feat: add chroma radiance support (#910)

* add chroma radiance support

* fix ci

* simply generate_init_latent

* workaround: avoid ggml cuda error

* format code

* add chroma radiance doc

README.md

@@ -35,9 +35,11 @@ API and command-line option may change frequently.***
   - Image Models
     - SD1.x, SD2.x, [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo)
     - SDXL, [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo)
+    - [Some SD1.x and SDXL distilled models](./docs/distilled_sd.md)
     - [SD3/SD3.5](./docs/sd3.md)
     - [Flux-dev/Flux-schnell](./docs/flux.md)
     - [Chroma](./docs/chroma.md)
+    - [Chroma1-Radiance](./docs/chroma_radiance.md)
     - [Qwen Image](./docs/qwen_image.md)
   - Image Edit Models
     - [FLUX.1-Kontext-dev](./docs/kontext.md)

conditioner.hpp

@@ -673,33 +673,80 @@ struct SD3CLIPEmbedder : public Conditioner {
                     bool offload_params_to_cpu,
                     const String2GGMLType& tensor_types = {})
         : clip_g_tokenizer(0) {
-        clip_l = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, false);
-        clip_g = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.clip_g.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false);
-        t5     = std::make_shared<T5Runner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.t5xxl.transformer");
+        bool use_clip_l = false;
+        bool use_clip_g = false;
+        bool use_t5     = false;
+        for (auto pair : tensor_types) {
+            if (pair.first.find("text_encoders.clip_l") != std::string::npos) {
+                use_clip_l = true;
+            } else if (pair.first.find("text_encoders.clip_g") != std::string::npos) {
+                use_clip_g = true;
+            } else if (pair.first.find("text_encoders.t5xxl") != std::string::npos) {
+                use_t5 = true;
+            }
+        }
+        if (!use_clip_l && !use_clip_g && !use_t5) {
+            LOG_WARN("IMPORTANT NOTICE: No text encoders provided, cannot process prompts!");
+            return;
+        }
+        if (use_clip_l) {
+            clip_l = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, false);
+        }
+        if (use_clip_g) {
+            clip_g = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.clip_g.transformer.text_model", OPEN_CLIP_VIT_BIGG_14, false);
+        }
+        if (use_t5) {
+            t5 = std::make_shared<T5Runner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.t5xxl.transformer");
+        }
     }
 
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
-        clip_l->get_param_tensors(tensors, "text_encoders.clip_l.transformer.text_model");
-        clip_g->get_param_tensors(tensors, "text_encoders.clip_g.transformer.text_model");
-        t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
+        if (clip_l) {
+            clip_l->get_param_tensors(tensors, "text_encoders.clip_l.transformer.text_model");
+        }
+        if (clip_g) {
+            clip_g->get_param_tensors(tensors, "text_encoders.clip_g.transformer.text_model");
+        }
+        if (t5) {
+            t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
+        }
     }
 
     void alloc_params_buffer() override {
-        clip_l->alloc_params_buffer();
-        clip_g->alloc_params_buffer();
-        t5->alloc_params_buffer();
+        if (clip_l) {
+            clip_l->alloc_params_buffer();
+        }
+        if (clip_g) {
+            clip_g->alloc_params_buffer();
+        }
+        if (t5) {
+            t5->alloc_params_buffer();
+        }
     }
 
     void free_params_buffer() override {
-        clip_l->free_params_buffer();
-        clip_g->free_params_buffer();
-        t5->free_params_buffer();
+        if (clip_l) {
+            clip_l->free_params_buffer();
+        }
+        if (clip_g) {
+            clip_g->free_params_buffer();
+        }
+        if (t5) {
+            t5->free_params_buffer();
+        }
     }
 
     size_t get_params_buffer_size() override {
-        size_t buffer_size = clip_l->get_params_buffer_size();
-        buffer_size += clip_g->get_params_buffer_size();
-        buffer_size += t5->get_params_buffer_size();
+        size_t buffer_size = 0;
+        if (clip_l) {
+            buffer_size += clip_l->get_params_buffer_size();
+        }
+        if (clip_g) {
+            buffer_size += clip_g->get_params_buffer_size();
+        }
+        if (t5) {
+            buffer_size += t5->get_params_buffer_size();
+        }
         return buffer_size;
     }
 
@@ -731,23 +778,32 @@ struct SD3CLIPEmbedder : public Conditioner {
         for (const auto& item : parsed_attention) {
             const std::string& curr_text = item.first;
             float curr_weight            = item.second;
-
-            std::vector<int> curr_tokens = clip_l_tokenizer.encode(curr_text, on_new_token_cb);
-            clip_l_tokens.insert(clip_l_tokens.end(), curr_tokens.begin(), curr_tokens.end());
-            clip_l_weights.insert(clip_l_weights.end(), curr_tokens.size(), curr_weight);
-
-            curr_tokens = clip_g_tokenizer.encode(curr_text, on_new_token_cb);
-            clip_g_tokens.insert(clip_g_tokens.end(), curr_tokens.begin(), curr_tokens.end());
-            clip_g_weights.insert(clip_g_weights.end(), curr_tokens.size(), curr_weight);
-
-            curr_tokens = t5_tokenizer.Encode(curr_text, true);
-            t5_tokens.insert(t5_tokens.end(), curr_tokens.begin(), curr_tokens.end());
-            t5_weights.insert(t5_weights.end(), curr_tokens.size(), curr_weight);
+            if (clip_l) {
+                std::vector<int> curr_tokens = clip_l_tokenizer.encode(curr_text, on_new_token_cb);
+                clip_l_tokens.insert(clip_l_tokens.end(), curr_tokens.begin(), curr_tokens.end());
+                clip_l_weights.insert(clip_l_weights.end(), curr_tokens.size(), curr_weight);
+            }
+            if (clip_g) {
+                std::vector<int> curr_tokens = clip_g_tokenizer.encode(curr_text, on_new_token_cb);
+                clip_g_tokens.insert(clip_g_tokens.end(), curr_tokens.begin(), curr_tokens.end());
+                clip_g_weights.insert(clip_g_weights.end(), curr_tokens.size(), curr_weight);
+            }
+            if (t5) {
+                std::vector<int> curr_tokens = t5_tokenizer.Encode(curr_text, true);
+                t5_tokens.insert(t5_tokens.end(), curr_tokens.begin(), curr_tokens.end());
+                t5_weights.insert(t5_weights.end(), curr_tokens.size(), curr_weight);
+            }
         }
 
-        clip_l_tokenizer.pad_tokens(clip_l_tokens, clip_l_weights, max_length, padding);
-        clip_g_tokenizer.pad_tokens(clip_g_tokens, clip_g_weights, max_length, padding);
-        t5_tokenizer.pad_tokens(t5_tokens, t5_weights, nullptr, max_length, padding);
+        if (clip_l) {
+            clip_l_tokenizer.pad_tokens(clip_l_tokens, clip_l_weights, max_length, padding);
+        }
+        if (clip_g) {
+            clip_g_tokenizer.pad_tokens(clip_g_tokens, clip_g_weights, max_length, padding);
+        }
+        if (t5) {
+            t5_tokenizer.pad_tokens(t5_tokens, t5_weights, nullptr, max_length, padding);
+        }
 
         // for (int i = 0; i < clip_l_tokens.size(); i++) {
         //     std::cout << clip_l_tokens[i] << ":" << clip_l_weights[i] << ", ";
@@ -795,10 +851,10 @@ struct SD3CLIPEmbedder : public Conditioner {
         std::vector<float> hidden_states_vec;
 
         size_t chunk_len   = 77;
-        size_t chunk_count = clip_l_tokens.size() / chunk_len;
+        size_t chunk_count = std::max(std::max(clip_l_tokens.size(), clip_g_tokens.size()), t5_tokens.size()) / chunk_len;
         for (int chunk_idx = 0; chunk_idx < chunk_count; chunk_idx++) {
             // clip_l
-            {
+            if (clip_l) {
                 std::vector<int> chunk_tokens(clip_l_tokens.begin() + chunk_idx * chunk_len,
                                               clip_l_tokens.begin() + (chunk_idx + 1) * chunk_len);
                 std::vector<float> chunk_weights(clip_l_weights.begin() + chunk_idx * chunk_len,
@@ -845,10 +901,17 @@ struct SD3CLIPEmbedder : public Conditioner {
                                     &pooled_l,
                                     work_ctx);
                 }
+            } else {
+                chunk_hidden_states_l = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 768, chunk_len);
+                ggml_set_f32(chunk_hidden_states_l, 0.f);
+                if (chunk_idx == 0) {
+                    pooled_l = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 768);
+                    ggml_set_f32(pooled_l, 0.f);
+                }
             }
 
             // clip_g
-            {
+            if (clip_g) {
                 std::vector<int> chunk_tokens(clip_g_tokens.begin() + chunk_idx * chunk_len,
                                               clip_g_tokens.begin() + (chunk_idx + 1) * chunk_len);
                 std::vector<float> chunk_weights(clip_g_weights.begin() + chunk_idx * chunk_len,
@@ -896,10 +959,17 @@ struct SD3CLIPEmbedder : public Conditioner {
                                     &pooled_g,
                                     work_ctx);
                 }
+            } else {
+                chunk_hidden_states_g = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 1280, chunk_len);
+                ggml_set_f32(chunk_hidden_states_g, 0.f);
+                if (chunk_idx == 0) {
+                    pooled_g = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 1280);
+                    ggml_set_f32(pooled_g, 0.f);
+                }
             }
 
             // t5
-            {
+            if (t5) {
                 std::vector<int> chunk_tokens(t5_tokens.begin() + chunk_idx * chunk_len,
                                               t5_tokens.begin() + (chunk_idx + 1) * chunk_len);
                 std::vector<float> chunk_weights(t5_weights.begin() + chunk_idx * chunk_len,
@@ -927,6 +997,9 @@ struct SD3CLIPEmbedder : public Conditioner {
                     float new_mean = ggml_tensor_mean(tensor);
                     ggml_tensor_scale(tensor, (original_mean / new_mean));
                 }
+            } else {
+                chunk_hidden_states_t5 = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 4096, chunk_len);
+                ggml_set_f32(chunk_hidden_states_t5, 0.f);
             }
 
             auto chunk_hidden_states_lg_pad = ggml_new_tensor_3d(work_ctx,
@@ -969,11 +1042,20 @@ struct SD3CLIPEmbedder : public Conditioner {
                                      ((float*)chunk_hidden_states->data) + ggml_nelements(chunk_hidden_states));
         }
 
-        hidden_states = vector_to_ggml_tensor(work_ctx, hidden_states_vec);
-        hidden_states = ggml_reshape_2d(work_ctx,
-                                        hidden_states,
-                                        chunk_hidden_states->ne[0],
-                                        ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);
+        if (hidden_states_vec.size() > 0) {
+            hidden_states = vector_to_ggml_tensor(work_ctx, hidden_states_vec);
+            hidden_states = ggml_reshape_2d(work_ctx,
+                                            hidden_states,
+                                            chunk_hidden_states->ne[0],
+                                            ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);
+        } else {
+            hidden_states = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 4096, 256);
+            ggml_set_f32(hidden_states, 0.f);
+        }
+        if (pooled == nullptr) {
+            pooled = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 2048);
+            ggml_set_f32(pooled, 0.f);
+        }
         return {hidden_states, pooled, nullptr};
     }
 
@@ -999,28 +1081,68 @@ struct FluxCLIPEmbedder : public Conditioner {
     FluxCLIPEmbedder(ggml_backend_t backend,
                      bool offload_params_to_cpu,
                      const String2GGMLType& tensor_types = {}) {
-        clip_l = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, true);
-        t5     = std::make_shared<T5Runner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.t5xxl.transformer");
+        bool use_clip_l = false;
+        bool use_t5     = false;
+        for (auto pair : tensor_types) {
+            if (pair.first.find("text_encoders.clip_l") != std::string::npos) {
+                use_clip_l = true;
+            } else if (pair.first.find("text_encoders.t5xxl") != std::string::npos) {
+                use_t5 = true;
+            }
+        }
+
+        if (!use_clip_l && !use_t5) {
+            LOG_WARN("IMPORTANT NOTICE: No text encoders provided, cannot process prompts!");
+            return;
+        }
+
+        if (use_clip_l) {
+            clip_l = std::make_shared<CLIPTextModelRunner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.clip_l.transformer.text_model", OPENAI_CLIP_VIT_L_14, true);
+        } else {
+            LOG_WARN("clip_l text encoder not found! Prompt adherence might be degraded.");
+        }
+        if (use_t5) {
+            t5 = std::make_shared<T5Runner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.t5xxl.transformer");
+        } else {
+            LOG_WARN("t5xxl text encoder not found! Prompt adherence might be degraded.");
+        }
     }
 
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
-        clip_l->get_param_tensors(tensors, "text_encoders.clip_l.transformer.text_model");
-        t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
+        if (clip_l) {
+            clip_l->get_param_tensors(tensors, "text_encoders.clip_l.transformer.text_model");
+        }
+        if (t5) {
+            t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
+        }
     }
 
     void alloc_params_buffer() override {
-        clip_l->alloc_params_buffer();
-        t5->alloc_params_buffer();
+        if (clip_l) {
+            clip_l->alloc_params_buffer();
+        }
+        if (t5) {
+            t5->alloc_params_buffer();
+        }
     }
 
     void free_params_buffer() override {
-        clip_l->free_params_buffer();
-        t5->free_params_buffer();
+        if (clip_l) {
+            clip_l->free_params_buffer();
+        }
+        if (t5) {
+            t5->free_params_buffer();
+        }
     }
 
     size_t get_params_buffer_size() override {
-        size_t buffer_size = clip_l->get_params_buffer_size();
-        buffer_size += t5->get_params_buffer_size();
+        size_t buffer_size = 0;
+        if (clip_l) {
+            buffer_size += clip_l->get_params_buffer_size();
+        }
+        if (t5) {
+            buffer_size += t5->get_params_buffer_size();
+        }
         return buffer_size;
     }
 
@@ -1050,18 +1172,24 @@ struct FluxCLIPEmbedder : public Conditioner {
         for (const auto& item : parsed_attention) {
             const std::string& curr_text = item.first;
             float curr_weight            = item.second;
-
-            std::vector<int> curr_tokens = clip_l_tokenizer.encode(curr_text, on_new_token_cb);
-            clip_l_tokens.insert(clip_l_tokens.end(), curr_tokens.begin(), curr_tokens.end());
-            clip_l_weights.insert(clip_l_weights.end(), curr_tokens.size(), curr_weight);
-
-            curr_tokens = t5_tokenizer.Encode(curr_text, true);
-            t5_tokens.insert(t5_tokens.end(), curr_tokens.begin(), curr_tokens.end());
-            t5_weights.insert(t5_weights.end(), curr_tokens.size(), curr_weight);
+            if (clip_l) {
+                std::vector<int> curr_tokens = clip_l_tokenizer.encode(curr_text, on_new_token_cb);
+                clip_l_tokens.insert(clip_l_tokens.end(), curr_tokens.begin(), curr_tokens.end());
+                clip_l_weights.insert(clip_l_weights.end(), curr_tokens.size(), curr_weight);
+            }
+            if (t5) {
+                std::vector<int> curr_tokens = t5_tokenizer.Encode(curr_text, true);
+                t5_tokens.insert(t5_tokens.end(), curr_tokens.begin(), curr_tokens.end());
+                t5_weights.insert(t5_weights.end(), curr_tokens.size(), curr_weight);
+            }
         }
 
-        clip_l_tokenizer.pad_tokens(clip_l_tokens, clip_l_weights, 77, padding);
-        t5_tokenizer.pad_tokens(t5_tokens, t5_weights, nullptr, max_length, padding);
+        if (clip_l) {
+            clip_l_tokenizer.pad_tokens(clip_l_tokens, clip_l_weights, 77, padding);
+        }
+        if (t5) {
+            t5_tokenizer.pad_tokens(t5_tokens, t5_weights, nullptr, max_length, padding);
+        }
 
         // for (int i = 0; i < clip_l_tokens.size(); i++) {
         //     std::cout << clip_l_tokens[i] << ":" << clip_l_weights[i] << ", ";
@@ -1096,35 +1224,37 @@ struct FluxCLIPEmbedder : public Conditioner {
         struct ggml_tensor* pooled              = nullptr;  // [768,]
         std::vector<float> hidden_states_vec;
 
-        size_t chunk_count = t5_tokens.size() / chunk_len;
+        size_t chunk_count = std::max(clip_l_tokens.size() > 0 ? chunk_len : 0, t5_tokens.size()) / chunk_len;
         for (int chunk_idx = 0; chunk_idx < chunk_count; chunk_idx++) {
             // clip_l
             if (chunk_idx == 0) {
-                size_t chunk_len_l = 77;
-                std::vector<int> chunk_tokens(clip_l_tokens.begin(),
-                                              clip_l_tokens.begin() + chunk_len_l);
-                std::vector<float> chunk_weights(clip_l_weights.begin(),
-                                                 clip_l_weights.begin() + chunk_len_l);
+                if (clip_l) {
+                    size_t chunk_len_l = 77;
+                    std::vector<int> chunk_tokens(clip_l_tokens.begin(),
+                                                  clip_l_tokens.begin() + chunk_len_l);
+                    std::vector<float> chunk_weights(clip_l_weights.begin(),
+                                                     clip_l_weights.begin() + chunk_len_l);
 
-                auto input_ids       = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
-                size_t max_token_idx = 0;
+                    auto input_ids       = vector_to_ggml_tensor_i32(work_ctx, chunk_tokens);
+                    size_t max_token_idx = 0;
 
-                auto it       = std::find(chunk_tokens.begin(), chunk_tokens.end(), clip_l_tokenizer.EOS_TOKEN_ID);
-                max_token_idx = std::min<size_t>(std::distance(chunk_tokens.begin(), it), chunk_tokens.size() - 1);
+                    auto it       = std::find(chunk_tokens.begin(), chunk_tokens.end(), clip_l_tokenizer.EOS_TOKEN_ID);
+                    max_token_idx = std::min<size_t>(std::distance(chunk_tokens.begin(), it), chunk_tokens.size() - 1);
 
-                clip_l->compute(n_threads,
-                                input_ids,
-                                0,
-                                nullptr,
-                                max_token_idx,
-                                true,
-                                clip_skip,
-                                &pooled,
-                                work_ctx);
+                    clip_l->compute(n_threads,
+                                    input_ids,
+                                    0,
+                                    nullptr,
+                                    max_token_idx,
+                                    true,
+                                    clip_skip,
+                                    &pooled,
+                                    work_ctx);
+                }
             }
 
             // t5
-            {
+            if (t5) {
                 std::vector<int> chunk_tokens(t5_tokens.begin() + chunk_idx * chunk_len,
                                               t5_tokens.begin() + (chunk_idx + 1) * chunk_len);
                 std::vector<float> chunk_weights(t5_weights.begin() + chunk_idx * chunk_len,
@@ -1152,6 +1282,9 @@ struct FluxCLIPEmbedder : public Conditioner {
                     float new_mean = ggml_tensor_mean(tensor);
                     ggml_tensor_scale(tensor, (original_mean / new_mean));
                 }
+            } else {
+                chunk_hidden_states = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 4096, chunk_len);
+                ggml_set_f32(chunk_hidden_states, 0.f);
             }
 
             int64_t t1 = ggml_time_ms();
@@ -1168,11 +1301,20 @@ struct FluxCLIPEmbedder : public Conditioner {
                                      ((float*)chunk_hidden_states->data) + ggml_nelements(chunk_hidden_states));
         }
 
-        hidden_states = vector_to_ggml_tensor(work_ctx, hidden_states_vec);
-        hidden_states = ggml_reshape_2d(work_ctx,
-                                        hidden_states,
-                                        chunk_hidden_states->ne[0],
-                                        ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);
+        if (hidden_states_vec.size() > 0) {
+            hidden_states = vector_to_ggml_tensor(work_ctx, hidden_states_vec);
+            hidden_states = ggml_reshape_2d(work_ctx,
+                                            hidden_states,
+                                            chunk_hidden_states->ne[0],
+                                            ggml_nelements(hidden_states) / chunk_hidden_states->ne[0]);
+        } else {
+            hidden_states = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 4096, 256);
+            ggml_set_f32(hidden_states, 0.f);
+        }
+        if (pooled == nullptr) {
+            pooled = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 768);
+            ggml_set_f32(pooled, 0.f);
+        }
         return {hidden_states, pooled, nullptr};
     }
 
@@ -1203,26 +1345,44 @@ struct T5CLIPEmbedder : public Conditioner {
                    int mask_pad                        = 1,
                    bool is_umt5                        = false)
         : use_mask(use_mask), mask_pad(mask_pad), t5_tokenizer(is_umt5) {
-        t5 = std::make_shared<T5Runner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.t5xxl.transformer", is_umt5);
+        bool use_t5 = false;
+        for (auto pair : tensor_types) {
+            if (pair.first.find("text_encoders.t5xxl") != std::string::npos) {
+                use_t5 = true;
+            }
+        }
+
+        if (!use_t5) {
+            LOG_WARN("IMPORTANT NOTICE: No text encoders provided, cannot process prompts!");
+            return;
+        } else {
+            t5 = std::make_shared<T5Runner>(backend, offload_params_to_cpu, tensor_types, "text_encoders.t5xxl.transformer", is_umt5);
+        }
     }
 
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
-        t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
+        if (t5) {
+            t5->get_param_tensors(tensors, "text_encoders.t5xxl.transformer");
+        }
     }
 
     void alloc_params_buffer() override {
-        t5->alloc_params_buffer();
+        if (t5) {
+            t5->alloc_params_buffer();
+        }
     }
 
     void free_params_buffer() override {
-        t5->free_params_buffer();
+        if (t5) {
+            t5->free_params_buffer();
+        }
     }
 
     size_t get_params_buffer_size() override {
         size_t buffer_size = 0;
-
-        buffer_size += t5->get_params_buffer_size();
-
+        if (t5) {
+            buffer_size += t5->get_params_buffer_size();
+        }
         return buffer_size;
     }
 
@@ -1248,17 +1408,18 @@ struct T5CLIPEmbedder : public Conditioner {
         std::vector<int> t5_tokens;
         std::vector<float> t5_weights;
         std::vector<float> t5_mask;
-        for (const auto& item : parsed_attention) {
-            const std::string& curr_text = item.first;
-            float curr_weight            = item.second;
+        if (t5) {
+            for (const auto& item : parsed_attention) {
+                const std::string& curr_text = item.first;
+                float curr_weight            = item.second;
 
-            std::vector<int> curr_tokens = t5_tokenizer.Encode(curr_text, true);
-            t5_tokens.insert(t5_tokens.end(), curr_tokens.begin(), curr_tokens.end());
-            t5_weights.insert(t5_weights.end(), curr_tokens.size(), curr_weight);
+                std::vector<int> curr_tokens = t5_tokenizer.Encode(curr_text, true);
+                t5_tokens.insert(t5_tokens.end(), curr_tokens.begin(), curr_tokens.end());
+                t5_weights.insert(t5_weights.end(), curr_tokens.size(), curr_weight);
+            }
+
+            t5_tokenizer.pad_tokens(t5_tokens, t5_weights, &t5_mask, max_length, padding);
         }
-
-        t5_tokenizer.pad_tokens(t5_tokens, t5_weights, &t5_mask, max_length, padding);
-
         return {t5_tokens, t5_weights, t5_mask};
     }
 
@@ -1282,6 +1443,13 @@ struct T5CLIPEmbedder : public Conditioner {
                                              std::tuple<std::vector<int>, std::vector<float>, std::vector<float>> token_and_weights,
                                              int clip_skip,
                                              bool zero_out_masked = false) {
+        if (!t5) {
+            auto hidden_states = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 4096, 256);
+            ggml_set_f32(hidden_states, 0.f);
+            auto t5_attn_mask = ggml_new_tensor_1d(work_ctx, GGML_TYPE_F32, 256);
+            ggml_set_f32(t5_attn_mask, -HUGE_VALF);
+            return {hidden_states, t5_attn_mask, nullptr};
+        }
         auto& t5_tokens        = std::get<0>(token_and_weights);
         auto& t5_weights       = std::get<1>(token_and_weights);
         auto& t5_attn_mask_vec = std::get<2>(token_and_weights);

docs/chroma_radiance.md

@@ -0,0 +1,21 @@
+# How to Use
+
+## Download weights
+
+- Download Chroma1-Radiance
+    - safetensors: https://huggingface.co/lodestones/Chroma1-Radiance/tree/main
+    - gguf: https://huggingface.co/silveroxides/Chroma1-Radiance-GGUF/tree/main
+
+- Download t5xxl
+    - safetensors: https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp16.safetensors
+
+## Examples
+
+```
+.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Chroma1-Radiance-v0.4-Q8_0.gguf --t5xxl ..\..\ComfyUI\models\clip\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'chroma  radiance cpp'" --cfg-scale 4.0 --sampling-method euler -v
+```
+
+<img alt="Chroma1-Radiance" src="../assets/flux/chroma1-radiance.png" />
+
+
+

docs/distilled_sd.md

@@ -0,0 +1,86 @@
+# Running distilled models: SSD1B and SD1.x with tiny U-Nets
+
+## Preface
+
+This kind of models have a reduced U-Net part. 
+Unlike other SDXL models the U-Net of SSD1B has only one middle block and lesser attention layers in up and down blocks, resulting in relatively smaller files. Running these models saves more than 33% of the time. For more details, refer to Segmind's paper on https://arxiv.org/abs/2401.02677v1 .
+Unlike other SD 1.x models Tiny-UNet models consist of only 6 U-Net blocks, resulting in relatively smaller files (approximately 1 GB). Running these models saves almost 50% of the time. For more details, refer to the paper: https://arxiv.org/pdf/2305.15798.pdf .
+
+## SSD1B
+
+Unfortunately not all of this models follow the standard model parameter naming mapping. 
+Anyway there are some very useful SSD1B models available online, such as:
+
+ * https://huggingface.co/segmind/SSD-1B/resolve/main/SSD-1B-A1111.safetensors
+ * https://huggingface.co/hassenhamdi/SSD-1B-fp8_e4m3fn/resolve/main/SSD-1B_fp8_e4m3fn.safetensors 
+
+Also there are useful LORAs available:
+
+ * https://huggingface.co/seungminh/lora-swarovski-SSD-1B/resolve/main/pytorch_lora_weights.safetensors
+ * https://huggingface.co/kylielee505/mylcmlorassd/resolve/main/pytorch_lora_weights.safetensors   
+
+You can use this files **out-of-the-box** - unlike models in next section.
+
+
+## SD1.x with tiny U-Nets
+
+There are some Tiny SD 1.x models available online, such as:
+
+ * https://huggingface.co/segmind/tiny-sd
+ * https://huggingface.co/segmind/portrait-finetuned
+ * https://huggingface.co/nota-ai/bk-sdm-tiny
+
+These models need some conversion, for example because partially tensors are **non contiguous** stored. To create a usable checkpoint file, follow these **easy** steps:
+
+### Download model from Hugging Face
+
+Download the model using Python on your computer, for example this way:
+
+```python
+import torch
+from diffusers import StableDiffusionPipeline
+pipe = StableDiffusionPipeline.from_pretrained("segmind/tiny-sd")
+unet=pipe.unet
+for param in unet.parameters():
+    param.data = param.data.contiguous()     # <- important here
+pipe.save_pretrained("segmindtiny-sd", safe_serialization=True)
+```
+
+### Convert that to a ckpt file 
+
+To convert the downloaded model to a checkpoint file, you need another Python script. Download the conversion script from here:
+
+ * https://raw.githubusercontent.com/huggingface/diffusers/refs/heads/main/scripts/convert_diffusers_to_original_stable_diffusion.py
+
+
+### Run convert script
+
+Now, run that conversion script:
+
+```bash
+python convert_diffusers_to_original_stable_diffusion.py \
+	--model_path  ./segmindtiny-sd \
+	--checkpoint_path ./segmind_tiny-sd.ckpt --half
+```
+
+The file **segmind_tiny-sd.ckpt**  will be generated and is now ready to use with sd.cpp
+
+You can follow a similar process for other models mentioned above from Hugging Face. 
+
+
+### Another ckpt file on the net
+
+There is another model file available online: 
+
+ * https://huggingface.co/ClashSAN/small-sd/resolve/main/tinySDdistilled.ckpt
+ 
+If you want to use that, you have to adjust some **non-contiguous tensors** first:
+
+```python
+import torch
+ckpt = torch.load("tinySDdistilled.ckpt", map_location=torch.device('cpu'))
+for key, value in ckpt['state_dict'].items():
+    if isinstance(value, torch.Tensor):
+        ckpt['state_dict'][key] = value.contiguous()
+torch.save(ckpt, "tinySDdistilled_fixed.ckpt")
+```

flux.hpp

@@ -399,7 +399,7 @@ namespace Flux {
 
         ModulationOut get_distil_mod(struct ggml_context* ctx, struct ggml_tensor* vec) {
             int64_t offset = 3 * idx;
-            return {ctx, vec, offset};
+            return ModulationOut(ctx, vec, offset);
         }
 
         struct ggml_tensor* forward(struct ggml_context* ctx,
@@ -549,7 +549,135 @@ namespace Flux {
         }
     };
 
+    struct NerfEmbedder : public GGMLBlock {
+        NerfEmbedder(int64_t in_channels,
+                     int64_t hidden_size_input,
+                     int64_t max_freqs) {
+            blocks["embedder.0"] = std::make_shared<Linear>(in_channels + max_freqs * max_freqs, hidden_size_input);
+        }
+
+        struct ggml_tensor* forward(struct ggml_context* ctx,
+                                    struct ggml_tensor* x,
+                                    struct ggml_tensor* dct) {
+            // x: (B, P^2, C)
+            // dct: (1, P^2, max_freqs^2)
+            // return: (B, P^2, hidden_size_input)
+            auto embedder = std::dynamic_pointer_cast<Linear>(blocks["embedder.0"]);
+
+            dct = ggml_repeat_4d(ctx, dct, dct->ne[0], dct->ne[1], x->ne[2], x->ne[3]);
+            x   = ggml_concat(ctx, x, dct, 0);
+            x   = embedder->forward(ctx, x);
+
+            return x;
+        }
+    };
+
+    struct NerfGLUBlock : public GGMLBlock {
+        int64_t mlp_ratio;
+        NerfGLUBlock(int64_t hidden_size_s,
+                     int64_t hidden_size_x,
+                     int64_t mlp_ratio)
+            : mlp_ratio(mlp_ratio) {
+            int64_t total_params      = 3 * hidden_size_x * hidden_size_x * mlp_ratio;
+            blocks["param_generator"] = std::make_shared<Linear>(hidden_size_s, total_params);
+            blocks["norm"]            = std::make_shared<RMSNorm>(hidden_size_x);
+        }
+
+        struct ggml_tensor* forward(struct ggml_context* ctx,
+                                    struct ggml_tensor* x,
+                                    struct ggml_tensor* s) {
+            // x: (batch_size, n_token, hidden_size_x)
+            // s: (batch_size, hidden_size_s)
+            // return: (batch_size, n_token, hidden_size_x)
+            auto param_generator = std::dynamic_pointer_cast<Linear>(blocks["param_generator"]);
+            auto norm            = std::dynamic_pointer_cast<RMSNorm>(blocks["norm"]);
+
+            int64_t batch_size    = x->ne[2];
+            int64_t hidden_size_x = x->ne[0];
+
+            auto mlp_params = param_generator->forward(ctx, s);
+            auto fc_params  = ggml_chunk(ctx, mlp_params, 3, 0);
+            auto fc1_gate   = ggml_reshape_3d(ctx, fc_params[0], hidden_size_x * mlp_ratio, hidden_size_x, batch_size);
+            auto fc1_value  = ggml_reshape_3d(ctx, fc_params[1], hidden_size_x * mlp_ratio, hidden_size_x, batch_size);
+            auto fc2        = ggml_reshape_3d(ctx, fc_params[2], hidden_size_x, mlp_ratio * hidden_size_x, batch_size);
+
+            fc1_gate  = ggml_cont(ctx, ggml_torch_permute(ctx, fc1_gate, 1, 0, 2, 3));  // [batch_size, hidden_size_x*mlp_ratio, hidden_size_x]
+            fc1_gate  = ggml_l2_norm(ctx, fc1_gate, 1e-12f);
+            fc1_value = ggml_cont(ctx, ggml_torch_permute(ctx, fc1_value, 1, 0, 2, 3));  // [batch_size, hidden_size_x*mlp_ratio, hidden_size_x]
+            fc1_value = ggml_l2_norm(ctx, fc1_value, 1e-12f);
+            fc2       = ggml_cont(ctx, ggml_torch_permute(ctx, fc2, 1, 0, 2, 3));  // [batch_size, hidden_size_x, hidden_size_x*mlp_ratio]
+            fc2       = ggml_l2_norm(ctx, fc2, 1e-12f);
+
+            auto res_x = x;
+            x          = norm->forward(ctx, x);  // [batch_size, n_token, hidden_size_x]
+
+            auto x1 = ggml_mul_mat(ctx, fc1_gate, x);  // [batch_size, n_token, hidden_size_x*mlp_ratio]
+            x1      = ggml_silu_inplace(ctx, x1);
+
+            auto x2 = ggml_mul_mat(ctx, fc1_value, x);  // [batch_size, n_token, hidden_size_x*mlp_ratio]
+
+            x = ggml_mul_inplace(ctx, x1, x2);  // [batch_size, n_token, hidden_size_x*mlp_ratio]
+
+            x = ggml_mul_mat(ctx, fc2, x);  // [batch_size, n_token, hidden_size_x]
+
+            x = ggml_add_inplace(ctx, x, res_x);
+
+            return x;
+        }
+    };
+
+    struct NerfFinalLayer : public GGMLBlock {
+        NerfFinalLayer(int64_t hidden_size,
+                       int64_t out_channels) {
+            blocks["norm"]   = std::make_shared<RMSNorm>(hidden_size);
+            blocks["linear"] = std::make_shared<Linear>(hidden_size, out_channels);
+        }
+
+        struct ggml_tensor* forward(struct ggml_context* ctx,
+                                    struct ggml_tensor* x) {
+            auto norm   = std::dynamic_pointer_cast<RMSNorm>(blocks["norm"]);
+            auto linear = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
+
+            x = norm->forward(ctx, x);
+            x = linear->forward(ctx, x);
+
+            return x;
+        }
+    };
+
+    struct NerfFinalLayerConv : public GGMLBlock {
+        NerfFinalLayerConv(int64_t hidden_size,
+                           int64_t out_channels) {
+            blocks["norm"] = std::make_shared<RMSNorm>(hidden_size);
+            blocks["conv"] = std::make_shared<Conv2d>(hidden_size, out_channels, std::pair{3, 3}, std::pair{1, 1}, std::pair{1, 1});
+        }
+
+        struct ggml_tensor* forward(struct ggml_context* ctx,
+                                    struct ggml_tensor* x) {
+            // x: [N, C, H, W]
+            auto norm = std::dynamic_pointer_cast<RMSNorm>(blocks["norm"]);
+            auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
+
+            x = ggml_cont(ctx, ggml_torch_permute(ctx, x, 2, 0, 1, 3));  // [N, H, W, C]
+            x = norm->forward(ctx, x);
+            x = ggml_cont(ctx, ggml_torch_permute(ctx, x, 1, 2, 0, 3));  // [N, C, H, W]
+            x = conv->forward(ctx, x);
+
+            return x;
+        }
+    };
+
+    struct ChromaRadianceParams {
+        int64_t nerf_hidden_size = 64;
+        int64_t nerf_mlp_ratio   = 4;
+        int64_t nerf_depth       = 4;
+        int64_t nerf_max_freqs   = 8;
+    };
+
     struct FluxParams {
+        SDVersion version           = VERSION_FLUX;
+        bool is_chroma              = false;
+        int64_t patch_size          = 2;
         int64_t in_channels         = 64;
         int64_t out_channels        = 64;
         int64_t vec_in_dim          = 768;
@@ -565,8 +693,8 @@ namespace Flux {
         bool qkv_bias               = true;
         bool guidance_embed         = true;
         bool flash_attn             = true;
-        bool is_chroma              = false;
-        SDVersion version           = VERSION_FLUX;
+        int64_t in_dim              = 64;
+        ChromaRadianceParams chroma_radiance_params;
     };
 
     struct Flux : public GGMLBlock {
@@ -575,53 +703,89 @@ namespace Flux {
         Flux() {}
         Flux(FluxParams params)
             : params(params) {
-            blocks["img_in"] = std::shared_ptr<GGMLBlock>(new Linear(params.in_channels, params.hidden_size, true));
-            if (params.is_chroma) {
-                blocks["distilled_guidance_layer"] = std::shared_ptr<GGMLBlock>(new ChromaApproximator(params.in_channels, params.hidden_size));
+            if (params.version == VERSION_CHROMA_RADIANCE) {
+                std::pair<int, int> kernel_size = {(int)params.patch_size, (int)params.patch_size};
+                std::pair<int, int> stride      = kernel_size;
+
+                blocks["img_in_patch"] = std::make_shared<Conv2d>(params.in_channels,
+                                                                  params.hidden_size,
+                                                                  kernel_size,
+                                                                  stride);
             } else {
-                blocks["time_in"]   = std::shared_ptr<GGMLBlock>(new MLPEmbedder(256, params.hidden_size));
-                blocks["vector_in"] = std::shared_ptr<GGMLBlock>(new MLPEmbedder(params.vec_in_dim, params.hidden_size));
+                blocks["img_in"] = std::make_shared<Linear>(params.in_channels, params.hidden_size, true);
+            }
+            if (params.is_chroma) {
+                blocks["distilled_guidance_layer"] = std::make_shared<ChromaApproximator>(params.in_dim, params.hidden_size);
+            } else {
+                blocks["time_in"]   = std::make_shared<MLPEmbedder>(256, params.hidden_size);
+                blocks["vector_in"] = std::make_shared<MLPEmbedder>(params.vec_in_dim, params.hidden_size);
                 if (params.guidance_embed) {
-                    blocks["guidance_in"] = std::shared_ptr<GGMLBlock>(new MLPEmbedder(256, params.hidden_size));
+                    blocks["guidance_in"] = std::make_shared<MLPEmbedder>(256, params.hidden_size);
                 }
             }
-            blocks["txt_in"] = std::shared_ptr<GGMLBlock>(new Linear(params.context_in_dim, params.hidden_size, true));
+            blocks["txt_in"] = std::make_shared<Linear>(params.context_in_dim, params.hidden_size, true);
 
             for (int i = 0; i < params.depth; i++) {
-                blocks["double_blocks." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new DoubleStreamBlock(params.hidden_size,
-                                                                                                                params.num_heads,
-                                                                                                                params.mlp_ratio,
-                                                                                                                i,
-                                                                                                                params.qkv_bias,
-                                                                                                                params.flash_attn,
-                                                                                                                params.is_chroma));
+                blocks["double_blocks." + std::to_string(i)] = std::make_shared<DoubleStreamBlock>(params.hidden_size,
+                                                                                                   params.num_heads,
+                                                                                                   params.mlp_ratio,
+                                                                                                   i,
+                                                                                                   params.qkv_bias,
+                                                                                                   params.flash_attn,
+                                                                                                   params.is_chroma);
             }
 
             for (int i = 0; i < params.depth_single_blocks; i++) {
-                blocks["single_blocks." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new SingleStreamBlock(params.hidden_size,
-                                                                                                                params.num_heads,
-                                                                                                                params.mlp_ratio,
-                                                                                                                i,
-                                                                                                                0.f,
-                                                                                                                params.flash_attn,
-                                                                                                                params.is_chroma));
+                blocks["single_blocks." + std::to_string(i)] = std::make_shared<SingleStreamBlock>(params.hidden_size,
+                                                                                                   params.num_heads,
+                                                                                                   params.mlp_ratio,
+                                                                                                   i,
+                                                                                                   0.f,
+                                                                                                   params.flash_attn,
+                                                                                                   params.is_chroma);
             }
 
-            blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new LastLayer(params.hidden_size, 1, params.out_channels, params.is_chroma));
+            if (params.version == VERSION_CHROMA_RADIANCE) {
+                blocks["nerf_image_embedder"] = std::make_shared<NerfEmbedder>(params.in_channels,
+                                                                               params.chroma_radiance_params.nerf_hidden_size,
+                                                                               params.chroma_radiance_params.nerf_max_freqs);
+
+                for (int i = 0; i < params.chroma_radiance_params.nerf_depth; i++) {
+                    blocks["nerf_blocks." + std::to_string(i)] = std::make_shared<NerfGLUBlock>(params.hidden_size,
+                                                                                                params.chroma_radiance_params.nerf_hidden_size,
+                                                                                                params.chroma_radiance_params.nerf_mlp_ratio);
+                }
+
+                blocks["nerf_final_layer_conv"] = std::make_shared<NerfFinalLayerConv>(params.chroma_radiance_params.nerf_hidden_size,
+                                                                                       params.in_channels);
+
+            } else {
+                blocks["final_layer"] = std::make_shared<LastLayer>(params.hidden_size, 1, params.out_channels, params.is_chroma);
+            }
+        }
+
+        struct ggml_tensor* pad_to_patch_size(struct ggml_context* ctx,
+                                              struct ggml_tensor* x) {
+            int64_t W = x->ne[0];
+            int64_t H = x->ne[1];
+
+            int pad_h = (params.patch_size - H % params.patch_size) % params.patch_size;
+            int pad_w = (params.patch_size - W % params.patch_size) % params.patch_size;
+            x         = ggml_pad(ctx, x, pad_w, pad_h, 0, 0);  // [N, C, H + pad_h, W + pad_w]
+            return x;
         }
 
         struct ggml_tensor* patchify(struct ggml_context* ctx,
-                                     struct ggml_tensor* x,
-                                     int64_t patch_size) {
+                                     struct ggml_tensor* x) {
             // x: [N, C, H, W]
             // return: [N, h*w, C * patch_size * patch_size]
             int64_t N = x->ne[3];
             int64_t C = x->ne[2];
             int64_t H = x->ne[1];
             int64_t W = x->ne[0];
-            int64_t p = patch_size;
-            int64_t h = H / patch_size;
-            int64_t w = W / patch_size;
+            int64_t p = params.patch_size;
+            int64_t h = H / params.patch_size;
+            int64_t w = W / params.patch_size;
 
             GGML_ASSERT(h * p == H && w * p == W);
 
@@ -633,18 +797,25 @@ namespace Flux {
             return x;
         }
 
+        struct ggml_tensor* process_img(struct ggml_context* ctx,
+                                        struct ggml_tensor* x) {
+            // img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+            x = pad_to_patch_size(ctx, x);
+            x = patchify(ctx, x);
+            return x;
+        }
+
         struct ggml_tensor* unpatchify(struct ggml_context* ctx,
                                        struct ggml_tensor* x,
                                        int64_t h,
-                                       int64_t w,
-                                       int64_t patch_size) {
+                                       int64_t w) {
             // x: [N, h*w, C*patch_size*patch_size]
             // return: [N, C, H, W]
             int64_t N = x->ne[2];
-            int64_t C = x->ne[0] / patch_size / patch_size;
-            int64_t H = h * patch_size;
-            int64_t W = w * patch_size;
-            int64_t p = patch_size;
+            int64_t C = x->ne[0] / params.patch_size / params.patch_size;
+            int64_t H = h * params.patch_size;
+            int64_t W = w * params.patch_size;
+            int64_t p = params.patch_size;
 
             GGML_ASSERT(C * p * p == x->ne[0]);
 
@@ -671,7 +842,10 @@ namespace Flux {
             auto txt_in      = std::dynamic_pointer_cast<Linear>(blocks["txt_in"]);
             auto final_layer = std::dynamic_pointer_cast<LastLayer>(blocks["final_layer"]);
 
-            img = img_in->forward(ctx, img);
+            if (img_in) {
+                img = img_in->forward(ctx, img);
+            }
+
             struct ggml_tensor* vec;
             struct ggml_tensor* txt_img_mask = nullptr;
             if (params.is_chroma) {
@@ -682,7 +856,7 @@ namespace Flux {
 
                 // auto mod_index_arange  = ggml_arange(ctx, 0, (float)mod_index_length, 1);
                 // ggml_arange tot working on a lot of backends, precomputing it on CPU instead
-                GGML_ASSERT(arange != nullptr);
+                GGML_ASSERT(mod_index_arange != nullptr);
                 auto modulation_index = ggml_nn_timestep_embedding(ctx, mod_index_arange, 32, 10000, 1000.f);  // [1, 344, 32]
 
                 // Batch broadcast (will it ever be useful)
@@ -749,52 +923,96 @@ namespace Flux {
                                    txt_img->nb[2] * txt->ne[1]);           // [n_img_token, N, hidden_size]
             img     = ggml_cont(ctx, ggml_permute(ctx, img, 0, 2, 1, 3));  // [N, n_img_token, hidden_size]
 
-            img = final_layer->forward(ctx, img, vec);  // (N, T, patch_size ** 2 * out_channels)
+            if (final_layer) {
+                img = final_layer->forward(ctx, img, vec);  // (N, T, patch_size ** 2 * out_channels)
+            }
+
             return img;
         }
 
-        struct ggml_tensor* process_img(struct ggml_context* ctx,
-                                        struct ggml_tensor* x) {
-            int64_t W          = x->ne[0];
-            int64_t H          = x->ne[1];
-            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;
-            x                  = ggml_pad(ctx, x, pad_w, pad_h, 0, 0);  // [N, C, H + pad_h, W + pad_w]
-
-            // 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]
-            return img;
-        }
-
-        struct ggml_tensor* forward(struct ggml_context* ctx,
-                                    ggml_backend_t backend,
-                                    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,
-                                    struct ggml_tensor* mod_index_arange  = nullptr,
-                                    std::vector<ggml_tensor*> ref_latents = {},
-                                    std::vector<int> skip_layers          = {}) {
-            // Forward pass of DiT.
-            // 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: nullptr, 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)
-            // return: (N, C, H, W)
-
+        struct ggml_tensor* forward_chroma_radiance(struct ggml_context* ctx,
+                                                    ggml_backend_t backend,
+                                                    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,
+                                                    struct ggml_tensor* mod_index_arange  = nullptr,
+                                                    struct ggml_tensor* dct               = nullptr,
+                                                    std::vector<ggml_tensor*> ref_latents = {},
+                                                    std::vector<int> skip_layers          = {}) {
             GGML_ASSERT(x->ne[3] == 1);
 
             int64_t W          = x->ne[0];
             int64_t H          = x->ne[1];
             int64_t C          = x->ne[2];
-            int64_t patch_size = 2;
+            int64_t patch_size = params.patch_size;
+            int pad_h          = (patch_size - H % patch_size) % patch_size;
+            int pad_w          = (patch_size - W % patch_size) % patch_size;
+
+            auto img      = pad_to_patch_size(ctx, x);
+            auto orig_img = img;
+
+            auto img_in_patch = std::dynamic_pointer_cast<Conv2d>(blocks["img_in_patch"]);
+
+            img = img_in_patch->forward(ctx, img);                                             // [N, hidden_size, H/patch_size, W/patch_size]
+            img = ggml_reshape_3d(ctx, img, img->ne[0] * img->ne[1], img->ne[2], img->ne[3]);  // [N, hidden_size, H/patch_size*W/patch_size]
+            img = ggml_cont(ctx, ggml_torch_permute(ctx, img, 1, 0, 2, 3));                    // [N, H/patch_size*W/patch_size, hidden_size]
+
+            auto out = forward_orig(ctx, backend, img, context, timestep, y, guidance, pe, mod_index_arange, skip_layers);  // [N, n_img_token, hidden_size]
+
+            // nerf decode
+            auto nerf_image_embedder   = std::dynamic_pointer_cast<NerfEmbedder>(blocks["nerf_image_embedder"]);
+            auto nerf_final_layer_conv = std::dynamic_pointer_cast<NerfFinalLayerConv>(blocks["nerf_final_layer_conv"]);
+
+            auto nerf_pixels    = patchify(ctx, orig_img);  // [N, num_patches, C * patch_size * patch_size]
+            int64_t num_patches = nerf_pixels->ne[1];
+            nerf_pixels         = ggml_reshape_3d(ctx,
+                                                  nerf_pixels,
+                                                  nerf_pixels->ne[0] / C,
+                                                  C,
+                                                  nerf_pixels->ne[1] * nerf_pixels->ne[2]);          // [N*num_patches, C, patch_size*patch_size]
+            nerf_pixels         = ggml_cont(ctx, ggml_torch_permute(ctx, nerf_pixels, 1, 0, 2, 3));  // [N*num_patches, patch_size*patch_size, C]
+
+            auto nerf_hidden = ggml_reshape_2d(ctx, out, out->ne[0], out->ne[1] * out->ne[2]);  // [N*num_patches, hidden_size]
+            auto img_dct     = nerf_image_embedder->forward(ctx, nerf_pixels, dct);             // [N*num_patches, patch_size*patch_size, nerf_hidden_size]
+
+            for (int i = 0; i < params.chroma_radiance_params.nerf_depth; i++) {
+                auto block = std::dynamic_pointer_cast<NerfGLUBlock>(blocks["nerf_blocks." + std::to_string(i)]);
+
+                img_dct = block->forward(ctx, img_dct, nerf_hidden);
+            }
+
+            img_dct = ggml_cont(ctx, ggml_torch_permute(ctx, img_dct, 1, 0, 2, 3));                                               // [N*num_patches, nerf_hidden_size, patch_size*patch_size]
+            img_dct = ggml_reshape_3d(ctx, img_dct, img_dct->ne[0] * img_dct->ne[1], num_patches, img_dct->ne[2] / num_patches);  // [N, num_patches, nerf_hidden_size*patch_size*patch_size]
+            img_dct = unpatchify(ctx, img_dct, (H + pad_h) / patch_size, (W + pad_w) / patch_size);                               // [N, nerf_hidden_size, H, W]
+
+            out = nerf_final_layer_conv->forward(ctx, img_dct);  // [N, C, H, W]
+
+            return out;
+        }
+
+        struct ggml_tensor* forward_flux_chroma(struct ggml_context* ctx,
+                                                ggml_backend_t backend,
+                                                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,
+                                                struct ggml_tensor* mod_index_arange  = nullptr,
+                                                struct ggml_tensor* dct               = nullptr,
+                                                std::vector<ggml_tensor*> ref_latents = {},
+                                                std::vector<int> skip_layers          = {}) {
+            GGML_ASSERT(x->ne[3] == 1);
+
+            int64_t W          = x->ne[0];
+            int64_t H          = x->ne[1];
+            int64_t C          = x->ne[2];
+            int64_t patch_size = params.patch_size;
             int pad_h          = (patch_size - H % patch_size) % patch_size;
             int pad_w          = (patch_size - W % patch_size) % patch_size;
 
@@ -816,21 +1034,16 @@ namespace Flux {
                 ggml_tensor* mask    = ggml_view_4d(ctx, c_concat, c_concat->ne[0], c_concat->ne[1], 1, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * C);
                 ggml_tensor* control = 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], c_concat->nb[2] * (C + 1));
 
-                masked  = ggml_pad(ctx, masked, pad_w, pad_h, 0, 0);
-                mask    = ggml_pad(ctx, mask, pad_w, pad_h, 0, 0);
-                control = ggml_pad(ctx, control, pad_w, pad_h, 0, 0);
-
-                masked  = patchify(ctx, masked, patch_size);
-                mask    = patchify(ctx, mask, patch_size);
-                control = patchify(ctx, control, patch_size);
+                masked  = process_img(ctx, masked);
+                mask    = process_img(ctx, mask);
+                control = process_img(ctx, control);
 
                 img = ggml_concat(ctx, img, ggml_concat(ctx, ggml_concat(ctx, masked, mask, 0), control, 0), 0);
             } else if (params.version == VERSION_FLUX_CONTROLS) {
                 GGML_ASSERT(c_concat != nullptr);
 
-                ggml_tensor* control = ggml_pad(ctx, c_concat, pad_w, pad_h, 0, 0);
-                control              = patchify(ctx, control, patch_size);
-                img                  = ggml_concat(ctx, img, control, 0);
+                auto control = process_img(ctx, c_concat);
+                img          = ggml_concat(ctx, img, control, 0);
             }
 
             if (ref_latents.size() > 0) {
@@ -849,10 +1062,63 @@ namespace Flux {
             }
 
             // rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)
-            out = unpatchify(ctx, out, (H + pad_h) / patch_size, (W + pad_w) / patch_size, patch_size);  // [N, C, H + pad_h, W + pad_w]
-
+            out = unpatchify(ctx, out, (H + pad_h) / patch_size, (W + pad_w) / patch_size);  // [N, C, H + pad_h, W + pad_w]
             return out;
         }
+
+        struct ggml_tensor* forward(struct ggml_context* ctx,
+                                    ggml_backend_t backend,
+                                    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,
+                                    struct ggml_tensor* mod_index_arange  = nullptr,
+                                    struct ggml_tensor* dct               = nullptr,
+                                    std::vector<ggml_tensor*> ref_latents = {},
+                                    std::vector<int> skip_layers          = {}) {
+            // Forward pass of DiT.
+            // 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: nullptr, 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)
+            // return: (N, C, H, W)
+
+            if (params.version == VERSION_CHROMA_RADIANCE) {
+                return forward_chroma_radiance(ctx,
+                                               backend,
+                                               x,
+                                               timestep,
+                                               context,
+                                               c_concat,
+                                               y,
+                                               guidance,
+                                               pe,
+                                               mod_index_arange,
+                                               dct,
+                                               ref_latents,
+                                               skip_layers);
+            } else {
+                return forward_flux_chroma(ctx,
+                                           backend,
+                                           x,
+                                           timestep,
+                                           context,
+                                           c_concat,
+                                           y,
+                                           guidance,
+                                           pe,
+                                           mod_index_arange,
+                                           dct,
+                                           ref_latents,
+                                           skip_layers);
+            }
+        }
     };
 
     struct FluxRunner : public GGMLRunner {
@@ -860,7 +1126,8 @@ namespace Flux {
         FluxParams flux_params;
         Flux flux;
         std::vector<float> pe_vec;
-        std::vector<float> mod_index_arange_vec;  // for cache
+        std::vector<float> mod_index_arange_vec;
+        std::vector<float> dct_vec;
         SDVersion version;
         bool use_mask = false;
 
@@ -883,6 +1150,9 @@ namespace Flux {
                 flux_params.in_channels = 128;
             } else if (version == VERSION_FLEX_2) {
                 flux_params.in_channels = 196;
+            } else if (version == VERSION_CHROMA_RADIANCE) {
+                flux_params.in_channels = 3;
+                flux_params.patch_size  = 16;
             }
             for (auto pair : tensor_types) {
                 std::string tensor_name = pair.first;
@@ -933,6 +1203,56 @@ namespace Flux {
             flux.get_param_tensors(tensors, prefix);
         }
 
+        std::vector<float> fetch_dct_pos(int patch_size, int max_freqs) {
+            const float PI = 3.14159265358979323846f;
+
+            std::vector<float> pos(patch_size);
+            for (int i = 0; i < patch_size; ++i) {
+                pos[i] = static_cast<float>(i) / static_cast<float>(patch_size - 1);
+            }
+
+            std::vector<float> pos_x(patch_size * patch_size);
+            std::vector<float> pos_y(patch_size * patch_size);
+            for (int i = 0; i < patch_size; ++i) {
+                for (int j = 0; j < patch_size; ++j) {
+                    pos_x[i * patch_size + j] = pos[j];
+                    pos_y[i * patch_size + j] = pos[i];
+                }
+            }
+
+            std::vector<float> freqs(max_freqs);
+            for (int i = 0; i < max_freqs; ++i) {
+                freqs[i] = static_cast<float>(i);
+            }
+
+            std::vector<float> coeffs(max_freqs * max_freqs);
+            for (int fx = 0; fx < max_freqs; ++fx) {
+                for (int fy = 0; fy < max_freqs; ++fy) {
+                    coeffs[fx * max_freqs + fy] = 1.0f / (1.0f + freqs[fx] * freqs[fy]);
+                }
+            }
+
+            int num_positions = patch_size * patch_size;
+            int num_features  = max_freqs * max_freqs;
+            std::vector<float> dct(num_positions * num_features);
+
+            for (int p = 0; p < num_positions; ++p) {
+                float px = pos_x[p];
+                float py = pos_y[p];
+
+                for (int fx = 0; fx < max_freqs; ++fx) {
+                    float cx = std::cos(px * freqs[fx] * PI);
+                    for (int fy = 0; fy < max_freqs; ++fy) {
+                        float cy                                      = std::cos(py * freqs[fy] * PI);
+                        float val                                     = cx * cy * coeffs[fx * max_freqs + fy];
+                        dct[p * num_features + (fx * max_freqs + fy)] = val;
+                    }
+                }
+            }
+
+            return dct;
+        }
+
         struct ggml_cgraph* build_graph(struct ggml_tensor* x,
                                         struct ggml_tensor* timesteps,
                                         struct ggml_tensor* context,
@@ -946,6 +1266,7 @@ namespace Flux {
             struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, FLUX_GRAPH_SIZE, false);
 
             struct ggml_tensor* mod_index_arange = nullptr;
+            struct ggml_tensor* dct              = nullptr;  // for chroma radiance
 
             x       = to_backend(x);
             context = to_backend(context);
@@ -976,7 +1297,7 @@ namespace Flux {
 
             pe_vec      = Rope::gen_flux_pe(x->ne[1],
                                             x->ne[0],
-                                            2,
+                                            flux_params.patch_size,
                                             x->ne[3],
                                             context->ne[1],
                                             ref_latents,
@@ -991,6 +1312,17 @@ namespace Flux {
             // pe->data = nullptr;
             set_backend_tensor_data(pe, pe_vec.data());
 
+            if (version == VERSION_CHROMA_RADIANCE) {
+                int64_t patch_size     = flux_params.patch_size;
+                int64_t nerf_max_freqs = flux_params.chroma_radiance_params.nerf_max_freqs;
+                dct_vec                = fetch_dct_pos(patch_size, nerf_max_freqs);
+                dct                    = ggml_new_tensor_2d(compute_ctx, GGML_TYPE_F32, nerf_max_freqs * nerf_max_freqs, patch_size * patch_size);
+                // dct->data = dct_vec.data();
+                // print_ggml_tensor(dct);
+                // dct->data = nullptr;
+                set_backend_tensor_data(dct, dct_vec.data());
+            }
+
             struct ggml_tensor* out = flux.forward(compute_ctx,
                                                    runtime_backend,
                                                    x,
@@ -1001,6 +1333,7 @@ namespace Flux {
                                                    guidance,
                                                    pe,
                                                    mod_index_arange,
+                                                   dct,
                                                    ref_latents,
                                                    skip_layers);
 
@@ -1035,7 +1368,7 @@ namespace Flux {
 
         void test() {
             struct ggml_init_params params;
-            params.mem_size   = static_cast<size_t>(20 * 1024 * 1024);  // 20 MB
+            params.mem_size   = static_cast<size_t>(1024 * 1024) * 1024;  // 1GB
             params.mem_buffer = nullptr;
             params.no_alloc   = false;
 
@@ -1046,22 +1379,25 @@ namespace Flux {
                 // cpu f16:
                 // cuda f16: nan
                 // cuda q8_0: pass
-                auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 16, 16, 16, 1);
-                ggml_set_f32(x, 0.01f);
+                // auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 16, 16, 16, 1);
+                // ggml_set_f32(x, 0.01f);
+                auto x = load_tensor_from_file(work_ctx, "chroma_x.bin");
                 // print_ggml_tensor(x);
 
-                std::vector<float> timesteps_vec(1, 999.f);
+                std::vector<float> timesteps_vec(1, 1.f);
                 auto timesteps = vector_to_ggml_tensor(work_ctx, timesteps_vec);
 
-                std::vector<float> guidance_vec(1, 3.5f);
+                std::vector<float> guidance_vec(1, 0.f);
                 auto guidance = vector_to_ggml_tensor(work_ctx, guidance_vec);
 
-                auto context = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 4096, 256, 1);
-                ggml_set_f32(context, 0.01f);
+                // auto context = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 4096, 256, 1);
+                // ggml_set_f32(context, 0.01f);
+                auto context = load_tensor_from_file(work_ctx, "chroma_context.bin");
                 // print_ggml_tensor(context);
 
-                auto y = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 768, 1);
-                ggml_set_f32(y, 0.01f);
+                // auto y = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 768, 1);
+                // ggml_set_f32(y, 0.01f);
+                auto y = nullptr;
                 // print_ggml_tensor(y);
 
                 struct ggml_tensor* out = nullptr;
@@ -1076,32 +1412,44 @@ namespace Flux {
         }
 
         static void load_from_file_and_test(const std::string& file_path) {
-            // ggml_backend_t backend    = ggml_backend_cuda_init(0);
-            ggml_backend_t backend           = ggml_backend_cpu_init();
-            ggml_type model_data_type        = GGML_TYPE_Q8_0;
-            std::shared_ptr<FluxRunner> flux = std::make_shared<FluxRunner>(backend, false);
-            {
-                LOG_INFO("loading from '%s'", file_path.c_str());
+            // ggml_backend_t backend = ggml_backend_cuda_init(0);
+            ggml_backend_t backend    = ggml_backend_cpu_init();
+            ggml_type model_data_type = GGML_TYPE_Q8_0;
 
-                flux->alloc_params_buffer();
-                std::map<std::string, ggml_tensor*> tensors;
-                flux->get_param_tensors(tensors, "model.diffusion_model");
-
-                ModelLoader model_loader;
-                if (!model_loader.init_from_file(file_path, "model.diffusion_model.")) {
-                    LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
-                    return;
-                }
-
-                bool success = model_loader.load_tensors(tensors);
-
-                if (!success) {
-                    LOG_ERROR("load tensors from model loader failed");
-                    return;
-                }
-
-                LOG_INFO("flux model loaded");
+            ModelLoader model_loader;
+            if (!model_loader.init_from_file(file_path, "model.diffusion_model.")) {
+                LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
+                return;
             }
+
+            auto tensor_types = model_loader.tensor_storages_types;
+            for (auto& item : tensor_types) {
+                // LOG_DEBUG("%s %u", item.first.c_str(), item.second);
+                if (ends_with(item.first, "weight")) {
+                    // item.second = model_data_type;
+                }
+            }
+
+            std::shared_ptr<FluxRunner> flux = std::make_shared<FluxRunner>(backend,
+                                                                            false,
+                                                                            tensor_types,
+                                                                            "model.diffusion_model",
+                                                                            VERSION_CHROMA_RADIANCE,
+                                                                            false,
+                                                                            true);
+
+            flux->alloc_params_buffer();
+            std::map<std::string, ggml_tensor*> tensors;
+            flux->get_param_tensors(tensors, "model.diffusion_model");
+
+            bool success = model_loader.load_tensors(tensors);
+
+            if (!success) {
+                LOG_ERROR("load tensors from model loader failed");
+                return;
+            }
+
+            LOG_INFO("flux model loaded");
             flux->test();
         }
     };

ggml_extend.hpp

@@ -954,7 +954,16 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_linear(struct ggml_context* ctx,
     if (scale != 1.f) {
         x = ggml_scale(ctx, x, scale);
     }
-    x = ggml_mul_mat(ctx, w, x);
+    if (x->ne[2] * x->ne[3] > 1024) {
+        // workaround: avoid ggml cuda error
+        int64_t ne2 = x->ne[2];
+        int64_t ne3 = x->ne[3];
+        x           = ggml_reshape_2d(ctx, x, x->ne[0], x->ne[1] * x->ne[2] * x->ne[3]);
+        x           = ggml_mul_mat(ctx, w, x);
+        x           = ggml_reshape_4d(ctx, x, x->ne[0], x->ne[1] / ne2 / ne3, ne2, ne3);
+    } else {
+        x = ggml_mul_mat(ctx, w, x);
+    }
     if (force_prec_f32) {
         ggml_mul_mat_set_prec(x, GGML_PREC_F32);
     }

model.cpp

@@ -330,6 +330,10 @@ std::string convert_cond_model_name(const std::string& name) {
         return new_name;
     }
 
+    if (new_name == "model.text_projection.weight") {
+        new_name = "transformer.text_model.text_projection";
+    }
+
     if (open_clip_to_hf_clip_model.find(new_name) != open_clip_to_hf_clip_model.end()) {
         new_name = open_clip_to_hf_clip_model[new_name];
     }
@@ -623,6 +627,14 @@ std::string convert_tensor_name(std::string name) {
     if (starts_with(name, "diffusion_model")) {
         name = "model." + name;
     }
+    if (starts_with(name, "model.diffusion_model.up_blocks.0.attentions.0.")) {
+        name.replace(0, sizeof("model.diffusion_model.up_blocks.0.attentions.0.") - 1,
+                     "model.diffusion_model.output_blocks.0.1.");
+    }
+    if (starts_with(name, "model.diffusion_model.up_blocks.0.attentions.1.")) {
+        name.replace(0, sizeof("model.diffusion_model.up_blocks.0.attentions.1.") - 1,
+                     "model.diffusion_model.output_blocks.1.1.");
+    }
     // size_t pos = name.find("lora_A");
     // if (pos != std::string::npos) {
     //     name.replace(pos, strlen("lora_A"), "lora_up");
@@ -1766,7 +1778,6 @@ bool ModelLoader::model_is_unet() {
 
 SDVersion ModelLoader::get_sd_version() {
     TensorStorage token_embedding_weight, input_block_weight;
-    bool input_block_checked = false;
 
     bool has_multiple_encoders = false;
     bool is_unet               = false;
@@ -1776,14 +1787,15 @@ SDVersion ModelLoader::get_sd_version() {
     bool is_wan                      = false;
     int64_t patch_embedding_channels = 0;
     bool has_img_emb                 = false;
+    bool has_middle_block_1          = false;
 
     for (auto& tensor_storage : tensor_storages) {
-        if (!(is_xl || is_flux)) {
+        if (!(is_xl)) {
             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.nerf_final_layer_conv.") != std::string::npos) {
+                return VERSION_CHROMA_RADIANCE;
             }
             if (tensor_storage.name.find("model.diffusion_model.joint_blocks.") != std::string::npos) {
                 return VERSION_SD3;
@@ -1800,28 +1812,29 @@ SDVersion ModelLoader::get_sd_version() {
             if (tensor_storage.name.find("model.diffusion_model.img_emb") != std::string::npos) {
                 has_img_emb = true;
             }
-            if (tensor_storage.name.find("model.diffusion_model.input_blocks.") != std::string::npos || tensor_storage.name.find("unet.down_blocks.") != std::string::npos) {
+            if (tensor_storage.name.find("model.diffusion_model.input_blocks.") != std::string::npos ||
+                tensor_storage.name.find("unet.down_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 || tensor_storage.name.find("te.1") != std::string::npos) {
+            if (tensor_storage.name.find("conditioner.embedders.1") != std::string::npos ||
+                tensor_storage.name.find("cond_stage_model.1") != std::string::npos ||
+                tensor_storage.name.find("te.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.find("model.diffusion_model.middle_block.1.") != std::string::npos ||
+            tensor_storage.name.find("unet.mid_block.resnets.1.") != std::string::npos) {
+            has_middle_block_1 = true;
+        }
         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" ||
@@ -1831,12 +1844,10 @@ 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" || tensor_storage.name == "unet.conv_in.weight") {
-            input_block_weight  = tensor_storage;
-            input_block_checked = true;
-            if (is_xl || is_flux) {
-                break;
-            }
+        if (tensor_storage.name == "model.diffusion_model.input_blocks.0.0.weight" ||
+            tensor_storage.name == "model.diffusion_model.img_in.weight" ||
+            tensor_storage.name == "unet.conv_in.weight") {
+            input_block_weight = tensor_storage;
         }
     }
     if (is_wan) {
@@ -1858,6 +1869,9 @@ SDVersion ModelLoader::get_sd_version() {
         if (is_ip2p) {
             return VERSION_SDXL_PIX2PIX;
         }
+        if (!has_middle_block_1) {
+            return VERSION_SDXL_SSD1B;
+        }
         return VERSION_SDXL;
     }
 
@@ -1881,6 +1895,9 @@ SDVersion ModelLoader::get_sd_version() {
         if (is_ip2p) {
             return VERSION_SD1_PIX2PIX;
         }
+        if (!has_middle_block_1) {
+            return VERSION_SD1_TINY_UNET;
+        }
         return VERSION_SD1;
     } else if (token_embedding_weight.ne[0] == 1024) {
         if (is_inpaint) {

model.h

@@ -23,17 +23,20 @@ enum SDVersion {
     VERSION_SD1,
     VERSION_SD1_INPAINT,
     VERSION_SD1_PIX2PIX,
+    VERSION_SD1_TINY_UNET,
     VERSION_SD2,
     VERSION_SD2_INPAINT,
     VERSION_SDXL,
     VERSION_SDXL_INPAINT,
     VERSION_SDXL_PIX2PIX,
+    VERSION_SDXL_SSD1B,
     VERSION_SVD,
     VERSION_SD3,
     VERSION_FLUX,
     VERSION_FLUX_FILL,
     VERSION_FLUX_CONTROLS,
     VERSION_FLEX_2,
+    VERSION_CHROMA_RADIANCE,
     VERSION_WAN2,
     VERSION_WAN2_2_I2V,
     VERSION_WAN2_2_TI2V,
@@ -42,7 +45,7 @@ enum SDVersion {
 };
 
 static inline bool sd_version_is_sd1(SDVersion version) {
-    if (version == VERSION_SD1 || version == VERSION_SD1_INPAINT || version == VERSION_SD1_PIX2PIX) {
+    if (version == VERSION_SD1 || version == VERSION_SD1_INPAINT || version == VERSION_SD1_PIX2PIX || version == VERSION_SD1_TINY_UNET) {
         return true;
     }
     return false;
@@ -56,7 +59,7 @@ static inline bool sd_version_is_sd2(SDVersion version) {
 }
 
 static inline bool sd_version_is_sdxl(SDVersion version) {
-    if (version == VERSION_SDXL || version == VERSION_SDXL_INPAINT || version == VERSION_SDXL_PIX2PIX) {
+    if (version == VERSION_SDXL || version == VERSION_SDXL_INPAINT || version == VERSION_SDXL_PIX2PIX || version == VERSION_SDXL_SSD1B) {
         return true;
     }
     return false;
@@ -70,7 +73,11 @@ static inline bool sd_version_is_sd3(SDVersion version) {
 }
 
 static inline bool sd_version_is_flux(SDVersion version) {
-    if (version == VERSION_FLUX || version == VERSION_FLUX_FILL || version == VERSION_FLUX_CONTROLS || version == VERSION_FLEX_2) {
+    if (version == VERSION_FLUX ||
+        version == VERSION_FLUX_FILL ||
+        version == VERSION_FLUX_CONTROLS ||
+        version == VERSION_FLEX_2 ||
+        version == VERSION_CHROMA_RADIANCE) {
         return true;
     }
     return false;

qwen_image.hpp

@@ -649,7 +649,7 @@ namespace Qwen {
 
         static void load_from_file_and_test(const std::string& file_path) {
             // cuda q8: pass
-            // cuda q8 fa: nan
+            // cuda q8 fa: pass
             // ggml_backend_t backend    = ggml_backend_cuda_init(0);
             ggml_backend_t backend    = ggml_backend_cpu_init();
             ggml_type model_data_type = GGML_TYPE_Q8_0;

stable-diffusion.cpp

@@ -28,17 +28,20 @@ const char* model_version_to_str[] = {
     "SD 1.x",
     "SD 1.x Inpaint",
     "Instruct-Pix2Pix",
+    "SD 1.x Tiny UNet",
     "SD 2.x",
     "SD 2.x Inpaint",
     "SDXL",
     "SDXL Inpaint",
     "SDXL Instruct-Pix2Pix",
+    "SDXL (SSD1B)",
     "SVD",
     "SD3.x",
     "Flux",
     "Flux Fill",
     "Flux Control",
     "Flex.2",
+    "Chroma Radiance",
     "Wan 2.x",
     "Wan 2.2 I2V",
     "Wan 2.2 TI2V",
@@ -492,6 +495,9 @@ public:
                                                                         version);
                 first_stage_model->alloc_params_buffer();
                 first_stage_model->get_param_tensors(tensors, "first_stage_model");
+            } else if (version == VERSION_CHROMA_RADIANCE) {
+                first_stage_model = std::make_shared<FakeVAE>(vae_backend,
+                                                              offload_params_to_cpu);
             } else if (!use_tiny_autoencoder) {
                 first_stage_model = std::make_shared<AutoEncoderKL>(vae_backend,
                                                                     offload_params_to_cpu,
@@ -1039,7 +1045,7 @@ public:
         struct ggml_tensor* c_concat = nullptr;
         {
             if (zero_out_masked) {
-                c_concat = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width / 8, height / 8, 4, 1);
+                c_concat = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width / get_vae_scale_factor(), height / get_vae_scale_factor(), 4, 1);
                 ggml_set_f32(c_concat, 0.f);
             } else {
                 ggml_tensor* init_img = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 3, 1);
@@ -1373,6 +1379,53 @@ public:
         return x;
     }
 
+    int get_vae_scale_factor() {
+        int vae_scale_factor = 8;
+        if (version == VERSION_WAN2_2_TI2V) {
+            vae_scale_factor = 16;
+        } else if (version == VERSION_CHROMA_RADIANCE) {
+            vae_scale_factor = 1;
+        }
+        return vae_scale_factor;
+    }
+
+    int get_latent_channel() {
+        int latent_channel = 4;
+        if (sd_version_is_dit(version)) {
+            if (version == VERSION_WAN2_2_TI2V) {
+                latent_channel = 48;
+            } else if (version == VERSION_CHROMA_RADIANCE) {
+                latent_channel = 3;
+            } else {
+                latent_channel = 16;
+            }
+        }
+        return latent_channel;
+    }
+
+    ggml_tensor* generate_init_latent(ggml_context* work_ctx,
+                                      int width,
+                                      int height,
+                                      int frames = 1,
+                                      bool video = false) {
+        int vae_scale_factor = get_vae_scale_factor();
+        int W                = width / vae_scale_factor;
+        int H                = height / vae_scale_factor;
+        int T                = frames;
+        if (sd_version_is_wan(version)) {
+            T = ((T - 1) / 4) + 1;
+        }
+        int C = get_latent_channel();
+        ggml_tensor* init_latent;
+        if (video) {
+            init_latent = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, T, C);
+        } else {
+            init_latent = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, C, 1);
+        }
+        ggml_set_f32(init_latent, shift_factor);
+        return init_latent;
+    }
+
     void process_latent_in(ggml_tensor* latent) {
         if (sd_version_is_wan(version) || sd_version_is_qwen_image(version)) {
             GGML_ASSERT(latent->ne[3] == 16 || latent->ne[3] == 48);
@@ -1408,6 +1461,8 @@ public:
                     }
                 }
             }
+        } else if (version == VERSION_CHROMA_RADIANCE) {
+            // pass
         } else {
             ggml_tensor_iter(latent, [&](ggml_tensor* latent, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
                 float value = ggml_tensor_get_f32(latent, i0, i1, i2, i3);
@@ -1452,6 +1507,8 @@ public:
                     }
                 }
             }
+        } else if (version == VERSION_CHROMA_RADIANCE) {
+            // pass
         } else {
             ggml_tensor_iter(latent, [&](ggml_tensor* latent, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
                 float value = ggml_tensor_get_f32(latent, i0, i1, i2, i3);
@@ -1493,11 +1550,11 @@ public:
     ggml_tensor* vae_encode(ggml_context* work_ctx, ggml_tensor* x, bool encode_video = false) {
         int64_t t0          = ggml_time_ms();
         ggml_tensor* result = nullptr;
-        int W               = x->ne[0] / 8;
-        int H               = x->ne[1] / 8;
+        int W               = x->ne[0] / get_vae_scale_factor();
+        int H               = x->ne[1] / get_vae_scale_factor();
+        int C               = get_latent_channel();
         if (vae_tiling_params.enabled && !encode_video) {
             // TODO wan2.2 vae support?
-            int C = sd_version_is_dit(version) ? 16 : 4;
             int ne2;
             int ne3;
             if (sd_version_is_qwen_image(version)) {
@@ -1584,7 +1641,10 @@ public:
 
     ggml_tensor* get_first_stage_encoding(ggml_context* work_ctx, ggml_tensor* vae_output) {
         ggml_tensor* latent;
-        if (use_tiny_autoencoder || sd_version_is_qwen_image(version) || sd_version_is_wan(version)) {
+        if (use_tiny_autoencoder ||
+            sd_version_is_qwen_image(version) ||
+            sd_version_is_wan(version) ||
+            version == VERSION_CHROMA_RADIANCE) {
             latent = vae_output;
         } else if (version == VERSION_SD1_PIX2PIX) {
             latent = ggml_view_3d(work_ctx,
@@ -1611,18 +1671,14 @@ public:
     }
 
     ggml_tensor* decode_first_stage(ggml_context* work_ctx, ggml_tensor* x, bool decode_video = false) {
-        int64_t W           = x->ne[0] * 8;
-        int64_t H           = x->ne[1] * 8;
+        int64_t W           = x->ne[0] * get_vae_scale_factor();
+        int64_t H           = x->ne[1] * get_vae_scale_factor();
         int64_t C           = 3;
         ggml_tensor* result = nullptr;
         if (decode_video) {
             int T = x->ne[2];
             if (sd_version_is_wan(version)) {
                 T = ((T - 1) * 4) + 1;
-                if (version == VERSION_WAN2_2_TI2V) {
-                    W = x->ne[0] * 16;
-                    H = x->ne[1] * 16;
-                }
             }
             result = ggml_new_tensor_4d(work_ctx,
                                         GGML_TYPE_F32,
@@ -2233,16 +2289,9 @@ sd_image_t* generate_image_internal(sd_ctx_t* sd_ctx,
 
     // Sample
     std::vector<struct ggml_tensor*> final_latents;  // collect latents to decode
-    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;
-    } else if (sd_version_is_qwen_image(sd_ctx->sd->version)) {
-        C = 16;
-    }
-    int W = width / 8;
-    int H = height / 8;
+    int C = sd_ctx->sd->get_latent_channel();
+    int W = width / sd_ctx->sd->get_vae_scale_factor();
+    int H = height / sd_ctx->sd->get_vae_scale_factor();
     LOG_INFO("sampling using %s method", sampling_methods_str[sample_method]);
 
     struct ggml_tensor* control_latent = nullptr;
@@ -2420,51 +2469,11 @@ sd_image_t* generate_image_internal(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 frames = 1,
-                                  bool video = false) {
-    int C = 4;
-    int T = frames;
-    int W = width / 8;
-    int H = height / 8;
-    if (sd_version_is_sd3(sd_ctx->sd->version)) {
-        C = 16;
-    } else if (sd_version_is_flux(sd_ctx->sd->version)) {
-        C = 16;
-    } else if (sd_version_is_qwen_image(sd_ctx->sd->version)) {
-        C = 16;
-    } else if (sd_version_is_wan(sd_ctx->sd->version)) {
-        C = 16;
-        T = ((T - 1) / 4) + 1;
-        if (sd_ctx->sd->version == VERSION_WAN2_2_TI2V) {
-            C = 48;
-            W = width / 16;
-            H = height / 16;
-        }
-    }
-    ggml_tensor* init_latent;
-    if (video) {
-        init_latent = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, W, H, T, C);
-    } else {
-        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* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_gen_params) {
     sd_ctx->sd->vae_tiling_params = sd_img_gen_params->vae_tiling_params;
     int width                     = sd_img_gen_params->width;
     int height                    = sd_img_gen_params->height;
+    int vae_scale_factor          = sd_ctx->sd->get_vae_scale_factor();
     if (sd_version_is_dit(sd_ctx->sd->version)) {
         if (width % 16 || height % 16) {
             LOG_ERROR("Image dimensions must be must be a multiple of 16 on each axis for %s models. (Got %dx%d)",
@@ -2560,20 +2569,20 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
                                                1);
             for (int ix = 0; ix < masked_latent->ne[0]; ix++) {
                 for (int iy = 0; iy < masked_latent->ne[1]; iy++) {
-                    int mx = ix * 8;
-                    int my = iy * 8;
+                    int mx = ix * vae_scale_factor;
+                    int my = iy * vae_scale_factor;
                     if (sd_ctx->sd->version == VERSION_FLUX_FILL) {
                         for (int k = 0; k < masked_latent->ne[2]; k++) {
                             float v = ggml_tensor_get_f32(masked_latent, ix, iy, k);
                             ggml_tensor_set_f32(concat_latent, 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++) {
+                        for (int x = 0; x < vae_scale_factor; x++) {
+                            for (int y = 0; y < vae_scale_factor; 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(concat_latent, m, ix, iy, masked_latent->ne[2] + x * 8 + y);
+                                // TODO: check if the way the mask is flattened is correct (is it supposed to be x*vae_scale_factor+y or x+vae_scale_factor*y?)
+                                // python code was using "b (h vae_scale_factor) (w vae_scale_factor) -> b (vae_scale_factor vae_scale_factor) h w"
+                                ggml_tensor_set_f32(concat_latent, m, ix, iy, masked_latent->ne[2] + x * vae_scale_factor + y);
                             }
                         }
                     } else if (sd_ctx->sd->version == VERSION_FLEX_2) {
@@ -2596,11 +2605,11 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
 
         {
             // LOG_WARN("Inpainting with a base model is not great");
-            denoise_mask = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width / 8, height / 8, 1, 1);
+            denoise_mask = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width / vae_scale_factor, height / vae_scale_factor, 1, 1);
             for (int ix = 0; ix < denoise_mask->ne[0]; ix++) {
                 for (int iy = 0; iy < denoise_mask->ne[1]; iy++) {
-                    int mx  = ix * 8;
-                    int my  = iy * 8;
+                    int mx  = ix * vae_scale_factor;
+                    int my  = iy * vae_scale_factor;
                     float m = ggml_tensor_get_f32(mask_img, mx, my);
                     ggml_tensor_set_f32(denoise_mask, m, ix, iy);
                 }
@@ -2611,7 +2620,7 @@ sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_g
         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");
         }
-        init_latent = generate_init_latent(sd_ctx, work_ctx, width, height);
+        init_latent = sd_ctx->sd->generate_init_latent(work_ctx, width, height);
     }
 
     sd_guidance_params_t guidance = sd_img_gen_params->sample_params.guidance;
@@ -2739,6 +2748,8 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
     int sample_steps = sd_vid_gen_params->sample_params.sample_steps;
     LOG_INFO("generate_video %dx%dx%d", width, height, frames);
 
+    int vae_scale_factor = sd_ctx->sd->get_vae_scale_factor();
+
     sd_ctx->sd->init_scheduler(sd_vid_gen_params->sample_params.scheduler);
 
     int high_noise_sample_steps = 0;
@@ -2836,7 +2847,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
             ggml_tensor_set_f32(image, value, i0, i1, i2, i3);
         });
 
-        concat_latent = sd_ctx->sd->encode_first_stage(work_ctx, image);  // [b*c, t, h/8, w/8]
+        concat_latent = sd_ctx->sd->encode_first_stage(work_ctx, image);  // [b*c, t, h/vae_scale_factor, w/vae_scale_factor]
 
         int64_t t2 = ggml_time_ms();
         LOG_INFO("encode_first_stage completed, taking %" PRId64 " ms", t2 - t1);
@@ -2846,7 +2857,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
                                                       concat_latent->ne[0],
                                                       concat_latent->ne[1],
                                                       concat_latent->ne[2],
-                                                      4);  // [b*4, t, w/8, h/8]
+                                                      4);  // [b*4, t, w/vae_scale_factor, h/vae_scale_factor]
         ggml_tensor_iter(concat_mask, [&](ggml_tensor* concat_mask, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
             float value = 0.0f;
             if (i2 == 0 && sd_vid_gen_params->init_image.data) {  // start image
@@ -2857,7 +2868,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
             ggml_tensor_set_f32(concat_mask, value, i0, i1, i2, i3);
         });
 
-        concat_latent = ggml_tensor_concat(work_ctx, concat_mask, concat_latent, 3);  // [b*(c+4), t, h/8, w/8]
+        concat_latent = ggml_tensor_concat(work_ctx, concat_mask, concat_latent, 3);  // [b*(c+4), t, h/vae_scale_factor, w/vae_scale_factor]
     } else if (sd_ctx->sd->diffusion_model->get_desc() == "Wan2.2-TI2V-5B" && sd_vid_gen_params->init_image.data) {
         LOG_INFO("IMG2VID");
 
@@ -2868,7 +2879,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
 
         auto init_image_latent = sd_ctx->sd->vae_encode(work_ctx, init_img);  // [b*c, 1, h/16, w/16]
 
-        init_latent  = generate_init_latent(sd_ctx, work_ctx, width, height, frames, true);
+        init_latent  = sd_ctx->sd->generate_init_latent(work_ctx, width, height, frames, true);
         denoise_mask = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, init_latent->ne[0], init_latent->ne[1], init_latent->ne[2], 1);
         ggml_set_f32(denoise_mask, 1.f);
 
@@ -2925,8 +2936,8 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
             ggml_tensor_set_f32(reactive, reactive_value, i0, i1, i2, i3);
         });
 
-        inactive = sd_ctx->sd->encode_first_stage(work_ctx, inactive);  // [b*c, t, h/8, w/8]
-        reactive = sd_ctx->sd->encode_first_stage(work_ctx, reactive);  // [b*c, t, h/8, w/8]
+        inactive = sd_ctx->sd->encode_first_stage(work_ctx, inactive);  // [b*c, t, h/vae_scale_factor, w/vae_scale_factor]
+        reactive = sd_ctx->sd->encode_first_stage(work_ctx, reactive);  // [b*c, t, h/vae_scale_factor, w/vae_scale_factor]
 
         int64_t length = inactive->ne[2];
         if (ref_image_latent) {
@@ -2934,7 +2945,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
             frames        = (length - 1) * 4 + 1;
             ref_image_num = 1;
         }
-        vace_context = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, inactive->ne[0], inactive->ne[1], length, 96);  // [b*96, t, h/8, w/8]
+        vace_context = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, inactive->ne[0], inactive->ne[1], length, 96);  // [b*96, t, h/vae_scale_factor, w/vae_scale_factor]
         ggml_tensor_iter(vace_context, [&](ggml_tensor* vace_context, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
             float value;
             if (i3 < 32) {
@@ -2951,7 +2962,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
                 if (ref_image_latent && i2 == 0) {
                     value = 0.f;
                 } else {
-                    int64_t vae_stride        = 8;
+                    int64_t vae_stride        = vae_scale_factor;
                     int64_t mask_height_index = i1 * vae_stride + (i3 - 32) / vae_stride;
                     int64_t mask_width_index  = i0 * vae_stride + (i3 - 32) % vae_stride;
                     value                     = ggml_tensor_get_f32(mask, mask_width_index, mask_height_index, i2 - ref_image_num, 0);
@@ -2964,7 +2975,7 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
     }
 
     if (init_latent == nullptr) {
-        init_latent = generate_init_latent(sd_ctx, work_ctx, width, height, frames, true);
+        init_latent = sd_ctx->sd->generate_init_latent(work_ctx, width, height, frames, true);
     }
 
     // Get learned condition
@@ -2995,16 +3006,10 @@ SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* s
         sd_ctx->sd->cond_stage_model->free_params_buffer();
     }
 
-    int W = width / 8;
-    int H = height / 8;
+    int W = width / vae_scale_factor;
+    int H = height / vae_scale_factor;
     int T = init_latent->ne[2];
-    int C = 16;
-
-    if (sd_ctx->sd->version == VERSION_WAN2_2_TI2V) {
-        W = width / 16;
-        H = height / 16;
-        C = 48;
-    }
+    int C = sd_ctx->sd->get_latent_channel();
 
     struct ggml_tensor* final_latent;
     struct ggml_tensor* x_t   = init_latent;

unet.hpp

@@ -204,6 +204,9 @@ public:
             adm_in_channels   = 768;
             num_head_channels = 64;
             num_heads         = -1;
+        } else if (version == VERSION_SD1_TINY_UNET) {
+            num_res_blocks = 1;
+            channel_mult   = {1, 2, 4};
         }
         if (sd_version_is_inpaint(version)) {
             in_channels = 9;
@@ -270,13 +273,22 @@ public:
                         n_head = ch / d_head;
                     }
                     std::string name = "input_blocks." + std::to_string(input_block_idx) + ".1";
-                    blocks[name]     = std::shared_ptr<GGMLBlock>(get_attention_layer(ch,
-                                                                                      n_head,
-                                                                                      d_head,
-                                                                                      transformer_depth[i],
-                                                                                      context_dim));
+                    int td           = transformer_depth[i];
+                    if (version == VERSION_SDXL_SSD1B) {
+                        if (i == 2) {
+                            td = 4;
+                        }
+                    }
+                    blocks[name] = std::shared_ptr<GGMLBlock>(get_attention_layer(ch,
+                                                                                  n_head,
+                                                                                  d_head,
+                                                                                  td,
+                                                                                  context_dim));
                 }
                 input_block_chans.push_back(ch);
+                if (version == VERSION_SD1_TINY_UNET) {
+                    input_block_idx++;
+                }
             }
             if (i != len_mults - 1) {
                 input_block_idx += 1;
@@ -295,14 +307,17 @@ public:
             d_head = num_head_channels;
             n_head = ch / d_head;
         }
-        blocks["middle_block.0"] = std::shared_ptr<GGMLBlock>(get_resblock(ch, time_embed_dim, ch));
-        blocks["middle_block.1"] = std::shared_ptr<GGMLBlock>(get_attention_layer(ch,
-                                                                                  n_head,
-                                                                                  d_head,
-                                                                                  transformer_depth[transformer_depth.size() - 1],
-                                                                                  context_dim));
-        blocks["middle_block.2"] = std::shared_ptr<GGMLBlock>(get_resblock(ch, time_embed_dim, ch));
-
+        if (version != VERSION_SD1_TINY_UNET) {
+            blocks["middle_block.0"] = std::shared_ptr<GGMLBlock>(get_resblock(ch, time_embed_dim, ch));
+            if (version != VERSION_SDXL_SSD1B) {
+                blocks["middle_block.1"] = std::shared_ptr<GGMLBlock>(get_attention_layer(ch,
+                                                                                          n_head,
+                                                                                          d_head,
+                                                                                          transformer_depth[transformer_depth.size() - 1],
+                                                                                          context_dim));
+                blocks["middle_block.2"] = std::shared_ptr<GGMLBlock>(get_resblock(ch, time_embed_dim, ch));
+            }
+        }
         // output_blocks
         int output_block_idx = 0;
         for (int i = (int)len_mults - 1; i >= 0; i--) {
@@ -324,12 +339,27 @@ public:
                         n_head = ch / d_head;
                     }
                     std::string name = "output_blocks." + std::to_string(output_block_idx) + ".1";
-                    blocks[name]     = std::shared_ptr<GGMLBlock>(get_attention_layer(ch, n_head, d_head, transformer_depth[i], context_dim));
+                    int td           = transformer_depth[i];
+                    if (version == VERSION_SDXL_SSD1B) {
+                        if (i == 2 && (j == 0 || j == 1)) {
+                            td = 4;
+                        }
+                        if (i == 1 && (j == 1 || j == 2)) {
+                            td = 1;
+                        }
+                    }
+                    blocks[name] = std::shared_ptr<GGMLBlock>(get_attention_layer(ch, n_head, d_head, td, context_dim));
 
                     up_sample_idx++;
                 }
 
                 if (i > 0 && j == num_res_blocks) {
+                    if (version == VERSION_SD1_TINY_UNET) {
+                        output_block_idx++;
+                        if (output_block_idx == 2) {
+                            up_sample_idx = 1;
+                        }
+                    }
                     std::string name = "output_blocks." + std::to_string(output_block_idx) + "." + std::to_string(up_sample_idx);
                     blocks[name]     = std::shared_ptr<GGMLBlock>(new UpSampleBlock(ch, ch));
 
@@ -463,6 +493,9 @@ public:
                 }
                 hs.push_back(h);
             }
+            if (version == VERSION_SD1_TINY_UNET) {
+                input_block_idx++;
+            }
             if (i != len_mults - 1) {
                 ds *= 2;
                 input_block_idx += 1;
@@ -477,10 +510,13 @@ public:
         // [N, 4*model_channels, h/8, w/8]
 
         // middle_block
-        h = resblock_forward("middle_block.0", ctx, h, emb, num_video_frames);                      // [N, 4*model_channels, h/8, w/8]
-        h = attention_layer_forward("middle_block.1", ctx, backend, h, context, num_video_frames);  // [N, 4*model_channels, h/8, w/8]
-        h = resblock_forward("middle_block.2", ctx, h, emb, num_video_frames);                      // [N, 4*model_channels, h/8, w/8]
-
+        if (version != VERSION_SD1_TINY_UNET) {
+            h = resblock_forward("middle_block.0", ctx, h, emb, num_video_frames);  // [N, 4*model_channels, h/8, w/8]
+            if (version != VERSION_SDXL_SSD1B) {
+                h = attention_layer_forward("middle_block.1", ctx, backend, h, context, num_video_frames);  // [N, 4*model_channels, h/8, w/8]
+                h = resblock_forward("middle_block.2", ctx, h, emb, num_video_frames);                      // [N, 4*model_channels, h/8, w/8]
+            }
+        }
         if (controls.size() > 0) {
             auto cs = ggml_scale_inplace(ctx, controls[controls.size() - 1], control_strength);
             h       = ggml_add(ctx, h, cs);  // middle control
@@ -516,6 +552,12 @@ public:
                 }
 
                 if (i > 0 && j == num_res_blocks) {
+                    if (version == VERSION_SD1_TINY_UNET) {
+                        output_block_idx++;
+                        if (output_block_idx == 2) {
+                            up_sample_idx = 1;
+                        }
+                    }
                     std::string name = "output_blocks." + std::to_string(output_block_idx) + "." + std::to_string(up_sample_idx);
                     auto block       = std::dynamic_pointer_cast<UpSampleBlock>(blocks[name]);
 

vae.hpp

@@ -533,6 +533,30 @@ struct VAE : public GGMLRunner {
     virtual void set_conv2d_scale(float scale) { SD_UNUSED(scale); };
 };
 
+struct FakeVAE : public VAE {
+    FakeVAE(ggml_backend_t backend, bool offload_params_to_cpu)
+        : VAE(backend, offload_params_to_cpu) {}
+    void compute(const int n_threads,
+                 struct ggml_tensor* z,
+                 bool decode_graph,
+                 struct ggml_tensor** output,
+                 struct ggml_context* output_ctx) override {
+        if (*output == nullptr && output_ctx != nullptr) {
+            *output = ggml_dup_tensor(output_ctx, z);
+        }
+        ggml_tensor_iter(z, [&](ggml_tensor* z, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
+            float value = ggml_tensor_get_f32(z, i0, i1, i2, i3);
+            ggml_tensor_set_f32(*output, value, i0, i1, i2, i3);
+        });
+    }
+
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) override {}
+
+    std::string get_desc() override {
+        return "fake_vae";
+    }
+};
+
 struct AutoEncoderKL : public VAE {
     bool decode_only = true;
     AutoencodingEngine ae;