feat: add native CUDA Sol-Attn support

2026-11-10 02:46:32 +00:00 · 2026-09-21 21:15:00 +08:00
512 changed files with 6930 additions and 48878 deletions
@@ -1,8 +1,7 @@
 build*/
 docs/
 test/
-tests/

 .cache/
 *.swp
-models/
+models/
@@ -1,2 +0,0 @@
-/start-server.sh text eol=lf
-/start-server.bat text eol=crlf
@@ -16,7 +16,6 @@ on:
        ".github/workflows/**",
        ".dockerignore",
        "docker/Dockerfile*",
-        "scripts/ci-cuda-cache.sh",
        "**/CMakeLists.txt",
        "**/Makefile",
        "**/*.h",
@@ -24,13 +23,9 @@ on:
        "**/*.c",
        "**/*.cpp",
        "**/*.cu",
-        "**/*.cuh",
        "ggml",
        "examples/server/frontend",
        "examples/server/frontend/**",
-        "examples/server/recipes/*.json",
-        "start-server.sh",
-        "start-server.bat",
      ]
  pull_request:
    types: [opened, synchronize, reopened]
@@ -39,7 +34,6 @@ on:
        ".github/workflows/**",
        ".dockerignore",
        "docker/Dockerfile*",
-        "scripts/ci-cuda-cache.sh",
        "**/CMakeLists.txt",
        "**/Makefile",
        "**/*.h",
@@ -47,21 +41,14 @@ on:
        "**/*.c",
        "**/*.cpp",
        "**/*.cu",
-        "**/*.cuh",
        "ggml",
        "examples/server/frontend",
        "examples/server/frontend/**",
-        "examples/server/recipes/*.json",
-        "start-server.sh",
-        "start-server.bat",
      ]

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

-permissions:
-  contents: read
-
 concurrency:
  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
  cancel-in-progress: true
@@ -73,17 +60,17 @@ jobs:
    steps:
      - name: Clone
        id: checkout
-        uses: actions/checkout@v6
+        uses: actions/checkout@v3
        with:
          submodules: recursive

      - name: Setup Node
-        uses: actions/setup-node@v5
+        uses: actions/setup-node@v4
        with:
-          node-version: 24
+          node-version: 20

      - name: Setup pnpm
-        uses: pnpm/action-setup@v5
+        uses: pnpm/action-setup@v4
        with:
          version: 10.15.1

@@ -104,8 +91,7 @@ jobs:
      - name: Get commit hash
        id: commit
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        shell: bash
-        run: echo "short=${GITHUB_SHA:0:7}" >> "$GITHUB_OUTPUT"
+        uses: prompt/actions-commit-hash@v2

      - name: Fetch system info
        id: system-info
@@ -121,12 +107,11 @@ jobs:
        run: |
          cp ggml/LICENSE ./build/bin/ggml.txt
          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
-          chmod +x start-server.sh
-          zip -j sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip ./build/bin/* ./start-server.sh ./start-server.bat
+          zip -j sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip ./build/bin/*

      - name: Upload artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v4
        with:
          name: sd-${{ 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 }}.zip
          path: |
@@ -138,17 +123,17 @@ jobs:
    steps:
      - name: Clone
        id: checkout
-        uses: actions/checkout@v6
+        uses: actions/checkout@v3
        with:
          submodules: recursive

      - name: Setup Node
-        uses: actions/setup-node@v5
+        uses: actions/setup-node@v4
        with:
-          node-version: 24
+          node-version: 20

      - name: Setup pnpm
-        uses: pnpm/action-setup@v5
+        uses: pnpm/action-setup@v4
        with:
          version: 10.15.1

@@ -169,8 +154,7 @@ jobs:
      - name: Get commit hash
        id: commit
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        shell: bash
-        run: echo "short=${GITHUB_SHA:0:7}" >> "$GITHUB_OUTPUT"
+        uses: prompt/actions-commit-hash@v2

      - name: Fetch system info
        id: system-info
@@ -186,12 +170,11 @@ jobs:
        run: |
          cp ggml/LICENSE ./build/bin/ggml.txt
          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
-          chmod +x start-server.sh
-          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/* ./start-server.sh ./start-server.bat
+          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: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v4
        with:
          name: sd-${{ 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: |
@@ -239,27 +222,25 @@ jobs:
          submodules: recursive

      - name: Setup Node
-        uses: actions/setup-node@v5
+        uses: actions/setup-node@v4
        with:
-          node-version: 24
+          node-version: 20

      - name: Setup pnpm
-        uses: pnpm/action-setup@v5
+        uses: pnpm/action-setup@v4
        with:
          version: 10.15.1

      - name: Get commit hash
        id: commit
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        shell: bash
-        run: echo "short=${GITHUB_SHA:0:7}" >> "$GITHUB_OUTPUT"
+        uses: prompt/actions-commit-hash@v2

      - name: Set up Docker Buildx
-        id: buildx
-        uses: docker/setup-buildx-action@v4
+        uses: docker/setup-buildx-action@v3

      - name: Log in to the container registry
-        uses: docker/login-action@v4
+        uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
@@ -267,7 +248,7 @@ jobs:

      - name: Extract metadata for Docker
        id: meta
-        uses: docker/metadata-action@v6
+        uses: docker/metadata-action@v5
        with:
          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}

@@ -278,24 +259,9 @@ jobs:
          # if set to "true" but frees about 6 GB
          tool-cache: false

-      - name: Cache CUDA compilation
-        if: matrix.variant == 'cuda'
-        uses: actions/cache@v5
-        with:
-          path: ${{ runner.temp }}/cuda-ccache
-          key: cuda-ccache-${{ runner.os }}-${{ runner.arch }}-${{ hashFiles('docker/Dockerfile.cuda') }}-${{ github.run_id }}-${{ github.run_attempt }}
-          restore-keys: |
-            cuda-ccache-${{ runner.os }}-${{ runner.arch }}-${{ hashFiles('docker/Dockerfile.cuda') }}-
-
-      - name: Restore CUDA compiler cache mount
-        if: matrix.variant == 'cuda'
-        env:
-          CUDA_CACHE_BUILDER: ${{ steps.buildx.outputs.name }}
-        run: bash scripts/ci-cuda-cache.sh restore
-
      - name: Build and push Docker image
        id: build-push
-        uses: docker/build-push-action@v7
+        uses: docker/build-push-action@v6
        with:
          context: .
          platforms: ${{ matrix.platform }}
@@ -306,29 +272,23 @@ jobs:
          annotations: ${{ steps.meta.outputs.annotations }}
          build-args: ${{ matrix.build-args }}

-      - name: Export CUDA compiler cache mount
-        if: matrix.variant == 'cuda'
-        env:
-          CUDA_CACHE_BUILDER: ${{ steps.buildx.outputs.name }}
-        run: bash scripts/ci-cuda-cache.sh save
-
  macOS-latest-cmake:
    runs-on: macos-latest

    steps:
      - name: Clone
        id: checkout
-        uses: actions/checkout@v6
+        uses: actions/checkout@v3
        with:
          submodules: recursive

      - name: Setup Node
-        uses: actions/setup-node@v5
+        uses: actions/setup-node@v4
        with:
-          node-version: 24
+          node-version: 20

      - name: Setup pnpm
-        uses: pnpm/action-setup@v5
+        uses: pnpm/action-setup@v4
        with:
          version: 10.15.1

@@ -349,8 +309,7 @@ jobs:
      - name: Get commit hash
        id: commit
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        shell: bash
-        run: echo "short=${GITHUB_SHA:0:7}" >> "$GITHUB_OUTPUT"
+        uses: prompt/actions-commit-hash@v2

      - name: Fetch system info
        id: system-info
@@ -366,12 +325,11 @@ jobs:
        run: |
          cp ggml/LICENSE ./build/bin/ggml.txt
          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
-          chmod +x start-server.sh
-          zip -j sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip ./build/bin/* ./start-server.sh ./start-server.bat
+          zip -j sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip ./build/bin/*

      - name: Upload artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v4
        with:
          name: sd-${{ 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 }}.zip
          path: |
@@ -389,44 +347,35 @@ jobs:
          - build: "cpu"
            defines: "-DGGML_NATIVE=OFF -DSD_BUILD_SHARED_LIBS=ON -DSD_BUILD_SHARED_GGML_LIB=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON"
          - build: "cuda12"
-            defines: "-DSD_CUDA=ON -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache -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\"' -DGGML_NATIVE=OFF -DSD_BUILD_SHARED_GGML_LIB=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON"
+            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\"' -DGGML_NATIVE=OFF -DSD_BUILD_SHARED_GGML_LIB=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON"
          - build: "vulkan"
            defines: "-DSD_VULKAN=ON -DSD_BUILD_SHARED_LIBS=ON -DGGML_NATIVE=OFF -DSD_BUILD_SHARED_GGML_LIB=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON"
    steps:
      - name: Clone
        id: checkout
-        uses: actions/checkout@v6
+        uses: actions/checkout@v3
        with:
          submodules: recursive

      - name: Setup Node
-        uses: actions/setup-node@v5
+        uses: actions/setup-node@v4
        with:
-          node-version: 24
+          node-version: 20

      - name: Setup pnpm
-        uses: pnpm/action-setup@v5
+        uses: pnpm/action-setup@v4
        with:
          version: 10.15.1

      - name: Install cuda-toolkit
        id: cuda-toolkit
        if: ${{ matrix.build == 'cuda12' }}
-        uses: Jimver/cuda-toolkit@v0.2.35
+        uses: Jimver/cuda-toolkit@v0.2.22
        with:
          cuda: "12.8.1"
          method: "network"
          sub-packages: '["nvcc", "cudart", "cublas", "cublas_dev", "thrust", "visual_studio_integration"]'

-      - name: Cache CUDA compilation
-        if: matrix.build == 'cuda12'
-        uses: ggml-org/ccache-action@v1.2.21
-        with:
-          variant: ccache
-          install: binary
-          key: windows-cuda-12.8.1-x64
-          max-size: "2G"
-
      - name: Install Vulkan SDK
        id: get_vulkan
        if: ${{ matrix.build == 'vulkan' }}
@@ -436,15 +385,13 @@ jobs:
          Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}"
          Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin"

+      - name: Activate MSVC environment
+        id: msvc_dev_cmd
+        uses: ilammy/msvc-dev-cmd@v1
+
      - name: Build
        id: cmake_build
-        shell: pwsh
        run: |
-          $vswhere = "${env:ProgramFiles(x86)}\Microsoft Visual Studio\Installer\vswhere.exe"
-          $vsPath = & $vswhere -latest -products * -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath
-          if (-not $vsPath) { throw "Visual Studio C++ tools not found" }
-          & "$vsPath\Common7\Tools\Launch-VsDevShell.ps1" -Arch amd64 -HostArch amd64 -SkipAutomaticLocation
-
          mkdir build
          cd build
          cmake .. -DCMAKE_CXX_FLAGS='/bigobj' -G Ninja -DCMAKE_C_COMPILER=cl.exe -DCMAKE_CXX_COMPILER=cl.exe -DCMAKE_BUILD_TYPE=Release ${{ matrix.defines }}
@@ -453,8 +400,7 @@ jobs:
      - name: Get commit hash
        id: commit
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        shell: bash
-        run: echo "short=${GITHUB_SHA:0:7}" >> "$GITHUB_OUTPUT"
+        uses: prompt/actions-commit-hash@v2

      - name: Pack artifacts
        id: pack_artifacts
@@ -474,10 +420,7 @@ jobs:
            ls .\build\bin
            throw "Can't find stable-diffusion.dll"
          }
-          if ("${{ matrix.build }}" -eq "cuda12") {
-            Copy-Item thirdparty/cutlass/LICENSE.txt (Join-Path (Split-Path -Parent $filePath) 'cutlass.txt')
-          }
-          7z a sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip $filePath .\start-server.sh .\start-server.bat
+          7z a sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip $filePath

      - name: Copy and pack Cuda runtime
        id: pack_cuda_runtime
@@ -490,7 +433,7 @@ jobs:

      - name: Upload Cuda runtime
        if: ${{ matrix.build == 'cuda12' && (github.event_name == 'push' && github.ref == 'refs/heads/master' || github.event.inputs.create_release == 'true') }}
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v4
        with:
          name: sd-cudart-sd-bin-win-cu12-x64.zip
          path: |
@@ -498,7 +441,7 @@ jobs:

      - name: Upload artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v4
        with:
          name: sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip
          path: |
@@ -508,33 +451,33 @@ jobs:
    runs-on: windows-2022

    env:
-      ROCM_VERSION: "10.1.0"
-      gpu_targets: "gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1103;gfx1150;gfx1151;gfx1152;gfx1153;gfx1200;gfx1201"
+      ROCM_VERSION: "7.14.0"
+      GPU_TARGETS: "gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1103;gfx1150;gfx1151;gfx1152;gfx1153;gfx1200;gfx1201"

    steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v3
        with:
          submodules: recursive

      - name: Setup Node
-        uses: actions/setup-node@v5
+        uses: actions/setup-node@v4
        with:
-          node-version: 24
+          node-version: 20

      - name: Setup pnpm
-        uses: pnpm/action-setup@v5
+        uses: pnpm/action-setup@v4
        with:
          version: 10.15.1

      - name: Cache ROCm Installation
        id: cache-rocm
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: C:\TheRock\build
          key: rocm-wheels-${{ env.ROCM_VERSION }}-${{ runner.os }}

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
        with:
          key: windows-rocm-${{ env.ROCM_VERSION }}-x64
          evict-old-files: 1d
@@ -555,7 +498,7 @@ jobs:

          write-host "Installing ROCm wheels for multi-arch support"
          # Install ROCm wheels for multi-arch support (this may take several minutes)
-          python -m pip install --index-url https://stable.repo.amd.com/rocm/whl-next/ "rocm[libraries,devel]==${{env.ROCM_VERSION}}"
+          python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{env.ROCM_VERSION}}"

          # Pre-expand the devel tree so it is included in the cache
          write-host "Initializing ROCm devel tree"
@@ -615,29 +558,16 @@ jobs:
      - name: Get commit hash
        id: commit
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        shell: bash
-        run: echo "short=${GITHUB_SHA:0:7}" >> "$GITHUB_OUTPUT"
-
-      - name: Bundle HIP runtime libraries
-        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        run: |
-          $ErrorActionPreference = "Stop"
-          $binPath = (rocm-sdk path --bin).Trim()
-          if (-not $binPath) { throw "rocm-sdk path --bin returned empty" }
-          foreach ($dll in @("amdhip64_7.dll", "rocm_kpack.dll", "amd_comgr.dll")) {
-            $f = Get-ChildItem -Path $binPath -Filter $dll -ErrorAction SilentlyContinue
-            if (-not $f) { throw "no match for $dll in $binPath" }
-            Copy-Item $f.FullName -Destination .\build\bin\ -Force
-          }
+        uses: pr-mpt/actions-commit-hash@v2

      - name: Pack artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
        run: |
-          7z a sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-${{ env.ROCM_VERSION }}-x64.zip .\build\bin\* .\start-server.sh .\start-server.bat
+          7z a sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-${{ env.ROCM_VERSION }}-x64.zip .\build\bin\*

      - name: Upload artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v4
        with:
          name: sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-${{ env.ROCM_VERSION }}-x64.zip
          path: |
@@ -652,9 +582,9 @@ jobs:
    strategy:
      matrix:
        include:
-          - ROCM_VERSION: "10.1.0"
+          - ROCM_VERSION: "7.14.0"
            gpu_targets: "gfx900;gfx906;gfx908;gfx90a;gfx90c;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1103;gfx1150;gfx1151;gfx1152;gfx1153;gfx1200;gfx1201"
-            build: 'x64'
+            build: x64

    steps:
      - name: Clone
@@ -664,20 +594,10 @@ jobs:
          submodules: recursive

      - name: ccache
-        uses: ggml-org/ccache-action@v1.2.21
+        uses: ggml-org/ccache-action@v1.2.16
        with:
          key: ubuntu-rocm-cmake-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
          evict-old-files: 1d
-          max-size: "2G"
-
-      - name: Tune ccache for reinstalled ROCm toolchain
-        run: |
-          # ROCm is pip-installed fresh each run, so the clang binary's mtime
-          # changes every time. With the default compiler_check=mtime that
-          # invalidates the cache; hash compiler contents instead so warm
-          # builds hit.
-          ccache --set-config=compiler_check=content
-          ccache --set-config=sloppiness=time_macros,include_file_mtime,include_file_ctime

      - name: Dependencies
        id: depends
@@ -714,7 +634,7 @@ jobs:
          # libraries = HIP runtime and CMake configs needed for linking
          # devel = compilers, headers, static libs
          python -m pip install --upgrade pip
-          python -m pip install --index-url https://stable.repo.amd.com/rocm/whl-next/ "rocm[libraries,devel]==${{matrix.ROCM_VERSION}}"
+          python -m pip install --index-url https://repo.amd.com/rocm/whl-multi-arch/ "rocm[libraries,devel]==${{matrix.ROCM_VERSION}}"

          # Get ROCm installation paths using the rocm-sdk CLI tool
          ROCM_PATH=$(rocm-sdk path --root)
@@ -737,12 +657,12 @@ jobs:
      # setup-node installs into /opt/hostedtoolcache, which is removed above.
      # Keep Node/pnpm setup after disk cleanup so the server frontend can be embedded.
      - name: Setup Node
-        uses: actions/setup-node@v5
+        uses: actions/setup-node@v4
        with:
-          node-version: 24
+          node-version: 20

      - name: Setup pnpm
-        uses: pnpm/action-setup@v5
+        uses: pnpm/action-setup@v4
        with:
          version: 10.15.1

@@ -771,8 +691,7 @@ jobs:
      - name: Get commit hash
        id: commit
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        shell: bash
-        run: echo "short=${GITHUB_SHA:0:7}" >> "$GITHUB_OUTPUT"
+        uses: prompt/actions-commit-hash@v2

      - name: Prepare artifacts
        id: prepare_artifacts
@@ -796,12 +715,11 @@ jobs:
        run: |
          cp ggml/LICENSE ./build/bin/ggml.txt
          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
-          chmod +x start-server.sh
-          zip -y -r sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm-${{ matrix.ROCM_VERSION }}.zip ./build/bin ./start-server.sh ./start-server.bat
+          zip -y -r sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm-${{ matrix.ROCM_VERSION }}.zip ./build/bin

      - name: Upload artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v4
        with:
          name: sd-${{ 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-${{ matrix.ROCM_VERSION }}.zip
          path: |
@@ -812,9 +730,6 @@ jobs:

    runs-on: ubuntu-latest

-    permissions:
-      contents: write
-
    needs:
      - ubuntu-latest-cmake
      - ubuntu-latest-cmake-vulkan
@@ -826,13 +741,13 @@ jobs:

    steps:
      - name: Clone
-        uses: actions/checkout@v6
+        uses: actions/checkout@v3
        with:
          fetch-depth: 0

      - name: Download artifacts
        id: download-artifact
-        uses: actions/download-artifact@v7
+        uses: actions/download-artifact@v4
        with:
          path: ./artifact
          pattern: sd-*
@@ -845,47 +760,36 @@ jobs:

      - name: Get commit hash
        id: commit
-        shell: bash
-        run: echo "short=${GITHUB_SHA:0:7}" >> "$GITHUB_OUTPUT"
+        uses: prompt/actions-commit-hash@v2

      - name: Create release
        id: create_release
        if: ${{ github.event_name == 'workflow_dispatch' || github.ref_name == 'master' }}
-        uses: actions/github-script@v9
+        uses: anzz1/action-create-release@v1
        env:
-          RELEASE_TAG: ${{ format('{0}-{1}-{2}', env.BRANCH_NAME, steps.commit_count.outputs.count, steps.commit.outputs.short) }}
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        with:
-          script: |
-            const { data: release } = await github.rest.repos.createRelease({
-              ...context.repo,
-              tag_name: process.env.RELEASE_TAG,
-              name: process.env.RELEASE_TAG,
-              target_commitish: context.sha,
-              draft: false,
-              prerelease: false,
-            });
-            core.setOutput('id', release.id);
+          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@v9
-        env:
-          RELEASE_ID: ${{ steps.create_release.outputs.id }}
+        uses: actions/github-script@v3
        with:
+          github-token: ${{secrets.GITHUB_TOKEN}}
          script: |
            const path = require('path');
-            const fs = require('fs').promises;
-            const release_id = Number(process.env.RELEASE_ID);
-            for (const file of await fs.readdir('./artifact')) {
+            const fs = require('fs');
+            const release_id = '${{ steps.create_release.outputs.id }}';
+            for (let file of await fs.readdirSync('./artifact')) {
              if (path.extname(file) === '.zip') {
                console.log('uploadReleaseAsset', file);
-                await github.rest.repos.uploadReleaseAsset({
+                await github.repos.uploadReleaseAsset({
                  owner: context.repo.owner,
                  repo: context.repo.repo,
                  release_id: release_id,
                  name: file,
-                  data: await fs.readFile(`./artifact/${file}`)
+                  data: await fs.readFileSync(`./artifact/${file}`)
                });
              }
            }
@@ -1,61 +0,0 @@
-name: Close PRs from organization forks
-
-on:
-  pull_request_target:
-    types: [opened, reopened]
-
-permissions:
-  pull-requests: write
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number }}
-  cancel-in-progress: false
-
-jobs:
-  close-organization-fork-pr:
-    if: >-
-      github.event.pull_request.head.repo.owner.type == 'Organization' &&
-      github.event.pull_request.head.repo.id != github.event.pull_request.base.repo.id
-    runs-on: ubuntu-latest
-    timeout-minutes: 5
-    steps:
-      - name: Explain the contribution policy and close the PR
-        uses: actions/github-script@v9
-        with:
-          script: |
-            const { data: pr } = await github.rest.pulls.get({
-              ...context.repo,
-              pull_number: context.issue.number,
-            });
-            const headRepo = pr.head.repo;
-            if (pr.state !== 'open' || !headRepo ||
-                headRepo.id === pr.base.repo.id || headRepo.owner.type !== 'Organization') {
-              return;
-            }
-
-            const marker = '<!-- organization-fork-policy -->';
-            const comments = await github.paginate(github.rest.issues.listComments, {
-              ...context.repo,
-              issue_number: pr.number,
-              per_page: 100,
-            });
-            const alreadyExplained = comments.some(comment =>
-              comment.user?.login === 'github-actions[bot]' && comment.body?.includes(marker));
-            if (!alreadyExplained) {
-              await github.rest.issues.createComment({
-                ...context.repo,
-                issue_number: pr.number,
-                body: [
-                  marker,
-                  'This repository requires contributions from forks to use a personal fork with **Allow edits from maintainers** enabled.',
-                  'GitHub does not support this option for organization-owned forks, so this PR is being closed automatically.',
-                  'Please open a new PR from a fork in your personal GitHub account and enable **Allow edits from maintainers** so maintainers can help update the branch.',
-                  'See [the GitHub documentation](https://docs.github.com/en/pull-requests/how-tos/work-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork).',
-                ].join('\n\n'),
-              });
-            }
-            await github.rest.pulls.update({
-              ...context.repo,
-              pull_number: pr.number,
-              state: 'closed',
-            });
@@ -2,16 +2,11 @@ build*/
 cmake-build-*/
 test/
 tests/
-logs/
 .vscode/
 .idea/
 .cache/
 *.swp
 *.bat
-!/start-server.bat
-/start-server.local.sh
-/start-server.local.bat
-/start-server.local.ps1
 *.bin
 *.exe
 *.gguf
@@ -10,6 +10,3 @@
 [submodule "thirdparty/libwebm"]
 	path = thirdparty/libwebm
 	url = https://github.com/webmproject/libwebm.git
-[submodule "thirdparty/cutlass"]
-	path = thirdparty/cutlass
-	url = https://github.com/NVIDIA/cutlass.git
@@ -83,8 +83,6 @@ documentation.
  and debugging.
 * `examples/common/` - shared example support code.
 * `examples/server/` - server application built on top of the library.
-  Before modifying built-in recipes, read and follow
-  [the built-in recipe policy](examples/server/recipes/README.md).
 * `examples/server/frontend/` - git submodule containing independent frontend
  code. Avoid modifying it unless explicitly requested.

@@ -9,28 +9,13 @@ if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
 endif()

 if (MSVC)
-    cmake_host_system_information(RESULT SD_LOGICAL_CORES QUERY NUMBER_OF_LOGICAL_CORES)
-    if (SD_LOGICAL_CORES GREATER 8)
-        math(EXPR SD_DEFAULT_MSVC_MP_COUNT "${SD_LOGICAL_CORES} - 2")
-    elseif (SD_LOGICAL_CORES GREATER 2)
-        math(EXPR SD_DEFAULT_MSVC_MP_COUNT "${SD_LOGICAL_CORES} - 1")
-    else()
-        set(SD_DEFAULT_MSVC_MP_COUNT "${SD_LOGICAL_CORES}")
-    endif()
-    if (NOT SD_DEFAULT_MSVC_MP_COUNT GREATER 0)
-        set(SD_DEFAULT_MSVC_MP_COUNT 1)
-    endif()
-    set(SD_MSVC_MP_COUNT "${SD_DEFAULT_MSVC_MP_COUNT}" CACHE STRING "Maximum parallel MSVC compilations per project")
-    if (NOT SD_MSVC_MP_COUNT MATCHES "^[1-9][0-9]*$" OR SD_MSVC_MP_COUNT GREATER 65536)
-        message(FATAL_ERROR "SD_MSVC_MP_COUNT must be an integer between 1 and 65536")
-    endif()
    add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
    add_compile_definitions(_SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING)
    # /MP is MSVC-only: icx rejects it outright once offloading is enabled.
    add_compile_options(
-        $<$<AND:$<COMPILE_LANGUAGE:C>,$<C_COMPILER_ID:MSVC>>:/MP${SD_MSVC_MP_COUNT}>
+        $<$<AND:$<COMPILE_LANGUAGE:C>,$<C_COMPILER_ID:MSVC>>:/MP>
        $<$<COMPILE_LANGUAGE:C>:/utf-8>
-        $<$<AND:$<COMPILE_LANGUAGE:CXX>,$<CXX_COMPILER_ID:MSVC>>:/MP${SD_MSVC_MP_COUNT}>
+        $<$<AND:$<COMPILE_LANGUAGE:CXX>,$<CXX_COMPILER_ID:MSVC>>:/MP>
        $<$<COMPILE_LANGUAGE:CXX>:/utf-8>
    )
 endif()
@@ -126,12 +111,6 @@ if(SD_CUDA)
 endif()

 if(SD_METAL)
-    if(NOT SD_USE_UPSTREAM_GGML)
-        if(CMAKE_VERSION VERSION_LESS 3.16)
-            message(FATAL_ERROR "SD_METAL with SD_USE_UPSTREAM_GGML=OFF requires CMake 3.16 or later for OBJC and OBJCXX support")
-        endif()
-        enable_language(OBJC OBJCXX)
-    endif()
    message("-- Use Metal as backend stable-diffusion")
    set(GGML_METAL ON)
 endif()
@@ -246,10 +225,6 @@ file(GLOB SD_LIB_SOURCES CONFIGURE_DEPENDS
    "src/core/*.h"
    "src/core/*.cpp"
    "src/core/*.hpp"
-    "src/core/cpu/*.h"
-    "src/core/cpu/*.cpp"
-    "src/core/probe/*.h"
-    "src/core/probe/*.cpp"
    "src/extensions/*.h"
    "src/extensions/*.cpp"
    "src/extensions/*.hpp"
@@ -366,63 +341,10 @@ if (SD_CUDA)
    # Keep the driver stub on downstream link lines when no driver is installed.
    target_link_libraries(${SD_LIB} PUBLIC CUDA::cuda_driver)
    set_property(SOURCE src/core/ggml_extend_backend.cpp APPEND PROPERTY COMPILE_DEFINITIONS SD_USE_CUDA)
-    if (NOT SD_USE_UPSTREAM_GGML)
-        include(cmake/cutlass.cmake)
-        enable_language(CUDA)
-        set_target_properties(${SD_LIB} PROPERTIES CUDA_STANDARD 17 CUDA_STANDARD_REQUIRED ON)
-        if (SD_CUDA_CUTLASS_PATH)
-            target_sources(${SD_LIB} PRIVATE src/core/cuda/cute_attention.cu src/core/cuda/i8_projection.cu src/core/cuda/i8_gated_residual.cu src/core/cuda/i8_gemm.cu src/core/cuda/bf16_gemm.cu)
-            set_source_files_properties(src/core/cuda/cute_attention.cu src/core/cuda/i8_projection.cu src/core/cuda/i8_gated_residual.cu src/core/cuda/i8_gemm.cu src/core/cuda/bf16_gemm.cu PROPERTIES
-                INCLUDE_DIRECTORIES "${SD_CUDA_CUTLASS_PATH}/include"
-                COMPILE_OPTIONS "--use_fast_math;--extended-lambda;--expt-relaxed-constexpr")
-            if (MSVC)
-                set_property(SOURCE src/core/cuda/cute_attention.cu src/core/cuda/i8_projection.cu src/core/cuda/i8_gated_residual.cu src/core/cuda/i8_gemm.cu src/core/cuda/bf16_gemm.cu APPEND PROPERTY COMPILE_OPTIONS -Xcompiler=/Zc:preprocessor,/Zc:__cplusplus)
-            endif()
-            if (GGML_CUDA_FORCE_CUBLAS)
-                set_property(SOURCE src/core/cuda/i8_projection.cu src/core/cuda/i8_gated_residual.cu src/core/cuda/i8_gemm.cu src/core/cuda/bf16_gemm.cu APPEND PROPERTY COMPILE_DEFINITIONS SD_CUDA_FORCE_CUBLAS)
-            endif()
-        endif()
-    endif()
 endif()
-set(SD_BACKEND_REGISTRATIONS)
-if (SD_METAL AND NOT SD_USE_UPSTREAM_GGML)
-    add_subdirectory(src/core/metal)
-    target_link_libraries(${SD_LIB} PRIVATE "-framework Foundation" "-framework Metal")
-    list(APPEND SD_BACKEND_REGISTRATIONS sd_ggml_register_metal_i8)
-endif()
-if (NOT SD_USE_UPSTREAM_GGML AND (SD_CUDA OR SD_HIPBLAS OR SD_MUSA))
-    if(SD_HIPBLAS)
-        include(cmake/hip.cmake)
-    endif()
-    add_subdirectory(src/core/cuda)
-    foreach(VENDOR CUDA HIP MUSA)
-        if(SD_${VENDOR} OR (VENDOR STREQUAL "HIP" AND SD_HIPBLAS))
-            string(TOLOWER "${VENDOR}" NAMESPACE)
-            list(APPEND SD_BACKEND_REGISTRATIONS "sd_${NAMESPACE}::register_extensions")
-        endif()
-    endforeach()
-endif()
-if (SD_VULKAN AND NOT SD_USE_UPSTREAM_GGML)
-    add_subdirectory(src/core/vulkan)
-    list(APPEND SD_BACKEND_REGISTRATIONS sd_ggml_register_vulkan_rope sd_ggml_register_vulkan_i8 sd_ggml_register_vulkan_modulate sd_ggml_register_vulkan_rms_affine)
-endif()
-if(SD_BACKEND_REGISTRATIONS)
-    string(REPLACE ";" ",\n    " SD_BACKEND_REGISTRATIONS "${SD_BACKEND_REGISTRATIONS}")
-    configure_file(cmake/backend_extensions.h.in generated/sd_backend_extensions.h @ONLY)
-    set_property(SOURCE src/core/ggml_extensions.cpp APPEND PROPERTY INCLUDE_DIRECTORIES "${CMAKE_CURRENT_BINARY_DIR}/generated")
-    set_property(SOURCE src/core/ggml_extensions.cpp APPEND PROPERTY COMPILE_DEFINITIONS SD_BACKEND_EXTENSIONS)
-endif()
-if (SD_HIPBLAS)
-    find_package(hip REQUIRED CONFIG HINTS "$ENV{ROCM_PATH}" /opt/rocm)
-    target_link_libraries(${SD_LIB} PRIVATE hip::host)
-    set_property(SOURCE src/core/ggml_extend_backend.cpp APPEND PROPERTY COMPILE_DEFINITIONS SD_USE_HIP)
-    if (GGML_HIP_MMQ_MFMA)
-        set_property(SOURCE src/core/ggml_extend_backend.cpp APPEND PROPERTY COMPILE_DEFINITIONS SD_HIP_MFMA)
-    endif()
-endif()
-target_include_directories(${SD_LIB} PUBLIC include)
-target_include_directories(${SD_LIB} PRIVATE src src/core)
-target_include_directories(${SD_LIB} PUBLIC thirdparty)
+target_include_directories(${SD_LIB} PUBLIC . src include)
+target_include_directories(${SD_LIB} PRIVATE src/core)
+target_include_directories(${SD_LIB} PUBLIC . thirdparty)
 target_compile_features(${SD_LIB} PUBLIC c_std_11 cxx_std_17)


@@ -12,10 +12,6 @@ If you want to update a third-party dependency, please open an issue first inste

 ## Pull Requests

-When contributing from a fork, use a fork under your personal GitHub account and enable **Allow edits from maintainers**. This lets maintainers make follow-up fixes directly on the PR branch.
-
-PRs from organization-owned forks are automatically closed when opened or reopened because GitHub does not support this maintainer-edit option for those forks. Submit the changes from a personal fork instead. See [GitHub's documentation](https://docs.github.com/en/pull-requests/how-tos/work-with-forks/allowing-changes-to-a-pull-request-branch-created-from-a-fork).
-
 Keep each PR focused on one clear change. Large or overly complex PRs are harder to review and may not be merged.

 Do not include test code or test scripts in commits or PRs. Keep them local and report verification results in the PR description.
@@ -29,33 +25,6 @@ PRs should include:
 - Linked issue/PR context when applicable.
 - Screenshots or sample outputs for UI/visual behavior changes.

-## Documentation
-
-Write model usage guides in `docs/` for users. Focus on supported weights,
-setup, runnable commands, parameter meanings, output behavior, and practical
-limits such as memory requirements. Include technical details only when they
-help users choose settings or troubleshoot a problem.
-
-Keep tensor layouts, internal class names, kernel implementation details,
-algorithm derivations, and development history out of usage guides. Keep task-specific
-profiling logs, numerical comparison procedures, and test artifacts local, and
-summarize relevant verification results in the PR description.
-
-## README UI Listings
-
-The [UIs list in the README](README.md#uis) only includes open-source projects that use `stable-diffusion.cpp` as a backend. Do not submit PRs to add closed-source UIs.
-
-## Server Recipes
-
-Before adding or changing a built-in server recipe, read and follow the
-[built-in recipe policy](examples/server/recipes/README.md). The policy governs
-native editing, community editing LoRAs, distinct task checkpoints, external
-adapters, native components and layer-count variants. Do not add recipes solely
-for style or acceleration LoRAs, separately stored native components, or added
-or removed model layers. Explain the task, component compatibility or capability
-difference that justifies a new recipe, and verify its required components,
-feature declarations and embedded API representation.
-
 ## Code Style

 Format code according to the repository style before submitting changes.
@@ -15,8 +15,6 @@ API and command-line option may change frequently.***

 ## 🔥Important News

-* **2026/10/16** 🚀 stable-diffusion.cpp adds **Day-0 support for MOSS-Image-Base / MOSS-Image-SFT**
-* **2026/10/16** 🚀 stable-diffusion.cpp adds **Day-0 support for FLUX.3 Image Klein / Klein Base**
 * **2026/09/20** 🚀 stable-diffusion.cpp adds **Day-0 support for Qwen-Image-2.1**
 * **2026/08/20** 🚀 stable-diffusion.cpp now supports **LTX-2.5**
 * **2026/08/04** 🚀 stable-diffusion.cpp adds **Day-1 support for MiniMax-H3**
@@ -46,9 +44,6 @@ API and command-line option may change frequently.***
    - [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.3 Image Klein / Klein Base](./docs/flux3.md)
-    - [MOSS-Image-Base / MOSS-Image-SFT](./docs/moss_image.md)
-    - [Fibo 2.0 Dev](./docs/fibo2.md)
    - [Lens](./docs/lens.md)
    - [Chroma](./docs/chroma.md)
    - [Chroma1-Radiance](./docs/chroma_radiance.md)
@@ -57,9 +52,7 @@ API and command-line option may change frequently.***
    - [PiD](./docs/pid.md)
    - [LongCat Image](./docs/longcat_image.md)
    - [Z-Image](./docs/z_image.md)
-    - [Z-Image L2P](./docs/z_image_l2p.md)
    - [MiniT2I](./docs/minit2i.md)
-    - [P-H3-Image](./docs/p_h3_image.md)
    - [SenseNova U1.5](./docs/sensenova_u1.md)
    - [Ovis-Image](./docs/ovis_image.md)
    - [Anima](./docs/anima.md)
@@ -71,9 +64,6 @@ API and command-line option may change frequently.***
    - [HiDream-O1-Image](./docs/hidream_o1_image.md)
    - [Ideogram4](./docs/ideogram4.md)
    - [LLaDA-Image](./docs/llada_image.md)
-    - [Ming-Image Design](./docs/ming_image.md)
-    - [PixArt](./docs/pixart.md)
-    - [Iris-3B](./docs/iris.md)
  - [Image Edit Models](./docs/edit.md)
    - [FLUX.1-Kontext-dev](./docs/kontext.md)
    - [Qwen Image Edit series](./docs/qwen_image_edit.md)
@@ -81,16 +71,12 @@ API and command-line option may change frequently.***
    - [Boogu Image Edit](./docs/boogu_image.md)
    - [Mage-Flow-Edit](./docs/mage_flow.md#image-editing)
    - [LLaDA-Image Edit](./docs/llada_image.md#image-editing)
-    - [Fibo 2.0 Dev / Edit Turbo](./docs/fibo2.md#editing-and-turbo)
  - Video Models
    - [Wan2.1/Wan2.2](./docs/wan.md)
    - [MiniMax-H3](./docs/minimax_h3.md)
    - [LTX-2.3/LTX-2.5](./docs/ltx2.md)
    - [HunyuanVideo 1.5](./docs/hunyuan_video.md)
    - [LingBot-Video](./docs/lingbot_video.md)
-  - Video Upscale Models
-    - [RelayVSR](./docs/relay_vsr.md)
-    - [SeedVR2](./docs/seedvr2.md)
  - [PhotoMaker](./docs/photo_maker.md) support.
  - [IP-Adapter](./docs/ip_adapter.md) support (SD 1.5 and SDXL, including Plus)
  - Control Net support with SD 1.5
@@ -193,6 +179,7 @@ These projects wrap `stable-diffusion.cpp` for easier use in other languages/fra
 These projects use `stable-diffusion.cpp` as a backend for their image generation.

 - [GIMP Plugins](https://github.com/themanyone/gimp-plugins)
+- [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)
@@ -200,10 +187,6 @@ These projects use `stable-diffusion.cpp` as a backend for their image generatio
 - [LocalAI](https://github.com/mudler/LocalAI)
 - [Neural-Pixel](https://github.com/Luiz-Alcantara/Neural-Pixel)
 - [KoboldCpp](https://github.com/LostRuins/koboldcpp)
- [Kokon](https://github.com/GabrieleLozupone/kokon)
- [NodeTool](https://github.com/nodetool-ai/nodetool)
- [Quo](https://github.com/quo-ai/quo)
- [Prisme](https://github.com/d3Ita/prisme)

 ## Contributors

@@ -1,17 +0,0 @@
-#ifndef __SD_BACKEND_EXTENSIONS_H__
-#define __SD_BACKEND_EXTENSIONS_H__
-
-#include "core/cuda/registration.h"
-
-void sd_ggml_register_metal_i8(ggml_backend_dev_t device);
-
-void sd_ggml_register_vulkan_rope(ggml_backend_dev_t device);
-void sd_ggml_register_vulkan_i8(ggml_backend_dev_t device);
-void sd_ggml_register_vulkan_rms_affine(ggml_backend_dev_t device);
-void sd_ggml_register_vulkan_modulate(ggml_backend_dev_t device);
-
-static void (*const sd_backend_extensions[])(ggml_backend_dev_t) = {
-    @SD_BACKEND_REGISTRATIONS@
-};
-
-#endif
@@ -1,20 +0,0 @@
-set(sd_cutlass_default "${PROJECT_SOURCE_DIR}/thirdparty/cutlass")
-if(NOT DEFINED SD_CUDA_CUTLASS_PATH AND DEFINED GGML_CUDA_CUTLASS_PATH)
-    get_filename_component(sd_old_cutlass_default "${SD_GGML_SOURCE_DIR}/src/ggml-cuda/vendors/cutlass" ABSOLUTE)
-    if(NOT GGML_CUDA_CUTLASS_PATH STREQUAL sd_old_cutlass_default)
-        set(sd_cutlass_default "${GGML_CUDA_CUTLASS_PATH}")
-    endif()
-    message(STATUS "Migrating GGML_CUDA_CUTLASS_PATH to SD_CUDA_CUTLASS_PATH")
-endif()
-set(SD_CUDA_CUTLASS_PATH "${sd_cutlass_default}" CACHE PATH "sd: CUTLASS source directory for INT8 GEMM and CuTe attention, empty to disable")
-unset(GGML_CUDA_CUTLASS_PATH CACHE)
-if(SD_CUDA_CUTLASS_PATH)
-    if(NOT EXISTS "${SD_CUDA_CUTLASS_PATH}/include/cutlass/gemm/kernel/default_gemm_universal_with_visitor.h" OR
-       NOT EXISTS "${SD_CUDA_CUTLASS_PATH}/include/cute/tensor.hpp")
-        message(FATAL_ERROR
-            "CUTLASS headers not found at ${SD_CUDA_CUTLASS_PATH}. "
-            "Run git submodule update --init --recursive from the sd.cpp repository root, "
-            "set SD_CUDA_CUTLASS_PATH to a CUTLASS source tree (tested with 3.9.2), "
-            "or set SD_CUDA_CUTLASS_PATH to an empty string to disable CUTLASS.")
-    endif()
-endif()
@@ -1,3 +0,0 @@
-file(READ "${INPUT}" SPIRV HEX)
-string(REGEX REPLACE "(..)(..)(..)(..)" "0x\\4\\3\\2\\1," SPIRV_WORDS "${SPIRV}")
-file(WRITE "${OUTPUT}" "#include <cstdint>\nnamespace sd_vulkan_shaders {\nstatic const uint32_t ${NAME}[] = {${SPIRV_WORDS}};\n}\n")
@@ -9,19 +9,17 @@ if(NOT TARGET ggml)
    add_library(ggml ALIAS ggml::ggml)
 endif()

-if(SD_USE_UPSTREAM_GGML)
-    get_target_property(sd_ggml_imported ggml IMPORTED)
-    if(sd_ggml_imported)
-        set(sd_ggml_private_include "${SD_GGML_SOURCE_DIR}/src")
-    else()
-        get_target_property(sd_ggml_private_include ggml SOURCE_DIR)
-    endif()
-    if(NOT EXISTS "${sd_ggml_private_include}/ggml-impl.h")
-        message(FATAL_ERROR "Set SD_GGML_SOURCE_DIR to the source tree matching the selected ggml library (ggml-impl.h is required).")
-    endif()
-    target_include_directories(${SD_LIB} PRIVATE "${sd_ggml_private_include}")
-    set_property(TARGET ${SD_LIB} PROPERTY SD_GGML_PRIVATE_INCLUDE_DIR "${sd_ggml_private_include}")
+get_target_property(sd_ggml_imported ggml IMPORTED)
+if(sd_ggml_imported)
+    set(sd_ggml_private_include "${SD_GGML_SOURCE_DIR}/src")
+else()
+    get_target_property(sd_ggml_private_include ggml SOURCE_DIR)
 endif()
+if(NOT EXISTS "${sd_ggml_private_include}/ggml-impl.h")
+    message(FATAL_ERROR "Set SD_GGML_SOURCE_DIR to the source tree matching the selected ggml library (ggml-impl.h is required).")
+endif()
+target_include_directories(${SD_LIB} PRIVATE "${sd_ggml_private_include}")
+set_property(TARGET ${SD_LIB} PROPERTY SD_GGML_PRIVATE_INCLUDE_DIR "${sd_ggml_private_include}")

 if(SD_USE_UPSTREAM_GGML)
    target_compile_definitions(${SD_LIB} PUBLIC SD_USE_UPSTREAM_GGML)
@@ -1,12 +0,0 @@
-if(NOT WIN32 AND NOT CMAKE_CXX_COMPILER MATCHES "hipcc")
-    if(NOT CMAKE_HIP_ARCHITECTURES)
-        if(GPU_TARGETS)
-            set(CMAKE_HIP_ARCHITECTURES ${GPU_TARGETS})
-        elseif(AMDGPU_TARGETS)
-            set(CMAKE_HIP_ARCHITECTURES ${AMDGPU_TARGETS})
-        endif()
-    endif()
-    # HIP objects are linked by the parent target, so initialize its link rules here.
-    enable_language(HIP)
-    set_property(TARGET ${SD_LIB} PROPERTY HIP_ARCHITECTURES "${CMAKE_HIP_ARCHITECTURES}")
-endif()
@@ -8,7 +8,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends build-essential
    curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key -o /tmp/nodesource-repo.gpg.key && \
    gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg /tmp/nodesource-repo.gpg.key && \
    rm /tmp/nodesource-repo.gpg.key && \
-    echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_24.x nodistro main" > /etc/apt/sources.list.d/nodesource.list && \
+    echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_20.x nodistro main" > /etc/apt/sources.list.d/nodesource.list && \
    apt-get update && \
    apt-get install -y --no-install-recommends nodejs && \
    npm install -g pnpm@10.15.1 && \
@@ -40,6 +40,4 @@ RUN printf '#!/bin/sh\nexec /sd.cpp/bin/sd-cli "$@"\n' > /sd-cli && \
    printf '#!/bin/sh\nexec /sd.cpp/bin/sd-server "$@"\n' > /sd-server && \
    chmod +x /sd-cli /sd-server

-WORKDIR /sd.cpp
-
 ENTRYPOINT [ "/sd-cli" ]
@@ -1,9 +1,7 @@
-# syntax=docker/dockerfile:1
-
 ARG CUDA_VERSION=12.6.3
 ARG UBUNTU_VERSION=24.04

-FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} AS build
+FROM nvidia/cuda:${CUDA_VERSION}-cudnn-devel-ubuntu${UBUNTU_VERSION} AS build

 # sd-server embeds the web UI at build time, so the build image needs Node/pnpm.
 RUN apt-get update && apt-get install -y --no-install-recommends build-essential git ccache cmake ca-certificates curl gnupg && \
@@ -11,7 +9,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends build-essential
    curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key -o /tmp/nodesource-repo.gpg.key && \
    gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg /tmp/nodesource-repo.gpg.key && \
    rm /tmp/nodesource-repo.gpg.key && \
-    echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_24.x nodistro main" > /etc/apt/sources.list.d/nodesource.list && \
+    echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_20.x nodistro main" > /etc/apt/sources.list.d/nodesource.list && \
    apt-get update && \
    apt-get install -y --no-install-recommends nodejs && \
    npm install -g pnpm@10.15.1 && \
@@ -27,15 +25,8 @@ ARG CUDA_ARCHITECTURES=""
 ARG GGML_CUDA_FA_ALL_QUANTS=""
 ARG GGML_CUDA_ENABLE_DYNAMIC_CPU_BACKENDS=ON

-ENV CCACHE_DIR=/root/.cache/ccache \
-    CCACHE_MAXSIZE=2G \
-    CCACHE_COMPILERCHECK=content
-
 RUN set -- \
-        -DSD_CUDA=ON \
-        -DCMAKE_C_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
-        -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache; \
+        -DSD_CUDA=ON; \
    if [ "${GGML_CUDA_ENABLE_DYNAMIC_CPU_BACKENDS}" = "ON" ]; then \
        set -- "$@" \
            -DSD_BUILD_SHARED_LIBS=ON \
@@ -53,24 +44,17 @@ RUN set -- \
        set -- "$@" "-DGGML_CUDA_FA_ALL_QUANTS=${GGML_CUDA_FA_ALL_QUANTS}"; \
    fi; \
    cmake . -B ./build "$@"
-RUN --mount=type=cache,target=/root/.cache/ccache \
-    ccache --zero-stats && \
-    cmake --build ./build --config Release -j$(nproc) && \
-    ccache --cleanup && \
-    ccache --show-stats
+RUN cmake --build ./build --config Release -j$(nproc)

-FROM nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} AS runtime
+FROM nvidia/cuda:${CUDA_VERSION}-cudnn-runtime-ubuntu${UBUNTU_VERSION} AS runtime

 RUN apt-get update && \
    apt-get install --yes --no-install-recommends libgomp1 && \
    apt-get clean

 COPY --from=build /sd.cpp/build/bin /sd.cpp/bin
-COPY --from=build /sd.cpp/thirdparty/cutlass/LICENSE.txt /sd.cpp/bin/cutlass.txt
 RUN printf '#!/bin/sh\nexec /sd.cpp/bin/sd-cli "$@"\n' > /sd-cli && \
    printf '#!/bin/sh\nexec /sd.cpp/bin/sd-server "$@"\n' > /sd-server && \
    chmod +x /sd-cli /sd-server

-WORKDIR /sd.cpp
-
 ENTRYPOINT [ "/sd-cli" ]
@@ -1,6 +1,7 @@
-ARG MUSA_IMAGE=registry.mthreads.com/mcconline/inference/pytorch:2.9.1.post1-py3.10-musa5.2.0-mp31-devel-ubuntu22.04-amd64
+ARG MUSA_VERSION=rc4.2.0
+ARG UBUNTU_VERSION=22.04

-FROM ${MUSA_IMAGE} AS build
+FROM mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}-amd64 as build

 # sd-server embeds the web UI at build time, so the build image needs Node/pnpm.
 RUN apt-get update && apt-get install -y --no-install-recommends ccache cmake git ca-certificates curl gnupg && \
@@ -8,7 +9,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends ccache cmake gi
    curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key -o /tmp/nodesource-repo.gpg.key && \
    gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg /tmp/nodesource-repo.gpg.key && \
    rm /tmp/nodesource-repo.gpg.key && \
-    echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_24.x nodistro main" > /etc/apt/sources.list.d/nodesource.list && \
+    echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_20.x nodistro main" > /etc/apt/sources.list.d/nodesource.list && \
    apt-get update && \
    apt-get install -y --no-install-recommends nodejs && \
    npm install -g pnpm@10.15.1 && \
@@ -21,8 +22,9 @@ 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 \
-        -DMUSA_ARCHITECTURES=31 \
        -DSD_BUILD_SHARED_LIBS=ON \
        -DGGML_NATIVE=OFF \
        -DSD_BUILD_SHARED_GGML_LIB=ON \
@@ -33,14 +35,11 @@ RUN mkdir build && cd build && \
        -DCMAKE_BUILD_TYPE=Release && \
    cmake --build . --config Release

-FROM ${MUSA_IMAGE} AS runtime
+FROM mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64 as runtime

 COPY --from=build /sd.cpp/build/bin /sd.cpp/bin
 RUN printf '#!/bin/sh\nexec /sd.cpp/bin/sd-cli "$@"\n' > /sd-cli && \
    printf '#!/bin/sh\nexec /sd.cpp/bin/sd-server "$@"\n' > /sd-server && \
    chmod +x /sd-cli /sd-server

-WORKDIR /sd.cpp
-
 ENTRYPOINT [ "/sd-cli" ]
-CMD []
@@ -1,50 +0,0 @@
-FROM rocm/dev-ubuntu-24.04 AS build
-
-# sd-server embeds the web UI at build time, so the build image needs Node/pnpm.
-RUN apt-get update && apt-get install -y --no-install-recommends \
-        build-essential git cmake ninja-build ccache ca-certificates curl gnupg && \
-    mkdir -p /etc/apt/keyrings && \
-    curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key -o /tmp/nodesource-repo.gpg.key && \
-    gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg /tmp/nodesource-repo.gpg.key && \
-    rm /tmp/nodesource-repo.gpg.key && \
-    echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_24.x nodistro main" > /etc/apt/sources.list.d/nodesource.list && \
-    apt-get update && apt-get install -y --no-install-recommends nodejs && \
-    npm install -g pnpm@10.15.1 && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-
-WORKDIR /sd.cpp
-
-# ROCm's clang is not on PATH in Docker RUN shells (profile scripts are not loaded).
-ENV PATH="/opt/rocm/llvm/bin:$PATH"
-
-COPY . .
-
-ARG AMDGPU_TARGETS=""
-
-RUN cmake . -B ./build -G Ninja \
-    -DCMAKE_C_COMPILER=clang \
-    -DCMAKE_CXX_COMPILER=clang++ \
-    -DSD_HIPBLAS=ON \
-    -DGGML_NATIVE=OFF \
-    -DAMDGPU_TARGETS="${AMDGPU_TARGETS}" \
-    -DCMAKE_BUILD_TYPE=Release \
-    -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
-    -DCMAKE_POSITION_INDEPENDENT_CODE=ON
-RUN cmake --build ./build --config Release --parallel
-
-FROM rocm/dev-ubuntu-24.04 AS runtime
-
-# ROCm clang links against its own OpenMP runtime in /opt/rocm/llvm/lib,
-# and the ROCm runtime libs (hipblas, rocblas, amdhip64) live in /opt/rocm/lib.
-ENV LD_LIBRARY_PATH="/opt/rocm/llvm/lib:/opt/rocm/lib:$LD_LIBRARY_PATH"
-
-RUN apt-get update && \
-    apt-get install --yes --no-install-recommends libgomp1 && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-
-COPY --from=build /sd.cpp/build/bin /sd.cpp/bin
-RUN printf '#!/bin/sh\nexec /sd.cpp/bin/sd-cli "$@"\n' > /sd-cli && \
-    printf '#!/bin/sh\nexec /sd.cpp/bin/sd-server "$@"\n' > /sd-server && \
-    chmod +x /sd-cli /sd-server
-
-ENTRYPOINT [ "/sd-cli" ]
@@ -9,7 +9,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends cmake ca-certif
    curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key -o /tmp/nodesource-repo.gpg.key && \
    gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg /tmp/nodesource-repo.gpg.key && \
    rm /tmp/nodesource-repo.gpg.key && \
-    echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_24.x nodistro main" > /etc/apt/sources.list.d/nodesource.list && \
+    echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_20.x nodistro main" > /etc/apt/sources.list.d/nodesource.list && \
    apt-get update && \
    apt-get install -y --no-install-recommends nodejs && \
    npm install -g pnpm@10.15.1 && \
@@ -29,6 +29,4 @@ FROM intel/oneapi-basekit:${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

-WORKDIR /sd.cpp
-
 ENTRYPOINT [ "/sd-cli" ]
@@ -8,7 +8,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends build-essential
    curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key -o /tmp/nodesource-repo.gpg.key && \
    gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg /tmp/nodesource-repo.gpg.key && \
    rm /tmp/nodesource-repo.gpg.key && \
-    echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_24.x nodistro main" > /etc/apt/sources.list.d/nodesource.list && \
+    echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_20.x nodistro main" > /etc/apt/sources.list.d/nodesource.list && \
    apt-get update && \
    apt-get install -y --no-install-recommends nodejs && \
    npm install -g pnpm@10.15.1 && \
@@ -41,6 +41,4 @@ RUN printf '#!/bin/sh\nexec /sd.cpp/bin/sd-cli "$@"\n' > /sd-cli && \
    printf '#!/bin/sh\nexec /sd.cpp/bin/sd-server "$@"\n' > /sd-server && \
    chmod +x /sd-cli /sd-server

-WORKDIR /sd.cpp
-
 ENTRYPOINT [ "/sd-cli" ]
@@ -1,10 +1,9 @@
 # ADetailer

 `sd-cli` can run a YOLOv8 object detector on an existing or newly generated
-image, and `sd-server` can run it after image generation. Both perform a cropped
-inpaint pass for every detected object. The implementation supports YOLOv8
-detection checkpoints. YOLOv8 segmentation and MediaPipe models are not
-supported yet.
+image and perform a cropped inpaint pass for every detected object. The first
+implementation supports YOLOv8 detection checkpoints. YOLOv8 segmentation and
+MediaPipe models are not supported yet.

 ## Convert a detector

@@ -69,36 +68,6 @@ ADetailer can also run automatically after normal image generation:
  --extra-ad-args "confidence=0.3,denoising_strength=0.4,inpaint_width=512,inpaint_height=512"
 ```

-The same options can be supplied when starting `sd-server`. When `--ad-model` is set, its detector runs after OpenAI-compatible and native asynchronous image generation. SDAPI requests enable ADetailer through `alwayson_scripts`, as described below. If no object is detected, the original image is returned unchanged. RGBA output keeps its original alpha channel, including when detected regions are repaired. Detector initialization or detail-pass failures are reported as request failures.
-
-Server image-generation requests limit base, hires, and detail-pass dimensions to 4096 pixels per side. ADetailer step overrides are limited to 1–100; `steps=0` in `extra_ad_args` keeps inheriting the main generation steps. These limits also apply to inherited dimensions and `extra_ad_args` overrides. Requests exceeding the limits return a parameter error before generation. CLI limits are unchanged.
-
-### SDAPI requests
-
-`/sdapi/v1/txt2img` and `/sdapi/v1/img2img` accept the WebUI ADetailer argument format:
-
-```json
-{
-  "prompt": "portrait photo",
-  "alwayson_scripts": {
-    "ADetailer": {
-      "args": [true, false, {
-        "ad_model": "face_yolov8n.pt",
-        "ad_prompt": "[PROMPT], detailed face",
-        "ad_use_steps": true,
-        "ad_steps": 20
-      }]
-    }
-  }
-}
-```
-
-The detector must be configured at server startup with `--ad-model`. `ad_model` must match that file's basename; the original `.pt` basename is also accepted for a converted detector. Requests cannot load arbitrary detector paths. Omitting the script, setting its first argument to `false`, selecting `"None"`, or setting `ad_tab_enable` to `false` disables detailing. Dictionary-only argument arrays are also accepted. Multiple enabled dictionaries run in order using the configured detector.
-
-Supported settings include prompts, confidence, mask filtering by area or confidence, mask count and area ratios, dilation/erosion, offsets, mask merging/inversion, blur, denoising strength, masked or full-image inpainting, padding, and optional overrides for inpaint dimensions, steps, CFG, sampler/scheduler, and CLIP skip. The corresponding `ad_use_*` flags control whether overrides apply. Otherwise dimensions, steps, CFG, sampler, scheduler, and CLIP skip inherit the main request. Denoising strength defaults to `0.4`. Pixel mask settings are limited to the larger output dimension, with a minimum limit of 32 and a maximum of 16384 pixels.
-
-Model/VAE switching, non-default noise multipliers, face restoration, ControlNet, and skipping the initial img2img pass are not supported; active requests for these features return an error. Unknown settings and invalid active values also return errors. During detailing, `/sdapi/v1/progress` remains active with an `adetailer` job; its percentage and preview stay fixed until detailing finishes.
-
 An empty ADetailer prompt inherits the main prompt. `[PROMPT]` inserts the main
 prompt, `[SEP]` assigns different prompts to consecutive masks, and `[SKIP]`
 skips the corresponding mask.
@@ -113,7 +82,6 @@ All settings other than the detector path and prompts are passed through
 | `nms` | `0.45` | NMS IoU threshold |
 | `max_detections` | `100` | Maximum detections retained after NMS |
 | `mask_k_largest` | `0` | Keep only the largest K masks; zero keeps all |
-| `mask_filter_method` | `area` | Rank retained masks by `area` or `confidence` |
 | `mask_min_ratio` | `0` | Minimum bbox area relative to the image |
 | `mask_max_ratio` | `1` | Maximum bbox area relative to the image |
 | `dilate_erode` | `4` | Positive values dilate; negative values erode |
@@ -122,7 +90,6 @@ All settings other than the detector path and prompts are passed through
 | `merge_masks`, `invert_mask` | `false` | Boolean alternatives to `mask_mode` |
 | `mask_blur` | `4` | Final composite feather radius |
 | `inpaint_padding` | `32` | Padding around the detected region |
-| `inpaint_only_masked` | `true` | Crop to the detected region; `false` uses the full image with the detection mask |
 | `inpaint_width`, `inpaint_height` | mode-specific | `512x512` after generation; input/output size in `adetailer` mode |
 | `denoising_strength` | mode-specific | `0.4` after generation; inherits `--strength` in `adetailer` mode |
 | `steps` | `0` | Detail steps; zero inherits the main generation |
@@ -15,7 +15,7 @@
 ## Examples

 ```sh
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\anima-preview.safetensors --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_3_06b_base.safetensors  -p "a lovely cat holding a sign says 'anima.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\anima-preview.safetensors --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_3_06b_base.safetensors  -p "a lovely cat holding a sign says 'anima.cpp'" --cfg-scale 6.0 --sampling-method euler -v --offload-to-cpu --diffusion-fa
 ```

 <img alt="anima image example" src="../assets/anima/example.png" />
@@ -51,7 +51,7 @@ SD 1.5 defaults (euler_a, low CFG) produces noise-like output.
 .\bin\Release\sd-cli.exe -M vid_gen \
    --model ..\models\checkpoints\realisticVisionV60B1.safetensors \
    --motion-module ..\models\animatediff\mm_sd15_v3.safetensors \
-    --offload-to-cpu \
+    --offload-to-cpu --diffusion-fa \
    -p "a red apple on a wooden table" \
    --cfg-scale 8.0 --sampling-method euler --scheduler discrete \
    -H 512 -W 512 --video-frames 8 --fps 8 --steps 20 -s 42 \
@@ -66,7 +66,7 @@ use layer streaming:
 .\bin\Release\sd-cli.exe -M vid_gen \
    --model ..\models\checkpoints\realisticVisionV60B1.safetensors \
    --motion-module ..\models\animatediff\mm_sd15_v3.safetensors \
-    --offload-to-cpu \
+    --offload-to-cpu --diffusion-fa \
    -p "photo of coastline, rocks, storm weather, wind, waves, lightning" \
    --cfg-scale 8.0 --sampling-method euler --scheduler discrete \
    -H 384 -W 384 --video-frames 16 --fps 8 --steps 20 -s 42 \
@@ -79,7 +79,7 @@ Low-VRAM streaming (verified with a 2 GiB cap on RTX 3060):
 .\bin\Release\sd-cli.exe -M vid_gen \
    --model ..\models\checkpoints\realisticVisionV60B1.safetensors \
    --motion-module ..\models\animatediff\mm_sd15_v3.safetensors \
-    --max-vram 2.0 \
+    --max-vram 2.0 --diffusion-fa \
    -p "photo of coastline, rocks, storm weather, wind, waves, lightning" \
    --cfg-scale 8.0 --sampling-method euler --scheduler discrete \
    -H 384 -W 384 --video-frames 8 --fps 8 --steps 20 -s 42 \
@@ -135,10 +135,7 @@ regardless of argument order, even with `--auto-fit on`.

 Auto-fit preserves explicit `--backend` assignments, including per-module
 assignments and device lists. For modules without a runtime assignment, it chooses
-the discrete GPU with the largest positive memory budget (the first device on a
-tie), falling back to integrated GPUs when no discrete GPU has a positive budget.
-An integrated GPU's shared system memory does not give it priority over a discrete
-GPU. Explicit assignments such as `--backend Vulkan1` still select that device.
+the GPU with the largest available memory budget (the first device on a tie).
 It then derives parameter placements from the model metadata, each module's
 compute devices, and the remaining memory budgets. The chosen backend
 specifications are printed.
@@ -159,14 +156,8 @@ the runner's graph-cut capacity checks.

 Runtime capacity checks also leave 512 MiB of currently free device memory for
 backend scratch buffers and pipelines, including with explicit backend assignments.
-They cap free-memory reports by the device's total memory minus tracked
-resident allocations. Vulkan reports exceeding total memory are rejected because
-its heap-budget subtraction can underflow. Other backends use the cap instead of
-treating such reports as zero free memory. Failed checks log the reported free and
-total memory alongside tracked weight and runtime allocations.
-With `--mmap`, device-backed mappings count toward these budgets at their full
-mapped-file size, once per device buffer even when multiple parameter blocks
-share it. Mappings retained in the loader cache continue to count.
+They cap stale free-memory reports by the device's total memory minus tracked
+resident allocations and reject reports that exceed the device's total memory.

 Components are considered in `diffusion`, `te`, `vae` order so that repeatedly
 used diffusion weights have priority. Each component's weights use the first
@@ -218,126 +209,6 @@ If a VAE decode fails, decoding retries with spatial tiling even when `--auto-fi
 is off; supported video decoders try temporal tiling first and can then add
 spatial tiling. Spatial retries use half-size tiles along each latent dimension.

-## Direct convolution (`--conv-direct on|off|auto`)
-
-`--conv-direct` defaults to `auto` and accepts module assignments for
-`diffusion`, `vae` (including preview VAE), and `controlnet`, using the same
-aliases and precedence rules as `--flash-attn`:
-
-```shell
-sd-cli -m model.safetensors -p "a cat" --conv-direct auto,vae=off
-sd-cli -m model.safetensors -p "a cat" --conv-direct off,diffusion=on
-```
-
- `on` requests direct 2D and 3D convolution.
- `off` uses the existing im2col-based convolution paths.
- `auto` first checks support for a representative F16-weight/F32-input direct
-  convolution graph. Vulkan then benchmarks direct convolution against im2col
-  using two small shapes per dimension, with warmup, output validation, and five
-  interleaved measurements per shape. Direct convolution must improve the sum of
-  median times by at least 5%; a failed probe defaults to `off`.
-  Other backends retain their implicit GEMM checks without benchmarking:
-  CUDA 2D requires a compiled Turing-or-newer MMA architecture; HIP 2D requires
-  the supported CDNA MFMA or RDNA WMMA architectures described below for flash
-  attention. MUSA 2D has a SIMT implementation. CUDA, HIP, and MUSA 3D also have
-  a SIMT implementation, so they do not require MMA. Other backends default to
-  `off`. Results and decision logs are cached per compute backend and dimension
-  in the context, as with flash attention. Each dimension is selected independently,
-  and all compute devices in a module must qualify. Parameter-storage devices do
-  not affect this choice.
-
-These are backend-level defaults. GGML still chooses the actual kernel based on
-weight type, shape, and hardware; enabling direct convolution does not guarantee
-that every operation uses implicit GEMM. The CUDA-family implicit GEMM paths
-require F16 weights and can use ordinary direct kernels for other cases.
-For 3D convolution, a supported direct kernel is also used as a fallback when
-the im2col decomposition is unsupported for the actual tensor sizes, including
-when an intermediate reshape or matrix multiplication exceeds backend limits.
-This option does not change the separately created image upscaler context.
-
-Repeating `--conv-direct` replaces the previous assignment string. Existing
-`--diffusion-conv-direct` (diffusion and ControlNet) and `--vae-conv-direct`
-flags are deprecated and emit a warning, but remain supported when
-`--conv-direct` is omitted. Use `--conv-direct auto,diffusion=on,controlnet=on`
-and `--conv-direct auto,vae=on`, respectively. An explicit
-`--conv-direct` takes precedence over both legacy flags regardless of order.
-Library callers can set the appended `sd_ctx_params_t::conv_direct_mode` field
-to the same string; null or empty selects `auto` while honoring legacy true
-flags. The context copies the string.
-
-## Flash attention (`--flash-attn on|off|auto`)
-
-`--flash-attn` defaults to `auto`. Selection happens while creating the model
-context, before inference, and is cached once per compute backend in that context.
-Parameter-storage backends are not benchmarked.
-
-The option also accepts module assignments, using the same module aliases as
-`--backend`:
-
-```shell
-sd-cli -m model.safetensors -p "a cat" --flash-attn auto,te=off,vae=on
-sd-cli -m model.safetensors -p "a cat" --flash-attn off,diffusion=auto
-```
-
-Supported modules are `diffusion`, `te`, `clip_vision`, `vae`, `controlnet`, and
-`audio_encoder`. Modules without an assignment use the default (`auto` if no
-default is supplied). A bare mode or `all=`, `default=`, or `*=` sets the default;
-module-specific settings take precedence regardless of their position. Within
-one assignment string, the last setting for the same module or default wins.
-Repeating `--flash-attn` replaces the entire previous string.
-
-Only modules set to `auto` need backend probing. An explicit `on` or `off` for
-one module does not change another module's decision, even when they share a
-backend. Modules without configurable attention are rejected instead of silently
-ignoring their settings.
-
- `on` requests flash attention for all models. Unsupported attention operations
-  still fall back to ordinary attention.
- `off` disables flash attention, including the flash-attention fallback from
-  SageAttention. SageAttention itself remains controlled by `--sage-attn`.
- `auto` enables CPU and supported CUDA MMA configurations without timing a
-  benchmark. CUDA uses both the device's capability and the compiled GGML
-  architectures. HIP also skips timing on known CDNA MFMA architectures
-  (`gfx908`, `gfx90a`, `gfx940`/`gfx941`/`gfx942`, `gfx950`) and RDNA 3/3.5/4
-  WMMA architectures (`gfx11xx`/`gfx12xx`). The CDNA shortcut requires
-  `GGML_HIP_MMQ_MFMA` to be enabled at build time. All shortcuts still check
-  support for the FA probe graph. Vulkan devices whose description identifies
-  NVIDIA RTX hardware (GeForce RTX, TITAN RTX, Quadro RTX and professional RTX)
-  also skip timing. This is a device-name heuristic and needs no CUDA runtime.
-  Unsupported graphs are disabled; other configurations, including older or
-  unknown HIP architectures and other Vulkan devices, are benchmarked.
-
-The probe compares ordinary and flash attention through the same attention
-builder used by models, including layout conversion and F16 conversion. It uses
-two small self-attention graphs (512 tokens with head size 64, and 1024 tokens
-with head size 128, both with four heads), checks their outputs, warms up each
-path, then alternates five synchronized measurements. Flash attention is enabled
-when the sum of its median times is at least 5% lower. These representative
-shapes are a startup heuristic, not a guarantee for every model or resolution.
-Allocation/execution failures and inconclusive probes select `off`. Timing stops
-starting new measurement pairs after one second of measured computation;
-backend initialization, warmup and an in-flight pair can take longer.
-
-Different modules can select different results. A multi-device model enables
-flash attention only if all of its compute devices select `on`, preserving the
-model's existing single boolean setting. This conservative rule also covers
-row-split configurations, which can fall back to layer split during initialization.
-
-`--fa` and `--diffusion-fa` are deprecated and emit a warning when used.
-Use `--flash-attn on` and `--flash-attn off,diffusion=on`, respectively.
-Both aliases write the same mode string as `--flash-attn`; the last occurrence
-of any of these options wins. Library callers
-can set `sd_ctx_params_t::flash_attn_mode` to the same string, including
-`"on"`, `"off"`, `"auto"`, or `"auto,te=off,vae=on"`.
-A null or empty string uses automatic selection unless a
-legacy `flash_attn` or `diffusion_flash_attn` boolean is set. The context copies
-the string; no new enum is required.
-
-```shell
-sd-cli -m model.safetensors -p "a cat" --flash-attn auto
-sd-cli -m model.safetensors -p "a cat" --flash-attn off
-```
-
 ## Modules

 | Module | Purpose | Accepted names |
@@ -1,50 +0,0 @@
-# Adding backend performance probes
-
-`src/core/probe/backend_probe.h` provides the shared measurement machinery. Operator
-policy stays separate: `src/core/probe/flash_attn.cpp`, for example, defines attention
-shapes and its CPU/CUDA/HIP/Vulkan shortcuts, then calls `sd_run_backend_probe` for unknown
-configurations. `src/core/probe/conv_direct.cpp` uses metadata-only probe graphs
-to check direct 2D/3D convolution support, then enables known implicit GEMM
-paths on non-Vulkan backends. Vulkan compares direct convolution with im2col
-using the shared measurement machinery.
-
-For each representative shape, create a `BackendProbeCase`:
-
- `build(graph, candidate)` creates inputs and either the reference or candidate
-  graph in `graph.ctx`, registers input tensors in `graph.inputs`, and assigns
-  `graph.output`. Leave the output null if the requested implementation is
-  unavailable; do not silently benchmark its fallback as the candidate.
- By default, inputs receive deterministic F32 data, with the same seed for both
-  variants. Supply `initialize` for F16 convolution weights, integer inputs, or
-  operator-specific values. It runs after allocation, outside timed execution.
-  Both variants must receive equivalent logical inputs.
- Default output validation requires matching shapes and contiguous F32 outputs,
-  checks finiteness, and applies absolute and relative tolerances. Supply
-  `compare` for other output types or validation rules.
-
-Graphs must be safe to execute repeatedly without modifying their inputs.
-`BackendProbeGraph` owns its GGML context and buffer, so all return paths release
-probe resources. Constructors allocate metadata only; `supported()` can also be
-used before a known-architecture shortcut, without allocating device tensors.
-
-`BackendProbeOptions` controls graph metadata capacity, warmup count, measurement
-count, measured-time budget, minimum free device memory, comparison tolerances,
-and minimum improvement. Warmup and allocation are excluded from timing. The
-runner alternates the two variants, synchronizes the backend around execution,
-and compares sums of per-case medians. The time budget is checked before each
-measurement pair; it cannot interrupt an in-flight backend operation.
-
-Only use `candidate_faster` when `completed` is true. On incomplete probes,
-`error` reports why comparison failed and the timing totals may be partial.
-The caller handles logging, known architecture rules, fallback policy, and
-context-lifetime caching. Keep caches separate by operation (or key a shared
-cache by both operation and backend); an attention result must not select a
-convolution implementation. If shapes or settings vary within one context,
-include them in the corresponding cache key as well.
-
-Convolution results are cached separately for 2D and 3D, once per compute backend
-in the context, just like attention results. Each probe logs its backend, decision,
-and reason; mixed-device modules log when their compute devices disagree.
-
-Module assignment parsing remains in `src/core/ggml_extend_backend.cpp`, next
-to backend assignment parsing. The probe directory does not parse CLI/API options.
@@ -17,7 +17,7 @@ Boogu Image uses a Boogu diffusion transformer, the FLUX VAE, and Qwen3-VL as th
 ### Boogu Image Base

 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\boogu_image_base_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\models\vae\ae.sft -p "a lovely cat" -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\boogu_image_base_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\models\vae\ae.sft -p "a lovely cat" --diffusion-fa -v --offload-to-cpu
 ```

 <img width="256" alt="Boogu Image Base example" src="../assets/boogu/example.png" />
@@ -25,7 +25,7 @@ Boogu Image uses a Boogu diffusion transformer, the FLUX VAE, and Qwen3-VL as th
 ### Boogu Image Edit

 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\boogu_image_edit_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\mmproj-Qwen3VL-8B-Instruct-F16.gguf --vae ..\models\vae\ae.sft -v --offload-to-cpu -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'boogu.cpp'"
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\boogu_image_edit_bf16.safetensors --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\mmproj-Qwen3VL-8B-Instruct-F16.gguf --vae ..\models\vae\ae.sft --diffusion-fa -v --offload-to-cpu -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'boogu.cpp'"
 ```

 <img width="256" alt="Boogu Image Edit example" src="../assets/boogu/edit_example.png" />
@@ -2,44 +2,20 @@

 ## Get the Code

-```shell
+```
 git clone --recursive https://github.com/leejet/stable-diffusion.cpp
 cd stable-diffusion.cpp
 ```

-If you already cloned the repository without submodules, initialize them from the repository root:
+- If you have already cloned the repository, you can use the following command to update the repository to the latest code.

-```shell
-git submodule update --init --recursive
 ```
-
-To update an existing checkout and its submodules:
-
-```shell
 cd stable-diffusion.cpp
 git pull origin master
-git submodule update --init --recursive
+git submodule init
+git submodule update
 ```

-## MSVC compilation parallelism
-
-MSVC builds limit parallel C/C++ compilation within each project by default.
-For `n` logical processors, the limit is `n` when `n <= 2`, `n - 1` when
-`3 <= n <= 8`, and `n - 2` when `n > 8`. The default is calculated when the
-build directory is first configured with this setting. Override it with
-`-DSD_MSVC_MP_COUNT=4`, or pass `-USD_MSVC_MP_COUNT` to recalculate it.
-
-For Visual Studio builds, keep project-level parallelism at one to avoid
-multiplying the per-project limit:
-
-```shell
-cmake --build build --config Release --parallel 1
-```
-
-This setting controls MSVC's `/MP` option, not CUDA compiler parallelism or a
-hard CPU usage limit. Other build tools, such as Ninja, schedule compilation
-jobs separately; use `cmake --build build --parallel N` to limit those jobs.
-
 ## Selecting a GGML source tree

 By default, sd.cpp builds the patched GGML submodule in `ggml/`. To build with
@@ -51,10 +27,8 @@ cmake -S . -B build-upstream -DSD_USE_UPSTREAM_GGML=ON -DSD_GGML_SOURCE_DIR=../g
 cmake --build build-upstream --config Release
 ```

-Upstream compatibility mode uses the selected library and matching private
-headers. The patched GGML build uses public headers, including `ggml-ext.h` and
-`ggml-graph.h`. Backend options such as `-DSD_CUDA=ON` apply to the selected tree
-as usual.
+The selected source tree supplies both the library and its private headers.
+Backend options such as `-DSD_CUDA=ON` apply to the selected tree as usual.

 `SD_USE_UPSTREAM_GGML` defaults to `OFF`, which enables the patched GGML
 extensions. Set it to `ON` when using upstream GGML; it selects the compatibility
@@ -71,12 +45,10 @@ creating an inference context. Ordinary floating-point and shared GGML
 quantization types remain available, subject to backend operator support.

 `SD_USE_SYSTEM_GGML=ON` instead links an installed GGML CMake package, located
-with `ggml_DIR` or `CMAKE_PREFIX_PATH`. An installed patched package must include
-the extension and graph APIs; its source tree is not required for private
-headers. The installed library must use the same ABI settings as sd.cpp,
-including `GGML_MAX_NAME`. For an upstream package, also set
-`SD_USE_UPSTREAM_GGML=ON` and point `SD_GGML_SOURCE_DIR` to its matching source
-tree for the compatibility layer's private headers.
+with `ggml_DIR` or `CMAKE_PREFIX_PATH`. In that mode, `SD_GGML_SOURCE_DIR` must
+point to the matching source tree for private headers. The installed library
+must use the same ABI settings as sd.cpp, including `GGML_MAX_NAME`.
+Set `SD_USE_UPSTREAM_GGML=ON` as well if the installed package is upstream GGML.

 ## WebP and WebM Support in Examples

@@ -118,8 +90,8 @@ cmake --build . --config Release

 ## Build with CUDA

-The sd.cpp SageAttention extension is included when using CUDA with patched
-GGML (`SD_USE_UPSTREAM_GGML=OFF`).
+Native SageAttention is included when using CUDA with patched GGML
+(`SD_USE_UPSTREAM_GGML=OFF`).
 See [SageAttention](sage_attention.md) for GPU requirements and `--sage-attn` usage.

 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.
@@ -130,28 +102,6 @@ cmake .. -DSD_CUDA=ON
 cmake --build . --config Release
 ```

-CUDA extensions use sd.cpp's `thirdparty/cutlass` submodule by default
-for eligible INT8 GEMM and CuTe attention operations. If its headers are missing,
-CMake stops with an initialization hint; it does not download them automatically.
-Run `git submodule update --init --recursive` from the repository root before
-configuring.
-
-To build with the existing CUDA implementations without CUTLASS, configure from
-the repository root with:
-
-```shell
-cmake -S . -B build -DSD_CUDA=ON -DSD_CUDA_CUTLASS_PATH:PATH=
-cmake --build build --config Release
-```
-
-Alternatively, set `SD_CUDA_CUTLASS_PATH` to another local CUTLASS source
-directory (tested with 3.9.2). Existing CMake caches retain their configured path;
-pass `-USD_CUDA_CUTLASS_PATH` when reconfiguring to restore the bundled default.
-Existing `GGML_CUDA_CUTLASS_PATH` caches are migrated on first configuration:
-the old bundled path becomes `thirdparty/cutlass`, while custom paths and an
-empty disabled value are preserved. An explicitly set `SD_CUDA_CUTLASS_PATH`
-takes precedence. Non-CUDA builds do not require CUTLASS.
-
 ## Build with HipBLAS

 This provides GPU acceleration using AMD GPU. Make sure to have the ROCm toolkit installed.
@@ -169,16 +119,14 @@ cmake --build . --config Release

 ## Build with MUSA

-This provides GPU acceleration using Moore Threads GPUs. The configuration below follows upstream validation with MUSA SDK 5.2.0 and MTT S5000 (PH1, `mp_31`). Install that toolkit before building. Older SDKs such as 4.2 may lack the CUB headers required by the current backend.
+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 -DMUSA_ARCHITECTURES=31 -DCMAKE_BUILD_TYPE=Release
+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
 ```

-The MUSA Docker image uses the same SDK 5.2.0 development image as upstream for both build and runtime stages, and targets `mp_31` only. It includes the development tools and PyTorch from that base image, so it is larger than a minimal runtime image. Other GPU architectures require separate validation.
-
 ## 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.
@@ -2,16 +2,6 @@

 Caching methods accelerate diffusion inference by reusing intermediate computations when changes between steps are small.

-### Conditioning Cache
-
-Conditioning results are cached per model context using an LRU cache. The default
-capacity is **0 (disabled) for `sd-cli`** and **4 entries for `sd-server` and the C
-API**. Set `--conditioning-cache-size N` to change the limit; `0` disables caching.
-For example, `sd-cli -m model.safetensors -p "a cat" --conditioning-cache-size 4`
-enables the cache in the CLI. The C API option is
-`sd_ctx_params_t::conditioning_cache_size`, initialized by `sd_ctx_params_init()`.
-This cache is independent of the diffusion-step `--cache-mode` options below.
-
 ### Cache Modes

 | Mode | Target | Description |
@@ -18,45 +18,18 @@ docker run --rm --init -v /path/to/models:/models -v /path/to/output/:/output -p

 ## Building using Docker

-Initialize the submodules from the repository root before building. Docker copies
-the local checkout, so its build context must include the submodule contents:
-
 ```shell
-git submodule update --init --recursive
 docker build -f docker/Dockerfile -t sd .
 ```

 ## Building variants using Docker

-### CUDA (NVIDIA GPU):
-
-```shell
-docker build -f docker/Dockerfile.cuda -t sd-cuda .
-```
-
-To select GPU architectures, pass `--build-arg "CUDA_ARCHITECTURES=89"` (for
-example, RTX 4090). Run the image on a host configured with NVIDIA Container
-Toolkit, using `docker run --rm --gpus all ... sd-cuda [args...]`.
-
-### Vulkan:
+Vulkan:

 ```shell
 docker build -f docker/Dockerfile.vulkan -t sd .
 ```

-### ROCm (AMD GPU):
-
-Defining one or multiple targets with `--build-arg AMDGPU_TARGETS` is strongly recommended.
-Building the image will otherwise likely fail or build for a target that does not fit your
-hardware.
-
-```shell
-# Build for one target
-docker build -f docker/Dockerfile.rocm --build-arg "AMDGPU_TARGETS=gfx1100" -t sd .
-# Build for multiple targets
-docker build -f docker/Dockerfile.rocm --build-arg "AMDGPU_TARGETS=gfx1030;gfx1100" -t sd .
-```
-
 ## Run locally built image's CLI

 ```shell
@@ -64,21 +37,3 @@ docker run --rm -v /path/to/models:/models -v /path/to/output/:/output sd [args.
 # For example
 # 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
 ```
-
-### ROCm (AMD GPU)
-
-The user that executes this command needs to be in the `video` group and have read and write
-access to `/dev/dri` and `/dev/kfd`.
-
-`--device` and `--group-add` allow the container access to the GPU.
-
-```shell
-docker run --rm --device=/dev/dri --device=/dev/kfd --group-add video \
-  -v /path/to/models:/models -v /path/to/output/:/output \
-  sd [args...]
-# For example
-# docker run --rm --device=/dev/dri --device=/dev/kfd --group-add video \
-#   -v ./models:/models -v ./build:/output \
-#   sd -m /models/sd-v1-4.ckpt -v -o /output/output.png \
-#   -p "a lovely cat"
-```
@@ -14,12 +14,10 @@ Depending on the architecture, different models handle reference images differen
 | [**Qwen Image Edit**](./qwen_image_edit.md) | `qwen` |
 | **Qwen Image LAYERED** | `qwen_layered` |
 | [**Flux.2 [Dev] / Flux.2 [Klein]**](./flux2.md) | `flux2` |
-| [**FLUX.3 Image Klein / Klein Base**](./flux3.md) | `flux2` |
 | [**Boogu Image Edit**](./boogu_image.md) | `z_image_omni` |
 | **Krea2 (Community Edit LoRAs)** | `krea2_ostris_edit` |
 | [**Mage-Flow-Edit**](./mage_flow.md#image-editing) | `mage_flow` |
 | [**LLaDA-Image**](./llada_image.md#image-editing) | `llada_image` |
-| [**Fibo 2.0 Dev / Edit Turbo**](./fibo2.md#examples) | `fibo2` |
 | **Anima (Community Edit LoRAs)** | `cosmos_reference` |

 Stable-diffusion.spp also supports basic Unet-based editing models like instruct-pix2pix or CosXL-Edit. This document is not about those.
@@ -28,9 +26,6 @@ Stable-diffusion.spp also supports basic Unet-based editing models like instruct

 ## Configuring Reference Modes (`--ref-image-args`)

-For a one-time input transform before reference presets and model processing,
-including cropping, padding, and resizing algorithms, see [Image preprocessing](./image_preprocessing.md).
-
 Different DiT-based editing models require different configurations to process reference images correctly (e.g., whether to use a Vision Language Model (VLM) encoder or pass VAE-encoded images directly to the DiT).

 To simplify this, we provide **Presets**. By default, the system automatically selects the best preset based on the model architecture. However, you can override this using the `--ref-image-args` argument.
@@ -51,7 +46,6 @@ The `--ref-image-args` argument accepts a comma-separated list of key-value pair
 | `flux_kontext` | FLUX.1 Kontext |
 | `longcat` | LongCat Image Edit |
 | `flux2` | FLUX.2 models |
-| `fibo2` | Fibo 2.0 Dev / Edit Turbo |
 | `qwen` | Qwen Image Edit |
 | `qwen_layered` | Qwen Image Layered |
 | `z_image_omni` | Boogu, Z-Image Omni |
@@ -75,19 +69,11 @@ If presets are insufficient, you can manually configure the following parameters
 | `ref_index_mode` | string | Behavior of the RoPE index. | `fixed`, `increase`, `decrease` |
 | `force_ref_timestep_zero` | bool | Forces timestep=0 for reference tokens. | `true`, `false` (Krea2 only) |
 | `resize_before_vae` | bool | Whether reference images are resized before VAE encoding. | `true`, `false` |
-| `resize_vae_to_target` | bool | Use output dimensions for every reference, overriding area-based sizing. | `true`, `false` |
-| `resize_single_ref_to_target` | bool | Use output dimensions when exactly one reference is supplied. | `true`, `false` |
-| `vae_size_buckets` | bool | Choose aligned dimensions within ±10% of the requested area, minimizing aspect-ratio cropping. Ignored when using output dimensions. | `true`, `false` |
-| `vae_center_crop` | bool | Scale to cover the selected dimensions with Lanczos, then center-crop. Otherwise resize directly. | `true`, `false` |
-| `vae_input_max_pixels` | int | VAE area budget, also capped by output area in ordinary resizing. With size buckets, this is the target area and output area does not cap it. | Integer |
-| `vlm_use_vae_input` | bool | Reuse the resized/cropped VAE input for the VLM, before the VLM's own resize. Has no effect when `resize_before_vae=false`. | `true`, `false` |
+| `vae_input_max_pixels` | int | Maximum pixel area for VAE reference inputs. | Integer |
 | `vlm_resize_mode` | string | How to resize VLM reference inputs. | `longest_side`, `area`, `none` |
 | `vlm_max_size` | int | Maximum VLM input size; interpreted according to `vlm_resize_mode`. | Integer |
 | `vlm_min_size` | int | Minimum VLM input size; interpreted according to `vlm_resize_mode`. | Integer |
 | `vlm_size` | int | Shortcut to set both VLM min and max size to the same value. | Integer |
-| `ref_image_size` | string | MiniMax-H3 reference image sizing before VAE encoding. `match` scales each reference down to the generation's pixel area; `max` uses a 2048 px short edge. Only references above the limit are downscaled; each axis is then snapped to the nearest 32 px multiple (an axis of at least 16 px grows by at most 16 px, and references are never narrower than 32 px). | `match`, `max` |
-
-`ref_image_size` applies to `vid_gen` with MiniMax-H3 Ref2VA. Reference latents are attended to at every sampling step, so `max` can be several times slower.

 ### Preset Default Values

@@ -98,7 +84,6 @@ For a technical overview of how each preset is configured, see the table below.
 | `flux_kontext` | No | `fixed` | `none` | |
 | `longcat` | Yes | `fixed` | `area` | |
 | `flux2` | No | `increase` | `none` | |
-| `fibo2` | Yes | `increase` | `longest` | Shared reference crops; clean reference timesteps; see [Fibo 2.0](./fibo2.md) |
 | `qwen` | Yes | `increase` | `area` | |
 | `qwen_layered` | Yes | `decrease` | `area` | |
 | `mage_flow` | Yes | `increase` | `longest` | `vlm_max_size = 384`, VAE input resized to target |
@@ -110,5 +95,3 @@ For a technical overview of how each preset is configured, see the table below.
 **Additional Default Notes:**
 - **VLM Input Sizes:** For most presets, `vlm_max_size` and `vlm_min_size` are set to `-1`, meaning the values are model-dependent and handled automatically. In `area` mode they represent pixel area; in `longest_side` mode they represent a side length in pixels.
 - **VAE Input Size:** `vae_input_max_pixels` defaults to $1024 \times 1024$ pixels (`1048576`).
- **VAE Geometry:** `resize_vae_to_target` defaults to `true` for Mage-Flow and LLaDA-Image. Fibo enables `resize_single_ref_to_target`, `vae_size_buckets`, and `vae_center_crop`. These options are otherwise `false`. Bucket dimensions use the model's alignment (normally 16, Qwen Image 32), with a minimum side of four alignment units; if no bucket fits, output dimensions are used.
- **Shared Encoder Input:** `vlm_use_vae_input` defaults to `true` for Fibo and Qwen Image 2.1, otherwise `false`. All these options can be overridden individually after selecting a preset.
@@ -21,7 +21,7 @@ You can run ERNIE-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — o
 ### ERNIE-Image-Turbo

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\ernie-image-turbo.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\ministral-3-3b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 8 -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\ernie-image-turbo.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\ministral-3-3b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 8 -v --offload-to-cpu --diffusion-fa
 ```

 <img width="256" alt="ERNIE-Image Turbo example" src="../assets/ernie_image/turbo_example.png" />
@@ -29,7 +29,7 @@ You can run ERNIE-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — o
 ### ERNIE-Image

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\ernie-image-UD-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\ministral-3-3b.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\ernie-image-UD-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\ministral-3-3b.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu --diffusion-fa
 ```

 <img width="256" alt="ERNIE-Image example" src="../assets/ernie_image/example.png" />
@@ -2,10 +2,6 @@

 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.

-MoSR models with the PixelShuffle upsampler, such as [4xNomos2_hq_mosr.pth](https://github.com/Phhofm/models/releases/download/4xNomos2_hq_mosr/4xNomos2_hq_mosr.pth), are also supported. The architecture is detected from the weights, so they are used the same way as ESRGAN models.
-
-RGBA images, including Qwen Image 2.1 output, keep their alpha channel during model upscaling and hires fix. The upscaler processes the RGB channels; the alpha channel is resized with bilinear interpolation and recombined with the upscaled image.
-
 - Specify the model path using the `--upscale-model PATH` parameter. example:

 ```bash
@@ -1,53 +0,0 @@
-# How to Use
-
-Fibo 2.0 uses a Fibo diffusion transformer, the standard FLUX.2 VAE, and Qwen3-VL 4B as the LLM text encoder. The Qwen tokenizer is built in.
-
-## Download weights
-
- Download Fibo 2.0 Dev
-    - safetensors: https://huggingface.co/Comfy-Org/Fibo-2.0/tree/main/diffusion_models
- Download Fibo 2.0 Edit Turbo
-    - safetensors: https://huggingface.co/Comfy-Org/Fibo-2.0/tree/main/diffusion_models
- Download vae
-    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/main
- Download Qwen3-VL 4B
-    - safetensors: https://huggingface.co/Comfy-Org/Fibo-2.0/tree/main/text_encoders
-    - gguf: https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/tree/main
-
-The original Diffusers weights from [briaai/Fibo-2.0-dev](https://huggingface.co/briaai/Fibo-2.0-dev) and [briaai/Fibo-2.0-edit-turbo](https://huggingface.co/briaai/Fibo-2.0-edit-turbo) are also supported. Pass the transformer's `diffusion_pytorch_model.safetensors.index.json` to `--diffusion-model` and keep its shards alongside it.
-
-INT8 convrot weights load directly; see [INT8 convrot](./int8_convrot.md) for backend requirements.
-
-## Examples
-
-### Fibo 2.0 Dev
-
-Dev supports both text-to-image generation and reference-image editing.
-
-```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\fibo2_dev_int8_convrot.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\Qwen3-VL-4B-Instruct-mmproj-BF16.gguf --vae ..\models\vae\flux2_ae.safetensors -p "a lovely cat holding a sign says 'fibo2.cpp'" --cfg-scale 5.0 --sampling-method euler -v  --offload-to-cpu
-```
-
-<img src="../assets/fibo2/t2i.png" />
-
-```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\fibo2_dev_int8_convrot.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\Qwen3-VL-4B-Instruct-mmproj-BF16.gguf --vae ..\models\vae\flux2_ae.safetensors -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'fibo2.cpp'" --cfg-scale 5.0 --sampling-method euler -v  --offload-to-cpu
-```
-
-<img src="../assets/fibo2/dev_edit.png" />
-
-### Fibo 2.0 Edit Turbo
-
-Edit Turbo is distilled for reference-image editing in 4 steps with CFG 1.
-
-```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\fibo2_edit_turbo_int8_convrot.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\Qwen3-VL-4B-Instruct-mmproj-BF16.gguf --vae ..\models\vae\flux2_ae.safetensors -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'fibo2.cpp'" --cfg-scale 1 --steps 4 --sampling-method euler -v  --offload-to-cpu
-```
-
-The BF16 files accept the same options. Use dimensions divisible by 16. Repeat `-r` to use multiple reference images; instructions can refer to `<image_1>`, `<image_2>`, etc. See [Image Editing](./edit.md) for reference-image preprocessing options. Separate GGUF text encoders need their vision projector passed with `--llm_vision` for editing.
-
-The automatic `fibo2` reference preset combines `resize_single_ref_to_target=true`, `vae_size_buckets=true`, `vae_center_crop=true`, and `vlm_use_vae_input=true`. A single reference is scaled to cover the output dimensions and center-cropped. Multiple references use dimensions divisible by 16, chosen within ±10% of one megapixel to minimize cropping for each image's aspect ratio; `vae_input_max_pixels` overrides that target area. The VAE and vision encoder receive the same crops, with smaller copies used by the vision encoder. These are generic reference-image options and can be overridden individually: for example, `--ref-image-args vae_center_crop=false` uses direct resizing, while `resize_before_vae=false` skips this automatic crop and resize step entirely.
-
-The vision encoder defaults to `vlm_resize_mode=longest_side,vlm_max_size=768`. Override these with `--ref-image-args`, for example `vlm_max_size=512`, or use `vlm_resize_mode=none` to skip size-based scaling. Vision inputs are still aligned to the encoder's patch grid.
-
-Plain prompts and structured JSON captions are accepted. This integration uses the caption-only text-rendering path (`render_text_images=False`); it does not generate auxiliary glyph images or expand prompts with Bria VGL. Native Fibo mask preprocessing is not connected to `--mask`.
@@ -16,7 +16,7 @@
 ### Examples

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux2-dev-Q4_K_S.gguf --vae ..\models\vae\flux2_ae.safetensors  --llm ..\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 --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux2-dev-Q4_K_S.gguf --vae ..\models\vae\flux2_ae.safetensors  --llm ..\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" />
@@ -42,19 +42,19 @@
 ### Examples

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\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  ..\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\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 --offload-to-cpu --steps 4
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\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  ..\models\diffusion_models\flux-2-klein-base-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-base-4b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\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" />
@@ -78,19 +78,19 @@
 ### Examples

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\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  ..\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\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 --offload-to-cpu --steps 4
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\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  ..\models\diffusion_models\flux-2-klein-base-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\models\text_encoders\qwen_3_8b.safetensors -p "a lovely cat" --cfg-scale 4.0 --steps 20 -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-2-klein-base-9b.safetensors --vae ..\models\vae\flux2_ae.safetensors  --llm ..\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" />
@@ -1,50 +0,0 @@
-# How to Use
-
-FLUX.3 Image Klein uses a FLUX.3 diffusion transformer, the FLUX.2 VAE, and Qwen3-VL 4B as the LLM text encoder.
-
-## Download weights
-
- Download FLUX.3 Image Klein
-    - safetensors: https://huggingface.co/black-forest-labs/flux-3-image-klein/tree/main
-    - gguf: https://huggingface.co/leejet/flux-3-image-klein-GGUF/tree/main
- Download FLUX.3 Image Klein Base
-    - safetensors: https://huggingface.co/black-forest-labs/flux-3-image-klein-base/tree/main
-    - gguf: https://huggingface.co/leejet/flux-3-image-klein-base-GGUF/tree/main
- Download vae
-    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/main
-    - The `ae.safetensors` included with FLUX.3 can also be used.
- Download Qwen3-VL 4B
-    - safetensors: https://huggingface.co/Comfy-Org/Krea-2/tree/main/text_encoders
-    - gguf: https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/tree/main
-
-Use the root `model.safetensors` for the diffusion model.
-
-## Examples
-
-### FLUX.3 Image Klein
-
-The distilled model uses 16 steps and CFG 1.0.
-
-```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\flux-3-image-klein.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors -p "a lovely cat holding a sign says 'flux3.cpp'" --cfg-scale 1.0 --steps 16 --sampling-method euler -v --offload-to-cpu
-```
-
-<img src="../assets/flux3/flux3-image-klein.png" />
-
-```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\flux-3-image-klein.safetensors --vae ..\models\vae\flux2_ae.safetensors --llm  ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'flux3-image-klein.cpp'" --cfg-scale 1.0 --sampling-method euler -v  --offload-to-cpu --steps 16
-```
-
-<img src="../assets/flux3/flux3-image-klein-edit.png" />
-
-Repeat `-r` to use multiple reference images, up to 10. See [Image Editing](./edit.md) for reference-image preprocessing options.
-
-### FLUX.3 Image Klein Base
-
-```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\flux-3-image-klein-base.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors -p "a lovely cat holding a sign says 'flux3.cpp'" --cfg-scale 3.5 --steps 20 --sampling-method euler -v --offload-to-cpu
-```
-
-<img src="../assets/flux3/flux3-image-klein-base.png" />
-
-The default discrete scheduler uses the upstream time shift of 6.93 for both models. Use `--flow-shift` to override it.
@@ -1,165 +0,0 @@
-# Generation results and metadata
-
-`generate_image()`, `generate_video()`, `adetail_image()` and `upscale()` return
-owned results containing the media and its generation record. This is a breaking
-C API change: applications and bindings must update their calls and rebuild.
-
-```c
-sd_img_gen_params_t params;
-sd_img_gen_params_init(&params);
-params.prompt = "a small house";
-
-sd_image_generation_result_t* result = NULL;
-if (generate_image(ctx, &params, &result)) {
-    for (int i = 0; i < result->count; ++i) {
-        const sd_image_result_t* item = &result->items[i];
-        /* Consume item->image and item->generation_info together. */
-    }
-    free_sd_image_generation_result(result);
-}
-```
-
-`result_out` is required and is set to `NULL` on failure. A successful result
-owns its pixels and UTF-8 JSON strings. The video result additionally owns its
-audio and contains the effective `fps` and `frame_count`. Results remain valid
-after freeing the generation context. Use the matching library result destructor;
-do not separately free fields unless transferring ownership and clearing them.
-Both result destructors accept `NULL`.
-
-## Reproduction parameters
-
-The `sdcpp.generation/v1` JSON is a snapshot of generation parameters for the
-same loaded context and the same input images, masks, reference media and other
-assets. It is created after `GenerationRequest` resolves the request, before
-preprocessing or sampling. A failed call returns no result or metadata.
-
-Fields follow the C generation parameter structs: `prompt`, `negative_prompt`,
-`width`, `height`, `seed`, `sample_params`, `hires`, `cache`,
-`vae_tiling_params`, `image_preprocess`, and the image/video-specific options.
-`kind` distinguishes image and video parameters. There is no inferred `task`.
-Enums use their API string names. Seeds are decimal strings to preserve their
-full integer range. Omitted optional fields retain the parameter initializer's
-defaults, including automatic values represented by infinity in the C API.
-
-Disabled optional features are omitted entirely: `cache`, `hires`, VAE tiling,
-zero/nonpositive `shifted_timestep`, inactive SLG, false circular axes, and empty
-preprocessing/reference arguments. SLG layers remain present when `slg_uncond`
-is enabled even with zero SLG scale. Enabled caches store their mode's parameters;
-TaylorSeer retains the shared DBCache thresholds and limits used by the runtime.
-Image CFG at its neutral value of 1 is omitted and resolves back to 1 by default.
-
-Image strength, ControlNet/IP-Adapter strength and PhotoMaker settings are stored
-when their corresponding input images are supplied. PuLID requires an embedding
-path. Layer count is stored for layered generation; video VACE and high-noise
-settings require those models. High-noise settings omit scheduler, custom sigmas,
-flow shift and timestep shifting because that pass shares the base configuration.
-Explicit nonempty argument strings are retained without rewriting their contents.
-Core sampling values such as seed, sampler, scheduler, steps and guidance remain
-explicit even when equal to their defaults. ADetailer always retains its inpaint
-strength because its input crops are created later.
-Model-required VAE tiling is resolved before serialization, so forced tiling
-(such as MiniMax-H3) remains present even if the caller did not enable it.
-
-The request fixes the seed, dimensions, clip skip, guidance, sampler, scheduler
-and eta before taking the snapshot. `custom_sigmas` is stored only when supplied
-as an input. Automatically computed schedules, preprocessing transforms,
-conditioning/cache decisions, detector regions and output statistics are not
-stored: they are recomputed from the same parameters and inputs.
-
-Each image records the seed used to sample its latent. `batch_count` is omitted;
-replay image parameters with `batch_count = 1`. Layers or frames decoded from
-the same latent share its seed, so layered generation reproduces that group of
-layers. Video frames share the video request. Partial batch cancellation returns
-metadata only for completed outputs. Full cancellation returns no result.
-
-The current image pipeline prepares input latents once using the starting seed
-and shares them across a batch. With a stochastic VAE encoder, replaying a later
-image alone using its sampling seed can change those input latents. Per-image
-seeds therefore do not guarantee independent reproduction for these paths.
-
-To replay a record, initialize the matching C parameter struct, populate its
-fields from the JSON, restore the same input buffers, and call generation with
-the same context. Keep custom-sigma/SLG arrays and string storage alive for the
-call. Paths supplied as generation parameters (LoRAs, hires models and identity
-embeddings) are retained. `models` identifies successfully loaded context model
-files by component (`model`, `diffusion_model`, `clip_l`, `vae`, etc.), using
-filenames with extensions and omitting directories. Optional components disabled
-for the request and preview-only TAE are omitted. WebUI text includes `Model`
-(the diffusion model when supplied separately, otherwise the checkpoint, without
-the extension) and `VAE` for a separate active VAE. These names are identifiers,
-not hashes or sufficient configuration to rebuild a context; replay still requires
-the same model files and context settings. The JSON does not embed media buffers
-or model weights.
-
-When `--lora-model-dir` is configured, CLI/server output records express LoRA
-paths relative to that directory, preserving subdirectories and file extensions
-and using `/` separators. This applies to file metadata, API responses and
-ADetailer records. Resolve these paths against the same LoRA directory before
-passing them back to the C API. Core results retain the supplied loading paths.
-Files outside the directory retain `..` components; paths on another volume,
-or records without a configured LoRA directory, retain their original paths.
-
-## Postprocessing
-
-Upscale records `upscale.upscale_factor`. ADetailer records `adetailer` and
-`inpaint_params`, with a concrete starting seed chosen before detection. These
-operations require the same upscaler/detector context and source image on replay.
-They do not store detection results or per-region generation traces.
-
-The CLI/server append operation parameter snapshots to `postprocess` in call
-order. The source generation parameters remain unchanged. Frame postprocessing
-uses the same operation parameters for each video frame. Standalone
-postprocessing does not invent source diffusion parameters for an input image.
-
-## File metadata
-
-Image encoders accept the native record and derive WebUI infotext from it.
-Images use the existing metadata locations and combined text layout:
-`<WebUI infotext>, SDCPP: <native JSON>`. Standalone postprocessing without
-source diffusion parameters stores only native JSON. Video metadata stores
-only native JSON.
-
-| Container | Metadata location |
-| --- | --- |
-| PNG | `tEXt`, keyword `parameters` |
-| JPEG | COM segment, `parameters` label followed by a NUL byte |
-| WebP image / animated WebP | XMP property `sdcpp:parameters` |
-| AVI | RIFF INFO `ICMT` |
-| WebM | `COMMENT` tag |
-
-Inspect images or videos with `sd-cli -M metadata --image <file>`; the same
-command supports all containers above, with `--metadata-format json` for a
-structured report. Video metadata inspection does not require a model or video
-decoder.
-Matroska containers are also supported, including `.webm` outputs whose audio
-codec causes the muxer to select the Matroska document type.
-
-The XMP namespace is `https://github.com/leejet/stable-diffusion.cpp/ns/1.0/`.
-PNG and JPEG retain the existing UTF-8 text payload convention. Memory encoding
-and file saving use the same implementation. The core result and API
-`generation_info` fields remain pure JSON; the combined layout applies only
-to embedded image metadata.
-
-WebUI conversion is lossy: known samplers and schedules have explicit name
-mappings; unsupported ones use `SDCPP Sampler`/`SDCPP Schedule` fields. Hires
-keeps the base request size and separate configured hires size/steps. The native
-record retains the full generation parameters. Standalone postprocessing has no WebUI
-infotext because its original diffusion settings are unknown.
-
-WebUI prompt text appends ordinary LoRAs as `<lora:filename_without_extension:weight>`.
-Directory components are omitted to match WebUI's filename lookup; the native
-`loras` array retains relative paths and weights. High-noise-only LoRAs and names
-that cannot be represented by this tag syntax remain in the native record only.
-The native prompt is not modified, and postprocessing LoRAs are not added to the
-source generation prompt.
-
-JPEG requires the COM payload, including the label and terminating NUL bytes,
-to fit within 65,533 bytes. Larger records fail encoding with an error rather
-than being truncated. Use PNG or WebP for larger records.
-
-Disabling file metadata omits both image representations, but does not disable
-the core generation record or its API response. Native async image results and
-OpenAI image response items contain `generation_info`; video results contain a
-sequence-level `generation_info`. The SD API `info` object contains per-output
-`generation_info` alongside derived `infotexts` and each image's seed. Its outer
-`parameters` field continues to echo the request.
@@ -1,550 +0,0 @@
-# ggml operator extensions
-
-The patched ggml exposes `ggml-ext.h` for application-owned operators, kernel
-overrides, and graph fusion rules. Registrations are in-process and scoped to a
-backend device. Register them before capability queries or graph allocation;
-callback code and user data must remain alive until all dependent graphs and
-backends have been destroyed. Registration names are unique, and duplicate
-registrations fail rather than replacing live implementations.
-
-## Operator definitions
-
-`ggml_ext_register_op` registers a versioned name, attribute size, and an output
-metadata inference callback. `ggml_ext_op_create` creates an ordinary ggml tensor
-with explicit input dependencies. The initial interface supports one contiguous,
-out-of-place output with an existing `ggml_type`.
-
-Attributes are copied into the unused portion of `op_params`. They are small,
-plain values; use `memcpy` when reading attributes that need stricter alignment.
-Auxiliary tensors must appear in `src`, not in an attribute pointer or user data.
-Registered operators use `GGML_OP_CUSTOM` internally, with an identifiable
-callback marker. Unsupported devices do not mistake them for CPU callbacks.
-These process-local descriptors are not a graph serialization format.
-
-## Kernel selection
-
-`ggml_ext_register_kernel(device, &desc)` associates a kernel with a built-in
-`ggml_op`, or with a registered custom operator. `supports` describes correctness
-constraints; `preferred` optionally decides whether to override an available
-native implementation. Higher priorities are considered first. When native
-execution is unsupported, a supported extension can run regardless of its
-preference callback.
-
-Capability queries include registered single-node implementations, so the
-scheduler can select a device even when its native implementation rejects the
-operator. Callbacks inspect metadata only and must not recursively call the
-public capability query for the same device and operator. A fusion-only
-registration does not claim single-node support.
-
-`ggml_ext_dev_supports_native_op` queries the original device implementation
-without extension dispatch. Fusion policies can use it to select a supported
-extension even when their performance preference normally favors native kernels.
-
-`prepare` creates optional host-side plan state and reports workspace bytes.
-It can run during memory measurement, before tensor addresses are assigned, and
-must not launch work or allocate device buffers. Workspace is allocated by ggml
-before execution and included in scheduler memory estimates and buffer-size
-reports. `execute` receives the backend, workspace buffer, and current tensor
-addresses. `destroy` releases prepared state. A preparation or execution error
-is propagated; execution never retries a native implementation after launch.
-
-Workspace is scratch storage shared by sequential extension steps in a plan;
-its capacity is the maximum request, not the sum across nodes. Contents are not
-preserved between calls. Queue work in backend execution order, and finish any
-private-stream use before subsequent backend work can access the workspace.
-Persistent state belongs in `prepare`/`destroy` state or explicit tensor inputs.
-
-Overriding a built-in operator preserves its numerical and aliasing contract,
-including allocator reuse of input storage. Explicit views/in-place nodes are
-not intercepted by this first interface. Rebuild the scheduler allocation after
-changing graph metadata, selection policy, or registrations.
-
-## Fusion
-
-A `match` callback returns the number of consecutive nodes it can replace.
-The scheduler freezes this selection before allocating tensors. ggml checks
-compute flags, external uses, output flags, and views; the initial implementation
-accepts a conservative set of pure operators with only the final node exposed
-as the fused output. The rule remains responsible for mathematical equivalence,
-dtype, shape, layout, and hardware constraints. Internal views are accepted when
-their storage belongs to the fused subgraph. In-place intermediates are accepted
-only when their storage owner is an earlier node in the same match. External
-consumers and output flags still prevent eliminating those results. The final
-output must own storage, use a terminal `CPY`, or write back to an earlier owning
-node within the match. Internal writeback must cover exactly the owner's full
-shape, type, strides and storage offset, without external consumers of that
-owner. Output flags propagated to that owner are retained. Terminal `CPY` keeps
-its original destination and write semantics. The extension must handle input/output storage overlap, including
-allocator reuse, with scratch space when necessary. Other in-place writes are
-not eligible for extension fusion when they target storage outside the match.
-
-An optional `optimize` callback runs before matching and allocation. Use
-`ggml_ext_graph_delay` to move a producer range after independent nodes. The
-helper rejects dependencies, unknown side effects, and writes that alias moved
-inputs or outputs. It can cross independent copies and known in-place operators
-when their storage does not alias the moved inputs or outputs. Owning dtype
-conversions can also be moved or crossed.
-Reordering must preserve edges and tensor metadata. Fusion legality is checked
-again after reordering.
-
-External inputs stay allocated through the final node. Native graph optimization
-and execution operate on the remaining contiguous ranges, so native fusion cannot
-consume nodes selected by an extension. Fused intermediates are excluded from
-allocation through the backend's `skip_alloc` callback and
-`ggml_gallocr_set_skipped_tensors`. Their metadata and graph edges remain available,
-but execution must not access their data or buffers. Storage backing the final
-output is retained, including an internal owner used by an in-place final write.
-The skipped set is replaced for each allocation graph; changing which tensors are skipped
-invalidates cached allocation assignments even when tensor shapes are unchanged.
-
-Automatic extension fusion is enabled in scheduler execution with one pipeline
-copy and without evaluation callbacks. Multiple pipeline copies and evaluation
-callbacks retain individual operations. Direct backend graph computation supports
-single-node extensions; it does not fuse an already allocated graph. Direct calls
-use temporary plans and synchronize before releasing them; scheduler plans and
-workspaces are reused across executions.
-
-Use the backend graph APIs or the scheduler. The legacy CPU `ggml_graph_compute`
-and backend `graph_plan_*` interfaces do not dispatch these extensions.
-
-## Backend services
-
-Packed ComfyUI INT4 linear layers use `sd.linear_w4_convrot.v1`, built by
-`ggml_ext_mul_mat_w4_convrot`. The operator consumes byte-packed I8 weights and the
-explicit output of `sd.quantize_i8_convrot.v1`; F32 callers get a quantization
-node during graph construction. Logical output dimensions are stored in the
-operator attributes, so backend copies of the packed activation do not need to
-retain the producer's op marker. W4A4 uses row scales; W4A8 additionally exposes
-the codebook, channel scales, and FP8 relative scales as tensor dependencies.
-CPU, CUDA/HIP/MUSA, and Vulkan register kernels through the same extension API
-as INT8. No ggml submodule changes are required.
-
-Services are discovered through `ggml_backend_reg_get_proc_address`:
-
-| Service | Purpose |
-| --- | --- |
-| `ggml_ext_cpu_parallel_v1` | Run an application callback on the CPU backend's configured thread pool, with its abort callback. |
-| `ggml_ext_cuda_context_v1` | Borrow the current CUDA device and stream, ordered with native work. |
-| `ggml_ext_cuda_device_v1` | Get the physical CUDA ordinal for capability checks before backend creation, including virtual ggml devices. |
-| `ggml_ext_cuda_blas_v1` | Borrow the current stream's cuBLAS handle during execution, with host scalar pointers. |
-| `ggml_ext_gpu_device_v1` | Query CUDA/HIP/MUSA runtime kind, physical ordinal, shared-memory limit, warp size, and INT8 BLAS capability. |
-| `ggml_ext_gpu_context_v1` | Borrow the matching runtime's device and execution stream. |
-| `ggml_ext_gpu_blas_v1` | Borrow the matching cuBLAS, hipBLAS, or muBLAS handle on that stream. |
-| `ggml_ext_metal_device_v1` | Borrow the Metal device for application pipeline compilation. |
-| `ggml_ext_metal_buffer_v1` | Resolve a backend buffer range to its Metal buffer and offset, including private storage and split allocations. |
-| `ggml_ext_metal_submit_v1` | Encode application commands on a backend-owned command buffer, ordered with native Metal work and tracked by backend synchronization. |
-
-The borrowed cuBLAS handle remains owned by the backend. Extensions must not retain
-or destroy it, or change its stream, pointer mode, math mode, or workspace. This
-CUDA-specific BLAS service is retained for existing callers; the runtime-aware
-services support CUDA, HIP, and MUSA with the same ownership rules. Extensions
-call vendor runtime and BLAS APIs directly. These services do not execute GEMM,
-quantization, or model fusion on the application's behalf.
-
-Workspace is a backend buffer. Its base is a CUDA device pointer on CUDA; callers
-must use the representation appropriate to their backend. Vulkan extensions use
-the command recording services described below.
-
-CUDA INT8 Linear, RoPE, ConvRot, RMS affine and INT8 gated residual kernels omit output
-scratch when allocation guarantees that a fresh owning output cannot overlap
-live inputs. Existing outputs and views retain overlap protection. Internal
-GEMM and quantization workspace is still reserved when required; kernels also
-handle a null workspace when the entire step needs no scratch.
-
-Compute workspace measurement preserves the storage identities and offsets of
-external tensors, including views. Separate allocations must not share a synthetic
-buffer identity: that can invent overlap, overestimate scratch, and repeatedly
-rebuild a workspace whose actual allocation is smaller than the estimate.
-
-## sd.cpp integration
-
-`src/core/ggml_extensions.cpp` and `.h` contain the shared operator schemas,
-validation, and backend registration entry point. Graph construction helpers live
-in `src/core/ggml_extend.cpp` and `.h`; they take `GGMLRunnerContext*` so backend
-selection, compatibility workarounds, and shared ConvRot nodes use the same
-context as the calling model. Per-call precision, scaling, attention, and padding
-options remain explicit. Model blocks retain weight loading and adapter
-orchestration, while the shared helpers own FP8 fallback, embedding lookup,
-full/partial RoPE, grouped convolution, and backend-dependent repeat handling.
-LoRA/LoKr construction, n-mode products, and Kronecker products stay in
-`src/model/adapter/lora_ops.cpp` and `.h`, with the same runner-context interface.
-CPU kernels and fusion rules live in `src/core/cpu/`, with registration collected
-in `cpu/registration.cpp`. CUDA-style and Vulkan implementations live in their
-respective backend directories.
-
-`src/core/metal/int8.mm` registers Metal INT8 linear and ConvRot kernels. Their
-Metal source is embedded at build time and compiled for the selected device
-during registration. The public `ggml-ext-metal.h` services expose borrowed
-objects without private backend headers. The submit callback only encodes work;
-GGML owns submission, cancellation checks, command-buffer lifetime, and
-asynchronous error tracking. Rotation scratch and any overlapping output use
-the scheduler workspace. No full floating-point weight copy is allocated.
-When runtime compilation supports Metal 4 TensorOps, INT8 multiplication uses
-`matmul2d` with INT32 cooperative results and fused scale/bias restoration.
-The SIMD-group implementation remains the fallback for older systems and
-devices where the TensorOps pipeline is unavailable.
-TensorOps multiplies at most 1024 pairs per partial result and combines those
-results with wrapping integer addition. This preserves exact INT32 semantics
-even when TensorOps uses floating-point arithmetic internally.
-
-`ggml_ext_modulate(ctx, x, shift, scale)` emits `sd.modulate.v1` for F32 inputs
-when the runner context uses CPU, CUDA/HIP/MUSA, or Vulkan.
-Its explicit inputs are `x`, `scale`, and optional `shift`; it computes
-`(x + x * scale) + shift` with separate FP32 multiply/add rounding. The output
-is contiguous and out of place. Inputs may be strided and use ggml's
-dimension-wise repetition rules. Callers prepare token, batch, or frame layouts
-before constructing the operation; normalization and residual gates remain
-separate operations. CPU, CUDA/HIP/MUSA, and Vulkan have standalone kernels.
-Other backends (including Metal, SYCL, and OpenCL), upstream ggml builds, and
-other input types retain the original MUL/ADD graph.
-
-ConvRot fusion matches the Modulate node directly, including scale-only
-modulation, instead of recognizing its former MUL/ADD/ADD decomposition.
-CUDA accepts contiguous per-channel parameters for `Modulate -> ConvRot` and
-`RMSNorm -> Mul(weight) -> Modulate -> ConvRot`. Vulkan limits the first fusion
-to widths 4096/6144/5376 and the RMSNorm fusion to width 5376.
-Other layouts execute the standalone operation followed by ConvRot. The
-gated-residual MUL/ADD fusion remains available for residual branches.
-
-`src/core/cpu/rope.cpp` registers the CPU implementation of
-`sd.rope_apply.v1` and a
-`RMSNorm -> Mul -> RoPE` fusion. The fusion accepts a one-dimensional weight
-vector. Both interleaved and split-half RoPE, partial rotation, and strided input
-rows are supported. Model construction uses the custom operator on CPU, CUDA,
-HIP, MUSA, and Vulkan. Other backends can use the scheduler's CPU fallback.
-The former `ggml_rope_apply` API, builtin opcode, and CPU implementation have
-been removed from ggml.
-
-`src/core/cuda/rope.cu` supplies standalone CUDA RoPE
-and `RMSNorm -> Mul -> RoPE [-> Scale -> FP16 Copy]` fusion. CuTe attention shares
-its RMSNorm/RoPE kernel. Partial rotation and arbitrary float-aligned input and
-position-encoding strides are supported by the standalone kernel; RMSNorm fusion
-requires contiguous position encoding and weights. Copy fusion uses workspace
-when the destination overlaps an input. These registrations do not require
-CUTLASS. The old CUDA/HIP/MUSA RoPE dispatch is removed.
-
-Shared CUDA-style kernels live in `src/core/cuda/` and are compiled separately
-for each enabled runtime. `SD_CUDA_NAMESPACE` expands to `sd_cuda`, `sd_hip`, or
-`sd_musa`, so implementations can coexist. `runtime.h` handles the small runtime
-differences, including logical 32-lane shuffles on HIP. The application defines
-and launches its kernels directly; it includes only public ggml headers.
-CMake generates the registration table from enabled backend options. The shared
-entry point uses one `SD_BACKEND_EXTENSIONS` macro; backend registrars verify the
-device kind, and disabled backends add no symbol references.
-
-`SD_USE_UPSTREAM_GGML` excludes these registrations. The stable-diffusion public C
-API is unchanged. INT8 ConvRot retains its existing packed activation layout and
-separate weight scale tensor.
-
-`src/core/cuda/cute_attention.cu` supplies CuTe Flash Attention on CUDA when
-CUTLASS headers are configured. It registers an override of `FLASH_ATTN_EXT`,
-with the existing sequence-length preference and native fallback. It also
-registers Q preparation (`RMSNorm -> Mul -> RoPE`), output scaling, and packed
-ConvRot output fusion. These kernels use only the public extension API, borrowed
-CUDA stream, and scheduler-owned workspace; they do not use ggml's CUDA pool or
-private conversion helpers. The existing CUDA fusion and CuTe disable environment
-variables are honored.
-Aligned contiguous FP16 K/V inputs are read directly without reserving conversion
-buffers. Fresh F32 outputs are written by the attention epilogue, retaining the
-original FP16 rounding before output scaling. Packed ConvRot and preallocated or
-view outputs retain the intermediate half buffer.
-
-`src/core/cuda/i8_projection.cu` supplies the INT8 GEMM projection epilogues:
-`GEMM -> Reshape -> RMSNorm -> Mul -> RoPE`, optional scaling and FP16 copy,
-and K/V prefix concatenation with FP16 output. CuTe can consume a directly
-produced FP16 Q without materializing the FP32 GEMM or RoPE intermediates.
-Independent affine operands, including broadcasted constants, are computed before
-their normalization/projection
-chain when graph dependencies allow it. The attention optimizer can then move
-the complete five-node projection when safe.
-These rules accept custom RoPE nodes. They preserve the original
-large-matrix selection thresholds, full interleaved head dimension 128, packed
-activation layout, and separate weight scales. External consumers prevent fusion;
-aliasing terminal copies use scheduler workspace. The CUDA projection kernels
-and matchers now live in sd.cpp. `GGML_CUDA_FORCE_CUBLAS` disables these epilogues.
-
-`src/core/ggml_extensions.cpp` defines `sd.linear_i8.v1` and
-`sd.quantize_i8_convrot.v1`. `src/core/cpu/int8.cpp` supplies their CPU
-implementations. The CPU kernels use the
-configured thread pool and scheduler workspace; the old CPU INT8 GEMM and ConvRot
-implementations have been removed from ggml.
-Model code keeps calling `ggml_ext_linear_i8_tensorwise`,
-`ggml_ext_mul_mat_i8_tensorwise`, and `ggml_ext_quantize_i8_convrot`. The latter two
-constructors are declared and implemented in sd.cpp and create custom nodes.
-Their former ggml declarations, implementations, ConvRot opcode, and native
-backend paths have been removed. CPU, CUDA/HIP/MUSA, and Vulkan register kernels
-and fusion rules through the extension interfaces.
-
-For already quantized activations, `ggml_ext_mul_mat_i8_tensorwise` accepts ordinary
-I8 weight and activation tensors. Its optional final `input_scale` parameter is a
-separate F32 tensor with one value per activation row. Weight scales have one
-value per output channel; weight scales and bias can be null. Existing calls
-omit `input_scale` and retain their original argument order. It computes `float(int32_dot(W, X)) * X_scale * W_scale + bias`, in that
-order. All scales are explicit graph dependencies; there is no quantized-tensor
-wrapper. Shapes use ggml's low-to-high dimension order. Call
-`sd_ggml_register_extensions` before scheduling these custom nodes.
-
-`ggml_ext_mul_mat_i8_tensorwise` also accepts F32 activations (quantized into temporary
-workspace) or the output of `ggml_ext_quantize_i8_convrot`. ConvRot keeps the existing
-packed I8 activation rows, padded to a multiple of four rows, followed by F32 row
-scales. Weight scales remain separate. CPU ConvRot groups are 1, 4, 16, 64, or 256.
-Unsupported layouts and malformed scale shapes are rejected. Separate CPU quantize
-and GEMM phases propagate cancellation, and overlapping outputs use workspace.
-
-`src/core/cuda/int8.cu` implements the same custom schemas on CUDA. It uses the
-borrowed cuBLAS handle for I8-by-I8-to-I32 GEMM and application-owned quantization,
-ConvRot, and rescaling kernels. F32 inputs are quantized into workspace; ordinary
-I8 inputs retain separate scale tensors and use a padded temporary only when
-needed by cuBLAS. Packed ConvRot inputs are consumed directly. Large ConvRot rows
-use multiple kernels when they exceed shared memory. Aliasing outputs use scratch.
-CUDA GEMM requires Turing or newer, contiguous tensors, K/N divisible by four,
-float-aligned inputs, and row counts that fit a padded signed 32-bit dimension;
-CUDA ConvRot uses groups of 256.
-Unsupported custom nodes can fall back to CPU through the scheduler.
-
-When CUTLASS is configured, `src/core/cuda/i8_gemm.cu` supplies dense and SwiGLU
-INT8 GEMM kernels on Ampere or newer. It preserves the native tile selection,
-small-row policy, Stream-K scheduling, Ada column bands, and separate scaling
-order. The same extension matches `GEMM -> SwiGLU [-> ConvRot]` and row
-concatenation for custom GEMM nodes. Weight scales remain explicit
-inputs, including the gate/up scale permutation recorded on every SwiGLU launch.
-Two independent INT8 GEMMs sharing a quantized input can also fuse with split
-SwiGLU. A paired-column iterator reads the separate gate/up weight allocations,
-and the GEMM epilogue writes only the activated output. Both intermediate
-activations are omitted through the generic fusion allocation interface; weights
-are neither copied nor concatenated. This path requires bias-free branches,
-compatible layouts and alignments, and no external intermediate consumers.
-Only interleaved weight scales and any Stream-K workspace remain temporary.
-Plans reserve bounded Stream-K workspace, cache occupancy on first execution,
-and initialize barriers on every launch, so CUDA Graph replay observes updated
-inputs and scales. External consumers retain ordinary execution; overlapping
-outputs use workspace. For fresh outputs, the CUTLASS SwiGLU epilogue writes
-an allocator-managed tensor that the subsequent ConvRot launch consumes. This
-keeps the same GPU launches while allowing its storage to be reused by other
-operations. The combined SwiGLU/ConvRot path retains its workspace intermediate
-for other cases. Shapes or alignments unsuitable for CUTLASS use cuBLAS.
-Custom I8 nodes also retain cuBLAS when the reduction could overflow I32, to
-preserve their previous accumulation behavior. Fusion disable and force-cuBLAS
-settings are honored. No CUTLASS headers are needed for the cuBLAS path.
-
-The native CUDA/HIP INT8 GEMM, standalone ConvRot, and associated fusion paths
-have been removed from ggml. The shared application implementation calls cuBLAS
-or hipBLAS directly. HIP retains the existing architecture eligibility policy;
-MUSA does not advertise INT8 BLAS support and uses scheduler fallback for GEMM.
-Standalone ConvRot kernels are compiled for all three runtimes. CUTLASS
-implementations remain CUDA-specific.
-
-`src/core/cuda/i8_gated_residual.cu` implements
-`INT8 GEMM -> [Scale] -> Mul(gate) -> Add(residual) -> [Concat]` as a registered
-CUDA fusion, for packed ConvRot inputs and ordinary I8 nodes with separate activation
-scales. The corresponding native CUDA fusion rules and CUTLASS residual epilogue
-have been removed from ggml. Its optimizer moves independent gate producers ahead
-of GEMM and optional scaling through `ggml_ext_graph_delay`, before fusion matching.
-The former native INT8 affine reorder rule has also moved into this extension.
-It preserves the original tile selection, small-row
-threshold, Stream-K scheduling, Ada column bands, and separate FP32 rounding of
-gate multiplication and residual addition. The plan bounds Stream-K workspace
-over possible occupancy values using host metadata; execution verifies the
-actual requirement against that bound. Device occupancy is cached on first
-execution; each launch rebuilds parameters for the current tensor addresses and
-initializes its Stream-K barriers. Overlapping outputs use scratch, except
-for an exact in-place residual without concatenation. External consumers and
-unsupported layouts retain ordinary operator execution. Fusion and force-cuBLAS
-disable settings are honored.
-
-`src/core/cuda/convrot.cu` registers CUDA fusion for `Scale -> ConvRot`,
-`Norm -> Mul -> ConvRot`, `Modulate -> ConvRot`,
-`RMSNorm -> Mul(weight) -> Modulate -> ConvRot`, `SwiGLU -> ConvRot`, and
-`SiLU/Sigmoid -> Mul -> ConvRot`.
-ConvRot outputs are custom nodes. SwiGLU accepts packed,
-swapped, or separate gate/up inputs. Independent modulation and up-branch
-producers are moved ahead of their consumers through the safe graph-delay API.
-Reordering keeps an owning producer together with in-place consumers, such as
-Krea2's RMSNorm weight multiplication, so the alias checks can preserve their
-storage relationship.
-The application kernel preserves the original reduction order, thread selection,
-ConvRot row padding, and separate scale tail. Prepared plans cache operation
-metadata, use the borrowed CUDA stream, and reserve scheduler workspace for
-overlapping outputs. Long rows or external intermediate consumers retain
-ordinary execution. These fusions do not require CUTLASS and honor
-`GGML_CUDA_DISABLE_FUSION`.
-
-The corresponding scale, normalization, and unary-activation fusion rules have
-been removed from ggml for CUDA and HIP. GEMM/SwiGLU/ConvRot chains are selected by the
-application INT8 extension before the standalone activation fusion.
-
-`src/core/cuda/affine.cu` registers FP32 `Mul(gate) -> Add(residual)`,
-`Concat -> [Permute] -> FP16 Copy`, and `Contiguous -> Scale -> Permute -> FP16
-Copy` fusions. Gate multiplication and residual addition retain separate FP32
-rounding. Copy layouts and alignment requirements match the original CUDA
-kernels; unsupported layouts, external intermediate consumers, self-residual
-Add inputs, and fusion disabling retain ordinary operators. Internal in-place
-Add outputs are accepted when they overwrite the complete fused Mul allocation. Exact in-place residual
-or input addresses are safe for the pointwise kernel; partial overlap, broadcast
-gate overlap, and FP16 copy overlap use scheduler workspace. These registrations
-require no CUTLASS, compile for CUDA/HIP/MUSA, and support reusable CUDA Graph
-replay. Their former native fusion implementations have been removed from ggml.
-
-CUDA kernels can opt into device replay with `ggml_ext_register_kernel_ex` and
-`GGML_EXT_KERNEL_CAPTURE_SAFE`. The original registration function retains its
-per-execution callback behavior, and the descriptor ABI is unchanged. Opting in
-means the callback enqueues replayable device work: it has no required
-per-execution host side effects or decisions based on tensor contents, and any
-private streams rejoin the borrowed stream. Lazy setup must complete during the
-first ordinary execution. Tensor metadata, attributes, and host plan parameters
-remain frozen until scheduler reallocation; tensor contents may change normally.
-
-When every selected extension opts in, the CUDA backend can capture the whole
-scheduled plan, including its native fragments, into one CUDA Graph. The first
-execution warms up kernels and library state while retaining native fragment
-caches. The next stable execution captures
-and launches the graph; later calls replay it. Changing a tensor address or the
-execution stream invalidates the capture and repeats warmup, even when the graph
-UID is unchanged. Capture failures propagate without retrying partially executed
-work. An enclosing stream capture records the plan directly instead of starting
-a nested capture.
-
-`GGML_EXT_KERNEL_REPLAY_REUSABLE` additionally permits reuse across scheduler
-plan rebuilds and requires `CAPTURE_SAFE`. Recorded work must depend only on
-complete tensor metadata, addresses, the device and immutable registration data.
-Captured resources must belong to the backend or the supplied workspace, never
-per-plan host state. Workspace contents must be initialized by recorded device
-work on each execution. Prepare/destroy still run for every rebuilt host plan.
-The backend cache retains only the executor and workspace; it compares graph
-structure, tensor metadata/addresses, selected kernels and workspace requirements
-exactly, without dereferencing old graph objects. A new metadata arena can reuse
-an equivalent capture. Each backend retains at most 16 retired plans and 256 MiB
-of their workspaces, evicting the oldest entries first. Larger workspaces retain
-plan-local capture only. Backend destruction synchronizes and releases the cache.
-This allows sd.cpp's per-step scheduler rebuilds to retain device replay.
-
-Mixed plans containing an extension without the flag retain ordinary dispatch.
-Evaluation callbacks, multiple pipeline copies, and direct backend graph calls
-also retain their previous behavior. `GGML_CUDA_DISABLE_GRAPHS` disables whole-plan
-capture as well as native CUDA Graph caching. Native operations that cannot be
-captured retain the existing fragment execution path. Whole-plan replay requires
-CUDA VMM so native scratch addresses remain valid for the backend lifetime;
-legacy scratch pools retain the existing fragment path. HIP and MUSA do not yet
-provide this plan executor. sd.cpp's CUDA registrations opt in to reusable replay.
-
-## Vulkan execution services
-
-`ggml-ext-vulkan.h` exposes `ggml_ext_vulkan_v1` through the Vulkan backend
-registry. The versioned function table reports device limits and subgroup
-capabilities, resolves tensor/workspace byte ranges, dispatches application
-SPIR-V, and copies nonoverlapping buffer ranges. Extensions do not include
-private ggml Vulkan headers or own backend command buffers and descriptors.
-
-Shaders use the `main` entry point, consecutive storage-buffer bindings starting
-at zero, and consecutive 32-bit specialization constants. Dispatch accepts actual
-workgroup counts. The backend validates ranges and dispatch limits, caches the
-complete shader/configuration per device, and releases pipelines with that
-device. Push constants and specialization values are copied; the backend does
-not retain application execution state. Pipeline creation occurs on execution,
-never during host-only plan preparation.
-
-Bindings identify either a tensor or a backend buffer, with a byte offset and
-optional byte count. `buffer_info` returns an opaque storage identity and byte
-range for alias checks. Vulkan descriptor offsets are rounded down to the
-device's required alignment; shaders must add the returned `shader_offset`.
-Workspace bindings follow the same rules. Invalid or foreign-device ranges are
-rejected before recording commands. Kernel bounds and shader capability
-requirements remain the extension's responsibility.
-
-Dispatch and copy use the backend's existing compute command context, descriptor
-pool, transfer waits, and memory barriers. They preserve ordering with native
-fragments and invalidate cached converted operands after writes. Normal backend
-synchronization completes queued work before host access or resource release.
-Vulkan copy ranges must be equal in size, nonoverlapping, and aligned to four
-bytes. There is no per-dispatch host wait or extension-owned queue.
-
-Scheduled Vulkan plans share submission counters and FLOP thresholds across
-native fragments and extension callbacks. Native scratch is initialized once
-per plan invocation, with its offsets retained across fragments. Fragment
-boundaries do not force a
-queue submission, and the final pending commands are submitted when the plan
-returns. Extension dispatches and copies count toward the device's node limit.
-Every callback still executes on every invocation; this batching neither
-captures device replay nor retains plan state across scheduler rebuilds.
-Callback errors propagate without retry. Evaluation callbacks, direct backend
-calls, synchronous devices, serialized diagnostics, and timestamp profiling
-retain fragment execution.
-
-`src/core/vulkan/rope.cpp` registers the custom RoPE implementation,
-plus `RMSNorm -> Mul -> RoPE` and `RMSNorm -> Mul -> RoPE -> Scale -> FP16 Copy`
-fusion. These kernels retain the original strides, partial-rotation behavior,
-128-channel subgroup reduction, and separate arithmetic rounding. Their SPIR-V
-is generated from application shaders with the native RoPE compilation policy;
-SPIR-V optimization is disabled because it changes the reduction results.
-Safe graph-delay rules bring independent producers ahead of fusion candidates.
-External intermediate consumers retain ordinary operators, and overlapping
-outputs use plan workspace. `GGML_VK_DISABLE_FUSION` is honored.
-
-The corresponding Vulkan shader sources and native fusion rules have been
-removed from ggml. Unsupported custom nodes retain scheduler fallback to
-another supported backend.
-
-RoPE Apply, INT8/ConvRot, CuTe attention, SageAttention, and Sol-Attn use
-application-owned implementations. This interface does not add dynamic dtypes.
-
-`src/core/vulkan/int8.cpp` owns Vulkan INT8 GEMM, ConvRot, and their fusion rules.
-The Vulkan compute metadata service exposes shared-memory limits, subgroup
-arithmetic, and cooperative-matrix properties; shape selection stays in sd.cpp.
-Shader variants preserve the former tiling and compilation policy, including
-SwiGLU, residual, row-concatenation, and modulation paths. Kernels use public
-Vulkan dispatch, buffer, copy, and workspace services without private backend
-headers. Vulkan currently handles packed group-256 activations with separate
-weight scales; unsupported custom nodes fall back to CPU.
-
-## SageAttention
-
-The `ggml_ext_sage_attn` entry point is declared and implemented in sd.cpp's
-graph construction helpers. It constructs `sd.sage_attn.v1` with explicit Q/K/V inputs
-and scale/mode attributes. ggml has no Sage-specific opcode, allocation rule, or
-backend dispatch branch.
-
-`src/core/cuda/sage_attention.cu` and its `sageattention/` device headers own the
-CUDA implementation. Quantization and temporary attention output use scheduler
-workspace, rather than an oversized output tensor. The implementation obtains
-the backend stream through the public GPU service and converts the final FP16
-output with a local CUDA kernel. It retains the SM80 compatibility path and the
-SageAttention2/2++ toolkit and architecture requirements. Capability checks use
-the application's compiled CUDA target as well as the physical device.
-
-CPU, HIP, MUSA, and Vulkan do not implement this NVIDIA-specific kernel. Existing
-model-level capability checks retain flash/default attention for unsupported
-layers; a custom Sage node itself does not silently change algorithms.
-
-## Sol-Attn
-
-The `ggml_ext_sol_attn` entry point lives in the common sd.cpp graph construction
-files and constructs `sd.sol_attn.v1`. It accepts contiguous F32 Q/K/V tensors
-with shape `[128, tokens, heads, batch]`, a positive finite scale, and finite tau.
-The output remains F32 `[128, heads, tokens, batch]`. No current sd.cpp model or
-CLI path calls this entry point; the migration preserves its low-level behavior.
-
-`src/core/cuda/sol_attention.cu` owns packing, block summaries, threshold
-selection, and the BF16 tensor-core kernel. Temporary storage is separate
-scheduler workspace, so the output allocation contains only the output tensor.
-The kernel uses the public backend stream service and direct CUDA/PTX operations.
-Registration requires CUDA 12 or newer, an SM80-or-newer device, and an application
-kernel compiled for SM80 or newer. CPU, HIP, MUSA, and Vulkan do not advertise
-support. ggml no longer contains a Sol-Attn opcode, constructor, allocation rule,
-or backend kernel.
-
-## Public graph access
-
-`ggml-graph.h` exposes leaf enumeration, append-only leaf construction, recorded
-use counts, mapped inference-graph copying, use-count copying for segments, and
-backend execution of a node range. Mapped copies retain node order and consumer
-counts from the original graph, including consumers outside a graph view. The
-caller owns tensor copies and remaps their storage and edges; invalid mappings
-leave the destination graph unchanged. Range execution uses already allocated
-tensors and retains the original graph's consumer counts.
-
-Workspace measurement, graph cuts, and evaluation callbacks use these APIs.
-Normal sd.cpp builds require only ggml's public headers. Compatibility with an
-upstream ggml lacking this header is isolated in `src/core/ggml_compat.cpp`; that
-optional build still requires matching upstream private headers. The compatibility
-layer also retains the workaround for old Metal backends wrapping failed host
-mappings. The patched Metal backend returns null directly when mapping fails.
@@ -18,7 +18,7 @@ and ByT5 Small GlyphXL for glyph-aware text conditioning.
 ## Text-to-video example

 ```shell
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\hunyuanvideo1.5_720p_t2v_fp16.safetensors --vae ..\models\vae\hunyuanvideo15_vae_fp16.safetensors --llm ..\models\text_encoders\qwen_2.5_vl_7b.safetensors --t5xxl ..\models\text_encoders\byt5_small_glyphxl_fp16.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -W 1280 -H 720 --offload-to-cpu --video-frames 33 --vae-tiling
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\hunyuanvideo1.5_720p_t2v_fp16.safetensors --vae ..\models\vae\hunyuanvideo15_vae_fp16.safetensors --llm ..\models\text_encoders\qwen_2.5_vl_7b.safetensors --t5xxl ..\models\text_encoders\byt5_small_glyphxl_fp16.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -W 1280 -H 720 --offload-to-cpu --diffusion-fa --video-frames 33 --vae-tiling
 ```

 <video src=../assets/hunyuan_video/hy1.5_t2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -49,7 +49,7 @@ If you want lower VRAM usage, you can change the quantization from q8_0 to a low
 ## Examples

 ```sh
-.\bin\Release\sd-cli.exe --diffusion-model ideogram4-Q8_0.gguf --uncond-diffusion-model ideogram4_uncond-Q8_0.gguf --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors -p '{"high_level_description":"A square 1024 x 1024 luxury fashion magazine cover featuring exactly one short chubby fluffy cat as the main model. The cat sits on a soft ivory studio floor, facing the viewer with a stylish calm expression, wearing tiny black sunglasses, a red silk scarf, and a small gold collar charm. In front of the cat on the floor is a wide horizontal luxury nameplate that clearly reads ideogram4.cpp. The whole design feels premium, fashionable, clean, and editorial.","style_description":{"aesthetics":"luxury fashion magazine cover, high-end pet couture campaign, minimalist editorial design, elegant studio photography, soft paper texture, refined typography, fashionable and polished","lighting":"Soft diffused studio lighting, gentle spotlight on the cat, subtle floor shadow, warm ivory highlights, clean separation between subject and background","photo":"high-resolution fashion editorial photography look, front-facing cat portrait, crisp fur details, glossy sunglasses, clear readable nameplate text, shallow depth of field","medium":"mixed media fashion photography and premium editorial graphic design","color_palette":["#F4EFE7","#111111","#D8B56D","#B73A3A","#FFFFFF","#8A7A6A"]},"compositional_deconstruction":{"canvas":"Square 1024 x 1024 canvas with a normal upright orientation. Do not rotate the poster or any text. Use a clean fashion magazine cover layout.","background":"Warm ivory studio backdrop with subtle paper grain, a soft spotlight gradient, faint floor shadow, and a few minimal gold editorial lines. The background is spacious, premium, and uncluttered.","layout":"Top center has a small elegant headline. Center area features one cat as the main fashion model. Lower foreground has a wide horizontal luxury nameplate placed on the floor in front of the cat. Bottom center has a small footer. All text is horizontal, upright, and readable left to right.","elements":[{"type":"text","desc":"Top center headline reading LOOK WHAT I FOUND in a refined high-fashion serif font. The headline is horizontal, centered, elegant, and secondary to the nameplate text."},{"type":"obj","desc":"Exactly one short chubby fluffy cat sitting in the center like a luxury fashion model. The cat has a large round head, compact body, short legs, soft detailed fur, expressive eyes, and a calm confident pose. The cat is cute and rounded, not tall, not stretched, not duplicated."},{"type":"obj","desc":"Tiny glossy black sunglasses worn naturally by the cat, slightly oversized but still showing the cat face clearly. The sunglasses add a chic fashion-editorial attitude."},{"type":"obj","desc":"A red silk scarf tied neatly around the cat neck, with soft folds and a couture feeling. The scarf must not cover the cat face or the nameplate."},{"type":"obj","desc":"A small gold collar charm or fashion accessory under the scarf, subtle and premium, adding a luxury campaign detail."},{"type":"obj","desc":"In the lower foreground, place a wide horizontal luxury nameplate on the floor in front of the cat. The nameplate is low, flat, landscape-oriented, much wider than tall, like a fashion show seat card or premium display plaque. It is centered, front-facing, level, and fully visible. It must not become vertical, tall, standing, rotated, or side-facing."},{"type":"text","desc":"Print the exact text ideogram4.cpp only on the wide horizontal nameplate. Use clean bold black lettering, perfectly spelled, lowercase, with the number 4 and .cpp extension. The text must fit completely inside the nameplate, stay horizontal, and be readable from left to right."},{"type":"obj","desc":"Add sparse premium editorial accents around the edges: thin gold lines, small code brackets, tiny cursor marks, subtle dots, and minimal geometric details. No extra cats, no stickers, no animal faces, no busy decorations."},{"type":"text","desc":"Bottom center footer reading tiny paws, big compile energy in a small refined monospace or editorial font. The footer is horizontal, centered, understated, and much smaller than the nameplate text."}]}}' -v --offload-to-cpu -H 1024 -W 1024
+.\bin\Release\sd-cli.exe --diffusion-model ideogram4-Q8_0.gguf --uncond-diffusion-model ideogram4_uncond-Q8_0.gguf --llm ..\models\text_encoders\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors -p '{"high_level_description":"A square 1024 x 1024 luxury fashion magazine cover featuring exactly one short chubby fluffy cat as the main model. The cat sits on a soft ivory studio floor, facing the viewer with a stylish calm expression, wearing tiny black sunglasses, a red silk scarf, and a small gold collar charm. In front of the cat on the floor is a wide horizontal luxury nameplate that clearly reads ideogram4.cpp. The whole design feels premium, fashionable, clean, and editorial.","style_description":{"aesthetics":"luxury fashion magazine cover, high-end pet couture campaign, minimalist editorial design, elegant studio photography, soft paper texture, refined typography, fashionable and polished","lighting":"Soft diffused studio lighting, gentle spotlight on the cat, subtle floor shadow, warm ivory highlights, clean separation between subject and background","photo":"high-resolution fashion editorial photography look, front-facing cat portrait, crisp fur details, glossy sunglasses, clear readable nameplate text, shallow depth of field","medium":"mixed media fashion photography and premium editorial graphic design","color_palette":["#F4EFE7","#111111","#D8B56D","#B73A3A","#FFFFFF","#8A7A6A"]},"compositional_deconstruction":{"canvas":"Square 1024 x 1024 canvas with a normal upright orientation. Do not rotate the poster or any text. Use a clean fashion magazine cover layout.","background":"Warm ivory studio backdrop with subtle paper grain, a soft spotlight gradient, faint floor shadow, and a few minimal gold editorial lines. The background is spacious, premium, and uncluttered.","layout":"Top center has a small elegant headline. Center area features one cat as the main fashion model. Lower foreground has a wide horizontal luxury nameplate placed on the floor in front of the cat. Bottom center has a small footer. All text is horizontal, upright, and readable left to right.","elements":[{"type":"text","desc":"Top center headline reading LOOK WHAT I FOUND in a refined high-fashion serif font. The headline is horizontal, centered, elegant, and secondary to the nameplate text."},{"type":"obj","desc":"Exactly one short chubby fluffy cat sitting in the center like a luxury fashion model. The cat has a large round head, compact body, short legs, soft detailed fur, expressive eyes, and a calm confident pose. The cat is cute and rounded, not tall, not stretched, not duplicated."},{"type":"obj","desc":"Tiny glossy black sunglasses worn naturally by the cat, slightly oversized but still showing the cat face clearly. The sunglasses add a chic fashion-editorial attitude."},{"type":"obj","desc":"A red silk scarf tied neatly around the cat neck, with soft folds and a couture feeling. The scarf must not cover the cat face or the nameplate."},{"type":"obj","desc":"A small gold collar charm or fashion accessory under the scarf, subtle and premium, adding a luxury campaign detail."},{"type":"obj","desc":"In the lower foreground, place a wide horizontal luxury nameplate on the floor in front of the cat. The nameplate is low, flat, landscape-oriented, much wider than tall, like a fashion show seat card or premium display plaque. It is centered, front-facing, level, and fully visible. It must not become vertical, tall, standing, rotated, or side-facing."},{"type":"text","desc":"Print the exact text ideogram4.cpp only on the wide horizontal nameplate. Use clean bold black lettering, perfectly spelled, lowercase, with the number 4 and .cpp extension. The text must fit completely inside the nameplate, stay horizontal, and be readable from left to right."},{"type":"obj","desc":"Add sparse premium editorial accents around the edges: thin gold lines, small code brackets, tiny cursor marks, subtle dots, and minimal geometric details. No extra cats, no stickers, no animal faces, no busy decorations."},{"type":"text","desc":"Bottom center footer reading tiny paws, big compile energy in a small refined monospace or editorial font. The footer is horizontal, centered, understated, and much smaller than the nameplate text."}]}}'  --diffusion-fa -v --offload-to-cpu -H 1024 -W 1024
 ```

 <img alt="ideogram4 image example" src="../assets/ideogram4/example.png" />
@@ -1,180 +0,0 @@
-# Image preprocessing
-
-Use `--image-preprocess` to transform each image input once, before generation:
-
-```sh
-sd-cli ... \
-  --image-preprocess "target=init,mode=crop-resize,filter=lanczos,antialias=true" \
-  --image-preprocess "target=mask,filter=nearest-exact" \
-  --image-preprocess "target=ref,index=0,mode=fit-pad,width=768,height=768,filter=bicubic"
-```
-
-CLI and server image loaders decode at the original resolution. The generation
-entry point merges input defaults with user rules and prepares one transformed
-image per input. The original pipeline then consumes those images, including
-its mandatory canvas adaptation, reference resizing, and encoder preprocessing.
-
-```text
-native-resolution image
-  -> input defaults + user overrides
-  -> one input transform
-  -> original generation pipeline and model-specific processing
-```
-
-These rules do not override internal VAE, CLIP/VLM, ControlNet, or pixel-patch preprocessing.
-`--ref-image-args` retains its existing meaning and runs after this input transform.
-
-## Inputs and defaults
-
-| `target` | Input | Default geometry | Indexed? |
-| --- | --- | --- | --- |
-| `init` | img2img image or video first frame | Center crop to the generation aspect ratio, then resize | No |
-| `end` | Video last frame | Center crop, then resize | No |
-| `mask` | Inpainting mask | Inherit init geometry; otherwise center crop, then resize | No |
-| `control` | Control image | Center crop, then resize | No |
-| `ref` | Reference images | Preserve source dimensions | Yes |
-| `ip-adapter` | IP-Adapter image | Preserve source dimensions | No |
-| `id` | PhotoMaker identity images | Preserve source dimensions | Yes |
-| `control-frame` | Control video frames | Center crop, then resize | Yes |
-
-Canvas defaults use the aligned generation dimensions. Reference, IP-Adapter,
-and identity inputs use their original dimensions unless overridden. Default
-resampling is nearest for images and nearest-exact for masks.
-
-These defaults are shared by CLI, server, and C API. Moving geometry out of
-the loaders replaces the previous CLI/server BOX/sRGB resizing, so default
-pixels are not guaranteed to match earlier builds.
-
-Reference video and audio preprocessing are outside these image rules.
-Preprocessing options apply to `img_gen` and `vid_gen`, not standalone upscale
-or ADetailer mode. ADetailer clears the user's rules for its internal crops.
-
-## Rules
-
-Rules are comma-separated `key=value` lists. Repeat the CLI option or separate
-rules with semicolons. Every rule requires a `target` and at least one option.
-Rule syntax and input compatibility are checked when image/video generation
-starts. Unknown keys, invalid values, duplicate keys in a rule, missing images,
-and out-of-range indices cause generation to fail with an error log.
-
-Omit `index` to configure every image of that type; otherwise use a zero-based
-index. CLI directory inputs follow filename order. Indexed rules override
-type-wide rules field by field, regardless of order. At equal specificity,
-the last value for a field wins. `auto` selects the input preset.
-
-| `mode` | Input transform |
-| --- | --- |
-| `auto` | Use the input's default geometry |
-| `none` | Keep source dimensions without resizing, cropping, or padding |
-| `stretch` | Resize to the target dimensions |
-| `crop` | Crop a target-sized rectangle without resizing; fail if the source is too small |
-| `crop-resize` | Crop to the target aspect ratio, then resize |
-| `fit-pad` | Fit the entire image inside the target dimensions, preserving aspect ratio, then pad |
-
-`width` and `height` must be specified together as positive integers. They
-override the input transform's dimensions, not the generation or encoder size.
-For a native-size preset, specifying dimensions without a mode selects stretch.
-`mode=none` with explicit dimensions different from the source is contradictory
-and is rejected.
-
-`anchor=center|top|bottom|left|right` selects crop/padding placement.
-`pad_color=#RRGGBB` or `#RRGGBBAA` selects padding, defaulting to opaque black.
-A grayscale mask uses the first color component.
-
-`filter=auto|nearest|nearest-exact|bilinear|bicubic|lanczos` selects resampling.
-`antialias=auto|true|false` enables antialiasing automatically for filtered
-downscaling; explicit true requires bilinear, bicubic, or Lanczos.
-Filtered RGBA resizing uses premultiplied alpha.
-
-`canny=true|false` enables edge detection for any supported image target,
-defaulting to `false`. It runs once after geometry, before the original
-generation pipeline, including with `mode=none`. Grayscale, grayscale-alpha,
-RGB, and RGBA inputs are supported; alpha is preserved.
-
-Each input has its own Canny setting. Indexed rules can enable or disable it
-for individual references, identity images, or video control frames.
-
-```sh
--image-preprocess "target=init,mode=fit-pad,canny=true"
--image-preprocess "target=ref,index=0,mode=none,canny=true"
--image-preprocess "target=control-frame,index=2,canny=true"
-```
-
-If init and mask source dimensions differ, the mask is first center-cropped to
-the init source aspect ratio and resized to the init source dimensions using
-nearest-exact interpolation. The mask then inherits the init crop, resize, and
-padding coordinates, while retaining its own filter, padding value, and Canny
-setting for that transform. Explicit mask geometry is evaluated after this
-alignment; conflicting geometry is rejected. An omitted mask remains absent
-until the original pipeline creates its default mask.
-
-## Downstream behavior
-
-`mode=none` only skips the input geometry transform. For example:
-
-```sh
--image-preprocess "target=init,mode=none" \
--image-preprocess "target=ref,mode=none"
-```
-
-The init image is still adapted to the generation canvas by the original
-pipeline. Reference images still follow `--ref-image-args` and model-specific
-resizing. CLIP retains its fixed input dimensions and normalization. HiDream-O1
-retains its original pixel-reference and visual preprocessing.
-
-Existing sharing between consumers is preserved: for example, Wan img2video
-uses the same adapted first frame for VAE conditioning and CLIP. High-resolution
-passes reuse the prepared images and apply their original size adaptation;
-they do not apply the user's crop a second time.
-
-To disable reference resizing before VAE encoding, use
-`--ref-image-args "resize_before_vae=false"` or the server field
-`"ref_image_args": "resize_before_vae=false"`. This is separate from
-`target=ref,mode=none`, which only skips input geometry. Model constraints
-still apply; MiniMax-H3 requires 32-pixel-aligned references and warns instead
-of honoring the switch.
-
-## Server requests
-
-Native image/video requests and SDAPI accept `image_preprocess` as a string or
-an array of rule strings:
-
-```json
-{
-  "image_preprocess": [
-    "target=init,mode=fit-pad,filter=bicubic",
-    "target=mask,filter=nearest-exact",
-    "target=ref,index=0,mode=none"
-  ]
-}
-```
-
-OpenAI-compatible requests accept it through
-`<sd_cpp_extra_args>{...}</sd_cpp_extra_args>` in the prompt.
-Request rules replace server-default rules. Generation metadata records the
-user rules for replay with the same input images; image encodings and channel conventions are unchanged.
-
-## C API
-
-Set `image_preprocess` on the existing image/video generation parameters.
-Generation returns media and actual parameters together; see [generation metadata](generation_metadata.md):
-
-```c
-sd_img_gen_params_t params;
-sd_img_gen_params_init(&params);
-/* Set prompt, original-resolution input images, and generation options. */
-params.image_preprocess.rules = "target=init,mode=crop-resize,filter=lanczos;"
-                                "target=mask,filter=nearest-exact";
-sd_image_generation_result_t* result = NULL;
-bool ok = generate_image(ctx, &params, &result);
-/* Consume result->items and their generation_info on success. */
-free_sd_image_generation_result(result);
-```
-
-Both generation parameter initializers set `image_preprocess.rules` to `NULL`,
-selecting input presets. Rule strings are borrowed for the synchronous call.
-The library owns temporary transformed pixels; caller images and arrays are
-not modified. Add `canny=true` to the desired target's rule in
-`image_preprocess.rules` to enable Canny.
-
-The parameter structs have grown; applications and bindings must be rebuilt.
@@ -2,11 +2,7 @@

 sd.cpp can load and execute ComfyUI `int8_tensorwise` safetensors with `convrot` metadata directly. The stored INT8 weights are not converted to another weight type at load time.

-This uses sd.cpp's INT8 tensorwise/convrot operators through the patched GGML
-extension API. Schemas and graph builders live in sd.cpp, alongside the
-backend kernels and fusion rules; GGML supplies generic dispatch and workspace
-services. See [operator extensions](ggml_extensions.md) for the implementation
-and the preserved INT8 API entry points.
+This requires the INT8 tensorwise/convrot extensions in the patched GGML.
 Builds with `SD_USE_UPSTREAM_GGML=ON` reject these files during loading.

 ## Checkpoint format
@@ -88,86 +84,22 @@ The floating-point output is reconstructed as
 Y[r, o] ~= A[r, o] * s_x[r] * s_w[o] + b[o]
 ```

-The packed runtime activation tensor contains the I8 activation rows and their floating-point row scales. Linear layers with an input scale of `1` that share the same input and convrot group size reuse this packed tensor, avoiding repeated rotation and activation quantization within the graph. For other input scales, activations are scaled before explicit convrot quantization, and the linear output is unscaled before adding bias.
+The packed runtime activation tensor contains the I8 activation rows and their floating-point row scales. Linear layers that share the same input and convrot group size reuse this packed tensor, avoiding repeated rotation and activation quantization within the graph.

 ## Backend support

 - CPU provides the portable regular Hadamard, activation quantization, INT8 matrix multiplication, and scale restoration implementations.
- Metal supports INT8 tensorwise and convrot on Apple silicon (Apple GPU family 7 or newer). It keeps weights in INT8 and rotates and quantizes activations on the GPU. On macOS/iOS 26 or newer, it probes Metal TensorOps `matmul2d` with INT8 inputs and INT32 cooperative results. It combines 1024-product partial sums using wrapping integer addition to preserve exact accumulation on older GPUs, then applies scales and bias in registers. Older systems or unavailable TensorOps pipelines use SIMD-group matrix multiplication: INT8 tiles are converted to FP16, and FP32 partial sums cover at most 256 products before being combined as integers, preserving the I32 accumulation contract. Convrot groups 1, 4, 16, 64, and 256 are supported. These kernels require the patched GGML Metal extension services and are enabled by `SD_METAL=ON`.
- NVIDIA CUDA devices with compute capability 7.5 or newer use the native accelerated path. For H256, CUDA fuses the rotation, row-wise maximum reduction, and activation quantization. Eligible shapes use the CUTLASS INT8 implementation when enabled; other supported shapes use cuBLAS for I8 x I8 to I32 GEMM and a CUDA kernel for scale restoration and bias addition.
- AMD HIP devices in the CDNA, RDNA3 (including RDNA3.5), and RDNA4 families use the same INT8/H256 kernels with hipBLAS for I8 x I8 to I32 GEMM. Other AMD architectures fall back to CPU for INT8 matrix multiplication.
- MUSA shares the CUDA-style H256 activation kernels. INT8 GEMM falls back to CPU because the MUSA backend does not advertise INT8 BLAS support.
- Vulkan provides H256 activation quantization and INT8 matrix multiplication when the build and device support accelerated packed INT8 dot products. Unsupported configurations and GPU backends without these kernels use the backend scheduler to fall back to CPU.
+- NVIDIA CUDA devices with compute capability 7.5 or newer use the native accelerated path. For H256, CUDA fuses the rotation, row-wise maximum reduction, and activation quantization. It uses cuBLAS for I8 x I8 to I32 GEMM and a CUDA kernel for scale restoration and bias addition.
+- Vulkan and other GPU backends do not currently have dedicated INT8 convrot kernels. They use the backend scheduler to fall back to CPU, which is expected to be substantially slower than the CUDA path.

 LoRA adapters are applied at runtime without modifying the INT8 weights. The INT8 convrot path computes the base linear output, while LoRA, LoHa, LoKr, and raw weight-difference adapters compute their output corrections from the original, unrotated activation and add them to the base output. `--lora-apply-mode auto` selects this path for models containing INT8 tensorwise weights. If `immediately` is requested, sd.cpp falls back to runtime application because merging an adapter would require dequantizing and rotating its weight update, then recalculating the per-row scales and requantizing the result.

-The shared CUDA/HIP/MUSA convrot activation paths support H64 and H256. Vulkan also supports H64 activation quantization; its INT8 matrix multiplication path currently requires H256.
-
-## Packed INT4 convrot
-
-ComfyUI `convrot_w4a4` and `asym_w4a8_int8` safetensors load directly, including
-checkpoints that mix packed INT4, INT8 tensorwise, and floating-point layers.
-Packed weights retain their checkpoint byte layout; they are not expanded or
-converted to GGUF Q4 blocks at load time.
-
- `convrot_w4a4`: signed 4-bit weights, low nibble first, with F32 per-output
-  `weight_scale`. Activations use the same INT8 convrot quantization as above;
-  the format name does not select a native INT4 activation kernel.
- `asym_w4a8_int8`: 4-bit indices into `weight_codebook` (16 F32 values), F32
-  `weight_s_channel`, and F8_E4M3 `weight_s_rel` per 16 input features. The
-  metadata must specify `group_size: 16`. CPU evaluates the floating-point
-  codebook; GPU kernels requantize it to INT8 with `amax / 127`, introducing a
-  small additional rounding error.
-
-Both formats imply convrot rotation even without a `convrot` flag; missing
-`convrot_groupsize` defaults to 256.
-
-Both formats reuse the convrot activation cache when the input scale is `1` and
-produce F32 outputs with optional bias. CPU provides a portable implementation.
-CUDA/HIP/MUSA use shared packed-weight kernels without requiring INT8 BLAS.
-With CUTLASS enabled, NVIDIA
-SM80 and newer devices use mixed-input S8 x S4 GEMM for eligible W4A4 shapes,
-reusing the INT8 scale epilogue without expanding the full weight tensor. W4A8
-uses CUTLASS integer MMA per 16-value group with FP32 scale restoration.
-Other CUDA shapes and builds retain the packed byte-dot path. Set
-`GGML_CUDA_W4_FORCE_DP4A=1` to compare against that path. Supported AMD targets
-use packed byte-dot instructions, with portable byte arithmetic for other targets.
-Vulkan requires integer dot product support and at least 13312 bytes of shared
-memory for W4A4 or 16452 bytes for W4A8. GPU W4 kernels require the logical input
-width to be divisible by 64; H64 and H256 are supported. When supported by the
-compiler and device, Vulkan uses NV cooperative matrix integer GEMM. W4A4
-decodes packed weights directly into cooperative matrices for aligned input
-and output widths. Complete activation tiles and the final partial tile use
-separate shader specializations. Other W4A4 shapes use double-buffered weight
-and activation tiles (20480 bytes of shared memory, or 30720 for larger tiles).
-W4A8 stages both weights and activations in shared memory in K=64 tiles,
-reusing them for each 16-value group's integer
-MMA and FP32 scale restoration. FP8 scales use a shared lookup table, and
-expanding projections visit activation tiles in groups of weight panels for
-cache reuse. Adjacent groups share a K=32 activation fragment, with
-complementary zero padding in the weights to preserve each group's scale.
-This path uses 17476 bytes of shared memory, or 22596 for larger tiles.
-Both paths use 256-thread workgroups and keep the model's
-weights packed; neither expands a full layer in device memory. Other Vulkan
-devices retain the packed dot-product path; set `GGML_VK_W4_FORCE_DP4A=1` to compare
-against it. FP32 accumulation order can differ between these paths.
-Other shapes use CPU fallback. HIP W4A8 selects wave64 WMMA by the active
-compilation target's intrinsic support, preferring the GFX12 fragment layout
-over the GFX11 layout.
-There is no GPU architecture allowlist: newer targets supporting either
-intrinsic automatically use its matching layout. Selection happens per device
-image, including multi-architecture builds; targets supporting neither intrinsic
-retain the integer-dot fallback. W4A4 retains integer dot products on HIP.
-
-LoRA application remains at runtime, using the original unrotated activations
-and logical weight dimensions. Packed weights and their codebook/scale tensors
-are excluded from weight type conversion. `SD_USE_UPSTREAM_GGML` builds reject
-these formats because they require the application extension API.
+The dedicated CUDA convrot activation path currently requires a group size of `256`; other supported group sizes use CPU execution.

 ## Example

 ComfyUI INT8 convrot safetensors can be passed to `--diffusion-model` without conversion:

 ```powershell
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\krea2_turbo_int8_convrot.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p "a lovely cat holding a sign says 'krea2.cpp'" --steps 8 --cfg-scale 1 -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\krea2_turbo_int8_convrot.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p "a lovely cat holding a sign says 'krea2.cpp'" --steps 8 --cfg-scale 1 --diffusion-fa -v --offload-to-cpu
 ```
@@ -48,7 +48,7 @@ sd-cli -m ..\models\sd_v1.5.safetensors --clip_vision ..\models\clip_vision_h.sa
 ## Example (SDXL)

 ```
-sd-cli -m ..\models\sdxl.safetensors --clip_vision ..\models\clip_vision_h.safetensors --ip-adapter ..\models\ip-adapter_sdxl_vit-h.safetensors --ip-adapter-image ..\assets\reference.png --ip-adapter-strength 0.8 -p "a woman, best quality" -n "lowres, bad anatomy" --cfg-scale 6 --steps 25 --sampling-method dpm++2m --scheduler karras -W 1024 -H 1024 --vae-tiling
+sd-cli -m ..\models\sdxl.safetensors --clip_vision ..\models\clip_vision_h.safetensors --ip-adapter ..\models\ip-adapter_sdxl_vit-h.safetensors --ip-adapter-image ..\assets\reference.png --ip-adapter-strength 0.8 -p "a woman, best quality" -n "lowres, bad anatomy" --cfg-scale 6 --steps 25 --sampling-method dpm++2m --scheduler karras -W 1024 -H 1024 --diffusion-fa --vae-tiling
 ```

 The SDXL VAE decode at 1024x1024 is memory heavy; add `--vae-tiling` (and
@@ -83,5 +83,5 @@ Add the usual Control Net options to keep the reference appearance while
 controlling the pose:

 ```
-sd-cli -m ..\models\sdxl.safetensors --clip_vision ..\models\clip_vision_h.safetensors --ip-adapter ..\models\ip-adapter_sdxl_vit-h.safetensors --ip-adapter-image ..\assets\character.png --ip-adapter-strength 0.9 --control-net ..\models\OpenPoseXL2.safetensors --control-image ..\assets\pose.png --control-strength 0.8 -p "a character, side view" --cfg-scale 6 --steps 25 -W 1024 -H 1024 --vae-tiling
+sd-cli -m ..\models\sdxl.safetensors --clip_vision ..\models\clip_vision_h.safetensors --ip-adapter ..\models\ip-adapter_sdxl_vit-h.safetensors --ip-adapter-image ..\assets\character.png --ip-adapter-strength 0.9 --control-net ..\models\OpenPoseXL2.safetensors --control-image ..\assets\pose.png --control-strength 0.8 -p "a character, side view" --cfg-scale 6 --steps 25 -W 1024 -H 1024 --diffusion-fa --vae-tiling
 ```
@@ -1,24 +0,0 @@
-# How to Use
-
-Iris-3B generates images directly in pixel space and uses Qwen3-VL-4B-Instruct as the text encoder. No VAE is required.
-
-## Download weights
-
- Download Iris-3B
-    - safetensors: https://huggingface.co/speridlabs/iris-3b/tree/main (`model.safetensors` in the root directory)
- Download Qwen3-VL-4B-Instruct
-    - safetensors: https://huggingface.co/Comfy-Org/Krea-2/tree/main/text_encoders
-    - gguf: https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/tree/main
-
-## Examples
-
-```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\iris-3b.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf -p "a lovely cat" --cfg-scale 3 -H 1024 -W 1024
-```
-
-<img width="256" alt="iris-3b example" src="../assets/iris/example.png" />
-
-## Notes
-
- Width and height must be multiples of 16. Do not pass `--vae`.
- Captions are limited to 300 tokens including the assistant-turn suffix. Positive and negative prompts use the same template.
@@ -21,7 +21,7 @@ Krea2 uses a Krea2 diffusion transformer, the Wan2.1 VAE, and Qwen3-VL 4B as the
 ### Krea2

 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Krea-2-Raw-Q8_0.gguf --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p "a lovely cat holding a sign says 'krea2.cpp'" -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Krea-2-Raw-Q8_0.gguf --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p "a lovely cat holding a sign says 'krea2.cpp'"  --diffusion-fa -v --offload-to-cpu
 ```

 <img width="256" alt="Krea2 Raw example" src="../assets/krea2/example.png" />
@@ -22,7 +22,7 @@ Lens and Lens Turbo require an external GPT-OSS `tokenizer.json` matching the te
 ### Lens

 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\lens_bf16.safetensors --llm "..\models\text_encoders\gpt-oss-20b-UD-Q8_K_XL.gguf" --tokenizer ..\models\tokenizers\tokenizer_gpt_oss.json --vae ..\models\vae\flux2_ae.safetensors --cfg-scale 5.0  -p "A crystal dragon soaring through an aurora borealis sky, its entire body made of transparent faceted crystal refracting the green and purple aurora light into rainbow spectra, ice particles trailing from its wings, high fantasy digital art" -v
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\lens_bf16.safetensors --llm "..\models\text_encoders\gpt-oss-20b-UD-Q8_K_XL.gguf" --tokenizer ..\models\tokenizers\tokenizer_gpt_oss.json --vae ..\models\vae\flux2_ae.safetensors --cfg-scale 5.0  -p "A crystal dragon soaring through an aurora borealis sky, its entire body made of transparent faceted crystal refracting the green and purple aurora light into rainbow spectra, ice particles trailing from its wings, high fantasy digital art" --diffusion-fa -v
 ```

 <img width="256" alt="Lens example" src="../assets/lens/example.png" />
@@ -30,7 +30,7 @@ Lens and Lens Turbo require an external GPT-OSS `tokenizer.json` matching the te
 ### Lens Turbo

 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\lens_turbo_bf16.safetensors --llm "..\models\text_encoders\gpt-oss-20b-UD-Q8_K_XL.gguf" --tokenizer ..\models\tokenizers\tokenizer_gpt_oss.json --vae ..\models\vae\flux2_ae.safetensors --cfg-scale 1.0  -p "A crystal dragon soaring through an aurora borealis sky, its entire body made of transparent faceted crystal refracting the green and purple aurora light into rainbow spectra, ice particles trailing from its wings, high fantasy digital art" -v --steps 4
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\lens_turbo_bf16.safetensors --llm "..\models\text_encoders\gpt-oss-20b-UD-Q8_K_XL.gguf" --tokenizer ..\models\tokenizers\tokenizer_gpt_oss.json --vae ..\models\vae\flux2_ae.safetensors --cfg-scale 1.0  -p "A crystal dragon soaring through an aurora borealis sky, its entire body made of transparent faceted crystal refracting the green and purple aurora light into rainbow spectra, ice particles trailing from its wings, high fantasy digital art" --diffusion-fa -v --steps 4
 ```

 <img width="256" alt="Lens Turbo example" src="../assets/lens/turbo_example.png" />
@@ -28,5 +28,5 @@ Lingbot Video uses a Lingbot diffusion transformer, the Wan2.1 VAE, and Qwen3-VL
 ### LingBot-Video T2V

 ```sh
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\lingbot-video-dens-1.3b.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p '{"caption":{"comprehensive_description":"A lovely cat sits comfortably on a soft cushion near a sunlit window, looking calm, gentle, and adorable. The cat has soft fluffy fur, bright expressive eyes, small rounded ears, delicate whiskers, and a relaxed posture. Warm daylight falls across the cat from one side, creating soft highlights on the fur and a cozy glow around the scene. The background is softly blurred, showing hints of a peaceful indoor home environment with warm tones and gentle natural light. The overall atmosphere is cute, tender, serene, and photorealistic, emphasizing the cat''s charming appearance, soft texture, and affectionate presence.","camera_info":{"color":"Warm","frame_size":"Close Up","shot_type_angle":"Eye level","lens_size":"Medium Lens","composition":"Centered balanced","lighting":"Soft light","lighting_type":"Daylight"},"world_knowledge":[],"prominent_elements":[{"name":"lovely cat","description":"A cute and gentle domestic cat with soft fluffy fur, expressive eyes, and a calm relaxed presence.","location":"center of the frame","relative_size":"large","shape_and_color":"Small animal body with rounded face, triangular ears, bright eyes, and soft fur in warm natural tones","texture":"soft, fluffy, silky","appearance_details":"The cat has clean well-groomed fur, delicate whiskers, small ears, a cute nose, and bright attentive eyes. Its expression appears calm, affectionate, and slightly curious.","relationship":"Acts as the main subject and emotional focal point of the scene.","orientation":"facing the camera","pose":"sitting comfortably with a relaxed posture","expression":"gentle, adorable, calm, slightly curious","clothing":"","gender":"","skin_tone_and_texture":""},{"name":"cat eyes","description":"Bright expressive eyes that give the cat a sweet and affectionate appearance.","location":"upper center of the cat face","relative_size":"small","shape_and_color":"Round almond-like eyes with glossy reflections","texture":"clear, glossy, reflective","appearance_details":"The eyes catch the soft daylight, creating small natural highlights that make the cat look vivid and alive.","relationship":"Enhance the emotional charm and cuteness of the cat.","orientation":"looking toward the camera","pose":"","expression":"soft and attentive","clothing":"","gender":"","skin_tone_and_texture":""},{"name":"soft cushion","description":"A comfortable cushion or blanket where the cat is resting.","location":"bottom portion of the frame","relative_size":"medium","shape_and_color":"Soft rounded fabric surface in light warm neutral tones","texture":"plush, fabric, cozy","appearance_details":"The cushion gently supports the cat and adds a comfortable home-like feeling to the scene.","relationship":"Provides a cozy resting place for the cat.","orientation":"horizontal","pose":"","expression":"","clothing":"","gender":"","skin_tone_and_texture":""},{"name":"sunlit indoor background","description":"A softly blurred indoor background with warm daylight and peaceful home atmosphere.","location":"behind the cat, filling the upper and side areas of the frame","relative_size":"large","shape_and_color":"Soft abstract shapes in warm beige, cream, and pale golden tones","texture":"soft, blurry, bokeh-like","appearance_details":"The background is intentionally out of focus, keeping attention on the cat while creating a cozy and serene mood.","relationship":"Provides a warm and gentle environment that supports the cute domestic scene.","orientation":"upright","pose":"","expression":"","clothing":"","gender":"","skin_tone_and_texture":"","is_cluster":true,"number_of_objects":"numerous"}]}}' -n '{"universal_negative":{"visual_quality":["low quality","worst quality","blurry","pixelated","jpeg artifacts","low resolution","unstable color","color flicker","underexposed","overexposed","invisible subject","subject hidden in darkness"],"artistic_style":["painting","illustration","drawing","cartoon","3d render","cgi","sketch","digital art"],"composition_and_content":["text","watermark","signature","logo","subtitles","pillarboxed","side bars","portrait image in landscape frame"],"temporal_and_motion_stability":["flickering","jittery","motion blur","temporal inconsistency","warping","morphing","incoherent motion","unnatural movement","static object with sudden jump","frame-to-frame inconsistency"],"material_and_structure":["plastic-like glass","unrealistic texture","deformed bottle","liquid freezing improperly","distorted reflections"]}}' --offload-to-cpu --cfg-scale 3 --video-frames 33 -v
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\lingbot-video-dens-1.3b.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\wan_2.1_vae.safetensors -p '{"caption":{"comprehensive_description":"A lovely cat sits comfortably on a soft cushion near a sunlit window, looking calm, gentle, and adorable. The cat has soft fluffy fur, bright expressive eyes, small rounded ears, delicate whiskers, and a relaxed posture. Warm daylight falls across the cat from one side, creating soft highlights on the fur and a cozy glow around the scene. The background is softly blurred, showing hints of a peaceful indoor home environment with warm tones and gentle natural light. The overall atmosphere is cute, tender, serene, and photorealistic, emphasizing the cat''s charming appearance, soft texture, and affectionate presence.","camera_info":{"color":"Warm","frame_size":"Close Up","shot_type_angle":"Eye level","lens_size":"Medium Lens","composition":"Centered balanced","lighting":"Soft light","lighting_type":"Daylight"},"world_knowledge":[],"prominent_elements":[{"name":"lovely cat","description":"A cute and gentle domestic cat with soft fluffy fur, expressive eyes, and a calm relaxed presence.","location":"center of the frame","relative_size":"large","shape_and_color":"Small animal body with rounded face, triangular ears, bright eyes, and soft fur in warm natural tones","texture":"soft, fluffy, silky","appearance_details":"The cat has clean well-groomed fur, delicate whiskers, small ears, a cute nose, and bright attentive eyes. Its expression appears calm, affectionate, and slightly curious.","relationship":"Acts as the main subject and emotional focal point of the scene.","orientation":"facing the camera","pose":"sitting comfortably with a relaxed posture","expression":"gentle, adorable, calm, slightly curious","clothing":"","gender":"","skin_tone_and_texture":""},{"name":"cat eyes","description":"Bright expressive eyes that give the cat a sweet and affectionate appearance.","location":"upper center of the cat face","relative_size":"small","shape_and_color":"Round almond-like eyes with glossy reflections","texture":"clear, glossy, reflective","appearance_details":"The eyes catch the soft daylight, creating small natural highlights that make the cat look vivid and alive.","relationship":"Enhance the emotional charm and cuteness of the cat.","orientation":"looking toward the camera","pose":"","expression":"soft and attentive","clothing":"","gender":"","skin_tone_and_texture":""},{"name":"soft cushion","description":"A comfortable cushion or blanket where the cat is resting.","location":"bottom portion of the frame","relative_size":"medium","shape_and_color":"Soft rounded fabric surface in light warm neutral tones","texture":"plush, fabric, cozy","appearance_details":"The cushion gently supports the cat and adds a comfortable home-like feeling to the scene.","relationship":"Provides a cozy resting place for the cat.","orientation":"horizontal","pose":"","expression":"","clothing":"","gender":"","skin_tone_and_texture":""},{"name":"sunlit indoor background","description":"A softly blurred indoor background with warm daylight and peaceful home atmosphere.","location":"behind the cat, filling the upper and side areas of the frame","relative_size":"large","shape_and_color":"Soft abstract shapes in warm beige, cream, and pale golden tones","texture":"soft, blurry, bokeh-like","appearance_details":"The background is intentionally out of focus, keeping attention on the cat while creating a cozy and serene mood.","relationship":"Provides a warm and gentle environment that supports the cute domestic scene.","orientation":"upright","pose":"","expression":"","clothing":"","gender":"","skin_tone_and_texture":"","is_cluster":true,"number_of_objects":"numerous"}]}}' -n '{"universal_negative":{"visual_quality":["low quality","worst quality","blurry","pixelated","jpeg artifacts","low resolution","unstable color","color flicker","underexposed","overexposed","invisible subject","subject hidden in darkness"],"artistic_style":["painting","illustration","drawing","cartoon","3d render","cgi","sketch","digital art"],"composition_and_content":["text","watermark","signature","logo","subtitles","pillarboxed","side bars","portrait image in landscape frame"],"temporal_and_motion_stability":["flickering","jittery","motion blur","temporal inconsistency","warping","morphing","incoherent motion","unnatural movement","static object with sudden jump","frame-to-frame inconsistency"],"material_and_structure":["plastic-like glass","unrealistic texture","deformed bottle","liquid freezing improperly","distorted reflections"]}}' --diffusion-fa --offload-to-cpu --cfg-scale 3 --video-frames 33 -v
 ```
@@ -122,6 +122,7 @@ save_file(merged, "llada_connectors.safetensors")
  --height 1024 \
  --steps 4 \
  --cfg-scale 1.0 \
+  --diffusion-fa \
  --output output.png
 ```

@@ -144,7 +145,7 @@ only in the positive branch. Editing requires connectors that include the SigVQ
  unchanged instead of applying the instruction; LLaDA-Image-Turbo edits correctly at both.
 - Editing runs the reference and the target in one sequence, so it needs roughly twice the
  tokens of text to image at the same size. On 12 GB, editing at 1024x1024 needs
-  flash attention; without it the diffusion graph does not fit.
+  `--diffusion-fa`; without it the diffusion graph does not fit.
 - The weights total about 16 GB, but segmented execution streams them, so a much smaller
  budget works. At 512x512, `--max-vram 6` costs almost nothing over unconstrained execution,
  and `--max-vram 3` still produces byte-identical output at roughly 2.5x the time.
@@ -24,7 +24,7 @@ LongCat uses quoted text for character-level text rendering. Put target text ins
 ### LongCat Image

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\LongCat-Image-Q4_K_M.gguf --vae ..\models\vae\ae.sft --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf -p "a lovely cat holding a sign says 'longcat.cpp'" --cfg-scale 5.0 --sampling-method euler --flow-shift 3 -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\LongCat-Image-Q4_K_M.gguf --vae ..\models\vae\ae.sft --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf -p "a lovely cat holding a sign says 'longcat.cpp'" --cfg-scale 5.0 --sampling-method euler --flow-shift 3 -v --offload-to-cpu --diffusion-fa
 ```

 <img alt="longcat example" src="../assets/longcat/example.png" />
@@ -55,7 +55,7 @@ To run the text encoder quantized, convert it once with sd-cli:
 ### LTX-2.3 dev T2V

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "worst quality, low quality, blurry, distorted, artifacts" -W 1280 -H 720 --offload-to-cpu --video-frames 33 --fps 24 -o t2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v -n "worst quality, low quality, blurry, distorted, artifacts" -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 33 --fps 24 -o t2v.webm
 ```

 <video
@@ -67,7 +67,7 @@ To run the text encoder quantized, convert it once with sd-cli:
 ### LTX-2.3 dev I2V

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v  -W 1280 -H 720 --offload-to-cpu --video-frames 33 -i ..\assets\ernie_image\turbo_example.png -o i2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v  -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\ernie_image\turbo_example.png -o i2v.webm
 ```

 <video
@@ -79,7 +79,7 @@ To run the text encoder quantized, convert it once with sd-cli:
 ### LTX-2.3 dev FLF2V

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -v  -W 1280 -H 720 --offload-to-cpu --video-frames 33 --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png -o flf2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors  -p "glass flower blossom" --cfg-scale 6.0 --sampling-method euler -v  -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 33 --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png -o flf2v.webm
 ```

 <video
@@ -95,7 +95,7 @@ LTX spatial latent upscale runs a model-backed x2 latent upsampler between the l
 Put `ltx-2.3-spatial-upscaler-x2-1.1.safetensors` under the directory passed to `--hires-upscalers-dir`, then use the model name without path or extension in `--hires-upscaler`.

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors --hires-upscalers-dir ..\models\latent_upscale_models --hires-upscaler ltx-2.3-spatial-upscaler-x2-1.1 --hires --hires-steps 4 -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v  -W 640 -H 360 --offload-to-cpu --video-frames 33 -i ..\assets\ernie_image\turbo_example.png -o hires_i2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors --hires-upscalers-dir ..\models\latent_upscale_models --hires-upscaler ltx-2.3-spatial-upscaler-x2-1.1 --hires --hires-steps 4 -p "a lovely cat" --cfg-scale 6.0 --sampling-method euler -v  -W 640 -H 360 --diffusion-fa --offload-to-cpu --video-frames 33 -i ..\assets\ernie_image\turbo_example.png -o hires_i2v.webm
 ```

 By default, the hires refine pass uses the main sampler and scheduler, then trims the second-pass sigma schedule by `--hires-denoising-strength` (`0.7` by default). To reproduce a ComfyUI-style explicit refine schedule, pass custom hires sigmas:
@@ -113,13 +113,13 @@ By default, the hires refine pass uses the main sampler and scheduler, then trim
 ### LTX-2.5 dev T2V

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\ltx-2.5-22b-dev-transformer-Q8_0.gguf --vae ..\models\vae\ltx-2.5-video-vae-conv-bf16.safetensors --audio-vae ..\models\vae\ltx-2.5-audio-vae-bf16.safetensors --llm ..\models\text_encoders\gemma4-12b-with-proj-ltx-2.5-Q8_0.gguf -p "A wide aerial shot of a red vintage convertible driving along a coastal cliff road at sunset, waves crashing below" --cfg-scale 3.0 --sampling-method euler -v -n "worst quality, low quality, blurry, distorted, artifacts" -W 1280 -H 720 --offload-to-cpu --video-frames 121 --fps 24 -o t2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\ltx-2.5-22b-dev-transformer-Q8_0.gguf --vae ..\models\vae\ltx-2.5-video-vae-conv-bf16.safetensors --audio-vae ..\models\vae\ltx-2.5-audio-vae-bf16.safetensors --llm ..\models\text_encoders\gemma4-12b-with-proj-ltx-2.5-Q8_0.gguf -p "A wide aerial shot of a red vintage convertible driving along a coastal cliff road at sunset, waves crashing below" --cfg-scale 3.0 --sampling-method euler -v -n "worst quality, low quality, blurry, distorted, artifacts" -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 121 --fps 24 -o t2v.webm
 ```

 ### LTX-2.5 dev I2V

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\ltx-2.5-22b-dev-transformer-Q8_0.gguf --vae ..\models\vae\ltx-2.5-video-vae-conv-bf16.safetensors --audio-vae ..\models\vae\ltx-2.5-audio-vae-bf16.safetensors --llm ..\models\text_encoders\gemma4-12b-with-proj-ltx-2.5-Q8_0.gguf -p "a lovely cat blinking slowly, gentle camera push in" --cfg-scale 3.0 --sampling-method euler -v -W 1280 -H 720 --offload-to-cpu --video-frames 121 -i ..\assets\ernie_image\turbo_example.png -o i2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\ltx-2.5-22b-dev-transformer-Q8_0.gguf --vae ..\models\vae\ltx-2.5-video-vae-conv-bf16.safetensors --audio-vae ..\models\vae\ltx-2.5-audio-vae-bf16.safetensors --llm ..\models\text_encoders\gemma4-12b-with-proj-ltx-2.5-Q8_0.gguf -p "a lovely cat blinking slowly, gentle camera push in" --cfg-scale 3.0 --sampling-method euler -v -W 1280 -H 720 --diffusion-fa --offload-to-cpu --video-frames 121 -i ..\assets\ernie_image\turbo_example.png -o i2v.webm
 ```

 ### LTX-2.5 spatial latent upscale
@@ -129,7 +129,7 @@ Works exactly like the LTX-2.3 upscaler described below; put
 pass its name without path or extension to `--hires-upscaler`.

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\ltx-2.5-22b-dev-transformer-Q8_0.gguf --vae ..\models\vae\ltx-2.5-video-vae-conv-bf16.safetensors --audio-vae ..\models\vae\ltx-2.5-audio-vae-bf16.safetensors --llm ..\models\text_encoders\gemma4-12b-with-proj-ltx-2.5-Q8_0.gguf --hires-upscalers-dir ..\models\latent_upscale_models --hires-upscaler ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0 --hires --hires-steps 6 -p "a lovely cat" --cfg-scale 3.0 --sampling-method euler -v -W 640 -H 360 --offload-to-cpu --video-frames 121 -o hires_t2v.webm
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\ltx-2.5-22b-dev-transformer-Q8_0.gguf --vae ..\models\vae\ltx-2.5-video-vae-conv-bf16.safetensors --audio-vae ..\models\vae\ltx-2.5-audio-vae-bf16.safetensors --llm ..\models\text_encoders\gemma4-12b-with-proj-ltx-2.5-Q8_0.gguf --hires-upscalers-dir ..\models\latent_upscale_models --hires-upscaler ltx-2.5-latent-spatial-upscaler-x2-bf16-1.0 --hires --hires-steps 6 -p "a lovely cat" --cfg-scale 3.0 --sampling-method euler -v -W 640 -H 360 --diffusion-fa --offload-to-cpu --video-frames 121 -o hires_t2v.webm
 ```

 ## Not implemented
@@ -4,8 +4,20 @@

 ## Download weights

- Download Mage-Flow diffusion from https://huggingface.co/Comfy-Org/Mage-Flow/tree/main/diffusion_models
- Download Mage-Flow vae from https://huggingface.co/Comfy-Org/Mage-Flow/tree/main/vae
+- Download Mage-Flow
+    - safetensors: https://huggingface.co/microsoft/Mage-Flow/tree/main/transformer
+- Download Mage-Flow-Base
+    - safetensors: https://huggingface.co/microsoft/Mage-Flow-Base/tree/main/transformer
+- Download Mage-Flow-Turbo
+    - safetensors: https://huggingface.co/microsoft/Mage-Flow-Turbo/tree/main/transformer
+- Download Mage-Flow-Edit
+    - safetensors: https://huggingface.co/microsoft/Mage-Flow-Edit/tree/main/transformer
+- Download Mage-Flow-Edit-Turbo
+    - safetensors: https://huggingface.co/microsoft/Mage-Flow-Edit-Turbo/tree/main/transformer
+- Download Mage-Flow-Edit-Base
+    - safetensors: https://huggingface.co/microsoft/Mage-Flow-Edit-Base/tree/main/transformer
+- Download Mage-Flow vae
+    - safetensors: https://huggingface.co/microsoft/Mage-Flow/tree/main/vae
 - Download Qwen3-VL 4B
    - safetensors: https://huggingface.co/Comfy-Org/Krea-2/tree/main/text_encoders
    - gguf: https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF/tree/main
@@ -15,7 +27,7 @@
 Use 30 steps for Base models and 4 steps with `--cfg-scale 1` for Turbo models. Image dimensions must be multiples of 16; the official checkpoints are trained for native resolutions from 512 to 2048 pixels.

 ```bash
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Mage-Flow-Turbo.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\mage_vae.safetensors -p "a lovely cat holding a sign says 'mage.cpp'" --cfg-scale 1.0 --steps 4 -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Mage-Flow-Turbo.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --vae ..\models\vae\mage_vae.safetensors -p "a lovely cat holding a sign says 'mage.cpp'" --cfg-scale 1.0 --steps 4 --diffusion-fa -v --offload-to-cpu
 ```

 <img width="256" alt="Mage-Flow example" src="../assets/mage_flow/example.png" />
@@ -27,7 +39,7 @@ Mage-Flow-Edit accepts one or more reference images. The default `mage_flow` ref
 For the Turbo edit checkpoint, use 4 steps and `--cfg-scale 1`.

 ```bash
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Mage-Flow-Edit.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\Qwen3-VL-4B-Instruct-mmproj-BF16.gguf --vae ..\models\vae\mage_vae.safetensors -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'mage.cpp'" --cfg-scale 4.0 --sampling-method euler -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\Mage-Flow-Edit.safetensors --llm ..\models\text_encoders\Qwen3-VL-4B-Instruct-Q4_K_M.gguf --llm_vision ..\models\text_encoders\Qwen3-VL-4B-Instruct-mmproj-BF16.gguf --vae ..\models\vae\mage_vae.safetensors -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'mage.cpp'" --cfg-scale 4.0 --sampling-method euler -v --diffusion-fa --offload-to-cpu
 ```

 <img width="256" alt="Mage-Flow-Edit example" src="../assets/mage_flow/edit_example.png" />
@@ -1,24 +0,0 @@
-# Ming-Image
-
-[Ming-Image](https://github.com/inclusionAI/Ming-Image) 0.1 Design uses a 6B diffusion transformer (DiT), Ling-mini-2.0 for text conditioning, and the Ming-Image VAE. Text-to-image generation with RGBA output is supported.
-
-## Download weights
-
- Download Ming-Image 0.1 Design DiT
-    - safetensors: https://huggingface.co/Comfy-Org/Ming-Image/tree/main/diffusion_models
- Download Ling-mini-2.0 BF16
-    - safetensors: https://huggingface.co/Comfy-Org/Ming-Image/tree/main/text_encoders
- Download Ming-Image VAE
-    - safetensors: https://huggingface.co/Comfy-Org/Ming-Image/tree/main/vae
- Download Ling tokenizer
-    - tokenizer.json: https://huggingface.co/inclusionAI/Ming-Image-0.1-Design/blob/main/mllm/tokenizer.json
-
-The example below uses `ming_image_0.1_design_bf16.safetensors` for the DiT. You can also use `ming_image_0.1_design_int8_convrot.safetensors` with [INT8 convrot support](int8_convrot.md). Use the BF16 text encoder.
-
-## Text-to-image
-
-Pass the Ling `tokenizer.json` with `--tokenizer` and use the matching Ming-Image VAE. Image dimensions must be multiples of 16. Save the output as PNG to preserve the alpha channel.
-
-```bash
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\ming_image_0.1_design_bf16.safetensors --llm ..\models\text_encoders\ming_image_0.1_ling_mini_2.0_bf16.safetensors --vae ..\models\vae\ming_image_vae_bf16.safetensors --tokenizer ..\models\text_encoders\tokenizer.json -p "A cheerful orange cat sticker, transparent background" --width 1024 --height 1024 --steps 12 --cfg-scale 1 --sampling-method euler -v --offload-to-cpu -o ming_image.png
-```
@@ -38,7 +38,7 @@ are detected from their weights.
 ## Text-to-audio-video

 ```sh
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_fl2va-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "A cute American Shorthair silver tabby kitten surfs on a tropical ocean wave, riding a white surfboard with the clear text 'sd.cpp' on it. Cinematic tracking shot, realistic water, bright sunlight, smooth motion, and consistent character appearance. Add upbeat tropical surf-rock background music with cheerful drums and guitar, synchronized with the kitten’s energetic surfing." --cfg-scale 1.0 -v -W 864 -H 480 --offload-to-cpu --rng cpu --fps 24 --video-frames 56
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_fl2va-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "A cute American Shorthair silver tabby kitten surfs on a tropical ocean wave, riding a white surfboard with the clear text 'sd.cpp' on it. Cinematic tracking shot, realistic water, bright sunlight, smooth motion, and consistent character appearance. Add upbeat tropical surf-rock background music with cheerful drums and guitar, synchronized with the kitten’s energetic surfing." --cfg-scale 1.0 -v -W 864 -H 480 --diffusion-fa --offload-to-cpu --rng cpu --fps 24 --video-frames 56
 ```

 <video src=../assets/minimax-h3/t2av.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -51,7 +51,7 @@ decoded audio track.
 Add `--init-img` for I2VA, or both `--init-img` and `--end-img` for FL2VA:

 ```sh
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_fl2va-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "a lovely cat" -i ..\assets\ernie_image\turbo_example.png --cfg-scale 1.0 -v -W 864 -H 480 --offload-to-cpu --rng cpu --fps 24 --video-frames 56
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_fl2va-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "a lovely cat" -i ..\assets\ernie_image\turbo_example.png --cfg-scale 1.0 -v -W 864 -H 480 --diffusion-fa --offload-to-cpu --rng cpu --fps 24 --video-frames 56
 ```

 <video src=../assets/minimax-h3/i2av.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -62,7 +62,7 @@ Ref2VA accepts any combination of reference images, reference videos, paired
 video soundtracks, and standalone audio references:

 ```sh
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_ref2va_pruned-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "Use the cat from <Picture 1> as the main character. Keep the cat’s appearance, fur color, facial features, and identity consistent with the reference image. Create a 2-second cinematic video: start with an extreme close-up shot of the cat’s face, focusing on its cute expression and detailed fur texture. The camera slowly rotates around the cat’s head, creating a dynamic reveal. Then smoothly pull back and zoom out to reveal the full scene: the cat is standing confidently on a surfboard, riding ocean waves. Water splashes around the board, sea breeze gently moves the cat’s fur, and the cat maintains a cute and fearless expression while surfing. Smooth camera movement, cinematic orbit shot, seamless zoom-out transition, low-angle wide shot, realistic ocean environment, golden sunlight, dynamic waves, high-quality realistic style, natural motion, no distortion, keep the cat’s identity unchanged." -r ..\assets\ernie_image\turbo_example.png --cfg-scale 1.0 -v -W 864 -H 480 --offload-to-cpu --rng cpu --fps 24 --video-frames 56
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_ref2va_pruned-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "Use the cat from <Picture 1> as the main character. Keep the cat’s appearance, fur color, facial features, and identity consistent with the reference image. Create a 2-second cinematic video: start with an extreme close-up shot of the cat’s face, focusing on its cute expression and detailed fur texture. The camera slowly rotates around the cat’s head, creating a dynamic reveal. Then smoothly pull back and zoom out to reveal the full scene: the cat is standing confidently on a surfboard, riding ocean waves. Water splashes around the board, sea breeze gently moves the cat’s fur, and the cat maintains a cute and fearless expression while surfing. Smooth camera movement, cinematic orbit shot, seamless zoom-out transition, low-angle wide shot, realistic ocean environment, golden sunlight, dynamic waves, high-quality realistic style, natural motion, no distortion, keep the cat’s identity unchanged." -r ..\assets\ernie_image\turbo_example.png --cfg-scale 1.0 -v -W 864 -H 480 --diffusion-fa --offload-to-cpu --rng cpu --fps 24 --video-frames 56
 ```

 <video src=../assets/minimax-h3/r2av.mp4 controls="controls" muted="muted" type="video/mp4"></video>
@@ -80,107 +80,17 @@ latents condition the diffusion transformer. Paired video and audio references
 share the same timeline. Ref2VA cannot be combined with `--init-img` or
 `--end-img` in one request.

-Reference images keep their aspect ratio and are only downscaled when they
-exceed the configured limit; neither mode scales a reference up. The limit is
-selected by `ref_image_size`. The default, `match`, uses the pixel area of the
-requested generation canvas. Pass `--ref-image-args "ref_image_size=max"` (or
-`ref_image_size` in `sd_vid_gen_params_t.ref_image_args`) to use a 2048 px short
-edge instead, which preserves more identity detail at a higher sampling cost:
-reference latents are attended at every sampling step.
-
-Whichever limit applies, each axis is then snapped to the nearest 32-pixel
-multiple. An axis of at least 16 pixels can therefore grow by up to 16 pixels
-past the scaled size (a 48-pixel axis becomes 64), and references are never
-narrower than 32 pixels.
-`resize_before_vae=false` is not supported here; MiniMax-H3 keeps references on
-the 32-pixel grid and reports a warning.
+Reference images keep their aspect ratio and are only downscaled when their
+pixel area exceeds the requested generation canvas.

 The C API exposes the same inputs through `ref_images`, `ref_videos`, and
 `ref_audios` in `sd_vid_gen_params_t`. Each `sd_ref_video_t` supplies its own
 frame rate and optional soundtrack; non-24-fps inputs are resampled internally.

-## Latent hires second pass (two-stage sampling)
-
-MiniMax-H3 supports `--hires` with two interchangeable second-stage upscalers:
-
- an interpolating latent upscaler (`Latent`, `Latent (nearest)`,
-  `Latent (bicubic)`, ...): after the first pass finishes, the packed AV latent
-  is resampled in latent space to `--hires-width` x `--hires-height` and
-  refined by a second sampling pass;
- a model-backed upscaler (`--hires-upscaler <name>` + `--hires-upscalers-dir`):
-  a H3-shaped learned latent upscaler checkpoint runs between the passes,
-  like the LTX spatial upsampler. This is the sd.cpp equivalent of the
-  two-sampler workflows used for H3 in node-based frontends (e.g. the
-  community 3D-conv latent upscaler from
-  [LBH-123-AI/Minimax_h3_latent_Upscaler](https://huggingface.co/LBH-123-AI/Minimax_h3_latent_Upscaler)).
-
-Draft at a low megapixel count (e.g. 0.4 MP) and refine at a higher one
-(e.g. 0.8 MP) instead of paying for the full resolution in every step:
-
-```sh
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\minimax_h3_fl2va-Q4_K_M.gguf --vae ..\models\vae\minimax_h3_video_vae_fp16.safetensors --audio-vae ..\models\vae\minimax_h3_audio_vae_fp32.safetensors --llm ..\models\text_encoders\qwen3vl_32b_minimax_h3-Q4_K_M.gguf -p "a lovely cat" --cfg-scale 1.0 -v -W 864 -H 480 --diffusion-fa --offload-to-cpu --rng cpu --video-frames 56 --hires --hires-upscaler "Latent (bicubic)" --hires-width 1216 --hires-height 672 --hires-steps 4 --hires-denoising-strength 0.4
-```
-
-or with the learned upscaler:
-
-```sh
-.\bin\Release\sd-cli.exe -M vid_gen ... --hires --hires-upscalers-dir ..\models\latent_upscale_models --hires-upscaler h3-latent-upscaler-3d --hires-width 1216 --hires-height 672 --hires-steps 4 --hires-denoising-strength 0.4
-```
-
-Notes for this mode:
-
- The learned upscaler expects a second normalization of the diffusion latent,
-  matching its training convention. The runner applies and reverses this extra
-  transform internally; callers should pass the usual H3 diffusion latents.
- The frame count is never changed by the second stage, so the packed audio
-  latent length stays valid and the generated soundtrack is preserved.
- Width and height still have to be multiples of 32; the hires target is
-  aligned like any other resolution. The learned upscaler additionally wants
-  an even latent size on both axes, which 32-pixel targets provide.
- `--hires-sigmas` can replace `--hires-denoising-strength` when the second
-  stage should resume from an explicit sigma schedule, which is how most
-  two-stage H3 workflows split the sampler.
- Keyframe conditioning (`--init-img` / `--end-img`) is not available together
-  with hires yet: those latents are encoded at the first stage resolution and
-  would have to be re-encoded for the hires canvas. Use T2VA or Ref2VA.
- The second stage runs at the larger latent, so it is the memory-hungry half;
-  combine it with quantization, `--vae-tiling` and `--offload-to-cpu` on
-  small cards.
- The learned upscaler is scale-conditional: the target-to-source ratio is
-  fed into the network as an embedding, 1.2-2.0x is the trained domain, and
-  downscaling is rejected.
-
 ## Shape and runtime notes

 - Width and height are aligned upward to a multiple of 32.
 - Frame count is aligned upward to the `17k + 5` grid, with a minimum of 5.
 - MiniMax-H3 runs at 24 fps; another requested value is overridden.
- Attention head grouping is controlled by
-  `--model-args minimax_h3_attn_chunk_budget=2`, in GiB (default: `2`).
-  The budget estimates the size of a single FP32 Q/K tensor per group:
-  `head_dim * tokens * batch * 4 bytes * heads_per_group`. The group size adapts
-  to the sequence length, with a minimum of one head; inputs that fit use all
-  heads together. Set `0` to disable grouping, or a smaller budget such as
-  `0.25` to reduce peak activation memory for long sequences at the cost of
-  extra launches and concatenation. Each head still attends to the full sequence.
-  This is a grouping target, not a cap on total attention memory or VRAM usage;
-  other intermediates, backend workspace, and weights require additional memory.
-  `--max-vram` and `--offload-to-cpu` control weight residency, but the active
-  block's working memory must also fit within the VRAM budget.
 - The default video flow shift is 12. The audio stream is mapped internally to
  its shift of 3, so the regular samplers can operate on the packed AV latent.
-
-## HyperFlow H3 Flash
-
-[HyperFlow H3 Flash](https://huggingface.co/VideoRebirthAI/hyperflow-h3-flash)
-uses the shared components and input options described in this guide. Replace
-`--diffusion-model` with the matching HyperFlow FL2VA or Ref2VA checkpoint and
-use these sampling options:
-
-```sh
--sampling-method euler --cfg-scale 1 --sigmas hyperflow-h3-flash-6
-```
-
-Use `hyperflow-h3-flash-4` or `hyperflow-h3-flash-8` for 4 or 8 steps. These
-presets also work with `--hires-sigmas`; their length determines the step count.
-They already include video flow shift 12, so keep the default flow shift.
@@ -43,5 +43,6 @@ MiniT2I uses a MiniT2I diffusion transformer and `google/flan-t5-large` as the t
  --height 512 \
  --seed 42 \
  --sampling-method euler \
+  --diffusion-fa \
  --output minit2i_cuda.png
 ```
@@ -1,7 +1,7 @@
 # Model Configuration Conventions

-This document describes model configuration structs, weight-based configuration
-detection, and the definitions used to integrate models into the runtime.
+This document describes the conventions for model configuration structs and
+weight-based configuration detection.

 ## Config Types

@@ -60,8 +60,6 @@ static UNetConfig detect_from_weights(const String2TensorStorage& tensor_storage
 ```

 Use `TensorStorage` metadata, especially `n_dims` and `ne`, to infer shapes.
-Use `logical_ne0()` for Linear input widths; packed INT4 weights store half of
-the logical width in `ne[0]`. Keep raw `ne` values for storage layout calculations.
 Do not load or parse tensor data for config detection.

 Detection should respect `prefix`. For nested weights, construct full names from
@@ -118,113 +116,3 @@ struct.
 If a model has no weight-derived config today, it may still provide
 `detect_from_weights` for API consistency, but it should not print a config
 detection log unless it actually derives values from weights.
-
-## Model Integration Definitions
-
-Every `SDVersion` has an explicit `ModelDefinition` in
-`src/model/integrations/`. Related variants share an integration file and runner
-factory. Network blocks and their weight-derived configuration remain under
-`src/model/diffusion/`; reusable text encoder implementations remain under
-`src/model/te/`. Weight-name conversion remains in `src/name_conversion.cpp`.
-
-To add a model:
-
-1. Add its identity to `SDVersion` without renumbering existing values.
-2. Implement the network and `*Config::detect_from_weights` following the
-   conventions above, or reuse an existing runner.
-3. Add a definition in its integration file, including its component
-   factory, traits, geometry, and sampling policy.
-4. Add the family's explicit registration in `src/model_registry.cpp`. For a new
-   variant of a registered family, add its definition to that family's list.
-5. Add version detection signatures and any required weight-name mappings in
-   `src/model_loader.cpp` and `src/name_conversion.cpp` respectively. Update shared
-   conditioning or runtime code only when existing behavior cannot serve the model.
-
-Use the existing family integration as the starting point for a variant. For a
-new family, `src/model/integrations/moss_image.cpp` is a compact example of a
-conditioner/diffusion factory and its definition. Definitions collect model
-construction and defaults; they do not replace version detection, name conversion,
-or model-specific inference algorithms.
-
-The registry checks for missing, duplicate, and invalid version registrations.
-Explicit references keep integrations reachable in static-library builds. The
-existing CMake glob includes integration sources automatically. No legacy
-builder, display-name table, or prediction-default fallback needs updating.
-
-### Detection And Weight Conversion
-
-Version detection remains in `ModelLoader::get_sd_version()` in
-`src/model_loader.cpp`. Add model signatures there and preserve the order of
-overlapping checks. Detection uses tensor names and metadata, and can run before
-and after name conversion, so signatures must work at both stages. Do not load
-tensor data to identify an architecture. The registry looks up definitions by
-the detected version; it does not perform detection.
-
-Weight-name mappings and their version-based dispatch remain centralized in
-`src/name_conversion.cpp`. Add new mappings there when needed. Inference, GGUF
-export, quantization, and LoRA conversion share the `convert_tensor_name` entry
-point; preserve its prefix normalization and component conversion order.
-
-### Components And Conditioning
-
-The core factory receives `model_builders::Context`, the tokenizer configuration,
-and the output runners. Backend checks, optional IP-Adapter setup, scale
-overrides, and tokenizer usage validation remain in the common builder. A
-factory returns failure if any required additional backend cannot initialize.
-
-LLM architecture selection, tokenizer requirements, prompt preparation, and
-encoding remain centralized in `src/conditioning/conditioner.hpp`. Integration
-factories instantiate the conditioner with the model version; models requiring
-new LLM conditioning behavior still need changes in that shared implementation.
-
-`vae_kind` is the single source of truth for the VAE family; compatibility
-helpers derive their results from it. `KL`, `FLUX`, and `FLUX2` share the
-`AutoEncoderKL` implementation while retaining their distinct latent formats.
-The related traits have separate responsibilities:
-
- `supports_video_generation` describes the model's video generation capability.
- `video_tae` selects `TinyVideoAutoEncoder` instead of `TinyImageAutoEncoder`.
-  Image models can use a video TAE, so this is independent of video generation.
- `fake_vae` selects FakeVAE for pixel-space models. It is separate from
-  `vae_kind`, which also controls latent-format compatibility helpers.
- `vae_format_override` allows PiD's VAE format override.
-
-`configure_vae` handles model-specific KL adjustments such as
-PixArt's scale and SDXL's convolution scale. VAE compatibility traits also drive
-latent transformations and previews through the existing helper functions.
-
-### Sampling And Geometry
-
-`ModelSamplingPolicy` groups the defaults first: `default_prediction`,
-`default_flow_shift`, `default_sample_method`, and `default_scheduler`. Set only
-the values that differ from the struct defaults. `prediction_from_weights` and
-`flow_shift_from_weights` preserve checkpoint-dependent defaults during automatic
-prediction selection; `probe_v_prediction` enables SD2's runtime v-prediction
-probe. Explicit prediction settings take precedence over automatic selection.
-MOSS's default unconditional skip layer stays
-in the diffusion engine, with active unconditional SLG taking precedence.
-
-The resolved `prediction_t` selects the denoiser directly. Flux3, MiniMax H3 AV,
-and Ming-Image use `FLUX3_FLOW_PRED`, `H3_AV_FLOW_PRED`, and
-`MING_IMAGE_FLOW_PRED`; their CLI overrides are `flux3_flow`, `h3_av_flow`, and
-`ming_image_flow`. Explicit `sd3_flow` always selects `DiscreteFlowDenoiser`,
-and `flux_flow` always selects `FluxFlowDenoiser`, regardless of model version.
-Automatic selection retains each model's default implementation. New prediction
-values are appended before `PREDICTION_COUNT`, preserving existing valid values;
-clients using the automatic-selection sentinel must rebuild with the new header.
-
-`ModelGeometry` declares `diffusion_down_factor`, `latent_channels`, and
-`image_channels`. `ref_image_preset` defaults to `"default"`; set it only for models
-that need another reference-image preset.
-
-The common pipeline executes these policies; specialized video, multimodal,
-cache, and extension algorithms remain in their existing runtime components.
-Model-specific diffusion extra parameters are assembled in the diffusion engine.
-Video frame conversion and alignment also remain in the diffusion engine.
-`get_vae_scale_factor()` retains PiD's pixel-space special case and otherwise
-uses the VAE's scale factor.
-
-Validate migrations against the previous implementation using synthetic metadata,
-name conversions, prompt preparation, default/override selection, and focused
-builds. Real generation checks are still needed to validate numerical behavior
-when changing network or encoding algorithms.
@@ -1,37 +0,0 @@
-# How to Use
-
-MOSS-Image uses a 5B diffusion transformer, the FLUX.2 VAE, and Qwen3.5 9B as the LLM text encoder. Both Base and SFT are supported. Prompt enhancement is not included.
-
-## Download weights
-
- Download MOSS-Image Base or SFT
-    - safetensors: https://huggingface.co/Comfy-Org/MOSS-Image/tree/main/diffusion_models
-    - gguf:
-        - Base: https://huggingface.co/leejet/MOSS-Image-Base-GGUF/tree/main
-        - SFT: https://huggingface.co/leejet/MOSS-Image-SFT-GGUF/tree/main
- Download vae
-    - safetensors: https://huggingface.co/Comfy-Org/MOSS-Image/tree/main/vae
- Download Qwen3.5 9B
-    - gguf: https://huggingface.co/unsloth/Qwen3.5-9B-GGUF/tree/main
- Download `tokenizer.json`
-    - https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base/tree/main/tokenizer
-
-Pass the external `tokenizer.json` with `--tokenizer`; Qwen3.5 uses a different vocabulary from the embedded Qwen tokenizer.
-
-## Examples
-
-### MOSS-Image Base
-
-```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\moss_image_base_int8_convrot_attn.safetensors --llm ..\models\text_encoders\Qwen3.5-9B-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors --tokenizer ..\models\tokenizers\tokenizer_qwen3.5.json --cfg-scale 3.0 -p "a lovely cat holding a sign says 'moss-image.cpp'" -v --offload-to-cpu -H 1024 -W 1024
-```
-
-### MOSS-Image SFT
-
-```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\moss_image_sft_int8_convrot_attn.safetensors --llm ..\models\text_encoders\Qwen3.5-9B-Q4_K_M.gguf --vae ..\models\vae\flux2_ae.safetensors --tokenizer ..\models\tokenizers\tokenizer_qwen3.5.json --cfg-scale 3.0 -p "a lovely cat holding a sign says 'moss-image.cpp'" -v --offload-to-cpu -H 1024 -W 1024
-```
-
-<img src="../assets/moss/sft.png" />
-
-Use dimensions divisible by 16. The default discrete scheduler uses the upstream time shift of 5. Use `--flow-shift` to override it. The negative CFG branch skips DiT layer 22 (zero-based), matching the upstream pipeline.
@@ -13,7 +13,7 @@
 ## Examples

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ovis_image-Q4_0.gguf --vae ..\models\vae\ae.sft  --llm ..\models\text_encoders\ovis_2.5.safetensors -p "a lovely cat" --cfg-scale 5.0 -v --offload-to-cpu
+.\bin\Release\sd-cli.exe --diffusion-model  ovis_image-Q4_0.gguf --vae ..\models\vae\ae.sft  --llm ..\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" />
@@ -1,34 +0,0 @@
-# How to Use
-
-P-H3-Image supports text-to-image and reference-image editing. It uses a MiniMax-H3 diffusion transformer, a single-frame VAE, and Qwen3-VL-4B with a text connector. Prompt enhancement is not included.
-
-## Download weights
-
- Download P-H3-Image
-    - safetensors: https://huggingface.co/PrunaAI/P-H3-Image-Comfy/tree/main/diffusion_models
- Download P-H3-Image VAE
-    - safetensors: https://huggingface.co/PrunaAI/P-H3-Image-Comfy/tree/main/vae
- Download Qwen3-VL-4B with the text connector
-    - https://huggingface.co/PrunaAI/P-H3-Image-Comfy/tree/main/text_encoders
-
-Alternatively, reuse Qwen3-VL-4B, including its vision tower, with the [separate text connector](https://huggingface.co/PrunaAI/P-H3-Image/blob/main/text_connector/diffusion_pytorch_model.safetensors). Save the connector as `p-h3-image_text_connector.safetensors` and pass it with `--embeddings-connectors`.
-
-Use the P-H3 image VAE; the MiniMax-H3 video VAE is not compatible. No audio VAE is needed.
-
-## Examples
-
-### Text-to-image
-
-```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\p-h3-image_int8_convrot.safetensors --llm ..\models\text_encoders\qwen3vl_4b_bf16.safetensors --embeddings-connectors ..\models\text_encoders\p-h3-image_text_connector.safetensors --vae ..\models\vae\p-h3-image_vae_bf16.safetensors --cfg-scale 1.0 --steps 8 --sampling-method euler --sigmas p-h3-image -p "a lovely cat holding a sign says 'p-h3-image.cpp'" -v --offload-to-cpu --rng cpu -H 2912 -W 2912
-```
-
-### Image editing
-
-```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\p-h3-image_int8_convrot.safetensors --llm ..\models\text_encoders\qwen3vl_4b_bf16.safetensors --embeddings-connectors ..\models\text_encoders\p-h3-image_text_connector.safetensors --vae ..\models\vae\p-h3-image_vae_bf16.safetensors --cfg-scale 1.0 --steps 8 --sampling-method euler --sigmas p-h3-image -r source.png -r reference.png -p "Edit <Picture 1>. Give the cat the scarf from <Picture 2>, preserving its face and the background." -v --offload-to-cpu --rng cpu -H 2912 -W 2912
-```
-
-Reference images are numbered in argument order as `<Picture 1>`, `<Picture 2>`, and so on. A plain edit instruction gets `Edit <Picture 1>. ` prepended automatically. Prompts starting with `Edit <Picture N>` or `subject_definitions:` are used directly.
-
-Use dimensions divisible by 32. The upstream default is 2912 × 2912; smaller canvases reduce memory use. Use CFG 1 and `--sigmas p-h3-image` for the upstream 8-step schedule. Keep the default VAE tiling size of 256 pixels.
@@ -1,60 +0,0 @@
-# Position embedding cache
-
-`sd::PECache` stores CPU-side `sd::Tensor<float>` data. Each model runner owns
-its cache and calls the existing `make_input()` on every graph build. Cache
-hits skip PE generation; allocation and upload remain ordinary graph-input
-operations. `GGMLRunner` has no PE-specific code or resource hooks.
-
-The cache keeps the most recent key and tensor for each named PE input. A
-matching key returns the existing tensor by reference. Changing its parameters
-replaces that input's cached result. Different named inputs have independent
-storage. Alternating two layouts under the same name recomputes their PE;
-there is no LRU, device-buffer cache, memory budget, or graph lifecycle callback.
-CPU data lives with the model runner, including across `runner_end()` calls,
-and is released when its named entry is replaced or the cache is destroyed.
-
-## Adding a model
-
-Include `core/pe_cache.h` and declare a member:
-
-```cpp
-sd::PECache pe_cache_;
-```
-
-Construct the key before generating PE and place the generation code in the
-lazy callback:
-
-```cpp
-sd::PECacheKey key("model.image_pe");
-key.add(height).add(width).add(head_dim).add(theta)
-    .add(circular_x_enabled).add(circular_y_enabled);
-auto pe = make_input(pe_cache_.get_or_create(key, [&]() {
-    auto values = generate_position_embedding(/* existing arguments */);
-    return sd::Tensor<float>({2, 2, head_dim / 2, token_count}, std::move(values));
-}));
-```
-
-Keys must cover every variable affecting PE values or shape: geometry, text
-length, frequency settings, reference-image ordering and offsets, circular
-settings, and video/audio positions or frame rates as applicable.
-`add_shape()` records all four ggml dimensions, including trailing singleton
-dimensions. `add_shapes()` records an ordered list of reference shapes.
-`add(sd::Tensor<T>)` records both shape and values for explicit position inputs.
-Keys compare their complete encoded fields rather than a data pointer or hash.
-
-Use distinct names for PE inputs that coexist in a graph, such as Anima's
-adapter query and key PE. `make_input()` defers reading the CPU data until graph
-execution: do not replace a named entry while an unfinished graph references
-it. Adding or updating other named entries does not invalidate its storage.
-Return tensor shapes with the contiguous dimension first, and preserve each
-model's existing F32 values, layout, and final transformations.
-
-## Current integrations
-
-The cache is used by MiniT2I, the Flux runner and its variants, Flux3, Qwen Image,
-Qwen Image 2.1, Z-Image and L2P, Anima, Boogu, ERNIE Image, Fibo2, Ideogram4,
-Iris, Krea2, Lens, LLaDA Image, MAGE Flow, Ming Image, MOSS Image, PiD, PixArt,
-Wan, Hunyuan Video, LingBot Video, and LTX audio/video.
-
-Position embeddings stored as model weights and graph-side RoPE operations
-remain on their existing paths, including MiniMax H3 and SenseNova U1.
@@ -5,15 +5,10 @@ eg.:
 - flux 768x768 ~600mb
 - SD2 768x768 ~1400mb

-`--flash-attn on|off|auto` controls flash attention. Its effect on speed depends
-on GPU architecture, backend, driver, model, and tensor shapes. Automatic
-selection uses backend rules and, for some paths, small probes; it does not
-guarantee the fastest end-to-end generation. Compare `on` and `off` with the
-same model and generation settings over repeated runs, separating startup/probe
-time from steady-state generation time. See [backend selection](backend.md)
-for automatic selection rules and per-module overrides.
+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).

-Watch for:
+Run by adding `--diffusion-fa` to the arguments and watch for:
 ```
 [INFO ] stable-diffusion.cpp:312  - Using flash attention in the diffusion model
 ```
@@ -26,77 +21,6 @@ CPU fallback. It excludes weights and cache buffers. Within a runner lifecycle,
 the summary is printed only on the first graph or when backend capacities or the
 segment count change.

-## Tune direct convolution.
-
-`--conv-direct on|off|auto` selects direct convolution or the im2col-based
-path. Performance depends on GPU architecture, backend, driver, model, and
-tensor shapes, so enabling direct convolution can either improve or reduce speed.
-
-Automatic selection uses small benchmarks on Vulkan and implicit GEMM checks
-on other backends. It does not guarantee the fastest end-to-end generation.
-Compare `on` and `off` with the same model and generation settings over
-repeated runs, keeping flash attention unchanged and separating startup/probe
-time from steady-state generation time. See [backend selection](backend.md)
-for automatic selection rules and per-module overrides.
-
-## Run conditional and unconditional CFG in one batched UNet forward.
-
-For UNet models, the conditional and unconditional guidance branches are
-concatenated into a single batch of two and run through one UNet forward per
-step instead of two separate forwards. This is enabled by default whenever the
-run qualifies for it.
-
-Use `--batched-cfg off` to force separate conditional and unconditional
-forwards.
-
-## Use VAE tiling to reduce encode and decode memory usage.
-
-`--vae-tiling` enables spatial tiling for both VAE encoding and decoding. The
-default tile size is 256x256 **image pixels**, independent of the VAE scale factor:
-
-```shell
--vae-tiling --vae-tile-size 256x256 --vae-tile-overlap 0.5
-```
-
-`--vae-tile-size` accepts one size or `WIDTHxHEIGHT`. A zero dimension uses the
-256-pixel default. Sizes are rounded down to a multiple of the VAE scale factor
-and capped at the current input dimensions. Explicit sizes below four latent
-pixels per axis (or the full axis when it is smaller) are rejected. Encoding and
-decoding use the same spatial sizes, without an additional encoding multiplier.
-Inputs that fit within a tile are processed as one tile.
-
-For a 512x512 image with the default 50% overlap, both encoding and decoding use
-3x3 tiles. A 256-pixel tile corresponds to 32 latent pixels for an 8x VAE, 16 for
-a 16x VAE, and 8 for a 32x VAE. Smaller tiles reduce each graph's memory demand,
-but overlapping work can increase processing time and tiling can affect image
-quality, especially during encoding. Use larger tiles when more context is needed.
-
-`--vae-relative-tile-size` overrides the absolute size on each axis with a positive
-value. Values up to and including 1 specify a fraction of the current input size;
-values greater than 1 specify a target number of tiles per axis, accounting for
-overlap. For example, `0.5x0.5` uses half the width and height in both encode and
-decode. The target overlap is clamped to 0 through 0.5 and the actual overlap is
-adjusted to fit the image. Size and overlap options require `--vae-tiling`.
-
-**Migration:** `--vae-tile-size` and the C/JSON fields `tile_size_w` and
-`tile_size_h` now use image pixels instead of latent units. The C/JSON fields
-`tile_size_x/y` have been renamed to `tile_size_w/h`, and `rel_size_x/y` to
-`rel_size_w/h`. The command-line option names are unchanged. For example, an old
-decode tile size of 32 corresponds to 256 pixels for an 8x VAE or 512 pixels for a
-16x VAE. Encoding no longer enlarges explicit or relative tile sizes.
-
-The main VAE decode path retries allocation failures with smaller tiles, even
-without `--vae-tiling`. Supported video VAEs first try temporal tiling; spatial
-retries use at most 256-pixel tiles initially and then halve the effective tile
-dimensions down to the minimum size. Each spatial retry must reduce the effective
-tile size. These runtime adjustments do not change the caller's parameters.
-Execution failures are not retried, and encoding has no automatic OOM retry.
-
-`--temporal-tiling` remains independent of spatial tiling. MiniMax H3 always uses
-spatial tiling (256x256 pixels and 25% overlap by default) and its own temporal
-windows. With `--vae-tiling`, its overlap follows `--vae-tile-overlap`; explicit
-spatial sizes are honored.
-
 ## 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.
@@ -139,12 +63,6 @@ See [backend selection](./backend.md) for full syntax.

 When a graph has cut markers and its missing weights plus incremental compute workspace exceed the available device headroom, it runs its fixed segment list in order. A reusable monolithic compute buffer is not counted as a new allocation. An explicit `--max-vram` budget deducts already-resident managed weights and compute/cache buffers registered by every runner sharing the device, so later graph runs remain segmented when the full graph exceeds the budget. The current segment's weights are pinned during compute, and the next parameter-bearing segment is prefetched when the device supports asynchronous transfer. No opt-in streaming flag is required.

-When choosing between monolithic and segmented execution, the runner requires
-an additional 128 MiB of headroom in both available device memory and any explicit
-managed budget. This planning headroom absorbs small allocation estimate changes;
-subsequent capacity checks can consume it while still preserving the 512 MiB device
-scratch reserve and respecting the managed budget.
-
 - `--max-vram <GiB>` optionally lowers the live-memory limit. A positive value is a managed per-device budget, `0` uses the device's current free memory without an explicit budget, and a negative value snapshots free memory at startup while reserving that many GiB (`--max-vram -1` reserves about 1 GiB). Driver contexts and unrelated external allocations remain outside the managed budget.
 - `--disable-prefetch` disables asynchronous next-segment prefetch while retaining synchronous loading, eviction, and segmented execution.
 - `--disable-segmented-compute` forces monolithic graph execution for diagnostics or compatibility, even when the automatic memory check would select segments.
@@ -159,25 +77,6 @@ allocator/scheduler capacity. Compatible gallocr reservations are reused across
 graphs; idle workspaces can be reclaimed under pressure and are freed at runner
 completion. Cross-graph caches survive individual graphs, but cut buffers do not.

-On discrete Vulkan devices, runners reuse released device buffers of the same
-size to reduce allocation overhead during segmented execution. This idle buffer
-cache is limited to 1 GiB per device and is reclaimed before allocating a new
-size or at runner completion. It is outside the managed `--max-vram` budget,
-so physical VRAM usage can be higher. Set `GGML_VK_REUSE_BUFFERS=0` to disable
-it. Unified-memory and host-memory modes do not use this cache.
-
-Discrete NVIDIA and non-GCN AMD Vulkan devices use a separate transfer queue by
-default when available and graphics queue use is disabled, so weight prefetch can overlap compute. Set
-`GGML_VK_ASYNC_USE_TRANSFER_QUEUE=0` to disable it.
-
-On discrete NVIDIA devices, allocation chunks can grow up to 4 GiB, bounded by the device's buffer and allocation
-limits, to reduce fragmentation within large graph workspaces. Other devices
-retain the 1 GiB default. `GGML_VK_SUBALLOCATION_BLOCK_SIZE` overrides this limit
-in bytes on any vendor; for example, `1073741824` selects 1 GiB and `4294967296`
-selects 4 GiB, subject to device limits. This is a soft chunk limit, not a reservation
-or a change to `--max-vram`: a single oversized tensor or exhausting the allocator's
-chunk count can require a larger allocation, which may fail.
-
 The recommended shape for "biggest model my card can host" is:

 ```shell
@@ -27,7 +27,7 @@ PiD and PiD 1.5 require an external Gemma 2 `tokenizer.json` matching the text e
 ## Examples

 ```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\pid_flux1_512_to_2048_4step_bf16.safetensors --llm "..\models\text_encoders\gemma_2_2b_it_elm_bf16.safetensors" --tokenizer ..\models\tokenizers\tokenizer_gemma2.json --vae ..\models\vae\ae.sft --vae-format flux --cfg-scale 1.0  -p "a lovely cat" -r ..\assets\ernie_image\turbo_example.png -v --steps 4 -H 2048 -W 2048 --rng cpu
+.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\pid_flux1_512_to_2048_4step_bf16.safetensors --llm "..\models\text_encoders\gemma_2_2b_it_elm_bf16.safetensors" --tokenizer ..\models\tokenizers\tokenizer_gemma2.json --vae ..\models\vae\ae.sft --vae-format flux --cfg-scale 1.0  -p "a lovely cat" -r ..\assets\ernie_image\turbo_example.png --diffusion-fa -v --steps 4 -H 2048 -W 2048 --rng cpu
 ```

 Before:
@@ -1,46 +0,0 @@
-# How to Use
-
-You can run PixArt-α / PixArt-Σ with stable-diffusion.cpp.
-
-PixArt is a DiT-based text-to-image model family conditioned by a T5-XXL text
-encoder and a 4-channel VAE: SDXL-style for PixArt-Σ and SD1.x-style for PixArt-α.
-
-## Download weights
-
- Download the transformer (diffusion model)
-    - PixArt-Σ XL-2 1024-MS: https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS/tree/main/transformer
-    - PixArt-α XL-2 1024-MS: https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS/tree/main/transformer
- Download the T5-XXL text encoder
-    - safetensors: https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS/tree/main/text_encoder
- Download the VAE
-    - PixArt-Σ: https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS/tree/main/vae
-    - PixArt-α: https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS/tree/main/vae
-    - Use the VAE matching the checkpoint's latent space. For TAE decoding or
-      preview, use TAESDXL for PixArt-Σ and TAESD for PixArt-α.
- Tokenizer: the T5 vocabulary is embedded; no extra tokenizer file is needed.
-
-## Examples
-
-```
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\pixart_sigma_xl2_1024_ms.safetensors --t5xxl ..\models\text_encoders\t5xxl.safetensors --vae ..\models\vae\pixart_vae.safetensors -p "a lovely cat" --cfg-scale 4.5 -W 1024 -H 1024 --steps 20 -v
-```
-
-## Notes
-
- The VAE scaling factor defaults to `0.13025` for PixArt-Σ. PixArt-α
-  checkpoints with resolution micro-condition weights use `0.18215`.
-  PixArt-α 512 has the same tensor layout as PixArt-Σ, so it requires an
-  explicit override: `--model-args "pixart_vae_scale_factor=0.18215"`.
-  This argument can also override the scale for other compatible checkpoints.
- PixArt-Σ checkpoints compute 2D sincos positional embeddings at runtime;
-  the trained grid is 64x64 patches with an interpolation scale of 2.
-  For checkpoints trained at a different resolution, the positional embedding
-  parameters can be adjusted via model args:
-  `--model-args "pixart_pos_embed_base_size=<trained grid>,pixart_interpolation_scale=<scale>"`
-  (e.g. `pixart_pos_embed_base_size=32,pixart_interpolation_scale=1,pixart_vae_scale_factor=0.18215` for
-  PixArt-α XL-2 512).
- Checkpoints carrying resolution/aspect-ratio micro-condition weights are
-  detected but those conditions are not applied yet; a warning is logged and
-  generation proceeds with the timestep embedding only.
- The transformer predicts 8 channels (noise + learned variance); only the
-  noise half is used for sampling, matching the reference implementation.
@@ -14,7 +14,7 @@
 ## Examples

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\qwen-image-Q8_0.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\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 --flow-shift 3
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\qwen-image-Q8_0.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\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" />
@@ -16,8 +16,6 @@ Qwen Image 2.1 supports text-to-image generation and image editing, using Qwen3-

 Use `qwen_image_2.1_vae_bf16.safetensors` with this model. The earlier Qwen Image and Wan 2.2 VAE weights are not interchangeable with the Qwen Image 2.1 VAE weights.

-For faster encoding, decoding, or previews, see [TAEQI2.1](taesd.md#qwen-image-21-taeqi21).
-
 ## Examples

 Run the following commands from the build directory. Use image dimensions divisible by 32. The resolution-dependent flow schedule is selected automatically.
@@ -32,16 +30,6 @@ Run the following commands from the build directory. Use image dimensions divisi

 To use GGUF diffusion weights, set `--diffusion-model` to the path of a file such as `qwen_image_2.1-Q4_K.gguf`.

-### Turbo
-
-For [Qwen Image 2.1 Turbo](https://huggingface.co/Qwen/Qwen-Image-2.1-Turbo), use the Turbo diffusion weights and select its fixed 8-step sigma schedule with `--sigmas qwen-image-2.1-turbo`. For example, add these sampling options to the command above:
-
-```text
--sigmas qwen-image-2.1-turbo --steps 8 --cfg-scale 1.0 --sampling-method euler
-```
-
-The preset is equivalent to `--sigmas 1.0,0.978453,0.95418,0.926626,0.89508,0.845148,0.704534,0.414568,0.0`. It only supplies the sigma values; CFG scale and sampling method remain independently configurable. As with an explicit sigma list, the sigma count determines the actual number of sampling steps. The same preset name is also accepted by `--hires-sigmas` for the highres fix second pass.
-
 ### Image editing

 Pass the reference image with `-r` and describe the edit in `-p`. Vision weights are required; the example below loads them separately with `--llm_vision`.
@@ -51,49 +39,3 @@ Pass the reference image with `-r` and describe the edit in `-p`. Vision weights
 ```

 For multiple reference images, repeat `-r` in the desired order, for example `-r first.png -r second.png`.
-
-### Prefix cache
-
-By default, the first denoising call for each fixed condition saves the text and reference-image keys and values from every transformer layer. Later calls only compute the target-image tokens. Positive and negative conditions use separate caches, which are released when sampling ends.
-
-Set `qwen_image_2_1_prefix_cache_type` in `--model-args` to `auto` or a type name using the same parser and case-sensitive names as `--type`:
-
- `auto` (default): use FP16 only when Flash Attention is enabled, Sage Attention is disabled, the attention scale is unchanged, and every attention operation in the cache-writing or cache-reading graph selects Flash Attention after backend support checks. If an operation falls back, rebuild the prefix in FP32 before executing and keep FP32 for the rest of that sampling run.
- `f32`: always store FP32 keys and values.
- `f16`: always store FP16 keys and values, including with ordinary attention or custom attention scaling. This saves cache memory but can introduce additional rounding error.
- Other types, such as `bf16`, `q4_1`, `q5_0`, `q5_1`, `q8_0`, `q4_K`, `q6_K`, `iq4_nl`, and `iq4_xs`: use the requested storage type if the ggml build provides runtime conversion to and from FP32. Quantization is lossy and must be selected explicitly; `auto` never selects a quantized type.
-
-Cache data is packed into contiguous rows of `hidden_size` elements before conversion, so 256-element quantization blocks work with the model's 128-element attention heads without padding. The type's block size must divide `hidden_size`. Unknown types, types lacking runtime conversion (for example `q8_1` and several IQ formats), and incompatible block sizes are ignored with a warning, leaving the previous setting or the default `auto` unchanged.
-
-For example, use `--model-args qwen_image_2_1_prefix_cache_type=q8_0` to enable 8-bit cache storage. Cached keys and values are converted back to the attention input type before concatenating with the current target tokens. This reduces persistent cache memory; attention working buffers still use floating-point values, and conversion adds work on each step. Backends without the required conversion operations use the existing CPU fallback.
-
-For the default 32-layer model, a prefix of 4096 tokens takes approximately the following memory per condition, excluding weights, working buffers, and allocation overhead:
-
-| Cache type | Memory |
-| --- | ---: |
-| `f32` | 4 GiB |
-| `f16` | 2 GiB |
-| `q8_0` | 1.0625 GiB |
-| `q4_0` | 0.5625 GiB |
-
-The runner accounts for the cache when checking the memory budget. If a cached execution runs out of memory, it releases the prefix caches, disables caching for the rest of that sampling run, and retries the full sequence once. Per-step conditioning extensions currently use the full-sequence path.
-
-Disable this optimization with `--model-args qwen_image_2_1_prefix_cache=false`. It reuses step-independent activations; numerical results can still differ slightly because the matrix sizes change.
-
-### Alpha channel
-
-This model supports alpha channel output. As the model determines whether to output a regular image or with transparency through the prompt, according to [official recommendation](https://github.com/QwenLM/Qwen-Image-2.1#transparent-image-generation-rgba), use the following prompt format for better results:
-
-> `This is an RGBA image with transparency. <your description>. The image has alpha channel and the background is transparent.`
-
-Since transparency is decided by the prompt rather than by the input or an explicit switch, the same format applies equally to editing, whether or not the reference image itself has an alpha channel. Note that alpha is kept only in `.png` and `.webp` outputs; saving as `.jpg` drops the transparency.
-
-Here are some examples ran with Q6_K quantization:
-| Input | Prompt | Output |
-| --- | --- | --- |
-| ![Qwen Image 2.1 alpha input example 1](../assets/qwen/qwen-image-2.1-alpha-in1.png) | This is an RGBA image with transparency. Replace the text "BLOOM" with "Qwen Image 2.1", keeping the same font of the original text. The image has alpha channel and the background is transparent. | ![Qwen Image 2.1 alpha output example 1](../assets/qwen/qwen-image-2.1-alpha-out1.png) |
-| ![Qwen Image 2.1 alpha input example 2](../assets/logo.png) | This is an RGBA image with transparency. Remove the background of the image, keeping only the text and cat. The image has alpha channel and the background is transparent. | ![Qwen Image 2.1 alpha output example 2](../assets/qwen/qwen-image-2.1-alpha-out2.png) |
-
-### Other features
-
-Other features of the model could be found on the [model card from QwenLM/Qwen-Image-2.1 repo](https://github.com/QwenLM/Qwen-Image-2.1), including 2 finetuned prompt rewriting Qwen3.5-9B model.
@@ -23,7 +23,7 @@
 ### Qwen Image Edit

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\Qwen_Image_Edit-Q8_0.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --seed 1118877715456453
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\Qwen_Image_Edit-Q8_0.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\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" />
@@ -32,7 +32,7 @@
 ### Qwen Image Edit 2509

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\Qwen-Image-Edit-2509-Q4_K_S.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf --llm_vision ..\models\text_encoders\Qwen2.5-VL-7B-Instruct.mmproj-Q8_0.gguf --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'Qwen Image Edit 2509'"
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\Qwen-Image-Edit-2509-Q4_K_S.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\Qwen2.5-VL-7B-Instruct-Q8_0.gguf --llm_vision ..\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" />
@@ -42,7 +42,7 @@
 To use the new Qwen Image Edit 2511 mode, `--model-args qwen_image_zero_cond_t=true` must be enabled; otherwise, image editing quality will degrade significantly.

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\qwen-image-edit-2511-Q4_K_M.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\models\text_encoders\qwen_2.5_vl_7b.safetensors --cfg-scale 2.5 --sampling-method euler -v --offload-to-cpu --flow-shift 3 -r ..\assets\flux\flux1-dev-q8_0.png -p "change 'flux.cpp' to 'edit.cpp'" --model-args qwen_image_zero_cond_t=true
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\qwen-image-edit-2511-Q4_K_M.gguf --vae ..\models\vae\qwen_image_vae.safetensors  --llm ..\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'" --model-args qwen_image_zero_cond_t=true
 ```

 <img alt="qwen_image_edit_2509" src="../assets/qwen/qwen_image_edit_2511.png" />
@@ -1,50 +0,0 @@
-# RelayVSR image and video super-resolution
-
-[RelayVSR](https://github.com/kopperx/RelayVSR) upscales images and videos by
-2x or 4x with `-M vid_upscale`.
-
-## Weights
-
-[ComfyUI safetensors weights](https://huggingface.co/Comfy-Org/RelayVSR) load
-directly. Select the matching pair for the requested scale:
-
-| `--upscale-factor` | `--diffusion-model` | `--vae` |
-| --- | --- | --- |
-| 2 | `relayvsr_x2_lightx2v_int8_convrot.safetensors` | `relayvsr_flash_vae_x2_fp32.safetensors` |
-| 4 | `relayvsr_x4_int8_convrot.safetensors` | `relayvsr_flash_vae_x4_fp32.safetensors` |
-
-No separate text encoder or ordinary Wan VAE is needed.
-
-## Usage
-
-For a video, extract numbered frames into a directory, then run:
-
-```sh
-sd-cli -M vid_upscale \
-  --diffusion-model models/diffusion_models/relayvsr_x2_lightx2v_int8_convrot.safetensors \
-  --vae models/vae/relayvsr_flash_vae_x2_fp32.safetensors \
-  --input-frames input_frames --upscale-factor 2 --fps 24 \
-  --vsr-window-size 16 -s 42 -o output.avi
-```
-
-Frames are read in filename order. Set `--fps` to the source video's frame rate.
-Video outputs support AVI, WebM, and animated WebP. To save individual frames,
-use an output pattern such as `-o output/%04d.png`.
-
-For a single image, replace `--input-frames input_frames` with `-i input.png`
-and use `-o output.png` (or JPEG/WebP). No FPS setting is needed for an image.
-`-i` / `--init-img` and `--input-frames` are mutually exclusive.
-
-## Settings and limits
-
- `--upscale-factor` is required. Use 2 or 4 with the corresponding weights.
-  Output width and height are exactly the input dimensions times the scale.
- `--vsr-window-size` sets the reference-frame window, including both endpoints.
-  The default 16 selects reference frames 0, 15, 30, and so on, and always
-  includes the final frame. Every input frame appears once in the output.
- `--flash-attn auto` and `--conv-direct auto` are enabled by default. See
-  [backend selection](backend.md) to choose a device or weight placement.
- Sampler, scheduler, and step settings do not configure RelayVSR.
-  `--vsr-temporal-chunk-size` is a SeedVR2 option.
- Long videos require enough system memory to hold the input and output frames.
-  Audio is not copied to the output.
@@ -131,7 +131,7 @@ If you want to allow connections from other machines on the network:

 > **Drivers & Hardware:** Ensure the Server machine has the necessary drivers installed and functional (e.g., Nvidia Drivers for CUDA, Vulkan SDK, or Metal). If no devices are found, the server will simply fallback to CPU usage.

-### Step B: Check if the client is able to connect to the server and see the available devices
+<!-- ### Step B: Check if the client is able to connect to the server and see the available devices

 We're assuming the server is running on your local machine, and listening on the default port `50052`. If it's running on a different machine, you can replace `localhost` with the IP address of the server.

@@ -147,15 +147,15 @@ Example output:
 (Client built without GPU acceleration, two GPUs available on the server)

 ```
+List of available GGML devices:
+Name    Description
+-------------------
 CPU     AMD Ryzen 9 5900X 12-Core Processor
 RPC0    localhost:50052
 RPC1    localhost:50052
-```
+``` -->

-The options can be given in either order; no model is required. The client exits
-with an error if RPC support is unavailable or a server cannot provide devices.
-
-### Step C: Run with RPC device
+### Step B: Run with RPC device

 If everything is working correctly, you can now run the client while offloading some or all of the work to the RPC server.

@@ -217,4 +217,4 @@ The client will map these servers to sequential device IDs (e.g., RPC0 from the

 ## 6. Performance Considerations

-RPC performance is heavily dependent on network bandwidth, as large weights and activations must be transferred back and forth over the network, especially for large models, or when using high resolutions. For best results, ensure your network connection is stable and has sufficient bandwidth (>1Gbps recommended). This shoumd not be a concern if you are running the server and client on the same machine, as the data transfer will happen over the loopback interface.
+RPC performance is heavily dependent on network bandwidth, as large weights and activations must be transferred back and forth over the network, especially for large models, or when using high resolutions. For best results, ensure your network connection is stable and has sufficient bandwidth (>1Gbps recommended). This shoumd not be a concern if you are running the server and client on the same machine, as the data transfer will happen over the loopback interface.
@@ -4,9 +4,8 @@
 the high-noise diffusion model when present. Python, PyTorch, and Triton are not
 required at build time or runtime.

-The sd.cpp CUDA extension registers SageAttention through GGML's generic
-operator API. It selects a kernel supported by the GPU, compiled CUDA target,
-and toolkit:
+The CUDA backend automatically selects a kernel supported by both the GPU and
+the compiled CUDA toolkit:

 | GPU / toolkit | Implementation |
 | --- | --- |
@@ -38,12 +37,12 @@ rebuild with CUDA 12.8 or newer to use SageAttention2++.

 ## Use

-Add `--sage-attn` to enable SageAttention. For example,
+Replace `--diffusion-fa` with `--sage-attn` in an existing command. For example,
 from the build directory:

 ```powershell
 .\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 --offload-to-cpu --video-frames 33 --sage-attn
+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 --sage-attn
 ```

 SageAttention currently handles unmasked attention with head dimensions from
@@ -52,7 +51,9 @@ and multiple batches. Dimensions below 64 are zero-padded to 64; dimensions
 between 65 and 127 are zero-padded to 128. The original softmax scale is preserved,
 and the output is cropped back to the original dimension. Other attention
 operations fall back to FlashAttention when supported, then ordinary attention.
-SageAttention takes precedence over FlashAttention in diffusion. Existing attention scaling overrides remain effective.
+SageAttention takes precedence in diffusion
+when combined with `--fa` or `--diffusion-fa`; `--fa` continues to control other
+modules. Existing attention scaling overrides remain effective.

 Attention quantization changes numerical results. Compare image quality and
 end-to-end generation time using the same seed, dimensions, and sampling
@@ -1,69 +0,0 @@
-# SeedVR2 image and video super-resolution
-
-SeedVR2 upscales images and videos with `-M vid_upscale`. Support is experimental.
-
-## Weights
-
-These [ComfyUI safetensors weights](https://huggingface.co/Comfy-Org/SeedVR2)
-load directly, without conversion or a separate text encoder:
-
-| Model | `--diffusion-model` | `--vae` |
-| --- | --- | --- |
-| 3B | `seedvr2_3b_int8_convrot.safetensors` | `seedvr2_ema_vae_fp16.safetensors` |
-| 7B Sharp | `seedvr2_7b_sharp_int8_convrot.safetensors` | `seedvr2_ema_vae_fp16.safetensors` |
-
-Use the matching SeedVR2 VAE for either model. The 7B model requires more memory.
-
-## Upscale an image
-
-```sh
-sd-cli -M vid_upscale \
-  --diffusion-model models/diffusion_models/seedvr2_3b_int8_convrot.safetensors \
-  --vae models/vae/seedvr2_ema_vae_fp16.safetensors \
-  -i input.png --upscale-factor 2 -s 42 -o restored.png
-```
-
-`-i` / `--init-img` accepts a single image. Outputs can be PNG, JPEG, or WebP.
-No FPS or temporal chunk setting is needed for an image.
-
-## Upscale a video
-
-Extract the video into numbered frames, then pass the frame directory:
-
-```sh
-mkdir frames
-ffmpeg -i input.mp4 -fps_mode passthrough frames/%06d.png
-sd-cli -M vid_upscale \
-  --diffusion-model models/diffusion_models/seedvr2_3b_int8_convrot.safetensors \
-  --vae models/vae/seedvr2_ema_vae_fp16.safetensors \
-  --input-frames frames --upscale-factor 2 --fps 24 \
-  --vsr-temporal-chunk-size 1 -s 42 -o restored.avi
-```
-
-Set `--fps` to the source video's frame rate; 24 is only the example above.
-Frames are read in filename order. Use either `-i` or `--input-frames`, not both.
-
-Video outputs support AVI, WebM, and animated WebP. To save individual frames,
-use an output pattern such as `-o restored/%06d.png`.
-
-## Settings and limits
-
- `--upscale-factor` is required and must be a positive integer. Output width
-  and height are exactly the input dimensions multiplied by this value.
- `--vsr-temporal-chunk-size` controls how many video frames are processed
-  together. Valid values are 1, 5, 9, 13, and so on; the default is 5. A value of
-  1 processes each frame independently and uses less memory. Larger values use
-  neighboring frames but require more memory. Chunk boundaries may be visible.
- Frame count and order are preserved, including a shorter final chunk. Audio
-  is not copied to the output.
- If you run out of GPU memory, try `--vsr-temporal-chunk-size 1`. A five-frame
-  chunk at 1728x960 output resolution exceeded 24 GB of GPU memory in testing.
- `--flash-attn auto` and `--conv-direct auto` are enabled by default. See
-  [backend selection](backend.md) to choose a device or weight placement.
- SeedVR2 does not use sampler, scheduler, or step settings.
-  `--vsr-window-size` is a RelayVSR option and does not affect SeedVR2.
-
-3B has been tested on CUDA and CPU; 7B has been tested on CUDA with small inputs.
-Other backends have not been tested with this integration.
-
-Model reference: [ByteDance-Seed/SeedVR](https://github.com/ByteDance-Seed/SeedVR).
@@ -32,19 +32,19 @@ The dispatcher picks `alpha` from the filename (`turbo` substring => 1.0, otherw
 ### 1B / 2B turbo

 ```
-./build/bin/sd-cli --diffusion-model /path/to/sefi_1b_turbo.safetensors --vae /path/to/flux2_ae.safetensors --llm /path/to/qwen3_vl_2b.safetensors -p "a photograph of an orange tabby cat sitting on a couch" --cfg-scale 1.0 --steps 4 -W 1024 -H 1024 -s 42 --offload-to-cpu -o out.png
+./build/bin/sd-cli --diffusion-model /path/to/sefi_1b_turbo.safetensors --vae /path/to/flux2_ae.safetensors --llm /path/to/qwen3_vl_2b.safetensors -p "a photograph of an orange tabby cat sitting on a couch" --cfg-scale 1.0 --steps 4 -W 1024 -H 1024 -s 42 --diffusion-fa --offload-to-cpu -o out.png
 ```

 ### 1B / 2B base

 ```
-./build/bin/sd-cli --diffusion-model /path/to/sefi_1b_base.safetensors --vae /path/to/flux2_ae.safetensors --llm /path/to/qwen3_vl_2b.safetensors -p "a photograph of an orange tabby cat sitting on a couch" --cfg-scale 4.0 --steps 50 -W 1024 -H 1024 -s 42 --offload-to-cpu -o out.png
+./build/bin/sd-cli --diffusion-model /path/to/sefi_1b_base.safetensors --vae /path/to/flux2_ae.safetensors --llm /path/to/qwen3_vl_2b.safetensors -p "a photograph of an orange tabby cat sitting on a couch" --cfg-scale 4.0 --steps 50 -W 1024 -H 1024 -s 42 --diffusion-fa --offload-to-cpu -o out.png
 ```

 ### 5B (needs streaming on 12 GiB VRAM)

 ```
-./build/bin/sd-cli --diffusion-model /path/to/sefi_5b_turbo.safetensors --vae /path/to/flux2_ae.safetensors --llm /path/to/qwen3_vl_4b.safetensors -p "a photograph of an orange tabby cat sitting on a couch" --cfg-scale 1.0 --steps 4 -W 1024 -H 1024 -s 42 --max-vram 8 --offload-to-cpu -o out.png
+./build/bin/sd-cli --diffusion-model /path/to/sefi_5b_turbo.safetensors --vae /path/to/flux2_ae.safetensors --llm /path/to/qwen3_vl_4b.safetensors -p "a photograph of an orange tabby cat sitting on a couch" --cfg-scale 1.0 --steps 4 -W 1024 -H 1024 -s 42 --diffusion-fa --max-vram 8 --offload-to-cpu -o out.png
 ```

 <img alt="SeFi-Image 5B turbo example" src="../assets/sefi_image/example.png" />
@@ -28,6 +28,7 @@ and the tokenizer files.
  --seed 42 \
  --sampling-method euler \
  --rng cuda \
+  --fa \
  --output output.png
 ```

@@ -0,0 +1,106 @@
+# Sol-Attn
+
+`--sol-attn` enables native CUDA Sol-Attn in the diffusion model, including the
+high-noise diffusion model when present. It uses the shared attention dispatcher
+without classifying tokens as text, images, or video. Python, PyTorch, Triton,
+and CuTe DSL are not needed to build or run it.
+
+This implementation follows the diagonal-threshold algorithm in
+[NVlabs/Sana's Sol-Attn](https://github.com/NVlabs/Sana/tree/sol-engine/techniques/sparse_backends/sol_attn).
+It summarizes 64-token KV blocks, selects exact blocks using proxy scores and
+an online threshold, and approximates the remaining blocks using their K means
+and V sums. Adjacent blocks remain exact. Both contributions share an online
+softmax normalizer. Q/K/V and probability tiles use BF16 Tensor Cores with FP32
+accumulation; the BF16 result is returned through the existing FP32 interface.
+
+## Build
+
+Use patched GGML, CUDA Toolkit 12.0 or newer, and an NVIDIA GPU with compute
+capability 8.0 or newer. Compile kernels for the target GPU:
+
+```sh
+cmake -S . -B build -DSD_CUDA=ON -DSD_USE_UPSTREAM_GGML=OFF
+cmake --build build --config Release
+```
+
+The feature is compiled with the CUDA backend; no separate build option is
+required. Upstream GGML and non-CUDA backends do not support it. A system GGML
+must provide the matching patched API and CUDA implementation. Tensor-parallel
+row splitting is not supported; layer splitting requires supported devices.
+
+## Use
+
+Add `--sol-attn` to an existing generation command:
+
+```sh
+sd-cli ... --sol-attn
+sd-cli ... --sol-attn --sol-attn-tau 1.0
+```
+
+The default threshold coefficient is `1.0`. Larger coefficients select fewer
+blocks for exact attention. The coefficient must be finite; zero does not mean
+dense attention. Omit `--sol-attn` to disable the feature.
+
+The native kernel supports unmasked, noncausal attention with head dimension
+128, equal Q/K/V sequence lengths and head counts, and multiple batches. Other
+attention operations fall back to FlashAttention when available, then ordinary
+attention. Existing attention scaling overrides remain effective. `--fa` and
+`--diffusion-fa` may be used together with Sol-Attn; `--sage-attn` is mutually
+exclusive. Text encoders and VAEs retain their existing attention selection.
+
+Initialization reports an error if the requested diffusion backend cannot run
+Sol-Attn. Graph logs report the number of Sol-Attn and FlashAttention nodes and
+warn when no Sol-Attn nodes are selected. CUDA execution errors are not silently
+converted into dense attention.
+
+This is approximate attention. Validate quality and end-to-end speed with the
+same prompt, seed, dimensions, frame count, and sampling settings. Include
+packing, preprocessing, offload, and decode time in comparisons. Short sequences
+may not benefit. Upstream combined pipeline speedups are not measurements of
+this native kernel. Exact-covariance thresholds, text sinks, Morton ordering,
+and step/layer schedules are not implemented.
+
+## Validation
+
+On an RTX 4090 with CUDA 12.4, Wan 2.1 T2V 1.3B was tested at 832x480,
+33 frames, 20 Euler steps, seed 42, CFG 6, and flow shift 3, using the prompt
+`a lovely cat` and the same negative prompt for every run:
+
+| Attention | Sampling time | Total process time |
+| --- | ---: | ---: |
+| FlashAttention | 45.73 s | 74.63 s |
+| Sol-Attn, tau 1 | 37.17 s | 66.20 s |
+| Sol-Attn, tau 0 | 40.66 s | 68.50 s |
+
+These are single-run measurements. The graph selected 30 Sol-Attn nodes and
+30 FlashAttention nodes. At tau 1, sampled video frames showed washed-out
+colors and reduced detail. Tau 0 improved clarity in this example, but still
+changed the composition. Neither setting guarantees the baseline's quality.
+For this Wan command, `--sol-attn --sol-attn-tau 0` is a more conservative
+starting point. In the one-frame case, tau 1 increased warm sampling time from
+0.140 to 0.148 seconds per step.
+
+Validation also covered 15 numerical reference cases, 11 layout/scaling/fallback
+cases, CUDA memory checking, and 36 existing SageAttention regression cases.
+CLI and server CUDA builds and the upstream GGML CPU library build passed.
+Other GPU architectures, multi-GPU execution, and other models have not been
+tested.
+
+## Library API
+
+Configure Sol-Attn in `sd_ctx_params_t` before creating the context:
+
+```cpp
+sd_ctx_params_t params;
+sd_ctx_params_init(&params);
+// Set model paths and other context options here.
+params.sol_attn = true;
+params.sol_attn_tau = 1.0f;
+sd_ctx_t* ctx = new_sd_ctx(&params);
+```
+
+`sd_ctx_params_init` defaults `sol_attn` to false and `sol_attn_tau` to 1.0.
+`new_sd_ctx` returns null for a nonfinite threshold, unavailable requested
+backends, or a conflict with SageAttention. The context owns a copy of these
+settings; changing the input structure after creation does not reconfigure it.
+Applications must be rebuilt against the updated `sd_ctx_params_t` definition.
@@ -37,17 +37,3 @@ sd.cpp also supports [TAEHV](https://github.com/madebyollin/taehv) (#937), which
  ```

 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.
-
-### Qwen Image 2.1 (TAEQI2.1)
-
-For Qwen Image 2.1, use [taeqi2_1](https://github.com/madebyollin/taesd), which supports 64-channel latents, 16x spatial scaling, and RGBA images.
-
-Download the official [safetensors weights](https://huggingface.co/madebyollin/taeqi2_1/blob/main/taeqi2_1.safetensors) directly; no conversion is needed:
-
-```bash
-curl -L -o taeqi2_1.safetensors https://huggingface.co/madebyollin/taeqi2_1/resolve/main/taeqi2_1.safetensors
-```
-
-Replace `--vae PATH` with `--taesd taeqi2_1.safetensors` in the [Qwen Image 2.1 examples](qwen_image_2.1.md) to use it for encoding and decoding. For previews only, keep `--vae PATH` and add `--taesd taeqi2_1.safetensors --taesd-preview-only --preview tae`.
-
-`sd-server` accepts the same `--taesd` and `--taesd-preview-only` options; the preview method is selected per request instead of via `--preview`.
@@ -1,14 +1,5 @@
 # Troubleshooting

-## Video model used in image generation mode
-
-If generation reports that a model cannot be run with `generate_image()`, add
-`--mode vid_gen` to the CLI command. `--video-frames` alone does not select video
-mode. Video models require this mode even when generating a single frame.
-Library callers must use `generate_video()` for these models; use
-`sd_ctx_supports_image_generation()` and `sd_ctx_supports_video_generation()` to
-check the available generation modes.
-
 ## Completely black or white images or videos / NaNs

 Some ggml backends can encounter numerical overflow during inference, producing
@@ -40,7 +31,8 @@ the intended output scale:
 - `--linear-scale` scales Linear inputs before matrix multiplication and rescales
  the result.
 - `--attn-scale` scales attention keys and values (K/V). It takes effect only in
-  the Flash Attention path, when selected and supported by the backend.
+  the Flash Attention path, where `--fa` or `--diffusion-fa` is enabled and the
+  backend supports it.

 The two values can be set independently and apply across model components. The
 default `0` preserves each model's built-in settings; `1` explicitly disables the
@@ -1,7 +1,5 @@
 # How to Use

-Wan models require `-M vid_gen`, including single-frame generation. `--video-frames` alone does not select video mode. Library callers must use `generate_video()` instead of `generate_image()`.
-
 ## Download weights

 - Download Wan
@@ -40,9 +38,6 @@ Wan models require `-M vid_gen`, including single-frame generation. `--video-fra
            - safetensors: https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/tree/main/split_files/diffusion_models
            - gguf: https://huggingface.co/QuantStack/Wan2.2-S2V-14B-GGUF/tree/main
            - int8_convrot safetensors: https://huggingface.co/noctrex/Wan2.2-S2V-14B-int8_convrot
-        - Wan2.2 VACE-Fun A14B
-            - safetensors: https://huggingface.co/alibaba-pai/Wan2.2-VACE-Fun-A14B
-            - gguf: https://huggingface.co/QuantStack/Wan2.2-VACE-Fun-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
@@ -67,7 +62,7 @@ Wan models require `-M vid_gen`, including single-frame generation. `--video-fra
 ### Wan2.1 T2V 1.3B

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1_t2v_1.3B_fp16.safetensors --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1_t2v_1.3B_fp16.safetensors --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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>
@@ -75,7 +70,7 @@ Wan models require `-M vid_gen`, including single-frame generation. `--video-fra
 ### Wan2.1 T2V 14B

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-t2v-14b-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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  --offload-to-cpu --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-t2v-14b-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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>
@@ -85,7 +80,7 @@ Wan models require `-M vid_gen`, including single-frame generation. `--video-fra
 ### Wan2.1 I2V 14B

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-i2v-14b-480p-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\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 --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-i2v-14b-480p-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\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>
@@ -93,7 +88,7 @@ Wan models require `-M vid_gen`, including single-frame generation. `--video-fra
 ### Wan2.2 T2V A14B

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 --offload-to-cpu --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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>
@@ -101,7 +96,7 @@ Wan models require `-M vid_gen`, including single-frame generation. `--video-fra
 ### Wan2.2 I2V A14B

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 --offload-to-cpu --video-frames 33 --offload-to-cpu -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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>
@@ -114,7 +109,7 @@ wav2vec2 audio encoder. Wan2.2 S2V requires the wan_2.1 vae (16 channel), not
 the wan2.2 vae.

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_s2v-14B-Q8_0.gguf --audio-encoder ..\models\audio_encoders\wav2vec2_large_english_fp16.safetensors --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a person is talking" --cfg-scale 6.0 --steps 20 --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 --offload-to-cpu --vae-tiling --video-frames 81 -i ..\assets\cat_with_sd_cpp_42.png --audio .\input\speech.wav --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_s2v-14B-Q8_0.gguf --audio-encoder ..\models\audio_encoders\wav2vec2_large_english_fp16.safetensors --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf  -p "a person is talking" --cfg-scale 6.0 --steps 20 --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 --vae-tiling --video-frames 81 -i ..\assets\cat_with_sd_cpp_42.png --audio .\input\speech.wav --flow-shift 3.0
 ```

 Notes:
@@ -151,7 +146,7 @@ Notes:
 ### Wan2.2 T2V A14B T2I

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 --offload-to-cpu --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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" />
@@ -159,7 +154,7 @@ Notes:
 ### Wan2.2 T2V 14B with Lora

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 --offload-to-cpu --lora-model-dir ..\models\loras --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-T2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 ..\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>
@@ -171,7 +166,7 @@ Notes:
 #### T2V

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\models\vae\wan2.2_vae.safetensors --t5xxl ..\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 --offload-to-cpu --video-frames 33 --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\models\vae\wan2.2_vae.safetensors --t5xxl ..\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>
@@ -179,17 +174,15 @@ Notes:
 #### I2V

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\models\vae\wan2.2_vae.safetensors --t5xxl ..\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 --offload-to-cpu --video-frames 33 -i ..\assets\cat_with_sd_cpp_42.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.2_ti2v_5B_fp16.safetensors --vae ..\models\vae\wan2.2_vae.safetensors --t5xxl ..\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>

-TI2V 5B can decode multiple latent frames together when temporal tiling is enabled. For example, `--temporal-tiling --vae-tiling --vae-tile-size 256x256 --extra-tiling-args "temporal_tile_frames=4,temporal_decode_frames=2"` keeps up to four latent frames in each temporal tile and decodes up to two at once, with spatial tiles of 256×256 pixels. `temporal_decode_frames` defaults to `1`, preserving frame-by-frame decoding; other model versions ignore it. Increasing it requires more memory. Adjust the spatial tile size to fit the available memory.
-
 ### Wan2.1 FLF2V 14B

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-flf2v-14b-720p-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\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 --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-flf2v-14b-720p-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\models\text_encoders\umt5-xxl-encoder-Q8_0.gguf --clip_vision ..\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
 ```


@@ -198,7 +191,7 @@ TI2V 5B can decode multiple latent frames together when temporal tiling is enabl
 ### Wan2.2 FLF2V 14B

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 --video-frames 33 --offload-to-cpu --init-img ..\..\ComfyUI\input\start_image.png --end-img ..\..\ComfyUI\input\end_image.png --flow-shift 3.0
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf --high-noise-diffusion-model  ..\models\diffusion_models\Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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>
@@ -208,7 +201,7 @@ TI2V 5B can decode multiple latent frames together when temporal tiling is enabl
 #### T2V

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 --video-frames 1 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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>
@@ -217,7 +210,7 @@ TI2V 5B can decode multiple latent frames together when temporal tiling is enabl
 #### R2V

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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>
@@ -228,7 +221,7 @@ TI2V 5B can decode multiple latent frames together when temporal tiling is enabl
 ```
 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  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\wan2.1-vace-1.3b-q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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>
@@ -238,7 +231,7 @@ ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\fr
 #### T2V

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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>
@@ -247,7 +240,7 @@ ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\fr
 #### R2V

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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>
@@ -257,29 +250,7 @@ ffmpeg -i ..\..\ComfyUI\input\post+depth.mp4 -qscale:v 1 -vf fps=8 post+depth\fr
 #### V2V

 ```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 -i ..\..\ComfyUI\input\dance_girl.jpg --control-video ./post+depth --video-frames 33 --offload-to-cpu
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\models\diffusion_models\Wan2.1_14B_VACE-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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>
-
-### Wan2.2 VACE-Fun A14B
-
-VACE-Fun runs as a MoE pair: `--diffusion-model` takes the low-noise expert and
-`--high-noise-diffusion-model` the high-noise one. Reference-to-video uses `-i`
-for the reference image, same as Wan2.1 VACE.
-
-#### R2V
-
-```
-.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model ..\models\diffusion_models\Wan2.2-VACE-Fun-A14B-low-noise-Q8_0.gguf --high-noise-diffusion-model ..\models\diffusion_models\Wan2.2-VACE-Fun-A14B-high-noise-Q8_0.gguf --vae ..\models\vae\wan_2.1_vae.safetensors --t5xxl ..\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 -i ..\assets\cat_with_sd_cpp_42.png --video-frames 33 --offload-to-cpu
-```
-
-<video src=../assets/wan/Wan2.2_A14B_vace_r2v.mp4 controls="controls" muted="muted" type="video/mp4"></video>
-
-#### T2V
-
-Same command without `-i` (VACE context is synthesized from an empty control
-video, like Wan2.1 VACE t2v).
-
-> On GPUs with ~12 GB VRAM, VACE also needs `--vae-tiling` — the control-video
-> encode can exceed the budget otherwise.
@@ -21,7 +21,7 @@ You can run Z-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — or ev
 ### Z-Image-Turbo

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  z_image_turbo-Q3_K.gguf --vae ..\models\vae\ae.sft  --llm ..\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 -H 1024 -W 512 --steps 8
+.\bin\Release\sd-cli.exe --diffusion-model  z_image_turbo-Q3_K.gguf --vae ..\models\vae\ae.sft  --llm ..\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 --steps 8
 ```

 <img width="256" alt="z-image example" src="../assets/z_image/q3_K.png" />
@@ -29,7 +29,7 @@ You can run Z-Image with stable-diffusion.cpp on GPUs with 4GB of VRAM — or ev
 ### Z-Image-Base

 ```
-.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\z_image_bf16.safetensors --vae ..\models\vae\ae.sft  --llm ..\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 -H 1024 -W 512
+.\bin\Release\sd-cli.exe --diffusion-model  ..\models\diffusion_models\z_image_bf16.safetensors --vae ..\models\vae\ae.sft  --llm ..\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" />
@@ -1,21 +0,0 @@
-# Z-Image L2P
-
-[L2P](https://nju-pcalab.github.io/projects/L2P/) (Latent-to-Pixel) transfers Z-Image-Turbo to pixel space. The VAE is replaced by 16x16 patch tokenization on RGB pixels and a small convolutional local decoder that turns the last DiT hidden states back into pixels, so no VAE is needed. The text encoder is the Qwen3-4B used by Z-Image-Turbo.
-
-## Download weights
-
- Download L2P (1K)
-    - safetensors: https://huggingface.co/zhen-nan/L2P/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
-
-## Text-to-image
-
-Do not pass `--vae`. Image dimensions must be multiples of 16. The reference pipeline uses 30 steps with a CFG scale of 2.0.
-
-The released checkpoint keeps part of the transformer in F32. Pass `--type bf16` to load it in BF16, as the reference pipeline runs it; this reduces the diffusion model from about 18.6 GB to 11.8 GB.
-
-```bash
-.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\model-1k-merge.safetensors --llm ..\models\text_encoders\qwen_3_4b.safetensors -p "an origami pig on fire in the middle of a dark room with a pentagram on the floor" --cfg-scale 2.0 --steps 30 --sampling-method euler -W 1024 -H 1024 --type bf16 -v -o z_image_l2p.png
-```
@@ -2,10 +2,8 @@ set(TARGET sd-cli)

 add_executable(${TARGET}
    ../common/common.cpp
-    ../common/generation_metadata.cpp
    ../common/log.cpp
    ../common/media_io.cpp
-    ../common/media_metadata.cpp
    image_metadata.cpp
    main.cpp
 )
@@ -11,34 +11,14 @@ Logging defaults to `info`. Use `--log-level <level>` to select `debug`, `verbos
 messages at that level and all less detailed levels. `-v` and `--verbose` are
 equivalent to `--log-level verbose`. If repeated, the last logging option wins.

-`--flash-attn on|off|auto` defaults to `auto`, selecting flash attention for each
-compute backend during context creation. See [Backend selection](../../docs/backend.md#flash-attention---flash-attn-onoffauto)
-for probing behavior.
-Use module assignments such as `--flash-attn auto,te=off,vae=on` to override
-individual modules.
-
-`--conv-direct on|off|auto` also defaults to `auto`, enabling direct convolution
-on backends with implicit GEMM implementations. Use `--conv-direct auto,vae=off`
-for module overrides; supported modules are `diffusion`, `vae`, and `controlnet`.
-See [Direct convolution](../../docs/backend.md#direct-convolution---conv-direct-onoffauto).
-
 For direct image repair or automatic post-generation YOLOv8 detection followed by cropped inpainting, see
 [ADetailer](../../docs/adetailer.md).

-Use repeatable `--image-preprocess` rules to select resizing, cropping, padding,
-and resampling separately for each image input. Add `canny=true` to any input
-rule for edge detection. See
-[Image preprocessing](../../docs/image_preprocessing.md) for input selectors,
-input defaults, downstream model processing, mask alignment, and examples.
-
-Metadata mode inspects PNG, JPEG, WebP (including animated WebP), AVI, WebM, and Matroska
-container metadata without loading any model. Use `--image` for both images and videos:
+Metadata mode inspects PNG/JPEG container metadata without loading any model:

 ```bash
 ./bin/sd-cli -M metadata --image ./output.png
 ./bin/sd-cli -M metadata --image ./output.jpg --metadata-format json
-./bin/sd-cli -M metadata --image ./output.webm
-./bin/sd-cli -M metadata --image ./output.avi --metadata-format json
 ./bin/sd-cli -M metadata --image ./output.png --metadata-raw
 ./bin/sd-cli -M metadata --image ./output.png --metadata-all
 ```
@@ -46,6 +26,6 @@ container metadata without loading any model. Use `--image` for both images and
 For completely black or white images or videos, NaNs, and the `--linear-scale` /
 `--attn-scale` workaround, see [Troubleshooting](../../docs/troubleshooting.md).

-Generation outputs embed the core [generation record](../../docs/generation_metadata.md).
-Images retain the native JSON and an additional WebUI-compatible infotext.
-`--disable-image-metadata` omits both from files.
+For native CUDA sparse attention in the diffusion model, use `--sol-attn`.
+See [Sol-Attn](../../docs/sol_attention.md) for requirements, supported shapes,
+and the `--sol-attn-tau` threshold coefficient.
@@ -2,14 +2,11 @@

 #include <algorithm>
 #include <cctype>
-#include <codecvt>
 #include <cstdint>
 #include <cstring>
-#include <filesystem>
 #include <fstream>
 #include <iomanip>
 #include <iostream>
-#include <locale>
 #include <map>
 #include <set>
 #include <sstream>
@@ -279,7 +276,7 @@ namespace {
    }

    bool read_file(const std::string& path, std::vector<uint8_t>& data, std::string& error) {
-        std::ifstream fin(std::filesystem::u8path(path), std::ios::binary);
+        std::ifstream fin(path, std::ios::binary);
        if (!fin) {
            error = "failed to open file: " + path;
            return false;
@@ -393,39 +390,8 @@ namespace {
                               json& report,
                               std::string& error);

-    json parse_exif_user_comment(const uint8_t* data, size_t size) {
-        if (size >= 8 && memcmp(data, "ASCII\0\0\0", 8) == 0) {
-            return trim_trailing_nuls(bytes_to_string(data + 8, data + size));
-        }
-        if (size < 8 || memcmp(data, "UNICODE\0", 8) != 0) {
-            return json{{"raw_hex_preview", hex_preview(data, size)}};
-        }
-        size_t offset = 8;
-        // WebUI's piexif writer uses big-endian UTF-16 without a BOM.
-        bool little_endian = false;
-        if (size >= 10 && ((data[8] == 0xfe && data[9] == 0xff) || (data[8] == 0xff && data[9] == 0xfe))) {
-            little_endian = data[8] == 0xff;
-            offset        = 10;
-        }
-        if ((size - offset) % 2 != 0) {
-            return json{{"error", "truncated UTF-16 UserComment"}, {"raw_hex_preview", hex_preview(data, size)}};
-        }
-        std::u16string text;
-        for (; offset < size; offset += 2) {
-            text.push_back(static_cast<char16_t>(little_endian ? data[offset] | (uint16_t(data[offset + 1]) << 8)
-                                                               : (uint16_t(data[offset]) << 8) | data[offset + 1]));
-        }
-        try {
-            std::wstring_convert<std::codecvt_utf8_utf16<char16_t>, char16_t> converter;
-            return trim_trailing_nuls(converter.to_bytes(text));
-        } catch (const std::range_error&) {
-            return json{{"error", "invalid UTF-16 UserComment"}, {"raw_hex_preview", hex_preview(data, size)}};
-        }
-    }
-
    json parse_exif_value(const std::vector<uint8_t>& data,
                          size_t value_offset,
-                          uint16_t tag,
                          uint16_t type,
                          uint32_t count,
                          bool little_endian,
@@ -499,9 +465,8 @@ namespace {
                break;
            }
            case 7:
-                value = tag == 0x9286 ? parse_exif_user_comment(data.data() + value_offset, count)
-                                      : json(bytes_to_string(data.data() + value_offset,
-                                                             data.data() + value_offset + count));
+                value = bytes_to_string(data.data() + value_offset,
+                                        data.data() + value_offset + count);
                break;
            case 9: {
                if (count == 1) {
@@ -625,7 +590,6 @@ namespace {

            tag_json["value"] = parse_exif_value(data,
                                                 value_offset,
-                                                 tag,
                                                 type,
                                                 value_count,
                                                 little_endian,
@@ -1133,178 +1097,6 @@ namespace {
        return true;
    }

-    static bool parse_avi_chunks(const std::vector<uint8_t>& data, size_t offset, size_t end, bool info, bool include_raw, json& result, std::string& error) {
-        while (offset < end) {
-            if (end - offset < 8) {
-                error = "truncated AVI chunk header";
-                return false;
-            }
-            const auto name   = bytes_to_string(data.data() + offset, data.data() + offset + 4);
-            const size_t size = read_u32_le(data, offset + 4);
-            offset += 8;
-            if (size > end - offset || (size & 1u) > end - offset - size) {
-                error = "AVI chunk exceeds its container";
-                return false;
-            }
-            json entry = {{"entry_type", "chunk"}, {"name", name}, {"length", size}, {"metadata_like", info}};
-            if (name == "LIST") {
-                if (size < 4) {
-                    error = "truncated AVI LIST type";
-                    return false;
-                }
-                const auto type            = bytes_to_string(data.data() + offset, data.data() + offset + 4);
-                entry["data"]["list_type"] = type;
-                if (type == "INFO" && !info &&
-                    !parse_avi_chunks(data, offset + 4, offset + size, true, include_raw, result, error))
-                    return false;
-            } else if (info) {
-                entry["data"]["text"] = trim_trailing_nuls(bytes_to_string(data.data() + offset, data.data() + offset + size));
-            } else {
-                append_raw_preview(entry, data.data() + offset, size, include_raw);
-            }
-            result["entries"].push_back(std::move(entry));
-            offset += size + (size & 1u);
-        }
-        return true;
-    }
-
-    static bool parse_avi(const std::vector<uint8_t>& data, bool include_raw, json& result, std::string& error) {
-        result        = {{"format", "AVI"}, {"entries", json::array()}};
-        size_t offset = 0;
-        while (offset < data.size()) {
-            if (data.size() - offset < 12 || memcmp(data.data() + offset, "RIFF", 4) != 0 ||
-                memcmp(data.data() + offset + 8, offset == 0 ? "AVI " : "AVIX", 4) != 0) {
-                error = "invalid AVI RIFF header";
-                return false;
-            }
-            const size_t size = read_u32_le(data, offset + 4);
-            if (size < 4 || size > data.size() - offset - 8 ||
-                (size & 1u) > data.size() - offset - 8 - size) {
-                error = "AVI RIFF size exceeds file size";
-                return false;
-            }
-            if (!parse_avi_chunks(data, offset + 12, offset + 8 + size, false, include_raw, result, error))
-                return false;
-            offset += 8 + size + (size & 1u);
-        }
-        return true;
-    }
-
-    static bool read_ebml_uint(const std::vector<uint8_t>& data, size_t& offset, size_t end, bool id, uint64_t& value, bool& unknown) {
-        if (offset == end || data[offset] == 0)
-            return false;
-        uint8_t mask  = 0x80;
-        size_t length = 1;
-        while (!(data[offset] & mask)) {
-            mask >>= 1;
-            ++length;
-        }
-        if (length > (id ? 4u : 8u) || length > end - offset)
-            return false;
-        value = data[offset++] & (id ? 0xff : mask - 1);
-        for (size_t i = 1; i < length; ++i)
-            value = (value << 8) | data[offset++];
-        unknown = !id && value == (uint64_t(1) << (7 * length)) - 1;
-        return true;
-    }
-
-    static bool is_webm_segment_child(uint64_t id) {
-        return id == 0x114D9B74 || id == 0x1549A966 || id == 0x1654AE6B ||
-               id == 0x1F43B675 || id == 0x1C53BB6B || id == 0x1941A469 ||
-               id == 0x1043A770 || id == 0x1254C367;
-    }
-
-    static bool parse_webm_elements(const std::vector<uint8_t>& data, size_t& offset, size_t end, uint64_t parent, bool unknown_cluster, int depth, bool include_raw, json& result, std::string& error) {
-        if (depth > 16) {
-            error = "WebM tag nesting exceeds limit";
-            return false;
-        }
-        std::string tag_name;
-        std::string tag_text;
-        bool has_text = false;
-        while (offset < end) {
-            const auto start = offset;
-            uint64_t id, size;
-            bool unknown;
-            if (!read_ebml_uint(data, offset, end, true, id, unknown)) {
-                error = "invalid or truncated WebM element ID";
-                return false;
-            }
-            // An unknown-size Cluster ends at the next Segment-level element.
-            if (unknown_cluster && is_webm_segment_child(id)) {
-                offset = start;
-                return true;
-            }
-            if (!read_ebml_uint(data, offset, end, false, size, unknown) ||
-                (!unknown && size > end - offset)) {
-                error = "invalid or truncated WebM element size";
-                return false;
-            }
-            if (unknown && id != 0x18538067 && id != 0x1F43B675) {
-                error = "unsupported unknown-size WebM element";
-                return false;
-            }
-            const size_t stop    = unknown ? end : offset + static_cast<size_t>(size);
-            const bool container = (parent == 0 && (id == 0x1A45DFA3 || id == 0x18538067)) ||
-                                   (parent == 0x18538067 && (id == 0x1254C367 || (id == 0x1F43B675 && unknown))) ||
-                                   (parent == 0x1254C367 && id == 0x7373) ||
-                                   ((parent == 0x7373 || parent == 0x67C8) && id == 0x67C8);
-            if (parent == 0 && id == 0x18538067)
-                result["has_segment"] = true;
-            if (parent == 0x1A45DFA3 && id == 0x4282) {
-                const auto type = bytes_to_string(data.data() + offset, data.data() + stop);
-                if (type != "webm" && type != "matroska") {
-                    error = "EBML document is not WebM or Matroska";
-                    return false;
-                }
-                result["doc_type"] = type;
-                result["format"]   = type == "webm" ? "WEBM" : "MATROSKA";
-            }
-            if (parent == 0x67C8 && (id == 0x45A3 || id == 0x4487)) {
-                auto value = bytes_to_string(data.data() + offset, data.data() + stop);
-                if (id == 0x45A3)
-                    tag_name = std::move(value);
-                else {
-                    tag_text = std::move(value);
-                    has_text = true;
-                }
-            }
-            if (parent == 0 || parent == 0x18538067) {
-                std::ostringstream name;
-                name << "0x" << std::hex << std::uppercase << id;
-                json entry = {{"entry_type", "element"}, {"name", name.str()}, {"length", stop - offset}, {"metadata_like", false}};
-                if (unknown)
-                    entry["unknown_size"] = true;
-                append_raw_preview(entry, data.data() + offset, stop - offset, include_raw);
-                result["entries"].push_back(std::move(entry));
-            }
-            if (container) {
-                if (!parse_webm_elements(data, offset, stop, id, id == 0x1F43B675 && unknown,
-                                         depth + 1, include_raw, result, error))
-                    return false;
-            } else {
-                offset = stop;
-            }
-        }
-        if (parent == 0x67C8 && has_text) {
-            result["entries"].push_back({{"entry_type", "tag"}, {"name", tag_name}, {"metadata_like", true}, {"data", {{"text", tag_text}}}});
-        }
-        return true;
-    }
-
-    static bool parse_webm(const std::vector<uint8_t>& data, bool include_raw, json& result, std::string& error) {
-        result        = {{"format", "WEBM"}, {"entries", json::array()}};
-        size_t offset = 0;
-        if (!parse_webm_elements(data, offset, data.size(), 0, false, 0, include_raw, result, error))
-            return false;
-        if (!result.contains("doc_type") || !result.value("has_segment", false)) {
-            error = "missing WebM document type or Segment";
-            return false;
-        }
-        result.erase("has_segment");
-        return true;
-    }
-
    std::string abbreviate(const std::string& value, bool brief) {
        if (!brief || value.size() <= 240) {
            return value;
@@ -1323,7 +1115,7 @@ namespace {
            return;
        }
        if (value.is_primitive()) {
-            out << prefix << key << ": " << value.dump(-1, ' ', false, json::error_handler_t::replace) << "\n";
+            out << prefix << key << ": " << value.dump() << "\n";
            return;
        }
        if (value.is_array()) {
@@ -1332,7 +1124,7 @@ namespace {
                if (item.is_string()) {
                    out << prefix << "  - " << abbreviate(item.get<std::string>(), brief) << "\n";
                } else if (item.is_primitive()) {
-                    out << prefix << "  - " << item.dump(-1, ' ', false, json::error_handler_t::replace) << "\n";
+                    out << prefix << "  - " << item.dump() << "\n";
                } else {
                    out << prefix << "  -\n";
                    for (auto it = item.begin(); it != item.end(); ++it) {
@@ -1365,12 +1157,10 @@ namespace {
        }

        for (const auto& entry : entries) {
-            const auto type   = entry.value("entry_type", "");
-            const auto name   = entry.value("name", entry.value("marker", "unknown"));
-            const char* label = type == "chunk" ? "Chunk" : type == "tag"   ? "Tag"
-                                                        : type == "element" ? "Element"
-                                                                            : "Segment";
-            out << label << ": " << name << "\n";
+            const bool is_chunk    = entry.value("entry_type", "") == "chunk";
+            const std::string name = is_chunk ? entry.value("name", "unknown")
+                                              : entry.value("marker", "unknown");
+            out << (is_chunk ? "Chunk: " : "Segment: ") << name << "\n";
            for (auto it = entry.begin(); it != entry.end(); ++it) {
                if (it.key() == "entry_type" || it.key() == "name" || it.key() == "marker" ||
                    it.key() == "metadata_like") {
@@ -1420,13 +1210,7 @@ namespace {
            return parse_webp(data, include_raw, report, error);
        }

-        if (data.size() >= 12 && memcmp(data.data(), "RIFF", 4) == 0 &&
-            memcmp(data.data() + 8, "AVI ", 4) == 0)
-            return parse_avi(data, include_raw, report, error);
-        if (data.size() >= 4 && read_u32_be(data, 0) == 0x1A45DFA3)
-            return parse_webm(data, include_raw, report, error);
-
-        error = "unsupported media format; only PNG, JPEG, WebP, AVI, WebM, and Matroska are supported";
+        error = "unsupported image format; only PNG, JPEG, and WebP are supported";
        return false;
    }

@@ -1445,7 +1229,7 @@ bool print_image_metadata(const std::string& image_path,

    if (options.output_format == MetadataOutputFormat::JSON) {
        visible_report["file"] = image_path;
-        out << visible_report.dump(2, ' ', false, json::error_handler_t::replace) << "\n";
+        out << visible_report.dump(2) << "\n";
    } else {
        print_text_report(image_path, visible_report, options.include_structural, options.brief, out);
    }
@@ -3,7 +3,6 @@
 #include <time.h>
 #include <algorithm>
 #include <cctype>
-#include <climits>
 #include <filesystem>
 #include <functional>
 #include <iostream>
@@ -34,24 +33,22 @@ const char* previews_str[] = {
 std::regex format_specifier_regex("(?:[^%]|^)(?:%%)*(%\\d{0,3}d)");

 struct SDCliParams {
-    std::string input_frames;
-    int video_upscale_factor    = 0;
-    int vsr_window_size         = 16;
-    int vsr_temporal_chunk_size = 5;
-    SDMode mode                 = IMG_GEN;
-    std::string output_path     = "output.png";
-    int output_begin_idx        = -1;
-    int compression_quality     = 90;
+    SDMode mode             = IMG_GEN;
+    std::string output_path = "output.png";
+    int output_begin_idx    = -1;
+    int compression_quality = 90;
    std::string image_path;
    std::string metadata_format = "text";

    sd_log_level_t log_level = SD_LOG_INFO;
+    bool canny_preprocess    = false;
    bool convert_name        = false;

    preview_t preview_method = PREVIEW_NONE;
    int preview_interval     = 1;
    std::string preview_path = "preview.png";
    int preview_fps          = 16;
+    bool taesd_preview       = false;
    bool preview_noisy       = false;
    bool color               = false;
    bool metadata_raw        = false;
@@ -67,7 +64,6 @@ struct SDCliParams {
        ArgOptions options;

        options.string_options = {
-            {"", "--input-frames", "directory of ordered RGB frames for vid_upscale (use -i for a single image)", 0, &input_frames},
            {"-o",
             "--output",
             "path to write result image to. you can use printf-style %d format specifiers for image sequences (default: ./output.png) (eg. output_%03d.png). Single-file video outputs support .avi, .webm, and animated .webp",
@@ -75,7 +71,7 @@ struct SDCliParams {
             &output_path},
            {"",
             "--image",
-             "path to the image or video to inspect (for metadata mode)",
+             "path to the image to inspect (for metadata mode)",
             0,
             &image_path},
            {"",
@@ -96,9 +92,6 @@ struct SDCliParams {
        };

        options.int_options = {
-            {"", "--upscale-factor", "required positive video upscale factor (RelayVSR: 2 or 4; SeedVR2: integer)", &video_upscale_factor},
-            {"", "--vsr-window-size", "RelayVSR window including both endpoints (default: 16)", &vsr_window_size},
-            {"", "--vsr-temporal-chunk-size", "SeedVR2 input frames per independent chunk: 1 or 4n+1 (default: 5)", &vsr_temporal_chunk_size},
            {"",
             "--preview-interval",
             "preview interval: in each sampling pass, positive N updates every Nth denoiser step and -N previews only completed logical step N; 0 previews the final completed step of the first pass (base-resolution or high-noise). Default: 1",
@@ -114,6 +107,10 @@ struct SDCliParams {
        };

        options.bool_options = {
+            {"",
+             "--canny",
+             "apply canny preprocessor (edge detection)",
+             true, &canny_preprocess},
            {"",
             "--convert-name",
             "convert tensor name (for convert mode)",
@@ -122,6 +119,10 @@ struct SDCliParams {
             "--color",
             "colors the logging tags according to level",
             true, &color},
+            {"",
+             "--taesd-preview-only",
+             std::string("prevents usage of taesd for decoding the final image. (for use with --preview ") + previews_str[PREVIEW_TAE] + ")",
+             true, &taesd_preview},
            {"",
             "--preview-noisy",
             "enables previewing noisy inputs of the models rather than the denoised outputs",
@@ -192,14 +193,14 @@ struct SDCliParams {
            if (++index >= argc) {
                return -1;
            }
-            imatrix_in.push_back(argv_to_utf8(index, argv));
+            imatrix_in.push_back(argv[index]);
            return 1;
        };

        options.manual_options = {
            {"-M",
             "--mode",
-             "run mode, one of [img_gen, adetailer, vid_gen, vid_upscale, upscale, convert, metadata], default: img_gen",
+             "run mode, one of [img_gen, adetailer, vid_gen, upscale, convert, metadata], default: img_gen",
             on_mode_arg},
            {"",
             "--preview",
@@ -229,10 +230,6 @@ struct SDCliParams {
    }

    bool validate() {
-        if (mode == VID_UPSCALE && video_upscale_factor <= 0) {
-            LOG_ERROR("vid_upscale requires a positive --upscale-factor");
-            return false;
-        }
        if (mode != METADATA) {
            if (output_path.length() == 0) {
                LOG_ERROR("error: the following arguments are required: output_path");
@@ -240,7 +237,7 @@ struct SDCliParams {
            }
        } else {
            if (image_path.empty()) {
-                LOG_ERROR("error: metadata mode needs an image or video path (--image)");
+                LOG_ERROR("error: metadata mode needs an image path (--image)");
                return false;
            }
            if (metadata_format != "text" && metadata_format != "json") {
@@ -271,11 +268,13 @@ struct SDCliParams {
            << "  metadata_format: \"" << metadata_format << "\",\n"
            << "  log_level: " << log_level_name(log_level) << ",\n"
            << "  color: " << (color ? "true" : "false") << ",\n"
+            << "  canny_preprocess: " << (canny_preprocess ? "true" : "false") << ",\n"
            << "  convert_name: " << (convert_name ? "true" : "false") << ",\n"
            << "  preview_method: " << previews_str[preview_method] << ",\n"
            << "  preview_interval: " << preview_interval << ",\n"
            << "  preview_path: \"" << preview_path << "\",\n"
            << "  preview_fps: " << preview_fps << ",\n"
+            << "  taesd_preview: " << (taesd_preview ? "true" : "false") << ",\n"
            << "  preview_noisy: " << (preview_noisy ? "true" : "false") << ",\n"
            << "  imatrix_out: \"" << imatrix_out << "\",\n"
            << "  metadata_raw: " << (metadata_raw ? "true" : "false") << ",\n"
@@ -308,10 +307,6 @@ void parse_args(int argc, const char** argv, SDCliParams& cli_params, SDContextP
    log_level = cli_params.log_level;
    log_color = cli_params.color;

-    if (ctx_params.list_devices) {
-        exit(ctx_params.print_devices() ? 0 : 1);
-    }
-
    bool valid = cli_params.resolve_and_validate();
    if (valid && cli_params.mode != METADATA) {
        valid = ctx_params.resolve_and_validate(cli_params.mode) &&
@@ -333,15 +328,16 @@ void sd_log_cb(enum sd_log_level_t level, const char* log, void* data) {

 bool load_images_from_dir(const std::string dir,
                          std::vector<SDImageOwner>& images,
-                          int max_image_num = 0) {
-    const fs::path dir_path = fs::u8path(dir);
-    if (!fs::exists(dir_path) || !fs::is_directory(dir_path)) {
+                          int expected_width  = 0,
+                          int expected_height = 0,
+                          int max_image_num   = 0) {
+    if (!fs::exists(dir) || !fs::is_directory(dir)) {
        LOG_ERROR("'%s' is not a valid directory\n", dir.c_str());
        return false;
    }

    std::vector<fs::directory_entry> entries;
-    for (const auto& entry : fs::directory_iterator(dir_path)) {
+    for (const auto& entry : fs::directory_iterator(dir)) {
        if (entry.is_regular_file()) {
            entries.push_back(entry);
        }
@@ -349,20 +345,19 @@ bool load_images_from_dir(const std::string dir,

    std::sort(entries.begin(), entries.end(),
              [](const fs::directory_entry& a, const fs::directory_entry& b) {
-                  return a.path().filename().u8string() < b.path().filename().u8string();
+                  return a.path().filename().string() < b.path().filename().string();
              });

    for (const auto& entry : entries) {
-        std::string path = entry.path().u8string();
-        std::string ext  = entry.path().extension().u8string();
+        std::string path = entry.path().string();
+        std::string ext  = entry.path().extension().string();
        std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);

        if (ext == ".jpg" || ext == ".jpeg" || ext == ".png" || ext == ".bmp" || ext == ".webp") {
            LOG_VERBOSE("load image %zu from '%s'", images.size(), path.c_str());
            int width             = 0;
            int height            = 0;
-            int loaded_channel    = 0;
-            uint8_t* image_buffer = load_image_from_file(path.c_str(), width, height, loaded_channel, 0, 0);
+            uint8_t* image_buffer = load_image_from_file(path.c_str(), width, height, expected_width, expected_height);
            if (image_buffer == nullptr) {
                LOG_ERROR("load image from '%s' failed", path.c_str());
                return false;
@@ -370,7 +365,7 @@ bool load_images_from_dir(const std::string dir,

            images.emplace_back(sd_image_t{(uint32_t)width,
                                           (uint32_t)height,
-                                           (uint32_t)loaded_channel,
+                                           3,
                                           image_buffer});

            if (max_image_num > 0 && static_cast<int>(images.size()) >= max_image_num) {
@@ -409,19 +404,19 @@ void step_callback(int step, int frame_count, sd_image_t* image, bool is_noisy,
    // is_noisy is set to true if the preview corresponds to noisy latents, false if it's denoised latents
    // unused in this app, it will either be always noisy or always denoised here
    if (frame_count == 1) {
-        fs::path path = fs::u8path(cli_params->preview_path);
-        if (encoded_image_format_from_path(path.u8string()) == EncodedImageFormat::UNKNOWN)
+        fs::path path = cli_params->preview_path;
+        if (encoded_image_format_from_path(path.string()) == EncodedImageFormat::UNKNOWN)
            path += ".png";
-        if (std::regex_search(path.u8string(), format_specifier_regex))
-            path = fs::u8path(format_frame_idx(path.u8string(), continuous_preview_counter++));
-        if (!write_image_to_file(path.u8string(),
+        if (std::regex_search(path.string(), format_specifier_regex))
+            path = fs::path(format_frame_idx(path.string(), continuous_preview_counter++));
+        if (!write_image_to_file(path.string(),
                                 image->data,
                                 image->width,
                                 image->height,
                                 image->channel,
                                 "",
                                 cli_params->compression_quality)) {
-            LOG_ERROR("save preview image to '%s' failed", path.u8string().c_str());
+            LOG_ERROR("save preview image to '%s' failed", path.string().c_str());
        }
    } else {
        int fps = cli_params->preview_method == PREVIEW_PROJ ? cli_params->preview_fps / 4 : cli_params->preview_fps;
@@ -432,12 +427,12 @@ void step_callback(int step, int frame_count, sd_image_t* image, bool is_noisy,
 }

 static fs::path get_video_audio_sidecar_path(const SDCliParams& cli_params) {
-    fs::path out_path     = fs::u8path(cli_params.output_path);
+    fs::path out_path     = cli_params.output_path;
    fs::path base_path    = out_path;
    fs::path ext          = out_path.has_extension() ? out_path.extension() : fs::path{};
-    std::string ext_lower = ext.u8string();
+    std::string ext_lower = ext.string();
    std::transform(ext_lower.begin(), ext_lower.end(), ext_lower.begin(), ::tolower);
-    const EncodedImageFormat output_format = encoded_image_format_from_path(out_path.u8string());
+    const EncodedImageFormat output_format = encoded_image_format_from_path(out_path.string());
    if (!ext.empty()) {
        if (output_format == EncodedImageFormat::JPEG ||
            output_format == EncodedImageFormat::PNG ||
@@ -454,23 +449,22 @@ static fs::path get_video_audio_sidecar_path(const SDCliParams& cli_params) {
 bool save_results(const SDCliParams& cli_params,
                  const SDContextParams& ctx_params,
                  const SDGenerationParams& gen_params,
-                  SDImageVec& results,
+                  sd_image_t* results,
                  int num_results,
-                  const sd_audio_t* generated_audio,
-                  const std::string& video_info) {
-    if (results.empty() || num_results <= 0) {
+                  const sd_audio_t* generated_audio = nullptr) {
+    if (results == nullptr || num_results <= 0) {
        return false;
    }

    namespace fs      = std::filesystem;
-    fs::path out_path = fs::u8path(cli_params.output_path);
+    fs::path out_path = cli_params.output_path;

    if (!out_path.parent_path().empty()) {
        std::error_code ec;
        fs::create_directories(out_path.parent_path(), ec);
        if (ec) {
            LOG_ERROR("failed to create directory '%s': %s",
-                      out_path.parent_path().u8string().c_str(), ec.message().c_str());
+                      out_path.parent_path().string().c_str(), ec.message().c_str());
            return false;
        }
    }
@@ -478,9 +472,9 @@ bool save_results(const SDCliParams& cli_params,
    fs::path base_path = out_path;
    fs::path ext       = out_path.has_extension() ? out_path.extension() : fs::path{};

-    std::string ext_lower = ext.u8string();
+    std::string ext_lower = ext.string();
    std::transform(ext_lower.begin(), ext_lower.end(), ext_lower.begin(), ::tolower);
-    const EncodedImageFormat output_format = encoded_image_format_from_path(out_path.u8string());
+    const EncodedImageFormat output_format = encoded_image_format_from_path(out_path.string());
    if (!ext.empty()) {
        if (output_format == EncodedImageFormat::JPEG ||
            output_format == EncodedImageFormat::PNG ||
@@ -501,11 +495,13 @@ bool save_results(const SDCliParams& cli_params,
        if (!img.data)
            return false;

-        std::string params = gen_params.embed_image_metadata
-                                 ? results.generation_info(idx)
-                                 : "";
-        const bool ok      = write_image_to_file(path.u8string(), img.data, img.width, img.height, img.channel, params, cli_params.compression_quality);
-        LOG_INFO("save result image %d to '%s' (%s)", idx, path.u8string().c_str(), ok ? "success" : "failure");
+        int images_per_batch        = gen_params.batch_count > 0 ? std::max(1, num_results / gen_params.batch_count) : 1;
+        const int64_t metadata_seed = cli_params.mode == VID_GEN ? gen_params.seed : gen_params.seed + idx / images_per_batch;
+        std::string params          = gen_params.embed_image_metadata
+                                          ? get_image_params(ctx_params, gen_params, metadata_seed, cli_params.mode)
+                                          : "";
+        const bool ok               = write_image_to_file(path.string(), img.data, img.width, img.height, img.channel, params, cli_params.compression_quality);
+        LOG_INFO("save result image %d to '%s' (%s)", idx, path.string().c_str(), ok ? "success" : "failure");
        return ok;
    };

@@ -513,14 +509,14 @@ bool save_results(const SDCliParams& cli_params,
        if (generated_audio == nullptr) {
            return;
        }
-        if (write_wav_to_file(wav_path.u8string(),
+        if (write_wav_to_file(wav_path.string(),
                              generated_audio->data,
                              generated_audio->sample_count,
                              generated_audio->channels,
                              generated_audio->sample_rate)) {
-            LOG_INFO("save result audio to '%s'", wav_path.u8string().c_str());
+            LOG_INFO("save result audio to '%s'", wav_path.string().c_str());
        } else {
-            LOG_WARN("failed to save result audio to '%s'", wav_path.u8string().c_str());
+            LOG_WARN("failed to save result audio to '%s'", wav_path.string().c_str());
        }
    };

@@ -533,7 +529,7 @@ bool save_results(const SDCliParams& cli_params,
        pattern += ext;

        for (int i = 0; i < num_results; ++i) {
-            fs::path img_path = fs::u8path(format_frame_idx(pattern.u8string(), output_begin_idx + i));
+            fs::path img_path = format_frame_idx(pattern.string(), output_begin_idx + i);
            if (write_image(img_path, i)) {
                sucessful_reults++;
            }
@@ -542,22 +538,20 @@ bool save_results(const SDCliParams& cli_params,
        return sucessful_reults != 0;
    }

-    const bool video_upscale_output = cli_params.mode == VID_UPSCALE &&
-                                      (gen_params.init_image_path.empty() || ext_lower == ".avi" || ext_lower == ".webm");
-    if ((cli_params.mode == VID_GEN && num_results > 1) || video_upscale_output) {
+    if (cli_params.mode == VID_GEN && num_results > 1) {
        if (ext_lower != ".avi" && ext_lower != ".webp" && ext_lower != ".webm")
            ext = ".avi";
        std::string params = gen_params.embed_image_metadata
-                                 ? video_info
+                                 ? get_image_params(ctx_params, gen_params, gen_params.seed, cli_params.mode)
                                 : "";

        fs::path video_path = base_path;
        video_path += ext;
-        std::string final_ext_lower = ext.u8string();
+        std::string final_ext_lower = ext.string();
        std::transform(final_ext_lower.begin(), final_ext_lower.end(), final_ext_lower.begin(), ::tolower);
        const bool mux_audio = generated_audio != nullptr && (final_ext_lower == ".avi" || final_ext_lower == ".webm");
-        if (create_video_from_sd_images(video_path.u8string().c_str(), results.data(), num_results, gen_params.fps, cli_params.compression_quality, mux_audio ? generated_audio : nullptr, params) == 0) {
-            LOG_INFO("save result video to '%s'", video_path.u8string().c_str());
+        if (create_video_from_sd_images(video_path.string().c_str(), results, num_results, gen_params.fps, cli_params.compression_quality, mux_audio ? generated_audio : nullptr, params) == 0) {
+            LOG_INFO("save result video to '%s'", video_path.string().c_str());
            if (generated_audio != nullptr && !mux_audio) {
                fs::path wav_path = video_path;
                wav_path.replace_extension(".wav");
@@ -565,7 +559,7 @@ bool save_results(const SDCliParams& cli_params,
            }
            return true;
        } else {
-            LOG_ERROR("Failed to save result video to '%s'", video_path.u8string().c_str());
+            LOG_ERROR("Failed to save result video to '%s'", video_path.string().c_str());
            return false;
        }
    }
@@ -621,29 +615,30 @@ static bool apply_adetailer(sd_ctx_t* sd_ctx,
            continue;
        }
        sd_img_gen_params_t ad_generation_params = img_gen_params;
-        ad_generation_params.seed                = generation_seed(results.generation_info(i), img_gen_params.seed);
+        ad_generation_params.seed                = img_gen_params.seed + i;
        if (mode == IMG_GEN) {
            ad_generation_params.width    = 512;
            ad_generation_params.height   = 512;
            ad_generation_params.strength = 0.4f;
        }
-        sd_image_generation_result_t* detailed = nullptr;
+        sd_image_t* detailed_images = nullptr;
+        int detailed_count          = 0;
        if (!adetail_image(ad_ctx.get(),
                           sd_ctx,
                           results[i],
                           &ad_params,
                           &ad_generation_params,
-                           &detailed) ||
-            detailed == nullptr || detailed->count <= 0 || detailed->items[0].image.data == nullptr) {
-            free_sd_image_generation_result(detailed);
+                           &detailed_images,
+                           &detailed_count) ||
+            detailed_count <= 0 || detailed_images == nullptr || detailed_images[0].data == nullptr) {
+            free_sd_images(detailed_images, detailed_count);
            LOG_ERROR("ADetailer failed for image %d", i + 1);
            return false;
        }
-        results.append_generation_info(i, detailed->items[0].generation_info, ctx_params.lora_model_dir);
        free(results[i].data);
-        results[i]               = detailed->items[0].image;
-        detailed->items[0].image = {};
-        free_sd_image_generation_result(detailed);
+        results[i]         = detailed_images[0];
+        detailed_images[0] = {0, 0, 0, nullptr};
+        free_sd_images(detailed_images, detailed_count);
    }
    return true;
 }
@@ -656,11 +651,10 @@ int main(int argc, const char* argv[]) {

    SDCliParams cli_params;
    SDContextParams ctx_params;
-    ctx_params.conditioning_cache_size = 0;
    SDGenerationParams gen_params;

-    sd_set_log_callback(sd_log_cb, (void*)&cli_params);
    parse_args(argc, argv, cli_params, ctx_params, gen_params);
+    sd_set_log_callback(sd_log_cb, (void*)&cli_params);

    if (cli_params.mode == METADATA) {
        MetadataReadOptions options;
@@ -679,72 +673,6 @@ int main(int argc, const char* argv[]) {
        return 0;
    }

-    if (cli_params.mode == VID_UPSCALE) {
-        if (cli_params.input_frames.empty() == gen_params.init_image_path.empty()) {
-            LOG_ERROR("vid_upscale requires exactly one input: -i/--init-img or --input-frames");
-            return 1;
-        }
-        ctx_params.prepare_backend_assignments();
-        if (ctx_params.diffusion_model_path.empty() || ctx_params.vae_path.empty() || gen_params.fps <= 0) {
-            LOG_ERROR("vid_upscale requires --diffusion-model, --vae and positive --fps");
-            return 1;
-        }
-        std::vector<SDImageOwner> owned_frames;
-        if (!gen_params.init_image_path.empty()) {
-            owned_frames.emplace_back();
-            if (!load_sd_image_from_file(owned_frames.back().put(), gen_params.init_image_path.c_str(), 0, 0, 3)) {
-                LOG_ERROR("load image from '%s' failed", gen_params.init_image_path.c_str());
-                return 1;
-            }
-        } else if (!load_images_from_dir(cli_params.input_frames, owned_frames) || owned_frames.empty()) {
-            LOG_ERROR("vid_upscale input contains no readable frames");
-            return 1;
-        }
-        if (owned_frames.size() > size_t(INT_MAX))
-            return 1;
-        std::vector<sd_image_t> frames;
-        for (const auto& frame : owned_frames)
-            frames.push_back(frame.get());
-        sd_video_upscaler_ctx_params_t params;
-        sd_video_upscaler_ctx_params_init(&params);
-        params.diffusion_model_path  = ctx_params.diffusion_model_path.c_str();
-        params.vae_path              = ctx_params.vae_path.c_str();
-        params.upscale_factor        = cli_params.video_upscale_factor;
-        params.n_threads             = ctx_params.n_threads;
-        params.backend               = ctx_params.effective_backend.c_str();
-        params.params_backend        = ctx_params.effective_params_backend.c_str();
-        params.flash_attn_mode       = ctx_params.flash_attn_mode.c_str();
-        std::string conv_direct_mode = ctx_params.conv_direct_mode;
-        if (conv_direct_mode.empty()) {
-            conv_direct_mode = "auto";
-            if (ctx_params.diffusion_conv_direct)
-                conv_direct_mode += ",diffusion=on";
-            if (ctx_params.vae_conv_direct)
-                conv_direct_mode += ",vae=on";
-        }
-        params.conv_direct_mode = conv_direct_mode.c_str();
-        std::unique_ptr<video_upscaler_ctx_t, decltype(&free_video_upscaler_ctx)> upscaler(
-            new_video_upscaler_ctx(&params),
-            free_video_upscaler_ctx);
-        if (!upscaler)
-            return 1;
-        sd_video_generation_result_t* output = nullptr;
-        sd_video_upscale_params_t upscale_params;
-        sd_video_upscale_params_init(&upscale_params);
-        upscale_params.frames              = frames.data();
-        upscale_params.frame_count         = int(frames.size());
-        upscale_params.fps                 = gen_params.fps;
-        upscale_params.window_size         = cli_params.vsr_window_size;
-        upscale_params.temporal_chunk_size = cli_params.vsr_temporal_chunk_size;
-        upscale_params.seed                = gen_params.seed;
-        if (!upscale_video(upscaler.get(), &upscale_params, &output))
-            return 1;
-        std::string video_info = output->generation_info;
-        SDImageVec results;
-        results.adopt_video(output);
-        return save_results(cli_params, ctx_params, gen_params, results, int(results.size()), nullptr, video_info) ? 0 : 1;
-    }
-
    if (!gen_params.ad_model_path.empty() && cli_params.mode != IMG_GEN && cli_params.mode != ADETAILER) {
        LOG_ERROR("--ad-model is only supported in image generation and adetailer modes");
        return 1;
@@ -774,12 +702,12 @@ int main(int argc, const char* argv[]) {

    LOG_VERBOSE("version: %s", version_string().c_str());
    LOG_VERBOSE("%s", sd_get_system_info());
-    LOG_DEBUG("%s", cli_params.to_string().c_str());
-    LOG_DEBUG("%s", ctx_params.to_string().c_str());
-    LOG_DEBUG("%s", gen_params.to_string().c_str());
+    LOG_VERBOSE("%s", cli_params.to_string().c_str());
+    LOG_VERBOSE("%s", ctx_params.to_string().c_str());
+    LOG_VERBOSE("%s", gen_params.to_string().c_str());

    if (!cli_params.imatrix_out.empty()) {
-        if (fs::exists(fs::u8path(cli_params.imatrix_out)) &&
+        if (fs::exists(cli_params.imatrix_out) &&
            std::find(cli_params.imatrix_in.begin(), cli_params.imatrix_in.end(), cli_params.imatrix_out) == cli_params.imatrix_in.end()) {
            LOG_WARN("imatrix file '%s' already exists and will be overwritten", cli_params.imatrix_out.c_str());
        }
@@ -794,15 +722,6 @@ int main(int argc, const char* argv[]) {
    }

    if (cli_params.mode == CONVERT) {
-        std::vector<sd_lora_t> loras;
-        loras.reserve(gen_params.lora_map.size() + gen_params.high_noise_lora_map.size());
-        for (const auto& [path, multiplier] : gen_params.lora_map) {
-            loras.push_back({false, multiplier, path.c_str()});
-        }
-        for (const auto& [path, multiplier] : gen_params.high_noise_lora_map) {
-            loras.push_back({true, multiplier, path.c_str()});
-        }
-
        bool success = convert_with_components(ctx_params.model_path.c_str(),
                                               ctx_params.clip_l_path.c_str(),
                                               ctx_params.clip_g_path.c_str(),
@@ -813,9 +732,7 @@ int main(int argc, const char* argv[]) {
                                               ctx_params.wtype,
                                               ctx_params.tensor_type_rules.c_str(),
                                               cli_params.convert_name,
-                                               ctx_params.n_threads,
-                                               loras.empty() ? nullptr : loras.data(),
-                                               (int)loras.size());
+                                               ctx_params.n_threads);
        if (!success) {
            LOG_ERROR("convert '%s'/'%s' to '%s' failed",
                      ctx_params.model_path.c_str(),
@@ -833,8 +750,16 @@ int main(int argc, const char* argv[]) {

    auto load_image_and_update_size = [&](const std::string& path,
                                          SDImageOwner& image,
+                                          bool resize_image    = true,
                                          int expected_channel = 3) -> bool {
-        if (!load_sd_image_from_file(image.put(), path.c_str(), 0, 0, expected_channel)) {
+        int expected_width  = 0;
+        int expected_height = 0;
+        if (resize_image && gen_params.width_and_height_are_set()) {
+            expected_width  = gen_params.width;
+            expected_height = gen_params.height;
+        }
+
+        if (!load_sd_image_from_file(image.put(), path.c_str(), expected_width, expected_height, expected_channel)) {
            LOG_ERROR("load image from '%s' failed", path.c_str());
            return false;
        }
@@ -856,8 +781,7 @@ int main(int argc, const char* argv[]) {
    };

    if (gen_params.init_image_path.size() > 0) {
-        const bool native_init = cli_params.mode == IMG_GEN || cli_params.mode == ADETAILER;
-        if (!load_image_and_update_size(gen_params.init_image_path, gen_params.init_image, native_init ? 0 : 3)) {
+        if (!load_image_and_update_size(gen_params.init_image_path, gen_params.init_image)) {
            return 1;
        }
    }
@@ -871,8 +795,8 @@ int main(int argc, const char* argv[]) {
    if (gen_params.ref_image_paths.size() > 0) {
        gen_params.ref_images.clear();
        for (auto& path : gen_params.ref_image_paths) {
-            SDImageOwner ref_image({0, 0, 0, nullptr});
-            if (!load_image_and_update_size(path, ref_image, 0)) {
+            SDImageOwner ref_image({0, 0, 3, nullptr});
+            if (!load_image_and_update_size(path, ref_image, false)) {
                return 1;
            }
            gen_params.ref_images.push_back(std::move(ref_image));
@@ -913,22 +837,41 @@ int main(int argc, const char* argv[]) {
    if (gen_params.mask_image_path.size() > 0) {
        if (!load_sd_image_from_file(gen_params.mask_image.put(),
                                     gen_params.mask_image_path.c_str(),
-                                     0,
-                                     0,
+                                     gen_params.get_resolved_width(),
+                                     gen_params.get_resolved_height(),
                                     1)) {
            LOG_ERROR("load image from '%s' failed", gen_params.mask_image_path.c_str());
            return 1;
        }
+    } else {
+        sd_image_t generated_mask = {0, 0, 1, nullptr};
+        generated_mask.data       = (uint8_t*)malloc(gen_params.get_resolved_width() * gen_params.get_resolved_height());
+        if (generated_mask.data == nullptr) {
+            LOG_ERROR("malloc mask image failed");
+            return 1;
+        }
+        generated_mask.width  = gen_params.get_resolved_width();
+        generated_mask.height = gen_params.get_resolved_height();
+        memset(generated_mask.data, 255, gen_params.get_resolved_width() * gen_params.get_resolved_height());
+        gen_params.mask_image.reset(generated_mask);
    }

    if (gen_params.control_image_path.size() > 0) {
        if (!load_sd_image_from_file(gen_params.control_image.put(),
                                     gen_params.control_image_path.c_str(),
-                                     0,
-                                     0)) {
+                                     gen_params.get_resolved_width(),
+                                     gen_params.get_resolved_height())) {
            LOG_ERROR("load image from '%s' failed", gen_params.control_image_path.c_str());
            return 1;
        }
+        if (cli_params.canny_preprocess) {  // apply preprocessor
+            preprocess_canny(gen_params.control_image.get(),
+                             0.08f,
+                             0.08f,
+                             0.8f,
+                             1.0f,
+                             false);
+        }
    }

    if (gen_params.ip_adapter_image_path.size() > 0) {
@@ -945,6 +888,8 @@ int main(int argc, const char* argv[]) {
        gen_params.control_frames.clear();
        if (!load_images_from_dir(gen_params.control_video_path,
                                  gen_params.control_frames,
+                                  gen_params.get_resolved_width(),
+                                  gen_params.get_resolved_height(),
                                  gen_params.video_frames)) {
            return 1;
        }
@@ -953,17 +898,19 @@ int main(int argc, const char* argv[]) {
    if (!gen_params.pm_id_images_dir.empty()) {
        gen_params.pm_id_images.clear();
        if (!load_images_from_dir(gen_params.pm_id_images_dir,
-                                  gen_params.pm_id_images)) {
+                                  gen_params.pm_id_images,
+                                  0,
+                                  0,
+                                  0)) {
            return 1;
        }
    }

-    sd_ctx_params_t sd_ctx_params = ctx_params.to_sd_ctx_params_t();
+    sd_ctx_params_t sd_ctx_params = ctx_params.to_sd_ctx_params_t(cli_params.taesd_preview);

    SDImageVec results;
    int num_results             = 0;
    sd_audio_t* generated_audio = nullptr;
-    std::string video_info;

    if (cli_params.mode == UPSCALE) {
        num_results = 1;
@@ -976,6 +923,18 @@ int main(int argc, const char* argv[]) {
            return 1;
        }

+        if (gen_params.sample_params.sample_method == SAMPLE_METHOD_COUNT) {
+            gen_params.sample_params.sample_method = sd_get_default_sample_method(sd_ctx.get());
+        }
+
+        if (gen_params.high_noise_sample_params.sample_method == SAMPLE_METHOD_COUNT) {
+            gen_params.high_noise_sample_params.sample_method = sd_get_default_sample_method(sd_ctx.get());
+        }
+
+        if (gen_params.sample_params.scheduler == SCHEDULER_COUNT) {
+            gen_params.sample_params.scheduler = sd_get_default_scheduler(sd_ctx.get(), gen_params.sample_params.sample_method);
+        }
+
        sd_img_gen_params_t img_gen_params{};
        const bool use_img_gen_params = cli_params.mode == IMG_GEN || cli_params.mode == ADETAILER;
        if (use_img_gen_params) {
@@ -983,24 +942,23 @@ int main(int argc, const char* argv[]) {
        }

        if (cli_params.mode == IMG_GEN) {
-            sd_image_generation_result_t* generated = nullptr;
-            generate_image(sd_ctx.get(), &img_gen_params, &generated);
-            results.adopt(generated, ctx_params.lora_model_dir);
-            num_results = results.count();
+            sd_image_t* generated_images = nullptr;
+            if (!generate_image(sd_ctx.get(), &img_gen_params, &generated_images, &num_results)) {
+                generated_images = nullptr;
+                num_results      = 0;
+            }
+            results.adopt(generated_images, num_results);
        } else if (cli_params.mode == ADETAILER) {
            num_results = 1;
            results.push_back(gen_params.init_image.release());
        } else if (cli_params.mode == VID_GEN) {
-            sd_vid_gen_params_t vid_gen_params      = gen_params.to_sd_vid_gen_params_t();
-            sd_video_generation_result_t* generated = nullptr;
-            if (generate_video(sd_ctx.get(), &vid_gen_params, &generated)) {
-                gen_params.fps = cli_params.preview_fps = generated->fps;
-                generated_audio                         = generated->audio;
-                generated->audio                        = nullptr;
-                num_results                             = generated->frame_count;
-                results.adopt_video(generated, ctx_params.lora_model_dir);
-                video_info = results.generation_info(0);
+            sd_vid_gen_params_t vid_gen_params = gen_params.to_sd_vid_gen_params_t();
+            sd_image_t* generated_video        = nullptr;
+            if (!generate_video(sd_ctx.get(), &vid_gen_params, &generated_video, &num_results, &generated_audio, &cli_params.preview_fps)) {
+                generated_video = nullptr;
            }
+            gen_params.fps = cli_params.preview_fps;
+            results.adopt(generated_video, num_results);
        }

        if (!results) {
@@ -1040,20 +998,21 @@ int main(int argc, const char* argv[]) {
                SDImageOwner current_image(results[i]);
                results[i] = {0, 0, 0, nullptr};
                for (int u = 0; u < gen_params.upscale_repeats; ++u) {
-                    sd_image_generation_result_t* upscaled = nullptr;
-                    bool upscale_ok                        = upscale(upscaler_ctx.get(),
-                                                                     current_image.get(),
-                                                                     upscale_factor,
-                                                                     &upscaled);
-                    if (!upscale_ok || !upscaled || upscaled->count <= 0 || upscaled->items[0].image.data == nullptr) {
-                        free_sd_image_generation_result(upscaled);
+                    sd_image_t* upscaled_images = nullptr;
+                    int upscaled_count          = 0;
+                    bool upscale_ok             = upscale(upscaler_ctx.get(),
+                                                          current_image.get(),
+                                                          upscale_factor,
+                                                          &upscaled_images,
+                                                          &upscaled_count);
+                    if (!upscale_ok || upscaled_count <= 0 || upscaled_images[0].data == nullptr) {
+                        free_sd_images(upscaled_images, upscaled_count);
                        LOG_ERROR("upscale failed");
                        break;
                    }
-                    results.append_generation_info(i, upscaled->items[0].generation_info);
-                    sd_image_t upscaled_image = upscaled->items[0].image;
-                    upscaled->items[0].image  = {};
-                    free_sd_image_generation_result(upscaled);
+                    sd_image_t upscaled_image = upscaled_images[0];
+                    upscaled_images[0]        = {0, 0, 0, nullptr};
+                    free_sd_images(upscaled_images, upscaled_count);
                    current_image.reset(upscaled_image);
                }
                results[i] = current_image.release();  // Set the final upscaled image as the result
@@ -1061,9 +1020,7 @@ int main(int argc, const char* argv[]) {
        }
    }

-    if (!video_info.empty() && results.count() > 0)
-        video_info = results.generation_info(0);
-    if (!save_results(cli_params, ctx_params, gen_params, results, num_results, generated_audio, video_info)) {
+    if (!save_results(cli_params, ctx_params, gen_params, results.data(), num_results, generated_audio)) {
        free_sd_audio(generated_audio);
        return 1;
    }
@@ -1,8 +1,6 @@
 #ifndef __EXAMPLES_COMMON_COMMON_H__
 #define __EXAMPLES_COMMON_COMMON_H__

-#include "generation_metadata.h"
-
 #include <cmath>
 #include <cstdint>
 #include <functional>
@@ -14,15 +12,11 @@
 #include "resource_owners.hpp"
 #include "stable-diffusion.h"

-// The command-line argument at `index` as UTF-8 (on Windows, read again from
-// the UTF-16 command line: argv itself is in the ANSI code page).
-std::string argv_to_utf8(int index, const char** argv);
-
 #define SAFE_STR(s) ((s) ? (s) : "")
 #define BOOL_STR(b) ((b) ? "true" : "false")

 extern const char* const modes_str[];
-#define SD_ALL_MODES_STR "img_gen, adetailer, vid_gen, convert, upscale, metadata, vid_upscale"
+#define SD_ALL_MODES_STR "img_gen, adetailer, vid_gen, convert, upscale, metadata"

 enum SDMode {
    IMG_GEN,
@@ -31,7 +25,6 @@ enum SDMode {
    CONVERT,
    UPSCALE,
    METADATA,
-    VID_UPSCALE,
    MODE_COUNT
 };

@@ -123,8 +116,7 @@ bool decode_base64_image(const std::string& encoded_input,
                         SDImageOwner& out_image);

 struct SDContextParams {
-    int n_threads               = -1;
-    int conditioning_cache_size = 4;
+    int n_threads = -1;
    std::string model_path;
    std::string clip_l_path;
    std::string clip_g_path;
@@ -164,26 +156,25 @@ struct SDContextParams {
    bool disable_prefetch          = false;
    bool disable_segmented_compute = false;
    bool eager_load                = false;
-    bool batched_cfg               = true;
    std::string backend;
    std::string params_backend;
    std::string split_mode;
    std::string model_args;
    bool auto_fit = true;
    std::string rpc_servers;
-    bool list_devices = false;
    std::string effective_backend;
    std::string effective_params_backend;
-    bool enable_mmap            = false;
-    bool control_net_cpu        = false;
-    bool clip_on_cpu            = false;
-    bool vae_on_cpu             = false;
-    std::string flash_attn_mode = "auto";
-    std::string conv_direct_mode;  // Empty selects auto while honoring legacy convolution flags.
+    bool enable_mmap           = false;
+    bool control_net_cpu       = false;
+    bool clip_on_cpu           = false;
+    bool vae_on_cpu            = false;
+    bool flash_attn            = false;
+    bool diffusion_flash_attn  = false;
    bool sage_attn             = false;
+    bool sol_attn              = false;
+    float sol_attn_tau         = 1.f;
    bool diffusion_conv_direct = false;
    bool vae_conv_direct       = false;
-    bool taesd_preview_only    = false;

    prediction_t prediction           = PREDICTION_COUNT;
    lora_apply_mode_t lora_apply_mode = LORA_APPLY_AUTO;
@@ -194,14 +185,13 @@ struct SDContextParams {

    float flow_shift = INFINITY;
    ArgOptions get_options();
-    bool print_devices() const;
    void build_embedding_map();
    void prepare_backend_assignments();
    bool resolve(SDMode mode);
    bool validate(SDMode mode);
    bool resolve_and_validate(SDMode mode);
    std::string to_string() const;
-    sd_ctx_params_t to_sd_ctx_params_t();
+    sd_ctx_params_t to_sd_ctx_params_t(bool taesd_preview);
 };

 struct SDGenerationParams {
@@ -212,23 +202,23 @@ struct SDGenerationParams {
    std::string ad_prompt;
    std::string ad_negative_prompt;
    std::string extra_ad_args;
-    int clip_skip             = -1;  // <= 0 represents unspecified
-    int width                 = -1;
-    int height                = -1;
-    int batch_count           = 1;
-    int qwen_image_layers     = 3;
-    int64_t seed              = 42;
-    float strength            = 0.75f;
-    float control_strength    = 0.9f;
-    float ip_adapter_strength = 1.0f;
-    bool increase_ref_index   = false;
-    bool embed_image_metadata = true;
+    int clip_skip              = -1;  // <= 0 represents unspecified
+    int width                  = -1;
+    int height                 = -1;
+    int batch_count            = 1;
+    int qwen_image_layers      = 3;
+    int64_t seed               = 42;
+    float strength             = 0.75f;
+    float control_strength     = 0.9f;
+    float ip_adapter_strength  = 1.0f;
+    bool auto_resize_ref_image = true;
+    bool increase_ref_index    = false;
+    bool embed_image_metadata  = true;

    std::string init_image_path;
    std::string end_image_path;
    std::string mask_image_path;
    std::string control_image_path;
-    std::string control_type;
    std::string ip_adapter_image_path;
    std::vector<std::string> ref_image_paths;
    std::vector<std::string> ref_video_paths;
@@ -259,7 +249,6 @@ struct SDGenerationParams {
    std::string extra_tiling_args;

    std::string ref_image_args;
-    std::string image_preprocess;

    std::string pm_id_images_dir;
    std::string pm_id_embed_path;
@@ -293,7 +282,6 @@ struct SDGenerationParams {
    std::string prompt_with_lora;  // for metadata record only
    std::vector<sd_lora_t> lora_vec;
    sd_hires_upscaler_t resolved_hires_upscaler;
-    sd_control_type_t resolved_control_type = SD_CONTROL_TYPE_NONE;

    // Owned execution payload.
    SDImageOwner init_image;
@@ -324,14 +312,13 @@ struct SDGenerationParams {
    ArgOptions get_options();
    bool from_json_str(const std::string& json_str,
                       const std::function<std::string(const std::string&)>& lora_path_resolver = {});
-    bool parse_image_preprocess_json(const std::string& json_str);
    bool initialize_cache_params();
-    bool extract_and_remove_lora(const std::string& lora_model_dir);
+    void extract_and_remove_lora(const std::string& lora_model_dir);
    bool width_and_height_are_set() const;
    void set_width_and_height_if_unset(int w, int h);
    int get_resolved_width() const;
    int get_resolved_height() const;
-    bool resolve(const std::string& lora_model_dir, const std::string& hires_upscalers_dir, bool strict = false, bool validate_missing_loras = false);
+    bool resolve(const std::string& lora_model_dir, const std::string& hires_upscalers_dir, bool strict = false);
    bool validate(SDMode mode);
    bool resolve_and_validate(SDMode mode,
                              const std::string& lora_model_dir,
@@ -343,4 +330,13 @@ struct SDGenerationParams {
 };

 std::string version_string();
+std::string build_sdcpp_image_metadata_json(const SDContextParams& ctx_params,
+                                            const SDGenerationParams& gen_params,
+                                            int64_t seed,
+                                            SDMode mode = IMG_GEN);
+std::string get_image_params(const SDContextParams& ctx_params,
+                             const SDGenerationParams& gen_params,
+                             int64_t seed,
+                             SDMode mode = IMG_GEN);
+
 #endif  // __EXAMPLES_COMMON_COMMON_H__
@@ -1,164 +0,0 @@
-#include "generation_metadata.h"
-
-#include <cctype>
-#include <filesystem>
-#include <map>
-
-#include "json.hpp"
-
-static void relativize_lora_paths(nlohmann::json& info, const std::filesystem::path& root) {
-    if (info.contains("loras")) {
-        for (auto& lora : info["loras"]) {
-            const auto path = lora.value("path", "");
-            if (path.empty())
-                continue;
-            std::error_code error;
-            const auto absolute = std::filesystem::absolute(std::filesystem::u8path(path), error);
-            if (error)
-                continue;
-            const auto relative = absolute.lexically_normal().lexically_relative(root);
-            if (!relative.empty())
-                lora["path"] = relative.generic_u8string();
-        }
-    }
-    if (info.contains("inpaint_params"))
-        relativize_lora_paths(info["inpaint_params"], root);
-    if (info.contains("postprocess")) {
-        for (auto& operation : info["postprocess"])
-            relativize_lora_paths(operation, root);
-    }
-}
-
-std::string generation_info_with_relative_lora_paths(const std::string& info, const std::string& lora_model_dir) {
-    if (info.empty() || lora_model_dir.empty())
-        return info;
-    std::error_code error;
-    const auto root = std::filesystem::absolute(std::filesystem::u8path(lora_model_dir), error);
-    if (error)
-        return info;
-    auto result = nlohmann::json::parse(info);
-    relativize_lora_paths(result, root.lexically_normal());
-    return result.dump();
-}
-
-std::string merge_generation_info(const std::string& source, const std::string& operation) {
-    if (source.empty())
-        return operation;
-    auto result     = nlohmann::json::parse(source);
-    const auto next = nlohmann::json::parse(operation);
-    result["postprocess"].push_back(next);
-    return result.dump();
-}
-
-int64_t generation_seed(const std::string& info, int64_t fallback) {
-    if (info.empty())
-        return fallback;
-    const auto value   = nlohmann::json::parse(info);
-    const auto& params = value.contains("inpaint_params") ? value.at("inpaint_params") : value;
-    return params.contains("seed") ? std::stoll(params.at("seed").get<std::string>()) : fallback;
-}
-
-std::string generation_info_to_webui(const std::string& text) {
-    using nlohmann::json;
-    if (text.empty())
-        return "";
-    const auto info = json::parse(text);
-    if (info.value("schema", "") != "sdcpp.generation/v1")
-        return "";
-    // Standalone postprocessing has no source diffusion settings to import.
-    if (!info.contains("sample_params"))
-        return "";
-    const auto& sampling = info.at("sample_params");
-    std::string result   = info.value("prompt", "");
-    if (info.contains("loras")) {
-        bool prompt_trimmed = false;
-        for (const auto& lora : info.at("loras")) {
-            // WebUI tags cannot restrict a LoRA to the high-noise model.
-            if (lora.value("is_high_noise", false))
-                continue;
-            auto name            = lora.at("path").get<std::string>();
-            const auto separator = name.find_last_of("/\\");
-            if (separator != std::string::npos)
-                name.erase(0, separator + 1);
-            const auto extension = name.find_last_of('.');
-            if (extension != std::string::npos && extension > 0)
-                name.erase(extension);
-            if (name.empty() || name.find_first_of(":<>\r\n") != std::string::npos)
-                continue;
-            if (!result.empty()) {
-                if (!prompt_trimmed) {
-                    while (!result.empty() && std::isspace(static_cast<unsigned char>(result.back())))
-                        result.pop_back();
-                    prompt_trimmed = true;
-                }
-                if (!result.empty())
-                    result += " ";
-            }
-            result += "<lora:" + name + ":" + lora.at("multiplier").dump() + ">";
-        }
-    }
-    const auto negative = info.value("negative_prompt", "");
-    if (!negative.empty())
-        result += "\nNegative prompt: " + negative;
-    result += "\n";
-    bool first       = true;
-    const auto field = [&](const char* key, const json& value) {
-        std::string v = value.is_string() ? value.get<std::string>() : value.dump();
-        if (v.find_first_of(",\n\r:\"") != std::string::npos)
-            v = json(v).dump(-1, ' ', false);
-        if (!first)
-            result += ", ";
-        first = false;
-        result += std::string(key) + ": " + v;
-    };
-    field("Steps", sampling.contains("custom_sigmas") ? json(sampling.at("custom_sigmas").size() - 1) : sampling.at("sample_steps"));
-    static const std::map<std::string, std::string> samplers = {
-        {"euler", "Euler"}, {"euler_a", "Euler a"}, {"heun", "Heun"}, {"dpm2", "DPM2"}, {"dpm++2s_a", "DPM++ 2S a"}, {"dpm++2m", "DPM++ 2M"}, {"dpm++2m_sde", "DPM++ 2M SDE"}, {"lcm", "LCM"}};
-    const auto method = sampling.at("sample_method").get<std::string>();
-    auto mapped       = samplers.find(method);
-    field(mapped != samplers.end() ? "Sampler" : "SDCPP Sampler", mapped != samplers.end() ? mapped->second : method);
-    static const std::map<std::string, std::string> schedules = {
-        {"discrete", "Normal"}, {"karras", "Karras"}, {"exponential", "Exponential"}, {"sgm_uniform", "SGM Uniform"}, {"simple", "Simple"}, {"ddim_uniform", "DDIM"}, {"beta", "Beta"}};
-    if (sampling.contains("scheduler")) {
-        const auto scheduler = sampling.at("scheduler").get<std::string>();
-        auto mapped_schedule = schedules.find(scheduler);
-        field(mapped_schedule != schedules.end() ? "Schedule type" : "SDCPP Schedule", mapped_schedule != schedules.end() ? mapped_schedule->second : scheduler);
-    }
-    field("CFG scale", sampling.at("guidance").at("txt_cfg"));
-    field("Seed", info.at("seed"));
-    field("Size", std::to_string(info.at("width").get<int>()) + "x" + std::to_string(info.at("height").get<int>()));
-    if (info.contains("models")) {
-        const auto& models   = info.at("models");
-        auto model           = models.value("diffusion_model", models.value("model", ""));
-        const auto extension = model.find_last_of('.');
-        if (extension != std::string::npos && extension > 0)
-            model.erase(extension);
-        if (!model.empty())
-            field("Model", model);
-        if (models.contains("vae"))
-            field("VAE", models.at("vae"));
-    }
-    if (sampling.contains("eta"))
-        field("Eta", sampling.at("eta"));
-    if (info.value("clip_skip", 0) > 0)
-        field("Clip skip", info.at("clip_skip"));
-    if (info.contains("strength"))
-        field("Denoising strength", info.at("strength"));
-    if (info.contains("hires") && info.at("hires").value("enabled", false)) {
-        const auto& hires = info.at("hires");
-        field("Hires resize", std::to_string(hires.at("target_width").get<int>()) + "x" + std::to_string(hires.at("target_height").get<int>()));
-        if (hires.value("steps", 0) > 0)
-            field("Hires steps", hires.at("steps"));
-        field("Denoising strength", hires.at("denoising_strength"));
-        field("Hires upscaler", hires.at("upscaler"));
-    }
-    field("Version", "stable-diffusion.cpp");
-    return result;
-}
-
-std::string generation_info_to_image_metadata(const std::string& info) {
-    if (info.empty())
-        return "";
-    const auto parameters = generation_info_to_webui(info);
-    return parameters.empty() ? info : parameters + ", SDCPP: " + info;
-}
@@ -1,13 +0,0 @@
-#ifndef __EXAMPLES_COMMON_GENERATION_METADATA_H__
-#define __EXAMPLES_COMMON_GENERATION_METADATA_H__
-
-#include <cstdint>
-#include <string>
-
-std::string merge_generation_info(const std::string& source, const std::string& operation);
-std::string generation_info_with_relative_lora_paths(const std::string& info, const std::string& lora_model_dir);
-int64_t generation_seed(const std::string& info, int64_t fallback = -1);
-std::string generation_info_to_webui(const std::string& text);
-std::string generation_info_to_image_metadata(const std::string& info);
-
-#endif  // __EXAMPLES_COMMON_GENERATION_METADATA_H__
@@ -91,34 +91,34 @@ void log_print(enum sd_log_level_t level, const char* log, sd_log_level_t min_le
    switch (level) {
        case SD_LOG_DEBUG:
            tag_color = 37;
-            level_str = "D";
+            level_str = "DEBUG";
            break;
        case SD_LOG_VERBOSE:
            tag_color = 37;
-            level_str = "V";
+            level_str = "VERBOSE";
            break;
        case SD_LOG_INFO:
            tag_color = 34;
-            level_str = "I";
+            level_str = "INFO";
            break;
        case SD_LOG_WARN:
            tag_color = 35;
-            level_str = "W";
+            level_str = "WARN";
            break;
        case SD_LOG_ERROR:
            tag_color = 31;
-            level_str = "E";
+            level_str = "ERROR";
            break;
        default:
            tag_color = 33;
-            level_str = "?";
+            level_str = "?????";
            break;
    }

    if (color) {
-        fprintf(out_stream, "\033[%d;1m[%s]\033[0m ", tag_color, level_str);
+        fprintf(out_stream, "\033[%d;1m[%-7s]\033[0m ", tag_color, level_str);
    } else {
-        fprintf(out_stream, "[%s] ", level_str);
+        fprintf(out_stream, "[%-7s] ", level_str);
    }
    fflush(out_stream);
    print_utf8(out_stream, log);
@@ -131,15 +131,18 @@ void example_log_printf(sd_log_level_t level, const char* file, int line, const
    va_list args;
    va_start(args, format);

-    char log_buffer[LOG_BUFFER_SIZE + 1] = {};
-    vsnprintf(log_buffer, sizeof(log_buffer), format, args);
-    va_end(args);
+    static char log_buffer[LOG_BUFFER_SIZE + 1];
+    int written = snprintf(log_buffer, LOG_BUFFER_SIZE, "%s:%-4d - ", sd_basename(file).c_str(), line);

-    std::string message = log_buffer;
-    while (!message.empty() && (message.back() == '\n' || message.back() == '\r')) {
-        message.pop_back();
+    if (written >= 0 && written < static_cast<int>(LOG_BUFFER_SIZE)) {
+        vsnprintf(log_buffer + written, LOG_BUFFER_SIZE - written, format, args);
+    }
+    size_t len = strlen(log_buffer);
+    if (len == 0 || log_buffer[len - 1] != '\n') {
+        strncat(log_buffer, "\n", LOG_BUFFER_SIZE - len);
    }
-    message += " --- " + sd_basename(file) + ":" + std::to_string(line) + '\n';

-    log_print(level, message.c_str(), log_level, log_color);
+    log_print(level, log_buffer, log_level, log_color);
+
+    va_end(args);
 }
@@ -1,7 +1,5 @@
 #include "media_io.h"
-#include "generation_metadata.h"
 #include "log.h"
-#include "media_metadata.h"
 #include "resource_owners.hpp"

 #include <algorithm>
@@ -17,12 +15,10 @@

 #define STB_IMAGE_IMPLEMENTATION
 #define STB_IMAGE_STATIC
-#define STBI_WINDOWS_UTF8
 #include "stb_image.h"

 #define STB_IMAGE_WRITE_IMPLEMENTATION
 #define STB_IMAGE_WRITE_STATIC
-#define STBIW_WINDOWS_UTF8
 #include "stb_image_write.h"

 #define STB_IMAGE_RESIZE_IMPLEMENTATION
@@ -151,7 +147,7 @@ private:
 #endif

 bool read_binary_file_bytes(const char* path, std::vector<uint8_t>& data) {
-    std::ifstream fin(fs::u8path(path), std::ios::binary);
+    std::ifstream fin(fs::path(path), std::ios::binary);
    if (!fin) {
        return false;
    }
@@ -174,7 +170,7 @@ bool read_binary_file_bytes(const char* path, std::vector<uint8_t>& data) {
 }

 bool write_binary_file_bytes(const std::string& path, const std::vector<uint8_t>& data) {
-    std::ofstream fout(fs::u8path(path), std::ios::binary);
+    std::ofstream fout(fs::path(path), std::ios::binary);
    if (!fout) {
        return false;
    }
@@ -265,10 +261,6 @@ uint8_t* decode_webp_image_to_buffer(const uint8_t* data,
    height               = features.height;
    source_channel_count = features.has_alpha ? 4 : 3;

-    if (expected_channel == 0) {
-        expected_channel = source_channel_count;
-    }
-
    const size_t pixel_count = static_cast<size_t>(width) * static_cast<size_t>(height);

    if (expected_channel == 1) {
@@ -318,22 +310,23 @@ uint8_t* decode_webp_image_to_buffer(const uint8_t* data,
    return output.release();
 }

-static bool embed_webp_metadata(std::vector<uint8_t>& image, const std::string& parameters) {
-    if (parameters.empty())
-        return true;
-    WebPData data{image.data(), image.size()};
-    WebPMuxPtr mux(WebPMuxCreate(&data, 1));
-    if (!mux)
-        return false;
-    const auto xmp = generation_xmp(parameters);
-    WebPData xmp_data{reinterpret_cast<const uint8_t*>(xmp.data()), xmp.size()};
-    if (WebPMuxSetChunk(mux.get(), "XMP ", &xmp_data, 1) != WEBP_MUX_OK)
-        return false;
-    WebPDataGuard assembled;
-    if (WebPMuxAssemble(mux.get(), &assembled.data) != WEBP_MUX_OK)
-        return false;
-    image.assign(assembled.data.bytes, assembled.data.bytes + assembled.data.size);
-    return true;
+std::string build_webp_xmp_packet(const std::string& parameters) {
+    if (parameters.empty()) {
+        return "";
+    }
+
+    const std::string escaped_parameters = xml_escape(parameters);
+    return "<?xpacket begin=\"\" id=\"W5M0MpCehiHzreSzNTczkc9d\"?>\n"
+           "<x:xmpmeta xmlns:x=\"adobe:ns:meta/\">\n"
+           "  <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n"
+           "    <rdf:Description xmlns:sdcpp=\"https://github.com/leejet/stable-diffusion.cpp/ns/1.0/\">\n"
+           "      <sdcpp:parameters>" +
+           escaped_parameters +
+           "</sdcpp:parameters>\n"
+           "    </rdf:Description>\n"
+           "  </rdf:RDF>\n"
+           "</x:xmpmeta>\n"
+           "<?xpacket end=\"w\"?>";
 }

 bool encode_webp_image_to_vector(const uint8_t* image,
@@ -377,7 +370,37 @@ bool encode_webp_image_to_vector(const uint8_t* image,

    out.assign(encoded.get(), encoded.get() + encoded_size);

-    return embed_webp_metadata(out, generation_info_to_image_metadata(parameters));
+    if (parameters.empty()) {
+        return true;
+    }
+
+    WebPData image_data;
+    WebPDataInit(&image_data);
+    WebPDataGuard assembled_data;
+
+    image_data.bytes = out.data();
+    image_data.size  = out.size();
+
+    WebPMuxPtr mux(WebPMuxNew());
+    if (mux == nullptr) {
+        return false;
+    }
+
+    const std::string xmp_packet = build_webp_xmp_packet(parameters);
+    WebPData xmp_data;
+    WebPDataInit(&xmp_data);
+    xmp_data.bytes = reinterpret_cast<const uint8_t*>(xmp_packet.data());
+    xmp_data.size  = xmp_packet.size();
+
+    const bool ok = WebPMuxSetImage(mux.get(), &image_data, 1) == WEBP_MUX_OK &&
+                    WebPMuxSetChunk(mux.get(), "XMP ", &xmp_data, 1) == WEBP_MUX_OK &&
+                    WebPMuxAssemble(mux.get(), &assembled_data.data) == WEBP_MUX_OK;
+
+    if (ok) {
+        out.assign(assembled_data.data.bytes, assembled_data.data.bytes + assembled_data.data.size);
+    }
+
+    return ok;
 }

 #ifdef SD_USE_WEBM
@@ -458,8 +481,7 @@ uint8_t* load_image_common(bool from_memory,
                           int& height,
                           int expected_width,
                           int expected_height,
-                           int expected_channel,
-                           int& out_channel) {
+                           int expected_channel) {
    const char* image_path;
    FreeUniquePtr<uint8_t> image_buffer;
    int source_channel_count = 0;
@@ -516,32 +538,6 @@ uint8_t* load_image_common(bool from_memory,
        LOG_ERROR("load image from '%s' failed", image_path);
        return nullptr;
    }
-    if (expected_channel == 0) {
-        expected_channel = source_channel_count == 2 ? 4 : (source_channel_count == 1 ? 3 : source_channel_count);
-        if (expected_channel != source_channel_count) {
-            FreeUniquePtr<uint8_t> promoted((uint8_t*)malloc((size_t)width * height * expected_channel));
-            if (promoted == nullptr) {
-                LOG_ERROR("error: allocate memory for channel promotion, image_path = %s", image_path);
-                return nullptr;
-            }
-            const size_t pixel_count = (size_t)width * (size_t)height;
-            for (size_t i = 0; i < pixel_count; ++i) {
-                if (source_channel_count == 1) {
-                    promoted.get()[i * 3 + 0] = image_buffer.get()[i];
-                    promoted.get()[i * 3 + 1] = image_buffer.get()[i];
-                    promoted.get()[i * 3 + 2] = image_buffer.get()[i];
-                } else {
-                    promoted.get()[i * 4 + 0] = image_buffer.get()[i * 2];
-                    promoted.get()[i * 4 + 1] = image_buffer.get()[i * 2];
-                    promoted.get()[i * 4 + 2] = image_buffer.get()[i * 2];
-                    promoted.get()[i * 4 + 3] = image_buffer.get()[i * 2 + 1];
-                }
-            }
-            image_buffer         = std::move(promoted);
-            source_channel_count = expected_channel;
-        }
-    }
-    // stb reports the source channel count even when it converts the output.
    if (source_channel_count < expected_channel) {
        fprintf(stderr,
                "the number of channels for the input image must be >= %d,"
@@ -601,7 +597,7 @@ uint8_t* load_image_common(bool from_memory,
        }
        stbir_resize(image_buffer.get(), width, height, 0,
                     resized_image_buffer.get(), expected_width, expected_height, 0, STBIR_TYPE_UINT8,
-                     expected_channel, expected_channel == 4 ? 3 : STBIR_ALPHA_CHANNEL_NONE, 0,
+                     expected_channel, STBIR_ALPHA_CHANNEL_NONE, 0,
                     STBIR_EDGE_CLAMP, STBIR_EDGE_CLAMP,
                     STBIR_FILTER_BOX, STBIR_FILTER_BOX,
                     STBIR_COLORSPACE_SRGB, nullptr);
@@ -609,7 +605,6 @@ uint8_t* load_image_common(bool from_memory,
        height       = expected_height;
        image_buffer = std::move(resized_image_buffer);
    }
-    out_channel = expected_channel;
    return image_buffer.release();
 }

@@ -687,7 +682,7 @@ static std::pair<uint64_t, uint64_t> audio_sample_range_for_video_frame(const sd
 }

 EncodedImageFormat encoded_image_format_from_path(const std::string& path) {
-    std::string ext = fs::u8path(path).extension().u8string();
+    std::string ext = fs::path(path).extension().string();
    std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);

    if (ext == ".jpg" || ext == ".jpeg" || ext == ".jpe") {
@@ -710,7 +705,6 @@ std::vector<uint8_t> encode_image_to_vector(EncodedImageFormat format,
                                            const std::string& parameters,
                                            int quality) {
    std::vector<uint8_t> buffer;
-    const auto metadata = format == EncodedImageFormat::WEBP ? std::string() : generation_info_to_image_metadata(parameters);

    auto write_func = [&buffer](void* context, void* data, int size) {
        (void)context;
@@ -733,7 +727,7 @@ std::vector<uint8_t> encode_image_to_vector(EncodedImageFormat format,
            result = stbi_write_jpg_to_func(c_func, &ctx, width, height, channels, image, quality);
            break;
        case EncodedImageFormat::PNG:
-            result = stbi_ext_write_png_to_func(c_func, &ctx, width, height, channels, image, width * channels, metadata.empty() ? nullptr : metadata.c_str());
+            result = stbi_ext_write_png_to_func(c_func, &ctx, width, height, channels, image, width * channels, parameters.empty() ? nullptr : parameters.c_str());
            break;
        case EncodedImageFormat::WEBP:
 #ifdef SD_USE_WEBP
@@ -751,12 +745,9 @@ std::vector<uint8_t> encode_image_to_vector(EncodedImageFormat format,
            break;
    }

-    if (result && format == EncodedImageFormat::JPEG && !embed_jpeg_metadata(buffer, metadata)) {
-        LOG_ERROR("generation metadata exceeds the container capacity or the encoded image is invalid");
-        result = 0;
-    }
-    if (!result)
+    if (!result) {
        buffer.clear();
+    }
    return buffer;
 }

@@ -769,18 +760,27 @@ bool write_image_to_file(const std::string& path,
                         int quality) {
    const EncodedImageFormat format = encoded_image_format_from_path(path);

-    const auto encoded = encode_image_to_vector(format, image, width, height, channels, parameters, quality);
-    return !encoded.empty() && write_binary_file_bytes(path, encoded);
+    switch (format) {
+        case EncodedImageFormat::JPEG:
+            return stbi_write_jpg(path.c_str(), width, height, channels, image, quality, parameters.empty() ? nullptr : parameters.c_str()) != 0;
+        case EncodedImageFormat::PNG:
+            return stbi_write_png(path.c_str(), width, height, channels, image, 0, parameters.empty() ? nullptr : parameters.c_str()) != 0;
+        case EncodedImageFormat::WEBP: {
+            const std::vector<uint8_t> encoded = encode_image_to_vector(format, image, width, height, channels, parameters, quality);
+            return !encoded.empty() && write_binary_file_bytes(path, encoded);
+        }
+        default:
+            return false;
+    }
 }

 uint8_t* load_image_from_file(const char* image_path,
                              int& width,
                              int& height,
-                              int& out_channel,
                              int expected_width,
                              int expected_height,
                              int expected_channel) {
-    return load_image_common(false, image_path, 0, width, height, expected_width, expected_height, expected_channel, out_channel);
+    return load_image_common(false, image_path, 0, width, height, expected_width, expected_height, expected_channel);
 }

 bool load_sd_image_from_file(sd_image_t* image,
@@ -790,14 +790,13 @@ bool load_sd_image_from_file(sd_image_t* image,
                             int expected_channel) {
    int width;
    int height;
-    int resolved_channel = expected_channel;
-    image->data          = load_image_common(false, image_path, 0, width, height, expected_width, expected_height, expected_channel, resolved_channel);
+    image->data = load_image_common(false, image_path, 0, width, height, expected_width, expected_height, expected_channel);
    if (image->data == nullptr) {
        return false;
    }
    image->width   = width;
    image->height  = height;
-    image->channel = resolved_channel;
+    image->channel = expected_channel;
    return true;
 }

@@ -805,11 +804,10 @@ uint8_t* load_image_from_memory(const char* image_bytes,
                                int len,
                                int& width,
                                int& height,
-                                int& out_channel,
                                int expected_width,
                                int expected_height,
                                int expected_channel) {
-    return load_image_common(true, image_bytes, len, width, height, expected_width, expected_height, expected_channel, out_channel);
+    return load_image_common(true, image_bytes, len, width, height, expected_width, expected_height, expected_channel);
 }

 static void append_avi_metadata(std::vector<uint8_t>& data, const std::string& parameters) {
@@ -1349,10 +1347,7 @@ std::vector<uint8_t> create_video_from_sd_images_to_vector(const std::string& ou

 #ifdef SD_USE_WEBP
    if (format == "webp") {
-        auto data = create_animated_webp_from_sd_images_to_vector(images, num_images, fps, quality);
-        if (!data.empty() && !embed_webp_metadata(data, parameters))
-            return {};
-        return data;
+        return create_animated_webp_from_sd_images_to_vector(images, num_images, fps, quality);
    }
 #endif

@@ -1383,7 +1378,7 @@ bool write_wav_to_file(const std::string& path,
        return false;
    }

-    std::ofstream file(fs::u8path(path), std::ios::binary);
+    std::ofstream file(path, std::ios::binary);
    if (!file.is_open()) {
        return false;
    }
@@ -1437,94 +1432,70 @@ static uint32_t read_le32(const uint8_t* data) {
           (static_cast<uint32_t>(data[3]) << 24);
 }

-/** Loads a WAV file from disk and delegates decoding to load_wav_from_memory(). */
 bool load_wav_from_file(const std::string& path,
                        std::vector<float>& interleaved_samples,
                        uint32_t& sample_rate,
                        uint32_t& channels) {
-    std::ifstream file(fs::u8path(path), std::ios::binary);
-    if (!file) {
-        interleaved_samples.clear();
-        sample_rate = 0;
-        channels    = 0;
-        return false;
-    }
-    file.seekg(0, std::ios::end);
-    const std::streamoff file_size = file.tellg();
-    file.seekg(0, std::ios::beg);
-    if (file_size < 0) {
-        interleaved_samples.clear();
-        sample_rate = 0;
-        channels    = 0;
-        return false;
-    }
-    std::vector<uint8_t> buffer(static_cast<size_t>(file_size));
-    if (file_size > 0 && !file.read(reinterpret_cast<char*>(buffer.data()), file_size)) {
-        interleaved_samples.clear();
-        sample_rate = 0;
-        channels    = 0;
-        return false;
-    }
-    return load_wav_from_memory(buffer.data(), buffer.size(), interleaved_samples, sample_rate, channels);
-}
-
-/** Parses a RIFF/WAVE buffer in place; compares chunk sizes against the remaining
- * bytes so payload-controlled sizes cannot wrap size_t on 32-bit builds. */
-bool load_wav_from_memory(const uint8_t* data,
-                          size_t size,
-                          std::vector<float>& interleaved_samples,
-                          uint32_t& sample_rate,
-                          uint32_t& channels) {
    interleaved_samples.clear();
    sample_rate = 0;
    channels    = 0;

-    if (data == nullptr ||
-        size < 12 ||
-        std::memcmp(data, "RIFF", 4) != 0 ||
-        std::memcmp(data + 8, "WAVE", 4) != 0) {
+    std::ifstream file(path, std::ios::binary);
+    uint8_t riff_header[12];
+    if (!file.read(reinterpret_cast<char*>(riff_header), sizeof(riff_header)) ||
+        std::memcmp(riff_header, "RIFF", 4) != 0 ||
+        std::memcmp(riff_header + 8, "WAVE", 4) != 0) {
        return false;
    }

    uint16_t audio_format    = 0;
    uint16_t bits_per_sample = 0;
    uint16_t block_align     = 0;
-    size_t data_pos          = SIZE_MAX;
+    std::streampos data_pos  = std::streampos(-1);
    uint32_t data_size       = 0;

-    size_t pos = 12;
-    while (pos + 8 <= size) {
-        uint32_t chunk_size    = read_le32(data + pos + 4);
-        size_t chunk_data_pos  = pos + 8;
-        const size_t remaining = size - chunk_data_pos;  // safe: chunk_data_pos <= size
-        // Chunk sizes come from the payload, so compare against the remaining bytes
-        // instead of adding them to offsets (avoids size_t wrap on 32-bit builds).
-        const bool data_fits = static_cast<size_t>(chunk_size) <= remaining;
+    while (file.good()) {
+        uint8_t chunk_header[8];
+        if (!file.read(reinterpret_cast<char*>(chunk_header), sizeof(chunk_header))) {
+            break;
+        }
+        uint32_t chunk_size           = read_le32(chunk_header + 4);
+        std::streampos chunk_data_pos = file.tellg();

-        if (std::memcmp(data + pos, "fmt ", 4) == 0) {
-            if (chunk_size < 16 || !data_fits) {
+        if (std::memcmp(chunk_header, "fmt ", 4) == 0) {
+            if (chunk_size < 16) {
                return false;
            }
-            audio_format    = read_le16(data + chunk_data_pos);
-            channels        = read_le16(data + chunk_data_pos + 2);
-            sample_rate     = read_le32(data + chunk_data_pos + 4);
-            block_align     = read_le16(data + chunk_data_pos + 12);
-            bits_per_sample = read_le16(data + chunk_data_pos + 14);
-            if (audio_format == 0xfffe && chunk_size >= 40) {
-                audio_format = read_le16(data + chunk_data_pos + 24);
+            std::vector<uint8_t> fmt(chunk_size);
+            if (!file.read(reinterpret_cast<char*>(fmt.data()), chunk_size)) {
+                return false;
            }
-        } else if (std::memcmp(data + pos, "data", 4) == 0) {
+            audio_format    = read_le16(fmt.data());
+            channels        = read_le16(fmt.data() + 2);
+            sample_rate     = read_le32(fmt.data() + 4);
+            block_align     = read_le16(fmt.data() + 12);
+            bits_per_sample = read_le16(fmt.data() + 14);
+            if (audio_format == 0xfffe && chunk_size >= 40) {
+                audio_format = read_le16(fmt.data() + 24);
+            }
+        } else if (std::memcmp(chunk_header, "data", 4) == 0) {
            data_pos  = chunk_data_pos;
            data_size = chunk_size;
+            file.seekg(chunk_size, std::ios::cur);
+        } else {
+            file.seekg(chunk_size, std::ios::cur);
+        }
+
+        if (!file.good()) {
+            break;
+        }
+        if ((chunk_size & 1) != 0) {
+            file.seekg(1, std::ios::cur);
        }
-        // Only advance past a chunk whose size and pad byte fully fit (keeps the sum
-        // overflow-free); a chunk truncated at the buffer end terminates the walk.
-        const bool chunk_fits = data_fits && ((chunk_size & 1) == 0 || static_cast<size_t>(chunk_size) < remaining);
-        pos                   = chunk_fits ? chunk_data_pos + static_cast<size_t>(chunk_size) + (chunk_size & 1) : size;
    }

    const uint32_t bytes_per_sample = (bits_per_sample + 7) / 8;
-    if (data_pos == SIZE_MAX || data_size == 0 || channels == 0 || sample_rate == 0 ||
+    if (data_pos == std::streampos(-1) || data_size == 0 || channels == 0 || sample_rate == 0 ||
        block_align == 0 || bytes_per_sample == 0 || block_align < channels * bytes_per_sample ||
        (audio_format != 1 && audio_format != 3)) {
        return false;
@@ -1534,14 +1505,16 @@ bool load_wav_from_memory(const uint8_t* data,
    if (frame_count == 0 || frame_count > SIZE_MAX / channels) {
        return false;
    }
-    if (static_cast<size_t>(data_size) > size - data_pos) {
+    std::vector<uint8_t> pcm(data_size);
+    file.clear();
+    file.seekg(data_pos);
+    if (!file.read(reinterpret_cast<char*>(pcm.data()), data_size)) {
        return false;
    }
-    const uint8_t* pcm = data + data_pos;

    interleaved_samples.resize(static_cast<size_t>(frame_count * channels));
    for (uint64_t frame = 0; frame < frame_count; ++frame) {
-        const uint8_t* frame_data = pcm + frame * block_align;
+        const uint8_t* frame_data = pcm.data() + frame * block_align;
        for (uint32_t channel = 0; channel < channels; ++channel) {
            const uint8_t* sample_data = frame_data + channel * bytes_per_sample;
            float sample               = 0.0f;
@@ -16,7 +16,6 @@ enum class EncodedImageFormat {

 EncodedImageFormat encoded_image_format_from_path(const std::string& path);

-// parameters is the core sdcpp.generation/v1 JSON; empty disables both metadata formats.
 std::vector<uint8_t> encode_image_to_vector(EncodedImageFormat format,
                                            const uint8_t* image,
                                            int width,
@@ -33,12 +32,9 @@ bool write_image_to_file(const std::string& path,
                         const std::string& parameters = "",
                         int quality                   = 90);

-// expected_channel == 0 preserves native channels (grayscale -> RGB, gray+alpha -> RGBA).
-// out_channel receives the output channel count.
 uint8_t* load_image_from_file(const char* image_path,
                              int& width,
                              int& height,
-                              int& out_channel,
                              int expected_width   = 0,
                              int expected_height  = 0,
                              int expected_channel = 3);
@@ -53,7 +49,6 @@ uint8_t* load_image_from_memory(const char* image_bytes,
                                int len,
                                int& width,
                                int& height,
-                                int& out_channel,
                                int expected_width   = 0,
                                int expected_height  = 0,
                                int expected_channel = 3);
@@ -126,11 +121,4 @@ bool load_wav_from_file(const std::string& path,
                        uint32_t& sample_rate,
                        uint32_t& channels);

-/** Decodes a WAV buffer (PCM 8/16/24/32 or IEEE float 32/64) into interleaved samples. */
-bool load_wav_from_memory(const uint8_t* data,
-                          size_t size,
-                          std::vector<float>& interleaved_samples,
-                          uint32_t& sample_rate,
-                          uint32_t& channels);
-
 #endif  // __MEDIA_IO_H__
@@ -1,47 +0,0 @@
-#include "media_metadata.h"
-
-std::string generation_xmp(const std::string& info) {
-    std::string escaped;
-    for (char c : info) {
-        switch (c) {
-            case '&':
-                escaped += "&amp;";
-                break;
-            case '<':
-                escaped += "&lt;";
-                break;
-            case '>':
-                escaped += "&gt;";
-                break;
-            case '"':
-                escaped += "&quot;";
-                break;
-            case '\'':
-                escaped += "&apos;";
-                break;
-            default:
-                escaped += c;
-                break;
-        }
-    }
-    return "<x:xmpmeta xmlns:x=\"adobe:ns:meta/\"><rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">"
-           "<rdf:Description rdf:about=\"\" xmlns:sdcpp=\"https://github.com/leejet/stable-diffusion.cpp/ns/1.0/\">"
-           "<sdcpp:parameters>" +
-           escaped + "</sdcpp:parameters></rdf:Description></rdf:RDF></x:xmpmeta>";
-}
-
-bool embed_jpeg_metadata(std::vector<uint8_t>& image, const std::string& parameters) {
-    if (parameters.empty())
-        return true;
-    const char label[]    = "parameters";
-    const size_t overhead = sizeof(label) + 1;
-    if (image.size() < 2 || parameters.size() > 65533 - overhead)
-        return false;
-    const size_t length          = overhead + parameters.size() + 2;
-    std::vector<uint8_t> segment = {0xff, 0xfe, static_cast<uint8_t>(length >> 8), static_cast<uint8_t>(length)};
-    segment.insert(segment.end(), label, label + sizeof(label));
-    segment.insert(segment.end(), parameters.begin(), parameters.end());
-    segment.push_back(0);
-    image.insert(image.begin() + 2, segment.begin(), segment.end());
-    return true;
-}