feat: add flux2 small decoder support (#1402 )

fix: correct double increment on flow denoisers sigma calculations (#1372 )
chore: normalize text files to utf-8 without bom (#1394 )
2026-10-03 02:38:25 +08:00 · 2026-04-08 23:13:25 +08:00 · 2026-04-08 23:13:05 +08:00 · 2026-04-06 21:25:34 +08:00 · 2026-04-06 21:11:57 +08:00 · 2026-04-06 20:33:46 +08:00
120 changed files with 24659 additions and 14876 deletions
@@ -1,4 +1,5 @@
 build*/
+docs/
 test/

 .cache/
@@ -21,11 +21,13 @@ on:
        "**/*.c",
        "**/*.cpp",
        "**/*.cu",
+        "examples/server/frontend/**",
      ]
  pull_request:
    types: [opened, synchronize, reopened]
    paths:
      [
+        ".github/workflows/**",
        "**/CMakeLists.txt",
        "**/Makefile",
        "**/*.h",
@@ -33,11 +35,16 @@ on:
        "**/*.c",
        "**/*.cpp",
        "**/*.cu",
+        "examples/server/frontend/**",
      ]

 env:
  BRANCH_NAME: ${{ github.head_ref || github.ref_name }}

+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
  ubuntu-latest-cmake:
    runs-on: ubuntu-latest
@@ -49,6 +56,16 @@ jobs:
        with:
          submodules: recursive

+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 10.15.1
+
      - name: Dependencies
        id: depends
        run: |
@@ -66,7 +83,7 @@ jobs:
      - name: Get commit hash
        id: commit
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2

      - name: Fetch system info
        id: system-info
@@ -92,6 +109,144 @@ jobs:
          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

+  ubuntu-latest-cmake-vulkan:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v3
+        with:
+          submodules: recursive
+
+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 10.15.1
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential libvulkan-dev glslc
+
+      - name: Build
+        id: cmake_build
+        run: |
+          mkdir build
+          cd build
+          cmake .. -DSD_BUILD_SHARED_LIBS=ON -DSD_VULKAN=ON
+          cmake --build . --config Release
+
+      - name: Get commit hash
+        id: commit
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: prompt/actions-commit-hash@v2
+
+      - name: Fetch system info
+        id: system-info
+        run: |
+          echo "CPU_ARCH=`uname -m`" >> "$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 }}-vulkan.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-vulkan.zip
+          path: |
+            sd-${{ 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 }}-vulkan.zip
+
+  build-and-push-docker-images:
+    name: Build and push container images
+    runs-on: ubuntu-latest
+
+    permissions:
+      contents: read
+      packages: write
+      id-token: write
+      attestations: write
+      artifact-metadata: write
+
+    strategy:
+      matrix:
+        variant: [musa, sycl, vulkan, cuda]
+
+    env:
+      REGISTRY: ghcr.io
+      IMAGE_NAME: ${{ github.repository }}
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v6
+        with:
+          submodules: recursive
+
+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 10.15.1
+
+      - name: Get commit hash
+        id: commit
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: prompt/actions-commit-hash@v2
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Log in to the container registry
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+
+      - name: Free Disk Space (Ubuntu)
+        uses: jlumbroso/free-disk-space@v1.3.1
+        with:
+          # this might remove tools that are actually needed,
+          # if set to "true" but frees about 6 GB
+          tool-cache: false
+
+      - name: Build and push Docker image
+        id: build-push
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          platforms: linux/amd64
+          push: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+          file: Dockerfile.${{ matrix.variant }}
+          tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ env.BRANCH_NAME }}-${{ matrix.variant }}
+          labels: ${{ steps.meta.outputs.labels }}
+          annotations: ${{ steps.meta.outputs.annotations }}
+
  macOS-latest-cmake:
    runs-on: macos-latest

@@ -102,6 +257,16 @@ jobs:
        with:
          submodules: recursive

+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 10.15.1
+
      - name: Dependencies
        id: depends
        run: |
@@ -119,7 +284,7 @@ jobs:
      - name: Get commit hash
        id: commit
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2

      - name: Fetch system info
        id: system-info
@@ -146,7 +311,7 @@ jobs:
            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-2025
+    runs-on: windows-2022

    env:
      VULKAN_VERSION: 1.4.328.1
@@ -163,8 +328,8 @@ jobs:
          - build: "avx512"
            defines: "-DGGML_NATIVE=OFF -DGGML_AVX512=ON -DGGML_AVX=ON -DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
          - build: "cuda12"
-            defines: "-DSD_CUDA=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_CUDA_ARCHITECTURES='61;70;75;80;86;89;90;100;120'"
-          - build: 'vulkan'
+            defines: "-DSD_CUDA=ON -DSD_BUILD_SHARED_LIBS=ON -DCMAKE_CUDA_ARCHITECTURES='61;70;75;80;86;89;90;100;120' -DCMAKE_CUDA_FLAGS='-Xcudafe \"--diag_suppress=177\" -Xcudafe \"--diag_suppress=550\"'"
+          - build: "vulkan"
            defines: "-DSD_VULKAN=ON -DSD_BUILD_SHARED_LIBS=ON"
    steps:
      - name: Clone
@@ -173,6 +338,16 @@ jobs:
        with:
          submodules: recursive

+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 10.15.1
+
      - name: Install cuda-toolkit
        id: cuda-toolkit
        if: ${{ matrix.build == 'cuda12' }}
@@ -191,13 +366,17 @@ jobs:
          Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}"
          Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin"

+      - name: Activate MSVC environment
+        id: msvc_dev_cmd
+        uses: ilammy/msvc-dev-cmd@v1
+
      - name: Build
        id: cmake_build
        run: |
          mkdir build
          cd build
-          cmake .. ${{ matrix.defines }}
-          cmake --build . --config Release
+          cmake .. -DCMAKE_CXX_FLAGS='/bigobj' -G Ninja -DCMAKE_C_COMPILER=cl.exe -DCMAKE_CXX_COMPILER=cl.exe -DCMAKE_BUILD_TYPE=Release ${{ matrix.defines }}
+          cmake --build .

      - name: Check AVX512F support
        id: check_avx512f
@@ -215,7 +394,7 @@ jobs:
      - name: Get commit hash
        id: commit
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2

      - name: Pack artifacts
        id: pack_artifacts
@@ -274,6 +453,16 @@ jobs:
        with:
          submodules: recursive

+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 10.15.1
+
      - name: Cache ROCm Installation
        id: cache-rocm
        uses: actions/cache@v4
@@ -338,7 +527,7 @@ jobs:
      - name: Get commit hash
        id: commit
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2

      - name: Pack artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
@@ -360,6 +549,156 @@ jobs:
          path: |
            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-x64.zip

+  ubuntu-latest-rocm:
+    runs-on: ubuntu-latest
+    container: rocm/dev-ubuntu-24.04:7.2
+
+    env:
+      ROCM_VERSION: "7.2"
+      UBUNTU_VERSION: "24.04"
+      GPU_TARGETS: "gfx1151;gfx1150;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
+
+    steps:
+      - run: apt-get update && apt-get install -y git
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          submodules: recursive
+
+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 10.15.1
+
+      - name: Free disk space
+        run: |
+          # Remove preinstalled SDKs and caches not needed for this job
+          sudo rm -rf /usr/share/dotnet || true
+          sudo rm -rf /usr/local/lib/android || true
+          sudo rm -rf /opt/ghc || true
+          sudo rm -rf /usr/local/.ghcup || true
+          sudo rm -rf /opt/hostedtoolcache || true
+
+          # Remove old package lists and caches
+          sudo rm -rf /var/lib/apt/lists/* || true
+          sudo apt clean
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt install -y \
+            cmake \
+            hip-dev \
+            hipblas-dev \
+            ninja-build \
+            rocm-dev \
+            zip
+          # Clean apt caches to recover disk space
+          sudo apt clean
+          sudo rm -rf /var/lib/apt/lists/* || true
+
+      - name: Setup ROCm Environment
+        run: |
+          # Add ROCm to PATH for current session
+          echo "/opt/rocm/bin" >> $GITHUB_PATH
+
+          # Build regex pattern from ${{ env.GPU_TARGETS }} (match target as substring)
+          TARGET_REGEX="($(printf '%s' "${{ env.GPU_TARGETS }}" | sed 's/;/|/g'))"
+
+          # Remove library files for architectures we're not building for to save disk space
+          echo "Cleaning up unneeded architecture files..."
+          cd /opt/rocm/lib/rocblas/library
+          # Keep only our target architectures
+          for file in *; do
+            if printf '%s' "$file" | grep -q 'gfx'; then
+              if ! printf '%s' "$file" | grep -Eq "$TARGET_REGEX"; then
+                echo "Removing $file" &&
+                sudo rm -f "$file";
+              fi
+            fi
+          done
+
+          cd /opt/rocm/lib/hipblaslt/library
+          for file in *; do
+            if printf '%s' "$file" | grep -q 'gfx'; then
+              if ! printf '%s' "$file" | grep -Eq "$TARGET_REGEX"; then
+                echo "Removing $file" &&
+                sudo rm -f "$file";
+              fi
+            fi
+          done
+
+      - name: Build
+        id: cmake_build
+        run: |
+          mkdir build
+          cd build
+          cmake .. -G Ninja \
+            -DCMAKE_CXX_COMPILER=amdclang++ \
+            -DCMAKE_C_COMPILER=amdclang \
+            -DCMAKE_BUILD_TYPE=Release \
+            -DSD_HIPBLAS=ON \
+            -DGPU_TARGETS="${{ env.GPU_TARGETS }}" \
+            -DAMDGPU_TARGETS="${{ env.GPU_TARGETS }}" \
+            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
+            -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+            -DSD_BUILD_SHARED_LIBS=ON
+          cmake --build . --config Release
+
+      - name: Get commit hash
+        id: commit
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: prompt/actions-commit-hash@v2
+
+      - name: Prepare artifacts
+        id: prepare_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          # Copy licenses
+          cp ggml/LICENSE ./build/bin/ggml.txt
+          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
+
+          # Move ROCm runtime libraries (to avoid double space consumption)
+          sudo mv /opt/rocm/lib/librocsparse.so* ./build/bin/
+          sudo mv /opt/rocm/lib/libhsa-runtime64.so* ./build/bin/
+          sudo mv /opt/rocm/lib/libamdhip64.so* ./build/bin/
+          sudo mv /opt/rocm/lib/libhipblas.so* ./build/bin/
+          sudo mv /opt/rocm/lib/libhipblaslt.so* ./build/bin/
+          sudo mv /opt/rocm/lib/librocblas.so* ./build/bin/
+          sudo mv /opt/rocm/lib/rocblas/ ./build/bin/
+          sudo mv /opt/rocm/lib/hipblaslt/ ./build/bin/
+
+      - name: Fetch system info
+        id: system-info
+        run: |
+          echo "CPU_ARCH=`uname -m`" >> "$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: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          cp ggml/LICENSE ./build/bin/ggml.txt
+          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
+          zip -y -r sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm.zip ./build/bin
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm.zip
+          path: |
+            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm.zip
+
  release:
    if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}

@@ -367,6 +706,9 @@ jobs:

    needs:
      - ubuntu-latest-cmake
+      - ubuntu-latest-cmake-vulkan
+      - ubuntu-latest-rocm
+      - build-and-push-docker-images
      - macOS-latest-cmake
      - windows-latest-cmake
      - windows-latest-cmake-hip
@@ -392,7 +734,7 @@ jobs:

      - name: Get commit hash
        id: commit
-        uses: pr-mpt/actions-commit-hash@v2
+        uses: prompt/actions-commit-hash@v2

      - name: Create release
        id: create_release
@@ -1,3 +1,12 @@
 [submodule "ggml"]
    path = ggml
 	url = https://github.com/ggml-org/ggml.git
+[submodule "examples/server/frontend"]
+	path = examples/server/frontend
+	url = https://github.com/leejet/stable-ui.git
+[submodule "thirdparty/libwebp"]
+	path = thirdparty/libwebp
+	url = https://github.com/webmproject/libwebp.git
+[submodule "thirdparty/libwebm"]
+	path = thirdparty/libwebm
+	url = https://github.com/webmproject/libwebm.git
@@ -8,6 +8,11 @@ if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
    set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
 endif()

+if (MSVC)
+    add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
+    add_compile_definitions(_SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING)
+endif()
+
 set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)

@@ -17,6 +22,26 @@ else()
    set(SD_STANDALONE OFF)
 endif()

+set(SD_SUBMODULE_WEBP FALSE)
+if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/thirdparty/libwebp/CMakeLists.txt")
+    set(SD_SUBMODULE_WEBP TRUE)
+endif()
+if(SD_SUBMODULE_WEBP)
+    set(SD_WEBP_DEFAULT ON)
+else()
+    set(SD_WEBP_DEFAULT ${SD_USE_SYSTEM_WEBP})
+endif()
+
+set(SD_SUBMODULE_WEBM FALSE)
+if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/thirdparty/libwebm/CMakeLists.txt")
+    set(SD_SUBMODULE_WEBM TRUE)
+endif()
+if(SD_SUBMODULE_WEBM)
+    set(SD_WEBM_DEFAULT ON)
+else()
+    set(SD_WEBM_DEFAULT ${SD_USE_SYSTEM_WEBM})
+endif()
+
 #
 # Option list
 #
@@ -24,6 +49,10 @@ endif()
 # general
 #option(SD_BUILD_TESTS                "sd: build tests"    ${SD_STANDALONE})
 option(SD_BUILD_EXAMPLES             "sd: build examples" ${SD_STANDALONE})
+option(SD_WEBP                       "sd: enable WebP image I/O support" ${SD_WEBP_DEFAULT})
+option(SD_USE_SYSTEM_WEBP            "sd: link against system libwebp" OFF)
+option(SD_WEBM                       "sd: enable WebM video output support" ${SD_WEBM_DEFAULT})
+option(SD_USE_SYSTEM_WEBM            "sd: link against system libwebm" OFF)
 option(SD_CUDA                       "sd: cuda backend" OFF)
 option(SD_HIPBLAS                    "sd: rocm backend" OFF)
 option(SD_METAL                      "sd: metal backend" OFF)
@@ -31,7 +60,6 @@ option(SD_VULKAN                     "sd: vulkan backend" OFF)
 option(SD_OPENCL                     "sd: opencl backend" OFF)
 option(SD_SYCL                       "sd: sycl backend" OFF)
 option(SD_MUSA                       "sd: musa backend" 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_SHARED_GGML_LIB      "sd: build ggml as a separate shared lib" OFF)
 option(SD_USE_SYSTEM_GGML            "sd: use system-installed GGML library" OFF)
@@ -65,26 +93,71 @@ if (SD_HIPBLAS)
    message("-- Use HIPBLAS as backend stable-diffusion")
    set(GGML_HIP ON)
    add_definitions(-DSD_USE_CUDA)
-    if(SD_FAST_SOFTMAX)
-        set(GGML_CUDA_FAST_SOFTMAX ON)
-    endif()
 endif ()

 if(SD_MUSA)
    message("-- Use MUSA as backend stable-diffusion")
    set(GGML_MUSA ON)
    add_definitions(-DSD_USE_CUDA)
-    if(SD_FAST_SOFTMAX)
-        set(GGML_CUDA_FAST_SOFTMAX ON)
+endif()
+
+if(SD_WEBP)
+    if(NOT SD_SUBMODULE_WEBP AND NOT SD_USE_SYSTEM_WEBP)
+        message(FATAL_ERROR "WebP support enabled but no source found.
+          Either initialize the submodule:\n  git submodule update --init thirdparty/libwebp\n\n"
+          "Or link against system library:\n  cmake (...) -DSD_USE_SYSTEM_WEBP=ON")
+    endif()
+    if(SD_USE_SYSTEM_WEBP)
+        find_package(WebP REQUIRED)
+        add_library(webp ALIAS WebP::webp)
+        # libwebp CMake target naming is not consistent across versions/distros.
+        # Some export WebP::libwebpmux, others export WebP::webpmux.
+        if(TARGET WebP::libwebpmux)
+            add_library(libwebpmux ALIAS WebP::libwebpmux)
+        elseif(TARGET WebP::webpmux)
+            add_library(libwebpmux ALIAS WebP::webpmux)
+        else()
+            message(FATAL_ERROR
+                "Could not find a compatible webpmux target in system WebP package. "
+                "Expected WebP::libwebpmux or WebP::webpmux."
+            )
+        endif()
+    endif()
+endif()
+
+if(SD_WEBM)
+    if(NOT SD_WEBP)
+        message(FATAL_ERROR "SD_WEBM requires SD_WEBP because WebM output reuses libwebp VP8 encoding.")
+    endif()
+    if(NOT SD_SUBMODULE_WEBM AND NOT SD_USE_SYSTEM_WEBM)
+        message(FATAL_ERROR "WebM support enabled but no source found.
+          Either initialize the submodule:\n  git submodule update --init thirdparty/libwebm\n\n"
+          "Or link against system library:\n  cmake (...) -DSD_USE_SYSTEM_WEBM=ON")
+    endif()
+    if(SD_USE_SYSTEM_WEBM)
+        find_path(WEBM_INCLUDE_DIR
+            NAMES mkvmuxer/mkvmuxer.h mkvparser/mkvparser.h common/webmids.h
+            PATH_SUFFIXES webm
+            REQUIRED)
+        find_library(WEBM_LIBRARY
+            NAMES webm libwebm
+            REQUIRED)
+
+        add_library(webm UNKNOWN IMPORTED)
+        set_target_properties(webm PROPERTIES
+            IMPORTED_LOCATION "${WEBM_LIBRARY}"
+            INTERFACE_INCLUDE_DIRECTORIES "${WEBM_INCLUDE_DIR}")
    endif()
 endif()

 set(SD_LIB stable-diffusion)

 file(GLOB SD_LIB_SOURCES
-    "*.h"
-    "*.cpp"
-    "*.hpp"
+    "src/*.h"
+    "src/*.cpp"
+    "src/*.hpp"
+    "src/vocab/*.h"
+    "src/vocab/*.cpp"
 )

 find_program(GIT_EXE NAMES git git.exe NO_CMAKE_FIND_ROOT_PATH)
@@ -114,7 +187,7 @@ endif()
 message(STATUS "stable-diffusion.cpp commit ${SDCPP_BUILD_COMMIT}")

 set_property(
-  SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp
+  SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/src/version.cpp
  APPEND PROPERTY COMPILE_DEFINITIONS
  SDCPP_BUILD_COMMIT=${SDCPP_BUILD_COMMIT} SDCPP_BUILD_VERSION=${SDCPP_BUILD_VERSION}
 )
@@ -177,6 +250,7 @@ endif()
 add_subdirectory(thirdparty)

 target_link_libraries(${SD_LIB} PUBLIC ggml zip)
+target_include_directories(${SD_LIB} PUBLIC . include)
 target_include_directories(${SD_LIB} PUBLIC . thirdparty)
 target_compile_features(${SD_LIB} PUBLIC c_std_11 cxx_std_17)

@@ -185,7 +259,7 @@ if (SD_BUILD_EXAMPLES)
    add_subdirectory(examples)
 endif()

-set(SD_PUBLIC_HEADERS stable-diffusion.h)
+set(SD_PUBLIC_HEADERS include/stable-diffusion.h)
 set_target_properties(${SD_LIB} PROPERTIES PUBLIC_HEADER "${SD_PUBLIC_HEADERS}")

 install(TARGETS ${SD_LIB} LIBRARY PUBLIC_HEADER)
@@ -1,4 +1,4 @@
-ARG UBUNTU_VERSION=22.04
+ARG UBUNTU_VERSION=24.04

 FROM ubuntu:$UBUNTU_VERSION AS build

@@ -18,5 +18,6 @@ RUN apt-get update && \
    apt-get clean

 COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
+COPY --from=build /sd.cpp/build/bin/sd-server /sd-server

 ENTRYPOINT [ "/sd-cli" ]
@@ -0,0 +1,25 @@
+ARG CUDA_VERSION=12.6.3
+ARG UBUNTU_VERSION=24.04
+
+FROM nvidia/cuda:${CUDA_VERSION}-cudnn-devel-ubuntu${UBUNTU_VERSION} AS build
+
+RUN apt-get update && apt-get install -y --no-install-recommends build-essential git ccache cmake
+
+WORKDIR /sd.cpp
+
+COPY . .
+
+ARG CUDACXX=/usr/local/cuda/bin/nvcc
+RUN cmake . -B ./build -DSD_CUDA=ON
+RUN cmake --build ./build --config Release -j$(nproc)
+
+FROM nvidia/cuda:${CUDA_VERSION}-cudnn-runtime-ubuntu${UBUNTU_VERSION} AS runtime
+
+RUN apt-get update && \
+    apt-get install --yes --no-install-recommends libgomp1 && \
+    apt-get clean
+
+COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
+COPY --from=build /sd.cpp/build/bin/sd-server /sd-server
+
+ENTRYPOINT [ "/sd-cli" ]
@@ -19,5 +19,6 @@ RUN mkdir build && cd build && \
 FROM mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64 as runtime

 COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
+COPY --from=build /sd.cpp/build/bin/sd-server /sd-server

 ENTRYPOINT [ "/sd-cli" ]
@@ -15,5 +15,6 @@ RUN mkdir build && cd build && \
 FROM intel/oneapi-basekit:${SYCL_VERSION}-devel-ubuntu24.04 AS runtime

 COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
+COPY --from=build /sd.cpp/build/bin/sd-server /sd-server

 ENTRYPOINT [ "/sd-cli" ]
@@ -0,0 +1,23 @@
+ARG UBUNTU_VERSION=24.04
+
+FROM ubuntu:$UBUNTU_VERSION AS build
+
+RUN apt-get update && apt-get install -y --no-install-recommends build-essential git cmake libvulkan-dev glslc
+
+WORKDIR /sd.cpp
+
+COPY . .
+
+RUN cmake . -B ./build -DSD_VULKAN=ON
+RUN cmake --build ./build --config Release --parallel
+
+FROM ubuntu:$UBUNTU_VERSION AS runtime
+
+RUN apt-get update && \
+    apt-get install --yes --no-install-recommends libgomp1 libvulkan1 mesa-vulkan-drivers && \
+    apt-get clean
+
+COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
+COPY --from=build /sd.cpp/build/bin/sd-server /sd-server
+
+ENTRYPOINT [ "/sd-cli" ]
@@ -15,6 +15,9 @@ API and command-line option may change frequently.***

 ## 🔥Important News

+* **2026/01/18** 🚀 stable-diffusion.cpp now supports **FLUX.2-klein**  
+  👉 Details: [PR #1193](https://github.com/leejet/stable-diffusion.cpp/pull/1193)
+
 * **2025/12/01** 🚀 stable-diffusion.cpp now supports **Z-Image**  
  👉 Details: [PR #1020](https://github.com/leejet/stable-diffusion.cpp/pull/1020)

@@ -43,16 +46,17 @@ API and command-line option may change frequently.***
    - SDXL, [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo)
    - [Some SD1.x and SDXL distilled models](./docs/distilled_sd.md)
    - [SD3/SD3.5](./docs/sd3.md)
-    - [FlUX.1-dev/FlUX.1-schnell](./docs/flux.md)
-    - [FLUX.2-dev](./docs/flux2.md)
+    - [FLUX.1-dev/FLUX.1-schnell](./docs/flux.md)
+    - [FLUX.2-dev/FLUX.2-klein](./docs/flux2.md)
    - [Chroma](./docs/chroma.md)
    - [Chroma1-Radiance](./docs/chroma_radiance.md)
    - [Qwen Image](./docs/qwen_image.md)
    - [Z-Image](./docs/z_image.md)
    - [Ovis-Image](./docs/ovis_image.md)
+    - [Anima](./docs/anima.md)
  - Image Edit Models
    - [FLUX.1-Kontext-dev](./docs/kontext.md)
-    - [Qwen Image Edit/Qwen Image Edit 2509](./docs/qwen_image_edit.md)
+    - [Qwen Image Edit series](./docs/qwen_image_edit.md)
  - Video Models
    - [Wan2.1/Wan2.2](./docs/wan.md)
  - [PhotoMaker](https://github.com/TencentARC/PhotoMaker) support.
@@ -70,7 +74,7 @@ API and command-line option may change frequently.***
  - SYCL
 - Supported weight formats
  - Pytorch checkpoint (`.ckpt` or `.pth`)
-  - Safetensors (`./safetensors`)
+  - Safetensors (`.safetensors`)
  - GGUF (`.gguf`)
 - Supported platforms
    - Linux
@@ -127,15 +131,16 @@ If you want to improve performance or reduce VRAM/RAM usage, please refer to [pe

 - [SD1.x/SD2.x/SDXL](./docs/sd.md)
 - [SD3/SD3.5](./docs/sd3.md)
- [FlUX.1-dev/FlUX.1-schnell](./docs/flux.md)
- [FLUX.2-dev](./docs/flux2.md)
+- [FLUX.1-dev/FLUX.1-schnell](./docs/flux.md)
+- [FLUX.2-dev/FLUX.2-klein](./docs/flux2.md)
 - [FLUX.1-Kontext-dev](./docs/kontext.md)
 - [Chroma](./docs/chroma.md)
 - [🔥Qwen Image](./docs/qwen_image.md)
- [🔥Qwen Image Edit/Qwen Image Edit 2509](./docs/qwen_image_edit.md)
+- [🔥Qwen Image Edit series](./docs/qwen_image_edit.md)
 - [🔥Wan2.1/Wan2.2](./docs/wan.md)
 - [🔥Z-Image](./docs/z_image.md)
 - [Ovis-Image](./docs/ovis_image.md)
+- [Anima](./docs/anima.md)
 - [LoRA](./docs/lora.md)
 - [LCM/LCM-LoRA](./docs/lcm.md)
 - [Using PhotoMaker to personalize image generation](./docs/photo_maker.md)
@@ -143,6 +148,7 @@ If you want to improve performance or reduce VRAM/RAM usage, please refer to [pe
 - [Using TAESD to faster decoding](./docs/taesd.md)
 - [Docker](./docs/docker.md)
 - [Quantization and GGUF](./docs/quantization_and_gguf.md)
+- [Inference acceleration via caching](./docs/caching.md)

 ## Bindings

@@ -1,424 +0,0 @@
-#ifndef __DIFFUSION_MODEL_H__
-#define __DIFFUSION_MODEL_H__
-
-#include "flux.hpp"
-#include "mmdit.hpp"
-#include "qwen_image.hpp"
-#include "unet.hpp"
-#include "wan.hpp"
-#include "z_image.hpp"
-
-struct DiffusionParams {
-    struct ggml_tensor* x                     = nullptr;
-    struct ggml_tensor* timesteps             = nullptr;
-    struct ggml_tensor* context               = nullptr;
-    struct ggml_tensor* c_concat              = nullptr;
-    struct ggml_tensor* y                     = nullptr;
-    struct ggml_tensor* guidance              = nullptr;
-    std::vector<ggml_tensor*> ref_latents     = {};
-    bool increase_ref_index                   = false;
-    int num_video_frames                      = -1;
-    std::vector<struct ggml_tensor*> controls = {};
-    float control_strength                    = 0.f;
-    struct ggml_tensor* vace_context          = nullptr;
-    float vace_strength                       = 1.f;
-    std::vector<int> skip_layers              = {};
-};
-
-struct DiffusionModel {
-    virtual std::string get_desc()                                                      = 0;
-    virtual bool compute(int n_threads,
-                         DiffusionParams diffusion_params,
-                         struct ggml_tensor** output     = nullptr,
-                         struct ggml_context* output_ctx = nullptr)                     = 0;
-    virtual void alloc_params_buffer()                                                  = 0;
-    virtual void free_params_buffer()                                                   = 0;
-    virtual void free_compute_buffer()                                                  = 0;
-    virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) = 0;
-    virtual size_t get_params_buffer_size()                                             = 0;
-    virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter){};
-    virtual int64_t get_adm_in_channels()             = 0;
-    virtual void set_flash_attn_enabled(bool enabled) = 0;
-};
-
-struct UNetModel : public DiffusionModel {
-    UNetModelRunner unet;
-
-    UNetModel(ggml_backend_t backend,
-              bool offload_params_to_cpu,
-              const String2TensorStorage& tensor_storage_map = {},
-              SDVersion version                              = VERSION_SD1)
-        : unet(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model", version) {
-    }
-
-    std::string get_desc() override {
-        return unet.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        unet.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        unet.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        unet.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
-        unet.get_param_tensors(tensors, "model.diffusion_model");
-    }
-
-    size_t get_params_buffer_size() override {
-        return unet.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        unet.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return unet.unet.adm_in_channels;
-    }
-
-    void set_flash_attn_enabled(bool enabled) {
-        unet.set_flash_attention_enabled(enabled);
-    }
-
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
-        return unet.compute(n_threads,
-                            diffusion_params.x,
-                            diffusion_params.timesteps,
-                            diffusion_params.context,
-                            diffusion_params.c_concat,
-                            diffusion_params.y,
-                            diffusion_params.num_video_frames,
-                            diffusion_params.controls,
-                            diffusion_params.control_strength, output, output_ctx);
-    }
-};
-
-struct MMDiTModel : public DiffusionModel {
-    MMDiTRunner mmdit;
-
-    MMDiTModel(ggml_backend_t backend,
-               bool offload_params_to_cpu,
-               const String2TensorStorage& tensor_storage_map = {})
-        : mmdit(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model") {
-    }
-
-    std::string get_desc() override {
-        return mmdit.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        mmdit.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        mmdit.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        mmdit.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
-        mmdit.get_param_tensors(tensors, "model.diffusion_model");
-    }
-
-    size_t get_params_buffer_size() override {
-        return mmdit.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        mmdit.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return 768 + 1280;
-    }
-
-    void set_flash_attn_enabled(bool enabled) {
-        mmdit.set_flash_attention_enabled(enabled);
-    }
-
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
-        return mmdit.compute(n_threads,
-                             diffusion_params.x,
-                             diffusion_params.timesteps,
-                             diffusion_params.context,
-                             diffusion_params.y,
-                             output,
-                             output_ctx,
-                             diffusion_params.skip_layers);
-    }
-};
-
-struct FluxModel : public DiffusionModel {
-    Flux::FluxRunner flux;
-
-    FluxModel(ggml_backend_t backend,
-              bool offload_params_to_cpu,
-              const String2TensorStorage& tensor_storage_map = {},
-              SDVersion version                              = VERSION_FLUX,
-              bool use_mask                                  = false)
-        : flux(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model", version, use_mask) {
-    }
-
-    std::string get_desc() override {
-        return flux.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        flux.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        flux.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        flux.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
-        flux.get_param_tensors(tensors, "model.diffusion_model");
-    }
-
-    size_t get_params_buffer_size() override {
-        return flux.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        flux.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return 768;
-    }
-
-    void set_flash_attn_enabled(bool enabled) {
-        flux.set_flash_attention_enabled(enabled);
-    }
-
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
-        return flux.compute(n_threads,
-                            diffusion_params.x,
-                            diffusion_params.timesteps,
-                            diffusion_params.context,
-                            diffusion_params.c_concat,
-                            diffusion_params.y,
-                            diffusion_params.guidance,
-                            diffusion_params.ref_latents,
-                            diffusion_params.increase_ref_index,
-                            output,
-                            output_ctx,
-                            diffusion_params.skip_layers);
-    }
-};
-
-struct WanModel : public DiffusionModel {
-    std::string prefix;
-    WAN::WanRunner wan;
-
-    WanModel(ggml_backend_t backend,
-             bool offload_params_to_cpu,
-             const String2TensorStorage& tensor_storage_map = {},
-             const std::string prefix                       = "model.diffusion_model",
-             SDVersion version                              = VERSION_WAN2)
-        : prefix(prefix), wan(backend, offload_params_to_cpu, tensor_storage_map, prefix, version) {
-    }
-
-    std::string get_desc() override {
-        return wan.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        wan.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        wan.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        wan.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
-        wan.get_param_tensors(tensors, prefix);
-    }
-
-    size_t get_params_buffer_size() override {
-        return wan.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        wan.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return 768;
-    }
-
-    void set_flash_attn_enabled(bool enabled) {
-        wan.set_flash_attention_enabled(enabled);
-    }
-
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
-        return wan.compute(n_threads,
-                           diffusion_params.x,
-                           diffusion_params.timesteps,
-                           diffusion_params.context,
-                           diffusion_params.y,
-                           diffusion_params.c_concat,
-                           nullptr,
-                           diffusion_params.vace_context,
-                           diffusion_params.vace_strength,
-                           output,
-                           output_ctx);
-    }
-};
-
-struct QwenImageModel : public DiffusionModel {
-    std::string prefix;
-    Qwen::QwenImageRunner qwen_image;
-
-    QwenImageModel(ggml_backend_t backend,
-                   bool offload_params_to_cpu,
-                   const String2TensorStorage& tensor_storage_map = {},
-                   const std::string prefix                       = "model.diffusion_model",
-                   SDVersion version                              = VERSION_QWEN_IMAGE)
-        : prefix(prefix), qwen_image(backend, offload_params_to_cpu, tensor_storage_map, prefix, version) {
-    }
-
-    std::string get_desc() override {
-        return qwen_image.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        qwen_image.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        qwen_image.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        qwen_image.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
-        qwen_image.get_param_tensors(tensors, prefix);
-    }
-
-    size_t get_params_buffer_size() override {
-        return qwen_image.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        qwen_image.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return 768;
-    }
-
-    void set_flash_attn_enabled(bool enabled) {
-        qwen_image.set_flash_attention_enabled(enabled);
-    }
-
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
-        return qwen_image.compute(n_threads,
-                                  diffusion_params.x,
-                                  diffusion_params.timesteps,
-                                  diffusion_params.context,
-                                  diffusion_params.ref_latents,
-                                  true,  // increase_ref_index
-                                  output,
-                                  output_ctx);
-    }
-};
-
-struct ZImageModel : public DiffusionModel {
-    std::string prefix;
-    ZImage::ZImageRunner z_image;
-
-    ZImageModel(ggml_backend_t backend,
-                bool offload_params_to_cpu,
-                const String2TensorStorage& tensor_storage_map = {},
-                const std::string prefix                       = "model.diffusion_model",
-                SDVersion version                              = VERSION_Z_IMAGE)
-        : prefix(prefix), z_image(backend, offload_params_to_cpu, tensor_storage_map, prefix, version) {
-    }
-
-    std::string get_desc() override {
-        return z_image.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        z_image.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        z_image.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        z_image.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
-        z_image.get_param_tensors(tensors, prefix);
-    }
-
-    size_t get_params_buffer_size() override {
-        return z_image.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        z_image.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return 768;
-    }
-
-    void set_flash_attn_enabled(bool enabled) {
-        z_image.set_flash_attention_enabled(enabled);
-    }
-
-    bool compute(int n_threads,
-                 DiffusionParams diffusion_params,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) override {
-        return z_image.compute(n_threads,
-                               diffusion_params.x,
-                               diffusion_params.timesteps,
-                               diffusion_params.context,
-                               diffusion_params.ref_latents,
-                               true,  // increase_ref_index
-                               output,
-                               output_ctx);
-    }
-};
-
-#endif
@@ -0,0 +1,21 @@
+# How to Use
+
+## Download weights
+
+- Download Anima
+    - safetensors: https://huggingface.co/circlestone-labs/Anima/tree/main/split_files/diffusion_models
+    - gguf: https://huggingface.co/Bedovyy/Anima-GGUF/tree/main
+    - gguf Anima2: https://huggingface.co/JusteLeo/Anima2-GGUF/tree/main
+- Download vae
+    - safetensors: https://huggingface.co/circlestone-labs/Anima/tree/main/split_files/vae
+- Download Qwen3-0.6B-Base
+    - safetensors: https://huggingface.co/circlestone-labs/Anima/tree/main/split_files/text_encoders
+    - gguf: https://huggingface.co/mradermacher/Qwen3-0.6B-Base-GGUF/tree/main
+
+## Examples
+
+```sh
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\anima-preview.safetensors --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_06b_base.safetensors  -p "a lovely cat holding a sign says 'anima.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu --diffusion-fa
+```
+
+<img alt="anima image example" src="../assets/anima/example.png" />
@@ -16,6 +16,26 @@ git submodule init
 git submodule update
 ```

+## WebP and WebM Support in Examples
+
+The example applications (`examples/cli` and `examples/server`) use `libwebp` to support WebP image I/O, and `examples/cli` can also use `libwebm` for `.webm` video output. Both are enabled by default. WebM output currently reuses `libwebp` to encode each frame as VP8 before muxing with `libwebm`.
+
+If you do not want WebP/WebM support, you can disable them at configure time:
+
+```shell
+mkdir build && cd build
+cmake .. -DSD_WEBP=OFF -DSD_WEBM=OFF
+cmake --build . --config Release
+```
+
+If the submodules are not available, you can also link against system packages instead:
+
+```shell
+mkdir build && cd build
+cmake .. -DSD_USE_SYSTEM_WEBP=ON -DSD_USE_SYSTEM_WEBM=ON
+cmake --build . --config Release
+```
+
 ## Build (CPU only)

 If you don't have a GPU or CUDA installed, you can build a CPU-only version.
@@ -0,0 +1,141 @@
+## Caching
+
+Caching methods accelerate diffusion inference by reusing intermediate computations when changes between steps are small.
+
+### Cache Modes
+
+| Mode | Target | Description |
+|------|--------|-------------|
+| `ucache` | UNET models | Condition-level caching with error tracking |
+| `easycache` | DiT models | Condition-level cache |
+| `dbcache` | DiT models | Block-level L1 residual threshold |
+| `taylorseer` | DiT models | Taylor series approximation |
+| `cache-dit` | DiT models | Combined DBCache + TaylorSeer |
+| `spectrum` | UNET and DiT models | Chebyshev + Taylor output forecasting |
+
+### UCache (UNET Models)
+
+UCache caches the residual difference (output - input) and reuses it when input changes are below threshold.
+
+```bash
+sd-cli -m model.safetensors -p "a cat" --cache-mode ucache --cache-option "threshold=1.5"
+```
+
+#### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `threshold` | Error threshold for reuse decision | 1.0 |
+| `start` | Start caching at this percent of steps | 0.15 |
+| `end` | Stop caching at this percent of steps | 0.95 |
+| `decay` | Error decay rate (0-1) | 1.0 |
+| `relative` | Scale threshold by output norm (0/1) | 1 |
+| `reset` | Reset error after computing (0/1) | 1 |
+
+#### Reset Parameter
+
+The `reset` parameter controls error accumulation behavior:
+
+- `reset=1` (default): Resets accumulated error after each computed step. More aggressive caching, works well with most samplers.
+- `reset=0`: Keeps error accumulated. More conservative, recommended for `euler_a` sampler.
+
+### EasyCache (DiT Models)
+
+Condition-level caching for DiT models. Caches and reuses outputs when input changes are below threshold.
+
+```bash
+--cache-mode easycache --cache-option "threshold=0.3"
+```
+
+#### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `threshold` | Input change threshold for reuse | 0.2 |
+| `start` | Start caching at this percent of steps | 0.15 |
+| `end` | Stop caching at this percent of steps | 0.95 |
+
+### Cache-DIT (DiT Models)
+
+For DiT models like FLUX and QWEN, use block-level caching modes.
+
+#### DBCache
+
+Caches blocks based on L1 residual difference threshold:
+
+```bash
+--cache-mode dbcache --cache-option "threshold=0.25,warmup=4"
+```
+
+#### TaylorSeer
+
+Uses Taylor series approximation to predict block outputs:
+
+```bash
+--cache-mode taylorseer
+```
+
+#### Cache-DIT (Combined)
+
+Combines DBCache and TaylorSeer:
+
+```bash
+--cache-mode cache-dit
+```
+
+#### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `Fn` | Front blocks to always compute | 8 |
+| `Bn` | Back blocks to always compute | 0 |
+| `threshold` | L1 residual difference threshold | 0.08 |
+| `warmup` | Steps before caching starts | 8 |
+
+#### SCM Options
+
+Steps Computation Mask controls which steps can be cached:
+
+```bash
+--scm-mask "1,1,1,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,1"
+```
+
+Mask values: `1` = compute, `0` = can cache.
+
+| Policy | Description |
+|--------|-------------|
+| `dynamic` | Check threshold before caching |
+| `static` | Always cache on cacheable steps |
+
+```bash
+--scm-policy dynamic
+```
+
+### Spectrum (UNET and DiT Models)
+
+Spectrum uses Chebyshev polynomial fitting blended with Taylor extrapolation to predict denoised outputs, skipping entire forward passes. Based on the paper [Spectrum: Adaptive Spectral Feature Forecasting for Efficient Diffusion Sampling](https://github.com/tingyu215/Spectrum).
+
+```bash
+sd-cli -m model.safetensors -p "a cat" --cache-mode spectrum
+```
+
+#### Parameters
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `w` | Chebyshev vs Taylor blend weight (0=Taylor, 1=Chebyshev) | 0.40 |
+| `m` | Chebyshev polynomial degree | 3 |
+| `lam` | Ridge regression regularization | 1.0 |
+| `window` | Initial window size (compute every N steps) | 2 |
+| `flex` | Window growth per computed step after warmup | 0.50 |
+| `warmup` | Steps to always compute before caching starts | 4 |
+| `stop` | Stop caching at this fraction of total steps | 0.9 |
+
+```
+
+### Performance Tips
+
+- Start with default thresholds and adjust based on output quality
+- Lower threshold = better quality, less speedup
+- Higher threshold = more speedup, potential quality loss
+- More steps generally means more caching opportunities
@@ -1,8 +1,8 @@
-# Running distilled models: SSD1B and SDx.x with tiny U-Nets
+# Running distilled models: SSD1B, Vega and SDx.x with tiny U-Nets

 ## Preface 

-These models feature a reduced U-Net architecture. Unlike standard SDXL models, the SSD-1B U-Net contains only one middle block and fewer attention layers in its up- and down-blocks, resulting in significantly smaller file sizes. Using these models can reduce inference time by more than 33%. For more details, refer to Segmind's paper: https://arxiv.org/abs/2401.02677v1.
+These models feature a reduced U-Net architecture. Unlike standard SDXL models, the SSD-1B and Vega U-Net contains only one middle block and fewer attention layers in its up- and down-blocks, resulting in significantly smaller file sizes. Using these models can reduce inference time by more than 33%. For more details, refer to Segmind's paper: https://arxiv.org/abs/2401.02677v1.
 Similarly, SD1.x- and SD2.x-style models with a tiny U-Net consist of only 6 U-Net blocks, leading to very small files and time savings of up to 50%. For more information, see the paper: https://arxiv.org/pdf/2305.15798.pdf.

 ## SSD1B
@@ -17,7 +17,17 @@ Useful LoRAs are also available:
 * https://huggingface.co/seungminh/lora-swarovski-SSD-1B/resolve/main/pytorch_lora_weights.safetensors
 * https://huggingface.co/kylielee505/mylcmlorassd/resolve/main/pytorch_lora_weights.safetensors

-These files can be used out-of-the-box, unlike the models described in the next section.
+## Vega
+
+Segmind's Vega model is available online here:
+
+ * https://huggingface.co/segmind/Segmind-Vega/resolve/main/segmind-vega.safetensors
+ 
+VegaRT is an example for an LCM-LoRA:
+
+ * https://huggingface.co/segmind/Segmind-VegaRT/resolve/main/pytorch_lora_weights.safetensors
+
+Both files can be used out-of-the-box, unlike the models described in next sections.


 ## SD1.x, SD2.x with tiny U-Nets
@@ -83,7 +93,7 @@ python convert_diffusers_to_original_stable_diffusion.py \
 The file segmind_tiny-sd.ckpt will be generated and is now ready for use with sd.cpp. You can follow a similar process for the other models mentioned above.


-### Another available .ckpt file:
+##### Another available .ckpt file:

 * https://huggingface.co/ClashSAN/small-sd/resolve/main/tinySDdistilled.ckpt

@@ -97,3 +107,31 @@ for key, value in ckpt['state_dict'].items():
        ckpt['state_dict'][key] = value.contiguous()
 torch.save(ckpt, "tinySDdistilled_fixed.ckpt")
 ```
+
+
+### SDXS-512
+
+Another very tiny and **incredibly fast**  model is SDXS by IDKiro et al.  The authors refer to it as *"Real-Time One-Step Latent Diffusion Models with Image Conditions"*. For details read the paper: https://arxiv.org/pdf/2403.16627 . Once again the authors removed some more blocks of U-Net part and unlike other SD1 models they use an adjusted _AutoEncoderTiny_ instead of default _AutoEncoderKL_ for the VAE part.
+
+##### 1. Download the diffusers model from  Hugging Face using Python:
+
+```python
+from diffusers import StableDiffusionPipeline
+pipe = StableDiffusionPipeline.from_pretrained("IDKiro/sdxs-512-dreamshaper")
+pipe.save_pretrained(save_directory="sdxs")
+```
+##### 2. Create a safetensors file
+
+```bash
+python convert_diffusers_to_original_stable_diffusion.py \
+    --model_path  sdxs  --checkpoint_path sdxs.safetensors --half --use_safetensors
+```
+
+##### 3. Run the model as follows:
+
+```bash
+~/stable-diffusion.cpp/build/bin/sd-cli -m sdxs.safetensors -p "portrait of a lovely cat" \
+  --cfg-scale 1 --steps 1
+```
+
+Both options: ``` --cfg-scale 1 ``` and  ``` --steps 1 ``` are mandatory here.                                                 
@@ -1,15 +1,39 @@
-## Docker
+# Docker

-### Building using Docker
+## Run CLI
+
+```shell
+docker run --rm -v /path/to/models:/models -v /path/to/output/:/output ghcr.io/leejet/stable-diffusion.cpp:master [args...]
+# For example
+# docker run --rm -v ./models:/models -v ./build:/output ghcr.io/leejet/stable-diffusion.cpp:master -m /models/sd-v1-4.ckpt -p "a lovely cat" -v -o /output/output.png
+```
+
+## Run server
+
+```shell
+docker run --rm --init -v /path/to/models:/models -v /path/to/output/:/output -p "1234:1234" --entrypoint "/sd-server" ghcr.io/leejet/stable-diffusion.cpp:master [args...]
+# For example
+# docker run --rm --init -v ./models:/models -v ./build:/output -p "1234:1234" --entrypoint "/sd-server" ghcr.io/leejet/stable-diffusion.cpp:master -m /models/sd-v1-4.ckpt -p "a lovely cat" -v -o /output/output.png
+```
+
+## Building using Docker

 ```shell
 docker build -t sd .
 ```

-### Run
+## Building variants using Docker
+
+Vulkan:

 ```shell
-docker run -v /path/to/models:/models -v /path/to/output/:/output sd-cli [args...]
+docker build -f Dockerfile.vulkan -t sd .
+```
+
+## Run locally built image's CLI
+
+```shell
+docker run --rm -v /path/to/models:/models -v /path/to/output/:/output sd [args...]
 # For example
-# docker run -v ./models:/models -v ./build:/output sd-cli -m /models/sd-v1-4.ckpt -p "a lovely cat" -v -o /output/output.png
-```
+# docker run --rm -v ./models:/models -v ./build:/output sd -m /models/sd-v1-4.ckpt -p "a lovely cat" -v -o /output/output.png
+```
@@ -1,6 +1,6 @@
 ## Using ESRGAN to upscale results

-You can use ESRGAN to upscale the generated images. At the moment, only the [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth) model is supported. Support for more models of this architecture will be added soon.
+You can use ESRGAN—such as the model [RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)—to upscale the generated images and improve their overall resolution and clarity.

 - Specify the model path using the `--upscale-model PATH` parameter. example:

@@ -1,15 +1,19 @@
 # How to Use

-## Download weights
+## Flux.2-dev
+
+### Download weights

 - Download FLUX.2-dev
    - gguf: https://huggingface.co/city96/FLUX.2-dev-gguf/tree/main
 - Download vae
    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/main
+- Download FLUX.2-small-decoder (full_encoder_small_decoder.safetensors) as an alternative VAE option
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-small-decoder/tree/main
 - Download Mistral-Small-3.2-24B-Instruct-2506-GGUF
    - gguf: https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF/tree/main

-## Examples
+### Examples

 ```
 .\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux2-dev-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf -r .\kontext_input.png -p "change 'flux.cpp' to 'flux2-dev.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu
@@ -17,5 +21,76 @@

 <img alt="flux2 example" src="../assets/flux2/example.png" />

+## Flux.2 klein 4B / Flux.2 klein base 4B

+### Download weights

+- Download FLUX.2-klein-4B
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-klein-4B
+    - gguf: https://huggingface.co/leejet/FLUX.2-klein-4B-GGUF/tree/main
+- Download FLUX.2-klein-base-4B
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B
+    - gguf: https://huggingface.co/leejet/FLUX.2-klein-base-4B-GGUF/tree/main
+- Download vae
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/main
+- Download FLUX.2-small-decoder (full_encoder_small_decoder.safetensors) as an alternative VAE option
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-small-decoder/tree/main
+- Download Qwen3 4b
+    - safetensors: https://huggingface.co/Comfy-Org/flux2-klein-4B/tree/main/split_files/text_encoders
+    - gguf: https://huggingface.co/unsloth/Qwen3-4B-GGUF/tree/main
+
+### Examples
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu --diffusion-fa
+```
+
+<img alt="flux2-klein-4b" src="../assets/flux2/flux2-klein-4b.png" />
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_4b.safetensors -r .\kontext_input.png -p "change 'flux.cpp' to 'klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu --steps 4
+```
+
+<img alt="flux2-klein-4b-edit" src="../assets/flux2/flux2-klein-4b-edit.png" />
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-base-4b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu --diffusion-fa
+```
+
+<img alt="flux2-klein-base-4b" src="../assets/flux2/flux2-klein-base-4b.png" />
+
+## Flux.2 klein 9B / Flux.2 klein base 9B
+
+### Download weights
+
+- Download FLUX.2-klein-9B
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-klein-9B
+    - gguf: https://huggingface.co/leejet/FLUX.2-klein-9B-GGUF/tree/main
+- Download FLUX.2-klein-base-9B
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9B
+    - gguf: https://huggingface.co/leejet/FLUX.2-klein-base-9B-GGUF/tree/main
+- Download vae
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/main
+- Download Qwen3 8B
+    - safetensors: https://huggingface.co/Comfy-Org/flux2-klein-9B/tree/main/split_files/text_encoders
+    - gguf: https://huggingface.co/unsloth/Qwen3-8B-GGUF/tree/main
+
+### Examples
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu --diffusion-fa
+```
+
+<img alt="flux2-klein-9b" src="../assets/flux2/flux2-klein-9b.png" />
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_8b.safetensors -r .\kontext_input.png -p "change 'flux.cpp' to 'klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu --steps 4
+```
+
+<img alt="flux2-klein-9b-edit" src="../assets/flux2/flux2-klein-9b-edit.png" />
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\flux-2-klein-base-9b.safetensors --vae ..\..\ComfyUI\models\vae\flux2_ae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu --diffusion-fa
+```
+
+<img alt="flux2-klein-base-9b" src="../assets/flux2/flux2-klein-base-9b.png" />
@@ -9,6 +9,9 @@
    - Qwen Image Edit 2509
        - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models
        - gguf: https://huggingface.co/QuantStack/Qwen-Image-Edit-2509-GGUF/tree/main
+    - Qwen Image Edit 2511
+        - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models
+        - gguf: https://huggingface.co/unsloth/Qwen-Image-Edit-2511-GGUF/tree/main
 - Download vae
    - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/vae
 - Download qwen_2.5_vl 7b
@@ -32,4 +35,14 @@
 .\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen-Image-Edit-2509-Q4_K_S.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf --llm_vision ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct.mmproj-Q8_0.gguf --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'Qwen Image Edit 2509'"
 ```

-<img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2509.png" />
+<img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2509.png" />
+
+### Qwen Image Edit 2511
+
+To use the new Qwen Image Edit 2511 mode, the  `--qwen-image-zero-cond-t` flag must be enabled; otherwise, image editing quality will degrade significantly.
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\qwen-image-edit-2511-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'"  --qwen-image-zero-cond-t
+```
+
+<img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2511.png" />
@@ -14,4 +14,26 @@ curl -L -O https://huggingface.co/madebyollin/taesd/resolve/main/diffusion_pytor

 ```bash
 sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat" --taesd ../models/diffusion_pytorch_model.safetensors
-```
+```
+
+### Qwen-Image and wan (TAEHV)
+
+sd.cpp also supports [TAEHV](https://github.com/madebyollin/taehv) (#937), which can be used for Qwen-Image and wan.
+
+- For **Qwen-Image and wan2.1 and wan2.2-A14B**, download the wan2.1 tae [safetensors weights](https://github.com/madebyollin/taehv/blob/main/safetensors/taew2_1.safetensors)
+  
+  Or curl
+  
+  ```bash
+  curl -L -O https://github.com/madebyollin/taehv/raw/refs/heads/main/safetensors/taew2_1.safetensors
+  ```
+
+- For **wan2.2-TI2V-5B**, use the wan2.2 tae [safetensors weights](https://github.com/madebyollin/taehv/blob/main/safetensors/taew2_2.safetensors)
+  
+  Or curl
+  
+  ```bash
+  curl -L -O https://github.com/madebyollin/taehv/raw/refs/heads/main/safetensors/taew2_2.safetensors
+  ```
+
+Then simply replace the `--vae xxx.safetensors` with `--tae xxx.safetensors` in the commands. If it still out of VRAM, add `--vae-conv-direct` to your command though might be slower.
@@ -39,6 +39,9 @@
        - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors
    - wan_2.2_vae (for Wan2.2 TI2V 5B only)
        - safetensors: https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/blob/main/split_files/vae/wan2.2_vae.safetensors
+
+    > Wan models vae requires really much VRAM! If you do not have enough VRAM, please try tae instead, though the results may be poorer. For tae usage, please refer to [taesd](taesd.md)
+
 - Download umt5_xxl
    - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/text_encoders/umt5_xxl_fp16.safetensors
    - gguf: https://huggingface.co/city96/umt5-xxl-encoder-gguf/tree/main
@@ -7,6 +7,9 @@ You can run Z-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — or ev
 - Download Z-Image-Turbo
    - safetensors: https://huggingface.co/Comfy-Org/z_image_turbo/tree/main/split_files/diffusion_models
    - gguf: https://huggingface.co/leejet/Z-Image-Turbo-GGUF/tree/main
+- Download Z-Image
+    - safetensors: https://huggingface.co/Comfy-Org/z_image/tree/main/split_files/diffusion_models
+    - gguf: https://huggingface.co/unsloth/Z-Image-GGUF/tree/main
 - Download vae
    - safetensors: https://huggingface.co/black-forest-labs/FLUX.1-schnell/tree/main
 - Download Qwen3 4b
@@ -15,12 +18,22 @@ You can run Z-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — or ev

 ## Examples

+### Z-Image-Turbo
+
 ```
 .\bin\Release\sd-cli.exe --diffusion-model  z_image_turbo-Q3_K.gguf --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\Qwen3-4B-Instruct-2507-Q4_K_M.gguf -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 1.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512
 ```

 <img width="256" alt="z-image example" src="../assets/z_image/q3_K.png" />

+### Z-Image-Base
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\z_image_bf16.safetensors --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\qwen_3_4b.safetensors -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512
+```
+
+<img width="256" alt="z-image example" src="../assets/z_image/base_bf16.png" />
+
 ## Comparison of Different Quantization Types

 | bf16 | q8_0 | q6_K | q5_0 | q4_K | q4_0 | q3_K | q2_K|
@@ -1,6 +1,20 @@
 set(TARGET sd-cli)

-add_executable(${TARGET} main.cpp)
+add_executable(${TARGET}
+    ../common/common.cpp
+    ../common/log.cpp
+    ../common/media_io.cpp
+    image_metadata.cpp
+    main.cpp
+)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE stable-diffusion ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PUBLIC c_std_11 cxx_std_17)
+target_link_libraries(${TARGET} PRIVATE stable-diffusion zip ${CMAKE_THREAD_LIBS_INIT})
+if(SD_WEBP)
+    target_compile_definitions(${TARGET} PRIVATE SD_USE_WEBP)
+    target_link_libraries(${TARGET} PRIVATE webp libwebpmux)
+endif()
+if(SD_WEBM)
+    target_compile_definitions(${TARGET} PRIVATE SD_USE_WEBM)
+    target_link_libraries(${TARGET} PRIVATE webm)
+endif()
+target_compile_features(${TARGET} PUBLIC c_std_11 cxx_std_17)
@@ -4,16 +4,24 @@
 usage: ./bin/sd-cli  [options]

 CLI Options:
-  -o, --output <string>       path to write result image to (default: ./output.png)
-  --preview-path <string>     path to write preview image to (default: ./preview.png)
+  -o, --output <string>       path to write result image to. you can use printf-style %d format specifiers for image sequences (default:
+                              ./output.png) (eg. output_%03d.png). For video generation, single-file outputs support .avi, .webm, and animated .webp
+  --preview-path <string>     path to write preview image to (default: ./preview.png). Multi-frame previews support .avi, .webm, and animated .webp
  --preview-interval <int>    interval in denoising steps between consecutive updates of the image preview file (default is 1, meaning updating at
                              every step)
+  --output-begin-idx <int>    starting index for output image sequence, must be non-negative (default 0 if specified %d in output path, 1 otherwise)
+  --image <string>            path to the image to inspect (for metadata mode)
+  --metadata-format <string>  metadata output format, one of [text, json] (default: text)
  --canny                     apply canny preprocessor (edge detection)
+  --convert-name              convert tensor name (for convert mode)
  -v, --verbose               print extra info
  --color                     colors the logging tags according to level
  --taesd-preview-only        prevents usage of taesd for decoding the final image. (for use with --preview tae)
  --preview-noisy             enables previewing noisy inputs of the models rather than the denoised outputs
-  -M, --mode                  run mode, one of [img_gen, vid_gen, upscale, convert], default: img_gen
+  --metadata-raw              include raw hex previews for unparsed metadata payloads
+  --metadata-brief            truncate long metadata text values in text output
+  --metadata-all              include structural/container entries such as IHDR, IDAT, and non-metadata JPEG segments
+  -M, --mode                  run mode, one of [img_gen, vid_gen, upscale, convert, metadata], default: img_gen
  --preview                   preview method. must be one of the following [none, proj, tae, vae] (default is none)
  -h, --help                  show this help message and exit

@@ -42,17 +50,22 @@ Context Options:
                                           CPU physical cores
  --chroma-t5-mask-pad <int>               t5 mask pad size of chroma
  --vae-tile-overlap <float>               tile overlap for vae tiling, in fraction of tile size (default: 0.5)
-  --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
  --vae-tiling                             process vae in tiles to reduce memory usage
  --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
  --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
+  --mmap                                   whether to memory-map model
  --control-net-cpu                        keep controlnet in cpu (for low vram)
  --clip-on-cpu                            keep clip in cpu (for low vram)
  --vae-on-cpu                             keep vae in cpu (for low vram)
-  --diffusion-fa                           use flash attention in the diffusion model
+  --fa                                     use flash attention
+  --diffusion-fa                           use flash attention in the diffusion model only
  --diffusion-conv-direct                  use ggml_conv2d_direct in the diffusion model
  --vae-conv-direct                        use ggml_conv2d_direct in the vae model
+  --circular                               enable circular padding for convolutions
+  --circularx                              enable circular RoPE wrapping on x-axis (width) only
+  --circulary                              enable circular RoPE wrapping on y-axis (height) only
  --chroma-disable-dit-mask                disable dit mask for chroma
+  --qwen-image-zero-cond-t                 enable zero_cond_t for qwen image
  --chroma-enable-t5-mask                  enable t5 mask for chroma
  --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). If not specified, the default is the
                                           type of the weight file
@@ -93,6 +106,7 @@ Generation Options:
  --timestep-shift <int>                   shift timestep for NitroFusion models (default: 0). recommended N for NitroSD-Realism around 250 and 500 for
                                           NitroSD-Vibrant
  --upscale-repeats <int>                  Run the ESRGAN upscaler this many times (default: 1)
+  --upscale-tile-size <int>                tile size for ESRGAN upscaling (default: 128)
  --cfg-scale <float>                      unconditional guidance scale: (default: 7.0)
  --img-cfg-scale <float>                  image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)
  --guidance <float>                       distilled guidance scale for models with guidance input (default: 3.5)
@@ -100,14 +114,15 @@ Generation Options:
                                           medium
  --skip-layer-start <float>               SLG enabling point (default: 0.01)
  --skip-layer-end <float>                 SLG disabling point (default: 0.2)
-  --eta <float>                            eta in DDIM, only for DDIM and TCD (default: 0)
+  --eta <float>                            noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a and dpm++2s_a)
+  --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
  --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
  --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input (default: 3.5)
  --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
  --high-noise-skip-layer-start <float>    (high noise) SLG enabling point (default: 0.01)
  --high-noise-skip-layer-end <float>      (high noise) SLG disabling point (default: 0.2)
-  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM and TCD (default: 0)
+  --high-noise-eta <float>                 (high noise) noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a and dpm++2s_a)
  --strength <float>                       strength for noising/unnoising (default: 0.75)
  --pm-style-strength <float>
  --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
@@ -115,16 +130,35 @@ Generation Options:
  --vace-strength <float>                  wan vace strength
  --increase-ref-index                     automatically increase the indices of references images based on the order they are listed (starting with 1).
  --disable-auto-resize-ref-image          disable auto resize of ref images
+  --disable-image-metadata                 do not embed generation metadata on image files
  -s, --seed                               RNG seed (default: 42, use random seed for < 0)
  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
-                                           tcd] (default: euler for Flux/SD3/Wan, euler_a otherwise)
+                                           tcd, res_multistep, res_2s] (default: euler for Flux/SD3/Wan, euler_a
+                                           otherwise)
  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
-                                           ddim_trailing, tcd] default: euler for Flux/SD3/Wan, euler_a otherwise
-  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, lcm],
-                                           default: discrete
+                                           ddim_trailing, tcd, res_multistep, res_2s] default: euler for Flux/SD3/Wan,
+                                           euler_a otherwise
+  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple,
+                                           kl_optimal, lcm, bong_tangent], default: discrete
  --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
  --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
  --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
  -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
-  --easycache                              enable EasyCache for DiT models with optional "threshold,start_percent,end_percent" (default: 0.2,0.15,0.95)
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level),
+                                           'spectrum' (UNET/DiT Chebyshev+Taylor forecasting)
+  --cache-option                           named cache params (key=value format, comma-separated). easycache/ucache:
+                                           threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=;
+                                           spectrum: w=,m=,lam=,window=,flex=,warmup=,stop=. Examples:
+                                           "threshold=0.25" or "threshold=1.5,reset=0" or "w=0.4,window=2"
+  --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g., "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
+  --scm-policy                             SCM policy: 'dynamic' (default) or 'static'
+```
+
+Metadata mode inspects PNG/JPEG container metadata without loading any model:
+
+```bash
+./bin/sd-cli -M metadata --image ./output.png
+./bin/sd-cli -M metadata --image ./output.jpg --metadata-format json
+./bin/sd-cli -M metadata --image ./output.png --metadata-raw
+./bin/sd-cli -M metadata --image ./output.png --metadata-all
 ```
@@ -1,217 +0,0 @@
-#ifndef __AVI_WRITER_H__
-#define __AVI_WRITER_H__
-
-#include <cstdint>
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-
-#include "stable-diffusion.h"
-
-#ifndef INCLUDE_STB_IMAGE_WRITE_H
-#include "stb_image_write.h"
-#endif
-
-typedef struct {
-    uint32_t offset;
-    uint32_t size;
-} avi_index_entry;
-
-// Write 32-bit little-endian integer
-void write_u32_le(FILE* f, uint32_t val) {
-    fwrite(&val, 4, 1, f);
-}
-
-// Write 16-bit little-endian integer
-void write_u16_le(FILE* f, uint16_t val) {
-    fwrite(&val, 2, 1, f);
-}
-
-/**
- * Create an MJPG AVI file from an array of sd_image_t images.
- * Images are encoded to JPEG using stb_image_write.
- *
- * @param filename Output AVI file name.
- * @param images Array of input images.
- * @param num_images Number of images in the array.
- * @param fps Frames per second for the video.
- * @param quality JPEG quality (0-100).
- * @return 0 on success, -1 on failure.
- */
-int create_mjpg_avi_from_sd_images(const char* filename, sd_image_t* images, int num_images, int fps, int quality = 90) {
-    if (num_images == 0) {
-        fprintf(stderr, "Error: Image array is empty.\n");
-        return -1;
-    }
-
-    FILE* f = fopen(filename, "wb");
-    if (!f) {
-        perror("Error opening file for writing");
-        return -1;
-    }
-
-    uint32_t width    = images[0].width;
-    uint32_t height   = images[0].height;
-    uint32_t channels = images[0].channel;
-    if (channels != 3 && channels != 4) {
-        fprintf(stderr, "Error: Unsupported channel count: %u\n", channels);
-        fclose(f);
-        return -1;
-    }
-
-    // --- RIFF AVI Header ---
-    fwrite("RIFF", 4, 1, f);
-    long riff_size_pos = ftell(f);
-    write_u32_le(f, 0);  // Placeholder for file size
-    fwrite("AVI ", 4, 1, f);
-
-    // 'hdrl' LIST (header list)
-    fwrite("LIST", 4, 1, f);
-    write_u32_le(f, 4 + 8 + 56 + 8 + 4 + 8 + 56 + 8 + 40);
-    fwrite("hdrl", 4, 1, f);
-
-    // 'avih' chunk (AVI main header)
-    fwrite("avih", 4, 1, f);
-    write_u32_le(f, 56);
-    write_u32_le(f, 1000000 / fps);       // Microseconds per frame
-    write_u32_le(f, 0);                   // Max bytes per second
-    write_u32_le(f, 0);                   // Padding granularity
-    write_u32_le(f, 0x110);               // Flags (HASINDEX | ISINTERLEAVED)
-    write_u32_le(f, num_images);          // Total frames
-    write_u32_le(f, 0);                   // Initial frames
-    write_u32_le(f, 1);                   // Number of streams
-    write_u32_le(f, width * height * 3);  // Suggested buffer size
-    write_u32_le(f, width);
-    write_u32_le(f, height);
-    write_u32_le(f, 0);  // Reserved
-    write_u32_le(f, 0);  // Reserved
-    write_u32_le(f, 0);  // Reserved
-    write_u32_le(f, 0);  // Reserved
-
-    // 'strl' LIST (stream list)
-    fwrite("LIST", 4, 1, f);
-    write_u32_le(f, 4 + 8 + 56 + 8 + 40);
-    fwrite("strl", 4, 1, f);
-
-    // 'strh' chunk (stream header)
-    fwrite("strh", 4, 1, f);
-    write_u32_le(f, 56);
-    fwrite("vids", 4, 1, f);              // Stream type: video
-    fwrite("MJPG", 4, 1, f);              // Codec: Motion JPEG
-    write_u32_le(f, 0);                   // Flags
-    write_u16_le(f, 0);                   // Priority
-    write_u16_le(f, 0);                   // Language
-    write_u32_le(f, 0);                   // Initial frames
-    write_u32_le(f, 1);                   // Scale
-    write_u32_le(f, fps);                 // Rate
-    write_u32_le(f, 0);                   // Start
-    write_u32_le(f, num_images);          // Length
-    write_u32_le(f, width * height * 3);  // Suggested buffer size
-    write_u32_le(f, (uint32_t)-1);        // Quality
-    write_u32_le(f, 0);                   // Sample size
-    write_u16_le(f, 0);                   // rcFrame.left
-    write_u16_le(f, 0);                   // rcFrame.top
-    write_u16_le(f, 0);                   // rcFrame.right
-    write_u16_le(f, 0);                   // rcFrame.bottom
-
-    // 'strf' chunk (stream format: BITMAPINFOHEADER)
-    fwrite("strf", 4, 1, f);
-    write_u32_le(f, 40);
-    write_u32_le(f, 40);  // biSize
-    write_u32_le(f, width);
-    write_u32_le(f, height);
-    write_u16_le(f, 1);                   // biPlanes
-    write_u16_le(f, 24);                  // biBitCount
-    fwrite("MJPG", 4, 1, f);              // biCompression (FOURCC)
-    write_u32_le(f, width * height * 3);  // biSizeImage
-    write_u32_le(f, 0);                   // XPelsPerMeter
-    write_u32_le(f, 0);                   // YPelsPerMeter
-    write_u32_le(f, 0);                   // Colors used
-    write_u32_le(f, 0);                   // Colors important
-
-    // 'movi' LIST (video frames)
-    // long movi_list_pos = ftell(f);
-    fwrite("LIST", 4, 1, f);
-    long movi_size_pos = ftell(f);
-    write_u32_le(f, 0);  // Placeholder for movi size
-    fwrite("movi", 4, 1, f);
-
-    avi_index_entry* index = (avi_index_entry*)malloc(sizeof(avi_index_entry) * num_images);
-    if (!index) {
-        fclose(f);
-        return -1;
-    }
-
-    // Encode and write each frame as JPEG
-    struct {
-        uint8_t* buf;
-        size_t size;
-    } jpeg_data;
-
-    for (int i = 0; i < num_images; i++) {
-        jpeg_data.buf  = nullptr;
-        jpeg_data.size = 0;
-
-        // Callback function to collect JPEG data into memory
-        auto write_to_buf = [](void* context, void* data, int size) {
-            auto jd = (decltype(jpeg_data)*)context;
-            jd->buf = (uint8_t*)realloc(jd->buf, jd->size + size);
-            memcpy(jd->buf + jd->size, data, size);
-            jd->size += size;
-        };
-
-        // Encode to JPEG in memory
-        stbi_write_jpg_to_func(
-            write_to_buf,
-            &jpeg_data,
-            images[i].width,
-            images[i].height,
-            channels,
-            images[i].data,
-            quality);
-
-        // Write '00dc' chunk (video frame)
-        fwrite("00dc", 4, 1, f);
-        write_u32_le(f, jpeg_data.size);
-        index[i].offset = ftell(f) - 8;
-        index[i].size   = jpeg_data.size;
-        fwrite(jpeg_data.buf, 1, jpeg_data.size, f);
-
-        // Align to even byte size
-        if (jpeg_data.size % 2)
-            fputc(0, f);
-
-        free(jpeg_data.buf);
-    }
-
-    // Finalize 'movi' size
-    long cur_pos   = ftell(f);
-    long movi_size = cur_pos - movi_size_pos - 4;
-    fseek(f, movi_size_pos, SEEK_SET);
-    write_u32_le(f, movi_size);
-    fseek(f, cur_pos, SEEK_SET);
-
-    // Write 'idx1' index
-    fwrite("idx1", 4, 1, f);
-    write_u32_le(f, num_images * 16);
-    for (int i = 0; i < num_images; i++) {
-        fwrite("00dc", 4, 1, f);
-        write_u32_le(f, 0x10);
-        write_u32_le(f, index[i].offset);
-        write_u32_le(f, index[i].size);
-    }
-
-    // Finalize RIFF size
-    cur_pos        = ftell(f);
-    long file_size = cur_pos - riff_size_pos - 4;
-    fseek(f, riff_size_pos, SEEK_SET);
-    write_u32_le(f, file_size);
-    fseek(f, cur_pos, SEEK_SET);
-
-    fclose(f);
-    free(index);
-
-    return 0;
-}
-
-#endif  // __AVI_WRITER_H__
@@ -0,0 +1,21 @@
+#pragma once
+
+#include <iosfwd>
+#include <string>
+
+enum class MetadataOutputFormat {
+    TEXT,
+    JSON,
+};
+
+struct MetadataReadOptions {
+    MetadataOutputFormat output_format = MetadataOutputFormat::TEXT;
+    bool include_raw                   = false;
+    bool brief                         = false;
+    bool include_structural            = false;
+};
+
+bool print_image_metadata(const std::string& image_path,
+                          const MetadataReadOptions& options,
+                          std::ostream& out,
+                          std::string& error);
@@ -15,9 +15,12 @@
 // #include "preprocessing.hpp"
 #include "stable-diffusion.h"

-#include "common/common.hpp"
+#include "common/common.h"
+#include "common/media_io.h"
+#include "common/resource_owners.hpp"
+#include "image_metadata.h"

-#include "avi_writer.h"
+namespace fs = std::filesystem;

 const char* previews_str[] = {
    "none",
@@ -26,13 +29,18 @@ const char* previews_str[] = {
    "vae",
 };

+std::regex format_specifier_regex("(?:[^%]|^)(?:%%)*(%\\d{0,3}d)");
+
 struct SDCliParams {
    SDMode mode             = IMG_GEN;
    std::string output_path = "output.png";
+    int output_begin_idx    = -1;
+    std::string image_path;
+    std::string metadata_format = "text";

    bool verbose          = false;
-    bool version          = false;
    bool canny_preprocess = false;
+    bool convert_name     = false;

    preview_t preview_method = PREVIEW_NONE;
    int preview_interval     = 1;
@@ -41,6 +49,9 @@ struct SDCliParams {
    bool taesd_preview       = false;
    bool preview_noisy       = false;
    bool color               = false;
+    bool metadata_raw        = false;
+    bool metadata_brief      = false;
+    bool metadata_all        = false;

    bool normal_exit = false;

@@ -50,11 +61,19 @@ struct SDCliParams {
        options.string_options = {
            {"-o",
             "--output",
-             "path to write result image to (default: ./output.png)",
+             "path to write result image to. you can use printf-style %d format specifiers for image sequences (default: ./output.png) (eg. output_%03d.png). Single-file video outputs support .avi, .webm, and animated .webp",
             &output_path},
+            {"",
+             "--image",
+             "path to the image to inspect (for metadata mode)",
+             &image_path},
+            {"",
+             "--metadata-format",
+             "metadata output format, one of [text, json] (default: text)",
+             &metadata_format},
            {"",
             "--preview-path",
-             "path to write preview image to (default: ./preview.png)",
+             "path to write preview image to (default: ./preview.png). Multi-frame previews support .avi, .webm, and animated .webp",
             &preview_path},
        };

@@ -63,6 +82,10 @@ struct SDCliParams {
             "--preview-interval",
             "interval in denoising steps between consecutive updates of the image preview file (default is 1, meaning updating at every step)",
             &preview_interval},
+            {"",
+             "--output-begin-idx",
+             "starting index for output image sequence, must be non-negative (default 0 if specified %d in output path, 1 otherwise)",
+             &output_begin_idx},
        };

        options.bool_options = {
@@ -70,14 +93,14 @@ struct SDCliParams {
             "--canny",
             "apply canny preprocessor (edge detection)",
             true, &canny_preprocess},
+            {"",
+             "--convert-name",
+             "convert tensor name (for convert mode)",
+             true, &convert_name},
            {"-v",
             "--verbose",
             "print extra info",
             true, &verbose},
-            {"",
-             "--version",
-             "print stable-diffusion.cpp version",
-             true, &version},
            {"",
             "--color",
             "colors the logging tags according to level",
@@ -90,6 +113,18 @@ struct SDCliParams {
             "--preview-noisy",
             "enables previewing noisy inputs of the models rather than the denoised outputs",
             true, &preview_noisy},
+            {"",
+             "--metadata-raw",
+             "include raw hex previews for unparsed metadata payloads",
+             true, &metadata_raw},
+            {"",
+             "--metadata-brief",
+             "truncate long metadata text values in text output",
+             true, &metadata_brief},
+            {"",
+             "--metadata-all",
+             "include structural/container entries such as IHDR, IDAT, and non-metadata JPEG segments",
+             true, &metadata_all},

        };

@@ -142,7 +177,7 @@ struct SDCliParams {
        options.manual_options = {
            {"-M",
             "--mode",
-             "run mode, one of [img_gen, vid_gen, upscale, convert], default: img_gen",
+             "run mode, one of [img_gen, vid_gen, upscale, convert, metadata], default: img_gen",
             on_mode_arg},
            {"",
             "--preview",
@@ -158,7 +193,7 @@ struct SDCliParams {
    };

    bool process_and_check() {
-        if (output_path.length() == 0) {
+        if (mode != METADATA && output_path.length() == 0) {
            LOG_ERROR("error: the following arguments are required: output_path");
            return false;
        }
@@ -167,6 +202,16 @@ struct SDCliParams {
            if (output_path == "output.png") {
                output_path = "output.gguf";
            }
+        } else if (mode == METADATA) {
+            if (image_path.empty()) {
+                LOG_ERROR("error: metadata mode needs an image path (--image)");
+                return false;
+            }
+            if (metadata_format != "text" && metadata_format != "json") {
+                LOG_ERROR("error: invalid metadata format %s, must be one of [text, json]",
+                          metadata_format.c_str());
+                return false;
+            }
        }
        return true;
    }
@@ -176,15 +221,21 @@ struct SDCliParams {
        oss << "SDCliParams {\n"
            << "  mode: " << modes_str[mode] << ",\n"
            << "  output_path: \"" << output_path << "\",\n"
+            << "  image_path: \"" << image_path << "\",\n"
+            << "  metadata_format: \"" << metadata_format << "\",\n"
            << "  verbose: " << (verbose ? "true" : "false") << ",\n"
            << "  color: " << (color ? "true" : "false") << ",\n"
            << "  canny_preprocess: " << (canny_preprocess ? "true" : "false") << ",\n"
+            << "  convert_name: " << (convert_name ? "true" : "false") << ",\n"
            << "  preview_method: " << previews_str[preview_method] << ",\n"
            << "  preview_interval: " << preview_interval << ",\n"
            << "  preview_path: \"" << preview_path << "\",\n"
            << "  preview_fps: " << preview_fps << ",\n"
            << "  taesd_preview: " << (taesd_preview ? "true" : "false") << ",\n"
-            << "  preview_noisy: " << (preview_noisy ? "true" : "false") << "\n"
+            << "  preview_noisy: " << (preview_noisy ? "true" : "false") << ",\n"
+            << "  metadata_raw: " << (metadata_raw ? "true" : "false") << ",\n"
+            << "  metadata_brief: " << (metadata_brief ? "true" : "false") << ",\n"
+            << "  metadata_all: " << (metadata_all ? "true" : "false") << "\n"
            << "}";
        return oss.str();
    }
@@ -209,78 +260,25 @@ void parse_args(int argc, const char** argv, SDCliParams& cli_params, SDContextP
        exit(cli_params.normal_exit ? 0 : 1);
    }

-    if (!cli_params.process_and_check() ||
-        !ctx_params.process_and_check(cli_params.mode) ||
-        !gen_params.process_and_check(cli_params.mode, ctx_params.lora_model_dir)) {
+    bool valid = cli_params.process_and_check();
+    if (valid && cli_params.mode != METADATA) {
+        valid = ctx_params.process_and_check(cli_params.mode) &&
+                gen_params.process_and_check(cli_params.mode, ctx_params.lora_model_dir);
+    }
+
+    if (!valid) {
        print_usage(argc, argv, options_vec);
        exit(1);
    }
 }

-std::string get_image_params(const SDCliParams& cli_params, const SDContextParams& ctx_params, const SDGenerationParams& gen_params, int64_t seed) {
-    std::string parameter_string = gen_params.prompt_with_lora + "\n";
-    if (gen_params.negative_prompt.size() != 0) {
-        parameter_string += "Negative prompt: " + gen_params.negative_prompt + "\n";
-    }
-    parameter_string += "Steps: " + std::to_string(gen_params.sample_params.sample_steps) + ", ";
-    parameter_string += "CFG scale: " + std::to_string(gen_params.sample_params.guidance.txt_cfg) + ", ";
-    if (gen_params.sample_params.guidance.slg.scale != 0 && gen_params.skip_layers.size() != 0) {
-        parameter_string += "SLG scale: " + std::to_string(gen_params.sample_params.guidance.txt_cfg) + ", ";
-        parameter_string += "Skip layers: [";
-        for (const auto& layer : gen_params.skip_layers) {
-            parameter_string += std::to_string(layer) + ", ";
-        }
-        parameter_string += "], ";
-        parameter_string += "Skip layer start: " + std::to_string(gen_params.sample_params.guidance.slg.layer_start) + ", ";
-        parameter_string += "Skip layer end: " + std::to_string(gen_params.sample_params.guidance.slg.layer_end) + ", ";
-    }
-    parameter_string += "Guidance: " + std::to_string(gen_params.sample_params.guidance.distilled_guidance) + ", ";
-    parameter_string += "Eta: " + std::to_string(gen_params.sample_params.eta) + ", ";
-    parameter_string += "Seed: " + std::to_string(seed) + ", ";
-    parameter_string += "Size: " + std::to_string(gen_params.width) + "x" + std::to_string(gen_params.height) + ", ";
-    parameter_string += "Model: " + sd_basename(ctx_params.model_path) + ", ";
-    parameter_string += "RNG: " + std::string(sd_rng_type_name(ctx_params.rng_type)) + ", ";
-    if (ctx_params.sampler_rng_type != RNG_TYPE_COUNT) {
-        parameter_string += "Sampler RNG: " + std::string(sd_rng_type_name(ctx_params.sampler_rng_type)) + ", ";
-    }
-    parameter_string += "Sampler: " + std::string(sd_sample_method_name(gen_params.sample_params.sample_method));
-    if (!gen_params.custom_sigmas.empty()) {
-        parameter_string += ", Custom Sigmas: [";
-        for (size_t i = 0; i < gen_params.custom_sigmas.size(); ++i) {
-            std::ostringstream oss;
-            oss << std::fixed << std::setprecision(4) << gen_params.custom_sigmas[i];
-            parameter_string += oss.str() + (i == gen_params.custom_sigmas.size() - 1 ? "" : ", ");
-        }
-        parameter_string += "]";
-    } else if (gen_params.sample_params.scheduler != SCHEDULER_COUNT) {  // Only show schedule if not using custom sigmas
-        parameter_string += " " + std::string(sd_scheduler_name(gen_params.sample_params.scheduler));
-    }
-    parameter_string += ", ";
-    for (const auto& te : {ctx_params.clip_l_path, ctx_params.clip_g_path, ctx_params.t5xxl_path, ctx_params.llm_path, ctx_params.llm_vision_path}) {
-        if (!te.empty()) {
-            parameter_string += "TE: " + sd_basename(te) + ", ";
-        }
-    }
-    if (!ctx_params.diffusion_model_path.empty()) {
-        parameter_string += "Unet: " + sd_basename(ctx_params.diffusion_model_path) + ", ";
-    }
-    if (!ctx_params.vae_path.empty()) {
-        parameter_string += "VAE: " + sd_basename(ctx_params.vae_path) + ", ";
-    }
-    if (gen_params.clip_skip != -1) {
-        parameter_string += "Clip skip: " + std::to_string(gen_params.clip_skip) + ", ";
-    }
-    parameter_string += "Version: stable-diffusion.cpp";
-    return parameter_string;
-}
-
 void sd_log_cb(enum sd_log_level_t level, const char* log, void* data) {
    SDCliParams* cli_params = (SDCliParams*)data;
    log_print(level, log, cli_params->verbose, cli_params->color);
 }

 bool load_images_from_dir(const std::string dir,
-                          std::vector<sd_image_t>& images,
+                          SDImageVec& images,
                          int expected_width  = 0,
                          int expected_height = 0,
                          int max_image_num   = 0,
@@ -307,7 +305,7 @@ bool load_images_from_dir(const std::string dir,
        std::string ext  = entry.path().extension().string();
        std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);

-        if (ext == ".jpg" || ext == ".jpeg" || ext == ".png" || ext == ".bmp") {
+        if (ext == ".jpg" || ext == ".jpeg" || ext == ".png" || ext == ".bmp" || ext == ".webp") {
            LOG_DEBUG("load image %zu from '%s'", images.size(), path.c_str());
            int width             = 0;
            int height            = 0;
@@ -322,7 +320,7 @@ bool load_images_from_dir(const std::string dir,
                              3,
                              image_buffer});

-            if (max_image_num > 0 && images.size() >= max_image_num) {
+            if (max_image_num > 0 && static_cast<int>(images.size()) >= max_image_num) {
                break;
            }
        }
@@ -337,12 +335,144 @@ void step_callback(int step, int frame_count, sd_image_t* image, bool is_noisy,
    // is_noisy is set to true if the preview corresponds to noisy latents, false if it's denoised latents
    // unused in this app, it will either be always noisy or always denoised here
    if (frame_count == 1) {
-        stbi_write_png(cli_params->preview_path.c_str(), image->width, image->height, image->channel, image->data, 0);
+        if (!write_image_to_file(cli_params->preview_path,
+                                 image->data,
+                                 image->width,
+                                 image->height,
+                                 image->channel)) {
+            LOG_ERROR("save preview image to '%s' failed", cli_params->preview_path.c_str());
+        }
    } else {
-        create_mjpg_avi_from_sd_images(cli_params->preview_path.c_str(), image, frame_count, cli_params->preview_fps);
+        if (create_video_from_sd_images(cli_params->preview_path.c_str(), image, frame_count, cli_params->preview_fps) != 0) {
+            LOG_ERROR("save preview video to '%s' failed", cli_params->preview_path.c_str());
+        }
    }
 }

+std::string format_frame_idx(std::string pattern, int frame_idx) {
+    std::smatch match;
+    std::string result = pattern;
+    while (std::regex_search(result, match, format_specifier_regex)) {
+        std::string specifier = match.str(1);
+        char buffer[32];
+        snprintf(buffer, sizeof(buffer), specifier.c_str(), frame_idx);
+        result.replace(match.position(1), match.length(1), buffer);
+    }
+
+    // Then replace all '%%' with '%'
+    size_t pos = 0;
+    while ((pos = result.find("%%", pos)) != std::string::npos) {
+        result.replace(pos, 2, "%");
+        pos += 1;
+    }
+    return result;
+}
+
+bool save_results(const SDCliParams& cli_params,
+                  const SDContextParams& ctx_params,
+                  const SDGenerationParams& gen_params,
+                  sd_image_t* results,
+                  int num_results) {
+    if (results == nullptr || num_results <= 0) {
+        return false;
+    }
+
+    namespace fs      = std::filesystem;
+    fs::path out_path = cli_params.output_path;
+
+    if (!out_path.parent_path().empty()) {
+        std::error_code ec;
+        fs::create_directories(out_path.parent_path(), ec);
+        if (ec) {
+            LOG_ERROR("failed to create directory '%s': %s",
+                      out_path.parent_path().string().c_str(), ec.message().c_str());
+            return false;
+        }
+    }
+
+    fs::path base_path = out_path;
+    fs::path ext       = out_path.has_extension() ? out_path.extension() : fs::path{};
+
+    std::string ext_lower = ext.string();
+    std::transform(ext_lower.begin(), ext_lower.end(), ext_lower.begin(), ::tolower);
+    const EncodedImageFormat output_format = encoded_image_format_from_path(out_path.string());
+    if (!ext.empty()) {
+        if (output_format == EncodedImageFormat::JPEG ||
+            output_format == EncodedImageFormat::PNG ||
+            output_format == EncodedImageFormat::WEBP ||
+            ext_lower == ".avi" ||
+            ext_lower == ".webm") {
+            base_path.replace_extension();
+        }
+    }
+
+    int output_begin_idx = cli_params.output_begin_idx;
+    if (output_begin_idx < 0) {
+        output_begin_idx = 0;
+    }
+
+    auto write_image = [&](const fs::path& path, int idx) {
+        const sd_image_t& img = results[idx];
+        if (!img.data)
+            return false;
+
+        std::string params = gen_params.embed_image_metadata
+                                 ? get_image_params(ctx_params, gen_params, gen_params.seed + idx)
+                                 : "";
+        const bool ok      = write_image_to_file(path.string(), img.data, img.width, img.height, img.channel, params, 90);
+        LOG_INFO("save result image %d to '%s' (%s)", idx, path.string().c_str(), ok ? "success" : "failure");
+        return ok;
+    };
+
+    int sucessful_reults = 0;
+
+    if (std::regex_search(cli_params.output_path, format_specifier_regex)) {
+        if (output_format == EncodedImageFormat::UNKNOWN)
+            ext = ".png";
+        fs::path pattern = base_path;
+        pattern += ext;
+
+        for (int i = 0; i < num_results; ++i) {
+            fs::path img_path = format_frame_idx(pattern.string(), output_begin_idx + i);
+            if (write_image(img_path, i)) {
+                sucessful_reults++;
+            }
+        }
+        LOG_INFO("%d/%d images saved", sucessful_reults, num_results);
+        return sucessful_reults != 0;
+    }
+
+    if (cli_params.mode == VID_GEN && num_results > 1) {
+        if (ext_lower != ".avi" && ext_lower != ".webp" && ext_lower != ".webm")
+            ext = ".avi";
+        fs::path video_path = base_path;
+        video_path += ext;
+        if (create_video_from_sd_images(video_path.string().c_str(), results, num_results, gen_params.fps) == 0) {
+            LOG_INFO("save result video to '%s'", video_path.string().c_str());
+            return true;
+        } else {
+            LOG_ERROR("Failed to save result video to '%s'", video_path.string().c_str());
+            return false;
+        }
+    }
+
+    if (output_format == EncodedImageFormat::UNKNOWN)
+        ext = ".png";
+
+    for (int i = 0; i < num_results; ++i) {
+        fs::path img_path = base_path;
+        if (num_results > 1) {
+            img_path += "_" + std::to_string(output_begin_idx + i);
+        }
+        img_path += ext;
+        if (write_image(img_path, i)) {
+            sucessful_reults++;
+        }
+    }
+    LOG_INFO("%d/%d images saved", sucessful_reults, num_results);
+    return sucessful_reults != 0;
+}
+
 int main(int argc, const char* argv[]) {
    if (argc > 1 && std::string(argv[1]) == "--version") {
        std::cout << version_string() << "\n";
@@ -354,9 +484,27 @@ int main(int argc, const char* argv[]) {
    SDGenerationParams gen_params;

    parse_args(argc, argv, cli_params, ctx_params, gen_params);
-    if (cli_params.verbose || cli_params.version) {
-        std::cout << version_string() << "\n";
+    sd_set_log_callback(sd_log_cb, (void*)&cli_params);
+    log_verbose = cli_params.verbose;
+    log_color   = cli_params.color;
+
+    if (cli_params.mode == METADATA) {
+        MetadataReadOptions options;
+        options.output_format      = cli_params.metadata_format == "json"
+                                         ? MetadataOutputFormat::JSON
+                                         : MetadataOutputFormat::TEXT;
+        options.include_raw        = cli_params.metadata_raw;
+        options.brief              = cli_params.metadata_brief;
+        options.include_structural = cli_params.metadata_all;
+
+        std::string error;
+        if (!print_image_metadata(cli_params.image_path, options, std::cout, error)) {
+            LOG_ERROR("%s", error.c_str());
+            return 1;
+        }
+        return 0;
    }
+
    if (gen_params.video_frames > 4) {
        size_t last_dot_pos   = cli_params.preview_path.find_last_of(".");
        std::string base_path = cli_params.preview_path;
@@ -374,9 +522,6 @@ int main(int argc, const char* argv[]) {
    if (cli_params.preview_method == PREVIEW_PROJ)
        cli_params.preview_fps /= 4;

-    sd_set_log_callback(sd_log_cb, (void*)&cli_params);
-    log_verbose = cli_params.verbose;
-    log_color   = cli_params.color;
    sd_set_preview_callback(step_callback,
                            cli_params.preview_method,
                            cli_params.preview_interval,
@@ -384,19 +529,19 @@ int main(int argc, const char* argv[]) {
                            cli_params.preview_noisy,
                            (void*)&cli_params);

-    if (cli_params.verbose) {
-        LOG_INFO("%s", sd_get_system_info());
-        LOG_INFO("%s", cli_params.to_string().c_str());
-        LOG_INFO("%s", ctx_params.to_string().c_str());
-        LOG_INFO("%s", gen_params.to_string().c_str());
-    }
+    LOG_DEBUG("version: %s", version_string().c_str());
+    LOG_DEBUG("%s", sd_get_system_info());
+    LOG_DEBUG("%s", cli_params.to_string().c_str());
+    LOG_DEBUG("%s", ctx_params.to_string().c_str());
+    LOG_DEBUG("%s", gen_params.to_string().c_str());

    if (cli_params.mode == CONVERT) {
        bool success = convert(ctx_params.model_path.c_str(),
                               ctx_params.vae_path.c_str(),
                               cli_params.output_path.c_str(),
                               ctx_params.wtype,
-                               ctx_params.tensor_type_rules.c_str());
+                               ctx_params.tensor_type_rules.c_str(),
+                               cli_params.convert_name);
        if (!success) {
            LOG_ERROR("convert '%s'/'%s' to '%s' failed",
                      ctx_params.model_path.c_str(),
@@ -412,94 +557,92 @@ int main(int argc, const char* argv[]) {
        }
    }

-    bool vae_decode_only     = true;
-    sd_image_t init_image    = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
-    sd_image_t end_image     = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
-    sd_image_t control_image = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 3, nullptr};
-    sd_image_t mask_image    = {(uint32_t)gen_params.width, (uint32_t)gen_params.height, 1, nullptr};
-    std::vector<sd_image_t> ref_images;
-    std::vector<sd_image_t> pmid_images;
-    std::vector<sd_image_t> control_frames;
+    bool vae_decode_only = true;
+    SDImageOwner init_image({0, 0, 3, nullptr});
+    SDImageOwner end_image({0, 0, 3, nullptr});
+    SDImageOwner control_image({0, 0, 3, nullptr});
+    SDImageOwner mask_image({0, 0, 1, nullptr});
+    SDImageVec ref_images;
+    SDImageVec pmid_images;
+    SDImageVec control_frames;

-    auto release_all_resources = [&]() {
-        free(init_image.data);
-        free(end_image.data);
-        free(control_image.data);
-        free(mask_image.data);
-        for (auto image : ref_images) {
-            free(image.data);
-            image.data = nullptr;
+    auto load_image_and_update_size = [&](const std::string& path,
+                                          SDImageOwner& image,
+                                          bool resize_image    = true,
+                                          int expected_channel = 3) -> bool {
+        int expected_width  = 0;
+        int expected_height = 0;
+        if (resize_image && gen_params.width_and_height_are_set()) {
+            expected_width  = gen_params.width;
+            expected_height = gen_params.height;
        }
-        ref_images.clear();
-        for (auto image : pmid_images) {
-            free(image.data);
-            image.data = nullptr;
+
+        if (!load_sd_image_from_file(image.put(), path.c_str(), expected_width, expected_height, expected_channel)) {
+            LOG_ERROR("load image from '%s' failed", path.c_str());
+            return false;
        }
-        pmid_images.clear();
-        for (auto image : control_frames) {
-            free(image.data);
-            image.data = nullptr;
-        }
-        control_frames.clear();
+
+        gen_params.set_width_and_height_if_unset(image.get().width, image.get().height);
+        return true;
    };

    if (gen_params.init_image_path.size() > 0) {
        vae_decode_only = false;
-
-        int width       = 0;
-        int height      = 0;
-        init_image.data = load_image_from_file(gen_params.init_image_path.c_str(), width, height, gen_params.width, gen_params.height);
-        if (init_image.data == nullptr) {
-            LOG_ERROR("load image from '%s' failed", gen_params.init_image_path.c_str());
-            release_all_resources();
+        if (!load_image_and_update_size(gen_params.init_image_path, init_image)) {
            return 1;
        }
    }

    if (gen_params.end_image_path.size() > 0) {
        vae_decode_only = false;
-
-        int width      = 0;
-        int height     = 0;
-        end_image.data = load_image_from_file(gen_params.end_image_path.c_str(), width, height, gen_params.width, gen_params.height);
-        if (end_image.data == nullptr) {
-            LOG_ERROR("load image from '%s' failed", gen_params.end_image_path.c_str());
-            release_all_resources();
+        if (!load_image_and_update_size(gen_params.end_image_path, end_image)) {
            return 1;
        }
    }

+    if (gen_params.ref_image_paths.size() > 0) {
+        vae_decode_only = false;
+        for (auto& path : gen_params.ref_image_paths) {
+            SDImageOwner ref_image({0, 0, 3, nullptr});
+            if (!load_image_and_update_size(path, ref_image, false)) {
+                return 1;
+            }
+            ref_images.push_back(std::move(ref_image));
+        }
+    }
+
    if (gen_params.mask_image_path.size() > 0) {
-        int c           = 0;
-        int width       = 0;
-        int height      = 0;
-        mask_image.data = load_image_from_file(gen_params.mask_image_path.c_str(), width, height, gen_params.width, gen_params.height, 1);
-        if (mask_image.data == nullptr) {
+        if (!load_sd_image_from_file(mask_image.put(),
+                                     gen_params.mask_image_path.c_str(),
+                                     gen_params.get_resolved_width(),
+                                     gen_params.get_resolved_height(),
+                                     1)) {
            LOG_ERROR("load image from '%s' failed", gen_params.mask_image_path.c_str());
-            release_all_resources();
            return 1;
        }
    } else {
-        mask_image.data = (uint8_t*)malloc(gen_params.width * gen_params.height);
-        memset(mask_image.data, 255, gen_params.width * gen_params.height);
-        if (mask_image.data == nullptr) {
+        sd_image_t generated_mask = {0, 0, 1, nullptr};
+        generated_mask.data       = (uint8_t*)malloc(gen_params.get_resolved_width() * gen_params.get_resolved_height());
+        if (generated_mask.data == nullptr) {
            LOG_ERROR("malloc mask image failed");
-            release_all_resources();
            return 1;
        }
+        generated_mask.width  = gen_params.get_resolved_width();
+        generated_mask.height = gen_params.get_resolved_height();
+        memset(generated_mask.data, 255, gen_params.get_resolved_width() * gen_params.get_resolved_height());
+        mask_image.reset(generated_mask);
    }

    if (gen_params.control_image_path.size() > 0) {
-        int width          = 0;
-        int height         = 0;
-        control_image.data = load_image_from_file(gen_params.control_image_path.c_str(), width, height, gen_params.width, gen_params.height);
-        if (control_image.data == nullptr) {
+        if (!load_sd_image_from_file(control_image.put(),
+                                     gen_params.control_image_path.c_str(),
+                                     gen_params.get_resolved_width(),
+                                     gen_params.get_resolved_height())) {
            LOG_ERROR("load image from '%s' failed", gen_params.control_image_path.c_str());
-            release_all_resources();
            return 1;
        }
        if (cli_params.canny_preprocess) {  // apply preprocessor
-            preprocess_canny(control_image,
+            preprocess_canny(control_image.get(),
                             0.08f,
                             0.08f,
                             0.8f,
@@ -508,32 +651,13 @@ int main(int argc, const char* argv[]) {
        }
    }

-    if (gen_params.ref_image_paths.size() > 0) {
-        vae_decode_only = false;
-        for (auto& path : gen_params.ref_image_paths) {
-            int width             = 0;
-            int height            = 0;
-            uint8_t* image_buffer = load_image_from_file(path.c_str(), width, height);
-            if (image_buffer == nullptr) {
-                LOG_ERROR("load image from '%s' failed", path.c_str());
-                release_all_resources();
-                return 1;
-            }
-            ref_images.push_back({(uint32_t)width,
-                                  (uint32_t)height,
-                                  3,
-                                  image_buffer});
-        }
-    }
-
    if (!gen_params.control_video_path.empty()) {
        if (!load_images_from_dir(gen_params.control_video_path,
                                  control_frames,
-                                  gen_params.width,
-                                  gen_params.height,
+                                  gen_params.get_resolved_width(),
+                                  gen_params.get_resolved_height(),
                                  gen_params.video_frames,
                                  cli_params.verbose)) {
-            release_all_resources();
            return 1;
        }
    }
@@ -545,7 +669,6 @@ int main(int argc, const char* argv[]) {
                                  0,
                                  0,
                                  cli_params.verbose)) {
-            release_all_resources();
            return 1;
        }
    }
@@ -556,39 +679,30 @@ int main(int argc, const char* argv[]) {

    sd_ctx_params_t sd_ctx_params = ctx_params.to_sd_ctx_params_t(vae_decode_only, true, cli_params.taesd_preview);

-    sd_image_t* results = nullptr;
-    int num_results     = 0;
+    SDImageVec results;
+    int num_results = 0;

    if (cli_params.mode == UPSCALE) {
        num_results = 1;
-        results     = (sd_image_t*)calloc(num_results, sizeof(sd_image_t));
-        if (results == nullptr) {
-            LOG_INFO("failed to allocate results array");
-            release_all_resources();
-            return 1;
-        }
-
-        results[0]      = init_image;
-        init_image.data = nullptr;
+        results.push_back(init_image.release());
    } else {
-        sd_ctx_t* sd_ctx = new_sd_ctx(&sd_ctx_params);
+        SDCtxPtr sd_ctx(new_sd_ctx(&sd_ctx_params));

        if (sd_ctx == nullptr) {
            LOG_INFO("new_sd_ctx_t failed");
-            release_all_resources();
            return 1;
        }

        if (gen_params.sample_params.sample_method == SAMPLE_METHOD_COUNT) {
-            gen_params.sample_params.sample_method = sd_get_default_sample_method(sd_ctx);
+            gen_params.sample_params.sample_method = sd_get_default_sample_method(sd_ctx.get());
        }

        if (gen_params.high_noise_sample_params.sample_method == SAMPLE_METHOD_COUNT) {
-            gen_params.high_noise_sample_params.sample_method = sd_get_default_sample_method(sd_ctx);
+            gen_params.high_noise_sample_params.sample_method = sd_get_default_sample_method(sd_ctx.get());
        }

        if (gen_params.sample_params.scheduler == SCHEDULER_COUNT) {
-            gen_params.sample_params.scheduler = sd_get_default_scheduler(sd_ctx);
+            gen_params.sample_params.scheduler = sd_get_default_scheduler(sd_ctx.get(), gen_params.sample_params.sample_method);
        }

        if (cli_params.mode == IMG_GEN) {
@@ -598,19 +712,19 @@ int main(int argc, const char* argv[]) {
                gen_params.prompt.c_str(),
                gen_params.negative_prompt.c_str(),
                gen_params.clip_skip,
-                init_image,
+                init_image.get(),
                ref_images.data(),
                (int)ref_images.size(),
                gen_params.auto_resize_ref_image,
                gen_params.increase_ref_index,
-                mask_image,
-                gen_params.width,
-                gen_params.height,
+                mask_image.get(),
+                gen_params.get_resolved_width(),
+                gen_params.get_resolved_height(),
                gen_params.sample_params,
                gen_params.strength,
                gen_params.seed,
                gen_params.batch_count,
-                control_image,
+                control_image.get(),
                gen_params.control_strength,
                {
                    pmid_images.data(),
@@ -618,12 +732,12 @@ int main(int argc, const char* argv[]) {
                    gen_params.pm_id_embed_path.c_str(),
                    gen_params.pm_style_strength,
                },  // pm_params
-                ctx_params.vae_tiling_params,
-                gen_params.easycache_params,
+                gen_params.vae_tiling_params,
+                gen_params.cache_params,
            };

-            results     = generate_image(sd_ctx, &img_gen_params);
            num_results = gen_params.batch_count;
+            results.adopt(generate_image(sd_ctx.get(), &img_gen_params), num_results);
        } else if (cli_params.mode == VID_GEN) {
            sd_vid_gen_params_t vid_gen_params = {
                gen_params.lora_vec.data(),
@@ -631,12 +745,12 @@ int main(int argc, const char* argv[]) {
                gen_params.prompt.c_str(),
                gen_params.negative_prompt.c_str(),
                gen_params.clip_skip,
-                init_image,
-                end_image,
+                init_image.get(),
+                end_image.get(),
                control_frames.data(),
                (int)control_frames.size(),
-                gen_params.width,
-                gen_params.height,
+                gen_params.get_resolved_width(),
+                gen_params.get_resolved_height(),
                gen_params.sample_params,
                gen_params.high_noise_sample_params,
                gen_params.moe_boundary,
@@ -644,28 +758,27 @@ int main(int argc, const char* argv[]) {
                gen_params.seed,
                gen_params.video_frames,
                gen_params.vace_strength,
-                gen_params.easycache_params,
+                gen_params.vae_tiling_params,
+                gen_params.cache_params,
            };

-            results = generate_video(sd_ctx, &vid_gen_params, &num_results);
+            sd_image_t* generated_video = generate_video(sd_ctx.get(), &vid_gen_params, &num_results);
+            results.adopt(generated_video, num_results);
        }

-        if (results == nullptr) {
+        if (!results) {
            LOG_ERROR("generate failed");
-            free_sd_ctx(sd_ctx);
            return 1;
        }
-
-        free_sd_ctx(sd_ctx);
    }

    int upscale_factor = 4;  // unused for RealESRGAN_x4plus_anime_6B.pth
    if (ctx_params.esrgan_path.size() > 0 && gen_params.upscale_repeats > 0) {
-        upscaler_ctx_t* upscaler_ctx = new_upscaler_ctx(ctx_params.esrgan_path.c_str(),
-                                                        ctx_params.offload_params_to_cpu,
-                                                        ctx_params.diffusion_conv_direct,
-                                                        ctx_params.n_threads,
-                                                        gen_params.upscale_tile_size);
+        UpscalerCtxPtr upscaler_ctx(new_upscaler_ctx(ctx_params.esrgan_path.c_str(),
+                                                     ctx_params.offload_params_to_cpu,
+                                                     ctx_params.diffusion_conv_direct,
+                                                     ctx_params.n_threads,
+                                                     gen_params.upscale_tile_size));

        if (upscaler_ctx == nullptr) {
            LOG_ERROR("new_upscaler_ctx failed");
@@ -674,91 +787,24 @@ int main(int argc, const char* argv[]) {
                if (results[i].data == nullptr) {
                    continue;
                }
-                sd_image_t current_image = results[i];
+                SDImageOwner current_image(results[i]);
+                results[i] = {0, 0, 0, nullptr};
                for (int u = 0; u < gen_params.upscale_repeats; ++u) {
-                    sd_image_t upscaled_image = upscale(upscaler_ctx, current_image, upscale_factor);
-                    if (upscaled_image.data == nullptr) {
+                    SDImageOwner upscaled_image(upscale(upscaler_ctx.get(), current_image.get(), upscale_factor));
+                    if (upscaled_image.get().data == nullptr) {
                        LOG_ERROR("upscale failed");
                        break;
                    }
-                    free(current_image.data);
-                    current_image = upscaled_image;
+                    current_image = std::move(upscaled_image);
                }
-                results[i] = current_image;  // Set the final upscaled image as the result
+                results[i] = current_image.release();  // Set the final upscaled image as the result
            }
        }
    }

-    // create directory if not exists
-    {
-        const fs::path out_path = cli_params.output_path;
-        if (const fs::path out_dir = out_path.parent_path(); !out_dir.empty()) {
-            std::error_code ec;
-            fs::create_directories(out_dir, ec);  // OK if already exists
-            if (ec) {
-                LOG_ERROR("failed to create directory '%s': %s",
-                          out_dir.string().c_str(), ec.message().c_str());
-                return 1;
-            }
-        }
+    if (!save_results(cli_params, ctx_params, gen_params, results.data(), num_results)) {
+        return 1;
    }

-    std::string base_path;
-    std::string file_ext;
-    std::string file_ext_lower;
-    bool is_jpg;
-    size_t last_dot_pos   = cli_params.output_path.find_last_of(".");
-    size_t last_slash_pos = std::min(cli_params.output_path.find_last_of("/"),
-                                     cli_params.output_path.find_last_of("\\"));
-    if (last_dot_pos != std::string::npos && (last_slash_pos == std::string::npos || last_dot_pos > last_slash_pos)) {  // filename has extension
-        base_path = cli_params.output_path.substr(0, last_dot_pos);
-        file_ext = file_ext_lower = cli_params.output_path.substr(last_dot_pos);
-        std::transform(file_ext.begin(), file_ext.end(), file_ext_lower.begin(), ::tolower);
-        is_jpg = (file_ext_lower == ".jpg" || file_ext_lower == ".jpeg" || file_ext_lower == ".jpe");
-    } else {
-        base_path = cli_params.output_path;
-        file_ext = file_ext_lower = "";
-        is_jpg                    = false;
-    }
-
-    if (cli_params.mode == VID_GEN && num_results > 1) {
-        std::string vid_output_path = cli_params.output_path;
-        if (file_ext_lower == ".png") {
-            vid_output_path = base_path + ".avi";
-        }
-        create_mjpg_avi_from_sd_images(vid_output_path.c_str(), results, num_results, gen_params.fps);
-        LOG_INFO("save result MJPG AVI video to '%s'\n", vid_output_path.c_str());
-    } else {
-        // appending ".png" to absent or unknown extension
-        if (!is_jpg && file_ext_lower != ".png") {
-            base_path += file_ext;
-            file_ext = ".png";
-        }
-        for (int i = 0; i < num_results; i++) {
-            if (results[i].data == nullptr) {
-                continue;
-            }
-            int write_ok;
-            std::string final_image_path = i > 0 ? base_path + "_" + std::to_string(i + 1) + file_ext : base_path + file_ext;
-            if (is_jpg) {
-                write_ok = stbi_write_jpg(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
-                                          results[i].data, 90, get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + i).c_str());
-                LOG_INFO("save result JPEG image to '%s' (%s)", final_image_path.c_str(), write_ok == 0 ? "failure" : "success");
-            } else {
-                write_ok = stbi_write_png(final_image_path.c_str(), results[i].width, results[i].height, results[i].channel,
-                                          results[i].data, 0, get_image_params(cli_params, ctx_params, gen_params, gen_params.seed + i).c_str());
-                LOG_INFO("save result PNG image to '%s' (%s)", final_image_path.c_str(), write_ok == 0 ? "failure" : "success");
-            }
-        }
-    }
-
-    for (int i = 0; i < num_results; i++) {
-        free(results[i].data);
-        results[i].data = nullptr;
-    }
-    free(results);
-
-    release_all_resources();
-
    return 0;
-}
+}
@@ -0,0 +1,207 @@
+#ifndef __EXAMPLES_COMMON_COMMON_H__
+#define __EXAMPLES_COMMON_COMMON_H__
+
+#include <cmath>
+#include <cstdint>
+#include <functional>
+#include <map>
+#include <string>
+#include <vector>
+
+#include "log.h"
+#include "stable-diffusion.h"
+
+#define SAFE_STR(s) ((s) ? (s) : "")
+#define BOOL_STR(b) ((b) ? "true" : "false")
+
+extern const char* const modes_str[];
+#define SD_ALL_MODES_STR "img_gen, vid_gen, convert, upscale, metadata"
+
+enum SDMode {
+    IMG_GEN,
+    VID_GEN,
+    CONVERT,
+    UPSCALE,
+    METADATA,
+    MODE_COUNT
+};
+
+struct StringOption {
+    std::string short_name;
+    std::string long_name;
+    std::string desc;
+    std::string* target;
+};
+
+struct IntOption {
+    std::string short_name;
+    std::string long_name;
+    std::string desc;
+    int* target;
+};
+
+struct FloatOption {
+    std::string short_name;
+    std::string long_name;
+    std::string desc;
+    float* target;
+};
+
+struct BoolOption {
+    std::string short_name;
+    std::string long_name;
+    std::string desc;
+    bool keep_true;
+    bool* target;
+};
+
+struct ManualOption {
+    std::string short_name;
+    std::string long_name;
+    std::string desc;
+    std::function<int(int argc, const char** argv, int index)> cb;
+};
+
+struct ArgOptions {
+    std::vector<StringOption> string_options;
+    std::vector<IntOption> int_options;
+    std::vector<FloatOption> float_options;
+    std::vector<BoolOption> bool_options;
+    std::vector<ManualOption> manual_options;
+
+    static std::string wrap_text(const std::string& text, size_t width, size_t indent);
+    void print() const;
+};
+
+bool parse_options(int argc, const char** argv, const std::vector<ArgOptions>& options_list);
+
+struct SDContextParams {
+    int n_threads = -1;
+    std::string model_path;
+    std::string clip_l_path;
+    std::string clip_g_path;
+    std::string clip_vision_path;
+    std::string t5xxl_path;
+    std::string llm_path;
+    std::string llm_vision_path;
+    std::string diffusion_model_path;
+    std::string high_noise_diffusion_model_path;
+    std::string vae_path;
+    std::string taesd_path;
+    std::string esrgan_path;
+    std::string control_net_path;
+    std::string embedding_dir;
+    std::string photo_maker_path;
+    sd_type_t wtype = SD_TYPE_COUNT;
+    std::string tensor_type_rules;
+    std::string lora_model_dir = ".";
+
+    std::map<std::string, std::string> embedding_map;
+    std::vector<sd_embedding_t> embedding_vec;
+
+    rng_type_t rng_type         = CUDA_RNG;
+    rng_type_t sampler_rng_type = RNG_TYPE_COUNT;
+    bool offload_params_to_cpu  = false;
+    bool enable_mmap            = false;
+    bool control_net_cpu        = false;
+    bool clip_on_cpu            = false;
+    bool vae_on_cpu             = false;
+    bool flash_attn             = false;
+    bool diffusion_flash_attn   = false;
+    bool diffusion_conv_direct  = false;
+    bool vae_conv_direct        = false;
+
+    bool circular   = false;
+    bool circular_x = false;
+    bool circular_y = false;
+
+    bool chroma_use_dit_mask = true;
+    bool chroma_use_t5_mask  = false;
+    int chroma_t5_mask_pad   = 1;
+
+    bool qwen_image_zero_cond_t = false;
+
+    prediction_t prediction           = PREDICTION_COUNT;
+    lora_apply_mode_t lora_apply_mode = LORA_APPLY_AUTO;
+
+    bool force_sdxl_vae_conv_scale = false;
+
+    float flow_shift = INFINITY;
+    ArgOptions get_options();
+    void build_embedding_map();
+    bool process_and_check(SDMode mode);
+    std::string to_string() const;
+    sd_ctx_params_t to_sd_ctx_params_t(bool vae_decode_only, bool free_params_immediately, bool taesd_preview);
+};
+
+struct SDGenerationParams {
+    std::string prompt;
+    std::string prompt_with_lora;  // for metadata record only
+    std::string negative_prompt;
+    int clip_skip   = -1;  // <= 0 represents unspecified
+    int width       = -1;
+    int height      = -1;
+    int batch_count = 1;
+    std::string init_image_path;
+    std::string end_image_path;
+    std::string mask_image_path;
+    std::string control_image_path;
+    std::vector<std::string> ref_image_paths;
+    std::string control_video_path;
+    bool auto_resize_ref_image = true;
+    bool increase_ref_index    = false;
+    bool embed_image_metadata  = true;
+
+    std::vector<int> skip_layers = {7, 8, 9};
+    sd_sample_params_t sample_params;
+
+    std::vector<int> high_noise_skip_layers = {7, 8, 9};
+    sd_sample_params_t high_noise_sample_params;
+
+    std::vector<float> custom_sigmas;
+
+    std::string cache_mode;
+    std::string cache_option;
+    std::string scm_mask;
+    bool scm_policy_dynamic = true;
+    sd_cache_params_t cache_params{};
+
+    float moe_boundary  = 0.875f;
+    int video_frames    = 1;
+    int fps             = 16;
+    float vace_strength = 1.f;
+
+    float strength         = 0.75f;
+    float control_strength = 0.9f;
+
+    int64_t seed = 42;
+
+    sd_tiling_params_t vae_tiling_params = {false, 0, 0, 0.5f, 0.0f, 0.0f};
+
+    // Photo Maker
+    std::string pm_id_images_dir;
+    std::string pm_id_embed_path;
+    float pm_style_strength = 20.f;
+
+    int upscale_repeats   = 1;
+    int upscale_tile_size = 128;
+
+    std::map<std::string, float> lora_map;
+    std::map<std::string, float> high_noise_lora_map;
+    std::vector<sd_lora_t> lora_vec;
+    SDGenerationParams();
+    ArgOptions get_options();
+    bool from_json_str(const std::string& json_str);
+    void extract_and_remove_lora(const std::string& lora_model_dir);
+    bool width_and_height_are_set() const;
+    void set_width_and_height_if_unset(int w, int h);
+    int get_resolved_width() const;
+    int get_resolved_height() const;
+    bool process_and_check(SDMode mode, const std::string& lora_model_dir);
+    std::string to_string() const;
+};
+
+std::string version_string();
+std::string get_image_params(const SDContextParams& ctx_params, const SDGenerationParams& gen_params, int64_t seed);
+
+#endif  // __EXAMPLES_COMMON_COMMON_H__
@@ -0,0 +1,115 @@
+#include "log.h"
+
+#include <vector>
+
+bool log_verbose = false;
+bool log_color   = false;
+
+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;
+}
+
+void print_utf8(FILE* stream, const char* utf8) {
+    if (!utf8) {
+        return;
+    }
+
+#ifdef _WIN32
+    HANDLE h = (stream == stderr)
+                   ? GetStdHandle(STD_ERROR_HANDLE)
+                   : GetStdHandle(STD_OUTPUT_HANDLE);
+
+    DWORD mode;
+    BOOL is_console = GetConsoleMode(h, &mode);
+
+    if (is_console) {
+        int wlen = MultiByteToWideChar(CP_UTF8, 0, utf8, -1, NULL, 0);
+        if (wlen <= 0) {
+            return;
+        }
+
+        std::vector<wchar_t> wbuf(static_cast<size_t>(wlen));
+
+        MultiByteToWideChar(CP_UTF8, 0, utf8, -1, wbuf.data(), wlen);
+
+        DWORD written;
+        WriteConsoleW(h, wbuf.data(), wlen - 1, &written, NULL);
+    } else {
+        DWORD written;
+        WriteFile(h, utf8, (DWORD)strlen(utf8), &written, NULL);
+    }
+#else
+    fputs(utf8, stream);
+#endif
+}
+
+void log_print(enum sd_log_level_t level, const char* log, bool verbose, bool color) {
+    int tag_color;
+    const char* level_str;
+    FILE* out_stream = (level == SD_LOG_ERROR) ? stderr : stdout;
+
+    if (!log || (!verbose && level <= SD_LOG_DEBUG)) {
+        return;
+    }
+
+    switch (level) {
+        case SD_LOG_DEBUG:
+            tag_color = 37;
+            level_str = "DEBUG";
+            break;
+        case SD_LOG_INFO:
+            tag_color = 34;
+            level_str = "INFO";
+            break;
+        case SD_LOG_WARN:
+            tag_color = 35;
+            level_str = "WARN";
+            break;
+        case SD_LOG_ERROR:
+            tag_color = 31;
+            level_str = "ERROR";
+            break;
+        default:
+            tag_color = 33;
+            level_str = "?????";
+            break;
+    }
+
+    if (color) {
+        fprintf(out_stream, "\033[%d;1m[%-5s]\033[0m ", tag_color, level_str);
+    } else {
+        fprintf(out_stream, "[%-5s] ", level_str);
+    }
+    print_utf8(out_stream, log);
+    fflush(out_stream);
+}
+
+void example_log_printf(sd_log_level_t level, const char* file, int line, const char* format, ...) {
+    constexpr size_t LOG_BUFFER_SIZE = 4096;
+
+    va_list args;
+    va_start(args, format);
+
+    static char log_buffer[LOG_BUFFER_SIZE + 1];
+    int written = snprintf(log_buffer, LOG_BUFFER_SIZE, "%s:%-4d - ", sd_basename(file).c_str(), line);
+
+    if (written >= 0 && written < static_cast<int>(LOG_BUFFER_SIZE)) {
+        vsnprintf(log_buffer + written, LOG_BUFFER_SIZE - written, format, args);
+    }
+    size_t len = strlen(log_buffer);
+    if (len == 0 || log_buffer[len - 1] != '\n') {
+        strncat(log_buffer, "\n", LOG_BUFFER_SIZE - len);
+    }
+
+    log_print(level, log_buffer, log_verbose, log_color);
+
+    va_end(args);
+}
@@ -0,0 +1,32 @@
+#ifndef __EXAMPLE_LOG_H__
+#define __EXAMPLE_LOG_H__
+
+#include <cstdarg>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <string>
+
+#if defined(_WIN32)
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#endif  // _WIN32
+
+#include "stable-diffusion.h"
+
+extern bool log_verbose;
+extern bool log_color;
+
+std::string sd_basename(const std::string& path);
+void print_utf8(FILE* stream, const char* utf8);
+void log_print(sd_log_level_t level, const char* log, bool verbose, bool color);
+void example_log_printf(sd_log_level_t level, const char* file, int line, const char* format, ...);
+
+#define LOG_DEBUG(format, ...) example_log_printf(SD_LOG_DEBUG, __FILE__, __LINE__, format, ##__VA_ARGS__)
+#define LOG_INFO(format, ...) example_log_printf(SD_LOG_INFO, __FILE__, __LINE__, format, ##__VA_ARGS__)
+#define LOG_WARN(format, ...) example_log_printf(SD_LOG_WARN, __FILE__, __LINE__, format, ##__VA_ARGS__)
+#define LOG_ERROR(format, ...) example_log_printf(SD_LOG_ERROR, __FILE__, __LINE__, format, ##__VA_ARGS__)
+
+#endif  // __EXAMPLE_LOG_H__
@@ -0,0 +1,84 @@
+#ifndef __MEDIA_IO_H__
+#define __MEDIA_IO_H__
+
+#include <cstdint>
+#include <string>
+#include <vector>
+
+#include "stable-diffusion.h"
+
+enum class EncodedImageFormat {
+    JPEG,
+    PNG,
+    WEBP,
+    UNKNOWN,
+};
+
+EncodedImageFormat encoded_image_format_from_path(const std::string& path);
+
+std::vector<uint8_t> encode_image_to_vector(EncodedImageFormat format,
+                                            const uint8_t* image,
+                                            int width,
+                                            int height,
+                                            int channels,
+                                            const std::string& parameters = "",
+                                            int quality                   = 90);
+
+bool write_image_to_file(const std::string& path,
+                         const uint8_t* image,
+                         int width,
+                         int height,
+                         int channels,
+                         const std::string& parameters = "",
+                         int quality                   = 90);
+
+uint8_t* load_image_from_file(const char* image_path,
+                              int& width,
+                              int& height,
+                              int expected_width   = 0,
+                              int expected_height  = 0,
+                              int expected_channel = 3);
+
+bool load_sd_image_from_file(sd_image_t* image,
+                             const char* image_path,
+                             int expected_width   = 0,
+                             int expected_height  = 0,
+                             int expected_channel = 3);
+
+uint8_t* load_image_from_memory(const char* image_bytes,
+                                int len,
+                                int& width,
+                                int& height,
+                                int expected_width   = 0,
+                                int expected_height  = 0,
+                                int expected_channel = 3);
+
+int create_mjpg_avi_from_sd_images(const char* filename,
+                                   sd_image_t* images,
+                                   int num_images,
+                                   int fps,
+                                   int quality = 90);
+
+#ifdef SD_USE_WEBP
+int create_animated_webp_from_sd_images(const char* filename,
+                                        sd_image_t* images,
+                                        int num_images,
+                                        int fps,
+                                        int quality = 90);
+#endif
+
+#ifdef SD_USE_WEBM
+int create_webm_from_sd_images(const char* filename,
+                               sd_image_t* images,
+                               int num_images,
+                               int fps,
+                               int quality = 90);
+#endif
+
+int create_video_from_sd_images(const char* filename,
+                                sd_image_t* images,
+                                int num_images,
+                                int fps,
+                                int quality = 90);
+
+#endif  // __MEDIA_IO_H__
@@ -0,0 +1,207 @@
+#ifndef __EXAMPLE_RESOURCE_OWNERS_H__
+#define __EXAMPLE_RESOURCE_OWNERS_H__
+
+#include <cstdio>
+#include <cstdlib>
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "stable-diffusion.h"
+
+struct FreeDeleter {
+    void operator()(void* ptr) const {
+        free(ptr);
+    }
+};
+
+struct FileCloser {
+    void operator()(FILE* file) const {
+        if (file != nullptr) {
+            fclose(file);
+        }
+    }
+};
+
+struct SDCtxDeleter {
+    void operator()(sd_ctx_t* ctx) const {
+        if (ctx != nullptr) {
+            free_sd_ctx(ctx);
+        }
+    }
+};
+
+struct UpscalerCtxDeleter {
+    void operator()(upscaler_ctx_t* ctx) const {
+        if (ctx != nullptr) {
+            free_upscaler_ctx(ctx);
+        }
+    }
+};
+
+template <typename T>
+using FreeUniquePtr = std::unique_ptr<T, FreeDeleter>;
+
+using FilePtr        = std::unique_ptr<FILE, FileCloser>;
+using SDCtxPtr       = std::unique_ptr<sd_ctx_t, SDCtxDeleter>;
+using UpscalerCtxPtr = std::unique_ptr<upscaler_ctx_t, UpscalerCtxDeleter>;
+
+class SDImageOwner {
+public:
+    SDImageOwner() = default;
+    explicit SDImageOwner(sd_image_t image)
+        : image_(image) {
+    }
+
+    SDImageOwner(const SDImageOwner&)            = delete;
+    SDImageOwner& operator=(const SDImageOwner&) = delete;
+
+    SDImageOwner(SDImageOwner&& other) noexcept
+        : image_(other.release()) {
+    }
+
+    SDImageOwner& operator=(SDImageOwner&& other) noexcept {
+        if (this != &other) {
+            reset();
+            image_ = other.release();
+        }
+        return *this;
+    }
+
+    ~SDImageOwner() {
+        reset();
+    }
+
+    sd_image_t* put() {
+        if (image_.data != nullptr) {
+            free(image_.data);
+            image_.data = nullptr;
+        }
+        image_.width  = 0;
+        image_.height = 0;
+        return &image_;
+    }
+
+    sd_image_t& get() {
+        return image_;
+    }
+
+    const sd_image_t& get() const {
+        return image_;
+    }
+
+    sd_image_t release() {
+        sd_image_t image = image_;
+        image_           = {0, 0, 0, nullptr};
+        return image;
+    }
+
+    void reset(sd_image_t image = {0, 0, 0, nullptr}) {
+        if (image_.data != nullptr) {
+            free(image_.data);
+        }
+        image_ = image;
+    }
+
+private:
+    sd_image_t image_ = {0, 0, 0, nullptr};
+};
+
+class SDImageVec {
+public:
+    SDImageVec() = default;
+
+    SDImageVec(const SDImageVec&)            = delete;
+    SDImageVec& operator=(const SDImageVec&) = delete;
+
+    SDImageVec(SDImageVec&& other) noexcept
+        : images_(std::move(other.images_)) {
+    }
+
+    SDImageVec& operator=(SDImageVec&& other) noexcept {
+        if (this != &other) {
+            clear();
+            images_ = std::move(other.images_);
+        }
+        return *this;
+    }
+
+    ~SDImageVec() {
+        clear();
+    }
+
+    void push_back(sd_image_t image) {
+        images_.push_back(image);
+    }
+
+    void push_back(SDImageOwner&& image) {
+        images_.push_back(image.release());
+    }
+
+    void reserve(size_t count) {
+        images_.reserve(count);
+    }
+
+    void adopt(sd_image_t* images, int count) {
+        clear();
+        if (images == nullptr || count <= 0) {
+            free(images);
+            return;
+        }
+
+        images_.reserve(static_cast<size_t>(count));
+        for (int i = 0; i < count; ++i) {
+            images_.push_back(images[i]);
+        }
+        free(images);
+    }
+
+    size_t size() const {
+        return images_.size();
+    }
+
+    bool empty() const {
+        return images_.empty();
+    }
+
+    explicit operator bool() const {
+        return !images_.empty();
+    }
+
+    sd_image_t* data() {
+        return images_.data();
+    }
+
+    const sd_image_t* data() const {
+        return images_.data();
+    }
+
+    sd_image_t& operator[](size_t index) {
+        return images_[index];
+    }
+
+    const sd_image_t& operator[](size_t index) const {
+        return images_[index];
+    }
+
+    std::vector<sd_image_t>& raw() {
+        return images_;
+    }
+
+    const std::vector<sd_image_t>& raw() const {
+        return images_;
+    }
+
+    void clear() {
+        for (sd_image_t& image : images_) {
+            free(image.data);
+            image.data = nullptr;
+        }
+        images_.clear();
+    }
+
+private:
+    std::vector<sd_image_t> images_;
+};
+
+#endif  // __EXAMPLE_RESOURCE_OWNERS_H__
@@ -1,6 +1,92 @@
 set(TARGET sd-server)

-add_executable(${TARGET} main.cpp)
+option(SD_SERVER_BUILD_FRONTEND "Build server frontend with pnpm" ON)
+
+set(FRONTEND_DIR "${CMAKE_CURRENT_SOURCE_DIR}/frontend")
+set(GENERATED_HTML_HEADER "${FRONTEND_DIR}/dist/gen_index_html.h")
+
+set(HAVE_FRONTEND_BUILD OFF)
+
+if(SD_SERVER_BUILD_FRONTEND AND EXISTS "${FRONTEND_DIR}")
+    if(WIN32)
+        find_program(PNPM_EXECUTABLE NAMES pnpm.cmd pnpm)
+    else()
+        find_program(PNPM_EXECUTABLE NAMES pnpm)
+    endif()
+
+    if(PNPM_EXECUTABLE)
+        message(STATUS "Frontend dir found: ${FRONTEND_DIR}")
+        message(STATUS "pnpm found: ${PNPM_EXECUTABLE}")
+
+        set(HAVE_FRONTEND_BUILD ON)
+
+        add_custom_target(${TARGET}_frontend_install
+            COMMAND "${PNPM_EXECUTABLE}" -C "${FRONTEND_DIR}" install
+            WORKING_DIRECTORY "${FRONTEND_DIR}"
+            COMMENT "Installing frontend dependencies"
+            VERBATIM
+        )
+
+        add_custom_target(${TARGET}_frontend_build
+            COMMAND "${PNPM_EXECUTABLE}" -C "${FRONTEND_DIR}" run build
+            WORKING_DIRECTORY "${FRONTEND_DIR}"
+            COMMENT "Building frontend"
+            VERBATIM
+        )
+
+        add_custom_target(${TARGET}_frontend_header
+            COMMAND "${PNPM_EXECUTABLE}" -C "${FRONTEND_DIR}" run build:header
+            WORKING_DIRECTORY "${FRONTEND_DIR}"
+            COMMENT "Generating gen_index_html.h"
+            VERBATIM
+        )
+
+        add_dependencies(${TARGET}_frontend_build ${TARGET}_frontend_install)
+        add_dependencies(${TARGET}_frontend_header ${TARGET}_frontend_build)
+
+        add_custom_target(${TARGET}_frontend
+            DEPENDS ${TARGET}_frontend_header
+        )
+
+        set_source_files_properties("${GENERATED_HTML_HEADER}" PROPERTIES GENERATED TRUE)
+    else()
+        message(WARNING "pnpm not found, frontend build disabled")
+    endif()
+else()
+    message(STATUS "Frontend disabled or directory not found: ${FRONTEND_DIR}")
+endif()
+
+add_executable(${TARGET}
+    ../common/common.cpp
+    ../common/log.cpp
+    ../common/media_io.cpp
+    main.cpp
+)
+
+if(HAVE_FRONTEND_BUILD)
+    add_dependencies(${TARGET} ${TARGET}_frontend)
+    target_sources(${TARGET} PRIVATE "${GENERATED_HTML_HEADER}")
+    target_include_directories(${TARGET} PRIVATE "${FRONTEND_DIR}/dist")
+    target_compile_definitions(${TARGET} PRIVATE HAVE_INDEX_HTML)
+    message(STATUS "HAVE_INDEX_HTML enabled")
+else()
+    message(STATUS "HAVE_INDEX_HTML disabled")
+endif()
+
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE stable-diffusion ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PUBLIC c_std_11 cxx_std_17)
+if(SD_WEBP)
+    target_compile_definitions(${TARGET} PRIVATE SD_USE_WEBP)
+    target_link_libraries(${TARGET} PRIVATE webp libwebpmux)
+endif()
+if(SD_WEBM)
+    target_compile_definitions(${TARGET} PRIVATE SD_USE_WEBM)
+    target_link_libraries(${TARGET} PRIVATE webm)
+endif()
+
+# due to httplib; it contains a pragma for MSVC, but other things need explicit flags
+if(WIN32 AND NOT MSVC)
+    target_link_libraries(${TARGET} PRIVATE ws2_32)
+endif()
+
+target_compile_features(${TARGET} PUBLIC c_std_11 cxx_std_17)
@@ -1,14 +1,104 @@
+# Frontend
+
+## Build with Frontend
+
+The server can optionally build the web frontend and embed it into the binary as `gen_index_html.h`.
+
+### Requirements
+
+Install the following tools:
+
+* **Node.js** ≥ 22.18
+  https://nodejs.org/
+
+* **pnpm** ≥ 10
+  Install via npm:
+
+```bash
+npm install -g pnpm
+```
+
+Verify installation:
+
+```bash
+node -v
+pnpm -v
+```
+
+### Install frontend dependencies
+
+Go to the frontend directory and install dependencies:
+
+```bash
+cd examples/server/frontend
+pnpm install
+```
+
+### Build the server with CMake
+
+Enable the frontend build option when configuring CMake:
+
+```bash
+cmake -B build -DSD_SERVER_BUILD_FRONTEND=ON
+cmake --build build --config Release
+```
+
+If `pnpm` is available, the build system will automatically run:
+
+```
+pnpm run build
+pnpm run build:header
+```
+
+and embed the generated frontend into the server binary.
+
+## Frontend Repository
+
+The web frontend is maintained in a **separate repository**, https://github.com/leejet/stable-ui.
+
+If you want to modify the UI or frontend logic, please submit pull requests to the **frontend repository**.
+
+This repository (`stable-diffusion.cpp`) only vendors the frontend periodically. Changes from the frontend repo are synchronized:
+
+* approximately **every 1–2 weeks**, or
+* when there are **major frontend updates**
+
+Because of this, frontend changes will **not appear here immediately** after being merged upstream.
+
+## Using an external frontend
+
+By default, the server uses the **embedded frontend** generated during the build (`gen_index_html.h`).
+
+You can also serve a custom frontend file instead of the embedded one by using:
+
+```bash
+--serve-html-path <path-to-index.html>
+```
+
+For example:
+
+```bash
+sd-server --serve-html-path ./index.html
+```
+
+In this case, the server will load and serve the specified `index.html` file instead of the embedded frontend. This is useful when:
+
+* developing or testing frontend changes
+* using a custom UI
+* avoiding rebuilding the binary after frontend modifications
+
 # Run

 ```
 usage: ./bin/sd-server  [options]

 Svr Options:
-  -l, --listen-ip <string>    server listen ip (default: 127.0.0.1)
-  --listen-port <int>         server listen port (default: 1234)
-  -v, --verbose               print extra info
-  --color                     colors the logging tags according to level
-  -h, --help                  show this help message and exit
+  -l, --listen-ip <string>      server listen ip (default: 127.0.0.1)        
+  --serve-html-path <string>    path to HTML file to serve at root (optional)
+  --listen-port <int>           server listen port (default: 1234)
+  -v, --verbose                 print extra info
+  --color                       colors the logging tags according to level   
+  -h, --help                    show this help message and exit

 Context Options:
  -m, --model <string>                     path to full model
@@ -35,17 +125,22 @@ Context Options:
                                           CPU physical cores
  --chroma-t5-mask-pad <int>               t5 mask pad size of chroma
  --vae-tile-overlap <float>               tile overlap for vae tiling, in fraction of tile size (default: 0.5)
-  --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
  --vae-tiling                             process vae in tiles to reduce memory usage
  --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
  --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
+  --mmap                                   whether to memory-map model
  --control-net-cpu                        keep controlnet in cpu (for low vram)
  --clip-on-cpu                            keep clip in cpu (for low vram)
  --vae-on-cpu                             keep vae in cpu (for low vram)
-  --diffusion-fa                           use flash attention in the diffusion model
+  --fa                                     use flash attention
+  --diffusion-fa                           use flash attention in the diffusion model only
  --diffusion-conv-direct                  use ggml_conv2d_direct in the diffusion model
  --vae-conv-direct                        use ggml_conv2d_direct in the vae model
+  --circular                               enable circular padding for convolutions
+  --circularx                              enable circular RoPE wrapping on x-axis (width) only
+  --circulary                              enable circular RoPE wrapping on y-axis (height) only
  --chroma-disable-dit-mask                disable dit mask for chroma
+  --qwen-image-zero-cond-t                 enable zero_cond_t for qwen image
  --chroma-enable-t5-mask                  enable t5 mask for chroma
  --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). If not specified, the default is the
                                           type of the weight file
@@ -94,14 +189,15 @@ Default Generation Options:
                                           medium
  --skip-layer-start <float>               SLG enabling point (default: 0.01)
  --skip-layer-end <float>                 SLG disabling point (default: 0.2)
-  --eta <float>                            eta in DDIM, only for DDIM and TCD (default: 0)
+  --eta <float>                            noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a and dpm++2s_a)
+  --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
  --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
  --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input (default: 3.5)
  --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
  --high-noise-skip-layer-start <float>    (high noise) SLG enabling point (default: 0.01)
  --high-noise-skip-layer-end <float>      (high noise) SLG disabling point (default: 0.2)
-  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM and TCD (default: 0)
+  --high-noise-eta <float>                 (high noise) noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a and dpm++2s_a)
  --strength <float>                       strength for noising/unnoising (default: 0.75)
  --pm-style-strength <float>
  --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
@@ -109,16 +205,24 @@ Default Generation Options:
  --vace-strength <float>                  wan vace strength
  --increase-ref-index                     automatically increase the indices of references images based on the order they are listed (starting with 1).
  --disable-auto-resize-ref-image          disable auto resize of ref images
+  --disable-image-metadata                 do not embed generation metadata on image files
  -s, --seed                               RNG seed (default: 42, use random seed for < 0)
  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
-                                           tcd] (default: euler for Flux/SD3/Wan, euler_a otherwise)
+                                           tcd, res_multistep, res_2s] (default: euler for Flux/SD3/Wan, euler_a
+                                           otherwise)
  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
-                                           ddim_trailing, tcd] default: euler for Flux/SD3/Wan, euler_a otherwise
-  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple, lcm],
-                                           default: discrete
+                                           ddim_trailing, tcd, res_multistep, res_2s] default: euler for Flux/SD3/Wan,
+                                           euler_a otherwise
+  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple,
+                                           kl_optimal, lcm, bong_tangent], default: discrete
  --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
  --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
  --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
  -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
-  --easycache                              enable EasyCache for DiT models with optional "threshold,start_percent,end_percent" (default: 0.2,0.15,0.95)
-```
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level), 'spectrum' (UNET/DiT Chebyshev+Taylor forecasting)
+  --cache-option                           named cache params (key=value format, comma-separated). easycache/ucache:
+                                           threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=. Examples:
+                                           "threshold=0.25" or "threshold=1.5,reset=0"
+  --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g., "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
+  --scm-policy                             SCM policy: 'dynamic' (default) or 'static'
+```
@@ -1,4 +1,6 @@
-for f in *.cpp *.h *.hpp examples/cli/*.cpp examples/common/*.hpp examples/cli/*.h examples/server/*.cpp; do
+for f in src/*.cpp src/*.h src/*.hpp src/vocab/*.h src/vocab/*.cpp \
+         examples/cli/*.cpp examples/cli/*.h examples/server/*.cpp \
+         examples/common/*.hpp examples/common/*.h examples/common/*.cpp; do
  [[ "$f" == vocab* ]] && continue
  echo "formatting '$f'"
  # if [ "$f" != "stable-diffusion.h" ]; then
@@ -48,6 +48,8 @@ enum sample_method_t {
    LCM_SAMPLE_METHOD,
    DDIM_TRAILING_SAMPLE_METHOD,
    TCD_SAMPLE_METHOD,
+    RES_MULTISTEP_SAMPLE_METHOD,
+    RES_2S_SAMPLE_METHOD,
    SAMPLE_METHOD_COUNT
 };

@@ -60,7 +62,9 @@ enum scheduler_t {
    SGM_UNIFORM_SCHEDULER,
    SIMPLE_SCHEDULER,
    SMOOTHSTEP_SCHEDULER,
+    KL_OPTIMAL_SCHEDULER,
    LCM_SCHEDULER,
+    BONG_TANGENT_SCHEDULER,
    SCHEDULER_COUNT
 };

@@ -116,7 +120,8 @@ enum sd_type_t {
    // SD_TYPE_IQ4_NL_4_8 = 37,
    // SD_TYPE_IQ4_NL_8_8 = 38,
    SD_TYPE_MXFP4 = 39,  // MXFP4 (1 block)
-    SD_TYPE_COUNT = 40,
+    SD_TYPE_NVFP4 = 40,  // NVFP4 (4 blocks, E4M3 scale)
+    SD_TYPE_COUNT = 41,
 };

 enum sd_log_level_t {
@@ -181,18 +186,22 @@ typedef struct {
    enum prediction_t prediction;
    enum lora_apply_mode_t lora_apply_mode;
    bool offload_params_to_cpu;
+    bool enable_mmap;
    bool keep_clip_on_cpu;
    bool keep_control_net_on_cpu;
    bool keep_vae_on_cpu;
+    bool flash_attn;
    bool diffusion_flash_attn;
    bool tae_preview_only;
    bool diffusion_conv_direct;
    bool vae_conv_direct;
+    bool circular_x;
+    bool circular_y;
    bool force_sdxl_vae_conv_scale;
    bool chroma_use_dit_mask;
    bool chroma_use_t5_mask;
    int chroma_t5_mask_pad;
-    float flow_shift;
+    bool qwen_image_zero_cond_t;
 } sd_ctx_params_t;

 typedef struct {
@@ -226,6 +235,7 @@ typedef struct {
    int shifted_timestep;
    float* custom_sigmas;
    int custom_sigmas_count;
+    float flow_shift;
 } sd_sample_params_t;

 typedef struct {
@@ -235,12 +245,42 @@ typedef struct {
    float style_strength;
 } sd_pm_params_t;  // photo maker

+enum sd_cache_mode_t {
+    SD_CACHE_DISABLED = 0,
+    SD_CACHE_EASYCACHE,
+    SD_CACHE_UCACHE,
+    SD_CACHE_DBCACHE,
+    SD_CACHE_TAYLORSEER,
+    SD_CACHE_CACHE_DIT,
+    SD_CACHE_SPECTRUM,
+};
+
 typedef struct {
-    bool enabled;
+    enum sd_cache_mode_t mode;
    float reuse_threshold;
    float start_percent;
    float end_percent;
-} sd_easycache_params_t;
+    float error_decay_rate;
+    bool use_relative_threshold;
+    bool reset_error_on_compute;
+    int Fn_compute_blocks;
+    int Bn_compute_blocks;
+    float residual_diff_threshold;
+    int max_warmup_steps;
+    int max_cached_steps;
+    int max_continuous_cached_steps;
+    int taylorseer_n_derivatives;
+    int taylorseer_skip_interval;
+    const char* scm_mask;
+    bool scm_policy_dynamic;
+    float spectrum_w;
+    int spectrum_m;
+    float spectrum_lam;
+    int spectrum_window_size;
+    float spectrum_flex_window;
+    int spectrum_warmup_steps;
+    float spectrum_stop_percent;
+} sd_cache_params_t;

 typedef struct {
    bool is_high_noise;
@@ -270,7 +310,7 @@ typedef struct {
    float control_strength;
    sd_pm_params_t pm_params;
    sd_tiling_params_t vae_tiling_params;
-    sd_easycache_params_t easycache;
+    sd_cache_params_t cache;
 } sd_img_gen_params_t;

 typedef struct {
@@ -292,7 +332,8 @@ typedef struct {
    int64_t seed;
    int video_frames;
    float vace_strength;
-    sd_easycache_params_t easycache;
+    sd_tiling_params_t vae_tiling_params;
+    sd_cache_params_t cache;
 } sd_vid_gen_params_t;

 typedef struct sd_ctx_t sd_ctx_t;
@@ -322,7 +363,7 @@ SD_API enum preview_t str_to_preview(const char* str);
 SD_API const char* sd_lora_apply_mode_name(enum lora_apply_mode_t mode);
 SD_API enum lora_apply_mode_t str_to_lora_apply_mode(const char* str);

-SD_API void sd_easycache_params_init(sd_easycache_params_t* easycache_params);
+SD_API void sd_cache_params_init(sd_cache_params_t* cache_params);

 SD_API void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params);
 SD_API char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params);
@@ -334,7 +375,7 @@ SD_API void sd_sample_params_init(sd_sample_params_t* sample_params);
 SD_API char* sd_sample_params_to_str(const sd_sample_params_t* sample_params);

 SD_API enum sample_method_t sd_get_default_sample_method(const sd_ctx_t* sd_ctx);
-SD_API enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx);
+SD_API enum scheduler_t sd_get_default_scheduler(const sd_ctx_t* sd_ctx, enum sample_method_t sample_method);

 SD_API void sd_img_gen_params_init(sd_img_gen_params_t* sd_img_gen_params);
 SD_API char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_params);
@@ -362,7 +403,8 @@ SD_API bool convert(const char* input_path,
                    const char* vae_path,
                    const char* output_path,
                    enum sd_type_t output_type,
-                    const char* tensor_type_rules);
+                    const char* tensor_type_rules,
+                    bool convert_name);

 SD_API bool preprocess_canny(sd_image_t image,
                             float high_threshold,
@@ -1,226 +0,0 @@
-#ifndef __PREPROCESSING_HPP__
-#define __PREPROCESSING_HPP__
-
-#include "ggml_extend.hpp"
-#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                 = 80 * input->ne[0] * input->ne[1];  // 20M for 512x512
-    params.mem_buffer               = nullptr;
-    params.no_alloc                 = false;
-    struct ggml_context* ctx0       = ggml_init(params);
-    struct ggml_tensor* kernel_fp16 = ggml_new_tensor_4d(ctx0, GGML_TYPE_F16, kernel->ne[0], kernel->ne[1], 1, 1);
-    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);
-    ggml_free(ctx0);
-}
-
-void gaussian_kernel(struct ggml_tensor* kernel) {
-    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++) {
-        float gx = -ks_mid + y;
-        for (int x = 0; x < kernel->ne[1]; x++) {
-            float gy = -ks_mid + x;
-            float k_ = expf(-((gx * gx + gy * gy) / (2.0f * powf(sigma, 2.0f)))) * normal;
-            ggml_ext_tensor_set_f32(kernel, k_, x, y);
-        }
-    }
-}
-
-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_ext_tensor_get_f32(rgb_img, ix, iy);
-            float g    = ggml_ext_tensor_get_f32(rgb_img, ix, iy, 1);
-            float b    = ggml_ext_tensor_get_f32(rgb_img, ix, iy, 2);
-            float gray = 0.2989f * r + 0.5870f * g + 0.1140f * b;
-            ggml_ext_tensor_set_f32(grayscale, gray, ix, iy);
-        }
-    }
-}
-
-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++) {
-        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;
-    for (int i = 0; i < n_elements; i++) {
-        dh[i] = atan2f(dy[i], dx[i]);
-    }
-}
-
-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++) {
-        max = dg[i] > max ? dg[i] : max;
-    }
-    max = 1.0f / max;
-    for (int i = 0; i < n_elements; i++) {
-        dg[i] *= max;
-    }
-}
-
-void non_max_supression(struct ggml_tensor* result, struct ggml_tensor* G, struct ggml_tensor* D) {
-    for (int iy = 1; iy < result->ne[1] - 1; iy++) {
-        for (int ix = 1; ix < result->ne[0] - 1; ix++) {
-            float angle = ggml_ext_tensor_get_f32(D, ix, iy) * 180.0f / M_PI_;
-            angle       = angle < 0.0f ? angle += 180.0f : angle;
-            float q     = 1.0f;
-            float r     = 1.0f;
-
-            // angle 0
-            if ((0 >= angle && angle < 22.5f) || (157.5f >= angle && angle <= 180)) {
-                q = ggml_ext_tensor_get_f32(G, ix, iy + 1);
-                r = ggml_ext_tensor_get_f32(G, ix, iy - 1);
-            }
-            // angle 45
-            else if (22.5f >= angle && angle < 67.5f) {
-                q = ggml_ext_tensor_get_f32(G, ix + 1, iy - 1);
-                r = ggml_ext_tensor_get_f32(G, ix - 1, iy + 1);
-            }
-            // angle 90
-            else if (67.5f >= angle && angle < 112.5) {
-                q = ggml_ext_tensor_get_f32(G, ix + 1, iy);
-                r = ggml_ext_tensor_get_f32(G, ix - 1, iy);
-            }
-            // angle 135
-            else if (112.5 >= angle && angle < 157.5f) {
-                q = ggml_ext_tensor_get_f32(G, ix - 1, iy - 1);
-                r = ggml_ext_tensor_get_f32(G, ix + 1, iy + 1);
-            }
-
-            float cur = ggml_ext_tensor_get_f32(G, ix, iy);
-            if ((cur >= q) && (cur >= r)) {
-                ggml_ext_tensor_set_f32(result, cur, ix, iy);
-            } else {
-                ggml_ext_tensor_set_f32(result, 0.0f, ix, iy);
-            }
-        }
-    }
-}
-
-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;
-    for (int i = 0; i < n_elements; i++) {
-        max = imd[i] > max ? imd[i] : max;
-    }
-    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
-            imd[i] = strong;
-        } 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) {
-                ggml_ext_tensor_set_f32(img, ggml_ext_tensor_get_f32(img, ix, iy), ix, iy);
-            } else {
-                ggml_ext_tensor_set_f32(img, 0.0f, ix, iy);
-            }
-        }
-    }
-
-    // hysteresis
-    for (int iy = 1; iy < img->ne[1] - 1; iy++) {
-        for (int ix = 1; ix < img->ne[0] - 1; ix++) {
-            float imd_v = ggml_ext_tensor_get_f32(img, ix, iy);
-            if (imd_v == weak) {
-                if (ggml_ext_tensor_get_f32(img, ix + 1, iy - 1) == strong || ggml_ext_tensor_get_f32(img, ix + 1, iy) == strong ||
-                    ggml_ext_tensor_get_f32(img, ix, iy - 1) == strong || ggml_ext_tensor_get_f32(img, ix, iy + 1) == strong ||
-                    ggml_ext_tensor_get_f32(img, ix - 1, iy - 1) == strong || ggml_ext_tensor_get_f32(img, ix - 1, iy) == strong) {
-                    ggml_ext_tensor_set_f32(img, strong, ix, iy);
-                } else {
-                    ggml_ext_tensor_set_f32(img, 0.0f, ix, iy);
-                }
-            }
-        }
-    }
-}
-
-bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold, float weak, float strong, bool inverse) {
-    struct ggml_init_params params;
-    params.mem_size               = static_cast<size_t>(40 * img.width * img.height);  // 10MB for 512x512
-    params.mem_buffer             = nullptr;
-    params.no_alloc               = false;
-    struct ggml_context* work_ctx = ggml_init(params);
-
-    if (!work_ctx) {
-        LOG_ERROR("ggml_init() failed");
-        return false;
-    }
-
-    float kX[9] = {
-        -1, 0, 1,
-        -2, 0, 2,
-        -1, 0, 1};
-
-    float kY[9] = {
-        1, 2, 1,
-        0, 0, 0,
-        -1, -2, -1};
-
-    // generate kernel
-    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);
-    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, img.width, img.height, 3, 1);
-    struct ggml_tensor* image_gray = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, img.width, img.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);
-    sd_image_to_ggml_tensor(img, image);
-    grayscale(image, image_gray);
-    convolve(image_gray, image_gray, gkernel, 2);
-    convolve(image_gray, iX, sf_kx, 1);
-    convolve(image_gray, iY, sf_ky, 1);
-    prop_hypot(iX, iY, G);
-    normalize_tensor(G);
-    prop_arctan2(iX, iY, tetha);
-    non_max_supression(image_gray, G, tetha);
-    threshold_hystersis(image_gray, high_threshold, low_threshold, weak, strong);
-    // to RGB channels
-    for (int iy = 0; iy < img.height; iy++) {
-        for (int ix = 0; ix < img.width; ix++) {
-            float gray = ggml_ext_tensor_get_f32(image_gray, ix, iy);
-            gray       = inverse ? 1.0f - gray : gray;
-            ggml_ext_tensor_set_f32(image, gray, ix, iy);
-            ggml_ext_tensor_set_f32(image, gray, ix, iy, 1);
-            ggml_ext_tensor_set_f32(image, gray, ix, iy, 2);
-        }
-    }
-    ggml_tensor_to_sd_image(image, img.data);
-    ggml_free(work_ctx);
-    return true;
-}
-
-#endif  // __PREPROCESSING_HPP__
@@ -1,88 +1,88 @@
-import os
-import sys
-
-import numpy as np
-import torch
-from diffusers.utils import load_image
-# pip install insightface==0.7.3
-from insightface.app import FaceAnalysis
-from insightface.data import get_image as ins_get_image
-from safetensors.torch import save_file
-
-### 
-# https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/165#issue-2055829543
-###
-class FaceAnalysis2(FaceAnalysis):
-    # NOTE: allows setting det_size for each detection call.
-    # the model allows it but the wrapping code from insightface
-    # doesn't show it, and people end up loading duplicate models
-    # for different sizes where there is absolutely no need to
-    def get(self, img, max_num=0, det_size=(640, 640)):
-        if det_size is not None:
-            self.det_model.input_size = det_size
-
-        return super().get(img, max_num)
-
-def analyze_faces(face_analysis: FaceAnalysis, img_data: np.ndarray, det_size=(640, 640)):
-    # NOTE: try detect faces, if no faces detected, lower det_size until it does
-    detection_sizes = [None] + [(size, size) for size in range(640, 256, -64)] + [(256, 256)]
-
-    for size in detection_sizes:
-        faces = face_analysis.get(img_data, det_size=size)
-        if len(faces) > 0:
-            return faces
-
-    return []
-
-if __name__ == "__main__":
-    #face_detector = FaceAnalysis2(providers=['CUDAExecutionProvider'], allowed_modules=['detection', 'recognition'])
-    face_detector = FaceAnalysis2(providers=['CPUExecutionProvider'], allowed_modules=['detection', 'recognition'])
-    face_detector.prepare(ctx_id=0, det_size=(640, 640))
-    #input_folder_name = './scarletthead_woman'
-    input_folder_name = sys.argv[1]
-    image_basename_list = os.listdir(input_folder_name)
-    image_path_list = sorted([os.path.join(input_folder_name, basename) for basename in image_basename_list])
-
-    input_id_images = []
-    for image_path in image_path_list:
-        input_id_images.append(load_image(image_path))
-    
-    id_embed_list = []
-    
-    for img in input_id_images:
-        img = np.array(img)
-        img = img[:, :, ::-1]
-        faces = analyze_faces(face_detector, img)
-        if len(faces) > 0:
-            id_embed_list.append(torch.from_numpy((faces[0]['embedding'])))
-    
-    if len(id_embed_list) == 0:
-        raise ValueError(f"No face detected in input image pool")
-    
-    id_embeds = torch.stack(id_embed_list)    
-    
-    # for r in id_embeds:
-    #     print(r)
-    # #torch.save(id_embeds, input_folder_name+'/id_embeds.pt');
-    # weights = dict()
-    # weights["id_embeds"] = id_embeds
-    # save_file(weights, input_folder_name+'/id_embeds.safetensors')
-
-    binary_data = id_embeds.numpy().tobytes()
-    two = 4
-    zero = 0
-    one = 1
-    tensor_name = "id_embeds"
-# Write binary data to a file
-    with open(input_folder_name+'/id_embeds.bin', "wb") as f:
-        f.write(two.to_bytes(4, byteorder='little'))
-        f.write((len(tensor_name)).to_bytes(4, byteorder='little'))
-        f.write(zero.to_bytes(4, byteorder='little'))
-        f.write((id_embeds.shape[1]).to_bytes(4, byteorder='little'))
-        f.write((id_embeds.shape[0]).to_bytes(4, byteorder='little'))
-        f.write(one.to_bytes(4, byteorder='little'))
-        f.write(one.to_bytes(4, byteorder='little'))
-        f.write(tensor_name.encode('ascii'))
-        f.write(binary_data)
-
+import os
+import sys
+
+import numpy as np
+import torch
+from diffusers.utils import load_image
+# pip install insightface==0.7.3
+from insightface.app import FaceAnalysis
+from insightface.data import get_image as ins_get_image
+from safetensors.torch import save_file
+
+### 
+# https://github.com/cubiq/ComfyUI_IPAdapter_plus/issues/165#issue-2055829543
+###
+class FaceAnalysis2(FaceAnalysis):
+    # NOTE: allows setting det_size for each detection call.
+    # the model allows it but the wrapping code from insightface
+    # doesn't show it, and people end up loading duplicate models
+    # for different sizes where there is absolutely no need to
+    def get(self, img, max_num=0, det_size=(640, 640)):
+        if det_size is not None:
+            self.det_model.input_size = det_size
+
+        return super().get(img, max_num)
+
+def analyze_faces(face_analysis: FaceAnalysis, img_data: np.ndarray, det_size=(640, 640)):
+    # NOTE: try detect faces, if no faces detected, lower det_size until it does
+    detection_sizes = [None] + [(size, size) for size in range(640, 256, -64)] + [(256, 256)]
+
+    for size in detection_sizes:
+        faces = face_analysis.get(img_data, det_size=size)
+        if len(faces) > 0:
+            return faces
+
+    return []
+
+if __name__ == "__main__":
+    #face_detector = FaceAnalysis2(providers=['CUDAExecutionProvider'], allowed_modules=['detection', 'recognition'])
+    face_detector = FaceAnalysis2(providers=['CPUExecutionProvider'], allowed_modules=['detection', 'recognition'])
+    face_detector.prepare(ctx_id=0, det_size=(640, 640))
+    #input_folder_name = './scarletthead_woman'
+    input_folder_name = sys.argv[1]
+    image_basename_list = os.listdir(input_folder_name)
+    image_path_list = sorted([os.path.join(input_folder_name, basename) for basename in image_basename_list])
+
+    input_id_images = []
+    for image_path in image_path_list:
+        input_id_images.append(load_image(image_path))
+    
+    id_embed_list = []
+    
+    for img in input_id_images:
+        img = np.array(img)
+        img = img[:, :, ::-1]
+        faces = analyze_faces(face_detector, img)
+        if len(faces) > 0:
+            id_embed_list.append(torch.from_numpy((faces[0]['embedding'])))
+    
+    if len(id_embed_list) == 0:
+        raise ValueError(f"No face detected in input image pool")
+    
+    id_embeds = torch.stack(id_embed_list)    
+    
+    # for r in id_embeds:
+    #     print(r)
+    # #torch.save(id_embeds, input_folder_name+'/id_embeds.pt');
+    # weights = dict()
+    # weights["id_embeds"] = id_embeds
+    # save_file(weights, input_folder_name+'/id_embeds.safetensors')
+
+    binary_data = id_embeds.numpy().tobytes()
+    two = 4
+    zero = 0
+    one = 1
+    tensor_name = "id_embeds"
+# Write binary data to a file
+    with open(input_folder_name+'/id_embeds.bin', "wb") as f:
+        f.write(two.to_bytes(4, byteorder='little'))
+        f.write((len(tensor_name)).to_bytes(4, byteorder='little'))
+        f.write(zero.to_bytes(4, byteorder='little'))
+        f.write((id_embeds.shape[1]).to_bytes(4, byteorder='little'))
+        f.write((id_embeds.shape[0]).to_bytes(4, byteorder='little'))
+        f.write(one.to_bytes(4, byteorder='little'))
+        f.write(one.to_bytes(4, byteorder='little'))
+        f.write(tensor_name.encode('ascii'))
+        f.write(binary_data)
+
    
@@ -0,0 +1,683 @@
+#ifndef __ANIMA_HPP__
+#define __ANIMA_HPP__
+
+#include <cmath>
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "common_block.hpp"
+#include "flux.hpp"
+#include "rope.hpp"
+
+namespace Anima {
+    constexpr int ANIMA_GRAPH_SIZE = 65536;
+
+    __STATIC_INLINE__ ggml_tensor* apply_gate(ggml_context* ctx,
+                                              ggml_tensor* x,
+                                              ggml_tensor* gate) {
+        gate = ggml_reshape_3d(ctx, gate, gate->ne[0], 1, gate->ne[1]);  // [N, 1, C]
+        return ggml_mul(ctx, x, gate);
+    }
+
+    struct XEmbedder : public GGMLBlock {
+    public:
+        XEmbedder(int64_t in_dim, int64_t out_dim) {
+            blocks["proj.1"] = std::make_shared<Linear>(in_dim, out_dim, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj.1"]);
+            return proj->forward(ctx, x);
+        }
+    };
+
+    struct TimestepEmbedder : public GGMLBlock {
+    public:
+        TimestepEmbedder(int64_t in_dim, int64_t out_dim) {
+            blocks["1.linear_1"] = std::make_shared<Linear>(in_dim, in_dim, false);
+            blocks["1.linear_2"] = std::make_shared<Linear>(in_dim, out_dim, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["1.linear_1"]);
+            auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["1.linear_2"]);
+
+            x = linear_1->forward(ctx, x);
+            x = ggml_silu_inplace(ctx->ggml_ctx, x);
+            x = linear_2->forward(ctx, x);
+            return x;
+        }
+    };
+
+    struct AdaLayerNormZero : public GGMLBlock {
+    protected:
+        int64_t in_features;
+
+    public:
+        AdaLayerNormZero(int64_t in_features, int64_t hidden_features = 256)
+            : in_features(in_features) {
+            blocks["norm"] = std::make_shared<LayerNorm>(in_features, 1e-6f, false, false);
+            blocks["1"]    = std::make_shared<Linear>(in_features, hidden_features, false);
+            blocks["2"]    = std::make_shared<Linear>(hidden_features, 3 * in_features, false);
+        }
+
+        std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                      ggml_tensor* hidden_states,
+                                                      ggml_tensor* embedded_timestep,
+                                                      ggml_tensor* temb = nullptr) {
+            auto norm     = std::dynamic_pointer_cast<LayerNorm>(blocks["norm"]);
+            auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["1"]);
+            auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["2"]);
+
+            auto emb = ggml_silu(ctx->ggml_ctx, embedded_timestep);
+            emb      = linear_1->forward(ctx, emb);
+            emb      = linear_2->forward(ctx, emb);  // [N, 3*C]
+
+            if (temb != nullptr) {
+                emb = ggml_add(ctx->ggml_ctx, emb, temb);
+            }
+
+            auto emb_chunks = ggml_ext_chunk(ctx->ggml_ctx, emb, 3, 0);
+            auto shift      = emb_chunks[0];
+            auto scale      = emb_chunks[1];
+            auto gate       = emb_chunks[2];
+
+            auto x = norm->forward(ctx, hidden_states);
+            x      = Flux::modulate(ctx->ggml_ctx, x, shift, scale);
+
+            return {x, gate};
+        }
+    };
+
+    struct AdaLayerNorm : public GGMLBlock {
+    protected:
+        int64_t embedding_dim;
+
+    public:
+        AdaLayerNorm(int64_t in_features, int64_t hidden_features = 256)
+            : embedding_dim(in_features) {
+            blocks["norm"] = std::make_shared<LayerNorm>(in_features, 1e-6f, false, false);
+            blocks["1"]    = std::make_shared<Linear>(in_features, hidden_features, false);
+            blocks["2"]    = std::make_shared<Linear>(hidden_features, 2 * in_features, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* hidden_states,
+                             ggml_tensor* embedded_timestep,
+                             ggml_tensor* temb = nullptr) {
+            auto norm     = std::dynamic_pointer_cast<LayerNorm>(blocks["norm"]);
+            auto linear_1 = std::dynamic_pointer_cast<Linear>(blocks["1"]);
+            auto linear_2 = std::dynamic_pointer_cast<Linear>(blocks["2"]);
+
+            auto emb = ggml_silu(ctx->ggml_ctx, embedded_timestep);
+            emb      = linear_1->forward(ctx, emb);
+            emb      = linear_2->forward(ctx, emb);  // [N, 2*C]
+
+            if (temb != nullptr) {
+                auto temb_2c = ggml_view_2d(ctx->ggml_ctx, temb, 2 * embedding_dim, temb->ne[1], temb->nb[1], 0);
+                emb          = ggml_add(ctx->ggml_ctx, emb, temb_2c);
+            }
+
+            auto emb_chunks = ggml_ext_chunk(ctx->ggml_ctx, emb, 2, 0);
+            auto shift      = emb_chunks[0];
+            auto scale      = emb_chunks[1];
+
+            auto x = norm->forward(ctx, hidden_states);
+            x      = Flux::modulate(ctx->ggml_ctx, x, shift, scale);
+            return x;
+        }
+    };
+
+    struct AnimaAttention : public GGMLBlock {
+    protected:
+        int64_t num_heads;
+        int64_t head_dim;
+        std::string out_proj_name;
+
+    public:
+        AnimaAttention(int64_t query_dim,
+                       int64_t context_dim,
+                       int64_t num_heads,
+                       int64_t head_dim,
+                       const std::string& out_proj_name = "output_proj")
+            : num_heads(num_heads), head_dim(head_dim), out_proj_name(out_proj_name) {
+            int64_t inner_dim = num_heads * head_dim;
+
+            blocks["q_proj"]            = std::make_shared<Linear>(query_dim, inner_dim, false);
+            blocks["k_proj"]            = std::make_shared<Linear>(context_dim, inner_dim, false);
+            blocks["v_proj"]            = std::make_shared<Linear>(context_dim, inner_dim, false);
+            blocks["q_norm"]            = std::make_shared<RMSNorm>(head_dim, 1e-6f);
+            blocks["k_norm"]            = std::make_shared<RMSNorm>(head_dim, 1e-6f);
+            blocks[this->out_proj_name] = std::make_shared<Linear>(inner_dim, query_dim, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* hidden_states,
+                             ggml_tensor* encoder_hidden_states = nullptr,
+                             ggml_tensor* pe_q                  = nullptr,
+                             ggml_tensor* pe_k                  = nullptr) {
+            if (encoder_hidden_states == nullptr) {
+                encoder_hidden_states = hidden_states;
+            }
+
+            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 q_norm   = std::dynamic_pointer_cast<RMSNorm>(blocks["q_norm"]);
+            auto k_norm   = std::dynamic_pointer_cast<RMSNorm>(blocks["k_norm"]);
+            auto out_proj = std::dynamic_pointer_cast<Linear>(blocks[out_proj_name]);
+
+            auto q = q_proj->forward(ctx, hidden_states);
+            auto k = k_proj->forward(ctx, encoder_hidden_states);
+            auto v = v_proj->forward(ctx, encoder_hidden_states);
+
+            int64_t N   = q->ne[2];
+            int64_t L_q = q->ne[1];
+            int64_t L_k = k->ne[1];
+
+            auto q4 = ggml_reshape_4d(ctx->ggml_ctx, q, head_dim, num_heads, L_q, N);  // [N, L_q, H, D]
+            auto k4 = ggml_reshape_4d(ctx->ggml_ctx, k, head_dim, num_heads, L_k, N);  // [N, L_k, H, D]
+            auto v4 = ggml_reshape_4d(ctx->ggml_ctx, v, head_dim, num_heads, L_k, N);  // [N, L_k, H, D]
+
+            q4 = q_norm->forward(ctx, q4);
+            k4 = k_norm->forward(ctx, k4);
+
+            ggml_tensor* attn_out = nullptr;
+            if (pe_q != nullptr || pe_k != nullptr) {
+                if (pe_q == nullptr) {
+                    pe_q = pe_k;
+                }
+                if (pe_k == nullptr) {
+                    pe_k = pe_q;
+                }
+                auto q_rope = Rope::apply_rope(ctx->ggml_ctx, q4, pe_q, false);
+                auto k_rope = Rope::apply_rope(ctx->ggml_ctx, k4, pe_k, false);
+                attn_out    = ggml_ext_attention_ext(ctx->ggml_ctx,
+                                                     ctx->backend,
+                                                     q_rope,
+                                                     k_rope,
+                                                     v4,
+                                                     num_heads,
+                                                     nullptr,
+                                                     true,
+                                                     ctx->flash_attn_enabled);
+            } else {
+                auto q_flat = ggml_reshape_3d(ctx->ggml_ctx, q4, head_dim * num_heads, L_q, N);
+                auto k_flat = ggml_reshape_3d(ctx->ggml_ctx, k4, head_dim * num_heads, L_k, N);
+                attn_out    = ggml_ext_attention_ext(ctx->ggml_ctx,
+                                                     ctx->backend,
+                                                     q_flat,
+                                                     k_flat,
+                                                     v,
+                                                     num_heads,
+                                                     nullptr,
+                                                     false,
+                                                     ctx->flash_attn_enabled);
+            }
+
+            return out_proj->forward(ctx, attn_out);
+        }
+    };
+
+    struct AnimaMLP : public GGMLBlock {
+    public:
+        AnimaMLP(int64_t dim, int64_t hidden_dim) {
+            blocks["layer1"] = std::make_shared<Linear>(dim, hidden_dim, false);
+            blocks["layer2"] = std::make_shared<Linear>(hidden_dim, dim, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto layer1 = std::dynamic_pointer_cast<Linear>(blocks["layer1"]);
+            auto layer2 = std::dynamic_pointer_cast<Linear>(blocks["layer2"]);
+
+            x = layer1->forward(ctx, x);
+            x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
+            x = layer2->forward(ctx, x);
+            return x;
+        }
+    };
+
+    struct AdapterMLP : public GGMLBlock {
+    public:
+        AdapterMLP(int64_t dim, int64_t hidden_dim) {
+            blocks["0"] = std::make_shared<Linear>(dim, hidden_dim, true);
+            blocks["2"] = std::make_shared<Linear>(hidden_dim, dim, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto layer0 = std::dynamic_pointer_cast<Linear>(blocks["0"]);
+            auto layer2 = std::dynamic_pointer_cast<Linear>(blocks["2"]);
+
+            x = layer0->forward(ctx, x);
+            x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
+            x = layer2->forward(ctx, x);
+            return x;
+        }
+    };
+
+    struct LLMAdapterBlock : public GGMLBlock {
+    public:
+        LLMAdapterBlock(int64_t model_dim = 1024, int64_t source_dim = 1024, int64_t num_heads = 16, int64_t head_dim = 64) {
+            blocks["norm_self_attn"]  = std::make_shared<RMSNorm>(model_dim, 1e-6f);
+            blocks["self_attn"]       = std::make_shared<AnimaAttention>(model_dim, model_dim, num_heads, head_dim, "o_proj");
+            blocks["norm_cross_attn"] = std::make_shared<RMSNorm>(model_dim, 1e-6f);
+            blocks["cross_attn"]      = std::make_shared<AnimaAttention>(model_dim, source_dim, num_heads, head_dim, "o_proj");
+            blocks["norm_mlp"]        = std::make_shared<RMSNorm>(model_dim, 1e-6f);
+            blocks["mlp"]             = std::make_shared<AdapterMLP>(model_dim, model_dim * 4);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* context,
+                             ggml_tensor* target_pe,
+                             ggml_tensor* context_pe) {
+            auto norm_self_attn  = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_self_attn"]);
+            auto self_attn       = std::dynamic_pointer_cast<AnimaAttention>(blocks["self_attn"]);
+            auto norm_cross_attn = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_cross_attn"]);
+            auto cross_attn      = std::dynamic_pointer_cast<AnimaAttention>(blocks["cross_attn"]);
+            auto norm_mlp        = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_mlp"]);
+            auto mlp             = std::dynamic_pointer_cast<AdapterMLP>(blocks["mlp"]);
+
+            auto h = norm_self_attn->forward(ctx, x);
+            h      = self_attn->forward(ctx, h, nullptr, target_pe, target_pe);
+            x      = ggml_add(ctx->ggml_ctx, x, h);
+
+            h = norm_cross_attn->forward(ctx, x);
+            h = cross_attn->forward(ctx, h, context, target_pe, context_pe);
+            x = ggml_add(ctx->ggml_ctx, x, h);
+
+            h = norm_mlp->forward(ctx, x);
+            h = mlp->forward(ctx, h);
+            x = ggml_add(ctx->ggml_ctx, x, h);
+
+            return x;
+        }
+    };
+
+    struct LLMAdapter : public GGMLBlock {
+    protected:
+        int num_layers;
+
+    public:
+        LLMAdapter(int64_t source_dim = 1024,
+                   int64_t target_dim = 1024,
+                   int64_t model_dim  = 1024,
+                   int num_layers     = 6,
+                   int num_heads      = 16)
+            : num_layers(num_layers) {
+            int64_t head_dim = model_dim / num_heads;
+
+            blocks["embed"] = std::make_shared<Embedding>(32128, target_dim);
+            for (int i = 0; i < num_layers; i++) {
+                blocks["blocks." + std::to_string(i)] =
+                    std::make_shared<LLMAdapterBlock>(model_dim, source_dim, num_heads, head_dim);
+            }
+            blocks["out_proj"] = std::make_shared<Linear>(model_dim, target_dim, true);
+            blocks["norm"]     = std::make_shared<RMSNorm>(target_dim, 1e-6f);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* source_hidden_states,
+                             ggml_tensor* target_input_ids,
+                             ggml_tensor* target_pe,
+                             ggml_tensor* source_pe) {
+            GGML_ASSERT(target_input_ids != nullptr);
+            if (ggml_n_dims(target_input_ids) == 1) {
+                target_input_ids = ggml_reshape_2d(ctx->ggml_ctx, target_input_ids, target_input_ids->ne[0], 1);
+            }
+
+            auto embed    = std::dynamic_pointer_cast<Embedding>(blocks["embed"]);
+            auto out_proj = std::dynamic_pointer_cast<Linear>(blocks["out_proj"]);
+            auto norm     = std::dynamic_pointer_cast<RMSNorm>(blocks["norm"]);
+
+            auto x = embed->forward(ctx, target_input_ids);  // [N, target_len, target_dim]
+
+            for (int i = 0; i < num_layers; i++) {
+                auto block = std::dynamic_pointer_cast<LLMAdapterBlock>(blocks["blocks." + std::to_string(i)]);
+                x          = block->forward(ctx, x, source_hidden_states, target_pe, source_pe);
+            }
+
+            x = out_proj->forward(ctx, x);
+            x = norm->forward(ctx, x);
+            return x;
+        }
+    };
+
+    struct TransformerBlock : public GGMLBlock {
+    public:
+        TransformerBlock(int64_t hidden_size,
+                         int64_t text_embed_dim,
+                         int64_t num_heads,
+                         int64_t head_dim,
+                         int64_t mlp_ratio      = 4,
+                         int64_t adaln_lora_dim = 256) {
+            blocks["adaln_modulation_self_attn"]  = std::make_shared<AdaLayerNormZero>(hidden_size, adaln_lora_dim);
+            blocks["self_attn"]                   = std::make_shared<AnimaAttention>(hidden_size, hidden_size, num_heads, head_dim);
+            blocks["adaln_modulation_cross_attn"] = std::make_shared<AdaLayerNormZero>(hidden_size, adaln_lora_dim);
+            blocks["cross_attn"]                  = std::make_shared<AnimaAttention>(hidden_size, text_embed_dim, num_heads, head_dim);
+            blocks["adaln_modulation_mlp"]        = std::make_shared<AdaLayerNormZero>(hidden_size, adaln_lora_dim);
+            blocks["mlp"]                         = std::make_shared<AnimaMLP>(hidden_size, hidden_size * mlp_ratio);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* hidden_states,
+                             ggml_tensor* encoder_hidden_states,
+                             ggml_tensor* embedded_timestep,
+                             ggml_tensor* temb,
+                             ggml_tensor* image_pe) {
+            auto norm1 = std::dynamic_pointer_cast<AdaLayerNormZero>(blocks["adaln_modulation_self_attn"]);
+            auto attn1 = std::dynamic_pointer_cast<AnimaAttention>(blocks["self_attn"]);
+            auto norm2 = std::dynamic_pointer_cast<AdaLayerNormZero>(blocks["adaln_modulation_cross_attn"]);
+            auto attn2 = std::dynamic_pointer_cast<AnimaAttention>(blocks["cross_attn"]);
+            auto norm3 = std::dynamic_pointer_cast<AdaLayerNormZero>(blocks["adaln_modulation_mlp"]);
+            auto mlp   = std::dynamic_pointer_cast<AnimaMLP>(blocks["mlp"]);
+
+            auto [normed1, gate1] = norm1->forward(ctx, hidden_states, embedded_timestep, temb);
+            auto h                = attn1->forward(ctx, normed1, nullptr, image_pe, image_pe);
+            hidden_states         = ggml_add(ctx->ggml_ctx, hidden_states, apply_gate(ctx->ggml_ctx, h, gate1));
+
+            auto [normed2, gate2] = norm2->forward(ctx, hidden_states, embedded_timestep, temb);
+            h                     = attn2->forward(ctx, normed2, encoder_hidden_states, nullptr, nullptr);
+            hidden_states         = ggml_add(ctx->ggml_ctx, hidden_states, apply_gate(ctx->ggml_ctx, h, gate2));
+
+            auto [normed3, gate3] = norm3->forward(ctx, hidden_states, embedded_timestep, temb);
+            h                     = mlp->forward(ctx, normed3);
+            hidden_states         = ggml_add(ctx->ggml_ctx, hidden_states, apply_gate(ctx->ggml_ctx, h, gate3));
+
+            return hidden_states;
+        }
+    };
+
+    struct FinalLayer : public GGMLBlock {
+    protected:
+        int64_t hidden_size;
+        int64_t patch_size;
+        int64_t out_channels;
+
+    public:
+        FinalLayer(int64_t hidden_size, int64_t patch_size, int64_t out_channels)
+            : hidden_size(hidden_size), patch_size(patch_size), out_channels(out_channels) {
+            blocks["adaln_modulation"] = std::make_shared<AdaLayerNorm>(hidden_size, 256);
+            blocks["linear"]           = std::make_shared<Linear>(hidden_size, patch_size * patch_size * out_channels, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* hidden_states,
+                             ggml_tensor* embedded_timestep,
+                             ggml_tensor* temb) {
+            auto adaln  = std::dynamic_pointer_cast<AdaLayerNorm>(blocks["adaln_modulation"]);
+            auto linear = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
+
+            hidden_states = adaln->forward(ctx, hidden_states, embedded_timestep, temb);
+            hidden_states = linear->forward(ctx, hidden_states);
+            return hidden_states;
+        }
+    };
+
+    struct AnimaNet : public GGMLBlock {
+    public:
+        int64_t in_channels       = 16;
+        int64_t out_channels      = 16;
+        int64_t hidden_size       = 2048;
+        int64_t text_embed_dim    = 1024;
+        int64_t num_heads         = 16;
+        int64_t head_dim          = 128;
+        int patch_size            = 2;
+        int64_t num_layers        = 28;
+        std::vector<int> axes_dim = {44, 42, 42};
+        int theta                 = 10000;
+
+    public:
+        AnimaNet() = default;
+        explicit AnimaNet(int64_t num_layers)
+            : num_layers(num_layers) {
+            blocks["x_embedder"]       = std::make_shared<XEmbedder>((in_channels + 1) * patch_size * patch_size, hidden_size);
+            blocks["t_embedder"]       = std::make_shared<TimestepEmbedder>(hidden_size, hidden_size * 3);
+            blocks["t_embedding_norm"] = std::make_shared<RMSNorm>(hidden_size, 1e-6f);
+            for (int i = 0; i < num_layers; i++) {
+                blocks["blocks." + std::to_string(i)] = std::make_shared<TransformerBlock>(hidden_size,
+                                                                                           text_embed_dim,
+                                                                                           num_heads,
+                                                                                           head_dim);
+            }
+            blocks["final_layer"] = std::make_shared<FinalLayer>(hidden_size, patch_size, out_channels);
+            blocks["llm_adapter"] = std::make_shared<LLMAdapter>(1024, 1024, 1024, 6, 16);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* timestep,
+                             ggml_tensor* encoder_hidden_states,
+                             ggml_tensor* image_pe,
+                             ggml_tensor* t5_ids       = nullptr,
+                             ggml_tensor* t5_weights   = nullptr,
+                             ggml_tensor* adapter_q_pe = nullptr,
+                             ggml_tensor* adapter_k_pe = nullptr) {
+            GGML_ASSERT(x->ne[3] == 1);
+
+            auto x_embedder       = std::dynamic_pointer_cast<XEmbedder>(blocks["x_embedder"]);
+            auto t_embedder       = std::dynamic_pointer_cast<TimestepEmbedder>(blocks["t_embedder"]);
+            auto t_embedding_norm = std::dynamic_pointer_cast<RMSNorm>(blocks["t_embedding_norm"]);
+            auto final_layer      = std::dynamic_pointer_cast<FinalLayer>(blocks["final_layer"]);
+            auto llm_adapter      = std::dynamic_pointer_cast<LLMAdapter>(blocks["llm_adapter"]);
+
+            int64_t W = x->ne[0];
+            int64_t H = x->ne[1];
+
+            auto padding_mask = ggml_ext_zeros(ctx->ggml_ctx, x->ne[0], x->ne[1], 1, x->ne[3]);
+            x                 = ggml_concat(ctx->ggml_ctx, x, padding_mask, 2);  // [N, C + 1, H, W]
+
+            x = DiT::pad_and_patchify(ctx, x, patch_size, patch_size);  // [N, h*w, (C+1)*ph*pw]
+
+            x = x_embedder->forward(ctx, x);
+
+            auto timestep_proj     = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep, static_cast<int>(hidden_size));
+            auto temb              = t_embedder->forward(ctx, timestep_proj);
+            auto embedded_timestep = t_embedding_norm->forward(ctx, timestep_proj);
+
+            if (t5_ids != nullptr) {
+                auto adapted_context = llm_adapter->forward(ctx, encoder_hidden_states, t5_ids, adapter_q_pe, adapter_k_pe);
+                if (t5_weights != nullptr) {
+                    auto w = t5_weights;
+                    if (ggml_n_dims(w) == 1) {
+                        w = ggml_reshape_3d(ctx->ggml_ctx, w, 1, w->ne[0], 1);
+                    }
+                    w               = ggml_repeat_4d(ctx->ggml_ctx, w, adapted_context->ne[0], adapted_context->ne[1], adapted_context->ne[2], 1);
+                    adapted_context = ggml_mul(ctx->ggml_ctx, adapted_context, w);
+                }
+                if (adapted_context->ne[1] < 512) {
+                    auto pad_ctx    = ggml_ext_zeros(ctx->ggml_ctx,
+                                                     adapted_context->ne[0],
+                                                     512 - adapted_context->ne[1],
+                                                     adapted_context->ne[2],
+                                                     1);
+                    adapted_context = ggml_concat(ctx->ggml_ctx, adapted_context, pad_ctx, 1);
+                } else if (adapted_context->ne[1] > 512) {
+                    adapted_context = ggml_ext_slice(ctx->ggml_ctx, adapted_context, 1, 0, 512);
+                }
+                encoder_hidden_states = adapted_context;
+            }
+
+            for (int i = 0; i < num_layers; i++) {
+                auto block = std::dynamic_pointer_cast<TransformerBlock>(blocks["blocks." + std::to_string(i)]);
+                x          = block->forward(ctx, x, encoder_hidden_states, embedded_timestep, temb, image_pe);
+            }
+
+            x = final_layer->forward(ctx, x, embedded_timestep, temb);  // [N, h*w, ph*pw*C]
+
+            x = DiT::unpatchify_and_crop(ctx->ggml_ctx, x, H, W, patch_size, patch_size, false);  // [N, C, H, W]
+
+            return x;
+        }
+    };
+
+    struct AnimaRunner : public GGMLRunner {
+    public:
+        std::vector<float> image_pe_vec;
+        std::vector<float> adapter_q_pe_vec;
+        std::vector<float> adapter_k_pe_vec;
+        AnimaNet net;
+
+        AnimaRunner(ggml_backend_t backend,
+                    bool offload_params_to_cpu,
+                    const String2TensorStorage& tensor_storage_map = {},
+                    const std::string prefix                       = "model.diffusion_model")
+            : GGMLRunner(backend, offload_params_to_cpu) {
+            int64_t num_layers    = 0;
+            std::string layer_tag = prefix + ".net.blocks.";
+            for (const auto& kv : tensor_storage_map) {
+                const std::string& tensor_name = kv.first;
+                size_t pos                     = tensor_name.find(layer_tag);
+                if (pos == std::string::npos) {
+                    continue;
+                }
+                size_t start = pos + layer_tag.size();
+                size_t end   = tensor_name.find('.', start);
+                if (end == std::string::npos) {
+                    continue;
+                }
+                int64_t layer_id = atoll(tensor_name.substr(start, end - start).c_str());
+                num_layers       = std::max(num_layers, layer_id + 1);
+            }
+            if (num_layers <= 0) {
+                num_layers = 28;
+            }
+            LOG_INFO("anima net layers: %" PRId64, num_layers);
+
+            net = AnimaNet(num_layers);
+            net.init(params_ctx, tensor_storage_map, prefix + ".net");
+        }
+
+        std::string get_desc() override {
+            return "anima";
+        }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
+            net.get_param_tensors(tensors, prefix + ".net");
+        }
+
+        static std::vector<float> gen_1d_rope_pe_vec(int64_t seq_len, int dim, float theta = 10000.f) {
+            std::vector<float> pos(seq_len);
+            for (int64_t i = 0; i < seq_len; i++) {
+                pos[i] = static_cast<float>(i);
+            }
+            auto rope_emb = Rope::rope(pos, dim, theta);
+            return Rope::flatten(rope_emb);
+        }
+
+        static float calc_ntk_factor(float extrapolation_ratio, int axis_dim) {
+            if (extrapolation_ratio == 1.0f || axis_dim <= 2) {
+                return 1.0f;
+            }
+            return std::pow(extrapolation_ratio, static_cast<float>(axis_dim) / static_cast<float>(axis_dim - 2));
+        }
+
+        static std::vector<float> gen_anima_image_pe_vec(int bs,
+                                                         int h,
+                                                         int w,
+                                                         int patch_size,
+                                                         int theta,
+                                                         const std::vector<int>& axes_dim,
+                                                         float h_extrapolation_ratio,
+                                                         float w_extrapolation_ratio,
+                                                         float t_extrapolation_ratio) {
+            static const std::vector<ggml_tensor*> empty_ref_latents;
+            auto ids = Rope::gen_flux_ids(h,
+                                          w,
+                                          patch_size,
+                                          bs,
+                                          static_cast<int>(axes_dim.size()),
+                                          0,
+                                          {},
+                                          empty_ref_latents,
+                                          false,
+                                          1.0f);
+
+            std::vector<float> axis_thetas = {
+                static_cast<float>(theta) * calc_ntk_factor(t_extrapolation_ratio, axes_dim[0]),
+                static_cast<float>(theta) * calc_ntk_factor(h_extrapolation_ratio, axes_dim[1]),
+                static_cast<float>(theta) * calc_ntk_factor(w_extrapolation_ratio, axes_dim[2]),
+            };
+            return Rope::embed_nd(ids, bs, axis_thetas, axes_dim);
+        }
+
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor    = {},
+                                 const sd::Tensor<int32_t>& t5_ids_tensor   = {},
+                                 const sd::Tensor<float>& t5_weights_tensor = {}) {
+            ggml_tensor* x          = make_input(x_tensor);
+            ggml_tensor* timesteps  = make_input(timesteps_tensor);
+            ggml_tensor* context    = make_optional_input(context_tensor);
+            ggml_tensor* t5_ids     = make_optional_input(t5_ids_tensor);
+            ggml_tensor* t5_weights = make_optional_input(t5_weights_tensor);
+            GGML_ASSERT(x->ne[3] == 1);
+            ggml_cgraph* gf = new_graph_custom(ANIMA_GRAPH_SIZE);
+
+            int64_t pad_h = (net.patch_size - x->ne[1] % net.patch_size) % net.patch_size;
+            int64_t pad_w = (net.patch_size - x->ne[0] % net.patch_size) % net.patch_size;
+            int64_t h_pad = x->ne[1] + pad_h;
+            int64_t w_pad = x->ne[0] + pad_w;
+
+            image_pe_vec          = gen_anima_image_pe_vec(1,
+                                                           static_cast<int>(h_pad),
+                                                           static_cast<int>(w_pad),
+                                                           static_cast<int>(net.patch_size),
+                                                           net.theta,
+                                                           net.axes_dim,
+                                                           4.0f,
+                                                           4.0f,
+                                                           1.0f);
+            int64_t image_pos_len = static_cast<int64_t>(image_pe_vec.size()) / (2 * 2 * (net.head_dim / 2));
+            auto image_pe         = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, net.head_dim / 2, image_pos_len);
+            set_backend_tensor_data(image_pe, image_pe_vec.data());
+
+            ggml_tensor* adapter_q_pe = nullptr;
+            ggml_tensor* adapter_k_pe = nullptr;
+            if (t5_ids != nullptr) {
+                int64_t target_len = t5_ids->ne[0];
+                int64_t source_len = context->ne[1];
+
+                adapter_q_pe_vec = gen_1d_rope_pe_vec(target_len, 64, 10000.f);
+                adapter_k_pe_vec = gen_1d_rope_pe_vec(source_len, 64, 10000.f);
+
+                int64_t target_pos_len = static_cast<int64_t>(adapter_q_pe_vec.size()) / (2 * 2 * 32);
+                int64_t source_pos_len = static_cast<int64_t>(adapter_k_pe_vec.size()) / (2 * 2 * 32);
+
+                adapter_q_pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, 32, target_pos_len);
+                adapter_k_pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, 32, source_pos_len);
+                set_backend_tensor_data(adapter_q_pe, adapter_q_pe_vec.data());
+                set_backend_tensor_data(adapter_k_pe, adapter_k_pe_vec.data());
+            }
+
+            auto runner_ctx = get_context();
+            auto out        = net.forward(&runner_ctx,
+                                          x,
+                                          timesteps,
+                                          context,
+                                          image_pe,
+                                          t5_ids,
+                                          t5_weights,
+                                          adapter_q_pe,
+                                          adapter_k_pe);
+
+            ggml_build_forward_expand(gf, out);
+            return gf;
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context    = {},
+                                  const sd::Tensor<int32_t>& t5_ids   = {},
+                                  const sd::Tensor<float>& t5_weights = {}) {
+            auto get_graph = [&]() -> ggml_cgraph* {
+                return build_graph(x, timesteps, context, t5_ids, t5_weights);
+            };
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+        }
+    };
+}  // namespace Anima
+
+#endif  // __ANIMA_HPP__
@@ -1,8 +1,7 @@
-#ifndef __VAE_HPP__
-#define __VAE_HPP__
+#ifndef __AUTO_ENCODER_KL_HPP__
+#define __AUTO_ENCODER_KL_HPP__

-#include "common.hpp"
-#include "ggml_extend.hpp"
+#include "vae.hpp"

 /*================================================== AutoEncoderKL ===================================================*/

@@ -30,7 +29,7 @@ public:
        }
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
        // x: [N, in_channels, h, w]
        // t_emb is always None
        auto norm1 = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm1"]);
@@ -66,7 +65,7 @@ protected:
    int64_t in_channels;
    bool use_linear;

-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") {
        auto iter = tensor_storage_map.find(prefix + "proj_out.weight");
        if (iter != tensor_storage_map.end()) {
            if (iter->second.n_dims == 4 && use_linear) {
@@ -102,7 +101,7 @@ public:
        }
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
        // x: [N, in_channels, h, w]
        auto norm     = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm"]);
        auto q_proj   = std::dynamic_pointer_cast<UnaryBlock>(blocks["q"]);
@@ -127,8 +126,6 @@ public:
            q = q_proj->forward(ctx, h_);  // [N, h * w, in_channels]
            k = k_proj->forward(ctx, h_);  // [N, h * w, in_channels]
            v = v_proj->forward(ctx, h_);  // [N, h * w, in_channels]
-
-            v = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, v, 1, 0, 2, 3));  // [N, in_channels, h * w]
        } else {
            q = q_proj->forward(ctx, h_);                                              // [N, in_channels, h, w]
            q = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, q, 1, 2, 0, 3));  // [N, h, w, in_channels]
@@ -138,11 +135,12 @@ public:
            k = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, k, 1, 2, 0, 3));  // [N, h, w, in_channels]
            k = ggml_reshape_3d(ctx->ggml_ctx, k, c, h * w, n);                        // [N, h * w, in_channels]

-            v = v_proj->forward(ctx, h_);                        // [N, in_channels, h, w]
-            v = ggml_reshape_3d(ctx->ggml_ctx, v, h * w, c, n);  // [N, in_channels, h * w]
+            v = v_proj->forward(ctx, h_);                                              // [N, in_channels, h, w]
+            v = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, v, 1, 2, 0, 3));  // [N, h, w, in_channels]
+            v = ggml_reshape_3d(ctx->ggml_ctx, v, c, h * w, n);                        // [N, h * w, in_channels]
        }

-        h_ = ggml_ext_attention(ctx->ggml_ctx, q, k, v, false);  // [N, h * w, in_channels]
+        h_ = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, 1, nullptr, false, ctx->flash_attn_enabled);

        if (use_linear) {
            h_ = proj_out->forward(ctx, h_);  // [N, h * w, in_channels]
@@ -166,27 +164,27 @@ public:
    AE3DConv(int64_t in_channels,
             int64_t out_channels,
             std::pair<int, int> kernel_size,
-             int64_t video_kernel_size    = 3,
+             int video_kernel_size        = 3,
             std::pair<int, int> stride   = {1, 1},
             std::pair<int, int> padding  = {0, 0},
             std::pair<int, int> dilation = {1, 1},
             bool bias                    = true)
        : Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias) {
-        int64_t kernel_padding  = video_kernel_size / 2;
-        blocks["time_mix_conv"] = std::shared_ptr<GGMLBlock>(new Conv3dnx1x1(out_channels,
-                                                                             out_channels,
-                                                                             video_kernel_size,
-                                                                             1,
-                                                                             kernel_padding));
+        int kernel_padding      = video_kernel_size / 2;
+        blocks["time_mix_conv"] = std::shared_ptr<GGMLBlock>(new Conv3d(out_channels,
+                                                                        out_channels,
+                                                                        {video_kernel_size, 1, 1},
+                                                                        {1, 1, 1},
+                                                                        {kernel_padding, 0, 0}));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x) override {
        // timesteps always None
        // skip_video always False
        // x: [N, IC, IH, IW]
        // result: [N, OC, OH, OW]
-        auto time_mix_conv = std::dynamic_pointer_cast<Conv3dnx1x1>(blocks["time_mix_conv"]);
+        auto time_mix_conv = std::dynamic_pointer_cast<Conv3d>(blocks["time_mix_conv"]);

        x = Conv2d::forward(ctx, x);
        // timesteps = x.shape[0]
@@ -210,7 +208,7 @@ public:

 class VideoResnetBlock : public ResnetBlock {
 protected:
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
        enum ggml_type wtype = get_type(prefix + "mix_factor", tensor_storage_map, GGML_TYPE_F32);
        params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
    }
@@ -229,7 +227,7 @@ public:
        blocks["time_stack"] = std::shared_ptr<GGMLBlock>(new ResBlock(out_channels, 0, out_channels, {video_kernel_size, 1}, 3, false, true));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
        // x: [N, in_channels, h, w] aka [b*t, in_channels, h, w]
        // return: [N, out_channels, h, w] aka [b*t, out_channels, h, w]
        // t_emb is always None
@@ -254,8 +252,8 @@ public:

        float alpha = get_alpha();
        x           = ggml_add(ctx->ggml_ctx,
-                               ggml_scale(ctx->ggml_ctx, x, alpha),
-                               ggml_scale(ctx->ggml_ctx, x_mix, 1.0f - alpha));
+                               ggml_ext_scale(ctx->ggml_ctx, x, alpha),
+                               ggml_ext_scale(ctx->ggml_ctx, x_mix, 1.0f - alpha));

        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
        x = ggml_reshape_4d(ctx->ggml_ctx, x, W, H, C, T * B);                     // b t c (h w) -> (b t) c h w
@@ -319,7 +317,7 @@ public:
        blocks["conv_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(block_in, double_z ? z_channels * 2 : z_channels, {3, 3}, {1, 1}, {1, 1}));
    }

-    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    virtual ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [N, in_channels, h, w]

        auto conv_in     = std::dynamic_pointer_cast<Conv2d>(blocks["conv_in"]);
@@ -409,8 +407,8 @@ public:
          z_channels(z_channels),
          video_decoder(video_decoder),
          video_kernel_size(video_kernel_size) {
-        size_t num_resolutions = ch_mult.size();
-        int block_in           = ch * ch_mult[num_resolutions - 1];
+        int num_resolutions = static_cast<int>(ch_mult.size());
+        int block_in        = ch * ch_mult[num_resolutions - 1];

        blocks["conv_in"] = std::shared_ptr<GGMLBlock>(new Conv2d(z_channels, block_in, {3, 3}, {1, 1}, {1, 1}));

@@ -437,7 +435,7 @@ public:
        blocks["conv_out"] = get_conv_out(block_in, out_ch, {3, 3}, {1, 1}, {1, 1});
    }

-    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
+    virtual ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* z) {
        // z: [N, z_channels, h, w]
        // alpha is always 0
        // merge_strategy is always learned
@@ -461,7 +459,7 @@ public:
        h = mid_block_2->forward(ctx, h);  // [N, block_in, h, w]

        // upsampling
-        size_t num_resolutions = ch_mult.size();
+        int num_resolutions = static_cast<int>(ch_mult.size());
        for (int i = num_resolutions - 1; i >= 0; i--) {
            for (int j = 0; j < num_res_blocks + 1; j++) {
                std::string name = "up." + std::to_string(i) + ".block." + std::to_string(j);
@@ -485,7 +483,7 @@ public:
 };

 // ldm.models.autoencoder.AutoencoderKL
-class AutoencodingEngine : public GGMLBlock {
+class AutoEncoderKLModel : public GGMLBlock {
 protected:
    SDVersion version;
    bool decode_only       = true;
@@ -503,11 +501,36 @@ protected:
        bool double_z            = true;
    } dd_config;

+    static std::string get_tensor_name(const std::string& prefix, const std::string& name) {
+        return prefix.empty() ? name : prefix + "." + name;
+    }
+
+    void detect_decoder_ch(const String2TensorStorage& tensor_storage_map,
+                           const std::string& prefix,
+                           int& decoder_ch) {
+        auto conv_in_iter = tensor_storage_map.find(get_tensor_name(prefix, "decoder.conv_in.weight"));
+        if (conv_in_iter != tensor_storage_map.end() && conv_in_iter->second.n_dims >= 4 && conv_in_iter->second.ne[3] > 0) {
+            int last_ch_mult             = dd_config.ch_mult.back();
+            int64_t conv_in_out_channels = conv_in_iter->second.ne[3];
+            if (last_ch_mult > 0 && conv_in_out_channels % last_ch_mult == 0) {
+                decoder_ch = static_cast<int>(conv_in_out_channels / last_ch_mult);
+                LOG_INFO("vae decoder: ch = %d", decoder_ch);
+            } else {
+                LOG_WARN("vae decoder: failed to infer ch from %s (%" PRId64 " / %d)",
+                         get_tensor_name(prefix, "decoder.conv_in.weight").c_str(),
+                         conv_in_out_channels,
+                         last_ch_mult);
+            }
+        }
+    }
+
 public:
-    AutoencodingEngine(SDVersion version          = VERSION_SD1,
-                       bool decode_only           = true,
-                       bool use_linear_projection = false,
-                       bool use_video_decoder     = false)
+    AutoEncoderKLModel(SDVersion version                              = VERSION_SD1,
+                       bool decode_only                               = true,
+                       bool use_linear_projection                     = false,
+                       bool use_video_decoder                         = false,
+                       const String2TensorStorage& tensor_storage_map = {},
+                       const std::string& prefix                      = "")
        : version(version), decode_only(decode_only), use_video_decoder(use_video_decoder) {
        if (sd_version_is_dit(version)) {
            if (sd_version_is_flux2(version)) {
@@ -521,7 +544,9 @@ public:
        if (use_video_decoder) {
            use_quant = false;
        }
-        blocks["decoder"] = std::shared_ptr<GGMLBlock>(new Decoder(dd_config.ch,
+        int decoder_ch = dd_config.ch;
+        detect_decoder_ch(tensor_storage_map, prefix, decoder_ch);
+        blocks["decoder"] = std::shared_ptr<GGMLBlock>(new Decoder(decoder_ch,
                                                                   dd_config.out_ch,
                                                                   dd_config.ch_mult,
                                                                   dd_config.num_res_blocks,
@@ -551,7 +576,7 @@ public:
        }
    }

-    struct ggml_tensor* decode(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
+    ggml_tensor* decode(GGMLRunnerContext* ctx, ggml_tensor* z) {
        // z: [N, z_channels, h, w]
        if (sd_version_is_flux2(version)) {
            // [N, C*p*p, h, w] -> [N, C, h*p, w*p]
@@ -583,7 +608,7 @@ public:
        return h;
    }

-    struct ggml_tensor* encode(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* encode(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [N, in_channels, h, w]
        auto encoder = std::dynamic_pointer_cast<Encoder>(blocks["encoder"]);

@@ -612,48 +637,21 @@ public:
        }
        return z;
    }
-};

-struct VAE : public GGMLRunner {
-    VAE(ggml_backend_t backend, bool offload_params_to_cpu)
-        : GGMLRunner(backend, offload_params_to_cpu) {}
-    virtual bool compute(const int n_threads,
-                         struct ggml_tensor* z,
-                         bool decode_graph,
-                         struct ggml_tensor** output,
-                         struct ggml_context* output_ctx)                                                         = 0;
-    virtual void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) = 0;
-    virtual void set_conv2d_scale(float scale) { SD_UNUSED(scale); };
-};
-
-struct FakeVAE : public VAE {
-    FakeVAE(ggml_backend_t backend, bool offload_params_to_cpu)
-        : VAE(backend, offload_params_to_cpu) {}
-    bool compute(const int n_threads,
-                 struct ggml_tensor* z,
-                 bool decode_graph,
-                 struct ggml_tensor** output,
-                 struct ggml_context* output_ctx) override {
-        if (*output == nullptr && output_ctx != nullptr) {
-            *output = ggml_dup_tensor(output_ctx, z);
+    int get_encoder_output_channels() {
+        int factor = dd_config.double_z ? 2 : 1;
+        if (sd_version_is_flux2(version)) {
+            return dd_config.z_channels * 4;
        }
-        ggml_ext_tensor_iter(z, [&](ggml_tensor* z, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
-            float value = ggml_ext_tensor_get_f32(z, i0, i1, i2, i3);
-            ggml_ext_tensor_set_f32(*output, value, i0, i1, i2, i3);
-        });
-        return true;
-    }
-
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) override {}
-
-    std::string get_desc() override {
-        return "fake_vae";
+        return dd_config.z_channels * factor;
    }
 };

 struct AutoEncoderKL : public VAE {
-    bool decode_only = true;
-    AutoencodingEngine ae;
+    float scale_factor = 1.f;
+    float shift_factor = 0.f;
+    bool decode_only   = true;
+    AutoEncoderKLModel ae;

    AutoEncoderKL(ggml_backend_t backend,
                  bool offload_params_to_cpu,
@@ -662,7 +660,23 @@ struct AutoEncoderKL : public VAE {
                  bool decode_only       = false,
                  bool use_video_decoder = false,
                  SDVersion version      = VERSION_SD1)
-        : decode_only(decode_only), VAE(backend, offload_params_to_cpu) {
+        : decode_only(decode_only), VAE(version, backend, offload_params_to_cpu) {
+        if (sd_version_is_sd1(version) || sd_version_is_sd2(version)) {
+            scale_factor = 0.18215f;
+            shift_factor = 0.f;
+        } else if (sd_version_is_sdxl(version)) {
+            scale_factor = 0.13025f;
+            shift_factor = 0.f;
+        } else if (sd_version_is_sd3(version)) {
+            scale_factor = 1.5305f;
+            shift_factor = 0.0609f;
+        } else if (sd_version_is_flux(version) || sd_version_is_z_image(version)) {
+            scale_factor = 0.3611f;
+            shift_factor = 0.1159f;
+        } else if (sd_version_is_flux2(version)) {
+            scale_factor = 1.0f;
+            shift_factor = 0.f;
+        }
        bool use_linear_projection = false;
        for (const auto& [name, tensor_storage] : tensor_storage_map) {
            if (!starts_with(name, prefix)) {
@@ -675,7 +689,7 @@ struct AutoEncoderKL : public VAE {
                break;
            }
        }
-        ae = AutoencodingEngine(version, decode_only, use_linear_projection, use_video_decoder);
+        ae = AutoEncoderKLModel(version, decode_only, use_linear_projection, use_video_decoder, tensor_storage_map, prefix);
        ae.init(params_ctx, tensor_storage_map, prefix);
    }

@@ -694,63 +708,150 @@ struct AutoEncoderKL : public VAE {
        return "vae";
    }

-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) override {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) override {
        ae.get_param_tensors(tensors, prefix);
    }

-    struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
-        struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
-
-        z = to_backend(z);
+    ggml_cgraph* build_graph(const sd::Tensor<float>& z_tensor, bool decode_graph) {
+        ggml_cgraph* gf = ggml_new_graph(compute_ctx);
+        ggml_tensor* z  = make_input(z_tensor);

        auto runner_ctx = get_context();

-        struct ggml_tensor* out = decode_graph ? ae.decode(&runner_ctx, z) : ae.encode(&runner_ctx, z);
+        ggml_tensor* out = decode_graph ? ae.decode(&runner_ctx, z) : ae.encode(&runner_ctx, z);

        ggml_build_forward_expand(gf, out);

        return gf;
    }

-    bool compute(const int n_threads,
-                 struct ggml_tensor* z,
-                 bool decode_graph,
-                 struct ggml_tensor** output,
-                 struct ggml_context* output_ctx = nullptr) override {
+    sd::Tensor<float> _compute(const int n_threads,
+                               const sd::Tensor<float>& z,
+                               bool decode_graph) override {
        GGML_ASSERT(!decode_only || decode_graph);
-        auto get_graph = [&]() -> struct ggml_cgraph* {
+        auto get_graph = [&]() -> ggml_cgraph* {
            return build_graph(z, decode_graph);
        };
-        // ggml_set_f32(z, 0.5f);
-        // print_ggml_tensor(z);
-        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), z.dim());
+    }
+
+    sd::Tensor<float> gaussian_latent_sample(const sd::Tensor<float>& moments, std::shared_ptr<RNG> rng) {
+        // ldm.modules.distributions.distributions.DiagonalGaussianDistribution.sample
+        auto chunks               = sd::ops::chunk(moments, 2, 2);
+        const auto& mean          = chunks[0];
+        const auto& logvar        = chunks[1];
+        sd::Tensor<float> stddev  = sd::ops::exp(0.5f * sd::ops::clamp(logvar, -30.0f, 20.0f));
+        sd::Tensor<float> noise   = sd::Tensor<float>::randn_like(mean, rng);
+        sd::Tensor<float> latents = mean + stddev * noise;
+        return latents;
+    }
+
+    sd::Tensor<float> vae_output_to_latents(const sd::Tensor<float>& vae_output, std::shared_ptr<RNG> rng) override {
+        if (sd_version_is_flux2(version)) {
+            return vae_output;
+        } else if (version == VERSION_SD1_PIX2PIX) {
+            return sd::ops::chunk(vae_output, 2, 2)[0];
+        } else {
+            return gaussian_latent_sample(vae_output, rng);
+        }
+    }
+
+    std::pair<sd::Tensor<float>, sd::Tensor<float>> get_latents_mean_std(const sd::Tensor<float>& latents, int channel_dim) {
+        GGML_ASSERT(channel_dim >= 0 && static_cast<size_t>(channel_dim) < static_cast<size_t>(latents.dim()));
+        if (sd_version_is_flux2(version)) {
+            GGML_ASSERT(latents.shape()[channel_dim] == 128);
+            std::vector<int64_t> stats_shape(static_cast<size_t>(latents.dim()), 1);
+            stats_shape[static_cast<size_t>(channel_dim)] = latents.shape()[channel_dim];
+
+            auto mean_tensor = sd::Tensor<float>::from_vector({-0.0676f, -0.0715f, -0.0753f, -0.0745f, 0.0223f, 0.0180f, 0.0142f, 0.0184f,
+                                                               -0.0001f, -0.0063f, -0.0002f, -0.0031f, -0.0272f, -0.0281f, -0.0276f, -0.0290f,
+                                                               -0.0769f, -0.0672f, -0.0902f, -0.0892f, 0.0168f, 0.0152f, 0.0079f, 0.0086f,
+                                                               0.0083f, 0.0015f, 0.0003f, -0.0043f, -0.0439f, -0.0419f, -0.0438f, -0.0431f,
+                                                               -0.0102f, -0.0132f, -0.0066f, -0.0048f, -0.0311f, -0.0306f, -0.0279f, -0.0180f,
+                                                               0.0030f, 0.0015f, 0.0126f, 0.0145f, 0.0347f, 0.0338f, 0.0337f, 0.0283f,
+                                                               0.0020f, 0.0047f, 0.0047f, 0.0050f, 0.0123f, 0.0081f, 0.0081f, 0.0146f,
+                                                               0.0681f, 0.0679f, 0.0767f, 0.0732f, -0.0462f, -0.0474f, -0.0392f, -0.0511f,
+                                                               -0.0528f, -0.0477f, -0.0470f, -0.0517f, -0.0317f, -0.0316f, -0.0345f, -0.0283f,
+                                                               0.0510f, 0.0445f, 0.0578f, 0.0458f, -0.0412f, -0.0458f, -0.0487f, -0.0467f,
+                                                               -0.0088f, -0.0106f, -0.0088f, -0.0046f, -0.0376f, -0.0432f, -0.0436f, -0.0499f,
+                                                               0.0118f, 0.0166f, 0.0203f, 0.0279f, 0.0113f, 0.0129f, 0.0016f, 0.0072f,
+                                                               -0.0118f, -0.0018f, -0.0141f, -0.0054f, -0.0091f, -0.0138f, -0.0145f, -0.0187f,
+                                                               0.0323f, 0.0305f, 0.0259f, 0.0300f, 0.0540f, 0.0614f, 0.0495f, 0.0590f,
+                                                               -0.0511f, -0.0603f, -0.0478f, -0.0524f, -0.0227f, -0.0274f, -0.0154f, -0.0255f,
+                                                               -0.0572f, -0.0565f, -0.0518f, -0.0496f, 0.0116f, 0.0054f, 0.0163f, 0.0104f});
+            mean_tensor.reshape_(stats_shape);
+            auto std_tensor = sd::Tensor<float>::from_vector({1.8029f, 1.7786f, 1.7868f, 1.7837f, 1.7717f, 1.7590f, 1.7610f, 1.7479f,
+                                                              1.7336f, 1.7373f, 1.7340f, 1.7343f, 1.8626f, 1.8527f, 1.8629f, 1.8589f,
+                                                              1.7593f, 1.7526f, 1.7556f, 1.7583f, 1.7363f, 1.7400f, 1.7355f, 1.7394f,
+                                                              1.7342f, 1.7246f, 1.7392f, 1.7304f, 1.7551f, 1.7513f, 1.7559f, 1.7488f,
+                                                              1.8449f, 1.8454f, 1.8550f, 1.8535f, 1.8240f, 1.7813f, 1.7854f, 1.7945f,
+                                                              1.8047f, 1.7876f, 1.7695f, 1.7676f, 1.7782f, 1.7667f, 1.7925f, 1.7848f,
+                                                              1.7579f, 1.7407f, 1.7483f, 1.7368f, 1.7961f, 1.7998f, 1.7920f, 1.7925f,
+                                                              1.7780f, 1.7747f, 1.7727f, 1.7749f, 1.7526f, 1.7447f, 1.7657f, 1.7495f,
+                                                              1.7775f, 1.7720f, 1.7813f, 1.7813f, 1.8162f, 1.8013f, 1.8023f, 1.8033f,
+                                                              1.7527f, 1.7331f, 1.7563f, 1.7482f, 1.7610f, 1.7507f, 1.7681f, 1.7613f,
+                                                              1.7665f, 1.7545f, 1.7828f, 1.7726f, 1.7896f, 1.7999f, 1.7864f, 1.7760f,
+                                                              1.7613f, 1.7625f, 1.7560f, 1.7577f, 1.7783f, 1.7671f, 1.7810f, 1.7799f,
+                                                              1.7201f, 1.7068f, 1.7265f, 1.7091f, 1.7793f, 1.7578f, 1.7502f, 1.7455f,
+                                                              1.7587f, 1.7500f, 1.7525f, 1.7362f, 1.7616f, 1.7572f, 1.7444f, 1.7430f,
+                                                              1.7509f, 1.7610f, 1.7634f, 1.7612f, 1.7254f, 1.7135f, 1.7321f, 1.7226f,
+                                                              1.7664f, 1.7624f, 1.7718f, 1.7664f, 1.7457f, 1.7441f, 1.7569f, 1.7530f});
+            std_tensor.reshape_(stats_shape);
+            return {std::move(mean_tensor), std::move(std_tensor)};
+        } else {
+            GGML_ABORT("unknown version %d", version);
+        }
+    }
+
+    sd::Tensor<float> diffusion_to_vae_latents(const sd::Tensor<float>& latents) override {
+        if (sd_version_is_flux2(version)) {
+            int channel_dim                = 2;
+            auto [mean_tensor, std_tensor] = get_latents_mean_std(latents, channel_dim);
+            return (latents * std_tensor) / scale_factor + mean_tensor;
+        }
+        return (latents / scale_factor) + shift_factor;
+    }
+
+    sd::Tensor<float> vae_to_diffusion_latents(const sd::Tensor<float>& latents) override {
+        if (sd_version_is_flux2(version)) {
+            int channel_dim                = 2;
+            auto [mean_tensor, std_tensor] = get_latents_mean_std(latents, channel_dim);
+            return ((latents - mean_tensor) * scale_factor) / std_tensor;
+        }
+        return (latents - shift_factor) * scale_factor;
+    }
+
+    int get_encoder_output_channels(int input_channels) {
+        return ae.get_encoder_output_channels();
    }

    void test() {
-        struct ggml_init_params params;
+        ggml_init_params params;
        params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
        params.mem_buffer = nullptr;
        params.no_alloc   = false;

-        struct ggml_context* work_ctx = ggml_init(params);
-        GGML_ASSERT(work_ctx != nullptr);
+        ggml_context* ctx = ggml_init(params);
+        GGML_ASSERT(ctx != nullptr);

        {
            // CPU, x{1, 3, 64, 64}: Pass
            // CUDA, x{1, 3, 64, 64}: Pass, but sill get wrong result for some image, may be due to interlnal nan
            // CPU, x{2, 3, 64, 64}: Wrong result
            // CUDA, x{2, 3, 64, 64}: Wrong result, and different from CPU result
-            auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 64, 64, 3, 2);
-            ggml_set_f32(x, 0.5f);
-            print_ggml_tensor(x);
-            struct ggml_tensor* out = nullptr;
+            sd::Tensor<float> x({64, 64, 3, 2});
+            x.fill_(0.5f);
+            print_sd_tensor(x);
+            sd::Tensor<float> out;

-            int t0 = ggml_time_ms();
-            compute(8, x, false, &out, work_ctx);
-            int t1 = ggml_time_ms();
+            int64_t t0   = ggml_time_ms();
+            auto out_opt = _compute(8, x, false);
+            int64_t t1   = ggml_time_ms();

-            print_ggml_tensor(out);
-            LOG_DEBUG("encode test done in %dms", t1 - t0);
+            GGML_ASSERT(!out_opt.empty());
+            out = std::move(out_opt);
+            print_sd_tensor(out);
+            LOG_DEBUG("encode test done in %lldms", t1 - t0);
        }

        if (false) {
@@ -758,19 +859,21 @@ struct AutoEncoderKL : public VAE {
            // CUDA, z{1, 4, 8, 8}: Pass
            // CPU, z{3, 4, 8, 8}: Wrong result
            // CUDA, z{3, 4, 8, 8}: Wrong result, and different from CPU result
-            auto z = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 8, 8, 4, 1);
-            ggml_set_f32(z, 0.5f);
-            print_ggml_tensor(z);
-            struct ggml_tensor* out = nullptr;
+            sd::Tensor<float> z({8, 8, 4, 1});
+            z.fill_(0.5f);
+            print_sd_tensor(z);
+            sd::Tensor<float> out;

-            int t0 = ggml_time_ms();
-            compute(8, z, true, &out, work_ctx);
-            int t1 = ggml_time_ms();
+            int64_t t0   = ggml_time_ms();
+            auto out_opt = _compute(8, z, true);
+            int64_t t1   = ggml_time_ms();

-            print_ggml_tensor(out);
-            LOG_DEBUG("decode test done in %dms", t1 - t0);
+            GGML_ASSERT(!out_opt.empty());
+            out = std::move(out_opt);
+            print_sd_tensor(out);
+            LOG_DEBUG("decode test done in %lldms", t1 - t0);
        }
    };
 };

-#endif
+#endif  // __AUTO_ENCODER_KL_HPP__
@@ -0,0 +1,896 @@
+#ifndef __CACHE_DIT_HPP__
+#define __CACHE_DIT_HPP__
+
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "condition_cache_utils.hpp"
+#include "ggml_extend.hpp"
+#include "tensor.hpp"
+
+struct DBCacheConfig {
+    bool enabled                        = false;
+    int Fn_compute_blocks               = 8;
+    int Bn_compute_blocks               = 0;
+    float residual_diff_threshold       = 0.08f;
+    int max_warmup_steps                = 8;
+    int max_cached_steps                = -1;
+    int max_continuous_cached_steps     = -1;
+    float max_accumulated_residual_diff = -1.0f;
+    std::vector<int> steps_computation_mask;
+    bool scm_policy_dynamic = true;
+};
+
+struct TaylorSeerConfig {
+    bool enabled            = false;
+    int n_derivatives       = 1;
+    int max_warmup_steps    = 2;
+    int skip_interval_steps = 1;
+};
+
+struct CacheDitConfig {
+    DBCacheConfig dbcache;
+    TaylorSeerConfig taylorseer;
+    int double_Fn_blocks = -1;
+    int double_Bn_blocks = -1;
+    int single_Fn_blocks = -1;
+    int single_Bn_blocks = -1;
+};
+
+struct TaylorSeerState {
+    int n_derivatives      = 1;
+    int current_step       = -1;
+    int last_computed_step = -1;
+    std::vector<std::vector<float>> dY_prev;
+    std::vector<std::vector<float>> dY_current;
+
+    void init(int n_deriv, size_t hidden_size) {
+        n_derivatives = n_deriv;
+        int order     = n_derivatives + 1;
+        dY_prev.resize(order);
+        dY_current.resize(order);
+        for (int i = 0; i < order; i++) {
+            dY_prev[i].clear();
+            dY_current[i].clear();
+        }
+        current_step       = -1;
+        last_computed_step = -1;
+    }
+
+    void reset() {
+        for (auto& v : dY_prev)
+            v.clear();
+        for (auto& v : dY_current)
+            v.clear();
+        current_step       = -1;
+        last_computed_step = -1;
+    }
+
+    bool can_approximate() const {
+        return last_computed_step >= n_derivatives && !dY_prev.empty() && !dY_prev[0].empty();
+    }
+
+    void update_derivatives(const float* Y, size_t size, int step) {
+        int order = n_derivatives + 1;
+        dY_prev   = dY_current;
+        dY_current[0].resize(size);
+        for (size_t i = 0; i < size; i++) {
+            dY_current[0][i] = Y[i];
+        }
+
+        int window = step - last_computed_step;
+        if (window <= 0)
+            window = 1;
+
+        for (int d = 0; d < n_derivatives; d++) {
+            if (!dY_prev[d].empty() && dY_prev[d].size() == size) {
+                dY_current[d + 1].resize(size);
+                for (size_t i = 0; i < size; i++) {
+                    dY_current[d + 1][i] = (dY_current[d][i] - dY_prev[d][i]) / static_cast<float>(window);
+                }
+            } else {
+                dY_current[d + 1].clear();
+            }
+        }
+
+        current_step       = step;
+        last_computed_step = step;
+    }
+
+    void approximate(float* output, size_t size, int target_step) const {
+        if (!can_approximate() || dY_prev[0].size() != size) {
+            return;
+        }
+
+        int elapsed = target_step - last_computed_step;
+        if (elapsed <= 0)
+            elapsed = 1;
+
+        std::fill(output, output + size, 0.0f);
+        float factorial = 1.0f;
+        int order       = static_cast<int>(dY_prev.size());
+
+        for (int o = 0; o < order; o++) {
+            if (dY_prev[o].empty() || dY_prev[o].size() != size)
+                continue;
+            if (o > 0)
+                factorial *= static_cast<float>(o);
+            float coeff = ::powf(static_cast<float>(elapsed), static_cast<float>(o)) / factorial;
+            for (size_t i = 0; i < size; i++) {
+                output[i] += coeff * dY_prev[o][i];
+            }
+        }
+    }
+};
+
+struct BlockCacheEntry {
+    std::vector<float> residual_img;
+    std::vector<float> residual_txt;
+    std::vector<float> residual;
+    std::vector<float> prev_img;
+    std::vector<float> prev_txt;
+    std::vector<float> prev_output;
+    bool has_prev = false;
+};
+
+struct CacheDitState {
+    CacheDitConfig config;
+    bool initialized = false;
+
+    int total_double_blocks = 0;
+    int total_single_blocks = 0;
+    size_t hidden_size      = 0;
+
+    int current_step     = -1;
+    int total_steps      = 0;
+    int warmup_remaining = 0;
+    std::vector<int> cached_steps;
+    int continuous_cached_steps     = 0;
+    float accumulated_residual_diff = 0.0f;
+
+    std::vector<BlockCacheEntry> double_block_cache;
+    std::vector<BlockCacheEntry> single_block_cache;
+
+    std::vector<float> Fn_residual_img;
+    std::vector<float> Fn_residual_txt;
+    std::vector<float> prev_Fn_residual_img;
+    std::vector<float> prev_Fn_residual_txt;
+    bool has_prev_Fn_residual = false;
+
+    std::vector<float> Bn_buffer_img;
+    std::vector<float> Bn_buffer_txt;
+    std::vector<float> Bn_buffer;
+    bool has_Bn_buffer = false;
+
+    TaylorSeerState taylor_state;
+
+    bool can_cache_this_step  = false;
+    bool is_caching_this_step = false;
+
+    int total_blocks_computed = 0;
+    int total_blocks_cached   = 0;
+
+    void init(const CacheDitConfig& cfg, int num_double_blocks, int num_single_blocks, size_t h_size) {
+        config              = cfg;
+        total_double_blocks = num_double_blocks;
+        total_single_blocks = num_single_blocks;
+        hidden_size         = h_size;
+
+        initialized = cfg.dbcache.enabled || cfg.taylorseer.enabled;
+
+        if (!initialized)
+            return;
+
+        warmup_remaining = cfg.dbcache.max_warmup_steps;
+        double_block_cache.resize(total_double_blocks);
+        single_block_cache.resize(total_single_blocks);
+
+        if (cfg.taylorseer.enabled) {
+            taylor_state.init(cfg.taylorseer.n_derivatives, h_size);
+        }
+
+        reset_runtime();
+    }
+
+    void reset_runtime() {
+        current_step     = -1;
+        total_steps      = 0;
+        warmup_remaining = config.dbcache.max_warmup_steps;
+        cached_steps.clear();
+        continuous_cached_steps   = 0;
+        accumulated_residual_diff = 0.0f;
+
+        for (auto& entry : double_block_cache) {
+            entry.residual_img.clear();
+            entry.residual_txt.clear();
+            entry.prev_img.clear();
+            entry.prev_txt.clear();
+            entry.has_prev = false;
+        }
+
+        for (auto& entry : single_block_cache) {
+            entry.residual.clear();
+            entry.prev_output.clear();
+            entry.has_prev = false;
+        }
+
+        Fn_residual_img.clear();
+        Fn_residual_txt.clear();
+        prev_Fn_residual_img.clear();
+        prev_Fn_residual_txt.clear();
+        has_prev_Fn_residual = false;
+
+        Bn_buffer_img.clear();
+        Bn_buffer_txt.clear();
+        Bn_buffer.clear();
+        has_Bn_buffer = false;
+
+        taylor_state.reset();
+
+        can_cache_this_step  = false;
+        is_caching_this_step = false;
+
+        total_blocks_computed = 0;
+        total_blocks_cached   = 0;
+    }
+
+    bool enabled() const {
+        return initialized && (config.dbcache.enabled || config.taylorseer.enabled);
+    }
+
+    void begin_step(int step_index, float sigma = 0.0f) {
+        if (!enabled())
+            return;
+        if (step_index == current_step)
+            return;
+
+        current_step = step_index;
+        total_steps++;
+
+        bool in_warmup = warmup_remaining > 0;
+        if (in_warmup) {
+            warmup_remaining--;
+        }
+
+        bool scm_allows_cache = true;
+        if (!config.dbcache.steps_computation_mask.empty()) {
+            if (step_index < static_cast<int>(config.dbcache.steps_computation_mask.size())) {
+                scm_allows_cache = (config.dbcache.steps_computation_mask[step_index] == 0);
+                if (!config.dbcache.scm_policy_dynamic && scm_allows_cache) {
+                    can_cache_this_step  = true;
+                    is_caching_this_step = false;
+                    return;
+                }
+            }
+        }
+
+        bool max_cached_ok = (config.dbcache.max_cached_steps < 0) ||
+                             (static_cast<int>(cached_steps.size()) < config.dbcache.max_cached_steps);
+
+        bool max_cont_ok = (config.dbcache.max_continuous_cached_steps < 0) ||
+                           (continuous_cached_steps < config.dbcache.max_continuous_cached_steps);
+
+        bool accum_ok = (config.dbcache.max_accumulated_residual_diff < 0.0f) ||
+                        (accumulated_residual_diff < config.dbcache.max_accumulated_residual_diff);
+
+        can_cache_this_step  = !in_warmup && scm_allows_cache && max_cached_ok && max_cont_ok && accum_ok && has_prev_Fn_residual;
+        is_caching_this_step = false;
+    }
+
+    void end_step(bool was_cached) {
+        if (was_cached) {
+            cached_steps.push_back(current_step);
+            continuous_cached_steps++;
+        } else {
+            continuous_cached_steps = 0;
+        }
+    }
+
+    static float calculate_residual_diff(const float* prev, const float* curr, size_t size) {
+        if (size == 0)
+            return 0.0f;
+
+        float sum_diff = 0.0f;
+        float sum_abs  = 0.0f;
+
+        for (size_t i = 0; i < size; i++) {
+            sum_diff += std::fabs(prev[i] - curr[i]);
+            sum_abs += std::fabs(prev[i]);
+        }
+
+        return sum_diff / (sum_abs + 1e-6f);
+    }
+
+    static float calculate_residual_diff(const std::vector<float>& prev, const std::vector<float>& curr) {
+        if (prev.size() != curr.size() || prev.empty())
+            return 1.0f;
+        return calculate_residual_diff(prev.data(), curr.data(), prev.size());
+    }
+
+    int get_double_Fn_blocks() const {
+        return (config.double_Fn_blocks >= 0) ? config.double_Fn_blocks : config.dbcache.Fn_compute_blocks;
+    }
+
+    int get_double_Bn_blocks() const {
+        return (config.double_Bn_blocks >= 0) ? config.double_Bn_blocks : config.dbcache.Bn_compute_blocks;
+    }
+
+    int get_single_Fn_blocks() const {
+        return (config.single_Fn_blocks >= 0) ? config.single_Fn_blocks : config.dbcache.Fn_compute_blocks;
+    }
+
+    int get_single_Bn_blocks() const {
+        return (config.single_Bn_blocks >= 0) ? config.single_Bn_blocks : config.dbcache.Bn_compute_blocks;
+    }
+
+    bool is_Fn_double_block(int block_idx) const {
+        return block_idx < get_double_Fn_blocks();
+    }
+
+    bool is_Bn_double_block(int block_idx) const {
+        int Bn = get_double_Bn_blocks();
+        return Bn > 0 && block_idx >= (total_double_blocks - Bn);
+    }
+
+    bool is_Mn_double_block(int block_idx) const {
+        return !is_Fn_double_block(block_idx) && !is_Bn_double_block(block_idx);
+    }
+
+    bool is_Fn_single_block(int block_idx) const {
+        return block_idx < get_single_Fn_blocks();
+    }
+
+    bool is_Bn_single_block(int block_idx) const {
+        int Bn = get_single_Bn_blocks();
+        return Bn > 0 && block_idx >= (total_single_blocks - Bn);
+    }
+
+    bool is_Mn_single_block(int block_idx) const {
+        return !is_Fn_single_block(block_idx) && !is_Bn_single_block(block_idx);
+    }
+
+    void store_Fn_residual(const float* img, const float* txt, size_t img_size, size_t txt_size, const float* input_img, const float* input_txt) {
+        Fn_residual_img.resize(img_size);
+        Fn_residual_txt.resize(txt_size);
+
+        for (size_t i = 0; i < img_size; i++) {
+            Fn_residual_img[i] = img[i] - input_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            Fn_residual_txt[i] = txt[i] - input_txt[i];
+        }
+    }
+
+    bool check_cache_decision() {
+        if (!can_cache_this_step) {
+            is_caching_this_step = false;
+            return false;
+        }
+
+        if (!has_prev_Fn_residual || prev_Fn_residual_img.empty()) {
+            is_caching_this_step = false;
+            return false;
+        }
+
+        float diff_img = calculate_residual_diff(prev_Fn_residual_img, Fn_residual_img);
+        float diff_txt = calculate_residual_diff(prev_Fn_residual_txt, Fn_residual_txt);
+        float diff     = (diff_img + diff_txt) / 2.0f;
+
+        if (diff < config.dbcache.residual_diff_threshold) {
+            is_caching_this_step = true;
+            accumulated_residual_diff += diff;
+            return true;
+        }
+
+        is_caching_this_step = false;
+        return false;
+    }
+
+    void update_prev_Fn_residual() {
+        prev_Fn_residual_img = Fn_residual_img;
+        prev_Fn_residual_txt = Fn_residual_txt;
+        has_prev_Fn_residual = !prev_Fn_residual_img.empty();
+    }
+
+    void store_double_block_residual(int block_idx, const float* img, const float* txt, size_t img_size, size_t txt_size, const float* prev_img, const float* prev_txt) {
+        if (block_idx < 0 || block_idx >= static_cast<int>(double_block_cache.size()))
+            return;
+
+        BlockCacheEntry& entry = double_block_cache[block_idx];
+
+        entry.residual_img.resize(img_size);
+        entry.residual_txt.resize(txt_size);
+        for (size_t i = 0; i < img_size; i++) {
+            entry.residual_img[i] = img[i] - prev_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            entry.residual_txt[i] = txt[i] - prev_txt[i];
+        }
+
+        entry.prev_img.resize(img_size);
+        entry.prev_txt.resize(txt_size);
+        for (size_t i = 0; i < img_size; i++) {
+            entry.prev_img[i] = img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            entry.prev_txt[i] = txt[i];
+        }
+        entry.has_prev = true;
+    }
+
+    void apply_double_block_cache(int block_idx, float* img, float* txt, size_t img_size, size_t txt_size) {
+        if (block_idx < 0 || block_idx >= static_cast<int>(double_block_cache.size()))
+            return;
+
+        const BlockCacheEntry& entry = double_block_cache[block_idx];
+        if (entry.residual_img.size() != img_size || entry.residual_txt.size() != txt_size)
+            return;
+
+        for (size_t i = 0; i < img_size; i++) {
+            img[i] += entry.residual_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            txt[i] += entry.residual_txt[i];
+        }
+
+        total_blocks_cached++;
+    }
+
+    void store_single_block_residual(int block_idx, const float* output, size_t size, const float* input) {
+        if (block_idx < 0 || block_idx >= static_cast<int>(single_block_cache.size()))
+            return;
+
+        BlockCacheEntry& entry = single_block_cache[block_idx];
+
+        entry.residual.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.residual[i] = output[i] - input[i];
+        }
+
+        entry.prev_output.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.prev_output[i] = output[i];
+        }
+        entry.has_prev = true;
+    }
+
+    void apply_single_block_cache(int block_idx, float* output, size_t size) {
+        if (block_idx < 0 || block_idx >= static_cast<int>(single_block_cache.size()))
+            return;
+
+        const BlockCacheEntry& entry = single_block_cache[block_idx];
+        if (entry.residual.size() != size)
+            return;
+
+        for (size_t i = 0; i < size; i++) {
+            output[i] += entry.residual[i];
+        }
+
+        total_blocks_cached++;
+    }
+
+    void store_Bn_buffer(const float* img, const float* txt, size_t img_size, size_t txt_size, const float* Bn_start_img, const float* Bn_start_txt) {
+        Bn_buffer_img.resize(img_size);
+        Bn_buffer_txt.resize(txt_size);
+
+        for (size_t i = 0; i < img_size; i++) {
+            Bn_buffer_img[i] = img[i] - Bn_start_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            Bn_buffer_txt[i] = txt[i] - Bn_start_txt[i];
+        }
+        has_Bn_buffer = true;
+    }
+
+    void apply_Bn_buffer(float* img, float* txt, size_t img_size, size_t txt_size) {
+        if (!has_Bn_buffer)
+            return;
+        if (Bn_buffer_img.size() != img_size || Bn_buffer_txt.size() != txt_size)
+            return;
+
+        for (size_t i = 0; i < img_size; i++) {
+            img[i] += Bn_buffer_img[i];
+        }
+        for (size_t i = 0; i < txt_size; i++) {
+            txt[i] += Bn_buffer_txt[i];
+        }
+    }
+
+    void taylor_update(const float* hidden_state, size_t size) {
+        if (!config.taylorseer.enabled)
+            return;
+        taylor_state.update_derivatives(hidden_state, size, current_step);
+    }
+
+    bool taylor_can_approximate() const {
+        return config.taylorseer.enabled && taylor_state.can_approximate();
+    }
+
+    void taylor_approximate(float* output, size_t size) {
+        if (!config.taylorseer.enabled)
+            return;
+        taylor_state.approximate(output, size, current_step);
+    }
+
+    bool should_use_taylor_this_step() const {
+        if (!config.taylorseer.enabled)
+            return false;
+        if (current_step < config.taylorseer.max_warmup_steps)
+            return false;
+
+        int interval = config.taylorseer.skip_interval_steps;
+        if (interval <= 0)
+            interval = 1;
+
+        return (current_step % (interval + 1)) != 0;
+    }
+
+    void log_metrics() const {
+        if (!enabled())
+            return;
+
+        int total_blocks  = total_blocks_computed + total_blocks_cached;
+        float cache_ratio = (total_blocks > 0) ? (static_cast<float>(total_blocks_cached) / total_blocks * 100.0f) : 0.0f;
+
+        float step_cache_ratio = (total_steps > 0) ? (static_cast<float>(cached_steps.size()) / total_steps * 100.0f) : 0.0f;
+
+        LOG_INFO("CacheDIT: steps_cached=%zu/%d (%.1f%%), blocks_cached=%d/%d (%.1f%%), accum_diff=%.4f",
+                 cached_steps.size(), total_steps, step_cache_ratio,
+                 total_blocks_cached, total_blocks, cache_ratio,
+                 accumulated_residual_diff);
+    }
+
+    std::string get_summary() const {
+        char buf[256];
+        snprintf(buf, sizeof(buf),
+                 "CacheDIT[thresh=%.2f]: cached %zu/%d steps, %d/%d blocks",
+                 config.dbcache.residual_diff_threshold,
+                 cached_steps.size(), total_steps,
+                 total_blocks_cached, total_blocks_computed + total_blocks_cached);
+        return std::string(buf);
+    }
+};
+
+inline std::vector<int> parse_scm_mask(const std::string& mask_str) {
+    std::vector<int> mask;
+    if (mask_str.empty())
+        return mask;
+
+    size_t pos   = 0;
+    size_t start = 0;
+    while ((pos = mask_str.find(',', start)) != std::string::npos) {
+        std::string token = mask_str.substr(start, pos - start);
+        mask.push_back(std::stoi(token));
+        start = pos + 1;
+    }
+    if (start < mask_str.length()) {
+        mask.push_back(std::stoi(mask_str.substr(start)));
+    }
+
+    return mask;
+}
+
+inline std::vector<int> generate_scm_mask(
+    const std::vector<int>& compute_bins,
+    const std::vector<int>& cache_bins,
+    int total_steps) {
+    std::vector<int> mask;
+    size_t c_idx = 0, cache_idx = 0;
+
+    while (static_cast<int>(mask.size()) < total_steps) {
+        if (c_idx < compute_bins.size()) {
+            for (int i = 0; i < compute_bins[c_idx] && static_cast<int>(mask.size()) < total_steps; i++) {
+                mask.push_back(1);
+            }
+            c_idx++;
+        }
+        if (cache_idx < cache_bins.size()) {
+            for (int i = 0; i < cache_bins[cache_idx] && static_cast<int>(mask.size()) < total_steps; i++) {
+                mask.push_back(0);
+            }
+            cache_idx++;
+        }
+        if (c_idx >= compute_bins.size() && cache_idx >= cache_bins.size())
+            break;
+    }
+
+    if (!mask.empty()) {
+        mask.back() = 1;
+    }
+
+    return mask;
+}
+
+inline void parse_dbcache_options(const std::string& opts, DBCacheConfig& cfg) {
+    if (opts.empty())
+        return;
+
+    int Fn = 8, Bn = 0, warmup = 8, max_cached = -1, max_cont = -1;
+    float thresh = 0.08f;
+
+    sscanf(opts.c_str(), "%d,%d,%f,%d,%d,%d",
+           &Fn, &Bn, &thresh, &warmup, &max_cached, &max_cont);
+
+    cfg.Fn_compute_blocks           = Fn;
+    cfg.Bn_compute_blocks           = Bn;
+    cfg.residual_diff_threshold     = thresh;
+    cfg.max_warmup_steps            = warmup;
+    cfg.max_cached_steps            = max_cached;
+    cfg.max_continuous_cached_steps = max_cont;
+}
+
+inline void parse_taylorseer_options(const std::string& opts, TaylorSeerConfig& cfg) {
+    if (opts.empty())
+        return;
+
+    int n_deriv = 1, warmup = 2, interval = 1;
+    sscanf(opts.c_str(), "%d,%d,%d", &n_deriv, &warmup, &interval);
+
+    cfg.n_derivatives       = n_deriv;
+    cfg.max_warmup_steps    = warmup;
+    cfg.skip_interval_steps = interval;
+}
+
+struct CacheDitConditionState {
+    DBCacheConfig config;
+    TaylorSeerConfig taylor_config;
+    bool initialized = false;
+
+    int current_step_index = -1;
+    bool step_active       = false;
+    bool skip_current_step = false;
+    bool initial_step      = true;
+    int warmup_remaining   = 0;
+    std::vector<int> cached_steps;
+    int continuous_cached_steps     = 0;
+    float accumulated_residual_diff = 0.0f;
+    int total_steps_skipped         = 0;
+
+    const void* anchor_condition = nullptr;
+
+    struct CacheEntry {
+        std::vector<float> diff;
+        std::vector<float> prev_input;
+        std::vector<float> prev_output;
+        bool has_prev = false;
+    };
+    std::unordered_map<const void*, CacheEntry> cache_diffs;
+
+    TaylorSeerState taylor_state;
+
+    float start_sigma = std::numeric_limits<float>::max();
+    float end_sigma   = 0.0f;
+
+    void reset_runtime() {
+        current_step_index = -1;
+        step_active        = false;
+        skip_current_step  = false;
+        initial_step       = true;
+        warmup_remaining   = config.max_warmup_steps;
+        cached_steps.clear();
+        continuous_cached_steps   = 0;
+        accumulated_residual_diff = 0.0f;
+        total_steps_skipped       = 0;
+        anchor_condition          = nullptr;
+        cache_diffs.clear();
+        taylor_state.reset();
+    }
+
+    void init(const DBCacheConfig& dbcfg, const TaylorSeerConfig& tcfg) {
+        config        = dbcfg;
+        taylor_config = tcfg;
+        initialized   = dbcfg.enabled || tcfg.enabled;
+        reset_runtime();
+
+        if (taylor_config.enabled) {
+            taylor_state.init(taylor_config.n_derivatives, 0);
+        }
+    }
+
+    void set_sigmas(const std::vector<float>& sigmas) {
+        if (!initialized || sigmas.size() < 2)
+            return;
+
+        float start_percent = 0.15f;
+        float end_percent   = 0.95f;
+
+        size_t n_steps    = sigmas.size() - 1;
+        size_t start_step = static_cast<size_t>(start_percent * n_steps);
+        size_t end_step   = static_cast<size_t>(end_percent * n_steps);
+
+        if (start_step >= n_steps)
+            start_step = n_steps - 1;
+        if (end_step >= n_steps)
+            end_step = n_steps - 1;
+
+        start_sigma = sigmas[start_step];
+        end_sigma   = sigmas[end_step];
+
+        if (start_sigma < end_sigma) {
+            std::swap(start_sigma, end_sigma);
+        }
+    }
+
+    bool enabled() const {
+        return initialized && (config.enabled || taylor_config.enabled);
+    }
+
+    void begin_step(int step_index, float sigma) {
+        if (!enabled())
+            return;
+        if (step_index == current_step_index)
+            return;
+
+        current_step_index = step_index;
+        skip_current_step  = false;
+        step_active        = false;
+
+        if (sigma > start_sigma)
+            return;
+        if (!(sigma > end_sigma))
+            return;
+
+        step_active = true;
+
+        if (warmup_remaining > 0) {
+            warmup_remaining--;
+            return;
+        }
+
+        if (!config.steps_computation_mask.empty()) {
+            if (step_index < static_cast<int>(config.steps_computation_mask.size())) {
+                if (config.steps_computation_mask[step_index] == 1) {
+                    return;
+                }
+            }
+        }
+
+        if (config.max_cached_steps >= 0 &&
+            static_cast<int>(cached_steps.size()) >= config.max_cached_steps) {
+            return;
+        }
+
+        if (config.max_continuous_cached_steps >= 0 &&
+            continuous_cached_steps >= config.max_continuous_cached_steps) {
+            return;
+        }
+    }
+
+    bool step_is_active() const {
+        return enabled() && step_active;
+    }
+
+    bool is_step_skipped() const {
+        return enabled() && step_active && skip_current_step;
+    }
+
+    bool has_cache(const void* cond) const {
+        auto it = cache_diffs.find(cond);
+        return it != cache_diffs.end() && !it->second.diff.empty();
+    }
+
+    void update_cache(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
+        CacheEntry& entry = cache_diffs[cond];
+        if (!sd::store_condition_cache_diff(&entry.diff, input, output)) {
+            entry.prev_input.clear();
+            entry.prev_output.clear();
+            entry.has_prev = false;
+            return;
+        }
+
+        size_t size              = static_cast<size_t>(output.numel());
+        const float* input_data  = input.data();
+        const float* output_data = output.data();
+        entry.prev_input.resize(size);
+        entry.prev_output.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.prev_input[i]  = input_data[i];
+            entry.prev_output[i] = output_data[i];
+        }
+        entry.has_prev = true;
+    }
+
+    void apply_cache(const void* cond,
+                     const sd::Tensor<float>& input,
+                     sd::Tensor<float>* output) {
+        auto it = cache_diffs.find(cond);
+        if (it == cache_diffs.end() || it->second.diff.empty())
+            return;
+        sd::apply_condition_cache_diff(it->second.diff, input, output);
+    }
+
+    bool before_condition(const void* cond, const sd::Tensor<float>& input, sd::Tensor<float>* output, float sigma, int step_index) {
+        if (!enabled() || step_index < 0)
+            return false;
+
+        if (step_index != current_step_index) {
+            begin_step(step_index, sigma);
+        }
+
+        if (!step_active)
+            return false;
+
+        if (initial_step) {
+            anchor_condition = cond;
+            initial_step     = false;
+        }
+
+        bool is_anchor = (cond == anchor_condition);
+
+        if (skip_current_step) {
+            if (has_cache(cond)) {
+                apply_cache(cond, input, output);
+                return true;
+            }
+            return false;
+        }
+
+        if (!is_anchor)
+            return false;
+
+        auto it = cache_diffs.find(cond);
+        if (it == cache_diffs.end() || !it->second.has_prev)
+            return false;
+
+        size_t ne = static_cast<size_t>(input.numel());
+        if (it->second.prev_input.size() != ne)
+            return false;
+
+        const float* input_data = input.data();
+        float diff              = CacheDitState::calculate_residual_diff(
+                         it->second.prev_input.data(), input_data, ne);
+
+        float effective_threshold = config.residual_diff_threshold;
+        if (config.Fn_compute_blocks > 0) {
+            float fn_confidence = 1.0f + 0.02f * (config.Fn_compute_blocks - 8);
+            fn_confidence       = std::max(0.5f, std::min(2.0f, fn_confidence));
+            effective_threshold *= fn_confidence;
+        }
+        if (config.Bn_compute_blocks > 0) {
+            float bn_quality = 1.0f - 0.03f * config.Bn_compute_blocks;
+            bn_quality       = std::max(0.5f, std::min(1.0f, bn_quality));
+            effective_threshold *= bn_quality;
+        }
+
+        if (diff < effective_threshold) {
+            skip_current_step = true;
+            total_steps_skipped++;
+            cached_steps.push_back(current_step_index);
+            continuous_cached_steps++;
+            accumulated_residual_diff += diff;
+            apply_cache(cond, input, output);
+            return true;
+        }
+
+        continuous_cached_steps = 0;
+        return false;
+    }
+
+    void after_condition(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
+        if (!step_is_active())
+            return;
+
+        update_cache(cond, input, output);
+
+        if (cond == anchor_condition && taylor_config.enabled) {
+            taylor_state.update_derivatives(output.data(), static_cast<size_t>(output.numel()), current_step_index);
+        }
+    }
+
+    void log_metrics() const {
+        if (!enabled())
+            return;
+
+        LOG_INFO("CacheDIT: steps_skipped=%d/%d (%.1f%%), accum_residual_diff=%.4f",
+                 total_steps_skipped,
+                 current_step_index + 1,
+                 (current_step_index > 0) ? (100.0f * total_steps_skipped / (current_step_index + 1)) : 0.0f,
+                 accumulated_residual_diff);
+    }
+};
+
+#endif
@@ -4,6 +4,7 @@
 #include "ggml_extend.hpp"
 #include "model.h"
 #include "tokenize_util.h"
+#include "vocab/vocab.h"

 /*================================================== CLIPTokenizer ===================================================*/

@@ -110,7 +111,7 @@ public:
        if (merges_utf8_str.size() > 0) {
            load_from_merges(merges_utf8_str);
        } else {
-            load_from_merges(ModelLoader::load_merges());
+            load_from_merges(load_clip_merges());
        }
        add_special_token("<|startoftext|>");
        add_special_token("<|endoftext|>");
@@ -296,7 +297,7 @@ public:
                    size_t max_length = 0,
                    bool padding      = false) {
        if (max_length > 0 && padding) {
-            size_t n = std::ceil(tokens.size() * 1.0 / (max_length - 2));
+            size_t n = static_cast<size_t>(std::ceil(tokens.size() * 1.0 / (max_length - 2)));
            if (n == 0) {
                n = 1;
            }
@@ -472,16 +473,16 @@ public:
        }
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [N, n_token, d_model]
        auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
        auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);

        x = fc1->forward(ctx, x);
        if (use_gelu) {
-            x = ggml_gelu_inplace(ctx->ggml_ctx, x);
+            x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
        } else {
-            x = ggml_gelu_quick_inplace(ctx->ggml_ctx, x);
+            x = ggml_ext_gelu_quick(ctx->ggml_ctx, x, true);
        }
        x = fc2->forward(ctx, x);
        return x;
@@ -510,7 +511,7 @@ public:
        blocks["mlp"] = std::shared_ptr<GGMLBlock>(new CLIPMLP(d_model, intermediate_size));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, bool mask = true) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* mask = nullptr) {
        // x: [N, n_token, d_model]
        auto self_attn   = std::dynamic_pointer_cast<MultiheadAttention>(blocks["self_attn"]);
        auto layer_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm1"]);
@@ -525,10 +526,10 @@ public:

 struct CLIPEncoder : public GGMLBlock {
 protected:
-    int64_t n_layer;
+    int n_layer;

 public:
-    CLIPEncoder(int64_t n_layer,
+    CLIPEncoder(int n_layer,
                int64_t d_model,
                int64_t n_head,
                int64_t intermediate_size,
@@ -540,10 +541,10 @@ public:
        }
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                int clip_skip = -1,
-                                bool mask     = true) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* mask = nullptr,
+                         int clip_skip     = -1) {
        // x: [N, n_token, d_model]
        int layer_idx = n_layer - 1;
        // LOG_DEBUG("clip_skip %d", clip_skip);
@@ -572,7 +573,7 @@ protected:
    int64_t num_positions;
    bool force_clip_f32;

-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
        enum ggml_type token_wtype = GGML_TYPE_F32;
        if (!force_clip_f32) {
            token_wtype = get_type(prefix + "token_embedding.weight", tensor_storage_map, GGML_TYPE_F32);
@@ -596,13 +597,13 @@ public:
          force_clip_f32(force_clip_f32) {
    }

-    struct ggml_tensor* get_token_embed_weight() {
+    ggml_tensor* get_token_embed_weight() {
        return params["token_embedding.weight"];
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* input_ids,
-                                struct ggml_tensor* custom_embed_weight) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* input_ids,
+                         ggml_tensor* custom_embed_weight) {
        // input_ids: [N, n_token]
        auto token_embed_weight    = params["token_embedding.weight"];
        auto position_embed_weight = params["position_embedding.weight"];
@@ -623,13 +624,13 @@ public:
 class CLIPVisionEmbeddings : public GGMLBlock {
 protected:
    int64_t embed_dim;
-    int64_t num_channels;
-    int64_t patch_size;
-    int64_t image_size;
-    int64_t num_patches;
+    int num_channels;
+    int patch_size;
+    int image_size;
+    int num_patches;
    int64_t num_positions;

-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
        enum ggml_type patch_wtype    = GGML_TYPE_F16;
        enum ggml_type class_wtype    = GGML_TYPE_F32;
        enum ggml_type position_wtype = GGML_TYPE_F32;
@@ -641,9 +642,9 @@ protected:

 public:
    CLIPVisionEmbeddings(int64_t embed_dim,
-                         int64_t num_channels = 3,
-                         int64_t patch_size   = 14,
-                         int64_t image_size   = 224)
+                         int num_channels = 3,
+                         int patch_size   = 14,
+                         int image_size   = 224)
        : embed_dim(embed_dim),
          num_channels(num_channels),
          patch_size(patch_size),
@@ -652,7 +653,7 @@ public:
        num_positions = num_patches + 1;
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* pixel_values) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* pixel_values) {
        // pixel_values: [N, num_channels, image_size, image_size]
        // return: [N, num_positions, embed_dim]
        GGML_ASSERT(pixel_values->ne[0] == image_size && pixel_values->ne[1] == image_size && pixel_values->ne[2] == num_channels);
@@ -662,20 +663,20 @@ public:
        auto position_embed_weight = params["position_embedding.weight"];

        // concat(patch_embedding, class_embedding) + position_embedding
-        struct ggml_tensor* patch_embedding;
+        ggml_tensor* patch_embedding;
        int64_t N       = pixel_values->ne[3];
        patch_embedding = ggml_ext_conv_2d(ctx->ggml_ctx, pixel_values, patch_embed_weight, nullptr, patch_size, patch_size);  // [N, embed_dim, image_size // pacht_size, image_size // pacht_size]
        patch_embedding = ggml_reshape_3d(ctx->ggml_ctx, patch_embedding, num_patches, embed_dim, N);                          // [N, embed_dim, num_patches]
        patch_embedding = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, patch_embedding, 1, 0, 2, 3));                  // [N, num_patches, embed_dim]
        patch_embedding = ggml_reshape_4d(ctx->ggml_ctx, patch_embedding, 1, embed_dim, num_patches, N);                       // [N, num_patches, embed_dim, 1]

-        struct ggml_tensor* class_embedding = ggml_new_tensor_2d(ctx->ggml_ctx, GGML_TYPE_F32, embed_dim, N);
-        class_embedding                     = ggml_repeat(ctx->ggml_ctx, class_embed_weight, class_embedding);      // [N, embed_dim]
-        class_embedding                     = ggml_reshape_4d(ctx->ggml_ctx, class_embedding, 1, embed_dim, 1, N);  // [N, 1, embed_dim, 1]
+        ggml_tensor* class_embedding = ggml_new_tensor_2d(ctx->ggml_ctx, GGML_TYPE_F32, embed_dim, N);
+        class_embedding              = ggml_repeat(ctx->ggml_ctx, class_embed_weight, class_embedding);      // [N, embed_dim]
+        class_embedding              = ggml_reshape_4d(ctx->ggml_ctx, class_embedding, 1, embed_dim, 1, N);  // [N, 1, embed_dim, 1]

-        struct ggml_tensor* x = ggml_concat(ctx->ggml_ctx, class_embedding, patch_embedding, 2);  // [N, num_positions, embed_dim, 1]
-        x                     = ggml_reshape_3d(ctx->ggml_ctx, x, embed_dim, num_positions, N);   // [N, num_positions, embed_dim]
-        x                     = ggml_add(ctx->ggml_ctx, x, position_embed_weight);
+        ggml_tensor* x = ggml_concat(ctx->ggml_ctx, class_embedding, patch_embedding, 2);  // [N, num_positions, embed_dim, 1]
+        x              = ggml_reshape_3d(ctx->ggml_ctx, x, embed_dim, num_positions, N);   // [N, num_positions, embed_dim]
+        x              = ggml_add(ctx->ggml_ctx, x, position_embed_weight);
        return x;  // [N, num_positions, embed_dim]
    }
 };
@@ -692,7 +693,7 @@ enum CLIPVersion {

 class CLIPTextModel : public GGMLBlock {
 protected:
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
        if (version == OPEN_CLIP_VIT_BIGG_14) {
            enum ggml_type wtype      = GGML_TYPE_F32;
            params["text_projection"] = ggml_new_tensor_2d(ctx, wtype, projection_dim, hidden_size);
@@ -733,24 +734,25 @@ public:
        blocks["final_layer_norm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
    }

-    struct ggml_tensor* get_token_embed_weight() {
+    ggml_tensor* get_token_embed_weight() {
        auto embeddings = std::dynamic_pointer_cast<CLIPEmbeddings>(blocks["embeddings"]);
        return embeddings->get_token_embed_weight();
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* input_ids,
-                                struct ggml_tensor* tkn_embeddings,
-                                size_t max_token_idx = 0,
-                                bool return_pooled   = false,
-                                int clip_skip        = -1) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* input_ids,
+                         ggml_tensor* tkn_embeddings,
+                         ggml_tensor* mask    = nullptr,
+                         size_t max_token_idx = 0,
+                         bool return_pooled   = false,
+                         int clip_skip        = -1) {
        // input_ids: [N, n_token]
        auto embeddings       = std::dynamic_pointer_cast<CLIPEmbeddings>(blocks["embeddings"]);
        auto encoder          = std::dynamic_pointer_cast<CLIPEncoder>(blocks["encoder"]);
        auto final_layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["final_layer_norm"]);

        auto x = embeddings->forward(ctx, input_ids, tkn_embeddings);  // [N, n_token, hidden_size]
-        x      = encoder->forward(ctx, x, return_pooled ? -1 : clip_skip, true);
+        x      = encoder->forward(ctx, x, mask, return_pooled ? -1 : clip_skip);
        if (return_pooled || with_final_ln) {
            x = final_layer_norm->forward(ctx, x);
        }
@@ -802,10 +804,10 @@ public:
        blocks["post_layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* pixel_values,
-                                bool return_pooled = true,
-                                int clip_skip      = -1) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* pixel_values,
+                         bool return_pooled = true,
+                         int clip_skip      = -1) {
        // pixel_values: [N, num_channels, image_size, image_size]
        auto embeddings     = std::dynamic_pointer_cast<CLIPVisionEmbeddings>(blocks["embeddings"]);
        auto pre_layernorm  = std::dynamic_pointer_cast<LayerNorm>(blocks["pre_layernorm"]);
@@ -814,10 +816,11 @@ public:

        auto x = embeddings->forward(ctx, pixel_values);  // [N, num_positions, embed_dim]
        x      = pre_layernorm->forward(ctx, x);
-        x      = encoder->forward(ctx, x, clip_skip, false);
-        // print_ggml_tensor(x, true, "ClipVisionModel x: ");
+        x      = encoder->forward(ctx, x, nullptr, clip_skip);
+
        auto last_hidden_state = x;
-        x                      = post_layernorm->forward(ctx, x);  // [N, n_token, hidden_size]
+
+        x = post_layernorm->forward(ctx, x);  // [N, n_token, hidden_size]

        GGML_ASSERT(x->ne[3] == 1);
        if (return_pooled) {
@@ -836,7 +839,7 @@ protected:
    int64_t out_features;
    bool transpose_weight;

-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
        enum ggml_type wtype = get_type(prefix + "weight", tensor_storage_map, GGML_TYPE_F32);
        if (transpose_weight) {
            params["weight"] = ggml_new_tensor_2d(ctx, wtype, out_features, in_features);
@@ -853,8 +856,8 @@ public:
          out_features(out_features),
          transpose_weight(transpose_weight) {}

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
-        struct ggml_tensor* w = params["weight"];
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+        ggml_tensor* w = params["weight"];
        if (transpose_weight) {
            w = ggml_cont(ctx->ggml_ctx, ggml_transpose(ctx->ggml_ctx, w));
        }
@@ -883,10 +886,10 @@ public:
        blocks["visual_projection"] = std::shared_ptr<GGMLBlock>(new CLIPProjection(hidden_size, projection_dim, transpose_proj_w));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* pixel_values,
-                                bool return_pooled = true,
-                                int clip_skip      = -1) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* pixel_values,
+                         bool return_pooled = true,
+                         int clip_skip      = -1) {
        // pixel_values: [N, num_channels, image_size, image_size]
        // return: [N, projection_dim] if return_pooled else [N, n_token, hidden_size]
        auto vision_model      = std::dynamic_pointer_cast<CLIPVisionModel>(blocks["vision_model"]);
@@ -905,6 +908,8 @@ public:
 struct CLIPTextModelRunner : public GGMLRunner {
    CLIPTextModel model;

+    std::vector<float> attention_mask_vec;
+
    CLIPTextModelRunner(ggml_backend_t backend,
                        bool offload_params_to_cpu,
                        const String2TensorStorage& tensor_storage_map,
@@ -931,16 +936,17 @@ struct CLIPTextModelRunner : public GGMLRunner {
        return "clip";
    }

-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
        model.get_param_tensors(tensors, prefix);
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* input_ids,
-                                struct ggml_tensor* embeddings,
-                                size_t max_token_idx = 0,
-                                bool return_pooled   = false,
-                                int clip_skip        = -1) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* input_ids,
+                         ggml_tensor* embeddings,
+                         ggml_tensor* mask,
+                         size_t max_token_idx = 0,
+                         bool return_pooled   = false,
+                         int clip_skip        = -1) {
        size_t N       = input_ids->ne[1];
        size_t n_token = input_ids->ne[0];
        if (input_ids->ne[0] > model.n_token) {
@@ -948,20 +954,19 @@ struct CLIPTextModelRunner : public GGMLRunner {
            input_ids = ggml_reshape_2d(ctx->ggml_ctx, input_ids, model.n_token, input_ids->ne[0] / model.n_token);
        }

-        return model.forward(ctx, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
+        return model.forward(ctx, input_ids, embeddings, mask, max_token_idx, return_pooled, clip_skip);
    }

-    struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
-                                    int num_custom_embeddings    = 0,
-                                    void* custom_embeddings_data = nullptr,
-                                    size_t max_token_idx         = 0,
-                                    bool return_pooled           = false,
-                                    int clip_skip                = -1) {
-        struct ggml_cgraph* gf = new_graph_custom(2048);
+    ggml_cgraph* build_graph(const sd::Tensor<int32_t>& input_ids_tensor,
+                             int num_custom_embeddings    = 0,
+                             void* custom_embeddings_data = nullptr,
+                             size_t max_token_idx         = 0,
+                             bool return_pooled           = false,
+                             int clip_skip                = -1) {
+        ggml_cgraph* gf        = new_graph_custom(2048);
+        ggml_tensor* input_ids = make_input(input_ids_tensor);

-        input_ids = to_backend(input_ids);
-
-        struct ggml_tensor* embeddings = nullptr;
+        ggml_tensor* embeddings = nullptr;

        if (num_custom_embeddings > 0 && custom_embeddings_data != nullptr) {
            auto token_embed_weight = model.get_token_embed_weight();
@@ -975,28 +980,44 @@ struct CLIPTextModelRunner : public GGMLRunner {
            embeddings = ggml_concat(compute_ctx, token_embed_weight, custom_embeddings, 1);
        }

+        int n_tokens = static_cast<int>(input_ids->ne[0]);
+        attention_mask_vec.resize(n_tokens * n_tokens);
+        for (int i0 = 0; i0 < n_tokens; i0++) {
+            for (int i1 = 0; i1 < n_tokens; i1++) {
+                float value = 0.f;
+                if (i0 > i1) {
+                    value = -INFINITY;
+                }
+                attention_mask_vec[i1 * n_tokens + i0] = value;
+            }
+        }
+        auto attention_mask = ggml_new_tensor_2d(compute_ctx, GGML_TYPE_F32, n_tokens, n_tokens);
+        set_backend_tensor_data(attention_mask, attention_mask_vec.data());
+
        auto runner_ctx = get_context();

-        struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, embeddings, max_token_idx, return_pooled, clip_skip);
+        ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, embeddings, attention_mask, max_token_idx, return_pooled, clip_skip);

        ggml_build_forward_expand(gf, hidden_states);

        return gf;
    }

-    bool compute(const int n_threads,
-                 struct ggml_tensor* input_ids,
-                 int num_custom_embeddings,
-                 void* custom_embeddings_data,
-                 size_t max_token_idx,
-                 bool return_pooled,
-                 int clip_skip,
-                 ggml_tensor** output,
-                 ggml_context* output_ctx = nullptr) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
+    sd::Tensor<float> compute(const int n_threads,
+                              const sd::Tensor<int32_t>& input_ids,
+                              int num_custom_embeddings,
+                              void* custom_embeddings_data,
+                              size_t max_token_idx,
+                              bool return_pooled,
+                              int clip_skip) {
+        auto get_graph = [&]() -> ggml_cgraph* {
            return build_graph(input_ids, num_custom_embeddings, custom_embeddings_data, max_token_idx, return_pooled, clip_skip);
        };
-        return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
+        auto result = GGMLRunner::compute<float>(get_graph, n_threads, true);
+        if (return_pooled) {
+            return take_or_empty(std::move(result));
+        }
+        return restore_trailing_singleton_dims(std::move(result), 3);
    }
 };

@@ -1,5 +1,5 @@
-#ifndef __COMMON_HPP__
-#define __COMMON_HPP__
+#ifndef __COMMON_BLOCK_HPP__
+#define __COMMON_BLOCK_HPP__

 #include "ggml_extend.hpp"

@@ -23,12 +23,12 @@ public:
        }
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [N, channels, h, w]
        if (vae_downsample) {
            auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);

-            x = ggml_pad(ctx->ggml_ctx, x, 1, 1, 0, 0);
+            x = ggml_ext_pad(ctx->ggml_ctx, x, 1, 1, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
            x = conv->forward(ctx, x);
        } else {
            auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["op"]);
@@ -52,7 +52,7 @@ public:
        blocks["conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [N, channels, h, w]
        auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);

@@ -80,7 +80,7 @@ protected:
                                       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));
+            return std::shared_ptr<GGMLBlock>(new Conv3d(in_channels, out_channels, {kernel_size.first, 1, 1}, {1, 1, 1}, {padding.first, 0, 0}));
        } else {
            return std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, out_channels, kernel_size, {1, 1}, padding));
        }
@@ -121,7 +121,7 @@ public:
        }
    }

-    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, struct ggml_tensor* emb = nullptr) {
+    virtual ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* emb = nullptr) {
        // For dims==3, we reduce dimension from 5d to 4d by merging h and w, in order not to change ggml
        // [N, c, t, h, w] => [N, c, t, h * w]
        // x: [N, channels, h, w] if dims == 2 else [N, channels, t, h, w]
@@ -188,7 +188,7 @@ public:
        blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(dim_in, dim_out * 2));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
        // x: [ne3, ne2, ne1, dim_in]
        // return: [ne3, ne2, ne1, dim_out]
        auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
@@ -200,7 +200,7 @@ public:

        gate = ggml_cont(ctx->ggml_ctx, gate);

-        gate = ggml_gelu_inplace(ctx->ggml_ctx, gate);
+        gate = ggml_ext_gelu(ctx->ggml_ctx, gate, true);

        x = ggml_mul(ctx->ggml_ctx, x, gate);  // [ne3, ne2, ne1, dim_out]

@@ -214,13 +214,13 @@ public:
        blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(dim_in, dim_out, bias));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
        // x: [ne3, ne2, ne1, dim_in]
        // return: [ne3, ne2, ne1, dim_out]
        auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);

        x = proj->forward(ctx, x);
-        x = ggml_gelu_inplace(ctx->ggml_ctx, x);
+        x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
        return x;
    }
 };
@@ -258,7 +258,7 @@ public:
        blocks["net.2"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, dim_out, true, false, force_prec_f32, scale));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [ne3, ne2, ne1, dim]
        // return: [ne3, ne2, ne1, dim_out]

@@ -297,9 +297,9 @@ public:
        // to_out_1 is nn.Dropout(), skip for inference
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* context) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* context) {
        // x: [N, n_token, query_dim]
        // context: [N, n_context, context_dim]
        // return: [N, n_token, query_dim]
@@ -317,7 +317,7 @@ public:
        auto k = to_k->forward(ctx, context);  // [N, n_context, inner_dim]
        auto v = to_v->forward(ctx, context);  // [N, n_context, inner_dim]

-        x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, inner_dim]
+        x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, inner_dim]

        x = to_out_0->forward(ctx, x);  // [N, n_token, query_dim]
        return x;
@@ -355,9 +355,9 @@ public:
        }
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* context) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* context) {
        // x: [N, n_token, query_dim]
        // context: [N, n_context, context_dim]
        // return: [N, n_token, query_dim]
@@ -406,7 +406,7 @@ protected:
    int64_t context_dim = 768;  // hidden_size, 1024 for VERSION_SD2
    bool use_linear     = false;

-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") {
        auto iter = tensor_storage_map.find(prefix + "proj_out.weight");
        if (iter != tensor_storage_map.end()) {
            int64_t inner_dim = n_head * d_head;
@@ -456,9 +456,9 @@ public:
        }
    }

-    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                        struct ggml_tensor* x,
-                                        struct ggml_tensor* context) {
+    virtual ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                 ggml_tensor* x,
+                                 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"]);
@@ -510,7 +510,7 @@ public:

 class AlphaBlender : public GGMLBlock {
 protected:
-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
        // Get the type of the "mix_factor" tensor from the input tensors map with the specified prefix
        enum ggml_type wtype = GGML_TYPE_F32;
        params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
@@ -530,23 +530,23 @@ public:
        // since mix_factor.shape is [1,], we don't need rearrange using  rearrange_pattern
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x_spatial,
-                                struct ggml_tensor* x_temporal) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x_spatial,
+                         ggml_tensor* x_temporal) {
        // image_only_indicator is always tensor([0.])
        float alpha = get_alpha();
        auto x      = ggml_add(ctx->ggml_ctx,
-                               ggml_scale(ctx->ggml_ctx, x_spatial, alpha),
-                               ggml_scale(ctx->ggml_ctx, x_temporal, 1.0f - alpha));
+                               ggml_ext_scale(ctx->ggml_ctx, x_spatial, alpha),
+                               ggml_ext_scale(ctx->ggml_ctx, x_temporal, 1.0f - alpha));
        return x;
    }
 };

 class VideoResBlock : public ResBlock {
 public:
-    VideoResBlock(int channels,
-                  int emb_channels,
-                  int out_channels,
+    VideoResBlock(int64_t channels,
+                  int64_t emb_channels,
+                  int64_t out_channels,
                  std::pair<int, int> kernel_size = {3, 3},
                  int64_t video_kernel_size       = 3,
                  int dims                        = 2)  // always 2
@@ -555,10 +555,10 @@ public:
        blocks["time_mixer"] = std::shared_ptr<GGMLBlock>(new AlphaBlender());
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* emb,
-                                int num_video_frames) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         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.])
@@ -590,4 +590,4 @@ public:
    }
 };

-#endif  // __COMMON_HPP__
+#endif  // __COMMON_BLOCK_HPP__
@@ -0,0 +1,108 @@
+#ifndef __COMMON_DIT_HPP__
+#define __COMMON_DIT_HPP__
+
+#include "ggml_extend.hpp"
+
+namespace DiT {
+    inline ggml_tensor* patchify(ggml_context* ctx,
+                                 ggml_tensor* x,
+                                 int pw,
+                                 int ph,
+                                 bool patch_last = true) {
+        // x: [N, C, H, W]
+        // return: [N, h*w, C*ph*pw] if patch_last else [N, h*w, ph*pw*C]
+        int64_t N = x->ne[3];
+        int64_t C = x->ne[2];
+        int64_t H = x->ne[1];
+        int64_t W = x->ne[0];
+        int64_t h = H / ph;
+        int64_t w = W / pw;
+
+        GGML_ASSERT(h * ph == H && w * pw == W);
+
+        x = ggml_reshape_4d(ctx, x, pw, w, ph, h * C * N);     // [N*C*h, ph, w, pw]
+        x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N*C*h, w, ph, pw]
+        x = ggml_reshape_4d(ctx, x, pw * ph, w * h, C, N);     // [N, C, h*w, ph*pw]
+        if (patch_last) {
+            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N, h*w, C, ph*pw]
+            x = ggml_reshape_3d(ctx, x, pw * ph * C, w * h, N);    // [N, h*w, C*ph*pw]
+        } else {
+            x = ggml_cont(ctx, ggml_ext_torch_permute(ctx, x, 2, 0, 1, 3));  // [N, h*w, C, ph*pw]
+            x = ggml_reshape_3d(ctx, x, C * pw * ph, w * h, N);              // [N, h*w, ph*pw*C]
+        }
+        return x;
+    }
+
+    inline ggml_tensor* unpatchify(ggml_context* ctx,
+                                   ggml_tensor* x,
+                                   int64_t h,
+                                   int64_t w,
+                                   int ph,
+                                   int pw,
+                                   bool patch_last = true) {
+        // x: [N, h*w, C*ph*pw] if patch_last else [N, h*w, ph*pw*C]
+        // return: [N, C, H, W]
+        int64_t N = x->ne[2];
+        int64_t C = x->ne[0] / ph / pw;
+        int64_t H = h * ph;
+        int64_t W = w * pw;
+
+        GGML_ASSERT(C * ph * pw == x->ne[0]);
+
+        if (patch_last) {
+            x = ggml_reshape_4d(ctx, x, pw * ph, C, w * h, N);     // [N, h*w, C, ph*pw]
+            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N, C, h*w, ph*pw]
+        } else {
+            x = ggml_reshape_4d(ctx, x, C, pw * ph, w * h, N);     // [N, h*w, ph*pw, C]
+            x = ggml_cont(ctx, ggml_permute(ctx, x, 2, 0, 1, 3));  // [N, C, h*w, ph*pw]
+        }
+
+        x = ggml_reshape_4d(ctx, x, pw, ph, w, h * C * N);     // [N*C*h, w, ph, pw]
+        x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N*C*h, ph, w, pw]
+        x = ggml_reshape_4d(ctx, x, W, H, C, N);               // [N, C, h*ph, w*pw]
+
+        return x;
+    }
+
+    inline ggml_tensor* pad_to_patch_size(GGMLRunnerContext* ctx,
+                                          ggml_tensor* x,
+                                          int ph,
+                                          int pw) {
+        int64_t W = x->ne[0];
+        int64_t H = x->ne[1];
+
+        int pad_h = (ph - H % ph) % ph;
+        int pad_w = (pw - W % pw) % pw;
+        x         = ggml_ext_pad(ctx->ggml_ctx, x, pad_w, pad_h, 0, 0, ctx->circular_x_enabled, ctx->circular_y_enabled);
+        return x;
+    }
+
+    inline ggml_tensor* pad_and_patchify(GGMLRunnerContext* ctx,
+                                         ggml_tensor* x,
+                                         int ph,
+                                         int pw,
+                                         bool patch_last = true) {
+        x = pad_to_patch_size(ctx, x, ph, pw);
+        x = patchify(ctx->ggml_ctx, x, ph, pw, patch_last);
+        return x;
+    }
+
+    inline ggml_tensor* unpatchify_and_crop(ggml_context* ctx,
+                                            ggml_tensor* x,
+                                            int64_t H,
+                                            int64_t W,
+                                            int ph,
+                                            int pw,
+                                            bool patch_last = true) {
+        int pad_h = (ph - H % ph) % ph;
+        int pad_w = (pw - W % pw) % pw;
+        int64_t h = ((H + pad_h) / ph);
+        int64_t w = ((W + pad_w) / pw);
+        x         = unpatchify(ctx, x, h, w, ph, pw, patch_last);  // [N, C, H + pad_h, W + pad_w]
+        x         = ggml_ext_slice(ctx, x, 1, 0, H);               // [N, C, H, W + pad_w]
+        x         = ggml_ext_slice(ctx, x, 0, 0, W);               // [N, C, H, W]
+        return x;
+    }
+}  // namespace DiT
+
+#endif  // __COMMON_DIT_HPP__
@@ -0,0 +1,64 @@
+#ifndef __CONDITION_CACHE_UTILS_HPP__
+#define __CONDITION_CACHE_UTILS_HPP__
+
+#include <vector>
+
+#include "tensor.hpp"
+
+namespace sd {
+
+    inline bool store_condition_cache_diff(std::vector<float>* diff,
+                                           const sd::Tensor<float>& input,
+                                           const sd::Tensor<float>& output) {
+        if (diff == nullptr || input.empty() || output.empty()) {
+            return false;
+        }
+
+        size_t input_size  = static_cast<size_t>(input.numel());
+        size_t output_size = static_cast<size_t>(output.numel());
+        if (input_size == 0 || input_size != output_size) {
+            diff->clear();
+            return false;
+        }
+
+        const float* input_data  = input.data();
+        const float* output_data = output.data();
+        if (input_data == nullptr || output_data == nullptr) {
+            diff->clear();
+            return false;
+        }
+
+        diff->resize(output_size);
+        for (size_t i = 0; i < output_size; ++i) {
+            (*diff)[i] = output_data[i] - input_data[i];
+        }
+        return true;
+    }
+
+    inline bool apply_condition_cache_diff(const std::vector<float>& diff,
+                                           const sd::Tensor<float>& input,
+                                           sd::Tensor<float>* output) {
+        if (output == nullptr || input.empty() || diff.empty()) {
+            return false;
+        }
+
+        size_t input_size = static_cast<size_t>(input.numel());
+        if (input_size == 0 || diff.size() != input_size) {
+            return false;
+        }
+
+        *output            = input;
+        float* output_data = output->data();
+        if (output_data == nullptr) {
+            return false;
+        }
+
+        for (size_t i = 0; i < input_size; ++i) {
+            output_data[i] += diff[i];
+        }
+        return true;
+    }
+
+}  // namespace sd
+
+#endif  // __CONDITION_CACHE_UTILS_HPP__
@@ -1,8 +1,7 @@
 #ifndef __CONTROL_HPP__
 #define __CONTROL_HPP__

-#include "common.hpp"
-#include "ggml_extend.hpp"
+#include "common_block.hpp"
 #include "model.h"

 #define CONTROL_NET_GRAPH_SIZE 1536
@@ -165,26 +164,26 @@ public:
        blocks["middle_block_out.0"] = std::shared_ptr<GGMLBlock>(make_zero_conv(ch));
    }

-    struct ggml_tensor* resblock_forward(std::string name,
-                                         GGMLRunnerContext* ctx,
-                                         struct ggml_tensor* x,
-                                         struct ggml_tensor* emb) {
+    ggml_tensor* resblock_forward(std::string name,
+                                  GGMLRunnerContext* ctx,
+                                  ggml_tensor* x,
+                                  ggml_tensor* emb) {
        auto block = std::dynamic_pointer_cast<ResBlock>(blocks[name]);
        return block->forward(ctx, x, emb);
    }

-    struct ggml_tensor* attention_layer_forward(std::string name,
-                                                GGMLRunnerContext* ctx,
-                                                struct ggml_tensor* x,
-                                                struct ggml_tensor* context) {
+    ggml_tensor* attention_layer_forward(std::string name,
+                                         GGMLRunnerContext* ctx,
+                                         ggml_tensor* x,
+                                         ggml_tensor* context) {
        auto block = std::dynamic_pointer_cast<SpatialTransformer>(blocks[name]);
        return block->forward(ctx, x, context);
    }

-    struct ggml_tensor* input_hint_block_forward(GGMLRunnerContext* ctx,
-                                                 struct ggml_tensor* hint,
-                                                 struct ggml_tensor* emb,
-                                                 struct ggml_tensor* context) {
+    ggml_tensor* input_hint_block_forward(GGMLRunnerContext* ctx,
+                                          ggml_tensor* hint,
+                                          ggml_tensor* emb,
+                                          ggml_tensor* context) {
        int num_input_blocks = 15;
        auto h               = hint;
        for (int i = 0; i < num_input_blocks; i++) {
@@ -199,13 +198,13 @@ public:
        return h;
    }

-    std::vector<struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                             struct ggml_tensor* x,
-                                             struct ggml_tensor* hint,
-                                             struct ggml_tensor* guided_hint,
-                                             struct ggml_tensor* timesteps,
-                                             struct ggml_tensor* context,
-                                             struct ggml_tensor* y = nullptr) {
+    std::vector<ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                      ggml_tensor* x,
+                                      ggml_tensor* hint,
+                                      ggml_tensor* guided_hint,
+                                      ggml_tensor* timesteps,
+                                      ggml_tensor* context,
+                                      ggml_tensor* y = nullptr) {
        // x: [N, in_channels, h, w] or [N, in_channels/2, h, w]
        // timesteps: [N,]
        // context: [N, max_position, hidden_size] or [1, max_position, hidden_size]. for example, [N, 77, 768]
@@ -247,7 +246,7 @@ public:
            emb = ggml_add(ctx->ggml_ctx, emb, label_emb);  // [N, time_embed_dim]
        }

-        std::vector<struct ggml_tensor*> outs;
+        std::vector<ggml_tensor*> outs;

        if (guided_hint == nullptr) {
            guided_hint = input_hint_block_forward(ctx, hint, emb, context);
@@ -311,11 +310,13 @@ struct ControlNet : public GGMLRunner {
    SDVersion version = VERSION_SD1;
    ControlNetBlock control_net;

-    ggml_backend_buffer_t control_buffer = nullptr;  // keep control output tensors in backend memory
+    ggml_backend_buffer_t control_buffer = nullptr;
    ggml_context* control_ctx            = nullptr;
-    std::vector<struct ggml_tensor*> controls;  // (12 input block outputs, 1 middle block output) SD 1.5
-    struct ggml_tensor* guided_hint = nullptr;  // guided_hint cache, for faster inference
-    bool guided_hint_cached         = false;
+    std::vector<ggml_tensor*> control_outputs_ggml;
+    ggml_tensor* guided_hint_output_ggml = nullptr;
+    std::vector<sd::Tensor<float>> controls;
+    sd::Tensor<float> guided_hint;
+    bool guided_hint_cached = false;

    ControlNet(ggml_backend_t backend,
               bool offload_params_to_cpu,
@@ -329,23 +330,23 @@ struct ControlNet : public GGMLRunner {
        free_control_ctx();
    }

-    void alloc_control_ctx(std::vector<struct ggml_tensor*> outs) {
-        struct ggml_init_params params;
+    void alloc_control_ctx(std::vector<ggml_tensor*> outs) {
+        ggml_init_params params;
        params.mem_size   = static_cast<size_t>(outs.size() * ggml_tensor_overhead()) + 1024 * 1024;
        params.mem_buffer = nullptr;
        params.no_alloc   = true;
        control_ctx       = ggml_init(params);

-        controls.resize(outs.size() - 1);
+        control_outputs_ggml.resize(outs.size() - 1);

        size_t control_buffer_size = 0;

-        guided_hint = ggml_dup_tensor(control_ctx, outs[0]);
-        control_buffer_size += ggml_nbytes(guided_hint);
+        guided_hint_output_ggml = ggml_dup_tensor(control_ctx, outs[0]);
+        control_buffer_size += ggml_nbytes(guided_hint_output_ggml);

        for (int i = 0; i < outs.size() - 1; i++) {
-            controls[i] = ggml_dup_tensor(control_ctx, outs[i + 1]);
-            control_buffer_size += ggml_nbytes(controls[i]);
+            control_outputs_ggml[i] = ggml_dup_tensor(control_ctx, outs[i + 1]);
+            control_buffer_size += ggml_nbytes(control_outputs_ggml[i]);
        }

        control_buffer = ggml_backend_alloc_ctx_tensors(control_ctx, runtime_backend);
@@ -362,8 +363,10 @@ struct ControlNet : public GGMLRunner {
            ggml_free(control_ctx);
            control_ctx = nullptr;
        }
-        guided_hint        = nullptr;
-        guided_hint_cached = false;
+        guided_hint_output_ggml = nullptr;
+        guided_hint_cached      = false;
+        guided_hint             = {};
+        control_outputs_ggml.clear();
        controls.clear();
    }

@@ -371,33 +374,37 @@ struct ControlNet : public GGMLRunner {
        return "control_net";
    }

-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
        control_net.get_param_tensors(tensors, prefix);
    }

-    struct ggml_cgraph* build_graph(struct ggml_tensor* x,
-                                    struct ggml_tensor* hint,
-                                    struct ggml_tensor* timesteps,
-                                    struct ggml_tensor* context,
-                                    struct ggml_tensor* y = nullptr) {
-        struct ggml_cgraph* gf = new_graph_custom(CONTROL_NET_GRAPH_SIZE);
+    ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                             const sd::Tensor<float>& hint_tensor,
+                             const sd::Tensor<float>& timesteps_tensor,
+                             const sd::Tensor<float>& context_tensor = {},
+                             const sd::Tensor<float>& y_tensor       = {}) {
+        ggml_cgraph* gf = new_graph_custom(CONTROL_NET_GRAPH_SIZE);

-        x = to_backend(x);
-        if (guided_hint_cached) {
-            hint = nullptr;
+        ggml_tensor* x         = make_input(x_tensor);
+        ggml_tensor* hint      = nullptr;
+        ggml_tensor* timesteps = make_input(timesteps_tensor);
+        ggml_tensor* context   = make_optional_input(context_tensor);
+        ggml_tensor* y         = make_optional_input(y_tensor);
+
+        ggml_tensor* guided_hint_input = nullptr;
+        if (guided_hint_cached && !guided_hint.empty()) {
+            guided_hint_input = make_input(guided_hint);
+            hint              = nullptr;
        } else {
-            hint = to_backend(hint);
+            hint = make_input(hint_tensor);
        }
-        context   = to_backend(context);
-        y         = to_backend(y);
-        timesteps = to_backend(timesteps);

        auto runner_ctx = get_context();

        auto outs = control_net.forward(&runner_ctx,
                                        x,
                                        hint,
-                                        guided_hint_cached ? guided_hint : nullptr,
+                                        guided_hint_input,
                                        timesteps,
                                        context,
                                        y);
@@ -406,36 +413,46 @@ struct ControlNet : public GGMLRunner {
            alloc_control_ctx(outs);
        }

-        ggml_build_forward_expand(gf, ggml_cpy(compute_ctx, outs[0], guided_hint));
+        ggml_build_forward_expand(gf, ggml_cpy(compute_ctx, outs[0], guided_hint_output_ggml));
        for (int i = 0; i < outs.size() - 1; i++) {
-            ggml_build_forward_expand(gf, ggml_cpy(compute_ctx, outs[i + 1], controls[i]));
+            ggml_build_forward_expand(gf, ggml_cpy(compute_ctx, outs[i + 1], control_outputs_ggml[i]));
        }

        return gf;
    }

-    bool compute(int n_threads,
-                 struct ggml_tensor* x,
-                 struct ggml_tensor* hint,
-                 struct ggml_tensor* timesteps,
-                 struct ggml_tensor* context,
-                 struct ggml_tensor* y,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr) {
+    std::optional<std::vector<sd::Tensor<float>>> compute(int n_threads,
+                                                          const sd::Tensor<float>& x,
+                                                          const sd::Tensor<float>& hint,
+                                                          const sd::Tensor<float>& timesteps,
+                                                          const sd::Tensor<float>& context = {},
+                                                          const sd::Tensor<float>& y       = {}) {
        // x: [N, in_channels, h, w]
        // timesteps: [N, ]
        // context: [N, max_position, hidden_size]([N, 77, 768]) or [1, max_position, hidden_size]
        // y: [N, adm_in_channels] or [1, adm_in_channels]
-        auto get_graph = [&]() -> struct ggml_cgraph* {
+        auto get_graph = [&]() -> ggml_cgraph* {
            return build_graph(x, hint, timesteps, context, y);
        };

-        bool res = GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
-        if (res) {
-            // cache guided_hint
-            guided_hint_cached = true;
+        auto compute_result = GGMLRunner::compute<float>(get_graph, n_threads, false);
+        if (!compute_result.has_value()) {
+            return std::nullopt;
        }
-        return res;
+
+        if (guided_hint_output_ggml != nullptr) {
+            guided_hint = restore_trailing_singleton_dims(sd::make_sd_tensor_from_ggml<float>(guided_hint_output_ggml),
+                                                          4);
+        }
+        controls.clear();
+        controls.reserve(control_outputs_ggml.size());
+        for (ggml_tensor* control : control_outputs_ggml) {
+            auto control_host = restore_trailing_singleton_dims(sd::make_sd_tensor_from_ggml<float>(control), 4);
+            GGML_ASSERT(!control_host.empty());
+            controls.push_back(std::move(control_host));
+        }
+        guided_hint_cached = true;
+        return controls;
    }

    bool load_from_file(const std::string& file_path, int n_threads) {
@@ -463,4 +480,4 @@ struct ControlNet : public GGMLRunner {
    }
 };

-#endif  // __CONTROL_HPP__
+#endif  // __CONTROL_HPP__
@@ -0,0 +1,519 @@
+#ifndef __DIFFUSION_MODEL_H__
+#define __DIFFUSION_MODEL_H__
+
+#include <optional>
+#include "anima.hpp"
+#include "flux.hpp"
+#include "mmdit.hpp"
+#include "qwen_image.hpp"
+#include "tensor_ggml.hpp"
+#include "unet.hpp"
+#include "wan.hpp"
+#include "z_image.hpp"
+
+struct DiffusionParams {
+    const sd::Tensor<float>* x                        = nullptr;
+    const sd::Tensor<float>* timesteps                = nullptr;
+    const sd::Tensor<float>* context                  = nullptr;
+    const sd::Tensor<float>* c_concat                 = nullptr;
+    const sd::Tensor<float>* y                        = nullptr;
+    const sd::Tensor<int32_t>* t5_ids                 = nullptr;
+    const sd::Tensor<float>* t5_weights               = nullptr;
+    const sd::Tensor<float>* guidance                 = nullptr;
+    const std::vector<sd::Tensor<float>>* ref_latents = nullptr;
+    bool increase_ref_index                           = false;
+    int num_video_frames                              = -1;
+    const std::vector<sd::Tensor<float>>* controls    = nullptr;
+    float control_strength                            = 0.f;
+    const sd::Tensor<float>* vace_context             = nullptr;
+    float vace_strength                               = 1.f;
+    const std::vector<int>* skip_layers               = nullptr;
+};
+
+template <typename T>
+static inline const sd::Tensor<T>& tensor_or_empty(const sd::Tensor<T>* tensor) {
+    static const sd::Tensor<T> kEmpty;
+    return tensor != nullptr ? *tensor : kEmpty;
+}
+
+struct DiffusionModel {
+    virtual std::string get_desc()                                               = 0;
+    virtual sd::Tensor<float> compute(int n_threads,
+                                      const DiffusionParams& diffusion_params)   = 0;
+    virtual void alloc_params_buffer()                                           = 0;
+    virtual void free_params_buffer()                                            = 0;
+    virtual void free_compute_buffer()                                           = 0;
+    virtual void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) = 0;
+    virtual size_t get_params_buffer_size()                                      = 0;
+    virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter){};
+    virtual int64_t get_adm_in_channels()                            = 0;
+    virtual void set_flash_attention_enabled(bool enabled)           = 0;
+    virtual void set_circular_axes(bool circular_x, bool circular_y) = 0;
+};
+
+struct UNetModel : public DiffusionModel {
+    UNetModelRunner unet;
+
+    UNetModel(ggml_backend_t backend,
+              bool offload_params_to_cpu,
+              const String2TensorStorage& tensor_storage_map = {},
+              SDVersion version                              = VERSION_SD1)
+        : unet(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model", version) {
+    }
+
+    std::string get_desc() override {
+        return unet.get_desc();
+    }
+
+    void alloc_params_buffer() override {
+        unet.alloc_params_buffer();
+    }
+
+    void free_params_buffer() override {
+        unet.free_params_buffer();
+    }
+
+    void free_compute_buffer() override {
+        unet.free_compute_buffer();
+    }
+
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        unet.get_param_tensors(tensors, "model.diffusion_model");
+    }
+
+    size_t get_params_buffer_size() override {
+        return unet.get_params_buffer_size();
+    }
+
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        unet.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
+        return unet.unet.adm_in_channels;
+    }
+
+    void set_flash_attention_enabled(bool enabled) {
+        unet.set_flash_attention_enabled(enabled);
+    }
+
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        unet.set_circular_axes(circular_x, circular_y);
+    }
+
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        static const std::vector<sd::Tensor<float>> empty_controls;
+        return unet.compute(n_threads,
+                            *diffusion_params.x,
+                            *diffusion_params.timesteps,
+                            tensor_or_empty(diffusion_params.context),
+                            tensor_or_empty(diffusion_params.c_concat),
+                            tensor_or_empty(diffusion_params.y),
+                            diffusion_params.num_video_frames,
+                            diffusion_params.controls ? *diffusion_params.controls : empty_controls,
+                            diffusion_params.control_strength);
+    }
+};
+
+struct MMDiTModel : public DiffusionModel {
+    MMDiTRunner mmdit;
+
+    MMDiTModel(ggml_backend_t backend,
+               bool offload_params_to_cpu,
+               const String2TensorStorage& tensor_storage_map = {})
+        : mmdit(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model") {
+    }
+
+    std::string get_desc() override {
+        return mmdit.get_desc();
+    }
+
+    void alloc_params_buffer() override {
+        mmdit.alloc_params_buffer();
+    }
+
+    void free_params_buffer() override {
+        mmdit.free_params_buffer();
+    }
+
+    void free_compute_buffer() override {
+        mmdit.free_compute_buffer();
+    }
+
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        mmdit.get_param_tensors(tensors, "model.diffusion_model");
+    }
+
+    size_t get_params_buffer_size() override {
+        return mmdit.get_params_buffer_size();
+    }
+
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        mmdit.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
+        return 768 + 1280;
+    }
+
+    void set_flash_attention_enabled(bool enabled) {
+        mmdit.set_flash_attention_enabled(enabled);
+    }
+
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        mmdit.set_circular_axes(circular_x, circular_y);
+    }
+
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        static const std::vector<int> empty_skip_layers;
+        return mmdit.compute(n_threads,
+                             *diffusion_params.x,
+                             *diffusion_params.timesteps,
+                             tensor_or_empty(diffusion_params.context),
+                             tensor_or_empty(diffusion_params.y),
+                             diffusion_params.skip_layers ? *diffusion_params.skip_layers : empty_skip_layers);
+    }
+};
+
+struct FluxModel : public DiffusionModel {
+    Flux::FluxRunner flux;
+
+    FluxModel(ggml_backend_t backend,
+              bool offload_params_to_cpu,
+              const String2TensorStorage& tensor_storage_map = {},
+              SDVersion version                              = VERSION_FLUX,
+              bool use_mask                                  = false)
+        : flux(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model", version, use_mask) {
+    }
+
+    std::string get_desc() override {
+        return flux.get_desc();
+    }
+
+    void alloc_params_buffer() override {
+        flux.alloc_params_buffer();
+    }
+
+    void free_params_buffer() override {
+        flux.free_params_buffer();
+    }
+
+    void free_compute_buffer() override {
+        flux.free_compute_buffer();
+    }
+
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        flux.get_param_tensors(tensors, "model.diffusion_model");
+    }
+
+    size_t get_params_buffer_size() override {
+        return flux.get_params_buffer_size();
+    }
+
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        flux.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
+        return 768;
+    }
+
+    void set_flash_attention_enabled(bool enabled) {
+        flux.set_flash_attention_enabled(enabled);
+    }
+
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        flux.set_circular_axes(circular_x, circular_y);
+    }
+
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        static const std::vector<sd::Tensor<float>> empty_ref_latents;
+        static const std::vector<int> empty_skip_layers;
+        return flux.compute(n_threads,
+                            *diffusion_params.x,
+                            *diffusion_params.timesteps,
+                            tensor_or_empty(diffusion_params.context),
+                            tensor_or_empty(diffusion_params.c_concat),
+                            tensor_or_empty(diffusion_params.y),
+                            tensor_or_empty(diffusion_params.guidance),
+                            diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
+                            diffusion_params.increase_ref_index,
+                            diffusion_params.skip_layers ? *diffusion_params.skip_layers : empty_skip_layers);
+    }
+};
+
+struct AnimaModel : public DiffusionModel {
+    std::string prefix;
+    Anima::AnimaRunner anima;
+
+    AnimaModel(ggml_backend_t backend,
+               bool offload_params_to_cpu,
+               const String2TensorStorage& tensor_storage_map = {},
+               const std::string prefix                       = "model.diffusion_model")
+        : prefix(prefix), anima(backend, offload_params_to_cpu, tensor_storage_map, prefix) {
+    }
+
+    std::string get_desc() override {
+        return anima.get_desc();
+    }
+
+    void alloc_params_buffer() override {
+        anima.alloc_params_buffer();
+    }
+
+    void free_params_buffer() override {
+        anima.free_params_buffer();
+    }
+
+    void free_compute_buffer() override {
+        anima.free_compute_buffer();
+    }
+
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        anima.get_param_tensors(tensors, prefix);
+    }
+
+    size_t get_params_buffer_size() override {
+        return anima.get_params_buffer_size();
+    }
+
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        anima.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
+        return 768;
+    }
+
+    void set_flash_attention_enabled(bool enabled) {
+        anima.set_flash_attention_enabled(enabled);
+    }
+
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        anima.set_circular_axes(circular_x, circular_y);
+    }
+
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        return anima.compute(n_threads,
+                             *diffusion_params.x,
+                             *diffusion_params.timesteps,
+                             tensor_or_empty(diffusion_params.context),
+                             tensor_or_empty(diffusion_params.t5_ids),
+                             tensor_or_empty(diffusion_params.t5_weights));
+    }
+};
+
+struct WanModel : public DiffusionModel {
+    std::string prefix;
+    WAN::WanRunner wan;
+
+    WanModel(ggml_backend_t backend,
+             bool offload_params_to_cpu,
+             const String2TensorStorage& tensor_storage_map = {},
+             const std::string prefix                       = "model.diffusion_model",
+             SDVersion version                              = VERSION_WAN2)
+        : prefix(prefix), wan(backend, offload_params_to_cpu, tensor_storage_map, prefix, version) {
+    }
+
+    std::string get_desc() override {
+        return wan.get_desc();
+    }
+
+    void alloc_params_buffer() override {
+        wan.alloc_params_buffer();
+    }
+
+    void free_params_buffer() override {
+        wan.free_params_buffer();
+    }
+
+    void free_compute_buffer() override {
+        wan.free_compute_buffer();
+    }
+
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        wan.get_param_tensors(tensors, prefix);
+    }
+
+    size_t get_params_buffer_size() override {
+        return wan.get_params_buffer_size();
+    }
+
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        wan.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
+        return 768;
+    }
+
+    void set_flash_attention_enabled(bool enabled) {
+        wan.set_flash_attention_enabled(enabled);
+    }
+
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        wan.set_circular_axes(circular_x, circular_y);
+    }
+
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        return wan.compute(n_threads,
+                           *diffusion_params.x,
+                           *diffusion_params.timesteps,
+                           tensor_or_empty(diffusion_params.context),
+                           tensor_or_empty(diffusion_params.y),
+                           tensor_or_empty(diffusion_params.c_concat),
+                           sd::Tensor<float>(),
+                           tensor_or_empty(diffusion_params.vace_context),
+                           diffusion_params.vace_strength);
+    }
+};
+
+struct QwenImageModel : public DiffusionModel {
+    std::string prefix;
+    Qwen::QwenImageRunner qwen_image;
+
+    QwenImageModel(ggml_backend_t backend,
+                   bool offload_params_to_cpu,
+                   const String2TensorStorage& tensor_storage_map = {},
+                   const std::string prefix                       = "model.diffusion_model",
+                   SDVersion version                              = VERSION_QWEN_IMAGE,
+                   bool zero_cond_t                               = false)
+        : prefix(prefix), qwen_image(backend, offload_params_to_cpu, tensor_storage_map, prefix, version, zero_cond_t) {
+    }
+
+    std::string get_desc() override {
+        return qwen_image.get_desc();
+    }
+
+    void alloc_params_buffer() override {
+        qwen_image.alloc_params_buffer();
+    }
+
+    void free_params_buffer() override {
+        qwen_image.free_params_buffer();
+    }
+
+    void free_compute_buffer() override {
+        qwen_image.free_compute_buffer();
+    }
+
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        qwen_image.get_param_tensors(tensors, prefix);
+    }
+
+    size_t get_params_buffer_size() override {
+        return qwen_image.get_params_buffer_size();
+    }
+
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        qwen_image.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
+        return 768;
+    }
+
+    void set_flash_attention_enabled(bool enabled) {
+        qwen_image.set_flash_attention_enabled(enabled);
+    }
+
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        qwen_image.set_circular_axes(circular_x, circular_y);
+    }
+
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        static const std::vector<sd::Tensor<float>> empty_ref_latents;
+        return qwen_image.compute(n_threads,
+                                  *diffusion_params.x,
+                                  *diffusion_params.timesteps,
+                                  tensor_or_empty(diffusion_params.context),
+                                  diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
+                                  true);
+    }
+};
+
+struct ZImageModel : public DiffusionModel {
+    std::string prefix;
+    ZImage::ZImageRunner z_image;
+
+    ZImageModel(ggml_backend_t backend,
+                bool offload_params_to_cpu,
+                const String2TensorStorage& tensor_storage_map = {},
+                const std::string prefix                       = "model.diffusion_model",
+                SDVersion version                              = VERSION_Z_IMAGE)
+        : prefix(prefix), z_image(backend, offload_params_to_cpu, tensor_storage_map, prefix, version) {
+    }
+
+    std::string get_desc() override {
+        return z_image.get_desc();
+    }
+
+    void alloc_params_buffer() override {
+        z_image.alloc_params_buffer();
+    }
+
+    void free_params_buffer() override {
+        z_image.free_params_buffer();
+    }
+
+    void free_compute_buffer() override {
+        z_image.free_compute_buffer();
+    }
+
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+        z_image.get_param_tensors(tensors, prefix);
+    }
+
+    size_t get_params_buffer_size() override {
+        return z_image.get_params_buffer_size();
+    }
+
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        z_image.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
+        return 768;
+    }
+
+    void set_flash_attention_enabled(bool enabled) {
+        z_image.set_flash_attention_enabled(enabled);
+    }
+
+    void set_circular_axes(bool circular_x, bool circular_y) override {
+        z_image.set_circular_axes(circular_x, circular_y);
+    }
+
+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        static const std::vector<sd::Tensor<float>> empty_ref_latents;
+        return z_image.compute(n_threads,
+                               *diffusion_params.x,
+                               *diffusion_params.timesteps,
+                               tensor_or_empty(diffusion_params.context),
+                               diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
+                               true);
+    }
+};
+
+#endif
@@ -1,10 +1,15 @@
+#ifndef __EASYCACHE_HPP__
+#define __EASYCACHE_HPP__
+
 #include <cmath>
 #include <limits>
 #include <unordered_map>
 #include <vector>

+#include "condition_cache_utils.hpp"
 #include "denoiser.hpp"
 #include "ggml_extend.hpp"
+#include "tensor.hpp"

 struct EasyCacheConfig {
    bool enabled          = false;
@@ -19,15 +24,15 @@ struct EasyCacheCacheEntry {

 struct EasyCacheState {
    EasyCacheConfig config;
-    Denoiser* denoiser                  = nullptr;
-    float start_sigma                   = std::numeric_limits<float>::max();
-    float end_sigma                     = 0.0f;
-    bool initialized                    = false;
-    bool initial_step                   = true;
-    bool skip_current_step              = false;
-    bool step_active                    = false;
-    const SDCondition* anchor_condition = nullptr;
-    std::unordered_map<const SDCondition*, EasyCacheCacheEntry> cache_diffs;
+    Denoiser* denoiser           = nullptr;
+    float start_sigma            = std::numeric_limits<float>::max();
+    float end_sigma              = 0.0f;
+    bool initialized             = false;
+    bool initial_step            = true;
+    bool skip_current_step       = false;
+    bool step_active             = false;
+    const void* anchor_condition = nullptr;
+    std::unordered_map<const void*, EasyCacheCacheEntry> cache_diffs;
    std::vector<float> prev_input;
    std::vector<float> prev_output;
    float output_prev_norm                = 0.0f;
@@ -120,41 +125,30 @@ struct EasyCacheState {
        return enabled() && step_active && skip_current_step;
    }

-    bool has_cache(const SDCondition* cond) const {
+    bool has_cache(const void* cond) const {
        auto it = cache_diffs.find(cond);
        return it != cache_diffs.end() && !it->second.diff.empty();
    }

-    void update_cache(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+    void update_cache(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
        EasyCacheCacheEntry& entry = cache_diffs[cond];
-        size_t ne                  = static_cast<size_t>(ggml_nelements(output));
-        entry.diff.resize(ne);
-        float* out_data = (float*)output->data;
-        float* in_data  = (float*)input->data;
-        for (size_t i = 0; i < ne; ++i) {
-            entry.diff[i] = out_data[i] - in_data[i];
-        }
+        sd::store_condition_cache_diff(&entry.diff, input, output);
    }

-    void apply_cache(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+    void apply_cache(const void* cond, const sd::Tensor<float>& input, sd::Tensor<float>* output) {
        auto it = cache_diffs.find(cond);
        if (it == cache_diffs.end() || it->second.diff.empty()) {
            return;
        }
-        copy_ggml_tensor(output, input);
-        float* out_data                = (float*)output->data;
-        const std::vector<float>& diff = it->second.diff;
-        for (size_t i = 0; i < diff.size(); ++i) {
-            out_data[i] += diff[i];
-        }
+        sd::apply_condition_cache_diff(it->second.diff, input, output);
    }

-    bool before_condition(const SDCondition* cond,
-                          ggml_tensor* input,
-                          ggml_tensor* output,
+    bool before_condition(const void* cond,
+                          const sd::Tensor<float>& input,
+                          sd::Tensor<float>* output,
                          float sigma,
                          int step_index) {
-        if (!enabled() || step_index < 0) {
+        if (!enabled() || step_index < 0 || output == nullptr) {
            return false;
        }
        if (step_index != current_step_index) {
@@ -181,12 +175,12 @@ struct EasyCacheState {
        if (!has_prev_input || !has_prev_output || !has_cache(cond)) {
            return false;
        }
-        size_t ne = static_cast<size_t>(ggml_nelements(input));
+        size_t ne = static_cast<size_t>(input.numel());
        if (prev_input.size() != ne) {
            return false;
        }
-        float* input_data = (float*)input->data;
-        last_input_change = 0.0f;
+        const float* input_data = input.data();
+        last_input_change       = 0.0f;
        for (size_t i = 0; i < ne; ++i) {
            last_input_change += std::fabs(input_data[i] - prev_input[i]);
        }
@@ -211,7 +205,7 @@ struct EasyCacheState {
        return false;
    }

-    void after_condition(const SDCondition* cond, ggml_tensor* input, ggml_tensor* output) {
+    void after_condition(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
        if (!step_is_active()) {
            return;
        }
@@ -220,16 +214,16 @@ struct EasyCacheState {
            return;
        }

-        size_t ne      = static_cast<size_t>(ggml_nelements(input));
-        float* in_data = (float*)input->data;
+        size_t ne            = static_cast<size_t>(input.numel());
+        const float* in_data = input.data();
        prev_input.resize(ne);
        for (size_t i = 0; i < ne; ++i) {
            prev_input[i] = in_data[i];
        }
        has_prev_input = true;

-        float* out_data     = (float*)output->data;
-        float output_change = 0.0f;
+        const float* out_data = output.data();
+        float output_change   = 0.0f;
        if (has_prev_output && prev_output.size() == ne) {
            for (size_t i = 0; i < ne; ++i) {
                output_change += std::fabs(out_data[i] - prev_output[i]);
@@ -262,4 +256,6 @@ struct EasyCacheState {
        cumulative_change_rate = 0.0f;
        has_last_input_change  = false;
    }
-};
+};
+
+#endif
@@ -27,11 +27,11 @@ public:
        blocks["conv5"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat + 4 * num_grow_ch, num_feat, {3, 3}, {1, 1}, {1, 1}));
    }

-    struct ggml_tensor* lrelu(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* lrelu(GGMLRunnerContext* ctx, ggml_tensor* x) {
        return ggml_leaky_relu(ctx->ggml_ctx, x, 0.2f, true);
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [n, num_feat, h, w]
        // return: [n, num_feat, h, w]

@@ -51,7 +51,7 @@ public:
        x_cat      = ggml_concat(ctx->ggml_ctx, x_cat, x4, 2);
        auto x5    = conv5->forward(ctx, x_cat);

-        x5 = ggml_add(ctx->ggml_ctx, ggml_scale(ctx->ggml_ctx, x5, 0.2f), x);
+        x5 = ggml_add(ctx->ggml_ctx, ggml_ext_scale(ctx->ggml_ctx, x5, 0.2f), x);
        return x5;
    }
 };
@@ -64,7 +64,7 @@ public:
        blocks["rdb3"] = std::shared_ptr<GGMLBlock>(new ResidualDenseBlock(num_feat, num_grow_ch));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [n, num_feat, h, w]
        // return: [n, num_feat, h, w]

@@ -76,7 +76,7 @@ public:
        out      = rdb2->forward(ctx, out);
        out      = rdb3->forward(ctx, out);

-        out = ggml_add(ctx->ggml_ctx, ggml_scale(ctx->ggml_ctx, out, 0.2f), x);
+        out = ggml_add(ctx->ggml_ctx, ggml_ext_scale(ctx->ggml_ctx, out, 0.2f), x);
        return out;
    }
 };
@@ -112,11 +112,11 @@ public:
    int get_scale() { return scale; }
    int get_num_block() { return num_block; }

-    struct ggml_tensor* lrelu(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* lrelu(GGMLRunnerContext* ctx, ggml_tensor* x) {
        return ggml_leaky_relu(ctx->ggml_ctx, x, 0.2f, true);
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [n, num_in_ch, h, w]
        // return: [n, num_out_ch, h*scale, w*scale]
        auto conv_first = std::dynamic_pointer_cast<Conv2d>(blocks["conv_first"]);
@@ -341,28 +341,25 @@ struct ESRGAN : public GGMLRunner {
        return success;
    }

-    struct ggml_cgraph* build_graph(struct ggml_tensor* x) {
+    ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor) {
        if (!rrdb_net)
            return nullptr;
        constexpr int kGraphNodes = 1 << 16;  // 65k
-        struct ggml_cgraph* gf    = new_graph_custom(kGraphNodes);
-        x                         = to_backend(x);
+        ggml_cgraph* gf           = new_graph_custom(kGraphNodes);
+        ggml_tensor* x            = make_input(x_tensor);

-        auto runner_ctx         = get_context();
-        struct ggml_tensor* out = rrdb_net->forward(&runner_ctx, x);
+        auto runner_ctx  = get_context();
+        ggml_tensor* out = rrdb_net->forward(&runner_ctx, x);
        ggml_build_forward_expand(gf, out);
        return gf;
    }

-    bool compute(const int n_threads,
-                 struct ggml_tensor* x,
-                 ggml_tensor** output,
-                 ggml_context* output_ctx = nullptr) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(x);
-        };
-        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+    sd::Tensor<float> compute(const int n_threads,
+                              const sd::Tensor<float>& x) {
+        auto get_graph = [&]() -> ggml_cgraph* { return build_graph(x); };
+        auto result    = restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+        return result;
    }
 };

-#endif  // __ESRGAN_HPP__
+#endif  // __ESRGAN_HPP__
@@ -151,7 +151,7 @@ private:
        }

        if (n_dims > GGML_MAX_DIMS) {
-            for (int i = GGML_MAX_DIMS; i < n_dims; i++) {
+            for (uint32_t i = GGML_MAX_DIMS; i < n_dims; i++) {
                info.shape[GGML_MAX_DIMS - 1] *= info.shape[i];  // stack to last dim;
            }
            info.shape.resize(GGML_MAX_DIMS);
@@ -1,234 +1,300 @@
-#include <cstddef>
-#include <cstdint>
-#include "ggml.h"
-
-const float wan_21_latent_rgb_proj[16][3] = {
-    {0.015123f, -0.148418f, 0.479828f},
-    {0.003652f, -0.010680f, -0.037142f},
-    {0.212264f, 0.063033f, 0.016779f},
-    {0.232999f, 0.406476f, 0.220125f},
-    {-0.051864f, -0.082384f, -0.069396f},
-    {0.085005f, -0.161492f, 0.010689f},
-    {-0.245369f, -0.506846f, -0.117010f},
-    {-0.151145f, 0.017721f, 0.007207f},
-    {-0.293239f, -0.207936f, -0.421135f},
-    {-0.187721f, 0.050783f, 0.177649f},
-    {-0.013067f, 0.265964f, 0.166578f},
-    {0.028327f, 0.109329f, 0.108642f},
-    {-0.205343f, 0.043991f, 0.148914f},
-    {0.014307f, -0.048647f, -0.007219f},
-    {0.217150f, 0.053074f, 0.319923f},
-    {0.155357f, 0.083156f, 0.064780f}};
-float wan_21_latent_rgb_bias[3] = {-0.270270f, -0.234976f, -0.456853f};
-
-const float wan_22_latent_rgb_proj[48][3] = {
-    {0.017126f, -0.027230f, -0.019257f},
-    {-0.113739f, -0.028715f, -0.022885f},
-    {-0.000106f, 0.021494f, 0.004629f},
-    {-0.013273f, -0.107137f, -0.033638f},
-    {-0.000381f, 0.000279f, 0.025877f},
-    {-0.014216f, -0.003975f, 0.040528f},
-    {0.001638f, -0.000748f, 0.011022f},
-    {0.029238f, -0.006697f, 0.035933f},
-    {0.021641f, -0.015874f, 0.040531f},
-    {-0.101984f, -0.070160f, -0.028855f},
-    {0.033207f, -0.021068f, 0.002663f},
-    {-0.104711f, 0.121673f, 0.102981f},
-    {0.082647f, -0.004991f, 0.057237f},
-    {-0.027375f, 0.031581f, 0.006868f},
-    {-0.045434f, 0.029444f, 0.019287f},
-    {-0.046572f, -0.012537f, 0.006675f},
-    {0.074709f, 0.033690f, 0.025289f},
-    {-0.008251f, -0.002745f, -0.006999f},
-    {0.012685f, -0.061856f, -0.048658f},
-    {0.042304f, -0.007039f, 0.000295f},
-    {-0.007644f, -0.060843f, -0.033142f},
-    {0.159909f, 0.045628f, 0.367541f},
-    {0.095171f, 0.086438f, 0.010271f},
-    {0.006812f, 0.019643f, 0.029637f},
-    {0.003467f, -0.010705f, 0.014252f},
-    {-0.099681f, -0.066272f, -0.006243f},
-    {0.047357f, 0.037040f, 0.000185f},
-    {-0.041797f, -0.089225f, -0.032257f},
-    {0.008928f, 0.017028f, 0.018684f},
-    {-0.042255f, 0.016045f, 0.006849f},
-    {0.011268f, 0.036462f, 0.037387f},
-    {0.011553f, -0.016375f, -0.048589f},
-    {0.046266f, -0.027189f, 0.056979f},
-    {0.009640f, -0.017576f, 0.030324f},
-    {-0.045794f, -0.036083f, -0.010616f},
-    {0.022418f, 0.039783f, -0.032939f},
-    {-0.052714f, -0.015525f, 0.007438f},
-    {0.193004f, 0.223541f, 0.264175f},
-    {-0.059406f, -0.008188f, 0.022867f},
-    {-0.156742f, -0.263791f, -0.007385f},
-    {-0.015717f, 0.016570f, 0.033969f},
-    {0.037969f, 0.109835f, 0.200449f},
-    {-0.000782f, -0.009566f, -0.008058f},
-    {0.010709f, 0.052960f, -0.044195f},
-    {0.017271f, 0.045839f, 0.034569f},
-    {0.009424f, 0.013088f, -0.001714f},
-    {-0.024805f, -0.059378f, -0.033756f},
-    {-0.078293f, 0.029070f, 0.026129f}};
-float wan_22_latent_rgb_bias[3] = {0.013160f, -0.096492f, -0.071323f};
-
-const float flux_latent_rgb_proj[16][3] = {
-    {-0.041168f, 0.019917f, 0.097253f},
-    {0.028096f, 0.026730f, 0.129576f},
-    {0.065618f, -0.067950f, -0.014651f},
-    {-0.012998f, -0.014762f, 0.081251f},
-    {0.078567f, 0.059296f, -0.024687f},
-    {-0.015987f, -0.003697f, 0.005012f},
-    {0.033605f, 0.138999f, 0.068517f},
-    {-0.024450f, -0.063567f, -0.030101f},
-    {-0.040194f, -0.016710f, 0.127185f},
-    {0.112681f, 0.088764f, -0.041940f},
-    {-0.023498f, 0.093664f, 0.025543f},
-    {0.082899f, 0.048320f, 0.007491f},
-    {0.075712f, 0.074139f, 0.081965f},
-    {-0.143501f, 0.018263f, -0.136138f},
-    {-0.025767f, -0.082035f, -0.040023f},
-    {-0.111849f, -0.055589f, -0.032361f}};
-float flux_latent_rgb_bias[3] = {0.024600f, -0.006937f, -0.008089f};
-
-const float flux2_latent_rgb_proj[32][3] = {
-    {0.000736f, -0.008385f, -0.019710f},
-    {-0.001352f, -0.016392f, 0.020693f},
-    {-0.006376f, 0.002428f, 0.036736f},
-    {0.039384f, 0.074167f, 0.119789f},
-    {0.007464f, -0.005705f, -0.004734f},
-    {-0.004086f, 0.005287f, -0.000409f},
-    {-0.032835f, 0.050802f, -0.028120f},
-    {-0.003158f, -0.000835f, 0.000406f},
-    {-0.112840f, -0.084337f, -0.023083f},
-    {0.001462f, -0.006656f, 0.000549f},
-    {-0.009980f, -0.007480f, 0.009702f},
-    {0.032540f, 0.000214f, -0.061388f},
-    {0.011023f, 0.000694f, 0.007143f},
-    {-0.001468f, -0.006723f, -0.001678f},
-    {-0.005921f, -0.010320f, -0.003907f},
-    {-0.028434f, 0.027584f, 0.018457f},
-    {0.014349f, 0.011523f, 0.000441f},
-    {0.009874f, 0.003081f, 0.001507f},
-    {0.002218f, 0.005712f, 0.001563f},
-    {0.053010f, -0.019844f, 0.008683f},
-    {-0.002507f, 0.005384f, 0.000938f},
-    {-0.002177f, -0.011366f, 0.003559f},
-    {-0.000261f, 0.015121f, -0.003240f},
-    {-0.003944f, -0.002083f, 0.005043f},
-    {-0.009138f, 0.011336f, 0.003781f},
-    {0.011429f, 0.003985f, -0.003855f},
-    {0.010518f, -0.005586f, 0.010131f},
-    {0.007883f, 0.002912f, -0.001473f},
-    {-0.003318f, -0.003160f, 0.003684f},
-    {-0.034560f, -0.008740f, 0.012996f},
-    {0.000166f, 0.001079f, -0.012153f},
-    {0.017772f, 0.000937f, -0.011953f}};
-float flux2_latent_rgb_bias[3] = {-0.028738f, -0.098463f, -0.107619f};
-
-// This one was taken straight from
-// https://github.com/Stability-AI/sd3.5/blob/8565799a3b41eb0c7ba976d18375f0f753f56402/sd3_impls.py#L288-L303
-// (MiT Licence)
-const float sd3_latent_rgb_proj[16][3] = {
-    {-0.0645f, 0.0177f, 0.1052f},
-    {0.0028f, 0.0312f, 0.0650f},
-    {0.1848f, 0.0762f, 0.0360f},
-    {0.0944f, 0.0360f, 0.0889f},
-    {0.0897f, 0.0506f, -0.0364f},
-    {-0.0020f, 0.1203f, 0.0284f},
-    {0.0855f, 0.0118f, 0.0283f},
-    {-0.0539f, 0.0658f, 0.1047f},
-    {-0.0057f, 0.0116f, 0.0700f},
-    {-0.0412f, 0.0281f, -0.0039f},
-    {0.1106f, 0.1171f, 0.1220f},
-    {-0.0248f, 0.0682f, -0.0481f},
-    {0.0815f, 0.0846f, 0.1207f},
-    {-0.0120f, -0.0055f, -0.0867f},
-    {-0.0749f, -0.0634f, -0.0456f},
-    {-0.1418f, -0.1457f, -0.1259f},
-};
-float sd3_latent_rgb_bias[3] = {0, 0, 0};
-
-const float sdxl_latent_rgb_proj[4][3] = {
-    {0.258303f, 0.277640f, 0.329699f},
-    {-0.299701f, 0.105446f, 0.014194f},
-    {0.050522f, 0.186163f, -0.143257f},
-    {-0.211938f, -0.149892f, -0.080036f}};
-float sdxl_latent_rgb_bias[3] = {0.144381f, -0.033313f, 0.007061f};
-
-const float sd_latent_rgb_proj[4][3] = {
-    {0.337366f, 0.216344f, 0.257386f},
-    {0.165636f, 0.386828f, 0.046994f},
-    {-0.267803f, 0.237036f, 0.223517f},
-    {-0.178022f, -0.200862f, -0.678514f}};
-float sd_latent_rgb_bias[3] = {-0.017478f, -0.055834f, -0.105825f};
-
-void preview_latent_video(uint8_t* buffer, struct ggml_tensor* latents, const float (*latent_rgb_proj)[3], const float latent_rgb_bias[3], int patch_size) {
-    size_t buffer_head = 0;
-
-    uint32_t latent_width  = latents->ne[0];
-    uint32_t latent_height = latents->ne[1];
-    uint32_t dim           = latents->ne[ggml_n_dims(latents) - 1];
-    uint32_t frames        = 1;
-    if (ggml_n_dims(latents) == 4) {
-        frames = latents->ne[2];
-    }
-
-    uint32_t rgb_width  = latent_width * patch_size;
-    uint32_t rgb_height = latent_height * patch_size;
-
-    uint32_t unpatched_dim = dim / (patch_size * patch_size);
-
-    for (int k = 0; k < frames; k++) {
-        for (int rgb_x = 0; rgb_x < rgb_width; rgb_x++) {
-            for (int rgb_y = 0; rgb_y < rgb_height; rgb_y++) {
-                int latent_x = rgb_x / patch_size;
-                int latent_y = rgb_y / patch_size;
-
-                int channel_offset = 0;
-                if (patch_size > 1) {
-                    channel_offset = ((rgb_y % patch_size) * patch_size + (rgb_x % patch_size));
-                }
-
-                size_t latent_id = (latent_x * latents->nb[0] + latent_y * latents->nb[1] + k * latents->nb[2]);
-
-                // should be incremented by 1 for each pixel
-                size_t pixel_id = k * rgb_width * rgb_height + rgb_y * rgb_width + rgb_x;
-
-                float r = 0, g = 0, b = 0;
-                if (latent_rgb_proj != nullptr) {
-                    for (int d = 0; d < unpatched_dim; d++) {
-                        float value = *(float*)((char*)latents->data + latent_id + (d * patch_size * patch_size + channel_offset) * latents->nb[ggml_n_dims(latents) - 1]);
-                        r += value * latent_rgb_proj[d][0];
-                        g += value * latent_rgb_proj[d][1];
-                        b += value * latent_rgb_proj[d][2];
-                    }
-                } else {
-                    // interpret first 3 channels as RGB
-                    r = *(float*)((char*)latents->data + latent_id + 0 * latents->nb[ggml_n_dims(latents) - 1]);
-                    g = *(float*)((char*)latents->data + latent_id + 1 * latents->nb[ggml_n_dims(latents) - 1]);
-                    b = *(float*)((char*)latents->data + latent_id + 2 * latents->nb[ggml_n_dims(latents) - 1]);
-                }
-                if (latent_rgb_bias != nullptr) {
-                    // bias
-                    r += latent_rgb_bias[0];
-                    g += latent_rgb_bias[1];
-                    b += latent_rgb_bias[2];
-                }
-                // change range
-                r = r * .5f + .5f;
-                g = g * .5f + .5f;
-                b = b * .5f + .5f;
-
-                // clamp rgb values to [0,1] range
-                r = r >= 0 ? r <= 1 ? r : 1 : 0;
-                g = g >= 0 ? g <= 1 ? g : 1 : 0;
-                b = b >= 0 ? b <= 1 ? b : 1 : 0;
-
-                buffer[pixel_id * 3 + 0] = (uint8_t)(r * 255);
-                buffer[pixel_id * 3 + 1] = (uint8_t)(g * 255);
-                buffer[pixel_id * 3 + 2] = (uint8_t)(b * 255);
-            }
-        }
-    }
-}
+#include <algorithm>
+#include <cstddef>
+#include <cstdint>
+#include "ggml.h"
+#include "tensor.hpp"
+
+const float wan_21_latent_rgb_proj[16][3] = {
+    {0.015123f, -0.148418f, 0.479828f},
+    {0.003652f, -0.010680f, -0.037142f},
+    {0.212264f, 0.063033f, 0.016779f},
+    {0.232999f, 0.406476f, 0.220125f},
+    {-0.051864f, -0.082384f, -0.069396f},
+    {0.085005f, -0.161492f, 0.010689f},
+    {-0.245369f, -0.506846f, -0.117010f},
+    {-0.151145f, 0.017721f, 0.007207f},
+    {-0.293239f, -0.207936f, -0.421135f},
+    {-0.187721f, 0.050783f, 0.177649f},
+    {-0.013067f, 0.265964f, 0.166578f},
+    {0.028327f, 0.109329f, 0.108642f},
+    {-0.205343f, 0.043991f, 0.148914f},
+    {0.014307f, -0.048647f, -0.007219f},
+    {0.217150f, 0.053074f, 0.319923f},
+    {0.155357f, 0.083156f, 0.064780f}};
+float wan_21_latent_rgb_bias[3] = {-0.270270f, -0.234976f, -0.456853f};
+
+const float wan_22_latent_rgb_proj[48][3] = {
+    {0.017126f, -0.027230f, -0.019257f},
+    {-0.113739f, -0.028715f, -0.022885f},
+    {-0.000106f, 0.021494f, 0.004629f},
+    {-0.013273f, -0.107137f, -0.033638f},
+    {-0.000381f, 0.000279f, 0.025877f},
+    {-0.014216f, -0.003975f, 0.040528f},
+    {0.001638f, -0.000748f, 0.011022f},
+    {0.029238f, -0.006697f, 0.035933f},
+    {0.021641f, -0.015874f, 0.040531f},
+    {-0.101984f, -0.070160f, -0.028855f},
+    {0.033207f, -0.021068f, 0.002663f},
+    {-0.104711f, 0.121673f, 0.102981f},
+    {0.082647f, -0.004991f, 0.057237f},
+    {-0.027375f, 0.031581f, 0.006868f},
+    {-0.045434f, 0.029444f, 0.019287f},
+    {-0.046572f, -0.012537f, 0.006675f},
+    {0.074709f, 0.033690f, 0.025289f},
+    {-0.008251f, -0.002745f, -0.006999f},
+    {0.012685f, -0.061856f, -0.048658f},
+    {0.042304f, -0.007039f, 0.000295f},
+    {-0.007644f, -0.060843f, -0.033142f},
+    {0.159909f, 0.045628f, 0.367541f},
+    {0.095171f, 0.086438f, 0.010271f},
+    {0.006812f, 0.019643f, 0.029637f},
+    {0.003467f, -0.010705f, 0.014252f},
+    {-0.099681f, -0.066272f, -0.006243f},
+    {0.047357f, 0.037040f, 0.000185f},
+    {-0.041797f, -0.089225f, -0.032257f},
+    {0.008928f, 0.017028f, 0.018684f},
+    {-0.042255f, 0.016045f, 0.006849f},
+    {0.011268f, 0.036462f, 0.037387f},
+    {0.011553f, -0.016375f, -0.048589f},
+    {0.046266f, -0.027189f, 0.056979f},
+    {0.009640f, -0.017576f, 0.030324f},
+    {-0.045794f, -0.036083f, -0.010616f},
+    {0.022418f, 0.039783f, -0.032939f},
+    {-0.052714f, -0.015525f, 0.007438f},
+    {0.193004f, 0.223541f, 0.264175f},
+    {-0.059406f, -0.008188f, 0.022867f},
+    {-0.156742f, -0.263791f, -0.007385f},
+    {-0.015717f, 0.016570f, 0.033969f},
+    {0.037969f, 0.109835f, 0.200449f},
+    {-0.000782f, -0.009566f, -0.008058f},
+    {0.010709f, 0.052960f, -0.044195f},
+    {0.017271f, 0.045839f, 0.034569f},
+    {0.009424f, 0.013088f, -0.001714f},
+    {-0.024805f, -0.059378f, -0.033756f},
+    {-0.078293f, 0.029070f, 0.026129f}};
+float wan_22_latent_rgb_bias[3] = {0.013160f, -0.096492f, -0.071323f};
+
+const float flux_latent_rgb_proj[16][3] = {
+    {-0.041168f, 0.019917f, 0.097253f},
+    {0.028096f, 0.026730f, 0.129576f},
+    {0.065618f, -0.067950f, -0.014651f},
+    {-0.012998f, -0.014762f, 0.081251f},
+    {0.078567f, 0.059296f, -0.024687f},
+    {-0.015987f, -0.003697f, 0.005012f},
+    {0.033605f, 0.138999f, 0.068517f},
+    {-0.024450f, -0.063567f, -0.030101f},
+    {-0.040194f, -0.016710f, 0.127185f},
+    {0.112681f, 0.088764f, -0.041940f},
+    {-0.023498f, 0.093664f, 0.025543f},
+    {0.082899f, 0.048320f, 0.007491f},
+    {0.075712f, 0.074139f, 0.081965f},
+    {-0.143501f, 0.018263f, -0.136138f},
+    {-0.025767f, -0.082035f, -0.040023f},
+    {-0.111849f, -0.055589f, -0.032361f}};
+float flux_latent_rgb_bias[3] = {0.024600f, -0.006937f, -0.008089f};
+
+const float flux2_latent_rgb_proj[32][3] = {
+    {0.000736f, -0.008385f, -0.019710f},
+    {-0.001352f, -0.016392f, 0.020693f},
+    {-0.006376f, 0.002428f, 0.036736f},
+    {0.039384f, 0.074167f, 0.119789f},
+    {0.007464f, -0.005705f, -0.004734f},
+    {-0.004086f, 0.005287f, -0.000409f},
+    {-0.032835f, 0.050802f, -0.028120f},
+    {-0.003158f, -0.000835f, 0.000406f},
+    {-0.112840f, -0.084337f, -0.023083f},
+    {0.001462f, -0.006656f, 0.000549f},
+    {-0.009980f, -0.007480f, 0.009702f},
+    {0.032540f, 0.000214f, -0.061388f},
+    {0.011023f, 0.000694f, 0.007143f},
+    {-0.001468f, -0.006723f, -0.001678f},
+    {-0.005921f, -0.010320f, -0.003907f},
+    {-0.028434f, 0.027584f, 0.018457f},
+    {0.014349f, 0.011523f, 0.000441f},
+    {0.009874f, 0.003081f, 0.001507f},
+    {0.002218f, 0.005712f, 0.001563f},
+    {0.053010f, -0.019844f, 0.008683f},
+    {-0.002507f, 0.005384f, 0.000938f},
+    {-0.002177f, -0.011366f, 0.003559f},
+    {-0.000261f, 0.015121f, -0.003240f},
+    {-0.003944f, -0.002083f, 0.005043f},
+    {-0.009138f, 0.011336f, 0.003781f},
+    {0.011429f, 0.003985f, -0.003855f},
+    {0.010518f, -0.005586f, 0.010131f},
+    {0.007883f, 0.002912f, -0.001473f},
+    {-0.003318f, -0.003160f, 0.003684f},
+    {-0.034560f, -0.008740f, 0.012996f},
+    {0.000166f, 0.001079f, -0.012153f},
+    {0.017772f, 0.000937f, -0.011953f}};
+float flux2_latent_rgb_bias[3] = {-0.028738f, -0.098463f, -0.107619f};
+
+// This one was taken straight from
+// https://github.com/Stability-AI/sd3.5/blob/8565799a3b41eb0c7ba976d18375f0f753f56402/sd3_impls.py#L288-L303
+// (MiT Licence)
+const float sd3_latent_rgb_proj[16][3] = {
+    {-0.0645f, 0.0177f, 0.1052f},
+    {0.0028f, 0.0312f, 0.0650f},
+    {0.1848f, 0.0762f, 0.0360f},
+    {0.0944f, 0.0360f, 0.0889f},
+    {0.0897f, 0.0506f, -0.0364f},
+    {-0.0020f, 0.1203f, 0.0284f},
+    {0.0855f, 0.0118f, 0.0283f},
+    {-0.0539f, 0.0658f, 0.1047f},
+    {-0.0057f, 0.0116f, 0.0700f},
+    {-0.0412f, 0.0281f, -0.0039f},
+    {0.1106f, 0.1171f, 0.1220f},
+    {-0.0248f, 0.0682f, -0.0481f},
+    {0.0815f, 0.0846f, 0.1207f},
+    {-0.0120f, -0.0055f, -0.0867f},
+    {-0.0749f, -0.0634f, -0.0456f},
+    {-0.1418f, -0.1457f, -0.1259f},
+};
+float sd3_latent_rgb_bias[3] = {0, 0, 0};
+
+const float sdxl_latent_rgb_proj[4][3] = {
+    {0.258303f, 0.277640f, 0.329699f},
+    {-0.299701f, 0.105446f, 0.014194f},
+    {0.050522f, 0.186163f, -0.143257f},
+    {-0.211938f, -0.149892f, -0.080036f}};
+float sdxl_latent_rgb_bias[3] = {0.144381f, -0.033313f, 0.007061f};
+
+const float sd_latent_rgb_proj[4][3] = {
+    {0.337366f, 0.216344f, 0.257386f},
+    {0.165636f, 0.386828f, 0.046994f},
+    {-0.267803f, 0.237036f, 0.223517f},
+    {-0.178022f, -0.200862f, -0.678514f}};
+float sd_latent_rgb_bias[3] = {-0.017478f, -0.055834f, -0.105825f};
+
+void preview_latent_video(uint8_t* buffer, ggml_tensor* latents, const float (*latent_rgb_proj)[3], const float latent_rgb_bias[3], int patch_size) {
+    size_t buffer_head = 0;
+
+    uint32_t latent_width  = static_cast<uint32_t>(latents->ne[0]);
+    uint32_t latent_height = static_cast<uint32_t>(latents->ne[1]);
+    uint32_t dim           = static_cast<uint32_t>(latents->ne[ggml_n_dims(latents) - 1]);
+    uint32_t frames        = 1;
+    if (ggml_n_dims(latents) == 4) {
+        frames = static_cast<uint32_t>(latents->ne[2]);
+    }
+
+    uint32_t rgb_width  = latent_width * patch_size;
+    uint32_t rgb_height = latent_height * patch_size;
+
+    uint32_t unpatched_dim = dim / (patch_size * patch_size);
+
+    for (uint32_t k = 0; k < frames; k++) {
+        for (uint32_t rgb_x = 0; rgb_x < rgb_width; rgb_x++) {
+            for (uint32_t rgb_y = 0; rgb_y < rgb_height; rgb_y++) {
+                int latent_x = rgb_x / patch_size;
+                int latent_y = rgb_y / patch_size;
+
+                int channel_offset = 0;
+                if (patch_size > 1) {
+                    channel_offset = ((rgb_y % patch_size) * patch_size + (rgb_x % patch_size));
+                }
+
+                size_t latent_id = (latent_x * latents->nb[0] + latent_y * latents->nb[1] + k * latents->nb[2]);
+
+                // should be incremented by 1 for each pixel
+                size_t pixel_id = k * rgb_width * rgb_height + rgb_y * rgb_width + rgb_x;
+
+                float r = 0, g = 0, b = 0;
+                if (latent_rgb_proj != nullptr) {
+                    for (uint32_t d = 0; d < unpatched_dim; d++) {
+                        float value = *(float*)((char*)latents->data + latent_id + (d * patch_size * patch_size + channel_offset) * latents->nb[ggml_n_dims(latents) - 1]);
+                        r += value * latent_rgb_proj[d][0];
+                        g += value * latent_rgb_proj[d][1];
+                        b += value * latent_rgb_proj[d][2];
+                    }
+                } else {
+                    // interpret first 3 channels as RGB
+                    r = *(float*)((char*)latents->data + latent_id + 0 * latents->nb[ggml_n_dims(latents) - 1]);
+                    g = *(float*)((char*)latents->data + latent_id + 1 * latents->nb[ggml_n_dims(latents) - 1]);
+                    b = *(float*)((char*)latents->data + latent_id + 2 * latents->nb[ggml_n_dims(latents) - 1]);
+                }
+                if (latent_rgb_bias != nullptr) {
+                    // bias
+                    r += latent_rgb_bias[0];
+                    g += latent_rgb_bias[1];
+                    b += latent_rgb_bias[2];
+                }
+                // change range
+                r = r * .5f + .5f;
+                g = g * .5f + .5f;
+                b = b * .5f + .5f;
+
+                // clamp rgb values to [0,1] range
+                r = r >= 0 ? r <= 1 ? r : 1 : 0;
+                g = g >= 0 ? g <= 1 ? g : 1 : 0;
+                b = b >= 0 ? b <= 1 ? b : 1 : 0;
+
+                buffer[pixel_id * 3 + 0] = (uint8_t)(r * 255);
+                buffer[pixel_id * 3 + 1] = (uint8_t)(g * 255);
+                buffer[pixel_id * 3 + 2] = (uint8_t)(b * 255);
+            }
+        }
+    }
+}
+
+static inline bool preview_latent_tensor_is_video(const sd::Tensor<float>& latents) {
+    return latents.dim() == 5;
+}
+
+void preview_latent_video(uint8_t* buffer, const sd::Tensor<float>& latents, const float (*latent_rgb_proj)[3], const float latent_rgb_bias[3], int patch_size) {
+    uint32_t latent_width  = static_cast<uint32_t>(latents.shape()[0]);
+    uint32_t latent_height = static_cast<uint32_t>(latents.shape()[1]);
+    bool is_video          = preview_latent_tensor_is_video(latents);
+    uint32_t frames        = is_video ? static_cast<uint32_t>(latents.shape()[2]) : 1;
+    uint32_t dim           = is_video ? static_cast<uint32_t>(latents.shape()[3]) : static_cast<uint32_t>(latents.shape()[2]);
+
+    uint32_t rgb_width     = latent_width * patch_size;
+    uint32_t rgb_height    = latent_height * patch_size;
+    uint32_t unpatched_dim = dim / (patch_size * patch_size);
+
+    for (uint32_t k = 0; k < frames; k++) {
+        for (uint32_t rgb_x = 0; rgb_x < rgb_width; rgb_x++) {
+            for (uint32_t rgb_y = 0; rgb_y < rgb_height; rgb_y++) {
+                uint32_t latent_x = rgb_x / patch_size;
+                uint32_t latent_y = rgb_y / patch_size;
+
+                uint32_t channel_offset = 0;
+                if (patch_size > 1) {
+                    channel_offset = ((rgb_y % patch_size) * patch_size + (rgb_x % patch_size));
+                }
+
+                size_t pixel_id   = k * rgb_width * rgb_height + rgb_y * rgb_width + rgb_x;
+                auto latent_value = [&](uint32_t latent_channel) -> float {
+                    return is_video
+                               ? latents.values()[latent_x + latent_width * (latent_y + latent_height * (k + frames * latent_channel))]
+                               : latents.values()[latent_x + latent_width * (latent_y + latent_height * latent_channel)];
+                };
+
+                float r = 0.f, g = 0.f, b = 0.f;
+                if (latent_rgb_proj != nullptr) {
+                    for (uint32_t d = 0; d < unpatched_dim; d++) {
+                        uint32_t latent_channel = d * patch_size * patch_size + channel_offset;
+                        float value             = latent_value(latent_channel);
+                        r += value * latent_rgb_proj[d][0];
+                        g += value * latent_rgb_proj[d][1];
+                        b += value * latent_rgb_proj[d][2];
+                    }
+                } else {
+                    r = latent_value(0);
+                    g = latent_value(1);
+                    b = latent_value(2);
+                }
+                if (latent_rgb_bias != nullptr) {
+                    r += latent_rgb_bias[0];
+                    g += latent_rgb_bias[1];
+                    b += latent_rgb_bias[2];
+                }
+                r = std::min(1.0f, std::max(0.0f, r * .5f + .5f));
+                g = std::min(1.0f, std::max(0.0f, g * .5f + .5f));
+                b = std::min(1.0f, std::max(0.0f, b * .5f + .5f));
+
+                buffer[pixel_id * 3 + 0] = (uint8_t)(r * 255);
+                buffer[pixel_id * 3 + 1] = (uint8_t)(g * 255);
+                buffer[pixel_id * 3 + 2] = (uint8_t)(b * 255);
+            }
+        }
+    }
+}
@@ -19,6 +19,7 @@
 #include "json.hpp"
 #include "rope.hpp"
 #include "tokenize_util.h"
+#include "vocab/vocab.h"

 namespace LLM {
    constexpr int LLM_GRAPH_SIZE = 10240;
@@ -193,16 +194,17 @@ namespace LLM {
                        bool padding      = false) {
            if (add_bos_token) {
                tokens.insert(tokens.begin(), BOS_TOKEN_ID);
+                weights.insert(weights.begin(), 1.f);
            }
            if (max_length > 0 && padding) {
-                size_t n = std::ceil(tokens.size() * 1.0 / max_length);
+                size_t n = static_cast<size_t>(std::ceil(tokens.size() * 1.f / max_length));
                if (n == 0) {
                    n = 1;
                }
                size_t length = max_length * n;
                LOG_DEBUG("token length: %llu", length);
                tokens.insert(tokens.end(), length - tokens.size(), PAD_TOKEN_ID);
-                weights.insert(weights.end(), length - weights.size(), 1.0);
+                weights.insert(weights.end(), length - weights.size(), 1.f);
            }
        }

@@ -365,7 +367,7 @@ namespace LLM {
            if (merges_utf8_str.size() > 0) {
                load_from_merges(merges_utf8_str);
            } else {
-                load_from_merges(ModelLoader::load_qwen2_merges());
+                load_from_merges(load_qwen2_merges());
            }
        }
    };
@@ -377,7 +379,7 @@ namespace LLM {

            try {
                vocab = nlohmann::json::parse(vocab_utf8_str);
-            } catch (const nlohmann::json::parse_error& e) {
+            } catch (const nlohmann::json::parse_error&) {
                GGML_ABORT("invalid vocab json str");
            }
            for (const auto& [key, value] : vocab.items()) {
@@ -386,7 +388,7 @@ namespace LLM {
                encoder[token]       = i;
                decoder[i]           = token;
            }
-            encoder_len = vocab.size();
+            encoder_len = static_cast<int>(vocab.size());
            LOG_DEBUG("vocab size: %d", encoder_len);

            auto byte_unicode_pairs = bytes_to_unicode();
@@ -466,7 +468,7 @@ namespace LLM {
            if (merges_utf8_str.size() > 0 && vocab_utf8_str.size() > 0) {
                load_from_merges(merges_utf8_str, vocab_utf8_str);
            } else {
-                load_from_merges(ModelLoader::load_mistral_merges(), ModelLoader::load_mistral_vocab_json());
+                load_from_merges(load_mistral_merges(), load_mistral_vocab_json());
            }
        }
    };
@@ -485,16 +487,16 @@ namespace LLM {
    };

    struct LLMVisionParams {
-        int64_t num_layers                  = 32;
+        int num_layers                      = 32;
        int64_t hidden_size                 = 1280;
        int64_t intermediate_size           = 3420;
-        int64_t num_heads                   = 16;
+        int num_heads                       = 16;
        int64_t in_channels                 = 3;
        int64_t out_hidden_size             = 3584;
-        int64_t temporal_patch_size         = 2;
-        int64_t patch_size                  = 14;
-        int64_t spatial_merge_size          = 2;
-        int64_t window_size                 = 112;
+        int temporal_patch_size             = 2;
+        int patch_size                      = 14;
+        int spatial_merge_size              = 2;
+        int window_size                     = 112;
        std::set<int> fullatt_block_indexes = {7, 15, 23, 31};
    };

@@ -503,9 +505,9 @@ namespace LLM {
        int64_t num_layers        = 28;
        int64_t hidden_size       = 3584;
        int64_t intermediate_size = 18944;
-        int64_t num_heads         = 28;
-        int64_t num_kv_heads      = 4;
-        int64_t head_dim          = 128;
+        int num_heads             = 28;
+        int num_kv_heads          = 4;
+        int head_dim              = 128;
        bool qkv_bias             = true;
        bool qk_norm              = false;
        int64_t vocab_size        = 152064;
@@ -521,7 +523,7 @@ namespace LLM {
            blocks["down_proj"] = std::shared_ptr<GGMLBlock>(new Linear(intermediate_size, hidden_size, bias));
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
            // x: [N, n_token, hidden_size]
            auto gate_proj = std::dynamic_pointer_cast<Linear>(blocks["gate_proj"]);
            auto up_proj   = std::dynamic_pointer_cast<Linear>(blocks["up_proj"]);
@@ -581,7 +583,7 @@ namespace LLM {
            }
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
            // x: [N*grid_t*grid_h*grid_w, in_channels, temporal_patch_size*patch_size*patch_size]
            // return: [N*grid_t*grid_h*grid_w, embed_dim]
            x = ggml_reshape_4d(ctx->ggml_ctx,
@@ -630,7 +632,7 @@ namespace LLM {
            blocks["mlp.2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, dim));
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
            auto ln_q  = std::dynamic_pointer_cast<RMSNorm>(blocks["ln_q"]);
            auto mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["mlp.0"]);
            auto mlp_2 = std::dynamic_pointer_cast<Linear>(blocks["mlp.2"]);
@@ -638,7 +640,7 @@ namespace LLM {
            x = ln_q->forward(ctx, x);
            x = ggml_reshape_2d(ctx->ggml_ctx, x, hidden_size, ggml_nelements(x) / hidden_size);
            x = mlp_0->forward(ctx, x);
-            x = ggml_gelu(ctx->ggml_ctx, x);
+            x = ggml_ext_gelu(ctx->ggml_ctx, x);
            x = mlp_2->forward(ctx, x);
            return x;
        }
@@ -647,15 +649,15 @@ namespace LLM {
    struct VisionAttention : public GGMLBlock {
    protected:
        bool llama_cpp_style;
-        int64_t head_dim;
-        int64_t num_heads;
+        int head_dim;
+        int num_heads;

    public:
        VisionAttention(bool llama_cpp_style,
                        int64_t hidden_size,
-                        int64_t num_heads)
+                        int num_heads)
            : llama_cpp_style(llama_cpp_style), num_heads(num_heads) {
-            head_dim = hidden_size / num_heads;
+            head_dim = static_cast<int>(hidden_size / num_heads);
            GGML_ASSERT(num_heads * head_dim == hidden_size);
            if (llama_cpp_style) {
                blocks["q_proj"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, hidden_size));
@@ -667,10 +669,10 @@ namespace LLM {
            blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, hidden_size));
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* pe,
-                                    struct ggml_tensor* mask = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* pe,
+                             ggml_tensor* mask = nullptr) {
            // x: [N, n_token, hidden_size]
            int64_t n_token = x->ne[1];
            int64_t N       = x->ne[2];
@@ -709,7 +711,7 @@ namespace LLM {
        VisionBlock(bool llama_cpp_style,
                    int64_t hidden_size,
                    int64_t intermediate_size,
-                    int64_t num_heads,
+                    int num_heads,
                    float eps = 1e-6f) {
            blocks["attn"]  = std::shared_ptr<GGMLBlock>(new VisionAttention(llama_cpp_style, hidden_size, num_heads));
            blocks["mlp"]   = std::shared_ptr<GGMLBlock>(new MLP(hidden_size, intermediate_size, true));
@@ -717,10 +719,10 @@ namespace LLM {
            blocks["norm2"] = std::shared_ptr<GGMLBlock>(new RMSNorm(hidden_size, eps));
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* pe,
-                                    struct ggml_tensor* mask = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* pe,
+                             ggml_tensor* mask = nullptr) {
            // x: [N, n_token, hidden_size]
            auto attn  = std::dynamic_pointer_cast<VisionAttention>(blocks["attn"]);
            auto mlp   = std::dynamic_pointer_cast<MLP>(blocks["mlp"]);
@@ -743,22 +745,22 @@ namespace LLM {

    struct VisionModel : public GGMLBlock {
    protected:
-        int64_t num_layers;
-        int64_t spatial_merge_size;
+        int num_layers;
+        int spatial_merge_size;
        std::set<int> fullatt_block_indexes;

    public:
        VisionModel(bool llama_cpp_style,
-                    int64_t num_layers,
+                    int num_layers,
                    int64_t in_channels,
                    int64_t hidden_size,
                    int64_t out_hidden_size,
                    int64_t intermediate_size,
-                    int64_t num_heads,
-                    int64_t spatial_merge_size,
-                    int64_t patch_size,
-                    int64_t temporal_patch_size,
-                    int64_t window_size,
+                    int num_heads,
+                    int spatial_merge_size,
+                    int patch_size,
+                    int temporal_patch_size,
+                    int window_size,
                    std::set<int> fullatt_block_indexes = {7, 15, 23, 31},
                    float eps                           = 1e-6f)
            : num_layers(num_layers), fullatt_block_indexes(std::move(fullatt_block_indexes)), spatial_merge_size(spatial_merge_size) {
@@ -777,12 +779,12 @@ namespace LLM {
            blocks["merger"] = std::shared_ptr<GGMLBlock>(new PatchMerger(out_hidden_size, hidden_size, spatial_merge_size));
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* pixel_values,
-                                    struct ggml_tensor* pe,
-                                    struct ggml_tensor* window_index,
-                                    struct ggml_tensor* window_inverse_index,
-                                    struct ggml_tensor* window_mask) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* pixel_values,
+                             ggml_tensor* pe,
+                             ggml_tensor* window_index,
+                             ggml_tensor* window_inverse_index,
+                             ggml_tensor* window_mask) {
            // pixel_values: [grid_t*(H/mh/ph)*(W/mw/pw)*mh*mw, C*pt*ph*pw]
            // window_index: [grid_t*(H/mh/ph)*(W/mw/pw)]
            // window_inverse_index: [grid_t*(H/mh/ph)*(W/mw/pw)]
@@ -817,7 +819,7 @@ namespace LLM {
    struct Attention : public GGMLBlock {
    protected:
        LLMArch arch;
-        int64_t head_dim;
+        int head_dim;
        int64_t num_heads;
        int64_t num_kv_heads;
        bool qk_norm;
@@ -835,9 +837,10 @@ namespace LLM {
            }
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* input_pos) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* input_pos,
+                             ggml_tensor* attention_mask = nullptr) {
            // x: [N, n_token, hidden_size]
            int64_t n_token = x->ne[1];
            int64_t N       = x->ne[2];
@@ -880,7 +883,7 @@ namespace LLM {
            k = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, k, 0, 2, 1, 3));  // [N, num_kv_heads, n_token, head_dim]
            k = ggml_reshape_3d(ctx->ggml_ctx, k, k->ne[0], k->ne[1], k->ne[2] * k->ne[3]);      // [N*num_kv_heads, n_token, head_dim]

-            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, nullptr, true, true, false);  // [N, n_token, hidden_size]
+            x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, attention_mask, true, false);  // [N, n_token, hidden_size]

            x = out_proj->forward(ctx, x);  // [N, n_token, hidden_size]
            return x;
@@ -896,9 +899,10 @@ namespace LLM {
            blocks["post_attention_layernorm"] = std::make_shared<RMSNorm>(params.hidden_size, params.rms_norm_eps);
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* input_pos) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* input_pos,
+                             ggml_tensor* attention_mask = nullptr) {
            // x: [N, n_token, hidden_size]
            auto self_attn                = std::dynamic_pointer_cast<Attention>(blocks["self_attn"]);
            auto mlp                      = std::dynamic_pointer_cast<MLP>(blocks["mlp"]);
@@ -907,7 +911,7 @@ namespace LLM {

            auto residual = x;
            x             = input_layernorm->forward(ctx, x);
-            x             = self_attn->forward(ctx, x, input_pos);
+            x             = self_attn->forward(ctx, x, input_pos, attention_mask);
            x             = ggml_add_inplace(ctx->ggml_ctx, x, residual);

            residual = x;
@@ -933,11 +937,12 @@ namespace LLM {
            blocks["norm"] = std::shared_ptr<GGMLBlock>(new RMSNorm(params.hidden_size, params.rms_norm_eps));
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* input_ids,
-                                    struct ggml_tensor* input_pos,
-                                    std::vector<std::pair<int, ggml_tensor*>> image_embeds,
-                                    std::set<int> out_layers) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* input_ids,
+                             ggml_tensor* input_pos,
+                             ggml_tensor* attention_mask,
+                             std::vector<std::pair<int, ggml_tensor*>> image_embeds,
+                             std::set<int> out_layers) {
            // input_ids: [N, n_token]
            // return: [N, n_token, hidden_size]

@@ -990,7 +995,7 @@ namespace LLM {
            for (int i = 0; i < num_layers; i++) {
                auto block = std::dynamic_pointer_cast<TransformerBlock>(blocks["layers." + std::to_string(i)]);

-                x = block->forward(ctx, x, input_pos);
+                x = block->forward(ctx, x, input_pos, attention_mask);
                if (out_layers.find(i + 1) != out_layers.end()) {
                    intermediate_outputs.push_back(x);
                }
@@ -1033,24 +1038,25 @@ namespace LLM {
            }
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* input_ids,
-                                    struct ggml_tensor* input_pos,
-                                    std::vector<std::pair<int, ggml_tensor*>> image_embeds,
-                                    std::set<int> out_layers) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* input_ids,
+                             ggml_tensor* input_pos,
+                             ggml_tensor* attention_mask,
+                             std::vector<std::pair<int, ggml_tensor*>> image_embeds,
+                             std::set<int> out_layers) {
            // input_ids: [N, n_token]
            auto model = std::dynamic_pointer_cast<TextModel>(blocks["model"]);

-            auto x = model->forward(ctx, input_ids, input_pos, image_embeds, out_layers);
+            auto x = model->forward(ctx, input_ids, input_pos, attention_mask, image_embeds, out_layers);
            return x;
        }

-        struct ggml_tensor* vision_forward(GGMLRunnerContext* ctx,
-                                           struct ggml_tensor* pixel_values,
-                                           struct ggml_tensor* pe,
-                                           struct ggml_tensor* window_index,
-                                           struct ggml_tensor* window_inverse_index,
-                                           struct ggml_tensor* window_mask) {
+        ggml_tensor* vision_forward(GGMLRunnerContext* ctx,
+                                    ggml_tensor* pixel_values,
+                                    ggml_tensor* pe,
+                                    ggml_tensor* window_index,
+                                    ggml_tensor* window_inverse_index,
+                                    ggml_tensor* window_mask) {
            GGML_ASSERT(enable_vision);
            auto vision_model = std::dynamic_pointer_cast<VisionModel>(blocks["visual"]);
            return vision_model->forward(ctx, pixel_values, pe, window_index, window_inverse_index, window_mask);
@@ -1063,6 +1069,7 @@ namespace LLM {
        LLM model;

        std::vector<int> input_pos_vec;
+        std::vector<float> attention_mask_vec;
        std::vector<float> window_mask_vec;
        std::vector<int> window_index_vec;
        std::vector<int> window_inverse_index_vec;
@@ -1150,38 +1157,41 @@ namespace LLM {
            return llm_arch_to_str[static_cast<int>(params.arch)];
        }

-        void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
            model.get_param_tensors(tensors, prefix);
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* input_ids,
-                                    struct ggml_tensor* input_pos,
-                                    std::vector<std::pair<int, ggml_tensor*>> image_embeds,
-                                    std::set<int> out_layers) {
-            auto hidden_states = model.forward(ctx, input_ids, input_pos, image_embeds, out_layers);  // [N, n_token, hidden_size]
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* input_ids,
+                             ggml_tensor* input_pos,
+                             ggml_tensor* attention_mask,
+                             std::vector<std::pair<int, ggml_tensor*>> image_embeds,
+                             std::set<int> out_layers) {
+            auto hidden_states = model.forward(ctx, input_ids, input_pos, attention_mask, image_embeds, out_layers);  // [N, n_token, hidden_size]
            return hidden_states;
        }

-        struct ggml_tensor* vision_forward(GGMLRunnerContext* ctx,
-                                           struct ggml_tensor* pixel_values,
-                                           struct ggml_tensor* input_pos,
-                                           struct ggml_tensor* window_index,
-                                           struct ggml_tensor* window_inverse_index,
-                                           struct ggml_tensor* window_mask) {
+        ggml_tensor* vision_forward(GGMLRunnerContext* ctx,
+                                    ggml_tensor* pixel_values,
+                                    ggml_tensor* input_pos,
+                                    ggml_tensor* window_index,
+                                    ggml_tensor* window_inverse_index,
+                                    ggml_tensor* window_mask) {
            auto hidden_states = model.vision_forward(ctx, pixel_values, input_pos, window_index, window_inverse_index, window_mask);
            return hidden_states;
        }

-        struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
-                                        std::vector<std::pair<int, ggml_tensor*>> image_embeds,
-                                        std::set<int> out_layers) {
-            struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
-
-            input_ids = to_backend(input_ids);
-
-            for (auto& image_embed : image_embeds) {
-                image_embed.second = to_backend(image_embed.second);
+        ggml_cgraph* build_graph(const sd::Tensor<int32_t>& input_ids_tensor,
+                                 const sd::Tensor<float>& attention_mask_tensor,
+                                 const std::vector<std::pair<int, sd::Tensor<float>>>& image_embeds_tensor,
+                                 std::set<int> out_layers) {
+            ggml_cgraph* gf        = ggml_new_graph(compute_ctx);
+            ggml_tensor* input_ids = make_input(input_ids_tensor);
+            std::vector<std::pair<int, ggml_tensor*>> image_embeds;
+            image_embeds.reserve(image_embeds_tensor.size());
+            for (const auto& [idx, embed_tensor] : image_embeds_tensor) {
+                ggml_tensor* embed = make_input(embed_tensor);
+                image_embeds.emplace_back(idx, embed);
            }

            int64_t n_tokens = input_ids->ne[0];
@@ -1205,37 +1215,54 @@ namespace LLM {
                                                input_pos_vec.size());
            set_backend_tensor_data(input_pos, input_pos_vec.data());

+            ggml_tensor* attention_mask = nullptr;
+            if (!attention_mask_tensor.empty()) {
+                attention_mask = make_input(attention_mask_tensor);
+            } else {
+                attention_mask_vec.resize(n_tokens * n_tokens);
+                for (int i0 = 0; i0 < n_tokens; i0++) {
+                    for (int i1 = 0; i1 < n_tokens; i1++) {
+                        float value = 0.f;
+                        if (i0 > i1) {
+                            value = -INFINITY;
+                        }
+                        attention_mask_vec[i1 * n_tokens + i0] = value;
+                    }
+                }
+                attention_mask = ggml_new_tensor_2d(compute_ctx, GGML_TYPE_F32, n_tokens, n_tokens);
+                set_backend_tensor_data(attention_mask, attention_mask_vec.data());
+            }
+
            auto runner_ctx = get_context();

-            struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, input_pos, image_embeds, out_layers);
+            ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, input_pos, attention_mask, image_embeds, out_layers);

            ggml_build_forward_expand(gf, hidden_states);

            return gf;
        }

-        bool compute(const int n_threads,
-                     struct ggml_tensor* input_ids,
-                     std::vector<std::pair<int, ggml_tensor*>> image_embeds,
-                     std::set<int> out_layers,
-                     ggml_tensor** output,
-                     ggml_context* output_ctx = nullptr) {
-            auto get_graph = [&]() -> struct ggml_cgraph* {
-                return build_graph(input_ids, image_embeds, out_layers);
+        sd::Tensor<float> compute(const int n_threads,
+                                  const sd::Tensor<int32_t>& input_ids,
+                                  const sd::Tensor<float>& attention_mask,
+                                  const std::vector<std::pair<int, sd::Tensor<float>>>& image_embeds,
+                                  std::set<int> out_layers) {
+            auto get_graph = [&]() -> ggml_cgraph* {
+                return build_graph(input_ids, attention_mask, image_embeds, out_layers);
            };
-            return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
+            return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, true));
        }

        int64_t get_num_image_tokens(int64_t t, int64_t h, int64_t w) {
-            int grid_t     = 1;
-            int grid_h     = h / params.vision.patch_size;
-            int grid_w     = w / params.vision.patch_size;
-            int llm_grid_h = grid_h / params.vision.spatial_merge_size;
-            int llm_grid_w = grid_w / params.vision.spatial_merge_size;
+            int64_t grid_t     = 1;
+            int64_t grid_h     = h / params.vision.patch_size;
+            int64_t grid_w     = w / params.vision.patch_size;
+            int64_t llm_grid_h = grid_h / params.vision.spatial_merge_size;
+            int64_t llm_grid_w = grid_w / params.vision.spatial_merge_size;
            return grid_t * grid_h * grid_w;
        }

-        struct ggml_tensor* process_image(struct ggml_context* ctx, struct ggml_tensor* image) {
+        ggml_tensor* process_image(ggml_context* ctx, ggml_tensor* image) {
            // image: [C, H, W]
            // return: [grid_t*(H/mh/ph)*(W/mw/pw)*mh*mw, C*pt*ph*pw], grid_t == 1
            int64_t C  = image->ne[2];
@@ -1262,21 +1289,20 @@ namespace LLM {
            return image;
        }

-        struct ggml_cgraph* build_encode_image_graph(struct ggml_tensor* image) {
-            struct ggml_cgraph* gf = new_graph_custom(LLM_GRAPH_SIZE);
+        ggml_cgraph* build_encode_image_graph(const sd::Tensor<float>& image_tensor) {
+            ggml_cgraph* gf    = new_graph_custom(LLM_GRAPH_SIZE);
+            ggml_tensor* image = make_input(image_tensor);

            GGML_ASSERT(image->ne[1] % (params.vision.patch_size * params.vision.spatial_merge_size) == 0);
            GGML_ASSERT(image->ne[0] % (params.vision.patch_size * params.vision.spatial_merge_size) == 0);

            int grid_t                 = 1;
-            int grid_h                 = image->ne[1] / params.vision.patch_size;
-            int grid_w                 = image->ne[0] / params.vision.patch_size;
+            int grid_h                 = static_cast<int>(image->ne[1]) / params.vision.patch_size;
+            int grid_w                 = static_cast<int>(image->ne[0]) / params.vision.patch_size;
            int llm_grid_h             = grid_h / params.vision.spatial_merge_size;
            int llm_grid_w             = grid_w / params.vision.spatial_merge_size;
            int vit_merger_window_size = params.vision.window_size / params.vision.patch_size / params.vision.spatial_merge_size;

-            image = to_backend(image);
-
            auto pixel_values = process_image(compute_ctx, image);

            // window index
@@ -1358,14 +1384,14 @@ namespace LLM {
            set_backend_tensor_data(window_mask, window_mask_vec.data());

            // pe
-            int head_dim = params.vision.hidden_size / params.vision.num_heads;
+            int head_dim = static_cast<int>(params.vision.hidden_size / params.vision.num_heads);
            pe_vec       = Rope::gen_qwen2vl_pe(grid_h,
                                                grid_w,
                                                params.vision.spatial_merge_size,
                                                window_inverse_index_vec,
-                                                10000.f,
+                                                10000,
                                                {head_dim / 2, head_dim / 2});
-            int pos_len  = pe_vec.size() / head_dim / 2;
+            int pos_len  = static_cast<int>(pe_vec.size() / head_dim / 2);
            // LOG_DEBUG("pos_len %d", pos_len);
            auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, head_dim / 2, pos_len);
            // pe->data = pe_vec.data();
@@ -1373,26 +1399,24 @@ namespace LLM {
            // pe->data = nullptr;
            set_backend_tensor_data(pe, pe_vec.data());

-            auto runnter_ctx                  = get_context();
-            struct ggml_tensor* hidden_states = vision_forward(&runnter_ctx,
-                                                               pixel_values,
-                                                               pe,
-                                                               window_index,
-                                                               window_inverse_index,
-                                                               window_mask);
+            auto runnter_ctx           = get_context();
+            ggml_tensor* hidden_states = vision_forward(&runnter_ctx,
+                                                        pixel_values,
+                                                        pe,
+                                                        window_index,
+                                                        window_inverse_index,
+                                                        window_mask);
            ggml_build_forward_expand(gf, hidden_states);

            return gf;
        }

-        void encode_image(const int n_threads,
-                          struct ggml_tensor* image,
-                          ggml_tensor** output,
-                          ggml_context* output_ctx = nullptr) {
-            auto get_graph = [&]() -> struct ggml_cgraph* {
+        sd::Tensor<float> encode_image(const int n_threads,
+                                       const sd::Tensor<float>& image) {
+            auto get_graph = [&]() -> ggml_cgraph* {
                return build_encode_image_graph(image);
            };
-            GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+            return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, false));
        }
    };

@@ -1414,7 +1438,7 @@ namespace LLM {
            }
        }

-        void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
            model.get_param_tensors(tensors, prefix);
        }

@@ -1466,44 +1490,46 @@ namespace LLM {
        }

        void test() {
-            struct ggml_init_params params;
+            ggml_init_params params;
            params.mem_size   = static_cast<size_t>(1024 * 1024) * 1024;  // 1GB
            params.mem_buffer = nullptr;
            params.no_alloc   = false;

-            struct ggml_context* work_ctx = ggml_init(params);
-            GGML_ASSERT(work_ctx != nullptr);
+            ggml_context* ctx = ggml_init(params);
+            GGML_ASSERT(ctx != nullptr);
            bool test_mistral          = false;
            bool test_qwen3            = true;
            bool test_vit              = false;
            bool test_decoder_with_vit = false;

            if (test_decoder_with_vit) {
-                ggml_tensor* image_embed = nullptr;
+                sd::Tensor<float> image_embed;
                {
-                    auto image = load_tensor_from_file(work_ctx, "qwen2vl_normalized.bin");
-                    print_ggml_tensor(image, false, "image");
-                    struct ggml_tensor* out = nullptr;
+                    auto image = sd::load_tensor_from_file_as_tensor<float>("qwen2vl_normalized.bin");
+                    print_sd_tensor(image, false, "image");
+                    sd::Tensor<float> out;

-                    int t0 = ggml_time_ms();
-                    model.encode_image(8, image, &out, work_ctx);
-                    int t1 = ggml_time_ms();
+                    int64_t t0   = ggml_time_ms();
+                    auto out_opt = model.encode_image(8, image);
+                    int64_t t1   = ggml_time_ms();

-                    print_ggml_tensor(out, false, "image_embed");
+                    GGML_ASSERT(!out_opt.empty());
+                    out = std::move(out_opt);
+                    print_sd_tensor(out, false, "image_embed");
                    image_embed = out;
-                    LOG_DEBUG("llm encode_image test done in %dms", t1 - t0);
+                    LOG_DEBUG("llm encode_image test done in %lldms", t1 - t0);
                }

                std::string placeholder  = "<|image_pad|>";
                std::string img_prompt   = "Picture 1: <|vision_start|>";  // [24669, 220, 16, 25, 220, 151652]
-                int64_t num_image_tokens = image_embed->ne[1];
+                int64_t num_image_tokens = image_embed.shape()[1];
                img_prompt.reserve(num_image_tokens * placeholder.size());
                for (int i = 0; i < num_image_tokens; i++) {
                    img_prompt += placeholder;
                }
                img_prompt += "<|vision_end|>";

-                std::vector<std::pair<int, ggml_tensor*>> image_embeds;
+                std::vector<std::pair<int, sd::Tensor<float>>> image_embeds;
                image_embeds.emplace_back(64, image_embed);

                std::pair<int, int> prompt_attn_range;
@@ -1521,32 +1547,36 @@ namespace LLM {
                    printf("%d ", token);
                }
                printf("\n");
-                auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
-                struct ggml_tensor* out = nullptr;
+                auto input_ids = sd::Tensor<int32_t>::from_vector(tokens);
+                sd::Tensor<float> out;

-                int t0 = ggml_time_ms();
-                model.compute(8, input_ids, image_embeds, {}, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t0   = ggml_time_ms();
+                auto out_opt = model.compute(8, input_ids, sd::Tensor<float>(), image_embeds, {});
+                int64_t t1   = ggml_time_ms();

-                print_ggml_tensor(out);
-                LOG_DEBUG("llm test done in %dms", t1 - t0);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
+                LOG_DEBUG("llm test done in %lldms", t1 - t0);
            } else if (test_vit) {
-                // auto image = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 280, 280, 3);
+                // auto image = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 280, 280, 3);
                // ggml_set_f32(image, 0.f);
-                auto image = load_tensor_from_file(work_ctx, "qwen2vl_normalized.bin");
-                print_ggml_tensor(image, false, "image");
-                struct ggml_tensor* out = nullptr;
+                auto image = sd::load_tensor_from_file_as_tensor<float>("qwen2vl_normalized.bin");
+                print_sd_tensor(image, false, "image");
+                sd::Tensor<float> out;

-                int t0 = ggml_time_ms();
-                model.encode_image(8, image, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t0   = ggml_time_ms();
+                auto out_opt = model.encode_image(8, image);
+                int64_t t1   = ggml_time_ms();

-                print_ggml_tensor(out, false, "out");
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out, false, "out");

-                // auto ref_out = load_tensor_from_file(work_ctx, "qwen2vl.bin");
+                // auto ref_out = load_tensor_from_file(ctx, "qwen2vl.bin");
                // ggml_ext_tensor_diff(ref_out, out, 0.01f);

-                LOG_DEBUG("llm test done in %dms", t1 - t0);
+                LOG_DEBUG("llm test done in %lldms", t1 - t0);
            } else if (test_mistral) {
                std::pair<int, int> prompt_attn_range;
                std::string text        = "[SYSTEM_PROMPT]You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object\nattribution and actions without speculation.[/SYSTEM_PROMPT][INST]";
@@ -1561,15 +1591,17 @@ namespace LLM {
                    printf("%d ", token);
                }
                printf("\n");
-                auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
-                struct ggml_tensor* out = nullptr;
+                auto input_ids = sd::Tensor<int32_t>::from_vector(tokens);
+                sd::Tensor<float> out;

-                int t0 = ggml_time_ms();
-                model.compute(8, input_ids, {}, {10, 20, 30}, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t0   = ggml_time_ms();
+                auto out_opt = model.compute(8, input_ids, sd::Tensor<float>(), {}, {10, 20, 30});
+                int64_t t1   = ggml_time_ms();

-                print_ggml_tensor(out);
-                LOG_DEBUG("llm test done in %dms", t1 - t0);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
+                LOG_DEBUG("llm test done in %lldms", t1 - t0);
            } else if (test_qwen3) {
                std::pair<int, int> prompt_attn_range;
                std::string text        = "<|im_start|>user\n";
@@ -1584,15 +1616,17 @@ namespace LLM {
                    printf("%d ", token);
                }
                printf("\n");
-                auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
-                struct ggml_tensor* out = nullptr;
+                auto input_ids = sd::Tensor<int32_t>::from_vector(tokens);
+                sd::Tensor<float> out;

-                int t0 = ggml_time_ms();
-                model.compute(8, input_ids, {}, {35}, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t0   = ggml_time_ms();
+                auto out_opt = model.compute(8, input_ids, sd::Tensor<float>(), {}, {35});
+                int64_t t1   = ggml_time_ms();

-                print_ggml_tensor(out);
-                LOG_DEBUG("llm test done in %dms", t1 - t0);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
+                LOG_DEBUG("llm test done in %lldms", t1 - t0);
            } else {
                std::pair<int, int> prompt_attn_range;
                std::string text        = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n";
@@ -1607,15 +1641,17 @@ namespace LLM {
                    printf("%d ", token);
                }
                printf("\n");
-                auto input_ids          = vector_to_ggml_tensor_i32(work_ctx, tokens);
-                struct ggml_tensor* out = nullptr;
+                auto input_ids = sd::Tensor<int32_t>::from_vector(tokens);
+                sd::Tensor<float> out;

-                int t0 = ggml_time_ms();
-                model.compute(8, input_ids, {}, {}, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t0   = ggml_time_ms();
+                auto out_opt = model.compute(8, input_ids, sd::Tensor<float>(), {}, {});
+                int64_t t1   = ggml_time_ms();

-                print_ggml_tensor(out);
-                LOG_DEBUG("llm test done in %dms", t1 - t0);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
+                LOG_DEBUG("llm test done in %lldms", t1 - t0);
            }
        }

@@ -9,7 +9,7 @@
 struct LoraModel : public GGMLRunner {
    std::string lora_id;
    float multiplier = 1.0f;
-    std::unordered_map<std::string, struct ggml_tensor*> lora_tensors;
+    std::unordered_map<std::string, ggml_tensor*> lora_tensors;
    std::map<ggml_tensor*, ggml_tensor*> original_tensor_to_final_tensor;
    std::set<std::string> applied_lora_tensors;
    std::string file_path;
@@ -76,13 +76,13 @@ struct LoraModel : public GGMLRunner {
        }

        for (const auto& pair : tensors_to_create) {
-            const auto& name         = pair.first;
-            const auto& ts           = pair.second;
-            struct ggml_tensor* real = ggml_new_tensor(params_ctx,
-                                                       ts.type,
-                                                       ts.n_dims,
-                                                       ts.ne);
-            lora_tensors[name]       = real;
+            const auto& name   = pair.first;
+            const auto& ts     = pair.second;
+            ggml_tensor* real  = ggml_new_tensor(params_ctx,
+                                                 ts.type,
+                                                 ts.n_dims,
+                                                 ts.ne);
+            lora_tensors[name] = real;
        }

        alloc_params_buffer();
@@ -195,7 +195,7 @@ struct LoraModel : public GGMLRunner {
            scale_value *= multiplier;

            auto curr_updown = ggml_ext_merge_lora(ctx, lora_down, lora_up, lora_mid);
-            curr_updown      = ggml_scale_inplace(ctx, curr_updown, scale_value);
+            curr_updown      = ggml_ext_scale(ctx, curr_updown, scale_value, true);

            if (updown == nullptr) {
                updown = curr_updown;
@@ -235,7 +235,7 @@ struct LoraModel : public GGMLRunner {
            float scale_value = 1.0f;
            scale_value *= multiplier;

-            curr_updown = ggml_scale_inplace(ctx, curr_updown, scale_value);
+            curr_updown = ggml_ext_scale(ctx, curr_updown, scale_value, true);

            if (updown == nullptr) {
                updown = curr_updown;
@@ -337,10 +337,10 @@ struct LoraModel : public GGMLRunner {
            }
            scale_value *= multiplier;

-            struct ggml_tensor* updown_1 = ggml_ext_merge_lora(ctx, hada_1_down, hada_1_up, hada_1_mid);
-            struct ggml_tensor* updown_2 = ggml_ext_merge_lora(ctx, hada_2_down, hada_2_up, hada_2_mid);
-            auto curr_updown             = ggml_mul_inplace(ctx, updown_1, updown_2);
-            curr_updown                  = ggml_scale_inplace(ctx, curr_updown, scale_value);
+            ggml_tensor* updown_1 = ggml_ext_merge_lora(ctx, hada_1_down, hada_1_up, hada_1_mid);
+            ggml_tensor* updown_2 = ggml_ext_merge_lora(ctx, hada_2_down, hada_2_up, hada_2_mid);
+            auto curr_updown      = ggml_mul_inplace(ctx, updown_1, updown_2);
+            curr_updown           = ggml_ext_scale(ctx, curr_updown, scale_value, true);
            if (updown == nullptr) {
                updown = curr_updown;
            } else {
@@ -456,7 +456,7 @@ struct LoraModel : public GGMLRunner {
            scale_value *= multiplier;

            auto curr_updown = ggml_ext_kronecker(ctx, lokr_w1, lokr_w2);
-            curr_updown      = ggml_scale_inplace(ctx, curr_updown, scale_value);
+            curr_updown      = ggml_ext_scale(ctx, curr_updown, scale_value, true);

            if (updown == nullptr) {
                updown = curr_updown;
@@ -468,10 +468,10 @@ struct LoraModel : public GGMLRunner {
        return updown;
    }

-    ggml_tensor* get_weight_diff(const std::string& model_tensor_name, ggml_context* ctx, ggml_tensor* model_tensor, bool with_lora = true) {
+    ggml_tensor* get_weight_diff(const std::string& model_tensor_name, ggml_context* ctx, ggml_tensor* model_tensor, bool with_lora_and_lokr = true) {
        // lora
        ggml_tensor* diff = nullptr;
-        if (with_lora) {
+        if (with_lora_and_lokr) {
            diff = get_lora_weight_diff(model_tensor_name, ctx);
        }
        // diff
@@ -483,7 +483,7 @@ struct LoraModel : public GGMLRunner {
            diff = get_loha_weight_diff(model_tensor_name, ctx);
        }
        // lokr
-        if (diff == nullptr) {
+        if (diff == nullptr && with_lora_and_lokr) {
            diff = get_lokr_weight_diff(model_tensor_name, ctx);
        }
        if (diff != nullptr) {
@@ -514,6 +514,108 @@ struct LoraModel : public GGMLRunner {
            } else {
                key = model_tensor_name + "." + std::to_string(index);
            }
+            bool is_conv2d = forward_params.op_type == WeightAdapter::ForwardParams::op_type_t::OP_CONV2D;
+
+            std::string lokr_w1_name   = "lora." + key + ".lokr_w1";
+            std::string lokr_w1_a_name = "lora." + key + ".lokr_w1_a";
+            // if either of these is found, then we have a lokr lora
+            auto iter   = lora_tensors.find(lokr_w1_name);
+            auto iter_a = lora_tensors.find(lokr_w1_a_name);
+            if (iter != lora_tensors.end() || iter_a != lora_tensors.end()) {
+                std::string lokr_w1_b_name = "lora." + key + ".lokr_w1_b";
+                std::string lokr_w2_name   = "lora." + key + ".lokr_w2";
+                std::string lokr_w2_a_name = "lora." + key + ".lokr_w2_a";
+                std::string lokr_w2_b_name = "lora." + key + ".lokr_w2_b";
+                std::string alpha_name     = "lora." + key + ".alpha";
+
+                ggml_tensor* lokr_w1   = nullptr;
+                ggml_tensor* lokr_w1_a = nullptr;
+                ggml_tensor* lokr_w1_b = nullptr;
+                ggml_tensor* lokr_w2   = nullptr;
+                ggml_tensor* lokr_w2_a = nullptr;
+                ggml_tensor* lokr_w2_b = nullptr;
+
+                if (iter != lora_tensors.end()) {
+                    lokr_w1 = iter->second;
+                }
+                iter = iter_a;
+                if (iter != lora_tensors.end()) {
+                    lokr_w1_a = iter->second;
+                }
+                iter = lora_tensors.find(lokr_w1_b_name);
+                if (iter != lora_tensors.end()) {
+                    lokr_w1_b = iter->second;
+                }
+
+                iter = lora_tensors.find(lokr_w2_name);
+                if (iter != lora_tensors.end()) {
+                    lokr_w2 = iter->second;
+                    if (is_conv2d && lokr_w2->type != GGML_TYPE_F16) {
+                        lokr_w2 = ggml_cast(ctx, lokr_w2, GGML_TYPE_F16);
+                    }
+                }
+                iter = lora_tensors.find(lokr_w2_a_name);
+                if (iter != lora_tensors.end()) {
+                    lokr_w2_a = iter->second;
+                    if (is_conv2d && lokr_w2_a->type != GGML_TYPE_F16) {
+                        lokr_w2_a = ggml_cast(ctx, lokr_w2_a, GGML_TYPE_F16);
+                    }
+                }
+                iter = lora_tensors.find(lokr_w2_b_name);
+                if (iter != lora_tensors.end()) {
+                    lokr_w2_b = iter->second;
+                    if (is_conv2d && lokr_w2_b->type != GGML_TYPE_F16) {
+                        lokr_w2_b = ggml_cast(ctx, lokr_w2_b, GGML_TYPE_F16);
+                    }
+                }
+
+                int rank = 1;
+                if (lokr_w1_b) {
+                    rank = (int)lokr_w1_b->ne[ggml_n_dims(lokr_w1_b) - 1];
+                }
+                if (lokr_w2_b) {
+                    rank = (int)lokr_w2_b->ne[ggml_n_dims(lokr_w2_b) - 1];
+                }
+
+                float scale_value = 1.0f;
+                iter              = lora_tensors.find(alpha_name);
+                if (iter != lora_tensors.end()) {
+                    float alpha = ggml_ext_backend_tensor_get_f32(iter->second);
+                    scale_value = alpha / rank;
+                    applied_lora_tensors.insert(alpha_name);
+                }
+
+                if (rank == 1) {
+                    scale_value = 1.0f;
+                }
+                scale_value *= multiplier;
+
+                auto curr_out_diff = ggml_ext_lokr_forward(ctx, x, lokr_w1, lokr_w1_a, lokr_w1_b, lokr_w2, lokr_w2_a, lokr_w2_b, is_conv2d, forward_params.conv2d, scale_value);
+                if (out_diff == nullptr) {
+                    out_diff = curr_out_diff;
+                } else {
+                    out_diff = ggml_concat(ctx, out_diff, curr_out_diff, 0);
+                }
+
+                if (lokr_w1)
+                    applied_lora_tensors.insert(lokr_w1_name);
+                if (lokr_w1_a)
+                    applied_lora_tensors.insert(lokr_w1_a_name);
+                if (lokr_w1_b)
+                    applied_lora_tensors.insert(lokr_w1_b_name);
+                if (lokr_w2)
+                    applied_lora_tensors.insert(lokr_w2_name);
+                if (lokr_w2_a)
+                    applied_lora_tensors.insert(lokr_w2_name);
+                if (lokr_w2_b)
+                    applied_lora_tensors.insert(lokr_w2_b_name);
+                applied_lora_tensors.insert(alpha_name);
+
+                index++;
+                continue;
+            }
+
+            // not a lokr, normal lora path

            std::string lora_down_name = "lora." + key + ".lora_down";
            std::string lora_up_name   = "lora." + key + ".lora_up";
@@ -525,9 +627,7 @@ struct LoraModel : public GGMLRunner {
            ggml_tensor* lora_mid  = nullptr;
            ggml_tensor* lora_down = nullptr;

-            bool is_conv2d = forward_params.op_type == WeightAdapter::ForwardParams::op_type_t::OP_CONV2D;
-
-            auto iter = lora_tensors.find(lora_up_name);
+            iter = lora_tensors.find(lora_up_name);
            if (iter != lora_tensors.end()) {
                lora_up = iter->second;
                if (is_conv2d && lora_up->type != GGML_TYPE_F16) {
@@ -599,6 +699,8 @@ struct LoraModel : public GGMLRunner {
                                      forward_params.conv2d.d0,
                                      forward_params.conv2d.d1,
                                      forward_params.conv2d.direct,
+                                      forward_params.conv2d.circular_x,
+                                      forward_params.conv2d.circular_y,
                                      forward_params.conv2d.scale);
                if (lora_mid) {
                    lx = ggml_ext_conv_2d(ctx,
@@ -612,6 +714,8 @@ struct LoraModel : public GGMLRunner {
                                          1,
                                          1,
                                          forward_params.conv2d.direct,
+                                          forward_params.conv2d.circular_x,
+                                          forward_params.conv2d.circular_y,
                                          forward_params.conv2d.scale);
                }
                lx = ggml_ext_conv_2d(ctx,
@@ -625,10 +729,12 @@ struct LoraModel : public GGMLRunner {
                                      1,
                                      1,
                                      forward_params.conv2d.direct,
+                                      forward_params.conv2d.circular_x,
+                                      forward_params.conv2d.circular_y,
                                      forward_params.conv2d.scale);
            }

-            auto curr_out_diff = ggml_scale_inplace(ctx, lx, scale_value);
+            auto curr_out_diff = ggml_ext_scale(ctx, lx, scale_value, true);

            if (out_diff == nullptr) {
                out_diff = curr_out_diff;
@@ -641,9 +747,9 @@ struct LoraModel : public GGMLRunner {
        return out_diff;
    }

-    struct ggml_cgraph* build_lora_graph(const std::map<std::string, ggml_tensor*>& model_tensors, SDVersion version) {
+    ggml_cgraph* build_lora_graph(const std::map<std::string, ggml_tensor*>& model_tensors, SDVersion version) {
        size_t lora_graph_size = LORA_GRAPH_BASE_SIZE + lora_tensors.size() * 10;
-        struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, lora_graph_size, false);
+        ggml_cgraph* gf        = ggml_new_graph_custom(compute_ctx, lora_graph_size, false);

        preprocess_lora_tensors(model_tensors);

@@ -682,11 +788,11 @@ struct LoraModel : public GGMLRunner {
        return gf;
    }

-    void apply(std::map<std::string, struct ggml_tensor*> model_tensors, SDVersion version, int n_threads) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
+    void apply(std::map<std::string, ggml_tensor*> model_tensors, SDVersion version, int n_threads) {
+        auto get_graph = [&]() -> ggml_cgraph* {
            return build_lora_graph(model_tensors, version);
        };
-        GGMLRunner::compute(get_graph, n_threads, false);
+        GGMLRunner::compute<float>(get_graph, n_threads, false, true);
        stat();
        for (auto item : original_tensor_to_final_tensor) {
            ggml_tensor* original_tensor = item.first;
@@ -735,9 +841,9 @@ public:
        : lora_models(lora_models) {
    }

-    ggml_tensor* patch_weight(ggml_context* ctx, ggml_tensor* weight, const std::string& weight_name, bool with_lora) {
+    ggml_tensor* patch_weight(ggml_context* ctx, ggml_tensor* weight, const std::string& weight_name, bool with_lora_and_lokr) {
        for (auto& lora_model : lora_models) {
-            ggml_tensor* diff = lora_model->get_weight_diff(weight_name, ctx, weight, with_lora);
+            ggml_tensor* diff = lora_model->get_weight_diff(weight_name, ctx, weight, with_lora_and_lokr);
            if (diff == nullptr) {
                continue;
            }
@@ -779,6 +885,8 @@ public:
                                   forward_params.conv2d.d0,
                                   forward_params.conv2d.d1,
                                   forward_params.conv2d.direct,
+                                   forward_params.conv2d.circular_x,
+                                   forward_params.conv2d.circular_y,
                                   forward_params.conv2d.scale);
        }
        for (auto& lora_model : lora_models) {
@@ -1,8 +1,7 @@
 #ifndef __LTXV_HPP__
 #define __LTXV_HPP__

-#include "common.hpp"
-#include "ggml_extend.hpp"
+#include "common_block.hpp"

 namespace LTXV {

@@ -27,9 +26,9 @@ namespace LTXV {
                                                                     bias));
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    bool causal = true) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             bool causal = true) {
            // x: [N*IC, ID, IH, IW]
            // result: [N*OC, OD, OH, OW]
            auto conv = std::dynamic_pointer_cast<Conv3d>(blocks["conv"]);
@@ -27,13 +27,13 @@ public:
        blocks["fc2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_features, out_features, bias));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [N, n_token, in_features]
        auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
        auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);

        x = fc1->forward(ctx, x);
-        x = ggml_gelu_inplace(ctx->ggml_ctx, x);
+        x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
        x = fc2->forward(ctx, x);
        return x;
    }
@@ -72,7 +72,7 @@ public:
                                                               bias));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [N, C, H, W]
        // return: [N, H*W, embed_dim]
        auto proj = std::dynamic_pointer_cast<Conv2d>(blocks["proj"]);
@@ -97,12 +97,12 @@ public:
 struct TimestepEmbedder : public GGMLBlock {
    // Embeds scalar timesteps into vector representations.
 protected:
-    int64_t frequency_embedding_size;
+    int frequency_embedding_size;

 public:
    TimestepEmbedder(int64_t hidden_size,
-                     int64_t frequency_embedding_size = 256,
-                     int64_t out_channels             = 0)
+                     int frequency_embedding_size = 256,
+                     int64_t out_channels         = 0)
        : frequency_embedding_size(frequency_embedding_size) {
        if (out_channels <= 0) {
            out_channels = hidden_size;
@@ -111,7 +111,7 @@ public:
        blocks["mlp.2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, out_channels, true, true));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* t) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* t) {
        // t: [N, ]
        // return: [N, hidden_size]
        auto mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["mlp.0"]);
@@ -135,7 +135,7 @@ public:
        blocks["mlp.2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, hidden_size, true, true));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [N, input_dim]
        // return: [N, hidden_size]
        auto mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["mlp.0"]);
@@ -167,15 +167,15 @@ public:
            blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(dim, dim));
        }
        if (qk_norm == "rms") {
-            blocks["ln_q"] = std::shared_ptr<GGMLBlock>(new RMSNorm(d_head, 1.0e-6));
-            blocks["ln_k"] = std::shared_ptr<GGMLBlock>(new RMSNorm(d_head, 1.0e-6));
+            blocks["ln_q"] = std::shared_ptr<GGMLBlock>(new RMSNorm(d_head, 1.0e-6f));
+            blocks["ln_k"] = std::shared_ptr<GGMLBlock>(new RMSNorm(d_head, 1.0e-6f));
        } else if (qk_norm == "ln") {
-            blocks["ln_q"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_head, 1.0e-6));
-            blocks["ln_k"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_head, 1.0e-6));
+            blocks["ln_q"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_head, 1.0e-6f));
+            blocks["ln_k"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_head, 1.0e-6f));
        }
    }

-    std::vector<struct ggml_tensor*> pre_attention(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    std::vector<ggml_tensor*> pre_attention(GGMLRunnerContext* ctx, ggml_tensor* x) {
        auto qkv_proj = std::dynamic_pointer_cast<Linear>(blocks["qkv"]);

        auto qkv         = qkv_proj->forward(ctx, x);
@@ -198,7 +198,7 @@ public:
        return {q, k, v};
    }

-    struct ggml_tensor* post_attention(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* post_attention(GGMLRunnerContext* ctx, ggml_tensor* x) {
        GGML_ASSERT(!pre_only);

        auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
@@ -208,19 +208,19 @@ public:
    }

    // x: [N, n_token, dim]
-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x) {
        auto qkv = pre_attention(ctx, x);
-        x        = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
-        x        = post_attention(ctx, x);                                                                                                                  // [N, n_token, dim]
+        x        = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
+        x        = post_attention(ctx, x);                                                                                                           // [N, n_token, dim]
        return x;
    }
 };

-__STATIC_INLINE__ struct ggml_tensor* modulate(struct ggml_context* ctx,
-                                               struct ggml_tensor* x,
-                                               struct ggml_tensor* shift,
-                                               struct ggml_tensor* scale) {
+__STATIC_INLINE__ ggml_tensor* modulate(ggml_context* ctx,
+                                        ggml_tensor* x,
+                                        ggml_tensor* shift,
+                                        ggml_tensor* scale) {
    // x: [N, L, C]
    // scale: [N, C]
    // shift: [N, C]
@@ -274,8 +274,8 @@ public:
    }

    std::tuple<std::vector<ggml_tensor*>, std::vector<ggml_tensor*>, std::vector<ggml_tensor*>> pre_attention_x(GGMLRunnerContext* ctx,
-                                                                                                                struct ggml_tensor* x,
-                                                                                                                struct ggml_tensor* c) {
+                                                                                                                ggml_tensor* x,
+                                                                                                                ggml_tensor* c) {
        GGML_ASSERT(self_attn);
        // x: [N, n_token, hidden_size]
        // c: [N, hidden_size]
@@ -284,23 +284,19 @@ public:
        auto attn2              = std::dynamic_pointer_cast<SelfAttention>(blocks["attn2"]);
        auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);

-        int64_t n_mods = 9;
-        auto m         = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));         // [N, n_mods * hidden_size]
-        m              = ggml_reshape_3d(ctx->ggml_ctx, m, c->ne[0], n_mods, c->ne[1]);         // [N, n_mods, hidden_size]
-        m              = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, m, 0, 2, 1, 3));  // [n_mods, N, hidden_size]
+        int n_mods = 9;
+        auto m     = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));  // [N, n_mods * hidden_size]
+        auto m_vec = ggml_ext_chunk(ctx->ggml_ctx, m, n_mods, 0);

-        int64_t offset = m->nb[1] * m->ne[1];
-        auto shift_msa = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
-        auto scale_msa = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
-        auto gate_msa  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 2);  // [N, hidden_size]
-
-        auto shift_mlp = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 3);  // [N, hidden_size]
-        auto scale_mlp = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 4);  // [N, hidden_size]
-        auto gate_mlp  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 5);  // [N, hidden_size]
-
-        auto shift_msa2 = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 6);  // [N, hidden_size]
-        auto scale_msa2 = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 7);  // [N, hidden_size]
-        auto gate_msa2  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 8);  // [N, hidden_size]
+        auto shift_msa  = m_vec[0];  // [N, hidden_size]
+        auto scale_msa  = m_vec[1];  // [N, hidden_size]
+        auto gate_msa   = m_vec[2];  // [N, hidden_size]
+        auto shift_mlp  = m_vec[3];  // [N, hidden_size]
+        auto scale_mlp  = m_vec[4];  // [N, hidden_size]
+        auto gate_mlp   = m_vec[5];  // [N, hidden_size]
+        auto shift_msa2 = m_vec[6];  // [N, hidden_size]
+        auto scale_msa2 = m_vec[7];  // [N, hidden_size]
+        auto gate_msa2  = m_vec[8];  // [N, hidden_size]

        auto x_norm = norm1->forward(ctx, x);

@@ -313,31 +309,29 @@ public:
        return {qkv, qkv2, {x, gate_msa, shift_mlp, scale_mlp, gate_mlp, gate_msa2}};
    }

-    std::pair<std::vector<struct ggml_tensor*>, std::vector<struct ggml_tensor*>> pre_attention(GGMLRunnerContext* ctx,
-                                                                                                struct ggml_tensor* x,
-                                                                                                struct ggml_tensor* c) {
+    std::pair<std::vector<ggml_tensor*>, std::vector<ggml_tensor*>> pre_attention(GGMLRunnerContext* ctx,
+                                                                                  ggml_tensor* x,
+                                                                                  ggml_tensor* c) {
        // x: [N, n_token, hidden_size]
        // c: [N, hidden_size]
        auto norm1              = std::dynamic_pointer_cast<LayerNorm>(blocks["norm1"]);
        auto attn               = std::dynamic_pointer_cast<SelfAttention>(blocks["attn"]);
        auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);

-        int64_t n_mods = 6;
+        int n_mods = 6;
        if (pre_only) {
            n_mods = 2;
        }
-        auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));         // [N, n_mods * hidden_size]
-        m      = ggml_reshape_3d(ctx->ggml_ctx, m, c->ne[0], n_mods, c->ne[1]);         // [N, n_mods, hidden_size]
-        m      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, m, 0, 2, 1, 3));  // [n_mods, N, hidden_size]
+        auto m     = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));  // [N, n_mods * hidden_size]
+        auto m_vec = ggml_ext_chunk(ctx->ggml_ctx, m, n_mods, 0);

-        int64_t offset = m->nb[1] * m->ne[1];
-        auto shift_msa = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
-        auto scale_msa = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
+        auto shift_msa = m_vec[0];  // [N, hidden_size]
+        auto scale_msa = m_vec[1];  // [N, hidden_size]
        if (!pre_only) {
-            auto gate_msa  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 2);  // [N, hidden_size]
-            auto shift_mlp = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 3);  // [N, hidden_size]
-            auto scale_mlp = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 4);  // [N, hidden_size]
-            auto gate_mlp  = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 5);  // [N, hidden_size]
+            auto gate_msa  = m_vec[2];  // [N, hidden_size]
+            auto shift_mlp = m_vec[3];  // [N, hidden_size]
+            auto scale_mlp = m_vec[4];  // [N, hidden_size]
+            auto gate_mlp  = m_vec[5];  // [N, hidden_size]

            auto attn_in = modulate(ctx->ggml_ctx, norm1->forward(ctx, x), shift_msa, scale_msa);

@@ -352,15 +346,15 @@ public:
        }
    }

-    struct ggml_tensor* post_attention_x(GGMLRunnerContext* ctx,
-                                         struct ggml_tensor* attn_out,
-                                         struct ggml_tensor* attn2_out,
-                                         struct ggml_tensor* x,
-                                         struct ggml_tensor* gate_msa,
-                                         struct ggml_tensor* shift_mlp,
-                                         struct ggml_tensor* scale_mlp,
-                                         struct ggml_tensor* gate_mlp,
-                                         struct ggml_tensor* gate_msa2) {
+    ggml_tensor* post_attention_x(GGMLRunnerContext* ctx,
+                                  ggml_tensor* attn_out,
+                                  ggml_tensor* attn2_out,
+                                  ggml_tensor* x,
+                                  ggml_tensor* gate_msa,
+                                  ggml_tensor* shift_mlp,
+                                  ggml_tensor* scale_mlp,
+                                  ggml_tensor* gate_mlp,
+                                  ggml_tensor* gate_msa2) {
        // attn_out: [N, n_token, hidden_size]
        // x: [N, n_token, hidden_size]
        // gate_msa: [N, hidden_size]
@@ -390,13 +384,13 @@ public:
        return x;
    }

-    struct ggml_tensor* post_attention(GGMLRunnerContext* ctx,
-                                       struct ggml_tensor* attn_out,
-                                       struct ggml_tensor* x,
-                                       struct ggml_tensor* gate_msa,
-                                       struct ggml_tensor* shift_mlp,
-                                       struct ggml_tensor* scale_mlp,
-                                       struct ggml_tensor* gate_mlp) {
+    ggml_tensor* post_attention(GGMLRunnerContext* ctx,
+                                ggml_tensor* attn_out,
+                                ggml_tensor* x,
+                                ggml_tensor* gate_msa,
+                                ggml_tensor* shift_mlp,
+                                ggml_tensor* scale_mlp,
+                                ggml_tensor* gate_mlp) {
        // attn_out: [N, n_token, hidden_size]
        // x: [N, n_token, hidden_size]
        // gate_msa: [N, hidden_size]
@@ -422,9 +416,9 @@ public:
        return x;
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* c) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* c) {
        // x: [N, n_token, hidden_size]
        // c: [N, hidden_size]
        // return: [N, n_token, hidden_size]
@@ -439,8 +433,8 @@ public:
            auto qkv2          = std::get<1>(qkv_intermediates);
            auto intermediates = std::get<2>(qkv_intermediates);

-            auto attn_out  = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, false, ctx->flash_attn_enabled);     // [N, n_token, dim]
-            auto attn2_out = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv2[0], qkv2[1], qkv2[2], num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
+            auto attn_out  = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, ctx->flash_attn_enabled);     // [N, n_token, dim]
+            auto attn2_out = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv2[0], qkv2[1], qkv2[2], num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
            x              = post_attention_x(ctx,
                                              attn_out,
                                              attn2_out,
@@ -456,7 +450,7 @@ public:
            auto qkv               = qkv_intermediates.first;
            auto intermediates     = qkv_intermediates.second;

-            auto attn_out = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
+            auto attn_out = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, dim]
            x             = post_attention(ctx,
                                           attn_out,
                                           intermediates[0],
@@ -469,11 +463,11 @@ public:
    }
 };

-__STATIC_INLINE__ std::pair<struct ggml_tensor*, struct ggml_tensor*>
+__STATIC_INLINE__ std::pair<ggml_tensor*, ggml_tensor*>
 block_mixing(GGMLRunnerContext* ctx,
-             struct ggml_tensor* context,
-             struct ggml_tensor* x,
-             struct ggml_tensor* c,
+             ggml_tensor* context,
+             ggml_tensor* x,
+             ggml_tensor* c,
             std::shared_ptr<DismantledBlock> context_block,
             std::shared_ptr<DismantledBlock> x_block) {
    // context: [N, n_context, hidden_size]
@@ -495,31 +489,29 @@ block_mixing(GGMLRunnerContext* ctx,
        x_qkv                    = x_qkv_intermediates.first;
        x_intermediates          = x_qkv_intermediates.second;
    }
-    std::vector<struct ggml_tensor*> qkv;
+    std::vector<ggml_tensor*> qkv;
    for (int i = 0; i < 3; i++) {
        qkv.push_back(ggml_concat(ctx->ggml_ctx, context_qkv[i], x_qkv[i], 1));
    }

-    auto attn         = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], x_block->num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_context + n_token, hidden_size]
-    attn              = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, attn, 0, 2, 1, 3));                                                                          // [n_context + n_token, N, hidden_size]
+    auto attn = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, qkv[0], qkv[1], qkv[2], x_block->num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_context + n_token, hidden_size]
+
    auto context_attn = ggml_view_3d(ctx->ggml_ctx,
                                     attn,
                                     attn->ne[0],
-                                     attn->ne[1],
                                     context->ne[1],
+                                     attn->ne[2],
                                     attn->nb[1],
                                     attn->nb[2],
-                                     0);                                                                  // [n_context, N, hidden_size]
-    context_attn      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, context_attn, 0, 2, 1, 3));  // [N, n_context, hidden_size]
+                                     0);  // [N, n_context, hidden_size]
    auto x_attn       = ggml_view_3d(ctx->ggml_ctx,
                                     attn,
                                     attn->ne[0],
-                                     attn->ne[1],
                                     x->ne[1],
+                                     attn->ne[2],
                                     attn->nb[1],
                                     attn->nb[2],
-                                     attn->nb[2] * context->ne[1]);                                 // [n_token, N, hidden_size]
-    x_attn            = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x_attn, 0, 2, 1, 3));  // [N, n_token, hidden_size]
+                                     context->ne[1] * attn->nb[1]);  // [N, n_token, hidden_size]

    if (!context_block->pre_only) {
        context = context_block->post_attention(ctx,
@@ -534,7 +526,7 @@ block_mixing(GGMLRunnerContext* ctx,
    }

    if (x_block->self_attn) {
-        auto attn2 = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, x_qkv2[0], x_qkv2[1], x_qkv2[2], x_block->num_heads, nullptr, false, false, ctx->flash_attn_enabled);  // [N, n_token, hidden_size]
+        auto attn2 = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, x_qkv2[0], x_qkv2[1], x_qkv2[2], x_block->num_heads, nullptr, false, ctx->flash_attn_enabled);  // [N, n_token, hidden_size]

        x = x_block->post_attention_x(ctx,
                                      x_attn,
@@ -571,10 +563,10 @@ public:
        blocks["x_block"]       = std::shared_ptr<GGMLBlock>(new DismantledBlock(hidden_size, num_heads, mlp_ratio, qk_norm, qkv_bias, false, self_attn_x));
    }

-    std::pair<struct ggml_tensor*, struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                                                struct ggml_tensor* context,
-                                                                struct ggml_tensor* x,
-                                                                struct ggml_tensor* c) {
+    std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                  ggml_tensor* context,
+                                                  ggml_tensor* x,
+                                                  ggml_tensor* c) {
        auto context_block = std::dynamic_pointer_cast<DismantledBlock>(blocks["context_block"]);
        auto x_block       = std::dynamic_pointer_cast<DismantledBlock>(blocks["x_block"]);

@@ -594,9 +586,9 @@ public:
        blocks["adaLN_modulation.1"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, 2 * hidden_size));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* c) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* c) {
        // x: [N, n_token, hidden_size]
        // c: [N, hidden_size]
        // return: [N, n_token, patch_size * patch_size * out_channels]
@@ -604,13 +596,10 @@ public:
        auto linear             = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
        auto adaLN_modulation_1 = std::dynamic_pointer_cast<Linear>(blocks["adaLN_modulation.1"]);

-        auto m = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));         // [N, 2 * hidden_size]
-        m      = ggml_reshape_3d(ctx->ggml_ctx, m, c->ne[0], 2, c->ne[1]);              // [N, 2, hidden_size]
-        m      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, m, 0, 2, 1, 3));  // [2, N, hidden_size]
-
-        int64_t offset = m->nb[1] * m->ne[1];
-        auto shift     = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 0);  // [N, hidden_size]
-        auto scale     = ggml_view_2d(ctx->ggml_ctx, m, m->ne[0], m->ne[1], m->nb[1], offset * 1);  // [N, hidden_size]
+        auto m     = adaLN_modulation_1->forward(ctx, ggml_silu(ctx->ggml_ctx, c));  // [N, 2 * hidden_size]
+        auto m_vec = ggml_ext_chunk(ctx->ggml_ctx, m, 2, 0);
+        auto shift = m_vec[0];  // [N, hidden_size]
+        auto scale = m_vec[1];  // [N, hidden_size]

        x = modulate(ctx->ggml_ctx, norm_final->forward(ctx, x), shift, scale);
        x = linear->forward(ctx, x);
@@ -623,7 +612,7 @@ struct MMDiT : public GGMLBlock {
    // Diffusion model with a Transformer backbone.
 protected:
    int64_t input_size               = -1;
-    int64_t patch_size               = 2;
+    int patch_size                   = 2;
    int64_t in_channels              = 16;
    int64_t d_self                   = -1;  // >=0 for MMdiT-X
    int64_t depth                    = 24;
@@ -637,7 +626,7 @@ protected:
    int64_t hidden_size;
    std::string qk_norm;

-    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
+    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
        enum ggml_type wtype = GGML_TYPE_F32;
        params["pos_embed"]  = ggml_new_tensor_3d(ctx, wtype, hidden_size, num_patchs, 1);
    }
@@ -716,8 +705,8 @@ public:
        blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new FinalLayer(hidden_size, patch_size, out_channels));
    }

-    struct ggml_tensor*
-    cropped_pos_embed(struct ggml_context* ctx,
+    ggml_tensor*
+    cropped_pos_embed(ggml_context* ctx,
                      int64_t h,
                      int64_t w) {
        auto pos_embed = params["pos_embed"];
@@ -756,33 +745,11 @@ public:
        return spatial_pos_embed;
    }

-    struct ggml_tensor* unpatchify(struct ggml_context* ctx,
-                                   struct ggml_tensor* x,
-                                   int64_t h,
-                                   int64_t w) {
-        // x: [N, H*W, patch_size * patch_size * C]
-        // return: [N, C, H, W]
-        int64_t n = x->ne[2];
-        int64_t c = out_channels;
-        int64_t p = patch_size;
-        h         = (h + 1) / p;
-        w         = (w + 1) / p;
-
-        GGML_ASSERT(h * w == x->ne[1]);
-
-        x = ggml_reshape_4d(ctx, x, c, p * p, w * h, n);       // [N, H*W, P*P, C]
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 2, 0, 1, 3));  // [N, C, H*W, P*P]
-        x = ggml_reshape_4d(ctx, x, p, p, w, h * c * n);       // [N*C*H, W, P, P]
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N*C*H, P, W, P]
-        x = ggml_reshape_4d(ctx, x, p * w, p * h, c, n);       // [N, C, H*P, W*P]
-        return x;
-    }
-
-    struct ggml_tensor* forward_core_with_concat(GGMLRunnerContext* ctx,
-                                                 struct ggml_tensor* x,
-                                                 struct ggml_tensor* c_mod,
-                                                 struct ggml_tensor* context,
-                                                 std::vector<int> skip_layers = std::vector<int>()) {
+    ggml_tensor* forward_core_with_concat(GGMLRunnerContext* ctx,
+                                          ggml_tensor* x,
+                                          ggml_tensor* c_mod,
+                                          ggml_tensor* context,
+                                          std::vector<int> skip_layers = std::vector<int>()) {
        // x: [N, H*W, hidden_size]
        // context: [N, n_context, d_context]
        // c: [N, hidden_size]
@@ -807,12 +774,12 @@ public:
        return x;
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* t,
-                                struct ggml_tensor* y        = nullptr,
-                                struct ggml_tensor* context  = nullptr,
-                                std::vector<int> skip_layers = std::vector<int>()) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* t,
+                         ggml_tensor* y               = nullptr,
+                         ggml_tensor* context         = nullptr,
+                         std::vector<int> skip_layers = std::vector<int>()) {
        // Forward pass of DiT.
        // x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
        // t: (N,) tensor of diffusion timesteps
@@ -822,11 +789,11 @@ public:
        auto x_embedder = std::dynamic_pointer_cast<PatchEmbed>(blocks["x_embedder"]);
        auto t_embedder = std::dynamic_pointer_cast<TimestepEmbedder>(blocks["t_embedder"]);

-        int64_t w = x->ne[0];
-        int64_t h = x->ne[1];
+        int64_t W = x->ne[0];
+        int64_t H = x->ne[1];

        auto patch_embed = x_embedder->forward(ctx, x);                      // [N, H*W, hidden_size]
-        auto pos_embed   = cropped_pos_embed(ctx->ggml_ctx, h, w);           // [1, H*W, hidden_size]
+        auto pos_embed   = cropped_pos_embed(ctx->ggml_ctx, H, W);           // [1, H*W, hidden_size]
        x                = ggml_add(ctx->ggml_ctx, patch_embed, pos_embed);  // [N, H*W, hidden_size]

        auto c = t_embedder->forward(ctx, t);  // [N, hidden_size]
@@ -845,7 +812,7 @@ public:

        x = forward_core_with_concat(ctx, x, c, context, skip_layers);  // (N, H*W, patch_size ** 2 * out_channels)

-        x = unpatchify(ctx->ggml_ctx, x, h, w);  // [N, C, H, W]
+        x = DiT::unpatchify_and_crop(ctx->ggml_ctx, x, H, W, patch_size, patch_size, /*patch_last*/ false);  // [N, C, H, W]

        return x;
    }
@@ -865,90 +832,94 @@ struct MMDiTRunner : public GGMLRunner {
        return "mmdit";
    }

-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
        mmdit.get_param_tensors(tensors, prefix);
    }

-    struct ggml_cgraph* build_graph(struct ggml_tensor* x,
-                                    struct ggml_tensor* timesteps,
-                                    struct ggml_tensor* context,
-                                    struct ggml_tensor* y,
-                                    std::vector<int> skip_layers = std::vector<int>()) {
-        struct ggml_cgraph* gf = new_graph_custom(MMDIT_GRAPH_SIZE);
+    ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                             const sd::Tensor<float>& timesteps_tensor,
+                             const sd::Tensor<float>& context_tensor = {},
+                             const sd::Tensor<float>& y_tensor       = {},
+                             std::vector<int> skip_layers            = std::vector<int>()) {
+        ggml_cgraph* gf = new_graph_custom(MMDIT_GRAPH_SIZE);

-        x         = to_backend(x);
-        context   = to_backend(context);
-        y         = to_backend(y);
-        timesteps = to_backend(timesteps);
+        ggml_tensor* x         = make_input(x_tensor);
+        ggml_tensor* timesteps = make_input(timesteps_tensor);
+        ggml_tensor* context   = make_optional_input(context_tensor);
+        ggml_tensor* y         = make_optional_input(y_tensor);

-        auto runner_ctx         = get_context();
-        struct ggml_tensor* out = mmdit.forward(&runner_ctx,
-                                                x,
-                                                timesteps,
-                                                y,
-                                                context,
-                                                skip_layers);
+        auto runner_ctx  = get_context();
+        ggml_tensor* out = mmdit.forward(&runner_ctx,
+                                         x,
+                                         timesteps,
+                                         y,
+                                         context,
+                                         skip_layers);

        ggml_build_forward_expand(gf, out);

        return gf;
    }

-    bool compute(int n_threads,
-                 struct ggml_tensor* x,
-                 struct ggml_tensor* timesteps,
-                 struct ggml_tensor* context,
-                 struct ggml_tensor* y,
-                 struct ggml_tensor** output     = nullptr,
-                 struct ggml_context* output_ctx = nullptr,
-                 std::vector<int> skip_layers    = std::vector<int>()) {
+    sd::Tensor<float> compute(int n_threads,
+                              const sd::Tensor<float>& x,
+                              const sd::Tensor<float>& timesteps,
+                              const sd::Tensor<float>& context = {},
+                              const sd::Tensor<float>& y       = {},
+                              std::vector<int> skip_layers     = std::vector<int>()) {
        // x: [N, in_channels, h, w]
        // timesteps: [N, ]
        // context: [N, max_position, hidden_size]([N, 154, 4096]) or [1, max_position, hidden_size]
        // y: [N, adm_in_channels] or [1, adm_in_channels]
-        auto get_graph = [&]() -> struct ggml_cgraph* {
+        auto get_graph = [&]() -> ggml_cgraph* {
            return build_graph(x, timesteps, context, y, skip_layers);
        };

-        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
    }

    void test() {
-        struct ggml_init_params params;
+        ggml_init_params params;
        params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
        params.mem_buffer = nullptr;
        params.no_alloc   = false;

-        struct ggml_context* work_ctx = ggml_init(params);
-        GGML_ASSERT(work_ctx != nullptr);
+        ggml_context* ctx = ggml_init(params);
+        GGML_ASSERT(ctx != nullptr);

        {
            // cpu f16: pass
            // cpu f32: pass
            // cuda f16: pass
            // cuda f32: pass
-            auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 128, 128, 16, 1);
+            sd::Tensor<float> x({128, 128, 16, 1});
            std::vector<float> timesteps_vec(1, 999.f);
-            auto timesteps = vector_to_ggml_tensor(work_ctx, timesteps_vec);
-            ggml_set_f32(x, 0.01f);
+            auto timesteps = sd::Tensor<float>::from_vector(timesteps_vec);
+            x.fill_(0.01f);
            // print_ggml_tensor(x);

-            auto context = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 4096, 154, 1);
-            ggml_set_f32(context, 0.01f);
+            sd::Tensor<float> context({4096, 154, 1});
+            context.fill_(0.01f);
            // print_ggml_tensor(context);

-            auto y = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 2048, 1);
-            ggml_set_f32(y, 0.01f);
+            sd::Tensor<float> y({2048, 1});
+            y.fill_(0.01f);
            // print_ggml_tensor(y);

-            struct ggml_tensor* out = nullptr;
+            sd::Tensor<float> out;

-            int t0 = ggml_time_ms();
-            compute(8, x, timesteps, context, y, &out, work_ctx);
-            int t1 = ggml_time_ms();
+            int64_t t0   = ggml_time_ms();
+            auto out_opt = compute(8,
+                                   x,
+                                   timesteps,
+                                   context,
+                                   y);
+            int64_t t1   = ggml_time_ms();

-            print_ggml_tensor(out);
-            LOG_DEBUG("mmdit test done in %dms", t1 - t0);
+            GGML_ASSERT(!out_opt.empty());
+            out = std::move(out_opt);
+            print_sd_tensor(out);
+            LOG_DEBUG("mmdit test done in %lldms", t1 - t0);
        }
    }

@@ -983,4 +954,4 @@ struct MMDiTRunner : public GGMLRunner {
    }
 };

-#endif
+#endif
@@ -16,10 +16,6 @@
 #include "model.h"
 #include "stable-diffusion.h"
 #include "util.h"
-#include "vocab.hpp"
-#include "vocab_mistral.hpp"
-#include "vocab_qwen.hpp"
-#include "vocab_umt5.hpp"

 #include "ggml-alloc.h"
 #include "ggml-backend.h"
@@ -166,43 +162,7 @@ uint16_t f8_e4m3_to_f16(uint8_t f8) {
 }

 uint16_t f8_e5m2_to_f16(uint8_t fp8) {
-    uint8_t sign     = (fp8 >> 7) & 0x1;
-    uint8_t exponent = (fp8 >> 2) & 0x1F;
-    uint8_t mantissa = fp8 & 0x3;
-
-    uint16_t fp16_sign = sign << 15;
-    uint16_t fp16_exponent;
-    uint16_t fp16_mantissa;
-
-    if (exponent == 0 && mantissa == 0) {  // zero
-        return fp16_sign;
-    }
-
-    if (exponent == 0x1F) {  // NAN and INF
-        fp16_exponent = 0x1F;
-        fp16_mantissa = mantissa ? (mantissa << 8) : 0;
-        return fp16_sign | (fp16_exponent << 10) | fp16_mantissa;
-    }
-
-    if (exponent == 0) {  // subnormal numbers
-        fp16_mantissa = (mantissa << 8);
-        return fp16_sign | fp16_mantissa;
-    }
-
-    // normal numbers
-    int16_t true_exponent = (int16_t)exponent - 15 + 15;
-    if (true_exponent <= 0) {
-        fp16_exponent = 0;
-        fp16_mantissa = (mantissa << 8);
-    } else if (true_exponent >= 0x1F) {
-        fp16_exponent = 0x1F;
-        fp16_mantissa = 0;
-    } else {
-        fp16_exponent = (uint16_t)true_exponent;
-        fp16_mantissa = mantissa << 8;
-    }
-
-    return fp16_sign | (fp16_exponent << 10) | fp16_mantissa;
+    return static_cast<uint16_t>(fp8) << 8;
 }

 void f8_e4m3_to_f16_vec(uint8_t* src, uint16_t* dst, int64_t n) {
@@ -291,7 +251,7 @@ void ModelLoader::add_tensor_storage(const TensorStorage& tensor_storage) {
 }

 bool is_zip_file(const std::string& file_path) {
-    struct zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
+    zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
    if (zip == nullptr) {
        return false;
    }
@@ -376,7 +336,11 @@ bool ModelLoader::init_from_file(const std::string& file_path, const std::string
        LOG_INFO("load %s using checkpoint format", file_path.c_str());
        return init_from_ckpt_file(file_path, prefix);
    } else {
-        LOG_WARN("unknown format %s", file_path.c_str());
+        if (file_exists(file_path)) {
+            LOG_WARN("unknown format %s", file_path.c_str());
+        } else {
+            LOG_WARN("file %s not found", file_path.c_str());
+        }
        return false;
    }
 }
@@ -436,7 +400,7 @@ bool ModelLoader::init_from_gguf_file(const std::string& file_path, const std::s
                name,
                gguf_tensor_info.type,
                gguf_tensor_info.shape.data(),
-                gguf_tensor_info.shape.size(),
+                static_cast<int>(gguf_tensor_info.shape.size()),
                file_index,
                data_offset + gguf_tensor_info.offset);

@@ -448,14 +412,14 @@ bool ModelLoader::init_from_gguf_file(const std::string& file_path, const std::s
        return true;
    }

-    int n_tensors = gguf_get_n_tensors(ctx_gguf_);
+    int n_tensors = static_cast<int>(gguf_get_n_tensors(ctx_gguf_));

    size_t total_size  = 0;
    size_t data_offset = gguf_get_data_offset(ctx_gguf_);
    for (int i = 0; i < n_tensors; i++) {
-        std::string name          = gguf_get_tensor_name(ctx_gguf_, i);
-        struct ggml_tensor* dummy = ggml_get_tensor(ctx_meta_, name.c_str());
-        size_t offset             = data_offset + gguf_get_tensor_offset(ctx_gguf_, i);
+        std::string name   = gguf_get_tensor_name(ctx_gguf_, i);
+        ggml_tensor* dummy = ggml_get_tensor(ctx_meta_, name.c_str());
+        size_t offset      = data_offset + gguf_get_tensor_offset(ctx_gguf_, i);

        // LOG_DEBUG("%s", name.c_str());

@@ -812,7 +776,7 @@ struct PickleTensorReader {
        }
    }

-    void read_string(const std::string& str, struct zip_t* zip, std::string dir) {
+    void read_string(const std::string& str, zip_t* zip, std::string dir) {
        if (str == "storage") {
            read_global_type = true;
        } else if (str != "state_dict") {
@@ -995,7 +959,7 @@ bool ModelLoader::init_from_ckpt_file(const std::string& file_path, const std::s
    file_paths_.push_back(file_path);
    size_t file_index = file_paths_.size() - 1;

-    struct zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
+    zip_t* zip = zip_open(file_path.c_str(), 0, 'r');
    if (zip == nullptr) {
        LOG_ERROR("failed to open '%s'", file_path.c_str());
        return false;
@@ -1034,10 +998,14 @@ SDVersion ModelLoader::get_sd_version() {

    bool is_xl                       = false;
    bool is_flux                     = false;
+    bool is_flux2                    = false;
+    bool has_single_block_47         = false;
    bool is_wan                      = false;
    int64_t patch_embedding_channels = 0;
    bool has_img_emb                 = false;
    bool has_middle_block_1          = false;
+    bool has_output_block_311        = false;
+    bool has_output_block_71         = false;

    for (auto& [name, tensor_storage] : tensor_storage_map) {
        if (!(is_xl)) {
@@ -1053,8 +1021,14 @@ SDVersion ModelLoader::get_sd_version() {
            if (tensor_storage.name.find("model.diffusion_model.transformer_blocks.0.img_mod.1.weight") != std::string::npos) {
                return VERSION_QWEN_IMAGE;
            }
+            if (tensor_storage.name.find("llm_adapter.blocks.0.cross_attn.q_proj.weight") != std::string::npos) {
+                return VERSION_ANIMA;
+            }
            if (tensor_storage.name.find("model.diffusion_model.double_stream_modulation_img.lin.weight") != std::string::npos) {
-                return VERSION_FLUX2;
+                is_flux2 = true;
+            }
+            if (tensor_storage.name.find("single_blocks.47.linear1.weight") != std::string::npos) {
+                has_single_block_47 = true;
            }
            if (tensor_storage.name.find("model.diffusion_model.double_blocks.0.img_mlp.gate_proj.weight") != std::string::npos) {
                return VERSION_OVIS_IMAGE;
@@ -1094,6 +1068,14 @@ SDVersion ModelLoader::get_sd_version() {
            tensor_storage.name.find("unet.mid_block.resnets.1.") != std::string::npos) {
            has_middle_block_1 = true;
        }
+        if (tensor_storage.name.find("model.diffusion_model.output_blocks.3.1.transformer_blocks.1") != std::string::npos ||
+            tensor_storage.name.find("unet.up_blocks.1.attentions.0.transformer_blocks.1") != std::string::npos) {
+            has_output_block_311 = true;
+        }
+        if (tensor_storage.name.find("model.diffusion_model.output_blocks.7.1") != std::string::npos ||
+            tensor_storage.name.find("unet.up_blocks.2.attentions.1") != std::string::npos) {
+            has_output_block_71 = true;
+        }
        if (tensor_storage.name == "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight" ||
            tensor_storage.name == "cond_stage_model.model.token_embedding.weight" ||
            tensor_storage.name == "text_model.embeddings.token_embedding.weight" ||
@@ -1129,12 +1111,15 @@ SDVersion ModelLoader::get_sd_version() {
            return VERSION_SDXL_PIX2PIX;
        }
        if (!has_middle_block_1) {
+            if (!has_output_block_311) {
+                return VERSION_SDXL_VEGA;
+            }
            return VERSION_SDXL_SSD1B;
        }
        return VERSION_SDXL;
    }

-    if (is_flux) {
+    if (is_flux && !is_flux2) {
        if (input_block_weight.ne[0] == 384) {
            return VERSION_FLUX_FILL;
        }
@@ -1147,6 +1132,13 @@ SDVersion ModelLoader::get_sd_version() {
        return VERSION_FLUX;
    }

+    if (is_flux2) {
+        if (has_single_block_47) {
+            return VERSION_FLUX2;
+        }
+        return VERSION_FLUX2_KLEIN;
+    }
+
    if (token_embedding_weight.ne[0] == 768) {
        if (is_inpaint) {
            return VERSION_SD1_INPAINT;
@@ -1155,6 +1147,9 @@ SDVersion ModelLoader::get_sd_version() {
            return VERSION_SD1_PIX2PIX;
        }
        if (!has_middle_block_1) {
+            if (!has_output_block_71) {
+                return VERSION_SDXS;
+            }
            return VERSION_SD1_TINY_UNET;
        }
        return VERSION_SD1;
@@ -1310,42 +1305,13 @@ void ModelLoader::set_wtype_override(ggml_type wtype, std::string tensor_type_ru
    }
 }

-std::string ModelLoader::load_merges() {
-    std::string merges_utf8_str(reinterpret_cast<const char*>(merges_utf8_c_str), sizeof(merges_utf8_c_str));
-    return merges_utf8_str;
-}
-
-std::string ModelLoader::load_qwen2_merges() {
-    std::string merges_utf8_str(reinterpret_cast<const char*>(qwen2_merges_utf8_c_str), sizeof(qwen2_merges_utf8_c_str));
-    return merges_utf8_str;
-}
-
-std::string ModelLoader::load_mistral_merges() {
-    std::string merges_utf8_str(reinterpret_cast<const char*>(mistral_merges_utf8_c_str), sizeof(mistral_merges_utf8_c_str));
-    return merges_utf8_str;
-}
-
-std::string ModelLoader::load_mistral_vocab_json() {
-    std::string json_str(reinterpret_cast<const char*>(mistral_vocab_json_utf8_c_str), sizeof(mistral_vocab_json_utf8_c_str));
-    return json_str;
-}
-
-std::string ModelLoader::load_t5_tokenizer_json() {
-    std::string json_str(reinterpret_cast<const char*>(t5_tokenizer_json_str), sizeof(t5_tokenizer_json_str));
-    return json_str;
-}
-
-std::string ModelLoader::load_umt5_tokenizer_json() {
-    std::string json_str(reinterpret_cast<const char*>(umt5_tokenizer_json_str), sizeof(umt5_tokenizer_json_str));
-    return json_str;
-}
-
-bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads_p) {
+bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads_p, bool enable_mmap) {
    int64_t process_time_ms = 0;
    std::atomic<int64_t> read_time_ms(0);
    std::atomic<int64_t> memcpy_time_ms(0);
    std::atomic<int64_t> copy_to_backend_time_ms(0);
    std::atomic<int64_t> convert_time_ms(0);
+    std::atomic<uint64_t> bytes_processed(0);

    int num_threads_to_use = n_threads_p > 0 ? n_threads_p : sd_get_num_physical_cores();
    LOG_DEBUG("using %d threads for model loading", num_threads_to_use);
@@ -1390,6 +1356,15 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
            }
        }

+        std::unique_ptr<MmapWrapper> mmapped;
+        if (enable_mmap && !is_zip) {
+            LOG_DEBUG("using mmap for I/O");
+            mmapped = MmapWrapper::create(file_path);
+            if (!mmapped) {
+                LOG_WARN("failed to memory-map '%s'", file_path.c_str());
+            }
+        }
+
        int n_threads = is_zip ? 1 : std::min(num_threads_to_use, (int)file_tensors.size());
        if (n_threads < 1) {
            n_threads = 1;
@@ -1403,7 +1378,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
        for (int i = 0; i < n_threads; ++i) {
            workers.emplace_back([&, file_path, is_zip]() {
                std::ifstream file;
-                struct zip_t* zip = nullptr;
+                zip_t* zip = nullptr;
                if (is_zip) {
                    zip = zip_open(file_path.c_str(), 0, 'r');
                    if (zip == nullptr) {
@@ -1411,7 +1386,7 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
                        failed = true;
                        return;
                    }
-                } else {
+                } else if (!mmapped) {
                    file.open(file_path, std::ios::binary);
                    if (!file.is_open()) {
                        LOG_ERROR("failed to open '%s'", file_path.c_str());
@@ -1464,6 +1439,11 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
                                zip_entry_noallocread(zip, (void*)buf, n);
                            }
                            zip_entry_close(zip);
+                        } else if (mmapped) {
+                            if (!mmapped->copy_data(buf, n, tensor_storage.offset)) {
+                                LOG_ERROR("read tensor data failed: '%s'", file_path.c_str());
+                                failed = true;
+                            }
                        } else {
                            file.seekg(tensor_storage.offset);
                            file.read(buf, n);
@@ -1543,6 +1523,8 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
                        t1 = ggml_time_ms();
                        copy_to_backend_time_ms.fetch_add(t1 - t0);
                    }
+
+                    bytes_processed.fetch_add((uint64_t)nbytes_to_read);
                }
                if (zip != nullptr) {
                    zip_close(zip);
@@ -1555,8 +1537,12 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
            if (current_idx >= file_tensors.size() || failed) {
                break;
            }
-            size_t curr_num = total_tensors_processed + current_idx;
-            pretty_progress(curr_num, total_tensors_to_process, (ggml_time_ms() - t_start) / 1000.0f / (curr_num + 1e-6f));
+            size_t curr_num       = total_tensors_processed + current_idx;
+            float elapsed_seconds = (ggml_time_ms() - t_start) / 1000.0f;
+            pretty_bytes_progress(static_cast<int>(curr_num),
+                                  static_cast<int>(total_tensors_to_process),
+                                  bytes_processed.load(),
+                                  elapsed_seconds);
            std::this_thread::sleep_for(std::chrono::milliseconds(200));
        }

@@ -1569,7 +1555,10 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
            break;
        }
        total_tensors_processed += file_tensors.size();
-        pretty_progress(total_tensors_processed, total_tensors_to_process, (ggml_time_ms() - t_start) / 1000.0f / (total_tensors_processed + 1e-6f));
+        pretty_bytes_progress(static_cast<int>(total_tensors_processed),
+                              static_cast<int>(total_tensors_to_process),
+                              bytes_processed.load(),
+                              (ggml_time_ms() - t_start) / 1000.0f);
        if (total_tensors_processed < total_tensors_to_process) {
            printf("\n");
        }
@@ -1586,9 +1575,10 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
    return success;
 }

-bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tensors,
+bool ModelLoader::load_tensors(std::map<std::string, ggml_tensor*>& tensors,
                               std::set<std::string> ignore_tensors,
-                               int n_threads) {
+                               int n_threads,
+                               bool enable_mmap) {
    std::set<std::string> tensor_names_in_file;
    std::mutex tensor_names_mutex;
    auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
@@ -1599,7 +1589,7 @@ bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tenso
            tensor_names_in_file.insert(name);
        }

-        struct ggml_tensor* real;
+        ggml_tensor* real;
        if (tensors.find(name) != tensors.end()) {
            real = tensors[name];
        } else {
@@ -1631,7 +1621,7 @@ bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tenso
        return true;
    };

-    bool success = load_tensors(on_new_tensor_cb, n_threads);
+    bool success = load_tensors(on_new_tensor_cb, n_threads, enable_mmap);
    if (!success) {
        LOG_ERROR("load tensors from file failed");
        return false;
@@ -1737,6 +1727,13 @@ bool ModelLoader::save_to_gguf_file(const std::string& file_path, ggml_type type
        // tensor_storage.ne[0], tensor_storage.ne[1], tensor_storage.ne[2], tensor_storage.ne[3],
        // tensor->n_dims, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);

+        if (!tensor->data) {
+            GGML_ASSERT(ggml_nelements(tensor) == 0);
+            // avoid crashing the gguf writer by setting a dummy pointer for zero-sized tensors
+            LOG_DEBUG("setting dummy pointer for zero-sized tensor %s", name.c_str());
+            tensor->data = ggml_get_mem_buffer(ggml_ctx);
+        }
+
        *dst_tensor = tensor;

        gguf_add_tensor(gguf_ctx, tensor);
@@ -1776,7 +1773,12 @@ int64_t ModelLoader::get_params_mem_size(ggml_backend_t backend, ggml_type type)
    return mem_size;
 }

-bool convert(const char* input_path, const char* vae_path, const char* output_path, sd_type_t output_type, const char* tensor_type_rules) {
+bool convert(const char* input_path,
+             const char* vae_path,
+             const char* output_path,
+             sd_type_t output_type,
+             const char* tensor_type_rules,
+             bool convert_name) {
    ModelLoader model_loader;

    if (!model_loader.init_from_file(input_path)) {
@@ -1790,7 +1792,9 @@ bool convert(const char* input_path, const char* vae_path, const char* output_pa
            return false;
        }
    }
-    model_loader.convert_tensors_name();
+    if (convert_name) {
+        model_loader.convert_tensors_name();
+    }
    bool success = model_loader.save_to_gguf_file(output_path, (ggml_type)output_type, tensor_type_rules);
    return success;
 }
@@ -28,9 +28,11 @@ enum SDVersion {
    VERSION_SD2,
    VERSION_SD2_INPAINT,
    VERSION_SD2_TINY_UNET,
+    VERSION_SDXS,
    VERSION_SDXL,
    VERSION_SDXL_INPAINT,
    VERSION_SDXL_PIX2PIX,
+    VERSION_SDXL_VEGA,
    VERSION_SDXL_SSD1B,
    VERSION_SVD,
    VERSION_SD3,
@@ -43,14 +45,16 @@ enum SDVersion {
    VERSION_WAN2_2_I2V,
    VERSION_WAN2_2_TI2V,
    VERSION_QWEN_IMAGE,
+    VERSION_ANIMA,
    VERSION_FLUX2,
+    VERSION_FLUX2_KLEIN,
    VERSION_Z_IMAGE,
    VERSION_OVIS_IMAGE,
    VERSION_COUNT,
 };

 static inline bool sd_version_is_sd1(SDVersion version) {
-    if (version == VERSION_SD1 || version == VERSION_SD1_INPAINT || version == VERSION_SD1_PIX2PIX || version == VERSION_SD1_TINY_UNET) {
+    if (version == VERSION_SD1 || version == VERSION_SD1_INPAINT || version == VERSION_SD1_PIX2PIX || version == VERSION_SD1_TINY_UNET || version == VERSION_SDXS) {
        return true;
    }
    return false;
@@ -64,7 +68,7 @@ static inline bool sd_version_is_sd2(SDVersion version) {
 }

 static inline bool sd_version_is_sdxl(SDVersion version) {
-    if (version == VERSION_SDXL || version == VERSION_SDXL_INPAINT || version == VERSION_SDXL_PIX2PIX || version == VERSION_SDXL_SSD1B) {
+    if (version == VERSION_SDXL || version == VERSION_SDXL_INPAINT || version == VERSION_SDXL_PIX2PIX || version == VERSION_SDXL_SSD1B || version == VERSION_SDXL_VEGA) {
        return true;
    }
    return false;
@@ -99,7 +103,7 @@ static inline bool sd_version_is_flux(SDVersion version) {
 }

 static inline bool sd_version_is_flux2(SDVersion version) {
-    if (version == VERSION_FLUX2) {
+    if (version == VERSION_FLUX2 || version == VERSION_FLUX2_KLEIN) {
        return true;
    }
    return false;
@@ -119,6 +123,13 @@ static inline bool sd_version_is_qwen_image(SDVersion version) {
    return false;
 }

+static inline bool sd_version_is_anima(SDVersion version) {
+    if (version == VERSION_ANIMA) {
+        return true;
+    }
+    return false;
+}
+
 static inline bool sd_version_is_z_image(SDVersion version) {
    if (version == VERSION_Z_IMAGE) {
        return true;
@@ -143,6 +154,7 @@ static inline bool sd_version_is_dit(SDVersion version) {
        sd_version_is_sd3(version) ||
        sd_version_is_wan(version) ||
        sd_version_is_qwen_image(version) ||
+        sd_version_is_anima(version) ||
        sd_version_is_z_image(version)) {
        return true;
    }
@@ -310,10 +322,11 @@ public:
    std::map<ggml_type, uint32_t> get_vae_wtype_stat();
    String2TensorStorage& get_tensor_storage_map() { return tensor_storage_map; }
    void set_wtype_override(ggml_type wtype, std::string tensor_type_rules = "");
-    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads = 0);
-    bool load_tensors(std::map<std::string, struct ggml_tensor*>& tensors,
+    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads = 0, bool use_mmap = false);
+    bool load_tensors(std::map<std::string, ggml_tensor*>& tensors,
                      std::set<std::string> ignore_tensors = {},
-                      int n_threads                        = 0);
+                      int n_threads                        = 0,
+                      bool use_mmap                        = false);

    std::vector<std::string> get_tensor_names() const {
        std::vector<std::string> names;
@@ -327,13 +340,6 @@ public:
    bool tensor_should_be_converted(const TensorStorage& tensor_storage, ggml_type type);
    int64_t get_params_mem_size(ggml_backend_t backend, ggml_type type = GGML_TYPE_COUNT);
    ~ModelLoader() = default;
-
-    static std::string load_merges();
-    static std::string load_qwen2_merges();
-    static std::string load_mistral_merges();
-    static std::string load_mistral_vocab_json();
-    static std::string load_t5_tokenizer_json();
-    static std::string load_umt5_tokenizer_json();
 };

 #endif  // __MODEL_H__
@@ -653,6 +653,14 @@ std::string convert_diffusers_dit_to_original_lumina2(std::string name) {
    return name;
 }

+std::string convert_other_dit_to_original_anima(std::string name) {
+    static const std::string anima_net_prefix = "net.";
+    if (!starts_with(name, anima_net_prefix)) {
+        name = anima_net_prefix + name;
+    }
+    return name;
+}
+
 std::string convert_diffusion_model_name(std::string name, std::string prefix, SDVersion version) {
    if (sd_version_is_sd1(version) || sd_version_is_sd2(version)) {
        name = convert_diffusers_unet_to_original_sd1(name);
@@ -664,6 +672,8 @@ std::string convert_diffusion_model_name(std::string name, std::string prefix, S
        name = convert_diffusers_dit_to_original_flux(name);
    } else if (sd_version_is_z_image(version)) {
        name = convert_diffusers_dit_to_original_lumina2(name);
+    } else if (sd_version_is_anima(version)) {
+        name = convert_other_dit_to_original_anima(name);
    }
    return name;
 }
@@ -835,12 +845,14 @@ std::string convert_sep_to_dot(std::string name) {
        "proj_out",
        "transformer_blocks",
        "single_transformer_blocks",
+        "single_blocks",
        "diffusion_model",
        "cond_stage_model",
        "first_stage_model",
        "conv_in",
        "conv_out",
        "lora_down",
+        "lora_mid",
        "lora_up",
        "diff_b",
        "hada_w1_a",
@@ -876,7 +888,18 @@ std::string convert_sep_to_dot(std::string name) {
        "ff_context",
        "norm_added_q",
        "norm_added_v",
-        "to_add_out"};
+        "to_add_out",
+        "txt_mod",
+        "img_mod",
+        "txt_mlp",
+        "img_mlp",
+        "proj_mlp",
+        "wi_0",
+        "wi_1",
+        "norm1_context",
+        "ff_context",
+        "x_embedder",
+    };

    // record the positions of underscores that should NOT be replaced
    std::unordered_set<size_t> protected_positions;
@@ -948,6 +971,7 @@ bool is_first_stage_model_name(const std::string& name) {
 std::string convert_tensor_name(std::string name, SDVersion version) {
    bool is_lora                             = false;
    bool is_lycoris_underline                = false;
+    bool is_underline                        = false;
    std::vector<std::string> lora_prefix_vec = {
        "lora.lora.",
        "lora.lora_",
@@ -955,12 +979,27 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
        "lora.lycoris.",
        "lora.",
    };
+    std::vector<std::string> underline_lora_prefix_vec = {
+        "unet_",
+        "te_",
+        "te1_",
+        "te2_",
+        "te3_",
+        "vae_",
+    };
    for (const auto& prefix : lora_prefix_vec) {
        if (starts_with(name, prefix)) {
            is_lora = true;
            name    = name.substr(prefix.size());
            if (contains(prefix, "lycoris_")) {
                is_lycoris_underline = true;
+            } else {
+                for (const auto& underline_lora_prefix : underline_lora_prefix_vec) {
+                    if (starts_with(name, underline_lora_prefix)) {
+                        is_underline = true;
+                        break;
+                    }
+                }
            }
            break;
        }
@@ -969,10 +1008,13 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
    if (is_lora) {
        std::map<std::string, std::string> lora_suffix_map = {
            {".lora_down.weight", ".weight.lora_down"},
+            {".lora_mid.weight", ".weight.lora_mid"},
            {".lora_up.weight", ".weight.lora_up"},
            {".lora.down.weight", ".weight.lora_down"},
+            {".lora.mid.weight", ".weight.lora_mid"},
            {".lora.up.weight", ".weight.lora_up"},
            {"_lora.down.weight", ".weight.lora_down"},
+            {"_lora.mid.weight", ".weight.lora_mid"},
            {"_lora.up.weight", ".weight.lora_up"},
            {".lora_A.weight", ".weight.lora_down"},
            {".lora_B.weight", ".weight.lora_up"},
@@ -1020,12 +1062,14 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
            }
        }

-        if (sd_version_is_unet(version) || is_lycoris_underline) {
+        // LOG_DEBUG("name %s %d", name.c_str(), version);
+
+        if (sd_version_is_unet(version) || is_underline || is_lycoris_underline) {
            name = convert_sep_to_dot(name);
        }
    }

-    std::vector<std::pair<std::string, std::string>> prefix_map = {
+    std::unordered_map<std::string, std::string> prefix_map = {
        {"diffusion_model.", "model.diffusion_model."},
        {"unet.", "model.diffusion_model."},
        {"transformer.", "model.diffusion_model."},  // dit
@@ -1040,8 +1084,13 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
        // {"te2.text_model.encoder.layers.", "cond_stage_model.1.model.transformer.resblocks."},
        {"te2.", "cond_stage_model.1.transformer."},
        {"te1.", "cond_stage_model.transformer."},
+        {"te3.", "text_encoders.t5xxl.transformer."},
    };

+    if (sd_version_is_flux(version)) {
+        prefix_map["te1."] = "text_encoders.clip_l.transformer.";
+    }
+
    replace_with_prefix_map(name, prefix_map);

    // diffusion model
@@ -1071,7 +1120,11 @@ std::string convert_tensor_name(std::string name, SDVersion version) {
        for (const auto& prefix : first_stage_model_prefix_vec) {
            if (starts_with(name, prefix)) {
                name = convert_first_stage_model_name(name.substr(prefix.size()), prefix);
-                name = prefix + name;
+                if (version == VERSION_SDXS) {
+                    name = "tae." + name;
+                } else {
+                    name = prefix + name;
+                }
                break;
            }
        }
@@ -21,19 +21,19 @@ public:
        blocks["layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(in_dim));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
        // x: [N, channels, h, w]

        auto fc1        = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
        auto fc2        = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
        auto layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["layernorm"]);

-        struct ggml_tensor* r = x;
+        ggml_tensor* r = x;
        // x = ggml_ext_layer_norm(ctx, x, ln_w, ln_b);
        x = layer_norm->forward(ctx, x);
        // x = ggml_add(ctx, ggml_mul_mat(ctx, fc1_w, x),  fc1_b);
        x = fc1->forward(ctx, x);
-        x = ggml_gelu_inplace(ctx->ggml_ctx, x);
+        x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
        x = fc2->forward(ctx, x);
        // x = ggml_add(ctx, ggml_mul_mat(ctx, fc2_w, x),  fc2_b);
        if (use_residue)
@@ -54,8 +54,8 @@ public:
        blocks["1"]   = std::shared_ptr<GGMLBlock>(new Mlp(dim, inner_dim, dim, false));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x) {
        auto norm = std::dynamic_pointer_cast<LayerNorm>(blocks["0"]);
        auto ff   = std::dynamic_pointer_cast<Mlp>(blocks["1"]);

@@ -72,7 +72,7 @@ struct PerceiverAttention : public GGMLBlock {
    int heads;     // = heads
 public:
    PerceiverAttention(int dim, int dim_h = 64, int h = 8)
-        : scale(powf(dim_h, -0.5)), dim_head(dim_h), heads(h) {
+        : scale(powf(static_cast<float>(dim_h), -0.5f)), dim_head(dim_h), heads(h) {
        int inner_dim    = dim_head * heads;
        blocks["norm1"]  = std::shared_ptr<GGMLBlock>(new LayerNorm(dim));
        blocks["norm2"]  = std::shared_ptr<GGMLBlock>(new LayerNorm(dim));
@@ -81,9 +81,9 @@ public:
        blocks["to_out"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, dim, false));
    }

-    struct ggml_tensor* reshape_tensor(struct ggml_context* ctx,
-                                       struct ggml_tensor* x,
-                                       int heads) {
+    ggml_tensor* reshape_tensor(ggml_context* ctx,
+                                ggml_tensor* x,
+                                int heads) {
        int64_t ne[4];
        for (int i = 0; i < 4; ++i)
            ne[i] = x->ne[i];
@@ -92,17 +92,17 @@ public:
        return x;
    }

-    std::vector<struct ggml_tensor*> chunk_half(struct ggml_context* ctx,
-                                                struct ggml_tensor* x) {
+    std::vector<ggml_tensor*> chunk_half(ggml_context* ctx,
+                                         ggml_tensor* x) {
        auto tlo = ggml_view_4d(ctx, x, x->ne[0] / 2, x->ne[1], x->ne[2], x->ne[3], x->nb[1], x->nb[2], x->nb[3], 0);
        auto tli = ggml_view_4d(ctx, x, x->ne[0] / 2, x->ne[1], x->ne[2], x->ne[3], x->nb[1], x->nb[2], x->nb[3], x->nb[0] * x->ne[0] / 2);
        return {ggml_cont(ctx, tlo),
                ggml_cont(ctx, tli)};
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* latents) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* latents) {
        // x (torch.Tensor): image features
        //     shape (b, n1, D)
        // latent (torch.Tensor): latent features
@@ -129,8 +129,8 @@ public:
        k             = reshape_tensor(ctx->ggml_ctx, k, heads);
        v             = reshape_tensor(ctx->ggml_ctx, v, heads);
        scale         = 1.f / sqrt(sqrt((float)dim_head));
-        k             = ggml_scale_inplace(ctx->ggml_ctx, k, scale);
-        q             = ggml_scale_inplace(ctx->ggml_ctx, q, scale);
+        k             = ggml_ext_scale(ctx->ggml_ctx, k, scale, true);
+        q             = ggml_ext_scale(ctx->ggml_ctx, q, scale, true);
        // auto weight = ggml_mul_mat(ctx, q, k);
        auto weight = ggml_mul_mat(ctx->ggml_ctx, k, q);  // NOTE order of mul is opposite to pytorch

@@ -176,9 +176,9 @@ public:
        }
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* latents,
-                                struct ggml_tensor* x) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* latents,
+                         ggml_tensor* x) {
        // x: [N, channels, h, w]
        auto proj_in  = std::dynamic_pointer_cast<Linear>(blocks["proj_in"]);
        auto proj_out = std::dynamic_pointer_cast<Linear>(blocks["proj_out"]);
@@ -225,19 +225,19 @@ public:
            4));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* x,
-                                struct ggml_tensor* last_hidden_state) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* x,
+                         ggml_tensor* last_hidden_state) {
        // x: [N, channels, h, w]
        auto token_proj          = std::dynamic_pointer_cast<Mlp>(blocks["token_proj"]);
        auto token_norm          = std::dynamic_pointer_cast<LayerNorm>(blocks["token_norm"]);
        auto perceiver_resampler = std::dynamic_pointer_cast<FacePerceiverResampler>(blocks["perceiver_resampler"]);

-        x                       = token_proj->forward(ctx, x);
-        int64_t nel             = ggml_nelements(x);
-        x                       = ggml_reshape_3d(ctx->ggml_ctx, x, cross_attention_dim, num_tokens, nel / (cross_attention_dim * num_tokens));
-        x                       = token_norm->forward(ctx, x);
-        struct ggml_tensor* out = perceiver_resampler->forward(ctx, x, last_hidden_state);
+        x                = token_proj->forward(ctx, x);
+        int64_t nel      = ggml_nelements(x);
+        x                = ggml_reshape_3d(ctx->ggml_ctx, x, cross_attention_dim, num_tokens, nel / (cross_attention_dim * num_tokens));
+        x                = token_norm->forward(ctx, x);
+        ggml_tensor* out = perceiver_resampler->forward(ctx, x, last_hidden_state);
        if (use_residul)
            out = ggml_add(ctx->ggml_ctx, x, out);
        return out;
@@ -256,9 +256,9 @@ public:
        blocks["layer_norm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(embed_dim));
    }

-    struct ggml_tensor* fuse_fn(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* prompt_embeds,
-                                struct ggml_tensor* id_embeds) {
+    ggml_tensor* fuse_fn(GGMLRunnerContext* ctx,
+                         ggml_tensor* prompt_embeds,
+                         ggml_tensor* id_embeds) {
        auto mlp1       = std::dynamic_pointer_cast<FuseBlock>(blocks["mlp1"]);
        auto mlp2       = std::dynamic_pointer_cast<FuseBlock>(blocks["mlp2"]);
        auto layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm"]);
@@ -273,24 +273,24 @@ public:
        return stacked_id_embeds;
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* prompt_embeds,
-                                struct ggml_tensor* id_embeds,
-                                struct ggml_tensor* class_tokens_mask,
-                                struct ggml_tensor* class_tokens_mask_pos,
-                                struct ggml_tensor* left,
-                                struct ggml_tensor* right) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* prompt_embeds,
+                         ggml_tensor* id_embeds,
+                         ggml_tensor* class_tokens_mask,
+                         ggml_tensor* class_tokens_mask_pos,
+                         ggml_tensor* left,
+                         ggml_tensor* right) {
        // x: [N, channels, h, w]

-        struct ggml_tensor* valid_id_embeds = id_embeds;
+        ggml_tensor* valid_id_embeds = id_embeds;
        // # slice out the image token embeddings
        ggml_set_name(class_tokens_mask_pos, "class_tokens_mask_pos");
        ggml_set_name(prompt_embeds, "prompt_embeds");
-        struct ggml_tensor* image_token_embeds = ggml_get_rows(ctx->ggml_ctx, prompt_embeds, class_tokens_mask_pos);
+        ggml_tensor* image_token_embeds = ggml_get_rows(ctx->ggml_ctx, prompt_embeds, class_tokens_mask_pos);
        ggml_set_name(image_token_embeds, "image_token_embeds");
-        valid_id_embeds                       = ggml_reshape_2d(ctx->ggml_ctx, valid_id_embeds, valid_id_embeds->ne[0],
-                                                                ggml_nelements(valid_id_embeds) / valid_id_embeds->ne[0]);
-        struct ggml_tensor* stacked_id_embeds = fuse_fn(ctx, image_token_embeds, valid_id_embeds);
+        valid_id_embeds                = ggml_reshape_2d(ctx->ggml_ctx, valid_id_embeds, valid_id_embeds->ne[0],
+                                                         ggml_nelements(valid_id_embeds) / valid_id_embeds->ne[0]);
+        ggml_tensor* stacked_id_embeds = fuse_fn(ctx, image_token_embeds, valid_id_embeds);

        if (left && right) {
            stacked_id_embeds = ggml_concat(ctx->ggml_ctx, left, stacked_id_embeds, 1);
@@ -301,10 +301,10 @@ public:
            stacked_id_embeds = ggml_concat(ctx->ggml_ctx, stacked_id_embeds, right, 1);
        }

-        class_tokens_mask                         = ggml_cont(ctx->ggml_ctx, ggml_transpose(ctx->ggml_ctx, class_tokens_mask));
-        class_tokens_mask                         = ggml_repeat(ctx->ggml_ctx, class_tokens_mask, prompt_embeds);
-        prompt_embeds                             = ggml_mul(ctx->ggml_ctx, prompt_embeds, class_tokens_mask);
-        struct ggml_tensor* updated_prompt_embeds = ggml_add(ctx->ggml_ctx, prompt_embeds, stacked_id_embeds);
+        class_tokens_mask                  = ggml_cont(ctx->ggml_ctx, ggml_transpose(ctx->ggml_ctx, class_tokens_mask));
+        class_tokens_mask                  = ggml_repeat(ctx->ggml_ctx, class_tokens_mask, prompt_embeds);
+        prompt_embeds                      = ggml_mul(ctx->ggml_ctx, prompt_embeds, class_tokens_mask);
+        ggml_tensor* updated_prompt_embeds = ggml_add(ctx->ggml_ctx, prompt_embeds, stacked_id_embeds);
        ggml_set_name(updated_prompt_embeds, "updated_prompt_embeds");
        return updated_prompt_embeds;
    }
@@ -317,22 +317,22 @@ struct PhotoMakerIDEncoderBlock : public CLIPVisionModelProjection {
        blocks["fuse_module"]         = std::shared_ptr<GGMLBlock>(new FuseModule(2048));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* id_pixel_values,
-                                struct ggml_tensor* prompt_embeds,
-                                struct ggml_tensor* class_tokens_mask,
-                                struct ggml_tensor* class_tokens_mask_pos,
-                                struct ggml_tensor* left,
-                                struct ggml_tensor* right) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* id_pixel_values,
+                         ggml_tensor* prompt_embeds,
+                         ggml_tensor* class_tokens_mask,
+                         ggml_tensor* class_tokens_mask_pos,
+                         ggml_tensor* left,
+                         ggml_tensor* right) {
        // x: [N, channels, h, w]
        auto vision_model        = std::dynamic_pointer_cast<CLIPVisionModel>(blocks["vision_model"]);
        auto visual_projection   = std::dynamic_pointer_cast<CLIPProjection>(blocks["visual_projection"]);
        auto visual_projection_2 = std::dynamic_pointer_cast<Linear>(blocks["visual_projection_2"]);
        auto fuse_module         = std::dynamic_pointer_cast<FuseModule>(blocks["fuse_module"]);

-        struct ggml_tensor* shared_id_embeds = vision_model->forward(ctx, id_pixel_values);          // [N, hidden_size]
-        struct ggml_tensor* id_embeds        = visual_projection->forward(ctx, shared_id_embeds);    // [N, proj_dim(768)]
-        struct ggml_tensor* id_embeds_2      = visual_projection_2->forward(ctx, shared_id_embeds);  // [N, 1280]
+        ggml_tensor* shared_id_embeds = vision_model->forward(ctx, id_pixel_values);          // [N, hidden_size]
+        ggml_tensor* id_embeds        = visual_projection->forward(ctx, shared_id_embeds);    // [N, proj_dim(768)]
+        ggml_tensor* id_embeds_2      = visual_projection_2->forward(ctx, shared_id_embeds);  // [N, 1280]

        id_embeds   = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, id_embeds, 2, 0, 1, 3));
        id_embeds_2 = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, id_embeds_2, 2, 0, 1, 3));
@@ -340,12 +340,12 @@ struct PhotoMakerIDEncoderBlock : public CLIPVisionModelProjection {
        id_embeds = ggml_concat(ctx->ggml_ctx, id_embeds, id_embeds_2, 2);  // [batch_size, seq_length, 1, 2048] check whether concat at dim 2 is right
        id_embeds = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, id_embeds, 1, 2, 0, 3));

-        struct ggml_tensor* updated_prompt_embeds = fuse_module->forward(ctx,
-                                                                         prompt_embeds,
-                                                                         id_embeds,
-                                                                         class_tokens_mask,
-                                                                         class_tokens_mask_pos,
-                                                                         left, right);
+        ggml_tensor* updated_prompt_embeds = fuse_module->forward(ctx,
+                                                                  prompt_embeds,
+                                                                  id_embeds,
+                                                                  class_tokens_mask,
+                                                                  class_tokens_mask_pos,
+                                                                  left, right);
        return updated_prompt_embeds;
    }
 };
@@ -365,29 +365,29 @@ struct PhotoMakerIDEncoder_CLIPInsightfaceExtendtokenBlock : public CLIPVisionMo
                                                                                        num_tokens));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                struct ggml_tensor* id_pixel_values,
-                                struct ggml_tensor* prompt_embeds,
-                                struct ggml_tensor* class_tokens_mask,
-                                struct ggml_tensor* class_tokens_mask_pos,
-                                struct ggml_tensor* id_embeds,
-                                struct ggml_tensor* left,
-                                struct ggml_tensor* right) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx,
+                         ggml_tensor* id_pixel_values,
+                         ggml_tensor* prompt_embeds,
+                         ggml_tensor* class_tokens_mask,
+                         ggml_tensor* class_tokens_mask_pos,
+                         ggml_tensor* id_embeds,
+                         ggml_tensor* left,
+                         ggml_tensor* right) {
        // x: [N, channels, h, w]
        auto vision_model      = std::dynamic_pointer_cast<CLIPVisionModel>(blocks["vision_model"]);
        auto fuse_module       = std::dynamic_pointer_cast<FuseModule>(blocks["fuse_module"]);
        auto qformer_perceiver = std::dynamic_pointer_cast<QFormerPerceiver>(blocks["qformer_perceiver"]);

-        // struct ggml_tensor* last_hidden_state = vision_model->forward(ctx, id_pixel_values);          // [N, hidden_size]
-        struct ggml_tensor* last_hidden_state = vision_model->forward(ctx, id_pixel_values, false);  // [N, hidden_size]
-        id_embeds                             = qformer_perceiver->forward(ctx, id_embeds, last_hidden_state);
+        // ggml_tensor* last_hidden_state = vision_model->forward(ctx, id_pixel_values);          // [N, hidden_size]
+        ggml_tensor* last_hidden_state = vision_model->forward(ctx, id_pixel_values, false);  // [N, hidden_size]
+        id_embeds                      = qformer_perceiver->forward(ctx, id_embeds, last_hidden_state);

-        struct ggml_tensor* updated_prompt_embeds = fuse_module->forward(ctx,
-                                                                         prompt_embeds,
-                                                                         id_embeds,
-                                                                         class_tokens_mask,
-                                                                         class_tokens_mask_pos,
-                                                                         left, right);
+        ggml_tensor* updated_prompt_embeds = fuse_module->forward(ctx,
+                                                                  prompt_embeds,
+                                                                  id_embeds,
+                                                                  class_tokens_mask,
+                                                                  class_tokens_mask_pos,
+                                                                  left, right);
        return updated_prompt_embeds;
    }
 };
@@ -436,18 +436,17 @@ public:
        return pm_version;
    }

-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
        if (pm_version == PM_VERSION_1)
            id_encoder.get_param_tensors(tensors, prefix);
        else if (pm_version == PM_VERSION_2)
            id_encoder2.get_param_tensors(tensors, prefix);
    }

-    struct ggml_cgraph* build_graph(  // struct ggml_allocr* allocr,
-        struct ggml_tensor* id_pixel_values,
-        struct ggml_tensor* prompt_embeds,
-        std::vector<bool>& class_tokens_mask,
-        struct ggml_tensor* id_embeds) {
+    ggml_cgraph* build_graph(const sd::Tensor<float>& id_pixel_values_tensor,
+                             const sd::Tensor<float>& prompt_embeds_tensor,
+                             std::vector<bool>& class_tokens_mask,
+                             const sd::Tensor<float>& id_embeds_tensor = {}) {
        ctm.clear();
        ctmf16.clear();
        ctmpos.clear();
@@ -458,20 +457,20 @@ public:

        auto runner_ctx = get_context();

-        struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
+        ggml_cgraph* gf = ggml_new_graph(compute_ctx);
+
+        ggml_tensor* id_pixel_values = make_input(id_pixel_values_tensor);
+        ggml_tensor* prompt_embeds   = make_input(prompt_embeds_tensor);
+        ggml_tensor* id_embeds       = make_optional_input(id_embeds_tensor);

        int64_t hidden_size = prompt_embeds->ne[0];
        int64_t seq_length  = prompt_embeds->ne[1];
        ggml_type type      = GGML_TYPE_F32;

-        struct ggml_tensor* class_tokens_mask_d = ggml_new_tensor_1d(runner_ctx.ggml_ctx, type, class_tokens_mask.size());
+        ggml_tensor* class_tokens_mask_d = ggml_new_tensor_1d(runner_ctx.ggml_ctx, type, class_tokens_mask.size());

-        struct ggml_tensor* id_pixel_values_d = to_backend(id_pixel_values);
-        struct ggml_tensor* prompt_embeds_d   = to_backend(prompt_embeds);
-        struct ggml_tensor* id_embeds_d       = to_backend(id_embeds);
-
-        struct ggml_tensor* left  = nullptr;
-        struct ggml_tensor* right = nullptr;
+        ggml_tensor* left  = nullptr;
+        ggml_tensor* right = nullptr;
        for (int i = 0; i < class_tokens_mask.size(); i++) {
            if (class_tokens_mask[i]) {
                // printf(" 1,");
@@ -495,7 +494,7 @@ public:
            right = ggml_new_tensor_3d(runner_ctx.ggml_ctx, type,
                                       hidden_size, seq_length - ctmpos[ctmpos.size() - 1] - 1, 1);
        }
-        struct ggml_tensor* class_tokens_mask_pos = ggml_new_tensor_1d(runner_ctx.ggml_ctx, GGML_TYPE_I32, ctmpos.size());
+        ggml_tensor* class_tokens_mask_pos = ggml_new_tensor_1d(runner_ctx.ggml_ctx, GGML_TYPE_I32, ctmpos.size());

        {
            if (type == GGML_TYPE_F16)
@@ -526,21 +525,21 @@ public:
                }
            }
        }
-        struct ggml_tensor* updated_prompt_embeds = nullptr;
+        ggml_tensor* updated_prompt_embeds = nullptr;
        if (pm_version == PM_VERSION_1)
            updated_prompt_embeds = id_encoder.forward(&runner_ctx,
-                                                       id_pixel_values_d,
-                                                       prompt_embeds_d,
+                                                       id_pixel_values,
+                                                       prompt_embeds,
                                                       class_tokens_mask_d,
                                                       class_tokens_mask_pos,
                                                       left, right);
        else if (pm_version == PM_VERSION_2)
            updated_prompt_embeds = id_encoder2.forward(&runner_ctx,
-                                                        id_pixel_values_d,
-                                                        prompt_embeds_d,
+                                                        id_pixel_values,
+                                                        prompt_embeds,
                                                        class_tokens_mask_d,
                                                        class_tokens_mask_pos,
-                                                        id_embeds_d,
+                                                        id_embeds,
                                                        left, right);

        ggml_build_forward_expand(gf, updated_prompt_embeds);
@@ -548,25 +547,21 @@ public:
        return gf;
    }

-    bool compute(const int n_threads,
-                 struct ggml_tensor* id_pixel_values,
-                 struct ggml_tensor* prompt_embeds,
-                 struct ggml_tensor* id_embeds,
-                 std::vector<bool>& class_tokens_mask,
-                 struct ggml_tensor** updated_prompt_embeds,
-                 ggml_context* output_ctx) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
-            // return build_graph(compute_allocr, id_pixel_values, prompt_embeds, class_tokens_mask);
+    sd::Tensor<float> compute(const int n_threads,
+                              const sd::Tensor<float>& id_pixel_values,
+                              const sd::Tensor<float>& prompt_embeds,
+                              const sd::Tensor<float>& id_embeds,
+                              std::vector<bool>& class_tokens_mask) {
+        auto get_graph = [&]() -> ggml_cgraph* {
            return build_graph(id_pixel_values, prompt_embeds, class_tokens_mask, id_embeds);
        };

-        // GGMLRunner::compute(get_graph, n_threads, updated_prompt_embeds);
-        return GGMLRunner::compute(get_graph, n_threads, true, updated_prompt_embeds, output_ctx);
+        return take_or_empty(GGMLRunner::compute<float>(get_graph, n_threads, true));
    }
 };

 struct PhotoMakerIDEmbed : public GGMLRunner {
-    std::map<std::string, struct ggml_tensor*> tensors;
+    std::map<std::string, ggml_tensor*> tensors;
    std::string file_path;
    ModelLoader* model_loader;
    bool load_failed = false;
@@ -606,11 +601,11 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
            }
            if (dry_run) {
                std::lock_guard<std::mutex> lock(tensor_mutex);
-                struct ggml_tensor* real = ggml_new_tensor(params_ctx,
-                                                           tensor_storage.type,
-                                                           tensor_storage.n_dims,
-                                                           tensor_storage.ne);
-                tensors[name]            = real;
+                ggml_tensor* real = ggml_new_tensor(params_ctx,
+                                                    tensor_storage.type,
+                                                    tensor_storage.n_dims,
+                                                    tensor_storage.ne);
+                tensors[name]     = real;
            } else {
                auto real   = tensors[name];
                *dst_tensor = real;
@@ -629,8 +624,8 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
        return true;
    }

-    struct ggml_tensor* get() {
-        std::map<std::string, struct ggml_tensor*>::iterator pos;
+    ggml_tensor* get() {
+        std::map<std::string, ggml_tensor*>::iterator pos;
        pos = tensors.find("pmid.id_embeds");
        if (pos != tensors.end())
            return pos->second;
@@ -0,0 +1,278 @@
+#ifndef __PREPROCESSING_HPP__
+#define __PREPROCESSING_HPP__
+
+#include <cmath>
+#include <limits>
+
+#include "ggml_extend.hpp"
+
+#define M_PI_ 3.14159265358979323846f
+
+static inline int64_t preprocessing_offset_4d(const sd::Tensor<float>& tensor, int64_t i0, int64_t i1 = 0, int64_t i2 = 0, int64_t i3 = 0) {
+    const auto& shape = tensor.shape();
+    int64_t n0        = shape.size() > 0 ? shape[0] : 1;
+    int64_t n1        = shape.size() > 1 ? shape[1] : 1;
+    int64_t n2        = shape.size() > 2 ? shape[2] : 1;
+    return ((i3 * n2 + i2) * n1 + i1) * n0 + i0;
+}
+
+static inline float preprocessing_get_4d(const sd::Tensor<float>& tensor, int64_t i0, int64_t i1 = 0, int64_t i2 = 0, int64_t i3 = 0) {
+    return tensor.values()[static_cast<size_t>(preprocessing_offset_4d(tensor, i0, i1, i2, i3))];
+}
+
+static inline void preprocessing_set_4d(sd::Tensor<float>& tensor, float value, int64_t i0, int64_t i1 = 0, int64_t i2 = 0, int64_t i3 = 0) {
+    tensor.values()[static_cast<size_t>(preprocessing_offset_4d(tensor, i0, i1, i2, i3))] = value;
+}
+
+static inline sd::Tensor<float> sd_image_to_preprocessing_tensor(sd_image_t image) {
+    sd::Tensor<float> tensor({static_cast<int64_t>(image.width), static_cast<int64_t>(image.height), static_cast<int64_t>(image.channel), 1});
+    for (uint32_t y = 0; y < image.height; ++y) {
+        for (uint32_t x = 0; x < image.width; ++x) {
+            for (uint32_t c = 0; c < image.channel; ++c) {
+                preprocessing_set_4d(tensor, sd_image_get_f32(image, x, y, c), x, y, c, 0);
+            }
+        }
+    }
+    return tensor;
+}
+
+static inline void preprocessing_tensor_to_sd_image(const sd::Tensor<float>& tensor, uint8_t* image_data) {
+    GGML_ASSERT(tensor.dim() == 4);
+    GGML_ASSERT(tensor.shape()[3] == 1);
+    GGML_ASSERT(image_data != nullptr);
+
+    int width   = static_cast<int>(tensor.shape()[0]);
+    int height  = static_cast<int>(tensor.shape()[1]);
+    int channel = static_cast<int>(tensor.shape()[2]);
+    for (int y = 0; y < height; ++y) {
+        for (int x = 0; x < width; ++x) {
+            for (int c = 0; c < channel; ++c) {
+                float value                               = preprocessing_get_4d(tensor, x, y, c, 0);
+                value                                     = std::min(1.0f, std::max(0.0f, value));
+                image_data[(y * width + x) * channel + c] = static_cast<uint8_t>(std::round(value * 255.0f));
+            }
+        }
+    }
+}
+
+static inline sd::Tensor<float> gaussian_kernel_tensor(int kernel_size) {
+    sd::Tensor<float> kernel({kernel_size, kernel_size, 1, 1});
+    int ks_mid   = kernel_size / 2;
+    float sigma  = 1.4f;
+    float normal = 1.f / (2.0f * M_PI_ * std::pow(sigma, 2.0f));
+    for (int y = 0; y < kernel_size; ++y) {
+        float gx = static_cast<float>(-ks_mid + y);
+        for (int x = 0; x < kernel_size; ++x) {
+            float gy = static_cast<float>(-ks_mid + x);
+            float k  = std::exp(-((gx * gx + gy * gy) / (2.0f * std::pow(sigma, 2.0f)))) * normal;
+            preprocessing_set_4d(kernel, k, x, y, 0, 0);
+        }
+    }
+    return kernel;
+}
+
+static inline sd::Tensor<float> convolve_tensor(const sd::Tensor<float>& input, const sd::Tensor<float>& kernel, int padding) {
+    GGML_ASSERT(input.dim() == 4);
+    GGML_ASSERT(kernel.dim() == 4);
+    GGML_ASSERT(input.shape()[3] == 1);
+    GGML_ASSERT(kernel.shape()[2] == 1);
+    GGML_ASSERT(kernel.shape()[3] == 1);
+
+    sd::Tensor<float> output(input.shape());
+    int64_t width    = input.shape()[0];
+    int64_t height   = input.shape()[1];
+    int64_t channels = input.shape()[2];
+    int64_t kernel_w = kernel.shape()[0];
+    int64_t kernel_h = kernel.shape()[1];
+
+    for (int64_t c = 0; c < channels; ++c) {
+        for (int64_t y = 0; y < height; ++y) {
+            for (int64_t x = 0; x < width; ++x) {
+                float sum = 0.0f;
+                for (int64_t ky = 0; ky < kernel_h; ++ky) {
+                    int64_t iy = y + ky - padding;
+                    if (iy < 0 || iy >= height) {
+                        continue;
+                    }
+                    for (int64_t kx = 0; kx < kernel_w; ++kx) {
+                        int64_t ix = x + kx - padding;
+                        if (ix < 0 || ix >= width) {
+                            continue;
+                        }
+                        sum += preprocessing_get_4d(input, ix, iy, c, 0) * preprocessing_get_4d(kernel, kx, ky, 0, 0);
+                    }
+                }
+                preprocessing_set_4d(output, sum, x, y, c, 0);
+            }
+        }
+    }
+    return output;
+}
+
+static inline sd::Tensor<float> grayscale_tensor(const sd::Tensor<float>& rgb_img) {
+    GGML_ASSERT(rgb_img.dim() == 4);
+    GGML_ASSERT(rgb_img.shape()[2] >= 3);
+    sd::Tensor<float> grayscale({rgb_img.shape()[0], rgb_img.shape()[1], 1, rgb_img.shape()[3]});
+    for (int64_t iy = 0; iy < rgb_img.shape()[1]; ++iy) {
+        for (int64_t ix = 0; ix < rgb_img.shape()[0]; ++ix) {
+            float r    = preprocessing_get_4d(rgb_img, ix, iy, 0, 0);
+            float g    = preprocessing_get_4d(rgb_img, ix, iy, 1, 0);
+            float b    = preprocessing_get_4d(rgb_img, ix, iy, 2, 0);
+            float gray = 0.2989f * r + 0.5870f * g + 0.1140f * b;
+            preprocessing_set_4d(grayscale, gray, ix, iy, 0, 0);
+        }
+    }
+    return grayscale;
+}
+
+static inline sd::Tensor<float> tensor_hypot(const sd::Tensor<float>& x, const sd::Tensor<float>& y) {
+    sd::tensor_check_same_shape(x, y);
+    sd::Tensor<float> out(x.shape());
+    for (int64_t i = 0; i < out.numel(); ++i) {
+        out[i] = std::sqrt(x[i] * x[i] + y[i] * y[i]);
+    }
+    return out;
+}
+
+static inline sd::Tensor<float> tensor_arctan2(const sd::Tensor<float>& x, const sd::Tensor<float>& y) {
+    sd::tensor_check_same_shape(x, y);
+    sd::Tensor<float> out(x.shape());
+    for (int64_t i = 0; i < out.numel(); ++i) {
+        out[i] = std::atan2(y[i], x[i]);
+    }
+    return out;
+}
+
+static inline void normalize_tensor(sd::Tensor<float>* g) {
+    GGML_ASSERT(g != nullptr);
+    if (g->empty()) {
+        return;
+    }
+    float max_value = -std::numeric_limits<float>::infinity();
+    for (int64_t i = 0; i < g->numel(); ++i) {
+        max_value = std::max(max_value, (*g)[i]);
+    }
+    if (max_value == 0.0f || !std::isfinite(max_value)) {
+        return;
+    }
+    *g *= (1.0f / max_value);
+}
+
+static inline sd::Tensor<float> non_max_supression(const sd::Tensor<float>& G, const sd::Tensor<float>& D) {
+    GGML_ASSERT(G.shape() == D.shape());
+    sd::Tensor<float> result = sd::Tensor<float>::zeros(G.shape());
+    for (int64_t iy = 1; iy < result.shape()[1] - 1; ++iy) {
+        for (int64_t ix = 1; ix < result.shape()[0] - 1; ++ix) {
+            float angle = preprocessing_get_4d(D, ix, iy, 0, 0) * 180.0f / M_PI_;
+            angle       = angle < 0.0f ? angle + 180.0f : angle;
+            float q     = 1.0f;
+            float r     = 1.0f;
+
+            if ((0 >= angle && angle < 22.5f) || (157.5f >= angle && angle <= 180.0f)) {
+                q = preprocessing_get_4d(G, ix, iy + 1, 0, 0);
+                r = preprocessing_get_4d(G, ix, iy - 1, 0, 0);
+            } else if (22.5f >= angle && angle < 67.5f) {
+                q = preprocessing_get_4d(G, ix + 1, iy - 1, 0, 0);
+                r = preprocessing_get_4d(G, ix - 1, iy + 1, 0, 0);
+            } else if (67.5f >= angle && angle < 112.5f) {
+                q = preprocessing_get_4d(G, ix + 1, iy, 0, 0);
+                r = preprocessing_get_4d(G, ix - 1, iy, 0, 0);
+            } else if (112.5f >= angle && angle < 157.5f) {
+                q = preprocessing_get_4d(G, ix - 1, iy - 1, 0, 0);
+                r = preprocessing_get_4d(G, ix + 1, iy + 1, 0, 0);
+            }
+
+            float cur = preprocessing_get_4d(G, ix, iy, 0, 0);
+            preprocessing_set_4d(result, (cur >= q && cur >= r) ? cur : 0.0f, ix, iy, 0, 0);
+        }
+    }
+    return result;
+}
+
+static inline void threshold_hystersis(sd::Tensor<float>* img, float high_threshold, float low_threshold, float weak, float strong) {
+    GGML_ASSERT(img != nullptr);
+    if (img->empty()) {
+        return;
+    }
+    float max_value = -std::numeric_limits<float>::infinity();
+    for (int64_t i = 0; i < img->numel(); ++i) {
+        max_value = std::max(max_value, (*img)[i]);
+    }
+
+    float ht = max_value * high_threshold;
+    float lt = ht * low_threshold;
+    for (int64_t i = 0; i < img->numel(); ++i) {
+        float img_v = (*img)[i];
+        if (img_v >= ht) {
+            (*img)[i] = strong;
+        } else if (img_v <= ht && img_v >= lt) {
+            (*img)[i] = weak;
+        }
+    }
+
+    for (int64_t iy = 0; iy < img->shape()[1]; ++iy) {
+        for (int64_t ix = 0; ix < img->shape()[0]; ++ix) {
+            if (!(ix >= 3 && ix <= img->shape()[0] - 3 && iy >= 3 && iy <= img->shape()[1] - 3)) {
+                preprocessing_set_4d(*img, 0.0f, ix, iy, 0, 0);
+            }
+        }
+    }
+
+    for (int64_t iy = 1; iy < img->shape()[1] - 1; ++iy) {
+        for (int64_t ix = 1; ix < img->shape()[0] - 1; ++ix) {
+            float imd_v = preprocessing_get_4d(*img, ix, iy, 0, 0);
+            if (imd_v == weak) {
+                bool has_strong_neighbor =
+                    preprocessing_get_4d(*img, ix + 1, iy - 1, 0, 0) == strong ||
+                    preprocessing_get_4d(*img, ix + 1, iy, 0, 0) == strong ||
+                    preprocessing_get_4d(*img, ix, iy - 1, 0, 0) == strong ||
+                    preprocessing_get_4d(*img, ix, iy + 1, 0, 0) == strong ||
+                    preprocessing_get_4d(*img, ix - 1, iy - 1, 0, 0) == strong ||
+                    preprocessing_get_4d(*img, ix - 1, iy, 0, 0) == strong;
+                preprocessing_set_4d(*img, has_strong_neighbor ? strong : 0.0f, ix, iy, 0, 0);
+            }
+        }
+    }
+}
+
+bool preprocess_canny(sd_image_t img, float high_threshold, float low_threshold, float weak, float strong, bool inverse) {
+    float kX[9] = {
+        -1, 0, 1,
+        -2, 0, 2,
+        -1, 0, 1};
+
+    float kY[9] = {
+        1, 2, 1,
+        0, 0, 0,
+        -1, -2, -1};
+
+    sd::Tensor<float> gkernel = gaussian_kernel_tensor(5);
+    sd::Tensor<float> sf_kx({3, 3, 1, 1}, std::vector<float>(kX, kX + 9));
+    sd::Tensor<float> sf_ky({3, 3, 1, 1}, std::vector<float>(kY, kY + 9));
+
+    sd::Tensor<float> image      = sd_image_to_preprocessing_tensor(img);
+    sd::Tensor<float> image_gray = grayscale_tensor(image);
+    image_gray                   = convolve_tensor(image_gray, gkernel, 2);
+    sd::Tensor<float> iX         = convolve_tensor(image_gray, sf_kx, 1);
+    sd::Tensor<float> iY         = convolve_tensor(image_gray, sf_ky, 1);
+    sd::Tensor<float> G          = tensor_hypot(iX, iY);
+    normalize_tensor(&G);
+    sd::Tensor<float> theta = tensor_arctan2(iX, iY);
+    image_gray              = non_max_supression(G, theta);
+    threshold_hystersis(&image_gray, high_threshold, low_threshold, weak, strong);
+
+    for (uint32_t iy = 0; iy < img.height; ++iy) {
+        for (uint32_t ix = 0; ix < img.width; ++ix) {
+            float gray = preprocessing_get_4d(image_gray, ix, iy, 0, 0);
+            gray       = inverse ? 1.0f - gray : gray;
+            for (uint32_t c = 0; c < img.channel; ++c) {
+                preprocessing_set_4d(image, gray, ix, iy, c, 0);
+            }
+        }
+    }
+
+    preprocessing_tensor_to_sd_image(image, img.data);
+    return true;
+}
+
+#endif  // __PREPROCESSING_HPP__
@@ -3,9 +3,8 @@

 #include <memory>

-#include "common.hpp"
+#include "common_block.hpp"
 #include "flux.hpp"
-#include "ggml_extend.hpp"

 namespace Qwen {
    constexpr int QWEN_IMAGE_GRAPH_SIZE = 20480;
@@ -27,9 +26,9 @@ namespace Qwen {
            blocks["linear_2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, out_dim, sample_proj_bias));
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* sample,
-                                    struct ggml_tensor* condition = nullptr) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* sample,
+                             ggml_tensor* condition = nullptr) {
            if (condition != nullptr) {
                auto cond_proj = std::dynamic_pointer_cast<Linear>(blocks["cond_proj"]);
                sample         = ggml_add(ctx->ggml_ctx, sample, cond_proj->forward(ctx, condition));
@@ -50,8 +49,8 @@ namespace Qwen {
            blocks["timestep_embedder"] = std::shared_ptr<GGMLBlock>(new TimestepEmbedding(256, embedding_dim));
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* timesteps) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* timesteps) {
            // timesteps: [N,]
            // return: [N, embedding_dim]
            auto timestep_embedder = std::dynamic_pointer_cast<TimestepEmbedding>(blocks["timestep_embedder"]);
@@ -108,10 +107,10 @@ namespace Qwen {
        }

        std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                                      struct ggml_tensor* img,
-                                                      struct ggml_tensor* txt,
-                                                      struct ggml_tensor* pe,
-                                                      struct ggml_tensor* mask = nullptr) {
+                                                      ggml_tensor* img,
+                                                      ggml_tensor* txt,
+                                                      ggml_tensor* pe,
+                                                      ggml_tensor* mask = nullptr) {
            // img: [N, n_img_token, hidden_size]
            // txt: [N, n_txt_token, hidden_size]
            // pe: [n_img_token + n_txt_token, d_head/2, 2, 2]
@@ -162,26 +161,25 @@ namespace Qwen {
            auto k = ggml_concat(ctx->ggml_ctx, txt_k, img_k, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]
            auto v = ggml_concat(ctx->ggml_ctx, txt_v, img_v, 2);  // [N, n_txt_token + n_img_token, n_head, d_head]

-            auto attn         = Rope::attention(ctx, q, k, v, pe, mask, (1.0f / 128.f));                  // [N, n_txt_token + n_img_token, n_head*d_head]
-            attn              = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, attn, 0, 2, 1, 3));  // [n_txt_token + n_img_token, N, hidden_size]
+            auto attn         = Rope::attention(ctx, q, k, v, pe, mask, (1.0f / 128.f));  // [N, n_txt_token + n_img_token, n_head*d_head]
            auto txt_attn_out = ggml_view_3d(ctx->ggml_ctx,
                                             attn,
                                             attn->ne[0],
-                                             attn->ne[1],
                                             txt->ne[1],
+                                             attn->ne[2],
                                             attn->nb[1],
                                             attn->nb[2],
-                                             0);                                                                  // [n_txt_token, N, hidden_size]
-            txt_attn_out      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, txt_attn_out, 0, 2, 1, 3));  // [N, n_txt_token, hidden_size]
+                                             0);  // [N, n_txt_token, n_head*d_head]
            auto img_attn_out = ggml_view_3d(ctx->ggml_ctx,
                                             attn,
                                             attn->ne[0],
-                                             attn->ne[1],
                                             img->ne[1],
+                                             attn->ne[2],
                                             attn->nb[1],
                                             attn->nb[2],
-                                             attn->nb[2] * txt->ne[1]);                                           // [n_img_token, N, hidden_size]
-            img_attn_out      = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, img_attn_out, 0, 2, 1, 3));  // [N, n_img_token, hidden_size]
+                                             txt->ne[1] * attn->nb[1]);  // [N, n_img_token, n_head*d_head]
+            img_attn_out      = ggml_cont(ctx->ggml_ctx, img_attn_out);
+            txt_attn_out      = ggml_cont(ctx->ggml_ctx, txt_attn_out);

            img_attn_out = to_out_0->forward(ctx, img_attn_out);
            txt_attn_out = to_add_out->forward(ctx, txt_attn_out);
@@ -191,11 +189,16 @@ namespace Qwen {
    };

    class QwenImageTransformerBlock : public GGMLBlock {
+    protected:
+        bool zero_cond_t;
+
    public:
        QwenImageTransformerBlock(int64_t dim,
                                  int64_t num_attention_heads,
                                  int64_t attention_head_dim,
-                                  float eps = 1e-6) {
+                                  float eps        = 1e-6,
+                                  bool zero_cond_t = false)
+            : zero_cond_t(zero_cond_t) {
            // img_mod.0 is nn.SiLU()
            blocks["img_mod.1"] = std::shared_ptr<GGMLBlock>(new Linear(dim, 6 * dim, true));

@@ -208,7 +211,7 @@ namespace Qwen {

            blocks["txt_norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(dim, eps, false));
            blocks["txt_norm2"] = std::shared_ptr<GGMLBlock>(new LayerNorm(dim, eps, false));
-            blocks["txt_mlp"]   = std::shared_ptr<GGMLBlock>(new FeedForward(dim, dim, 4, FeedForward::Activation::GELU));
+            blocks["txt_mlp"]   = std::shared_ptr<GGMLBlock>(new FeedForward(dim, dim, 4, FeedForward::Activation::GELU, true));

            blocks["attn"] = std::shared_ptr<GGMLBlock>(new QwenImageAttention(dim,
                                                                               attention_head_dim,
@@ -220,11 +223,37 @@ namespace Qwen {
                                                                               eps));
        }

+        std::vector<ggml_tensor*> get_mod_params_vec(ggml_context* ctx, ggml_tensor* mod_params, ggml_tensor* index = nullptr) {
+            // index: [N, n_img_token]
+            // mod_params: [N, hidden_size * 12]
+            if (index == nullptr) {
+                return ggml_ext_chunk(ctx, mod_params, 6, 0);
+            }
+            mod_params          = ggml_reshape_1d(ctx, mod_params, ggml_nelements(mod_params));
+            auto mod_params_vec = ggml_ext_chunk(ctx, mod_params, 12, 0);
+            index               = ggml_reshape_3d(ctx, index, 1, index->ne[0], index->ne[1]);                                      // [N, n_img_token, 1]
+            index               = ggml_repeat_4d(ctx, index, mod_params_vec[0]->ne[0], index->ne[1], index->ne[2], index->ne[3]);  // [N, n_img_token, hidden_size]
+            std::vector<ggml_tensor*> mod_results;
+            for (int i = 0; i < 6; i++) {
+                auto mod_0 = mod_params_vec[i];
+                auto mod_1 = mod_params_vec[i + 6];
+
+                // mod_result = torch.where(index == 0, mod_0, mod_1)
+                // mod_result = (1 - index)*mod_0 + index*mod_1
+                mod_0           = ggml_sub(ctx, ggml_repeat(ctx, mod_0, index), ggml_mul(ctx, index, mod_0));  // [N, n_img_token, hidden_size]
+                mod_1           = ggml_mul(ctx, index, mod_1);                                                 // [N, n_img_token, hidden_size]
+                auto mod_result = ggml_add(ctx, mod_0, mod_1);
+                mod_results.push_back(mod_result);
+            }
+            return mod_results;
+        }
+
        virtual std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
-                                                              struct ggml_tensor* img,
-                                                              struct ggml_tensor* txt,
-                                                              struct ggml_tensor* t_emb,
-                                                              struct ggml_tensor* pe) {
+                                                              ggml_tensor* img,
+                                                              ggml_tensor* txt,
+                                                              ggml_tensor* t_emb,
+                                                              ggml_tensor* pe,
+                                                              ggml_tensor* modulate_index = nullptr) {
            // img: [N, n_img_token, hidden_size]
            // txt: [N, n_txt_token, hidden_size]
            // pe: [n_img_token + n_txt_token, d_head/2, 2, 2]
@@ -244,14 +273,18 @@ namespace Qwen {

            auto img_mod_params    = ggml_silu(ctx->ggml_ctx, t_emb);
            img_mod_params         = img_mod_1->forward(ctx, img_mod_params);
-            auto img_mod_param_vec = ggml_ext_chunk(ctx->ggml_ctx, img_mod_params, 6, 0);
+            auto img_mod_param_vec = get_mod_params_vec(ctx->ggml_ctx, img_mod_params, modulate_index);
+
+            if (zero_cond_t) {
+                t_emb = ggml_ext_chunk(ctx->ggml_ctx, t_emb, 2, 1)[0];
+            }

            auto txt_mod_params    = ggml_silu(ctx->ggml_ctx, t_emb);
            txt_mod_params         = txt_mod_1->forward(ctx, txt_mod_params);
-            auto txt_mod_param_vec = ggml_ext_chunk(ctx->ggml_ctx, txt_mod_params, 6, 0);
+            auto txt_mod_param_vec = get_mod_params_vec(ctx->ggml_ctx, txt_mod_params);

            auto img_normed    = img_norm1->forward(ctx, img);
-            auto img_modulated = Flux::modulate(ctx->ggml_ctx, img_normed, img_mod_param_vec[0], img_mod_param_vec[1]);
+            auto img_modulated = Flux::modulate(ctx->ggml_ctx, img_normed, img_mod_param_vec[0], img_mod_param_vec[1], modulate_index != nullptr);
            auto img_gate1     = img_mod_param_vec[2];

            auto txt_normed    = txt_norm1->forward(ctx, txt);
@@ -264,7 +297,7 @@ namespace Qwen {
            txt = ggml_add(ctx->ggml_ctx, txt, ggml_mul(ctx->ggml_ctx, txt_attn_output, txt_gate1));

            auto img_normed2    = img_norm2->forward(ctx, img);
-            auto img_modulated2 = Flux::modulate(ctx->ggml_ctx, img_normed2, img_mod_param_vec[3], img_mod_param_vec[4]);
+            auto img_modulated2 = Flux::modulate(ctx->ggml_ctx, img_normed2, img_mod_param_vec[3], img_mod_param_vec[4], modulate_index != nullptr);
            auto img_gate2      = img_mod_param_vec[5];

            auto txt_normed2    = txt_norm2->forward(ctx, txt);
@@ -292,9 +325,9 @@ namespace Qwen {
            blocks["linear"] = std::shared_ptr<GGMLBlock>(new Linear(conditioning_embedding_dim, embedding_dim * 2, bias));
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* c) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* c) {
            // x: [N, n_token, hidden_size]
            // c: [N, hidden_size]
            // return: [N, n_token, patch_size * patch_size * out_channels]
@@ -315,16 +348,17 @@ namespace Qwen {
    };

    struct QwenImageParams {
-        int64_t patch_size          = 2;
+        int patch_size              = 2;
        int64_t in_channels         = 64;
        int64_t out_channels        = 16;
-        int64_t num_layers          = 60;
+        int num_layers              = 60;
        int64_t attention_head_dim  = 128;
        int64_t num_attention_heads = 24;
        int64_t joint_attention_dim = 3584;
-        float theta                 = 10000;
+        int theta                   = 10000;
        std::vector<int> axes_dim   = {16, 56, 56};
-        int64_t axes_dim_sum        = 128;
+        int axes_dim_sum            = 128;
+        bool zero_cond_t            = false;
    };

    class QwenImageModel : public GGMLBlock {
@@ -346,7 +380,8 @@ namespace Qwen {
                auto block                                        = std::shared_ptr<GGMLBlock>(new QwenImageTransformerBlock(inner_dim,
                                                                                                                             params.num_attention_heads,
                                                                                                                             params.attention_head_dim,
-                                                                                                                             1e-6f));
+                                                                                                                             1e-6f,
+                                                                                                                             params.zero_cond_t));
                blocks["transformer_blocks." + std::to_string(i)] = block;
            }

@@ -354,74 +389,12 @@ namespace Qwen {
            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, params.patch_size * params.patch_size * params.out_channels));
        }

-        struct ggml_tensor* pad_to_patch_size(struct ggml_context* ctx,
-                                              struct ggml_tensor* x) {
-            int64_t W = x->ne[0];
-            int64_t H = x->ne[1];
-
-            int pad_h = (params.patch_size - H % params.patch_size) % params.patch_size;
-            int pad_w = (params.patch_size - W % params.patch_size) % params.patch_size;
-            x         = ggml_pad(ctx, x, pad_w, pad_h, 0, 0);  // [N, C, H + pad_h, W + pad_w]
-            return x;
-        }
-
-        struct ggml_tensor* patchify(struct ggml_context* ctx,
-                                     struct ggml_tensor* x) {
-            // x: [N, C, H, W]
-            // return: [N, h*w, C * patch_size * patch_size]
-            int64_t N = x->ne[3];
-            int64_t C = x->ne[2];
-            int64_t H = x->ne[1];
-            int64_t W = x->ne[0];
-            int64_t p = params.patch_size;
-            int64_t h = H / params.patch_size;
-            int64_t w = W / params.patch_size;
-
-            GGML_ASSERT(h * p == H && w * p == W);
-
-            x = ggml_reshape_4d(ctx, x, p, w, p, h * C * N);       // [N*C*h, p, w, p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N*C*h, w, p, p]
-            x = ggml_reshape_4d(ctx, x, p * p, w * h, C, N);       // [N, C, h*w, p*p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N, h*w, C, p*p]
-            x = ggml_reshape_3d(ctx, x, p * p * C, w * h, N);      // [N, h*w, C*p*p]
-            return x;
-        }
-
-        struct ggml_tensor* process_img(struct ggml_context* ctx,
-                                        struct ggml_tensor* x) {
-            x = pad_to_patch_size(ctx, x);
-            x = patchify(ctx, x);
-            return x;
-        }
-
-        struct ggml_tensor* unpatchify(struct ggml_context* ctx,
-                                       struct ggml_tensor* x,
-                                       int64_t h,
-                                       int64_t w) {
-            // x: [N, h*w, C*patch_size*patch_size]
-            // return: [N, C, H, W]
-            int64_t N = x->ne[2];
-            int64_t C = x->ne[0] / params.patch_size / params.patch_size;
-            int64_t H = h * params.patch_size;
-            int64_t W = w * params.patch_size;
-            int64_t p = params.patch_size;
-
-            GGML_ASSERT(C * p * p == x->ne[0]);
-
-            x = ggml_reshape_4d(ctx, x, p * p, C, w * h, N);       // [N, h*w, C, p*p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N, C, h*w, p*p]
-            x = ggml_reshape_4d(ctx, x, p, p, w, h * C * N);       // [N*C*h, w, p, p]
-            x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // [N*C*h, p, w, p]
-            x = ggml_reshape_4d(ctx, x, W, H, C, N);               // [N, C, h*p, w*p]
-
-            return x;
-        }
-
-        struct ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
-                                         struct ggml_tensor* x,
-                                         struct ggml_tensor* timestep,
-                                         struct ggml_tensor* context,
-                                         struct ggml_tensor* pe) {
+        ggml_tensor* forward_orig(GGMLRunnerContext* ctx,
+                                  ggml_tensor* x,
+                                  ggml_tensor* timestep,
+                                  ggml_tensor* context,
+                                  ggml_tensor* pe,
+                                  ggml_tensor* modulate_index = nullptr) {
            auto time_text_embed = std::dynamic_pointer_cast<QwenTimestepProjEmbeddings>(blocks["time_text_embed"]);
            auto txt_norm        = std::dynamic_pointer_cast<RMSNorm>(blocks["txt_norm"]);
            auto img_in          = std::dynamic_pointer_cast<Linear>(blocks["img_in"]);
@@ -430,30 +403,39 @@ namespace Qwen {
            auto proj_out        = std::dynamic_pointer_cast<Linear>(blocks["proj_out"]);

            auto t_emb = time_text_embed->forward(ctx, timestep);
-            auto img   = img_in->forward(ctx, x);
-            auto txt   = txt_norm->forward(ctx, context);
-            txt        = txt_in->forward(ctx, txt);
+            if (params.zero_cond_t) {
+                auto t_emb_0 = time_text_embed->forward(ctx, ggml_ext_zeros_like(ctx->ggml_ctx, timestep));
+                t_emb        = ggml_concat(ctx->ggml_ctx, t_emb, t_emb_0, 1);
+            }
+            auto img = img_in->forward(ctx, x);
+            auto txt = txt_norm->forward(ctx, context);
+            txt      = txt_in->forward(ctx, txt);

            for (int i = 0; i < params.num_layers; i++) {
                auto block = std::dynamic_pointer_cast<QwenImageTransformerBlock>(blocks["transformer_blocks." + std::to_string(i)]);

-                auto result = block->forward(ctx, img, txt, t_emb, pe);
+                auto result = block->forward(ctx, img, txt, t_emb, pe, modulate_index);
                img         = result.first;
                txt         = result.second;
            }

+            if (params.zero_cond_t) {
+                t_emb = ggml_ext_chunk(ctx->ggml_ctx, t_emb, 2, 1)[0];
+            }
+
            img = norm_out->forward(ctx, img, t_emb);
            img = proj_out->forward(ctx, img);

            return img;
        }

-        struct ggml_tensor* forward(GGMLRunnerContext* ctx,
-                                    struct ggml_tensor* x,
-                                    struct ggml_tensor* timestep,
-                                    struct ggml_tensor* context,
-                                    struct ggml_tensor* pe,
-                                    std::vector<ggml_tensor*> ref_latents = {}) {
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* timestep,
+                             ggml_tensor* context,
+                             ggml_tensor* pe,
+                             std::vector<ggml_tensor*> ref_latents = {},
+                             ggml_tensor* modulate_index           = nullptr) {
            // Forward pass of DiT.
            // x: [N, C, H, W]
            // timestep: [N,]
@@ -466,20 +448,17 @@ namespace Qwen {
            int64_t C = x->ne[2];
            int64_t N = x->ne[3];

-            auto img            = process_img(ctx->ggml_ctx, x);
-            uint64_t img_tokens = img->ne[1];
+            auto img           = DiT::pad_and_patchify(ctx, x, params.patch_size, params.patch_size);
+            int64_t img_tokens = img->ne[1];

            if (ref_latents.size() > 0) {
                for (ggml_tensor* ref : ref_latents) {
-                    ref = process_img(ctx->ggml_ctx, ref);
+                    ref = DiT::pad_and_patchify(ctx, ref, params.patch_size, params.patch_size);
                    img = ggml_concat(ctx->ggml_ctx, img, ref, 1);
                }
            }

-            int64_t h_len = ((H + (params.patch_size / 2)) / params.patch_size);
-            int64_t w_len = ((W + (params.patch_size / 2)) / params.patch_size);
-
-            auto out = forward_orig(ctx, img, timestep, context, pe);  // [N, h_len*w_len, ph*pw*C]
+            auto out = forward_orig(ctx, img, timestep, context, pe, modulate_index);  // [N, h_len*w_len, ph*pw*C]

            if (out->ne[1] > img_tokens) {
                out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, out, 0, 2, 1, 3));  // [num_tokens, N, C * patch_size * patch_size]
@@ -487,11 +466,7 @@ namespace Qwen {
                out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, out, 0, 2, 1, 3));  // [N, h*w, C * patch_size * patch_size]
            }

-            out = unpatchify(ctx->ggml_ctx, out, h_len, w_len);  // [N, C, H + pad_h, W + pad_w]
-
-            // slice
-            out = ggml_ext_slice(ctx->ggml_ctx, out, 1, 0, H);  // [N, C, H, W + pad_w]
-            out = ggml_ext_slice(ctx->ggml_ctx, out, 0, 0, W);  // [N, C, H, W]
+            out = DiT::unpatchify_and_crop(ctx->ggml_ctx, out, H, W, params.patch_size, params.patch_size);  // [N, C, H, W]

            return out;
        }
@@ -502,19 +477,25 @@ namespace Qwen {
        QwenImageParams qwen_image_params;
        QwenImageModel qwen_image;
        std::vector<float> pe_vec;
+        std::vector<float> modulate_index_vec;
        SDVersion version;

        QwenImageRunner(ggml_backend_t backend,
                        bool offload_params_to_cpu,
                        const String2TensorStorage& tensor_storage_map = {},
                        const std::string prefix                       = "",
-                        SDVersion version                              = VERSION_QWEN_IMAGE)
+                        SDVersion version                              = VERSION_QWEN_IMAGE,
+                        bool zero_cond_t                               = false)
            : GGMLRunner(backend, offload_params_to_cpu) {
-            qwen_image_params.num_layers = 0;
+            qwen_image_params.num_layers  = 0;
+            qwen_image_params.zero_cond_t = zero_cond_t;
            for (auto pair : tensor_storage_map) {
                std::string tensor_name = pair.first;
                if (tensor_name.find(prefix) == std::string::npos)
                    continue;
+                if (tensor_name.find("__index_timestep_zero__") != std::string::npos) {
+                    qwen_image_params.zero_cond_t = true;
+                }
                size_t pos = tensor_name.find("transformer_blocks.");
                if (pos != std::string::npos) {
                    tensor_name = tensor_name.substr(pos);  // remove prefix
@@ -529,6 +510,9 @@ namespace Qwen {
                }
            }
            LOG_INFO("qwen_image_params.num_layers: %ld", qwen_image_params.num_layers);
+            if (qwen_image_params.zero_cond_t) {
+                LOG_INFO("use zero_cond_t");
+            }
            qwen_image = QwenImageModel(qwen_image_params);
            qwen_image.init(params_ctx, tensor_storage_map, prefix);
        }
@@ -537,36 +521,39 @@ namespace Qwen {
            return "qwen_image";
        }

-        void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
            qwen_image.get_param_tensors(tensors, prefix);
        }

-        struct ggml_cgraph* build_graph(struct ggml_tensor* x,
-                                        struct ggml_tensor* timesteps,
-                                        struct ggml_tensor* context,
-                                        std::vector<ggml_tensor*> ref_latents = {},
-                                        bool increase_ref_index               = false) {
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor,
+                                 const std::vector<sd::Tensor<float>>& ref_latents_tensor = {},
+                                 bool increase_ref_index                                  = false) {
+            ggml_cgraph* gf        = new_graph_custom(QWEN_IMAGE_GRAPH_SIZE);
+            ggml_tensor* x         = make_input(x_tensor);
+            ggml_tensor* timesteps = make_input(timesteps_tensor);
            GGML_ASSERT(x->ne[3] == 1);
-            struct ggml_cgraph* gf = new_graph_custom(QWEN_IMAGE_GRAPH_SIZE);
-
-            x         = to_backend(x);
-            context   = to_backend(context);
-            timesteps = to_backend(timesteps);
-
-            for (int i = 0; i < ref_latents.size(); i++) {
-                ref_latents[i] = to_backend(ref_latents[i]);
+            GGML_ASSERT(!context_tensor.empty());
+            ggml_tensor* context = make_input(context_tensor);
+            std::vector<ggml_tensor*> ref_latents;
+            ref_latents.reserve(ref_latents_tensor.size());
+            for (const auto& ref_latent_tensor : ref_latents_tensor) {
+                ref_latents.push_back(make_input(ref_latent_tensor));
            }

-            pe_vec      = Rope::gen_qwen_image_pe(x->ne[1],
-                                                  x->ne[0],
+            pe_vec      = Rope::gen_qwen_image_pe(static_cast<int>(x->ne[1]),
+                                                  static_cast<int>(x->ne[0]),
                                                  qwen_image_params.patch_size,
-                                                  x->ne[3],
-                                                  context->ne[1],
+                                                  static_cast<int>(x->ne[3]),
+                                                  static_cast<int>(context->ne[1]),
                                                  ref_latents,
                                                  increase_ref_index,
                                                  qwen_image_params.theta,
+                                                  circular_y_enabled,
+                                                  circular_x_enabled,
                                                  qwen_image_params.axes_dim);
-            int pos_len = pe_vec.size() / qwen_image_params.axes_dim_sum / 2;
+            int pos_len = static_cast<int>(pe_vec.size() / qwen_image_params.axes_dim_sum / 2);
            // LOG_DEBUG("pos_len %d", pos_len);
            auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, qwen_image_params.axes_dim_sum / 2, pos_len);
            // pe->data = pe_vec.data();
@@ -574,69 +561,100 @@ namespace Qwen {
            // pe->data = nullptr;
            set_backend_tensor_data(pe, pe_vec.data());

+            ggml_tensor* modulate_index = nullptr;
+            if (qwen_image_params.zero_cond_t) {
+                modulate_index_vec.clear();
+
+                int64_t h_len          = ((x->ne[1] + (qwen_image_params.patch_size / 2)) / qwen_image_params.patch_size);
+                int64_t w_len          = ((x->ne[0] + (qwen_image_params.patch_size / 2)) / qwen_image_params.patch_size);
+                int64_t num_img_tokens = h_len * w_len;
+
+                modulate_index_vec.insert(modulate_index_vec.end(), num_img_tokens, 0.f);
+                int64_t num_ref_img_tokens = 0;
+                for (ggml_tensor* ref : ref_latents) {
+                    int64_t h_len = ((ref->ne[1] + (qwen_image_params.patch_size / 2)) / qwen_image_params.patch_size);
+                    int64_t w_len = ((ref->ne[0] + (qwen_image_params.patch_size / 2)) / qwen_image_params.patch_size);
+
+                    num_ref_img_tokens += h_len * w_len;
+                }
+
+                if (num_ref_img_tokens > 0) {
+                    modulate_index_vec.insert(modulate_index_vec.end(), num_ref_img_tokens, 1.f);
+                }
+
+                modulate_index = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_F32, modulate_index_vec.size());
+                set_backend_tensor_data(modulate_index, modulate_index_vec.data());
+            }
+
            auto runner_ctx = get_context();

-            struct ggml_tensor* out = qwen_image.forward(&runner_ctx,
-                                                         x,
-                                                         timesteps,
-                                                         context,
-                                                         pe,
-                                                         ref_latents);
+            ggml_tensor* out = qwen_image.forward(&runner_ctx,
+                                                  x,
+                                                  timesteps,
+                                                  context,
+                                                  pe,
+                                                  ref_latents,
+                                                  modulate_index);

            ggml_build_forward_expand(gf, out);

            return gf;
        }

-        bool compute(int n_threads,
-                     struct ggml_tensor* x,
-                     struct ggml_tensor* timesteps,
-                     struct ggml_tensor* context,
-                     std::vector<ggml_tensor*> ref_latents = {},
-                     bool increase_ref_index               = false,
-                     struct ggml_tensor** output           = nullptr,
-                     struct ggml_context* output_ctx       = nullptr) {
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context,
+                                  const std::vector<sd::Tensor<float>>& ref_latents = {},
+                                  bool increase_ref_index                           = false) {
            // x: [N, in_channels, h, w]
            // timesteps: [N, ]
            // context: [N, max_position, hidden_size]
-            auto get_graph = [&]() -> struct ggml_cgraph* {
+            auto get_graph = [&]() -> ggml_cgraph* {
                return build_graph(x, timesteps, context, ref_latents, increase_ref_index);
            };

-            return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
        }

        void test() {
-            struct ggml_init_params params;
+            ggml_init_params params;
            params.mem_size   = static_cast<size_t>(1024 * 1024) * 1024;  // 1GB
            params.mem_buffer = nullptr;
            params.no_alloc   = false;

-            struct ggml_context* work_ctx = ggml_init(params);
-            GGML_ASSERT(work_ctx != nullptr);
+            ggml_context* ctx = ggml_init(params);
+            GGML_ASSERT(ctx != nullptr);

            {
-                // auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 16, 16, 16, 1);
+                // auto x = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 16, 16, 16, 1);
                // ggml_set_f32(x, 0.01f);
-                auto x = load_tensor_from_file(work_ctx, "./qwen_image_x.bin");
-                print_ggml_tensor(x);
+                auto x = sd::load_tensor_from_file_as_tensor<float>("./qwen_image_x.bin");
+                print_sd_tensor(x);

                std::vector<float> timesteps_vec(1, 1000.f);
-                auto timesteps = vector_to_ggml_tensor(work_ctx, timesteps_vec);
+                auto timesteps = sd::Tensor<float>::from_vector(timesteps_vec);

-                // auto context = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 3584, 256, 1);
+                // auto context = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 3584, 256, 1);
                // ggml_set_f32(context, 0.01f);
-                auto context = load_tensor_from_file(work_ctx, "./qwen_image_context.bin");
-                print_ggml_tensor(context);
+                auto context = sd::load_tensor_from_file_as_tensor<float>("./qwen_image_context.bin");
+                print_sd_tensor(context);

-                struct ggml_tensor* out = nullptr;
+                sd::Tensor<float> out;

-                int t0 = ggml_time_ms();
-                compute(8, x, timesteps, context, {}, false, &out, work_ctx);
-                int t1 = ggml_time_ms();
+                int64_t t0   = ggml_time_ms();
+                auto out_opt = compute(8,
+                                       x,
+                                       timesteps,
+                                       context,
+                                       {},
+                                       false);
+                int64_t t1   = ggml_time_ms();

-                print_ggml_tensor(out);
-                LOG_DEBUG("qwen_image test done in %dms", t1 - t0);
+                GGML_ASSERT(!out_opt.empty());
+                out = std::move(out_opt);
+                print_sd_tensor(out);
+                LOG_DEBUG("qwen_image test done in %lldms", t1 - t0);
            }
        }

@@ -684,4 +702,4 @@ namespace Qwen {

 }  // namespace name

-#endif  // __QWEN_IMAGE_HPP__
+#endif  // __QWEN_IMAGE_HPP__
@@ -90,7 +90,7 @@ class MT19937RNG : public RNG {
            float u1    = 1.0f - data[j];
            float u2    = data[j + 8];
            float r     = std::sqrt(-2.0f * std::log(u1));
-            float theta = 2.0f * 3.14159265358979323846 * u2;
+            float theta = 2.0f * 3.14159265358979323846f * u2;
            data[j]     = r * std::cos(theta) * std + mean;
            data[j + 8] = r * std::sin(theta) * std + mean;
        }
@@ -1,6 +1,8 @@
 #ifndef __ROPE_HPP__
 #define __ROPE_HPP__

+#include <algorithm>
+#include <cmath>
 #include <vector>
 #include "ggml_extend.hpp"

@@ -20,11 +22,11 @@ namespace Rope {
    }

    __STATIC_INLINE__ std::vector<std::vector<float>> transpose(const std::vector<std::vector<float>>& mat) {
-        int rows = mat.size();
-        int cols = mat[0].size();
+        size_t rows = mat.size();
+        size_t cols = mat[0].size();
        std::vector<std::vector<float>> transposed(cols, std::vector<float>(rows));
-        for (int i = 0; i < rows; ++i) {
-            for (int j = 0; j < cols; ++j) {
+        for (size_t i = 0; i < rows; ++i) {
+            for (size_t j = 0; j < cols; ++j) {
                transposed[j][i] = mat[i][j];
            }
        }
@@ -39,7 +41,10 @@ namespace Rope {
        return flat_vec;
    }

-    __STATIC_INLINE__ std::vector<std::vector<float>> rope(const std::vector<float>& pos, int dim, int theta) {
+    __STATIC_INLINE__ std::vector<std::vector<float>> rope(const std::vector<float>& pos,
+                                                           int dim,
+                                                           float theta,
+                                                           const std::vector<int>& axis_wrap_dims = {}) {
        assert(dim % 2 == 0);
        int half_dim = dim / 2;

@@ -47,14 +52,31 @@ namespace Rope {

        std::vector<float> omega(half_dim);
        for (int i = 0; i < half_dim; ++i) {
-            omega[i] = 1.0 / std::pow(theta, scale[i]);
+            omega[i] = 1.0f / ::powf(1.f * theta, scale[i]);
        }

-        int pos_size = pos.size();
+        size_t pos_size = pos.size();
        std::vector<std::vector<float>> out(pos_size, std::vector<float>(half_dim));
-        for (int i = 0; i < pos_size; ++i) {
-            for (int j = 0; j < half_dim; ++j) {
-                out[i][j] = pos[i] * omega[j];
+        for (size_t i = 0; i < pos_size; ++i) {
+            for (size_t j = 0; j < half_dim; ++j) {
+                float angle = pos[i] * omega[j];
+                if (!axis_wrap_dims.empty()) {
+                    size_t wrap_size = axis_wrap_dims.size();
+                    // mod batch size since we only store this for one item in the batch
+                    size_t wrap_idx = wrap_size > 0 ? (i % wrap_size) : 0;
+                    int wrap_dim    = axis_wrap_dims[wrap_idx];
+                    if (wrap_dim > 0) {
+                        constexpr float TWO_PI = 6.28318530717958647692f;
+                        float cycles           = omega[j] * wrap_dim / TWO_PI;
+                        // closest periodic harmonic, necessary to ensure things neatly tile
+                        // without this round, things don't tile at the boundaries and you end up
+                        // with the model knowing what is "center"
+                        float rounded = std::round(cycles);
+                        angle         = pos[i] * TWO_PI * rounded / wrap_dim;
+                    }
+                }
+
+                out[i][j] = angle;
            }
        }

@@ -77,7 +99,7 @@ namespace Rope {
        for (int dim = 0; dim < axes_dim_num; dim++) {
            if (arange_dims.find(dim) != arange_dims.end()) {
                for (int i = 0; i < bs * context_len; i++) {
-                    txt_ids[i][dim] = (i % context_len);
+                    txt_ids[i][dim] = 1.f * (i % context_len);
                }
            }
        }
@@ -89,20 +111,29 @@ namespace Rope {
                                                                       int patch_size,
                                                                       int bs,
                                                                       int axes_dim_num,
-                                                                       int index    = 0,
-                                                                       int h_offset = 0,
-                                                                       int w_offset = 0) {
+                                                                       int index       = 0,
+                                                                       int h_offset    = 0,
+                                                                       int w_offset    = 0,
+                                                                       bool scale_rope = false) {
        int h_len = (h + (patch_size / 2)) / patch_size;
        int w_len = (w + (patch_size / 2)) / patch_size;

        std::vector<std::vector<float>> img_ids(h_len * w_len, std::vector<float>(axes_dim_num, 0.0));

-        std::vector<float> row_ids = linspace<float>(h_offset, h_len - 1 + h_offset, h_len);
-        std::vector<float> col_ids = linspace<float>(w_offset, w_len - 1 + w_offset, w_len);
+        int h_start = h_offset;
+        int w_start = w_offset;
+
+        if (scale_rope) {
+            h_start -= h_len / 2;
+            w_start -= w_len / 2;
+        }
+
+        std::vector<float> row_ids = linspace<float>(1.f * h_start, 1.f * h_start + h_len - 1, h_len);
+        std::vector<float> col_ids = linspace<float>(1.f * w_start, 1.f * w_start + w_len - 1, w_len);

        for (int i = 0; i < h_len; ++i) {
            for (int j = 0; j < w_len; ++j) {
-                img_ids[i * w_len + j][0] = index;
+                img_ids[i * w_len + j][0] = 1.f * index;
                img_ids[i * w_len + j][1] = row_ids[i];
                img_ids[i * w_len + j][2] = col_ids[j];
            }
@@ -136,11 +167,12 @@ namespace Rope {

    __STATIC_INLINE__ std::vector<float> embed_nd(const std::vector<std::vector<float>>& ids,
                                                  int bs,
-                                                  int theta,
-                                                  const std::vector<int>& axes_dim) {
+                                                  const std::vector<float>& axis_thetas,
+                                                  const std::vector<int>& axes_dim,
+                                                  const std::vector<std::vector<int>>& wrap_dims = {}) {
        std::vector<std::vector<float>> trans_ids = transpose(ids);
        size_t pos_len                            = ids.size() / bs;
-        int num_axes                              = axes_dim.size();
+        size_t num_axes                           = axes_dim.size();
        // for (int i = 0; i < pos_len; i++) {
        //     std::cout << trans_ids[0][i] << " " << trans_ids[1][i] << " " << trans_ids[2][i] << std::endl;
        // }
@@ -150,9 +182,18 @@ namespace Rope {
            emb_dim += d / 2;

        std::vector<std::vector<float>> emb(bs * pos_len, std::vector<float>(emb_dim * 2 * 2, 0.0));
-        int offset = 0;
-        for (int i = 0; i < num_axes; ++i) {
-            std::vector<std::vector<float>> rope_emb = rope(trans_ids[i], axes_dim[i], theta);  // [bs*pos_len, axes_dim[i]/2 * 2 * 2]
+        size_t offset = 0;
+        for (size_t i = 0; i < num_axes; ++i) {
+            std::vector<int> axis_wrap_dims;
+            if (!wrap_dims.empty() && i < (int)wrap_dims.size()) {
+                axis_wrap_dims = wrap_dims[i];
+            }
+            float axis_theta = 10000.0f;
+            if (!axis_thetas.empty()) {
+                axis_theta = axis_thetas[std::min(i, axis_thetas.size() - 1)];
+            }
+            std::vector<std::vector<float>> rope_emb =
+                rope(trans_ids[i], axes_dim[i], axis_theta, axis_wrap_dims);  // [bs*pos_len, axes_dim[i]/2 * 2 * 2]
            for (int b = 0; b < bs; ++b) {
                for (int j = 0; j < pos_len; ++j) {
                    for (int k = 0; k < rope_emb[0].size(); ++k) {
@@ -166,43 +207,55 @@ namespace Rope {
        return flatten(emb);
    }

+    __STATIC_INLINE__ std::vector<float> embed_nd(const std::vector<std::vector<float>>& ids,
+                                                  int bs,
+                                                  float theta,
+                                                  const std::vector<int>& axes_dim,
+                                                  const std::vector<std::vector<int>>& wrap_dims = {}) {
+        std::vector<float> axis_thetas(axes_dim.size(), theta);
+        return embed_nd(ids, bs, axis_thetas, axes_dim, wrap_dims);
+    }
+
    __STATIC_INLINE__ std::vector<std::vector<float>> gen_refs_ids(int patch_size,
                                                                   int bs,
                                                                   int axes_dim_num,
                                                                   const std::vector<ggml_tensor*>& ref_latents,
                                                                   bool increase_ref_index,
-                                                                   float ref_index_scale) {
+                                                                   float ref_index_scale,
+                                                                   bool scale_rope) {
        std::vector<std::vector<float>> ids;
-        uint64_t curr_h_offset = 0;
-        uint64_t curr_w_offset = 0;
-        int index              = 1;
+        int curr_h_offset = 0;
+        int curr_w_offset = 0;
+        int index         = 1;
        for (ggml_tensor* ref : ref_latents) {
-            uint64_t h_offset = 0;
-            uint64_t w_offset = 0;
+            int h_offset = 0;
+            int w_offset = 0;
            if (!increase_ref_index) {
                if (ref->ne[1] + curr_h_offset > ref->ne[0] + curr_w_offset) {
                    w_offset = curr_w_offset;
                } else {
                    h_offset = curr_h_offset;
                }
+                scale_rope = false;
            }

-            auto ref_ids = gen_flux_img_ids(ref->ne[1],
-                                            ref->ne[0],
+            auto ref_ids = gen_flux_img_ids(static_cast<int>(ref->ne[1]),
+                                            static_cast<int>(ref->ne[0]),
                                            patch_size,
                                            bs,
                                            axes_dim_num,
                                            static_cast<int>(index * ref_index_scale),
                                            h_offset,
-                                            w_offset);
+                                            w_offset,
+                                            scale_rope);
            ids          = concat_ids(ids, ref_ids, bs);

            if (increase_ref_index) {
                index++;
            }

-            curr_h_offset = std::max(curr_h_offset, ref->ne[1] + h_offset);
-            curr_w_offset = std::max(curr_w_offset, ref->ne[0] + w_offset);
+            curr_h_offset = std::max(curr_h_offset, static_cast<int>(ref->ne[1]) + h_offset);
+            curr_w_offset = std::max(curr_w_offset, static_cast<int>(ref->ne[0]) + w_offset);
        }
        return ids;
    }
@@ -222,7 +275,7 @@ namespace Rope {

        auto ids = concat_ids(txt_ids, img_ids, bs);
        if (ref_latents.size() > 0) {
-            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, ref_index_scale);
+            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, ref_index_scale, false);
            ids           = concat_ids(ids, refs_ids, bs);
        }
        return ids;
@@ -239,6 +292,8 @@ namespace Rope {
                                                     bool increase_ref_index,
                                                     float ref_index_scale,
                                                     int theta,
+                                                     bool circular_h,
+                                                     bool circular_w,
                                                     const std::vector<int>& axes_dim) {
        std::vector<std::vector<float>> ids = gen_flux_ids(h,
                                                           w,
@@ -250,7 +305,47 @@ namespace Rope {
                                                           ref_latents,
                                                           increase_ref_index,
                                                           ref_index_scale);
-        return embed_nd(ids, bs, theta, axes_dim);
+        std::vector<std::vector<int>> wrap_dims;
+        if ((circular_h || circular_w) && bs > 0 && axes_dim.size() >= 3) {
+            int h_len = (h + (patch_size / 2)) / patch_size;
+            int w_len = (w + (patch_size / 2)) / patch_size;
+            if (h_len > 0 && w_len > 0) {
+                size_t pos_len = ids.size() / bs;
+                wrap_dims.assign(axes_dim.size(), std::vector<int>(pos_len, 0));
+                size_t cursor           = context_len;  // text first
+                const size_t img_tokens = static_cast<size_t>(h_len) * static_cast<size_t>(w_len);
+                for (size_t token_i = 0; token_i < img_tokens; ++token_i) {
+                    if (circular_h) {
+                        wrap_dims[1][cursor + token_i] = h_len;
+                    }
+                    if (circular_w) {
+                        wrap_dims[2][cursor + token_i] = w_len;
+                    }
+                }
+                cursor += img_tokens;
+                // reference latents
+                for (ggml_tensor* ref : ref_latents) {
+                    if (ref == nullptr) {
+                        continue;
+                    }
+                    int ref_h         = static_cast<int>(ref->ne[1]);
+                    int ref_w         = static_cast<int>(ref->ne[0]);
+                    int ref_h_l       = (ref_h + (patch_size / 2)) / patch_size;
+                    int ref_w_l       = (ref_w + (patch_size / 2)) / patch_size;
+                    size_t ref_tokens = static_cast<size_t>(ref_h_l) * static_cast<size_t>(ref_w_l);
+                    for (size_t token_i = 0; token_i < ref_tokens; ++token_i) {
+                        if (circular_h) {
+                            wrap_dims[1][cursor + token_i] = ref_h_l;
+                        }
+                        if (circular_w) {
+                            wrap_dims[2][cursor + token_i] = ref_w_l;
+                        }
+                    }
+                    cursor += ref_tokens;
+                }
+            }
+        }
+        return embed_nd(ids, bs, static_cast<float>(theta), axes_dim, wrap_dims);
    }

    __STATIC_INLINE__ std::vector<std::vector<float>> gen_qwen_image_ids(int h,
@@ -263,7 +358,7 @@ namespace Rope {
        int h_len        = (h + (patch_size / 2)) / patch_size;
        int w_len        = (w + (patch_size / 2)) / patch_size;
        int txt_id_start = std::max(h_len, w_len);
-        auto txt_ids     = linspace<float>(txt_id_start, context_len + txt_id_start, context_len);
+        auto txt_ids     = linspace<float>(1.f * txt_id_start, 1.f * context_len + txt_id_start, context_len);
        std::vector<std::vector<float>> txt_ids_repeated(bs * context_len, std::vector<float>(3));
        for (int i = 0; i < bs; ++i) {
            for (int j = 0; j < txt_ids.size(); ++j) {
@@ -271,10 +366,10 @@ namespace Rope {
            }
        }
        int axes_dim_num = 3;
-        auto img_ids     = gen_flux_img_ids(h, w, patch_size, bs, axes_dim_num);
+        auto img_ids     = gen_flux_img_ids(h, w, patch_size, bs, axes_dim_num, 0, 0, 0, true);
        auto ids         = concat_ids(txt_ids_repeated, img_ids, bs);
        if (ref_latents.size() > 0) {
-            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, 1.f);
+            auto refs_ids = gen_refs_ids(patch_size, bs, axes_dim_num, ref_latents, increase_ref_index, 1.f, true);
            ids           = concat_ids(ids, refs_ids, bs);
        }
        return ids;
@@ -289,9 +384,57 @@ namespace Rope {
                                                           const std::vector<ggml_tensor*>& ref_latents,
                                                           bool increase_ref_index,
                                                           int theta,
+                                                           bool circular_h,
+                                                           bool circular_w,
                                                           const std::vector<int>& axes_dim) {
        std::vector<std::vector<float>> ids = gen_qwen_image_ids(h, w, patch_size, bs, context_len, ref_latents, increase_ref_index);
-        return embed_nd(ids, bs, theta, axes_dim);
+        std::vector<std::vector<int>> wrap_dims;
+        // This logic simply stores the (pad and patch_adjusted) sizes of images so we can make sure rope correctly tiles
+        if ((circular_h || circular_w) && bs > 0 && axes_dim.size() >= 3) {
+            int pad_h = (patch_size - (h % patch_size)) % patch_size;
+            int pad_w = (patch_size - (w % patch_size)) % patch_size;
+            int h_len = (h + pad_h) / patch_size;
+            int w_len = (w + pad_w) / patch_size;
+            if (h_len > 0 && w_len > 0) {
+                const size_t total_tokens = ids.size();
+                // Track per-token wrap lengths for the row/column axes so only spatial tokens become periodic.
+                wrap_dims.assign(axes_dim.size(), std::vector<int>(total_tokens / bs, 0));
+                size_t cursor           = context_len;  // ignore text tokens
+                const size_t img_tokens = static_cast<size_t>(h_len) * static_cast<size_t>(w_len);
+                for (size_t token_i = 0; token_i < img_tokens; ++token_i) {
+                    if (circular_h) {
+                        wrap_dims[1][cursor + token_i] = h_len;
+                    }
+                    if (circular_w) {
+                        wrap_dims[2][cursor + token_i] = w_len;
+                    }
+                }
+                cursor += img_tokens;
+                // For each reference image, store wrap sizes as well
+                for (ggml_tensor* ref : ref_latents) {
+                    if (ref == nullptr) {
+                        continue;
+                    }
+                    int ref_h           = static_cast<int>(ref->ne[1]);
+                    int ref_w           = static_cast<int>(ref->ne[0]);
+                    int ref_pad_h       = (patch_size - (ref_h % patch_size)) % patch_size;
+                    int ref_pad_w       = (patch_size - (ref_w % patch_size)) % patch_size;
+                    int ref_h_len       = (ref_h + ref_pad_h) / patch_size;
+                    int ref_w_len       = (ref_w + ref_pad_w) / patch_size;
+                    size_t ref_n_tokens = static_cast<size_t>(ref_h_len) * static_cast<size_t>(ref_w_len);
+                    for (size_t token_i = 0; token_i < ref_n_tokens; ++token_i) {
+                        if (circular_h) {
+                            wrap_dims[1][cursor + token_i] = ref_h_len;
+                        }
+                        if (circular_w) {
+                            wrap_dims[2][cursor + token_i] = ref_w_len;
+                        }
+                    }
+                    cursor += ref_n_tokens;
+                }
+            }
+        }
+        return embed_nd(ids, bs, static_cast<float>(theta), axes_dim, wrap_dims);
    }

    __STATIC_INLINE__ std::vector<std::vector<float>> gen_vid_ids(int t,
@@ -310,9 +453,9 @@ namespace Rope {

        std::vector<std::vector<float>> vid_ids(t_len * h_len * w_len, std::vector<float>(3, 0.0));

-        std::vector<float> t_ids = linspace<float>(t_offset, t_len - 1 + t_offset, t_len);
-        std::vector<float> h_ids = linspace<float>(h_offset, h_len - 1 + h_offset, h_len);
-        std::vector<float> w_ids = linspace<float>(w_offset, w_len - 1 + w_offset, w_len);
+        std::vector<float> t_ids = linspace<float>(1.f * t_offset, 1.f * t_len - 1 + t_offset, t_len);
+        std::vector<float> h_ids = linspace<float>(1.f * h_offset, 1.f * h_len - 1 + h_offset, h_len);
+        std::vector<float> w_ids = linspace<float>(1.f * w_offset, 1.f * w_len - 1 + w_offset, w_len);

        for (int i = 0; i < t_len; ++i) {
            for (int j = 0; j < h_len; ++j) {
@@ -345,7 +488,7 @@ namespace Rope {
                                                    int theta,
                                                    const std::vector<int>& axes_dim) {
        std::vector<std::vector<float>> ids = gen_vid_ids(t, h, w, pt, ph, pw, bs);
-        return embed_nd(ids, bs, theta, axes_dim);
+        return embed_nd(ids, bs, static_cast<float>(theta), axes_dim);
    }

    __STATIC_INLINE__ std::vector<std::vector<float>> gen_qwen2vl_ids(int grid_h,
@@ -363,8 +506,8 @@ namespace Rope {

                        GGML_ASSERT(i < grid_h * grid_w);

-                        ids[i][0] = ih + iy;
-                        ids[i][1] = iw + ix;
+                        ids[i][0] = static_cast<float>(ih + iy);
+                        ids[i][1] = static_cast<float>(iw + ix);
                        index++;
                    }
                }
@@ -381,7 +524,7 @@ namespace Rope {
                                                        int theta,
                                                        const std::vector<int>& axes_dim) {
        std::vector<std::vector<float>> ids = gen_qwen2vl_ids(grid_h, grid_w, merge_size, window_index);
-        return embed_nd(ids, 1, theta, axes_dim);
+        return embed_nd(ids, 1, static_cast<float>(theta), axes_dim);
    }

    __STATIC_INLINE__ int bound_mod(int a, int m) {
@@ -428,15 +571,39 @@ namespace Rope {
                                                        const std::vector<ggml_tensor*>& ref_latents,
                                                        bool increase_ref_index,
                                                        int theta,
+                                                        bool circular_h,
+                                                        bool circular_w,
                                                        const std::vector<int>& axes_dim) {
        std::vector<std::vector<float>> ids = gen_z_image_ids(h, w, patch_size, bs, context_len, seq_multi_of, ref_latents, increase_ref_index);
-        return embed_nd(ids, bs, theta, axes_dim);
+        std::vector<std::vector<int>> wrap_dims;
+        if ((circular_h || circular_w) && bs > 0 && axes_dim.size() >= 3) {
+            int pad_h = (patch_size - (h % patch_size)) % patch_size;
+            int pad_w = (patch_size - (w % patch_size)) % patch_size;
+            int h_len = (h + pad_h) / patch_size;
+            int w_len = (w + pad_w) / patch_size;
+            if (h_len > 0 && w_len > 0) {
+                size_t pos_len = ids.size() / bs;
+                wrap_dims.assign(axes_dim.size(), std::vector<int>(pos_len, 0));
+                size_t cursor     = context_len + bound_mod(context_len, seq_multi_of);  // skip text (and its padding)
+                size_t img_tokens = static_cast<size_t>(h_len) * static_cast<size_t>(w_len);
+                for (size_t token_i = 0; token_i < img_tokens; ++token_i) {
+                    if (circular_h) {
+                        wrap_dims[1][cursor + token_i] = h_len;
+                    }
+                    if (circular_w) {
+                        wrap_dims[2][cursor + token_i] = w_len;
+                    }
+                }
+            }
+        }
+
+        return embed_nd(ids, bs, static_cast<float>(theta), axes_dim, wrap_dims);
    }

-    __STATIC_INLINE__ struct ggml_tensor* apply_rope(struct ggml_context* ctx,
-                                                     struct ggml_tensor* x,
-                                                     struct ggml_tensor* pe,
-                                                     bool rope_interleaved = true) {
+    __STATIC_INLINE__ ggml_tensor* apply_rope(ggml_context* ctx,
+                                              ggml_tensor* x,
+                                              ggml_tensor* pe,
+                                              bool rope_interleaved = true) {
        // x: [N, L, n_head, d_head]
        // pe: [L, d_head/2, 2, 2], [[cos, -sin], [sin, cos]]
        int64_t d_head = x->ne[0];
@@ -474,21 +641,21 @@ namespace Rope {
        return x_out;
    }

-    __STATIC_INLINE__ struct ggml_tensor* attention(GGMLRunnerContext* ctx,
-                                                    struct ggml_tensor* q,
-                                                    struct ggml_tensor* k,
-                                                    struct ggml_tensor* v,
-                                                    struct ggml_tensor* pe,
-                                                    struct ggml_tensor* mask,
-                                                    float kv_scale        = 1.0f,
-                                                    bool rope_interleaved = true) {
+    __STATIC_INLINE__ ggml_tensor* attention(GGMLRunnerContext* ctx,
+                                             ggml_tensor* q,
+                                             ggml_tensor* k,
+                                             ggml_tensor* v,
+                                             ggml_tensor* pe,
+                                             ggml_tensor* mask,
+                                             float kv_scale        = 1.0f,
+                                             bool rope_interleaved = true) {
        // q,k,v: [N, L, n_head, d_head]
        // pe: [L, d_head/2, 2, 2]
        // return: [N, L, n_head*d_head]
        q = apply_rope(ctx->ggml_ctx, q, pe, rope_interleaved);  // [N*n_head, L, d_head]
        k = apply_rope(ctx->ggml_ctx, k, pe, rope_interleaved);  // [N*n_head, L, d_head]

-        auto x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, v->ne[1], mask, false, true, ctx->flash_attn_enabled, kv_scale);  // [N, L, n_head*d_head]
+        auto x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, v->ne[1], mask, true, ctx->flash_attn_enabled, kv_scale);  // [N, L, n_head*d_head]
        return x;
    }
 };  // namespace Rope
@@ -0,0 +1,361 @@
+#include "sample-cache.h"
+
+namespace sd_sample {
+
+    static float get_cache_reuse_threshold(const sd_cache_params_t& params) {
+        float reuse_threshold = params.reuse_threshold;
+        if (reuse_threshold == INFINITY) {
+            if (params.mode == SD_CACHE_EASYCACHE) {
+                reuse_threshold = 0.2f;
+            } else if (params.mode == SD_CACHE_UCACHE) {
+                reuse_threshold = 1.0f;
+            }
+        }
+        return std::max(0.0f, reuse_threshold);
+    }
+
+    bool SampleCacheRuntime::easycache_enabled() const {
+        return mode == SampleCacheMode::EASYCACHE;
+    }
+
+    bool SampleCacheRuntime::ucache_enabled() const {
+        return mode == SampleCacheMode::UCACHE;
+    }
+
+    bool SampleCacheRuntime::cachedit_enabled() const {
+        return mode == SampleCacheMode::CACHEDIT;
+    }
+
+    static bool has_valid_cache_percent_range(const sd_cache_params_t& cache_params) {
+        if (cache_params.mode != SD_CACHE_EASYCACHE && cache_params.mode != SD_CACHE_UCACHE) {
+            return true;
+        }
+
+        return cache_params.start_percent >= 0.0f &&
+               cache_params.start_percent < 1.0f &&
+               cache_params.end_percent > 0.0f &&
+               cache_params.end_percent <= 1.0f &&
+               cache_params.start_percent < cache_params.end_percent;
+    }
+
+    static void init_easycache_runtime(SampleCacheRuntime& runtime,
+                                       SDVersion version,
+                                       const sd_cache_params_t& cache_params,
+                                       Denoiser* denoiser) {
+        if (!sd_version_is_dit(version)) {
+            LOG_WARN("EasyCache requested but not supported for this model type");
+            return;
+        }
+
+        EasyCacheConfig config;
+        config.enabled         = true;
+        config.reuse_threshold = get_cache_reuse_threshold(cache_params);
+        config.start_percent   = cache_params.start_percent;
+        config.end_percent     = cache_params.end_percent;
+
+        runtime.easycache.init(config, denoiser);
+        if (!runtime.easycache.enabled()) {
+            LOG_WARN("EasyCache requested but could not be initialized for this run");
+            return;
+        }
+
+        runtime.mode = SampleCacheMode::EASYCACHE;
+        LOG_INFO("EasyCache enabled - threshold: %.3f, start: %.2f, end: %.2f",
+                 config.reuse_threshold,
+                 config.start_percent,
+                 config.end_percent);
+    }
+
+    static void init_ucache_runtime(SampleCacheRuntime& runtime,
+                                    SDVersion version,
+                                    const sd_cache_params_t& cache_params,
+                                    Denoiser* denoiser,
+                                    const std::vector<float>& sigmas) {
+        if (!sd_version_is_unet(version)) {
+            LOG_WARN("UCache requested but not supported for this model type (only UNET models)");
+            return;
+        }
+
+        UCacheConfig config;
+        config.enabled                = true;
+        config.reuse_threshold        = get_cache_reuse_threshold(cache_params);
+        config.start_percent          = cache_params.start_percent;
+        config.end_percent            = cache_params.end_percent;
+        config.error_decay_rate       = std::max(0.0f, std::min(1.0f, cache_params.error_decay_rate));
+        config.use_relative_threshold = cache_params.use_relative_threshold;
+        config.reset_error_on_compute = cache_params.reset_error_on_compute;
+
+        runtime.ucache.init(config, denoiser);
+        if (!runtime.ucache.enabled()) {
+            LOG_WARN("UCache requested but could not be initialized for this run");
+            return;
+        }
+
+        runtime.ucache.set_sigmas(sigmas);
+        runtime.mode = SampleCacheMode::UCACHE;
+        LOG_INFO("UCache enabled - threshold: %.3f, start: %.2f, end: %.2f, decay: %.2f, relative: %s, reset: %s",
+                 config.reuse_threshold,
+                 config.start_percent,
+                 config.end_percent,
+                 config.error_decay_rate,
+                 config.use_relative_threshold ? "true" : "false",
+                 config.reset_error_on_compute ? "true" : "false");
+    }
+
+    static void init_cachedit_runtime(SampleCacheRuntime& runtime,
+                                      SDVersion version,
+                                      const sd_cache_params_t& cache_params,
+                                      const std::vector<float>& sigmas) {
+        if (!sd_version_is_dit(version)) {
+            LOG_WARN("CacheDIT requested but not supported for this model type (only DiT models)");
+            return;
+        }
+
+        DBCacheConfig dbcfg;
+        dbcfg.enabled                     = (cache_params.mode == SD_CACHE_DBCACHE || cache_params.mode == SD_CACHE_CACHE_DIT);
+        dbcfg.Fn_compute_blocks           = cache_params.Fn_compute_blocks;
+        dbcfg.Bn_compute_blocks           = cache_params.Bn_compute_blocks;
+        dbcfg.residual_diff_threshold     = cache_params.residual_diff_threshold;
+        dbcfg.max_warmup_steps            = cache_params.max_warmup_steps;
+        dbcfg.max_cached_steps            = cache_params.max_cached_steps;
+        dbcfg.max_continuous_cached_steps = cache_params.max_continuous_cached_steps;
+        if (cache_params.scm_mask != nullptr && strlen(cache_params.scm_mask) > 0) {
+            dbcfg.steps_computation_mask = parse_scm_mask(cache_params.scm_mask);
+        }
+        dbcfg.scm_policy_dynamic = cache_params.scm_policy_dynamic;
+
+        TaylorSeerConfig tcfg;
+        tcfg.enabled             = (cache_params.mode == SD_CACHE_TAYLORSEER || cache_params.mode == SD_CACHE_CACHE_DIT);
+        tcfg.n_derivatives       = cache_params.taylorseer_n_derivatives;
+        tcfg.skip_interval_steps = cache_params.taylorseer_skip_interval;
+
+        runtime.cachedit.init(dbcfg, tcfg);
+        if (!runtime.cachedit.enabled()) {
+            LOG_WARN("CacheDIT requested but could not be initialized for this run");
+            return;
+        }
+
+        runtime.cachedit.set_sigmas(sigmas);
+        runtime.mode = SampleCacheMode::CACHEDIT;
+        LOG_INFO("CacheDIT enabled - mode: %s, Fn: %d, Bn: %d, threshold: %.3f, warmup: %d",
+                 cache_params.mode == SD_CACHE_CACHE_DIT ? "DBCache+TaylorSeer" : (cache_params.mode == SD_CACHE_DBCACHE ? "DBCache" : "TaylorSeer"),
+                 dbcfg.Fn_compute_blocks,
+                 dbcfg.Bn_compute_blocks,
+                 dbcfg.residual_diff_threshold,
+                 dbcfg.max_warmup_steps);
+    }
+
+    static void init_spectrum_runtime(SampleCacheRuntime& runtime,
+                                      SDVersion version,
+                                      const sd_cache_params_t& cache_params,
+                                      const std::vector<float>& sigmas) {
+        if (!sd_version_is_unet(version) && !sd_version_is_dit(version)) {
+            LOG_WARN("Spectrum requested but not supported for this model type (only UNET and DiT models)");
+            return;
+        }
+
+        SpectrumConfig config;
+        config.w            = cache_params.spectrum_w;
+        config.m            = cache_params.spectrum_m;
+        config.lam          = cache_params.spectrum_lam;
+        config.window_size  = cache_params.spectrum_window_size;
+        config.flex_window  = cache_params.spectrum_flex_window;
+        config.warmup_steps = cache_params.spectrum_warmup_steps;
+        config.stop_percent = cache_params.spectrum_stop_percent;
+
+        size_t total_steps = sigmas.size() > 0 ? sigmas.size() - 1 : 0;
+        runtime.spectrum.init(config, total_steps);
+        runtime.spectrum_enabled = true;
+
+        LOG_INFO("Spectrum enabled - w: %.2f, m: %d, lam: %.2f, window: %d, flex: %.2f, warmup: %d, stop: %.0f%%",
+                 config.w, config.m, config.lam,
+                 config.window_size, config.flex_window,
+                 config.warmup_steps, config.stop_percent * 100.0f);
+    }
+
+    SampleCacheRuntime init_sample_cache_runtime(SDVersion version,
+                                                 const sd_cache_params_t* cache_params,
+                                                 Denoiser* denoiser,
+                                                 const std::vector<float>& sigmas) {
+        SampleCacheRuntime runtime;
+        if (cache_params == nullptr || cache_params->mode == SD_CACHE_DISABLED) {
+            return runtime;
+        }
+
+        if (!has_valid_cache_percent_range(*cache_params)) {
+            LOG_WARN("Cache disabled due to invalid percent range (start=%.3f, end=%.3f)",
+                     cache_params->start_percent,
+                     cache_params->end_percent);
+            return runtime;
+        }
+
+        switch (cache_params->mode) {
+            case SD_CACHE_EASYCACHE:
+                init_easycache_runtime(runtime, version, *cache_params, denoiser);
+                break;
+            case SD_CACHE_UCACHE:
+                init_ucache_runtime(runtime, version, *cache_params, denoiser, sigmas);
+                break;
+            case SD_CACHE_DBCACHE:
+            case SD_CACHE_TAYLORSEER:
+            case SD_CACHE_CACHE_DIT:
+                init_cachedit_runtime(runtime, version, *cache_params, sigmas);
+                break;
+            case SD_CACHE_SPECTRUM:
+                init_spectrum_runtime(runtime, version, *cache_params, sigmas);
+                break;
+            default:
+                break;
+        }
+
+        return runtime;
+    }
+
+    SampleStepCacheDispatcher::SampleStepCacheDispatcher(SampleCacheRuntime& runtime, int step, float sigma)
+        : runtime(runtime), step(step), sigma(sigma), step_index(step > 0 ? (step - 1) : -1) {
+        if (step_index < 0) {
+            return;
+        }
+
+        switch (runtime.mode) {
+            case SampleCacheMode::EASYCACHE:
+                runtime.easycache.begin_step(step_index, sigma);
+                break;
+            case SampleCacheMode::UCACHE:
+                runtime.ucache.begin_step(step_index, sigma);
+                break;
+            case SampleCacheMode::CACHEDIT:
+                runtime.cachedit.begin_step(step_index, sigma);
+                break;
+            case SampleCacheMode::NONE:
+                break;
+        }
+    }
+
+    bool SampleStepCacheDispatcher::before_condition(const void* condition,
+                                                     const sd::Tensor<float>& input,
+                                                     sd::Tensor<float>* output) {
+        if (step_index < 0 || condition == nullptr || output == nullptr) {
+            return false;
+        }
+
+        switch (runtime.mode) {
+            case SampleCacheMode::EASYCACHE:
+                return runtime.easycache.before_condition(condition, input, output, sigma, step_index);
+            case SampleCacheMode::UCACHE:
+                return runtime.ucache.before_condition(condition, input, output, sigma, step_index);
+            case SampleCacheMode::CACHEDIT:
+                return runtime.cachedit.before_condition(condition, input, output, sigma, step_index);
+            case SampleCacheMode::NONE:
+                return false;
+        }
+
+        return false;
+    }
+
+    void SampleStepCacheDispatcher::after_condition(const void* condition,
+                                                    const sd::Tensor<float>& input,
+                                                    const sd::Tensor<float>& output) {
+        if (step_index < 0 || condition == nullptr) {
+            return;
+        }
+
+        switch (runtime.mode) {
+            case SampleCacheMode::EASYCACHE:
+                runtime.easycache.after_condition(condition, input, output);
+                break;
+            case SampleCacheMode::UCACHE:
+                runtime.ucache.after_condition(condition, input, output);
+                break;
+            case SampleCacheMode::CACHEDIT:
+                runtime.cachedit.after_condition(condition, input, output);
+                break;
+            case SampleCacheMode::NONE:
+                break;
+        }
+    }
+
+    bool SampleStepCacheDispatcher::is_step_skipped() const {
+        switch (runtime.mode) {
+            case SampleCacheMode::EASYCACHE:
+                return runtime.easycache.is_step_skipped();
+            case SampleCacheMode::UCACHE:
+                return runtime.ucache.is_step_skipped();
+            case SampleCacheMode::CACHEDIT:
+                return runtime.cachedit.is_step_skipped();
+            case SampleCacheMode::NONE:
+                return false;
+        }
+
+        return false;
+    }
+
+    void log_sample_cache_summary(const SampleCacheRuntime& runtime, size_t total_steps) {
+        if (runtime.easycache_enabled()) {
+            if (runtime.easycache.total_steps_skipped > 0 && total_steps > 0) {
+                if (runtime.easycache.total_steps_skipped < static_cast<int>(total_steps)) {
+                    double speedup = static_cast<double>(total_steps) /
+                                     static_cast<double>(total_steps - runtime.easycache.total_steps_skipped);
+                    LOG_INFO("EasyCache skipped %d/%zu steps (%.2fx estimated speedup)",
+                             runtime.easycache.total_steps_skipped,
+                             total_steps,
+                             speedup);
+                } else {
+                    LOG_INFO("EasyCache skipped %d/%zu steps",
+                             runtime.easycache.total_steps_skipped,
+                             total_steps);
+                }
+            } else if (total_steps > 0) {
+                LOG_INFO("EasyCache completed without skipping steps");
+            }
+        }
+
+        if (runtime.ucache_enabled()) {
+            if (runtime.ucache.total_steps_skipped > 0 && total_steps > 0) {
+                if (runtime.ucache.total_steps_skipped < static_cast<int>(total_steps)) {
+                    double speedup = static_cast<double>(total_steps) /
+                                     static_cast<double>(total_steps - runtime.ucache.total_steps_skipped);
+                    LOG_INFO("UCache skipped %d/%zu steps (%.2fx estimated speedup)",
+                             runtime.ucache.total_steps_skipped,
+                             total_steps,
+                             speedup);
+                } else {
+                    LOG_INFO("UCache skipped %d/%zu steps",
+                             runtime.ucache.total_steps_skipped,
+                             total_steps);
+                }
+            } else if (total_steps > 0) {
+                LOG_INFO("UCache completed without skipping steps");
+            }
+        }
+
+        if (runtime.cachedit_enabled()) {
+            if (runtime.cachedit.total_steps_skipped > 0 && total_steps > 0) {
+                if (runtime.cachedit.total_steps_skipped < static_cast<int>(total_steps)) {
+                    double speedup = static_cast<double>(total_steps) /
+                                     static_cast<double>(total_steps - runtime.cachedit.total_steps_skipped);
+                    LOG_INFO("CacheDIT skipped %d/%zu steps (%.2fx estimated speedup)",
+                             runtime.cachedit.total_steps_skipped,
+                             total_steps,
+                             speedup);
+                } else {
+                    LOG_INFO("CacheDIT skipped %d/%zu steps",
+                             runtime.cachedit.total_steps_skipped,
+                             total_steps);
+                }
+            } else if (total_steps > 0) {
+                LOG_INFO("CacheDIT completed without skipping steps");
+            }
+        }
+
+        if (runtime.spectrum_enabled && runtime.spectrum.total_steps_skipped > 0 && total_steps > 0) {
+            double speedup = static_cast<double>(total_steps) /
+                             static_cast<double>(total_steps - runtime.spectrum.total_steps_skipped);
+            LOG_INFO("Spectrum skipped %d/%zu steps (%.2fx estimated speedup)",
+                     runtime.spectrum.total_steps_skipped,
+                     total_steps,
+                     speedup);
+        }
+    }
+
+}  // namespace sd_sample
@@ -0,0 +1,61 @@
+#ifndef __SAMPLE_CACHE_H__
+#define __SAMPLE_CACHE_H__
+
+#include <vector>
+
+#include "cache_dit.hpp"
+#include "denoiser.hpp"
+#include "easycache.hpp"
+#include "model.h"
+#include "spectrum.hpp"
+#include "tensor.hpp"
+#include "ucache.hpp"
+#include "util.h"
+
+namespace sd_sample {
+
+    enum class SampleCacheMode {
+        NONE,
+        EASYCACHE,
+        UCACHE,
+        CACHEDIT,
+    };
+
+    struct SampleCacheRuntime {
+        SampleCacheMode mode = SampleCacheMode::NONE;
+
+        EasyCacheState easycache;
+        UCacheState ucache;
+        CacheDitConditionState cachedit;
+        SpectrumState spectrum;
+
+        bool spectrum_enabled = false;
+
+        bool easycache_enabled() const;
+        bool ucache_enabled() const;
+        bool cachedit_enabled() const;
+    };
+
+    struct SampleStepCacheDispatcher {
+        SampleCacheRuntime& runtime;
+        int step;
+        float sigma;
+        int step_index;
+
+        SampleStepCacheDispatcher(SampleCacheRuntime& runtime, int step, float sigma);
+
+        bool before_condition(const void* condition, const sd::Tensor<float>& input, sd::Tensor<float>* output);
+        void after_condition(const void* condition, const sd::Tensor<float>& input, const sd::Tensor<float>& output);
+        bool is_step_skipped() const;
+    };
+
+    SampleCacheRuntime init_sample_cache_runtime(SDVersion version,
+                                                 const sd_cache_params_t* cache_params,
+                                                 Denoiser* denoiser,
+                                                 const std::vector<float>& sigmas);
+
+    void log_sample_cache_summary(const SampleCacheRuntime& runtime, size_t total_steps);
+
+}  // namespace sd_sample
+
+#endif  // __SAMPLE_CACHE_H__
@@ -0,0 +1,187 @@
+#ifndef __SPECTRUM_HPP__
+#define __SPECTRUM_HPP__
+
+#include <cmath>
+#include <cstring>
+#include <vector>
+
+#include "ggml_extend.hpp"
+#include "tensor.hpp"
+
+struct SpectrumConfig {
+    float w            = 0.40f;
+    int m              = 3;
+    float lam          = 1.0f;
+    int window_size    = 2;
+    float flex_window  = 0.50f;
+    int warmup_steps   = 4;
+    float stop_percent = 0.9f;
+};
+
+struct SpectrumState {
+    SpectrumConfig config;
+    int cnt                 = 0;
+    int num_cached          = 0;
+    float curr_ws           = 2.0f;
+    int K                   = 6;
+    int stop_step           = 0;
+    int total_steps_skipped = 0;
+
+    std::vector<std::vector<float>> H_buf;
+    std::vector<float> T_buf;
+
+    void init(const SpectrumConfig& cfg, size_t total_steps) {
+        config              = cfg;
+        cnt                 = 0;
+        num_cached          = 0;
+        curr_ws             = (float)cfg.window_size;
+        K                   = std::max(cfg.m + 1, 6);
+        stop_step           = (int)(cfg.stop_percent * (float)total_steps);
+        total_steps_skipped = 0;
+        H_buf.clear();
+        T_buf.clear();
+    }
+
+    float taus(int step_cnt) const {
+        return (step_cnt / 50.0f) * 2.0f - 1.0f;
+    }
+
+    bool should_predict() {
+        if (cnt < config.warmup_steps)
+            return false;
+        if (stop_step > 0 && cnt >= stop_step)
+            return false;
+        if ((int)H_buf.size() < 2)
+            return false;
+
+        int ws = std::max(1, (int)std::floor(curr_ws));
+        return (num_cached + 1) % ws != 0;
+    }
+
+    void update(const sd::Tensor<float>& denoised) {
+        H_buf.emplace_back(denoised.data(), denoised.data() + denoised.numel());
+        T_buf.push_back(taus(cnt));
+
+        while ((int)H_buf.size() > K) {
+            H_buf.erase(H_buf.begin());
+            T_buf.erase(T_buf.begin());
+        }
+
+        if (cnt >= config.warmup_steps)
+            curr_ws += config.flex_window;
+
+        num_cached = 0;
+        cnt++;
+    }
+
+    void predict(sd::Tensor<float>* denoised) {
+        GGML_ASSERT(denoised != nullptr);
+        int64_t F    = (int64_t)H_buf[0].size();
+        int K_curr   = (int)H_buf.size();
+        int M1       = config.m + 1;
+        float tau_at = taus(cnt);
+
+        std::vector<float> X(K_curr * M1);
+        for (int i = 0; i < K_curr; i++) {
+            X[i * M1] = 1.0f;
+            if (M1 > 1)
+                X[i * M1 + 1] = T_buf[i];
+            for (int j = 2; j < M1; j++)
+                X[i * M1 + j] = 2.0f * T_buf[i] * X[i * M1 + j - 1] - X[i * M1 + j - 2];
+        }
+
+        std::vector<float> x_star(M1);
+        x_star[0] = 1.0f;
+        if (M1 > 1)
+            x_star[1] = tau_at;
+        for (int j = 2; j < M1; j++)
+            x_star[j] = 2.0f * tau_at * x_star[j - 1] - x_star[j - 2];
+
+        std::vector<float> XtX(M1 * M1, 0.0f);
+        for (int i = 0; i < M1; i++) {
+            for (int j = 0; j < M1; j++) {
+                float sum = 0.0f;
+                for (int k = 0; k < K_curr; k++)
+                    sum += X[k * M1 + i] * X[k * M1 + j];
+                XtX[i * M1 + j] = sum + (i == j ? config.lam : 0.0f);
+            }
+        }
+
+        std::vector<float> L(M1 * M1, 0.0f);
+        if (!cholesky_decompose(XtX.data(), L.data(), M1)) {
+            float trace = 0.0f;
+            for (int i = 0; i < M1; i++)
+                trace += XtX[i * M1 + i];
+            for (int i = 0; i < M1; i++)
+                XtX[i * M1 + i] += 1e-4f * trace / M1;
+            cholesky_decompose(XtX.data(), L.data(), M1);
+        }
+
+        std::vector<float> v(M1);
+        cholesky_solve(L.data(), x_star.data(), v.data(), M1);
+
+        std::vector<float> weights(K_curr, 0.0f);
+        for (int k = 0; k < K_curr; k++)
+            for (int j = 0; j < M1; j++)
+                weights[k] += X[k * M1 + j] * v[j];
+
+        float* out          = denoised->data();
+        float w_cheb        = config.w;
+        float w_taylor      = 1.0f - w_cheb;
+        const float* h_last = H_buf.back().data();
+        const float* h_prev = H_buf[H_buf.size() - 2].data();
+
+        for (int64_t f = 0; f < F; f++) {
+            float pred_cheb = 0.0f;
+            for (int k = 0; k < K_curr; k++)
+                pred_cheb += weights[k] * H_buf[k][f];
+
+            float pred_taylor = h_last[f] + 0.5f * (h_last[f] - h_prev[f]);
+
+            out[f] = w_taylor * pred_taylor + w_cheb * pred_cheb;
+        }
+
+        num_cached++;
+        total_steps_skipped++;
+        cnt++;
+    }
+
+private:
+    static bool cholesky_decompose(const float* A, float* L, int n) {
+        std::memset(L, 0, n * n * sizeof(float));
+        for (int i = 0; i < n; i++) {
+            for (int j = 0; j <= i; j++) {
+                float sum = 0.0f;
+                for (int k = 0; k < j; k++)
+                    sum += L[i * n + k] * L[j * n + k];
+                if (i == j) {
+                    float diag = A[i * n + i] - sum;
+                    if (diag <= 0.0f)
+                        return false;
+                    L[i * n + j] = std::sqrt(diag);
+                } else {
+                    L[i * n + j] = (A[i * n + j] - sum) / L[j * n + j];
+                }
+            }
+        }
+        return true;
+    }
+
+    static void cholesky_solve(const float* L, const float* b, float* x, int n) {
+        std::vector<float> y(n);
+        for (int i = 0; i < n; i++) {
+            float sum = 0.0f;
+            for (int j = 0; j < i; j++)
+                sum += L[i * n + j] * y[j];
+            y[i] = (b[i] - sum) / L[i * n + i];
+        }
+        for (int i = n - 1; i >= 0; i--) {
+            float sum = 0.0f;
+            for (int j = i + 1; j < n; j++)
+                sum += L[j * n + i] * x[j];
+            x[i] = (y[i] - sum) / L[i * n + i];
+        }
+    }
+};
+
+#endif  // __SPECTRUM_HPP__
@@ -17,22 +17,43 @@ class TAEBlock : public UnaryBlock {
 protected:
    int n_in;
    int n_out;
+    bool use_midblock_gn;

 public:
-    TAEBlock(int n_in, int n_out)
-        : n_in(n_in), n_out(n_out) {
+    TAEBlock(int n_in, int n_out, bool use_midblock_gn = false)
+        : n_in(n_in), n_out(n_out), use_midblock_gn(use_midblock_gn) {
        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));
        }
+        if (use_midblock_gn) {
+            int n_gn         = n_in * 4;
+            blocks["pool.0"] = std::shared_ptr<GGMLBlock>(new Conv2d(n_in, n_gn, {1, 1}, {1, 1}, {0, 0}, {1, 1}, false));
+            blocks["pool.1"] = std::shared_ptr<GGMLBlock>(new GroupNorm(4, n_gn));
+            // pool.2 is ReLU, handled in forward
+            blocks["pool.3"] = std::shared_ptr<GGMLBlock>(new Conv2d(n_gn, n_in, {1, 1}, {1, 1}, {0, 0}, {1, 1}, false));
+        }
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
        // x: [n, n_in, h, w]
        // return: [n, n_out, h, w]

+        if (use_midblock_gn) {
+            auto pool_0 = std::dynamic_pointer_cast<Conv2d>(blocks["pool.0"]);
+            auto pool_1 = std::dynamic_pointer_cast<GroupNorm>(blocks["pool.1"]);
+            auto pool_3 = std::dynamic_pointer_cast<Conv2d>(blocks["pool.3"]);
+
+            auto p = pool_0->forward(ctx, x);
+            p      = pool_1->forward(ctx, p);
+            p      = ggml_relu_inplace(ctx->ggml_ctx, p);
+            p      = pool_3->forward(ctx, p);
+
+            x = ggml_add(ctx->ggml_ctx, x, p);
+        }
+
        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"]);
@@ -62,7 +83,7 @@ class TinyEncoder : public UnaryBlock {
    int num_blocks  = 3;

 public:
-    TinyEncoder(int z_channels = 4)
+    TinyEncoder(int z_channels = 4, bool use_midblock_gn = false)
        : z_channels(z_channels) {
        int index                       = 0;
        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, channels, {3, 3}, {1, 1}, {1, 1}));
@@ -80,13 +101,13 @@ public:

        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 TAEBlock(channels, channels, use_midblock_gn));
        }

        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, z_channels, {3, 3}, {1, 1}, {1, 1}));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
        // x: [n, in_channels, h, w]
        // return: [n, z_channels, h/8, w/8]

@@ -107,7 +128,7 @@ class TinyDecoder : public UnaryBlock {
    int num_blocks   = 3;

 public:
-    TinyDecoder(int z_channels = 4)
+    TinyDecoder(int z_channels = 4, bool use_midblock_gn = false)
        : z_channels(z_channels) {
        int index = 0;

@@ -115,7 +136,7 @@ public:
        index++;  // nn.ReLU()

        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 TAEBlock(channels, channels, use_midblock_gn));
        }
        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));
@@ -136,13 +157,13 @@ public:
        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* z) override {
        // z: [n, z_channels, h, w]
        // return: [n, out_channels, h*8, w*8]

-        auto h = ggml_scale(ctx->ggml_ctx, z, 1.0f / 3.0f);
+        auto h = ggml_ext_scale(ctx->ggml_ctx, z, 1.0f / 3.0f);
        h      = ggml_tanh_inplace(ctx->ggml_ctx, h);
-        h      = ggml_scale(ctx->ggml_ctx, h, 3.0f);
+        h      = ggml_ext_scale(ctx->ggml_ctx, h, 3.0f);

        for (int i = 0; i < num_blocks * 3 + 10; i++) {
            if (blocks.find(std::to_string(i)) == blocks.end()) {
@@ -171,7 +192,7 @@ public:
        blocks["conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels * stride, channels, {1, 1}, {1, 1}, {0, 0}, {1, 1}, false));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
        auto conv = std::dynamic_pointer_cast<UnaryBlock>(blocks["conv"]);
        auto h    = x;
        if (stride != 1) {
@@ -191,7 +212,7 @@ public:
        blocks["conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, channels * stride, {1, 1}, {1, 1}, {0, 0}, {1, 1}, false));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
        auto conv = std::dynamic_pointer_cast<UnaryBlock>(blocks["conv"]);
        auto h    = conv->forward(ctx, x);
        if (stride != 1) {
@@ -215,7 +236,7 @@ public:
        }
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, struct ggml_tensor* past) {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* past) {
        // x: [n, channels, h, w]
        auto conv0 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.0"]);
        auto conv1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv.2"]);
@@ -239,10 +260,10 @@ public:
    }
 };

-struct ggml_tensor* patchify(struct ggml_context* ctx,
-                             struct ggml_tensor* x,
-                             int64_t patch_size,
-                             int64_t b = 1) {
+ggml_tensor* patchify(ggml_context* ctx,
+                      ggml_tensor* x,
+                      int64_t patch_size,
+                      int64_t b = 1) {
    // x: [f, b*c, h*q, w*r]
    // return: [f, b*c*r*q, h, w]
    if (patch_size == 1) {
@@ -268,10 +289,10 @@ struct ggml_tensor* patchify(struct ggml_context* ctx,
    return x;
 }

-struct ggml_tensor* unpatchify(struct ggml_context* ctx,
-                               struct ggml_tensor* x,
-                               int64_t patch_size,
-                               int64_t b = 1) {
+ggml_tensor* unpatchify(ggml_context* ctx,
+                        ggml_tensor* x,
+                        int64_t patch_size,
+                        int64_t b = 1) {
    // x: [f, b*c*r*q, h, w]
    // return: [f, b*c, h*q, w*r]
    if (patch_size == 1) {
@@ -318,7 +339,7 @@ public:
        blocks[std::to_string(index)] = std::shared_ptr<GGMLBlock>(new Conv2d(hidden, z_channels, {3, 3}, {1, 1}, {1, 1}));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* z) override {
        auto first_conv = std::dynamic_pointer_cast<Conv2d>(blocks["0"]);

        if (patch_size > 1) {
@@ -375,14 +396,15 @@ public:
        blocks[std::to_string(index++)] = std::shared_ptr<GGMLBlock>(new Conv2d(channels[num_layers], out_channels * patch_size * patch_size, {3, 3}, {1, 1}, {1, 1}));
    }

-    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* z) override {
+    ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* z) override {
        auto first_conv = std::dynamic_pointer_cast<Conv2d>(blocks["1"]);

        // Clamp()
-        auto h = ggml_scale_inplace(ctx->ggml_ctx,
-                                    ggml_tanh_inplace(ctx->ggml_ctx,
-                                                      ggml_scale(ctx->ggml_ctx, z, 1.0f / 3.0f)),
-                                    3.0f);
+        auto h = ggml_ext_scale(ctx->ggml_ctx,
+                                ggml_tanh_inplace(ctx->ggml_ctx,
+                                                  ggml_ext_scale(ctx->ggml_ctx, z, 1.0f / 3.0f)),
+                                3.0f,
+                                true);

        h         = first_conv->forward(ctx, h);
        h         = ggml_relu_inplace(ctx->ggml_ctx, h);
@@ -420,11 +442,13 @@ protected:
    bool decode_only;
    SDVersion version;

+public:
+    int z_channels = 16;
+
 public:
    TAEHV(bool decode_only = true, SDVersion version = VERSION_WAN2)
        : decode_only(decode_only), version(version) {
-        int z_channels = 16;
-        int patch      = 1;
+        int patch = 1;
        if (version == VERSION_WAN2_2_TI2V) {
            z_channels = 48;
            patch      = 2;
@@ -435,7 +459,7 @@ public:
        }
    }

-    struct ggml_tensor* decode(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
+    ggml_tensor* decode(GGMLRunnerContext* ctx, ggml_tensor* z) {
        auto decoder = std::dynamic_pointer_cast<TinyVideoDecoder>(blocks["decoder"]);
        if (sd_version_is_wan(version)) {
            // (W, H, C, T) -> (W, H, T, C)
@@ -449,7 +473,7 @@ public:
        return result;
    }

-    struct ggml_tensor* encode(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* encode(GGMLRunnerContext* ctx, ggml_tensor* x) {
        auto encoder = std::dynamic_pointer_cast<TinyVideoEncoder>(blocks["encoder"]);
        // (W, H, T, C) -> (W, H, C, T)
        x                  = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));
@@ -470,45 +494,50 @@ public:
 class TAESD : public GGMLBlock {
 protected:
    bool decode_only;
+    bool taef2 = false;
+
+public:
+    int z_channels = 4;

 public:
    TAESD(bool decode_only = true, SDVersion version = VERSION_SD1)
        : decode_only(decode_only) {
-        int z_channels = 4;
+        bool use_midblock_gn = false;
+        taef2                = sd_version_is_flux2(version);
+
        if (sd_version_is_dit(version)) {
            z_channels = 16;
        }
-        blocks["decoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyDecoder(z_channels));
+        if (taef2) {
+            z_channels      = 32;
+            use_midblock_gn = true;
+        }
+        blocks["decoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyDecoder(z_channels, use_midblock_gn));

        if (!decode_only) {
-            blocks["encoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyEncoder(z_channels));
+            blocks["encoder.layers"] = std::shared_ptr<GGMLBlock>(new TinyEncoder(z_channels, use_midblock_gn));
        }
    }

-    struct ggml_tensor* decode(GGMLRunnerContext* ctx, struct ggml_tensor* z) {
+    ggml_tensor* decode(GGMLRunnerContext* ctx, ggml_tensor* z) {
        auto decoder = std::dynamic_pointer_cast<TinyDecoder>(blocks["decoder.layers"]);
+        if (taef2) {
+            z = unpatchify(ctx->ggml_ctx, z, 2);
+        }
        return decoder->forward(ctx, z);
    }

-    struct ggml_tensor* encode(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+    ggml_tensor* encode(GGMLRunnerContext* ctx, ggml_tensor* x) {
        auto encoder = std::dynamic_pointer_cast<TinyEncoder>(blocks["encoder.layers"]);
-        return encoder->forward(ctx, x);
+        auto z       = encoder->forward(ctx, x);
+        if (taef2) {
+            z = patchify(ctx->ggml_ctx, z, 2);
+        }
+        return z;
    }
 };

-struct TinyAutoEncoder : public GGMLRunner {
-    TinyAutoEncoder(ggml_backend_t backend, bool offload_params_to_cpu)
-        : GGMLRunner(backend, offload_params_to_cpu) {}
-    virtual bool compute(const int n_threads,
-                         struct ggml_tensor* z,
-                         bool decode_graph,
-                         struct ggml_tensor** output,
-                         struct ggml_context* output_ctx = nullptr) = 0;
-
-    virtual bool load_from_file(const std::string& file_path, int n_threads) = 0;
-};
-
-struct TinyImageAutoEncoder : public TinyAutoEncoder {
+struct TinyImageAutoEncoder : public VAE {
    TAESD taesd;
    bool decode_only = false;

@@ -520,7 +549,8 @@ struct TinyImageAutoEncoder : public TinyAutoEncoder {
                         SDVersion version = VERSION_SD1)
        : decode_only(decoder_only),
          taesd(decoder_only, version),
-          TinyAutoEncoder(backend, offload_params_to_cpu) {
+          VAE(version, backend, offload_params_to_cpu) {
+        scale_input = false;
        taesd.init(params_ctx, tensor_storage_map, prefix);
    }

@@ -528,56 +558,48 @@ struct TinyImageAutoEncoder : public TinyAutoEncoder {
        return "taesd";
    }

-    bool load_from_file(const std::string& file_path, int n_threads) {
-        LOG_INFO("loading taesd from '%s', decode_only = %s", file_path.c_str(), decode_only ? "true" : "false");
-        alloc_params_buffer();
-        std::map<std::string, ggml_tensor*> taesd_tensors;
-        taesd.get_param_tensors(taesd_tensors);
-        std::set<std::string> ignore_tensors;
-        if (decode_only) {
-            ignore_tensors.insert("encoder.");
-        }
-
-        ModelLoader model_loader;
-        if (!model_loader.init_from_file_and_convert_name(file_path)) {
-            LOG_ERROR("init taesd model loader from file failed: '%s'", file_path.c_str());
-            return false;
-        }
-
-        bool success = model_loader.load_tensors(taesd_tensors, ignore_tensors, n_threads);
-
-        if (!success) {
-            LOG_ERROR("load tae tensors from model loader failed");
-            return false;
-        }
-
-        LOG_INFO("taesd model loaded");
-        return success;
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
+        taesd.get_param_tensors(tensors, prefix);
    }

-    struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
-        struct ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
-        z                       = to_backend(z);
-        auto runner_ctx         = get_context();
-        struct ggml_tensor* out = decode_graph ? taesd.decode(&runner_ctx, z) : taesd.encode(&runner_ctx, z);
+    sd::Tensor<float> vae_output_to_latents(const sd::Tensor<float>& vae_output, std::shared_ptr<RNG> rng) override {
+        SD_UNUSED(rng);
+        return vae_output;
+    }
+
+    sd::Tensor<float> diffusion_to_vae_latents(const sd::Tensor<float>& latents) override {
+        return latents;
+    }
+
+    sd::Tensor<float> vae_to_diffusion_latents(const sd::Tensor<float>& latents) override {
+        return latents;
+    }
+
+    int get_encoder_output_channels(int input_channels) {
+        return taesd.z_channels;
+    }
+
+    ggml_cgraph* build_graph(const sd::Tensor<float>& z_tensor, bool decode_graph) {
+        ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
+        ggml_tensor* z   = make_input(z_tensor);
+        auto runner_ctx  = get_context();
+        ggml_tensor* out = decode_graph ? taesd.decode(&runner_ctx, z) : taesd.encode(&runner_ctx, z);
        ggml_build_forward_expand(gf, out);
        return gf;
    }

-    bool compute(const int n_threads,
-                 struct ggml_tensor* z,
-                 bool decode_graph,
-                 struct ggml_tensor** output,
-                 struct ggml_context* output_ctx = nullptr) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(z, decode_graph);
+    sd::Tensor<float> _compute(const int n_threads,
+                               const sd::Tensor<float>& z_tensor,
+                               bool decode_graph) override {
+        auto get_graph = [&]() -> ggml_cgraph* {
+            return build_graph(z_tensor, decode_graph);
        };

-        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), z_tensor.dim());
    }
 };

-struct TinyVideoAutoEncoder : public TinyAutoEncoder {
+struct TinyVideoAutoEncoder : public VAE {
    TAEHV taehv;
    bool decode_only = false;

@@ -589,7 +611,8 @@ struct TinyVideoAutoEncoder : public TinyAutoEncoder {
                         SDVersion version = VERSION_WAN2)
        : decode_only(decoder_only),
          taehv(decoder_only, version),
-          TinyAutoEncoder(backend, offload_params_to_cpu) {
+          VAE(version, backend, offload_params_to_cpu) {
+        scale_input = false;
        taehv.init(params_ctx, tensor_storage_map, prefix);
    }

@@ -597,53 +620,45 @@ struct TinyVideoAutoEncoder : public TinyAutoEncoder {
        return "taehv";
    }

-    bool load_from_file(const std::string& file_path, int n_threads) {
-        LOG_INFO("loading taehv from '%s', decode_only = %s", file_path.c_str(), decode_only ? "true" : "false");
-        alloc_params_buffer();
-        std::map<std::string, ggml_tensor*> taehv_tensors;
-        taehv.get_param_tensors(taehv_tensors);
-        std::set<std::string> ignore_tensors;
-        if (decode_only) {
-            ignore_tensors.insert("encoder.");
-        }
-
-        ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
-            LOG_ERROR("init taehv model loader from file failed: '%s'", file_path.c_str());
-            return false;
-        }
-
-        bool success = model_loader.load_tensors(taehv_tensors, ignore_tensors, n_threads);
-
-        if (!success) {
-            LOG_ERROR("load tae tensors from model loader failed");
-            return false;
-        }
-
-        LOG_INFO("taehv model loaded");
-        return success;
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string prefix) {
+        taehv.get_param_tensors(tensors, prefix);
    }

-    struct ggml_cgraph* build_graph(struct ggml_tensor* z, bool decode_graph) {
-        struct ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
-        z                       = to_backend(z);
-        auto runner_ctx         = get_context();
-        struct ggml_tensor* out = decode_graph ? taehv.decode(&runner_ctx, z) : taehv.encode(&runner_ctx, z);
+    sd::Tensor<float> vae_output_to_latents(const sd::Tensor<float>& vae_output, std::shared_ptr<RNG> rng) override {
+        SD_UNUSED(rng);
+        return vae_output;
+    }
+
+    sd::Tensor<float> diffusion_to_vae_latents(const sd::Tensor<float>& latents) override {
+        return latents;
+    }
+
+    sd::Tensor<float> vae_to_diffusion_latents(const sd::Tensor<float>& latents) override {
+        return latents;
+    }
+
+    int get_encoder_output_channels(int input_channels) {
+        return taehv.z_channels;
+    }
+
+    ggml_cgraph* build_graph(const sd::Tensor<float>& z_tensor, bool decode_graph) {
+        ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
+        ggml_tensor* z   = make_input(z_tensor);
+        auto runner_ctx  = get_context();
+        ggml_tensor* out = decode_graph ? taehv.decode(&runner_ctx, z) : taehv.encode(&runner_ctx, z);
        ggml_build_forward_expand(gf, out);
        return gf;
    }

-    bool compute(const int n_threads,
-                 struct ggml_tensor* z,
-                 bool decode_graph,
-                 struct ggml_tensor** output,
-                 struct ggml_context* output_ctx = nullptr) {
-        auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(z, decode_graph);
+    sd::Tensor<float> _compute(const int n_threads,
+                               const sd::Tensor<float>& z_tensor,
+                               bool decode_graph) override {
+        auto get_graph = [&]() -> ggml_cgraph* {
+            return build_graph(z_tensor, decode_graph);
        };

-        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), z_tensor.dim());
    }
 };

-#endif  // __TAE_HPP__
+#endif  // __TAE_HPP__
@@ -0,0 +1,127 @@
+#ifndef __SD_TENSOR_GGML_HPP__
+#define __SD_TENSOR_GGML_HPP__
+
+#include <array>
+#include <cstring>
+#include <fstream>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+
+#include "ggml.h"
+#include "tensor.hpp"
+
+namespace sd {
+
+    template <typename T>
+    struct GGMLTypeTraits;
+
+    template <>
+    struct GGMLTypeTraits<float> {
+        static constexpr ggml_type type = GGML_TYPE_F32;
+    };
+
+    template <>
+    struct GGMLTypeTraits<ggml_fp16_t> {
+        static constexpr ggml_type type = GGML_TYPE_F16;
+    };
+
+    template <>
+    struct GGMLTypeTraits<int32_t> {
+        static constexpr ggml_type type = GGML_TYPE_I32;
+    };
+
+    template <>
+    struct GGMLTypeTraits<int64_t> {
+        static constexpr ggml_type type = GGML_TYPE_I64;
+    };
+
+    inline std::vector<int64_t> shape_from_ggml(const ggml_tensor* tensor) {
+        std::vector<int64_t> shape;
+        shape.reserve(static_cast<size_t>(ggml_n_dims(tensor)));
+        for (int i = 0; i < ggml_n_dims(tensor); ++i) {
+            shape.push_back(tensor->ne[i]);
+        }
+        return shape;
+    }
+
+    template <typename T>
+    inline Tensor<T> make_sd_tensor_from_ggml(const ggml_tensor* tensor) {
+        if (tensor == nullptr) {
+            return {};
+        }
+        if (tensor->type != GGMLTypeTraits<T>::type) {
+            GGML_ABORT("ggml tensor type does not match sd::Tensor type");
+        }
+        Tensor<T> result(shape_from_ggml(tensor));
+        if (tensor->buffer != nullptr) {
+            ggml_backend_tensor_get(tensor, result.data(), 0, ggml_nbytes(tensor));
+        } else {
+            std::memcpy(result.data(), tensor->data, ggml_nbytes(tensor));
+        }
+        return result;
+    }
+
+    template <typename T>
+    inline ggml_tensor* make_ggml_tensor(ggml_context* ctx, const Tensor<T>& tensor, bool copy_data = true) {
+        GGML_ASSERT(tensor.dim() > 0 && tensor.dim() <= 5);
+
+        int n_dims = std::min(static_cast<int>(tensor.dim()), GGML_MAX_DIMS);
+
+        std::array<int64_t, GGML_MAX_DIMS> ne = {1, 1, 1, 1};
+        for (int64_t i = 0; i < n_dims; ++i) {
+            ne[static_cast<size_t>(i)] = tensor.shape()[static_cast<size_t>(i)];
+        }
+
+        if (tensor.dim() == 5) {
+            ne[3] *= tensor.shape()[4];
+        }
+
+        ggml_tensor* result = ggml_new_tensor(ctx, GGMLTypeTraits<T>::type, n_dims, ne.data());
+        if (copy_data && tensor.numel() > 0) {
+            std::memcpy(result->data, tensor.data(), static_cast<size_t>(ggml_nbytes(result)));
+        }
+        return result;
+    }
+
+    template <typename T>
+    inline Tensor<T> load_tensor_from_file_as_tensor(const std::string& file_path) {
+        std::ifstream file(file_path, std::ios::binary);
+        if (!file.is_open()) {
+            throw std::runtime_error("failed to open tensor file: " + file_path);
+        }
+
+        int32_t n_dims = 0;
+        int32_t length = 0;
+        int32_t ttype  = 0;
+        file.read(reinterpret_cast<char*>(&n_dims), sizeof(n_dims));
+        file.read(reinterpret_cast<char*>(&length), sizeof(length));
+        file.read(reinterpret_cast<char*>(&ttype), sizeof(ttype));
+        if (!file.good()) {
+            throw std::runtime_error("incomplete tensor file header: " + file_path);
+        }
+        if (static_cast<ggml_type>(ttype) != GGMLTypeTraits<T>::type) {
+            throw std::invalid_argument("tensor file type does not match requested sd::Tensor type");
+        }
+
+        std::vector<int64_t> shape(4, 1);
+        for (int i = 0; i < n_dims; ++i) {
+            int32_t dim = 1;
+            file.read(reinterpret_cast<char*>(&dim), sizeof(dim));
+            shape[static_cast<size_t>(i)] = dim;
+        }
+        std::string name(static_cast<size_t>(length), '\0');
+        file.read(name.data(), length);
+
+        shape.resize(static_cast<size_t>(n_dims));
+        Tensor<T> tensor(shape);
+        file.read(reinterpret_cast<char*>(tensor.data()), static_cast<std::streamsize>(tensor.numel() * sizeof(T)));
+        if (!file.good()) {
+            throw std::runtime_error("incomplete tensor file data: " + file_path);
+        }
+        return tensor;
+    }
+
+}  // namespace sd
+
+#endif
@@ -0,0 +1,423 @@
+#ifndef __UCACHE_HPP__
+#define __UCACHE_HPP__
+
+#include <cmath>
+#include <limits>
+#include <unordered_map>
+#include <vector>
+
+#include "condition_cache_utils.hpp"
+#include "denoiser.hpp"
+#include "ggml_extend.hpp"
+#include "tensor.hpp"
+
+struct UCacheConfig {
+    bool enabled                = false;
+    float reuse_threshold       = 1.0f;
+    float start_percent         = 0.15f;
+    float end_percent           = 0.95f;
+    float error_decay_rate      = 1.0f;
+    bool use_relative_threshold = true;
+    bool adaptive_threshold     = true;
+    float early_step_multiplier = 0.5f;
+    float late_step_multiplier  = 1.5f;
+    float relative_norm_gain    = 1.6f;
+    bool reset_error_on_compute = true;
+};
+
+struct UCacheCacheEntry {
+    std::vector<float> diff;
+};
+
+struct UCacheState {
+    UCacheConfig config;
+    Denoiser* denoiser           = nullptr;
+    float start_sigma            = std::numeric_limits<float>::max();
+    float end_sigma              = 0.0f;
+    bool initialized             = false;
+    bool initial_step            = true;
+    bool skip_current_step       = false;
+    bool step_active             = false;
+    const void* anchor_condition = nullptr;
+    std::unordered_map<const void*, UCacheCacheEntry> cache_diffs;
+    std::vector<float> prev_input;
+    std::vector<float> prev_output;
+    float output_prev_norm                = 0.0f;
+    bool has_prev_input                   = false;
+    bool has_prev_output                  = false;
+    bool has_output_prev_norm             = false;
+    bool has_relative_transformation_rate = false;
+    float relative_transformation_rate    = 0.0f;
+    float last_input_change               = 0.0f;
+    bool has_last_input_change            = false;
+    float output_change_ema               = 0.0f;
+    bool has_output_change_ema            = false;
+    int total_steps_skipped               = 0;
+    int current_step_index                = -1;
+    int steps_computed_since_active       = 0;
+    int expected_total_steps              = 0;
+    int consecutive_skipped_steps         = 0;
+    float accumulated_error               = 0.0f;
+
+    struct BlockMetrics {
+        float sum_transformation_rate = 0.0f;
+        float sum_output_norm         = 0.0f;
+        int sample_count              = 0;
+        float min_change_rate         = std::numeric_limits<float>::max();
+        float max_change_rate         = 0.0f;
+
+        void reset() {
+            sum_transformation_rate = 0.0f;
+            sum_output_norm         = 0.0f;
+            sample_count            = 0;
+            min_change_rate         = std::numeric_limits<float>::max();
+            max_change_rate         = 0.0f;
+        }
+
+        void record(float change_rate, float output_norm) {
+            if (std::isfinite(change_rate) && change_rate > 0.0f) {
+                sum_transformation_rate += change_rate;
+                sum_output_norm += output_norm;
+                sample_count++;
+                if (change_rate < min_change_rate)
+                    min_change_rate = change_rate;
+                if (change_rate > max_change_rate)
+                    max_change_rate = change_rate;
+            }
+        }
+
+        float avg_transformation_rate() const {
+            return (sample_count > 0) ? (sum_transformation_rate / sample_count) : 0.0f;
+        }
+
+        float avg_output_norm() const {
+            return (sample_count > 0) ? (sum_output_norm / sample_count) : 0.0f;
+        }
+    };
+    BlockMetrics block_metrics;
+    int total_active_steps = 0;
+
+    void reset_runtime() {
+        initial_step      = true;
+        skip_current_step = false;
+        step_active       = false;
+        anchor_condition  = nullptr;
+        cache_diffs.clear();
+        prev_input.clear();
+        prev_output.clear();
+        output_prev_norm                 = 0.0f;
+        has_prev_input                   = false;
+        has_prev_output                  = false;
+        has_output_prev_norm             = false;
+        has_relative_transformation_rate = false;
+        relative_transformation_rate     = 0.0f;
+        last_input_change                = 0.0f;
+        has_last_input_change            = false;
+        output_change_ema                = 0.0f;
+        has_output_change_ema            = false;
+        total_steps_skipped              = 0;
+        current_step_index               = -1;
+        steps_computed_since_active      = 0;
+        expected_total_steps             = 0;
+        consecutive_skipped_steps        = 0;
+        accumulated_error                = 0.0f;
+        block_metrics.reset();
+        total_active_steps = 0;
+    }
+
+    void init(const UCacheConfig& cfg, Denoiser* d) {
+        config      = cfg;
+        denoiser    = d;
+        initialized = cfg.enabled && d != nullptr;
+        reset_runtime();
+        if (initialized) {
+            start_sigma = percent_to_sigma(config.start_percent);
+            end_sigma   = percent_to_sigma(config.end_percent);
+        }
+    }
+
+    void set_sigmas(const std::vector<float>& sigmas) {
+        if (!initialized || sigmas.size() < 2) {
+            return;
+        }
+        size_t n_steps       = sigmas.size() - 1;
+        expected_total_steps = static_cast<int>(n_steps);
+
+        size_t start_step = static_cast<size_t>(config.start_percent * n_steps);
+        size_t end_step   = static_cast<size_t>(config.end_percent * n_steps);
+
+        if (start_step >= n_steps)
+            start_step = n_steps - 1;
+        if (end_step >= n_steps)
+            end_step = n_steps - 1;
+
+        start_sigma = sigmas[start_step];
+        end_sigma   = sigmas[end_step];
+
+        if (start_sigma < end_sigma) {
+            std::swap(start_sigma, end_sigma);
+        }
+    }
+
+    bool enabled() const {
+        return initialized && config.enabled;
+    }
+
+    float percent_to_sigma(float percent) const {
+        if (!denoiser) {
+            return 0.0f;
+        }
+        if (percent <= 0.0f) {
+            return std::numeric_limits<float>::max();
+        }
+        if (percent >= 1.0f) {
+            return 0.0f;
+        }
+        float t = (1.0f - percent) * (TIMESTEPS - 1);
+        return denoiser->t_to_sigma(t);
+    }
+
+    void begin_step(int step_index, float sigma) {
+        if (!enabled()) {
+            return;
+        }
+        if (step_index == current_step_index) {
+            return;
+        }
+        current_step_index    = step_index;
+        skip_current_step     = false;
+        has_last_input_change = false;
+        step_active           = false;
+
+        if (sigma > start_sigma) {
+            return;
+        }
+        if (!(sigma > end_sigma)) {
+            return;
+        }
+        step_active = true;
+        total_active_steps++;
+    }
+
+    bool step_is_active() const {
+        return enabled() && step_active;
+    }
+
+    bool is_step_skipped() const {
+        return enabled() && step_active && skip_current_step;
+    }
+
+    float get_adaptive_threshold(int estimated_total_steps = 0) const {
+        float base_threshold = config.reuse_threshold;
+
+        if (!config.adaptive_threshold) {
+            return base_threshold;
+        }
+
+        int effective_total = estimated_total_steps;
+        if (effective_total <= 0) {
+            effective_total = expected_total_steps;
+        }
+        if (effective_total <= 0) {
+            effective_total = std::max(20, steps_computed_since_active * 2);
+        }
+
+        float progress = (effective_total > 0) ? (static_cast<float>(steps_computed_since_active) / effective_total) : 0.0f;
+        progress       = std::max(0.0f, std::min(1.0f, progress));
+
+        float multiplier = 1.0f;
+        if (progress < 0.2f) {
+            multiplier = config.early_step_multiplier;
+        } else if (progress > 0.8f) {
+            multiplier = config.late_step_multiplier;
+        }
+
+        return base_threshold * multiplier;
+    }
+
+    bool has_cache(const void* cond) const {
+        auto it = cache_diffs.find(cond);
+        return it != cache_diffs.end() && !it->second.diff.empty();
+    }
+
+    void update_cache(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
+        UCacheCacheEntry& entry = cache_diffs[cond];
+        sd::store_condition_cache_diff(&entry.diff, input, output);
+    }
+
+    void apply_cache(const void* cond, const sd::Tensor<float>& input, sd::Tensor<float>* output) {
+        auto it = cache_diffs.find(cond);
+        if (it == cache_diffs.end() || it->second.diff.empty()) {
+            return;
+        }
+        sd::apply_condition_cache_diff(it->second.diff, input, output);
+    }
+
+    bool before_condition(const void* cond,
+                          const sd::Tensor<float>& input,
+                          sd::Tensor<float>* output,
+                          float sigma,
+                          int step_index) {
+        if (!enabled() || step_index < 0 || output == nullptr) {
+            return false;
+        }
+        if (step_index != current_step_index) {
+            begin_step(step_index, sigma);
+        }
+        if (!step_active) {
+            return false;
+        }
+
+        if (initial_step) {
+            anchor_condition = cond;
+            initial_step     = false;
+        }
+
+        bool is_anchor = (cond == anchor_condition);
+
+        if (skip_current_step) {
+            if (has_cache(cond)) {
+                apply_cache(cond, input, output);
+                return true;
+            }
+            return false;
+        }
+
+        if (!is_anchor) {
+            return false;
+        }
+
+        if (!has_prev_input || !has_prev_output || !has_cache(cond)) {
+            return false;
+        }
+
+        size_t ne = static_cast<size_t>(input.numel());
+        if (prev_input.size() != ne) {
+            return false;
+        }
+
+        const float* input_data = input.data();
+        last_input_change       = 0.0f;
+        for (size_t i = 0; i < ne; ++i) {
+            last_input_change += std::fabs(input_data[i] - prev_input[i]);
+        }
+        if (ne > 0) {
+            last_input_change /= static_cast<float>(ne);
+        }
+        has_last_input_change = true;
+
+        if (has_output_prev_norm && has_relative_transformation_rate &&
+            last_input_change > 0.0f && output_prev_norm > 0.0f) {
+            float approx_output_change = relative_transformation_rate * last_input_change;
+            float approx_output_change_rate;
+            if (config.use_relative_threshold) {
+                float base_scale          = std::max(output_prev_norm, 1e-6f);
+                float dyn_scale           = has_output_change_ema
+                                                ? std::max(output_change_ema * std::max(1.0f, config.relative_norm_gain), 1e-6f)
+                                                : base_scale;
+                float scale               = std::sqrt(base_scale * dyn_scale);
+                approx_output_change_rate = approx_output_change / scale;
+            } else {
+                approx_output_change_rate = approx_output_change;
+            }
+            // Increase estimated error with skip horizon to avoid long extrapolation streaks
+            approx_output_change_rate *= (1.0f + 0.50f * consecutive_skipped_steps);
+            accumulated_error = accumulated_error * config.error_decay_rate + approx_output_change_rate;
+
+            float effective_threshold = get_adaptive_threshold();
+            if (!config.use_relative_threshold && output_prev_norm > 0.0f) {
+                effective_threshold = effective_threshold * output_prev_norm;
+            }
+
+            if (accumulated_error < effective_threshold) {
+                skip_current_step = true;
+                total_steps_skipped++;
+                consecutive_skipped_steps++;
+                apply_cache(cond, input, output);
+                return true;
+            } else if (config.reset_error_on_compute) {
+                accumulated_error = 0.0f;
+            }
+        }
+
+        return false;
+    }
+
+    void after_condition(const void* cond, const sd::Tensor<float>& input, const sd::Tensor<float>& output) {
+        if (!step_is_active()) {
+            return;
+        }
+
+        update_cache(cond, input, output);
+
+        if (cond != anchor_condition) {
+            return;
+        }
+        steps_computed_since_active++;
+        consecutive_skipped_steps = 0;
+
+        size_t ne            = static_cast<size_t>(input.numel());
+        const float* in_data = input.data();
+        prev_input.resize(ne);
+        for (size_t i = 0; i < ne; ++i) {
+            prev_input[i] = in_data[i];
+        }
+        has_prev_input = true;
+
+        const float* out_data = output.data();
+        float output_change   = 0.0f;
+        if (has_prev_output && prev_output.size() == ne) {
+            for (size_t i = 0; i < ne; ++i) {
+                output_change += std::fabs(out_data[i] - prev_output[i]);
+            }
+            if (ne > 0) {
+                output_change /= static_cast<float>(ne);
+            }
+        }
+        if (std::isfinite(output_change) && output_change > 0.0f) {
+            if (!has_output_change_ema) {
+                output_change_ema     = output_change;
+                has_output_change_ema = true;
+            } else {
+                output_change_ema = 0.8f * output_change_ema + 0.2f * output_change;
+            }
+        }
+
+        prev_output.resize(ne);
+        for (size_t i = 0; i < ne; ++i) {
+            prev_output[i] = out_data[i];
+        }
+        has_prev_output = true;
+
+        float mean_abs = 0.0f;
+        for (size_t i = 0; i < ne; ++i) {
+            mean_abs += std::fabs(out_data[i]);
+        }
+        output_prev_norm     = (ne > 0) ? (mean_abs / static_cast<float>(ne)) : 0.0f;
+        has_output_prev_norm = output_prev_norm > 0.0f;
+
+        if (has_last_input_change && last_input_change > 0.0f && output_change > 0.0f) {
+            float rate = output_change / last_input_change;
+            if (std::isfinite(rate)) {
+                relative_transformation_rate     = rate;
+                has_relative_transformation_rate = true;
+                block_metrics.record(rate, output_prev_norm);
+            }
+        }
+
+        has_last_input_change = false;
+    }
+
+    void log_block_metrics() const {
+        if (block_metrics.sample_count > 0) {
+            LOG_INFO("UCacheBlockMetrics: samples=%d, avg_rate=%.4f, min=%.4f, max=%.4f, avg_norm=%.4f",
+                     block_metrics.sample_count,
+                     block_metrics.avg_transformation_rate(),
+                     block_metrics.min_change_rate,
+                     block_metrics.max_change_rate,
+                     block_metrics.avg_output_norm());
+        }
+    }
+};
+
+#endif  // __UCACHE_HPP__
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
leejet	e8323cabb0	feat: add flux2 small decoder support (#1402 )	2026-04-08 23:13:25 +08:00
Wagner Bruna	dd753729cc	fix: correct double increment on flow denoisers sigma calculations (#1372 )	2026-04-08 23:13:05 +08:00
leejet	8afbeb6ba9	chore: normalize text files to utf-8 without bom (#1394 )	2026-04-06 21:25:34 +08:00
leejet	5bf438d568	refactor: split examples common into header and source (#1393 )	2026-04-06 21:11:57 +08:00
leejet	359eb8b8de	refactor: apply RAII ownership to examples (#1392 )	2026-04-06 20:33:46 +08:00
leejet	7397ddaa86	feat: add webm support (#1391 )	2026-04-06 01:49:28 +08:00
stduhpfandleejet	9369ab759f	feat: inpaint improvements (#1357 ) * inpaint: get max pixel max instead of single sample * inpaint: masked diffusion for inpainting models with inflated mask * refactor tensor interpolate nearest-like reduction paths and generalize max_pool_2d --------- Co-authored-by: leejet <leejet714@gmail.com>	2026-04-06 00:44:26 +08:00
Wagner Brunaandleejet	687a81f251	chore: make libwebp optional and support system libwebp (#1387 ) Co-authored-by: leejet <leejet714@gmail.com>	2026-04-05 23:52:05 +08:00
leejet	87ecb95cbc	feat: add webp support (#1384 )	2026-04-02 01:36:11 +08:00
Wagner Brunaandleejet	99c1de379b	feat: ancestral sampler implementations for flow models (#1374 ) * feat: add support for the eta parameter to ancestral samplers * feat: Euler Ancestral sampler implementation for flow models * refine flow ancestral sampling and normalize eta defaults --------- Co-authored-by: leejet <leejet714@gmail.com>	2026-04-02 01:35:29 +08:00
leejet	09b12d5f6d	feat(cli): add metadata inspection mode (#1381 )	2026-04-01 00:52:03 +08:00
leejet	6dfe945958	fix: use resolved image size in embedded metadata (#1382 )	2026-03-31 23:55:49 +08:00
leejet	bf0216765a	feat: show tensor loading progress in MB/s or GB/s (#1380 )	2026-03-31 23:06:44 +08:00
Wagner Bruna	4fe7a35939	feat(server): add generation metadata to png images (#1217 )	2026-03-31 23:06:27 +08:00
Jan Ekström	4d5232083f	chore(server): link winsock2 for non-MSVC windows (#1378 )	2026-03-31 22:10:34 +08:00
leejet	1d6cb0f8c3	refactor: split and simplify sample_k_diffusion samplers (#1377 )	2026-03-31 00:32:14 +08:00
leejet	83e8f6f0af	refactor(server): split server endpoint registration (#1376 )	2026-03-31 00:02:03 +08:00
leejet	8d878872d9	fix: disable extra T5 mask padding for Wan (#1375 )	2026-03-30 23:10:07 +08:00
Wagner Bruna	02dd5e5dd2	refactor: move VAE tiling parameters to SDGenerationParams (#1261 )	2026-03-30 23:09:38 +08:00
leejet	8f2967c006	sync: update ggml	2026-03-30 23:00:53 +08:00
leejet	f16a110f87	refactor: migrate generation pipeline to sd::Tensor (#1373 )	2026-03-30 00:19:25 +08:00
stduhpf	ed88e215a2	refactor: simplify f8_e5m2_to_f16 function a little bit (#1358 )	2026-03-30 00:14:33 +08:00
Wagner Bruna	6293ab5aaf	docs: update Spectrum info about DiT models (#1360 )	2026-03-30 00:12:57 +08:00
leejet	545fac4f3f	refactor: simplify sample cache flow (#1350 )	2026-03-17 00:28:03 +08:00
Tay	5265a5efa1	perf(z-image): switch to fused SwiGLU kernel (#1302 )	2026-03-17 00:27:46 +08:00
leejet	84cbd88df1	style: remove redundant struct qualifiers for consistent C/C++ type usage (#1349 )	2026-03-16 22:17:22 +08:00
Daniele	997bb11fb6	fix: correct encoder channels for flux2 (#1346 )	2026-03-16 22:16:43 +08:00
leejet	862a6586cb	feat: add embedded WebUI (#1207 )	2026-03-16 00:26:57 +08:00
leejet	61d8331ef3	ci: avoid cuda docker build timeout by using -j16	2026-03-15 18:39:29 +08:00
leejet	acc3bf1fdc	refactor: optimize the VAE architecture (#1345 )	2026-03-15 16:57:42 +08:00
Kevin Nause	83eabd7c01	ci: add CUDA Dockerfile (#1314 )	2026-03-15 16:46:01 +08:00
Wagner Bruna	630ee03f23	refactor: move all cache parameter defaults to the library (#1327 )	2026-03-15 16:43:46 +08:00
Wagner Bruna	f6968bc589	chore: remove SD_FAST_SOFTMAX build flag (#1338 )	2026-03-15 16:42:47 +08:00
rmatif	adfef62900	feat: add generic DiT support to spectrum cache (#1336 )	2026-03-15 16:41:05 +08:00
JusteLeo	6fa7ca9317	docs: add Anima2 gguf download link to anima.md (#1335 )	2026-03-15 16:40:14 +08:00
leejet	d6dd6d7b55	refactor: remove ununsed encode_video (#1332 )	2026-03-10 00:36:09 +08:00
rmatif	dea4980f4e	feat: add spectrum caching method (#1322 )	2026-03-10 00:35:32 +08:00
leejet	c8fb3d2458	fix: resolve SD1 Pix2Pix issue (#1329 )	2026-03-08 00:28:05 +08:00
stduhpf	3d33caaef8	fix: make tiling work better when using circular (#1299 )	2026-03-08 00:25:07 +08:00
WinkelCode	9b424db0f4	ci: change workflow owner of "actions-commit-hash" from "pr-mpt" to "prompt" (#1323 )	2026-03-08 00:23:23 +08:00
rmatif	d95062737e	fix: ucache: normalize reuse error (#1313 )	2026-03-04 23:50:45 +08:00
Korsar13	7c880f80c7	fix: avoid sd-server memory leak (#1316 )	2026-03-04 23:47:38 +08:00
leejet	aaa8a51bd8	docs: update sd-cli/sd-server docs	2026-03-04 00:41:17 +08:00
leejet	ba35dd734e	refactor: introduce ggml_ext_zeros_like/ggml_ext_ones_like (#1312 )	2026-03-04 00:36:52 +08:00
bssrdf	d41f5fff69	perf: improved flux attention qkv unpacking (#1306 )	2026-03-04 00:36:32 +08:00
Korsar13	810ef0cf76	fix: reset weight adapter for models if no loras in request (#1307 )	2026-03-04 00:34:07 +08:00
leejet	5792c66879	feat: support some non-standard Anima weight names (#1305 )	2026-03-01 22:01:29 +08:00
Wagner Bruna	39d54702a6	feat: accept legacy image parameter on v1/images/edits (#1270 )	2026-03-01 22:00:50 +08:00
Wagner Bruna	60889bc9a1	fix: correct sdapi LoRA file handling (#1276 )	2026-03-01 21:57:06 +08:00
leejet	e64baa3611	refactor: reuse DiT's patchify/unpatchify functions (#1304 )	2026-03-01 21:44:51 +08:00
leejet	cec4aedcfd	docs: add anima docs	2026-03-01 15:32:25 +08:00
rmatif	4cdfff5ff2	feat: add Anima support (#1296 )	2026-03-01 15:23:18 +08:00
leejet	0752cc9d3a	fix: resolve image quality degradation issue (#1297 )	2026-02-26 00:26:21 +08:00
Wagner Brunaandleejet	b314d80ad0	feat: turn flow_shift into a generation parameter (#1289 ) * feat: turn flow_shift into a generation parameter * format code * simplify set_shift/set_parameters * fix sd_sample_params_to_str * remove unused variable * update docs --------- Co-authored-by: leejet <leejet714@gmail.com>	2026-02-26 00:26:04 +08:00
leejet	c9cd49701a	fix: safely handle whitespace and consecutive newlines (#1288 )	2026-02-19 20:54:42 +08:00
akleine	c5eb1e4137	fix: avoid black images if using an invalid VAE (for SDXL) (#1273 )	2026-02-19 20:54:18 +08:00
leejet	636d3cb6ff	refactor: reorganize the vocab file structure (#1271 )	2026-02-11 00:44:17 +08:00
Wagner Bruna	adea272225	feat(server): use image and command-line dimensions by default on server (#1262 )	2026-02-11 00:42:50 +08:00
Mario Limonciello	45ce78a3ae	ci: correct rocm artifact of linux (#1269 )	2026-02-10 23:19:28 +08:00
leejet	28ef93c0e1	refactor: reorganize the file structure (#1266 )	2026-02-10 23:13:35 +08:00
leejet	3296545090	feat: add extra_c_crossattns support for llm embedder (#1265 )	2026-02-10 00:00:17 +08:00
akleine	d60fb27560	fix: avoid unwanted file extension changes (#1257 )	2026-02-09 23:59:43 +08:00
Wagner Bruna	c7ccafbd6f	fix: correct sdapi handling of cfg_scale and steps (#1260 )	2026-02-09 23:34:19 +08:00
stduhpf	aa0b899397	fix: improve handling of VAE decode failures (#1222 )	2026-02-09 23:29:41 +08:00
Mario Limonciello	5e264372ce	ci: add a github action to generate a Linux ROCm artifact (#1258 )	2026-02-09 23:23:06 +08:00
leejet	f0f641a142	feat(server): add lora support to sdapi (#1256 )	2026-02-08 00:11:16 +08:00
stduhpf	9f56833e14	feat: optimize LoKr at runtime (#1233 )	2026-02-08 00:08:09 +08:00
Roj234	65891d74cc	fix: avoid the issue of NaN for qwen-image on certain devices (#1249 )	2026-02-04 23:49:05 +08:00
leejet	f957fa3d2a	feat: add --fa option (#1242 )	2026-02-01 21:44:54 +08:00
leejet	c252e03c6b	sync: update ggml	2026-02-01 20:54:23 +08:00
rmatif	e63daba33d	feat: add res_multistep, res_2s sampler and bong tangent scheduler (#1234 )	2026-02-01 20:05:27 +08:00
stduhpf	3959109281	fix: improve LoCon support with other naming conventions (#1239 )	2026-02-01 20:00:16 +08:00
leejet	e411520407	docs: add z-image-base example	2026-01-28 21:47:36 +08:00
leejet	43e829f219	refactor: unify the processing of attention mask (#1230 )	2026-01-26 00:33:34 +08:00
leejet	7837232631	perf: make dit faster (#1228 )	2026-01-25 22:50:10 +08:00
Equious	4ccce027b2	fix: correct mask and control image loading in cli (#1229 )	2026-01-25 22:47:52 +08:00
leejet	fa61ea744d	fix: set default lora_model_dir to . (#1224 )	2026-01-23 22:13:59 +08:00
leejet	5e4579c11d	feat: use image width and height when not explicitly set (#1206 )	2026-01-22 23:54:41 +08:00
Wagner Bruna	329571131d	chore: clarify warning about missing model files (#1219 )	2026-01-21 22:34:11 +08:00
leejet	a48b4a3ade	docs: add FLUX.2-klein support to news	2026-01-19 23:56:50 +08:00
stduhpf	b87fe13afd	feat: support new chroma radiance "x0_x32_proto" (#1209 )	2026-01-19 23:51:26 +08:00
Oleg Skutte	e50e1f253d	feat: add taef2 support (#1211 )	2026-01-19 23:39:36 +08:00
leejet	c6206fb351	fix: set VAE conv scale for all SDXL variants	2026-01-19 23:21:48 +08:00
akleine	639091fbe9	feat: add support for Segmind's Vega model (#1195 )	2026-01-19 23:15:47 +08:00
leejet	9293016c9d	docs: update esrgan.md	2026-01-19 23:00:50 +08:00
leejet	2efd19978d	fix: use Unix timestamp for field instead of ISO string (#1205 )	2026-01-19 00:21:29 +08:00
Wagner Brunaandleejet	61659ef299	feat: add basic sdapi support to sd-server (#1197 ) * feat: add basic sdapi support to sd-server Compatible with AUTOMATIC1111 / Forge. * fix img2img with no mask * add more parameter validation * eliminate MSVC warnings --------- Co-authored-by: leejet <leejet714@gmail.com>	2026-01-19 00:21:11 +08:00
leejet	9565c7f6bd	add support for flux2 klein (#1193 ) * add support for flux2 klein 4b * add support for flux2 klein 8b * use attention_mask in Flux.2 klein LLMEmbedder * update docs	2026-01-18 01:17:33 +08:00
Wagner Bruna	fbce16e02d	fix: avoid undefined behavior on image mask allocation failure (#1198 )	2026-01-18 01:14:56 +08:00
akleineandleejet	7010bb4dff	feat: support for SDXS-512 model (#1180 ) * feat: add U-Net specials of SDXS * docs: update distilled_sd.md for SDXS-512 * feat: for SDXS use AutoencoderTiny as the primary VAE * docs: update distilled_sd.md for SDXS-512 * fix: SDXS code cleaning after review by stduhpf * format code * fix sdxs with --taesd-preview-only --------- Co-authored-by: leejet <leejet714@gmail.com>	2026-01-14 01:14:57 +08:00
Wagner Bruna	48d3161a8d	feat: add sd-server API support for steps, sampler and scheduler (#1173 )	2026-01-14 00:34:27 +08:00
Weiqi Gao	271b594e74	sync: update ggml (#1187 )	2026-01-14 00:28:55 +08:00
leejet	885e62ea82	refactor: replace ggml_ext_attention with ggml_ext_attention_ext (#1185 )	2026-01-11 16:34:13 +08:00
rmatifandleejet	0e52afc651	feat: enable vae tiling for vid gen (#1152 ) * enable vae tiling for vid gen * format code * eliminate compilation warning --------- Co-authored-by: leejet <leejet714@gmail.com>	2026-01-08 23:23:05 +08:00
leejet	27b5f17401	ci: only push Docker images on master or release	2026-01-08 23:03:32 +08:00
Flavio Bizzarri	dfe6d6c664	fix: missing newline after seed in sd_img_gen_params_to_str (#1183 )	2026-01-08 22:52:22 +08:00
leejet	9be0b91927	docs: fix safetensors file extension notation	2026-01-06 23:31:03 +08:00
evanreichard	e7e83ed4d1	fix(server): use has_file for mask multipart detection (#1178 )	2026-01-06 23:16:05 +08:00
Wagner Bruna	c5602a676c	feat: prioritize gguf and safetensors formats for embeddings and LoRAs (#1169 )	2026-01-05 23:58:09 +08:00
Nuno	c34730d9b4	chore: downgrade ubuntu base image in musa container image (#1176 ) Signed-off-by: rare-magma <rare-magma@posteo.eu>	2026-01-05 23:56:34 +08:00
Nunoandleejet	fdcacc1ebb	ci: cancel old github action runs (#1172 ) * ci: cancel old github action runs Signed-off-by: rare-magma <rare-magma@posteo.eu> * ci: adjust concurrency to avoid canceling non-PR workflows --------- Signed-off-by: rare-magma <rare-magma@posteo.eu> Co-authored-by: leejet <leejet714@gmail.com>	2026-01-05 23:52:34 +08:00
Nuno	496ec9421e	chore: add Linux Vulkan build and Docker image workflows (#1164 )	2026-01-05 23:42:12 +08:00
leejet	05006cd6e1	chore: use CMAKE_BUILD_TYPE (#1175 )	2026-01-05 23:29:22 +08:00
leejet	b90b1ee9cf	chore: eliminate compilation warnings under MSVC (#1170 )	2026-01-04 22:26:57 +08:00
leejet	2cef4badb8	chore: use Release build for windows-latest-cmake	2026-01-04 22:26:09 +08:00
Daniele	a119a4da9a	fix: avoid issues when sigma_min is close to 0 (#1138 )	2026-01-04 22:05:01 +08:00
Jay4242	6eefd2d49a	feat: support random seed flag (#1163 )	2026-01-04 21:57:50 +08:00
leejet	4ff2c8c74b	refactor: simplify logic for saving results (#1149 )	2025-12-28 23:27:27 +08:00
leejet	51bd9c8004	chore: reformat named cache params description into single line	2025-12-28 22:53:07 +08:00
Wagner Bruna	d0d836ae74	feat: support mmap for model loading (#1059 )	2025-12-28 22:38:29 +08:00
leejet	a2d83dd0c8	refactor: move pmid condition logic into get_pmid_condition (#1148 )	2025-12-27 16:48:15 +08:00
Wagner Bruna	cc107714d7	fix: consistently pass 2nd-order samplers half steps as negatives (#1095 )	2025-12-27 15:54:18 +08:00
leejet	37c9860b79	fix: handle redirected UTF-8 output correctly on Windows (#1147 )	2025-12-27 15:43:19 +08:00
leejet	ccb6b0ac9d	feat: add __index_timestep_zero__ support (#1146 )	2025-12-26 22:07:40 +08:00
Weiqi Gao	df4efe26bd	feat: add png sequence output for vid_gen (#1117 )	2025-12-26 22:06:13 +08:00
leejet	860a78e248	fix: avoid crash when using taesd for preview only (#1141 )	2025-12-24 23:30:12 +08:00
leejet	a0adcfb148	feat: add support for qwen image edit 2511 (#1096 )	2025-12-24 23:00:08 +08:00
leejet	3d5fdd7b37	feat: add support for more underline loras (#1135 )	2025-12-24 22:59:23 +08:00
Weiqi Gao	3e6c428c27	chore: use Ninja on Windows to speed up build process (#1120 )	2025-12-24 22:53:17 +08:00
张春乔	96fcb13fc0	feat: add --serve-html-path option to example server (#1123 )	2025-12-24 22:43:09 +08:00
leejet	3e812460cf	fix: correct ggml_pad_ext (#1133 )	2025-12-23 21:37:07 +08:00
leejet	98916e8256	docs: update README.md	2025-12-22 23:58:28 +08:00
rmatif	298b11069f	feat: add more caching methods (#1066 )	2025-12-22 23:52:11 +08:00
leejet	30a91138f8	fix: add the missing }	2025-12-21 21:53:38 +08:00
leejet	c6937ba44a	fix: correct the parsing of --convert-name opotion	2025-12-21 21:47:50 +08:00
leejet	ca5b1969a8	feat: do not convert tensor names by default in convert mode (#1122 )	2025-12-21 18:40:10 +08:00
Phylliida Dev Phylliida leejet	50ff966445	feat: add seamless texture generation support (#914 ) * global bool * reworked circular to global flag * cleaner implementation of tiling support in sd cpp * cleaned rope * working simplified but still need wraps * Further clean of rope * resolve flux conflict * switch to pad op circular only * Set ggml to most recent * Revert ggml temp * Update ggml to most recent * Revert unneded flux change * move circular flag to the GGMLRunnerContext * Pass through circular param in all places where conv is called * fix of constant and minor cleanup * Added back --circular option * Conv2d circular in vae and various models * Fix temporal padding for qwen image and other vaes * Z Image circular tiling * x and y axis seamless only * First attempt at chroma seamless x and y * refactor into pure x and y, almost there * Fix crash on chroma * Refactor into cleaner variable choices * Removed redundant set_circular_enabled * Sync ggml * simplify circular parameter * format code * no need to perform circular pad on the clip * simplify circular_axes setting * unify function naming * remove unnecessary member variables * simplify rope --------- Co-authored-by: Phylliida <phylliidadev@gmail.com> Co-authored-by: leejet <leejet714@gmail.com>	2025-12-21 18:06:47 +08:00
leejet	88ec9d30b1	feat: add scale_rope support (#1121 )	2025-12-21 15:40:21 +08:00
stduhpf	60abda56e0	feat: select vulkan device with env variable (#629 )	2025-12-21 15:35:38 +08:00
stduhpfandleejet	23fce0bd84	feat: add support for Chroma Radiance x0 (#1091 ) * Add x0 Flux pred (+prepare for others) * Fix convert models with empty tensors * patch_32 exp support attempt * improve support for patch_32 * follow official pipeline --------- Co-authored-by: leejet <leejet714@gmail.com>	2025-12-20 00:55:57 +08:00
Wagner Bruna	7c88c4765c	chore: give feedback about cfg values smaller than 1 (#1088 )	2025-12-19 23:41:52 +08:00
Weiqi Gao	1f77545cf8	docs: document usage of tae for VRAM reduction using wan (#1108 )	2025-12-19 23:31:09 +08:00
leejet	8e9f3a4d9e	feat: add support for underline style lora of flux (#1103 ) * feat: add support for underline style lora of flux * add support for underline style lora of t5 * add more protected tokens	2025-12-18 21:44:16 +08:00
Wagner Brunaandleejet	78e15bd4af	feat: default to LCM scheduler for LCM sampling (#1109 ) * feat: default to LCM scheduler for LCM sampling * fix bug and attempt to get default scheduler for vid_gen when none is set --------- Co-authored-by: leejet <leejet714@gmail.com>	2025-12-18 21:43:39 +08:00
Daniele	97cf2efe45	feat: add KL Optimal scheduler (#1098 )	2025-12-18 21:02:55 +08:00
leejet	bda7fab9f2	chore: remove unused debug code	2025-12-17 23:43:37 +08:00
leejet	c2e18c86e8	fix: make flash attn work with high noise diffusion model (#1111 )	2025-12-17 23:28:59 +08:00
leejet	c3ad6a13e1	refactor: optimize the printing of version log (#1102 )	2025-12-16 23:11:27 +08:00