refactor: add user data pointer to the image preview callback (#1001)

* add flux2 support

* rename qwenvl to llm

* add Flux2FlowDenoiser

* update docs

.clang-tidy

@@ -0,0 +1,10 @@
+Checks: >
+  modernize-make-shared,
+  modernize-use-nullptr,
+  modernize-use-override,
+  modernize-pass-by-value,
+  modernize-return-braced-init-list,
+  modernize-deprecated-headers,
+HeaderFilterRegex: '^$'
+WarningsAsErrors: ''
+FormatStyle: none

.github/workflows/build.yml

@@ -65,7 +65,7 @@ jobs:
 
       - name: Get commit hash
         id: commit
-        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/main' ) || github.event.inputs.create_release == 'true' }}
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         uses: pr-mpt/actions-commit-hash@v2
 
       - name: Fetch system info
@@ -118,7 +118,7 @@ jobs:
 
       - name: Get commit hash
         id: commit
-        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/main' ) || github.event.inputs.create_release == 'true' }}
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         uses: pr-mpt/actions-commit-hash@v2
 
       - name: Fetch system info
@@ -164,8 +164,6 @@ jobs:
             defines: "-DGGML_NATIVE=OFF -DGGML_AVX512=ON -DGGML_AVX=ON -DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
           - build: "cuda12"
             defines: "-DSD_CUDA=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_CUDA_ARCHITECTURES=90;89;86;80;75"
-          # - build: "rocm5.5"
-          #   defines: '-G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS="gfx1100;gfx1102;gfx1030" -DSD_BUILD_SHARED_LIBS=ON'
           - build: 'vulkan'
             defines: "-DSD_VULKAN=ON -DSD_BUILD_SHARED_LIBS=ON"
     steps:
@@ -184,22 +182,9 @@ jobs:
           method: "network"
           sub-packages: '["nvcc", "cudart", "cublas", "cublas_dev", "thrust", "visual_studio_integration"]'
 
-      - name: Install rocm-toolkit
-        id: rocm-toolkit
-        if: ${{ matrix.build == 'rocm5.5' }}
-        uses: Cyberhan123/rocm-toolkit@v0.1.0
-        with:
-          rocm: "5.5.0"
-
-      - name: Install Ninja
-        id: install-ninja
-        if: ${{ matrix.build == 'rocm5.5' }}
-        uses: urkle/action-get-ninja@v1
-        with:
-          version: 1.11.1
       - name: Install Vulkan SDK
         id: get_vulkan
-        if: ${{ matrix.build == 'vulkan' }} https://sdk.lunarg.com/sdk/download/1.4.328.1/windows/vulkansdk-windows-X64-1.4.328.1.exe
+        if: ${{ matrix.build == 'vulkan' }}
         run: |
           curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/vulkansdk-windows-X64-${env:VULKAN_VERSION}.exe"
           & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install
@@ -277,6 +262,104 @@ jobs:
           path: |
             sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip
 
+  windows-latest-cmake-hip:
+    runs-on: windows-2022
+
+    env:
+      HIPSDK_INSTALLER_VERSION: "25.Q3"
+      GPU_TARGETS: "gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"
+
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          submodules: recursive
+
+      - name: Cache ROCm Installation
+        id: cache-rocm
+        uses: actions/cache@v4
+        with:
+          path: C:\Program Files\AMD\ROCm
+          key: rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.16
+        with:
+          key: windows-latest-cmake-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-x64
+          evict-old-files: 1d
+
+      - name: Install ROCm
+        if: steps.cache-rocm.outputs.cache-hit != 'true'
+        run: |
+          $ErrorActionPreference = "Stop"
+          write-host "Downloading AMD HIP SDK Installer"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          write-host "Installing AMD HIP SDK"
+          $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru
+          $completed = $proc.WaitForExit(600000)
+          if (-not $completed) {
+              Write-Error "ROCm installation timed out after 10 minutes. Killing the process"
+              $proc.Kill()
+              exit 1
+          }
+          if ($proc.ExitCode -ne 0) {
+              Write-Error "ROCm installation failed with exit code $($proc.ExitCode)"
+              exit 1
+          }
+          write-host "Completed AMD HIP SDK installation"
+
+      - name: Verify ROCm
+        run: |
+          # Find and test ROCm installation
+          $clangPath = Get-ChildItem 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Select-Object -First 1
+          if (-not $clangPath) {
+            Write-Error "ROCm installation not found"
+            exit 1
+          }
+          & $clangPath.FullName --version
+          # Set HIP_PATH environment variable for later steps
+          echo "HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)" >> $env:GITHUB_ENV
+
+      - name: Build
+        run: |
+          mkdir build
+          cd build
+          $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
+          cmake .. `
+            -G "Unix Makefiles" `
+            -DSD_HIPBLAS=ON `
+            -DSD_BUILD_SHARED_LIBS=ON `
+            -DGGML_NATIVE=OFF `
+            -DCMAKE_C_COMPILER=clang `
+            -DCMAKE_CXX_COMPILER=clang++ `
+            -DCMAKE_BUILD_TYPE=Release `
+            -DGPU_TARGETS="${{ env.GPU_TARGETS }}"
+          cmake --build . --config Release --parallel ${env:NUMBER_OF_PROCESSORS}
+
+      - name: Get commit hash
+        id: commit
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: pr-mpt/actions-commit-hash@v2
+
+      - name: Pack artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          md "build\bin\rocblas\library\"
+          md "build\bin\hipblaslt\library"
+          cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\"
+          cp "${env:HIP_PATH}\bin\hipblaslt.dll" "build\bin\"
+          cp "${env:HIP_PATH}\bin\rocblas.dll" "build\bin\"
+          cp "${env:HIP_PATH}\bin\rocblas\library\*" "build\bin\rocblas\library\"
+          cp "${env:HIP_PATH}\bin\hipblaslt\library\*" "build\bin\hipblaslt\library\"
+          7z a sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-x64.zip .\build\bin\*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-x64.zip
+          path: |
+            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-x64.zip
+
   release:
     if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
 
@@ -286,6 +369,7 @@ jobs:
       - ubuntu-latest-cmake
       - macOS-latest-cmake
       - windows-latest-cmake
+      - windows-latest-cmake-hip
 
     steps:
       - name: Clone

.gitignore

@@ -12,3 +12,4 @@ test/
 output*.png
 models*
 *.log
+preview.png

README.md

@@ -29,15 +29,18 @@ API and command-line option may change frequently.***
 
 ## Features
 
-- Plain C/C++ implementation based on [ggml](https://github.com/ggerganov/ggml), working in the same way as [llama.cpp](https://github.com/ggerganov/llama.cpp)
+- Plain C/C++ implementation based on [ggml](https://github.com/ggml-org/ggml), working in the same way as [llama.cpp](https://github.com/ggml-org/llama.cpp)
 - Super lightweight and without external dependencies
 - Supported models
   - 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)
+    - [FlUX.1-dev/FlUX.1-schnell](./docs/flux.md)
+    - [FLUX.2-dev](./docs/flux2.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)
@@ -79,7 +82,9 @@ API and command-line option may change frequently.***
     - [`DPM++ 2M v2`](https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8457)
     - `DPM++ 2S a`
     - [`LCM`](https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13952)
-- Cross-platform reproducibility (`--rng cuda`, consistent with the `stable-diffusion-webui GPU RNG`)
+- Cross-platform reproducibility
+    - `--rng cuda`, default, consistent with the `stable-diffusion-webui GPU RNG`
+    - `--rng cpu`, consistent with the `comfyui RNG`
 - Embedds generation parameters into png output as webui-compatible text string
 
 ## Quick Start
@@ -114,7 +119,8 @@ If you want to improve performance or reduce VRAM/RAM usage, please refer to [pe
 
 - [SD1.x/SD2.x/SDXL](./docs/sd.md)
 - [SD3/SD3.5](./docs/sd3.md)
-- [Flux-dev/Flux-schnell](./docs/flux.md)
+- [FlUX.1-dev/FlUX.1-schnell](./docs/flux.md)
+- [FLUX.2-dev](./docs/flux2.md)
 - [FLUX.1-Kontext-dev](./docs/kontext.md)
 - [Chroma](./docs/chroma.md)
 - [🔥Qwen Image](./docs/qwen_image.md)
@@ -150,6 +156,7 @@ These projects use `stable-diffusion.cpp` as a backend for their image generatio
 - [sd.cpp-webui](https://github.com/daniandtheweb/sd.cpp-webui)
 - [LocalAI](https://github.com/mudler/LocalAI)
 - [Neural-Pixel](https://github.com/Luiz-Alcantara/Neural-Pixel)
+- [KoboldCpp](https://github.com/LostRuins/koboldcpp)
 
 ## Contributors
 
@@ -163,7 +170,7 @@ Thank you to all the people who have already contributed to stable-diffusion.cpp
 
 ## References
 
-- [ggml](https://github.com/ggerganov/ggml)
+- [ggml](https://github.com/ggml-org/ggml)
 - [diffusers](https://github.com/huggingface/diffusers)
 - [stable-diffusion](https://github.com/CompVis/stable-diffusion)
 - [sd3-ref](https://github.com/Stability-AI/sd3-ref)

clip.hpp

@@ -451,16 +451,16 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [N, n_token, d_model]
         auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
         auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
 
         x = fc1->forward(ctx, x);
         if (use_gelu) {
-            x = ggml_gelu_inplace(ctx, x);
+            x = ggml_gelu_inplace(ctx->ggml_ctx, x);
         } else {
-            x = ggml_gelu_quick_inplace(ctx, x);
+            x = ggml_gelu_quick_inplace(ctx->ggml_ctx, x);
         }
         x = fc2->forward(ctx, x);
         return x;
@@ -476,11 +476,12 @@ protected:
 public:
     CLIPLayer(int64_t d_model,
               int64_t n_head,
-              int64_t intermediate_size)
+              int64_t intermediate_size,
+              bool proj_in = false)
         : d_model(d_model),
           n_head(n_head),
           intermediate_size(intermediate_size) {
-        blocks["self_attn"] = std::shared_ptr<GGMLBlock>(new MultiheadAttention(d_model, n_head, true, true));
+        blocks["self_attn"] = std::shared_ptr<GGMLBlock>(new MultiheadAttention(d_model, n_head, true, true, proj_in));
 
         blocks["layer_norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_model));
         blocks["layer_norm2"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_model));
@@ -488,15 +489,15 @@ public:
         blocks["mlp"] = std::shared_ptr<GGMLBlock>(new CLIPMLP(d_model, intermediate_size));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, ggml_backend_t backend, struct ggml_tensor* x, bool mask = true) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, bool mask = true) {
         // x: [N, n_token, d_model]
         auto self_attn   = std::dynamic_pointer_cast<MultiheadAttention>(blocks["self_attn"]);
         auto layer_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm1"]);
         auto layer_norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm2"]);
         auto mlp         = std::dynamic_pointer_cast<CLIPMLP>(blocks["mlp"]);
 
-        x = ggml_add(ctx, x, self_attn->forward(ctx, backend, layer_norm1->forward(ctx, x), mask));
-        x = ggml_add(ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
+        x = ggml_add(ctx->ggml_ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
+        x = ggml_add(ctx->ggml_ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
         return x;
     }
 };
@@ -509,16 +510,16 @@ public:
     CLIPEncoder(int64_t n_layer,
                 int64_t d_model,
                 int64_t n_head,
-                int64_t intermediate_size)
+                int64_t intermediate_size,
+                bool proj_in = false)
         : n_layer(n_layer) {
         for (int i = 0; i < n_layer; i++) {
             std::string name = "layers." + std::to_string(i);
-            blocks[name]     = std::shared_ptr<GGMLBlock>(new CLIPLayer(d_model, n_head, intermediate_size));
+            blocks[name]     = std::shared_ptr<GGMLBlock>(new CLIPLayer(d_model, n_head, intermediate_size, proj_in));
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
                                 int clip_skip = -1,
                                 bool mask     = true) {
@@ -536,7 +537,7 @@ public:
             }
             std::string name = "layers." + std::to_string(i);
             auto layer       = std::dynamic_pointer_cast<CLIPLayer>(blocks[name]);
-            x                = layer->forward(ctx, backend, x, mask);  // [N, n_token, d_model]
+            x                = layer->forward(ctx, x, mask);  // [N, n_token, d_model]
             // LOG_DEBUG("layer %d", i);
         }
         return x;
@@ -550,10 +551,10 @@ protected:
     int64_t num_positions;
     bool force_clip_f32;
 
-    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
         enum ggml_type token_wtype = GGML_TYPE_F32;
         if (!force_clip_f32) {
-            token_wtype = get_type(prefix + "token_embedding.weight", tensor_types, GGML_TYPE_F32);
+            token_wtype = get_type(prefix + "token_embedding.weight", tensor_storage_map, GGML_TYPE_F32);
             if (!support_get_rows(token_wtype)) {
                 token_wtype = GGML_TYPE_F32;
             }
@@ -578,7 +579,7 @@ public:
         return params["token_embedding.weight"];
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* input_ids,
                                 struct ggml_tensor* custom_embed_weight) {
         // input_ids: [N, n_token]
@@ -586,12 +587,12 @@ public:
         auto position_embed_weight = params["position_embedding.weight"];
 
         GGML_ASSERT(input_ids->ne[0] == position_embed_weight->ne[1]);
-        input_ids            = ggml_reshape_3d(ctx, input_ids, input_ids->ne[0], 1, input_ids->ne[1]);
-        auto token_embedding = ggml_get_rows(ctx, custom_embed_weight != NULL ? custom_embed_weight : token_embed_weight, input_ids);
-        token_embedding      = ggml_reshape_3d(ctx, token_embedding, token_embedding->ne[0], token_embedding->ne[1], token_embedding->ne[3]);
+        input_ids            = ggml_reshape_3d(ctx->ggml_ctx, input_ids, input_ids->ne[0], 1, input_ids->ne[1]);
+        auto token_embedding = ggml_get_rows(ctx->ggml_ctx, custom_embed_weight != nullptr ? custom_embed_weight : token_embed_weight, input_ids);
+        token_embedding      = ggml_reshape_3d(ctx->ggml_ctx, token_embedding, token_embedding->ne[0], token_embedding->ne[1], token_embedding->ne[3]);
 
         // token_embedding + position_embedding
-        auto x = ggml_add(ctx,
+        auto x = ggml_add(ctx->ggml_ctx,
                           token_embedding,
                           position_embed_weight);  // [N, n_token, embed_dim]
         return x;
@@ -606,7 +607,8 @@ protected:
     int64_t image_size;
     int64_t num_patches;
     int64_t num_positions;
-    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
+
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
         enum ggml_type patch_wtype    = GGML_TYPE_F16;
         enum ggml_type class_wtype    = GGML_TYPE_F32;
         enum ggml_type position_wtype = GGML_TYPE_F32;
@@ -629,7 +631,7 @@ public:
         num_positions = num_patches + 1;
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* pixel_values) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* pixel_values) {
         // pixel_values: [N, num_channels, image_size, image_size]
         // return: [N, num_positions, embed_dim]
         GGML_ASSERT(pixel_values->ne[0] == image_size && pixel_values->ne[1] == image_size && pixel_values->ne[2] == num_channels);
@@ -641,18 +643,18 @@ public:
         // concat(patch_embedding, class_embedding) + position_embedding
         struct ggml_tensor* patch_embedding;
         int64_t N       = pixel_values->ne[3];
-        patch_embedding = ggml_nn_conv_2d(ctx, pixel_values, patch_embed_weight, NULL, patch_size, patch_size);  // [N, embed_dim, image_size // pacht_size, image_size // pacht_size]
-        patch_embedding = ggml_reshape_3d(ctx, patch_embedding, num_patches, embed_dim, N);                      // [N, embed_dim, num_patches]
-        patch_embedding = ggml_cont(ctx, ggml_permute(ctx, patch_embedding, 1, 0, 2, 3));                        // [N, num_patches, embed_dim]
-        patch_embedding = ggml_reshape_4d(ctx, patch_embedding, 1, embed_dim, num_patches, N);                   // [N, num_patches, embed_dim, 1]
+        patch_embedding = ggml_ext_conv_2d(ctx->ggml_ctx, pixel_values, patch_embed_weight, nullptr, patch_size, patch_size);  // [N, embed_dim, image_size // pacht_size, image_size // pacht_size]
+        patch_embedding = ggml_reshape_3d(ctx->ggml_ctx, patch_embedding, num_patches, embed_dim, N);                          // [N, embed_dim, num_patches]
+        patch_embedding = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, patch_embedding, 1, 0, 2, 3));                  // [N, num_patches, embed_dim]
+        patch_embedding = ggml_reshape_4d(ctx->ggml_ctx, patch_embedding, 1, embed_dim, num_patches, N);                       // [N, num_patches, embed_dim, 1]
 
-        struct ggml_tensor* class_embedding = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, embed_dim, N);
-        class_embedding                     = ggml_repeat(ctx, class_embed_weight, class_embedding);      // [N, embed_dim]
-        class_embedding                     = ggml_reshape_4d(ctx, class_embedding, 1, embed_dim, 1, N);  // [N, 1, embed_dim, 1]
+        struct ggml_tensor* class_embedding = ggml_new_tensor_2d(ctx->ggml_ctx, GGML_TYPE_F32, embed_dim, N);
+        class_embedding                     = ggml_repeat(ctx->ggml_ctx, class_embed_weight, class_embedding);      // [N, embed_dim]
+        class_embedding                     = ggml_reshape_4d(ctx->ggml_ctx, class_embedding, 1, embed_dim, 1, N);  // [N, 1, embed_dim, 1]
 
-        struct ggml_tensor* x = ggml_concat(ctx, class_embedding, patch_embedding, 2);  // [N, num_positions, embed_dim, 1]
-        x                     = ggml_reshape_3d(ctx, x, embed_dim, num_positions, N);   // [N, num_positions, embed_dim]
-        x                     = ggml_add(ctx, x, position_embed_weight);
+        struct ggml_tensor* x = ggml_concat(ctx->ggml_ctx, class_embedding, patch_embedding, 2);  // [N, num_positions, embed_dim, 1]
+        x                     = ggml_reshape_3d(ctx->ggml_ctx, x, embed_dim, num_positions, N);   // [N, num_positions, embed_dim]
+        x                     = ggml_add(ctx->ggml_ctx, x, position_embed_weight);
         return x;  // [N, num_positions, embed_dim]
     }
 };
@@ -669,7 +671,7 @@ enum CLIPVersion {
 
 class CLIPTextModel : public GGMLBlock {
 protected:
-    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
         if (version == OPEN_CLIP_VIT_BIGG_14) {
             enum ggml_type wtype      = GGML_TYPE_F32;
             params["text_projection"] = ggml_new_tensor_2d(ctx, wtype, projection_dim, hidden_size);
@@ -690,7 +692,8 @@ public:
 
     CLIPTextModel(CLIPVersion version = OPENAI_CLIP_VIT_L_14,
                   bool with_final_ln  = true,
-                  bool force_clip_f32 = false)
+                  bool force_clip_f32 = false,
+                  bool proj_in        = false)
         : version(version), with_final_ln(with_final_ln) {
         if (version == OPEN_CLIP_VIT_H_14) {
             hidden_size       = 1024;
@@ -705,7 +708,7 @@ public:
         }
 
         blocks["embeddings"]       = std::shared_ptr<GGMLBlock>(new CLIPEmbeddings(hidden_size, vocab_size, n_token, force_clip_f32));
-        blocks["encoder"]          = std::shared_ptr<GGMLBlock>(new CLIPEncoder(n_layer, hidden_size, n_head, intermediate_size));
+        blocks["encoder"]          = std::shared_ptr<GGMLBlock>(new CLIPEncoder(n_layer, hidden_size, n_head, intermediate_size, proj_in));
         blocks["final_layer_norm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
     }
 
@@ -714,8 +717,7 @@ public:
         return embeddings->get_token_embed_weight();
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* input_ids,
                                 struct ggml_tensor* tkn_embeddings,
                                 size_t max_token_idx = 0,
@@ -727,16 +729,16 @@ public:
         auto final_layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["final_layer_norm"]);
 
         auto x = embeddings->forward(ctx, input_ids, tkn_embeddings);  // [N, n_token, hidden_size]
-        x      = encoder->forward(ctx, backend, x, return_pooled ? -1 : clip_skip, true);
+        x      = encoder->forward(ctx, x, return_pooled ? -1 : clip_skip, true);
         if (return_pooled || with_final_ln) {
             x = final_layer_norm->forward(ctx, x);
         }
 
         if (return_pooled) {
             auto text_projection = params["text_projection"];
-            ggml_tensor* pooled  = ggml_view_1d(ctx, x, hidden_size, x->nb[1] * max_token_idx);
-            if (text_projection != NULL) {
-                pooled = ggml_nn_linear(ctx, pooled, text_projection, NULL);
+            ggml_tensor* pooled  = ggml_view_1d(ctx->ggml_ctx, x, hidden_size, x->nb[1] * max_token_idx);
+            if (text_projection != nullptr) {
+                pooled = ggml_ext_linear(ctx->ggml_ctx, pooled, text_projection, nullptr);
             } else {
                 LOG_DEBUG("identity projection");
             }
@@ -760,7 +762,7 @@ public:
     int32_t n_layer           = 24;
 
 public:
-    CLIPVisionModel(CLIPVersion version = OPENAI_CLIP_VIT_L_14) {
+    CLIPVisionModel(CLIPVersion version = OPENAI_CLIP_VIT_L_14, bool proj_in = false) {
         if (version == OPEN_CLIP_VIT_H_14) {
             hidden_size       = 1280;
             intermediate_size = 5120;
@@ -775,12 +777,11 @@ public:
 
         blocks["embeddings"]     = std::shared_ptr<GGMLBlock>(new CLIPVisionEmbeddings(hidden_size, num_channels, patch_size, image_size));
         blocks["pre_layernorm"]  = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
-        blocks["encoder"]        = std::shared_ptr<GGMLBlock>(new CLIPEncoder(n_layer, hidden_size, n_head, intermediate_size));
+        blocks["encoder"]        = std::shared_ptr<GGMLBlock>(new CLIPEncoder(n_layer, hidden_size, n_head, intermediate_size, proj_in));
         blocks["post_layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* pixel_values,
                                 bool return_pooled = true,
                                 int clip_skip      = -1) {
@@ -792,14 +793,14 @@ public:
 
         auto x = embeddings->forward(ctx, pixel_values);  // [N, num_positions, embed_dim]
         x      = pre_layernorm->forward(ctx, x);
-        x      = encoder->forward(ctx, backend, x, clip_skip, false);
+        x      = encoder->forward(ctx, x, clip_skip, false);
         // print_ggml_tensor(x, true, "ClipVisionModel x: ");
         auto last_hidden_state = x;
         x                      = post_layernorm->forward(ctx, x);  // [N, n_token, hidden_size]
 
         GGML_ASSERT(x->ne[3] == 1);
         if (return_pooled) {
-            ggml_tensor* pooled = ggml_cont(ctx, ggml_view_2d(ctx, x, x->ne[0], x->ne[2], x->nb[2], 0));
+            ggml_tensor* pooled = ggml_cont(ctx->ggml_ctx, ggml_view_2d(ctx->ggml_ctx, x, x->ne[0], x->ne[2], x->nb[2], 0));
             return pooled;  // [N, hidden_size]
         } else {
             // return x;  // [N, n_token, hidden_size]
@@ -814,8 +815,8 @@ protected:
     int64_t out_features;
     bool transpose_weight;
 
-    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
-        enum ggml_type wtype = get_type(prefix + "weight", tensor_types, GGML_TYPE_F32);
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        enum ggml_type wtype = get_type(prefix + "weight", tensor_storage_map, GGML_TYPE_F32);
         if (transpose_weight) {
             params["weight"] = ggml_new_tensor_2d(ctx, wtype, out_features, in_features);
         } else {
@@ -831,12 +832,12 @@ public:
           out_features(out_features),
           transpose_weight(transpose_weight) {}
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         struct ggml_tensor* w = params["weight"];
         if (transpose_weight) {
-            w = ggml_cont(ctx, ggml_transpose(ctx, w));
+            w = ggml_cont(ctx->ggml_ctx, ggml_transpose(ctx->ggml_ctx, w));
         }
-        return ggml_nn_linear(ctx, x, w, NULL);
+        return ggml_ext_linear(ctx->ggml_ctx, x, w, nullptr);
     }
 };
 
@@ -848,7 +849,8 @@ public:
 
 public:
     CLIPVisionModelProjection(CLIPVersion version   = OPENAI_CLIP_VIT_L_14,
-                              bool transpose_proj_w = false) {
+                              bool transpose_proj_w = false,
+                              bool proj_in          = false) {
         if (version == OPEN_CLIP_VIT_H_14) {
             hidden_size    = 1280;
             projection_dim = 1024;
@@ -856,12 +858,11 @@ public:
             hidden_size = 1664;
         }
 
-        blocks["vision_model"]      = std::shared_ptr<GGMLBlock>(new CLIPVisionModel(version));
+        blocks["vision_model"]      = std::shared_ptr<GGMLBlock>(new CLIPVisionModel(version, proj_in));
         blocks["visual_projection"] = std::shared_ptr<GGMLBlock>(new CLIPProjection(hidden_size, projection_dim, transpose_proj_w));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* pixel_values,
                                 bool return_pooled = true,
                                 int clip_skip      = -1) {
@@ -870,7 +871,7 @@ public:
         auto vision_model      = std::dynamic_pointer_cast<CLIPVisionModel>(blocks["vision_model"]);
         auto visual_projection = std::dynamic_pointer_cast<CLIPProjection>(blocks["visual_projection"]);
 
-        auto x = vision_model->forward(ctx, backend, pixel_values, return_pooled, clip_skip);  // [N, hidden_size] or [N, n_token, hidden_size]
+        auto x = vision_model->forward(ctx, pixel_values, return_pooled, clip_skip);  // [N, hidden_size] or [N, n_token, hidden_size]
 
         if (return_pooled) {
             x = visual_projection->forward(ctx, x);  // [N, projection_dim]
@@ -885,16 +886,27 @@ struct CLIPTextModelRunner : public GGMLRunner {
 
     CLIPTextModelRunner(ggml_backend_t backend,
                         bool offload_params_to_cpu,
-                        const String2GGMLType& tensor_types,
+                        const String2TensorStorage& tensor_storage_map,
                         const std::string prefix,
                         CLIPVersion version = OPENAI_CLIP_VIT_L_14,
                         bool with_final_ln  = true,
                         bool force_clip_f32 = false)
-        : GGMLRunner(backend, offload_params_to_cpu), model(version, with_final_ln, force_clip_f32) {
-        model.init(params_ctx, tensor_types, prefix);
+        : GGMLRunner(backend, offload_params_to_cpu) {
+        bool proj_in = false;
+        for (const auto& [name, tensor_storage] : tensor_storage_map) {
+            if (!starts_with(name, prefix)) {
+                continue;
+            }
+            if (contains(name, "self_attn.in_proj")) {
+                proj_in = true;
+                break;
+            }
+        }
+        model = CLIPTextModel(version, with_final_ln, force_clip_f32, proj_in);
+        model.init(params_ctx, tensor_storage_map, prefix);
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return "clip";
     }
 
@@ -902,8 +914,7 @@ struct CLIPTextModelRunner : public GGMLRunner {
         model.get_param_tensors(tensors, prefix);
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* input_ids,
                                 struct ggml_tensor* embeddings,
                                 size_t max_token_idx = 0,
@@ -913,25 +924,25 @@ struct CLIPTextModelRunner : public GGMLRunner {
         size_t n_token = input_ids->ne[0];
         if (input_ids->ne[0] > model.n_token) {
             GGML_ASSERT(input_ids->ne[0] % model.n_token == 0);
-            input_ids = ggml_reshape_2d(ctx, input_ids, model.n_token, input_ids->ne[0] / model.n_token);
+            input_ids = ggml_reshape_2d(ctx->ggml_ctx, input_ids, model.n_token, input_ids->ne[0] / model.n_token);
         }
 
-        return model.forward(ctx, backend, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
+        return model.forward(ctx, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
     }
 
     struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
                                     int num_custom_embeddings    = 0,
-                                    void* custom_embeddings_data = NULL,
+                                    void* custom_embeddings_data = nullptr,
                                     size_t max_token_idx         = 0,
                                     bool return_pooled           = false,
                                     int clip_skip                = -1) {
-        struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
+        struct ggml_cgraph* gf = new_graph_custom(2048);
 
         input_ids = to_backend(input_ids);
 
-        struct ggml_tensor* embeddings = NULL;
+        struct ggml_tensor* embeddings = nullptr;
 
-        if (num_custom_embeddings > 0 && custom_embeddings_data != NULL) {
+        if (num_custom_embeddings > 0 && custom_embeddings_data != nullptr) {
             auto token_embed_weight = model.get_token_embed_weight();
             auto custom_embeddings  = ggml_new_tensor_2d(compute_ctx,
                                                          token_embed_weight->type,
@@ -943,7 +954,9 @@ struct CLIPTextModelRunner : public GGMLRunner {
             embeddings = ggml_concat(compute_ctx, token_embed_weight, custom_embeddings, 1);
         }
 
-        struct ggml_tensor* hidden_states = forward(compute_ctx, runtime_backend, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
+        auto runner_ctx = get_context();
+
+        struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
 
         ggml_build_forward_expand(gf, hidden_states);
 
@@ -958,7 +971,7 @@ struct CLIPTextModelRunner : public GGMLRunner {
                  bool return_pooled,
                  int clip_skip,
                  ggml_tensor** output,
-                 ggml_context* output_ctx = NULL) {
+                 ggml_context* output_ctx = nullptr) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
             return build_graph(input_ids, num_custom_embeddings, custom_embeddings_data, max_token_idx, return_pooled, clip_skip);
         };

common.hpp

@@ -23,12 +23,12 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [N, channels, h, w]
         if (vae_downsample) {
             auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
 
-            x = ggml_pad(ctx, x, 1, 1, 0, 0);
+            x = ggml_pad(ctx->ggml_ctx, x, 1, 1, 0, 0);
             x = conv->forward(ctx, x);
         } else {
             auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["op"]);
@@ -52,12 +52,12 @@ public:
         blocks["conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [N, channels, h, w]
         auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
 
-        x = ggml_upscale(ctx, x, 2, GGML_SCALE_MODE_NEAREST);  // [N, channels, h*2, w*2]
-        x = conv->forward(ctx, x);                             // [N, out_channels, h*2, w*2]
+        x = ggml_upscale(ctx->ggml_ctx, x, 2, GGML_SCALE_MODE_NEAREST);  // [N, channels, h*2, w*2]
+        x = conv->forward(ctx, x);                                       // [N, out_channels, h*2, w*2]
         return x;
     }
 };
@@ -121,7 +121,7 @@ public:
         }
     }
 
-    virtual struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* emb = NULL) {
+    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, struct ggml_tensor* emb = nullptr) {
         // For dims==3, we reduce dimension from 5d to 4d by merging h and w, in order not to change ggml
         // [N, c, t, h, w] => [N, c, t, h * w]
         // x: [N, channels, h, w] if dims == 2 else [N, channels, t, h, w]
@@ -131,38 +131,38 @@ public:
         auto out_layers_0 = std::dynamic_pointer_cast<GroupNorm32>(blocks["out_layers.0"]);
         auto out_layers_3 = std::dynamic_pointer_cast<UnaryBlock>(blocks["out_layers.3"]);
 
-        if (emb == NULL) {
+        if (emb == nullptr) {
             GGML_ASSERT(skip_t_emb);
         }
 
         // in_layers
         auto h = in_layers_0->forward(ctx, x);
-        h      = ggml_silu_inplace(ctx, h);
+        h      = ggml_silu_inplace(ctx->ggml_ctx, h);
         h      = in_layers_2->forward(ctx, h);  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
 
         // emb_layers
         if (!skip_t_emb) {
             auto emb_layer_1 = std::dynamic_pointer_cast<Linear>(blocks["emb_layers.1"]);
 
-            auto emb_out = ggml_silu(ctx, emb);
+            auto emb_out = ggml_silu(ctx->ggml_ctx, emb);
             emb_out      = emb_layer_1->forward(ctx, emb_out);  // [N, out_channels] if dims == 2 else [N, t, out_channels]
 
             if (dims == 2) {
-                emb_out = ggml_reshape_4d(ctx, emb_out, 1, 1, emb_out->ne[0], emb_out->ne[1]);  // [N, out_channels, 1, 1]
+                emb_out = ggml_reshape_4d(ctx->ggml_ctx, emb_out, 1, 1, emb_out->ne[0], emb_out->ne[1]);  // [N, out_channels, 1, 1]
             } else {
-                emb_out = ggml_reshape_4d(ctx, emb_out, 1, emb_out->ne[0], emb_out->ne[1], emb_out->ne[2]);  // [N, t, out_channels, 1]
+                emb_out = ggml_reshape_4d(ctx->ggml_ctx, emb_out, 1, emb_out->ne[0], emb_out->ne[1], emb_out->ne[2]);  // [N, t, out_channels, 1]
                 if (exchange_temb_dims) {
                     // emb_out = rearrange(emb_out, "b t c ... -> b c t ...")
-                    emb_out = ggml_cont(ctx, ggml_permute(ctx, emb_out, 0, 2, 1, 3));  // [N, out_channels, t, 1]
+                    emb_out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, emb_out, 0, 2, 1, 3));  // [N, out_channels, t, 1]
                 }
             }
 
-            h = ggml_add(ctx, h, emb_out);  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
+            h = ggml_add(ctx->ggml_ctx, h, emb_out);  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
         }
 
         // out_layers
         h = out_layers_0->forward(ctx, h);
-        h = ggml_silu_inplace(ctx, h);
+        h = ggml_silu_inplace(ctx->ggml_ctx, h);
         // dropout, skip for inference
         h = out_layers_3->forward(ctx, h);
 
@@ -172,7 +172,7 @@ public:
             x                    = skip_connection->forward(ctx, x);  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
         }
 
-        h = ggml_add(ctx, h, x);
+        h = ggml_add(ctx->ggml_ctx, h, x);
         return h;  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
     }
 };
@@ -182,35 +182,25 @@ protected:
     int64_t dim_in;
     int64_t dim_out;
 
-    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, std::string prefix = "") {
-        enum ggml_type wtype      = get_type(prefix + "proj.weight", tensor_types, GGML_TYPE_F32);
-        enum ggml_type bias_wtype = GGML_TYPE_F32;
-        params["proj.weight"]     = ggml_new_tensor_2d(ctx, wtype, dim_in, dim_out * 2);
-        params["proj.bias"]       = ggml_new_tensor_1d(ctx, bias_wtype, dim_out * 2);
-    }
-
 public:
     GEGLU(int64_t dim_in, int64_t dim_out)
-        : dim_in(dim_in), dim_out(dim_out) {}
+        : dim_in(dim_in), dim_out(dim_out) {
+        blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(dim_in, dim_out * 2));
+    }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [ne3, ne2, ne1, dim_in]
         // return: [ne3, ne2, ne1, dim_out]
-        struct ggml_tensor* w = params["proj.weight"];
-        struct ggml_tensor* b = params["proj.bias"];
+        auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
 
-        auto x_w    = ggml_view_2d(ctx, w, w->ne[0], w->ne[1] / 2, w->nb[1], 0);                        // [dim_out, dim_in]
-        auto x_b    = ggml_view_1d(ctx, b, b->ne[0] / 2, 0);                                            // [dim_out, dim_in]
-        auto gate_w = ggml_view_2d(ctx, w, w->ne[0], w->ne[1] / 2, w->nb[1], w->nb[1] * w->ne[1] / 2);  // [dim_out, ]
-        auto gate_b = ggml_view_1d(ctx, b, b->ne[0] / 2, b->nb[0] * b->ne[0] / 2);                      // [dim_out, ]
+        x          = proj->forward(ctx, x);  // [ne3, ne2, ne1, dim_out*2]
+        auto x_vec = ggml_ext_chunk(ctx->ggml_ctx, x, 2, 0);
+        x          = x_vec[0];  // [ne3, ne2, ne1, dim_out]
+        auto gate  = x_vec[1];  // [ne3, ne2, ne1, dim_out]
 
-        auto x_in = x;
-        x         = ggml_nn_linear(ctx, x_in, x_w, x_b);        // [ne3, ne2, ne1, dim_out]
-        auto gate = ggml_nn_linear(ctx, x_in, gate_w, gate_b);  // [ne3, ne2, ne1, dim_out]
+        gate = ggml_gelu_inplace(ctx->ggml_ctx, gate);
 
-        gate = ggml_gelu_inplace(ctx, gate);
-
-        x = ggml_mul(ctx, x, gate);  // [ne3, ne2, ne1, dim_out]
+        x = ggml_mul(ctx->ggml_ctx, x, gate);  // [ne3, ne2, ne1, dim_out]
 
         return x;
     }
@@ -222,13 +212,13 @@ public:
         blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(dim_in, dim_out, bias));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [ne3, ne2, ne1, dim_in]
         // return: [ne3, ne2, ne1, dim_out]
         auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
 
         x = proj->forward(ctx, x);
-        x = ggml_gelu_inplace(ctx, x);
+        x = ggml_gelu_inplace(ctx->ggml_ctx, x);
         return x;
     }
 };
@@ -252,17 +242,21 @@ public:
         }
 
         // net_1 is nn.Dropout(), skip for inference
-        float scale = 1.f;
+        bool force_prec_f32 = false;
+        float scale         = 1.f;
         if (precision_fix) {
             scale = 1.f / 128.f;
+#ifdef SD_USE_VULKAN
+            force_prec_f32 = true;
+#endif
         }
         // The purpose of the scale here is to prevent NaN issues in certain situations.
         // For example, when using Vulkan without enabling force_prec_f32,
         // or when using CUDA but the weights are k-quants.
-        blocks["net.2"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, dim_out, true, false, false, scale));
+        blocks["net.2"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, dim_out, true, false, force_prec_f32, scale));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [ne3, ne2, ne1, dim]
         // return: [ne3, ne2, ne1, dim_out]
 
@@ -281,19 +275,16 @@ protected:
     int64_t context_dim;
     int64_t n_head;
     int64_t d_head;
-    bool flash_attn;
 
 public:
     CrossAttention(int64_t query_dim,
                    int64_t context_dim,
                    int64_t n_head,
-                   int64_t d_head,
-                   bool flash_attn = false)
+                   int64_t d_head)
         : n_head(n_head),
           d_head(d_head),
           query_dim(query_dim),
-          context_dim(context_dim),
-          flash_attn(flash_attn) {
+          context_dim(context_dim) {
         int64_t inner_dim = d_head * n_head;
 
         blocks["to_q"] = std::shared_ptr<GGMLBlock>(new Linear(query_dim, inner_dim, false));
@@ -304,8 +295,7 @@ public:
         // to_out_1 is nn.Dropout(), skip for inference
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* context) {
         // x: [N, n_token, query_dim]
@@ -325,7 +315,7 @@ public:
         auto k = to_k->forward(ctx, context);  // [N, n_context, inner_dim]
         auto v = to_v->forward(ctx, context);  // [N, n_context, inner_dim]
 
-        x = ggml_nn_attention_ext(ctx, backend, q, k, v, n_head, NULL, false, false, flash_attn);  // [N, n_token, inner_dim]
+        x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, inner_dim]
 
         x = to_out_0->forward(ctx, x);  // [N, n_token, query_dim]
         return x;
@@ -343,16 +333,15 @@ public:
                           int64_t n_head,
                           int64_t d_head,
                           int64_t context_dim,
-                          bool ff_in      = false,
-                          bool flash_attn = false)
+                          bool ff_in = false)
         : n_head(n_head), d_head(d_head), ff_in(ff_in) {
         // disable_self_attn is always False
         // disable_temporal_crossattention is always False
         // switch_temporal_ca_to_sa is always False
         // inner_dim is always None or equal to dim
         // gated_ff is always True
-        blocks["attn1"] = std::shared_ptr<GGMLBlock>(new CrossAttention(dim, dim, n_head, d_head, flash_attn));
-        blocks["attn2"] = std::shared_ptr<GGMLBlock>(new CrossAttention(dim, context_dim, n_head, d_head, flash_attn));
+        blocks["attn1"] = std::shared_ptr<GGMLBlock>(new CrossAttention(dim, dim, n_head, d_head));
+        blocks["attn2"] = std::shared_ptr<GGMLBlock>(new CrossAttention(dim, context_dim, n_head, d_head));
         blocks["ff"]    = std::shared_ptr<GGMLBlock>(new FeedForward(dim, dim));
         blocks["norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(dim));
         blocks["norm2"] = std::shared_ptr<GGMLBlock>(new LayerNorm(dim));
@@ -364,8 +353,7 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* context) {
         // x: [N, n_token, query_dim]
@@ -387,21 +375,21 @@ public:
             x           = norm_in->forward(ctx, x);
             x           = ff_in->forward(ctx, x);
             // self.is_res is always True
-            x = ggml_add(ctx, x, x_skip);
+            x = ggml_add(ctx->ggml_ctx, x, x_skip);
         }
 
         auto r = x;
         x      = norm1->forward(ctx, x);
-        x      = attn1->forward(ctx, backend, x, x);  // self-attention
-        x      = ggml_add(ctx, x, r);
+        x      = attn1->forward(ctx, x, x);  // self-attention
+        x      = ggml_add(ctx->ggml_ctx, x, r);
         r      = x;
         x      = norm2->forward(ctx, x);
-        x      = attn2->forward(ctx, backend, x, context);  // cross-attention
-        x      = ggml_add(ctx, x, r);
+        x      = attn2->forward(ctx, x, context);  // cross-attention
+        x      = ggml_add(ctx->ggml_ctx, x, r);
         r      = x;
         x      = norm3->forward(ctx, x);
         x      = ff->forward(ctx, x);
-        x      = ggml_add(ctx, x, r);
+        x      = ggml_add(ctx->ggml_ctx, x, r);
 
         return x;
     }
@@ -414,6 +402,23 @@ protected:
     int64_t d_head;
     int64_t depth       = 1;    // 1
     int64_t context_dim = 768;  // hidden_size, 1024 for VERSION_SD2
+    bool use_linear     = false;
+
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") {
+        auto iter = tensor_storage_map.find(prefix + "proj_out.weight");
+        if (iter != tensor_storage_map.end()) {
+            int64_t inner_dim = n_head * d_head;
+            if (iter->second.n_dims == 4 && use_linear) {
+                use_linear         = false;
+                blocks["proj_in"]  = std::make_shared<Conv2d>(in_channels, inner_dim, std::pair{1, 1});
+                blocks["proj_out"] = std::make_shared<Conv2d>(inner_dim, in_channels, std::pair{1, 1});
+            } else if (iter->second.n_dims == 2 && !use_linear) {
+                use_linear         = true;
+                blocks["proj_in"]  = std::make_shared<Linear>(in_channels, inner_dim);
+                blocks["proj_out"] = std::make_shared<Linear>(inner_dim, in_channels);
+            }
+        }
+    }
 
 public:
     SpatialTransformer(int64_t in_channels,
@@ -421,35 +426,42 @@ public:
                        int64_t d_head,
                        int64_t depth,
                        int64_t context_dim,
-                       bool flash_attn = false)
+                       bool use_linear)
         : in_channels(in_channels),
           n_head(n_head),
           d_head(d_head),
           depth(depth),
-          context_dim(context_dim) {
-        // We will convert unet transformer linear to conv2d 1x1 when loading the weights, so use_linear is always False
+          context_dim(context_dim),
+          use_linear(use_linear) {
         // disable_self_attn is always False
         int64_t inner_dim = n_head * d_head;  // in_channels
         blocks["norm"]    = std::shared_ptr<GGMLBlock>(new GroupNorm32(in_channels));
-        blocks["proj_in"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, inner_dim, {1, 1}));
+        if (use_linear) {
+            blocks["proj_in"] = std::shared_ptr<GGMLBlock>(new Linear(in_channels, inner_dim));
+        } else {
+            blocks["proj_in"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, inner_dim, {1, 1}));
+        }
 
         for (int i = 0; i < depth; i++) {
             std::string name = "transformer_blocks." + std::to_string(i);
-            blocks[name]     = std::shared_ptr<GGMLBlock>(new BasicTransformerBlock(inner_dim, n_head, d_head, context_dim, false, flash_attn));
+            blocks[name]     = std::shared_ptr<GGMLBlock>(new BasicTransformerBlock(inner_dim, n_head, d_head, context_dim, false));
         }
 
-        blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(inner_dim, in_channels, {1, 1}));
+        if (use_linear) {
+            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, in_channels));
+        } else {
+            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(inner_dim, in_channels, {1, 1}));
+        }
     }
 
-    virtual struct ggml_tensor* forward(struct ggml_context* ctx,
-                                        ggml_backend_t backend,
+    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                         struct ggml_tensor* x,
                                         struct ggml_tensor* context) {
         // x: [N, in_channels, h, w]
         // context: [N, max_position(aka n_token), hidden_size(aka context_dim)]
         auto norm     = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm"]);
-        auto proj_in  = std::dynamic_pointer_cast<Conv2d>(blocks["proj_in"]);
-        auto proj_out = std::dynamic_pointer_cast<Conv2d>(blocks["proj_out"]);
+        auto proj_in  = std::dynamic_pointer_cast<UnaryBlock>(blocks["proj_in"]);
+        auto proj_out = std::dynamic_pointer_cast<UnaryBlock>(blocks["proj_out"]);
 
         auto x_in         = x;
         int64_t n         = x->ne[3];
@@ -458,32 +470,45 @@ public:
         int64_t inner_dim = n_head * d_head;
 
         x = norm->forward(ctx, x);
-        x = proj_in->forward(ctx, x);  // [N, inner_dim, h, w]
-
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 1, 2, 0, 3));  // [N, h, w, inner_dim]
-        x = ggml_reshape_3d(ctx, x, inner_dim, w * h, n);      // [N, h * w, inner_dim]
+        if (use_linear) {
+            x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 2, 0, 3));  // [N, h, w, inner_dim]
+            x = ggml_reshape_3d(ctx->ggml_ctx, x, inner_dim, w * h, n);                // [N, h * w, inner_dim]
+            x = proj_in->forward(ctx, x);                                              // [N, inner_dim, h, w]
+        } else {
+            x = proj_in->forward(ctx, x);                                              // [N, inner_dim, h, w]
+            x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 2, 0, 3));  // [N, h, w, inner_dim]
+            x = ggml_reshape_3d(ctx->ggml_ctx, x, inner_dim, w * h, n);                // [N, h * w, inner_dim]
+        }
 
         for (int i = 0; i < depth; i++) {
             std::string name       = "transformer_blocks." + std::to_string(i);
             auto transformer_block = std::dynamic_pointer_cast<BasicTransformerBlock>(blocks[name]);
 
-            x = transformer_block->forward(ctx, backend, x, context);
+            x = transformer_block->forward(ctx, x, context);
         }
 
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 1, 0, 2, 3));  // [N, inner_dim, h * w]
-        x = ggml_reshape_4d(ctx, x, w, h, inner_dim, n);       // [N, inner_dim, h, w]
+        if (use_linear) {
+            // proj_out
+            x = proj_out->forward(ctx, x);  // [N, in_channels, h, w]
 
-        // proj_out
-        x = proj_out->forward(ctx, x);  // [N, in_channels, h, w]
+            x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [N, inner_dim, h * w]
+            x = ggml_reshape_4d(ctx->ggml_ctx, x, w, h, inner_dim, n);                 // [N, inner_dim, h, w]
+        } else {
+            x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [N, inner_dim, h * w]
+            x = ggml_reshape_4d(ctx->ggml_ctx, x, w, h, inner_dim, n);                 // [N, inner_dim, h, w]
 
-        x = ggml_add(ctx, x, x_in);
+            // proj_out
+            x = proj_out->forward(ctx, x);  // [N, in_channels, h, w]
+        }
+
+        x = ggml_add(ctx->ggml_ctx, x, x_in);
         return x;
     }
 };
 
 class AlphaBlender : public GGMLBlock {
 protected:
-    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, std::string prefix = "") {
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
         // Get the type of the "mix_factor" tensor from the input tensors map with the specified prefix
         enum ggml_type wtype = GGML_TYPE_F32;
         params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
@@ -492,7 +517,7 @@ protected:
     float get_alpha() {
         // image_only_indicator is always tensor([0.]) and since mix_factor.shape is [1,]
         // so learned_with_images is same as learned
-        float alpha = ggml_backend_tensor_get_f32(params["mix_factor"]);
+        float alpha = ggml_ext_backend_tensor_get_f32(params["mix_factor"]);
         return sigmoid(alpha);
     }
 
@@ -503,14 +528,14 @@ public:
         // since mix_factor.shape is [1,], we don't need rearrange using  rearrange_pattern
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x_spatial,
                                 struct ggml_tensor* x_temporal) {
         // image_only_indicator is always tensor([0.])
         float alpha = get_alpha();
-        auto x      = ggml_add(ctx,
-                               ggml_scale(ctx, x_spatial, alpha),
-                               ggml_scale(ctx, x_temporal, 1.0f - alpha));
+        auto x      = ggml_add(ctx->ggml_ctx,
+                               ggml_scale(ctx->ggml_ctx, x_spatial, alpha),
+                               ggml_scale(ctx->ggml_ctx, x_temporal, 1.0f - alpha));
         return x;
     }
 };
@@ -528,7 +553,7 @@ public:
         blocks["time_mixer"] = std::shared_ptr<GGMLBlock>(new AlphaBlender());
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* emb,
                                 int num_video_frames) {
@@ -546,18 +571,18 @@ public:
         int64_t H = x->ne[1];
         int64_t W = x->ne[0];
 
-        x          = ggml_reshape_4d(ctx, x, W * H, C, T, B);           // (b t) c h w -> b t c (h w)
-        x          = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
+        x          = ggml_reshape_4d(ctx->ggml_ctx, x, W * H, C, T, B);                     // (b t) c h w -> b t c (h w)
+        x          = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
         auto x_mix = x;
 
-        emb = ggml_reshape_4d(ctx, emb, emb->ne[0], T, B, emb->ne[3]);  // (b t) ... -> b t ...
+        emb = ggml_reshape_4d(ctx->ggml_ctx, emb, emb->ne[0], T, B, emb->ne[3]);  // (b t) ... -> b t ...
 
         x = time_stack->forward(ctx, x, emb);  // b t c (h w)
 
         x = time_mixer->forward(ctx, x_mix, x);  // b t c (h w)
 
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
-        x = ggml_reshape_4d(ctx, x, W, H, C, T * B);           // b t c (h w) -> (b t) c h w
+        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
+        x = ggml_reshape_4d(ctx->ggml_ctx, x, W, H, C, T * B);                     // b t c (h w) -> (b t) c h w
 
         return x;
     }

control.hpp

@@ -27,6 +27,7 @@ protected:
     int num_heads                          = 8;
     int num_head_channels                  = -1;   // channels // num_heads
     int context_dim                        = 768;  // 1024 for VERSION_SD2, 2048 for VERSION_SDXL
+    bool use_linear_projection             = false;
 
 public:
     int model_channels  = 320;
@@ -82,7 +83,7 @@ public:
                                        int64_t d_head,
                                        int64_t depth,
                                        int64_t context_dim) -> SpatialTransformer* {
-            return new SpatialTransformer(in_channels, n_head, d_head, depth, context_dim);
+            return new SpatialTransformer(in_channels, n_head, d_head, depth, context_dim, use_linear_projection);
         };
 
         auto make_zero_conv = [&](int64_t channels) {
@@ -165,7 +166,7 @@ public:
     }
 
     struct ggml_tensor* resblock_forward(std::string name,
-                                         struct ggml_context* ctx,
+                                         GGMLRunnerContext* ctx,
                                          struct ggml_tensor* x,
                                          struct ggml_tensor* emb) {
         auto block = std::dynamic_pointer_cast<ResBlock>(blocks[name]);
@@ -173,15 +174,14 @@ public:
     }
 
     struct ggml_tensor* attention_layer_forward(std::string name,
-                                                struct ggml_context* ctx,
-                                                ggml_backend_t backend,
+                                                GGMLRunnerContext* ctx,
                                                 struct ggml_tensor* x,
                                                 struct ggml_tensor* context) {
         auto block = std::dynamic_pointer_cast<SpatialTransformer>(blocks[name]);
-        return block->forward(ctx, backend, x, context);
+        return block->forward(ctx, x, context);
     }
 
-    struct ggml_tensor* input_hint_block_forward(struct ggml_context* ctx,
+    struct ggml_tensor* input_hint_block_forward(GGMLRunnerContext* ctx,
                                                  struct ggml_tensor* hint,
                                                  struct ggml_tensor* emb,
                                                  struct ggml_tensor* context) {
@@ -193,33 +193,32 @@ public:
 
                 h = block->forward(ctx, h);
             } else {
-                h = ggml_silu_inplace(ctx, h);
+                h = ggml_silu_inplace(ctx->ggml_ctx, h);
             }
         }
         return h;
     }
 
-    std::vector<struct ggml_tensor*> forward(struct ggml_context* ctx,
-                                             ggml_backend_t backend,
+    std::vector<struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
                                              struct ggml_tensor* x,
                                              struct ggml_tensor* hint,
                                              struct ggml_tensor* guided_hint,
                                              struct ggml_tensor* timesteps,
                                              struct ggml_tensor* context,
-                                             struct ggml_tensor* y = NULL) {
+                                             struct ggml_tensor* y = nullptr) {
         // x: [N, in_channels, h, w] or [N, in_channels/2, h, w]
         // timesteps: [N,]
         // context: [N, max_position, hidden_size] or [1, max_position, hidden_size]. for example, [N, 77, 768]
         // y: [N, adm_in_channels] or [1, adm_in_channels]
-        if (context != NULL) {
+        if (context != nullptr) {
             if (context->ne[2] != x->ne[3]) {
-                context = ggml_repeat(ctx, context, ggml_new_tensor_3d(ctx, GGML_TYPE_F32, context->ne[0], context->ne[1], x->ne[3]));
+                context = ggml_repeat(ctx->ggml_ctx, context, ggml_new_tensor_3d(ctx->ggml_ctx, GGML_TYPE_F32, context->ne[0], context->ne[1], x->ne[3]));
             }
         }
 
-        if (y != NULL) {
+        if (y != nullptr) {
             if (y->ne[1] != x->ne[3]) {
-                y = ggml_repeat(ctx, y, ggml_new_tensor_2d(ctx, GGML_TYPE_F32, y->ne[0], x->ne[3]));
+                y = ggml_repeat(ctx->ggml_ctx, y, ggml_new_tensor_2d(ctx->ggml_ctx, GGML_TYPE_F32, y->ne[0], x->ne[3]));
             }
         }
 
@@ -230,27 +229,27 @@ public:
 
         auto middle_block_out = std::dynamic_pointer_cast<Conv2d>(blocks["middle_block_out.0"]);
 
-        auto t_emb = ggml_nn_timestep_embedding(ctx, timesteps, model_channels);  // [N, model_channels]
+        auto t_emb = ggml_ext_timestep_embedding(ctx->ggml_ctx, timesteps, model_channels);  // [N, model_channels]
 
         auto emb = time_embed_0->forward(ctx, t_emb);
-        emb      = ggml_silu_inplace(ctx, emb);
+        emb      = ggml_silu_inplace(ctx->ggml_ctx, emb);
         emb      = time_embed_2->forward(ctx, emb);  // [N, time_embed_dim]
 
         // SDXL/SVD
-        if (y != NULL) {
+        if (y != nullptr) {
             auto label_embed_0 = std::dynamic_pointer_cast<Linear>(blocks["label_emb.0.0"]);
             auto label_embed_2 = std::dynamic_pointer_cast<Linear>(blocks["label_emb.0.2"]);
 
             auto label_emb = label_embed_0->forward(ctx, y);
-            label_emb      = ggml_silu_inplace(ctx, label_emb);
+            label_emb      = ggml_silu_inplace(ctx->ggml_ctx, label_emb);
             label_emb      = label_embed_2->forward(ctx, label_emb);  // [N, time_embed_dim]
 
-            emb = ggml_add(ctx, emb, label_emb);  // [N, time_embed_dim]
+            emb = ggml_add(ctx->ggml_ctx, emb, label_emb);  // [N, time_embed_dim]
         }
 
         std::vector<struct ggml_tensor*> outs;
 
-        if (guided_hint == NULL) {
+        if (guided_hint == nullptr) {
             guided_hint = input_hint_block_forward(ctx, hint, emb, context);
         }
         outs.push_back(guided_hint);
@@ -259,7 +258,7 @@ public:
 
         // input block 0
         auto h = input_blocks_0_0->forward(ctx, x);
-        h      = ggml_add(ctx, h, guided_hint);
+        h      = ggml_add(ctx->ggml_ctx, h, guided_hint);
         outs.push_back(zero_convs_0->forward(ctx, h));
 
         // input block 1-11
@@ -274,7 +273,7 @@ public:
                 h                = resblock_forward(name, ctx, h, emb);  // [N, mult*model_channels, h, w]
                 if (std::find(attention_resolutions.begin(), attention_resolutions.end(), ds) != attention_resolutions.end()) {
                     std::string name = "input_blocks." + std::to_string(input_block_idx) + ".1";
-                    h                = attention_layer_forward(name, ctx, backend, h, context);  // [N, mult*model_channels, h, w]
+                    h                = attention_layer_forward(name, ctx, h, context);  // [N, mult*model_channels, h, w]
                 }
 
                 auto zero_conv = std::dynamic_pointer_cast<Conv2d>(blocks["zero_convs." + std::to_string(input_block_idx) + ".0"]);
@@ -298,9 +297,9 @@ public:
         // [N, 4*model_channels, h/8, w/8]
 
         // middle_block
-        h = resblock_forward("middle_block.0", ctx, h, emb);                      // [N, 4*model_channels, h/8, w/8]
-        h = attention_layer_forward("middle_block.1", ctx, backend, h, context);  // [N, 4*model_channels, h/8, w/8]
-        h = resblock_forward("middle_block.2", ctx, h, emb);                      // [N, 4*model_channels, h/8, w/8]
+        h = resblock_forward("middle_block.0", ctx, h, emb);             // [N, 4*model_channels, h/8, w/8]
+        h = attention_layer_forward("middle_block.1", ctx, h, context);  // [N, 4*model_channels, h/8, w/8]
+        h = resblock_forward("middle_block.2", ctx, h, emb);             // [N, 4*model_channels, h/8, w/8]
 
         // out
         outs.push_back(middle_block_out->forward(ctx, h));
@@ -312,39 +311,28 @@ struct ControlNet : public GGMLRunner {
     SDVersion version = VERSION_SD1;
     ControlNetBlock control_net;
 
-    ggml_backend_buffer_t control_buffer = NULL;  // keep control output tensors in backend memory
-    ggml_context* control_ctx            = NULL;
+    ggml_backend_buffer_t control_buffer = nullptr;  // keep control output tensors in backend memory
+    ggml_context* control_ctx            = nullptr;
     std::vector<struct ggml_tensor*> controls;  // (12 input block outputs, 1 middle block output) SD 1.5
-    struct ggml_tensor* guided_hint = NULL;     // guided_hint cache, for faster inference
+    struct ggml_tensor* guided_hint = nullptr;  // guided_hint cache, for faster inference
     bool guided_hint_cached         = false;
 
     ControlNet(ggml_backend_t backend,
                bool offload_params_to_cpu,
-               const String2GGMLType& tensor_types = {},
-               SDVersion version                   = VERSION_SD1)
+               const String2TensorStorage& tensor_storage_map = {},
+               SDVersion version                              = VERSION_SD1)
         : GGMLRunner(backend, offload_params_to_cpu), control_net(version) {
-        control_net.init(params_ctx, tensor_types, "");
+        control_net.init(params_ctx, tensor_storage_map, "");
     }
 
-    void enable_conv2d_direct() {
-        std::vector<GGMLBlock*> blocks;
-        control_net.get_all_blocks(blocks);
-        for (auto block : blocks) {
-            if (block->get_desc() == "Conv2d") {
-                auto conv_block = (Conv2d*)block;
-                conv_block->enable_direct();
-            }
-        }
-    }
-
-    ~ControlNet() {
+    ~ControlNet() override {
         free_control_ctx();
     }
 
     void alloc_control_ctx(std::vector<struct ggml_tensor*> outs) {
         struct ggml_init_params params;
         params.mem_size   = static_cast<size_t>(outs.size() * ggml_tensor_overhead()) + 1024 * 1024;
-        params.mem_buffer = NULL;
+        params.mem_buffer = nullptr;
         params.no_alloc   = true;
         control_ctx       = ggml_init(params);
 
@@ -366,20 +354,20 @@ struct ControlNet : public GGMLRunner {
     }
 
     void free_control_ctx() {
-        if (control_buffer != NULL) {
+        if (control_buffer != nullptr) {
             ggml_backend_buffer_free(control_buffer);
-            control_buffer = NULL;
+            control_buffer = nullptr;
         }
-        if (control_ctx != NULL) {
+        if (control_ctx != nullptr) {
             ggml_free(control_ctx);
-            control_ctx = NULL;
+            control_ctx = nullptr;
         }
-        guided_hint        = NULL;
+        guided_hint        = nullptr;
         guided_hint_cached = false;
         controls.clear();
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return "control_net";
     }
 
@@ -391,12 +379,12 @@ struct ControlNet : public GGMLRunner {
                                     struct ggml_tensor* hint,
                                     struct ggml_tensor* timesteps,
                                     struct ggml_tensor* context,
-                                    struct ggml_tensor* y = NULL) {
-        struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, CONTROL_NET_GRAPH_SIZE, false);
+                                    struct ggml_tensor* y = nullptr) {
+        struct ggml_cgraph* gf = new_graph_custom(CONTROL_NET_GRAPH_SIZE);
 
         x = to_backend(x);
         if (guided_hint_cached) {
-            hint = NULL;
+            hint = nullptr;
         } else {
             hint = to_backend(hint);
         }
@@ -404,16 +392,17 @@ struct ControlNet : public GGMLRunner {
         y         = to_backend(y);
         timesteps = to_backend(timesteps);
 
-        auto outs = control_net.forward(compute_ctx,
-                                        runtime_backend,
+        auto runner_ctx = get_context();
+
+        auto outs = control_net.forward(&runner_ctx,
                                         x,
                                         hint,
-                                        guided_hint_cached ? guided_hint : NULL,
+                                        guided_hint_cached ? guided_hint : nullptr,
                                         timesteps,
                                         context,
                                         y);
 
-        if (control_ctx == NULL) {
+        if (control_ctx == nullptr) {
             alloc_control_ctx(outs);
         }
 
@@ -431,8 +420,8 @@ struct ControlNet : public GGMLRunner {
                  struct ggml_tensor* timesteps,
                  struct ggml_tensor* context,
                  struct ggml_tensor* y,
-                 struct ggml_tensor** output     = NULL,
-                 struct ggml_context* output_ctx = NULL) {
+                 struct ggml_tensor** output     = nullptr,
+                 struct ggml_context* output_ctx = nullptr) {
         // x: [N, in_channels, h, w]
         // timesteps: [N, ]
         // context: [N, max_position, hidden_size]([N, 77, 768]) or [1, max_position, hidden_size]
@@ -453,7 +442,7 @@ struct ControlNet : public GGMLRunner {
         std::set<std::string> ignore_tensors;
 
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
+        if (!model_loader.init_from_file_and_convert_name(file_path)) {
             LOG_ERROR("init control net model loader from file failed: '%s'", file_path.c_str());
             return false;
         }

denoiser.hpp

@@ -11,15 +11,14 @@
 #define TIMESTEPS 1000
 #define FLUX_TIMESTEPS 1000
 
-struct SigmaSchedule {
-    int version = 0;
+struct SigmaScheduler {
     typedef std::function<float(float)> t_to_sigma_t;
 
     virtual std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) = 0;
 };
 
-struct DiscreteSchedule : SigmaSchedule {
-    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) {
+struct DiscreteScheduler : SigmaScheduler {
+    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
         std::vector<float> result;
 
         int t_max = TIMESTEPS - 1;
@@ -42,8 +41,8 @@ struct DiscreteSchedule : SigmaSchedule {
     }
 };
 
-struct ExponentialSchedule : SigmaSchedule {
-    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) {
+struct ExponentialScheduler : SigmaScheduler {
+    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
         std::vector<float> sigmas;
 
         // Calculate step size
@@ -149,8 +148,11 @@ std::vector<float> log_linear_interpolation(std::vector<float> sigma_in,
 /*
 https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
 */
-struct AYSSchedule : SigmaSchedule {
-    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) {
+struct AYSScheduler : SigmaScheduler {
+    SDVersion version;
+    explicit AYSScheduler(SDVersion version)
+        : version(version) {}
+    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
         const std::vector<float> noise_levels[] = {
             /* SD1.5 */
             {14.6146412293f, 6.4745760956f, 3.8636745985f, 2.6946151520f,
@@ -169,19 +171,19 @@ struct AYSSchedule : SigmaSchedule {
         std::vector<float> results(n + 1);
 
         if (sd_version_is_sd2((SDVersion)version)) {
-            LOG_WARN("AYS not designed for SD2.X models");
+            LOG_WARN("AYS_SCHEDULER not designed for SD2.X models");
         } /* fallthrough */
         else if (sd_version_is_sd1((SDVersion)version)) {
-            LOG_INFO("AYS using SD1.5 noise levels");
+            LOG_INFO("AYS_SCHEDULER using SD1.5 noise levels");
             inputs = noise_levels[0];
         } else if (sd_version_is_sdxl((SDVersion)version)) {
-            LOG_INFO("AYS using SDXL noise levels");
+            LOG_INFO("AYS_SCHEDULER using SDXL noise levels");
             inputs = noise_levels[1];
         } else if (version == VERSION_SVD) {
-            LOG_INFO("AYS using SVD noise levels");
+            LOG_INFO("AYS_SCHEDULER using SVD noise levels");
             inputs = noise_levels[2];
         } else {
-            LOG_ERROR("Version not compatible with AYS scheduler");
+            LOG_ERROR("Version not compatible with AYS_SCHEDULER scheduler");
             return results;
         }
 
@@ -203,8 +205,8 @@ struct AYSSchedule : SigmaSchedule {
 /*
  * GITS Scheduler: https://github.com/zju-pi/diff-sampler/tree/main/gits-main
  */
-struct GITSSchedule : SigmaSchedule {
-    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) {
+struct GITSScheduler : SigmaScheduler {
+    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
         if (sigma_max <= 0.0f) {
             return std::vector<float>{};
         }
@@ -232,7 +234,7 @@ struct GITSSchedule : SigmaSchedule {
     }
 };
 
-struct SGMUniformSchedule : SigmaSchedule {
+struct SGMUniformScheduler : SigmaScheduler {
     std::vector<float> get_sigmas(uint32_t n, float sigma_min_in, float sigma_max_in, t_to_sigma_t t_to_sigma_func) override {
         std::vector<float> result;
         if (n == 0) {
@@ -251,8 +253,25 @@ struct SGMUniformSchedule : SigmaSchedule {
     }
 };
 
-struct KarrasSchedule : SigmaSchedule {
-    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) {
+struct LCMScheduler : SigmaScheduler {
+    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
+        std::vector<float> result;
+        result.reserve(n + 1);
+        const int original_steps = 50;
+        const int k              = TIMESTEPS / original_steps;
+        for (int i = 0; i < n; i++) {
+            // the rounding ensures we match the training schedule of the LCM model
+            int index    = (i * original_steps) / n;
+            int timestep = (original_steps - index) * k - 1;
+            result.push_back(t_to_sigma(timestep));
+        }
+        result.push_back(0.0f);
+        return result;
+    }
+};
+
+struct KarrasScheduler : SigmaScheduler {
+    std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
         // These *COULD* be function arguments here,
         // but does anybody ever bother to touch them?
         float rho = 7.f;
@@ -270,7 +289,7 @@ struct KarrasSchedule : SigmaSchedule {
     }
 };
 
-struct SimpleSchedule : SigmaSchedule {
+struct SimpleScheduler : SigmaScheduler {
     std::vector<float> get_sigmas(uint32_t n, float sigma_min, float sigma_max, t_to_sigma_t t_to_sigma) override {
         std::vector<float> result_sigmas;
 
@@ -299,8 +318,8 @@ struct SimpleSchedule : SigmaSchedule {
     }
 };
 
-// Close to Beta Schedule, but increadably simple in code.
-struct SmoothStepSchedule : SigmaSchedule {
+// Close to Beta Scheduler, but increadably simple in code.
+struct SmoothStepScheduler : SigmaScheduler {
     static constexpr float smoothstep(float x) {
         return x * x * (3.0f - 2.0f * x);
     }
@@ -329,7 +348,6 @@ struct SmoothStepSchedule : SigmaSchedule {
 };
 
 struct Denoiser {
-    std::shared_ptr<SigmaSchedule> scheduler                                                 = std::make_shared<DiscreteSchedule>();
     virtual float sigma_min()                                                                = 0;
     virtual float sigma_max()                                                                = 0;
     virtual float sigma_to_t(float sigma)                                                    = 0;
@@ -338,8 +356,51 @@ struct Denoiser {
     virtual ggml_tensor* noise_scaling(float sigma, ggml_tensor* noise, ggml_tensor* latent) = 0;
     virtual ggml_tensor* inverse_noise_scaling(float sigma, ggml_tensor* latent)             = 0;
 
-    virtual std::vector<float> get_sigmas(uint32_t n) {
+    virtual std::vector<float> get_sigmas(uint32_t n, int /*image_seq_len*/, scheduler_t scheduler_type, SDVersion version) {
         auto bound_t_to_sigma = std::bind(&Denoiser::t_to_sigma, this, std::placeholders::_1);
+        std::shared_ptr<SigmaScheduler> scheduler;
+        switch (scheduler_type) {
+            case DISCRETE_SCHEDULER:
+                LOG_INFO("get_sigmas with discrete scheduler");
+                scheduler = std::make_shared<DiscreteScheduler>();
+                break;
+            case KARRAS_SCHEDULER:
+                LOG_INFO("get_sigmas with Karras scheduler");
+                scheduler = std::make_shared<KarrasScheduler>();
+                break;
+            case EXPONENTIAL_SCHEDULER:
+                LOG_INFO("get_sigmas exponential scheduler");
+                scheduler = std::make_shared<ExponentialScheduler>();
+                break;
+            case AYS_SCHEDULER:
+                LOG_INFO("get_sigmas with Align-Your-Steps scheduler");
+                scheduler = std::make_shared<AYSScheduler>(version);
+                break;
+            case GITS_SCHEDULER:
+                LOG_INFO("get_sigmas with GITS scheduler");
+                scheduler = std::make_shared<GITSScheduler>();
+                break;
+            case SGM_UNIFORM_SCHEDULER:
+                LOG_INFO("get_sigmas with SGM Uniform scheduler");
+                scheduler = std::make_shared<SGMUniformScheduler>();
+                break;
+            case SIMPLE_SCHEDULER:
+                LOG_INFO("get_sigmas with Simple scheduler");
+                scheduler = std::make_shared<SimpleScheduler>();
+                break;
+            case SMOOTHSTEP_SCHEDULER:
+                LOG_INFO("get_sigmas with SmoothStep scheduler");
+                scheduler = std::make_shared<SmoothStepScheduler>();
+                break;
+            case LCM_SCHEDULER:
+                LOG_INFO("get_sigmas with LCM scheduler");
+                scheduler = std::make_shared<LCMScheduler>();
+                break;
+            default:
+                LOG_INFO("get_sigmas with discrete scheduler (default)");
+                scheduler = std::make_shared<DiscreteScheduler>();
+                break;
+        }
         return scheduler->get_sigmas(n, sigma_min(), sigma_max(), bound_t_to_sigma);
     }
 };
@@ -350,15 +411,15 @@ struct CompVisDenoiser : public Denoiser {
 
     float sigma_data = 1.0f;
 
-    float sigma_min() {
+    float sigma_min() override {
         return sigmas[0];
     }
 
-    float sigma_max() {
+    float sigma_max() override {
         return sigmas[TIMESTEPS - 1];
     }
 
-    float sigma_to_t(float sigma) {
+    float sigma_to_t(float sigma) override {
         float log_sigma = std::log(sigma);
         std::vector<float> dists;
         dists.reserve(TIMESTEPS);
@@ -384,7 +445,7 @@ struct CompVisDenoiser : public Denoiser {
         return t;
     }
 
-    float t_to_sigma(float t) {
+    float t_to_sigma(float t) override {
         int low_idx     = static_cast<int>(std::floor(t));
         int high_idx    = static_cast<int>(std::ceil(t));
         float w         = t - static_cast<float>(low_idx);
@@ -392,7 +453,7 @@ struct CompVisDenoiser : public Denoiser {
         return std::exp(log_sigma);
     }
 
-    std::vector<float> get_scalings(float sigma) {
+    std::vector<float> get_scalings(float sigma) override {
         float c_skip = 1.0f;
         float c_out  = -sigma;
         float c_in   = 1.0f / std::sqrt(sigma * sigma + sigma_data * sigma_data);
@@ -400,19 +461,19 @@ struct CompVisDenoiser : public Denoiser {
     }
 
     // this function will modify noise/latent
-    ggml_tensor* noise_scaling(float sigma, ggml_tensor* noise, ggml_tensor* latent) {
-        ggml_tensor_scale(noise, sigma);
-        ggml_tensor_add(latent, noise);
+    ggml_tensor* noise_scaling(float sigma, ggml_tensor* noise, ggml_tensor* latent) override {
+        ggml_ext_tensor_scale_inplace(noise, sigma);
+        ggml_ext_tensor_add_inplace(latent, noise);
         return latent;
     }
 
-    ggml_tensor* inverse_noise_scaling(float sigma, ggml_tensor* latent) {
+    ggml_tensor* inverse_noise_scaling(float sigma, ggml_tensor* latent) override {
         return latent;
     }
 };
 
 struct CompVisVDenoiser : public CompVisDenoiser {
-    std::vector<float> get_scalings(float sigma) {
+    std::vector<float> get_scalings(float sigma) override {
         float c_skip = sigma_data * sigma_data / (sigma * sigma + sigma_data * sigma_data);
         float c_out  = -sigma * sigma_data / std::sqrt(sigma * sigma + sigma_data * sigma_data);
         float c_in   = 1.0f / std::sqrt(sigma * sigma + sigma_data * sigma_data);
@@ -426,22 +487,21 @@ struct EDMVDenoiser : public CompVisVDenoiser {
 
     EDMVDenoiser(float min_sigma = 0.002, float max_sigma = 120.0)
         : min_sigma(min_sigma), max_sigma(max_sigma) {
-        scheduler = std::make_shared<ExponentialSchedule>();
     }
 
-    float t_to_sigma(float t) {
+    float t_to_sigma(float t) override {
         return std::exp(t * 4 / (float)TIMESTEPS);
     }
 
-    float sigma_to_t(float s) {
+    float sigma_to_t(float s) override {
         return 0.25 * std::log(s);
     }
 
-    float sigma_min() {
+    float sigma_min() override {
         return min_sigma;
     }
 
-    float sigma_max() {
+    float sigma_max() override {
         return max_sigma;
     }
 };
@@ -470,24 +530,24 @@ struct DiscreteFlowDenoiser : public Denoiser {
         }
     }
 
-    float sigma_min() {
+    float sigma_min() override {
         return sigmas[0];
     }
 
-    float sigma_max() {
+    float sigma_max() override {
         return sigmas[TIMESTEPS - 1];
     }
 
-    float sigma_to_t(float sigma) {
+    float sigma_to_t(float sigma) override {
         return sigma * 1000.f;
     }
 
-    float t_to_sigma(float t) {
+    float t_to_sigma(float t) override {
         t = t + 1;
         return time_snr_shift(shift, t / 1000.f);
     }
 
-    std::vector<float> get_scalings(float sigma) {
+    std::vector<float> get_scalings(float sigma) override {
         float c_skip = 1.0f;
         float c_out  = -sigma;
         float c_in   = 1.0f;
@@ -495,15 +555,15 @@ struct DiscreteFlowDenoiser : public Denoiser {
     }
 
     // this function will modify noise/latent
-    ggml_tensor* noise_scaling(float sigma, ggml_tensor* noise, ggml_tensor* latent) {
-        ggml_tensor_scale(noise, sigma);
-        ggml_tensor_scale(latent, 1.0f - sigma);
-        ggml_tensor_add(latent, noise);
+    ggml_tensor* noise_scaling(float sigma, ggml_tensor* noise, ggml_tensor* latent) override {
+        ggml_ext_tensor_scale_inplace(noise, sigma);
+        ggml_ext_tensor_scale_inplace(latent, 1.0f - sigma);
+        ggml_ext_tensor_add_inplace(latent, noise);
         return latent;
     }
 
-    ggml_tensor* inverse_noise_scaling(float sigma, ggml_tensor* latent) {
-        ggml_tensor_scale(latent, 1.0f / (1.0f - sigma));
+    ggml_tensor* inverse_noise_scaling(float sigma, ggml_tensor* latent) override {
+        ggml_ext_tensor_scale_inplace(latent, 1.0f / (1.0f - sigma));
         return latent;
     }
 };
@@ -522,31 +582,35 @@ struct FluxFlowDenoiser : public Denoiser {
         set_parameters(shift);
     }
 
-    void set_parameters(float shift = 1.15f) {
+    void set_shift(float shift) {
         this->shift = shift;
-        for (int i = 1; i < TIMESTEPS + 1; i++) {
-            sigmas[i - 1] = t_to_sigma(i / TIMESTEPS * TIMESTEPS);
+    }
+
+    void set_parameters(float shift) {
+        set_shift(shift);
+        for (int i = 0; i < TIMESTEPS; i++) {
+            sigmas[i] = t_to_sigma(i);
         }
     }
 
-    float sigma_min() {
+    float sigma_min() override {
         return sigmas[0];
     }
 
-    float sigma_max() {
+    float sigma_max() override {
         return sigmas[TIMESTEPS - 1];
     }
 
-    float sigma_to_t(float sigma) {
+    float sigma_to_t(float sigma) override {
         return sigma;
     }
 
-    float t_to_sigma(float t) {
+    float t_to_sigma(float t) override {
         t = t + 1;
         return flux_time_shift(shift, 1.0f, t / TIMESTEPS);
     }
 
-    std::vector<float> get_scalings(float sigma) {
+    std::vector<float> get_scalings(float sigma) override {
         float c_skip = 1.0f;
         float c_out  = -sigma;
         float c_in   = 1.0f;
@@ -554,19 +618,51 @@ struct FluxFlowDenoiser : public Denoiser {
     }
 
     // this function will modify noise/latent
-    ggml_tensor* noise_scaling(float sigma, ggml_tensor* noise, ggml_tensor* latent) {
-        ggml_tensor_scale(noise, sigma);
-        ggml_tensor_scale(latent, 1.0f - sigma);
-        ggml_tensor_add(latent, noise);
+    ggml_tensor* noise_scaling(float sigma, ggml_tensor* noise, ggml_tensor* latent) override {
+        ggml_ext_tensor_scale_inplace(noise, sigma);
+        ggml_ext_tensor_scale_inplace(latent, 1.0f - sigma);
+        ggml_ext_tensor_add_inplace(latent, noise);
         return latent;
     }
 
-    ggml_tensor* inverse_noise_scaling(float sigma, ggml_tensor* latent) {
-        ggml_tensor_scale(latent, 1.0f / (1.0f - sigma));
+    ggml_tensor* inverse_noise_scaling(float sigma, ggml_tensor* latent) override {
+        ggml_ext_tensor_scale_inplace(latent, 1.0f / (1.0f - sigma));
         return latent;
     }
 };
 
+struct Flux2FlowDenoiser : public FluxFlowDenoiser {
+    Flux2FlowDenoiser() = default;
+
+    float compute_empirical_mu(uint32_t n, int image_seq_len) {
+        const float a1 = 8.73809524e-05f;
+        const float b1 = 1.89833333f;
+        const float a2 = 0.00016927f;
+        const float b2 = 0.45666666f;
+
+        if (image_seq_len > 4300) {
+            float mu = a2 * image_seq_len + b2;
+            return mu;
+        }
+
+        float m_200 = a2 * image_seq_len + b2;
+        float m_10  = a1 * image_seq_len + b1;
+
+        float a  = (m_200 - m_10) / 190.0f;
+        float b  = m_200 - 200.0f * a;
+        float mu = a * n + b;
+
+        return mu;
+    }
+
+    std::vector<float> get_sigmas(uint32_t n, int image_seq_len, scheduler_t scheduler_type, SDVersion version) override {
+        float mu = compute_empirical_mu(n, image_seq_len);
+        LOG_DEBUG("Flux2FlowDenoiser: set shift to %.3f", mu);
+        set_shift(mu);
+        return Denoiser::get_sigmas(n, image_seq_len, scheduler_type, version);
+    }
+};
+
 typedef std::function<ggml_tensor*(ggml_tensor*, float, int)> denoise_cb_t;
 
 // k diffusion reverse ODE: dx = (x - D(x;\sigma)) / \sigma dt; \sigma(t) = t
@@ -580,7 +676,7 @@ static void sample_k_diffusion(sample_method_t method,
     size_t steps = sigmas.size() - 1;
     // sample_euler_ancestral
     switch (method) {
-        case EULER_A: {
+        case EULER_A_SAMPLE_METHOD: {
             struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, x);
             struct ggml_tensor* d     = ggml_dup_tensor(work_ctx, x);
 
@@ -620,7 +716,7 @@ static void sample_k_diffusion(sample_method_t method,
 
                 if (sigmas[i + 1] > 0) {
                     // x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
-                    ggml_tensor_set_f32_randn(noise, rng);
+                    ggml_ext_im_set_randn_f32(noise, rng);
                     // noise = load_tensor_from_file(work_ctx, "./rand" + std::to_string(i+1) + ".bin");
                     {
                         float* vec_x     = (float*)x->data;
@@ -633,7 +729,7 @@ static void sample_k_diffusion(sample_method_t method,
                 }
             }
         } break;
-        case EULER:  // Implemented without any sigma churn
+        case EULER_SAMPLE_METHOD:  // Implemented without any sigma churn
         {
             struct ggml_tensor* d = ggml_dup_tensor(work_ctx, x);
 
@@ -666,7 +762,7 @@ static void sample_k_diffusion(sample_method_t method,
                 }
             }
         } break;
-        case HEUN: {
+        case HEUN_SAMPLE_METHOD: {
             struct ggml_tensor* d  = ggml_dup_tensor(work_ctx, x);
             struct ggml_tensor* x2 = ggml_dup_tensor(work_ctx, x);
 
@@ -716,7 +812,7 @@ static void sample_k_diffusion(sample_method_t method,
                 }
             }
         } break;
-        case DPM2: {
+        case DPM2_SAMPLE_METHOD: {
             struct ggml_tensor* d  = ggml_dup_tensor(work_ctx, x);
             struct ggml_tensor* x2 = ggml_dup_tensor(work_ctx, x);
 
@@ -768,7 +864,7 @@ static void sample_k_diffusion(sample_method_t method,
             }
 
         } break;
-        case DPMPP2S_A: {
+        case DPMPP2S_A_SAMPLE_METHOD: {
             struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, x);
             struct ggml_tensor* x2    = ggml_dup_tensor(work_ctx, x);
 
@@ -820,7 +916,7 @@ static void sample_k_diffusion(sample_method_t method,
 
                 // Noise addition
                 if (sigmas[i + 1] > 0) {
-                    ggml_tensor_set_f32_randn(noise, rng);
+                    ggml_ext_im_set_randn_f32(noise, rng);
                     {
                         float* vec_x     = (float*)x->data;
                         float* vec_noise = (float*)noise->data;
@@ -832,7 +928,7 @@ static void sample_k_diffusion(sample_method_t method,
                 }
             }
         } break;
-        case DPMPP2M:  // DPM++ (2M) from Karras et al (2022)
+        case DPMPP2M_SAMPLE_METHOD:  // DPM++ (2M) from Karras et al (2022)
         {
             struct ggml_tensor* old_denoised = ggml_dup_tensor(work_ctx, x);
 
@@ -871,7 +967,7 @@ static void sample_k_diffusion(sample_method_t method,
                 }
             }
         } break;
-        case DPMPP2Mv2:  // Modified DPM++ (2M) from https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8457
+        case DPMPP2Mv2_SAMPLE_METHOD:  // Modified DPM++ (2M) from https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8457
         {
             struct ggml_tensor* old_denoised = ggml_dup_tensor(work_ctx, x);
 
@@ -914,7 +1010,7 @@ static void sample_k_diffusion(sample_method_t method,
                 }
             }
         } break;
-        case IPNDM:  // iPNDM sampler from https://github.com/zju-pi/diff-sampler/tree/main/diff-solvers-main
+        case IPNDM_SAMPLE_METHOD:  // iPNDM sampler from https://github.com/zju-pi/diff-sampler/tree/main/diff-solvers-main
         {
             int max_order       = 4;
             ggml_tensor* x_next = x;
@@ -989,7 +1085,7 @@ static void sample_k_diffusion(sample_method_t method,
                 }
             }
         } break;
-        case IPNDM_V:  // iPNDM_v sampler from https://github.com/zju-pi/diff-sampler/tree/main/diff-solvers-main
+        case IPNDM_V_SAMPLE_METHOD:  // iPNDM_v sampler from https://github.com/zju-pi/diff-sampler/tree/main/diff-solvers-main
         {
             int max_order = 4;
             std::vector<ggml_tensor*> buffer_model;
@@ -1063,7 +1159,7 @@ static void sample_k_diffusion(sample_method_t method,
                 d_cur = ggml_dup_tensor(work_ctx, x_next);
             }
         } break;
-        case LCM:  // Latent Consistency Models
+        case LCM_SAMPLE_METHOD:  // Latent Consistency Models
         {
             struct ggml_tensor* noise = ggml_dup_tensor(work_ctx, x);
             struct ggml_tensor* d     = ggml_dup_tensor(work_ctx, x);
@@ -1085,7 +1181,7 @@ static void sample_k_diffusion(sample_method_t method,
 
                 if (sigmas[i + 1] > 0) {
                     // x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1])
-                    ggml_tensor_set_f32_randn(noise, rng);
+                    ggml_ext_im_set_randn_f32(noise, rng);
                     // noise = load_tensor_from_file(res_ctx, "./rand" + std::to_string(i+1) + ".bin");
                     {
                         float* vec_x     = (float*)x->data;
@@ -1098,8 +1194,8 @@ static void sample_k_diffusion(sample_method_t method,
                 }
             }
         } break;
-        case DDIM_TRAILING:  // Denoising Diffusion Implicit Models
-                             // with the "trailing" timestep spacing
+        case DDIM_TRAILING_SAMPLE_METHOD:  // Denoising Diffusion Implicit Models
+                                           // with the "trailing" timestep spacing
         {
             // See J. Song et al., "Denoising Diffusion Implicit
             // Models", arXiv:2010.02502 [cs.LG]
@@ -1109,7 +1205,7 @@ static void sample_k_diffusion(sample_method_t method,
             // end beta) (which unfortunately k-diffusion's data
             // structure hides from the denoiser), and the sigmas are
             // also needed to invert the behavior of CompVisDenoiser
-            // (k-diffusion's LMSDiscreteScheduler)
+            // (k-diffusion's LMSDiscreteSchedulerr)
             float beta_start = 0.00085f;
             float beta_end   = 0.0120f;
             std::vector<double> alphas_cumprod;
@@ -1137,7 +1233,7 @@ static void sample_k_diffusion(sample_method_t method,
 
             for (int i = 0; i < steps; i++) {
                 // The "trailing" DDIM timestep, see S. Lin et al.,
-                // "Common Diffusion Noise Schedules and Sample Steps
+                // "Common Diffusion Noise Schedulers and Sample Steps
                 // are Flawed", arXiv:2305.08891 [cs], p. 4, Table
                 // 2. Most variables below follow Diffusers naming
                 //
@@ -1276,7 +1372,7 @@ static void sample_k_diffusion(sample_method_t method,
                     }
                 }
                 if (eta > 0) {
-                    ggml_tensor_set_f32_randn(variance_noise, rng);
+                    ggml_ext_im_set_randn_f32(variance_noise, rng);
                     float* vec_variance_noise =
                         (float*)variance_noise->data;
                     float* vec_x = (float*)x->data;
@@ -1292,8 +1388,8 @@ static void sample_k_diffusion(sample_method_t method,
                 // factor c_in.
             }
         } break;
-        case TCD:  // Strategic Stochastic Sampling (Algorithm 4) in
-                   // Trajectory Consistency Distillation
+        case TCD_SAMPLE_METHOD:  // Strategic Stochastic Sampling (Algorithm 4) in
+                                 // Trajectory Consistency Distillation
         {
             // See J. Zheng et al., "Trajectory Consistency
             // Distillation: Improved Latent Consistency Distillation
@@ -1444,7 +1540,7 @@ static void sample_k_diffusion(sample_method_t method,
                 if (eta > 0 && i != steps - 1) {
                     // In this case, x is still pred_noised_sample,
                     // continue in-place
-                    ggml_tensor_set_f32_randn(noise, rng);
+                    ggml_ext_im_set_randn_f32(noise, rng);
                     float* vec_x     = (float*)x->data;
                     float* vec_noise = (float*)noise->data;
                     for (int j = 0; j < ggml_nelements(x); j++) {

diffusion_model.hpp

@@ -8,18 +8,18 @@
 #include "wan.hpp"
 
 struct DiffusionParams {
-    struct ggml_tensor* x                     = NULL;
-    struct ggml_tensor* timesteps             = NULL;
-    struct ggml_tensor* context               = NULL;
-    struct ggml_tensor* c_concat              = NULL;
-    struct ggml_tensor* y                     = NULL;
-    struct ggml_tensor* guidance              = NULL;
+    struct ggml_tensor* x                     = nullptr;
+    struct ggml_tensor* timesteps             = nullptr;
+    struct ggml_tensor* context               = nullptr;
+    struct ggml_tensor* c_concat              = nullptr;
+    struct ggml_tensor* y                     = nullptr;
+    struct ggml_tensor* guidance              = nullptr;
     std::vector<ggml_tensor*> ref_latents     = {};
     bool increase_ref_index                   = false;
     int num_video_frames                      = -1;
     std::vector<struct ggml_tensor*> controls = {};
     float control_strength                    = 0.f;
-    struct ggml_tensor* vace_context          = NULL;
+    struct ggml_tensor* vace_context          = nullptr;
     float vace_strength                       = 1.f;
     std::vector<int> skip_layers              = {};
 };
@@ -28,14 +28,16 @@ struct DiffusionModel {
     virtual std::string get_desc()                                                      = 0;
     virtual void compute(int n_threads,
                          DiffusionParams diffusion_params,
-                         struct ggml_tensor** output     = NULL,
-                         struct ggml_context* output_ctx = NULL)                        = 0;
+                         struct ggml_tensor** output     = nullptr,
+                         struct ggml_context* output_ctx = nullptr)                     = 0;
     virtual void alloc_params_buffer()                                                  = 0;
     virtual void free_params_buffer()                                                   = 0;
     virtual void free_compute_buffer()                                                  = 0;
     virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) = 0;
     virtual size_t get_params_buffer_size()                                             = 0;
-    virtual int64_t get_adm_in_channels()                                               = 0;
+    virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter){};
+    virtual int64_t get_adm_in_channels()             = 0;
+    virtual void set_flash_attn_enabled(bool enabled) = 0;
 };
 
 struct UNetModel : public DiffusionModel {
@@ -43,44 +45,51 @@ struct UNetModel : public DiffusionModel {
 
     UNetModel(ggml_backend_t backend,
               bool offload_params_to_cpu,
-              const String2GGMLType& tensor_types = {},
-              SDVersion version                   = VERSION_SD1,
-              bool flash_attn                     = false)
-        : unet(backend, offload_params_to_cpu, tensor_types, "model.diffusion_model", version, flash_attn) {
+              const String2TensorStorage& tensor_storage_map = {},
+              SDVersion version                              = VERSION_SD1)
+        : unet(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model", version) {
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return unet.get_desc();
     }
 
-    void alloc_params_buffer() {
+    void alloc_params_buffer() override {
         unet.alloc_params_buffer();
     }
 
-    void free_params_buffer() {
+    void free_params_buffer() override {
         unet.free_params_buffer();
     }
 
-    void free_compute_buffer() {
+    void free_compute_buffer() override {
         unet.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
         unet.get_param_tensors(tensors, "model.diffusion_model");
     }
 
-    size_t get_params_buffer_size() {
+    size_t get_params_buffer_size() override {
         return unet.get_params_buffer_size();
     }
 
-    int64_t get_adm_in_channels() {
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        unet.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
         return unet.unet.adm_in_channels;
     }
 
+    void set_flash_attn_enabled(bool enabled) {
+        unet.set_flash_attention_enabled(enabled);
+    }
+
     void compute(int n_threads,
                  DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = NULL,
-                 struct ggml_context* output_ctx = NULL) {
+                 struct ggml_tensor** output     = nullptr,
+                 struct ggml_context* output_ctx = nullptr) override {
         return unet.compute(n_threads,
                             diffusion_params.x,
                             diffusion_params.timesteps,
@@ -98,43 +107,50 @@ struct MMDiTModel : public DiffusionModel {
 
     MMDiTModel(ggml_backend_t backend,
                bool offload_params_to_cpu,
-               bool flash_attn                     = false,
-               const String2GGMLType& tensor_types = {})
-        : mmdit(backend, offload_params_to_cpu, flash_attn, tensor_types, "model.diffusion_model") {
+               const String2TensorStorage& tensor_storage_map = {})
+        : mmdit(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model") {
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return mmdit.get_desc();
     }
 
-    void alloc_params_buffer() {
+    void alloc_params_buffer() override {
         mmdit.alloc_params_buffer();
     }
 
-    void free_params_buffer() {
+    void free_params_buffer() override {
         mmdit.free_params_buffer();
     }
 
-    void free_compute_buffer() {
+    void free_compute_buffer() override {
         mmdit.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
         mmdit.get_param_tensors(tensors, "model.diffusion_model");
     }
 
-    size_t get_params_buffer_size() {
+    size_t get_params_buffer_size() override {
         return mmdit.get_params_buffer_size();
     }
 
-    int64_t get_adm_in_channels() {
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        mmdit.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
         return 768 + 1280;
     }
 
+    void set_flash_attn_enabled(bool enabled) {
+        mmdit.set_flash_attention_enabled(enabled);
+    }
+
     void compute(int n_threads,
                  DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = NULL,
-                 struct ggml_context* output_ctx = NULL) {
+                 struct ggml_tensor** output     = nullptr,
+                 struct ggml_context* output_ctx = nullptr) override {
         return mmdit.compute(n_threads,
                              diffusion_params.x,
                              diffusion_params.timesteps,
@@ -151,45 +167,52 @@ struct FluxModel : public DiffusionModel {
 
     FluxModel(ggml_backend_t backend,
               bool offload_params_to_cpu,
-              const String2GGMLType& tensor_types = {},
-              SDVersion version                   = VERSION_FLUX,
-              bool flash_attn                     = false,
-              bool use_mask                       = false)
-        : flux(backend, offload_params_to_cpu, tensor_types, "model.diffusion_model", version, flash_attn, use_mask) {
+              const String2TensorStorage& tensor_storage_map = {},
+              SDVersion version                              = VERSION_FLUX,
+              bool use_mask                                  = false)
+        : flux(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model", version, use_mask) {
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return flux.get_desc();
     }
 
-    void alloc_params_buffer() {
+    void alloc_params_buffer() override {
         flux.alloc_params_buffer();
     }
 
-    void free_params_buffer() {
+    void free_params_buffer() override {
         flux.free_params_buffer();
     }
 
-    void free_compute_buffer() {
+    void free_compute_buffer() override {
         flux.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
         flux.get_param_tensors(tensors, "model.diffusion_model");
     }
 
-    size_t get_params_buffer_size() {
+    size_t get_params_buffer_size() override {
         return flux.get_params_buffer_size();
     }
 
-    int64_t get_adm_in_channels() {
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        flux.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
         return 768;
     }
 
+    void set_flash_attn_enabled(bool enabled) {
+        flux.set_flash_attention_enabled(enabled);
+    }
+
     void compute(int n_threads,
                  DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = NULL,
-                 struct ggml_context* output_ctx = NULL) {
+                 struct ggml_tensor** output     = nullptr,
+                 struct ggml_context* output_ctx = nullptr) override {
         return flux.compute(n_threads,
                             diffusion_params.x,
                             diffusion_params.timesteps,
@@ -211,52 +234,59 @@ struct WanModel : public DiffusionModel {
 
     WanModel(ggml_backend_t backend,
              bool offload_params_to_cpu,
-             const String2GGMLType& tensor_types = {},
-             const std::string prefix            = "model.diffusion_model",
-             SDVersion version                   = VERSION_WAN2,
-             bool flash_attn                     = false)
-        : prefix(prefix), wan(backend, offload_params_to_cpu, tensor_types, prefix, version, flash_attn) {
+             const String2TensorStorage& tensor_storage_map = {},
+             const std::string prefix                       = "model.diffusion_model",
+             SDVersion version                              = VERSION_WAN2)
+        : prefix(prefix), wan(backend, offload_params_to_cpu, tensor_storage_map, prefix, version) {
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return wan.get_desc();
     }
 
-    void alloc_params_buffer() {
+    void alloc_params_buffer() override {
         wan.alloc_params_buffer();
     }
 
-    void free_params_buffer() {
+    void free_params_buffer() override {
         wan.free_params_buffer();
     }
 
-    void free_compute_buffer() {
+    void free_compute_buffer() override {
         wan.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
         wan.get_param_tensors(tensors, prefix);
     }
 
-    size_t get_params_buffer_size() {
+    size_t get_params_buffer_size() override {
         return wan.get_params_buffer_size();
     }
 
-    int64_t get_adm_in_channels() {
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        wan.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
         return 768;
     }
 
+    void set_flash_attn_enabled(bool enabled) {
+        wan.set_flash_attention_enabled(enabled);
+    }
+
     void compute(int n_threads,
                  DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = NULL,
-                 struct ggml_context* output_ctx = NULL) {
+                 struct ggml_tensor** output     = nullptr,
+                 struct ggml_context* output_ctx = nullptr) override {
         return wan.compute(n_threads,
                            diffusion_params.x,
                            diffusion_params.timesteps,
                            diffusion_params.context,
                            diffusion_params.y,
                            diffusion_params.c_concat,
-                           NULL,
+                           nullptr,
                            diffusion_params.vace_context,
                            diffusion_params.vace_strength,
                            output,
@@ -270,45 +300,52 @@ struct QwenImageModel : public DiffusionModel {
 
     QwenImageModel(ggml_backend_t backend,
                    bool offload_params_to_cpu,
-                   const String2GGMLType& tensor_types = {},
-                   const std::string prefix            = "model.diffusion_model",
-                   SDVersion version                   = VERSION_QWEN_IMAGE,
-                   bool flash_attn                     = false)
-        : prefix(prefix), qwen_image(backend, offload_params_to_cpu, tensor_types, prefix, version, flash_attn) {
+                   const String2TensorStorage& tensor_storage_map = {},
+                   const std::string prefix                       = "model.diffusion_model",
+                   SDVersion version                              = VERSION_QWEN_IMAGE)
+        : prefix(prefix), qwen_image(backend, offload_params_to_cpu, tensor_storage_map, prefix, version) {
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return qwen_image.get_desc();
     }
 
-    void alloc_params_buffer() {
+    void alloc_params_buffer() override {
         qwen_image.alloc_params_buffer();
     }
 
-    void free_params_buffer() {
+    void free_params_buffer() override {
         qwen_image.free_params_buffer();
     }
 
-    void free_compute_buffer() {
+    void free_compute_buffer() override {
         qwen_image.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
         qwen_image.get_param_tensors(tensors, prefix);
     }
 
-    size_t get_params_buffer_size() {
+    size_t get_params_buffer_size() override {
         return qwen_image.get_params_buffer_size();
     }
 
-    int64_t get_adm_in_channels() {
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        qwen_image.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
         return 768;
     }
 
+    void set_flash_attn_enabled(bool enabled) {
+        qwen_image.set_flash_attention_enabled(enabled);
+    }
+
     void compute(int n_threads,
                  DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = NULL,
-                 struct ggml_context* output_ctx = NULL) {
+                 struct ggml_tensor** output     = nullptr,
+                 struct ggml_context* output_ctx = nullptr) override {
         return qwen_image.compute(n_threads,
                                   diffusion_params.x,
                                   diffusion_params.timesteps,

docs/build.md

@@ -157,7 +157,7 @@ ninja
 
 ## Build with SYCL
 
-Using SYCL makes the computation run on the Intel GPU. Please make sure you have installed the related driver and [Intel® oneAPI Base toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) before start. More details and steps can refer to [llama.cpp SYCL backend](https://github.com/ggerganov/llama.cpp/blob/master/docs/backend/SYCL.md#linux).
+Using SYCL makes the computation run on the Intel GPU. Please make sure you have installed the related driver and [Intel® oneAPI Base toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) before start. More details and steps can refer to [llama.cpp SYCL backend](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/SYCL.md#linux).
 
 ```shell
 # Export relevant ENV variables

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 +1,99 @@
+# Running distilled models: SSD1B and SDx.x with tiny U-Nets
+
+## Preface 
+
+These models feature a reduced U-Net architecture. Unlike standard SDXL models, the SSD-1B U-Net contains only one middle block and fewer attention layers in its up- and down-blocks, resulting in significantly smaller file sizes. Using these models can reduce inference time by more than 33%. For more details, refer to Segmind's paper: https://arxiv.org/abs/2401.02677v1.
+Similarly, SD1.x- and SD2.x-style models with a tiny U-Net consist of only 6 U-Net blocks, leading to very small files and time savings of up to 50%. For more information, see the paper: https://arxiv.org/pdf/2305.15798.pdf.
+
+## SSD1B
+
+Note that not all of these models follow the standard parameter naming conventions. However, several useful SSD-1B models are 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
+
+Useful LoRAs are also 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
+
+These files can be used out-of-the-box, unlike the models described in the next section.
+
+
+## SD1.x, SD2.x with tiny U-Nets
+
+These models require conversion before use. You will need a Python script provided by the diffusers team, available on GitHub:
+
+ * https://raw.githubusercontent.com/huggingface/diffusers/refs/heads/main/scripts/convert_diffusers_to_original_stable_diffusion.py
+
+### SD2.x
+
+NotaAI provides the following model online:
+
+* https://huggingface.co/nota-ai/bk-sdm-v2-tiny
+
+Creating a .safetensors file involves two steps. First, run this short Python script to download the model from Hugging Face:
+
+```python
+from diffusers import StableDiffusionPipeline
+pipe = StableDiffusionPipeline.from_pretrained("nota-ai/bk-sdm-v2-tiny",cache_dir="./")
+```
+
+Second, create the .safetensors file by running:
+
+```bash
+python convert_diffusers_to_original_stable_diffusion.py \
+      --model_path  models--nota-ai--bk-sdm-v2-tiny/snapshots/68277af553777858cd47e133f92e4db47321bc74 \
+      --checkpoint_path bk-sdm-v2-tiny.safetensors --half --use_safetensors
+```
+
+This will generate the **file bk-sdm-v2-tiny.safetensors**, which is now ready for use with sd.cpp.
+
+### SD1.x
+
+Several Tiny SD 1.x models are 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 also require conversion, partly because some tensors are stored in a non-contiguous manner. To create a usable checkpoint file, follow these simple steps:
+Download and prepare the model using Python: 
+
+##### 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)
+```
+
+##### Run the 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 for use with sd.cpp. You can follow a similar process for the other models mentioned above.
+
+
+### Another available .ckpt file:
+
+ * https://huggingface.co/ClashSAN/small-sd/resolve/main/tinySDdistilled.ckpt
+
+To use this file, you must first adjust its non-contiguous tensors:
+
+```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")
+```

docs/flux2.md

@@ -0,0 +1,21 @@
+# How to Use
+
+## Download weights
+
+- Download FLUX.2-dev
+    - gguf: https://huggingface.co/city96/FLUX.2-dev-gguf/tree/main
+- Download vae
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/main
+- Download Mistral-Small-3.2-24B-Instruct-2506-GGUF
+    - gguf: https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF/tree/main
+
+## Examples
+
+```
+.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux2-dev-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf -r .\kontext_input.png -p "change 'flux.cpp' to 'flux2-dev.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu
+```
+
+<img alt="flux2 example" src="../assets/flux2/example.png" />
+
+
+

docs/lora.md

@@ -12,38 +12,15 @@ Here's a simple example:
 
 `../models/marblesh.safetensors` or `../models/marblesh.ckpt` will be applied to the model
 
-# Support matrix
+# Lora Apply Mode
 
-> ℹ️ CUDA `get_rows` support is defined here:  
-> [ggml-org/ggml/src/ggml-cuda/getrows.cu#L156](https://github.com/ggml-org/ggml/blob/7dee1d6a1e7611f238d09be96738388da97c88ed/src/ggml-cuda/getrows.cu#L156)  
-> Currently only the basic types + Q4/Q5/Q8 are implemented. K-quants are **not** supported.
+There are two ways to apply LoRA: **immediately** and **at_runtime**. You can specify it using the `--lora-apply-mode` parameter.
 
-NOTE: The other backends may have different support.
+By default, the mode is selected automatically:
+
+* If the model weights contain any quantized parameters, the **at_runtime** mode is used;
+* Otherwise, the **immediately** mode is used.
+
+The **immediately** mode may have precision and compatibility issues with quantized parameters, but it usually offers faster inference speed and, in some cases, lower memory usage.
+In contrast, the **at_runtime** mode provides better compatibility and higher precision, but inference may be slower and memory usage may be higher in some cases.
 
-| Quant / Type | CUDA | Vulkan |
-|--------------|------|--------|
-| F32          | ✔️   | ✔️   |
-| F16          | ✔️   | ✔️   |
-| BF16         | ✔️   | ✔️   |
-| I32          | ✔️   | ❌   |
-| Q4_0         | ✔️   | ✔️   |
-| Q4_1         | ✔️   | ✔️   |
-| Q5_0         | ✔️   | ✔️   |
-| Q5_1         | ✔️   | ✔️   |
-| Q8_0         | ✔️   | ✔️   |
-| Q2_K         | ❌   | ❌   |
-| Q3_K         | ❌   | ❌   |
-| Q4_K         | ❌   | ❌   |
-| Q5_K         | ❌   | ❌   |
-| Q6_K         | ❌   | ❌   |
-| Q8_K         | ❌   | ❌   |
-| IQ1_S        | ❌   | ✔️   |
-| IQ1_M        | ❌   | ✔️   |
-| IQ2_XXS      | ❌   | ✔️   |
-| IQ2_XS       | ❌   | ✔️   |
-| IQ2_S        | ❌   | ✔️   |
-| IQ3_XXS      | ❌   | ✔️   |
-| IQ3_S        | ❌   | ✔️   |
-| IQ4_XS       | ❌   | ✔️   |
-| IQ4_NL       | ❌   | ✔️   |
-| MXFP4        | ❌   | ✔️   |

docs/photo_maker.md

@@ -40,7 +40,7 @@ Running PMV2 is now a two-step process:
 ```
 python face_detect.py input_image_dir
 ```
-An ```id_embeds.safetensors``` file will be generated in ```input_images_dir```
+An ```id_embeds.bin``` file will be generated in ```input_images_dir```
 
 **Note: this step is only needed to run once; the same ```id_embeds``` can be reused**
 
@@ -48,6 +48,6 @@ An ```id_embeds.safetensors``` file will be generated in ```input_images_dir```
 
   You can download ```photomaker-v2.safetensors``` from [here](https://huggingface.co/bssrdf/PhotoMakerV2)
 
-- All the command line parameters from Version 1 remain the same for Version 2
+- All the command line parameters from Version 1 remain the same for Version 2 plus one extra pointing to a valid ```id_embeds``` file:  --pm-id-embed-path [path_to__id_embeds.bin] 
 
 

docs/qwen_image.md

@@ -14,7 +14,7 @@
 ## Examples
 
 ```
-.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\qwen-image-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --qwen2vl ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf  -p '一个穿着"QWEN"标志的T恤的中国美女正拿着黑色的马克笔面相镜头微笑。她身后的玻璃板上手写体写着 “一、Qwen-Image的技术路线： 探索视觉生成基础模型的极限，开创理解与生成一体化的未来。二、Qwen-Image的模型特色：1、复杂文字渲染。支持中英渲染、自动布局； 2、精准图像编辑。支持文字编辑、物体增减、风格变换。三、Qwen-Image的未来愿景：赋能专业内容创作、助力生成式AI发展。”' --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu -H 1024 -W 1024 --diffusion-fa --flow-shift 3
+.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\qwen-image-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf  -p '一个穿着"QWEN"标志的T恤的中国美女正拿着黑色的马克笔面相镜头微笑。她身后的玻璃板上手写体写着 “一、Qwen-Image的技术路线： 探索视觉生成基础模型的极限，开创理解与生成一体化的未来。二、Qwen-Image的模型特色：1、复杂文字渲染。支持中英渲染、自动布局； 2、精准图像编辑。支持文字编辑、物体增减、风格变换。三、Qwen-Image的未来愿景：赋能专业内容创作、助力生成式AI发展。”' --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu -H 1024 -W 1024 --diffusion-fa --flow-shift 3
 ```
 
 <img alt="qwen example" src="../assets/qwen/example.png" />

docs/qwen_image_edit.md

@@ -20,7 +20,7 @@
 ### Qwen Image Edit
 
 ```
-.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen_Image_Edit-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --qwen2vl ..\..\ComfyUI\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --seed 1118877715456453
+.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen_Image_Edit-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --seed 1118877715456453
 ```
 
 <img alt="qwen_image_edit" src="../assets/qwen/qwen_image_edit.png" />
@@ -29,7 +29,7 @@
 ### Qwen Image Edit 2509
 
 ```
-.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen-Image-Edit-2509-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --qwen2vl ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf --qwen2vl_vision ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct.mmproj-Q8_0.gguf --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'Qwen Image Edit 2509'"
+.\bin\Release\sd.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen-Image-Edit-2509-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf --llm_vision ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct.mmproj-Q8_0.gguf --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'Qwen Image Edit 2509'"
 ```
 
 <img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2509.png" />

docs/taesd.md

@@ -7,7 +7,7 @@ You can use TAESD to accelerate the decoding of latent images by following these
 Or curl
 
 ```bash
-curl -L -O https://huggingface.co/madebyollin/taesd/blob/main/diffusion_pytorch_model.safetensors
+curl -L -O https://huggingface.co/madebyollin/taesd/resolve/main/diffusion_pytorch_model.safetensors
 ```
 
 - Specify the model path using the `--taesd PATH` parameter. example:

easycache.hpp

@@ -0,0 +1,265 @@
+#include <cmath>
+#include <limits>
+#include <unordered_map>
+#include <vector>
+
+#include "denoiser.hpp"
+#include "ggml_extend.hpp"
+
+struct EasyCacheConfig {
+    bool enabled          = false;
+    float reuse_threshold = 0.2f;
+    float start_percent   = 0.15f;
+    float end_percent     = 0.95f;
+};
+
+struct EasyCacheCacheEntry {
+    std::vector<float> diff;
+};
+
+struct EasyCacheState {
+    EasyCacheConfig config;
+    Denoiser* denoiser                  = nullptr;
+    float start_sigma                   = std::numeric_limits<float>::max();
+    float end_sigma                     = 0.0f;
+    bool initialized                    = false;
+    bool initial_step                   = true;
+    bool skip_current_step              = false;
+    bool step_active                    = false;
+    const SDCondition* anchor_condition = nullptr;
+    std::unordered_map<const SDCondition*, EasyCacheCacheEntry> cache_diffs;
+    std::vector<float> prev_input;
+    std::vector<float> prev_output;
+    float output_prev_norm                = 0.0f;
+    bool has_prev_input                   = false;
+    bool has_prev_output                  = false;
+    bool has_output_prev_norm             = false;
+    bool has_relative_transformation_rate = false;
+    float relative_transformation_rate    = 0.0f;
+    float cumulative_change_rate          = 0.0f;
+    float last_input_change               = 0.0f;
+    bool has_last_input_change            = false;
+    int total_steps_skipped               = 0;
+    int current_step_index                = -1;
+
+    void reset_runtime() {
+        initial_step      = true;
+        skip_current_step = false;
+        step_active       = false;
+        anchor_condition  = nullptr;
+        cache_diffs.clear();
+        prev_input.clear();
+        prev_output.clear();
+        output_prev_norm                 = 0.0f;
+        has_prev_input                   = false;
+        has_prev_output                  = false;
+        has_output_prev_norm             = false;
+        has_relative_transformation_rate = false;
+        relative_transformation_rate     = 0.0f;
+        cumulative_change_rate           = 0.0f;
+        last_input_change                = 0.0f;
+        has_last_input_change            = false;
+        total_steps_skipped              = 0;
+        current_step_index               = -1;
+    }
+
+    void init(const EasyCacheConfig& cfg, Denoiser* d) {
+        config      = cfg;
+        denoiser    = d;
+        initialized = cfg.enabled && d != nullptr;
+        reset_runtime();
+        if (initialized) {
+            start_sigma = percent_to_sigma(config.start_percent);
+            end_sigma   = percent_to_sigma(config.end_percent);
+        }
+    }
+
+    bool enabled() const {
+        return initialized && config.enabled;
+    }
+
+    float percent_to_sigma(float percent) const {
+        if (!denoiser) {
+            return 0.0f;
+        }
+        if (percent <= 0.0f) {
+            return std::numeric_limits<float>::max();
+        }
+        if (percent >= 1.0f) {
+            return 0.0f;
+        }
+        float t = (1.0f - percent) * (TIMESTEPS - 1);
+        return denoiser->t_to_sigma(t);
+    }
+
+    void begin_step(int step_index, float sigma) {
+        if (!enabled()) {
+            return;
+        }
+        if (step_index == current_step_index) {
+            return;
+        }
+        current_step_index    = step_index;
+        skip_current_step     = false;
+        has_last_input_change = false;
+        step_active           = false;
+        if (sigma > start_sigma) {
+            return;
+        }
+        if (!(sigma > end_sigma)) {
+            return;
+        }
+        step_active = true;
+    }
+
+    bool step_is_active() const {
+        return enabled() && step_active;
+    }
+
+    bool is_step_skipped() const {
+        return enabled() && step_active && skip_current_step;
+    }
+
+    bool has_cache(const SDCondition* cond) const {
+        auto it = cache_diffs.find(cond);
+        return it != cache_diffs.end() && !it->second.diff.empty();
+    }
+
+    void update_cache(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+        EasyCacheCacheEntry& entry = cache_diffs[cond];
+        size_t ne                  = static_cast<size_t>(ggml_nelements(output));
+        entry.diff.resize(ne);
+        float* out_data = (float*)output->data;
+        float* in_data  = (float*)input->data;
+        for (size_t i = 0; i < ne; ++i) {
+            entry.diff[i] = out_data[i] - in_data[i];
+        }
+    }
+
+    void apply_cache(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+        auto it = cache_diffs.find(cond);
+        if (it == cache_diffs.end() || it->second.diff.empty()) {
+            return;
+        }
+        copy_ggml_tensor(output, input);
+        float* out_data                = (float*)output->data;
+        const std::vector<float>& diff = it->second.diff;
+        for (size_t i = 0; i < diff.size(); ++i) {
+            out_data[i] += diff[i];
+        }
+    }
+
+    bool before_condition(const SDCondition* cond,
+                          ggml_tensor* input,
+                          ggml_tensor* output,
+                          float sigma,
+                          int step_index) {
+        if (!enabled() || step_index < 0) {
+            return false;
+        }
+        if (step_index != current_step_index) {
+            begin_step(step_index, sigma);
+        }
+        if (!step_active) {
+            return false;
+        }
+        if (initial_step) {
+            anchor_condition = cond;
+            initial_step     = false;
+        }
+        bool is_anchor = (cond == anchor_condition);
+        if (skip_current_step) {
+            if (has_cache(cond)) {
+                apply_cache(cond, input, output);
+                return true;
+            }
+            return false;
+        }
+        if (!is_anchor) {
+            return false;
+        }
+        if (!has_prev_input || !has_prev_output || !has_cache(cond)) {
+            return false;
+        }
+        size_t ne = static_cast<size_t>(ggml_nelements(input));
+        if (prev_input.size() != ne) {
+            return false;
+        }
+        float* input_data = (float*)input->data;
+        last_input_change = 0.0f;
+        for (size_t i = 0; i < ne; ++i) {
+            last_input_change += std::fabs(input_data[i] - prev_input[i]);
+        }
+        if (ne > 0) {
+            last_input_change /= static_cast<float>(ne);
+        }
+        has_last_input_change = true;
+
+        if (has_output_prev_norm && has_relative_transformation_rate && last_input_change > 0.0f && output_prev_norm > 0.0f) {
+            float approx_output_change_rate = (relative_transformation_rate * last_input_change) / output_prev_norm;
+            cumulative_change_rate += approx_output_change_rate;
+            if (cumulative_change_rate < config.reuse_threshold) {
+                skip_current_step = true;
+                total_steps_skipped++;
+                apply_cache(cond, input, output);
+                return true;
+            } else {
+                cumulative_change_rate = 0.0f;
+            }
+        }
+
+        return false;
+    }
+
+    void after_condition(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+        if (!step_is_active()) {
+            return;
+        }
+        update_cache(cond, input, output);
+        if (cond != anchor_condition) {
+            return;
+        }
+
+        size_t ne      = static_cast<size_t>(ggml_nelements(input));
+        float* in_data = (float*)input->data;
+        prev_input.resize(ne);
+        for (size_t i = 0; i < ne; ++i) {
+            prev_input[i] = in_data[i];
+        }
+        has_prev_input = true;
+
+        float* out_data     = (float*)output->data;
+        float output_change = 0.0f;
+        if (has_prev_output && prev_output.size() == ne) {
+            for (size_t i = 0; i < ne; ++i) {
+                output_change += std::fabs(out_data[i] - prev_output[i]);
+            }
+            if (ne > 0) {
+                output_change /= static_cast<float>(ne);
+            }
+        }
+
+        prev_output.resize(ne);
+        for (size_t i = 0; i < ne; ++i) {
+            prev_output[i] = out_data[i];
+        }
+        has_prev_output = true;
+
+        float mean_abs = 0.0f;
+        for (size_t i = 0; i < ne; ++i) {
+            mean_abs += std::fabs(out_data[i]);
+        }
+        output_prev_norm     = (ne > 0) ? (mean_abs / static_cast<float>(ne)) : 0.0f;
+        has_output_prev_norm = output_prev_norm > 0.0f;
+
+        if (has_last_input_change && last_input_change > 0.0f && output_change > 0.0f) {
+            float rate = output_change / last_input_change;
+            if (std::isfinite(rate)) {
+                relative_transformation_rate     = rate;
+                has_relative_transformation_rate = true;
+            }
+        }
+        cumulative_change_rate = 0.0f;
+        has_last_input_change  = false;
+    }
+};

esrgan.hpp

@@ -27,11 +27,11 @@ public:
         blocks["conv5"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat + 4 * num_grow_ch, num_feat, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* lrelu(struct ggml_context* ctx, struct ggml_tensor* x) {
-        return ggml_leaky_relu(ctx, x, 0.2f, true);
+    struct ggml_tensor* lrelu(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        return ggml_leaky_relu(ctx->ggml_ctx, x, 0.2f, true);
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [n, num_feat, h, w]
         // return: [n, num_feat, h, w]
 
@@ -42,16 +42,16 @@ public:
         auto conv5 = std::dynamic_pointer_cast<Conv2d>(blocks["conv5"]);
 
         auto x1    = lrelu(ctx, conv1->forward(ctx, x));
-        auto x_cat = ggml_concat(ctx, x, x1, 2);
+        auto x_cat = ggml_concat(ctx->ggml_ctx, x, x1, 2);
         auto x2    = lrelu(ctx, conv2->forward(ctx, x_cat));
-        x_cat      = ggml_concat(ctx, x_cat, x2, 2);
+        x_cat      = ggml_concat(ctx->ggml_ctx, x_cat, x2, 2);
         auto x3    = lrelu(ctx, conv3->forward(ctx, x_cat));
-        x_cat      = ggml_concat(ctx, x_cat, x3, 2);
+        x_cat      = ggml_concat(ctx->ggml_ctx, x_cat, x3, 2);
         auto x4    = lrelu(ctx, conv4->forward(ctx, x_cat));
-        x_cat      = ggml_concat(ctx, x_cat, x4, 2);
+        x_cat      = ggml_concat(ctx->ggml_ctx, x_cat, x4, 2);
         auto x5    = conv5->forward(ctx, x_cat);
 
-        x5 = ggml_add(ctx, ggml_scale(ctx, x5, 0.2f), x);
+        x5 = ggml_add(ctx->ggml_ctx, ggml_scale(ctx->ggml_ctx, x5, 0.2f), x);
         return x5;
     }
 };
@@ -64,7 +64,7 @@ public:
         blocks["rdb3"] = std::shared_ptr<GGMLBlock>(new ResidualDenseBlock(num_feat, num_grow_ch));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [n, num_feat, h, w]
         // return: [n, num_feat, h, w]
 
@@ -76,7 +76,7 @@ public:
         out      = rdb2->forward(ctx, out);
         out      = rdb3->forward(ctx, out);
 
-        out = ggml_add(ctx, ggml_scale(ctx, out, 0.2f), x);
+        out = ggml_add(ctx->ggml_ctx, ggml_scale(ctx->ggml_ctx, out, 0.2f), x);
         return out;
     }
 };
@@ -112,11 +112,11 @@ public:
     int get_scale() { return scale; }
     int get_num_block() { return num_block; }
 
-    struct ggml_tensor* lrelu(struct ggml_context* ctx, struct ggml_tensor* x) {
-        return ggml_leaky_relu(ctx, x, 0.2f, true);
+    struct ggml_tensor* lrelu(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        return ggml_leaky_relu(ctx->ggml_ctx, x, 0.2f, true);
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [n, num_in_ch, h, w]
         // return: [n, num_out_ch, h*scale, w*scale]
         auto conv_first = std::dynamic_pointer_cast<Conv2d>(blocks["conv_first"]);
@@ -133,14 +133,14 @@ public:
             body_feat = block->forward(ctx, body_feat);
         }
         body_feat = conv_body->forward(ctx, body_feat);
-        feat      = ggml_add(ctx, feat, body_feat);
+        feat      = ggml_add(ctx->ggml_ctx, feat, body_feat);
         // upsample
         if (scale >= 2) {
             auto conv_up1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up1"]);
-            feat          = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
+            feat          = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx->ggml_ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
             if (scale == 4) {
                 auto conv_up2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up2"]);
-                feat          = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
+                feat          = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx->ggml_ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
             }
         }
         // for all scales
@@ -156,25 +156,12 @@ struct ESRGAN : public GGMLRunner {
 
     ESRGAN(ggml_backend_t backend,
            bool offload_params_to_cpu,
-           const String2GGMLType& tensor_types = {})
+           const String2TensorStorage& tensor_storage_map = {})
         : GGMLRunner(backend, offload_params_to_cpu) {
         // rrdb_net will be created in load_from_file
     }
 
-    void enable_conv2d_direct() {
-        if (!rrdb_net)
-            return;
-        std::vector<GGMLBlock*> blocks;
-        rrdb_net->get_all_blocks(blocks);
-        for (auto block : blocks) {
-            if (block->get_desc() == "Conv2d") {
-                auto conv_block = (Conv2d*)block;
-                conv_block->enable_direct();
-            }
-        }
-    }
-
-    std::string get_desc() {
+    std::string get_desc() override {
         return "esrgan";
     }
 
@@ -182,7 +169,7 @@ struct ESRGAN : public GGMLRunner {
         LOG_INFO("loading esrgan from '%s'", file_path.c_str());
 
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
+        if (!model_loader.init_from_file_and_convert_name(file_path)) {
             LOG_ERROR("init esrgan model loader from file failed: '%s'", file_path.c_str());
             return false;
         }
@@ -357,9 +344,11 @@ struct ESRGAN : public GGMLRunner {
         if (!rrdb_net)
             return nullptr;
         constexpr int kGraphNodes = 1 << 16;  // 65k
-        struct ggml_cgraph* gf    = ggml_new_graph_custom(compute_ctx, kGraphNodes, /*grads*/ false);
+        struct ggml_cgraph* gf    = new_graph_custom(kGraphNodes);
         x                         = to_backend(x);
-        struct ggml_tensor* out   = rrdb_net->forward(compute_ctx, x);
+
+        auto runner_ctx         = get_context();
+        struct ggml_tensor* out = rrdb_net->forward(&runner_ctx, x);
         ggml_build_forward_expand(gf, out);
         return gf;
     }
@@ -367,7 +356,7 @@ struct ESRGAN : public GGMLRunner {
     void compute(const int n_threads,
                  struct ggml_tensor* x,
                  ggml_tensor** output,
-                 ggml_context* output_ctx = NULL) {
+                 ggml_context* output_ctx = nullptr) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
             return build_graph(x);
         };

examples/cli/README.md

@@ -1,114 +1,124 @@
 # Run
 
 ```
-usage: ./bin/sd [arguments]
+usage: ./bin/sd  [options]
 
-arguments:
-  -h, --help                         show this help message and exit
-  -M, --mode [MODE]                  run mode, one of: [img_gen, vid_gen, upscale, convert], default: img_gen
-  -t, --threads N                    number of threads to use during computation (default: -1)
-                                     If threads <= 0, then threads will be set to the number of CPU physical cores
-  --offload-to-cpu                   place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
-  -m, --model [MODEL]                path to full model
-  --diffusion-model                  path to the standalone diffusion model
-  --high-noise-diffusion-model       path to the standalone high noise diffusion model
-  --clip_l                           path to the clip-l text encoder
-  --clip_g                           path to the clip-g text encoder
-  --clip_vision                      path to the clip-vision encoder
-  --t5xxl                            path to the t5xxl text encoder
-  --qwen2vl                          path to the qwen2vl text encoder
-  --qwen2vl_vision                   path to the qwen2vl vit
-  --vae [VAE]                        path to vae
-  --taesd [TAESD_PATH]               path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
-  --control-net [CONTROL_PATH]       path to control net model
-  --embd-dir [EMBEDDING_PATH]        path to embeddings
-  --upscale-model [ESRGAN_PATH]      path to esrgan model. For img_gen mode, upscale images after generate, just RealESRGAN_x4plus_anime_6B supported by now
-  --upscale-repeats                  Run the ESRGAN upscaler this many times (default 1)
-  --type [TYPE]                      weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K)
-                                     If not specified, the default is the type of the weight file
-  --tensor-type-rules [EXPRESSION]   weight type per tensor pattern (example: "^vae\.=f16,model\.=q8_0")
-  --lora-model-dir [DIR]             lora model directory
-  -i, --init-img [IMAGE]             path to the init image, required by img2img
-  --mask [MASK]                      path to the mask image, required by img2img with mask
-  -i, --end-img [IMAGE]              path to the end image, required by flf2v
-  --control-image [IMAGE]            path to image condition, control net
-  -r, --ref-image [PATH]             reference image for Flux Kontext models (can be used multiple times)
-  --control-video [PATH]             path to control video frames, It must be a directory path.
-                                     The video frames inside should be stored as images in lexicographical (character) order
-                                     For example, if the control video path is `frames`, the directory contain images such as 00.png, 01.png, ... etc.
-  --increase-ref-index               automatically increase the indices of references images based on the order they are listed (starting with 1).
-  --disable-auto-resize-ref-image    disable auto resize of ref images
-  -o, --output OUTPUT                path to write result image to (default: ./output.png)
-  -p, --prompt [PROMPT]              the prompt to render
-  -n, --negative-prompt PROMPT       the negative prompt (default: "")
-  --cfg-scale SCALE                  unconditional guidance scale: (default: 7.0)
-  --img-cfg-scale SCALE              image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)
-  --guidance SCALE                   distilled guidance scale for models with guidance input (default: 3.5)
-  --slg-scale SCALE                  skip layer guidance (SLG) scale, only for DiT models: (default: 0)
-                                     0 means disabled, a value of 2.5 is nice for sd3.5 medium
-  --eta SCALE                        eta in DDIM, only for DDIM and TCD: (default: 0)
-  --skip-layers LAYERS               Layers to skip for SLG steps: (default: [7,8,9])
-  --skip-layer-start START           SLG enabling point: (default: 0.01)
-  --skip-layer-end END               SLG disabling point: (default: 0.2)
-  --scheduler {discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple} Denoiser sigma scheduler (default: discrete)
-  --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd}
-                                     sampling method (default: "euler" for Flux/SD3/Wan, "euler_a" otherwise)
-  --timestep-shift N                 shift timestep for NitroFusion models, default: 0, recommended N for NitroSD-Realism around 250 and 500 for NitroSD-Vibrant
-  --steps  STEPS                     number of sample steps (default: 20)
-  --high-noise-cfg-scale SCALE       (high noise) unconditional guidance scale: (default: 7.0)
-  --high-noise-img-cfg-scale SCALE   (high noise) image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)
-  --high-noise-guidance SCALE        (high noise) distilled guidance scale for models with guidance input (default: 3.5)
-  --high-noise-slg-scale SCALE       (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
-                                     0 means disabled, a value of 2.5 is nice for sd3.5 medium
-  --high-noise-eta SCALE             (high noise) eta in DDIM, only for DDIM and TCD: (default: 0)
-  --high-noise-skip-layers LAYERS    (high noise) Layers to skip for SLG steps: (default: [7,8,9])
-  --high-noise-skip-layer-start      (high noise) SLG enabling point: (default: 0.01)
-  --high-noise-skip-layer-end END    (high noise) SLG disabling point: (default: 0.2)
-  --high-noise-scheduler {discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple} Denoiser sigma scheduler (default: discrete)
-  --high-noise-sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd}
-                                     (high noise) sampling method (default: "euler_a")
-  --high-noise-steps  STEPS          (high noise) number of sample steps (default: -1 = auto)
-                                     SLG will be enabled at step int([STEPS]*[START]) and disabled at int([STEPS]*[END])
-  --strength STRENGTH                strength for noising/unnoising (default: 0.75)
-  --control-strength STRENGTH        strength to apply Control Net (default: 0.9)
-                                     1.0 corresponds to full destruction of information in init image
-  -H, --height H                     image height, in pixel space (default: 512)
-  -W, --width W                      image width, in pixel space (default: 512)
-  --rng {std_default, cuda}          RNG (default: cuda)
-  -s SEED, --seed SEED               RNG seed (default: 42, use random seed for < 0)
-  -b, --batch-count COUNT            number of images to generate
-  --prediction {eps, v, edm_v, sd3_flow, flux_flow} Prediction type override
-  --clip-skip N                      ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1)
-                                     <= 0 represents unspecified, will be 1 for SD1.x, 2 for SD2.x
-  --vae-tiling                       process vae in tiles to reduce memory usage
-  --vae-tile-size [X]x[Y]            tile size for vae tiling (default: 32x32)
-  --vae-relative-tile-size [X]x[Y]   relative tile size for vae tiling, in fraction of image size if < 1, in number of tiles per dim if >=1 (overrides --vae-tile-size)
-  --vae-tile-overlap OVERLAP         tile overlap for vae tiling, in fraction of tile size (default: 0.5)
-  --force-sdxl-vae-conv-scale        force use of conv scale on sdxl vae
-  --vae-on-cpu                       keep vae in cpu (for low vram)
-  --clip-on-cpu                      keep clip in cpu (for low vram)
-  --diffusion-fa                     use flash attention in the diffusion model (for low vram)
-                                     Might lower quality, since it implies converting k and v to f16.
-                                     This might crash if it is not supported by the backend.
-  --diffusion-conv-direct            use Conv2d direct in the diffusion model
-                                     This might crash if it is not supported by the backend.
-  --vae-conv-direct                  use Conv2d direct in the vae model (should improve the performance)
-                                     This might crash if it is not supported by the backend.
-  --control-net-cpu                  keep controlnet in cpu (for low vram)
-  --canny                            apply canny preprocessor (edge detection)
-  --color                            colors the logging tags according to level
-  --chroma-disable-dit-mask          disable dit mask for chroma
-  --chroma-enable-t5-mask            enable t5 mask for chroma
-  --chroma-t5-mask-pad  PAD_SIZE     t5 mask pad size of chroma
-  --video-frames                     video frames (default: 1)
-  --fps                              fps (default: 24)
-  --moe-boundary BOUNDARY            timestep boundary for Wan2.2 MoE model. (default: 0.875)
-                                     only enabled if `--high-noise-steps` is set to -1
-  --flow-shift SHIFT                 shift value for Flow models like SD3.x or WAN (default: auto)
-  --vace-strength                    wan vace strength
-  --photo-maker                      path to PHOTOMAKER model
-  --pm-id-images-dir [DIR]           path to PHOTOMAKER input id images dir
-  --pm-id-embed-path [PATH]          path to PHOTOMAKER v2 id embed
-  --pm-style-strength                strength for keeping PHOTOMAKER input identity (default: 20)
-  -v, --verbose                      print extra info
-```
+Options:
+  -m, --model <string>                     path to full model
+  --clip_l <string>                        path to the clip-l text encoder
+  --clip_g <string>                        path to the clip-g text encoder
+  --clip_vision <string>                   path to the clip-vision encoder
+  --t5xxl <string>                         path to the t5xxl text encoder
+  --llm <string>                           path to the llm text encoder. For example: (qwenvl2.5 for qwen-image, mistral-small3.2 for flux2, ...)
+  --llm_vision <string>                    path to the llm vit
+  --qwen2vl <string>                       alias of --llm. Deprecated.
+  --qwen2vl_vision <string>                alias of --llm_vision. Deprecated.
+  --diffusion-model <string>               path to the standalone diffusion model
+  --high-noise-diffusion-model <string>    path to the standalone high noise diffusion model
+  --vae <string>                           path to standalone vae model
+  --taesd <string>                         path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
+  --control-net <string>                   path to control net model
+  --embd-dir <string>                      embeddings directory
+  --lora-model-dir <string>                lora model directory
+  -i, --init-img <string>                  path to the init image
+  --end-img <string>                       path to the end image, required by flf2v
+  --tensor-type-rules <string>             weight type per tensor pattern (example: "^vae\.=f16,model\.=q8_0")
+  --photo-maker <string>                   path to PHOTOMAKER model
+  --pm-id-images-dir <string>              path to PHOTOMAKER input id images dir
+  --pm-id-embed-path <string>              path to PHOTOMAKER v2 id embed
+  --mask <string>                          path to the mask image
+  --control-image <string>                 path to control image, control net
+  --control-video <string>                 path to control video frames, It must be a directory path. The video frames inside should be stored as images in
+                                           lexicographical (character) order. For example, if the control video path is
+                                           `frames`, the directory contain images such as 00.png, 01.png, ... etc.
+  -o, --output <string>                    path to write result image to (default: ./output.png)
+  -p, --prompt <string>                    the prompt to render
+  -n, --negative-prompt <string>           the negative prompt (default: "")
+  --preview-path <string>                  path to write preview image to (default: ./preview.png)
+  --upscale-model <string>                 path to esrgan model.
+  -t, --threads <int>                      number of threads to use during computation (default: -1). If threads <= 0, then threads will be set to the number of
+                                           CPU physical cores
+  --upscale-repeats <int>                  Run the ESRGAN upscaler this many times (default: 1)
+  -H, --height <int>                       image height, in pixel space (default: 512)
+  -W, --width <int>                        image width, in pixel space (default: 512)
+  --steps <int>                            number of sample steps (default: 20)
+  --high-noise-steps <int>                 (high noise) number of sample steps (default: -1 = auto)
+  --clip-skip <int>                        ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1). <= 0 represents unspecified,
+                                           will be 1 for SD1.x, 2 for SD2.x
+  -b, --batch-count <int>                  batch count
+  --chroma-t5-mask-pad <int>               t5 mask pad size of chroma
+  --video-frames <int>                     video frames (default: 1)
+  --fps <int>                              fps (default: 24)
+  --timestep-shift <int>                   shift timestep for NitroFusion models (default: 0). recommended N for NitroSD-Realism around 250 and 500 for
+                                           NitroSD-Vibrant
+  --preview-interval <int>                 interval in denoising steps between consecutive updates of the image preview file (default is 1, meaning updating at
+                                           every step)
+  --cfg-scale <float>                      unconditional guidance scale: (default: 7.0)
+  --img-cfg-scale <float>                  image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)
+  --guidance <float>                       distilled guidance scale for models with guidance input (default: 3.5)
+  --slg-scale <float>                      skip layer guidance (SLG) scale, only for DiT models: (default: 0). 0 means disabled, a value of 2.5 is nice for sd3.5
+                                           medium
+  --skip-layer-start <float>               SLG enabling point (default: 0.01)
+  --skip-layer-end <float>                 SLG disabling point (default: 0.2)
+  --eta <float>                            eta in DDIM, only for DDIM and TCD (default: 0)
+  --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
+  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
+  --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input (default: 3.5)
+  --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
+  --high-noise-skip-layer-start <float>    (high noise) SLG enabling point (default: 0.01)
+  --high-noise-skip-layer-end <float>      (high noise) SLG disabling point (default: 0.2)
+  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM and TCD (default: 0)
+  --strength <float>                       strength for noising/unnoising (default: 0.75)
+  --pm-style-strength <float>
+  --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
+  --moe-boundary <float>                   timestep boundary for Wan2.2 MoE model. (default: 0.875). Only enabled if `--high-noise-steps` is set to -1
+  --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
+  --vace-strength <float>                  wan vace strength
+  --vae-tile-overlap <float>               tile overlap for vae tiling, in fraction of tile size (default: 0.5)
+  --vae-tiling                             process vae in tiles to reduce memory usage
+  --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
+  --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
+  --control-net-cpu                        keep controlnet in cpu (for low vram)
+  --clip-on-cpu                            keep clip in cpu (for low vram)
+  --vae-on-cpu                             keep vae in cpu (for low vram)
+  --diffusion-fa                           use flash attention in the diffusion model
+  --diffusion-conv-direct                  use ggml_conv2d_direct in the diffusion model
+  --vae-conv-direct                        use ggml_conv2d_direct in the vae model
+  --canny                                  apply canny preprocessor (edge detection)
+  -v, --verbose                            print extra info
+  --color                                  colors the logging tags according to level
+  --chroma-disable-dit-mask                disable dit mask for chroma
+  --chroma-enable-t5-mask                  enable t5 mask for chroma
+  --increase-ref-index                     automatically increase the indices of references images based on the order they are listed (starting with 1).
+  --disable-auto-resize-ref-image          disable auto resize of ref images
+  --taesd-preview-only                     prevents usage of taesd for decoding the final image. (for use with --preview tae)
+  --preview-noisy                          enables previewing noisy inputs of the models rather than the denoised outputs
+  -M, --mode                               run mode, one of [img_gen, vid_gen, upscale, convert], default: img_gen
+  --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). If not specified, the default is the
+                                           type of the weight file
+  --rng                                    RNG, one of [std_default, cuda, cpu], default: cuda(sd-webui), cpu(comfyui)
+  --sampler-rng                            sampler RNG, one of [std_default, cuda, cpu]. If not specified, use --rng
+  -s, --seed                               RNG seed (default: 42, use random seed for < 0)
+  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
+                                           tcd] (default: euler for Flux/SD3/Wan, euler_a otherwise)
+  --prediction                             prediction type override, one of [eps, v, edm_v, sd3_flow, flux_flow, flux2_flow]
+  --lora-apply-mode                        the way to apply LoRA, one of [auto, immediately, at_runtime], default is auto. In auto mode, if the model weights
+                                           contain any quantized parameters, the at_runtime mode will be used; otherwise,
+                                           immediately will be used.The immediately mode may have precision and
+                                           compatibility issues with quantized parameters, but it usually offers faster inference
+                                           speed and, in some cases, lower memory usage. The at_runtime mode, on the
+                                           other hand, is exactly the opposite.
+  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, lcm],
+                                           default: discrete
+  --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
+  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
+                                           ddim_trailing, tcd] default: euler for Flux/SD3/Wan, euler_a otherwise
+  --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
+  -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
+  -h, --help                               show this help message and exit
+  --vae-tile-size                          tile size for vae tiling, format [X]x[Y] (default: 32x32)
+  --vae-relative-tile-size                 relative tile size for vae tiling, format [X]x[Y], in fraction of image size if < 1, in number of tiles per dim if >=1
+                                           (overrides --vae-tile-size)
+  --preview                                preview method. must be one of the following [none, proj, tae, vae] (default is none)
+  --easycache                              enable EasyCache for DiT models with optional "threshold,start_percent,end_percent" (default: 0.2,0.15,0.95)
+```

examples/cli/avi_writer.h

@@ -1,10 +1,10 @@
 #ifndef __AVI_WRITER_H__
 #define __AVI_WRITER_H__
 
-#include <stdint.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
+#include <cstdint>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
 
 #include "stable-diffusion.h"
 
@@ -130,7 +130,7 @@ int create_mjpg_avi_from_sd_images(const char* filename, sd_image_t* images, int
     write_u32_le(f, 0);                   // Colors important
 
     // 'movi' LIST (video frames)
-    long movi_list_pos = ftell(f);
+    // long movi_list_pos = ftell(f);
     fwrite("LIST", 4, 1, f);
     long movi_size_pos = ftell(f);
     write_u32_le(f, 0);  // Placeholder for movi size
@@ -149,7 +149,7 @@ int create_mjpg_avi_from_sd_images(const char* filename, sd_image_t* images, int
     } jpeg_data;
 
     for (int i = 0; i < num_images; i++) {
-        jpeg_data.buf  = NULL;
+        jpeg_data.buf  = nullptr;
         jpeg_data.size = 0;
 
         // Callback function to collect JPEG data into memory

format-code.sh

@@ -1,5 +1,8 @@
 for f in *.cpp *.h *.hpp examples/cli/*.cpp examples/cli/*.h; do
   [[ "$f" == vocab* ]] && continue
   echo "formatting '$f'"
+  # if [ "$f" != "stable-diffusion.h" ]; then
+  #   clang-tidy -fix -p build_linux/ "$f"
+  # fi
   clang-format -style=file -i "$f"
 done

latent-preview.h

@@ -0,0 +1,173 @@
+#include <cstddef>
+#include <cstdint>
+#include "ggml.h"
+
+const float wan_21_latent_rgb_proj[16][3] = {
+    {0.015123f, -0.148418f, 0.479828f},
+    {0.003652f, -0.010680f, -0.037142f},
+    {0.212264f, 0.063033f, 0.016779f},
+    {0.232999f, 0.406476f, 0.220125f},
+    {-0.051864f, -0.082384f, -0.069396f},
+    {0.085005f, -0.161492f, 0.010689f},
+    {-0.245369f, -0.506846f, -0.117010f},
+    {-0.151145f, 0.017721f, 0.007207f},
+    {-0.293239f, -0.207936f, -0.421135f},
+    {-0.187721f, 0.050783f, 0.177649f},
+    {-0.013067f, 0.265964f, 0.166578f},
+    {0.028327f, 0.109329f, 0.108642f},
+    {-0.205343f, 0.043991f, 0.148914f},
+    {0.014307f, -0.048647f, -0.007219f},
+    {0.217150f, 0.053074f, 0.319923f},
+    {0.155357f, 0.083156f, 0.064780f}};
+float wan_21_latent_rgb_bias[3] = {-0.270270f, -0.234976f, -0.456853f};
+
+const float wan_22_latent_rgb_proj[48][3] = {
+    {0.017126f, -0.027230f, -0.019257f},
+    {-0.113739f, -0.028715f, -0.022885f},
+    {-0.000106f, 0.021494f, 0.004629f},
+    {-0.013273f, -0.107137f, -0.033638f},
+    {-0.000381f, 0.000279f, 0.025877f},
+    {-0.014216f, -0.003975f, 0.040528f},
+    {0.001638f, -0.000748f, 0.011022f},
+    {0.029238f, -0.006697f, 0.035933f},
+    {0.021641f, -0.015874f, 0.040531f},
+    {-0.101984f, -0.070160f, -0.028855f},
+    {0.033207f, -0.021068f, 0.002663f},
+    {-0.104711f, 0.121673f, 0.102981f},
+    {0.082647f, -0.004991f, 0.057237f},
+    {-0.027375f, 0.031581f, 0.006868f},
+    {-0.045434f, 0.029444f, 0.019287f},
+    {-0.046572f, -0.012537f, 0.006675f},
+    {0.074709f, 0.033690f, 0.025289f},
+    {-0.008251f, -0.002745f, -0.006999f},
+    {0.012685f, -0.061856f, -0.048658f},
+    {0.042304f, -0.007039f, 0.000295f},
+    {-0.007644f, -0.060843f, -0.033142f},
+    {0.159909f, 0.045628f, 0.367541f},
+    {0.095171f, 0.086438f, 0.010271f},
+    {0.006812f, 0.019643f, 0.029637f},
+    {0.003467f, -0.010705f, 0.014252f},
+    {-0.099681f, -0.066272f, -0.006243f},
+    {0.047357f, 0.037040f, 0.000185f},
+    {-0.041797f, -0.089225f, -0.032257f},
+    {0.008928f, 0.017028f, 0.018684f},
+    {-0.042255f, 0.016045f, 0.006849f},
+    {0.011268f, 0.036462f, 0.037387f},
+    {0.011553f, -0.016375f, -0.048589f},
+    {0.046266f, -0.027189f, 0.056979f},
+    {0.009640f, -0.017576f, 0.030324f},
+    {-0.045794f, -0.036083f, -0.010616f},
+    {0.022418f, 0.039783f, -0.032939f},
+    {-0.052714f, -0.015525f, 0.007438f},
+    {0.193004f, 0.223541f, 0.264175f},
+    {-0.059406f, -0.008188f, 0.022867f},
+    {-0.156742f, -0.263791f, -0.007385f},
+    {-0.015717f, 0.016570f, 0.033969f},
+    {0.037969f, 0.109835f, 0.200449f},
+    {-0.000782f, -0.009566f, -0.008058f},
+    {0.010709f, 0.052960f, -0.044195f},
+    {0.017271f, 0.045839f, 0.034569f},
+    {0.009424f, 0.013088f, -0.001714f},
+    {-0.024805f, -0.059378f, -0.033756f},
+    {-0.078293f, 0.029070f, 0.026129f}};
+float wan_22_latent_rgb_bias[3] = {0.013160f, -0.096492f, -0.071323f};
+
+const float flux_latent_rgb_proj[16][3] = {
+    {-0.041168f, 0.019917f, 0.097253f},
+    {0.028096f, 0.026730f, 0.129576f},
+    {0.065618f, -0.067950f, -0.014651f},
+    {-0.012998f, -0.014762f, 0.081251f},
+    {0.078567f, 0.059296f, -0.024687f},
+    {-0.015987f, -0.003697f, 0.005012f},
+    {0.033605f, 0.138999f, 0.068517f},
+    {-0.024450f, -0.063567f, -0.030101f},
+    {-0.040194f, -0.016710f, 0.127185f},
+    {0.112681f, 0.088764f, -0.041940f},
+    {-0.023498f, 0.093664f, 0.025543f},
+    {0.082899f, 0.048320f, 0.007491f},
+    {0.075712f, 0.074139f, 0.081965f},
+    {-0.143501f, 0.018263f, -0.136138f},
+    {-0.025767f, -0.082035f, -0.040023f},
+    {-0.111849f, -0.055589f, -0.032361f}};
+float flux_latent_rgb_bias[3] = {0.024600f, -0.006937f, -0.008089f};
+
+// This one was taken straight from
+// https://github.com/Stability-AI/sd3.5/blob/8565799a3b41eb0c7ba976d18375f0f753f56402/sd3_impls.py#L288-L303
+// (MiT Licence)
+const float sd3_latent_rgb_proj[16][3] = {
+    {-0.0645f, 0.0177f, 0.1052f},
+    {0.0028f, 0.0312f, 0.0650f},
+    {0.1848f, 0.0762f, 0.0360f},
+    {0.0944f, 0.0360f, 0.0889f},
+    {0.0897f, 0.0506f, -0.0364f},
+    {-0.0020f, 0.1203f, 0.0284f},
+    {0.0855f, 0.0118f, 0.0283f},
+    {-0.0539f, 0.0658f, 0.1047f},
+    {-0.0057f, 0.0116f, 0.0700f},
+    {-0.0412f, 0.0281f, -0.0039f},
+    {0.1106f, 0.1171f, 0.1220f},
+    {-0.0248f, 0.0682f, -0.0481f},
+    {0.0815f, 0.0846f, 0.1207f},
+    {-0.0120f, -0.0055f, -0.0867f},
+    {-0.0749f, -0.0634f, -0.0456f},
+    {-0.1418f, -0.1457f, -0.1259f},
+};
+float sd3_latent_rgb_bias[3] = {0, 0, 0};
+
+const float sdxl_latent_rgb_proj[4][3] = {
+    {0.258303f, 0.277640f, 0.329699f},
+    {-0.299701f, 0.105446f, 0.014194f},
+    {0.050522f, 0.186163f, -0.143257f},
+    {-0.211938f, -0.149892f, -0.080036f}};
+float sdxl_latent_rgb_bias[3] = {0.144381f, -0.033313f, 0.007061f};
+
+const float sd_latent_rgb_proj[4][3] = {
+    {0.337366f, 0.216344f, 0.257386f},
+    {0.165636f, 0.386828f, 0.046994f},
+    {-0.267803f, 0.237036f, 0.223517f},
+    {-0.178022f, -0.200862f, -0.678514f}};
+float sd_latent_rgb_bias[3] = {-0.017478f, -0.055834f, -0.105825f};
+
+void preview_latent_video(uint8_t* buffer, struct ggml_tensor* latents, const float (*latent_rgb_proj)[3], const float latent_rgb_bias[3], int width, int height, int frames, int dim) {
+    size_t buffer_head = 0;
+    for (int k = 0; k < frames; k++) {
+        for (int j = 0; j < height; j++) {
+            for (int i = 0; i < width; i++) {
+                size_t latent_id = (i * latents->nb[0] + j * latents->nb[1] + k * latents->nb[2]);
+                float r = 0, g = 0, b = 0;
+                if (latent_rgb_proj != nullptr) {
+                    for (int d = 0; d < dim; d++) {
+                        float value = *(float*)((char*)latents->data + latent_id + d * latents->nb[ggml_n_dims(latents) - 1]);
+                        r += value * latent_rgb_proj[d][0];
+                        g += value * latent_rgb_proj[d][1];
+                        b += value * latent_rgb_proj[d][2];
+                    }
+                } else {
+                    // interpret first 3 channels as RGB
+                    r = *(float*)((char*)latents->data + latent_id + 0 * latents->nb[ggml_n_dims(latents) - 1]);
+                    g = *(float*)((char*)latents->data + latent_id + 1 * latents->nb[ggml_n_dims(latents) - 1]);
+                    b = *(float*)((char*)latents->data + latent_id + 2 * latents->nb[ggml_n_dims(latents) - 1]);
+                }
+                if (latent_rgb_bias != nullptr) {
+                    // bias
+                    r += latent_rgb_bias[0];
+                    g += latent_rgb_bias[1];
+                    b += latent_rgb_bias[2];
+                }
+                // change range
+                r = r * .5f + .5f;
+                g = g * .5f + .5f;
+                b = b * .5f + .5f;
+
+                // clamp rgb values to [0,1] range
+                r = r >= 0 ? r <= 1 ? r : 1 : 0;
+                g = g >= 0 ? g <= 1 ? g : 1 : 0;
+                b = b >= 0 ? b <= 1 ? b : 1 : 0;
+
+                buffer[buffer_head++] = (uint8_t)(r * 255);
+                buffer[buffer_head++] = (uint8_t)(g * 255);
+                buffer[buffer_head++] = (uint8_t)(b * 255);
+            }
+        }
+    }
+}

ltxv.hpp

@@ -13,10 +13,10 @@ namespace LTXV {
     public:
         CausalConv3d(int64_t in_channels,
                      int64_t out_channels,
-                     int kernel_size        = 3,
-                     std::tuple<int> stride = {1, 1, 1},
-                     int dilation           = 1,
-                     bool bias              = true) {
+                     int kernel_size                  = 3,
+                     std::tuple<int, int, int> stride = {1, 1, 1},
+                     int dilation                     = 1,
+                     bool bias                        = true) {
             time_kernel_size = kernel_size / 2;
             blocks["conv"]   = std::shared_ptr<GGMLBlock>(new Conv3d(in_channels,
                                                                      out_channels,
@@ -27,7 +27,7 @@ namespace LTXV {
                                                                      bias));
         }
 
-        struct ggml_tensor* forward(struct ggml_context* ctx,
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                     struct ggml_tensor* x,
                                     bool causal = true) {
             // x: [N*IC, ID, IH, IW]

mmdit.hpp

@@ -1,6 +1,8 @@
 #ifndef __MMDIT_HPP__
 #define __MMDIT_HPP__
 
+#include <memory>
+
 #include "ggml_extend.hpp"
 #include "model.h"
 
@@ -25,13 +27,13 @@ public:
         blocks["fc2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_features, out_features, bias));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [N, n_token, in_features]
         auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
         auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
 
         x = fc1->forward(ctx, x);
-        x = ggml_gelu_inplace(ctx, x);
+        x = ggml_gelu_inplace(ctx->ggml_ctx, x);
         x = fc2->forward(ctx, x);
         return x;
     }
@@ -70,7 +72,7 @@ public:
                                                                bias));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [N, C, H, W]
         // return: [N, H*W, embed_dim]
         auto proj = std::dynamic_pointer_cast<Conv2d>(blocks["proj"]);
@@ -80,13 +82,13 @@ public:
             int64_t H = x->ne[1];
             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);  // TODO: reflect pad mode
+            x         = ggml_pad(ctx->ggml_ctx, x, pad_w, pad_h, 0, 0);  // TODO: reflect pad mode
         }
         x = proj->forward(ctx, x);
 
         if (flatten) {
-            x = ggml_reshape_3d(ctx, x, x->ne[0] * x->ne[1], x->ne[2], x->ne[3]);
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 1, 0, 2, 3));
+            x = ggml_reshape_3d(ctx->ggml_ctx, x, x->ne[0] * x->ne[1], x->ne[2], x->ne[3]);
+            x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));
         }
         return x;
     }
@@ -105,16 +107,16 @@ public:
         blocks["mlp.2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, hidden_size, true, true));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* t) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* t) {
         // t: [N, ]
         // return: [N, hidden_size]
         auto mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["mlp.0"]);
         auto mlp_2 = std::dynamic_pointer_cast<Linear>(blocks["mlp.2"]);
 
-        auto t_freq = ggml_nn_timestep_embedding(ctx, t, frequency_embedding_size);  // [N, frequency_embedding_size]
+        auto t_freq = ggml_ext_timestep_embedding(ctx->ggml_ctx, t, frequency_embedding_size);  // [N, frequency_embedding_size]
 
         auto t_emb = mlp_0->forward(ctx, t_freq);
-        t_emb      = ggml_silu_inplace(ctx, t_emb);
+        t_emb      = ggml_silu_inplace(ctx->ggml_ctx, t_emb);
         t_emb      = mlp_2->forward(ctx, t_emb);
         return t_emb;
     }
@@ -129,14 +131,14 @@ public:
         blocks["mlp.2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, hidden_size, true, true));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [N, input_dim]
         // return: [N, hidden_size]
         auto mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["mlp.0"]);
         auto mlp_2 = std::dynamic_pointer_cast<Linear>(blocks["mlp.2"]);
 
         x = mlp_0->forward(ctx, x);
-        x = ggml_silu_inplace(ctx, x);
+        x = ggml_silu_inplace(ctx->ggml_ctx, x);
         x = mlp_2->forward(ctx, x);
         return x;
     }
@@ -147,16 +149,14 @@ public:
     int64_t num_heads;
     bool pre_only;
     std::string qk_norm;
-    bool flash_attn;
 
 public:
     SelfAttention(int64_t dim,
                   int64_t num_heads   = 8,
                   std::string qk_norm = "",
                   bool qkv_bias       = false,
-                  bool pre_only       = false,
-                  bool flash_attn     = false)
-        : num_heads(num_heads), pre_only(pre_only), qk_norm(qk_norm), flash_attn(flash_attn) {
+                  bool pre_only       = false)
+        : num_heads(num_heads), pre_only(pre_only), qk_norm(qk_norm) {
         int64_t d_head = dim / num_heads;
         blocks["qkv"]  = std::shared_ptr<GGMLBlock>(new Linear(dim, dim * 3, qkv_bias));
         if (!pre_only) {
@@ -171,15 +171,15 @@ public:
         }
     }
 
-    std::vector<struct ggml_tensor*> pre_attention(struct ggml_context* ctx, struct ggml_tensor* x) {
+    std::vector<struct ggml_tensor*> pre_attention(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         auto qkv_proj = std::dynamic_pointer_cast<Linear>(blocks["qkv"]);
 
         auto qkv         = qkv_proj->forward(ctx, x);
-        auto qkv_vec     = split_qkv(ctx, qkv);
+        auto qkv_vec     = split_qkv(ctx->ggml_ctx, qkv);
         int64_t head_dim = qkv_vec[0]->ne[0] / num_heads;
-        auto q           = ggml_reshape_4d(ctx, qkv_vec[0], head_dim, num_heads, qkv_vec[0]->ne[1], qkv_vec[0]->ne[2]);  // [N, n_token, n_head, d_head]
-        auto k           = ggml_reshape_4d(ctx, qkv_vec[1], head_dim, num_heads, qkv_vec[1]->ne[1], qkv_vec[1]->ne[2]);  // [N, n_token, n_head, d_head]
-        auto v           = qkv_vec[2];                                                                                   // [N, n_token, n_head*d_head]
+        auto q           = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[0], head_dim, num_heads, qkv_vec[0]->ne[1], qkv_vec[0]->ne[2]);  // [N, n_token, n_head, d_head]
+        auto k           = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[1], head_dim, num_heads, qkv_vec[1]->ne[1], qkv_vec[1]->ne[2]);  // [N, n_token, n_head, d_head]
+        auto v           = qkv_vec[2];                                                                                             // [N, n_token, n_head*d_head]
 
         if (qk_norm == "rms" || qk_norm == "ln") {
             auto ln_q = std::dynamic_pointer_cast<UnaryBlock>(blocks["ln_q"]);
@@ -188,13 +188,13 @@ public:
             k         = ln_k->forward(ctx, k);
         }
 
-        q = ggml_reshape_3d(ctx, q, q->ne[0] * q->ne[1], q->ne[2], q->ne[3]);  // [N, n_token, n_head*d_head]
-        k = ggml_reshape_3d(ctx, k, k->ne[0] * k->ne[1], k->ne[2], k->ne[3]);  // [N, n_token, n_head*d_head]
+        q = ggml_reshape_3d(ctx->ggml_ctx, q, q->ne[0] * q->ne[1], q->ne[2], q->ne[3]);  // [N, n_token, n_head*d_head]
+        k = ggml_reshape_3d(ctx->ggml_ctx, k, k->ne[0] * k->ne[1], k->ne[2], k->ne[3]);  // [N, n_token, n_head*d_head]
 
         return {q, k, v};
     }
 
-    struct ggml_tensor* post_attention(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* post_attention(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         GGML_ASSERT(!pre_only);
 
         auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
@@ -204,12 +204,11 @@ public:
     }
 
     // x: [N, n_token, dim]
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x) {
         auto qkv = pre_attention(ctx, x);
-        x        = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], num_heads, NULL, false, false, true);  // [N, n_token, dim]
-        x        = post_attention(ctx, x);                                                                            // [N, n_token, dim]
+        x        = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
+        x        = post_attention(ctx, x);                                                                                                                  // [N, n_token, dim]
         return x;
     }
 };
@@ -234,7 +233,6 @@ public:
     int64_t num_heads;
     bool pre_only;
     bool self_attn;
-    bool flash_attn;
 
 public:
     DismantledBlock(int64_t hidden_size,
@@ -243,17 +241,16 @@ public:
                     std::string qk_norm = "",
                     bool qkv_bias       = false,
                     bool pre_only       = false,
-                    bool self_attn      = false,
-                    bool flash_attn     = false)
+                    bool self_attn      = false)
         : num_heads(num_heads), pre_only(pre_only), self_attn(self_attn) {
         // rmsnorm is always Flase
         // scale_mod_only is always Flase
         // swiglu is always Flase
         blocks["norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size, 1e-06f, false));
-        blocks["attn"]  = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, pre_only, flash_attn));
+        blocks["attn"]  = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, pre_only));
 
         if (self_attn) {
-            blocks["attn2"] = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, false, flash_attn));
+            blocks["attn2"] = std::shared_ptr<GGMLBlock>(new SelfAttention(hidden_size, num_heads, qk_norm, qkv_bias, false));
         }
 
         if (!pre_only) {
@@ -272,9 +269,9 @@ public:
         blocks["adaLN_modulation.1"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, n_mods * hidden_size));
     }
 
-    std::tuple<std::vector<struct ggml_tensor*>, std::vector<struct ggml_tensor*>, std::vector<struct ggml_tensor*>> pre_attention_x(struct ggml_context* ctx,
-                                                                                                                                     struct ggml_tensor* x,
-                                                                                                                                     struct ggml_tensor* c) {
+    std::tuple<std::vector<ggml_tensor*>, std::vector<ggml_tensor*>, std::vector<ggml_tensor*>> pre_attention_x(GGMLRunnerContext* ctx,
+                                                                                                                struct ggml_tensor* x,
+                                                                                                                struct ggml_tensor* c) {
         GGML_ASSERT(self_attn);
         // x: [N, n_token, hidden_size]
         // c: [N, hidden_size]
@@ -284,35 +281,35 @@ public:
         auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);
 
         int64_t n_mods = 9;
-        auto m         = adaLN_modulation_1->forward(ctx, ggml_silu(ctx, c));  // [N, n_mods * hidden_size]
-        m              = ggml_reshape_3d(ctx, m, c->ne[0], n_mods, c->ne[1]);  // [N, n_mods, hidden_size]
-        m              = ggml_cont(ctx, ggml_permute(ctx, m, 0, 2, 1, 3));     // [n_mods, N, hidden_size]
+        auto m         = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));         // [N, n_mods * hidden_size]
+        m              = ggml_reshape_3d(ctx->ggml_ctx, m, c->ne[0], n_mods, c->ne[1]);         // [N, n_mods, hidden_size]
+        m              = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, m, 0, 2, 1, 3));  // [n_mods, N, hidden_size]
 
         int64_t offset = m->nb[1] * m->ne[1];
-        auto shift_msa = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
-        auto scale_msa = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
-        auto gate_msa  = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 2);  // [N, hidden_size]
+        auto shift_msa = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
+        auto scale_msa = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
+        auto gate_msa  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 2);  // [N, hidden_size]
 
-        auto shift_mlp = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 3);  // [N, hidden_size]
-        auto scale_mlp = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 4);  // [N, hidden_size]
-        auto gate_mlp  = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 5);  // [N, hidden_size]
+        auto shift_mlp = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 3);  // [N, hidden_size]
+        auto scale_mlp = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 4);  // [N, hidden_size]
+        auto gate_mlp  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 5);  // [N, hidden_size]
 
-        auto shift_msa2 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 6);  // [N, hidden_size]
-        auto scale_msa2 = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 7);  // [N, hidden_size]
-        auto gate_msa2  = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 8);  // [N, hidden_size]
+        auto shift_msa2 = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 6);  // [N, hidden_size]
+        auto scale_msa2 = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 7);  // [N, hidden_size]
+        auto gate_msa2  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 8);  // [N, hidden_size]
 
         auto x_norm = norm1->forward(ctx, x);
 
-        auto attn_in = modulate(ctx, x_norm, shift_msa, scale_msa);
+        auto attn_in = modulate(ctx->ggml_ctx, x_norm, shift_msa, scale_msa);
         auto qkv     = attn->pre_attention(ctx, attn_in);
 
-        auto attn2_in = modulate(ctx, x_norm, shift_msa2, scale_msa2);
+        auto attn2_in = modulate(ctx->ggml_ctx, x_norm, shift_msa2, scale_msa2);
         auto qkv2     = attn2->pre_attention(ctx, attn2_in);
 
         return {qkv, qkv2, {x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2}};
     }
 
-    std::pair<std::vector<struct ggml_tensor*>, std::vector<struct ggml_tensor*>> pre_attention(struct ggml_context* ctx,
+    std::pair<std::vector<struct ggml_tensor*>, std::vector<struct ggml_tensor*>> pre_attention(GGMLRunnerContext* ctx,
                                                                                                 struct ggml_tensor* x,
                                                                                                 struct ggml_tensor* c) {
         // x: [N, n_token, hidden_size]
@@ -325,33 +322,33 @@ public:
         if (pre_only) {
             n_mods = 2;
         }
-        auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx, c));  // [N, n_mods * hidden_size]
-        m      = ggml_reshape_3d(ctx, m, c->ne[0], n_mods, c->ne[1]);  // [N, n_mods, hidden_size]
-        m      = ggml_cont(ctx, ggml_permute(ctx, m, 0, 2, 1, 3));     // [n_mods, N, hidden_size]
+        auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));         // [N, n_mods * hidden_size]
+        m      = ggml_reshape_3d(ctx->ggml_ctx, m, c->ne[0], n_mods, c->ne[1]);         // [N, n_mods, hidden_size]
+        m      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, m, 0, 2, 1, 3));  // [n_mods, N, hidden_size]
 
         int64_t offset = m->nb[1] * m->ne[1];
-        auto shift_msa = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
-        auto scale_msa = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
+        auto shift_msa = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
+        auto scale_msa = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
         if (!pre_only) {
-            auto gate_msa  = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 2);  // [N, hidden_size]
-            auto shift_mlp = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 3);  // [N, hidden_size]
-            auto scale_mlp = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 4);  // [N, hidden_size]
-            auto gate_mlp  = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 5);  // [N, hidden_size]
+            auto gate_msa  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 2);  // [N, hidden_size]
+            auto shift_mlp = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 3);  // [N, hidden_size]
+            auto scale_mlp = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 4);  // [N, hidden_size]
+            auto gate_mlp  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 5);  // [N, hidden_size]
 
-            auto attn_in = modulate(ctx, norm1->forward(ctx, x), shift_msa, scale_msa);
+            auto attn_in = modulate(ctx->ggml_ctx, norm1->forward(ctx, x), shift_msa, scale_msa);
 
             auto qkv = attn->pre_attention(ctx, attn_in);
 
             return {qkv, {x, gate_msa, shift_mlp, scale_mlp, gate_mlp}};
         } else {
-            auto attn_in = modulate(ctx, norm1->forward(ctx, x), shift_msa, scale_msa);
+            auto attn_in = modulate(ctx->ggml_ctx, norm1->forward(ctx, x), shift_msa, scale_msa);
             auto qkv     = attn->pre_attention(ctx, attn_in);
 
-            return {qkv, {NULL, NULL, NULL, NULL, NULL}};
+            return {qkv, {nullptr, nullptr, nullptr, nullptr, nullptr}};
         }
     }
 
-    struct ggml_tensor* post_attention_x(struct ggml_context* ctx,
+    struct ggml_tensor* post_attention_x(GGMLRunnerContext* ctx,
                                          struct ggml_tensor* attn_out,
                                          struct ggml_tensor* attn2_out,
                                          struct ggml_tensor* x,
@@ -374,22 +371,22 @@ public:
         auto norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["norm2"]);
         auto mlp   = std::dynamic_pointer_cast<Mlp>(blocks["mlp"]);
 
-        gate_msa  = ggml_reshape_3d(ctx, gate_msa, gate_msa->ne[0], 1, gate_msa->ne[1]);     // [N, 1, hidden_size]
-        gate_mlp  = ggml_reshape_3d(ctx, gate_mlp, gate_mlp->ne[0], 1, gate_mlp->ne[1]);     // [N, 1, hidden_size]
-        gate_msa2 = ggml_reshape_3d(ctx, gate_msa2, gate_msa2->ne[0], 1, gate_msa2->ne[1]);  // [N, 1, hidden_size]
+        gate_msa  = ggml_reshape_3d(ctx->ggml_ctx, gate_msa, gate_msa->ne[0], 1, gate_msa->ne[1]);     // [N, 1, hidden_size]
+        gate_mlp  = ggml_reshape_3d(ctx->ggml_ctx, gate_mlp, gate_mlp->ne[0], 1, gate_mlp->ne[1]);     // [N, 1, hidden_size]
+        gate_msa2 = ggml_reshape_3d(ctx->ggml_ctx, gate_msa2, gate_msa2->ne[0], 1, gate_msa2->ne[1]);  // [N, 1, hidden_size]
 
         attn_out  = attn->post_attention(ctx, attn_out);
         attn2_out = attn2->post_attention(ctx, attn2_out);
 
-        x            = ggml_add(ctx, x, ggml_mul(ctx, attn_out, gate_msa));
-        x            = ggml_add(ctx, x, ggml_mul(ctx, attn2_out, gate_msa2));
-        auto mlp_out = mlp->forward(ctx, modulate(ctx, norm2->forward(ctx, x), shift_mlp, scale_mlp));
-        x            = ggml_add(ctx, x, ggml_mul(ctx, mlp_out, gate_mlp));
+        x            = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, attn_out, gate_msa));
+        x            = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, attn2_out, gate_msa2));
+        auto mlp_out = mlp->forward(ctx, modulate(ctx->ggml_ctx, norm2->forward(ctx, x), shift_mlp, scale_mlp));
+        x            = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, mlp_out, gate_mlp));
 
         return x;
     }
 
-    struct ggml_tensor* post_attention(struct ggml_context* ctx,
+    struct ggml_tensor* post_attention(GGMLRunnerContext* ctx,
                                        struct ggml_tensor* attn_out,
                                        struct ggml_tensor* x,
                                        struct ggml_tensor* gate_msa,
@@ -409,20 +406,19 @@ public:
         auto norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["norm2"]);
         auto mlp   = std::dynamic_pointer_cast<Mlp>(blocks["mlp"]);
 
-        gate_msa = ggml_reshape_3d(ctx, gate_msa, gate_msa->ne[0], 1, gate_msa->ne[1]);  // [N, 1, hidden_size]
-        gate_mlp = ggml_reshape_3d(ctx, gate_mlp, gate_mlp->ne[0], 1, gate_mlp->ne[1]);  // [N, 1, hidden_size]
+        gate_msa = ggml_reshape_3d(ctx->ggml_ctx, gate_msa, gate_msa->ne[0], 1, gate_msa->ne[1]);  // [N, 1, hidden_size]
+        gate_mlp = ggml_reshape_3d(ctx->ggml_ctx, gate_mlp, gate_mlp->ne[0], 1, gate_mlp->ne[1]);  // [N, 1, hidden_size]
 
         attn_out = attn->post_attention(ctx, attn_out);
 
-        x            = ggml_add(ctx, x, ggml_mul(ctx, attn_out, gate_msa));
-        auto mlp_out = mlp->forward(ctx, modulate(ctx, norm2->forward(ctx, x), shift_mlp, scale_mlp));
-        x            = ggml_add(ctx, x, ggml_mul(ctx, mlp_out, gate_mlp));
+        x            = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, attn_out, gate_msa));
+        auto mlp_out = mlp->forward(ctx, modulate(ctx->ggml_ctx, norm2->forward(ctx, x), shift_mlp, scale_mlp));
+        x            = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, mlp_out, gate_mlp));
 
         return x;
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* c) {
         // x: [N, n_token, hidden_size]
@@ -439,8 +435,8 @@ public:
             auto qkv2          = std::get<1>(qkv_intermediates);
             auto intermediates = std::get<2>(qkv_intermediates);
 
-            auto attn_out  = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], num_heads, NULL, false, false, flash_attn);     // [N, n_token, dim]
-            auto attn2_out = ggml_nn_attention_ext(ctx, backend, qkv2[0], qkv2[1], qkv2[2], num_heads, NULL, false, false, flash_attn);  // [N, n_token, dim]
+            auto attn_out  = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, false, ctx->flash_attn_enabled);     // [N, n_token, dim]
+            auto attn2_out = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv2[0], qkv2[1], qkv2[2], num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
             x              = post_attention_x(ctx,
                                               attn_out,
                                               attn2_out,
@@ -456,7 +452,7 @@ public:
             auto qkv               = qkv_intermediates.first;
             auto intermediates     = qkv_intermediates.second;
 
-            auto attn_out = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], num_heads, NULL, false, false, flash_attn);  // [N, n_token, dim]
+            auto attn_out = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
             x             = post_attention(ctx,
                                            attn_out,
                                            intermediates[0],
@@ -470,9 +466,7 @@ public:
 };
 
 __STATIC_INLINE__ std::pair<struct ggml_tensor*, struct ggml_tensor*>
-block_mixing(struct ggml_context* ctx,
-             ggml_backend_t backend,
-             bool flash_attn,
+block_mixing(GGMLRunnerContext* ctx,
              struct ggml_tensor* context,
              struct ggml_tensor* x,
              struct ggml_tensor* c,
@@ -499,29 +493,29 @@ block_mixing(struct ggml_context* ctx,
     }
     std::vector<struct ggml_tensor*> qkv;
     for (int i = 0; i < 3; i++) {
-        qkv.push_back(ggml_concat(ctx, context_qkv[i], x_qkv[i], 1));
+        qkv.push_back(ggml_concat(ctx->ggml_ctx, context_qkv[i], x_qkv[i], 1));
     }
 
-    auto attn         = ggml_nn_attention_ext(ctx, backend, qkv[0], qkv[1], qkv[2], x_block->num_heads, NULL, false, false, flash_attn);  // [N, n_context + n_token, hidden_size]
-    attn              = ggml_cont(ctx, ggml_permute(ctx, attn, 0, 2, 1, 3));                                                              // [n_context + n_token, N, hidden_size]
-    auto context_attn = ggml_view_3d(ctx,
+    auto attn         = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], x_block->num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_context + n_token, hidden_size]
+    attn              = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, attn, 0, 2, 1, 3));                                                                          // [n_context + n_token, N, hidden_size]
+    auto context_attn = ggml_view_3d(ctx->ggml_ctx,
                                      attn,
                                      attn->ne[0],
                                      attn->ne[1],
                                      context->ne[1],
                                      attn->nb[1],
                                      attn->nb[2],
-                                     0);                                              // [n_context, N, hidden_size]
-    context_attn      = ggml_cont(ctx, ggml_permute(ctx, context_attn, 0, 2, 1, 3));  // [N, n_context, hidden_size]
-    auto x_attn       = ggml_view_3d(ctx,
+                                     0);                                                                  // [n_context, N, hidden_size]
+    context_attn      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, context_attn, 0, 2, 1, 3));  // [N, n_context, hidden_size]
+    auto x_attn       = ggml_view_3d(ctx->ggml_ctx,
                                      attn,
                                      attn->ne[0],
                                      attn->ne[1],
                                      x->ne[1],
                                      attn->nb[1],
                                      attn->nb[2],
-                                     attn->nb[2] * context->ne[1]);             // [n_token, N, hidden_size]
-    x_attn            = ggml_cont(ctx, ggml_permute(ctx, x_attn, 0, 2, 1, 3));  // [N, n_token, hidden_size]
+                                     attn->nb[2] * context->ne[1]);                                 // [n_token, N, hidden_size]
+    x_attn            = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x_attn, 0, 2, 1, 3));  // [N, n_token, hidden_size]
 
     if (!context_block->pre_only) {
         context = context_block->post_attention(ctx,
@@ -532,11 +526,11 @@ block_mixing(struct ggml_context* ctx,
                                                 context_intermediates[3],
                                                 context_intermediates[4]);
     } else {
-        context = NULL;
+        context = nullptr;
     }
 
     if (x_block->self_attn) {
-        auto attn2 = ggml_nn_attention_ext(ctx, backend, x_qkv2[0], x_qkv2[1], x_qkv2[2], x_block->num_heads);  // [N, n_token, hidden_size]
+        auto attn2 = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, x_qkv2[0], x_qkv2[1], x_qkv2[2], x_block->num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, hidden_size]
 
         x = x_block->post_attention_x(ctx,
                                       x_attn,
@@ -561,8 +555,6 @@ block_mixing(struct ggml_context* ctx,
 }
 
 struct JointBlock : public GGMLBlock {
-    bool flash_attn;
-
 public:
     JointBlock(int64_t hidden_size,
                int64_t num_heads,
@@ -570,22 +562,19 @@ public:
                std::string qk_norm = "",
                bool qkv_bias       = false,
                bool pre_only       = false,
-               bool self_attn_x    = false,
-               bool flash_attn     = false)
-        : flash_attn(flash_attn) {
-        blocks["context_block"] = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, pre_only, false, flash_attn));
-        blocks["x_block"]       = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, false, self_attn_x, flash_attn));
+               bool self_attn_x    = false) {
+        blocks["context_block"] = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, pre_only, false));
+        blocks["x_block"]       = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, false, self_attn_x));
     }
 
-    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx,
-                                                                ggml_backend_t backend,
+    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
                                                                 struct ggml_tensor* context,
                                                                 struct ggml_tensor* x,
                                                                 struct ggml_tensor* c) {
         auto context_block = std::dynamic_pointer_cast<DismantledBlock>(blocks["context_block"]);
         auto x_block       = std::dynamic_pointer_cast<DismantledBlock>(blocks["x_block"]);
 
-        return block_mixing(ctx, backend, flash_attn, context, x, c, context_block, x_block);
+        return block_mixing(ctx, context, x, c, context_block, x_block);
     }
 };
 
@@ -601,7 +590,7 @@ public:
         blocks["adaLN_modulation.1"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, 2 * hidden_size));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* c) {
         // x: [N, n_token, hidden_size]
@@ -611,15 +600,15 @@ public:
         auto linear             = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
         auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);
 
-        auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx, c));  // [N, 2 * hidden_size]
-        m      = ggml_reshape_3d(ctx, m, c->ne[0], 2, c->ne[1]);       // [N, 2, hidden_size]
-        m      = ggml_cont(ctx, ggml_permute(ctx, m, 0, 2, 1, 3));     // [2, N, hidden_size]
+        auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));         // [N, 2 * hidden_size]
+        m      = ggml_reshape_3d(ctx->ggml_ctx, m, c->ne[0], 2, c->ne[1]);              // [N, 2, hidden_size]
+        m      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, m, 0, 2, 1, 3));  // [2, N, hidden_size]
 
         int64_t offset = m->nb[1] * m->ne[1];
-        auto shift     = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
-        auto scale     = ggml_view_2d(ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
+        auto shift     = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
+        auto scale     = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
 
-        x = modulate(ctx, norm_final->forward(ctx, x), shift, scale);
+        x = modulate(ctx->ggml_ctx, norm_final->forward(ctx, x), shift, scale);
         x = linear->forward(ctx, x);
 
         return x;
@@ -643,16 +632,14 @@ protected:
     int64_t context_embedder_out_dim = 1536;
     int64_t hidden_size;
     std::string qk_norm;
-    bool flash_attn = false;
 
-    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, std::string prefix = "") {
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
         enum ggml_type wtype = GGML_TYPE_F32;
         params["pos_embed"]  = ggml_new_tensor_3d(ctx, wtype, hidden_size, num_patchs, 1);
     }
 
 public:
-    MMDiT(bool flash_attn = false, const String2GGMLType& tensor_types = {})
-        : flash_attn(flash_attn) {
+    MMDiT(const String2TensorStorage& tensor_storage_map = {}) {
         // input_size is always None
         // learn_sigma is always False
         // register_length is alwalys 0
@@ -665,8 +652,7 @@ public:
         // pos_embed_offset is not used
         // context_embedder_config is always {'target': 'torch.nn.Linear', 'params': {'in_features': 4096, 'out_features': 1536}}
 
-        // read tensors from tensor_types
-        for (auto pair : tensor_types) {
+        for (auto pair : tensor_storage_map) {
             std::string tensor_name = pair.first;
             if (tensor_name.find("model.diffusion_model.") == std::string::npos)
                 continue;
@@ -720,8 +706,7 @@ public:
                                                                                                     qk_norm,
                                                                                                     true,
                                                                                                     i == depth - 1,
-                                                                                                    i <= d_self,
-                                                                                                    flash_attn));
+                                                                                                    i <= d_self));
         }
 
         blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new FinalLayer(hidden_size, patch_size, out_channels));
@@ -789,8 +774,7 @@ public:
         return x;
     }
 
-    struct ggml_tensor* forward_core_with_concat(struct ggml_context* ctx,
-                                                 ggml_backend_t backend,
+    struct ggml_tensor* forward_core_with_concat(GGMLRunnerContext* ctx,
                                                  struct ggml_tensor* x,
                                                  struct ggml_tensor* c_mod,
                                                  struct ggml_tensor* context,
@@ -809,7 +793,7 @@ public:
 
             auto block = std::dynamic_pointer_cast<JointBlock>(blocks["joint_blocks." + std::to_string(i)]);
 
-            auto context_x = block->forward(ctx, backend, context, x, c_mod);
+            auto context_x = block->forward(ctx, context, x, c_mod);
             context        = context_x.first;
             x              = context_x.second;
         }
@@ -819,12 +803,11 @@ public:
         return x;
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* t,
-                                struct ggml_tensor* y        = NULL,
-                                struct ggml_tensor* context  = NULL,
+                                struct ggml_tensor* y        = nullptr,
+                                struct ggml_tensor* context  = nullptr,
                                 std::vector<int> skip_layers = std::vector<int>()) {
         // Forward pass of DiT.
         // x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
@@ -838,27 +821,27 @@ public:
         int64_t w = x->ne[0];
         int64_t h = x->ne[1];
 
-        auto patch_embed = x_embedder->forward(ctx, x);            // [N, H*W, hidden_size]
-        auto pos_embed   = cropped_pos_embed(ctx, h, w);           // [1, H*W, hidden_size]
-        x                = ggml_add(ctx, patch_embed, pos_embed);  // [N, H*W, hidden_size]
+        auto patch_embed = x_embedder->forward(ctx, x);                      // [N, H*W, hidden_size]
+        auto pos_embed   = cropped_pos_embed(ctx->ggml_ctx, h, w);           // [1, H*W, hidden_size]
+        x                = ggml_add(ctx->ggml_ctx, patch_embed, pos_embed);  // [N, H*W, hidden_size]
 
         auto c = t_embedder->forward(ctx, t);  // [N, hidden_size]
-        if (y != NULL && adm_in_channels != -1) {
+        if (y != nullptr && adm_in_channels != -1) {
             auto y_embedder = std::dynamic_pointer_cast<VectorEmbedder>(blocks["y_embedder"]);
 
             y = y_embedder->forward(ctx, y);  // [N, hidden_size]
-            c = ggml_add(ctx, c, y);
+            c = ggml_add(ctx->ggml_ctx, c, y);
         }
 
-        if (context != NULL) {
+        if (context != nullptr) {
             auto context_embedder = std::dynamic_pointer_cast<Linear>(blocks["context_embedder"]);
 
             context = context_embedder->forward(ctx, context);  // [N, L, D] aka [N, L, 1536]
         }
 
-        x = forward_core_with_concat(ctx, backend, x, c, context, skip_layers);  // (N, H*W, patch_size ** 2 * out_channels)
+        x = forward_core_with_concat(ctx, x, c, context, skip_layers);  // (N, H*W, patch_size ** 2 * out_channels)
 
-        x = unpatchify(ctx, x, h, w);  // [N, C, H, W]
+        x = unpatchify(ctx->ggml_ctx, x, h, w);  // [N, C, H, W]
 
         return x;
     }
@@ -868,14 +851,13 @@ struct MMDiTRunner : public GGMLRunner {
 
     MMDiTRunner(ggml_backend_t backend,
                 bool offload_params_to_cpu,
-                bool flash_attn,
-                const String2GGMLType& tensor_types = {},
-                const std::string prefix            = "")
-        : GGMLRunner(backend, offload_params_to_cpu), mmdit(flash_attn, tensor_types) {
-        mmdit.init(params_ctx, tensor_types, prefix);
+                const String2TensorStorage& tensor_storage_map = {},
+                const std::string prefix                       = "")
+        : GGMLRunner(backend, offload_params_to_cpu), mmdit(tensor_storage_map) {
+        mmdit.init(params_ctx, tensor_storage_map, prefix);
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return "mmdit";
     }
 
@@ -888,15 +870,15 @@ struct MMDiTRunner : public GGMLRunner {
                                     struct ggml_tensor* context,
                                     struct ggml_tensor* y,
                                     std::vector<int> skip_layers = std::vector<int>()) {
-        struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, MMDIT_GRAPH_SIZE, false);
+        struct ggml_cgraph* gf = new_graph_custom(MMDIT_GRAPH_SIZE);
 
         x         = to_backend(x);
         context   = to_backend(context);
         y         = to_backend(y);
         timesteps = to_backend(timesteps);
 
-        struct ggml_tensor* out = mmdit.forward(compute_ctx,
-                                                runtime_backend,
+        auto runner_ctx         = get_context();
+        struct ggml_tensor* out = mmdit.forward(&runner_ctx,
                                                 x,
                                                 timesteps,
                                                 y,
@@ -913,8 +895,8 @@ struct MMDiTRunner : public GGMLRunner {
                  struct ggml_tensor* timesteps,
                  struct ggml_tensor* context,
                  struct ggml_tensor* y,
-                 struct ggml_tensor** output     = NULL,
-                 struct ggml_context* output_ctx = NULL,
+                 struct ggml_tensor** output     = nullptr,
+                 struct ggml_context* output_ctx = nullptr,
                  std::vector<int> skip_layers    = std::vector<int>()) {
         // x: [N, in_channels, h, w]
         // timesteps: [N, ]
@@ -930,11 +912,11 @@ struct MMDiTRunner : public GGMLRunner {
     void test() {
         struct ggml_init_params params;
         params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
-        params.mem_buffer = NULL;
+        params.mem_buffer = nullptr;
         params.no_alloc   = false;
 
         struct ggml_context* work_ctx = ggml_init(params);
-        GGML_ASSERT(work_ctx != NULL);
+        GGML_ASSERT(work_ctx != nullptr);
 
         {
             // cpu f16: pass
@@ -955,7 +937,7 @@ struct MMDiTRunner : public GGMLRunner {
             ggml_set_f32(y, 0.01f);
             // print_ggml_tensor(y);
 
-            struct ggml_tensor* out = NULL;
+            struct ggml_tensor* out = nullptr;
 
             int t0 = ggml_time_ms();
             compute(8, x, timesteps, context, y, &out, work_ctx);
@@ -970,7 +952,7 @@ struct MMDiTRunner : public GGMLRunner {
         // ggml_backend_t backend    = ggml_backend_cuda_init(0);
         ggml_backend_t backend             = ggml_backend_cpu_init();
         ggml_type model_data_type          = GGML_TYPE_F16;
-        std::shared_ptr<MMDiTRunner> mmdit = std::shared_ptr<MMDiTRunner>(new MMDiTRunner(backend, false, false));
+        std::shared_ptr<MMDiTRunner> mmdit = std::make_shared<MMDiTRunner>(backend, false);
         {
             LOG_INFO("loading from '%s'", file_path.c_str());
 
@@ -979,7 +961,7 @@ struct MMDiTRunner : public GGMLRunner {
             mmdit->get_param_tensors(tensors, "model.diffusion_model");
 
             ModelLoader model_loader;
-            if (!model_loader.init_from_file(file_path)) {
+            if (!model_loader.init_from_file_and_convert_name(file_path)) {
                 LOG_ERROR("init model loader from file failed: '%s'", file_path.c_str());
                 return;
             }

model.h

@@ -8,12 +8,14 @@
 #include <sstream>
 #include <string>
 #include <tuple>
+#include <utility>
 #include <vector>
 
 #include "ggml-backend.h"
 #include "ggml.h"
 #include "gguf.h"
 #include "json.hpp"
+#include "ordered_map.hpp"
 #include "zip.h"
 
 #define SD_MAX_DIMS 5
@@ -22,40 +24,54 @@ enum SDVersion {
     VERSION_SD1,
     VERSION_SD1_INPAINT,
     VERSION_SD1_PIX2PIX,
+    VERSION_SD1_TINY_UNET,
     VERSION_SD2,
     VERSION_SD2_INPAINT,
+    VERSION_SD2_TINY_UNET,
     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,
     VERSION_QWEN_IMAGE,
+    VERSION_FLUX2,
     VERSION_COUNT,
 };
 
 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;
 }
 
 static inline bool sd_version_is_sd2(SDVersion version) {
-    if (version == VERSION_SD2 || version == VERSION_SD2_INPAINT) {
+    if (version == VERSION_SD2 || version == VERSION_SD2_INPAINT || version == VERSION_SD2_TINY_UNET) {
         return true;
     }
     return false;
 }
 
 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;
+}
+
+static inline bool sd_version_is_unet(SDVersion version) {
+    if (sd_version_is_sd1(version) ||
+        sd_version_is_sd2(version) ||
+        sd_version_is_sdxl(version)) {
         return true;
     }
     return false;
@@ -69,7 +85,18 @@ 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;
+}
+
+static inline bool sd_version_is_flux2(SDVersion version) {
+    if (version == VERSION_FLUX2) {
         return true;
     }
     return false;
@@ -90,7 +117,11 @@ static inline bool sd_version_is_qwen_image(SDVersion version) {
 }
 
 static inline bool sd_version_is_inpaint(SDVersion version) {
-    if (version == VERSION_SD1_INPAINT || version == VERSION_SD2_INPAINT || version == VERSION_SDXL_INPAINT || version == VERSION_FLUX_FILL || version == VERSION_FLEX_2) {
+    if (version == VERSION_SD1_INPAINT ||
+        version == VERSION_SD2_INPAINT ||
+        version == VERSION_SDXL_INPAINT ||
+        version == VERSION_FLUX_FILL ||
+        version == VERSION_FLEX_2) {
         return true;
     }
     return false;
@@ -98,6 +129,7 @@ static inline bool sd_version_is_inpaint(SDVersion version) {
 
 static inline bool sd_version_is_dit(SDVersion version) {
     if (sd_version_is_flux(version) ||
+        sd_version_is_flux2(version) ||
         sd_version_is_sd3(version) ||
         sd_version_is_wan(version) ||
         sd_version_is_qwen_image(version)) {
@@ -126,6 +158,7 @@ enum PMVersion {
 struct TensorStorage {
     std::string name;
     ggml_type type          = GGML_TYPE_F32;
+    ggml_type expected_type = GGML_TYPE_COUNT;
     bool is_bf16            = false;
     bool is_f8_e4m3         = false;
     bool is_f8_e5m2         = false;
@@ -140,8 +173,8 @@ struct TensorStorage {
 
     TensorStorage() = default;
 
-    TensorStorage(const std::string& name, ggml_type type, const int64_t* ne, int n_dims, size_t file_index, size_t offset = 0)
-        : name(name), type(type), n_dims(n_dims), file_index(file_index), offset(offset) {
+    TensorStorage(std::string name, ggml_type type, const int64_t* ne, int n_dims, size_t file_index, size_t offset = 0)
+        : name(std::move(name)), type(type), n_dims(n_dims), file_index(file_index), offset(offset) {
         for (int i = 0; i < n_dims; i++) {
             this->ne[i] = ne[i];
         }
@@ -234,12 +267,15 @@ struct TensorStorage {
 
 typedef std::function<bool(const TensorStorage&, ggml_tensor**)> on_new_tensor_cb_t;
 
-typedef std::map<std::string, enum ggml_type> String2GGMLType;
+typedef OrderedMap<std::string, TensorStorage> String2TensorStorage;
 
 class ModelLoader {
 protected:
+    SDVersion version_ = VERSION_COUNT;
     std::vector<std::string> file_paths_;
-    std::vector<TensorStorage> tensor_storages;
+    String2TensorStorage tensor_storage_map;
+
+    void add_tensor_storage(const TensorStorage& tensor_storage);
 
     bool parse_data_pkl(uint8_t* buffer,
                         size_t buffer_size,
@@ -254,16 +290,18 @@ protected:
     bool init_from_diffusers_file(const std::string& file_path, const std::string& prefix = "");
 
 public:
-    String2GGMLType tensor_storages_types;
-
     bool init_from_file(const std::string& file_path, const std::string& prefix = "");
-    bool model_is_unet();
+    void convert_tensors_name();
+    bool init_from_file_and_convert_name(const std::string& file_path,
+                                         const std::string& prefix = "",
+                                         SDVersion version         = VERSION_COUNT);
     SDVersion get_sd_version();
     std::map<ggml_type, uint32_t> get_wtype_stat();
     std::map<ggml_type, uint32_t> get_conditioner_wtype_stat();
     std::map<ggml_type, uint32_t> get_diffusion_model_wtype_stat();
     std::map<ggml_type, uint32_t> get_vae_wtype_stat();
-    void set_wtype_override(ggml_type wtype, std::string prefix = "");
+    String2TensorStorage& get_tensor_storage_map() { return tensor_storage_map; }
+    void set_wtype_override(ggml_type wtype, std::string tensor_type_rules = "");
     bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads = 0);
     bool load_tensors(std::map<std::string, struct ggml_tensor*>& tensors,
                       std::set<std::string> ignore_tensors = {},
@@ -271,8 +309,8 @@ public:
 
     std::vector<std::string> get_tensor_names() const {
         std::vector<std::string> names;
-        for (const auto& ts : tensor_storages) {
-            names.push_back(ts.name);
+        for (const auto& [name, tensor_storage] : tensor_storage_map) {
+            names.push_back(name);
         }
         return names;
     }
@@ -284,6 +322,8 @@ public:
 
     static std::string load_merges();
     static std::string load_qwen2_merges();
+    static std::string load_mistral_merges();
+    static std::string load_mistral_vocab_json();
     static std::string load_t5_tokenizer_json();
     static std::string load_umt5_tokenizer_json();
 };

name_conversion.h

@@ -0,0 +1,14 @@
+#ifndef __NAME_CONVERSTION_H__
+#define __NAME_CONVERSTION_H__
+
+#include <string>
+
+#include "model.h"
+
+bool is_cond_stage_model_name(const std::string& name);
+bool is_diffusion_model_name(const std::string& name);
+bool is_first_stage_model_name(const std::string& name);
+
+std::string convert_tensor_name(std::string name, SDVersion version);
+
+#endif  // __NAME_CONVERSTION_H__

ordered_map.hpp

@@ -0,0 +1,177 @@
+#ifndef __ORDERED_MAP_HPP__
+#define __ORDERED_MAP_HPP__
+
+#include <iostream>
+#include <list>
+#include <string>
+#include <unordered_map>
+
+#include <initializer_list>
+#include <iterator>
+#include <list>
+#include <stdexcept>
+#include <unordered_map>
+#include <utility>
+
+template <typename Key, typename T>
+class OrderedMap {
+public:
+    using key_type        = Key;
+    using mapped_type     = T;
+    using value_type      = std::pair<const Key, T>;
+    using list_type       = std::list<value_type>;
+    using size_type       = typename list_type::size_type;
+    using difference_type = typename list_type::difference_type;
+    using iterator        = typename list_type::iterator;
+    using const_iterator  = typename list_type::const_iterator;
+
+private:
+    list_type data_;
+    std::unordered_map<Key, iterator> index_;
+
+public:
+    // --- constructors ---
+    OrderedMap() = default;
+
+    OrderedMap(std::initializer_list<value_type> init) {
+        for (const auto& kv : init)
+            insert(kv);
+    }
+
+    OrderedMap(const OrderedMap&)                = default;
+    OrderedMap(OrderedMap&&) noexcept            = default;
+    OrderedMap& operator=(const OrderedMap&)     = default;
+    OrderedMap& operator=(OrderedMap&&) noexcept = default;
+
+    // --- element access ---
+    T& at(const Key& key) {
+        auto it = index_.find(key);
+        if (it == index_.end())
+            throw std::out_of_range("OrderedMap::at: key not found");
+        return it->second->second;
+    }
+
+    const T& at(const Key& key) const {
+        auto it = index_.find(key);
+        if (it == index_.end())
+            throw std::out_of_range("OrderedMap::at: key not found");
+        return it->second->second;
+    }
+
+    T& operator[](const Key& key) {
+        auto it = index_.find(key);
+        if (it == index_.end()) {
+            data_.emplace_back(key, T{});
+            auto iter   = std::prev(data_.end());
+            index_[key] = iter;
+            return iter->second;
+        }
+        return it->second->second;
+    }
+
+    // --- iterators ---
+    iterator begin() noexcept { return data_.begin(); }
+    const_iterator begin() const noexcept { return data_.begin(); }
+    const_iterator cbegin() const noexcept { return data_.cbegin(); }
+
+    iterator end() noexcept { return data_.end(); }
+    const_iterator end() const noexcept { return data_.end(); }
+    const_iterator cend() const noexcept { return data_.cend(); }
+
+    // --- capacity ---
+    bool empty() const noexcept { return data_.empty(); }
+    size_type size() const noexcept { return data_.size(); }
+
+    // --- modifiers ---
+    void clear() noexcept {
+        data_.clear();
+        index_.clear();
+    }
+
+    std::pair<iterator, bool> insert(const value_type& value) {
+        auto it = index_.find(value.first);
+        if (it != index_.end()) {
+            return {it->second, false};
+        }
+        data_.push_back(value);
+        auto iter           = std::prev(data_.end());
+        index_[value.first] = iter;
+        return {iter, true};
+    }
+
+    std::pair<iterator, bool> insert(value_type&& value) {
+        auto it = index_.find(value.first);
+        if (it != index_.end()) {
+            return {it->second, false};
+        }
+        data_.push_back(std::move(value));
+        auto iter           = std::prev(data_.end());
+        index_[iter->first] = iter;
+        return {iter, true};
+    }
+
+    void erase(const Key& key) {
+        auto it = index_.find(key);
+        if (it != index_.end()) {
+            data_.erase(it->second);
+            index_.erase(it);
+        }
+    }
+
+    iterator erase(iterator pos) {
+        index_.erase(pos->first);
+        return data_.erase(pos);
+    }
+
+    // --- lookup ---
+    size_type count(const Key& key) const {
+        return index_.count(key);
+    }
+
+    iterator find(const Key& key) {
+        auto it = index_.find(key);
+        if (it == index_.end())
+            return data_.end();
+        return it->second;
+    }
+
+    const_iterator find(const Key& key) const {
+        auto it = index_.find(key);
+        if (it == index_.end())
+            return data_.end();
+        return it->second;
+    }
+
+    bool contains(const Key& key) const {
+        return index_.find(key) != index_.end();
+    }
+
+    // --- comparison ---
+    bool operator==(const OrderedMap& other) const {
+        return data_ == other.data_;
+    }
+
+    bool operator!=(const OrderedMap& other) const {
+        return !(*this == other);
+    }
+
+    template <typename... Args>
+    std::pair<iterator, bool> emplace(Args&&... args) {
+        value_type value(std::forward<Args>(args)...);
+        auto it = index_.find(value.first);
+        if (it != index_.end()) {
+            return {it->second, false};
+        }
+        data_.push_back(std::move(value));
+        auto iter           = std::prev(data_.end());
+        index_[iter->first] = iter;
+        return {iter, true};
+    }
+
+    void swap(OrderedMap& other) noexcept {
+        data_.swap(other.data_);
+        index_.swap(other.index_);
+    }
+};
+
+#endif  // __ORDERED_MAP_HPP__

pmid.hpp

@@ -21,7 +21,7 @@ public:
         blocks["layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(in_dim));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [N, channels, h, w]
 
         auto fc1        = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
@@ -29,15 +29,15 @@ public:
         auto layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["layernorm"]);
 
         struct ggml_tensor* r = x;
-        // x = ggml_nn_layer_norm(ctx, x, ln_w, ln_b);
+        // x = ggml_ext_layer_norm(ctx, x, ln_w, ln_b);
         x = layer_norm->forward(ctx, x);
         // x = ggml_add(ctx, ggml_mul_mat(ctx, fc1_w, x),  fc1_b);
         x = fc1->forward(ctx, x);
-        x = ggml_gelu_inplace(ctx, x);
+        x = ggml_gelu_inplace(ctx->ggml_ctx, x);
         x = fc2->forward(ctx, x);
         // x = ggml_add(ctx, ggml_mul_mat(ctx, fc2_w, x),  fc2_b);
         if (use_residue)
-            x = ggml_add(ctx, x, r);
+            x = ggml_add(ctx->ggml_ctx, x, r);
         return x;
     }
 };
@@ -54,7 +54,7 @@ public:
         blocks["1"]   = std::shared_ptr<GGMLBlock>(new Mlp(dim, inner_dim, dim, false));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x) {
         auto norm = std::dynamic_pointer_cast<LayerNorm>(blocks["0"]);
         auto ff   = std::dynamic_pointer_cast<Mlp>(blocks["1"]);
@@ -100,7 +100,7 @@ public:
                 ggml_cont(ctx, tli)};
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* latents) {
         // x (torch.Tensor): image features
@@ -118,33 +118,33 @@ public:
         auto to_q  = std::dynamic_pointer_cast<Linear>(blocks["to_q"]);
         auto q     = to_q->forward(ctx, latents);
 
-        auto kv_input = ggml_concat(ctx, x, latents, 1);
+        auto kv_input = ggml_concat(ctx->ggml_ctx, x, latents, 1);
         auto to_kv    = std::dynamic_pointer_cast<Linear>(blocks["to_kv"]);
         auto kv       = to_kv->forward(ctx, kv_input);
-        auto k        = ggml_view_4d(ctx, kv, kv->ne[0] / 2, kv->ne[1], kv->ne[2], kv->ne[3], kv->nb[1] / 2, kv->nb[2] / 2, kv->nb[3] / 2, 0);
-        auto v        = ggml_view_4d(ctx, kv, kv->ne[0] / 2, kv->ne[1], kv->ne[2], kv->ne[3], kv->nb[1] / 2, kv->nb[2] / 2, kv->nb[3] / 2, kv->nb[0] * (kv->ne[0] / 2));
-        k             = ggml_cont(ctx, k);
-        v             = ggml_cont(ctx, v);
-        q             = reshape_tensor(ctx, q, heads);
-        k             = reshape_tensor(ctx, k, heads);
-        v             = reshape_tensor(ctx, v, heads);
+        auto k        = ggml_view_4d(ctx->ggml_ctx, kv, kv->ne[0] / 2, kv->ne[1], kv->ne[2], kv->ne[3], kv->nb[1] / 2, kv->nb[2] / 2, kv->nb[3] / 2, 0);
+        auto v        = ggml_view_4d(ctx->ggml_ctx, kv, kv->ne[0] / 2, kv->ne[1], kv->ne[2], kv->ne[3], kv->nb[1] / 2, kv->nb[2] / 2, kv->nb[3] / 2, kv->nb[0] * (kv->ne[0] / 2));
+        k             = ggml_cont(ctx->ggml_ctx, k);
+        v             = ggml_cont(ctx->ggml_ctx, v);
+        q             = reshape_tensor(ctx->ggml_ctx, q, heads);
+        k             = reshape_tensor(ctx->ggml_ctx, k, heads);
+        v             = reshape_tensor(ctx->ggml_ctx, v, heads);
         scale         = 1.f / sqrt(sqrt((float)dim_head));
-        k             = ggml_scale_inplace(ctx, k, scale);
-        q             = ggml_scale_inplace(ctx, q, scale);
+        k             = ggml_scale_inplace(ctx->ggml_ctx, k, scale);
+        q             = ggml_scale_inplace(ctx->ggml_ctx, q, scale);
         // auto weight = ggml_mul_mat(ctx, q, k);
-        auto weight = ggml_mul_mat(ctx, k, q);  // NOTE order of mul is opposite to pytorch
+        auto weight = ggml_mul_mat(ctx->ggml_ctx, k, q);  // NOTE order of mul is opposite to pytorch
 
         // GGML's softmax() is equivalent to pytorch's softmax(x, dim=-1)
         // in this case, dimension along which Softmax will be computed is the last dim
         // in torch and the first dim in GGML, consistent with the convention that pytorch's
         // last dimension (varying most rapidly) corresponds to GGML's first (varying most rapidly).
         // weight = ggml_soft_max(ctx, weight);
-        weight = ggml_soft_max_inplace(ctx, weight);
-        v      = ggml_cont(ctx, ggml_transpose(ctx, v));
+        weight = ggml_soft_max_inplace(ctx->ggml_ctx, weight);
+        v      = ggml_cont(ctx->ggml_ctx, ggml_transpose(ctx->ggml_ctx, v));
         // auto out = ggml_mul_mat(ctx, weight, v);
-        auto out    = ggml_mul_mat(ctx, v, weight);  // NOTE order of mul is opposite to pytorch
-        out         = ggml_cont(ctx, ggml_permute(ctx, out, 0, 2, 1, 3));
-        out         = ggml_reshape_3d(ctx, out, ne[0], ne[1], ggml_nelements(out) / (ne[0] * ne[1]));
+        auto out    = ggml_mul_mat(ctx->ggml_ctx, v, weight);  // NOTE order of mul is opposite to pytorch
+        out         = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, out, 0, 2, 1, 3));
+        out         = ggml_reshape_3d(ctx->ggml_ctx, out, ne[0], ne[1], ggml_nelements(out) / (ne[0] * ne[1]));
         auto to_out = std::dynamic_pointer_cast<Linear>(blocks["to_out"]);
         out         = to_out->forward(ctx, out);
         return out;
@@ -176,7 +176,7 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* latents,
                                 struct ggml_tensor* x) {
         // x: [N, channels, h, w]
@@ -191,9 +191,9 @@ public:
             name             = "layers." + std::to_string(i) + ".1";
             auto ff          = std::dynamic_pointer_cast<PMFeedForward>(blocks[name]);
             auto t           = attn->forward(ctx, x, latents);
-            latents          = ggml_add(ctx, t, latents);
+            latents          = ggml_add(ctx->ggml_ctx, t, latents);
             t                = ff->forward(ctx, latents);
-            latents          = ggml_add(ctx, t, latents);
+            latents          = ggml_add(ctx->ggml_ctx, t, latents);
         }
         latents = proj_out->forward(ctx, latents);
         latents = norm_out->forward(ctx, latents);
@@ -225,7 +225,7 @@ public:
             4));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* last_hidden_state) {
         // x: [N, channels, h, w]
@@ -235,11 +235,11 @@ public:
 
         x                       = token_proj->forward(ctx, x);
         int64_t nel             = ggml_nelements(x);
-        x                       = ggml_reshape_3d(ctx, x, cross_attention_dim, num_tokens, nel / (cross_attention_dim * num_tokens));
+        x                       = ggml_reshape_3d(ctx->ggml_ctx, x, cross_attention_dim, num_tokens, nel / (cross_attention_dim * num_tokens));
         x                       = token_norm->forward(ctx, x);
         struct ggml_tensor* out = perceiver_resampler->forward(ctx, x, last_hidden_state);
         if (use_residul)
-            out = ggml_add(ctx, x, out);
+            out = ggml_add(ctx->ggml_ctx, x, out);
         return out;
     }
 };
@@ -256,24 +256,24 @@ public:
         blocks["layer_norm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(embed_dim));
     }
 
-    struct ggml_tensor* fuse_fn(struct ggml_context* ctx,
+    struct ggml_tensor* fuse_fn(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* prompt_embeds,
                                 struct ggml_tensor* id_embeds) {
         auto mlp1       = std::dynamic_pointer_cast<FuseBlock>(blocks["mlp1"]);
         auto mlp2       = std::dynamic_pointer_cast<FuseBlock>(blocks["mlp2"]);
         auto layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm"]);
 
-        auto stacked_id_embeds = ggml_concat(ctx, prompt_embeds, id_embeds, 0);
+        auto stacked_id_embeds = ggml_concat(ctx->ggml_ctx, prompt_embeds, id_embeds, 0);
 
         stacked_id_embeds = mlp1->forward(ctx, stacked_id_embeds);
-        stacked_id_embeds = ggml_add(ctx, stacked_id_embeds, prompt_embeds);
+        stacked_id_embeds = ggml_add(ctx->ggml_ctx, stacked_id_embeds, prompt_embeds);
         stacked_id_embeds = mlp2->forward(ctx, stacked_id_embeds);
         stacked_id_embeds = layer_norm->forward(ctx, stacked_id_embeds);
 
         return stacked_id_embeds;
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* prompt_embeds,
                                 struct ggml_tensor* id_embeds,
                                 struct ggml_tensor* class_tokens_mask,
@@ -286,25 +286,25 @@ public:
         // # slice out the image token embeddings
         ggml_set_name(class_tokens_mask_pos, "class_tokens_mask_pos");
         ggml_set_name(prompt_embeds, "prompt_embeds");
-        struct ggml_tensor* image_token_embeds = ggml_get_rows(ctx, prompt_embeds, class_tokens_mask_pos);
+        struct ggml_tensor* image_token_embeds = ggml_get_rows(ctx->ggml_ctx, prompt_embeds, class_tokens_mask_pos);
         ggml_set_name(image_token_embeds, "image_token_embeds");
-        valid_id_embeds                       = ggml_reshape_2d(ctx, valid_id_embeds, valid_id_embeds->ne[0],
+        valid_id_embeds                       = ggml_reshape_2d(ctx->ggml_ctx, valid_id_embeds, valid_id_embeds->ne[0],
                                                                 ggml_nelements(valid_id_embeds) / valid_id_embeds->ne[0]);
         struct ggml_tensor* stacked_id_embeds = fuse_fn(ctx, image_token_embeds, valid_id_embeds);
 
         if (left && right) {
-            stacked_id_embeds = ggml_concat(ctx, left, stacked_id_embeds, 1);
-            stacked_id_embeds = ggml_concat(ctx, stacked_id_embeds, right, 1);
+            stacked_id_embeds = ggml_concat(ctx->ggml_ctx, left, stacked_id_embeds, 1);
+            stacked_id_embeds = ggml_concat(ctx->ggml_ctx, stacked_id_embeds, right, 1);
         } else if (left) {
-            stacked_id_embeds = ggml_concat(ctx, left, stacked_id_embeds, 1);
+            stacked_id_embeds = ggml_concat(ctx->ggml_ctx, left, stacked_id_embeds, 1);
         } else if (right) {
-            stacked_id_embeds = ggml_concat(ctx, stacked_id_embeds, right, 1);
+            stacked_id_embeds = ggml_concat(ctx->ggml_ctx, stacked_id_embeds, right, 1);
         }
 
-        class_tokens_mask                         = ggml_cont(ctx, ggml_transpose(ctx, class_tokens_mask));
-        class_tokens_mask                         = ggml_repeat(ctx, class_tokens_mask, prompt_embeds);
-        prompt_embeds                             = ggml_mul(ctx, prompt_embeds, class_tokens_mask);
-        struct ggml_tensor* updated_prompt_embeds = ggml_add(ctx, prompt_embeds, stacked_id_embeds);
+        class_tokens_mask                         = ggml_cont(ctx->ggml_ctx, ggml_transpose(ctx->ggml_ctx, class_tokens_mask));
+        class_tokens_mask                         = ggml_repeat(ctx->ggml_ctx, class_tokens_mask, prompt_embeds);
+        prompt_embeds                             = ggml_mul(ctx->ggml_ctx, prompt_embeds, class_tokens_mask);
+        struct ggml_tensor* updated_prompt_embeds = ggml_add(ctx->ggml_ctx, prompt_embeds, stacked_id_embeds);
         ggml_set_name(updated_prompt_embeds, "updated_prompt_embeds");
         return updated_prompt_embeds;
     }
@@ -317,8 +317,7 @@ struct PhotoMakerIDEncoderBlock : public CLIPVisionModelProjection {
         blocks["fuse_module"]         = std::shared_ptr<GGMLBlock>(new FuseModule(2048));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* id_pixel_values,
                                 struct ggml_tensor* prompt_embeds,
                                 struct ggml_tensor* class_tokens_mask,
@@ -331,15 +330,15 @@ struct PhotoMakerIDEncoderBlock : public CLIPVisionModelProjection {
         auto visual_projection_2 = std::dynamic_pointer_cast<Linear>(blocks["visual_projection_2"]);
         auto fuse_module         = std::dynamic_pointer_cast<FuseModule>(blocks["fuse_module"]);
 
-        struct ggml_tensor* shared_id_embeds = vision_model->forward(ctx, backend, id_pixel_values);  // [N, hidden_size]
-        struct ggml_tensor* id_embeds        = visual_projection->forward(ctx, shared_id_embeds);     // [N, proj_dim(768)]
-        struct ggml_tensor* id_embeds_2      = visual_projection_2->forward(ctx, shared_id_embeds);   // [N, 1280]
+        struct ggml_tensor* shared_id_embeds = vision_model->forward(ctx, id_pixel_values);          // [N, hidden_size]
+        struct ggml_tensor* id_embeds        = visual_projection->forward(ctx, shared_id_embeds);    // [N, proj_dim(768)]
+        struct ggml_tensor* id_embeds_2      = visual_projection_2->forward(ctx, shared_id_embeds);  // [N, 1280]
 
-        id_embeds   = ggml_cont(ctx, ggml_permute(ctx, id_embeds, 2, 0, 1, 3));
-        id_embeds_2 = ggml_cont(ctx, ggml_permute(ctx, id_embeds_2, 2, 0, 1, 3));
+        id_embeds   = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, id_embeds, 2, 0, 1, 3));
+        id_embeds_2 = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, id_embeds_2, 2, 0, 1, 3));
 
-        id_embeds = ggml_concat(ctx, id_embeds, id_embeds_2, 2);  // [batch_size, seq_length, 1, 2048] check whether concat at dim 2 is right
-        id_embeds = ggml_cont(ctx, ggml_permute(ctx, id_embeds, 1, 2, 0, 3));
+        id_embeds = ggml_concat(ctx->ggml_ctx, id_embeds, id_embeds_2, 2);  // [batch_size, seq_length, 1, 2048] check whether concat at dim 2 is right
+        id_embeds = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, id_embeds, 1, 2, 0, 3));
 
         struct ggml_tensor* updated_prompt_embeds = fuse_module->forward(ctx,
                                                                          prompt_embeds,
@@ -366,8 +365,7 @@ struct PhotoMakerIDEncoder_CLIPInsightfaceExtendtokenBlock : public CLIPVisionMo
                                                                                         num_tokens));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* id_pixel_values,
                                 struct ggml_tensor* prompt_embeds,
                                 struct ggml_tensor* class_tokens_mask,
@@ -381,7 +379,7 @@ struct PhotoMakerIDEncoder_CLIPInsightfaceExtendtokenBlock : public CLIPVisionMo
         auto qformer_perceiver = std::dynamic_pointer_cast<QFormerPerceiver>(blocks["qformer_perceiver"]);
 
         // struct ggml_tensor* last_hidden_state = vision_model->forward(ctx, id_pixel_values);          // [N, hidden_size]
-        struct ggml_tensor* last_hidden_state = vision_model->forward(ctx, backend, id_pixel_values, false);  // [N, hidden_size]
+        struct ggml_tensor* last_hidden_state = vision_model->forward(ctx, id_pixel_values, false);  // [N, hidden_size]
         id_embeds                             = qformer_perceiver->forward(ctx, id_embeds, last_hidden_state);
 
         struct ggml_tensor* updated_prompt_embeds = fuse_module->forward(ctx,
@@ -414,7 +412,7 @@ public:
 public:
     PhotoMakerIDEncoder(ggml_backend_t backend,
                         bool offload_params_to_cpu,
-                        const String2GGMLType& tensor_types,
+                        const String2TensorStorage& tensor_storage_map,
                         const std::string prefix,
                         SDVersion version = VERSION_SDXL,
                         PMVersion pm_v    = PM_VERSION_1,
@@ -424,9 +422,9 @@ public:
           pm_version(pm_v),
           style_strength(sty) {
         if (pm_version == PM_VERSION_1) {
-            id_encoder.init(params_ctx, tensor_types, prefix);
+            id_encoder.init(params_ctx, tensor_storage_map, prefix);
         } else if (pm_version == PM_VERSION_2) {
-            id_encoder2.init(params_ctx, tensor_types, prefix);
+            id_encoder2.init(params_ctx, tensor_storage_map, prefix);
         }
     }
 
@@ -458,7 +456,7 @@ public:
         zeros_right.clear();
         zeros_right_16.clear();
 
-        ggml_context* ctx0 = compute_ctx;
+        auto runner_ctx = get_context();
 
         struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
 
@@ -466,14 +464,14 @@ public:
         int64_t seq_length  = prompt_embeds->ne[1];
         ggml_type type      = GGML_TYPE_F32;
 
-        struct ggml_tensor* class_tokens_mask_d = ggml_new_tensor_1d(ctx0, type, class_tokens_mask.size());
+        struct ggml_tensor* class_tokens_mask_d = ggml_new_tensor_1d(runner_ctx.ggml_ctx, type, class_tokens_mask.size());
 
         struct ggml_tensor* id_pixel_values_d = to_backend(id_pixel_values);
         struct ggml_tensor* prompt_embeds_d   = to_backend(prompt_embeds);
         struct ggml_tensor* id_embeds_d       = to_backend(id_embeds);
 
-        struct ggml_tensor* left  = NULL;
-        struct ggml_tensor* right = NULL;
+        struct ggml_tensor* left  = nullptr;
+        struct ggml_tensor* right = nullptr;
         for (int i = 0; i < class_tokens_mask.size(); i++) {
             if (class_tokens_mask[i]) {
                 // printf(" 1,");
@@ -488,16 +486,16 @@ public:
         }
         // printf("\n");
         if (ctmpos[0] > 0) {
-            // left = ggml_new_tensor_3d(ctx0, type, hidden_size, 1, ctmpos[0]);
-            left = ggml_new_tensor_3d(ctx0, type, hidden_size, ctmpos[0], 1);
+            // left = ggml_new_tensor_3d(runner_ctx.ggml_ctx, type, hidden_size, 1, ctmpos[0]);
+            left = ggml_new_tensor_3d(runner_ctx.ggml_ctx, type, hidden_size, ctmpos[0], 1);
         }
         if (ctmpos[ctmpos.size() - 1] < seq_length - 1) {
-            // right = ggml_new_tensor_3d(ctx0, type,
+            // right = ggml_new_tensor_3d(runner_ctx.ggml_ctx, type,
             //                            hidden_size, 1, seq_length - ctmpos[ctmpos.size() - 1] - 1);
-            right = ggml_new_tensor_3d(ctx0, type,
+            right = ggml_new_tensor_3d(runner_ctx.ggml_ctx, type,
                                        hidden_size, seq_length - ctmpos[ctmpos.size() - 1] - 1, 1);
         }
-        struct ggml_tensor* class_tokens_mask_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ctmpos.size());
+        struct ggml_tensor* class_tokens_mask_pos = ggml_new_tensor_1d(runner_ctx.ggml_ctx, GGML_TYPE_I32, ctmpos.size());
 
         {
             if (type == GGML_TYPE_F16)
@@ -528,18 +526,16 @@ public:
                 }
             }
         }
-        struct ggml_tensor* updated_prompt_embeds = NULL;
+        struct ggml_tensor* updated_prompt_embeds = nullptr;
         if (pm_version == PM_VERSION_1)
-            updated_prompt_embeds = id_encoder.forward(ctx0,
-                                                       runtime_backend,
+            updated_prompt_embeds = id_encoder.forward(&runner_ctx,
                                                        id_pixel_values_d,
                                                        prompt_embeds_d,
                                                        class_tokens_mask_d,
                                                        class_tokens_mask_pos,
                                                        left, right);
         else if (pm_version == PM_VERSION_2)
-            updated_prompt_embeds = id_encoder2.forward(ctx0,
-                                                        runtime_backend,
+            updated_prompt_embeds = id_encoder2.forward(&runner_ctx,
                                                         id_pixel_values_d,
                                                         prompt_embeds_d,
                                                         class_tokens_mask_d,
@@ -582,7 +578,7 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
                       const std::string& file_path = "",
                       const std::string& prefix    = "")
         : file_path(file_path), GGMLRunner(backend, offload_params_to_cpu), model_loader(ml) {
-        if (!model_loader->init_from_file(file_path, prefix)) {
+        if (!model_loader->init_from_file_and_convert_name(file_path, prefix)) {
             load_failed = true;
         }
     }
@@ -638,7 +634,7 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
         pos = tensors.find("pmid.id_embeds");
         if (pos != tensors.end())
             return pos->second;
-        return NULL;
+        return nullptr;
     }
 };
 

preprocessing.hpp

@@ -7,7 +7,7 @@
 void convolve(struct ggml_tensor* input, struct ggml_tensor* output, struct ggml_tensor* kernel, int padding) {
     struct ggml_init_params params;
     params.mem_size                 = 80 * input->ne[0] * input->ne[1];  // 20M for 512x512
-    params.mem_buffer               = NULL;
+    params.mem_buffer               = nullptr;
     params.no_alloc                 = false;
     struct ggml_context* ctx0       = ggml_init(params);
     struct ggml_tensor* kernel_fp16 = ggml_new_tensor_4d(ctx0, GGML_TYPE_F16, kernel->ne[0], kernel->ne[1], 1, 1);
@@ -28,7 +28,7 @@ void gaussian_kernel(struct ggml_tensor* kernel) {
         for (int x = 0; x < kernel->ne[1]; x++) {
             float gy = -ks_mid + x;
             float k_ = expf(-((gx * gx + gy * gy) / (2.0f * powf(sigma, 2.0f)))) * normal;
-            ggml_tensor_set_f32(kernel, k_, x, y);
+            ggml_ext_tensor_set_f32(kernel, k_, x, y);
         }
     }
 }
@@ -36,11 +36,11 @@ void gaussian_kernel(struct ggml_tensor* kernel) {
 void grayscale(struct ggml_tensor* rgb_img, struct ggml_tensor* grayscale) {
     for (int iy = 0; iy < rgb_img->ne[1]; iy++) {
         for (int ix = 0; ix < rgb_img->ne[0]; ix++) {
-            float r    = ggml_tensor_get_f32(rgb_img, ix, iy);
-            float g    = ggml_tensor_get_f32(rgb_img, ix, iy, 1);
-            float b    = ggml_tensor_get_f32(rgb_img, ix, iy, 2);
+            float r    = ggml_ext_tensor_get_f32(rgb_img, ix, iy);
+            float g    = ggml_ext_tensor_get_f32(rgb_img, ix, iy, 1);
+            float b    = ggml_ext_tensor_get_f32(rgb_img, ix, iy, 2);
             float gray = 0.2989f * r + 0.5870f * g + 0.1140f * b;
-            ggml_tensor_set_f32(grayscale, gray, ix, iy);
+            ggml_ext_tensor_set_f32(grayscale, gray, ix, iy);
         }
     }
 }
@@ -81,37 +81,37 @@ void normalize_tensor(struct ggml_tensor* g) {
 void non_max_supression(struct ggml_tensor* result, struct ggml_tensor* G, struct ggml_tensor* D) {
     for (int iy = 1; iy < result->ne[1] - 1; iy++) {
         for (int ix = 1; ix < result->ne[0] - 1; ix++) {
-            float angle = ggml_tensor_get_f32(D, ix, iy) * 180.0f / M_PI_;
+            float angle = ggml_ext_tensor_get_f32(D, ix, iy) * 180.0f / M_PI_;
             angle       = angle < 0.0f ? angle += 180.0f : angle;
             float q     = 1.0f;
             float r     = 1.0f;
 
             // angle 0
             if ((0 >= angle && angle < 22.5f) || (157.5f >= angle && angle <= 180)) {
-                q = ggml_tensor_get_f32(G, ix, iy + 1);
-                r = ggml_tensor_get_f32(G, ix, iy - 1);
+                q = ggml_ext_tensor_get_f32(G, ix, iy + 1);
+                r = ggml_ext_tensor_get_f32(G, ix, iy - 1);
             }
             // angle 45
             else if (22.5f >= angle && angle < 67.5f) {
-                q = ggml_tensor_get_f32(G, ix + 1, iy - 1);
-                r = ggml_tensor_get_f32(G, ix - 1, iy + 1);
+                q = ggml_ext_tensor_get_f32(G, ix + 1, iy - 1);
+                r = ggml_ext_tensor_get_f32(G, ix - 1, iy + 1);
             }
             // angle 90
             else if (67.5f >= angle && angle < 112.5) {
-                q = ggml_tensor_get_f32(G, ix + 1, iy);
-                r = ggml_tensor_get_f32(G, ix - 1, iy);
+                q = ggml_ext_tensor_get_f32(G, ix + 1, iy);
+                r = ggml_ext_tensor_get_f32(G, ix - 1, iy);
             }
             // angle 135
             else if (112.5 >= angle && angle < 157.5f) {
-                q = ggml_tensor_get_f32(G, ix - 1, iy - 1);
-                r = ggml_tensor_get_f32(G, ix + 1, iy + 1);
+                q = ggml_ext_tensor_get_f32(G, ix - 1, iy - 1);
+                r = ggml_ext_tensor_get_f32(G, ix + 1, iy + 1);
             }
 
-            float cur = ggml_tensor_get_f32(G, ix, iy);
+            float cur = ggml_ext_tensor_get_f32(G, ix, iy);
             if ((cur >= q) && (cur >= r)) {
-                ggml_tensor_set_f32(result, cur, ix, iy);
+                ggml_ext_tensor_set_f32(result, cur, ix, iy);
             } else {
-                ggml_tensor_set_f32(result, 0.0f, ix, iy);
+                ggml_ext_tensor_set_f32(result, 0.0f, ix, iy);
             }
         }
     }
@@ -138,9 +138,9 @@ void threshold_hystersis(struct ggml_tensor* img, float high_threshold, float lo
     for (int iy = 0; iy < img->ne[1]; iy++) {
         for (int ix = 0; ix < img->ne[0]; ix++) {
             if (ix >= 3 && ix <= img->ne[0] - 3 && iy >= 3 && iy <= img->ne[1] - 3) {
-                ggml_tensor_set_f32(img, ggml_tensor_get_f32(img, ix, iy), ix, iy);
+                ggml_ext_tensor_set_f32(img, ggml_ext_tensor_get_f32(img, ix, iy), ix, iy);
             } else {
-                ggml_tensor_set_f32(img, 0.0f, ix, iy);
+                ggml_ext_tensor_set_f32(img, 0.0f, ix, iy);
             }
         }
     }
@@ -148,14 +148,14 @@ void threshold_hystersis(struct ggml_tensor* img, float high_threshold, float lo
     // hysteresis
     for (int iy = 1; iy < img->ne[1] - 1; iy++) {
         for (int ix = 1; ix < img->ne[0] - 1; ix++) {
-            float imd_v = ggml_tensor_get_f32(img, ix, iy);
+            float imd_v = ggml_ext_tensor_get_f32(img, ix, iy);
             if (imd_v == weak) {
-                if (ggml_tensor_get_f32(img, ix + 1, iy - 1) == strong || ggml_tensor_get_f32(img, ix + 1, iy) == strong ||
-                    ggml_tensor_get_f32(img, ix, iy - 1) == strong || ggml_tensor_get_f32(img, ix, iy + 1) == strong ||
-                    ggml_tensor_get_f32(img, ix - 1, iy - 1) == strong || ggml_tensor_get_f32(img, ix - 1, iy) == strong) {
-                    ggml_tensor_set_f32(img, strong, ix, iy);
+                if (ggml_ext_tensor_get_f32(img, ix + 1, iy - 1) == strong || ggml_ext_tensor_get_f32(img, ix + 1, iy) == strong ||
+                    ggml_ext_tensor_get_f32(img, ix, iy - 1) == strong || ggml_ext_tensor_get_f32(img, ix, iy + 1) == strong ||
+                    ggml_ext_tensor_get_f32(img, ix - 1, iy - 1) == strong || ggml_ext_tensor_get_f32(img, ix - 1, iy) == strong) {
+                    ggml_ext_tensor_set_f32(img, strong, ix, iy);
                 } else {
-                    ggml_tensor_set_f32(img, 0.0f, ix, iy);
+                    ggml_ext_tensor_set_f32(img, 0.0f, ix, iy);
                 }
             }
         }
@@ -165,7 +165,7 @@ void threshold_hystersis(struct ggml_tensor* img, float high_threshold, float lo
 bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold, float weak, float strong, bool inverse) {
     struct ggml_init_params params;
     params.mem_size               = static_cast<size_t>(40 * img.width * img.height);  // 10MB for 512x512
-    params.mem_buffer             = NULL;
+    params.mem_buffer             = nullptr;
     params.no_alloc               = false;
     struct ggml_context* work_ctx = ggml_init(params);
 
@@ -198,7 +198,7 @@ bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold,
     struct ggml_tensor* iY         = ggml_dup_tensor(work_ctx, image_gray);
     struct ggml_tensor* G          = ggml_dup_tensor(work_ctx, image_gray);
     struct ggml_tensor* tetha      = ggml_dup_tensor(work_ctx, image_gray);
-    sd_image_to_tensor(img, image);
+    sd_image_to_ggml_tensor(img, image);
     grayscale(image, image_gray);
     convolve(image_gray, image_gray, gkernel, 2);
     convolve(image_gray, iX, sf_kx, 1);
@@ -211,14 +211,14 @@ bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold,
     // to RGB channels
     for (int iy = 0; iy < img.height; iy++) {
         for (int ix = 0; ix < img.width; ix++) {
-            float gray = ggml_tensor_get_f32(image_gray, ix, iy);
+            float gray = ggml_ext_tensor_get_f32(image_gray, ix, iy);
             gray       = inverse ? 1.0f - gray : gray;
-            ggml_tensor_set_f32(image, gray, ix, iy);
-            ggml_tensor_set_f32(image, gray, ix, iy, 1);
-            ggml_tensor_set_f32(image, gray, ix, iy, 2);
+            ggml_ext_tensor_set_f32(image, gray, ix, iy);
+            ggml_ext_tensor_set_f32(image, gray, ix, iy, 1);
+            ggml_ext_tensor_set_f32(image, gray, ix, iy, 2);
         }
     }
-    sd_tensor_to_image(image, img.data);
+    ggml_tensor_to_sd_image(image, img.data);
     ggml_free(work_ctx);
     return true;
 }

qwen_image.hpp

@@ -1,6 +1,8 @@
 #ifndef __QWEN_IMAGE_HPP__
 #define __QWEN_IMAGE_HPP__
 
+#include <memory>
+
 #include "common.hpp"
 #include "flux.hpp"
 #include "ggml_extend.hpp"
@@ -25,18 +27,18 @@ namespace Qwen {
             blocks["linear_2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, out_dim, sample_proj_bias));
         }
 
-        struct ggml_tensor* forward(struct ggml_context* ctx,
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                     struct ggml_tensor* sample,
                                     struct ggml_tensor* condition = nullptr) {
             if (condition != nullptr) {
                 auto cond_proj = std::dynamic_pointer_cast<Linear>(blocks["cond_proj"]);
-                sample         = ggml_add(ctx, sample, cond_proj->forward(ctx, condition));
+                sample         = ggml_add(ctx->ggml_ctx, sample, cond_proj->forward(ctx, condition));
             }
             auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["linear_1"]);
             auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["linear_2"]);
 
             sample = linear_1->forward(ctx, sample);
-            sample = ggml_silu_inplace(ctx, sample);
+            sample = ggml_silu_inplace(ctx->ggml_ctx, sample);
             sample = linear_2->forward(ctx, sample);
             return sample;
         }
@@ -48,13 +50,13 @@ namespace Qwen {
             blocks["timestep_embedder"] = std::shared_ptr<GGMLBlock>(new TimestepEmbedding(256, embedding_dim));
         }
 
-        struct ggml_tensor* forward(struct ggml_context* ctx,
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                     struct ggml_tensor* timesteps) {
             // timesteps: [N,]
             // return: [N, embedding_dim]
             auto timestep_embedder = std::dynamic_pointer_cast<TimestepEmbedding>(blocks["timestep_embedder"]);
 
-            auto timesteps_proj = ggml_nn_timestep_embedding(ctx, timesteps, 256, 10000, 1.f);
+            auto timesteps_proj = ggml_ext_timestep_embedding(ctx->ggml_ctx, timesteps, 256, 10000, 1.f);
             auto timesteps_emb  = timestep_embedder->forward(ctx, timesteps_proj);
             return timesteps_emb;
         }
@@ -63,7 +65,6 @@ namespace Qwen {
     struct QwenImageAttention : public GGMLBlock {
     protected:
         int64_t dim_head;
-        bool flash_attn;
 
     public:
         QwenImageAttention(int64_t query_dim,
@@ -73,9 +74,8 @@ namespace Qwen {
                            int64_t out_context_dim = 0,
                            bool bias               = true,
                            bool out_bias           = true,
-                           float eps               = 1e-6,
-                           bool flash_attn         = false)
-            : dim_head(dim_head), flash_attn(flash_attn) {
+                           float eps               = 1e-6)
+            : dim_head(dim_head) {
             int64_t inner_dim = out_dim > 0 ? out_dim : dim_head * num_heads;
             out_dim           = out_dim > 0 ? out_dim : query_dim;
             out_context_dim   = out_context_dim > 0 ? out_context_dim : query_dim;
@@ -94,17 +94,20 @@ namespace Qwen {
             blocks["norm_added_q"] = std::shared_ptr<GGMLBlock>(new RMSNorm(dim_head, eps));
             blocks["norm_added_k"] = std::shared_ptr<GGMLBlock>(new RMSNorm(dim_head, eps));
 
-            float scale = 1.f / 32.f;
+            float scale         = 1.f / 32.f;
+            bool force_prec_f32 = false;
+#ifdef SD_USE_VULKAN
+            force_prec_f32 = true;
+#endif
             // The purpose of the scale here is to prevent NaN issues in certain situations.
             // For example when using CUDA but the weights are k-quants (not all prompts).
-            blocks["to_out.0"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, out_dim, out_bias, false, false, scale));
+            blocks["to_out.0"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, out_dim, out_bias, false, force_prec_f32, scale));
             // to_out.1 is nn.Dropout
 
             blocks["to_add_out"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, out_context_dim, out_bias, false, false, scale));
         }
 
-        std::pair<ggml_tensor*, ggml_tensor*> forward(struct ggml_context* ctx,
-                                                      ggml_backend_t backend,
+        std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
                                                       struct ggml_tensor* img,
                                                       struct ggml_tensor* txt,
                                                       struct ggml_tensor* pe,
@@ -136,49 +139,49 @@ namespace Qwen {
 
             auto img_q        = to_q->forward(ctx, img);
             int64_t num_heads = img_q->ne[0] / dim_head;
-            img_q             = ggml_reshape_4d(ctx, img_q, dim_head, num_heads, n_img_token, N);  // [N, n_img_token, n_head, d_head]
+            img_q             = ggml_reshape_4d(ctx->ggml_ctx, img_q, dim_head, num_heads, n_img_token, N);  // [N, n_img_token, n_head, d_head]
             auto img_k        = to_k->forward(ctx, img);
-            img_k             = ggml_reshape_4d(ctx, img_k, dim_head, num_heads, n_img_token, N);  // [N, n_img_token, n_head, d_head]
+            img_k             = ggml_reshape_4d(ctx->ggml_ctx, img_k, dim_head, num_heads, n_img_token, N);  // [N, n_img_token, n_head, d_head]
             auto img_v        = to_v->forward(ctx, img);
-            img_v             = ggml_reshape_4d(ctx, img_v, dim_head, num_heads, n_img_token, N);  // [N, n_img_token, n_head, d_head]
+            img_v             = ggml_reshape_4d(ctx->ggml_ctx, img_v, dim_head, num_heads, n_img_token, N);  // [N, n_img_token, n_head, d_head]
 
             img_q = norm_q->forward(ctx, img_q);
             img_k = norm_k->forward(ctx, img_k);
 
             auto txt_q = add_q_proj->forward(ctx, txt);
-            txt_q      = ggml_reshape_4d(ctx, txt_q, dim_head, num_heads, n_txt_token, N);  // [N, n_txt_token, n_head, d_head]
+            txt_q      = ggml_reshape_4d(ctx->ggml_ctx, txt_q, dim_head, num_heads, n_txt_token, N);  // [N, n_txt_token, n_head, d_head]
             auto txt_k = add_k_proj->forward(ctx, txt);
-            txt_k      = ggml_reshape_4d(ctx, txt_k, dim_head, num_heads, n_txt_token, N);  // [N, n_txt_token, n_head, d_head]
+            txt_k      = ggml_reshape_4d(ctx->ggml_ctx, txt_k, dim_head, num_heads, n_txt_token, N);  // [N, n_txt_token, n_head, d_head]
             auto txt_v = add_v_proj->forward(ctx, txt);
-            txt_v      = ggml_reshape_4d(ctx, txt_v, dim_head, num_heads, n_txt_token, N);  // [N, n_txt_token, n_head, d_head]
+            txt_v      = ggml_reshape_4d(ctx->ggml_ctx, txt_v, dim_head, num_heads, n_txt_token, N);  // [N, n_txt_token, n_head, d_head]
 
             txt_q = norm_added_q->forward(ctx, txt_q);
             txt_k = norm_added_k->forward(ctx, txt_k);
 
-            auto q = ggml_concat(ctx, txt_q, img_q, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
-            auto k = ggml_concat(ctx, txt_k, img_k, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
-            auto v = ggml_concat(ctx, txt_v, img_v, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
+            auto q = ggml_concat(ctx->ggml_ctx, txt_q, img_q, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
+            auto k = ggml_concat(ctx->ggml_ctx, txt_k, img_k, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
+            auto v = ggml_concat(ctx->ggml_ctx, txt_v, img_v, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
 
-            auto attn         = Rope::attention(ctx, backend, q, k, v, pe, mask, flash_attn, (1.0f / 128.f));  // [N, n_txt_token + n_img_token, n_head*d_head]
-            attn              = ggml_cont(ctx, ggml_permute(ctx, attn, 0, 2, 1, 3));                           // [n_txt_token + n_img_token, N, hidden_size]
-            auto txt_attn_out = ggml_view_3d(ctx,
+            auto attn         = Rope::attention(ctx, q, k, v, pe, mask, (1.0f / 128.f));                  // [N, n_txt_token + n_img_token, n_head*d_head]
+            attn              = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, attn, 0, 2, 1, 3));  // [n_txt_token + n_img_token, N, hidden_size]
+            auto txt_attn_out = ggml_view_3d(ctx->ggml_ctx,
                                              attn,
                                              attn->ne[0],
                                              attn->ne[1],
                                              txt->ne[1],
                                              attn->nb[1],
                                              attn->nb[2],
-                                             0);                                              // [n_txt_token, N, hidden_size]
-            txt_attn_out      = ggml_cont(ctx, ggml_permute(ctx, txt_attn_out, 0, 2, 1, 3));  // [N, n_txt_token, hidden_size]
-            auto img_attn_out = ggml_view_3d(ctx,
+                                             0);                                                                  // [n_txt_token, N, hidden_size]
+            txt_attn_out      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, txt_attn_out, 0, 2, 1, 3));  // [N, n_txt_token, hidden_size]
+            auto img_attn_out = ggml_view_3d(ctx->ggml_ctx,
                                              attn,
                                              attn->ne[0],
                                              attn->ne[1],
                                              img->ne[1],
                                              attn->nb[1],
                                              attn->nb[2],
-                                             attn->nb[2] * txt->ne[1]);                       // [n_img_token, N, hidden_size]
-            img_attn_out      = ggml_cont(ctx, ggml_permute(ctx, img_attn_out, 0, 2, 1, 3));  // [N, n_img_token, hidden_size]
+                                             attn->nb[2] * txt->ne[1]);                                           // [n_img_token, N, hidden_size]
+            img_attn_out      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, img_attn_out, 0, 2, 1, 3));  // [N, n_img_token, hidden_size]
 
             img_attn_out = to_out_0->forward(ctx, img_attn_out);
             txt_attn_out = to_add_out->forward(ctx, txt_attn_out);
@@ -192,8 +195,7 @@ namespace Qwen {
         QwenImageTransformerBlock(int64_t dim,
                                   int64_t num_attention_heads,
                                   int64_t attention_head_dim,
-                                  float eps       = 1e-6,
-                                  bool flash_attn = false) {
+                                  float eps = 1e-6) {
             // img_mod.0 is nn.SiLU()
             blocks["img_mod.1"] = std::shared_ptr<GGMLBlock>(new Linear(dim, 6 * dim, true));
 
@@ -215,12 +217,10 @@ namespace Qwen {
                                                                                0,     // out_context-dim
                                                                                true,  // bias
                                                                                true,  // out_bias
-                                                                               eps,
-                                                                               flash_attn));
+                                                                               eps));
         }
 
-        virtual std::pair<ggml_tensor*, ggml_tensor*> forward(struct ggml_context* ctx,
-                                                              ggml_backend_t backend,
+        virtual std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
                                                               struct ggml_tensor* img,
                                                               struct ggml_tensor* txt,
                                                               struct ggml_tensor* t_emb,
@@ -242,40 +242,40 @@ namespace Qwen {
 
             auto attn = std::dynamic_pointer_cast<QwenImageAttention>(blocks["attn"]);
 
-            auto img_mod_params    = ggml_silu(ctx, t_emb);
+            auto img_mod_params    = ggml_silu(ctx->ggml_ctx, t_emb);
             img_mod_params         = img_mod_1->forward(ctx, img_mod_params);
-            auto img_mod_param_vec = ggml_chunk(ctx, img_mod_params, 6, 0);
+            auto img_mod_param_vec = ggml_ext_chunk(ctx->ggml_ctx, img_mod_params, 6, 0);
 
-            auto txt_mod_params    = ggml_silu(ctx, t_emb);
+            auto txt_mod_params    = ggml_silu(ctx->ggml_ctx, t_emb);
             txt_mod_params         = txt_mod_1->forward(ctx, txt_mod_params);
-            auto txt_mod_param_vec = ggml_chunk(ctx, txt_mod_params, 6, 0);
+            auto txt_mod_param_vec = ggml_ext_chunk(ctx->ggml_ctx, txt_mod_params, 6, 0);
 
             auto img_normed    = img_norm1->forward(ctx, img);
-            auto img_modulated = Flux::modulate(ctx, img_normed, img_mod_param_vec[0], img_mod_param_vec[1]);
+            auto img_modulated = Flux::modulate(ctx->ggml_ctx, img_normed, img_mod_param_vec[0], img_mod_param_vec[1]);
             auto img_gate1     = img_mod_param_vec[2];
 
             auto txt_normed    = txt_norm1->forward(ctx, txt);
-            auto txt_modulated = Flux::modulate(ctx, txt_normed, txt_mod_param_vec[0], txt_mod_param_vec[1]);
+            auto txt_modulated = Flux::modulate(ctx->ggml_ctx, txt_normed, txt_mod_param_vec[0], txt_mod_param_vec[1]);
             auto txt_gate1     = txt_mod_param_vec[2];
 
-            auto [img_attn_output, txt_attn_output] = attn->forward(ctx, backend, img_modulated, txt_modulated, pe);
+            auto [img_attn_output, txt_attn_output] = attn->forward(ctx, img_modulated, txt_modulated, pe);
 
-            img = ggml_add(ctx, img, ggml_mul(ctx, img_attn_output, img_gate1));
-            txt = ggml_add(ctx, txt, ggml_mul(ctx, txt_attn_output, txt_gate1));
+            img = ggml_add(ctx->ggml_ctx, img, ggml_mul(ctx->ggml_ctx, img_attn_output, img_gate1));
+            txt = ggml_add(ctx->ggml_ctx, txt, ggml_mul(ctx->ggml_ctx, txt_attn_output, txt_gate1));
 
             auto img_normed2    = img_norm2->forward(ctx, img);
-            auto img_modulated2 = Flux::modulate(ctx, img_normed2, img_mod_param_vec[3], img_mod_param_vec[4]);
+            auto img_modulated2 = Flux::modulate(ctx->ggml_ctx, img_normed2, img_mod_param_vec[3], img_mod_param_vec[4]);
             auto img_gate2      = img_mod_param_vec[5];
 
             auto txt_normed2    = txt_norm2->forward(ctx, txt);
-            auto txt_modulated2 = Flux::modulate(ctx, txt_normed2, txt_mod_param_vec[3], txt_mod_param_vec[4]);
+            auto txt_modulated2 = Flux::modulate(ctx->ggml_ctx, txt_normed2, txt_mod_param_vec[3], txt_mod_param_vec[4]);
             auto txt_gate2      = txt_mod_param_vec[5];
 
             auto img_mlp_out = img_mlp->forward(ctx, img_modulated2);
             auto txt_mlp_out = txt_mlp->forward(ctx, txt_modulated2);
 
-            img = ggml_add(ctx, img, ggml_mul(ctx, img_mlp_out, img_gate2));
-            txt = ggml_add(ctx, txt, ggml_mul(ctx, txt_mlp_out, txt_gate2));
+            img = ggml_add(ctx->ggml_ctx, img, ggml_mul(ctx->ggml_ctx, img_mlp_out, img_gate2));
+            txt = ggml_add(ctx->ggml_ctx, txt, ggml_mul(ctx->ggml_ctx, txt_mlp_out, txt_gate2));
 
             return {img, txt};
         }
@@ -292,7 +292,7 @@ namespace Qwen {
             blocks["linear"] = std::shared_ptr<GGMLBlock>(new Linear(conditioning_embedding_dim, embedding_dim * 2, bias));
         }
 
-        struct ggml_tensor* forward(struct ggml_context* ctx,
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                     struct ggml_tensor* x,
                                     struct ggml_tensor* c) {
             // x: [N, n_token, hidden_size]
@@ -302,13 +302,13 @@ namespace Qwen {
             auto norm   = std::dynamic_pointer_cast<LayerNorm>(blocks["norm"]);
             auto linear = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
 
-            auto emb   = linear->forward(ctx, ggml_silu(ctx, c));
-            auto mods  = ggml_chunk(ctx, emb, 2, 0);
+            auto emb   = linear->forward(ctx, ggml_silu(ctx->ggml_ctx, c));
+            auto mods  = ggml_ext_chunk(ctx->ggml_ctx, emb, 2, 0);
             auto scale = mods[0];
             auto shift = mods[1];
 
             x = norm->forward(ctx, x);
-            x = Flux::modulate(ctx, x, shift, scale);
+            x = Flux::modulate(ctx->ggml_ctx, x, shift, scale);
 
             return x;
         }
@@ -325,7 +325,6 @@ namespace Qwen {
         float theta                 = 10000;
         std::vector<int> axes_dim   = {16, 56, 56};
         int64_t axes_dim_sum        = 128;
-        bool flash_attn             = false;
     };
 
     class QwenImageModel : public GGMLBlock {
@@ -347,8 +346,7 @@ namespace Qwen {
                 auto block                                        = std::shared_ptr<GGMLBlock>(new QwenImageTransformerBlock(inner_dim,
                                                                                                                              params.num_attention_heads,
                                                                                                                              params.attention_head_dim,
-                                                                                                                             1e-6f,
-                                                                                                                             params.flash_attn));
+                                                                                                                             1e-6f));
                 blocks["transformer_blocks." + std::to_string(i)] = block;
             }
 
@@ -419,8 +417,7 @@ namespace Qwen {
             return x;
         }
 
-        struct ggml_tensor* forward_orig(struct ggml_context* ctx,
-                                         ggml_backend_t backend,
+        struct ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
                                          struct ggml_tensor* x,
                                          struct ggml_tensor* timestep,
                                          struct ggml_tensor* context,
@@ -440,7 +437,7 @@ namespace Qwen {
             for (int i = 0; i < params.num_layers; i++) {
                 auto block = std::dynamic_pointer_cast<QwenImageTransformerBlock>(blocks["transformer_blocks." + std::to_string(i)]);
 
-                auto result = block->forward(ctx, backend, img, txt, t_emb, pe);
+                auto result = block->forward(ctx, img, txt, t_emb, pe);
                 img         = result.first;
                 txt         = result.second;
             }
@@ -451,8 +448,7 @@ namespace Qwen {
             return img;
         }
 
-        struct ggml_tensor* forward(struct ggml_context* ctx,
-                                    ggml_backend_t backend,
+        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                     struct ggml_tensor* x,
                                     struct ggml_tensor* timestep,
                                     struct ggml_tensor* context,
@@ -470,32 +466,32 @@ namespace Qwen {
             int64_t C = x->ne[2];
             int64_t N = x->ne[3];
 
-            auto img            = process_img(ctx, x);
+            auto img            = process_img(ctx->ggml_ctx, x);
             uint64_t img_tokens = img->ne[1];
 
             if (ref_latents.size() > 0) {
                 for (ggml_tensor* ref : ref_latents) {
-                    ref = process_img(ctx, ref);
-                    img = ggml_concat(ctx, img, ref, 1);
+                    ref = process_img(ctx->ggml_ctx, ref);
+                    img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
                 }
             }
 
             int64_t h_len = ((H + (params.patch_size / 2)) / params.patch_size);
             int64_t w_len = ((W + (params.patch_size / 2)) / params.patch_size);
 
-            auto out = forward_orig(ctx, backend, img, timestep, context, pe);  // [N, h_len*w_len, ph*pw*C]
+            auto out = forward_orig(ctx, img, timestep, context, pe);  // [N, h_len*w_len, ph*pw*C]
 
             if (out->ne[1] > img_tokens) {
-                out = ggml_cont(ctx, ggml_permute(ctx, out, 0, 2, 1, 3));  // [num_tokens, N, C * patch_size * patch_size]
-                out = ggml_view_3d(ctx, out, out->ne[0], out->ne[1], img_tokens, out->nb[1], out->nb[2], 0);
-                out = ggml_cont(ctx, ggml_permute(ctx, out, 0, 2, 1, 3));  // [N, h*w, C * patch_size * patch_size]
+                out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, out, 0, 2, 1, 3));  // [num_tokens, N, C * patch_size * patch_size]
+                out = ggml_view_3d(ctx->ggml_ctx, out, out->ne[0], out->ne[1], img_tokens, out->nb[1], out->nb[2], 0);
+                out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, out, 0, 2, 1, 3));  // [N, h*w, C * patch_size * patch_size]
             }
 
-            out = unpatchify(ctx, out, h_len, w_len);  // [N, C, H + pad_h, W + pad_w]
+            out = unpatchify(ctx->ggml_ctx, out, h_len, w_len);  // [N, C, H + pad_h, W + pad_w]
 
             // slice
-            out = ggml_slice(ctx, out, 1, 0, H);  // [N, C, H, W + pad_w]
-            out = ggml_slice(ctx, out, 0, 0, W);  // [N, C, H, W]
+            out = ggml_ext_slice(ctx->ggml_ctx, out, 1, 0, H);  // [N, C, H, W + pad_w]
+            out = ggml_ext_slice(ctx->ggml_ctx, out, 0, 0, W);  // [N, C, H, W]
 
             return out;
         }
@@ -510,14 +506,12 @@ namespace Qwen {
 
         QwenImageRunner(ggml_backend_t backend,
                         bool offload_params_to_cpu,
-                        const String2GGMLType& tensor_types = {},
-                        const std::string prefix            = "",
-                        SDVersion version                   = VERSION_QWEN_IMAGE,
-                        bool flash_attn                     = false)
+                        const String2TensorStorage& tensor_storage_map = {},
+                        const std::string prefix                       = "",
+                        SDVersion version                              = VERSION_QWEN_IMAGE)
             : GGMLRunner(backend, offload_params_to_cpu) {
-            qwen_image_params.flash_attn = flash_attn;
             qwen_image_params.num_layers = 0;
-            for (auto pair : tensor_types) {
+            for (auto pair : tensor_storage_map) {
                 std::string tensor_name = pair.first;
                 if (tensor_name.find(prefix) == std::string::npos)
                     continue;
@@ -534,12 +528,12 @@ namespace Qwen {
                     continue;
                 }
             }
-            LOG_ERROR("qwen_image_params.num_layers: %ld", qwen_image_params.num_layers);
+            LOG_INFO("qwen_image_params.num_layers: %ld", qwen_image_params.num_layers);
             qwen_image = QwenImageModel(qwen_image_params);
-            qwen_image.init(params_ctx, tensor_types, prefix);
+            qwen_image.init(params_ctx, tensor_storage_map, prefix);
         }
 
-        std::string get_desc() {
+        std::string get_desc() override {
             return "qwen_image";
         }
 
@@ -553,7 +547,7 @@ namespace Qwen {
                                         std::vector<ggml_tensor*> ref_latents = {},
                                         bool increase_ref_index               = false) {
             GGML_ASSERT(x->ne[3] == 1);
-            struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, QWEN_IMAGE_GRAPH_SIZE, false);
+            struct ggml_cgraph* gf = new_graph_custom(QWEN_IMAGE_GRAPH_SIZE);
 
             x         = to_backend(x);
             context   = to_backend(context);
@@ -577,11 +571,12 @@ namespace Qwen {
             auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, qwen_image_params.axes_dim_sum / 2, pos_len);
             // pe->data = pe_vec.data();
             // print_ggml_tensor(pe, true, "pe");
-            // pe->data = NULL;
+            // pe->data = nullptr;
             set_backend_tensor_data(pe, pe_vec.data());
 
-            struct ggml_tensor* out = qwen_image.forward(compute_ctx,
-                                                         runtime_backend,
+            auto runner_ctx = get_context();
+
+            struct ggml_tensor* out = qwen_image.forward(&runner_ctx,
                                                          x,
                                                          timesteps,
                                                          context,
@@ -599,8 +594,8 @@ namespace Qwen {
                      struct ggml_tensor* context,
                      std::vector<ggml_tensor*> ref_latents = {},
                      bool increase_ref_index               = false,
-                     struct ggml_tensor** output           = NULL,
-                     struct ggml_context* output_ctx       = NULL) {
+                     struct ggml_tensor** output           = nullptr,
+                     struct ggml_context* output_ctx       = nullptr) {
             // x: [N, in_channels, h, w]
             // timesteps: [N, ]
             // context: [N, max_position, hidden_size]
@@ -614,11 +609,11 @@ namespace Qwen {
         void test() {
             struct ggml_init_params params;
             params.mem_size   = static_cast<size_t>(1024 * 1024) * 1024;  // 1GB
-            params.mem_buffer = NULL;
+            params.mem_buffer = nullptr;
             params.no_alloc   = false;
 
             struct ggml_context* work_ctx = ggml_init(params);
-            GGML_ASSERT(work_ctx != NULL);
+            GGML_ASSERT(work_ctx != nullptr);
 
             {
                 // auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 16, 16, 16, 1);
@@ -634,7 +629,7 @@ namespace Qwen {
                 auto context = load_tensor_from_file(work_ctx, "./qwen_image_context.bin");
                 print_ggml_tensor(context);
 
-                struct ggml_tensor* out = NULL;
+                struct ggml_tensor* out = nullptr;
 
                 int t0 = ggml_time_ms();
                 compute(8, x, timesteps, context, {}, false, &out, work_ctx);
@@ -647,31 +642,29 @@ 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;
 
             ModelLoader model_loader;
-            if (!model_loader.init_from_file(file_path, "model.diffusion_model.")) {
+            if (!model_loader.init_from_file_and_convert_name(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;
+            auto& tensor_storage_map = model_loader.get_tensor_storage_map();
+            for (auto& [name, tensor_storage] : tensor_storage_map) {
+                if (ends_with(name, "weight")) {
+                    tensor_storage.expected_type = model_data_type;
                 }
             }
 
-            std::shared_ptr<QwenImageRunner> qwen_image = std::shared_ptr<QwenImageRunner>(new QwenImageRunner(backend,
-                                                                                                               false,
-                                                                                                               tensor_types,
-                                                                                                               "model.diffusion_model",
-                                                                                                               VERSION_QWEN_IMAGE,
-                                                                                                               true));
+            std::shared_ptr<QwenImageRunner> qwen_image = std::make_shared<QwenImageRunner>(backend,
+                                                                                            false,
+                                                                                            tensor_storage_map,
+                                                                                            "model.diffusion_model",
+                                                                                            VERSION_QWEN_IMAGE);
 
             qwen_image->alloc_params_buffer();
             std::map<std::string, ggml_tensor*> tensors;

rng.hpp

@@ -15,11 +15,11 @@ private:
     std::default_random_engine generator;
 
 public:
-    void manual_seed(uint64_t seed) {
+    void manual_seed(uint64_t seed) override {
         generator.seed((unsigned int)seed);
     }
 
-    std::vector<float> randn(uint32_t n) {
+    std::vector<float> randn(uint32_t n) override {
         std::vector<float> result;
         float mean   = 0.0;
         float stddev = 1.0;

rng_mt19937.hpp

@@ -0,0 +1,147 @@
+#ifndef __RNG_MT19937_HPP__
+#define __RNG_MT19937_HPP__
+
+#include <cmath>
+#include <vector>
+
+#include "rng.hpp"
+
+// RNG imitiating torch cpu randn on CPU.
+// Port from pytorch, original license: https://github.com/pytorch/pytorch/blob/d01a7b0241ed1c4cded7e7ca097249feb343f072/LICENSE
+// Ref: https://github.com/pytorch/pytorch/blob/d01a7b0241ed1c4cded7e7ca097249feb343f072/aten/src/ATen/core/TransformationHelper.h, for uniform_real
+// Ref: https://github.com/pytorch/pytorch/blob/d01a7b0241ed1c4cded7e7ca097249feb343f072/aten/src/ATen/native/cpu/DistributionTemplates.h, for normal_kernel/normal_fill/normal_fill_16
+// Ref: https://github.com/pytorch/pytorch/blob/d01a7b0241ed1c4cded7e7ca097249feb343f072/aten/src/ATen/core/MT19937RNGEngine.h, for mt19937_engine
+// Ref: https://github.com/pytorch/pytorch/blob/d01a7b0241ed1c4cded7e7ca097249feb343f072/aten/src/ATen/core/DistributionsHelper.h, for uniform_real_distribution/normal_distribution
+class MT19937RNG : public RNG {
+    static const int N             = 624;
+    static const int M             = 397;
+    static const uint32_t MATRIX_A = 0x9908b0dfU;
+    static const uint32_t UMASK    = 0x80000000U;
+    static const uint32_t LMASK    = 0x7fffffffU;
+
+    struct State {
+        uint64_t seed_;
+        int left_;
+        bool seeded_;
+        uint32_t next_;
+        std::array<uint32_t, N> state_;
+        bool has_next_gauss = false;
+        double next_gauss   = 0.0f;
+    };
+
+    State s;
+
+    uint32_t mix_bits(uint32_t u, uint32_t v) { return (u & UMASK) | (v & LMASK); }
+    uint32_t twist(uint32_t u, uint32_t v) { return (mix_bits(u, v) >> 1) ^ ((v & 1) ? MATRIX_A : 0); }
+    void next_state() {
+        uint32_t* p = s.state_.data();
+        s.left_     = N;
+        s.next_     = 0;
+        for (int j = N - M + 1; --j; p++)
+            p[0] = p[M] ^ twist(p[0], p[1]);
+        for (int j = M; --j; p++)
+            p[0] = p[M - N] ^ twist(p[0], p[1]);
+        p[0] = p[M - N] ^ twist(p[0], s.state_[0]);
+    }
+
+    uint32_t rand_uint32() {
+        if (--s.left_ == 0)
+            next_state();
+        uint32_t y = s.state_[s.next_++];
+        y ^= (y >> 11);
+        y ^= (y << 7) & 0x9d2c5680U;
+        y ^= (y << 15) & 0xefc60000U;
+        y ^= (y >> 18);
+        return y;
+    }
+
+    uint64_t rand_uint64() {
+        uint64_t high = (uint64_t)rand_uint32();
+        uint64_t low  = (uint64_t)rand_uint32();
+        return (high << 32) | low;
+    }
+
+    template <typename T, typename V>
+    T uniform_real(V val, T from, T to) {
+        constexpr auto MASK    = static_cast<V>((static_cast<uint64_t>(1) << std::numeric_limits<T>::digits) - 1);
+        constexpr auto DIVISOR = static_cast<T>(1) / (static_cast<uint64_t>(1) << std::numeric_limits<T>::digits);
+        T x                    = (val & MASK) * DIVISOR;
+        return (x * (to - from) + from);
+    }
+
+    double normal_double_value(double mean, double std) {
+        if (s.has_next_gauss) {
+            s.has_next_gauss = false;
+            return s.next_gauss;
+        }
+        double u1 = uniform_real(rand_uint64(), 0., 1.);  // double
+        double u2 = uniform_real(rand_uint64(), 0., 1.);  // double
+
+        double r         = std::sqrt(-2.0 * std::log1p(-u2));
+        double theta     = 2.0 * 3.14159265358979323846 * u1;
+        double value     = r * std::cos(theta) * std + mean;
+        s.next_gauss     = r * std::sin(theta) * std + mean;
+        s.has_next_gauss = true;
+        return value;
+    }
+
+    void normal_fill_16(float* data, float mean, float std) {
+        for (int j = 0; j < 8; ++j) {
+            float u1    = 1.0f - data[j];
+            float u2    = data[j + 8];
+            float r     = std::sqrt(-2.0f * std::log(u1));
+            float theta = 2.0f * 3.14159265358979323846 * u2;
+            data[j]     = r * std::cos(theta) * std + mean;
+            data[j + 8] = r * std::sin(theta) * std + mean;
+        }
+    }
+
+    void randn(float* data, int64_t size, float mean = 0.0f, float std = 1.0f) {
+        if (size >= 16) {
+            for (int64_t i = 0; i < size; i++) {
+                data[i] = uniform_real(rand_uint32(), 0.f, 1.f);
+            }
+            for (int64_t i = 0; i < size - 15; i += 16) {
+                normal_fill_16(data + i, mean, std);
+            }
+            if (size % 16 != 0) {
+                // Recompute the last 16 values.
+                data = data + size - 16;
+                for (int64_t i = 0; i < 16; i++) {
+                    data[i] = uniform_real(rand_uint32(), 0.f, 1.f);
+                }
+                normal_fill_16(data, mean, std);
+            }
+        } else {
+            // Strange handling, hard to understand, but keeping it consistent with PyTorch.
+            for (int64_t i = 0; i < size; i++) {
+                data[i] = (float)normal_double_value(mean, std);
+            }
+        }
+    }
+
+public:
+    MT19937RNG(uint64_t seed = 0) { manual_seed(seed); }
+
+    void manual_seed(uint64_t seed) override {
+        s.seed_     = seed;
+        s.seeded_   = true;
+        s.state_[0] = (uint32_t)(seed & 0xffffffffU);
+        for (int j = 1; j < N; j++) {
+            uint32_t prev = s.state_[j - 1];
+            s.state_[j]   = 1812433253U * (prev ^ (prev >> 30)) + j;
+        }
+        s.left_          = 1;
+        s.next_          = 0;
+        s.has_next_gauss = false;
+    }
+
+    std::vector<float> randn(uint32_t n) override {
+        std::vector<float> out;
+        out.resize(n);
+        randn((float*)out.data(), out.size());
+        return out;
+    }
+};
+
+#endif  // __RNG_MT19937_HPP__

rng_philox.hpp

@@ -93,12 +93,12 @@ public:
         this->offset = 0;
     }
 
-    void manual_seed(uint64_t seed) {
+    void manual_seed(uint64_t seed) override {
         this->seed   = seed;
         this->offset = 0;
     }
 
-    std::vector<float> randn(uint32_t n) {
+    std::vector<float> randn(uint32_t n) override {
         std::vector<std::vector<uint32_t>> counter(4, std::vector<uint32_t>(n, 0));
         for (uint32_t i = 0; i < n; i++) {
             counter[0][i] = this->offset;

rope.hpp

@@ -72,15 +72,28 @@ namespace Rope {
     }
 
     // Generate IDs for image patches and text
-    __STATIC_INLINE__ std::vector<std::vector<float>> gen_txt_ids(int bs, int context_len) {
-        return std::vector<std::vector<float>>(bs * context_len, std::vector<float>(3, 0.0));
+    __STATIC_INLINE__ std::vector<std::vector<float>> gen_flux_txt_ids(int bs, int context_len, int axes_dim_num) {
+        auto txt_ids = std::vector<std::vector<float>>(bs * context_len, std::vector<float>(axes_dim_num, 0.0f));
+        if (axes_dim_num == 4) {
+            for (int i = 0; i < bs * context_len; i++) {
+                txt_ids[i][3] = (i % context_len);
+            }
+        }
+        return txt_ids;
     }
 
-    __STATIC_INLINE__ std::vector<std::vector<float>> gen_img_ids(int h, int w, int patch_size, int bs, int index = 0, int h_offset = 0, int w_offset = 0) {
+    __STATIC_INLINE__ std::vector<std::vector<float>> gen_flux_img_ids(int h,
+                                                                       int w,
+                                                                       int patch_size,
+                                                                       int bs,
+                                                                       int axes_dim_num,
+                                                                       int index    = 0,
+                                                                       int h_offset = 0,
+                                                                       int w_offset = 0) {
         int h_len = (h + (patch_size / 2)) / patch_size;
         int w_len = (w + (patch_size / 2)) / patch_size;
 
-        std::vector<std::vector<float>> img_ids(h_len * w_len, std::vector<float>(3, 0.0));
+        std::vector<std::vector<float>> img_ids(h_len * w_len, std::vector<float>(axes_dim_num, 0.0));
 
         std::vector<float> row_ids = linspace<float>(h_offset, h_len - 1 + h_offset, h_len);
         std::vector<float> col_ids = linspace<float>(w_offset, w_len - 1 + w_offset, w_len);
@@ -153,8 +166,10 @@ namespace Rope {
 
     __STATIC_INLINE__ std::vector<std::vector<float>> gen_refs_ids(int patch_size,
                                                                    int bs,
+                                                                   int axes_dim_num,
                                                                    const std::vector<ggml_tensor*>& ref_latents,
-                                                                   bool increase_ref_index) {
+                                                                   bool increase_ref_index,
+                                                                   float ref_index_scale) {
         std::vector<std::vector<float>> ids;
         uint64_t curr_h_offset = 0;
         uint64_t curr_w_offset = 0;
@@ -170,7 +185,14 @@ namespace Rope {
                 }
             }
 
-            auto ref_ids = gen_img_ids(ref->ne[1], ref->ne[0], patch_size, bs, index, h_offset, w_offset);
+            auto ref_ids = gen_flux_img_ids(ref->ne[1],
+                                            ref->ne[0],
+                                            patch_size,
+                                            bs,
+                                            axes_dim_num,
+                                            static_cast<int>(index * ref_index_scale),
+                                            h_offset,
+                                            w_offset);
             ids          = concat_ids(ids, ref_ids, bs);
 
             if (increase_ref_index) {
@@ -187,15 +209,17 @@ namespace Rope {
                                                                    int w,
                                                                    int patch_size,
                                                                    int bs,
+                                                                   int axes_dim_num,
                                                                    int context_len,
                                                                    const std::vector<ggml_tensor*>& ref_latents,
-                                                                   bool increase_ref_index) {
-        auto txt_ids = gen_txt_ids(bs, context_len);
-        auto img_ids = gen_img_ids(h, w, patch_size, bs);
+                                                                   bool increase_ref_index,
+                                                                   float ref_index_scale) {
+        auto txt_ids = gen_flux_txt_ids(bs, context_len, axes_dim_num);
+        auto img_ids = gen_flux_img_ids(h, w, patch_size, bs, axes_dim_num);
 
         auto ids = concat_ids(txt_ids, img_ids, bs);
         if (ref_latents.size() > 0) {
-            auto refs_ids = gen_refs_ids(patch_size, bs, ref_latents, increase_ref_index);
+            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, ref_index_scale);
             ids           = concat_ids(ids, refs_ids, bs);
         }
         return ids;
@@ -209,9 +233,18 @@ namespace Rope {
                                                      int context_len,
                                                      const std::vector<ggml_tensor*>& ref_latents,
                                                      bool increase_ref_index,
+                                                     float ref_index_scale,
                                                      int theta,
                                                      const std::vector<int>& axes_dim) {
-        std::vector<std::vector<float>> ids = gen_flux_ids(h, w, patch_size, bs, context_len, ref_latents, increase_ref_index);
+        std::vector<std::vector<float>> ids = gen_flux_ids(h,
+                                                           w,
+                                                           patch_size,
+                                                           bs,
+                                                           static_cast<int>(axes_dim.size()),
+                                                           context_len,
+                                                           ref_latents,
+                                                           increase_ref_index,
+                                                           ref_index_scale);
         return embed_nd(ids, bs, theta, axes_dim);
     }
 
@@ -232,10 +265,11 @@ namespace Rope {
                 txt_ids_repeated[i * txt_ids.size() + j] = {txt_ids[j], txt_ids[j], txt_ids[j]};
             }
         }
-        auto img_ids = gen_img_ids(h, w, patch_size, bs);
-        auto ids     = concat_ids(txt_ids_repeated, img_ids, bs);
+        int axes_dim_num = 3;
+        auto img_ids     = gen_flux_img_ids(h, w, patch_size, bs, axes_dim_num);
+        auto ids         = concat_ids(txt_ids_repeated, img_ids, bs);
         if (ref_latents.size() > 0) {
-            auto refs_ids = gen_refs_ids(patch_size, bs, ref_latents, increase_ref_index);
+            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, 1.f);
             ids           = concat_ids(ids, refs_ids, bs);
         }
         return ids;
@@ -360,8 +394,8 @@ namespace Rope {
             x = ggml_reshape_4d(ctx, x, 2, d_head / 2, L, n_head * N);  // [N * n_head, L, d_head/2, 2]
             x = ggml_cont(ctx, ggml_permute(ctx, x, 3, 0, 1, 2));       // [2, N * n_head, L, d_head/2]
         } else {
-            x = ggml_reshape_4d(ctx, x, d_head / 2, 2, L, n_head * N);   // [N * n_head, L, 2, d_head/2]
-            x = ggml_cont(ctx, ggml_torch_permute(ctx, x, 0, 2, 3, 1));  // [2, N * n_head, L, d_head/2]
+            x = ggml_reshape_4d(ctx, x, d_head / 2, 2, L, n_head * N);       // [N * n_head, L, 2, d_head/2]
+            x = ggml_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 3, 1));  // [2, N * n_head, L, d_head/2]
         }
 
         int64_t offset = x->nb[2] * x->ne[2];
@@ -386,23 +420,21 @@ namespace Rope {
         return x_out;
     }
 
-    __STATIC_INLINE__ struct ggml_tensor* attention(struct ggml_context* ctx,
-                                                    ggml_backend_t backend,
+    __STATIC_INLINE__ struct ggml_tensor* attention(GGMLRunnerContext* ctx,
                                                     struct ggml_tensor* q,
                                                     struct ggml_tensor* k,
                                                     struct ggml_tensor* v,
                                                     struct ggml_tensor* pe,
                                                     struct ggml_tensor* mask,
-                                                    bool flash_attn,
                                                     float kv_scale        = 1.0f,
                                                     bool rope_interleaved = true) {
         // q,k,v: [N, L, n_head, d_head]
         // pe: [L, d_head/2, 2, 2]
         // return: [N, L, n_head*d_head]
-        q = apply_rope(ctx, q, pe, rope_interleaved);  // [N*n_head, L, d_head]
-        k = apply_rope(ctx, k, pe, rope_interleaved);  // [N*n_head, L, d_head]
+        q = apply_rope(ctx->ggml_ctx, q, pe, rope_interleaved);  // [N*n_head, L, d_head]
+        k = apply_rope(ctx->ggml_ctx, k, pe, rope_interleaved);  // [N*n_head, L, d_head]
 
-        auto x = ggml_nn_attention_ext(ctx, backend, q, k, v, v->ne[1], mask, false, true, flash_attn, kv_scale);  // [N, L, n_head*d_head]
+        auto x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, v->ne[1], mask, false, true, ctx->flash_attn_enabled, kv_scale);  // [N, L, n_head*d_head]
         return x;
     }
 };  // namespace Rope

stable-diffusion.h

@@ -31,37 +31,37 @@ extern "C" {
 enum rng_type_t {
     STD_DEFAULT_RNG,
     CUDA_RNG,
+    CPU_RNG,
     RNG_TYPE_COUNT
 };
 
 enum sample_method_t {
-    SAMPLE_METHOD_DEFAULT,
-    EULER,
-    HEUN,
-    DPM2,
-    DPMPP2S_A,
-    DPMPP2M,
-    DPMPP2Mv2,
-    IPNDM,
-    IPNDM_V,
-    LCM,
-    DDIM_TRAILING,
-    TCD,
-    EULER_A,
+    EULER_SAMPLE_METHOD,
+    EULER_A_SAMPLE_METHOD,
+    HEUN_SAMPLE_METHOD,
+    DPM2_SAMPLE_METHOD,
+    DPMPP2S_A_SAMPLE_METHOD,
+    DPMPP2M_SAMPLE_METHOD,
+    DPMPP2Mv2_SAMPLE_METHOD,
+    IPNDM_SAMPLE_METHOD,
+    IPNDM_V_SAMPLE_METHOD,
+    LCM_SAMPLE_METHOD,
+    DDIM_TRAILING_SAMPLE_METHOD,
+    TCD_SAMPLE_METHOD,
     SAMPLE_METHOD_COUNT
 };
 
 enum scheduler_t {
-    DEFAULT,
-    DISCRETE,
-    KARRAS,
-    EXPONENTIAL,
-    AYS,
-    GITS,
-    SGM_UNIFORM,
-    SIMPLE,
-    SMOOTHSTEP,
-    SCHEDULE_COUNT
+    DISCRETE_SCHEDULER,
+    KARRAS_SCHEDULER,
+    EXPONENTIAL_SCHEDULER,
+    AYS_SCHEDULER,
+    GITS_SCHEDULER,
+    SGM_UNIFORM_SCHEDULER,
+    SIMPLE_SCHEDULER,
+    SMOOTHSTEP_SCHEDULER,
+    LCM_SCHEDULER,
+    SCHEDULER_COUNT
 };
 
 enum prediction_t {
@@ -71,6 +71,7 @@ enum prediction_t {
     EDM_V_PRED,
     SD3_FLOW_PRED,
     FLUX_FLOW_PRED,
+    FLUX2_FLOW_PRED,
     PREDICTION_COUNT
 };
 
@@ -126,6 +127,21 @@ enum sd_log_level_t {
     SD_LOG_ERROR
 };
 
+enum preview_t {
+    PREVIEW_NONE,
+    PREVIEW_PROJ,
+    PREVIEW_TAE,
+    PREVIEW_VAE,
+    PREVIEW_COUNT
+};
+
+enum lora_apply_mode_t {
+    LORA_APPLY_AUTO,
+    LORA_APPLY_IMMEDIATELY,
+    LORA_APPLY_AT_RUNTIME,
+    LORA_APPLY_MODE_COUNT,
+};
+
 typedef struct {
     bool enabled;
     int tile_size_x;
@@ -141,8 +157,8 @@ typedef struct {
     const char* clip_g_path;
     const char* clip_vision_path;
     const char* t5xxl_path;
-    const char* qwen2vl_path;
-    const char* qwen2vl_vision_path;
+    const char* llm_path;
+    const char* llm_vision_path;
     const char* diffusion_model_path;
     const char* high_noise_diffusion_model_path;
     const char* vae_path;
@@ -151,17 +167,21 @@ typedef struct {
     const char* lora_model_dir;
     const char* embedding_dir;
     const char* photo_maker_path;
+    const char* tensor_type_rules;
     bool vae_decode_only;
     bool free_params_immediately;
     int n_threads;
     enum sd_type_t wtype;
     enum rng_type_t rng_type;
+    enum rng_type_t sampler_rng_type;
     enum prediction_t prediction;
+    enum lora_apply_mode_t lora_apply_mode;
     bool offload_params_to_cpu;
     bool keep_clip_on_cpu;
     bool keep_control_net_on_cpu;
     bool keep_vae_on_cpu;
     bool diffusion_flash_attn;
+    bool tae_preview_only;
     bool diffusion_conv_direct;
     bool vae_conv_direct;
     bool force_sdxl_vae_conv_scale;
@@ -209,6 +229,13 @@ typedef struct {
     float style_strength;
 } sd_pm_params_t;  // photo maker
 
+typedef struct {
+    bool enabled;
+    float reuse_threshold;
+    float start_percent;
+    float end_percent;
+} sd_easycache_params_t;
+
 typedef struct {
     const char* prompt;
     const char* negative_prompt;
@@ -229,6 +256,7 @@ typedef struct {
     float control_strength;
     sd_pm_params_t pm_params;
     sd_tiling_params_t vae_tiling_params;
+    sd_easycache_params_t easycache;
 } sd_img_gen_params_t;
 
 typedef struct {
@@ -248,15 +276,18 @@ typedef struct {
     int64_t seed;
     int video_frames;
     float vace_strength;
+    sd_easycache_params_t easycache;
 } sd_vid_gen_params_t;
 
 typedef struct sd_ctx_t sd_ctx_t;
 
 typedef void (*sd_log_cb_t)(enum sd_log_level_t level, const char* text, void* data);
 typedef void (*sd_progress_cb_t)(int step, int steps, float time, void* data);
+typedef void (*sd_preview_cb_t)(int step, int frame_count, sd_image_t* frames, bool is_noisy, void* data);
 
 SD_API void sd_set_log_callback(sd_log_cb_t sd_log_cb, void* data);
 SD_API void sd_set_progress_callback(sd_progress_cb_t cb, void* data);
+SD_API void sd_set_preview_callback(sd_preview_cb_t cb, enum preview_t mode, int interval, bool denoised, bool noisy, void* data);
 SD_API int32_t get_num_physical_cores();
 SD_API const char* sd_get_system_info();
 
@@ -266,21 +297,29 @@ SD_API const char* sd_rng_type_name(enum rng_type_t rng_type);
 SD_API enum rng_type_t str_to_rng_type(const char* str);
 SD_API const char* sd_sample_method_name(enum sample_method_t sample_method);
 SD_API enum sample_method_t str_to_sample_method(const char* str);
-SD_API const char* sd_schedule_name(enum scheduler_t scheduler);
-SD_API enum scheduler_t str_to_schedule(const char* str);
+SD_API const char* sd_scheduler_name(enum scheduler_t scheduler);
+SD_API enum scheduler_t str_to_scheduler(const char* str);
 SD_API const char* sd_prediction_name(enum prediction_t prediction);
 SD_API enum prediction_t str_to_prediction(const char* str);
+SD_API const char* sd_preview_name(enum preview_t preview);
+SD_API enum preview_t str_to_preview(const char* str);
+SD_API const char* sd_lora_apply_mode_name(enum lora_apply_mode_t mode);
+SD_API enum lora_apply_mode_t str_to_lora_apply_mode(const char* str);
+
+SD_API void sd_easycache_params_init(sd_easycache_params_t* easycache_params);
 
 SD_API void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params);
 SD_API char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params);
 
 SD_API sd_ctx_t* new_sd_ctx(const sd_ctx_params_t* sd_ctx_params);
 SD_API void free_sd_ctx(sd_ctx_t* sd_ctx);
-SD_API enum sample_method_t sd_get_default_sample_method(const sd_ctx_t* sd_ctx);
 
 SD_API void sd_sample_params_init(sd_sample_params_t* sample_params);
 SD_API char* sd_sample_params_to_str(const sd_sample_params_t* sample_params);
 
+SD_API enum sample_method_t sd_get_default_sample_method(const sd_ctx_t* sd_ctx);
+SD_API enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx);
+
 SD_API void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params);
 SD_API char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params);
 SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_gen_params);

t5.hpp

@@ -1,7 +1,7 @@
 #ifndef __T5_HPP__
 #define __T5_HPP__
 
-#include <float.h>
+#include <cfloat>
 #include <limits>
 #include <map>
 #include <memory>
@@ -461,7 +461,7 @@ protected:
     int64_t hidden_size;
     float eps;
 
-    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
         enum ggml_type wtype = GGML_TYPE_F32;
         params["weight"]     = ggml_new_tensor_1d(ctx, wtype, hidden_size);
     }
@@ -472,10 +472,10 @@ public:
         : hidden_size(hidden_size),
           eps(eps) {}
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         struct ggml_tensor* w = params["weight"];
-        x                     = ggml_rms_norm(ctx, x, eps);
-        x                     = ggml_mul(ctx, x, w);
+        x                     = ggml_rms_norm(ctx->ggml_ctx, x, eps);
+        x                     = ggml_mul(ctx->ggml_ctx, x, w);
         return x;
     }
 };
@@ -487,13 +487,13 @@ public:
         blocks["wo"] = std::shared_ptr<GGMLBlock>(new Linear(ff_dim, model_dim, false));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [N, n_token, model_dim]
         auto wi = std::dynamic_pointer_cast<Linear>(blocks["wi"]);
         auto wo = std::dynamic_pointer_cast<Linear>(blocks["wo"]);
 
         x = wi->forward(ctx, x);
-        x = ggml_relu_inplace(ctx, x);
+        x = ggml_relu_inplace(ctx->ggml_ctx, x);
         x = wo->forward(ctx, x);
         return x;
     }
@@ -509,15 +509,15 @@ public:
         blocks["wo"] = std::shared_ptr<GGMLBlock>(new Linear(ff_dim, model_dim, false, false, false, scale));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [N, n_token, model_dim]
         auto wi_0 = std::dynamic_pointer_cast<Linear>(blocks["wi_0"]);
         auto wi_1 = std::dynamic_pointer_cast<Linear>(blocks["wi_1"]);
         auto wo   = std::dynamic_pointer_cast<Linear>(blocks["wo"]);
 
-        auto hidden_gelu   = ggml_gelu_inplace(ctx, wi_0->forward(ctx, x));
+        auto hidden_gelu   = ggml_gelu_inplace(ctx->ggml_ctx, wi_0->forward(ctx, x));
         auto hidden_linear = wi_1->forward(ctx, x);
-        x                  = ggml_mul_inplace(ctx, hidden_gelu, hidden_linear);
+        x                  = ggml_mul_inplace(ctx->ggml_ctx, hidden_gelu, hidden_linear);
         x                  = wo->forward(ctx, x);
         return x;
     }
@@ -530,14 +530,14 @@ public:
         blocks["layer_norm"]     = std::shared_ptr<GGMLBlock>(new T5LayerNorm(model_dim));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [N, n_token, model_dim]
         auto DenseReluDense = std::dynamic_pointer_cast<T5DenseGatedActDense>(blocks["DenseReluDense"]);
         auto layer_norm     = std::dynamic_pointer_cast<T5LayerNorm>(blocks["layer_norm"]);
 
         auto forwarded_states = layer_norm->forward(ctx, x);
         forwarded_states      = DenseReluDense->forward(ctx, forwarded_states);
-        x                     = ggml_add_inplace(ctx, forwarded_states, x);
+        x                     = ggml_add_inplace(ctx->ggml_ctx, forwarded_states, x);
         return x;
     }
 };
@@ -569,22 +569,21 @@ public:
         }
     }
 
-    struct ggml_tensor* compute_bias(struct ggml_context* ctx,
+    struct ggml_tensor* compute_bias(GGMLRunnerContext* ctx,
                                      struct ggml_tensor* relative_position_bucket) {
         auto relative_attention_bias = std::dynamic_pointer_cast<Embedding>(blocks["relative_attention_bias"]);
 
-        auto values = relative_attention_bias->forward(ctx, relative_position_bucket);  // shape (query_length, key_length, num_heads)
-        values      = ggml_cont(ctx, ggml_permute(ctx, values, 2, 0, 1, 3));            // shape (1, num_heads, query_length, key_length)
+        auto values = relative_attention_bias->forward(ctx, relative_position_bucket);            // shape (query_length, key_length, num_heads)
+        values      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, values, 2, 0, 1, 3));  // shape (1, num_heads, query_length, key_length)
         return values;
     }
 
     // x: [N, n_token, model_dim]
-    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx,
-                                                                ggml_backend_t backend,
+    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
                                                                 struct ggml_tensor* x,
-                                                                struct ggml_tensor* past_bias                = NULL,
-                                                                struct ggml_tensor* mask                     = NULL,
-                                                                struct ggml_tensor* relative_position_bucket = NULL) {
+                                                                struct ggml_tensor* past_bias                = nullptr,
+                                                                struct ggml_tensor* mask                     = nullptr,
+                                                                struct ggml_tensor* relative_position_bucket = nullptr) {
         auto q_proj   = std::dynamic_pointer_cast<Linear>(blocks["q"]);
         auto k_proj   = std::dynamic_pointer_cast<Linear>(blocks["k"]);
         auto v_proj   = std::dynamic_pointer_cast<Linear>(blocks["v"]);
@@ -597,21 +596,21 @@ public:
         auto k = k_proj->forward(ctx, x);
         auto v = v_proj->forward(ctx, x);
 
-        if (using_relative_attention_bias && relative_position_bucket != NULL) {
+        if (using_relative_attention_bias && relative_position_bucket != nullptr) {
             past_bias = compute_bias(ctx, relative_position_bucket);
         }
-        if (past_bias != NULL) {
-            if (mask != NULL) {
-                mask = ggml_repeat(ctx, mask, past_bias);
-                mask = ggml_add(ctx, mask, past_bias);
+        if (past_bias != nullptr) {
+            if (mask != nullptr) {
+                mask = ggml_repeat(ctx->ggml_ctx, mask, past_bias);
+                mask = ggml_add(ctx->ggml_ctx, mask, past_bias);
             } else {
                 mask = past_bias;
             }
         }
 
-        k = ggml_scale_inplace(ctx, k, sqrt(d_head));
+        k = ggml_scale_inplace(ctx->ggml_ctx, k, sqrt(d_head));
 
-        x = ggml_nn_attention_ext(ctx, backend, q, k, v, num_heads, mask);  // [N, n_token, d_head * n_head]
+        x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, mask);  // [N, n_token, d_head * n_head]
 
         x = out_proj->forward(ctx, x);  // [N, n_token, model_dim]
         return {x, past_bias};
@@ -629,22 +628,21 @@ public:
         blocks["layer_norm"]    = std::shared_ptr<GGMLBlock>(new T5LayerNorm(model_dim));
     }
 
-    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx,
-                                                                ggml_backend_t backend,
+    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
                                                                 struct ggml_tensor* x,
-                                                                struct ggml_tensor* past_bias                = NULL,
-                                                                struct ggml_tensor* mask                     = NULL,
-                                                                struct ggml_tensor* relative_position_bucket = NULL) {
+                                                                struct ggml_tensor* past_bias                = nullptr,
+                                                                struct ggml_tensor* mask                     = nullptr,
+                                                                struct ggml_tensor* relative_position_bucket = nullptr) {
         // x: [N, n_token, model_dim]
         auto SelfAttention = std::dynamic_pointer_cast<T5Attention>(blocks["SelfAttention"]);
         auto layer_norm    = std::dynamic_pointer_cast<T5LayerNorm>(blocks["layer_norm"]);
 
         auto normed_hidden_state = layer_norm->forward(ctx, x);
-        auto ret                 = SelfAttention->forward(ctx, backend, normed_hidden_state, past_bias, mask, relative_position_bucket);
+        auto ret                 = SelfAttention->forward(ctx, normed_hidden_state, past_bias, mask, relative_position_bucket);
         auto output              = ret.first;
         past_bias                = ret.second;
 
-        x = ggml_add_inplace(ctx, output, x);
+        x = ggml_add_inplace(ctx->ggml_ctx, output, x);
         return {x, past_bias};
     }
 };
@@ -656,17 +654,16 @@ public:
         blocks["layer.1"] = std::shared_ptr<GGMLBlock>(new T5LayerFF(model_dim, ff_dim));
     }
 
-    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(struct ggml_context* ctx,
-                                                                ggml_backend_t backend,
+    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
                                                                 struct ggml_tensor* x,
-                                                                struct ggml_tensor* past_bias                = NULL,
-                                                                struct ggml_tensor* mask                     = NULL,
-                                                                struct ggml_tensor* relative_position_bucket = NULL) {
+                                                                struct ggml_tensor* past_bias                = nullptr,
+                                                                struct ggml_tensor* mask                     = nullptr,
+                                                                struct ggml_tensor* relative_position_bucket = nullptr) {
         // x: [N, n_token, model_dim]
         auto layer_0 = std::dynamic_pointer_cast<T5LayerSelfAttention>(blocks["layer.0"]);
         auto layer_1 = std::dynamic_pointer_cast<T5LayerFF>(blocks["layer.1"]);
 
-        auto ret  = layer_0->forward(ctx, backend, x, past_bias, mask, relative_position_bucket);
+        auto ret  = layer_0->forward(ctx, x, past_bias, mask, relative_position_bucket);
         x         = ret.first;
         past_bias = ret.second;
         x         = layer_1->forward(ctx, x);
@@ -692,17 +689,16 @@ public:
         blocks["final_layer_norm"] = std::shared_ptr<GGMLBlock>(new T5LayerNorm(model_dim));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
-                                struct ggml_tensor* past_bias                = NULL,
-                                struct ggml_tensor* attention_mask           = NULL,
-                                struct ggml_tensor* relative_position_bucket = NULL) {
+                                struct ggml_tensor* past_bias                = nullptr,
+                                struct ggml_tensor* attention_mask           = nullptr,
+                                struct ggml_tensor* relative_position_bucket = nullptr) {
         // x: [N, n_token, model_dim]
         for (int i = 0; i < num_layers; i++) {
             auto block = std::dynamic_pointer_cast<T5Block>(blocks["block." + std::to_string(i)]);
 
-            auto ret  = block->forward(ctx, backend, x, past_bias, attention_mask, relative_position_bucket);
+            auto ret  = block->forward(ctx, x, past_bias, attention_mask, relative_position_bucket);
             x         = ret.first;
             past_bias = ret.second;
         }
@@ -740,19 +736,18 @@ public:
                                                                      params.model_dim));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* input_ids,
-                                struct ggml_tensor* past_bias                = NULL,
-                                struct ggml_tensor* attention_mask           = NULL,
-                                struct ggml_tensor* relative_position_bucket = NULL) {
+                                struct ggml_tensor* past_bias                = nullptr,
+                                struct ggml_tensor* attention_mask           = nullptr,
+                                struct ggml_tensor* relative_position_bucket = nullptr) {
         // input_ids: [N, n_token]
 
         auto shared  = std::dynamic_pointer_cast<Embedding>(blocks["shared"]);
         auto encoder = std::dynamic_pointer_cast<T5Stack>(blocks["encoder"]);
 
         auto x = shared->forward(ctx, input_ids);
-        x      = encoder->forward(ctx, backend, x, past_bias, attention_mask, relative_position_bucket);
+        x      = encoder->forward(ctx, x, past_bias, attention_mask, relative_position_bucket);
         return x;
     }
 };
@@ -764,7 +759,7 @@ struct T5Runner : public GGMLRunner {
 
     T5Runner(ggml_backend_t backend,
              bool offload_params_to_cpu,
-             const String2GGMLType& tensor_types,
+             const String2TensorStorage& tensor_storage_map,
              const std::string prefix,
              bool is_umt5 = false)
         : GGMLRunner(backend, offload_params_to_cpu) {
@@ -773,10 +768,10 @@ struct T5Runner : public GGMLRunner {
             params.relative_attention = false;
         }
         model = T5(params);
-        model.init(params_ctx, tensor_types, prefix);
+        model.init(params_ctx, tensor_storage_map, prefix);
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return "t5";
     }
 
@@ -784,20 +779,19 @@ struct T5Runner : public GGMLRunner {
         model.get_param_tensors(tensors, prefix);
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* input_ids,
                                 struct ggml_tensor* relative_position_bucket,
-                                struct ggml_tensor* attention_mask = NULL) {
+                                struct ggml_tensor* attention_mask = nullptr) {
         size_t N       = input_ids->ne[1];
         size_t n_token = input_ids->ne[0];
 
-        auto hidden_states = model.forward(ctx, backend, input_ids, NULL, attention_mask, relative_position_bucket);  // [N, n_token, model_dim]
+        auto hidden_states = model.forward(ctx, input_ids, nullptr, attention_mask, relative_position_bucket);  // [N, n_token, model_dim]
         return hidden_states;
     }
 
     struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
-                                    struct ggml_tensor* attention_mask = NULL) {
+                                    struct ggml_tensor* attention_mask = nullptr) {
         struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
 
         input_ids      = to_backend(input_ids);
@@ -818,7 +812,8 @@ struct T5Runner : public GGMLRunner {
                                                            input_ids->ne[0]);
         set_backend_tensor_data(relative_position_bucket, relative_position_bucket_vec.data());
 
-        struct ggml_tensor* hidden_states = forward(compute_ctx, runtime_backend, input_ids, relative_position_bucket, attention_mask);
+        auto runner_ctx                   = get_context();
+        struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, relative_position_bucket, attention_mask);
 
         ggml_build_forward_expand(gf, hidden_states);
 
@@ -829,7 +824,7 @@ struct T5Runner : public GGMLRunner {
                  struct ggml_tensor* input_ids,
                  struct ggml_tensor* attention_mask,
                  ggml_tensor** output,
-                 ggml_context* output_ctx = NULL) {
+                 ggml_context* output_ctx = nullptr) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
             return build_graph(input_ids, attention_mask);
         };
@@ -910,10 +905,10 @@ struct T5Embedder {
 
     T5Embedder(ggml_backend_t backend,
                bool offload_params_to_cpu,
-               const String2GGMLType& tensor_types = {},
-               const std::string prefix            = "",
-               bool is_umt5                        = false)
-        : model(backend, offload_params_to_cpu, tensor_types, prefix, is_umt5), tokenizer(is_umt5) {
+               const String2TensorStorage& tensor_storage_map = {},
+               const std::string prefix                       = "",
+               bool is_umt5                                   = false)
+        : model(backend, offload_params_to_cpu, tensor_storage_map, prefix, is_umt5), tokenizer(is_umt5) {
     }
 
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
@@ -968,11 +963,11 @@ struct T5Embedder {
     void test() {
         struct ggml_init_params params;
         params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
-        params.mem_buffer = NULL;
+        params.mem_buffer = nullptr;
         params.no_alloc   = false;
 
         struct ggml_context* work_ctx = ggml_init(params);
-        GGML_ASSERT(work_ctx != NULL);
+        GGML_ASSERT(work_ctx != nullptr);
 
         {
             std::string text("a lovely cat");
@@ -987,7 +982,7 @@ struct T5Embedder {
             printf("\n");
             auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
             auto attention_mask     = vector_to_ggml_tensor(work_ctx, masks);
-            struct ggml_tensor* out = NULL;
+            struct ggml_tensor* out = nullptr;
 
             int t0 = ggml_time_ms();
             model.compute(8, input_ids, attention_mask, &out, work_ctx);
@@ -1009,20 +1004,19 @@ struct T5Embedder {
         ggml_type model_data_type = GGML_TYPE_F16;
 
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
+        if (!model_loader.init_from_file_and_convert_name(file_path)) {
             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;
+        auto& tensor_storage_map = model_loader.get_tensor_storage_map();
+        for (auto& [name, tensor_storage] : tensor_storage_map) {
+            if (ends_with(name, "weight")) {
+                tensor_storage.expected_type = model_data_type;
             }
         }
 
-        std::shared_ptr<T5Embedder> t5 = std::shared_ptr<T5Embedder>(new T5Embedder(backend, false, tensor_types, "", true));
+        std::shared_ptr<T5Embedder> t5 = std::make_shared<T5Embedder>(backend, false, tensor_storage_map, "", true);
 
         t5->alloc_params_buffer();
         std::map<std::string, ggml_tensor*> tensors;

tae.hpp

@@ -29,7 +29,7 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [n, n_in, h, w]
         // return: [n, n_out, h, w]
 
@@ -38,9 +38,9 @@ public:
         auto conv_4 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.4"]);
 
         auto h = conv_0->forward(ctx, x);
-        h      = ggml_relu_inplace(ctx, h);
+        h      = ggml_relu_inplace(ctx->ggml_ctx, h);
         h      = conv_2->forward(ctx, h);
-        h      = ggml_relu_inplace(ctx, h);
+        h      = ggml_relu_inplace(ctx->ggml_ctx, h);
         h      = conv_4->forward(ctx, h);
 
         if (n_in != n_out) {
@@ -49,8 +49,8 @@ public:
             x = skip->forward(ctx, x);
         }
 
-        h = ggml_add(ctx, h, x);
-        h = ggml_relu_inplace(ctx, h);
+        h = ggml_add(ctx->ggml_ctx, h, x);
+        h = ggml_relu_inplace(ctx->ggml_ctx, h);
         return h;
     }
 };
@@ -86,7 +86,7 @@ public:
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, z_channels, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [n, in_channels, h, w]
         // return: [n, z_channels, h/8, w/8]
 
@@ -136,20 +136,20 @@ public:
         blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* z) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) override {
         // z: [n, z_channels, h, w]
         // return: [n, out_channels, h*8, w*8]
 
-        auto h = ggml_scale(ctx, z, 1.0f / 3.0f);
-        h      = ggml_tanh_inplace(ctx, h);
-        h      = ggml_scale(ctx, h, 3.0f);
+        auto h = ggml_scale(ctx->ggml_ctx, z, 1.0f / 3.0f);
+        h      = ggml_tanh_inplace(ctx->ggml_ctx, h);
+        h      = ggml_scale(ctx->ggml_ctx, h, 3.0f);
 
         for (int i = 0; i < num_blocks * 3 + 10; i++) {
             if (blocks.find(std::to_string(i)) == blocks.end()) {
                 if (i == 1) {
-                    h = ggml_relu_inplace(ctx, h);
+                    h = ggml_relu_inplace(ctx->ggml_ctx, h);
                 } else {
-                    h = ggml_upscale(ctx, h, 2, GGML_SCALE_MODE_NEAREST);
+                    h = ggml_upscale(ctx->ggml_ctx, h, 2, GGML_SCALE_MODE_NEAREST);
                 }
                 continue;
             }
@@ -180,12 +180,12 @@ public:
         }
     }
 
-    struct ggml_tensor* decode(struct ggml_context* ctx, struct ggml_tensor* z) {
+    struct ggml_tensor* decode(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
         auto decoder = std::dynamic_pointer_cast<TinyDecoder>(blocks["decoder.layers"]);
         return decoder->forward(ctx, z);
     }
 
-    struct ggml_tensor* encode(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* encode(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         auto encoder = std::dynamic_pointer_cast<TinyEncoder>(blocks["encoder.layers"]);
         return encoder->forward(ctx, x);
     }
@@ -197,28 +197,17 @@ struct TinyAutoEncoder : public GGMLRunner {
 
     TinyAutoEncoder(ggml_backend_t backend,
                     bool offload_params_to_cpu,
-                    const String2GGMLType& tensor_types,
+                    const String2TensorStorage& tensor_storage_map,
                     const std::string prefix,
                     bool decoder_only = true,
                     SDVersion version = VERSION_SD1)
         : decode_only(decoder_only),
           taesd(decoder_only, version),
           GGMLRunner(backend, offload_params_to_cpu) {
-        taesd.init(params_ctx, tensor_types, prefix);
+        taesd.init(params_ctx, tensor_storage_map, prefix);
     }
 
-    void enable_conv2d_direct() {
-        std::vector<GGMLBlock*> blocks;
-        taesd.get_all_blocks(blocks);
-        for (auto block : blocks) {
-            if (block->get_desc() == "Conv2d") {
-                auto conv_block = (Conv2d*)block;
-                conv_block->enable_direct();
-            }
-        }
-    }
-
-    std::string get_desc() {
+    std::string get_desc() override {
         return "taesd";
     }
 
@@ -233,7 +222,7 @@ struct TinyAutoEncoder : public GGMLRunner {
         }
 
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
+        if (!model_loader.init_from_file_and_convert_name(file_path)) {
             LOG_ERROR("init taesd model loader from file failed: '%s'", file_path.c_str());
             return false;
         }
@@ -252,7 +241,8 @@ struct TinyAutoEncoder : public GGMLRunner {
     struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
         struct ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
         z                       = to_backend(z);
-        struct ggml_tensor* out = decode_graph ? taesd.decode(compute_ctx, z) : taesd.encode(compute_ctx, z);
+        auto runner_ctx         = get_context();
+        struct ggml_tensor* out = decode_graph ? taesd.decode(&runner_ctx, z) : taesd.encode(&runner_ctx, z);
         ggml_build_forward_expand(gf, out);
         return gf;
     }
@@ -261,7 +251,7 @@ struct TinyAutoEncoder : public GGMLRunner {
                  struct ggml_tensor* z,
                  bool decode_graph,
                  struct ggml_tensor** output,
-                 struct ggml_context* output_ctx = NULL) {
+                 struct ggml_context* output_ctx = nullptr) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
             return build_graph(z, decode_graph);
         };

tokenize_util.cpp

@@ -811,6 +811,8 @@ bool starts_with(const std::vector<char32_t>& text,
     return std::equal(prefix.begin(), prefix.end(), text.begin() + index);
 }
 
+// mistral: [^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+
+// qwen2: (?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+
 std::vector<std::string> token_split(const std::string& text) {
     std::vector<std::string> tokens;
     auto cps = utf8_to_codepoints(text);

unet.hpp

@@ -7,7 +7,7 @@
 
 /*==================================================== UnetModel =====================================================*/
 
-#define UNET_GRAPH_SIZE 10240
+#define UNET_GRAPH_SIZE 102400
 
 class SpatialVideoTransformer : public SpatialTransformer {
 protected:
@@ -20,9 +20,10 @@ public:
                             int64_t d_head,
                             int64_t depth,
                             int64_t context_dim,
+                            bool use_linear,
                             int64_t time_depth            = 1,
                             int64_t max_time_embed_period = 10000)
-        : SpatialTransformer(in_channels, n_head, d_head, depth, context_dim),
+        : SpatialTransformer(in_channels, n_head, d_head, depth, context_dim, use_linear),
           max_time_embed_period(max_time_embed_period) {
         // We will convert unet transformer linear to conv2d 1x1 when loading the weights, so use_linear is always False
         // use_spatial_context is always True
@@ -60,8 +61,7 @@ public:
         blocks["time_mixer"] = std::shared_ptr<GGMLBlock>(new AlphaBlender());
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* context,
                                 int timesteps) {
@@ -92,7 +92,7 @@ public:
         auto time_context    = context;  // [b*t, n_context, context_dim]
         auto spatial_context = context;
         // time_context_first_timestep = time_context[::timesteps]
-        auto time_context_first_timestep = ggml_view_3d(ctx,
+        auto time_context_first_timestep = ggml_view_3d(ctx->ggml_ctx,
                                                         time_context,
                                                         time_context->ne[0],
                                                         time_context->ne[1],
@@ -100,26 +100,26 @@ public:
                                                         time_context->nb[1],
                                                         time_context->nb[2],
                                                         0);  // [b, n_context, context_dim]
-        time_context                     = ggml_new_tensor_3d(ctx, GGML_TYPE_F32,
+        time_context                     = ggml_new_tensor_3d(ctx->ggml_ctx, GGML_TYPE_F32,
                                                               time_context_first_timestep->ne[0],
                                                               time_context_first_timestep->ne[1],
                                                               time_context_first_timestep->ne[2] * h * w);
-        time_context                     = ggml_repeat(ctx, time_context_first_timestep, time_context);  // [b*h*w, n_context, context_dim]
+        time_context                     = ggml_repeat(ctx->ggml_ctx, time_context_first_timestep, time_context);  // [b*h*w, n_context, context_dim]
 
         x = norm->forward(ctx, x);
         x = proj_in->forward(ctx, x);  // [N, inner_dim, h, w]
 
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 1, 2, 0, 3));  // [N, h, w, inner_dim]
-        x = ggml_reshape_3d(ctx, x, inner_dim, w * h, n);      // [N, h * w, inner_dim]
+        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 2, 0, 3));  // [N, h, w, inner_dim]
+        x = ggml_reshape_3d(ctx->ggml_ctx, x, inner_dim, w * h, n);                // [N, h * w, inner_dim]
 
-        auto num_frames = ggml_arange(ctx, 0, timesteps, 1);
+        auto num_frames = ggml_arange(ctx->ggml_ctx, 0, timesteps, 1);
         // since b is 1, no need to do repeat
-        auto t_emb = ggml_nn_timestep_embedding(ctx, num_frames, in_channels, max_time_embed_period);  // [N, in_channels]
+        auto t_emb = ggml_ext_timestep_embedding(ctx->ggml_ctx, num_frames, in_channels, max_time_embed_period);  // [N, in_channels]
 
         auto emb = time_pos_embed_0->forward(ctx, t_emb);
-        emb      = ggml_silu_inplace(ctx, emb);
-        emb      = time_pos_embed_2->forward(ctx, emb);                   // [N, in_channels]
-        emb      = ggml_reshape_3d(ctx, emb, emb->ne[0], 1, emb->ne[1]);  // [N, 1, in_channels]
+        emb      = ggml_silu_inplace(ctx->ggml_ctx, emb);
+        emb      = time_pos_embed_2->forward(ctx, emb);                             // [N, in_channels]
+        emb      = ggml_reshape_3d(ctx->ggml_ctx, emb, emb->ne[0], 1, emb->ne[1]);  // [N, 1, in_channels]
 
         for (int i = 0; i < depth; i++) {
             std::string transformer_name = "transformer_blocks." + std::to_string(i);
@@ -128,11 +128,11 @@ public:
             auto block     = std::dynamic_pointer_cast<BasicTransformerBlock>(blocks[transformer_name]);
             auto mix_block = std::dynamic_pointer_cast<BasicTransformerBlock>(blocks[time_stack_name]);
 
-            x = block->forward(ctx, backend, x, spatial_context);  // [N, h * w, inner_dim]
+            x = block->forward(ctx, x, spatial_context);  // [N, h * w, inner_dim]
 
             // in_channels == inner_dim
             auto x_mix = x;
-            x_mix      = ggml_add(ctx, x_mix, emb);  // [N, h * w, inner_dim]
+            x_mix      = ggml_add(ctx->ggml_ctx, x_mix, emb);  // [N, h * w, inner_dim]
 
             int64_t N = x_mix->ne[2];
             int64_t T = timesteps;
@@ -140,26 +140,26 @@ public:
             int64_t S = x_mix->ne[1];
             int64_t C = x_mix->ne[0];
 
-            x_mix = ggml_reshape_4d(ctx, x_mix, C, S, T, B);               // (b t) s c -> b t s c
-            x_mix = ggml_cont(ctx, ggml_permute(ctx, x_mix, 0, 2, 1, 3));  // b t s c -> b s t c
-            x_mix = ggml_reshape_3d(ctx, x_mix, C, T, S * B);              // b s t c -> (b s) t c
+            x_mix = ggml_reshape_4d(ctx->ggml_ctx, x_mix, C, S, T, B);                         // (b t) s c -> b t s c
+            x_mix = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x_mix, 0, 2, 1, 3));  // b t s c -> b s t c
+            x_mix = ggml_reshape_3d(ctx->ggml_ctx, x_mix, C, T, S * B);                        // b s t c -> (b s) t c
 
-            x_mix = mix_block->forward(ctx, backend, x_mix, time_context);  // [B * h * w, T, inner_dim]
+            x_mix = mix_block->forward(ctx, x_mix, time_context);  // [B * h * w, T, inner_dim]
 
-            x_mix = ggml_reshape_4d(ctx, x_mix, C, T, S, B);               // (b s) t c -> b s t c
-            x_mix = ggml_cont(ctx, ggml_permute(ctx, x_mix, 0, 2, 1, 3));  // b s t c -> b t s c
-            x_mix = ggml_reshape_3d(ctx, x_mix, C, S, T * B);              // b t s c -> (b t) s c
+            x_mix = ggml_reshape_4d(ctx->ggml_ctx, x_mix, C, T, S, B);                         // (b s) t c -> b s t c
+            x_mix = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x_mix, 0, 2, 1, 3));  // b s t c -> b t s c
+            x_mix = ggml_reshape_3d(ctx->ggml_ctx, x_mix, C, S, T * B);                        // b t s c -> (b t) s c
 
             x = time_mixer->forward(ctx, x, x_mix);  // [N, h * w, inner_dim]
         }
 
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 1, 0, 2, 3));  // [N, inner_dim, h * w]
-        x = ggml_reshape_4d(ctx, x, w, h, inner_dim, n);       // [N, inner_dim, h, w]
+        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [N, inner_dim, h * w]
+        x = ggml_reshape_4d(ctx->ggml_ctx, x, w, h, inner_dim, n);                 // [N, inner_dim, h, w]
 
         // proj_out
         x = proj_out->forward(ctx, x);  // [N, in_channels, h, w]
 
-        x = ggml_add(ctx, x, x_in);
+        x = ggml_add(ctx->ggml_ctx, x, x_in);
         return x;
     }
 };
@@ -179,17 +179,20 @@ protected:
     int num_heads                          = 8;
     int num_head_channels                  = -1;   // channels // num_heads
     int context_dim                        = 768;  // 1024 for VERSION_SD2, 2048 for VERSION_SDXL
+    bool use_linear_projection             = false;
+    bool tiny_unet                         = false;
 
 public:
     int model_channels  = 320;
     int adm_in_channels = 2816;  // only for VERSION_SDXL/SVD
 
-    UnetModelBlock(SDVersion version = VERSION_SD1, const String2GGMLType& tensor_types = {}, bool flash_attn = false)
+    UnetModelBlock(SDVersion version = VERSION_SD1, const String2TensorStorage& tensor_storage_map = {})
         : version(version) {
         if (sd_version_is_sd2(version)) {
-            context_dim       = 1024;
-            num_head_channels = 64;
-            num_heads         = -1;
+            context_dim           = 1024;
+            num_head_channels     = 64;
+            num_heads             = -1;
+            use_linear_projection = true;
         } else if (sd_version_is_sdxl(version)) {
             context_dim           = 2048;
             attention_resolutions = {4, 2};
@@ -197,19 +200,26 @@ public:
             transformer_depth     = {1, 2, 10};
             num_head_channels     = 64;
             num_heads             = -1;
+            use_linear_projection = true;
         } else if (version == VERSION_SVD) {
-            in_channels       = 8;
-            out_channels      = 4;
-            context_dim       = 1024;
-            adm_in_channels   = 768;
-            num_head_channels = 64;
-            num_heads         = -1;
+            in_channels           = 8;
+            out_channels          = 4;
+            context_dim           = 1024;
+            adm_in_channels       = 768;
+            num_head_channels     = 64;
+            num_heads             = -1;
+            use_linear_projection = true;
         }
         if (sd_version_is_inpaint(version)) {
             in_channels = 9;
         } else if (sd_version_is_unet_edit(version)) {
             in_channels = 8;
         }
+        if (version == VERSION_SD1_TINY_UNET || version == VERSION_SD2_TINY_UNET) {
+            num_res_blocks = 1;
+            channel_mult   = {1, 2, 4};
+            tiny_unet      = true;
+        }
 
         // dims is always 2
         // use_temporal_attention is always True for SVD
@@ -247,9 +257,9 @@ public:
                                        int64_t depth,
                                        int64_t context_dim) -> SpatialTransformer* {
             if (version == VERSION_SVD) {
-                return new SpatialVideoTransformer(in_channels, n_head, d_head, depth, context_dim);
+                return new SpatialVideoTransformer(in_channels, n_head, d_head, depth, context_dim, use_linear_projection);
             } else {
-                return new SpatialTransformer(in_channels, n_head, d_head, depth, context_dim, flash_attn);
+                return new SpatialTransformer(in_channels, n_head, d_head, depth, context_dim, use_linear_projection);
             }
         };
 
@@ -270,13 +280,22 @@ 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 (tiny_unet) {
+                    input_block_idx++;
+                }
             }
             if (i != len_mults - 1) {
                 input_block_idx += 1;
@@ -295,14 +314,17 @@ 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 (!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 +346,27 @@ 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 (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));
 
@@ -347,7 +384,7 @@ public:
     }
 
     struct ggml_tensor* resblock_forward(std::string name,
-                                         struct ggml_context* ctx,
+                                         GGMLRunnerContext* ctx,
                                          struct ggml_tensor* x,
                                          struct ggml_tensor* emb,
                                          int num_video_frames) {
@@ -363,29 +400,27 @@ public:
     }
 
     struct ggml_tensor* attention_layer_forward(std::string name,
-                                                struct ggml_context* ctx,
-                                                ggml_backend_t backend,
+                                                GGMLRunnerContext* ctx,
                                                 struct ggml_tensor* x,
                                                 struct ggml_tensor* context,
                                                 int timesteps) {
         if (version == VERSION_SVD) {
             auto block = std::dynamic_pointer_cast<SpatialVideoTransformer>(blocks[name]);
 
-            return block->forward(ctx, backend, x, context, timesteps);
+            return block->forward(ctx, x, context, timesteps);
         } else {
             auto block = std::dynamic_pointer_cast<SpatialTransformer>(blocks[name]);
 
-            return block->forward(ctx, backend, x, context);
+            return block->forward(ctx, x, context);
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                ggml_backend_t backend,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* timesteps,
                                 struct ggml_tensor* context,
-                                struct ggml_tensor* c_concat              = NULL,
-                                struct ggml_tensor* y                     = NULL,
+                                struct ggml_tensor* c_concat              = nullptr,
+                                struct ggml_tensor* y                     = nullptr,
                                 int num_video_frames                      = -1,
                                 std::vector<struct ggml_tensor*> controls = {},
                                 float control_strength                    = 0.f) {
@@ -395,22 +430,22 @@ public:
         // c_concat: [N, in_channels, h, w] or [1, in_channels, h, w]
         // y: [N, adm_in_channels] or [1, adm_in_channels]
         // return: [N, out_channels, h, w]
-        if (context != NULL) {
+        if (context != nullptr) {
             if (context->ne[2] != x->ne[3]) {
-                context = ggml_repeat(ctx, context, ggml_new_tensor_3d(ctx, GGML_TYPE_F32, context->ne[0], context->ne[1], x->ne[3]));
+                context = ggml_repeat(ctx->ggml_ctx, context, ggml_new_tensor_3d(ctx->ggml_ctx, GGML_TYPE_F32, context->ne[0], context->ne[1], x->ne[3]));
             }
         }
 
-        if (c_concat != NULL) {
+        if (c_concat != nullptr) {
             if (c_concat->ne[3] != x->ne[3]) {
-                c_concat = ggml_repeat(ctx, c_concat, x);
+                c_concat = ggml_repeat(ctx->ggml_ctx, c_concat, x);
             }
-            x = ggml_concat(ctx, x, c_concat, 2);
+            x = ggml_concat(ctx->ggml_ctx, x, c_concat, 2);
         }
 
-        if (y != NULL) {
+        if (y != nullptr) {
             if (y->ne[1] != x->ne[3]) {
-                y = ggml_repeat(ctx, y, ggml_new_tensor_2d(ctx, GGML_TYPE_F32, y->ne[0], x->ne[3]));
+                y = ggml_repeat(ctx->ggml_ctx, y, ggml_new_tensor_2d(ctx->ggml_ctx, GGML_TYPE_F32, y->ne[0], x->ne[3]));
             }
         }
 
@@ -421,22 +456,22 @@ public:
         auto out_0 = std::dynamic_pointer_cast<GroupNorm32>(blocks["out.0"]);
         auto out_2 = std::dynamic_pointer_cast<Conv2d>(blocks["out.2"]);
 
-        auto t_emb = ggml_nn_timestep_embedding(ctx, timesteps, model_channels);  // [N, model_channels]
+        auto t_emb = ggml_ext_timestep_embedding(ctx->ggml_ctx, timesteps, model_channels);  // [N, model_channels]
 
         auto emb = time_embed_0->forward(ctx, t_emb);
-        emb      = ggml_silu_inplace(ctx, emb);
+        emb      = ggml_silu_inplace(ctx->ggml_ctx, emb);
         emb      = time_embed_2->forward(ctx, emb);  // [N, time_embed_dim]
 
         // SDXL/SVD
-        if (y != NULL) {
+        if (y != nullptr) {
             auto label_embed_0 = std::dynamic_pointer_cast<Linear>(blocks["label_emb.0.0"]);
             auto label_embed_2 = std::dynamic_pointer_cast<Linear>(blocks["label_emb.0.2"]);
 
             auto label_emb = label_embed_0->forward(ctx, y);
-            label_emb      = ggml_silu_inplace(ctx, label_emb);
+            label_emb      = ggml_silu_inplace(ctx->ggml_ctx, label_emb);
             label_emb      = label_embed_2->forward(ctx, label_emb);  // [N, time_embed_dim]
 
-            emb = ggml_add(ctx, emb, label_emb);  // [N, time_embed_dim]
+            emb = ggml_add(ctx->ggml_ctx, emb, label_emb);  // [N, time_embed_dim]
         }
 
         // input_blocks
@@ -459,10 +494,13 @@ public:
                 h                = resblock_forward(name, ctx, h, emb, num_video_frames);  // [N, mult*model_channels, h, w]
                 if (std::find(attention_resolutions.begin(), attention_resolutions.end(), ds) != attention_resolutions.end()) {
                     std::string name = "input_blocks." + std::to_string(input_block_idx) + ".1";
-                    h                = attention_layer_forward(name, ctx, backend, h, context, num_video_frames);  // [N, mult*model_channels, h, w]
+                    h                = attention_layer_forward(name, ctx, h, context, num_video_frames);  // [N, mult*model_channels, h, w]
                 }
                 hs.push_back(h);
             }
+            if (tiny_unet) {
+                input_block_idx++;
+            }
             if (i != len_mults - 1) {
                 ds *= 2;
                 input_block_idx += 1;
@@ -477,13 +515,16 @@ 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 (!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, 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
+            auto cs = ggml_scale_inplace(ctx->ggml_ctx, controls[controls.size() - 1], control_strength);
+            h       = ggml_add(ctx->ggml_ctx, h, cs);  // middle control
         }
         int control_offset = controls.size() - 2;
 
@@ -495,12 +536,12 @@ public:
                 hs.pop_back();
 
                 if (controls.size() > 0) {
-                    auto cs = ggml_scale_inplace(ctx, controls[control_offset], control_strength);
-                    h_skip  = ggml_add(ctx, h_skip, cs);  // control net condition
+                    auto cs = ggml_scale_inplace(ctx->ggml_ctx, controls[control_offset], control_strength);
+                    h_skip  = ggml_add(ctx->ggml_ctx, h_skip, cs);  // control net condition
                     control_offset--;
                 }
 
-                h = ggml_concat(ctx, h, h_skip, 2);
+                h = ggml_concat(ctx->ggml_ctx, h, h_skip, 2);
 
                 std::string name = "output_blocks." + std::to_string(output_block_idx) + ".0";
 
@@ -510,12 +551,18 @@ public:
                 if (std::find(attention_resolutions.begin(), attention_resolutions.end(), ds) != attention_resolutions.end()) {
                     std::string name = "output_blocks." + std::to_string(output_block_idx) + ".1";
 
-                    h = attention_layer_forward(name, ctx, backend, h, context, num_video_frames);
+                    h = attention_layer_forward(name, ctx, h, context, num_video_frames);
 
                     up_sample_idx++;
                 }
 
                 if (i > 0 && j == num_res_blocks) {
+                    if (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]);
 
@@ -530,7 +577,7 @@ public:
 
         // out
         h = out_0->forward(ctx, h);
-        h = ggml_silu_inplace(ctx, h);
+        h = ggml_silu_inplace(ctx->ggml_ctx, h);
         h = out_2->forward(ctx, h);
         ggml_set_name(h, "bench-end");
         return h;  // [N, out_channels, h, w]
@@ -542,27 +589,14 @@ struct UNetModelRunner : public GGMLRunner {
 
     UNetModelRunner(ggml_backend_t backend,
                     bool offload_params_to_cpu,
-                    const String2GGMLType& tensor_types,
+                    const String2TensorStorage& tensor_storage_map,
                     const std::string prefix,
-                    SDVersion version = VERSION_SD1,
-                    bool flash_attn   = false)
-        : GGMLRunner(backend, offload_params_to_cpu), unet(version, tensor_types, flash_attn) {
-        unet.init(params_ctx, tensor_types, prefix);
+                    SDVersion version = VERSION_SD1)
+        : GGMLRunner(backend, offload_params_to_cpu), unet(version, tensor_storage_map) {
+        unet.init(params_ctx, tensor_storage_map, prefix);
     }
 
-    void enable_conv2d_direct() {
-        std::vector<GGMLBlock*> blocks;
-        unet.get_all_blocks(blocks);
-        for (auto block : blocks) {
-            if (block->get_desc() == "Conv2d") {
-                LOG_DEBUG("block %s", block->get_desc().c_str());
-                auto conv_block = (Conv2d*)block;
-                conv_block->enable_direct();
-            }
-        }
-    }
-
-    std::string get_desc() {
+    std::string get_desc() override {
         return "unet";
     }
 
@@ -573,12 +607,12 @@ struct UNetModelRunner : public GGMLRunner {
     struct ggml_cgraph* build_graph(struct ggml_tensor* x,
                                     struct ggml_tensor* timesteps,
                                     struct ggml_tensor* context,
-                                    struct ggml_tensor* c_concat              = NULL,
-                                    struct ggml_tensor* y                     = NULL,
+                                    struct ggml_tensor* c_concat              = nullptr,
+                                    struct ggml_tensor* y                     = nullptr,
                                     int num_video_frames                      = -1,
                                     std::vector<struct ggml_tensor*> controls = {},
                                     float control_strength                    = 0.f) {
-        struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, UNET_GRAPH_SIZE, false);
+        struct ggml_cgraph* gf = new_graph_custom(UNET_GRAPH_SIZE);
 
         if (num_video_frames == -1) {
             num_video_frames = x->ne[3];
@@ -594,8 +628,9 @@ struct UNetModelRunner : public GGMLRunner {
             controls[i] = to_backend(controls[i]);
         }
 
-        struct ggml_tensor* out = unet.forward(compute_ctx,
-                                               runtime_backend,
+        auto runner_ctx = get_context();
+
+        struct ggml_tensor* out = unet.forward(&runner_ctx,
                                                x,
                                                timesteps,
                                                context,
@@ -619,8 +654,8 @@ struct UNetModelRunner : public GGMLRunner {
                  int num_video_frames                      = -1,
                  std::vector<struct ggml_tensor*> controls = {},
                  float control_strength                    = 0.f,
-                 struct ggml_tensor** output               = NULL,
-                 struct ggml_context* output_ctx           = NULL) {
+                 struct ggml_tensor** output               = nullptr,
+                 struct ggml_context* output_ctx           = nullptr) {
         // x: [N, in_channels, h, w]
         // timesteps: [N, ]
         // context: [N, max_position, hidden_size]([N, 77, 768]) or [1, max_position, hidden_size]
@@ -636,11 +671,11 @@ struct UNetModelRunner : public GGMLRunner {
     void test() {
         struct ggml_init_params params;
         params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
-        params.mem_buffer = NULL;
+        params.mem_buffer = nullptr;
         params.no_alloc   = false;
 
         struct ggml_context* work_ctx = ggml_init(params);
-        GGML_ASSERT(work_ctx != NULL);
+        GGML_ASSERT(work_ctx != nullptr);
 
         {
             // CPU, num_video_frames = 1, x{num_video_frames, 8, 8, 8}: Pass
@@ -663,10 +698,10 @@ struct UNetModelRunner : public GGMLRunner {
             ggml_set_f32(y, 0.5f);
             // print_ggml_tensor(y);
 
-            struct ggml_tensor* out = NULL;
+            struct ggml_tensor* out = nullptr;
 
             int t0 = ggml_time_ms();
-            compute(8, x, timesteps, context, NULL, y, num_video_frames, {}, 0.f, &out, work_ctx);
+            compute(8, x, timesteps, context, nullptr, y, num_video_frames, {}, 0.f, &out, work_ctx);
             int t1 = ggml_time_ms();
 
             print_ggml_tensor(out);

upscaler.cpp

@@ -4,7 +4,7 @@
 #include "stable-diffusion.h"
 
 struct UpscalerGGML {
-    ggml_backend_t backend    = NULL;  // general backend
+    ggml_backend_t backend    = nullptr;  // general backend
     ggml_type model_data_type = GGML_TYPE_F16;
     std::shared_ptr<ESRGAN> esrgan_upscaler;
     std::string esrgan_path;
@@ -42,7 +42,7 @@ struct UpscalerGGML {
         backend = ggml_backend_sycl_init(0);
 #endif
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(esrgan_path)) {
+        if (!model_loader.init_from_file_and_convert_name(esrgan_path)) {
             LOG_ERROR("init model loader from file failed: '%s'", esrgan_path.c_str());
         }
         model_loader.set_wtype_override(model_data_type);
@@ -51,9 +51,9 @@ struct UpscalerGGML {
             backend = ggml_backend_cpu_init();
         }
         LOG_INFO("Upscaler weight type: %s", ggml_type_name(model_data_type));
-        esrgan_upscaler = std::make_shared<ESRGAN>(backend, offload_params_to_cpu, model_loader.tensor_storages_types);
+        esrgan_upscaler = std::make_shared<ESRGAN>(backend, offload_params_to_cpu, model_loader.get_tensor_storage_map());
         if (direct) {
-            esrgan_upscaler->enable_conv2d_direct();
+            esrgan_upscaler->set_conv2d_direct_enabled(true);
         }
         if (!esrgan_upscaler->load_from_file(esrgan_path, n_threads)) {
             return false;
@@ -63,7 +63,7 @@ struct UpscalerGGML {
 
     sd_image_t upscale(sd_image_t input_image, uint32_t upscale_factor) {
         // upscale_factor, unused for RealESRGAN_x4plus_anime_6B.pth
-        sd_image_t upscaled_image = {0, 0, 0, NULL};
+        sd_image_t upscaled_image = {0, 0, 0, nullptr};
         int output_width          = (int)input_image.width * esrgan_upscaler->scale;
         int output_height         = (int)input_image.height * esrgan_upscaler->scale;
         LOG_INFO("upscaling from (%i x %i) to (%i x %i)",
@@ -71,7 +71,7 @@ struct UpscalerGGML {
 
         struct ggml_init_params params;
         params.mem_size   = static_cast<size_t>(1024 * 1024) * 1024;  // 1G
-        params.mem_buffer = NULL;
+        params.mem_buffer = nullptr;
         params.no_alloc   = false;
 
         // draft context
@@ -82,7 +82,7 @@ struct UpscalerGGML {
         }
         // LOG_DEBUG("upscale work buffer size: %.2f MB", params.mem_size / 1024.f / 1024.f);
         ggml_tensor* input_image_tensor = ggml_new_tensor_4d(upscale_ctx, GGML_TYPE_F32, input_image.width, input_image.height, 3, 1);
-        sd_image_to_tensor(input_image, input_image_tensor);
+        sd_image_to_ggml_tensor(input_image, input_image_tensor);
 
         ggml_tensor* upscaled = ggml_new_tensor_4d(upscale_ctx, GGML_TYPE_F32, output_width, output_height, 3, 1);
         auto on_tiling        = [&](ggml_tensor* in, ggml_tensor* out, bool init) {
@@ -91,8 +91,8 @@ struct UpscalerGGML {
         int64_t t0 = ggml_time_ms();
         sd_tiling(input_image_tensor, upscaled, esrgan_upscaler->scale, esrgan_upscaler->tile_size, 0.25f, on_tiling);
         esrgan_upscaler->free_compute_buffer();
-        ggml_tensor_clamp(upscaled, 0.f, 1.f);
-        uint8_t* upscaled_data = sd_tensor_to_image(upscaled);
+        ggml_ext_tensor_clamp_inplace(upscaled, 0.f, 1.f);
+        uint8_t* upscaled_data = ggml_tensor_to_sd_image(upscaled);
         ggml_free(upscale_ctx);
         int64_t t3 = ggml_time_ms();
         LOG_INFO("input_image_tensor upscaled, taking %.2fs", (t3 - t0) / 1000.0f);
@@ -107,7 +107,7 @@ struct UpscalerGGML {
 };
 
 struct upscaler_ctx_t {
-    UpscalerGGML* upscaler = NULL;
+    UpscalerGGML* upscaler = nullptr;
 };
 
 upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path_c_str,
@@ -115,21 +115,21 @@ upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path_c_str,
                                  bool direct,
                                  int n_threads) {
     upscaler_ctx_t* upscaler_ctx = (upscaler_ctx_t*)malloc(sizeof(upscaler_ctx_t));
-    if (upscaler_ctx == NULL) {
-        return NULL;
+    if (upscaler_ctx == nullptr) {
+        return nullptr;
     }
     std::string esrgan_path(esrgan_path_c_str);
 
     upscaler_ctx->upscaler = new UpscalerGGML(n_threads, direct);
-    if (upscaler_ctx->upscaler == NULL) {
-        return NULL;
+    if (upscaler_ctx->upscaler == nullptr) {
+        return nullptr;
     }
 
     if (!upscaler_ctx->upscaler->load_from_file(esrgan_path, offload_params_to_cpu, n_threads)) {
         delete upscaler_ctx->upscaler;
-        upscaler_ctx->upscaler = NULL;
+        upscaler_ctx->upscaler = nullptr;
         free(upscaler_ctx);
-        return NULL;
+        return nullptr;
     }
     return upscaler_ctx;
 }
@@ -139,16 +139,16 @@ sd_image_t upscale(upscaler_ctx_t* upscaler_ctx, sd_image_t input_image, uint32_
 }
 
 int get_upscale_factor(upscaler_ctx_t* upscaler_ctx) {
-    if (upscaler_ctx == NULL || upscaler_ctx->upscaler == NULL || upscaler_ctx->upscaler->esrgan_upscaler == NULL) {
+    if (upscaler_ctx == nullptr || upscaler_ctx->upscaler == nullptr || upscaler_ctx->upscaler->esrgan_upscaler == nullptr) {
         return 1;
     }
     return upscaler_ctx->upscaler->esrgan_upscaler->scale;
 }
 
 void free_upscaler_ctx(upscaler_ctx_t* upscaler_ctx) {
-    if (upscaler_ctx->upscaler != NULL) {
+    if (upscaler_ctx->upscaler != nullptr) {
         delete upscaler_ctx->upscaler;
-        upscaler_ctx->upscaler = NULL;
+        upscaler_ctx->upscaler = nullptr;
     }
     free(upscaler_ctx);
 }

util.cpp

@@ -1,10 +1,11 @@
 #include "util.h"
-#include <stdarg.h>
 #include <algorithm>
 #include <cmath>
 #include <codecvt>
+#include <cstdarg>
 #include <fstream>
 #include <locale>
+#include <regex>
 #include <sstream>
 #include <string>
 #include <thread>
@@ -26,9 +27,6 @@
 #include "ggml.h"
 #include "stable-diffusion.h"
 
-#define STB_IMAGE_RESIZE_IMPLEMENTATION
-#include "stb_image_resize.h"
-
 bool ends_with(const std::string& str, const std::string& ending) {
     if (str.length() >= ending.length()) {
         return (str.compare(str.length() - ending.length(), ending.length(), ending) == 0);
@@ -64,7 +62,7 @@ std::string format(const char* fmt, ...) {
     va_list ap2;
     va_start(ap, fmt);
     va_copy(ap2, ap);
-    int size = vsnprintf(NULL, 0, fmt, ap);
+    int size = vsnprintf(nullptr, 0, fmt, ap);
     std::vector<char> buf(size + 1);
     int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
     va_end(ap2);
@@ -170,11 +168,11 @@ int32_t get_num_physical_cores() {
 #elif defined(__APPLE__) && defined(__MACH__)
     int32_t num_physical_cores;
     size_t len = sizeof(num_physical_cores);
-    int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
+    int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, nullptr, 0);
     if (result == 0) {
         return num_physical_cores;
     }
-    result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
+    result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, nullptr, 0);
     if (result == 0) {
         return num_physical_cores;
     }
@@ -185,8 +183,15 @@ int32_t get_num_physical_cores() {
     return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
 }
 
-static sd_progress_cb_t sd_progress_cb = NULL;
-void* sd_progress_cb_data              = NULL;
+static sd_progress_cb_t sd_progress_cb = nullptr;
+void* sd_progress_cb_data              = nullptr;
+
+static sd_preview_cb_t sd_preview_cb = nullptr;
+static void* sd_preview_cb_data      = nullptr;
+preview_t sd_preview_mode            = PREVIEW_NONE;
+int sd_preview_interval              = 1;
+bool sd_preview_denoised             = true;
+bool sd_preview_noisy                = false;
 
 std::u32string utf8_to_utf32(const std::string& utf8_str) {
     std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
@@ -296,8 +301,8 @@ std::string trim(const std::string& s) {
     return rtrim(ltrim(s));
 }
 
-static sd_log_cb_t sd_log_cb = NULL;
-void* sd_log_cb_data         = NULL;
+static sd_log_cb_t sd_log_cb = nullptr;
+void* sd_log_cb_data         = nullptr;
 
 #define LOG_BUFFER_SIZE 4096
 
@@ -331,6 +336,41 @@ void sd_set_progress_callback(sd_progress_cb_t cb, void* data) {
     sd_progress_cb      = cb;
     sd_progress_cb_data = data;
 }
+void sd_set_preview_callback(sd_preview_cb_t cb, preview_t mode, int interval, bool denoised, bool noisy, void* data) {
+    sd_preview_cb       = cb;
+    sd_preview_cb_data  = data;
+    sd_preview_mode     = mode;
+    sd_preview_interval = interval;
+    sd_preview_denoised = denoised;
+    sd_preview_noisy    = noisy;
+}
+
+sd_preview_cb_t sd_get_preview_callback() {
+    return sd_preview_cb;
+}
+void* sd_get_preview_callback_data() {
+    return sd_preview_cb_data;
+}
+
+preview_t sd_get_preview_mode() {
+    return sd_preview_mode;
+}
+int sd_get_preview_interval() {
+    return sd_preview_interval;
+}
+bool sd_should_preview_denoised() {
+    return sd_preview_denoised;
+}
+bool sd_should_preview_noisy() {
+    return sd_preview_noisy;
+}
+
+sd_progress_cb_t sd_get_progress_callback() {
+    return sd_progress_cb;
+}
+void* sd_get_progress_callback_data() {
+    return sd_progress_cb_data;
+}
 const char* sd_get_system_info() {
     static char buffer[1024];
     std::stringstream ss;
@@ -513,6 +553,8 @@ sd_image_f32_t clip_preprocess(sd_image_f32_t image, int target_width, int targe
 //   (abc) - increases attention to abc by a multiplier of 1.1
 //   (abc:3.12) - increases attention to abc by a multiplier of 3.12
 //   [abc] - decreases attention to abc by a multiplier of 1.1
+//   BREAK - separates the prompt into conceptually distinct parts for sequential processing
+//   B - internal helper pattern; prevents 'B' in 'BREAK' from being consumed as normal text
 //   \( - literal character '('
 //   \[ - literal character '['
 //   \) - literal character ')'
@@ -548,7 +590,7 @@ std::vector<std::pair<std::string, float>> parse_prompt_attention(const std::str
     float round_bracket_multiplier  = 1.1f;
     float square_bracket_multiplier = 1 / 1.1f;
 
-    std::regex re_attention(R"(\\\(|\\\)|\\\[|\\\]|\\\\|\\|\(|\[|:([+-]?[.\d]+)\)|\)|\]|[^\\()\[\]:]+|:)");
+    std::regex re_attention(R"(\\\(|\\\)|\\\[|\\\]|\\\\|\\|\(|\[|:([+-]?[.\d]+)\)|\)|\]|\bBREAK\b|[^\\()\[\]:B]+|:|\bB)");
     std::regex re_break(R"(\s*\bBREAK\b\s*)");
 
     auto multiply_range = [&](int start_position, float multiplier) {
@@ -557,7 +599,7 @@ std::vector<std::pair<std::string, float>> parse_prompt_attention(const std::str
         }
     };
 
-    std::smatch m;
+    std::smatch m, m2;
     std::string remaining_text = text;
 
     while (std::regex_search(remaining_text, m, re_attention)) {
@@ -581,6 +623,8 @@ std::vector<std::pair<std::string, float>> parse_prompt_attention(const std::str
             square_brackets.pop_back();
         } else if (text == "\\(") {
             res.push_back({text.substr(1), 1.0f});
+        } else if (std::regex_search(text, m2, re_break)) {
+            res.push_back({"BREAK", -1.0f});
         } else {
             res.push_back({text, 1.0f});
         }
@@ -611,4 +655,4 @@ std::vector<std::pair<std::string, float>> parse_prompt_attention(const std::str
     }
 
     return res;
-}
+}

util.h

@@ -54,6 +54,16 @@ std::string trim(const std::string& s);
 
 std::vector<std::pair<std::string, float>> parse_prompt_attention(const std::string& text);
 
+sd_progress_cb_t sd_get_progress_callback();
+void* sd_get_progress_callback_data();
+
+sd_preview_cb_t sd_get_preview_callback();
+void* sd_get_preview_callback_data();
+preview_t sd_get_preview_mode();
+int sd_get_preview_interval();
+bool sd_should_preview_denoised();
+bool sd_should_preview_noisy();
+
 #define LOG_DEBUG(format, ...) log_printf(SD_LOG_DEBUG, __FILE__, __LINE__, format, ##__VA_ARGS__)
 #define LOG_INFO(format, ...) log_printf(SD_LOG_INFO, __FILE__, __LINE__, format, ##__VA_ARGS__)
 #define LOG_WARN(format, ...) log_printf(SD_LOG_WARN, __FILE__, __LINE__, format, ##__VA_ARGS__)

vae.hpp

@@ -30,7 +30,7 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [N, in_channels, h, w]
         // t_emb is always None
         auto norm1 = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm1"]);
@@ -40,12 +40,12 @@ public:
 
         auto h = x;
         h      = norm1->forward(ctx, h);
-        h      = ggml_silu_inplace(ctx, h);  // swish
+        h      = ggml_silu_inplace(ctx->ggml_ctx, h);  // swish
         h      = conv1->forward(ctx, h);
         // return h;
 
         h = norm2->forward(ctx, h);
-        h = ggml_silu_inplace(ctx, h);  // swish
+        h = ggml_silu_inplace(ctx->ggml_ctx, h);  // swish
         // dropout, skip for inference
         h = conv2->forward(ctx, h);
 
@@ -56,7 +56,7 @@ public:
             x = nin_shortcut->forward(ctx, x);  // [N, out_channels, h, w]
         }
 
-        h = ggml_add(ctx, h, x);
+        h = ggml_add(ctx->ggml_ctx, h, x);
         return h;  // [N, out_channels, h, w]
     }
 };
@@ -64,25 +64,51 @@ public:
 class AttnBlock : public UnaryBlock {
 protected:
     int64_t in_channels;
+    bool use_linear;
 
-public:
-    AttnBlock(int64_t in_channels)
-        : in_channels(in_channels) {
-        blocks["norm"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(in_channels));
-        blocks["q"]    = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
-        blocks["k"]    = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
-        blocks["v"]    = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
-
-        blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") {
+        auto iter = tensor_storage_map.find(prefix + "proj_out.weight");
+        if (iter != tensor_storage_map.end()) {
+            if (iter->second.n_dims == 4 && use_linear) {
+                use_linear         = false;
+                blocks["q"]        = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
+                blocks["k"]        = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
+                blocks["v"]        = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
+                blocks["proj_out"] = std::make_shared<Conv2d>(in_channels, in_channels, std::pair{1, 1});
+            } else if (iter->second.n_dims == 2 && !use_linear) {
+                use_linear         = true;
+                blocks["q"]        = std::make_shared<Linear>(in_channels, in_channels);
+                blocks["k"]        = std::make_shared<Linear>(in_channels, in_channels);
+                blocks["v"]        = std::make_shared<Linear>(in_channels, in_channels);
+                blocks["proj_out"] = std::make_shared<Linear>(in_channels, in_channels);
+            }
+        }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+public:
+    AttnBlock(int64_t in_channels, bool use_linear)
+        : in_channels(in_channels), use_linear(use_linear) {
+        blocks["norm"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(in_channels));
+        if (use_linear) {
+            blocks["q"]        = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
+            blocks["k"]        = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
+            blocks["v"]        = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
+            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Linear(in_channels, in_channels));
+        } else {
+            blocks["q"]        = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+            blocks["k"]        = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+            blocks["v"]        = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, in_channels, {1, 1}));
+        }
+    }
+
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [N, in_channels, h, w]
         auto norm     = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm"]);
-        auto q_proj   = std::dynamic_pointer_cast<Conv2d>(blocks["q"]);
-        auto k_proj   = std::dynamic_pointer_cast<Conv2d>(blocks["k"]);
-        auto v_proj   = std::dynamic_pointer_cast<Conv2d>(blocks["v"]);
-        auto proj_out = std::dynamic_pointer_cast<Conv2d>(blocks["proj_out"]);
+        auto q_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["q"]);
+        auto k_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["k"]);
+        auto v_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["v"]);
+        auto proj_out = std::dynamic_pointer_cast<UnaryBlock>(blocks["proj_out"]);
 
         auto h_ = norm->forward(ctx, x);
 
@@ -91,25 +117,46 @@ public:
         const int64_t h = h_->ne[1];
         const int64_t w = h_->ne[0];
 
-        auto q = q_proj->forward(ctx, h_);                          // [N, in_channels, h, w]
-        q      = ggml_cont(ctx, ggml_permute(ctx, q, 1, 2, 0, 3));  // [N, h, w, in_channels]
-        q      = ggml_reshape_3d(ctx, q, c, h * w, n);              // [N, h * w, in_channels]
+        ggml_tensor* q;
+        ggml_tensor* k;
+        ggml_tensor* v;
+        if (use_linear) {
+            h_ = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, h_, 1, 2, 0, 3));  // [N, h, w, in_channels]
+            h_ = ggml_reshape_3d(ctx->ggml_ctx, h_, c, h * w, n);                        // [N, h * w, in_channels]
 
-        auto k = k_proj->forward(ctx, h_);                          // [N, in_channels, h, w]
-        k      = ggml_cont(ctx, ggml_permute(ctx, k, 1, 2, 0, 3));  // [N, h, w, in_channels]
-        k      = ggml_reshape_3d(ctx, k, c, h * w, n);              // [N, h * w, in_channels]
+            q = q_proj->forward(ctx, h_);  // [N, h * w, in_channels]
+            k = k_proj->forward(ctx, h_);  // [N, h * w, in_channels]
+            v = v_proj->forward(ctx, h_);  // [N, h * w, in_channels]
 
-        auto v = v_proj->forward(ctx, h_);              // [N, in_channels, h, w]
-        v      = ggml_reshape_3d(ctx, v, h * w, c, n);  // [N, in_channels, h * w]
+            v = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, v, 1, 0, 2, 3));  // [N, in_channels, h * w]
+        } else {
+            q = q_proj->forward(ctx, h_);                                              // [N, in_channels, h, w]
+            q = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, q, 1, 2, 0, 3));  // [N, h, w, in_channels]
+            q = ggml_reshape_3d(ctx->ggml_ctx, q, c, h * w, n);                        // [N, h * w, in_channels]
 
-        h_ = ggml_nn_attention(ctx, q, k, v, false);  // [N, h * w, in_channels]
+            k = k_proj->forward(ctx, h_);                                              // [N, in_channels, h, w]
+            k = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, k, 1, 2, 0, 3));  // [N, h, w, in_channels]
+            k = ggml_reshape_3d(ctx->ggml_ctx, k, c, h * w, n);                        // [N, h * w, in_channels]
 
-        h_ = ggml_cont(ctx, ggml_permute(ctx, h_, 1, 0, 2, 3));  // [N, in_channels, h * w]
-        h_ = ggml_reshape_4d(ctx, h_, w, h, c, n);               // [N, in_channels, h, w]
+            v = v_proj->forward(ctx, h_);                        // [N, in_channels, h, w]
+            v = ggml_reshape_3d(ctx->ggml_ctx, v, h * w, c, n);  // [N, in_channels, h * w]
+        }
 
-        h_ = proj_out->forward(ctx, h_);  // [N, in_channels, h, w]
+        h_ = ggml_ext_attention(ctx->ggml_ctx, q, k, v, false);  // [N, h * w, in_channels]
 
-        h_ = ggml_add(ctx, h_, x);
+        if (use_linear) {
+            h_ = proj_out->forward(ctx, h_);  // [N, h * w, in_channels]
+
+            h_ = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, h_, 1, 0, 2, 3));  // [N, in_channels, h * w]
+            h_ = ggml_reshape_4d(ctx->ggml_ctx, h_, w, h, c, n);                         // [N, in_channels, h, w]
+        } else {
+            h_ = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, h_, 1, 0, 2, 3));  // [N, in_channels, h * w]
+            h_ = ggml_reshape_4d(ctx->ggml_ctx, h_, w, h, c, n);                         // [N, in_channels, h, w]
+
+            h_ = proj_out->forward(ctx, h_);  // [N, in_channels, h, w]
+        }
+
+        h_ = ggml_add(ctx->ggml_ctx, h_, x);
         return h_;
     }
 };
@@ -133,8 +180,8 @@ public:
                                                                              kernel_padding));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
-                                struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                struct ggml_tensor* x) override {
         // timesteps always None
         // skip_video always False
         // x: [N, IC, IH, IW]
@@ -152,24 +199,24 @@ public:
         int64_t H = x->ne[1];
         int64_t W = x->ne[0];
 
-        x = ggml_reshape_4d(ctx, x, W * H, C, T, B);           // (b t) c h w -> b t c (h w)
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
-        x = time_mix_conv->forward(ctx, x);                    // [B, OC, T, OH * OW]
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
-        x = ggml_reshape_4d(ctx, x, W, H, C, T * B);           // b t c (h w) -> (b t) c h w
-        return x;                                              // [B*T, OC, OH, OW]
+        x = ggml_reshape_4d(ctx->ggml_ctx, x, W * H, C, T, B);                     // (b t) c h w -> b t c (h w)
+        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
+        x = time_mix_conv->forward(ctx, x);                                        // [B, OC, T, OH * OW]
+        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
+        x = ggml_reshape_4d(ctx->ggml_ctx, x, W, H, C, T * B);                     // b t c (h w) -> (b t) c h w
+        return x;                                                                  // [B*T, OC, OH, OW]
     }
 };
 
 class VideoResnetBlock : public ResnetBlock {
 protected:
-    void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
-        enum ggml_type wtype = get_type(prefix + "mix_factor", tensor_types, GGML_TYPE_F32);
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        enum ggml_type wtype = get_type(prefix + "mix_factor", tensor_storage_map, GGML_TYPE_F32);
         params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
     }
 
     float get_alpha() {
-        float alpha = ggml_backend_tensor_get_f32(params["mix_factor"]);
+        float alpha = ggml_ext_backend_tensor_get_f32(params["mix_factor"]);
         return sigmoid(alpha);
     }
 
@@ -182,7 +229,7 @@ public:
         blocks["time_stack"] = std::shared_ptr<GGMLBlock>(new ResBlock(out_channels, 0, out_channels, {video_kernel_size, 1}, 3, false, true));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [N, in_channels, h, w] aka [b*t, in_channels, h, w]
         // return: [N, out_channels, h, w] aka [b*t, out_channels, h, w]
         // t_emb is always None
@@ -199,19 +246,19 @@ public:
         int64_t H = x->ne[1];
         int64_t W = x->ne[0];
 
-        x          = ggml_reshape_4d(ctx, x, W * H, C, T, B);           // (b t) c h w -> b t c (h w)
-        x          = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
+        x          = ggml_reshape_4d(ctx->ggml_ctx, x, W * H, C, T, B);                     // (b t) c h w -> b t c (h w)
+        x          = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
         auto x_mix = x;
 
         x = time_stack->forward(ctx, x);  // b t c (h w)
 
         float alpha = get_alpha();
-        x           = ggml_add(ctx,
-                               ggml_scale(ctx, x, alpha),
-                               ggml_scale(ctx, x_mix, 1.0f - alpha));
+        x           = ggml_add(ctx->ggml_ctx,
+                               ggml_scale(ctx->ggml_ctx, x, alpha),
+                               ggml_scale(ctx->ggml_ctx, x_mix, 1.0f - alpha));
 
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
-        x = ggml_reshape_4d(ctx, x, W, H, C, T * B);           // b t c (h w) -> (b t) c h w
+        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
+        x = ggml_reshape_4d(ctx->ggml_ctx, x, W, H, C, T * B);                     // b t c (h w) -> (b t) c h w
 
         return x;
     }
@@ -233,7 +280,8 @@ public:
             int num_res_blocks,
             int in_channels,
             int z_channels,
-            bool double_z = true)
+            bool double_z              = true,
+            bool use_linear_projection = false)
         : ch(ch),
           ch_mult(ch_mult),
           num_res_blocks(num_res_blocks),
@@ -264,14 +312,14 @@ public:
         }
 
         blocks["mid.block_1"] = std::shared_ptr<GGMLBlock>(new ResnetBlock(block_in, block_in));
-        blocks["mid.attn_1"]  = std::shared_ptr<GGMLBlock>(new AttnBlock(block_in));
+        blocks["mid.attn_1"]  = std::shared_ptr<GGMLBlock>(new AttnBlock(block_in, use_linear_projection));
         blocks["mid.block_2"] = std::shared_ptr<GGMLBlock>(new ResnetBlock(block_in, block_in));
 
         blocks["norm_out"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(block_in));
         blocks["conv_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(block_in, double_z ? z_channels * 2 : z_channels, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    virtual struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [N, in_channels, h, w]
 
         auto conv_in     = std::dynamic_pointer_cast<Conv2d>(blocks["conv_in"]);
@@ -307,8 +355,8 @@ public:
 
         // end
         h = norm_out->forward(ctx, h);
-        h = ggml_silu_inplace(ctx, h);  // nonlinearity/swish
-        h = conv_out->forward(ctx, h);  // [N, z_channels*2, h, w]
+        h = ggml_silu_inplace(ctx->ggml_ctx, h);  // nonlinearity/swish
+        h = conv_out->forward(ctx, h);            // [N, z_channels*2, h, w]
         return h;
     }
 };
@@ -351,8 +399,9 @@ public:
             std::vector<int> ch_mult,
             int num_res_blocks,
             int z_channels,
-            bool video_decoder    = false,
-            int video_kernel_size = 3)
+            bool use_linear_projection = false,
+            bool video_decoder         = false,
+            int video_kernel_size      = 3)
         : ch(ch),
           out_ch(out_ch),
           ch_mult(ch_mult),
@@ -366,7 +415,7 @@ public:
         blocks["conv_in"] = std::shared_ptr<GGMLBlock>(new Conv2d(z_channels, block_in, {3, 3}, {1, 1}, {1, 1}));
 
         blocks["mid.block_1"] = get_resnet_block(block_in, block_in);
-        blocks["mid.attn_1"]  = std::shared_ptr<GGMLBlock>(new AttnBlock(block_in));
+        blocks["mid.attn_1"]  = std::shared_ptr<GGMLBlock>(new AttnBlock(block_in, use_linear_projection));
         blocks["mid.block_2"] = get_resnet_block(block_in, block_in);
 
         for (int i = num_resolutions - 1; i >= 0; i--) {
@@ -388,7 +437,7 @@ public:
         blocks["conv_out"] = get_conv_out(block_in, out_ch, {3, 3}, {1, 1}, {1, 1});
     }
 
-    virtual struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* z) {
+    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
         // z: [N, z_channels, h, w]
         // alpha is always 0
         // merge_strategy is always learned
@@ -429,8 +478,8 @@ public:
         }
 
         h = norm_out->forward(ctx, h);
-        h = ggml_silu_inplace(ctx, h);  // nonlinearity/swish
-        h = conv_out->forward(ctx, h);  // [N, out_ch, h*8, w*8]
+        h = ggml_silu_inplace(ctx->ggml_ctx, h);  // nonlinearity/swish
+        h = conv_out->forward(ctx, h);            // [N, out_ch, h*8, w*8]
         return h;
     }
 };
@@ -438,6 +487,7 @@ public:
 // ldm.models.autoencoder.AutoencoderKL
 class AutoencodingEngine : public GGMLBlock {
 protected:
+    SDVersion version;
     bool decode_only       = true;
     bool use_video_decoder = false;
     bool use_quant         = true;
@@ -454,13 +504,19 @@ protected:
     } dd_config;
 
 public:
-    AutoencodingEngine(bool decode_only       = true,
-                       bool use_video_decoder = false,
-                       SDVersion version      = VERSION_SD1)
-        : decode_only(decode_only), use_video_decoder(use_video_decoder) {
+    AutoencodingEngine(SDVersion version          = VERSION_SD1,
+                       bool decode_only           = true,
+                       bool use_linear_projection = false,
+                       bool use_video_decoder     = false)
+        : version(version), decode_only(decode_only), use_video_decoder(use_video_decoder) {
         if (sd_version_is_dit(version)) {
-            dd_config.z_channels = 16;
-            use_quant            = false;
+            if (sd_version_is_flux2(version)) {
+                dd_config.z_channels = 32;
+                embed_dim            = 32;
+            } else {
+                use_quant            = false;
+                dd_config.z_channels = 16;
+            }
         }
         if (use_video_decoder) {
             use_quant = false;
@@ -470,6 +526,7 @@ public:
                                                                    dd_config.ch_mult,
                                                                    dd_config.num_res_blocks,
                                                                    dd_config.z_channels,
+                                                                   use_linear_projection,
                                                                    use_video_decoder));
         if (use_quant) {
             blocks["post_quant_conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(dd_config.z_channels,
@@ -482,7 +539,8 @@ public:
                                                                        dd_config.num_res_blocks,
                                                                        dd_config.in_channels,
                                                                        dd_config.z_channels,
-                                                                       dd_config.double_z));
+                                                                       dd_config.double_z,
+                                                                       use_linear_projection));
             if (use_quant) {
                 int factor = dd_config.double_z ? 2 : 1;
 
@@ -493,8 +551,26 @@ public:
         }
     }
 
-    struct ggml_tensor* decode(struct ggml_context* ctx, struct ggml_tensor* z) {
+    struct ggml_tensor* decode(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
         // z: [N, z_channels, h, w]
+        if (sd_version_is_flux2(version)) {
+            // [N, C*p*p, h, w] -> [N, C, h*p, w*p]
+            int64_t p = 2;
+
+            int64_t N = z->ne[3];
+            int64_t C = z->ne[2] / p / p;
+            int64_t h = z->ne[1];
+            int64_t w = z->ne[0];
+            int64_t H = h * p;
+            int64_t W = w * p;
+
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, w * h, p * p, C, N);                           // [N, C, p*p, h*w]
+            z = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, z, 1, 0, 2, 3));  // [N, C, h*w, p*p]
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, p, p, w, h * C * N);                           // [N*C*h, w, p, p]
+            z = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, z, 0, 2, 1, 3));  // [N*C*h, p, w, p]
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, W, H, C, N);                                   // [N, C, h*p, w*p]
+        }
+
         if (use_quant) {
             auto post_quant_conv = std::dynamic_pointer_cast<Conv2d>(blocks["post_quant_conv"]);
             z                    = post_quant_conv->forward(ctx, z);  // [N, z_channels, h, w]
@@ -507,16 +583,34 @@ public:
         return h;
     }
 
-    struct ggml_tensor* encode(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* encode(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [N, in_channels, h, w]
         auto encoder = std::dynamic_pointer_cast<Encoder>(blocks["encoder"]);
 
-        auto h = encoder->forward(ctx, x);  // [N, 2*z_channels, h/8, w/8]
+        auto z = encoder->forward(ctx, x);  // [N, 2*z_channels, h/8, w/8]
         if (use_quant) {
             auto quant_conv = std::dynamic_pointer_cast<Conv2d>(blocks["quant_conv"]);
-            h               = quant_conv->forward(ctx, h);  // [N, 2*embed_dim, h/8, w/8]
+            z               = quant_conv->forward(ctx, z);  // [N, 2*embed_dim, h/8, w/8]
         }
-        return h;
+        if (sd_version_is_flux2(version)) {
+            z = ggml_ext_chunk(ctx->ggml_ctx, z, 2, 2)[0];
+
+            // [N, C, H, W] -> [N, C*p*p, H/p, W/p]
+            int64_t p = 2;
+            int64_t N = z->ne[3];
+            int64_t C = z->ne[2];
+            int64_t H = z->ne[1];
+            int64_t W = z->ne[0];
+            int64_t h = H / p;
+            int64_t w = W / p;
+
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, p, w, p, h * C * N);                 // [N*C*h, p, w, p]
+            z = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, z, 0, 2, 1, 3));  // [N*C*h, w, p, p]
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, p * p, w * h, C, N);                 // [N, C, h*w, p*p]
+            z = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, z, 1, 0, 2, 3));  // [N, C, p*p, h*w]
+            z = ggml_reshape_4d(ctx->ggml_ctx, z, w, h, p * p * C, N);                 // [N, C*p*p, h*w]
+        }
+        return z;
     }
 };
 
@@ -529,37 +623,62 @@ struct VAE : public GGMLRunner {
                          struct ggml_tensor** output,
                          struct ggml_context* output_ctx)                                                         = 0;
     virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) = 0;
-    virtual void enable_conv2d_direct(){};
     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_ext_tensor_iter(z, [&](ggml_tensor* z, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
+            float value = ggml_ext_tensor_get_f32(z, i0, i1, i2, i3);
+            ggml_ext_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;
 
     AutoEncoderKL(ggml_backend_t backend,
                   bool offload_params_to_cpu,
-                  const String2GGMLType& tensor_types,
+                  const String2TensorStorage& tensor_storage_map,
                   const std::string prefix,
                   bool decode_only       = false,
                   bool use_video_decoder = false,
                   SDVersion version      = VERSION_SD1)
-        : decode_only(decode_only), ae(decode_only, use_video_decoder, version), VAE(backend, offload_params_to_cpu) {
-        ae.init(params_ctx, tensor_types, prefix);
-    }
-
-    void enable_conv2d_direct() {
-        std::vector<GGMLBlock*> blocks;
-        ae.get_all_blocks(blocks);
-        for (auto block : blocks) {
-            if (block->get_desc() == "Conv2d") {
-                auto conv_block = (Conv2d*)block;
-                conv_block->enable_direct();
+        : decode_only(decode_only), VAE(backend, offload_params_to_cpu) {
+        bool use_linear_projection = false;
+        for (const auto& [name, tensor_storage] : tensor_storage_map) {
+            if (!starts_with(name, prefix)) {
+                continue;
+            }
+            if (ends_with(name, "attn_1.proj_out.weight")) {
+                if (tensor_storage.n_dims == 2) {
+                    use_linear_projection = true;
+                }
+                break;
             }
         }
+        ae = AutoencodingEngine(version, decode_only, use_linear_projection, use_video_decoder);
+        ae.init(params_ctx, tensor_storage_map, prefix);
     }
 
-    void set_conv2d_scale(float scale) {
+    void set_conv2d_scale(float scale) override {
         std::vector<GGMLBlock*> blocks;
         ae.get_all_blocks(blocks);
         for (auto block : blocks) {
@@ -570,11 +689,11 @@ struct AutoEncoderKL : public VAE {
         }
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return "vae";
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) override {
         ae.get_param_tensors(tensors, prefix);
     }
 
@@ -583,7 +702,9 @@ struct AutoEncoderKL : public VAE {
 
         z = to_backend(z);
 
-        struct ggml_tensor* out = decode_graph ? ae.decode(compute_ctx, z) : ae.encode(compute_ctx, z);
+        auto runner_ctx = get_context();
+
+        struct ggml_tensor* out = decode_graph ? ae.decode(&runner_ctx, z) : ae.encode(&runner_ctx, z);
 
         ggml_build_forward_expand(gf, out);
 
@@ -594,7 +715,7 @@ struct AutoEncoderKL : public VAE {
                  struct ggml_tensor* z,
                  bool decode_graph,
                  struct ggml_tensor** output,
-                 struct ggml_context* output_ctx = NULL) {
+                 struct ggml_context* output_ctx = nullptr) override {
         GGML_ASSERT(!decode_only || decode_graph);
         auto get_graph = [&]() -> struct ggml_cgraph* {
             return build_graph(z, decode_graph);
@@ -607,11 +728,11 @@ struct AutoEncoderKL : public VAE {
     void test() {
         struct ggml_init_params params;
         params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
-        params.mem_buffer = NULL;
+        params.mem_buffer = nullptr;
         params.no_alloc   = false;
 
         struct ggml_context* work_ctx = ggml_init(params);
-        GGML_ASSERT(work_ctx != NULL);
+        GGML_ASSERT(work_ctx != nullptr);
 
         {
             // CPU, x{1, 3, 64, 64}: Pass
@@ -621,7 +742,7 @@ struct AutoEncoderKL : public VAE {
             auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 64, 64, 3, 2);
             ggml_set_f32(x, 0.5f);
             print_ggml_tensor(x);
-            struct ggml_tensor* out = NULL;
+            struct ggml_tensor* out = nullptr;
 
             int t0 = ggml_time_ms();
             compute(8, x, false, &out, work_ctx);
@@ -639,7 +760,7 @@ struct AutoEncoderKL : public VAE {
             auto z = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 8, 8, 4, 1);
             ggml_set_f32(z, 0.5f);
             print_ggml_tensor(z);
-            struct ggml_tensor* out = NULL;
+            struct ggml_tensor* out = nullptr;
 
             int t0 = ggml_time_ms();
             compute(8, z, true, &out, work_ctx);