fix: fix metal build issues (#183)

---------

Co-authored-by: leejet <leejet714@gmail.com>

.clang-format

@@ -3,7 +3,6 @@ UseTab: Never
 IndentWidth: 4
 TabWidth: 4
 AllowShortIfStatementsOnASingleLine: false
-IndentCaseLabels: false
 ColumnLimit: 0
 AccessModifierOffset: -4
 NamespaceIndentation: All

.github/workflows/build.yml

@@ -30,7 +30,6 @@ jobs:
         with:
           submodules: recursive
 
-
       - name: Dependencies
         id: depends
         run: |
@@ -42,14 +41,37 @@ jobs:
         run: |
           mkdir build
           cd build
-          cmake ..
+          cmake .. -DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON
           cmake --build . --config Release
 
-      #- name: Test
-        #id: cmake_test
-        #run: |
-          #cd build
-          #ctest --verbose --timeout 900
+      - name: Get commit hash
+        id: commit
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/main' ) || github.event.inputs.create_release == 'true' }}
+        uses: pr-mpt/actions-commit-hash@v2
+
+      - name: Fetch system info
+        id: system-info
+        run: |
+          echo "CPU_ARCH=`uname -m`" >> "$GITHUB_OUTPUT"
+          echo "OS_NAME=`lsb_release -s -i`" >> "$GITHUB_OUTPUT"
+          echo "OS_VERSION=`lsb_release -s -r`" >> "$GITHUB_OUTPUT"
+          echo "OS_TYPE=`uname -s`" >> "$GITHUB_OUTPUT"
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          cp ggml/LICENSE ./build/bin/ggml.txt
+          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
+          zip -j sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v3
+        with:
+          path: |
+            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip
+
 
   macOS-latest-cmake:
     runs-on: macos-latest
@@ -63,9 +85,8 @@ jobs:
 
       - name: Dependencies
         id: depends
-        continue-on-error: true
         run: |
-          brew update
+          brew install zip
 
       - name: Build
         id: cmake_build
@@ -73,14 +94,37 @@ jobs:
           sysctl -a
           mkdir build
           cd build
-          cmake ..
+          cmake .. -DGGML_AVX2=ON -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" -DSD_BUILD_SHARED_LIBS=ON
           cmake --build . --config Release
 
-      #- name: Test
-        #id: cmake_test
-        #run: |
-          #cd build
-          #ctest --verbose --timeout 900
+      - name: Get commit hash
+        id: commit
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/main' ) || github.event.inputs.create_release == 'true' }}
+        uses: pr-mpt/actions-commit-hash@v2
+
+      - name: Fetch system info
+        id: system-info
+        run: |
+          echo "CPU_ARCH=`uname -m`" >> "$GITHUB_OUTPUT"
+          echo "OS_NAME=`sw_vers -productName`" >> "$GITHUB_OUTPUT"
+          echo "OS_VERSION=`sw_vers -productVersion`" >> "$GITHUB_OUTPUT"
+          echo "OS_TYPE=`uname -s`" >> "$GITHUB_OUTPUT"
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          cp ggml/LICENSE ./build/bin/ggml.txt
+          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
+          zip -j sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v3
+        with:
+          path: |
+            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip
+
 
   windows-latest-cmake:
     runs-on: windows-latest
@@ -89,14 +133,17 @@ jobs:
       matrix:
         include:
           - build: 'noavx'
-            defines: '-DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF'
+            defines: '-DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DSD_BUILD_SHARED_LIBS=ON'
           - build: 'avx2'
-            defines: '-DGGML_AVX2=ON'
+            defines: '-DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON'
           - build: 'avx'
-            defines: '-DGGML_AVX2=OFF'
+            defines: '-DGGML_AVX2=OFF -DSD_BUILD_SHARED_LIBS=ON'
           - build: 'avx512'
-            defines: '-DGGML_AVX512=ON'
-
+            defines: '-DGGML_AVX512=ON -DSD_BUILD_SHARED_LIBS=ON'
+          - build: 'cuda12'
+            defines: '-DSD_CUBLAS=ON -DSD_BUILD_SHARED_LIBS=ON'
+          - 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'
     steps:
       - name: Clone
         id: checkout
@@ -104,6 +151,29 @@ jobs:
         with:
           submodules: recursive
 
+      - name: Install cuda-toolkit
+        id: cuda-toolkit
+        if: ${{ matrix.build == 'cuda12' }}
+        uses: Jimver/cuda-toolkit@v0.2.11
+        with:
+          cuda: '12.2.0'
+          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: Build
         id: cmake_build
         run: |
@@ -125,12 +195,6 @@ jobs:
           & $cl /O2 /GS- /kernel avx512f.c /link /nodefaultlib /entry:main
           .\avx512f.exe && echo "AVX512F: YES" && ( echo HAS_AVX512F=1 >> $env:GITHUB_ENV ) || echo "AVX512F: NO"
 
-      #- name: Test
-        #id: cmake_test
-        #run: |
-          #cd build
-          #ctest -C Release --verbose --timeout 900
-
       - name: Get commit hash
         id: commit
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
@@ -140,9 +204,37 @@ jobs:
         id: pack_artifacts
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         run: |
-          Copy-Item ggml/LICENSE .\build\bin\Release\ggml.txt
-          Copy-Item LICENSE .\build\bin\Release\stable-diffusion.cpp.txt
-          7z a sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip .\build\bin\Release\*
+          $filePath = ".\build\bin\Release\*"
+          if (Test-Path $filePath) {
+            echo "Exists at path $filePath"
+            Copy-Item ggml/LICENSE .\build\bin\Release\ggml.txt
+            Copy-Item LICENSE .\build\bin\Release\stable-diffusion.cpp.txt
+          } elseif (Test-Path ".\build\bin\stable-diffusion.dll") {
+          $filePath = ".\build\bin\*"
+            echo "Exists at path $filePath"
+            Copy-Item ggml/LICENSE .\build\bin\ggml.txt
+            Copy-Item LICENSE .\build\bin\stable-diffusion.cpp.txt
+          } else {
+            ls .\build\bin
+            throw "Can't find stable-diffusion.dll"
+          }
+          7z a sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip $filePath
+
+      - name: Copy and pack Cuda runtime
+        id: pack_cuda_runtime
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' && matrix.build == 'cuda12' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          echo "Cuda install location: ${{steps.cuda-toolkit.outputs.CUDA_PATH}}"
+          $dst='.\build\bin\cudart\'
+          robocopy "${{steps.cuda-toolkit.outputs.CUDA_PATH}}\bin" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
+          7z a cudart-sd-bin-win-cu12-x64.zip $dst\*
+
+      - name: Upload Cuda runtime
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' && matrix.build == 'cuda12' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v3
+        with:
+          path: |
+            cudart-sd-bin-win-cu12-x64.zip
 
       - name: Upload artifacts
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}

CMakeLists.txt

@@ -28,7 +28,8 @@ option(SD_CUBLAS                     "sd: cuda backend" OFF)
 option(SD_HIPBLAS                    "sd: rocm backend" OFF)
 option(SD_METAL                      "sd: metal backend" OFF)
 option(SD_FLASH_ATTN                 "sd: use flash attention for x4 less memory usage" OFF)
-option(BUILD_SHARED_LIBS             "sd: build shared libs" OFF)
+option(SD_FAST_SOFTMAX               "sd: x1.5 faster softmax, indeterministic (sometimes, same seed don't generate same image), cuda only" OFF)
+option(SD_BUILD_SHARED_LIBS          "sd: build shared libs" OFF)
 #option(SD_BUILD_SERVER               "sd: build server example"                           ON)
 
 if(SD_CUBLAS)
@@ -59,16 +60,24 @@ endif()
 
 set(SD_LIB stable-diffusion)
 
-add_library(${SD_LIB} stable-diffusion.h stable-diffusion.cpp model.h model.cpp util.h util.cpp upscaler.cpp
-             ggml_extend.hpp clip.hpp common.hpp unet.hpp tae.hpp esrgan.hpp lora.hpp denoiser.hpp rng.hpp rng_philox.hpp)
+file(GLOB SD_LIB_SOURCES 
+    "*.h"
+    "*.cpp"
+    "*.hpp"
+)
 
-if(BUILD_SHARED_LIBS)
+# we can get only one share lib
+if(SD_BUILD_SHARED_LIBS)
     message("Build shared library")
+    set(BUILD_SHARED_LIBS OFF)
+    message(${SD_LIB_SOURCES})
+    add_library(${SD_LIB} SHARED ${SD_LIB_SOURCES})
     add_definitions(-DSD_BUILD_SHARED_LIB)
     target_compile_definitions(${SD_LIB} PRIVATE -DSD_BUILD_DLL)
     set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 else()
     message("Build static library")
+    add_library(${SD_LIB} STATIC ${SD_LIB_SOURCES})
 endif()
 
 

README.md

@@ -148,7 +148,7 @@ cmake --build . --config Release
 ### Run
 
 ```
-usage: ./bin/sd [arguments]
+usage: ./build/bin/sd [arguments]
 
 arguments:
   -h, --help                         show this help message and exit
@@ -161,6 +161,7 @@ arguments:
   --control-net [CONTROL_PATH]       path to control net model
   --embd-dir [EMBEDDING_PATH]        path to embeddings.
   --upscale-model [ESRGAN_PATH]      path to esrgan model. 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 (f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0)
                                      If not specified, the default is the type of the weight file.
   --lora-model-dir [DIR]             lora model directory
@@ -186,6 +187,7 @@ arguments:
                                      <= 0 represents unspecified, will be 1 for SD1.x, 2 for SD2.x
   --vae-tiling                       process vae in tiles to reduce memory usage
   --control-net-cpu                  keep controlnet in cpu (for low vram)
+  --canny                            apply canny preprocessor (edge detection)
   -v, --verbose                      print extra info
 ```
 
@@ -329,6 +331,7 @@ Thank you to all the people who have already contributed to stable-diffusion.cpp
 - [stable-diffusion](https://github.com/CompVis/stable-diffusion)
 - [stable-diffusion-stability-ai](https://github.com/Stability-AI/stablediffusion)
 - [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
+- [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
 - [k-diffusion](https://github.com/crowsonkb/k-diffusion)
 - [latent-consistency-model](https://github.com/luosiallen/latent-consistency-model)
 - [generative-models](https://github.com/Stability-AI/generative-models/)

common.hpp

@@ -3,541 +3,527 @@
 
 #include "ggml_extend.hpp"
 
-struct DownSample {
-    // hparams
+class DownSampleBlock : public GGMLBlock {
+protected:
     int channels;
     int out_channels;
+    bool vae_downsample;
 
-    // conv2d params
-    struct ggml_tensor* op_w;  // [out_channels, channels, 3, 3]
-    struct ggml_tensor* op_b;  // [out_channels,]
-
-    bool vae_downsample = false;
-
-    size_t calculate_mem_size(ggml_type wtype) {
-        size_t mem_size = 0;
-        mem_size += ggml_row_size(GGML_TYPE_F16, out_channels * channels * 3 * 3);  // op_w
-        mem_size += ggml_row_size(GGML_TYPE_F32, out_channels);                     // op_b
-        return mem_size;
-    }
-
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        op_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, out_channels);
-        op_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
-    }
-
-    void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+public:
+    DownSampleBlock(int channels,
+                    int out_channels,
+                    bool vae_downsample = false)
+        : channels(channels),
+          out_channels(out_channels),
+          vae_downsample(vae_downsample) {
         if (vae_downsample) {
-            tensors[prefix + "conv.weight"] = op_w;
-            tensors[prefix + "conv.bias"]   = op_b;
+            blocks["conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, out_channels, {3, 3}, {2, 2}, {0, 0}));
         } else {
-            tensors[prefix + "op.weight"] = op_w;
-            tensors[prefix + "op.bias"]   = op_b;
+            blocks["op"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, out_channels, {3, 3}, {2, 2}, {1, 1}));
         }
     }
 
     struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
         // x: [N, channels, h, w]
-        struct ggml_tensor* c = NULL;
         if (vae_downsample) {
-            c = ggml_pad(ctx, x, 1, 1, 0, 0);
-            c = ggml_nn_conv_2d(ctx, c, op_w, op_b, 2, 2, 0, 0);
+            auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
+
+            x = ggml_pad(ctx, x, 1, 1, 0, 0);
+            x = conv->forward(ctx, x);
         } else {
-            c = ggml_nn_conv_2d(ctx, x, op_w, op_b, 2, 2, 1, 1);
+            auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["op"]);
+
+            x = conv->forward(ctx, x);
         }
-        return c;  // [N, out_channels, h/2, w/2]
+        return x;  // [N, out_channels, h/2, w/2]
     }
 };
 
-struct UpSample {
-    // hparams
+class UpSampleBlock : public GGMLBlock {
+protected:
     int channels;
     int out_channels;
 
-    // conv2d params
-    struct ggml_tensor* conv_w;  // [out_channels, channels, 3, 3]
-    struct ggml_tensor* conv_b;  // [out_channels,]
-
-    size_t calculate_mem_size(ggml_type wtype) {
-        size_t mem_size = 0;
-        mem_size += ggml_row_size(GGML_TYPE_F16, out_channels * channels * 3 * 3);  // op_w
-        mem_size += ggml_row_size(GGML_TYPE_F32, out_channels);                     // op_b
-        return mem_size;
-    }
-
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        conv_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, out_channels);
-        conv_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
-    }
-
-    void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
-        tensors[prefix + "conv.weight"] = conv_w;
-        tensors[prefix + "conv.bias"]   = conv_b;
+public:
+    UpSampleBlock(int channels,
+                  int out_channels)
+        : channels(channels),
+          out_channels(out_channels) {
+        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) {
         // x: [N, channels, h, w]
-        x = ggml_upscale(ctx, x, 2);                              // [N, channels, h*2, w*2]
-        x = ggml_nn_conv_2d(ctx, x, conv_w, conv_b, 1, 1, 1, 1);  // [N, out_channels, h*2, w*2]
+        auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
+
+        x = ggml_upscale(ctx, x, 2);  // [N, channels, h*2, w*2]
+        x = conv->forward(ctx, x);    // [N, out_channels, h*2, w*2]
         return x;
     }
 };
 
-struct ResBlock {
+class ResBlock : public GGMLBlock {
+protected:
     // network hparams
-    int channels;      // model_channels * (1, 1, 1, 2, 2, 4, 4, 4)
-    int emb_channels;  // time_embed_dim
-    int out_channels;  // mult * model_channels
+    int64_t channels;      // model_channels * (1, 1, 1, 2, 2, 4, 4, 4)
+    int64_t emb_channels;  // time_embed_dim
+    int64_t out_channels;  // mult * model_channels
+    std::pair<int, int> kernel_size;
+    int dims;
+    bool skip_t_emb;
+    bool exchange_temb_dims;
 
-    // network params
-    // in_layers
-    struct ggml_tensor* in_layer_0_w;  // [channels, ]
-    struct ggml_tensor* in_layer_0_b;  // [channels, ]
-    // in_layer_1 is nn.SILU()
-    struct ggml_tensor* in_layer_2_w;  // [out_channels, channels, 3, 3]
-    struct ggml_tensor* in_layer_2_b;  // [out_channels, ]
-
-    // emb_layers
-    // emb_layer_0 is nn.SILU()
-    struct ggml_tensor* emb_layer_1_w;  // [out_channels, emb_channels]
-    struct ggml_tensor* emb_layer_1_b;  // [out_channels, ]
-
-    // out_layers
-    struct ggml_tensor* out_layer_0_w;  // [out_channels, ]
-    struct ggml_tensor* out_layer_0_b;  // [out_channels, ]
-    // out_layer_1 is nn.SILU()
-    // out_layer_2 is nn.Dropout(), p = 0 for inference
-    struct ggml_tensor* out_layer_3_w;  // [out_channels, out_channels, 3, 3]
-    struct ggml_tensor* out_layer_3_b;  // [out_channels, ]
-
-    // skip connection, only if out_channels != channels
-    struct ggml_tensor* skip_w;  // [out_channels, channels, 1, 1]
-    struct ggml_tensor* skip_b;  // [out_channels, ]
-
-    size_t calculate_mem_size(ggml_type wtype) {
-        size_t mem_size = 0;
-        mem_size += 2 * ggml_row_size(GGML_TYPE_F32, channels);                         // in_layer_0_w/b
-        mem_size += ggml_row_size(GGML_TYPE_F16, out_channels * channels * 3 * 3);      // in_layer_2_w
-        mem_size += 5 * ggml_row_size(GGML_TYPE_F32, out_channels);                     // in_layer_2_b/emb_layer_1_b/out_layer_0_w/out_layer_0_b/out_layer_3_b
-        mem_size += ggml_row_size(wtype, out_channels * emb_channels);                  // emb_layer_1_w
-        mem_size += ggml_row_size(GGML_TYPE_F16, out_channels * out_channels * 3 * 3);  // out_layer_3_w
-
-        if (out_channels != channels) {
-            mem_size += ggml_row_size(GGML_TYPE_F16, out_channels * channels * 1 * 1);  // skip_w
-            mem_size += ggml_row_size(GGML_TYPE_F32, out_channels);                     // skip_b
-        }
-        return mem_size;
-    }
-
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        in_layer_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, channels);
-        in_layer_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, channels);
-        in_layer_2_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, out_channels);
-        in_layer_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
-
-        emb_layer_1_w = ggml_new_tensor_2d(ctx, wtype, emb_channels, out_channels);
-        emb_layer_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
-
-        out_layer_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
-        out_layer_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
-        out_layer_3_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, out_channels, out_channels);
-        out_layer_3_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
-
-        if (out_channels != channels) {
-            skip_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, 1, channels, out_channels);
-            skip_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
+    std::shared_ptr<GGMLBlock> conv_nd(int dims,
+                                       int64_t in_channels,
+                                       int64_t out_channels,
+                                       std::pair<int, int> kernel_size,
+                                       std::pair<int, int> padding) {
+        GGML_ASSERT(dims == 2 || dims == 3);
+        if (dims == 3) {
+            return std::shared_ptr<GGMLBlock>(new Conv3dnx1x1(in_channels, out_channels, kernel_size.first, 1, padding.first));
+        } else {
+            return std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, out_channels, kernel_size, {1, 1}, padding));
         }
     }
 
-    void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
-        tensors[prefix + "in_layers.0.weight"] = in_layer_0_w;
-        tensors[prefix + "in_layers.0.bias"]   = in_layer_0_b;
-        tensors[prefix + "in_layers.2.weight"] = in_layer_2_w;
-        tensors[prefix + "in_layers.2.bias"]   = in_layer_2_b;
+public:
+    ResBlock(int64_t channels,
+             int64_t emb_channels,
+             int64_t out_channels,
+             std::pair<int, int> kernel_size = {3, 3},
+             int dims                        = 2,
+             bool exchange_temb_dims         = false,
+             bool skip_t_emb                 = false)
+        : channels(channels),
+          emb_channels(emb_channels),
+          out_channels(out_channels),
+          kernel_size(kernel_size),
+          dims(dims),
+          skip_t_emb(skip_t_emb),
+          exchange_temb_dims(exchange_temb_dims) {
+        std::pair<int, int> padding = {kernel_size.first / 2, kernel_size.second / 2};
+        blocks["in_layers.0"]       = std::shared_ptr<GGMLBlock>(new GroupNorm32(channels));
+        // in_layer_1 is nn.SILU()
+        blocks["in_layers.2"] = conv_nd(dims, channels, out_channels, kernel_size, padding);
 
-        tensors[prefix + "emb_layers.1.weight"] = emb_layer_1_w;
-        tensors[prefix + "emb_layers.1.bias"]   = emb_layer_1_b;
+        if (!skip_t_emb) {
+            // emb_layer_0 is nn.SILU()
+            blocks["emb_layers.1"] = std::shared_ptr<GGMLBlock>(new Linear(emb_channels, out_channels));
+        }
 
-        tensors[prefix + "out_layers.0.weight"] = out_layer_0_w;
-        tensors[prefix + "out_layers.0.bias"]   = out_layer_0_b;
-        tensors[prefix + "out_layers.3.weight"] = out_layer_3_w;
-        tensors[prefix + "out_layers.3.bias"]   = out_layer_3_b;
+        blocks["out_layers.0"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(out_channels));
+        // out_layer_1 is nn.SILU()
+        // out_layer_2 is nn.Dropout(), skip for inference
+        blocks["out_layers.3"] = conv_nd(dims, out_channels, out_channels, kernel_size, padding);
 
         if (out_channels != channels) {
-            tensors[prefix + "skip_connection.weight"] = skip_w;
-            tensors[prefix + "skip_connection.bias"]   = skip_b;
+            blocks["skip_connection"] = conv_nd(dims, channels, out_channels, {1, 1}, {0, 0});
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* emb) {
-        // x: [N, channels, h, w]
-        // emb: [N, emb_channels]
+    virtual struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* emb = NULL) {
+        // 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]
+        // emb: [N, emb_channels] if dims == 2 else [N, t, emb_channels]
+        auto in_layers_0  = std::dynamic_pointer_cast<GroupNorm32>(blocks["in_layers.0"]);
+        auto in_layers_2  = std::dynamic_pointer_cast<UnaryBlock>(blocks["in_layers.2"]);
+        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) {
+            GGML_ASSERT(skip_t_emb);
+        }
 
         // in_layers
-        auto h = ggml_nn_group_norm(ctx, x, in_layer_0_w, in_layer_0_b);
+        auto h = in_layers_0->forward(ctx, x);
         h      = ggml_silu_inplace(ctx, h);
-        h      = ggml_nn_conv_2d(ctx, h, in_layer_2_w, in_layer_2_b, 1, 1, 1, 1);  // [N, out_channels, h, w]
+        h      = in_layers_2->forward(ctx, h);  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
 
         // emb_layers
-        auto emb_out = ggml_silu(ctx, emb);
-        emb_out      = ggml_nn_linear(ctx, emb_out, emb_layer_1_w, emb_layer_1_b);           // [N, out_channels]
-        emb_out      = ggml_reshape_4d(ctx, emb_out, 1, 1, emb_out->ne[0], emb_out->ne[1]);  // [N, out_channels, 1, 1]
+        if (!skip_t_emb) {
+            auto emb_layer_1 = std::dynamic_pointer_cast<Linear>(blocks["emb_layers.1"]);
+
+            auto emb_out = ggml_silu(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]
+            } 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]
+                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]
+                }
+            }
+
+            h = ggml_add(ctx, h, emb_out);  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
+        }
 
         // out_layers
-        h = ggml_add(ctx, h, emb_out);
-        h = ggml_nn_group_norm(ctx, h, out_layer_0_w, out_layer_0_b);
+        h = out_layers_0->forward(ctx, h);
         h = ggml_silu_inplace(ctx, h);
-
         // dropout, skip for inference
-
-        h = ggml_nn_conv_2d(ctx, h, out_layer_3_w, out_layer_3_b, 1, 1, 1, 1);  // [N, out_channels, h, w]
+        h = out_layers_3->forward(ctx, h);
 
         // skip connection
         if (out_channels != channels) {
-            x = ggml_nn_conv_2d(ctx, x, skip_w, skip_b);  // [N, out_channels, h, w]
+            auto skip_connection = std::dynamic_pointer_cast<UnaryBlock>(blocks["skip_connection"]);
+            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);
-        return h;  // [N, out_channels, h, w]
+        return h;  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
     }
 };
 
-struct SpatialTransformer {
-    int in_channels;        // mult * model_channels
-    int n_head;             // num_heads
-    int d_head;             // in_channels // n_heads
-    int depth       = 1;    // 1
-    int context_dim = 768;  // hidden_size, 1024 for VERSION_2_x
+class GEGLU : public GGMLBlock {
+protected:
+    int64_t dim_in;
+    int64_t dim_out;
 
-    // group norm
-    struct ggml_tensor* norm_w;  // [in_channels,]
-    struct ggml_tensor* norm_b;  // [in_channels,]
-
-    // proj_in
-    struct ggml_tensor* proj_in_w;  // [in_channels, in_channels, 1, 1]
-    struct ggml_tensor* proj_in_b;  // [in_channels,]
-
-    // transformer
-    struct Transformer {
-        // layer norm 1
-        struct ggml_tensor* norm1_w;  // [in_channels, ]
-        struct ggml_tensor* norm1_b;  // [in_channels, ]
-
-        // attn1
-        struct ggml_tensor* attn1_q_w;  // [in_channels, in_channels]
-        struct ggml_tensor* attn1_k_w;  // [in_channels, in_channels]
-        struct ggml_tensor* attn1_v_w;  // [in_channels, in_channels]
-
-        struct ggml_tensor* attn1_out_w;  // [in_channels, in_channels]
-        struct ggml_tensor* attn1_out_b;  // [in_channels, ]
-
-        // layer norm 2
-        struct ggml_tensor* norm2_w;  // [in_channels, ]
-        struct ggml_tensor* norm2_b;  // [in_channels, ]
-
-        // attn2
-        struct ggml_tensor* attn2_q_w;  // [in_channels, in_channels]
-        struct ggml_tensor* attn2_k_w;  // [in_channels, context_dim]
-        struct ggml_tensor* attn2_v_w;  // [in_channels, context_dim]
-
-        struct ggml_tensor* attn2_out_w;  // [in_channels, in_channels]
-        struct ggml_tensor* attn2_out_b;  // [in_channels, ]
-
-        // layer norm 3
-        struct ggml_tensor* norm3_w;  // [in_channels, ]
-        struct ggml_tensor* norm3_b;  // [in_channels, ]
-
-        // ff
-        struct ggml_tensor* ff_0_proj_w;  // [in_channels * 4 * 2, in_channels]
-        struct ggml_tensor* ff_0_proj_b;  // [in_channels * 4 * 2]
-
-        struct ggml_tensor* ff_2_w;  // [in_channels, in_channels * 4]
-        struct ggml_tensor* ff_2_b;  // [in_channels,]
-    };
-
-    std::vector<Transformer> transformers;
-
-    // proj_out
-    struct ggml_tensor* proj_out_w;  // [in_channels, in_channels, 1, 1]
-    struct ggml_tensor* proj_out_b;  // [in_channels,]
-
-    SpatialTransformer(int depth = 1)
-        : depth(depth) {
-        transformers.resize(depth);
+    void init_params(struct ggml_context* ctx, ggml_type wtype) {
+        params["proj.weight"] = ggml_new_tensor_2d(ctx, wtype, dim_in, dim_out * 2);
+        params["proj.bias"]   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, dim_out * 2);
     }
 
-    int get_num_tensors() {
-        return depth * 20 + 7;
+public:
+    GEGLU(int64_t dim_in, int64_t dim_out)
+        : dim_in(dim_in), dim_out(dim_out) {}
+
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+        // 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 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, ]
+
+        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, gate);
+
+        x = ggml_mul(ctx, x, gate);  // [ne3, ne2, ne1, dim_out]
+
+        return x;
+    }
+};
+
+class FeedForward : public GGMLBlock {
+public:
+    FeedForward(int64_t dim,
+                int64_t dim_out,
+                int64_t mult = 4) {
+        int64_t inner_dim = dim * mult;
+
+        blocks["net.0"] = std::shared_ptr<GGMLBlock>(new GEGLU(dim, inner_dim));
+        // net_1 is nn.Dropout(), skip for inference
+        blocks["net.2"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, dim_out));
     }
 
-    size_t calculate_mem_size(ggml_type wtype) {
-        size_t mem_size = 0;
-        mem_size += 2 * ggml_row_size(GGML_TYPE_F32, in_channels);                        // norm_w/norm_b
-        mem_size += 2 * ggml_row_size(GGML_TYPE_F16, in_channels * in_channels * 1 * 1);  // proj_in_w/proj_out_w
-        mem_size += 2 * ggml_row_size(GGML_TYPE_F32, in_channels);                        // proj_in_b/proj_out_b
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+        // x: [ne3, ne2, ne1, dim]
+        // return: [ne3, ne2, ne1, dim_out]
 
-        // transformer
-        for (auto& transformer : transformers) {
-            mem_size += 6 * ggml_row_size(GGML_TYPE_F32, in_channels);            // norm1-3_w/b
-            mem_size += 6 * ggml_row_size(wtype, in_channels * in_channels);      // attn1_q/k/v/out_w attn2_q/out_w
-            mem_size += 2 * ggml_row_size(wtype, in_channels * context_dim);      // attn2_k/v_w
-            mem_size += ggml_row_size(wtype, in_channels * 4 * 2 * in_channels );  // ff_0_proj_w
-            mem_size += ggml_row_size(GGML_TYPE_F32, in_channels * 4 * 2);        // ff_0_proj_b
-            mem_size += ggml_row_size(wtype, in_channels * 4 * in_channels);      // ff_2_w
-            mem_size += ggml_row_size(GGML_TYPE_F32, in_channels);                // ff_2_b
-        }
-        return mem_size;
+        auto net_0 = std::dynamic_pointer_cast<GEGLU>(blocks["net.0"]);
+        auto net_2 = std::dynamic_pointer_cast<Linear>(blocks["net.2"]);
+
+        x = net_0->forward(ctx, x);  // [ne3, ne2, ne1, inner_dim]
+        x = net_2->forward(ctx, x);  // [ne3, ne2, ne1, dim_out]
+        return x;
     }
+};
 
-    void init_params(struct ggml_context* ctx, ggml_allocr* alloc, ggml_type wtype) {
-        norm_w    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
-        norm_b    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
-        proj_in_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, 1, in_channels, in_channels);
-        proj_in_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
+class CrossAttention : public GGMLBlock {
+protected:
+    int64_t query_dim;
+    int64_t context_dim;
+    int64_t n_head;
+    int64_t d_head;
 
-        proj_out_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, 1, in_channels, in_channels);
-        proj_out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
+public:
+    CrossAttention(int64_t query_dim,
+                   int64_t context_dim,
+                   int64_t n_head,
+                   int64_t d_head)
+        : n_head(n_head),
+          d_head(d_head),
+          query_dim(query_dim),
+          context_dim(context_dim) {
+        int64_t inner_dim = d_head * n_head;
 
-        // transformer
-        for (auto& transformer : transformers) {
-            transformer.norm1_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
-            transformer.norm1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
+        blocks["to_q"] = std::shared_ptr<GGMLBlock>(new Linear(query_dim, inner_dim, false));
+        blocks["to_k"] = std::shared_ptr<GGMLBlock>(new Linear(context_dim, inner_dim, false));
+        blocks["to_v"] = std::shared_ptr<GGMLBlock>(new Linear(context_dim, inner_dim, false));
 
-            transformer.attn1_q_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels);
-            transformer.attn1_k_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels);
-            transformer.attn1_v_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels);
-
-            transformer.attn1_out_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels);
-            transformer.attn1_out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
-
-            transformer.norm2_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
-            transformer.norm2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
-
-            transformer.attn2_q_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels);
-            transformer.attn2_k_w = ggml_new_tensor_2d(ctx, wtype, context_dim, in_channels);
-            transformer.attn2_v_w = ggml_new_tensor_2d(ctx, wtype, context_dim, in_channels);
-
-            transformer.attn2_out_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels);
-            transformer.attn2_out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
-
-            transformer.norm3_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
-            transformer.norm3_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
-
-            transformer.ff_0_proj_w = ggml_new_tensor_2d(ctx, wtype, in_channels, in_channels * 4 * 2);
-            transformer.ff_0_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels * 4 * 2);
-
-            transformer.ff_2_w = ggml_new_tensor_2d(ctx, wtype, in_channels * 4, in_channels);
-            transformer.ff_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
-        }
-    }
-
-    void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
-        tensors[prefix + "norm.weight"]    = norm_w;
-        tensors[prefix + "norm.bias"]      = norm_b;
-        tensors[prefix + "proj_in.weight"] = proj_in_w;
-        tensors[prefix + "proj_in.bias"]   = proj_in_b;
-
-        // transformer
-        for (int i = 0; i < transformers.size(); i++) {
-            auto& transformer                                 = transformers[i];
-            std::string transformer_prefix                    = prefix + "transformer_blocks." + std::to_string(i) + ".";
-            tensors[transformer_prefix + "attn1.to_q.weight"] = transformer.attn1_q_w;
-            tensors[transformer_prefix + "attn1.to_k.weight"] = transformer.attn1_k_w;
-            tensors[transformer_prefix + "attn1.to_v.weight"] = transformer.attn1_v_w;
-
-            tensors[transformer_prefix + "attn1.to_out.0.weight"] = transformer.attn1_out_w;
-            tensors[transformer_prefix + "attn1.to_out.0.bias"]   = transformer.attn1_out_b;
-
-            tensors[transformer_prefix + "ff.net.0.proj.weight"] = transformer.ff_0_proj_w;
-            tensors[transformer_prefix + "ff.net.0.proj.bias"]   = transformer.ff_0_proj_b;
-            tensors[transformer_prefix + "ff.net.2.weight"]      = transformer.ff_2_w;
-            tensors[transformer_prefix + "ff.net.2.bias"]        = transformer.ff_2_b;
-
-            tensors[transformer_prefix + "attn2.to_q.weight"] = transformer.attn2_q_w;
-            tensors[transformer_prefix + "attn2.to_k.weight"] = transformer.attn2_k_w;
-            tensors[transformer_prefix + "attn2.to_v.weight"] = transformer.attn2_v_w;
-
-            tensors[transformer_prefix + "attn2.to_out.0.weight"] = transformer.attn2_out_w;
-            tensors[transformer_prefix + "attn2.to_out.0.bias"]   = transformer.attn2_out_b;
-
-            tensors[transformer_prefix + "norm1.weight"] = transformer.norm1_w;
-            tensors[transformer_prefix + "norm1.bias"]   = transformer.norm1_b;
-            tensors[transformer_prefix + "norm2.weight"] = transformer.norm2_w;
-            tensors[transformer_prefix + "norm2.bias"]   = transformer.norm2_b;
-            tensors[transformer_prefix + "norm3.weight"] = transformer.norm3_w;
-            tensors[transformer_prefix + "norm3.bias"]   = transformer.norm3_b;
-        }
-
-        tensors[prefix + "proj_out.weight"] = proj_out_w;
-        tensors[prefix + "proj_out.bias"]   = proj_out_b;
+        blocks["to_out.0"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, query_dim));
+        // to_out_1 is nn.Dropout(), skip for inference
     }
 
     struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* context) {
-        // x: [N, in_channels, h, w]
-        // context: [N, max_position, hidden_size(aka context_dim)]
-        auto x_in = x;
-        x         = ggml_nn_group_norm(ctx, x, norm_w, norm_b);
-        // proj_in
-        x = ggml_nn_conv_2d(ctx, x, proj_in_w, proj_in_b);  // [N, in_channels, h, w]
+        // x: [N, n_token, query_dim]
+        // context: [N, n_context, context_dim]
+        // return: [N, n_token, query_dim]
+        auto to_q     = std::dynamic_pointer_cast<Linear>(blocks["to_q"]);
+        auto to_k     = std::dynamic_pointer_cast<Linear>(blocks["to_k"]);
+        auto to_v     = std::dynamic_pointer_cast<Linear>(blocks["to_v"]);
+        auto to_out_0 = std::dynamic_pointer_cast<Linear>(blocks["to_out.0"]);
 
-        // transformer
-        const int64_t n            = x->ne[3];
-        const int64_t c            = x->ne[2];
-        const int64_t h            = x->ne[1];
-        const int64_t w            = x->ne[0];
-        const int64_t max_position = context->ne[1];
-        x                          = ggml_cont(ctx, ggml_permute(ctx, x, 1, 2, 0, 3));  // [N, h, w, in_channels]
+        int64_t n         = x->ne[2];
+        int64_t n_token   = x->ne[1];
+        int64_t n_context = context->ne[1];
+        int64_t inner_dim = d_head * n_head;
 
-        for (auto& transformer : transformers) {
-            auto r = x;
-            // layer norm 1
-            x = ggml_reshape_2d(ctx, x, c, w * h * n);
-            x = ggml_nn_layer_norm(ctx, x, transformer.norm1_w, transformer.norm1_b);
+        auto q = to_q->forward(ctx, x);                                 // [N, n_token, inner_dim]
+        q      = ggml_reshape_4d(ctx, q, d_head, n_head, n_token, n);   // [N, n_token, n_head, d_head]
+        q      = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3));      // [N, n_head, n_token, d_head]
+        q      = ggml_reshape_3d(ctx, q, d_head, n_token, n_head * n);  // [N * n_head, n_token, d_head]
 
-            // self-attention
-            {
-                x                     = ggml_reshape_2d(ctx, x, c, h * w * n);        // [N * h * w, in_channels]
-                struct ggml_tensor* q = ggml_mul_mat(ctx, transformer.attn1_q_w, x);  // [N * h * w, in_channels]
-#if !defined(SD_USE_FLASH_ATTENTION) || defined(SD_USE_CUBLAS) || defined(SD_USE_METAL)
-                q = ggml_scale_inplace(ctx, q, 1.0f / sqrt((float)d_head));
-#endif
-                q = ggml_reshape_4d(ctx, q, d_head, n_head, h * w, n);   // [N, h * w, n_head, d_head]
-                q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3));    // [N, n_head, h * w, d_head]
-                q = ggml_reshape_3d(ctx, q, d_head, h * w, n_head * n);  // [N * n_head, h * w, d_head]
+        auto k = to_k->forward(ctx, context);                             // [N, n_context, inner_dim]
+        k      = ggml_reshape_4d(ctx, k, d_head, n_head, n_context, n);   // [N, n_context, n_head, d_head]
+        k      = ggml_cont(ctx, ggml_permute(ctx, k, 0, 2, 1, 3));        // [N, n_head, n_context, d_head]
+        k      = ggml_reshape_3d(ctx, k, d_head, n_context, n_head * n);  // [N * n_head, n_context, d_head]
 
-                struct ggml_tensor* k = ggml_mul_mat(ctx, transformer.attn1_k_w, x);         // [N * h * w, in_channels]
-                k                     = ggml_reshape_4d(ctx, k, d_head, n_head, h * w, n);   // [N, h * w, n_head, d_head]
-                k                     = ggml_cont(ctx, ggml_permute(ctx, k, 0, 2, 1, 3));    // [N, n_head, h * w, d_head]
-                k                     = ggml_reshape_3d(ctx, k, d_head, h * w, n_head * n);  // [N * n_head, h * w, d_head]
+        auto v = to_v->forward(ctx, context);                             // [N, n_context, inner_dim]
+        v      = ggml_reshape_4d(ctx, v, d_head, n_head, n_context, n);   // [N, n_context, n_head, d_head]
+        v      = ggml_cont(ctx, ggml_permute(ctx, v, 1, 2, 0, 3));        // [N, n_head, d_head, n_context]
+        v      = ggml_reshape_3d(ctx, v, n_context, d_head, n_head * n);  // [N * n_head, d_head, n_context]
 
-                struct ggml_tensor* v = ggml_mul_mat(ctx, transformer.attn1_v_w, x);         // [N * h * w, in_channels]
-                v                     = ggml_reshape_4d(ctx, v, d_head, n_head, h * w, n);   // [N, h * w, n_head, d_head]
-                v                     = ggml_cont(ctx, ggml_permute(ctx, v, 1, 2, 0, 3));    // [N, n_head, d_head, h * w]
-                v                     = ggml_reshape_3d(ctx, v, h * w, d_head, n_head * n);  // [N * n_head, d_head, h * w]
+        auto kqv = ggml_nn_attention(ctx, q, k, v, false);  // [N * n_head, n_token, d_head]
+        kqv      = ggml_reshape_4d(ctx, kqv, d_head, n_token, n_head, n);
+        kqv      = ggml_cont(ctx, ggml_permute(ctx, kqv, 0, 2, 1, 3));  // [N, n_token, n_head, d_head]
 
-#if defined(SD_USE_FLASH_ATTENTION) && !defined(SD_USE_CUBLAS) && !defined(SD_USE_METAL)
-                struct ggml_tensor* kqv = ggml_flash_attn(ctx, q, k, v, false);  // [N * n_head, h * w, d_head]
-#else
-                struct ggml_tensor* kq = ggml_mul_mat(ctx, k, q);  // [N * n_head, h * w, h * w]
-                // kq = ggml_diag_mask_inf_inplace(ctx, kq, 0);
-                kq = ggml_soft_max_inplace(ctx, kq);
+        x = ggml_reshape_3d(ctx, kqv, d_head * n_head, n_token, n);  // [N, n_token, inner_dim]
 
-                struct ggml_tensor* kqv = ggml_mul_mat(ctx, v, kq);  // [N * n_head, h * w, d_head]
-#endif
-                kqv = ggml_reshape_4d(ctx, kqv, d_head, h * w, n_head, n);
-                kqv = ggml_cont(ctx, ggml_permute(ctx, kqv, 0, 2, 1, 3));  // [N, h * w, n_head, d_head]
+        x = to_out_0->forward(ctx, x);  // [N, n_token, query_dim]
+        return x;
+    }
+};
 
-                // x = ggml_cpy(ctx, kqv, ggml_new_tensor_2d(ctx, GGML_TYPE_F32, d_head * n_head, h * w * n));
-                x = ggml_reshape_2d(ctx, kqv, d_head * n_head, h * w * n);
+class BasicTransformerBlock : public GGMLBlock {
+protected:
+    int64_t n_head;
+    int64_t d_head;
+    bool ff_in;
 
-                x = ggml_nn_linear(ctx, x, transformer.attn1_out_w, transformer.attn1_out_b);
+public:
+    BasicTransformerBlock(int64_t dim,
+                          int64_t n_head,
+                          int64_t d_head,
+                          int64_t context_dim,
+                          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));
+        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));
+        blocks["norm3"] = std::shared_ptr<GGMLBlock>(new LayerNorm(dim));
 
-                x = ggml_reshape_4d(ctx, x, c, w, h, n);
-            }
+        if (ff_in) {
+            blocks["norm_in"] = std::shared_ptr<GGMLBlock>(new LayerNorm(dim));
+            blocks["ff_in"]   = std::shared_ptr<GGMLBlock>(new FeedForward(dim, dim));
+        }
+    }
 
-            x = ggml_add(ctx, x, r);
-            r = x;
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* context) {
+        // x: [N, n_token, query_dim]
+        // context: [N, n_context, context_dim]
+        // return: [N, n_token, query_dim]
 
-            // layer norm 2
-            x = ggml_nn_layer_norm(ctx, x, transformer.norm2_w, transformer.norm2_b);
+        auto attn1 = std::dynamic_pointer_cast<CrossAttention>(blocks["attn1"]);
+        auto attn2 = std::dynamic_pointer_cast<CrossAttention>(blocks["attn2"]);
+        auto ff    = std::dynamic_pointer_cast<FeedForward>(blocks["ff"]);
+        auto norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["norm1"]);
+        auto norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["norm2"]);
+        auto norm3 = std::dynamic_pointer_cast<LayerNorm>(blocks["norm3"]);
 
-            // cross-attention
-            {
-                x                     = ggml_reshape_2d(ctx, x, c, h * w * n);                                           // [N * h * w, in_channels]
-                context               = ggml_reshape_2d(ctx, context, context->ne[0], context->ne[1] * context->ne[2]);  // [N * max_position, hidden_size]
-                struct ggml_tensor* q = ggml_mul_mat(ctx, transformer.attn2_q_w, x);                                     // [N * h * w, in_channels]
-#if !defined(SD_USE_FLASH_ATTENTION) || defined(SD_USE_CUBLAS) || defined(SD_USE_METAL)
-                q = ggml_scale_inplace(ctx, q, 1.0f / sqrt((float)d_head));
-#endif
-                q = ggml_reshape_4d(ctx, q, d_head, n_head, h * w, n);   // [N, h * w, n_head, d_head]
-                q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3));    // [N, n_head, h * w, d_head]
-                q = ggml_reshape_3d(ctx, q, d_head, h * w, n_head * n);  // [N * n_head, h * w, d_head]
+        if (ff_in) {
+            auto norm_in = std::dynamic_pointer_cast<LayerNorm>(blocks["norm_in"]);
+            auto ff_in   = std::dynamic_pointer_cast<FeedForward>(blocks["ff_in"]);
 
-                struct ggml_tensor* k = ggml_mul_mat(ctx, transformer.attn2_k_w, context);          // [N * max_position, in_channels]
-                k                     = ggml_reshape_4d(ctx, k, d_head, n_head, max_position, n);   // [N, max_position, n_head, d_head]
-                k                     = ggml_cont(ctx, ggml_permute(ctx, k, 0, 2, 1, 3));           // [N, n_head, max_position, d_head]
-                k                     = ggml_reshape_3d(ctx, k, d_head, max_position, n_head * n);  // [N * n_head, max_position, d_head]
-
-                struct ggml_tensor* v = ggml_mul_mat(ctx, transformer.attn2_v_w, context);          // [N * max_position, in_channels]
-                v                     = ggml_reshape_4d(ctx, v, d_head, n_head, max_position, n);   // [N, max_position, n_head, d_head]
-                v                     = ggml_cont(ctx, ggml_permute(ctx, v, 1, 2, 0, 3));           // [N, n_head, d_head, max_position]
-                v                     = ggml_reshape_3d(ctx, v, max_position, d_head, n_head * n);  // [N * n_head, d_head, max_position]
-#if defined(SD_USE_FLASH_ATTENTION) && !defined(SD_USE_CUBLAS) && !defined(SD_USE_METAL)
-                struct ggml_tensor* kqv = ggml_flash_attn(ctx, q, k, v, false);  // [N * n_head, h * w, d_head]
-#else
-                struct ggml_tensor* kq  = ggml_mul_mat(ctx, k, q);   // [N * n_head, h * w, max_position]
-                // kq = ggml_diag_mask_inf_inplace(ctx, kq, 0);
-                kq = ggml_soft_max_inplace(ctx, kq);
-
-                struct ggml_tensor* kqv = ggml_mul_mat(ctx, v, kq);  // [N * n_head, h * w, d_head]
-#endif
-                kqv = ggml_reshape_4d(ctx, kqv, d_head, h * w, n_head, n);
-                kqv = ggml_cont(ctx, ggml_permute(ctx, kqv, 0, 2, 1, 3));
-
-                // x = ggml_cpy(ctx, kqv, ggml_new_tensor_2d(ctx, GGML_TYPE_F32, d_head * n_head, h * w * n)); // [N * h * w, in_channels]
-                x = ggml_reshape_2d(ctx, kqv, d_head * n_head, h * w * n);  // [N * h * w, in_channels]
-
-                x = ggml_nn_linear(ctx, x, transformer.attn2_out_w, transformer.attn2_out_b);
-
-                x = ggml_reshape_4d(ctx, x, c, w, h, n);
-            }
-
-            x = ggml_add(ctx, x, r);
-            r = x;
-
-            // layer norm 3
-            x = ggml_reshape_2d(ctx, x, c, h * w * n);  // [N * h * w, in_channels]
-            x = ggml_nn_layer_norm(ctx, x, transformer.norm3_w, transformer.norm3_b);
-
-            // ff
-            {
-                // GEGLU
-                auto x_w    = ggml_view_2d(ctx,
-                                        transformer.ff_0_proj_w,
-                                        transformer.ff_0_proj_w->ne[0],
-                                        transformer.ff_0_proj_w->ne[1] / 2,
-                                        transformer.ff_0_proj_w->nb[1],
-                                        0);  // [in_channels * 4, in_channels]
-                auto x_b    = ggml_view_1d(ctx,
-                                        transformer.ff_0_proj_b,
-                                        transformer.ff_0_proj_b->ne[0] / 2,
-                                        0);  // [in_channels * 4, in_channels]
-                auto gate_w = ggml_view_2d(ctx,
-                                           transformer.ff_0_proj_w,
-                                           transformer.ff_0_proj_w->ne[0],
-                                           transformer.ff_0_proj_w->ne[1] / 2,
-                                           transformer.ff_0_proj_w->nb[1],
-                                           transformer.ff_0_proj_w->nb[1] * transformer.ff_0_proj_w->ne[1] / 2);  // [in_channels * 4, ]
-                auto gate_b = ggml_view_1d(ctx,
-                                           transformer.ff_0_proj_b,
-                                           transformer.ff_0_proj_b->ne[0] / 2,
-                                           transformer.ff_0_proj_b->nb[0] * transformer.ff_0_proj_b->ne[0] / 2);  // [in_channels * 4, ]
-                x           = ggml_reshape_2d(ctx, x, c, w * h * n);
-                auto x_in   = x;
-                x           = ggml_nn_linear(ctx, x_in, x_w, x_b);        // [N * h * w, in_channels * 4]
-                auto gate   = ggml_nn_linear(ctx, x_in, gate_w, gate_b);  // [N * h * w, in_channels * 4]
-
-                gate = ggml_gelu_inplace(ctx, gate);
-
-                x = ggml_mul(ctx, x, gate);  // [N * h * w, in_channels * 4]
-                // fc
-                x = ggml_nn_linear(ctx, x, transformer.ff_2_w, transformer.ff_2_b);  // [N * h * w, in_channels]
-            }
-
-            x = ggml_reshape_4d(ctx, x, c, w, h, n);  // [N, h, w, in_channels]
-
-            // residual
-            x = ggml_add(ctx, x, r);
+            auto x_skip = x;
+            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_cont(ctx, ggml_permute(ctx, x, 2, 0, 1, 3));  // [N, in_channels, h, w]
+        auto r = x;
+        x      = norm1->forward(ctx, x);
+        x      = attn1->forward(ctx, x, x);  // self-attention
+        x      = ggml_add(ctx, x, r);
+        r      = x;
+        x      = norm2->forward(ctx, x);
+        x      = attn2->forward(ctx, x, context);  // cross-attention
+        x      = ggml_add(ctx, x, r);
+        r      = x;
+        x      = norm3->forward(ctx, x);
+        x      = ff->forward(ctx, x);
+        x      = ggml_add(ctx, x, r);
+
+        return x;
+    }
+};
+
+class SpatialTransformer : public GGMLBlock {
+protected:
+    int64_t in_channels;  // mult * model_channels
+    int64_t n_head;
+    int64_t d_head;
+    int64_t depth       = 1;    // 1
+    int64_t context_dim = 768;  // hidden_size, 1024 for VERSION_2_x
+
+public:
+    SpatialTransformer(int64_t in_channels,
+                       int64_t n_head,
+                       int64_t d_head,
+                       int64_t depth,
+                       int64_t context_dim)
+        : 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
+        // 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}));
+
+        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));
+        }
+
+        blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(inner_dim, in_channels, {1, 1}));
+    }
+
+    virtual struct ggml_tensor* forward(struct ggml_context* 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 x_in         = x;
+        int64_t n         = x->ne[3];
+        int64_t h         = x->ne[1];
+        int64_t w         = x->ne[0];
+        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]
+
+        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, 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]
 
         // proj_out
-        x = ggml_nn_conv_2d(ctx, x, proj_out_w, proj_out_b);  // [N, in_channels, h, w]
+        x = proj_out->forward(ctx, x);  // [N, in_channels, h, w]
 
         x = ggml_add(ctx, x, x_in);
         return x;
     }
 };
 
+class AlphaBlender : public GGMLBlock {
+protected:
+    void init_params(struct ggml_context* ctx, ggml_type wtype) {
+        params["mix_factor"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+    }
+
+    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"]);
+        return sigmoid(alpha);
+    }
+
+public:
+    AlphaBlender() {
+        // merge_strategy is always learned_with_images
+        // for inference, we don't need to set alpha
+        // 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* 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));
+        return x;
+    }
+};
+
+class VideoResBlock : public ResBlock {
+public:
+    VideoResBlock(int channels,
+                  int emb_channels,
+                  int out_channels,
+                  std::pair<int, int> kernel_size = {3, 3},
+                  int64_t video_kernel_size       = 3,
+                  int dims                        = 2)  // always 2
+        : ResBlock(channels, emb_channels, out_channels, kernel_size, dims) {
+        blocks["time_stack"] = std::shared_ptr<GGMLBlock>(new ResBlock(out_channels, emb_channels, out_channels, kernel_size, 3, true));
+        blocks["time_mixer"] = std::shared_ptr<GGMLBlock>(new AlphaBlender());
+    }
+
+    struct ggml_tensor* forward(struct ggml_context* ctx,
+                                struct ggml_tensor* x,
+                                struct ggml_tensor* emb,
+                                int num_video_frames) {
+        // x: [N, channels, h, w] aka [b*t, channels, h, w]
+        // emb: [N, emb_channels] aka [b*t, emb_channels]
+        // image_only_indicator is always tensor([0.])
+        auto time_stack = std::dynamic_pointer_cast<ResBlock>(blocks["time_stack"]);
+        auto time_mixer = std::dynamic_pointer_cast<AlphaBlender>(blocks["time_mixer"]);
+
+        x = ResBlock::forward(ctx, x, emb);
+
+        int64_t T = num_video_frames;
+        int64_t B = x->ne[3] / T;
+        int64_t C = x->ne[2];
+        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)
+        auto x_mix = x;
+
+        emb = ggml_reshape_4d(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
+
+        return x;
+    }
+};
+
 #endif  // __COMMON_HPP__

esrgan.hpp

@@ -12,279 +12,160 @@
 
 */
 
-struct ResidualDenseBlock {
-    int num_features;
+class ResidualDenseBlock : public GGMLBlock {
+protected:
+    int num_feat;
     int num_grow_ch;
-    ggml_tensor* conv1_w;  // [num_grow_ch, num_features, 3, 3]
-    ggml_tensor* conv1_b;  // [num_grow_ch]
 
-    ggml_tensor* conv2_w;  // [num_grow_ch, num_features + num_grow_ch, 3, 3]
-    ggml_tensor* conv2_b;  // [num_grow_ch]
-
-    ggml_tensor* conv3_w;  // [num_grow_ch, num_features + 2 * num_grow_ch, 3, 3]
-    ggml_tensor* conv3_b;  // [num_grow_ch]
-
-    ggml_tensor* conv4_w;  // [num_grow_ch, num_features + 3 * num_grow_ch, 3, 3]
-    ggml_tensor* conv4_b;  // [num_grow_ch]
-
-    ggml_tensor* conv5_w;  // [num_features, num_features + 4 * num_grow_ch, 3, 3]
-    ggml_tensor* conv5_b;  // [num_features]
-
-    ResidualDenseBlock() {}
-
-    ResidualDenseBlock(int num_feat, int n_grow_ch) {
-        num_features = num_feat;
-        num_grow_ch  = n_grow_ch;
+public:
+    ResidualDenseBlock(int num_feat = 64, int num_grow_ch = 32)
+        : num_feat(num_feat), num_grow_ch(num_grow_ch) {
+        blocks["conv1"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_grow_ch, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv2"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat + num_grow_ch, num_grow_ch, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv3"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv4"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv5"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat + 4 * num_grow_ch, num_feat, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    size_t calculate_mem_size() {
-        size_t mem_size = num_features * num_grow_ch * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv1_w
-        mem_size += num_grow_ch * ggml_type_size(GGML_TYPE_F32);                               // conv1_b
-
-        mem_size += (num_features + num_grow_ch) * num_grow_ch * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv2_w
-        mem_size += num_grow_ch * ggml_type_size(GGML_TYPE_F32);                                         // conv2_b
-
-        mem_size += (num_features + 2 * num_grow_ch) * num_grow_ch * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv3_w
-        mem_size += num_grow_ch * ggml_type_size(GGML_TYPE_F32);                                             // conv3_w
-
-        mem_size += (num_features + 3 * num_grow_ch) * num_grow_ch * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv4_w
-        mem_size += num_grow_ch * ggml_type_size(GGML_TYPE_F32);                                             // conv4_w
-
-        mem_size += (num_features + 4 * num_grow_ch) * num_features * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv5_w
-        mem_size += num_features * ggml_type_size(GGML_TYPE_F32);                                             // conv5_w
-
-        return mem_size;
+    struct ggml_tensor* lrelu(struct ggml_context* ctx, struct ggml_tensor* x) {
+        return ggml_leaky_relu(ctx, x, 0.2f, true);
     }
 
-    int get_num_tensors() {
-        int num_tensors = 10;
-        return num_tensors;
-    }
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+        // x: [n, num_feat, h, w]
+        // return: [n, num_feat, h, w]
 
-    void init_params(ggml_context* ctx) {
-        conv1_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, num_features, num_grow_ch);
-        conv1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, num_grow_ch);
-        conv2_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, num_features + num_grow_ch, num_grow_ch);
-        conv2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, num_grow_ch);
-        conv3_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, num_features + 2 * num_grow_ch, num_grow_ch);
-        conv3_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, num_grow_ch);
-        conv4_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, num_features + 3 * num_grow_ch, num_grow_ch);
-        conv4_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, num_grow_ch);
-        conv5_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, num_features + 4 * num_grow_ch, num_features);
-        conv5_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, num_features);
-    }
+        auto conv1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv1"]);
+        auto conv2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv2"]);
+        auto conv3 = std::dynamic_pointer_cast<Conv2d>(blocks["conv3"]);
+        auto conv4 = std::dynamic_pointer_cast<Conv2d>(blocks["conv4"]);
+        auto conv5 = std::dynamic_pointer_cast<Conv2d>(blocks["conv5"]);
 
-    void map_by_name(std::map<std::string, ggml_tensor*>& tensors, std::string prefix) {
-        tensors[prefix + "conv1.weight"] = conv1_w;
-        tensors[prefix + "conv1.bias"]   = conv1_b;
+        auto x1    = lrelu(ctx, conv1->forward(ctx, x));
+        auto x_cat = ggml_concat(ctx, x, x1);
+        auto x2    = lrelu(ctx, conv2->forward(ctx, x_cat));
+        x_cat      = ggml_concat(ctx, x_cat, x2);
+        auto x3    = lrelu(ctx, conv3->forward(ctx, x_cat));
+        x_cat      = ggml_concat(ctx, x_cat, x3);
+        auto x4    = lrelu(ctx, conv4->forward(ctx, x_cat));
+        x_cat      = ggml_concat(ctx, x_cat, x4);
+        auto x5    = conv5->forward(ctx, x_cat);
 
-        tensors[prefix + "conv2.weight"] = conv2_w;
-        tensors[prefix + "conv2.bias"]   = conv2_b;
-
-        tensors[prefix + "conv3.weight"] = conv3_w;
-        tensors[prefix + "conv3.bias"]   = conv3_b;
-
-        tensors[prefix + "conv4.weight"] = conv4_w;
-        tensors[prefix + "conv4.bias"]   = conv4_b;
-
-        tensors[prefix + "conv5.weight"] = conv5_w;
-        tensors[prefix + "conv5.bias"]   = conv5_b;
-    }
-
-    ggml_tensor* forward(ggml_context* ctx, float out_scale, ggml_tensor* x /* feat */) {
-        // x1 = self.lrelu(self.conv1(x))
-        ggml_tensor* x1 = ggml_nn_conv_2d(ctx, x, conv1_w, conv1_b, 1, 1, 1, 1);
-        x1              = ggml_leaky_relu(ctx, x1, 0.2f, true);
-
-        // x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
-        ggml_tensor* x_cat = ggml_concat(ctx, x, x1);
-        ggml_tensor* x2    = ggml_nn_conv_2d(ctx, x_cat, conv2_w, conv2_b, 1, 1, 1, 1);
-        x2                 = ggml_leaky_relu(ctx, x2, 0.2f, true);
-
-        // x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
-        x_cat           = ggml_concat(ctx, x_cat, x2);
-        ggml_tensor* x3 = ggml_nn_conv_2d(ctx, x_cat, conv3_w, conv3_b, 1, 1, 1, 1);
-        x3              = ggml_leaky_relu(ctx, x3, 0.2f, true);
-
-        // x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
-        x_cat           = ggml_concat(ctx, x_cat, x3);
-        ggml_tensor* x4 = ggml_nn_conv_2d(ctx, x_cat, conv4_w, conv4_b, 1, 1, 1, 1);
-        x4              = ggml_leaky_relu(ctx, x4, 0.2f, true);
-
-        // self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        x_cat           = ggml_concat(ctx, x_cat, x4);
-        ggml_tensor* x5 = ggml_nn_conv_2d(ctx, x_cat, conv5_w, conv5_b, 1, 1, 1, 1);
-
-        // return x5 * 0.2 + x
-        x5 = ggml_add(ctx, ggml_scale(ctx, x5, out_scale), x);
+        x5 = ggml_add(ctx, ggml_scale(ctx, x5, 0.2f), x);
         return x5;
     }
 };
 
-struct EsrganBlock {
-    ResidualDenseBlock rd_blocks[3];
-    int num_residual_blocks = 3;
-
-    EsrganBlock() {}
-
-    EsrganBlock(int num_feat, int num_grow_ch) {
-        for (int i = 0; i < num_residual_blocks; i++) {
-            rd_blocks[i] = ResidualDenseBlock(num_feat, num_grow_ch);
-        }
+class RRDB : public GGMLBlock {
+public:
+    RRDB(int num_feat, int num_grow_ch = 32) {
+        blocks["rdb1"] = std::shared_ptr<GGMLBlock>(new ResidualDenseBlock(num_feat, num_grow_ch));
+        blocks["rdb2"] = std::shared_ptr<GGMLBlock>(new ResidualDenseBlock(num_feat, num_grow_ch));
+        blocks["rdb3"] = std::shared_ptr<GGMLBlock>(new ResidualDenseBlock(num_feat, num_grow_ch));
     }
 
-    int get_num_tensors() {
-        int num_tensors = 0;
-        for (int i = 0; i < num_residual_blocks; i++) {
-            num_tensors += rd_blocks[i].get_num_tensors();
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+        // x: [n, num_feat, h, w]
+        // return: [n, num_feat, h, w]
+
+        auto rdb1 = std::dynamic_pointer_cast<ResidualDenseBlock>(blocks["rdb1"]);
+        auto rdb2 = std::dynamic_pointer_cast<ResidualDenseBlock>(blocks["rdb2"]);
+        auto rdb3 = std::dynamic_pointer_cast<ResidualDenseBlock>(blocks["rdb3"]);
+
+        auto out = rdb1->forward(ctx, x);
+        out      = rdb2->forward(ctx, out);
+        out      = rdb3->forward(ctx, out);
+
+        out = ggml_add(ctx, ggml_scale(ctx, out, 0.2f), x);
+        return out;
+    }
+};
+
+class RRDBNet : public GGMLBlock {
+protected:
+    int scale       = 4;  // default RealESRGAN_x4plus_anime_6B
+    int num_block   = 6;  // default RealESRGAN_x4plus_anime_6B
+    int num_in_ch   = 3;
+    int num_out_ch  = 3;
+    int num_feat    = 64;  // default RealESRGAN_x4plus_anime_6B
+    int num_grow_ch = 32;  // default RealESRGAN_x4plus_anime_6B
+
+public:
+    RRDBNet() {
+        blocks["conv_first"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_in_ch, num_feat, {3, 3}, {1, 1}, {1, 1}));
+        for (int i = 0; i < num_block; i++) {
+            std::string name = "body." + std::to_string(i);
+            blocks[name]     = std::shared_ptr<GGMLBlock>(new RRDB(num_feat, num_grow_ch));
         }
-        return num_tensors;
+        blocks["conv_body"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
+        // upsample
+        blocks["conv_up1"]  = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv_up2"]  = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv_hr"]   = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv_last"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_out_ch, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    size_t calculate_mem_size() {
-        size_t mem_size = 0;
-        for (int i = 0; i < num_residual_blocks; i++) {
-            mem_size += rd_blocks[i].calculate_mem_size();
-        }
-        return mem_size;
+    struct ggml_tensor* lrelu(struct ggml_context* ctx, struct ggml_tensor* x) {
+        return ggml_leaky_relu(ctx, x, 0.2f, true);
     }
 
-    void init_params(ggml_context* ctx) {
-        for (int i = 0; i < num_residual_blocks; i++) {
-            rd_blocks[i].init_params(ctx);
-        }
-    }
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+        // x: [n, num_in_ch, h, w]
+        // return: [n, num_out_ch, h*4, w*4]
+        auto conv_first = std::dynamic_pointer_cast<Conv2d>(blocks["conv_first"]);
+        auto conv_body  = std::dynamic_pointer_cast<Conv2d>(blocks["conv_body"]);
+        auto conv_up1   = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up1"]);
+        auto conv_up2   = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up2"]);
+        auto conv_hr    = std::dynamic_pointer_cast<Conv2d>(blocks["conv_hr"]);
+        auto conv_last  = std::dynamic_pointer_cast<Conv2d>(blocks["conv_last"]);
 
-    void map_by_name(std::map<std::string, ggml_tensor*>& tensors, std::string prefix) {
-        for (int i = 0; i < num_residual_blocks; i++) {
-            rd_blocks[i].map_by_name(tensors, prefix + "rdb" + std::to_string(i + 1) + ".");
-        }
-    }
+        auto feat      = conv_first->forward(ctx, x);
+        auto body_feat = feat;
+        for (int i = 0; i < num_block; i++) {
+            std::string name = "body." + std::to_string(i);
+            auto block       = std::dynamic_pointer_cast<RRDB>(blocks[name]);
 
-    ggml_tensor* forward(ggml_context* ctx, float out_scale, ggml_tensor* x) {
-        ggml_tensor* out = x;
-        for (int i = 0; i < num_residual_blocks; i++) {
-            // out = self.rdb...(x)
-            out = rd_blocks[i].forward(ctx, out_scale, out);
+            body_feat = block->forward(ctx, body_feat);
         }
-        // return out * 0.2 + x
-        out = ggml_add(ctx, ggml_scale(ctx, out, out_scale), x);
+        body_feat = conv_body->forward(ctx, body_feat);
+        feat      = ggml_add(ctx, feat, body_feat);
+        // upsample
+        feat     = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx, feat, 2)));
+        feat     = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx, feat, 2)));
+        auto out = conv_last->forward(ctx, lrelu(ctx, conv_hr->forward(ctx, feat)));
         return out;
     }
 };
 
 struct ESRGAN : public GGMLModule {
-    int scale        = 4;  // default RealESRGAN_x4plus_anime_6B
-    int num_blocks   = 6;  // default RealESRGAN_x4plus_anime_6B
-    int in_channels  = 3;
-    int out_channels = 3;
-    int num_features = 64;   // default RealESRGAN_x4plus_anime_6B
-    int num_grow_ch  = 32;   // default RealESRGAN_x4plus_anime_6B
-    int tile_size    = 128;  // avoid cuda OOM for 4gb VRAM
+    RRDBNet rrdb_net;
+    int scale     = 4;
+    int tile_size = 128;  // avoid cuda OOM for 4gb VRAM
 
-    ggml_tensor* conv_first_w;  // [num_features, in_channels, 3, 3]
-    ggml_tensor* conv_first_b;  // [num_features]
-
-    EsrganBlock body_blocks[6];
-    ggml_tensor* conv_body_w;  // [num_features, num_features, 3, 3]
-    ggml_tensor* conv_body_b;  // [num_features]
-
-    // upsample
-    ggml_tensor* conv_up1_w;  // [num_features, num_features, 3, 3]
-    ggml_tensor* conv_up1_b;  // [num_features]
-    ggml_tensor* conv_up2_w;  // [num_features, num_features, 3, 3]
-    ggml_tensor* conv_up2_b;  // [num_features]
-
-    ggml_tensor* conv_hr_w;    // [num_features, num_features, 3, 3]
-    ggml_tensor* conv_hr_b;    // [num_features]
-    ggml_tensor* conv_last_w;  // [out_channels, num_features, 3, 3]
-    ggml_tensor* conv_last_b;  // [out_channels]
-
-    bool decode_only = false;
-
-    ESRGAN() {
-        name = "esrgan";
-        for (int i = 0; i < num_blocks; i++) {
-            body_blocks[i] = EsrganBlock(num_features, num_grow_ch);
-        }
+    ESRGAN(ggml_backend_t backend,
+           ggml_type wtype)
+        : GGMLModule(backend, wtype) {
+        rrdb_net.init(params_ctx, wtype);
     }
 
-    size_t calculate_mem_size() {
-        size_t mem_size = num_features * in_channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_first_w
-        mem_size += num_features * ggml_type_size(GGML_TYPE_F32);                              // conv_first_b
-
-        for (int i = 0; i < num_blocks; i++) {
-            mem_size += body_blocks[i].calculate_mem_size();
-        }
-
-        mem_size += num_features * num_features * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_body_w
-        mem_size += num_features * ggml_type_size(GGML_TYPE_F32);                         // conv_body_w
-
-        // upsample
-        mem_size += num_features * num_features * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_up1_w
-        mem_size += num_features * ggml_type_size(GGML_TYPE_F32);                         // conv_up1_b
-
-        mem_size += num_features * num_features * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_up2_w
-        mem_size += num_features * ggml_type_size(GGML_TYPE_F32);                         // conv_up2_b
-
-        mem_size += num_features * num_features * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_hr_w
-        mem_size += num_features * ggml_type_size(GGML_TYPE_F32);                         // conv_hr_b
-
-        mem_size += out_channels * num_features * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_last_w
-        mem_size += out_channels * ggml_type_size(GGML_TYPE_F32);                         // conv_last_b
-        return mem_size;
+    std::string get_desc() {
+        return "esrgan";
     }
 
-    size_t get_num_tensors() {
-        size_t num_tensors = 12;
-        for (int i = 0; i < num_blocks; i++) {
-            num_tensors += body_blocks[i].get_num_tensors();
-        }
-        return num_tensors;
+    size_t get_params_mem_size() {
+        return rrdb_net.get_params_mem_size();
     }
 
-    void init_params() {
-        ggml_allocr* alloc = ggml_allocr_new_from_buffer(params_buffer);
-        conv_first_w       = ggml_new_tensor_4d(params_ctx, GGML_TYPE_F16, 3, 3, in_channels, num_features);
-        conv_first_b       = ggml_new_tensor_1d(params_ctx, GGML_TYPE_F32, num_features);
-        conv_body_w        = ggml_new_tensor_4d(params_ctx, GGML_TYPE_F16, 3, 3, num_features, num_features);
-        conv_body_b        = ggml_new_tensor_1d(params_ctx, GGML_TYPE_F32, num_features);
-        conv_up1_w         = ggml_new_tensor_4d(params_ctx, GGML_TYPE_F16, 3, 3, num_features, num_features);
-        conv_up1_b         = ggml_new_tensor_1d(params_ctx, GGML_TYPE_F32, num_features);
-        conv_up2_w         = ggml_new_tensor_4d(params_ctx, GGML_TYPE_F16, 3, 3, num_features, num_features);
-        conv_up2_b         = ggml_new_tensor_1d(params_ctx, GGML_TYPE_F32, num_features);
-        conv_hr_w          = ggml_new_tensor_4d(params_ctx, GGML_TYPE_F16, 3, 3, num_features, num_features);
-        conv_hr_b          = ggml_new_tensor_1d(params_ctx, GGML_TYPE_F32, num_features);
-        conv_last_w        = ggml_new_tensor_4d(params_ctx, GGML_TYPE_F16, 3, 3, num_features, out_channels);
-        conv_last_b        = ggml_new_tensor_1d(params_ctx, GGML_TYPE_F32, out_channels);
-
-        for (int i = 0; i < num_blocks; i++) {
-            body_blocks[i].init_params(params_ctx);
-        }
-
-        // alloc all tensors linked to this context
-        for (struct ggml_tensor* t = ggml_get_first_tensor(params_ctx); t != NULL; t = ggml_get_next_tensor(params_ctx, t)) {
-            if (t->data == NULL) {
-                ggml_allocr_alloc(alloc, t);
-            }
-        }
-        ggml_allocr_free(alloc);
+    size_t get_params_num() {
+        return rrdb_net.get_params_num();
     }
 
-    bool load_from_file(const std::string& file_path, ggml_backend_t backend) {
+    bool load_from_file(const std::string& file_path) {
         LOG_INFO("loading esrgan from '%s'", file_path.c_str());
 
-        if (!alloc_params_buffer(backend)) {
-            return false;
-        }
-
+        alloc_params_buffer();
         std::map<std::string, ggml_tensor*> esrgan_tensors;
-
-        // prepare memory for the weights
-        {
-            init_params();
-            map_by_name(esrgan_tensors);
-        }
+        rrdb_net.get_param_tensors(esrgan_tensors);
 
         ModelLoader model_loader;
         if (!model_loader.init_from_file(file_path)) {
@@ -303,115 +184,22 @@ struct ESRGAN : public GGMLModule {
         return success;
     }
 
-    void map_by_name(std::map<std::string, ggml_tensor*>& tensors) {
-        tensors["conv_first.weight"] = conv_first_w;
-        tensors["conv_first.bias"]   = conv_first_b;
-
-        for (int i = 0; i < num_blocks; i++) {
-            body_blocks[i].map_by_name(tensors, "body." + std::to_string(i) + ".");
-        }
-
-        tensors["conv_body.weight"] = conv_body_w;
-        tensors["conv_body.bias"]   = conv_body_b;
-
-        tensors["conv_up1.weight"] = conv_up1_w;
-        tensors["conv_up1.bias"]   = conv_up1_b;
-        tensors["conv_up2.weight"] = conv_up2_w;
-        tensors["conv_up2.bias"]   = conv_up2_b;
-        tensors["conv_hr.weight"]  = conv_hr_w;
-        tensors["conv_hr.bias"]    = conv_hr_b;
-
-        tensors["conv_last.weight"] = conv_last_w;
-        tensors["conv_last.bias"]   = conv_last_b;
-    }
-
-    ggml_tensor* forward(ggml_context* ctx0, float out_scale, ggml_tensor* x /* feat */) {
-        // feat = self.conv_first(feat)
-        auto h = ggml_nn_conv_2d(ctx0, x, conv_first_w, conv_first_b, 1, 1, 1, 1);
-
-        auto body_h = h;
-        // self.body(feat)
-        for (int i = 0; i < num_blocks; i++) {
-            body_h = body_blocks[i].forward(ctx0, out_scale, body_h);
-        }
-
-        // body_feat = self.conv_body(self.body(feat))
-        body_h = ggml_nn_conv_2d(ctx0, body_h, conv_body_w, conv_body_b, 1, 1, 1, 1);
-
-        // feat = feat + body_feat
-        h = ggml_add(ctx0, h, body_h);
-
-        // upsample
-        // feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        h = ggml_upscale(ctx0, h, 2);
-        h = ggml_nn_conv_2d(ctx0, h, conv_up1_w, conv_up1_b, 1, 1, 1, 1);
-        h = ggml_leaky_relu(ctx0, h, 0.2f, true);
-
-        // feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
-        h = ggml_upscale(ctx0, h, 2);
-        h = ggml_nn_conv_2d(ctx0, h, conv_up2_w, conv_up2_b, 1, 1, 1, 1);
-        h = ggml_leaky_relu(ctx0, h, 0.2f, true);
-
-        // out = self.conv_last(self.lrelu(self.conv_hr(feat)))
-        h = ggml_nn_conv_2d(ctx0, h, conv_hr_w, conv_hr_b, 1, 1, 1, 1);
-        h = ggml_leaky_relu(ctx0, h, 0.2f, true);
-
-        h = ggml_nn_conv_2d(ctx0, h, conv_last_w, conv_last_b, 1, 1, 1, 1);
-        return h;
-    }
-
     struct ggml_cgraph* build_graph(struct ggml_tensor* x) {
-        // since we are using ggml-alloc, this buffer only needs enough space to hold the ggml_tensor and ggml_cgraph structs, but not the tensor data
-        static size_t buf_size = ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
-        static std::vector<uint8_t> buf(buf_size);
-
-        struct ggml_init_params params = {
-            /*.mem_size   =*/buf_size,
-            /*.mem_buffer =*/buf.data(),
-            /*.no_alloc   =*/true,  // the tensors will be allocated later by ggml_allocr_alloc_graph()
-        };
-
-        struct ggml_context* ctx0 = ggml_init(params);
-
-        struct ggml_cgraph* gf = ggml_new_graph(ctx0);
-
-        struct ggml_tensor* x_ = NULL;
-        float out_scale = 0.2f;
-
-        // it's performing a compute, check if backend isn't cpu
-        if (!ggml_backend_is_cpu(backend)) {
-            // pass input tensors to gpu memory
-            x_ = ggml_dup_tensor(ctx0, x);
-            ggml_allocr_alloc(compute_allocr, x_);
-
-            // pass data to device backend
-            if (!ggml_allocr_is_measure(compute_allocr)) {
-                ggml_backend_tensor_set(x_, x->data, 0, ggml_nbytes(x));
-            }
-        } else {
-            x_ = x;
-        }
-
-        struct ggml_tensor* out = forward(ctx0, out_scale, x);
-
+        struct ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
+        x                       = to_backend(x);
+        struct ggml_tensor* out = rrdb_net.forward(compute_ctx, x);
         ggml_build_forward_expand(gf, out);
-        ggml_free(ctx0);
-
         return gf;
     }
 
-    void alloc_compute_buffer(struct ggml_tensor* x) {
+    void compute(const int n_threads,
+                 struct ggml_tensor* x,
+                 ggml_tensor** output,
+                 ggml_context* output_ctx = NULL) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
             return build_graph(x);
         };
-        GGMLModule::alloc_compute_buffer(get_graph);
-    }
-
-    void compute(struct ggml_tensor* work_result, const int n_threads, struct ggml_tensor* x) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(x);
-        };
-        GGMLModule::compute(get_graph, n_threads, work_result);
+        GGMLModule::compute(get_graph, n_threads, false, output, output_ctx);
     }
 };
 

examples/cli/main.cpp

@@ -6,8 +6,8 @@
 #include <string>
 #include <vector>
 
+// #include "preprocessing.hpp"
 #include "stable-diffusion.h"
-#include "preprocessing.hpp"
 
 #define STB_IMAGE_IMPLEMENTATION
 #include "stb_image.h"
@@ -43,12 +43,14 @@ const char* schedule_str[] = {
 const char* modes_str[] = {
     "txt2img",
     "img2img",
+    "img2vid",
     "convert",
 };
 
 enum SDMode {
     TXT2IMG,
     IMG2IMG,
+    IMG2VID,
     CONVERT,
     MODE_COUNT
 };
@@ -71,23 +73,30 @@ struct SDParams {
 
     std::string prompt;
     std::string negative_prompt;
+    float min_cfg   = 1.0f;
     float cfg_scale = 7.0f;
     int clip_skip   = -1;  // <= 0 represents unspecified
     int width       = 512;
     int height      = 512;
     int batch_count = 1;
 
+    int video_frames         = 6;
+    int motion_bucket_id     = 127;
+    int fps                  = 6;
+    float augmentation_level = 0.f;
+
     sample_method_t sample_method = EULER_A;
     schedule_t schedule           = DEFAULT;
     int sample_steps              = 20;
     float strength                = 0.75f;
-    float control_strength     = 0.9f;
+    float control_strength        = 0.9f;
     rng_type_t rng_type           = CUDA_RNG;
     int64_t seed                  = 42;
     bool verbose                  = false;
     bool vae_tiling               = false;
     bool control_net_cpu          = false;
     bool canny_preprocess         = false;
+    int upscale_repeats           = 1;
 };
 
 void print_params(SDParams params) {
@@ -108,6 +117,7 @@ void print_params(SDParams params) {
     printf("    strength(control): %.2f\n", params.control_strength);
     printf("    prompt:            %s\n", params.prompt.c_str());
     printf("    negative_prompt:   %s\n", params.negative_prompt.c_str());
+    printf("    min_cfg:           %.2f\n", params.min_cfg);
     printf("    cfg_scale:         %.2f\n", params.cfg_scale);
     printf("    clip_skip:         %d\n", params.clip_skip);
     printf("    width:             %d\n", params.width);
@@ -120,6 +130,7 @@ void print_params(SDParams params) {
     printf("    seed:              %ld\n", params.seed);
     printf("    batch_count:       %d\n", params.batch_count);
     printf("    vae_tiling:        %s\n", params.vae_tiling ? "true" : "false");
+    printf("    upscale_repeats:   %d\n", params.upscale_repeats);
 }
 
 void print_usage(int argc, const char* argv[]) {
@@ -136,6 +147,7 @@ void print_usage(int argc, const char* argv[]) {
     printf("  --control-net [CONTROL_PATH]       path to control net model\n");
     printf("  --embd-dir [EMBEDDING_PATH]        path to embeddings.\n");
     printf("  --upscale-model [ESRGAN_PATH]      path to esrgan model. Upscale images after generate, just RealESRGAN_x4plus_anime_6B supported by now.\n");
+    printf("  --upscale-repeats                  Run the ESRGAN upscaler this many times (default 1)\n");
     printf("  --type [TYPE]                      weight type (f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0)\n");
     printf("                                     If not specified, the default is the type of the weight file.\n");
     printf("  --lora-model-dir [DIR]             lora model directory\n");
@@ -190,7 +202,8 @@ void parse_args(int argc, const char** argv, SDParams& params) {
                 }
             }
             if (mode_found == -1) {
-                fprintf(stderr, "error: invalid mode %s, must be one of [txt2img, img2img]\n",
+                fprintf(stderr,
+                        "error: invalid mode %s, must be one of [txt2img, img2img, img2vid, convert]\n",
                         mode_selected);
                 exit(1);
             }
@@ -231,7 +244,7 @@ void parse_args(int argc, const char** argv, SDParams& params) {
                 break;
             }
             params.embeddings_path = argv[i];
-        }  else if (arg == "--type") {
+        } else if (arg == "--type") {
             if (++i >= argc) {
                 invalid_arg = true;
                 break;
@@ -286,6 +299,16 @@ void parse_args(int argc, const char** argv, SDParams& params) {
                 break;
             }
             params.prompt = argv[i];
+        } else if (arg == "--upscale-repeats") {
+            if (++i >= argc) {
+                invalid_arg = true;
+                break;
+            }
+            params.upscale_repeats = std::stoi(argv[i]);
+            if (params.upscale_repeats < 1) {
+                fprintf(stderr, "error: upscale multiplier must be at least 1\n");
+                exit(1);
+            }
         } else if (arg == "-n" || arg == "--negative-prompt") {
             if (++i >= argc) {
                 invalid_arg = true;
@@ -338,7 +361,7 @@ void parse_args(int argc, const char** argv, SDParams& params) {
             params.vae_tiling = true;
         } else if (arg == "--control-net-cpu") {
             params.control_net_cpu = true;
-        }  else if (arg == "--canny") {
+        } else if (arg == "--canny") {
             params.canny_preprocess = true;
         } else if (arg == "-b" || arg == "--batch-count") {
             if (++i >= argc) {
@@ -420,7 +443,7 @@ void parse_args(int argc, const char** argv, SDParams& params) {
         params.n_threads = get_num_physical_cores();
     }
 
-    if (params.mode != CONVERT && params.prompt.length() == 0) {
+    if (params.mode != CONVERT && params.mode != IMG2VID && params.prompt.length() == 0) {
         fprintf(stderr, "error: the following arguments are required: prompt\n");
         print_usage(argc, argv);
         exit(1);
@@ -432,7 +455,7 @@ void parse_args(int argc, const char** argv, SDParams& params) {
         exit(1);
     }
 
-    if (params.mode == IMG2IMG && params.input_path.length() == 0) {
+    if ((params.mode == IMG2IMG || params.mode == IMG2VID) && params.input_path.length() == 0) {
         fprintf(stderr, "error: when using the img2img mode, the following arguments are required: init-img\n");
         print_usage(argc, argv);
         exit(1);
@@ -476,6 +499,18 @@ void parse_args(int argc, const char** argv, SDParams& params) {
     }
 }
 
+static std::string sd_basename(const std::string& path) {
+    size_t pos = path.find_last_of('/');
+    if (pos != std::string::npos) {
+        return path.substr(pos + 1);
+    }
+    pos = path.find_last_of('\\');
+    if (pos != std::string::npos) {
+        return path.substr(pos + 1);
+    }
+    return path;
+}
+
 std::string get_image_params(SDParams params, int64_t seed) {
     std::string parameter_string = params.prompt + "\n";
     if (params.negative_prompt.size() != 0) {
@@ -539,9 +574,14 @@ int main(int argc, const char* argv[]) {
         }
     }
 
+    if (params.mode == IMG2VID) {
+        fprintf(stderr, "SVD support is broken, do not use it!!!\n");
+        return 1;
+    }
+
     bool vae_decode_only        = true;
     uint8_t* input_image_buffer = NULL;
-    if (params.mode == IMG2IMG) {
+    if (params.mode == IMG2IMG || params.mode == IMG2VID) {
         vae_decode_only = false;
 
         int c              = 0;
@@ -590,20 +630,26 @@ int main(int argc, const char* argv[]) {
     sd_image_t* results;
     if (params.mode == TXT2IMG) {
         sd_image_t* control_image = NULL;
-        if(params.controlnet_path.size() > 0 && params.control_image_path.size() > 0) {
-            int c = 0;
+        if (params.controlnet_path.size() > 0 && params.control_image_path.size() > 0) {
+            int c              = 0;
             input_image_buffer = stbi_load(params.control_image_path.c_str(), &params.width, &params.height, &c, 3);
-            if(input_image_buffer == NULL) {
+            if (input_image_buffer == NULL) {
                 fprintf(stderr, "load image from '%s' failed\n", params.control_image_path.c_str());
                 return 1;
             }
             control_image = new sd_image_t{(uint32_t)params.width,
-                                            (uint32_t)params.height,
-                                            3,
-                                            input_image_buffer};
-            if(params.canny_preprocess)  { // apply preprocessor
-                LOG_INFO("Applying canny preprocessor");
-                control_image->data = preprocess_canny(control_image->data, control_image->width, control_image->height);
+                                           (uint32_t)params.height,
+                                           3,
+                                           input_image_buffer};
+            if (params.canny_preprocess) {  // apply preprocessor
+                control_image->data = preprocess_canny(control_image->data,
+                                                       control_image->width,
+                                                       control_image->height,
+                                                       0.08f,
+                                                       0.08f,
+                                                       0.8f,
+                                                       1.0f,
+                                                       false);
             }
         }
         results = txt2img(sd_ctx,
@@ -625,19 +671,57 @@ int main(int argc, const char* argv[]) {
                                   3,
                                   input_image_buffer};
 
-        results = img2img(sd_ctx,
-                          input_image,
-                          params.prompt.c_str(),
-                          params.negative_prompt.c_str(),
-                          params.clip_skip,
-                          params.cfg_scale,
-                          params.width,
-                          params.height,
-                          params.sample_method,
-                          params.sample_steps,
-                          params.strength,
-                          params.seed,
-                          params.batch_count);
+        if (params.mode == IMG2VID) {
+            results = img2vid(sd_ctx,
+                              input_image,
+                              params.width,
+                              params.height,
+                              params.video_frames,
+                              params.motion_bucket_id,
+                              params.fps,
+                              params.augmentation_level,
+                              params.min_cfg,
+                              params.cfg_scale,
+                              params.sample_method,
+                              params.sample_steps,
+                              params.strength,
+                              params.seed);
+            if (results == NULL) {
+                printf("generate failed\n");
+                free_sd_ctx(sd_ctx);
+                return 1;
+            }
+            size_t last            = params.output_path.find_last_of(".");
+            std::string dummy_name = last != std::string::npos ? params.output_path.substr(0, last) : params.output_path;
+            for (int i = 0; i < params.video_frames; i++) {
+                if (results[i].data == NULL) {
+                    continue;
+                }
+                std::string final_image_path = i > 0 ? dummy_name + "_" + std::to_string(i + 1) + ".png" : dummy_name + ".png";
+                stbi_write_png(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
+                               results[i].data, 0, get_image_params(params, params.seed + i).c_str());
+                printf("save result image to '%s'\n", final_image_path.c_str());
+                free(results[i].data);
+                results[i].data = NULL;
+            }
+            free(results);
+            free_sd_ctx(sd_ctx);
+            return 0;
+        } else {
+            results = img2img(sd_ctx,
+                              input_image,
+                              params.prompt.c_str(),
+                              params.negative_prompt.c_str(),
+                              params.clip_skip,
+                              params.cfg_scale,
+                              params.width,
+                              params.height,
+                              params.sample_method,
+                              params.sample_steps,
+                              params.strength,
+                              params.seed,
+                              params.batch_count);
+        }
     }
 
     if (results == NULL) {
@@ -647,7 +731,7 @@ int main(int argc, const char* argv[]) {
     }
 
     int upscale_factor = 4;  // unused for RealESRGAN_x4plus_anime_6B.pth
-    if (params.esrgan_path.size() > 0) {
+    if (params.esrgan_path.size() > 0 && params.upscale_repeats > 0) {
         upscaler_ctx_t* upscaler_ctx = new_upscaler_ctx(params.esrgan_path.c_str(),
                                                         params.n_threads,
                                                         params.wtype);
@@ -659,13 +743,17 @@ int main(int argc, const char* argv[]) {
                 if (results[i].data == NULL) {
                     continue;
                 }
-                sd_image_t upscaled_image = upscale(upscaler_ctx, results[i], upscale_factor);
-                if (upscaled_image.data == NULL) {
-                    printf("upscale failed\n");
-                    continue;
+                sd_image_t current_image = results[i];
+                for (int u = 0; u < params.upscale_repeats; ++u) {
+                    sd_image_t upscaled_image = upscale(upscaler_ctx, current_image, upscale_factor);
+                    if (upscaled_image.data == NULL) {
+                        printf("upscale failed\n");
+                        break;
+                    }
+                    free(current_image.data);
+                    current_image = upscaled_image;
                 }
-                free(results[i].data);
-                results[i] = upscaled_image;
+                results[i] = current_image;  // Set the final upscaled image as the result
             }
         }
     }

ggml_extend.hpp

@@ -11,6 +11,7 @@
 #include <iostream>
 #include <iterator>
 #include <map>
+#include <memory>
 #include <random>
 #include <regex>
 #include <set>
@@ -65,9 +66,11 @@ __STATIC_INLINE__ void ggml_tensor_set_f32(struct ggml_tensor* tensor, float val
 }
 
 __STATIC_INLINE__ float ggml_tensor_get_f32(const ggml_tensor* tensor, int l, int k = 0, int j = 0, int i = 0) {
-    // float value;
-    // ggml_backend_tensor_get(tensor, &value, i * tensor->nb[3] + j * tensor->nb[2] + k * tensor->nb[1] + l * tensor->nb[0], sizeof(float));
-    // return value;
+    if (tensor->buffer != NULL) {
+        float value;
+        ggml_backend_tensor_get(tensor, &value, i * tensor->nb[3] + j * tensor->nb[2] + k * tensor->nb[1] + l * tensor->nb[0], sizeof(float));
+        return value;
+    }
     GGML_ASSERT(tensor->nb[0] == sizeof(float));
     return *(float*)((char*)(tensor->data) + i * tensor->nb[3] + j * tensor->nb[2] + k * tensor->nb[1] + l * tensor->nb[0]);
 }
@@ -183,7 +186,7 @@ __STATIC_INLINE__ void copy_ggml_tensor(struct ggml_tensor* dst, struct ggml_ten
         LOG_ERROR("ggml_init() failed");
         return;
     }
-    ggml_tensor* final = ggml_cpy_inplace(ctx, src, dst);
+    ggml_tensor* final = ggml_cpy(ctx, src, dst);
 
     struct ggml_cgraph* graph = ggml_new_graph(ctx);
     ggml_build_forward_expand(graph, final);
@@ -191,6 +194,10 @@ __STATIC_INLINE__ void copy_ggml_tensor(struct ggml_tensor* dst, struct ggml_ten
     ggml_free(ctx);
 }
 
+__STATIC_INLINE__ float sigmoid(float x) {
+    return 1 / (1.0f + expf(-x));
+}
+
 // SPECIAL OPERATIONS WITH TENSORS
 
 __STATIC_INLINE__ uint8_t* sd_tensor_to_image(struct ggml_tensor* input) {
@@ -211,7 +218,8 @@ __STATIC_INLINE__ uint8_t* sd_tensor_to_image(struct ggml_tensor* input) {
 }
 
 __STATIC_INLINE__ void sd_image_to_tensor(const uint8_t* image_data,
-                                          struct ggml_tensor* output) {
+                                          struct ggml_tensor* output,
+                                          bool scale = true) {
     int64_t width    = output->ne[0];
     int64_t height   = output->ne[1];
     int64_t channels = output->ne[2];
@@ -219,8 +227,31 @@ __STATIC_INLINE__ void sd_image_to_tensor(const uint8_t* image_data,
     for (int iy = 0; iy < height; iy++) {
         for (int ix = 0; ix < width; ix++) {
             for (int k = 0; k < channels; k++) {
-                int value = *(image_data + iy * width * channels + ix * channels + k);
-                ggml_tensor_set_f32(output, value / 255.0f, ix, iy, k);
+                float value = *(image_data + iy * width * channels + ix * channels + k);
+                if (scale) {
+                    value /= 255.f;
+                }
+                ggml_tensor_set_f32(output, value, ix, iy, k);
+            }
+        }
+    }
+}
+
+__STATIC_INLINE__ void sd_image_f32_to_tensor(const float* image_data,
+                                              struct ggml_tensor* output,
+                                              bool scale = true) {
+    int64_t width    = output->ne[0];
+    int64_t height   = output->ne[1];
+    int64_t channels = output->ne[2];
+    GGML_ASSERT(channels == 3 && output->type == GGML_TYPE_F32);
+    for (int iy = 0; iy < height; iy++) {
+        for (int ix = 0; ix < width; ix++) {
+            for (int k = 0; k < channels; k++) {
+                float value = *(image_data + iy * width * channels + ix * channels + k);
+                if (scale) {
+                    value /= 255.f;
+                }
+                ggml_tensor_set_f32(output, value, ix, iy, k);
             }
         }
     }
@@ -407,7 +438,9 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_linear(struct ggml_context* ctx,
                                                      struct ggml_tensor* w,
                                                      struct ggml_tensor* b) {
     x = ggml_mul_mat(ctx, w, x);
-    x = ggml_add(ctx, x, b);
+    if (b != NULL) {
+        x = ggml_add(ctx, x, b);
+    }
     return x;
 }
 
@@ -428,19 +461,103 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_conv_2d(struct ggml_context* ctx,
     x = ggml_conv_2d(ctx, w, x, s0, s1, p0, p1, d0, d1);
     if (b != NULL) {
         b = ggml_reshape_4d(ctx, b, 1, 1, b->ne[0], 1);
+        // b = ggml_repeat(ctx, b, x);
         x = ggml_add(ctx, x, b);
     }
     return x;
 }
 
+// w: [OC，IC, KD, 1 * 1]
+// x: [N, IC, IH, IW]
+// b: [OC,]
+// result: [N, OC, OH, OW]
+__STATIC_INLINE__ struct ggml_tensor* ggml_nn_conv_3d_nx1x1_bak(struct ggml_context* ctx,
+                                                                struct ggml_tensor* x,
+                                                                struct ggml_tensor* w,
+                                                                struct ggml_tensor* b,
+                                                                int s2 = 1,
+                                                                int p2 = 1,
+                                                                int d2 = 1) {
+    GGML_ASSERT(w->ne[0] == 1);
+    // timesteps = x.shape[0]
+    // x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps)
+    // x = conv3d(x)
+    // return rearrange(x, "b c t h w -> (b t) c h w")
+    int64_t T = x->ne[3];
+    int64_t B = x->ne[3] / T;
+    int64_t C = x->ne[2];
+    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_conv_2d(ctx, w, x, 1, s2, 0, p2, 1, d2);      // [B, OC, T, OH * OW]
+    if (b != NULL) {
+        b = ggml_reshape_4d(ctx, b, 1, 1, b->ne[0], 1);
+        x = ggml_add(ctx, x, b);
+    }
+    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]
+}
+
+// w: [OC，IC, KD, 1 * 1]
+// x: [N, IC, ID, IH*IW]
+// b: [OC,]
+// result: [N, OC, OD, OH*OW]
+__STATIC_INLINE__ struct ggml_tensor* ggml_nn_conv_3d_nx1x1(struct ggml_context* ctx,
+                                                            struct ggml_tensor* x,
+                                                            struct ggml_tensor* w,
+                                                            struct ggml_tensor* b,
+                                                            int s2 = 1,
+                                                            int p2 = 1,
+                                                            int d2 = 1) {
+    x = ggml_conv_2d(ctx, w, x, 1, s2, 0, p2, 1, d2);  // [N, OC, T, OH * OW]
+    if (b != NULL) {
+        b = ggml_reshape_4d(ctx, b, 1, 1, b->ne[0], 1);
+        x = ggml_add(ctx, x, b);
+    }
+    return x;  // [N, OC, T, OH * OW]
+}
+
+// q: [N * n_head, n_token, d_head]
+// k: [N * n_head, n_k, d_head]
+// v: [N * n_head, d_head, n_k]
+// return: [N * n_head, n_token, d_head]
+__STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention(struct ggml_context* ctx,
+                                                        struct ggml_tensor* q,
+                                                        struct ggml_tensor* k,
+                                                        struct ggml_tensor* v,
+                                                        bool mask = false) {
+#if defined(SD_USE_FLASH_ATTENTION) && !defined(SD_USE_CUBLAS) && !defined(SD_USE_METAL)
+    struct ggml_tensor* kqv = ggml_flash_attn(ctx, q, k, v, false);  // [N * n_head, n_token, d_head]
+#else
+    float d_head = (float)q->ne[0];
+
+    struct ggml_tensor* kq = ggml_mul_mat(ctx, k, q);  // [N * n_head, n_token, n_k]
+    kq                     = ggml_scale_inplace(ctx, kq, 1.0f / sqrt(d_head));
+    if (mask) {
+        kq = ggml_diag_mask_inf_inplace(ctx, kq, 0);
+    }
+    kq = ggml_soft_max_inplace(ctx, kq);
+
+    struct ggml_tensor* kqv = ggml_mul_mat(ctx, v, kq);  // [N * n_head, n_token, d_head]
+#endif
+    return kqv;
+}
+
 __STATIC_INLINE__ struct ggml_tensor* ggml_nn_layer_norm(struct ggml_context* ctx,
                                                          struct ggml_tensor* x,
                                                          struct ggml_tensor* w,
                                                          struct ggml_tensor* b,
                                                          float eps = EPS) {
     x = ggml_norm(ctx, x, eps);
-    x = ggml_mul(ctx, x, w);
-    x = ggml_add(ctx, x, b);
+    if (w != NULL) {
+        x = ggml_mul(ctx, x, w);
+        if (b != NULL) {
+            x = ggml_add(ctx, x, b);
+        }
+    }
     return x;
 }
 
@@ -449,20 +566,23 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_group_norm(struct ggml_context* ct
                                                          struct ggml_tensor* w,
                                                          struct ggml_tensor* b,
                                                          int num_groups = 32) {
-    if (ggml_n_dims(x) >= 3) {
+    if (ggml_n_dims(x) >= 3 && w != NULL && b != NULL) {
         w = ggml_reshape_4d(ctx, w, 1, 1, w->ne[0], 1);
         b = ggml_reshape_4d(ctx, b, 1, 1, b->ne[0], 1);
     }
 
     x = ggml_group_norm(ctx, x, num_groups);
-    x = ggml_mul(ctx, x, w);
-    x = ggml_add(ctx, x, b);
+    if (w != NULL && b != NULL) {
+        x = ggml_mul(ctx, x, w);
+        // b = ggml_repeat(ctx, b, x);
+        x = ggml_add(ctx, x, b);
+    }
     return x;
 }
 
 __STATIC_INLINE__ void ggml_backend_tensor_get_and_sync(ggml_backend_t backend, const struct ggml_tensor* tensor, void* data, size_t offset, size_t size) {
 #ifdef SD_USE_CUBLAS
-    if(!ggml_backend_is_cpu(backend)) {
+    if (!ggml_backend_is_cpu(backend)) {
         ggml_backend_tensor_get_async(backend, tensor, data, offset, size);
         ggml_backend_synchronize(backend);
     } else {
@@ -486,133 +606,269 @@ __STATIC_INLINE__ float ggml_backend_tensor_get_f32(ggml_tensor* tensor) {
     return value;
 }
 
+__STATIC_INLINE__ struct ggml_tensor* vector_to_ggml_tensor(struct ggml_context* ctx,
+                                                            const std::vector<float>& vec) {
+    struct ggml_tensor* t = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, vec.size());
+    memcpy(t->data, (const void*)vec.data(), ggml_nbytes(t));
+    return t;
+}
+
+__STATIC_INLINE__ struct ggml_tensor* vector_to_ggml_tensor_i32(struct ggml_context* ctx,
+                                                                const std::vector<int>& vec) {
+    struct ggml_tensor* t = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, vec.size());
+    memcpy(t->data, (const void*)vec.data(), ggml_nbytes(t));
+    return t;
+}
+
+__STATIC_INLINE__ std::vector<float> arange(float start, float end, float step = 1.f) {
+    std::vector<float> result;
+
+    for (float value = start; value < end; value += step) {
+        result.push_back(value);
+    }
+
+    return result;
+}
+
 // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
-__STATIC_INLINE__ void set_timestep_embedding(struct ggml_tensor* timesteps, struct ggml_tensor* embedding, int dim, int max_period = 10000) {
+__STATIC_INLINE__ std::vector<float> timestep_embedding(std::vector<float> timesteps,
+                                                        int dim,
+                                                        int max_period = 10000) {
     // timesteps: [N,]
-    // embedding: [dim, N]
+    // embedding: [N, dim]
+    size_t N        = timesteps.size();
+    int acutual_dim = dim;
+    if (dim % 2 != 0) {
+        acutual_dim = dim + 1;
+    }
+    std::vector<float> embedding(N * acutual_dim, 0.f);
     int half = dim / 2;
     std::vector<float> freqs(half);
     for (int i = 0; i < half; ++i) {
         freqs[i] = (float)std::exp(-std::log(max_period) * i / half);
     }
-    for (int i = 0; i < timesteps->ne[0]; ++i) {
+    for (int i = 0; i < N; ++i) {
         for (int j = 0; j < half; ++j) {
-            float arg = ggml_get_f32_1d(timesteps, i) * freqs[j];
-            ggml_tensor_set_f32(embedding, std::cos(arg), j, i);
-            ggml_tensor_set_f32(embedding, std::sin(arg), j + half, i);
+            float arg                             = timesteps[i] * freqs[j];
+            embedding[i * acutual_dim + j]        = std::cos(arg);
+            embedding[i * acutual_dim + j + half] = std::sin(arg);
         }
-        if (dim % 2 != 0) {
-            *(float*)((char*)embedding->data + i * embedding->nb[1] + dim * embedding->nb[0]) = 0;
-        }
-    }
-}
-
-__STATIC_INLINE__ struct ggml_tensor* new_timestep_embedding(struct ggml_context* ctx,
-                                                             struct ggml_allocr* allocr,
-                                                             struct ggml_tensor* timesteps,
-                                                             int dim,
-                                                             int max_period = 10000) {
-    // timesteps: [N,]
-    // embedding: [dim, N]
-    int acutual_dim = dim;
-    if (dim % 2 != 0) {
-        acutual_dim = dim + 1;
-    }
-    struct ggml_tensor* embedding = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, acutual_dim, timesteps->ne[0]);
-    if (allocr != NULL) {
-        ggml_allocr_alloc(allocr, embedding);
-    }
-    if (allocr != NULL && !ggml_allocr_is_measure(allocr)) {
-        set_timestep_embedding(timesteps, embedding, dim, max_period);
     }
     return embedding;
 }
 
+__STATIC_INLINE__ void set_timestep_embedding(std::vector<float> timesteps,
+                                              struct ggml_tensor* embedding,
+                                              int dim,
+                                              int max_period = 10000) {
+    std::vector<float> embedding_vec = timestep_embedding(timesteps, dim, max_period);
+    memcpy(((char*)embedding->data), ((char*)embedding_vec.data()), ggml_nbytes(embedding));
+}
+
+__STATIC_INLINE__ struct ggml_tensor* new_timestep_embedding(struct ggml_context* ctx,
+                                                             std::vector<float> timesteps,
+                                                             int dim,
+                                                             int max_period = 10000) {
+    // timesteps: [N,]
+    // embedding: [N, dim]
+    std::vector<float> embedding_vec = timestep_embedding(timesteps, dim, max_period);
+    int acutual_dim                  = dim;
+    if (dim % 2 != 0) {
+        acutual_dim = dim + 1;
+    }
+    struct ggml_tensor* embedding = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, acutual_dim, timesteps.size());
+    if (embedding->data != NULL) {
+        memcpy(((char*)embedding->data), ((char*)embedding_vec.data()), ggml_nbytes(embedding));
+    } else {
+        ggml_backend_tensor_set(embedding, embedding_vec.data(), 0, ggml_nbytes(embedding));
+    }
+    return embedding;
+}
+
+__STATIC_INLINE__ struct ggml_tensor* ggml_nn_timestep_embedding(
+    struct ggml_context* ctx,
+    struct ggml_tensor* timesteps,
+    int dim,
+    int max_period = 10000) {
+    return ggml_timestep_embedding(ctx, timesteps, dim, max_period);
+}
+
+// struct GGMLComputeGraph {
+//     virtual void init(struct ggml_context* ctx, ggml_type wtype) = 0;
+//     virtual std::string get_desc() = 0;
+//     virtual size_t get_params_mem_size()   = 0;
+//     virtual size_t get_params_num() = 0;
+//     virtual struct ggml_cgraph* get_ggml_cgraph() = 0;
+// };
+
+#define MAX_PARAMS_TENSOR_NUM 10240
+#define MAX_GRAPH_SIZE 10240
+
 struct GGMLModule {
+protected:
     typedef std::function<struct ggml_cgraph*()> get_graph_cb_t;
 
-    std::string name                     = "ggml module";
-    struct ggml_context* params_ctx      = NULL;
-    size_t params_buffer_size            = 0;
-    size_t compute_buffer_size           = 0;
-    ggml_backend_buffer_t params_buffer  = NULL;
-    ggml_backend_buffer_t compute_buffer = NULL;  // for compute
-    struct ggml_allocr* compute_allocr   = NULL;
+    struct ggml_context* params_ctx     = NULL;
+    ggml_backend_buffer_t params_buffer = NULL;
+
+    struct ggml_context* compute_ctx    = NULL;
+    struct ggml_gallocr* compute_allocr = NULL;
+
+    std::map<struct ggml_tensor*, const void*> backend_tensor_data_map;
 
     ggml_type wtype        = GGML_TYPE_F32;
     ggml_backend_t backend = NULL;
 
-    virtual size_t calculate_mem_size() = 0;
-    virtual size_t get_num_tensors()    = 0;
-
-    bool alloc_params_buffer(ggml_backend_t backend_, ggml_type wtype_ = GGML_TYPE_F32) {
-        backend            = backend_;
-        wtype              = wtype_;
-        params_buffer_size = 4 * 1024 * 1024;  // 10 MB, for padding
-        params_buffer_size += calculate_mem_size();
-        size_t num_tensors = get_num_tensors();
-
-        LOG_DEBUG("%s params backend buffer size = % 6.2f MB (%i tensors)",
-                  name.c_str(), params_buffer_size / (1024.0 * 1024.0), num_tensors);
-
+    void alloc_params_ctx() {
         struct ggml_init_params params;
-        params.mem_size   = static_cast<size_t>(num_tensors * ggml_tensor_overhead()) + 1 * 1024 * 1024;
+        params.mem_size   = static_cast<size_t>(MAX_PARAMS_TENSOR_NUM * ggml_tensor_overhead());
         params.mem_buffer = NULL;
         params.no_alloc   = true;
-        // LOG_DEBUG("mem_size %u ", params.mem_size);
 
         params_ctx = ggml_init(params);
-        if (!params_ctx) {
-            LOG_ERROR("ggml_init() failed");
-            return false;
-        }
-
-        params_buffer = ggml_backend_alloc_buffer(backend, params_buffer_size);
-        return true;
+        GGML_ASSERT(params_ctx != NULL);
     }
 
-    void free_params_buffer() {
+    void free_params_ctx() {
         if (params_ctx != NULL) {
             ggml_free(params_ctx);
             params_ctx = NULL;
         }
+    }
 
+    void alloc_compute_ctx() {
+        struct ggml_init_params params;
+        params.mem_size   = static_cast<size_t>(ggml_tensor_overhead() * MAX_GRAPH_SIZE + ggml_graph_overhead());
+        params.mem_buffer = NULL;
+        params.no_alloc   = true;
+
+        compute_ctx = ggml_init(params);
+        GGML_ASSERT(compute_ctx != NULL);
+    }
+
+    void free_compute_ctx() {
+        if (compute_ctx != NULL) {
+            ggml_free(compute_ctx);
+            compute_ctx = NULL;
+        }
+    }
+
+    bool alloc_compute_buffer(get_graph_cb_t get_graph) {
+        if (compute_allocr != NULL) {
+            return true;
+        }
+        reset_compute_ctx();
+        struct ggml_cgraph* gf = get_graph();
+        backend_tensor_data_map.clear();
+        compute_allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
+
+        if (!ggml_gallocr_reserve(compute_allocr, gf)) {
+            // failed to allocate the compute buffer
+            LOG_ERROR("%s: failed to allocate the compute buffer\n", get_desc().c_str());
+            free_compute_buffer();
+            return false;
+        }
+
+        // compute the required memory
+        size_t compute_buffer_size = ggml_gallocr_get_buffer_size(compute_allocr, 0);
+        LOG_DEBUG("%s compute buffer size: %.2f MB", get_desc().c_str(), compute_buffer_size / 1024.0 / 1024.0);
+        return true;
+    }
+
+    void cpy_data_to_backend_tensor() {
+        for (auto& kv : backend_tensor_data_map) {
+            auto tensor = kv.first;
+            auto data   = kv.second;
+
+            ggml_backend_tensor_set(tensor, data, 0, ggml_nbytes(tensor));
+        }
+
+        backend_tensor_data_map.clear();
+    }
+
+public:
+    virtual size_t get_params_mem_size() = 0;
+    virtual size_t get_params_num()      = 0;
+    virtual std::string get_desc()       = 0;
+
+    GGMLModule(ggml_backend_t backend, ggml_type wtype = GGML_TYPE_F32)
+        : backend(backend), wtype(wtype) {
+        alloc_params_ctx();
+    }
+
+    virtual ~GGMLModule() {
+        free_params_buffer();
+        free_compute_buffer();
+        free_params_ctx();
+        free_compute_ctx();
+    }
+
+    void reset_compute_ctx() {
+        free_compute_ctx();
+        alloc_compute_ctx();
+    }
+
+    bool alloc_params_buffer() {
+        size_t num_tensors = get_params_num();
+        params_buffer      = ggml_backend_alloc_ctx_tensors(params_ctx, backend);
+        if (params_buffer == NULL) {
+            LOG_ERROR("%s alloc params backend buffer failed", get_desc().c_str());
+            return false;
+        }
+        size_t params_buffer_size = ggml_backend_buffer_get_size(params_buffer);
+        LOG_DEBUG("%s params backend buffer size = % 6.2f MB (%i tensors)",
+                  get_desc().c_str(), params_buffer_size / (1024.0 * 1024.0), num_tensors);
+        return true;
+    }
+
+    void free_params_buffer() {
         if (params_buffer != NULL) {
             ggml_backend_buffer_free(params_buffer);
             params_buffer = NULL;
         }
     }
 
-    ~GGMLModule() {
-        free_params_buffer();
-    }
-
-    void alloc_compute_buffer(get_graph_cb_t get_graph) {
-        if (compute_buffer_size == 0) {
-            // alignment required by the backend
-            compute_allocr = ggml_allocr_new_measure_from_backend(backend);
-
-            struct ggml_cgraph* gf = get_graph();
-
-            // compute the required memory
-            compute_buffer_size = ggml_allocr_alloc_graph(compute_allocr, gf) + 1024 * 1024;
-
-            // recreate the allocator with the required memory
-            ggml_allocr_free(compute_allocr);
-
-            LOG_DEBUG("%s compute buffer size: %.2f MB", name.c_str(), compute_buffer_size / 1024.0 / 1024.0);
+    void free_compute_buffer() {
+        if (compute_allocr != NULL) {
+            ggml_gallocr_free(compute_allocr);
+            compute_allocr = NULL;
         }
-
-        compute_buffer = ggml_backend_alloc_buffer(backend, compute_buffer_size);
-        compute_allocr = ggml_allocr_new_from_buffer(compute_buffer);
     }
 
-    void compute(get_graph_cb_t get_graph, int n_threads, struct ggml_tensor* output = NULL) {
-        ggml_allocr_reset(compute_allocr);
+    // do copy after alloc graph
+    void set_backend_tensor_data(struct ggml_tensor* tensor, const void* data) {
+        backend_tensor_data_map[tensor] = data;
+    }
 
+    struct ggml_tensor* to_backend(struct ggml_tensor* tensor) {
+        GGML_ASSERT(compute_ctx != NULL);
+        if (tensor == NULL) {
+            return NULL;
+        }
+        // it's performing a compute, check if backend isn't cpu
+        if (!ggml_backend_is_cpu(backend) && tensor->backend == GGML_BACKEND_CPU) {
+            // pass input tensors to gpu memory
+            auto backend_tensor = ggml_dup_tensor(compute_ctx, tensor);
+
+            set_backend_tensor_data(backend_tensor, tensor->data);
+            return backend_tensor;
+        } else {
+            return tensor;
+        }
+    }
+
+    void compute(get_graph_cb_t get_graph,
+                 int n_threads,
+                 bool free_compute_buffer_immediately = true,
+                 struct ggml_tensor** output          = NULL,
+                 struct ggml_context* output_ctx      = NULL) {
+        alloc_compute_buffer(get_graph);
+        reset_compute_ctx();
         struct ggml_cgraph* gf = get_graph();
 
-        ggml_allocr_alloc_graph(compute_allocr, gf);
+        GGML_ASSERT(ggml_gallocr_alloc_graph(compute_allocr, gf));
+
+        cpy_data_to_backend_tensor();
 
         if (ggml_backend_is_cpu(backend)) {
             ggml_backend_cpu_set_n_threads(backend, n_threads);
@@ -631,15 +887,368 @@ struct GGMLModule {
 #endif
 
         if (output != NULL) {
-            ggml_backend_tensor_get_and_sync(backend, gf->nodes[gf->n_nodes - 1], output->data, 0, ggml_nbytes(output));
+            auto result = gf->nodes[gf->n_nodes - 1];
+            if (*output == NULL && output_ctx != NULL) {
+                *output = ggml_dup_tensor(output_ctx, result);
+            }
+            if (*output != NULL) {
+                ggml_backend_tensor_get_and_sync(backend, result, (*output)->data, 0, ggml_nbytes(*output));
+            }
+        }
+
+        if (free_compute_buffer_immediately) {
+            free_compute_buffer();
+        }
+    }
+};
+
+class GGMLBlock {
+private:
+    static char temp_buffer[1024 * 1024 * 10];
+    ggml_context* get_temp_ctx() {
+        struct ggml_init_params params;
+        params.mem_size   = sizeof(temp_buffer);
+        params.mem_buffer = temp_buffer;
+        params.no_alloc   = true;
+
+        ggml_context* temp_ctx = ggml_init(params);
+        GGML_ASSERT(temp_ctx != NULL);
+        return temp_ctx;
+    }
+
+protected:
+    typedef std::unordered_map<std::string, struct ggml_tensor*> ParameterMap;
+    typedef std::unordered_map<std::string, std::shared_ptr<GGMLBlock>> GGMLBlockMap;
+    GGMLBlockMap blocks;
+    ParameterMap params;
+
+    void init_blocks(struct ggml_context* ctx, ggml_type wtype) {
+        for (auto& pair : blocks) {
+            auto& block = pair.second;
+
+            block->init(ctx, wtype);
         }
     }
 
-    void free_compute_buffer() {
-        ggml_allocr_free(compute_allocr);
-        ggml_backend_buffer_free(compute_buffer);
-        compute_allocr      = NULL;
-        compute_buffer_size = 0;
+    virtual void init_params(struct ggml_context* ctx, ggml_type wtype) {}
+
+public:
+    void init(struct ggml_context* ctx, ggml_type wtype) {
+        init_blocks(ctx, wtype);
+        init_params(ctx, wtype);
+    }
+
+    std::tuple<size_t, size_t> get_params_info(ggml_type wtype) {
+        ggml_context* temp_ctx = get_temp_ctx();
+        init(temp_ctx, wtype);
+        size_t num_tensors = get_params_num();
+        size_t mem_size    = get_params_mem_size();
+        return {num_tensors, mem_size};
+    }
+
+    size_t get_params_num() {
+        size_t num_tensors = params.size();
+        for (auto& pair : blocks) {
+            auto& block = pair.second;
+
+            num_tensors += block->get_params_num();
+        }
+        return num_tensors;
+    };
+
+    size_t get_params_mem_size() {
+        size_t mem_size = 0;
+        for (auto& pair : blocks) {
+            auto& block = pair.second;
+
+            mem_size += block->get_params_mem_size();
+        }
+
+        for (auto& pair : params) {
+            mem_size += ggml_nbytes(pair.second);
+        }
+
+        return mem_size;
+    }
+
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, std::string prefix = "") {
+        if (prefix.size() > 0) {
+            prefix = prefix + ".";
+        }
+        for (auto& pair : blocks) {
+            auto& block = pair.second;
+
+            block->get_param_tensors(tensors, prefix + pair.first);
+        }
+
+        for (auto& pair : params) {
+            struct ggml_tensor* param = pair.second;
+
+            tensors[prefix + pair.first] = pair.second;
+        }
+    }
+};
+
+class UnaryBlock : public GGMLBlock {
+public:
+    virtual struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) = 0;
+};
+
+class Linear : public UnaryBlock {
+protected:
+    int64_t in_features;
+    int64_t out_features;
+    bool bias;
+
+    void init_params(struct ggml_context* ctx, ggml_type wtype) {
+        params["weight"] = ggml_new_tensor_2d(ctx, wtype, in_features, out_features);
+        if (bias) {
+            params["bias"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_features);
+        }
+    }
+
+public:
+    Linear(int64_t in_features,
+           int64_t out_features,
+           bool bias = true)
+        : in_features(in_features),
+          out_features(out_features),
+          bias(bias) {}
+
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+        struct ggml_tensor* w = params["weight"];
+        struct ggml_tensor* b = NULL;
+        if (bias) {
+            b = params["bias"];
+        }
+        return ggml_nn_linear(ctx, x, w, b);
+    }
+};
+
+class Conv2d : public UnaryBlock {
+protected:
+    int64_t in_channels;
+    int64_t out_channels;
+    std::pair<int, int> kernel_size;
+    std::pair<int, int> stride;
+    std::pair<int, int> padding;
+    std::pair<int, int> dilation;
+    bool bias;
+
+    void init_params(struct ggml_context* ctx, ggml_type wtype) {
+        params["weight"] = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kernel_size.second, kernel_size.first, in_channels, out_channels);
+        if (bias) {
+            params["bias"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
+        }
+    }
+
+public:
+    Conv2d(int64_t in_channels,
+           int64_t out_channels,
+           std::pair<int, int> kernel_size,
+           std::pair<int, int> stride   = {1, 1},
+           std::pair<int, int> padding  = {0, 0},
+           std::pair<int, int> dilation = {1, 1},
+           bool bias                    = true)
+        : in_channels(in_channels),
+          out_channels(out_channels),
+          kernel_size(kernel_size),
+          stride(stride),
+          padding(padding),
+          dilation(dilation),
+          bias(bias) {}
+
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+        struct ggml_tensor* w = params["weight"];
+        struct ggml_tensor* b = NULL;
+        if (bias) {
+            b = params["bias"];
+        }
+        return ggml_nn_conv_2d(ctx, x, w, b, stride.second, stride.first, padding.second, padding.first, dilation.second, dilation.first);
+    }
+};
+
+class Conv3dnx1x1 : public UnaryBlock {
+protected:
+    int64_t in_channels;
+    int64_t out_channels;
+    int64_t kernel_size;
+    int64_t stride;
+    int64_t padding;
+    int64_t dilation;
+    bool bias;
+
+    void init_params(struct ggml_context* ctx, ggml_type wtype) {
+        params["weight"] = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, kernel_size, in_channels, out_channels);  // 5d => 4d
+        if (bias) {
+            params["bias"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
+        }
+    }
+
+public:
+    Conv3dnx1x1(int64_t in_channels,
+                int64_t out_channels,
+                int64_t kernel_size,
+                int64_t stride   = 1,
+                int64_t padding  = 0,
+                int64_t dilation = 1,
+                bool bias        = true)
+        : in_channels(in_channels),
+          out_channels(out_channels),
+          kernel_size(kernel_size),
+          stride(stride),
+          padding(padding),
+          dilation(dilation),
+          bias(bias) {}
+
+    // x: [N, IC, ID, IH*IW]
+    // result: [N, OC, OD, OH*OW]
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+        struct ggml_tensor* w = params["weight"];
+        struct ggml_tensor* b = NULL;
+        if (bias) {
+            b = params["bias"];
+        }
+        return ggml_nn_conv_3d_nx1x1(ctx, x, w, b, stride, padding, dilation);
+    }
+};
+
+class LayerNorm : public UnaryBlock {
+protected:
+    int64_t normalized_shape;
+    float eps;
+    bool elementwise_affine;
+    bool bias;
+
+    void init_params(struct ggml_context* ctx, ggml_type wtype) {
+        if (elementwise_affine) {
+            params["weight"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, normalized_shape);
+            if (bias) {
+                params["bias"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, normalized_shape);
+            }
+        }
+    }
+
+public:
+    LayerNorm(int64_t normalized_shape,
+              float eps               = 1e-05f,
+              bool elementwise_affine = true,
+              bool bias               = true)
+        : normalized_shape(normalized_shape),
+          eps(eps),
+          elementwise_affine(elementwise_affine),
+          bias(bias) {}
+
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+        struct ggml_tensor* w = NULL;
+        struct ggml_tensor* b = NULL;
+
+        if (elementwise_affine) {
+            w = params["weight"];
+            if (bias) {
+                b = params["bias"];
+            }
+        }
+        return ggml_nn_layer_norm(ctx, x, w, b, eps);
+    }
+};
+
+class GroupNorm : public GGMLBlock {
+protected:
+    int64_t num_groups;
+    int64_t num_channels;
+    float eps;
+    bool affine;
+
+    void init_params(struct ggml_context* ctx, ggml_type wtype) {
+        if (affine) {
+            params["weight"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, num_channels);
+            params["bias"]   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, num_channels);
+        }
+    }
+
+public:
+    GroupNorm(int64_t num_groups,
+              int64_t num_channels,
+              float eps   = 1e-05f,
+              bool affine = true)
+        : num_groups(num_groups),
+          num_channels(num_channels),
+          eps(eps),
+          affine(affine) {}
+
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+        struct ggml_tensor* w = NULL;
+        struct ggml_tensor* b = NULL;
+        if (affine) {
+            w = params["weight"];
+            b = params["bias"];
+        }
+        return ggml_nn_group_norm(ctx, x, w, b, num_groups);
+    }
+};
+
+class GroupNorm32 : public GroupNorm {
+public:
+    GroupNorm32(int64_t num_channels)
+        : GroupNorm(32, num_channels, 1e-06f) {}
+};
+
+class MultiheadAttention : public GGMLBlock {
+protected:
+    int64_t embed_dim;
+    int64_t n_head;
+    bool bias;
+    bool mask;
+
+public:
+    MultiheadAttention(int64_t embed_dim,
+                       int64_t n_head,
+                       bool bias = true)
+        : embed_dim(embed_dim),
+          n_head(n_head),
+          bias(bias) {
+        blocks["q_proj"]   = std::shared_ptr<GGMLBlock>(new Linear(embed_dim, embed_dim, bias));
+        blocks["k_proj"]   = std::shared_ptr<GGMLBlock>(new Linear(embed_dim, embed_dim, bias));
+        blocks["v_proj"]   = std::shared_ptr<GGMLBlock>(new Linear(embed_dim, embed_dim, bias));
+        blocks["out_proj"] = std::shared_ptr<GGMLBlock>(new Linear(embed_dim, embed_dim, bias));
+    }
+
+    // x: [N, n_token, embed_dim]
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, bool mask = false) {
+        auto q_proj   = std::dynamic_pointer_cast<Linear>(blocks["q_proj"]);
+        auto k_proj   = std::dynamic_pointer_cast<Linear>(blocks["k_proj"]);
+        auto v_proj   = std::dynamic_pointer_cast<Linear>(blocks["v_proj"]);
+        auto out_proj = std::dynamic_pointer_cast<Linear>(blocks["out_proj"]);
+
+        int64_t N       = x->ne[2];
+        int64_t n_token = x->ne[1];
+        int64_t d_head  = embed_dim / n_head;
+
+        struct ggml_tensor* q = q_proj->forward(ctx, x);
+        q                     = ggml_reshape_4d(ctx, q, d_head, n_head, n_token, N);   // [N, n_token, n_head, d_head]
+        q                     = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3));      // [N, n_head, n_token, d_head]
+        q                     = ggml_reshape_3d(ctx, q, d_head, n_token, n_head * N);  // [N * n_head, n_token, d_head]
+
+        struct ggml_tensor* k = k_proj->forward(ctx, x);
+        k                     = ggml_reshape_4d(ctx, k, d_head, n_head, n_token, N);   // [N, n_token, n_head, d_head]
+        k                     = ggml_cont(ctx, ggml_permute(ctx, k, 0, 2, 1, 3));      // [N, n_head, n_token, d_head]
+        k                     = ggml_reshape_3d(ctx, k, d_head, n_token, n_head * N);  // [N * n_head, n_token, d_head]
+
+        struct ggml_tensor* v = v_proj->forward(ctx, x);
+        v                     = ggml_reshape_4d(ctx, v, d_head, n_head, n_token, N);   // [N, n_token, n_head, d_head]
+        v                     = ggml_cont(ctx, ggml_permute(ctx, v, 1, 2, 0, 3));      // [N, n_head, d_head, n_token]
+        v                     = ggml_reshape_3d(ctx, v, n_token, d_head, n_head * N);  // [N * n_head, d_head, n_token]
+
+        struct ggml_tensor* kqv = ggml_nn_attention(ctx, q, k, v, mask);  // [N * n_head, n_token, d_head]
+
+        kqv = ggml_reshape_4d(ctx, kqv, d_head, n_token, n_head, N);
+        kqv = ggml_cont(ctx, ggml_permute(ctx, kqv, 0, 2, 1, 3));  // [N, n_token, n_head, d_head]
+
+        x = ggml_reshape_3d(ctx, kqv, d_head * n_head, n_token, N);  // [N * n_token, d_head * n_head]
+
+        x = out_proj->forward(ctx, x);
+        return x;
     }
 };
 

lora.hpp

@@ -12,26 +12,28 @@ struct LoraModel : public GGMLModule {
     ModelLoader model_loader;
     bool load_failed = false;
 
-    LoraModel(const std::string file_path = "")
-        : file_path(file_path) {
-        name = "lora";
+    LoraModel(ggml_backend_t backend,
+              ggml_type wtype,
+              const std::string file_path = "")
+        : file_path(file_path), GGMLModule(backend, wtype) {
         if (!model_loader.init_from_file(file_path)) {
             load_failed = true;
         }
     }
 
-    size_t get_num_tensors() {
+    std::string get_desc() {
+        return "lora";
+    }
+
+    size_t get_params_num() {
         return LORA_GRAPH_SIZE;
     }
 
-    size_t calculate_mem_size() {
-        return model_loader.cal_mem_size(NULL);
+    size_t get_params_mem_size() {
+        return model_loader.get_params_mem_size(NULL);
     }
 
-    bool load_from_file(ggml_backend_t backend) {
-        if (!alloc_params_buffer(backend)) {
-            return false;
-        }
+    bool load_from_file() {
         LOG_INFO("loading LoRA from '%s'", file_path.c_str());
 
         if (load_failed) {
@@ -39,42 +41,35 @@ struct LoraModel : public GGMLModule {
             return false;
         }
 
-        ggml_allocr* alloc = ggml_allocr_new_from_buffer(params_buffer);
-
+        bool dry_run          = true;
         auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
             const std::string& name = tensor_storage.name;
 
-            struct ggml_tensor* real = ggml_new_tensor(params_ctx, tensor_storage.type, tensor_storage.n_dims, tensor_storage.ne);
-            ggml_allocr_alloc(alloc, real);
-
-            *dst_tensor = real;
-
-            lora_tensors[name] = real;
+            if (dry_run) {
+                struct ggml_tensor* real = ggml_new_tensor(params_ctx,
+                                                           tensor_storage.type,
+                                                           tensor_storage.n_dims,
+                                                           tensor_storage.ne);
+                lora_tensors[name]       = real;
+            } else {
+                auto real   = lora_tensors[name];
+                *dst_tensor = real;
+            }
             return true;
         };
 
+        model_loader.load_tensors(on_new_tensor_cb, backend);
+        alloc_params_buffer();
+
+        dry_run = false;
         model_loader.load_tensors(on_new_tensor_cb, backend);
 
         LOG_DEBUG("finished loaded lora");
-        ggml_allocr_free(alloc);
         return true;
     }
 
     struct ggml_cgraph* build_graph(std::map<std::string, struct ggml_tensor*> model_tensors) {
-        // make a graph to compute all lora, expected lora and models tensors are in the same backend
-        // since we are using ggml-alloc, this buffer only needs enough space to hold the ggml_tensor and ggml_cgraph structs, but not the tensor data
-        static size_t buf_size = ggml_tensor_overhead() * LORA_GRAPH_SIZE + ggml_graph_overhead();
-        static std::vector<uint8_t> buf(buf_size);
-
-        struct ggml_init_params params = {
-            /*.mem_size   =*/buf_size,
-            /*.mem_buffer =*/buf.data(),
-            /*.no_alloc   =*/true,  // the tensors will be allocated later by ggml_allocr_alloc_graph()
-        };
-        // LOG_DEBUG("mem_size %u ", params.mem_size);
-
-        struct ggml_context* ctx0 = ggml_init(params);
-        struct ggml_cgraph* gf    = ggml_new_graph_custom(ctx0, LORA_GRAPH_SIZE, false);
+        struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, LORA_GRAPH_SIZE, false);
 
         std::set<std::string> applied_lora_tensors;
         for (auto it : model_tensors) {
@@ -125,27 +120,27 @@ struct LoraModel : public GGMLModule {
 
             // flat lora tensors to multiply it
             int64_t lora_up_rows   = lora_up->ne[ggml_n_dims(lora_up) - 1];
-            lora_up                = ggml_reshape_2d(ctx0, lora_up, ggml_nelements(lora_up) / lora_up_rows, lora_up_rows);
+            lora_up                = ggml_reshape_2d(compute_ctx, lora_up, ggml_nelements(lora_up) / lora_up_rows, lora_up_rows);
             int64_t lora_down_rows = lora_down->ne[ggml_n_dims(lora_down) - 1];
-            lora_down              = ggml_reshape_2d(ctx0, lora_down, ggml_nelements(lora_down) / lora_down_rows, lora_down_rows);
+            lora_down              = ggml_reshape_2d(compute_ctx, lora_down, ggml_nelements(lora_down) / lora_down_rows, lora_down_rows);
 
             // ggml_mul_mat requires tensor b transposed
-            lora_down                  = ggml_cont(ctx0, ggml_transpose(ctx0, lora_down));
-            struct ggml_tensor* updown = ggml_mul_mat(ctx0, lora_up, lora_down);
-            updown                     = ggml_cont(ctx0, ggml_transpose(ctx0, updown));
-            updown                     = ggml_reshape(ctx0, updown, weight);
+            lora_down                  = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, lora_down));
+            struct ggml_tensor* updown = ggml_mul_mat(compute_ctx, lora_up, lora_down);
+            updown                     = ggml_cont(compute_ctx, ggml_transpose(compute_ctx, updown));
+            updown                     = ggml_reshape(compute_ctx, updown, weight);
             GGML_ASSERT(ggml_nelements(updown) == ggml_nelements(weight));
-            updown = ggml_scale_inplace(ctx0, updown, scale_value);
+            updown = ggml_scale_inplace(compute_ctx, updown, scale_value);
             ggml_tensor* final_weight;
             // if (weight->type != GGML_TYPE_F32 && weight->type != GGML_TYPE_F16) {
-            //     final_weight = ggml_new_tensor(ctx0, GGML_TYPE_F32, weight->n_dims, weight->ne);
-            //     final_weight = ggml_cpy_inplace(ctx0, weight, final_weight);
-            //     final_weight = ggml_add_inplace(ctx0, final_weight, updown);
-            //     final_weight = ggml_cpy_inplace(ctx0, final_weight, weight);
+            //     final_weight = ggml_new_tensor(compute_ctx, GGML_TYPE_F32, weight->n_dims, weight->ne);
+            //     final_weight = ggml_cpy_inplace(compute_ctx, weight, final_weight);
+            //     final_weight = ggml_add_inplace(compute_ctx, final_weight, updown);
+            //     final_weight = ggml_cpy_inplace(compute_ctx, final_weight, weight);
             // } else {
-            //     final_weight = ggml_add_inplace(ctx0, weight, updown);
+            //     final_weight = ggml_add_inplace(compute_ctx, weight, updown);
             // }
-            final_weight = ggml_add_inplace(ctx0, weight, updown);  // apply directly
+            final_weight = ggml_add_inplace(compute_ctx, weight, updown);  // apply directly
             ggml_build_forward_expand(gf, final_weight);
         }
 
@@ -158,20 +153,11 @@ struct LoraModel : public GGMLModule {
         return gf;
     }
 
-    void alloc_compute_buffer(std::map<std::string, struct ggml_tensor*> model_tensors) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(model_tensors);
-        };
-        GGMLModule::alloc_compute_buffer(get_graph);
-    }
-
     void apply(std::map<std::string, struct ggml_tensor*> model_tensors, int n_threads) {
-        alloc_compute_buffer(model_tensors);
-
         auto get_graph = [&]() -> struct ggml_cgraph* {
             return build_graph(model_tensors);
         };
-        GGMLModule::compute(get_graph, n_threads);
+        GGMLModule::compute(get_graph, n_threads, true);
     }
 };
 

model.cpp

@@ -108,6 +108,14 @@ std::unordered_map<std::string, std::string> open_clip_to_hf_clip_model = {
     {"model.positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"},
     {"model.token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"},
     {"model.text_projection", "transformer.text_model.text_projection"},
+    {"model.visual.class_embedding", "transformer.visual_model.embeddings.class_embedding"},
+    {"model.visual.conv1.weight", "transformer.visual_model.embeddings.patch_embedding.weight"},
+    {"model.visual.ln_post.bias", "transformer.visual_model.post_layernorm.bias"},
+    {"model.visual.ln_post.weight", "transformer.visual_model.post_layernorm.weight"},
+    {"model.visual.ln_pre.bias", "transformer.visual_model.pre_layernorm.bias"},
+    {"model.visual.ln_pre.weight", "transformer.visual_model.pre_layernorm.weight"},
+    {"model.visual.positional_embedding", "transformer.visual_model.embeddings.position_embedding.weight"},
+    {"model.visual.proj", "transformer.visual_model.visual_projection"},
 };
 
 std::unordered_map<std::string, std::string> open_clip_to_hk_clip_resblock = {
@@ -137,7 +145,10 @@ std::unordered_map<std::string, std::string> vae_decoder_name_map = {
 std::string convert_open_clip_to_hf_clip(const std::string& name) {
     std::string new_name = name;
     std::string prefix;
-    if (starts_with(new_name, "conditioner.embedders.0.")) {
+    if (starts_with(new_name, "conditioner.embedders.0.open_clip.")) {
+        prefix   = "cond_stage_model.";
+        new_name = new_name.substr(strlen("conditioner.embedders.0.open_clip."));
+    } else if (starts_with(new_name, "conditioner.embedders.0.")) {
         prefix   = "cond_stage_model.";
         new_name = new_name.substr(strlen("conditioner.embedders.0."));
     } else if (starts_with(new_name, "conditioner.embedders.1.")) {
@@ -149,25 +160,35 @@ std::string convert_open_clip_to_hf_clip(const std::string& name) {
     } else {
         return new_name;
     }
-    std::string open_clip_resblock_prefix = "model.transformer.resblocks.";
-    std::string hf_clip_resblock_prefix   = "transformer.text_model.encoder.layers.";
 
     if (open_clip_to_hf_clip_model.find(new_name) != open_clip_to_hf_clip_model.end()) {
         new_name = open_clip_to_hf_clip_model[new_name];
     }
 
-    if (new_name.find(open_clip_resblock_prefix) == 0) {
-        std::string remain = new_name.substr(open_clip_resblock_prefix.length());
-        std::string idx    = remain.substr(0, remain.find("."));
-        std::string suffix = remain.substr(idx.length() + 1);
+    std::string open_clip_resblock_prefix = "model.transformer.resblocks.";
+    std::string hf_clip_resblock_prefix   = "transformer.text_model.encoder.layers.";
 
-        if (suffix == "attn.in_proj_weight" || suffix == "attn.in_proj_bias") {
-            new_name = hf_clip_resblock_prefix + idx + "." + suffix;
-        } else if (open_clip_to_hk_clip_resblock.find(suffix) != open_clip_to_hk_clip_resblock.end()) {
-            std::string new_suffix = open_clip_to_hk_clip_resblock[suffix];
-            new_name               = hf_clip_resblock_prefix + idx + "." + new_suffix;
+    auto replace_suffix = [&]() {
+        if (new_name.find(open_clip_resblock_prefix) == 0) {
+            std::string remain = new_name.substr(open_clip_resblock_prefix.length());
+            std::string idx    = remain.substr(0, remain.find("."));
+            std::string suffix = remain.substr(idx.length() + 1);
+
+            if (suffix == "attn.in_proj_weight" || suffix == "attn.in_proj_bias") {
+                new_name = hf_clip_resblock_prefix + idx + "." + suffix;
+            } else if (open_clip_to_hk_clip_resblock.find(suffix) != open_clip_to_hk_clip_resblock.end()) {
+                std::string new_suffix = open_clip_to_hk_clip_resblock[suffix];
+                new_name               = hf_clip_resblock_prefix + idx + "." + new_suffix;
+            }
         }
-    }
+    };
+
+    replace_suffix();
+
+    open_clip_resblock_prefix = "model.visual.transformer.resblocks.";
+    hf_clip_resblock_prefix   = "transformer.visual_model.encoder.layers.";
+
+    replace_suffix();
 
     return prefix + new_name;
 }
@@ -375,7 +396,7 @@ std::string convert_tensor_name(const std::string& name) {
         new_name = convert_open_clip_to_hf_clip(name);
     } else if (starts_with(name, "first_stage_model.decoder")) {
         new_name = convert_vae_decoder_name(name);
-    } else if (starts_with(name, "control_model.")) { // for controlnet pth models
+    } else if (starts_with(name, "control_model.")) {  // for controlnet pth models
         size_t pos = name.find('.');
         if (pos != std::string::npos) {
             new_name = name.substr(pos + 1);
@@ -437,7 +458,7 @@ void preprocess_tensor(TensorStorage tensor_storage,
 
     tensor_storage.name = new_name;
 
-    if (new_name.find("transformer.text_model.encoder.layers.") != std::string::npos &&
+    if (new_name.find("cond_stage_model") != std::string::npos &&
         ends_with(new_name, "attn.in_proj_weight")) {
         size_t prefix_size = new_name.find("attn.in_proj_weight");
         std::string prefix = new_name.substr(0, prefix_size);
@@ -449,7 +470,7 @@ void preprocess_tensor(TensorStorage tensor_storage,
 
         processed_tensor_storages.insert(processed_tensor_storages.end(), chunks.begin(), chunks.end());
 
-    } else if (new_name.find("transformer.text_model.encoder.layers.") != std::string::npos &&
+    } else if (new_name.find("cond_stage_model") != std::string::npos &&
                ends_with(new_name, "attn.in_proj_bias")) {
         size_t prefix_size = new_name.find("attn.in_proj_bias");
         std::string prefix = new_name.substr(0, prefix_size);
@@ -477,7 +498,13 @@ void bf16_to_f32_vec(uint16_t* src, float* dst, int64_t n) {
     }
 }
 
-void convert_tensor(void* src, ggml_type src_type, void* dst, ggml_type dst_type, int n) {
+void convert_tensor(void* src,
+                    ggml_type src_type,
+                    void* dst,
+                    ggml_type dst_type,
+                    int nrows,
+                    int n_per_row) {
+    int n = nrows * n_per_row;
     if (src_type == dst_type) {
         size_t nbytes = n * ggml_type_size(src_type) / ggml_blck_size(src_type);
         memcpy(((char*)dst), ((char*)src), nbytes);
@@ -486,7 +513,9 @@ void convert_tensor(void* src, ggml_type src_type, void* dst, ggml_type dst_type
             ggml_fp32_to_fp16_row((float*)src, (ggml_fp16_t*)dst, n);
         } else {
             int64_t hist[16];
-            ggml_quantize_chunk(dst_type, (float*)src, dst, 0, n, hist);
+            std::vector<float> imatrix(n_per_row, 1.0f);  // dummy importance matrix
+            const float* im = imatrix.data();
+            ggml_quantize_chunk(dst_type, (float*)src, dst, 0, nrows, n_per_row, hist, im);
         }
     } else if (dst_type == GGML_TYPE_F32) {
         if (src_type == GGML_TYPE_F16) {
@@ -515,7 +544,9 @@ void convert_tensor(void* src, ggml_type src_type, void* dst, ggml_type dst_type
             ggml_fp32_to_fp16_row((float*)src_data_f32, (ggml_fp16_t*)dst, n);
         } else {
             int64_t hist[16];
-            ggml_quantize_chunk(dst_type, (float*)src_data_f32, dst, 0, n, hist);
+            std::vector<float> imatrix(n_per_row, 1.0f);  // dummy importance matrix
+            const float* im = imatrix.data();
+            ggml_quantize_chunk(dst_type, (float*)src_data_f32, dst, 0, nrows, n_per_row, hist, im);
         }
     }
 }
@@ -778,17 +809,26 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
             return false;
         }
 
-        if (shape.size() > 4) {
+        if (shape.size() > SD_MAX_DIMS) {
             LOG_ERROR("invalid tensor '%s'", name.c_str());
             return false;
         }
 
-        int n_dims    = (int)shape.size();
-        int64_t ne[4] = {1, 1, 1, 1};
+        int n_dims              = (int)shape.size();
+        int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
         for (int i = 0; i < n_dims; i++) {
             ne[i] = shape[i].get<int64_t>();
         }
 
+        if (n_dims == 5) {
+            if (ne[3] == 1 && ne[4] == 1) {
+                n_dims = 4;
+            } else {
+                LOG_ERROR("invalid tensor '%s'", name.c_str());
+                return false;
+            }
+        }
+
         TensorStorage tensor_storage(prefix + name, type, ne, n_dims, file_index, ST_HEADER_SIZE_LEN + header_size_ + begin);
 
         tensor_storage.reverse_ne();
@@ -803,6 +843,8 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
         }
 
         tensor_storages.push_back(tensor_storage);
+
+        // LOG_DEBUG("%s %s", tensor_storage.to_string().c_str(), dtype.c_str());
     }
 
     return true;
@@ -946,7 +988,7 @@ struct PickleTensorReader {
                 phase = READ_NAME;
             }
         } else if (phase == READ_DIMENS) {
-            if (tensor_storage.n_dims + 1 > 4) {  // too many dimens
+            if (tensor_storage.n_dims + 1 > SD_MAX_DIMS) {  // too many dimens
                 phase                 = READ_NAME;
                 tensor_storage.n_dims = 0;
             }
@@ -1181,7 +1223,6 @@ bool ModelLoader::init_from_ckpt_file(const std::string& file_path, const std::s
 }
 
 SDVersion ModelLoader::get_sd_version() {
-    // return VERSION_1_x;
     TensorStorage token_embedding_weight;
     for (auto& tensor_storage : tensor_storages) {
         if (tensor_storage.name.find("conditioner.embedders.1") != std::string::npos) {
@@ -1190,6 +1231,10 @@ SDVersion ModelLoader::get_sd_version() {
         if (tensor_storage.name.find("cond_stage_model.1") != std::string::npos) {
             return VERSION_XL;
         }
+        if (tensor_storage.name.find("model.diffusion_model.input_blocks.8.0.time_mixer.mix_factor") != std::string::npos) {
+            return VERSION_SVD;
+        }
+
         if (tensor_storage.name == "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight" ||
             tensor_storage.name == "cond_stage_model.model.token_embedding.weight" ||
             tensor_storage.name == "text_model.embeddings.token_embedding.weight" ||
@@ -1317,7 +1362,6 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
             if (tensor_storage.file_index != file_index) {
                 continue;
             }
-            // LOG_DEBUG("%s", tensor_storage.name.c_str());
 
             ggml_tensor* dst_tensor = NULL;
 
@@ -1353,7 +1397,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                     }
 
                     convert_tensor((void*)read_buffer.data(), tensor_storage.type, dst_tensor->data,
-                                   dst_tensor->type, (int)tensor_storage.nelements());
+                                   dst_tensor->type, (int)tensor_storage.nelements() / (int)tensor_storage.ne[0], (int)tensor_storage.ne[0]);
                 }
             } else {
                 read_buffer.resize(tensor_storage.nbytes());
@@ -1372,7 +1416,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                     convert_buffer.resize(ggml_nbytes(dst_tensor));
                     convert_tensor((void*)read_buffer.data(), tensor_storage.type,
                                    (void*)convert_buffer.data(), dst_tensor->type,
-                                   (int)tensor_storage.nelements());
+                                   (int)tensor_storage.nelements() / (int)tensor_storage.ne[0], (int)tensor_storage.ne[0]);
                     ggml_backend_tensor_set(dst_tensor, convert_buffer.data(), 0, ggml_nbytes(dst_tensor));
                 }
             }
@@ -1395,15 +1439,19 @@ bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tenso
     std::set<std::string> tensor_names_in_file;
     auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
         const std::string& name = tensor_storage.name;
+        // LOG_DEBUG("%s", tensor_storage.to_string().c_str());
         tensor_names_in_file.insert(name);
 
         struct ggml_tensor* real;
         if (tensors.find(name) != tensors.end()) {
             real = tensors[name];
         } else {
-            if (ignore_tensors.find(name) == ignore_tensors.end()) {
-                LOG_WARN("unknown tensor '%s' in model file", name.c_str());
+            for (auto& ignore_tensor : ignore_tensors) {
+                if (starts_with(name, ignore_tensor)) {
+                    return true;
+                }
             }
+            LOG_INFO("unknown tensor '%s' in model file", tensor_storage.to_string().c_str());
             return true;
         }
 
@@ -1462,7 +1510,7 @@ bool ModelLoader::save_to_gguf_file(const std::string& file_path, ggml_type type
     auto backend    = ggml_backend_cpu_init();
     size_t mem_size = 1 * 1024 * 1024;  // for padding
     mem_size += tensor_storages.size() * ggml_tensor_overhead();
-    mem_size += cal_mem_size(backend, type);
+    mem_size += get_params_mem_size(backend, type);
     LOG_INFO("model tensors mem size: %.2fMB", mem_size / 1024.f / 1024.f);
     ggml_context* ggml_ctx = ggml_init({mem_size, NULL, false});
 
@@ -1512,7 +1560,7 @@ bool ModelLoader::save_to_gguf_file(const std::string& file_path, ggml_type type
     return success;
 }
 
-int64_t ModelLoader::cal_mem_size(ggml_backend_t backend, ggml_type type) {
+int64_t ModelLoader::get_params_mem_size(ggml_backend_t backend, ggml_type type) {
     size_t alignment = 128;
     if (backend != NULL) {
         alignment = ggml_backend_get_alignment(backend);

model.h

@@ -5,6 +5,7 @@
 #include <map>
 #include <memory>
 #include <set>
+#include <sstream>
 #include <string>
 #include <vector>
 
@@ -13,19 +14,22 @@
 #include "json.hpp"
 #include "zip.h"
 
+#define SD_MAX_DIMS 5
+
 enum SDVersion {
     VERSION_1_x,
     VERSION_2_x,
     VERSION_XL,
+    VERSION_SVD,
     VERSION_COUNT,
 };
 
 struct TensorStorage {
     std::string name;
-    ggml_type type = GGML_TYPE_F32;
-    bool is_bf16   = false;
-    int64_t ne[4]  = {1, 1, 1, 1};
-    int n_dims     = 0;
+    ggml_type type          = GGML_TYPE_F32;
+    bool is_bf16            = false;
+    int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
+    int n_dims              = 0;
 
     size_t file_index = 0;
     int index_in_zip  = -1;  // >= means stored in a zip file
@@ -41,7 +45,11 @@ struct TensorStorage {
     }
 
     int64_t nelements() const {
-        return ne[0] * ne[1] * ne[2] * ne[3];
+        int64_t n = 1;
+        for (int i = 0; i < SD_MAX_DIMS; i++) {
+            n *= ne[i];
+        }
+        return n;
     }
 
     int64_t nbytes() const {
@@ -69,6 +77,7 @@ struct TensorStorage {
     std::vector<TensorStorage> chunk(size_t n) {
         std::vector<TensorStorage> chunks;
         size_t chunk_size = nbytes_to_read() / n;
+        // printf("%d/%d\n", chunk_size, nbytes_to_read());
         reverse_ne();
         for (int i = 0; i < n; i++) {
             TensorStorage chunk_i = *this;
@@ -82,7 +91,7 @@ struct TensorStorage {
     }
 
     void reverse_ne() {
-        int64_t new_ne[4] = {1, 1, 1, 1};
+        int64_t new_ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
         for (int i = 0; i < n_dims; i++) {
             new_ne[i] = ne[n_dims - 1 - i];
         }
@@ -90,6 +99,24 @@ struct TensorStorage {
             ne[i] = new_ne[i];
         }
     }
+
+    std::string to_string() const {
+        std::stringstream ss;
+        const char* type_name = ggml_type_name(type);
+        if (is_bf16) {
+            type_name = "bf16";
+        }
+        ss << name << " | " << type_name << " | ";
+        ss << n_dims << " [";
+        for (int i = 0; i < SD_MAX_DIMS; i++) {
+            ss << ne[i];
+            if (i != SD_MAX_DIMS - 1) {
+                ss << ", ";
+            }
+        }
+        ss << "]";
+        return ss.str();
+    }
 };
 
 typedef std::function<bool(const TensorStorage&, ggml_tensor**)> on_new_tensor_cb_t;
@@ -121,7 +148,7 @@ public:
                       ggml_backend_t backend,
                       std::set<std::string> ignore_tensors = {});
     bool save_to_gguf_file(const std::string& file_path, ggml_type type);
-    int64_t cal_mem_size(ggml_backend_t backend, ggml_type type = GGML_TYPE_COUNT);
+    int64_t get_params_mem_size(ggml_backend_t backend, ggml_type type = GGML_TYPE_COUNT);
     ~ModelLoader() = default;
 };
 #endif  // __MODEL_H__

preprocessing.hpp

@@ -2,17 +2,17 @@
 #define __PREPROCESSING_HPP__
 
 #include "ggml_extend.hpp"
-#define M_PI_       3.14159265358979323846
+#define M_PI_ 3.14159265358979323846
 
 void convolve(struct ggml_tensor* input, struct ggml_tensor* output, struct ggml_tensor* kernel, int padding) {
     struct ggml_init_params params;
-    params.mem_size = 20 * 1024 * 1024;  // 10
-    params.mem_buffer = NULL;
-    params.no_alloc   = false;
-    struct ggml_context* ctx0 = ggml_init(params);
+    params.mem_size                 = 20 * 1024 * 1024;  // 10
+    params.mem_buffer               = NULL;
+    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);
-    ggml_fp32_to_fp16_row((float*)kernel->data, (ggml_fp16_t*) kernel_fp16->data, ggml_nelements(kernel));
-    ggml_tensor* h = ggml_conv_2d(ctx0, kernel_fp16, input, 1, 1, padding, padding, 1, 1);
+    ggml_fp32_to_fp16_row((float*)kernel->data, (ggml_fp16_t*)kernel_fp16->data, ggml_nelements(kernel));
+    ggml_tensor* h  = ggml_conv_2d(ctx0, kernel_fp16, input, 1, 1, padding, padding, 1, 1);
     ggml_cgraph* gf = ggml_new_graph(ctx0);
     ggml_build_forward_expand(gf, ggml_cpy(ctx0, h, output));
     ggml_graph_compute_with_ctx(ctx0, gf, 1);
@@ -20,14 +20,14 @@ void convolve(struct ggml_tensor* input, struct ggml_tensor* output, struct ggml
 }
 
 void gaussian_kernel(struct ggml_tensor* kernel) {
-    int ks_mid = kernel->ne[0] / 2;
-    float sigma = 1.4f;
+    int ks_mid   = kernel->ne[0] / 2;
+    float sigma  = 1.4f;
     float normal = 1.f / (2.0f * M_PI_ * powf(sigma, 2.0f));
-    for(int y = 0; y < kernel->ne[0]; y++) {
+    for (int y = 0; y < kernel->ne[0]; y++) {
         float gx = -ks_mid + y;
-        for(int x = 0; x < kernel->ne[1]; x++) {
+        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;
+            float k_ = expf(-((gx * gx + gy * gy) / (2.0f * powf(sigma, 2.0f)))) * normal;
             ggml_tensor_set_f32(kernel, k_, x, y);
         }
     }
@@ -36,9 +36,9 @@ 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_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 gray = 0.2989f * r + 0.5870f * g + 0.1140f * b;
             ggml_tensor_set_f32(grayscale, gray, ix, iy);
         }
@@ -47,19 +47,19 @@ void grayscale(struct ggml_tensor* rgb_img, struct ggml_tensor* grayscale) {
 
 void prop_hypot(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tensor* h) {
     int n_elements = ggml_nelements(h);
-    float* dx = (float*)x->data;
-    float* dy = (float*)y->data;
-    float* dh = (float*)h->data;
-    for (int i = 0; i <n_elements; i++) {
+    float* dx      = (float*)x->data;
+    float* dy      = (float*)y->data;
+    float* dh      = (float*)h->data;
+    for (int i = 0; i < n_elements; i++) {
         dh[i] = sqrtf(dx[i] * dx[i] + dy[i] * dy[i]);
     }
 }
 
 void prop_arctan2(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tensor* h) {
     int n_elements = ggml_nelements(h);
-    float* dx = (float*)x->data;
-    float* dy = (float*)y->data;
-    float* dh = (float*)h->data;
+    float* dx      = (float*)x->data;
+    float* dy      = (float*)y->data;
+    float* dh      = (float*)h->data;
     for (int i = 0; i < n_elements; i++) {
         dh[i] = atan2f(dy[i], dx[i]);
     }
@@ -67,13 +67,13 @@ void prop_arctan2(struct ggml_tensor* x, struct ggml_tensor* y, struct ggml_tens
 
 void normalize_tensor(struct ggml_tensor* g) {
     int n_elements = ggml_nelements(g);
-    float* dg = (float*)g->data;
-    float max = -INFINITY;
-    for (int i = 0; i <n_elements; i++) {
+    float* dg      = (float*)g->data;
+    float max      = -INFINITY;
+    for (int i = 0; i < n_elements; i++) {
         max = dg[i] > max ? dg[i] : max;
     }
     max = 1.0f / max;
-    for (int i = 0; i <n_elements; i++) {
+    for (int i = 0; i < n_elements; i++) {
         dg[i] *= max;
     }
 }
@@ -82,12 +82,12 @@ void non_max_supression(struct ggml_tensor* result, struct ggml_tensor* G, struc
     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_;
-            angle = angle < 0.0f ? angle += 180.0f : angle;
-            float q = 1.0f;
-            float r = 1.0f;
+            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)){
+            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);
             }
@@ -117,27 +117,27 @@ void non_max_supression(struct ggml_tensor* result, struct ggml_tensor* G, struc
     }
 }
 
-void threshold_hystersis(struct ggml_tensor* img, float highThreshold, float lowThreshold, float weak, float strong) {
+void threshold_hystersis(struct ggml_tensor* img, float high_threshold, float low_threshold, float weak, float strong) {
     int n_elements = ggml_nelements(img);
-    float* imd = (float*)img->data;
-    float max = -INFINITY;
+    float* imd     = (float*)img->data;
+    float max      = -INFINITY;
     for (int i = 0; i < n_elements; i++) {
         max = imd[i] > max ? imd[i] : max;
     }
-    float ht = max * highThreshold;
-    float lt = ht * lowThreshold;
+    float ht = max * high_threshold;
+    float lt = ht * low_threshold;
     for (int i = 0; i < n_elements; i++) {
         float img_v = imd[i];
-        if(img_v >= ht) { // strong pixel
+        if (img_v >= ht) {  // strong pixel
             imd[i] = strong;
-        } else if(img_v <= ht && img_v >= lt) { // strong pixel
+        } else if (img_v <= ht && img_v >= lt) {  // strong pixel
             imd[i] = weak;
         }
     }
 
     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) {
+            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);
             } else {
                 ggml_tensor_set_f32(img, 0.0f, ix, iy);
@@ -149,8 +149,8 @@ void threshold_hystersis(struct ggml_tensor* img, float highThreshold, float low
     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);
-            if(imd_v == weak) {
-                if(ggml_tensor_get_f32(img, ix + 1, iy - 1) == strong || ggml_tensor_get_f32(img, ix + 1, iy) == strong ||
+            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);
@@ -162,11 +162,11 @@ void threshold_hystersis(struct ggml_tensor* img, float highThreshold, float low
     }
 }
 
-uint8_t* preprocess_canny(uint8_t* img, int width, int height, float highThreshold = 0.08f, float lowThreshold = 0.08f, float weak = 0.8f, float strong = 1.0f, bool inverse = false) {
+uint8_t* preprocess_canny(uint8_t* img, int width, int height, float high_threshold, float low_threshold, float weak, float strong, bool inverse) {
     struct ggml_init_params params;
-    params.mem_size = static_cast<size_t>(10 * 1024 * 1024);  // 10
-    params.mem_buffer = NULL;
-    params.no_alloc   = false;
+    params.mem_size               = static_cast<size_t>(10 * 1024 * 1024);  // 10
+    params.mem_buffer             = NULL;
+    params.no_alloc               = false;
     struct ggml_context* work_ctx = ggml_init(params);
 
     if (!work_ctx) {
@@ -177,29 +177,27 @@ uint8_t* preprocess_canny(uint8_t* img, int width, int height, float highThresho
     float kX[9] = {
         -1, 0, 1,
         -2, 0, 2,
-        -1, 0, 1
-    };
+        -1, 0, 1};
 
     float kY[9] = {
         1, 2, 1,
         0, 0, 0,
-        -1, -2, -1
-    };
+        -1, -2, -1};
 
     // generate kernel
-    int kernel_size = 5;
+    int kernel_size             = 5;
     struct ggml_tensor* gkernel = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, kernel_size, kernel_size, 1, 1);
-    struct ggml_tensor* sf_kx = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 3, 3, 1, 1);
+    struct ggml_tensor* sf_kx   = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 3, 3, 1, 1);
     memcpy(sf_kx->data, kX, ggml_nbytes(sf_kx));
     struct ggml_tensor* sf_ky = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 3, 3, 1, 1);
     memcpy(sf_ky->data, kY, ggml_nbytes(sf_ky));
     gaussian_kernel(gkernel);
-    struct ggml_tensor* image = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 3, 1);
+    struct ggml_tensor* image      = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 3, 1);
     struct ggml_tensor* image_gray = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, width, height, 1, 1);
-    struct ggml_tensor* iX = ggml_dup_tensor(work_ctx, image_gray);
-    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);
+    struct ggml_tensor* iX         = ggml_dup_tensor(work_ctx, image_gray);
+    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);
     grayscale(image, image_gray);
     convolve(image_gray, image_gray, gkernel, 2);
@@ -209,12 +207,12 @@ uint8_t* preprocess_canny(uint8_t* img, int width, int height, float highThresho
     normalize_tensor(G);
     prop_arctan2(iX, iY, tetha);
     non_max_supression(image_gray, G, tetha);
-    threshold_hystersis(image_gray, highThreshold, lowThreshold, weak, strong);
+    threshold_hystersis(image_gray, high_threshold, low_threshold, weak, strong);
     // to RGB channels
     for (int iy = 0; iy < height; iy++) {
         for (int ix = 0; ix < width; ix++) {
             float gray = ggml_tensor_get_f32(image_gray, ix, iy);
-            gray = inverse ? 1.0f - gray : gray;
+            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);
@@ -226,4 +224,4 @@ uint8_t* preprocess_canny(uint8_t* img, int width, int height, float highThresho
     return output;
 }
 
-#endif // __PREPROCESSING_HPP__
+#endif  // __PREPROCESSING_HPP__

stable-diffusion.h

@@ -65,13 +65,17 @@ enum sd_type_t {
     SD_TYPE_Q8_0 = 8,
     SD_TYPE_Q8_1 = 9,
     // k-quantizations
-    SD_TYPE_Q2_K = 10,
-    SD_TYPE_Q3_K = 11,
-    SD_TYPE_Q4_K = 12,
-    SD_TYPE_Q5_K = 13,
-    SD_TYPE_Q6_K = 14,
-    SD_TYPE_Q8_K = 15,
+    SD_TYPE_Q2_K    = 10,
+    SD_TYPE_Q3_K    = 11,
+    SD_TYPE_Q4_K    = 12,
+    SD_TYPE_Q5_K    = 13,
+    SD_TYPE_Q6_K    = 14,
+    SD_TYPE_Q8_K    = 15,
     SD_TYPE_IQ2_XXS = 16,
+    SD_TYPE_IQ2_XS  = 17,
+    SD_TYPE_IQ3_XXS = 18,
+    SD_TYPE_IQ1_S   = 19,
+    SD_TYPE_IQ4_NL  = 20,
     SD_TYPE_I8,
     SD_TYPE_I16,
     SD_TYPE_I32,
@@ -147,6 +151,21 @@ SD_API sd_image_t* img2img(sd_ctx_t* sd_ctx,
                            int64_t seed,
                            int batch_count);
 
+SD_API sd_image_t* img2vid(sd_ctx_t* sd_ctx,
+                           sd_image_t init_image,
+                           int width,
+                           int height,
+                           int video_frames,
+                           int motion_bucket_id,
+                           int fps,
+                           float augmentation_level,
+                           float min_cfg,
+                           float cfg_scale,
+                           enum sample_method_t sample_method,
+                           int sample_steps,
+                           float strength,
+                           int64_t seed);
+
 typedef struct upscaler_ctx_t upscaler_ctx_t;
 
 SD_API upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path,
@@ -158,6 +177,15 @@ SD_API sd_image_t upscale(upscaler_ctx_t* upscaler_ctx, sd_image_t input_image,
 
 SD_API bool convert(const char* input_path, const char* vae_path, const char* output_path, sd_type_t output_type);
 
+SD_API uint8_t* preprocess_canny(uint8_t* img,
+                                 int width,
+                                 int height,
+                                 float high_threshold,
+                                 float low_threshold,
+                                 float weak,
+                                 float strong,
+                                 bool inverse);
+
 #ifdef __cplusplus
 }
 #endif

tae.hpp

@@ -8,88 +8,45 @@
 /*
     ===================================    TinyAutoEncoder  ===================================
     References:
-    https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoder_tiny.py
+    https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoders/vae.py
     https://github.com/madebyollin/taesd/blob/main/taesd.py
 
 */
-struct TAEBlock {
-    int in_channels;
-    int out_channels;
 
-    // conv
-    ggml_tensor* conv_0_w;  // [in_channels, out_channels, 3, 3]
-    ggml_tensor* conv_0_b;  // [in_channels]
-    ggml_tensor* conv_1_w;  // [out_channels, out_channels, 3, 3]
-    ggml_tensor* conv_1_b;  // [out_channels]
-    ggml_tensor* conv_2_w;  // [out_channels, out_channels, 3, 3]
-    ggml_tensor* conv_2_b;  // [out_channels]
+class TAEBlock : public UnaryBlock {
+protected:
+    int n_in;
+    int n_out;
 
-    // skip
-    ggml_tensor* conv_skip_w;  // [in_channels, out_channels, 1, 1]
-
-    size_t calculate_mem_size() {
-        size_t mem_size = in_channels * out_channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_0_w
-        mem_size += in_channels * ggml_type_size(GGML_TYPE_F32);                               // conv_0_b
-        mem_size += out_channels * out_channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);       // conv_1_w
-        mem_size += out_channels * ggml_type_size(GGML_TYPE_F32);                              // conv_1_b
-        mem_size += out_channels * out_channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);       // conv_1_w
-        mem_size += out_channels * ggml_type_size(GGML_TYPE_F32);                              // conv_1_b
-        mem_size += out_channels * out_channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);       // conv_2_w
-        mem_size += out_channels * ggml_type_size(GGML_TYPE_F32);                              // conv_2_b
-
-        if (in_channels != out_channels) {
-            mem_size += in_channels * out_channels * ggml_type_size(GGML_TYPE_F16);  // conv_skip_w
-        }
-        return mem_size;
-    }
-
-    int get_num_tensors() {
-        return 6 + (in_channels != out_channels ? 1 : 0);
-    }
-
-    void init_params(ggml_context* ctx) {
-        conv_0_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, out_channels, in_channels);
-        conv_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
-
-        conv_1_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, out_channels, out_channels);
-        conv_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
-
-        conv_2_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, out_channels, out_channels);
-        conv_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_channels);
-
-        if (in_channels != out_channels) {
-            conv_skip_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, 1, out_channels, in_channels);
+public:
+    TAEBlock(int n_in, int n_out)
+        : n_in(n_in), n_out(n_out) {
+        blocks["conv.0"] = std::shared_ptr<GGMLBlock>(new Conv2d(n_in, n_out, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv.2"] = std::shared_ptr<GGMLBlock>(new Conv2d(n_out, n_out, {3, 3}, {1, 1}, {1, 1}));
+        blocks["conv.4"] = std::shared_ptr<GGMLBlock>(new Conv2d(n_out, n_out, {3, 3}, {1, 1}, {1, 1}));
+        if (n_in != n_out) {
+            blocks["skip"] = std::shared_ptr<GGMLBlock>(new Conv2d(n_in, n_out, {1, 1}, {1, 1}, {1, 1}, {1, 1}, false));
         }
     }
 
-    void map_by_name(std::map<std::string, ggml_tensor*>& tensors, std::string prefix) {
-        tensors[prefix + "conv.0.weight"] = conv_0_w;
-        tensors[prefix + "conv.0.bias"]   = conv_0_b;
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+        // x: [n, n_in, h, w]
+        // return: [n, n_out, h, w]
 
-        tensors[prefix + "conv.2.weight"] = conv_1_w;
-        tensors[prefix + "conv.2.bias"]   = conv_1_b;
+        auto conv_0 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.0"]);
+        auto conv_2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.2"]);
+        auto conv_4 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.4"]);
 
-        tensors[prefix + "conv.4.weight"] = conv_2_w;
-        tensors[prefix + "conv.4.bias"]   = conv_2_b;
+        auto h = conv_0->forward(ctx, x);
+        h      = ggml_relu_inplace(ctx, h);
+        h      = conv_2->forward(ctx, h);
+        h      = ggml_relu_inplace(ctx, h);
+        h      = conv_4->forward(ctx, h);
 
-        if (in_channels != out_channels) {
-            tensors[prefix + "skip.weight"] = conv_skip_w;
-        }
-    }
-
-    ggml_tensor* forward(ggml_context* ctx, ggml_tensor* x) {
-        // conv(n_in, n_out)
-        ggml_tensor* h;
-        h = ggml_nn_conv_2d(ctx, x, conv_0_w, conv_0_b, 1, 1, 1, 1);
-        h = ggml_relu_inplace(ctx, h);
-        h = ggml_nn_conv_2d(ctx, h, conv_1_w, conv_1_b, 1, 1, 1, 1);
-        h = ggml_relu_inplace(ctx, h);
-        h = ggml_nn_conv_2d(ctx, h, conv_2_w, conv_2_b, 1, 1, 1, 1);
-
-        // skip connection
-        if (in_channels != out_channels) {
-            // skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
-            x = ggml_nn_conv_2d(ctx, x, conv_skip_w, NULL, 1, 1, 1, 1);
+        if (n_in != n_out) {
+            auto skip = std::dynamic_pointer_cast<Conv2d>(blocks["skip"]);
+            LOG_DEBUG("skip");
+            x = skip->forward(ctx, x);
         }
 
         h = ggml_add(ctx, h, x);
@@ -98,412 +55,167 @@ struct TAEBlock {
     }
 };
 
-struct TinyEncoder {
+class TinyEncoder : public UnaryBlock {
     int in_channels = 3;
-    int z_channels  = 4;
     int channels    = 64;
+    int z_channels  = 4;
     int num_blocks  = 3;
 
-    // input
-    ggml_tensor* conv_input_w;  // [channels, in_channels, 3, 3]
-    ggml_tensor* conv_input_b;  // [channels]
-    TAEBlock initial_block;
-
-    ggml_tensor* conv_1_w;  // [channels, channels, 3, 3]
-    TAEBlock input_blocks[3];
-
-    // middle
-    ggml_tensor* conv_2_w;  // [channels, channels, 3, 3]
-    TAEBlock middle_blocks[3];
-
-    // output
-    ggml_tensor* conv_3_w;  // [channels, channels, 3, 3]
-    TAEBlock output_blocks[3];
-
-    // final
-    ggml_tensor* conv_final_w;  // [z_channels, channels, 3, 3]
-    ggml_tensor* conv_final_b;  // [z_channels]
-
+public:
     TinyEncoder() {
+        int index                       = 0;
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, channels, {3, 3}, {1, 1}, {1, 1}));
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
+
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, channels, {3, 3}, {2, 2}, {1, 1}, {1, 1}, false));
         for (int i = 0; i < num_blocks; i++) {
-            input_blocks[i].in_channels  = channels;
-            input_blocks[i].out_channels = channels;
-
-            middle_blocks[i].in_channels  = channels;
-            middle_blocks[i].out_channels = channels;
-
-            output_blocks[i].in_channels  = channels;
-            output_blocks[i].out_channels = channels;
+            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
         }
 
-        initial_block.in_channels  = channels;
-        initial_block.out_channels = channels;
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, channels, {3, 3}, {2, 2}, {1, 1}, {1, 1}, false));
+        for (int i = 0; i < num_blocks; i++) {
+            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
+        }
+
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, channels, {3, 3}, {2, 2}, {1, 1}, {1, 1}, false));
+        for (int i = 0; i < num_blocks; i++) {
+            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
+        }
+
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, z_channels, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    size_t calculate_mem_size() {
-        size_t mem_size = channels * in_channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_input_w
-        mem_size += channels * ggml_type_size(GGML_TYPE_F32);                              // conv_input_b
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+        // x: [n, in_channels, h, w]
+        // return: [n, z_channels, h/8, w/8]
 
-        mem_size += initial_block.calculate_mem_size();
+        for (int i = 0; i < num_blocks * 3 + 6; i++) {
+            auto block = std::dynamic_pointer_cast<UnaryBlock>(blocks[std::to_string(i)]);
 
-        mem_size += channels * channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_1_w
-        mem_size += channels * channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_2_w
-        mem_size += channels * channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_3_w
-
-        for (int i = 0; i < num_blocks; i++) {
-            mem_size += input_blocks[i].calculate_mem_size();
-            mem_size += middle_blocks[i].calculate_mem_size();
-            mem_size += output_blocks[i].calculate_mem_size();
-        }
-        mem_size += z_channels * channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_input_w
-        mem_size += z_channels * ggml_type_size(GGML_TYPE_F32);                     // conv_input_b
-        return mem_size;
-    }
-
-    int get_num_tensors() {
-        int num_tensors = 7;
-        for (int i = 0; i < num_blocks; i++) {
-            num_tensors += input_blocks[i].get_num_tensors();
-            num_tensors += middle_blocks[i].get_num_tensors();
-            num_tensors += output_blocks[i].get_num_tensors();
-        }
-        num_tensors += initial_block.get_num_tensors();
-        return num_tensors;
-    }
-
-    void init_params(ggml_context* ctx) {
-        conv_input_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, in_channels, channels);
-        conv_input_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, channels);
-
-        initial_block.init_params(ctx);
-
-        conv_1_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, channels);
-        conv_2_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, channels);
-        conv_3_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, channels);
-
-        conv_final_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, z_channels);
-        conv_final_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, z_channels);
-
-        for (int i = 0; i < num_blocks; i++) {
-            input_blocks[i].init_params(ctx);
-            middle_blocks[i].init_params(ctx);
-            output_blocks[i].init_params(ctx);
-        }
-    }
-
-    void map_by_name(std::map<std::string, ggml_tensor*>& tensors, std::string prefix) {
-        tensors[prefix + "0.weight"] = conv_input_w;
-        tensors[prefix + "0.bias"]   = conv_input_b;
-
-        initial_block.map_by_name(tensors, prefix + "1.");
-
-        tensors[prefix + "2.weight"] = conv_1_w;
-        for (int i = 0; i < num_blocks; i++) {
-            input_blocks[i].map_by_name(tensors, prefix + std::to_string(i + 3) + ".");
+            x = block->forward(ctx, x);
         }
 
-        tensors[prefix + "6.weight"] = conv_2_w;
-        for (int i = 0; i < num_blocks; i++) {
-            middle_blocks[i].map_by_name(tensors, prefix + std::to_string(i + 7) + ".");
-        }
-
-        tensors[prefix + "10.weight"] = conv_3_w;
-        for (int i = 0; i < num_blocks; i++) {
-            output_blocks[i].map_by_name(tensors, prefix + std::to_string(i + 11) + ".");
-        }
-
-        tensors[prefix + "14.weight"] = conv_final_w;
-        tensors[prefix + "14.bias"]   = conv_final_b;
-    }
-
-    ggml_tensor* forward(ggml_context* ctx, ggml_tensor* x) {
-        // conv(3, 64)
-        auto z = ggml_nn_conv_2d(ctx, x, conv_input_w, conv_input_b, 1, 1, 1, 1);
-
-        // Block(64, 64)
-        z = initial_block.forward(ctx, z);
-
-        // conv(64, 64, stride=2, bias=False)
-        z = ggml_nn_conv_2d(ctx, z, conv_1_w, NULL, 2, 2, 1, 1);
-
-        // Block(64, 64), Block(64, 64), Block(64, 64)
-        for (int i = 0; i < num_blocks; i++) {
-            z = input_blocks[i].forward(ctx, z);
-        }
-
-        // conv(64, 64, stride=2, bias=False)
-        z = ggml_nn_conv_2d(ctx, z, conv_2_w, NULL, 2, 2, 1, 1);
-
-        // Block(64, 64), Block(64, 64), Block(64, 64)
-        for (int i = 0; i < num_blocks; i++) {
-            z = middle_blocks[i].forward(ctx, z);
-        }
-
-        // conv(64, 64, stride=2, bias=False)
-        z = ggml_nn_conv_2d(ctx, z, conv_3_w, NULL, 2, 2, 1, 1);
-
-        // Block(64, 64), Block(64, 64), Block(64, 64)
-        for (int i = 0; i < num_blocks; i++) {
-            z = output_blocks[i].forward(ctx, z);
-        }
-
-        // conv(64, 4)
-        z = ggml_nn_conv_2d(ctx, z, conv_final_w, conv_final_b, 1, 1, 1, 1);
-        return z;
+        return x;
     }
 };
 
-struct TinyDecoder {
-    int z_channels      = 4;
-    int channels        = 64;
-    int output_channels = 3;
-    int num_blocks      = 3;
+class TinyDecoder : public UnaryBlock {
+    int z_channels   = 4;
+    int channels     = 64;
+    int out_channels = 3;
+    int num_blocks   = 3;
 
-    // input
-    ggml_tensor* conv_input_w;  // [channels, z_channels, 3, 3]
-    ggml_tensor* conv_input_b;  // [channels]
-    TAEBlock input_blocks[3];
-    ggml_tensor* conv_1_w;  // [channels, channels, 3, 3]
+public:
+    TinyDecoder(int index = 0) {
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(z_channels, channels, {3, 3}, {1, 1}, {1, 1}));
+        index++;  // nn.ReLU()
 
-    // middle
-    TAEBlock middle_blocks[3];
-    ggml_tensor* conv_2_w;  // [channels, channels, 3, 3]
-
-    // output
-    TAEBlock output_blocks[3];
-    ggml_tensor* conv_3_w;  // [channels, channels, 3, 3]
-
-    // final
-    TAEBlock final_block;
-    ggml_tensor* conv_final_w;  // [output_channels, channels, 3, 3]
-    ggml_tensor* conv_final_b;  // [output_channels]
-
-    TinyDecoder() {
         for (int i = 0; i < num_blocks; i++) {
-            input_blocks[i].in_channels  = channels;
-            input_blocks[i].out_channels = channels;
-
-            middle_blocks[i].in_channels  = channels;
-            middle_blocks[i].out_channels = channels;
-
-            output_blocks[i].in_channels  = channels;
-            output_blocks[i].out_channels = channels;
+            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
         }
+        index++;  // nn.Upsample()
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, channels, {3, 3}, {1, 1}, {1, 1}, {1, 1}, false));
 
-        final_block.in_channels  = channels;
-        final_block.out_channels = channels;
+        for (int i = 0; i < num_blocks; i++) {
+            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
+        }
+        index++;  // nn.Upsample()
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, channels, {3, 3}, {1, 1}, {1, 1}, {1, 1}, false));
+
+        for (int i = 0; i < num_blocks; i++) {
+            blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
+        }
+        index++;  // nn.Upsample()
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, channels, {3, 3}, {1, 1}, {1, 1}, {1, 1}, false));
+
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new TAEBlock(channels, channels));
+        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    size_t calculate_mem_size() {
-        size_t mem_size = channels * z_channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_input_w
-        mem_size += channels * ggml_type_size(GGML_TYPE_F32);                             // conv_input_b
+    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* z) {
+        // z: [n, z_channels, h, w]
+        // return: [n, out_channels, h*8, w*8]
 
-        for (int i = 0; i < num_blocks; i++) {
-            mem_size += input_blocks[i].calculate_mem_size();
-        }
-        mem_size += channels * channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_1_w
-
-        for (int i = 0; i < num_blocks; i++) {
-            mem_size += middle_blocks[i].calculate_mem_size();
-        }
-        mem_size += channels * channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_2_w
-
-        for (int i = 0; i < num_blocks; i++) {
-            mem_size += output_blocks[i].calculate_mem_size();
-        }
-        mem_size += channels * channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_3_w
-
-        mem_size += final_block.calculate_mem_size();
-        mem_size += output_channels * channels * 3 * 3 * ggml_type_size(GGML_TYPE_F16);  // conv_input_w
-        mem_size += output_channels * ggml_type_size(GGML_TYPE_F32);                     // conv_input_b
-        return mem_size;
-    }
-
-    int get_num_tensors() {
-        int num_tensors = 9;
-        for (int i = 0; i < num_blocks; i++) {
-            num_tensors += input_blocks[i].get_num_tensors();
-            num_tensors += middle_blocks[i].get_num_tensors();
-            num_tensors += output_blocks[i].get_num_tensors();
-        }
-        num_tensors += final_block.get_num_tensors();
-        return num_tensors;
-    }
-
-    void init_params(ggml_allocr* alloc, ggml_context* ctx) {
-        conv_input_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, z_channels, channels);
-        conv_input_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, channels);
-
-        conv_1_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, channels);
-        conv_2_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, channels);
-        conv_3_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, channels);
-
-        conv_final_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 3, 3, channels, output_channels);
-        conv_final_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, output_channels);
-
-        for (int i = 0; i < num_blocks; i++) {
-            input_blocks[i].init_params(ctx);
-            middle_blocks[i].init_params(ctx);
-            output_blocks[i].init_params(ctx);
-        }
-
-        final_block.init_params(ctx);
-    }
-
-    void map_by_name(std::map<std::string, ggml_tensor*>& tensors, std::string prefix) {
-        tensors[prefix + "0.weight"] = conv_input_w;
-        tensors[prefix + "0.bias"]   = conv_input_b;
-
-        for (int i = 0; i < num_blocks; i++) {
-            input_blocks[i].map_by_name(tensors, prefix + std::to_string(i + 2) + ".");
-        }
-
-        tensors[prefix + "6.weight"] = conv_1_w;
-        for (int i = 0; i < num_blocks; i++) {
-            middle_blocks[i].map_by_name(tensors, prefix + std::to_string(i + 7) + ".");
-        }
-
-        tensors[prefix + "11.weight"] = conv_2_w;
-        for (int i = 0; i < num_blocks; i++) {
-            output_blocks[i].map_by_name(tensors, prefix + std::to_string(i + 12) + ".");
-        }
-
-        tensors[prefix + "16.weight"] = conv_3_w;
-
-        final_block.map_by_name(tensors, prefix + "17.");
-
-        tensors[prefix + "18.weight"] = conv_final_w;
-        tensors[prefix + "18.bias"]   = conv_final_b;
-    }
-
-    ggml_tensor* forward(ggml_context* ctx, ggml_tensor* z) {
-        // torch.tanh(x / 3) * 3
         auto h = ggml_scale(ctx, z, 1.0f / 3.0f);
         h      = ggml_tanh_inplace(ctx, h);
         h      = ggml_scale(ctx, h, 3.0f);
 
-        // conv(4, 64)
-        h = ggml_nn_conv_2d(ctx, h, conv_input_w, conv_input_b, 1, 1, 1, 1);
+        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);
+                } else {
+                    h = ggml_upscale(ctx, h, 2);
+                }
+                continue;
+            }
+            auto block = std::dynamic_pointer_cast<UnaryBlock>(blocks[std::to_string(i)]);
 
-        // nn.ReLU()
-        h = ggml_relu_inplace(ctx, h);
-
-        // Block(64, 64), Block(64, 64), Block(64, 64)
-        for (int i = 0; i < num_blocks; i++) {
-            h = input_blocks[i].forward(ctx, h);
+            h = block->forward(ctx, h);
         }
 
-        // nn.Upsample(scale_factor=2)
-        h = ggml_upscale(ctx, h, 2);
-
-        // conv(64, 64, bias=False)
-        h = ggml_nn_conv_2d(ctx, h, conv_1_w, NULL, 1, 1, 1, 1);
-
-        // Block(64, 64), Block(64, 64), Block(64, 64)
-        for (int i = 0; i < num_blocks; i++) {
-            h = middle_blocks[i].forward(ctx, h);
-        }
-
-        // nn.Upsample(scale_factor=2)
-        h = ggml_upscale(ctx, h, 2);
-
-        // conv(64, 64, bias=False)
-        h = ggml_nn_conv_2d(ctx, h, conv_2_w, NULL, 1, 1, 1, 1);
-
-        // Block(64, 64), Block(64, 64), Block(64, 64)
-        for (int i = 0; i < num_blocks; i++) {
-            h = output_blocks[i].forward(ctx, h);
-        }
-
-        // nn.Upsample(scale_factor=2)
-        h = ggml_upscale(ctx, h, 2);
-
-        // conv(64, 64, bias=False)
-        h = ggml_nn_conv_2d(ctx, h, conv_3_w, NULL, 1, 1, 1, 1);
-
-        // Block(64, 64)
-        h = final_block.forward(ctx, h);
-
-        // conv(64, 3)
-        h = ggml_nn_conv_2d(ctx, h, conv_final_w, conv_final_b, 1, 1, 1, 1);
         return h;
     }
 };
 
+class TAESD : public GGMLBlock {
+protected:
+    bool decode_only;
+
+public:
+    TAESD(bool decode_only = true)
+        : decode_only(decode_only) {
+        blocks["decoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyDecoder());
+
+        if (!decode_only) {
+            blocks["encoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyEncoder());
+        }
+    }
+
+    struct ggml_tensor* decode(struct ggml_context* 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) {
+        auto encoder = std::dynamic_pointer_cast<TinyEncoder>(blocks["encoder.layers"]);
+        return encoder->forward(ctx, x);
+    }
+};
+
 struct TinyAutoEncoder : public GGMLModule {
-    TinyEncoder encoder;
-    TinyDecoder decoder;
+    TAESD taesd;
     bool decode_only = false;
 
-    TinyAutoEncoder(bool decoder_only_ = true)
-        : decode_only(decoder_only_) {
-        name = "tae";
+    TinyAutoEncoder(ggml_backend_t backend,
+                    ggml_type wtype,
+                    bool decoder_only = true)
+        : decode_only(decoder_only),
+          taesd(decode_only),
+          GGMLModule(backend, wtype) {
+        taesd.init(params_ctx, wtype);
     }
 
-    size_t calculate_mem_size() {
-        size_t mem_size = decoder.calculate_mem_size();
-        if (!decode_only) {
-            mem_size += encoder.calculate_mem_size();
-        }
-        mem_size += 1024;  // padding
-        return mem_size;
+    std::string get_desc() {
+        return "taesd";
     }
 
-    size_t get_num_tensors() {
-        size_t num_tensors = decoder.get_num_tensors();
-        if (!decode_only) {
-            num_tensors += encoder.get_num_tensors();
-        }
-        return num_tensors;
+    size_t get_params_mem_size() {
+        return taesd.get_params_mem_size();
     }
 
-    void init_params() {
-        ggml_allocr* alloc = ggml_allocr_new_from_buffer(params_buffer);
-        decoder.init_params(alloc, params_ctx);
-        if (!decode_only) {
-            encoder.init_params(params_ctx);
-        }
-
-        // alloc all tensors linked to this context
-        for (struct ggml_tensor* t = ggml_get_first_tensor(params_ctx); t != NULL; t = ggml_get_next_tensor(params_ctx, t)) {
-            if (t->data == NULL) {
-                ggml_allocr_alloc(alloc, t);
-            }
-        }
-        ggml_allocr_free(alloc);
+    size_t get_params_num() {
+        return taesd.get_params_num();
     }
 
-    void map_by_name(std::map<std::string, ggml_tensor*>& tensors) {
-        decoder.map_by_name(tensors, "decoder.layers.");
-        encoder.map_by_name(tensors, "encoder.layers.");
-    }
-
-    bool load_from_file(const std::string& file_path, ggml_backend_t backend) {
+    bool load_from_file(const std::string& file_path) {
         LOG_INFO("loading taesd from '%s'", file_path.c_str());
-
-        if (!alloc_params_buffer(backend)) {
-            return false;
-        }
-
+        alloc_params_buffer();
         std::map<std::string, ggml_tensor*> taesd_tensors;
-
-        // prepare memory for the weights
-        {
-            init_params();
-            map_by_name(taesd_tensors);
-        }
-
-        std::map<std::string, struct ggml_tensor*> tensors_need_to_load;
+        taesd.get_param_tensors(taesd_tensors);
         std::set<std::string> ignore_tensors;
-        for (auto& pair : taesd_tensors) {
-            const std::string& name = pair.first;
-
-            if (decode_only && starts_with(name, "encoder")) {
-                ignore_tensors.insert(name);
-                continue;
-            }
-
-            tensors_need_to_load.insert(pair);
+        if (decode_only) {
+            ignore_tensors.insert("encoder.");
         }
 
         ModelLoader model_loader;
@@ -512,7 +224,7 @@ struct TinyAutoEncoder : public GGMLModule {
             return false;
         }
 
-        bool success = model_loader.load_tensors(tensors_need_to_load, backend, ignore_tensors);
+        bool success = model_loader.load_tensors(taesd_tensors, backend, ignore_tensors);
 
         if (!success) {
             LOG_ERROR("load tae tensors from model loader failed");
@@ -524,57 +236,23 @@ struct TinyAutoEncoder : public GGMLModule {
     }
 
     struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
-        // since we are using ggml-alloc, this buffer only needs enough space to hold the ggml_tensor and ggml_cgraph structs, but not the tensor data
-        static size_t buf_size = ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
-        static std::vector<uint8_t> buf(buf_size);
-
-        struct ggml_init_params params = {
-            /*.mem_size   =*/buf_size,
-            /*.mem_buffer =*/buf.data(),
-            /*.no_alloc   =*/true,  // the tensors will be allocated later by ggml_allocr_alloc_graph()
-        };
-        // LOG_DEBUG("mem_size %u ", params.mem_size);
-
-        struct ggml_context* ctx0 = ggml_init(params);
-
-        struct ggml_cgraph* gf = ggml_new_graph(ctx0);
-
-        struct ggml_tensor* z_ = NULL;
-
-        // it's performing a compute, check if backend isn't cpu
-        if (!ggml_backend_is_cpu(backend)) {
-            // pass input tensors to gpu memory
-            z_ = ggml_dup_tensor(ctx0, z);
-            ggml_allocr_alloc(compute_allocr, z_);
-
-            // pass data to device backend
-            if (!ggml_allocr_is_measure(compute_allocr)) {
-                ggml_backend_tensor_set(z_, z->data, 0, ggml_nbytes(z));
-            }
-        } else {
-            z_ = z;
-        }
-
-        struct ggml_tensor* out = decode_graph ? decoder.forward(ctx0, z_) : encoder.forward(ctx0, z_);
-
+        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);
         ggml_build_forward_expand(gf, out);
-        ggml_free(ctx0);
-
         return gf;
     }
 
-    void alloc_compute_buffer(struct ggml_tensor* x, bool decode) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(x, decode);
-        };
-        GGMLModule::alloc_compute_buffer(get_graph);
-    }
-
-    void compute(struct ggml_tensor* work_result, int n_threads, struct ggml_tensor* z, bool decode_graph) {
+    void compute(const int n_threads,
+                 struct ggml_tensor* z,
+                 bool decode_graph,
+                 struct ggml_tensor** output,
+                 struct ggml_context* output_ctx = NULL) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
             return build_graph(z, decode_graph);
         };
-        GGMLModule::compute(get_graph, n_threads, work_result);
+
+        GGMLModule::compute(get_graph, n_threads, false, output, output_ctx);
     }
 };
 

upscaler.cpp

@@ -6,7 +6,7 @@
 struct UpscalerGGML {
     ggml_backend_t backend    = NULL;  // general backend
     ggml_type model_data_type = GGML_TYPE_F16;
-    ESRGAN esrgan_upscaler;
+    std::shared_ptr<ESRGAN> esrgan_upscaler;
     std::string esrgan_path;
     int n_threads;
 
@@ -21,7 +21,7 @@ struct UpscalerGGML {
 #endif
 #ifdef SD_USE_METAL
         LOG_DEBUG("Using Metal backend");
-        ggml_metal_log_set_callback(ggml_log_callback_default, nullptr);
+        ggml_backend_metal_log_set_callback(ggml_log_callback_default, nullptr);
         backend = ggml_backend_metal_init();
 #endif
 
@@ -30,7 +30,8 @@ struct UpscalerGGML {
             backend = ggml_backend_cpu_init();
         }
         LOG_INFO("Upscaler weight type: %s", ggml_type_name(model_data_type));
-        if (!esrgan_upscaler.load_from_file(esrgan_path, backend)) {
+        esrgan_upscaler = std::make_shared<ESRGAN>(backend, model_data_type);
+        if (!esrgan_upscaler->load_from_file(esrgan_path)) {
             return false;
         }
         return true;
@@ -39,8 +40,8 @@ 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};
-        int output_width          = (int)input_image.width * esrgan_upscaler.scale;
-        int output_height         = (int)input_image.height * esrgan_upscaler.scale;
+        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)",
                  input_image.width, input_image.height, output_width, output_height);
 
@@ -62,15 +63,11 @@ struct UpscalerGGML {
 
         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) {
-            if (init) {
-                esrgan_upscaler.alloc_compute_buffer(in);
-            } else {
-                esrgan_upscaler.compute(out, n_threads, in);
-            }
+            esrgan_upscaler->compute(n_threads, in, &out);
         };
         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();
+        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_free(upscale_ctx);

util.cpp

@@ -1,6 +1,7 @@
 #include "util.h"
-#include <algorithm>
 #include <stdarg.h>
+#include <algorithm>
+#include <cmath>
 #include <codecvt>
 #include <fstream>
 #include <locale>
@@ -9,6 +10,7 @@
 #include <thread>
 #include <unordered_set>
 #include <vector>
+#include "preprocessing.hpp"
 
 #if defined(__APPLE__) && defined(__MACH__)
 #include <sys/sysctl.h>
@@ -174,7 +176,7 @@ std::u32string unicode_value_to_utf32(int unicode_value) {
     return utf32_string;
 }
 
-std::string sd_basename(const std::string& path) {
+static std::string sd_basename(const std::string& path) {
     size_t pos = path.find_last_of('/');
     if (pos != std::string::npos) {
         return path.substr(pos + 1);
@@ -203,6 +205,9 @@ std::string path_join(const std::string& p1, const std::string& p2) {
 }
 
 void pretty_progress(int step, int steps, float time) {
+    if (step == 0) {
+        return;
+    }
     std::string progress = "  |";
     int max_progress     = 50;
     int32_t current      = (int32_t)(step * 1.f * max_progress / steps);
@@ -261,14 +266,14 @@ void log_printf(sd_log_level_t level, const char* file, int line, const char* fo
         level_str = "ERROR";
     }
 
-    static char log_buffer[LOG_BUFFER_SIZE];
+    static char log_buffer[LOG_BUFFER_SIZE + 1];
 
     int written = snprintf(log_buffer, LOG_BUFFER_SIZE, "[%s] %s:%-4d - ", level_str, sd_basename(file).c_str(), line);
 
     if (written >= 0 && written < LOG_BUFFER_SIZE) {
         vsnprintf(log_buffer + written, LOG_BUFFER_SIZE - written, format, args);
-        strncat(log_buffer, "\n", LOG_BUFFER_SIZE - strlen(log_buffer) - 1);
     }
+    strncat(log_buffer, "\n", LOG_BUFFER_SIZE - strlen(log_buffer));
 
     if (sd_log_cb) {
         sd_log_cb(level, log_buffer, sd_log_cb_data);
@@ -307,3 +312,152 @@ const char* sd_get_system_info() {
 const char* sd_type_name(enum sd_type_t type) {
     return ggml_type_name((ggml_type)type);
 }
+
+sd_image_f32_t sd_image_t_to_sd_image_f32_t(sd_image_t image) {
+    sd_image_f32_t converted_image;
+    converted_image.width   = image.width;
+    converted_image.height  = image.height;
+    converted_image.channel = image.channel;
+
+    // Allocate memory for float data
+    converted_image.data = (float*)malloc(image.width * image.height * image.channel * sizeof(float));
+
+    for (int i = 0; i < image.width * image.height * image.channel; i++) {
+        // Convert uint8_t to float
+        converted_image.data[i] = (float)image.data[i];
+    }
+
+    return converted_image;
+}
+
+// Function to perform double linear interpolation
+float interpolate(float v1, float v2, float v3, float v4, float x_ratio, float y_ratio) {
+    return v1 * (1 - x_ratio) * (1 - y_ratio) + v2 * x_ratio * (1 - y_ratio) + v3 * (1 - x_ratio) * y_ratio + v4 * x_ratio * y_ratio;
+}
+
+sd_image_f32_t resize_sd_image_f32_t(sd_image_f32_t image, int target_width, int target_height) {
+    sd_image_f32_t resized_image;
+    resized_image.width   = target_width;
+    resized_image.height  = target_height;
+    resized_image.channel = image.channel;
+
+    // Allocate memory for resized float data
+    resized_image.data = (float*)malloc(target_width * target_height * image.channel * sizeof(float));
+
+    for (int y = 0; y < target_height; y++) {
+        for (int x = 0; x < target_width; x++) {
+            float original_x = (float)x * image.width / target_width;
+            float original_y = (float)y * image.height / target_height;
+
+            int x1 = (int)original_x;
+            int y1 = (int)original_y;
+            int x2 = x1 + 1;
+            int y2 = y1 + 1;
+
+            for (int k = 0; k < image.channel; k++) {
+                float v1 = *(image.data + y1 * image.width * image.channel + x1 * image.channel + k);
+                float v2 = *(image.data + y1 * image.width * image.channel + x2 * image.channel + k);
+                float v3 = *(image.data + y2 * image.width * image.channel + x1 * image.channel + k);
+                float v4 = *(image.data + y2 * image.width * image.channel + x2 * image.channel + k);
+
+                float x_ratio = original_x - x1;
+                float y_ratio = original_y - y1;
+
+                float value = interpolate(v1, v2, v3, v4, x_ratio, y_ratio);
+
+                *(resized_image.data + y * target_width * image.channel + x * image.channel + k) = value;
+            }
+        }
+    }
+
+    return resized_image;
+}
+
+void normalize_sd_image_f32_t(sd_image_f32_t image, float means[3], float stds[3]) {
+    for (int y = 0; y < image.height; y++) {
+        for (int x = 0; x < image.width; x++) {
+            for (int k = 0; k < image.channel; k++) {
+                int index         = (y * image.width + x) * image.channel + k;
+                image.data[index] = (image.data[index] - means[k]) / stds[k];
+            }
+        }
+    }
+}
+
+// Constants for means and std
+float means[3] = {0.48145466, 0.4578275, 0.40821073};
+float stds[3]  = {0.26862954, 0.26130258, 0.27577711};
+
+// Function to clip and preprocess sd_image_f32_t
+sd_image_f32_t clip_preprocess(sd_image_f32_t image, int size) {
+    float scale = (float)size / fmin(image.width, image.height);
+
+    // Interpolation
+    int new_width       = (int)(scale * image.width);
+    int new_height      = (int)(scale * image.height);
+    float* resized_data = (float*)malloc(new_width * new_height * image.channel * sizeof(float));
+
+    for (int y = 0; y < new_height; y++) {
+        for (int x = 0; x < new_width; x++) {
+            float original_x = (float)x * image.width / new_width;
+            float original_y = (float)y * image.height / new_height;
+
+            int x1 = (int)original_x;
+            int y1 = (int)original_y;
+            int x2 = x1 + 1;
+            int y2 = y1 + 1;
+
+            for (int k = 0; k < image.channel; k++) {
+                float v1 = *(image.data + y1 * image.width * image.channel + x1 * image.channel + k);
+                float v2 = *(image.data + y1 * image.width * image.channel + x2 * image.channel + k);
+                float v3 = *(image.data + y2 * image.width * image.channel + x1 * image.channel + k);
+                float v4 = *(image.data + y2 * image.width * image.channel + x2 * image.channel + k);
+
+                float x_ratio = original_x - x1;
+                float y_ratio = original_y - y1;
+
+                float value = interpolate(v1, v2, v3, v4, x_ratio, y_ratio);
+
+                *(resized_data + y * new_width * image.channel + x * image.channel + k) = value;
+            }
+        }
+    }
+
+    // Clip and preprocess
+    int h = (new_height - size) / 2;
+    int w = (new_width - size) / 2;
+
+    sd_image_f32_t result;
+    result.width   = size;
+    result.height  = size;
+    result.channel = image.channel;
+    result.data    = (float*)malloc(size * size * image.channel * sizeof(float));
+
+    for (int k = 0; k < image.channel; k++) {
+        for (int i = 0; i < size; i++) {
+            for (int j = 0; j < size; j++) {
+                *(result.data + i * size * image.channel + j * image.channel + k) =
+                    fmin(fmax(*(resized_data + (i + h) * new_width * image.channel + (j + w) * image.channel + k), 0.0f), 255.0f) / 255.0f;
+            }
+        }
+    }
+
+    // Free allocated memory
+    free(resized_data);
+
+    // Normalize
+    for (int k = 0; k < image.channel; k++) {
+        for (int i = 0; i < size; i++) {
+            for (int j = 0; j < size; j++) {
+                // *(result.data + i * size * image.channel + j * image.channel + k) = 0.5f;
+                int offset  = i * size * image.channel + j * image.channel + k;
+                float value = *(result.data + offset);
+                value       = (value - means[k]) / stds[k];
+                // value = 0.5f;
+                *(result.data + offset) = value;
+            }
+        }
+    }
+
+    return result;
+}

util.h

@@ -21,7 +21,20 @@ std::u32string utf8_to_utf32(const std::string& utf8_str);
 std::string utf32_to_utf8(const std::u32string& utf32_str);
 std::u32string unicode_value_to_utf32(int unicode_value);
 
-std::string sd_basename(const std::string& path);
+typedef struct {
+    uint32_t width;
+    uint32_t height;
+    uint32_t channel;
+    float* data;
+} sd_image_f32_t;
+
+void normalize_sd_image_f32_t(sd_image_f32_t image, float means[3], float stds[3]);
+
+sd_image_f32_t sd_image_t_to_sd_image_f32_t(sd_image_t image);
+
+sd_image_f32_t resize_sd_image_f32_t(sd_image_f32_t image, int target_width, int target_height);
+
+sd_image_f32_t clip_preprocess(sd_image_f32_t image, int size);
 
 std::string path_join(const std::string& p1, const std::string& p2);