fix: fix a few typos on cli help and error messages (#714)

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
 

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

@@ -181,7 +181,7 @@ struct AYSSchedule : SigmaSchedule {
             LOG_INFO("AYS using SVD noise levels");
             inputs = noise_levels[2];
         } else {
-            LOG_ERROR("Version not compatable with AYS scheduler");
+            LOG_ERROR("Version not compatible with AYS scheduler");
             return results;
         }
 

examples/cli/main.cpp

@@ -60,6 +60,7 @@ const char* modes_str[] = {
     "edit",
     "convert",
 };
+#define SD_ALL_MODES_STR "txt2img, img2img, edit, convert"
 
 enum SDMode {
     TXT2IMG,
@@ -199,14 +200,18 @@ void print_usage(int argc, const char* argv[]) {
     printf("\n");
     printf("arguments:\n");
     printf("  -h, --help                         show this help message and exit\n");
-    printf("  -M, --mode [MODEL]                 run mode (txt2img or img2img or convert, default: txt2img)\n");
+    printf("  -M, --mode [MODE]                  run mode, one of:\n");
+    printf("                                     txt2img: generate an image from a text prompt (default)\n");
+    printf("                                     img2img: generate an image from a text prompt and an initial image (--init-img)\n");
+    printf("                                     edit:    modify an image (--ref-image) based on text instructions\n");
+    printf("                                     convert: convert a model file to gguf format, optionally with quantization\n");
     printf("  -t, --threads N                    number of threads to use during computation (default: -1)\n");
     printf("                                     If threads <= 0, then threads will be set to the number of CPU physical cores\n");
     printf("  -m, --model [MODEL]                path to full model\n");
     printf("  --diffusion-model                  path to the standalone diffusion model\n");
     printf("  --clip_l                           path to the clip-l text encoder\n");
     printf("  --clip_g                           path to the clip-g text encoder\n");
-    printf("  --t5xxl                            path to the the t5xxl text encoder\n");
+    printf("  --t5xxl                            path to the t5xxl text encoder\n");
     printf("  --vae [VAE]                        path to vae\n");
     printf("  --taesd [TAESD_PATH]               path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)\n");
     printf("  --control-net [CONTROL_PATH]       path to control net model\n");
@@ -222,7 +227,7 @@ void print_usage(int argc, const char* argv[]) {
     printf("  -i, --init-img [IMAGE]             path to the input image, required by img2img\n");
     printf("  --mask [MASK]                      path to the mask image, required by img2img with mask\n");
     printf("  --control-image [IMAGE]            path to image condition, control net\n");
-    printf("  -r, --ref_image [PATH]             reference image for Flux Kontext models (can be used multiple times) \n");
+    printf("  -r, --ref-image [PATH]             reference image for Flux Kontext models (can be used multiple times) \n");
     printf("  -o, --output OUTPUT                path to write result image to (default: ./output.png)\n");
     printf("  -p, --prompt [PROMPT]              the prompt to render\n");
     printf("  -n, --negative-prompt PROMPT       the negative prompt (default: \"\")\n");
@@ -291,8 +296,8 @@ void parse_args(int argc, const char** argv, SDParams& params) {
             }
             if (mode_found == -1) {
                 fprintf(stderr,
-                        "error: invalid mode %s, must be one of [txt2img, img2img, img2vid, convert]\n",
-                        mode_selected);
+                        "error: invalid mode %s, must be one of [%s]\n",
+                        mode_selected, SD_ALL_MODES_STR);
                 exit(1);
             }
             params.mode = (SDMode)mode_found;
@@ -1218,4 +1223,4 @@ int main(int argc, const char* argv[]) {
     free(input_image_buffer);
 
     return 0;
-}
+}

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;
         }