fix: break the line after skipping tensors in VAE (#591)

README.md

@@ -50,7 +50,7 @@ Inference of Stable Diffusion and Flux in pure C/C++
     - Linux
     - Mac OS
     - Windows
-    - Android (via Termux)
+    - Android (via Termux, [Local Diffusion](https://github.com/rmatif/Local-Diffusion))
 
 ### TODO
 
@@ -392,10 +392,12 @@ Using formats of different precisions will yield results of varying quality.
 
 These projects wrap `stable-diffusion.cpp` for easier use in other languages/frameworks.
 
-* Golang: [seasonjs/stable-diffusion](https://github.com/seasonjs/stable-diffusion)
+* Golang (non-cgo): [seasonjs/stable-diffusion](https://github.com/seasonjs/stable-diffusion)
+* Golang (cgo): [Binozo/GoStableDiffusion](https://github.com/Binozo/GoStableDiffusion)
 * C#: [DarthAffe/StableDiffusion.NET](https://github.com/DarthAffe/StableDiffusion.NET)
 * Python: [william-murray1204/stable-diffusion-cpp-python](https://github.com/william-murray1204/stable-diffusion-cpp-python)
 * Rust: [newfla/diffusion-rs](https://github.com/newfla/diffusion-rs)
+* Flutter/Dart: [rmatif/Local-Diffusion](https://github.com/rmatif/Local-Diffusion)
 
 ## UIs
 
@@ -404,6 +406,7 @@ These projects use `stable-diffusion.cpp` as a backend for their image generatio
 - [Jellybox](https://jellybox.com)
 - [Stable Diffusion GUI](https://github.com/fszontagh/sd.cpp.gui.wx)
 - [Stable Diffusion CLI-GUI](https://github.com/piallai/stable-diffusion.cpp)
+- [Local Diffusion](https://github.com/rmatif/Local-Diffusion)
 
 ## Contributors
 

common.hpp

@@ -57,7 +57,7 @@ public:
         auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
 
         x = ggml_upscale(ctx, x, 2, GGML_SCALE_MODE_NEAREST);  // [N, channels, h*2, w*2]
-        x = conv->forward(ctx, x);    // [N, out_channels, h*2, w*2]
+        x = conv->forward(ctx, x);                             // [N, out_channels, h*2, w*2]
         return x;
     }
 };

conditioner.hpp

@@ -459,8 +459,8 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                 if (sd_version_is_sdxl(version)) {
                     text_model2->compute(n_threads,
                                          input_ids2,
-                                         0,
-                                         NULL,
+                                         num_custom_embeddings,
+                                         token_embed_custom.data(),
                                          max_token_idx,
                                          false,
                                          &chunk_hidden_states2, work_ctx);
@@ -470,8 +470,8 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
                     if (chunk_idx == 0) {
                         text_model2->compute(n_threads,
                                              input_ids2,
-                                             0,
-                                             NULL,
+                                             num_custom_embeddings,
+                                             token_embed_custom.data(),
                                              max_token_idx,
                                              true,
                                              &pooled,

denoiser.hpp

@@ -347,12 +347,13 @@ struct EDMVDenoiser : public CompVisVDenoiser {
     float min_sigma = 0.002;
     float max_sigma = 120.0;
 
-    EDMVDenoiser(float min_sigma = 0.002, float max_sigma = 120.0) : min_sigma(min_sigma), max_sigma(max_sigma) {
+    EDMVDenoiser(float min_sigma = 0.002, float max_sigma = 120.0)
+        : min_sigma(min_sigma), max_sigma(max_sigma) {
         schedule = std::make_shared<ExponentialSchedule>();
     }
 
     float t_to_sigma(float t) {
-        return std::exp(t * 4/(float)TIMESTEPS);
+        return std::exp(t * 4 / (float)TIMESTEPS);
     }
 
     float sigma_to_t(float s) {

ggml_extend.hpp

@@ -118,7 +118,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_kronecker(ggml_context* ctx, struct g
                                      a->ne[1] * b->ne[1],
                                      a->ne[2] * b->ne[2],
                                      a->ne[3] * b->ne[3],
-                                     GGML_SCALE_MODE_NEAREST), 
+                                     GGML_SCALE_MODE_NEAREST),
                     b);
 }
 

model.cpp

@@ -181,6 +181,64 @@ std::unordered_map<std::string, std::string> pmid_v2_name_map = {
 std::string convert_open_clip_to_hf_clip(const std::string& name) {
     std::string new_name = name;
     std::string prefix;
+    if (contains(new_name, ".enc.")) {
+        // llama.cpp naming convention for T5
+        size_t pos = new_name.find(".enc.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 5, ".encoder.");
+        }
+        pos = new_name.find("blk.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 4, "block.");
+        }
+        pos = new_name.find("output_norm.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 12, "final_layer_norm.");
+        }
+        pos = new_name.find("attn_k.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 7, "layer.0.SelfAttention.k.");
+        }
+        pos = new_name.find("attn_v.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 7, "layer.0.SelfAttention.v.");
+        }
+        pos = new_name.find("attn_o.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 7, "layer.0.SelfAttention.o.");
+        }
+        pos = new_name.find("attn_q.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 7, "layer.0.SelfAttention.q.");
+        }
+        pos = new_name.find("attn_norm.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 10, "layer.0.layer_norm.");
+        }
+        pos = new_name.find("ffn_norm.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 9, "layer.1.layer_norm.");
+        }
+        pos = new_name.find("ffn_up.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 7, "layer.1.DenseReluDense.wi_1.");
+        }
+        pos = new_name.find("ffn_down.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 9, "layer.1.DenseReluDense.wo.");
+        }
+        pos = new_name.find("ffn_gate.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 9, "layer.1.DenseReluDense.wi_0.");
+        }
+        pos = new_name.find("attn_rel_b.");
+        if (pos != std::string::npos) {
+            new_name.replace(pos, 11, "layer.0.SelfAttention.relative_attention_bias.");
+        }
+    } else if (name == "text_encoders.t5xxl.transformer.token_embd.weight") {
+        new_name = "text_encoders.t5xxl.transformer.shared.weight";
+    }
+
     if (starts_with(new_name, "conditioner.embedders.0.open_clip.")) {
         prefix   = "cond_stage_model.";
         new_name = new_name.substr(strlen("conditioner.embedders.0.open_clip."));
@@ -1783,6 +1841,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
         };
         int tensor_count = 0;
         int64_t t1       = ggml_time_ms();
+        bool partial = false;
         for (auto& tensor_storage : processed_tensor_storages) {
             if (tensor_storage.file_index != file_index) {
                 ++tensor_count;
@@ -1864,15 +1923,21 @@ 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));
                 }
             }
+            size_t tensor_max = processed_tensor_storages.size();
             int64_t t2 = ggml_time_ms();
-            pretty_progress(++tensor_count, processed_tensor_storages.size(), (t2 - t1) / 1000.0f);
+            pretty_progress(++tensor_count, tensor_max, (t2 - t1) / 1000.0f);
             t1 = t2;
+            partial = tensor_count != tensor_max;
         }
 
         if (zip != NULL) {
             zip_close(zip);
         }
 
+        if (partial) {
+            printf("\n");
+        }
+
         if (!success) {
             break;
         }

stable-diffusion.cpp

@@ -1566,6 +1566,29 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx,
     return result_images;
 }
 
+ggml_tensor* generate_init_latent(sd_ctx_t* sd_ctx,
+                                  ggml_context* work_ctx,
+                                  int width,
+                                  int height) {
+    int C = 4;
+    if (sd_version_is_sd3(sd_ctx->sd->version)) {
+        C = 16;
+    } else if (sd_version_is_flux(sd_ctx->sd->version)) {
+        C = 16;
+    }
+    int W                    = width / 8;
+    int H                    = height / 8;
+    ggml_tensor* init_latent = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, C, 1);
+    if (sd_version_is_sd3(sd_ctx->sd->version)) {
+        ggml_set_f32(init_latent, 0.0609f);
+    } else if (sd_version_is_flux(sd_ctx->sd->version)) {
+        ggml_set_f32(init_latent, 0.1159f);
+    } else {
+        ggml_set_f32(init_latent, 0.f);
+    }
+    return init_latent;
+}
+
 sd_image_t* txt2img(sd_ctx_t* sd_ctx,
                     const char* prompt_c_str,
                     const char* negative_prompt_c_str,
@@ -1622,27 +1645,12 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
 
     std::vector<float> sigmas = sd_ctx->sd->denoiser->get_sigmas(sample_steps);
 
-    int C = 4;
-    if (sd_version_is_sd3(sd_ctx->sd->version)) {
-        C = 16;
-    } else if (sd_version_is_flux(sd_ctx->sd->version)) {
-        C = 16;
-    }
-    int W                    = width / 8;
-    int H                    = height / 8;
-    ggml_tensor* init_latent = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, C, 1);
-    if (sd_version_is_sd3(sd_ctx->sd->version)) {
-        ggml_set_f32(init_latent, 0.0609f);
-    } else if (sd_version_is_flux(sd_ctx->sd->version)) {
-        ggml_set_f32(init_latent, 0.1159f);
-    } else {
-        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");
     }
 
+    ggml_tensor* init_latent = generate_init_latent(sd_ctx, work_ctx, width, height);
+
     sd_image_t* result_images = generate_image(sd_ctx,
                                                work_ctx,
                                                init_latent,
@@ -2046,23 +2054,6 @@ sd_image_t* edit(sd_ctx_t* sd_ctx,
     }
     sd_ctx->sd->rng->manual_seed(seed);
 
-    int C = 4;
-    if (sd_version_is_sd3(sd_ctx->sd->version)) {
-        C = 16;
-    } else if (sd_version_is_flux(sd_ctx->sd->version)) {
-        C = 16;
-    }
-    int W                    = width / 8;
-    int H                    = height / 8;
-    ggml_tensor* init_latent = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, C, 1);
-    if (sd_version_is_sd3(sd_ctx->sd->version)) {
-        ggml_set_f32(init_latent, 0.0609f);
-    } else if (sd_version_is_flux(sd_ctx->sd->version)) {
-        ggml_set_f32(init_latent, 0.1159f);
-    } else {
-        ggml_set_f32(init_latent, 0.f);
-    }
-
     size_t t0 = ggml_time_ms();
 
     std::vector<struct ggml_tensor*> ref_latents;
@@ -2085,6 +2076,8 @@ sd_image_t* edit(sd_ctx_t* sd_ctx,
 
     std::vector<float> sigmas = sd_ctx->sd->denoiser->get_sigmas(sample_steps);
 
+    ggml_tensor* init_latent = generate_init_latent(sd_ctx, work_ctx, width, height);
+
     sd_image_t* result_images = generate_image(sd_ctx,
                                                work_ctx,
                                                init_latent,