CMakeLists.txt

@@ -119,10 +119,8 @@ endif()
 
 set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
 
-if (NOT SD_USE_SYSTEM_GGML)
-    # see https://github.com/ggerganov/ggml/pull/682
-    add_definitions(-DGGML_MAX_NAME=128)
-endif()
+# see https://github.com/ggerganov/ggml/pull/682
+add_definitions(-DGGML_MAX_NAME=128)
 
 # deps
 # Only add ggml if it hasn't been added yet

README.md

@@ -341,10 +341,6 @@ arguments:
   --diffusion-fa                     use flash attention in the diffusion model (for low vram)
                                      Might lower quality, since it implies converting k and v to f16.
                                      This might crash if it is not supported by the backend.
-  --diffusion-conv-direct            use Conv2d direct in the diffusion model
-                                     This might crash if it is not supported by the backend.
-  --vae-conv-direct                  use Conv2d direct in the vae model (should improve the performance)
-                                     This might crash if it is not supported by the backend.
   --control-net-cpu                  keep controlnet in cpu (for low vram)
   --canny                            apply canny preprocessor (edge detection)
   --color                            colors the logging tags according to level

control.hpp

@@ -323,17 +323,6 @@ struct ControlNet : public GGMLRunner {
         control_net.init(params_ctx, tensor_types, "");
     }
 
-    void enable_conv2d_direct() {
-        std::vector<GGMLBlock*> blocks;
-        control_net.get_all_blocks(blocks);
-        for (auto block : blocks) {
-            if (block->get_desc() == "Conv2d") {
-                auto conv_block = (Conv2d*)block;
-                conv_block->enable_direct();
-            }
-        }
-    }
-
     ~ControlNet() {
         free_control_ctx();
     }

esrgan.hpp

@@ -147,17 +147,6 @@ struct ESRGAN : public GGMLRunner {
         rrdb_net.init(params_ctx, tensor_types, "");
     }
 
-    void enable_conv2d_direct() {
-        std::vector<GGMLBlock*> blocks;
-        rrdb_net.get_all_blocks(blocks);
-        for (auto block : blocks) {
-            if (block->get_desc() == "Conv2d") {
-                auto conv_block = (Conv2d*)block;
-                conv_block->enable_direct();
-            }
-        }
-    }
-
     std::string get_desc() {
         return "esrgan";
     }

examples/cli/main.cpp

@@ -97,8 +97,6 @@ struct SDParams {
     bool clip_on_cpu              = false;
     bool vae_on_cpu               = false;
     bool diffusion_flash_attn     = false;
-    bool diffusion_conv_direct    = false;
-    bool vae_conv_direct          = false;
     bool canny_preprocess         = false;
     bool color                    = false;
     int upscale_repeats           = 1;
@@ -144,8 +142,6 @@ void print_params(SDParams params) {
     printf("    controlnet cpu:    %s\n", params.control_net_cpu ? "true" : "false");
     printf("    vae decoder on cpu:%s\n", params.vae_on_cpu ? "true" : "false");
     printf("    diffusion flash attention:%s\n", params.diffusion_flash_attn ? "true" : "false");
-    printf("    diffusion Conv2d direct:%s\n", params.diffusion_conv_direct ? "true" : "false");
-    printf("    vae Conv2d direct:%s\n", params.vae_conv_direct ? "true" : "false");
     printf("    strength(control): %.2f\n", params.control_strength);
     printf("    prompt:            %s\n", params.prompt.c_str());
     printf("    negative_prompt:   %s\n", params.negative_prompt.c_str());
@@ -236,10 +232,6 @@ void print_usage(int argc, const char* argv[]) {
     printf("  --diffusion-fa                     use flash attention in the diffusion model (for low vram)\n");
     printf("                                     Might lower quality, since it implies converting k and v to f16.\n");
     printf("                                     This might crash if it is not supported by the backend.\n");
-    printf("  --diffusion-conv-direct            use Conv2d direct in the diffusion model");
-    printf("                                     This might crash if it is not supported by the backend.\n");
-    printf("  --vae-conv-direct                  use Conv2d direct in the vae model (should improve the performance)");
-    printf("                                     This might crash if it is not supported by the backend.\n");
     printf("  --control-net-cpu                  keep controlnet in cpu (for low vram)\n");
     printf("  --canny                            apply canny preprocessor (edge detection)\n");
     printf("  --color                            colors the logging tags according to level\n");
@@ -430,8 +422,6 @@ void parse_args(int argc, const char** argv, SDParams& params) {
         {"", "--clip-on-cpu", "", true, &params.clip_on_cpu},
         {"", "--vae-on-cpu", "", true, &params.vae_on_cpu},
         {"", "--diffusion-fa", "", true, &params.diffusion_flash_attn},
-        {"", "--diffusion-conv-direct", "", true, &params.diffusion_conv_direct},
-        {"", "--vae-conv-direct", "", true, &params.vae_conv_direct},
         {"", "--canny", "", true, &params.canny_preprocess},
         {"-v", "--verbos", "", true, &params.verbose},
         {"", "--color", "", true, &params.color},
@@ -911,8 +901,6 @@ int main(int argc, const char* argv[]) {
         params.control_net_cpu,
         params.vae_on_cpu,
         params.diffusion_flash_attn,
-        params.diffusion_conv_direct,
-        params.vae_conv_direct,
         params.chroma_use_dit_mask,
         params.chroma_use_t5_mask,
         params.chroma_t5_mask_pad,
@@ -1024,8 +1012,7 @@ int main(int argc, const char* argv[]) {
     int upscale_factor = 4;  // unused for RealESRGAN_x4plus_anime_6B.pth
     if (params.esrgan_path.size() > 0 && params.upscale_repeats > 0) {
         upscaler_ctx_t* upscaler_ctx = new_upscaler_ctx(params.esrgan_path.c_str(),
-                                                        params.n_threads,
-                                                        params.diffusion_conv_direct);
+                                                        params.n_threads);
 
         if (upscaler_ctx == NULL) {
             printf("new_upscaler_ctx failed\n");

ggml

@@ -1 +1 @@
-Subproject commit 7dee1d6a1e7611f238d09be96738388da97c88ed
+Subproject commit b96890f3ab5ffbdbe56bc126df5366c34bd08d39

ggml_extend.hpp

@@ -56,8 +56,6 @@
 #define __STATIC_INLINE__ static inline
 #endif
 
-static_assert(GGML_MAX_NAME >= 128, "GGML_MAX_NAME must be at least 128");
-
 // n-mode trensor-matrix product
 // example: 2-mode product
 // A: [ne03, k, ne01, ne00]
@@ -708,25 +706,6 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_conv_2d(struct ggml_context* ctx,
     return x;
 }
 
-__STATIC_INLINE__ struct ggml_tensor* ggml_nn_conv_2d_direct(struct ggml_context* ctx,
-                                                             struct ggml_tensor* x,
-                                                             struct ggml_tensor* w,
-                                                             struct ggml_tensor* b,
-                                                             int s0 = 1,
-                                                             int s1 = 1,
-                                                             int p0 = 0,
-                                                             int p1 = 0,
-                                                             int d0 = 1,
-                                                             int d1 = 1) {
-    x = ggml_conv_2d_direct(ctx, w, x, s0, s1, p0, p1, d0, d1);
-    if (b != NULL) {
-        b = ggml_reshape_4d(ctx, b, 1, 1, b->ne[0], 1);
-        // b = ggml_repeat(ctx, b, x);
-        x = ggml_add(ctx, x, b);
-    }
-    return x;
-}
-
 // w: [OC，IC, KD, 1 * 1]
 // x: [N, IC, IH, IW]
 // b: [OC,]
@@ -1396,19 +1375,6 @@ public:
             tensors[prefix + pair.first] = pair.second;
         }
     }
-
-    virtual std::string get_desc() {
-        return "GGMLBlock";
-    }
-
-    void get_all_blocks(std::vector<GGMLBlock*>& result) {
-        result.push_back(this);
-        for (auto& block_iter : blocks) {
-            if (block_iter.second) {
-                block_iter.second->get_all_blocks(result);
-            }
-        }
-    }
 };
 
 class UnaryBlock : public GGMLBlock {
@@ -1498,7 +1464,6 @@ protected:
     std::pair<int, int> padding;
     std::pair<int, int> dilation;
     bool bias;
-    bool direct = false;
 
     void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types, const std::string prefix = "") {
         enum ggml_type wtype = GGML_TYPE_F16;
@@ -1525,25 +1490,13 @@ public:
           dilation(dilation),
           bias(bias) {}
 
-    void enable_direct() {
-        direct = true;
-    }
-
-    std::string get_desc() {
-        return "Conv2d";
-    }
-
     struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
         struct ggml_tensor* w = params["weight"];
         struct ggml_tensor* b = NULL;
         if (bias) {
             b = params["bias"];
         }
-        if (direct) {
-            return ggml_nn_conv_2d_direct(ctx, x, w, b, stride.second, stride.first, padding.second, padding.first, dilation.second, dilation.first);
-        } else {
-            return ggml_nn_conv_2d(ctx, x, w, b, stride.second, stride.first, padding.second, padding.first, dilation.second, dilation.first);
-        }
+        return ggml_nn_conv_2d(ctx, x, w, b, stride.second, stride.first, padding.second, padding.first, dilation.second, dilation.first);
     }
 };
 

stable-diffusion.cpp

@@ -374,10 +374,6 @@ public:
                                                               model_loader.tensor_storages_types,
                                                               version,
                                                               sd_ctx_params->diffusion_flash_attn);
-                if (sd_ctx_params->diffusion_conv_direct) {
-                    LOG_INFO("Using Conv2d direct in the diffusion model");
-                    std::dynamic_pointer_cast<UNetModel>(diffusion_model)->unet.enable_conv2d_direct();
-                }
             }
 
             cond_stage_model->alloc_params_buffer();
@@ -399,10 +395,6 @@ public:
                                                                     vae_decode_only,
                                                                     false,
                                                                     version);
-                if (sd_ctx_params->vae_conv_direct) {
-                    LOG_INFO("Using Conv2d direct in the vae model");
-                    first_stage_model->enable_conv2d_direct();
-                }
                 first_stage_model->alloc_params_buffer();
                 first_stage_model->get_param_tensors(tensors, "first_stage_model");
             } else {
@@ -411,10 +403,6 @@ public:
                                                                     "decoder.layers",
                                                                     vae_decode_only,
                                                                     version);
-                if (sd_ctx_params->vae_conv_direct) {
-                    LOG_INFO("Using Conv2d direct in the tae model");
-                    tae_first_stage->enable_conv2d_direct();
-                }
             }
             // first_stage_model->get_param_tensors(tensors, "first_stage_model.");
 
@@ -427,10 +415,6 @@ public:
                     controlnet_backend = backend;
                 }
                 control_net = std::make_shared<ControlNet>(controlnet_backend, model_loader.tensor_storages_types, version);
-                if (sd_ctx_params->diffusion_conv_direct) {
-                    LOG_INFO("Using Conv2d direct in the control net");
-                    control_net->enable_conv2d_direct();
-                }
             }
 
             if (strstr(SAFE_STR(sd_ctx_params->stacked_id_embed_dir), "v2")) {

stable-diffusion.h

@@ -134,8 +134,6 @@ typedef struct {
     bool keep_control_net_on_cpu;
     bool keep_vae_on_cpu;
     bool diffusion_flash_attn;
-    bool diffusion_conv_direct;
-    bool vae_conv_direct;
     bool chroma_use_dit_mask;
     bool chroma_use_t5_mask;
     int chroma_t5_mask_pad;
@@ -238,8 +236,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
 typedef struct upscaler_ctx_t upscaler_ctx_t;
 
 SD_API upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path,
-                                        int n_threads,
-                                        bool direct);
+                                        int n_threads);
 SD_API void free_upscaler_ctx(upscaler_ctx_t* upscaler_ctx);
 
 SD_API sd_image_t upscale(upscaler_ctx_t* upscaler_ctx, sd_image_t input_image, uint32_t upscale_factor);

tae.hpp

@@ -206,17 +206,6 @@ struct TinyAutoEncoder : public GGMLRunner {
         taesd.init(params_ctx, tensor_types, prefix);
     }
 
-    void enable_conv2d_direct() {
-        std::vector<GGMLBlock*> blocks;
-        taesd.get_all_blocks(blocks);
-        for (auto block : blocks) {
-            if (block->get_desc() == "Conv2d") {
-                auto conv_block = (Conv2d*)block;
-                conv_block->enable_direct();
-            }
-        }
-    }
-
     std::string get_desc() {
         return "taesd";
     }

unet.hpp

@@ -546,18 +546,6 @@ struct UNetModelRunner : public GGMLRunner {
         unet.init(params_ctx, tensor_types, prefix);
     }
 
-    void enable_conv2d_direct() {
-        std::vector<GGMLBlock*> blocks;
-        unet.get_all_blocks(blocks);
-        for (auto block : blocks) {
-            if (block->get_desc() == "Conv2d") {
-                LOG_DEBUG("block %s", block->get_desc().c_str());
-                auto conv_block = (Conv2d*)block;
-                conv_block->enable_direct();
-            }
-        }
-    }
-
     std::string get_desc() {
         return "unet";
     }

upscaler.cpp

@@ -9,12 +9,9 @@ struct UpscalerGGML {
     std::shared_ptr<ESRGAN> esrgan_upscaler;
     std::string esrgan_path;
     int n_threads;
-    bool direct = false;
 
-    UpscalerGGML(int n_threads,
-                 bool direct = false)
-        : n_threads(n_threads),
-          direct(direct) {
+    UpscalerGGML(int n_threads)
+        : n_threads(n_threads) {
     }
 
     bool load_from_file(const std::string& esrgan_path) {
@@ -50,9 +47,6 @@ struct UpscalerGGML {
         }
         LOG_INFO("Upscaler weight type: %s", ggml_type_name(model_data_type));
         esrgan_upscaler = std::make_shared<ESRGAN>(backend, model_loader.tensor_storages_types);
-        if (direct) {
-            esrgan_upscaler->enable_conv2d_direct();
-        }
         if (!esrgan_upscaler->load_from_file(esrgan_path)) {
             return false;
         }
@@ -110,15 +104,14 @@ struct upscaler_ctx_t {
 };
 
 upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path_c_str,
-                                 int n_threads,
-                                 bool direct = false) {
+                                 int n_threads) {
     upscaler_ctx_t* upscaler_ctx = (upscaler_ctx_t*)malloc(sizeof(upscaler_ctx_t));
     if (upscaler_ctx == NULL) {
         return NULL;
     }
     std::string esrgan_path(esrgan_path_c_str);
 
-    upscaler_ctx->upscaler = new UpscalerGGML(n_threads, direct);
+    upscaler_ctx->upscaler = new UpscalerGGML(n_threads);
     if (upscaler_ctx->upscaler == NULL) {
         return NULL;
     }

vae.hpp

@@ -534,17 +534,6 @@ struct AutoEncoderKL : public GGMLRunner {
         ae.init(params_ctx, tensor_types, prefix);
     }
 
-    void enable_conv2d_direct() {
-        std::vector<GGMLBlock*> blocks;
-        ae.get_all_blocks(blocks);
-        for (auto block : blocks) {
-            if (block->get_desc() == "Conv2d") {
-                auto conv_block = (Conv2d*)block;
-                conv_block->enable_direct();
-            }
-        }
-    }
-
     std::string get_desc() {
         return "vae";
     }