ci: add a github action to generate a Linux ROCm artifact (#1258)

* 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>

.clang-tidy

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

.dockerignore

@@ -1,4 +1,5 @@
 build*/
+docs/
 test/
 
 .cache/

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,73 @@
+name: 🐞 Bug Report
+description: Report a bug or unexpected behavior
+title: "[Bug] "
+labels: ["bug"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Please use this template and include as many details as possible to help us reproduce and fix the issue.
+  - type: textarea
+    id: commit
+    attributes:
+      label: Git commit
+      description: Which commit are you trying to compile?
+      placeholder: |
+        $git rev-parse HEAD
+        40a6a8710ec15b1b5db6b5a098409f6bc8f654a4
+    validations:
+      required: true
+  - type: input
+    id: os
+    attributes:
+      label: Operating System & Version
+      placeholder: e.g. “Ubuntu 22.04”, “Windows 11 23H2”, “macOS 14.3”
+    validations:
+      required: true
+  - type: dropdown
+    id: backends
+    attributes:
+        label: GGML backends
+        description: Which GGML backends do you know to be affected?
+        options: [CPU, CUDA, HIP, Metal, Musa, SYCL, Vulkan, OpenCL]
+        multiple: true
+    validations:
+      required: true
+  - type: input
+    id: cmd_arguments
+    attributes:
+      label: Command-line arguments used
+      placeholder: The full command line you ran (with all flags)
+    validations:
+      required: true
+  - type: textarea
+    id: steps_to_reproduce
+    attributes:
+      label: Steps to reproduce
+      placeholder: A step-by-step list of what you did
+    validations:
+      required: true
+  - type: textarea
+    id: expected_behavior
+    attributes:
+      label: What you expected to happen
+      placeholder: Describe the expected behavior or result
+    validations:
+      required: true
+  - type: textarea
+    id: actual_behavior
+    attributes:
+      label: What actually happened
+      placeholder: Describe what you saw instead (errors, logs, crash, etc.)
+    validations:
+      required: true
+  - type: textarea
+    id: logs_and_errors
+    attributes:
+      label: Logs / error messages / stack trace
+      placeholder: Paste complete logs or error output
+  - type: textarea
+    id: additional_info
+    attributes:
+      label: Additional context / environment details
+      placeholder: e.g. CPU model, GPU, RAM, model file versions, quantization type, etc.

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,33 @@
+name: 💡 Feature Request
+description: Suggest a new feature or improvement
+title: "[Feature] "
+labels: ["enhancement"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thank you for suggesting an improvement! Please fill in the fields below.
+  - type: input
+    id: summary
+    attributes:
+      label: Feature Summary
+      placeholder: A one-line summary of the feature you’d like
+    validations:
+      required: true
+  - type: textarea
+    id: description
+    attributes:
+      label: Detailed Description
+      placeholder: What problem does this solve? How do you expect it to work?
+    validations:
+      required: true
+  - type: textarea
+    id: alternatives
+    attributes:
+      label: Alternatives you considered
+      placeholder: Any alternative designs or workarounds you tried
+  - type: textarea
+    id: additional_context
+    attributes:
+      label: Additional context
+      placeholder: Any extra information (use cases, related functionalities, constraints)

.github/workflows/build.yml

@@ -38,6 +38,10 @@ on:
 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
@@ -65,7 +69,7 @@ jobs:
 
       - name: Get commit hash
         id: commit
-        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/main' ) || github.event.inputs.create_release == 'true' }}
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         uses: pr-mpt/actions-commit-hash@v2
 
       - name: Fetch system info
@@ -92,6 +96,123 @@ 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: 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: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: pr-mpt/actions-commit-hash@v2
+
+      - name: Fetch system info
+        id: system-info
+        run: |
+          echo "CPU_ARCH=`uname -m`" >> "$GITHUB_OUTPUT"
+          echo "OS_NAME=`lsb_release -s -i`" >> "$GITHUB_OUTPUT"
+          echo "OS_VERSION=`lsb_release -s -r`" >> "$GITHUB_OUTPUT"
+          echo "OS_TYPE=`uname -s`" >> "$GITHUB_OUTPUT"
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          cp ggml/LICENSE ./build/bin/ggml.txt
+          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
+          zip -j sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-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]
+
+    env:
+      REGISTRY: ghcr.io
+      IMAGE_NAME: ${{ github.repository }}
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v6
+        with:
+          submodules: recursive
+
+      - name: Get commit hash
+        id: commit
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: pr-mpt/actions-commit-hash@v2
+
+      - name: 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:
+          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
 
@@ -118,7 +239,7 @@ jobs:
 
       - name: Get commit hash
         id: commit
-        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/main' ) || github.event.inputs.create_release == 'true' }}
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         uses: pr-mpt/actions-commit-hash@v2
 
       - name: Fetch system info
@@ -146,27 +267,25 @@ 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-2019
+    runs-on: windows-2022
 
     env:
-      VULKAN_VERSION: 1.3.261.1
+      VULKAN_VERSION: 1.4.328.1
 
     strategy:
       matrix:
         include:
           - build: "noavx"
-            defines: "-DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DGGML_NATIVE=OFF -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DSD_BUILD_SHARED_LIBS=ON"
           - build: "avx2"
-            defines: "-DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DGGML_NATIVE=OFF -DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
           - build: "avx"
-            defines: "-DGGML_AVX2=OFF -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DGGML_NATIVE=OFF -DGGML_AVX=ON -DGGML_AVX2=OFF -DSD_BUILD_SHARED_LIBS=ON"
           - build: "avx512"
-            defines: "-DGGML_AVX512=ON -DSD_BUILD_SHARED_LIBS=ON"
+            defines: "-DGGML_NATIVE=OFF -DGGML_AVX512=ON -DGGML_AVX=ON -DGGML_AVX2=ON -DSD_BUILD_SHARED_LIBS=ON"
           - build: "cuda12"
-            defines: "-DSD_CUBLAS=ON -DSD_BUILD_SHARED_LIBS=ON"
-          - build: "rocm5.5"
-            defines: '-G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS="gfx1100;gfx1102;gfx1030" -DSD_BUILD_SHARED_LIBS=ON'
-          - 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
@@ -178,41 +297,32 @@ jobs:
       - name: Install cuda-toolkit
         id: cuda-toolkit
         if: ${{ matrix.build == 'cuda12' }}
-        uses: Jimver/cuda-toolkit@v0.2.11
+        uses: Jimver/cuda-toolkit@v0.2.22
         with:
-          cuda: "12.2.0"
+          cuda: "12.8.1"
           method: "network"
           sub-packages: '["nvcc", "cudart", "cublas", "cublas_dev", "thrust", "visual_studio_integration"]'
 
-      - name: Install rocm-toolkit
-        id: rocm-toolkit
-        if: ${{ matrix.build == 'rocm5.5' }}
-        uses: Cyberhan123/rocm-toolkit@v0.1.0
-        with:
-          rocm: "5.5.0"
-
-      - name: Install Ninja
-        id: install-ninja
-        if: ${{ matrix.build == 'rocm5.5' }}
-        uses: urkle/action-get-ninja@v1
-        with:
-          version: 1.11.1
       - name: Install Vulkan SDK
         id: get_vulkan
         if: ${{ matrix.build == 'vulkan' }}
         run: |
-          curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe"
+          curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/vulkansdk-windows-X64-${env:VULKAN_VERSION}.exe"
           & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install
           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
@@ -254,7 +364,7 @@ jobs:
 
       - name: Copy and pack Cuda runtime
         id: pack_cuda_runtime
-        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' && matrix.build == 'cuda12' ) || github.event.inputs.create_release == 'true' }}
+        if: ${{ matrix.build == 'cuda12' && (github.event_name == 'push' && github.ref == 'refs/heads/master' || github.event.inputs.create_release == 'true') }}
         run: |
           echo "Cuda install location: ${{steps.cuda-toolkit.outputs.CUDA_PATH}}"
           $dst='.\build\bin\cudart\'
@@ -262,7 +372,7 @@ jobs:
           7z a cudart-sd-bin-win-cu12-x64.zip $dst\*
 
       - name: Upload Cuda runtime
-        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' && matrix.build == 'cuda12' ) || github.event.inputs.create_release == 'true' }}
+        if: ${{ matrix.build == 'cuda12' && (github.event_name == 'push' && github.ref == 'refs/heads/master' || github.event.inputs.create_release == 'true') }}
         uses: actions/upload-artifact@v4
         with:
           name: sd-cudart-sd-bin-win-cu12-x64.zip
@@ -277,6 +387,251 @@ jobs:
           path: |
             sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip
 
+  windows-latest-cmake-hip:
+    runs-on: windows-2022
+
+    env:
+      HIPSDK_INSTALLER_VERSION: "25.Q3"
+      GPU_TARGETS: "gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"
+
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          submodules: recursive
+
+      - name: Cache ROCm Installation
+        id: cache-rocm
+        uses: actions/cache@v4
+        with:
+          path: C:\Program Files\AMD\ROCm
+          key: rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.16
+        with:
+          key: windows-latest-cmake-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-x64
+          evict-old-files: 1d
+
+      - name: Install ROCm
+        if: steps.cache-rocm.outputs.cache-hit != 'true'
+        run: |
+          $ErrorActionPreference = "Stop"
+          write-host "Downloading AMD HIP SDK Installer"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          write-host "Installing AMD HIP SDK"
+          $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru
+          $completed = $proc.WaitForExit(600000)
+          if (-not $completed) {
+              Write-Error "ROCm installation timed out after 10 minutes. Killing the process"
+              $proc.Kill()
+              exit 1
+          }
+          if ($proc.ExitCode -ne 0) {
+              Write-Error "ROCm installation failed with exit code $($proc.ExitCode)"
+              exit 1
+          }
+          write-host "Completed AMD HIP SDK installation"
+
+      - name: Verify ROCm
+        run: |
+          # Find and test ROCm installation
+          $clangPath = Get-ChildItem 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Select-Object -First 1
+          if (-not $clangPath) {
+            Write-Error "ROCm installation not found"
+            exit 1
+          }
+          & $clangPath.FullName --version
+          # Set HIP_PATH environment variable for later steps
+          echo "HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)" >> $env:GITHUB_ENV
+
+      - name: Build
+        run: |
+          mkdir build
+          cd build
+          $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
+          cmake .. `
+            -G "Unix Makefiles" `
+            -DSD_HIPBLAS=ON `
+            -DSD_BUILD_SHARED_LIBS=ON `
+            -DGGML_NATIVE=OFF `
+            -DCMAKE_C_COMPILER=clang `
+            -DCMAKE_CXX_COMPILER=clang++ `
+            -DCMAKE_BUILD_TYPE=Release `
+            -DGPU_TARGETS="${{ env.GPU_TARGETS }}"
+          cmake --build . --config Release --parallel ${env:NUMBER_OF_PROCESSORS}
+
+      - name: Get commit hash
+        id: commit
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: pr-mpt/actions-commit-hash@v2
+
+      - name: Pack artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          md "build\bin\rocblas\library\"
+          md "build\bin\hipblaslt\library"
+          cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\"
+          cp "${env:HIP_PATH}\bin\hipblaslt.dll" "build\bin\"
+          cp "${env:HIP_PATH}\bin\rocblas.dll" "build\bin\"
+          cp "${env:HIP_PATH}\bin\rocblas\library\*" "build\bin\rocblas\library\"
+          cp "${env:HIP_PATH}\bin\hipblaslt\library\*" "build\bin\hipblaslt\library\"
+          7z a sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-x64.zip .\build\bin\*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-x64.zip
+          path: |
+            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-x64.zip
+
+  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: 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" >> $GITHUB_PATH
+
+          # Build case pattern from GPU_TARGETS
+          PATTERN=$(printf '%s' "$GPU_TARGETS" | sed 's/;/\*|\*/g')
+          PATTERN="*${PATTERN}*"
+
+          # 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
+            case "$file" in
+            $PATTERN)
+              ;;
+            *)
+              sudo rm -f "$file" ;;
+            esac;
+          done
+
+          cd /opt/rocm/lib/hipblaslt/library
+          for file in *; do
+            case "$file" in
+              $PATTERN)
+                ;;
+              *)
+                sudo rm -f "$file" ;;
+            esac;
+          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: pr-mpt/actions-commit-hash@v2
+
+      - name: Prepare artifacts
+        id: prepare_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          # Copy licenses
+          cp ggml/LICENSE ./build/bin/ggml.txt
+          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
+
+          # Create directories for ROCm libraries
+          mkdir -p ./build/bin/rocblas/library
+          mkdir -p ./build/bin/hipblaslt/library
+
+          # Copy ROCm runtime libraries (use || true to continue if files don't exist)
+          cp /opt/rocm/lib/librocsparse.so* ./build/bin/ || true
+          cp /opt/rocm/lib/libhsa-runtime64.so* ./build/bin/ || true
+          cp /opt/rocm/lib/libamdhip64.so* ./build/bin/ || true
+          cp /opt/rocm/lib/libhipblas.so* ./build/bin/ || true
+          cp /opt/rocm/lib/libhipblaslt.so* ./build/bin/ || true
+          cp /opt/rocm/lib/librocblas.so* ./build/bin/ || true
+
+          # Copy library files (already filtered to target architectures)
+          cp /opt/rocm/lib/rocblas/library/* ./build/bin/rocblas/library/ || true
+          cp /opt/rocm/lib/hipblaslt/library/* ./build/bin/hipblaslt/library/ || true
+
+      - name: Fetch system info
+        id: system-info
+        run: |
+          echo "CPU_ARCH=`uname -m`" >> "$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 }}-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' }}
 
@@ -284,10 +639,19 @@ 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
 
     steps:
+      - name: Clone
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+
       - name: Download artifacts
         id: download-artifact
         uses: actions/download-artifact@v4
@@ -296,20 +660,27 @@ jobs:
           pattern: sd-*
           merge-multiple: true
 
+      - name: Get commit count
+        id: commit_count
+        run: |
+          echo "count=$(git rev-list --count HEAD)" >> $GITHUB_OUTPUT
+
       - name: Get commit hash
         id: commit
         uses: pr-mpt/actions-commit-hash@v2
 
       - name: Create release
         id: create_release
+        if: ${{ github.event_name == 'workflow_dispatch' || github.ref_name == 'master' }}
         uses: anzz1/action-create-release@v1
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         with:
-          tag_name: ${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}
+          tag_name: ${{ format('{0}-{1}-{2}', env.BRANCH_NAME, steps.commit_count.outputs.count, steps.commit.outputs.short) }}
 
       - name: Upload release
         id: upload_release
+        if: ${{ github.event_name == 'workflow_dispatch' || github.ref_name == 'master' }}
         uses: actions/github-script@v3
         with:
           github-token: ${{secrets.GITHUB_TOKEN}}

.gitignore

@@ -1,13 +1,15 @@
 build*/
+cmake-build-*/
 test/
 .vscode/
+.idea/
 .cache/
 *.swp
-.vscode/
 *.bat
 *.bin
 *.exe
 *.gguf
 output*.png
 models*
-*.log
+*.log
+preview.png

.gitmodules

@@ -1,3 +1,3 @@
 [submodule "ggml"]
     path = ggml
-	url = https://github.com/ggerganov/ggml.git
+	url = https://github.com/ggml-org/ggml.git

CMakeLists.txt

@@ -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)
 
@@ -24,97 +29,168 @@ endif()
 # general
 #option(SD_BUILD_TESTS                "sd: build tests"    ${SD_STANDALONE})
 option(SD_BUILD_EXAMPLES             "sd: build examples" ${SD_STANDALONE})
-option(SD_CUBLAS                     "sd: cuda backend" OFF)
+option(SD_CUDA                       "sd: cuda backend" OFF)
 option(SD_HIPBLAS                    "sd: rocm backend" OFF)
 option(SD_METAL                      "sd: metal backend" OFF)
 option(SD_VULKAN                     "sd: vulkan backend" OFF)
+option(SD_OPENCL                     "sd: opencl backend" OFF)
 option(SD_SYCL                       "sd: sycl backend" OFF)
-option(SD_FLASH_ATTN                 "sd: use flash attention for x4 less memory usage" 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)
 #option(SD_BUILD_SERVER               "sd: build server example"                           ON)
 
-if(SD_CUBLAS)
-    message("Use CUBLAS as backend stable-diffusion")
+if(SD_CUDA)
+    message("-- Use CUDA as backend stable-diffusion")
     set(GGML_CUDA ON)
-    add_definitions(-DSD_USE_CUBLAS)
+    add_definitions(-DSD_USE_CUDA)
 endif()
 
 if(SD_METAL)
-    message("Use Metal as backend stable-diffusion")
+    message("-- Use Metal as backend stable-diffusion")
     set(GGML_METAL ON)
     add_definitions(-DSD_USE_METAL)
 endif()
 
 if (SD_VULKAN)
-    message("Use Vulkan as backend stable-diffusion")
+    message("-- Use Vulkan as backend stable-diffusion")
     set(GGML_VULKAN ON)
     add_definitions(-DSD_USE_VULKAN)
 endif ()
 
+if (SD_OPENCL)
+    message("-- Use OpenCL as backend stable-diffusion")
+    set(GGML_OPENCL ON)
+    add_definitions(-DSD_USE_OPENCL)
+endif ()
+
 if (SD_HIPBLAS)
-    message("Use HIPBLAS as backend stable-diffusion")
-    set(GGML_HIPBLAS ON)
-    add_definitions(-DSD_USE_CUBLAS)
+    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_SYCL)
-    message("Use SYCL as backend stable-diffusion")
-    set(GGML_SYCL ON)
-    add_definitions(-DSD_USE_SYCL)
-endif()
-
-if(SD_FLASH_ATTN)
-    message("Use Flash Attention for memory optimization")
-    add_definitions(-DSD_USE_FLASH_ATTENTION)
+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()
 endif()
 
 set(SD_LIB stable-diffusion)
 
-file(GLOB SD_LIB_SOURCES 
+file(GLOB SD_LIB_SOURCES
     "*.h"
     "*.cpp"
     "*.hpp"
 )
 
-# we can get only one share lib
+find_program(GIT_EXE NAMES git git.exe NO_CMAKE_FIND_ROOT_PATH)
+if(GIT_EXE)
+    execute_process(COMMAND ${GIT_EXE} describe --tags --abbrev=7 --dirty=+
+        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
+        OUTPUT_VARIABLE SDCPP_BUILD_VERSION
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+        ERROR_QUIET
+    )
+    execute_process(COMMAND ${GIT_EXE} rev-parse --short HEAD
+        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
+        OUTPUT_VARIABLE SDCPP_BUILD_COMMIT
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+        ERROR_QUIET
+    )
+endif()
+
+if(NOT SDCPP_BUILD_VERSION)
+    set(SDCPP_BUILD_VERSION unknown)
+endif()
+message(STATUS "stable-diffusion.cpp version ${SDCPP_BUILD_VERSION}")
+
+if(NOT SDCPP_BUILD_COMMIT)
+    set(SDCPP_BUILD_COMMIT unknown)
+endif()
+message(STATUS "stable-diffusion.cpp commit ${SDCPP_BUILD_COMMIT}")
+
+set_property(
+  SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/version.cpp
+  APPEND PROPERTY COMPILE_DEFINITIONS
+  SDCPP_BUILD_COMMIT=${SDCPP_BUILD_COMMIT} SDCPP_BUILD_VERSION=${SDCPP_BUILD_VERSION}
+)
+
 if(SD_BUILD_SHARED_LIBS)
-    message("Build shared library")
+    message("-- Build shared library")
     message(${SD_LIB_SOURCES})
-    set(BUILD_SHARED_LIBS OFF)
+    if(NOT SD_BUILD_SHARED_GGML_LIB)
+        set(BUILD_SHARED_LIBS OFF)
+    endif()
     add_library(${SD_LIB} SHARED ${SD_LIB_SOURCES})
     add_definitions(-DSD_BUILD_SHARED_LIB)
     target_compile_definitions(${SD_LIB} PRIVATE -DSD_BUILD_DLL)
     set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 else()
-    message("Build static library")
-    set(BUILD_SHARED_LIBS OFF)
+    message("-- Build static library")
+    if(NOT SD_BUILD_SHARED_GGML_LIB)
+        set(BUILD_SHARED_LIBS OFF)
+    endif()
     add_library(${SD_LIB} STATIC ${SD_LIB_SOURCES})
 endif()
 
+if(SD_SYCL)
+    message("-- Use SYCL as backend stable-diffusion")
+    set(GGML_SYCL ON)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing -fsycl")
+    add_definitions(-DSD_USE_SYCL)
+    # disable fast-math on host, see:
+    # https://www.intel.com/content/www/us/en/docs/cpp-compiler/developer-guide-reference/2021-10/fp-model-fp.html
+    if (WIN32)
+        set(SYCL_COMPILE_OPTIONS /fp:precise)
+    else()
+        set(SYCL_COMPILE_OPTIONS -fp-model=precise)
+    endif()
+    message("-- Turn off fast-math for host in SYCL backend")
+    target_compile_options(${SD_LIB} PRIVATE ${SYCL_COMPILE_OPTIONS})
+endif()
 
 set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
 
-# see https://github.com/ggerganov/ggml/pull/682
-add_definitions(-DGGML_MAX_NAME=128)
+if (NOT SD_USE_SYSTEM_GGML)
+    # see https://github.com/ggerganov/ggml/pull/682
+    add_definitions(-DGGML_MAX_NAME=128)
+endif()
 
 # deps
 # Only add ggml if it hasn't been added yet
 if (NOT TARGET ggml)
-    add_subdirectory(ggml)
+    if (SD_USE_SYSTEM_GGML)
+        find_package(ggml REQUIRED)
+        if (NOT ggml_FOUND)
+            message(FATAL_ERROR "System-installed GGML library not found.")
+        endif()
+        add_library(ggml ALIAS ggml::ggml)
+    else()
+        add_subdirectory(ggml)
+    endif()
 endif()
 
 add_subdirectory(thirdparty)
 
 target_link_libraries(${SD_LIB} PUBLIC ggml zip)
 target_include_directories(${SD_LIB} PUBLIC . thirdparty)
-target_compile_features(${SD_LIB} PUBLIC cxx_std_11)
+target_compile_features(${SD_LIB} PUBLIC c_std_11 cxx_std_17)
 
 
 if (SD_BUILD_EXAMPLES)
     add_subdirectory(examples)
 endif()
 
+set(SD_PUBLIC_HEADERS stable-diffusion.h)
+set_target_properties(${SD_LIB} PROPERTIES PUBLIC_HEADER "${SD_PUBLIC_HEADERS}")
+
+install(TARGETS ${SD_LIB} LIBRARY PUBLIC_HEADER)

Dockerfile

@@ -1,17 +1,23 @@
-ARG UBUNTU_VERSION=22.04
+ARG UBUNTU_VERSION=24.04
 
-FROM ubuntu:$UBUNTU_VERSION as build
+FROM ubuntu:$UBUNTU_VERSION AS build
 
-RUN apt-get update && apt-get install -y build-essential git cmake
+RUN apt-get update && apt-get install -y --no-install-recommends build-essential git cmake
 
 WORKDIR /sd.cpp
 
 COPY . .
 
-RUN mkdir build && cd build && cmake .. && cmake --build . --config Release
+RUN cmake . -B ./build
+RUN cmake --build ./build --config Release --parallel
 
-FROM ubuntu:$UBUNTU_VERSION as runtime
+FROM ubuntu:$UBUNTU_VERSION AS runtime
 
-COPY --from=build /sd.cpp/build/bin/sd /sd
+RUN apt-get update && \
+    apt-get install --yes --no-install-recommends libgomp1 && \
+    apt-get clean
 
-ENTRYPOINT [ "/sd" ]
+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" ]

Dockerfile.musa

@@ -0,0 +1,24 @@
+ARG MUSA_VERSION=rc4.2.0
+ARG UBUNTU_VERSION=22.04
+
+FROM mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}-amd64 as build
+
+RUN apt-get update && apt-get install -y ccache cmake git
+
+WORKDIR /sd.cpp
+
+COPY . .
+
+RUN mkdir build && cd build && \
+    cmake .. -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ \
+        -DCMAKE_C_FLAGS="${CMAKE_C_FLAGS} -fopenmp -I/usr/lib/llvm-14/lib/clang/14.0.0/include -L/usr/lib/llvm-14/lib" \
+        -DCMAKE_CXX_FLAGS="${CMAKE_CXX_FLAGS} -fopenmp -I/usr/lib/llvm-14/lib/clang/14.0.0/include -L/usr/lib/llvm-14/lib" \
+        -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release && \
+    cmake --build . --config Release
+
+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" ]

Dockerfile.sycl

@@ -0,0 +1,20 @@
+ARG SYCL_VERSION=2025.1.0-0
+
+FROM intel/oneapi-basekit:${SYCL_VERSION}-devel-ubuntu24.04 AS build
+
+RUN apt-get update && apt-get install -y cmake
+
+WORKDIR /sd.cpp
+
+COPY . .
+
+RUN mkdir build && cd build && \
+    cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DSD_SYCL=ON -DCMAKE_BUILD_TYPE=Release && \
+    cmake --build . --config Release -j$(nproc)
+
+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" ]

Dockerfile.vulkan

@@ -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" ]

README.md

@@ -1,39 +1,89 @@
 <p align="center">
-  <img src="./assets/cat_with_sd_cpp_42.png" width="360x">
+  <img src="./assets/logo.png" width="360x">
 </p>
 
 # stable-diffusion.cpp
 
-Inference of Stable Diffusion and Flux in pure C/C++
+<div align="center">
+<a href="https://trendshift.io/repositories/9714" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9714" alt="leejet%2Fstable-diffusion.cpp | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+</div>
+
+Diffusion model(SD,Flux,Wan,...) inference in pure C/C++
+
+***Note that this project is under active development. \
+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)
+
+* **2025/11/30** 🚀 stable-diffusion.cpp now supports **FLUX.2-dev**  
+  👉 Details: [PR #1016](https://github.com/leejet/stable-diffusion.cpp/pull/1016)
+
+* **2025/10/13** 🚀 stable-diffusion.cpp now supports **Qwen-Image-Edit / Qwen-Image-Edit 2509**  
+  👉 Details: [PR #877](https://github.com/leejet/stable-diffusion.cpp/pull/877)
+
+* **2025/10/12** 🚀 stable-diffusion.cpp now supports **Qwen-Image**  
+  👉 Details: [PR #851](https://github.com/leejet/stable-diffusion.cpp/pull/851)
+
+* **2025/09/14** 🚀 stable-diffusion.cpp now supports **Wan2.1 Vace**  
+  👉 Details: [PR #819](https://github.com/leejet/stable-diffusion.cpp/pull/819)
+
+* **2025/09/06** 🚀 stable-diffusion.cpp now supports **Wan2.1 / Wan2.2**  
+  👉 Details: [PR #778](https://github.com/leejet/stable-diffusion.cpp/pull/778)
 
 ## Features
 
-- Plain C/C++ implementation based on [ggml](https://github.com/ggerganov/ggml), working in the same way as [llama.cpp](https://github.com/ggerganov/llama.cpp)
+- Plain C/C++ implementation based on [ggml](https://github.com/ggml-org/ggml), working in the same way as [llama.cpp](https://github.com/ggml-org/llama.cpp)
 - Super lightweight and without external dependencies
-- SD1.x, SD2.x, SDXL and SD3 support
-    - !!!The VAE in SDXL encounters NaN issues under FP16, but unfortunately, the ggml_conv_2d only operates under FP16. Hence, a parameter is needed to specify the VAE that has fixed the FP16 NaN issue. You can find it here: [SDXL VAE FP16 Fix](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/blob/main/sdxl_vae.safetensors).
-- [Flux-dev/Flux-schnell Support](./docs/flux.md)
-
-- [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo) and [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo) support
-- [PhotoMaker](https://github.com/TencentARC/PhotoMaker) support.
-- 16-bit, 32-bit float support
-- 2-bit, 3-bit, 4-bit, 5-bit and 8-bit integer quantization support
-- Accelerated memory-efficient CPU inference
-    - Only requires ~2.3GB when using txt2img with fp16 precision to generate a 512x512 image, enabling Flash Attention just requires ~1.8GB.
-- AVX, AVX2 and AVX512 support for x86 architectures
-- Full CUDA, Metal and SYCL backend for GPU acceleration.
-- Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs models
-    - No need to convert to `.ggml` or `.gguf` anymore!
-- Flash Attention for memory usage optimization (only cpu for now)
-- Original `txt2img` and `img2img` mode
+- Supported models
+  - Image Models
+    - SD1.x, SD2.x, [SD-Turbo](https://huggingface.co/stabilityai/sd-turbo)
+    - SDXL, [SDXL-Turbo](https://huggingface.co/stabilityai/sdxl-turbo)
+    - [Some SD1.x and SDXL distilled models](./docs/distilled_sd.md)
+    - [SD3/SD3.5](./docs/sd3.md)
+    - [FLUX.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)
+  - Image Edit Models
+    - [FLUX.1-Kontext-dev](./docs/kontext.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.
+  - Control Net support with SD 1.5
+  - LoRA support, same as [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#lora)
+  - Latent Consistency Models support (LCM/LCM-LoRA)
+  - Faster and memory efficient latent decoding with [TAESD](https://github.com/madebyollin/taesd)
+  - Upscale images generated with [ESRGAN](https://github.com/xinntao/Real-ESRGAN)
+- Supported backends
+  - CPU (AVX, AVX2 and AVX512 support for x86 architectures)
+  - CUDA
+  - Vulkan
+  - Metal
+  - OpenCL
+  - SYCL
+- Supported weight formats
+  - Pytorch checkpoint (`.ckpt` or `.pth`)
+  - Safetensors (`.safetensors`)
+  - GGUF (`.gguf`)
+- Supported platforms
+    - Linux
+    - Mac OS
+    - Windows
+    - Android (via Termux, [Local Diffusion](https://github.com/rmatif/Local-Diffusion))
+- Flash Attention for memory usage optimization
 - Negative prompt
 - [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) style tokenizer (not all the features, only token weighting for now)
-- LoRA support, same as [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#lora)
-- Latent Consistency Models support (LCM/LCM-LoRA)
-- Faster and memory efficient latent decoding with [TAESD](https://github.com/madebyollin/taesd)
-- Upscale images generated with [ESRGAN](https://github.com/xinntao/Real-ESRGAN)
 - VAE tiling processing for reduce memory usage
-- Control Net support with SD 1.5
 - Sampling method
     - `Euler A`
     - `Euler`
@@ -43,232 +93,52 @@ Inference of Stable Diffusion and Flux in pure C/C++
     - [`DPM++ 2M v2`](https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8457)
     - `DPM++ 2S a`
     - [`LCM`](https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13952)
-- Cross-platform reproducibility (`--rng cuda`, consistent with the `stable-diffusion-webui GPU RNG`)
+- Cross-platform reproducibility
+    - `--rng cuda`, default, consistent with the `stable-diffusion-webui GPU RNG`
+    - `--rng cpu`, consistent with the `comfyui RNG`
 - Embedds generation parameters into png output as webui-compatible text string
-- Supported platforms
-    - Linux
-    - Mac OS
-    - Windows
-    - Android (via Termux)
 
-### TODO
+## Quick Start
 
-- [ ] More sampling methods
-- [ ] Make inference faster
-    - The current implementation of ggml_conv_2d is slow and has high memory usage
-- [ ] Continuing to reduce memory usage (quantizing the weights of ggml_conv_2d)
-- [ ] Implement Inpainting support
+### Get the sd executable
 
-## Usage
+- Download pre-built binaries from the [releases page](https://github.com/leejet/stable-diffusion.cpp/releases)
+- Or build from source by following the [build guide](./docs/build.md)
 
-For most users, you can download the built executable program from the latest [release](https://github.com/leejet/stable-diffusion.cpp/releases/latest).
-If the built product does not meet your requirements, you can choose to build it manually.
+### Download model weights
 
-### Get the Code
+- download weights(.ckpt or .safetensors or .gguf). For example
+    - Stable Diffusion v1.5 from https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5 
 
-```
-git clone --recursive https://github.com/leejet/stable-diffusion.cpp
-cd stable-diffusion.cpp
-```
-
-- If you have already cloned the repository, you can use the following command to update the repository to the latest code.
-
-```
-cd stable-diffusion.cpp
-git pull origin master
-git submodule init
-git submodule update
-```
-
-### Download weights
-
-- download original weights(.ckpt or .safetensors). For example
-    - Stable Diffusion v1.4 from https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
-    - Stable Diffusion v1.5 from https://huggingface.co/runwayml/stable-diffusion-v1-5
-    - Stable Diffuison v2.1 from https://huggingface.co/stabilityai/stable-diffusion-2-1
-    - Stable Diffusion 3 2B from https://huggingface.co/stabilityai/stable-diffusion-3-medium
-
-    ```shell
-    curl -L -O https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
-    # curl -L -O https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors
-    # curl -L -O https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-nonema-pruned.safetensors
-    # curl -L -O https://huggingface.co/stabilityai/stable-diffusion-3-medium/resolve/main/sd3_medium_incl_clips_t5xxlfp16.safetensors
+    ```sh
+    curl -L -O https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors
     ```
 
-### Build
-
-#### Build from scratch
-
-```shell
-mkdir build
-cd build
-cmake ..
-cmake --build . --config Release
-```
-
-##### Using OpenBLAS
-
-```
-cmake .. -DGGML_OPENBLAS=ON
-cmake --build . --config Release
-```
-
-##### Using CUBLAS
-
-This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). Recommended to have at least 4 GB of VRAM.
-
-```
-cmake .. -DSD_CUBLAS=ON
-cmake --build . --config Release
-```
-
-##### Using HipBLAS
-This provides BLAS acceleration using the ROCm cores of your AMD GPU. Make sure to have the ROCm toolkit installed.
-
-Windows User Refer to [docs/hipBLAS_on_Windows.md](docs%2FhipBLAS_on_Windows.md) for a comprehensive guide.
-
-```
-cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=gfx1100
-cmake --build . --config Release
-```
-
-
-##### Using Metal
-
-Using Metal makes the computation run on the GPU. Currently, there are some issues with Metal when performing operations on very large matrices, making it highly inefficient at the moment. Performance improvements are expected in the near future.
-
-```
-cmake .. -DSD_METAL=ON
-cmake --build . --config Release
-```
-
-##### Using SYCL
-
-Using SYCL makes the computation run on the Intel GPU. Please make sure you have installed the related driver and [Intel® oneAPI Base toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) before start. More details and steps can refer to [llama.cpp SYCL backend](https://github.com/ggerganov/llama.cpp/blob/master/docs/backend/SYCL.md#linux).
-
-```
-# Export relevant ENV variables
-source /opt/intel/oneapi/setvars.sh
-
-# Option 1: Use FP32 (recommended for better performance in most cases)
-cmake .. -DSD_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
-
-# Option 2: Use FP16
-cmake .. -DSD_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
-
-cmake --build . --config Release
-```
-
-Example of text2img by using SYCL backend:
-
-- download `stable-diffusion` model weight, refer to [download-weight](#download-weights).
-
-- run `./bin/sd -m ../models/sd3_medium_incl_clips_t5xxlfp16.safetensors --cfg-scale 5 --steps 30 --sampling-method euler  -H 512 -W 512 --seed 42 -p "fantasy medieval village world inside a glass sphere , high detail, fantasy, realistic, light effect, hyper detail, volumetric lighting, cinematic, macro, depth of field, blur, red light and clouds from the back, highly detailed epic cinematic concept art cg render made in maya, blender and photoshop, octane render, excellent composition, dynamic dramatic cinematic lighting, aesthetic, very inspirational, world inside a glass sphere by james gurney by artgerm with james jean, joe fenton and tristan eaton by ross tran, fine details, 4k resolution"`
-
-<p align="center">
-  <img src="./assets/sycl_sd3_output.png" width="360x">
-</p>
-
-> [!NOTE]
-> Try to set smaller image height and width (for example, `-H 512 -W 512`) if you meet `Provided range is out of integer limits. Pass '-fno-sycl-id-queries-fit-in-int' to disable range check.`
-
-
-##### Using Flash Attention
-
-Enabling flash attention reduces memory usage by at least 400 MB. At the moment, it is not supported when CUBLAS is enabled because the kernel implementation is missing.
-
-```
-cmake .. -DSD_FLASH_ATTN=ON
-cmake --build . --config Release
-```
-
-### Run
-
-```
-usage: ./bin/sd [arguments]
-
-arguments:
-  -h, --help                         show this help message and exit
-  -M, --mode [MODEL]                 run mode (txt2img or img2img or convert, default: txt2img)
-  -t, --threads N                    number of threads to use during computation (default: -1).
-                                     If threads <= 0, then threads will be set to the number of CPU physical cores
-  -m, --model [MODEL]                path to full model
-  --diffusion-model                  path to the standalone diffusion model
-  --clip_l                           path to the clip-l text encoder
-  --t5xxl                            path to the the t5xxl text encoder.
-  --vae [VAE]                        path to vae
-  --taesd [TAESD_PATH]               path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
-  --control-net [CONTROL_PATH]       path to control net model
-  --embd-dir [EMBEDDING_PATH]        path to embeddings.
-  --stacked-id-embd-dir [DIR]        path to PHOTOMAKER stacked id embeddings.
-  --input-id-images-dir [DIR]        path to PHOTOMAKER input id images dir.
-  --normalize-input                  normalize PHOTOMAKER input id images
-  --upscale-model [ESRGAN_PATH]      path to esrgan model. Upscale images after generate, just RealESRGAN_x4plus_anime_6B supported by now.
-  --upscale-repeats                  Run the ESRGAN upscaler this many times (default 1)
-  --type [TYPE]                      weight type (f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_k, q3_k, q4_k)
-                                     If not specified, the default is the type of the weight file.
-  --lora-model-dir [DIR]             lora model directory
-  -i, --init-img [IMAGE]             path to the input image, required by img2img
-  --control-image [IMAGE]            path to image condition, control net
-  -o, --output OUTPUT                path to write result image to (default: ./output.png)
-  -p, --prompt [PROMPT]              the prompt to render
-  -n, --negative-prompt PROMPT       the negative prompt (default: "")
-  --cfg-scale SCALE                  unconditional guidance scale: (default: 7.0)
-  --strength STRENGTH                strength for noising/unnoising (default: 0.75)
-  --style-ratio STYLE-RATIO          strength for keeping input identity (default: 20%)
-  --control-strength STRENGTH        strength to apply Control Net (default: 0.9)
-                                     1.0 corresponds to full destruction of information in init image
-  -H, --height H                     image height, in pixel space (default: 512)
-  -W, --width W                      image width, in pixel space (default: 512)
-  --sampling-method {euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm}
-                                     sampling method (default: "euler_a")
-  --steps  STEPS                     number of sample steps (default: 20)
-  --rng {std_default, cuda}          RNG (default: cuda)
-  -s SEED, --seed SEED               RNG seed (default: 42, use random seed for < 0)
-  -b, --batch-count COUNT            number of images to generate.
-  --schedule {discrete, karras, exponential, ays, gits} Denoiser sigma schedule (default: discrete)
-  --clip-skip N                      ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1)
-                                     <= 0 represents unspecified, will be 1 for SD1.x, 2 for SD2.x
-  --vae-tiling                       process vae in tiles to reduce memory usage
-  --vae-on-cpu                       keep vae in cpu (for low vram)
-  --clip-on-cpu                      keep clip in cpu (for low vram).
-  --control-net-cpu                  keep controlnet in cpu (for low vram)
-  --canny                            apply canny preprocessor (edge detection)
-  --color                            Colors the logging tags according to level
-  -v, --verbose                      print extra info
-```
-
-#### txt2img example
+### Generate an image with just one command
 
 ```sh
-./bin/sd -m ../models/sd-v1-4.ckpt -p "a lovely cat"
-# ./bin/sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat"
-# ./bin/sd -m ../models/sd_xl_base_1.0.safetensors --vae ../models/sdxl_vae-fp16-fix.safetensors -H 1024 -W 1024 -p "a lovely cat" -v
-# ./bin/sd -m ../models/sd3_medium_incl_clips_t5xxlfp16.safetensors -H 1024 -W 1024 -p 'a lovely cat holding a sign says \"Stable Diffusion CPP\"' --cfg-scale 4.5 --sampling-method euler -v
-# ./bin/sd --diffusion-model  ../models/flux1-dev-q3_k.gguf --vae ../models/ae.sft --clip_l ../models/clip_l.safetensors --t5xxl ../models/t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'" --cfg-scale 1.0 --sampling-method euler -v
+./bin/sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat"
 ```
 
-Using formats of different precisions will yield results of varying quality.
+***For detailed command-line arguments, check out [cli doc](./examples/cli/README.md).***
 
-| f32  | f16  |q8_0  |q5_0  |q5_1  |q4_0  |q4_1  |
-| ----  |----  |----  |----  |----  |----  |----  |
-| ![](./assets/f32.png) |![](./assets/f16.png) |![](./assets/q8_0.png) |![](./assets/q5_0.png) |![](./assets/q5_1.png) |![](./assets/q4_0.png) |![](./assets/q4_1.png) |
+## Performance
 
-#### img2img example
-
-- `./output.png` is the image generated from the above txt2img pipeline
-
-
-```
-./bin/sd --mode img2img -m ../models/sd-v1-4.ckpt -p "cat with blue eyes" -i ./output.png -o ./img2img_output.png --strength 0.4
-```
-
-<p align="center">
-  <img src="./assets/img2img_output.png" width="256x">
-</p>
+If you want to improve performance or reduce VRAM/RAM usage, please refer to [performance guide](./docs/performance.md).
 
 ## More Guides
 
+- [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/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 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)
 - [LoRA](./docs/lora.md)
 - [LCM/LCM-LoRA](./docs/lcm.md)
 - [Using PhotoMaker to personalize image generation](./docs/photo_maker.md)
@@ -276,19 +146,31 @@ Using formats of different precisions will yield results of varying quality.
 - [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
 
 These projects wrap `stable-diffusion.cpp` for easier use in other languages/frameworks.
 
-* Golang: [seasonjs/stable-diffusion](https://github.com/seasonjs/stable-diffusion)
+* Golang (non-cgo): [seasonjs/stable-diffusion](https://github.com/seasonjs/stable-diffusion)
+* Golang (cgo): [Binozo/GoStableDiffusion](https://github.com/Binozo/GoStableDiffusion)
 * C#: [DarthAffe/StableDiffusion.NET](https://github.com/DarthAffe/StableDiffusion.NET)
+* Python: [william-murray1204/stable-diffusion-cpp-python](https://github.com/william-murray1204/stable-diffusion-cpp-python)
+* Rust: [newfla/diffusion-rs](https://github.com/newfla/diffusion-rs)
+* Flutter/Dart: [rmatif/Local-Diffusion](https://github.com/rmatif/Local-Diffusion)
 
 ## UIs
 
 These projects use `stable-diffusion.cpp` as a backend for their image generation.
 
 - [Jellybox](https://jellybox.com)
+- [Stable Diffusion GUI](https://github.com/fszontagh/sd.cpp.gui.wx)
+- [Stable Diffusion CLI-GUI](https://github.com/piallai/stable-diffusion.cpp)
+- [Local Diffusion](https://github.com/rmatif/Local-Diffusion)
+- [sd.cpp-webui](https://github.com/daniandtheweb/sd.cpp-webui)
+- [LocalAI](https://github.com/mudler/LocalAI)
+- [Neural-Pixel](https://github.com/Luiz-Alcantara/Neural-Pixel)
+- [KoboldCpp](https://github.com/LostRuins/koboldcpp)
 
 ## Contributors
 
@@ -296,9 +178,14 @@ Thank you to all the people who have already contributed to stable-diffusion.cpp
 
 [![Contributors](https://contrib.rocks/image?repo=leejet/stable-diffusion.cpp)](https://github.com/leejet/stable-diffusion.cpp/graphs/contributors)
 
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=leejet/stable-diffusion.cpp&type=Date)](https://star-history.com/#leejet/stable-diffusion.cpp&Date)
+
 ## References
 
-- [ggml](https://github.com/ggerganov/ggml)
+- [ggml](https://github.com/ggml-org/ggml)
+- [diffusers](https://github.com/huggingface/diffusers)
 - [stable-diffusion](https://github.com/CompVis/stable-diffusion)
 - [sd3-ref](https://github.com/Stability-AI/sd3-ref)
 - [stable-diffusion-stability-ai](https://github.com/Stability-AI/stablediffusion)
@@ -308,3 +195,5 @@ Thank you to all the people who have already contributed to stable-diffusion.cpp
 - [latent-consistency-model](https://github.com/luosiallen/latent-consistency-model)
 - [generative-models](https://github.com/Stability-AI/generative-models/)
 - [PhotoMaker](https://github.com/TencentARC/PhotoMaker)
+- [Wan2.1](https://github.com/Wan-Video/Wan2.1)
+- [Wan2.2](https://github.com/Wan-Video/Wan2.2)

cache_dit.hpp

@@ -0,0 +1,975 @@
+#ifndef __CACHE_DIT_HPP__
+#define __CACHE_DIT_HPP__
+
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "ggml_extend.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 std::vector<int> get_scm_preset(const std::string& preset, int total_steps) {
+    struct Preset {
+        std::vector<int> compute_bins;
+        std::vector<int> cache_bins;
+    };
+
+    Preset slow   = {{8, 3, 3, 2, 1, 1}, {1, 2, 2, 2, 3}};
+    Preset medium = {{6, 2, 2, 2, 2, 1}, {1, 3, 3, 3, 3}};
+    Preset fast   = {{6, 1, 1, 1, 1, 1}, {1, 3, 4, 5, 4}};
+    Preset ultra  = {{4, 1, 1, 1, 1}, {2, 5, 6, 7}};
+
+    Preset* p = nullptr;
+    if (preset == "slow" || preset == "s" || preset == "S")
+        p = &slow;
+    else if (preset == "medium" || preset == "m" || preset == "M")
+        p = &medium;
+    else if (preset == "fast" || preset == "f" || preset == "F")
+        p = &fast;
+    else if (preset == "ultra" || preset == "u" || preset == "U")
+        p = &ultra;
+    else
+        return {};
+
+    if (total_steps != 28 && total_steps > 0) {
+        float scale = static_cast<float>(total_steps) / 28.0f;
+        std::vector<int> scaled_compute, scaled_cache;
+
+        for (int v : p->compute_bins) {
+            scaled_compute.push_back(std::max(1, static_cast<int>(v * scale + 0.5f)));
+        }
+        for (int v : p->cache_bins) {
+            scaled_cache.push_back(std::max(1, static_cast<int>(v * scale + 0.5f)));
+        }
+
+        return generate_scm_mask(scaled_compute, scaled_cache, total_steps);
+    }
+
+    return generate_scm_mask(p->compute_bins, p->cache_bins, total_steps);
+}
+
+inline float get_preset_threshold(const std::string& preset) {
+    if (preset == "slow" || preset == "s" || preset == "S")
+        return 0.20f;
+    if (preset == "medium" || preset == "m" || preset == "M")
+        return 0.25f;
+    if (preset == "fast" || preset == "f" || preset == "F")
+        return 0.30f;
+    if (preset == "ultra" || preset == "u" || preset == "U")
+        return 0.34f;
+    return 0.08f;
+}
+
+inline int get_preset_warmup(const std::string& preset) {
+    if (preset == "slow" || preset == "s" || preset == "S")
+        return 8;
+    if (preset == "medium" || preset == "m" || preset == "M")
+        return 6;
+    if (preset == "fast" || preset == "f" || preset == "F")
+        return 6;
+    if (preset == "ultra" || preset == "u" || preset == "U")
+        return 4;
+    return 8;
+}
+
+inline int get_preset_Fn(const std::string& preset) {
+    if (preset == "slow" || preset == "s" || preset == "S")
+        return 8;
+    if (preset == "medium" || preset == "m" || preset == "M")
+        return 8;
+    if (preset == "fast" || preset == "f" || preset == "F")
+        return 6;
+    if (preset == "ultra" || preset == "u" || preset == "U")
+        return 4;
+    return 8;
+}
+
+inline int get_preset_Bn(const std::string& preset) {
+    (void)preset;
+    return 0;
+}
+
+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 float* input, const float* output, size_t size) {
+        CacheEntry& entry = cache_diffs[cond];
+        entry.diff.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.diff[i] = output[i] - input[i];
+        }
+
+        entry.prev_input.resize(size);
+        entry.prev_output.resize(size);
+        for (size_t i = 0; i < size; i++) {
+            entry.prev_input[i]  = input[i];
+            entry.prev_output[i] = output[i];
+        }
+        entry.has_prev = true;
+    }
+
+    void apply_cache(const void* cond, const float* input, float* output, size_t size) {
+        auto it = cache_diffs.find(cond);
+        if (it == cache_diffs.end() || it->second.diff.empty())
+            return;
+        if (it->second.diff.size() != size)
+            return;
+
+        for (size_t i = 0; i < size; i++) {
+            output[i] = input[i] + it->second.diff[i];
+        }
+    }
+
+    bool before_condition(const void* cond, struct ggml_tensor* input, struct ggml_tensor* output, float sigma, int step_index) {
+        if (!enabled() || step_index < 0)
+            return false;
+
+        if (step_index != current_step_index) {
+            begin_step(step_index, sigma);
+        }
+
+        if (!step_active)
+            return false;
+
+        if (initial_step) {
+            anchor_condition = cond;
+            initial_step     = false;
+        }
+
+        bool is_anchor = (cond == anchor_condition);
+
+        if (skip_current_step) {
+            if (has_cache(cond)) {
+                apply_cache(cond, (float*)input->data, (float*)output->data,
+                            static_cast<size_t>(ggml_nelements(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>(ggml_nelements(input));
+        if (it->second.prev_input.size() != ne)
+            return false;
+
+        float* input_data = (float*)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_data, (float*)output->data, ne);
+            return true;
+        }
+
+        continuous_cached_steps = 0;
+        return false;
+    }
+
+    void after_condition(const void* cond, struct ggml_tensor* input, struct ggml_tensor* output) {
+        if (!step_is_active())
+            return;
+
+        size_t ne = static_cast<size_t>(ggml_nelements(output));
+        update_cache(cond, (float*)input->data, (float*)output->data, ne);
+
+        if (cond == anchor_condition && taylor_config.enabled) {
+            taylor_state.update_derivatives((float*)output->data, ne, 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

clip.hpp

@@ -3,35 +3,11 @@
 
 #include "ggml_extend.hpp"
 #include "model.h"
+#include "tokenize_util.h"
 
 /*================================================== CLIPTokenizer ===================================================*/
 
-std::pair<std::unordered_map<std::string, float>, std::string> extract_and_remove_lora(std::string text) {
-    std::regex re("<lora:([^:]+):([^>]+)>");
-    std::smatch matches;
-    std::unordered_map<std::string, float> filename2multiplier;
-
-    while (std::regex_search(text, matches, re)) {
-        std::string filename = matches[1].str();
-        float multiplier     = std::stof(matches[2].str());
-
-        text = std::regex_replace(text, re, "", std::regex_constants::format_first_only);
-
-        if (multiplier == 0.f) {
-            continue;
-        }
-
-        if (filename2multiplier.find(filename) == filename2multiplier.end()) {
-            filename2multiplier[filename] = multiplier;
-        } else {
-            filename2multiplier[filename] += multiplier;
-        }
-    }
-
-    return std::make_pair(filename2multiplier, text);
-}
-
-std::vector<std::pair<int, std::u32string>> bytes_to_unicode() {
+__STATIC_INLINE__ std::vector<std::pair<int, std::u32string>> bytes_to_unicode() {
     std::vector<std::pair<int, std::u32string>> byte_unicode_pairs;
     std::set<int> byte_set;
     for (int b = static_cast<int>('!'); b <= static_cast<int>('~'); ++b) {
@@ -72,6 +48,8 @@ private:
     int encoder_len;
     int bpe_len;
 
+    std::vector<std::string> special_tokens;
+
 public:
     const std::string UNK_TOKEN = "<|endoftext|>";
     const std::string BOS_TOKEN = "<|startoftext|>";
@@ -117,6 +95,15 @@ private:
         return pairs;
     }
 
+    bool is_special_token(const std::string& token) {
+        for (auto& special_token : special_tokens) {
+            if (special_token == token) {
+                return true;
+            }
+        }
+        return false;
+    }
+
 public:
     CLIPTokenizer(int pad_token_id = 49407, const std::string& merges_utf8_str = "")
         : PAD_TOKEN_ID(pad_token_id) {
@@ -125,6 +112,8 @@ public:
         } else {
             load_from_merges(ModelLoader::load_merges());
         }
+        add_special_token("<|startoftext|>");
+        add_special_token("<|endoftext|>");
     }
 
     void load_from_merges(const std::string& merges_utf8_str) {
@@ -179,9 +168,9 @@ public:
 
         auto it = encoder.find(utf8_to_utf32("img</w>"));
         if (it != encoder.end()) {
-            LOG_DEBUG(" trigger word img already in vocab");
+            LOG_DEBUG("trigger word img already in vocab");
         } else {
-            LOG_DEBUG(" trigger word img not in vocab yet");
+            LOG_DEBUG("trigger word img not in vocab yet");
         }
 
         int rank = 0;
@@ -201,6 +190,10 @@ public:
         }
     }
 
+    void add_special_token(const std::string& token) {
+        special_tokens.push_back(token);
+    }
+
     std::u32string bpe(const std::u32string& token) {
         std::vector<std::u32string> word;
 
@@ -303,7 +296,7 @@ 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;
             }
@@ -343,6 +336,13 @@ public:
         }
     }
 
+    std::string clean_up_tokenization(std::string& text) {
+        std::regex pattern(R"( ,)");
+        // Replace " ," with ","
+        std::string result = std::regex_replace(text, pattern, ",");
+        return result;
+    }
+
     std::string decode(const std::vector<int>& tokens) {
         std::string text = "";
         for (int t : tokens) {
@@ -351,8 +351,12 @@ public:
             std::u32string ts = decoder[t];
             // printf("%d, %s \n", t,  utf32_to_utf8(ts).c_str());
             std::string s = utf32_to_utf8(ts);
-            if (s.length() >= 4 && ends_with(s, "</w>")) {
-                text += " " + s.replace(s.length() - 4, s.length() - 1, "");
+            if (s.length() >= 4) {
+                if (ends_with(s, "</w>")) {
+                    text += s.replace(s.length() - 4, s.length() - 1, "") + " ";
+                } else {
+                    text += s;
+                }
             } else {
                 text += " " + s;
             }
@@ -364,31 +368,61 @@ public:
 
         // std::string s((char *)bytes.data());
         // std::string s = "";
+        text = clean_up_tokenization(text);
         return trim(text);
     }
 
+    std::vector<std::string> token_split(const std::string& text) {
+        std::regex pat(R"('s|'t|'re|'ve|'m|'ll|'d|[[:alpha:]]+|[[:digit:]]|[^[:space:][:alpha:][:digit:]]+)",
+                       std::regex::icase);
+        std::sregex_iterator iter(text.begin(), text.end(), pat);
+        std::sregex_iterator end;
+
+        std::vector<std::string> result;
+        for (; iter != end; ++iter) {
+            result.emplace_back(iter->str());
+        }
+
+        return result;
+    }
+
     std::vector<int> encode(std::string text, on_new_token_cb_t on_new_token_cb) {
         std::string original_text = text;
         std::vector<int32_t> bpe_tokens;
         text = whitespace_clean(text);
         std::transform(text.begin(), text.end(), text.begin(), [](unsigned char c) { return std::tolower(c); });
 
-        std::regex pat(R"(<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[[:alpha:]]+|[[:digit:]]|[^[:space:][:alpha:][:digit:]]+)",
-                       std::regex::icase);
-
-        std::smatch matches;
         std::string str = text;
         std::vector<std::string> token_strs;
-        while (std::regex_search(str, matches, pat)) {
-            bool skip = on_new_token_cb(str, bpe_tokens);
-            if (skip) {
+
+        auto splited_texts = split_with_special_tokens(text, special_tokens);
+
+        for (auto& splited_text : splited_texts) {
+            LOG_DEBUG("token %s", splited_text.c_str());
+            if (is_special_token(splited_text)) {
+                LOG_DEBUG("special %s", splited_text.c_str());
+                bool skip = on_new_token_cb(splited_text, bpe_tokens);
+                if (skip) {
+                    token_strs.push_back(splited_text);
+                    continue;
+                }
                 continue;
             }
-            for (auto& token : matches) {
-                std::string token_str = token.str();
+
+            auto tokens = token_split(splited_text);
+            for (auto& token : tokens) {
+                if (on_new_token_cb != nullptr) {
+                    bool skip = on_new_token_cb(token, bpe_tokens);
+                    if (skip) {
+                        token_strs.push_back(token);
+                        continue;
+                    }
+                }
+
+                std::string token_str = token;
                 std::u32string utf32_token;
                 for (int i = 0; i < token_str.length(); i++) {
-                    char b = token_str[i];
+                    unsigned char b = token_str[i];
                     utf32_token += byte_encoder[b];
                 }
                 auto bpe_strs = bpe(utf32_token);
@@ -405,14 +439,13 @@ public:
                 bpe_tokens.push_back(encoder[bpe_str]);
                 token_strs.push_back(utf32_to_utf8(bpe_str));
             }
-            str = matches.suffix();
         }
-        std::stringstream ss;
-        ss << "[";
-        for (auto token : token_strs) {
-            ss << "\"" << token << "\", ";
-        }
-        ss << "]";
+        // std::stringstream ss;
+        // ss << "[";
+        // for (auto token : token_strs) {
+        //     ss << "\"" << token << "\", ";
+        // }
+        // ss << "]";
         // LOG_DEBUG("split prompt \"%s\" to tokens %s", original_text.c_str(), ss.str().c_str());
         // printf("split prompt \"%s\" to tokens %s \n", original_text.c_str(), ss.str().c_str());
         return bpe_tokens;
@@ -439,16 +472,16 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [N, n_token, d_model]
         auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
         auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
 
         x = fc1->forward(ctx, x);
         if (use_gelu) {
-            x = ggml_gelu_inplace(ctx, x);
+            x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
         } else {
-            x = ggml_gelu_quick_inplace(ctx, x);
+            x = ggml_ext_gelu_quick(ctx->ggml_ctx, x, true);
         }
         x = fc2->forward(ctx, x);
         return x;
@@ -464,11 +497,12 @@ protected:
 public:
     CLIPLayer(int64_t d_model,
               int64_t n_head,
-              int64_t intermediate_size)
+              int64_t intermediate_size,
+              bool proj_in = false)
         : d_model(d_model),
           n_head(n_head),
           intermediate_size(intermediate_size) {
-        blocks["self_attn"] = std::shared_ptr<GGMLBlock>(new MultiheadAttention(d_model, n_head, true, true));
+        blocks["self_attn"] = std::shared_ptr<GGMLBlock>(new MultiheadAttention(d_model, n_head, true, true, proj_in));
 
         blocks["layer_norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_model));
         blocks["layer_norm2"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_model));
@@ -476,36 +510,40 @@ public:
         blocks["mlp"] = std::shared_ptr<GGMLBlock>(new CLIPMLP(d_model, intermediate_size));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, bool mask = true) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, struct 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"]);
         auto layer_norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm2"]);
         auto mlp         = std::dynamic_pointer_cast<CLIPMLP>(blocks["mlp"]);
 
-        x = ggml_add(ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
-        x = ggml_add(ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
+        x = ggml_add(ctx->ggml_ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
+        x = ggml_add(ctx->ggml_ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
         return x;
     }
 };
 
 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)
+                int64_t intermediate_size,
+                bool proj_in = false)
         : n_layer(n_layer) {
         for (int i = 0; i < n_layer; i++) {
             std::string name = "layers." + std::to_string(i);
-            blocks[name]     = std::shared_ptr<GGMLBlock>(new CLIPLayer(d_model, n_head, intermediate_size));
+            blocks[name]     = std::shared_ptr<GGMLBlock>(new CLIPLayer(d_model, n_head, intermediate_size, proj_in));
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, int clip_skip = -1, bool mask = true) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                struct ggml_tensor* x,
+                                struct 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);
@@ -532,26 +570,37 @@ protected:
     int64_t embed_dim;
     int64_t vocab_size;
     int64_t num_positions;
+    bool force_clip_f32;
 
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        params["token_embedding.weight"]    = ggml_new_tensor_2d(ctx, wtype, embed_dim, vocab_size);
-        params["position_embedding.weight"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, embed_dim, num_positions);
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        enum ggml_type token_wtype = GGML_TYPE_F32;
+        if (!force_clip_f32) {
+            token_wtype = get_type(prefix + "token_embedding.weight", tensor_storage_map, GGML_TYPE_F32);
+            if (!support_get_rows(token_wtype)) {
+                token_wtype = GGML_TYPE_F32;
+            }
+        }
+        enum ggml_type position_wtype       = GGML_TYPE_F32;
+        params["token_embedding.weight"]    = ggml_new_tensor_2d(ctx, token_wtype, embed_dim, vocab_size);
+        params["position_embedding.weight"] = ggml_new_tensor_2d(ctx, position_wtype, embed_dim, num_positions);
     }
 
 public:
     CLIPEmbeddings(int64_t embed_dim,
                    int64_t vocab_size    = 49408,
-                   int64_t num_positions = 77)
+                   int64_t num_positions = 77,
+                   bool force_clip_f32   = false)
         : embed_dim(embed_dim),
           vocab_size(vocab_size),
-          num_positions(num_positions) {
+          num_positions(num_positions),
+          force_clip_f32(force_clip_f32) {
     }
 
     struct ggml_tensor* get_token_embed_weight() {
         return params["token_embedding.weight"];
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* input_ids,
                                 struct ggml_tensor* custom_embed_weight) {
         // input_ids: [N, n_token]
@@ -559,12 +608,12 @@ public:
         auto position_embed_weight = params["position_embedding.weight"];
 
         GGML_ASSERT(input_ids->ne[0] == position_embed_weight->ne[1]);
-        input_ids            = ggml_reshape_3d(ctx, input_ids, input_ids->ne[0], 1, input_ids->ne[1]);
-        auto token_embedding = ggml_get_rows(ctx, custom_embed_weight != NULL ? custom_embed_weight : token_embed_weight, input_ids);
-        token_embedding      = ggml_reshape_3d(ctx, token_embedding, token_embedding->ne[0], token_embedding->ne[1], token_embedding->ne[3]);
+        input_ids            = ggml_reshape_3d(ctx->ggml_ctx, input_ids, input_ids->ne[0], 1, input_ids->ne[1]);
+        auto token_embedding = ggml_get_rows(ctx->ggml_ctx, custom_embed_weight != nullptr ? custom_embed_weight : token_embed_weight, input_ids);
+        token_embedding      = ggml_reshape_3d(ctx->ggml_ctx, token_embedding, token_embedding->ne[0], token_embedding->ne[1], token_embedding->ne[3]);
 
         // token_embedding + position_embedding
-        auto x = ggml_add(ctx,
+        auto x = ggml_add(ctx->ggml_ctx,
                           token_embedding,
                           position_embed_weight);  // [N, n_token, embed_dim]
         return x;
@@ -574,23 +623,27 @@ 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, ggml_type wtype) {
-        params["patch_embedding.weight"]    = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, patch_size, patch_size, num_channels, embed_dim);
-        params["class_embedding"]           = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, embed_dim);
-        params["position_embedding.weight"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, embed_dim, num_positions);
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        enum ggml_type patch_wtype    = GGML_TYPE_F16;
+        enum ggml_type class_wtype    = GGML_TYPE_F32;
+        enum ggml_type position_wtype = GGML_TYPE_F32;
+
+        params["patch_embedding.weight"]    = ggml_new_tensor_4d(ctx, patch_wtype, patch_size, patch_size, num_channels, embed_dim);
+        params["class_embedding"]           = ggml_new_tensor_1d(ctx, class_wtype, embed_dim);
+        params["position_embedding.weight"] = ggml_new_tensor_2d(ctx, position_wtype, embed_dim, num_positions);
     }
 
 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),
@@ -599,7 +652,7 @@ public:
         num_positions = num_patches + 1;
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* pixel_values) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* pixel_values) {
         // pixel_values: [N, num_channels, image_size, image_size]
         // return: [N, num_positions, embed_dim]
         GGML_ASSERT(pixel_values->ne[0] == image_size && pixel_values->ne[1] == image_size && pixel_values->ne[2] == num_channels);
@@ -611,18 +664,18 @@ public:
         // concat(patch_embedding, class_embedding) + position_embedding
         struct ggml_tensor* patch_embedding;
         int64_t N       = pixel_values->ne[3];
-        patch_embedding = ggml_nn_conv_2d(ctx, pixel_values, patch_embed_weight, NULL, patch_size, patch_size);  // [N, embed_dim, image_size // pacht_size, image_size // pacht_size]
-        patch_embedding = ggml_reshape_3d(ctx, patch_embedding, num_patches, embed_dim, N);                      // [N, embed_dim, num_patches]
-        patch_embedding = ggml_cont(ctx, ggml_permute(ctx, patch_embedding, 1, 0, 2, 3));                        // [N, num_patches, embed_dim]
-        patch_embedding = ggml_reshape_4d(ctx, patch_embedding, 1, embed_dim, num_patches, N);                   // [N, num_patches, embed_dim, 1]
+        patch_embedding = ggml_ext_conv_2d(ctx->ggml_ctx, pixel_values, patch_embed_weight, nullptr, patch_size, patch_size);  // [N, embed_dim, image_size // pacht_size, image_size // pacht_size]
+        patch_embedding = ggml_reshape_3d(ctx->ggml_ctx, patch_embedding, num_patches, embed_dim, N);                          // [N, embed_dim, num_patches]
+        patch_embedding = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, patch_embedding, 1, 0, 2, 3));                  // [N, num_patches, embed_dim]
+        patch_embedding = ggml_reshape_4d(ctx->ggml_ctx, patch_embedding, 1, embed_dim, num_patches, N);                       // [N, num_patches, embed_dim, 1]
 
-        struct ggml_tensor* class_embedding = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, embed_dim, N);
-        class_embedding                     = ggml_repeat(ctx, class_embed_weight, class_embedding);      // [N, embed_dim]
-        class_embedding                     = ggml_reshape_4d(ctx, class_embedding, 1, embed_dim, 1, N);  // [N, 1, embed_dim, 1]
+        struct ggml_tensor* class_embedding = ggml_new_tensor_2d(ctx->ggml_ctx, GGML_TYPE_F32, embed_dim, N);
+        class_embedding                     = ggml_repeat(ctx->ggml_ctx, class_embed_weight, class_embedding);      // [N, embed_dim]
+        class_embedding                     = ggml_reshape_4d(ctx->ggml_ctx, class_embedding, 1, embed_dim, 1, N);  // [N, 1, embed_dim, 1]
 
-        struct ggml_tensor* x = ggml_concat(ctx, class_embedding, patch_embedding, 2);  // [N, num_positions, embed_dim, 1]
-        x                     = ggml_reshape_3d(ctx, x, embed_dim, num_positions, N);   // [N, num_positions, embed_dim]
-        x                     = ggml_add(ctx, x, position_embed_weight);
+        struct ggml_tensor* x = ggml_concat(ctx->ggml_ctx, class_embedding, patch_embedding, 2);  // [N, num_positions, embed_dim, 1]
+        x                     = ggml_reshape_3d(ctx->ggml_ctx, x, embed_dim, num_positions, N);   // [N, num_positions, embed_dim]
+        x                     = ggml_add(ctx->ggml_ctx, x, position_embed_weight);
         return x;  // [N, num_positions, embed_dim]
     }
 };
@@ -639,9 +692,10 @@ enum CLIPVersion {
 
 class CLIPTextModel : public GGMLBlock {
 protected:
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
         if (version == OPEN_CLIP_VIT_BIGG_14) {
-            params["text_projection"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, projection_dim, hidden_size);
+            enum ggml_type wtype      = GGML_TYPE_F32;
+            params["text_projection"] = ggml_new_tensor_2d(ctx, wtype, projection_dim, hidden_size);
         }
     }
 
@@ -655,12 +709,12 @@ public:
     int32_t n_head            = 12;
     int32_t n_layer           = 12;    // num_hidden_layers
     int32_t projection_dim    = 1280;  // only for OPEN_CLIP_VIT_BIGG_14
-    int32_t clip_skip         = -1;
     bool with_final_ln        = true;
 
     CLIPTextModel(CLIPVersion version = OPENAI_CLIP_VIT_L_14,
-                  int clip_skip_value = -1,
-                  bool with_final_ln  = true)
+                  bool with_final_ln  = true,
+                  bool force_clip_f32 = false,
+                  bool proj_in        = false)
         : version(version), with_final_ln(with_final_ln) {
         if (version == OPEN_CLIP_VIT_H_14) {
             hidden_size       = 1024;
@@ -673,46 +727,44 @@ public:
             n_head            = 20;
             n_layer           = 32;
         }
-        set_clip_skip(clip_skip_value);
 
-        blocks["embeddings"]       = std::shared_ptr<GGMLBlock>(new CLIPEmbeddings(hidden_size, vocab_size, n_token));
-        blocks["encoder"]          = std::shared_ptr<GGMLBlock>(new CLIPEncoder(n_layer, hidden_size, n_head, intermediate_size));
+        blocks["embeddings"]       = std::shared_ptr<GGMLBlock>(new CLIPEmbeddings(hidden_size, vocab_size, n_token, force_clip_f32));
+        blocks["encoder"]          = std::shared_ptr<GGMLBlock>(new CLIPEncoder(n_layer, hidden_size, n_head, intermediate_size, proj_in));
         blocks["final_layer_norm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
     }
 
-    void set_clip_skip(int skip) {
-        if (skip <= 0) {
-            return;
-        }
-        clip_skip = skip;
-    }
-
     struct 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(struct ggml_context* ctx,
+    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) {
+                                struct 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);
         }
 
         if (return_pooled) {
             auto text_projection = params["text_projection"];
-            ggml_tensor* pooled  = ggml_view_1d(ctx, x, hidden_size, x->nb[1] * max_token_idx);
-            pooled               = ggml_mul_mat(ctx, ggml_cont(ctx, ggml_transpose(ctx, text_projection)), pooled);
-            return pooled;
+            ggml_tensor* pooled  = ggml_view_1d(ctx->ggml_ctx, x, hidden_size, x->nb[1] * max_token_idx);
+            if (text_projection != nullptr) {
+                pooled = ggml_ext_linear(ctx->ggml_ctx, pooled, text_projection, nullptr);
+            } else {
+                LOG_DEBUG("identity projection");
+            }
+            return pooled;  // [hidden_size, 1, 1]
         }
 
         return x;  // [N, n_token, hidden_size]
@@ -732,7 +784,7 @@ public:
     int32_t n_layer           = 24;
 
 public:
-    CLIPVisionModel(CLIPVersion version = OPENAI_CLIP_VIT_L_14) {
+    CLIPVisionModel(CLIPVersion version = OPENAI_CLIP_VIT_L_14, bool proj_in = false) {
         if (version == OPEN_CLIP_VIT_H_14) {
             hidden_size       = 1280;
             intermediate_size = 5120;
@@ -747,11 +799,14 @@ public:
 
         blocks["embeddings"]     = std::shared_ptr<GGMLBlock>(new CLIPVisionEmbeddings(hidden_size, num_channels, patch_size, image_size));
         blocks["pre_layernorm"]  = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
-        blocks["encoder"]        = std::shared_ptr<GGMLBlock>(new CLIPEncoder(n_layer, hidden_size, n_head, intermediate_size));
+        blocks["encoder"]        = std::shared_ptr<GGMLBlock>(new CLIPEncoder(n_layer, hidden_size, n_head, intermediate_size, proj_in));
         blocks["post_layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* pixel_values, bool return_pooled = true) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                struct 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"]);
@@ -760,15 +815,19 @@ public:
 
         auto x = embeddings->forward(ctx, pixel_values);  // [N, num_positions, embed_dim]
         x      = pre_layernorm->forward(ctx, x);
-        x      = encoder->forward(ctx, x, -1, false);
-        x      = post_layernorm->forward(ctx, x);  // [N, n_token, hidden_size]
+        x      = encoder->forward(ctx, x, nullptr, clip_skip);
+
+        auto last_hidden_state = x;
+
+        x = post_layernorm->forward(ctx, x);  // [N, n_token, hidden_size]
 
         GGML_ASSERT(x->ne[3] == 1);
         if (return_pooled) {
-            ggml_tensor* pooled = ggml_cont(ctx, ggml_view_2d(ctx, x, x->ne[0], x->ne[2], x->nb[2], 0));
+            ggml_tensor* pooled = ggml_cont(ctx->ggml_ctx, ggml_view_2d(ctx->ggml_ctx, x, x->ne[0], x->ne[2], x->nb[2], 0));
             return pooled;  // [N, hidden_size]
         } else {
-            return x;  // [N, n_token, hidden_size]
+            // return x;  // [N, n_token, hidden_size]
+            return last_hidden_state;  // [N, n_token, hidden_size]
         }
     }
 };
@@ -779,9 +838,9 @@ protected:
     int64_t out_features;
     bool transpose_weight;
 
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") override {
+        enum ggml_type wtype = get_type(prefix + "weight", tensor_storage_map, GGML_TYPE_F32);
         if (transpose_weight) {
-            LOG_ERROR("transpose_weight");
             params["weight"] = ggml_new_tensor_2d(ctx, wtype, out_features, in_features);
         } else {
             params["weight"] = ggml_new_tensor_2d(ctx, wtype, in_features, out_features);
@@ -796,12 +855,12 @@ public:
           out_features(out_features),
           transpose_weight(transpose_weight) {}
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         struct ggml_tensor* w = params["weight"];
         if (transpose_weight) {
-            w = ggml_cont(ctx, ggml_transpose(ctx, w));
+            w = ggml_cont(ctx->ggml_ctx, ggml_transpose(ctx->ggml_ctx, w));
         }
-        return ggml_nn_linear(ctx, x, w, NULL);
+        return ggml_ext_linear(ctx->ggml_ctx, x, w, nullptr);
     }
 };
 
@@ -813,7 +872,8 @@ public:
 
 public:
     CLIPVisionModelProjection(CLIPVersion version   = OPENAI_CLIP_VIT_L_14,
-                              bool transpose_proj_w = false) {
+                              bool transpose_proj_w = false,
+                              bool proj_in          = false) {
         if (version == OPEN_CLIP_VIT_H_14) {
             hidden_size    = 1280;
             projection_dim = 1024;
@@ -821,104 +881,141 @@ public:
             hidden_size = 1664;
         }
 
-        blocks["vision_model"]      = std::shared_ptr<GGMLBlock>(new CLIPVisionModel(version));
+        blocks["vision_model"]      = std::shared_ptr<GGMLBlock>(new CLIPVisionModel(version, proj_in));
         blocks["visual_projection"] = std::shared_ptr<GGMLBlock>(new CLIPProjection(hidden_size, projection_dim, transpose_proj_w));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* pixel_values) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                struct 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]
+        // return: [N, projection_dim] if return_pooled else [N, n_token, hidden_size]
         auto vision_model      = std::dynamic_pointer_cast<CLIPVisionModel>(blocks["vision_model"]);
         auto visual_projection = std::dynamic_pointer_cast<CLIPProjection>(blocks["visual_projection"]);
 
-        auto x = vision_model->forward(ctx, pixel_values);  // [N, hidden_size]
-        x      = visual_projection->forward(ctx, x);        // [N, projection_dim]
+        auto x = vision_model->forward(ctx, pixel_values, return_pooled, clip_skip);  // [N, hidden_size] or [N, n_token, hidden_size]
 
-        return x;  // [N, projection_dim]
+        if (return_pooled) {
+            x = visual_projection->forward(ctx, x);  // [N, projection_dim]
+        }
+
+        return x;
     }
 };
 
 struct CLIPTextModelRunner : public GGMLRunner {
     CLIPTextModel model;
 
+    std::vector<float> attention_mask_vec;
+
     CLIPTextModelRunner(ggml_backend_t backend,
-                        ggml_type wtype,
+                        bool offload_params_to_cpu,
+                        const String2TensorStorage& tensor_storage_map,
+                        const std::string prefix,
                         CLIPVersion version = OPENAI_CLIP_VIT_L_14,
-                        int clip_skip_value = 1,
-                        bool with_final_ln  = true)
-        : GGMLRunner(backend, wtype), model(version, clip_skip_value, with_final_ln) {
-        model.init(params_ctx, wtype);
+                        bool with_final_ln  = true,
+                        bool force_clip_f32 = false)
+        : GGMLRunner(backend, offload_params_to_cpu) {
+        bool proj_in = false;
+        for (const auto& [name, tensor_storage] : tensor_storage_map) {
+            if (!starts_with(name, prefix)) {
+                continue;
+            }
+            if (contains(name, "self_attn.in_proj")) {
+                proj_in = true;
+                break;
+            }
+        }
+        model = CLIPTextModel(version, with_final_ln, force_clip_f32, proj_in);
+        model.init(params_ctx, tensor_storage_map, prefix);
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return "clip";
     }
 
-    void set_clip_skip(int clip_skip) {
-        model.set_clip_skip(clip_skip);
-    }
-
     void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
         model.get_param_tensors(tensors, prefix);
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* input_ids,
                                 struct ggml_tensor* embeddings,
+                                struct ggml_tensor* mask,
                                 size_t max_token_idx = 0,
-                                bool return_pooled   = false) {
+                                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) {
             GGML_ASSERT(input_ids->ne[0] % model.n_token == 0);
-            input_ids = ggml_reshape_2d(ctx, input_ids, model.n_token, input_ids->ne[0] / model.n_token);
+            input_ids = ggml_reshape_2d(ctx->ggml_ctx, input_ids, model.n_token, input_ids->ne[0] / model.n_token);
         }
 
-        return model.forward(ctx, input_ids, embeddings, max_token_idx, return_pooled);
+        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 = NULL,
+                                    void* custom_embeddings_data = nullptr,
                                     size_t max_token_idx         = 0,
-                                    bool return_pooled           = false) {
-        struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
+                                    bool return_pooled           = false,
+                                    int clip_skip                = -1) {
+        struct ggml_cgraph* gf = new_graph_custom(2048);
 
         input_ids = to_backend(input_ids);
 
-        struct ggml_tensor* embeddings = NULL;
+        struct ggml_tensor* embeddings = nullptr;
 
-        if (num_custom_embeddings > 0 && custom_embeddings_data != NULL) {
-            auto custom_embeddings = ggml_new_tensor_2d(compute_ctx,
-                                                        wtype,
-                                                        model.hidden_size,
-                                                        num_custom_embeddings);
+        if (num_custom_embeddings > 0 && custom_embeddings_data != nullptr) {
+            auto token_embed_weight = model.get_token_embed_weight();
+            auto custom_embeddings  = ggml_new_tensor_2d(compute_ctx,
+                                                         token_embed_weight->type,
+                                                         model.hidden_size,
+                                                         num_custom_embeddings);
             set_backend_tensor_data(custom_embeddings, custom_embeddings_data);
 
-            auto token_embed_weight = model.get_token_embed_weight();
             // concatenate custom embeddings
             embeddings = ggml_concat(compute_ctx, token_embed_weight, custom_embeddings, 1);
         }
 
-        struct ggml_tensor* hidden_states = forward(compute_ctx, input_ids, embeddings, max_token_idx, return_pooled);
+        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, attention_mask, max_token_idx, return_pooled, clip_skip);
 
         ggml_build_forward_expand(gf, hidden_states);
 
         return gf;
     }
 
-    void compute(const int n_threads,
+    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 = NULL) {
+                 ggml_context* output_ctx = nullptr) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
-            return build_graph(input_ids, num_custom_embeddings, custom_embeddings_data, max_token_idx, return_pooled);
+            return build_graph(input_ids, num_custom_embeddings, custom_embeddings_data, max_token_idx, return_pooled, clip_skip);
         };
-        GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
+        return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
     }
 };
 

common.hpp

@@ -23,12 +23,12 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [N, channels, h, w]
         if (vae_downsample) {
             auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
 
-            x = ggml_pad(ctx, x, 1, 1, 0, 0);
+            x = ggml_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"]);
@@ -52,12 +52,12 @@ public:
         blocks["conv"] = std::shared_ptr<GGMLBlock>(new Conv2d(channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [N, channels, h, w]
         auto conv = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
 
-        x = ggml_upscale(ctx, x, 2);  // [N, channels, h*2, w*2]
-        x = conv->forward(ctx, x);    // [N, out_channels, h*2, w*2]
+        x = ggml_upscale(ctx->ggml_ctx, x, 2, GGML_SCALE_MODE_NEAREST);  // [N, channels, h*2, w*2]
+        x = conv->forward(ctx, x);                                       // [N, out_channels, h*2, w*2]
         return x;
     }
 };
@@ -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(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* emb = NULL) {
+    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x, struct ggml_tensor* emb = nullptr) {
         // For dims==3, we reduce dimension from 5d to 4d by merging h and w, in order not to change ggml
         // [N, c, t, h, w] => [N, c, t, h * w]
         // x: [N, channels, h, w] if dims == 2 else [N, channels, t, h, w]
@@ -131,38 +131,38 @@ public:
         auto out_layers_0 = std::dynamic_pointer_cast<GroupNorm32>(blocks["out_layers.0"]);
         auto out_layers_3 = std::dynamic_pointer_cast<UnaryBlock>(blocks["out_layers.3"]);
 
-        if (emb == NULL) {
+        if (emb == nullptr) {
             GGML_ASSERT(skip_t_emb);
         }
 
         // in_layers
         auto h = in_layers_0->forward(ctx, x);
-        h      = ggml_silu_inplace(ctx, h);
+        h      = ggml_silu_inplace(ctx->ggml_ctx, h);
         h      = in_layers_2->forward(ctx, h);  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
 
         // emb_layers
         if (!skip_t_emb) {
             auto emb_layer_1 = std::dynamic_pointer_cast<Linear>(blocks["emb_layers.1"]);
 
-            auto emb_out = ggml_silu(ctx, emb);
+            auto emb_out = ggml_silu(ctx->ggml_ctx, emb);
             emb_out      = emb_layer_1->forward(ctx, emb_out);  // [N, out_channels] if dims == 2 else [N, t, out_channels]
 
             if (dims == 2) {
-                emb_out = ggml_reshape_4d(ctx, emb_out, 1, 1, emb_out->ne[0], emb_out->ne[1]);  // [N, out_channels, 1, 1]
+                emb_out = ggml_reshape_4d(ctx->ggml_ctx, emb_out, 1, 1, emb_out->ne[0], emb_out->ne[1]);  // [N, out_channels, 1, 1]
             } else {
-                emb_out = ggml_reshape_4d(ctx, emb_out, 1, emb_out->ne[0], emb_out->ne[1], emb_out->ne[2]);  // [N, t, out_channels, 1]
+                emb_out = ggml_reshape_4d(ctx->ggml_ctx, emb_out, 1, emb_out->ne[0], emb_out->ne[1], emb_out->ne[2]);  // [N, t, out_channels, 1]
                 if (exchange_temb_dims) {
                     // emb_out = rearrange(emb_out, "b t c ... -> b c t ...")
-                    emb_out = ggml_cont(ctx, ggml_permute(ctx, emb_out, 0, 2, 1, 3));  // [N, out_channels, t, 1]
+                    emb_out = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, emb_out, 0, 2, 1, 3));  // [N, out_channels, t, 1]
                 }
             }
 
-            h = ggml_add(ctx, h, emb_out);  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
+            h = ggml_add(ctx->ggml_ctx, h, emb_out);  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
         }
 
         // out_layers
         h = out_layers_0->forward(ctx, h);
-        h = ggml_silu_inplace(ctx, h);
+        h = ggml_silu_inplace(ctx->ggml_ctx, h);
         // dropout, skip for inference
         h = out_layers_3->forward(ctx, h);
 
@@ -172,65 +172,97 @@ public:
             x                    = skip_connection->forward(ctx, x);  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
         }
 
-        h = ggml_add(ctx, h, x);
+        h = ggml_add(ctx->ggml_ctx, h, x);
         return h;  // [N, out_channels, h, w] if dims == 2 else [N, out_channels, t, h, w]
     }
 };
 
-class GEGLU : public GGMLBlock {
+class GEGLU : public UnaryBlock {
 protected:
     int64_t dim_in;
     int64_t dim_out;
 
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        params["proj.weight"] = ggml_new_tensor_2d(ctx, wtype, dim_in, dim_out * 2);
-        params["proj.bias"]   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, dim_out * 2);
-    }
-
 public:
     GEGLU(int64_t dim_in, int64_t dim_out)
-        : dim_in(dim_in), dim_out(dim_out) {}
+        : dim_in(dim_in), dim_out(dim_out) {
+        blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(dim_in, dim_out * 2));
+    }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
         // x: [ne3, ne2, ne1, dim_in]
         // return: [ne3, ne2, ne1, dim_out]
-        struct ggml_tensor* w = params["proj.weight"];
-        struct ggml_tensor* b = params["proj.bias"];
+        auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
 
-        auto x_w    = ggml_view_2d(ctx, w, w->ne[0], w->ne[1] / 2, w->nb[1], 0);                        // [dim_out, dim_in]
-        auto x_b    = ggml_view_1d(ctx, b, b->ne[0] / 2, 0);                                            // [dim_out, dim_in]
-        auto gate_w = ggml_view_2d(ctx, w, w->ne[0], w->ne[1] / 2, w->nb[1], w->nb[1] * w->ne[1] / 2);  // [dim_out, ]
-        auto gate_b = ggml_view_1d(ctx, b, b->ne[0] / 2, b->nb[0] * b->ne[0] / 2);                      // [dim_out, ]
+        x          = proj->forward(ctx, x);  // [ne3, ne2, ne1, dim_out*2]
+        auto x_vec = ggml_ext_chunk(ctx->ggml_ctx, x, 2, 0, false);
+        x          = x_vec[0];  // [ne3, ne2, ne1, dim_out]
+        auto gate  = x_vec[1];  // [ne3, ne2, ne1, dim_out]
 
-        auto x_in = x;
-        x         = ggml_nn_linear(ctx, x_in, x_w, x_b);        // [ne3, ne2, ne1, dim_out]
-        auto gate = ggml_nn_linear(ctx, x_in, gate_w, gate_b);  // [ne3, ne2, ne1, dim_out]
+        gate = ggml_cont(ctx->ggml_ctx, gate);
 
-        gate = ggml_gelu_inplace(ctx, gate);
+        gate = ggml_ext_gelu(ctx->ggml_ctx, gate, true);
 
-        x = ggml_mul(ctx, x, gate);  // [ne3, ne2, ne1, dim_out]
+        x = ggml_mul(ctx->ggml_ctx, x, gate);  // [ne3, ne2, ne1, dim_out]
 
         return x;
     }
 };
 
+class GELU : public UnaryBlock {
+public:
+    GELU(int64_t dim_in, int64_t dim_out, bool bias = true) {
+        blocks["proj"] = std::shared_ptr<GGMLBlock>(new Linear(dim_in, dim_out, bias));
+    }
+
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) override {
+        // x: [ne3, ne2, ne1, dim_in]
+        // return: [ne3, ne2, ne1, dim_out]
+        auto proj = std::dynamic_pointer_cast<Linear>(blocks["proj"]);
+
+        x = proj->forward(ctx, x);
+        x = ggml_ext_gelu(ctx->ggml_ctx, x, true);
+        return x;
+    }
+};
+
 class FeedForward : public GGMLBlock {
 public:
+    enum class Activation {
+        GEGLU,
+        GELU
+    };
     FeedForward(int64_t dim,
                 int64_t dim_out,
-                int64_t mult = 4) {
+                int64_t mult          = 4,
+                Activation activation = Activation::GEGLU,
+                bool precision_fix    = false) {
         int64_t inner_dim = dim * mult;
+        if (activation == Activation::GELU) {
+            blocks["net.0"] = std::shared_ptr<GGMLBlock>(new GELU(dim, inner_dim));
+        } else {
+            blocks["net.0"] = std::shared_ptr<GGMLBlock>(new GEGLU(dim, inner_dim));
+        }
 
-        blocks["net.0"] = std::shared_ptr<GGMLBlock>(new GEGLU(dim, inner_dim));
         // net_1 is nn.Dropout(), skip for inference
-        blocks["net.2"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, dim_out));
+        bool force_prec_f32 = false;
+        float scale         = 1.f;
+        if (precision_fix) {
+            scale = 1.f / 128.f;
+#ifdef SD_USE_VULKAN
+            force_prec_f32 = true;
+#endif
+        }
+        // The purpose of the scale here is to prevent NaN issues in certain situations.
+        // For example, when using Vulkan without enabling force_prec_f32,
+        // or when using CUDA but the weights are k-quants.
+        blocks["net.2"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, dim_out, true, false, force_prec_f32, scale));
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [ne3, ne2, ne1, dim]
         // return: [ne3, ne2, ne1, dim_out]
 
-        auto net_0 = std::dynamic_pointer_cast<GEGLU>(blocks["net.0"]);
+        auto net_0 = std::dynamic_pointer_cast<UnaryBlock>(blocks["net.0"]);
         auto net_2 = std::dynamic_pointer_cast<Linear>(blocks["net.2"]);
 
         x = net_0->forward(ctx, x);  // [ne3, ne2, ne1, inner_dim]
@@ -265,7 +297,9 @@ public:
         // to_out_1 is nn.Dropout(), skip for inference
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* context) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                struct ggml_tensor* x,
+                                struct ggml_tensor* context) {
         // x: [N, n_token, query_dim]
         // context: [N, n_context, context_dim]
         // return: [N, n_token, query_dim]
@@ -283,7 +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_nn_attention_ext(ctx, q, k, v, n_head, NULL, false);  // [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;
@@ -321,7 +355,9 @@ public:
         }
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* context) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                struct ggml_tensor* x,
+                                struct ggml_tensor* context) {
         // x: [N, n_token, query_dim]
         // context: [N, n_context, context_dim]
         // return: [N, n_token, query_dim]
@@ -341,21 +377,21 @@ public:
             x           = norm_in->forward(ctx, x);
             x           = ff_in->forward(ctx, x);
             // self.is_res is always True
-            x = ggml_add(ctx, x, x_skip);
+            x = ggml_add(ctx->ggml_ctx, x, x_skip);
         }
 
         auto r = x;
         x      = norm1->forward(ctx, x);
         x      = attn1->forward(ctx, x, x);  // self-attention
-        x      = ggml_add(ctx, x, r);
+        x      = ggml_add(ctx->ggml_ctx, x, r);
         r      = x;
         x      = norm2->forward(ctx, x);
         x      = attn2->forward(ctx, x, context);  // cross-attention
-        x      = ggml_add(ctx, x, r);
+        x      = ggml_add(ctx->ggml_ctx, x, r);
         r      = x;
         x      = norm3->forward(ctx, x);
         x      = ff->forward(ctx, x);
-        x      = ggml_add(ctx, x, r);
+        x      = ggml_add(ctx->ggml_ctx, x, r);
 
         return x;
     }
@@ -368,38 +404,66 @@ protected:
     int64_t d_head;
     int64_t depth       = 1;    // 1
     int64_t context_dim = 768;  // hidden_size, 1024 for VERSION_SD2
+    bool use_linear     = false;
+
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, const std::string prefix = "") {
+        auto iter = tensor_storage_map.find(prefix + "proj_out.weight");
+        if (iter != tensor_storage_map.end()) {
+            int64_t inner_dim = n_head * d_head;
+            if (iter->second.n_dims == 4 && use_linear) {
+                use_linear         = false;
+                blocks["proj_in"]  = std::make_shared<Conv2d>(in_channels, inner_dim, std::pair{1, 1});
+                blocks["proj_out"] = std::make_shared<Conv2d>(inner_dim, in_channels, std::pair{1, 1});
+            } else if (iter->second.n_dims == 2 && !use_linear) {
+                use_linear         = true;
+                blocks["proj_in"]  = std::make_shared<Linear>(in_channels, inner_dim);
+                blocks["proj_out"] = std::make_shared<Linear>(inner_dim, in_channels);
+            }
+        }
+    }
 
 public:
     SpatialTransformer(int64_t in_channels,
                        int64_t n_head,
                        int64_t d_head,
                        int64_t depth,
-                       int64_t context_dim)
+                       int64_t context_dim,
+                       bool use_linear)
         : in_channels(in_channels),
           n_head(n_head),
           d_head(d_head),
           depth(depth),
-          context_dim(context_dim) {
-        // We will convert unet transformer linear to conv2d 1x1 when loading the weights, so use_linear is always False
+          context_dim(context_dim),
+          use_linear(use_linear) {
         // disable_self_attn is always False
         int64_t inner_dim = n_head * d_head;  // in_channels
         blocks["norm"]    = std::shared_ptr<GGMLBlock>(new GroupNorm32(in_channels));
-        blocks["proj_in"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, inner_dim, {1, 1}));
+        if (use_linear) {
+            blocks["proj_in"] = std::shared_ptr<GGMLBlock>(new Linear(in_channels, inner_dim));
+        } else {
+            blocks["proj_in"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, inner_dim, {1, 1}));
+        }
 
         for (int i = 0; i < depth; i++) {
             std::string name = "transformer_blocks." + std::to_string(i);
-            blocks[name]     = std::shared_ptr<GGMLBlock>(new BasicTransformerBlock(inner_dim, n_head, d_head, context_dim));
+            blocks[name]     = std::shared_ptr<GGMLBlock>(new BasicTransformerBlock(inner_dim, n_head, d_head, context_dim, false));
         }
 
-        blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(inner_dim, in_channels, {1, 1}));
+        if (use_linear) {
+            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, in_channels));
+        } else {
+            blocks["proj_out"] = std::shared_ptr<GGMLBlock>(new Conv2d(inner_dim, in_channels, {1, 1}));
+        }
     }
 
-    virtual struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, struct ggml_tensor* context) {
+    virtual struct ggml_tensor* forward(GGMLRunnerContext* ctx,
+                                        struct ggml_tensor* x,
+                                        struct ggml_tensor* context) {
         // x: [N, in_channels, h, w]
         // context: [N, max_position(aka n_token), hidden_size(aka context_dim)]
         auto norm     = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm"]);
-        auto proj_in  = std::dynamic_pointer_cast<Conv2d>(blocks["proj_in"]);
-        auto proj_out = std::dynamic_pointer_cast<Conv2d>(blocks["proj_out"]);
+        auto proj_in  = std::dynamic_pointer_cast<UnaryBlock>(blocks["proj_in"]);
+        auto proj_out = std::dynamic_pointer_cast<UnaryBlock>(blocks["proj_out"]);
 
         auto x_in         = x;
         int64_t n         = x->ne[3];
@@ -408,10 +472,15 @@ public:
         int64_t inner_dim = n_head * d_head;
 
         x = norm->forward(ctx, x);
-        x = proj_in->forward(ctx, x);  // [N, inner_dim, h, w]
-
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 1, 2, 0, 3));  // [N, h, w, inner_dim]
-        x = ggml_reshape_3d(ctx, x, inner_dim, w * h, n);      // [N, h * w, inner_dim]
+        if (use_linear) {
+            x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 2, 0, 3));  // [N, h, w, inner_dim]
+            x = ggml_reshape_3d(ctx->ggml_ctx, x, inner_dim, w * h, n);                // [N, h * w, inner_dim]
+            x = proj_in->forward(ctx, x);                                              // [N, inner_dim, h, w]
+        } else {
+            x = proj_in->forward(ctx, x);                                              // [N, inner_dim, h, w]
+            x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 2, 0, 3));  // [N, h, w, inner_dim]
+            x = ggml_reshape_3d(ctx->ggml_ctx, x, inner_dim, w * h, n);                // [N, h * w, inner_dim]
+        }
 
         for (int i = 0; i < depth; i++) {
             std::string name       = "transformer_blocks." + std::to_string(i);
@@ -420,27 +489,37 @@ public:
             x = transformer_block->forward(ctx, x, context);
         }
 
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 1, 0, 2, 3));  // [N, inner_dim, h * w]
-        x = ggml_reshape_4d(ctx, x, w, h, inner_dim, n);       // [N, inner_dim, h, w]
+        if (use_linear) {
+            // proj_out
+            x = proj_out->forward(ctx, x);  // [N, in_channels, h, w]
 
-        // proj_out
-        x = proj_out->forward(ctx, x);  // [N, in_channels, h, w]
+            x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [N, inner_dim, h * w]
+            x = ggml_reshape_4d(ctx->ggml_ctx, x, w, h, inner_dim, n);                 // [N, inner_dim, h, w]
+        } else {
+            x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 1, 0, 2, 3));  // [N, inner_dim, h * w]
+            x = ggml_reshape_4d(ctx->ggml_ctx, x, w, h, inner_dim, n);                 // [N, inner_dim, h, w]
 
-        x = ggml_add(ctx, x, x_in);
+            // proj_out
+            x = proj_out->forward(ctx, x);  // [N, in_channels, h, w]
+        }
+
+        x = ggml_add(ctx->ggml_ctx, x, x_in);
         return x;
     }
 };
 
 class AlphaBlender : public GGMLBlock {
 protected:
-    void init_params(struct ggml_context* ctx, ggml_type wtype) {
-        params["mix_factor"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
+    void init_params(struct ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
+        // Get the type of the "mix_factor" tensor from the input tensors map with the specified prefix
+        enum ggml_type wtype = GGML_TYPE_F32;
+        params["mix_factor"] = ggml_new_tensor_1d(ctx, wtype, 1);
     }
 
     float get_alpha() {
         // image_only_indicator is always tensor([0.]) and since mix_factor.shape is [1,]
         // so learned_with_images is same as learned
-        float alpha = ggml_backend_tensor_get_f32(params["mix_factor"]);
+        float alpha = ggml_ext_backend_tensor_get_f32(params["mix_factor"]);
         return sigmoid(alpha);
     }
 
@@ -451,23 +530,23 @@ public:
         // since mix_factor.shape is [1,], we don't need rearrange using  rearrange_pattern
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x_spatial,
                                 struct ggml_tensor* x_temporal) {
         // image_only_indicator is always tensor([0.])
         float alpha = get_alpha();
-        auto x      = ggml_add(ctx,
-                               ggml_scale(ctx, x_spatial, alpha),
-                               ggml_scale(ctx, x_temporal, 1.0f - alpha));
+        auto x      = ggml_add(ctx->ggml_ctx,
+                               ggml_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
@@ -476,7 +555,7 @@ public:
         blocks["time_mixer"] = std::shared_ptr<GGMLBlock>(new AlphaBlender());
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx,
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx,
                                 struct ggml_tensor* x,
                                 struct ggml_tensor* emb,
                                 int num_video_frames) {
@@ -494,21 +573,21 @@ public:
         int64_t H = x->ne[1];
         int64_t W = x->ne[0];
 
-        x          = ggml_reshape_4d(ctx, x, W * H, C, T, B);           // (b t) c h w -> b t c (h w)
-        x          = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
+        x          = ggml_reshape_4d(ctx->ggml_ctx, x, W * H, C, T, B);                     // (b t) c h w -> b t c (h w)
+        x          = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b t c (h w) -> b c t (h w)
         auto x_mix = x;
 
-        emb = ggml_reshape_4d(ctx, emb, emb->ne[0], T, B, emb->ne[3]);  // (b t) ... -> b t ...
+        emb = ggml_reshape_4d(ctx->ggml_ctx, emb, emb->ne[0], T, B, emb->ne[3]);  // (b t) ... -> b t ...
 
         x = time_stack->forward(ctx, x, emb);  // b t c (h w)
 
         x = time_mixer->forward(ctx, x_mix, x);  // b t c (h w)
 
-        x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
-        x = ggml_reshape_4d(ctx, x, W, H, C, T * B);           // b t c (h w) -> (b t) c h w
+        x = ggml_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));  // b c t (h w) -> b t c (h w)
+        x = ggml_reshape_4d(ctx->ggml_ctx, x, W, H, C, T * B);                     // b t c (h w) -> (b t) c h w
 
         return x;
     }
 };
 
-#endif  // __COMMON_HPP__
+#endif  // __COMMON_HPP__

control.hpp

@@ -27,6 +27,7 @@ protected:
     int num_heads                          = 8;
     int num_head_channels                  = -1;   // channels // num_heads
     int context_dim                        = 768;  // 1024 for VERSION_SD2, 2048 for VERSION_SDXL
+    bool use_linear_projection             = false;
 
 public:
     int model_channels  = 320;
@@ -34,11 +35,11 @@ public:
 
     ControlNetBlock(SDVersion version = VERSION_SD1)
         : version(version) {
-        if (version == VERSION_SD2) {
+        if (sd_version_is_sd2(version)) {
             context_dim       = 1024;
             num_head_channels = 64;
             num_heads         = -1;
-        } else if (version == VERSION_SDXL) {
+        } else if (sd_version_is_sdxl(version)) {
             context_dim           = 2048;
             attention_resolutions = {4, 2};
             channel_mult          = {1, 2, 4};
@@ -58,7 +59,7 @@ public:
         // time_embed_1 is nn.SiLU()
         blocks["time_embed.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim));
 
-        if (version == VERSION_SDXL || version == VERSION_SVD) {
+        if (sd_version_is_sdxl(version) || version == VERSION_SVD) {
             blocks["label_emb.0.0"] = std::shared_ptr<GGMLBlock>(new Linear(adm_in_channels, time_embed_dim));
             // label_emb_1 is nn.SiLU()
             blocks["label_emb.0.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim));
@@ -82,7 +83,7 @@ public:
                                        int64_t d_head,
                                        int64_t depth,
                                        int64_t context_dim) -> SpatialTransformer* {
-            return new SpatialTransformer(in_channels, n_head, d_head, depth, context_dim);
+            return new SpatialTransformer(in_channels, n_head, d_head, depth, context_dim, use_linear_projection);
         };
 
         auto make_zero_conv = [&](int64_t channels) {
@@ -165,7 +166,7 @@ public:
     }
 
     struct ggml_tensor* resblock_forward(std::string name,
-                                         struct ggml_context* ctx,
+                                         GGMLRunnerContext* ctx,
                                          struct ggml_tensor* x,
                                          struct ggml_tensor* emb) {
         auto block = std::dynamic_pointer_cast<ResBlock>(blocks[name]);
@@ -173,14 +174,14 @@ public:
     }
 
     struct ggml_tensor* attention_layer_forward(std::string name,
-                                                struct ggml_context* ctx,
+                                                GGMLRunnerContext* ctx,
                                                 struct ggml_tensor* x,
                                                 struct 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(struct ggml_context* ctx,
+    struct ggml_tensor* input_hint_block_forward(GGMLRunnerContext* ctx,
                                                  struct ggml_tensor* hint,
                                                  struct ggml_tensor* emb,
                                                  struct ggml_tensor* context) {
@@ -192,32 +193,32 @@ public:
 
                 h = block->forward(ctx, h);
             } else {
-                h = ggml_silu_inplace(ctx, h);
+                h = ggml_silu_inplace(ctx->ggml_ctx, h);
             }
         }
         return h;
     }
 
-    std::vector<struct ggml_tensor*> forward(struct ggml_context* ctx,
+    std::vector<struct ggml_tensor*> forward(GGMLRunnerContext* ctx,
                                              struct ggml_tensor* x,
                                              struct ggml_tensor* hint,
                                              struct ggml_tensor* guided_hint,
                                              struct ggml_tensor* timesteps,
                                              struct ggml_tensor* context,
-                                             struct ggml_tensor* y = NULL) {
+                                             struct ggml_tensor* y = nullptr) {
         // x: [N, in_channels, h, w] or [N, in_channels/2, h, w]
         // timesteps: [N,]
         // context: [N, max_position, hidden_size] or [1, max_position, hidden_size]. for example, [N, 77, 768]
         // y: [N, adm_in_channels] or [1, adm_in_channels]
-        if (context != NULL) {
+        if (context != nullptr) {
             if (context->ne[2] != x->ne[3]) {
-                context = ggml_repeat(ctx, context, ggml_new_tensor_3d(ctx, GGML_TYPE_F32, context->ne[0], context->ne[1], x->ne[3]));
+                context = ggml_repeat(ctx->ggml_ctx, context, ggml_new_tensor_3d(ctx->ggml_ctx, GGML_TYPE_F32, context->ne[0], context->ne[1], x->ne[3]));
             }
         }
 
-        if (y != NULL) {
+        if (y != nullptr) {
             if (y->ne[1] != x->ne[3]) {
-                y = ggml_repeat(ctx, y, ggml_new_tensor_2d(ctx, GGML_TYPE_F32, y->ne[0], x->ne[3]));
+                y = ggml_repeat(ctx->ggml_ctx, y, ggml_new_tensor_2d(ctx->ggml_ctx, GGML_TYPE_F32, y->ne[0], x->ne[3]));
             }
         }
 
@@ -228,27 +229,27 @@ public:
 
         auto middle_block_out = std::dynamic_pointer_cast<Conv2d>(blocks["middle_block_out.0"]);
 
-        auto t_emb = ggml_nn_timestep_embedding(ctx, timesteps, model_channels);  // [N, model_channels]
+        auto t_emb = ggml_ext_timestep_embedding(ctx->ggml_ctx, timesteps, model_channels);  // [N, model_channels]
 
         auto emb = time_embed_0->forward(ctx, t_emb);
-        emb      = ggml_silu_inplace(ctx, emb);
+        emb      = ggml_silu_inplace(ctx->ggml_ctx, emb);
         emb      = time_embed_2->forward(ctx, emb);  // [N, time_embed_dim]
 
         // SDXL/SVD
-        if (y != NULL) {
+        if (y != nullptr) {
             auto label_embed_0 = std::dynamic_pointer_cast<Linear>(blocks["label_emb.0.0"]);
             auto label_embed_2 = std::dynamic_pointer_cast<Linear>(blocks["label_emb.0.2"]);
 
             auto label_emb = label_embed_0->forward(ctx, y);
-            label_emb      = ggml_silu_inplace(ctx, label_emb);
+            label_emb      = ggml_silu_inplace(ctx->ggml_ctx, label_emb);
             label_emb      = label_embed_2->forward(ctx, label_emb);  // [N, time_embed_dim]
 
-            emb = ggml_add(ctx, emb, label_emb);  // [N, time_embed_dim]
+            emb = ggml_add(ctx->ggml_ctx, emb, label_emb);  // [N, time_embed_dim]
         }
 
         std::vector<struct ggml_tensor*> outs;
 
-        if (guided_hint == NULL) {
+        if (guided_hint == nullptr) {
             guided_hint = input_hint_block_forward(ctx, hint, emb, context);
         }
         outs.push_back(guided_hint);
@@ -257,7 +258,7 @@ public:
 
         // input block 0
         auto h = input_blocks_0_0->forward(ctx, x);
-        h      = ggml_add(ctx, h, guided_hint);
+        h      = ggml_add(ctx->ggml_ctx, h, guided_hint);
         outs.push_back(zero_convs_0->forward(ctx, h));
 
         // input block 1-11
@@ -310,27 +311,28 @@ struct ControlNet : public GGMLRunner {
     SDVersion version = VERSION_SD1;
     ControlNetBlock control_net;
 
-    ggml_backend_buffer_t control_buffer = NULL;  // keep control output tensors in backend memory
-    ggml_context* control_ctx            = NULL;
+    ggml_backend_buffer_t control_buffer = nullptr;  // keep control output tensors in backend memory
+    ggml_context* control_ctx            = nullptr;
     std::vector<struct ggml_tensor*> controls;  // (12 input block outputs, 1 middle block output) SD 1.5
-    struct ggml_tensor* guided_hint = NULL;     // guided_hint cache, for faster inference
+    struct ggml_tensor* guided_hint = nullptr;  // guided_hint cache, for faster inference
     bool guided_hint_cached         = false;
 
     ControlNet(ggml_backend_t backend,
-               ggml_type wtype,
-               SDVersion version = VERSION_SD1)
-        : GGMLRunner(backend, wtype), control_net(version) {
-        control_net.init(params_ctx, wtype);
+               bool offload_params_to_cpu,
+               const String2TensorStorage& tensor_storage_map = {},
+               SDVersion version                              = VERSION_SD1)
+        : GGMLRunner(backend, offload_params_to_cpu), control_net(version) {
+        control_net.init(params_ctx, tensor_storage_map, "");
     }
 
-    ~ControlNet() {
+    ~ControlNet() override {
         free_control_ctx();
     }
 
     void alloc_control_ctx(std::vector<struct ggml_tensor*> outs) {
         struct ggml_init_params params;
         params.mem_size   = static_cast<size_t>(outs.size() * ggml_tensor_overhead()) + 1024 * 1024;
-        params.mem_buffer = NULL;
+        params.mem_buffer = nullptr;
         params.no_alloc   = true;
         control_ctx       = ggml_init(params);
 
@@ -346,26 +348,26 @@ struct ControlNet : public GGMLRunner {
             control_buffer_size += ggml_nbytes(controls[i]);
         }
 
-        control_buffer = ggml_backend_alloc_ctx_tensors(control_ctx, backend);
+        control_buffer = ggml_backend_alloc_ctx_tensors(control_ctx, runtime_backend);
 
         LOG_DEBUG("control buffer size %.2fMB", control_buffer_size * 1.f / 1024.f / 1024.f);
     }
 
     void free_control_ctx() {
-        if (control_buffer != NULL) {
+        if (control_buffer != nullptr) {
             ggml_backend_buffer_free(control_buffer);
-            control_buffer = NULL;
+            control_buffer = nullptr;
         }
-        if (control_ctx != NULL) {
+        if (control_ctx != nullptr) {
             ggml_free(control_ctx);
-            control_ctx = NULL;
+            control_ctx = nullptr;
         }
-        guided_hint        = NULL;
+        guided_hint        = nullptr;
         guided_hint_cached = false;
         controls.clear();
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return "control_net";
     }
 
@@ -377,12 +379,12 @@ struct ControlNet : public GGMLRunner {
                                     struct ggml_tensor* hint,
                                     struct ggml_tensor* timesteps,
                                     struct ggml_tensor* context,
-                                    struct ggml_tensor* y = NULL) {
-        struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, CONTROL_NET_GRAPH_SIZE, false);
+                                    struct ggml_tensor* y = nullptr) {
+        struct ggml_cgraph* gf = new_graph_custom(CONTROL_NET_GRAPH_SIZE);
 
         x = to_backend(x);
         if (guided_hint_cached) {
-            hint = NULL;
+            hint = nullptr;
         } else {
             hint = to_backend(hint);
         }
@@ -390,15 +392,17 @@ struct ControlNet : public GGMLRunner {
         y         = to_backend(y);
         timesteps = to_backend(timesteps);
 
-        auto outs = control_net.forward(compute_ctx,
+        auto runner_ctx = get_context();
+
+        auto outs = control_net.forward(&runner_ctx,
                                         x,
                                         hint,
-                                        guided_hint_cached ? guided_hint : NULL,
+                                        guided_hint_cached ? guided_hint : nullptr,
                                         timesteps,
                                         context,
                                         y);
 
-        if (control_ctx == NULL) {
+        if (control_ctx == nullptr) {
             alloc_control_ctx(outs);
         }
 
@@ -410,14 +414,14 @@ struct ControlNet : public GGMLRunner {
         return gf;
     }
 
-    void compute(int n_threads,
+    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     = NULL,
-                 struct ggml_context* output_ctx = NULL) {
+                 struct ggml_tensor** output     = nullptr,
+                 struct ggml_context* output_ctx = nullptr) {
         // x: [N, in_channels, h, w]
         // timesteps: [N, ]
         // context: [N, max_position, hidden_size]([N, 77, 768]) or [1, max_position, hidden_size]
@@ -426,11 +430,15 @@ struct ControlNet : public GGMLRunner {
             return build_graph(x, hint, timesteps, context, y);
         };
 
-        GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
-        guided_hint_cached = true;
+        bool res = GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+        if (res) {
+            // cache guided_hint
+            guided_hint_cached = true;
+        }
+        return res;
     }
 
-    bool load_from_file(const std::string& file_path) {
+    bool load_from_file(const std::string& file_path, int n_threads) {
         LOG_INFO("loading control net from '%s'", file_path.c_str());
         alloc_params_buffer();
         std::map<std::string, ggml_tensor*> tensors;
@@ -438,12 +446,12 @@ struct ControlNet : public GGMLRunner {
         std::set<std::string> ignore_tensors;
 
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
+        if (!model_loader.init_from_file_and_convert_name(file_path)) {
             LOG_ERROR("init control net model loader from file failed: '%s'", file_path.c_str());
             return false;
         }
 
-        bool success = model_loader.load_tensors(tensors, backend, ignore_tensors);
+        bool success = model_loader.load_tensors(tensors, ignore_tensors, n_threads);
 
         if (!success) {
             LOG_ERROR("load control net tensors from model loader failed");

diffusion_model.hpp

@@ -3,75 +3,108 @@
 
 #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 void compute(int n_threads,
-                         struct ggml_tensor* x,
-                         struct ggml_tensor* timesteps,
-                         struct ggml_tensor* context,
-                         struct ggml_tensor* c_concat,
-                         struct ggml_tensor* y,
-                         struct ggml_tensor* guidance,
-                         int num_video_frames                      = -1,
-                         std::vector<struct ggml_tensor*> controls = {},
-                         float control_strength                    = 0.f,
-                         struct ggml_tensor** output               = NULL,
-                         struct ggml_context* output_ctx           = NULL)                        = 0;
+    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 int64_t get_adm_in_channels()                                               = 0;
+    virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter){};
+    virtual int64_t get_adm_in_channels()                            = 0;
+    virtual void set_flash_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,
-              ggml_type wtype,
-              SDVersion version = VERSION_SD1)
-        : unet(backend, wtype, version) {
+              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) {
     }
 
-    void alloc_params_buffer() {
+    std::string get_desc() override {
+        return unet.get_desc();
+    }
+
+    void alloc_params_buffer() override {
         unet.alloc_params_buffer();
     }
 
-    void free_params_buffer() {
+    void free_params_buffer() override {
         unet.free_params_buffer();
     }
 
-    void free_compute_buffer() {
+    void free_compute_buffer() override {
         unet.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
         unet.get_param_tensors(tensors, "model.diffusion_model");
     }
 
-    size_t get_params_buffer_size() {
+    size_t get_params_buffer_size() override {
         return unet.get_params_buffer_size();
     }
 
-    int64_t get_adm_in_channels() {
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        unet.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
         return unet.unet.adm_in_channels;
     }
 
-    void compute(int n_threads,
-                 struct ggml_tensor* x,
-                 struct ggml_tensor* timesteps,
-                 struct ggml_tensor* context,
-                 struct ggml_tensor* c_concat,
-                 struct ggml_tensor* y,
-                 struct ggml_tensor* guidance,
-                 int num_video_frames                      = -1,
-                 std::vector<struct ggml_tensor*> controls = {},
-                 float control_strength                    = 0.f,
-                 struct ggml_tensor** output               = NULL,
-                 struct ggml_context* output_ctx           = NULL) {
-        return unet.compute(n_threads, x, timesteps, context, c_concat, y, num_video_frames, controls, control_strength, output, output_ctx);
+    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);
+    }
+
+    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);
     }
 };
 
@@ -79,48 +112,63 @@ struct MMDiTModel : public DiffusionModel {
     MMDiTRunner mmdit;
 
     MMDiTModel(ggml_backend_t backend,
-               ggml_type wtype,
-               SDVersion version = VERSION_SD3_2B)
-        : mmdit(backend, wtype, version) {
+               bool offload_params_to_cpu,
+               const String2TensorStorage& tensor_storage_map = {})
+        : mmdit(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model") {
     }
 
-    void alloc_params_buffer() {
+    std::string get_desc() override {
+        return mmdit.get_desc();
+    }
+
+    void alloc_params_buffer() override {
         mmdit.alloc_params_buffer();
     }
 
-    void free_params_buffer() {
+    void free_params_buffer() override {
         mmdit.free_params_buffer();
     }
 
-    void free_compute_buffer() {
+    void free_compute_buffer() override {
         mmdit.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
         mmdit.get_param_tensors(tensors, "model.diffusion_model");
     }
 
-    size_t get_params_buffer_size() {
+    size_t get_params_buffer_size() override {
         return mmdit.get_params_buffer_size();
     }
 
-    int64_t get_adm_in_channels() {
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        mmdit.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
         return 768 + 1280;
     }
 
-    void compute(int n_threads,
-                 struct ggml_tensor* x,
-                 struct ggml_tensor* timesteps,
-                 struct ggml_tensor* context,
-                 struct ggml_tensor* c_concat,
-                 struct ggml_tensor* y,
-                 struct ggml_tensor* guidance,
-                 int num_video_frames                      = -1,
-                 std::vector<struct ggml_tensor*> controls = {},
-                 float control_strength                    = 0.f,
-                 struct ggml_tensor** output               = NULL,
-                 struct ggml_context* output_ctx           = NULL) {
-        return mmdit.compute(n_threads, x, timesteps, context, y, output, output_ctx);
+    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);
+    }
+
+    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);
     }
 };
 
@@ -128,49 +176,275 @@ struct FluxModel : public DiffusionModel {
     Flux::FluxRunner flux;
 
     FluxModel(ggml_backend_t backend,
-              ggml_type wtype,
-              SDVersion version = VERSION_FLUX_DEV)
-        : flux(backend, wtype, version) {
+              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) {
     }
 
-    void alloc_params_buffer() {
+    std::string get_desc() override {
+        return flux.get_desc();
+    }
+
+    void alloc_params_buffer() override {
         flux.alloc_params_buffer();
     }
 
-    void free_params_buffer() {
+    void free_params_buffer() override {
         flux.free_params_buffer();
     }
 
-    void free_compute_buffer() {
+    void free_compute_buffer() override {
         flux.free_compute_buffer();
     }
 
-    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) {
+    void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors) override {
         flux.get_param_tensors(tensors, "model.diffusion_model");
     }
 
-    size_t get_params_buffer_size() {
+    size_t get_params_buffer_size() override {
         return flux.get_params_buffer_size();
     }
 
-    int64_t get_adm_in_channels() {
+    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+        flux.set_weight_adapter(adapter);
+    }
+
+    int64_t get_adm_in_channels() override {
         return 768;
     }
 
-    void compute(int n_threads,
-                 struct ggml_tensor* x,
-                 struct ggml_tensor* timesteps,
-                 struct ggml_tensor* context,
-                 struct ggml_tensor* c_concat,
-                 struct ggml_tensor* y,
-                 struct ggml_tensor* guidance,
-                 int num_video_frames                      = -1,
-                 std::vector<struct ggml_tensor*> controls = {},
-                 float control_strength                    = 0.f,
-                 struct ggml_tensor** output               = NULL,
-                 struct ggml_context* output_ctx           = NULL) {
-        return flux.compute(n_threads, x, timesteps, context, y, guidance, output, output_ctx);
+    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);
+    }
+
+    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);
     }
 };
 
-#endif
+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_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);
+    }
+
+    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,
+                   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, 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_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);
+    }
+
+    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_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);
+    }
+
+    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

docs/build.md

@@ -0,0 +1,173 @@
+# Build from scratch
+
+## Get the Code
+
+```
+git clone --recursive https://github.com/leejet/stable-diffusion.cpp
+cd stable-diffusion.cpp
+```
+
+- If you have already cloned the repository, you can use the following command to update the repository to the latest code.
+
+```
+cd stable-diffusion.cpp
+git pull origin master
+git submodule init
+git submodule update
+```
+
+## Build (CPU only)
+
+If you don't have a GPU or CUDA installed, you can build a CPU-only version.
+
+```shell
+mkdir build && cd build
+cmake ..
+cmake --build . --config Release
+```
+
+## Build with OpenBLAS
+
+```shell
+mkdir build && cd build
+cmake .. -DGGML_OPENBLAS=ON
+cmake --build . --config Release
+```
+
+## Build with CUDA
+
+This provides GPU acceleration using NVIDIA GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). Recommended to have at least 4 GB of VRAM.
+
+```shell
+mkdir build && cd build
+cmake .. -DSD_CUDA=ON
+cmake --build . --config Release
+```
+
+## Build with HipBLAS
+
+This provides GPU acceleration using AMD GPU. Make sure to have the ROCm toolkit installed.
+To build for another GPU architecture than installed in your system, set `$GFX_NAME` manually to the desired architecture (replace first command). This is also necessary if your GPU is not officially supported by ROCm, for example you have to set `$GFX_NAME` manually to `gfx1030` for consumer RDNA2 cards.
+
+Windows User Refer to [docs/hipBLAS_on_Windows.md](docs%2FhipBLAS_on_Windows.md) for a comprehensive guide.
+
+```shell
+mkdir build && cd build
+if command -v rocminfo; then export GFX_NAME=$(rocminfo | awk '/ *Name: +gfx[1-9]/ {print $2; exit}'); else echo "rocminfo missing!"; fi
+if [ -z "${GFX_NAME}" ]; then echo "Error: Couldn't detect GPU!"; else echo "Building for GPU: ${GFX_NAME}"; fi
+cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DGPU_TARGETS=$GFX_NAME -DAMDGPU_TARGETS=$GFX_NAME -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON -DCMAKE_POSITION_INDEPENDENT_CODE=ON
+cmake --build . --config Release
+```
+
+## Build with MUSA
+
+This provides GPU acceleration using Moore Threads GPU. Make sure to have the MUSA toolkit installed.
+
+```shell
+mkdir build && cd build
+cmake .. -DCMAKE_C_COMPILER=/usr/local/musa/bin/clang -DCMAKE_CXX_COMPILER=/usr/local/musa/bin/clang++ -DSD_MUSA=ON -DCMAKE_BUILD_TYPE=Release
+cmake --build . --config Release
+```
+
+## Build with Metal
+
+Using Metal makes the computation run on the GPU. Currently, there are some issues with Metal when performing operations on very large matrices, making it highly inefficient at the moment. Performance improvements are expected in the near future.
+
+```shell
+mkdir build && cd build
+cmake .. -DSD_METAL=ON
+cmake --build . --config Release
+```
+
+## Build with Vulkan
+
+Install Vulkan SDK from https://www.lunarg.com/vulkan-sdk/.
+
+```shell
+mkdir build && cd build
+cmake .. -DSD_VULKAN=ON
+cmake --build . --config Release
+```
+
+## Build with OpenCL (for Adreno GPU)
+
+Currently, it supports only Adreno GPUs and is primarily optimized for Q4_0 type
+
+To build for Windows ARM please refers to [Windows 11 Arm64](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/OPENCL.md#windows-11-arm64)
+
+Building for Android:
+
+  Android NDK:
+       Download and install the Android NDK from the [official Android developer site](https://developer.android.com/ndk/downloads).
+
+Setup OpenCL Dependencies for NDK:
+
+You need to provide OpenCL headers and the ICD loader library to your NDK sysroot.
+
+*   OpenCL Headers:
+    ```bash
+    # In a temporary working directory
+    git clone https://github.com/KhronosGroup/OpenCL-Headers
+    cd OpenCL-Headers
+    # Replace <YOUR_NDK_PATH> with your actual NDK installation path
+    # e.g., cp -r CL /path/to/android-ndk-r26c/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
+    sudo cp -r CL <YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
+    cd ..
+    ```
+
+*   OpenCL ICD Loader:
+    ```shell
+    # In the same temporary working directory
+    git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader
+    cd OpenCL-ICD-Loader
+    mkdir build_ndk && cd build_ndk
+
+    # Replace <YOUR_NDK_PATH> in the CMAKE_TOOLCHAIN_FILE and OPENCL_ICD_LOADER_HEADERS_DIR
+    cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \
+      -DCMAKE_TOOLCHAIN_FILE=<YOUR_NDK_PATH>/build/cmake/android.toolchain.cmake \
+      -DOPENCL_ICD_LOADER_HEADERS_DIR=<YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \
+      -DANDROID_ABI=arm64-v8a \
+      -DANDROID_PLATFORM=24 \
+      -DANDROID_STL=c++_shared
+
+    ninja
+    # Replace <YOUR_NDK_PATH>
+    # e.g., cp libOpenCL.so /path/to/android-ndk-r26c/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
+    sudo cp libOpenCL.so <YOUR_NDK_PATH>/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
+    cd ../..
+    ```
+
+Build `stable-diffusion.cpp` for Android with OpenCL:
+
+```shell
+mkdir build-android && cd build-android
+
+# Replace <YOUR_NDK_PATH> with your actual NDK installation path
+# e.g., -DCMAKE_TOOLCHAIN_FILE=/path/to/android-ndk-r26c/build/cmake/android.toolchain.cmake
+cmake .. -G Ninja \
+  -DCMAKE_TOOLCHAIN_FILE=<YOUR_NDK_PATH>/build/cmake/android.toolchain.cmake \
+  -DANDROID_ABI=arm64-v8a \
+  -DANDROID_PLATFORM=android-28 \
+  -DGGML_OPENMP=OFF \
+  -DSD_OPENCL=ON
+
+ninja
+```
+*(Note: Don't forget to include `LD_LIBRARY_PATH=/vendor/lib64` in your command line before running the binary)*
+
+## Build with SYCL
+
+Using SYCL makes the computation run on the Intel GPU. Please make sure you have installed the related driver and [Intel® oneAPI Base toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) before start. More details and steps can refer to [llama.cpp SYCL backend](https://github.com/ggml-org/llama.cpp/blob/master/docs/backend/SYCL.md#linux).
+
+```shell
+# Export relevant ENV variables
+source /opt/intel/oneapi/setvars.sh
+
+# Option 1: Use FP32 (recommended for better performance in most cases)
+cmake .. -DSD_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+
+# Option 2: Use FP16
+cmake .. -DSD_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
+
+cmake --build . --config Release
+```

docs/caching.md

@@ -0,0 +1,126 @@
+## 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 |
+
+### 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 --cache-preset fast
+```
+
+#### 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 |
+
+#### Presets
+
+Available presets: `slow`, `medium`, `fast`, `ultra` (or `s`, `m`, `f`, `u`).
+
+```bash
+--cache-mode cache-dit --cache-preset fast
+```
+
+#### 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
+```
+
+### 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

docs/chroma.md

@@ -0,0 +1,33 @@
+# How to Use
+
+You can run Chroma using stable-diffusion.cpp with a GPU that has 6GB or even 4GB of VRAM, without needing to offload to RAM.
+
+## Download weights
+
+- Download Chroma
+    - If you don't want to do the conversion yourself, download the preconverted gguf model from [silveroxides/Chroma-GGUF](https://huggingface.co/silveroxides/Chroma-GGUF)
+    - Otherwise, download chroma's safetensors from [lodestones/Chroma](https://huggingface.co/lodestones/Chroma)
+- Download vae from https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/ae.safetensors
+- Download t5xxl from https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp16.safetensors
+
+## Convert Chroma weights
+
+You can download the preconverted gguf weights from [silveroxides/Chroma-GGUF](https://huggingface.co/silveroxides/Chroma-GGUF), this way you don't have to do the conversion yourself.
+
+```
+.\bin\Release\sd-cli.exe -M convert -m ..\..\ComfyUI\models\unet\chroma-unlocked-v40.safetensors -o ..\models\chroma-unlocked-v40-q8_0.gguf -v --type q8_0
+```
+
+## Run
+
+### Example
+For example:
+
+```
+ .\bin\Release\sd-cli.exe --diffusion-model  ..\models\chroma-unlocked-v40-q8_0.gguf --vae ..\models\ae.sft --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'chroma.cpp'" --cfg-scale 4.0 --sampling-method euler -v --chroma-disable-dit-mask --clip-on-cpu
+```
+
+![](../assets/flux/chroma_v40.png)
+
+
+

docs/chroma_radiance.md

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

docs/distilled_sd.md

@@ -0,0 +1,137 @@
+# 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 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
+
+Note that not all of these models follow the standard parameter naming conventions. However, several useful SSD-1B models are available online, such as:
+
+ * https://huggingface.co/segmind/SSD-1B/resolve/main/SSD-1B-A1111.safetensors
+ * https://huggingface.co/hassenhamdi/SSD-1B-fp8_e4m3fn/resolve/main/SSD-1B_fp8_e4m3fn.safetensors
+
+Useful LoRAs are also available:
+
+ * https://huggingface.co/seungminh/lora-swarovski-SSD-1B/resolve/main/pytorch_lora_weights.safetensors
+ * https://huggingface.co/kylielee505/mylcmlorassd/resolve/main/pytorch_lora_weights.safetensors
+
+## 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
+
+These models require conversion before use. You will need a Python script provided by the diffusers team, available on GitHub:
+
+ * https://raw.githubusercontent.com/huggingface/diffusers/refs/heads/main/scripts/convert_diffusers_to_original_stable_diffusion.py
+
+### SD2.x
+
+NotaAI provides the following model online:
+
+* https://huggingface.co/nota-ai/bk-sdm-v2-tiny
+
+Creating a .safetensors file involves two steps. First, run this short Python script to download the model from Hugging Face:
+
+```python
+from diffusers import StableDiffusionPipeline
+pipe = StableDiffusionPipeline.from_pretrained("nota-ai/bk-sdm-v2-tiny",cache_dir="./")
+```
+
+Second, create the .safetensors file by running:
+
+```bash
+python convert_diffusers_to_original_stable_diffusion.py \
+      --model_path  models--nota-ai--bk-sdm-v2-tiny/snapshots/68277af553777858cd47e133f92e4db47321bc74 \
+      --checkpoint_path bk-sdm-v2-tiny.safetensors --half --use_safetensors
+```
+
+This will generate the **file bk-sdm-v2-tiny.safetensors**, which is now ready for use with sd.cpp.
+
+### SD1.x
+
+Several Tiny SD 1.x models are available online, such as:
+
+ * https://huggingface.co/segmind/tiny-sd
+ * https://huggingface.co/segmind/portrait-finetuned
+ * https://huggingface.co/nota-ai/bk-sdm-tiny
+
+These models also require conversion, partly because some tensors are stored in a non-contiguous manner. To create a usable checkpoint file, follow these simple steps:
+Download and prepare the model using Python: 
+
+##### Download the model using Python on your computer, for example this way:
+
+```python
+import torch
+from diffusers import StableDiffusionPipeline
+pipe = StableDiffusionPipeline.from_pretrained("segmind/tiny-sd")
+unet=pipe.unet
+for param in unet.parameters():
+    param.data = param.data.contiguous()     # <- important here
+pipe.save_pretrained("segmindtiny-sd", safe_serialization=True)
+```
+
+##### Run the conversion script:
+
+```bash
+python convert_diffusers_to_original_stable_diffusion.py \
+      --model_path  ./segmindtiny-sd \
+      --checkpoint_path ./segmind_tiny-sd.ckpt --half
+```
+
+The file segmind_tiny-sd.ckpt will be generated and is now ready for use with sd.cpp. You can follow a similar process for the other models mentioned above.
+
+
+##### Another available .ckpt file:
+
+ * https://huggingface.co/ClashSAN/small-sd/resolve/main/tinySDdistilled.ckpt
+
+To use this file, you must first adjust its non-contiguous tensors:
+
+```python
+import torch
+ckpt = torch.load("tinySDdistilled.ckpt", map_location=torch.device('cpu'))
+for key, value in ckpt['state_dict'].items():
+    if isinstance(value, torch.Tensor):
+        ckpt['state_dict'][key] = value.contiguous()
+torch.save(ckpt, "tinySDdistilled_fixed.ckpt")
+```
+
+
+### 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.                                                 

docs/docker.md

@@ -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 [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 -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
+```

docs/esrgan.md

@@ -1,9 +1,9 @@
 ## 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:
 
 ```bash
-sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat" --upscale-model ../models/RealESRGAN_x4plus_anime_6B.pth
+sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat" --upscale-model ../models/RealESRGAN_x4plus_anime_6B.pth
 ```

docs/flux.md

@@ -15,9 +15,9 @@ You can run Flux using stable-diffusion.cpp with a GPU that has 6GB or even 4GB
 
 You can download the preconverted gguf weights from [FLUX.1-dev-gguf](https://huggingface.co/leejet/FLUX.1-dev-gguf) or [FLUX.1-schnell](https://huggingface.co/leejet/FLUX.1-schnell-gguf), this way you don't have to do the conversion yourself.
 
-Using fp16 will lead to overflow, but ggml's support for bf16 is not yet fully developed. Therefore, we need to convert flux to gguf format here, which also saves VRAM. For example:
+For example:
 ```
-.\bin\Release\sd.exe -M convert -m ..\..\ComfyUI\models\unet\flux1-dev.sft -o ..\models\flux1-dev-q8_0.gguf -v --type q8_0
+.\bin\Release\sd-cli.exe -M convert -m ..\..\ComfyUI\models\unet\flux1-dev.sft -o ..\models\flux1-dev-q8_0.gguf -v --type q8_0
 ```
 
 ## Run
@@ -28,7 +28,7 @@ Using fp16 will lead to overflow, but ggml's support for bf16 is not yet fully d
 For example:
 
 ```
- .\bin\Release\sd.exe --diffusion-model  ..\models\flux1-dev-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'" --cfg-scale 1.0 --sampling-method euler -v
+ .\bin\Release\sd-cli.exe --diffusion-model  ..\models\flux1-dev-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'" --cfg-scale 1.0 --sampling-method euler -v --clip-on-cpu
 ```
 
 Using formats of different precisions will yield results of varying quality.
@@ -44,7 +44,7 @@ Using formats of different precisions will yield results of varying quality.
 
 
 ```
- .\bin\Release\sd.exe --diffusion-model  ..\models\flux1-schnell-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'" --cfg-scale 1.0 --sampling-method euler -v --steps 4
+ .\bin\Release\sd-cli.exe --diffusion-model  ..\models\flux1-schnell-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'" --cfg-scale 1.0 --sampling-method euler -v --steps 4 --clip-on-cpu
 ```
 
 | q8_0  |
@@ -60,7 +60,7 @@ Since many flux LoRA training libraries have used various LoRA naming formats, i
 - LoRA model from https://huggingface.co/XLabs-AI/flux-lora-collection/tree/main (using comfy converted version!!!)
 
 ```
-.\bin\Release\sd.exe --diffusion-model  ..\models\flux1-dev-q8_0.gguf --vae ...\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'<lora:realism_lora_comfy_converted:1>" --cfg-scale 1.0 --sampling-method euler -v --lora-model-dir ../models
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\flux1-dev-q8_0.gguf --vae ...\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'<lora:realism_lora_comfy_converted:1>" --cfg-scale 1.0 --sampling-method euler -v --lora-model-dir ../models --clip-on-cpu
 ```
 
 ![output](../assets/flux/flux1-dev-q8_0%20with%20lora.png)

docs/flux2.md

@@ -0,0 +1,92 @@
+# How to Use
+
+## 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 Mistral-Small-3.2-24B-Instruct-2506-GGUF
+    - gguf: https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF/tree/main
+
+### Examples
+
+```
+.\bin\Release\sd-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
+```
+
+<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 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" />

docs/hipBLAS_on_Windows.md

@@ -82,4 +82,4 @@ cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_H
 cmake --build . --config Release
 ```
 
-If everything went OK, `build\bin\sd.exe` file should appear.
+If everything went OK, `build\bin\sd-cli.exe` file should appear.

docs/kontext.md

@@ -0,0 +1,39 @@
+# How to Use
+
+You can run Kontext using stable-diffusion.cpp with a GPU that has 6GB or even 4GB of VRAM, without needing to offload to RAM.
+
+## Download weights
+
+- Download Kontext
+    - If you don't want to do the conversion yourself, download the preconverted gguf model from [FLUX.1-Kontext-dev-GGUF](https://huggingface.co/QuantStack/FLUX.1-Kontext-dev-GGUF)
+    - Otherwise, download FLUX.1-Kontext-dev from https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev/blob/main/flux1-kontext-dev.safetensors
+- Download vae from https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/ae.safetensors
+- Download clip_l from https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/clip_l.safetensors
+- Download t5xxl from https://huggingface.co/comfyanonymous/flux_text_encoders/blob/main/t5xxl_fp16.safetensors
+
+## Convert Kontext weights
+
+You can download the preconverted gguf weights from [FLUX.1-Kontext-dev-GGUF](https://huggingface.co/QuantStack/FLUX.1-Kontext-dev-GGUF), this way you don't have to do the conversion yourself.
+
+```
+.\bin\Release\sd-cli.exe -M convert -m ..\..\ComfyUI\models\unet\flux1-kontext-dev.safetensors -o ..\models\flux1-kontext-dev-q8_0.gguf -v --type q8_0
+```
+
+## Run
+
+- `--cfg-scale` is recommended to be set to 1. 
+
+### Example
+For example:
+
+```
+ .\bin\Release\sd-cli.exe -r .\flux1-dev-q8_0.png --diffusion-model  ..\models\flux1-kontext-dev-q8_0.gguf --vae ..\models\ae.sft --clip_l ..\models\clip_l.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors -p "change 'flux.cpp' to 'kontext.cpp'" --cfg-scale 1.0 --sampling-method euler -v --clip-on-cpu
+```
+
+
+| ref_image | prompt  | output  |
+| ---- | ----  |----  |
+| ![](../assets/flux/flux1-dev-q8_0.png) | change 'flux.cpp' to 'kontext.cpp' |![](../assets/flux/kontext1_dev_output.png) |
+
+
+

docs/lcm.md

@@ -7,7 +7,7 @@
 Here's a simple example:
 
 ```
-./bin/sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat<lora:lcm-lora-sdv1-5:1>" --steps 4 --lora-model-dir ../models -v --cfg-scale 1
+./bin/sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat<lora:lcm-lora-sdv1-5:1>" --steps 4 --lora-model-dir ../models -v --cfg-scale 1
 ```
 
 | without LCM-LoRA (--cfg-scale 7)  | with LCM-LoRA (--cfg-scale 1)  |

docs/lora.md

@@ -7,7 +7,20 @@
 Here's a simple example:
 
 ```
-./bin/sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat<lora:marblesh:1>" --lora-model-dir ../models
+./bin/sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat<lora:marblesh:1>" --lora-model-dir ../models
 ```
 
-`../models/marblesh.safetensors` or `../models/marblesh.ckpt` will be applied to the model
+`../models/marblesh.safetensors` or `../models/marblesh.ckpt` will be applied to the model
+
+# Lora Apply Mode
+
+There are two ways to apply LoRA: **immediately** and **at_runtime**. You can specify it using the `--lora-apply-mode` parameter.
+
+By default, the mode is selected automatically:
+
+* If the model weights contain any quantized parameters, the **at_runtime** mode is used;
+* Otherwise, the **immediately** mode is used.
+
+The **immediately** mode may have precision and compatibility issues with quantized parameters, but it usually offers faster inference speed and, in some cases, lower memory usage.
+In contrast, the **at_runtime** mode provides better compatibility and higher precision, but inference may be slower and memory usage may be higher in some cases.
+

docs/ovis_image.md

@@ -0,0 +1,19 @@
+# How to Use
+
+## Download weights
+
+- Download Ovis-Image-7B
+    - safetensors: https://huggingface.co/Comfy-Org/Ovis-Image/tree/main/split_files/diffusion_models
+    - gguf: https://huggingface.co/leejet/Ovis-Image-7B-GGUF
+- Download vae
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.1-schnell/tree/main
+- Download Ovis 2.5
+    - safetensors: https://huggingface.co/Comfy-Org/Ovis-Image/tree/main/split_files/text_encoders
+
+## Examples
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ovis_image-Q4_0.gguf --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\ovis_2.5.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa
+```
+
+<img alt="ovis image example" src="../assets/ovis_image/example.png" />

docs/performance.md

@@ -0,0 +1,26 @@
+## Use Flash Attention to save memory and improve speed.
+
+Enabling flash attention for the diffusion model reduces memory usage by varying amounts of MB.
+eg.:
+ - flux 768x768 ~600mb
+ - SD2 768x768 ~1400mb
+
+For most backends, it slows things down, but for cuda it generally speeds it up too.
+At the moment, it is only supported for some models and some backends (like cpu, cuda/rocm, metal).
+
+Run by adding `--diffusion-fa` to the arguments and watch for:
+```
+[INFO ] stable-diffusion.cpp:312  - Using flash attention in the diffusion model
+```
+and the compute buffer shrink in the debug log:
+```
+[DEBUG] ggml_extend.hpp:1004 - flux compute buffer size: 650.00 MB(VRAM)
+```
+
+## Offload weights to the CPU to save VRAM without reducing generation speed.
+
+Using `--offload-to-cpu` allows you to offload weights to the CPU, saving VRAM without reducing generation speed.
+
+## Use quantization to reduce memory usage.
+
+[quantization](./quantization_and_gguf.md)

docs/photo_maker.md

@@ -6,16 +6,15 @@ You can use [PhotoMaker](https://github.com/TencentARC/PhotoMaker) to personaliz
 
 Download PhotoMaker model file (in safetensor format) [here](https://huggingface.co/bssrdf/PhotoMaker). The official release of the model file (in .bin format) does not work with ```stablediffusion.cpp```.
 
-- Specify the PhotoMaker model path using the `--stacked-id-embd-dir PATH` parameter.
-- Specify the input images path using the `--input-id-images-dir PATH` parameter.
-  - input images **must** have the same width and height for preprocessing (to be improved)
+- Specify the PhotoMaker model path using the `--photo-maker PATH` parameter.
+- Specify the input images path using the `--pm-id-images-dir PATH` parameter.
 
 In prompt, make sure you have a class word followed by the trigger word ```"img"``` (hard-coded for now). The class word could be one of ```"man, woman, girl, boy"```. If input ID images contain asian faces, add ```Asian``` before the class
 word.
 
 Another PhotoMaker specific parameter:
 
-- ```--style-ratio  (0-100)%```: default is 20 and 10-20 typically gets good results. Lower ratio means more faithfully following input ID (not necessarily better quality).
+- ```--pm-style-strength  (0-100)%```: default is 20 and 10-20 typically gets good results. Lower ratio means more faithfully following input ID (not necessarily better quality).
 
 Other parameters recommended for running Photomaker:
 
@@ -28,5 +27,27 @@ If on low memory GPUs (<= 8GB), recommend running with ```--vae-on-cpu``` option
 Example:
 
 ```bash
-bin/sd -m ../models/sdxlUnstableDiffusers_v11.safetensors  --vae ../models/sdxl_vae.safetensors --stacked-id-embd-dir ../models/photomaker-v1.safetensors --input-id-images-dir ../assets/photomaker_examples/scarletthead_woman -p "a girl img, retro futurism, retro game art style but extremely beautiful, intricate details, masterpiece, best quality, space-themed, cosmic, celestial, stars, galaxies, nebulas, planets, science fiction, highly detailed" -n "realistic, photo-realistic, worst quality, greyscale, bad anatomy, bad hands, error, text" --cfg-scale 5.0  --sampling-method euler -H 1024 -W 1024 --style-ratio 10 --vae-on-cpu -o output.png
-```
+bin/sd-cli -m ../models/sdxlUnstableDiffusers_v11.safetensors  --vae ../models/sdxl_vae.safetensors --photo-maker ../models/photomaker-v1.safetensors --pm-id-images-dir ../assets/photomaker_examples/scarletthead_woman -p "a girl img, retro futurism, retro game art style but extremely beautiful, intricate details, masterpiece, best quality, space-themed, cosmic, celestial, stars, galaxies, nebulas, planets, science fiction, highly detailed" -n "realistic, photo-realistic, worst quality, greyscale, bad anatomy, bad hands, error, text" --cfg-scale 5.0  --sampling-method euler -H 1024 -W 1024 --pm-style-strength 10 --vae-on-cpu --steps 50
+```
+
+## PhotoMaker Version 2
+
+[PhotoMaker Version 2 (PMV2)](https://github.com/TencentARC/PhotoMaker/blob/main/README_pmv2.md) has some key improvements. Unfortunately it has a very heavy dependency which makes running it a bit involved in ```SD.cpp```. 
+
+Running PMV2 is now a two-step process:
+
+- Run a python script ```face_detect.py``` to obtain **id_embeds** for the given input images
+```
+python face_detect.py input_image_dir
+```
+An ```id_embeds.bin``` file will be generated in ```input_images_dir```
+
+**Note: this step is only needed to run once; the same ```id_embeds``` can be reused**
+
+- Run the same command as in version 1 but replacing ```photomaker-v1.safetensors``` with ```photomaker-v2.safetensors```.
+
+  You can download ```photomaker-v2.safetensors``` from [here](https://huggingface.co/bssrdf/PhotoMakerV2)
+
+- All the command line parameters from Version 1 remain the same for Version 2 plus one extra pointing to a valid ```id_embeds``` file:  --pm-id-embed-path [path_to__id_embeds.bin] 
+
+

docs/quantization_and_gguf.md

@@ -23,5 +23,5 @@ You can also convert weights in the formats `ckpt/safetensors/diffusers` to gguf
 For example:
 
 ```sh
-./bin/sd -M convert -m ../models/v1-5-pruned-emaonly.safetensors -o  ../models/v1-5-pruned-emaonly.q8_0.gguf -v --type q8_0
+./bin/sd-cli -M convert -m ../models/v1-5-pruned-emaonly.safetensors -o  ../models/v1-5-pruned-emaonly.q8_0.gguf -v --type q8_0
 ```

docs/qwen_image.md

@@ -0,0 +1,23 @@
+# How to Use
+
+## Download weights
+
+- Download Qwen Image
+    - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/diffusion_models
+    - gguf: https://huggingface.co/QuantStack/Qwen-Image-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
+    - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders
+    - gguf: https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-GGUF/tree/main
+
+## Examples
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\qwen-image-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf  -p '一个穿着"QWEN"标志的T恤的中国美女正拿着黑色的马克笔面相镜头微笑。她身后的玻璃板上手写体写着 “一、Qwen-Image的技术路线： 探索视觉生成基础模型的极限，开创理解与生成一体化的未来。二、Qwen-Image的模型特色：1、复杂文字渲染。支持中英渲染、自动布局； 2、精准图像编辑。支持文字编辑、物体增减、风格变换。三、Qwen-Image的未来愿景：赋能专业内容创作、助力生成式AI发展。”' --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu -H 1024 -W 1024 --diffusion-fa --flow-shift 3
+```
+
+<img alt="qwen example" src="../assets/qwen/example.png" />
+
+
+

docs/qwen_image_edit.md

@@ -0,0 +1,48 @@
+# How to Use
+
+## Download weights
+
+- Download Qwen Image
+    - Qwen Image Edit
+        - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image-Edit_ComfyUI/tree/main/split_files/diffusion_models
+        - gguf: https://huggingface.co/QuantStack/Qwen-Image-Edit-GGUF/tree/main
+    - 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
+    - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders
+    - gguf: https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-GGUF/tree/main
+
+## Examples
+
+### Qwen Image Edit
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Qwen_Image_Edit-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\qwen_image_vae.safetensors  --llm ..\..\ComfyUI\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --diffusion-fa --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --seed 1118877715456453
+```
+
+<img alt="qwen_image_edit" src="../assets/qwen/qwen_image_edit.png" />
+
+
+### Qwen Image Edit 2509
+
+```
+.\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" />
+
+### 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" />

docs/sd.md

@@ -0,0 +1,37 @@
+## Download weights
+
+- download original weights(.ckpt or .safetensors). For example
+    - Stable Diffusion v1.4 from https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
+    - Stable Diffusion v1.5 from https://huggingface.co/runwayml/stable-diffusion-v1-5
+    - Stable Diffuison v2.1 from https://huggingface.co/stabilityai/stable-diffusion-2-1
+    - Stable Diffusion 3 2B from https://huggingface.co/stabilityai/stable-diffusion-3-medium
+
+### txt2img example
+
+```sh
+./bin/sd-cli -m ../models/sd-v1-4.ckpt -p "a lovely cat"
+# ./bin/sd-cli -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat"
+# ./bin/sd-cli -m ../models/sd_xl_base_1.0.safetensors --vae ../models/sdxl_vae-fp16-fix.safetensors -H 1024 -W 1024 -p "a lovely cat" -v
+# ./bin/sd-cli -m ../models/sd3_medium_incl_clips_t5xxlfp16.safetensors -H 1024 -W 1024 -p 'a lovely cat holding a sign says \"Stable Diffusion CPP\"' --cfg-scale 4.5 --sampling-method euler -v --clip-on-cpu
+# ./bin/sd-cli --diffusion-model  ../models/flux1-dev-q3_k.gguf --vae ../models/ae.sft --clip_l ../models/clip_l.safetensors --t5xxl ../models/t5xxl_fp16.safetensors  -p "a lovely cat holding a sign says 'flux.cpp'" --cfg-scale 1.0 --sampling-method euler -v --clip-on-cpu
+# ./bin/sd-cli -m  ..\models\sd3.5_large.safetensors --clip_l ..\models\clip_l.safetensors --clip_g ..\models\clip_g.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -H 1024 -W 1024 -p 'a lovely cat holding a sign says \"Stable diffusion 3.5 Large\"' --cfg-scale 4.5 --sampling-method euler -v --clip-on-cpu
+```
+
+Using formats of different precisions will yield results of varying quality.
+
+| f32  | f16  |q8_0  |q5_0  |q5_1  |q4_0  |q4_1  |
+| ----  |----  |----  |----  |----  |----  |----  |
+| ![](../assets/f32.png) |![](../assets/f16.png) |![](../assets/q8_0.png) |![](../assets/q5_0.png) |![](../assets/q5_1.png) |![](../assets/q4_0.png) |![](../assets/q4_1.png) |
+
+### img2img example
+
+- `./output.png` is the image generated from the above txt2img pipeline
+
+
+```
+./bin/sd-cli -m ../models/sd-v1-4.ckpt -p "cat with blue eyes" -i ./output.png -o ./img2img_output.png --strength 0.4
+```
+
+<p align="center">
+  <img src="../assets/img2img_output.png" width="256x">
+</p>

docs/sd3.md

@@ -0,0 +1,20 @@
+# How to Use
+
+## Download weights
+
+- Download sd3.5_large from https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/sd3.5_large.safetensors
+- Download clip_g from https://huggingface.co/Comfy-Org/stable-diffusion-3.5-fp8/blob/main/text_encoders/clip_g.safetensors
+- Download clip_l from https://huggingface.co/Comfy-Org/stable-diffusion-3.5-fp8/blob/main/text_encoders/clip_l.safetensors
+- Download t5xxl from https://huggingface.co/Comfy-Org/stable-diffusion-3.5-fp8/blob/main/text_encoders/t5xxl_fp16.safetensors
+
+
+## Run
+
+### SD3.5 Large
+For example:
+
+```
+.\bin\Release\sd-cli.exe -m  ..\models\sd3.5_large.safetensors --clip_l ..\models\clip_l.safetensors --clip_g ..\models\clip_g.safetensors --t5xxl ..\models\t5xxl_fp16.safetensors  -H 1024 -W 1024 -p 'a lovely cat holding a sign says \"Stable diffusion 3.5 Large\"' --cfg-scale 4.5 --sampling-method euler -v --clip-on-cpu
+```
+
+![](../assets/sd3.5_large.png)

docs/taesd.md

@@ -7,11 +7,33 @@ You can use TAESD to accelerate the decoding of latent images by following these
 Or curl
 
 ```bash
-curl -L -O https://huggingface.co/madebyollin/taesd/blob/main/diffusion_pytorch_model.safetensors
+curl -L -O https://huggingface.co/madebyollin/taesd/resolve/main/diffusion_pytorch_model.safetensors
 ```
 
 - Specify the model path using the `--taesd PATH` parameter. example:
 
 ```bash
-sd -m ../models/v1-5-pruned-emaonly.safetensors -p "a lovely cat" --taesd ../models/diffusion_pytorch_model.safetensors
-```
+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.

docs/wan.md

@@ -0,0 +1,207 @@
+# How to Use
+
+## Download weights
+
+- Download Wan
+    - Wan2.1
+        - Wan2.1 T2V 1.3B
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
+        - Wan2.1 T2V 14B
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/city96/Wan2.1-T2V-14B-gguf/tree/main
+        - Wan2.1 I2V 14B 480P
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/city96/Wan2.1-I2V-14B-480P-gguf/tree/main
+        - Wan2.1 I2V 14B 720P
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/city96/Wan2.1-I2V-14B-720P-gguf/tree/main
+        - Wan2.1 FLF2V 14B 720P
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/city96/Wan2.1-FLF2V-14B-720P-gguf/tree/main
+        - Wan2.1 VACE 1.3B
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/calcuis/wan-1.3b-gguf/tree/main
+        - Wan2.1 VACE 14B
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/QuantStack/Wan2.1_14B_VACE-GGUF/tree/main
+    - Wan2.2
+        - Wan2.2 TI2V 5B
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/QuantStack/Wan2.2-TI2V-5B-GGUF/tree/main
+        - Wan2.2 T2V A14B
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/QuantStack/Wan2.2-T2V-A14B-GGUF/tree/main
+        - Wan2.2 I2V A14B
+            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/tree/main/split_files/diffusion_models
+            - gguf: https://huggingface.co/QuantStack/Wan2.2-I2V-A14B-GGUF/tree/main
+- Download vae
+    - wan_2.1_vae (for all the wan model except Wan2.2 TI2V 5B)
+        - 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
+
+- Download clip_vison_h (for Wan2.1 I2V/FLF2V only)
+    - safetensors: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/clip_vision/clip_vision_h.safetensors
+
+
+## Examples
+
+### Wan2.1 T2V 1.3B
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1_t2v_1.3B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --video-frames 33 --flow-shift 3.0
+```
+
+<video src=../assets/wan/Wan2.1_1.3B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.1 T2V 14B
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-t2v-14b-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa  --offload-to-cpu --video-frames 33 --flow-shift 3.0
+```
+
+<video src=../assets/wan/Wan2.1_14B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+
+
+### Wan2.1 I2V 14B
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-i2v-14b-480p-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\..\ComfyUI\models\clip_vision\clip_vision_h.safetensors -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+```
+
+<video src=../assets/wan/Wan2.1_14B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.2 T2V A14B
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --video-frames 33 --flow-shift 3.0
+```
+
+<video src=../assets/wan/Wan2.2_14B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.2 I2V A14B
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+```
+
+<video src=../assets/wan/Wan2.2_14B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.2 T2V A14B T2I
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --flow-shift 3.0
+```
+
+<img width="832" height="480" alt="Wan2 2_14B_t2i" src="../assets/wan/Wan2.2_14B_t2i.png" />
+
+### Wan2.2 T2V 14B with Lora
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat<lora:wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise:1><lora:|high_noise|wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise:1>" --cfg-scale 3.5 --sampling-method euler --steps 4 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 4 -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --offload-to-cpu --lora-model-dir ..\..\ComfyUI\models\loras --video-frames 33 --flow-shift 3.0
+```
+
+<video src=../assets/wan/Wan2.2_14B_t2v_lora.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+
+
+### Wan2.2 TI2V 5B
+
+#### T2V
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan2.2_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --offload-to-cpu --video-frames 33 --flow-shift 3.0
+```
+
+<video src=../assets/wan/Wan2.2_5B_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+#### I2V
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\..\ComfyUI\models\vae\wan2.2_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+```
+
+<video src=../assets/wan/Wan2.2_5B_i2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.1 FLF2V 14B
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-flf2v-14b-720p-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\..\ComfyUI\models\clip_vision\clip_vision_h.safetensors -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
+```
+
+
+<video src=../assets/wan/Wan2.1_14B_flf2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.2 FLF2V 14B
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --cfg-scale 3.5 --sampling-method euler --steps 10 --high-noise-cfg-scale 3.5 --high-noise-sampling-method euler --high-noise-steps 8 -v -p "glass flower blossom" -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
+```
+
+<video src=../assets/wan/Wan2.2_14B_flf2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.1 VACE 1.3B
+
+#### T2V
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --video-frames 1 --offload-to-cpu
+```
+
+<video src=../assets/wan/Wan2.1_1.3B_vace_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+
+#### R2V
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
+```
+
+<video src=../assets/wan/Wan2.1_1.3B_vace_r2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+
+#### V2V
+
+```
+mkdir post+depth
+ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\frame_%04d.jpg
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
+```
+
+<video src=../assets/wan/Wan2.1_1.3B_vace_v2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+### Wan2.1 VACE 14B
+
+#### T2V
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa --video-frames 33 --offload-to-cpu
+```
+
+<video src=../assets/wan/Wan2.1_14B_vace_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+
+#### R2V
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 832 -H 480 --diffusion-fa -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
+```
+
+<video src=../assets/wan/Wan2.1_14B_vace_r2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
+
+
+
+#### V2V
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\..\ComfyUI\models\vae\wan_2.1_vae.safetensors --t5xxl ..\..\ComfyUI\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "The girl is dancing in a sea of flowers, slowly moving her hands. There is a close - up shot of her upper body. The character is surrounded by other transparent glass flowers in the style of Nicoletta Ceccoli, creating a beautiful, surreal, and emotionally expressive movie scene with a white. transparent feel and a dreamyl atmosphere." --cfg-scale 6.0 --sampling-method euler -v -n "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部， 畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" -W 480 -H 832 --diffusion-fa -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
+```
+
+<video src=../assets/wan/Wan2.1_14B_vace_v2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>

docs/z_image.md

@@ -0,0 +1,41 @@
+# How to Use
+
+You can run Z-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — or even less.
+
+## Download weights
+
+- 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
+    - safetensors: https://huggingface.co/Comfy-Org/z_image_turbo/tree/main/split_files/text_encoders
+    - gguf: https://huggingface.co/unsloth/Qwen3-4B-Instruct-2507-GGUF/tree/main
+
+## 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|
+|---|---|---|---|---|---|---|---|
+| <img width="256" alt="bf16" src="../assets/z_image/bf16.png" /> | <img width="256" alt="q8_0" src="../assets/z_image/q8_0.png" /> | <img width="256" alt="q6_K" src="../assets/z_image/q6_K.png" /> | <img width="256" alt="q5_0" src="../assets/z_image/q5_0.png" />  | <img width="256" alt="q4_K" src="../assets/z_image/q4_K.png" /> | <img width="256" alt="q4_0" src="../assets/z_image/q4_0.png" /> | <img width="256" alt="q3_K" src="../assets/z_image/q3_K.png" /> | <img width="256" alt="q2_K" src="../assets/z_image/q2_K.png" /> |

easycache.hpp

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

esrgan.hpp

@@ -27,11 +27,11 @@ public:
         blocks["conv5"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat + 4 * num_grow_ch, num_feat, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* lrelu(struct ggml_context* ctx, struct ggml_tensor* x) {
-        return ggml_leaky_relu(ctx, x, 0.2f, true);
+    struct ggml_tensor* lrelu(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        return ggml_leaky_relu(ctx->ggml_ctx, x, 0.2f, true);
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [n, num_feat, h, w]
         // return: [n, num_feat, h, w]
 
@@ -42,16 +42,16 @@ public:
         auto conv5 = std::dynamic_pointer_cast<Conv2d>(blocks["conv5"]);
 
         auto x1    = lrelu(ctx, conv1->forward(ctx, x));
-        auto x_cat = ggml_concat(ctx, x, x1, 2);
+        auto x_cat = ggml_concat(ctx->ggml_ctx, x, x1, 2);
         auto x2    = lrelu(ctx, conv2->forward(ctx, x_cat));
-        x_cat      = ggml_concat(ctx, x_cat, x2, 2);
+        x_cat      = ggml_concat(ctx->ggml_ctx, x_cat, x2, 2);
         auto x3    = lrelu(ctx, conv3->forward(ctx, x_cat));
-        x_cat      = ggml_concat(ctx, x_cat, x3, 2);
+        x_cat      = ggml_concat(ctx->ggml_ctx, x_cat, x3, 2);
         auto x4    = lrelu(ctx, conv4->forward(ctx, x_cat));
-        x_cat      = ggml_concat(ctx, x_cat, x4, 2);
+        x_cat      = ggml_concat(ctx->ggml_ctx, x_cat, x4, 2);
         auto x5    = conv5->forward(ctx, x_cat);
 
-        x5 = ggml_add(ctx, ggml_scale(ctx, x5, 0.2f), x);
+        x5 = ggml_add(ctx->ggml_ctx, ggml_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(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [n, num_feat, h, w]
         // return: [n, num_feat, h, w]
 
@@ -76,46 +76,51 @@ public:
         out      = rdb2->forward(ctx, out);
         out      = rdb3->forward(ctx, out);
 
-        out = ggml_add(ctx, ggml_scale(ctx, out, 0.2f), x);
+        out = ggml_add(ctx->ggml_ctx, ggml_ext_scale(ctx->ggml_ctx, out, 0.2f), x);
         return out;
     }
 };
 
 class RRDBNet : public GGMLBlock {
 protected:
-    int scale       = 4;  // default RealESRGAN_x4plus_anime_6B
-    int num_block   = 6;  // default RealESRGAN_x4plus_anime_6B
+    int scale       = 4;
+    int num_block   = 23;
     int num_in_ch   = 3;
     int num_out_ch  = 3;
-    int num_feat    = 64;  // default RealESRGAN_x4plus_anime_6B
-    int num_grow_ch = 32;  // default RealESRGAN_x4plus_anime_6B
+    int num_feat    = 64;
+    int num_grow_ch = 32;
 
 public:
-    RRDBNet() {
+    RRDBNet(int scale, int num_block, int num_in_ch, int num_out_ch, int num_feat, int num_grow_ch)
+        : scale(scale), num_block(num_block), num_in_ch(num_in_ch), num_out_ch(num_out_ch), num_feat(num_feat), num_grow_ch(num_grow_ch) {
         blocks["conv_first"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_in_ch, num_feat, {3, 3}, {1, 1}, {1, 1}));
         for (int i = 0; i < num_block; i++) {
             std::string name = "body." + std::to_string(i);
             blocks[name]     = std::shared_ptr<GGMLBlock>(new RRDB(num_feat, num_grow_ch));
         }
         blocks["conv_body"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
-        // upsample
-        blocks["conv_up1"]  = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
-        blocks["conv_up2"]  = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
+        if (scale >= 2) {
+            blocks["conv_up1"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
+        }
+        if (scale == 4) {
+            blocks["conv_up2"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
+        }
         blocks["conv_hr"]   = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_feat, {3, 3}, {1, 1}, {1, 1}));
         blocks["conv_last"] = std::shared_ptr<GGMLBlock>(new Conv2d(num_feat, num_out_ch, {3, 3}, {1, 1}, {1, 1}));
     }
 
-    struct ggml_tensor* lrelu(struct ggml_context* ctx, struct ggml_tensor* x) {
-        return ggml_leaky_relu(ctx, x, 0.2f, true);
+    int get_scale() { return scale; }
+    int get_num_block() { return num_block; }
+
+    struct ggml_tensor* lrelu(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
+        return ggml_leaky_relu(ctx->ggml_ctx, x, 0.2f, true);
     }
 
-    struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
+    struct ggml_tensor* forward(GGMLRunnerContext* ctx, struct ggml_tensor* x) {
         // x: [n, num_in_ch, h, w]
-        // return: [n, num_out_ch, h*4, w*4]
+        // return: [n, num_out_ch, h*scale, w*scale]
         auto conv_first = std::dynamic_pointer_cast<Conv2d>(blocks["conv_first"]);
         auto conv_body  = std::dynamic_pointer_cast<Conv2d>(blocks["conv_body"]);
-        auto conv_up1   = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up1"]);
-        auto conv_up2   = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up2"]);
         auto conv_hr    = std::dynamic_pointer_cast<Conv2d>(blocks["conv_hr"]);
         auto conv_last  = std::dynamic_pointer_cast<Conv2d>(blocks["conv_last"]);
 
@@ -128,70 +133,235 @@ public:
             body_feat = block->forward(ctx, body_feat);
         }
         body_feat = conv_body->forward(ctx, body_feat);
-        feat      = ggml_add(ctx, feat, body_feat);
+        feat      = ggml_add(ctx->ggml_ctx, feat, body_feat);
         // upsample
-        feat     = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx, feat, 2)));
-        feat     = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx, feat, 2)));
+        if (scale >= 2) {
+            auto conv_up1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up1"]);
+            feat          = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx->ggml_ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
+            if (scale == 4) {
+                auto conv_up2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up2"]);
+                feat          = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx->ggml_ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
+            }
+        }
+        // for all scales
         auto out = conv_last->forward(ctx, lrelu(ctx, conv_hr->forward(ctx, feat)));
         return out;
     }
 };
 
 struct ESRGAN : public GGMLRunner {
-    RRDBNet rrdb_net;
+    std::unique_ptr<RRDBNet> rrdb_net;
     int scale     = 4;
     int tile_size = 128;  // avoid cuda OOM for 4gb VRAM
 
     ESRGAN(ggml_backend_t backend,
-           ggml_type wtype)
-        : GGMLRunner(backend, wtype) {
-        rrdb_net.init(params_ctx, wtype);
+           bool offload_params_to_cpu,
+           int tile_size                                  = 128,
+           const String2TensorStorage& tensor_storage_map = {})
+        : GGMLRunner(backend, offload_params_to_cpu) {
+        this->tile_size = tile_size;
     }
 
-    std::string get_desc() {
+    std::string get_desc() override {
         return "esrgan";
     }
 
-    bool load_from_file(const std::string& file_path) {
+    bool load_from_file(const std::string& file_path, int n_threads) {
         LOG_INFO("loading esrgan from '%s'", file_path.c_str());
 
-        alloc_params_buffer();
-        std::map<std::string, ggml_tensor*> esrgan_tensors;
-        rrdb_net.get_param_tensors(esrgan_tensors);
-
         ModelLoader model_loader;
-        if (!model_loader.init_from_file(file_path)) {
+        if (!model_loader.init_from_file_and_convert_name(file_path)) {
             LOG_ERROR("init esrgan model loader from file failed: '%s'", file_path.c_str());
             return false;
         }
 
-        bool success = model_loader.load_tensors(esrgan_tensors, backend);
+        // Get tensor names
+        auto tensor_names = model_loader.get_tensor_names();
+
+        // Detect if it's ESRGAN format
+        bool is_ESRGAN = std::find(tensor_names.begin(), tensor_names.end(), "model.0.weight") != tensor_names.end();
+
+        // Detect parameters from tensor names
+        int detected_num_block = 0;
+        if (is_ESRGAN) {
+            for (const auto& name : tensor_names) {
+                if (name.find("model.1.sub.") == 0) {
+                    size_t first_dot = name.find('.', 12);
+                    if (first_dot != std::string::npos) {
+                        size_t second_dot = name.find('.', first_dot + 1);
+                        if (second_dot != std::string::npos && name.substr(first_dot + 1, 3) == "RDB") {
+                            try {
+                                int idx            = std::stoi(name.substr(12, first_dot - 12));
+                                detected_num_block = std::max(detected_num_block, idx + 1);
+                            } catch (...) {
+                            }
+                        }
+                    }
+                }
+            }
+        } else {
+            // Original format
+            for (const auto& name : tensor_names) {
+                if (name.find("body.") == 0) {
+                    size_t pos = name.find('.', 5);
+                    if (pos != std::string::npos) {
+                        try {
+                            int idx            = std::stoi(name.substr(5, pos - 5));
+                            detected_num_block = std::max(detected_num_block, idx + 1);
+                        } catch (...) {
+                        }
+                    }
+                }
+            }
+        }
+
+        int detected_scale = 4;  // default
+        if (is_ESRGAN) {
+            // For ESRGAN format, detect scale by highest model number
+            int max_model_num = 0;
+            for (const auto& name : tensor_names) {
+                if (name.find("model.") == 0) {
+                    size_t dot_pos = name.find('.', 6);
+                    if (dot_pos != std::string::npos) {
+                        try {
+                            int num       = std::stoi(name.substr(6, dot_pos - 6));
+                            max_model_num = std::max(max_model_num, num);
+                        } catch (...) {
+                        }
+                    }
+                }
+            }
+            if (max_model_num <= 4) {
+                detected_scale = 1;
+            } else if (max_model_num <= 7) {
+                detected_scale = 2;
+            } else {
+                detected_scale = 4;
+            }
+        } else {
+            // Original format
+            bool has_conv_up2 = std::any_of(tensor_names.begin(), tensor_names.end(), [](const std::string& name) {
+                return name == "conv_up2.weight";
+            });
+            bool has_conv_up1 = std::any_of(tensor_names.begin(), tensor_names.end(), [](const std::string& name) {
+                return name == "conv_up1.weight";
+            });
+            if (has_conv_up2) {
+                detected_scale = 4;
+            } else if (has_conv_up1) {
+                detected_scale = 2;
+            } else {
+                detected_scale = 1;
+            }
+        }
+
+        int detected_num_in_ch   = 3;
+        int detected_num_out_ch  = 3;
+        int detected_num_feat    = 64;
+        int detected_num_grow_ch = 32;
+
+        // Create RRDBNet with detected parameters
+        rrdb_net = std::make_unique<RRDBNet>(detected_scale, detected_num_block, detected_num_in_ch, detected_num_out_ch, detected_num_feat, detected_num_grow_ch);
+        rrdb_net->init(params_ctx, {}, "");
+
+        alloc_params_buffer();
+        std::map<std::string, ggml_tensor*> esrgan_tensors;
+        rrdb_net->get_param_tensors(esrgan_tensors);
+
+        bool success;
+        if (is_ESRGAN) {
+            // Build name mapping for ESRGAN format
+            std::map<std::string, std::string> expected_to_model;
+            expected_to_model["conv_first.weight"] = "model.0.weight";
+            expected_to_model["conv_first.bias"]   = "model.0.bias";
+
+            for (int i = 0; i < detected_num_block; i++) {
+                for (int j = 1; j <= 3; j++) {
+                    for (int k = 1; k <= 5; k++) {
+                        std::string expected_weight        = "body." + std::to_string(i) + ".rdb" + std::to_string(j) + ".conv" + std::to_string(k) + ".weight";
+                        std::string model_weight           = "model.1.sub." + std::to_string(i) + ".RDB" + std::to_string(j) + ".conv" + std::to_string(k) + ".0.weight";
+                        expected_to_model[expected_weight] = model_weight;
+
+                        std::string expected_bias        = "body." + std::to_string(i) + ".rdb" + std::to_string(j) + ".conv" + std::to_string(k) + ".bias";
+                        std::string model_bias           = "model.1.sub." + std::to_string(i) + ".RDB" + std::to_string(j) + ".conv" + std::to_string(k) + ".0.bias";
+                        expected_to_model[expected_bias] = model_bias;
+                    }
+                }
+            }
+
+            if (detected_scale == 1) {
+                expected_to_model["conv_body.weight"] = "model.1.sub." + std::to_string(detected_num_block) + ".weight";
+                expected_to_model["conv_body.bias"]   = "model.1.sub." + std::to_string(detected_num_block) + ".bias";
+                expected_to_model["conv_hr.weight"]   = "model.2.weight";
+                expected_to_model["conv_hr.bias"]     = "model.2.bias";
+                expected_to_model["conv_last.weight"] = "model.4.weight";
+                expected_to_model["conv_last.bias"]   = "model.4.bias";
+            } else {
+                expected_to_model["conv_body.weight"] = "model.1.sub." + std::to_string(detected_num_block) + ".weight";
+                expected_to_model["conv_body.bias"]   = "model.1.sub." + std::to_string(detected_num_block) + ".bias";
+                if (detected_scale >= 2) {
+                    expected_to_model["conv_up1.weight"] = "model.3.weight";
+                    expected_to_model["conv_up1.bias"]   = "model.3.bias";
+                }
+                if (detected_scale == 4) {
+                    expected_to_model["conv_up2.weight"]  = "model.6.weight";
+                    expected_to_model["conv_up2.bias"]    = "model.6.bias";
+                    expected_to_model["conv_hr.weight"]   = "model.8.weight";
+                    expected_to_model["conv_hr.bias"]     = "model.8.bias";
+                    expected_to_model["conv_last.weight"] = "model.10.weight";
+                    expected_to_model["conv_last.bias"]   = "model.10.bias";
+                } else if (detected_scale == 2) {
+                    expected_to_model["conv_hr.weight"]   = "model.5.weight";
+                    expected_to_model["conv_hr.bias"]     = "model.5.bias";
+                    expected_to_model["conv_last.weight"] = "model.7.weight";
+                    expected_to_model["conv_last.bias"]   = "model.7.bias";
+                }
+            }
+
+            std::map<std::string, ggml_tensor*> model_tensors;
+            for (auto& p : esrgan_tensors) {
+                auto it = expected_to_model.find(p.first);
+                if (it != expected_to_model.end()) {
+                    model_tensors[it->second] = p.second;
+                }
+            }
+
+            success = model_loader.load_tensors(model_tensors, {}, n_threads);
+        } else {
+            success = model_loader.load_tensors(esrgan_tensors, {}, n_threads);
+        }
 
         if (!success) {
             LOG_ERROR("load esrgan tensors from model loader failed");
             return false;
         }
 
-        LOG_INFO("esrgan model loaded");
+        scale = rrdb_net->get_scale();
+        LOG_INFO("esrgan model loaded with scale=%d, num_block=%d", scale, detected_num_block);
         return success;
     }
 
     struct ggml_cgraph* build_graph(struct ggml_tensor* x) {
-        struct ggml_cgraph* gf  = ggml_new_graph(compute_ctx);
-        x                       = to_backend(x);
-        struct ggml_tensor* out = rrdb_net.forward(compute_ctx, x);
+        if (!rrdb_net)
+            return nullptr;
+        constexpr int kGraphNodes = 1 << 16;  // 65k
+        struct ggml_cgraph* gf    = new_graph_custom(kGraphNodes);
+        x                         = to_backend(x);
+
+        auto runner_ctx         = get_context();
+        struct ggml_tensor* out = rrdb_net->forward(&runner_ctx, x);
         ggml_build_forward_expand(gf, out);
         return gf;
     }
 
-    void compute(const int n_threads,
+    bool compute(const int n_threads,
                  struct ggml_tensor* x,
                  ggml_tensor** output,
-                 ggml_context* output_ctx = NULL) {
+                 ggml_context* output_ctx = nullptr) {
         auto get_graph = [&]() -> struct ggml_cgraph* {
             return build_graph(x);
         };
-        GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
+        return GGMLRunner::compute(get_graph, n_threads, false, output, output_ctx);
     }
 };
 

examples/CMakeLists.txt

@@ -1,3 +1,4 @@
 include_directories(${CMAKE_CURRENT_SOURCE_DIR})
 
-add_subdirectory(cli)
+add_subdirectory(cli)
+add_subdirectory(server)

examples/cli/CMakeLists.txt

@@ -1,6 +1,6 @@
-set(TARGET sd)
+set(TARGET sd-cli)
 
 add_executable(${TARGET} main.cpp)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE stable-diffusion ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PUBLIC cxx_std_11)
+target_compile_features(${TARGET} PUBLIC c_std_11 cxx_std_17)

examples/cli/README.md

@@ -0,0 +1,144 @@
+# Run
+
+```
+usage: ./bin/sd-cli  [options]
+
+CLI Options:
+  -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)
+  --output-begin-idx <int>    starting index for output image sequence, must be non-negative (default 0 if specified %d in output path, 1 otherwise)
+  --preview-path <string>     path to write preview image to (default: ./preview.png)
+  --preview-interval <int>    interval in denoising steps between consecutive updates of the image preview file (default is 1, meaning updating at
+                              every step)
+  --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
+  --preview                   preview method. must be one of the following [none, proj, tae, vae] (default is none)
+  -h, --help                  show this help message and exit
+
+Context Options:
+  -m, --model <string>                     path to full model
+  --clip_l <string>                        path to the clip-l text encoder
+  --clip_g <string>                        path to the clip-g text encoder
+  --clip_vision <string>                   path to the clip-vision encoder
+  --t5xxl <string>                         path to the t5xxl text encoder
+  --llm <string>                           path to the llm text encoder. For example: (qwenvl2.5 for qwen-image, mistral-small3.2 for flux2, ...)
+  --llm_vision <string>                    path to the llm vit
+  --qwen2vl <string>                       alias of --llm. Deprecated.
+  --qwen2vl_vision <string>                alias of --llm_vision. Deprecated.
+  --diffusion-model <string>               path to the standalone diffusion model
+  --high-noise-diffusion-model <string>    path to the standalone high noise diffusion model
+  --vae <string>                           path to standalone vae model
+  --taesd <string>                         path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
+  --tae <string>                           alias of --taesd
+  --control-net <string>                   path to control net model
+  --embd-dir <string>                      embeddings directory
+  --lora-model-dir <string>                lora model directory
+  --tensor-type-rules <string>             weight type per tensor pattern (example: "^vae\.=f16,model\.=q8_0")
+  --photo-maker <string>                   path to PHOTOMAKER model
+  --upscale-model <string>                 path to esrgan model.
+  -t, --threads <int>                      number of threads to use during computation (default: -1). If threads <= 0, then threads will be set to the number of
+                                           CPU physical cores
+  --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)
+  --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
+  --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
+  --rng                                    RNG, one of [std_default, cuda, cpu], default: cuda(sd-webui), cpu(comfyui)
+  --sampler-rng                            sampler RNG, one of [std_default, cuda, cpu]. If not specified, use --rng
+  --prediction                             prediction type override, one of [eps, v, edm_v, sd3_flow, flux_flow, flux2_flow]
+  --lora-apply-mode                        the way to apply LoRA, one of [auto, immediately, at_runtime], default is auto. In auto mode, if the model weights
+                                           contain any quantized parameters, the at_runtime mode will be used; otherwise,
+                                           immediately will be used.The immediately mode may have precision and
+                                           compatibility issues with quantized parameters, but it usually offers faster inference
+                                           speed and, in some cases, lower memory usage. The at_runtime mode, on the
+                                           other hand, is exactly the opposite.
+  --vae-tile-size                          tile size for vae tiling, format [X]x[Y] (default: 32x32)
+  --vae-relative-tile-size                 relative tile size for vae tiling, format [X]x[Y], in fraction of image size if < 1, in number of tiles per dim if >=1
+                                           (overrides --vae-tile-size)
+
+Generation Options:
+  -p, --prompt <string>                    the prompt to render
+  -n, --negative-prompt <string>           the negative prompt (default: "")
+  -i, --init-img <string>                  path to the init image
+  --end-img <string>                       path to the end image, required by flf2v
+  --mask <string>                          path to the mask image
+  --control-image <string>                 path to control image, control net
+  --control-video <string>                 path to control video frames, It must be a directory path. The video frames inside should be stored as images in
+                                           lexicographical (character) order. For example, if the control video path is
+                                           `frames`, the directory contain images such as 00.png, 01.png, ... etc.
+  --pm-id-images-dir <string>              path to PHOTOMAKER input id images dir
+  --pm-id-embed-path <string>              path to PHOTOMAKER v2 id embed
+  -H, --height <int>                       image height, in pixel space (default: 512)
+  -W, --width <int>                        image width, in pixel space (default: 512)
+  --steps <int>                            number of sample steps (default: 20)
+  --high-noise-steps <int>                 (high noise) number of sample steps (default: -1 = auto)
+  --clip-skip <int>                        ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1). <= 0 represents unspecified,
+                                           will be 1 for SD1.x, 2 for SD2.x
+  -b, --batch-count <int>                  batch count
+  --video-frames <int>                     video frames (default: 1)
+  --fps <int>                              fps (default: 24)
+  --timestep-shift <int>                   shift timestep for NitroFusion models (default: 0). recommended N for NitroSD-Realism around 250 and 500 for
+                                           NitroSD-Vibrant
+  --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)
+  --slg-scale <float>                      skip layer guidance (SLG) scale, only for DiT models: (default: 0). 0 means disabled, a value of 2.5 is nice for sd3.5
+                                           medium
+  --skip-layer-start <float>               SLG enabling point (default: 0.01)
+  --skip-layer-end <float>                 SLG disabling point (default: 0.2)
+  --eta <float>                            eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
+  --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
+  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
+  --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input (default: 3.5)
+  --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
+  --high-noise-skip-layer-start <float>    (high noise) SLG enabling point (default: 0.01)
+  --high-noise-skip-layer-end <float>      (high noise) SLG disabling point (default: 0.2)
+  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
+  --strength <float>                       strength for noising/unnoising (default: 0.75)
+  --pm-style-strength <float>
+  --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
+  --moe-boundary <float>                   timestep boundary for Wan2.2 MoE model. (default: 0.875). Only enabled if `--high-noise-steps` is set to -1
+  --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
+  -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,
+                                           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, 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)
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)
+  --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"
+  --cache-preset                           cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'
+  --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'
+```

examples/cli/avi_writer.h

@@ -0,0 +1,217 @@
+#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, (uint32_t)jpeg_data.size);
+        index[i].offset = ftell(f) - 8;
+        index[i].size   = (uint32_t)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__

examples/server/CMakeLists.txt

@@ -0,0 +1,6 @@
+set(TARGET sd-server)
+
+add_executable(${TARGET} 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)

examples/server/README.md

@@ -0,0 +1,136 @@
+# 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)
+  --serve-html-path <string>  path to HTML file to serve at root (optional)
+  -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
+  --clip_l <string>                        path to the clip-l text encoder
+  --clip_g <string>                        path to the clip-g text encoder
+  --clip_vision <string>                   path to the clip-vision encoder
+  --t5xxl <string>                         path to the t5xxl text encoder
+  --llm <string>                           path to the llm text encoder. For example: (qwenvl2.5 for qwen-image, mistral-small3.2 for flux2, ...)
+  --llm_vision <string>                    path to the llm vit
+  --qwen2vl <string>                       alias of --llm. Deprecated.
+  --qwen2vl_vision <string>                alias of --llm_vision. Deprecated.
+  --diffusion-model <string>               path to the standalone diffusion model
+  --high-noise-diffusion-model <string>    path to the standalone high noise diffusion model
+  --vae <string>                           path to standalone vae model
+  --taesd <string>                         path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
+  --tae <string>                           alias of --taesd
+  --control-net <string>                   path to control net model
+  --embd-dir <string>                      embeddings directory
+  --lora-model-dir <string>                lora model directory
+  --tensor-type-rules <string>             weight type per tensor pattern (example: "^vae\.=f16,model\.=q8_0")
+  --photo-maker <string>                   path to PHOTOMAKER model
+  --upscale-model <string>                 path to esrgan model.
+  -t, --threads <int>                      number of threads to use during computation (default: -1). If threads <= 0, then threads will be set to the number of
+                                           CPU physical cores
+  --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
+  --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)
+  --mmap                                   whether to memory-map 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
+  --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
+  --rng                                    RNG, one of [std_default, cuda, cpu], default: cuda(sd-webui), cpu(comfyui)
+  --sampler-rng                            sampler RNG, one of [std_default, cuda, cpu]. If not specified, use --rng
+  --prediction                             prediction type override, one of [eps, v, edm_v, sd3_flow, flux_flow, flux2_flow]
+  --lora-apply-mode                        the way to apply LoRA, one of [auto, immediately, at_runtime], default is auto. In auto mode, if the model weights
+                                           contain any quantized parameters, the at_runtime mode will be used; otherwise,
+                                           immediately will be used.The immediately mode may have precision and
+                                           compatibility issues with quantized parameters, but it usually offers faster inference
+                                           speed and, in some cases, lower memory usage. The at_runtime mode, on the
+                                           other hand, is exactly the opposite.
+  --vae-tile-size                          tile size for vae tiling, format [X]x[Y] (default: 32x32)
+  --vae-relative-tile-size                 relative tile size for vae tiling, format [X]x[Y], in fraction of image size if < 1, in number of tiles per dim if >=1
+                                           (overrides --vae-tile-size)
+
+Default Generation Options:
+  -p, --prompt <string>                    the prompt to render
+  -n, --negative-prompt <string>           the negative prompt (default: "")
+  -i, --init-img <string>                  path to the init image
+  --end-img <string>                       path to the end image, required by flf2v
+  --mask <string>                          path to the mask image
+  --control-image <string>                 path to control image, control net
+  --control-video <string>                 path to control video frames, It must be a directory path. The video frames inside should be stored as images in
+                                           lexicographical (character) order. For example, if the control video path is
+                                           `frames`, the directory contain images such as 00.png, 01.png, ... etc.
+  --pm-id-images-dir <string>              path to PHOTOMAKER input id images dir
+  --pm-id-embed-path <string>              path to PHOTOMAKER v2 id embed
+  -H, --height <int>                       image height, in pixel space (default: 512)
+  -W, --width <int>                        image width, in pixel space (default: 512)
+  --steps <int>                            number of sample steps (default: 20)
+  --high-noise-steps <int>                 (high noise) number of sample steps (default: -1 = auto)
+  --clip-skip <int>                        ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1). <= 0 represents unspecified,
+                                           will be 1 for SD1.x, 2 for SD2.x
+  -b, --batch-count <int>                  batch count
+  --video-frames <int>                     video frames (default: 1)
+  --fps <int>                              fps (default: 24)
+  --timestep-shift <int>                   shift timestep for NitroFusion models (default: 0). recommended N for NitroSD-Realism around 250 and 500 for
+                                           NitroSD-Vibrant
+  --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)
+  --slg-scale <float>                      skip layer guidance (SLG) scale, only for DiT models: (default: 0). 0 means disabled, a value of 2.5 is nice for sd3.5
+                                           medium
+  --skip-layer-start <float>               SLG enabling point (default: 0.01)
+  --skip-layer-end <float>                 SLG disabling point (default: 0.2)
+  --eta <float>                            eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
+  --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
+  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
+  --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input (default: 3.5)
+  --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
+  --high-noise-skip-layer-start <float>    (high noise) SLG enabling point (default: 0.01)
+  --high-noise-skip-layer-end <float>      (high noise) SLG disabling point (default: 0.2)
+  --high-noise-eta <float>                 (high noise) eta in DDIM, only for DDIM/TCD/res_multistep/res_2s (default: 0)
+  --strength <float>                       strength for noising/unnoising (default: 0.75)
+  --pm-style-strength <float>
+  --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
+  --moe-boundary <float>                   timestep boundary for Wan2.2 MoE model. (default: 0.875). Only enabled if `--high-noise-steps` is set to -1
+  --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
+  -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,
+                                           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, 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)
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level)
+  --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"
+  --cache-preset                           cache-dit preset: 'slow'/'s', 'medium'/'m', 'fast'/'f', 'ultra'/'u'
+  --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'
+```

face_detect.py

@@ -0,0 +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)
+
+