chore: change SD_CUBLAS/SD_USE_CUBLAS to SD_CUDA/SD_USE_CUDA

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>

.github/workflows/build.yml

@@ -163,7 +163,7 @@ jobs:
           - build: "avx512"
             defines: "-DGGML_AVX512=ON -DSD_BUILD_SHARED_LIBS=ON"
           - build: "cuda12"
-            defines: "-DSD_CUBLAS=ON -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DSD_CUDA=ON -DSD_BUILD_SHARED_LIBS=ON"
           # - build: "rocm5.5"
           #   defines: '-G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS="gfx1100;gfx1102;gfx1030" -DSD_BUILD_SHARED_LIBS=ON'
           - build: 'vulkan'

CMakeLists.txt

@@ -24,19 +24,20 @@ endif()
 # general
 #option(SD_BUILD_TESTS                "sd: build tests"    ${SD_STANDALONE})
 option(SD_BUILD_EXAMPLES             "sd: build examples" ${SD_STANDALONE})
-option(SD_CUBLAS                     "sd: cuda backend" OFF)
+option(SD_CUDA                       "sd: cuda backend" OFF)
 option(SD_HIPBLAS                    "sd: rocm backend" OFF)
 option(SD_METAL                      "sd: metal backend" OFF)
 option(SD_VULKAN                     "sd: vulkan 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_SERVER               "sd: build server example"                           ON)
 
-if(SD_CUBLAS)
+if(SD_CUDA)
-    message("-- Use CUBLAS as backend stable-diffusion")
+    message("-- Use CUDA as backend stable-diffusion")
     set(GGML_CUDA ON)
-    add_definitions(-DSD_USE_CUBLAS)
+    add_definitions(-DSD_USE_CUDA)
 endif()
 
 if(SD_METAL)
@@ -54,15 +55,24 @@ endif ()
 if (SD_HIPBLAS)
     message("-- Use HIPBLAS as backend stable-diffusion")
     set(GGML_HIPBLAS ON)
-    add_definitions(-DSD_USE_CUBLAS)
+    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_CUBLAS)
+    if(SD_FAST_SOFTMAX)
+        set(GGML_CUDA_FAST_SOFTMAX ON)
+    endif()
+endif()
+
 set(SD_LIB stable-diffusion)
 
-file(GLOB SD_LIB_SOURCES 
+file(GLOB SD_LIB_SOURCES
     "*.h"
     "*.cpp"
     "*.hpp"

Dockerfile.musa

@@ -0,0 +1,19 @@
+ARG MUSA_VERSION=rc3.1.0
+
+FROM mthreads/musa:${MUSA_VERSION}-devel-ubuntu22.04 as build
+
+RUN apt-get update && apt-get install -y cmake
+
+WORKDIR /sd.cpp
+
+COPY . .
+
+RUN mkdir build && cd build && \
+    cmake .. -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release && \
+    cmake --build . --config Release
+
+FROM mthreads/musa:${MUSA_VERSION}-runtime-ubuntu22.04 as runtime
+
+COPY --from=build /sd.cpp/build/bin/sd /sd
+
+ENTRYPOINT [ "/sd" ]

README.md

@@ -113,12 +113,12 @@ cmake .. -DGGML_OPENBLAS=ON
 cmake --build . --config Release
 ```
 
-##### Using CUBLAS
+##### Using CUDA
 
 This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). Recommended to have at least 4 GB of VRAM.
 
 ```
-cmake .. -DSD_CUBLAS=ON
+cmake .. -DSD_CUDA=ON
 cmake --build . --config Release
 ```
 
@@ -132,6 +132,14 @@ cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_H
 cmake --build . --config Release
 ```
 
+##### Using MUSA
+
+This provides BLAS acceleration using the MUSA cores of your Moore Threads GPU. Make sure to have the MUSA toolkit installed.
+
+```bash
+cmake .. -DCMAKE_C_COMPILER=/usr/local/musa/bin/clang -DCMAKE_CXX_COMPILER=/usr/local/musa/bin/clang++ -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release
+cmake --build . --config Release
+```
 
 ##### Using Metal
 
@@ -232,6 +240,10 @@ arguments:
   -p, --prompt [PROMPT]              the prompt to render
   -n, --negative-prompt PROMPT       the negative prompt (default: "")
   --cfg-scale SCALE                  unconditional guidance scale: (default: 7.0)
+  --skip-layers LAYERS               Layers to skip for SLG steps: (default: [7,8,9])
+  --skip-layer-start START           SLG enabling point: (default: 0.01)
+  --skip-layer-end END               SLG disabling point: (default: 0.2)
+									 SLG will be enabled at step int([STEPS]*[START]) and disabled at int([STEPS]*[END])
   --strength STRENGTH                strength for noising/unnoising (default: 0.75)
   --style-ratio STYLE-RATIO          strength for keeping input identity (default: 20%)
   --control-strength STRENGTH        strength to apply Control Net (default: 0.9)

conditioner.hpp

@@ -61,18 +61,18 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                       SDVersion version = VERSION_SD1,
                                       PMVersion pv      = PM_VERSION_1,
                                       int clip_skip     = -1)
-        : version(version), pm_version(pv), tokenizer(version == VERSION_SD2 ? 0 : 49407), embd_dir(embd_dir) {
+        : version(version), pm_version(pv), tokenizer(sd_version_is_sd2(version) ? 0 : 49407), embd_dir(embd_dir) {
         if (clip_skip <= 0) {
             clip_skip = 1;
-            if (version == VERSION_SD2 || version == VERSION_SDXL) {
+            if (sd_version_is_sd2(version) || sd_version_is_sdxl(version)) {
                 clip_skip = 2;
             }
         }
-        if (version == VERSION_SD1) {
+        if (sd_version_is_sd1(version)) {
             text_model = std::make_shared<CLIPTextModelRunner>(backend, tensor_types, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, clip_skip);
-        } else if (version == VERSION_SD2) {
+        } else if (sd_version_is_sd2(version)) {
             text_model = std::make_shared<CLIPTextModelRunner>(backend, tensor_types, "cond_stage_model.transformer.text_model", OPEN_CLIP_VIT_H_14, clip_skip);
-        } else if (version == VERSION_SDXL) {
+        } else if (sd_version_is_sdxl(version)) {
             text_model  = std::make_shared<CLIPTextModelRunner>(backend, tensor_types, "cond_stage_model.transformer.text_model", OPENAI_CLIP_VIT_L_14, clip_skip, false);
             text_model2 = std::make_shared<CLIPTextModelRunner>(backend, tensor_types, "cond_stage_model.1.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, clip_skip, false);
         }
@@ -80,35 +80,35 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
 
     void set_clip_skip(int clip_skip) {
         text_model->set_clip_skip(clip_skip);
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             text_model2->set_clip_skip(clip_skip);
         }
     }
 
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
         text_model->get_param_tensors(tensors, "cond_stage_model.transformer.text_model");
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             text_model2->get_param_tensors(tensors, "cond_stage_model.1.transformer.text_model");
         }
     }
 
     void alloc_params_buffer() {
         text_model->alloc_params_buffer();
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             text_model2->alloc_params_buffer();
         }
     }
 
     void free_params_buffer() {
         text_model->free_params_buffer();
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             text_model2->free_params_buffer();
         }
     }
 
     size_t get_params_buffer_size() {
         size_t buffer_size = text_model->get_params_buffer_size();
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             buffer_size += text_model2->get_params_buffer_size();
         }
         return buffer_size;
@@ -402,7 +402,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
             auto input_ids                 = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
             struct ggml_tensor* input_ids2 = NULL;
             size_t max_token_idx           = 0;
-            if (version == VERSION_SDXL) {
+            if (sd_version_is_sdxl(version)) {
                 auto it = std::find(chunk_tokens.begin(), chunk_tokens.end(), tokenizer.EOS_TOKEN_ID);
                 if (it != chunk_tokens.end()) {
                     std::fill(std::next(it), chunk_tokens.end(), 0);
@@ -427,7 +427,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                     false,
                                     &chunk_hidden_states1,
                                     work_ctx);
-                if (version == VERSION_SDXL) {
+                if (sd_version_is_sdxl(version)) {
                     text_model2->compute(n_threads,
                                          input_ids2,
                                          0,
@@ -486,7 +486,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                                         ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);
 
         ggml_tensor* vec = NULL;
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             int out_dim = 256;
             vec         = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, adm_in_channels);
             // [0:1280]

control.hpp

@@ -34,11 +34,11 @@ public:
 
     ControlNetBlock(SDVersion version = VERSION_SD1)
         : version(version) {
-        if (version == VERSION_SD2) {
+        if (sd_version_is_sd2(version)) {
             context_dim       = 1024;
             num_head_channels = 64;
             num_heads         = -1;
-        } else if (version == VERSION_SDXL) {
+        } else if (sd_version_is_sdxl(version)) {
             context_dim           = 2048;
             attention_resolutions = {4, 2};
             channel_mult          = {1, 2, 4};
@@ -58,7 +58,7 @@ public:
         // time_embed_1 is nn.SiLU()
         blocks["time_embed.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim));
 
-        if (version == VERSION_SDXL || version == VERSION_SVD) {
+        if (sd_version_is_sdxl(version) || version == VERSION_SVD) {
             blocks["label_emb.0.0"] = std::shared_ptr<GGMLBlock>(new Linear(adm_in_channels, time_embed_dim));
             // label_emb_1 is nn.SiLU()
             blocks["label_emb.0.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim));

diffusion_model.hpp

@@ -133,8 +133,9 @@ struct FluxModel : public DiffusionModel {
 
     FluxModel(ggml_backend_t backend,
               std::map<std::string, enum ggml_type>& tensor_types,
-              bool flash_attn = false)
+              SDVersion version = VERSION_FLUX,
-        : flux(backend, tensor_types, "model.diffusion_model", flash_attn) {
+              bool flash_attn   = false)
+        : flux(backend, tensor_types, "model.diffusion_model", version, flash_attn) {
     }
 
     void alloc_params_buffer() {
@@ -174,7 +175,7 @@ struct FluxModel : public DiffusionModel {
                  struct ggml_tensor** output               = NULL,
                  struct ggml_context* output_ctx           = NULL,
                  std::vector<int> skip_layers              = std::vector<int>()) {
-        return flux.compute(n_threads, x, timesteps, context, y, guidance, output, output_ctx, skip_layers);
+        return flux.compute(n_threads, x, timesteps, context, c_concat, y, guidance, output, output_ctx, skip_layers);
     }
 };
 

examples/cli/main.cpp

@@ -85,6 +85,7 @@ struct SDParams {
     std::string lora_model_dir;
     std::string output_path = "output.png";
     std::string input_path;
+    std::string mask_path;
     std::string control_image_path;
 
     std::string prompt;
@@ -148,6 +149,7 @@ void print_params(SDParams params) {
     printf("    normalize input image :  %s\n", params.normalize_input ? "true" : "false");
     printf("    output_path:       %s\n", params.output_path.c_str());
     printf("    init_img:          %s\n", params.input_path.c_str());
+    printf("    mask_img:          %s\n", params.mask_path.c_str());
     printf("    control_image:     %s\n", params.control_image_path.c_str());
     printf("    clip on cpu:       %s\n", params.clip_on_cpu ? "true" : "false");
     printf("    controlnet cpu:    %s\n", params.control_net_cpu ? "true" : "false");
@@ -207,9 +209,9 @@ void print_usage(int argc, const char* argv[]) {
     printf("  --cfg-scale SCALE                  unconditional guidance scale: (default: 7.0)\n");
     printf("  --slg-scale SCALE                  skip layer guidance (SLG) scale, only for DiT models: (default: 0)\n");
     printf("                                     0 means disabled, a value of 2.5 is nice for sd3.5 medium\n");
-    printf("  --skip_layers LAYERS               Layers to skip for SLG steps: (default: [7,8,9])\n");
+    printf("  --skip-layers LAYERS               Layers to skip for SLG steps: (default: [7,8,9])\n");
-    printf("  --skip_layer_start START           SLG enabling point: (default: 0.01)\n");
+    printf("  --skip-layer-start START           SLG enabling point: (default: 0.01)\n");
-    printf("  --skip_layer_end END               SLG disabling point: (default: 0.2)\n");
+    printf("  --skip-layer-end END               SLG disabling point: (default: 0.2)\n");
     printf("                                     SLG will be enabled at step int([STEPS]*[START]) and disabled at int([STEPS]*[END])\n");
     printf("  --strength STRENGTH                strength for noising/unnoising (default: 0.75)\n");
     printf("  --style-ratio STYLE-RATIO          strength for keeping input identity (default: 20%%)\n");
@@ -384,6 +386,12 @@ void parse_args(int argc, const char** argv, SDParams& params) {
                 break;
             }
             params.input_path = argv[i];
+        } else if (arg == "--mask") {
+            if (++i >= argc) {
+                invalid_arg = true;
+                break;
+            }
+            params.mask_path = argv[i];
         } else if (arg == "--control-image") {
             if (++i >= argc) {
                 invalid_arg = true;
@@ -803,6 +811,8 @@ int main(int argc, const char* argv[]) {
     bool vae_decode_only          = true;
     uint8_t* input_image_buffer   = NULL;
     uint8_t* control_image_buffer = NULL;
+    uint8_t* mask_image_buffer    = NULL;
+
     if (params.mode == IMG2IMG || params.mode == IMG2VID) {
         vae_decode_only = false;
 
@@ -907,6 +917,18 @@ int main(int argc, const char* argv[]) {
         }
     }
 
+    if (params.mask_path != "") {
+        int c             = 0;
+        mask_image_buffer = stbi_load(params.mask_path.c_str(), &params.width, &params.height, &c, 1);
+    } else {
+        std::vector<uint8_t> arr(params.width * params.height, 255);
+        mask_image_buffer = arr.data();
+    }
+    sd_image_t mask_image = {(uint32_t)params.width,
+                             (uint32_t)params.height,
+                             1,
+                             mask_image_buffer};
+
     sd_image_t* results;
     if (params.mode == TXT2IMG) {
         results = txt2img(sd_ctx,
@@ -976,6 +998,7 @@ int main(int argc, const char* argv[]) {
         } else {
             results = img2img(sd_ctx,
                               input_image,
+                              mask_image,
                               params.prompt.c_str(),
                               params.negative_prompt.c_str(),
                               params.clip_skip,

flux.hpp

@@ -490,6 +490,7 @@ namespace Flux {
 
     struct FluxParams {
         int64_t in_channels         = 64;
+        int64_t out_channels        = 64;
         int64_t vec_in_dim          = 768;
         int64_t context_in_dim      = 4096;
         int64_t hidden_size         = 3072;
@@ -642,8 +643,7 @@ namespace Flux {
         Flux() {}
         Flux(FluxParams params)
             : params(params) {
-            int64_t out_channels = params.in_channels;
+            int64_t pe_dim = params.hidden_size / params.num_heads;
-            int64_t pe_dim       = params.hidden_size / params.num_heads;
 
             blocks["img_in"]    = std::shared_ptr<GGMLBlock>(new Linear(params.in_channels, params.hidden_size, true));
             blocks["time_in"]   = std::shared_ptr<GGMLBlock>(new MLPEmbedder(256, params.hidden_size));
@@ -669,7 +669,7 @@ namespace Flux {
                                                                                                                 params.flash_attn));
             }
 
-            blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new LastLayer(params.hidden_size, 1, out_channels));
+            blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new LastLayer(params.hidden_size, 1, params.out_channels));
         }
 
         struct ggml_tensor* patchify(struct ggml_context* ctx,
@@ -789,6 +789,7 @@ namespace Flux {
                                     struct ggml_tensor* x,
                                     struct ggml_tensor* timestep,
                                     struct ggml_tensor* context,
+                                    struct ggml_tensor* c_concat,
                                     struct ggml_tensor* y,
                                     struct ggml_tensor* guidance,
                                     struct ggml_tensor* pe,
@@ -797,6 +798,7 @@ namespace Flux {
             // x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
             // timestep: (N,) tensor of diffusion timesteps
             // context: (N, L, D)
+            // c_concat: NULL, or for (N,C+M, H, W) for Fill
             // y: (N, adm_in_channels) tensor of class labels
             // guidance: (N,)
             // pe: (L, d_head/2, 2, 2)
@@ -806,6 +808,7 @@ namespace Flux {
 
             int64_t W          = x->ne[0];
             int64_t H          = x->ne[1];
+            int64_t C          = x->ne[2];
             int64_t patch_size = 2;
             int pad_h          = (patch_size - H % patch_size) % patch_size;
             int pad_w          = (patch_size - W % patch_size) % patch_size;
@@ -814,6 +817,19 @@ namespace Flux {
             // img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
             auto img = patchify(ctx, x, patch_size);  // [N, h*w, C * patch_size * patch_size]
 
+            if (c_concat != NULL) {
+                ggml_tensor* masked = ggml_view_4d(ctx, c_concat, c_concat->ne[0], c_concat->ne[1], C, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], 0);
+                ggml_tensor* mask   = ggml_view_4d(ctx, c_concat, c_concat->ne[0], c_concat->ne[1], 8 * 8, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * C);
+
+                masked = ggml_pad(ctx, masked, pad_w, pad_h, 0, 0);
+                mask   = ggml_pad(ctx, mask, pad_w, pad_h, 0, 0);
+
+                masked = patchify(ctx, masked, patch_size);
+                mask   = patchify(ctx, mask, patch_size);
+
+                img = ggml_concat(ctx, img, ggml_concat(ctx, masked, mask, 0), 0);
+            }
+
             auto out = forward_orig(ctx, img, context, timestep, y, guidance, pe, skip_layers);  // [N, h*w, C * patch_size * patch_size]
 
             // rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)
@@ -834,12 +850,16 @@ namespace Flux {
         FluxRunner(ggml_backend_t backend,
                    std::map<std::string, enum ggml_type>& tensor_types = empty_tensor_types,
                    const std::string prefix                            = "",
+                   SDVersion version                                   = VERSION_FLUX,
                    bool flash_attn                                     = false)
             : GGMLRunner(backend) {
             flux_params.flash_attn          = flash_attn;
             flux_params.guidance_embed      = false;
             flux_params.depth               = 0;
             flux_params.depth_single_blocks = 0;
+            if (version == VERSION_FLUX_FILL) {
+                flux_params.in_channels = 384;
+            }
             for (auto pair : tensor_types) {
                 std::string tensor_name = pair.first;
                 if (tensor_name.find("model.diffusion_model.") == std::string::npos)
@@ -886,14 +906,18 @@ namespace Flux {
         struct ggml_cgraph* build_graph(struct ggml_tensor* x,
                                         struct ggml_tensor* timesteps,
                                         struct ggml_tensor* context,
+                                        struct ggml_tensor* c_concat,
                                         struct ggml_tensor* y,
                                         struct ggml_tensor* guidance,
                                         std::vector<int> skip_layers = std::vector<int>()) {
             GGML_ASSERT(x->ne[3] == 1);
             struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, FLUX_GRAPH_SIZE, false);
 
-            x         = to_backend(x);
+            x       = to_backend(x);
-            context   = to_backend(context);
+            context = to_backend(context);
+            if (c_concat != NULL) {
+                c_concat = to_backend(c_concat);
+            }
             y         = to_backend(y);
             timesteps = to_backend(timesteps);
             if (flux_params.guidance_embed) {
@@ -913,6 +937,7 @@ namespace Flux {
                                                    x,
                                                    timesteps,
                                                    context,
+                                                   c_concat,
                                                    y,
                                                    guidance,
                                                    pe,
@@ -927,6 +952,7 @@ namespace Flux {
                      struct ggml_tensor* x,
                      struct ggml_tensor* timesteps,
                      struct ggml_tensor* context,
+                     struct ggml_tensor* c_concat,
                      struct ggml_tensor* y,
                      struct ggml_tensor* guidance,
                      struct ggml_tensor** output     = NULL,
@@ -938,7 +964,7 @@ namespace Flux {
             // y: [N, adm_in_channels] or [1, adm_in_channels]
             // guidance: [N, ]
             auto get_graph = [&]() -> struct ggml_cgraph* {
-                return build_graph(x, timesteps, context, y, guidance, skip_layers);
+                return build_graph(x, timesteps, context, c_concat, y, guidance, skip_layers);
             };
 
             GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
@@ -978,7 +1004,7 @@ namespace Flux {
                 struct ggml_tensor* out = NULL;
 
                 int t0 = ggml_time_ms();
-                compute(8, x, timesteps, context, y, guidance, &out, work_ctx);
+                compute(8, x, timesteps, context, NULL, y, guidance, &out, work_ctx);
                 int t1 = ggml_time_ms();
 
                 print_ggml_tensor(out);

ggml_extend.hpp

@@ -27,7 +27,7 @@
 
 #include "model.h"
 
-#ifdef SD_USE_CUBLAS
+#ifdef SD_USE_CUDA
 #include "ggml-cuda.h"
 #endif
 
@@ -290,6 +290,42 @@ __STATIC_INLINE__ void sd_image_to_tensor(const uint8_t* image_data,
     }
 }
 
+__STATIC_INLINE__ void sd_mask_to_tensor(const uint8_t* image_data,
+                                         struct ggml_tensor* output,
+                                         bool scale = true) {
+    int64_t width    = output->ne[0];
+    int64_t height   = output->ne[1];
+    int64_t channels = output->ne[2];
+    GGML_ASSERT(channels == 1 && output->type == GGML_TYPE_F32);
+    for (int iy = 0; iy < height; iy++) {
+        for (int ix = 0; ix < width; ix++) {
+            float value = *(image_data + iy * width * channels + ix);
+            if (scale) {
+                value /= 255.f;
+            }
+            ggml_tensor_set_f32(output, value, ix, iy);
+        }
+    }
+}
+
+__STATIC_INLINE__ void sd_apply_mask(struct ggml_tensor* image_data,
+                                     struct ggml_tensor* mask,
+                                     struct ggml_tensor* output) {
+    int64_t width    = output->ne[0];
+    int64_t height   = output->ne[1];
+    int64_t channels = output->ne[2];
+    GGML_ASSERT(output->type == GGML_TYPE_F32);
+    for (int ix = 0; ix < width; ix++) {
+        for (int iy = 0; iy < height; iy++) {
+            float m = ggml_tensor_get_f32(mask, ix, iy);
+            for (int k = 0; k < channels; k++) {
+                float value = ((float)(m < 254.5/255)) * (ggml_tensor_get_f32(image_data, ix, iy, k) - .5) + .5;
+                ggml_tensor_set_f32(output, value, ix, iy, k);
+            }
+        }
+    }
+}
+
 __STATIC_INLINE__ void sd_mul_images_to_tensor(const uint8_t* image_data,
                                                struct ggml_tensor* output,
                                                int idx,
@@ -672,7 +708,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention(struct ggml_context* ctx
                                                         struct ggml_tensor* k,
                                                         struct ggml_tensor* v,
                                                         bool mask = false) {
-#if defined(SD_USE_FLASH_ATTENTION) && !defined(SD_USE_CUBLAS) && !defined(SD_USE_METAL) && !defined(SD_USE_VULKAN) && !defined(SD_USE_SYCL)
+#if defined(SD_USE_FLASH_ATTENTION) && !defined(SD_USE_CUDA) && !defined(SD_USE_METAL) && !defined(SD_USE_VULKAN) && !defined(SD_USE_SYCL)
     struct ggml_tensor* kqv = ggml_flash_attn(ctx, q, k, v, false);  // [N * n_head, n_token, d_head]
 #else
     float d_head           = (float)q->ne[0];
@@ -828,7 +864,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_group_norm(struct ggml_context* ct
 }
 
 __STATIC_INLINE__ void ggml_backend_tensor_get_and_sync(ggml_backend_t backend, const struct ggml_tensor* tensor, void* data, size_t offset, size_t size) {
-#if defined(SD_USE_CUBLAS) || defined(SD_USE_SYCL)
+#if defined(SD_USE_CUDA) || defined(SD_USE_SYCL)
     if (!ggml_backend_is_cpu(backend)) {
         ggml_backend_tensor_get_async(backend, tensor, data, offset, size);
         ggml_backend_synchronize(backend);
@@ -1138,13 +1174,7 @@ public:
             ggml_backend_cpu_set_n_threads(backend, n_threads);
         }
 
-#ifdef SD_USE_METAL
-        if (ggml_backend_is_metal(backend)) {
-            ggml_backend_metal_set_n_cb(backend, n_threads);
-        }
-#endif
         ggml_backend_graph_compute(backend, gf);
-
 #ifdef GGML_PERF
         ggml_graph_print(gf);
 #endif

lora.hpp

@@ -6,6 +6,90 @@
 #define LORA_GRAPH_SIZE 10240
 
 struct LoraModel : public GGMLRunner {
+    enum lora_t {
+        REGULAR      = 0,
+        DIFFUSERS    = 1,
+        DIFFUSERS_2  = 2,
+        DIFFUSERS_3  = 3,
+        TRANSFORMERS = 4,
+        LORA_TYPE_COUNT
+    };
+
+    const std::string lora_ups[LORA_TYPE_COUNT] = {
+        ".lora_up",
+        "_lora.up",
+        ".lora_B",
+        ".lora.up",
+        ".lora_linear_layer.up",
+    };
+
+    const std::string lora_downs[LORA_TYPE_COUNT] = {
+        ".lora_down",
+        "_lora.down",
+        ".lora_A",
+        ".lora.down",
+        ".lora_linear_layer.down",
+    };
+
+    const std::string lora_pre[LORA_TYPE_COUNT] = {
+        "lora.",
+        "",
+        "",
+        "",
+        "",
+    };
+
+    const std::map<std::string, std::string> alt_names = {
+        // mmdit
+        {"final_layer.adaLN_modulation.1", "norm_out.linear"},
+        {"pos_embed", "pos_embed.proj"},
+        {"final_layer.linear", "proj_out"},
+        {"y_embedder.mlp.0", "time_text_embed.text_embedder.linear_1"},
+        {"y_embedder.mlp.2", "time_text_embed.text_embedder.linear_2"},
+        {"t_embedder.mlp.0", "time_text_embed.timestep_embedder.linear_1"},
+        {"t_embedder.mlp.2", "time_text_embed.timestep_embedder.linear_2"},
+        {"x_block.mlp.fc1", "ff.net.0.proj"},
+        {"x_block.mlp.fc2", "ff.net.2"},
+        {"context_block.mlp.fc1", "ff_context.net.0.proj"},
+        {"context_block.mlp.fc2", "ff_context.net.2"},
+        {"x_block.adaLN_modulation.1", "norm1.linear"},
+        {"context_block.adaLN_modulation.1", "norm1_context.linear"},
+        {"context_block.attn.proj", "attn.to_add_out"},
+        {"x_block.attn.proj", "attn.to_out.0"},
+        {"x_block.attn2.proj", "attn2.to_out.0"},
+        // flux
+        // singlestream
+        {"linear2", "proj_out"},
+        {"modulation.lin", "norm.linear"},
+        // doublestream
+        {"txt_attn.proj", "attn.to_add_out"},
+        {"img_attn.proj", "attn.to_out.0"},
+        {"txt_mlp.0", "ff_context.net.0.proj"},
+        {"txt_mlp.2", "ff_context.net.2"},
+        {"img_mlp.0", "ff.net.0.proj"},
+        {"img_mlp.2", "ff.net.2"},
+        {"txt_mod.lin", "norm1_context.linear"},
+        {"img_mod.lin", "norm1.linear"},
+    };
+
+    const std::map<std::string, std::string> qkv_prefixes = {
+        // mmdit
+        {"context_block.attn.qkv", "attn.add_"},  // suffix "_proj"
+        {"x_block.attn.qkv", "attn.to_"},
+        {"x_block.attn2.qkv", "attn2.to_"},
+        // flux
+        // doublestream
+        {"txt_attn.qkv", "attn.add_"},  // suffix "_proj"
+        {"img_attn.qkv", "attn.to_"},
+    };
+    const std::map<std::string, std::string> qkvm_prefixes = {
+        // flux
+        // singlestream
+        {"linear1", ""},
+    };
+
+    const std::string* type_fingerprints = lora_ups;
+
     float multiplier = 1.0f;
     std::map<std::string, struct ggml_tensor*> lora_tensors;
     std::string file_path;
@@ -14,6 +98,7 @@ struct LoraModel : public GGMLRunner {
     bool applied                    = false;
     std::vector<int> zero_index_vec = {0};
     ggml_tensor* zero_index         = NULL;
+    enum lora_t type                = REGULAR;
 
     LoraModel(ggml_backend_t backend,
               const std::string& file_path = "",
@@ -44,6 +129,13 @@ struct LoraModel : public GGMLRunner {
                 // LOG_INFO("skipping LoRA tesnor '%s'", name.c_str());
                 return true;
             }
+            // LOG_INFO("%s", name.c_str());
+            for (int i = 0; i < LORA_TYPE_COUNT; i++) {
+                if (name.find(type_fingerprints[i]) != std::string::npos) {
+                    type = (lora_t)i;
+                    break;
+                }
+            }
 
             if (dry_run) {
                 struct ggml_tensor* real = ggml_new_tensor(params_ctx,
@@ -61,10 +153,12 @@ struct LoraModel : public GGMLRunner {
 
         model_loader.load_tensors(on_new_tensor_cb, backend);
         alloc_params_buffer();
-
+        // exit(0);
         dry_run = false;
         model_loader.load_tensors(on_new_tensor_cb, backend);
 
+        LOG_DEBUG("lora type: \"%s\"/\"%s\"", lora_downs[type].c_str(), lora_ups[type].c_str());
+
         LOG_DEBUG("finished loaded lora");
         return true;
     }
@@ -76,7 +170,66 @@ struct LoraModel : public GGMLRunner {
         return out;
     }
 
-    struct ggml_cgraph* build_lora_graph(std::map<std::string, struct ggml_tensor*> model_tensors) {
+    std::vector<std::string> to_lora_keys(std::string blk_name, SDVersion version) {
+        std::vector<std::string> keys;
+        // if (!sd_version_is_sd3(version) || blk_name != "model.diffusion_model.pos_embed") {
+        size_t k_pos = blk_name.find(".weight");
+        if (k_pos == std::string::npos) {
+            return keys;
+        }
+        blk_name = blk_name.substr(0, k_pos);
+        // }
+        keys.push_back(blk_name);
+        keys.push_back("lora." + blk_name);
+        if (sd_version_is_dit(version)) {
+            if (blk_name.find("model.diffusion_model") != std::string::npos) {
+                blk_name.replace(blk_name.find("model.diffusion_model"), sizeof("model.diffusion_model") - 1, "transformer");
+            }
+
+            if (blk_name.find(".single_blocks") != std::string::npos) {
+                blk_name.replace(blk_name.find(".single_blocks"), sizeof(".single_blocks") - 1, ".single_transformer_blocks");
+            }
+            if (blk_name.find(".double_blocks") != std::string::npos) {
+                blk_name.replace(blk_name.find(".double_blocks"), sizeof(".double_blocks") - 1, ".transformer_blocks");
+            }
+
+            if (blk_name.find(".joint_blocks") != std::string::npos) {
+                blk_name.replace(blk_name.find(".joint_blocks"), sizeof(".joint_blocks") - 1, ".transformer_blocks");
+            }
+
+            for (const auto& item : alt_names) {
+                size_t match = blk_name.find(item.first);
+                if (match != std::string::npos) {
+                    blk_name = blk_name.substr(0, match) + item.second;
+                }
+            }
+            for (const auto& prefix : qkv_prefixes) {
+                size_t match = blk_name.find(prefix.first);
+                if (match != std::string::npos) {
+                    std::string split_blk = "SPLIT|" + blk_name.substr(0, match) + prefix.second;
+                    keys.push_back(split_blk);
+                }
+            }
+            for (const auto& prefix : qkvm_prefixes) {
+                size_t match = blk_name.find(prefix.first);
+                if (match != std::string::npos) {
+                    std::string split_blk = "SPLIT_L|" + blk_name.substr(0, match) + prefix.second;
+                    keys.push_back(split_blk);
+                }
+            }
+        }
+        keys.push_back(blk_name);
+
+        std::vector<std::string> ret;
+        for (std::string& key : keys) {
+            ret.push_back(key);
+            replace_all_chars(key, '.', '_');
+            ret.push_back(key);
+        }
+        return ret;
+    }
+
+    struct ggml_cgraph* build_lora_graph(std::map<std::string, struct ggml_tensor*> model_tensors, SDVersion version) {
         struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, LORA_GRAPH_SIZE, false);
 
         zero_index = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_I32, 1);
@@ -88,91 +241,416 @@ struct LoraModel : public GGMLRunner {
             std::string k_tensor       = it.first;
             struct ggml_tensor* weight = model_tensors[it.first];
 
-            size_t k_pos = k_tensor.find(".weight");
+            std::vector<std::string> keys = to_lora_keys(k_tensor, version);
-            if (k_pos == std::string::npos) {
+            if (keys.size() == 0)
                 continue;
-            }
-            k_tensor = k_tensor.substr(0, k_pos);
-            replace_all_chars(k_tensor, '.', '_');
-            // LOG_DEBUG("k_tensor %s", k_tensor.c_str());
-            std::string lora_up_name = "lora." + k_tensor + ".lora_up.weight";
-            if (lora_tensors.find(lora_up_name) == lora_tensors.end()) {
-                if (k_tensor == "model_diffusion_model_output_blocks_2_2_conv") {
-                    // fix for some sdxl lora, like lcm-lora-xl
-                    k_tensor     = "model_diffusion_model_output_blocks_2_1_conv";
-                    lora_up_name = "lora." + k_tensor + ".lora_up.weight";
-                }
-            }
-
-            std::string lora_down_name = "lora." + k_tensor + ".lora_down.weight";
-            std::string alpha_name     = "lora." + k_tensor + ".alpha";
-            std::string scale_name     = "lora." + k_tensor + ".scale";
-
             ggml_tensor* lora_up   = NULL;
             ggml_tensor* lora_down = NULL;
+            for (auto& key : keys) {
+                std::string alpha_name         = "";
+                std::string scale_name         = "";
+                std::string split_q_scale_name = "";
+                std::string lora_down_name     = "";
+                std::string lora_up_name       = "";
 
-            if (lora_tensors.find(lora_up_name) != lora_tensors.end()) {
+                if (starts_with(key, "SPLIT|")) {
-                lora_up = lora_tensors[lora_up_name];
+                    key = key.substr(sizeof("SPLIT|") - 1);
+                    // TODO: Handle alphas
+                    std::string suffix  = "";
+                    auto split_q_d_name = lora_pre[type] + key + "q" + suffix + lora_downs[type] + ".weight";
+
+                    if (lora_tensors.find(split_q_d_name) == lora_tensors.end()) {
+                        suffix         = "_proj";
+                        split_q_d_name = lora_pre[type] + key + "q" + suffix + lora_downs[type] + ".weight";
+                    }
+                    if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
+                        // print_ggml_tensor(it.second, true);  //[3072, 21504, 1, 1]
+                        // find qkv and mlp up parts in LoRA model
+                        auto split_k_d_name = lora_pre[type] + key + "k" + suffix + lora_downs[type] + ".weight";
+                        auto split_v_d_name = lora_pre[type] + key + "v" + suffix + lora_downs[type] + ".weight";
+
+                        auto split_q_u_name = lora_pre[type] + key + "q" + suffix + lora_ups[type] + ".weight";
+                        auto split_k_u_name = lora_pre[type] + key + "k" + suffix + lora_ups[type] + ".weight";
+                        auto split_v_u_name = lora_pre[type] + key + "v" + suffix + lora_ups[type] + ".weight";
+
+                        auto split_q_scale_name = lora_pre[type] + key + "q" + suffix + ".scale";
+                        auto split_k_scale_name = lora_pre[type] + key + "k" + suffix + ".scale";
+                        auto split_v_scale_name = lora_pre[type] + key + "v" + suffix + ".scale";
+
+                        auto split_q_alpha_name = lora_pre[type] + key + "q" + suffix + ".alpha";
+                        auto split_k_alpha_name = lora_pre[type] + key + "k" + suffix + ".alpha";
+                        auto split_v_alpha_name = lora_pre[type] + key + "v" + suffix + ".alpha";
+
+                        ggml_tensor* lora_q_down = NULL;
+                        ggml_tensor* lora_q_up   = NULL;
+                        ggml_tensor* lora_k_down = NULL;
+                        ggml_tensor* lora_k_up   = NULL;
+                        ggml_tensor* lora_v_down = NULL;
+                        ggml_tensor* lora_v_up   = NULL;
+
+                        lora_q_down = to_f32(compute_ctx, lora_tensors[split_q_d_name]);
+
+                        if (lora_tensors.find(split_q_u_name) != lora_tensors.end()) {
+                            lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
+                        }
+
+                        if (lora_tensors.find(split_k_d_name) != lora_tensors.end()) {
+                            lora_k_down = to_f32(compute_ctx, lora_tensors[split_k_d_name]);
+                        }
+
+                        if (lora_tensors.find(split_k_u_name) != lora_tensors.end()) {
+                            lora_k_up = to_f32(compute_ctx, lora_tensors[split_k_u_name]);
+                        }
+
+                        if (lora_tensors.find(split_v_d_name) != lora_tensors.end()) {
+                            lora_v_down = to_f32(compute_ctx, lora_tensors[split_v_d_name]);
+                        }
+
+                        if (lora_tensors.find(split_v_u_name) != lora_tensors.end()) {
+                            lora_v_up = to_f32(compute_ctx, lora_tensors[split_v_u_name]);
+                        }
+
+                        float q_rank = lora_q_up->ne[0];
+                        float k_rank = lora_k_up->ne[0];
+                        float v_rank = lora_v_up->ne[0];
+
+                        float lora_q_scale = 1;
+                        float lora_k_scale = 1;
+                        float lora_v_scale = 1;
+
+                        if (lora_tensors.find(split_q_scale_name) != lora_tensors.end()) {
+                            lora_q_scale = ggml_backend_tensor_get_f32(lora_tensors[split_q_scale_name]);
+                            applied_lora_tensors.insert(split_q_scale_name);
+                        }
+                        if (lora_tensors.find(split_k_scale_name) != lora_tensors.end()) {
+                            lora_k_scale = ggml_backend_tensor_get_f32(lora_tensors[split_k_scale_name]);
+                            applied_lora_tensors.insert(split_k_scale_name);
+                        }
+                        if (lora_tensors.find(split_v_scale_name) != lora_tensors.end()) {
+                            lora_v_scale = ggml_backend_tensor_get_f32(lora_tensors[split_v_scale_name]);
+                            applied_lora_tensors.insert(split_v_scale_name);
+                        }
+
+                        if (lora_tensors.find(split_q_alpha_name) != lora_tensors.end()) {
+                            float lora_q_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_q_alpha_name]);
+                            applied_lora_tensors.insert(split_q_alpha_name);
+                            lora_q_scale = lora_q_alpha / q_rank;
+                        }
+                        if (lora_tensors.find(split_k_alpha_name) != lora_tensors.end()) {
+                            float lora_k_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_k_alpha_name]);
+                            applied_lora_tensors.insert(split_k_alpha_name);
+                            lora_k_scale = lora_k_alpha / k_rank;
+                        }
+                        if (lora_tensors.find(split_v_alpha_name) != lora_tensors.end()) {
+                            float lora_v_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_v_alpha_name]);
+                            applied_lora_tensors.insert(split_v_alpha_name);
+                            lora_v_scale = lora_v_alpha / v_rank;
+                        }
+
+                        ggml_scale_inplace(compute_ctx, lora_q_down, lora_q_scale);
+                        ggml_scale_inplace(compute_ctx, lora_k_down, lora_k_scale);
+                        ggml_scale_inplace(compute_ctx, lora_v_down, lora_v_scale);
+
+                        // print_ggml_tensor(lora_q_down, true);  //[3072, R, 1, 1]
+                        // print_ggml_tensor(lora_k_down, true);  //[3072, R, 1, 1]
+                        // print_ggml_tensor(lora_v_down, true);  //[3072, R, 1, 1]
+                        // print_ggml_tensor(lora_q_up, true);    //[R, 3072, 1, 1]
+                        // print_ggml_tensor(lora_k_up, true);    //[R, 3072, 1, 1]
+                        // print_ggml_tensor(lora_v_up, true);    //[R, 3072, 1, 1]
+
+                        // these need to be stitched together this way:
+                        //                          |q_up,0   ,0   |
+                        //                          |0   ,k_up,0   |
+                        //                          |0   ,0   ,v_up|
+                        // (q_down,k_down,v_down) . (q   ,k   ,v)
+
+                        // up_concat will be [9216, R*3, 1, 1]
+                        // down_concat will be [R*3, 3072, 1, 1]
+                        ggml_tensor* lora_down_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_down, lora_k_down, 1), lora_v_down, 1);
+
+                        ggml_tensor* z = ggml_dup_tensor(compute_ctx, lora_q_up);
+                        ggml_scale(compute_ctx, z, 0);
+                        ggml_tensor* zz = ggml_concat(compute_ctx, z, z, 1);
+
+                        ggml_tensor* q_up = ggml_concat(compute_ctx, lora_q_up, zz, 1);
+                        ggml_tensor* k_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, z, lora_k_up, 1), z, 1);
+                        ggml_tensor* v_up = ggml_concat(compute_ctx, zz, lora_v_up, 1);
+                        // print_ggml_tensor(q_up, true);  //[R, 9216, 1, 1]
+                        // print_ggml_tensor(k_up, true);  //[R, 9216, 1, 1]
+                        // print_ggml_tensor(v_up, true);  //[R, 9216, 1, 1]
+                        ggml_tensor* lora_up_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, q_up, k_up, 0), v_up, 0);
+                        // print_ggml_tensor(lora_up_concat, true);  //[R*3, 9216, 1, 1]
+
+                        lora_down = ggml_cont(compute_ctx, lora_down_concat);
+                        lora_up   = ggml_cont(compute_ctx, lora_up_concat);
+
+                        applied_lora_tensors.insert(split_q_u_name);
+                        applied_lora_tensors.insert(split_k_u_name);
+                        applied_lora_tensors.insert(split_v_u_name);
+
+                        applied_lora_tensors.insert(split_q_d_name);
+                        applied_lora_tensors.insert(split_k_d_name);
+                        applied_lora_tensors.insert(split_v_d_name);
+                    }
+                }
+                if (starts_with(key, "SPLIT_L|")) {
+                    key = key.substr(sizeof("SPLIT_L|") - 1);
+
+                    auto split_q_d_name = lora_pre[type] + key + "attn.to_q" + lora_downs[type] + ".weight";
+                    if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
+                        // print_ggml_tensor(it.second, true);  //[3072, 21504, 1, 1]
+                        // find qkv and mlp up parts in LoRA model
+                        auto split_k_d_name = lora_pre[type] + key + "attn.to_k" + lora_downs[type] + ".weight";
+                        auto split_v_d_name = lora_pre[type] + key + "attn.to_v" + lora_downs[type] + ".weight";
+
+                        auto split_q_u_name = lora_pre[type] + key + "attn.to_q" + lora_ups[type] + ".weight";
+                        auto split_k_u_name = lora_pre[type] + key + "attn.to_k" + lora_ups[type] + ".weight";
+                        auto split_v_u_name = lora_pre[type] + key + "attn.to_v" + lora_ups[type] + ".weight";
+
+                        auto split_m_d_name = lora_pre[type] + key + "proj_mlp" + lora_downs[type] + ".weight";
+                        auto split_m_u_name = lora_pre[type] + key + "proj_mlp" + lora_ups[type] + ".weight";
+
+                        auto split_q_scale_name = lora_pre[type] + key + "attn.to_q" + ".scale";
+                        auto split_k_scale_name = lora_pre[type] + key + "attn.to_k" + ".scale";
+                        auto split_v_scale_name = lora_pre[type] + key + "attn.to_v" + ".scale";
+                        auto split_m_scale_name = lora_pre[type] + key + "proj_mlp" + ".scale";
+
+                        auto split_q_alpha_name = lora_pre[type] + key + "attn.to_q" + ".alpha";
+                        auto split_k_alpha_name = lora_pre[type] + key + "attn.to_k" + ".alpha";
+                        auto split_v_alpha_name = lora_pre[type] + key + "attn.to_v" + ".alpha";
+                        auto split_m_alpha_name = lora_pre[type] + key + "proj_mlp" + ".alpha";
+
+                        ggml_tensor* lora_q_down = NULL;
+                        ggml_tensor* lora_q_up   = NULL;
+                        ggml_tensor* lora_k_down = NULL;
+                        ggml_tensor* lora_k_up   = NULL;
+                        ggml_tensor* lora_v_down = NULL;
+                        ggml_tensor* lora_v_up   = NULL;
+
+                        ggml_tensor* lora_m_down = NULL;
+                        ggml_tensor* lora_m_up   = NULL;
+
+                        lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
+
+                        if (lora_tensors.find(split_q_d_name) != lora_tensors.end()) {
+                            lora_q_down = to_f32(compute_ctx, lora_tensors[split_q_d_name]);
+                        }
+
+                        if (lora_tensors.find(split_q_u_name) != lora_tensors.end()) {
+                            lora_q_up = to_f32(compute_ctx, lora_tensors[split_q_u_name]);
+                        }
+
+                        if (lora_tensors.find(split_k_d_name) != lora_tensors.end()) {
+                            lora_k_down = to_f32(compute_ctx, lora_tensors[split_k_d_name]);
+                        }
+
+                        if (lora_tensors.find(split_k_u_name) != lora_tensors.end()) {
+                            lora_k_up = to_f32(compute_ctx, lora_tensors[split_k_u_name]);
+                        }
+
+                        if (lora_tensors.find(split_v_d_name) != lora_tensors.end()) {
+                            lora_v_down = to_f32(compute_ctx, lora_tensors[split_v_d_name]);
+                        }
+
+                        if (lora_tensors.find(split_v_u_name) != lora_tensors.end()) {
+                            lora_v_up = to_f32(compute_ctx, lora_tensors[split_v_u_name]);
+                        }
+
+                        if (lora_tensors.find(split_m_d_name) != lora_tensors.end()) {
+                            lora_m_down = to_f32(compute_ctx, lora_tensors[split_m_d_name]);
+                        }
+
+                        if (lora_tensors.find(split_m_u_name) != lora_tensors.end()) {
+                            lora_m_up = to_f32(compute_ctx, lora_tensors[split_m_u_name]);
+                        }
+
+                        float q_rank = lora_q_up->ne[0];
+                        float k_rank = lora_k_up->ne[0];
+                        float v_rank = lora_v_up->ne[0];
+                        float m_rank = lora_v_up->ne[0];
+
+                        float lora_q_scale = 1;
+                        float lora_k_scale = 1;
+                        float lora_v_scale = 1;
+                        float lora_m_scale = 1;
+
+                        if (lora_tensors.find(split_q_scale_name) != lora_tensors.end()) {
+                            lora_q_scale = ggml_backend_tensor_get_f32(lora_tensors[split_q_scale_name]);
+                            applied_lora_tensors.insert(split_q_scale_name);
+                        }
+                        if (lora_tensors.find(split_k_scale_name) != lora_tensors.end()) {
+                            lora_k_scale = ggml_backend_tensor_get_f32(lora_tensors[split_k_scale_name]);
+                            applied_lora_tensors.insert(split_k_scale_name);
+                        }
+                        if (lora_tensors.find(split_v_scale_name) != lora_tensors.end()) {
+                            lora_v_scale = ggml_backend_tensor_get_f32(lora_tensors[split_v_scale_name]);
+                            applied_lora_tensors.insert(split_v_scale_name);
+                        }
+                        if (lora_tensors.find(split_m_scale_name) != lora_tensors.end()) {
+                            lora_m_scale = ggml_backend_tensor_get_f32(lora_tensors[split_m_scale_name]);
+                            applied_lora_tensors.insert(split_m_scale_name);
+                        }
+
+                        if (lora_tensors.find(split_q_alpha_name) != lora_tensors.end()) {
+                            float lora_q_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_q_alpha_name]);
+                            applied_lora_tensors.insert(split_q_alpha_name);
+                            lora_q_scale = lora_q_alpha / q_rank;
+                        }
+                        if (lora_tensors.find(split_k_alpha_name) != lora_tensors.end()) {
+                            float lora_k_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_k_alpha_name]);
+                            applied_lora_tensors.insert(split_k_alpha_name);
+                            lora_k_scale = lora_k_alpha / k_rank;
+                        }
+                        if (lora_tensors.find(split_v_alpha_name) != lora_tensors.end()) {
+                            float lora_v_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_v_alpha_name]);
+                            applied_lora_tensors.insert(split_v_alpha_name);
+                            lora_v_scale = lora_v_alpha / v_rank;
+                        }
+                        if (lora_tensors.find(split_m_alpha_name) != lora_tensors.end()) {
+                            float lora_m_alpha = ggml_backend_tensor_get_f32(lora_tensors[split_m_alpha_name]);
+                            applied_lora_tensors.insert(split_m_alpha_name);
+                            lora_m_scale = lora_m_alpha / m_rank;
+                        }
+
+                        ggml_scale_inplace(compute_ctx, lora_q_down, lora_q_scale);
+                        ggml_scale_inplace(compute_ctx, lora_k_down, lora_k_scale);
+                        ggml_scale_inplace(compute_ctx, lora_v_down, lora_v_scale);
+                        ggml_scale_inplace(compute_ctx, lora_m_down, lora_m_scale);
+
+                        // print_ggml_tensor(lora_q_down, true);  //[3072, R, 1, 1]
+                        // print_ggml_tensor(lora_k_down, true);  //[3072, R, 1, 1]
+                        // print_ggml_tensor(lora_v_down, true);  //[3072, R, 1, 1]
+                        // print_ggml_tensor(lora_m_down, true);  //[3072, R, 1, 1]
+                        // print_ggml_tensor(lora_q_up, true);  //[R, 3072, 1, 1]
+                        // print_ggml_tensor(lora_k_up, true);  //[R, 3072, 1, 1]
+                        // print_ggml_tensor(lora_v_up, true);  //[R, 3072, 1, 1]
+                        // print_ggml_tensor(lora_m_up, true);  //[R, 12288, 1, 1]
+
+                        // these need to be stitched together this way:
+                        //                                 |q_up,0   ,0   ,0   |
+                        //                                 |0   ,k_up,0   ,0   |
+                        //                                 |0   ,0   ,v_up,0   |
+                        //                                 |0   ,0   ,0   ,m_up|
+                        // (q_down,k_down,v_down,m_down) . (q   ,k   ,v   ,m)
+
+                        // up_concat will be [21504, R*4, 1, 1]
+                        // down_concat will be [R*4, 3072, 1, 1]
+
+                        ggml_tensor* lora_down_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_down, lora_k_down, 1), ggml_concat(compute_ctx, lora_v_down, lora_m_down, 1), 1);
+                        // print_ggml_tensor(lora_down_concat, true);  //[3072, R*4, 1, 1]
+
+                        // this also means that if rank is bigger than 672, it is less memory efficient to do it this way (should be fine)
+                        // print_ggml_tensor(lora_q_up, true);  //[3072, R, 1, 1]
+                        ggml_tensor* z     = ggml_dup_tensor(compute_ctx, lora_q_up);
+                        ggml_tensor* mlp_z = ggml_dup_tensor(compute_ctx, lora_m_up);
+                        ggml_scale(compute_ctx, z, 0);
+                        ggml_scale(compute_ctx, mlp_z, 0);
+                        ggml_tensor* zz = ggml_concat(compute_ctx, z, z, 1);
+
+                        ggml_tensor* q_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, lora_q_up, zz, 1), mlp_z, 1);
+                        ggml_tensor* k_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, z, lora_k_up, 1), ggml_concat(compute_ctx, z, mlp_z, 1), 1);
+                        ggml_tensor* v_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, zz, lora_v_up, 1), mlp_z, 1);
+                        ggml_tensor* m_up = ggml_concat(compute_ctx, ggml_concat(compute_ctx, zz, z, 1), lora_m_up, 1);
+                        // print_ggml_tensor(q_up, true);  //[R, 21504, 1, 1]
+                        // print_ggml_tensor(k_up, true);  //[R, 21504, 1, 1]
+                        // print_ggml_tensor(v_up, true);  //[R, 21504, 1, 1]
+                        // print_ggml_tensor(m_up, true);  //[R, 21504, 1, 1]
+
+                        ggml_tensor* lora_up_concat = ggml_concat(compute_ctx, ggml_concat(compute_ctx, q_up, k_up, 0), ggml_concat(compute_ctx, v_up, m_up, 0), 0);
+                        // print_ggml_tensor(lora_up_concat, true);  //[R*4, 21504, 1, 1]
+
+                        lora_down = ggml_cont(compute_ctx, lora_down_concat);
+                        lora_up   = ggml_cont(compute_ctx, lora_up_concat);
+
+                        applied_lora_tensors.insert(split_q_u_name);
+                        applied_lora_tensors.insert(split_k_u_name);
+                        applied_lora_tensors.insert(split_v_u_name);
+                        applied_lora_tensors.insert(split_m_u_name);
+
+                        applied_lora_tensors.insert(split_q_d_name);
+                        applied_lora_tensors.insert(split_k_d_name);
+                        applied_lora_tensors.insert(split_v_d_name);
+                        applied_lora_tensors.insert(split_m_d_name);
+                    }
+                }
+                if (lora_up == NULL || lora_down == NULL) {
+                    lora_up_name = lora_pre[type] + key + lora_ups[type] + ".weight";
+                    if (lora_tensors.find(lora_up_name) == lora_tensors.end()) {
+                        if (key == "model_diffusion_model_output_blocks_2_2_conv") {
+                            // fix for some sdxl lora, like lcm-lora-xl
+                            key          = "model_diffusion_model_output_blocks_2_1_conv";
+                            lora_up_name = lora_pre[type] + key + lora_ups[type] + ".weight";
+                        }
+                    }
+
+                    lora_down_name = lora_pre[type] + key + lora_downs[type] + ".weight";
+                    alpha_name     = lora_pre[type] + key + ".alpha";
+                    scale_name     = lora_pre[type] + key + ".scale";
+
+                    if (lora_tensors.find(lora_up_name) != lora_tensors.end()) {
+                        lora_up = lora_tensors[lora_up_name];
+                    }
+
+                    if (lora_tensors.find(lora_down_name) != lora_tensors.end()) {
+                        lora_down = lora_tensors[lora_down_name];
+                    }
+                    applied_lora_tensors.insert(lora_up_name);
+                    applied_lora_tensors.insert(lora_down_name);
+                    applied_lora_tensors.insert(alpha_name);
+                    applied_lora_tensors.insert(scale_name);
+                }
+
+                if (lora_up == NULL || lora_down == NULL) {
+                    continue;
+                }
+                // calc_scale
+                int64_t dim       = lora_down->ne[ggml_n_dims(lora_down) - 1];
+                float scale_value = 1.0f;
+                if (lora_tensors.find(scale_name) != lora_tensors.end()) {
+                    scale_value = ggml_backend_tensor_get_f32(lora_tensors[scale_name]);
+                } else if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
+                    float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
+                    scale_value = alpha / dim;
+                }
+                scale_value *= multiplier;
+
+                // flat lora tensors to multiply it
+                int64_t lora_up_rows   = lora_up->ne[ggml_n_dims(lora_up) - 1];
+                lora_up                = ggml_reshape_2d(compute_ctx, lora_up, ggml_nelements(lora_up) / lora_up_rows, lora_up_rows);
+                int64_t lora_down_rows = lora_down->ne[ggml_n_dims(lora_down) - 1];
+                lora_down              = ggml_reshape_2d(compute_ctx, lora_down, ggml_nelements(lora_down) / lora_down_rows, lora_down_rows);
+
+                // ggml_mul_mat requires tensor b transposed
+                lora_down                  = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, lora_down));
+                struct ggml_tensor* updown = ggml_mul_mat(compute_ctx, lora_up, lora_down);
+                updown                     = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, updown));
+                updown                     = ggml_reshape(compute_ctx, updown, weight);
+                GGML_ASSERT(ggml_nelements(updown) == ggml_nelements(weight));
+                updown = ggml_scale_inplace(compute_ctx, updown, scale_value);
+                ggml_tensor* final_weight;
+                if (weight->type != GGML_TYPE_F32 && weight->type != GGML_TYPE_F16) {
+                    // final_weight = ggml_new_tensor(compute_ctx, GGML_TYPE_F32, ggml_n_dims(weight), weight->ne);
+                    // final_weight = ggml_cpy(compute_ctx, weight, final_weight);
+                    final_weight = to_f32(compute_ctx, weight);
+                    final_weight = ggml_add_inplace(compute_ctx, final_weight, updown);
+                    final_weight = ggml_cpy(compute_ctx, final_weight, weight);
+                } else {
+                    final_weight = ggml_add_inplace(compute_ctx, weight, updown);
+                }
+                // final_weight = ggml_add_inplace(compute_ctx, weight, updown);  // apply directly
+                ggml_build_forward_expand(gf, final_weight);
+                break;
             }
-
-            if (lora_tensors.find(lora_down_name) != lora_tensors.end()) {
-                lora_down = lora_tensors[lora_down_name];
-            }
-
-            if (lora_up == NULL || lora_down == NULL) {
-                continue;
-            }
-
-            applied_lora_tensors.insert(lora_up_name);
-            applied_lora_tensors.insert(lora_down_name);
-            applied_lora_tensors.insert(alpha_name);
-            applied_lora_tensors.insert(scale_name);
-
-            // calc_cale
-            int64_t dim       = lora_down->ne[ggml_n_dims(lora_down) - 1];
-            float scale_value = 1.0f;
-            if (lora_tensors.find(scale_name) != lora_tensors.end()) {
-                scale_value = ggml_backend_tensor_get_f32(lora_tensors[scale_name]);
-            } else if (lora_tensors.find(alpha_name) != lora_tensors.end()) {
-                float alpha = ggml_backend_tensor_get_f32(lora_tensors[alpha_name]);
-                scale_value = alpha / dim;
-            }
-            scale_value *= multiplier;
-
-            // flat lora tensors to multiply it
-            int64_t lora_up_rows   = lora_up->ne[ggml_n_dims(lora_up) - 1];
-            lora_up                = ggml_reshape_2d(compute_ctx, lora_up, ggml_nelements(lora_up) / lora_up_rows, lora_up_rows);
-            int64_t lora_down_rows = lora_down->ne[ggml_n_dims(lora_down) - 1];
-            lora_down              = ggml_reshape_2d(compute_ctx, lora_down, ggml_nelements(lora_down) / lora_down_rows, lora_down_rows);
-
-            // ggml_mul_mat requires tensor b transposed
-            lora_down                  = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, lora_down));
-            struct ggml_tensor* updown = ggml_mul_mat(compute_ctx, lora_up, lora_down);
-            updown                     = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, updown));
-            updown                     = ggml_reshape(compute_ctx, updown, weight);
-            GGML_ASSERT(ggml_nelements(updown) == ggml_nelements(weight));
-            updown = ggml_scale_inplace(compute_ctx, updown, scale_value);
-            ggml_tensor* final_weight;
-            if (weight->type != GGML_TYPE_F32 && weight->type != GGML_TYPE_F16) {
-                // final_weight = ggml_new_tensor(compute_ctx, GGML_TYPE_F32, ggml_n_dims(weight), weight->ne);
-                // final_weight = ggml_cpy(compute_ctx, weight, final_weight);
-                final_weight = to_f32(compute_ctx, weight);
-                final_weight = ggml_add_inplace(compute_ctx, final_weight, updown);
-                final_weight = ggml_cpy(compute_ctx, final_weight, weight);
-            } else {
-                final_weight = ggml_add_inplace(compute_ctx, weight, updown);
-            }
-            // final_weight = ggml_add_inplace(compute_ctx, weight, updown);  // apply directly
-            ggml_build_forward_expand(gf, final_weight);
         }
-
         size_t total_lora_tensors_count   = 0;
         size_t applied_lora_tensors_count = 0;
 
         for (auto& kv : lora_tensors) {
             total_lora_tensors_count++;
             if (applied_lora_tensors.find(kv.first) == applied_lora_tensors.end()) {
-                LOG_WARN("unused lora tensor %s", kv.first.c_str());
+                LOG_WARN("unused lora tensor |%s|", kv.first.c_str());
+                print_ggml_tensor(kv.second, true);
+                // exit(0);
             } else {
                 applied_lora_tensors_count++;
             }
@@ -191,9 +669,9 @@ struct LoraModel : public GGMLRunner {
         return gf;
     }
 
-    void apply(std::map<std::string, struct ggml_tensor*> model_tensors, int n_threads) {
+    void apply(std::map<std::string, struct ggml_tensor*> model_tensors, SDVersion version, int n_threads) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_lora_graph(model_tensors);
+            return build_lora_graph(model_tensors, version);
         };
         GGMLRunner::compute(get_graph, n_threads, true);
     }

model.cpp

@@ -1458,24 +1458,49 @@ bool ModelLoader::init_from_ckpt_file(const std::string& file_path, const std::s
 }
 
 SDVersion ModelLoader::get_sd_version() {
-    TensorStorage token_embedding_weight;
+    TensorStorage token_embedding_weight, input_block_weight;
-    for (auto& tensor_storage : tensor_storages) {
+    bool input_block_checked = false;
-        if (tensor_storage.name.find("model.diffusion_model.double_blocks.") != std::string::npos) {
-            return VERSION_FLUX;
-        }
-        if (tensor_storage.name.find("model.diffusion_model.joint_blocks.") != std::string::npos) {
-            return VERSION_SD3;
-        }
-        if (tensor_storage.name.find("conditioner.embedders.1") != std::string::npos) {
-            return VERSION_SDXL;
-        }
-        if (tensor_storage.name.find("cond_stage_model.1") != std::string::npos) {
-            return VERSION_SDXL;
-        }
-        if (tensor_storage.name.find("model.diffusion_model.input_blocks.8.0.time_mixer.mix_factor") != std::string::npos) {
-            return VERSION_SVD;
-        }
 
+    bool has_multiple_encoders   = false;
+    bool is_unet = false;
+
+    bool is_xl = false;
+    bool is_flux = false;
+
+#define found_family (is_xl || is_flux)
+    for (auto& tensor_storage : tensor_storages) {
+        if (!found_family) {
+            if (tensor_storage.name.find("model.diffusion_model.double_blocks.") != std::string::npos) {
+                is_flux = true;
+                if (input_block_checked) {
+                    break;
+                }
+            }
+            if (tensor_storage.name.find("model.diffusion_model.joint_blocks.") != std::string::npos) {
+                return VERSION_SD3;
+            }
+            if (tensor_storage.name.find("model.diffusion_model.input_blocks.") != std::string::npos) {
+                is_unet = true;
+                if(has_multiple_encoders){
+                    is_xl = true;
+                    if (input_block_checked) {
+                        break;
+                    }
+                }
+            }
+            if (tensor_storage.name.find("conditioner.embedders.1") != std::string::npos || tensor_storage.name.find("cond_stage_model.1") != std::string::npos) {
+                has_multiple_encoders = true;
+                if(is_unet){
+                    is_xl = true;
+                    if (input_block_checked) {
+                        break;
+                    }
+                }
+            }
+            if (tensor_storage.name.find("model.diffusion_model.input_blocks.8.0.time_mixer.mix_factor") != std::string::npos) {
+                return VERSION_SVD;
+            }
+        }
         if (tensor_storage.name == "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight" ||
             tensor_storage.name == "cond_stage_model.model.token_embedding.weight" ||
             tensor_storage.name == "text_model.embeddings.token_embedding.weight" ||
@@ -1485,11 +1510,39 @@ SDVersion ModelLoader::get_sd_version() {
             token_embedding_weight = tensor_storage;
             // break;
         }
+        if (tensor_storage.name == "model.diffusion_model.input_blocks.0.0.weight" || tensor_storage.name == "model.diffusion_model.img_in.weight") {
+            input_block_weight  = tensor_storage;
+            input_block_checked = true;
+            if (found_family) {
+                break;
+            }
+        }
+    }
+    bool is_inpaint = input_block_weight.ne[2] == 9;
+    if (is_xl) {
+        if (is_inpaint) {
+            return VERSION_SDXL_INPAINT;
+        }
+        return VERSION_SDXL;
+    }
+
+    if (is_flux) {
+        is_inpaint = input_block_weight.ne[0] == 384;
+        if (is_inpaint) {
+            return VERSION_FLUX_FILL;
+        }
+        return VERSION_FLUX;
     }
 
     if (token_embedding_weight.ne[0] == 768) {
+        if (is_inpaint) {
+            return VERSION_SD1_INPAINT;
+        }
         return VERSION_SD1;
     } else if (token_embedding_weight.ne[0] == 1024) {
+        if (is_inpaint) {
+            return VERSION_SD2_INPAINT;
+        }
         return VERSION_SD2;
     }
     return VERSION_COUNT;
@@ -1695,9 +1748,11 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
             }
             return true;
         };
-
+        int tensor_count = 0;
+        int64_t t1       = ggml_time_ms();
         for (auto& tensor_storage : processed_tensor_storages) {
             if (tensor_storage.file_index != file_index) {
+                ++tensor_count;
                 continue;
             }
             ggml_tensor* dst_tensor = NULL;
@@ -1709,6 +1764,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
             }
 
             if (dst_tensor == NULL) {
+                ++tensor_count;
                 continue;
             }
 
@@ -1775,6 +1831,9 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                     ggml_backend_tensor_set(dst_tensor, convert_buffer.data(), 0, ggml_nbytes(dst_tensor));
                 }
             }
+            int64_t t2 = ggml_time_ms();
+            pretty_progress(++tensor_count, processed_tensor_storages.size(), (t2 - t1) / 1000.0f);
+            t1 = t2;
         }
 
         if (zip != NULL) {

model.h

@@ -19,16 +19,20 @@
 
 enum SDVersion {
     VERSION_SD1,
+    VERSION_SD1_INPAINT,
     VERSION_SD2,
+    VERSION_SD2_INPAINT,
     VERSION_SDXL,
+    VERSION_SDXL_INPAINT,
     VERSION_SVD,
     VERSION_SD3,
     VERSION_FLUX,
+    VERSION_FLUX_FILL,
     VERSION_COUNT,
 };
 
 static inline bool sd_version_is_flux(SDVersion version) {
-    if (version == VERSION_FLUX) {
+    if (version == VERSION_FLUX || version == VERSION_FLUX_FILL) {
         return true;
     }
     return false;
@@ -41,6 +45,34 @@ static inline bool sd_version_is_sd3(SDVersion version) {
     return false;
 }
 
+static inline bool sd_version_is_sd1(SDVersion version) {
+    if (version == VERSION_SD1 || version == VERSION_SD1_INPAINT) {
+        return true;
+    }
+    return false;
+}
+
+static inline bool sd_version_is_sd2(SDVersion version) {
+    if (version == VERSION_SD2 || version == VERSION_SD2_INPAINT) {
+        return true;
+    }
+    return false;
+}
+
+static inline bool sd_version_is_sdxl(SDVersion version) {
+    if (version == VERSION_SDXL || version == VERSION_SDXL_INPAINT) {
+        return true;
+    }
+    return false;
+}
+
+static inline bool sd_version_is_inpaint(SDVersion version) {
+    if (version == VERSION_SD1_INPAINT || version == VERSION_SD2_INPAINT || version == VERSION_SDXL_INPAINT || version == VERSION_FLUX_FILL) {
+        return true;
+    }
+    return false;
+}
+
 static inline bool sd_version_is_dit(SDVersion version) {
     if (sd_version_is_flux(version) || sd_version_is_sd3(version)) {
         return true;

stable-diffusion.cpp

@@ -26,11 +26,15 @@
 
 const char* model_version_to_str[] = {
     "SD 1.x",
+    "SD 1.x Inpaint",
     "SD 2.x",
+    "SD 2.x Inpaint",
     "SDXL",
+    "SDXL Inpaint",
     "SVD",
     "SD3.x",
-    "Flux"};
+    "Flux",
+    "Flux Fill"};
 
 const char* sampling_methods_str[] = {
     "Euler A",
@@ -155,13 +159,13 @@ public:
                         bool vae_on_cpu,
                         bool diffusion_flash_attn) {
         use_tiny_autoencoder = taesd_path.size() > 0;
-#ifdef SD_USE_CUBLAS
+#ifdef SD_USE_CUDA
         LOG_DEBUG("Using CUDA backend");
         backend = ggml_backend_cuda_init(0);
 #endif
 #ifdef SD_USE_METAL
         LOG_DEBUG("Using Metal backend");
-        ggml_backend_metal_log_set_callback(ggml_log_callback_default, nullptr);
+        ggml_log_set(ggml_log_callback_default, nullptr);
         backend = ggml_backend_metal_init();
 #endif
 #ifdef SD_USE_VULKAN
@@ -263,7 +267,7 @@ public:
             model_loader.set_wtype_override(wtype);
         }
 
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             vae_wtype = GGML_TYPE_F32;
             model_loader.set_wtype_override(GGML_TYPE_F32, "vae.");
         }
@@ -275,7 +279,7 @@ public:
 
         LOG_DEBUG("ggml tensor size = %d bytes", (int)sizeof(ggml_tensor));
 
-        if (version == VERSION_SDXL) {
+        if (sd_version_is_sdxl(version)) {
             scale_factor = 0.13025f;
             if (vae_path.size() == 0 && taesd_path.size() == 0) {
                 LOG_WARN(
@@ -329,7 +333,7 @@ public:
                 diffusion_model  = std::make_shared<MMDiTModel>(backend, model_loader.tensor_storages_types);
             } else if (sd_version_is_flux(version)) {
                 cond_stage_model = std::make_shared<FluxCLIPEmbedder>(clip_backend, model_loader.tensor_storages_types);
-                diffusion_model  = std::make_shared<FluxModel>(backend, model_loader.tensor_storages_types, diffusion_flash_attn);
+                diffusion_model  = std::make_shared<FluxModel>(backend, model_loader.tensor_storages_types, version, diffusion_flash_attn);
             } else {
                 if (id_embeddings_path.find("v2") != std::string::npos) {
                     cond_stage_model = std::make_shared<FrozenCLIPEmbedderWithCustomWords>(clip_backend, model_loader.tensor_storages_types, embeddings_path, version, PM_VERSION_2);
@@ -356,7 +360,7 @@ public:
                 first_stage_model->alloc_params_buffer();
                 first_stage_model->get_param_tensors(tensors, "first_stage_model");
             } else {
-                tae_first_stage = std::make_shared<TinyAutoEncoder>(backend, model_loader.tensor_storages_types, "decoder.layers", vae_decode_only);
+                tae_first_stage = std::make_shared<TinyAutoEncoder>(backend, model_loader.tensor_storages_types, "decoder.layers", vae_decode_only, version);
             }
             // first_stage_model->get_param_tensors(tensors, "first_stage_model.");
 
@@ -517,8 +521,8 @@ public:
 
         // check is_using_v_parameterization_for_sd2
         bool is_using_v_parameterization = false;
-        if (version == VERSION_SD2) {
+        if (sd_version_is_sd2(version)) {
-            if (is_using_v_parameterization_for_sd2(ctx)) {
+            if (is_using_v_parameterization_for_sd2(ctx, sd_version_is_inpaint(version))) {
                 is_using_v_parameterization = true;
             }
         } else if (version == VERSION_SVD) {
@@ -592,7 +596,7 @@ public:
         return true;
     }
 
-    bool is_using_v_parameterization_for_sd2(ggml_context* work_ctx) {
+    bool is_using_v_parameterization_for_sd2(ggml_context* work_ctx, bool is_inpaint = false) {
         struct ggml_tensor* x_t = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 8, 8, 4, 1);
         ggml_set_f32(x_t, 0.5);
         struct ggml_tensor* c = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 1024, 2, 1, 1);
@@ -600,9 +604,13 @@ public:
 
         struct ggml_tensor* timesteps = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 1);
         ggml_set_f32(timesteps, 999);
+
+        struct ggml_tensor* concat = is_inpaint ? ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 8, 8, 5, 1) : NULL;
+        ggml_set_f32(concat, 0);
+
         int64_t t0              = ggml_time_ms();
         struct ggml_tensor* out = ggml_dup_tensor(work_ctx, x_t);
-        diffusion_model->compute(n_threads, x_t, timesteps, c, NULL, NULL, NULL, -1, {}, 0.f, &out);
+        diffusion_model->compute(n_threads, x_t, timesteps, c, concat, NULL, NULL, -1, {}, 0.f, &out);
         diffusion_model->free_compute_buffer();
 
         double result = 0.f;
@@ -642,7 +650,8 @@ public:
         }
 
         lora.multiplier = multiplier;
-        lora.apply(tensors, n_threads);
+        // TODO: send version?
+        lora.apply(tensors, version, n_threads);
         lora.free_params_buffer();
 
         int64_t t1 = ggml_time_ms();
@@ -784,7 +793,20 @@ public:
                         std::vector<int> skip_layers = {},
                         float slg_scale              = 0,
                         float skip_layer_start       = 0.01,
-                        float skip_layer_end         = 0.2) {
+                        float skip_layer_end         = 0.2,
+                        ggml_tensor* noise_mask      = nullptr) {
+        LOG_DEBUG("Sample");
+        struct ggml_init_params params;
+        size_t data_size = ggml_row_size(init_latent->type, init_latent->ne[0]);
+        for (int i = 1; i < 4; i++) {
+            data_size *= init_latent->ne[i];
+        }
+        data_size += 1024;
+        params.mem_size       = data_size * 3;
+        params.mem_buffer     = NULL;
+        params.no_alloc       = false;
+        ggml_context* tmp_ctx = ggml_init(params);
+
         size_t steps = sigmas.size() - 1;
         // noise = load_tensor_from_file(work_ctx, "./rand0.bin");
         // print_ggml_tensor(noise);
@@ -943,6 +965,19 @@ public:
                 pretty_progress(step, (int)steps, (t1 - t0) / 1000000.f);
                 // LOG_INFO("step %d sampling completed taking %.2fs", step, (t1 - t0) * 1.0f / 1000000);
             }
+            if (noise_mask != nullptr) {
+                for (int64_t x = 0; x < denoised->ne[0]; x++) {
+                    for (int64_t y = 0; y < denoised->ne[1]; y++) {
+                        float mask = ggml_tensor_get_f32(noise_mask, x, y);
+                        for (int64_t k = 0; k < denoised->ne[2]; k++) {
+                            float init = ggml_tensor_get_f32(init_latent, x, y, k);
+                            float den  = ggml_tensor_get_f32(denoised, x, y, k);
+                            ggml_tensor_set_f32(denoised, init + mask * (den - init), x, y, k);
+                        }
+                    }
+                }
+            }
+
             return denoised;
         };
 
@@ -1166,7 +1201,8 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
                            std::vector<int> skip_layers = {},
                            float slg_scale              = 0,
                            float skip_layer_start       = 0.01,
-                           float skip_layer_end         = 0.2) {
+                           float skip_layer_end         = 0.2,
+                           ggml_tensor* masked_image    = NULL) {
     if (seed < 0) {
         // Generally, when using the provided command line, the seed is always >0.
         // However, to prevent potential issues if 'stable-diffusion.cpp' is invoked as a library
@@ -1206,7 +1242,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
     if (sd_ctx->sd->stacked_id) {
         if (!sd_ctx->sd->pmid_lora->applied) {
             t0 = ggml_time_ms();
-            sd_ctx->sd->pmid_lora->apply(sd_ctx->sd->tensors, sd_ctx->sd->n_threads);
+            sd_ctx->sd->pmid_lora->apply(sd_ctx->sd->tensors, sd_ctx->sd->version, sd_ctx->sd->n_threads);
             t1                             = ggml_time_ms();
             sd_ctx->sd->pmid_lora->applied = true;
             LOG_INFO("pmid_lora apply completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
@@ -1316,7 +1352,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
     SDCondition uncond;
     if (cfg_scale != 1.0) {
         bool force_zero_embeddings = false;
-        if (sd_ctx->sd->version == VERSION_SDXL && negative_prompt.size() == 0) {
+        if (sd_version_is_sdxl(sd_ctx->sd->version) && negative_prompt.size() == 0) {
             force_zero_embeddings = true;
         }
         uncond = sd_ctx->sd->cond_stage_model->get_learned_condition(work_ctx,
@@ -1353,6 +1389,39 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
     int W = width / 8;
     int H = height / 8;
     LOG_INFO("sampling using %s method", sampling_methods_str[sample_method]);
+    ggml_tensor* noise_mask = nullptr;
+    if (sd_version_is_inpaint(sd_ctx->sd->version)) {
+        if (masked_image == NULL) {
+            int64_t mask_channels = 1;
+            if (sd_ctx->sd->version == VERSION_FLUX_FILL) {
+                mask_channels = 8 * 8;  // flatten the whole mask
+            }
+            // no mask, set the whole image as masked
+            masked_image = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, init_latent->ne[0], init_latent->ne[1], mask_channels + init_latent->ne[2], 1);
+            for (int64_t x = 0; x < masked_image->ne[0]; x++) {
+                for (int64_t y = 0; y < masked_image->ne[1]; y++) {
+                    if (sd_ctx->sd->version == VERSION_FLUX_FILL) {
+                        // TODO: this might be wrong
+                        for (int64_t c = 0; c < init_latent->ne[2]; c++) {
+                            ggml_tensor_set_f32(masked_image, 0, x, y, c);
+                        }
+                        for (int64_t c = init_latent->ne[2]; c < masked_image->ne[2]; c++) {
+                            ggml_tensor_set_f32(masked_image, 1, x, y, c);
+                        }
+                    } else {
+                        ggml_tensor_set_f32(masked_image, 1, x, y, 0);
+                        for (int64_t c = 1; c < masked_image->ne[2]; c++) {
+                            ggml_tensor_set_f32(masked_image, 0, x, y, c);
+                        }
+                    }
+                }
+            }
+        }
+        cond.c_concat   = masked_image;
+        uncond.c_concat = masked_image;
+    } else {
+        noise_mask = masked_image;
+    }
     for (int b = 0; b < batch_count; b++) {
         int64_t sampling_start = ggml_time_ms();
         int64_t cur_seed       = seed + b;
@@ -1388,7 +1457,9 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
                                                      skip_layers,
                                                      slg_scale,
                                                      skip_layer_start,
-                                                     skip_layer_end);
+                                                     skip_layer_end,
+                                                     noise_mask);
+
         // struct ggml_tensor* x_0 = load_tensor_from_file(ctx, "samples_ddim.bin");
         // print_ggml_tensor(x_0);
         int64_t sampling_end = ggml_time_ms();
@@ -1510,6 +1581,10 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
         ggml_set_f32(init_latent, 0.f);
     }
 
+    if (sd_version_is_inpaint(sd_ctx->sd->version)) {
+        LOG_WARN("This is an inpainting model, this should only be used in img2img mode with a mask");
+    }
+
     sd_image_t* result_images = generate_image(sd_ctx,
                                                work_ctx,
                                                init_latent,
@@ -1543,6 +1618,7 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
 
 sd_image_t* img2img(sd_ctx_t* sd_ctx,
                     sd_image_t init_image,
+                    sd_image_t mask,
                     const char* prompt_c_str,
                     const char* negative_prompt_c_str,
                     int clip_skip,
@@ -1582,7 +1658,7 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
     if (sd_ctx->sd->stacked_id) {
         params.mem_size += static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
     }
-    params.mem_size += width * height * 3 * sizeof(float) * 2;
+    params.mem_size += width * height * 3 * sizeof(float) * 3;
     params.mem_size *= batch_count;
     params.mem_buffer = NULL;
     params.no_alloc   = false;
@@ -1603,7 +1679,70 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
     sd_ctx->sd->rng->manual_seed(seed);
 
     ggml_tensor* init_img = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 3, 1);
+    ggml_tensor* mask_img = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 1, 1);
+
+    sd_mask_to_tensor(mask.data, mask_img);
+
     sd_image_to_tensor(init_image.data, init_img);
+
+    ggml_tensor* masked_image;
+
+    if (sd_version_is_inpaint(sd_ctx->sd->version)) {
+        int64_t mask_channels = 1;
+        if (sd_ctx->sd->version == VERSION_FLUX_FILL) {
+            mask_channels = 8 * 8;  // flatten the whole mask
+        }
+        ggml_tensor* masked_img = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 3, 1);
+        sd_apply_mask(init_img, mask_img, masked_img);
+        ggml_tensor* masked_image_0 = NULL;
+        if (!sd_ctx->sd->use_tiny_autoencoder) {
+            ggml_tensor* moments = sd_ctx->sd->encode_first_stage(work_ctx, masked_img);
+            masked_image_0       = sd_ctx->sd->get_first_stage_encoding(work_ctx, moments);
+        } else {
+            masked_image_0 = sd_ctx->sd->encode_first_stage(work_ctx, masked_img);
+        }
+        masked_image = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, masked_image_0->ne[0], masked_image_0->ne[1], mask_channels + masked_image_0->ne[2], 1);
+        for (int ix = 0; ix < masked_image_0->ne[0]; ix++) {
+            for (int iy = 0; iy < masked_image_0->ne[1]; iy++) {
+                int mx = ix * 8;
+                int my = iy * 8;
+                if (sd_ctx->sd->version == VERSION_FLUX_FILL) {
+                    for (int k = 0; k < masked_image_0->ne[2]; k++) {
+                        float v = ggml_tensor_get_f32(masked_image_0, ix, iy, k);
+                        ggml_tensor_set_f32(masked_image, v, ix, iy, k);
+                    }
+                    // "Encode" 8x8 mask chunks into a flattened 1x64 vector, and concatenate to masked image
+                    for (int x = 0; x < 8; x++) {
+                        for (int y = 0; y < 8; y++) {
+                            float m = ggml_tensor_get_f32(mask_img, mx + x, my + y);
+                            // TODO: check if the way the mask is flattened is correct (is it supposed to be x*8+y or x+8*y?)
+                            // python code was using "b (h 8) (w 8) -> b (8 8) h w"
+                            ggml_tensor_set_f32(masked_image, m, ix, iy, masked_image_0->ne[2] + x * 8 + y);
+                        }
+                    }
+                } else {
+                    float m = ggml_tensor_get_f32(mask_img, mx, my);
+                    ggml_tensor_set_f32(masked_image, m, ix, iy, 0);
+                    for (int k = 0; k < masked_image_0->ne[2]; k++) {
+                        float v = ggml_tensor_get_f32(masked_image_0, ix, iy, k);
+                        ggml_tensor_set_f32(masked_image, v, ix, iy, k + mask_channels);
+                    }
+                }
+            }
+        }
+    } else {
+        // LOG_WARN("Inpainting with a base model is not great");
+        masked_image = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width / 8, height / 8, 1, 1);
+        for (int ix = 0; ix < masked_image->ne[0]; ix++) {
+            for (int iy = 0; iy < masked_image->ne[1]; iy++) {
+                int mx  = ix * 8;
+                int my  = iy * 8;
+                float m = ggml_tensor_get_f32(mask_img, mx, my);
+                ggml_tensor_set_f32(masked_image, m, ix, iy);
+            }
+        }
+    }
+
     ggml_tensor* init_latent = NULL;
     if (!sd_ctx->sd->use_tiny_autoencoder) {
         ggml_tensor* moments = sd_ctx->sd->encode_first_stage(work_ctx, init_img);
@@ -1611,12 +1750,15 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
     } else {
         init_latent = sd_ctx->sd->encode_first_stage(work_ctx, init_img);
     }
+
     print_ggml_tensor(init_latent, true);
     size_t t1 = ggml_time_ms();
     LOG_INFO("encode_first_stage completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
 
     std::vector<float> sigmas = sd_ctx->sd->denoiser->get_sigmas(sample_steps);
     size_t t_enc              = static_cast<size_t>(sample_steps * strength);
+    if (t_enc == sample_steps)
+        t_enc--;
     LOG_INFO("target t_enc is %zu steps", t_enc);
     std::vector<float> sigma_sched;
     sigma_sched.assign(sigmas.begin() + sample_steps - t_enc - 1, sigmas.end());
@@ -1643,7 +1785,8 @@ sd_image_t* img2img(sd_ctx_t* sd_ctx,
                                                skip_layers_vec,
                                                slg_scale,
                                                skip_layer_start,
-                                               skip_layer_end);
+                                               skip_layer_end,
+                                               masked_image);
 
     size_t t2 = ggml_time_ms();
 

stable-diffusion.h

@@ -174,6 +174,7 @@ SD_API sd_image_t* txt2img(sd_ctx_t* sd_ctx,
 
 SD_API sd_image_t* img2img(sd_ctx_t* sd_ctx,
                            sd_image_t init_image,
+                           sd_image_t mask_image,
                            const char* prompt,
                            const char* negative_prompt,
                            int clip_skip,

tae.hpp

@@ -62,7 +62,8 @@ class TinyEncoder : public UnaryBlock {
     int num_blocks  = 3;
 
 public:
-    TinyEncoder() {
+    TinyEncoder(int z_channels = 4)
+        : z_channels(z_channels) {
         int index                       = 0;
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, channels, {3, 3}, {1, 1}, {1, 1}));
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
@@ -106,7 +107,10 @@ class TinyDecoder : public UnaryBlock {
     int num_blocks   = 3;
 
 public:
-    TinyDecoder(int index = 0) {
+    TinyDecoder(int z_channels = 4)
+        : z_channels(z_channels) {
+        int index = 0;
+
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(z_channels, channels, {3, 3}, {1, 1}, {1, 1}));
         index++;  // nn.ReLU()
 
@@ -163,12 +167,16 @@ protected:
     bool decode_only;
 
 public:
-    TAESD(bool decode_only = true)
+    TAESD(bool decode_only = true, SDVersion version = VERSION_SD1)
         : decode_only(decode_only) {
-        blocks["decoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyDecoder());
+        int z_channels = 4;
+        if (sd_version_is_dit(version)) {
+            z_channels = 16;
+        }
+        blocks["decoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyDecoder(z_channels));
 
         if (!decode_only) {
-            blocks["encoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyEncoder());
+            blocks["encoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyEncoder(z_channels));
         }
     }
 
@@ -190,9 +198,10 @@ struct TinyAutoEncoder : public GGMLRunner {
     TinyAutoEncoder(ggml_backend_t backend,
                     std::map<std::string, enum ggml_type>& tensor_types,
                     const std::string prefix,
-                    bool decoder_only = true)
+                    bool decoder_only = true,
+                    SDVersion version = VERSION_SD1)
         : decode_only(decoder_only),
-          taesd(decode_only),
+          taesd(decode_only, version),
           GGMLRunner(backend) {
         taesd.init(params_ctx, tensor_types, prefix);
     }

unet.hpp

@@ -166,6 +166,7 @@ public:
 // ldm.modules.diffusionmodules.openaimodel.UNetModel
 class UnetModelBlock : public GGMLBlock {
 protected:
+    static std::map<std::string, enum ggml_type> empty_tensor_types;
     SDVersion version = VERSION_SD1;
     // network hparams
     int in_channels                        = 4;
@@ -183,13 +184,13 @@ public:
     int model_channels  = 320;
     int adm_in_channels = 2816;  // only for VERSION_SDXL/SVD
 
-    UnetModelBlock(SDVersion version = VERSION_SD1, bool flash_attn = false)
+    UnetModelBlock(SDVersion version = VERSION_SD1, std::map<std::string, enum ggml_type>& tensor_types = empty_tensor_types, bool flash_attn = false)
         : version(version) {
-        if (version == VERSION_SD2) {
+        if (sd_version_is_sd2(version)) {
             context_dim       = 1024;
             num_head_channels = 64;
             num_heads         = -1;
-        } else if (version == VERSION_SDXL) {
+        } else if (sd_version_is_sdxl(version)) {
             context_dim           = 2048;
             attention_resolutions = {4, 2};
             channel_mult          = {1, 2, 4};
@@ -204,6 +205,10 @@ public:
             num_head_channels = 64;
             num_heads         = -1;
         }
+        if (sd_version_is_inpaint(version)) {
+            in_channels = 9;
+        }
+
         // dims is always 2
         // use_temporal_attention is always True for SVD
 
@@ -211,7 +216,7 @@ public:
         // time_embed_1 is nn.SiLU()
         blocks["time_embed.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim));
 
-        if (version == VERSION_SDXL || version == VERSION_SVD) {
+        if (sd_version_is_sdxl(version) || version == VERSION_SVD) {
             blocks["label_emb.0.0"] = std::shared_ptr<GGMLBlock>(new Linear(adm_in_channels, time_embed_dim));
             // label_emb_1 is nn.SiLU()
             blocks["label_emb.0.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim));
@@ -536,7 +541,7 @@ struct UNetModelRunner : public GGMLRunner {
                     const std::string prefix,
                     SDVersion version = VERSION_SD1,
                     bool flash_attn   = false)
-        : GGMLRunner(backend), unet(version, flash_attn) {
+        : GGMLRunner(backend), unet(version, tensor_types, flash_attn) {
         unet.init(params_ctx, tensor_types, prefix);
     }
 
@@ -566,6 +571,7 @@ struct UNetModelRunner : public GGMLRunner {
         context   = to_backend(context);
         y         = to_backend(y);
         timesteps = to_backend(timesteps);
+        c_concat  = to_backend(c_concat);
 
         for (int i = 0; i < controls.size(); i++) {
             controls[i] = to_backend(controls[i]);

upscaler.cpp

@@ -15,13 +15,13 @@ struct UpscalerGGML {
     }
 
     bool load_from_file(const std::string& esrgan_path) {
-#ifdef SD_USE_CUBLAS
+#ifdef SD_USE_CUDA
         LOG_DEBUG("Using CUDA backend");
         backend = ggml_backend_cuda_init(0);
 #endif
 #ifdef SD_USE_METAL
         LOG_DEBUG("Using Metal backend");
-        ggml_backend_metal_log_set_callback(ggml_log_callback_default, nullptr);
+        ggml_log_set(ggml_log_callback_default, nullptr);
         backend = ggml_backend_metal_init();
 #endif
 #ifdef SD_USE_VULKAN

util.cpp

@@ -348,7 +348,7 @@ void pretty_progress(int step, int steps, float time) {
         }
     }
     progress += "|";
-    printf(time > 1.0f ? "\r%s %i/%i - %.2fs/it" : "\r%s %i/%i - %.2fit/s",
+    printf(time > 1.0f ? "\r%s %i/%i - %.2fs/it" : "\r%s %i/%i - %.2fit/s\033[K",
            progress.c_str(), step, steps,
            time > 1.0f || time == 0 ? time : (1.0f / time));
     fflush(stdout);  // for linux