struct BongTangentScheduler : SigmaScheduler {
+    static constexpr float kPi = 3.14159265358979323846f;
+
+    static std::vector<float> get_bong_tangent_sigmas(int steps, float slope, float pivot, float start, float end) {
+        std::vector<float> sigmas;
+        if (steps <= 0) {
+            return sigmas;
+        }
+
+        float smax   = ((2.0f / kPi) * atanf(-slope * (0.0f - pivot)) + 1.0f) * 0.5f;
+        float smin   = ((2.0f / kPi) * atanf(-slope * ((float)(steps - 1) - pivot)) + 1.0f) * 0.5f;
+        float srange = smax - smin;
+        float sscale = start - end;
+
+        sigmas.reserve(steps);
+
+        if (fabsf(srange) < 1e-8f) {
+            if (steps == 1) {
+                sigmas.push_back(start);
+                return sigmas;
+            }
+            for (int i = 0; i < steps; ++i) {
+                float t = (float)i / (float)(steps - 1);
+                sigmas.push_back(start + (end - start) * t);
+            }
+            return sigmas;
+        }
+
+        float inv_srange = 1.0f / srange;
+        for (int x = 0; x < steps; ++x) {
+            float v     = ((2.0f / kPi) * atanf(-slope * ((float)x - pivot)) + 1.0f) * 0.5f;
+            float sigma = ((v - smin) * inv_srange) * sscale + end;
+            sigmas.push_back(sigma);
+        }
+
+        return sigmas;
+    }
+
+    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t /*t_to_sigma*/) override {
+        std::vector<float> result;
+        if (n == 0) {
+            return result;
+        }
+
+        float start  = sigma_max;
+        float end    = sigma_min;
+        float middle = sigma_min + (sigma_max - sigma_min) * 0.5f;
+
+        float pivot_1 = 0.6f;
+        float pivot_2 = 0.6f;
+        float slope_1 = 0.2f;
+        float slope_2 = 0.2f;
+
+        int steps     = static_cast<int>(n) + 2;
+        int midpoint  = static_cast<int>(((float)steps * pivot_1 + (float)steps * pivot_2) * 0.5f);
+        int pivot_1_i = static_cast<int>((float)steps * pivot_1);
+        int pivot_2_i = static_cast<int>((float)steps * pivot_2);
+
+        float slope_scale = (float)steps / 40.0f;
+        slope_1           = slope_1 / slope_scale;
+        slope_2           = slope_2 / slope_scale;
+
+        int stage_2_len = steps - midpoint;
+        int stage_1_len = steps - stage_2_len;
+
+        std::vector<float> sigmas_1 = get_bong_tangent_sigmas(stage_1_len, slope_1, (float)pivot_1_i, start, middle);
+        std::vector<float> sigmas_2 = get_bong_tangent_sigmas(stage_2_len, slope_2, (float)(pivot_2_i - stage_1_len), middle, end);
+
+        if (!sigmas_1.empty()) {
+            sigmas_1.pop_back();
+        }
+
+        result.reserve(n + 1);
+        result.insert(result.end(), sigmas_1.begin(), sigmas_1.end());
+        result.insert(result.end(), sigmas_2.begin(), sigmas_2.end());
+
+        if (result.size() < n + 1) {
+            while (result.size() < n + 1) {
+                result.push_back(end);
+            }
+        } else if (result.size() > n + 1) {
+            result.resize(n + 1);
+        }
+
+        result[n] = 0.0f;
+        return result;
+    }
+};
+
 struct KLOptimalScheduler : SigmaScheduler {
     std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
         std::vector<float> sigmas;
@@ -431,6 +522,10 @@ struct Denoiser {
                 LOG_INFO("get_sigmas with SmoothStep scheduler");
                 scheduler = std::make_shared<SmoothStepScheduler>();
                 break;
+            case BONG_TANGENT_SCHEDULER:
+                LOG_INFO("get_sigmas with bong_tangent scheduler");
+                scheduler = std::make_shared<BongTangentScheduler>();
+                break;
             case KL_OPTIMAL_SCHEDULER:
                 LOG_INFO("get_sigmas with KL Optimal scheduler");
                 scheduler = std::make_shared<KLOptimalScheduler>();
@@ -1634,6 +1729,216 @@ static bool sample_k_diffusion(sample_method_t method,
                 }
             }
         } break;
+        case RES_MULTISTEP_SAMPLE_METHOD:  // Res Multistep sampler
+        {
+            struct ggml_tensor* noise        = ggml_dup_tensor(work_ctx, x);
+            struct ggml_tensor* old_denoised = ggml_dup_tensor(work_ctx, x);
+
+            bool have_old_sigma  = false;
+            float old_sigma_down = 0.0f;
+
+            auto t_fn     = [](float sigma) -> float { return -logf(sigma); };
+            auto sigma_fn = [](float t) -> float { return expf(-t); };
+            auto phi1_fn  = [](float t) -> float {
+                if (fabsf(t) < 1e-6f) {
+                    return 1.0f + t * 0.5f + (t * t) / 6.0f;
+                }
+                return (expf(t) - 1.0f) / t;
+            };
+            auto phi2_fn = [&](float t) -> float {
+                if (fabsf(t) < 1e-6f) {
+                    return 0.5f + t / 6.0f + (t * t) / 24.0f;
+                }
+                float phi1_val = phi1_fn(t);
+                return (phi1_val - 1.0f) / t;
+            };
+
+            for (int i = 0; i < steps; i++) {
+                ggml_tensor* denoised = model(x, sigmas[i], i + 1);
+                if (denoised == nullptr) {
+                    return false;
+                }
+
+                float sigma_from = sigmas[i];
+                float sigma_to   = sigmas[i + 1];
+                float sigma_up   = 0.0f;
+                float sigma_down = sigma_to;
+
+                if (eta > 0.0f) {
+                    float sigma_from_sq = sigma_from * sigma_from;
+                    float sigma_to_sq   = sigma_to * sigma_to;
+                    if (sigma_from_sq > 0.0f) {
+                        float term = sigma_to_sq * (sigma_from_sq - sigma_to_sq) / sigma_from_sq;
+                        if (term > 0.0f) {
+                            sigma_up = eta * std::sqrt(term);
+                        }
+                    }
+                    sigma_up            = std::min(sigma_up, sigma_to);
+                    float sigma_down_sq = sigma_to_sq - sigma_up * sigma_up;
+                    sigma_down          = sigma_down_sq > 0.0f ? std::sqrt(sigma_down_sq) : 0.0f;
+                }
+
+                if (sigma_down == 0.0f || !have_old_sigma) {
+                    float dt            = sigma_down - sigma_from;
+                    float* vec_x        = (float*)x->data;
+                    float* vec_denoised = (float*)denoised->data;
+
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        float d  = (vec_x[j] - vec_denoised[j]) / sigma_from;
+                        vec_x[j] = vec_x[j] + d * dt;
+                    }
+                } else {
+                    float t      = t_fn(sigma_from);
+                    float t_old  = t_fn(old_sigma_down);
+                    float t_next = t_fn(sigma_down);
+                    float t_prev = t_fn(sigmas[i - 1]);
+                    float h      = t_next - t;
+                    float c2     = (t_prev - t_old) / h;
+
+                    float phi1_val = phi1_fn(-h);
+                    float phi2_val = phi2_fn(-h);
+                    float b1       = phi1_val - phi2_val / c2;
+                    float b2       = phi2_val / c2;
+
+                    if (!std::isfinite(b1)) {
+                        b1 = 0.0f;
+                    }
+                    if (!std::isfinite(b2)) {
+                        b2 = 0.0f;
+                    }
+
+                    float sigma_h           = sigma_fn(h);
+                    float* vec_x            = (float*)x->data;
+                    float* vec_denoised     = (float*)denoised->data;
+                    float* vec_old_denoised = (float*)old_denoised->data;
+
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] = sigma_h * vec_x[j] + h * (b1 * vec_denoised[j] + b2 * vec_old_denoised[j]);
+                    }
+                }
+
+                if (sigmas[i + 1] > 0 && sigma_up > 0.0f) {
+                    ggml_ext_im_set_randn_f32(noise, rng);
+                    float* vec_x     = (float*)x->data;
+                    float* vec_noise = (float*)noise->data;
+
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] = vec_x[j] + vec_noise[j] * sigma_up;
+                    }
+                }
+
+                float* vec_old_denoised = (float*)old_denoised->data;
+                float* vec_denoised     = (float*)denoised->data;
+                for (int j = 0; j < ggml_nelements(x); j++) {
+                    vec_old_denoised[j] = vec_denoised[j];
+                }
+
+                old_sigma_down = sigma_down;
+                have_old_sigma = true;
+            }
+        } break;
+        case RES_2S_SAMPLE_METHOD:  // Res 2s sampler
+        {
+            struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, x);
+            struct ggml_tensor* x0    = ggml_dup_tensor(work_ctx, x);
+            struct ggml_tensor* x2    = ggml_dup_tensor(work_ctx, x);
+
+            const float c2 = 0.5f;
+            auto t_fn      = [](float sigma) -> float { return -logf(sigma); };
+            auto phi1_fn   = [](float t) -> float {
+                if (fabsf(t) < 1e-6f) {
+                    return 1.0f + t * 0.5f + (t * t) / 6.0f;
+                }
+                return (expf(t) - 1.0f) / t;
+            };
+            auto phi2_fn = [&](float t) -> float {
+                if (fabsf(t) < 1e-6f) {
+                    return 0.5f + t / 6.0f + (t * t) / 24.0f;
+                }
+                float phi1_val = phi1_fn(t);
+                return (phi1_val - 1.0f) / t;
+            };
+
+            for (int i = 0; i < steps; i++) {
+                float sigma_from = sigmas[i];
+                float sigma_to   = sigmas[i + 1];
+
+                ggml_tensor* denoised = model(x, sigma_from, -(i + 1));
+                if (denoised == nullptr) {
+                    return false;
+                }
+
+                float sigma_up   = 0.0f;
+                float sigma_down = sigma_to;
+                if (eta > 0.0f) {
+                    float sigma_from_sq = sigma_from * sigma_from;
+                    float sigma_to_sq   = sigma_to * sigma_to;
+                    if (sigma_from_sq > 0.0f) {
+                        float term = sigma_to_sq * (sigma_from_sq - sigma_to_sq) / sigma_from_sq;
+                        if (term > 0.0f) {
+                            sigma_up = eta * std::sqrt(term);
+                        }
+                    }
+                    sigma_up            = std::min(sigma_up, sigma_to);
+                    float sigma_down_sq = sigma_to_sq - sigma_up * sigma_up;
+                    sigma_down          = sigma_down_sq > 0.0f ? std::sqrt(sigma_down_sq) : 0.0f;
+                }
+
+                float* vec_x  = (float*)x->data;
+                float* vec_x0 = (float*)x0->data;
+                for (int j = 0; j < ggml_nelements(x); j++) {
+                    vec_x0[j] = vec_x[j];
+                }
+
+                if (sigma_down == 0.0f || sigma_from == 0.0f) {
+                    float* vec_denoised = (float*)denoised->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] = vec_denoised[j];
+                    }
+                } else {
+                    float t      = t_fn(sigma_from);
+                    float t_next = t_fn(sigma_down);
+                    float h      = t_next - t;
+
+                    float a21      = c2 * phi1_fn(-h * c2);
+                    float phi1_val = phi1_fn(-h);
+                    float phi2_val = phi2_fn(-h);
+                    float b2       = phi2_val / c2;
+                    float b1       = phi1_val - b2;
+
+                    float sigma_c2 = expf(-(t + h * c2));
+
+                    float* vec_denoised = (float*)denoised->data;
+                    float* vec_x2       = (float*)x2->data;
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        float eps1 = vec_denoised[j] - vec_x0[j];
+                        vec_x2[j]  = vec_x0[j] + h * a21 * eps1;
+                    }
+
+                    ggml_tensor* denoised2 = model(x2, sigma_c2, i + 1);
+                    if (denoised2 == nullptr) {
+                        return false;
+                    }
+                    float* vec_denoised2 = (float*)denoised2->data;
+
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        float eps1 = vec_denoised[j] - vec_x0[j];
+                        float eps2 = vec_denoised2[j] - vec_x0[j];
+                        vec_x[j]   = vec_x0[j] + h * (b1 * eps1 + b2 * eps2);
+                    }
+                }
+
+                if (sigmas[i + 1] > 0 && sigma_up > 0.0f) {
+                    ggml_ext_im_set_randn_f32(noise, rng);
+                    float* vec_x     = (float*)x->data;
+                    float* vec_noise = (float*)noise->data;
+
+                    for (int j = 0; j < ggml_nelements(x); j++) {
+                        vec_x[j] = vec_x[j] + vec_noise[j] * sigma_up;
+                    }
+                }
+            }
+        } break;
 
         default:
             LOG_ERROR("Attempting to sample with nonexisting sample method %i", method);
diff --git a/examples/cli/README.md b/examples/cli/README.md
index 84dd5c71..d6c36b8b 100644
--- a/examples/cli/README.md
+++ b/examples/cli/README.md
@@ -107,14 +107,14 @@ Generation Options:
                                            medium
   --skip-layer-start <float>               SLG enabling point (default: 0.01)
   --skip-layer-end <float>                 SLG disabling point (default: 0.2)
-  --eta <float>                            eta in DDIM, only for DDIM and TCD (default: 0)
+  --eta <float>                            eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
   --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
   --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
   --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input (default: 3.5)
   --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
   --high-noise-skip-layer-start <float>    (high noise) SLG enabling point (default: 0.01)
   --high-noise-skip-layer-end <float>      (high noise) SLG disabling point (default: 0.2)
-  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM and TCD (default: 0)
+  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
   --strength <float>                       strength for noising/unnoising (default: 0.75)
   --pm-style-strength <float>
   --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
@@ -123,12 +123,12 @@ Generation Options:
   --increase-ref-index                     automatically increase the indices of references images based on the order they are listed (starting with 1).
   --disable-auto-resize-ref-image          disable auto resize of ref images
   -s, --seed                               RNG seed (default: 42, use random seed for < 0)
-  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
-                                           tcd] (default: euler for Flux/SD3/Wan, euler_a otherwise)
-  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
-                                           ddim_trailing, tcd] default: euler for Flux/SD3/Wan, euler_a otherwise
+  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd,
+                                           res_multistep, res_2s] (default: euler for Flux/SD3/Wan, euler_a otherwise)
+  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
+                                           tcd, res_multistep, res_2s] default: euler for Flux/SD3/Wan, euler_a otherwise
   --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple,
-                                           kl_optimal, lcm], default: discrete
+                                           kl_optimal, lcm, bong_tangent], default: discrete
   --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
   --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
   --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index ba1b0d8d..11d30a3b 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -1478,17 +1478,17 @@ struct SDGenerationParams {
              on_seed_arg},
             {"",
              "--sampling-method",
-             "sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd] "
+             "sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s] "
              "(default: euler for Flux/SD3/Wan, euler_a otherwise)",
              on_sample_method_arg},
             {"",
              "--high-noise-sampling-method",
-             "(high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd]"
+             "(high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s]"
              " default: euler for Flux/SD3/Wan, euler_a otherwise",
              on_high_noise_sample_method_arg},
             {"",
              "--scheduler",
-             "denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm], default: discrete",
+             "denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, kl_optimal, lcm, bong_tangent], default: discrete",
              on_scheduler_arg},
             {"",
              "--sigmas",
diff --git a/examples/server/README.md b/examples/server/README.md
index 7e668157..354075cc 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -99,14 +99,14 @@ Default Generation Options:
                                            medium
   --skip-layer-start <float>               SLG enabling point (default: 0.01)
   --skip-layer-end <float>                 SLG disabling point (default: 0.2)
-  --eta <float>                            eta in DDIM, only for DDIM and TCD (default: 0)
+  --eta <float>                            eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
   --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
   --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
   --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input (default: 3.5)
   --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
   --high-noise-skip-layer-start <float>    (high noise) SLG enabling point (default: 0.01)
   --high-noise-skip-layer-end <float>      (high noise) SLG disabling point (default: 0.2)
-  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM and TCD (default: 0)
+  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
   --strength <float>                       strength for noising/unnoising (default: 0.75)
   --pm-style-strength <float>
   --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
@@ -115,12 +115,12 @@ Default Generation Options:
   --increase-ref-index                     automatically increase the indices of references images based on the order they are listed (starting with 1).
   --disable-auto-resize-ref-image          disable auto resize of ref images
   -s, --seed                               RNG seed (default: 42, use random seed for < 0)
-  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
-                                           tcd] (default: euler for Flux/SD3/Wan, euler_a otherwise)
-  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
-                                           ddim_trailing, tcd] default: euler for Flux/SD3/Wan, euler_a otherwise
+  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd,
+                                           res_multistep, res_2s] (default: euler for Flux/SD3/Wan, euler_a otherwise)
+  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
+                                           tcd, res_multistep, res_2s] default: euler for Flux/SD3/Wan, euler_a otherwise
   --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple,
-                                           kl_optimal, lcm], default: discrete
+                                           kl_optimal, lcm, bong_tangent], default: discrete
   --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
   --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
   --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index 76199ac6..def49975 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -785,7 +785,11 @@ int main(int argc, const char** argv) {
                     {"lcm", LCM_SAMPLE_METHOD},
                     {"ddim", DDIM_TRAILING_SAMPLE_METHOD},
                     {"dpm++ 2m", DPMPP2M_SAMPLE_METHOD},
-                    {"k_dpmpp_2m", DPMPP2M_SAMPLE_METHOD}};
+                    {"k_dpmpp_2m", DPMPP2M_SAMPLE_METHOD},
+                    {"res multistep", RES_MULTISTEP_SAMPLE_METHOD},
+                    {"k_res_multistep", RES_MULTISTEP_SAMPLE_METHOD},
+                    {"res 2s", RES_2S_SAMPLE_METHOD},
+                    {"k_res_2s", RES_2S_SAMPLE_METHOD}};
                 auto it            = hardcoded.find(name);
                 if (it != hardcoded.end()) return it->second;
                 return SAMPLE_METHOD_COUNT;
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index b181f994..44412edc 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -67,6 +67,8 @@ const char* sampling_methods_str[] = {
     "LCM",
     "DDIM \"trailing\"",
     "TCD",
+    "Res Multistep",
+    "Res 2s",
 };
 
 /*================================================== Helper Functions ================================================*/
@@ -2743,6 +2745,8 @@ const char* sample_method_to_str[] = {
     "lcm",
     "ddim_trailing",
     "tcd",
+    "res_multistep",
+    "res_2s",
 };
 
 const char* sd_sample_method_name(enum sample_method_t sample_method) {
@@ -2772,6 +2776,7 @@ const char* scheduler_to_str[] = {
     "smoothstep",
     "kl_optimal",
     "lcm",
+    "bong_tangent",
 };
 
 const char* sd_scheduler_name(enum scheduler_t scheduler) {
diff --git a/stable-diffusion.h b/stable-diffusion.h
index 8f040d2b..85768f40 100644
--- a/stable-diffusion.h
+++ b/stable-diffusion.h
@@ -48,6 +48,8 @@ enum sample_method_t {
     LCM_SAMPLE_METHOD,
     DDIM_TRAILING_SAMPLE_METHOD,
     TCD_SAMPLE_METHOD,
+    RES_MULTISTEP_SAMPLE_METHOD,
+    RES_2S_SAMPLE_METHOD,
     SAMPLE_METHOD_COUNT
 };
 
@@ -62,6 +64,7 @@ enum scheduler_t {
     SMOOTHSTEP_SCHEDULER,
     KL_OPTIMAL_SCHEDULER,
     LCM_SCHEDULER,
+    BONG_TANGENT_SCHEDULER,
     SCHEDULER_COUNT
 };
 

From c252e03c6b27c878c45d2d394617404ff5799bde Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 1 Feb 2026 20:54:23 +0800
Subject: [PATCH 68/97] sync: update ggml

---
 ggml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml b/ggml
index 8891ab6f..a8db410a 160000
--- a/ggml
+++ b/ggml
@@ -1 +1 @@
-Subproject commit 8891ab6fc742ac1198736d3da3b73c730e42af84
+Subproject commit a8db410a252c8c8f2d120c6f2e7133ebe032f35d

From f957fa3d2af4ac947a54fab22a1ddfe99042ba58 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 1 Feb 2026 21:44:54 +0800
Subject: [PATCH 69/97] feat: add --fa option (#1242)

---
 conditioner.hpp            | 39 ++++++++++++++++++++++++++++++++++++++
 diffusion_model.hpp        | 14 +++++++-------
 examples/cli/README.md     |  3 ++-
 examples/common/common.hpp |  9 ++++++++-
 examples/server/README.md  |  3 ++-
 ggml_extend.hpp            |  2 +-
 stable-diffusion.cpp       | 34 ++++++++++++++++++++++++---------
 stable-diffusion.h         |  1 +
 vae.hpp                    |  2 +-
 wan.hpp                    |  4 ++--
 10 files changed, 88 insertions(+), 23 deletions(-)

diff --git a/conditioner.hpp b/conditioner.hpp
index a4e84aa3..b1876954 100644
--- a/conditioner.hpp
+++ b/conditioner.hpp
@@ -34,6 +34,7 @@ struct Conditioner {
     virtual void free_params_buffer()                                                      = 0;
     virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors)    = 0;
     virtual size_t get_params_buffer_size()                                                = 0;
+    virtual void set_flash_attention_enabled(bool enabled)                                 = 0;
     virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) {}
     virtual std::tuple<SDCondition, std::vector<bool>> get_learned_condition_with_trigger(ggml_context* work_ctx,
                                                                                           int n_threads,
@@ -115,6 +116,13 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
         return buffer_size;
     }
 
+    void set_flash_attention_enabled(bool enabled) override {
+        text_model->set_flash_attention_enabled(enabled);
+        if (sd_version_is_sdxl(version)) {
+            text_model2->set_flash_attention_enabled(enabled);
+        }
+    }
+
     void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
         text_model->set_weight_adapter(adapter);
         if (sd_version_is_sdxl(version)) {
@@ -783,6 +791,18 @@ struct SD3CLIPEmbedder : public Conditioner {
         return buffer_size;
     }
 
+    void set_flash_attention_enabled(bool enabled) override {
+        if (clip_l) {
+            clip_l->set_flash_attention_enabled(enabled);
+        }
+        if (clip_g) {
+            clip_g->set_flash_attention_enabled(enabled);
+        }
+        if (t5) {
+            t5->set_flash_attention_enabled(enabled);
+        }
+    }
+
     void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
         if (clip_l) {
             clip_l->set_weight_adapter(adapter);
@@ -1191,6 +1211,15 @@ struct FluxCLIPEmbedder : public Conditioner {
         return buffer_size;
     }
 
+    void set_flash_attention_enabled(bool enabled) override {
+        if (clip_l) {
+            clip_l->set_flash_attention_enabled(enabled);
+        }
+        if (t5) {
+            t5->set_flash_attention_enabled(enabled);
+        }
+    }
+
     void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) {
         if (clip_l) {
             clip_l->set_weight_adapter(adapter);
@@ -1440,6 +1469,12 @@ struct T5CLIPEmbedder : public Conditioner {
         return buffer_size;
     }
 
+    void set_flash_attention_enabled(bool enabled) override {
+        if (t5) {
+            t5->set_flash_attention_enabled(enabled);
+        }
+    }
+
     void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
         if (t5) {
             t5->set_weight_adapter(adapter);
@@ -1650,6 +1685,10 @@ struct LLMEmbedder : public Conditioner {
         return buffer_size;
     }
 
+    void set_flash_attention_enabled(bool enabled) override {
+        llm->set_flash_attention_enabled(enabled);
+    }
+
     void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
         if (llm) {
             llm->set_weight_adapter(adapter);
diff --git a/diffusion_model.hpp b/diffusion_model.hpp
index 06cbecc2..3293ba9b 100644
--- a/diffusion_model.hpp
+++ b/diffusion_model.hpp
@@ -38,7 +38,7 @@ struct DiffusionModel {
     virtual size_t get_params_buffer_size()                                             = 0;
     virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter){};
     virtual int64_t get_adm_in_channels()                            = 0;
-    virtual void set_flash_attn_enabled(bool enabled)                = 0;
+    virtual void set_flash_attention_enabled(bool enabled)           = 0;
     virtual void set_circular_axes(bool circular_x, bool circular_y) = 0;
 };
 
@@ -84,7 +84,7 @@ struct UNetModel : public DiffusionModel {
         return unet.unet.adm_in_channels;
     }
 
-    void set_flash_attn_enabled(bool enabled) {
+    void set_flash_attention_enabled(bool enabled) {
         unet.set_flash_attention_enabled(enabled);
     }
 
@@ -149,7 +149,7 @@ struct MMDiTModel : public DiffusionModel {
         return 768 + 1280;
     }
 
-    void set_flash_attn_enabled(bool enabled) {
+    void set_flash_attention_enabled(bool enabled) {
         mmdit.set_flash_attention_enabled(enabled);
     }
 
@@ -215,7 +215,7 @@ struct FluxModel : public DiffusionModel {
         return 768;
     }
 
-    void set_flash_attn_enabled(bool enabled) {
+    void set_flash_attention_enabled(bool enabled) {
         flux.set_flash_attention_enabled(enabled);
     }
 
@@ -286,7 +286,7 @@ struct WanModel : public DiffusionModel {
         return 768;
     }
 
-    void set_flash_attn_enabled(bool enabled) {
+    void set_flash_attention_enabled(bool enabled) {
         wan.set_flash_attention_enabled(enabled);
     }
 
@@ -357,7 +357,7 @@ struct QwenImageModel : public DiffusionModel {
         return 768;
     }
 
-    void set_flash_attn_enabled(bool enabled) {
+    void set_flash_attention_enabled(bool enabled) {
         qwen_image.set_flash_attention_enabled(enabled);
     }
 
@@ -424,7 +424,7 @@ struct ZImageModel : public DiffusionModel {
         return 768;
     }
 
-    void set_flash_attn_enabled(bool enabled) {
+    void set_flash_attention_enabled(bool enabled) {
         z_image.set_flash_attention_enabled(enabled);
     }
 
diff --git a/examples/cli/README.md b/examples/cli/README.md
index d6c36b8b..78f8821a 100644
--- a/examples/cli/README.md
+++ b/examples/cli/README.md
@@ -52,7 +52,8 @@ Context Options:
   --control-net-cpu                        keep controlnet in cpu (for low vram)
   --clip-on-cpu                            keep clip in cpu (for low vram)
   --vae-on-cpu                             keep vae in cpu (for low vram)
-  --diffusion-fa                           use flash attention in the diffusion model
+  --fa                                     use flash attention
+  --diffusion-fa                           use flash attention in the diffusion model only
   --diffusion-conv-direct                  use ggml_conv2d_direct in the diffusion model
   --vae-conv-direct                        use ggml_conv2d_direct in the vae model
   --circular                               enable circular padding for convolutions
diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index 11d30a3b..50f35aed 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -457,6 +457,7 @@ struct SDContextParams {
     bool control_net_cpu        = false;
     bool clip_on_cpu            = false;
     bool vae_on_cpu             = false;
+    bool flash_attn             = false;
     bool diffusion_flash_attn   = false;
     bool diffusion_conv_direct  = false;
     bool vae_conv_direct        = false;
@@ -615,9 +616,13 @@ struct SDContextParams {
              "--vae-on-cpu",
              "keep vae in cpu (for low vram)",
              true, &vae_on_cpu},
+            {"",
+             "--fa",
+             "use flash attention",
+             true, &flash_attn},
             {"",
              "--diffusion-fa",
-             "use flash attention in the diffusion model",
+             "use flash attention in the diffusion model only",
              true, &diffusion_flash_attn},
             {"",
              "--diffusion-conv-direct",
@@ -904,6 +909,7 @@ struct SDContextParams {
             << "  control_net_cpu: " << (control_net_cpu ? "true" : "false") << ",\n"
             << "  clip_on_cpu: " << (clip_on_cpu ? "true" : "false") << ",\n"
             << "  vae_on_cpu: " << (vae_on_cpu ? "true" : "false") << ",\n"
+            << "  flash_attn: " << (flash_attn ? "true" : "false") << ",\n"
             << "  diffusion_flash_attn: " << (diffusion_flash_attn ? "true" : "false") << ",\n"
             << "  diffusion_conv_direct: " << (diffusion_conv_direct ? "true" : "false") << ",\n"
             << "  vae_conv_direct: " << (vae_conv_direct ? "true" : "false") << ",\n"
@@ -968,6 +974,7 @@ struct SDContextParams {
             clip_on_cpu,
             control_net_cpu,
             vae_on_cpu,
+            flash_attn,
             diffusion_flash_attn,
             taesd_preview,
             diffusion_conv_direct,
diff --git a/examples/server/README.md b/examples/server/README.md
index 354075cc..8a2f2e91 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -44,7 +44,8 @@ Context Options:
   --clip-on-cpu                            keep clip in cpu (for low vram)
   --vae-on-cpu                             keep vae in cpu (for low vram)
   --mmap                                   whether to memory-map model
-  --diffusion-fa                           use flash attention in the diffusion model
+  --fa                                     use flash attention
+  --diffusion-fa                           use flash attention in the diffusion model only
   --diffusion-conv-direct                  use ggml_conv2d_direct in the diffusion model
   --vae-conv-direct                        use ggml_conv2d_direct in the vae model
   --circular                               enable circular padding for convolutions
diff --git a/ggml_extend.hpp b/ggml_extend.hpp
index 7dac0373..193a2c39 100644
--- a/ggml_extend.hpp
+++ b/ggml_extend.hpp
@@ -2623,7 +2623,7 @@ public:
             v = v_proj->forward(ctx, x);
         }
 
-        x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, mask);  // [N, n_token, embed_dim]
+        x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, mask, false);  // [N, n_token, embed_dim]
 
         x = out_proj->forward(ctx, x);  // [N, n_token, embed_dim]
         return x;
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index 44412edc..f5c82b21 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -445,7 +445,7 @@ public:
                     }
                 }
                 if (is_chroma) {
-                    if (sd_ctx_params->diffusion_flash_attn && sd_ctx_params->chroma_use_dit_mask) {
+                    if ((sd_ctx_params->flash_attn || sd_ctx_params->diffusion_flash_attn) && sd_ctx_params->chroma_use_dit_mask) {
                         LOG_WARN(
                             "!!!It looks like you are using Chroma with flash attention. "
                             "This is currently unsupported. "
@@ -571,14 +571,6 @@ public:
                 }
             }
 
-            if (sd_ctx_params->diffusion_flash_attn) {
-                LOG_INFO("Using flash attention in the diffusion model");
-                diffusion_model->set_flash_attn_enabled(true);
-                if (high_noise_diffusion_model) {
-                    high_noise_diffusion_model->set_flash_attn_enabled(true);
-                }
-            }
-
             cond_stage_model->alloc_params_buffer();
             cond_stage_model->get_param_tensors(tensors);
 
@@ -725,6 +717,28 @@ public:
                 pmid_model->get_param_tensors(tensors, "pmid");
             }
 
+            if (sd_ctx_params->flash_attn) {
+                LOG_INFO("Using flash attention");
+                cond_stage_model->set_flash_attention_enabled(true);
+                if (clip_vision) {
+                    clip_vision->set_flash_attention_enabled(true);
+                }
+                if (first_stage_model) {
+                    first_stage_model->set_flash_attention_enabled(true);
+                }
+                if (tae_first_stage) {
+                    tae_first_stage->set_flash_attention_enabled(true);
+                }
+            }
+
+            if (sd_ctx_params->flash_attn || sd_ctx_params->diffusion_flash_attn) {
+                LOG_INFO("Using flash attention in the diffusion model");
+                diffusion_model->set_flash_attention_enabled(true);
+                if (high_noise_diffusion_model) {
+                    high_noise_diffusion_model->set_flash_attention_enabled(true);
+                }
+            }
+
             diffusion_model->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
             if (high_noise_diffusion_model) {
                 high_noise_diffusion_model->set_circular_axes(sd_ctx_params->circular_x, sd_ctx_params->circular_y);
@@ -2942,6 +2956,7 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              "keep_clip_on_cpu: %s\n"
              "keep_control_net_on_cpu: %s\n"
              "keep_vae_on_cpu: %s\n"
+             "flash_attn: %s\n"
              "diffusion_flash_attn: %s\n"
              "circular_x: %s\n"
              "circular_y: %s\n"
@@ -2973,6 +2988,7 @@ char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
              BOOL_STR(sd_ctx_params->keep_clip_on_cpu),
              BOOL_STR(sd_ctx_params->keep_control_net_on_cpu),
              BOOL_STR(sd_ctx_params->keep_vae_on_cpu),
+             BOOL_STR(sd_ctx_params->flash_attn),
              BOOL_STR(sd_ctx_params->diffusion_flash_attn),
              BOOL_STR(sd_ctx_params->circular_x),
              BOOL_STR(sd_ctx_params->circular_y),
diff --git a/stable-diffusion.h b/stable-diffusion.h
index 85768f40..cb966d7e 100644
--- a/stable-diffusion.h
+++ b/stable-diffusion.h
@@ -189,6 +189,7 @@ typedef struct {
     bool keep_clip_on_cpu;
     bool keep_control_net_on_cpu;
     bool keep_vae_on_cpu;
+    bool flash_attn;
     bool diffusion_flash_attn;
     bool tae_preview_only;
     bool diffusion_conv_direct;
diff --git a/vae.hpp b/vae.hpp
index 01b99e89..01081343 100644
--- a/vae.hpp
+++ b/vae.hpp
@@ -141,7 +141,7 @@ public:
             v = ggml_reshape_3d(ctx->ggml_ctx, v, c, h * w, n);                        // [N, h * w, in_channels]
         }
 
-        h_ = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, true, false);
+        h_ = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, true, ctx->flash_attn_enabled);
 
         if (use_linear) {
             h_ = proj_out->forward(ctx, h_);  // [N, h * w, in_channels]
diff --git a/wan.hpp b/wan.hpp
index 81959efc..7b105978 100644
--- a/wan.hpp
+++ b/wan.hpp
@@ -572,8 +572,8 @@ namespace WAN {
             auto v = qkv_vec[2];
             v      = ggml_reshape_3d(ctx->ggml_ctx, v, h * w, c, n);  // [t, c, h * w]
 
-            v = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, v, 1, 0, 2, 3));         // [t, h * w, c]
-            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, true, false);  // [t, h * w, c]
+            v = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, v, 1, 0, 2, 3));                           // [t, h * w, c]
+            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, true, ctx->flash_attn_enabled);  // [t, h * w, c]
 
             x = ggml_ext_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [t, c, h * w]
             x = ggml_reshape_4d(ctx->ggml_ctx, x, w, h, c, n);                             // [t, c, h, w]

From 65891d74cc1ae4fdb4b64c560cacd1e29628d7b3 Mon Sep 17 00:00:00 2001
From: Roj234 <82699138+roj234@users.noreply.github.com>
Date: Thu, 5 Feb 2026 00:49:05 +0900
Subject: [PATCH 70/97] fix: avoid the issue of NaN for qwen-image on certain
 devices (#1249)

---
 qwen_image.hpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qwen_image.hpp b/qwen_image.hpp
index dfa53978..3044eb45 100644
--- a/qwen_image.hpp
+++ b/qwen_image.hpp
@@ -212,7 +212,7 @@ namespace Qwen {
 
             blocks["txt_norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(dim, eps, false));
             blocks["txt_norm2"] = std::shared_ptr<GGMLBlock>(new LayerNorm(dim, eps, false));
-            blocks["txt_mlp"]   = std::shared_ptr<GGMLBlock>(new FeedForward(dim, dim, 4, FeedForward::Activation::GELU));
+            blocks["txt_mlp"]   = std::shared_ptr<GGMLBlock>(new FeedForward(dim, dim, 4, FeedForward::Activation::GELU, true));
 
             blocks["attn"] = std::shared_ptr<GGMLBlock>(new QwenImageAttention(dim,
                                                                                attention_head_dim,

From 9f56833e14e7c8e09c2716c3e79f276a37fc88ee Mon Sep 17 00:00:00 2001
From: stduhpf <stephduh@live.fr>
Date: Sat, 7 Feb 2026 17:08:09 +0100
Subject: [PATCH 71/97] feat: optimize LoKr at runtime (#1233)

---
 ggml_extend.hpp | 171 +++++++++++++++++++++++++++++++++++++++++++++++-
 lora.hpp        | 116 +++++++++++++++++++++++++++++---
 2 files changed, 278 insertions(+), 9 deletions(-)

diff --git a/ggml_extend.hpp b/ggml_extend.hpp
index 193a2c39..e3f991c7 100644
--- a/ggml_extend.hpp
+++ b/ggml_extend.hpp
@@ -1577,7 +1577,7 @@ struct WeightAdapter {
             bool force_prec_f32 = false;
             float scale         = 1.f;
         } linear;
-        struct {
+        struct conv2d_params_t {
             int s0          = 1;
             int s1          = 1;
             int p0          = 0;
@@ -2630,4 +2630,173 @@ public:
     }
 };
 
+__STATIC_INLINE__ struct ggml_tensor* ggml_ext_lokr_forward(
+    struct ggml_context* ctx,
+    struct ggml_tensor* h,    // Input: [q, batch] or [W, H, q, batch]
+    struct ggml_tensor* w1,   // Outer C (Full rank)
+    struct ggml_tensor* w1a,  // Outer A (Low rank part 1)
+    struct ggml_tensor* w1b,  // Outer B (Low rank part 2)
+    struct ggml_tensor* w2,   // Inner BA (Full rank)
+    struct ggml_tensor* w2a,  // Inner A (Low rank part 1)
+    struct ggml_tensor* w2b,  // Inner B (Low rank part 2)
+    bool is_conv,
+    WeightAdapter::ForwardParams::conv2d_params_t conv_params,
+    float scale) {
+    GGML_ASSERT((w1 != NULL || (w1a != NULL && w1b != NULL)));
+    GGML_ASSERT((w2 != NULL || (w2a != NULL && w2b != NULL)));
+
+    int uq = (w1 != NULL) ? (int)w1->ne[0] : (int)w1a->ne[0];
+    int up = (w1 != NULL) ? (int)w1->ne[1] : (int)w1b->ne[1];
+
+    int q_actual = is_conv ? (int)h->ne[2] : (int)h->ne[0];
+    int vq       = q_actual / uq;
+
+    int vp = (w2 != NULL) ? (is_conv ? (int)w2->ne[3] : (int)w2->ne[1])
+                          : (int)w2a->ne[1];
+    GGML_ASSERT(q_actual == (uq * vq) && "Input dimension mismatch for LoKR split");
+
+    struct ggml_tensor* hb;
+
+    if (!is_conv) {
+        int batch          = (int)h->ne[1];
+        int merge_batch_uq = batch;
+        int merge_batch_vp = batch;
+
+#if SD_USE_VULKAN
+        if (batch > 1) {
+            // no access to backend here, worst case is slightly worse perfs for other backends when built alongside Vulkan backend
+            int max_batch    = 65535;
+            int max_batch_uq = max_batch / uq;
+            merge_batch_uq   = 1;
+            for (int i = max_batch_uq; i > 0; i--) {
+                if (batch % i == 0) {
+                    merge_batch_uq = i;
+                    break;
+                }
+            }
+
+            int max_batch_vp = max_batch / vp;
+            merge_batch_vp   = 1;
+            for (int i = max_batch_vp; i > 0; i--) {
+                if (batch % i == 0) {
+                    merge_batch_vp = i;
+                    break;
+                }
+            }
+        }
+#endif
+
+        struct ggml_tensor* h_split = ggml_reshape_3d(ctx, h, vq, uq * merge_batch_uq, batch / merge_batch_uq);
+        if (w2 != NULL) {
+            hb = ggml_mul_mat(ctx, w2, h_split);
+        } else {
+            hb = ggml_mul_mat(ctx, w2b, ggml_mul_mat(ctx, w2a, h_split));
+        }
+
+        if (batch > 1) {
+            hb = ggml_reshape_3d(ctx, hb, vp, uq, batch);
+        }
+        struct ggml_tensor* hb_t = ggml_cont(ctx, ggml_transpose(ctx, hb));
+        hb_t                     = ggml_reshape_3d(ctx, hb_t, uq, vp * merge_batch_vp, batch / merge_batch_vp);
+
+        struct ggml_tensor* hc_t;
+        if (w1 != NULL) {
+            hc_t = ggml_mul_mat(ctx, w1, hb_t);
+        } else {
+            hc_t = ggml_mul_mat(ctx, w1b, ggml_mul_mat(ctx, w1a, hb_t));
+        }
+
+        if (batch > 1) {
+            hc_t = ggml_reshape_3d(ctx, hc_t, up, vp, batch);
+        }
+
+        struct ggml_tensor* hc  = ggml_transpose(ctx, hc_t);
+        struct ggml_tensor* out = ggml_reshape_2d(ctx, ggml_cont(ctx, hc), up * vp, batch);
+        return ggml_scale(ctx, out, scale);
+    } else {
+        int batch = (int)h->ne[3];
+        // 1. Reshape input: [W, H, vq*uq, batch] -> [W, H, vq, uq * batch]
+        struct ggml_tensor* h_split = ggml_reshape_4d(ctx, h, h->ne[0], h->ne[1], vq, uq * batch);
+
+        if (w2 != NULL) {
+            hb = ggml_ext_conv_2d(ctx, h_split, w2, nullptr,
+                                  conv_params.s0,
+                                  conv_params.s1,
+                                  conv_params.p0,
+                                  conv_params.p1,
+                                  conv_params.d0,
+                                  conv_params.d1,
+                                  conv_params.direct,
+                                  conv_params.circular_x,
+                                  conv_params.circular_y,
+                                  conv_params.scale);
+        } else {
+            // swap a and b order for conv lora
+            struct ggml_tensor* a = w2b;
+            struct ggml_tensor* b = w2a;
+
+            // unpack conv2d weights if needed
+            if (ggml_n_dims(a) < 4) {
+                int k = (int)sqrt(a->ne[0] / h_split->ne[2]);
+                GGML_ASSERT(k * k * h_split->ne[2] == a->ne[0]);
+                a = ggml_reshape_4d(ctx, a, k, k, a->ne[0] / (k * k), a->ne[1]);
+            } else if (a->ne[2] != h_split->ne[2]) {
+                int k = (int)sqrt(a->ne[2] / h_split->ne[2]);
+                GGML_ASSERT(k * k * h_split->ne[2] == a->ne[2]);
+                a = ggml_reshape_4d(ctx, a, a->ne[0] * k, a->ne[1] * k, a->ne[2] / (k * k), a->ne[3]);
+            }
+            struct ggml_tensor* ha = ggml_ext_conv_2d(ctx, h_split, a, nullptr,
+                                                      conv_params.s0,
+                                                      conv_params.s1,
+                                                      conv_params.p0,
+                                                      conv_params.p1,
+                                                      conv_params.d0,
+                                                      conv_params.d1,
+                                                      conv_params.direct,
+                                                      conv_params.circular_x,
+                                                      conv_params.circular_y,
+                                                      conv_params.scale);
+
+            // not supporting lora_mid here
+            hb = ggml_ext_conv_2d(ctx,
+                                  ha,
+                                  b,
+                                  nullptr,
+                                  1,
+                                  1,
+                                  0,
+                                  0,
+                                  1,
+                                  1,
+                                  conv_params.direct,
+                                  conv_params.circular_x,
+                                  conv_params.circular_y,
+                                  conv_params.scale);
+        }
+
+        // Current hb shape: [W_out, H_out, vp, uq * batch]
+        int w_out = (int)hb->ne[0];
+        int h_out = (int)hb->ne[1];
+
+        // struct ggml_tensor* hb_cat = ggml_reshape_4d(ctx, hb, w_out , h_out , vp * uq, batch);
+        // [W_out, H_out, vp * uq,  batch]
+        // Now left to compute (W1 kr Id) * hb_cat == (W1 kr W2) cv h
+
+        // merge the uq groups of size vp*w_out*h_out
+        struct ggml_tensor* hb_merged = ggml_reshape_2d(ctx, hb, w_out * h_out * vp, uq * batch);
+        struct ggml_tensor* hc_t;
+        struct ggml_tensor* hb_merged_t = ggml_cont(ctx, ggml_transpose(ctx, hb_merged));
+        if (w1 != NULL) {
+            // Would be great to be able to transpose w1 instead to avoid transposing both hb and hc
+            hc_t = ggml_mul_mat(ctx, w1, hb_merged_t);
+        } else {
+            hc_t = ggml_mul_mat(ctx, w1b, ggml_mul_mat(ctx, w1a, hb_merged_t));
+        }
+        struct ggml_tensor* hc = ggml_transpose(ctx, hc_t);
+        // ungroup
+        struct ggml_tensor* out = ggml_reshape_4d(ctx, ggml_cont(ctx, hc), w_out, h_out, up * vp, batch);
+        return ggml_scale(ctx, out, scale);
+    }
+}
+
 #endif  // __GGML_EXTEND__HPP__
diff --git a/lora.hpp b/lora.hpp
index e5d9906f..d2f91cd4 100644
--- a/lora.hpp
+++ b/lora.hpp
@@ -468,10 +468,10 @@ struct LoraModel : public GGMLRunner {
         return updown;
     }
 
-    ggml_tensor* get_weight_diff(const std::string& model_tensor_name, ggml_context* ctx, ggml_tensor* model_tensor, bool with_lora = true) {
+    ggml_tensor* get_weight_diff(const std::string& model_tensor_name, ggml_context* ctx, ggml_tensor* model_tensor, bool with_lora_and_lokr = true) {
         // lora
         ggml_tensor* diff = nullptr;
-        if (with_lora) {
+        if (with_lora_and_lokr) {
             diff = get_lora_weight_diff(model_tensor_name, ctx);
         }
         // diff
@@ -483,7 +483,7 @@ struct LoraModel : public GGMLRunner {
             diff = get_loha_weight_diff(model_tensor_name, ctx);
         }
         // lokr
-        if (diff == nullptr) {
+        if (diff == nullptr && with_lora_and_lokr) {
             diff = get_lokr_weight_diff(model_tensor_name, ctx);
         }
         if (diff != nullptr) {
@@ -514,6 +514,108 @@ struct LoraModel : public GGMLRunner {
             } else {
                 key = model_tensor_name + "." + std::to_string(index);
             }
+            bool is_conv2d = forward_params.op_type == WeightAdapter::ForwardParams::op_type_t::OP_CONV2D;
+
+            std::string lokr_w1_name   = "lora." + key + ".lokr_w1";
+            std::string lokr_w1_a_name = "lora." + key + ".lokr_w1_a";
+            // if either of these is found, then we have a lokr lora
+            auto iter   = lora_tensors.find(lokr_w1_name);
+            auto iter_a = lora_tensors.find(lokr_w1_a_name);
+            if (iter != lora_tensors.end() || iter_a != lora_tensors.end()) {
+                std::string lokr_w1_b_name = "lora." + key + ".lokr_w1_b";
+                std::string lokr_w2_name   = "lora." + key + ".lokr_w2";
+                std::string lokr_w2_a_name = "lora." + key + ".lokr_w2_a";
+                std::string lokr_w2_b_name = "lora." + key + ".lokr_w2_b";
+                std::string alpha_name     = "lora." + key + ".alpha";
+
+                ggml_tensor* lokr_w1   = nullptr;
+                ggml_tensor* lokr_w1_a = nullptr;
+                ggml_tensor* lokr_w1_b = nullptr;
+                ggml_tensor* lokr_w2   = nullptr;
+                ggml_tensor* lokr_w2_a = nullptr;
+                ggml_tensor* lokr_w2_b = nullptr;
+
+                if (iter != lora_tensors.end()) {
+                    lokr_w1 = iter->second;
+                }
+                iter = iter_a;
+                if (iter != lora_tensors.end()) {
+                    lokr_w1_a = iter->second;
+                }
+                iter = lora_tensors.find(lokr_w1_b_name);
+                if (iter != lora_tensors.end()) {
+                    lokr_w1_b = iter->second;
+                }
+
+                iter = lora_tensors.find(lokr_w2_name);
+                if (iter != lora_tensors.end()) {
+                    lokr_w2 = iter->second;
+                    if (is_conv2d && lokr_w2->type != GGML_TYPE_F16) {
+                        lokr_w2 = ggml_cast(ctx, lokr_w2, GGML_TYPE_F16);
+                    }
+                }
+                iter = lora_tensors.find(lokr_w2_a_name);
+                if (iter != lora_tensors.end()) {
+                    lokr_w2_a = iter->second;
+                    if (is_conv2d && lokr_w2_a->type != GGML_TYPE_F16) {
+                        lokr_w2_a = ggml_cast(ctx, lokr_w2_a, GGML_TYPE_F16);
+                    }
+                }
+                iter = lora_tensors.find(lokr_w2_b_name);
+                if (iter != lora_tensors.end()) {
+                    lokr_w2_b = iter->second;
+                    if (is_conv2d && lokr_w2_b->type != GGML_TYPE_F16) {
+                        lokr_w2_b = ggml_cast(ctx, lokr_w2_b, GGML_TYPE_F16);
+                    }
+                }
+
+                int rank = 1;
+                if (lokr_w1_b) {
+                    rank = (int)lokr_w1_b->ne[ggml_n_dims(lokr_w1_b) - 1];
+                }
+                if (lokr_w2_b) {
+                    rank = (int)lokr_w2_b->ne[ggml_n_dims(lokr_w2_b) - 1];
+                }
+
+                float scale_value = 1.0f;
+                iter              = lora_tensors.find(alpha_name);
+                if (iter != lora_tensors.end()) {
+                    float alpha = ggml_ext_backend_tensor_get_f32(iter->second);
+                    scale_value = alpha / rank;
+                    applied_lora_tensors.insert(alpha_name);
+                }
+
+                if (rank == 1) {
+                    scale_value = 1.0f;
+                }
+                scale_value *= multiplier;
+
+                auto curr_out_diff = ggml_ext_lokr_forward(ctx, x, lokr_w1, lokr_w1_a, lokr_w1_b, lokr_w2, lokr_w2_a, lokr_w2_b, is_conv2d, forward_params.conv2d, scale_value);
+                if (out_diff == nullptr) {
+                    out_diff = curr_out_diff;
+                } else {
+                    out_diff = ggml_concat(ctx, out_diff, curr_out_diff, 0);
+                }
+
+                if (lokr_w1)
+                    applied_lora_tensors.insert(lokr_w1_name);
+                if (lokr_w1_a)
+                    applied_lora_tensors.insert(lokr_w1_a_name);
+                if (lokr_w1_b)
+                    applied_lora_tensors.insert(lokr_w1_b_name);
+                if (lokr_w2)
+                    applied_lora_tensors.insert(lokr_w2_name);
+                if (lokr_w2_a)
+                    applied_lora_tensors.insert(lokr_w2_name);
+                if (lokr_w2_b)
+                    applied_lora_tensors.insert(lokr_w2_b_name);
+                applied_lora_tensors.insert(alpha_name);
+
+                index++;
+                continue;
+            }
+
+            // not a lokr, normal lora path
 
             std::string lora_down_name = "lora." + key + ".lora_down";
             std::string lora_up_name   = "lora." + key + ".lora_up";
@@ -525,9 +627,7 @@ struct LoraModel : public GGMLRunner {
             ggml_tensor* lora_mid  = nullptr;
             ggml_tensor* lora_down = nullptr;
 
-            bool is_conv2d = forward_params.op_type == WeightAdapter::ForwardParams::op_type_t::OP_CONV2D;
-
-            auto iter = lora_tensors.find(lora_up_name);
+            iter = lora_tensors.find(lora_up_name);
             if (iter != lora_tensors.end()) {
                 lora_up = iter->second;
                 if (is_conv2d && lora_up->type != GGML_TYPE_F16) {
@@ -741,9 +841,9 @@ public:
         : lora_models(lora_models) {
     }
 
-    ggml_tensor* patch_weight(ggml_context* ctx, ggml_tensor* weight, const std::string& weight_name, bool with_lora) {
+    ggml_tensor* patch_weight(ggml_context* ctx, ggml_tensor* weight, const std::string& weight_name, bool with_lora_and_lokr) {
         for (auto& lora_model : lora_models) {
-            ggml_tensor* diff = lora_model->get_weight_diff(weight_name, ctx, weight, with_lora);
+            ggml_tensor* diff = lora_model->get_weight_diff(weight_name, ctx, weight, with_lora_and_lokr);
             if (diff == nullptr) {
                 continue;
             }

From f0f641a142705798d5064ffd3808165d75723344 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 8 Feb 2026 00:11:16 +0800
Subject: [PATCH 72/97] feat(server): add lora support to sdapi (#1256)

---
 examples/server/main.cpp | 107 +++++++++++++++++++++++++++++++++++++--
 1 file changed, 104 insertions(+), 3 deletions(-)

diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index def49975..4de46fa6 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -263,6 +263,11 @@ void sd_log_cb(enum sd_log_level_t level, const char* log, void* data) {
     log_print(level, log, svr_params->verbose, svr_params->color);
 }
 
+struct LoraEntry {
+    std::string name;
+    std::string path;
+};
+
 int main(int argc, const char** argv) {
     if (argc > 1 && std::string(argv[1]) == "--version") {
         std::cout << version_string() << "\n";
@@ -293,6 +298,54 @@ int main(int argc, const char** argv) {
 
     std::mutex sd_ctx_mutex;
 
+    std::vector<LoraEntry> lora_cache;
+    std::mutex lora_mutex;
+
+    auto refresh_lora_cache = [&]() {
+        std::vector<LoraEntry> new_cache;
+
+        fs::path lora_dir = ctx_params.lora_model_dir;
+        if (fs::exists(lora_dir) && fs::is_directory(lora_dir)) {
+            auto is_lora_ext = [](const fs::path& p) {
+                auto ext = p.extension().string();
+                std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);
+                return ext == ".gguf" || ext == ".pt" || ext == ".pth" || ext == ".safetensors";
+            };
+
+            for (auto& entry : fs::recursive_directory_iterator(lora_dir)) {
+                if (!entry.is_regular_file())
+                    continue;
+                const fs::path& p = entry.path();
+                if (!is_lora_ext(p))
+                    continue;
+
+                LoraEntry e;
+                e.name          = p.stem().u8string();
+                std::string rel = fs::relative(p, lora_dir).u8string();
+                std::replace(rel.begin(), rel.end(), '\\', '/');
+                e.path = rel;
+
+                new_cache.push_back(std::move(e));
+            }
+        }
+
+        std::sort(new_cache.begin(), new_cache.end(),
+                  [](const LoraEntry& a, const LoraEntry& b) {
+                      return a.path < b.path;
+                  });
+
+        {
+            std::lock_guard<std::mutex> lock(lora_mutex);
+            lora_cache = std::move(new_cache);
+        }
+    };
+
+    auto is_valid_lora_path = [&](const std::string& path) -> bool {
+        std::lock_guard<std::mutex> lock(lora_mutex);
+        return std::any_of(lora_cache.begin(), lora_cache.end(),
+                           [&](const LoraEntry& e) { return e.path == path; });
+    };
+
     httplib::Server svr;
 
     svr.set_pre_routing_handler([](const httplib::Request& req, httplib::Response& res) {
@@ -312,7 +365,7 @@ int main(int argc, const char** argv) {
         return httplib::Server::HandlerResponse::Unhandled;
     });
 
-    // health
+    // root
     svr.Get("/", [&](const httplib::Request&, httplib::Response& res) {
         if (!svr_params.serve_html_path.empty()) {
             std::ifstream file(svr_params.serve_html_path);
@@ -767,6 +820,37 @@ int main(int argc, const char** argv) {
                 return bad("prompt required");
             }
 
+            std::vector<sd_lora_t> sd_loras;
+            std::vector<std::string> lora_path_storage;
+
+            if (j.contains("lora") && j["lora"].is_array()) {
+                for (const auto& item : j["lora"]) {
+                    if (!item.is_object()) {
+                        continue;
+                    }
+
+                    std::string path   = item.value("path", "");
+                    float multiplier   = item.value("multiplier", 1.0f);
+                    bool is_high_noise = item.value("is_high_noise", false);
+
+                    if (path.empty()) {
+                        return bad("lora.path required");
+                    }
+
+                    if (!is_valid_lora_path(path)) {
+                        return bad("invalid lora path: " + path);
+                    }
+
+                    lora_path_storage.push_back(path);
+                    sd_lora_t l;
+                    l.is_high_noise = is_high_noise;
+                    l.multiplier    = multiplier;
+                    l.path          = lora_path_storage.back().c_str();
+
+                    sd_loras.push_back(l);
+                }
+            }
+
             auto get_sample_method = [](std::string name) -> enum sample_method_t {
                 enum sample_method_t result = str_to_sample_method(name.c_str());
                 if (result != SAMPLE_METHOD_COUNT) return result;
@@ -894,8 +978,8 @@ int main(int argc, const char** argv) {
             }
 
             sd_img_gen_params_t img_gen_params = {
-                gen_params.lora_vec.data(),
-                static_cast<uint32_t>(gen_params.lora_vec.size()),
+                sd_loras.data(),
+                static_cast<uint32_t>(sd_loras.size()),
                 gen_params.prompt.c_str(),
                 gen_params.negative_prompt.c_str(),
                 gen_params.clip_skip,
@@ -987,6 +1071,23 @@ int main(int argc, const char** argv) {
         sdapi_any2img(req, res, true);
     });
 
+    svr.Get("/sdapi/v1/loras", [&](const httplib::Request&, httplib::Response& res) {
+        refresh_lora_cache();
+
+        json result = json::array();
+        {
+            std::lock_guard<std::mutex> lock(lora_mutex);
+            for (const auto& e : lora_cache) {
+                json item;
+                item["name"] = e.name;
+                item["path"] = e.path;
+                result.push_back(item);
+            }
+        }
+
+        res.set_content(result.dump(), "application/json");
+    });
+
     svr.Get("/sdapi/v1/samplers", [&](const httplib::Request&, httplib::Response& res) {
         std::vector<std::string> sampler_names;
         sampler_names.push_back("default");

From 5e264372ce02b2d53a8e79daae6a7b47ac17d285 Mon Sep 17 00:00:00 2001
From: Mario Limonciello <superm1@kernel.org>
Date: Mon, 9 Feb 2026 09:23:06 -0600
Subject: [PATCH 73/97] ci: add a github action to generate a Linux ROCm
 artifact (#1258)

---
 .github/workflows/build.yml | 148 ++++++++++++++++++++++++++++++++++++
 1 file changed, 148 insertions(+)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 76b17935..3c05caf5 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -485,6 +485,153 @@ jobs:
           path: |
             sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-x64.zip
 
+  ubuntu-latest-rocm:
+    runs-on: ubuntu-latest
+    container: rocm/dev-ubuntu-24.04:7.2
+
+    env:
+      ROCM_VERSION: "7.2"
+      UBUNTU_VERSION: "24.04"
+      GPU_TARGETS: "gfx1151;gfx1150;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
+
+    steps:
+      - run: apt-get update && apt-get install -y git
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          submodules: recursive
+
+      - name: Free disk space
+        run: |
+          # Remove preinstalled SDKs and caches not needed for this job
+          sudo rm -rf /usr/share/dotnet || true
+          sudo rm -rf /usr/local/lib/android || true
+          sudo rm -rf /opt/ghc || true
+          sudo rm -rf /usr/local/.ghcup || true
+          sudo rm -rf /opt/hostedtoolcache || true
+
+          # Remove old package lists and caches
+          sudo rm -rf /var/lib/apt/lists/* || true
+          sudo apt clean
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt install -y \
+            cmake \
+            hip-dev \
+            hipblas-dev \
+            ninja-build \
+            rocm-dev \
+            zip
+          # Clean apt caches to recover disk space
+          sudo apt clean
+          sudo rm -rf /var/lib/apt/lists/* || true
+
+      - name: Setup ROCm Environment
+        run: |
+          # Add ROCm to PATH for current session
+          echo "/opt/rocm/bin" >> $GITHUB_PATH
+
+          # Build case pattern from GPU_TARGETS
+          PATTERN=$(printf '%s' "$GPU_TARGETS" | sed 's/;/\*|\*/g')
+          PATTERN="*${PATTERN}*"
+
+          # Remove library files for architectures we're not building for to save disk space
+          echo "Cleaning up unneeded architecture files..."
+          cd /opt/rocm/lib/rocblas/library
+          # Keep only our target architectures
+          for file in *; do
+            case "$file" in
+            $PATTERN)
+              ;;
+            *)
+              sudo rm -f "$file" ;;
+            esac;
+          done
+
+          cd /opt/rocm/lib/hipblaslt/library
+          for file in *; do
+            case "$file" in
+              $PATTERN)
+                ;;
+              *)
+                sudo rm -f "$file" ;;
+            esac;
+          done
+
+      - name: Build
+        id: cmake_build
+        run: |
+          mkdir build
+          cd build
+          cmake .. -G Ninja \
+            -DCMAKE_CXX_COMPILER=amdclang++ \
+            -DCMAKE_C_COMPILER=amdclang \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DSD_HIPBLAS=ON \
+            -DGPU_TARGETS="${{ env.GPU_TARGETS }}" \
+            -DAMDGPU_TARGETS="${{ env.GPU_TARGETS }}" \
+            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
+            -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+            -DSD_BUILD_SHARED_LIBS=ON
+          cmake --build . --config Release
+
+      - name: Get commit hash
+        id: commit
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: pr-mpt/actions-commit-hash@v2
+
+      - name: Prepare artifacts
+        id: prepare_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          # Copy licenses
+          cp ggml/LICENSE ./build/bin/ggml.txt
+          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
+
+          # Create directories for ROCm libraries
+          mkdir -p ./build/bin/rocblas/library
+          mkdir -p ./build/bin/hipblaslt/library
+
+          # Copy ROCm runtime libraries (use || true to continue if files don't exist)
+          cp /opt/rocm/lib/librocsparse.so* ./build/bin/ || true
+          cp /opt/rocm/lib/libhsa-runtime64.so* ./build/bin/ || true
+          cp /opt/rocm/lib/libamdhip64.so* ./build/bin/ || true
+          cp /opt/rocm/lib/libhipblas.so* ./build/bin/ || true
+          cp /opt/rocm/lib/libhipblaslt.so* ./build/bin/ || true
+          cp /opt/rocm/lib/librocblas.so* ./build/bin/ || true
+
+          # Copy library files (already filtered to target architectures)
+          cp /opt/rocm/lib/rocblas/library/* ./build/bin/rocblas/library/ || true
+          cp /opt/rocm/lib/hipblaslt/library/* ./build/bin/hipblaslt/library/ || true
+
+      - name: Fetch system info
+        id: system-info
+        run: |
+          echo "CPU_ARCH=`uname -m`" >> "$GITHUB_OUTPUT"
+          echo "OS_NAME=`lsb_release -s -i`" >> "$GITHUB_OUTPUT"
+          echo "OS_VERSION=`lsb_release -s -r`" >> "$GITHUB_OUTPUT"
+          echo "OS_TYPE=`uname -s`" >> "$GITHUB_OUTPUT"
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          cp ggml/LICENSE ./build/bin/ggml.txt
+          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
+          zip -j sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm.zip
+          path: |
+            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm.zip
+
   release:
     if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
 
@@ -493,6 +640,7 @@ jobs:
     needs:
       - ubuntu-latest-cmake
       - ubuntu-latest-cmake-vulkan
+      - ubuntu-latest-rocm
       - build-and-push-docker-images
       - macOS-latest-cmake
       - windows-latest-cmake

From aa0b8993975e7a1e4f7039c06cf7e61b19eff57a Mon Sep 17 00:00:00 2001
From: stduhpf <stephduh@live.fr>
Date: Mon, 9 Feb 2026 16:29:41 +0100
Subject: [PATCH 74/97] fix: improve handling of VAE decode failures (#1222)

---
 examples/cli/main.cpp | 30 +++++++++++++++++++++---------
 ggml_extend.hpp       | 15 +++++++++------
 stable-diffusion.cpp  | 25 +++++++++++++++++--------
 upscaler.cpp          |  2 +-
 4 files changed, 48 insertions(+), 24 deletions(-)

diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index ab58ab5f..cc382d2f 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -409,7 +409,7 @@ bool save_results(const SDCliParams& cli_params,
     auto write_image = [&](const fs::path& path, int idx) {
         const sd_image_t& img = results[idx];
         if (!img.data)
-            return;
+            return false;
 
         std::string params = get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + idx);
         int ok             = 0;
@@ -419,8 +419,11 @@ bool save_results(const SDCliParams& cli_params,
             ok = stbi_write_png(path.string().c_str(), img.width, img.height, img.channel, img.data, 0, params.c_str());
         }
         LOG_INFO("save result image %d to '%s' (%s)", idx, path.string().c_str(), ok ? "success" : "failure");
+        return ok != 0;
     };
 
+    int sucessful_reults = 0;
+
     if (std::regex_search(cli_params.output_path, format_specifier_regex)) {
         if (!is_jpg && ext_lower != ".png")
             ext = ".png";
@@ -429,9 +432,12 @@ bool save_results(const SDCliParams& cli_params,
 
         for (int i = 0; i < num_results; ++i) {
             fs::path img_path = format_frame_idx(pattern.string(), output_begin_idx + i);
-            write_image(img_path, i);
+            if (write_image(img_path, i)) {
+                sucessful_reults++;
+            }
         }
-        return true;
+        LOG_INFO("%d/%d images saved", sucessful_reults, num_results);
+        return sucessful_reults != 0;
     }
 
     if (cli_params.mode == VID_GEN && num_results > 1) {
@@ -439,9 +445,13 @@ bool save_results(const SDCliParams& cli_params,
             ext = ".avi";
         fs::path video_path = base_path;
         video_path += ext;
-        create_mjpg_avi_from_sd_images(video_path.string().c_str(), results, num_results, gen_params.fps);
-        LOG_INFO("save result MJPG AVI video to '%s'", video_path.string().c_str());
-        return true;
+        if (create_mjpg_avi_from_sd_images(video_path.string().c_str(), results, num_results, gen_params.fps) == 0) {
+            LOG_INFO("save result MJPG AVI video to '%s'", video_path.string().c_str());
+            return true;
+        } else {
+            LOG_ERROR("Failed to save result MPG AVI video to '%s'", video_path.string().c_str());
+            return false;
+        }
     }
 
     if (!is_jpg && ext_lower != ".png")
@@ -453,10 +463,12 @@ bool save_results(const SDCliParams& cli_params,
             img_path += "_" + std::to_string(output_begin_idx + i);
         }
         img_path += ext;
-        write_image(img_path, i);
+        if (write_image(img_path, i)) {
+            sucessful_reults++;
+        }
     }
-
-    return true;
+    LOG_INFO("%d/%d images saved", sucessful_reults, num_results);
+    return sucessful_reults != 0;
 }
 
 int main(int argc, const char* argv[]) {
diff --git a/ggml_extend.hpp b/ggml_extend.hpp
index e3f991c7..6642cfd5 100644
--- a/ggml_extend.hpp
+++ b/ggml_extend.hpp
@@ -767,7 +767,7 @@ __STATIC_INLINE__ ggml_tensor* ggml_ext_silu_act(ggml_context* ctx, ggml_tensor*
     return x;
 }
 
-typedef std::function<void(ggml_tensor*, ggml_tensor*, bool)> on_tile_process;
+typedef std::function<bool(ggml_tensor*, ggml_tensor*, bool)> on_tile_process;
 
 __STATIC_INLINE__ void sd_tiling_calc_tiles(int& num_tiles_dim,
                                             float& tile_overlap_factor_dim,
@@ -918,12 +918,15 @@ __STATIC_INLINE__ void sd_tiling_non_square(ggml_tensor* input,
 
             int64_t t1 = ggml_time_ms();
             ggml_ext_tensor_split_2d(input, input_tile, x_in, y_in);
-            on_processing(input_tile, output_tile, false);
-            ggml_ext_tensor_merge_2d(output_tile, output, x_out, y_out, overlap_x_out, overlap_y_out, dx, dy);
+            if (on_processing(input_tile, output_tile, false)) {
+                ggml_ext_tensor_merge_2d(output_tile, output, x_out, y_out, overlap_x_out, overlap_y_out, dx, dy);
 
-            int64_t t2 = ggml_time_ms();
-            last_time  = (t2 - t1) / 1000.0f;
-            pretty_progress(tile_count, num_tiles, last_time);
+                int64_t t2 = ggml_time_ms();
+                last_time  = (t2 - t1) / 1000.0f;
+                pretty_progress(tile_count, num_tiles, last_time);
+            } else {
+                LOG_ERROR("Failed to process patch %d at (%d, %d)", tile_count, x, y);
+            }
             tile_count++;
         }
         last_x = false;
diff --git a/stable-diffusion.cpp b/stable-diffusion.cpp
index f5c82b21..19bdec8b 100644
--- a/stable-diffusion.cpp
+++ b/stable-diffusion.cpp
@@ -1558,7 +1558,7 @@ public:
                 if (vae_tiling_params.enabled) {
                     // split latent in 32x32 tiles and compute in several steps
                     auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
-                        first_stage_model->compute(n_threads, in, true, &out, nullptr);
+                        return first_stage_model->compute(n_threads, in, true, &out, nullptr);
                     };
                     silent_tiling(latents, result, get_vae_scale_factor(), 32, 0.5f, on_tiling);
 
@@ -1577,7 +1577,7 @@ public:
                 if (vae_tiling_params.enabled) {
                     // split latent in 64x64 tiles and compute in several steps
                     auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
-                        tae_first_stage->compute(n_threads, in, true, &out, nullptr);
+                        return tae_first_stage->compute(n_threads, in, true, &out, nullptr);
                     };
                     silent_tiling(latents, result, get_vae_scale_factor(), 64, 0.5f, on_tiling);
                 } else {
@@ -2546,7 +2546,7 @@ public:
                 LOG_DEBUG("VAE Tile size: %dx%d", tile_size_x, tile_size_y);
 
                 auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
-                    first_stage_model->compute(n_threads, in, false, &out, work_ctx);
+                    return first_stage_model->compute(n_threads, in, false, &out, work_ctx);
                 };
                 sd_tiling_non_square(x, result, vae_scale_factor, tile_size_x, tile_size_y, tile_overlap, on_tiling);
             } else {
@@ -2557,7 +2557,7 @@ public:
             if (vae_tiling_params.enabled && !encode_video) {
                 // split latent in 32x32 tiles and compute in several steps
                 auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
-                    tae_first_stage->compute(n_threads, in, false, &out, nullptr);
+                    return tae_first_stage->compute(n_threads, in, false, &out, nullptr);
                 };
                 sd_tiling(x, result, vae_scale_factor, 64, 0.5f, on_tiling);
             } else {
@@ -2675,11 +2675,15 @@ public:
 
                 // split latent in 32x32 tiles and compute in several steps
                 auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
-                    first_stage_model->compute(n_threads, in, true, &out, nullptr);
+                    return first_stage_model->compute(n_threads, in, true, &out, nullptr);
                 };
                 sd_tiling_non_square(x, result, vae_scale_factor, tile_size_x, tile_size_y, tile_overlap, on_tiling);
             } else {
-                first_stage_model->compute(n_threads, x, true, &result, work_ctx);
+                if(!first_stage_model->compute(n_threads, x, true, &result, work_ctx)){
+                    LOG_ERROR("Failed to decode latetnts");
+                    first_stage_model->free_compute_buffer();
+                    return nullptr;
+                }
             }
             first_stage_model->free_compute_buffer();
             process_vae_output_tensor(result);
@@ -2687,11 +2691,15 @@ public:
             if (vae_tiling_params.enabled) {
                 // split latent in 64x64 tiles and compute in several steps
                 auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
-                    tae_first_stage->compute(n_threads, in, true, &out);
+                    return tae_first_stage->compute(n_threads, in, true, &out);
                 };
                 sd_tiling(x, result, vae_scale_factor, 64, 0.5f, on_tiling);
             } else {
-                tae_first_stage->compute(n_threads, x, true, &result);
+                if(!tae_first_stage->compute(n_threads, x, true, &result)){
+                    LOG_ERROR("Failed to decode latetnts");
+                    tae_first_stage->free_compute_buffer();
+                    return nullptr;
+                }
             }
             tae_first_stage->free_compute_buffer();
         }
@@ -3461,6 +3469,7 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
         ggml_free(work_ctx);
         return nullptr;
     }
+    memset(result_images, 0, batch_count * sizeof(sd_image_t));
 
     for (size_t i = 0; i < decoded_images.size(); i++) {
         result_images[i].width   = width;
diff --git a/upscaler.cpp b/upscaler.cpp
index 29ac981e..fd0dc824 100644
--- a/upscaler.cpp
+++ b/upscaler.cpp
@@ -89,7 +89,7 @@ struct UpscalerGGML {
 
         ggml_tensor* upscaled = ggml_new_tensor_4d(upscale_ctx, GGML_TYPE_F32, output_width, output_height, 3, 1);
         auto on_tiling        = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
-            esrgan_upscaler->compute(n_threads, in, &out);
+            return esrgan_upscaler->compute(n_threads, in, &out);
         };
         int64_t t0 = ggml_time_ms();
         sd_tiling(input_image_tensor, upscaled, esrgan_upscaler->scale, esrgan_upscaler->tile_size, 0.25f, on_tiling);

From c7ccafbd6f7a20ab6a26045921418e59eff45667 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Mon, 9 Feb 2026 12:34:19 -0300
Subject: [PATCH 75/97] fix: correct sdapi handling of cfg_scale and steps
 (#1260)

---
 examples/server/main.cpp | 23 +++++++++++------------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index 4de46fa6..2173b1fb 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -786,8 +786,8 @@ int main(int argc, const char** argv) {
             std::string negative_prompt = j.value("negative_prompt", "");
             int width                   = j.value("width", 512);
             int height                  = j.value("height", 512);
-            int steps                   = j.value("steps", -1);
-            float cfg_scale             = j.value("cfg_scale", 7.f);
+            int steps                   = j.value("steps", default_gen_params.sample_params.sample_steps);
+            float cfg_scale             = j.value("cfg_scale", default_gen_params.sample_params.guidance.txt_cfg);
             int64_t seed                = j.value("seed", -1);
             int batch_size              = j.value("batch_size", 1);
             int clip_skip               = j.value("clip_skip", -1);
@@ -883,16 +883,15 @@ int main(int argc, const char** argv) {
 
             enum scheduler_t scheduler = str_to_scheduler(scheduler_name.c_str());
 
-            // avoid excessive resource usage
-
-            SDGenerationParams gen_params         = default_gen_params;
-            gen_params.prompt                     = prompt;
-            gen_params.negative_prompt            = negative_prompt;
-            gen_params.width                      = width;
-            gen_params.height                     = height;
-            gen_params.seed                       = seed;
-            gen_params.sample_params.sample_steps = steps;
-            gen_params.batch_count                = batch_size;
+            SDGenerationParams gen_params             = default_gen_params;
+            gen_params.prompt                         = prompt;
+            gen_params.negative_prompt                = negative_prompt;
+            gen_params.width                          = width;
+            gen_params.height                         = height;
+            gen_params.seed                           = seed;
+            gen_params.sample_params.sample_steps     = steps;
+            gen_params.batch_count                    = batch_size;
+            gen_params.sample_params.guidance.txt_cfg = cfg_scale;
 
             if (clip_skip > 0) {
                 gen_params.clip_skip = clip_skip;

From d60fb27560de3f9ab83e5380b34ca48e2975e61d Mon Sep 17 00:00:00 2001
From: akleine <alb.kleine@gmx.de>
Date: Mon, 9 Feb 2026 16:59:43 +0100
Subject: [PATCH 76/97] fix: avoid unwanted file extension changes (#1257)

---
 examples/cli/main.cpp | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/examples/cli/main.cpp b/examples/cli/main.cpp
index cc382d2f..f9e4928e 100644
--- a/examples/cli/main.cpp
+++ b/examples/cli/main.cpp
@@ -394,12 +394,15 @@ bool save_results(const SDCliParams& cli_params,
 
     fs::path base_path = out_path;
     fs::path ext       = out_path.has_extension() ? out_path.extension() : fs::path{};
-    if (!ext.empty())
-        base_path.replace_extension();
 
     std::string ext_lower = ext.string();
     std::transform(ext_lower.begin(), ext_lower.end(), ext_lower.begin(), ::tolower);
     bool is_jpg = (ext_lower == ".jpg" || ext_lower == ".jpeg" || ext_lower == ".jpe");
+    if (!ext.empty()) {
+        if (is_jpg || ext_lower == ".png") {
+            base_path.replace_extension();
+        }
+    }
 
     int output_begin_idx = cli_params.output_begin_idx;
     if (output_begin_idx < 0) {

From 32965450907c730ae8d205a53b9574a99906da31 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Tue, 10 Feb 2026 00:00:17 +0800
Subject: [PATCH 77/97] feat: add extra_c_crossattns support for llm embedder
 (#1265)

---
 conditioner.hpp | 375 +++++++++++++++++++++++++++---------------------
 1 file changed, 213 insertions(+), 162 deletions(-)

diff --git a/conditioner.hpp b/conditioner.hpp
index b1876954..4317ed18 100644
--- a/conditioner.hpp
+++ b/conditioner.hpp
@@ -10,9 +10,14 @@ struct SDCondition {
     struct ggml_tensor* c_vector    = nullptr;  // aka y
     struct ggml_tensor* c_concat    = nullptr;
 
+    std::vector<struct ggml_tensor*> extra_c_crossattns;
+
     SDCondition() = default;
-    SDCondition(struct ggml_tensor* c_crossattn, struct ggml_tensor* c_vector, struct ggml_tensor* c_concat)
-        : c_crossattn(c_crossattn), c_vector(c_vector), c_concat(c_concat) {}
+    SDCondition(struct ggml_tensor* c_crossattn,
+                struct ggml_tensor* c_vector,
+                struct ggml_tensor* c_concat,
+                const std::vector<struct ggml_tensor*>& extra_c_crossattns = {})
+        : c_crossattn(c_crossattn), c_vector(c_vector), c_concat(c_concat), extra_c_crossattns(extra_c_crossattns) {}
 };
 
 struct ConditionerParams {
@@ -1696,18 +1701,23 @@ struct LLMEmbedder : public Conditioner {
     }
 
     std::tuple<std::vector<int>, std::vector<float>> tokenize(std::string text,
-                                                              std::pair<int, int> attn_range,
+                                                              const std::pair<int, int>& attn_range,
                                                               size_t max_length = 0,
                                                               bool padding      = false) {
         std::vector<std::pair<std::string, float>> parsed_attention;
-        parsed_attention.emplace_back(text.substr(0, attn_range.first), 1.f);
-        if (attn_range.second - attn_range.first > 0) {
-            auto new_parsed_attention = parse_prompt_attention(text.substr(attn_range.first, attn_range.second - attn_range.first));
-            parsed_attention.insert(parsed_attention.end(),
-                                    new_parsed_attention.begin(),
-                                    new_parsed_attention.end());
+        if (attn_range.first >= 0 && attn_range.second > 0) {
+            parsed_attention.emplace_back(text.substr(0, attn_range.first), 1.f);
+            if (attn_range.second - attn_range.first > 0) {
+                auto new_parsed_attention = parse_prompt_attention(text.substr(attn_range.first, attn_range.second - attn_range.first));
+                parsed_attention.insert(parsed_attention.end(),
+                                        new_parsed_attention.begin(),
+                                        new_parsed_attention.end());
+            }
+            parsed_attention.emplace_back(text.substr(attn_range.second), 1.f);
+        } else {
+            parsed_attention.emplace_back(text, 1.f);
         }
-        parsed_attention.emplace_back(text.substr(attn_range.second), 1.f);
+
         {
             std::stringstream ss;
             ss << "[";
@@ -1738,156 +1748,27 @@ struct LLMEmbedder : public Conditioner {
         return {tokens, weights};
     }
 
-    SDCondition get_learned_condition(ggml_context* work_ctx,
-                                      int n_threads,
-                                      const ConditionerParams& conditioner_params) override {
-        std::string prompt;
-        std::vector<std::pair<int, ggml_tensor*>> image_embeds;
-        std::pair<int, int> prompt_attn_range;
-        int prompt_template_encode_start_idx = 34;
-        int max_length                       = 0;
-        std::set<int> out_layers;
-        std::vector<int> tokens;
-        std::vector<float> weights;
+    ggml_tensor* encode_prompt(ggml_context* work_ctx,
+                               int n_threads,
+                               const std::string prompt,
+                               const std::pair<int, int>& prompt_attn_range,
+                               int max_length,
+                               int min_length,
+                               std::vector<std::pair<int, ggml_tensor*>> image_embeds,
+                               const std::set<int>& out_layers,
+                               int prompt_template_encode_start_idx) {
+        auto tokens_and_weights = tokenize(prompt, prompt_attn_range);
+        auto& tokens            = std::get<0>(tokens_and_weights);
+        auto& weights           = std::get<1>(tokens_and_weights);
         std::vector<float> mask;
-        if (llm->enable_vision && conditioner_params.ref_images.size() > 0) {
-            LOG_INFO("QwenImageEditPlusPipeline");
-            prompt_template_encode_start_idx = 64;
-            int image_embed_idx              = 64 + 6;
-
-            int min_pixels          = 384 * 384;
-            int max_pixels          = 560 * 560;
-            std::string placeholder = "<|image_pad|>";
-            std::string img_prompt;
-
-            for (int i = 0; i < conditioner_params.ref_images.size(); i++) {
-                sd_image_f32_t image = sd_image_t_to_sd_image_f32_t(*conditioner_params.ref_images[i]);
-                double factor        = llm->params.vision.patch_size * llm->params.vision.spatial_merge_size;
-                int height           = image.height;
-                int width            = image.width;
-                int h_bar            = static_cast<int>(std::round(height / factor) * factor);
-                int w_bar            = static_cast<int>(std::round(width / factor) * factor);
-
-                if (static_cast<double>(h_bar) * w_bar > max_pixels) {
-                    double beta = std::sqrt((height * width) / static_cast<double>(max_pixels));
-                    h_bar       = std::max(static_cast<int>(factor),
-                                           static_cast<int>(std::floor(height / beta / factor)) * static_cast<int>(factor));
-                    w_bar       = std::max(static_cast<int>(factor),
-                                           static_cast<int>(std::floor(width / beta / factor)) * static_cast<int>(factor));
-                } else if (static_cast<double>(h_bar) * w_bar < min_pixels) {
-                    double beta = std::sqrt(static_cast<double>(min_pixels) / (height * width));
-                    h_bar       = static_cast<int>(std::ceil(height * beta / factor)) * static_cast<int>(factor);
-                    w_bar       = static_cast<int>(std::ceil(width * beta / factor)) * static_cast<int>(factor);
-                }
-
-                LOG_DEBUG("resize conditioner ref image %d from %dx%d to %dx%d", i, image.height, image.width, h_bar, w_bar);
-
-                sd_image_f32_t resized_image = clip_preprocess(image, w_bar, h_bar);
-                free(image.data);
-                image.data = nullptr;
-
-                ggml_tensor* image_tensor = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, resized_image.width, resized_image.height, 3, 1);
-                sd_image_f32_to_ggml_tensor(resized_image, image_tensor, false);
-                free(resized_image.data);
-                resized_image.data = nullptr;
-
-                ggml_tensor* image_embed = nullptr;
-                llm->encode_image(n_threads, image_tensor, &image_embed, work_ctx);
-                image_embeds.emplace_back(image_embed_idx, image_embed);
-                image_embed_idx += 1 + static_cast<int>(image_embed->ne[1]) + 6;
-
-                img_prompt += "Picture " + std::to_string(i + 1) + ": <|vision_start|>";  // [24669, 220, index, 25, 220, 151652]
-                int64_t num_image_tokens = image_embed->ne[1];
-                img_prompt.reserve(num_image_tokens * placeholder.size());
-                for (int j = 0; j < num_image_tokens; j++) {
-                    img_prompt += placeholder;
-                }
-                img_prompt += "<|vision_end|>";
-            }
-
-            prompt = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n";
-            prompt += img_prompt;
-
-            prompt_attn_range.first = static_cast<int>(prompt.size());
-            prompt += conditioner_params.text;
-            prompt_attn_range.second = static_cast<int>(prompt.size());
-
-            prompt += "<|im_end|>\n<|im_start|>assistant\n";
-        } else if (version == VERSION_FLUX2) {
-            prompt_template_encode_start_idx = 0;
-            out_layers                       = {10, 20, 30};
-
-            prompt = "[SYSTEM_PROMPT]You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object\nattribution and actions without speculation.[/SYSTEM_PROMPT][INST]";
-
-            prompt_attn_range.first = static_cast<int>(prompt.size());
-            prompt += conditioner_params.text;
-            prompt_attn_range.second = static_cast<int>(prompt.size());
-
-            prompt += "[/INST]";
-        } else if (sd_version_is_z_image(version)) {
-            prompt_template_encode_start_idx = 0;
-            out_layers                       = {35};  // -2
-
-            prompt = "<|im_start|>user\n";
-
-            prompt_attn_range.first = static_cast<int>(prompt.size());
-            prompt += conditioner_params.text;
-            prompt_attn_range.second = static_cast<int>(prompt.size());
-
-            prompt += "<|im_end|>\n<|im_start|>assistant\n";
-        } else if (version == VERSION_FLUX2_KLEIN) {
-            prompt_template_encode_start_idx = 0;
-            max_length                       = 512;
-            out_layers                       = {9, 18, 27};
-
-            prompt = "<|im_start|>user\n";
-
-            prompt_attn_range.first = static_cast<int>(prompt.size());
-            prompt += conditioner_params.text;
-            prompt_attn_range.second = static_cast<int>(prompt.size());
-
-            prompt += "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n";
-
-            auto tokens_and_weights = tokenize(prompt, prompt_attn_range, 0, false);
-            tokens                  = std::get<0>(tokens_and_weights);
-            weights                 = std::get<1>(tokens_and_weights);
 
+        if (max_length > 0 && tokens.size() < max_length) {
             mask.insert(mask.end(), tokens.size(), 1.f);
-            if (tokens.size() < max_length) {
-                mask.insert(mask.end(), max_length - tokens.size(), 0.f);
-                tokenizer->pad_tokens(tokens, weights, max_length, true);
-            }
-        } else if (version == VERSION_OVIS_IMAGE) {
-            prompt_template_encode_start_idx = 28;
-            max_length                       = prompt_template_encode_start_idx + 256;
-
-            prompt = "<|im_start|>user\nDescribe the image by detailing the color, quantity, text, shape, size, texture, spatial relationships of the objects and background:";
-
-            prompt_attn_range.first = static_cast<int>(prompt.size());
-            prompt += " " + conditioner_params.text;
-            prompt_attn_range.second = static_cast<int>(prompt.size());
-
-            prompt += "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n";
-        } else {
-            prompt_template_encode_start_idx = 34;
-
-            prompt = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n";
-
-            prompt_attn_range.first = static_cast<int>(prompt.size());
-            prompt += conditioner_params.text;
-            prompt_attn_range.second = static_cast<int>(prompt.size());
-
-            prompt += "<|im_end|>\n<|im_start|>assistant\n";
+            mask.insert(mask.end(), max_length - tokens.size(), 0.f);
+            tokenizer->pad_tokens(tokens, weights, max_length, true);
         }
 
-        if (tokens.empty()) {
-            auto tokens_and_weights = tokenize(prompt, prompt_attn_range, max_length, max_length > 0);
-            tokens                  = std::get<0>(tokens_and_weights);
-            weights                 = std::get<1>(tokens_and_weights);
-        }
-
-        int64_t t0                        = ggml_time_ms();
-        struct ggml_tensor* hidden_states = nullptr;  // [N, n_token, 3584]
+        struct ggml_tensor* hidden_states = nullptr;  // [N, n_token, hidden_size]
 
         auto input_ids = vector_to_ggml_tensor_i32(work_ctx, tokens);
 
@@ -1930,11 +1811,6 @@ struct LLMEmbedder : public Conditioner {
 
         GGML_ASSERT(hidden_states->ne[1] > prompt_template_encode_start_idx);
 
-        int64_t min_length = 0;
-        if (version == VERSION_FLUX2) {
-            min_length = 512;
-        }
-
         int64_t zero_pad_len = 0;
         if (min_length > 0) {
             if (hidden_states->ne[1] - prompt_template_encode_start_idx < min_length) {
@@ -1956,11 +1832,186 @@ struct LLMEmbedder : public Conditioner {
             ggml_ext_tensor_set_f32(new_hidden_states, value, i0, i1, i2, i3);
         });
 
-        // print_ggml_tensor(new_hidden_states);
+        return new_hidden_states;
+    }
+
+    SDCondition get_learned_condition(ggml_context* work_ctx,
+                                      int n_threads,
+                                      const ConditionerParams& conditioner_params) override {
+        std::string prompt;
+        std::pair<int, int> prompt_attn_range;
+        std::vector<std::string> extra_prompts;
+        std::vector<std::pair<int, int>> extra_prompts_attn_range;
+        std::vector<std::pair<int, ggml_tensor*>> image_embeds;
+        int prompt_template_encode_start_idx = 34;
+        int max_length                       = 0;  // pad tokens
+        int min_length                       = 0;  // zero pad hidden_states
+        std::set<int> out_layers;
+
+        int64_t t0 = ggml_time_ms();
+
+        if (sd_version_is_qwen_image(version)) {
+            if (llm->enable_vision && !conditioner_params.ref_images.empty()) {
+                LOG_INFO("QwenImageEditPlusPipeline");
+                prompt_template_encode_start_idx = 64;
+                int image_embed_idx              = 64 + 6;
+
+                int min_pixels          = 384 * 384;
+                int max_pixels          = 560 * 560;
+                std::string placeholder = "<|image_pad|>";
+                std::string img_prompt;
+
+                for (int i = 0; i < conditioner_params.ref_images.size(); i++) {
+                    sd_image_f32_t image = sd_image_t_to_sd_image_f32_t(*conditioner_params.ref_images[i]);
+                    double factor        = llm->params.vision.patch_size * llm->params.vision.spatial_merge_size;
+                    int height           = image.height;
+                    int width            = image.width;
+                    int h_bar            = static_cast<int>(std::round(height / factor) * factor);
+                    int w_bar            = static_cast<int>(std::round(width / factor) * factor);
+
+                    if (static_cast<double>(h_bar) * w_bar > max_pixels) {
+                        double beta = std::sqrt((height * width) / static_cast<double>(max_pixels));
+                        h_bar       = std::max(static_cast<int>(factor),
+                                               static_cast<int>(std::floor(height / beta / factor)) * static_cast<int>(factor));
+                        w_bar       = std::max(static_cast<int>(factor),
+                                               static_cast<int>(std::floor(width / beta / factor)) * static_cast<int>(factor));
+                    } else if (static_cast<double>(h_bar) * w_bar < min_pixels) {
+                        double beta = std::sqrt(static_cast<double>(min_pixels) / (height * width));
+                        h_bar       = static_cast<int>(std::ceil(height * beta / factor)) * static_cast<int>(factor);
+                        w_bar       = static_cast<int>(std::ceil(width * beta / factor)) * static_cast<int>(factor);
+                    }
+
+                    LOG_DEBUG("resize conditioner ref image %d from %dx%d to %dx%d", i, image.height, image.width, h_bar, w_bar);
+
+                    sd_image_f32_t resized_image = clip_preprocess(image, w_bar, h_bar);
+                    free(image.data);
+                    image.data = nullptr;
+
+                    ggml_tensor* image_tensor = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, resized_image.width, resized_image.height, 3, 1);
+                    sd_image_f32_to_ggml_tensor(resized_image, image_tensor, false);
+                    free(resized_image.data);
+                    resized_image.data = nullptr;
+
+                    ggml_tensor* image_embed = nullptr;
+                    llm->encode_image(n_threads, image_tensor, &image_embed, work_ctx);
+                    image_embeds.emplace_back(image_embed_idx, image_embed);
+                    image_embed_idx += 1 + static_cast<int>(image_embed->ne[1]) + 6;
+
+                    img_prompt += "Picture " + std::to_string(i + 1) + ": <|vision_start|>";  // [24669, 220, index, 25, 220, 151652]
+                    int64_t num_image_tokens = image_embed->ne[1];
+                    img_prompt.reserve(num_image_tokens * placeholder.size());
+                    for (int j = 0; j < num_image_tokens; j++) {
+                        img_prompt += placeholder;
+                    }
+                    img_prompt += "<|vision_end|>";
+                }
+
+                prompt = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n";
+                prompt += img_prompt;
+
+                prompt_attn_range.first = static_cast<int>(prompt.size());
+                prompt += conditioner_params.text;
+                prompt_attn_range.second = static_cast<int>(prompt.size());
+
+                prompt += "<|im_end|>\n<|im_start|>assistant\n";
+            } else {
+                prompt_template_encode_start_idx = 34;
+
+                prompt = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n";
+
+                prompt_attn_range.first = static_cast<int>(prompt.size());
+                prompt += conditioner_params.text;
+                prompt_attn_range.second = static_cast<int>(prompt.size());
+
+                prompt += "<|im_end|>\n<|im_start|>assistant\n";
+            }
+        } else if (version == VERSION_FLUX2) {
+            prompt_template_encode_start_idx = 0;
+            min_length                       = 512;
+            out_layers                       = {10, 20, 30};
+
+            prompt = "[SYSTEM_PROMPT]You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object\nattribution and actions without speculation.[/SYSTEM_PROMPT][INST]";
+
+            prompt_attn_range.first = static_cast<int>(prompt.size());
+            prompt += conditioner_params.text;
+            prompt_attn_range.second = static_cast<int>(prompt.size());
+
+            prompt += "[/INST]";
+        } else if (sd_version_is_z_image(version)) {
+            prompt_template_encode_start_idx = 0;
+            out_layers                       = {35};  // -2
+
+            if (!conditioner_params.ref_images.empty()) {
+                LOG_INFO("ZImageOmniPipeline");
+                prompt = "<|im_start|>user\n<|vision_start|>";
+                for (int i = 0; i < conditioner_params.ref_images.size() - 1; i++) {
+                    extra_prompts.push_back("<|vision_end|><|vision_start|>");
+                }
+                extra_prompts.push_back("<|vision_end|>" + conditioner_params.text + "<|im_end|>\n<|im_start|>assistant\n<|vision_start|>");
+                extra_prompts.push_back("<|vision_end|><|im_end|>");
+            } else {
+                prompt = "<|im_start|>user\n";
+
+                prompt_attn_range.first = static_cast<int>(prompt.size());
+                prompt += conditioner_params.text;
+                prompt_attn_range.second = static_cast<int>(prompt.size());
+
+                prompt += "<|im_end|>\n<|im_start|>assistant\n";
+            }
+        } else if (version == VERSION_FLUX2_KLEIN) {
+            prompt_template_encode_start_idx = 0;
+            max_length                       = 512;
+            out_layers                       = {9, 18, 27};
+
+            prompt = "<|im_start|>user\n";
+
+            prompt_attn_range.first = static_cast<int>(prompt.size());
+            prompt += conditioner_params.text;
+            prompt_attn_range.second = static_cast<int>(prompt.size());
+
+            prompt += "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n";
+        } else if (version == VERSION_OVIS_IMAGE) {
+            prompt_template_encode_start_idx = 28;
+            max_length                       = prompt_template_encode_start_idx + 256;
+
+            prompt = "<|im_start|>user\nDescribe the image by detailing the color, quantity, text, shape, size, texture, spatial relationships of the objects and background:";
+
+            prompt_attn_range.first = static_cast<int>(prompt.size());
+            prompt += " " + conditioner_params.text;
+            prompt_attn_range.second = static_cast<int>(prompt.size());
+
+            prompt += "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n";
+        } else {
+            GGML_ABORT("unknown version %d", version);
+        }
+
+        auto hidden_states = encode_prompt(work_ctx,
+                                           n_threads,
+                                           prompt,
+                                           prompt_attn_range,
+                                           max_length,
+                                           min_length,
+                                           image_embeds,
+                                           out_layers,
+                                           prompt_template_encode_start_idx);
+
+        std::vector<ggml_tensor*> extra_hidden_states_vec;
+        for (int i = 0; i < extra_prompts.size(); i++) {
+            auto extra_hidden_states = encode_prompt(work_ctx,
+                                                     n_threads,
+                                                     extra_prompts[i],
+                                                     extra_prompts_attn_range[i],
+                                                     max_length,
+                                                     min_length,
+                                                     image_embeds,
+                                                     out_layers,
+                                                     prompt_template_encode_start_idx);
+            extra_hidden_states_vec.push_back(extra_hidden_states);
+        }
 
         int64_t t1 = ggml_time_ms();
         LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
-        return {new_hidden_states, nullptr, nullptr};
+        return {hidden_states, nullptr, nullptr, extra_hidden_states_vec};
     }
 };
 

From 28ef93c0e172a29b0dcbd131ec8e34ecbd2b98e2 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Tue, 10 Feb 2026 23:13:35 +0800
Subject: [PATCH 78/97] refactor: reorganize the file structure (#1266)

---
 CMakeLists.txt                                |  11 +-
 .../stable-diffusion.h                        |   0
 face_detect.py => script/face_detect.py       | 174 +++----
 cache_dit.hpp => src/cache_dit.hpp            |   0
 clip.hpp => src/clip.hpp                      |   0
 common.hpp => src/common.hpp                  |   0
 conditioner.hpp => src/conditioner.hpp        |   0
 control.hpp => src/control.hpp                |   0
 denoiser.hpp => src/denoiser.hpp              |   0
 .../diffusion_model.hpp                       |   0
 easycache.hpp => src/easycache.hpp            |   0
 esrgan.hpp => src/esrgan.hpp                  |   0
 flux.hpp => src/flux.hpp                      |   0
 ggml_extend.hpp => src/ggml_extend.hpp        |   0
 gguf_reader.hpp => src/gguf_reader.hpp        |   0
 gits_noise.inl => src/gits_noise.inl          |   0
 latent-preview.h => src/latent-preview.h      | 468 +++++++++---------
 llm.hpp => src/llm.hpp                        |   0
 lora.hpp => src/lora.hpp                      |   0
 ltxv.hpp => src/ltxv.hpp                      |   0
 mmdit.hpp => src/mmdit.hpp                    |   0
 model.cpp => src/model.cpp                    |   0
 model.h => src/model.h                        |   0
 .../name_conversion.cpp                       |   0
 name_conversion.h => src/name_conversion.h    |   0
 ordered_map.hpp => src/ordered_map.hpp        |   0
 pmid.hpp => src/pmid.hpp                      |   0
 preprocessing.hpp => src/preprocessing.hpp    |   0
 qwen_image.hpp => src/qwen_image.hpp          |   0
 rng.hpp => src/rng.hpp                        |   0
 rng_mt19937.hpp => src/rng_mt19937.hpp        |   0
 rng_philox.hpp => src/rng_philox.hpp          |   0
 rope.hpp => src/rope.hpp                      |   0
 .../stable-diffusion.cpp                      |   0
 t5.hpp => src/t5.hpp                          |   0
 tae.hpp => src/tae.hpp                        |   0
 tokenize_util.cpp => src/tokenize_util.cpp    |   0
 tokenize_util.h => src/tokenize_util.h        |   0
 ucache.hpp => src/ucache.hpp                  |   0
 unet.hpp => src/unet.hpp                      |   0
 upscaler.cpp => src/upscaler.cpp              |   0
 util.cpp => src/util.cpp                      |   0
 util.h => src/util.h                          |   0
 vae.hpp => src/vae.hpp                        |   0
 version.cpp => src/version.cpp                |   0
 vocab.hpp => src/vocab.hpp                    |   0
 vocab_mistral.hpp => src/vocab_mistral.hpp    |   0
 vocab_qwen.hpp => src/vocab_qwen.hpp          |   0
 vocab_umt5.hpp => src/vocab_umt5.hpp          |   0
 wan.hpp => src/wan.hpp                        |   0
 z_image.hpp => src/z_image.hpp                |   0
 51 files changed, 327 insertions(+), 326 deletions(-)
 rename stable-diffusion.h => include/stable-diffusion.h (100%)
 rename face_detect.py => script/face_detect.py (97%)
 rename cache_dit.hpp => src/cache_dit.hpp (100%)
 rename clip.hpp => src/clip.hpp (100%)
 rename common.hpp => src/common.hpp (100%)
 rename conditioner.hpp => src/conditioner.hpp (100%)
 rename control.hpp => src/control.hpp (100%)
 rename denoiser.hpp => src/denoiser.hpp (100%)
 rename diffusion_model.hpp => src/diffusion_model.hpp (100%)
 rename easycache.hpp => src/easycache.hpp (100%)
 rename esrgan.hpp => src/esrgan.hpp (100%)
 rename flux.hpp => src/flux.hpp (100%)
 rename ggml_extend.hpp => src/ggml_extend.hpp (100%)
 rename gguf_reader.hpp => src/gguf_reader.hpp (100%)
 rename gits_noise.inl => src/gits_noise.inl (100%)
 rename latent-preview.h => src/latent-preview.h (97%)
 rename llm.hpp => src/llm.hpp (100%)
 rename lora.hpp => src/lora.hpp (100%)
 rename ltxv.hpp => src/ltxv.hpp (100%)
 rename mmdit.hpp => src/mmdit.hpp (100%)
 rename model.cpp => src/model.cpp (100%)
 rename model.h => src/model.h (100%)
 rename name_conversion.cpp => src/name_conversion.cpp (100%)
 rename name_conversion.h => src/name_conversion.h (100%)
 rename ordered_map.hpp => src/ordered_map.hpp (100%)
 rename pmid.hpp => src/pmid.hpp (100%)
 rename preprocessing.hpp => src/preprocessing.hpp (100%)
 rename qwen_image.hpp => src/qwen_image.hpp (100%)
 rename rng.hpp => src/rng.hpp (100%)
 rename rng_mt19937.hpp => src/rng_mt19937.hpp (100%)
 rename rng_philox.hpp => src/rng_philox.hpp (100%)
 rename rope.hpp => src/rope.hpp (100%)
 rename stable-diffusion.cpp => src/stable-diffusion.cpp (100%)
 rename t5.hpp => src/t5.hpp (100%)
 rename tae.hpp => src/tae.hpp (100%)
 rename tokenize_util.cpp => src/tokenize_util.cpp (100%)
 rename tokenize_util.h => src/tokenize_util.h (100%)
 rename ucache.hpp => src/ucache.hpp (100%)
 rename unet.hpp => src/unet.hpp (100%)
 rename upscaler.cpp => src/upscaler.cpp (100%)
 rename util.cpp => src/util.cpp (100%)
 rename util.h => src/util.h (100%)
 rename vae.hpp => src/vae.hpp (100%)
 rename version.cpp => src/version.cpp (100%)
 rename vocab.hpp => src/vocab.hpp (100%)
 rename vocab_mistral.hpp => src/vocab_mistral.hpp (100%)
 rename vocab_qwen.hpp => src/vocab_qwen.hpp (100%)
 rename vocab_umt5.hpp => src/vocab_umt5.hpp (100%)
 rename wan.hpp => src/wan.hpp (100%)
 rename z_image.hpp => src/z_image.hpp (100%)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index e731d95d..7b77225d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -87,9 +87,9 @@ endif()
 set(SD_LIB stable-diffusion)
 
 file(GLOB SD_LIB_SOURCES
-    "*.h"
-    "*.cpp"
-    "*.hpp"
+    "src/*.h"
+    "src/*.cpp"
+    "src/*.hpp"
 )
 
 find_program(GIT_EXE NAMES git git.exe NO_CMAKE_FIND_ROOT_PATH)
@@ -119,7 +119,7 @@ endif()
 message(STATUS "stable-diffusion.cpp commit ${SDCPP_BUILD_COMMIT}")
 
 set_property(
-  SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp
+  SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/src/version.cpp
   APPEND PROPERTY COMPILE_DEFINITIONS
   SDCPP_BUILD_COMMIT=${SDCPP_BUILD_COMMIT} SDCPP_BUILD_VERSION=${SDCPP_BUILD_VERSION}
 )
@@ -182,6 +182,7 @@ endif()
 add_subdirectory(thirdparty)
 
 target_link_libraries(${SD_LIB} PUBLIC ggml zip)
+target_include_directories(${SD_LIB} PUBLIC . include)
 target_include_directories(${SD_LIB} PUBLIC . thirdparty)
 target_compile_features(${SD_LIB} PUBLIC c_std_11 cxx_std_17)
 
@@ -190,7 +191,7 @@ if (SD_BUILD_EXAMPLES)
     add_subdirectory(examples)
 endif()
 
-set(SD_PUBLIC_HEADERS stable-diffusion.h)
+set(SD_PUBLIC_HEADERS include/stable-diffusion.h)
 set_target_properties(${SD_LIB} PROPERTIES PUBLIC_HEADER "${SD_PUBLIC_HEADERS}")
 
 install(TARGETS ${SD_LIB} LIBRARY PUBLIC_HEADER)
diff --git a/stable-diffusion.h b/include/stable-diffusion.h
similarity index 100%
rename from stable-diffusion.h
rename to include/stable-diffusion.h
diff --git a/face_detect.py b/script/face_detect.py
similarity index 97%
rename from face_detect.py
rename to script/face_detect.py
index 7131af31..e7a3eae1 100644
--- a/face_detect.py
+++ b/script/face_detect.py
@@ -1,88 +1,88 @@
-import os
-import sys
-
-import numpy as np
-import torch
-from diffusers.utils import load_image
-# pip install insightface==0.7.3
-from insightface.app import FaceAnalysis
-from insightface.data import get_image as ins_get_image
-from safetensors.torch import save_file
-
-### 
-# https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/165#issue-2055829543
-###
-class FaceAnalysis2(FaceAnalysis):
-    # NOTE: allows setting det_size for each detection call.
-    # the model allows it but the wrapping code from insightface
-    # doesn't show it, and people end up loading duplicate models
-    # for different sizes where there is absolutely no need to
-    def get(self, img, max_num=0, det_size=(640, 640)):
-        if det_size is not None:
-            self.det_model.input_size = det_size
-
-        return super().get(img, max_num)
-
-def analyze_faces(face_analysis: FaceAnalysis, img_data: np.ndarray, det_size=(640, 640)):
-    # NOTE: try detect faces, if no faces detected, lower det_size until it does
-    detection_sizes = [None] + [(size, size) for size in range(640, 256, -64)] + [(256, 256)]
-
-    for size in detection_sizes:
-        faces = face_analysis.get(img_data, det_size=size)
-        if len(faces) > 0:
-            return faces
-
-    return []
-
-if __name__ == "__main__":
-    #face_detector = FaceAnalysis2(providers=['CUDAExecutionProvider'], allowed_modules=['detection', 'recognition'])
-    face_detector = FaceAnalysis2(providers=['CPUExecutionProvider'], allowed_modules=['detection', 'recognition'])
-    face_detector.prepare(ctx_id=0, det_size=(640, 640))
-    #input_folder_name = './scarletthead_woman'
-    input_folder_name = sys.argv[1]
-    image_basename_list = os.listdir(input_folder_name)
-    image_path_list = sorted([os.path.join(input_folder_name, basename) for basename in image_basename_list])
-
-    input_id_images = []
-    for image_path in image_path_list:
-        input_id_images.append(load_image(image_path))
-    
-    id_embed_list = []
-    
-    for img in input_id_images:
-        img = np.array(img)
-        img = img[:, :, ::-1]
-        faces = analyze_faces(face_detector, img)
-        if len(faces) > 0:
-            id_embed_list.append(torch.from_numpy((faces[0]['embedding'])))
-    
-    if len(id_embed_list) == 0:
-        raise ValueError(f"No face detected in input image pool")
-    
-    id_embeds = torch.stack(id_embed_list)    
-    
-    # for r in id_embeds:
-    #     print(r)
-    # #torch.save(id_embeds, input_folder_name+'/id_embeds.pt');
-    # weights = dict()
-    # weights["id_embeds"] = id_embeds
-    # save_file(weights, input_folder_name+'/id_embeds.safetensors')
-
-    binary_data = id_embeds.numpy().tobytes()
-    two = 4
-    zero = 0
-    one = 1
-    tensor_name = "id_embeds"
-# Write binary data to a file
-    with open(input_folder_name+'/id_embeds.bin', "wb") as f:
-        f.write(two.to_bytes(4, byteorder='little'))
-        f.write((len(tensor_name)).to_bytes(4, byteorder='little'))
-        f.write(zero.to_bytes(4, byteorder='little'))
-        f.write((id_embeds.shape[1]).to_bytes(4, byteorder='little'))
-        f.write((id_embeds.shape[0]).to_bytes(4, byteorder='little'))
-        f.write(one.to_bytes(4, byteorder='little'))
-        f.write(one.to_bytes(4, byteorder='little'))
-        f.write(tensor_name.encode('ascii'))
-        f.write(binary_data)
-
+import os
+import sys
+
+import numpy as np
+import torch
+from diffusers.utils import load_image
+# pip install insightface==0.7.3
+from insightface.app import FaceAnalysis
+from insightface.data import get_image as ins_get_image
+from safetensors.torch import save_file
+
+### 
+# https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/165#issue-2055829543
+###
+class FaceAnalysis2(FaceAnalysis):
+    # NOTE: allows setting det_size for each detection call.
+    # the model allows it but the wrapping code from insightface
+    # doesn't show it, and people end up loading duplicate models
+    # for different sizes where there is absolutely no need to
+    def get(self, img, max_num=0, det_size=(640, 640)):
+        if det_size is not None:
+            self.det_model.input_size = det_size
+
+        return super().get(img, max_num)
+
+def analyze_faces(face_analysis: FaceAnalysis, img_data: np.ndarray, det_size=(640, 640)):
+    # NOTE: try detect faces, if no faces detected, lower det_size until it does
+    detection_sizes = [None] + [(size, size) for size in range(640, 256, -64)] + [(256, 256)]
+
+    for size in detection_sizes:
+        faces = face_analysis.get(img_data, det_size=size)
+        if len(faces) > 0:
+            return faces
+
+    return []
+
+if __name__ == "__main__":
+    #face_detector = FaceAnalysis2(providers=['CUDAExecutionProvider'], allowed_modules=['detection', 'recognition'])
+    face_detector = FaceAnalysis2(providers=['CPUExecutionProvider'], allowed_modules=['detection', 'recognition'])
+    face_detector.prepare(ctx_id=0, det_size=(640, 640))
+    #input_folder_name = './scarletthead_woman'
+    input_folder_name = sys.argv[1]
+    image_basename_list = os.listdir(input_folder_name)
+    image_path_list = sorted([os.path.join(input_folder_name, basename) for basename in image_basename_list])
+
+    input_id_images = []
+    for image_path in image_path_list:
+        input_id_images.append(load_image(image_path))
+    
+    id_embed_list = []
+    
+    for img in input_id_images:
+        img = np.array(img)
+        img = img[:, :, ::-1]
+        faces = analyze_faces(face_detector, img)
+        if len(faces) > 0:
+            id_embed_list.append(torch.from_numpy((faces[0]['embedding'])))
+    
+    if len(id_embed_list) == 0:
+        raise ValueError(f"No face detected in input image pool")
+    
+    id_embeds = torch.stack(id_embed_list)    
+    
+    # for r in id_embeds:
+    #     print(r)
+    # #torch.save(id_embeds, input_folder_name+'/id_embeds.pt');
+    # weights = dict()
+    # weights["id_embeds"] = id_embeds
+    # save_file(weights, input_folder_name+'/id_embeds.safetensors')
+
+    binary_data = id_embeds.numpy().tobytes()
+    two = 4
+    zero = 0
+    one = 1
+    tensor_name = "id_embeds"
+# Write binary data to a file
+    with open(input_folder_name+'/id_embeds.bin', "wb") as f:
+        f.write(two.to_bytes(4, byteorder='little'))
+        f.write((len(tensor_name)).to_bytes(4, byteorder='little'))
+        f.write(zero.to_bytes(4, byteorder='little'))
+        f.write((id_embeds.shape[1]).to_bytes(4, byteorder='little'))
+        f.write((id_embeds.shape[0]).to_bytes(4, byteorder='little'))
+        f.write(one.to_bytes(4, byteorder='little'))
+        f.write(one.to_bytes(4, byteorder='little'))
+        f.write(tensor_name.encode('ascii'))
+        f.write(binary_data)
+
     
\ No newline at end of file
diff --git a/cache_dit.hpp b/src/cache_dit.hpp
similarity index 100%
rename from cache_dit.hpp
rename to src/cache_dit.hpp
diff --git a/clip.hpp b/src/clip.hpp
similarity index 100%
rename from clip.hpp
rename to src/clip.hpp
diff --git a/common.hpp b/src/common.hpp
similarity index 100%
rename from common.hpp
rename to src/common.hpp
diff --git a/conditioner.hpp b/src/conditioner.hpp
similarity index 100%
rename from conditioner.hpp
rename to src/conditioner.hpp
diff --git a/control.hpp b/src/control.hpp
similarity index 100%
rename from control.hpp
rename to src/control.hpp
diff --git a/denoiser.hpp b/src/denoiser.hpp
similarity index 100%
rename from denoiser.hpp
rename to src/denoiser.hpp
diff --git a/diffusion_model.hpp b/src/diffusion_model.hpp
similarity index 100%
rename from diffusion_model.hpp
rename to src/diffusion_model.hpp
diff --git a/easycache.hpp b/src/easycache.hpp
similarity index 100%
rename from easycache.hpp
rename to src/easycache.hpp
diff --git a/esrgan.hpp b/src/esrgan.hpp
similarity index 100%
rename from esrgan.hpp
rename to src/esrgan.hpp
diff --git a/flux.hpp b/src/flux.hpp
similarity index 100%
rename from flux.hpp
rename to src/flux.hpp
diff --git a/ggml_extend.hpp b/src/ggml_extend.hpp
similarity index 100%
rename from ggml_extend.hpp
rename to src/ggml_extend.hpp
diff --git a/gguf_reader.hpp b/src/gguf_reader.hpp
similarity index 100%
rename from gguf_reader.hpp
rename to src/gguf_reader.hpp
diff --git a/gits_noise.inl b/src/gits_noise.inl
similarity index 100%
rename from gits_noise.inl
rename to src/gits_noise.inl
diff --git a/latent-preview.h b/src/latent-preview.h
similarity index 97%
rename from latent-preview.h
rename to src/latent-preview.h
index 76e17415..85c8e0dc 100644
--- a/latent-preview.h
+++ b/src/latent-preview.h
@@ -1,234 +1,234 @@
-#include <cstddef>
-#include <cstdint>
-#include "ggml.h"
-
-const float wan_21_latent_rgb_proj[16][3] = {
-    {0.015123f, -0.148418f, 0.479828f},
-    {0.003652f, -0.010680f, -0.037142f},
-    {0.212264f, 0.063033f, 0.016779f},
-    {0.232999f, 0.406476f, 0.220125f},
-    {-0.051864f, -0.082384f, -0.069396f},
-    {0.085005f, -0.161492f, 0.010689f},
-    {-0.245369f, -0.506846f, -0.117010f},
-    {-0.151145f, 0.017721f, 0.007207f},
-    {-0.293239f, -0.207936f, -0.421135f},
-    {-0.187721f, 0.050783f, 0.177649f},
-    {-0.013067f, 0.265964f, 0.166578f},
-    {0.028327f, 0.109329f, 0.108642f},
-    {-0.205343f, 0.043991f, 0.148914f},
-    {0.014307f, -0.048647f, -0.007219f},
-    {0.217150f, 0.053074f, 0.319923f},
-    {0.155357f, 0.083156f, 0.064780f}};
-float wan_21_latent_rgb_bias[3] = {-0.270270f, -0.234976f, -0.456853f};
-
-const float wan_22_latent_rgb_proj[48][3] = {
-    {0.017126f, -0.027230f, -0.019257f},
-    {-0.113739f, -0.028715f, -0.022885f},
-    {-0.000106f, 0.021494f, 0.004629f},
-    {-0.013273f, -0.107137f, -0.033638f},
-    {-0.000381f, 0.000279f, 0.025877f},
-    {-0.014216f, -0.003975f, 0.040528f},
-    {0.001638f, -0.000748f, 0.011022f},
-    {0.029238f, -0.006697f, 0.035933f},
-    {0.021641f, -0.015874f, 0.040531f},
-    {-0.101984f, -0.070160f, -0.028855f},
-    {0.033207f, -0.021068f, 0.002663f},
-    {-0.104711f, 0.121673f, 0.102981f},
-    {0.082647f, -0.004991f, 0.057237f},
-    {-0.027375f, 0.031581f, 0.006868f},
-    {-0.045434f, 0.029444f, 0.019287f},
-    {-0.046572f, -0.012537f, 0.006675f},
-    {0.074709f, 0.033690f, 0.025289f},
-    {-0.008251f, -0.002745f, -0.006999f},
-    {0.012685f, -0.061856f, -0.048658f},
-    {0.042304f, -0.007039f, 0.000295f},
-    {-0.007644f, -0.060843f, -0.033142f},
-    {0.159909f, 0.045628f, 0.367541f},
-    {0.095171f, 0.086438f, 0.010271f},
-    {0.006812f, 0.019643f, 0.029637f},
-    {0.003467f, -0.010705f, 0.014252f},
-    {-0.099681f, -0.066272f, -0.006243f},
-    {0.047357f, 0.037040f, 0.000185f},
-    {-0.041797f, -0.089225f, -0.032257f},
-    {0.008928f, 0.017028f, 0.018684f},
-    {-0.042255f, 0.016045f, 0.006849f},
-    {0.011268f, 0.036462f, 0.037387f},
-    {0.011553f, -0.016375f, -0.048589f},
-    {0.046266f, -0.027189f, 0.056979f},
-    {0.009640f, -0.017576f, 0.030324f},
-    {-0.045794f, -0.036083f, -0.010616f},
-    {0.022418f, 0.039783f, -0.032939f},
-    {-0.052714f, -0.015525f, 0.007438f},
-    {0.193004f, 0.223541f, 0.264175f},
-    {-0.059406f, -0.008188f, 0.022867f},
-    {-0.156742f, -0.263791f, -0.007385f},
-    {-0.015717f, 0.016570f, 0.033969f},
-    {0.037969f, 0.109835f, 0.200449f},
-    {-0.000782f, -0.009566f, -0.008058f},
-    {0.010709f, 0.052960f, -0.044195f},
-    {0.017271f, 0.045839f, 0.034569f},
-    {0.009424f, 0.013088f, -0.001714f},
-    {-0.024805f, -0.059378f, -0.033756f},
-    {-0.078293f, 0.029070f, 0.026129f}};
-float wan_22_latent_rgb_bias[3] = {0.013160f, -0.096492f, -0.071323f};
-
-const float flux_latent_rgb_proj[16][3] = {
-    {-0.041168f, 0.019917f, 0.097253f},
-    {0.028096f, 0.026730f, 0.129576f},
-    {0.065618f, -0.067950f, -0.014651f},
-    {-0.012998f, -0.014762f, 0.081251f},
-    {0.078567f, 0.059296f, -0.024687f},
-    {-0.015987f, -0.003697f, 0.005012f},
-    {0.033605f, 0.138999f, 0.068517f},
-    {-0.024450f, -0.063567f, -0.030101f},
-    {-0.040194f, -0.016710f, 0.127185f},
-    {0.112681f, 0.088764f, -0.041940f},
-    {-0.023498f, 0.093664f, 0.025543f},
-    {0.082899f, 0.048320f, 0.007491f},
-    {0.075712f, 0.074139f, 0.081965f},
-    {-0.143501f, 0.018263f, -0.136138f},
-    {-0.025767f, -0.082035f, -0.040023f},
-    {-0.111849f, -0.055589f, -0.032361f}};
-float flux_latent_rgb_bias[3] = {0.024600f, -0.006937f, -0.008089f};
-
-const float flux2_latent_rgb_proj[32][3] = {
-    {0.000736f, -0.008385f, -0.019710f},
-    {-0.001352f, -0.016392f, 0.020693f},
-    {-0.006376f, 0.002428f, 0.036736f},
-    {0.039384f, 0.074167f, 0.119789f},
-    {0.007464f, -0.005705f, -0.004734f},
-    {-0.004086f, 0.005287f, -0.000409f},
-    {-0.032835f, 0.050802f, -0.028120f},
-    {-0.003158f, -0.000835f, 0.000406f},
-    {-0.112840f, -0.084337f, -0.023083f},
-    {0.001462f, -0.006656f, 0.000549f},
-    {-0.009980f, -0.007480f, 0.009702f},
-    {0.032540f, 0.000214f, -0.061388f},
-    {0.011023f, 0.000694f, 0.007143f},
-    {-0.001468f, -0.006723f, -0.001678f},
-    {-0.005921f, -0.010320f, -0.003907f},
-    {-0.028434f, 0.027584f, 0.018457f},
-    {0.014349f, 0.011523f, 0.000441f},
-    {0.009874f, 0.003081f, 0.001507f},
-    {0.002218f, 0.005712f, 0.001563f},
-    {0.053010f, -0.019844f, 0.008683f},
-    {-0.002507f, 0.005384f, 0.000938f},
-    {-0.002177f, -0.011366f, 0.003559f},
-    {-0.000261f, 0.015121f, -0.003240f},
-    {-0.003944f, -0.002083f, 0.005043f},
-    {-0.009138f, 0.011336f, 0.003781f},
-    {0.011429f, 0.003985f, -0.003855f},
-    {0.010518f, -0.005586f, 0.010131f},
-    {0.007883f, 0.002912f, -0.001473f},
-    {-0.003318f, -0.003160f, 0.003684f},
-    {-0.034560f, -0.008740f, 0.012996f},
-    {0.000166f, 0.001079f, -0.012153f},
-    {0.017772f, 0.000937f, -0.011953f}};
-float flux2_latent_rgb_bias[3] = {-0.028738f, -0.098463f, -0.107619f};
-
-// This one was taken straight from
-// https://github.com/Stability-AI/sd3.5/blob/8565799a3b41eb0c7ba976d18375f0f753f56402/sd3_impls.py#L288-L303
-// (MiT Licence)
-const float sd3_latent_rgb_proj[16][3] = {
-    {-0.0645f, 0.0177f, 0.1052f},
-    {0.0028f, 0.0312f, 0.0650f},
-    {0.1848f, 0.0762f, 0.0360f},
-    {0.0944f, 0.0360f, 0.0889f},
-    {0.0897f, 0.0506f, -0.0364f},
-    {-0.0020f, 0.1203f, 0.0284f},
-    {0.0855f, 0.0118f, 0.0283f},
-    {-0.0539f, 0.0658f, 0.1047f},
-    {-0.0057f, 0.0116f, 0.0700f},
-    {-0.0412f, 0.0281f, -0.0039f},
-    {0.1106f, 0.1171f, 0.1220f},
-    {-0.0248f, 0.0682f, -0.0481f},
-    {0.0815f, 0.0846f, 0.1207f},
-    {-0.0120f, -0.0055f, -0.0867f},
-    {-0.0749f, -0.0634f, -0.0456f},
-    {-0.1418f, -0.1457f, -0.1259f},
-};
-float sd3_latent_rgb_bias[3] = {0, 0, 0};
-
-const float sdxl_latent_rgb_proj[4][3] = {
-    {0.258303f, 0.277640f, 0.329699f},
-    {-0.299701f, 0.105446f, 0.014194f},
-    {0.050522f, 0.186163f, -0.143257f},
-    {-0.211938f, -0.149892f, -0.080036f}};
-float sdxl_latent_rgb_bias[3] = {0.144381f, -0.033313f, 0.007061f};
-
-const float sd_latent_rgb_proj[4][3] = {
-    {0.337366f, 0.216344f, 0.257386f},
-    {0.165636f, 0.386828f, 0.046994f},
-    {-0.267803f, 0.237036f, 0.223517f},
-    {-0.178022f, -0.200862f, -0.678514f}};
-float sd_latent_rgb_bias[3] = {-0.017478f, -0.055834f, -0.105825f};
-
-void preview_latent_video(uint8_t* buffer, struct ggml_tensor* latents, const float (*latent_rgb_proj)[3], const float latent_rgb_bias[3], int patch_size) {
-    size_t buffer_head = 0;
-
-    uint32_t latent_width  = static_cast<uint32_t>(latents->ne[0]);
-    uint32_t latent_height = static_cast<uint32_t>(latents->ne[1]);
-    uint32_t dim           = static_cast<uint32_t>(latents->ne[ggml_n_dims(latents) - 1]);
-    uint32_t frames        = 1;
-    if (ggml_n_dims(latents) == 4) {
-        frames = static_cast<uint32_t>(latents->ne[2]);
-    }
-
-    uint32_t rgb_width  = latent_width * patch_size;
-    uint32_t rgb_height = latent_height * patch_size;
-
-    uint32_t unpatched_dim = dim / (patch_size * patch_size);
-
-    for (uint32_t k = 0; k < frames; k++) {
-        for (uint32_t rgb_x = 0; rgb_x < rgb_width; rgb_x++) {
-            for (uint32_t rgb_y = 0; rgb_y < rgb_height; rgb_y++) {
-                int latent_x = rgb_x / patch_size;
-                int latent_y = rgb_y / patch_size;
-
-                int channel_offset = 0;
-                if (patch_size > 1) {
-                    channel_offset = ((rgb_y % patch_size) * patch_size + (rgb_x % patch_size));
-                }
-
-                size_t latent_id = (latent_x * latents->nb[0] + latent_y * latents->nb[1] + k * latents->nb[2]);
-
-                // should be incremented by 1 for each pixel
-                size_t pixel_id = k * rgb_width * rgb_height + rgb_y * rgb_width + rgb_x;
-
-                float r = 0, g = 0, b = 0;
-                if (latent_rgb_proj != nullptr) {
-                    for (uint32_t d = 0; d < unpatched_dim; d++) {
-                        float value = *(float*)((char*)latents->data + latent_id + (d * patch_size * patch_size + channel_offset) * latents->nb[ggml_n_dims(latents) - 1]);
-                        r += value * latent_rgb_proj[d][0];
-                        g += value * latent_rgb_proj[d][1];
-                        b += value * latent_rgb_proj[d][2];
-                    }
-                } else {
-                    // interpret first 3 channels as RGB
-                    r = *(float*)((char*)latents->data + latent_id + 0 * latents->nb[ggml_n_dims(latents) - 1]);
-                    g = *(float*)((char*)latents->data + latent_id + 1 * latents->nb[ggml_n_dims(latents) - 1]);
-                    b = *(float*)((char*)latents->data + latent_id + 2 * latents->nb[ggml_n_dims(latents) - 1]);
-                }
-                if (latent_rgb_bias != nullptr) {
-                    // bias
-                    r += latent_rgb_bias[0];
-                    g += latent_rgb_bias[1];
-                    b += latent_rgb_bias[2];
-                }
-                // change range
-                r = r * .5f + .5f;
-                g = g * .5f + .5f;
-                b = b * .5f + .5f;
-
-                // clamp rgb values to [0,1] range
-                r = r >= 0 ? r <= 1 ? r : 1 : 0;
-                g = g >= 0 ? g <= 1 ? g : 1 : 0;
-                b = b >= 0 ? b <= 1 ? b : 1 : 0;
-
-                buffer[pixel_id * 3 + 0] = (uint8_t)(r * 255);
-                buffer[pixel_id * 3 + 1] = (uint8_t)(g * 255);
-                buffer[pixel_id * 3 + 2] = (uint8_t)(b * 255);
-            }
-        }
-    }
-}
+#include <cstddef>
+#include <cstdint>
+#include "ggml.h"
+
+const float wan_21_latent_rgb_proj[16][3] = {
+    {0.015123f, -0.148418f, 0.479828f},
+    {0.003652f, -0.010680f, -0.037142f},
+    {0.212264f, 0.063033f, 0.016779f},
+    {0.232999f, 0.406476f, 0.220125f},
+    {-0.051864f, -0.082384f, -0.069396f},
+    {0.085005f, -0.161492f, 0.010689f},
+    {-0.245369f, -0.506846f, -0.117010f},
+    {-0.151145f, 0.017721f, 0.007207f},
+    {-0.293239f, -0.207936f, -0.421135f},
+    {-0.187721f, 0.050783f, 0.177649f},
+    {-0.013067f, 0.265964f, 0.166578f},
+    {0.028327f, 0.109329f, 0.108642f},
+    {-0.205343f, 0.043991f, 0.148914f},
+    {0.014307f, -0.048647f, -0.007219f},
+    {0.217150f, 0.053074f, 0.319923f},
+    {0.155357f, 0.083156f, 0.064780f}};
+float wan_21_latent_rgb_bias[3] = {-0.270270f, -0.234976f, -0.456853f};
+
+const float wan_22_latent_rgb_proj[48][3] = {
+    {0.017126f, -0.027230f, -0.019257f},
+    {-0.113739f, -0.028715f, -0.022885f},
+    {-0.000106f, 0.021494f, 0.004629f},
+    {-0.013273f, -0.107137f, -0.033638f},
+    {-0.000381f, 0.000279f, 0.025877f},
+    {-0.014216f, -0.003975f, 0.040528f},
+    {0.001638f, -0.000748f, 0.011022f},
+    {0.029238f, -0.006697f, 0.035933f},
+    {0.021641f, -0.015874f, 0.040531f},
+    {-0.101984f, -0.070160f, -0.028855f},
+    {0.033207f, -0.021068f, 0.002663f},
+    {-0.104711f, 0.121673f, 0.102981f},
+    {0.082647f, -0.004991f, 0.057237f},
+    {-0.027375f, 0.031581f, 0.006868f},
+    {-0.045434f, 0.029444f, 0.019287f},
+    {-0.046572f, -0.012537f, 0.006675f},
+    {0.074709f, 0.033690f, 0.025289f},
+    {-0.008251f, -0.002745f, -0.006999f},
+    {0.012685f, -0.061856f, -0.048658f},
+    {0.042304f, -0.007039f, 0.000295f},
+    {-0.007644f, -0.060843f, -0.033142f},
+    {0.159909f, 0.045628f, 0.367541f},
+    {0.095171f, 0.086438f, 0.010271f},
+    {0.006812f, 0.019643f, 0.029637f},
+    {0.003467f, -0.010705f, 0.014252f},
+    {-0.099681f, -0.066272f, -0.006243f},
+    {0.047357f, 0.037040f, 0.000185f},
+    {-0.041797f, -0.089225f, -0.032257f},
+    {0.008928f, 0.017028f, 0.018684f},
+    {-0.042255f, 0.016045f, 0.006849f},
+    {0.011268f, 0.036462f, 0.037387f},
+    {0.011553f, -0.016375f, -0.048589f},
+    {0.046266f, -0.027189f, 0.056979f},
+    {0.009640f, -0.017576f, 0.030324f},
+    {-0.045794f, -0.036083f, -0.010616f},
+    {0.022418f, 0.039783f, -0.032939f},
+    {-0.052714f, -0.015525f, 0.007438f},
+    {0.193004f, 0.223541f, 0.264175f},
+    {-0.059406f, -0.008188f, 0.022867f},
+    {-0.156742f, -0.263791f, -0.007385f},
+    {-0.015717f, 0.016570f, 0.033969f},
+    {0.037969f, 0.109835f, 0.200449f},
+    {-0.000782f, -0.009566f, -0.008058f},
+    {0.010709f, 0.052960f, -0.044195f},
+    {0.017271f, 0.045839f, 0.034569f},
+    {0.009424f, 0.013088f, -0.001714f},
+    {-0.024805f, -0.059378f, -0.033756f},
+    {-0.078293f, 0.029070f, 0.026129f}};
+float wan_22_latent_rgb_bias[3] = {0.013160f, -0.096492f, -0.071323f};
+
+const float flux_latent_rgb_proj[16][3] = {
+    {-0.041168f, 0.019917f, 0.097253f},
+    {0.028096f, 0.026730f, 0.129576f},
+    {0.065618f, -0.067950f, -0.014651f},
+    {-0.012998f, -0.014762f, 0.081251f},
+    {0.078567f, 0.059296f, -0.024687f},
+    {-0.015987f, -0.003697f, 0.005012f},
+    {0.033605f, 0.138999f, 0.068517f},
+    {-0.024450f, -0.063567f, -0.030101f},
+    {-0.040194f, -0.016710f, 0.127185f},
+    {0.112681f, 0.088764f, -0.041940f},
+    {-0.023498f, 0.093664f, 0.025543f},
+    {0.082899f, 0.048320f, 0.007491f},
+    {0.075712f, 0.074139f, 0.081965f},
+    {-0.143501f, 0.018263f, -0.136138f},
+    {-0.025767f, -0.082035f, -0.040023f},
+    {-0.111849f, -0.055589f, -0.032361f}};
+float flux_latent_rgb_bias[3] = {0.024600f, -0.006937f, -0.008089f};
+
+const float flux2_latent_rgb_proj[32][3] = {
+    {0.000736f, -0.008385f, -0.019710f},
+    {-0.001352f, -0.016392f, 0.020693f},
+    {-0.006376f, 0.002428f, 0.036736f},
+    {0.039384f, 0.074167f, 0.119789f},
+    {0.007464f, -0.005705f, -0.004734f},
+    {-0.004086f, 0.005287f, -0.000409f},
+    {-0.032835f, 0.050802f, -0.028120f},
+    {-0.003158f, -0.000835f, 0.000406f},
+    {-0.112840f, -0.084337f, -0.023083f},
+    {0.001462f, -0.006656f, 0.000549f},
+    {-0.009980f, -0.007480f, 0.009702f},
+    {0.032540f, 0.000214f, -0.061388f},
+    {0.011023f, 0.000694f, 0.007143f},
+    {-0.001468f, -0.006723f, -0.001678f},
+    {-0.005921f, -0.010320f, -0.003907f},
+    {-0.028434f, 0.027584f, 0.018457f},
+    {0.014349f, 0.011523f, 0.000441f},
+    {0.009874f, 0.003081f, 0.001507f},
+    {0.002218f, 0.005712f, 0.001563f},
+    {0.053010f, -0.019844f, 0.008683f},
+    {-0.002507f, 0.005384f, 0.000938f},
+    {-0.002177f, -0.011366f, 0.003559f},
+    {-0.000261f, 0.015121f, -0.003240f},
+    {-0.003944f, -0.002083f, 0.005043f},
+    {-0.009138f, 0.011336f, 0.003781f},
+    {0.011429f, 0.003985f, -0.003855f},
+    {0.010518f, -0.005586f, 0.010131f},
+    {0.007883f, 0.002912f, -0.001473f},
+    {-0.003318f, -0.003160f, 0.003684f},
+    {-0.034560f, -0.008740f, 0.012996f},
+    {0.000166f, 0.001079f, -0.012153f},
+    {0.017772f, 0.000937f, -0.011953f}};
+float flux2_latent_rgb_bias[3] = {-0.028738f, -0.098463f, -0.107619f};
+
+// This one was taken straight from
+// https://github.com/Stability-AI/sd3.5/blob/8565799a3b41eb0c7ba976d18375f0f753f56402/sd3_impls.py#L288-L303
+// (MiT Licence)
+const float sd3_latent_rgb_proj[16][3] = {
+    {-0.0645f, 0.0177f, 0.1052f},
+    {0.0028f, 0.0312f, 0.0650f},
+    {0.1848f, 0.0762f, 0.0360f},
+    {0.0944f, 0.0360f, 0.0889f},
+    {0.0897f, 0.0506f, -0.0364f},
+    {-0.0020f, 0.1203f, 0.0284f},
+    {0.0855f, 0.0118f, 0.0283f},
+    {-0.0539f, 0.0658f, 0.1047f},
+    {-0.0057f, 0.0116f, 0.0700f},
+    {-0.0412f, 0.0281f, -0.0039f},
+    {0.1106f, 0.1171f, 0.1220f},
+    {-0.0248f, 0.0682f, -0.0481f},
+    {0.0815f, 0.0846f, 0.1207f},
+    {-0.0120f, -0.0055f, -0.0867f},
+    {-0.0749f, -0.0634f, -0.0456f},
+    {-0.1418f, -0.1457f, -0.1259f},
+};
+float sd3_latent_rgb_bias[3] = {0, 0, 0};
+
+const float sdxl_latent_rgb_proj[4][3] = {
+    {0.258303f, 0.277640f, 0.329699f},
+    {-0.299701f, 0.105446f, 0.014194f},
+    {0.050522f, 0.186163f, -0.143257f},
+    {-0.211938f, -0.149892f, -0.080036f}};
+float sdxl_latent_rgb_bias[3] = {0.144381f, -0.033313f, 0.007061f};
+
+const float sd_latent_rgb_proj[4][3] = {
+    {0.337366f, 0.216344f, 0.257386f},
+    {0.165636f, 0.386828f, 0.046994f},
+    {-0.267803f, 0.237036f, 0.223517f},
+    {-0.178022f, -0.200862f, -0.678514f}};
+float sd_latent_rgb_bias[3] = {-0.017478f, -0.055834f, -0.105825f};
+
+void preview_latent_video(uint8_t* buffer, struct ggml_tensor* latents, const float (*latent_rgb_proj)[3], const float latent_rgb_bias[3], int patch_size) {
+    size_t buffer_head = 0;
+
+    uint32_t latent_width  = static_cast<uint32_t>(latents->ne[0]);
+    uint32_t latent_height = static_cast<uint32_t>(latents->ne[1]);
+    uint32_t dim           = static_cast<uint32_t>(latents->ne[ggml_n_dims(latents) - 1]);
+    uint32_t frames        = 1;
+    if (ggml_n_dims(latents) == 4) {
+        frames = static_cast<uint32_t>(latents->ne[2]);
+    }
+
+    uint32_t rgb_width  = latent_width * patch_size;
+    uint32_t rgb_height = latent_height * patch_size;
+
+    uint32_t unpatched_dim = dim / (patch_size * patch_size);
+
+    for (uint32_t k = 0; k < frames; k++) {
+        for (uint32_t rgb_x = 0; rgb_x < rgb_width; rgb_x++) {
+            for (uint32_t rgb_y = 0; rgb_y < rgb_height; rgb_y++) {
+                int latent_x = rgb_x / patch_size;
+                int latent_y = rgb_y / patch_size;
+
+                int channel_offset = 0;
+                if (patch_size > 1) {
+                    channel_offset = ((rgb_y % patch_size) * patch_size + (rgb_x % patch_size));
+                }
+
+                size_t latent_id = (latent_x * latents->nb[0] + latent_y * latents->nb[1] + k * latents->nb[2]);
+
+                // should be incremented by 1 for each pixel
+                size_t pixel_id = k * rgb_width * rgb_height + rgb_y * rgb_width + rgb_x;
+
+                float r = 0, g = 0, b = 0;
+                if (latent_rgb_proj != nullptr) {
+                    for (uint32_t d = 0; d < unpatched_dim; d++) {
+                        float value = *(float*)((char*)latents->data + latent_id + (d * patch_size * patch_size + channel_offset) * latents->nb[ggml_n_dims(latents) - 1]);
+                        r += value * latent_rgb_proj[d][0];
+                        g += value * latent_rgb_proj[d][1];
+                        b += value * latent_rgb_proj[d][2];
+                    }
+                } else {
+                    // interpret first 3 channels as RGB
+                    r = *(float*)((char*)latents->data + latent_id + 0 * latents->nb[ggml_n_dims(latents) - 1]);
+                    g = *(float*)((char*)latents->data + latent_id + 1 * latents->nb[ggml_n_dims(latents) - 1]);
+                    b = *(float*)((char*)latents->data + latent_id + 2 * latents->nb[ggml_n_dims(latents) - 1]);
+                }
+                if (latent_rgb_bias != nullptr) {
+                    // bias
+                    r += latent_rgb_bias[0];
+                    g += latent_rgb_bias[1];
+                    b += latent_rgb_bias[2];
+                }
+                // change range
+                r = r * .5f + .5f;
+                g = g * .5f + .5f;
+                b = b * .5f + .5f;
+
+                // clamp rgb values to [0,1] range
+                r = r >= 0 ? r <= 1 ? r : 1 : 0;
+                g = g >= 0 ? g <= 1 ? g : 1 : 0;
+                b = b >= 0 ? b <= 1 ? b : 1 : 0;
+
+                buffer[pixel_id * 3 + 0] = (uint8_t)(r * 255);
+                buffer[pixel_id * 3 + 1] = (uint8_t)(g * 255);
+                buffer[pixel_id * 3 + 2] = (uint8_t)(b * 255);
+            }
+        }
+    }
+}
diff --git a/llm.hpp b/src/llm.hpp
similarity index 100%
rename from llm.hpp
rename to src/llm.hpp
diff --git a/lora.hpp b/src/lora.hpp
similarity index 100%
rename from lora.hpp
rename to src/lora.hpp
diff --git a/ltxv.hpp b/src/ltxv.hpp
similarity index 100%
rename from ltxv.hpp
rename to src/ltxv.hpp
diff --git a/mmdit.hpp b/src/mmdit.hpp
similarity index 100%
rename from mmdit.hpp
rename to src/mmdit.hpp
diff --git a/model.cpp b/src/model.cpp
similarity index 100%
rename from model.cpp
rename to src/model.cpp
diff --git a/model.h b/src/model.h
similarity index 100%
rename from model.h
rename to src/model.h
diff --git a/name_conversion.cpp b/src/name_conversion.cpp
similarity index 100%
rename from name_conversion.cpp
rename to src/name_conversion.cpp
diff --git a/name_conversion.h b/src/name_conversion.h
similarity index 100%
rename from name_conversion.h
rename to src/name_conversion.h
diff --git a/ordered_map.hpp b/src/ordered_map.hpp
similarity index 100%
rename from ordered_map.hpp
rename to src/ordered_map.hpp
diff --git a/pmid.hpp b/src/pmid.hpp
similarity index 100%
rename from pmid.hpp
rename to src/pmid.hpp
diff --git a/preprocessing.hpp b/src/preprocessing.hpp
similarity index 100%
rename from preprocessing.hpp
rename to src/preprocessing.hpp
diff --git a/qwen_image.hpp b/src/qwen_image.hpp
similarity index 100%
rename from qwen_image.hpp
rename to src/qwen_image.hpp
diff --git a/rng.hpp b/src/rng.hpp
similarity index 100%
rename from rng.hpp
rename to src/rng.hpp
diff --git a/rng_mt19937.hpp b/src/rng_mt19937.hpp
similarity index 100%
rename from rng_mt19937.hpp
rename to src/rng_mt19937.hpp
diff --git a/rng_philox.hpp b/src/rng_philox.hpp
similarity index 100%
rename from rng_philox.hpp
rename to src/rng_philox.hpp
diff --git a/rope.hpp b/src/rope.hpp
similarity index 100%
rename from rope.hpp
rename to src/rope.hpp
diff --git a/stable-diffusion.cpp b/src/stable-diffusion.cpp
similarity index 100%
rename from stable-diffusion.cpp
rename to src/stable-diffusion.cpp
diff --git a/t5.hpp b/src/t5.hpp
similarity index 100%
rename from t5.hpp
rename to src/t5.hpp
diff --git a/tae.hpp b/src/tae.hpp
similarity index 100%
rename from tae.hpp
rename to src/tae.hpp
diff --git a/tokenize_util.cpp b/src/tokenize_util.cpp
similarity index 100%
rename from tokenize_util.cpp
rename to src/tokenize_util.cpp
diff --git a/tokenize_util.h b/src/tokenize_util.h
similarity index 100%
rename from tokenize_util.h
rename to src/tokenize_util.h
diff --git a/ucache.hpp b/src/ucache.hpp
similarity index 100%
rename from ucache.hpp
rename to src/ucache.hpp
diff --git a/unet.hpp b/src/unet.hpp
similarity index 100%
rename from unet.hpp
rename to src/unet.hpp
diff --git a/upscaler.cpp b/src/upscaler.cpp
similarity index 100%
rename from upscaler.cpp
rename to src/upscaler.cpp
diff --git a/util.cpp b/src/util.cpp
similarity index 100%
rename from util.cpp
rename to src/util.cpp
diff --git a/util.h b/src/util.h
similarity index 100%
rename from util.h
rename to src/util.h
diff --git a/vae.hpp b/src/vae.hpp
similarity index 100%
rename from vae.hpp
rename to src/vae.hpp
diff --git a/version.cpp b/src/version.cpp
similarity index 100%
rename from version.cpp
rename to src/version.cpp
diff --git a/vocab.hpp b/src/vocab.hpp
similarity index 100%
rename from vocab.hpp
rename to src/vocab.hpp
diff --git a/vocab_mistral.hpp b/src/vocab_mistral.hpp
similarity index 100%
rename from vocab_mistral.hpp
rename to src/vocab_mistral.hpp
diff --git a/vocab_qwen.hpp b/src/vocab_qwen.hpp
similarity index 100%
rename from vocab_qwen.hpp
rename to src/vocab_qwen.hpp
diff --git a/vocab_umt5.hpp b/src/vocab_umt5.hpp
similarity index 100%
rename from vocab_umt5.hpp
rename to src/vocab_umt5.hpp
diff --git a/wan.hpp b/src/wan.hpp
similarity index 100%
rename from wan.hpp
rename to src/wan.hpp
diff --git a/z_image.hpp b/src/z_image.hpp
similarity index 100%
rename from z_image.hpp
rename to src/z_image.hpp

From 45ce78a3ae2fa15c9a2da053df30029b768d53f5 Mon Sep 17 00:00:00 2001
From: Mario Limonciello <superm1@kernel.org>
Date: Tue, 10 Feb 2026 09:19:28 -0600
Subject: [PATCH 79/97] ci: correct rocm artifact of linux (#1269)

---
 .github/workflows/build.yml | 55 ++++++++++++++++---------------------
 1 file changed, 24 insertions(+), 31 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 3c05caf5..666887d9 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -535,31 +535,30 @@ jobs:
           # Add ROCm to PATH for current session
           echo "/opt/rocm/bin" >> $GITHUB_PATH
 
-          # Build case pattern from GPU_TARGETS
-          PATTERN=$(printf '%s' "$GPU_TARGETS" | sed 's/;/\*|\*/g')
-          PATTERN="*${PATTERN}*"
+          # Build regex pattern from ${{ env.GPU_TARGETS }} (match target as substring)
+          TARGET_REGEX="($(printf '%s' "${{ env.GPU_TARGETS }}" | sed 's/;/|/g'))"
 
           # Remove library files for architectures we're not building for to save disk space
           echo "Cleaning up unneeded architecture files..."
           cd /opt/rocm/lib/rocblas/library
           # Keep only our target architectures
           for file in *; do
-            case "$file" in
-            $PATTERN)
-              ;;
-            *)
-              sudo rm -f "$file" ;;
-            esac;
+            if printf '%s' "$file" | grep -q 'gfx'; then
+              if ! printf '%s' "$file" | grep -Eq "$TARGET_REGEX"; then
+                echo "Removing $file" &&
+                sudo rm -f "$file";
+              fi
+            fi
           done
 
           cd /opt/rocm/lib/hipblaslt/library
           for file in *; do
-            case "$file" in
-              $PATTERN)
-                ;;
-              *)
-                sudo rm -f "$file" ;;
-            esac;
+            if printf '%s' "$file" | grep -q 'gfx'; then
+              if ! printf '%s' "$file" | grep -Eq "$TARGET_REGEX"; then
+                echo "Removing $file" &&
+                sudo rm -f "$file";
+              fi
+            fi
           done
 
       - name: Build
@@ -592,21 +591,15 @@ jobs:
           cp ggml/LICENSE ./build/bin/ggml.txt
           cp LICENSE ./build/bin/stable-diffusion.cpp.txt
 
-          # Create directories for ROCm libraries
-          mkdir -p ./build/bin/rocblas/library
-          mkdir -p ./build/bin/hipblaslt/library
-
-          # Copy ROCm runtime libraries (use || true to continue if files don't exist)
-          cp /opt/rocm/lib/librocsparse.so* ./build/bin/ || true
-          cp /opt/rocm/lib/libhsa-runtime64.so* ./build/bin/ || true
-          cp /opt/rocm/lib/libamdhip64.so* ./build/bin/ || true
-          cp /opt/rocm/lib/libhipblas.so* ./build/bin/ || true
-          cp /opt/rocm/lib/libhipblaslt.so* ./build/bin/ || true
-          cp /opt/rocm/lib/librocblas.so* ./build/bin/ || true
-
-          # Copy library files (already filtered to target architectures)
-          cp /opt/rocm/lib/rocblas/library/* ./build/bin/rocblas/library/ || true
-          cp /opt/rocm/lib/hipblaslt/library/* ./build/bin/hipblaslt/library/ || true
+          # Move ROCm runtime libraries (to avoid double space consumption)
+          sudo mv /opt/rocm/lib/librocsparse.so* ./build/bin/
+          sudo mv /opt/rocm/lib/libhsa-runtime64.so* ./build/bin/
+          sudo mv /opt/rocm/lib/libamdhip64.so* ./build/bin/
+          sudo mv /opt/rocm/lib/libhipblas.so* ./build/bin/
+          sudo mv /opt/rocm/lib/libhipblaslt.so* ./build/bin/
+          sudo mv /opt/rocm/lib/librocblas.so* ./build/bin/
+          sudo mv /opt/rocm/lib/rocblas/ ./build/bin/
+          sudo mv /opt/rocm/lib/hipblaslt/ ./build/bin/
 
       - name: Fetch system info
         id: system-info
@@ -622,7 +615,7 @@ jobs:
         run: |
           cp ggml/LICENSE ./build/bin/ggml.txt
           cp LICENSE ./build/bin/stable-diffusion.cpp.txt
-          zip -j sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm.zip ./build/bin/*
+          zip -y -r sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm.zip ./build/bin
 
       - name: Upload artifacts
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}

From adea27222559c929837e36ca31c097c71473c59c Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Tue, 10 Feb 2026 13:42:50 -0300
Subject: [PATCH 80/97] feat(server): use image and command-line dimensions by
 default on server (#1262)

---
 examples/server/main.cpp | 165 ++++++++++++++++++++++++++-------------
 1 file changed, 109 insertions(+), 56 deletions(-)

diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index 2173b1fb..0fb10c7a 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -404,8 +404,8 @@ int main(int argc, const char** argv) {
             std::string size          = j.value("size", "");
             std::string output_format = j.value("output_format", "png");
             int output_compression    = j.value("output_compression", 100);
-            int width                 = 512;
-            int height                = 512;
+            int width                 = default_gen_params.width > 0 ? default_gen_params.width : 512;
+            int height                = default_gen_params.width > 0 ? default_gen_params.height : 512;
             if (!size.empty()) {
                 auto pos = size.find('x');
                 if (pos != std::string::npos) {
@@ -593,7 +593,7 @@ int main(int argc, const char** argv) {
             n = std::clamp(n, 1, 8);
 
             std::string size = req.form.get_field("size");
-            int width = 512, height = 512;
+            int width = -1, height = -1;
             if (!size.empty()) {
                 auto pos = size.find('x');
                 if (pos != std::string::npos) {
@@ -650,15 +650,31 @@ int main(int argc, const char** argv) {
 
             LOG_DEBUG("%s\n", gen_params.to_string().c_str());
 
-            sd_image_t init_image    = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
-            sd_image_t control_image = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
+            sd_image_t init_image    = {0, 0, 3, nullptr};
+            sd_image_t control_image = {0, 0, 3, nullptr};
             std::vector<sd_image_t> pmid_images;
 
+            auto get_resolved_width = [&gen_params, &default_gen_params]() -> int {
+                if (gen_params.width > 0)
+                    return gen_params.width;
+                if (default_gen_params.width > 0)
+                    return default_gen_params.width;
+                return 512;
+            };
+            auto get_resolved_height = [&gen_params, &default_gen_params]() -> int {
+                if (gen_params.height > 0)
+                    return gen_params.height;
+                if (default_gen_params.height > 0)
+                    return default_gen_params.height;
+                return 512;
+            };
+
             std::vector<sd_image_t> ref_images;
             ref_images.reserve(images_bytes.size());
             for (auto& bytes : images_bytes) {
-                int img_w           = width;
-                int img_h           = height;
+                int img_w;
+                int img_h;
+
                 uint8_t* raw_pixels = load_image_from_memory(
                     reinterpret_cast<const char*>(bytes.data()),
                     static_cast<int>(bytes.size()),
@@ -670,22 +686,31 @@ int main(int argc, const char** argv) {
                 }
 
                 sd_image_t img{(uint32_t)img_w, (uint32_t)img_h, 3, raw_pixels};
+                gen_params.set_width_and_height_if_unset(img.width, img.height);
                 ref_images.push_back(img);
             }
 
             sd_image_t mask_image = {0};
             if (!mask_bytes.empty()) {
-                int mask_w        = width;
-                int mask_h        = height;
+                int expected_width  = 0;
+                int expected_height = 0;
+                if (gen_params.width_and_height_are_set()) {
+                    expected_width  = gen_params.width;
+                    expected_height = gen_params.height;
+                }
+                int mask_w;
+                int mask_h;
+
                 uint8_t* mask_raw = load_image_from_memory(
                     reinterpret_cast<const char*>(mask_bytes.data()),
                     static_cast<int>(mask_bytes.size()),
                     mask_w, mask_h,
-                    width, height, 1);
+                    expected_width, expected_height, 1);
                 mask_image = {(uint32_t)mask_w, (uint32_t)mask_h, 1, mask_raw};
+                gen_params.set_width_and_height_if_unset(mask_image.width, mask_image.height);
             } else {
-                mask_image.width   = width;
-                mask_image.height  = height;
+                mask_image.width   = get_resolved_width();
+                mask_image.height  = get_resolved_height();
                 mask_image.channel = 1;
                 mask_image.data    = nullptr;
             }
@@ -702,8 +727,8 @@ int main(int argc, const char** argv) {
                 gen_params.auto_resize_ref_image,
                 gen_params.increase_ref_index,
                 mask_image,
-                gen_params.width,
-                gen_params.height,
+                get_resolved_width(),
+                get_resolved_height(),
                 gen_params.sample_params,
                 gen_params.strength,
                 gen_params.seed,
@@ -886,8 +911,6 @@ int main(int argc, const char** argv) {
             SDGenerationParams gen_params             = default_gen_params;
             gen_params.prompt                         = prompt;
             gen_params.negative_prompt                = negative_prompt;
-            gen_params.width                          = width;
-            gen_params.height                         = height;
             gen_params.seed                           = seed;
             gen_params.sample_params.sample_steps     = steps;
             gen_params.batch_count                    = batch_size;
@@ -905,38 +928,66 @@ int main(int argc, const char** argv) {
                 gen_params.sample_params.scheduler = scheduler;
             }
 
+            // re-read to avoid applying 512 as default before the provided
+            // images and/or server command-line
+            gen_params.width  = j.value("width", -1);
+            gen_params.height = j.value("height", -1);
+
             LOG_DEBUG("%s\n", gen_params.to_string().c_str());
 
-            sd_image_t init_image    = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
-            sd_image_t control_image = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
-            sd_image_t mask_image    = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 1, nullptr};
+            sd_image_t init_image    = {0, 0, 3, nullptr};
+            sd_image_t control_image = {0, 0, 3, nullptr};
+            sd_image_t mask_image    = {0, 0, 1, nullptr};
             std::vector<uint8_t> mask_data;
             std::vector<sd_image_t> pmid_images;
             std::vector<sd_image_t> ref_images;
 
-            if (img2img) {
-                auto decode_image = [](sd_image_t& image, std::string encoded) -> bool {
-                    // remove data URI prefix if present ("data:image/png;base64,")
-                    auto comma_pos = encoded.find(',');
-                    if (comma_pos != std::string::npos) {
-                        encoded = encoded.substr(comma_pos + 1);
-                    }
-                    std::vector<uint8_t> img_data = base64_decode(encoded);
-                    if (!img_data.empty()) {
-                        int img_w         = image.width;
-                        int img_h         = image.height;
-                        uint8_t* raw_data = load_image_from_memory(
-                            (const char*)img_data.data(), (int)img_data.size(),
-                            img_w, img_h,
-                            image.width, image.height, image.channel);
-                        if (raw_data) {
-                            image = {(uint32_t)img_w, (uint32_t)img_h, image.channel, raw_data};
-                            return true;
-                        }
-                    }
-                    return false;
-                };
+            auto get_resolved_width = [&gen_params, &default_gen_params]() -> int {
+                if (gen_params.width > 0)
+                    return gen_params.width;
+                if (default_gen_params.width > 0)
+                    return default_gen_params.width;
+                return 512;
+            };
+            auto get_resolved_height = [&gen_params, &default_gen_params]() -> int {
+                if (gen_params.height > 0)
+                    return gen_params.height;
+                if (default_gen_params.height > 0)
+                    return default_gen_params.height;
+                return 512;
+            };
 
+            auto decode_image = [&gen_params](sd_image_t& image, std::string encoded) -> bool {
+                // remove data URI prefix if present ("data:image/png;base64,")
+                auto comma_pos = encoded.find(',');
+                if (comma_pos != std::string::npos) {
+                    encoded = encoded.substr(comma_pos + 1);
+                }
+                std::vector<uint8_t> img_data = base64_decode(encoded);
+                if (!img_data.empty()) {
+                    int expected_width  = 0;
+                    int expected_height = 0;
+                    if (gen_params.width_and_height_are_set()) {
+                        expected_width  = gen_params.width;
+                        expected_height = gen_params.height;
+                    }
+                    int img_w;
+                    int img_h;
+
+                    uint8_t* raw_data = load_image_from_memory(
+                        (const char*)img_data.data(), (int)img_data.size(),
+                        img_w, img_h,
+                        expected_width, expected_height, image.channel);
+                    if (raw_data) {
+                        image = {(uint32_t)img_w, (uint32_t)img_h, image.channel, raw_data};
+                        gen_params.set_width_and_height_if_unset(image.width, image.height);
+                        return true;
+                    }
+                }
+                return false;
+            };
+
+            if (img2img) {
                 if (j.contains("init_images") && j["init_images"].is_array() && !j["init_images"].empty()) {
                     std::string encoded = j["init_images"][0].get<std::string>();
                     decode_image(init_image, encoded);
@@ -952,23 +1003,15 @@ int main(int argc, const char** argv) {
                         }
                     }
                 } else {
-                    mask_data          = std::vector<uint8_t>(width * height, 255);
-                    mask_image.width   = width;
-                    mask_image.height  = height;
+                    int m_width        = get_resolved_width();
+                    int m_height       = get_resolved_height();
+                    mask_data          = std::vector<uint8_t>(m_width * m_height, 255);
+                    mask_image.width   = m_width;
+                    mask_image.height  = m_height;
                     mask_image.channel = 1;
                     mask_image.data    = mask_data.data();
                 }
 
-                if (j.contains("extra_images") && j["extra_images"].is_array()) {
-                    for (auto extra_image : j["extra_images"]) {
-                        std::string encoded  = extra_image.get<std::string>();
-                        sd_image_t tmp_image = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
-                        if (decode_image(tmp_image, encoded)) {
-                            ref_images.push_back(tmp_image);
-                        }
-                    }
-                }
-
                 float denoising_strength = j.value("denoising_strength", -1.f);
                 if (denoising_strength >= 0.f) {
                     denoising_strength  = std::min(denoising_strength, 1.0f);
@@ -976,6 +1019,16 @@ int main(int argc, const char** argv) {
                 }
             }
 
+            if (j.contains("extra_images") && j["extra_images"].is_array()) {
+                for (auto extra_image : j["extra_images"]) {
+                    std::string encoded  = extra_image.get<std::string>();
+                    sd_image_t tmp_image = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
+                    if (decode_image(tmp_image, encoded)) {
+                        ref_images.push_back(tmp_image);
+                    }
+                }
+            }
+
             sd_img_gen_params_t img_gen_params = {
                 sd_loras.data(),
                 static_cast<uint32_t>(sd_loras.size()),
@@ -988,8 +1041,8 @@ int main(int argc, const char** argv) {
                 gen_params.auto_resize_ref_image,
                 gen_params.increase_ref_index,
                 mask_image,
-                gen_params.width,
-                gen_params.height,
+                get_resolved_width(),
+                get_resolved_height(),
                 gen_params.sample_params,
                 gen_params.strength,
                 gen_params.seed,

From 636d3cb6ff25d1ffa7267e5f6dac9f2925945606 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Wed, 11 Feb 2026 00:44:17 +0800
Subject: [PATCH 81/97] refactor: reorganize the vocab file structure (#1271)

---
 CMakeLists.txt                               |  2 ++
 format-code.sh                               |  2 +-
 src/clip.hpp                                 |  3 +-
 src/llm.hpp                                  |  5 +--
 src/model.cpp                                | 34 -------------------
 src/model.h                                  |  7 ----
 src/stable-diffusion.cpp                     |  4 +--
 src/t5.hpp                                   |  5 +--
 src/{vocab.hpp => vocab/clip_t5.hpp}         |  4 +--
 src/{vocab_mistral.hpp => vocab/mistral.hpp} |  4 +--
 src/{vocab_qwen.hpp => vocab/qwen.hpp}       |  2 +-
 src/{vocab_umt5.hpp => vocab/umt5.hpp}       |  2 +-
 src/vocab/vocab.cpp                          | 35 ++++++++++++++++++++
 src/vocab/vocab.h                            | 13 ++++++++
 14 files changed, 67 insertions(+), 55 deletions(-)
 rename src/{vocab.hpp => vocab/clip_t5.hpp} (99%)
 rename src/{vocab_mistral.hpp => vocab/mistral.hpp} (99%)
 rename src/{vocab_qwen.hpp => vocab/qwen.hpp} (99%)
 rename src/{vocab_umt5.hpp => vocab/umt5.hpp} (99%)
 create mode 100644 src/vocab/vocab.cpp
 create mode 100644 src/vocab/vocab.h

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7b77225d..b90086ea 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -90,6 +90,8 @@ file(GLOB SD_LIB_SOURCES
     "src/*.h"
     "src/*.cpp"
     "src/*.hpp"
+    "src/vocab/*.h"
+    "src/vocab/*.cpp"
 )
 
 find_program(GIT_EXE NAMES git git.exe NO_CMAKE_FIND_ROOT_PATH)
diff --git a/format-code.sh b/format-code.sh
index d2a75bdc..ac5fd340 100644
--- a/format-code.sh
+++ b/format-code.sh
@@ -1,4 +1,4 @@
-for f in *.cpp *.h *.hpp examples/cli/*.cpp examples/common/*.hpp examples/cli/*.h examples/server/*.cpp; do
+for f in src/*.cpp src/*.h src/*.hpp src/vocab/*.h src/vocab/*.cpp examples/cli/*.cpp examples/common/*.hpp examples/cli/*.h examples/server/*.cpp; do
   [[ "$f" == vocab* ]] && continue
   echo "formatting '$f'"
   # if [ "$f" != "stable-diffusion.h" ]; then
diff --git a/src/clip.hpp b/src/clip.hpp
index 3fc65665..adecd4d2 100644
--- a/src/clip.hpp
+++ b/src/clip.hpp
@@ -4,6 +4,7 @@
 #include "ggml_extend.hpp"
 #include "model.h"
 #include "tokenize_util.h"
+#include "vocab/vocab.h"
 
 /*================================================== CLIPTokenizer ===================================================*/
 
@@ -110,7 +111,7 @@ public:
         if (merges_utf8_str.size() > 0) {
             load_from_merges(merges_utf8_str);
         } else {
-            load_from_merges(ModelLoader::load_merges());
+            load_from_merges(load_clip_merges());
         }
         add_special_token("<|startoftext|>");
         add_special_token("<|endoftext|>");
diff --git a/src/llm.hpp b/src/llm.hpp
index 31555751..5490f07c 100644
--- a/src/llm.hpp
+++ b/src/llm.hpp
@@ -19,6 +19,7 @@
 #include "json.hpp"
 #include "rope.hpp"
 #include "tokenize_util.h"
+#include "vocab/vocab.h"
 
 namespace LLM {
     constexpr int LLM_GRAPH_SIZE = 10240;
@@ -365,7 +366,7 @@ namespace LLM {
             if (merges_utf8_str.size() > 0) {
                 load_from_merges(merges_utf8_str);
             } else {
-                load_from_merges(ModelLoader::load_qwen2_merges());
+                load_from_merges(load_qwen2_merges());
             }
         }
     };
@@ -466,7 +467,7 @@ namespace LLM {
             if (merges_utf8_str.size() > 0 && vocab_utf8_str.size() > 0) {
                 load_from_merges(merges_utf8_str, vocab_utf8_str);
             } else {
-                load_from_merges(ModelLoader::load_mistral_merges(), ModelLoader::load_mistral_vocab_json());
+                load_from_merges(load_mistral_merges(), load_mistral_vocab_json());
             }
         }
     };
diff --git a/src/model.cpp b/src/model.cpp
index 253dd25c..58d71d9e 100644
--- a/src/model.cpp
+++ b/src/model.cpp
@@ -16,10 +16,6 @@
 #include "model.h"
 #include "stable-diffusion.h"
 #include "util.h"
-#include "vocab.hpp"
-#include "vocab_mistral.hpp"
-#include "vocab_qwen.hpp"
-#include "vocab_umt5.hpp"
 
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
@@ -1340,36 +1336,6 @@ void ModelLoader::set_wtype_override(ggml_type wtype, std::string tensor_type_ru
     }
 }
 
-std::string ModelLoader::load_merges() {
-    std::string merges_utf8_str(reinterpret_cast<const char*>(merges_utf8_c_str), sizeof(merges_utf8_c_str));
-    return merges_utf8_str;
-}
-
-std::string ModelLoader::load_qwen2_merges() {
-    std::string merges_utf8_str(reinterpret_cast<const char*>(qwen2_merges_utf8_c_str), sizeof(qwen2_merges_utf8_c_str));
-    return merges_utf8_str;
-}
-
-std::string ModelLoader::load_mistral_merges() {
-    std::string merges_utf8_str(reinterpret_cast<const char*>(mistral_merges_utf8_c_str), sizeof(mistral_merges_utf8_c_str));
-    return merges_utf8_str;
-}
-
-std::string ModelLoader::load_mistral_vocab_json() {
-    std::string json_str(reinterpret_cast<const char*>(mistral_vocab_json_utf8_c_str), sizeof(mistral_vocab_json_utf8_c_str));
-    return json_str;
-}
-
-std::string ModelLoader::load_t5_tokenizer_json() {
-    std::string json_str(reinterpret_cast<const char*>(t5_tokenizer_json_str), sizeof(t5_tokenizer_json_str));
-    return json_str;
-}
-
-std::string ModelLoader::load_umt5_tokenizer_json() {
-    std::string json_str(reinterpret_cast<const char*>(umt5_tokenizer_json_str), sizeof(umt5_tokenizer_json_str));
-    return json_str;
-}
-
 bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads_p, bool enable_mmap) {
     int64_t process_time_ms = 0;
     std::atomic<int64_t> read_time_ms(0);
diff --git a/src/model.h b/src/model.h
index e16ac3a0..0d7a85c6 100644
--- a/src/model.h
+++ b/src/model.h
@@ -331,13 +331,6 @@ public:
     bool tensor_should_be_converted(const TensorStorage& tensor_storage, ggml_type type);
     int64_t get_params_mem_size(ggml_backend_t backend, ggml_type type = GGML_TYPE_COUNT);
     ~ModelLoader() = default;
-
-    static std::string load_merges();
-    static std::string load_qwen2_merges();
-    static std::string load_mistral_merges();
-    static std::string load_mistral_vocab_json();
-    static std::string load_t5_tokenizer_json();
-    static std::string load_umt5_tokenizer_json();
 };
 
 #endif  // __MODEL_H__
diff --git a/src/stable-diffusion.cpp b/src/stable-diffusion.cpp
index 19bdec8b..c0ee1182 100644
--- a/src/stable-diffusion.cpp
+++ b/src/stable-diffusion.cpp
@@ -2679,7 +2679,7 @@ public:
                 };
                 sd_tiling_non_square(x, result, vae_scale_factor, tile_size_x, tile_size_y, tile_overlap, on_tiling);
             } else {
-                if(!first_stage_model->compute(n_threads, x, true, &result, work_ctx)){
+                if (!first_stage_model->compute(n_threads, x, true, &result, work_ctx)) {
                     LOG_ERROR("Failed to decode latetnts");
                     first_stage_model->free_compute_buffer();
                     return nullptr;
@@ -2695,7 +2695,7 @@ public:
                 };
                 sd_tiling(x, result, vae_scale_factor, 64, 0.5f, on_tiling);
             } else {
-                if(!tae_first_stage->compute(n_threads, x, true, &result)){
+                if (!tae_first_stage->compute(n_threads, x, true, &result)) {
                     LOG_ERROR("Failed to decode latetnts");
                     tae_first_stage->free_compute_buffer();
                     return nullptr;
diff --git a/src/t5.hpp b/src/t5.hpp
index fdac3475..d789c5bd 100644
--- a/src/t5.hpp
+++ b/src/t5.hpp
@@ -14,6 +14,7 @@
 #include "ggml_extend.hpp"
 #include "json.hpp"
 #include "model.h"
+#include "vocab/vocab.h"
 
 // Port from: https://github.com/google/sentencepiece/blob/master/src/unigram_model.h
 // and https://github.com/google/sentencepiece/blob/master/src/unigram_model.h.
@@ -341,9 +342,9 @@ protected:
 public:
     explicit T5UniGramTokenizer(bool is_umt5 = false) {
         if (is_umt5) {
-            InitializePieces(ModelLoader::load_umt5_tokenizer_json());
+            InitializePieces(load_umt5_tokenizer_json());
         } else {
-            InitializePieces(ModelLoader::load_t5_tokenizer_json());
+            InitializePieces(load_t5_tokenizer_json());
         }
 
         min_score_ = FLT_MAX;
diff --git a/src/vocab.hpp b/src/vocab/clip_t5.hpp
similarity index 99%
rename from src/vocab.hpp
rename to src/vocab/clip_t5.hpp
index 3902045e..8ba40783 100644
--- a/src/vocab.hpp
+++ b/src/vocab/clip_t5.hpp
@@ -1,4 +1,4 @@
-static unsigned char merges_utf8_c_str[] = {
+static const unsigned char clip_merges_utf8_c_str[] = {
     0x23,
     0x76,
     0x65,
@@ -524620,7 +524620,7 @@ static unsigned char merges_utf8_c_str[] = {
     0x0a,
 };
 
-static unsigned char t5_tokenizer_json_str[] = {
+static const unsigned char t5_tokenizer_json_str[] = {
     0x7b,
     0x0a,
     0x20,
diff --git a/src/vocab_mistral.hpp b/src/vocab/mistral.hpp
similarity index 99%
rename from src/vocab_mistral.hpp
rename to src/vocab/mistral.hpp
index 3eb8b259..5bfa873b 100644
--- a/src/vocab_mistral.hpp
+++ b/src/vocab/mistral.hpp
@@ -1,4 +1,4 @@
-unsigned char mistral_merges_utf8_c_str[] = {
+static const unsigned char mistral_merges_utf8_c_str[] = {
   0xc4, 0xa0, 0x20, 0xc4, 0xa0, 0x0a, 0xc4, 0xa0, 0x20, 0x74, 0x0a, 0x65,
   0x20, 0x72, 0x0a, 0x69, 0x20, 0x6e, 0x0a, 0xc4, 0xa0, 0x20, 0xc4, 0xa0,
   0xc4, 0xa0, 0xc4, 0xa0, 0x0a, 0xc4, 0xa0, 0xc4, 0xa0, 0x20, 0xc4, 0xa0,
@@ -260614,7 +260614,7 @@ unsigned char mistral_merges_utf8_c_str[] = {
   0xc3, 0xa5, 0xc4, 0xb2, 0xc4, 0xb0, 0x20, 0xc3, 0xa6, 0xc2, 0xb1, 0xc4,
   0xab, 0xc3, 0xa4, 0xc2, 0xb9, 0xc2, 0xa6, 0x0a,
 };
-unsigned char mistral_vocab_json_utf8_c_str[] = {
+static const unsigned char mistral_vocab_json_utf8_c_str[] = {
   0x7b, 0x22, 0x3c, 0x75, 0x6e, 0x6b, 0x3e, 0x22, 0x3a, 0x20, 0x30, 0x2c,
   0x20, 0x22, 0x3c, 0x73, 0x3e, 0x22, 0x3a, 0x20, 0x31, 0x2c, 0x20, 0x22,
   0x3c, 0x2f, 0x73, 0x3e, 0x22, 0x3a, 0x20, 0x32, 0x2c, 0x20, 0x22, 0x5b,
diff --git a/src/vocab_qwen.hpp b/src/vocab/qwen.hpp
similarity index 99%
rename from src/vocab_qwen.hpp
rename to src/vocab/qwen.hpp
index 2c5c7fe8..9db2339e 100644
--- a/src/vocab_qwen.hpp
+++ b/src/vocab/qwen.hpp
@@ -1,4 +1,4 @@
-unsigned char qwen2_merges_utf8_c_str[] = {
+static const unsigned char qwen2_merges_utf8_c_str[] = {
   0xc4, 0xa0, 0x20, 0xc4, 0xa0, 0x0a, 0xc4, 0xa0, 0xc4, 0xa0, 0x20, 0xc4,
   0xa0, 0xc4, 0xa0, 0x0a, 0x69, 0x20, 0x6e, 0x0a, 0xc4, 0xa0, 0x20, 0x74,
   0x0a, 0xc4, 0xa0, 0xc4, 0xa0, 0xc4, 0xa0, 0xc4, 0xa0, 0x20, 0xc4, 0xa0,
diff --git a/src/vocab_umt5.hpp b/src/vocab/umt5.hpp
similarity index 99%
rename from src/vocab_umt5.hpp
rename to src/vocab/umt5.hpp
index 22c581d5..a9f87a20 100644
--- a/src/vocab_umt5.hpp
+++ b/src/vocab/umt5.hpp
@@ -1,4 +1,4 @@
-unsigned char umt5_tokenizer_json_str[] = {
+static const unsigned char umt5_tokenizer_json_str[] = {
   0x7b, 0x22, 0x76, 0x65, 0x72, 0x73, 0x69, 0x6f, 0x6e, 0x22, 0x3a, 0x20,
   0x22, 0x31, 0x2e, 0x30, 0x22, 0x2c, 0x20, 0x22, 0x74, 0x72, 0x75, 0x6e,
   0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3a, 0x20, 0x6e, 0x75, 0x6c,
diff --git a/src/vocab/vocab.cpp b/src/vocab/vocab.cpp
new file mode 100644
index 00000000..63b28686
--- /dev/null
+++ b/src/vocab/vocab.cpp
@@ -0,0 +1,35 @@
+#include "vocab.h"
+#include "clip_t5.hpp"
+#include "mistral.hpp"
+#include "qwen.hpp"
+#include "umt5.hpp"
+
+std::string load_clip_merges() {
+    std::string merges_utf8_str(reinterpret_cast<const char*>(clip_merges_utf8_c_str), sizeof(clip_merges_utf8_c_str));
+    return merges_utf8_str;
+}
+
+std::string load_qwen2_merges() {
+    std::string merges_utf8_str(reinterpret_cast<const char*>(qwen2_merges_utf8_c_str), sizeof(qwen2_merges_utf8_c_str));
+    return merges_utf8_str;
+}
+
+std::string load_mistral_merges() {
+    std::string merges_utf8_str(reinterpret_cast<const char*>(mistral_merges_utf8_c_str), sizeof(mistral_merges_utf8_c_str));
+    return merges_utf8_str;
+}
+
+std::string load_mistral_vocab_json() {
+    std::string json_str(reinterpret_cast<const char*>(mistral_vocab_json_utf8_c_str), sizeof(mistral_vocab_json_utf8_c_str));
+    return json_str;
+}
+
+std::string load_t5_tokenizer_json() {
+    std::string json_str(reinterpret_cast<const char*>(t5_tokenizer_json_str), sizeof(t5_tokenizer_json_str));
+    return json_str;
+}
+
+std::string load_umt5_tokenizer_json() {
+    std::string json_str(reinterpret_cast<const char*>(umt5_tokenizer_json_str), sizeof(umt5_tokenizer_json_str));
+    return json_str;
+}
\ No newline at end of file
diff --git a/src/vocab/vocab.h b/src/vocab/vocab.h
new file mode 100644
index 00000000..cfa033a4
--- /dev/null
+++ b/src/vocab/vocab.h
@@ -0,0 +1,13 @@
+#ifndef __VOCAB_H__
+#define __VOCAB_H__
+
+#include <string>
+
+std::string load_clip_merges();
+std::string load_qwen2_merges();
+std::string load_mistral_merges();
+std::string load_mistral_vocab_json();
+std::string load_t5_tokenizer_json();
+std::string load_umt5_tokenizer_json();
+
+#endif  // __VOCAB_H__
\ No newline at end of file

From c5eb1e4137f22bcc6bf7b866d059b4e0638fb109 Mon Sep 17 00:00:00 2001
From: akleine <alb.kleine@gmx.de>
Date: Thu, 19 Feb 2026 13:54:18 +0100
Subject: [PATCH 82/97] fix: avoid black images if using an invalid VAE (for
 SDXL) (#1273)

---
 src/stable-diffusion.cpp | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/stable-diffusion.cpp b/src/stable-diffusion.cpp
index c0ee1182..94221d8e 100644
--- a/src/stable-diffusion.cpp
+++ b/src/stable-diffusion.cpp
@@ -107,6 +107,7 @@ public:
 
     SDVersion version;
     bool vae_decode_only         = false;
+    bool external_vae_is_invalid = false;
     bool free_params_immediately = false;
 
     std::shared_ptr<RNG> rng         = std::make_shared<PhiloxRNG>();
@@ -321,6 +322,7 @@ public:
             LOG_INFO("loading vae from '%s'", sd_ctx_params->vae_path);
             if (!model_loader.init_from_file(sd_ctx_params->vae_path, "vae.")) {
                 LOG_WARN("loading vae from '%s' failed", sd_ctx_params->vae_path);
+                external_vae_is_invalid = true;
             }
         }
 
@@ -619,10 +621,10 @@ public:
                         first_stage_model->set_conv2d_direct_enabled(true);
                     }
                     if (sd_version_is_sdxl(version) &&
-                        (strlen(SAFE_STR(sd_ctx_params->vae_path)) == 0 || sd_ctx_params->force_sdxl_vae_conv_scale)) {
+                        (strlen(SAFE_STR(sd_ctx_params->vae_path)) == 0 || sd_ctx_params->force_sdxl_vae_conv_scale || external_vae_is_invalid)) {
                         float vae_conv_2d_scale = 1.f / 32.f;
                         LOG_WARN(
-                            "No VAE specified with --vae or --force-sdxl-vae-conv-scale flag set, "
+                            "No valid VAE specified with --vae or --force-sdxl-vae-conv-scale flag set, "
                             "using Conv2D scale %.3f",
                             vae_conv_2d_scale);
                         first_stage_model->set_conv2d_scale(vae_conv_2d_scale);

From c9cd49701a91cb10a07f07512d1bb5b966f05c6e Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Thu, 19 Feb 2026 20:54:42 +0800
Subject: [PATCH 83/97] fix: safely handle whitespace and consecutive newlines
 (#1288)

---
 src/tokenize_util.cpp | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/src/tokenize_util.cpp b/src/tokenize_util.cpp
index bc0ff1d5..22cf8ae2 100644
--- a/src/tokenize_util.cpp
+++ b/src/tokenize_util.cpp
@@ -919,15 +919,21 @@ std::vector<std::string> token_split(const std::string& text) {
 
         // `\s*[\r\n]+|\s+(?!\S)|\s+`
         if (is_space(cp)) {
-            std::string token = codepoint_to_utf8(cp);
-            ++i;
+            std::string token;
+            bool saw_new_line = false;
 
             while (i < cps.size() && is_space(cps[i])) {
                 token += codepoint_to_utf8(cps[i]);
-                ++i;
+
                 if (cps[i] == U'\r' || cps[i] == U'\n') {
-                    break;
+                    saw_new_line = true;
+                } else {
+                    if (saw_new_line) {
+                        break;
+                    }
                 }
+
+                ++i;
             }
 
             tokens.push_back(token);

From b314d80ad0513fdee6ff78b8a575cb8c25ffb45d Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Wed, 25 Feb 2026 13:26:04 -0300
Subject: [PATCH 84/97] feat: turn flow_shift into a generation parameter
 (#1289)

* feat: turn flow_shift into a generation parameter

* format code

* simplify set_shift/set_parameters

* fix sd_sample_params_to_str

* remove unused variable

* update docs

---------

Co-authored-by: leejet <leejet714@gmail.com>
---
 examples/cli/README.md     |  2 +-
 examples/common/common.hpp | 11 ++++---
 examples/server/README.md  |  2 +-
 include/stable-diffusion.h |  2 +-
 src/denoiser.hpp           | 60 ++++++--------------------------------
 src/stable-diffusion.cpp   | 49 +++++++++++++++++++------------
 6 files changed, 48 insertions(+), 78 deletions(-)

diff --git a/examples/cli/README.md b/examples/cli/README.md
index 78f8821a..c79d2ab6 100644
--- a/examples/cli/README.md
+++ b/examples/cli/README.md
@@ -44,7 +44,6 @@ Context Options:
                                            CPU physical cores
   --chroma-t5-mask-pad <int>               t5 mask pad size of chroma
   --vae-tile-overlap <float>               tile overlap for vae tiling, in fraction of tile size (default: 0.5)
-  --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
   --vae-tiling                             process vae in tiles to reduce memory usage
   --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
   --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
@@ -109,6 +108,7 @@ Generation Options:
   --skip-layer-start <float>               SLG enabling point (default: 0.01)
   --skip-layer-end <float>                 SLG disabling point (default: 0.2)
   --eta <float>                            eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
+  --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
   --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
   --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
   --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input (default: 3.5)
diff --git a/examples/common/common.hpp b/examples/common/common.hpp
index 50f35aed..369c1f07 100644
--- a/examples/common/common.hpp
+++ b/examples/common/common.hpp
@@ -581,10 +581,6 @@ struct SDContextParams {
              "--vae-tile-overlap",
              "tile overlap for vae tiling, in fraction of tile size (default: 0.5)",
              &vae_tiling_params.target_overlap},
-            {"",
-             "--flow-shift",
-             "shift value for Flow models like SD3.x or WAN (default: auto)",
-             &flow_shift},
         };
 
         options.bool_options = {
@@ -903,7 +899,6 @@ struct SDContextParams {
             << "  photo_maker_path: \"" << photo_maker_path << "\",\n"
             << "  rng_type: " << sd_rng_type_name(rng_type) << ",\n"
             << "  sampler_rng_type: " << sd_rng_type_name(sampler_rng_type) << ",\n"
-            << "  flow_shift: " << (std::isinf(flow_shift) ? "INF" : std::to_string(flow_shift)) << "\n"
             << "  offload_params_to_cpu: " << (offload_params_to_cpu ? "true" : "false") << ",\n"
             << "  enable_mmap: " << (enable_mmap ? "true" : "false") << ",\n"
             << "  control_net_cpu: " << (control_net_cpu ? "true" : "false") << ",\n"
@@ -986,7 +981,6 @@ struct SDContextParams {
             chroma_use_t5_mask,
             chroma_t5_mask_pad,
             qwen_image_zero_cond_t,
-            flow_shift,
         };
         return sd_ctx_params;
     }
@@ -1206,6 +1200,10 @@ struct SDGenerationParams {
              "--eta",
              "eta in DDIM, only for DDIM and TCD (default: 0)",
              &sample_params.eta},
+            {"",
+             "--flow-shift",
+             "shift value for Flow models like SD3.x or WAN (default: auto)",
+             &sample_params.flow_shift},
             {"",
              "--high-noise-cfg-scale",
              "(high noise) unconditional guidance scale: (default: 7.0)",
@@ -1606,6 +1604,7 @@ struct SDGenerationParams {
         load_if_exists("cfg_scale", sample_params.guidance.txt_cfg);
         load_if_exists("img_cfg_scale", sample_params.guidance.img_cfg);
         load_if_exists("guidance", sample_params.guidance.distilled_guidance);
+        load_if_exists("flow_shift", sample_params.flow_shift);
 
         auto load_sampler_if_exists = [&](const char* key, enum sample_method_t& out) {
             if (j.contains(key) && j[key].is_string()) {
diff --git a/examples/server/README.md b/examples/server/README.md
index 8a2f2e91..38672900 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -36,7 +36,6 @@ Context Options:
                                            CPU physical cores
   --chroma-t5-mask-pad <int>               t5 mask pad size of chroma
   --vae-tile-overlap <float>               tile overlap for vae tiling, in fraction of tile size (default: 0.5)
-  --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
   --vae-tiling                             process vae in tiles to reduce memory usage
   --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
   --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
@@ -101,6 +100,7 @@ Default Generation Options:
   --skip-layer-start <float>               SLG enabling point (default: 0.01)
   --skip-layer-end <float>                 SLG disabling point (default: 0.2)
   --eta <float>                            eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
+  --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
   --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
   --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
   --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input (default: 3.5)
diff --git a/include/stable-diffusion.h b/include/stable-diffusion.h
index cb966d7e..51b2b329 100644
--- a/include/stable-diffusion.h
+++ b/include/stable-diffusion.h
@@ -201,7 +201,6 @@ typedef struct {
     bool chroma_use_t5_mask;
     int chroma_t5_mask_pad;
     bool qwen_image_zero_cond_t;
-    float flow_shift;
 } sd_ctx_params_t;
 
 typedef struct {
@@ -235,6 +234,7 @@ typedef struct {
     int shifted_timestep;
     float* custom_sigmas;
     int custom_sigmas_count;
+    float flow_shift;
 } sd_sample_params_t;
 
 typedef struct {
diff --git a/src/denoiser.hpp b/src/denoiser.hpp
index 7e99b84a..40bd7cb7 100644
--- a/src/denoiser.hpp
+++ b/src/denoiser.hpp
@@ -657,9 +657,8 @@ struct DiscreteFlowDenoiser : public Denoiser {
 
     float sigma_data = 1.0f;
 
-    DiscreteFlowDenoiser(float shift = 3.0f)
-        : shift(shift) {
-        set_parameters();
+    DiscreteFlowDenoiser(float shift = 3.0f) {
+        set_shift(shift);
     }
 
     void set_parameters() {
@@ -668,6 +667,11 @@ struct DiscreteFlowDenoiser : public Denoiser {
         }
     }
 
+    void set_shift(float shift) {
+        this->shift = shift;
+        set_parameters();
+    }
+
     float sigma_min() override {
         return sigmas[0];
     }
@@ -710,34 +714,8 @@ float flux_time_shift(float mu, float sigma, float t) {
     return ::expf(mu) / (::expf(mu) + ::powf((1.0f / t - 1.0f), sigma));
 }
 
-struct FluxFlowDenoiser : public Denoiser {
-    float sigmas[TIMESTEPS];
-    float shift = 1.15f;
-
-    float sigma_data = 1.0f;
-
-    FluxFlowDenoiser(float shift = 1.15f) {
-        set_parameters(shift);
-    }
-
-    void set_shift(float shift) {
-        this->shift = shift;
-    }
-
-    void set_parameters(float shift) {
-        set_shift(shift);
-        for (int i = 0; i < TIMESTEPS; i++) {
-            sigmas[i] = t_to_sigma(static_cast<float>(i));
-        }
-    }
-
-    float sigma_min() override {
-        return sigmas[0];
-    }
-
-    float sigma_max() override {
-        return sigmas[TIMESTEPS - 1];
-    }
+struct FluxFlowDenoiser : public DiscreteFlowDenoiser {
+    FluxFlowDenoiser() = default;
 
     float sigma_to_t(float sigma) override {
         return sigma;
@@ -747,26 +725,6 @@ struct FluxFlowDenoiser : public Denoiser {
         t = t + 1;
         return flux_time_shift(shift, 1.0f, t / TIMESTEPS);
     }
-
-    std::vector<float> get_scalings(float sigma) override {
-        float c_skip = 1.0f;
-        float c_out  = -sigma;
-        float c_in   = 1.0f;
-        return {c_skip, c_out, c_in};
-    }
-
-    // this function will modify noise/latent
-    ggml_tensor* noise_scaling(float sigma, ggml_tensor* noise, ggml_tensor* latent) override {
-        ggml_ext_tensor_scale_inplace(noise, sigma);
-        ggml_ext_tensor_scale_inplace(latent, 1.0f - sigma);
-        ggml_ext_tensor_add_inplace(latent, noise);
-        return latent;
-    }
-
-    ggml_tensor* inverse_noise_scaling(float sigma, ggml_tensor* latent) override {
-        ggml_ext_tensor_scale_inplace(latent, 1.0f / (1.0f - sigma));
-        return latent;
-    }
 };
 
 struct Flux2FlowDenoiser : public FluxFlowDenoiser {
diff --git a/src/stable-diffusion.cpp b/src/stable-diffusion.cpp
index 94221d8e..e704922a 100644
--- a/src/stable-diffusion.cpp
+++ b/src/stable-diffusion.cpp
@@ -115,6 +115,7 @@ public:
     int n_threads                    = -1;
     float scale_factor               = 0.18215f;
     float shift_factor               = 0.f;
+    float default_flow_shift         = INFINITY;
 
     std::shared_ptr<Conditioner> cond_stage_model;
     std::shared_ptr<FrozenCLIPVisionEmbedder> clip_vision;  // for svd or wan2.1 i2v
@@ -881,7 +882,6 @@ public:
         // init denoiser
         {
             prediction_t pred_type = sd_ctx_params->prediction;
-            float flow_shift       = sd_ctx_params->flow_shift;
 
             if (pred_type == PREDICTION_COUNT) {
                 if (sd_version_is_sd2(version)) {
@@ -906,22 +906,19 @@ public:
                            sd_version_is_qwen_image(version) ||
                            sd_version_is_z_image(version)) {
                     pred_type = FLOW_PRED;
-                    if (flow_shift == INFINITY) {
-                        if (sd_version_is_wan(version)) {
-                            flow_shift = 5.f;
-                        } else {
-                            flow_shift = 3.f;
-                        }
+                    if (sd_version_is_wan(version)) {
+                        default_flow_shift = 5.f;
+                    } else {
+                        default_flow_shift = 3.f;
                     }
                 } else if (sd_version_is_flux(version)) {
                     pred_type = FLUX_FLOW_PRED;
 
-                    if (flow_shift == INFINITY) {
-                        flow_shift = 1.0f;  // TODO: validate
-                        for (const auto& [name, tensor_storage] : tensor_storage_map) {
-                            if (starts_with(name, "model.diffusion_model.guidance_in.in_layer.weight")) {
-                                flow_shift = 1.15f;
-                            }
+                    default_flow_shift = 1.0f;  // TODO: validate
+                    for (const auto& [name, tensor_storage] : tensor_storage_map) {
+                        if (starts_with(name, "model.diffusion_model.guidance_in.in_layer.weight")) {
+                            default_flow_shift = 1.15f;
+                            break;
                         }
                     }
                 } else if (sd_version_is_flux2(version)) {
@@ -945,12 +942,12 @@ public:
                     break;
                 case FLOW_PRED: {
                     LOG_INFO("running in FLOW mode");
-                    denoiser = std::make_shared<DiscreteFlowDenoiser>(flow_shift);
+                    denoiser = std::make_shared<DiscreteFlowDenoiser>();
                     break;
                 }
                 case FLUX_FLOW_PRED: {
                     LOG_INFO("running in Flux FLOW mode");
-                    denoiser = std::make_shared<FluxFlowDenoiser>(flow_shift);
+                    denoiser = std::make_shared<FluxFlowDenoiser>();
                     break;
                 }
                 case FLUX2_FLOW_PRED: {
@@ -2711,6 +2708,16 @@ public:
         ggml_ext_tensor_clamp_inplace(result, 0.0f, 1.0f);
         return result;
     }
+
+    void set_flow_shift(float flow_shift = INFINITY) {
+        auto flow_denoiser = std::dynamic_pointer_cast<DiscreteFlowDenoiser>(denoiser);
+        if (flow_denoiser) {
+            if (flow_shift == INFINITY) {
+                flow_shift = default_flow_shift;
+            }
+            flow_denoiser->set_shift(flow_shift);
+        }
+    }
 };
 
 /*================================================= SD API ==================================================*/
@@ -2931,7 +2938,6 @@ void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params) {
     sd_ctx_params->chroma_use_dit_mask     = true;
     sd_ctx_params->chroma_use_t5_mask      = false;
     sd_ctx_params->chroma_t5_mask_pad      = 1;
-    sd_ctx_params->flow_shift              = INFINITY;
 }
 
 char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params) {
@@ -3023,6 +3029,7 @@ void sd_sample_params_init(sd_sample_params_t* sample_params) {
     sample_params->sample_steps                = 20;
     sample_params->custom_sigmas               = nullptr;
     sample_params->custom_sigmas_count         = 0;
+    sample_params->flow_shift                  = INFINITY;
 }
 
 char* sd_sample_params_to_str(const sd_sample_params_t* sample_params) {
@@ -3043,7 +3050,8 @@ char* sd_sample_params_to_str(const sd_sample_params_t* sample_params) {
              "sample_method: %s, "
              "sample_steps: %d, "
              "eta: %.2f, "
-             "shifted_timestep: %d)",
+             "shifted_timestep: %d, "
+             "flow_shift: %.2f)",
              sample_params->guidance.txt_cfg,
              std::isfinite(sample_params->guidance.img_cfg)
                  ? sample_params->guidance.img_cfg
@@ -3057,7 +3065,8 @@ char* sd_sample_params_to_str(const sd_sample_params_t* sample_params) {
              sd_sample_method_name(sample_params->sample_method),
              sample_params->sample_steps,
              sample_params->eta,
-             sample_params->shifted_timestep);
+             sample_params->shifted_timestep,
+             sample_params->flow_shift);
 
     return buf;
 }
@@ -3528,6 +3537,8 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
 
     size_t t0 = ggml_time_ms();
 
+    sd_ctx->sd->set_flow_shift(sd_img_gen_params->sample_params.flow_shift);
+
     // Apply lora
     sd_ctx->sd->apply_loras(sd_img_gen_params->loras, sd_img_gen_params->lora_count);
 
@@ -3803,6 +3814,8 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
     }
     LOG_INFO("generate_video %dx%dx%d", width, height, frames);
 
+    sd_ctx->sd->set_flow_shift(sd_vid_gen_params->sample_params.flow_shift);
+
     enum sample_method_t sample_method = sd_vid_gen_params->sample_params.sample_method;
     if (sample_method == SAMPLE_METHOD_COUNT) {
         sample_method = sd_get_default_sample_method(sd_ctx);

From 0752cc9d3a8ddee25b72ca65d061a28310d97c02 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Thu, 26 Feb 2026 00:26:21 +0800
Subject: [PATCH 85/97] fix: resolve image quality degradation issue (#1297)

---
 src/vae.hpp | 2 +-
 src/wan.hpp | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/vae.hpp b/src/vae.hpp
index 01081343..c627616c 100644
--- a/src/vae.hpp
+++ b/src/vae.hpp
@@ -141,7 +141,7 @@ public:
             v = ggml_reshape_3d(ctx->ggml_ctx, v, c, h * w, n);                        // [N, h * w, in_channels]
         }
 
-        h_ = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, true, ctx->flash_attn_enabled);
+        h_ = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, false, ctx->flash_attn_enabled);
 
         if (use_linear) {
             h_ = proj_out->forward(ctx, h_);  // [N, h * w, in_channels]
diff --git a/src/wan.hpp b/src/wan.hpp
index 7b105978..90de3bdd 100644
--- a/src/wan.hpp
+++ b/src/wan.hpp
@@ -572,8 +572,8 @@ namespace WAN {
             auto v = qkv_vec[2];
             v      = ggml_reshape_3d(ctx->ggml_ctx, v, h * w, c, n);  // [t, c, h * w]
 
-            v = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, v, 1, 0, 2, 3));                           // [t, h * w, c]
-            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, true, ctx->flash_attn_enabled);  // [t, h * w, c]
+            v = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, v, 1, 0, 2, 3));                            // [t, h * w, c]
+            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, false, ctx->flash_attn_enabled);  // [t, h * w, c]
 
             x = ggml_ext_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [t, c, h * w]
             x = ggml_reshape_4d(ctx->ggml_ctx, x, w, h, c, n);                             // [t, c, h, w]

From 4cdfff5ff29ddd433ef9176644b010f84518e0e6 Mon Sep 17 00:00:00 2001
From: rmatif <rmatif@proton.me>
Date: Sun, 1 Mar 2026 08:23:18 +0100
Subject: [PATCH 86/97] feat: add Anima support  (#1296)

---
 src/anima.hpp            | 774 +++++++++++++++++++++++++++++++++++++++
 src/conditioner.hpp      | 136 +++++++
 src/diffusion_model.hpp  |  67 ++++
 src/model.cpp            |   3 +
 src/model.h              |   9 +
 src/name_conversion.cpp  |   8 +
 src/rope.hpp             |  29 +-
 src/stable-diffusion.cpp |  33 +-
 8 files changed, 1042 insertions(+), 17 deletions(-)
 create mode 100644 src/anima.hpp

diff --git a/src/anima.hpp b/src/anima.hpp
new file mode 100644
index 00000000..2f4d8681
--- /dev/null
+++ b/src/anima.hpp
@@ -0,0 +1,774 @@
+#ifndef __ANIMA_HPP__
+#define __ANIMA_HPP__
+
+#include <cmath>
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "common.hpp"
+#include "flux.hpp"
+#include "ggml_extend.hpp"
+#include "rope.hpp"
+
+namespace Anima {
+    constexpr int ANIMA_GRAPH_SIZE = 65536;
+
+    __STATIC_INLINE__ struct ggml_tensor* patchify_2d(struct ggml_context* ctx,
+                                                      struct ggml_tensor* x,
+                                                      int64_t patch_size) {
+        // x: [W*r, H*q, T, C]
+        // return: [W, H, T, C*q*r]
+        if (patch_size == 1) {
+            return x;
+        }
+        GGML_ASSERT(x->ne[2] == 1);
+
+        int64_t W = x->ne[0];
+        int64_t H = x->ne[1];
+        int64_t T = x->ne[2];
+        int64_t C = x->ne[3];
+        int64_t p = patch_size;
+        int64_t h = H / p;
+        int64_t w = W / p;
+
+        GGML_ASSERT(T == 1);
+        GGML_ASSERT(h * p == H && w * p == W);
+
+        // Reuse Flux patchify layout on a [W, H, C, N] view.
+        x = ggml_reshape_4d(ctx, x, W, H, C, T);  // [W, H, C, N]
+
+        // Flux patchify: [N, C, H, W] -> [N, h*w, C*p*p]
+        x = ggml_reshape_4d(ctx, x, p, w, p, h * C * T);           // [p, w, p, h*C*N]
+        x = ggml_ext_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [p, p, w, h*C*N]
+        x = ggml_reshape_4d(ctx, x, p * p, w * h, C, T);           // [p*p, h*w, C, N]
+        x = ggml_ext_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [p*p, C, h*w, N]
+        x = ggml_reshape_3d(ctx, x, p * p * C, w * h, T);          // [C*p*p, h*w, N]
+
+        // Return [w, h, T, C*p*p]
+        x = ggml_reshape_4d(ctx, x, p * p * C, w, h, T);           // [C*p*p, w, h, N]
+        x = ggml_ext_cont(ctx, ggml_permute(ctx, x, 3, 0, 1, 2));  // [w, h, N, C*p*p]
+        return x;
+    }
+
+    __STATIC_INLINE__ struct ggml_tensor* unpatchify_2d(struct ggml_context* ctx,
+                                                        struct ggml_tensor* x,
+                                                        int64_t patch_size) {
+        // x: [W, H, T, C*q*r]
+        // return: [W*r, H*q, T, C]
+        if (patch_size == 1) {
+            return x;
+        }
+        GGML_ASSERT(x->ne[2] == 1);
+
+        int64_t w  = x->ne[0];
+        int64_t h  = x->ne[1];
+        int64_t T  = x->ne[2];
+        int64_t p  = patch_size;
+        int64_t nm = p * p;
+        int64_t Cp = x->ne[3];
+        int64_t C  = Cp / nm;
+        int64_t W  = w * p;
+        int64_t H  = h * p;
+
+        GGML_ASSERT(T == 1);
+        GGML_ASSERT(C * nm == Cp);
+
+        // [w, h, 1, C*p*p] -> [W, H, 1, C]
+        x = ggml_reshape_4d(ctx, x, w, h * C, p, p);                         // [w, h*C, p2, p1]
+        x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 2, 0, 3, 1));  // [p2, w, p1, h*C]
+        x = ggml_reshape_4d(ctx, x, W, H, T, C);                             // [W, H, 1, C]
+        return x;
+    }
+
+    __STATIC_INLINE__ struct ggml_tensor* apply_gate(struct ggml_context* ctx,
+                                                     struct ggml_tensor* x,
+                                                     struct ggml_tensor* gate) {
+        gate = ggml_reshape_3d(ctx, gate, gate->ne[0], 1, gate->ne[1]);  // [N, 1, C]
+        return ggml_mul(ctx, x, gate);
+    }
+
+    struct XEmbedder : public GGMLBlock {
+    public:
+        XEmbedder(int64_t in_dim, int64_t out_dim) {
+            blocks["proj.1"] = std::make_shared<Linear>(in_dim, out_dim, false);
+        }
+
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+            auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj.1"]);
+            return proj->forward(ctx, x);
+        }
+    };
+
+    struct TimestepEmbedder : public GGMLBlock {
+    public:
+        TimestepEmbedder(int64_t in_dim, int64_t out_dim) {
+            blocks["1.linear_1"] = std::make_shared<Linear>(in_dim, in_dim, false);
+            blocks["1.linear_2"] = std::make_shared<Linear>(in_dim, out_dim, false);
+        }
+
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+            auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["1.linear_1"]);
+            auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["1.linear_2"]);
+
+            x = linear_1->forward(ctx, x);
+            x = ggml_silu_inplace(ctx->ggml_ctx, x);
+            x = linear_2->forward(ctx, x);
+            return x;
+        }
+    };
+
+    struct AdaLayerNormZero : public GGMLBlock {
+    protected:
+        int64_t in_features;
+
+    public:
+        AdaLayerNormZero(int64_t in_features, int64_t hidden_features = 256)
+            : in_features(in_features) {
+            blocks["norm"] = std::make_shared<LayerNorm>(in_features, 1e-6f, false, false);
+            blocks["1"]    = std::make_shared<Linear>(in_features, hidden_features, false);
+            blocks["2"]    = std::make_shared<Linear>(hidden_features, 3 * in_features, false);
+        }
+
+        std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                                    struct ggml_tensor* hidden_states,
+                                                                    struct ggml_tensor* embedded_timestep,
+                                                                    struct ggml_tensor* temb = nullptr) {
+            auto norm     = std::dynamic_pointer_cast<LayerNorm>(blocks["norm"]);
+            auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["1"]);
+            auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["2"]);
+
+            auto emb = ggml_silu(ctx->ggml_ctx, embedded_timestep);
+            emb      = linear_1->forward(ctx, emb);
+            emb      = linear_2->forward(ctx, emb);  // [N, 3*C]
+
+            if (temb != nullptr) {
+                emb = ggml_add(ctx->ggml_ctx, emb, temb);
+            }
+
+            auto emb_chunks = ggml_ext_chunk(ctx->ggml_ctx, emb, 3, 0);
+            auto shift      = emb_chunks[0];
+            auto scale      = emb_chunks[1];
+            auto gate       = emb_chunks[2];
+
+            auto x = norm->forward(ctx, hidden_states);
+            x      = Flux::modulate(ctx->ggml_ctx, x, shift, scale);
+
+            return {x, gate};
+        }
+    };
+
+    struct AdaLayerNorm : public GGMLBlock {
+    protected:
+        int64_t embedding_dim;
+
+    public:
+        AdaLayerNorm(int64_t in_features, int64_t hidden_features = 256)
+            : embedding_dim(in_features) {
+            blocks["norm"] = std::make_shared<LayerNorm>(in_features, 1e-6f, false, false);
+            blocks["1"]    = std::make_shared<Linear>(in_features, hidden_features, false);
+            blocks["2"]    = std::make_shared<Linear>(hidden_features, 2 * in_features, false);
+        }
+
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                    struct ggml_tensor* hidden_states,
+                                    struct ggml_tensor* embedded_timestep,
+                                    struct ggml_tensor* temb = nullptr) {
+            auto norm     = std::dynamic_pointer_cast<LayerNorm>(blocks["norm"]);
+            auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["1"]);
+            auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["2"]);
+
+            auto emb = ggml_silu(ctx->ggml_ctx, embedded_timestep);
+            emb      = linear_1->forward(ctx, emb);
+            emb      = linear_2->forward(ctx, emb);  // [N, 2*C]
+
+            if (temb != nullptr) {
+                auto temb_2c = ggml_view_2d(ctx->ggml_ctx, temb, 2 * embedding_dim, temb->ne[1], temb->nb[1], 0);
+                emb          = ggml_add(ctx->ggml_ctx, emb, temb_2c);
+            }
+
+            auto emb_chunks = ggml_ext_chunk(ctx->ggml_ctx, emb, 2, 0);
+            auto shift      = emb_chunks[0];
+            auto scale      = emb_chunks[1];
+
+            auto x = norm->forward(ctx, hidden_states);
+            x      = Flux::modulate(ctx->ggml_ctx, x, shift, scale);
+            return x;
+        }
+    };
+
+    struct AnimaAttention : public GGMLBlock {
+    protected:
+        int64_t num_heads;
+        int64_t head_dim;
+        std::string out_proj_name;
+
+    public:
+        AnimaAttention(int64_t query_dim,
+                       int64_t context_dim,
+                       int64_t num_heads,
+                       int64_t head_dim,
+                       const std::string& out_proj_name = "output_proj")
+            : num_heads(num_heads), head_dim(head_dim), out_proj_name(out_proj_name) {
+            int64_t inner_dim = num_heads * head_dim;
+
+            blocks["q_proj"]            = std::make_shared<Linear>(query_dim, inner_dim, false);
+            blocks["k_proj"]            = std::make_shared<Linear>(context_dim, inner_dim, false);
+            blocks["v_proj"]            = std::make_shared<Linear>(context_dim, inner_dim, false);
+            blocks["q_norm"]            = std::make_shared<RMSNorm>(head_dim, 1e-6f);
+            blocks["k_norm"]            = std::make_shared<RMSNorm>(head_dim, 1e-6f);
+            blocks[this->out_proj_name] = std::make_shared<Linear>(inner_dim, query_dim, false);
+        }
+
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                    struct ggml_tensor* hidden_states,
+                                    struct ggml_tensor* encoder_hidden_states = nullptr,
+                                    struct ggml_tensor* pe_q                  = nullptr,
+                                    struct ggml_tensor* pe_k                  = nullptr) {
+            if (encoder_hidden_states == nullptr) {
+                encoder_hidden_states = hidden_states;
+            }
+
+            auto q_proj   = std::dynamic_pointer_cast<Linear>(blocks["q_proj"]);
+            auto k_proj   = std::dynamic_pointer_cast<Linear>(blocks["k_proj"]);
+            auto v_proj   = std::dynamic_pointer_cast<Linear>(blocks["v_proj"]);
+            auto q_norm   = std::dynamic_pointer_cast<RMSNorm>(blocks["q_norm"]);
+            auto k_norm   = std::dynamic_pointer_cast<RMSNorm>(blocks["k_norm"]);
+            auto out_proj = std::dynamic_pointer_cast<Linear>(blocks[out_proj_name]);
+
+            auto q = q_proj->forward(ctx, hidden_states);
+            auto k = k_proj->forward(ctx, encoder_hidden_states);
+            auto v = v_proj->forward(ctx, encoder_hidden_states);
+
+            int64_t N   = q->ne[2];
+            int64_t L_q = q->ne[1];
+            int64_t L_k = k->ne[1];
+
+            auto q4 = ggml_reshape_4d(ctx->ggml_ctx, q, head_dim, num_heads, L_q, N);  // [N, L_q, H, D]
+            auto k4 = ggml_reshape_4d(ctx->ggml_ctx, k, head_dim, num_heads, L_k, N);  // [N, L_k, H, D]
+            auto v4 = ggml_reshape_4d(ctx->ggml_ctx, v, head_dim, num_heads, L_k, N);  // [N, L_k, H, D]
+
+            q4 = q_norm->forward(ctx, q4);
+            k4 = k_norm->forward(ctx, k4);
+
+            struct ggml_tensor* attn_out = nullptr;
+            if (pe_q != nullptr || pe_k != nullptr) {
+                if (pe_q == nullptr) {
+                    pe_q = pe_k;
+                }
+                if (pe_k == nullptr) {
+                    pe_k = pe_q;
+                }
+                auto q_rope = Rope::apply_rope(ctx->ggml_ctx, q4, pe_q, false);
+                auto k_rope = Rope::apply_rope(ctx->ggml_ctx, k4, pe_k, false);
+                attn_out    = ggml_ext_attention_ext(ctx->ggml_ctx,
+                                                     ctx->backend,
+                                                     q_rope,
+                                                     k_rope,
+                                                     v4,
+                                                     num_heads,
+                                                     nullptr,
+                                                     true,
+                                                     ctx->flash_attn_enabled);
+            } else {
+                auto q_flat = ggml_reshape_3d(ctx->ggml_ctx, q4, head_dim * num_heads, L_q, N);
+                auto k_flat = ggml_reshape_3d(ctx->ggml_ctx, k4, head_dim * num_heads, L_k, N);
+                attn_out    = ggml_ext_attention_ext(ctx->ggml_ctx,
+                                                     ctx->backend,
+                                                     q_flat,
+                                                     k_flat,
+                                                     v,
+                                                     num_heads,
+                                                     nullptr,
+                                                     false,
+                                                     ctx->flash_attn_enabled);
+            }
+
+            return out_proj->forward(ctx, attn_out);
+        }
+    };
+
+    struct AnimaMLP : public GGMLBlock {
+    public:
+        AnimaMLP(int64_t dim, int64_t hidden_dim) {
+            blocks["layer1"] = std::make_shared<Linear>(dim, hidden_dim, false);
+            blocks["layer2"] = std::make_shared<Linear>(hidden_dim, dim, false);
+        }
+
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+            auto layer1 = std::dynamic_pointer_cast<Linear>(blocks["layer1"]);
+            auto layer2 = std::dynamic_pointer_cast<Linear>(blocks["layer2"]);
+
+            x = layer1->forward(ctx, x);
+            x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
+            x = layer2->forward(ctx, x);
+            return x;
+        }
+    };
+
+    struct AdapterMLP : public GGMLBlock {
+    public:
+        AdapterMLP(int64_t dim, int64_t hidden_dim) {
+            blocks["0"] = std::make_shared<Linear>(dim, hidden_dim, true);
+            blocks["2"] = std::make_shared<Linear>(hidden_dim, dim, true);
+        }
+
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+            auto layer0 = std::dynamic_pointer_cast<Linear>(blocks["0"]);
+            auto layer2 = std::dynamic_pointer_cast<Linear>(blocks["2"]);
+
+            x = layer0->forward(ctx, x);
+            x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
+            x = layer2->forward(ctx, x);
+            return x;
+        }
+    };
+
+    struct LLMAdapterBlock : public GGMLBlock {
+    public:
+        LLMAdapterBlock(int64_t model_dim = 1024, int64_t source_dim = 1024, int64_t num_heads = 16, int64_t head_dim = 64) {
+            blocks["norm_self_attn"]  = std::make_shared<RMSNorm>(model_dim, 1e-6f);
+            blocks["self_attn"]       = std::make_shared<AnimaAttention>(model_dim, model_dim, num_heads, head_dim, "o_proj");
+            blocks["norm_cross_attn"] = std::make_shared<RMSNorm>(model_dim, 1e-6f);
+            blocks["cross_attn"]      = std::make_shared<AnimaAttention>(model_dim, source_dim, num_heads, head_dim, "o_proj");
+            blocks["norm_mlp"]        = std::make_shared<RMSNorm>(model_dim, 1e-6f);
+            blocks["mlp"]             = std::make_shared<AdapterMLP>(model_dim, model_dim * 4);
+        }
+
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                    struct ggml_tensor* x,
+                                    struct ggml_tensor* context,
+                                    struct ggml_tensor* target_pe,
+                                    struct ggml_tensor* context_pe) {
+            auto norm_self_attn  = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_self_attn"]);
+            auto self_attn       = std::dynamic_pointer_cast<AnimaAttention>(blocks["self_attn"]);
+            auto norm_cross_attn = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_cross_attn"]);
+            auto cross_attn      = std::dynamic_pointer_cast<AnimaAttention>(blocks["cross_attn"]);
+            auto norm_mlp        = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_mlp"]);
+            auto mlp             = std::dynamic_pointer_cast<AdapterMLP>(blocks["mlp"]);
+
+            auto h = norm_self_attn->forward(ctx, x);
+            h      = self_attn->forward(ctx, h, nullptr, target_pe, target_pe);
+            x      = ggml_add(ctx->ggml_ctx, x, h);
+
+            h = norm_cross_attn->forward(ctx, x);
+            h = cross_attn->forward(ctx, h, context, target_pe, context_pe);
+            x = ggml_add(ctx->ggml_ctx, x, h);
+
+            h = norm_mlp->forward(ctx, x);
+            h = mlp->forward(ctx, h);
+            x = ggml_add(ctx->ggml_ctx, x, h);
+
+            return x;
+        }
+    };
+
+    struct LLMAdapter : public GGMLBlock {
+    protected:
+        int num_layers;
+
+    public:
+        LLMAdapter(int64_t source_dim = 1024,
+                   int64_t target_dim = 1024,
+                   int64_t model_dim  = 1024,
+                   int num_layers     = 6,
+                   int num_heads      = 16)
+            : num_layers(num_layers) {
+            int64_t head_dim = model_dim / num_heads;
+
+            blocks["embed"] = std::make_shared<Embedding>(32128, target_dim);
+            for (int i = 0; i < num_layers; i++) {
+                blocks["blocks." + std::to_string(i)] =
+                    std::make_shared<LLMAdapterBlock>(model_dim, source_dim, num_heads, head_dim);
+            }
+            blocks["out_proj"] = std::make_shared<Linear>(model_dim, target_dim, true);
+            blocks["norm"]     = std::make_shared<RMSNorm>(target_dim, 1e-6f);
+        }
+
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                    struct ggml_tensor* source_hidden_states,
+                                    struct ggml_tensor* target_input_ids,
+                                    struct ggml_tensor* target_pe,
+                                    struct ggml_tensor* source_pe) {
+            GGML_ASSERT(target_input_ids != nullptr);
+            if (ggml_n_dims(target_input_ids) == 1) {
+                target_input_ids = ggml_reshape_2d(ctx->ggml_ctx, target_input_ids, target_input_ids->ne[0], 1);
+            }
+
+            auto embed    = std::dynamic_pointer_cast<Embedding>(blocks["embed"]);
+            auto out_proj = std::dynamic_pointer_cast<Linear>(blocks["out_proj"]);
+            auto norm     = std::dynamic_pointer_cast<RMSNorm>(blocks["norm"]);
+
+            auto x = embed->forward(ctx, target_input_ids);  // [N, target_len, target_dim]
+
+            for (int i = 0; i < num_layers; i++) {
+                auto block = std::dynamic_pointer_cast<LLMAdapterBlock>(blocks["blocks." + std::to_string(i)]);
+                x          = block->forward(ctx, x, source_hidden_states, target_pe, source_pe);
+            }
+
+            x = out_proj->forward(ctx, x);
+            x = norm->forward(ctx, x);
+            return x;
+        }
+    };
+
+    struct TransformerBlock : public GGMLBlock {
+    public:
+        TransformerBlock(int64_t hidden_size,
+                         int64_t text_embed_dim,
+                         int64_t num_heads,
+                         int64_t head_dim,
+                         int64_t mlp_ratio      = 4,
+                         int64_t adaln_lora_dim = 256) {
+            blocks["adaln_modulation_self_attn"]  = std::make_shared<AdaLayerNormZero>(hidden_size, adaln_lora_dim);
+            blocks["self_attn"]                   = std::make_shared<AnimaAttention>(hidden_size, hidden_size, num_heads, head_dim);
+            blocks["adaln_modulation_cross_attn"] = std::make_shared<AdaLayerNormZero>(hidden_size, adaln_lora_dim);
+            blocks["cross_attn"]                  = std::make_shared<AnimaAttention>(hidden_size, text_embed_dim, num_heads, head_dim);
+            blocks["adaln_modulation_mlp"]        = std::make_shared<AdaLayerNormZero>(hidden_size, adaln_lora_dim);
+            blocks["mlp"]                         = std::make_shared<AnimaMLP>(hidden_size, hidden_size * mlp_ratio);
+        }
+
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                    struct ggml_tensor* hidden_states,
+                                    struct ggml_tensor* encoder_hidden_states,
+                                    struct ggml_tensor* embedded_timestep,
+                                    struct ggml_tensor* temb,
+                                    struct ggml_tensor* image_pe) {
+            auto norm1 = std::dynamic_pointer_cast<AdaLayerNormZero>(blocks["adaln_modulation_self_attn"]);
+            auto attn1 = std::dynamic_pointer_cast<AnimaAttention>(blocks["self_attn"]);
+            auto norm2 = std::dynamic_pointer_cast<AdaLayerNormZero>(blocks["adaln_modulation_cross_attn"]);
+            auto attn2 = std::dynamic_pointer_cast<AnimaAttention>(blocks["cross_attn"]);
+            auto norm3 = std::dynamic_pointer_cast<AdaLayerNormZero>(blocks["adaln_modulation_mlp"]);
+            auto mlp   = std::dynamic_pointer_cast<AnimaMLP>(blocks["mlp"]);
+
+            auto [normed1, gate1] = norm1->forward(ctx, hidden_states, embedded_timestep, temb);
+            auto h                = attn1->forward(ctx, normed1, nullptr, image_pe, image_pe);
+            hidden_states         = ggml_add(ctx->ggml_ctx, hidden_states, apply_gate(ctx->ggml_ctx, h, gate1));
+
+            auto [normed2, gate2] = norm2->forward(ctx, hidden_states, embedded_timestep, temb);
+            h                     = attn2->forward(ctx, normed2, encoder_hidden_states, nullptr, nullptr);
+            hidden_states         = ggml_add(ctx->ggml_ctx, hidden_states, apply_gate(ctx->ggml_ctx, h, gate2));
+
+            auto [normed3, gate3] = norm3->forward(ctx, hidden_states, embedded_timestep, temb);
+            h                     = mlp->forward(ctx, normed3);
+            hidden_states         = ggml_add(ctx->ggml_ctx, hidden_states, apply_gate(ctx->ggml_ctx, h, gate3));
+
+            return hidden_states;
+        }
+    };
+
+    struct FinalLayer : public GGMLBlock {
+    protected:
+        int64_t hidden_size;
+        int64_t patch_size;
+        int64_t out_channels;
+
+    public:
+        FinalLayer(int64_t hidden_size, int64_t patch_size, int64_t out_channels)
+            : hidden_size(hidden_size), patch_size(patch_size), out_channels(out_channels) {
+            blocks["adaln_modulation"] = std::make_shared<AdaLayerNorm>(hidden_size, 256);
+            blocks["linear"]           = std::make_shared<Linear>(hidden_size, patch_size * patch_size * out_channels, false);
+        }
+
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                    struct ggml_tensor* hidden_states,
+                                    struct ggml_tensor* embedded_timestep,
+                                    struct ggml_tensor* temb) {
+            auto adaln  = std::dynamic_pointer_cast<AdaLayerNorm>(blocks["adaln_modulation"]);
+            auto linear = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
+
+            hidden_states = adaln->forward(ctx, hidden_states, embedded_timestep, temb);
+            hidden_states = linear->forward(ctx, hidden_states);
+            return hidden_states;
+        }
+    };
+
+    struct AnimaNet : public GGMLBlock {
+    public:
+        int64_t in_channels       = 16;
+        int64_t out_channels      = 16;
+        int64_t hidden_size       = 2048;
+        int64_t text_embed_dim    = 1024;
+        int64_t num_heads         = 16;
+        int64_t head_dim          = 128;
+        int64_t patch_size        = 2;
+        int64_t num_layers        = 28;
+        std::vector<int> axes_dim = {44, 42, 42};
+        int theta                 = 10000;
+
+    public:
+        AnimaNet() = default;
+        explicit AnimaNet(int64_t num_layers)
+            : num_layers(num_layers) {
+            blocks["x_embedder"]       = std::make_shared<XEmbedder>((in_channels + 1) * patch_size * patch_size, hidden_size);
+            blocks["t_embedder"]       = std::make_shared<TimestepEmbedder>(hidden_size, hidden_size * 3);
+            blocks["t_embedding_norm"] = std::make_shared<RMSNorm>(hidden_size, 1e-6f);
+            for (int i = 0; i < num_layers; i++) {
+                blocks["blocks." + std::to_string(i)] = std::make_shared<TransformerBlock>(hidden_size,
+                                                                                           text_embed_dim,
+                                                                                           num_heads,
+                                                                                           head_dim);
+            }
+            blocks["final_layer"] = std::make_shared<FinalLayer>(hidden_size, patch_size, out_channels);
+            blocks["llm_adapter"] = std::make_shared<LLMAdapter>(1024, 1024, 1024, 6, 16);
+        }
+
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                    struct ggml_tensor* x,
+                                    struct ggml_tensor* timestep,
+                                    struct ggml_tensor* encoder_hidden_states,
+                                    struct ggml_tensor* image_pe,
+                                    struct ggml_tensor* t5_ids       = nullptr,
+                                    struct ggml_tensor* t5_weights   = nullptr,
+                                    struct ggml_tensor* adapter_q_pe = nullptr,
+                                    struct ggml_tensor* adapter_k_pe = nullptr) {
+            GGML_ASSERT(x->ne[3] == 1);
+
+            auto x_embedder       = std::dynamic_pointer_cast<XEmbedder>(blocks["x_embedder"]);
+            auto t_embedder       = std::dynamic_pointer_cast<TimestepEmbedder>(blocks["t_embedder"]);
+            auto t_embedding_norm = std::dynamic_pointer_cast<RMSNorm>(blocks["t_embedding_norm"]);
+            auto final_layer      = std::dynamic_pointer_cast<FinalLayer>(blocks["final_layer"]);
+            auto llm_adapter      = std::dynamic_pointer_cast<LLMAdapter>(blocks["llm_adapter"]);
+
+            int64_t W = x->ne[0];
+            int64_t H = x->ne[1];
+
+            x = ggml_reshape_4d(ctx->ggml_ctx, x, x->ne[0], x->ne[1], 1, x->ne[2] * x->ne[3]);  // [N*C, T, H, W] style
+
+            int64_t pad_h = (patch_size - H % patch_size) % patch_size;
+            int64_t pad_w = (patch_size - W % patch_size) % patch_size;
+            if (pad_h > 0 || pad_w > 0) {
+                x = ggml_ext_pad(ctx->ggml_ctx, x, static_cast<int>(pad_w), static_cast<int>(pad_h), 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
+            }
+
+            auto padding_mask = ggml_ext_zeros(ctx->ggml_ctx, x->ne[0], x->ne[1], x->ne[2], 1);
+            x                 = ggml_concat(ctx->ggml_ctx, x, padding_mask, 3);  // concat mask channel
+
+            x = patchify_2d(ctx->ggml_ctx, x, patch_size);  // [C*4, T, H/2, W/2]
+
+            int64_t w_len = x->ne[0];
+            int64_t h_len = x->ne[1];
+            int64_t t_len = x->ne[2];
+            x             = ggml_reshape_3d(ctx->ggml_ctx, x, x->ne[0] * x->ne[1] * x->ne[2], x->ne[3], 1);
+            x             = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [N, n_token, C]
+
+            x = x_embedder->forward(ctx, x);
+
+            auto timestep_proj     = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep, static_cast<int>(hidden_size));
+            auto temb              = t_embedder->forward(ctx, timestep_proj);
+            auto embedded_timestep = t_embedding_norm->forward(ctx, timestep_proj);
+
+            if (t5_ids != nullptr) {
+                auto adapted_context = llm_adapter->forward(ctx, encoder_hidden_states, t5_ids, adapter_q_pe, adapter_k_pe);
+                if (t5_weights != nullptr) {
+                    auto w = t5_weights;
+                    if (ggml_n_dims(w) == 1) {
+                        w = ggml_reshape_3d(ctx->ggml_ctx, w, 1, w->ne[0], 1);
+                    }
+                    w               = ggml_repeat_4d(ctx->ggml_ctx, w, adapted_context->ne[0], adapted_context->ne[1], adapted_context->ne[2], 1);
+                    adapted_context = ggml_mul(ctx->ggml_ctx, adapted_context, w);
+                }
+                if (adapted_context->ne[1] < 512) {
+                    auto pad_ctx    = ggml_ext_zeros(ctx->ggml_ctx,
+                                                     adapted_context->ne[0],
+                                                     512 - adapted_context->ne[1],
+                                                     adapted_context->ne[2],
+                                                     1);
+                    adapted_context = ggml_concat(ctx->ggml_ctx, adapted_context, pad_ctx, 1);
+                } else if (adapted_context->ne[1] > 512) {
+                    adapted_context = ggml_ext_slice(ctx->ggml_ctx, adapted_context, 1, 0, 512);
+                }
+                encoder_hidden_states = adapted_context;
+            }
+
+            for (int i = 0; i < num_layers; i++) {
+                auto block = std::dynamic_pointer_cast<TransformerBlock>(blocks["blocks." + std::to_string(i)]);
+                x          = block->forward(ctx, x, encoder_hidden_states, embedded_timestep, temb, image_pe);
+            }
+
+            x = final_layer->forward(ctx, x, embedded_timestep, temb);  // [N, n_token, C*4]
+
+            x = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [n_token, C*4, N]
+            x = ggml_reshape_4d(ctx->ggml_ctx, x, w_len, h_len, t_len, x->ne[1]);                    // [C*4, T, H/2, W/2]
+            x = unpatchify_2d(ctx->ggml_ctx, x, patch_size);                                         // [C, T, H, W]
+
+            x = ggml_ext_slice(ctx->ggml_ctx, x, 1, 0, H);                                  // [C, T, H, W + pad]
+            x = ggml_ext_slice(ctx->ggml_ctx, x, 0, 0, W);                                  // [C, T, H, W]
+            x = ggml_reshape_4d(ctx->ggml_ctx, x, x->ne[0], x->ne[1], x->ne[3], x->ne[2]);  // [N, C, H, W]
+
+            return x;
+        }
+    };
+
+    struct AnimaRunner : public GGMLRunner {
+    public:
+        std::vector<float> image_pe_vec;
+        std::vector<float> adapter_q_pe_vec;
+        std::vector<float> adapter_k_pe_vec;
+        AnimaNet net;
+
+        AnimaRunner(ggml_backend_t backend,
+                    bool offload_params_to_cpu,
+                    const String2TensorStorage& tensor_storage_map = {},
+                    const std::string prefix                       = "model.diffusion_model")
+            : GGMLRunner(backend, offload_params_to_cpu) {
+            int64_t num_layers    = 0;
+            std::string layer_tag = prefix + ".net.blocks.";
+            for (const auto& kv : tensor_storage_map) {
+                const std::string& tensor_name = kv.first;
+                size_t pos                     = tensor_name.find(layer_tag);
+                if (pos == std::string::npos) {
+                    continue;
+                }
+                size_t start = pos + layer_tag.size();
+                size_t end   = tensor_name.find('.', start);
+                if (end == std::string::npos) {
+                    continue;
+                }
+                int64_t layer_id = atoll(tensor_name.substr(start, end - start).c_str());
+                num_layers       = std::max(num_layers, layer_id + 1);
+            }
+            if (num_layers <= 0) {
+                num_layers = 28;
+            }
+            LOG_INFO("anima net layers: %" PRId64, num_layers);
+
+            net = AnimaNet(num_layers);
+            net.init(params_ctx, tensor_storage_map, prefix + ".net");
+        }
+
+        std::string get_desc() override {
+            return "anima";
+        }
+
+        void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+            net.get_param_tensors(tensors, prefix + ".net");
+        }
+
+        static std::vector<float> gen_1d_rope_pe_vec(int64_t seq_len, int dim, float theta = 10000.f) {
+            std::vector<float> pos(seq_len);
+            for (int64_t i = 0; i < seq_len; i++) {
+                pos[i] = static_cast<float>(i);
+            }
+            auto rope_emb = Rope::rope(pos, dim, theta);
+            return Rope::flatten(rope_emb);
+        }
+
+        static float calc_ntk_factor(float extrapolation_ratio, int axis_dim) {
+            if (extrapolation_ratio == 1.0f || axis_dim <= 2) {
+                return 1.0f;
+            }
+            return std::pow(extrapolation_ratio, static_cast<float>(axis_dim) / static_cast<float>(axis_dim - 2));
+        }
+
+        static std::vector<float> gen_anima_image_pe_vec(int bs,
+                                                         int h,
+                                                         int w,
+                                                         int patch_size,
+                                                         int theta,
+                                                         const std::vector<int>& axes_dim,
+                                                         float h_extrapolation_ratio,
+                                                         float w_extrapolation_ratio,
+                                                         float t_extrapolation_ratio) {
+            static const std::vector<ggml_tensor*> empty_ref_latents;
+            auto ids = Rope::gen_flux_ids(h,
+                                          w,
+                                          patch_size,
+                                          bs,
+                                          static_cast<int>(axes_dim.size()),
+                                          0,
+                                          {},
+                                          empty_ref_latents,
+                                          false,
+                                          1.0f);
+
+            std::vector<float> axis_thetas = {
+                static_cast<float>(theta) * calc_ntk_factor(t_extrapolation_ratio, axes_dim[0]),
+                static_cast<float>(theta) * calc_ntk_factor(h_extrapolation_ratio, axes_dim[1]),
+                static_cast<float>(theta) * calc_ntk_factor(w_extrapolation_ratio, axes_dim[2]),
+            };
+            return Rope::embed_nd(ids, bs, axis_thetas, axes_dim);
+        }
+
+        struct ggml_cgraph* build_graph(struct ggml_tensor* x,
+                                        struct ggml_tensor* timesteps,
+                                        struct ggml_tensor* context,
+                                        struct ggml_tensor* t5_ids     = nullptr,
+                                        struct ggml_tensor* t5_weights = nullptr) {
+            GGML_ASSERT(x->ne[3] == 1);
+            struct ggml_cgraph* gf = new_graph_custom(ANIMA_GRAPH_SIZE);
+
+            x          = to_backend(x);
+            timesteps  = to_backend(timesteps);
+            context    = to_backend(context);
+            t5_ids     = to_backend(t5_ids);
+            t5_weights = to_backend(t5_weights);
+
+            int64_t pad_h = (net.patch_size - x->ne[1] % net.patch_size) % net.patch_size;
+            int64_t pad_w = (net.patch_size - x->ne[0] % net.patch_size) % net.patch_size;
+            int64_t h_pad = x->ne[1] + pad_h;
+            int64_t w_pad = x->ne[0] + pad_w;
+
+            image_pe_vec          = gen_anima_image_pe_vec(1,
+                                                           static_cast<int>(h_pad),
+                                                           static_cast<int>(w_pad),
+                                                           static_cast<int>(net.patch_size),
+                                                           net.theta,
+                                                           net.axes_dim,
+                                                           4.0f,
+                                                           4.0f,
+                                                           1.0f);
+            int64_t image_pos_len = static_cast<int64_t>(image_pe_vec.size()) / (2 * 2 * (net.head_dim / 2));
+            auto image_pe         = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, net.head_dim / 2, image_pos_len);
+            set_backend_tensor_data(image_pe, image_pe_vec.data());
+
+            ggml_tensor* adapter_q_pe = nullptr;
+            ggml_tensor* adapter_k_pe = nullptr;
+            if (t5_ids != nullptr) {
+                int64_t target_len = t5_ids->ne[0];
+                int64_t source_len = context->ne[1];
+
+                adapter_q_pe_vec = gen_1d_rope_pe_vec(target_len, 64, 10000.f);
+                adapter_k_pe_vec = gen_1d_rope_pe_vec(source_len, 64, 10000.f);
+
+                int64_t target_pos_len = static_cast<int64_t>(adapter_q_pe_vec.size()) / (2 * 2 * 32);
+                int64_t source_pos_len = static_cast<int64_t>(adapter_k_pe_vec.size()) / (2 * 2 * 32);
+
+                adapter_q_pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, 32, target_pos_len);
+                adapter_k_pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, 32, source_pos_len);
+                set_backend_tensor_data(adapter_q_pe, adapter_q_pe_vec.data());
+                set_backend_tensor_data(adapter_k_pe, adapter_k_pe_vec.data());
+            }
+
+            auto runner_ctx = get_context();
+            auto out        = net.forward(&runner_ctx,
+                                          x,
+                                          timesteps,
+                                          context,
+                                          image_pe,
+                                          t5_ids,
+                                          t5_weights,
+                                          adapter_q_pe,
+                                          adapter_k_pe);
+
+            ggml_build_forward_expand(gf, out);
+            return gf;
+        }
+
+        bool compute(int n_threads,
+                     struct ggml_tensor* x,
+                     struct ggml_tensor* timesteps,
+                     struct ggml_tensor* context,
+                     struct ggml_tensor* t5_ids      = nullptr,
+                     struct ggml_tensor* t5_weights  = nullptr,
+                     struct ggml_tensor** output     = nullptr,
+                     struct ggml_context* output_ctx = nullptr) {
+            auto get_graph = [&]() -> struct ggml_cgraph* {
+                return build_graph(x, timesteps, context, t5_ids, t5_weights);
+            };
+            return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+        }
+    };
+}  // namespace Anima
+
+#endif  // __ANIMA_HPP__
diff --git a/src/conditioner.hpp b/src/conditioner.hpp
index 4317ed18..d4a3146b 100644
--- a/src/conditioner.hpp
+++ b/src/conditioner.hpp
@@ -1641,6 +1641,142 @@ struct T5CLIPEmbedder : public Conditioner {
     }
 };
 
+struct AnimaConditioner : public Conditioner {
+    std::shared_ptr<LLM::BPETokenizer> qwen_tokenizer;
+    T5UniGramTokenizer t5_tokenizer;
+    std::shared_ptr<LLM::LLMRunner> llm;
+
+    AnimaConditioner(ggml_backend_t backend,
+                     bool offload_params_to_cpu,
+                     const String2TensorStorage& tensor_storage_map = {}) {
+        qwen_tokenizer = std::make_shared<LLM::Qwen2Tokenizer>();
+        llm            = std::make_shared<LLM::LLMRunner>(LLM::LLMArch::QWEN3,
+                                               backend,
+                                               offload_params_to_cpu,
+                                               tensor_storage_map,
+                                               "text_encoders.llm",
+                                               false);
+    }
+
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
+        llm->get_param_tensors(tensors, "text_encoders.llm");
+    }
+
+    void alloc_params_buffer() override {
+        llm->alloc_params_buffer();
+    }
+
+    void free_params_buffer() override {
+        llm->free_params_buffer();
+    }
+
+    size_t get_params_buffer_size() override {
+        return llm->get_params_buffer_size();
+    }
+
+    void set_flash_attention_enabled(bool enabled) override {
+        llm->set_flash_attention_enabled(enabled);
+    }
+
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        llm->set_weight_adapter(adapter);
+    }
+
+    std::tuple<std::vector<int>, std::vector<float>, std::vector<int>, std::vector<float>> tokenize(std::string text) {
+        auto parsed_attention = parse_prompt_attention(text);
+
+        {
+            std::stringstream ss;
+            ss << "[";
+            for (const auto& item : parsed_attention) {
+                ss << "['" << item.first << "', " << item.second << "], ";
+            }
+            ss << "]";
+            LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
+        }
+
+        std::vector<int> qwen_tokens;
+        std::vector<float> qwen_weights;
+        std::vector<int> t5_tokens;
+        std::vector<float> t5_weights;
+
+        for (const auto& item : parsed_attention) {
+            const std::string& curr_text = item.first;
+            std::vector<int> curr_tokens = qwen_tokenizer->tokenize(curr_text, nullptr);
+            qwen_tokens.insert(qwen_tokens.end(), curr_tokens.begin(), curr_tokens.end());
+            // Anima uses uniform Qwen token weights.
+            qwen_weights.insert(qwen_weights.end(), curr_tokens.size(), 1.f);
+        }
+        if (qwen_tokens.empty()) {
+            qwen_tokens.push_back(151643);  // qwen3 pad token
+            qwen_weights.push_back(1.f);
+        }
+
+        for (const auto& item : parsed_attention) {
+            const std::string& curr_text = item.first;
+            float curr_weight            = item.second;
+            std::vector<int> curr_tokens = t5_tokenizer.Encode(curr_text, true);
+            t5_tokens.insert(t5_tokens.end(), curr_tokens.begin(), curr_tokens.end());
+            t5_weights.insert(t5_weights.end(), curr_tokens.size(), curr_weight);
+        }
+
+        return {qwen_tokens, qwen_weights, t5_tokens, t5_weights};
+    }
+
+    SDCondition get_learned_condition(ggml_context* work_ctx,
+                                      int n_threads,
+                                      const ConditionerParams& conditioner_params) override {
+        int64_t t0 = ggml_time_ms();
+
+        auto tokenized     = tokenize(conditioner_params.text);
+        auto& qwen_tokens  = std::get<0>(tokenized);
+        auto& qwen_weights = std::get<1>(tokenized);
+        auto& t5_tokens    = std::get<2>(tokenized);
+        auto& t5_weights   = std::get<3>(tokenized);
+
+        auto input_ids = vector_to_ggml_tensor_i32(work_ctx, qwen_tokens);
+
+        struct ggml_tensor* hidden_states = nullptr;  // [N, n_token, 1024]
+        llm->compute(n_threads,
+                     input_ids,
+                     nullptr,
+                     {},
+                     {},
+                     &hidden_states,
+                     work_ctx);
+
+        {
+            auto tensor         = hidden_states;
+            float original_mean = ggml_ext_tensor_mean(tensor);
+            for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
+                for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
+                    for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
+                        float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
+                        value *= qwen_weights[i1];
+                        ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
+                    }
+                }
+            }
+            float new_mean = ggml_ext_tensor_mean(tensor);
+            if (new_mean != 0.f) {
+                ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
+            }
+        }
+
+        struct ggml_tensor* t5_ids_tensor    = nullptr;
+        struct ggml_tensor* t5_weight_tensor = nullptr;
+        if (!t5_tokens.empty()) {
+            t5_ids_tensor    = vector_to_ggml_tensor_i32(work_ctx, t5_tokens);
+            t5_weight_tensor = vector_to_ggml_tensor(work_ctx, t5_weights);
+        }
+
+        int64_t t1 = ggml_time_ms();
+        LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
+
+        return {hidden_states, t5_weight_tensor, t5_ids_tensor};
+    }
+};
+
 struct LLMEmbedder : public Conditioner {
     SDVersion version;
     std::shared_ptr<LLM::BPETokenizer> tokenizer;
diff --git a/src/diffusion_model.hpp b/src/diffusion_model.hpp
index 3293ba9b..329bb9d9 100644
--- a/src/diffusion_model.hpp
+++ b/src/diffusion_model.hpp
@@ -1,6 +1,7 @@
 #ifndef __DIFFUSION_MODEL_H__
 #define __DIFFUSION_MODEL_H__
 
+#include "anima.hpp"
 #include "flux.hpp"
 #include "mmdit.hpp"
 #include "qwen_image.hpp"
@@ -242,6 +243,72 @@ struct FluxModel : public DiffusionModel {
     }
 };
 
+struct AnimaModel : public DiffusionModel {
+    std::string prefix;
+    Anima::AnimaRunner anima;
+
+    AnimaModel(ggml_backend_t backend,
+               bool offload_params_to_cpu,
+               const String2TensorStorage& tensor_storage_map = {},
+               const std::string prefix                       = "model.diffusion_model")
+        : prefix(prefix), anima(backend, offload_params_to_cpu, tensor_storage_map, prefix) {
+    }
+
+    std::string get_desc() override {
+        return anima.get_desc();
+    }
+
+    void alloc_params_buffer() override {
+        anima.alloc_params_buffer();
+    }
+
+    void free_params_buffer() override {
+        anima.free_params_buffer();
+    }
+
+    void free_compute_buffer() override {
+        anima.free_compute_buffer();
+    }
+
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
+        anima.get_param_tensors(tensors, prefix);
+    }
+
+    size_t get_params_buffer_size() override {
+        return anima.get_params_buffer_size();
+    }
+
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        anima.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
+        return 768;
+    }
+
+    void set_flash_attention_enabled(bool enabled) {
+        anima.set_flash_attention_enabled(enabled);
+    }
+
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        anima.set_circular_axes(circular_x, circular_y);
+    }
+
+    bool compute(int n_threads,
+                 DiffusionParams diffusion_params,
+                 struct ggml_tensor** output     = nullptr,
+                 struct ggml_context* output_ctx = nullptr) override {
+        return anima.compute(n_threads,
+                             diffusion_params.x,
+                             diffusion_params.timesteps,
+                             diffusion_params.context,
+                             diffusion_params.c_concat,
+                             diffusion_params.y,
+                             output,
+                             output_ctx);
+    }
+};
+
 struct WanModel : public DiffusionModel {
     std::string prefix;
     WAN::WanRunner wan;
diff --git a/src/model.cpp b/src/model.cpp
index 58d71d9e..24e3c566 100644
--- a/src/model.cpp
+++ b/src/model.cpp
@@ -1057,6 +1057,9 @@ SDVersion ModelLoader::get_sd_version() {
             if (tensor_storage.name.find("model.diffusion_model.transformer_blocks.0.img_mod.1.weight") != std::string::npos) {
                 return VERSION_QWEN_IMAGE;
             }
+            if (tensor_storage.name.find("model.diffusion_model.net.llm_adapter.blocks.0.cross_attn.q_proj.weight") != std::string::npos) {
+                return VERSION_ANIMA;
+            }
             if (tensor_storage.name.find("model.diffusion_model.double_stream_modulation_img.lin.weight") != std::string::npos) {
                 is_flux2 = true;
             }
diff --git a/src/model.h b/src/model.h
index 0d7a85c6..5b9ce18a 100644
--- a/src/model.h
+++ b/src/model.h
@@ -45,6 +45,7 @@ enum SDVersion {
     VERSION_WAN2_2_I2V,
     VERSION_WAN2_2_TI2V,
     VERSION_QWEN_IMAGE,
+    VERSION_ANIMA,
     VERSION_FLUX2,
     VERSION_FLUX2_KLEIN,
     VERSION_Z_IMAGE,
@@ -122,6 +123,13 @@ static inline bool sd_version_is_qwen_image(SDVersion version) {
     return false;
 }
 
+static inline bool sd_version_is_anima(SDVersion version) {
+    if (version == VERSION_ANIMA) {
+        return true;
+    }
+    return false;
+}
+
 static inline bool sd_version_is_z_image(SDVersion version) {
     if (version == VERSION_Z_IMAGE) {
         return true;
@@ -146,6 +154,7 @@ static inline bool sd_version_is_dit(SDVersion version) {
         sd_version_is_sd3(version) ||
         sd_version_is_wan(version) ||
         sd_version_is_qwen_image(version) ||
+        sd_version_is_anima(version) ||
         sd_version_is_z_image(version)) {
         return true;
     }
diff --git a/src/name_conversion.cpp b/src/name_conversion.cpp
index d3e863b8..a7839ab9 100644
--- a/src/name_conversion.cpp
+++ b/src/name_conversion.cpp
@@ -1094,6 +1094,14 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
         }
     }
 
+    if (is_lora && sd_version_is_anima(version)) {
+        static const std::string anima_diffusion_prefix = "model.diffusion_model.";
+        static const std::string anima_net_prefix       = "model.diffusion_model.net.";
+        if (starts_with(name, anima_diffusion_prefix) && !starts_with(name, anima_net_prefix)) {
+            name = anima_net_prefix + name.substr(anima_diffusion_prefix.size());
+        }
+    }
+
     // cond_stage_model
     {
         for (const auto& prefix : cond_stage_model_prefix_vec) {
diff --git a/src/rope.hpp b/src/rope.hpp
index 45e88c83..b26e4fcc 100644
--- a/src/rope.hpp
+++ b/src/rope.hpp
@@ -43,7 +43,7 @@ namespace Rope {
 
     __STATIC_INLINE__ std::vector<std::vector<float>> rope(const std::vector<float>& pos,
                                                            int dim,
-                                                           int theta,
+                                                           float theta,
                                                            const std::vector<int>& axis_wrap_dims = {}) {
         assert(dim % 2 == 0);
         int half_dim = dim / 2;
@@ -167,7 +167,7 @@ namespace Rope {
 
     __STATIC_INLINE__ std::vector<float> embed_nd(const std::vector<std::vector<float>>& ids,
                                                   int bs,
-                                                  int theta,
+                                                  const std::vector<float>& axis_thetas,
                                                   const std::vector<int>& axes_dim,
                                                   const std::vector<std::vector<int>>& wrap_dims = {}) {
         std::vector<std::vector<float>> trans_ids = transpose(ids);
@@ -188,8 +188,12 @@ namespace Rope {
             if (!wrap_dims.empty() && i < (int)wrap_dims.size()) {
                 axis_wrap_dims = wrap_dims[i];
             }
+            float axis_theta = 10000.0f;
+            if (!axis_thetas.empty()) {
+                axis_theta = axis_thetas[std::min(i, axis_thetas.size() - 1)];
+            }
             std::vector<std::vector<float>> rope_emb =
-                rope(trans_ids[i], axes_dim[i], theta, axis_wrap_dims);  // [bs*pos_len, axes_dim[i]/2 * 2 * 2]
+                rope(trans_ids[i], axes_dim[i], axis_theta, axis_wrap_dims);  // [bs*pos_len, axes_dim[i]/2 * 2 * 2]
             for (int b = 0; b < bs; ++b) {
                 for (int j = 0; j < pos_len; ++j) {
                     for (int k = 0; k < rope_emb[0].size(); ++k) {
@@ -203,6 +207,15 @@ namespace Rope {
         return flatten(emb);
     }
 
+    __STATIC_INLINE__ std::vector<float> embed_nd(const std::vector<std::vector<float>>& ids,
+                                                  int bs,
+                                                  float theta,
+                                                  const std::vector<int>& axes_dim,
+                                                  const std::vector<std::vector<int>>& wrap_dims = {}) {
+        std::vector<float> axis_thetas(axes_dim.size(), theta);
+        return embed_nd(ids, bs, axis_thetas, axes_dim, wrap_dims);
+    }
+
     __STATIC_INLINE__ std::vector<std::vector<float>> gen_refs_ids(int patch_size,
                                                                    int bs,
                                                                    int axes_dim_num,
@@ -332,7 +345,7 @@ namespace Rope {
                 }
             }
         }
-        return embed_nd(ids, bs, theta, axes_dim, wrap_dims);
+        return embed_nd(ids, bs, static_cast<float>(theta), axes_dim, wrap_dims);
     }
 
     __STATIC_INLINE__ std::vector<std::vector<float>> gen_qwen_image_ids(int h,
@@ -421,7 +434,7 @@ namespace Rope {
                 }
             }
         }
-        return embed_nd(ids, bs, theta, axes_dim, wrap_dims);
+        return embed_nd(ids, bs, static_cast<float>(theta), axes_dim, wrap_dims);
     }
 
     __STATIC_INLINE__ std::vector<std::vector<float>> gen_vid_ids(int t,
@@ -475,7 +488,7 @@ namespace Rope {
                                                     int theta,
                                                     const std::vector<int>& axes_dim) {
         std::vector<std::vector<float>> ids = gen_vid_ids(t, h, w, pt, ph, pw, bs);
-        return embed_nd(ids, bs, theta, axes_dim);
+        return embed_nd(ids, bs, static_cast<float>(theta), axes_dim);
     }
 
     __STATIC_INLINE__ std::vector<std::vector<float>> gen_qwen2vl_ids(int grid_h,
@@ -511,7 +524,7 @@ namespace Rope {
                                                         int theta,
                                                         const std::vector<int>& axes_dim) {
         std::vector<std::vector<float>> ids = gen_qwen2vl_ids(grid_h, grid_w, merge_size, window_index);
-        return embed_nd(ids, 1, theta, axes_dim);
+        return embed_nd(ids, 1, static_cast<float>(theta), axes_dim);
     }
 
     __STATIC_INLINE__ int bound_mod(int a, int m) {
@@ -584,7 +597,7 @@ namespace Rope {
             }
         }
 
-        return embed_nd(ids, bs, theta, axes_dim, wrap_dims);
+        return embed_nd(ids, bs, static_cast<float>(theta), axes_dim, wrap_dims);
     }
 
     __STATIC_INLINE__ struct ggml_tensor* apply_rope(struct ggml_context* ctx,
diff --git a/src/stable-diffusion.cpp b/src/stable-diffusion.cpp
index e704922a..d769d45c 100644
--- a/src/stable-diffusion.cpp
+++ b/src/stable-diffusion.cpp
@@ -48,6 +48,7 @@ const char* model_version_to_str[] = {
     "Wan 2.2 I2V",
     "Wan 2.2 TI2V",
     "Qwen Image",
+    "Anima",
     "Flux.2",
     "Flux.2 klein",
     "Z-Image",
@@ -405,6 +406,7 @@ public:
             shift_factor = 0.1159f;
         } else if (sd_version_is_wan(version) ||
                    sd_version_is_qwen_image(version) ||
+                   sd_version_is_anima(version) ||
                    sd_version_is_flux2(version)) {
             scale_factor = 1.0f;
             shift_factor = 0.f;
@@ -535,6 +537,14 @@ public:
                                                                    "model.diffusion_model",
                                                                    version,
                                                                    sd_ctx_params->qwen_image_zero_cond_t);
+            } else if (sd_version_is_anima(version)) {
+                cond_stage_model = std::make_shared<AnimaConditioner>(clip_backend,
+                                                                      offload_params_to_cpu,
+                                                                      tensor_storage_map);
+                diffusion_model  = std::make_shared<AnimaModel>(backend,
+                                                               offload_params_to_cpu,
+                                                               tensor_storage_map,
+                                                               "model.diffusion_model");
             } else if (sd_version_is_z_image(version)) {
                 cond_stage_model = std::make_shared<LLMEmbedder>(clip_backend,
                                                                  offload_params_to_cpu,
@@ -597,7 +607,7 @@ public:
             }
 
             if (!(use_tiny_autoencoder || version == VERSION_SDXS) || tae_preview_only) {
-                if (sd_version_is_wan(version) || sd_version_is_qwen_image(version)) {
+                if (sd_version_is_wan(version) || sd_version_is_qwen_image(version) || sd_version_is_anima(version)) {
                     first_stage_model = std::make_shared<WAN::WanVAERunner>(vae_backend,
                                                                             offload_params_to_cpu,
                                                                             tensor_storage_map,
@@ -635,7 +645,7 @@ public:
                 }
             }
             if (use_tiny_autoencoder || version == VERSION_SDXS) {
-                if (sd_version_is_wan(version) || sd_version_is_qwen_image(version)) {
+                if (sd_version_is_wan(version) || sd_version_is_qwen_image(version) || sd_version_is_anima(version)) {
                     tae_first_stage = std::make_shared<TinyVideoAutoEncoder>(vae_backend,
                                                                              offload_params_to_cpu,
                                                                              tensor_storage_map,
@@ -904,6 +914,7 @@ public:
                 } else if (sd_version_is_sd3(version) ||
                            sd_version_is_wan(version) ||
                            sd_version_is_qwen_image(version) ||
+                           sd_version_is_anima(version) ||
                            sd_version_is_z_image(version)) {
                     pred_type = FLOW_PRED;
                     if (sd_version_is_wan(version)) {
@@ -1503,7 +1514,7 @@ public:
                 } else if (sd_version_is_flux(version) || sd_version_is_z_image(version)) {
                     latent_rgb_proj = flux_latent_rgb_proj;
                     latent_rgb_bias = flux_latent_rgb_bias;
-                } else if (sd_version_is_wan(version) || sd_version_is_qwen_image(version)) {
+                } else if (sd_version_is_wan(version) || sd_version_is_qwen_image(version) || sd_version_is_anima(version)) {
                     latent_rgb_proj = wan_21_latent_rgb_proj;
                     latent_rgb_bias = wan_21_latent_rgb_bias;
                 } else {
@@ -1984,6 +1995,9 @@ public:
                 shifted_t             = std::max((int64_t)0, std::min((int64_t)(TIMESTEPS - 1), shifted_t));
                 LOG_DEBUG("shifting timestep from %.2f to %" PRId64 " (sigma: %.4f)", t, shifted_t, sigma);
                 timesteps_vec.assign(1, (float)shifted_t);
+            } else if (sd_version_is_anima(version)) {
+                // Anima uses normalized flow timesteps.
+                timesteps_vec.assign(1, t / static_cast<float>(TIMESTEPS));
             } else if (sd_version_is_z_image(version)) {
                 timesteps_vec.assign(1, 1000.f - t);
             } else {
@@ -2395,7 +2409,7 @@ public:
     }
 
     void process_latent_in(ggml_tensor* latent) {
-        if (sd_version_is_wan(version) || sd_version_is_qwen_image(version) || sd_version_is_flux2(version)) {
+        if (sd_version_is_wan(version) || sd_version_is_qwen_image(version) || sd_version_is_anima(version) || sd_version_is_flux2(version)) {
             int channel_dim = sd_version_is_flux2(version) ? 2 : 3;
             std::vector<float> latents_mean_vec;
             std::vector<float> latents_std_vec;
@@ -2434,7 +2448,7 @@ public:
     }
 
     void process_latent_out(ggml_tensor* latent) {
-        if (sd_version_is_wan(version) || sd_version_is_qwen_image(version) || sd_version_is_flux2(version)) {
+        if (sd_version_is_wan(version) || sd_version_is_qwen_image(version) || sd_version_is_anima(version) || sd_version_is_flux2(version)) {
             int channel_dim = sd_version_is_flux2(version) ? 2 : 3;
             std::vector<float> latents_mean_vec;
             std::vector<float> latents_std_vec;
@@ -2512,7 +2526,7 @@ public:
             // TODO wan2.2 vae support?
             int64_t ne2;
             int64_t ne3;
-            if (sd_version_is_qwen_image(version)) {
+            if (sd_version_is_qwen_image(version) || sd_version_is_anima(version)) {
                 ne2 = 1;
                 ne3 = C * x->ne[3];
             } else {
@@ -2530,7 +2544,7 @@ public:
             result = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, ne2, ne3);
         }
 
-        if (sd_version_is_qwen_image(version)) {
+        if (sd_version_is_qwen_image(version) || sd_version_is_anima(version)) {
             x = ggml_reshape_4d(work_ctx, x, x->ne[0], x->ne[1], 1, x->ne[2] * x->ne[3]);
         }
 
@@ -2603,6 +2617,7 @@ public:
         ggml_tensor* latent;
         if (use_tiny_autoencoder ||
             sd_version_is_qwen_image(version) ||
+            sd_version_is_anima(version) ||
             sd_version_is_wan(version) ||
             sd_version_is_flux2(version) ||
             version == VERSION_CHROMA_RADIANCE) {
@@ -2622,7 +2637,7 @@ public:
         if (!use_tiny_autoencoder) {
             process_latent_in(latent);
         }
-        if (sd_version_is_qwen_image(version)) {
+        if (sd_version_is_qwen_image(version) || sd_version_is_anima(version)) {
             latent = ggml_reshape_4d(work_ctx, latent, latent->ne[0], latent->ne[1], latent->ne[3], 1);
         }
         return latent;
@@ -2660,7 +2675,7 @@ public:
         }
         int64_t t0 = ggml_time_ms();
         if (!use_tiny_autoencoder) {
-            if (sd_version_is_qwen_image(version)) {
+            if (sd_version_is_qwen_image(version) || sd_version_is_anima(version)) {
                 x = ggml_reshape_4d(work_ctx, x, x->ne[0], x->ne[1], 1, x->ne[2] * x->ne[3]);
             }
             process_latent_out(x);

From cec4aedcfd542ac2493e584e695369edab214f87 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 1 Mar 2026 15:32:25 +0800
Subject: [PATCH 87/97] docs: add anima docs

---
 README.md                |   2 ++
 assets/anima/example.png | Bin 0 -> 235029 bytes
 docs/anima.md            |  20 ++++++++++++++++++++
 3 files changed, 22 insertions(+)
 create mode 100644 assets/anima/example.png
 create mode 100644 docs/anima.md

diff --git a/README.md b/README.md
index 84d0832c..b5bb4975 100644
--- a/README.md
+++ b/README.md
@@ -53,6 +53,7 @@ API and command-line option may change frequently.***
     - [Qwen Image](./docs/qwen_image.md)
     - [Z-Image](./docs/z_image.md)
     - [Ovis-Image](./docs/ovis_image.md)
+    - [Anima](./docs/anima.md)
   - Image Edit Models
     - [FLUX.1-Kontext-dev](./docs/kontext.md)
     - [Qwen Image Edit series](./docs/qwen_image_edit.md)
@@ -139,6 +140,7 @@ If you want to improve performance or reduce VRAM/RAM usage, please refer to [pe
 - [🔥Wan2.1/Wan2.2](./docs/wan.md)
 - [🔥Z-Image](./docs/z_image.md)
 - [Ovis-Image](./docs/ovis_image.md)
+- [Anima](./docs/anima.md)
 - [LoRA](./docs/lora.md)
 - [LCM/LCM-LoRA](./docs/lcm.md)
 - [Using PhotoMaker to personalize image generation](./docs/photo_maker.md)
diff --git a/assets/anima/example.png b/assets/anima/example.png
new file mode 100644
index 0000000000000000000000000000000000000000..ab91dbf2d4af31e8a8f1237a5dcb53ccd6ec9ab7
GIT binary patch
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diff --git a/docs/anima.md b/docs/anima.md
new file mode 100644
index 00000000..9c941785
--- /dev/null
+++ b/docs/anima.md
@@ -0,0 +1,20 @@
+# How to Use
+
+## Download weights
+
+- Download Anima
+    - safetensors: https://huggingface.co/circlestone-labs/Anima/tree/main/split_files/diffusion_models
+    - gguf: https://huggingface.co/Bedovyy/Anima-GGUF/tree/main
+- Download vae
+    - safetensors: https://huggingface.co/circlestone-labs/Anima/tree/main/split_files/vae
+- Download Qwen3-0.6B-Base
+    - safetensors: https://huggingface.co/circlestone-labs/Anima/tree/main/split_files/text_encoders
+    - gguf: https://huggingface.co/mradermacher/Qwen3-0.6B-Base-GGUF/tree/main
+
+## Examples
+
+```sh
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\anima-preview.safetensors --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_06b_base.safetensors  -p "a lovely cat holding a sign says 'anima.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu --diffusion-fa
+```
+
+<img alt="anima image example" src="../assets/anima/example.png" />
\ No newline at end of file

From e64baa3611216af3408bd1c2ea15bfdff761912a Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 1 Mar 2026 21:44:51 +0800
Subject: [PATCH 88/97] refactor: reuse DiT's patchify/unpatchify functions
 (#1304)

---
 src/anima.hpp                        | 102 ++-----------------------
 src/{common.hpp => common_block.hpp} |   6 +-
 src/common_dit.hpp                   | 108 +++++++++++++++++++++++++++
 src/control.hpp                      |   3 +-
 src/flux.hpp                         |  91 ++++------------------
 src/ltxv.hpp                         |   3 +-
 src/mmdit.hpp                        |  30 +-------
 src/qwen_image.hpp                   |  79 +-------------------
 src/unet.hpp                         |   3 +-
 src/vae.hpp                          |   3 +-
 src/wan.hpp                          |   3 +-
 src/z_image.hpp                      |  80 ++------------------
 12 files changed, 150 insertions(+), 361 deletions(-)
 rename src/{common.hpp => common_block.hpp} (99%)
 create mode 100644 src/common_dit.hpp

diff --git a/src/anima.hpp b/src/anima.hpp
index 2f4d8681..191a096d 100644
--- a/src/anima.hpp
+++ b/src/anima.hpp
@@ -6,81 +6,13 @@
 #include <utility>
 #include <vector>
 
-#include "common.hpp"
+#include "common_block.hpp"
 #include "flux.hpp"
-#include "ggml_extend.hpp"
 #include "rope.hpp"
 
 namespace Anima {
     constexpr int ANIMA_GRAPH_SIZE = 65536;
 
-    __STATIC_INLINE__ struct ggml_tensor* patchify_2d(struct ggml_context* ctx,
-                                                      struct ggml_tensor* x,
-                                                      int64_t patch_size) {
-        // x: [W*r, H*q, T, C]
-        // return: [W, H, T, C*q*r]
-        if (patch_size == 1) {
-            return x;
-        }
-        GGML_ASSERT(x->ne[2] == 1);
-
-        int64_t W = x->ne[0];
-        int64_t H = x->ne[1];
-        int64_t T = x->ne[2];
-        int64_t C = x->ne[3];
-        int64_t p = patch_size;
-        int64_t h = H / p;
-        int64_t w = W / p;
-
-        GGML_ASSERT(T == 1);
-        GGML_ASSERT(h * p == H && w * p == W);
-
-        // Reuse Flux patchify layout on a [W, H, C, N] view.
-        x = ggml_reshape_4d(ctx, x, W, H, C, T);  // [W, H, C, N]
-
-        // Flux patchify: [N, C, H, W] -> [N, h*w, C*p*p]
-        x = ggml_reshape_4d(ctx, x, p, w, p, h * C * T);           // [p, w, p, h*C*N]
-        x = ggml_ext_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [p, p, w, h*C*N]
-        x = ggml_reshape_4d(ctx, x, p * p, w * h, C, T);           // [p*p, h*w, C, N]
-        x = ggml_ext_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [p*p, C, h*w, N]
-        x = ggml_reshape_3d(ctx, x, p * p * C, w * h, T);          // [C*p*p, h*w, N]
-
-        // Return [w, h, T, C*p*p]
-        x = ggml_reshape_4d(ctx, x, p * p * C, w, h, T);           // [C*p*p, w, h, N]
-        x = ggml_ext_cont(ctx, ggml_permute(ctx, x, 3, 0, 1, 2));  // [w, h, N, C*p*p]
-        return x;
-    }
-
-    __STATIC_INLINE__ struct ggml_tensor* unpatchify_2d(struct ggml_context* ctx,
-                                                        struct ggml_tensor* x,
-                                                        int64_t patch_size) {
-        // x: [W, H, T, C*q*r]
-        // return: [W*r, H*q, T, C]
-        if (patch_size == 1) {
-            return x;
-        }
-        GGML_ASSERT(x->ne[2] == 1);
-
-        int64_t w  = x->ne[0];
-        int64_t h  = x->ne[1];
-        int64_t T  = x->ne[2];
-        int64_t p  = patch_size;
-        int64_t nm = p * p;
-        int64_t Cp = x->ne[3];
-        int64_t C  = Cp / nm;
-        int64_t W  = w * p;
-        int64_t H  = h * p;
-
-        GGML_ASSERT(T == 1);
-        GGML_ASSERT(C * nm == Cp);
-
-        // [w, h, 1, C*p*p] -> [W, H, 1, C]
-        x = ggml_reshape_4d(ctx, x, w, h * C, p, p);                         // [w, h*C, p2, p1]
-        x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 2, 0, 3, 1));  // [p2, w, p1, h*C]
-        x = ggml_reshape_4d(ctx, x, W, H, T, C);                             // [W, H, 1, C]
-        return x;
-    }
-
     __STATIC_INLINE__ struct ggml_tensor* apply_gate(struct ggml_context* ctx,
                                                      struct ggml_tensor* x,
                                                      struct ggml_tensor* gate) {
@@ -491,7 +423,7 @@ namespace Anima {
         int64_t text_embed_dim    = 1024;
         int64_t num_heads         = 16;
         int64_t head_dim          = 128;
-        int64_t patch_size        = 2;
+        int patch_size            = 2;
         int64_t num_layers        = 28;
         std::vector<int> axes_dim = {44, 42, 42};
         int theta                 = 10000;
@@ -533,24 +465,10 @@ namespace Anima {
             int64_t W = x->ne[0];
             int64_t H = x->ne[1];
 
-            x = ggml_reshape_4d(ctx->ggml_ctx, x, x->ne[0], x->ne[1], 1, x->ne[2] * x->ne[3]);  // [N*C, T, H, W] style
+            auto padding_mask = ggml_ext_zeros(ctx->ggml_ctx, x->ne[0], x->ne[1], 1, x->ne[3]);
+            x                 = ggml_concat(ctx->ggml_ctx, x, padding_mask, 2);  // [N, C + 1, H, W]
 
-            int64_t pad_h = (patch_size - H % patch_size) % patch_size;
-            int64_t pad_w = (patch_size - W % patch_size) % patch_size;
-            if (pad_h > 0 || pad_w > 0) {
-                x = ggml_ext_pad(ctx->ggml_ctx, x, static_cast<int>(pad_w), static_cast<int>(pad_h), 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
-            }
-
-            auto padding_mask = ggml_ext_zeros(ctx->ggml_ctx, x->ne[0], x->ne[1], x->ne[2], 1);
-            x                 = ggml_concat(ctx->ggml_ctx, x, padding_mask, 3);  // concat mask channel
-
-            x = patchify_2d(ctx->ggml_ctx, x, patch_size);  // [C*4, T, H/2, W/2]
-
-            int64_t w_len = x->ne[0];
-            int64_t h_len = x->ne[1];
-            int64_t t_len = x->ne[2];
-            x             = ggml_reshape_3d(ctx->ggml_ctx, x, x->ne[0] * x->ne[1] * x->ne[2], x->ne[3], 1);
-            x             = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [N, n_token, C]
+            x = DiT::pad_and_patchify(ctx, x, patch_size, patch_size);  // [N, h*w, (C+1)*ph*pw]
 
             x = x_embedder->forward(ctx, x);
 
@@ -586,15 +504,9 @@ namespace Anima {
                 x          = block->forward(ctx, x, encoder_hidden_states, embedded_timestep, temb, image_pe);
             }
 
-            x = final_layer->forward(ctx, x, embedded_timestep, temb);  // [N, n_token, C*4]
+            x = final_layer->forward(ctx, x, embedded_timestep, temb);  // [N, h*w, ph*pw*C]
 
-            x = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [n_token, C*4, N]
-            x = ggml_reshape_4d(ctx->ggml_ctx, x, w_len, h_len, t_len, x->ne[1]);                    // [C*4, T, H/2, W/2]
-            x = unpatchify_2d(ctx->ggml_ctx, x, patch_size);                                         // [C, T, H, W]
-
-            x = ggml_ext_slice(ctx->ggml_ctx, x, 1, 0, H);                                  // [C, T, H, W + pad]
-            x = ggml_ext_slice(ctx->ggml_ctx, x, 0, 0, W);                                  // [C, T, H, W]
-            x = ggml_reshape_4d(ctx->ggml_ctx, x, x->ne[0], x->ne[1], x->ne[3], x->ne[2]);  // [N, C, H, W]
+            x = DiT::unpatchify_and_crop(ctx->ggml_ctx, x, H, W, patch_size, patch_size, false);  // [N, C, H, W]
 
             return x;
         }
diff --git a/src/common.hpp b/src/common_block.hpp
similarity index 99%
rename from src/common.hpp
rename to src/common_block.hpp
index d9c823df..435afa4f 100644
--- a/src/common.hpp
+++ b/src/common_block.hpp
@@ -1,5 +1,5 @@
-#ifndef __COMMON_HPP__
-#define __COMMON_HPP__
+#ifndef __COMMON_BLOCK_HPP__
+#define __COMMON_BLOCK_HPP__
 
 #include "ggml_extend.hpp"
 
@@ -590,4 +590,4 @@ public:
     }
 };
 
-#endif  // __COMMON_HPP__
+#endif  // __COMMON_BLOCK_HPP__
diff --git a/src/common_dit.hpp b/src/common_dit.hpp
new file mode 100644
index 00000000..0e6f0f08
--- /dev/null
+++ b/src/common_dit.hpp
@@ -0,0 +1,108 @@
+#ifndef __COMMON_DIT_HPP__
+#define __COMMON_DIT_HPP__
+
+#include "ggml_extend.hpp"
+
+namespace DiT {
+    ggml_tensor* patchify(ggml_context* ctx,
+                          ggml_tensor* x,
+                          int pw,
+                          int ph,
+                          bool patch_last = true) {
+        // x: [N, C, H, W]
+        // return: [N, h*w, C*ph*pw] if patch_last else [N, h*w, ph*pw*C]
+        int64_t N = x->ne[3];
+        int64_t C = x->ne[2];
+        int64_t H = x->ne[1];
+        int64_t W = x->ne[0];
+        int64_t h = H / ph;
+        int64_t w = W / pw;
+
+        GGML_ASSERT(h * ph == H && w * pw == W);
+
+        x = ggml_reshape_4d(ctx, x, pw, w, ph, h * C * N);     // [N*C*h, ph, w, pw]
+        x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N*C*h, w, ph, pw]
+        x = ggml_reshape_4d(ctx, x, pw * ph, w * h, C, N);     // [N, C, h*w, ph*pw]
+        if (patch_last) {
+            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N, h*w, C, ph*pw]
+            x = ggml_reshape_3d(ctx, x, pw * ph * C, w * h, N);    // [N, h*w, C*ph*pw]
+        } else {
+            x = ggml_cont(ctx, ggml_ext_torch_permute(ctx, x, 2, 0, 1, 3));  // [N, h*w, C, ph*pw]
+            x = ggml_reshape_3d(ctx, x, C * pw * ph, w * h, N);              // [N, h*w, ph*pw*C]
+        }
+        return x;
+    }
+
+    ggml_tensor* unpatchify(ggml_context* ctx,
+                            ggml_tensor* x,
+                            int64_t h,
+                            int64_t w,
+                            int ph,
+                            int pw,
+                            bool patch_last = true) {
+        // x: [N, h*w, C*ph*pw] if patch_last else [N, h*w, ph*pw*C]
+        // return: [N, C, H, W]
+        int64_t N = x->ne[2];
+        int64_t C = x->ne[0] / ph / pw;
+        int64_t H = h * ph;
+        int64_t W = w * pw;
+
+        GGML_ASSERT(C * ph * pw == x->ne[0]);
+
+        if (patch_last) {
+            x = ggml_reshape_4d(ctx, x, pw * ph, C, w * h, N);     // [N, h*w, C, ph*pw]
+            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N, C, h*w, ph*pw]
+        } else {
+            x = ggml_reshape_4d(ctx, x, C, pw * ph, w * h, N);     // [N, h*w, ph*pw, C]
+            x = ggml_cont(ctx, ggml_permute(ctx, x, 2, 0, 1, 3));  // [N, C, h*w, ph*pw]
+        }
+
+        x = ggml_reshape_4d(ctx, x, pw, ph, w, h * C * N);     // [N*C*h, w, ph, pw]
+        x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N*C*h, ph, w, pw]
+        x = ggml_reshape_4d(ctx, x, W, H, C, N);               // [N, C, h*ph, w*pw]
+
+        return x;
+    }
+
+    ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
+                                   ggml_tensor* x,
+                                   int ph,
+                                   int pw) {
+        int64_t W = x->ne[0];
+        int64_t H = x->ne[1];
+
+        int pad_h = (ph - H % ph) % ph;
+        int pad_w = (pw - W % pw) % pw;
+        x         = ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
+        return x;
+    }
+
+    ggml_tensor* pad_and_patchify(GGMLRunnerContext* ctx,
+                                  ggml_tensor* x,
+                                  int ph,
+                                  int pw,
+                                  bool patch_last = true) {
+        x = pad_to_patch_size(ctx, x, ph, pw);
+        x = patchify(ctx->ggml_ctx, x, ph, pw, patch_last);
+        return x;
+    }
+
+    ggml_tensor* unpatchify_and_crop(ggml_context* ctx,
+                                     ggml_tensor* x,
+                                     int64_t H,
+                                     int64_t W,
+                                     int ph,
+                                     int pw,
+                                     bool patch_last = true) {
+        int pad_h = (ph - H % ph) % ph;
+        int pad_w = (pw - W % pw) % pw;
+        int64_t h = ((H + pad_h) / ph);
+        int64_t w = ((W + pad_w) / pw);
+        x         = unpatchify(ctx, x, h, w, ph, pw, patch_last);  // [N, C, H + pad_h, W + pad_w]
+        x         = ggml_ext_slice(ctx, x, 1, 0, H);               // [N, C, H, W + pad_w]
+        x         = ggml_ext_slice(ctx, x, 0, 0, W);               // [N, C, H, W]
+        return x;
+    }
+}  // namespace DiT
+
+#endif  // __COMMON_DIT_HPP__
\ No newline at end of file
diff --git a/src/control.hpp b/src/control.hpp
index f7842021..5bab0381 100644
--- a/src/control.hpp
+++ b/src/control.hpp
@@ -1,8 +1,7 @@
 #ifndef __CONTROL_HPP__
 #define __CONTROL_HPP__
 
-#include "common.hpp"
-#include "ggml_extend.hpp"
+#include "common_block.hpp"
 #include "model.h"
 
 #define CONTROL_NET_GRAPH_SIZE 1536
diff --git a/src/flux.hpp b/src/flux.hpp
index ff8c1899..37cbb126 100644
--- a/src/flux.hpp
+++ b/src/flux.hpp
@@ -4,7 +4,7 @@
 #include <memory>
 #include <vector>
 
-#include "ggml_extend.hpp"
+#include "common_dit.hpp"
 #include "model.h"
 #include "rope.hpp"
 
@@ -846,70 +846,6 @@ namespace Flux {
             }
         }
 
-        struct ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
-                                              struct ggml_tensor* x) {
-            int64_t W = x->ne[0];
-            int64_t H = x->ne[1];
-
-            int pad_h = (params.patch_size - H % params.patch_size) % params.patch_size;
-            int pad_w = (params.patch_size - W % params.patch_size) % params.patch_size;
-            x         = ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
-            return x;
-        }
-
-        struct ggml_tensor* patchify(struct ggml_context* ctx,
-                                     struct ggml_tensor* x) {
-            // x: [N, C, H, W]
-            // return: [N, h*w, C * patch_size * patch_size]
-            int64_t N = x->ne[3];
-            int64_t C = x->ne[2];
-            int64_t H = x->ne[1];
-            int64_t W = x->ne[0];
-            int64_t p = params.patch_size;
-            int64_t h = H / params.patch_size;
-            int64_t w = W / params.patch_size;
-
-            GGML_ASSERT(h * p == H && w * p == W);
-
-            x = ggml_reshape_4d(ctx, x, p, w, p, h * C * N);       // [N*C*h, p, w, p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N*C*h, w, p, p]
-            x = ggml_reshape_4d(ctx, x, p * p, w * h, C, N);       // [N, C, h*w, p*p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N, h*w, C, p*p]
-            x = ggml_reshape_3d(ctx, x, p * p * C, w * h, N);      // [N, h*w, C*p*p]
-            return x;
-        }
-
-        struct ggml_tensor* process_img(GGMLRunnerContext* ctx,
-                                        struct ggml_tensor* x) {
-            // img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
-            x = pad_to_patch_size(ctx, x);
-            x = patchify(ctx->ggml_ctx, x);
-            return x;
-        }
-
-        struct ggml_tensor* unpatchify(struct ggml_context* ctx,
-                                       struct ggml_tensor* x,
-                                       int64_t h,
-                                       int64_t w) {
-            // x: [N, h*w, C*patch_size*patch_size]
-            // return: [N, C, H, W]
-            int64_t N = x->ne[2];
-            int64_t C = x->ne[0] / params.patch_size / params.patch_size;
-            int64_t H = h * params.patch_size;
-            int64_t W = w * params.patch_size;
-            int64_t p = params.patch_size;
-
-            GGML_ASSERT(C * p * p == x->ne[0]);
-
-            x = ggml_reshape_4d(ctx, x, p * p, C, w * h, N);       // [N, h*w, C, p*p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N, C, h*w, p*p]
-            x = ggml_reshape_4d(ctx, x, p, p, w, h * C * N);       // [N*C*h, w, p, p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N*C*h, p, w, p]
-            x = ggml_reshape_4d(ctx, x, W, H, C, N);               // [N, C, h*p, w*p]
-
-            return x;
-        }
-
         struct ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
                                          struct ggml_tensor* img,
                                          struct ggml_tensor* txt,
@@ -1060,7 +996,7 @@ namespace Flux {
             int pad_h      = (patch_size - H % patch_size) % patch_size;
             int pad_w      = (patch_size - W % patch_size) % patch_size;
 
-            auto img      = pad_to_patch_size(ctx, x);
+            auto img      = DiT::pad_to_patch_size(ctx, x, params.patch_size, params.patch_size);
             auto orig_img = img;
 
             if (params.chroma_radiance_params.fake_patch_size_x2) {
@@ -1082,7 +1018,7 @@ namespace Flux {
             auto nerf_image_embedder   = std::dynamic_pointer_cast<NerfEmbedder>(blocks["nerf_image_embedder"]);
             auto nerf_final_layer_conv = std::dynamic_pointer_cast<NerfFinalLayerConv>(blocks["nerf_final_layer_conv"]);
 
-            auto nerf_pixels    = patchify(ctx->ggml_ctx, orig_img);  // [N, num_patches, C * patch_size * patch_size]
+            auto nerf_pixels    = DiT::patchify(ctx->ggml_ctx, orig_img, patch_size, patch_size);  // [N, num_patches, C * patch_size * patch_size]
             int64_t num_patches = nerf_pixels->ne[1];
             nerf_pixels         = ggml_reshape_3d(ctx->ggml_ctx,
                                                   nerf_pixels,
@@ -1102,7 +1038,7 @@ namespace Flux {
 
             img_dct = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, img_dct, 1, 0, 2, 3));                                 // [N*num_patches, nerf_hidden_size, patch_size*patch_size]
             img_dct = ggml_reshape_3d(ctx->ggml_ctx, img_dct, img_dct->ne[0] * img_dct->ne[1], num_patches, img_dct->ne[2] / num_patches);  // [N, num_patches, nerf_hidden_size*patch_size*patch_size]
-            img_dct = unpatchify(ctx->ggml_ctx, img_dct, (H + pad_h) / patch_size, (W + pad_w) / patch_size);                               // [N, nerf_hidden_size, H, W]
+            img_dct = DiT::unpatchify(ctx->ggml_ctx, img_dct, (H + pad_h) / patch_size, (W + pad_w) / patch_size, patch_size, patch_size);  // [N, nerf_hidden_size, H, W]
 
             out = nerf_final_layer_conv->forward(ctx, img_dct);  // [N, C, H, W]
 
@@ -1134,7 +1070,7 @@ namespace Flux {
             int pad_h      = (patch_size - H % patch_size) % patch_size;
             int pad_w      = (patch_size - W % patch_size) % patch_size;
 
-            auto img           = process_img(ctx, x);
+            auto img           = DiT::pad_and_patchify(ctx, x, patch_size, patch_size);
             int64_t img_tokens = img->ne[1];
 
             if (params.version == VERSION_FLUX_FILL) {
@@ -1142,8 +1078,8 @@ namespace Flux {
                 ggml_tensor* masked = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], C, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], 0);
                 ggml_tensor* mask   = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], 8 * 8, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * C);
 
-                masked = process_img(ctx, masked);
-                mask   = process_img(ctx, mask);
+                masked = DiT::pad_and_patchify(ctx, masked, patch_size, patch_size);
+                mask   = DiT::pad_and_patchify(ctx, mask, patch_size, patch_size);
 
                 img = ggml_concat(ctx->ggml_ctx, img, ggml_concat(ctx->ggml_ctx, masked, mask, 0), 0);
             } else if (params.version == VERSION_FLEX_2) {
@@ -1152,21 +1088,21 @@ namespace Flux {
                 ggml_tensor* mask    = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], 1, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * C);
                 ggml_tensor* control = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], C, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * (C + 1));
 
-                masked  = process_img(ctx, masked);
-                mask    = process_img(ctx, mask);
-                control = process_img(ctx, control);
+                masked  = DiT::pad_and_patchify(ctx, masked, patch_size, patch_size);
+                mask    = DiT::pad_and_patchify(ctx, mask, patch_size, patch_size);
+                control = DiT::pad_and_patchify(ctx, control, patch_size, patch_size);
 
                 img = ggml_concat(ctx->ggml_ctx, img, ggml_concat(ctx->ggml_ctx, ggml_concat(ctx->ggml_ctx, masked, mask, 0), control, 0), 0);
             } else if (params.version == VERSION_FLUX_CONTROLS) {
                 GGML_ASSERT(c_concat != nullptr);
 
-                auto control = process_img(ctx, c_concat);
+                auto control = DiT::pad_and_patchify(ctx, c_concat, patch_size, patch_size);
                 img          = ggml_concat(ctx->ggml_ctx, img, control, 0);
             }
 
             if (ref_latents.size() > 0) {
                 for (ggml_tensor* ref : ref_latents) {
-                    ref = process_img(ctx, ref);
+                    ref = DiT::pad_and_patchify(ctx, ref, patch_size, patch_size);
                     img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
                 }
             }
@@ -1178,8 +1114,7 @@ namespace Flux {
                 out = ggml_cont(ctx->ggml_ctx, out);
             }
 
-            // rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)
-            out = unpatchify(ctx->ggml_ctx, out, (H + pad_h) / patch_size, (W + pad_w) / patch_size);  // [N, C, H + pad_h, W + pad_w]
+            out = DiT::unpatchify_and_crop(ctx->ggml_ctx, out, H, W, patch_size, patch_size);  // [N, C, H, W]
             return out;
         }
 
diff --git a/src/ltxv.hpp b/src/ltxv.hpp
index 0a2877a8..9dcdd4b2 100644
--- a/src/ltxv.hpp
+++ b/src/ltxv.hpp
@@ -1,8 +1,7 @@
 #ifndef __LTXV_HPP__
 #define __LTXV_HPP__
 
-#include "common.hpp"
-#include "ggml_extend.hpp"
+#include "common_block.hpp"
 
 namespace LTXV {
 
diff --git a/src/mmdit.hpp b/src/mmdit.hpp
index 726f60c2..ba1c35d6 100644
--- a/src/mmdit.hpp
+++ b/src/mmdit.hpp
@@ -745,28 +745,6 @@ public:
         return spatial_pos_embed;
     }
 
-    struct ggml_tensor* unpatchify(struct ggml_context* ctx,
-                                   struct ggml_tensor* x,
-                                   int64_t h,
-                                   int64_t w) {
-        // x: [N, H*W, patch_size * patch_size * C]
-        // return: [N, C, H, W]
-        int64_t n = x->ne[2];
-        int64_t c = out_channels;
-        int64_t p = patch_size;
-        h         = (h + 1) / p;
-        w         = (w + 1) / p;
-
-        GGML_ASSERT(h * w == x->ne[1]);
-
-        x = ggml_reshape_4d(ctx, x, c, p * p, w * h, n);       // [N, H*W, P*P, C]
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 2, 0, 1, 3));  // [N, C, H*W, P*P]
-        x = ggml_reshape_4d(ctx, x, p, p, w, h * c * n);       // [N*C*H, W, P, P]
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N*C*H, P, W, P]
-        x = ggml_reshape_4d(ctx, x, p * w, p * h, c, n);       // [N, C, H*P, W*P]
-        return x;
-    }
-
     struct ggml_tensor* forward_core_with_concat(GGMLRunnerContext* ctx,
                                                  struct ggml_tensor* x,
                                                  struct ggml_tensor* c_mod,
@@ -811,11 +789,11 @@ public:
         auto x_embedder = std::dynamic_pointer_cast<PatchEmbed>(blocks["x_embedder"]);
         auto t_embedder = std::dynamic_pointer_cast<TimestepEmbedder>(blocks["t_embedder"]);
 
-        int64_t w = x->ne[0];
-        int64_t h = x->ne[1];
+        int64_t W = x->ne[0];
+        int64_t H = x->ne[1];
 
         auto patch_embed = x_embedder->forward(ctx, x);                      // [N, H*W, hidden_size]
-        auto pos_embed   = cropped_pos_embed(ctx->ggml_ctx, h, w);           // [1, H*W, hidden_size]
+        auto pos_embed   = cropped_pos_embed(ctx->ggml_ctx, H, W);           // [1, H*W, hidden_size]
         x                = ggml_add(ctx->ggml_ctx, patch_embed, pos_embed);  // [N, H*W, hidden_size]
 
         auto c = t_embedder->forward(ctx, t);  // [N, hidden_size]
@@ -834,7 +812,7 @@ public:
 
         x = forward_core_with_concat(ctx, x, c, context, skip_layers);  // (N, H*W, patch_size ** 2 * out_channels)
 
-        x = unpatchify(ctx->ggml_ctx, x, h, w);  // [N, C, H, W]
+        x = DiT::unpatchify_and_crop(ctx->ggml_ctx, x, H, W, patch_size, patch_size, /*patch_last*/ false);  // [N, C, H, W]
 
         return x;
     }
diff --git a/src/qwen_image.hpp b/src/qwen_image.hpp
index 3044eb45..8fff5e00 100644
--- a/src/qwen_image.hpp
+++ b/src/qwen_image.hpp
@@ -3,9 +3,8 @@
 
 #include <memory>
 
-#include "common.hpp"
+#include "common_block.hpp"
 #include "flux.hpp"
-#include "ggml_extend.hpp"
 
 namespace Qwen {
     constexpr int QWEN_IMAGE_GRAPH_SIZE = 20480;
@@ -390,69 +389,6 @@ namespace Qwen {
             blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, params.patch_size * params.patch_size * params.out_channels));
         }
 
-        struct ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
-                                              struct ggml_tensor* x) {
-            int64_t W = x->ne[0];
-            int64_t H = x->ne[1];
-
-            int pad_h = (params.patch_size - H % params.patch_size) % params.patch_size;
-            int pad_w = (params.patch_size - W % params.patch_size) % params.patch_size;
-            x         = ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
-            return x;
-        }
-
-        struct ggml_tensor* patchify(struct ggml_context* ctx,
-                                     struct ggml_tensor* x) {
-            // x: [N, C, H, W]
-            // return: [N, h*w, C * patch_size * patch_size]
-            int64_t N = x->ne[3];
-            int64_t C = x->ne[2];
-            int64_t H = x->ne[1];
-            int64_t W = x->ne[0];
-            int64_t p = params.patch_size;
-            int64_t h = H / params.patch_size;
-            int64_t w = W / params.patch_size;
-
-            GGML_ASSERT(h * p == H && w * p == W);
-
-            x = ggml_reshape_4d(ctx, x, p, w, p, h * C * N);       // [N*C*h, p, w, p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N*C*h, w, p, p]
-            x = ggml_reshape_4d(ctx, x, p * p, w * h, C, N);       // [N, C, h*w, p*p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N, h*w, C, p*p]
-            x = ggml_reshape_3d(ctx, x, p * p * C, w * h, N);      // [N, h*w, C*p*p]
-            return x;
-        }
-
-        struct ggml_tensor* process_img(GGMLRunnerContext* ctx,
-                                        struct ggml_tensor* x) {
-            x = pad_to_patch_size(ctx, x);
-            x = patchify(ctx->ggml_ctx, x);
-            return x;
-        }
-
-        struct ggml_tensor* unpatchify(struct ggml_context* ctx,
-                                       struct ggml_tensor* x,
-                                       int64_t h,
-                                       int64_t w) {
-            // x: [N, h*w, C*patch_size*patch_size]
-            // return: [N, C, H, W]
-            int64_t N = x->ne[2];
-            int64_t C = x->ne[0] / params.patch_size / params.patch_size;
-            int64_t H = h * params.patch_size;
-            int64_t W = w * params.patch_size;
-            int64_t p = params.patch_size;
-
-            GGML_ASSERT(C * p * p == x->ne[0]);
-
-            x = ggml_reshape_4d(ctx, x, p * p, C, w * h, N);       // [N, h*w, C, p*p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N, C, h*w, p*p]
-            x = ggml_reshape_4d(ctx, x, p, p, w, h * C * N);       // [N*C*h, w, p, p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N*C*h, p, w, p]
-            x = ggml_reshape_4d(ctx, x, W, H, C, N);               // [N, C, h*p, w*p]
-
-            return x;
-        }
-
         struct ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
                                          struct ggml_tensor* x,
                                          struct ggml_tensor* timestep,
@@ -512,19 +448,16 @@ namespace Qwen {
             int64_t C = x->ne[2];
             int64_t N = x->ne[3];
 
-            auto img           = process_img(ctx, x);
+            auto img           = DiT::pad_and_patchify(ctx, x, params.patch_size, params.patch_size);
             int64_t img_tokens = img->ne[1];
 
             if (ref_latents.size() > 0) {
                 for (ggml_tensor* ref : ref_latents) {
-                    ref = process_img(ctx, ref);
+                    ref = DiT::pad_and_patchify(ctx, ref, params.patch_size, params.patch_size);
                     img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
                 }
             }
 
-            int64_t h_len = ((H + (params.patch_size / 2)) / params.patch_size);
-            int64_t w_len = ((W + (params.patch_size / 2)) / params.patch_size);
-
             auto out = forward_orig(ctx, img, timestep, context, pe, modulate_index);  // [N, h_len*w_len, ph*pw*C]
 
             if (out->ne[1] > img_tokens) {
@@ -533,11 +466,7 @@ namespace Qwen {
                 out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, out, 0, 2, 1, 3));  // [N, h*w, C * patch_size * patch_size]
             }
 
-            out = unpatchify(ctx->ggml_ctx, out, h_len, w_len);  // [N, C, H + pad_h, W + pad_w]
-
-            // slice
-            out = ggml_ext_slice(ctx->ggml_ctx, out, 1, 0, H);  // [N, C, H, W + pad_w]
-            out = ggml_ext_slice(ctx->ggml_ctx, out, 0, 0, W);  // [N, C, H, W]
+            out = DiT::unpatchify_and_crop(ctx->ggml_ctx, out, H, W, params.patch_size, params.patch_size);  // [N, C, H, W]
 
             return out;
         }
diff --git a/src/unet.hpp b/src/unet.hpp
index 2dd79e0e..e0fd4c52 100644
--- a/src/unet.hpp
+++ b/src/unet.hpp
@@ -1,8 +1,7 @@
 #ifndef __UNET_HPP__
 #define __UNET_HPP__
 
-#include "common.hpp"
-#include "ggml_extend.hpp"
+#include "common_block.hpp"
 #include "model.h"
 
 /*==================================================== UnetModel =====================================================*/
diff --git a/src/vae.hpp b/src/vae.hpp
index c627616c..7ccba6ee 100644
--- a/src/vae.hpp
+++ b/src/vae.hpp
@@ -1,8 +1,7 @@
 #ifndef __VAE_HPP__
 #define __VAE_HPP__
 
-#include "common.hpp"
-#include "ggml_extend.hpp"
+#include "common_block.hpp"
 
 /*================================================== AutoEncoderKL ===================================================*/
 
diff --git a/src/wan.hpp b/src/wan.hpp
index 90de3bdd..d94fbd48 100644
--- a/src/wan.hpp
+++ b/src/wan.hpp
@@ -5,9 +5,8 @@
 #include <memory>
 #include <utility>
 
-#include "common.hpp"
+#include "common_block.hpp"
 #include "flux.hpp"
-#include "ggml_extend.hpp"
 #include "rope.hpp"
 #include "vae.hpp"
 
diff --git a/src/z_image.hpp b/src/z_image.hpp
index cee23833..8f405a59 100644
--- a/src/z_image.hpp
+++ b/src/z_image.hpp
@@ -346,69 +346,6 @@ namespace ZImage {
             blocks["final_layer"] = std::make_shared<FinalLayer>(z_image_params.hidden_size, z_image_params.patch_size, z_image_params.out_channels);
         }
 
-        struct ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
-                                              struct ggml_tensor* x) {
-            int64_t W = x->ne[0];
-            int64_t H = x->ne[1];
-
-            int pad_h = (z_image_params.patch_size - H % z_image_params.patch_size) % z_image_params.patch_size;
-            int pad_w = (z_image_params.patch_size - W % z_image_params.patch_size) % z_image_params.patch_size;
-            x         = ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
-            return x;
-        }
-
-        struct ggml_tensor* patchify(struct ggml_context* ctx,
-                                     struct ggml_tensor* x) {
-            // x: [N, C, H, W]
-            // return: [N, h*w, patch_size*patch_size*C]
-            int64_t N = x->ne[3];
-            int64_t C = x->ne[2];
-            int64_t H = x->ne[1];
-            int64_t W = x->ne[0];
-            int64_t p = z_image_params.patch_size;
-            int64_t h = H / z_image_params.patch_size;
-            int64_t w = W / z_image_params.patch_size;
-
-            GGML_ASSERT(h * p == H && w * p == W);
-
-            x = ggml_reshape_4d(ctx, x, p, w, p, h * C * N);                 // [N*C*h, p, w, p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));            // [N*C*h, w, p, p]
-            x = ggml_reshape_4d(ctx, x, p * p, w * h, C, N);                 // [N, C, h*w, p*p]
-            x = ggml_cont(ctx, ggml_ext_torch_permute(ctx, x, 2, 0, 1, 3));  // [N, h*w, C, p*p]
-            x = ggml_reshape_3d(ctx, x, C * p * p, w * h, N);                // [N, h*w, p*p*C]
-            return x;
-        }
-
-        struct ggml_tensor* process_img(GGMLRunnerContext* ctx,
-                                        struct ggml_tensor* x) {
-            x = pad_to_patch_size(ctx, x);
-            x = patchify(ctx->ggml_ctx, x);
-            return x;
-        }
-
-        struct ggml_tensor* unpatchify(struct ggml_context* ctx,
-                                       struct ggml_tensor* x,
-                                       int64_t h,
-                                       int64_t w) {
-            // x: [N, h*w, patch_size*patch_size*C]
-            // return: [N, C, H, W]
-            int64_t N = x->ne[2];
-            int64_t C = x->ne[0] / z_image_params.patch_size / z_image_params.patch_size;
-            int64_t H = h * z_image_params.patch_size;
-            int64_t W = w * z_image_params.patch_size;
-            int64_t p = z_image_params.patch_size;
-
-            GGML_ASSERT(C * p * p == x->ne[0]);
-
-            x = ggml_reshape_4d(ctx, x, C, p * p, w * h, N);                 // [N, h*w, p*p, C]
-            x = ggml_cont(ctx, ggml_ext_torch_permute(ctx, x, 1, 2, 0, 3));  // [N, C, h*w, p*p]
-            x = ggml_reshape_4d(ctx, x, p, p, w, h * C * N);                 // [N*C*h, w, p, p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));            // [N*C*h, p, w, p]
-            x = ggml_reshape_4d(ctx, x, W, H, C, N);                         // [N, C, h*p, w*p]
-
-            return x;
-        }
-
         struct ggml_tensor* forward_core(GGMLRunnerContext* ctx,
                                          struct ggml_tensor* x,
                                          struct ggml_tensor* timestep,
@@ -495,27 +432,22 @@ namespace ZImage {
             int64_t C = x->ne[2];
             int64_t N = x->ne[3];
 
-            auto img             = process_img(ctx, x);
+            int patch_size = z_image_params.patch_size;
+
+            auto img             = DiT::pad_and_patchify(ctx, x, patch_size, patch_size, false);
             uint64_t n_img_token = img->ne[1];
 
             if (ref_latents.size() > 0) {
                 for (ggml_tensor* ref : ref_latents) {
-                    ref = process_img(ctx, ref);
+                    ref = DiT::pad_and_patchify(ctx, ref, patch_size, patch_size, false);
                     img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
                 }
             }
 
-            int64_t h_len = ((H + (z_image_params.patch_size / 2)) / z_image_params.patch_size);
-            int64_t w_len = ((W + (z_image_params.patch_size / 2)) / z_image_params.patch_size);
-
             auto out = forward_core(ctx, img, timestep, context, pe);
 
-            out = ggml_ext_slice(ctx->ggml_ctx, out, 1, 0, n_img_token);  // [N, n_img_token, ph*pw*C]
-            out = unpatchify(ctx->ggml_ctx, out, h_len, w_len);           // [N, C, H + pad_h, W + pad_w]
-
-            // slice
-            out = ggml_ext_slice(ctx->ggml_ctx, out, 1, 0, H);  // [N, C, H, W + pad_w]
-            out = ggml_ext_slice(ctx->ggml_ctx, out, 0, 0, W);  // [N, C, H, W]
+            out = ggml_ext_slice(ctx->ggml_ctx, out, 1, 0, n_img_token);                              // [N, n_img_token, ph*pw*C]
+            out = DiT::unpatchify_and_crop(ctx->ggml_ctx, out, H, W, patch_size, patch_size, false);  // [N, C, H, W]
 
             out = ggml_ext_scale(ctx->ggml_ctx, out, -1.f);
 

From 60889bc9a1c8db8191ed0c378cf8965bbd99b61d Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Sun, 1 Mar 2026 10:57:06 -0300
Subject: [PATCH 89/97] fix: correct sdapi LoRA file handling (#1276)

---
 examples/server/main.cpp | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index 0fb10c7a..2212d812 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -266,6 +266,7 @@ void sd_log_cb(enum sd_log_level_t level, const char* log, void* data) {
 struct LoraEntry {
     std::string name;
     std::string path;
+    std::string fullpath;
 };
 
 int main(int argc, const char** argv) {
@@ -321,7 +322,8 @@ int main(int argc, const char** argv) {
 
                 LoraEntry e;
                 e.name          = p.stem().u8string();
-                std::string rel = fs::relative(p, lora_dir).u8string();
+                e.fullpath      = p.u8string();
+                std::string rel = p.lexically_relative(lora_dir).u8string();
                 std::replace(rel.begin(), rel.end(), '\\', '/');
                 e.path = rel;
 
@@ -340,10 +342,11 @@ int main(int argc, const char** argv) {
         }
     };
 
-    auto is_valid_lora_path = [&](const std::string& path) -> bool {
+    auto get_lora_full_path = [&](const std::string& path) -> std::string {
         std::lock_guard<std::mutex> lock(lora_mutex);
-        return std::any_of(lora_cache.begin(), lora_cache.end(),
+        auto it = std::find_if(lora_cache.begin(), lora_cache.end(),
                            [&](const LoraEntry& e) { return e.path == path; });
+        return (it != lora_cache.end()) ? it->fullpath : "";
     };
 
     httplib::Server svr;
@@ -862,11 +865,12 @@ int main(int argc, const char** argv) {
                         return bad("lora.path required");
                     }
 
-                    if (!is_valid_lora_path(path)) {
+                    std::string fullpath = get_lora_full_path(path);
+                    if (fullpath.empty()) {
                         return bad("invalid lora path: " + path);
                     }
 
-                    lora_path_storage.push_back(path);
+                    lora_path_storage.push_back(fullpath);
                     sd_lora_t l;
                     l.is_high_noise = is_high_noise;
                     l.multiplier    = multiplier;

From 39d54702a6fb90c903142711b6d872e9935855e6 Mon Sep 17 00:00:00 2001
From: Wagner Bruna <wbruna@users.noreply.github.com>
Date: Sun, 1 Mar 2026 11:00:50 -0300
Subject: [PATCH 90/97] feat: accept legacy image parameter on v1/images/edits
 (#1270)

---
 examples/server/main.cpp | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index 2212d812..46a1356d 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -568,7 +568,8 @@ int main(int argc, const char** argv) {
             std::string sd_cpp_extra_args_str = extract_and_remove_sd_cpp_extra_args(prompt);
 
             size_t image_count = req.form.get_file_count("image[]");
-            if (image_count == 0) {
+            bool has_legacy_image = req.form.has_file("image");
+            if (image_count == 0 && !has_legacy_image) {
                 res.status = 400;
                 res.set_content(R"({"error":"at least one image[] required"})", "application/json");
                 return;
@@ -579,6 +580,10 @@ int main(int argc, const char** argv) {
                 auto file = req.form.get_file("image[]", i);
                 images_bytes.emplace_back(file.content.begin(), file.content.end());
             }
+            if (image_count == 0 && has_legacy_image) {
+                auto file = req.form.get_file("image");
+                images_bytes.emplace_back(file.content.begin(), file.content.end());
+            }
 
             std::vector<uint8_t> mask_bytes;
             if (req.form.has_file("mask")) {

From 5792c668798083f9f6d57dac66fbc62ddfdac405 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Sun, 1 Mar 2026 22:01:29 +0800
Subject: [PATCH 91/97] feat: support some non-standard Anima weight names
 (#1305)

---
 src/model.cpp           |  2 +-
 src/name_conversion.cpp | 18 ++++++++++--------
 2 files changed, 11 insertions(+), 9 deletions(-)

diff --git a/src/model.cpp b/src/model.cpp
index 24e3c566..77b032c2 100644
--- a/src/model.cpp
+++ b/src/model.cpp
@@ -1057,7 +1057,7 @@ SDVersion ModelLoader::get_sd_version() {
             if (tensor_storage.name.find("model.diffusion_model.transformer_blocks.0.img_mod.1.weight") != std::string::npos) {
                 return VERSION_QWEN_IMAGE;
             }
-            if (tensor_storage.name.find("model.diffusion_model.net.llm_adapter.blocks.0.cross_attn.q_proj.weight") != std::string::npos) {
+            if (tensor_storage.name.find("llm_adapter.blocks.0.cross_attn.q_proj.weight") != std::string::npos) {
                 return VERSION_ANIMA;
             }
             if (tensor_storage.name.find("model.diffusion_model.double_stream_modulation_img.lin.weight") != std::string::npos) {
diff --git a/src/name_conversion.cpp b/src/name_conversion.cpp
index a7839ab9..3b3abfb6 100644
--- a/src/name_conversion.cpp
+++ b/src/name_conversion.cpp
@@ -653,6 +653,14 @@ std::string convert_diffusers_dit_to_original_lumina2(std::string name) {
     return name;
 }
 
+std::string convert_other_dit_to_original_anima(std::string name) {
+    static const std::string anima_net_prefix = "net.";
+    if (!starts_with(name, anima_net_prefix)) {
+        name = anima_net_prefix + name;
+    }
+    return name;
+}
+
 std::string convert_diffusion_model_name(std::string name, std::string prefix, SDVersion version) {
     if (sd_version_is_sd1(version) || sd_version_is_sd2(version)) {
         name = convert_diffusers_unet_to_original_sd1(name);
@@ -664,6 +672,8 @@ std::string convert_diffusion_model_name(std::string name, std::string prefix, S
         name = convert_diffusers_dit_to_original_flux(name);
     } else if (sd_version_is_z_image(version)) {
         name = convert_diffusers_dit_to_original_lumina2(name);
+    } else if (sd_version_is_anima(version)) {
+        name = convert_other_dit_to_original_anima(name);
     }
     return name;
 }
@@ -1094,14 +1104,6 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
         }
     }
 
-    if (is_lora && sd_version_is_anima(version)) {
-        static const std::string anima_diffusion_prefix = "model.diffusion_model.";
-        static const std::string anima_net_prefix       = "model.diffusion_model.net.";
-        if (starts_with(name, anima_diffusion_prefix) && !starts_with(name, anima_net_prefix)) {
-            name = anima_net_prefix + name.substr(anima_diffusion_prefix.size());
-        }
-    }
-
     // cond_stage_model
     {
         for (const auto& prefix : cond_stage_model_prefix_vec) {

From 810ef0cf76381fdf5e25e9a219ed0b83df0c9731 Mon Sep 17 00:00:00 2001
From: Korsar13 <44565681+Korsar13@users.noreply.github.com>
Date: Tue, 3 Mar 2026 21:34:07 +0500
Subject: [PATCH 92/97] fix: reset weight adapter for models if no loras in
 request (#1307)

---
 src/stable-diffusion.cpp | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/src/stable-diffusion.cpp b/src/stable-diffusion.cpp
index d769d45c..717fec18 100644
--- a/src/stable-diffusion.cpp
+++ b/src/stable-diffusion.cpp
@@ -1098,6 +1098,18 @@ public:
         cond_stage_lora_models.clear();
         diffusion_lora_models.clear();
         first_stage_lora_models.clear();
+        if (cond_stage_model) {
+            cond_stage_model->set_weight_adapter(nullptr);
+        }
+        if (diffusion_model) {
+            diffusion_model->set_weight_adapter(nullptr);
+        }
+        if (high_noise_diffusion_model) {
+            high_noise_diffusion_model->set_weight_adapter(nullptr);
+        }
+        if (first_stage_model) {
+            first_stage_model->set_weight_adapter(nullptr);
+        }
         if (lora_state.empty()) {
             return;
         }

From d41f5fff6999f804343207c7a346d07ac10300a4 Mon Sep 17 00:00:00 2001
From: bssrdf <merlintiger@hotmail.com>
Date: Tue, 3 Mar 2026 11:36:32 -0500
Subject: [PATCH 93/97] perf: improved flux attention qkv unpacking (#1306)

---
 src/flux.hpp | 25 +++++++++++++------------
 1 file changed, 13 insertions(+), 12 deletions(-)

diff --git a/src/flux.hpp b/src/flux.hpp
index 37cbb126..1204ae1e 100644
--- a/src/flux.hpp
+++ b/src/flux.hpp
@@ -103,11 +103,13 @@ namespace Flux {
             auto norm     = std::dynamic_pointer_cast<QKNorm>(blocks["norm"]);
 
             auto qkv         = qkv_proj->forward(ctx, x);
-            auto qkv_vec     = ggml_ext_chunk(ctx->ggml_ctx, qkv, 3, 0, true);
-            int64_t head_dim = qkv_vec[0]->ne[0] / num_heads;
-            auto q           = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[0], head_dim, num_heads, qkv_vec[0]->ne[1], qkv_vec[0]->ne[2]);
-            auto k           = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[1], head_dim, num_heads, qkv_vec[1]->ne[1], qkv_vec[1]->ne[2]);
-            auto v           = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[2], head_dim, num_heads, qkv_vec[2]->ne[1], qkv_vec[2]->ne[2]);
+            int64_t head_dim = qkv->ne[0] / 3 / num_heads;
+            auto q           = ggml_view_4d(ctx->ggml_ctx, qkv, head_dim, num_heads, qkv->ne[1], qkv->ne[2],
+                                            qkv->nb[0] * head_dim, qkv->nb[1], qkv->nb[2], 0);
+            auto k           = ggml_view_4d(ctx->ggml_ctx, qkv, head_dim, num_heads, qkv->ne[1], qkv->ne[2],
+                                            qkv->nb[0] * head_dim, qkv->nb[1], qkv->nb[2], (qkv->nb[0]) * qkv->ne[0] / 3);
+            auto v           = ggml_view_4d(ctx->ggml_ctx, qkv, head_dim, num_heads, qkv->ne[1], qkv->ne[2],
+                                            qkv->nb[0] * head_dim, qkv->nb[1], qkv->nb[2], (qkv->nb[0]) * 2 * qkv->ne[0] / 3);
             q                = norm->query_norm(ctx, q);
             k                = norm->key_norm(ctx, k);
             return {q, k, v};
@@ -491,15 +493,14 @@ namespace Flux {
             auto x_mod   = Flux::modulate(ctx->ggml_ctx, pre_norm->forward(ctx, x), mod.shift, mod.scale);
             auto qkv_mlp = linear1->forward(ctx, x_mod);  // [N, n_token, hidden_size * 3 + mlp_hidden_dim*mlp_mult_factor]
 
-            auto q = ggml_view_3d(ctx->ggml_ctx, qkv_mlp, hidden_size, qkv_mlp->ne[1], qkv_mlp->ne[2], qkv_mlp->nb[1], qkv_mlp->nb[2], 0);
-            auto k = ggml_view_3d(ctx->ggml_ctx, qkv_mlp, hidden_size, qkv_mlp->ne[1], qkv_mlp->ne[2], qkv_mlp->nb[1], qkv_mlp->nb[2], hidden_size * qkv_mlp->nb[0]);
-            auto v = ggml_view_3d(ctx->ggml_ctx, qkv_mlp, hidden_size, qkv_mlp->ne[1], qkv_mlp->ne[2], qkv_mlp->nb[1], qkv_mlp->nb[2], hidden_size * 2 * qkv_mlp->nb[0]);
-
             int64_t head_dim = hidden_size / num_heads;
 
-            q = ggml_reshape_4d(ctx->ggml_ctx, ggml_cont(ctx->ggml_ctx, q), head_dim, num_heads, q->ne[1], q->ne[2]);  // [N, n_token, n_head, d_head]
-            k = ggml_reshape_4d(ctx->ggml_ctx, ggml_cont(ctx->ggml_ctx, k), head_dim, num_heads, k->ne[1], k->ne[2]);  // [N, n_token, n_head, d_head]
-            v = ggml_reshape_4d(ctx->ggml_ctx, ggml_cont(ctx->ggml_ctx, v), head_dim, num_heads, v->ne[1], v->ne[2]);  // [N, n_token, n_head, d_head]
+            auto q = ggml_view_4d(ctx->ggml_ctx, qkv_mlp, head_dim, num_heads, qkv_mlp->ne[1], qkv_mlp->ne[2],
+                                  qkv_mlp->nb[0] * head_dim, qkv_mlp->nb[1], qkv_mlp->nb[2], 0);
+            auto k = ggml_view_4d(ctx->ggml_ctx, qkv_mlp, head_dim, num_heads, qkv_mlp->ne[1], qkv_mlp->ne[2],
+                                  qkv_mlp->nb[0] * head_dim, qkv_mlp->nb[1], qkv_mlp->nb[2], (qkv_mlp->nb[0]) * hidden_size);
+            auto v = ggml_view_4d(ctx->ggml_ctx, qkv_mlp, head_dim, num_heads, qkv_mlp->ne[1], qkv_mlp->ne[2],
+                                  qkv_mlp->nb[0] * head_dim, qkv_mlp->nb[1], qkv_mlp->nb[2], (qkv_mlp->nb[0]) * 2 * hidden_size);
 
             q         = norm->query_norm(ctx, q);
             k         = norm->key_norm(ctx, k);

From ba35dd734ee15cacf27931e225692c3d88966321 Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Wed, 4 Mar 2026 00:36:52 +0800
Subject: [PATCH 94/97] refactor: introduce
 ggml_ext_zeros_like/ggml_ext_ones_like (#1312)

---
 examples/server/main.cpp |  4 ++--
 src/ggml_extend.hpp      | 10 ++++++++++
 src/qwen_image.hpp       |  2 +-
 3 files changed, 13 insertions(+), 3 deletions(-)

diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index 46a1356d..e13346b9 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -345,7 +345,7 @@ int main(int argc, const char** argv) {
     auto get_lora_full_path = [&](const std::string& path) -> std::string {
         std::lock_guard<std::mutex> lock(lora_mutex);
         auto it = std::find_if(lora_cache.begin(), lora_cache.end(),
-                           [&](const LoraEntry& e) { return e.path == path; });
+                               [&](const LoraEntry& e) { return e.path == path; });
         return (it != lora_cache.end()) ? it->fullpath : "";
     };
 
@@ -567,7 +567,7 @@ int main(int argc, const char** argv) {
 
             std::string sd_cpp_extra_args_str = extract_and_remove_sd_cpp_extra_args(prompt);
 
-            size_t image_count = req.form.get_file_count("image[]");
+            size_t image_count    = req.form.get_file_count("image[]");
             bool has_legacy_image = req.form.has_file("image");
             if (image_count == 0 && !has_legacy_image) {
                 res.status = 400;
diff --git a/src/ggml_extend.hpp b/src/ggml_extend.hpp
index 6642cfd5..131d66fb 100644
--- a/src/ggml_extend.hpp
+++ b/src/ggml_extend.hpp
@@ -1219,6 +1219,11 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_zeros(struct ggml_context* ctx,
     return ggml_ext_full(ctx, 0.f, ne0, ne1, ne2, ne3);
 }
 
+__STATIC_INLINE__ struct ggml_tensor* ggml_ext_zeros_like(struct ggml_context* ctx,
+                                                          struct ggml_tensor* x) {
+    return ggml_ext_zeros(ctx, x->ne[0], x->ne[1], x->ne[2], x->ne[3]);
+}
+
 __STATIC_INLINE__ struct ggml_tensor* ggml_ext_ones(struct ggml_context* ctx,
                                                     int64_t ne0,
                                                     int64_t ne1,
@@ -1227,6 +1232,11 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_ones(struct ggml_context* ctx,
     return ggml_ext_full(ctx, 1.f, ne0, ne1, ne2, ne3);
 }
 
+__STATIC_INLINE__ struct ggml_tensor* ggml_ext_ones_like(struct ggml_context* ctx,
+                                                         struct ggml_tensor* x) {
+    return ggml_ext_ones(ctx, x->ne[0], x->ne[1], x->ne[2], x->ne[3]);
+}
+
 __STATIC_INLINE__ ggml_tensor* ggml_ext_cast_f32(ggml_context* ctx, ggml_tensor* a) {
 #ifdef SD_USE_VULKAN
     auto zero_index = ggml_get_tensor(ctx, "ggml_runner_build_in_tensor:zero_int");
diff --git a/src/qwen_image.hpp b/src/qwen_image.hpp
index 8fff5e00..2c70344c 100644
--- a/src/qwen_image.hpp
+++ b/src/qwen_image.hpp
@@ -404,7 +404,7 @@ namespace Qwen {
 
             auto t_emb = time_text_embed->forward(ctx, timestep);
             if (params.zero_cond_t) {
-                auto t_emb_0 = time_text_embed->forward(ctx, ggml_ext_zeros(ctx->ggml_ctx, timestep->ne[0], timestep->ne[1], timestep->ne[2], timestep->ne[3]));
+                auto t_emb_0 = time_text_embed->forward(ctx, ggml_ext_zeros_like(ctx->ggml_ctx, timestep));
                 t_emb        = ggml_concat(ctx->ggml_ctx, t_emb, t_emb_0, 1);
             }
             auto img = img_in->forward(ctx, x);

From aaa8a51bd86efb495edc2d38343f17255bd4cbdb Mon Sep 17 00:00:00 2001
From: leejet <leejet714@gmail.com>
Date: Wed, 4 Mar 2026 00:41:17 +0800
Subject: [PATCH 95/97] docs: update sd-cli/sd-server docs

---
 examples/cli/README.md    | 20 ++++++++++++--------
 examples/server/README.md | 29 ++++++++++++++++-------------
 2 files changed, 28 insertions(+), 21 deletions(-)

diff --git a/examples/cli/README.md b/examples/cli/README.md
index c79d2ab6..564e5ce0 100644
--- a/examples/cli/README.md
+++ b/examples/cli/README.md
@@ -4,11 +4,12 @@
 usage: ./bin/sd-cli  [options]
 
 CLI Options:
-  -o, --output <string>       path to write result image to. you can use printf-style %d format specifiers for image sequences (default: ./output.png) (eg. output_%03d.png)
-  --output-begin-idx <int>    starting index for output image sequence, must be non-negative (default 0 if specified %d in output path, 1 otherwise)
+  -o, --output <string>       path to write result image to. you can use printf-style %d format specifiers for image sequences (default:
+                              ./output.png) (eg. output_%03d.png)
   --preview-path <string>     path to write preview image to (default: ./preview.png)
   --preview-interval <int>    interval in denoising steps between consecutive updates of the image preview file (default is 1, meaning updating at
                               every step)
+  --output-begin-idx <int>    starting index for output image sequence, must be non-negative (default 0 if specified %d in output path, 1 otherwise)
   --canny                     apply canny preprocessor (edge detection)
   --convert-name              convert tensor name (for convert mode)
   -v, --verbose               print extra info
@@ -59,6 +60,7 @@ Context Options:
   --circularx                              enable circular RoPE wrapping on x-axis (width) only
   --circulary                              enable circular RoPE wrapping on y-axis (height) only
   --chroma-disable-dit-mask                disable dit mask for chroma
+  --qwen-image-zero-cond-t                 enable zero_cond_t for qwen image
   --chroma-enable-t5-mask                  enable t5 mask for chroma
   --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). If not specified, the default is the
                                            type of the weight file
@@ -107,7 +109,7 @@ Generation Options:
                                            medium
   --skip-layer-start <float>               SLG enabling point (default: 0.01)
   --skip-layer-end <float>                 SLG disabling point (default: 0.2)
-  --eta <float>                            eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
+  --eta <float>                            eta in DDIM, only for DDIM and TCD (default: 0)
   --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
   --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
   --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
@@ -115,7 +117,7 @@ Generation Options:
   --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
   --high-noise-skip-layer-start <float>    (high noise) SLG enabling point (default: 0.01)
   --high-noise-skip-layer-end <float>      (high noise) SLG disabling point (default: 0.2)
-  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
+  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM and TCD (default: 0)
   --strength <float>                       strength for noising/unnoising (default: 0.75)
   --pm-style-strength <float>
   --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
@@ -124,10 +126,12 @@ Generation Options:
   --increase-ref-index                     automatically increase the indices of references images based on the order they are listed (starting with 1).
   --disable-auto-resize-ref-image          disable auto resize of ref images
   -s, --seed                               RNG seed (default: 42, use random seed for < 0)
-  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd,
-                                           res_multistep, res_2s] (default: euler for Flux/SD3/Wan, euler_a otherwise)
-  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
-                                           tcd, res_multistep, res_2s] default: euler for Flux/SD3/Wan, euler_a otherwise
+  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
+                                           tcd, res_multistep, res_2s] (default: euler for Flux/SD3/Wan, euler_a
+                                           otherwise)
+  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
+                                           ddim_trailing, tcd, res_multistep, res_2s] default: euler for Flux/SD3/Wan,
+                                           euler_a otherwise
   --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple,
                                            kl_optimal, lcm, bong_tangent], default: discrete
   --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
diff --git a/examples/server/README.md b/examples/server/README.md
index 38672900..75544364 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -4,12 +4,12 @@
 usage: ./bin/sd-server  [options]
 
 Svr Options:
-  -l, --listen-ip <string>    server listen ip (default: 127.0.0.1)
-  --listen-port <int>         server listen port (default: 1234)
-  --serve-html-path <string>  path to HTML file to serve at root (optional)
-  -v, --verbose               print extra info
-  --color                     colors the logging tags according to level
-  -h, --help                  show this help message and exit
+  -l, --listen-ip <string>      server listen ip (default: 127.0.0.1)        
+  --serve-html-path <string>    path to HTML file to serve at root (optional)
+  --listen-port <int>           server listen port (default: 1234)
+  -v, --verbose                 print extra info
+  --color                       colors the logging tags according to level   
+  -h, --help                    show this help message and exit
 
 Context Options:
   -m, --model <string>                     path to full model
@@ -39,10 +39,10 @@ Context Options:
   --vae-tiling                             process vae in tiles to reduce memory usage
   --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
   --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
+  --mmap                                   whether to memory-map model
   --control-net-cpu                        keep controlnet in cpu (for low vram)
   --clip-on-cpu                            keep clip in cpu (for low vram)
   --vae-on-cpu                             keep vae in cpu (for low vram)
-  --mmap                                   whether to memory-map model
   --fa                                     use flash attention
   --diffusion-fa                           use flash attention in the diffusion model only
   --diffusion-conv-direct                  use ggml_conv2d_direct in the diffusion model
@@ -51,6 +51,7 @@ Context Options:
   --circularx                              enable circular RoPE wrapping on x-axis (width) only
   --circulary                              enable circular RoPE wrapping on y-axis (height) only
   --chroma-disable-dit-mask                disable dit mask for chroma
+  --qwen-image-zero-cond-t                 enable zero_cond_t for qwen image
   --chroma-enable-t5-mask                  enable t5 mask for chroma
   --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). If not specified, the default is the
                                            type of the weight file
@@ -99,7 +100,7 @@ Default Generation Options:
                                            medium
   --skip-layer-start <float>               SLG enabling point (default: 0.01)
   --skip-layer-end <float>                 SLG disabling point (default: 0.2)
-  --eta <float>                            eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
+  --eta <float>                            eta in DDIM, only for DDIM and TCD (default: 0)
   --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
   --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
   --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
@@ -107,7 +108,7 @@ Default Generation Options:
   --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
   --high-noise-skip-layer-start <float>    (high noise) SLG enabling point (default: 0.01)
   --high-noise-skip-layer-end <float>      (high noise) SLG disabling point (default: 0.2)
-  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
+  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM and TCD (default: 0)
   --strength <float>                       strength for noising/unnoising (default: 0.75)
   --pm-style-strength <float>
   --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
@@ -116,10 +117,12 @@ Default Generation Options:
   --increase-ref-index                     automatically increase the indices of references images based on the order they are listed (starting with 1).
   --disable-auto-resize-ref-image          disable auto resize of ref images
   -s, --seed                               RNG seed (default: 42, use random seed for < 0)
-  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd,
-                                           res_multistep, res_2s] (default: euler for Flux/SD3/Wan, euler_a otherwise)
-  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
-                                           tcd, res_multistep, res_2s] default: euler for Flux/SD3/Wan, euler_a otherwise
+  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
+                                           tcd, res_multistep, res_2s] (default: euler for Flux/SD3/Wan, euler_a
+                                           otherwise)
+  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
+                                           ddim_trailing, tcd, res_multistep, res_2s] default: euler for Flux/SD3/Wan,
+                                           euler_a otherwise
   --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple,
                                            kl_optimal, lcm, bong_tangent], default: discrete
   --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").

From 7c880f80c71d35deb9b5069e02b2f221139b3770 Mon Sep 17 00:00:00 2001
From: Korsar13 <44565681+Korsar13@users.noreply.github.com>
Date: Wed, 4 Mar 2026 20:47:38 +0500
Subject: [PATCH 96/97] fix: avoid sd-server memory leak (#1316)

---
 examples/server/main.cpp | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/examples/server/main.cpp b/examples/server/main.cpp
index e13346b9..cc9e66cc 100644
--- a/examples/server/main.cpp
+++ b/examples/server/main.cpp
@@ -269,6 +269,18 @@ struct LoraEntry {
     std::string fullpath;
 };
 
+void free_results(sd_image_t* result_images, int num_results) {
+    if (result_images) {
+        for (int i = 0; i < num_results; ++i) {
+            if (result_images[i].data) {
+                stbi_image_free(result_images[i].data);
+                result_images[i].data = nullptr;
+            }
+        }
+    }
+    free(result_images);
+}
+
 int main(int argc, const char** argv) {
     if (argc > 1 && std::string(argv[1]) == "--version") {
         std::cout << version_string() << "\n";
@@ -537,6 +549,7 @@ int main(int argc, const char** argv) {
                 item["b64_json"] = b64;
                 out["data"].push_back(item);
             }
+            free_results(results, num_results);
 
             res.set_content(out.dump(), "application/json");
             res.status = 200;
@@ -781,6 +794,7 @@ int main(int argc, const char** argv) {
                 item["b64_json"] = b64;
                 out["data"].push_back(item);
             }
+            free_results(results, num_results);
 
             res.set_content(out.dump(), "application/json");
             res.status = 200;
@@ -1101,6 +1115,7 @@ int main(int argc, const char** argv) {
                 std::string b64 = base64_encode(image_bytes);
                 out["images"].push_back(b64);
             }
+            free_results(results, num_results);
 
             res.set_content(out.dump(), "application/json");
             res.status = 200;

From d95062737e4bd72060595220c5545cce1b049af9 Mon Sep 17 00:00:00 2001
From: rmatif <rmatif@proton.me>
Date: Wed, 4 Mar 2026 16:50:45 +0100
Subject: [PATCH 97/97] fix: ucache: normalize reuse error  (#1313)

---
 src/ucache.hpp | 56 ++++++++++++++++++++++++++++++++++++++------------
 1 file changed, 43 insertions(+), 13 deletions(-)

diff --git a/src/ucache.hpp b/src/ucache.hpp
index 92e94a16..d3247618 100644
--- a/src/ucache.hpp
+++ b/src/ucache.hpp
@@ -19,6 +19,7 @@ struct UCacheConfig {
     bool adaptive_threshold     = true;
     float early_step_multiplier = 0.5f;
     float late_step_multiplier  = 1.5f;
+    float relative_norm_gain    = 1.6f;
     bool reset_error_on_compute = true;
 };
 
@@ -45,14 +46,16 @@ struct UCacheState {
     bool has_output_prev_norm             = false;
     bool has_relative_transformation_rate = false;
     float relative_transformation_rate    = 0.0f;
-    float cumulative_change_rate          = 0.0f;
     float last_input_change               = 0.0f;
     bool has_last_input_change            = false;
+    float output_change_ema               = 0.0f;
+    bool has_output_change_ema            = false;
     int total_steps_skipped               = 0;
     int current_step_index                = -1;
     int steps_computed_since_active       = 0;
+    int expected_total_steps              = 0;
+    int consecutive_skipped_steps         = 0;
     float accumulated_error               = 0.0f;
-    float reference_output_norm           = 0.0f;
 
     struct BlockMetrics {
         float sum_transformation_rate = 0.0f;
@@ -106,14 +109,16 @@ struct UCacheState {
         has_output_prev_norm             = false;
         has_relative_transformation_rate = false;
         relative_transformation_rate     = 0.0f;
-        cumulative_change_rate           = 0.0f;
         last_input_change                = 0.0f;
         has_last_input_change            = false;
+        output_change_ema                = 0.0f;
+        has_output_change_ema            = false;
         total_steps_skipped              = 0;
         current_step_index               = -1;
         steps_computed_since_active      = 0;
+        expected_total_steps             = 0;
+        consecutive_skipped_steps        = 0;
         accumulated_error                = 0.0f;
-        reference_output_norm            = 0.0f;
         block_metrics.reset();
         total_active_steps = 0;
     }
@@ -133,7 +138,8 @@ struct UCacheState {
         if (!initialized || sigmas.size() < 2) {
             return;
         }
-        size_t n_steps = sigmas.size() - 1;
+        size_t n_steps       = sigmas.size() - 1;
+        expected_total_steps = static_cast<int>(n_steps);
 
         size_t start_step = static_cast<size_t>(config.start_percent * n_steps);
         size_t end_step   = static_cast<size_t>(config.end_percent * n_steps);
@@ -207,11 +213,15 @@ struct UCacheState {
         }
 
         int effective_total = estimated_total_steps;
+        if (effective_total <= 0) {
+            effective_total = expected_total_steps;
+        }
         if (effective_total <= 0) {
             effective_total = std::max(20, steps_computed_since_active * 2);
         }
 
         float progress = (effective_total > 0) ? (static_cast<float>(steps_computed_since_active) / effective_total) : 0.0f;
+        progress       = std::max(0.0f, std::min(1.0f, progress));
 
         float multiplier = 1.0f;
         if (progress < 0.2f) {
@@ -309,17 +319,31 @@ struct UCacheState {
 
         if (has_output_prev_norm && has_relative_transformation_rate &&
             last_input_change > 0.0f && output_prev_norm > 0.0f) {
-            float approx_output_change_rate = (relative_transformation_rate * last_input_change) / output_prev_norm;
-            accumulated_error               = accumulated_error * config.error_decay_rate + approx_output_change_rate;
+            float approx_output_change = relative_transformation_rate * last_input_change;
+            float approx_output_change_rate;
+            if (config.use_relative_threshold) {
+                float base_scale          = std::max(output_prev_norm, 1e-6f);
+                float dyn_scale           = has_output_change_ema
+                                                ? std::max(output_change_ema * std::max(1.0f, config.relative_norm_gain), 1e-6f)
+                                                : base_scale;
+                float scale               = std::sqrt(base_scale * dyn_scale);
+                approx_output_change_rate = approx_output_change / scale;
+            } else {
+                approx_output_change_rate = approx_output_change;
+            }
+            // Increase estimated error with skip horizon to avoid long extrapolation streaks
+            approx_output_change_rate *= (1.0f + 0.50f * consecutive_skipped_steps);
+            accumulated_error = accumulated_error * config.error_decay_rate + approx_output_change_rate;
 
             float effective_threshold = get_adaptive_threshold();
-            if (config.use_relative_threshold && reference_output_norm > 0.0f) {
-                effective_threshold = effective_threshold * reference_output_norm;
+            if (!config.use_relative_threshold && output_prev_norm > 0.0f) {
+                effective_threshold = effective_threshold * output_prev_norm;
             }
 
             if (accumulated_error < effective_threshold) {
                 skip_current_step = true;
                 total_steps_skipped++;
+                consecutive_skipped_steps++;
                 apply_cache(cond, input, output);
                 return true;
             } else if (config.reset_error_on_compute) {
@@ -340,6 +364,8 @@ struct UCacheState {
         if (cond != anchor_condition) {
             return;
         }
+        steps_computed_since_active++;
+        consecutive_skipped_steps = 0;
 
         size_t ne      = static_cast<size_t>(ggml_nelements(input));
         float* in_data = (float*)input->data;
@@ -359,6 +385,14 @@ struct UCacheState {
                 output_change /= static_cast<float>(ne);
             }
         }
+        if (std::isfinite(output_change) && output_change > 0.0f) {
+            if (!has_output_change_ema) {
+                output_change_ema     = output_change;
+                has_output_change_ema = true;
+            } else {
+                output_change_ema = 0.8f * output_change_ema + 0.2f * output_change;
+            }
+        }
 
         prev_output.resize(ne);
         for (size_t i = 0; i < ne; ++i) {
@@ -373,10 +407,6 @@ struct UCacheState {
         output_prev_norm     = (ne > 0) ? (mean_abs / static_cast<float>(ne)) : 0.0f;
         has_output_prev_norm = output_prev_norm > 0.0f;
 
-        if (reference_output_norm == 0.0f) {
-            reference_output_norm = output_prev_norm;
-        }
-
         if (has_last_input_change && last_input_change > 0.0f && output_change > 0.0f) {
             float rate = output_change / last_input_change;
             if (std::isfinite(rate)) {