ci: avoid cuda docker build timeout by using -j16

.github/workflows/build.yml

@@ -162,7 +162,7 @@ jobs:
 
     strategy:
       matrix:
-        variant: [musa, sycl, vulkan]
+        variant: [musa, sycl, vulkan, cuda]
 
     env:
       REGISTRY: ghcr.io

CMakeLists.txt

@@ -36,7 +36,6 @@ option(SD_VULKAN                     "sd: vulkan backend" OFF)
 option(SD_OPENCL                     "sd: opencl backend" OFF)
 option(SD_SYCL                       "sd: sycl backend" OFF)
 option(SD_MUSA                       "sd: musa backend" OFF)
-option(SD_FAST_SOFTMAX               "sd: x1.5 faster softmax, indeterministic (sometimes, same seed don't generate same image), cuda only" OFF)
 option(SD_BUILD_SHARED_LIBS          "sd: build shared libs" OFF)
 option(SD_BUILD_SHARED_GGML_LIB      "sd: build ggml as a separate shared lib" OFF)
 option(SD_USE_SYSTEM_GGML            "sd: use system-installed GGML library" OFF)
@@ -70,18 +69,12 @@ if (SD_HIPBLAS)
     message("-- Use HIPBLAS as backend stable-diffusion")
     set(GGML_HIP ON)
     add_definitions(-DSD_USE_CUDA)
-    if(SD_FAST_SOFTMAX)
-        set(GGML_CUDA_FAST_SOFTMAX ON)
-    endif()
 endif ()
 
 if(SD_MUSA)
     message("-- Use MUSA as backend stable-diffusion")
     set(GGML_MUSA ON)
     add_definitions(-DSD_USE_CUDA)
-    if(SD_FAST_SOFTMAX)
-        set(GGML_CUDA_FAST_SOFTMAX ON)
-    endif()
 endif()
 
 set(SD_LIB stable-diffusion)

Dockerfile.cuda

@@ -0,0 +1,25 @@
+ARG CUDA_VERSION=12.6.3
+ARG UBUNTU_VERSION=24.04
+
+FROM nvidia/cuda:${CUDA_VERSION}-cudnn-devel-ubuntu${UBUNTU_VERSION} AS build
+
+RUN apt-get update && apt-get install -y --no-install-recommends build-essential git ccache cmake
+
+WORKDIR /sd.cpp
+
+COPY . .
+
+ARG CUDACXX=/usr/local/cuda/bin/nvcc
+RUN cmake . -B ./build -DSD_CUDA=ON
+RUN cmake --build ./build --config Release -j$(nproc)
+
+FROM nvidia/cuda:${CUDA_VERSION}-cudnn-runtime-ubuntu${UBUNTU_VERSION} AS runtime
+
+RUN apt-get update && \
+    apt-get install --yes --no-install-recommends libgomp1 && \
+    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" ]

docs/anima.md

@@ -5,6 +5,7 @@
 - Download Anima
     - safetensors: https://huggingface.co/circlestone-labs/Anima/tree/main/split_files/diffusion_models
     - gguf: https://huggingface.co/Bedovyy/Anima-GGUF/tree/main
+    - gguf Anima2: https://huggingface.co/JusteLeo/Anima2-GGUF/tree/main
 - Download vae
     - safetensors: https://huggingface.co/circlestone-labs/Anima/tree/main/split_files/vae
 - Download Qwen3-0.6B-Base
@@ -17,4 +18,4 @@
 .\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" />
+<img alt="anima image example" src="../assets/anima/example.png" />

docs/caching.md

@@ -80,7 +80,7 @@ Uses Taylor series approximation to predict block outputs:
 Combines DBCache and TaylorSeer:
 
 ```bash
---cache-mode cache-dit --cache-preset fast
+--cache-mode cache-dit
 ```
 
 #### Parameters
@@ -92,14 +92,6 @@ Combines DBCache and TaylorSeer:
 | `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:

examples/cli/README.md

@@ -139,12 +139,11 @@ 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),
-                                           'spectrum' (UNET Chebyshev+Taylor forecasting)
+                                           'spectrum' (UNET/DiT Chebyshev+Taylor forecasting)
   --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=;
                                            spectrum: w=,m=,lam=,window=,flex=,warmup=,stop=. Examples:
                                            "threshold=0.25" or "threshold=1.5,reset=0" or "w=0.4,window=2"
-  --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'
 ```

examples/common/common.hpp

@@ -1047,7 +1047,6 @@ struct SDGenerationParams {
 
     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{};
@@ -1461,21 +1460,6 @@ struct SDGenerationParams {
             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 = {
             {"-s",
              "--seed",
@@ -1513,16 +1497,12 @@ struct SDGenerationParams {
              on_ref_image_arg},
             {"",
              "--cache-mode",
-             "caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)",
+             "caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level), 'spectrum' (UNET/DiT Chebyshev+Taylor forecasting)",
              on_cache_mode_arg},
             {"",
              "--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\"",
+             "named cache params (key=value format, comma-separated). easycache/ucache: threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=; spectrum: w=,m=,lam=,window=,flex=,warmup=,stop=. 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",
@@ -1575,7 +1555,6 @@ struct SDGenerationParams {
         load_if_exists("negative_prompt", negative_prompt);
         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);
@@ -1810,48 +1789,17 @@ struct SDGenerationParams {
 
         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;
+                cache_params.mode = SD_CACHE_EASYCACHE;
             } 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;
+                cache_params.mode = SD_CACHE_UCACHE;
             } 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;
+                cache_params.mode = SD_CACHE_DBCACHE;
             } 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;
+                cache_params.mode = SD_CACHE_TAYLORSEER;
             } 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;
+                cache_params.mode = SD_CACHE_CACHE_DIT;
             } else if (cache_mode == "spectrum") {
-                cache_params.mode                  = SD_CACHE_SPECTRUM;
-                cache_params.spectrum_w            = 0.40f;
-                cache_params.spectrum_m            = 3;
-                cache_params.spectrum_lam          = 1.0f;
-                cache_params.spectrum_window_size  = 2;
-                cache_params.spectrum_flex_window  = 0.50f;
-                cache_params.spectrum_warmup_steps = 4;
-                cache_params.spectrum_stop_percent = 0.9f;
+                cache_params.mode = SD_CACHE_SPECTRUM;
             }
 
             if (!cache_option.empty()) {

examples/server/README.md

@@ -129,11 +129,10 @@ Default 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)
-  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level), 'spectrum' (UNET/DiT Chebyshev+Taylor forecasting)
   --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'
 ```

src/auto_encoder_kl.hpp

@@ -0,0 +1,930 @@
+#ifndef __AUTO_ENCODER_KL_HPP__
+#define __AUTO_ENCODER_KL_HPP__
+
+#include "vae.hpp"
+
+/*================================================== AutoEncoderKL ===================================================*/
+
+#define VAE_GRAPH_SIZE 20480
+
+class ResnetBlock : public UnaryBlock {
+protected:
+    int64_t in_channels;
+    int64_t out_channels;
+
+public:
+    ResnetBlock(int64_t in_channels,
+                int64_t out_channels)
+        : in_channels(in_channels),
+          out_channels(out_channels) {
+        // temb_channels is always 0
+        blocks["norm1"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(in_channels));
+        blocks["conv1"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
+
+        blocks["norm2"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(out_channels));
+        blocks["conv2"] = std::shared_ptr<GGMLBlock>(new Conv2d(out_channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
+
+        if (out_channels != in_channels) {
+            blocks["nin_shortcut"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, out_channels, {1, 1}));
+        }
+    }
+
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+        // x: [N, in_channels, h, w]
+        // t_emb is always None
+        auto norm1 = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm1"]);
+        auto conv1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv1"]);
+        auto norm2 = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm2"]);
+        auto conv2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv2"]);
+
+        auto h = x;
+        h      = norm1->forward(ctx, h);
+        h      = ggml_silu_inplace(ctx->ggml_ctx, h);  // swish
+        h      = conv1->forward(ctx, h);
+        // return h;
+
+        h = norm2->forward(ctx, h);
+        h = ggml_silu_inplace(ctx->ggml_ctx, h);  // swish
+        // dropout, skip for inference
+        h = conv2->forward(ctx, h);
+
+        // skip connection
+        if (out_channels != in_channels) {
+            auto nin_shortcut = std::dynamic_pointer_cast<Conv2d>(blocks["nin_shortcut"]);
+
+            x = nin_shortcut->forward(ctx, x);  // [N, out_channels, h, w]
+        }
+
+        h = ggml_add(ctx->ggml_ctx, h, x);
+        return h;  // [N, out_channels, h, w]
+    }
+};
+
+class AttnBlock : public UnaryBlock {
+protected:
+    int64_t in_channels;
+    bool use_linear;
+
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") {
+        auto iter = tensor_storage_map.find(prefix + "proj_out.weight");
+        if (iter != tensor_storage_map.end()) {
+            if (iter->second.n_dims == 4 && use_linear) {
+                use_linear         = false;
+                blocks["q"]        = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
+                blocks["k"]        = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
+                blocks["v"]        = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
+                blocks["proj_out"] = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
+            } else if (iter->second.n_dims == 2 && !use_linear) {
+                use_linear         = true;
+                blocks["q"]        = std::make_shared<Linear>(in_channels, in_channels);
+                blocks["k"]        = std::make_shared<Linear>(in_channels, in_channels);
+                blocks["v"]        = std::make_shared<Linear>(in_channels, in_channels);
+                blocks["proj_out"] = std::make_shared<Linear>(in_channels, in_channels);
+            }
+        }
+    }
+
+public:
+    AttnBlock(int64_t in_channels, bool use_linear)
+        : in_channels(in_channels), use_linear(use_linear) {
+        blocks["norm"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(in_channels));
+        if (use_linear) {
+            blocks["q"]        = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
+            blocks["k"]        = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
+            blocks["v"]        = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
+            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
+        } else {
+            blocks["q"]        = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+            blocks["k"]        = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+            blocks["v"]        = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+        }
+    }
+
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+        // x: [N, in_channels, h, w]
+        auto norm     = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm"]);
+        auto q_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["q"]);
+        auto k_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["k"]);
+        auto v_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["v"]);
+        auto proj_out = std::dynamic_pointer_cast<UnaryBlock>(blocks["proj_out"]);
+
+        auto h_ = norm->forward(ctx, x);
+
+        const int64_t n = h_->ne[3];
+        const int64_t c = h_->ne[2];
+        const int64_t h = h_->ne[1];
+        const int64_t w = h_->ne[0];
+
+        ggml_tensor* q;
+        ggml_tensor* k;
+        ggml_tensor* v;
+        if (use_linear) {
+            h_ = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, h_, 1, 2, 0, 3));  // [N, h, w, in_channels]
+            h_ = ggml_reshape_3d(ctx->ggml_ctx, h_, c, h * w, n);                        // [N, h * w, in_channels]
+
+            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]
+        } 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]
+            q = ggml_reshape_3d(ctx->ggml_ctx, q, c, h * w, n);                        // [N, h * w, in_channels]
+
+            k = k_proj->forward(ctx, h_);                                              // [N, in_channels, h, w]
+            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_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_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]
+
+            h_ = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, h_, 1, 0, 2, 3));  // [N, in_channels, h * w]
+            h_ = ggml_reshape_4d(ctx->ggml_ctx, h_, w, h, c, n);                         // [N, in_channels, h, w]
+        } else {
+            h_ = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, h_, 1, 0, 2, 3));  // [N, in_channels, h * w]
+            h_ = ggml_reshape_4d(ctx->ggml_ctx, h_, w, h, c, n);                         // [N, in_channels, h, w]
+
+            h_ = proj_out->forward(ctx, h_);  // [N, in_channels, h, w]
+        }
+
+        h_ = ggml_add(ctx->ggml_ctx, h_, x);
+        return h_;
+    }
+};
+
+class AE3DConv : public Conv2d {
+public:
+    AE3DConv(int64_t in_channels,
+             int64_t out_channels,
+             std::pair<int, int> kernel_size,
+             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) {
+        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,
+                                struct ggml_tensor* x) override {
+        // timesteps always None
+        // skip_video always False
+        // x: [N, IC, IH, IW]
+        // result: [N, OC, OH, OW]
+        auto time_mix_conv = std::dynamic_pointer_cast<Conv3d>(blocks["time_mix_conv"]);
+
+        x = Conv2d::forward(ctx, x);
+        // timesteps = x.shape[0]
+        // x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
+        // x = conv3d(x)
+        // return rearrange(x, "b c t h w -> (b t) c h w")
+        int64_t T = x->ne[3];
+        int64_t B = x->ne[3] / T;
+        int64_t C = x->ne[2];
+        int64_t H = x->ne[1];
+        int64_t W = x->ne[0];
+
+        x = ggml_reshape_4d(ctx->ggml_ctx, x, W * H, C, T, B);                     // (b t) c h w -> b t c (h w)
+        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
+        x = time_mix_conv->forward(ctx, x);                                        // [B, OC, T, OH * OW]
+        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
+        return x;                                                                  // [B*T, OC, OH, OW]
+    }
+};
+
+class VideoResnetBlock : public ResnetBlock {
+protected:
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        enum ggml_type wtype = get_type(prefix + "mix_factor", tensor_storage_map, GGML_TYPE_F32);
+        params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
+    }
+
+    float get_alpha() {
+        float alpha = ggml_ext_backend_tensor_get_f32(params["mix_factor"]);
+        return sigmoid(alpha);
+    }
+
+public:
+    VideoResnetBlock(int64_t in_channels,
+                     int64_t out_channels,
+                     int video_kernel_size = 3)
+        : ResnetBlock(in_channels, out_channels) {
+        // merge_strategy is always learned
+        blocks["time_stack"] = std::shared_ptr<GGMLBlock>(new ResBlock(out_channels, 0, out_channels, {video_kernel_size, 1}, 3, false, true));
+    }
+
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+        // x: [N, in_channels, h, w] aka [b*t, in_channels, h, w]
+        // return: [N, out_channels, h, w] aka [b*t, out_channels, h, w]
+        // t_emb is always None
+        // skip_video is always False
+        // timesteps is always None
+        auto time_stack = std::dynamic_pointer_cast<ResBlock>(blocks["time_stack"]);
+
+        x = ResnetBlock::forward(ctx, x);  // [N, out_channels, h, w]
+        // return x;
+
+        int64_t T = x->ne[3];
+        int64_t B = x->ne[3] / T;
+        int64_t C = x->ne[2];
+        int64_t H = x->ne[1];
+        int64_t W = x->ne[0];
+
+        x          = ggml_reshape_4d(ctx->ggml_ctx, x, W * H, C, T, B);                     // (b t) c h w -> b t c (h w)
+        x          = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
+        auto x_mix = x;
+
+        x = time_stack->forward(ctx, x);  // b t c (h w)
+
+        float alpha = get_alpha();
+        x           = ggml_add(ctx->ggml_ctx,
+                               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
+
+        return x;
+    }
+};
+
+// ldm.modules.diffusionmodules.model.Encoder
+class Encoder : public GGMLBlock {
+protected:
+    int ch                   = 128;
+    std::vector<int> ch_mult = {1, 2, 4, 4};
+    int num_res_blocks       = 2;
+    int in_channels          = 3;
+    int z_channels           = 4;
+    bool double_z            = true;
+
+public:
+    Encoder(int ch,
+            std::vector<int> ch_mult,
+            int num_res_blocks,
+            int in_channels,
+            int z_channels,
+            bool double_z              = true,
+            bool use_linear_projection = false)
+        : ch(ch),
+          ch_mult(ch_mult),
+          num_res_blocks(num_res_blocks),
+          in_channels(in_channels),
+          z_channels(z_channels),
+          double_z(double_z) {
+        blocks["conv_in"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, ch, {3, 3}, {1, 1}, {1, 1}));
+
+        size_t num_resolutions = ch_mult.size();
+
+        int block_in = 1;
+        for (int i = 0; i < num_resolutions; i++) {
+            if (i == 0) {
+                block_in = ch;
+            } else {
+                block_in = ch * ch_mult[i - 1];
+            }
+            int block_out = ch * ch_mult[i];
+            for (int j = 0; j < num_res_blocks; j++) {
+                std::string name = "down." + std::to_string(i) + ".block." + std::to_string(j);
+                blocks[name]     = std::shared_ptr<GGMLBlock>(new ResnetBlock(block_in, block_out));
+                block_in         = block_out;
+            }
+            if (i != num_resolutions - 1) {
+                std::string name = "down." + std::to_string(i) + ".downsample";
+                blocks[name]     = std::shared_ptr<GGMLBlock>(new DownSampleBlock(block_in, block_in, true));
+            }
+        }
+
+        blocks["mid.block_1"] = std::shared_ptr<GGMLBlock>(new ResnetBlock(block_in, block_in));
+        blocks["mid.attn_1"]  = std::shared_ptr<GGMLBlock>(new AttnBlock(block_in, use_linear_projection));
+        blocks["mid.block_2"] = std::shared_ptr<GGMLBlock>(new ResnetBlock(block_in, block_in));
+
+        blocks["norm_out"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(block_in));
+        blocks["conv_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(block_in, double_z ? z_channels * 2 : z_channels, {3, 3}, {1, 1}, {1, 1}));
+    }
+
+    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        // x: [N, in_channels, h, w]
+
+        auto conv_in     = std::dynamic_pointer_cast<Conv2d>(blocks["conv_in"]);
+        auto mid_block_1 = std::dynamic_pointer_cast<ResnetBlock>(blocks["mid.block_1"]);
+        auto mid_attn_1  = std::dynamic_pointer_cast<AttnBlock>(blocks["mid.attn_1"]);
+        auto mid_block_2 = std::dynamic_pointer_cast<ResnetBlock>(blocks["mid.block_2"]);
+        auto norm_out    = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm_out"]);
+        auto conv_out    = std::dynamic_pointer_cast<Conv2d>(blocks["conv_out"]);
+
+        auto h = conv_in->forward(ctx, x);  // [N, ch, h, w]
+
+        // downsampling
+        size_t num_resolutions = ch_mult.size();
+        for (int i = 0; i < num_resolutions; i++) {
+            for (int j = 0; j < num_res_blocks; j++) {
+                std::string name = "down." + std::to_string(i) + ".block." + std::to_string(j);
+                auto down_block  = std::dynamic_pointer_cast<ResnetBlock>(blocks[name]);
+
+                h = down_block->forward(ctx, h);
+            }
+            if (i != num_resolutions - 1) {
+                std::string name = "down." + std::to_string(i) + ".downsample";
+                auto down_sample = std::dynamic_pointer_cast<DownSampleBlock>(blocks[name]);
+
+                h = down_sample->forward(ctx, h);
+            }
+        }
+
+        // middle
+        h = mid_block_1->forward(ctx, h);
+        h = mid_attn_1->forward(ctx, h);
+        h = mid_block_2->forward(ctx, h);  // [N, block_in, h, w]
+
+        // end
+        h = norm_out->forward(ctx, h);
+        h = ggml_silu_inplace(ctx->ggml_ctx, h);  // nonlinearity/swish
+        h = conv_out->forward(ctx, h);            // [N, z_channels*2, h, w]
+        return h;
+    }
+};
+
+// ldm.modules.diffusionmodules.model.Decoder
+class Decoder : public GGMLBlock {
+protected:
+    int ch                   = 128;
+    int out_ch               = 3;
+    std::vector<int> ch_mult = {1, 2, 4, 4};
+    int num_res_blocks       = 2;
+    int z_channels           = 4;
+    bool video_decoder       = false;
+    int video_kernel_size    = 3;
+
+    virtual std::shared_ptr<GGMLBlock> get_conv_out(int64_t in_channels,
+                                                    int64_t out_channels,
+                                                    std::pair<int, int> kernel_size,
+                                                    std::pair<int, int> stride  = {1, 1},
+                                                    std::pair<int, int> padding = {0, 0}) {
+        if (video_decoder) {
+            return std::shared_ptr<GGMLBlock>(new AE3DConv(in_channels, out_channels, kernel_size, video_kernel_size, stride, padding));
+        } else {
+            return std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, out_channels, kernel_size, stride, padding));
+        }
+    }
+
+    virtual std::shared_ptr<GGMLBlock> get_resnet_block(int64_t in_channels,
+                                                        int64_t out_channels) {
+        if (video_decoder) {
+            return std::shared_ptr<GGMLBlock>(new VideoResnetBlock(in_channels, out_channels, video_kernel_size));
+        } else {
+            return std::shared_ptr<GGMLBlock>(new ResnetBlock(in_channels, out_channels));
+        }
+    }
+
+public:
+    Decoder(int ch,
+            int out_ch,
+            std::vector<int> ch_mult,
+            int num_res_blocks,
+            int z_channels,
+            bool use_linear_projection = false,
+            bool video_decoder         = false,
+            int video_kernel_size      = 3)
+        : ch(ch),
+          out_ch(out_ch),
+          ch_mult(ch_mult),
+          num_res_blocks(num_res_blocks),
+          z_channels(z_channels),
+          video_decoder(video_decoder),
+          video_kernel_size(video_kernel_size) {
+        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}));
+
+        blocks["mid.block_1"] = get_resnet_block(block_in, block_in);
+        blocks["mid.attn_1"]  = std::shared_ptr<GGMLBlock>(new AttnBlock(block_in, use_linear_projection));
+        blocks["mid.block_2"] = get_resnet_block(block_in, block_in);
+
+        for (int i = num_resolutions - 1; i >= 0; i--) {
+            int mult      = ch_mult[i];
+            int block_out = ch * mult;
+            for (int j = 0; j < num_res_blocks + 1; j++) {
+                std::string name = "up." + std::to_string(i) + ".block." + std::to_string(j);
+                blocks[name]     = get_resnet_block(block_in, block_out);
+
+                block_in = block_out;
+            }
+            if (i != 0) {
+                std::string name = "up." + std::to_string(i) + ".upsample";
+                blocks[name]     = std::shared_ptr<GGMLBlock>(new UpSampleBlock(block_in, block_in));
+            }
+        }
+
+        blocks["norm_out"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(block_in));
+        blocks["conv_out"] = get_conv_out(block_in, out_ch, {3, 3}, {1, 1}, {1, 1});
+    }
+
+    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
+        // z: [N, z_channels, h, w]
+        // alpha is always 0
+        // merge_strategy is always learned
+        // time_mode is always conv-only, so we need to replace conv_out_op/resnet_op to AE3DConv/VideoResBlock
+        // AttnVideoBlock will not be used
+        auto conv_in     = std::dynamic_pointer_cast<Conv2d>(blocks["conv_in"]);
+        auto mid_block_1 = std::dynamic_pointer_cast<ResnetBlock>(blocks["mid.block_1"]);
+        auto mid_attn_1  = std::dynamic_pointer_cast<AttnBlock>(blocks["mid.attn_1"]);
+        auto mid_block_2 = std::dynamic_pointer_cast<ResnetBlock>(blocks["mid.block_2"]);
+        auto norm_out    = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm_out"]);
+        auto conv_out    = std::dynamic_pointer_cast<Conv2d>(blocks["conv_out"]);
+
+        // conv_in
+        auto h = conv_in->forward(ctx, z);  // [N, block_in, h, w]
+
+        // middle
+        h = mid_block_1->forward(ctx, h);
+        // return h;
+
+        h = mid_attn_1->forward(ctx, h);
+        h = mid_block_2->forward(ctx, h);  // [N, block_in, h, w]
+
+        // upsampling
+        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);
+                auto up_block    = std::dynamic_pointer_cast<ResnetBlock>(blocks[name]);
+
+                h = up_block->forward(ctx, h);
+            }
+            if (i != 0) {
+                std::string name = "up." + std::to_string(i) + ".upsample";
+                auto up_sample   = std::dynamic_pointer_cast<UpSampleBlock>(blocks[name]);
+
+                h = up_sample->forward(ctx, h);
+            }
+        }
+
+        h = norm_out->forward(ctx, h);
+        h = ggml_silu_inplace(ctx->ggml_ctx, h);  // nonlinearity/swish
+        h = conv_out->forward(ctx, h);            // [N, out_ch, h*8, w*8]
+        return h;
+    }
+};
+
+// ldm.models.autoencoder.AutoencoderKL
+class AutoEncoderKLModel : public GGMLBlock {
+protected:
+    SDVersion version;
+    bool decode_only       = true;
+    bool use_video_decoder = false;
+    bool use_quant         = true;
+    int embed_dim          = 4;
+    struct {
+        int z_channels           = 4;
+        int resolution           = 256;
+        int in_channels          = 3;
+        int out_ch               = 3;
+        int ch                   = 128;
+        std::vector<int> ch_mult = {1, 2, 4, 4};
+        int num_res_blocks       = 2;
+        bool double_z            = true;
+    } dd_config;
+
+public:
+    AutoEncoderKLModel(SDVersion version          = VERSION_SD1,
+                       bool decode_only           = true,
+                       bool use_linear_projection = false,
+                       bool use_video_decoder     = false)
+        : version(version), decode_only(decode_only), use_video_decoder(use_video_decoder) {
+        if (sd_version_is_dit(version)) {
+            if (sd_version_is_flux2(version)) {
+                dd_config.z_channels = 32;
+                embed_dim            = 32;
+            } else {
+                use_quant            = false;
+                dd_config.z_channels = 16;
+            }
+        }
+        if (use_video_decoder) {
+            use_quant = false;
+        }
+        blocks["decoder"] = std::shared_ptr<GGMLBlock>(new Decoder(dd_config.ch,
+                                                                   dd_config.out_ch,
+                                                                   dd_config.ch_mult,
+                                                                   dd_config.num_res_blocks,
+                                                                   dd_config.z_channels,
+                                                                   use_linear_projection,
+                                                                   use_video_decoder));
+        if (use_quant) {
+            blocks["post_quant_conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(dd_config.z_channels,
+                                                                              embed_dim,
+                                                                              {1, 1}));
+        }
+        if (!decode_only) {
+            blocks["encoder"] = std::shared_ptr<GGMLBlock>(new Encoder(dd_config.ch,
+                                                                       dd_config.ch_mult,
+                                                                       dd_config.num_res_blocks,
+                                                                       dd_config.in_channels,
+                                                                       dd_config.z_channels,
+                                                                       dd_config.double_z,
+                                                                       use_linear_projection));
+            if (use_quant) {
+                int factor = dd_config.double_z ? 2 : 1;
+
+                blocks["quant_conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(embed_dim * factor,
+                                                                             dd_config.z_channels * factor,
+                                                                             {1, 1}));
+            }
+        }
+    }
+
+    struct ggml_tensor* decode(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
+        // z: [N, z_channels, h, w]
+        if (sd_version_is_flux2(version)) {
+            // [N, C*p*p, h, w] -> [N, C, h*p, w*p]
+            int64_t p = 2;
+
+            int64_t N = z->ne[3];
+            int64_t C = z->ne[2] / p / p;
+            int64_t h = z->ne[1];
+            int64_t w = z->ne[0];
+            int64_t H = h * p;
+            int64_t W = w * p;
+
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, w * h, p * p, C, N);                           // [N, C, p*p, h*w]
+            z = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, z, 1, 0, 2, 3));  // [N, C, h*w, p*p]
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, p, p, w, h * C * N);                           // [N*C*h, w, p, p]
+            z = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, z, 0, 2, 1, 3));  // [N*C*h, p, w, p]
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, W, H, C, N);                                   // [N, C, h*p, w*p]
+        }
+
+        if (use_quant) {
+            auto post_quant_conv = std::dynamic_pointer_cast<Conv2d>(blocks["post_quant_conv"]);
+            z                    = post_quant_conv->forward(ctx, z);  // [N, z_channels, h, w]
+        }
+        auto decoder = std::dynamic_pointer_cast<Decoder>(blocks["decoder"]);
+
+        ggml_set_name(z, "bench-start");
+        auto h = decoder->forward(ctx, z);
+        ggml_set_name(h, "bench-end");
+        return h;
+    }
+
+    struct ggml_tensor* encode(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        // x: [N, in_channels, h, w]
+        auto encoder = std::dynamic_pointer_cast<Encoder>(blocks["encoder"]);
+
+        auto z = encoder->forward(ctx, x);  // [N, 2*z_channels, h/8, w/8]
+        if (use_quant) {
+            auto quant_conv = std::dynamic_pointer_cast<Conv2d>(blocks["quant_conv"]);
+            z               = quant_conv->forward(ctx, z);  // [N, 2*embed_dim, h/8, w/8]
+        }
+        if (sd_version_is_flux2(version)) {
+            z = ggml_ext_chunk(ctx->ggml_ctx, z, 2, 2)[0];
+
+            // [N, C, H, W] -> [N, C*p*p, H/p, W/p]
+            int64_t p = 2;
+            int64_t N = z->ne[3];
+            int64_t C = z->ne[2];
+            int64_t H = z->ne[1];
+            int64_t W = z->ne[0];
+            int64_t h = H / p;
+            int64_t w = W / p;
+
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, p, w, p, h * C * N);                 // [N*C*h, p, w, p]
+            z = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, z, 0, 2, 1, 3));  // [N*C*h, w, p, p]
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, p * p, w * h, C, N);                 // [N, C, h*w, p*p]
+            z = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, z, 1, 0, 2, 3));  // [N, C, p*p, h*w]
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, w, h, p * p * C, N);                 // [N, C*p*p, h*w]
+        }
+        return z;
+    }
+
+    int get_encoder_output_channels() {
+        int factor = dd_config.double_z ? 2 : 1;
+        return dd_config.z_channels * factor;
+    }
+};
+
+struct AutoEncoderKL : public VAE {
+    float scale_factor = 1.f;
+    float shift_factor = 0.f;
+    bool decode_only   = true;
+    AutoEncoderKLModel ae;
+
+    AutoEncoderKL(ggml_backend_t backend,
+                  bool offload_params_to_cpu,
+                  const String2TensorStorage& tensor_storage_map,
+                  const std::string prefix,
+                  bool decode_only       = false,
+                  bool use_video_decoder = false,
+                  SDVersion version      = VERSION_SD1)
+        : decode_only(decode_only), VAE(version, backend, offload_params_to_cpu) {
+        if (sd_version_is_sd1(version) || sd_version_is_sd2(version)) {
+            scale_factor = 0.18215f;
+            shift_factor = 0.f;
+        } else if (sd_version_is_sdxl(version)) {
+            scale_factor = 0.13025f;
+            shift_factor = 0.f;
+        } else if (sd_version_is_sd3(version)) {
+            scale_factor = 1.5305f;
+            shift_factor = 0.0609f;
+        } else if (sd_version_is_flux(version) || sd_version_is_z_image(version)) {
+            scale_factor = 0.3611f;
+            shift_factor = 0.1159f;
+        } else if (sd_version_is_flux2(version)) {
+            scale_factor = 1.0f;
+            shift_factor = 0.f;
+        }
+        bool use_linear_projection = false;
+        for (const auto& [name, tensor_storage] : tensor_storage_map) {
+            if (!starts_with(name, prefix)) {
+                continue;
+            }
+            if (ends_with(name, "attn_1.proj_out.weight")) {
+                if (tensor_storage.n_dims == 2) {
+                    use_linear_projection = true;
+                }
+                break;
+            }
+        }
+        ae = AutoEncoderKLModel(version, decode_only, use_linear_projection, use_video_decoder);
+        ae.init(params_ctx, tensor_storage_map, prefix);
+    }
+
+    void set_conv2d_scale(float scale) override {
+        std::vector<GGMLBlock*> blocks;
+        ae.get_all_blocks(blocks);
+        for (auto block : blocks) {
+            if (block->get_desc() == "Conv2d") {
+                auto conv_block = (Conv2d*)block;
+                conv_block->set_scale(scale);
+            }
+        }
+    }
+
+    std::string get_desc() override {
+        return "vae";
+    }
+
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) override {
+        ae.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);
+
+        auto runner_ctx = get_context();
+
+        struct ggml_tensor* out = decode_graph ? ae.decode(&runner_ctx, z) : ae.encode(&runner_ctx, z);
+
+        ggml_build_forward_expand(gf, out);
+
+        return gf;
+    }
+
+    bool _compute(const int n_threads,
+                  struct ggml_tensor* z,
+                  bool decode_graph,
+                  struct ggml_tensor** output,
+                  struct ggml_context* output_ctx = nullptr) override {
+        GGML_ASSERT(!decode_only || decode_graph);
+        auto get_graph = [&]() -> struct ggml_cgraph* {
+            return build_graph(z, decode_graph);
+        };
+        // ggml_set_f32(z, 0.5f);
+        // print_ggml_tensor(z);
+        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+    }
+
+    ggml_tensor* gaussian_latent_sample(ggml_context* work_ctx, ggml_tensor* moments, std::shared_ptr<RNG> rng) {
+        // ldm.modules.distributions.distributions.DiagonalGaussianDistribution.sample
+        ggml_tensor* latents      = ggml_new_tensor_4d(work_ctx, moments->type, moments->ne[0], moments->ne[1], moments->ne[2] / 2, moments->ne[3]);
+        struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, latents);
+        ggml_ext_im_set_randn_f32(noise, rng);
+        {
+            float mean   = 0;
+            float logvar = 0;
+            float value  = 0;
+            float std_   = 0;
+            for (int i = 0; i < latents->ne[3]; i++) {
+                for (int j = 0; j < latents->ne[2]; j++) {
+                    for (int k = 0; k < latents->ne[1]; k++) {
+                        for (int l = 0; l < latents->ne[0]; l++) {
+                            mean   = ggml_ext_tensor_get_f32(moments, l, k, j, i);
+                            logvar = ggml_ext_tensor_get_f32(moments, l, k, j + (int)latents->ne[2], i);
+                            logvar = std::max(-30.0f, std::min(logvar, 20.0f));
+                            std_   = std::exp(0.5f * logvar);
+                            value  = mean + std_ * ggml_ext_tensor_get_f32(noise, l, k, j, i);
+                            // printf("%d %d %d %d -> %f\n", i, j, k, l, value);
+                            ggml_ext_tensor_set_f32(latents, value, l, k, j, i);
+                        }
+                    }
+                }
+            }
+        }
+        return latents;
+    }
+
+    ggml_tensor* vae_output_to_latents(ggml_context* work_ctx, ggml_tensor* vae_output, std::shared_ptr<RNG> rng) {
+        if (sd_version_is_flux2(version)) {
+            return vae_output;
+        } else if (version == VERSION_SD1_PIX2PIX) {
+            return ggml_view_3d(work_ctx,
+                                vae_output,
+                                vae_output->ne[0],
+                                vae_output->ne[1],
+                                vae_output->ne[2] / 2,
+                                vae_output->nb[1],
+                                vae_output->nb[2],
+                                0);
+        } else {
+            return gaussian_latent_sample(work_ctx, vae_output, rng);
+        }
+    }
+
+    void get_latents_mean_std_vec(ggml_tensor* latents, int channel_dim, std::vector<float>& latents_mean_vec, std::vector<float>& latents_std_vec) {
+        // flux2
+        if (sd_version_is_flux2(version)) {
+            GGML_ASSERT(latents->ne[channel_dim] == 128);
+            latents_mean_vec = {-0.0676f, -0.0715f, -0.0753f, -0.0745f, 0.0223f, 0.0180f, 0.0142f, 0.0184f,
+                                -0.0001f, -0.0063f, -0.0002f, -0.0031f, -0.0272f, -0.0281f, -0.0276f, -0.0290f,
+                                -0.0769f, -0.0672f, -0.0902f, -0.0892f, 0.0168f, 0.0152f, 0.0079f, 0.0086f,
+                                0.0083f, 0.0015f, 0.0003f, -0.0043f, -0.0439f, -0.0419f, -0.0438f, -0.0431f,
+                                -0.0102f, -0.0132f, -0.0066f, -0.0048f, -0.0311f, -0.0306f, -0.0279f, -0.0180f,
+                                0.0030f, 0.0015f, 0.0126f, 0.0145f, 0.0347f, 0.0338f, 0.0337f, 0.0283f,
+                                0.0020f, 0.0047f, 0.0047f, 0.0050f, 0.0123f, 0.0081f, 0.0081f, 0.0146f,
+                                0.0681f, 0.0679f, 0.0767f, 0.0732f, -0.0462f, -0.0474f, -0.0392f, -0.0511f,
+                                -0.0528f, -0.0477f, -0.0470f, -0.0517f, -0.0317f, -0.0316f, -0.0345f, -0.0283f,
+                                0.0510f, 0.0445f, 0.0578f, 0.0458f, -0.0412f, -0.0458f, -0.0487f, -0.0467f,
+                                -0.0088f, -0.0106f, -0.0088f, -0.0046f, -0.0376f, -0.0432f, -0.0436f, -0.0499f,
+                                0.0118f, 0.0166f, 0.0203f, 0.0279f, 0.0113f, 0.0129f, 0.0016f, 0.0072f,
+                                -0.0118f, -0.0018f, -0.0141f, -0.0054f, -0.0091f, -0.0138f, -0.0145f, -0.0187f,
+                                0.0323f, 0.0305f, 0.0259f, 0.0300f, 0.0540f, 0.0614f, 0.0495f, 0.0590f,
+                                -0.0511f, -0.0603f, -0.0478f, -0.0524f, -0.0227f, -0.0274f, -0.0154f, -0.0255f,
+                                -0.0572f, -0.0565f, -0.0518f, -0.0496f, 0.0116f, 0.0054f, 0.0163f, 0.0104f};
+            latents_std_vec  = {
+                 1.8029f, 1.7786f, 1.7868f, 1.7837f, 1.7717f, 1.7590f, 1.7610f, 1.7479f,
+                 1.7336f, 1.7373f, 1.7340f, 1.7343f, 1.8626f, 1.8527f, 1.8629f, 1.8589f,
+                 1.7593f, 1.7526f, 1.7556f, 1.7583f, 1.7363f, 1.7400f, 1.7355f, 1.7394f,
+                 1.7342f, 1.7246f, 1.7392f, 1.7304f, 1.7551f, 1.7513f, 1.7559f, 1.7488f,
+                 1.8449f, 1.8454f, 1.8550f, 1.8535f, 1.8240f, 1.7813f, 1.7854f, 1.7945f,
+                 1.8047f, 1.7876f, 1.7695f, 1.7676f, 1.7782f, 1.7667f, 1.7925f, 1.7848f,
+                 1.7579f, 1.7407f, 1.7483f, 1.7368f, 1.7961f, 1.7998f, 1.7920f, 1.7925f,
+                 1.7780f, 1.7747f, 1.7727f, 1.7749f, 1.7526f, 1.7447f, 1.7657f, 1.7495f,
+                 1.7775f, 1.7720f, 1.7813f, 1.7813f, 1.8162f, 1.8013f, 1.8023f, 1.8033f,
+                 1.7527f, 1.7331f, 1.7563f, 1.7482f, 1.7610f, 1.7507f, 1.7681f, 1.7613f,
+                 1.7665f, 1.7545f, 1.7828f, 1.7726f, 1.7896f, 1.7999f, 1.7864f, 1.7760f,
+                 1.7613f, 1.7625f, 1.7560f, 1.7577f, 1.7783f, 1.7671f, 1.7810f, 1.7799f,
+                 1.7201f, 1.7068f, 1.7265f, 1.7091f, 1.7793f, 1.7578f, 1.7502f, 1.7455f,
+                 1.7587f, 1.7500f, 1.7525f, 1.7362f, 1.7616f, 1.7572f, 1.7444f, 1.7430f,
+                 1.7509f, 1.7610f, 1.7634f, 1.7612f, 1.7254f, 1.7135f, 1.7321f, 1.7226f,
+                 1.7664f, 1.7624f, 1.7718f, 1.7664f, 1.7457f, 1.7441f, 1.7569f, 1.7530f};
+        } else {
+            GGML_ABORT("unknown version %d", version);
+        }
+    }
+
+    ggml_tensor* diffusion_to_vae_latents(ggml_context* work_ctx, ggml_tensor* latents) {
+        ggml_tensor* vae_latents = ggml_dup(work_ctx, latents);
+        if (sd_version_is_flux2(version)) {
+            int channel_dim = 2;
+            std::vector<float> latents_mean_vec;
+            std::vector<float> latents_std_vec;
+            get_latents_mean_std_vec(latents, channel_dim, latents_mean_vec, latents_std_vec);
+
+            float mean;
+            float std_;
+            for (int i = 0; i < latents->ne[3]; i++) {
+                if (channel_dim == 3) {
+                    mean = latents_mean_vec[i];
+                    std_ = latents_std_vec[i];
+                }
+                for (int j = 0; j < latents->ne[2]; j++) {
+                    if (channel_dim == 2) {
+                        mean = latents_mean_vec[j];
+                        std_ = latents_std_vec[j];
+                    }
+                    for (int k = 0; k < latents->ne[1]; k++) {
+                        for (int l = 0; l < latents->ne[0]; l++) {
+                            float value = ggml_ext_tensor_get_f32(latents, l, k, j, i);
+                            value       = value * std_ / scale_factor + mean;
+                            ggml_ext_tensor_set_f32(vae_latents, value, l, k, j, i);
+                        }
+                    }
+                }
+            }
+        } else {
+            ggml_ext_tensor_iter(latents, [&](ggml_tensor* latents, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
+                float value = ggml_ext_tensor_get_f32(latents, i0, i1, i2, i3);
+                value       = (value / scale_factor) + shift_factor;
+                ggml_ext_tensor_set_f32(vae_latents, value, i0, i1, i2, i3);
+            });
+        }
+        return vae_latents;
+    }
+
+    ggml_tensor* vae_to_diffuison_latents(ggml_context* work_ctx, ggml_tensor* latents) {
+        ggml_tensor* diffusion_latents = ggml_dup(work_ctx, latents);
+        if (sd_version_is_flux2(version)) {
+            int channel_dim = 2;
+            std::vector<float> latents_mean_vec;
+            std::vector<float> latents_std_vec;
+            get_latents_mean_std_vec(latents, channel_dim, latents_mean_vec, latents_std_vec);
+
+            float mean;
+            float std_;
+            for (int i = 0; i < latents->ne[3]; i++) {
+                if (channel_dim == 3) {
+                    mean = latents_mean_vec[i];
+                    std_ = latents_std_vec[i];
+                }
+                for (int j = 0; j < latents->ne[2]; j++) {
+                    if (channel_dim == 2) {
+                        mean = latents_mean_vec[j];
+                        std_ = latents_std_vec[j];
+                    }
+                    for (int k = 0; k < latents->ne[1]; k++) {
+                        for (int l = 0; l < latents->ne[0]; l++) {
+                            float value = ggml_ext_tensor_get_f32(latents, l, k, j, i);
+                            value       = (value - mean) * scale_factor / std_;
+                            ggml_ext_tensor_set_f32(diffusion_latents, value, l, k, j, i);
+                        }
+                    }
+                }
+            }
+        } else {
+            ggml_ext_tensor_iter(latents, [&](ggml_tensor* latents, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
+                float value = ggml_ext_tensor_get_f32(latents, i0, i1, i2, i3);
+                value       = (value - shift_factor) * scale_factor;
+                ggml_ext_tensor_set_f32(diffusion_latents, value, i0, i1, i2, i3);
+            });
+        }
+        return diffusion_latents;
+    }
+
+    int get_encoder_output_channels(int input_channels) {
+        return ae.get_encoder_output_channels();
+    }
+
+    void test() {
+        struct ggml_init_params params;
+        params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
+        params.mem_buffer = nullptr;
+        params.no_alloc   = false;
+
+        struct ggml_context* work_ctx = ggml_init(params);
+        GGML_ASSERT(work_ctx != nullptr);
+
+        {
+            // CPU, x{1, 3, 64, 64}: Pass
+            // CUDA, x{1, 3, 64, 64}: Pass, but sill get wrong result for some image, may be due to interlnal nan
+            // CPU, x{2, 3, 64, 64}: Wrong result
+            // CUDA, x{2, 3, 64, 64}: Wrong result, and different from CPU result
+            auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 64, 64, 3, 2);
+            ggml_set_f32(x, 0.5f);
+            print_ggml_tensor(x);
+            struct ggml_tensor* out = nullptr;
+
+            int64_t t0 = ggml_time_ms();
+            _compute(8, x, false, &out, work_ctx);
+            int64_t t1 = ggml_time_ms();
+
+            print_ggml_tensor(out);
+            LOG_DEBUG("encode test done in %lldms", t1 - t0);
+        }
+
+        if (false) {
+            // CPU, z{1, 4, 8, 8}: Pass
+            // CUDA, z{1, 4, 8, 8}: Pass
+            // CPU, z{3, 4, 8, 8}: Wrong result
+            // CUDA, z{3, 4, 8, 8}: Wrong result, and different from CPU result
+            auto z = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 8, 8, 4, 1);
+            ggml_set_f32(z, 0.5f);
+            print_ggml_tensor(z);
+            struct ggml_tensor* out = nullptr;
+
+            int64_t t0 = ggml_time_ms();
+            _compute(8, z, true, &out, work_ctx);
+            int64_t t1 = ggml_time_ms();
+
+            print_ggml_tensor(out);
+            LOG_DEBUG("decode test done in %lldms", t1 - t0);
+        }
+    };
+};
+
+#endif  // __AUTO_ENCODER_KL_HPP__

src/cache_dit.hpp

@@ -603,87 +603,6 @@ inline std::vector<int> generate_scm_mask(
     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;

src/ggml_extend.hpp

@@ -377,6 +377,12 @@ __STATIC_INLINE__ void copy_ggml_tensor(struct ggml_tensor* dst, struct ggml_ten
     ggml_free(ctx);
 }
 
+__STATIC_INLINE__ ggml_tensor* ggml_ext_dup_and_cpy_tensor(ggml_context* ctx, ggml_tensor* src) {
+    ggml_tensor* dup = ggml_dup_tensor(ctx, src);
+    copy_ggml_tensor(dup, src);
+    return dup;
+}
+
 __STATIC_INLINE__ float sigmoid(float x) {
     return 1 / (1.0f + expf(-x));
 }
@@ -637,7 +643,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_tensor_concat(struct ggml_context
 }
 
 // convert values from [0, 1] to [-1, 1]
-__STATIC_INLINE__ void process_vae_input_tensor(struct ggml_tensor* src) {
+__STATIC_INLINE__ void scale_to_minus1_1(struct ggml_tensor* src) {
     int64_t nelements = ggml_nelements(src);
     float* data       = (float*)src->data;
     for (int i = 0; i < nelements; i++) {
@@ -647,7 +653,7 @@ __STATIC_INLINE__ void process_vae_input_tensor(struct ggml_tensor* src) {
 }
 
 // convert values from [-1, 1] to [0, 1]
-__STATIC_INLINE__ void process_vae_output_tensor(struct ggml_tensor* src) {
+__STATIC_INLINE__ void scale_to_0_1(struct ggml_tensor* src) {
     int64_t nelements = ggml_nelements(src);
     float* data       = (float*)src->data;
     for (int i = 0; i < nelements; i++) {
@@ -834,7 +840,8 @@ __STATIC_INLINE__ void sd_tiling_non_square(ggml_tensor* input,
                                             const float tile_overlap_factor,
                                             const bool circular_x,
                                             const bool circular_y,
-                                            on_tile_process on_processing) {
+                                            on_tile_process on_processing,
+                                            bool slient = false) {
     output = ggml_set_f32(output, 0);
 
     int input_width   = (int)input->ne[0];
@@ -864,8 +871,10 @@ __STATIC_INLINE__ void sd_tiling_non_square(ggml_tensor* input,
     float tile_overlap_factor_y;
     sd_tiling_calc_tiles(num_tiles_y, tile_overlap_factor_y, small_height, p_tile_size_y, tile_overlap_factor, circular_y);
 
-    LOG_DEBUG("num tiles : %d, %d ", num_tiles_x, num_tiles_y);
-    LOG_DEBUG("optimal overlap : %f, %f (targeting %f)", tile_overlap_factor_x, tile_overlap_factor_y, tile_overlap_factor);
+    if (!slient) {
+        LOG_DEBUG("num tiles : %d, %d ", num_tiles_x, num_tiles_y);
+        LOG_DEBUG("optimal overlap : %f, %f (targeting %f)", tile_overlap_factor_x, tile_overlap_factor_y, tile_overlap_factor);
+    }
 
     int tile_overlap_x     = (int32_t)(p_tile_size_x * tile_overlap_factor_x);
     int non_tile_overlap_x = p_tile_size_x - tile_overlap_x;
@@ -896,7 +905,9 @@ __STATIC_INLINE__ void sd_tiling_non_square(ggml_tensor* input,
     params.mem_buffer = nullptr;
     params.no_alloc   = false;
 
-    LOG_DEBUG("tile work buffer size: %.2f MB", params.mem_size / 1024.f / 1024.f);
+    if (!slient) {
+        LOG_DEBUG("tile work buffer size: %.2f MB", params.mem_size / 1024.f / 1024.f);
+    }
 
     // draft context
     struct ggml_context* tiles_ctx = ggml_init(params);
@@ -909,8 +920,10 @@ __STATIC_INLINE__ void sd_tiling_non_square(ggml_tensor* input,
     ggml_tensor* input_tile  = ggml_new_tensor_4d(tiles_ctx, GGML_TYPE_F32, input_tile_size_x, input_tile_size_y, input->ne[2], input->ne[3]);
     ggml_tensor* output_tile = ggml_new_tensor_4d(tiles_ctx, GGML_TYPE_F32, output_tile_size_x, output_tile_size_y, output->ne[2], output->ne[3]);
     int num_tiles            = num_tiles_x * num_tiles_y;
-    LOG_DEBUG("processing %i tiles", num_tiles);
-    pretty_progress(0, num_tiles, 0.0f);
+    if (!slient) {
+        LOG_DEBUG("processing %i tiles", num_tiles);
+        pretty_progress(0, num_tiles, 0.0f);
+    }
     int tile_count = 1;
     bool last_y = false, last_x = false;
     float last_time = 0.0f;
@@ -960,8 +973,10 @@ __STATIC_INLINE__ void sd_tiling_non_square(ggml_tensor* input,
         }
         last_x = false;
     }
-    if (tile_count < num_tiles) {
-        pretty_progress(num_tiles, num_tiles, last_time);
+    if (!slient) {
+        if (tile_count < num_tiles) {
+            pretty_progress(num_tiles, num_tiles, last_time);
+        }
     }
     ggml_free(tiles_ctx);
 }

src/model.cpp

@@ -1104,10 +1104,12 @@ 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) {
+        if (tensor_storage.name.find("model.diffusion_model.output_blocks.3.1.transformer_blocks.1") != std::string::npos ||
+            tensor_storage.name.find("unet.up_blocks.1.attentions.0.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) {
+        if (tensor_storage.name.find("model.diffusion_model.output_blocks.7.1") != std::string::npos ||
+            tensor_storage.name.find("unet.up_blocks.2.attentions.1") != std::string::npos) {
             has_output_block_71 = true;
         }
         if (tensor_storage.name == "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight" ||

src/name_conversion.cpp

@@ -1120,7 +1120,11 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
         for (const auto& prefix : first_stage_model_prefix_vec) {
             if (starts_with(name, prefix)) {
                 name = convert_first_stage_model_name(name.substr(prefix.size()), prefix);
-                name = prefix + name;
+                if (version == VERSION_SDXS) {
+                    name = "tae." + name;
+                } else {
+                    name = prefix + name;
+                }
                 break;
             }
         }

src/stable-diffusion.cpp

@@ -7,6 +7,7 @@
 #include "stable-diffusion.h"
 #include "util.h"
 
+#include "auto_encoder_kl.hpp"
 #include "cache_dit.hpp"
 #include "conditioner.hpp"
 #include "control.hpp"
@@ -90,12 +91,17 @@ void calculate_alphas_cumprod(float* alphas_cumprod,
     }
 }
 
-void suppress_pp(int step, int steps, float time, void* data) {
-    (void)step;
-    (void)steps;
-    (void)time;
-    (void)data;
-    return;
+static float get_cache_reuse_threshold(const sd_cache_params_t& params) {
+    float reuse_threshold = params.reuse_threshold;
+    if (reuse_threshold == INFINITY) {
+        if (params.mode == SD_CACHE_EASYCACHE) {
+            reuse_threshold = 0.2;
+        }
+        else if (params.mode == SD_CACHE_UCACHE) {
+            reuse_threshold = 1.0;
+        }
+    }
+    return std::max(0.0f, reuse_threshold);
 }
 
 /*=============================================== StableDiffusionGGML ================================================*/
@@ -118,8 +124,6 @@ public:
     std::shared_ptr<RNG> rng         = std::make_shared<PhiloxRNG>();
     std::shared_ptr<RNG> sampler_rng = nullptr;
     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;
@@ -127,7 +131,7 @@ public:
     std::shared_ptr<DiffusionModel> diffusion_model;
     std::shared_ptr<DiffusionModel> high_noise_diffusion_model;
     std::shared_ptr<VAE> first_stage_model;
-    std::shared_ptr<TinyAutoEncoder> tae_first_stage;
+    std::shared_ptr<VAE> preview_vae;
     std::shared_ptr<ControlNet> control_net;
     std::shared_ptr<PhotoMakerIDEncoder> pmid_model;
     std::shared_ptr<LoraModel> pmid_lora;
@@ -138,7 +142,6 @@ public:
     bool apply_lora_immediately = false;
 
     std::string taesd_path;
-    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 use_pmid                        = false;
@@ -239,10 +242,10 @@ public:
         n_threads               = sd_ctx_params->n_threads;
         vae_decode_only         = sd_ctx_params->vae_decode_only;
         free_params_immediately = sd_ctx_params->free_params_immediately;
-        taesd_path              = SAFE_STR(sd_ctx_params->taesd_path);
-        use_tiny_autoencoder    = taesd_path.size() > 0;
         offload_params_to_cpu   = sd_ctx_params->offload_params_to_cpu;
 
+        bool use_tae = false;
+
         rng = get_rng(sd_ctx_params->rng_type);
         if (sd_ctx_params->sampler_rng_type != RNG_TYPE_COUNT && sd_ctx_params->sampler_rng_type != sd_ctx_params->rng_type) {
             sampler_rng = get_rng(sd_ctx_params->sampler_rng_type);
@@ -332,6 +335,14 @@ public:
             }
         }
 
+        if (strlen(SAFE_STR(sd_ctx_params->taesd_path)) > 0) {
+            LOG_INFO("loading tae from '%s'", sd_ctx_params->taesd_path);
+            if (!model_loader.init_from_file(sd_ctx_params->taesd_path, "tae.")) {
+                LOG_WARN("loading tae from '%s' failed", sd_ctx_params->taesd_path);
+            }
+            use_tae = true;
+        }
+
         model_loader.convert_tensors_name();
 
         version = model_loader.get_sd_version();
@@ -400,22 +411,6 @@ public:
             apply_lora_immediately = false;
         }
 
-        if (sd_version_is_sdxl(version)) {
-            scale_factor = 0.13025f;
-        } else if (sd_version_is_sd3(version)) {
-            scale_factor = 1.5305f;
-            shift_factor = 0.0609f;
-        } else if (sd_version_is_flux(version) || sd_version_is_z_image(version)) {
-            scale_factor = 0.3611f;
-            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;
-        }
-
         if (sd_version_is_control(version)) {
             // Might need vae encode for control cond
             vae_decode_only = false;
@@ -424,6 +419,7 @@ public:
         bool tae_preview_only = sd_ctx_params->tae_preview_only;
         if (version == VERSION_SDXS) {
             tae_preview_only = false;
+            use_tae          = true;
         }
 
         if (sd_ctx_params->circular_x || sd_ctx_params->circular_y) {
@@ -610,31 +606,46 @@ public:
                 vae_backend = backend;
             }
 
-            if (!(use_tiny_autoencoder || version == VERSION_SDXS) || tae_preview_only) {
-                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,
-                                                                            "first_stage_model",
-                                                                            vae_decode_only,
-                                                                            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);
+            auto create_tae = [&]() -> std::shared_ptr<VAE> {
+                if (sd_version_is_wan(version) ||
+                    sd_version_is_qwen_image(version) ||
+                    sd_version_is_anima(version)) {
+                    return std::make_shared<TinyVideoAutoEncoder>(vae_backend,
+                                                                  offload_params_to_cpu,
+                                                                  tensor_storage_map,
+                                                                  "decoder",
+                                                                  vae_decode_only,
+                                                                  version);
+
                 } else {
-                    first_stage_model = std::make_shared<AutoEncoderKL>(vae_backend,
+                    auto model = std::make_shared<TinyImageAutoEncoder>(vae_backend,
                                                                         offload_params_to_cpu,
                                                                         tensor_storage_map,
-                                                                        "first_stage_model",
+                                                                        "decoder.layers",
                                                                         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);
-                    }
+                    return model;
+                }
+            };
+
+            auto create_vae = [&]() -> std::shared_ptr<VAE> {
+                if (sd_version_is_wan(version) ||
+                    sd_version_is_qwen_image(version) ||
+                    sd_version_is_anima(version)) {
+                    return std::make_shared<WAN::WanVAERunner>(vae_backend,
+                                                               offload_params_to_cpu,
+                                                               tensor_storage_map,
+                                                               "first_stage_model",
+                                                               vae_decode_only,
+                                                               version);
+                } else {
+                    auto model = std::make_shared<AutoEncoderKL>(vae_backend,
+                                                                 offload_params_to_cpu,
+                                                                 tensor_storage_map,
+                                                                 "first_stage_model",
+                                                                 vae_decode_only,
+                                                                 false,
+                                                                 version);
                     if (sd_version_is_sdxl(version) &&
                         (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;
@@ -642,35 +653,40 @@ public:
                             "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);
+                        model->set_conv2d_scale(vae_conv_2d_scale);
                     }
-                    first_stage_model->alloc_params_buffer();
-                    first_stage_model->get_param_tensors(tensors, "first_stage_model");
+                    return model;
+                }
+            };
+
+            if (version == VERSION_CHROMA_RADIANCE) {
+                LOG_INFO("using FakeVAE");
+                first_stage_model = std::make_shared<FakeVAE>(version,
+                                                              vae_backend,
+                                                              offload_params_to_cpu);
+            } else if (use_tae && !tae_preview_only) {
+                LOG_INFO("using TAE for encoding / decoding");
+                first_stage_model = create_tae();
+                first_stage_model->alloc_params_buffer();
+                first_stage_model->get_param_tensors(tensors, "tae");
+            } else {
+                LOG_INFO("using VAE for encoding / decoding");
+                first_stage_model = create_vae();
+                first_stage_model->alloc_params_buffer();
+                first_stage_model->get_param_tensors(tensors, "first_stage_model");
+                if (use_tae && tae_preview_only) {
+                    LOG_INFO("using TAE for preview");
+                    preview_vae = create_tae();
+                    preview_vae->alloc_params_buffer();
+                    preview_vae->get_param_tensors(tensors, "tae");
                 }
             }
-            if (use_tiny_autoencoder || version == VERSION_SDXS) {
-                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,
-                                                                             "decoder",
-                                                                             vae_decode_only,
-                                                                             version);
-                } else {
-                    tae_first_stage = std::make_shared<TinyImageAutoEncoder>(vae_backend,
-                                                                             offload_params_to_cpu,
-                                                                             tensor_storage_map,
-                                                                             "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");
-                    tae_first_stage->set_conv2d_direct_enabled(true);
+
+            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 (preview_vae) {
+                    preview_vae->set_conv2d_direct_enabled(true);
                 }
             }
 
@@ -743,8 +759,8 @@ public:
                 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 (preview_vae) {
+                    preview_vae->set_flash_attention_enabled(true);
                 }
             }
 
@@ -782,7 +798,7 @@ public:
 
         std::set<std::string> ignore_tensors;
         tensors["alphas_cumprod"] = alphas_cumprod_tensor;
-        if (use_tiny_autoencoder) {
+        if (use_tae && !tae_preview_only) {
             ignore_tensors.insert("first_stage_model.");
         }
         if (use_pmid) {
@@ -796,6 +812,7 @@ public:
             ignore_tensors.insert("first_stage_model.encoder");
             ignore_tensors.insert("first_stage_model.conv1");
             ignore_tensors.insert("first_stage_model.quant");
+            ignore_tensors.insert("tae.encoder");
             ignore_tensors.insert("text_encoders.llm.visual.");
         }
         if (version == VERSION_OVIS_IMAGE) {
@@ -822,15 +839,9 @@ public:
                 unet_params_mem_size += high_noise_diffusion_model->get_params_buffer_size();
             }
             size_t vae_params_mem_size = 0;
-            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 || version == VERSION_SDXS) {
-                if (use_tiny_autoencoder && !tae_first_stage->load_from_file(taesd_path, n_threads)) {
-                    return false;
-                }
-                use_tiny_autoencoder = true;  // now the processing is identical for VERSION_SDXS
-                vae_params_mem_size  = tae_first_stage->get_params_buffer_size();
+            vae_params_mem_size        = first_stage_model->get_params_buffer_size();
+            if (preview_vae) {
+                vae_params_mem_size += preview_vae->get_params_buffer_size();
             }
             size_t control_net_params_mem_size = 0;
             if (control_net) {
@@ -983,7 +994,6 @@ public:
         }
 
         ggml_free(ctx);
-        use_tiny_autoencoder = use_tiny_autoencoder && !tae_preview_only;
         return true;
     }
 
@@ -1422,8 +1432,7 @@ public:
                     ggml_ext_tensor_scale_inplace(noise, augmentation_level);
                     ggml_ext_tensor_add_inplace(init_img, noise);
                 }
-                ggml_tensor* moments = vae_encode(work_ctx, init_img);
-                c_concat             = get_first_stage_encoding(work_ctx, moments);
+                c_concat = encode_first_stage(work_ctx, init_img);
             }
         }
 
@@ -1475,14 +1484,6 @@ public:
         }
     }
 
-    void silent_tiling(ggml_tensor* input, ggml_tensor* output, const int scale, const int tile_size, const float tile_overlap_factor, on_tile_process on_processing) {
-        sd_progress_cb_t cb = sd_get_progress_callback();
-        void* cbd           = sd_get_progress_callback_data();
-        sd_set_progress_callback((sd_progress_cb_t)suppress_pp, nullptr);
-        sd_tiling(input, output, scale, tile_size, tile_overlap_factor, circular_x, circular_y, on_processing);
-        sd_set_progress_callback(cb, cbd);
-    }
-
     void preview_image(ggml_context* work_ctx,
                        int step,
                        struct ggml_tensor* latents,
@@ -1575,37 +1576,14 @@ public:
             free(data);
             free(images);
         } else {
-            if (preview_mode == PREVIEW_VAE) {
-                process_latent_out(latents);
-                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) {
-                        return first_stage_model->compute(n_threads, in, true, &out, nullptr);
-                    };
-                    silent_tiling(latents, result, get_vae_scale_factor(), 32, 0.5f, on_tiling);
-
+            if (preview_mode == PREVIEW_VAE || preview_mode == PREVIEW_TAE) {
+                if (preview_vae) {
+                    latents = preview_vae->diffusion_to_vae_latents(work_ctx, latents);
+                    result  = preview_vae->decode(n_threads, work_ctx, latents, vae_tiling_params, false, circular_x, circular_y, result, true);
                 } else {
-                    first_stage_model->compute(n_threads, latents, true, &result, work_ctx);
+                    latents = first_stage_model->diffusion_to_vae_latents(work_ctx, latents);
+                    result  = first_stage_model->decode(n_threads, work_ctx, latents, vae_tiling_params, false, circular_x, circular_y, result, true);
                 }
-
-                first_stage_model->free_compute_buffer();
-                process_vae_output_tensor(result);
-                process_latent_in(latents);
-            } else if (preview_mode == PREVIEW_TAE) {
-                if (tae_first_stage == nullptr) {
-                    LOG_WARN("TAE not found for preview");
-                    return;
-                }
-                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) {
-                        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 {
-                    tae_first_stage->compute(n_threads, latents, true, &result, work_ctx);
-                }
-                tae_first_stage->free_compute_buffer();
             } else {
                 return;
             }
@@ -1715,7 +1693,7 @@ public:
                 } else {
                     EasyCacheConfig easycache_config;
                     easycache_config.enabled         = true;
-                    easycache_config.reuse_threshold = std::max(0.0f, cache_params->reuse_threshold);
+                    easycache_config.reuse_threshold = get_cache_reuse_threshold(*cache_params);
                     easycache_config.start_percent   = cache_params->start_percent;
                     easycache_config.end_percent     = cache_params->end_percent;
                     easycache_state.init(easycache_config, denoiser.get());
@@ -1736,7 +1714,7 @@ public:
                 } else {
                     UCacheConfig ucache_config;
                     ucache_config.enabled                = true;
-                    ucache_config.reuse_threshold        = std::max(0.0f, cache_params->reuse_threshold);
+                    ucache_config.reuse_threshold        = get_cache_reuse_threshold(*cache_params);
                     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));
@@ -1797,9 +1775,9 @@ public:
                     }
                 }
             } else if (cache_params->mode == SD_CACHE_SPECTRUM) {
-                bool spectrum_supported = sd_version_is_unet(version);
+                bool spectrum_supported = sd_version_is_unet(version) || sd_version_is_dit(version);
                 if (!spectrum_supported) {
-                    LOG_WARN("Spectrum requested but not supported for this model type (only UNET models)");
+                    LOG_WARN("Spectrum requested but not supported for this model type (only UNET and DiT models)");
                 } else {
                     SpectrumConfig spectrum_config;
                     spectrum_config.w            = cache_params->spectrum_w;
@@ -1829,8 +1807,7 @@ public:
         }
 
         size_t steps          = sigmas.size() - 1;
-        struct ggml_tensor* x = ggml_dup_tensor(work_ctx, init_latent);
-        copy_ggml_tensor(x, init_latent);
+        struct ggml_tensor* x = ggml_ext_dup_and_cpy_tensor(work_ctx, init_latent);
 
         if (noise) {
             x = denoiser->noise_scaling(sigmas[0], noise, x);
@@ -2351,15 +2328,7 @@ public:
     }
 
     int get_vae_scale_factor() {
-        int vae_scale_factor = 8;
-        if (version == VERSION_WAN2_2_TI2V) {
-            vae_scale_factor = 16;
-        } else if (sd_version_is_flux2(version)) {
-            vae_scale_factor = 16;
-        } else if (version == VERSION_CHROMA_RADIANCE) {
-            vae_scale_factor = 1;
-        }
-        return vae_scale_factor;
+        return first_stage_model->get_scale_factor();
     }
 
     int get_diffusion_model_down_factor() {
@@ -2414,383 +2383,28 @@ public:
         } else {
             init_latent = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, C, 1);
         }
-        ggml_set_f32(init_latent, shift_factor);
+        ggml_set_f32(init_latent, 0.f);
         return init_latent;
     }
 
-    void get_latents_mean_std_vec(ggml_tensor* latent, int channel_dim, std::vector<float>& latents_mean_vec, std::vector<float>& latents_std_vec) {
-        GGML_ASSERT(latent->ne[channel_dim] == 16 || latent->ne[channel_dim] == 48 || latent->ne[channel_dim] == 128);
-        if (latent->ne[channel_dim] == 16) {
-            latents_mean_vec = {-0.7571f, -0.7089f, -0.9113f, 0.1075f, -0.1745f, 0.9653f, -0.1517f, 1.5508f,
-                                0.4134f, -0.0715f, 0.5517f, -0.3632f, -0.1922f, -0.9497f, 0.2503f, -0.2921f};
-            latents_std_vec  = {2.8184f, 1.4541f, 2.3275f, 2.6558f, 1.2196f, 1.7708f, 2.6052f, 2.0743f,
-                                3.2687f, 2.1526f, 2.8652f, 1.5579f, 1.6382f, 1.1253f, 2.8251f, 1.9160f};
-        } else if (latent->ne[channel_dim] == 48) {
-            latents_mean_vec = {-0.2289f, -0.0052f, -0.1323f, -0.2339f, -0.2799f, 0.0174f, 0.1838f, 0.1557f,
-                                -0.1382f, 0.0542f, 0.2813f, 0.0891f, 0.1570f, -0.0098f, 0.0375f, -0.1825f,
-                                -0.2246f, -0.1207f, -0.0698f, 0.5109f, 0.2665f, -0.2108f, -0.2158f, 0.2502f,
-                                -0.2055f, -0.0322f, 0.1109f, 0.1567f, -0.0729f, 0.0899f, -0.2799f, -0.1230f,
-                                -0.0313f, -0.1649f, 0.0117f, 0.0723f, -0.2839f, -0.2083f, -0.0520f, 0.3748f,
-                                0.0152f, 0.1957f, 0.1433f, -0.2944f, 0.3573f, -0.0548f, -0.1681f, -0.0667f};
-            latents_std_vec  = {
-                 0.4765f, 1.0364f, 0.4514f, 1.1677f, 0.5313f, 0.4990f, 0.4818f, 0.5013f,
-                 0.8158f, 1.0344f, 0.5894f, 1.0901f, 0.6885f, 0.6165f, 0.8454f, 0.4978f,
-                 0.5759f, 0.3523f, 0.7135f, 0.6804f, 0.5833f, 1.4146f, 0.8986f, 0.5659f,
-                 0.7069f, 0.5338f, 0.4889f, 0.4917f, 0.4069f, 0.4999f, 0.6866f, 0.4093f,
-                 0.5709f, 0.6065f, 0.6415f, 0.4944f, 0.5726f, 1.2042f, 0.5458f, 1.6887f,
-                 0.3971f, 1.0600f, 0.3943f, 0.5537f, 0.5444f, 0.4089f, 0.7468f, 0.7744f};
-        } else if (latent->ne[channel_dim] == 128) {
-            // flux2
-            latents_mean_vec = {-0.0676f, -0.0715f, -0.0753f, -0.0745f, 0.0223f, 0.0180f, 0.0142f, 0.0184f,
-                                -0.0001f, -0.0063f, -0.0002f, -0.0031f, -0.0272f, -0.0281f, -0.0276f, -0.0290f,
-                                -0.0769f, -0.0672f, -0.0902f, -0.0892f, 0.0168f, 0.0152f, 0.0079f, 0.0086f,
-                                0.0083f, 0.0015f, 0.0003f, -0.0043f, -0.0439f, -0.0419f, -0.0438f, -0.0431f,
-                                -0.0102f, -0.0132f, -0.0066f, -0.0048f, -0.0311f, -0.0306f, -0.0279f, -0.0180f,
-                                0.0030f, 0.0015f, 0.0126f, 0.0145f, 0.0347f, 0.0338f, 0.0337f, 0.0283f,
-                                0.0020f, 0.0047f, 0.0047f, 0.0050f, 0.0123f, 0.0081f, 0.0081f, 0.0146f,
-                                0.0681f, 0.0679f, 0.0767f, 0.0732f, -0.0462f, -0.0474f, -0.0392f, -0.0511f,
-                                -0.0528f, -0.0477f, -0.0470f, -0.0517f, -0.0317f, -0.0316f, -0.0345f, -0.0283f,
-                                0.0510f, 0.0445f, 0.0578f, 0.0458f, -0.0412f, -0.0458f, -0.0487f, -0.0467f,
-                                -0.0088f, -0.0106f, -0.0088f, -0.0046f, -0.0376f, -0.0432f, -0.0436f, -0.0499f,
-                                0.0118f, 0.0166f, 0.0203f, 0.0279f, 0.0113f, 0.0129f, 0.0016f, 0.0072f,
-                                -0.0118f, -0.0018f, -0.0141f, -0.0054f, -0.0091f, -0.0138f, -0.0145f, -0.0187f,
-                                0.0323f, 0.0305f, 0.0259f, 0.0300f, 0.0540f, 0.0614f, 0.0495f, 0.0590f,
-                                -0.0511f, -0.0603f, -0.0478f, -0.0524f, -0.0227f, -0.0274f, -0.0154f, -0.0255f,
-                                -0.0572f, -0.0565f, -0.0518f, -0.0496f, 0.0116f, 0.0054f, 0.0163f, 0.0104f};
-            latents_std_vec  = {
-                 1.8029f, 1.7786f, 1.7868f, 1.7837f, 1.7717f, 1.7590f, 1.7610f, 1.7479f,
-                 1.7336f, 1.7373f, 1.7340f, 1.7343f, 1.8626f, 1.8527f, 1.8629f, 1.8589f,
-                 1.7593f, 1.7526f, 1.7556f, 1.7583f, 1.7363f, 1.7400f, 1.7355f, 1.7394f,
-                 1.7342f, 1.7246f, 1.7392f, 1.7304f, 1.7551f, 1.7513f, 1.7559f, 1.7488f,
-                 1.8449f, 1.8454f, 1.8550f, 1.8535f, 1.8240f, 1.7813f, 1.7854f, 1.7945f,
-                 1.8047f, 1.7876f, 1.7695f, 1.7676f, 1.7782f, 1.7667f, 1.7925f, 1.7848f,
-                 1.7579f, 1.7407f, 1.7483f, 1.7368f, 1.7961f, 1.7998f, 1.7920f, 1.7925f,
-                 1.7780f, 1.7747f, 1.7727f, 1.7749f, 1.7526f, 1.7447f, 1.7657f, 1.7495f,
-                 1.7775f, 1.7720f, 1.7813f, 1.7813f, 1.8162f, 1.8013f, 1.8023f, 1.8033f,
-                 1.7527f, 1.7331f, 1.7563f, 1.7482f, 1.7610f, 1.7507f, 1.7681f, 1.7613f,
-                 1.7665f, 1.7545f, 1.7828f, 1.7726f, 1.7896f, 1.7999f, 1.7864f, 1.7760f,
-                 1.7613f, 1.7625f, 1.7560f, 1.7577f, 1.7783f, 1.7671f, 1.7810f, 1.7799f,
-                 1.7201f, 1.7068f, 1.7265f, 1.7091f, 1.7793f, 1.7578f, 1.7502f, 1.7455f,
-                 1.7587f, 1.7500f, 1.7525f, 1.7362f, 1.7616f, 1.7572f, 1.7444f, 1.7430f,
-                 1.7509f, 1.7610f, 1.7634f, 1.7612f, 1.7254f, 1.7135f, 1.7321f, 1.7226f,
-                 1.7664f, 1.7624f, 1.7718f, 1.7664f, 1.7457f, 1.7441f, 1.7569f, 1.7530f};
-        }
-    }
-
-    void process_latent_in(ggml_tensor* latent) {
-        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;
-            get_latents_mean_std_vec(latent, channel_dim, latents_mean_vec, latents_std_vec);
-
-            float mean;
-            float std_;
-            for (int i = 0; i < latent->ne[3]; i++) {
-                if (channel_dim == 3) {
-                    mean = latents_mean_vec[i];
-                    std_ = latents_std_vec[i];
-                }
-                for (int j = 0; j < latent->ne[2]; j++) {
-                    if (channel_dim == 2) {
-                        mean = latents_mean_vec[i];
-                        std_ = latents_std_vec[i];
-                    }
-                    for (int k = 0; k < latent->ne[1]; k++) {
-                        for (int l = 0; l < latent->ne[0]; l++) {
-                            float value = ggml_ext_tensor_get_f32(latent, l, k, j, i);
-                            value       = (value - mean) * scale_factor / std_;
-                            ggml_ext_tensor_set_f32(latent, value, l, k, j, i);
-                        }
-                    }
-                }
-            }
-        } else if (version == VERSION_CHROMA_RADIANCE) {
-            // pass
-        } else {
-            ggml_ext_tensor_iter(latent, [&](ggml_tensor* latent, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
-                float value = ggml_ext_tensor_get_f32(latent, i0, i1, i2, i3);
-                value       = (value - shift_factor) * scale_factor;
-                ggml_ext_tensor_set_f32(latent, value, i0, i1, i2, i3);
-            });
-        }
-    }
-
-    void process_latent_out(ggml_tensor* latent) {
-        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;
-            get_latents_mean_std_vec(latent, channel_dim, latents_mean_vec, latents_std_vec);
-
-            float mean;
-            float std_;
-            for (int i = 0; i < latent->ne[3]; i++) {
-                if (channel_dim == 3) {
-                    mean = latents_mean_vec[i];
-                    std_ = latents_std_vec[i];
-                }
-                for (int j = 0; j < latent->ne[2]; j++) {
-                    if (channel_dim == 2) {
-                        mean = latents_mean_vec[i];
-                        std_ = latents_std_vec[i];
-                    }
-                    for (int k = 0; k < latent->ne[1]; k++) {
-                        for (int l = 0; l < latent->ne[0]; l++) {
-                            float value = ggml_ext_tensor_get_f32(latent, l, k, j, i);
-                            value       = value * std_ / scale_factor + mean;
-                            ggml_ext_tensor_set_f32(latent, value, l, k, j, i);
-                        }
-                    }
-                }
-            }
-        } else if (version == VERSION_CHROMA_RADIANCE) {
-            // pass
-        } else {
-            ggml_ext_tensor_iter(latent, [&](ggml_tensor* latent, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
-                float value = ggml_ext_tensor_get_f32(latent, i0, i1, i2, i3);
-                value       = (value / scale_factor) + shift_factor;
-                ggml_ext_tensor_set_f32(latent, value, i0, i1, i2, i3);
-            });
-        }
-    }
-
-    void get_tile_sizes(int& tile_size_x,
-                        int& tile_size_y,
-                        float& tile_overlap,
-                        const sd_tiling_params_t& params,
-                        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, int64_t latent_size) {
-            const int default_tile_size  = 32;
-            const int min_tile_dimension = 4;
-            int tile_size                = default_tile_size;
-            // factor <= 1 means simple fraction of the latent dimension
-            // factor > 1 means number of tiles across that dimension
-            if (factor > 0.f) {
-                if (factor > 1.0)
-                    factor = 1 / (factor - factor * tile_overlap + tile_overlap);
-                tile_size = static_cast<int>(std::round(latent_size * factor));
-            } else if (requested_size >= min_tile_dimension) {
-                tile_size = requested_size;
-            }
-            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);
-        tile_size_y = get_tile_size(params.tile_size_y, params.rel_size_y, latent_y);
-    }
-
-    ggml_tensor* vae_encode(ggml_context* work_ctx, ggml_tensor* x) {
-        int64_t t0                 = ggml_time_ms();
-        ggml_tensor* result        = nullptr;
-        const int vae_scale_factor = get_vae_scale_factor();
-        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) {
-            // TODO wan2.2 vae support?
-            int64_t ne2;
-            int64_t ne3;
-            if (sd_version_is_qwen_image(version) || sd_version_is_anima(version)) {
-                ne2 = 1;
-                ne3 = C * x->ne[3];
-            } else {
-                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 = out_channels;
-                ne3 = x->ne[3];
-            }
-            result = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, ne2, ne3);
-        }
-
-        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]);
-        }
-
-        if (!use_tiny_autoencoder) {
-            process_vae_input_tensor(x);
-            if (vae_tiling_params.enabled) {
-                float tile_overlap;
-                int tile_size_x, tile_size_y;
-                // multiply tile size for encode to keep the compute buffer size consistent
-                get_tile_sizes(tile_size_x, tile_size_y, tile_overlap, vae_tiling_params, W, H, 1.30539f);
-
-                LOG_DEBUG("VAE Tile size: %dx%d", tile_size_x, tile_size_y);
-
-                auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
-                    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, circular_x, circular_y, on_tiling);
-            } else {
-                first_stage_model->compute(n_threads, x, false, &result, work_ctx);
-            }
-            first_stage_model->free_compute_buffer();
-        } else {
-            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) {
-                    return tae_first_stage->compute(n_threads, in, false, &out, nullptr);
-                };
-                sd_tiling(x, result, vae_scale_factor, 64, 0.5f, circular_x, circular_y, on_tiling);
-            } else {
-                tae_first_stage->compute(n_threads, x, false, &result, work_ctx);
-            }
-            tae_first_stage->free_compute_buffer();
-        }
-
-        int64_t t1 = ggml_time_ms();
-        LOG_DEBUG("computing vae encode graph completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
-        return result;
-    }
-
-    ggml_tensor* gaussian_latent_sample(ggml_context* work_ctx, ggml_tensor* moments) {
-        // ldm.modules.distributions.distributions.DiagonalGaussianDistribution.sample
-        ggml_tensor* latent       = ggml_new_tensor_4d(work_ctx, moments->type, moments->ne[0], moments->ne[1], moments->ne[2] / 2, moments->ne[3]);
-        struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, latent);
-        ggml_ext_im_set_randn_f32(noise, rng);
-        {
-            float mean   = 0;
-            float logvar = 0;
-            float value  = 0;
-            float std_   = 0;
-            for (int i = 0; i < latent->ne[3]; i++) {
-                for (int j = 0; j < latent->ne[2]; j++) {
-                    for (int k = 0; k < latent->ne[1]; k++) {
-                        for (int l = 0; l < latent->ne[0]; l++) {
-                            mean   = ggml_ext_tensor_get_f32(moments, l, k, j, i);
-                            logvar = ggml_ext_tensor_get_f32(moments, l, k, j + (int)latent->ne[2], i);
-                            logvar = std::max(-30.0f, std::min(logvar, 20.0f));
-                            std_   = std::exp(0.5f * logvar);
-                            value  = mean + std_ * ggml_ext_tensor_get_f32(noise, l, k, j, i);
-                            // printf("%d %d %d %d -> %f\n", i, j, k, l, value);
-                            ggml_ext_tensor_set_f32(latent, value, l, k, j, i);
-                        }
-                    }
-                }
-            }
-        }
-        return latent;
-    }
-
-    ggml_tensor* get_first_stage_encoding(ggml_context* work_ctx, ggml_tensor* vae_output) {
-        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) {
-            latent = vae_output;
-        } else if (version == VERSION_SD1_PIX2PIX) {
-            latent = ggml_view_3d(work_ctx,
-                                  vae_output,
-                                  vae_output->ne[0],
-                                  vae_output->ne[1],
-                                  vae_output->ne[2] / 2,
-                                  vae_output->nb[1],
-                                  vae_output->nb[2],
-                                  0);
-        } else {
-            latent = gaussian_latent_sample(work_ctx, vae_output);
-        }
-        if (!use_tiny_autoencoder && version != VERSION_SD1_PIX2PIX) {
-            process_latent_in(latent);
-        }
-        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;
+    ggml_tensor* encode_to_vae_latents(ggml_context* work_ctx, ggml_tensor* x) {
+        ggml_tensor* vae_output = first_stage_model->encode(n_threads, work_ctx, x, vae_tiling_params, circular_x, circular_y);
+        ggml_tensor* latents    = first_stage_model->vae_output_to_latents(work_ctx, vae_output, rng);
+        return latents;
     }
 
     ggml_tensor* encode_first_stage(ggml_context* work_ctx, ggml_tensor* x) {
-        ggml_tensor* vae_output = vae_encode(work_ctx, x);
-        return get_first_stage_encoding(work_ctx, vae_output);
+        ggml_tensor* latents = encode_to_vae_latents(work_ctx, x);
+        if (version != VERSION_SD1_PIX2PIX) {
+            latents = first_stage_model->vae_to_diffuison_latents(work_ctx, latents);
+        }
+        return latents;
     }
 
     ggml_tensor* decode_first_stage(ggml_context* work_ctx, ggml_tensor* x, bool decode_video = false) {
-        const int vae_scale_factor = get_vae_scale_factor();
-        int64_t W                  = x->ne[0] * vae_scale_factor;
-        int64_t H                  = x->ne[1] * vae_scale_factor;
-        int64_t C                  = 3;
-        ggml_tensor* result        = nullptr;
-        if (decode_video) {
-            int64_t T = x->ne[2];
-            if (sd_version_is_wan(version)) {
-                T = ((T - 1) * 4) + 1;
-            }
-            result = ggml_new_tensor_4d(work_ctx,
-                                        GGML_TYPE_F32,
-                                        W,
-                                        H,
-                                        T,
-                                        3);
-        } else {
-            result = ggml_new_tensor_4d(work_ctx,
-                                        GGML_TYPE_F32,
-                                        W,
-                                        H,
-                                        C,
-                                        x->ne[3]);
-        }
-        int64_t t0 = ggml_time_ms();
-        if (!use_tiny_autoencoder) {
-            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);
-            // x = load_tensor_from_file(work_ctx, "wan_vae_z.bin");
-            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]);
-
-                LOG_DEBUG("VAE Tile size: %dx%d", tile_size_x, tile_size_y);
-
-                // split latent in 32x32 tiles and compute in several steps
-                auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
-                    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, circular_x, circular_y, on_tiling);
-            } else {
-                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);
-        } else {
-            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) {
-                    return tae_first_stage->compute(n_threads, in, true, &out);
-                };
-                sd_tiling(x, result, vae_scale_factor, 64, 0.5f, circular_x, circular_y, on_tiling);
-            } else {
-                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();
-        }
-
-        int64_t t1 = ggml_time_ms();
-        LOG_DEBUG("computing vae decode graph completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
-        ggml_ext_tensor_clamp_inplace(result, 0.0f, 1.0f);
-        return result;
+        x = first_stage_model->diffusion_to_vae_latents(work_ctx, x);
+        x = first_stage_model->decode(n_threads, work_ctx, x, vae_tiling_params, decode_video, circular_x, circular_y);
+        return x;
     }
 
     void set_flow_shift(float flow_shift = INFINITY) {
@@ -2983,7 +2597,7 @@ enum lora_apply_mode_t str_to_lora_apply_mode(const char* str) {
 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->reuse_threshold             = INFINITY;
     cache_params->start_percent               = 0.15f;
     cache_params->end_percent                 = 0.95f;
     cache_params->error_decay_rate            = 1.0f;
@@ -3229,7 +2843,7 @@ char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params) {
     snprintf(buf + strlen(buf), 4096 - strlen(buf),
              "cache: %s (threshold=%.3f, start=%.2f, end=%.2f)\n",
              cache_mode_str,
-             sd_img_gen_params->cache.reuse_threshold,
+             get_cache_reuse_threshold(sd_img_gen_params->cache),
              sd_img_gen_params->cache.start_percent,
              sd_img_gen_params->cache.end_percent);
     free(sample_params_str);
@@ -3560,7 +3174,7 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
 
     int64_t t4 = ggml_time_ms();
     LOG_INFO("decode_first_stage completed, taking %.2fs", (t4 - t3) * 1.0f / 1000);
-    if (sd_ctx->sd->free_params_immediately && !sd_ctx->sd->use_tiny_autoencoder) {
+    if (sd_ctx->sd->free_params_immediately) {
         sd_ctx->sd->first_stage_model->free_params_buffer();
     }
 
@@ -3609,15 +3223,15 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
         if (sd_ctx->sd->first_stage_model) {
             sd_ctx->sd->first_stage_model->set_circular_axes(sd_ctx->sd->circular_x, sd_ctx->sd->circular_y);
         }
-        if (sd_ctx->sd->tae_first_stage) {
-            sd_ctx->sd->tae_first_stage->set_circular_axes(sd_ctx->sd->circular_x, sd_ctx->sd->circular_y);
+        if (sd_ctx->sd->preview_vae) {
+            sd_ctx->sd->preview_vae->set_circular_axes(sd_ctx->sd->circular_x, sd_ctx->sd->circular_y);
         }
     } else {
         int tile_size_x, tile_size_y;
         float _overlap;
         int latent_size_x = width / sd_ctx->sd->get_vae_scale_factor();
         int latent_size_y = height / sd_ctx->sd->get_vae_scale_factor();
-        sd_ctx->sd->get_tile_sizes(tile_size_x, tile_size_y, _overlap, sd_img_gen_params->vae_tiling_params, latent_size_x, latent_size_y);
+        sd_ctx->sd->first_stage_model->get_tile_sizes(tile_size_x, tile_size_y, _overlap, sd_img_gen_params->vae_tiling_params, latent_size_x, latent_size_y);
 
         // force disable circular padding for vae if tiling is enabled unless latent is smaller than tile size
         // otherwise it will cause artifacts at the edges of the tiles
@@ -3627,8 +3241,8 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
         if (sd_ctx->sd->first_stage_model) {
             sd_ctx->sd->first_stage_model->set_circular_axes(sd_ctx->sd->circular_x, sd_ctx->sd->circular_y);
         }
-        if (sd_ctx->sd->tae_first_stage) {
-            sd_ctx->sd->tae_first_stage->set_circular_axes(sd_ctx->sd->circular_x, sd_ctx->sd->circular_y);
+        if (sd_ctx->sd->preview_vae) {
+            sd_ctx->sd->preview_vae->set_circular_axes(sd_ctx->sd->circular_x, sd_ctx->sd->circular_y);
         }
 
         // disable circular tiling if it's enabled for the VAE
@@ -4105,14 +3719,13 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
         sd_image_to_ggml_tensor(sd_vid_gen_params->init_image, init_img);
         init_img = ggml_reshape_4d(work_ctx, init_img, width, height, 1, 3);
 
-        auto init_image_latent = sd_ctx->sd->vae_encode(work_ctx, init_img);  // [b*c, 1, h/16, w/16]
+        auto init_image_latent = sd_ctx->sd->encode_to_vae_latents(work_ctx, init_img);  // [b*c, 1, h/16, w/16]
 
         init_latent  = sd_ctx->sd->generate_init_latent(work_ctx, width, height, frames, true);
         denoise_mask = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, init_latent->ne[0], init_latent->ne[1], init_latent->ne[2], 1);
         ggml_set_f32(denoise_mask, 1.f);
 
-        if (!sd_ctx->sd->use_tiny_autoencoder)
-            sd_ctx->sd->process_latent_out(init_latent);
+        init_latent = sd_ctx->sd->first_stage_model->diffusion_to_vae_latents(work_ctx, init_latent);
 
         ggml_ext_tensor_iter(init_image_latent, [&](ggml_tensor* t, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
             float value = ggml_ext_tensor_get_f32(t, i0, i1, i2, i3);
@@ -4122,8 +3735,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
             }
         });
 
-        if (!sd_ctx->sd->use_tiny_autoencoder)
-            sd_ctx->sd->process_latent_in(init_latent);
+        init_latent = sd_ctx->sd->first_stage_model->vae_to_diffuison_latents(work_ctx, init_latent);
 
         int64_t t2 = ggml_time_ms();
         LOG_INFO("encode_first_stage completed, taking %" PRId64 " ms", t2 - t1);
@@ -4346,7 +3958,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
     struct ggml_tensor* vid = sd_ctx->sd->decode_first_stage(work_ctx, final_latent, true);
     int64_t t5              = ggml_time_ms();
     LOG_INFO("decode_first_stage completed, taking %.2fs", (t5 - t4) * 1.0f / 1000);
-    if (sd_ctx->sd->free_params_immediately && !sd_ctx->sd->use_tiny_autoencoder) {
+    if (sd_ctx->sd->free_params_immediately) {
         sd_ctx->sd->first_stage_model->free_params_buffer();
     }
 

src/tae.hpp

@@ -442,11 +442,13 @@ protected:
     bool decode_only;
     SDVersion version;
 
+public:
+    int z_channels = 16;
+
 public:
     TAEHV(bool decode_only = true, SDVersion version = VERSION_WAN2)
         : decode_only(decode_only), version(version) {
-        int z_channels = 16;
-        int patch      = 1;
+        int patch = 1;
         if (version == VERSION_WAN2_2_TI2V) {
             z_channels = 48;
             patch      = 2;
@@ -494,10 +496,12 @@ protected:
     bool decode_only;
     bool taef2 = false;
 
+public:
+    int z_channels = 4;
+
 public:
     TAESD(bool decode_only = true, SDVersion version = VERSION_SD1)
         : decode_only(decode_only) {
-        int z_channels       = 4;
         bool use_midblock_gn = false;
         taef2                = sd_version_is_flux2(version);
 
@@ -533,20 +537,7 @@ public:
     }
 };
 
-struct TinyAutoEncoder : public GGMLRunner {
-    TinyAutoEncoder(ggml_backend_t backend, bool offload_params_to_cpu)
-        : GGMLRunner(backend, offload_params_to_cpu) {}
-    virtual bool compute(const int n_threads,
-                         struct ggml_tensor* z,
-                         bool decode_graph,
-                         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 void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) = 0;
-};
-
-struct TinyImageAutoEncoder : public TinyAutoEncoder {
+struct TinyImageAutoEncoder : public VAE {
     TAESD taesd;
     bool decode_only = false;
 
@@ -558,7 +549,8 @@ struct TinyImageAutoEncoder : public TinyAutoEncoder {
                          SDVersion version = VERSION_SD1)
         : decode_only(decoder_only),
           taesd(decoder_only, version),
-          TinyAutoEncoder(backend, offload_params_to_cpu) {
+          VAE(version, backend, offload_params_to_cpu) {
+        scale_input = false;
         taesd.init(params_ctx, tensor_storage_map, prefix);
     }
 
@@ -566,37 +558,26 @@ struct TinyImageAutoEncoder : public TinyAutoEncoder {
         return "taesd";
     }
 
-    bool load_from_file(const std::string& file_path, int n_threads) {
-        LOG_INFO("loading taesd from '%s', decode_only = %s", file_path.c_str(), decode_only ? "true" : "false");
-        alloc_params_buffer();
-        std::map<std::string, ggml_tensor*> taesd_tensors;
-        taesd.get_param_tensors(taesd_tensors);
-        std::set<std::string> ignore_tensors;
-        if (decode_only) {
-            ignore_tensors.insert("encoder.");
-        }
-
-        ModelLoader model_loader;
-        if (!model_loader.init_from_file_and_convert_name(file_path)) {
-            LOG_ERROR("init taesd model loader from file failed: '%s'", file_path.c_str());
-            return false;
-        }
-
-        bool success = model_loader.load_tensors(taesd_tensors, ignore_tensors, n_threads);
-
-        if (!success) {
-            LOG_ERROR("load tae tensors from model loader failed");
-            return false;
-        }
-
-        LOG_INFO("taesd model loaded");
-        return success;
-    }
-
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
         taesd.get_param_tensors(tensors, prefix);
     }
 
+    ggml_tensor* vae_output_to_latents(ggml_context* work_ctx, ggml_tensor* vae_output, std::shared_ptr<RNG> rng) {
+        return vae_output;
+    }
+
+    ggml_tensor* diffusion_to_vae_latents(ggml_context* work_ctx, ggml_tensor* latents) {
+        return ggml_ext_dup_and_cpy_tensor(work_ctx, latents);
+    }
+
+    ggml_tensor* vae_to_diffuison_latents(ggml_context* work_ctx, ggml_tensor* latents) {
+        return ggml_ext_dup_and_cpy_tensor(work_ctx, latents);
+    }
+
+    int get_encoder_output_channels(int input_channels) {
+        return taesd.z_channels;
+    }
+
     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);
@@ -606,11 +587,11 @@ struct TinyImageAutoEncoder : public TinyAutoEncoder {
         return gf;
     }
 
-    bool compute(const int n_threads,
-                 struct ggml_tensor* z,
-                 bool decode_graph,
-                 struct ggml_tensor** output,
-                 struct ggml_context* output_ctx = nullptr) {
+    bool _compute(const int n_threads,
+                  struct ggml_tensor* z,
+                  bool decode_graph,
+                  struct ggml_tensor** output,
+                  struct ggml_context* output_ctx = nullptr) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
             return build_graph(z, decode_graph);
         };
@@ -619,7 +600,7 @@ struct TinyImageAutoEncoder : public TinyAutoEncoder {
     }
 };
 
-struct TinyVideoAutoEncoder : public TinyAutoEncoder {
+struct TinyVideoAutoEncoder : public VAE {
     TAEHV taehv;
     bool decode_only = false;
 
@@ -631,7 +612,8 @@ struct TinyVideoAutoEncoder : public TinyAutoEncoder {
                          SDVersion version = VERSION_WAN2)
         : decode_only(decoder_only),
           taehv(decoder_only, version),
-          TinyAutoEncoder(backend, offload_params_to_cpu) {
+          VAE(version, backend, offload_params_to_cpu) {
+        scale_input = false;
         taehv.init(params_ctx, tensor_storage_map, prefix);
     }
 
@@ -639,37 +621,26 @@ struct TinyVideoAutoEncoder : public TinyAutoEncoder {
         return "taehv";
     }
 
-    bool load_from_file(const std::string& file_path, int n_threads) {
-        LOG_INFO("loading taehv from '%s', decode_only = %s", file_path.c_str(), decode_only ? "true" : "false");
-        alloc_params_buffer();
-        std::map<std::string, ggml_tensor*> taehv_tensors;
-        taehv.get_param_tensors(taehv_tensors);
-        std::set<std::string> ignore_tensors;
-        if (decode_only) {
-            ignore_tensors.insert("encoder.");
-        }
-
-        ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
-            LOG_ERROR("init taehv model loader from file failed: '%s'", file_path.c_str());
-            return false;
-        }
-
-        bool success = model_loader.load_tensors(taehv_tensors, ignore_tensors, n_threads);
-
-        if (!success) {
-            LOG_ERROR("load tae tensors from model loader failed");
-            return false;
-        }
-
-        LOG_INFO("taehv model loaded");
-        return success;
-    }
-
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
         taehv.get_param_tensors(tensors, prefix);
     }
 
+    ggml_tensor* vae_output_to_latents(ggml_context* work_ctx, ggml_tensor* vae_output, std::shared_ptr<RNG> rng) {
+        return vae_output;
+    }
+
+    ggml_tensor* diffusion_to_vae_latents(ggml_context* work_ctx, ggml_tensor* latents) {
+        return ggml_ext_dup_and_cpy_tensor(work_ctx, latents);
+    }
+
+    ggml_tensor* vae_to_diffuison_latents(ggml_context* work_ctx, ggml_tensor* latents) {
+        return ggml_ext_dup_and_cpy_tensor(work_ctx, latents);
+    }
+
+    int get_encoder_output_channels(int input_channels) {
+        return taehv.z_channels;
+    }
+
     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);
@@ -679,11 +650,11 @@ struct TinyVideoAutoEncoder : public TinyAutoEncoder {
         return gf;
     }
 
-    bool compute(const int n_threads,
-                 struct ggml_tensor* z,
-                 bool decode_graph,
-                 struct ggml_tensor** output,
-                 struct ggml_context* output_ctx = nullptr) {
+    bool _compute(const int n_threads,
+                  struct ggml_tensor* z,
+                  bool decode_graph,
+                  struct ggml_tensor** output,
+                  struct ggml_context* output_ctx = nullptr) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
             return build_graph(z, decode_graph);
         };

src/vae.hpp

@@ -3,635 +3,206 @@
 
 #include "common_block.hpp"
 
-/*================================================== AutoEncoderKL ===================================================*/
-
-#define VAE_GRAPH_SIZE 20480
-
-class ResnetBlock : public UnaryBlock {
-protected:
-    int64_t in_channels;
-    int64_t out_channels;
-
-public:
-    ResnetBlock(int64_t in_channels,
-                int64_t out_channels)
-        : in_channels(in_channels),
-          out_channels(out_channels) {
-        // temb_channels is always 0
-        blocks["norm1"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(in_channels));
-        blocks["conv1"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
-
-        blocks["norm2"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(out_channels));
-        blocks["conv2"] = std::shared_ptr<GGMLBlock>(new Conv2d(out_channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
-
-        if (out_channels != in_channels) {
-            blocks["nin_shortcut"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, out_channels, {1, 1}));
-        }
-    }
-
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
-        // x: [N, in_channels, h, w]
-        // t_emb is always None
-        auto norm1 = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm1"]);
-        auto conv1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv1"]);
-        auto norm2 = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm2"]);
-        auto conv2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv2"]);
-
-        auto h = x;
-        h      = norm1->forward(ctx, h);
-        h      = ggml_silu_inplace(ctx->ggml_ctx, h);  // swish
-        h      = conv1->forward(ctx, h);
-        // return h;
-
-        h = norm2->forward(ctx, h);
-        h = ggml_silu_inplace(ctx->ggml_ctx, h);  // swish
-        // dropout, skip for inference
-        h = conv2->forward(ctx, h);
-
-        // skip connection
-        if (out_channels != in_channels) {
-            auto nin_shortcut = std::dynamic_pointer_cast<Conv2d>(blocks["nin_shortcut"]);
-
-            x = nin_shortcut->forward(ctx, x);  // [N, out_channels, h, w]
-        }
-
-        h = ggml_add(ctx->ggml_ctx, h, x);
-        return h;  // [N, out_channels, h, w]
-    }
-};
-
-class AttnBlock : public UnaryBlock {
-protected:
-    int64_t in_channels;
-    bool use_linear;
-
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") {
-        auto iter = tensor_storage_map.find(prefix + "proj_out.weight");
-        if (iter != tensor_storage_map.end()) {
-            if (iter->second.n_dims == 4 && use_linear) {
-                use_linear         = false;
-                blocks["q"]        = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
-                blocks["k"]        = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
-                blocks["v"]        = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
-                blocks["proj_out"] = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
-            } else if (iter->second.n_dims == 2 && !use_linear) {
-                use_linear         = true;
-                blocks["q"]        = std::make_shared<Linear>(in_channels, in_channels);
-                blocks["k"]        = std::make_shared<Linear>(in_channels, in_channels);
-                blocks["v"]        = std::make_shared<Linear>(in_channels, in_channels);
-                blocks["proj_out"] = std::make_shared<Linear>(in_channels, in_channels);
-            }
-        }
-    }
-
-public:
-    AttnBlock(int64_t in_channels, bool use_linear)
-        : in_channels(in_channels), use_linear(use_linear) {
-        blocks["norm"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(in_channels));
-        if (use_linear) {
-            blocks["q"]        = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
-            blocks["k"]        = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
-            blocks["v"]        = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
-            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
-        } else {
-            blocks["q"]        = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
-            blocks["k"]        = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
-            blocks["v"]        = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
-            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
-        }
-    }
-
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
-        // x: [N, in_channels, h, w]
-        auto norm     = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm"]);
-        auto q_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["q"]);
-        auto k_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["k"]);
-        auto v_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["v"]);
-        auto proj_out = std::dynamic_pointer_cast<UnaryBlock>(blocks["proj_out"]);
-
-        auto h_ = norm->forward(ctx, x);
-
-        const int64_t n = h_->ne[3];
-        const int64_t c = h_->ne[2];
-        const int64_t h = h_->ne[1];
-        const int64_t w = h_->ne[0];
-
-        ggml_tensor* q;
-        ggml_tensor* k;
-        ggml_tensor* v;
-        if (use_linear) {
-            h_ = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, h_, 1, 2, 0, 3));  // [N, h, w, in_channels]
-            h_ = ggml_reshape_3d(ctx->ggml_ctx, h_, c, h * w, n);                        // [N, h * w, in_channels]
-
-            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]
-        } 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]
-            q = ggml_reshape_3d(ctx->ggml_ctx, q, c, h * w, n);                        // [N, h * w, in_channels]
-
-            k = k_proj->forward(ctx, h_);                                              // [N, in_channels, h, w]
-            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_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_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]
-
-            h_ = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, h_, 1, 0, 2, 3));  // [N, in_channels, h * w]
-            h_ = ggml_reshape_4d(ctx->ggml_ctx, h_, w, h, c, n);                         // [N, in_channels, h, w]
-        } else {
-            h_ = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, h_, 1, 0, 2, 3));  // [N, in_channels, h * w]
-            h_ = ggml_reshape_4d(ctx->ggml_ctx, h_, w, h, c, n);                         // [N, in_channels, h, w]
-
-            h_ = proj_out->forward(ctx, h_);  // [N, in_channels, h, w]
-        }
-
-        h_ = ggml_add(ctx->ggml_ctx, h_, x);
-        return h_;
-    }
-};
-
-class AE3DConv : public Conv2d {
-public:
-    AE3DConv(int64_t in_channels,
-             int64_t out_channels,
-             std::pair<int, int> kernel_size,
-             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) {
-        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,
-                                struct ggml_tensor* x) override {
-        // timesteps always None
-        // skip_video always False
-        // x: [N, IC, IH, IW]
-        // result: [N, OC, OH, OW]
-        auto time_mix_conv = std::dynamic_pointer_cast<Conv3d>(blocks["time_mix_conv"]);
-
-        x = Conv2d::forward(ctx, x);
-        // timesteps = x.shape[0]
-        // x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
-        // x = conv3d(x)
-        // return rearrange(x, "b c t h w -> (b t) c h w")
-        int64_t T = x->ne[3];
-        int64_t B = x->ne[3] / T;
-        int64_t C = x->ne[2];
-        int64_t H = x->ne[1];
-        int64_t W = x->ne[0];
-
-        x = ggml_reshape_4d(ctx->ggml_ctx, x, W * H, C, T, B);                     // (b t) c h w -> b t c (h w)
-        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
-        x = time_mix_conv->forward(ctx, x);                                        // [B, OC, T, OH * OW]
-        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
-        return x;                                                                  // [B*T, OC, OH, OW]
-    }
-};
-
-class VideoResnetBlock : public ResnetBlock {
-protected:
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
-        enum ggml_type wtype = get_type(prefix + "mix_factor", tensor_storage_map, GGML_TYPE_F32);
-        params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
-    }
-
-    float get_alpha() {
-        float alpha = ggml_ext_backend_tensor_get_f32(params["mix_factor"]);
-        return sigmoid(alpha);
-    }
-
-public:
-    VideoResnetBlock(int64_t in_channels,
-                     int64_t out_channels,
-                     int video_kernel_size = 3)
-        : ResnetBlock(in_channels, out_channels) {
-        // merge_strategy is always learned
-        blocks["time_stack"] = std::shared_ptr<GGMLBlock>(new ResBlock(out_channels, 0, out_channels, {video_kernel_size, 1}, 3, false, true));
-    }
-
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
-        // x: [N, in_channels, h, w] aka [b*t, in_channels, h, w]
-        // return: [N, out_channels, h, w] aka [b*t, out_channels, h, w]
-        // t_emb is always None
-        // skip_video is always False
-        // timesteps is always None
-        auto time_stack = std::dynamic_pointer_cast<ResBlock>(blocks["time_stack"]);
-
-        x = ResnetBlock::forward(ctx, x);  // [N, out_channels, h, w]
-        // return x;
-
-        int64_t T = x->ne[3];
-        int64_t B = x->ne[3] / T;
-        int64_t C = x->ne[2];
-        int64_t H = x->ne[1];
-        int64_t W = x->ne[0];
-
-        x          = ggml_reshape_4d(ctx->ggml_ctx, x, W * H, C, T, B);                     // (b t) c h w -> b t c (h w)
-        x          = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
-        auto x_mix = x;
-
-        x = time_stack->forward(ctx, x);  // b t c (h w)
-
-        float alpha = get_alpha();
-        x           = ggml_add(ctx->ggml_ctx,
-                               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
-
-        return x;
-    }
-};
-
-// ldm.modules.diffusionmodules.model.Encoder
-class Encoder : public GGMLBlock {
-protected:
-    int ch                   = 128;
-    std::vector<int> ch_mult = {1, 2, 4, 4};
-    int num_res_blocks       = 2;
-    int in_channels          = 3;
-    int z_channels           = 4;
-    bool double_z            = true;
-
-public:
-    Encoder(int ch,
-            std::vector<int> ch_mult,
-            int num_res_blocks,
-            int in_channels,
-            int z_channels,
-            bool double_z              = true,
-            bool use_linear_projection = false)
-        : ch(ch),
-          ch_mult(ch_mult),
-          num_res_blocks(num_res_blocks),
-          in_channels(in_channels),
-          z_channels(z_channels),
-          double_z(double_z) {
-        blocks["conv_in"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, ch, {3, 3}, {1, 1}, {1, 1}));
-
-        size_t num_resolutions = ch_mult.size();
-
-        int block_in = 1;
-        for (int i = 0; i < num_resolutions; i++) {
-            if (i == 0) {
-                block_in = ch;
-            } else {
-                block_in = ch * ch_mult[i - 1];
-            }
-            int block_out = ch * ch_mult[i];
-            for (int j = 0; j < num_res_blocks; j++) {
-                std::string name = "down." + std::to_string(i) + ".block." + std::to_string(j);
-                blocks[name]     = std::shared_ptr<GGMLBlock>(new ResnetBlock(block_in, block_out));
-                block_in         = block_out;
-            }
-            if (i != num_resolutions - 1) {
-                std::string name = "down." + std::to_string(i) + ".downsample";
-                blocks[name]     = std::shared_ptr<GGMLBlock>(new DownSampleBlock(block_in, block_in, true));
-            }
-        }
-
-        blocks["mid.block_1"] = std::shared_ptr<GGMLBlock>(new ResnetBlock(block_in, block_in));
-        blocks["mid.attn_1"]  = std::shared_ptr<GGMLBlock>(new AttnBlock(block_in, use_linear_projection));
-        blocks["mid.block_2"] = std::shared_ptr<GGMLBlock>(new ResnetBlock(block_in, block_in));
-
-        blocks["norm_out"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(block_in));
-        blocks["conv_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(block_in, double_z ? z_channels * 2 : z_channels, {3, 3}, {1, 1}, {1, 1}));
-    }
-
-    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
-        // x: [N, in_channels, h, w]
-
-        auto conv_in     = std::dynamic_pointer_cast<Conv2d>(blocks["conv_in"]);
-        auto mid_block_1 = std::dynamic_pointer_cast<ResnetBlock>(blocks["mid.block_1"]);
-        auto mid_attn_1  = std::dynamic_pointer_cast<AttnBlock>(blocks["mid.attn_1"]);
-        auto mid_block_2 = std::dynamic_pointer_cast<ResnetBlock>(blocks["mid.block_2"]);
-        auto norm_out    = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm_out"]);
-        auto conv_out    = std::dynamic_pointer_cast<Conv2d>(blocks["conv_out"]);
-
-        auto h = conv_in->forward(ctx, x);  // [N, ch, h, w]
-
-        // downsampling
-        size_t num_resolutions = ch_mult.size();
-        for (int i = 0; i < num_resolutions; i++) {
-            for (int j = 0; j < num_res_blocks; j++) {
-                std::string name = "down." + std::to_string(i) + ".block." + std::to_string(j);
-                auto down_block  = std::dynamic_pointer_cast<ResnetBlock>(blocks[name]);
-
-                h = down_block->forward(ctx, h);
-            }
-            if (i != num_resolutions - 1) {
-                std::string name = "down." + std::to_string(i) + ".downsample";
-                auto down_sample = std::dynamic_pointer_cast<DownSampleBlock>(blocks[name]);
-
-                h = down_sample->forward(ctx, h);
-            }
-        }
-
-        // middle
-        h = mid_block_1->forward(ctx, h);
-        h = mid_attn_1->forward(ctx, h);
-        h = mid_block_2->forward(ctx, h);  // [N, block_in, h, w]
-
-        // end
-        h = norm_out->forward(ctx, h);
-        h = ggml_silu_inplace(ctx->ggml_ctx, h);  // nonlinearity/swish
-        h = conv_out->forward(ctx, h);            // [N, z_channels*2, h, w]
-        return h;
-    }
-};
-
-// ldm.modules.diffusionmodules.model.Decoder
-class Decoder : public GGMLBlock {
-protected:
-    int ch                   = 128;
-    int out_ch               = 3;
-    std::vector<int> ch_mult = {1, 2, 4, 4};
-    int num_res_blocks       = 2;
-    int z_channels           = 4;
-    bool video_decoder       = false;
-    int video_kernel_size    = 3;
-
-    virtual std::shared_ptr<GGMLBlock> get_conv_out(int64_t in_channels,
-                                                    int64_t out_channels,
-                                                    std::pair<int, int> kernel_size,
-                                                    std::pair<int, int> stride  = {1, 1},
-                                                    std::pair<int, int> padding = {0, 0}) {
-        if (video_decoder) {
-            return std::shared_ptr<GGMLBlock>(new AE3DConv(in_channels, out_channels, kernel_size, video_kernel_size, stride, padding));
-        } else {
-            return std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, out_channels, kernel_size, stride, padding));
-        }
-    }
-
-    virtual std::shared_ptr<GGMLBlock> get_resnet_block(int64_t in_channels,
-                                                        int64_t out_channels) {
-        if (video_decoder) {
-            return std::shared_ptr<GGMLBlock>(new VideoResnetBlock(in_channels, out_channels, video_kernel_size));
-        } else {
-            return std::shared_ptr<GGMLBlock>(new ResnetBlock(in_channels, out_channels));
-        }
-    }
-
-public:
-    Decoder(int ch,
-            int out_ch,
-            std::vector<int> ch_mult,
-            int num_res_blocks,
-            int z_channels,
-            bool use_linear_projection = false,
-            bool video_decoder         = false,
-            int video_kernel_size      = 3)
-        : ch(ch),
-          out_ch(out_ch),
-          ch_mult(ch_mult),
-          num_res_blocks(num_res_blocks),
-          z_channels(z_channels),
-          video_decoder(video_decoder),
-          video_kernel_size(video_kernel_size) {
-        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}));
-
-        blocks["mid.block_1"] = get_resnet_block(block_in, block_in);
-        blocks["mid.attn_1"]  = std::shared_ptr<GGMLBlock>(new AttnBlock(block_in, use_linear_projection));
-        blocks["mid.block_2"] = get_resnet_block(block_in, block_in);
-
-        for (int i = num_resolutions - 1; i >= 0; i--) {
-            int mult      = ch_mult[i];
-            int block_out = ch * mult;
-            for (int j = 0; j < num_res_blocks + 1; j++) {
-                std::string name = "up." + std::to_string(i) + ".block." + std::to_string(j);
-                blocks[name]     = get_resnet_block(block_in, block_out);
-
-                block_in = block_out;
-            }
-            if (i != 0) {
-                std::string name = "up." + std::to_string(i) + ".upsample";
-                blocks[name]     = std::shared_ptr<GGMLBlock>(new UpSampleBlock(block_in, block_in));
-            }
-        }
-
-        blocks["norm_out"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(block_in));
-        blocks["conv_out"] = get_conv_out(block_in, out_ch, {3, 3}, {1, 1}, {1, 1});
-    }
-
-    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
-        // z: [N, z_channels, h, w]
-        // alpha is always 0
-        // merge_strategy is always learned
-        // time_mode is always conv-only, so we need to replace conv_out_op/resnet_op to AE3DConv/VideoResBlock
-        // AttnVideoBlock will not be used
-        auto conv_in     = std::dynamic_pointer_cast<Conv2d>(blocks["conv_in"]);
-        auto mid_block_1 = std::dynamic_pointer_cast<ResnetBlock>(blocks["mid.block_1"]);
-        auto mid_attn_1  = std::dynamic_pointer_cast<AttnBlock>(blocks["mid.attn_1"]);
-        auto mid_block_2 = std::dynamic_pointer_cast<ResnetBlock>(blocks["mid.block_2"]);
-        auto norm_out    = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm_out"]);
-        auto conv_out    = std::dynamic_pointer_cast<Conv2d>(blocks["conv_out"]);
-
-        // conv_in
-        auto h = conv_in->forward(ctx, z);  // [N, block_in, h, w]
-
-        // middle
-        h = mid_block_1->forward(ctx, h);
-        // return h;
-
-        h = mid_attn_1->forward(ctx, h);
-        h = mid_block_2->forward(ctx, h);  // [N, block_in, h, w]
-
-        // upsampling
-        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);
-                auto up_block    = std::dynamic_pointer_cast<ResnetBlock>(blocks[name]);
-
-                h = up_block->forward(ctx, h);
-            }
-            if (i != 0) {
-                std::string name = "up." + std::to_string(i) + ".upsample";
-                auto up_sample   = std::dynamic_pointer_cast<UpSampleBlock>(blocks[name]);
-
-                h = up_sample->forward(ctx, h);
-            }
-        }
-
-        h = norm_out->forward(ctx, h);
-        h = ggml_silu_inplace(ctx->ggml_ctx, h);  // nonlinearity/swish
-        h = conv_out->forward(ctx, h);            // [N, out_ch, h*8, w*8]
-        return h;
-    }
-};
-
-// ldm.models.autoencoder.AutoencoderKL
-class AutoencodingEngine : public GGMLBlock {
+struct VAE : public GGMLRunner {
 protected:
     SDVersion version;
-    bool decode_only       = true;
-    bool use_video_decoder = false;
-    bool use_quant         = true;
-    int embed_dim          = 4;
-    struct {
-        int z_channels           = 4;
-        int resolution           = 256;
-        int in_channels          = 3;
-        int out_ch               = 3;
-        int ch                   = 128;
-        std::vector<int> ch_mult = {1, 2, 4, 4};
-        int num_res_blocks       = 2;
-        bool double_z            = true;
-    } dd_config;
+    bool scale_input                                       = true;
+    virtual bool _compute(const int n_threads,
+                          struct ggml_tensor* z,
+                          bool decode_graph,
+                          struct ggml_tensor** output,
+                          struct ggml_context* output_ctx) = 0;
 
 public:
-    AutoencodingEngine(SDVersion version          = VERSION_SD1,
-                       bool decode_only           = true,
-                       bool use_linear_projection = false,
-                       bool use_video_decoder     = false)
-        : version(version), decode_only(decode_only), use_video_decoder(use_video_decoder) {
-        if (sd_version_is_dit(version)) {
-            if (sd_version_is_flux2(version)) {
-                dd_config.z_channels = 32;
-                embed_dim            = 32;
+    VAE(SDVersion version, ggml_backend_t backend, bool offload_params_to_cpu)
+        : version(version), GGMLRunner(backend, offload_params_to_cpu) {}
+
+    int get_scale_factor() {
+        int scale_factor = 8;
+        if (version == VERSION_WAN2_2_TI2V) {
+            scale_factor = 16;
+        } else if (sd_version_is_flux2(version)) {
+            scale_factor = 16;
+        } else if (version == VERSION_CHROMA_RADIANCE) {
+            scale_factor = 1;
+        }
+        return scale_factor;
+    }
+
+    virtual int get_encoder_output_channels(int input_channels) = 0;
+
+    void get_tile_sizes(int& tile_size_x,
+                        int& tile_size_y,
+                        float& tile_overlap,
+                        const sd_tiling_params_t& params,
+                        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, int64_t latent_size) {
+            const int default_tile_size  = 32;
+            const int min_tile_dimension = 4;
+            int tile_size                = default_tile_size;
+            // factor <= 1 means simple fraction of the latent dimension
+            // factor > 1 means number of tiles across that dimension
+            if (factor > 0.f) {
+                if (factor > 1.0)
+                    factor = 1 / (factor - factor * tile_overlap + tile_overlap);
+                tile_size = static_cast<int>(std::round(latent_size * factor));
+            } else if (requested_size >= min_tile_dimension) {
+                tile_size = requested_size;
+            }
+            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);
+        tile_size_y = get_tile_size(params.tile_size_y, params.rel_size_y, latent_y);
+    }
+
+    ggml_tensor* encode(int n_threads,
+                        ggml_context* work_ctx,
+                        ggml_tensor* x,
+                        sd_tiling_params_t tiling_params,
+                        bool circular_x = false,
+                        bool circular_y = false) {
+        int64_t t0             = ggml_time_ms();
+        ggml_tensor* result    = nullptr;
+        const int scale_factor = get_scale_factor();
+        int64_t W              = x->ne[0] / scale_factor;
+        int64_t H              = x->ne[1] / scale_factor;
+        int channel_dim        = sd_version_is_wan(version) ? 3 : 2;
+        int64_t C              = get_encoder_output_channels(static_cast<int>(x->ne[channel_dim]));
+        int64_t ne2;
+        int64_t ne3;
+        if (sd_version_is_wan(version)) {
+            int64_t T = x->ne[2];
+            ne2       = (T - 1) / 4 + 1;
+            ne3       = C;
+        } else {
+            ne2 = C;
+            ne3 = x->ne[3];
+        }
+        result = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, ne2, ne3);
+
+        if (scale_input) {
+            scale_to_minus1_1(x);
+        }
+
+        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]);
+        }
+
+        if (tiling_params.enabled) {
+            float tile_overlap;
+            int tile_size_x, tile_size_y;
+            // multiply tile size for encode to keep the compute buffer size consistent
+            get_tile_sizes(tile_size_x, tile_size_y, tile_overlap, tiling_params, W, H, 1.30539f);
+
+            LOG_DEBUG("VAE Tile size: %dx%d", tile_size_x, tile_size_y);
+
+            auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
+                return _compute(n_threads, in, false, &out, work_ctx);
+            };
+            sd_tiling_non_square(x, result, scale_factor, tile_size_x, tile_size_y, tile_overlap, circular_x, circular_y, on_tiling);
+        } else {
+            _compute(n_threads, x, false, &result, work_ctx);
+        }
+        free_compute_buffer();
+
+        int64_t t1 = ggml_time_ms();
+        LOG_DEBUG("computing vae encode graph completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
+        return result;
+    }
+
+    ggml_tensor* decode(int n_threads,
+                        ggml_context* work_ctx,
+                        ggml_tensor* x,
+                        sd_tiling_params_t tiling_params,
+                        bool decode_video   = false,
+                        bool circular_x     = false,
+                        bool circular_y     = false,
+                        ggml_tensor* result = nullptr,
+                        bool silent         = false) {
+        const int scale_factor = get_scale_factor();
+        int64_t W              = x->ne[0] * scale_factor;
+        int64_t H              = x->ne[1] * scale_factor;
+        int64_t C              = 3;
+        if (result == nullptr) {
+            if (decode_video) {
+                int64_t T = x->ne[2];
+                if (sd_version_is_wan(version)) {
+                    T = ((T - 1) * 4) + 1;
+                }
+                result = ggml_new_tensor_4d(work_ctx,
+                                            GGML_TYPE_F32,
+                                            W,
+                                            H,
+                                            T,
+                                            3);
             } else {
-                use_quant            = false;
-                dd_config.z_channels = 16;
+                result = ggml_new_tensor_4d(work_ctx,
+                                            GGML_TYPE_F32,
+                                            W,
+                                            H,
+                                            C,
+                                            x->ne[3]);
             }
         }
-        if (use_video_decoder) {
-            use_quant = false;
+        int64_t t0 = ggml_time_ms();
+        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]);
         }
-        blocks["decoder"] = std::shared_ptr<GGMLBlock>(new Decoder(dd_config.ch,
-                                                                   dd_config.out_ch,
-                                                                   dd_config.ch_mult,
-                                                                   dd_config.num_res_blocks,
-                                                                   dd_config.z_channels,
-                                                                   use_linear_projection,
-                                                                   use_video_decoder));
-        if (use_quant) {
-            blocks["post_quant_conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(dd_config.z_channels,
-                                                                              embed_dim,
-                                                                              {1, 1}));
-        }
-        if (!decode_only) {
-            blocks["encoder"] = std::shared_ptr<GGMLBlock>(new Encoder(dd_config.ch,
-                                                                       dd_config.ch_mult,
-                                                                       dd_config.num_res_blocks,
-                                                                       dd_config.in_channels,
-                                                                       dd_config.z_channels,
-                                                                       dd_config.double_z,
-                                                                       use_linear_projection));
-            if (use_quant) {
-                int factor = dd_config.double_z ? 2 : 1;
+        if (tiling_params.enabled) {
+            float tile_overlap;
+            int tile_size_x, tile_size_y;
+            get_tile_sizes(tile_size_x, tile_size_y, tile_overlap, tiling_params, x->ne[0], x->ne[1]);
 
-                blocks["quant_conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(embed_dim * factor,
-                                                                             dd_config.z_channels * factor,
-                                                                             {1, 1}));
+            if (!silent) {
+                LOG_DEBUG("VAE Tile size: %dx%d", tile_size_x, tile_size_y);
+            }
+
+            auto on_tiling = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
+                return _compute(n_threads, in, true, &out, nullptr);
+            };
+            sd_tiling_non_square(x, result, scale_factor, tile_size_x, tile_size_y, tile_overlap, circular_x, circular_y, on_tiling, silent);
+        } else {
+            if (!_compute(n_threads, x, true, &result, work_ctx)) {
+                LOG_ERROR("Failed to decode latetnts");
+                free_compute_buffer();
+                return nullptr;
             }
         }
+        free_compute_buffer();
+        if (scale_input) {
+            scale_to_0_1(result);
+        }
+        int64_t t1 = ggml_time_ms();
+        LOG_DEBUG("computing vae decode graph completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
+        ggml_ext_tensor_clamp_inplace(result, 0.0f, 1.0f);
+        return result;
     }
 
-    struct ggml_tensor* decode(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
-        // z: [N, z_channels, h, w]
-        if (sd_version_is_flux2(version)) {
-            // [N, C*p*p, h, w] -> [N, C, h*p, w*p]
-            int64_t p = 2;
-
-            int64_t N = z->ne[3];
-            int64_t C = z->ne[2] / p / p;
-            int64_t h = z->ne[1];
-            int64_t w = z->ne[0];
-            int64_t H = h * p;
-            int64_t W = w * p;
-
-            z = ggml_reshape_4d(ctx->ggml_ctx, z, w * h, p * p, C, N);                           // [N, C, p*p, h*w]
-            z = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, z, 1, 0, 2, 3));  // [N, C, h*w, p*p]
-            z = ggml_reshape_4d(ctx->ggml_ctx, z, p, p, w, h * C * N);                           // [N*C*h, w, p, p]
-            z = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, z, 0, 2, 1, 3));  // [N*C*h, p, w, p]
-            z = ggml_reshape_4d(ctx->ggml_ctx, z, W, H, C, N);                                   // [N, C, h*p, w*p]
-        }
-
-        if (use_quant) {
-            auto post_quant_conv = std::dynamic_pointer_cast<Conv2d>(blocks["post_quant_conv"]);
-            z                    = post_quant_conv->forward(ctx, z);  // [N, z_channels, h, w]
-        }
-        auto decoder = std::dynamic_pointer_cast<Decoder>(blocks["decoder"]);
-
-        ggml_set_name(z, "bench-start");
-        auto h = decoder->forward(ctx, z);
-        ggml_set_name(h, "bench-end");
-        return h;
-    }
-
-    struct ggml_tensor* encode(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
-        // x: [N, in_channels, h, w]
-        auto encoder = std::dynamic_pointer_cast<Encoder>(blocks["encoder"]);
-
-        auto z = encoder->forward(ctx, x);  // [N, 2*z_channels, h/8, w/8]
-        if (use_quant) {
-            auto quant_conv = std::dynamic_pointer_cast<Conv2d>(blocks["quant_conv"]);
-            z               = quant_conv->forward(ctx, z);  // [N, 2*embed_dim, h/8, w/8]
-        }
-        if (sd_version_is_flux2(version)) {
-            z = ggml_ext_chunk(ctx->ggml_ctx, z, 2, 2)[0];
-
-            // [N, C, H, W] -> [N, C*p*p, H/p, W/p]
-            int64_t p = 2;
-            int64_t N = z->ne[3];
-            int64_t C = z->ne[2];
-            int64_t H = z->ne[1];
-            int64_t W = z->ne[0];
-            int64_t h = H / p;
-            int64_t w = W / p;
-
-            z = ggml_reshape_4d(ctx->ggml_ctx, z, p, w, p, h * C * N);                 // [N*C*h, p, w, p]
-            z = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, z, 0, 2, 1, 3));  // [N*C*h, w, p, p]
-            z = ggml_reshape_4d(ctx->ggml_ctx, z, p * p, w * h, C, N);                 // [N, C, h*w, p*p]
-            z = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, z, 1, 0, 2, 3));  // [N, C, p*p, h*w]
-            z = ggml_reshape_4d(ctx->ggml_ctx, z, w, h, p * p * C, N);                 // [N, C*p*p, h*w]
-        }
-        return z;
-    }
-};
-
-struct VAE : public GGMLRunner {
-    VAE(ggml_backend_t backend, bool offload_params_to_cpu)
-        : GGMLRunner(backend, offload_params_to_cpu) {}
-    virtual bool compute(const int n_threads,
-                         struct ggml_tensor* z,
-                         bool decode_graph,
-                         struct ggml_tensor** output,
-                         struct ggml_context* output_ctx)                                                         = 0;
-    virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) = 0;
+    virtual ggml_tensor* vae_output_to_latents(ggml_context* work_ctx, ggml_tensor* vae_output, std::shared_ptr<RNG> rng) = 0;
+    virtual ggml_tensor* diffusion_to_vae_latents(ggml_context* work_ctx, ggml_tensor* latents)                           = 0;
+    virtual ggml_tensor* vae_to_diffuison_latents(ggml_context* work_ctx, ggml_tensor* latents)                           = 0;
+    virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix)         = 0;
     virtual void set_conv2d_scale(float scale) { SD_UNUSED(scale); };
 };
 
 struct FakeVAE : public VAE {
-    FakeVAE(ggml_backend_t backend, bool offload_params_to_cpu)
-        : VAE(backend, offload_params_to_cpu) {}
-    bool compute(const int n_threads,
-                 struct ggml_tensor* z,
-                 bool decode_graph,
-                 struct ggml_tensor** output,
-                 struct ggml_context* output_ctx) override {
+    FakeVAE(SDVersion version, ggml_backend_t backend, bool offload_params_to_cpu)
+        : VAE(version, backend, offload_params_to_cpu) {}
+
+    int get_encoder_output_channels(int input_channels) {
+        return input_channels;
+    }
+
+    bool _compute(const int n_threads,
+                  struct ggml_tensor* z,
+                  bool decode_graph,
+                  struct ggml_tensor** output,
+                  struct ggml_context* output_ctx) override {
         if (*output == nullptr && output_ctx != nullptr) {
             *output = ggml_dup_tensor(output_ctx, z);
         }
@@ -642,6 +213,18 @@ struct FakeVAE : public VAE {
         return true;
     }
 
+    ggml_tensor* vae_output_to_latents(ggml_context* work_ctx, ggml_tensor* vae_output, std::shared_ptr<RNG> rng) {
+        return vae_output;
+    }
+
+    ggml_tensor* diffusion_to_vae_latents(ggml_context* work_ctx, ggml_tensor* latents) {
+        return ggml_ext_dup_and_cpy_tensor(work_ctx, latents);
+    }
+
+    ggml_tensor* vae_to_diffuison_latents(ggml_context* work_ctx, ggml_tensor* latents) {
+        return ggml_ext_dup_and_cpy_tensor(work_ctx, latents);
+    }
+
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) override {}
 
     std::string get_desc() override {
@@ -649,126 +232,4 @@ struct FakeVAE : public VAE {
     }
 };
 
-struct AutoEncoderKL : public VAE {
-    bool decode_only = true;
-    AutoencodingEngine ae;
-
-    AutoEncoderKL(ggml_backend_t backend,
-                  bool offload_params_to_cpu,
-                  const String2TensorStorage& tensor_storage_map,
-                  const std::string prefix,
-                  bool decode_only       = false,
-                  bool use_video_decoder = false,
-                  SDVersion version      = VERSION_SD1)
-        : decode_only(decode_only), VAE(backend, offload_params_to_cpu) {
-        bool use_linear_projection = false;
-        for (const auto& [name, tensor_storage] : tensor_storage_map) {
-            if (!starts_with(name, prefix)) {
-                continue;
-            }
-            if (ends_with(name, "attn_1.proj_out.weight")) {
-                if (tensor_storage.n_dims == 2) {
-                    use_linear_projection = true;
-                }
-                break;
-            }
-        }
-        ae = AutoencodingEngine(version, decode_only, use_linear_projection, use_video_decoder);
-        ae.init(params_ctx, tensor_storage_map, prefix);
-    }
-
-    void set_conv2d_scale(float scale) override {
-        std::vector<GGMLBlock*> blocks;
-        ae.get_all_blocks(blocks);
-        for (auto block : blocks) {
-            if (block->get_desc() == "Conv2d") {
-                auto conv_block = (Conv2d*)block;
-                conv_block->set_scale(scale);
-            }
-        }
-    }
-
-    std::string get_desc() override {
-        return "vae";
-    }
-
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) override {
-        ae.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);
-
-        auto runner_ctx = get_context();
-
-        struct ggml_tensor* out = decode_graph ? ae.decode(&runner_ctx, z) : ae.encode(&runner_ctx, z);
-
-        ggml_build_forward_expand(gf, out);
-
-        return gf;
-    }
-
-    bool compute(const int n_threads,
-                 struct ggml_tensor* z,
-                 bool decode_graph,
-                 struct ggml_tensor** output,
-                 struct ggml_context* output_ctx = nullptr) override {
-        GGML_ASSERT(!decode_only || decode_graph);
-        auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(z, decode_graph);
-        };
-        // ggml_set_f32(z, 0.5f);
-        // print_ggml_tensor(z);
-        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
-    }
-
-    void test() {
-        struct ggml_init_params params;
-        params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
-        params.mem_buffer = nullptr;
-        params.no_alloc   = false;
-
-        struct ggml_context* work_ctx = ggml_init(params);
-        GGML_ASSERT(work_ctx != nullptr);
-
-        {
-            // CPU, x{1, 3, 64, 64}: Pass
-            // CUDA, x{1, 3, 64, 64}: Pass, but sill get wrong result for some image, may be due to interlnal nan
-            // CPU, x{2, 3, 64, 64}: Wrong result
-            // CUDA, x{2, 3, 64, 64}: Wrong result, and different from CPU result
-            auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 64, 64, 3, 2);
-            ggml_set_f32(x, 0.5f);
-            print_ggml_tensor(x);
-            struct ggml_tensor* out = nullptr;
-
-            int64_t t0 = ggml_time_ms();
-            compute(8, x, false, &out, work_ctx);
-            int64_t t1 = ggml_time_ms();
-
-            print_ggml_tensor(out);
-            LOG_DEBUG("encode test done in %lldms", t1 - t0);
-        }
-
-        if (false) {
-            // CPU, z{1, 4, 8, 8}: Pass
-            // CUDA, z{1, 4, 8, 8}: Pass
-            // CPU, z{3, 4, 8, 8}: Wrong result
-            // CUDA, z{3, 4, 8, 8}: Wrong result, and different from CPU result
-            auto z = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 8, 8, 4, 1);
-            ggml_set_f32(z, 0.5f);
-            print_ggml_tensor(z);
-            struct ggml_tensor* out = nullptr;
-
-            int64_t t0 = ggml_time_ms();
-            compute(8, z, true, &out, work_ctx);
-            int64_t t1 = ggml_time_ms();
-
-            print_ggml_tensor(out);
-            LOG_DEBUG("decode test done in %lldms", t1 - t0);
-        }
-    };
-};
-
-#endif
+#endif  // __VAE_HPP__

src/wan.hpp

@@ -1109,7 +1109,8 @@ namespace WAN {
     };
 
     struct WanVAERunner : public VAE {
-        bool decode_only = true;
+        float scale_factor = 1.0f;
+        bool decode_only   = true;
         WanVAE ae;
 
         WanVAERunner(ggml_backend_t backend,
@@ -1118,7 +1119,7 @@ namespace WAN {
                      const std::string prefix                       = "",
                      bool decode_only                               = false,
                      SDVersion version                              = VERSION_WAN2)
-            : decode_only(decode_only), ae(decode_only, version == VERSION_WAN2_2_TI2V), VAE(backend, offload_params_to_cpu) {
+            : decode_only(decode_only), ae(decode_only, version == VERSION_WAN2_2_TI2V), VAE(version, backend, offload_params_to_cpu) {
             ae.init(params_ctx, tensor_storage_map, prefix);
         }
 
@@ -1130,6 +1131,101 @@ namespace WAN {
             ae.get_param_tensors(tensors, prefix);
         }
 
+        ggml_tensor* vae_output_to_latents(ggml_context* work_ctx, ggml_tensor* vae_output, std::shared_ptr<RNG> rng) {
+            return vae_output;
+        }
+
+        void get_latents_mean_std_vec(ggml_tensor* latents, int channel_dim, std::vector<float>& latents_mean_vec, std::vector<float>& latents_std_vec) {
+            GGML_ASSERT(latents->ne[channel_dim] == 16 || latents->ne[channel_dim] == 48);
+            if (latents->ne[channel_dim] == 16) {  // Wan2.1 VAE
+                latents_mean_vec = {-0.7571f, -0.7089f, -0.9113f, 0.1075f, -0.1745f, 0.9653f, -0.1517f, 1.5508f,
+                                    0.4134f, -0.0715f, 0.5517f, -0.3632f, -0.1922f, -0.9497f, 0.2503f, -0.2921f};
+                latents_std_vec  = {2.8184f, 1.4541f, 2.3275f, 2.6558f, 1.2196f, 1.7708f, 2.6052f, 2.0743f,
+                                    3.2687f, 2.1526f, 2.8652f, 1.5579f, 1.6382f, 1.1253f, 2.8251f, 1.9160f};
+            } else if (latents->ne[channel_dim] == 48) {  // Wan2.2 VAE
+                latents_mean_vec = {-0.2289f, -0.0052f, -0.1323f, -0.2339f, -0.2799f, 0.0174f, 0.1838f, 0.1557f,
+                                    -0.1382f, 0.0542f, 0.2813f, 0.0891f, 0.1570f, -0.0098f, 0.0375f, -0.1825f,
+                                    -0.2246f, -0.1207f, -0.0698f, 0.5109f, 0.2665f, -0.2108f, -0.2158f, 0.2502f,
+                                    -0.2055f, -0.0322f, 0.1109f, 0.1567f, -0.0729f, 0.0899f, -0.2799f, -0.1230f,
+                                    -0.0313f, -0.1649f, 0.0117f, 0.0723f, -0.2839f, -0.2083f, -0.0520f, 0.3748f,
+                                    0.0152f, 0.1957f, 0.1433f, -0.2944f, 0.3573f, -0.0548f, -0.1681f, -0.0667f};
+                latents_std_vec  = {
+                     0.4765f, 1.0364f, 0.4514f, 1.1677f, 0.5313f, 0.4990f, 0.4818f, 0.5013f,
+                     0.8158f, 1.0344f, 0.5894f, 1.0901f, 0.6885f, 0.6165f, 0.8454f, 0.4978f,
+                     0.5759f, 0.3523f, 0.7135f, 0.6804f, 0.5833f, 1.4146f, 0.8986f, 0.5659f,
+                     0.7069f, 0.5338f, 0.4889f, 0.4917f, 0.4069f, 0.4999f, 0.6866f, 0.4093f,
+                     0.5709f, 0.6065f, 0.6415f, 0.4944f, 0.5726f, 1.2042f, 0.5458f, 1.6887f,
+                     0.3971f, 1.0600f, 0.3943f, 0.5537f, 0.5444f, 0.4089f, 0.7468f, 0.7744f};
+            }
+        }
+
+        ggml_tensor* diffusion_to_vae_latents(ggml_context* work_ctx, ggml_tensor* latents) {
+            ggml_tensor* vae_latents = ggml_dup(work_ctx, latents);
+            int channel_dim          = sd_version_is_wan(version) ? 3 : 2;
+            std::vector<float> latents_mean_vec;
+            std::vector<float> latents_std_vec;
+            get_latents_mean_std_vec(latents, channel_dim, latents_mean_vec, latents_std_vec);
+
+            float mean;
+            float std_;
+            for (int i = 0; i < latents->ne[3]; i++) {
+                if (channel_dim == 3) {
+                    mean = latents_mean_vec[i];
+                    std_ = latents_std_vec[i];
+                }
+                for (int j = 0; j < latents->ne[2]; j++) {
+                    if (channel_dim == 2) {
+                        mean = latents_mean_vec[j];
+                        std_ = latents_std_vec[j];
+                    }
+                    for (int k = 0; k < latents->ne[1]; k++) {
+                        for (int l = 0; l < latents->ne[0]; l++) {
+                            float value = ggml_ext_tensor_get_f32(latents, l, k, j, i);
+                            value       = value * std_ / scale_factor + mean;
+                            ggml_ext_tensor_set_f32(vae_latents, value, l, k, j, i);
+                        }
+                    }
+                }
+            }
+
+            return vae_latents;
+        }
+
+        ggml_tensor* vae_to_diffuison_latents(ggml_context* work_ctx, ggml_tensor* latents) {
+            ggml_tensor* diffusion_latents = ggml_dup(work_ctx, latents);
+            int channel_dim                = sd_version_is_wan(version) ? 3 : 2;
+            std::vector<float> latents_mean_vec;
+            std::vector<float> latents_std_vec;
+            get_latents_mean_std_vec(latents, channel_dim, latents_mean_vec, latents_std_vec);
+
+            float mean;
+            float std_;
+            for (int i = 0; i < latents->ne[3]; i++) {
+                if (channel_dim == 3) {
+                    mean = latents_mean_vec[i];
+                    std_ = latents_std_vec[i];
+                }
+                for (int j = 0; j < latents->ne[2]; j++) {
+                    if (channel_dim == 2) {
+                        mean = latents_mean_vec[j];
+                        std_ = latents_std_vec[j];
+                    }
+                    for (int k = 0; k < latents->ne[1]; k++) {
+                        for (int l = 0; l < latents->ne[0]; l++) {
+                            float value = ggml_ext_tensor_get_f32(latents, l, k, j, i);
+                            value       = (value - mean) * scale_factor / std_;
+                            ggml_ext_tensor_set_f32(diffusion_latents, value, l, k, j, i);
+                        }
+                    }
+                }
+            }
+            return diffusion_latents;
+        }
+
+        int get_encoder_output_channels(int input_channels) {
+            return static_cast<int>(ae.z_dim);
+        }
+
         struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
             struct ggml_cgraph* gf = new_graph_custom(10240 * z->ne[2]);
 
@@ -1173,11 +1269,11 @@ namespace WAN {
             return gf;
         }
 
-        bool compute(const int n_threads,
-                     struct ggml_tensor* z,
-                     bool decode_graph,
-                     struct ggml_tensor** output,
-                     struct ggml_context* output_ctx = nullptr) override {
+        bool _compute(const int n_threads,
+                      struct ggml_tensor* z,
+                      bool decode_graph,
+                      struct ggml_tensor** output,
+                      struct ggml_context* output_ctx = nullptr) override {
             if (true) {
                 auto get_graph = [&]() -> struct ggml_cgraph* {
                     return build_graph(z, decode_graph);
@@ -1249,7 +1345,7 @@ namespace WAN {
                 struct ggml_tensor* out = nullptr;
 
                 int64_t t0 = ggml_time_ms();
-                compute(8, z, true, &out, work_ctx);
+                _compute(8, z, true, &out, work_ctx);
                 int64_t t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);