refactor: extract Wan VAE implementation (#1614 )

refactor: unify model config detection (#1613 )
feat: add ideogram4 support (#1609 )
2026-10-14 05:10:44 +08:00 · 2026-06-07 01:33:49 +08:00 · 2026-06-07 01:05:12 +08:00 · 2026-06-06 16:34:16 +08:00 · 2026-06-06 16:32:03 +08:00 · 2026-06-06 16:23:44 +08:00
148 changed files with 26752 additions and 4344753 deletions
@@ -0,0 +1,15 @@
+## Summary
+
+<!-- Describe what changed and why. Keep the PR focused on one clear change. -->
+
+## Related Issue / Discussion
+
+<!-- Link related issues, discussions, or previous PRs if applicable. -->
+
+## Additional Information
+
+<!-- Add verification notes, screenshots, sample output, or other context when applicable. -->
+
+## Checklist
+
+- [ ] I have read and confirmed this PR follows the [contribution guidelines](https://github.com/leejet/stable-diffusion.cpp/blob/master/CONTRIBUTING.md).
@@ -14,6 +14,8 @@ on:
    paths:
      [
        ".github/workflows/**",
+        ".dockerignore",
+        "Dockerfile*",
        "**/CMakeLists.txt",
        "**/Makefile",
        "**/*.h",
@@ -29,6 +31,8 @@ on:
    paths:
      [
        ".github/workflows/**",
+        ".dockerignore",
+        "Dockerfile*",
        "**/CMakeLists.txt",
        "**/Makefile",
        "**/*.h",
@@ -135,7 +139,7 @@ jobs:
        id: depends
        run: |
          sudo apt-get update
-          sudo apt-get install build-essential libvulkan-dev glslc
+          sudo apt-get install build-essential libvulkan-dev glslc spirv-headers

      - name: Build
        id: cmake_build
@@ -177,7 +181,7 @@ jobs:
  build-and-push-docker-images:
    name: Build and push container images
    if: ${{ github.event_name != 'pull_request' }}
-    runs-on: ubuntu-latest
+    runs-on: ${{ matrix.runner }}

    permissions:
      contents: read
@@ -189,6 +193,20 @@ jobs:
    strategy:
      matrix:
        variant: [musa, sycl, vulkan, cuda]
+        platform: [linux/amd64]
+        runner: [ubuntu-latest]
+        build-args: [""]
+        tag-suffix: [""]
+        include:
+          - variant: cuda
+            platform: linux/arm64
+            runner: ubuntu-24.04-arm
+            tag-suffix: "-spark"
+            build-args: |
+              CUDA_VERSION=13.0.0
+              UBUNTU_VERSION=24.04
+              CUDA_ARCHITECTURES=121
+              GGML_CUDA_FA_ALL_QUANTS=ON

    env:
      REGISTRY: ghcr.io
@@ -243,12 +261,13 @@ jobs:
        uses: docker/build-push-action@v6
        with:
          context: .
-          platforms: linux/amd64
+          platforms: ${{ matrix.platform }}
          push: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
          file: Dockerfile.${{ matrix.variant }}
-          tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ env.BRANCH_NAME }}-${{ matrix.variant }}
+          tags: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ env.BRANCH_NAME }}-${{ matrix.variant }}${{ matrix.tag-suffix }}
          labels: ${{ steps.meta.outputs.labels }}
          annotations: ${{ steps.meta.outputs.annotations }}
+          build-args: ${{ matrix.build-args }}

  macOS-latest-cmake:
    runs-on: macos-latest
@@ -444,12 +463,129 @@ jobs:
          path: |
            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip

+  windows-latest-rocm:
+    runs-on: windows-2022
+
+    env:
+      ROCM_VERSION: "7.13.0"
+      GPU_TARGETS: "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
+
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          submodules: recursive
+
+      - name: Setup Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 10.15.1
+
+      - name: Cache ROCm Installation
+        id: cache-rocm
+        uses: actions/cache@v4
+        with:
+          path: C:\TheRock\build
+          key: rocm-${{ env.ROCM_VERSION }}-gfx1151-${{ runner.os }}
+
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.16
+        with:
+          key: windows-latest-rocm-${{ env.ROCM_VERSION }}-x64
+          evict-old-files: 1d
+
+      - name: Install ROCm
+        if: steps.cache-rocm.outputs.cache-hit != 'true'
+        run: |
+          $ErrorActionPreference = "Stop"
+          write-host "Downloading AMD ROCm ${{ env.ROCM_VERSION }} tarball"
+          Invoke-WebRequest -Uri "https://repo.amd.com/rocm/tarball/therock-dist-windows-gfx1151-${{ env.ROCM_VERSION }}.tar.gz" -OutFile "${env:RUNNER_TEMP}\rocm.tar.gz"
+          write-host "Extracting ROCm tarball"
+          mkdir C:\TheRock\build -Force
+          tar -xzf "${env:RUNNER_TEMP}\rocm.tar.gz" -C C:\TheRock\build --strip-components=1
+          write-host "Completed ROCm extraction"
+
+      - name: Setup ROCm Environment
+        run: |
+          $rocmPath = "C:\TheRock\build"
+          echo "HIP_PATH=$rocmPath" >> $env:GITHUB_ENV
+          echo "HIP_DEVICE_LIB_PATH=$rocmPath\lib\llvm\amdgcn\bitcode" >> $env:GITHUB_ENV
+          echo "HIP_PLATFORM=amd" >> $env:GITHUB_ENV
+          echo "LLVM_PATH=$rocmPath\lib\llvm" >> $env:GITHUB_ENV
+          echo "$rocmPath\bin" >> $env:GITHUB_PATH
+          echo "$rocmPath\lib\llvm\bin" >> $env:GITHUB_PATH
+
+      - name: Build
+        run: |
+          mkdir build
+          cd build
+          cmake .. `
+            -G "Unix Makefiles" `
+            -DCMAKE_PREFIX_PATH="${env:HIP_PATH}" `
+            -DSD_HIPBLAS=ON `
+            -DSD_BUILD_SHARED_LIBS=ON `
+            -DGGML_NATIVE=OFF `
+            -DCMAKE_C_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang.exe" `
+            -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang++.exe" `
+            -DCMAKE_HIP_COMPILER="${env:HIP_PATH}\lib\llvm\bin\clang.exe" `
+            -DHIP_PATH="${env:HIP_PATH}" `
+            -DCMAKE_BUILD_TYPE=Release `
+            -DGPU_TARGETS="${{ env.GPU_TARGETS }}"
+          cmake --build . --config Release --parallel ${env:NUMBER_OF_PROCESSORS}
+
+      - name: Get commit hash
+        id: commit
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: pr-mpt/actions-commit-hash@v2
+
+      - name: Pack artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          $ErrorActionPreference = "Stop"
+          $dst = "build\bin"
+          $rocmBin = Join-Path "${env:HIP_PATH}" "bin"
+          $requiredRocmPaths = @(
+            (Join-Path $rocmBin "rocblas.dll"),
+            (Join-Path $rocmBin "rocblas\library")
+          )
+          foreach ($path in $requiredRocmPaths) {
+            if (!(Test-Path $path)) {
+              throw "Missing ROCm runtime dependency: $path"
+            }
+          }
+
+          foreach ($pattern in @("rocblas*.dll", "hipblas*.dll", "libhipblas*.dll")) {
+            Copy-Item -Path (Join-Path $rocmBin $pattern) -Destination $dst -Force -ErrorAction SilentlyContinue
+          }
+
+          foreach ($dir in @("rocblas", "hipblaslt")) {
+            $src = Join-Path $rocmBin $dir
+            if (Test-Path $src) {
+              Copy-Item -Path $src -Destination $dst -Recurse -Force
+            }
+          }
+
+          7z a sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-${{ env.ROCM_VERSION }}-x64.zip .\build\bin\*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-${{ env.ROCM_VERSION }}-x64.zip
+          path: |
+            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-${{ env.ROCM_VERSION }}-x64.zip
+
  windows-latest-cmake-hip:
    runs-on: windows-2022

    env:
-      HIPSDK_INSTALLER_VERSION: "25.Q3"
-      GPU_TARGETS: "gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"
+      HIPSDK_INSTALLER_VERSION: "26.Q1"
+      ROCM_VERSION: "7.1.1"
+      GPU_TARGETS: "gfx1150;gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"

    steps:
      - uses: actions/checkout@v3
@@ -484,7 +620,7 @@ jobs:
        run: |
          $ErrorActionPreference = "Stop"
          write-host "Downloading AMD HIP SDK Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-Win11-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
          write-host "Installing AMD HIP SDK"
          $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru
          $completed = $proc.WaitForExit(600000)
@@ -537,47 +673,75 @@ jobs:
        run: |
          md "build\bin\rocblas\library\"
          md "build\bin\hipblaslt\library"
-          cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\"
-          cp "${env:HIP_PATH}\bin\hipblaslt.dll" "build\bin\"
+          cp "${env:HIP_PATH}\bin\libhipblas.dll" "build\bin\"
+          cp "${env:HIP_PATH}\bin\libhipblaslt.dll" "build\bin\"
          cp "${env:HIP_PATH}\bin\rocblas.dll" "build\bin\"
          cp "${env:HIP_PATH}\bin\rocblas\library\*" "build\bin\rocblas\library\"
          cp "${env:HIP_PATH}\bin\hipblaslt\library\*" "build\bin\hipblaslt\library\"
-          7z a sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-x64.zip .\build\bin\*
+          7z a sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-${{ env.ROCM_VERSION }}-x64.zip .\build\bin\*

      - name: Upload artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
        uses: actions/upload-artifact@v4
        with:
-          name: sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-x64.zip
+          name: sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-${{ env.ROCM_VERSION }}-x64.zip
          path: |
-            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-x64.zip
+            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-rocm-${{ env.ROCM_VERSION }}-x64.zip

  ubuntu-latest-rocm:
-    runs-on: ubuntu-latest
-    container: rocm/dev-ubuntu-24.04:7.2
+    runs-on: ubuntu-24.04

    env:
-      ROCM_VERSION: "7.2"
      UBUNTU_VERSION: "24.04"
-      GPU_TARGETS: "gfx1151;gfx1150;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
+
+    strategy:
+      matrix:
+        include:
+          - ROCM_VERSION: "7.2.1"
+            gpu_targets: "gfx908;gfx90a;gfx942;gfx1030;gfx1031;gfx1032;gfx1100;gfx1101;gfx1102;gfx1151;gfx1150;gfx1200;gfx1201"
+            build: 'x64'
+          - ROCM_VERSION: "7.13.0"
+            gpu_targets: "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1010;gfx1011;gfx1012;gfx1030;gfx1031;gfx1032;gfx1033;gfx1034;gfx1035;gfx1036;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1152;gfx1200;gfx1201"
+            build: x64

    steps:
-      - run: apt-get update && apt-get install -y git
      - name: Clone
        id: checkout
        uses: actions/checkout@v6
        with:
          submodules: recursive

-      - name: Setup Node
-        uses: actions/setup-node@v4
+      - name: ccache
+        uses: ggml-org/ccache-action@v1.2.16
        with:
-          node-version: 20
+          key: ubuntu-rocm-cmake-${{ matrix.ROCM_VERSION }}-${{ matrix.build }}
+          evict-old-files: 1d

-      - name: Setup pnpm
-        uses: pnpm/action-setup@v4
-        with:
-          version: 10.15.1
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt install -y build-essential cmake wget zip ninja-build
+
+      - name: Setup Legacy ROCm
+        if: matrix.ROCM_VERSION == '7.2.1'
+        id: legacy_env
+        run: |
+          sudo mkdir --parents --mode=0755 /etc/apt/keyrings
+          wget https://repo.radeon.com/rocm/rocm.gpg.key -O - | \
+            gpg --dearmor | sudo tee /etc/apt/keyrings/rocm.gpg > /dev/null
+
+          sudo tee /etc/apt/sources.list.d/rocm.list << EOF
+          deb [arch=amd64 signed-by=/etc/apt/keyrings/rocm.gpg] https://repo.radeon.com/rocm/apt/${{ matrix.ROCM_VERSION }} noble main
+          EOF
+
+          sudo tee /etc/apt/preferences.d/rocm-pin-600 << EOF
+          Package: *
+          Pin: release o=repo.radeon.com
+          Pin-Priority: 600
+          EOF
+
+          sudo apt update
+          sudo apt-get install -y libssl-dev rocm-hip-sdk

      - name: Free disk space
        run: |
@@ -592,51 +756,29 @@ jobs:
          sudo rm -rf /var/lib/apt/lists/* || true
          sudo apt clean

-      - name: Dependencies
-        id: depends
+      - name: Setup TheRock
+        if: matrix.ROCM_VERSION != '7.2.1'
+        id: therock_env
        run: |
-          sudo apt-get update
-          sudo apt install -y \
-            cmake \
-            hip-dev \
-            hipblas-dev \
-            ninja-build \
-            rocm-dev \
-            zip
-          # Clean apt caches to recover disk space
-          sudo apt clean
-          sudo rm -rf /var/lib/apt/lists/* || true
+          wget https://repo.amd.com/rocm/tarball/therock-dist-linux-gfx1151-${{ matrix.ROCM_VERSION }}.tar.gz
+          mkdir install
+          tar -xf *.tar.gz -C install
+          export ROCM_PATH=$(pwd)/install
+          echo ROCM_PATH=$ROCM_PATH >> $GITHUB_ENV
+          echo PATH=$PATH:$ROCM_PATH/bin >> $GITHUB_ENV
+          echo LD_LIBRARY_PATH=$ROCM_PATH/lib:$ROCM_PATH/llvm/lib:$ROCM_PATH/lib/rocprofiler-systems >> $GITHUB_ENV

-      - name: Setup ROCm Environment
-        run: |
-          # Add ROCm to PATH for current session
-          echo "/opt/rocm/bin" >> $GITHUB_PATH
+      # 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@v4
+        with:
+          node-version: 20

-          # Build regex pattern from ${{ env.GPU_TARGETS }} (match target as substring)
-          TARGET_REGEX="($(printf '%s' "${{ env.GPU_TARGETS }}" | sed 's/;/|/g'))"
-
-          # Remove library files for architectures we're not building for to save disk space
-          echo "Cleaning up unneeded architecture files..."
-          cd /opt/rocm/lib/rocblas/library
-          # Keep only our target architectures
-          for file in *; do
-            if printf '%s' "$file" | grep -q 'gfx'; then
-              if ! printf '%s' "$file" | grep -Eq "$TARGET_REGEX"; then
-                echo "Removing $file" &&
-                sudo rm -f "$file";
-              fi
-            fi
-          done
-
-          cd /opt/rocm/lib/hipblaslt/library
-          for file in *; do
-            if printf '%s' "$file" | grep -q 'gfx'; then
-              if ! printf '%s' "$file" | grep -Eq "$TARGET_REGEX"; then
-                echo "Removing $file" &&
-                sudo rm -f "$file";
-              fi
-            fi
-          done
+      - name: Setup pnpm
+        uses: pnpm/action-setup@v4
+        with:
+          version: 10.15.1

      - name: Build
        id: cmake_build
@@ -644,12 +786,12 @@ jobs:
          mkdir build
          cd build
          cmake .. -G Ninja \
-            -DCMAKE_CXX_COMPILER=amdclang++ \
-            -DCMAKE_C_COMPILER=amdclang \
+            -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" \
+            -DCMAKE_HIP_FLAGS="-mllvm --amdgpu-unroll-threshold-local=600" \
            -DCMAKE_BUILD_TYPE=Release \
            -DSD_HIPBLAS=ON \
-            -DGPU_TARGETS="${{ env.GPU_TARGETS }}" \
-            -DAMDGPU_TARGETS="${{ env.GPU_TARGETS }}" \
+            -DHIP_PLATFORM=amd \
+            -DGPU_TARGETS="${{ matrix.gpu_targets }}" \
            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
            -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
            -DSD_BUILD_SHARED_LIBS=ON
@@ -668,16 +810,6 @@ jobs:
          cp ggml/LICENSE ./build/bin/ggml.txt
          cp LICENSE ./build/bin/stable-diffusion.cpp.txt

-          # Move ROCm runtime libraries (to avoid double space consumption)
-          sudo mv /opt/rocm/lib/librocsparse.so* ./build/bin/
-          sudo mv /opt/rocm/lib/libhsa-runtime64.so* ./build/bin/
-          sudo mv /opt/rocm/lib/libamdhip64.so* ./build/bin/
-          sudo mv /opt/rocm/lib/libhipblas.so* ./build/bin/
-          sudo mv /opt/rocm/lib/libhipblaslt.so* ./build/bin/
-          sudo mv /opt/rocm/lib/librocblas.so* ./build/bin/
-          sudo mv /opt/rocm/lib/rocblas/ ./build/bin/
-          sudo mv /opt/rocm/lib/hipblaslt/ ./build/bin/
-
      - name: Fetch system info
        id: system-info
        run: |
@@ -692,15 +824,15 @@ jobs:
        run: |
          cp ggml/LICENSE ./build/bin/ggml.txt
          cp LICENSE ./build/bin/stable-diffusion.cpp.txt
-          zip -y -r sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm.zip ./build/bin
+          zip -y -r sd-${{ 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: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
        uses: actions/upload-artifact@v4
        with:
-          name: sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm.zip
+          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: |
-            sd-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-Ubuntu-${{ env.UBUNTU_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}-rocm.zip
+            sd-${{ 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

  release:
    if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
@@ -715,6 +847,7 @@ jobs:
      - macOS-latest-cmake
      - windows-latest-cmake
      - windows-latest-cmake-hip
+      - windows-latest-rocm

    steps:
      - name: Clone
@@ -0,0 +1,55 @@
+name: Close inactive PRs
+
+on:
+  schedule:
+    # Run daily. GitHub cron schedules use UTC.
+    - cron: "30 1 * * *"
+  workflow_dispatch:
+    inputs:
+      debug_only:
+        description: "Dry run: log intended actions without changing PRs"
+        required: false
+        default: false
+        type: boolean
+
+permissions:
+  issues: write
+  pull-requests: write
+
+concurrency:
+  group: ${{ github.workflow }}
+  cancel-in-progress: false
+
+jobs:
+  stale-prs:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Mark and close inactive PRs
+        uses: actions/stale@v10
+        with:
+          days-before-issue-stale: -1
+          days-before-issue-close: -1
+
+          days-before-pr-stale: 365
+          days-before-pr-close: 7
+
+          stale-pr-label: pr:inactive
+          close-pr-label: pr:auto-closed
+          exempt-pr-labels: pr:keep-open
+
+          stale-pr-message: >
+            This PR has been inactive for 365 days. If there is no new activity
+            within 7 days, it will be closed automatically. Comment, push new
+            commits, or remove the pr:inactive label to keep it open. Add
+            pr:keep-open to exempt it from future inactive PR cleanup.
+
+          close-pr-message: >
+            Closing this PR because it has had no activity for 7 days after
+            being marked inactive. If this is still useful or ready to move
+            forward, feel free to reopen it with fresh context or updated
+            details. Sorry for any inconvenience.
+
+          remove-pr-stale-when-updated: true
+          delete-branch: false
+          operations-per-run: 100
+          debug-only: ${{ github.event_name == 'workflow_dispatch' && inputs.debug_only || false }}
@@ -1,6 +1,6 @@
 [submodule "ggml"]
    path = ggml
-	url = https://github.com/ggml-org/ggml.git
+	url = https://github.com/leejet/ggml.git
 [submodule "examples/server/frontend"]
 	path = examples/server/frontend
 	url = https://github.com/leejet/sdcpp-webui.git
@@ -13,13 +13,40 @@ if (MSVC)
    add_compile_definitions(_SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING)
    add_compile_options(
        $<$<COMPILE_LANGUAGE:C>:/MP>
+        $<$<COMPILE_LANGUAGE:C>:/utf-8>
        $<$<COMPILE_LANGUAGE:CXX>:/MP>
+        $<$<COMPILE_LANGUAGE:CXX>:/utf-8>
    )
 endif()

 set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)

+if(APPLE)
+    function(sd_set_macos_rpaths target)
+        get_target_property(target_type ${target} TYPE)
+        if(target_type STREQUAL "EXECUTABLE")
+            set(runtime_paths "@executable_path" "@executable_path/../lib")
+        elseif(target_type STREQUAL "SHARED_LIBRARY" OR target_type STREQUAL "MODULE_LIBRARY")
+            set(runtime_paths "@loader_path" "@loader_path/../lib")
+            set_target_properties(${target} PROPERTIES
+                MACOSX_RPATH ON
+                INSTALL_NAME_DIR "@rpath"
+                BUILD_WITH_INSTALL_NAME_DIR ON
+            )
+        else()
+            return()
+        endif()
+
+        # Release artifacts zip the build output directly, so keep macOS rpaths relocatable.
+        set_target_properties(${target} PROPERTIES
+            BUILD_RPATH "${runtime_paths}"
+            INSTALL_RPATH "${runtime_paths}"
+            BUILD_WITH_INSTALL_RPATH ON
+        )
+    endfunction()
+endif()
+
 if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
    set(SD_STANDALONE ON)
 else()
@@ -69,40 +96,46 @@ option(SD_BUILD_SHARED_GGML_LIB      "sd: build ggml as a separate shared lib" O
 option(SD_USE_SYSTEM_GGML            "sd: use system-installed GGML library" OFF)
 #option(SD_BUILD_SERVER               "sd: build server example"                           ON)

+set(CMAKE_C_STANDARD 11)
+set(CMAKE_C_STANDARD_REQUIRED true)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED true)
+
 if(SD_CUDA)
    message("-- Use CUDA as backend stable-diffusion")
    set(GGML_CUDA ON)
-    add_definitions(-DSD_USE_CUDA)
 endif()

 if(SD_METAL)
    message("-- Use Metal as backend stable-diffusion")
    set(GGML_METAL ON)
-    add_definitions(-DSD_USE_METAL)
 endif()

 if (SD_VULKAN)
    message("-- Use Vulkan as backend stable-diffusion")
    set(GGML_VULKAN ON)
-    add_definitions(-DSD_USE_VULKAN)
 endif ()

 if (SD_OPENCL)
    message("-- Use OpenCL as backend stable-diffusion")
    set(GGML_OPENCL ON)
-    add_definitions(-DSD_USE_OPENCL)
 endif ()

 if (SD_HIPBLAS)
    message("-- Use HIPBLAS as backend stable-diffusion")
    set(GGML_HIP ON)
-    add_definitions(-DSD_USE_CUDA)
+    # ggml-hip's device-stub objects must be position-independent, or the
+    # default-PIE sd-cli link fails with `relocation R_X86_64_32 ... cannot be
+    # used when making a PIE object` on distros that default to PIE
+    # (Ubuntu 24.04, Fedora 40+, Debian 12+). The shared-library branch below
+    # already sets this; the static build (the HIP default) did not.
+    set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 endif ()

 if(SD_MUSA)
    message("-- Use MUSA as backend stable-diffusion")
    set(GGML_MUSA ON)
-    add_definitions(-DSD_USE_CUDA)
 endif()

 if(SD_WEBP)
@@ -112,19 +145,28 @@ if(SD_WEBP)
          "Or link against system library:\n  cmake (...) -DSD_USE_SYSTEM_WEBP=ON")
    endif()
    if(SD_USE_SYSTEM_WEBP)
-        find_package(WebP REQUIRED)
-        add_library(webp ALIAS WebP::webp)
-        # libwebp CMake target naming is not consistent across versions/distros.
-        # Some export WebP::libwebpmux, others export WebP::webpmux.
-        if(TARGET WebP::libwebpmux)
-            add_library(libwebpmux ALIAS WebP::libwebpmux)
-        elseif(TARGET WebP::webpmux)
-            add_library(libwebpmux ALIAS WebP::webpmux)
+        find_package(WebP)
+        if(WebP_FOUND)
+            add_library(webp ALIAS WebP::webp)
+            # libwebp CMake target naming is not consistent across versions/distros.
+            # Some export WebP::libwebpmux, others export WebP::webpmux.
+            if(TARGET WebP::libwebpmux)
+                add_library(libwebpmux ALIAS WebP::libwebpmux)
+            elseif(TARGET WebP::webpmux)
+                add_library(libwebpmux ALIAS WebP::webpmux)
+            else()
+                message(FATAL_ERROR
+                    "Could not find a compatible webpmux target in system WebP package. "
+                    "Expected WebP::libwebpmux or WebP::webpmux."
+                )
+            endif()
        else()
-            message(FATAL_ERROR
-                "Could not find a compatible webpmux target in system WebP package. "
-                "Expected WebP::libwebpmux or WebP::webpmux."
-            )
+            find_package(PkgConfig REQUIRED)
+            pkg_check_modules(WebP REQUIRED IMPORTED_TARGET GLOBAL libwebp)
+            pkg_check_modules(WebPMux REQUIRED IMPORTED_TARGET GLOBAL libwebpmux)
+            link_libraries(PkgConfig::WebP)
+            link_libraries(PkgConfig::WebPMux)
+            add_library(libwebpmux ALIAS PkgConfig::WebPMux)
        endif()
    endif()
 endif()
@@ -139,27 +181,37 @@ if(SD_WEBM)
          "Or link against system library:\n  cmake (...) -DSD_USE_SYSTEM_WEBM=ON")
    endif()
    if(SD_USE_SYSTEM_WEBM)
-        find_path(WEBM_INCLUDE_DIR
-            NAMES mkvmuxer/mkvmuxer.h mkvparser/mkvparser.h common/webmids.h
-            PATH_SUFFIXES webm
-            REQUIRED)
-        find_library(WEBM_LIBRARY
-            NAMES webm libwebm
-            REQUIRED)
+        find_package(PkgConfig)
+        if(PkgConfig_FOUND)
+            pkg_check_modules(WebM REQUIRED IMPORTED_TARGET GLOBAL libwebm)
+        endif()
+        if(PkgConfig_FOUND AND WebM_FOUND)
+            link_libraries(PkgConfig::WebM)
+        else()
+            find_path(WEBM_INCLUDE_DIR
+                NAMES mkvmuxer/mkvmuxer.h mkvparser/mkvparser.h common/webmids.h
+                PATH_SUFFIXES webm
+                REQUIRED)
+            find_library(WEBM_LIBRARY
+                NAMES webm libwebm
+                REQUIRED)

-        add_library(webm UNKNOWN IMPORTED)
-        set_target_properties(webm PROPERTIES
-            IMPORTED_LOCATION "${WEBM_LIBRARY}"
-            INTERFACE_INCLUDE_DIRECTORIES "${WEBM_INCLUDE_DIR}")
+            add_library(webm UNKNOWN IMPORTED)
+            set_target_properties(webm PROPERTIES
+                IMPORTED_LOCATION "${WEBM_LIBRARY}"
+                INTERFACE_INCLUDE_DIRECTORIES "${WEBM_INCLUDE_DIR}")
+        endif()
    endif()
 endif()

 set(SD_LIB stable-diffusion)

-file(GLOB SD_LIB_SOURCES
+file(GLOB SD_LIB_SOURCES CONFIGURE_DEPENDS
    "src/*.h"
    "src/*.cpp"
    "src/*.hpp"
+    "src/model_io/*.h"
+    "src/model_io/*.cpp"
    "src/tokenizers/*.h"
    "src/tokenizers/*.cpp"
    "src/tokenizers/vocab/*.h"
@@ -216,11 +268,14 @@ else()
    add_library(${SD_LIB} STATIC ${SD_LIB_SOURCES})
 endif()

+if(APPLE)
+    sd_set_macos_rpaths(${SD_LIB})
+endif()
+
 if(SD_SYCL)
    message("-- Use SYCL as backend stable-diffusion")
    set(GGML_SYCL ON)
    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing -fsycl")
-    add_definitions(-DSD_USE_SYCL)
    # disable fast-math on host, see:
    # https://www.intel.com/content/www/us/en/docs/cpp-compiler/developer-guide-reference/2021-10/fp-model-fp.html
    if (WIN32)
@@ -0,0 +1,67 @@
+# Contributing
+
+This document collects general contribution conventions for this repository.
+
+## Before You Start
+
+Before opening a PR, please search existing PRs to avoid duplicating ongoing work.
+
+For large-scale refactors or changes with broad impact, please open an issue first to discuss the approach before submitting a PR.
+
+If you want to update a third-party dependency, please open an issue first instead of submitting a direct PR. See [Dependency Updates](#dependency-updates) for details.
+
+## Pull Requests
+
+Keep each PR focused on one clear change. Large or overly complex PRs are harder to review and may not be merged.
+
+Follow Conventional Commit-style subjects seen in history: `feat:`, `fix:`, `refactor:`, `ci:`, `docs:`, `chore:`. Keep subjects imperative and scoped.
+
+PRs should include:
+
+- What changed and why (short problem/solution summary).
+- Verification evidence when applicable (commands and key outputs).
+- Linked issue/PR context when applicable.
+- Screenshots or sample outputs for UI/visual behavior changes.
+
+## Code Style
+
+Format code according to the repository style before submitting changes.
+
+Formatting follows `.clang-format` (Chromium base, 4-space indent, no tabs). Run `format-code.sh` before opening a PR. Keep C++ standard at C++17-compatible patterns used in this repo.
+
+Naming conventions:
+
+- Use `PascalCase` for class/struct/type names.
+- In `PascalCase` names, preserve common abbreviations in uppercase, for example `SD`, `API`, `HTTP`, `JSON`, `RGB`, `VAE`, `TAE`, `LoRA`, and `WebP`.
+- Use `snake_case` for functions, methods, variables, and file names unless an existing API requires a different style.
+- Use a trailing underscore for private data member names, for example `hidden_size_` or `tokenizer_`.
+- Use `.h` for C and C++ header files. Do not introduce new `.hpp` headers.
+- Use macro-based header include guards instead of `#pragma once`.
+- Format header include guards as `__SD_{PATH}__`, where `{PATH}` is the header path in uppercase snake case without the file extension. For example, `src/sample.h` should use `__SD_SAMPLE_H__`.
+- Do not introduce anonymous namespaces in new or modified code; prefer `static` file-local functions/variables or an explicit named namespace when scoping is needed.
+- In `class`/`struct` definitions, place data members before member functions unless an existing type already clearly follows a different pattern.
+- Keep `test_*.cpp` / `test_*.py` naming for tests.
+
+Some older code in the project may not fully follow the current conventions. Please do not submit PRs that only rewrite existing code to match style rules.
+
+When adding or modifying model implementations, follow the model config and weight detection conventions in [docs/model_config.md](docs/model_config.md).
+
+## AI-Assisted Contributions
+
+AI tools may be used to assist development, but contributors are responsible for the quality and correctness of the submitted code.
+
+If any part of a contribution was generated with AI assistance, the contributor must perform a thorough human review before submitting the PR and understand every changed line.
+
+Do not list AI tools as co-authors. The human contributor is the sole responsible author of the submitted code.
+
+Please do not submit AI-generated code that you do not understand, and do not include meaningless experiments, temporary test code, or unrelated generated output in a PR.
+
+## Dependency Updates
+
+Do not submit PRs that update `ggml`. `ggml` updates are performed only after local validation by the maintainer.
+
+Other third-party dependencies are not updated unless necessary. If you want to update a dependency, please open an issue first instead of submitting a direct PR.
+
+## Security & Configuration
+
+Do not commit model weights, secrets, or local absolute paths. Keep large binaries out of git unless intentionally tracked release assets.
@@ -2,7 +2,18 @@ ARG UBUNTU_VERSION=24.04

 FROM ubuntu:$UBUNTU_VERSION AS build

-RUN apt-get update && apt-get install -y --no-install-recommends build-essential git cmake
+# 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 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_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 && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*

 WORKDIR /sd.cpp

@@ -20,4 +31,4 @@ RUN apt-get update && \
 COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
 COPY --from=build /sd.cpp/build/bin/sd-server /sd-server

-ENTRYPOINT [ "/sd-cli" ]
+ENTRYPOINT [ "/sd-cli" ]
@@ -3,14 +3,31 @@ ARG UBUNTU_VERSION=24.04

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

-RUN apt-get update && apt-get install -y --no-install-recommends build-essential git ccache cmake
+# 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 && \
+    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_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 && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*

 WORKDIR /sd.cpp

 COPY . .

 ARG CUDACXX=/usr/local/cuda/bin/nvcc
-RUN cmake . -B ./build -DSD_CUDA=ON
+ARG CUDA_ARCHITECTURES=""
+ARG GGML_CUDA_FA_ALL_QUANTS=""
+
+RUN cmake . -B ./build \
+    -DSD_CUDA=ON \
+    ${CUDA_ARCHITECTURES:+-DCMAKE_CUDA_ARCHITECTURES="${CUDA_ARCHITECTURES}"} \
+    ${GGML_CUDA_FA_ALL_QUANTS:+-DGGML_CUDA_FA_ALL_QUANTS=${GGML_CUDA_FA_ALL_QUANTS}}
 RUN cmake --build ./build --config Release -j$(nproc)

 FROM nvidia/cuda:${CUDA_VERSION}-cudnn-runtime-ubuntu${UBUNTU_VERSION} AS runtime
@@ -3,7 +3,18 @@ ARG UBUNTU_VERSION=22.04

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

-RUN apt-get update && apt-get install -y ccache cmake git
+# 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 && \
+    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_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 && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*

 WORKDIR /sd.cpp

@@ -21,4 +32,4 @@ FROM mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}-amd64 as runt
 COPY --from=build /sd.cpp/build/bin/sd-cli /sd-cli
 COPY --from=build /sd.cpp/build/bin/sd-server /sd-server

-ENTRYPOINT [ "/sd-cli" ]
+ENTRYPOINT [ "/sd-cli" ]
@@ -1,8 +1,20 @@
-ARG SYCL_VERSION=2025.1.0-0
+# ggml SYCL hardware detection uses BMG G31/WCL architecture enums added in oneAPI 2025.3.
+ARG SYCL_VERSION=2025.3.2-0

 FROM intel/oneapi-basekit:${SYCL_VERSION}-devel-ubuntu24.04 AS build

-RUN apt-get update && apt-get install -y cmake
+# 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 cmake 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_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 && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*

 WORKDIR /sd.cpp

@@ -2,7 +2,18 @@ ARG UBUNTU_VERSION=24.04

 FROM ubuntu:$UBUNTU_VERSION AS build

-RUN apt-get update && apt-get install -y --no-install-recommends build-essential git cmake libvulkan-dev glslc
+# 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 libvulkan-dev glslc spirv-headers 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_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 && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*

 WORKDIR /sd.cpp

@@ -15,29 +15,18 @@ API and command-line option may change frequently.***

 ## 🔥Important News

+* **2026/06/04** 🚀 stable-diffusion.cpp now supports **Ideogram4**
+* **2026/05/31** 🚀 stable-diffusion.cpp now supports **PiD**
+* **2026/05/27** 🚀 stable-diffusion.cpp now supports **Lens**
+* **2026/05/17** 🚀 stable-diffusion.cpp now supports **LTX-2.3**
 * **2026/04/11** 🚀 stable-diffusion.cpp now uses a brand-new embedded web UI.  
-  👉 Details: [PR #1408](https://github.com/leejet/stable-diffusion.cpp/pull/1408)
-
 * **2026/01/18** 🚀 stable-diffusion.cpp now supports **FLUX.2-klein**  
-  👉 Details: [PR #1193](https://github.com/leejet/stable-diffusion.cpp/pull/1193)
-
 * **2025/12/01** 🚀 stable-diffusion.cpp now supports **Z-Image**  
-  👉 Details: [PR #1020](https://github.com/leejet/stable-diffusion.cpp/pull/1020)
-
 * **2025/11/30** 🚀 stable-diffusion.cpp now supports **FLUX.2-dev**  
-  👉 Details: [PR #1016](https://github.com/leejet/stable-diffusion.cpp/pull/1016)
-
 * **2025/10/13** 🚀 stable-diffusion.cpp now supports **Qwen-Image-Edit / Qwen-Image-Edit 2509**  
-  👉 Details: [PR #877](https://github.com/leejet/stable-diffusion.cpp/pull/877)
-
 * **2025/10/12** 🚀 stable-diffusion.cpp now supports **Qwen-Image**  
-  👉 Details: [PR #851](https://github.com/leejet/stable-diffusion.cpp/pull/851)
-
 * **2025/09/14** 🚀 stable-diffusion.cpp now supports **Wan2.1 Vace**  
-  👉 Details: [PR #819](https://github.com/leejet/stable-diffusion.cpp/pull/819)
-
 * **2025/09/06** 🚀 stable-diffusion.cpp now supports **Wan2.1 / Wan2.2**  
-  👉 Details: [PR #778](https://github.com/leejet/stable-diffusion.cpp/pull/778)

 ## Features

@@ -51,18 +40,25 @@ 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)
+    - [Lens](./docs/lens.md)
    - [Chroma](./docs/chroma.md)
    - [Chroma1-Radiance](./docs/chroma_radiance.md)
    - [Qwen Image](./docs/qwen_image.md)
+    - [PiD](./docs/pid.md)
+    - [LongCat Image](./docs/longcat_image.md)
    - [Z-Image](./docs/z_image.md)
    - [Ovis-Image](./docs/ovis_image.md)
    - [Anima](./docs/anima.md)
    - [ERNIE-Image](./docs/ernie_image.md)
+    - [HiDream-O1-Image](./docs/hidream_o1_image.md)
+    - [Ideogram4](./docs/ideogram4.md)
  - Image Edit Models
    - [FLUX.1-Kontext-dev](./docs/kontext.md)
    - [Qwen Image Edit series](./docs/qwen_image_edit.md)
+    - [LongCat Image Edit](./docs/longcat_image.md)
  - Video Models
    - [Wan2.1/Wan2.2](./docs/wan.md)
+    - [LTX-2.3](./docs/ltx2.md)
  - [PhotoMaker](https://github.com/TencentARC/PhotoMaker) support.
  - Control Net support with SD 1.5
  - LoRA support, same as [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#lora)
@@ -77,9 +73,10 @@ API and command-line option may change frequently.***
  - OpenCL
  - SYCL
 - Supported weight formats
-  - Pytorch checkpoint (`.ckpt` or `.pth`)
+  - Pytorch checkpoint (`.ckpt` or `.pth` or `.pt`)
  - Safetensors (`.safetensors`)
  - GGUF (`.gguf`)
+- Convert mode supports converting model weights to `.gguf` or `.safetensors`
 - Supported platforms
    - Linux
    - Mac OS
@@ -131,9 +128,11 @@ API and command-line option may change frequently.***
 ## Performance

 If you want to improve performance or reduce VRAM/RAM usage, please refer to [performance guide](./docs/performance.md).
+For runtime and parameter backend placement, see the [backend selection guide](./docs/backend.md).

 ## More Guides

+- [Backend selection](./docs/backend.md)
 - [SD1.x/SD2.x/SDXL](./docs/sd.md)
 - [SD3/SD3.5](./docs/sd3.md)
 - [FLUX.1-dev/FLUX.1-schnell](./docs/flux.md)
@@ -143,10 +142,14 @@ If you want to improve performance or reduce VRAM/RAM usage, please refer to [pe
 - [🔥Qwen Image](./docs/qwen_image.md)
 - [🔥Qwen Image Edit series](./docs/qwen_image_edit.md)
 - [🔥Wan2.1/Wan2.2](./docs/wan.md)
+- [🔥LTX-2.3](./docs/ltx2.md)
 - [🔥Z-Image](./docs/z_image.md)
 - [Ovis-Image](./docs/ovis_image.md)
 - [Anima](./docs/anima.md)
 - [ERNIE-Image](./docs/ernie_image.md)
+- [HiDream-O1-Image](./docs/hidream_o1_image.md)
+- [Lens](./docs/lens.md)
+- [LongCat Image / LongCat Image Edit](./docs/longcat_image.md)
 - [LoRA](./docs/lora.md)
 - [LCM/LCM-LoRA](./docs/lcm.md)
 - [Using PhotoMaker to personalize image generation](./docs/photo_maker.md)
@@ -162,6 +165,7 @@ These projects wrap `stable-diffusion.cpp` for easier use in other languages/fra

 * Golang (non-cgo): [seasonjs/stable-diffusion](https://github.com/seasonjs/stable-diffusion)
 * Golang (cgo): [Binozo/GoStableDiffusion](https://github.com/Binozo/GoStableDiffusion)
+* Golang (non-cgo): [l8bloom/gosd](https://github.com/l8bloom/gosd)
 * C#: [DarthAffe/StableDiffusion.NET](https://github.com/DarthAffe/StableDiffusion.NET)
 * Python: [william-murray1204/stable-diffusion-cpp-python](https://github.com/william-murray1204/stable-diffusion-cpp-python)
 * Rust: [newfla/diffusion-rs](https://github.com/newfla/diffusion-rs)
@@ -0,0 +1,122 @@
+# Backend selection
+
+`stable-diffusion.cpp` has two backend assignments:
+
+- `--backend` selects the runtime backend used to execute model graphs.
+- `--params-backend` selects the backend used to allocate model parameters.
+
+If `--params-backend` is not set, parameters use the same backend as their module runtime backend.
+
+## Syntax
+
+A backend assignment can be a single backend name:
+
+```shell
+sd-cli -m model.safetensors -p "a cat" --backend cpu
+```
+
+This applies to every module that does not have a more specific assignment.
+
+Assignments can also target individual modules:
+
+```shell
+sd-cli -m model.safetensors -p "a cat" --backend te=cpu,vae=cuda0,diffusion=vulkan0
+```
+
+The same syntax is used for parameter placement:
+
+```shell
+sd-cli -m model.safetensors -p "a cat" --backend cuda0 --params-backend te=cpu,vae=cpu
+```
+
+Module names are case-insensitive. Hyphens and underscores in module names are ignored, so `clip_vision`, `clip-vision`, and `clipvision` are equivalent.
+
+`all=`, `default=`, and `*=` can be used to set the default backend inside a mixed assignment:
+
+```shell
+sd-cli -m model.safetensors -p "a cat" --backend all=cuda0,te=cpu
+```
+
+## Modules
+
+| Module | Purpose | Accepted names |
+| --- | --- | --- |
+| `diffusion` | UNet, DiT, MMDiT, Flux, Wan, Qwen Image, and other diffusion models | `diffusion`, `model`, `unet`, `dit` |
+| `te` | Text encoders and conditioners | `te`, `clip`, `text`, `textencoder`, `textencoders`, `conditioner`, `cond`, `llm`, `t5`, `t5xxl` |
+| `clip_vision` | CLIP vision encoder | `clip_vision`, `clipvision`, `clip-vision`, `vision` |
+| `vae` | VAE and TAE | `vae`, `firststage`, `autoencoder`, `tae` |
+| `controlnet` | ControlNet | `controlnet`, `control` |
+| `photomaker` | PhotoMaker ID encoder and PhotoMaker LoRA | `photomaker`, `photomakerid`, `pmid`, `photo` |
+| `upscaler` | ESRGAN upscaler | `upscaler`, `esrgan`, `hires` |
+
+`te` is the preferred module name for text encoders. `clip` is kept as an accepted alias because many existing commands and model names use CLIP terminology.
+
+## Backend names
+
+Backend names are resolved against the GGML backend device list. Matching is case-insensitive and accepts exact names or unique prefixes, so common values include names such as:
+
+- `cpu`
+- `cuda0`
+- `vulkan0`
+- `metal`
+
+The special values `auto`, `default`, and an empty backend name select the default backend. The default preference is GPU, then integrated GPU, then CPU.
+
+The special value `gpu` selects the first GPU backend, falling back to the first integrated GPU backend.
+
+## Runtime backend vs. parameter backend
+
+The runtime backend controls where graph execution runs. The parameter backend controls where model weights are allocated.
+
+For example:
+
+```shell
+sd-cli -m model.safetensors -p "a cat" --backend cuda0 --params-backend cpu
+```
+
+This runs all modules on `cuda0`, but stores parameters in CPU RAM. During execution, parameters are moved to the runtime backend as needed.
+
+Per-module assignments can be mixed:
+
+```shell
+sd-cli -m model.safetensors -p "a cat" --backend diffusion=cuda0,te=cpu,vae=cpu --params-backend diffusion=cuda0,te=cpu,vae=cpu
+```
+
+This keeps text encoding and VAE execution on CPU while the diffusion model runs on GPU.
+
+## Backend sharing and lifetime
+
+Backends are managed by `SDBackendManager`.
+
+Within one manager, backend instances are cached by resolved backend device name. If multiple modules request the same backend, they share the same `ggml_backend_t`.
+
+For example:
+
+```shell
+--backend te=cpu,vae=cpu
+```
+
+uses one shared CPU backend for both `te` and `vae` runtime execution.
+
+Runtime and parameter assignments also share the same backend cache. If `--backend diffusion=cuda0` and `--params-backend diffusion=cuda0` resolve to the same device, both use the same backend instance.
+
+`SDBackendManager` owns the backend instances and frees them when the context or upscaler is destroyed. Model runners receive non-owning runtime and parameter backend pointers and do not free them.
+
+## Compatibility flags
+
+The older CPU placement flags are still supported:
+
+- `--clip-on-cpu`
+- `--vae-on-cpu`
+- `--control-net-cpu`
+- `--offload-to-cpu`
+
+`--clip-on-cpu`, `--vae-on-cpu`, and `--control-net-cpu` affect runtime backend assignment only when `--backend` is not set. They map to `te=cpu`, `vae=cpu`, and `controlnet=cpu`.
+
+`--offload-to-cpu` affects parameter backend assignment only when `--params-backend` is not set. It is equivalent to:
+
+```shell
+--params-backend cpu
+```
+
+Explicit `--backend` and `--params-backend` assignments are preferred for new commands.
@@ -102,6 +102,11 @@ cmake --build . --config Release
 ## Build with Vulkan

 Install Vulkan SDK from https://www.lunarg.com/vulkan-sdk/.
+On Ubuntu, install the Vulkan development packages and SPIR-V headers:
+
+```shell
+sudo apt-get install build-essential libvulkan-dev glslc spirv-headers
+```

 ```shell
 mkdir build && cd build
@@ -131,8 +131,6 @@ sd-cli -m model.safetensors -p "a cat" --cache-mode spectrum
 | `warmup` | Steps to always compute before caching starts | 4 |
 | `stop` | Stop caching at this fraction of total steps | 0.9 |

-```
-
 ### Performance Tips

 - Start with default thresholds and adjust based on output quality
@@ -0,0 +1,20 @@
+# How to Use
+
+## Download weights
+
+- Download HiDream-O1-Image-Dev
+    - safetensors: https://huggingface.co/Comfy-Org/HiDream-O1-Image/tree/main/checkpoints
+- Download HiDream-O1-Image
+    - safetensors: https://huggingface.co/Comfy-Org/HiDream-O1-Image/tree/main/checkpoints
+
+## Examples
+
+### HiDream-O1-Image-Dev
+
+```
+.\bin\Release\sd-cli.exe -m  ..\..\ComfyUI\models\diffusion_models\hidream_o1_image_dev_bf16.safetensors -p "a lovely cat holding a sign says 
+'hidream o1 cpp'" --cfg-scale 1.0  -v -H 1024 -W 1024
+```
+
+<img width="256" alt="HiDream-O1-Image-Dev example" src="../assets/hidream-o1/dev_example.png" />
+
@@ -26,12 +26,12 @@ Fortunately, `AMD` provides complete help documentation, you can use the help do

 Then we must set `ROCM` as environment variables before running cmake.

-Usually if you install according to the official tutorial and do not modify the ROCM path, then there is a high probability that it is here `C:\Program Files\AMD\ROCm\5.5\bin`
+Usually if you install according to the official tutorial and do not modify the ROCM path, then there is a high probability that it is here `C:\Program Files\AMD\ROCm\7.1.1\bin`

 This is what I use to set the clang:
 ```Commandline
-set CC=C:\Program Files\AMD\ROCm\5.5\bin\clang.exe
-set CXX=C:\Program Files\AMD\ROCm\5.5\bin\clang++.exe
+set CC=C:\Program Files\AMD\ROCm\7.1.1\bin\clang.exe
+set CXX=C:\Program Files\AMD\ROCm\7.1.1\bin\clang++.exe
 ```

 ## Ninja
@@ -46,7 +46,7 @@ set ninja=C:\Program Files\ninja\ninja.exe
 ## Building stable-diffusion.cpp

 The thing different from the regular CPU build is `-DSD_HIPBLAS=ON` ,
-`-G "Ninja"`, `-DCMAKE_C_COMPILER=clang`, `-DCMAKE_CXX_COMPILER=clang++`, `-DAMDGPU_TARGETS=gfx1100`
+`-G "Ninja"`, `-DCMAKE_C_COMPILER=clang`, `-DCMAKE_CXX_COMPILER=clang++`, `-DAMDGPU_TARGETS=gfx1150;gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032`

 >**Notice**: check the `clang` and `clang++` information:
 ```Commandline
@@ -59,26 +59,29 @@ If you see like this, we can continue:
 clang version 17.0.0 (git@github.amd.com:Compute-Mirrors/llvm-project e3201662d21c48894f2156d302276eb1cf47c7be)
 Target: x86_64-pc-windows-msvc
 Thread model: posix
-InstalledDir: C:\Program Files\AMD\ROCm\5.5\bin
+InstalledDir: C:\Program Files\AMD\ROCm\7.1.1\bin
 ```

 ```
 clang version 17.0.0 (git@github.amd.com:Compute-Mirrors/llvm-project e3201662d21c48894f2156d302276eb1cf47c7be)
 Target: x86_64-pc-windows-msvc
 Thread model: posix
-InstalledDir: C:\Program Files\AMD\ROCm\5.5\bin
+InstalledDir: C:\Program Files\AMD\ROCm\7.1.1\bin
 ```

->**Notice** that the `gfx1100` is the GPU architecture of my GPU, you can change it to your GPU architecture. Click here to see your architecture [LLVM Target](https://rocm.docs.amd.com/en/latest/release/windows_support.html#windows-supported-gpus)
+>**Notice** that the GPU targets are now compatible with multiple GPU architectures (ROCm 7.1.1 targets). You can change them to match your GPU architecture. Click here to see your architecture [LLVM Target](https://rocm.docs.amd.com/en/latest/release/windows_support.html#windows-supported-gpus)

-My GPU is AMD Radeon™ RX 7900 XTX Graphics, so I set it to `gfx1100`.
+Examples:
+- AMD Radeon™ RX 7900 XTX Graphics: `gfx1100`
+- AMD Radeon™ RX 7900 XT Graphics: `gfx1101`
+- AMD Radeon™ RX 7900 GRE Graphics: `gfx1102`

 option:

 ```commandline
 mkdir build
 cd build
-cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS=gfx1100
+cmake .. -G "Ninja" -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DSD_HIPBLAS=ON -DCMAKE_BUILD_TYPE=Release -DAMDGPU_TARGETS="gfx1150;gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"
 cmake --build . --config Release
 ```

@@ -0,0 +1,40 @@
+# How to Use
+
+## Download weights
+
+- Download Ideogram4
+    - safetensors: https://huggingface.co/ideogram-ai/ideogram-4-fp8/tree/main/transformer
+- Download Ideogram4 uncond
+    - safetensors: https://huggingface.co/ideogram-ai/ideogram-4-fp8/tree/main/unconditional_transformer
+- Download vae
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/main
+- Download Qwen3-VL-8B-Instruct
+    - gguf: https://huggingface.co/unsloth/Qwen3-VL-8B-Instruct-GGUF/tree/main
+
+## Convert weights
+
+fp8 scale -> bf16
+
+```
+python .\convert_fp8_scale_to_bf16.py --input .\ideogram4_fp8.safetensors --output ideogram4_bf16.safetensors
+python .\convert_fp8_scale_to_bf16.py --input .\ideogram4_uncond_fp8.safetensors --output ideogram4_uncond_bf16.safetensors
+```
+
+bf16 -> q8
+
+```
+.\bin\Release\sd-cli.exe -M convert -m ideogram4_bf16.safetensors -o ideogram4-Q8_0.gguf --tensor-type-rules "^layers.*adaln_modulation.*weight=q8_0,layers.*attention.o.*weight=q8_0,layers.*attention.qkv.*weight=q8_0,layers.*feed_forward.*weight=q8_0" -v
+
+.\bin\Release\sd-cli.exe -M convert -m ideogram4_uncond_bf16.safetensors -o ideogram4_uncond-Q8_0.gguf --tensor-type-rules "^layers.*adaln_modulation.*weight=q8_0,layers.*attention.o.*weight=q8_0,layers.*attention.qkv.*weight=q8_0,layers.*feed_forward.*weight=q8_0" -v
+```
+
+If you want lower VRAM usage, you can change the quantization from q8_0 to a lower-level quantization, such as q4_0.
+
+
+## Examples
+
+```sh
+.\bin\Release\sd-cli.exe --diffusion-model ideogram4-Q8_0.gguf --uncond-diffusion-model ideogram4_uncond-Q8_0.gguf --llm ..\..\llm\Qwen3VL-8B-Instruct-Q4_K_M.gguf --vae ..\..\ComfyUI\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" />
@@ -0,0 +1,32 @@
+# How to Use
+
+Lens uses a Lens diffusion transformer, the FLUX.2 VAE, and GPT-OSS-20B as the LLM text encoder.
+
+## Download weights
+
+- Download Lens
+    - safetensors: https://huggingface.co/Comfy-Org/Lens/tree/main/diffusion_models
+- Download Lens Turbo
+    - safetensors: https://huggingface.co/Comfy-Org/Lens/tree/main/diffusion_models
+- Download vae
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.2-dev/tree/main
+- Download GPT-OSS-20B
+    - gguf: https://huggingface.co/unsloth/gpt-oss-20b-GGUF/tree/main
+
+## Examples
+
+### Lens
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model ..\..\ComfyUI\models\diffusion_models\lens_bf16.safetensors --llm "..\..\llm\gpt-oss-20b-UD-Q8_K_XL.gguf" --vae ..\..\ComfyUI\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" />
+
+### Lens Turbo
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model ..\..\ComfyUI\models\diffusion_models\lens_turbo_bf16.safetensors --llm "..\..\llm\gpt-oss-20b-UD-Q8_K_XL.gguf" --vae ..\..\ComfyUI\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" />
@@ -0,0 +1,30 @@
+# How to Use
+
+LongCat-Image uses a LongCat diffusion transformer, the FLUX VAE, and Qwen2.5-VL as the LLM text encoder.
+
+## Download weights
+
+- Download LongCat Image
+    - safetensors: https://huggingface.co/Comfy-Org/LongCat-Image/tree/main/split_files/diffusion_models
+    - gguf: https://huggingface.co/vantagewithai/LongCat-Image-GGUF/tree/main/comfy
+- Download LongCat Image Edit
+    - LongCat Image Edit Turbo: https://huggingface.co/meituan-longcat/LongCat-Image-Edit-Turbo
+    - gguf: https://huggingface.co/vantagewithai/LongCat-Image-Edit-GGUF/tree/main
+- Download vae
+    - safetensors: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/ae.safetensors
+- Download qwen_2.5_vl 7b
+    - safetensors: https://huggingface.co/Comfy-Org/Qwen-Image_ComfyUI/tree/main/split_files/text_encoders
+    - gguf: https://huggingface.co/mradermacher/Qwen2.5-VL-7B-Instruct-GGUF/tree/main
+    - For image editing with GGUF text encoders, also download the matching mmproj file and pass it with `--llm_vision`.
+
+## Run
+
+LongCat uses quoted text for character-level text rendering. Put target text inside single quotes, double quotes, or Chinese quotes.
+
+### LongCat Image
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model  ..\..\ComfyUI\models\diffusion_models\LongCat-Image-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\ae.sft --llm ..\..\ComfyUI\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" />
@@ -0,0 +1,77 @@
+# How to Use
+
+## Download weights
+
+- Download LTX-2.3
+    - safetensors: https://huggingface.co/Kijai/LTX2.3_comfy/tree/main/diffusion_models
+    - gguf: https://huggingface.co/unsloth/LTX-2.3-GGUF/tree/main
+- Download gemma-3-12b-it
+    - gguf: https://huggingface.co/unsloth/gemma-3-12b-it-GGUF/tree/main
+- Download embeddings connectors
+    - safetensors: https://huggingface.co/unsloth/LTX-2.3-GGUF/tree/main/text_encoders
+- Download vae
+    - safetensors: https://huggingface.co/unsloth/LTX-2.3-GGUF/tree/main/vae
+- Download audio vae
+    - safetensors: https://huggingface.co/unsloth/LTX-2.3-GGUF/tree/main/vae
+- Download LTX spatial latent upscaler
+    - safetensors: https://huggingface.co/Lightricks/LTX-2.3/resolve/main/ltx-2.3-spatial-upscaler-x2-1.1.safetensors
+
+## Examples
+
+### LTX-2.3 dev T2V
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\..\ComfyUI\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\..\ComfyUI\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
+  src="../assets/ltx2/t2v.webm"
+  controls
+  muted
+  style="max-width: 100%; height: auto;"></video>
+
+### LTX-2.3 dev I2V
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\..\ComfyUI\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\..\ComfyUI\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
+  src="../assets/ltx2/i2v.webm"
+  controls
+  muted
+  style="max-width: 100%; height: auto;"></video>
+
+### LTX-2.3 dev FLF2V
+
+```
+.\bin\Release\sd-cli.exe -M vid_gen --diffusion-model  ..\..\ComfyUI\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\..\ComfyUI\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\..\ComfyUI\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
+  src="../assets/ltx2/flf2v.webm"
+  controls
+  muted
+  style="max-width: 100%; height: auto;"></video>
+
+### LTX-2.3 spatial latent upscale
+
+LTX spatial latent upscale runs a model-backed x2 latent upsampler between the low-resolution video pass and the high-resolution refine pass. `-W` and `-H` are the pre-upscale generation size; the spatial upsampler produces x2 latent dimensions.
+
+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  ..\..\ComfyUI\models\diffusion_models\ltx-2.3-22b-dev-UD-Q4_K_M.gguf --vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_video_vae.safetensors --audio-vae ..\..\ComfyUI\models\vae\ltx-2.3-22b-dev_audio_vae.safetensors --llm ..\..\ComfyUI\models\text_encoders\gemma-3-12b-it-qat-UD-Q4_K_XL.gguf --embeddings-connectors ..\..\ComfyUI\models\text_encoders\ltx-2.3-22b-dev_embeddings_connectors.safetensors --hires-upscalers-dir ..\..\ComfyUI\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:
+
+```
+--hires-sigmas "0.85,0.725,0.421875,0.0"
+```
+
+<video
+  src="../assets/ltx2/hires_i2v.webm"
+  controls
+  muted
+  style="max-width: 100%; height: auto;"></video>
@@ -0,0 +1,118 @@
+# Model Configuration Conventions
+
+This document describes the conventions for model configuration structs and
+weight-based configuration detection.
+
+## Config Types
+
+Model configuration should live in a model-specific `*Config` struct.
+
+Examples:
+
+- `ZImageConfig`
+- `UNetConfig`
+- `MMDiTConfig`
+- `LLMConfig`
+
+Preserve established acronym casing in type names, such as `UNet`, `MMDiT`,
+`LLM`, `VAE`, and `T5`.
+
+Place the config struct near the top of the model header, before the main model
+blocks and runner types that consume it.
+
+## Config Variables
+
+Variables and members that hold a config should be named `config`.
+
+Examples:
+
+```cpp
+UNetConfig config;
+UnetModelBlock unet;
+
+MMDiTRunner(...)
+    : DiffusionModelRunner(backend, params_backend, prefix),
+      config(MMDiTConfig::detect_from_weights(tensor_storage_map, prefix)),
+      mmdit(config) {
+}
+```
+
+Avoid alternate names such as `params`, `params_cfg`, `model_params`, or
+model-specific aliases unless an existing public API requires them.
+
+## Weight Detection
+
+If a model can derive configuration from loaded weight metadata, expose that
+logic as a static method on the config type:
+
+```cpp
+static XxxConfig detect_from_weights(const String2TensorStorage& tensor_storage_map,
+                                     const std::string& prefix);
+```
+
+Additional selector arguments are allowed when required by an existing model
+family, for example `SDVersion version` or an architecture enum:
+
+```cpp
+static UNetConfig detect_from_weights(const String2TensorStorage& tensor_storage_map,
+                                      const std::string& prefix,
+                                      SDVersion version = VERSION_SD1);
+```
+
+Use `TensorStorage` metadata, especially `n_dims` and `ne`, to infer shapes.
+Do not load or parse tensor data for config detection.
+
+Detection should respect `prefix`. For nested weights, construct full names from
+`prefix + "." + suffix` or filter entries with `starts_with(name, prefix)`.
+
+Do not add persistent config fields such as `inferred_from_weights` only to
+record whether detection happened. If the function needs to decide whether to
+print a debug line, keep that as local control flow inside `detect_from_weights`.
+
+## Logging
+
+When config values are inferred from weights, print one `LOG_DEBUG` line at the
+end of `detect_from_weights`.
+
+Example:
+
+```cpp
+LOG_DEBUG("llm: num_layers = %" PRId64 ", vocab_size = %" PRId64 ", hidden_size = %" PRId64 ", intermediate_size = %" PRId64,
+          config.num_layers,
+          config.vocab_size,
+          config.hidden_size,
+          config.intermediate_size);
+```
+
+Only print the config detection log when the function actually inferred values
+from weights. Do not duplicate the same config summary in runner constructors or
+model loading code.
+
+Use the correct format specifiers for field types, such as `%" PRId64 "` for
+`int64_t` and `%d` for `int`.
+
+## Runner And Model Responsibilities
+
+Runners should detect the config once and pass it into the model block:
+
+```cpp
+struct XxxRunner : public DiffusionModelRunner {
+    XxxConfig config;
+    XxxModel model;
+
+    XxxRunner(..., const String2TensorStorage& tensor_storage_map, const std::string prefix)
+        : DiffusionModelRunner(backend, params_backend, prefix),
+          config(XxxConfig::detect_from_weights(tensor_storage_map, prefix)),
+          model(config) {
+        model.init(params_ctx, tensor_storage_map, prefix);
+    }
+};
+```
+
+Model blocks should consume `config` directly instead of re-scanning weights in
+their constructors. Keep config-derived behavior centralized in the config
+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.
@@ -0,0 +1,39 @@
+# How to Use
+
+PiD is NVIDIA's Pixel Diffusion Decoder. It replaces the usual VAE decode or decode-then-upscale path with a pixel-space diffusion decoder conditioned on a
+source latent and text prompt.
+
+In stable-diffusion.cpp, PiD currently runs as an image edit pipeline: provide a reference image with `-r`/`--ref-image`, encode that image with a matching VAE, then let the PiD diffusion model decode/upscale directly to RGB.
+
+## Download weights
+
+- Download PiD
+    - safetensors: https://huggingface.co/Comfy-Org/PixelDiT/tree/main/diffusion_models
+- Download Gemma 2 2B
+    - safetensors: https://huggingface.co/Comfy-Org/PixelDiT/tree/main/text_encoders
+- Download the VAE that matches the PiD checkpoint backbone
+    - safetensors: https://huggingface.co/nvidia/PiD/tree/main/checkpoints
+    - Flux / Z-Image PiD: use the Flux VAE and pass `--vae-format flux`
+    - SD3 PiD: use the SD3 VAE and pass `--vae-format sd3`
+    - Flux.2 PiD: use the Flux.2 VAE and pass `--vae-format flux2`
+
+The official PiD model card should be checked before use. At the time of the initial PiD release, the official weights are under the NSCLv1 non-commercial license.
+
+## Examples
+
+```
+.\bin\Release\sd-cli.exe --diffusion-model ..\..\ComfyUI\models\diffusion_models\pid_flux1_512_to_2048_4step_bf16.safetensors --llm "..\..\ComfyUI\models\text_encoders\gemma_2_2b_it_elm_bf16.safetensors" --vae ..\..\ComfyUI\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:
+
+<img width="256" alt="ERNIE-Image Turbo example" src="../assets/ernie_image/turbo_example.png" />
+
+After:
+<img width="1024" alt="PiD example" src="../assets/pid/example.png" />
+
+## Notes
+
+- `-r`/`--ref-image` is required. PiD uses the first reference image as the source latent condition.
+- `--vae-format` should match the VAE latent layout used by the PiD checkpoint. This is important when using standalone VAE files because the PiD diffusion
+  checkpoint alone does not identify the VAE format.
@@ -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 ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\Qwen3-4B-Instruct-2507-Q4_K_M.gguf -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 1.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512
+.\bin\Release\sd-cli.exe --diffusion-model  z_image_turbo-Q3_K.gguf --vae ..\..\ComfyUI\models\vae\ae.sft  --llm ..\..\ComfyUI\models\text_encoders\Qwen3-4B-Instruct-2507-Q4_K_M.gguf -p "A cinematic, melancholic photograph of a solitary hooded figure walking through a sprawling, rain-slicked metropolis at night. The city lights are a chaotic blur of neon orange and cool blue, reflecting on the wet asphalt. The scene evokes a sense of being a single component in a vast machine. Superimposed over the image in a sleek, modern, slightly glitched font is the philosophical quote: 'THE CITY IS A CIRCUIT BOARD, AND I AM A BROKEN TRANSISTOR.' -- moody, atmospheric, profound, dark academic" --cfg-scale 1.0 -v --offload-to-cpu --diffusion-fa -H 1024 -W 512 --steps 8
 ```

 <img width="256" alt="z-image example" src="../assets/z_image/q3_K.png" />
@@ -7,6 +7,13 @@ add_executable(${TARGET}
    image_metadata.cpp
    main.cpp
 )
+if(APPLE)
+    sd_set_macos_rpaths(${TARGET})
+endif()
+target_include_directories(${TARGET} PRIVATE
+    "${CMAKE_CURRENT_SOURCE_DIR}/.."
+    "${PROJECT_SOURCE_DIR}/src"
+)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE stable-diffusion zip ${CMAKE_THREAD_LIBS_INIT})
 if(SD_WEBP)
@@ -4,26 +4,29 @@
 usage: ./bin/sd-cli  [options]

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

 Context Options:
  -m, --model <string>                     path to full model
@@ -31,28 +34,34 @@ Context Options:
  --clip_g <string>                        path to the clip-g text encoder
  --clip_vision <string>                   path to the clip-vision encoder
  --t5xxl <string>                         path to the t5xxl text encoder
-  --llm <string>                           path to the llm text encoder. For example: (qwenvl2.5 for qwen-image, mistral-small3.2 for flux2, ...)
+  --llm <string>                           path to the llm text encoder. For example: (qwenvl2.5 for qwen-image,
+                                           mistral-small3.2 for flux2, ...)
  --llm_vision <string>                    path to the llm vit
  --qwen2vl <string>                       alias of --llm. Deprecated.
  --qwen2vl_vision <string>                alias of --llm_vision. Deprecated.
  --diffusion-model <string>               path to the standalone diffusion model
  --high-noise-diffusion-model <string>    path to the standalone high noise diffusion model
+  --uncond-diffusion-model <string>        path to the standalone unconditional diffusion model, currently used by
+                                           Ideogram4 CFG
  --vae <string>                           path to standalone vae model
  --taesd <string>                         path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
  --tae <string>                           alias of --taesd
  --control-net <string>                   path to control net model
  --embd-dir <string>                      embeddings directory
  --lora-model-dir <string>                lora model directory
+  --hires-upscalers-dir <string>           highres fix upscaler model directory
  --tensor-type-rules <string>             weight type per tensor pattern (example: "^vae\.=f16,model\.=q8_0")
  --photo-maker <string>                   path to PHOTOMAKER model
  --upscale-model <string>                 path to esrgan model.
-  -t, --threads <int>                      number of threads to use during computation (default: -1). If threads <= 0, then threads will be set to the number of
-                                           CPU physical cores
+  -t, --threads <int>                      number of threads to use during computation (default: -1). If threads <= 0,
+                                           then threads will be set to the number of CPU physical cores
  --chroma-t5-mask-pad <int>               t5 mask pad size of chroma
-  --vae-tile-overlap <float>               tile overlap for vae tiling, in fraction of tile size (default: 0.5)
-  --vae-tiling                             process vae in tiles to reduce memory usage
+  --max-vram <float>                       maximum VRAM budget in GiB for graph-cut segmented execution. 0 disables
+                                           graph splitting; a negative value auto-detects free VRAM, sparing the
+                                           specified value (e.g. -0.5 will keep at least 0.5 GiB free)
  --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
-  --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
+  --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM
+                                           when needed
  --mmap                                   whether to memory-map model
  --control-net-cpu                        keep controlnet in cpu (for low vram)
  --clip-on-cpu                            keep clip in cpu (for low vram)
@@ -67,20 +76,19 @@ Context Options:
  --chroma-disable-dit-mask                disable dit mask for chroma
  --qwen-image-zero-cond-t                 enable zero_cond_t for qwen image
  --chroma-enable-t5-mask                  enable t5 mask for chroma
-  --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). If not specified, the default is the
-                                           type of the weight file
+  --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K,
+                                           q4_K). If not specified, the default is the type of the weight file
  --rng                                    RNG, one of [std_default, cuda, cpu], default: cuda(sd-webui), cpu(comfyui)
  --sampler-rng                            sampler RNG, one of [std_default, cuda, cpu]. If not specified, use --rng
-  --prediction                             prediction type override, one of [eps, v, edm_v, sd3_flow, flux_flow, flux2_flow]
-  --lora-apply-mode                        the way to apply LoRA, one of [auto, immediately, at_runtime], default is auto. In auto mode, if the model weights
-                                           contain any quantized parameters, the at_runtime mode will be used; otherwise,
-                                           immediately will be used.The immediately mode may have precision and
-                                           compatibility issues with quantized parameters, but it usually offers faster inference
-                                           speed and, in some cases, lower memory usage. The at_runtime mode, on the
-                                           other hand, is exactly the opposite.
-  --vae-tile-size                          tile size for vae tiling, format [X]x[Y] (default: 32x32)
-  --vae-relative-tile-size                 relative tile size for vae tiling, format [X]x[Y], in fraction of image size if < 1, in number of tiles per dim if >=1
-                                           (overrides --vae-tile-size)
+  --prediction                             prediction type override, one of [eps, v, edm_v, sd3_flow, flux_flow,
+                                           flux2_flow]
+  --lora-apply-mode                        the way to apply LoRA, one of [auto, immediately, at_runtime], default is
+                                           auto. In auto mode, if the model weights contain any quantized parameters,
+                                           the at_runtime mode will be used; otherwise, immediately will be used.The
+                                           immediately mode may have precision and compatibility issues with quantized
+                                           parameters, but it usually offers faster inference speed and, in some cases,
+                                           lower memory usage. The at_runtime mode, on the other hand, is exactly the
+                                           opposite.

 Generation Options:
  -p, --prompt <string>                    the prompt to render
@@ -89,69 +97,108 @@ Generation Options:
  --end-img <string>                       path to the end image, required by flf2v
  --mask <string>                          path to the mask image
  --control-image <string>                 path to control image, control net
-  --control-video <string>                 path to control video frames, It must be a directory path. The video frames inside should be stored as images in
-                                           lexicographical (character) order. For example, if the control video path is
-                                           `frames`, the directory contain images such as 00.png, 01.png, ... etc.
+  --control-video <string>                 path to control video frames, It must be a directory path. The video frames
+                                           inside should be stored as images in lexicographical (character) order. For
+                                           example, if the control video path is `frames`, the directory contain images
+                                           such as 00.png, 01.png, ... etc.
  --pm-id-images-dir <string>              path to PHOTOMAKER input id images dir
  --pm-id-embed-path <string>              path to PHOTOMAKER v2 id embed
+  --hires-upscaler <string>                highres fix upscaler, Lanczos, Nearest, Latent, Latent (nearest), Latent
+                                           (nearest-exact), Latent (antialiased), Latent (bicubic), Latent (bicubic
+                                           antialiased), or a model name under --hires-upscalers-dir (default: Latent)
+  --extra-sample-args <string>             extra sampler/scheduler/guidance args, key=value list. APG supports apg_eta,
+                                           apg_momentum, apg_norm_threshold, apg_norm_threshold_smoothing; SLG supports
+                                           slg_uncond; lcm supports noise_clip_std, noise_scale_start, noise_scale_end;
+                                           ltx2 supports max_shift, base_shift, stretch, terminal; euler_ge supports gamma
+  --extra-tiling-args <string>             extra VAE tiling args, key=value list. LTX video VAE supports
+                                           temporal_tile_frames (default: 4), temporal_tile_overlap (default: 1)
  -H, --height <int>                       image height, in pixel space (default: 512)
  -W, --width <int>                        image width, in pixel space (default: 512)
  --steps <int>                            number of sample steps (default: 20)
  --high-noise-steps <int>                 (high noise) number of sample steps (default: -1 = auto)
-  --clip-skip <int>                        ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1). <= 0 represents unspecified,
-                                           will be 1 for SD1.x, 2 for SD2.x
+  --clip-skip <int>                        ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer
+                                           (default: -1). <= 0 represents unspecified, will be 1 for SD1.x, 2 for SD2.x
  -b, --batch-count <int>                  batch count
  --video-frames <int>                     video frames (default: 1)
  --fps <int>                              fps (default: 24)
-  --timestep-shift <int>                   shift timestep for NitroFusion models (default: 0). recommended N for NitroSD-Realism around 250 and 500 for
-                                           NitroSD-Vibrant
+  --timestep-shift <int>                   shift timestep for NitroFusion models (default: 0). recommended N for
+                                           NitroSD-Realism around 250 and 500 for NitroSD-Vibrant
  --upscale-repeats <int>                  Run the ESRGAN upscaler this many times (default: 1)
  --upscale-tile-size <int>                tile size for ESRGAN upscaling (default: 128)
+  --hires-width <int>                      highres fix target width, 0 to use --hires-scale (default: 0)
+  --hires-height <int>                     highres fix target height, 0 to use --hires-scale (default: 0)
+  --hires-steps <int>                      highres fix second pass sample steps, 0 to reuse --steps (default: 0)
+  --hires-upscale-tile-size <int>          highres fix upscaler tile size, reserved for model-backed upscalers (default:
+                                           128)
  --cfg-scale <float>                      unconditional guidance scale: (default: 7.0)
-  --img-cfg-scale <float>                  image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)
+  --img-cfg-scale <float>                  image guidance scale for inpaint or image edit models: (default: same as
+                                           --cfg-scale)
  --guidance <float>                       distilled guidance scale for models with guidance input (default: 3.5)
-  --slg-scale <float>                      skip layer guidance (SLG) scale, only for DiT models: (default: 0). 0 means disabled, a value of 2.5 is nice for sd3.5
-                                           medium
+  --slg-scale <float>                      skip layer guidance (SLG) scale, only for DiT models: (default: 0). 0 means
+                                           disabled, a value of 2.5 is nice for sd3.5 medium
  --skip-layer-start <float>               SLG enabling point (default: 0.01)
  --skip-layer-end <float>                 SLG disabling point (default: 0.2)
-  --eta <float>                            noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a, er_sde and dpm++2s_a)
+  --eta <float>                            noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and
+                                           res_2s; 1 for euler_a, er_sde and dpm++2s_a)
  --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
  --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
-  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
-  --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input (default: 3.5)
-  --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
+  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or image edit models (default:
+                                           same as --cfg-scale)
+  --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input
+                                           (default: 3.5)
+  --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default:
+                                           0)
  --high-noise-skip-layer-start <float>    (high noise) SLG enabling point (default: 0.01)
  --high-noise-skip-layer-end <float>      (high noise) SLG disabling point (default: 0.2)
-  --high-noise-eta <float>                 (high noise) noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a, er_sde and dpm++2s_a)
+  --high-noise-eta <float>                 (high noise) noise multiplier (default: 0 for ddim_trailing, tcd,
+                                           res_multistep and res_2s; 1 for euler_a, er_sde and dpm++2s_a)
  --strength <float>                       strength for noising/unnoising (default: 0.75)
  --pm-style-strength <float>
-  --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
-  --moe-boundary <float>                   timestep boundary for Wan2.2 MoE model. (default: 0.875). Only enabled if `--high-noise-steps` is set to -1
+  --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full
+                                           destruction of information in init image
+  --moe-boundary <float>                   timestep boundary for Wan2.2 MoE model. (default: 0.875). Only enabled if
+                                           `--high-noise-steps` is set to -1
  --vace-strength <float>                  wan vace strength
-  --increase-ref-index                     automatically increase the indices of references images based on the order they are listed (starting with 1).
+  --vae-tile-overlap <float>               tile overlap for vae tiling, in fraction of tile size (default: 0.5)
+  --hires-scale <float>                    highres fix scale when target size is not set (default: 2.0)
+  --hires-denoising-strength <float>       highres fix second pass denoising strength (default: 0.7)
+  --increase-ref-index                     automatically increase the indices of references images based on the order
+                                           they are listed (starting with 1).
  --disable-auto-resize-ref-image          disable auto resize of ref images
  --disable-image-metadata                 do not embed generation metadata on image files
+  --vae-tiling                             process vae in tiles to reduce memory usage
+  --temporal-tiling                        enable temporal tiling for LTX video VAE decode
+  --hires                                  enable highres fix
  -s, --seed                               RNG seed (default: 42, use random seed for < 0)
-  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
-                                           tcd, res_multistep, res_2s, er_sde] (default: euler for Flux/SD3/Wan, euler_a
-                                           otherwise)
-  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
-                                           ddim_trailing, tcd, res_multistep, res_2s, er_sde] default: euler for Flux/SD3/Wan,
-                                           euler_a otherwise
-  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple,
-                                           kl_optimal, lcm, bong_tangent], default: discrete
-  --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
+  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m,
+                                           dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s,
+                                           er_sde, euler_cfg_pp, euler_a_cfg_pp] (default: euler for Flux/SD3/Wan, euler_a otherwise)
+  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a,
+                                           dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep,
+                                           res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp] default: euler for Flux/SD3/Wan, euler_a otherwise
+  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits,
+                                           smoothstep, sgm_uniform, simple, kl_optimal, lcm, bong_tangent, ltx2], default:
+                                           model-specific
+  --sigmas                                 custom sigma values for the sampler, comma-separated (e.g.,
+                                           "14.61,7.8,3.5,0.0").
+  --hires-sigmas                           custom sigma values for the highres fix second pass, comma-separated (e.g.,
+                                           "0.85,0.725,0.421875,0.0").
  --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
  --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
  -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
-  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level),
-                                           'spectrum' (UNET/DiT Chebyshev+Taylor forecasting)
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET),
+                                           'dbcache'/'taylorseer'/'cache-dit' (DiT block-level), 'spectrum' (UNET/DiT
+                                           Chebyshev+Taylor forecasting)
  --cache-option                           named cache params (key=value format, comma-separated). easycache/ucache:
-                                           threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=;
-                                           spectrum: w=,m=,lam=,window=,flex=,warmup=,stop=. Examples:
-                                           "threshold=0.25" or "threshold=1.5,reset=0" or "w=0.4,window=2"
-  --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g., "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
+                                           threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit:
+                                           Fn=,Bn=,threshold=,warmup=; spectrum: w=,m=,lam=,window=,flex=,warmup=,stop=.
+                                           Examples: "threshold=0.25" or "threshold=1.5,reset=0"
+  --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g.,
+                                           "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
  --scm-policy                             SCM policy: 'dynamic' (default) or 'static'
+  --vae-tile-size                          tile size for vae tiling, format [X]x[Y] (default: 32x32)
+  --vae-relative-tile-size                 relative tile size for vae tiling, format [X]x[Y], in fraction of image size
+                                           if < 1, in number of tiles per dim if >=1 (overrides --vae-tile-size)
 ```

 Metadata mode inspects PNG/JPEG container metadata without loading any model:
@@ -169,8 +169,9 @@ struct SDCliParams {
            return 1;
        };

-        auto on_help_arg = [&](int argc, const char** argv, int index) {
+        auto on_help_arg = [&](int argc, const char** argv, int index, bool& valid) {
            normal_exit = true;
+            valid       = true;
            return -1;
        };

@@ -278,7 +279,9 @@ void parse_args(int argc, const char** argv, SDCliParams& cli_params, SDContextP
    bool valid = cli_params.resolve_and_validate();
    if (valid && cli_params.mode != METADATA) {
        valid = ctx_params.resolve_and_validate(cli_params.mode) &&
-                gen_params.resolve_and_validate(cli_params.mode, ctx_params.lora_model_dir);
+                gen_params.resolve_and_validate(cli_params.mode,
+                                                ctx_params.lora_model_dir,
+                                                ctx_params.hires_upscalers_dir);
    }

    if (!valid) {
@@ -383,11 +386,32 @@ std::string format_frame_idx(std::string pattern, int frame_idx) {
    return result;
 }

+static fs::path get_video_audio_sidecar_path(const SDCliParams& cli_params) {
+    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.string();
+    std::transform(ext_lower.begin(), ext_lower.end(), ext_lower.begin(), ::tolower);
+    const EncodedImageFormat output_format = encoded_image_format_from_path(out_path.string());
+    if (!ext.empty()) {
+        if (output_format == EncodedImageFormat::JPEG ||
+            output_format == EncodedImageFormat::PNG ||
+            output_format == EncodedImageFormat::WEBP ||
+            ext_lower == ".avi" ||
+            ext_lower == ".webm") {
+            base_path.replace_extension();
+        }
+    }
+    base_path += ".wav";
+    return base_path;
+}
+
 bool save_results(const SDCliParams& cli_params,
                  const SDContextParams& ctx_params,
                  const SDGenerationParams& gen_params,
                  sd_image_t* results,
-                  int num_results) {
+                  int num_results,
+                  const sd_audio_t* generated_audio = nullptr) {
    if (results == nullptr || num_results <= 0) {
        return false;
    }
@@ -431,14 +455,30 @@ bool save_results(const SDCliParams& cli_params,
        if (!img.data)
            return false;

-        std::string params = gen_params.embed_image_metadata
-                                 ? get_image_params(ctx_params, gen_params, gen_params.seed + idx)
-                                 : "";
-        const bool ok      = write_image_to_file(path.string(), img.data, img.width, img.height, img.channel, params, 90);
+        const int64_t metadata_seed = cli_params.mode == VID_GEN ? gen_params.seed : gen_params.seed + idx;
+        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, 90);
        LOG_INFO("save result image %d to '%s' (%s)", idx, path.string().c_str(), ok ? "success" : "failure");
        return ok;
    };

+    auto write_audio_sidecar = [&](const fs::path& wav_path) {
+        if (generated_audio == nullptr) {
+            return;
+        }
+        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.string().c_str());
+        } else {
+            LOG_WARN("failed to save result audio to '%s'", wav_path.string().c_str());
+        }
+    };
+
    int sucessful_reults = 0;

    if (std::regex_search(cli_params.output_path, format_specifier_regex)) {
@@ -462,8 +502,16 @@ bool save_results(const SDCliParams& cli_params,
            ext = ".avi";
        fs::path video_path = base_path;
        video_path += ext;
-        if (create_video_from_sd_images(video_path.string().c_str(), results, num_results, gen_params.fps) == 0) {
+        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.string().c_str(), results, num_results, gen_params.fps, 90, mux_audio ? generated_audio : nullptr) == 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");
+                write_audio_sidecar(wav_path);
+            }
            return true;
        } else {
            LOG_ERROR("Failed to save result video to '%s'", video_path.string().c_str());
@@ -485,6 +533,9 @@ bool save_results(const SDCliParams& cli_params,
        }
    }
    LOG_INFO("%d/%d images saved", sucessful_reults, num_results);
+    if (generated_audio != nullptr) {
+        write_audio_sidecar(get_video_audio_sidecar_path(cli_params));
+    }
    return sucessful_reults != 0;
 }

@@ -688,10 +739,18 @@ int main(int argc, const char* argv[]) {
        vae_decode_only = false;
    }

+    if (gen_params.hires_enabled &&
+        (gen_params.resolved_hires_upscaler == SD_HIRES_UPSCALER_MODEL ||
+         gen_params.resolved_hires_upscaler == SD_HIRES_UPSCALER_LANCZOS ||
+         gen_params.resolved_hires_upscaler == SD_HIRES_UPSCALER_NEAREST)) {
+        vae_decode_only = false;
+    }
+
    sd_ctx_params_t sd_ctx_params = ctx_params.to_sd_ctx_params_t(vae_decode_only, true, cli_params.taesd_preview);

    SDImageVec results;
-    int num_results = 0;
+    int num_results             = 0;
+    sd_audio_t* generated_audio = nullptr;

    if (cli_params.mode == UPSCALE) {
        num_results = 1;
@@ -723,7 +782,10 @@ int main(int argc, const char* argv[]) {
            results.adopt(generate_image(sd_ctx.get(), &img_gen_params), num_results);
        } else if (cli_params.mode == VID_GEN) {
            sd_vid_gen_params_t vid_gen_params = gen_params.to_sd_vid_gen_params_t();
-            sd_image_t* generated_video        = generate_video(sd_ctx.get(), &vid_gen_params, &num_results);
+            sd_image_t* generated_video        = nullptr;
+            if (!generate_video(sd_ctx.get(), &vid_gen_params, &generated_video, &num_results, &generated_audio)) {
+                generated_video = nullptr;
+            }
            results.adopt(generated_video, num_results);
        }

@@ -739,7 +801,9 @@ int main(int argc, const char* argv[]) {
                                                     ctx_params.offload_params_to_cpu,
                                                     ctx_params.diffusion_conv_direct,
                                                     ctx_params.n_threads,
-                                                     gen_params.upscale_tile_size));
+                                                     gen_params.upscale_tile_size,
+                                                     ctx_params.backend.c_str(),
+                                                     ctx_params.params_backend.c_str()));

        if (upscaler_ctx == nullptr) {
            LOG_ERROR("new_upscaler_ctx failed");
@@ -763,9 +827,12 @@ int main(int argc, const char* argv[]) {
        }
    }

-    if (!save_results(cli_params, ctx_params, gen_params, results.data(), num_results)) {
+    if (!save_results(cli_params, ctx_params, gen_params, results.data(), num_results, generated_audio)) {
+        free_sd_audio(generated_audio);
        return 1;
    }

+    free_sd_audio(generated_audio);
+
    return 0;
 }
@@ -56,11 +56,42 @@ struct BoolOption {
    bool* target;
 };

+struct ManualFunction {
+    std::function<int(int, const char**, int, bool&)> _func;
+
+    ManualFunction() = default;
+
+    ManualFunction(std::function<int(int argc, const char** argv, int index, bool& valid)> func)
+        : _func(std::move(func)) {
+    }
+
+    template <typename F>
+    ManualFunction(F func)
+        : _func(make_function(func)) {
+    }
+
+    int operator()(int argc, const char** argv, int index, bool& valid) const {
+        return _func(argc, argv, index, valid);
+    }
+
+private:
+    template <typename F>
+    static std::function<int(int, const char**, int, bool&)> make_function(F func) {
+        if constexpr (std::is_invocable_v<F, int, const char**, int, bool&>) {
+            return func;
+        } else {
+            return [func](int argc, const char** argv, int index, bool&) {
+                return func(argc, argv, index);
+            };
+        }
+    }
+};
+
 struct ManualOption {
    std::string short_name;
    std::string long_name;
    std::string desc;
-    std::function<int(int argc, const char** argv, int index)> cb;
+    ManualFunction cb;
 };

 struct ArgOptions {
@@ -92,7 +123,11 @@ struct SDContextParams {
    std::string llm_vision_path;
    std::string diffusion_model_path;
    std::string high_noise_diffusion_model_path;
+    std::string uncond_diffusion_model_path;
+    std::string embeddings_connectors_path;
    std::string vae_path;
+    std::string vae_format = "auto";
+    std::string audio_vae_path;
    std::string taesd_path;
    std::string esrgan_path;
    std::string control_net_path;
@@ -101,6 +136,7 @@ struct SDContextParams {
    sd_type_t wtype = SD_TYPE_COUNT;
    std::string tensor_type_rules;
    std::string lora_model_dir = ".";
+    std::string hires_upscalers_dir;

    std::map<std::string, std::string> embedding_map;
    std::vector<sd_embedding_t> embedding_vec;
@@ -108,14 +144,18 @@ struct SDContextParams {
    rng_type_t rng_type         = CUDA_RNG;
    rng_type_t sampler_rng_type = RNG_TYPE_COUNT;
    bool offload_params_to_cpu  = false;
-    bool enable_mmap            = false;
-    bool control_net_cpu        = false;
-    bool clip_on_cpu            = false;
-    bool vae_on_cpu             = false;
-    bool flash_attn             = false;
-    bool diffusion_flash_attn   = false;
-    bool diffusion_conv_direct  = false;
-    bool vae_conv_direct        = false;
+    float max_vram              = 0.f;
+    bool stream_layers          = false;
+    std::string backend;
+    std::string params_backend;
+    bool enable_mmap           = false;
+    bool control_net_cpu       = false;
+    bool clip_on_cpu           = false;
+    bool vae_on_cpu            = false;
+    bool flash_attn            = false;
+    bool diffusion_flash_attn  = false;
+    bool diffusion_conv_direct = false;
+    bool vae_conv_direct       = false;

    bool circular   = false;
    bool circular_x = false;
@@ -166,6 +206,8 @@ struct SDGenerationParams {

    sd_sample_params_t sample_params;
    sd_sample_params_t high_noise_sample_params;
+    std::string extra_sample_args;
+    std::string high_noise_extra_sample_args;
    std::vector<int> skip_layers            = {7, 8, 9};
    std::vector<int> high_noise_skip_layers = {7, 8, 9};

@@ -181,7 +223,8 @@ struct SDGenerationParams {
    int video_frames                     = 1;
    int fps                              = 16;
    float vace_strength                  = 1.f;
-    sd_tiling_params_t vae_tiling_params = {false, 0, 0, 0.5f, 0.0f, 0.0f};
+    sd_tiling_params_t vae_tiling_params = {false, false, 0, 0, 0.5f, 0.0f, 0.0f, nullptr};
+    std::string extra_tiling_args;

    std::string pm_id_images_dir;
    std::string pm_id_embed_path;
@@ -190,12 +233,24 @@ struct SDGenerationParams {
    int upscale_repeats   = 1;
    int upscale_tile_size = 128;

+    bool hires_enabled         = false;
+    std::string hires_upscaler = "Latent";
+    std::string hires_upscaler_model_path;
+    float hires_scale              = 2.f;
+    int hires_width                = 0;
+    int hires_height               = 0;
+    int hires_steps                = 0;
+    float hires_denoising_strength = 0.7f;
+    int hires_upscale_tile_size    = 128;
+    std::vector<float> hires_custom_sigmas;
+
    std::map<std::string, float> lora_map;
    std::map<std::string, float> high_noise_lora_map;

    // Derived and normalized fields.
    std::string prompt_with_lora;  // for metadata record only
    std::vector<sd_lora_t> lora_vec;
+    sd_hires_upscaler_t resolved_hires_upscaler;

    // Owned execution payload.
    SDImageOwner init_image;
@@ -225,15 +280,25 @@ struct SDGenerationParams {
    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, bool strict = 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, bool strict = false);
+    bool resolve_and_validate(SDMode mode,
+                              const std::string& lora_model_dir,
+                              const std::string& hires_upscalers_dir,
+                              bool strict = false);
    sd_img_gen_params_t to_sd_img_gen_params_t();
    sd_vid_gen_params_t to_sd_vid_gen_params_t();
    std::string to_string() const;
 };

 std::string version_string();
-std::string get_image_params(const SDContextParams& ctx_params, const SDGenerationParams& gen_params, int64_t seed);
+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__
@@ -613,6 +613,13 @@ typedef struct {
    uint32_t size;
 } avi_index_entry;

+typedef struct {
+    char fourcc[4];
+    uint32_t flags;
+    uint32_t offset;
+    uint32_t size;
+} avi_chunk_index_entry;
+
 void write_u32_le(FILE* f, uint32_t val) {
    fwrite(&val, 4, 1, f);
 }
@@ -647,6 +654,33 @@ void write_fourcc(std::vector<uint8_t>& data, const char* fourcc) {
    data.insert(data.end(), fourcc, fourcc + 4);
 }

+static std::vector<uint8_t> audio_to_pcm16_bytes(const sd_audio_t* audio) {
+    if (audio == nullptr || audio->data == nullptr || audio->sample_count == 0 || audio->channels == 0 || audio->sample_rate == 0) {
+        return {};
+    }
+
+    const size_t pcm_samples = static_cast<size_t>(audio->sample_count) * static_cast<size_t>(audio->channels);
+    std::vector<uint8_t> bytes(pcm_samples * sizeof(int16_t));
+    auto* pcm = reinterpret_cast<int16_t*>(bytes.data());
+    for (size_t i = 0; i < pcm_samples; ++i) {
+        const float sample = std::clamp(audio->data[i], -1.0f, 1.0f);
+        pcm[i]             = static_cast<int16_t>(std::lrint(sample * 32767.0f));
+    }
+    return bytes;
+}
+
+static std::pair<uint64_t, uint64_t> audio_sample_range_for_video_frame(const sd_audio_t* audio, int frame_idx, int num_frames, int fps) {
+    if (audio == nullptr || fps <= 0 || num_frames <= 0) {
+        return {0, 0};
+    }
+    const uint64_t total = audio->sample_count;
+    const uint64_t start = static_cast<uint64_t>((static_cast<long double>(frame_idx) * total) / num_frames);
+    const uint64_t end   = frame_idx + 1 == num_frames
+                               ? total
+                               : static_cast<uint64_t>((static_cast<long double>(frame_idx + 1) * total) / num_frames);
+    return {start, std::max(start, end)};
+}
+
 EncodedImageFormat encoded_image_format_from_path(const std::string& path) {
    std::string ext = fs::path(path).extension().string();
    std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);
@@ -776,7 +810,7 @@ uint8_t* load_image_from_memory(const char* image_bytes,
    return load_image_common(true, image_bytes, len, width, height, expected_width, expected_height, expected_channel);
 }

-std::vector<uint8_t> create_mjpg_avi_from_sd_images_to_vector(sd_image_t* images, int num_images, int fps, int quality) {
+std::vector<uint8_t> create_mjpg_avi_from_sd_images_to_vector(sd_image_t* images, int num_images, int fps, int quality, const sd_audio_t* audio) {
    if (num_images == 0) {
        fprintf(stderr, "Error: Image array is empty.\n");
        return {};
@@ -793,7 +827,13 @@ std::vector<uint8_t> create_mjpg_avi_from_sd_images_to_vector(sd_image_t* images
    // stb_image_write changes JPEG sampling behavior above quality 90.
    // MJPG AVI playback is more compatible when we keep the encoder on the
    // <= 90 path.
-    const int mjpg_quality = std::clamp(quality, 1, 90);
+    const int mjpg_quality               = std::clamp(quality, 1, 90);
+    const bool has_audio                 = audio != nullptr && audio->data != nullptr && audio->sample_count > 0 && audio->channels > 0 && audio->sample_rate > 0;
+    const std::vector<uint8_t> audio_pcm = audio_to_pcm16_bytes(audio);
+    const uint16_t audio_bits_per_sample = 16;
+    const uint16_t audio_block_align     = has_audio ? static_cast<uint16_t>(audio->channels * (audio_bits_per_sample / 8)) : 0;
+    const uint32_t audio_byte_rate       = has_audio ? static_cast<uint32_t>(audio->sample_rate * audio_block_align) : 0;
+    const uint32_t audio_data_size       = has_audio ? static_cast<uint32_t>(audio_pcm.size()) : 0;

    std::vector<uint8_t> avi_data;
    avi_data.reserve(static_cast<size_t>(num_images) * 1024);
@@ -804,7 +844,11 @@ std::vector<uint8_t> create_mjpg_avi_from_sd_images_to_vector(sd_image_t* images
    write_fourcc(avi_data, "AVI ");

    write_fourcc(avi_data, "LIST");
-    write_u32_le(avi_data, 4 + 8 + 56 + 8 + 4 + 8 + 56 + 8 + 40);
+    uint32_t hdrl_size = 4 + 8 + 56 + 8 + 4 + 8 + 56 + 8 + 40;
+    if (has_audio) {
+        hdrl_size += 8 + (4 + 8 + 56 + 8 + 16);
+    }
+    write_u32_le(avi_data, hdrl_size);
    write_fourcc(avi_data, "hdrl");

    write_fourcc(avi_data, "avih");
@@ -815,7 +859,7 @@ std::vector<uint8_t> create_mjpg_avi_from_sd_images_to_vector(sd_image_t* images
    write_u32_le(avi_data, 0x110);
    write_u32_le(avi_data, num_images);
    write_u32_le(avi_data, 0);
-    write_u32_le(avi_data, 1);
+    write_u32_le(avi_data, has_audio ? 2 : 1);
    write_u32_le(avi_data, width * height * 3);
    write_u32_le(avi_data, width);
    write_u32_le(avi_data, height);
@@ -862,12 +906,48 @@ std::vector<uint8_t> create_mjpg_avi_from_sd_images_to_vector(sd_image_t* images
    write_u32_le(avi_data, 0);
    write_u32_le(avi_data, 0);

+    if (has_audio) {
+        write_fourcc(avi_data, "LIST");
+        write_u32_le(avi_data, 4 + 8 + 56 + 8 + 16);
+        write_fourcc(avi_data, "strl");
+
+        write_fourcc(avi_data, "strh");
+        write_u32_le(avi_data, 56);
+        write_fourcc(avi_data, "auds");
+        write_u32_le(avi_data, 0);
+        write_u32_le(avi_data, 0);
+        write_u16_le(avi_data, 0);
+        write_u16_le(avi_data, 0);
+        write_u32_le(avi_data, 0);
+        write_u32_le(avi_data, audio_block_align);
+        write_u32_le(avi_data, audio_byte_rate);
+        write_u32_le(avi_data, 0);
+        write_u32_le(avi_data, static_cast<uint32_t>(audio->sample_count));
+        write_u32_le(avi_data, audio_data_size);
+        write_u32_le(avi_data, static_cast<uint32_t>(-1));
+        write_u32_le(avi_data, audio_block_align);
+        write_u16_le(avi_data, 0);
+        write_u16_le(avi_data, 0);
+        write_u16_le(avi_data, 0);
+        write_u16_le(avi_data, 0);
+
+        write_fourcc(avi_data, "strf");
+        write_u32_le(avi_data, 16);
+        write_u16_le(avi_data, 1);
+        write_u16_le(avi_data, static_cast<uint16_t>(audio->channels));
+        write_u32_le(avi_data, audio->sample_rate);
+        write_u32_le(avi_data, audio_byte_rate);
+        write_u16_le(avi_data, audio_block_align);
+        write_u16_le(avi_data, audio_bits_per_sample);
+    }
+
    write_fourcc(avi_data, "LIST");
    const size_t movi_size_pos = avi_data.size();
    write_u32_le(avi_data, 0);
    write_fourcc(avi_data, "movi");

-    std::vector<avi_index_entry> index(static_cast<size_t>(num_images));
+    std::vector<avi_chunk_index_entry> index;
+    index.reserve(static_cast<size_t>(num_images) + (has_audio ? 1 : 0));
    std::vector<uint8_t> jpeg_data;

    for (int i = 0; i < num_images; i++) {
@@ -884,27 +964,46 @@ std::vector<uint8_t> create_mjpg_avi_from_sd_images_to_vector(sd_image_t* images
            return {};
        }

-        index[i].offset = static_cast<uint32_t>(avi_data.size());
+        avi_chunk_index_entry video_entry = {};
+        memcpy(video_entry.fourcc, "00dc", 4);
+        video_entry.flags  = 0x10;
+        video_entry.offset = static_cast<uint32_t>(avi_data.size());
        write_fourcc(avi_data, "00dc");
        write_u32_le(avi_data, static_cast<uint32_t>(jpeg_data.size()));
-        index[i].size = (uint32_t)jpeg_data.size();
+        video_entry.size = static_cast<uint32_t>(jpeg_data.size());
        avi_data.insert(avi_data.end(), jpeg_data.begin(), jpeg_data.end());
+        index.push_back(video_entry);

        if (jpeg_data.size() % 2) {
            avi_data.push_back(0);
        }
    }

+    if (has_audio && !audio_pcm.empty()) {
+        avi_chunk_index_entry audio_entry = {};
+        memcpy(audio_entry.fourcc, "01wb", 4);
+        audio_entry.flags  = 0;
+        audio_entry.offset = static_cast<uint32_t>(avi_data.size());
+        audio_entry.size   = static_cast<uint32_t>(audio_pcm.size());
+        write_fourcc(avi_data, "01wb");
+        write_u32_le(avi_data, static_cast<uint32_t>(audio_pcm.size()));
+        avi_data.insert(avi_data.end(), audio_pcm.begin(), audio_pcm.end());
+        index.push_back(audio_entry);
+        if (audio_pcm.size() % 2 != 0) {
+            avi_data.push_back(0);
+        }
+    }
+
    const size_t movi_size = avi_data.size() - movi_size_pos - 4;
    patch_u32_le(avi_data, movi_size_pos, static_cast<uint32_t>(movi_size));

    write_fourcc(avi_data, "idx1");
-    write_u32_le(avi_data, num_images * 16);
-    for (int i = 0; i < num_images; i++) {
-        write_fourcc(avi_data, "00dc");
-        write_u32_le(avi_data, 0x10);
-        write_u32_le(avi_data, index[i].offset);
-        write_u32_le(avi_data, index[i].size);
+    write_u32_le(avi_data, static_cast<uint32_t>(index.size() * 16));
+    for (const auto& entry : index) {
+        write_fourcc(avi_data, entry.fourcc);
+        write_u32_le(avi_data, entry.flags);
+        write_u32_le(avi_data, entry.offset);
+        write_u32_le(avi_data, entry.size);
    }

    const size_t file_size = avi_data.size() - riff_size_pos - 4;
@@ -913,8 +1012,8 @@ std::vector<uint8_t> create_mjpg_avi_from_sd_images_to_vector(sd_image_t* images
    return avi_data;
 }

-int create_mjpg_avi_from_sd_images(const char* filename, sd_image_t* images, int num_images, int fps, int quality) {
-    std::vector<uint8_t> avi_data = create_mjpg_avi_from_sd_images_to_vector(images, num_images, fps, quality);
+int create_mjpg_avi_from_sd_images(const char* filename, sd_image_t* images, int num_images, int fps, int quality, const sd_audio_t* audio) {
+    std::vector<uint8_t> avi_data = create_mjpg_avi_from_sd_images_to_vector(images, num_images, fps, quality, audio);
    if (avi_data.empty()) {
        return -1;
    }
@@ -1044,7 +1143,7 @@ int create_animated_webp_from_sd_images(const char* filename, sd_image_t* images
 #endif

 #ifdef SD_USE_WEBM
-std::vector<uint8_t> create_webm_from_sd_images_to_vector(sd_image_t* images, int num_images, int fps, int quality) {
+std::vector<uint8_t> create_webm_from_sd_images_to_vector(sd_image_t* images, int num_images, int fps, int quality, const sd_audio_t* audio) {
    if (num_images == 0) {
        fprintf(stderr, "Error: Image array is empty.\n");
        return {};
@@ -1089,6 +1188,25 @@ std::vector<uint8_t> create_webm_from_sd_images_to_vector(sd_image_t* images, in
            video_track->set_display_height(static_cast<uint64_t>(height));
            video_track->set_frame_rate(static_cast<double>(fps));
        }
+
+        uint64_t audio_track_number    = 0;
+        std::vector<uint8_t> audio_pcm = audio_to_pcm16_bytes(audio);
+        if (audio != nullptr && !audio_pcm.empty()) {
+            audio_track_number = segment.AddAudioTrack(static_cast<int32_t>(audio->sample_rate), static_cast<int32_t>(audio->channels), 0);
+            if (audio_track_number == 0) {
+                fprintf(stderr, "Error: Failed to add audio track.\n");
+                return -1;
+            }
+            auto* audio_track = static_cast<mkvmuxer::AudioTrack*>(segment.GetTrackByNumber(audio_track_number));
+            if (audio_track == nullptr) {
+                fprintf(stderr, "Error: Failed to get audio track.\n");
+                return -1;
+            }
+            audio_track->set_codec_id("A_PCM/INT/LIT");
+            audio_track->set_bit_depth(16);
+            audio_track->set_sample_rate(static_cast<double>(audio->sample_rate));
+            audio_track->set_channels(audio->channels);
+        }
        segment.GetSegmentInfo()->set_writing_app("stable-diffusion.cpp");
        segment.GetSegmentInfo()->set_muxing_app("stable-diffusion.cpp");

@@ -1118,6 +1236,23 @@ std::vector<uint8_t> create_webm_from_sd_images_to_vector(sd_image_t* images, in
                return -1;
            }

+            if (audio_track_number != 0) {
+                auto [audio_begin, audio_end] = audio_sample_range_for_video_frame(audio, i, num_images, fps);
+                const uint64_t frame_samples  = audio_end - audio_begin;
+                if (frame_samples > 0) {
+                    const uint64_t frame_bytes = frame_samples * audio->channels * sizeof(int16_t);
+                    const uint8_t* frame_ptr   = audio_pcm.data() + audio_begin * audio->channels * sizeof(int16_t);
+                    if (!segment.AddFrame(frame_ptr,
+                                          frame_bytes,
+                                          audio_track_number,
+                                          timestamp_ns,
+                                          true)) {
+                        fprintf(stderr, "Error: Failed to mux audio chunk %d into WebM.\n", i);
+                        return -1;
+                    }
+                }
+            }
+
            timestamp_ns += frame_duration_ns;
        }

@@ -1133,8 +1268,8 @@ std::vector<uint8_t> create_webm_from_sd_images_to_vector(sd_image_t* images, in
    return writer.data();
 }

-int create_webm_from_sd_images(const char* filename, sd_image_t* images, int num_images, int fps, int quality) {
-    std::vector<uint8_t> webm_data = create_webm_from_sd_images_to_vector(images, num_images, fps, quality);
+int create_webm_from_sd_images(const char* filename, sd_image_t* images, int num_images, int fps, int quality, const sd_audio_t* audio) {
+    std::vector<uint8_t> webm_data = create_webm_from_sd_images_to_vector(images, num_images, fps, quality, audio);
    if (webm_data.empty()) {
        return -1;
    }
@@ -1150,7 +1285,8 @@ std::vector<uint8_t> create_video_from_sd_images_to_vector(const std::string& ou
                                                           sd_image_t* images,
                                                           int num_images,
                                                           int fps,
-                                                           int quality) {
+                                                           int quality,
+                                                           const sd_audio_t* audio) {
    std::string format = output_format;
    std::transform(format.begin(), format.end(), format.begin(),
                   [](unsigned char c) { return static_cast<char>(tolower(c)); });
@@ -1160,7 +1296,7 @@ std::vector<uint8_t> create_video_from_sd_images_to_vector(const std::string& ou

 #ifdef SD_USE_WEBM
    if (format == "webm") {
-        return create_webm_from_sd_images_to_vector(images, num_images, fps, quality);
+        return create_webm_from_sd_images_to_vector(images, num_images, fps, quality, audio);
    }
 #endif

@@ -1170,14 +1306,14 @@ std::vector<uint8_t> create_video_from_sd_images_to_vector(const std::string& ou
    }
 #endif

-    return create_mjpg_avi_from_sd_images_to_vector(images, num_images, fps, quality);
+    return create_mjpg_avi_from_sd_images_to_vector(images, num_images, fps, quality, audio);
 }

-int create_video_from_sd_images(const char* filename, sd_image_t* images, int num_images, int fps, int quality) {
+int create_video_from_sd_images(const char* filename, sd_image_t* images, int num_images, int fps, int quality, const sd_audio_t* audio) {
    std::string path                = filename ? filename : "";
    auto pos                        = path.find_last_of('.');
    std::string ext                 = pos == std::string::npos ? "" : path.substr(pos);
-    std::vector<uint8_t> video_data = create_video_from_sd_images_to_vector(ext, images, num_images, fps, quality);
+    std::vector<uint8_t> video_data = create_video_from_sd_images_to_vector(ext, images, num_images, fps, quality, audio);
    if (video_data.empty()) {
        return -1;
    }
@@ -1187,3 +1323,54 @@ int create_video_from_sd_images(const char* filename, sd_image_t* images, int nu
    }
    return 0;
 }
+
+bool write_wav_to_file(const std::string& path,
+                       const float* interleaved_samples,
+                       uint64_t sample_count,
+                       uint32_t channels,
+                       uint32_t sample_rate) {
+    if (interleaved_samples == nullptr || sample_count == 0 || channels == 0 || sample_rate == 0) {
+        return false;
+    }
+
+    std::ofstream file(path, std::ios::binary);
+    if (!file.is_open()) {
+        return false;
+    }
+
+    uint32_t bits_per_sample  = 16;
+    uint32_t bytes_per_sample = bits_per_sample / 8;
+    uint32_t block_align      = channels * bytes_per_sample;
+    uint32_t byte_rate        = sample_rate * block_align;
+    uint32_t data_size        = static_cast<uint32_t>(sample_count * channels * bytes_per_sample);
+    uint32_t riff_size        = 36 + data_size;
+
+    file.write("RIFF", 4);
+    file.write(reinterpret_cast<const char*>(&riff_size), sizeof(riff_size));
+    file.write("WAVE", 4);
+    file.write("fmt ", 4);
+
+    uint32_t fmt_size            = 16;
+    uint16_t audio_format        = 1;
+    uint16_t wav_channels        = static_cast<uint16_t>(channels);
+    uint16_t wav_block_align     = static_cast<uint16_t>(block_align);
+    uint16_t wav_bits_per_sample = static_cast<uint16_t>(bits_per_sample);
+    file.write(reinterpret_cast<const char*>(&fmt_size), sizeof(fmt_size));
+    file.write(reinterpret_cast<const char*>(&audio_format), sizeof(audio_format));
+    file.write(reinterpret_cast<const char*>(&wav_channels), sizeof(wav_channels));
+    file.write(reinterpret_cast<const char*>(&sample_rate), sizeof(sample_rate));
+    file.write(reinterpret_cast<const char*>(&byte_rate), sizeof(byte_rate));
+    file.write(reinterpret_cast<const char*>(&wav_block_align), sizeof(wav_block_align));
+    file.write(reinterpret_cast<const char*>(&wav_bits_per_sample), sizeof(wav_bits_per_sample));
+
+    file.write("data", 4);
+    file.write(reinterpret_cast<const char*>(&data_size), sizeof(data_size));
+
+    std::vector<int16_t> pcm(sample_count * channels);
+    for (size_t i = 0; i < pcm.size(); ++i) {
+        float sample = std::max(-1.0f, std::min(1.0f, interleaved_samples[i]));
+        pcm[i]       = static_cast<int16_t>(std::lrint(sample * 32767.0f));
+    }
+    file.write(reinterpret_cast<const char*>(pcm.data()), static_cast<std::streamsize>(pcm.size() * sizeof(int16_t)));
+    return file.good();
+}
@@ -57,11 +57,13 @@ int create_mjpg_avi_from_sd_images(const char* filename,
                                   sd_image_t* images,
                                   int num_images,
                                   int fps,
-                                   int quality = 90);
+                                   int quality             = 90,
+                                   const sd_audio_t* audio = nullptr);
 std::vector<uint8_t> create_mjpg_avi_from_sd_images_to_vector(sd_image_t* images,
                                                              int num_images,
                                                              int fps,
-                                                              int quality = 90);
+                                                              int quality             = 90,
+                                                              const sd_audio_t* audio = nullptr);

 #ifdef SD_USE_WEBP
 int create_animated_webp_from_sd_images(const char* filename,
@@ -80,22 +82,32 @@ int create_webm_from_sd_images(const char* filename,
                               sd_image_t* images,
                               int num_images,
                               int fps,
-                               int quality = 90);
+                               int quality             = 90,
+                               const sd_audio_t* audio = nullptr);
 std::vector<uint8_t> create_webm_from_sd_images_to_vector(sd_image_t* images,
                                                          int num_images,
                                                          int fps,
-                                                          int quality = 90);
+                                                          int quality             = 90,
+                                                          const sd_audio_t* audio = nullptr);
 #endif

 int create_video_from_sd_images(const char* filename,
                                sd_image_t* images,
                                int num_images,
                                int fps,
-                                int quality = 90);
+                                int quality             = 90,
+                                const sd_audio_t* audio = nullptr);
 std::vector<uint8_t> create_video_from_sd_images_to_vector(const std::string& output_format,
                                                           sd_image_t* images,
                                                           int num_images,
                                                           int fps,
-                                                           int quality = 90);
+                                                           int quality             = 90,
+                                                           const sd_audio_t* audio = nullptr);
+
+bool write_wav_to_file(const std::string& path,
+                       const float* interleaved_samples,
+                       uint64_t sample_count,
+                       uint32_t channels,
+                       uint32_t sample_rate);

 #endif  // __MEDIA_IO_H__
@@ -74,6 +74,9 @@ add_executable(${TARGET}
    routes_sdapi.cpp
    routes_sdcpp.cpp
 )
+if(APPLE)
+    sd_set_macos_rpaths(${TARGET})
+endif()

 if(HAVE_FRONTEND_BUILD)
    add_dependencies(${TARGET} ${TARGET}_frontend)
@@ -123,11 +123,11 @@ In this case, the server will load and serve the specified `index.html` file ins
 usage: ./bin/sd-server  [options]

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

 Context Options:
@@ -136,28 +136,34 @@ Context Options:
  --clip_g <string>                        path to the clip-g text encoder
  --clip_vision <string>                   path to the clip-vision encoder
  --t5xxl <string>                         path to the t5xxl text encoder
-  --llm <string>                           path to the llm text encoder. For example: (qwenvl2.5 for qwen-image, mistral-small3.2 for flux2, ...)
+  --llm <string>                           path to the llm text encoder. For example: (qwenvl2.5 for qwen-image,
+                                           mistral-small3.2 for flux2, ...)
  --llm_vision <string>                    path to the llm vit
  --qwen2vl <string>                       alias of --llm. Deprecated.
  --qwen2vl_vision <string>                alias of --llm_vision. Deprecated.
  --diffusion-model <string>               path to the standalone diffusion model
  --high-noise-diffusion-model <string>    path to the standalone high noise diffusion model
+  --uncond-diffusion-model <string>        path to the standalone unconditional diffusion model, currently used by
+                                           Ideogram4 CFG
  --vae <string>                           path to standalone vae model
  --taesd <string>                         path to taesd. Using Tiny AutoEncoder for fast decoding (low quality)
  --tae <string>                           alias of --taesd
  --control-net <string>                   path to control net model
  --embd-dir <string>                      embeddings directory
  --lora-model-dir <string>                lora model directory
+  --hires-upscalers-dir <string>           highres fix upscaler model directory
  --tensor-type-rules <string>             weight type per tensor pattern (example: "^vae\.=f16,model\.=q8_0")
  --photo-maker <string>                   path to PHOTOMAKER model
  --upscale-model <string>                 path to esrgan model.
-  -t, --threads <int>                      number of threads to use during computation (default: -1). If threads <= 0, then threads will be set to the number of
-                                           CPU physical cores
+  -t, --threads <int>                      number of threads to use during computation (default: -1). If threads <= 0,
+                                           then threads will be set to the number of CPU physical cores
  --chroma-t5-mask-pad <int>               t5 mask pad size of chroma
-  --vae-tile-overlap <float>               tile overlap for vae tiling, in fraction of tile size (default: 0.5)
-  --vae-tiling                             process vae in tiles to reduce memory usage
+  --max-vram <float>                       maximum VRAM budget in GiB for graph-cut segmented execution. 0 disables
+                                           graph splitting; a negative value auto-detects free VRAM, sparing the
+                                           specified value (e.g. -0.5 will keep at least 0.5 GiB free)
  --force-sdxl-vae-conv-scale              force use of conv scale on sdxl vae
-  --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM when needed
+  --offload-to-cpu                         place the weights in RAM to save VRAM, and automatically load them into VRAM
+                                           when needed
  --mmap                                   whether to memory-map model
  --control-net-cpu                        keep controlnet in cpu (for low vram)
  --clip-on-cpu                            keep clip in cpu (for low vram)
@@ -172,20 +178,19 @@ Context Options:
  --chroma-disable-dit-mask                disable dit mask for chroma
  --qwen-image-zero-cond-t                 enable zero_cond_t for qwen image
  --chroma-enable-t5-mask                  enable t5 mask for chroma
-  --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K, q4_K). If not specified, the default is the
-                                           type of the weight file
+  --type                                   weight type (examples: f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0, q2_K, q3_K,
+                                           q4_K). If not specified, the default is the type of the weight file
  --rng                                    RNG, one of [std_default, cuda, cpu], default: cuda(sd-webui), cpu(comfyui)
  --sampler-rng                            sampler RNG, one of [std_default, cuda, cpu]. If not specified, use --rng
-  --prediction                             prediction type override, one of [eps, v, edm_v, sd3_flow, flux_flow, flux2_flow]
-  --lora-apply-mode                        the way to apply LoRA, one of [auto, immediately, at_runtime], default is auto. In auto mode, if the model weights
-                                           contain any quantized parameters, the at_runtime mode will be used; otherwise,
-                                           immediately will be used.The immediately mode may have precision and
-                                           compatibility issues with quantized parameters, but it usually offers faster inference
-                                           speed and, in some cases, lower memory usage. The at_runtime mode, on the
-                                           other hand, is exactly the opposite.
-  --vae-tile-size                          tile size for vae tiling, format [X]x[Y] (default: 32x32)
-  --vae-relative-tile-size                 relative tile size for vae tiling, format [X]x[Y], in fraction of image size if < 1, in number of tiles per dim if >=1
-                                           (overrides --vae-tile-size)
+  --prediction                             prediction type override, one of [eps, v, edm_v, sd3_flow, flux_flow,
+                                           flux2_flow]
+  --lora-apply-mode                        the way to apply LoRA, one of [auto, immediately, at_runtime], default is
+                                           auto. In auto mode, if the model weights contain any quantized parameters,
+                                           the at_runtime mode will be used; otherwise, immediately will be used.The
+                                           immediately mode may have precision and compatibility issues with quantized
+                                           parameters, but it usually offers faster inference speed and, in some cases,
+                                           lower memory usage. The at_runtime mode, on the other hand, is exactly the
+                                           opposite.

 Default Generation Options:
  -p, --prompt <string>                    the prompt to render
@@ -194,65 +199,106 @@ Default Generation Options:
  --end-img <string>                       path to the end image, required by flf2v
  --mask <string>                          path to the mask image
  --control-image <string>                 path to control image, control net
-  --control-video <string>                 path to control video frames, It must be a directory path. The video frames inside should be stored as images in
-                                           lexicographical (character) order. For example, if the control video path is
-                                           `frames`, the directory contain images such as 00.png, 01.png, ... etc.
+  --control-video <string>                 path to control video frames, It must be a directory path. The video frames
+                                           inside should be stored as images in lexicographical (character) order. For
+                                           example, if the control video path is `frames`, the directory contain images
+                                           such as 00.png, 01.png, ... etc.
  --pm-id-images-dir <string>              path to PHOTOMAKER input id images dir
  --pm-id-embed-path <string>              path to PHOTOMAKER v2 id embed
+  --hires-upscaler <string>                highres fix upscaler, Lanczos, Nearest, Latent, Latent (nearest), Latent
+                                           (nearest-exact), Latent (antialiased), Latent (bicubic), Latent (bicubic
+                                           antialiased), or a model name under --hires-upscalers-dir (default: Latent)
+  --extra-sample-args <string>             extra sampler/scheduler/guidance args, key=value list. APG supports apg_eta,
+                                           apg_momentum, apg_norm_threshold, apg_norm_threshold_smoothing; SLG supports
+                                           slg_uncond; lcm supports noise_clip_std, noise_scale_start, noise_scale_end;
+                                           ltx2 supports max_shift, base_shift, stretch, terminal; euler_ge supports gamma
+  --extra-tiling-args <string>             extra VAE tiling args, key=value list. LTX video VAE supports
+                                           temporal_tile_frames (default: 4), temporal_tile_overlap (default: 1)
  -H, --height <int>                       image height, in pixel space (default: 512)
  -W, --width <int>                        image width, in pixel space (default: 512)
  --steps <int>                            number of sample steps (default: 20)
  --high-noise-steps <int>                 (high noise) number of sample steps (default: -1 = auto)
-  --clip-skip <int>                        ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer (default: -1). <= 0 represents unspecified,
-                                           will be 1 for SD1.x, 2 for SD2.x
+  --clip-skip <int>                        ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer
+                                           (default: -1). <= 0 represents unspecified, will be 1 for SD1.x, 2 for SD2.x
  -b, --batch-count <int>                  batch count
  --video-frames <int>                     video frames (default: 1)
  --fps <int>                              fps (default: 24)
-  --timestep-shift <int>                   shift timestep for NitroFusion models (default: 0). recommended N for NitroSD-Realism around 250 and 500 for
-                                           NitroSD-Vibrant
+  --timestep-shift <int>                   shift timestep for NitroFusion models (default: 0). recommended N for
+                                           NitroSD-Realism around 250 and 500 for NitroSD-Vibrant
  --upscale-repeats <int>                  Run the ESRGAN upscaler this many times (default: 1)
  --upscale-tile-size <int>                tile size for ESRGAN upscaling (default: 128)
+  --hires-width <int>                      highres fix target width, 0 to use --hires-scale (default: 0)
+  --hires-height <int>                     highres fix target height, 0 to use --hires-scale (default: 0)
+  --hires-steps <int>                      highres fix second pass sample steps, 0 to reuse --steps (default: 0)
+  --hires-upscale-tile-size <int>          highres fix upscaler tile size, reserved for model-backed upscalers (default:
+                                           128)
  --cfg-scale <float>                      unconditional guidance scale: (default: 7.0)
-  --img-cfg-scale <float>                  image guidance scale for inpaint or instruct-pix2pix models: (default: same as --cfg-scale)
+  --img-cfg-scale <float>                  image guidance scale for inpaint or image edit models: (default: same as
+                                           --cfg-scale)
  --guidance <float>                       distilled guidance scale for models with guidance input (default: 3.5)
-  --slg-scale <float>                      skip layer guidance (SLG) scale, only for DiT models: (default: 0). 0 means disabled, a value of 2.5 is nice for sd3.5
-                                           medium
+  --slg-scale <float>                      skip layer guidance (SLG) scale, only for DiT models: (default: 0). 0 means
+                                           disabled, a value of 2.5 is nice for sd3.5 medium
  --skip-layer-start <float>               SLG enabling point (default: 0.01)
  --skip-layer-end <float>                 SLG disabling point (default: 0.2)
-  --eta <float>                            noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a, er_sde and dpm++2s_a)
+  --eta <float>                            noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and
+                                           res_2s; 1 for euler_a, er_sde and dpm++2s_a)
  --flow-shift <float>                     shift value for Flow models like SD3.x or WAN (default: auto)
  --high-noise-cfg-scale <float>           (high noise) unconditional guidance scale: (default: 7.0)
-  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or instruct-pix2pix models (default: same as --cfg-scale)
-  --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input (default: 3.5)
-  --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default: 0)
+  --high-noise-img-cfg-scale <float>       (high noise) image guidance scale for inpaint or image edit models (default:
+                                           same as --cfg-scale)
+  --high-noise-guidance <float>            (high noise) distilled guidance scale for models with guidance input
+                                           (default: 3.5)
+  --high-noise-slg-scale <float>           (high noise) skip layer guidance (SLG) scale, only for DiT models: (default:
+                                           0)
  --high-noise-skip-layer-start <float>    (high noise) SLG enabling point (default: 0.01)
  --high-noise-skip-layer-end <float>      (high noise) SLG disabling point (default: 0.2)
-  --high-noise-eta <float>                 (high noise) noise multiplier (default: 0 for ddim_trailing, tcd, res_multistep and res_2s; 1 for euler_a, er_sde and dpm++2s_a)
+  --high-noise-eta <float>                 (high noise) noise multiplier (default: 0 for ddim_trailing, tcd,
+                                           res_multistep and res_2s; 1 for euler_a, er_sde and dpm++2s_a)
  --strength <float>                       strength for noising/unnoising (default: 0.75)
  --pm-style-strength <float>
-  --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full destruction of information in init image
-  --moe-boundary <float>                   timestep boundary for Wan2.2 MoE model. (default: 0.875). Only enabled if `--high-noise-steps` is set to -1
+  --control-strength <float>               strength to apply Control Net (default: 0.9). 1.0 corresponds to full
+                                           destruction of information in init image
+  --moe-boundary <float>                   timestep boundary for Wan2.2 MoE model. (default: 0.875). Only enabled if
+                                           `--high-noise-steps` is set to -1
  --vace-strength <float>                  wan vace strength
-  --increase-ref-index                     automatically increase the indices of references images based on the order they are listed (starting with 1).
+  --vae-tile-overlap <float>               tile overlap for vae tiling, in fraction of tile size (default: 0.5)
+  --hires-scale <float>                    highres fix scale when target size is not set (default: 2.0)
+  --hires-denoising-strength <float>       highres fix second pass denoising strength (default: 0.7)
+  --increase-ref-index                     automatically increase the indices of references images based on the order
+                                           they are listed (starting with 1).
  --disable-auto-resize-ref-image          disable auto resize of ref images
  --disable-image-metadata                 do not embed generation metadata on image files
+  --vae-tiling                             process vae in tiles to reduce memory usage
+  --temporal-tiling                        enable temporal tiling for LTX video VAE decode
+  --hires                                  enable highres fix
  -s, --seed                               RNG seed (default: 42, use random seed for < 0)
-  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing,
-                                           tcd, res_multistep, res_2s, er_sde] (default: euler for Flux/SD3/Wan, euler_a
-                                           otherwise)
-  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm,
-                                           ddim_trailing, tcd, res_multistep, res_2s, er_sde] default: euler for Flux/SD3/Wan,
-                                           euler_a otherwise
-  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits, smoothstep, sgm_uniform, simple,
-                                           kl_optimal, lcm, bong_tangent], default: discrete
-  --sigmas                                 custom sigma values for the sampler, comma-separated (e.g., "14.61,7.8,3.5,0.0").
+  --sampling-method                        sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a, dpm++2m,
+                                           dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep, res_2s,
+                                           er_sde, euler_cfg_pp, euler_a_cfg_pp] (default: euler for Flux/SD3/Wan, euler_a otherwise)
+  --high-noise-sampling-method             (high noise) sampling method, one of [euler, euler_a, heun, dpm2, dpm++2s_a,
+                                           dpm++2m, dpm++2mv2, ipndm, ipndm_v, lcm, ddim_trailing, tcd, res_multistep,
+                                           res_2s, er_sde, euler_cfg_pp, euler_a_cfg_pp] default: euler for Flux/SD3/Wan, euler_a otherwise
+  --scheduler                              denoiser sigma scheduler, one of [discrete, karras, exponential, ays, gits,
+                                           smoothstep, sgm_uniform, simple, kl_optimal, lcm, bong_tangent, ltx2], default:
+                                           model-specific
+  --sigmas                                 custom sigma values for the sampler, comma-separated (e.g.,
+                                           "14.61,7.8,3.5,0.0").
+  --hires-sigmas                           custom sigma values for the highres fix second pass, comma-separated (e.g.,
+                                           "0.85,0.725,0.421875,0.0").
  --skip-layers                            layers to skip for SLG steps (default: [7,8,9])
  --high-noise-skip-layers                 (high noise) layers to skip for SLG steps (default: [7,8,9])
  -r, --ref-image                          reference image for Flux Kontext models (can be used multiple times)
-  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET), 'dbcache'/'taylorseer'/'cache-dit' (DiT block-level), 'spectrum' (UNET/DiT Chebyshev+Taylor forecasting)
+  --cache-mode                             caching method: 'easycache' (DiT), 'ucache' (UNET),
+                                           'dbcache'/'taylorseer'/'cache-dit' (DiT block-level), 'spectrum' (UNET/DiT
+                                           Chebyshev+Taylor forecasting)
  --cache-option                           named cache params (key=value format, comma-separated). easycache/ucache:
-                                           threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit: Fn=,Bn=,threshold=,warmup=. Examples:
-                                           "threshold=0.25" or "threshold=1.5,reset=0"
-  --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g., "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
+                                           threshold=,start=,end=,decay=,relative=,reset=; dbcache/taylorseer/cache-dit:
+                                           Fn=,Bn=,threshold=,warmup=; spectrum: w=,m=,lam=,window=,flex=,warmup=,stop=.
+                                           Examples: "threshold=0.25" or "threshold=1.5,reset=0"
+  --scm-mask                               SCM steps mask for cache-dit: comma-separated 0/1 (e.g.,
+                                           "1,1,1,0,0,1,0,0,1,0") - 1=compute, 0=can cache
  --scm-policy                             SCM policy: 'dynamic' (default) or 'static'
+  --vae-tile-size                          tile size for vae tiling, format [X]x[Y] (default: 32x32)
+  --vae-relative-tile-size                 relative tile size for vae tiling, format [X]x[Y], in fraction of image size
+                                           if < 1, in number of tiles per dim if >=1 (overrides --vae-tile-size)
 ```
@@ -38,6 +38,8 @@ Current generation-related endpoints include:
 - `POST /sdapi/v1/txt2img`
 - `POST /sdapi/v1/img2img`
 - `GET /sdapi/v1/loras`
+- `GET /sdapi/v1/upscalers`
+- `GET /sdapi/v1/latent-upscale-modes`
 - `GET /sdapi/v1/samplers`
 - `GET /sdapi/v1/schedulers`
 - `GET /sdapi/v1/sd-models`
@@ -216,6 +218,13 @@ Currently supported request fields:
 | `scheduler` | `string` | Scheduler name |
 | `lora` | `array<object>` | Structured LoRA list |
 | `extra_images` | `array<string>` | Base64 or data URL images |
+| `enable_hr` | `boolean` | Enable highres fix for `txt2img` |
+| `hr_upscaler` | `string` | `Lanczos`, `Nearest`, a latent mode such as `Latent (nearest-exact)`, or an upscaler model name from `/sdapi/v1/upscalers` |
+| `hr_scale` | `number` | Highres scale when resize target is not set |
+| `hr_resize_x` | `integer` | Highres target width, `0` to use scale |
+| `hr_resize_y` | `integer` | Highres target height, `0` to use scale |
+| `hr_steps` | `integer` | Highres second-pass sample steps, `0` to reuse `steps` |
+| `denoising_strength` | `number` | Highres denoising strength for `txt2img` |

 Native extension fields:

@@ -241,6 +250,8 @@ Currently supported request fields:
 | `inpainting_mask_invert` | `integer` or `boolean` | Treated as invert flag |
 | `denoising_strength` | `number` | Clamped to `0.0..1.0` |

+Highres fix fields are currently handled for `txt2img`; `img2img` uses `denoising_strength` as image-to-image strength.
+
 Native extension fields:

 - any `sdcpp API` fields embedded through `sd_cpp_extra_args` inside `prompt`
@@ -258,6 +269,8 @@ Response fields:
 Currently exposed:

 - `GET /sdapi/v1/loras`
+- `GET /sdapi/v1/upscalers`
+- `GET /sdapi/v1/latent-upscale-modes`
 - `GET /sdapi/v1/samplers`
 - `GET /sdapi/v1/schedulers`
 - `GET /sdapi/v1/sd-models`
@@ -272,6 +285,26 @@ Response fields:
 | `[].name` | `string` | Display name derived from file stem |
 | `[].path` | `string` | Relative path under the configured LoRA directory |

+`GET /sdapi/v1/upscalers`
+
+| Field | Type | Notes |
+| --- | --- | --- |
+| `[].name` | `string` | Built-in name or model stem |
+| `[].model_name` | `string \| null` | Model family label for model-backed upscalers |
+| `[].model_path` | `string \| null` | Absolute model path for model-backed upscalers |
+| `[].model_url` | `string \| null` | Currently always null |
+| `[].scale` | `integer` | Currently `4` |
+
+Built-in entries include `None`, `Lanczos`, and `Nearest`. Model-backed entries are scanned from the top level of `--hires-upscalers-dir`; subdirectories are not scanned.
+
+`GET /sdapi/v1/latent-upscale-modes`
+
+| Field | Type | Notes |
+| --- | --- | --- |
+| `[].name` | `string` | WebUI-compatible latent upscale mode name |
+
+Built-in latent modes include `Latent`, `Latent (nearest)`, `Latent (nearest-exact)`, `Latent (antialiased)`, `Latent (bicubic)`, and `Latent (bicubic antialiased)`.
+
 `GET /sdapi/v1/samplers`

 | Field | Type | Notes |
@@ -388,6 +421,7 @@ Top-level fields:
 | `samplers` | `array<string>` | Available sampling methods |
 | `schedulers` | `array<string>` | Available schedulers |
 | `loras` | `array<object>` | Available LoRA entries |
+| `upscalers` | `array<object>` | Available model-backed highres upscalers |
 | `limits` | `object` | Shared queue and size limits |

 `model`
@@ -424,6 +458,14 @@ Shared nested fields:
 | `loras[].name` | `string` |
 | `loras[].path` | `string` |

+`upscalers`
+
+| Field | Type | Notes |
+| --- | --- | --- |
+| `upscalers[].name` | `string` | Built-in name or model stem; use this value in `hires.upscaler` |
+
+Built-in entries include `None`, `Lanczos`, `Nearest`, `Latent`, `Latent (nearest)`, `Latent (nearest-exact)`, `Latent (antialiased)`, `Latent (bicubic)`, and `Latent (bicubic antialiased)`. Model-backed entries are scanned from the top level of `--hires-upscalers-dir`; subdirectories are not scanned.
+
 `limits`

 | Field | Type |
@@ -462,11 +504,13 @@ Shared default fields used by both `img_gen` and `vid_gen`:
 | `sample_params.guidance.slg.scale` | `number` |
 | `vae_tiling_params` | `object` |
 | `vae_tiling_params.enabled` | `boolean` |
+| `vae_tiling_params.temporal_tiling` | `boolean` |
 | `vae_tiling_params.tile_size_x` | `integer` |
 | `vae_tiling_params.tile_size_y` | `integer` |
 | `vae_tiling_params.target_overlap` | `number` |
 | `vae_tiling_params.rel_size_x` | `number` |
 | `vae_tiling_params.rel_size_y` | `number` |
+| `vae_tiling_params.extra_tiling_args` | `string` |
 | `cache_mode` | `string` |
 | `cache_option` | `string` |
 | `scm_mask` | `string` |
@@ -474,6 +518,8 @@ Shared default fields used by both `img_gen` and `vid_gen`:
 | `output_format` | `string` |
 | `output_compression` | `integer` |

+`vae_tiling_params.extra_tiling_args` accepts a key=value list. For LTX video VAE temporal tiling, `temporal_tile_frames` defaults to `4` and `temporal_tile_overlap` defaults to `1`.
+
 `img_gen`-specific default fields:

 | Field | Type |
@@ -482,6 +528,16 @@ Shared default fields used by both `img_gen` and `vid_gen`:
 | `auto_resize_ref_image` | `boolean` |
 | `increase_ref_index` | `boolean` |
 | `control_strength` | `number` |
+| `hires` | `object` |
+| `hires.enabled` | `boolean` |
+| `hires.upscaler` | `string` |
+| `hires.scale` | `number` |
+| `hires.target_width` | `integer` |
+| `hires.target_height` | `integer` |
+| `hires.steps` | `integer` |
+| `hires.denoising_strength` | `number` |
+| `hires.custom_sigmas` | `array<number>` |
+| `hires.upscale_tile_size` | `integer` |

 `vid_gen`-specific default fields:

@@ -514,6 +570,7 @@ Fields returned in `features_by_mode.img_gen`:
 - `ref_images`
 - `lora`
 - `vae_tiling`
+- `hires`
 - `cache`
 - `cancel_queued`
 - `cancel_generating`
@@ -625,14 +682,27 @@ Example:
  },

  "lora": [],
+  "hires": {
+    "enabled": false,
+    "upscaler": "Latent",
+    "scale": 2.0,
+    "target_width": 0,
+    "target_height": 0,
+    "steps": 0,
+    "denoising_strength": 0.7,
+    "custom_sigmas": [],
+    "upscale_tile_size": 128
+  },

  "vae_tiling_params": {
    "enabled": false,
+    "temporal_tiling": false,
    "tile_size_x": 0,
    "tile_size_y": 0,
    "target_overlap": 0.5,
    "rel_size_x": 0.0,
-    "rel_size_y": 0.0
+    "rel_size_y": 0.0,
+    "extra_tiling_args": ""
  },

  "cache_mode": "disabled",
@@ -729,12 +799,32 @@ Other native fields:

 | Field | Type |
 | --- | --- |
+| `hires` | `object` |
+| `hires.enabled` | `boolean` |
+| `hires.upscaler` | `string` |
+| `hires.scale` | `number` |
+| `hires.target_width` | `integer` |
+| `hires.target_height` | `integer` |
+| `hires.steps` | `integer` |
+| `hires.denoising_strength` | `number` |
+| `hires.custom_sigmas` | `array<number>` |
+| `hires.upscale_tile_size` | `integer` |
 | `vae_tiling_params` | `object` |
+| `vae_tiling_params.enabled` | `boolean` |
+| `vae_tiling_params.temporal_tiling` | `boolean` |
+| `vae_tiling_params.tile_size_x` | `integer` |
+| `vae_tiling_params.tile_size_y` | `integer` |
+| `vae_tiling_params.target_overlap` | `number` |
+| `vae_tiling_params.rel_size_x` | `number` |
+| `vae_tiling_params.rel_size_y` | `number` |
+| `vae_tiling_params.extra_tiling_args` | `string` |
 | `cache_mode` | `string` |
 | `cache_option` | `string` |
 | `scm_mask` | `string` |
 | `scm_policy_dynamic` | `boolean` |

+For `hires.upscaler`, use `Lanczos`, `Nearest`, `Latent`, `Latent (nearest)`, `Latent (nearest-exact)`, `Latent (antialiased)`, `Latent (bicubic)`, `Latent (bicubic antialiased)`, or an `upscalers[].name` value from `GET /sdcpp/v1/capabilities`. Model-backed upscalers are resolved as `--hires-upscalers-dir / (name + ext)` and must live directly in that directory. `hires.custom_sigmas`, when present, overrides the generated second-pass hires sigma schedule; otherwise the hires schedule is trimmed by `hires.denoising_strength`.
+
 HTTP-only output fields:

 | Field | Type |
@@ -936,11 +1026,13 @@ Example:

  "vae_tiling_params": {
    "enabled": false,
+    "temporal_tiling": false,
    "tile_size_x": 0,
    "tile_size_y": 0,
    "target_overlap": 0.5,
    "rel_size_x": 0.0,
-    "rel_size_y": 0.0
+    "rel_size_y": 0.0,
+    "extra_tiling_args": ""
  },

  "cache_mode": "disabled",
@@ -1058,6 +1150,14 @@ Other native fields:
 | Field | Type |
 | --- | --- |
 | `vae_tiling_params` | `object` |
+| `vae_tiling_params.enabled` | `boolean` |
+| `vae_tiling_params.temporal_tiling` | `boolean` |
+| `vae_tiling_params.tile_size_x` | `integer` |
+| `vae_tiling_params.tile_size_y` | `integer` |
+| `vae_tiling_params.target_overlap` | `number` |
+| `vae_tiling_params.rel_size_x` | `number` |
+| `vae_tiling_params.rel_size_y` | `number` |
+| `vae_tiling_params.extra_tiling_args` | `string` |
 | `cache_mode` | `string` |
 | `cache_option` | `string` |
 | `scm_mask` | `string` |
@@ -231,16 +231,21 @@ bool execute_vid_gen_job(ServerRuntime& runtime,
    sd_vid_gen_params_t params = job.vid_gen.to_sd_vid_gen_params_t();

    SDImageVec results;
-    int num_results = 0;
+    int num_results             = 0;
+    sd_audio_t* generated_audio = nullptr;

    {
        std::lock_guard<std::mutex> lock(*runtime.sd_ctx_mutex);
-        sd_image_t* raw_results = generate_video(runtime.sd_ctx, &params, &num_results);
+        sd_image_t* raw_results = nullptr;
+        if (!generate_video(runtime.sd_ctx, &params, &raw_results, &num_results, &generated_audio)) {
+            raw_results = nullptr;
+        }
        results.adopt(raw_results, num_results);
    }

    num_results = results.count();
    if (num_results <= 0) {
+        free_sd_audio(generated_audio);
        error_message = "generate_video returned no results";
        return false;
    }
@@ -249,7 +254,9 @@ bool execute_vid_gen_job(ServerRuntime& runtime,
                                                                             results.data(),
                                                                             num_results,
                                                                             job.vid_gen.gen_params.fps,
-                                                                             job.vid_gen.output_compression);
+                                                                             job.vid_gen.output_compression,
+                                                                             generated_audio);
+    free_sd_audio(generated_audio);
    if (video_bytes.empty()) {
        error_message = "failed to encode generated video container";
        return false;
@@ -48,7 +48,9 @@ static void parse_args(int argc,

    if (!svr_params.resolve_and_validate() ||
        !ctx_params.resolve_and_validate(IMG_GEN) ||
-        !default_gen_params.resolve_and_validate(IMG_GEN, ctx_params.lora_model_dir)) {
+        !default_gen_params.resolve_and_validate(IMG_GEN,
+                                                 ctx_params.lora_model_dir,
+                                                 ctx_params.hires_upscalers_dir)) {
        print_usage(argv[0], options_vec);
        exit(1);
    }
@@ -95,6 +97,8 @@ int main(int argc, const char** argv) {

    std::vector<LoraEntry> lora_cache;
    std::mutex lora_mutex;
+    std::vector<UpscalerEntry> upscaler_cache;
+    std::mutex upscaler_mutex;
    AsyncJobManager async_job_manager;
    ServerRuntime runtime = {
        sd_ctx.get(),
@@ -104,6 +108,8 @@ int main(int argc, const char** argv) {
        &default_gen_params,
        &lora_cache,
        &lora_mutex,
+        &upscaler_cache,
+        &upscaler_mutex,
        &async_job_manager,
    };

@@ -139,7 +145,7 @@ int main(int argc, const char** argv) {
    register_sdapi_endpoints(svr, runtime);
    register_sdcpp_api_endpoints(svr, runtime);

-    LOG_INFO("listening on: %s:%d\n", svr_params.listen_ip.c_str(), svr_params.listen_port);
+    LOG_INFO("listening on: http://%s:%d\n", svr_params.listen_ip.c_str(), svr_params.listen_port);
    svr.listen(svr_params.listen_ip, svr_params.listen_port);

    {
@@ -70,7 +70,7 @@ static bool build_openai_generation_request(const httplib::Request& req,
    }

    // Intentionally disable prompt-embedded LoRA tag parsing for server APIs.
-    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", true)) {
+    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", runtime.ctx_params->hires_upscalers_dir, true)) {
        error_message = "invalid params";
        return false;
    }
@@ -212,7 +212,7 @@ static bool build_openai_edit_request(const httplib::Request& req,
    }

    // Intentionally disable prompt-embedded LoRA tag parsing for server APIs.
-    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", true)) {
+    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", runtime.ctx_params->hires_upscalers_dir, true)) {
        error_message = "invalid params";
        return false;
    }
@@ -1,6 +1,7 @@
 #include "routes.h"

 #include <algorithm>
+#include <cctype>
 #include <cstring>
 #include <regex>
 #include <string_view>
@@ -35,14 +36,20 @@ static fs::path resolve_display_model_path(const ServerRuntime& runtime) {
    return {};
 }

+static std::string lower_ascii(std::string value) {
+    std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c) {
+        return static_cast<char>(std::tolower(c));
+    });
+    return value;
+}
+
 static enum sample_method_t get_sdapi_sample_method(std::string name) {
    enum sample_method_t result = str_to_sample_method(name.c_str());
    if (result != SAMPLE_METHOD_COUNT) {
        return result;
    }

-    std::transform(name.begin(), name.end(), name.begin(),
-                   [](unsigned char c) { return static_cast<char>(std::tolower(c)); });
+    name = lower_ascii(name);
    static const std::unordered_map<std::string_view, sample_method_t> hardcoded{
        {"euler a", EULER_A_SAMPLE_METHOD},
        {"k_euler_a", EULER_A_SAMPLE_METHOD},
@@ -60,6 +67,10 @@ static enum sample_method_t get_sdapi_sample_method(std::string name) {
        {"k_res_multistep", RES_MULTISTEP_SAMPLE_METHOD},
        {"res 2s", RES_2S_SAMPLE_METHOD},
        {"k_res_2s", RES_2S_SAMPLE_METHOD},
+        {"euler_cfg_pp", EULER_CFG_PP_SAMPLE_METHOD},
+        {"k_euler_cfg_pp", EULER_CFG_PP_SAMPLE_METHOD},
+        {"euler_a_cfg_pp", EULER_CFG_PP_SAMPLE_METHOD},
+        {"k_euler_a_cfg_pp", EULER_CFG_PP_SAMPLE_METHOD},
    };
    auto it = hardcoded.find(name);
    return it != hardcoded.end() ? it->second : SAMPLE_METHOD_COUNT;
@@ -114,6 +125,18 @@ static bool build_sdapi_img_gen_request(const json& j,
    request.gen_params.width                          = j.value("width", -1);
    request.gen_params.height                         = j.value("height", -1);

+    if (!img2img && j.value("enable_hr", false)) {
+        request.gen_params.hires_enabled = true;
+        request.gen_params.hires_scale   = j.value("hr_scale", request.gen_params.hires_scale);
+        request.gen_params.hires_width   = j.value("hr_resize_x", request.gen_params.hires_width);
+        request.gen_params.hires_height  = j.value("hr_resize_y", request.gen_params.hires_height);
+        request.gen_params.hires_steps   = j.value("hr_steps", request.gen_params.hires_steps);
+        request.gen_params.hires_denoising_strength =
+            j.value("denoising_strength", request.gen_params.hires_denoising_strength);
+
+        request.gen_params.hires_upscaler = j.value("hr_upscaler", request.gen_params.hires_upscaler);
+    }
+
    std::string sd_cpp_extra_args_str = extract_and_remove_sd_cpp_extra_args(request.gen_params.prompt);
    if (!sd_cpp_extra_args_str.empty() && !request.gen_params.from_json_str(sd_cpp_extra_args_str)) {
        error_message = "invalid sd_cpp_extra_args";
@@ -228,7 +251,7 @@ static bool build_sdapi_img_gen_request(const json& j,
    }

    // Intentionally disable prompt-embedded LoRA tag parsing for server APIs.
-    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", true)) {
+    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", runtime.ctx_params->hires_upscalers_dir, true)) {
        error_message = "invalid params";
        return false;
    }
@@ -347,6 +370,52 @@ void register_sdapi_endpoints(httplib::Server& svr, ServerRuntime& rt) {
        res.set_content(result.dump(), "application/json");
    });

+    svr.Get("/sdapi/v1/upscalers", [runtime](const httplib::Request&, httplib::Response& res) {
+        refresh_upscaler_cache(*runtime);
+
+        auto make_builtin = [](const char* name) {
+            json item;
+            item["name"]       = name;
+            item["model_name"] = nullptr;
+            item["model_path"] = nullptr;
+            item["model_url"]  = nullptr;
+            item["scale"]      = 4;
+            return item;
+        };
+
+        json result = json::array();
+        result.push_back(make_builtin("None"));
+        result.push_back(make_builtin("Lanczos"));
+        result.push_back(make_builtin("Nearest"));
+
+        {
+            std::lock_guard<std::mutex> lock(*runtime->upscaler_mutex);
+            for (const auto& e : *runtime->upscaler_cache) {
+                json item;
+                item["name"]       = e.name;
+                item["model_name"] = e.model_name;
+                item["model_path"] = e.fullpath;
+                item["model_url"]  = nullptr;
+                item["scale"]      = e.scale;
+                result.push_back(item);
+            }
+        }
+
+        res.set_content(result.dump(), "application/json");
+    });
+
+    svr.Get("/sdapi/v1/latent-upscale-modes", [](const httplib::Request&, httplib::Response& res) {
+        json result = json::array({
+            {{"name", "Latent"}},
+            {{"name", "Latent (nearest)"}},
+            {{"name", "Latent (nearest-exact)"}},
+            {{"name", "Latent (antialiased)"}},
+            {{"name", "Latent (bicubic)"}},
+            {{"name", "Latent (bicubic antialiased)"}},
+        });
+        res.set_content(result.dump(), "application/json");
+    });
+
    svr.Get("/sdapi/v1/samplers", [runtime](const httplib::Request&, httplib::Response& res) {
        std::vector<std::string> sampler_names;
        sampler_names.push_back("default");
@@ -56,11 +56,13 @@ static const char* capability_sample_method_name(enum sample_method_t sample_met
 static json make_vae_tiling_json(const sd_tiling_params_t& params) {
    return {
        {"enabled", params.enabled},
+        {"temporal_tiling", params.temporal_tiling},
        {"tile_size_x", params.tile_size_x},
        {"tile_size_y", params.tile_size_y},
        {"target_overlap", params.target_overlap},
        {"rel_size_x", params.rel_size_x},
        {"rel_size_y", params.rel_size_y},
+        {"extra_tiling_args", params.extra_tiling_args ? params.extra_tiling_args : ""},
    };
 }

@@ -100,6 +102,20 @@ static json make_sample_params_json(const sd_sample_params_t& sample_params, con
    };
 }

+static json make_hires_json(const SDGenerationParams& defaults) {
+    return {
+        {"enabled", defaults.hires_enabled},
+        {"upscaler", defaults.hires_upscaler},
+        {"scale", defaults.hires_scale},
+        {"target_width", defaults.hires_width},
+        {"target_height", defaults.hires_height},
+        {"steps", defaults.hires_steps},
+        {"denoising_strength", defaults.hires_denoising_strength},
+        {"custom_sigmas", defaults.hires_custom_sigmas},
+        {"upscale_tile_size", defaults.hires_upscale_tile_size},
+    };
+}
+
 static json make_img_gen_defaults_json(const SDGenerationParams& defaults, const std::string& output_format) {
    return {
        {"prompt", defaults.prompt},
@@ -114,6 +130,7 @@ static json make_img_gen_defaults_json(const SDGenerationParams& defaults, const
        {"increase_ref_index", defaults.increase_ref_index},
        {"control_strength", defaults.control_strength},
        {"sample_params", make_sample_params_json(defaults.sample_params, defaults.skip_layers)},
+        {"hires", make_hires_json(defaults)},
        {"vae_tiling_params", make_vae_tiling_json(defaults.vae_tiling_params)},
        {"cache_mode", defaults.cache_mode},
        {"cache_option", defaults.cache_option},
@@ -139,6 +156,7 @@ static json make_vid_gen_defaults_json(const SDGenerationParams& defaults, const
        {"vace_strength", defaults.vace_strength},
        {"sample_params", make_sample_params_json(defaults.sample_params, defaults.skip_layers)},
        {"high_noise_sample_params", make_sample_params_json(defaults.high_noise_sample_params, defaults.high_noise_skip_layers)},
+        {"hires", make_hires_json(defaults)},
        {"vae_tiling_params", make_vae_tiling_json(defaults.vae_tiling_params)},
        {"cache_mode", defaults.cache_mode},
        {"cache_option", defaults.cache_option},
@@ -157,6 +175,7 @@ static json make_img_gen_features_json() {
        {"ref_images", true},
        {"lora", true},
        {"vae_tiling", true},
+        {"hires", true},
        {"cache", true},
        {"cancel_queued", true},
        {"cancel_generating", false},
@@ -179,6 +198,7 @@ static json make_vid_gen_features_json() {

 static json make_capabilities_json(ServerRuntime& runtime) {
    refresh_lora_cache(runtime);
+    refresh_upscaler_cache(runtime);

    AsyncJobManager& manager  = *runtime.async_job_manager;
    const auto& defaults      = *runtime.default_gen_params;
@@ -190,6 +210,7 @@ static json make_capabilities_json(ServerRuntime& runtime) {
    json image_output_formats = supported_img_output_formats();
    json video_output_formats = supported_vid_output_formats();
    json available_loras      = json::array();
+    json available_upscalers  = json::array();
    json supported_modes      = json::array();

    for (int i = 0; i < SAMPLE_METHOD_COUNT; ++i) {
@@ -210,6 +231,42 @@ static json make_capabilities_json(ServerRuntime& runtime) {
        }
    }

+    available_upscalers.push_back({
+        {"name", "None"},
+    });
+    available_upscalers.push_back({
+        {"name", "Lanczos"},
+    });
+    available_upscalers.push_back({
+        {"name", "Nearest"},
+    });
+    available_upscalers.push_back({
+        {"name", "Latent"},
+    });
+    available_upscalers.push_back({
+        {"name", "Latent (nearest)"},
+    });
+    available_upscalers.push_back({
+        {"name", "Latent (nearest-exact)"},
+    });
+    available_upscalers.push_back({
+        {"name", "Latent (antialiased)"},
+    });
+    available_upscalers.push_back({
+        {"name", "Latent (bicubic)"},
+    });
+    available_upscalers.push_back({
+        {"name", "Latent (bicubic antialiased)"},
+    });
+    {
+        std::lock_guard<std::mutex> lock(*runtime.upscaler_mutex);
+        for (const auto& entry : *runtime.upscaler_cache) {
+            available_upscalers.push_back({
+                {"name", entry.name},
+            });
+        }
+    }
+
    if (supports_img) {
        supported_modes.push_back("img_gen");
    }
@@ -284,6 +341,7 @@ static json make_capabilities_json(ServerRuntime& runtime) {
    result["features"]               = top_level_features;
    result["features_by_mode"]       = features_by_mode;
    result["loras"]                  = available_loras;
+    result["upscalers"]              = available_upscalers;
    return result;
 }

@@ -307,7 +365,7 @@ static bool parse_img_gen_request(const json& body,
        return false;
    }
    // Intentionally disable prompt-embedded LoRA tag parsing for server APIs.
-    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", true)) {
+    if (!request.gen_params.resolve_and_validate(IMG_GEN, "", runtime.ctx_params->hires_upscalers_dir, true)) {
        error_message = "invalid generation parameters";
        return false;
    }
@@ -334,7 +392,7 @@ static bool parse_vid_gen_request(const json& body,
        return false;
    }
    // Intentionally disable prompt-embedded LoRA tag parsing for server APIs.
-    if (!request.gen_params.resolve_and_validate(VID_GEN, "", true)) {
+    if (!request.gen_params.resolve_and_validate(VID_GEN, "", runtime.ctx_params->hires_upscalers_dir, true)) {
        error_message = "invalid generation parameters";
        return false;
    }
@@ -1,6 +1,7 @@
 #include "runtime.h"

 #include <algorithm>
+#include <cctype>
 #include <chrono>
 #include <cstdlib>
 #include <filesystem>
@@ -13,6 +14,18 @@

 namespace fs = std::filesystem;

+static std::string lower_ascii(std::string value) {
+    std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c) {
+        return static_cast<char>(std::tolower(c));
+    });
+    return value;
+}
+
+static bool is_supported_model_ext(const fs::path& p) {
+    auto ext = lower_ascii(p.extension().string());
+    return ext == ".gguf" || ext == ".pt" || ext == ".pth" || ext == ".safetensors";
+}
+
 static const std::string k_base64_chars =
    "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
    "abcdefghijklmnopqrstuvwxyz"
@@ -190,8 +203,9 @@ ArgOptions SDSvrParams::get_options() {
        {"", "--color", "colors the logging tags according to level", true, &color},
    };

-    auto on_help_arg = [&](int, const char**, int) {
+    auto on_help_arg = [&](int, const char**, int, bool& valid) {
        normal_exit = true;
+        valid       = true;
        return -1;
    };

@@ -241,20 +255,12 @@ void refresh_lora_cache(ServerRuntime& rt) {

    fs::path lora_dir = rt.ctx_params->lora_model_dir;
    if (fs::exists(lora_dir) && fs::is_directory(lora_dir)) {
-        auto is_lora_ext = [](const fs::path& p) {
-            auto ext = p.extension().string();
-            std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c) {
-                return static_cast<char>(std::tolower(c));
-            });
-            return ext == ".gguf" || ext == ".pt" || ext == ".pth" || ext == ".safetensors";
-        };
-
-        for (auto& entry : fs::recursive_directory_iterator(lora_dir)) {
+        for (auto& entry : fs::recursive_directory_iterator(lora_dir, fs::directory_options::skip_permission_denied)) {
            if (!entry.is_regular_file()) {
                continue;
            }
            const fs::path& p = entry.path();
-            if (!is_lora_ext(p)) {
+            if (!is_supported_model_ext(p)) {
                continue;
            }

@@ -286,6 +292,40 @@ std::string get_lora_full_path(ServerRuntime& rt, const std::string& path) {
    return it != rt.lora_cache->end() ? it->fullpath : "";
 }

+void refresh_upscaler_cache(ServerRuntime& rt) {
+    std::vector<UpscalerEntry> new_cache;
+
+    fs::path upscaler_dir = rt.ctx_params->hires_upscalers_dir;
+    if (fs::exists(upscaler_dir) && fs::is_directory(upscaler_dir)) {
+        for (auto& entry : fs::directory_iterator(upscaler_dir)) {
+            if (!entry.is_regular_file()) {
+                continue;
+            }
+            const fs::path& p = entry.path();
+            if (!is_supported_model_ext(p)) {
+                continue;
+            }
+
+            UpscalerEntry upscaler_entry;
+            upscaler_entry.name       = p.stem().u8string();
+            upscaler_entry.fullpath   = fs::absolute(p).lexically_normal().u8string();
+            upscaler_entry.model_name = "ESRGAN_4x";
+            upscaler_entry.path       = p.filename().u8string();
+
+            new_cache.push_back(std::move(upscaler_entry));
+        }
+    }
+
+    std::sort(new_cache.begin(), new_cache.end(), [](const UpscalerEntry& a, const UpscalerEntry& b) {
+        return a.name < b.name;
+    });
+
+    {
+        std::lock_guard<std::mutex> lock(*rt.upscaler_mutex);
+        *rt.upscaler_cache = std::move(new_cache);
+    }
+}
+
 int64_t unix_timestamp_now() {
    return std::chrono::duration_cast<std::chrono::seconds>(
               std::chrono::system_clock::now().time_since_epoch())
@@ -37,6 +37,14 @@ struct LoraEntry {
    std::string fullpath;
 };

+struct UpscalerEntry {
+    std::string name;
+    std::string path;
+    std::string fullpath;
+    std::string model_name;
+    int scale = 4;
+};
+
 struct ServerRuntime {
    sd_ctx_t* sd_ctx;
    std::mutex* sd_ctx_mutex;
@@ -45,6 +53,8 @@ struct ServerRuntime {
    const SDGenerationParams* default_gen_params;
    std::vector<LoraEntry>* lora_cache;
    std::mutex* lora_mutex;
+    std::vector<UpscalerEntry>* upscaler_cache;
+    std::mutex* upscaler_mutex;
    AsyncJobManager* async_job_manager;
 };

@@ -86,4 +96,5 @@ bool runtime_supports_generation_mode(const ServerRuntime& runtime, SDMode mode)
 std::string unsupported_generation_mode_error(SDMode mode);
 void refresh_lora_cache(ServerRuntime& rt);
 std::string get_lora_full_path(ServerRuntime& rt, const std::string& path);
+void refresh_upscaler_cache(ServerRuntime& rt);
 int64_t unix_timestamp_now();
@@ -1,5 +1,5 @@
 for f in src/*.cpp src/*.h src/*.hpp src/tokenizers/*.h src/tokenizers/*.cpp src/tokenizers/vocab/*.h src/tokenizers/vocab/*.cpp \
-         examples/cli/*.cpp examples/cli/*.h examples/server/*.cpp \
+         src/model_io/*.h src/model_io/*.cpp examples/cli/*.cpp examples/cli/*.h examples/server/*.cpp \
         examples/common/*.hpp examples/common/*.h examples/common/*.cpp; do
  [[ "$f" == vocab* ]] && continue
  echo "formatting '$f'"
@@ -51,6 +51,9 @@ enum sample_method_t {
    RES_MULTISTEP_SAMPLE_METHOD,
    RES_2S_SAMPLE_METHOD,
    ER_SDE_SAMPLE_METHOD,
+    EULER_CFG_PP_SAMPLE_METHOD,
+    EULER_A_CFG_PP_SAMPLE_METHOD,
+    EULER_GE_SAMPLE_METHOD,
    SAMPLE_METHOD_COUNT
 };

@@ -66,6 +69,7 @@ enum scheduler_t {
    KL_OPTIMAL_SCHEDULER,
    LCM_SCHEDULER,
    BONG_TANGENT_SCHEDULER,
+    LTX2_SCHEDULER,
    SCHEDULER_COUNT
 };

@@ -122,7 +126,8 @@ enum sd_type_t {
    // SD_TYPE_IQ4_NL_8_8 = 38,
    SD_TYPE_MXFP4 = 39,  // MXFP4 (1 block)
    SD_TYPE_NVFP4 = 40,  // NVFP4 (4 blocks, E4M3 scale)
-    SD_TYPE_COUNT = 41,
+    SD_TYPE_Q1_0  = 41,
+    SD_TYPE_COUNT = 42,
 };

 enum sd_log_level_t {
@@ -149,11 +154,13 @@ enum lora_apply_mode_t {

 typedef struct {
    bool enabled;
+    bool temporal_tiling;
    int tile_size_x;
    int tile_size_y;
    float target_overlap;
    float rel_size_x;
    float rel_size_y;
+    const char* extra_tiling_args;
 } sd_tiling_params_t;

 typedef struct {
@@ -161,6 +168,14 @@ typedef struct {
    const char* path;
 } sd_embedding_t;

+enum sd_vae_format_t {
+    SD_VAE_FORMAT_AUTO = -1,
+    SD_VAE_FORMAT_FLUX,
+    SD_VAE_FORMAT_SD3,
+    SD_VAE_FORMAT_FLUX2,
+    SD_VAE_FORMAT_COUNT,
+};
+
 typedef struct {
    const char* model_path;
    const char* clip_l_path;
@@ -171,7 +186,10 @@ typedef struct {
    const char* llm_vision_path;
    const char* diffusion_model_path;
    const char* high_noise_diffusion_model_path;
+    const char* uncond_diffusion_model_path;
+    const char* embeddings_connectors_path;
    const char* vae_path;
+    const char* audio_vae_path;
    const char* taesd_path;
    const char* control_net_path;
    const sd_embedding_t* embeddings;
@@ -203,8 +221,20 @@ typedef struct {
    bool chroma_use_t5_mask;
    int chroma_t5_mask_pad;
    bool qwen_image_zero_cond_t;
+    enum sd_vae_format_t vae_format;
+    float max_vram;  // GiB budget for graph-cut segmented param offload (0 = disabled, -1 = auto free VRAM minus 1 GiB)
+    bool stream_layers;  // Enable residency+prefetch streaming on top of --max-vram (no effect without --max-vram)
+    const char* backend;
+    const char* params_backend;
 } sd_ctx_params_t;

+typedef struct {
+    uint32_t sample_rate;
+    uint32_t channels;
+    uint64_t sample_count;
+    float* data;
+} sd_audio_t;
+
 typedef struct {
    uint32_t width;
    uint32_t height;
@@ -237,6 +267,7 @@ typedef struct {
    float* custom_sigmas;
    int custom_sigmas_count;
    float flow_shift;
+    const char* extra_sample_args;
 } sd_sample_params_t;

 typedef struct {
@@ -289,6 +320,34 @@ typedef struct {
    const char* path;
 } sd_lora_t;

+enum sd_hires_upscaler_t {
+    SD_HIRES_UPSCALER_NONE,
+    SD_HIRES_UPSCALER_LATENT,
+    SD_HIRES_UPSCALER_LATENT_NEAREST,
+    SD_HIRES_UPSCALER_LATENT_NEAREST_EXACT,
+    SD_HIRES_UPSCALER_LATENT_ANTIALIASED,
+    SD_HIRES_UPSCALER_LATENT_BICUBIC,
+    SD_HIRES_UPSCALER_LATENT_BICUBIC_ANTIALIASED,
+    SD_HIRES_UPSCALER_LANCZOS,
+    SD_HIRES_UPSCALER_NEAREST,
+    SD_HIRES_UPSCALER_MODEL,
+    SD_HIRES_UPSCALER_COUNT,
+};
+
+typedef struct {
+    bool enabled;
+    enum sd_hires_upscaler_t upscaler;
+    const char* model_path;
+    float scale;
+    int target_width;
+    int target_height;
+    int steps;
+    float denoising_strength;
+    int upscale_tile_size;
+    float* custom_sigmas;
+    int custom_sigmas_count;
+} sd_hires_params_t;
+
 typedef struct {
    const sd_lora_t* loras;
    uint32_t lora_count;
@@ -312,6 +371,7 @@ typedef struct {
    sd_pm_params_t pm_params;
    sd_tiling_params_t vae_tiling_params;
    sd_cache_params_t cache;
+    sd_hires_params_t hires;
 } sd_img_gen_params_t;

 typedef struct {
@@ -332,9 +392,11 @@ typedef struct {
    float strength;
    int64_t seed;
    int video_frames;
+    int fps;
    float vace_strength;
    sd_tiling_params_t vae_tiling_params;
    sd_cache_params_t cache;
+    sd_hires_params_t hires;
 } sd_vid_gen_params_t;

 typedef struct sd_ctx_t sd_ctx_t;
@@ -365,14 +427,18 @@ SD_API const char* sd_preview_name(enum preview_t preview);
 SD_API enum preview_t str_to_preview(const char* str);
 SD_API const char* sd_lora_apply_mode_name(enum lora_apply_mode_t mode);
 SD_API enum lora_apply_mode_t str_to_lora_apply_mode(const char* str);
+SD_API const char* sd_hires_upscaler_name(enum sd_hires_upscaler_t upscaler);
+SD_API enum sd_hires_upscaler_t str_to_sd_hires_upscaler(const char* str);

 SD_API void sd_cache_params_init(sd_cache_params_t* cache_params);
+SD_API void sd_hires_params_init(sd_hires_params_t* hires_params);

 SD_API void sd_ctx_params_init(sd_ctx_params_t* sd_ctx_params);
 SD_API char* sd_ctx_params_to_str(const sd_ctx_params_t* sd_ctx_params);

 SD_API sd_ctx_t* new_sd_ctx(const sd_ctx_params_t* sd_ctx_params);
 SD_API void free_sd_ctx(sd_ctx_t* sd_ctx);
+SD_API void free_sd_audio(sd_audio_t* audio);

 SD_API void sd_sample_params_init(sd_sample_params_t* sample_params);
 SD_API char* sd_sample_params_to_str(const sd_sample_params_t* sample_params);
@@ -385,7 +451,11 @@ SD_API char* sd_img_gen_params_to_str(const sd_img_gen_params_t* sd_img_gen_para
 SD_API sd_image_t* generate_image(sd_ctx_t* sd_ctx, const sd_img_gen_params_t* sd_img_gen_params);

 SD_API void sd_vid_gen_params_init(sd_vid_gen_params_t* sd_vid_gen_params);
-SD_API sd_image_t* generate_video(sd_ctx_t* sd_ctx, const sd_vid_gen_params_t* sd_vid_gen_params, int* num_frames_out);
+SD_API bool generate_video(sd_ctx_t* sd_ctx,
+                           const sd_vid_gen_params_t* sd_vid_gen_params,
+                           sd_image_t** frames_out,
+                           int* num_frames_out,
+                           sd_audio_t** audio_out);

 typedef struct upscaler_ctx_t upscaler_ctx_t;

@@ -393,7 +463,9 @@ SD_API upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path,
                                        bool offload_params_to_cpu,
                                        bool direct,
                                        int n_threads,
-                                        int tile_size);
+                                        int tile_size,
+                                        const char* backend,
+                                        const char* params_backend);
 SD_API void free_upscaler_ctx(upscaler_ctx_t* upscaler_ctx);

 SD_API sd_image_t upscale(upscaler_ctx_t* upscaler_ctx,
@@ -0,0 +1,283 @@
+#!/usr/bin/env python
+import argparse
+import json
+import math
+import os
+import struct
+from collections import Counter
+from pathlib import Path
+
+import torch
+from safetensors import safe_open
+
+
+FLOAT_DTYPES = {
+    "BF16",
+    "F16",
+    "F32",
+    "F64",
+    "F8_E4M3",
+    "F8_E4M3FN",
+    "F8_E5M2",
+}
+
+FP8_DTYPES = {
+    "F8_E4M3",
+    "F8_E4M3FN",
+    "F8_E5M2",
+}
+
+DTYPE_SIZES = {
+    "BOOL": 1,
+    "U8": 1,
+    "I8": 1,
+    "F8_E4M3": 1,
+    "F8_E4M3FN": 1,
+    "F8_E5M2": 1,
+    "U16": 2,
+    "I16": 2,
+    "F16": 2,
+    "BF16": 2,
+    "U32": 4,
+    "I32": 4,
+    "F32": 4,
+    "U64": 8,
+    "I64": 8,
+    "F64": 8,
+}
+
+
+def read_safetensors_header(path: Path):
+    with path.open("rb") as f:
+        header_len = struct.unpack("<Q", f.read(8))[0]
+        header = f.read(header_len).decode("utf-8").rstrip()
+    return json.loads(header)
+
+
+def numel(shape):
+    return math.prod(shape) if shape else 1
+
+
+def scale_key_for_weight(name: str):
+    if name.endswith(".weight"):
+        return name[:-len(".weight")] + ".weight_scale"
+    if name.endswith("weight"):
+        return name + "_scale"
+    return None
+
+
+def tensor_nbytes(dtype: str, shape):
+    return numel(shape) * DTYPE_SIZES[dtype]
+
+
+def build_output_plan(header):
+    entries = {k: v for k, v in header.items() if k != "__metadata__"}
+    paired_scale_keys = set()
+    plan = []
+
+    for name, info in entries.items():
+        scale_key = scale_key_for_weight(name)
+        if info["dtype"] in FP8_DTYPES and scale_key in entries:
+            paired_scale_keys.add(scale_key)
+
+    for name, info in entries.items():
+        if name in paired_scale_keys:
+            continue
+
+        dtype = info["dtype"]
+        shape = info["shape"]
+        scale_key = scale_key_for_weight(name)
+
+        if dtype in FP8_DTYPES and scale_key in entries:
+            scale_info = entries[scale_key]
+            plan.append(
+                {
+                    "name": name,
+                    "source_dtype": dtype,
+                    "output_dtype": "BF16",
+                    "shape": shape,
+                    "mode": "fp8_scaled_weight",
+                    "scale_key": scale_key,
+                }
+            )
+            continue
+
+        if dtype in FLOAT_DTYPES:
+            plan.append(
+                {
+                    "name": name,
+                    "source_dtype": dtype,
+                    "output_dtype": "BF16",
+                    "shape": shape,
+                    "mode": "float_to_bf16",
+                }
+            )
+        else:
+            plan.append(
+                {
+                    "name": name,
+                    "source_dtype": dtype,
+                    "output_dtype": dtype,
+                    "shape": shape,
+                    "mode": "copy",
+                }
+            )
+
+    metadata = dict(header.get("__metadata__", {}) or {})
+    metadata["format"] = "pt"
+    metadata["conversion"] = "fp8_weight_scale_to_bf16"
+
+    output_header = {"__metadata__": metadata}
+    offset = 0
+    for item in plan:
+        size = tensor_nbytes(item["output_dtype"], item["shape"])
+        output_header[item["name"]] = {
+            "dtype": item["output_dtype"],
+            "shape": item["shape"],
+            "data_offsets": [offset, offset + size],
+        }
+        offset += size
+
+    return plan, output_header, offset
+
+
+def write_tensor_bytes(out, tensor):
+    tensor = tensor.detach().cpu().contiguous()
+    if tensor.numel() == 0:
+        return
+    if tensor.dtype == torch.bfloat16:
+        tensor.view(torch.uint16).numpy().tofile(out)
+    elif tensor.dtype in (getattr(torch, "float8_e4m3fn", None), getattr(torch, "float8_e5m2", None)):
+        tensor.view(torch.uint8).numpy().tofile(out)
+    else:
+        tensor.numpy().tofile(out)
+
+
+def scale_view_for_chunk(scale, chunk, first_dim_start=0, first_dim_end=None):
+    scale = scale.to(torch.float32)
+
+    if scale.numel() == 1:
+        return scale.reshape((1,) * chunk.ndim)
+
+    if chunk.ndim > 0 and scale.ndim == 1:
+        if first_dim_end is not None and scale.shape[0] >= first_dim_end:
+            scale = scale[first_dim_start:first_dim_end]
+        if scale.shape[0] == chunk.shape[0]:
+            return scale.reshape((scale.shape[0],) + (1,) * (chunk.ndim - 1))
+
+    return scale
+
+
+def write_scaled_fp8_weight(out, weight, scale, chunk_rows):
+    if weight.ndim == 0:
+        result = weight.to(torch.float32) * scale_view_for_chunk(scale, weight)
+        write_tensor_bytes(out, result.to(torch.bfloat16))
+        return
+
+    rows = weight.shape[0]
+    for start in range(0, rows, chunk_rows):
+        end = min(start + chunk_rows, rows)
+        chunk = weight[start:end].to(torch.float32)
+        scale_view = scale_view_for_chunk(scale, chunk, start, end)
+        result = chunk * scale_view
+        write_tensor_bytes(out, result.to(torch.bfloat16))
+
+
+def write_float_as_bf16(out, tensor, chunk_rows):
+    if tensor.dtype == torch.bfloat16:
+        write_tensor_bytes(out, tensor)
+        return
+
+    if tensor.ndim == 0:
+        write_tensor_bytes(out, tensor.to(torch.bfloat16))
+        return
+
+    rows = tensor.shape[0]
+    for start in range(0, rows, chunk_rows):
+        end = min(start + chunk_rows, rows)
+        write_tensor_bytes(out, tensor[start:end].to(torch.bfloat16))
+
+
+def convert(input_path: Path, output_path: Path, chunk_rows: int, dry_run: bool):
+    header = read_safetensors_header(input_path)
+    plan, output_header, data_size = build_output_plan(header)
+
+    source_counts = Counter(item["source_dtype"] for item in plan)
+    output_counts = Counter(item["output_dtype"] for item in plan)
+    scaled_count = sum(item["mode"] == "fp8_scaled_weight" for item in plan)
+    dropped_scales = sum(item["mode"] == "fp8_scaled_weight" for item in plan)
+    header_bytes = json.dumps(output_header, separators=(",", ":")).encode("utf-8")
+    expected_size = 8 + len(header_bytes) + data_size
+
+    print(f"input:  {input_path}")
+    print(f"output: {output_path}")
+    print(f"tensors written: {len(plan)}")
+    print(f"scaled fp8 weights dequantized: {scaled_count}")
+    print(f"weight_scale tensors dropped: {dropped_scales}")
+    print(f"source dtypes: {dict(sorted(source_counts.items()))}")
+    print(f"output dtypes: {dict(sorted(output_counts.items()))}")
+    print(f"expected output size: {expected_size / (1024 ** 3):.2f} GiB")
+
+    if dry_run:
+        return
+
+    if output_path.exists():
+        raise FileExistsError(f"{output_path} already exists; pass --overwrite to replace it")
+
+    tmp_path = output_path.with_suffix(output_path.suffix + ".tmp")
+    if tmp_path.exists():
+        raise FileExistsError(f"{tmp_path} already exists; remove it or choose another output")
+
+    with safe_open(str(input_path), framework="pt", device="cpu") as sf, tmp_path.open("wb") as out:
+        out.write(struct.pack("<Q", len(header_bytes)))
+        out.write(header_bytes)
+
+        for index, item in enumerate(plan, 1):
+            name = item["name"]
+            print(f"[{index:04d}/{len(plan):04d}] {name} -> {item['output_dtype']}")
+
+            tensor = sf.get_tensor(name)
+            if item["mode"] == "fp8_scaled_weight":
+                scale = sf.get_tensor(item["scale_key"])
+                write_scaled_fp8_weight(out, tensor, scale, chunk_rows)
+            elif item["mode"] == "float_to_bf16":
+                write_float_as_bf16(out, tensor, chunk_rows)
+            else:
+                write_tensor_bytes(out, tensor)
+
+        actual_size = out.tell()
+
+    if actual_size != expected_size:
+        tmp_path.unlink(missing_ok=True)
+        raise RuntimeError(f"wrote {actual_size} bytes, expected {expected_size} bytes")
+
+    tmp_path.replace(output_path)
+    print("done")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Convert an fp8 safetensors checkpoint with weight_scale tensors to bf16."
+    )
+    parser.add_argument("--input", default="ideogram4_fp8.safetensors", type=Path)
+    parser.add_argument("--output", default="ideogram4_bf16.safetensors", type=Path)
+    parser.add_argument("--chunk-rows", default=1024, type=int)
+    parser.add_argument("--dry-run", action="store_true")
+    parser.add_argument("--overwrite", action="store_true")
+    args = parser.parse_args()
+
+    input_path = args.input.resolve()
+    output_path = args.output.resolve()
+
+    if args.chunk_rows < 1:
+        raise ValueError("--chunk-rows must be >= 1")
+    if not input_path.exists():
+        raise FileNotFoundError(input_path)
+    if args.overwrite and output_path.exists():
+        output_path.unlink()
+
+    convert(input_path, output_path, args.chunk_rows, args.dry_run)
+
+
+if __name__ == "__main__":
+    main()
@@ -1,18 +1,61 @@
 #ifndef __ANIMA_HPP__
 #define __ANIMA_HPP__

+#include <algorithm>
 #include <cmath>
 #include <memory>
 #include <utility>
 #include <vector>

 #include "common_block.hpp"
+#include "diffusion_model.hpp"
 #include "flux.hpp"
 #include "rope.hpp"

 namespace Anima {
    constexpr int ANIMA_GRAPH_SIZE = 65536;

+    struct AnimaConfig {
+        int64_t in_channels       = 16;
+        int64_t out_channels      = 16;
+        int64_t hidden_size       = 2048;
+        int64_t text_embed_dim    = 1024;
+        int64_t num_heads         = 16;
+        int64_t head_dim          = 128;
+        int patch_size            = 2;
+        int64_t num_layers        = 28;
+        std::vector<int> axes_dim = {44, 42, 42};
+        int theta                 = 10000;
+
+        static AnimaConfig detect_from_weights(const String2TensorStorage& tensor_storage_map, const std::string& prefix) {
+            AnimaConfig config;
+            int64_t detected_layers = 0;
+            std::string layer_tag   = prefix.empty() ? "blocks." : prefix + ".blocks.";
+            for (const auto& [name, _] : tensor_storage_map) {
+                size_t pos = name.find(layer_tag);
+                if (pos == std::string::npos) {
+                    continue;
+                }
+                size_t start = pos + layer_tag.size();
+                size_t end   = name.find('.', start);
+                if (end == std::string::npos) {
+                    continue;
+                }
+                int64_t layer_id = atoll(name.substr(start, end - start).c_str());
+                detected_layers  = std::max(detected_layers, layer_id + 1);
+            }
+            if (detected_layers > 0) {
+                config.num_layers = detected_layers;
+                LOG_DEBUG("anima: num_layers = %" PRId64 ", hidden_size = %" PRId64 ", num_heads = %" PRId64 ", head_dim = %" PRId64,
+                          config.num_layers,
+                          config.hidden_size,
+                          config.num_heads,
+                          config.head_dim);
+            }
+            return config;
+        }
+    };
+
    __STATIC_INLINE__ ggml_tensor* apply_gate(ggml_context* ctx,
                                              ggml_tensor* x,
                                              ggml_tensor* gate) {
@@ -417,31 +460,22 @@ namespace Anima {

    struct AnimaNet : public GGMLBlock {
    public:
-        int64_t in_channels       = 16;
-        int64_t out_channels      = 16;
-        int64_t hidden_size       = 2048;
-        int64_t text_embed_dim    = 1024;
-        int64_t num_heads         = 16;
-        int64_t head_dim          = 128;
-        int patch_size            = 2;
-        int64_t num_layers        = 28;
-        std::vector<int> axes_dim = {44, 42, 42};
-        int theta                 = 10000;
+        AnimaConfig config;

    public:
        AnimaNet() = default;
-        explicit AnimaNet(int64_t num_layers)
-            : num_layers(num_layers) {
-            blocks["x_embedder"]       = std::make_shared<XEmbedder>((in_channels + 1) * patch_size * patch_size, hidden_size);
-            blocks["t_embedder"]       = std::make_shared<TimestepEmbedder>(hidden_size, hidden_size * 3);
-            blocks["t_embedding_norm"] = std::make_shared<RMSNorm>(hidden_size, 1e-6f);
-            for (int i = 0; i < num_layers; i++) {
-                blocks["blocks." + std::to_string(i)] = std::make_shared<TransformerBlock>(hidden_size,
-                                                                                           text_embed_dim,
-                                                                                           num_heads,
-                                                                                           head_dim);
+        explicit AnimaNet(AnimaConfig config)
+            : config(config) {
+            blocks["x_embedder"]       = std::make_shared<XEmbedder>((config.in_channels + 1) * config.patch_size * config.patch_size, config.hidden_size);
+            blocks["t_embedder"]       = std::make_shared<TimestepEmbedder>(config.hidden_size, config.hidden_size * 3);
+            blocks["t_embedding_norm"] = std::make_shared<RMSNorm>(config.hidden_size, 1e-6f);
+            for (int i = 0; i < config.num_layers; i++) {
+                blocks["blocks." + std::to_string(i)] = std::make_shared<TransformerBlock>(config.hidden_size,
+                                                                                           config.text_embed_dim,
+                                                                                           config.num_heads,
+                                                                                           config.head_dim);
            }
-            blocks["final_layer"] = std::make_shared<FinalLayer>(hidden_size, patch_size, out_channels);
+            blocks["final_layer"] = std::make_shared<FinalLayer>(config.hidden_size, config.patch_size, config.out_channels);
            blocks["llm_adapter"] = std::make_shared<LLMAdapter>(1024, 1024, 1024, 6, 16);
        }

@@ -468,11 +502,11 @@ namespace Anima {
            auto padding_mask = ggml_ext_zeros(ctx->ggml_ctx, x->ne[0], x->ne[1], 1, x->ne[3]);
            x                 = ggml_concat(ctx->ggml_ctx, x, padding_mask, 2);  // [N, C + 1, H, W]

-            x = DiT::pad_and_patchify(ctx, x, patch_size, patch_size);  // [N, h*w, (C+1)*ph*pw]
+            x = DiT::pad_and_patchify(ctx, x, config.patch_size, config.patch_size);  // [N, h*w, (C+1)*ph*pw]

            x = x_embedder->forward(ctx, x);

-            auto timestep_proj     = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep, static_cast<int>(hidden_size));
+            auto timestep_proj     = ggml_ext_timestep_embedding(ctx->ggml_ctx, timestep, static_cast<int>(config.hidden_size));
            auto temb              = t_embedder->forward(ctx, timestep_proj);
            auto embedded_timestep = t_embedding_norm->forward(ctx, timestep_proj);

@@ -499,53 +533,40 @@ namespace Anima {
                encoder_hidden_states = adapted_context;
            }

-            for (int i = 0; i < num_layers; i++) {
+            sd::ggml_graph_cut::mark_graph_cut(x, "anima.prelude", "x");
+            sd::ggml_graph_cut::mark_graph_cut(embedded_timestep, "anima.prelude", "embedded_timestep");
+            sd::ggml_graph_cut::mark_graph_cut(temb, "anima.prelude", "temb");
+            sd::ggml_graph_cut::mark_graph_cut(encoder_hidden_states, "anima.prelude", "context");
+
+            for (int i = 0; i < config.num_layers; i++) {
                auto block = std::dynamic_pointer_cast<TransformerBlock>(blocks["blocks." + std::to_string(i)]);
                x          = block->forward(ctx, x, encoder_hidden_states, embedded_timestep, temb, image_pe);
+                sd::ggml_graph_cut::mark_graph_cut(x, "anima.blocks." + std::to_string(i), "x");
            }

            x = final_layer->forward(ctx, x, embedded_timestep, temb);  // [N, h*w, ph*pw*C]

-            x = DiT::unpatchify_and_crop(ctx->ggml_ctx, x, H, W, patch_size, patch_size, false);  // [N, C, H, W]
+            x = DiT::unpatchify_and_crop(ctx->ggml_ctx, x, H, W, config.patch_size, config.patch_size, false);  // [N, C, H, W]

            return x;
        }
    };

-    struct AnimaRunner : public GGMLRunner {
+    struct AnimaRunner : public DiffusionModelRunner {
    public:
        std::vector<float> image_pe_vec;
        std::vector<float> adapter_q_pe_vec;
        std::vector<float> adapter_k_pe_vec;
+        AnimaConfig config;
        AnimaNet net;

        AnimaRunner(ggml_backend_t backend,
-                    bool offload_params_to_cpu,
+                    ggml_backend_t params_backend,
                    const String2TensorStorage& tensor_storage_map = {},
                    const std::string prefix                       = "model.diffusion_model")
-            : GGMLRunner(backend, offload_params_to_cpu) {
-            int64_t num_layers    = 0;
-            std::string layer_tag = prefix + ".net.blocks.";
-            for (const auto& kv : tensor_storage_map) {
-                const std::string& tensor_name = kv.first;
-                size_t pos                     = tensor_name.find(layer_tag);
-                if (pos == std::string::npos) {
-                    continue;
-                }
-                size_t start = pos + layer_tag.size();
-                size_t end   = tensor_name.find('.', start);
-                if (end == std::string::npos) {
-                    continue;
-                }
-                int64_t layer_id = atoll(tensor_name.substr(start, end - start).c_str());
-                num_layers       = std::max(num_layers, layer_id + 1);
-            }
-            if (num_layers <= 0) {
-                num_layers = 28;
-            }
-            LOG_INFO("anima net layers: %" PRId64, num_layers);
-
-            net = AnimaNet(num_layers);
+            : DiffusionModelRunner(backend, params_backend, prefix),
+              config(AnimaConfig::detect_from_weights(tensor_storage_map, prefix + ".net")) {
+            net = AnimaNet(config);
            net.init(params_ctx, tensor_storage_map, prefix + ".net");
        }

@@ -553,7 +574,7 @@ namespace Anima {
            return "anima";
        }

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

@@ -592,7 +613,8 @@ namespace Anima {
                                          {},
                                          empty_ref_latents,
                                          false,
-                                          1.0f);
+                                          1.0f,
+                                          false);

            std::vector<float> axis_thetas = {
                static_cast<float>(theta) * calc_ntk_factor(t_extrapolation_ratio, axes_dim[0]),
@@ -615,22 +637,22 @@ namespace Anima {
            GGML_ASSERT(x->ne[3] == 1);
            ggml_cgraph* gf = new_graph_custom(ANIMA_GRAPH_SIZE);

-            int64_t pad_h = (net.patch_size - x->ne[1] % net.patch_size) % net.patch_size;
-            int64_t pad_w = (net.patch_size - x->ne[0] % net.patch_size) % net.patch_size;
+            int64_t pad_h = (config.patch_size - x->ne[1] % config.patch_size) % config.patch_size;
+            int64_t pad_w = (config.patch_size - x->ne[0] % config.patch_size) % config.patch_size;
            int64_t h_pad = x->ne[1] + pad_h;
            int64_t w_pad = x->ne[0] + pad_w;

            image_pe_vec          = gen_anima_image_pe_vec(1,
                                                           static_cast<int>(h_pad),
                                                           static_cast<int>(w_pad),
-                                                           static_cast<int>(net.patch_size),
-                                                           net.theta,
-                                                           net.axes_dim,
+                                                           static_cast<int>(config.patch_size),
+                                                           config.theta,
+                                                           config.axes_dim,
                                                           4.0f,
                                                           4.0f,
                                                           1.0f);
-            int64_t image_pos_len = static_cast<int64_t>(image_pe_vec.size()) / (2 * 2 * (net.head_dim / 2));
-            auto image_pe         = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, net.head_dim / 2, image_pos_len);
+            int64_t image_pos_len = static_cast<int64_t>(image_pe_vec.size()) / (2 * 2 * (config.head_dim / 2));
+            auto image_pe         = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.head_dim / 2, image_pos_len);
            set_backend_tensor_data(image_pe, image_pe_vec.data());

            ggml_tensor* adapter_q_pe = nullptr;
@@ -677,6 +699,19 @@ namespace Anima {
            };
            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const DiffusionParams& diffusion_params) override {
+            GGML_ASSERT(diffusion_params.x != nullptr);
+            GGML_ASSERT(diffusion_params.timesteps != nullptr);
+            const auto* extra = diffusion_extra_as<AnimaDiffusionExtra>(diffusion_params);
+            return compute(n_threads,
+                           *diffusion_params.x,
+                           *diffusion_params.timesteps,
+                           tensor_or_empty(diffusion_params.context),
+                           tensor_or_empty(extra->t5_ids),
+                           tensor_or_empty(extra->t5_weights));
+        }
    };
 }  // namespace Anima

@@ -328,6 +328,7 @@ public:
        auto conv_out    = std::dynamic_pointer_cast<Conv2d>(blocks["conv_out"]);

        auto h = conv_in->forward(ctx, x);  // [N, ch, h, w]
+        // sd::ggml_graph_cut::mark_graph_cut(h, "vae.encoder.prelude", "h");

        // downsampling
        size_t num_resolutions = ch_mult.size();
@@ -337,12 +338,14 @@ public:
                auto down_block  = std::dynamic_pointer_cast<ResnetBlock>(blocks[name]);

                h = down_block->forward(ctx, h);
+                // sd::ggml_graph_cut::mark_graph_cut(h, "vae.encoder.down." + std::to_string(i) + ".block." + std::to_string(j), "h");
            }
            if (i != num_resolutions - 1) {
                std::string name = "down." + std::to_string(i) + ".downsample";
                auto down_sample = std::dynamic_pointer_cast<DownSampleBlock>(blocks[name]);

                h = down_sample->forward(ctx, h);
+                // sd::ggml_graph_cut::mark_graph_cut(h, "vae.encoder.down." + std::to_string(i) + ".downsample", "h");
            }
        }

@@ -350,6 +353,7 @@ public:
        h = mid_block_1->forward(ctx, h);
        h = mid_attn_1->forward(ctx, h);
        h = mid_block_2->forward(ctx, h);  // [N, block_in, h, w]
+        // sd::ggml_graph_cut::mark_graph_cut(h, "vae.encoder.mid", "h");

        // end
        h = norm_out->forward(ctx, h);
@@ -450,6 +454,7 @@ public:

        // conv_in
        auto h = conv_in->forward(ctx, z);  // [N, block_in, h, w]
+        // sd::ggml_graph_cut::mark_graph_cut(h, "vae.decoder.prelude", "h");

        // middle
        h = mid_block_1->forward(ctx, h);
@@ -457,6 +462,7 @@ public:

        h = mid_attn_1->forward(ctx, h);
        h = mid_block_2->forward(ctx, h);  // [N, block_in, h, w]
+        // sd::ggml_graph_cut::mark_graph_cut(h, "vae.decoder.mid", "h");

        // upsampling
        int num_resolutions = static_cast<int>(ch_mult.size());
@@ -466,12 +472,14 @@ public:
                auto up_block    = std::dynamic_pointer_cast<ResnetBlock>(blocks[name]);

                h = up_block->forward(ctx, h);
+                // sd::ggml_graph_cut::mark_graph_cut(h, "vae.decoder.up." + std::to_string(i) + ".block." + std::to_string(j), "h");
            }
            if (i != 0) {
                std::string name = "up." + std::to_string(i) + ".upsample";
                auto up_sample   = std::dynamic_pointer_cast<UpSampleBlock>(blocks[name]);

                h = up_sample->forward(ctx, h);
+                // sd::ggml_graph_cut::mark_graph_cut(h, "vae.decoder.up." + std::to_string(i) + ".upsample", "h");
            }
        }

@@ -599,6 +607,7 @@ public:
        if (use_quant) {
            auto post_quant_conv = std::dynamic_pointer_cast<Conv2d>(blocks["post_quant_conv"]);
            z                    = post_quant_conv->forward(ctx, z);  // [N, z_channels, h, w]
+            // sd::ggml_graph_cut::mark_graph_cut(z, "vae.decode.prelude", "z");
        }
        auto decoder = std::dynamic_pointer_cast<Decoder>(blocks["decoder"]);

@@ -616,6 +625,7 @@ public:
        if (use_quant) {
            auto quant_conv = std::dynamic_pointer_cast<Conv2d>(blocks["quant_conv"]);
            z               = quant_conv->forward(ctx, z);  // [N, 2*embed_dim, h/8, w/8]
+            // sd::ggml_graph_cut::mark_graph_cut(z, "vae.encode.final", "z");
        }
        if (sd_version_uses_flux2_vae(version)) {
            z = ggml_ext_chunk(ctx->ggml_ctx, z, 2, 2)[0];
@@ -654,13 +664,13 @@ struct AutoEncoderKL : public VAE {
    AutoEncoderKLModel ae;

    AutoEncoderKL(ggml_backend_t backend,
-                  bool offload_params_to_cpu,
+                  ggml_backend_t params_backend,
                  const String2TensorStorage& tensor_storage_map,
                  const std::string prefix,
                  bool decode_only       = false,
                  bool use_video_decoder = false,
                  SDVersion version      = VERSION_SD1)
-        : decode_only(decode_only), VAE(version, backend, offload_params_to_cpu) {
+        : decode_only(decode_only), VAE(version, backend, params_backend) {
        if (sd_version_is_sd1(version) || sd_version_is_sd2(version)) {
            scale_factor = 0.18215f;
            shift_factor = 0.f;
@@ -670,7 +680,7 @@ struct AutoEncoderKL : public VAE {
        } else if (sd_version_is_sd3(version)) {
            scale_factor = 1.5305f;
            shift_factor = 0.0609f;
-        } else if (sd_version_is_flux(version) || sd_version_is_z_image(version)) {
+        } else if (sd_version_is_flux(version) || sd_version_is_z_image(version) || sd_version_is_longcat(version)) {
            scale_factor = 0.3611f;
            shift_factor = 0.1159f;
        } else if (sd_version_uses_flux2_vae(version)) {
@@ -95,8 +95,9 @@ public:

    ggml_tensor* forward(GGMLRunnerContext* ctx,
                         ggml_tensor* x,
-                         ggml_tensor* mask = nullptr,
-                         int clip_skip     = -1) {
+                         ggml_tensor* mask                   = nullptr,
+                         int clip_skip                       = -1,
+                         const std::string& graph_cut_prefix = "") {
        // x: [N, n_token, d_model]
        int layer_idx = n_layer - 1;
        // LOG_DEBUG("clip_skip %d", clip_skip);
@@ -112,6 +113,9 @@ public:
            std::string name = "layers." + std::to_string(i);
            auto layer       = std::dynamic_pointer_cast<CLIPLayer>(blocks[name]);
            x                = layer->forward(ctx, x, mask);  // [N, n_token, d_model]
+            if (!graph_cut_prefix.empty()) {
+                sd::ggml_graph_cut::mark_graph_cut(x, graph_cut_prefix + ".layers." + std::to_string(i), "x");
+            }
            // LOG_DEBUG("layer %d", i);
        }
        return x;
@@ -304,7 +308,8 @@ public:
        auto final_layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["final_layer_norm"]);

        auto x = embeddings->forward(ctx, input_ids, tkn_embeddings);  // [N, n_token, hidden_size]
-        x      = encoder->forward(ctx, x, mask, return_pooled ? -1 : clip_skip);
+        sd::ggml_graph_cut::mark_graph_cut(x, "clip_text.prelude", "x");
+        x = encoder->forward(ctx, x, mask, return_pooled ? -1 : clip_skip, "clip_text");
        if (return_pooled || with_final_ln) {
            x = final_layer_norm->forward(ctx, x);
        }
@@ -368,7 +373,8 @@ public:

        auto x = embeddings->forward(ctx, pixel_values);  // [N, num_positions, embed_dim]
        x      = pre_layernorm->forward(ctx, x);
-        x      = encoder->forward(ctx, x, nullptr, clip_skip);
+        sd::ggml_graph_cut::mark_graph_cut(x, "clip_vision.prelude", "x");
+        x = encoder->forward(ctx, x, nullptr, clip_skip, "clip_vision");

        auto last_hidden_state = x;

@@ -463,13 +469,13 @@ struct CLIPTextModelRunner : public GGMLRunner {
    std::vector<float> attention_mask_vec;

    CLIPTextModelRunner(ggml_backend_t backend,
-                        bool offload_params_to_cpu,
+                        ggml_backend_t params_backend,
                        const String2TensorStorage& tensor_storage_map,
                        const std::string prefix,
                        CLIPVersion version = OPENAI_CLIP_VIT_L_14,
                        bool with_final_ln  = true,
                        bool force_clip_f32 = false)
-        : GGMLRunner(backend, offload_params_to_cpu) {
+        : GGMLRunner(backend, params_backend) {
        bool proj_in = false;
        for (const auto& [name, tensor_storage] : tensor_storage_map) {
            if (!starts_with(name, prefix)) {
@@ -1,7 +1,9 @@
 #ifndef __COMMON_BLOCK_HPP__
 #define __COMMON_BLOCK_HPP__

+#include "ggml-backend.h"
 #include "ggml_extend.hpp"
+#include "util.h"

 class DownSampleBlock : public GGMLBlock {
 protected:
@@ -248,9 +250,6 @@ public:
        float scale         = 1.f;
        if (precision_fix) {
            scale = 1.f / 128.f;
-#ifdef SD_USE_VULKAN
-            force_prec_f32 = true;
-#endif
        }
        // The purpose of the scale here is to prevent NaN issues in certain situations.
        // For example, when using Vulkan without enabling force_prec_f32,
@@ -264,6 +263,9 @@ public:

        auto net_0 = std::dynamic_pointer_cast<UnaryBlock>(blocks["net.0"]);
        auto net_2 = std::dynamic_pointer_cast<Linear>(blocks["net.2"]);
+        if (sd_backend_is(ctx->backend, "Vulkan")) {
+            net_2->set_force_prec_f32(true);
+        }

        x = net_0->forward(ctx, x);  // [ne3, ne2, ne1, inner_dim]
        x = net_2->forward(ctx, x);  // [ne3, ne2, ne1, dim_out]
@@ -103,6 +103,64 @@ namespace DiT {
        x         = ggml_ext_slice(ctx, x, 0, 0, W);               // [N, C, H, W]
        return x;
    }
+
+    inline ggml_tensor* patchify(ggml_context* ctx,
+                                 ggml_tensor* x,
+                                 int pt,
+                                 int ph,
+                                 int pw,
+                                 int64_t N = 1) {
+        // x: [N*C, T, H, W]
+        // return: [N, h*w, C*pt*ph*pw]
+        int64_t C     = x->ne[3] / N;
+        int64_t T     = x->ne[2];
+        int64_t H     = x->ne[1];
+        int64_t W     = x->ne[0];
+        int64_t t_len = T / pt;
+        int64_t h_len = H / ph;
+        int64_t w_len = W / pw;
+
+        GGML_ASSERT(C * N == x->ne[3]);
+        GGML_ASSERT(t_len * pt == T && h_len * ph == H && w_len * pw == W);
+
+        x = ggml_reshape_4d(ctx, x, pw * w_len, ph * h_len, pt, t_len * C * N);      // [N*C*t_len, pt, h_len*ph, w_len*pw]
+        x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 1, 3));          // [N*C*t_len, h_len*ph, pt, w_len*pw]
+        x = ggml_reshape_4d(ctx, x, pw * w_len, pt, ph, h_len * t_len * C * N);      // [N*C*t_len*h_len, ph, pt, w_len*pw]
+        x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 1, 3));          // [N*C*t_len*h_len, pt, ph, w_len*pw]
+        x = ggml_reshape_4d(ctx, x, pw, w_len, ph * pt, h_len * t_len * C * N);      // [N*C*t_len*h_len, pt*ph, w_len, pw]
+        x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 1, 3));          // [N*C*t_len*h_len, w_len, pt*ph, pw]
+        x = ggml_reshape_4d(ctx, x, pw * ph * pt, w_len * h_len * t_len, C, N);      // [N, C, t_len*h_len*w_len, pt*ph*pw]
+        x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 1, 3));          // [N, t_len*h_len*w_len, C, pt*ph*pw]
+        x = ggml_reshape_4d(ctx, x, pw * ph * pt * C, w_len * h_len * t_len, N, 1);  // [N, t_len*h_len*w_len, C*pt*ph*pw]
+        return x;
+    }
+
+    inline ggml_tensor* unpatchify(ggml_context* ctx,
+                                   ggml_tensor* x,
+                                   int64_t t_len,
+                                   int64_t h_len,
+                                   int64_t w_len,
+                                   int pt,
+                                   int ph,
+                                   int pw) {
+        // x: [N, t_len*h_len*w_len, pt*ph*pw*C]
+        // return: [N*C, t_len*pt, h_len*ph, w_len*pw]
+        int64_t N = x->ne[3];
+        int64_t C = x->ne[0] / pt / ph / pw;
+
+        GGML_ASSERT(C * pt * ph * pw == x->ne[0]);
+
+        x = ggml_reshape_4d(ctx, x, C, pw * ph * pt, w_len * h_len * t_len, N);  // [N, t_len*h_len*w_len, pt*ph*pw, C]
+        x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 1, 2, 0, 3));      // [N, C, t_len*h_len*w_len, pt*ph*pw]
+        x = ggml_reshape_4d(ctx, x, pw, ph * pt, w_len, h_len * t_len * C * N);  // [N*C*t_len*h_len, w_len, pt*ph, pw]
+        x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 1, 3));      // [N*C*t_len*h_len, pt*ph, w_len, pw]
+        x = ggml_reshape_4d(ctx, x, pw * w_len, ph, pt, h_len * t_len * C * N);  // [N*C*t_len*h_len, pt, ph, w_len*pw]
+        x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 1, 3));      // [N*C*t_len*h_len, ph, pt, w_len*pw]
+        x = ggml_reshape_4d(ctx, x, pw * w_len, pt, ph * h_len, t_len * C * N);  // [N*C*t_len, h_len*ph, pt, w_len*pw]
+        x = ggml_ext_cont(ctx, ggml_ext_torch_permute(ctx, x, 0, 2, 1, 3));      // [N*C*t_len, pt, h_len*ph, w_len*pw]
+        x = ggml_reshape_4d(ctx, x, pw * w_len, ph * h_len, pt * t_len, C * N);  // [N*C, t_len*pt, h_len*ph, w_len*pw]
+        return x;
+    }
 }  // namespace DiT

 #endif  // __COMMON_DIT_HPP__
@@ -319,10 +319,10 @@ struct ControlNet : public GGMLRunner {
    bool guided_hint_cached = false;

    ControlNet(ggml_backend_t backend,
-               bool offload_params_to_cpu,
+               ggml_backend_t params_backend,
               const String2TensorStorage& tensor_storage_map = {},
               SDVersion version                              = VERSION_SD1)
-        : GGMLRunner(backend, offload_params_to_cpu), control_net(version) {
+        : GGMLRunner(backend, params_backend), control_net(version) {
        control_net.init(params_ctx, tensor_storage_map, "");
    }

@@ -457,7 +457,11 @@ struct ControlNet : public GGMLRunner {

    bool load_from_file(const std::string& file_path, int n_threads) {
        LOG_INFO("loading control net from '%s'", file_path.c_str());
-        alloc_params_buffer();
+        if (!alloc_params_buffer()) {
+            LOG_ERROR("control net model buffer allocation failed");
+            return false;
+        }
+
        std::map<std::string, ggml_tensor*> tensors;
        control_net.get_param_tensors(tensors);
        std::set<std::string> ignore_tensors;
@@ -0,0 +1,138 @@
+#include <cstring>
+#include <mutex>
+#include <regex>
+#include <vector>
+
+#include "model.h"
+#include "model_io/gguf_io.h"
+#include "model_io/safetensors_io.h"
+#include "util.h"
+
+#include "ggml_extend_backend.h"
+
+static ggml_type get_export_tensor_type(ModelLoader& model_loader,
+                                        const TensorStorage& tensor_storage,
+                                        ggml_type type,
+                                        const TensorTypeRules& tensor_type_rules) {
+    const std::string& name = tensor_storage.name;
+    ggml_type tensor_type   = tensor_storage.type;
+    ggml_type dst_type      = type;
+
+    for (const auto& tensor_type_rule : tensor_type_rules) {
+        std::regex pattern(tensor_type_rule.first);
+        if (std::regex_search(name, pattern)) {
+            dst_type = tensor_type_rule.second;
+            break;
+        }
+    }
+
+    if (model_loader.tensor_should_be_converted(tensor_storage, dst_type)) {
+        tensor_type = dst_type;
+    }
+
+    return tensor_type;
+}
+
+static bool load_tensors_for_export(ModelLoader& model_loader,
+                                    ggml_context* ggml_ctx,
+                                    ggml_type type,
+                                    const TensorTypeRules& tensor_type_rules,
+                                    std::vector<TensorWriteInfo>& tensors) {
+    std::mutex tensor_mutex;
+    auto on_new_tensor_cb = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) -> bool {
+        const std::string& name = tensor_storage.name;
+        ggml_type tensor_type   = get_export_tensor_type(model_loader, tensor_storage, type, tensor_type_rules);
+
+        std::lock_guard<std::mutex> lock(tensor_mutex);
+        ggml_tensor* tensor = ggml_new_tensor(ggml_ctx, tensor_type, tensor_storage.n_dims, tensor_storage.ne);
+        if (tensor == nullptr) {
+            LOG_ERROR("ggml_new_tensor failed");
+            return false;
+        }
+        ggml_set_name(tensor, name.c_str());
+
+        if (!tensor->data) {
+            GGML_ASSERT(ggml_nelements(tensor) == 0);
+            // Avoid crashing writers by setting a dummy pointer for zero-sized tensors.
+            LOG_DEBUG("setting dummy pointer for zero-sized tensor %s", name.c_str());
+            tensor->data = ggml_get_mem_buffer(ggml_ctx);
+        }
+
+        TensorWriteInfo write_info;
+        write_info.tensor = tensor;
+        write_info.n_dims = tensor_storage.n_dims;
+        for (int i = 0; i < tensor_storage.n_dims; ++i) {
+            write_info.ne[i] = tensor_storage.ne[i];
+        }
+
+        *dst_tensor = tensor;
+        tensors.push_back(std::move(write_info));
+
+        return true;
+    };
+
+    bool success = model_loader.load_tensors(on_new_tensor_cb);
+    LOG_INFO("load tensors done");
+    return success;
+}
+
+bool convert(const char* input_path,
+             const char* vae_path,
+             const char* output_path,
+             sd_type_t output_type,
+             const char* tensor_type_rules,
+             bool convert_name) {
+    ModelLoader model_loader;
+
+    if (!model_loader.init_from_file(input_path)) {
+        LOG_ERROR("init model loader from file failed: '%s'", input_path);
+        return false;
+    }
+
+    if (vae_path != nullptr && strlen(vae_path) > 0) {
+        if (!model_loader.init_from_file(vae_path, "vae.")) {
+            LOG_ERROR("init model loader from file failed: '%s'", vae_path);
+            return false;
+        }
+    }
+    if (convert_name) {
+        model_loader.convert_tensors_name();
+    }
+
+    ggml_type type             = (ggml_type)output_type;
+    bool output_is_safetensors = ends_with(output_path, ".safetensors");
+    TensorTypeRules type_rules = parse_tensor_type_rules(tensor_type_rules);
+
+    auto backend    = sd_backend_cpu_init();
+    size_t mem_size = 1 * 1024 * 1024;  // for padding
+    mem_size += model_loader.get_tensor_storage_map().size() * ggml_tensor_overhead();
+    mem_size += model_loader.get_params_mem_size(backend, type);
+    LOG_INFO("model tensors mem size: %.2fMB", mem_size / 1024.f / 1024.f);
+    ggml_context* ggml_ctx = ggml_init({mem_size, nullptr, false});
+
+    if (ggml_ctx == nullptr) {
+        LOG_ERROR("ggml_init failed for converter");
+        ggml_backend_free(backend);
+        return false;
+    }
+
+    std::vector<TensorWriteInfo> tensors;
+    bool success = load_tensors_for_export(model_loader, ggml_ctx, type, type_rules, tensors);
+    ggml_backend_free(backend);
+
+    std::string error;
+    if (success) {
+        if (output_is_safetensors) {
+            success = write_safetensors_file(output_path, tensors, &error);
+        } else {
+            success = write_gguf_file(output_path, tensors, &error);
+        }
+    }
+
+    if (!success && !error.empty()) {
+        LOG_ERROR("%s", error.c_str());
+    }
+
+    ggml_free(ggml_ctx);
+    return success;
+}
@@ -1,11 +1,16 @@
 #ifndef __DENOISER_HPP__
 #define __DENOISER_HPP__

+#include <algorithm>
+#include <cctype>
 #include <cmath>
+#include <functional>
+#include <string>
 #include <utility>

 #include "ggml_extend.hpp"
 #include "gits_noise.inl"
+#include "guidance.h"
 #include "tensor.hpp"

 /*================================================= CompVisDenoiser ==================================================*/
@@ -479,6 +484,81 @@ struct KLOptimalScheduler : SigmaScheduler {
    }
 };

+struct LTX2Scheduler : SigmaScheduler {
+    int token_count  = 4096;
+    float max_shift  = 2.05f;
+    float base_shift = 0.95f;
+    bool stretch     = true;
+    float terminal   = 0.1f;
+
+    explicit LTX2Scheduler(int token_count, const char* extra_sample_args = nullptr)
+        : token_count(token_count > 0 ? token_count : 4096) {
+        parse_extra_sample_args(extra_sample_args);
+    }
+
+    void parse_extra_sample_args(const char* extra_sample_args) {
+        for (const auto& [key, value] : parse_key_value_args(extra_sample_args, "ltx2 scheduler arg")) {
+            if (key == "max_shift") {
+                if (!parse_strict_float(value, max_shift)) {
+                    LOG_WARN("ignoring invalid ltx2 scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            } else if (key == "base_shift") {
+                if (!parse_strict_float(value, base_shift)) {
+                    LOG_WARN("ignoring invalid ltx2 scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            } else if (key == "terminal") {
+                if (!parse_strict_float(value, terminal)) {
+                    LOG_WARN("ignoring invalid ltx2 scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            } else if (key == "stretch") {
+                if (!parse_strict_bool(value, stretch)) {
+                    LOG_WARN("ignoring invalid ltx2 scheduler arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            }
+        }
+    }
+
+    std::vector<float> get_sigmas(uint32_t n, float /*sigma_min*/, float /*sigma_max*/, t_to_sigma_t /*t_to_sigma*/) override {
+        std::vector<float> sigmas;
+        if (n == 0) {
+            sigmas.push_back(0.0f);
+            return sigmas;
+        }
+
+        constexpr float base_shift_anchor = 1024.0f;
+        constexpr float max_shift_anchor  = 4096.0f;
+        float m                           = (max_shift - base_shift) / (max_shift_anchor - base_shift_anchor);
+        float b                           = base_shift - m * base_shift_anchor;
+        float sigma_shift                 = static_cast<float>(token_count) * m + b;
+        float exp_shift                   = std::exp(sigma_shift);
+        float target_terminal             = std::clamp(terminal, 0.0f, 0.99f);
+
+        LOG_DEBUG("LTX2 scheduler: tokens=%d, shift=%.4f, stretch=%d, terminal=%.4f", token_count, sigma_shift, stretch ? 1 : 0, target_terminal);
+
+        sigmas.reserve(n + 1);
+        for (uint32_t i = 0; i <= n; ++i) {
+            float sigma = 1.0f - static_cast<float>(i) / static_cast<float>(n);
+            if (sigma != 0.0f) {
+                sigma = exp_shift / (exp_shift + (1.0f / sigma - 1.0f));
+            }
+            sigmas.push_back(sigma);
+        }
+
+        if (stretch && sigmas.size() > 2) {
+            float one_minus_last = 1.0f - sigmas[n - 1];
+            float scale_factor   = one_minus_last / (1.0f - target_terminal);
+            if (scale_factor > 1e-8f) {
+                for (uint32_t i = 0; i < n; ++i) {
+                    sigmas[i] = 1.0f - (1.0f - sigmas[i]) / scale_factor;
+                }
+            }
+        }
+
+        sigmas[n] = 0.0f;
+        return sigmas;
+    }
+};
+
 struct Denoiser {
    virtual float sigma_min()                                                        = 0;
    virtual float sigma_max()                                                        = 0;
@@ -491,7 +571,7 @@ struct Denoiser {
    virtual sd::Tensor<float> inverse_noise_scaling(float sigma,
                                                    const sd::Tensor<float>& latent) = 0;

-    virtual std::vector<float> get_sigmas(uint32_t n, int /*image_seq_len*/, scheduler_t scheduler_type, SDVersion version) {
+    virtual std::vector<float> get_sigmas(uint32_t n, int image_seq_len, scheduler_t scheduler_type, SDVersion version, const char* extra_sample_args = nullptr) {
        auto bound_t_to_sigma = std::bind(&Denoiser::t_to_sigma, this, std::placeholders::_1);
        std::shared_ptr<SigmaScheduler> scheduler;
        switch (scheduler_type) {
@@ -539,6 +619,10 @@ struct Denoiser {
                LOG_INFO("get_sigmas with LCM scheduler");
                scheduler = std::make_shared<LCMScheduler>();
                break;
+            case LTX2_SCHEDULER:
+                LOG_INFO("get_sigmas with LTX2 scheduler");
+                scheduler = std::make_shared<LTX2Scheduler>(image_seq_len, extra_sample_args);
+                break;
            default:
                LOG_INFO("get_sigmas with discrete scheduler (default)");
                scheduler = std::make_shared<DiscreteScheduler>();
@@ -744,15 +828,15 @@ struct Flux2FlowDenoiser : public FluxFlowDenoiser {
        return mu;
    }

-    std::vector<float> get_sigmas(uint32_t n, int image_seq_len, scheduler_t scheduler_type, SDVersion version) override {
+    std::vector<float> get_sigmas(uint32_t n, int image_seq_len, scheduler_t scheduler_type, SDVersion version, const char* extra_sample_args = nullptr) override {
        float mu = compute_empirical_mu(n, image_seq_len);
        LOG_DEBUG("Flux2FlowDenoiser: set shift to %.3f", mu);
        set_shift(mu);
-        return Denoiser::get_sigmas(n, image_seq_len, scheduler_type, version);
+        return Denoiser::get_sigmas(n, image_seq_len, scheduler_type, version, extra_sample_args);
    }
 };

-typedef std::function<sd::Tensor<float>(const sd::Tensor<float>&, float, int)> denoise_cb_t;
+typedef std::function<sd::guidance::GuiderOutput(const sd::Tensor<float>&, float, int)> denoise_cb_t;

 static std::pair<float, float> get_ancestral_step(float sigma_from,
                                                  float sigma_to,
@@ -808,48 +892,48 @@ static std::tuple<float, float, float> get_ancestral_step_flow(float sigma_from,
    return {sigma_down, sigma_up, alpha_scale};
 }

+static std::tuple<float, float, float> get_ancestral_step(float sigma_from,
+                                                          float sigma_to,
+                                                          float eta,
+                                                          bool is_flow_denoiser) {
+    if (is_flow_denoiser) {
+        return get_ancestral_step_flow(sigma_from, sigma_to, eta);
+    } else {
+        auto [sigma_down, sigma_up] = get_ancestral_step(sigma_from, sigma_to, eta);
+        return {sigma_down, sigma_up, 1.0f};
+    }
+}
+
 static sd::Tensor<float> sample_euler_ancestral(denoise_cb_t model,
                                                sd::Tensor<float> x,
                                                const std::vector<float>& sigmas,
-                                                std::shared_ptr<RNG> rng,
-                                                float eta) {
+                                                std::shared_ptr<RNG> rng = nullptr,
+                                                bool is_flow_denoiser    = false,
+                                                float eta                = 0.f) {
    int steps = static_cast<int>(sigmas.size()) - 1;
    for (int i = 0; i < steps; i++) {
        float sigma       = sigmas[i];
+        float sigma_to    = sigmas[i + 1];
        auto denoised_opt = model(x, sigma, i + 1);
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> denoised  = std::move(denoised_opt);
-        sd::Tensor<float> d         = (x - denoised) / sigma;
-        auto [sigma_down, sigma_up] = get_ancestral_step(sigmas[i], sigmas[i + 1], eta);
-        x += d * (sigma_down - sigmas[i]);
-        if (sigmas[i + 1] > 0) {
-            x += sd::Tensor<float>::randn_like(x, rng) * sigma_up;
-        }
-    }
-    return x;
-}
-
-static sd::Tensor<float> sample_euler_flow(denoise_cb_t model,
-                                           sd::Tensor<float> x,
-                                           const std::vector<float>& sigmas,
-                                           std::shared_ptr<RNG> rng,
-                                           float eta) {
-    int steps = static_cast<int>(sigmas.size()) - 1;
-    for (int i = 0; i < steps; i++) {
-        float sigma       = sigmas[i];
-        auto denoised_opt = model(x, sigma, i + 1);
-        if (denoised_opt.empty()) {
-            return {};
-        }
-        sd::Tensor<float> denoised               = std::move(denoised_opt);
-        auto [sigma_down, sigma_up, alpha_scale] = get_ancestral_step_flow(sigma, sigmas[i + 1], eta);
-        float sigma_ratio                        = sigma_down / sigma;
-        x                                        = sigma_ratio * x + (1.0f - sigma_ratio) * denoised;
-
-        if (sigma_up > 0.0f) {
-            x = alpha_scale * x + sd::Tensor<float>::randn_like(x, rng) * sigma_up;
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);
+        if (sigma_to == 0.f) {
+            x = denoised;
+        } else if (eta == 0.f) {
+            float sigma_ratio = sigma_to / sigma;
+            x                 = sigma_ratio * x + (1.0 - sigma_ratio) * denoised;
+        } else {
+            auto [sigma_down, sigma_up, alpha_scale] = get_ancestral_step(sigma, sigma_to, eta, is_flow_denoiser);
+            float sigma_ratio                        = sigma_down / sigma;
+            x                                        = sigma_ratio * x + (1.0f - sigma_ratio) * denoised;
+            if (sigma_up > 0.f) {
+                if (is_flow_denoiser) {
+                    x *= alpha_scale;
+                }
+                x += sd::Tensor<float>::randn_like(x, rng) * sigma_up;
+            }
        }
    }
    return x;
@@ -862,10 +946,10 @@ static sd::Tensor<float> sample_euler(denoise_cb_t model,
    for (int i = 0; i < steps; i++) {
        float sigma       = sigmas[i];
        auto denoised_opt = model(x, sigma, i + 1);
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> denoised = std::move(denoised_opt);
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);
        sd::Tensor<float> d        = (x - denoised) / sigma;
        x += d * (sigmas[i + 1] - sigma);
    }
@@ -878,10 +962,10 @@ static sd::Tensor<float> sample_heun(denoise_cb_t model,
    int steps = static_cast<int>(sigmas.size()) - 1;
    for (int i = 0; i < steps; i++) {
        auto denoised_opt = model(x, sigmas[i], -(i + 1));
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> denoised = std::move(denoised_opt);
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);
        sd::Tensor<float> d        = (x - denoised) / sigmas[i];
        float dt                   = sigmas[i + 1] - sigmas[i];
        if (sigmas[i + 1] == 0) {
@@ -889,10 +973,10 @@ static sd::Tensor<float> sample_heun(denoise_cb_t model,
        } else {
            sd::Tensor<float> x2 = x + d * dt;
            auto denoised2_opt   = model(x2, sigmas[i + 1], i + 1);
-            if (denoised2_opt.empty()) {
+            if (denoised2_opt.pred.empty()) {
                return {};
            }
-            sd::Tensor<float> denoised2 = std::move(denoised2_opt);
+            sd::Tensor<float> denoised2 = std::move(denoised2_opt.pred);
            d                           = (d + (x2 - denoised2) / sigmas[i + 1]) / 2.0f;
            x += d * dt;
        }
@@ -906,10 +990,10 @@ static sd::Tensor<float> sample_dpm2(denoise_cb_t model,
    int steps = static_cast<int>(sigmas.size()) - 1;
    for (int i = 0; i < steps; i++) {
        auto denoised_opt = model(x, sigmas[i], -(i + 1));
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> denoised = std::move(denoised_opt);
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);
        sd::Tensor<float> d        = (x - denoised) / sigmas[i];
        if (sigmas[i + 1] == 0) {
            x += d * (sigmas[i + 1] - sigmas[i]);
@@ -919,10 +1003,10 @@ static sd::Tensor<float> sample_dpm2(denoise_cb_t model,
            float dt_2           = sigmas[i + 1] - sigmas[i];
            sd::Tensor<float> x2 = x + d * dt_1;
            auto denoised2_opt   = model(x2, sigma_mid, i + 1);
-            if (denoised2_opt.empty()) {
+            if (denoised2_opt.pred.empty()) {
                return {};
            }
-            sd::Tensor<float> denoised2 = std::move(denoised2_opt);
+            sd::Tensor<float> denoised2 = std::move(denoised2_opt.pred);
            x += ((x2 - denoised2) / sigma_mid) * dt_2;
        }
    }
@@ -940,10 +1024,10 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral(denoise_cb_t model,
    int steps = static_cast<int>(sigmas.size()) - 1;
    for (int i = 0; i < steps; i++) {
        auto denoised_opt = model(x, sigmas[i], -(i + 1));
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> denoised  = std::move(denoised_opt);
+        sd::Tensor<float> denoised  = std::move(denoised_opt.pred);
        auto [sigma_down, sigma_up] = get_ancestral_step(sigmas[i], sigmas[i + 1], eta);

        if (sigma_down == 0) {
@@ -956,10 +1040,10 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral(denoise_cb_t model,
            float sigma_s        = sigma_fn(s);
            sd::Tensor<float> x2 = (sigma_s / sigma_fn(t)) * x - (exp(-h * 0.5f) - 1) * denoised;
            auto denoised2_opt   = model(x2, sigma_s, i + 1);
-            if (denoised2_opt.empty()) {
+            if (denoised2_opt.pred.empty()) {
                return {};
            }
-            sd::Tensor<float> denoised2 = std::move(denoised2_opt);
+            sd::Tensor<float> denoised2 = std::move(denoised2_opt.pred);
            x                           = (sigma_fn(t_next) / sigma_fn(t)) * x - (exp(-h) - 1) * denoised2;
        }

@@ -977,16 +1061,16 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,
                                                        float eta = 1.0f) {
    int steps = static_cast<int>(sigmas.size()) - 1;
    for (int i = 0; i < steps; i++) {
-        float sigma = sigmas[i];
+        float sigma    = sigmas[i];
        float sigma_to = sigmas[i + 1];

        bool opt_first_step = (1.0 - sigma < 1e-6);

        auto denoised_opt = model(x, sigma, (opt_first_step ? 1 : -1) * (i + 1));
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> denoised = std::move(denoised_opt);
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);

        if (sigma_to == 0.0f) {
            // Euler method (final step, no noise)
@@ -1040,20 +1124,20 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,
                // and sigma_s = sigma_fn(s) = 1.0f / (exp(s) + 1.0f)

                float exp_s = std::sqrt(((1 - sigma) / sigma) * ((1 - sigma_down) / sigma_down));
-                sigma_s = 1.0f / (exp_s + 1.0f);
+                sigma_s     = 1.0f / (exp_s + 1.0f);

                float sigma_s_i_ratio = sigma_s / sigma;
-                sd::Tensor<float> u = (x * sigma_s_i_ratio) + (denoised * (1.0f - sigma_s_i_ratio));
+                sd::Tensor<float> u   = (x * sigma_s_i_ratio) + (denoised * (1.0f - sigma_s_i_ratio));

                auto denoised2_opt = model(u, sigma_s, i + 1);
-                if (denoised2_opt.empty()) {
+                if (denoised2_opt.pred.empty()) {
                    return {};
                }
-                D_i = std::move(denoised2_opt);
+                D_i = std::move(denoised2_opt.pred);
            }

            float sigma_down_i_ratio = sigma_down / sigma;
-            x = (x * sigma_down_i_ratio) + (D_i * (1.0f - sigma_down_i_ratio));
+            x                        = (x * sigma_down_i_ratio) + (D_i * (1.0f - sigma_down_i_ratio));

            if (sigma_to > 0.0f && eta > 0.0f) {
                x = alpha_scale * x + sd::Tensor<float>::randn_like(x, rng) * sigma_up;
@@ -1064,8 +1148,6 @@ static sd::Tensor<float> sample_dpmpp_2s_ancestral_flow(denoise_cb_t model,
    return x;
 }

-
-
 static sd::Tensor<float> sample_dpmpp_2m(denoise_cb_t model,
                                         sd::Tensor<float> x,
                                         const std::vector<float>& sigmas) {
@@ -1075,10 +1157,10 @@ static sd::Tensor<float> sample_dpmpp_2m(denoise_cb_t model,
    int steps = static_cast<int>(sigmas.size()) - 1;
    for (int i = 0; i < steps; i++) {
        auto denoised_opt = model(x, sigmas[i], i + 1);
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> denoised = std::move(denoised_opt);
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);
        float t                    = t_fn(sigmas[i]);
        float t_next               = t_fn(sigmas[i + 1]);
        float h                    = t_next - t;
@@ -1107,10 +1189,10 @@ static sd::Tensor<float> sample_dpmpp_2m_v2(denoise_cb_t model,
    int steps = static_cast<int>(sigmas.size()) - 1;
    for (int i = 0; i < steps; i++) {
        auto denoised_opt = model(x, sigmas[i], i + 1);
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> denoised = std::move(denoised_opt);
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);
        float t                    = t_fn(sigmas[i]);
        float t_next               = t_fn(sigmas[i + 1]);
        float h                    = t_next - t;
@@ -1134,23 +1216,85 @@ static sd::Tensor<float> sample_dpmpp_2m_v2(denoise_cb_t model,
    return x;
 }

+using SamplerExtraArgs = KeyValueArgs;
+
 static sd::Tensor<float> sample_lcm(denoise_cb_t model,
                                    sd::Tensor<float> x,
                                    const std::vector<float>& sigmas,
                                    std::shared_ptr<RNG> rng,
-                                    bool is_flow_denoiser) {
+                                    bool is_flow_denoiser,
+                                    const SamplerExtraArgs& extra_sample_args) {
+    struct LCMSampleArgs {
+        float noise_clip_std    = 0.0f;
+        float noise_scale_start = 1.0f;
+        float noise_scale_end   = 1.0f;
+    };
+
+    LCMSampleArgs args;
+    bool noise_scale_end_was_set   = false;
+    bool noise_scale_start_was_set = false;
+
+    for (const auto& [key, value] : extra_sample_args) {
+        float parsed = 0.0f;
+        if (key == "noise_clip_std") {
+            if (!parse_strict_float(value, parsed)) {
+                LOG_WARN("ignoring invalid lcm extra sample arg '%s=%s'", key.c_str(), value.c_str());
+                continue;
+            }
+            args.noise_clip_std = parsed;
+        } else if (key == "noise_scale_start") {
+            if (!parse_strict_float(value, parsed)) {
+                LOG_WARN("ignoring invalid lcm extra sample arg '%s=%s'", key.c_str(), value.c_str());
+                continue;
+            }
+            args.noise_scale_start    = parsed;
+            noise_scale_start_was_set = true;
+        } else if (key == "noise_scale_end") {
+            if (!parse_strict_float(value, parsed)) {
+                LOG_WARN("ignoring invalid lcm extra sample arg '%s=%s'", key.c_str(), value.c_str());
+                continue;
+            }
+            args.noise_scale_end    = parsed;
+            noise_scale_end_was_set = true;
+        }
+    }
+
+    if (noise_scale_start_was_set && !noise_scale_end_was_set) {
+        args.noise_scale_end = args.noise_scale_start;
+    }
+
    int steps = static_cast<int>(sigmas.size()) - 1;
    for (int i = 0; i < steps; i++) {
        auto denoised_opt = model(x, sigmas[i], i + 1);
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        x = std::move(denoised_opt);
+        x = std::move(denoised_opt.pred);
        if (sigmas[i + 1] > 0) {
            if (is_flow_denoiser) {
                x *= (1 - sigmas[i + 1]);
            }
-            x += sd::Tensor<float>::randn_like(x, rng) * sigmas[i + 1];
+            auto noise = sd::Tensor<float>::randn_like(x, rng);
+            if (args.noise_clip_std > 0.0f && noise.numel() > 0) {
+                double mean = 0.0;
+                for (int64_t j = 0; j < noise.numel(); ++j) {
+                    mean += static_cast<double>(noise[j]);
+                }
+                mean /= static_cast<double>(noise.numel());
+
+                double variance = 0.0;
+                for (int64_t j = 0; j < noise.numel(); ++j) {
+                    double centered = static_cast<double>(noise[j]) - mean;
+                    variance += centered * centered;
+                }
+                variance /= static_cast<double>(noise.numel());
+
+                float clip_val = args.noise_clip_std * static_cast<float>(std::sqrt(variance));
+                noise          = sd::ops::clamp(noise, -clip_val, clip_val);
+            }
+            float t           = steps > 1 ? static_cast<float>(i) / static_cast<float>(steps - 1) : 0.0f;
+            float noise_scale = args.noise_scale_start + (args.noise_scale_end - args.noise_scale_start) * t;
+            x += noise * (sigmas[i + 1] * noise_scale);
        }
    }
    return x;
@@ -1168,10 +1312,10 @@ static sd::Tensor<float> sample_ipndm(denoise_cb_t model,
        float sigma_next = sigmas[i + 1];

        auto denoised_opt = model(x, sigma, i + 1);
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> denoised = std::move(denoised_opt);
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);

        sd::Tensor<float> d_cur = (x - denoised) / sigma;
        int order               = std::min(max_order, i + 1);
@@ -1212,10 +1356,10 @@ static sd::Tensor<float> sample_ipndm_v(denoise_cb_t model,
        float t_next = sigmas[i + 1];

        auto denoised_opt = model(x, sigma, i + 1);
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> denoised = std::move(denoised_opt);
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);

        sd::Tensor<float> d_cur = (x - denoised) / sigma;
        int order               = std::min(max_order, i + 1);
@@ -1249,6 +1393,7 @@ static sd::Tensor<float> sample_res_multistep(denoise_cb_t model,
                                              sd::Tensor<float> x,
                                              const std::vector<float>& sigmas,
                                              std::shared_ptr<RNG> rng,
+                                              bool is_flow_denoiser,
                                              float eta) {
    sd::Tensor<float> old_denoised = x;
    bool have_old_sigma            = false;
@@ -1273,14 +1418,15 @@ static sd::Tensor<float> sample_res_multistep(denoise_cb_t model,
    int steps = static_cast<int>(sigmas.size()) - 1;
    for (int i = 0; i < steps; i++) {
        auto denoised_opt = model(x, sigmas[i], i + 1);
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> denoised = std::move(denoised_opt);
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);

-        float sigma_from            = sigmas[i];
-        float sigma_to              = sigmas[i + 1];
-        auto [sigma_down, sigma_up] = get_ancestral_step(sigma_from, sigma_to, eta);
+        float sigma_from = sigmas[i];
+        float sigma_to   = sigmas[i + 1];
+
+        auto [sigma_down, sigma_up, alpha_scale] = get_ancestral_step(sigma_from, sigma_to, eta, is_flow_denoiser);

        if (sigma_down == 0.0f || !have_old_sigma) {
            x += ((x - denoised) / sigma_from) * (sigma_down - sigma_from);
@@ -1307,7 +1453,10 @@ static sd::Tensor<float> sample_res_multistep(denoise_cb_t model,
            x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised);
        }

-        if (sigmas[i + 1] > 0 && sigma_up > 0.0f) {
+        if (sigma_to > 0.0f && sigma_up > 0.0f) {
+            if (is_flow_denoiser) {
+                x *= alpha_scale;
+            }
            x += sd::Tensor<float>::randn_like(x, rng) * sigma_up;
        }

@@ -1322,6 +1471,7 @@ static sd::Tensor<float> sample_res_2s(denoise_cb_t model,
                                       sd::Tensor<float> x,
                                       const std::vector<float>& sigmas,
                                       std::shared_ptr<RNG> rng,
+                                       bool is_flow_denoiser,
                                       float eta) {
    const float c2 = 0.5f;
    auto t_fn      = [](float sigma) -> float { return -logf(sigma); };
@@ -1345,12 +1495,12 @@ static sd::Tensor<float> sample_res_2s(denoise_cb_t model,
        float sigma_to   = sigmas[i + 1];

        auto denoised_opt = model(x, sigma_from, -(i + 1));
-        if (denoised_opt.empty()) {
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> denoised = std::move(denoised_opt);
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);

-        auto [sigma_down, sigma_up] = get_ancestral_step(sigma_from, sigma_to, eta);
+        auto [sigma_down, sigma_up, alpha_scale] = get_ancestral_step(sigma_from, sigma_to, eta, is_flow_denoiser);

        sd::Tensor<float> x0 = x;
        if (sigma_down == 0.0f || sigma_from == 0.0f) {
@@ -1371,15 +1521,18 @@ static sd::Tensor<float> sample_res_2s(denoise_cb_t model,
            sd::Tensor<float> x2   = x0 + eps1 * (h * a21);

            auto denoised2_opt = model(x2, sigma_c2, i + 1);
-            if (denoised2_opt.empty()) {
+            if (denoised2_opt.pred.empty()) {
                return {};
            }
-            sd::Tensor<float> denoised2 = std::move(denoised2_opt);
+            sd::Tensor<float> denoised2 = std::move(denoised2_opt.pred);
            sd::Tensor<float> eps2      = denoised2 - x0;
            x                           = x0 + h * (b1 * eps1 + b2 * eps2);
        }

-        if (sigmas[i + 1] > 0 && sigma_up > 0.0f) {
+        if (sigma_to > 0.0f && sigma_up > 0.0f) {
+            if (is_flow_denoiser) {
+                x *= alpha_scale;
+            }
            x += sd::Tensor<float>::randn_like(x, rng) * sigma_up;
        }
    }
@@ -1444,10 +1597,11 @@ static sd::Tensor<float> sample_er_sde(denoise_cb_t model,

    int steps = static_cast<int>(sigmas.size()) - 1;
    for (int i = 0; i < steps; i++) {
-        sd::Tensor<float> denoised = model(x, sigmas[i], i + 1);
-        if (denoised.empty()) {
+        auto denoised_opt = model(x, sigmas[i], i + 1);
+        if (denoised_opt.pred.empty()) {
            return {};
        }
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);

        int stage_used = std::min(max_stage, i + 1);

@@ -1520,68 +1674,6 @@ static sd::Tensor<float> sample_er_sde(denoise_cb_t model,
    return x;
 }

-static sd::Tensor<float> sample_ddim_trailing(denoise_cb_t model,
-                                              sd::Tensor<float> x,
-                                              const std::vector<float>& sigmas,
-                                              std::shared_ptr<RNG> rng,
-                                              float eta) {
-    float beta_start = 0.00085f;
-    float beta_end   = 0.0120f;
-    std::vector<double> alphas_cumprod(TIMESTEPS);
-    std::vector<double> compvis_sigmas(TIMESTEPS);
-    for (int i = 0; i < TIMESTEPS; i++) {
-        alphas_cumprod[i] =
-            (i == 0 ? 1.0f : alphas_cumprod[i - 1]) *
-            (1.0f -
-             std::pow(sqrtf(beta_start) +
-                          (sqrtf(beta_end) - sqrtf(beta_start)) *
-                              ((float)i / (TIMESTEPS - 1)),
-                      2));
-        compvis_sigmas[i] =
-            std::sqrt((1 - alphas_cumprod[i]) / alphas_cumprod[i]);
-    }
-
-    int steps = static_cast<int>(sigmas.size()) - 1;
-    for (int i = 0; i < steps; i++) {
-        int timestep      = static_cast<int>(roundf(TIMESTEPS - i * ((float)TIMESTEPS / steps))) - 1;
-        int prev_timestep = timestep - TIMESTEPS / steps;
-        float sigma       = static_cast<float>(compvis_sigmas[timestep]);
-        if (i == 0) {
-            x *= std::sqrt(sigma * sigma + 1) / sigma;
-        } else {
-            x *= std::sqrt(sigma * sigma + 1);
-        }
-
-        auto model_output_opt = model(x, sigma, i + 1);
-        if (model_output_opt.empty()) {
-            return {};
-        }
-        sd::Tensor<float> model_output = std::move(model_output_opt);
-        model_output                   = (x - model_output) * (1.0f / sigma);
-
-        float alpha_prod_t      = static_cast<float>(alphas_cumprod[timestep]);
-        float alpha_prod_t_prev = static_cast<float>(prev_timestep >= 0 ? alphas_cumprod[prev_timestep] : alphas_cumprod[0]);
-        float beta_prod_t       = 1.0f - alpha_prod_t;
-
-        sd::Tensor<float> pred_original_sample = ((x / std::sqrt(sigma * sigma + 1)) -
-                                                  std::sqrt(beta_prod_t) * model_output) *
-                                                 (1.0f / std::sqrt(alpha_prod_t));
-
-        float beta_prod_t_prev = 1.0f - alpha_prod_t_prev;
-        float variance         = (beta_prod_t_prev / beta_prod_t) *
-                         (1.0f - alpha_prod_t / alpha_prod_t_prev);
-        float std_dev_t = eta * std::sqrt(variance);
-
-        x = std::sqrt(alpha_prod_t_prev) * pred_original_sample +
-            std::sqrt(1.0f - alpha_prod_t_prev - std::pow(std_dev_t, 2)) * model_output;
-
-        if (eta > 0) {
-            x += std_dev_t * sd::Tensor<float>::randn_like(x, rng);
-        }
-    }
-    return x;
-}
-
 static sd::Tensor<float> sample_tcd(denoise_cb_t model,
                                    sd::Tensor<float> x,
                                    const std::vector<float>& sigmas,
@@ -1603,43 +1695,152 @@ static sd::Tensor<float> sample_tcd(denoise_cb_t model,
            std::sqrt((1 - alphas_cumprod[i]) / alphas_cumprod[i]);
    }

-    int original_steps = 50;
-    int steps          = static_cast<int>(sigmas.size()) - 1;
-    for (int i = 0; i < steps; i++) {
-        int timestep      = TIMESTEPS - 1 - (TIMESTEPS / original_steps) * (int)floor(i * ((float)original_steps / steps));
-        int prev_timestep = i >= steps - 1 ? 0 : TIMESTEPS - 1 - (TIMESTEPS / original_steps) * (int)floor((i + 1) * ((float)original_steps / steps));
-        int timestep_s    = (int)floor((1 - eta) * prev_timestep);
-        float sigma       = static_cast<float>(compvis_sigmas[timestep]);
-
-        if (i == 0) {
-            x *= std::sqrt(sigma * sigma + 1) / sigma;
-        } else {
-            x *= std::sqrt(sigma * sigma + 1);
+    auto get_timestep_from_sigma = [&](float s) -> int {
+        auto it = std::lower_bound(compvis_sigmas.begin(), compvis_sigmas.end(), s);
+        if (it == compvis_sigmas.begin())
+            return 0;
+        if (it == compvis_sigmas.end())
+            return TIMESTEPS - 1;
+        int idx_high = static_cast<int>(std::distance(compvis_sigmas.begin(), it));
+        int idx_low  = idx_high - 1;
+        if (std::abs(compvis_sigmas[idx_high] - s) < std::abs(compvis_sigmas[idx_low] - s)) {
+            return idx_high;
        }
+        return idx_low;
+    };

-        auto model_output_opt = model(x, sigma, i + 1);
-        if (model_output_opt.empty()) {
+    int steps = static_cast<int>(sigmas.size()) - 1;
+    for (int i = 0; i < steps; i++) {
+        float sigma_to    = sigmas[i + 1];
+        int prev_timestep = get_timestep_from_sigma(sigma_to);
+        int timestep_s    = (int)floor((1 - eta) * prev_timestep);
+        float sigma       = sigmas[i];
+
+        auto denoised_opt = model(x, sigma, i + 1);
+        if (denoised_opt.pred.empty()) {
            return {};
        }
-        sd::Tensor<float> model_output = std::move(model_output_opt);
-        model_output                   = (x - model_output) * (1.0f / sigma);
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);
+        sd::Tensor<float> d        = (x - denoised) / sigma;

-        float alpha_prod_t      = static_cast<float>(alphas_cumprod[timestep]);
+        float alpha_prod_t      = 1.0f / (sigma * sigma + 1.0f);
        float beta_prod_t       = 1.0f - alpha_prod_t;
-        float alpha_prod_t_prev = static_cast<float>(prev_timestep >= 0 ? alphas_cumprod[prev_timestep] : alphas_cumprod[0]);
+        float alpha_prod_t_prev = 1.0f / (sigma_to * sigma_to + 1.0f);
        float alpha_prod_s      = static_cast<float>(alphas_cumprod[timestep_s]);
        float beta_prod_s       = 1.0f - alpha_prod_s;

-        sd::Tensor<float> pred_original_sample = ((x / std::sqrt(sigma * sigma + 1)) -
-                                                  std::sqrt(beta_prod_t) * model_output) *
-                                                 (1.0f / std::sqrt(alpha_prod_t));
+        x = std::sqrt(alpha_prod_s / alpha_prod_t_prev) * denoised +
+            std::sqrt(beta_prod_s / alpha_prod_t_prev) * d;

-        x = std::sqrt(alpha_prod_s) * pred_original_sample +
-            std::sqrt(beta_prod_s) * model_output;
-
-        if (eta > 0 && i != steps - 1) {
+        if (eta > 0 && sigma_to > 0.0f) {
            x = std::sqrt(alpha_prod_t_prev / alpha_prod_s) * x +
-                std::sqrt(1.0f - alpha_prod_t_prev / alpha_prod_s) * sd::Tensor<float>::randn_like(x, rng);
+                std::sqrt(1.0f / alpha_prod_t_prev - 1.0f / alpha_prod_s) * sd::Tensor<float>::randn_like(x, rng);
+        }
+    }
+    return x;
+}
+
+static sd::Tensor<float> sample_euler_cfg_pp(denoise_cb_t model,
+                                             sd::Tensor<float> x,
+                                             const std::vector<float>& sigmas) {
+    int steps = static_cast<int>(sigmas.size()) - 1;
+    for (int i = 0; i < steps; i++) {
+        float sigma       = sigmas[i];
+        auto denoised_opt = model(x, sigma, i + 1);
+        if (denoised_opt.pred.empty() || denoised_opt.pred_uncond.empty()) {
+            return {};
+        }
+
+        sd::Tensor<float> denoised        = std::move(denoised_opt.pred);
+        sd::Tensor<float> uncond_denoised = std::move(denoised_opt.pred_uncond);
+        sd::Tensor<float> d               = (x - uncond_denoised) / sigma;
+
+        x = denoised + d * sigmas[i + 1];
+    }
+    return x;
+}
+
+static sd::Tensor<float> sample_euler_ancestral_cfg_pp(denoise_cb_t model,
+                                                       sd::Tensor<float> x,
+                                                       const std::vector<float>& sigmas,
+                                                       std::shared_ptr<RNG> rng,
+                                                       float eta) {
+    int steps = static_cast<int>(sigmas.size()) - 1;
+    for (int i = 0; i < steps; i++) {
+        float sigma       = sigmas[i];
+        auto denoised_opt = model(x, sigma, i + 1);
+        if (denoised_opt.pred.empty() || denoised_opt.pred_uncond.empty()) {
+            return {};
+        }
+
+        sd::Tensor<float> denoised        = std::move(denoised_opt.pred);
+        sd::Tensor<float> uncond_denoised = std::move(denoised_opt.pred_uncond);
+        sd::Tensor<float> d               = (x - uncond_denoised) / sigma;
+
+        auto [sigma_down, sigma_up] = get_ancestral_step(sigmas[i], sigmas[i + 1], eta);
+
+        x = denoised + d * sigma_down;
+
+        if (sigmas[i + 1] > 0) {
+            x += sd::Tensor<float>::randn_like(x, rng) * sigma_up;
+        }
+    }
+    return x;
+}
+
+// https://github.com/ToyotaResearchInstitute/gradient-estimation-sampler
+static sd::Tensor<float> sample_gradient_estimation(denoise_cb_t model,
+                                                    sd::Tensor<float> x,
+                                                    const std::vector<float>& sigmas,
+                                                    std::shared_ptr<RNG> rng,
+                                                    bool is_flow_denoiser,
+                                                    float eta,
+                                                    const SamplerExtraArgs& extra_sample_args) {
+    float ge_gamma = 2.0f;
+
+    for (const auto& [key, value] : extra_sample_args) {
+        if (key == "gamma") {
+            float parsed = 0.0f;
+            if (!parse_strict_float(value, parsed)) {
+                LOG_WARN("ignoring invalid euler_ge extra sample arg '%s=%s'", key.c_str(), value.c_str());
+                continue;
+            }
+            LOG_DEBUG("setting euler_ge gamma to %.2f", parsed);
+            ge_gamma = parsed;
+        }
+    }
+
+    int steps = static_cast<int>(sigmas.size()) - 1;
+    sd::Tensor<float> old_d;
+    bool has_old_d = false;
+    for (int i = 0; i < steps; i++) {
+        float sigma       = sigmas[i];
+        float sigma_to    = sigmas[i + 1];
+        auto denoised_opt = model(x, sigma, i + 1);
+        if (denoised_opt.pred.empty()) {
+            return {};
+        }
+        sd::Tensor<float> denoised = std::move(denoised_opt.pred);
+        if (sigma_to == 0.f) {
+            x = denoised;
+        } else {
+            auto [sigma_down, sigma_up, alpha_scale] = get_ancestral_step(sigma, sigma_to, eta, is_flow_denoiser);
+            sd::Tensor<float> d                      = (x - denoised) / sigma;
+            float dt                                 = sigma_down - sigma;
+            if (has_old_d) {
+                sd::Tensor<float> d_bar = d * ge_gamma + old_d * (1.0f - ge_gamma);
+                x += d_bar * dt;
+            } else {
+                x += d * dt;
+            }
+            old_d     = std::move(d);
+            has_old_d = true;
+            if (sigma_up > 0.f) {
+                if (is_flow_denoiser) {
+                    x *= alpha_scale;
+                }
+                x += sd::Tensor<float>::randn_like(x, rng) * sigma_up;
+            }
        }
    }
    return x;
@@ -1652,13 +1853,12 @@ static sd::Tensor<float> sample_k_diffusion(sample_method_t method,
                                            std::vector<float> sigmas,
                                            std::shared_ptr<RNG> rng,
                                            float eta,
-                                            bool is_flow_denoiser) {
+                                            bool is_flow_denoiser,
+                                            const char* extra_sample_args) {
+    SamplerExtraArgs extra_args = parse_key_value_args(extra_sample_args, "extra sample arg");
    switch (method) {
        case EULER_A_SAMPLE_METHOD:
-            if (is_flow_denoiser)
-                return sample_euler_flow(model, std::move(x), sigmas, rng, eta);
-            else
-                return sample_euler_ancestral(model, std::move(x), sigmas, rng, eta);
+            return sample_euler_ancestral(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);
        case EULER_SAMPLE_METHOD:
            return sample_euler(model, std::move(x), sigmas);
        case HEUN_SAMPLE_METHOD:
@@ -1675,21 +1875,28 @@ static sd::Tensor<float> sample_k_diffusion(sample_method_t method,
        case DPMPP2Mv2_SAMPLE_METHOD:
            return sample_dpmpp_2m_v2(model, std::move(x), sigmas);
        case LCM_SAMPLE_METHOD:
-            return sample_lcm(model, std::move(x), sigmas, rng, is_flow_denoiser);
+            return sample_lcm(model, std::move(x), sigmas, rng, is_flow_denoiser, extra_args);
        case IPNDM_SAMPLE_METHOD:
            return sample_ipndm(model, std::move(x), sigmas);
        case IPNDM_V_SAMPLE_METHOD:
            return sample_ipndm_v(model, std::move(x), sigmas);
        case RES_MULTISTEP_SAMPLE_METHOD:
-            return sample_res_multistep(model, std::move(x), sigmas, rng, eta);
+            return sample_res_multistep(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);
        case RES_2S_SAMPLE_METHOD:
-            return sample_res_2s(model, std::move(x), sigmas, rng, eta);
+            return sample_res_2s(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);
        case ER_SDE_SAMPLE_METHOD:
            return sample_er_sde(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);
        case DDIM_TRAILING_SAMPLE_METHOD:
-            return sample_ddim_trailing(model, std::move(x), sigmas, rng, eta);
+            // DDIM is equivalent to Euler Ancestral with the Simple scheduler
+            return sample_euler_ancestral(model, std::move(x), sigmas, rng, is_flow_denoiser, eta);
        case TCD_SAMPLE_METHOD:
            return sample_tcd(model, std::move(x), sigmas, rng, eta);
+        case EULER_CFG_PP_SAMPLE_METHOD:
+            return sample_euler_cfg_pp(model, std::move(x), sigmas);
+        case EULER_A_CFG_PP_SAMPLE_METHOD:
+            return sample_euler_ancestral_cfg_pp(model, std::move(x), sigmas, rng, eta);
+        case EULER_GE_SAMPLE_METHOD:
+            return sample_gradient_estimation(model, std::move(x), sigmas, rng, is_flow_denoiser, eta, extra_args);
        default:
            return {};
    }
@@ -1,16 +1,62 @@
 #ifndef __DIFFUSION_MODEL_H__
 #define __DIFFUSION_MODEL_H__

-#include <optional>
-#include "anima.hpp"
-#include "ernie_image.hpp"
-#include "flux.hpp"
-#include "mmdit.hpp"
-#include "qwen_image.hpp"
+#include <string>
+#include <utility>
+#include <variant>
+
+#include "ggml_extend.hpp"
 #include "tensor_ggml.hpp"
-#include "unet.hpp"
-#include "wan.hpp"
-#include "z_image.hpp"
+
+struct UNetDiffusionExtra {
+    int num_video_frames                           = -1;
+    const std::vector<sd::Tensor<float>>* controls = nullptr;
+    float control_strength                         = 0.f;
+};
+
+struct SkipLayerDiffusionExtra {
+    const std::vector<int>* skip_layers = nullptr;
+};
+
+struct FluxDiffusionExtra {
+    const sd::Tensor<float>* guidance   = nullptr;
+    const std::vector<int>* skip_layers = nullptr;
+};
+
+struct AnimaDiffusionExtra {
+    const sd::Tensor<int32_t>* t5_ids   = nullptr;
+    const sd::Tensor<float>* t5_weights = nullptr;
+};
+
+struct WanDiffusionExtra {
+    const sd::Tensor<float>* vace_context = nullptr;
+    float vace_strength                   = 1.f;
+};
+
+struct HiDreamO1DiffusionExtra {
+    const sd::Tensor<int32_t>* input_ids                               = nullptr;
+    const sd::Tensor<int32_t>* input_pos                               = nullptr;
+    const sd::Tensor<int32_t>* token_types                             = nullptr;
+    const sd::Tensor<int32_t>* vinput_mask                             = nullptr;
+    const std::vector<std::pair<int, sd::Tensor<float>>>* image_embeds = nullptr;
+};
+
+struct LTXAVDiffusionExtra {
+    const sd::Tensor<float>* audio_x         = nullptr;
+    const sd::Tensor<float>* audio_timesteps = nullptr;
+    int audio_length                         = 0;
+    float frame_rate                         = 24.f;
+    const sd::Tensor<float>* video_positions = nullptr;
+};
+
+using DiffusionExtraParams = std::variant<std::monostate,
+                                          UNetDiffusionExtra,
+                                          SkipLayerDiffusionExtra,
+                                          FluxDiffusionExtra,
+                                          AnimaDiffusionExtra,
+                                          WanDiffusionExtra,
+                                          HiDreamO1DiffusionExtra,
+                                          LTXAVDiffusionExtra>;

 struct DiffusionParams {
    const sd::Tensor<float>* x                        = nullptr;
@@ -18,565 +64,44 @@ struct DiffusionParams {
    const sd::Tensor<float>* context                  = nullptr;
    const sd::Tensor<float>* c_concat                 = nullptr;
    const sd::Tensor<float>* y                        = nullptr;
-    const sd::Tensor<int32_t>* t5_ids                 = nullptr;
-    const sd::Tensor<float>* t5_weights               = nullptr;
-    const sd::Tensor<float>* guidance                 = nullptr;
    const std::vector<sd::Tensor<float>>* ref_latents = nullptr;
    bool increase_ref_index                           = false;
-    int num_video_frames                              = -1;
-    const std::vector<sd::Tensor<float>>* controls    = nullptr;
-    float control_strength                            = 0.f;
-    const sd::Tensor<float>* vace_context             = nullptr;
-    float vace_strength                               = 1.f;
-    const std::vector<int>* skip_layers               = nullptr;
+    DiffusionExtraParams extra                        = std::monostate{};
 };

+template <typename T>
+static inline const T* diffusion_extra_as(const DiffusionParams& params) {
+    const auto* extra = std::get_if<T>(&params.extra);
+    GGML_ASSERT(extra != nullptr);
+    return extra;
+}
+
 template <typename T>
 static inline const sd::Tensor<T>& tensor_or_empty(const sd::Tensor<T>* tensor) {
    static const sd::Tensor<T> kEmpty;
    return tensor != nullptr ? *tensor : kEmpty;
 }

-struct DiffusionModel {
-    virtual std::string get_desc()                                               = 0;
+struct DiffusionModelRunner : public GGMLRunner {
+protected:
+    std::string prefix;
+
+public:
+    DiffusionModelRunner(ggml_backend_t backend,
+                         ggml_backend_t params_backend,
+                         const std::string& prefix)
+        : GGMLRunner(backend, params_backend),
+          prefix(prefix) {}
+
    virtual sd::Tensor<float> compute(int n_threads,
-                                      const DiffusionParams& diffusion_params)   = 0;
-    virtual void alloc_params_buffer()                                           = 0;
-    virtual void free_params_buffer()                                            = 0;
-    virtual void free_compute_buffer()                                           = 0;
-    virtual void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) = 0;
-    virtual size_t get_params_buffer_size()                                      = 0;
-    virtual void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter){};
-    virtual int64_t get_adm_in_channels()                            = 0;
-    virtual void set_flash_attention_enabled(bool enabled)           = 0;
-    virtual void set_circular_axes(bool circular_x, bool circular_y) = 0;
-};
+                                      const DiffusionParams& diffusion_params) = 0;

-struct UNetModel : public DiffusionModel {
-    UNetModelRunner unet;
-
-    UNetModel(ggml_backend_t backend,
-              bool offload_params_to_cpu,
-              const String2TensorStorage& tensor_storage_map = {},
-              SDVersion version                              = VERSION_SD1)
-        : unet(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model", version) {
-    }
-
-    std::string get_desc() override {
-        return unet.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        unet.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        unet.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        unet.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
-        unet.get_param_tensors(tensors, "model.diffusion_model");
-    }
-
-    size_t get_params_buffer_size() override {
-        return unet.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        unet.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return unet.unet.adm_in_channels;
-    }
-
-    void set_flash_attention_enabled(bool enabled) {
-        unet.set_flash_attention_enabled(enabled);
-    }
-
-    void set_circular_axes(bool circular_x, bool circular_y) override {
-        unet.set_circular_axes(circular_x, circular_y);
-    }
-
-    sd::Tensor<float> compute(int n_threads,
-                              const DiffusionParams& diffusion_params) override {
-        GGML_ASSERT(diffusion_params.x != nullptr);
-        GGML_ASSERT(diffusion_params.timesteps != nullptr);
-        static const std::vector<sd::Tensor<float>> empty_controls;
-        return unet.compute(n_threads,
-                            *diffusion_params.x,
-                            *diffusion_params.timesteps,
-                            tensor_or_empty(diffusion_params.context),
-                            tensor_or_empty(diffusion_params.c_concat),
-                            tensor_or_empty(diffusion_params.y),
-                            diffusion_params.num_video_frames,
-                            diffusion_params.controls ? *diffusion_params.controls : empty_controls,
-                            diffusion_params.control_strength);
-    }
-};
-
-struct MMDiTModel : public DiffusionModel {
-    MMDiTRunner mmdit;
-
-    MMDiTModel(ggml_backend_t backend,
-               bool offload_params_to_cpu,
-               const String2TensorStorage& tensor_storage_map = {})
-        : mmdit(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model") {
-    }
-
-    std::string get_desc() override {
-        return mmdit.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        mmdit.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        mmdit.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        mmdit.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
-        mmdit.get_param_tensors(tensors, "model.diffusion_model");
-    }
-
-    size_t get_params_buffer_size() override {
-        return mmdit.get_params_buffer_size();
+    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) {
+        get_param_tensors(tensors, prefix);
    }

-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        mmdit.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return 768 + 1280;
-    }
-
-    void set_flash_attention_enabled(bool enabled) {
-        mmdit.set_flash_attention_enabled(enabled);
-    }
-
-    void set_circular_axes(bool circular_x, bool circular_y) override {
-        mmdit.set_circular_axes(circular_x, circular_y);
-    }
-
-    sd::Tensor<float> compute(int n_threads,
-                              const DiffusionParams& diffusion_params) override {
-        GGML_ASSERT(diffusion_params.x != nullptr);
-        GGML_ASSERT(diffusion_params.timesteps != nullptr);
-        static const std::vector<int> empty_skip_layers;
-        return mmdit.compute(n_threads,
-                             *diffusion_params.x,
-                             *diffusion_params.timesteps,
-                             tensor_or_empty(diffusion_params.context),
-                             tensor_or_empty(diffusion_params.y),
-                             diffusion_params.skip_layers ? *diffusion_params.skip_layers : empty_skip_layers);
-    }
-};
-
-struct FluxModel : public DiffusionModel {
-    Flux::FluxRunner flux;
-
-    FluxModel(ggml_backend_t backend,
-              bool offload_params_to_cpu,
-              const String2TensorStorage& tensor_storage_map = {},
-              SDVersion version                              = VERSION_FLUX,
-              bool use_mask                                  = false)
-        : flux(backend, offload_params_to_cpu, tensor_storage_map, "model.diffusion_model", version, use_mask) {
-    }
-
-    std::string get_desc() override {
-        return flux.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        flux.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        flux.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        flux.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
-        flux.get_param_tensors(tensors, "model.diffusion_model");
-    }
-
-    size_t get_params_buffer_size() override {
-        return flux.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        flux.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return 768;
-    }
-
-    void set_flash_attention_enabled(bool enabled) {
-        flux.set_flash_attention_enabled(enabled);
-    }
-
-    void set_circular_axes(bool circular_x, bool circular_y) override {
-        flux.set_circular_axes(circular_x, circular_y);
-    }
-
-    sd::Tensor<float> compute(int n_threads,
-                              const DiffusionParams& diffusion_params) override {
-        GGML_ASSERT(diffusion_params.x != nullptr);
-        GGML_ASSERT(diffusion_params.timesteps != nullptr);
-        static const std::vector<sd::Tensor<float>> empty_ref_latents;
-        static const std::vector<int> empty_skip_layers;
-        return flux.compute(n_threads,
-                            *diffusion_params.x,
-                            *diffusion_params.timesteps,
-                            tensor_or_empty(diffusion_params.context),
-                            tensor_or_empty(diffusion_params.c_concat),
-                            tensor_or_empty(diffusion_params.y),
-                            tensor_or_empty(diffusion_params.guidance),
-                            diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
-                            diffusion_params.increase_ref_index,
-                            diffusion_params.skip_layers ? *diffusion_params.skip_layers : empty_skip_layers);
-    }
-};
-
-struct AnimaModel : public DiffusionModel {
-    std::string prefix;
-    Anima::AnimaRunner anima;
-
-    AnimaModel(ggml_backend_t backend,
-               bool offload_params_to_cpu,
-               const String2TensorStorage& tensor_storage_map = {},
-               const std::string prefix                       = "model.diffusion_model")
-        : prefix(prefix), anima(backend, offload_params_to_cpu, tensor_storage_map, prefix) {
-    }
-
-    std::string get_desc() override {
-        return anima.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        anima.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        anima.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        anima.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
-        anima.get_param_tensors(tensors, prefix);
-    }
-
-    size_t get_params_buffer_size() override {
-        return anima.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        anima.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return 768;
-    }
-
-    void set_flash_attention_enabled(bool enabled) {
-        anima.set_flash_attention_enabled(enabled);
-    }
-
-    void set_circular_axes(bool circular_x, bool circular_y) override {
-        anima.set_circular_axes(circular_x, circular_y);
-    }
-
-    sd::Tensor<float> compute(int n_threads,
-                              const DiffusionParams& diffusion_params) override {
-        GGML_ASSERT(diffusion_params.x != nullptr);
-        GGML_ASSERT(diffusion_params.timesteps != nullptr);
-        return anima.compute(n_threads,
-                             *diffusion_params.x,
-                             *diffusion_params.timesteps,
-                             tensor_or_empty(diffusion_params.context),
-                             tensor_or_empty(diffusion_params.t5_ids),
-                             tensor_or_empty(diffusion_params.t5_weights));
-    }
-};
-
-struct WanModel : public DiffusionModel {
-    std::string prefix;
-    WAN::WanRunner wan;
-
-    WanModel(ggml_backend_t backend,
-             bool offload_params_to_cpu,
-             const String2TensorStorage& tensor_storage_map = {},
-             const std::string prefix                       = "model.diffusion_model",
-             SDVersion version                              = VERSION_WAN2)
-        : prefix(prefix), wan(backend, offload_params_to_cpu, tensor_storage_map, prefix, version) {
-    }
-
-    std::string get_desc() override {
-        return wan.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        wan.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        wan.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        wan.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
-        wan.get_param_tensors(tensors, prefix);
-    }
-
-    size_t get_params_buffer_size() override {
-        return wan.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        wan.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return 768;
-    }
-
-    void set_flash_attention_enabled(bool enabled) {
-        wan.set_flash_attention_enabled(enabled);
-    }
-
-    void set_circular_axes(bool circular_x, bool circular_y) override {
-        wan.set_circular_axes(circular_x, circular_y);
-    }
-
-    sd::Tensor<float> compute(int n_threads,
-                              const DiffusionParams& diffusion_params) override {
-        GGML_ASSERT(diffusion_params.x != nullptr);
-        GGML_ASSERT(diffusion_params.timesteps != nullptr);
-        return wan.compute(n_threads,
-                           *diffusion_params.x,
-                           *diffusion_params.timesteps,
-                           tensor_or_empty(diffusion_params.context),
-                           tensor_or_empty(diffusion_params.y),
-                           tensor_or_empty(diffusion_params.c_concat),
-                           sd::Tensor<float>(),
-                           tensor_or_empty(diffusion_params.vace_context),
-                           diffusion_params.vace_strength);
-    }
-};
-
-struct QwenImageModel : public DiffusionModel {
-    std::string prefix;
-    Qwen::QwenImageRunner qwen_image;
-
-    QwenImageModel(ggml_backend_t backend,
-                   bool offload_params_to_cpu,
-                   const String2TensorStorage& tensor_storage_map = {},
-                   const std::string prefix                       = "model.diffusion_model",
-                   SDVersion version                              = VERSION_QWEN_IMAGE,
-                   bool zero_cond_t                               = false)
-        : prefix(prefix), qwen_image(backend, offload_params_to_cpu, tensor_storage_map, prefix, version, zero_cond_t) {
-    }
-
-    std::string get_desc() override {
-        return qwen_image.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        qwen_image.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        qwen_image.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        qwen_image.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
-        qwen_image.get_param_tensors(tensors, prefix);
-    }
-
-    size_t get_params_buffer_size() override {
-        return qwen_image.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        qwen_image.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return 768;
-    }
-
-    void set_flash_attention_enabled(bool enabled) {
-        qwen_image.set_flash_attention_enabled(enabled);
-    }
-
-    void set_circular_axes(bool circular_x, bool circular_y) override {
-        qwen_image.set_circular_axes(circular_x, circular_y);
-    }
-
-    sd::Tensor<float> compute(int n_threads,
-                              const DiffusionParams& diffusion_params) override {
-        GGML_ASSERT(diffusion_params.x != nullptr);
-        GGML_ASSERT(diffusion_params.timesteps != nullptr);
-        static const std::vector<sd::Tensor<float>> empty_ref_latents;
-        return qwen_image.compute(n_threads,
-                                  *diffusion_params.x,
-                                  *diffusion_params.timesteps,
-                                  tensor_or_empty(diffusion_params.context),
-                                  diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
-                                  true);
-    }
-};
-
-struct ZImageModel : public DiffusionModel {
-    std::string prefix;
-    ZImage::ZImageRunner z_image;
-
-    ZImageModel(ggml_backend_t backend,
-                bool offload_params_to_cpu,
-                const String2TensorStorage& tensor_storage_map = {},
-                const std::string prefix                       = "model.diffusion_model",
-                SDVersion version                              = VERSION_Z_IMAGE)
-        : prefix(prefix), z_image(backend, offload_params_to_cpu, tensor_storage_map, prefix, version) {
-    }
-
-    std::string get_desc() override {
-        return z_image.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        z_image.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        z_image.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        z_image.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
-        z_image.get_param_tensors(tensors, prefix);
-    }
-
-    size_t get_params_buffer_size() override {
-        return z_image.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        z_image.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return 768;
-    }
-
-    void set_flash_attention_enabled(bool enabled) {
-        z_image.set_flash_attention_enabled(enabled);
-    }
-
-    void set_circular_axes(bool circular_x, bool circular_y) override {
-        z_image.set_circular_axes(circular_x, circular_y);
-    }
-
-    sd::Tensor<float> compute(int n_threads,
-                              const DiffusionParams& diffusion_params) override {
-        GGML_ASSERT(diffusion_params.x != nullptr);
-        GGML_ASSERT(diffusion_params.timesteps != nullptr);
-        static const std::vector<sd::Tensor<float>> empty_ref_latents;
-        return z_image.compute(n_threads,
-                               *diffusion_params.x,
-                               *diffusion_params.timesteps,
-                               tensor_or_empty(diffusion_params.context),
-                               diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
-                               true);
-    }
-};
-
-struct ErnieImageModel : public DiffusionModel {
-    std::string prefix;
-    ErnieImage::ErnieImageRunner ernie_image;
-
-    ErnieImageModel(ggml_backend_t backend,
-                    bool offload_params_to_cpu,
-                    const String2TensorStorage& tensor_storage_map = {},
-                    const std::string prefix                       = "model.diffusion_model")
-        : prefix(prefix), ernie_image(backend, offload_params_to_cpu, tensor_storage_map, prefix) {
-    }
-
-    std::string get_desc() override {
-        return ernie_image.get_desc();
-    }
-
-    void alloc_params_buffer() override {
-        ernie_image.alloc_params_buffer();
-    }
-
-    void free_params_buffer() override {
-        ernie_image.free_params_buffer();
-    }
-
-    void free_compute_buffer() override {
-        ernie_image.free_compute_buffer();
-    }
-
-    void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
-        ernie_image.get_param_tensors(tensors, prefix);
-    }
-
-    size_t get_params_buffer_size() override {
-        return ernie_image.get_params_buffer_size();
-    }
-
-    void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
-        ernie_image.set_weight_adapter(adapter);
-    }
-
-    int64_t get_adm_in_channels() override {
-        return 768;
-    }
-
-    void set_flash_attention_enabled(bool enabled) {
-        ernie_image.set_flash_attention_enabled(enabled);
-    }
-
-    void set_circular_axes(bool circular_x, bool circular_y) override {
-        ernie_image.set_circular_axes(circular_x, circular_y);
-    }
-
-    sd::Tensor<float> compute(int n_threads,
-                              const DiffusionParams& diffusion_params) override {
-        GGML_ASSERT(diffusion_params.x != nullptr);
-        GGML_ASSERT(diffusion_params.timesteps != nullptr);
-        return ernie_image.compute(n_threads,
-                                   *diffusion_params.x,
-                                   *diffusion_params.timesteps,
-                                   tensor_or_empty(diffusion_params.context));
-    }
+    virtual void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors,
+                                   const std::string& prefix) = 0;
 };

 #endif
@@ -5,6 +5,7 @@
 #include <vector>

 #include "common_dit.hpp"
+#include "diffusion_model.hpp"
 #include "flux.hpp"
 #include "qwen_image.hpp"
 #include "rope.hpp"
@@ -12,6 +13,76 @@
 namespace ErnieImage {
    constexpr int ERNIE_IMAGE_GRAPH_SIZE = 40960;

+    struct ErnieImageConfig {
+        int64_t hidden_size       = 4096;
+        int64_t num_heads         = 32;
+        int64_t num_layers        = 36;
+        int64_t ffn_hidden_size   = 12288;
+        int64_t in_channels       = 128;
+        int64_t out_channels      = 128;
+        int patch_size            = 1;
+        int64_t text_in_dim       = 3072;
+        int theta                 = 256;
+        std::vector<int> axes_dim = {32, 48, 48};
+        int axes_dim_sum          = 128;
+        float eps                 = 1e-6f;
+
+        static ErnieImageConfig detect_from_weights(const String2TensorStorage& tensor_storage_map, const std::string& prefix) {
+            ErnieImageConfig config;
+            config.num_layers         = 0;
+            int64_t detected_head_dim = 0;
+            for (const auto& [name, tensor_storage] : tensor_storage_map) {
+                if (!starts_with(name, prefix)) {
+                    continue;
+                }
+                if (ends_with(name, "x_embedder.proj.weight") && tensor_storage.n_dims == 4) {
+                    config.patch_size  = static_cast<int>(tensor_storage.ne[0]);
+                    config.in_channels = tensor_storage.ne[2];
+                    config.hidden_size = tensor_storage.ne[3];
+                } else if (ends_with(name, "text_proj.weight") && tensor_storage.n_dims == 2) {
+                    config.text_in_dim = tensor_storage.ne[0];
+                } else if (ends_with(name, "layers.0.self_attention.norm_q.weight")) {
+                    detected_head_dim = tensor_storage.ne[0];
+                } else if (ends_with(name, "layers.0.mlp.gate_proj.weight") && tensor_storage.n_dims == 2) {
+                    config.ffn_hidden_size = tensor_storage.ne[1];
+                } else if (ends_with(name, "final_linear.weight") && tensor_storage.n_dims == 2) {
+                    int64_t out_dim     = tensor_storage.ne[1];
+                    int64_t patch_area  = config.patch_size * config.patch_size;
+                    config.out_channels = out_dim / patch_area;
+                }
+
+                size_t pos = name.find("layers.");
+                if (pos != std::string::npos) {
+                    auto items = split_string(name.substr(pos), '.');
+                    if (items.size() > 1) {
+                        int block_index = atoi(items[1].c_str());
+                        if (block_index + 1 > config.num_layers) {
+                            config.num_layers = block_index + 1;
+                        }
+                    }
+                }
+            }
+            if (config.num_layers == 0) {
+                config.num_layers = 36;
+            }
+            if (detected_head_dim > 0) {
+                config.num_heads = config.hidden_size / detected_head_dim;
+            }
+            config.axes_dim_sum = 0;
+            for (int axis_dim : config.axes_dim) {
+                config.axes_dim_sum += axis_dim;
+            }
+            LOG_DEBUG("ernie_image: num_layers = %" PRId64 ", hidden_size = %" PRId64 ", num_heads = %" PRId64 ", ffn_hidden_size = %" PRId64 ", in_channels = %" PRId64 ", out_channels = %" PRId64,
+                      config.num_layers,
+                      config.hidden_size,
+                      config.num_heads,
+                      config.ffn_hidden_size,
+                      config.in_channels,
+                      config.out_channels);
+            return config;
+        }
+    };
+
    __STATIC_INLINE__ ggml_tensor* timestep_embedding_sin_cos(ggml_context* ctx,
                                                              ggml_tensor* timesteps,
                                                              int dim,
@@ -207,51 +278,36 @@ namespace ErnieImage {
        }
    };

-    struct ErnieImageParams {
-        int64_t hidden_size       = 4096;
-        int64_t num_heads         = 32;
-        int64_t num_layers        = 36;
-        int64_t ffn_hidden_size   = 12288;
-        int64_t in_channels       = 128;
-        int64_t out_channels      = 128;
-        int patch_size            = 1;
-        int64_t text_in_dim       = 3072;
-        int theta                 = 256;
-        std::vector<int> axes_dim = {32, 48, 48};
-        int axes_dim_sum          = 128;
-        float eps                 = 1e-6f;
-    };
-
    class ErnieImageModel : public GGMLBlock {
    public:
-        ErnieImageParams params;
+        ErnieImageConfig config;

        ErnieImageModel() = default;
-        ErnieImageModel(ErnieImageParams params)
-            : params(params) {
-            blocks["x_embedder.proj"] = std::make_shared<Conv2d>(params.in_channels,
-                                                                 params.hidden_size,
-                                                                 std::pair<int, int>{params.patch_size, params.patch_size},
-                                                                 std::pair<int, int>{params.patch_size, params.patch_size},
+        ErnieImageModel(ErnieImageConfig config)
+            : config(config) {
+            blocks["x_embedder.proj"] = std::make_shared<Conv2d>(config.in_channels,
+                                                                 config.hidden_size,
+                                                                 std::pair<int, int>{config.patch_size, config.patch_size},
+                                                                 std::pair<int, int>{config.patch_size, config.patch_size},
                                                                 std::pair<int, int>{0, 0},
                                                                 std::pair<int, int>{1, 1},
                                                                 true);
-            if (params.text_in_dim != params.hidden_size) {
-                blocks["text_proj"] = std::make_shared<Linear>(params.text_in_dim, params.hidden_size, false);
+            if (config.text_in_dim != config.hidden_size) {
+                blocks["text_proj"] = std::make_shared<Linear>(config.text_in_dim, config.hidden_size, false);
            }
-            blocks["time_embedding"]     = std::make_shared<Qwen::TimestepEmbedding>(params.hidden_size, params.hidden_size);
-            blocks["adaLN_modulation.1"] = std::make_shared<Linear>(params.hidden_size, 6 * params.hidden_size, true);
+            blocks["time_embedding"]     = std::make_shared<Qwen::TimestepEmbedding>(config.hidden_size, config.hidden_size);
+            blocks["adaLN_modulation.1"] = std::make_shared<Linear>(config.hidden_size, 6 * config.hidden_size, true);

-            for (int i = 0; i < params.num_layers; i++) {
-                blocks["layers." + std::to_string(i)] = std::make_shared<ErnieImageSharedAdaLNBlock>(params.hidden_size,
-                                                                                                     params.num_heads,
-                                                                                                     params.ffn_hidden_size,
-                                                                                                     params.eps);
+            for (int i = 0; i < config.num_layers; i++) {
+                blocks["layers." + std::to_string(i)] = std::make_shared<ErnieImageSharedAdaLNBlock>(config.hidden_size,
+                                                                                                     config.num_heads,
+                                                                                                     config.ffn_hidden_size,
+                                                                                                     config.eps);
            }

-            blocks["final_norm"]   = std::make_shared<ErnieImageAdaLNContinuous>(params.hidden_size, params.eps);
-            blocks["final_linear"] = std::make_shared<Linear>(params.hidden_size,
-                                                              params.patch_size * params.patch_size * params.out_channels,
+            blocks["final_norm"]   = std::make_shared<ErnieImageAdaLNContinuous>(config.hidden_size, config.eps);
+            blocks["final_linear"] = std::make_shared<Linear>(config.hidden_size,
+                                                              config.patch_size * config.patch_size * config.out_channels,
                                                              true);
        }

@@ -264,12 +320,12 @@ namespace ErnieImage {
            // context: [N, text_tokens, 3072]
            // pe: [image_tokens + text_tokens, head_dim/2, 2, 2]
            GGML_ASSERT(context != nullptr);
-            GGML_ASSERT(x->ne[1] % params.patch_size == 0 && x->ne[0] % params.patch_size == 0);
+            GGML_ASSERT(x->ne[1] % config.patch_size == 0 && x->ne[0] % config.patch_size == 0);

            int64_t W     = x->ne[0];
            int64_t H     = x->ne[1];
-            int64_t Hp    = H / params.patch_size;
-            int64_t Wp    = W / params.patch_size;
+            int64_t Hp    = H / config.patch_size;
+            int64_t Wp    = W / config.patch_size;
            int64_t n_img = Hp * Wp;
            int64_t N     = x->ne[3];

@@ -291,20 +347,23 @@ namespace ErnieImage {

            auto hidden_states = ggml_concat(ctx->ggml_ctx, img, txt, 1);  // [N, image_tokens + text_tokens, hidden_size]

-            auto sample = timestep_embedding_sin_cos(ctx->ggml_ctx, timestep, static_cast<int>(params.hidden_size));
+            auto sample = timestep_embedding_sin_cos(ctx->ggml_ctx, timestep, static_cast<int>(config.hidden_size));
            auto c      = time_embedding->forward(ctx, sample);  // [N, hidden_size]

            auto mod_params = adaLN_mod->forward(ctx, ggml_silu(ctx->ggml_ctx, c));  // [N, 6 * hidden_size]
-            auto chunks     = ggml_ext_chunk(ctx->ggml_ctx, mod_params, 6, 0);
+            sd::ggml_graph_cut::mark_graph_cut(hidden_states, "ernie_image.prelude", "hidden_states");
+            // sd::ggml_graph_cut::mark_graph_cut(mod_params, "ernie_image.prelude", "mod_params");
+            auto chunks = ggml_ext_chunk(ctx->ggml_ctx, mod_params, 6, 0);
            std::vector<ggml_tensor*> temb;
            temb.reserve(6);
            for (auto chunk : chunks) {
                temb.push_back(ggml_reshape_3d(ctx->ggml_ctx, chunk, chunk->ne[0], 1, chunk->ne[1]));  // [N, 1, hidden_size]
            }

-            for (int i = 0; i < params.num_layers; i++) {
+            for (int i = 0; i < config.num_layers; i++) {
                auto layer    = std::dynamic_pointer_cast<ErnieImageSharedAdaLNBlock>(blocks["layers." + std::to_string(i)]);
                hidden_states = layer->forward(ctx, hidden_states, pe, temb);
+                sd::ggml_graph_cut::mark_graph_cut(hidden_states, "ernie_image.layers." + std::to_string(i), "hidden_states");
            }

            hidden_states = final_norm->forward(ctx, hidden_states, c);
@@ -315,74 +374,25 @@ namespace ErnieImage {
                                       patches,
                                       Hp,
                                       Wp,
-                                       params.patch_size,
-                                       params.patch_size,
+                                       config.patch_size,
+                                       config.patch_size,
                                       false);  // [N, out_channels, H, W]
            return out;
        }
    };

-    struct ErnieImageRunner : public GGMLRunner {
-        ErnieImageParams ernie_params;
+    struct ErnieImageRunner : public DiffusionModelRunner {
+        ErnieImageConfig config;
        ErnieImageModel ernie_image;
        std::vector<float> pe_vec;

        ErnieImageRunner(ggml_backend_t backend,
-                         bool offload_params_to_cpu,
+                         ggml_backend_t params_backend,
                         const String2TensorStorage& tensor_storage_map = {},
                         const std::string prefix                       = "")
-            : GGMLRunner(backend, offload_params_to_cpu) {
-            ernie_params.num_layers = 0;
-            for (const auto& [name, tensor_storage] : tensor_storage_map) {
-                if (!starts_with(name, prefix)) {
-                    continue;
-                }
-                if (ends_with(name, "x_embedder.proj.weight") && tensor_storage.n_dims == 4) {
-                    ernie_params.patch_size  = static_cast<int>(tensor_storage.ne[0]);
-                    ernie_params.in_channels = tensor_storage.ne[2];
-                    ernie_params.hidden_size = tensor_storage.ne[3];
-                } else if (ends_with(name, "text_proj.weight") && tensor_storage.n_dims == 2) {
-                    ernie_params.text_in_dim = tensor_storage.ne[0];
-                } else if (ends_with(name, "layers.0.self_attention.norm_q.weight")) {
-                    int64_t head_dim       = tensor_storage.ne[0];
-                    ernie_params.num_heads = ernie_params.hidden_size / head_dim;
-                } else if (ends_with(name, "layers.0.mlp.gate_proj.weight") && tensor_storage.n_dims == 2) {
-                    ernie_params.ffn_hidden_size = tensor_storage.ne[1];
-                } else if (ends_with(name, "final_linear.weight") && tensor_storage.n_dims == 2) {
-                    int64_t out_dim           = tensor_storage.ne[1];
-                    ernie_params.out_channels = out_dim / ernie_params.patch_size / ernie_params.patch_size;
-                }
-
-                size_t pos = name.find("layers.");
-                if (pos != std::string::npos) {
-                    std::string layer_name = name.substr(pos);
-                    auto items             = split_string(layer_name, '.');
-                    if (items.size() > 1) {
-                        int block_index = atoi(items[1].c_str());
-                        if (block_index + 1 > ernie_params.num_layers) {
-                            ernie_params.num_layers = block_index + 1;
-                        }
-                    }
-                }
-            }
-            if (ernie_params.num_layers == 0) {
-                ernie_params.num_layers = 36;
-            }
-            ernie_params.axes_dim_sum = 0;
-            for (int axis_dim : ernie_params.axes_dim) {
-                ernie_params.axes_dim_sum += axis_dim;
-            }
-
-            LOG_INFO("ernie_image: layers = %" PRId64 ", hidden_size = %" PRId64 ", heads = %" PRId64
-                     ", ffn_hidden_size = %" PRId64 ", in_channels = %" PRId64 ", out_channels = %" PRId64,
-                     ernie_params.num_layers,
-                     ernie_params.hidden_size,
-                     ernie_params.num_heads,
-                     ernie_params.ffn_hidden_size,
-                     ernie_params.in_channels,
-                     ernie_params.out_channels);
-
-            ernie_image = ErnieImageModel(ernie_params);
+            : DiffusionModelRunner(backend, params_backend, prefix),
+              config(ErnieImageConfig::detect_from_weights(tensor_storage_map, prefix)) {
+            ernie_image = ErnieImageModel(config);
            ernie_image.init(params_ctx, tensor_storage_map, prefix);
        }

@@ -390,7 +400,7 @@ namespace ErnieImage {
            return "ernie_image";
        }

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

@@ -406,15 +416,15 @@ namespace ErnieImage {

            pe_vec      = Rope::gen_ernie_image_pe(static_cast<int>(x->ne[1]),
                                                   static_cast<int>(x->ne[0]),
-                                                   ernie_params.patch_size,
+                                                   config.patch_size,
                                                   static_cast<int>(x->ne[3]),
                                                   static_cast<int>(context->ne[1]),
-                                                   ernie_params.theta,
+                                                   config.theta,
                                                   circular_y_enabled,
                                                   circular_x_enabled,
-                                                   ernie_params.axes_dim);
-            int pos_len = static_cast<int>(pe_vec.size() / ernie_params.axes_dim_sum / 2);
-            auto pe     = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, ernie_params.axes_dim_sum, 1, pos_len, 2);
+                                                   config.axes_dim);
+            int pos_len = static_cast<int>(pe_vec.size() / config.axes_dim_sum / 2);
+            auto pe     = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, config.axes_dim_sum, 1, pos_len, 2);
            set_backend_tensor_data(pe, pe_vec.data());

            auto runner_ctx  = get_context();
@@ -432,6 +442,16 @@ namespace ErnieImage {
            };
            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const DiffusionParams& diffusion_params) override {
+            GGML_ASSERT(diffusion_params.x != nullptr);
+            GGML_ASSERT(diffusion_params.timesteps != nullptr);
+            return compute(n_threads,
+                           *diffusion_params.x,
+                           *diffusion_params.timesteps,
+                           tensor_or_empty(diffusion_params.context));
+        }
    };
 }  // namespace ErnieImage

@@ -124,27 +124,33 @@ public:
        auto conv_hr    = std::dynamic_pointer_cast<Conv2d>(blocks["conv_hr"]);
        auto conv_last  = std::dynamic_pointer_cast<Conv2d>(blocks["conv_last"]);

-        auto feat      = conv_first->forward(ctx, x);
+        auto feat = conv_first->forward(ctx, x);
+        sd::ggml_graph_cut::mark_graph_cut(feat, "esrgan.prelude", "feat");
        auto body_feat = feat;
        for (int i = 0; i < num_block; i++) {
            std::string name = "body." + std::to_string(i);
            auto block       = std::dynamic_pointer_cast<RRDB>(blocks[name]);

            body_feat = block->forward(ctx, body_feat);
+            sd::ggml_graph_cut::mark_graph_cut(body_feat, "esrgan.body." + std::to_string(i), "feat");
        }
        body_feat = conv_body->forward(ctx, body_feat);
        feat      = ggml_add(ctx->ggml_ctx, feat, body_feat);
+        sd::ggml_graph_cut::mark_graph_cut(feat, "esrgan.body.out", "feat");
        // upsample
        if (scale >= 2) {
            auto conv_up1 = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up1"]);
            feat          = lrelu(ctx, conv_up1->forward(ctx, ggml_upscale(ctx->ggml_ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
+            sd::ggml_graph_cut::mark_graph_cut(feat, "esrgan.up1", "feat");
            if (scale == 4) {
                auto conv_up2 = std::dynamic_pointer_cast<Conv2d>(blocks["conv_up2"]);
                feat          = lrelu(ctx, conv_up2->forward(ctx, ggml_upscale(ctx->ggml_ctx, feat, 2, GGML_SCALE_MODE_NEAREST)));
+                sd::ggml_graph_cut::mark_graph_cut(feat, "esrgan.up2", "feat");
            }
        }
        // for all scales
        auto out = conv_last->forward(ctx, lrelu(ctx, conv_hr->forward(ctx, feat)));
+        sd::ggml_graph_cut::mark_graph_cut(out, "esrgan.final", "out");
        return out;
    }
 };
@@ -155,10 +161,10 @@ struct ESRGAN : public GGMLRunner {
    int tile_size = 128;  // avoid cuda OOM for 4gb VRAM

    ESRGAN(ggml_backend_t backend,
-           bool offload_params_to_cpu,
+           ggml_backend_t params_backend,
           int tile_size                                  = 128,
           const String2TensorStorage& tensor_storage_map = {})
-        : GGMLRunner(backend, offload_params_to_cpu) {
+        : GGMLRunner(backend, params_backend) {
        this->tile_size = tile_size;
    }

@@ -264,7 +270,11 @@ struct ESRGAN : public GGMLRunner {
        rrdb_net = std::make_unique<RRDBNet>(detected_scale, detected_num_block, detected_num_in_ch, detected_num_out_ch, detected_num_feat, detected_num_grow_ch);
        rrdb_net->init(params_ctx, {}, "");

-        alloc_params_buffer();
+        if (!alloc_params_buffer()) {
+            LOG_ERROR("esrgan model buffer allocation failed");
+            return false;
+        }
+
        std::map<std::string, ggml_tensor*> esrgan_tensors;
        rrdb_net->get_param_tensors(esrgan_tensors);

@@ -5,6 +5,7 @@
 #include <vector>

 #include "common_dit.hpp"
+#include "diffusion_model.hpp"
 #include "model.h"
 #include "rope.hpp"

@@ -12,6 +13,155 @@

 namespace Flux {

+    struct ChromaRadianceConfig {
+        int64_t nerf_hidden_size = 64;
+        int nerf_mlp_ratio       = 4;
+        int nerf_depth           = 4;
+        int nerf_max_freqs       = 8;
+        bool use_x0              = false;
+        bool fake_patch_size_x2  = false;
+    };
+
+    struct FluxConfig {
+        SDVersion version         = VERSION_FLUX;
+        bool is_chroma            = false;
+        int patch_size            = 2;
+        int64_t in_channels       = 64;
+        int64_t out_channels      = 64;
+        int64_t vec_in_dim        = 768;
+        int64_t context_in_dim    = 4096;
+        int64_t hidden_size       = 3072;
+        float mlp_ratio           = 4.0f;
+        int num_heads             = 24;
+        int depth                 = 19;
+        int depth_single_blocks   = 38;
+        std::vector<int> axes_dim = {16, 56, 56};
+        int axes_dim_sum          = 128;
+        int theta                 = 10000;
+        bool qkv_bias             = true;
+        bool guidance_embed       = true;
+        int64_t in_dim            = 64;
+        bool disable_bias         = false;
+        bool share_modulation     = false;
+        bool semantic_txt_norm    = false;
+        bool use_yak_mlp          = false;
+        bool use_mlp_silu_act     = false;
+        float ref_index_scale     = 1.f;
+        ChromaRadianceConfig chroma_radiance_params;
+
+        static FluxConfig detect_from_weights(const String2TensorStorage& tensor_storage_map,
+                                              const std::string& prefix,
+                                              SDVersion version = VERSION_FLUX) {
+            FluxConfig config;
+            config.version             = version;
+            config.guidance_embed      = false;
+            config.depth               = 0;
+            config.depth_single_blocks = 0;
+            if (version == VERSION_FLUX_FILL) {
+                config.in_channels = 384;
+            } else if (version == VERSION_FLUX_CONTROLS) {
+                config.in_channels = 128;
+            } else if (version == VERSION_FLEX_2) {
+                config.in_channels = 196;
+            } else if (version == VERSION_CHROMA_RADIANCE) {
+                config.in_channels = 3;
+                config.patch_size  = 16;
+            } else if (version == VERSION_OVIS_IMAGE) {
+                config.semantic_txt_norm = true;
+                config.use_yak_mlp       = true;
+                config.vec_in_dim        = 0;
+            } else if (sd_version_is_flux2(version)) {
+                config.in_channels      = 128;
+                config.patch_size       = 1;
+                config.out_channels     = 128;
+                config.mlp_ratio        = 3.f;
+                config.theta            = 2000;
+                config.axes_dim         = {32, 32, 32, 32};
+                config.vec_in_dim       = 0;
+                config.qkv_bias         = false;
+                config.disable_bias     = true;
+                config.share_modulation = true;
+                config.ref_index_scale  = 10.f;
+                config.use_mlp_silu_act = true;
+            } else if (sd_version_is_longcat(version)) {
+                config.context_in_dim = 3584;
+                config.vec_in_dim     = 0;
+            }
+
+            int64_t head_dim                   = 0;
+            int64_t actual_radiance_patch_size = -1;
+            for (const auto& [name, tensor_storage] : tensor_storage_map) {
+                if (!starts_with(name, prefix)) {
+                    continue;
+                }
+                if (name.find("guidance_in.in_layer.weight") != std::string::npos) {
+                    config.guidance_embed = true;
+                }
+                if (name.find("__x0__") != std::string::npos) {
+                    LOG_DEBUG("using x0 prediction");
+                    config.chroma_radiance_params.use_x0 = true;
+                }
+                if (name.find("__32x32__") != std::string::npos) {
+                    LOG_DEBUG("using patch size 32");
+                    config.patch_size = 32;
+                }
+                if (name.find("img_in_patch.weight") != std::string::npos) {
+                    actual_radiance_patch_size = tensor_storage.ne[0];
+                    LOG_DEBUG("actual radiance patch size: %" PRId64, actual_radiance_patch_size);
+                }
+                if (name.find("distilled_guidance_layer.in_proj.weight") != std::string::npos) {
+                    config.is_chroma = true;
+                }
+                size_t db = name.find("double_blocks.");
+                if (db != std::string::npos) {
+                    std::string block_name = name.substr(db);
+                    int block_depth        = atoi(block_name.substr(14, block_name.find(".", 14)).c_str());
+                    if (block_depth + 1 > config.depth) {
+                        config.depth = block_depth + 1;
+                    }
+                }
+                size_t sb = name.find("single_blocks.");
+                if (sb != std::string::npos) {
+                    std::string block_name = name.substr(sb);
+                    int block_depth        = atoi(block_name.substr(14, block_name.find(".", 14)).c_str());
+                    if (block_depth + 1 > config.depth_single_blocks) {
+                        config.depth_single_blocks = block_depth + 1;
+                    }
+                }
+                if (ends_with(name, "txt_in.weight")) {
+                    config.context_in_dim = tensor_storage.ne[0];
+                    config.hidden_size    = tensor_storage.ne[1];
+                }
+                if (ends_with(name, "single_blocks.0.norm.key_norm.scale")) {
+                    head_dim = tensor_storage.ne[0];
+                }
+                if (ends_with(name, "double_blocks.0.txt_attn.norm.key_norm.scale")) {
+                    head_dim = tensor_storage.ne[0];
+                }
+            }
+            if (actual_radiance_patch_size > 0 && actual_radiance_patch_size != config.patch_size) {
+                GGML_ASSERT(config.patch_size == 2 * actual_radiance_patch_size);
+                LOG_DEBUG("using fake x2 patch size");
+                config.chroma_radiance_params.fake_patch_size_x2 = true;
+            }
+            if (head_dim > 0) {
+                config.num_heads = static_cast<int>(config.hidden_size / head_dim);
+            }
+            config.axes_dim_sum = 0;
+            for (int axis_dim : config.axes_dim) {
+                config.axes_dim_sum += axis_dim;
+            }
+            LOG_DEBUG("flux: depth = %d, depth_single_blocks = %d, guidance_embed = %s, context_in_dim = %" PRId64 ", hidden_size = %" PRId64 ", num_heads = %d",
+                      config.depth,
+                      config.depth_single_blocks,
+                      config.guidance_embed ? "true" : "false",
+                      config.context_in_dim,
+                      config.hidden_size,
+                      config.num_heads);
+            return config;
+        }
+    };
+
    struct MLPEmbedder : public UnaryBlock {
    public:
        MLPEmbedder(int64_t in_dim, int64_t hidden_dim, bool bias = true) {
@@ -446,7 +596,6 @@ namespace Flux {
            if (use_yak_mlp || use_mlp_silu_act) {
                mlp_mult_factor = 2;
            }
-
            blocks["linear1"]  = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, hidden_size * 3 + mlp_hidden_dim * mlp_mult_factor, mlp_proj_bias));
            blocks["linear2"]  = std::shared_ptr<GGMLBlock>(new Linear(hidden_size + mlp_hidden_dim, hidden_size, mlp_proj_bias));
            blocks["norm"]     = std::shared_ptr<GGMLBlock>(new QKNorm(head_dim));
@@ -723,127 +872,90 @@ namespace Flux {
        }
    };

-    struct ChromaRadianceParams {
-        int64_t nerf_hidden_size = 64;
-        int nerf_mlp_ratio       = 4;
-        int nerf_depth           = 4;
-        int nerf_max_freqs       = 8;
-        bool use_x0              = false;
-        bool fake_patch_size_x2  = false;
-    };
-
-    struct FluxParams {
-        SDVersion version         = VERSION_FLUX;
-        bool is_chroma            = false;
-        int patch_size            = 2;
-        int64_t in_channels       = 64;
-        int64_t out_channels      = 64;
-        int64_t vec_in_dim        = 768;
-        int64_t context_in_dim    = 4096;
-        int64_t hidden_size       = 3072;
-        float mlp_ratio           = 4.0f;
-        int num_heads             = 24;
-        int depth                 = 19;
-        int depth_single_blocks   = 38;
-        std::vector<int> axes_dim = {16, 56, 56};
-        int axes_dim_sum          = 128;
-        int theta                 = 10000;
-        bool qkv_bias             = true;
-        bool guidance_embed       = true;
-        int64_t in_dim            = 64;
-        bool disable_bias         = false;
-        bool share_modulation     = false;
-        bool semantic_txt_norm    = false;
-        bool use_yak_mlp          = false;
-        bool use_mlp_silu_act     = false;
-        float ref_index_scale     = 1.f;
-        ChromaRadianceParams chroma_radiance_params;
-    };
-
    struct Flux : public GGMLBlock {
    public:
-        FluxParams params;
+        FluxConfig config;
        Flux() {}
-        Flux(FluxParams params)
-            : params(params) {
-            if (params.version == VERSION_CHROMA_RADIANCE) {
-                std::pair<int, int> kernel_size = {params.patch_size, params.patch_size};
-                if (params.chroma_radiance_params.fake_patch_size_x2) {
-                    kernel_size = {params.patch_size / 2, params.patch_size / 2};
+        Flux(FluxConfig config)
+            : config(config) {
+            if (config.version == VERSION_CHROMA_RADIANCE) {
+                std::pair<int, int> kernel_size = {config.patch_size, config.patch_size};
+                if (config.chroma_radiance_params.fake_patch_size_x2) {
+                    kernel_size = {config.patch_size / 2, config.patch_size / 2};
                }
                std::pair<int, int> stride = kernel_size;

-                blocks["img_in_patch"] = std::make_shared<Conv2d>(params.in_channels,
-                                                                  params.hidden_size,
+                blocks["img_in_patch"] = std::make_shared<Conv2d>(config.in_channels,
+                                                                  config.hidden_size,
                                                                  kernel_size,
                                                                  stride);
            } else {
-                blocks["img_in"] = std::make_shared<Linear>(params.in_channels, params.hidden_size, !params.disable_bias);
+                blocks["img_in"] = std::make_shared<Linear>(config.in_channels, config.hidden_size, !config.disable_bias);
            }
-            if (params.is_chroma) {
-                blocks["distilled_guidance_layer"] = std::make_shared<ChromaApproximator>(params.in_dim, params.hidden_size);
+            if (config.is_chroma) {
+                blocks["distilled_guidance_layer"] = std::make_shared<ChromaApproximator>(config.in_dim, config.hidden_size);
            } else {
-                blocks["time_in"] = std::make_shared<MLPEmbedder>(256, params.hidden_size, !params.disable_bias);
-                if (params.vec_in_dim > 0) {
-                    blocks["vector_in"] = std::make_shared<MLPEmbedder>(params.vec_in_dim, params.hidden_size, !params.disable_bias);
+                blocks["time_in"] = std::make_shared<MLPEmbedder>(256, config.hidden_size, !config.disable_bias);
+                if (config.vec_in_dim > 0) {
+                    blocks["vector_in"] = std::make_shared<MLPEmbedder>(config.vec_in_dim, config.hidden_size, !config.disable_bias);
                }
-                if (params.guidance_embed) {
-                    blocks["guidance_in"] = std::make_shared<MLPEmbedder>(256, params.hidden_size, !params.disable_bias);
+                if (config.guidance_embed) {
+                    blocks["guidance_in"] = std::make_shared<MLPEmbedder>(256, config.hidden_size, !config.disable_bias);
                }
            }
-            if (params.semantic_txt_norm) {
-                blocks["txt_norm"] = std::make_shared<RMSNorm>(params.context_in_dim);
+            if (config.semantic_txt_norm) {
+                blocks["txt_norm"] = std::make_shared<RMSNorm>(config.context_in_dim);
            }
-            blocks["txt_in"] = std::make_shared<Linear>(params.context_in_dim, params.hidden_size, !params.disable_bias);
+            blocks["txt_in"] = std::make_shared<Linear>(config.context_in_dim, config.hidden_size, !config.disable_bias);

-            for (int i = 0; i < params.depth; i++) {
-                blocks["double_blocks." + std::to_string(i)] = std::make_shared<DoubleStreamBlock>(params.hidden_size,
-                                                                                                   params.num_heads,
-                                                                                                   params.mlp_ratio,
+            for (int i = 0; i < config.depth; i++) {
+                blocks["double_blocks." + std::to_string(i)] = std::make_shared<DoubleStreamBlock>(config.hidden_size,
+                                                                                                   config.num_heads,
+                                                                                                   config.mlp_ratio,
                                                                                                   i,
-                                                                                                   params.qkv_bias,
-                                                                                                   params.is_chroma,
-                                                                                                   params.share_modulation,
-                                                                                                   !params.disable_bias,
-                                                                                                   params.use_yak_mlp,
-                                                                                                   params.use_mlp_silu_act);
+                                                                                                   config.qkv_bias,
+                                                                                                   config.is_chroma,
+                                                                                                   config.share_modulation,
+                                                                                                   !config.disable_bias,
+                                                                                                   config.use_yak_mlp,
+                                                                                                   config.use_mlp_silu_act);
            }

-            for (int i = 0; i < params.depth_single_blocks; i++) {
-                blocks["single_blocks." + std::to_string(i)] = std::make_shared<SingleStreamBlock>(params.hidden_size,
-                                                                                                   params.num_heads,
-                                                                                                   params.mlp_ratio,
+            for (int i = 0; i < config.depth_single_blocks; i++) {
+                blocks["single_blocks." + std::to_string(i)] = std::make_shared<SingleStreamBlock>(config.hidden_size,
+                                                                                                   config.num_heads,
+                                                                                                   config.mlp_ratio,
                                                                                                   i,
                                                                                                   0.f,
-                                                                                                   params.is_chroma,
-                                                                                                   params.share_modulation,
-                                                                                                   !params.disable_bias,
-                                                                                                   params.use_yak_mlp,
-                                                                                                   params.use_mlp_silu_act);
+                                                                                                   config.is_chroma,
+                                                                                                   config.share_modulation,
+                                                                                                   !config.disable_bias,
+                                                                                                   config.use_yak_mlp,
+                                                                                                   config.use_mlp_silu_act);
            }

-            if (params.version == VERSION_CHROMA_RADIANCE) {
-                blocks["nerf_image_embedder"] = std::make_shared<NerfEmbedder>(params.in_channels,
-                                                                               params.chroma_radiance_params.nerf_hidden_size,
-                                                                               params.chroma_radiance_params.nerf_max_freqs);
+            if (config.version == VERSION_CHROMA_RADIANCE) {
+                blocks["nerf_image_embedder"] = std::make_shared<NerfEmbedder>(config.in_channels,
+                                                                               config.chroma_radiance_params.nerf_hidden_size,
+                                                                               config.chroma_radiance_params.nerf_max_freqs);

-                for (int i = 0; i < params.chroma_radiance_params.nerf_depth; i++) {
-                    blocks["nerf_blocks." + std::to_string(i)] = std::make_shared<NerfGLUBlock>(params.hidden_size,
-                                                                                                params.chroma_radiance_params.nerf_hidden_size,
-                                                                                                params.chroma_radiance_params.nerf_mlp_ratio);
+                for (int i = 0; i < config.chroma_radiance_params.nerf_depth; i++) {
+                    blocks["nerf_blocks." + std::to_string(i)] = std::make_shared<NerfGLUBlock>(config.hidden_size,
+                                                                                                config.chroma_radiance_params.nerf_hidden_size,
+                                                                                                config.chroma_radiance_params.nerf_mlp_ratio);
                }

-                blocks["nerf_final_layer_conv"] = std::make_shared<NerfFinalLayerConv>(params.chroma_radiance_params.nerf_hidden_size,
-                                                                                       params.in_channels);
+                blocks["nerf_final_layer_conv"] = std::make_shared<NerfFinalLayerConv>(config.chroma_radiance_params.nerf_hidden_size,
+                                                                                       config.in_channels);

            } else {
-                blocks["final_layer"] = std::make_shared<LastLayer>(params.hidden_size, 1, params.out_channels, params.is_chroma, !params.disable_bias);
+                blocks["final_layer"] = std::make_shared<LastLayer>(config.hidden_size, 1, config.out_channels, config.is_chroma, !config.disable_bias);
            }

-            if (params.share_modulation) {
-                blocks["double_stream_modulation_img"] = std::make_shared<Modulation>(params.hidden_size, true, !params.disable_bias);
-                blocks["double_stream_modulation_txt"] = std::make_shared<Modulation>(params.hidden_size, true, !params.disable_bias);
-                blocks["single_stream_modulation"]     = std::make_shared<Modulation>(params.hidden_size, false, !params.disable_bias);
+            if (config.share_modulation) {
+                blocks["double_stream_modulation_img"] = std::make_shared<Modulation>(config.hidden_size, true, !config.disable_bias);
+                blocks["double_stream_modulation_txt"] = std::make_shared<Modulation>(config.hidden_size, true, !config.disable_bias);
+                blocks["single_stream_modulation"]     = std::make_shared<Modulation>(config.hidden_size, false, !config.disable_bias);
            }
        }

@@ -866,7 +978,7 @@ namespace Flux {

            ggml_tensor* vec;
            ggml_tensor* txt_img_mask = nullptr;
-            if (params.is_chroma) {
+            if (config.is_chroma) {
                int64_t mod_index_length = 344;
                auto approx              = std::dynamic_pointer_cast<ChromaApproximator>(blocks["distilled_guidance_layer"]);
                auto distill_timestep    = ggml_ext_timestep_embedding(ctx->ggml_ctx, timesteps, 16, 10000, 1000.f);
@@ -894,7 +1006,7 @@ namespace Flux {
            } else {
                auto time_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["time_in"]);
                vec          = time_in->forward(ctx, ggml_ext_timestep_embedding(ctx->ggml_ctx, timesteps, 256, 10000, 1000.f));
-                if (params.guidance_embed) {
+                if (config.guidance_embed) {
                    GGML_ASSERT(guidance != nullptr);
                    auto guidance_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["guidance_in"]);
                    // bf16 and fp16 result is different
@@ -902,7 +1014,7 @@ namespace Flux {
                    vec       = ggml_add(ctx->ggml_ctx, vec, guidance_in->forward(ctx, g_in));
                }

-                if (params.vec_in_dim > 0) {
+                if (config.vec_in_dim > 0) {
                    auto vector_in = std::dynamic_pointer_cast<MLPEmbedder>(blocks["vector_in"]);
                    vec            = ggml_add(ctx->ggml_ctx, vec, vector_in->forward(ctx, y));
                }
@@ -911,7 +1023,7 @@ namespace Flux {
            std::vector<ModulationOut> ds_img_mods;
            std::vector<ModulationOut> ds_txt_mods;
            std::vector<ModulationOut> ss_mods;
-            if (params.share_modulation) {
+            if (config.share_modulation) {
                auto double_stream_modulation_img = std::dynamic_pointer_cast<Modulation>(blocks["double_stream_modulation_img"]);
                auto double_stream_modulation_txt = std::dynamic_pointer_cast<Modulation>(blocks["double_stream_modulation_txt"]);
                auto single_stream_modulation     = std::dynamic_pointer_cast<Modulation>(blocks["single_stream_modulation"]);
@@ -921,15 +1033,18 @@ namespace Flux {
                ss_mods     = single_stream_modulation->forward(ctx, vec);
            }

-            if (params.semantic_txt_norm) {
+            if (config.semantic_txt_norm) {
                auto semantic_txt_norm = std::dynamic_pointer_cast<RMSNorm>(blocks["txt_norm"]);

                txt = semantic_txt_norm->forward(ctx, txt);
            }

            txt = txt_in->forward(ctx, txt);
+            sd::ggml_graph_cut::mark_graph_cut(img, "flux.prelude", "img");
+            sd::ggml_graph_cut::mark_graph_cut(txt, "flux.prelude", "txt");
+            sd::ggml_graph_cut::mark_graph_cut(vec, "flux.prelude", "vec");

-            for (int i = 0; i < params.depth; i++) {
+            for (int i = 0; i < config.depth; i++) {
                if (skip_layers.size() > 0 && std::find(skip_layers.begin(), skip_layers.end(), i) != skip_layers.end()) {
                    continue;
                }
@@ -939,16 +1054,19 @@ namespace Flux {
                auto img_txt = block->forward(ctx, img, txt, vec, pe, txt_img_mask, ds_img_mods, ds_txt_mods);
                img          = img_txt.first;   // [N, n_img_token, hidden_size]
                txt          = img_txt.second;  // [N, n_txt_token, hidden_size]
+                sd::ggml_graph_cut::mark_graph_cut(img, "flux.double_blocks." + std::to_string(i), "img");
+                sd::ggml_graph_cut::mark_graph_cut(txt, "flux.double_blocks." + std::to_string(i), "txt");
            }

            auto txt_img = ggml_concat(ctx->ggml_ctx, txt, img, 1);  // [N, n_txt_token + n_img_token, hidden_size]
-            for (int i = 0; i < params.depth_single_blocks; i++) {
-                if (skip_layers.size() > 0 && std::find(skip_layers.begin(), skip_layers.end(), i + params.depth) != skip_layers.end()) {
+            for (int i = 0; i < config.depth_single_blocks; i++) {
+                if (skip_layers.size() > 0 && std::find(skip_layers.begin(), skip_layers.end(), i + config.depth) != skip_layers.end()) {
                    continue;
                }
                auto block = std::dynamic_pointer_cast<SingleStreamBlock>(blocks["single_blocks." + std::to_string(i)]);

                txt_img = block->forward(ctx, txt_img, vec, pe, txt_img_mask, ss_mods);
+                sd::ggml_graph_cut::mark_graph_cut(txt_img, "flux.single_blocks." + std::to_string(i), "txt_img");
            }

            img = ggml_view_3d(ctx->ggml_ctx,
@@ -993,14 +1111,14 @@ namespace Flux {
            int64_t W      = x->ne[0];
            int64_t H      = x->ne[1];
            int64_t C      = x->ne[2];
-            int patch_size = params.patch_size;
+            int patch_size = config.patch_size;
            int pad_h      = (patch_size - H % patch_size) % patch_size;
            int pad_w      = (patch_size - W % patch_size) % patch_size;

-            auto img      = DiT::pad_to_patch_size(ctx, x, params.patch_size, params.patch_size);
+            auto img      = DiT::pad_to_patch_size(ctx, x, config.patch_size, config.patch_size);
            auto orig_img = img;

-            if (params.chroma_radiance_params.fake_patch_size_x2) {
+            if (config.chroma_radiance_params.fake_patch_size_x2) {
                // It's supposed to be using GGML_SCALE_MODE_NEAREST, but this seems more stable
                // Maybe the implementation of nearest-neighbor interpolation in ggml behaves differently than the one in PyTorch?
                // img = F.interpolate(img, size=(H//2, W//2), mode="nearest")
@@ -1031,7 +1149,7 @@ namespace Flux {
            auto nerf_hidden = ggml_reshape_2d(ctx->ggml_ctx, out, out->ne[0], out->ne[1] * out->ne[2]);  // [N*num_patches, hidden_size]
            auto img_dct     = nerf_image_embedder->forward(ctx, nerf_pixels, dct);                       // [N*num_patches, patch_size*patch_size, nerf_hidden_size]

-            for (int i = 0; i < params.chroma_radiance_params.nerf_depth; i++) {
+            for (int i = 0; i < config.chroma_radiance_params.nerf_depth; i++) {
                auto block = std::dynamic_pointer_cast<NerfGLUBlock>(blocks["nerf_blocks." + std::to_string(i)]);

                img_dct = block->forward(ctx, img_dct, nerf_hidden);
@@ -1043,7 +1161,7 @@ namespace Flux {

            out = nerf_final_layer_conv->forward(ctx, img_dct);  // [N, C, H, W]

-            if (params.chroma_radiance_params.use_x0) {
+            if (config.chroma_radiance_params.use_x0) {
                out = _apply_x0_residual(ctx, out, orig_img, timestep);
            }

@@ -1067,14 +1185,14 @@ namespace Flux {
            int64_t W      = x->ne[0];
            int64_t H      = x->ne[1];
            int64_t C      = x->ne[2];
-            int patch_size = params.patch_size;
+            int patch_size = config.patch_size;
            int pad_h      = (patch_size - H % patch_size) % patch_size;
            int pad_w      = (patch_size - W % patch_size) % patch_size;

            auto img           = DiT::pad_and_patchify(ctx, x, patch_size, patch_size);
            int64_t img_tokens = img->ne[1];

-            if (params.version == VERSION_FLUX_FILL) {
+            if (config.version == VERSION_FLUX_FILL) {
                GGML_ASSERT(c_concat != nullptr);
                ggml_tensor* masked = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], C, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], 0);
                ggml_tensor* mask   = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], 8 * 8, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * C);
@@ -1083,7 +1201,7 @@ namespace Flux {
                mask   = DiT::pad_and_patchify(ctx, mask, patch_size, patch_size);

                img = ggml_concat(ctx->ggml_ctx, img, ggml_concat(ctx->ggml_ctx, masked, mask, 0), 0);
-            } else if (params.version == VERSION_FLEX_2) {
+            } else if (config.version == VERSION_FLEX_2) {
                GGML_ASSERT(c_concat != nullptr);
                ggml_tensor* masked  = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], C, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], 0);
                ggml_tensor* mask    = ggml_view_4d(ctx->ggml_ctx, c_concat, c_concat->ne[0], c_concat->ne[1], 1, 1, c_concat->nb[1], c_concat->nb[2], c_concat->nb[3], c_concat->nb[2] * C);
@@ -1094,7 +1212,7 @@ namespace Flux {
                control = DiT::pad_and_patchify(ctx, control, patch_size, patch_size);

                img = ggml_concat(ctx->ggml_ctx, img, ggml_concat(ctx->ggml_ctx, ggml_concat(ctx->ggml_ctx, masked, mask, 0), control, 0), 0);
-            } else if (params.version == VERSION_FLUX_CONTROLS) {
+            } else if (config.version == VERSION_FLUX_CONTROLS) {
                GGML_ASSERT(c_concat != nullptr);

                auto control = DiT::pad_and_patchify(ctx, c_concat, patch_size, patch_size);
@@ -1141,7 +1259,7 @@ namespace Flux {
            // pe: (L, d_head/2, 2, 2)
            // return: (N, C, H, W)

-            if (params.version == VERSION_CHROMA_RADIANCE) {
+            if (config.version == VERSION_CHROMA_RADIANCE) {
                return forward_chroma_radiance(ctx,
                                               x,
                                               timestep,
@@ -1171,9 +1289,9 @@ namespace Flux {
        }
    };

-    struct FluxRunner : public GGMLRunner {
+    struct FluxRunner : public DiffusionModelRunner {
    public:
-        FluxParams flux_params;
+        FluxConfig config;
        Flux flux;
        std::vector<float> pe_vec;
        std::vector<float> mod_index_arange_vec;
@@ -1183,116 +1301,20 @@ namespace Flux {
        bool use_mask = false;

        FluxRunner(ggml_backend_t backend,
-                   bool offload_params_to_cpu,
+                   ggml_backend_t params_backend,
                   const String2TensorStorage& tensor_storage_map = {},
                   const std::string prefix                       = "",
                   SDVersion version                              = VERSION_FLUX,
                   bool use_mask                                  = false)
-            : GGMLRunner(backend, offload_params_to_cpu), version(version), use_mask(use_mask) {
-            flux_params.version             = version;
-            flux_params.guidance_embed      = false;
-            flux_params.depth               = 0;
-            flux_params.depth_single_blocks = 0;
-            if (version == VERSION_FLUX_FILL) {
-                flux_params.in_channels = 384;
-            } else if (version == VERSION_FLUX_CONTROLS) {
-                flux_params.in_channels = 128;
-            } else if (version == VERSION_FLEX_2) {
-                flux_params.in_channels = 196;
-            } else if (version == VERSION_CHROMA_RADIANCE) {
-                flux_params.in_channels = 3;
-                flux_params.patch_size  = 16;
-            } else if (version == VERSION_OVIS_IMAGE) {
-                flux_params.semantic_txt_norm = true;
-                flux_params.use_yak_mlp       = true;
-                flux_params.vec_in_dim        = 0;
-            } else if (sd_version_is_flux2(version)) {
-                flux_params.in_channels      = 128;
-                flux_params.patch_size       = 1;
-                flux_params.out_channels     = 128;
-                flux_params.mlp_ratio        = 3.f;
-                flux_params.theta            = 2000;
-                flux_params.axes_dim         = {32, 32, 32, 32};
-                flux_params.vec_in_dim       = 0;
-                flux_params.qkv_bias         = false;
-                flux_params.disable_bias     = true;
-                flux_params.share_modulation = true;
-                flux_params.ref_index_scale  = 10.f;
-                flux_params.use_mlp_silu_act = true;
-            }
-            int64_t head_dim                   = 0;
-            int64_t actual_radiance_patch_size = -1;
-            for (auto pair : tensor_storage_map) {
-                std::string tensor_name = pair.first;
-                if (!starts_with(tensor_name, prefix))
-                    continue;
-                if (tensor_name.find("guidance_in.in_layer.weight") != std::string::npos) {
-                    flux_params.guidance_embed = true;
-                }
-                if (tensor_name.find("__x0__") != std::string::npos) {
-                    LOG_DEBUG("using x0 prediction");
-                    flux_params.chroma_radiance_params.use_x0 = true;
-                }
-                if (tensor_name.find("__32x32__") != std::string::npos) {
-                    LOG_DEBUG("using patch size 32");
-                    flux_params.patch_size = 32;
-                }
-                if (tensor_name.find("img_in_patch.weight") != std::string::npos) {
-                    actual_radiance_patch_size = pair.second.ne[0];
-                    LOG_DEBUG("actual radiance patch size: %d", actual_radiance_patch_size);
-                }
-                if (tensor_name.find("distilled_guidance_layer.in_proj.weight") != std::string::npos) {
-                    // Chroma
-                    flux_params.is_chroma = true;
-                }
-                size_t db = tensor_name.find("double_blocks.");
-                if (db != std::string::npos) {
-                    tensor_name     = tensor_name.substr(db);  // remove prefix
-                    int block_depth = atoi(tensor_name.substr(14, tensor_name.find(".", 14)).c_str());
-                    if (block_depth + 1 > flux_params.depth) {
-                        flux_params.depth = block_depth + 1;
-                    }
-                }
-                size_t sb = tensor_name.find("single_blocks.");
-                if (sb != std::string::npos) {
-                    tensor_name     = tensor_name.substr(sb);  // remove prefix
-                    int block_depth = atoi(tensor_name.substr(14, tensor_name.find(".", 14)).c_str());
-                    if (block_depth + 1 > flux_params.depth_single_blocks) {
-                        flux_params.depth_single_blocks = block_depth + 1;
-                    }
-                }
-                if (ends_with(tensor_name, "txt_in.weight")) {
-                    flux_params.context_in_dim = pair.second.ne[0];
-                    flux_params.hidden_size    = pair.second.ne[1];
-                }
-                if (ends_with(tensor_name, "single_blocks.0.norm.key_norm.scale")) {
-                    head_dim = pair.second.ne[0];
-                }
-                if (ends_with(tensor_name, "double_blocks.0.txt_attn.norm.key_norm.scale")) {
-                    head_dim = pair.second.ne[0];
-                }
-            }
-            if (actual_radiance_patch_size > 0 && actual_radiance_patch_size != flux_params.patch_size) {
-                GGML_ASSERT(flux_params.patch_size == 2 * actual_radiance_patch_size);
-                LOG_DEBUG("using fake x2 patch size");
-                flux_params.chroma_radiance_params.fake_patch_size_x2 = true;
-            }
-
-            flux_params.num_heads = static_cast<int>(flux_params.hidden_size / head_dim);
-
-            LOG_INFO("flux: depth = %d, depth_single_blocks = %d, guidance_embed = %s, context_in_dim = %" PRId64
-                     ", hidden_size = %" PRId64 ", num_heads = %d",
-                     flux_params.depth,
-                     flux_params.depth_single_blocks,
-                     flux_params.guidance_embed ? "true" : "false",
-                     flux_params.context_in_dim,
-                     flux_params.hidden_size,
-                     flux_params.num_heads);
-            if (flux_params.is_chroma) {
+            : DiffusionModelRunner(backend, params_backend, prefix),
+              config(FluxConfig::detect_from_weights(tensor_storage_map, prefix, version)),
+              version(version),
+              use_mask(use_mask) {
+            if (config.is_chroma) {
                LOG_INFO("Using pruned modulation (Chroma)");
            }

-            flux = Flux(flux_params);
+            flux = Flux(config);
            flux.init(params_ctx, tensor_storage_map, prefix);
        }

@@ -1300,7 +1322,7 @@ namespace Flux {
            return "flux";
        }

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

@@ -1368,10 +1390,10 @@ namespace Flux {
            ggml_tensor* context   = make_optional_input(context_tensor);
            ggml_tensor* c_concat  = make_optional_input(c_concat_tensor);
            ggml_tensor* y         = make_optional_input(y_tensor);
-            if (flux_params.guidance_embed || flux_params.is_chroma) {
+            if (config.guidance_embed || config.is_chroma) {
                if (!guidance_tensor.empty()) {
                    this->guidance_tensor = guidance_tensor;
-                    if (flux_params.is_chroma) {
+                    if (config.is_chroma) {
                        this->guidance_tensor.fill_(0.f);
                    }
                }
@@ -1389,7 +1411,7 @@ namespace Flux {
            ggml_tensor* mod_index_arange = nullptr;
            ggml_tensor* dct              = nullptr;  // for chroma radiance

-            if (flux_params.is_chroma) {
+            if (config.is_chroma) {
                if (!use_mask) {
                    y = nullptr;
                }
@@ -1406,31 +1428,31 @@ namespace Flux {
            } else if (version == VERSION_OVIS_IMAGE) {
                txt_arange_dims = {1, 2};
            }
-
            pe_vec      = Rope::gen_flux_pe(static_cast<int>(x->ne[1]),
                                            static_cast<int>(x->ne[0]),
-                                            flux_params.patch_size,
+                                            config.patch_size,
                                            static_cast<int>(x->ne[3]),
                                            static_cast<int>(context->ne[1]),
                                            txt_arange_dims,
                                            ref_latents,
                                            increase_ref_index,
-                                            flux_params.ref_index_scale,
-                                            flux_params.theta,
+                                            config.ref_index_scale,
+                                            config.theta,
                                            circular_y_enabled,
                                            circular_x_enabled,
-                                            flux_params.axes_dim);
-            int pos_len = static_cast<int>(pe_vec.size() / flux_params.axes_dim_sum / 2);
+                                            config.axes_dim,
+                                            sd_version_is_longcat(version));
+            int pos_len = static_cast<int>(pe_vec.size() / config.axes_dim_sum / 2);
            // LOG_DEBUG("pos_len %d", pos_len);
-            auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, flux_params.axes_dim_sum / 2, pos_len);
+            auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.axes_dim_sum / 2, pos_len);
            // pe->data = pe_vec.data();
            // print_ggml_tensor(pe);
            // pe->data = nullptr;
            set_backend_tensor_data(pe, pe_vec.data());

            if (version == VERSION_CHROMA_RADIANCE) {
-                int patch_size     = flux_params.patch_size;
-                int nerf_max_freqs = flux_params.chroma_radiance_params.nerf_max_freqs;
+                int patch_size     = config.patch_size;
+                int nerf_max_freqs = config.chroma_radiance_params.nerf_max_freqs;
                dct_vec            = fetch_dct_pos(patch_size, nerf_max_freqs);
                dct                = ggml_new_tensor_2d(compute_ctx, GGML_TYPE_F32, nerf_max_freqs * nerf_max_freqs, patch_size * patch_size);
                // dct->data = dct_vec.data();
@@ -1482,6 +1504,25 @@ namespace Flux {
            return result;
        }

+        sd::Tensor<float> compute(int n_threads,
+                                  const DiffusionParams& diffusion_params) override {
+            GGML_ASSERT(diffusion_params.x != nullptr);
+            GGML_ASSERT(diffusion_params.timesteps != nullptr);
+            const auto* extra = diffusion_extra_as<FluxDiffusionExtra>(diffusion_params);
+            static const std::vector<sd::Tensor<float>> empty_ref_latents;
+            static const std::vector<int> empty_skip_layers;
+            return compute(n_threads,
+                           *diffusion_params.x,
+                           *diffusion_params.timesteps,
+                           tensor_or_empty(diffusion_params.context),
+                           tensor_or_empty(diffusion_params.c_concat),
+                           tensor_or_empty(diffusion_params.y),
+                           tensor_or_empty(extra->guidance),
+                           diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_ref_latents,
+                           diffusion_params.increase_ref_index,
+                           extra->skip_layers ? *extra->skip_layers : empty_skip_layers);
+        }
+
        void test() {
            ggml_init_params params;
            params.mem_size   = static_cast<size_t>(1024 * 1024) * 1024;  // 1GB
@@ -1539,7 +1580,7 @@ namespace Flux {

        static void load_from_file_and_test(const std::string& file_path) {
            // ggml_backend_t backend = ggml_backend_cuda_init(0);
-            ggml_backend_t backend    = ggml_backend_cpu_init();
+            ggml_backend_t backend    = sd_backend_cpu_init();
            ggml_type model_data_type = GGML_TYPE_COUNT;

            ModelLoader model_loader;
@@ -1558,13 +1599,17 @@ namespace Flux {
            }

            std::shared_ptr<FluxRunner> flux = std::make_shared<FluxRunner>(backend,
-                                                                            false,
+                                                                            backend,
                                                                            tensor_storage_map,
                                                                            "model.diffusion_model",
                                                                            VERSION_FLUX2,
                                                                            false);

-            flux->alloc_params_buffer();
+            if (!flux->alloc_params_buffer()) {
+                LOG_ERROR("flux model allocation failed");
+                return;
+            }
+
            std::map<std::string, ggml_tensor*> tensors;
            flux->get_param_tensors(tensors, "model.diffusion_model");

@@ -0,0 +1,656 @@
+#include "ggml_extend_backend.h"
+
+#include <algorithm>
+#include <cctype>
+#include <cstdlib>
+#include <mutex>
+#include <sstream>
+#include <stdexcept>
+#include <vector>
+
+#include "stable-diffusion.h"
+#include "util.h"
+
+static std::string trim_copy(const std::string& value) {
+    size_t begin = 0;
+    while (begin < value.size() && std::isspace(static_cast<unsigned char>(value[begin]))) {
+        ++begin;
+    }
+    size_t end = value.size();
+    while (end > begin && std::isspace(static_cast<unsigned char>(value[end - 1]))) {
+        --end;
+    }
+    return value.substr(begin, end - begin);
+}
+
+static std::string lower_copy(std::string value) {
+    std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c) {
+        return static_cast<char>(std::tolower(c));
+    });
+    return value;
+}
+
+static std::vector<std::string> split_copy(const std::string& value, char delimiter) {
+    std::vector<std::string> parts;
+    std::string part;
+    std::istringstream stream(value);
+    while (std::getline(stream, part, delimiter)) {
+        parts.push_back(part);
+    }
+    return parts;
+}
+
+static bool is_default_backend_token(const std::string& name) {
+    const std::string lower = lower_copy(trim_copy(name));
+    return lower.empty() || lower == "default" || lower == "auto";
+}
+
+static bool parse_backend_module(const std::string& raw_name, SDBackendModule* module) {
+    std::string name = lower_copy(trim_copy(raw_name));
+    name.erase(std::remove(name.begin(), name.end(), '-'), name.end());
+    name.erase(std::remove(name.begin(), name.end(), '_'), name.end());
+
+    if (name == "diffusion" || name == "model" || name == "unet" || name == "dit") {
+        *module = SDBackendModule::DIFFUSION;
+        return true;
+    }
+    if (name == "te" || name == "clip" || name == "text" || name == "textencoder" || name == "textencoders" || name == "conditioner" || name == "cond" || name == "llm" || name == "t5" || name == "t5xxl") {
+        *module = SDBackendModule::TE;
+        return true;
+    }
+    if (name == "clipvision" || name == "vision") {
+        *module = SDBackendModule::CLIP_VISION;
+        return true;
+    }
+    if (name == "vae" || name == "firststage" || name == "autoencoder" || name == "tae") {
+        *module = SDBackendModule::VAE;
+        return true;
+    }
+    if (name == "controlnet" || name == "control") {
+        *module = SDBackendModule::CONTROL_NET;
+        return true;
+    }
+    if (name == "photomaker" || name == "photomakerid" || name == "pmid" || name == "photo") {
+        *module = SDBackendModule::PHOTOMAKER;
+        return true;
+    }
+    if (name == "upscaler" || name == "esrgan" || name == "hires") {
+        *module = SDBackendModule::UPSCALER;
+        return true;
+    }
+    return false;
+}
+
+static std::string module_assignment_name(const SDBackendAssignment& assignment, SDBackendModule module) {
+    auto it = assignment.module_names.find(module);
+    if (it != assignment.module_names.end()) {
+        return it->second;
+    }
+    return assignment.default_name;
+}
+
+static std::string backend_cache_key(ggml_backend_t backend) {
+    if (backend == nullptr) {
+        return "";
+    }
+    ggml_backend_dev_t dev = ggml_backend_get_device(backend);
+    if (dev != nullptr) {
+        return lower_copy(ggml_backend_dev_name(dev));
+    }
+    const char* backend_name = ggml_backend_name(backend);
+    return backend_name != nullptr ? lower_copy(backend_name) : "";
+}
+
+static std::string resolve_first_device_by_type(enum ggml_backend_dev_type type) {
+    ggml_backend_dev_t dev = ggml_backend_dev_by_type(type);
+    if (dev == nullptr) {
+        return "";
+    }
+    return ggml_backend_dev_name(dev);
+}
+
+static ggml_backend_buffer_t ggml_backend_tensor_buffer(const struct ggml_tensor* tensor) {
+    if (tensor == nullptr) {
+        return nullptr;
+    }
+
+    return tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+}
+
+static bool ggml_backend_tensor_is_host_accessible(const struct ggml_tensor* tensor) {
+    if (tensor == nullptr || tensor->data == nullptr) {
+        return false;
+    }
+
+    ggml_backend_buffer_t buffer = ggml_backend_tensor_buffer(tensor);
+    return buffer == nullptr || ggml_backend_buffer_is_host(buffer);
+}
+
+static size_t ggml_backend_tensor_offset(const struct ggml_tensor* tensor, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
+    return static_cast<size_t>(i0 * tensor->nb[0] + i1 * tensor->nb[1] + i2 * tensor->nb[2] + i3 * tensor->nb[3]);
+}
+
+template <typename T>
+static void ggml_backend_tensor_write_scalar(const struct ggml_tensor* tensor, int64_t i0, int64_t i1, int64_t i2, int64_t i3, T value) {
+    const size_t offset = ggml_backend_tensor_offset(tensor, i0, i1, i2, i3);
+
+    if (ggml_backend_tensor_is_host_accessible(tensor)) {
+        auto* dst = reinterpret_cast<T*>(reinterpret_cast<char*>(tensor->data) + offset);
+        *dst      = value;
+        return;
+    }
+
+    ggml_backend_tensor_set(const_cast<struct ggml_tensor*>(tensor), &value, offset, sizeof(T));
+}
+
+static void ggml_set_f32_nd(const struct ggml_tensor* tensor, int64_t i0, int64_t i1, int64_t i2, int64_t i3, float value) {
+    switch (tensor->type) {
+        case GGML_TYPE_I8:
+            ggml_backend_tensor_write_scalar(tensor, i0, i1, i2, i3, static_cast<int8_t>(value));
+            break;
+        case GGML_TYPE_I16:
+            ggml_backend_tensor_write_scalar(tensor, i0, i1, i2, i3, static_cast<int16_t>(value));
+            break;
+        case GGML_TYPE_I32:
+            ggml_backend_tensor_write_scalar(tensor, i0, i1, i2, i3, static_cast<int32_t>(value));
+            break;
+        case GGML_TYPE_F16:
+            ggml_backend_tensor_write_scalar(tensor, i0, i1, i2, i3, ggml_fp32_to_fp16(value));
+            break;
+        case GGML_TYPE_BF16:
+            ggml_backend_tensor_write_scalar(tensor, i0, i1, i2, i3, ggml_fp32_to_bf16(value));
+            break;
+        case GGML_TYPE_F32:
+            ggml_backend_tensor_write_scalar(tensor, i0, i1, i2, i3, value);
+            break;
+        default:
+            GGML_ABORT("fatal error");
+    }
+}
+
+void ggml_ext_im_set_f32_1d(const struct ggml_tensor* tensor, int i, float value) {
+    if (!ggml_is_contiguous(tensor)) {
+        int64_t id[4] = {0, 0, 0, 0};
+        ggml_unravel_index(tensor, i, &id[0], &id[1], &id[2], &id[3]);
+        ggml_set_f32_nd(tensor, id[0], id[1], id[2], id[3], value);
+        return;
+    }
+
+    switch (tensor->type) {
+        case GGML_TYPE_I8:
+            ggml_backend_tensor_write_scalar(tensor, i, 0, 0, 0, static_cast<int8_t>(value));
+            break;
+        case GGML_TYPE_I16:
+            ggml_backend_tensor_write_scalar(tensor, i, 0, 0, 0, static_cast<int16_t>(value));
+            break;
+        case GGML_TYPE_I32:
+            ggml_backend_tensor_write_scalar(tensor, i, 0, 0, 0, static_cast<int32_t>(value));
+            break;
+        case GGML_TYPE_F16:
+            ggml_backend_tensor_write_scalar(tensor, i, 0, 0, 0, ggml_fp32_to_fp16(value));
+            break;
+        case GGML_TYPE_BF16:
+            ggml_backend_tensor_write_scalar(tensor, i, 0, 0, 0, ggml_fp32_to_bf16(value));
+            break;
+        case GGML_TYPE_F32:
+            ggml_backend_tensor_write_scalar(tensor, i, 0, 0, 0, value);
+            break;
+        default:
+            GGML_ABORT("fatal error");
+    }
+}
+
+static void ggml_backend_load_all_once() {
+    // If the registry already has devices and the CPU backend is present,
+    // assume either static registration or explicit host-side preloading has
+    // completed and avoid rescanning the default paths.
+    if (ggml_backend_dev_count() > 0 && ggml_backend_reg_by_name("CPU") != nullptr) {
+        return;
+    }
+    // In dynamic-backend mode the backend modules are discovered at runtime,
+    // so we must load them before asking for the CPU backend or its proc table.
+    // If the host preloaded only a subset of backends, allow one default-path
+    // scan so missing modules can still be discovered.
+    static std::once_flag once;
+    std::call_once(once, []() {
+        if (ggml_backend_dev_count() > 0 && ggml_backend_reg_by_name("CPU") != nullptr) {
+            return;
+        }
+        ggml_backend_load_all();
+    });
+}
+
+bool sd_backend_is(ggml_backend_t backend, const std::string& name) {
+    if (!backend) {
+        return false;
+    }
+    ggml_backend_dev_t dev = ggml_backend_get_device(backend);
+    if (!dev) {
+        return false;
+    }
+    std::string dev_name = ggml_backend_dev_name(dev);
+    return lower_copy(dev_name).find(lower_copy(name)) != std::string::npos;
+}
+
+static std::string get_default_backend_name() {
+    ggml_backend_load_all_once();
+    // should pick the same backend preference as ggml_backend_init_best
+    std::string name = resolve_first_device_by_type(GGML_BACKEND_DEVICE_TYPE_GPU);
+    if (!name.empty()) {
+        return name;
+    }
+    name = resolve_first_device_by_type(GGML_BACKEND_DEVICE_TYPE_IGPU);
+    if (!name.empty()) {
+        return name;
+    }
+    return resolve_first_device_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+}
+
+static std::string sd_resolve_backend_name(const std::string& name) {
+    ggml_backend_load_all_once();
+    std::string requested = trim_copy(name);
+    std::string lower     = lower_copy(requested);
+
+    if (is_default_backend_token(lower)) {
+        return get_default_backend_name();
+    }
+    if (lower == "gpu") {
+        std::string result = resolve_first_device_by_type(GGML_BACKEND_DEVICE_TYPE_GPU);
+        if (!result.empty()) {
+            return result;
+        }
+        return resolve_first_device_by_type(GGML_BACKEND_DEVICE_TYPE_IGPU);
+    }
+
+    const size_t device_count = ggml_backend_dev_count();
+    for (size_t i = 0; i < device_count; ++i) {
+        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
+        std::string dev_name   = ggml_backend_dev_name(dev);
+        if (lower_copy(dev_name) == lower) {
+            return dev_name;
+        }
+    }
+
+    for (size_t i = 0; i < device_count; ++i) {
+        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
+        std::string dev_name   = ggml_backend_dev_name(dev);
+        std::string dev_lower  = lower_copy(dev_name);
+        if (dev_lower.rfind(lower, 0) == 0) {
+            return dev_name;
+        }
+    }
+
+    return "";
+}
+
+static bool backend_name_exists(const std::string& name) {
+    return !sd_resolve_backend_name(name).empty();
+}
+
+static ggml_backend_t init_named_backend(const std::string& name) {
+    ggml_backend_load_all_once();
+    LOG_DEBUG("Initializing backend: %s", name.c_str());
+    if (trim_copy(name).empty()) {
+        return ggml_backend_init_best();
+    }
+
+    std::string resolved = sd_resolve_backend_name(name);
+    if (resolved.empty()) {
+        return nullptr;
+    }
+    return ggml_backend_init_by_name(resolved.c_str(), nullptr);
+}
+
+bool sd_backend_is_cpu(ggml_backend_t backend) {
+    if (backend == nullptr) {
+        return false;
+    }
+    auto dev = ggml_backend_get_device(backend);
+    return dev != nullptr && ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU;
+}
+
+ggml_backend_t sd_backend_cpu_init() {
+    ggml_backend_load_all_once();
+    return ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
+}
+
+bool sd_backend_cpu_set_n_threads(ggml_backend_t backend, int n_threads) {
+    if (backend == nullptr) {
+        return false;
+    }
+    auto dev = ggml_backend_get_device(backend);
+    if (dev != nullptr && ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
+        auto reg                           = ggml_backend_dev_backend_reg(dev);
+        auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t)ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads");
+        if (ggml_backend_set_n_threads_fn != nullptr) {
+            ggml_backend_set_n_threads_fn(backend, n_threads);
+            return true;
+        }
+    }
+    return false;
+}
+
+const char* sd_get_system_info() {
+    static std::string cache_info = []() -> std::string {
+        ggml_backend_load_all_once();
+        std::stringstream ss;
+        ss << "System Info: \n";
+        auto dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+        if (dev != nullptr) {
+            auto reg                          = ggml_backend_dev_backend_reg(dev);
+            auto ggml_backend_get_features_fn = (ggml_backend_get_features_t)ggml_backend_reg_get_proc_address(reg, "ggml_backend_get_features");
+            if (ggml_backend_get_features_fn != nullptr) {
+                ggml_backend_feature* feat = ggml_backend_get_features_fn(reg);
+                while (feat->name && feat->value) {
+                    ss << "   " << feat->name << " = " << feat->value << " | ";
+                    feat++;
+                }
+            } else {
+                LOG_WARN("unable to get CPU features");
+            }
+        } else {
+            LOG_WARN("unable to get CPU features");
+        }
+        return ss.str();
+    }();
+    return cache_info.c_str();
+}
+
+static ggml_backend_t sd_get_default_backend() {
+    ggml_backend_load_all_once();
+    static std::once_flag once;
+    std::call_once(once, []() {
+        size_t dev_count = ggml_backend_dev_count();
+        if (dev_count == 0) {
+            LOG_ERROR("No devices found!");
+        } else {
+            LOG_DEBUG("Found %zu backend devices:", dev_count);
+            for (size_t i = 0; i < dev_count; ++i) {
+                auto dev = ggml_backend_dev_get(i);
+                LOG_DEBUG("#%zu: %s", i, ggml_backend_dev_name(dev));
+            }
+        }
+    });
+
+    ggml_backend_t backend   = nullptr;
+    const char* SD_VK_DEVICE = getenv("SD_VK_DEVICE");
+    if (SD_VK_DEVICE != nullptr) {
+        std::string sd_vk_device_str = SD_VK_DEVICE;
+        try {
+            unsigned long long device  = std::stoull(sd_vk_device_str);
+            std::string vk_device_name = "Vulkan" + std::to_string(device);
+            if (backend_name_exists(vk_device_name)) {
+                LOG_INFO("Selecting %s as main device by env var SD_VK_DEVICE", vk_device_name.c_str());
+                backend = init_named_backend(vk_device_name);
+                if (!backend) {
+                    LOG_WARN("Device %s requested by SD_VK_DEVICE failed to init. Falling back to the default device.", vk_device_name.c_str());
+                }
+            } else {
+                LOG_WARN("Device %s requested by SD_VK_DEVICE was not found. Falling back to the default device.", vk_device_name.c_str());
+            }
+        } catch (const std::invalid_argument&) {
+            LOG_WARN("SD_VK_DEVICE environment variable is not a valid integer (%s). Falling back to the default device.", SD_VK_DEVICE);
+        } catch (const std::out_of_range&) {
+            LOG_WARN("SD_VK_DEVICE environment variable value is out of range for `unsigned long long` type (%s). Falling back to the default device.", SD_VK_DEVICE);
+        }
+    }
+
+    if (!backend) {
+        std::string dev_name = get_default_backend_name();
+        backend              = init_named_backend(dev_name);
+        if (!backend && !dev_name.empty()) {
+            LOG_WARN("device %s failed to init", dev_name.c_str());
+        }
+    }
+
+    if (!backend) {
+        LOG_WARN("loading CPU backend");
+        backend = sd_backend_cpu_init();
+    }
+
+    if (sd_backend_is_cpu(backend)) {
+        LOG_DEBUG("Using CPU backend");
+    }
+
+    return backend;
+}
+
+static bool sd_parse_backend_assignment(const std::string& spec, SDBackendAssignment* assignment, std::string* error) {
+    if (assignment == nullptr) {
+        return false;
+    }
+
+    *assignment          = {};
+    const std::string in = trim_copy(spec);
+    if (in.empty()) {
+        return true;
+    }
+
+    for (const std::string& raw_part : split_copy(in, ',')) {
+        const std::string part = trim_copy(raw_part);
+        if (part.empty()) {
+            continue;
+        }
+
+        const size_t eq = part.find('=');
+        if (eq == std::string::npos) {
+            assignment->set_default(part);
+            continue;
+        }
+
+        const std::string key   = trim_copy(part.substr(0, eq));
+        const std::string value = trim_copy(part.substr(eq + 1));
+        if (key.empty() || value.empty()) {
+            if (error != nullptr) {
+                *error = "invalid backend assignment '" + part + "'";
+            }
+            return false;
+        }
+
+        const std::string key_lower = lower_copy(key);
+        if (key_lower == "all" || key_lower == "default" || key_lower == "*") {
+            assignment->set_default(value);
+            continue;
+        }
+
+        SDBackendModule module = SDBackendModule::DIFFUSION;
+        if (!parse_backend_module(key, &module)) {
+            if (error != nullptr) {
+                *error = "unknown backend module '" + key + "'";
+            }
+            return false;
+        }
+        assignment->set_module(module, value);
+    }
+    return true;
+}
+
+bool SDBackendAssignment::empty() const {
+    return default_name.empty() && module_names.empty();
+}
+
+std::string SDBackendAssignment::get(SDBackendModule module) const {
+    return module_assignment_name(*this, module);
+}
+
+void SDBackendAssignment::set_default(const std::string& name) {
+    default_name = trim_copy(name);
+}
+
+void SDBackendAssignment::set_module(SDBackendModule module, const std::string& name) {
+    module_names[module] = trim_copy(name);
+}
+
+void SDBackendHandleDeleter::operator()(ggml_backend_t backend) const {
+    ggml_backend_free(backend);
+}
+
+SDBackendManager::~SDBackendManager() {
+    reset();
+}
+
+void SDBackendManager::reset() {
+    backends_.clear();
+    runtime_assignment_ = {};
+    params_assignment_  = {};
+}
+
+ggml_backend_t SDBackendManager::runtime_backend(SDBackendModule module) {
+    return init_cached_backend(runtime_assignment_.get(module));
+}
+
+ggml_backend_t SDBackendManager::params_backend(SDBackendModule module) {
+    std::string name = params_assignment_.get(module);
+    if (name.empty()) {
+        return runtime_backend(module);
+    }
+    return init_cached_backend(name);
+}
+
+bool SDBackendManager::runtime_backend_is_cpu(SDBackendModule module) {
+    return sd_backend_is_cpu(runtime_backend(module));
+}
+
+bool SDBackendManager::params_backend_is_cpu(SDBackendModule module) {
+    return sd_backend_is_cpu(params_backend(module));
+}
+
+bool SDBackendManager::runtime_backend_supports_host_buffer(SDBackendModule module) {
+    ggml_backend_t backend = runtime_backend(module);
+    if (backend == nullptr) {
+        return false;
+    }
+    if (sd_backend_is_cpu(backend)) {
+        return true;
+    }
+    ggml_backend_dev_t dev = ggml_backend_get_device(backend);
+    if (dev == nullptr) {
+        return false;
+    }
+    ggml_backend_dev_props props;
+    ggml_backend_dev_get_props(dev, &props);
+    return props.caps.buffer_from_host_ptr;
+}
+
+bool SDBackendManager::init(const char* backend_spec,
+                            const char* params_backend_spec,
+                            bool offload_params_to_cpu,
+                            bool keep_clip_on_cpu,
+                            bool keep_vae_on_cpu,
+                            bool keep_control_net_on_cpu,
+                            std::string* error) {
+    reset();
+
+    if (!sd_parse_backend_assignment(SAFE_STR(backend_spec), &runtime_assignment_, error)) {
+        return false;
+    }
+    if (!sd_parse_backend_assignment(SAFE_STR(params_backend_spec), &params_assignment_, error)) {
+        return false;
+    }
+
+    if (runtime_assignment_.empty()) {
+        if (keep_clip_on_cpu) {
+            runtime_assignment_.set_module(SDBackendModule::TE, "cpu");
+        }
+        if (keep_vae_on_cpu) {
+            runtime_assignment_.set_module(SDBackendModule::VAE, "cpu");
+        }
+        if (keep_control_net_on_cpu) {
+            runtime_assignment_.set_module(SDBackendModule::CONTROL_NET, "cpu");
+        }
+    }
+
+    if (params_assignment_.empty() && offload_params_to_cpu) {
+        params_assignment_.set_default("cpu");
+    }
+
+    return validate(error);
+}
+
+bool SDBackendManager::validate(std::string* error) const {
+    auto validate_name = [&](const std::string& name) -> bool {
+        if (is_default_backend_token(name)) {
+            return true;
+        }
+        if (!sd_resolve_backend_name(name).empty()) {
+            return true;
+        }
+        if (error != nullptr) {
+            *error = "backend '" + name + "' was not found";
+        }
+        return false;
+    };
+
+    if (!validate_name(runtime_assignment_.default_name) ||
+        !validate_name(params_assignment_.default_name)) {
+        return false;
+    }
+    for (const auto& kv : runtime_assignment_.module_names) {
+        if (!validate_name(kv.second)) {
+            return false;
+        }
+    }
+    for (const auto& kv : params_assignment_.module_names) {
+        if (!validate_name(kv.second)) {
+            return false;
+        }
+    }
+    return true;
+}
+
+ggml_backend_t SDBackendManager::init_cached_backend(const std::string& name) {
+    std::string resolved   = sd_resolve_backend_name(name);
+    std::string key        = lower_copy(resolved);
+    ggml_backend_t backend = nullptr;
+
+    if (!key.empty()) {
+        auto it = backends_.find(key);
+        if (it != backends_.end()) {
+            return it->second.get();
+        }
+    } else if (!is_default_backend_token(name)) {
+        LOG_ERROR("backend '%s' was not found", name.c_str());
+        return nullptr;
+    }
+
+    backend = is_default_backend_token(name) ? sd_get_default_backend() : init_named_backend(resolved);
+    if (backend == nullptr) {
+        LOG_ERROR("failed to initialize backend '%s'", name.c_str());
+        return nullptr;
+    }
+
+    std::string actual_key = backend_cache_key(backend);
+    if (actual_key.empty()) {
+        actual_key = !key.empty() ? key : lower_copy(trim_copy(name));
+    }
+
+    auto it = backends_.find(actual_key);
+    if (it != backends_.end()) {
+        ggml_backend_free(backend);
+        return it->second.get();
+    }
+
+    SDBackendHandle handle(backend);
+    backends_.emplace(actual_key, std::move(handle));
+    return backend;
+}
+
+const char* sd_backend_module_name(SDBackendModule module) {
+    switch (module) {
+        case SDBackendModule::DIFFUSION:
+            return "diffusion";
+        case SDBackendModule::TE:
+            return "te";
+        case SDBackendModule::CLIP_VISION:
+            return "clip_vision";
+        case SDBackendModule::VAE:
+            return "vae";
+        case SDBackendModule::CONTROL_NET:
+            return "controlnet";
+        case SDBackendModule::PHOTOMAKER:
+            return "photomaker";
+        case SDBackendModule::UPSCALER:
+            return "upscaler";
+    }
+    return "unknown";
+}
@@ -0,0 +1,79 @@
+#ifndef __SD_GGML_EXTEND_BACKEND_H__
+#define __SD_GGML_EXTEND_BACKEND_H__
+
+#include <cstdint>
+#include <cstring>
+#include <memory>
+#include <string>
+#include <unordered_map>
+
+#include "ggml-backend.h"
+#include "ggml.h"
+
+enum class SDBackendModule {
+    DIFFUSION,
+    TE,
+    CLIP_VISION,
+    VAE,
+    CONTROL_NET,
+    PHOTOMAKER,
+    UPSCALER,
+};
+
+struct SDBackendAssignment {
+    std::string default_name;
+    std::unordered_map<SDBackendModule, std::string> module_names;
+
+    bool empty() const;
+    std::string get(SDBackendModule module) const;
+    void set_default(const std::string& name);
+    void set_module(SDBackendModule module, const std::string& name);
+};
+
+struct SDBackendHandleDeleter {
+    void operator()(ggml_backend_t backend) const;
+};
+
+using SDBackendHandle = std::unique_ptr<struct ggml_backend, SDBackendHandleDeleter>;
+
+class SDBackendManager {
+private:
+    SDBackendAssignment runtime_assignment_;
+    SDBackendAssignment params_assignment_;
+    std::unordered_map<std::string, SDBackendHandle> backends_;
+
+public:
+    SDBackendManager() = default;
+    ~SDBackendManager();
+
+    SDBackendManager(const SDBackendManager&)            = delete;
+    SDBackendManager& operator=(const SDBackendManager&) = delete;
+
+    bool init(const char* backend_spec,
+              const char* params_backend_spec,
+              bool offload_params_to_cpu,
+              bool keep_clip_on_cpu,
+              bool keep_vae_on_cpu,
+              bool keep_control_net_on_cpu,
+              std::string* error);
+    void reset();
+
+    ggml_backend_t runtime_backend(SDBackendModule module);
+    ggml_backend_t params_backend(SDBackendModule module);
+
+    bool runtime_backend_is_cpu(SDBackendModule module);
+    bool params_backend_is_cpu(SDBackendModule module);
+    bool runtime_backend_supports_host_buffer(SDBackendModule module);
+
+private:
+    bool validate(std::string* error) const;
+    ggml_backend_t init_cached_backend(const std::string& name);
+};
+
+bool sd_backend_is(ggml_backend_t backend, const std::string& name);
+bool sd_backend_is_cpu(ggml_backend_t backend);
+ggml_backend_t sd_backend_cpu_init();
+bool sd_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
+const char* sd_backend_module_name(SDBackendModule module);
+void ggml_ext_im_set_f32_1d(const struct ggml_tensor* tensor, int i, float value);
+#endif
@@ -0,0 +1,806 @@
+#include "ggml_graph_cut.h"
+
+#include <algorithm>
+#include <cstring>
+#include <map>
+#include <set>
+#include <sstream>
+#include <stack>
+#include <unordered_map>
+
+#include "ggml-alloc.h"
+#include "ggml-backend.h"
+#include "util.h"
+
+#include "../ggml/src/ggml-impl.h"
+
+namespace sd::ggml_graph_cut {
+
+    static constexpr double MAX_VRAM_BYTES_PER_GIB = 1024.0 * 1024.0 * 1024.0;
+
+    static std::string graph_cut_tensor_display_name(const ggml_tensor* tensor) {
+        if (tensor == nullptr) {
+            return "<null>";
+        }
+        if (tensor->name[0] != '\0') {
+            return tensor->name;
+        }
+        return sd_format("<tensor@%p>", (const void*)tensor);
+    }
+
+    static int graph_leaf_index(ggml_cgraph* gf, const ggml_tensor* tensor) {
+        GGML_ASSERT(gf != nullptr);
+        GGML_ASSERT(tensor != nullptr);
+        for (int i = 0; i < gf->n_leafs; ++i) {
+            if (gf->leafs[i] == tensor) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+    static bool is_params_tensor(const std::unordered_set<const ggml_tensor*>& params_tensor_set,
+                                 const ggml_tensor* tensor) {
+        if (tensor == nullptr) {
+            return false;
+        }
+        return params_tensor_set.find(tensor) != params_tensor_set.end();
+    }
+
+    static int graph_node_index_by_name(ggml_cgraph* gf, const char* name) {
+        GGML_ASSERT(gf != nullptr);
+        if (name == nullptr || name[0] == '\0') {
+            return -1;
+        }
+        const int n_nodes = ggml_graph_n_nodes(gf);
+        for (int i = 0; i < n_nodes; ++i) {
+            ggml_tensor* node = ggml_graph_node(gf, i);
+            if (node != nullptr && std::strcmp(node->name, name) == 0) {
+                return i;
+            }
+        }
+        return -1;
+    }
+
+    static Plan::InputShape input_shape(const ggml_tensor* tensor) {
+        Plan::InputShape shape;
+        if (tensor == nullptr) {
+            return shape;
+        }
+        shape.type = tensor->type;
+        for (int i = 0; i < GGML_MAX_DIMS; ++i) {
+            shape.ne[static_cast<size_t>(i)] = tensor->ne[i];
+        }
+        return shape;
+    }
+
+    static size_t graph_cut_segment_vram_bytes(const Segment& segment) {
+        return segment.compute_buffer_size +
+               segment.input_param_bytes +
+               segment.input_previous_cut_bytes +
+               segment.output_bytes;
+    }
+
+    size_t max_vram_gib_to_bytes(float max_vram) {
+        if (max_vram <= 0.f) {
+            return 0;
+        }
+        return static_cast<size_t>(static_cast<double>(max_vram) * MAX_VRAM_BYTES_PER_GIB);
+    }
+
+    static float max_vram_bytes_to_gib(size_t max_vram_bytes) {
+        return static_cast<float>(static_cast<double>(max_vram_bytes) / MAX_VRAM_BYTES_PER_GIB);
+    }
+
+    static size_t resolve_auto_max_vram_bytes(float spare_vram, ggml_backend_t backend) {
+        if (backend == nullptr) {
+            LOG_WARN("--max-vram < 0 requested, but no backend is available; disabling graph splitting");
+            return 0;
+        }
+
+        ggml_backend_dev_t dev = ggml_backend_get_device(backend);
+        if (dev == nullptr) {
+            LOG_WARN("--max-vram < 0 requested, but no backend device is available; disabling graph splitting");
+            return 0;
+        }
+        if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
+            LOG_WARN("--max-vram < 0 requested, but the main backend is CPU; disabling graph splitting");
+            return 0;
+        }
+
+        size_t free_vram  = 0;
+        size_t total_vram = 0;
+        ggml_backend_dev_memory(dev, &free_vram, &total_vram);
+        size_t spare_bytes = static_cast<size_t>(MAX_VRAM_BYTES_PER_GIB * spare_vram);
+
+        if (free_vram <= spare_bytes) {
+            LOG_WARN("--max-vram < 0 requested, but free VRAM is %.2f GiB; reserving %.2f GiB leaves no graph budget",
+                     free_vram / MAX_VRAM_BYTES_PER_GIB, spare_vram);
+            return 0;
+        }
+
+        const size_t max_vram_bytes = free_vram - spare_bytes;
+        LOG_INFO("--max-vram < 0 auto-detected %.2f GiB free VRAM (%.2f GiB total), reserving %.2f GiB; using %.2f GiB",
+                 free_vram / MAX_VRAM_BYTES_PER_GIB,
+                 total_vram / MAX_VRAM_BYTES_PER_GIB,
+                 spare_vram,
+                 max_vram_bytes / MAX_VRAM_BYTES_PER_GIB);
+        return max_vram_bytes;
+    }
+
+    float resolve_max_vram_gib(float max_vram, ggml_backend_t backend) {
+        if (max_vram >= 0.f) {
+            return max_vram;
+        }
+        return max_vram_bytes_to_gib(resolve_auto_max_vram_bytes(-max_vram, backend));
+    }
+
+    static Segment make_segment_seed(const Plan& plan,
+                                     size_t start_segment_index,
+                                     size_t end_segment_index) {
+        GGML_ASSERT(start_segment_index < plan.segments.size());
+        GGML_ASSERT(end_segment_index < plan.segments.size());
+        GGML_ASSERT(start_segment_index <= end_segment_index);
+
+        Segment seed;
+        const auto& start_segment  = plan.segments[start_segment_index];
+        const auto& target_segment = plan.segments[end_segment_index];
+        std::unordered_set<int> seen_output_node_indices;
+        for (size_t seg_idx = start_segment_index; seg_idx <= end_segment_index; ++seg_idx) {
+            for (int output_node_index : plan.segments[seg_idx].output_node_indices) {
+                if (seen_output_node_indices.insert(output_node_index).second) {
+                    seed.output_node_indices.push_back(output_node_index);
+                }
+            }
+        }
+        if (start_segment_index == end_segment_index) {
+            seed.group_name = target_segment.group_name;
+        } else {
+            seed.group_name = sd_format("%s..%s",
+                                        start_segment.group_name.c_str(),
+                                        target_segment.group_name.c_str());
+        }
+        return seed;
+    }
+
+    static void build_segment(ggml_cgraph* gf,
+                              Plan& plan,
+                              Segment& segment,
+                              const std::unordered_map<const ggml_tensor*, int>& producer_index,
+                              std::unordered_set<int>& available_cut_output_node_indices,
+                              ggml_backend_t backend,
+                              const std::unordered_set<const ggml_tensor*>& params_tensor_set,
+                              const char* log_desc) {
+        std::set<int> internal_nodes;
+        std::unordered_set<const ggml_tensor*> input_seen;
+        std::vector<Segment::InputRef> input_refs;
+
+        std::stack<ggml_tensor*> work_stack;
+        for (int output_node_index : segment.output_node_indices) {
+            ggml_tensor* output = ggml_graph_node(gf, output_node_index);
+            if (output != nullptr) {
+                work_stack.push(output);
+            }
+        }
+
+        while (!work_stack.empty()) {
+            ggml_tensor* tensor = work_stack.top();
+            work_stack.pop();
+
+            if (tensor == nullptr) {
+                continue;
+            }
+
+            auto producer_it = producer_index.find(tensor);
+            if (producer_it == producer_index.end()) {
+                if (input_seen.insert(tensor).second) {
+                    Segment::InputRef input_ref;
+                    input_ref.type         = is_params_tensor(params_tensor_set, tensor) ? Segment::INPUT_PARAM : Segment::INPUT_EXTERNAL;
+                    input_ref.display_name = graph_cut_tensor_display_name(tensor);
+                    input_ref.leaf_index   = graph_leaf_index(gf, tensor);
+                    input_refs.push_back(std::move(input_ref));
+                }
+                continue;
+            }
+
+            int node_idx = producer_it->second;
+            if (available_cut_output_node_indices.find(node_idx) != available_cut_output_node_indices.end()) {
+                if (input_seen.insert(tensor).second) {
+                    Segment::InputRef input_ref;
+                    input_ref.type         = Segment::INPUT_PREVIOUS_CUT;
+                    input_ref.display_name = graph_cut_tensor_display_name(tensor);
+                    input_ref.node_index   = node_idx;
+                    input_refs.push_back(std::move(input_ref));
+                }
+                continue;
+            }
+
+            if (!internal_nodes.insert(node_idx).second) {
+                continue;
+            }
+
+            ggml_tensor* node = ggml_graph_node(gf, node_idx);
+            for (int src_idx = 0; src_idx < GGML_MAX_SRC; ++src_idx) {
+                if (node->src[src_idx] != nullptr) {
+                    work_stack.push(node->src[src_idx]);
+                }
+            }
+        }
+
+        if (!internal_nodes.empty()) {
+            segment.internal_node_indices.assign(internal_nodes.begin(), internal_nodes.end());
+        }
+
+        std::sort(input_refs.begin(),
+                  input_refs.end(),
+                  [](const Segment::InputRef& a, const Segment::InputRef& b) {
+                      if (a.type != b.type) {
+                          return a.type < b.type;
+                      }
+                      return a.display_name < b.display_name;
+                  });
+        segment.input_refs = input_refs;
+        for (const auto& input : input_refs) {
+            ggml_tensor* current_input = input_tensor(gf, input);
+            size_t tensor_bytes        = current_input == nullptr
+                                             ? 0
+                                             : (input.type == Segment::INPUT_PREVIOUS_CUT
+                                                    ? cache_tensor_bytes(current_input)
+                                                    : ggml_nbytes(current_input));
+            switch (input.type) {
+                case Segment::INPUT_PREVIOUS_CUT:
+                    segment.input_previous_cut_bytes += tensor_bytes;
+                    break;
+                case Segment::INPUT_PARAM:
+                    segment.input_param_bytes += tensor_bytes;
+                    break;
+                case Segment::INPUT_EXTERNAL:
+                default:
+                    segment.input_external_bytes += tensor_bytes;
+                    break;
+            }
+        }
+        for (int output_node_index : segment.output_node_indices) {
+            ggml_tensor* output = ggml_graph_node(gf, output_node_index);
+            segment.output_bytes += cache_tensor_bytes(output);
+        }
+        segment.compute_buffer_size = measure_segment_compute_buffer(backend, gf, segment, log_desc);
+
+        for (int output_node_index : segment.output_node_indices) {
+            available_cut_output_node_indices.insert(output_node_index);
+        }
+        plan.segments.push_back(std::move(segment));
+    }
+
+    bool is_graph_cut_tensor(const ggml_tensor* tensor) {
+        if (tensor == nullptr || tensor->name[0] == '\0') {
+            return false;
+        }
+        return std::strncmp(tensor->name, GGML_RUNNER_CUT_PREFIX, std::strlen(GGML_RUNNER_CUT_PREFIX)) == 0;
+    }
+
+    std::string make_graph_cut_name(const std::string& group, const std::string& output) {
+        return std::string(GGML_RUNNER_CUT_PREFIX) + group + "|" + output;
+    }
+
+    void mark_graph_cut(ggml_tensor* tensor, const std::string& group, const std::string& output) {
+        if (tensor == nullptr) {
+            return;
+        }
+        auto name = make_graph_cut_name(group, output);
+        ggml_set_name(tensor, name.c_str());
+    }
+
+    int leaf_count(ggml_cgraph* gf) {
+        GGML_ASSERT(gf != nullptr);
+        return gf->n_leafs;
+    }
+
+    ggml_tensor* leaf_tensor(ggml_cgraph* gf, int leaf_index) {
+        GGML_ASSERT(gf != nullptr);
+        if (leaf_index < 0 || leaf_index >= gf->n_leafs) {
+            return nullptr;
+        }
+        return gf->leafs[leaf_index];
+    }
+
+    ggml_backend_buffer_t tensor_buffer(const ggml_tensor* tensor) {
+        if (tensor == nullptr) {
+            return nullptr;
+        }
+        return tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+    }
+
+    ggml_tensor* cache_source_tensor(ggml_tensor* tensor) {
+        if (tensor == nullptr) {
+            return nullptr;
+        }
+        if (tensor_buffer(tensor) == nullptr && tensor->src[0] != nullptr &&
+            ggml_nelements(tensor->src[0]) == ggml_nelements(tensor) &&
+            ggml_nbytes(tensor->src[0]) == ggml_nbytes(tensor)) {
+            return cache_source_tensor(tensor->src[0]);
+        }
+        return tensor->view_src ? tensor->view_src : tensor;
+    }
+
+    size_t cache_tensor_bytes(const ggml_tensor* tensor) {
+        if (tensor == nullptr) {
+            return 0;
+        }
+        const ggml_tensor* cache_src = tensor->view_src ? tensor->view_src : tensor;
+        return ggml_nbytes(cache_src);
+    }
+
+    bool plan_matches_graph(ggml_cgraph* gf, const Plan& plan) {
+        GGML_ASSERT(gf != nullptr);
+        if (ggml_graph_n_nodes(gf) != plan.n_nodes || gf->n_leafs != plan.n_leafs) {
+            return false;
+        }
+        for (const auto& input_shape_ref : plan.input_shapes) {
+            if (input_shape_ref.leaf_index < 0 || input_shape_ref.leaf_index >= gf->n_leafs) {
+                return false;
+            }
+            ggml_tensor* leaf = gf->leafs[input_shape_ref.leaf_index];
+            if (leaf == nullptr || input_shape_ref.type != leaf->type) {
+                return false;
+            }
+            for (int d = 0; d < GGML_MAX_DIMS; ++d) {
+                if (input_shape_ref.ne[static_cast<size_t>(d)] != leaf->ne[d]) {
+                    return false;
+                }
+            }
+        }
+        return true;
+    }
+
+    ggml_tensor* output_tensor(ggml_cgraph* gf, const Segment& segment, size_t output_index) {
+        GGML_ASSERT(gf != nullptr);
+        if (output_index >= segment.output_node_indices.size()) {
+            return nullptr;
+        }
+        int node_index = segment.output_node_indices[output_index];
+        if (node_index < 0 || node_index >= ggml_graph_n_nodes(gf)) {
+            return nullptr;
+        }
+        return ggml_graph_node(gf, node_index);
+    }
+
+    ggml_tensor* input_tensor(ggml_cgraph* gf, const Segment::InputRef& input_ref) {
+        GGML_ASSERT(gf != nullptr);
+        if (input_ref.type == Segment::INPUT_PREVIOUS_CUT) {
+            if (input_ref.node_index < 0 || input_ref.node_index >= ggml_graph_n_nodes(gf)) {
+                return nullptr;
+            }
+            return ggml_graph_node(gf, input_ref.node_index);
+        }
+        if (input_ref.leaf_index < 0 || input_ref.leaf_index >= gf->n_leafs) {
+            return nullptr;
+        }
+        return leaf_tensor(gf, input_ref.leaf_index);
+    }
+
+    std::vector<ggml_tensor*> param_tensors(ggml_cgraph* gf, const Segment& segment) {
+        GGML_ASSERT(gf != nullptr);
+        std::vector<ggml_tensor*> tensors;
+        std::unordered_set<ggml_tensor*> seen_tensors;
+        tensors.reserve(segment.input_refs.size());
+        seen_tensors.reserve(segment.input_refs.size());
+        for (const auto& input_ref : segment.input_refs) {
+            if (input_ref.type != Segment::INPUT_PARAM) {
+                continue;
+            }
+            ggml_tensor* tensor = input_tensor(gf, input_ref);
+            if (tensor == nullptr) {
+                continue;
+            }
+            if (seen_tensors.insert(tensor).second) {
+                tensors.push_back(tensor);
+            }
+        }
+        return tensors;
+    }
+
+    std::vector<ggml_tensor*> runtime_param_tensors(ggml_cgraph* gf, const Segment& segment, const char* log_desc) {
+        std::vector<ggml_tensor*> tensors = param_tensors(gf, segment);
+        std::vector<ggml_tensor*> filtered_tensors;
+        filtered_tensors.reserve(tensors.size());
+        for (ggml_tensor* tensor : tensors) {
+            if (tensor_buffer(tensor) == nullptr) {
+                LOG_WARN("%s graph cut skipping param input without buffer: segment=%s tensor=%s",
+                         log_desc == nullptr ? "unknown" : log_desc,
+                         segment.group_name.c_str(),
+                         tensor->name);
+                continue;
+            }
+            filtered_tensors.push_back(tensor);
+        }
+        return filtered_tensors;
+    }
+
+    std::unordered_set<std::string> collect_future_input_names(ggml_cgraph* gf,
+                                                               const Plan& plan,
+                                                               size_t current_segment_index) {
+        GGML_ASSERT(gf != nullptr);
+        std::unordered_set<std::string> future_input_names;
+        for (size_t seg_idx = current_segment_index + 1; seg_idx < plan.segments.size(); ++seg_idx) {
+            const auto& segment = plan.segments[seg_idx];
+            for (const auto& input_ref : segment.input_refs) {
+                if (input_ref.type != Segment::INPUT_PREVIOUS_CUT) {
+                    continue;
+                }
+                ggml_tensor* current_input = input_tensor(gf, input_ref);
+                if (current_input != nullptr && current_input->name[0] != '\0') {
+                    future_input_names.insert(current_input->name);
+                }
+            }
+        }
+        return future_input_names;
+    }
+
+    ggml_cgraph* build_segment_graph(ggml_cgraph* gf,
+                                     const Segment& segment,
+                                     ggml_context** graph_ctx_out) {
+        GGML_ASSERT(gf != nullptr);
+        GGML_ASSERT(graph_ctx_out != nullptr);
+
+        const size_t graph_size = segment.internal_node_indices.size() + segment.input_refs.size() + 8;
+        ggml_init_params params = {
+            /*.mem_size   =*/ggml_graph_overhead_custom(graph_size, false) + 1024,
+            /*.mem_buffer =*/nullptr,
+            /*.no_alloc   =*/true,
+        };
+        ggml_context* graph_ctx = ggml_init(params);
+        GGML_ASSERT(graph_ctx != nullptr);
+        ggml_cgraph* segment_graph = ggml_new_graph_custom(graph_ctx, graph_size, false);
+        GGML_ASSERT(segment_graph != nullptr);
+
+        for (const auto& input : segment.input_refs) {
+            ggml_tensor* current_input = input_tensor(gf, input);
+            if (current_input == nullptr) {
+                continue;
+            }
+            GGML_ASSERT(segment_graph->n_leafs < segment_graph->size);
+            segment_graph->leafs[segment_graph->n_leafs++] = current_input;
+        }
+
+        for (int output_node_index : segment.output_node_indices) {
+            ggml_tensor* output = ggml_graph_node(gf, output_node_index);
+            if (output == nullptr) {
+                continue;
+            }
+            ggml_set_output(output);
+        }
+        for (int node_idx : segment.internal_node_indices) {
+            ggml_graph_add_node(segment_graph, ggml_graph_node(gf, node_idx));
+        }
+        *graph_ctx_out = graph_ctx;
+        return segment_graph;
+    }
+
+    size_t measure_segment_compute_buffer(ggml_backend_t backend,
+                                          ggml_cgraph* gf,
+                                          const Segment& segment,
+                                          const char* log_desc) {
+        GGML_ASSERT(backend != nullptr);
+        GGML_ASSERT(gf != nullptr);
+        if (segment.internal_node_indices.empty()) {
+            return 0;
+        }
+
+        ggml_context* graph_ctx    = nullptr;
+        ggml_cgraph* segment_graph = build_segment_graph(gf, segment, &graph_ctx);
+        ggml_gallocr_t allocr      = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
+
+        size_t sizes[1] = {0};
+        ggml_gallocr_reserve_n_size(
+            allocr,
+            segment_graph,
+            nullptr,
+            nullptr,
+            sizes);
+        size_t buffer_size = sizes[0];
+
+        ggml_gallocr_free(allocr);
+        ggml_free(graph_ctx);
+        return buffer_size;
+    }
+
+    Plan build_plan(ggml_backend_t backend,
+                    ggml_cgraph* gf,
+                    const std::unordered_set<const ggml_tensor*>& params_tensor_set,
+                    const char* log_desc) {
+        GGML_ASSERT(backend != nullptr);
+        GGML_ASSERT(gf != nullptr);
+        Plan plan;
+        plan.available    = true;
+        const int n_nodes = ggml_graph_n_nodes(gf);
+        if (n_nodes <= 0) {
+            return plan;
+        }
+        plan.n_nodes = n_nodes;
+        plan.n_leafs = gf->n_leafs;
+        for (int i = 0; i < gf->n_leafs; ++i) {
+            ggml_tensor* leaf = gf->leafs[i];
+            if (is_params_tensor(params_tensor_set, leaf)) {
+                continue;
+            }
+            auto shape       = input_shape(leaf);
+            shape.leaf_index = i;
+            plan.input_shapes.push_back(shape);
+        }
+
+        std::unordered_map<const ggml_tensor*, int> producer_index;
+        producer_index.reserve(static_cast<size_t>(n_nodes));
+        for (int i = 0; i < n_nodes; ++i) {
+            producer_index[ggml_graph_node(gf, i)] = i;
+        }
+
+        std::vector<Segment> grouped_segments;
+        std::unordered_map<std::string, size_t> group_to_segment;
+        for (int i = 0; i < n_nodes; ++i) {
+            ggml_tensor* node = ggml_graph_node(gf, i);
+            if (!is_graph_cut_tensor(node)) {
+                continue;
+            }
+
+            plan.has_cuts = true;
+            std::string full_name(node->name);
+            std::string payload = full_name.substr(std::strlen(GGML_RUNNER_CUT_PREFIX));
+            size_t sep          = payload.find('|');
+            std::string group   = sep == std::string::npos ? payload : payload.substr(0, sep);
+
+            auto it = group_to_segment.find(group);
+            if (it == group_to_segment.end()) {
+                Segment segment;
+                segment.group_name = group;
+                segment.output_node_indices.push_back(i);
+                group_to_segment[group] = grouped_segments.size();
+                grouped_segments.push_back(std::move(segment));
+            } else {
+                auto& segment = grouped_segments[it->second];
+                segment.output_node_indices.push_back(i);
+            }
+        }
+
+        if (!plan.has_cuts) {
+            return plan;
+        }
+
+        std::unordered_set<int> available_cut_output_node_indices;
+        available_cut_output_node_indices.reserve(static_cast<size_t>(n_nodes));
+        for (auto& segment : grouped_segments) {
+            build_segment(gf,
+                          plan,
+                          segment,
+                          producer_index,
+                          available_cut_output_node_indices,
+                          backend,
+                          params_tensor_set,
+                          log_desc);
+        }
+
+        int final_output_index = graph_node_index_by_name(gf, "ggml_runner_final_result_tensor");
+        if (final_output_index < 0) {
+            final_output_index = n_nodes - 1;
+        }
+        ggml_tensor* final_output = final_output_index >= 0 ? ggml_graph_node(gf, final_output_index) : nullptr;
+        if (final_output != nullptr && available_cut_output_node_indices.find(final_output_index) == available_cut_output_node_indices.end()) {
+            Segment final_segment;
+            final_segment.group_name = "ggml_runner.final";
+            final_segment.output_node_indices.push_back(final_output_index);
+            build_segment(gf,
+                          plan,
+                          final_segment,
+                          producer_index,
+                          available_cut_output_node_indices,
+                          backend,
+                          params_tensor_set,
+                          log_desc);
+        }
+
+        return plan;
+    }
+
+    Plan apply_max_vram_budget(ggml_cgraph* gf,
+                               const Plan& base_plan,
+                               size_t max_graph_vram_bytes,
+                               ggml_backend_t backend,
+                               const std::unordered_set<const ggml_tensor*>& params_tensor_set,
+                               const char* log_desc) {
+        GGML_ASSERT(backend != nullptr);
+        GGML_ASSERT(gf != nullptr);
+        int64_t t_budget_begin = ggml_time_ms();
+        if (max_graph_vram_bytes == 0 || !base_plan.has_cuts || base_plan.segments.size() <= 1) {
+            return base_plan;
+        }
+
+        const int n_nodes = ggml_graph_n_nodes(gf);
+        std::unordered_map<const ggml_tensor*, int> producer_index;
+        producer_index.reserve(static_cast<size_t>(n_nodes));
+        for (int i = 0; i < n_nodes; ++i) {
+            producer_index[ggml_graph_node(gf, i)] = i;
+        }
+
+        Plan merged_plan;
+        merged_plan.available = true;
+        merged_plan.has_cuts  = base_plan.has_cuts;
+        merged_plan.valid     = base_plan.valid;
+        merged_plan.n_nodes   = base_plan.n_nodes;
+        merged_plan.n_leafs   = base_plan.n_leafs;
+
+        std::unordered_set<int> available_cut_output_node_indices;
+        available_cut_output_node_indices.reserve(static_cast<size_t>(n_nodes));
+
+        size_t start_segment_index = 0;
+        while (start_segment_index < base_plan.segments.size()) {
+            Plan single_plan;
+            auto single_available_cut_output_node_indices = available_cut_output_node_indices;
+            auto single_seed                              = make_segment_seed(base_plan,
+                                                                              start_segment_index,
+                                                                              start_segment_index);
+            build_segment(gf,
+                          single_plan,
+                          single_seed,
+                          producer_index,
+                          single_available_cut_output_node_indices,
+                          backend,
+                          params_tensor_set,
+                          log_desc);
+            GGML_ASSERT(!single_plan.segments.empty());
+
+            size_t best_end_segment_index = start_segment_index;
+            bool can_merge_next_segment   = graph_cut_segment_vram_bytes(single_plan.segments.back()) <= max_graph_vram_bytes;
+
+            while (can_merge_next_segment && best_end_segment_index + 1 < base_plan.segments.size()) {
+                const size_t next_end_segment_index = best_end_segment_index + 1;
+                Plan candidate_plan;
+                auto candidate_available_cut_output_node_indices = available_cut_output_node_indices;
+                auto candidate_seed                              = make_segment_seed(base_plan,
+                                                                                     start_segment_index,
+                                                                                     next_end_segment_index);
+                build_segment(gf,
+                              candidate_plan,
+                              candidate_seed,
+                              producer_index,
+                              candidate_available_cut_output_node_indices,
+                              backend,
+                              params_tensor_set,
+                              log_desc);
+                GGML_ASSERT(!candidate_plan.segments.empty());
+
+                const auto& candidate_segment = candidate_plan.segments.back();
+                if (graph_cut_segment_vram_bytes(candidate_segment) > max_graph_vram_bytes) {
+                    break;
+                }
+
+                best_end_segment_index = next_end_segment_index;
+            }
+
+            auto best_seed = make_segment_seed(base_plan,
+                                               start_segment_index,
+                                               best_end_segment_index);
+            build_segment(gf,
+                          merged_plan,
+                          best_seed,
+                          producer_index,
+                          available_cut_output_node_indices,
+                          backend,
+                          params_tensor_set,
+                          log_desc);
+            start_segment_index = best_end_segment_index + 1;
+        }
+
+        if (log_desc != nullptr && merged_plan.segments.size() != base_plan.segments.size()) {
+            LOG_INFO("%s graph cut max_vram=%.2f MB merged %zu segments -> %zu segments",
+                     log_desc,
+                     max_graph_vram_bytes / 1024.0 / 1024.0,
+                     base_plan.segments.size(),
+                     merged_plan.segments.size());
+        }
+
+        if (log_desc != nullptr) {
+            LOG_DEBUG("%s graph cut max_vram budget merge took %lld ms",
+                      log_desc,
+                      ggml_time_ms() - t_budget_begin);
+        }
+
+        return merged_plan;
+    }
+
+    Plan resolve_plan(ggml_backend_t backend,
+                      ggml_cgraph* gf,
+                      PlanCache* cache,
+                      size_t max_graph_vram_bytes,
+                      const std::unordered_set<const ggml_tensor*>& params_tensor_set,
+                      const char* log_desc) {
+        GGML_ASSERT(backend != nullptr);
+        GGML_ASSERT(gf != nullptr);
+        GGML_ASSERT(cache != nullptr);
+
+        int64_t t_prepare_begin = ggml_time_ms();
+        Plan base_plan;
+        int64_t t_plan_begin = ggml_time_ms();
+        if (cache->graph_cut_plan.available && plan_matches_graph(gf, cache->graph_cut_plan)) {
+            base_plan = cache->graph_cut_plan;
+        } else {
+            base_plan                                = build_plan(backend, gf, params_tensor_set, log_desc);
+            cache->graph_cut_plan                    = base_plan;
+            cache->graph_cut_plan.available          = true;
+            cache->budgeted_graph_cut_plan.available = false;
+            if (log_desc != nullptr) {
+                LOG_INFO("%s build cached graph cut plan done (taking %lld ms)", log_desc, ggml_time_ms() - t_plan_begin);
+            }
+        }
+
+        Plan resolved_plan = base_plan;
+        if (max_graph_vram_bytes > 0 && base_plan.has_cuts) {
+            if (cache->budgeted_graph_cut_plan.available &&
+                cache->budgeted_graph_cut_plan_max_vram_bytes == max_graph_vram_bytes &&
+                plan_matches_graph(gf, cache->budgeted_graph_cut_plan)) {
+                resolved_plan = cache->budgeted_graph_cut_plan;
+            } else {
+                resolved_plan                                 = apply_max_vram_budget(gf,
+                                                                                      base_plan,
+                                                                                      max_graph_vram_bytes,
+                                                                                      backend,
+                                                                                      params_tensor_set,
+                                                                                      log_desc);
+                cache->budgeted_graph_cut_plan                = resolved_plan;
+                cache->budgeted_graph_cut_plan.available      = true;
+                cache->budgeted_graph_cut_plan_max_vram_bytes = max_graph_vram_bytes;
+            }
+        }
+        return resolved_plan;
+    }
+
+    void annotate_residency(Plan& plan, size_t max_graph_vram_bytes) {
+        // Cached plans may be reused with a smaller live budget.
+        for (auto& seg : plan.segments) {
+            seg.residency = SegmentResidency::STREAMED;
+        }
+        if (max_graph_vram_bytes == 0 || plan.segments.size() < 2) {
+            return;
+        }
+
+        bool any_param_bearing = false;
+        for (const auto& seg : plan.segments) {
+            if (seg.input_param_bytes > 0) {
+                any_param_bearing = true;
+                break;
+            }
+        }
+        if (!any_param_bearing) {
+            return;
+        }
+
+        // Leave room for the largest active streamed segment.
+        size_t worst_streamed_footprint = 0;
+        for (const auto& seg : plan.segments) {
+            const size_t seg_footprint = seg.input_param_bytes +
+                                         seg.compute_buffer_size +
+                                         seg.output_bytes +
+                                         seg.input_previous_cut_bytes +
+                                         seg.input_external_bytes;
+            if (seg_footprint > worst_streamed_footprint) {
+                worst_streamed_footprint = seg_footprint;
+            }
+        }
+        constexpr size_t safety = 512ull * 1024 * 1024;
+        const size_t reserved   = safety + worst_streamed_footprint;
+
+        if (max_graph_vram_bytes <= reserved) {
+            return;
+        }
+        const size_t available = max_graph_vram_bytes - reserved;
+
+        size_t cumulative = 0;
+        for (auto& seg : plan.segments) {
+            if (cumulative + seg.input_param_bytes > available) {
+                break;
+            }
+            seg.residency = SegmentResidency::RESIDENT;
+            cumulative += seg.input_param_bytes;
+        }
+    }
+
+}  // namespace sd::ggml_graph_cut
@@ -0,0 +1,117 @@
+#ifndef __SD_GGML_GRAPH_CUT_H__
+#define __SD_GGML_GRAPH_CUT_H__
+
+#include <array>
+#include <cstdint>
+#include <string>
+#include <unordered_set>
+#include <vector>
+
+#include "ggml-backend.h"
+#include "ggml.h"
+
+namespace sd::ggml_graph_cut {
+
+    // Streaming residency for a segment's params.
+    enum class SegmentResidency : uint8_t {
+        STREAMED = 0,
+        RESIDENT = 1,
+    };
+
+    struct Segment {
+        enum InputType {
+            INPUT_EXTERNAL = 0,
+            INPUT_PREVIOUS_CUT,
+            INPUT_PARAM,
+        };
+
+        struct InputRef {
+            InputType type = INPUT_EXTERNAL;
+            std::string display_name;
+            int leaf_index = -1;
+            int node_index = -1;
+        };
+
+        size_t compute_buffer_size      = 0;
+        size_t output_bytes             = 0;
+        size_t input_external_bytes     = 0;
+        size_t input_previous_cut_bytes = 0;
+        size_t input_param_bytes        = 0;
+        std::string group_name;
+        std::vector<int> internal_node_indices;
+        std::vector<int> output_node_indices;
+        std::vector<InputRef> input_refs;
+        SegmentResidency residency = SegmentResidency::STREAMED;
+    };
+
+    struct Plan {
+        struct InputShape {
+            int leaf_index                        = -1;
+            ggml_type type                        = GGML_TYPE_COUNT;
+            std::array<int64_t, GGML_MAX_DIMS> ne = {0, 0, 0, 0};
+        };
+
+        bool available = false;
+        bool has_cuts  = false;
+        bool valid     = true;
+        int n_nodes    = 0;
+        int n_leafs    = 0;
+        std::vector<InputShape> input_shapes;
+        std::vector<Segment> segments;
+    };
+
+    struct PlanCache {
+        Plan graph_cut_plan;
+        Plan budgeted_graph_cut_plan;
+        size_t budgeted_graph_cut_plan_max_vram_bytes = 0;
+    };
+
+    static constexpr const char* GGML_RUNNER_CUT_PREFIX = "ggml_runner_cut:";
+
+    bool is_graph_cut_tensor(const ggml_tensor* tensor);
+    std::string make_graph_cut_name(const std::string& group, const std::string& output);
+    void mark_graph_cut(ggml_tensor* tensor, const std::string& group, const std::string& output);
+    int leaf_count(ggml_cgraph* gf);
+    ggml_tensor* leaf_tensor(ggml_cgraph* gf, int leaf_index);
+    ggml_backend_buffer_t tensor_buffer(const ggml_tensor* tensor);
+    ggml_tensor* cache_source_tensor(ggml_tensor* tensor);
+    size_t cache_tensor_bytes(const ggml_tensor* tensor);
+    bool plan_matches_graph(ggml_cgraph* gf, const Plan& plan);
+    ggml_tensor* output_tensor(ggml_cgraph* gf, const Segment& segment, size_t output_index);
+    ggml_tensor* input_tensor(ggml_cgraph* gf, const Segment::InputRef& input_ref);
+    std::vector<ggml_tensor*> param_tensors(ggml_cgraph* gf, const Segment& segment);
+    std::vector<ggml_tensor*> runtime_param_tensors(ggml_cgraph* gf, const Segment& segment, const char* log_desc);
+    std::unordered_set<std::string> collect_future_input_names(ggml_cgraph* gf,
+                                                               const Plan& plan,
+                                                               size_t current_segment_index);
+    ggml_cgraph* build_segment_graph(ggml_cgraph* gf,
+                                     const Segment& segment,
+                                     ggml_context** graph_ctx_out);
+    size_t measure_segment_compute_buffer(ggml_backend_t backend,
+                                          ggml_cgraph* gf,
+                                          const Segment& segment,
+                                          const char* log_desc);
+    size_t max_vram_gib_to_bytes(float max_vram);
+    float resolve_max_vram_gib(float max_vram, ggml_backend_t backend);
+    Plan build_plan(ggml_backend_t backend,
+                    ggml_cgraph* gf,
+                    const std::unordered_set<const ggml_tensor*>& params_tensor_set,
+                    const char* log_desc);
+    Plan apply_max_vram_budget(ggml_cgraph* gf,
+                               const Plan& base_plan,
+                               size_t max_graph_vram_bytes,
+                               ggml_backend_t backend,
+                               const std::unordered_set<const ggml_tensor*>& params_tensor_set,
+                               const char* log_desc);
+    Plan resolve_plan(ggml_backend_t backend,
+                      ggml_cgraph* gf,
+                      PlanCache* cache,
+                      size_t max_graph_vram_bytes,
+                      const std::unordered_set<const ggml_tensor*>& params_tensor_set,
+                      const char* log_desc);
+
+    // Mark leading segments resident when they fit after streamed-segment headroom.
+    void annotate_residency(Plan& plan, size_t max_graph_vram_bytes);
+}  // namespace sd::ggml_graph_cut
+
+#endif
@@ -0,0 +1,259 @@
+#include "guidance.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstdlib>
+#include <string>
+#include <utility>
+
+#include "util.h"
+
+namespace sd::guidance {
+
+    static bool has_tensor(const sd::Tensor<float>* tensor) {
+        return tensor != nullptr && !tensor->empty();
+    }
+
+    bool is_adaptive_projected_guidance_enabled(const AdaptiveProjectedGuidanceParams& params) {
+        return params.eta != 1.0f || params.momentum != 0.0f || params.norm_threshold > 0.0f;
+    }
+
+    AdaptiveProjectedGuidanceParams parse_adaptive_projected_guidance_args(const char* extra_sample_args) {
+        AdaptiveProjectedGuidanceParams params;
+        for (const auto& [key, value] : parse_key_value_args(extra_sample_args, "extra sample arg")) {
+            float parsed = 0.0f;
+            if (key == "apg_eta") {
+                if (!parse_strict_float(value, parsed)) {
+                    LOG_WARN("ignoring invalid APG extra sample arg '%s=%s'", key.c_str(), value.c_str());
+                    continue;
+                }
+                params.eta = parsed;
+            } else if (key == "apg_momentum") {
+                if (!parse_strict_float(value, parsed)) {
+                    LOG_WARN("ignoring invalid APG extra sample arg '%s=%s'", key.c_str(), value.c_str());
+                    continue;
+                }
+                params.momentum = parsed;
+            } else if (key == "apg_norm_threshold") {
+                if (!parse_strict_float(value, parsed)) {
+                    LOG_WARN("ignoring invalid APG extra sample arg '%s=%s'", key.c_str(), value.c_str());
+                    continue;
+                }
+                params.norm_threshold = parsed;
+            } else if (key == "apg_norm_threshold_smoothing") {
+                if (!parse_strict_float(value, parsed)) {
+                    LOG_WARN("ignoring invalid APG extra sample arg '%s=%s'", key.c_str(), value.c_str());
+                    continue;
+                }
+                params.norm_threshold_smoothing = parsed;
+            }
+        }
+        return params;
+    }
+
+    bool parse_skip_layer_guidance_uncond_arg(const char* extra_sample_args) {
+        bool uncond = false;
+        for (const auto& [key, value] : parse_key_value_args(extra_sample_args, "extra sample arg")) {
+            if (key == "slg_uncond") {
+                if (!parse_strict_bool(value, uncond)) {
+                    LOG_WARN("ignoring invalid SLG extra sample arg '%s=%s'", key.c_str(), value.c_str());
+                }
+            }
+        }
+        return uncond;
+    }
+
+    ClassifierFreeGuidance::ClassifierFreeGuidance(float guidance_scale,
+                                                   float image_guidance_scale)
+        : guidance_scale_(guidance_scale),
+          image_guidance_scale_(image_guidance_scale) {
+    }
+
+    GuiderOutput ClassifierFreeGuidance::forward(const GuidanceInput& input,
+                                                 GuiderOutput previous) const {
+        (void)previous;
+
+        GuiderOutput output;
+        if (!has_tensor(input.pred_cond)) {
+            return output;
+        }
+
+        const sd::Tensor<float>& pred_cond = *input.pred_cond;
+        output.pred                        = pred_cond;
+        if (has_tensor(input.pred_uncond)) {
+            const sd::Tensor<float>& pred_uncond = *input.pred_uncond;
+            if (has_tensor(input.pred_img_uncond)) {
+                const sd::Tensor<float>& pred_img_uncond = *input.pred_img_uncond;
+                output.pred                              = pred_img_uncond +
+                              image_guidance_scale_ * (pred_uncond - pred_img_uncond) +
+                              guidance_scale_ * (pred_cond - pred_uncond);
+
+            } else {
+                output.pred = pred_uncond + guidance_scale_ * (pred_cond - pred_uncond);
+            }
+        } else if (has_tensor(input.pred_img_uncond)) {
+            const sd::Tensor<float>& pred_img_uncond = *input.pred_img_uncond;
+            output.pred                              = pred_img_uncond + guidance_scale_ * (pred_cond - pred_img_uncond);
+        }
+
+        return output;
+    }
+
+    AdaptiveProjectedGuidance::AdaptiveProjectedGuidance(float guidance_scale,
+                                                         float image_guidance_scale,
+                                                         AdaptiveProjectedGuidanceParams params)
+        : guidance_scale_(guidance_scale),
+          image_guidance_scale_(image_guidance_scale),
+          params_(params) {
+    }
+
+    static sd::Tensor<float> calculate_guidance_delta(const sd::Tensor<float>& pred_cond,
+                                                      const sd::Tensor<float>* pred_uncond,
+                                                      const sd::Tensor<float>* pred_img_uncond,
+                                                      float guidance_scale,
+                                                      float image_guidance_scale) {
+        if (pred_img_uncond != nullptr) {
+            if (pred_uncond != nullptr && guidance_scale == 1.0f) {
+                return *pred_uncond - *pred_img_uncond;
+            }
+            if (pred_uncond != nullptr) {
+                return pred_cond +
+                       (*pred_uncond * (image_guidance_scale - guidance_scale) +
+                        *pred_img_uncond * (1.0f - image_guidance_scale)) /
+                           (guidance_scale - 1.0f);
+            }
+            return pred_cond - *pred_img_uncond;
+        }
+        return pred_cond - *pred_uncond;
+    }
+
+    GuiderOutput AdaptiveProjectedGuidance::forward(const GuidanceInput& input,
+                                                    GuiderOutput previous) const {
+        (void)previous;
+
+        GuiderOutput output;
+        if (!has_tensor(input.pred_cond)) {
+            return output;
+        }
+
+        const sd::Tensor<float>& pred_cond = *input.pred_cond;
+        output.pred                        = pred_cond;
+        if (has_tensor(input.pred_uncond)) {
+            const sd::Tensor<float>& pred_uncond = *input.pred_uncond;
+            if (has_tensor(input.pred_img_uncond)) {
+                const sd::Tensor<float>& pred_img_uncond = *input.pred_img_uncond;
+                output.pred                              = pred_img_uncond +
+                              image_guidance_scale_ * (pred_uncond - pred_img_uncond) +
+                              guidance_scale_ * (pred_cond - pred_uncond);
+            } else {
+                output.pred = pred_uncond + guidance_scale_ * (pred_cond - pred_uncond);
+            }
+        } else if (has_tensor(input.pred_img_uncond)) {
+            const sd::Tensor<float>& pred_img_uncond = *input.pred_img_uncond;
+            output.pred                              = pred_img_uncond + guidance_scale_ * (pred_cond - pred_img_uncond);
+        }
+        if (!has_tensor(input.pred_uncond) && !has_tensor(input.pred_img_uncond)) {
+            return output;
+        }
+
+        const sd::Tensor<float>* pred_uncond     = input.pred_uncond;
+        const sd::Tensor<float>* pred_img_uncond = input.pred_img_uncond;
+
+        sd::Tensor<float> deltas = calculate_guidance_delta(pred_cond,
+                                                            pred_uncond,
+                                                            pred_img_uncond,
+                                                            guidance_scale_,
+                                                            image_guidance_scale_);
+        if (params_.momentum != 0.0f) {
+            if (momentum_buffer_.shape() != deltas.shape()) {
+                momentum_buffer_ = sd::Tensor<float>::zeros_like(deltas);
+            }
+            deltas += params_.momentum * momentum_buffer_;
+            momentum_buffer_ = deltas;
+        }
+
+        float diff_norm = 0.0f;
+        if (params_.norm_threshold > 0.0f) {
+            diff_norm = std::sqrt((deltas * deltas).sum());
+        }
+
+        float apg_scale_factor = 1.0f;
+        if (params_.norm_threshold > 0.0f) {
+            if (diff_norm > 0.0f) {
+                if (params_.norm_threshold_smoothing <= 0.0f) {
+                    apg_scale_factor = std::min(1.0f, params_.norm_threshold / diff_norm);
+                } else {
+                    float x          = params_.norm_threshold / diff_norm;
+                    apg_scale_factor = x / std::pow(1.0f + std::pow(x, 1.0f / params_.norm_threshold_smoothing),
+                                                    params_.norm_threshold_smoothing);
+                }
+            }
+        }
+
+        deltas *= apg_scale_factor;
+        if (params_.eta != 1.0f) {
+            float cond_norm_sq = (pred_cond * pred_cond).sum();
+            if (cond_norm_sq != 0.0f) {
+                float projection_scale = (pred_cond * deltas).sum() / cond_norm_sq;
+                deltas += (params_.eta - 1.0f) * (projection_scale * pred_cond);
+            }
+        }
+
+        output.pred = pred_cond;
+        if (pred_uncond != nullptr) {
+            if (guidance_scale_ != 1.0f) {
+                output.pred = pred_cond + (guidance_scale_ - 1.0f) * deltas;
+            } else if (pred_img_uncond != nullptr) {
+                output.pred = pred_cond + (image_guidance_scale_ - 1.0f) * deltas;
+            }
+        } else if (pred_img_uncond != nullptr) {
+            output.pred = *pred_img_uncond + guidance_scale_ * deltas;
+        }
+
+        return output;
+    }
+
+    SkipLayerGuidance::SkipLayerGuidance(std::vector<int> layers,
+                                         float scale,
+                                         float start,
+                                         float stop)
+        : layers_(std::move(layers)),
+          scale_(scale),
+          start_(start),
+          stop_(stop) {
+    }
+
+    bool SkipLayerGuidance::is_enabled_for_step(const GuidanceInput& input) const {
+        if (layers_.empty() || input.schedule_size == 0) {
+            return false;
+        }
+
+        int start_step = static_cast<int>(start_ * static_cast<float>(input.schedule_size));
+        int stop_step  = static_cast<int>(stop_ * static_cast<float>(input.schedule_size));
+        return input.step > start_step && input.step < stop_step;
+    }
+
+    const std::vector<int>& SkipLayerGuidance::layers() const {
+        return layers_;
+    }
+
+    GuiderOutput SkipLayerGuidance::forward(const GuidanceInput& input,
+                                            GuiderOutput output) const {
+        if (scale_ == 0.0f || !is_enabled_for_step(input) || !input.predict_skip_layer) {
+            return output;
+        }
+
+        if (output.pred.empty() || !has_tensor(input.pred_cond)) {
+            return GuiderOutput();
+        }
+
+        output.pred_skip_layer = input.predict_skip_layer();
+        if (output.pred_skip_layer.empty()) {
+            return GuiderOutput();
+        }
+
+        output.pred += (*input.pred_cond - output.pred_skip_layer) * scale_;
+        return output;
+    }
+
+}  // namespace sd::guidance
@@ -0,0 +1,96 @@
+#ifndef __SD_GUIDANCE_H__
+#define __SD_GUIDANCE_H__
+
+#include <cstddef>
+#include <functional>
+#include <vector>
+
+#include "tensor.hpp"
+
+namespace sd::guidance {
+
+    struct GuiderOutput {
+        sd::Tensor<float> pred;
+        sd::Tensor<float> pred_cond;
+        sd::Tensor<float> pred_uncond;
+        sd::Tensor<float> pred_img_cond;
+        sd::Tensor<float> pred_skip_layer;
+    };
+
+    struct AdaptiveProjectedGuidanceParams {
+        float eta                      = 1.0f;
+        float momentum                 = 0.0f;
+        float norm_threshold           = 0.0f;
+        float norm_threshold_smoothing = 0.0f;
+    };
+
+    AdaptiveProjectedGuidanceParams parse_adaptive_projected_guidance_args(const char* extra_sample_args);
+    bool is_adaptive_projected_guidance_enabled(const AdaptiveProjectedGuidanceParams& params);
+    bool parse_skip_layer_guidance_uncond_arg(const char* extra_sample_args);
+
+    struct GuidanceInput {
+        int step                                 = 0;
+        size_t schedule_size                     = 0;
+        const sd::Tensor<float>* pred_cond       = nullptr;
+        const sd::Tensor<float>* pred_uncond     = nullptr;
+        const sd::Tensor<float>* pred_img_uncond = nullptr;
+
+        std::function<sd::Tensor<float>()> predict_skip_layer;
+    };
+
+    class BaseGuidance {
+    public:
+        virtual ~BaseGuidance()                                   = default;
+        virtual GuiderOutput forward(const GuidanceInput& input,
+                                     GuiderOutput previous) const = 0;
+    };
+
+    class ClassifierFreeGuidance : public BaseGuidance {
+        float guidance_scale_       = 1.0f;
+        float image_guidance_scale_ = 1.0f;
+
+    public:
+        ClassifierFreeGuidance(float guidance_scale,
+                               float image_guidance_scale);
+
+        GuiderOutput forward(const GuidanceInput& input,
+                             GuiderOutput previous) const override;
+    };
+
+    class AdaptiveProjectedGuidance : public BaseGuidance {
+        float guidance_scale_       = 1.0f;
+        float image_guidance_scale_ = 1.0f;
+        AdaptiveProjectedGuidanceParams params_;
+        mutable sd::Tensor<float> momentum_buffer_;
+
+    public:
+        AdaptiveProjectedGuidance(float guidance_scale,
+                                  float image_guidance_scale,
+                                  AdaptiveProjectedGuidanceParams params);
+
+        GuiderOutput forward(const GuidanceInput& input,
+                             GuiderOutput previous) const override;
+    };
+
+    class SkipLayerGuidance : public BaseGuidance {
+        std::vector<int> layers_;
+        float scale_ = 0.0f;
+        float start_ = 0.0f;
+        float stop_  = 1.0f;
+
+    public:
+        SkipLayerGuidance(std::vector<int> layers,
+                          float scale,
+                          float start,
+                          float stop);
+
+        bool is_enabled_for_step(const GuidanceInput& input) const;
+        const std::vector<int>& layers() const;
+
+        GuiderOutput forward(const GuidanceInput& input,
+                             GuiderOutput previous) const override;
+    };
+
+}  // namespace sd::guidance
+
+#endif  // __SD_GUIDANCE_H__
@@ -0,0 +1,681 @@
+#ifndef __SD_HIDREAM_O1_H__
+#define __SD_HIDREAM_O1_H__
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+#include <cstring>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "common_dit.hpp"
+#include "conditioner.hpp"
+#include "diffusion_model.hpp"
+#include "llm.hpp"
+#include "util.h"
+
+namespace HiDreamO1 {
+    constexpr int HIDREAM_O1_GRAPH_SIZE = 32768;
+    constexpr int PATCH_SIZE            = 32;
+    constexpr int TIMESTEP_TOKEN_NUM    = 1;
+    constexpr int IMAGE_TOKEN_ID        = 151655;
+    constexpr int VISION_START_TOKEN_ID = 151652;
+
+    struct HiDreamO1Config {
+        LLM::LLMConfig llm;
+        int patch_size = PATCH_SIZE;
+
+        static HiDreamO1Config detect_from_weights(const String2TensorStorage& tensor_storage_map, const std::string& prefix) {
+            (void)tensor_storage_map;
+            (void)prefix;
+            HiDreamO1Config config;
+            config.llm.arch                           = LLM::LLMArch::QWEN3_VL;
+            config.llm.hidden_size                    = 4096;
+            config.llm.intermediate_size              = 12288;
+            config.llm.num_layers                     = 36;
+            config.llm.num_heads                      = 32;
+            config.llm.num_kv_heads                   = 8;
+            config.llm.head_dim                       = 128;
+            config.llm.qkv_bias                       = false;
+            config.llm.qk_norm                        = true;
+            config.llm.vocab_size                     = 151936;
+            config.llm.rms_norm_eps                   = 1e-6f;
+            config.llm.vision.arch                    = LLM::LLMVisionArch::QWEN3_VL;
+            config.llm.vision.num_layers              = 27;
+            config.llm.vision.hidden_size             = 1152;
+            config.llm.vision.intermediate_size       = 4304;
+            config.llm.vision.num_heads               = 16;
+            config.llm.vision.out_hidden_size         = 4096;
+            config.llm.vision.patch_size              = 16;
+            config.llm.vision.spatial_merge_size      = 2;
+            config.llm.vision.temporal_patch_size     = 2;
+            config.llm.vision.num_position_embeddings = 2304;
+            return config;
+        }
+    };
+
+    static inline std::string repeat_special_token(const std::string& token, int64_t count) {
+        std::string out;
+        out.reserve(static_cast<size_t>(count) * token.size());
+        for (int64_t i = 0; i < count; ++i) {
+            out += token;
+        }
+        return out;
+    }
+
+    static inline std::pair<int, int> calculate_dimensions(int max_size, double ratio) {
+        int width  = static_cast<int>(std::sqrt(max_size * max_size * ratio));
+        int height = static_cast<int>(width / ratio);
+        width      = (width / PATCH_SIZE) * PATCH_SIZE;
+        height     = (height / PATCH_SIZE) * PATCH_SIZE;
+        width      = std::max(width, PATCH_SIZE);
+        height     = std::max(height, PATCH_SIZE);
+        return {width, height};
+    }
+
+    static inline sd::Tensor<float> resize_to_area(const sd::Tensor<float>& image, int image_size) {
+        int64_t width  = image.shape()[0];
+        int64_t height = image.shape()[1];
+        int64_t s_max  = static_cast<int64_t>(image_size) * image_size;
+        double scale   = std::sqrt(static_cast<double>(s_max) / static_cast<double>(width * height));
+
+        std::vector<std::pair<int64_t, int64_t>> sizes = {
+            {(static_cast<int64_t>(std::llround(width * scale)) / PATCH_SIZE) * PATCH_SIZE, (static_cast<int64_t>(std::llround(height * scale)) / PATCH_SIZE) * PATCH_SIZE},
+            {(static_cast<int64_t>(std::llround(width * scale)) / PATCH_SIZE) * PATCH_SIZE, (static_cast<int64_t>(std::floor(height * scale)) / PATCH_SIZE) * PATCH_SIZE},
+            {(static_cast<int64_t>(std::floor(width * scale)) / PATCH_SIZE) * PATCH_SIZE, (static_cast<int64_t>(std::llround(height * scale)) / PATCH_SIZE) * PATCH_SIZE},
+            {(static_cast<int64_t>(std::floor(width * scale)) / PATCH_SIZE) * PATCH_SIZE, (static_cast<int64_t>(std::floor(height * scale)) / PATCH_SIZE) * PATCH_SIZE},
+        };
+        std::sort(sizes.begin(), sizes.end(), [](const auto& a, const auto& b) {
+            return a.first * a.second > b.first * b.second;
+        });
+
+        std::pair<int64_t, int64_t> new_size = sizes.back();
+        for (const auto& size : sizes) {
+            if (size.first > 0 && size.second > 0 && size.first * size.second <= s_max) {
+                new_size = size;
+                break;
+            }
+        }
+
+        double s1 = static_cast<double>(width) / static_cast<double>(new_size.first);
+        double s2 = static_cast<double>(height) / static_cast<double>(new_size.second);
+        sd::Tensor<float> resized;
+        if (s1 < s2) {
+            int64_t resized_h = static_cast<int64_t>(std::llround(height / s1));
+            resized           = sd::ops::interpolate(image,
+                                                     {new_size.first, resized_h, image.shape()[2], image.shape()[3]},
+                                                     sd::ops::InterpolateMode::Bicubic);
+            int64_t top       = (resized_h - new_size.second) / 2;
+            resized           = sd::ops::slice(resized, 1, top, top + new_size.second);
+        } else {
+            int64_t resized_w = static_cast<int64_t>(std::llround(width / s2));
+            resized           = sd::ops::interpolate(image,
+                                                     {resized_w, new_size.second, image.shape()[2], image.shape()[3]},
+                                                     sd::ops::InterpolateMode::Bicubic);
+            int64_t left      = (resized_w - new_size.first) / 2;
+            resized           = sd::ops::slice(resized, 0, left, left + new_size.first);
+        }
+        return resized;
+    }
+
+    static inline std::vector<int32_t> build_position_ids(const std::vector<int32_t>& input_ids,
+                                                          const std::vector<std::array<int32_t, 3>>& image_grids,
+                                                          const std::vector<int32_t>& skip_vision_start_token) {
+        std::vector<int32_t> position_ids(4 * input_ids.size(), 0);
+        int image_index = 0;
+        int st          = 0;
+        int fix_point   = 4096;
+        std::vector<int32_t> out_t;
+        std::vector<int32_t> out_h;
+        std::vector<int32_t> out_w;
+
+        while (st < static_cast<int>(input_ids.size())) {
+            int ed = st;
+            while (ed < static_cast<int>(input_ids.size()) && input_ids[ed] != IMAGE_TOKEN_ID) {
+                ed++;
+            }
+
+            if (ed >= static_cast<int>(input_ids.size())) {
+                int st_idx = out_t.empty() ? 0 : (*std::max_element(out_t.begin(), out_t.end()) + 1);
+                for (int i = 0; i < static_cast<int>(input_ids.size()) - st; ++i) {
+                    out_t.push_back(st_idx + i);
+                    out_h.push_back(st_idx + i);
+                    out_w.push_back(st_idx + i);
+                }
+                break;
+            }
+
+            int text_len = std::max(0, ed - st - skip_vision_start_token[image_index]);
+            int st_idx   = out_t.empty() ? 0 : (*std::max_element(out_t.begin(), out_t.end()) + 1);
+            for (int i = 0; i < text_len; ++i) {
+                out_t.push_back(st_idx + i);
+                out_h.push_back(st_idx + i);
+                out_w.push_back(st_idx + i);
+            }
+
+            auto grid = image_grids[image_index];
+            int base;
+            if (skip_vision_start_token[image_index]) {
+                if (fix_point > 0) {
+                    base      = fix_point;
+                    fix_point = 0;
+                } else {
+                    base = st_idx;
+                }
+            } else {
+                base = text_len + st_idx;
+            }
+            for (int32_t ti = 0; ti < grid[0]; ++ti) {
+                for (int32_t hi = 0; hi < grid[1]; ++hi) {
+                    for (int32_t wi = 0; wi < grid[2]; ++wi) {
+                        out_t.push_back(base + ti);
+                        out_h.push_back(base + hi);
+                        out_w.push_back(base + wi);
+                    }
+                }
+            }
+
+            st = ed + grid[0] * grid[1] * grid[2];
+            image_index++;
+        }
+
+        GGML_ASSERT(out_t.size() == input_ids.size());
+        for (size_t i = 0; i < input_ids.size(); ++i) {
+            // ggml IMROPE consumes 4 flattened position streams:
+            //   [t, h, w, e]
+            // llama.cpp's generic Qwen-VL fallback expands text positions as
+            // [pos, pos, pos, 0]. Keep the extra stream zeroed here too.
+            position_ids[i]                        = out_t[i];
+            position_ids[input_ids.size() + i]     = out_h[i];
+            position_ids[input_ids.size() * 2 + i] = out_w[i];
+            position_ids[input_ids.size() * 3 + i] = 0;
+        }
+        return position_ids;
+    }
+
+    struct TimestepEmbedder : public GGMLBlock {
+        int frequency_embedding_size = 256;
+
+        TimestepEmbedder(int64_t hidden_size) {
+            blocks["mlp.0"] = std::make_shared<Linear>(frequency_embedding_size, hidden_size, true);
+            blocks["mlp.2"] = std::make_shared<Linear>(hidden_size, hidden_size, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* t) {
+            auto mlp_0 = std::dynamic_pointer_cast<Linear>(blocks["mlp.0"]);
+            auto mlp_2 = std::dynamic_pointer_cast<Linear>(blocks["mlp.2"]);
+            auto emb   = ggml_ext_timestep_embedding(ctx->ggml_ctx, t, frequency_embedding_size, 10000, 1000.0f);
+            emb        = mlp_0->forward(ctx, emb);
+            emb        = ggml_silu_inplace(ctx->ggml_ctx, emb);
+            emb        = mlp_2->forward(ctx, emb);
+            return emb;
+        }
+    };
+
+    struct BottleneckPatchEmbed : public GGMLBlock {
+        BottleneckPatchEmbed(int64_t in_dim, int64_t pca_dim, int64_t embed_dim) {
+            blocks["proj1"] = std::make_shared<Linear>(in_dim, pca_dim, false);
+            blocks["proj2"] = std::make_shared<Linear>(pca_dim, embed_dim, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto proj1 = std::dynamic_pointer_cast<Linear>(blocks["proj1"]);
+            auto proj2 = std::dynamic_pointer_cast<Linear>(blocks["proj2"]);
+            return proj2->forward(ctx, proj1->forward(ctx, x));
+        }
+    };
+
+    struct FinalLayer : public GGMLBlock {
+        FinalLayer(int64_t hidden_size, int64_t out_dim) {
+            blocks["linear"] = std::make_shared<Linear>(hidden_size, out_dim, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto linear = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
+            return linear->forward(ctx, x);
+        }
+    };
+
+    struct HiDreamO1Model : public GGMLBlock {
+        HiDreamO1Config config;
+
+        HiDreamO1Model() = default;
+        explicit HiDreamO1Model(HiDreamO1Config config)
+            : config(std::move(config)) {
+            blocks["language_model"] = std::make_shared<LLM::TextModel>(this->config.llm);
+            blocks["t_embedder1"]    = std::make_shared<TimestepEmbedder>(this->config.llm.hidden_size);
+            blocks["x_embedder"]     = std::make_shared<BottleneckPatchEmbed>(this->config.patch_size * this->config.patch_size * 3,
+                                                                          this->config.llm.hidden_size / 4,
+                                                                          this->config.llm.hidden_size);
+            blocks["final_layer2"]   = std::make_shared<FinalLayer>(this->config.llm.hidden_size,
+                                                                  this->config.patch_size * this->config.patch_size * 3);
+        }
+
+        std::shared_ptr<LLM::TextModel> text_model() {
+            return std::dynamic_pointer_cast<LLM::TextModel>(blocks["language_model"]);
+        }
+
+        std::shared_ptr<TimestepEmbedder> timestep_embedder() {
+            return std::dynamic_pointer_cast<TimestepEmbedder>(blocks["t_embedder1"]);
+        }
+
+        std::shared_ptr<BottleneckPatchEmbed> patch_embedder() {
+            return std::dynamic_pointer_cast<BottleneckPatchEmbed>(blocks["x_embedder"]);
+        }
+
+        std::shared_ptr<FinalLayer> final_layer() {
+            return std::dynamic_pointer_cast<FinalLayer>(blocks["final_layer2"]);
+        }
+    };
+
+    struct HiDreamO1VisionRunner : public GGMLRunner {
+        HiDreamO1Config config;
+        std::shared_ptr<LLM::VisionModel> model;
+
+        std::vector<int> window_index_vec;
+        std::vector<int> window_inverse_index_vec;
+        std::vector<float> window_mask_vec;
+        std::vector<float> pe_vec;
+        std::array<std::vector<int32_t>, 4> pos_embed_idx_data_;
+        std::array<std::vector<float>, 4> pos_embed_weight_data_;
+
+        HiDreamO1VisionRunner(ggml_backend_t backend,
+                              ggml_backend_t params_backend,
+                              const String2TensorStorage& tensor_storage_map = {},
+                              const std::string& prefix                      = "model.visual")
+            : GGMLRunner(backend, params_backend),
+              config(HiDreamO1Config::detect_from_weights(tensor_storage_map, prefix)),
+              model(std::make_shared<LLM::VisionModel>(false, config.llm.vision)) {
+            model->init(params_ctx, tensor_storage_map, prefix);
+        }
+
+        std::string get_desc() override {
+            return "hidream_o1_vision";
+        }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string& prefix = "model.visual") {
+            model->get_param_tensors(tensors, prefix);
+        }
+
+        ggml_tensor* encode_image(GGMLRunnerContext* runner_ctx, ggml_tensor* image) {
+            return LLM::LLMRunner::encode_image_common(this,
+                                                       compute_ctx,
+                                                       runner_ctx,
+                                                       image,
+                                                       config.llm.vision,
+                                                       model,
+                                                       window_index_vec,
+                                                       window_inverse_index_vec,
+                                                       window_mask_vec,
+                                                       pe_vec,
+                                                       pos_embed_idx_data_,
+                                                       pos_embed_weight_data_);
+        }
+
+        ggml_cgraph* build_graph(const sd::Tensor<float>& image_tensor) {
+            ggml_cgraph* gf    = new_graph_custom(HIDREAM_O1_GRAPH_SIZE);
+            ggml_tensor* image = make_input(image_tensor);
+            auto runner_ctx    = get_context();
+            auto image_embeds  = encode_image(&runner_ctx, image);
+            ggml_build_forward_expand(gf, image_embeds);
+            return gf;
+        }
+
+        sd::Tensor<float> compute(int n_threads, const sd::Tensor<float>& image) {
+            auto get_graph = [&]() {
+                return build_graph(image);
+            };
+            auto output = GGMLRunner::compute<float>(get_graph, n_threads, false);
+            return output.has_value() ? std::move(output.value()) : sd::Tensor<float>();
+        }
+    };
+
+    struct HiDreamO1Runner : public DiffusionModelRunner {
+        HiDreamO1Config config;
+        HiDreamO1Model model;
+
+        std::vector<float> attention_mask_vec;
+
+        HiDreamO1Runner(ggml_backend_t backend,
+                        ggml_backend_t params_backend,
+                        const String2TensorStorage& tensor_storage_map = {},
+                        const std::string& prefix                      = "model")
+            : DiffusionModelRunner(backend, params_backend, prefix),
+              config(HiDreamO1Config::detect_from_weights(tensor_storage_map, prefix)) {
+            model = HiDreamO1Model(config);
+            model.init(params_ctx, tensor_storage_map, prefix);
+        }
+
+        std::string get_desc() override {
+            return "hidream_o1";
+        }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string& prefix) override {
+            model.get_param_tensors(tensors, prefix);
+        }
+
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timestep_tensor,
+                                 const sd::Tensor<int32_t>& input_ids_tensor,
+                                 const sd::Tensor<int32_t>& input_pos_tensor,
+                                 const sd::Tensor<int32_t>& token_types_tensor,
+                                 const sd::Tensor<int32_t>& vinput_mask_tensor,
+                                 const std::vector<std::pair<int, sd::Tensor<float>>>& image_embeds_tensor,
+                                 const std::vector<sd::Tensor<float>>& ref_images) {
+            ggml_cgraph* gf        = new_graph_custom(HIDREAM_O1_GRAPH_SIZE);
+            ggml_tensor* x         = make_input(x_tensor);
+            ggml_tensor* timestep  = make_input(timestep_tensor);
+            ggml_tensor* input_ids = make_input(input_ids_tensor);
+            ggml_tensor* input_pos = make_input(input_pos_tensor);
+
+            auto text_model   = model.text_model();
+            auto t_embedder1  = model.timestep_embedder();
+            auto x_embedder   = model.patch_embedder();
+            auto final_layer2 = model.final_layer();
+
+            std::vector<ggml_tensor*> ref_image_tensors;
+            for (const auto& image : ref_images) {
+                ref_image_tensors.push_back(make_input(image));
+            }
+
+            attention_mask_vec    = std::vector<float>(static_cast<size_t>(token_types_tensor.shape()[0] * token_types_tensor.shape()[0]), 0.0f);
+            int64_t total_seq_len = token_types_tensor.shape()[0];
+            for (int64_t query = 0; query < total_seq_len; ++query) {
+                bool is_gen = token_types_tensor.values()[static_cast<size_t>(query)] > 0;
+                for (int64_t key = 0; key < total_seq_len; ++key) {
+                    if (!is_gen && key > query) {
+                        attention_mask_vec[static_cast<size_t>(query * total_seq_len + key)] = -INFINITY;
+                    }
+                }
+            }
+            auto attention_mask = ggml_new_tensor_2d(compute_ctx, GGML_TYPE_F32, total_seq_len, total_seq_len);
+            set_backend_tensor_data(attention_mask, attention_mask_vec.data());
+
+            auto runner_ctx = get_context();
+            auto txt        = text_model->embed(&runner_ctx, input_ids);
+            std::vector<std::pair<int, ggml_tensor*>> image_embeds;
+            image_embeds.reserve(image_embeds_tensor.size());
+            for (const auto& image_embed : image_embeds_tensor) {
+                image_embeds.emplace_back(image_embed.first, make_input(image_embed.second));
+            }
+            txt = LLM::splice_image_embeds(&runner_ctx, txt, image_embeds);
+
+            auto t_emb          = t_embedder1->forward(&runner_ctx, timestep);
+            int64_t txt_seq_len = input_ids->ne[0];
+            if (txt_seq_len > 1) {
+                auto prefix = ggml_ext_slice(compute_ctx, txt, 1, 0, txt_seq_len - 1);
+                txt         = ggml_concat(compute_ctx, prefix, ggml_reshape_3d(compute_ctx, t_emb, t_emb->ne[0], 1, 1), 1);
+            } else {
+                txt = ggml_reshape_3d(compute_ctx, t_emb, t_emb->ne[0], 1, 1);
+            }
+
+            auto vinputs          = DiT::pad_and_patchify(&runner_ctx, x, PATCH_SIZE, PATCH_SIZE);
+            int64_t target_tokens = vinputs->ne[1];
+            for (ggml_tensor* ref_image : ref_image_tensors) {
+                auto ref = DiT::pad_and_patchify(&runner_ctx, ref_image, PATCH_SIZE, PATCH_SIZE);
+                vinputs  = ggml_concat(compute_ctx, vinputs, ref, 1);
+            }
+            auto vis = x_embedder->forward(&runner_ctx, vinputs);
+
+            auto inputs_embeds = ggml_concat(compute_ctx, txt, vis, 1);
+            auto hidden_states = text_model->forward_embeds(&runner_ctx, inputs_embeds, input_pos, attention_mask, {});
+            auto x_pred_all    = final_layer2->forward(&runner_ctx, hidden_states);
+
+            int64_t x_pred_start = txt_seq_len;
+            if (!vinput_mask_tensor.empty()) {
+                int64_t seq_len      = static_cast<int64_t>(vinput_mask_tensor.shape()[0]);
+                int64_t first_vinput = 0;
+                while (first_vinput < seq_len && vinput_mask_tensor.values()[static_cast<size_t>(first_vinput)] == 0) {
+                    first_vinput++;
+                }
+                x_pred_start = first_vinput;
+            }
+            auto x_pred = ggml_ext_slice(compute_ctx, x_pred_all, 1, x_pred_start, x_pred_start + target_tokens);
+            x_pred      = DiT::unpatchify_and_crop(compute_ctx, x_pred, x->ne[1], x->ne[0], PATCH_SIZE, PATCH_SIZE);
+
+            float sigma = 1.0f - timestep_tensor.values()[0];
+            sigma       = std::max(1e-6f, sigma);
+            auto out    = ggml_scale(compute_ctx, ggml_sub(compute_ctx, x, x_pred), 1.0f / sigma);
+
+            ggml_build_forward_expand(gf, out);
+            return gf;
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timestep,
+                                  const sd::Tensor<int32_t>& input_ids,
+                                  const sd::Tensor<int32_t>& input_pos,
+                                  const sd::Tensor<int32_t>& token_types,
+                                  const sd::Tensor<int32_t>& vinput_mask,
+                                  const std::vector<std::pair<int, sd::Tensor<float>>>& image_embeds,
+                                  const std::vector<sd::Tensor<float>>& ref_images) {
+            auto get_graph = [&]() {
+                return build_graph(x, timestep, input_ids, input_pos, token_types, vinput_mask, image_embeds, ref_images);
+            };
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const DiffusionParams& diffusion_params) override {
+            GGML_ASSERT(diffusion_params.x != nullptr);
+            GGML_ASSERT(diffusion_params.timesteps != nullptr);
+            const auto* extra = diffusion_extra_as<HiDreamO1DiffusionExtra>(diffusion_params);
+            GGML_ASSERT(extra != nullptr);
+            GGML_ASSERT(extra->input_ids != nullptr);
+            GGML_ASSERT(extra->input_pos != nullptr);
+            GGML_ASSERT(extra->token_types != nullptr);
+            static const std::vector<sd::Tensor<float>> empty_images;
+            static const std::vector<std::pair<int, sd::Tensor<float>>> empty_image_embeds;
+            return compute(n_threads,
+                           *diffusion_params.x,
+                           *diffusion_params.timesteps,
+                           *extra->input_ids,
+                           *extra->input_pos,
+                           *extra->token_types,
+                           tensor_or_empty(extra->vinput_mask),
+                           extra->image_embeds ? *extra->image_embeds : empty_image_embeds,
+                           diffusion_params.ref_latents ? *diffusion_params.ref_latents : empty_images);
+        }
+    };
+
+    struct HiDreamO1Conditioner : public Conditioner {
+        Qwen2Tokenizer tokenizer;
+        std::shared_ptr<HiDreamO1VisionRunner> vision_runner;
+
+        HiDreamO1Conditioner(ggml_backend_t backend,
+                             ggml_backend_t params_backend,
+                             const String2TensorStorage& tensor_storage_map = {})
+            : vision_runner(std::make_shared<HiDreamO1VisionRunner>(backend, params_backend, tensor_storage_map)) {}
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors) override {
+            vision_runner->get_param_tensors(tensors);
+        }
+
+        bool alloc_params_buffer() override {
+            if (!vision_runner->alloc_params_buffer()) {
+                return false;
+            }
+            return true;
+        }
+
+        void free_params_buffer() override {
+            vision_runner->free_params_buffer();
+        }
+
+        size_t get_params_buffer_size() override {
+            return vision_runner->get_params_buffer_size();
+        }
+
+        void set_max_graph_vram_bytes(size_t max_graph_vram_bytes) override {
+            vision_runner->set_max_graph_vram_bytes(max_graph_vram_bytes);
+        }
+
+        void set_flash_attention_enabled(bool enabled) override {
+            vision_runner->set_flash_attention_enabled(enabled);
+        }
+
+        void set_weight_adapter(const std::shared_ptr<WeightAdapter>& adapter) override {
+            vision_runner->set_weight_adapter(adapter);
+        }
+
+        SDCondition get_learned_condition(int n_threads,
+                                          const ConditionerParams& conditioner_params) override {
+            SDCondition result;
+
+            int width                = conditioner_params.width;
+            int height               = conditioner_params.height;
+            int64_t target_image_len = static_cast<int64_t>(width / PATCH_SIZE) * static_cast<int64_t>(height / PATCH_SIZE);
+
+            std::vector<sd::Tensor<float>> ref_images;
+            if (conditioner_params.ref_images != nullptr) {
+                ref_images = *conditioner_params.ref_images;
+            }
+
+            std::vector<std::pair<int, sd::Tensor<float>>> vlm_images;
+            std::vector<std::array<int32_t, 3>> image_grids;
+            std::vector<int32_t> skip_vision_start;
+
+            std::string prompt = "<|im_start|>user\n";
+
+            if (ref_images.empty()) {
+                prompt += conditioner_params.text;
+                prompt += "<|im_end|>\n<|im_start|>assistant\n<|boi_token|><|tms_token|>";
+                auto input_ids = tokenizer.encode(prompt, nullptr);
+
+                std::vector<int32_t> input_ids_pad = input_ids;
+                input_ids_pad.push_back(VISION_START_TOKEN_ID);
+                input_ids_pad.insert(input_ids_pad.end(), target_image_len - 1, IMAGE_TOKEN_ID);
+
+                image_grids.push_back({1, static_cast<int32_t>(height / PATCH_SIZE), static_cast<int32_t>(width / PATCH_SIZE)});
+                skip_vision_start.push_back(1);
+
+                std::vector<int32_t> token_types(input_ids_pad.size(), 0);
+                int txt_seq_len = static_cast<int>(input_ids.size());
+                int bgn         = txt_seq_len - TIMESTEP_TOKEN_NUM;
+                for (int i = bgn; i < static_cast<int>(token_types.size()); ++i) {
+                    token_types[i] = 1;
+                }
+
+                auto position_ids = build_position_ids(input_ids_pad, image_grids, skip_vision_start);
+
+                std::vector<int64_t> input_shape{static_cast<int64_t>(input_ids.size())};
+                std::vector<int64_t> position_shape{static_cast<int64_t>(input_ids_pad.size() * 4)};
+                std::vector<int64_t> token_type_shape{static_cast<int64_t>(token_types.size())};
+                std::vector<int32_t> vinput_mask(token_types.size(), 0);
+                for (int64_t i = txt_seq_len; i < static_cast<int64_t>(vinput_mask.size()); ++i) {
+                    vinput_mask[static_cast<size_t>(i)] = 1;
+                }
+                std::vector<int64_t> vinput_mask_shape{static_cast<int64_t>(vinput_mask.size())};
+
+                result.c_input_ids    = sd::Tensor<int32_t>(input_shape, std::move(input_ids));
+                result.c_position_ids = sd::Tensor<int32_t>(position_shape, position_ids);
+                result.c_token_types  = sd::Tensor<int32_t>(token_type_shape, std::move(token_types));
+                result.c_vinput_mask  = sd::Tensor<int32_t>(vinput_mask_shape, std::move(vinput_mask));
+                return result;
+            }
+
+            int K = static_cast<int>(ref_images.size());
+            int max_size;
+            if (K == 1) {
+                max_size = std::max(height, width);
+            } else if (K == 2) {
+                max_size = std::max(height, width) * 48 / 64;
+            } else if (K <= 4) {
+                max_size = std::max(height, width) / 2;
+            } else if (K <= 8) {
+                max_size = std::max(height, width) * 24 / 64;
+            } else {
+                max_size = std::max(height, width) / 4;
+            }
+
+            int cond_img_size;
+            if (K <= 4) {
+                cond_img_size = 384;
+            } else if (K <= 8) {
+                cond_img_size = 384 * 48 / 64;
+            } else {
+                cond_img_size = 384 / 2;
+            }
+
+            for (const auto& ref_image : ref_images) {
+                auto resized_ref = resize_to_area(ref_image, max_size);
+                resized_ref      = sd::ops::clamp(resized_ref, 0.0f, 1.0f);
+
+                // VLM image: Qwen3-VL expects mean=[0.5]/std=[0.5] (i.e. range [-1,1]),
+                // not CLIP normalization. Resize the already-resized ref directly to
+                // (cond_w, cond_h) to match the Python pipeline's pil_r.resize().
+                auto dims                   = calculate_dimensions(cond_img_size,
+                                                                   static_cast<double>(resized_ref.shape()[0]) / static_cast<double>(resized_ref.shape()[1]));
+                sd::Tensor<float> vlm_image = sd::ops::interpolate(
+                    resized_ref,
+                    {dims.first, dims.second, resized_ref.shape()[2], resized_ref.shape()[3]});
+                vlm_image            = vlm_image * 2.0f - 1.0f;
+                int64_t image_tokens = static_cast<int64_t>(dims.first / PATCH_SIZE) * static_cast<int64_t>(dims.second / PATCH_SIZE);
+
+                auto patch_img = resized_ref * 2.0f - 1.0f;
+                result.c_ref_images.push_back(std::move(patch_img));
+                int64_t prompt_start = static_cast<int64_t>(tokenizer.encode(prompt + "<|vision_start|>", nullptr).size());
+                prompt += "<|vision_start|>";
+                prompt += repeat_special_token("<|image_pad|>", image_tokens);
+                prompt += "<|vision_end|>";
+                vlm_images.emplace_back(static_cast<int>(prompt_start), std::move(vlm_image));
+                image_grids.push_back({1, dims.second / PATCH_SIZE, dims.first / PATCH_SIZE});
+                skip_vision_start.push_back(0);
+            }
+
+            prompt += conditioner_params.text;
+            prompt += "<|im_end|>\n<|im_start|>assistant\n<|boi_token|><|tms_token|>";
+            auto input_ids = tokenizer.encode(prompt, nullptr);
+
+            std::vector<int32_t> input_ids_pad = input_ids;
+            input_ids_pad.push_back(VISION_START_TOKEN_ID);
+            input_ids_pad.insert(input_ids_pad.end(), target_image_len - 1, IMAGE_TOKEN_ID);
+            image_grids.push_back({1, static_cast<int32_t>(height / PATCH_SIZE), static_cast<int32_t>(width / PATCH_SIZE)});
+            skip_vision_start.push_back(1);
+
+            for (const auto& ref_image : result.c_ref_images) {
+                int64_t ref_len = static_cast<int64_t>(ref_image.shape()[0] / PATCH_SIZE) * static_cast<int64_t>(ref_image.shape()[1] / PATCH_SIZE);
+                input_ids_pad.push_back(VISION_START_TOKEN_ID);
+                input_ids_pad.insert(input_ids_pad.end(), ref_len - 1, IMAGE_TOKEN_ID);
+                image_grids.push_back({1, static_cast<int32_t>(ref_image.shape()[1] / PATCH_SIZE), static_cast<int32_t>(ref_image.shape()[0] / PATCH_SIZE)});
+                skip_vision_start.push_back(1);
+            }
+
+            std::vector<int32_t> token_types(input_ids_pad.size(), 0);
+            int txt_seq_len = static_cast<int>(input_ids.size());
+            int bgn         = txt_seq_len - TIMESTEP_TOKEN_NUM;
+            for (int i = bgn; i < static_cast<int>(token_types.size()); ++i) {
+                token_types[i] = 1;
+            }
+
+            std::vector<int64_t> input_shape{static_cast<int64_t>(input_ids.size())};
+            std::vector<int64_t> position_shape{static_cast<int64_t>(input_ids_pad.size() * 4)};
+            std::vector<int64_t> token_type_shape{static_cast<int64_t>(token_types.size())};
+            std::vector<int32_t> vinput_mask(token_types.size(), 0);
+            for (int i = txt_seq_len; i < static_cast<int>(vinput_mask.size()); ++i) {
+                vinput_mask[static_cast<size_t>(i)] = 1;
+            }
+            std::vector<int64_t> vinput_mask_shape{static_cast<int64_t>(vinput_mask.size())};
+
+            result.c_input_ids    = sd::Tensor<int32_t>(input_shape, std::move(input_ids));
+            result.c_position_ids = sd::Tensor<int32_t>(position_shape, build_position_ids(input_ids_pad, image_grids, skip_vision_start));
+            result.c_token_types  = sd::Tensor<int32_t>(token_type_shape, std::move(token_types));
+            result.c_vinput_mask  = sd::Tensor<int32_t>(vinput_mask_shape, std::move(vinput_mask));
+            result.c_image_embeds.reserve(vlm_images.size());
+            for (const auto& vlm_image : vlm_images) {
+                auto image_embed = vision_runner->compute(n_threads, vlm_image.second);
+                if (image_embed.empty()) {
+                    LOG_ERROR("hidream_o1 conditioner: encode VLM image failed");
+                    return SDCondition();
+                }
+                result.c_image_embeds.emplace_back(vlm_image.first, std::move(image_embed));
+            }
+            return result;
+        }
+    };
+}  // namespace HiDreamO1
+
+#endif  // __SD_HIDREAM_O1_H__
@@ -0,0 +1,531 @@
+#ifndef __IDEOGRAM4_HPP__
+#define __IDEOGRAM4_HPP__
+
+#include <algorithm>
+#include <cmath>
+#include <cstdlib>
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "diffusion_model.hpp"
+#include "ggml_extend.hpp"
+#include "ggml_graph_cut.h"
+#include "rope.hpp"
+
+namespace Ideogram4 {
+    constexpr int IDEOGRAM4_GRAPH_SIZE    = 65536;
+    constexpr int OUTPUT_IMAGE_INDICATOR  = 2;
+    constexpr int IMAGE_POSITION_OFFSET   = 65536;
+    constexpr int DEFAULT_MROPE_SECTION_T = 24;
+    constexpr int DEFAULT_MROPE_SECTION_H = 20;
+    constexpr int DEFAULT_MROPE_SECTION_W = 20;
+    constexpr int TIMESTEP_MAX_PERIOD     = 10000;
+    constexpr int LLM_HIDDEN_STATE_LAYERS = 13;
+
+    struct Ideogram4Config {
+        int64_t emb_dim                = 4608;
+        int64_t num_layers             = 34;
+        int64_t num_heads              = 18;
+        int64_t intermediate_size      = 12288;
+        int64_t adanln_dim             = 512;
+        int64_t in_channels            = 128;
+        int64_t llm_features_dim       = 53248;
+        int64_t rope_theta             = 5000000;
+        float norm_eps                 = 1e-5f;
+        int patch_size                 = 2;
+        int ae_channels                = 32;
+        std::vector<int> mrope_section = {DEFAULT_MROPE_SECTION_T,
+                                          DEFAULT_MROPE_SECTION_H,
+                                          DEFAULT_MROPE_SECTION_W};
+
+        static Ideogram4Config detect_from_weights(const String2TensorStorage& tensor_storage_map,
+                                                   const std::string& prefix) {
+            Ideogram4Config config;
+            int64_t detected_layers  = 0;
+            std::string layer_prefix = prefix.empty() ? "layers." : prefix + ".layers.";
+            for (const auto& [name, _] : tensor_storage_map) {
+                if (name.find(layer_prefix) != 0) {
+                    continue;
+                }
+                std::string tail = name.substr(layer_prefix.size());
+                size_t dot       = tail.find('.');
+                if (dot == std::string::npos) {
+                    continue;
+                }
+                int layer_idx   = std::atoi(tail.substr(0, dot).c_str());
+                detected_layers = std::max<int64_t>(detected_layers, layer_idx + 1);
+            }
+            if (detected_layers > 0) {
+                config.num_layers = detected_layers;
+                LOG_DEBUG("ideogram4: num_layers = %" PRId64 ", emb_dim = %" PRId64 ", num_heads = %" PRId64 ", intermediate_size = %" PRId64,
+                          config.num_layers,
+                          config.emb_dim,
+                          config.num_heads,
+                          config.intermediate_size);
+            }
+            return config;
+        }
+    };
+
+    __STATIC_INLINE__ ggml_tensor* timestep_embedding_sin_cos(ggml_context* ctx,
+                                                              ggml_tensor* timesteps,
+                                                              int dim) {
+        GGML_ASSERT(dim % 2 == 0);
+        auto embedding = ggml_ext_timestep_embedding(ctx, timesteps, dim, TIMESTEP_MAX_PERIOD, 10.f);
+        auto chunks    = ggml_ext_chunk(ctx, embedding, 2, 0);
+        return ggml_concat(ctx, chunks[1], chunks[0], 0);
+    }
+
+    __STATIC_INLINE__ ggml_tensor* to_token_modulation(ggml_context* ctx, ggml_tensor* x) {
+        // [N, C] -> [N, 1, C] in PyTorch layout.
+        if (ggml_n_dims(x) < 3 || x->ne[1] != 1) {
+            x = ggml_reshape_3d(ctx, x, x->ne[0], 1, x->ne[1]);
+        }
+        return x;
+    }
+
+    __STATIC_INLINE__ ggml_tensor* interleave_hidden_state_layers(ggml_context* ctx, ggml_tensor* x) {
+        // Match upstream stack(...).permute(1, 2, 3, 0).reshape(...):
+        // [layers * hidden, tokens, batch] -> [hidden * layers, tokens, batch].
+        GGML_ASSERT(x->ne[0] % LLM_HIDDEN_STATE_LAYERS == 0);
+        const int64_t hidden_size = x->ne[0] / LLM_HIDDEN_STATE_LAYERS;
+        const int64_t token_count = x->ne[1];
+        const int64_t batch_count = x->ne[2];
+
+        x = ggml_reshape_4d(ctx, x, hidden_size, LLM_HIDDEN_STATE_LAYERS, token_count, batch_count);
+        x = ggml_cont(ctx, ggml_permute(ctx, x, 1, 0, 2, 3));
+        return ggml_reshape_3d(ctx, x, hidden_size * LLM_HIDDEN_STATE_LAYERS, token_count, batch_count);
+    }
+
+    __STATIC_INLINE__ ggml_tensor* modulate(ggml_context* ctx, ggml_tensor* x, ggml_tensor* scale) {
+        scale = to_token_modulation(ctx, scale);
+        return ggml_add(ctx, x, ggml_mul(ctx, x, scale));
+    }
+
+    __STATIC_INLINE__ ggml_tensor* patchify(ggml_context* ctx, ggml_tensor* x, const Ideogram4Config& config) {
+        // x: [N, 128, H, W] with channel order [ae, ph, pw].
+        // return: [N, H*W, 128] with token channel order [ph, pw, ae].
+        const int64_t W = x->ne[0];
+        const int64_t H = x->ne[1];
+        const int64_t C = x->ne[2];
+        const int64_t N = x->ne[3];
+
+        GGML_ASSERT(N == 1);
+        GGML_ASSERT(C == config.ae_channels * config.patch_size * config.patch_size);
+
+        x = ggml_cont(ctx, x);
+        x = ggml_reshape_4d(ctx, x, W * H, config.patch_size, config.patch_size, config.ae_channels);
+        x = ggml_cont(ctx, ggml_permute(ctx, x, 3, 1, 2, 0));
+        x = ggml_reshape_3d(ctx, x, C, W * H, N);
+        return x;
+    }
+
+    __STATIC_INLINE__ ggml_tensor* unpatchify(ggml_context* ctx,
+                                              ggml_tensor* x,
+                                              int64_t H,
+                                              int64_t W,
+                                              const Ideogram4Config& config) {
+        const int64_t C = x->ne[0];
+        const int64_t N = x->ne[2];
+
+        GGML_ASSERT(N == 1);
+        GGML_ASSERT(C == config.ae_channels * config.patch_size * config.patch_size);
+        GGML_ASSERT(x->ne[1] == H * W);
+
+        x = ggml_reshape_4d(ctx, x, config.ae_channels, config.patch_size, config.patch_size, H * W);
+        x = ggml_cont(ctx, ggml_permute(ctx, x, 3, 1, 2, 0));
+        x = ggml_reshape_4d(ctx, x, W, H, C, N);
+        return x;
+    }
+
+    __STATIC_INLINE__ std::shared_ptr<Linear> make_linear(int64_t in_features,
+                                                          int64_t out_features,
+                                                          bool bias = true) {
+        return std::make_shared<Linear>(in_features, out_features, bias, false, false, 1.f, true);
+    }
+
+    __STATIC_INLINE__ std::vector<float> gen_ideogram4_pe(int grid_h,
+                                                          int grid_w,
+                                                          int bs,
+                                                          int context_len,
+                                                          int head_dim,
+                                                          int rope_theta,
+                                                          const std::vector<int>& mrope_section) {
+        GGML_ASSERT(bs == 1);
+        std::vector<std::vector<float>> ids(static_cast<size_t>(bs) * (context_len + grid_h * grid_w),
+                                            std::vector<float>(3, 0.f));
+
+        for (int i = 0; i < context_len; ++i) {
+            ids[i] = {static_cast<float>(i), static_cast<float>(i), static_cast<float>(i)};
+        }
+
+        int cursor = context_len;
+        for (int y = 0; y < grid_h; ++y) {
+            for (int x = 0; x < grid_w; ++x) {
+                ids[cursor++] = {static_cast<float>(IMAGE_POSITION_OFFSET),
+                                 static_cast<float>(IMAGE_POSITION_OFFSET + y),
+                                 static_cast<float>(IMAGE_POSITION_OFFSET + x)};
+            }
+        }
+
+        return Rope::embed_interleaved_mrope(ids, bs, static_cast<float>(rope_theta), head_dim, mrope_section);
+    }
+
+    class Ideogram4Attention : public GGMLBlock {
+    protected:
+        int64_t hidden_size;
+        int64_t num_heads;
+        int64_t head_dim;
+
+    public:
+        Ideogram4Attention(int64_t hidden_size, int64_t num_heads, float eps)
+            : hidden_size(hidden_size), num_heads(num_heads), head_dim(hidden_size / num_heads) {
+            GGML_ASSERT(hidden_size % num_heads == 0);
+            blocks["qkv"]    = make_linear(hidden_size, hidden_size * 3, false);
+            blocks["norm_q"] = std::make_shared<RMSNorm>(head_dim, eps);
+            blocks["norm_k"] = std::make_shared<RMSNorm>(head_dim, eps);
+            blocks["o"]      = make_linear(hidden_size, hidden_size, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* pe,
+                             ggml_tensor* mask = nullptr) {
+            int64_t n_token = x->ne[1];
+            int64_t N       = x->ne[2];
+
+            auto qkv_proj = std::dynamic_pointer_cast<Linear>(blocks["qkv"]);
+            auto norm_q   = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_q"]);
+            auto norm_k   = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_k"]);
+            auto out_proj = std::dynamic_pointer_cast<Linear>(blocks["o"]);
+
+            auto qkv     = qkv_proj->forward(ctx, x);
+            auto qkv_vec = split_qkv(ctx->ggml_ctx, qkv);
+            auto q       = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[0], head_dim, num_heads, n_token, N);
+            auto k       = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[1], head_dim, num_heads, n_token, N);
+            auto v       = ggml_reshape_4d(ctx->ggml_ctx, qkv_vec[2], head_dim, num_heads, n_token, N);
+
+            q = norm_q->forward(ctx, q);
+            k = norm_k->forward(ctx, k);
+
+            x = Rope::attention(ctx, q, k, v, pe, mask, 1.f / 128.f, false);
+            x = out_proj->forward(ctx, x);
+            return x;
+        }
+    };
+
+    class Ideogram4MLP : public GGMLBlock {
+    public:
+        Ideogram4MLP(int64_t dim, int64_t hidden_dim) {
+            blocks["w1"] = make_linear(dim, hidden_dim, false);
+            blocks["w2"] = make_linear(hidden_dim, dim, false);
+            blocks["w3"] = make_linear(dim, hidden_dim, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto w1 = std::dynamic_pointer_cast<Linear>(blocks["w1"]);
+            auto w2 = std::dynamic_pointer_cast<Linear>(blocks["w2"]);
+            auto w3 = std::dynamic_pointer_cast<Linear>(blocks["w3"]);
+
+            auto x1 = ggml_silu(ctx->ggml_ctx, w1->forward(ctx, x));
+            auto x3 = w3->forward(ctx, x);
+            x       = ggml_mul(ctx->ggml_ctx, x1, x3);
+            x       = w2->forward(ctx, x);
+            return x;
+        }
+    };
+
+    class Ideogram4TransformerBlock : public GGMLBlock {
+    public:
+        Ideogram4TransformerBlock(const Ideogram4Config& config) {
+            blocks["attention"]        = std::make_shared<Ideogram4Attention>(config.emb_dim, config.num_heads, config.norm_eps);
+            blocks["feed_forward"]     = std::make_shared<Ideogram4MLP>(config.emb_dim, config.intermediate_size);
+            blocks["attention_norm1"]  = std::make_shared<RMSNorm>(config.emb_dim, config.norm_eps);
+            blocks["ffn_norm1"]        = std::make_shared<RMSNorm>(config.emb_dim, config.norm_eps);
+            blocks["attention_norm2"]  = std::make_shared<RMSNorm>(config.emb_dim, config.norm_eps);
+            blocks["ffn_norm2"]        = std::make_shared<RMSNorm>(config.emb_dim, config.norm_eps);
+            blocks["adaln_modulation"] = make_linear(config.adanln_dim, 4 * config.emb_dim, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* pe,
+                             ggml_tensor* adaln_input,
+                             ggml_tensor* mask = nullptr) {
+            auto attention        = std::dynamic_pointer_cast<Ideogram4Attention>(blocks["attention"]);
+            auto feed_forward     = std::dynamic_pointer_cast<Ideogram4MLP>(blocks["feed_forward"]);
+            auto attention_norm1  = std::dynamic_pointer_cast<RMSNorm>(blocks["attention_norm1"]);
+            auto ffn_norm1        = std::dynamic_pointer_cast<RMSNorm>(blocks["ffn_norm1"]);
+            auto attention_norm2  = std::dynamic_pointer_cast<RMSNorm>(blocks["attention_norm2"]);
+            auto ffn_norm2        = std::dynamic_pointer_cast<RMSNorm>(blocks["ffn_norm2"]);
+            auto adaln_modulation = std::dynamic_pointer_cast<Linear>(blocks["adaln_modulation"]);
+
+            auto mod       = adaln_modulation->forward(ctx, adaln_input);
+            auto mods      = ggml_ext_chunk(ctx->ggml_ctx, mod, 4, 0);
+            auto scale_msa = mods[0];
+            auto gate_msa  = to_token_modulation(ctx->ggml_ctx, ggml_tanh(ctx->ggml_ctx, mods[1]));
+            auto scale_mlp = mods[2];
+            auto gate_mlp  = to_token_modulation(ctx->ggml_ctx, ggml_tanh(ctx->ggml_ctx, mods[3]));
+
+            auto attn_out = attention_norm1->forward(ctx, x);
+            attn_out      = modulate(ctx->ggml_ctx, attn_out, scale_msa);
+            attn_out      = attention->forward(ctx, attn_out, pe, mask);
+            attn_out      = attention_norm2->forward(ctx, attn_out);
+            x             = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, attn_out, gate_msa));
+
+            auto ffn_out = ffn_norm1->forward(ctx, x);
+            ffn_out      = modulate(ctx->ggml_ctx, ffn_out, scale_mlp);
+            ffn_out      = feed_forward->forward(ctx, ffn_out);
+            ffn_out      = ffn_norm2->forward(ctx, ffn_out);
+            x            = ggml_add(ctx->ggml_ctx, x, ggml_mul(ctx->ggml_ctx, ffn_out, gate_mlp));
+
+            return x;
+        }
+    };
+
+    class Ideogram4EmbedScalar : public GGMLBlock {
+    protected:
+        int64_t dim;
+
+    public:
+        Ideogram4EmbedScalar(int64_t dim)
+            : dim(dim) {
+            blocks["mlp_in"]  = make_linear(dim, dim, true);
+            blocks["mlp_out"] = make_linear(dim, dim, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto mlp_in  = std::dynamic_pointer_cast<Linear>(blocks["mlp_in"]);
+            auto mlp_out = std::dynamic_pointer_cast<Linear>(blocks["mlp_out"]);
+
+            x = timestep_embedding_sin_cos(ctx->ggml_ctx, x, static_cast<int>(dim));
+            x = ggml_silu(ctx->ggml_ctx, mlp_in->forward(ctx, x));
+            x = mlp_out->forward(ctx, x);
+            return x;
+        }
+    };
+
+    class Ideogram4FinalLayer : public GGMLBlock {
+    public:
+        Ideogram4FinalLayer(const Ideogram4Config& config) {
+            blocks["norm_final"]       = std::make_shared<LayerNorm>(config.emb_dim, 1e-6f, false);
+            blocks["linear"]           = make_linear(config.emb_dim, config.in_channels, true);
+            blocks["adaln_modulation"] = make_linear(config.adanln_dim, config.emb_dim, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* c) {
+            auto norm_final       = std::dynamic_pointer_cast<LayerNorm>(blocks["norm_final"]);
+            auto linear           = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
+            auto adaln_modulation = std::dynamic_pointer_cast<Linear>(blocks["adaln_modulation"]);
+
+            auto scale = adaln_modulation->forward(ctx, ggml_silu(ctx->ggml_ctx, c));
+            x          = norm_final->forward(ctx, x);
+            x          = modulate(ctx->ggml_ctx, x, scale);
+            x          = linear->forward(ctx, x);
+            return x;
+        }
+    };
+
+    class Ideogram4Transformer : public GGMLBlock {
+    protected:
+        Ideogram4Config config;
+
+    public:
+        Ideogram4Transformer() = default;
+        explicit Ideogram4Transformer(Ideogram4Config config)
+            : config(std::move(config)) {
+            blocks["input_proj"]            = make_linear(this->config.in_channels, this->config.emb_dim, true);
+            blocks["llm_cond_norm"]         = std::make_shared<RMSNorm>(this->config.llm_features_dim, 1e-6f);
+            blocks["llm_cond_proj"]         = make_linear(this->config.llm_features_dim, this->config.emb_dim, true);
+            blocks["t_embedding"]           = std::make_shared<Ideogram4EmbedScalar>(this->config.emb_dim);
+            blocks["adaln_proj"]            = make_linear(this->config.emb_dim, this->config.adanln_dim, true);
+            blocks["embed_image_indicator"] = std::make_shared<Embedding>(2, this->config.emb_dim);
+
+            for (int i = 0; i < this->config.num_layers; ++i) {
+                blocks["layers." + std::to_string(i)] = std::make_shared<Ideogram4TransformerBlock>(this->config);
+            }
+            blocks["final_layer"] = std::make_shared<Ideogram4FinalLayer>(this->config);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* timestep,
+                             ggml_tensor* context,
+                             ggml_tensor* pe,
+                             ggml_tensor* image_indicator_ids) {
+            int64_t W = x->ne[0];
+            int64_t H = x->ne[1];
+            int64_t N = x->ne[3];
+            GGML_ASSERT(N == 1);
+
+            auto input_proj            = std::dynamic_pointer_cast<Linear>(blocks["input_proj"]);
+            auto llm_cond_norm         = std::dynamic_pointer_cast<RMSNorm>(blocks["llm_cond_norm"]);
+            auto llm_cond_proj         = std::dynamic_pointer_cast<Linear>(blocks["llm_cond_proj"]);
+            auto t_embedding           = std::dynamic_pointer_cast<Ideogram4EmbedScalar>(blocks["t_embedding"]);
+            auto adaln_proj            = std::dynamic_pointer_cast<Linear>(blocks["adaln_proj"]);
+            auto embed_image_indicator = std::dynamic_pointer_cast<Embedding>(blocks["embed_image_indicator"]);
+            auto final_layer           = std::dynamic_pointer_cast<Ideogram4FinalLayer>(blocks["final_layer"]);
+
+            auto img = patchify(ctx->ggml_ctx, x, config);
+            img      = input_proj->forward(ctx, img);
+
+            ggml_tensor* h      = img;
+            int64_t context_len = 0;
+            if (context != nullptr) {
+                if (ggml_n_dims(context) < 3) {
+                    context = ggml_reshape_3d(ctx->ggml_ctx, context, context->ne[0], context->ne[1], 1);
+                }
+                context     = interleave_hidden_state_layers(ctx->ggml_ctx, context);
+                context_len = context->ne[1];
+                auto txt    = llm_cond_norm->forward(ctx, context);
+                txt         = llm_cond_proj->forward(ctx, txt);
+                h           = ggml_concat(ctx->ggml_ctx, txt, img, 1);
+            }
+
+            auto indicator_embedding = embed_image_indicator->forward(ctx, image_indicator_ids);
+            h                        = ggml_add(ctx->ggml_ctx, h, indicator_embedding);
+
+            auto t_cond      = t_embedding->forward(ctx, timestep);
+            auto adaln_input = ggml_silu(ctx->ggml_ctx, adaln_proj->forward(ctx, t_cond));
+
+            for (int i = 0; i < config.num_layers; ++i) {
+                auto block = std::dynamic_pointer_cast<Ideogram4TransformerBlock>(blocks["layers." + std::to_string(i)]);
+                h          = block->forward(ctx, h, pe, adaln_input, nullptr);
+                sd::ggml_graph_cut::mark_graph_cut(h, "ideogram4.layers." + std::to_string(i), "hidden");
+            }
+
+            h = final_layer->forward(ctx, h, adaln_input);
+            if (context_len > 0) {
+                h = ggml_ext_slice(ctx->ggml_ctx, h, 1, context_len, h->ne[1]);
+            }
+
+            h = unpatchify(ctx->ggml_ctx, h, H, W, config);
+            h = ggml_ext_scale(ctx->ggml_ctx, h, -1.f);
+            return h;
+        }
+    };
+
+    class Ideogram4Runner : public DiffusionModelRunner {
+    protected:
+        bool should_use_uncond_model(const DiffusionParams& diffusion_params) const {
+            return has_uncond_model &&
+                   diffusion_params.context == nullptr &&
+                   diffusion_params.y != nullptr &&
+                   !diffusion_params.y->empty();
+        }
+
+    public:
+        Ideogram4Config config;
+        Ideogram4Transformer model;
+        Ideogram4Transformer uncond_model;
+        bool has_uncond_model = false;
+        std::string uncond_prefix;
+        std::vector<float> pe_vec;
+        std::vector<int32_t> image_indicator_vec;
+
+        Ideogram4Runner(ggml_backend_t backend,
+                        ggml_backend_t params_backend,
+                        const String2TensorStorage& tensor_storage_map = {},
+                        const std::string prefix                       = "")
+            : DiffusionModelRunner(backend, params_backend, prefix),
+              config(Ideogram4Config::detect_from_weights(tensor_storage_map, prefix)),
+              uncond_prefix(prefix + ".uncond") {
+            model = Ideogram4Transformer(config);
+            model.init(params_ctx, tensor_storage_map, prefix);
+            for (const auto& pair : tensor_storage_map) {
+                const std::string& name = pair.first;
+                if (starts_with(name, uncond_prefix)) {
+                    has_uncond_model = true;
+                    break;
+                }
+            }
+            if (has_uncond_model) {
+                LOG_DEBUG("using uncond model");
+                uncond_model = Ideogram4Transformer(config);
+                uncond_model.init(params_ctx, tensor_storage_map, uncond_prefix);
+            }
+        }
+
+        std::string get_desc() override {
+            return "ideogram4";
+        }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string& prefix) override {
+            model.get_param_tensors(tensors, prefix);
+            if (has_uncond_model) {
+                uncond_model.get_param_tensors(tensors, this->uncond_prefix);
+            }
+        }
+
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor,
+                                 bool use_uncond_model = false) {
+            ggml_cgraph* gf        = new_graph_custom(IDEOGRAM4_GRAPH_SIZE);
+            ggml_tensor* x         = make_input(x_tensor);
+            ggml_tensor* timesteps = make_input(timesteps_tensor);
+            GGML_ASSERT(x->ne[3] == 1);
+            Ideogram4Transformer& active_model = use_uncond_model ? uncond_model : model;
+
+            ggml_tensor* context = nullptr;
+            int64_t context_len  = 0;
+            if (!context_tensor.empty()) {
+                context     = make_input(context_tensor);
+                context_len = context->ne[1];
+            }
+
+            int64_t grid_w   = x->ne[0];
+            int64_t grid_h   = x->ne[1];
+            int64_t pos_len  = context_len + grid_h * grid_w;
+            int64_t head_dim = config.emb_dim / config.num_heads;
+
+            pe_vec  = gen_ideogram4_pe(static_cast<int>(grid_h),
+                                       static_cast<int>(grid_w),
+                                       static_cast<int>(x->ne[3]),
+                                       static_cast<int>(context_len),
+                                       static_cast<int>(head_dim),
+                                       static_cast<int>(config.rope_theta),
+                                       config.mrope_section);
+            auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, head_dim / 2, pos_len);
+            set_backend_tensor_data(pe, pe_vec.data());
+
+            image_indicator_vec.assign(static_cast<size_t>(pos_len), 1);
+            for (int64_t i = 0; i < context_len; ++i) {
+                image_indicator_vec[static_cast<size_t>(i)] = 0;
+            }
+            auto indicator = ggml_new_tensor_2d(compute_ctx, GGML_TYPE_I32, pos_len, x->ne[3]);
+            set_backend_tensor_data(indicator, image_indicator_vec.data());
+
+            auto runner_ctx  = get_context();
+            ggml_tensor* out = active_model.forward(&runner_ctx, x, timesteps, context, pe, indicator);
+            ggml_build_forward_expand(gf, out);
+            return gf;
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context,
+                                  bool use_uncond_model = false) {
+            auto get_graph = [&]() -> ggml_cgraph* {
+                return build_graph(x, timesteps, context, use_uncond_model);
+            };
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const DiffusionParams& diffusion_params) override {
+            GGML_ASSERT(diffusion_params.x != nullptr);
+            GGML_ASSERT(diffusion_params.timesteps != nullptr);
+            bool use_uncond_model = should_use_uncond_model(diffusion_params);
+            return compute(n_threads,
+                           *diffusion_params.x,
+                           *diffusion_params.timesteps,
+                           tensor_or_empty(diffusion_params.context),
+                           use_uncond_model);
+        }
+    };
+}  // namespace Ideogram4
+
+#endif  // __IDEOGRAM4_HPP__
@@ -4,6 +4,138 @@
 #include "ggml.h"
 #include "tensor.hpp"

+const float ltxav_latent_rgb_proj[128][3] = {
+    {-0.0293802f, -0.0362516f, -0.0291386f},
+    {0.0117735f, 0.0223435f, 0.018856f},
+    {0.00922335f, 0.0145666f, 0.0038772f},
+    {0.0227299f, 0.0109122f, 0.0131384f},
+    {0.00192413f, 0.0024648f, 0.00689245f},
+    {-0.0105576f, -0.0135933f, -0.00873841f},
+    {-0.0310222f, -0.0396358f, -0.0408445f},
+    {0.0149737f, 0.0316323f, 0.03415f},
+    {0.0027752f, 0.00814889f, 0.0108575f},
+    {-0.000678017f, -0.00180589f, -0.0161684f},
+    {0.0153964f, 0.0159774f, 0.0186479f},
+    {-0.0222799f, -0.0202068f, -0.0181082f},
+    {0.0128696f, 0.00754416f, -0.00673279f},
+    {0.0142729f, 0.00448099f, -0.00193934f},
+    {-0.014066f, -0.0193755f, -0.0160104f},
+    {-0.0176785f, -0.015903f, -0.0152621f},
+    {0.0307381f, 0.0292082f, 0.0328668f},
+    {0.0332928f, 0.0368629f, 0.0440893f},
+    {0.0186304f, 0.0124069f, 0.0160734f},
+    {0.00477787f, -0.00315658f, -0.000145702f},
+    {0.0183099f, 0.0122593f, 0.00599732f},
+    {-0.0194551f, -0.0183924f, -0.0147465f},
+    {0.0025732f, 0.00442582f, 0.0173176f},
+    {-0.0169423f, -0.0293863f, -0.0225908f},
+    {-0.021228f, -0.0265094f, -0.0253049f},
+    {0.0327111f, 0.0187133f, 0.0266184f},
+    {-0.0226425f, -0.0313781f, -0.0414356f},
+    {-0.0163142f, -0.0146144f, -0.0171793f},
+    {0.0192183f, 0.0108411f, 0.00829186f},
+    {-0.032246f, -0.0274846f, -0.0287434f},
+    {0.00345399f, 0.0115567f, 0.015288f},
+    {0.000972292f, 0.00331303f, 0.0110501f},
+    {0.000939494f, -0.00705084f, -0.00979449f},
+    {0.0405155f, 0.0339534f, 0.0419513f},
+    {0.0198596f, 0.0186626f, 0.0213766f},
+    {-0.00982375f, -0.00880439f, -0.00470429f},
+    {-0.0313707f, -0.0258098f, -0.0211663f},
+    {0.0144159f, 0.0117896f, 0.0141573f},
+    {0.0164571f, 0.0149178f, 0.00921599f},
+    {0.0436184f, 0.0346583f, 0.0360647f},
+    {-0.00289744f, -0.000752502f, 0.000675415f},
+    {-0.00621715f, -0.000558851f, 0.0135814f},
+    {-0.00817579f, -0.0113584f, -0.00556793f},
+    {0.00965067f, 0.0178221f, 0.015821f},
+    {0.0211832f, 0.0180827f, 0.0154707f},
+    {-0.00412858f, -0.00374182f, 0.0029568f},
+    {-0.0175603f, -0.0226242f, -0.0279012f},
+    {-0.00437471f, -0.00668329f, 0.000164887f},
+    {-0.0355983f, -0.0419093f, -0.0383065f},
+    {0.0144314f, 0.0192514f, 0.0175639f},
+    {-0.0130693f, -0.00569884f, -0.00341647f},
+    {-0.00184689f, 0.00189034f, -0.00190561f},
+    {0.019457f, 0.00842282f, 0.0123738f},
+    {-0.00477146f, -0.00206932f, 0.00283336f},
+    {-0.0364544f, -0.0256141f, -0.0322336f},
+    {-0.0295634f, -0.0295048f, -0.021057f},
+    {0.0144484f, 0.0191862f, 0.0112445f},
+    {0.0536406f, 0.0582376f, 0.0570966f},
+    {0.0085178f, 0.00748455f, 0.00995162f},
+    {-0.0136637f, -0.0172914f, -0.0195978f},
+    {-0.0339128f, -0.0392692f, -0.0355216f},
+    {0.00612855f, 0.00568303f, -0.00212333f},
+    {-0.0029225f, 0.00668819f, 0.0122131f},
+    {0.00841843f, 0.000181587f, -0.00650644f},
+    {-0.00514432f, 0.0127043f, 0.0168049f},
+    {-0.00997384f, -0.00602262f, -0.0164031f},
+    {0.0233226f, 0.033254f, 0.0307266f},
+    {-0.0110201f, -0.0164169f, -0.0161829f},
+    {-0.0195952f, -0.0177943f, -0.0115377f},
+    {-0.00523918f, -0.00452043f, 0.00267397f},
+    {0.0313464f, 0.0288241f, 0.0262496f},
+    {0.0324018f, 0.0339792f, 0.0312209f},
+    {-0.0163247f, -0.0230503f, -0.0263239f},
+    {0.000420577f, -0.00535659f, -0.00663426f},
+    {-0.012897f, -0.00203767f, -0.000622678f},
+    {-0.0632956f, -0.0651325f, -0.0584479f},
+    {-0.00426634f, -0.0150098f, -0.00719348f},
+    {0.00476109f, 0.00674315f, 0.00895472f},
+    {0.0129384f, 0.0158352f, 0.00963773f},
+    {-0.0333379f, -0.0410522f, -0.0317462f},
+    {0.00344054f, 0.00275915f, 0.00355732f},
+    {0.0209062f, 0.0273453f, 0.0222967f},
+    {0.00827287f, 0.00223045f, 0.00325844f},
+    {-0.0149132f, -0.0183973f, -0.0199781f},
+    {-0.0100786f, -0.0103681f, -0.00218224f},
+    {-0.00791409f, -0.00405153f, -0.00599893f},
+    {0.0176126f, 0.00618342f, -6.6569e-05f},
+    {0.00942486f, -0.00206494f, -0.00580324f},
+    {0.00678093f, -0.00291742f, -0.000921195f},
+    {-0.0221992f, -0.00483162f, -0.000848514f},
+    {-0.0151587f, -0.0157166f, -0.0107302f},
+    {0.00909646f, 0.0171985f, 0.0169785f},
+    {0.0127224f, 0.0170612f, 0.0303428f},
+    {0.0196562f, 0.00212451f, 0.0127744f},
+    {0.0233013f, 0.0228994f, 0.0108387f},
+    {0.00520761f, 0.00992992f, 0.0066267f},
+    {-3.77736e-05f, 0.00460229f, -0.00475132f},
+    {-0.0311763f, -0.0453566f, -0.0486901f},
+    {0.0195798f, 0.0281246f, 0.0180102f},
+    {-0.0174149f, -0.0240867f, -0.0188785f},
+    {0.000104658f, 0.00659008f, 0.0144594f},
+    {-0.00311086f, -0.0241426f, -0.0244164f},
+    {0.0336462f, 0.0305173f, 0.0331101f},
+    {0.0613625f, 0.066561f, 0.0610198f},
+    {-0.0286757f, -0.0325401f, -0.0338036f},
+    {0.0141534f, 0.0188266f, 0.0253059f},
+    {-0.00548197f, -0.00170198f, 0.00561745f},
+    {-0.0117872f, -0.00763218f, -0.0145037f},
+    {-0.0253304f, -0.0245217f, -0.0144905f},
+    {-0.00393624f, 0.00350048f, 0.00765561f},
+    {0.0113625f, 0.00561576f, -0.0113672f},
+    {-0.0301278f, -0.0261472f, -0.0301903f},
+    {0.016863f, 0.0173781f, 0.0170916f},
+    {-0.00495108f, 0.00686749f, 0.00282767f},
+    {0.00125409f, -0.00378072f, -0.00264117f},
+    {-0.00264001f, -0.00529772f, -0.0113109f},
+    {-0.054888f, -0.0575461f, -0.0509146f},
+    {-0.019442f, -0.0232916f, -0.0258637f},
+    {0.0133362f, 0.0161808f, 0.00917951f},
+    {-0.0349002f, -0.0372642f, -0.0466206f},
+    {-0.00216926f, 0.00208738f, 0.00766492f},
+    {0.0268528f, 0.0301179f, 0.0228579f},
+    {0.0226176f, 0.021536f, 0.023152f},
+    {-0.0110646f, -0.00511349f, -0.0137346f},
+    {-0.0098424f, -0.00218176f, 0.00414545f},
+    {0.00200216f, 0.00441732f, -0.0136515f},
+    {0.00695946f, 0.00313109f, -0.00379435f},
+    {0.0188377f, 0.0144059f, 0.0229724f},
+};
+float ltxav_latent_rgb_bias[3] = {0.043849f, 0.0201085f, 0.0150286f};
+
 const float wan_21_latent_rgb_proj[16][3] = {
    {0.015123f, -0.148418f, 0.479828f},
    {0.003652f, -0.010680f, -0.037142f},
@@ -0,0 +1,132 @@
+#include "layer_registry.h"
+
+#include <utility>
+
+#include "util.h"
+
+namespace sd::layer_registry {
+
+    void LayerRegistry::register_layer(const std::string& name, ggml_tensor* tensor) {
+        auto& info = layers_[name];
+        info.tensors.push_back(tensor);
+        info.bytes += ggml_nbytes(tensor);
+    }
+
+    bool LayerRegistry::move_layer_to_gpu(const std::string& name) {
+        auto it = layers_.find(name);
+        if (it == layers_.end())
+            return false;
+
+        LayerInfo& info = it->second;
+        if (info.on_gpu)
+            return true;
+        if (gpu_backend_ == nullptr || cpu_backend_ == nullptr) {
+            LOG_ERROR("layer_registry: backends not set; cannot move '%s' to GPU",
+                      name.c_str());
+            return false;
+        }
+        if (info.tensors.empty()) {
+            info.on_gpu = true;
+            return true;
+        }
+
+        // 1. Build a no_alloc context big enough to hold one twin tensor per CPU
+        //    tensor, plus a little overhead.
+        const size_t ctx_size = info.tensors.size() * ggml_tensor_overhead() + 1024;
+        ggml_init_params ctx_params{ctx_size, /*mem_buffer=*/nullptr, /*no_alloc=*/true};
+        ggml_context* twin_ctx = ggml_init(ctx_params);
+        if (twin_ctx == nullptr) {
+            LOG_ERROR("layer_registry: failed to allocate twin context for '%s'",
+                      name.c_str());
+            return false;
+        }
+
+        // 2. Create one GPU twin per CPU tensor. The twin shares the original
+        //    name so any name-based lookup keeps working.
+        std::vector<ggml_tensor*> gpu_twins;
+        gpu_twins.reserve(info.tensors.size());
+        for (ggml_tensor* cpu_t : info.tensors) {
+            ggml_tensor* twin = ggml_dup_tensor(twin_ctx, cpu_t);
+            if (cpu_t->name[0] != '\0') {
+                ggml_set_name(twin, cpu_t->name);
+            }
+            gpu_twins.push_back(twin);
+        }
+
+        // 3. Back the twins with a GPU buffer in one alloc call.
+        ggml_backend_buffer_t gpu_buffer = ggml_backend_alloc_ctx_tensors(twin_ctx, gpu_backend_);
+        if (gpu_buffer == nullptr) {
+            LOG_ERROR("layer_registry: failed to allocate GPU buffer for '%s'",
+                      name.c_str());
+            ggml_free(twin_ctx);
+            return false;
+        }
+
+        // 4. H2D copy + sync.
+        for (size_t i = 0; i < info.tensors.size(); ++i) {
+            ggml_backend_tensor_copy(info.tensors[i], gpu_twins[i]);
+        }
+        ggml_backend_synchronize(gpu_backend_);
+
+        // 5. Swap buffer/data/extra so the originals now point at GPU memory.
+        for (size_t i = 0; i < info.tensors.size(); ++i) {
+            std::swap(info.tensors[i]->buffer, gpu_twins[i]->buffer);
+            std::swap(info.tensors[i]->data, gpu_twins[i]->data);
+            std::swap(info.tensors[i]->extra, gpu_twins[i]->extra);
+        }
+
+        info.gpu_twins  = std::move(gpu_twins);
+        info.twin_ctx   = twin_ctx;
+        info.gpu_buffer = gpu_buffer;
+        info.on_gpu     = true;
+        return true;
+    }
+
+    bool LayerRegistry::move_layer_to_cpu(const std::string& name) {
+        auto it = layers_.find(name);
+        if (it == layers_.end())
+            return false;
+
+        LayerInfo& info = it->second;
+        if (!info.on_gpu)
+            return true;
+        if (info.tensors.size() != info.gpu_twins.size()) {
+            LOG_ERROR("layer_registry: twin/tensor count mismatch for '%s'",
+                      name.c_str());
+            return false;
+        }
+
+        // 1. Swap back: originals point at CPU memory again.
+        for (size_t i = 0; i < info.tensors.size(); ++i) {
+            if (info.gpu_twins[i] == nullptr)
+                continue;
+            std::swap(info.tensors[i]->buffer, info.gpu_twins[i]->buffer);
+            std::swap(info.tensors[i]->data, info.gpu_twins[i]->data);
+            std::swap(info.tensors[i]->extra, info.gpu_twins[i]->extra);
+        }
+
+        // 2. Free the GPU buffer + twin context.
+        if (info.gpu_buffer != nullptr) {
+            ggml_backend_buffer_free(info.gpu_buffer);
+            info.gpu_buffer = nullptr;
+        }
+        if (info.twin_ctx != nullptr) {
+            ggml_free(info.twin_ctx);
+            info.twin_ctx = nullptr;
+        }
+        info.gpu_twins.clear();
+        info.on_gpu = false;
+        return true;
+    }
+
+    bool LayerRegistry::is_layer_on_gpu(const std::string& name) const {
+        auto it = layers_.find(name);
+        return it != layers_.end() && it->second.on_gpu;
+    }
+
+    size_t LayerRegistry::get_layer_size(const std::string& name) const {
+        auto it = layers_.find(name);
+        return it != layers_.end() ? it->second.bytes : 0;
+    }
+
+}  // namespace sd::layer_registry
@@ -0,0 +1,50 @@
+#ifndef __LAYER_REGISTRY_H__
+#define __LAYER_REGISTRY_H__
+
+#include <map>
+#include <set>
+#include <string>
+#include <vector>
+
+#include "ggml-backend.h"
+#include "ggml.h"
+
+namespace sd::layer_registry {
+
+    struct LayerInfo {
+        std::vector<ggml_tensor*> tensors;
+        std::vector<ggml_tensor*> gpu_twins;
+        ggml_context* twin_ctx           = nullptr;
+        ggml_backend_buffer_t gpu_buffer = nullptr;
+        bool on_gpu                      = false;
+        size_t bytes                     = 0;
+    };
+
+    class LayerRegistry {
+    public:
+        LayerRegistry() = default;
+        LayerRegistry(ggml_backend_t gpu_backend, ggml_backend_t cpu_backend)
+            : gpu_backend_(gpu_backend), cpu_backend_(cpu_backend) {}
+
+        void set_backends(ggml_backend_t gpu_backend, ggml_backend_t cpu_backend) {
+            gpu_backend_ = gpu_backend;
+            cpu_backend_ = cpu_backend;
+        }
+        void register_layer(const std::string& name, ggml_tensor* tensor);
+        bool move_layer_to_gpu(const std::string& name);
+        bool move_layer_to_cpu(const std::string& name);
+        bool is_layer_on_gpu(const std::string& name) const;
+        size_t get_layer_size(const std::string& name) const;
+        size_t get_layer_count() const { return layers_.size(); }
+
+        const std::map<std::string, LayerInfo>& layers() const { return layers_; }
+
+    private:
+        ggml_backend_t gpu_backend_ = nullptr;
+        ggml_backend_t cpu_backend_ = nullptr;
+        std::map<std::string, LayerInfo> layers_;
+    };
+
+}  // namespace sd::layer_registry
+
+#endif
@@ -0,0 +1,426 @@
+#ifndef __SD_LENS_HPP__
+#define __SD_LENS_HPP__
+
+#include <memory>
+#include <vector>
+
+#include "common_block.hpp"
+#include "diffusion_model.hpp"
+#include "flux.hpp"
+#include "qwen_image.hpp"
+#include "rope.hpp"
+
+namespace Lens {
+    constexpr int LENS_GRAPH_SIZE = 40960;
+
+    struct LensConfig {
+        int patch_size              = 2;
+        int64_t in_channels         = 128;
+        int64_t out_channels        = 32;
+        int num_layers              = 48;
+        int64_t attention_head_dim  = 64;
+        int64_t num_attention_heads = 24;
+        int64_t joint_attention_dim = 2880;
+        int selected_layer_count    = 4;
+        int theta                   = 10000;
+        std::vector<int> axes_dim   = {8, 28, 28};
+        int axes_dim_sum            = 64;
+
+        static LensConfig detect_from_weights(const String2TensorStorage& tensor_storage_map, const std::string& prefix) {
+            LensConfig config;
+            config.num_layers = 0;
+            for (const auto& [name, tensor_storage] : tensor_storage_map) {
+                if (!starts_with(name, prefix)) {
+                    continue;
+                }
+                if (ends_with(name, "img_in.weight") && tensor_storage.n_dims == 2) {
+                    config.in_channels = tensor_storage.ne[0];
+                    int64_t inner_dim  = tensor_storage.ne[1];
+                    if (config.attention_head_dim > 0) {
+                        config.num_attention_heads = inner_dim / config.attention_head_dim;
+                    }
+                } else if (ends_with(name, "txt_in.weight") && tensor_storage.n_dims == 2) {
+                    config.selected_layer_count = static_cast<int>(tensor_storage.ne[0] / config.joint_attention_dim);
+                } else if (ends_with(name, "proj_out.weight") && tensor_storage.n_dims == 2) {
+                    int64_t patch_area  = config.patch_size * config.patch_size;
+                    config.out_channels = tensor_storage.ne[1] / patch_area;
+                } else if (ends_with(name, "transformer_blocks.0.attn.norm_q.weight") && tensor_storage.n_dims == 1) {
+                    config.attention_head_dim = tensor_storage.ne[0];
+                }
+
+                size_t pos = name.find("transformer_blocks.");
+                if (pos != std::string::npos) {
+                    auto items = split_string(name.substr(pos), '.');
+                    if (items.size() > 1) {
+                        int block_index = atoi(items[1].c_str());
+                        if (block_index + 1 > config.num_layers) {
+                            config.num_layers = block_index + 1;
+                        }
+                    }
+                }
+            }
+            if (config.num_layers == 0) {
+                config.num_layers = 48;
+            }
+            config.axes_dim_sum = 0;
+            for (int axis_dim : config.axes_dim) {
+                config.axes_dim_sum += axis_dim;
+            }
+            LOG_DEBUG("lens: num_layers = %d, selected_layer_count = %d, hidden_size = %" PRId64 ", num_attention_heads = %" PRId64 ", attention_head_dim = %" PRId64 ", in_channels = %" PRId64 ", out_channels = %" PRId64,
+                      config.num_layers,
+                      config.selected_layer_count,
+                      config.num_attention_heads * config.attention_head_dim,
+                      config.num_attention_heads,
+                      config.attention_head_dim,
+                      config.in_channels,
+                      config.out_channels);
+            return config;
+        }
+    };
+
+    struct LensTimestepProjEmbeddings : public GGMLBlock {
+        LensTimestepProjEmbeddings(int64_t embedding_dim) {
+            blocks["timestep_embedder"] = std::make_shared<Qwen::TimestepEmbedding>(256, embedding_dim);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* timesteps) {
+            auto timestep_embedder = std::dynamic_pointer_cast<Qwen::TimestepEmbedding>(blocks["timestep_embedder"]);
+            auto timesteps_proj    = ggml_ext_timestep_embedding(ctx->ggml_ctx, timesteps, 256, 10000, 1000.f);
+            return timestep_embedder->forward(ctx, timesteps_proj);
+        }
+    };
+
+    struct LensGateMLP : public GGMLBlock {
+        LensGateMLP(int64_t dim, int64_t hidden_dim) {
+            blocks["w1"] = std::make_shared<Linear>(dim, hidden_dim, false);
+            blocks["w2"] = std::make_shared<Linear>(hidden_dim, dim, false);
+            blocks["w3"] = std::make_shared<Linear>(dim, hidden_dim, false);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto w1 = std::dynamic_pointer_cast<Linear>(blocks["w1"]);
+            auto w2 = std::dynamic_pointer_cast<Linear>(blocks["w2"]);
+            auto w3 = std::dynamic_pointer_cast<Linear>(blocks["w3"]);
+
+            auto gate = ggml_silu(ctx->ggml_ctx, w1->forward(ctx, x));
+            auto up   = w3->forward(ctx, x);
+            x         = ggml_mul(ctx->ggml_ctx, gate, up);
+            return w2->forward(ctx, x);
+        }
+    };
+
+    struct LensJointAttention : public GGMLBlock {
+        int64_t dim_head;
+        int64_t num_heads;
+
+        LensJointAttention(int64_t query_dim,
+                           int64_t dim_head,
+                           int64_t num_heads,
+                           float eps = 1e-5f)
+            : dim_head(dim_head), num_heads(num_heads) {
+            int64_t inner_dim = dim_head * num_heads;
+            blocks["img_qkv"] = std::make_shared<Linear>(query_dim, inner_dim * 3, true);
+            blocks["txt_qkv"] = std::make_shared<Linear>(query_dim, inner_dim * 3, true);
+
+            blocks["norm_q"]       = std::make_shared<RMSNorm>(dim_head, eps);
+            blocks["norm_k"]       = std::make_shared<RMSNorm>(dim_head, eps);
+            blocks["norm_added_q"] = std::make_shared<RMSNorm>(dim_head, eps);
+            blocks["norm_added_k"] = std::make_shared<RMSNorm>(dim_head, eps);
+
+            blocks["to_out.0"]   = std::make_shared<Linear>(inner_dim, query_dim, true);
+            blocks["to_add_out"] = std::make_shared<Linear>(inner_dim, query_dim, true);
+        }
+
+        std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                      ggml_tensor* img,
+                                                      ggml_tensor* txt,
+                                                      ggml_tensor* pe,
+                                                      ggml_tensor* mask = nullptr) {
+            auto img_qkv    = std::dynamic_pointer_cast<Linear>(blocks["img_qkv"]);
+            auto txt_qkv    = std::dynamic_pointer_cast<Linear>(blocks["txt_qkv"]);
+            auto norm_q     = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_q"]);
+            auto norm_k     = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_k"]);
+            auto norm_add_q = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_added_q"]);
+            auto norm_add_k = std::dynamic_pointer_cast<RMSNorm>(blocks["norm_added_k"]);
+            auto to_out_0   = std::dynamic_pointer_cast<Linear>(blocks["to_out.0"]);
+            auto to_add_out = std::dynamic_pointer_cast<Linear>(blocks["to_add_out"]);
+            int64_t n_img   = img->ne[1];
+            int64_t n_txt   = txt->ne[1];
+            int64_t N       = img->ne[2];
+            int64_t inner   = dim_head * num_heads;
+
+            auto img_qkv_vec = split_qkv(ctx->ggml_ctx, img_qkv->forward(ctx, img));
+            auto txt_qkv_vec = split_qkv(ctx->ggml_ctx, txt_qkv->forward(ctx, txt));
+
+            auto img_q = ggml_reshape_4d(ctx->ggml_ctx, img_qkv_vec[0], dim_head, num_heads, n_img, N);
+            auto img_k = ggml_reshape_4d(ctx->ggml_ctx, img_qkv_vec[1], dim_head, num_heads, n_img, N);
+            auto img_v = ggml_reshape_4d(ctx->ggml_ctx, img_qkv_vec[2], dim_head, num_heads, n_img, N);
+
+            img_q = norm_q->forward(ctx, img_q);
+            img_k = norm_k->forward(ctx, img_k);
+
+            auto txt_q = ggml_reshape_4d(ctx->ggml_ctx, txt_qkv_vec[0], dim_head, num_heads, n_txt, N);
+            auto txt_k = ggml_reshape_4d(ctx->ggml_ctx, txt_qkv_vec[1], dim_head, num_heads, n_txt, N);
+            auto txt_v = ggml_reshape_4d(ctx->ggml_ctx, txt_qkv_vec[2], dim_head, num_heads, n_txt, N);
+
+            txt_q = norm_add_q->forward(ctx, txt_q);
+            txt_k = norm_add_k->forward(ctx, txt_k);
+
+            auto q = ggml_concat(ctx->ggml_ctx, img_q, txt_q, 2);
+            auto k = ggml_concat(ctx->ggml_ctx, img_k, txt_k, 2);
+            auto v = ggml_concat(ctx->ggml_ctx, img_v, txt_v, 2);
+
+            auto attn = Rope::attention(ctx, q, k, v, pe, mask, (1.0f / 128.f));
+
+            auto img_attn_out = ggml_view_3d(ctx->ggml_ctx,
+                                             attn,
+                                             inner,
+                                             n_img,
+                                             N,
+                                             attn->nb[1],
+                                             attn->nb[2],
+                                             0);
+            auto txt_attn_out = ggml_view_3d(ctx->ggml_ctx,
+                                             attn,
+                                             inner,
+                                             n_txt,
+                                             N,
+                                             attn->nb[1],
+                                             attn->nb[2],
+                                             n_img * attn->nb[1]);
+
+            img_attn_out = to_out_0->forward(ctx, ggml_cont(ctx->ggml_ctx, img_attn_out));
+            txt_attn_out = to_add_out->forward(ctx, ggml_cont(ctx->ggml_ctx, txt_attn_out));
+            return {img_attn_out, txt_attn_out};
+        }
+    };
+
+    struct LensTransformerBlock : public GGMLBlock {
+        LensTransformerBlock(int64_t dim,
+                             int64_t num_attention_heads,
+                             int64_t attention_head_dim,
+                             float eps = 1e-6f) {
+            int64_t mlp_hidden_dim = dim / 3 * 8;
+            blocks["img_mod.1"]    = std::make_shared<Linear>(dim, 6 * dim, true);
+            blocks["txt_mod.1"]    = std::make_shared<Linear>(dim, 6 * dim, true);
+            blocks["img_norm1"]    = std::make_shared<RMSNorm>(dim, eps);
+            blocks["img_norm2"]    = std::make_shared<RMSNorm>(dim, eps);
+            blocks["txt_norm1"]    = std::make_shared<RMSNorm>(dim, eps);
+            blocks["txt_norm2"]    = std::make_shared<RMSNorm>(dim, eps);
+            blocks["img_mlp"]      = std::make_shared<LensGateMLP>(dim, mlp_hidden_dim);
+            blocks["txt_mlp"]      = std::make_shared<LensGateMLP>(dim, mlp_hidden_dim);
+            blocks["attn"]         = std::make_shared<LensJointAttention>(dim, attention_head_dim, num_attention_heads);
+        }
+
+        std::pair<ggml_tensor*, ggml_tensor*> forward(GGMLRunnerContext* ctx,
+                                                      ggml_tensor* img,
+                                                      ggml_tensor* txt,
+                                                      ggml_tensor* t_emb,
+                                                      ggml_tensor* pe) {
+            auto img_mod_1 = std::dynamic_pointer_cast<Linear>(blocks["img_mod.1"]);
+            auto txt_mod_1 = std::dynamic_pointer_cast<Linear>(blocks["txt_mod.1"]);
+            auto img_norm1 = std::dynamic_pointer_cast<RMSNorm>(blocks["img_norm1"]);
+            auto img_norm2 = std::dynamic_pointer_cast<RMSNorm>(blocks["img_norm2"]);
+            auto txt_norm1 = std::dynamic_pointer_cast<RMSNorm>(blocks["txt_norm1"]);
+            auto txt_norm2 = std::dynamic_pointer_cast<RMSNorm>(blocks["txt_norm2"]);
+            auto img_mlp   = std::dynamic_pointer_cast<LensGateMLP>(blocks["img_mlp"]);
+            auto txt_mlp   = std::dynamic_pointer_cast<LensGateMLP>(blocks["txt_mlp"]);
+            auto attn      = std::dynamic_pointer_cast<LensJointAttention>(blocks["attn"]);
+
+            auto temb = ggml_silu(ctx->ggml_ctx, t_emb);
+
+            auto img_mod_params = img_mod_1->forward(ctx, temb);
+            auto img_mod_vec    = ggml_ext_chunk(ctx->ggml_ctx, img_mod_params, 6, 0);
+            auto txt_mod_params = txt_mod_1->forward(ctx, temb);
+            auto txt_mod_vec    = ggml_ext_chunk(ctx->ggml_ctx, txt_mod_params, 6, 0);
+
+            auto img_normed    = img_norm1->forward(ctx, img);
+            auto img_modulated = Flux::modulate(ctx->ggml_ctx, img_normed, img_mod_vec[0], img_mod_vec[1]);
+            auto txt_normed    = txt_norm1->forward(ctx, txt);
+            auto txt_modulated = Flux::modulate(ctx->ggml_ctx, txt_normed, txt_mod_vec[0], txt_mod_vec[1]);
+
+            auto [img_attn_output, txt_attn_output] = attn->forward(ctx, img_modulated, txt_modulated, pe);
+
+            img = ggml_add(ctx->ggml_ctx, img, ggml_mul(ctx->ggml_ctx, img_attn_output, img_mod_vec[2]));
+            txt = ggml_add(ctx->ggml_ctx, txt, ggml_mul(ctx->ggml_ctx, txt_attn_output, txt_mod_vec[2]));
+
+            auto img_normed2    = img_norm2->forward(ctx, img);
+            auto img_modulated2 = Flux::modulate(ctx->ggml_ctx, img_normed2, img_mod_vec[3], img_mod_vec[4]);
+            auto txt_normed2    = txt_norm2->forward(ctx, txt);
+            auto txt_modulated2 = Flux::modulate(ctx->ggml_ctx, txt_normed2, txt_mod_vec[3], txt_mod_vec[4]);
+
+            img = ggml_add(ctx->ggml_ctx, img, ggml_mul(ctx->ggml_ctx, img_mlp->forward(ctx, img_modulated2), img_mod_vec[5]));
+            txt = ggml_add(ctx->ggml_ctx, txt, ggml_mul(ctx->ggml_ctx, txt_mlp->forward(ctx, txt_modulated2), txt_mod_vec[5]));
+            return {img, txt};
+        }
+    };
+
+    struct LensAdaLayerNormContinuous : public GGMLBlock {
+        int64_t hidden_size;
+        float eps;
+
+        LensAdaLayerNormContinuous(int64_t hidden_size, float eps = 1e-6f)
+            : hidden_size(hidden_size), eps(eps) {
+            blocks["linear"] = std::make_shared<Linear>(hidden_size, hidden_size * 2, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x, ggml_tensor* conditioning) {
+            auto linear = std::dynamic_pointer_cast<Linear>(blocks["linear"]);
+            auto mods   = ggml_ext_chunk(ctx->ggml_ctx, linear->forward(ctx, ggml_silu(ctx->ggml_ctx, conditioning)), 2, 0);
+            auto scale  = mods[0];
+            auto shift  = mods[1];
+            x           = ggml_norm(ctx->ggml_ctx, x, eps);
+            return Flux::modulate(ctx->ggml_ctx, x, shift, scale);
+        }
+    };
+
+    class LensModel : public GGMLBlock {
+    public:
+        LensConfig config;
+
+        LensModel() = default;
+        LensModel(LensConfig config)
+            : config(config) {
+            int64_t inner_dim         = config.num_attention_heads * config.attention_head_dim;
+            blocks["time_text_embed"] = std::make_shared<LensTimestepProjEmbeddings>(inner_dim);
+            blocks["img_in"]          = std::make_shared<Linear>(config.in_channels, inner_dim, true);
+            blocks["txt_in"]          = std::make_shared<Linear>(config.joint_attention_dim * config.selected_layer_count, inner_dim, true);
+            for (int i = 0; i < config.selected_layer_count; ++i) {
+                blocks["txt_norm." + std::to_string(i)] = std::make_shared<RMSNorm>(config.joint_attention_dim, 1e-5f);
+            }
+            for (int i = 0; i < config.num_layers; ++i) {
+                blocks["transformer_blocks." + std::to_string(i)] = std::make_shared<LensTransformerBlock>(inner_dim,
+                                                                                                           config.num_attention_heads,
+                                                                                                           config.attention_head_dim);
+            }
+            blocks["norm_out"] = std::make_shared<LensAdaLayerNormContinuous>(inner_dim, 1e-6f);
+            blocks["proj_out"] = std::make_shared<Linear>(inner_dim, config.patch_size * config.patch_size * config.out_channels, true);
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx,
+                             ggml_tensor* x,
+                             ggml_tensor* timestep,
+                             ggml_tensor* context,
+                             ggml_tensor* pe) {
+            GGML_ASSERT(context != nullptr);
+            int64_t W = x->ne[0];
+            int64_t H = x->ne[1];
+            int64_t C = x->ne[2];
+            int64_t N = x->ne[3];
+
+            auto time_text_embed = std::dynamic_pointer_cast<LensTimestepProjEmbeddings>(blocks["time_text_embed"]);
+            auto img_in          = std::dynamic_pointer_cast<Linear>(blocks["img_in"]);
+            auto txt_in          = std::dynamic_pointer_cast<Linear>(blocks["txt_in"]);
+            auto norm_out        = std::dynamic_pointer_cast<LensAdaLayerNormContinuous>(blocks["norm_out"]);
+            auto proj_out        = std::dynamic_pointer_cast<Linear>(blocks["proj_out"]);
+
+            auto t_emb = time_text_embed->forward(ctx, timestep);
+
+            auto img = ggml_reshape_3d(ctx->ggml_ctx, x, W * H, C, N);
+            img      = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, img, 1, 0, 2, 3));
+            img      = img_in->forward(ctx, img);
+
+            std::vector<ggml_tensor*> txt_chunks = ggml_ext_chunk(ctx->ggml_ctx, context, config.selected_layer_count, 0);
+            ggml_tensor* txt                     = nullptr;
+            for (int i = 0; i < config.selected_layer_count; ++i) {
+                auto txt_norm = std::dynamic_pointer_cast<RMSNorm>(blocks["txt_norm." + std::to_string(i)]);
+                auto chunk    = txt_norm->forward(ctx, txt_chunks[i]);
+                txt           = txt == nullptr ? chunk : ggml_concat(ctx->ggml_ctx, txt, chunk, 0);
+            }
+            txt = txt_in->forward(ctx, txt);
+
+            sd::ggml_graph_cut::mark_graph_cut(img, "lens.prelude", "img");
+            sd::ggml_graph_cut::mark_graph_cut(txt, "lens.prelude", "txt");
+
+            for (int i = 0; i < config.num_layers; ++i) {
+                auto block = std::dynamic_pointer_cast<LensTransformerBlock>(blocks["transformer_blocks." + std::to_string(i)]);
+                auto out   = block->forward(ctx, img, txt, t_emb, pe);
+                img        = out.first;
+                txt        = out.second;
+                sd::ggml_graph_cut::mark_graph_cut(img, "lens.transformer_blocks." + std::to_string(i), "img");
+                sd::ggml_graph_cut::mark_graph_cut(txt, "lens.transformer_blocks." + std::to_string(i), "txt");
+            }
+
+            img = norm_out->forward(ctx, img, t_emb);
+            img = proj_out->forward(ctx, img);
+
+            auto out = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, img, 1, 0, 2, 3));
+            out      = ggml_reshape_4d(ctx->ggml_ctx, out, W, H, config.patch_size * config.patch_size * config.out_channels, N);
+            return out;
+        }
+    };
+
+    struct LensRunner : public DiffusionModelRunner {
+        LensConfig config;
+        LensModel lens;
+        std::vector<float> pe_vec;
+
+        LensRunner(ggml_backend_t backend,
+                   ggml_backend_t params_backend,
+                   const String2TensorStorage& tensor_storage_map = {},
+                   const std::string prefix                       = "")
+            : DiffusionModelRunner(backend, params_backend, prefix),
+              config(LensConfig::detect_from_weights(tensor_storage_map, prefix)) {
+            lens = LensModel(config);
+            lens.init(params_ctx, tensor_storage_map, prefix);
+        }
+
+        std::string get_desc() override {
+            return "lens";
+        }
+
+        void get_param_tensors(std::map<std::string, ggml_tensor*>& tensors, const std::string& prefix) override {
+            lens.get_param_tensors(tensors, prefix);
+        }
+
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor,
+                                 const sd::Tensor<float>& timesteps_tensor,
+                                 const sd::Tensor<float>& context_tensor) {
+            ggml_cgraph* gf        = new_graph_custom(LENS_GRAPH_SIZE);
+            ggml_tensor* x         = make_input(x_tensor);
+            ggml_tensor* timesteps = make_input(timesteps_tensor);
+            GGML_ASSERT(x->ne[3] == 1);
+            GGML_ASSERT(!context_tensor.empty());
+            ggml_tensor* context = make_input(context_tensor);
+
+            pe_vec      = Rope::gen_lens_pe(static_cast<int>(x->ne[1]),
+                                            static_cast<int>(x->ne[0]),
+                                            static_cast<int>(x->ne[3]),
+                                            static_cast<int>(context->ne[1]),
+                                            config.theta,
+                                            circular_y_enabled,
+                                            circular_x_enabled,
+                                            config.axes_dim);
+            int pos_len = static_cast<int>(pe_vec.size() / config.axes_dim_sum / 2);
+            auto pe     = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.axes_dim_sum / 2, pos_len);
+            set_backend_tensor_data(pe, pe_vec.data());
+
+            auto runner_ctx  = get_context();
+            ggml_tensor* out = lens.forward(&runner_ctx, x, timesteps, context, pe);
+            ggml_build_forward_expand(gf, out);
+            return gf;
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const sd::Tensor<float>& x,
+                                  const sd::Tensor<float>& timesteps,
+                                  const sd::Tensor<float>& context) {
+            auto get_graph = [&]() -> ggml_cgraph* {
+                return build_graph(x, timesteps, context);
+            };
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
+        }
+
+        sd::Tensor<float> compute(int n_threads,
+                                  const DiffusionParams& diffusion_params) override {
+            GGML_ASSERT(diffusion_params.x != nullptr);
+            GGML_ASSERT(diffusion_params.timesteps != nullptr);
+            return compute(n_threads,
+                           *diffusion_params.x,
+                           *diffusion_params.timesteps,
+                           tensor_or_empty(diffusion_params.context));
+        }
+    };
+}  // namespace Lens
+
+#endif  // __SD_LENS_HPP__
@@ -22,10 +22,11 @@ struct LoraModel : public GGMLRunner {

    LoraModel(const std::string& lora_id,
              ggml_backend_t backend,
+              ggml_backend_t params_backend,
              const std::string& file_path = "",
              std::string prefix           = "",
              SDVersion version            = VERSION_COUNT)
-        : lora_id(lora_id), file_path(file_path), GGMLRunner(backend, false) {
+        : lora_id(lora_id), file_path(file_path), GGMLRunner(backend, params_backend) {
        prefix = "lora." + prefix;
        if (!model_loader.init_from_file_and_convert_name(file_path, prefix, version)) {
            load_failed = true;
@@ -85,7 +86,10 @@ struct LoraModel : public GGMLRunner {
            lora_tensors[name] = real;
        }

-        alloc_params_buffer();
+        if (!alloc_params_buffer()) {
+            LOG_ERROR("lora model buffer allocation failed");
+            return false;
+        }

        dry_run = false;
        model_loader.load_tensors(on_new_tensor_cb, n_threads);
@@ -129,7 +133,7 @@ struct LoraModel : public GGMLRunner {
        }
    }

-    ggml_tensor* get_lora_weight_diff(const std::string& model_tensor_name, ggml_context* ctx) {
+    ggml_tensor* get_lora_weight_diff(const std::string& model_tensor_name, ggml_context* ctx, ggml_backend_t backend) {
        ggml_tensor* updown = nullptr;
        int index           = 0;
        while (true) {
@@ -152,17 +156,17 @@ struct LoraModel : public GGMLRunner {

            auto iter = lora_tensors.find(lora_up_name);
            if (iter != lora_tensors.end()) {
-                lora_up = ggml_ext_cast_f32(ctx, iter->second);
+                lora_up = ggml_ext_cast_f32(ctx, backend, iter->second);
            }

            iter = lora_tensors.find(lora_mid_name);
            if (iter != lora_tensors.end()) {
-                lora_mid = ggml_ext_cast_f32(ctx, iter->second);
+                lora_mid = ggml_ext_cast_f32(ctx, backend, iter->second);
            }

            iter = lora_tensors.find(lora_down_name);
            if (iter != lora_tensors.end()) {
-                lora_down = ggml_ext_cast_f32(ctx, iter->second);
+                lora_down = ggml_ext_cast_f32(ctx, backend, iter->second);
            }

            if (lora_up == nullptr || lora_down == nullptr) {
@@ -208,7 +212,7 @@ struct LoraModel : public GGMLRunner {
        return updown;
    }

-    ggml_tensor* get_raw_weight_diff(const std::string& model_tensor_name, ggml_context* ctx) {
+    ggml_tensor* get_raw_weight_diff(const std::string& model_tensor_name, ggml_context* ctx, ggml_backend_t backend) {
        ggml_tensor* updown = nullptr;
        int index           = 0;
        while (true) {
@@ -225,7 +229,7 @@ struct LoraModel : public GGMLRunner {

            auto iter = lora_tensors.find(diff_name);
            if (iter != lora_tensors.end()) {
-                curr_updown = ggml_ext_cast_f32(ctx, iter->second);
+                curr_updown = ggml_ext_cast_f32(ctx, backend, iter->second);
            } else {
                break;
            }
@@ -248,7 +252,7 @@ struct LoraModel : public GGMLRunner {
        return updown;
    }

-    ggml_tensor* get_loha_weight_diff(const std::string& model_tensor_name, ggml_context* ctx) {
+    ggml_tensor* get_loha_weight_diff(const std::string& model_tensor_name, ggml_context* ctx, ggml_backend_t backend) {
        ggml_tensor* updown = nullptr;
        int index           = 0;
        while (true) {
@@ -276,33 +280,33 @@ struct LoraModel : public GGMLRunner {

            auto iter = lora_tensors.find(hada_1_down_name);
            if (iter != lora_tensors.end()) {
-                hada_1_down = ggml_ext_cast_f32(ctx, iter->second);
+                hada_1_down = ggml_ext_cast_f32(ctx, backend, iter->second);
            }

            iter = lora_tensors.find(hada_1_up_name);
            if (iter != lora_tensors.end()) {
-                hada_1_up = ggml_ext_cast_f32(ctx, iter->second);
+                hada_1_up = ggml_ext_cast_f32(ctx, backend, iter->second);
            }

            iter = lora_tensors.find(hada_1_mid_name);
            if (iter != lora_tensors.end()) {
-                hada_1_mid = ggml_ext_cast_f32(ctx, iter->second);
+                hada_1_mid = ggml_ext_cast_f32(ctx, backend, iter->second);
                hada_1_up  = ggml_cont(ctx, ggml_transpose(ctx, hada_1_up));
            }

            iter = lora_tensors.find(hada_2_down_name);
            if (iter != lora_tensors.end()) {
-                hada_2_down = ggml_ext_cast_f32(ctx, iter->second);
+                hada_2_down = ggml_ext_cast_f32(ctx, backend, iter->second);
            }

            iter = lora_tensors.find(hada_2_up_name);
            if (iter != lora_tensors.end()) {
-                hada_2_up = ggml_ext_cast_f32(ctx, iter->second);
+                hada_2_up = ggml_ext_cast_f32(ctx, backend, iter->second);
            }

            iter = lora_tensors.find(hada_2_mid_name);
            if (iter != lora_tensors.end()) {
-                hada_2_mid = ggml_ext_cast_f32(ctx, iter->second);
+                hada_2_mid = ggml_ext_cast_f32(ctx, backend, iter->second);
                hada_2_up  = ggml_cont(ctx, ggml_transpose(ctx, hada_2_up));
            }

@@ -351,7 +355,7 @@ struct LoraModel : public GGMLRunner {
        return updown;
    }

-    ggml_tensor* get_lokr_weight_diff(const std::string& model_tensor_name, ggml_context* ctx) {
+    ggml_tensor* get_lokr_weight_diff(const std::string& model_tensor_name, ggml_context* ctx, ggml_backend_t backend) {
        ggml_tensor* updown = nullptr;
        int index           = 0;
        while (true) {
@@ -378,24 +382,24 @@ struct LoraModel : public GGMLRunner {

            auto iter = lora_tensors.find(lokr_w1_name);
            if (iter != lora_tensors.end()) {
-                lokr_w1 = ggml_ext_cast_f32(ctx, iter->second);
+                lokr_w1 = ggml_ext_cast_f32(ctx, backend, iter->second);
            }

            iter = lora_tensors.find(lokr_w2_name);
            if (iter != lora_tensors.end()) {
-                lokr_w2 = ggml_ext_cast_f32(ctx, iter->second);
+                lokr_w2 = ggml_ext_cast_f32(ctx, backend, iter->second);
            }

            int64_t rank = 1;
            if (lokr_w1 == nullptr) {
                iter = lora_tensors.find(lokr_w1_a_name);
                if (iter != lora_tensors.end()) {
-                    lokr_w1_a = ggml_ext_cast_f32(ctx, iter->second);
+                    lokr_w1_a = ggml_ext_cast_f32(ctx, backend, iter->second);
                }

                iter = lora_tensors.find(lokr_w1_b_name);
                if (iter != lora_tensors.end()) {
-                    lokr_w1_b = ggml_ext_cast_f32(ctx, iter->second);
+                    lokr_w1_b = ggml_ext_cast_f32(ctx, backend, iter->second);
                }

                if (lokr_w1_a == nullptr || lokr_w1_b == nullptr) {
@@ -410,12 +414,12 @@ struct LoraModel : public GGMLRunner {
            if (lokr_w2 == nullptr) {
                iter = lora_tensors.find(lokr_w2_a_name);
                if (iter != lora_tensors.end()) {
-                    lokr_w2_a = ggml_ext_cast_f32(ctx, iter->second);
+                    lokr_w2_a = ggml_ext_cast_f32(ctx, backend, iter->second);
                }

                iter = lora_tensors.find(lokr_w2_b_name);
                if (iter != lora_tensors.end()) {
-                    lokr_w2_b = ggml_ext_cast_f32(ctx, iter->second);
+                    lokr_w2_b = ggml_ext_cast_f32(ctx, backend, iter->second);
                }

                if (lokr_w2_a == nullptr || lokr_w2_b == nullptr) {
@@ -468,23 +472,23 @@ struct LoraModel : public GGMLRunner {
        return updown;
    }

-    ggml_tensor* get_weight_diff(const std::string& model_tensor_name, ggml_context* ctx, ggml_tensor* model_tensor, bool with_lora_and_lokr = true) {
+    ggml_tensor* get_weight_diff(const std::string& model_tensor_name, ggml_backend_t backend, ggml_context* ctx, ggml_tensor* model_tensor, bool with_lora_and_lokr = true) {
        // lora
        ggml_tensor* diff = nullptr;
        if (with_lora_and_lokr) {
-            diff = get_lora_weight_diff(model_tensor_name, ctx);
+            diff = get_lora_weight_diff(model_tensor_name, ctx, backend);
        }
        // diff
        if (diff == nullptr) {
-            diff = get_raw_weight_diff(model_tensor_name, ctx);
+            diff = get_raw_weight_diff(model_tensor_name, ctx, backend);
        }
        // loha
        if (diff == nullptr) {
-            diff = get_loha_weight_diff(model_tensor_name, ctx);
+            diff = get_loha_weight_diff(model_tensor_name, ctx, backend);
        }
        // lokr
        if (diff == nullptr && with_lora_and_lokr) {
-            diff = get_lokr_weight_diff(model_tensor_name, ctx);
+            diff = get_lokr_weight_diff(model_tensor_name, ctx, backend);
        }
        if (diff != nullptr) {
            if (ggml_nelements(diff) < ggml_nelements(model_tensor)) {
@@ -502,6 +506,7 @@ struct LoraModel : public GGMLRunner {
    }

    ggml_tensor* get_out_diff(ggml_context* ctx,
+                              ggml_backend_t backend,
                              ggml_tensor* x,
                              WeightAdapter::ForwardParams forward_params,
                              const std::string& model_tensor_name) {
@@ -590,7 +595,7 @@ struct LoraModel : public GGMLRunner {
                }
                scale_value *= multiplier;

-                auto curr_out_diff = ggml_ext_lokr_forward(ctx, x, lokr_w1, lokr_w1_a, lokr_w1_b, lokr_w2, lokr_w2_a, lokr_w2_b, is_conv2d, forward_params.conv2d, scale_value);
+                auto curr_out_diff = ggml_ext_lokr_forward(ctx, backend, x, lokr_w1, lokr_w1_a, lokr_w1_b, lokr_w2, lokr_w2_a, lokr_w2_b, is_conv2d, forward_params.conv2d, scale_value);
                if (out_diff == nullptr) {
                    out_diff = curr_out_diff;
                } else {
@@ -761,27 +766,27 @@ struct LoraModel : public GGMLRunner {
            ggml_tensor* model_tensor     = it.second;

            // lora
-            ggml_tensor* diff = get_weight_diff(model_tensor_name, compute_ctx, model_tensor);
+            ggml_tensor* diff = get_weight_diff(model_tensor_name, runtime_backend, compute_ctx, model_tensor);
            if (diff == nullptr) {
                continue;
            }

            ggml_tensor* original_tensor = model_tensor;
-            if (!ggml_backend_is_cpu(runtime_backend) && ggml_backend_buffer_is_host(original_tensor->buffer)) {
+            if (!sd_backend_is_cpu(runtime_backend) && ggml_backend_buffer_is_host(original_tensor->buffer)) {
                model_tensor = ggml_dup_tensor(compute_ctx, model_tensor);
                set_backend_tensor_data(model_tensor, original_tensor->data);
            }

            ggml_tensor* final_tensor;
            if (model_tensor->type != GGML_TYPE_F32 && model_tensor->type != GGML_TYPE_F16) {
-                final_tensor = ggml_ext_cast_f32(compute_ctx, model_tensor);
+                final_tensor = ggml_ext_cast_f32(compute_ctx, runtime_backend, model_tensor);
                final_tensor = ggml_add_inplace(compute_ctx, final_tensor, diff);
                final_tensor = ggml_cpy(compute_ctx, final_tensor, model_tensor);
            } else {
                final_tensor = ggml_add_inplace(compute_ctx, model_tensor, diff);
            }
            ggml_build_forward_expand(gf, final_tensor);
-            if (!ggml_backend_is_cpu(runtime_backend) && ggml_backend_buffer_is_host(original_tensor->buffer)) {
+            if (!sd_backend_is_cpu(runtime_backend) && ggml_backend_buffer_is_host(original_tensor->buffer)) {
                original_tensor_to_final_tensor[original_tensor] = final_tensor;
            }
        }
@@ -841,34 +846,35 @@ public:
        : lora_models(lora_models) {
    }

-    ggml_tensor* patch_weight(ggml_context* ctx, ggml_tensor* weight, const std::string& weight_name, bool with_lora_and_lokr) {
+    ggml_tensor* patch_weight(ggml_context* ctx, ggml_backend_t backend, ggml_tensor* weight, const std::string& weight_name, bool with_lora_and_lokr) {
        for (auto& lora_model : lora_models) {
-            ggml_tensor* diff = lora_model->get_weight_diff(weight_name, ctx, weight, with_lora_and_lokr);
+            ggml_tensor* diff = lora_model->get_weight_diff(weight_name, backend, ctx, weight, with_lora_and_lokr);
            if (diff == nullptr) {
                continue;
            }

            if (weight->type != GGML_TYPE_F32 && weight->type != GGML_TYPE_F16) {
-                weight = ggml_ext_cast_f32(ctx, weight);
+                weight = ggml_ext_cast_f32(ctx, backend, weight);
            }
            weight = ggml_add(ctx, weight, diff);
        }
        return weight;
    }

-    ggml_tensor* patch_weight(ggml_context* ctx, ggml_tensor* weight, const std::string& weight_name) override {
-        return patch_weight(ctx, weight, weight_name, true);
+    ggml_tensor* patch_weight(ggml_context* ctx, ggml_backend_t backend, ggml_tensor* weight, const std::string& weight_name) override {
+        return patch_weight(ctx, backend, weight, weight_name, true);
    }

    ggml_tensor* forward_with_lora(ggml_context* ctx,
+                                   ggml_backend_t backend,
                                   ggml_tensor* x,
                                   ggml_tensor* w,
                                   ggml_tensor* b,
                                   const std::string& prefix,
                                   WeightAdapter::ForwardParams forward_params) override {
-        w = patch_weight(ctx, w, prefix + "weight", false);
+        w = patch_weight(ctx, backend, w, prefix + "weight", false);
        if (b) {
-            b = patch_weight(ctx, b, prefix + "bias", false);
+            b = patch_weight(ctx, backend, b, prefix + "bias", false);
        }
        ggml_tensor* out;
        if (forward_params.op_type == ForwardParams::op_type_t::OP_LINEAR) {
@@ -890,7 +896,7 @@ public:
                                   forward_params.conv2d.scale);
        }
        for (auto& lora_model : lora_models) {
-            ggml_tensor* out_diff = lora_model->get_out_diff(ctx, x, forward_params, prefix + "weight");
+            ggml_tensor* out_diff = lora_model->get_out_diff(ctx, backend, x, forward_params, prefix + "weight");
            if (out_diff == nullptr) {
                continue;
            }
@@ -0,0 +1,551 @@
+#ifndef __SD_LTX_LATENT_UPSCALER_HPP__
+#define __SD_LTX_LATENT_UPSCALER_HPP__
+
+#include <cinttypes>
+#include <cmath>
+#include <cstdlib>
+#include <map>
+#include <memory>
+#include <set>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "common_dit.hpp"
+#include "ggml_extend.hpp"
+#include "ggml_graph_cut.h"
+#include "model.h"
+#include "util.h"
+
+namespace LTXVUpsampler {
+    constexpr int LTX_UPSAMPLER_GRAPH_SIZE = 10240;
+
+    struct LatentUpsamplerConfig {
+        int64_t in_channels      = 128;
+        int64_t mid_channels     = 1024;
+        int num_blocks_per_stage = 4;
+        int dims                 = 3;
+        bool spatial_upsample    = true;
+        bool temporal_upsample   = false;
+        bool rational_resampler  = false;
+        float spatial_scale      = 2.f;
+        int spatial_up_num       = 2;
+        int spatial_down_den     = 1;
+        int temporal_up_factor   = 1;
+    };
+
+    static inline bool has_tensor(const String2TensorStorage& tensor_storage_map,
+                                  const std::string& name) {
+        return tensor_storage_map.find(name) != tensor_storage_map.end();
+    }
+
+    static inline int64_t get_tensor_ne(const String2TensorStorage& tensor_storage_map,
+                                        const std::string& name,
+                                        int axis,
+                                        int64_t fallback) {
+        auto it = tensor_storage_map.find(name);
+        if (it == tensor_storage_map.end() || axis < 0 || axis >= GGML_MAX_DIMS) {
+            return fallback;
+        }
+        return it->second.ne[axis];
+    }
+
+    static inline int64_t get_tensor_ne0(const String2TensorStorage& tensor_storage_map,
+                                         const std::string& name,
+                                         int64_t fallback) {
+        return get_tensor_ne(tensor_storage_map, name, 0, fallback);
+    }
+
+    static inline int count_module_blocks(const String2TensorStorage& tensor_storage_map,
+                                          const std::string& module_name) {
+        int max_block            = -1;
+        const std::string prefix = module_name + ".";
+        for (const auto& pair : tensor_storage_map) {
+            const std::string& name = pair.first;
+            if (name.find(prefix) != 0) {
+                continue;
+            }
+            size_t begin = prefix.size();
+            size_t end   = name.find('.', begin);
+            if (end == std::string::npos) {
+                continue;
+            }
+            int index = atoi(name.substr(begin, end - begin).c_str());
+            max_block = std::max(max_block, index);
+        }
+        return max_block + 1;
+    }
+
+    static inline LatentUpsamplerConfig detect_config_from_weights(const String2TensorStorage& tensor_storage_map) {
+        LatentUpsamplerConfig config;
+        config.mid_channels = get_tensor_ne0(tensor_storage_map, "initial_norm.weight", config.mid_channels);
+        config.in_channels  = get_tensor_ne0(tensor_storage_map, "final_conv.bias", config.in_channels);
+        int detected_blocks = count_module_blocks(tensor_storage_map, "res_blocks");
+        if (detected_blocks > 0) {
+            config.num_blocks_per_stage = detected_blocks;
+        }
+        config.rational_resampler      = has_tensor(tensor_storage_map, "upsampler.conv.weight");
+        int64_t upsampler_out_channels = get_tensor_ne0(tensor_storage_map, "upsampler.0.bias", 0);
+        config.spatial_upsample        = config.rational_resampler || upsampler_out_channels == 4 * config.mid_channels;
+        config.temporal_upsample       = upsampler_out_channels == 2 * config.mid_channels;
+        if (config.temporal_upsample) {
+            config.temporal_up_factor = 2;
+        }
+        if (config.rational_resampler) {
+            int64_t out_channels = get_tensor_ne(tensor_storage_map,
+                                                 "upsampler.conv.weight",
+                                                 3,
+                                                 config.mid_channels * 9);
+            if (config.mid_channels > 0 && out_channels % config.mid_channels == 0) {
+                int64_t ratio = out_channels / config.mid_channels;
+                int num       = static_cast<int>(std::round(std::sqrt(static_cast<double>(ratio))));
+                if (num > 0 && static_cast<int64_t>(num) * num == ratio) {
+                    config.spatial_up_num = num;
+                }
+            }
+            if (config.spatial_up_num == 3) {
+                config.spatial_down_den = 2;
+                config.spatial_scale    = 1.5f;
+            } else if (config.spatial_up_num == 4) {
+                config.spatial_down_den = 1;
+                config.spatial_scale    = 4.f;
+            } else {
+                config.spatial_down_den = 1;
+                config.spatial_scale    = static_cast<float>(config.spatial_up_num);
+            }
+        }
+        return config;
+    }
+
+    class VideoGroupNorm : public GGMLBlock {
+    protected:
+        int num_groups;
+        int64_t num_channels;
+        float eps;
+        std::string prefix;
+
+        void init_params(ggml_context* ctx,
+                         const String2TensorStorage& tensor_storage_map = {},
+                         const std::string prefix                       = "") override {
+            SD_UNUSED(tensor_storage_map);
+            this->prefix     = prefix;
+            params["weight"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, num_channels);
+            params["bias"]   = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, num_channels);
+        }
+
+    public:
+        VideoGroupNorm(int num_groups, int64_t num_channels, float eps = 1e-05f)
+            : num_groups(num_groups),
+              num_channels(num_channels),
+              eps(eps) {}
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            // LTX video latent layout is [W, H, T, C]. ggml_group_norm treats ne[2]
+            // as channels, so fold only H/T internally and restore the same layout.
+            GGML_ASSERT(x->ne[3] == num_channels);
+            const int64_t W = x->ne[0];
+            const int64_t H = x->ne[1];
+            const int64_t T = x->ne[2];
+            x               = ggml_ext_cont(ctx->ggml_ctx, x);
+            x               = ggml_reshape_4d(ctx->ggml_ctx, x, W, H * T, num_channels, 1);
+            x               = ggml_group_norm(ctx->ggml_ctx, x, num_groups, eps);
+
+            ggml_tensor* weight = params["weight"];
+            ggml_tensor* bias   = params["bias"];
+            if (ctx->weight_adapter) {
+                weight = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, ctx->backend, weight, prefix + "weight");
+                bias   = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, ctx->backend, bias, prefix + "bias");
+            }
+            weight = ggml_reshape_4d(ctx->ggml_ctx, weight, 1, 1, num_channels, 1);
+            bias   = ggml_reshape_4d(ctx->ggml_ctx, bias, 1, 1, num_channels, 1);
+            x      = ggml_mul_inplace(ctx->ggml_ctx, x, weight);
+            x      = ggml_add_inplace(ctx->ggml_ctx, x, bias);
+            return ggml_reshape_4d(ctx->ggml_ctx, x, W, H, T, num_channels);
+        }
+    };
+
+    class ResBlock : public GGMLBlock {
+    public:
+        ResBlock(int64_t channels, int dims = 3) {
+            GGML_ASSERT(dims == 3);
+            blocks["conv1"] = std::shared_ptr<GGMLBlock>(new Conv3d(channels, channels, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+            blocks["norm1"] = std::shared_ptr<GGMLBlock>(new VideoGroupNorm(32, channels));
+            blocks["conv2"] = std::shared_ptr<GGMLBlock>(new Conv3d(channels, channels, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}));
+            blocks["norm2"] = std::shared_ptr<GGMLBlock>(new VideoGroupNorm(32, channels));
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto conv1 = std::dynamic_pointer_cast<Conv3d>(blocks["conv1"]);
+            auto norm1 = std::dynamic_pointer_cast<VideoGroupNorm>(blocks["norm1"]);
+            auto conv2 = std::dynamic_pointer_cast<Conv3d>(blocks["conv2"]);
+            auto norm2 = std::dynamic_pointer_cast<VideoGroupNorm>(blocks["norm2"]);
+
+            ggml_tensor* residual = x;
+
+            x = conv1->forward(ctx, x);
+            x = norm1->forward(ctx, x);
+            x = ggml_silu_inplace(ctx->ggml_ctx, x);
+            x = conv2->forward(ctx, x);
+            x = norm2->forward(ctx, x);
+            x = ggml_add(ctx->ggml_ctx, x, residual);
+            return ggml_silu(ctx->ggml_ctx, x);
+        }
+    };
+
+    class PixelShuffleND : public UnaryBlock {
+    protected:
+        int upscale_factor;
+
+    public:
+        explicit PixelShuffleND(int upscale_factor)
+            : upscale_factor(upscale_factor) {}
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+            GGML_ASSERT(upscale_factor > 0);
+            int64_t h = x->ne[1];
+            int64_t w = x->ne[0];
+            GGML_ASSERT(x->ne[2] % (upscale_factor * upscale_factor) == 0);
+            // x: [b*f, c*p1*p2, h, w] -> [b*f, c, h*p1, w*p2]
+            x = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 2, 0, 1, 3));  // [b*f, h, w, c*p1*p2]
+            x = ggml_reshape_3d(ctx->ggml_ctx, x, x->ne[0], x->ne[1] * x->ne[2], x->ne[3]);          // [b*f, h*w, c*p1*p2]
+            return DiT::unpatchify(ctx->ggml_ctx, x, h, w, upscale_factor, upscale_factor, true);
+        }
+    };
+
+    class TemporalPixelShuffleND : public UnaryBlock {
+    protected:
+        int upscale_factor;
+
+    public:
+        explicit TemporalPixelShuffleND(int upscale_factor)
+            : upscale_factor(upscale_factor) {}
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
+            GGML_ASSERT(upscale_factor > 0);
+            GGML_ASSERT(x->ne[3] % upscale_factor == 0);
+            const int64_t W = x->ne[0];
+            const int64_t H = x->ne[1];
+            const int64_t F = x->ne[2];
+            const int64_t C = x->ne[3] / upscale_factor;
+
+            // x: [b, c*p, f, h, w] -> [b, c, f*p, h, w]
+            x = ggml_ext_cont(ctx->ggml_ctx, x);
+            x = ggml_reshape_4d(ctx->ggml_ctx, x, W * H, F, upscale_factor, C);
+            x = ggml_ext_cont(ctx->ggml_ctx, ggml_permute(ctx->ggml_ctx, x, 0, 2, 1, 3));
+            return ggml_reshape_4d(ctx->ggml_ctx, x, W, H, F * upscale_factor, C);
+        }
+    };
+
+    class BlurDownsample : public GGMLBlock {
+    protected:
+        int64_t channels;
+        int stride;
+        ggml_tensor* kernel = nullptr;
+        std::vector<float> kernel_data;
+
+        void init_params(ggml_context* ctx,
+                         const String2TensorStorage& tensor_storage_map = {},
+                         const std::string prefix                       = "") override {
+            SD_UNUSED(tensor_storage_map);
+            if (stride == 1) {
+                return;
+            }
+            kernel           = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 5, 5, 1, channels);
+            std::string name = prefix + "kernel";
+            ggml_set_name(kernel, name.c_str());
+
+            static const float binomial[5] = {1.f, 4.f, 6.f, 4.f, 1.f};
+            kernel_data.resize(static_cast<size_t>(5 * 5 * channels));
+            for (int64_t c = 0; c < channels; ++c) {
+                for (int y = 0; y < 5; ++y) {
+                    for (int x = 0; x < 5; ++x) {
+                        kernel_data[static_cast<size_t>(x + 5 * (y + 5 * c))] =
+                            binomial[y] * binomial[x] / 256.f;
+                    }
+                }
+            }
+        }
+
+    public:
+        BlurDownsample(int64_t channels, int stride)
+            : channels(channels),
+              stride(stride) {
+            GGML_ASSERT(stride >= 1);
+        }
+
+        void load_fixed_tensors() {
+            if (kernel == nullptr || kernel_data.empty()) {
+                return;
+            }
+            ggml_backend_tensor_set(kernel, kernel_data.data(), 0, kernel_data.size() * sizeof(float));
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            if (stride == 1) {
+                return x;
+            }
+            GGML_ASSERT(kernel != nullptr);
+            GGML_ASSERT(x->ne[2] == channels);
+            if (ctx->conv2d_direct_enabled) {
+                return ggml_conv_2d_dw_direct(ctx->ggml_ctx, kernel, x, stride, stride, 2, 2, 1, 1);
+            }
+            return ggml_conv_2d_dw(ctx->ggml_ctx, kernel, x, stride, stride, 2, 2, 1, 1);
+        }
+    };
+
+    class SpatialRationalResampler : public GGMLBlock {
+    protected:
+        int64_t mid_channels;
+        int num;
+        int den;
+
+    public:
+        SpatialRationalResampler(int64_t mid_channels, int num, int den)
+            : mid_channels(mid_channels),
+              num(num),
+              den(den) {
+            GGML_ASSERT(num >= 1);
+            GGML_ASSERT(den >= 1);
+            blocks["conv"]          = std::shared_ptr<GGMLBlock>(new Conv2d(mid_channels, num * num * mid_channels, {3, 3}, {1, 1}, {1, 1}));
+            blocks["pixel_shuffle"] = std::shared_ptr<GGMLBlock>(new PixelShuffleND(num));
+            blocks["blur_down"]     = std::shared_ptr<GGMLBlock>(new BlurDownsample(mid_channels, den));
+        }
+
+        void load_fixed_tensors() {
+            auto blur_down = std::dynamic_pointer_cast<BlurDownsample>(blocks["blur_down"]);
+            blur_down->load_fixed_tensors();
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            auto conv          = std::dynamic_pointer_cast<Conv2d>(blocks["conv"]);
+            auto pixel_shuffle = std::dynamic_pointer_cast<PixelShuffleND>(blocks["pixel_shuffle"]);
+            auto blur_down     = std::dynamic_pointer_cast<BlurDownsample>(blocks["blur_down"]);
+
+            // rearrange(x, "b c f h w -> (b f) c h w")
+            x = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));
+            x = conv->forward(ctx, x);
+            x = pixel_shuffle->forward(ctx, x);
+            x = blur_down->forward(ctx, x);
+            return ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));
+        }
+    };
+
+    class LatentUpsampler : public GGMLBlock {
+    public:
+        LatentUpsamplerConfig config;
+
+        explicit LatentUpsampler(LatentUpsamplerConfig config)
+            : config(std::move(config)) {
+            GGML_ASSERT(this->config.dims == 3);
+            GGML_ASSERT(this->config.spatial_upsample || this->config.temporal_upsample);
+
+            blocks["initial_conv"] = std::shared_ptr<GGMLBlock>(new Conv3d(this->config.in_channels,
+                                                                           this->config.mid_channels,
+                                                                           {3, 3, 3},
+                                                                           {1, 1, 1},
+                                                                           {1, 1, 1}));
+            blocks["initial_norm"] = std::shared_ptr<GGMLBlock>(new VideoGroupNorm(32, this->config.mid_channels));
+            for (int i = 0; i < this->config.num_blocks_per_stage; ++i) {
+                blocks["res_blocks." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new ResBlock(this->config.mid_channels, this->config.dims));
+            }
+            if (this->config.rational_resampler) {
+                blocks["upsampler"] = std::shared_ptr<GGMLBlock>(new SpatialRationalResampler(this->config.mid_channels,
+                                                                                              this->config.spatial_up_num,
+                                                                                              this->config.spatial_down_den));
+            } else if (this->config.temporal_upsample) {
+                blocks["upsampler.0"] = std::shared_ptr<GGMLBlock>(new Conv3d(this->config.mid_channels,
+                                                                              this->config.temporal_up_factor * this->config.mid_channels,
+                                                                              {3, 3, 3},
+                                                                              {1, 1, 1},
+                                                                              {1, 1, 1}));
+                blocks["upsampler.1"] = std::shared_ptr<GGMLBlock>(new TemporalPixelShuffleND(this->config.temporal_up_factor));
+            } else {
+                blocks["upsampler.0"] = std::shared_ptr<GGMLBlock>(new Conv2d(this->config.mid_channels,
+                                                                              4 * this->config.mid_channels,
+                                                                              {3, 3},
+                                                                              {1, 1},
+                                                                              {1, 1}));
+                blocks["upsampler.1"] = std::shared_ptr<GGMLBlock>(new PixelShuffleND(2));
+            }
+            for (int i = 0; i < this->config.num_blocks_per_stage; ++i) {
+                blocks["post_upsample_res_blocks." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new ResBlock(this->config.mid_channels, this->config.dims));
+            }
+            blocks["final_conv"] = std::shared_ptr<GGMLBlock>(new Conv3d(this->config.mid_channels,
+                                                                         this->config.in_channels,
+                                                                         {3, 3, 3},
+                                                                         {1, 1, 1},
+                                                                         {1, 1, 1}));
+        }
+
+        ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) {
+            // x: [b, c, f, h, w]
+            // return: [b, c, scaled_f, scaled_h, scaled_w]
+            auto initial_conv = std::dynamic_pointer_cast<Conv3d>(blocks["initial_conv"]);
+            auto initial_norm = std::dynamic_pointer_cast<VideoGroupNorm>(blocks["initial_norm"]);
+            auto final_conv   = std::dynamic_pointer_cast<Conv3d>(blocks["final_conv"]);
+
+            x = initial_conv->forward(ctx, x);
+            x = initial_norm->forward(ctx, x);
+            x = ggml_silu(ctx->ggml_ctx, x);
+            sd::ggml_graph_cut::mark_graph_cut(x, "ltx_latent_upsampler.initial", "x");
+
+            for (int i = 0; i < config.num_blocks_per_stage; ++i) {
+                auto block = std::dynamic_pointer_cast<ResBlock>(blocks["res_blocks." + std::to_string(i)]);
+                x          = block->forward(ctx, x);
+                sd::ggml_graph_cut::mark_graph_cut(x, "ltx_latent_upsampler.res_blocks." + std::to_string(i), "x");
+            }
+
+            if (config.rational_resampler) {
+                auto upsampler = std::dynamic_pointer_cast<SpatialRationalResampler>(blocks["upsampler"]);
+                x              = upsampler->forward(ctx, x);
+            } else if (config.temporal_upsample) {
+                auto upsample_conv = std::dynamic_pointer_cast<Conv3d>(blocks["upsampler.0"]);
+                auto pixel_shuffle = std::dynamic_pointer_cast<TemporalPixelShuffleND>(blocks["upsampler.1"]);
+                x                  = upsample_conv->forward(ctx, x);                    // [b, c*2, f, h, w]
+                x                  = pixel_shuffle->forward(ctx, x);                    // [b, c, f*2, h, w]
+                x                  = ggml_ext_slice(ctx->ggml_ctx, x, 2, 1, x->ne[2]);  // x[:, :, 1:, :, :]
+            } else {
+                auto upsample_conv = std::dynamic_pointer_cast<Conv2d>(blocks["upsampler.0"]);
+                auto pixel_shuffle = std::dynamic_pointer_cast<PixelShuffleND>(blocks["upsampler.1"]);
+
+                // rearrange(x, "b c f h w -> (b f) c h w"),
+                x = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));  // [b*f, c, h, w]
+                x = upsample_conv->forward(ctx, x);                                                      // [b*f, c*4, h, w]
+                x = pixel_shuffle->forward(ctx, x);                                                      // [b*f, c, h*2, w*2]
+                x = ggml_ext_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, x, 0, 1, 3, 2));  // [b*c, f, h, w]
+            }
+            sd::ggml_graph_cut::mark_graph_cut(x, "ltx_latent_upsampler.spatial_up", "x");
+
+            for (int i = 0; i < config.num_blocks_per_stage; ++i) {
+                auto block = std::dynamic_pointer_cast<ResBlock>(blocks["post_upsample_res_blocks." + std::to_string(i)]);
+                x          = block->forward(ctx, x);
+                sd::ggml_graph_cut::mark_graph_cut(x, "ltx_latent_upsampler.post_blocks." + std::to_string(i), "x");
+            }
+
+            x = final_conv->forward(ctx, x);
+            sd::ggml_graph_cut::mark_graph_cut(x, "ltx_latent_upsampler.final", "x");
+            return x;
+        }
+
+        void load_fixed_tensors() {
+            if (!config.rational_resampler) {
+                return;
+            }
+            auto upsampler = std::dynamic_pointer_cast<SpatialRationalResampler>(blocks["upsampler"]);
+            upsampler->load_fixed_tensors();
+        }
+    };
+
+    struct LatentUpsamplerRunner : public GGMLRunner {
+        std::unique_ptr<LatentUpsampler> model;
+
+        LatentUpsamplerRunner(ggml_backend_t backend,
+                              ggml_backend_t params_backend)
+            : GGMLRunner(backend, params_backend) {}
+
+        std::string get_desc() override {
+            return "ltx_latent_upsampler";
+        }
+
+        bool load_from_file(const std::string& file_path, int n_threads) {
+            LOG_INFO("loading LTX latent upsampler from '%s'", file_path.c_str());
+            ModelLoader model_loader;
+            if (!model_loader.init_from_file(file_path)) {
+                LOG_ERROR("init LTX latent upsampler model loader from file failed: '%s'", file_path.c_str());
+                return false;
+            }
+
+            const auto& tensor_storage_map = model_loader.get_tensor_storage_map();
+            bool has_regular_upsampler     = has_tensor(tensor_storage_map, "upsampler.0.weight");
+            bool has_rational_spatial      = has_tensor(tensor_storage_map, "upsampler.conv.weight");
+            if (!has_tensor(tensor_storage_map, "post_upsample_res_blocks.0.conv2.bias") ||
+                (!has_regular_upsampler && !has_rational_spatial)) {
+                LOG_ERROR("unsupported LTX latent upsampler weights: expected upsampler tensors");
+                return false;
+            }
+
+            LatentUpsamplerConfig config = detect_config_from_weights(tensor_storage_map);
+            if (config.dims != 3 || (!config.spatial_upsample && !config.temporal_upsample) ||
+                config.spatial_up_num < 1 || config.spatial_down_den < 1 || config.temporal_up_factor < 1) {
+                LOG_ERROR("unsupported LTX latent upsampler config: dims=%d spatial=%d temporal=%d rational=%d scale=%.3f temporal_factor=%d",
+                          config.dims,
+                          config.spatial_upsample,
+                          config.temporal_upsample,
+                          config.rational_resampler,
+                          config.spatial_scale,
+                          config.temporal_up_factor);
+                return false;
+            }
+
+            model = std::make_unique<LatentUpsampler>(config);
+            model->init(params_ctx, tensor_storage_map, "");
+            if (!alloc_params_buffer()) {
+                LOG_ERROR("LTX latent upsampler params buffer allocation failed");
+                return false;
+            }
+
+            std::map<std::string, ggml_tensor*> tensors;
+            model->get_param_tensors(tensors);
+            std::set<std::string> ignore_tensors;
+            if (config.rational_resampler) {
+                ignore_tensors.insert("upsampler.blur_down.kernel");
+            }
+            if (!model_loader.load_tensors(tensors, ignore_tensors, n_threads)) {
+                LOG_ERROR("load LTX latent upsampler tensors failed");
+                return false;
+            }
+            model->load_fixed_tensors();
+
+            LOG_INFO("LTX latent upsampler loaded: in_channels=%" PRId64 ", mid_channels=%" PRId64 ", blocks=%d, scale=%.3f, temporal_factor=%d, rational=%d",
+                     config.in_channels,
+                     config.mid_channels,
+                     config.num_blocks_per_stage,
+                     config.spatial_scale,
+                     config.temporal_up_factor,
+                     config.rational_resampler);
+            return true;
+        }
+
+        ggml_cgraph* build_graph(const sd::Tensor<float>& x_tensor) {
+            if (!model) {
+                return nullptr;
+            }
+            ggml_cgraph* gf  = new_graph_custom(LTX_UPSAMPLER_GRAPH_SIZE);
+            ggml_tensor* x   = make_input(x_tensor);
+            auto runner_ctx  = get_context();
+            ggml_tensor* out = model->forward(&runner_ctx, x);
+            ggml_build_forward_expand(gf, out);
+            return gf;
+        }
+
+        sd::Tensor<float> compute(const int n_threads,
+                                  const sd::Tensor<float>& x) {
+            if (!model) {
+                LOG_ERROR("LTX latent upsampler is not loaded");
+                return {};
+            }
+            if (x.dim() != 4 && x.dim() != 5) {
+                LOG_ERROR("LTX latent upsampler expects 4D or 5D video latent, got dim=%lld",
+                          (long long)x.dim());
+                return {};
+            }
+            if (x.dim() == 5 && x.shape()[4] != 1) {
+                LOG_ERROR("LTX latent upsampler currently supports batch size 1, got batch=%lld",
+                          (long long)x.shape()[4]);
+                return {};
+            }
+            if (x.shape()[3] != model->config.in_channels) {
+                LOG_ERROR("LTX latent upsampler expected %" PRId64 " channels, got %lld",
+                          model->config.in_channels,
+                          (long long)x.shape()[3]);
+                return {};
+            }
+            size_t expected_dim = static_cast<size_t>(x.dim());
+            auto get_graph      = [&]() -> ggml_cgraph* { return build_graph(x); };
+            return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), expected_dim);
+        }
+    };
+
+}  // namespace LTXVUpsampler
+
+#endif  // __SD_LTX_LATENT_UPSCALER_HPP__
@@ -1,13 +1,139 @@
 #ifndef __MMDIT_HPP__
 #define __MMDIT_HPP__

+#include <algorithm>
 #include <memory>
+#include <string>
+#include <vector>

+#include "diffusion_model.hpp"
 #include "ggml_extend.hpp"
 #include "model.h"

 #define MMDIT_GRAPH_SIZE 10240

+struct MMDiTConfig {
+    int64_t input_size               = -1;
+    int patch_size                   = 2;
+    int64_t in_channels              = 16;
+    int64_t d_self                   = -1;  // >=0 for MMdiT-X
+    int64_t depth                    = 24;
+    float mlp_ratio                  = 4.0f;
+    int64_t adm_in_channels          = 2048;
+    int64_t out_channels             = 16;
+    int64_t pos_embed_max_size       = 192;
+    int64_t num_patches              = 36864;  // 192 * 192
+    int64_t context_size             = 4096;
+    int64_t context_embedder_out_dim = 1536;
+    int64_t hidden_size              = 1536;
+    std::string qk_norm;
+
+    static MMDiTConfig detect_from_weights(const String2TensorStorage& tensor_storage_map, const std::string& prefix) {
+        MMDiTConfig config;
+        bool has_weight_config = false;
+        bool has_pos_embed     = false;
+        bool has_hidden_size   = false;
+        bool has_context_embed = false;
+
+        for (const auto& [name, tensor_storage] : tensor_storage_map) {
+            if (!starts_with(name, prefix)) {
+                continue;
+            }
+
+            if (name.find("x_embedder.proj.weight") != std::string::npos && tensor_storage.n_dims == 4) {
+                has_weight_config  = true;
+                has_hidden_size    = true;
+                config.patch_size  = static_cast<int>(tensor_storage.ne[0]);
+                config.in_channels = tensor_storage.ne[2];
+                config.hidden_size = tensor_storage.ne[3];
+            } else if (name.find("t_embedder.mlp.0.weight") != std::string::npos && tensor_storage.n_dims == 2) {
+                has_weight_config  = true;
+                has_hidden_size    = true;
+                config.hidden_size = tensor_storage.ne[1];
+            } else if (name.find("y_embedder.mlp.0.weight") != std::string::npos && tensor_storage.n_dims == 2) {
+                has_weight_config      = true;
+                has_hidden_size        = true;
+                config.adm_in_channels = tensor_storage.ne[0];
+                config.hidden_size     = tensor_storage.ne[1];
+            } else if (name.find("context_embedder.weight") != std::string::npos && tensor_storage.n_dims == 2) {
+                has_weight_config               = true;
+                has_context_embed               = true;
+                config.context_size             = tensor_storage.ne[0];
+                config.context_embedder_out_dim = tensor_storage.ne[1];
+            } else if (name.find("final_layer.linear.weight") != std::string::npos && tensor_storage.n_dims == 2) {
+                has_weight_config  = true;
+                has_hidden_size    = true;
+                config.hidden_size = tensor_storage.ne[0];
+                int64_t patch_area = static_cast<int64_t>(config.patch_size) * config.patch_size;
+                if (patch_area > 0) {
+                    config.out_channels = tensor_storage.ne[1] / patch_area;
+                }
+            } else if (name.find("pos_embed") != std::string::npos && tensor_storage.n_dims == 3) {
+                has_weight_config  = true;
+                has_pos_embed      = true;
+                has_hidden_size    = true;
+                config.hidden_size = tensor_storage.ne[0];
+                config.num_patches = tensor_storage.ne[1];
+                for (int64_t size = 1; size * size <= config.num_patches; size++) {
+                    if (size * size == config.num_patches) {
+                        config.pos_embed_max_size = size;
+                        break;
+                    }
+                }
+            }
+
+            size_t jb = name.find("joint_blocks.");
+            if (jb == std::string::npos) {
+                continue;
+            }
+
+            has_weight_config      = true;
+            std::string block_name = name.substr(jb);
+            int64_t block_depth    = atoi(block_name.substr(13, block_name.find(".", 13)).c_str());
+            if (block_depth + 1 > config.depth) {
+                config.depth = block_depth + 1;
+            }
+            if (block_name.find("attn.ln") != std::string::npos) {
+                if (block_name.find(".bias") != std::string::npos) {
+                    config.qk_norm = "ln";
+                } else {
+                    config.qk_norm = "rms";
+                }
+            }
+            if (block_name.find("attn2") != std::string::npos) {
+                if (block_depth > config.d_self) {
+                    config.d_self = block_depth;
+                }
+            }
+        }
+
+        if (!has_pos_embed && config.d_self >= 0) {
+            config.pos_embed_max_size *= 2;
+            config.num_patches *= 4;
+        }
+        if (!has_hidden_size || config.hidden_size <= 0) {
+            config.hidden_size = 64 * config.depth;
+        }
+        if (!has_context_embed || config.context_embedder_out_dim <= 0) {
+            config.context_embedder_out_dim = config.hidden_size;
+        }
+
+        if (has_weight_config) {
+            LOG_DEBUG("mmdit: num_layers = %" PRId64 ", num_mmdit_x_layers = %" PRId64 ", hidden_size = %" PRId64 ", patch_size = %d, in_channels = %" PRId64 ", out_channels = %" PRId64 ", context_size = %" PRId64 ", adm_in_channels = %" PRId64 ", qk_norm = %s",
+                      config.depth,
+                      config.d_self + 1,
+                      config.hidden_size,
+                      config.patch_size,
+                      config.in_channels,
+                      config.out_channels,
+                      config.context_size,
+                      config.adm_in_channels,
+                      config.qk_norm.empty() ? "none" : config.qk_norm.c_str());
+        }
+        return config;
+    }
+};
+
 struct Mlp : public GGMLBlock {
 public:
    Mlp(int64_t in_features,
@@ -611,28 +737,16 @@ public:
 struct MMDiT : public GGMLBlock {
    // Diffusion model with a Transformer backbone.
 protected:
-    int64_t input_size               = -1;
-    int patch_size                   = 2;
-    int64_t in_channels              = 16;
-    int64_t d_self                   = -1;  // >=0 for MMdiT-X
-    int64_t depth                    = 24;
-    float mlp_ratio                  = 4.0f;
-    int64_t adm_in_channels          = 2048;
-    int64_t out_channels             = 16;
-    int64_t pos_embed_max_size       = 192;
-    int64_t num_patchs               = 36864;  // 192 * 192
-    int64_t context_size             = 4096;
-    int64_t context_embedder_out_dim = 1536;
-    int64_t hidden_size;
-    std::string qk_norm;
-
    void init_params(ggml_context* ctx, const String2TensorStorage& tensor_storage_map = {}, std::string prefix = "") override {
        enum ggml_type wtype = GGML_TYPE_F32;
-        params["pos_embed"]  = ggml_new_tensor_3d(ctx, wtype, hidden_size, num_patchs, 1);
+        params["pos_embed"]  = ggml_new_tensor_3d(ctx, wtype, config.hidden_size, config.num_patches, 1);
    }

 public:
-    MMDiT(const String2TensorStorage& tensor_storage_map = {}) {
+    MMDiTConfig config;
+
+    explicit MMDiT(MMDiTConfig config = {})
+        : config(config) {
        // input_size is always None
        // learn_sigma is always False
        // register_length is alwalys 0
@@ -645,64 +759,30 @@ public:
        // pos_embed_offset is not used
        // context_embedder_config is always {'target': 'torch.nn.Linear', 'params': {'in_features': 4096, 'out_features': 1536}}

-        for (auto pair : tensor_storage_map) {
-            std::string tensor_name = pair.first;
-            if (tensor_name.find("model.diffusion_model.") == std::string::npos)
-                continue;
-            size_t jb = tensor_name.find("joint_blocks.");
-            if (jb != std::string::npos) {
-                tensor_name     = tensor_name.substr(jb);  // remove prefix
-                int block_depth = atoi(tensor_name.substr(13, tensor_name.find(".", 13)).c_str());
-                if (block_depth + 1 > depth) {
-                    depth = block_depth + 1;
-                }
-                if (tensor_name.find("attn.ln") != std::string::npos) {
-                    if (tensor_name.find(".bias") != std::string::npos) {
-                        qk_norm = "ln";
-                    } else {
-                        qk_norm = "rms";
-                    }
-                }
-                if (tensor_name.find("attn2") != std::string::npos) {
-                    if (block_depth > d_self) {
-                        d_self = block_depth;
-                    }
-                }
-            }
+        blocks["x_embedder"] = std::shared_ptr<GGMLBlock>(new PatchEmbed(config.input_size,
+                                                                         config.patch_size,
+                                                                         config.in_channels,
+                                                                         config.hidden_size,
+                                                                         true));
+        blocks["t_embedder"] = std::shared_ptr<GGMLBlock>(new TimestepEmbedder(config.hidden_size));
+
+        if (config.adm_in_channels != -1) {
+            blocks["y_embedder"] = std::shared_ptr<GGMLBlock>(new VectorEmbedder(config.adm_in_channels, config.hidden_size));
        }

-        if (d_self >= 0) {
-            pos_embed_max_size *= 2;
-            num_patchs *= 4;
-        }
+        blocks["context_embedder"] = std::shared_ptr<GGMLBlock>(new Linear(config.context_size, config.context_embedder_out_dim, true, true));

-        LOG_INFO("MMDiT layers: %d (including %d MMDiT-x layers)", depth, d_self + 1);
-
-        int64_t default_out_channels = in_channels;
-        hidden_size                  = 64 * depth;
-        context_embedder_out_dim     = 64 * depth;
-        int64_t num_heads            = depth;
-
-        blocks["x_embedder"] = std::shared_ptr<GGMLBlock>(new PatchEmbed(input_size, patch_size, in_channels, hidden_size, true));
-        blocks["t_embedder"] = std::shared_ptr<GGMLBlock>(new TimestepEmbedder(hidden_size));
-
-        if (adm_in_channels != -1) {
-            blocks["y_embedder"] = std::shared_ptr<GGMLBlock>(new VectorEmbedder(adm_in_channels, hidden_size));
-        }
-
-        blocks["context_embedder"] = std::shared_ptr<GGMLBlock>(new Linear(4096, context_embedder_out_dim, true, true));
-
-        for (int i = 0; i < depth; i++) {
-            blocks["joint_blocks." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new JointBlock(hidden_size,
-                                                                                                    num_heads,
-                                                                                                    mlp_ratio,
-                                                                                                    qk_norm,
+        for (int i = 0; i < config.depth; i++) {
+            blocks["joint_blocks." + std::to_string(i)] = std::shared_ptr<GGMLBlock>(new JointBlock(config.hidden_size,
+                                                                                                    config.depth,
+                                                                                                    config.mlp_ratio,
+                                                                                                    config.qk_norm,
                                                                                                    true,
-                                                                                                    i == depth - 1,
-                                                                                                    i <= d_self));
+                                                                                                    i == config.depth - 1,
+                                                                                                    i <= config.d_self));
        }

-        blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new FinalLayer(hidden_size, patch_size, out_channels));
+        blocks["final_layer"] = std::shared_ptr<GGMLBlock>(new FinalLayer(config.hidden_size, config.patch_size, config.out_channels));
    }

    ggml_tensor*
@@ -711,22 +791,22 @@ public:
                      int64_t w) {
        auto pos_embed = params["pos_embed"];

-        h = (h + 1) / patch_size;
-        w = (w + 1) / patch_size;
+        h = (h + 1) / config.patch_size;
+        w = (w + 1) / config.patch_size;

-        GGML_ASSERT(h <= pos_embed_max_size && h > 0);
-        GGML_ASSERT(w <= pos_embed_max_size && w > 0);
+        GGML_ASSERT(h <= config.pos_embed_max_size && h > 0);
+        GGML_ASSERT(w <= config.pos_embed_max_size && w > 0);

-        int64_t top  = (pos_embed_max_size - h) / 2;
-        int64_t left = (pos_embed_max_size - w) / 2;
+        int64_t top  = (config.pos_embed_max_size - h) / 2;
+        int64_t left = (config.pos_embed_max_size - w) / 2;

-        auto spatial_pos_embed = ggml_reshape_3d(ctx, pos_embed, hidden_size, pos_embed_max_size, pos_embed_max_size);
+        auto spatial_pos_embed = ggml_reshape_3d(ctx, pos_embed, config.hidden_size, config.pos_embed_max_size, config.pos_embed_max_size);

        // spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :]
        spatial_pos_embed = ggml_view_3d(ctx,
                                         spatial_pos_embed,
-                                         hidden_size,
-                                         pos_embed_max_size,
+                                         config.hidden_size,
+                                         config.pos_embed_max_size,
                                         h,
                                         spatial_pos_embed->nb[1],
                                         spatial_pos_embed->nb[2],
@@ -734,14 +814,14 @@ public:
        spatial_pos_embed = ggml_cont(ctx, ggml_permute(ctx, spatial_pos_embed, 0, 2, 1, 3));  // [pos_embed_max_size, h, hidden_size]
        spatial_pos_embed = ggml_view_3d(ctx,
                                         spatial_pos_embed,
-                                         hidden_size,
+                                         config.hidden_size,
                                         h,
                                         w,
                                         spatial_pos_embed->nb[1],
                                         spatial_pos_embed->nb[2],
-                                         spatial_pos_embed->nb[2] * left);                     // [w, h, hidden_size]
-        spatial_pos_embed = ggml_cont(ctx, ggml_permute(ctx, spatial_pos_embed, 0, 2, 1, 3));  // [h, w, hidden_size]
-        spatial_pos_embed = ggml_reshape_3d(ctx, spatial_pos_embed, hidden_size, h * w, 1);    // [1, h*w, hidden_size]
+                                         spatial_pos_embed->nb[2] * left);                          // [w, h, hidden_size]
+        spatial_pos_embed = ggml_cont(ctx, ggml_permute(ctx, spatial_pos_embed, 0, 2, 1, 3));       // [h, w, hidden_size]
+        spatial_pos_embed = ggml_reshape_3d(ctx, spatial_pos_embed, config.hidden_size, h * w, 1);  // [1, h*w, hidden_size]
        return spatial_pos_embed;
    }

@@ -756,7 +836,7 @@ public:
        // return: [N, N*W, patch_size * patch_size * out_channels]
        auto final_layer = std::dynamic_pointer_cast<FinalLayer>(blocks["final_layer"]);

-        for (int i = 0; i < depth; i++) {
+        for (int i = 0; i < config.depth; i++) {
            // skip iteration if i is in skip_layers
            if (skip_layers.size() > 0 && std::find(skip_layers.begin(), skip_layers.end(), i) != skip_layers.end()) {
                continue;
@@ -767,6 +847,8 @@ public:
            auto context_x = block->forward(ctx, context, x, c_mod);
            context        = context_x.first;
            x              = context_x.second;
+            sd::ggml_graph_cut::mark_graph_cut(context, "mmdit.joint_blocks." + std::to_string(i), "context");
+            sd::ggml_graph_cut::mark_graph_cut(x, "mmdit.joint_blocks." + std::to_string(i), "x");
        }

        x = final_layer->forward(ctx, x, c_mod);  // (N, T, patch_size ** 2 * out_channels)
@@ -797,7 +879,7 @@ public:
        x                = ggml_add(ctx->ggml_ctx, patch_embed, pos_embed);  // [N, H*W, hidden_size]

        auto c = t_embedder->forward(ctx, t);  // [N, hidden_size]
-        if (y != nullptr && adm_in_channels != -1) {
+        if (y != nullptr && config.adm_in_channels != -1) {
            auto y_embedder = std::dynamic_pointer_cast<VectorEmbedder>(blocks["y_embedder"]);

            y = y_embedder->forward(ctx, y);  // [N, hidden_size]
@@ -809,22 +891,30 @@ public:

            context = context_embedder->forward(ctx, context);  // [N, L, D] aka [N, L, 1536]
        }
+        sd::ggml_graph_cut::mark_graph_cut(x, "mmdit.prelude", "x");
+        sd::ggml_graph_cut::mark_graph_cut(c, "mmdit.prelude", "c");
+        if (context != nullptr) {
+            sd::ggml_graph_cut::mark_graph_cut(context, "mmdit.prelude", "context");
+        }

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

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

        return x;
    }
 };
-struct MMDiTRunner : public GGMLRunner {
+struct MMDiTRunner : public DiffusionModelRunner {
+    MMDiTConfig config;
    MMDiT mmdit;

    MMDiTRunner(ggml_backend_t backend,
-                bool offload_params_to_cpu,
+                ggml_backend_t params_backend,
                const String2TensorStorage& tensor_storage_map = {},
                const std::string prefix                       = "")
-        : GGMLRunner(backend, offload_params_to_cpu), mmdit(tensor_storage_map) {
+        : DiffusionModelRunner(backend, params_backend, prefix),
+          config(MMDiTConfig::detect_from_weights(tensor_storage_map, prefix)),
+          mmdit(config) {
        mmdit.init(params_ctx, tensor_storage_map, prefix);
    }

@@ -832,7 +922,7 @@ struct MMDiTRunner : public GGMLRunner {
        return "mmdit";
    }

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

@@ -878,6 +968,20 @@ struct MMDiTRunner : public GGMLRunner {
        return restore_trailing_singleton_dims(GGMLRunner::compute<float>(get_graph, n_threads, false), x.dim());
    }

+    sd::Tensor<float> compute(int n_threads,
+                              const DiffusionParams& diffusion_params) override {
+        GGML_ASSERT(diffusion_params.x != nullptr);
+        GGML_ASSERT(diffusion_params.timesteps != nullptr);
+        const auto* extra = diffusion_extra_as<SkipLayerDiffusionExtra>(diffusion_params);
+        static const std::vector<int> empty_skip_layers;
+        return compute(n_threads,
+                       *diffusion_params.x,
+                       *diffusion_params.timesteps,
+                       tensor_or_empty(diffusion_params.context),
+                       tensor_or_empty(diffusion_params.y),
+                       extra->skip_layers ? *extra->skip_layers : empty_skip_layers);
+    }
+
    void test() {
        ggml_init_params params;
        params.mem_size   = static_cast<size_t>(10 * 1024 * 1024);  // 10 MB
@@ -925,13 +1029,17 @@ struct MMDiTRunner : public GGMLRunner {

    static void load_from_file_and_test(const std::string& file_path) {
        // ggml_backend_t backend    = ggml_backend_cuda_init(0);
-        ggml_backend_t backend             = ggml_backend_cpu_init();
+        ggml_backend_t backend             = sd_backend_cpu_init();
        ggml_type model_data_type          = GGML_TYPE_F16;
-        std::shared_ptr<MMDiTRunner> mmdit = std::make_shared<MMDiTRunner>(backend, false);
+        std::shared_ptr<MMDiTRunner> mmdit = std::make_shared<MMDiTRunner>(backend, backend);
        {
            LOG_INFO("loading from '%s'", file_path.c_str());

-            mmdit->alloc_params_buffer();
+            if (!mmdit->alloc_params_buffer()) {
+                LOG_ERROR("mmdit embeds buffer allocation failed");
+                return;
+            }
+
            std::map<std::string, ggml_tensor*> tensors;
            mmdit->get_param_tensors(tensors, "model.diffusion_model");

@@ -5,20 +5,13 @@
 #include <map>
 #include <memory>
 #include <set>
-#include <sstream>
 #include <string>
-#include <tuple>
-#include <utility>
 #include <vector>

 #include "ggml-backend.h"
 #include "ggml.h"
-#include "gguf.h"
-#include "json.hpp"
+#include "model_io/tensor_storage.h"
 #include "ordered_map.hpp"
-#include "zip.h"
-
-#define SD_MAX_DIMS 5

 enum SDVersion {
    VERSION_SD1,
@@ -49,9 +42,15 @@ enum SDVersion {
    VERSION_ANIMA,
    VERSION_FLUX2,
    VERSION_FLUX2_KLEIN,
+    VERSION_LTXAV,
+    VERSION_HIDREAM_O1,
    VERSION_Z_IMAGE,
    VERSION_OVIS_IMAGE,
    VERSION_ERNIE_IMAGE,
+    VERSION_LENS,
+    VERSION_LONGCAT,
+    VERSION_PID,
+    VERSION_IDEOGRAM4,
    VERSION_COUNT,
 };

@@ -111,6 +110,13 @@ static inline bool sd_version_is_flux2(SDVersion version) {
    return false;
 }

+static inline bool sd_version_is_ltxav(SDVersion version) {
+    if (version == VERSION_LTXAV) {
+        return true;
+    }
+    return false;
+}
+
 static inline bool sd_version_is_wan(SDVersion version) {
    if (version == VERSION_WAN2 || version == VERSION_WAN2_2_I2V || version == VERSION_WAN2_2_TI2V) {
        return true;
@@ -139,6 +145,13 @@ static inline bool sd_version_is_z_image(SDVersion version) {
    return false;
 }

+static inline bool sd_version_is_longcat(SDVersion version) {
+    if (version == VERSION_LONGCAT) {
+        return true;
+    }
+    return false;
+}
+
 static inline bool sd_version_is_ernie_image(SDVersion version) {
    if (version == VERSION_ERNIE_IMAGE) {
        return true;
@@ -146,8 +159,29 @@ static inline bool sd_version_is_ernie_image(SDVersion version) {
    return false;
 }

+static inline bool sd_version_is_lens(SDVersion version) {
+    if (version == VERSION_LENS) {
+        return true;
+    }
+    return false;
+}
+
+static inline bool sd_version_is_pid(SDVersion version) {
+    if (version == VERSION_PID) {
+        return true;
+    }
+    return false;
+}
+
+static inline bool sd_version_is_ideogram4(SDVersion version) {
+    if (version == VERSION_IDEOGRAM4) {
+        return true;
+    }
+    return false;
+}
+
 static inline bool sd_version_uses_flux2_vae(SDVersion version) {
-    if (sd_version_is_flux2(version) || sd_version_is_ernie_image(version)) {
+    if (sd_version_is_flux2(version) || sd_version_is_ernie_image(version) || sd_version_is_lens(version) || sd_version_is_ideogram4(version)) {
        return true;
    }
    return false;
@@ -167,12 +201,18 @@ static inline bool sd_version_is_inpaint(SDVersion version) {
 static inline bool sd_version_is_dit(SDVersion version) {
    if (sd_version_is_flux(version) ||
        sd_version_is_flux2(version) ||
+        sd_version_is_ltxav(version) ||
        sd_version_is_sd3(version) ||
        sd_version_is_wan(version) ||
        sd_version_is_qwen_image(version) ||
+        version == VERSION_HIDREAM_O1 ||
        sd_version_is_anima(version) ||
        sd_version_is_z_image(version) ||
-        sd_version_is_ernie_image(version)) {
+        sd_version_is_ernie_image(version) ||
+        sd_version_is_lens(version) ||
+        sd_version_is_longcat(version) ||
+        sd_version_is_pid(version) ||
+        sd_version_is_ideogram4(version)) {
        return true;
    }
    return false;
@@ -195,135 +235,40 @@ enum PMVersion {
    PM_VERSION_2,
 };

-struct TensorStorage {
-    std::string name;
-    ggml_type type          = GGML_TYPE_F32;
-    ggml_type expected_type = GGML_TYPE_COUNT;
-    bool is_f8_e4m3         = false;
-    bool is_f8_e5m2         = false;
-    bool is_f64             = false;
-    bool is_i64             = false;
-    int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
-    int n_dims              = 0;
+typedef OrderedMap<std::string, TensorStorage> String2TensorStorage;
+using TensorTypeRules = std::vector<std::pair<std::string, ggml_type>>;

-    size_t file_index = 0;
-    int index_in_zip  = -1;  // >= means stored in a zip file
-    uint64_t offset   = 0;   // offset in file
+TensorTypeRules parse_tensor_type_rules(const std::string& tensor_type_rules);

-    TensorStorage() = default;
+class MmapWrapper;

-    TensorStorage(std::string name, ggml_type type, const int64_t* ne, int n_dims, size_t file_index, size_t offset = 0)
-        : name(std::move(name)), type(type), n_dims(n_dims), file_index(file_index), offset(offset) {
-        for (int i = 0; i < n_dims; i++) {
-            this->ne[i] = ne[i];
-        }
-    }
-
-    int64_t nelements() const {
-        int64_t n = 1;
-        for (int i = 0; i < SD_MAX_DIMS; i++) {
-            n *= ne[i];
-        }
-        return n;
-    }
-
-    int64_t nbytes() const {
-        return nelements() * ggml_type_size(type) / ggml_blck_size(type);
-    }
-
-    int64_t nbytes_to_read() const {
-        if (is_f8_e4m3 || is_f8_e5m2) {
-            return nbytes() / 2;
-        } else if (is_f64 || is_i64) {
-            return nbytes() * 2;
-        } else {
-            return nbytes();
-        }
-    }
-
-    void unsqueeze() {
-        if (n_dims == 2) {
-            n_dims = 4;
-            ne[3]  = ne[1];
-            ne[2]  = ne[0];
-            ne[1]  = 1;
-            ne[0]  = 1;
-        }
-    }
-
-    std::vector<TensorStorage> chunk(size_t n) {
-        std::vector<TensorStorage> chunks;
-        uint64_t chunk_size = nbytes_to_read() / n;
-        // printf("%d/%d\n", chunk_size, nbytes_to_read());
-        reverse_ne();
-        for (size_t i = 0; i < n; i++) {
-            TensorStorage chunk_i = *this;
-            chunk_i.ne[0]         = ne[0] / n;
-            chunk_i.offset        = offset + i * chunk_size;
-            chunk_i.reverse_ne();
-            chunks.push_back(chunk_i);
-        }
-        reverse_ne();
-        return chunks;
-    }
-
-    void reverse_ne() {
-        int64_t new_ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
-        for (int i = 0; i < n_dims; i++) {
-            new_ne[i] = ne[n_dims - 1 - i];
-        }
-        for (int i = 0; i < n_dims; i++) {
-            ne[i] = new_ne[i];
-        }
-    }
-
-    std::string to_string() const {
-        std::stringstream ss;
-        const char* type_name = ggml_type_name(type);
-        if (is_f8_e4m3) {
-            type_name = "f8_e4m3";
-        } else if (is_f8_e5m2) {
-            type_name = "f8_e5m2";
-        } else if (is_f64) {
-            type_name = "f64";
-        } else if (is_i64) {
-            type_name = "i64";
-        }
-        ss << name << " | " << type_name << " | ";
-        ss << n_dims << " [";
-        for (int i = 0; i < SD_MAX_DIMS; i++) {
-            ss << ne[i];
-            if (i != SD_MAX_DIMS - 1) {
-                ss << ", ";
-            }
-        }
-        ss << "]";
-        return ss.str();
-    }
+struct ModelFileData {
+    std::string path;
+    std::vector<TensorStorage> tensors;
+    std::shared_ptr<MmapWrapper> mmapped;
+    std::shared_ptr<struct ggml_backend_buffer> mmbuffer;
+    bool is_zip;
 };

-typedef std::function<bool(const TensorStorage&, ggml_tensor**)> on_new_tensor_cb_t;
-
-typedef OrderedMap<std::string, TensorStorage> String2TensorStorage;
+struct MmapTensorStore {
+    std::shared_ptr<MmapWrapper> mmapped;
+    std::shared_ptr<struct ggml_backend_buffer> mmbuffer;
+};

 class ModelLoader {
 protected:
    SDVersion version_ = VERSION_COUNT;
    std::vector<std::string> file_paths_;
+    std::vector<ModelFileData> file_data;
+    bool model_files_processed = false;
    String2TensorStorage tensor_storage_map;

    void add_tensor_storage(const TensorStorage& tensor_storage);

-    bool parse_data_pkl(uint8_t* buffer,
-                        size_t buffer_size,
-                        zip_t* zip,
-                        std::string dir,
-                        size_t file_index,
-                        const std::string prefix);
-
    bool init_from_gguf_file(const std::string& file_path, const std::string& prefix = "");
    bool init_from_safetensors_file(const std::string& file_path, const std::string& prefix = "");
-    bool init_from_ckpt_file(const std::string& file_path, const std::string& prefix = "");
+    bool init_from_torch_zip_file(const std::string& file_path, const std::string& prefix = "");
+    bool init_from_torch_legacy_file(const std::string& file_path, const std::string& prefix = "");
    bool init_from_diffusers_file(const std::string& file_path, const std::string& prefix = "");

 public:
@@ -339,6 +284,10 @@ public:
    std::map<ggml_type, uint32_t> get_vae_wtype_stat();
    String2TensorStorage& get_tensor_storage_map() { return tensor_storage_map; }
    void set_wtype_override(ggml_type wtype, std::string tensor_type_rules = "");
+    void process_model_files(bool enable_mmap = false, bool writable_mmap = true);
+    std::vector<MmapTensorStore> mmap_tensors(std::map<std::string, ggml_tensor*>& tensors,
+                                              std::set<std::string> ignore_tensors = {},
+                                              bool writable                        = true);
    bool load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_threads = 0, bool use_mmap = false);
    bool load_tensors(std::map<std::string, ggml_tensor*>& tensors,
                      std::set<std::string> ignore_tensors = {},
@@ -353,7 +302,6 @@ public:
        return names;
    }

-    bool save_to_gguf_file(const std::string& file_path, ggml_type type, const std::string& tensor_type_rules);
    bool tensor_should_be_converted(const TensorStorage& tensor_storage, ggml_type type);
    int64_t get_params_mem_size(ggml_backend_t backend, ggml_type type = GGML_TYPE_COUNT);
    ~ModelLoader() = default;
@@ -0,0 +1,57 @@
+#ifndef __SD_MODEL_IO_BINARY_IO_H__
+#define __SD_MODEL_IO_BINARY_IO_H__
+
+#include <cstdint>
+#include <ostream>
+
+namespace model_io {
+
+    inline int32_t read_int(const uint8_t* buffer) {
+        uint32_t value = 0;
+        value |= static_cast<uint32_t>(buffer[3]) << 24;
+        value |= static_cast<uint32_t>(buffer[2]) << 16;
+        value |= static_cast<uint32_t>(buffer[1]) << 8;
+        value |= static_cast<uint32_t>(buffer[0]);
+        return static_cast<int32_t>(value);
+    }
+
+    inline uint16_t read_short(const uint8_t* buffer) {
+        uint16_t value = 0;
+        value |= static_cast<uint16_t>(buffer[1]) << 8;
+        value |= static_cast<uint16_t>(buffer[0]);
+        return value;
+    }
+
+    inline uint64_t read_u64(const uint8_t* buffer) {
+        uint64_t value = 0;
+        value |= static_cast<uint64_t>(buffer[7]) << 56;
+        value |= static_cast<uint64_t>(buffer[6]) << 48;
+        value |= static_cast<uint64_t>(buffer[5]) << 40;
+        value |= static_cast<uint64_t>(buffer[4]) << 32;
+        value |= static_cast<uint64_t>(buffer[3]) << 24;
+        value |= static_cast<uint64_t>(buffer[2]) << 16;
+        value |= static_cast<uint64_t>(buffer[1]) << 8;
+        value |= static_cast<uint64_t>(buffer[0]);
+        return value;
+    }
+
+    inline void write_u64(std::ostream& stream, uint64_t value) {
+        uint8_t buffer[8];
+        for (int i = 0; i < 8; ++i) {
+            buffer[i] = static_cast<uint8_t>((value >> (8 * i)) & 0xFF);
+        }
+        stream.write((const char*)buffer, sizeof(buffer));
+    }
+
+    inline int find_char(const uint8_t* buffer, int len, char c) {
+        for (int pos = 0; pos < len; pos++) {
+            if (buffer[pos] == (uint8_t)c) {
+                return pos;
+            }
+        }
+        return -1;
+    }
+
+}  // namespace model_io
+
+#endif  // __SD_MODEL_IO_BINARY_IO_H__
@@ -0,0 +1,123 @@
+#include "gguf_io.h"
+
+#include <cstdint>
+#include <fstream>
+#include <string>
+#include <vector>
+
+#include "gguf.h"
+#include "gguf_reader_ext.h"
+#include "util.h"
+
+static void set_error(std::string* error, const std::string& message) {
+    if (error != nullptr) {
+        *error = message;
+    }
+}
+
+bool is_gguf_file(const std::string& file_path) {
+    std::ifstream file(file_path, std::ios::binary);
+    if (!file.is_open()) {
+        return false;
+    }
+
+    char magic[4];
+
+    file.read(magic, sizeof(magic));
+    if (!file) {
+        return false;
+    }
+    for (uint32_t i = 0; i < sizeof(magic); i++) {
+        if (magic[i] != GGUF_MAGIC[i]) {
+            return false;
+        }
+    }
+
+    return true;
+}
+
+bool read_gguf_file(const std::string& file_path,
+                    std::vector<TensorStorage>& tensor_storages,
+                    std::string* error) {
+    tensor_storages.clear();
+
+    gguf_context* ctx_gguf_ = nullptr;
+    ggml_context* ctx_meta_ = nullptr;
+
+    ctx_gguf_ = gguf_init_from_file(file_path.c_str(), {true, &ctx_meta_});
+    if (!ctx_gguf_) {
+        GGUFReader gguf_reader;
+        if (!gguf_reader.load(file_path)) {
+            set_error(error, "failed to open '" + file_path + "' with GGUFReader");
+            return false;
+        }
+
+        size_t data_offset = gguf_reader.data_offset();
+        for (const auto& gguf_tensor_info : gguf_reader.tensors()) {
+            TensorStorage tensor_storage(
+                gguf_tensor_info.name,
+                gguf_tensor_info.type,
+                gguf_tensor_info.shape.data(),
+                static_cast<int>(gguf_tensor_info.shape.size()),
+                0,
+                data_offset + gguf_tensor_info.offset);
+
+            tensor_storages.push_back(tensor_storage);
+        }
+
+        return true;
+    }
+
+    int n_tensors = static_cast<int>(gguf_get_n_tensors(ctx_gguf_));
+
+    size_t data_offset = gguf_get_data_offset(ctx_gguf_);
+    for (int i = 0; i < n_tensors; i++) {
+        std::string name   = gguf_get_tensor_name(ctx_gguf_, i);
+        ggml_tensor* dummy = ggml_get_tensor(ctx_meta_, name.c_str());
+        size_t offset      = data_offset + gguf_get_tensor_offset(ctx_gguf_, i);
+
+        TensorStorage tensor_storage(name, dummy->type, dummy->ne, ggml_n_dims(dummy), 0, offset);
+
+        if (ggml_nbytes(dummy) != tensor_storage.nbytes()) {
+            gguf_free(ctx_gguf_);
+            ggml_free(ctx_meta_);
+            set_error(error, "size mismatch for tensor '" + name + "'");
+            return false;
+        }
+
+        tensor_storages.push_back(tensor_storage);
+    }
+
+    gguf_free(ctx_gguf_);
+    ggml_free(ctx_meta_);
+
+    return true;
+}
+
+bool write_gguf_file(const std::string& file_path,
+                     const std::vector<TensorWriteInfo>& tensors,
+                     std::string* error) {
+    gguf_context* gguf_ctx = gguf_init_empty();
+    if (gguf_ctx == nullptr) {
+        set_error(error, "gguf_init_empty failed");
+        return false;
+    }
+
+    for (const TensorWriteInfo& write_tensor : tensors) {
+        ggml_tensor* tensor = write_tensor.tensor;
+        if (tensor == nullptr) {
+            set_error(error, "null tensor cannot be written to GGUF");
+            gguf_free(gguf_ctx);
+            return false;
+        }
+        gguf_add_tensor(gguf_ctx, tensor);
+    }
+
+    LOG_INFO("trying to save tensors to %s", file_path.c_str());
+    bool success = gguf_write_to_file(gguf_ctx, file_path.c_str(), false);
+    if (!success) {
+        set_error(error, "failed to write GGUF file '" + file_path + "'");
+    }
+    gguf_free(gguf_ctx);
+    return success;
+}
@@ -0,0 +1,17 @@
+#ifndef __SD_MODEL_IO_GGUF_IO_H__
+#define __SD_MODEL_IO_GGUF_IO_H__
+
+#include <string>
+#include <vector>
+
+#include "tensor_storage.h"
+
+bool is_gguf_file(const std::string& file_path);
+bool read_gguf_file(const std::string& file_path,
+                    std::vector<TensorStorage>& tensor_storages,
+                    std::string* error = nullptr);
+bool write_gguf_file(const std::string& file_path,
+                     const std::vector<TensorWriteInfo>& tensors,
+                     std::string* error = nullptr);
+
+#endif  // __SD_MODEL_IO_GGUF_IO_H__
@@ -1,5 +1,5 @@
-#ifndef __GGUF_READER_HPP__
-#define __GGUF_READER_HPP__
+#ifndef __SD_MODEL_IO_GGUF_READER_EXT_H__
+#define __SD_MODEL_IO_GGUF_READER_EXT_H__

 #include <cstdint>
 #include <fstream>
@@ -231,4 +231,4 @@ public:
    size_t data_offset() const { return data_offset_; }
 };

-#endif  // __GGUF_READER_HPP__
+#endif  // __SD_MODEL_IO_GGUF_READER_EXT_H__
@@ -0,0 +1,21 @@
+#ifndef __SD_MODEL_IO_PICKLE_IO_H__
+#define __SD_MODEL_IO_PICKLE_IO_H__
+
+#include <cstddef>
+#include <cstdint>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "tensor_storage.h"
+
+bool skip_pickle_object(const uint8_t* buffer, size_t buffer_size, size_t* object_size);
+bool pickle_object_is_torch_magic_number(const uint8_t* buffer, size_t buffer_size);
+bool parse_pickle_uint32_object(const uint8_t* buffer, size_t buffer_size, uint32_t* value);
+bool parse_torch_state_dict_pickle(const uint8_t* buffer,
+                                   size_t buffer_size,
+                                   std::vector<TensorStorage>& tensor_storages,
+                                   std::unordered_map<std::string, uint64_t>& storage_nbytes,
+                                   std::string* error = nullptr);
+
+#endif  // __SD_MODEL_IO_PICKLE_IO_H__
@@ -0,0 +1,316 @@
+#include "safetensors_io.h"
+
+#include <cstdint>
+#include <exception>
+#include <fstream>
+#include <string>
+#include <vector>
+
+#include "binary_io.h"
+#include "json.hpp"
+#include "util.h"
+
+static constexpr size_t ST_HEADER_SIZE_LEN = 8;
+
+static void set_error(std::string* error, const std::string& message) {
+    if (error != nullptr) {
+        *error = message;
+    }
+}
+
+bool is_safetensors_file(const std::string& file_path) {
+    std::ifstream file(file_path, std::ios::binary);
+    if (!file.is_open()) {
+        return false;
+    }
+
+    // get file size
+    file.seekg(0, file.end);
+    size_t file_size_ = file.tellg();
+    file.seekg(0, file.beg);
+
+    // read header size
+    if (file_size_ <= ST_HEADER_SIZE_LEN) {
+        return false;
+    }
+
+    uint8_t header_size_buf[ST_HEADER_SIZE_LEN];
+    file.read((char*)header_size_buf, ST_HEADER_SIZE_LEN);
+    if (!file) {
+        return false;
+    }
+
+    size_t header_size_ = model_io::read_u64(header_size_buf);
+    if (header_size_ >= file_size_ || header_size_ <= 2) {
+        return false;
+    }
+
+    // read header
+    std::vector<char> header_buf;
+    header_buf.resize(header_size_ + 1);
+    header_buf[header_size_] = '\0';
+    file.read(header_buf.data(), header_size_);
+    if (!file) {
+        return false;
+    }
+    try {
+        nlohmann::json header_ = nlohmann::json::parse(header_buf.data());
+    } catch (const std::exception&) {
+        return false;
+    }
+    return true;
+}
+
+static ggml_type safetensors_dtype_to_ggml_type(const std::string& dtype) {
+    ggml_type ttype = GGML_TYPE_COUNT;
+    if (dtype == "F16") {
+        ttype = GGML_TYPE_F16;
+    } else if (dtype == "BF16") {
+        ttype = GGML_TYPE_BF16;
+    } else if (dtype == "F32") {
+        ttype = GGML_TYPE_F32;
+    } else if (dtype == "F64") {
+        ttype = GGML_TYPE_F32;
+    } else if (dtype == "F8_E4M3") {
+        ttype = GGML_TYPE_F16;
+    } else if (dtype == "F8_E5M2") {
+        ttype = GGML_TYPE_F16;
+    } else if (dtype == "I32") {
+        ttype = GGML_TYPE_I32;
+    } else if (dtype == "I64") {
+        ttype = GGML_TYPE_I32;
+    }
+    return ttype;
+}
+
+// https://huggingface.co/docs/safetensors/index
+bool read_safetensors_file(const std::string& file_path,
+                           std::vector<TensorStorage>& tensor_storages,
+                           std::string* error) {
+    std::ifstream file(file_path, std::ios::binary);
+    if (!file.is_open()) {
+        set_error(error, "failed to open '" + file_path + "'");
+        return false;
+    }
+
+    // get file size
+    file.seekg(0, file.end);
+    size_t file_size_ = file.tellg();
+    file.seekg(0, file.beg);
+
+    // read header size
+    if (file_size_ <= ST_HEADER_SIZE_LEN) {
+        set_error(error, "invalid safetensor file '" + file_path + "'");
+        return false;
+    }
+
+    uint8_t header_size_buf[ST_HEADER_SIZE_LEN];
+    file.read((char*)header_size_buf, ST_HEADER_SIZE_LEN);
+    if (!file) {
+        set_error(error, "read safetensors header size failed: '" + file_path + "'");
+        return false;
+    }
+
+    size_t header_size_ = model_io::read_u64(header_size_buf);
+    if (header_size_ >= file_size_) {
+        set_error(error, "invalid safetensor file '" + file_path + "'");
+        return false;
+    }
+
+    // read header
+    std::vector<char> header_buf;
+    header_buf.resize(header_size_ + 1);
+    header_buf[header_size_] = '\0';
+    file.read(header_buf.data(), header_size_);
+    if (!file) {
+        set_error(error, "read safetensors header failed: '" + file_path + "'");
+        return false;
+    }
+
+    nlohmann::json header_;
+    try {
+        header_ = nlohmann::json::parse(header_buf.data());
+    } catch (const std::exception&) {
+        set_error(error, "parsing safetensors header failed: '" + file_path + "'");
+        return false;
+    }
+
+    tensor_storages.clear();
+    for (auto& item : header_.items()) {
+        std::string name           = item.key();
+        nlohmann::json tensor_info = item.value();
+        // LOG_DEBUG("%s %s\n", name.c_str(), tensor_info.dump().c_str());
+
+        if (name == "__metadata__") {
+            continue;
+        }
+
+        std::string dtype    = tensor_info["dtype"];
+        nlohmann::json shape = tensor_info["shape"];
+
+        if (dtype == "U8") {
+            continue;
+        }
+
+        size_t begin = tensor_info["data_offsets"][0].get<size_t>();
+        size_t end   = tensor_info["data_offsets"][1].get<size_t>();
+
+        ggml_type type = safetensors_dtype_to_ggml_type(dtype);
+        if (type == GGML_TYPE_COUNT) {
+            set_error(error, "unsupported dtype '" + dtype + "' (tensor '" + name + "')");
+            return false;
+        }
+
+        if (shape.size() > SD_MAX_DIMS) {
+            set_error(error, "invalid tensor '" + name + "'");
+            return false;
+        }
+
+        int n_dims              = (int)shape.size();
+        int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
+        for (int i = 0; i < n_dims; i++) {
+            ne[i] = shape[i].get<int64_t>();
+        }
+
+        if (n_dims == 5) {
+            n_dims = 4;
+            ne[0]  = ne[0] * ne[1];
+            ne[1]  = ne[2];
+            ne[2]  = ne[3];
+            ne[3]  = ne[4];
+        }
+
+        // ggml_n_dims returns 1 for scalars
+        if (n_dims == 0) {
+            n_dims = 1;
+        }
+
+        TensorStorage tensor_storage(name, type, ne, n_dims, 0, ST_HEADER_SIZE_LEN + header_size_ + begin);
+        tensor_storage.reverse_ne();
+
+        size_t tensor_data_size = end - begin;
+
+        bool tensor_size_ok;
+        if (dtype == "F8_E4M3") {
+            tensor_storage.is_f8_e4m3 = true;
+            // f8 -> f16
+            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size * 2);
+        } else if (dtype == "F8_E5M2") {
+            tensor_storage.is_f8_e5m2 = true;
+            // f8 -> f16
+            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size * 2);
+        } else if (dtype == "F64") {
+            tensor_storage.is_f64 = true;
+            // f64 -> f32
+            tensor_size_ok = (tensor_storage.nbytes() * 2 == tensor_data_size);
+        } else if (dtype == "I64") {
+            tensor_storage.is_i64 = true;
+            // i64 -> i32
+            tensor_size_ok = (tensor_storage.nbytes() * 2 == tensor_data_size);
+        } else {
+            tensor_size_ok = (tensor_storage.nbytes() == tensor_data_size);
+        }
+        if (!tensor_size_ok) {
+            set_error(error, "size mismatch for tensor '" + name + "' (" + dtype + ")");
+            return false;
+        }
+
+        tensor_storages.push_back(tensor_storage);
+
+        // LOG_DEBUG("%s %s", tensor_storage.to_string().c_str(), dtype.c_str());
+    }
+
+    return true;
+}
+
+static bool ggml_type_to_safetensors_dtype(ggml_type type, std::string* dtype) {
+    switch (type) {
+        case GGML_TYPE_F16:
+            *dtype = "F16";
+            return true;
+        case GGML_TYPE_BF16:
+            *dtype = "BF16";
+            return true;
+        case GGML_TYPE_F32:
+            *dtype = "F32";
+            return true;
+        case GGML_TYPE_I32:
+            *dtype = "I32";
+            return true;
+        default:
+            return false;
+    }
+}
+
+bool write_safetensors_file(const std::string& file_path,
+                            const std::vector<TensorWriteInfo>& tensors,
+                            std::string* error) {
+    nlohmann::ordered_json header = nlohmann::ordered_json::object();
+
+    uint64_t data_offset = 0;
+    for (const TensorWriteInfo& write_tensor : tensors) {
+        ggml_tensor* tensor = write_tensor.tensor;
+        if (tensor == nullptr) {
+            set_error(error, "null tensor cannot be written to safetensors");
+            return false;
+        }
+
+        const std::string name = ggml_get_name(tensor);
+        std::string dtype;
+        if (!ggml_type_to_safetensors_dtype(tensor->type, &dtype)) {
+            set_error(error,
+                      "unsupported safetensors dtype '" + std::string(ggml_type_name(tensor->type)) +
+                          "' for tensor '" + name + "'");
+            return false;
+        }
+
+        const uint64_t tensor_nbytes = ggml_nbytes(tensor);
+
+        nlohmann::ordered_json json_tensor_info = nlohmann::ordered_json::object();
+        json_tensor_info["dtype"]               = dtype;
+
+        nlohmann::ordered_json shape = nlohmann::ordered_json::array();
+        for (int i = 0; i < write_tensor.n_dims; ++i) {
+            shape.push_back(write_tensor.ne[write_tensor.n_dims - 1 - i]);
+        }
+        json_tensor_info["shape"] = shape;
+
+        nlohmann::ordered_json data_offsets = nlohmann::ordered_json::array();
+        data_offsets.push_back(data_offset);
+        data_offsets.push_back(data_offset + tensor_nbytes);
+        json_tensor_info["data_offsets"] = data_offsets;
+
+        header[name] = json_tensor_info;
+        data_offset += tensor_nbytes;
+    }
+
+    const std::string header_str = header.dump();
+
+    std::ofstream file(file_path, std::ios::binary);
+    if (!file.is_open()) {
+        set_error(error, "failed to open '" + file_path + "' for writing");
+        return false;
+    }
+
+    LOG_INFO("trying to save tensors to %s", file_path.c_str());
+    model_io::write_u64(file, header_str.size());
+    file.write(header_str.data(), header_str.size());
+    if (!file) {
+        set_error(error, "failed to write safetensors header to '" + file_path + "'");
+        return false;
+    }
+
+    for (const TensorWriteInfo& write_tensor : tensors) {
+        ggml_tensor* tensor        = write_tensor.tensor;
+        const std::string name     = ggml_get_name(tensor);
+        const size_t tensor_nbytes = ggml_nbytes(tensor);
+        file.write((const char*)tensor->data, tensor_nbytes);
+        if (!file) {
+            set_error(error,
+                      "failed to write tensor '" + name + "' to '" + file_path + "'");
+            return false;
+        }
+    }
+
+    return true;
+}
@@ -0,0 +1,17 @@
+#ifndef __SD_MODEL_IO_SAFETENSORS_IO_H__
+#define __SD_MODEL_IO_SAFETENSORS_IO_H__
+
+#include <string>
+#include <vector>
+
+#include "tensor_storage.h"
+
+bool is_safetensors_file(const std::string& file_path);
+bool read_safetensors_file(const std::string& file_path,
+                           std::vector<TensorStorage>& tensor_storages,
+                           std::string* error = nullptr);
+bool write_safetensors_file(const std::string& file_path,
+                            const std::vector<TensorWriteInfo>& tensors,
+                            std::string* error = nullptr);
+
+#endif  // __SD_MODEL_IO_SAFETENSORS_IO_H__
@@ -0,0 +1,132 @@
+#ifndef __SD_TENSOR_STORAGE_H__
+#define __SD_TENSOR_STORAGE_H__
+
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <sstream>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "ggml.h"
+
+#define SD_MAX_DIMS 5
+
+struct TensorStorage {
+    std::string name;
+    ggml_type type          = GGML_TYPE_F32;
+    ggml_type expected_type = GGML_TYPE_COUNT;
+    bool is_f8_e4m3         = false;
+    bool is_f8_e5m2         = false;
+    bool is_f64             = false;
+    bool is_i64             = false;
+    int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
+    int n_dims              = 0;
+
+    std::string storage_key;
+    size_t file_index = 0;
+    int index_in_zip  = -1;  // >= means stored in a zip file
+    uint64_t offset   = 0;   // offset in file
+
+    TensorStorage() = default;
+
+    TensorStorage(std::string name, ggml_type type, const int64_t* ne, int n_dims, size_t file_index, size_t offset = 0)
+        : name(std::move(name)), type(type), n_dims(n_dims), file_index(file_index), offset(offset) {
+        for (int i = 0; i < n_dims; i++) {
+            this->ne[i] = ne[i];
+        }
+    }
+
+    int64_t nelements() const {
+        int64_t n = 1;
+        for (int i = 0; i < SD_MAX_DIMS; i++) {
+            n *= ne[i];
+        }
+        return n;
+    }
+
+    int64_t nbytes() const {
+        return nelements() * ggml_type_size(type) / ggml_blck_size(type);
+    }
+
+    int64_t nbytes_to_read() const {
+        if (is_f8_e4m3 || is_f8_e5m2) {
+            return nbytes() / 2;
+        } else if (is_f64 || is_i64) {
+            return nbytes() * 2;
+        } else {
+            return nbytes();
+        }
+    }
+
+    void unsqueeze() {
+        if (n_dims == 2) {
+            n_dims = 4;
+            ne[3]  = ne[1];
+            ne[2]  = ne[0];
+            ne[1]  = 1;
+            ne[0]  = 1;
+        }
+    }
+
+    std::vector<TensorStorage> chunk(size_t n) {
+        std::vector<TensorStorage> chunks;
+        uint64_t chunk_size = nbytes_to_read() / n;
+        // printf("%d/%d\n", chunk_size, nbytes_to_read());
+        reverse_ne();
+        for (size_t i = 0; i < n; i++) {
+            TensorStorage chunk_i = *this;
+            chunk_i.ne[0]         = ne[0] / n;
+            chunk_i.offset        = offset + i * chunk_size;
+            chunk_i.reverse_ne();
+            chunks.push_back(chunk_i);
+        }
+        reverse_ne();
+        return chunks;
+    }
+
+    void reverse_ne() {
+        int64_t new_ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
+        for (int i = 0; i < n_dims; i++) {
+            new_ne[i] = ne[n_dims - 1 - i];
+        }
+        for (int i = 0; i < n_dims; i++) {
+            ne[i] = new_ne[i];
+        }
+    }
+
+    std::string to_string() const {
+        std::stringstream ss;
+        const char* type_name = ggml_type_name(type);
+        if (is_f8_e4m3) {
+            type_name = "f8_e4m3";
+        } else if (is_f8_e5m2) {
+            type_name = "f8_e5m2";
+        } else if (is_f64) {
+            type_name = "f64";
+        } else if (is_i64) {
+            type_name = "i64";
+        }
+        ss << name << " | " << type_name << " | ";
+        ss << n_dims << " [";
+        for (int i = 0; i < SD_MAX_DIMS; i++) {
+            ss << ne[i];
+            if (i != SD_MAX_DIMS - 1) {
+                ss << ", ";
+            }
+        }
+        ss << "]";
+        return ss.str();
+    }
+};
+
+struct TensorWriteInfo {
+    int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
+    int n_dims              = 0;
+    ggml_tensor* tensor     = nullptr;
+};
+
+typedef std::function<bool(const TensorStorage&, ggml_tensor**)> on_new_tensor_cb_t;
+
+#endif  // __SD_TENSOR_STORAGE_H__
@@ -0,0 +1,252 @@
+#include "torch_legacy_io.h"
+
+#include <algorithm>
+#include <cstdint>
+#include <fstream>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "pickle_io.h"
+#include "util.h"
+
+// torch.save format background:
+//
+//   - Before PyTorch 1.6.0, torch.save used this legacy non-zip format by
+//     default.
+//   - Since PyTorch 1.6.0, torch.save defaults to an uncompressed ZIP64 archive
+//     containing data.pkl, data/, version, and, since PyTorch 2.1.0, byteorder.
+//   - The old format can still be produced explicitly with:
+//       torch.save(obj, path, _use_new_zipfile_serialization=False)
+//
+// Whether obj is a state_dict or a whole nn.Module does not change the outer
+// container format selected by torch.save. It changes the pickled object inside:
+//
+//   - state_dict: usually an OrderedDict[str, Tensor]. pickle_io.cpp supports a
+//     restricted subset of this layout because tensor metadata and raw storages
+//     can be recovered without executing pickle callables.
+//   - whole module/checkpoint object: arbitrary Python object graph. This may
+//     require importing user classes and executing pickle GLOBAL/REDUCE rebuild
+//     logic, so it is intentionally not supported here.
+//
+// Legacy non-zip PyTorch files are not a single pickle object:
+//
+//   1. pickle object: PyTorch legacy magic number
+//   2. pickle object: legacy protocol version, expected to be 1001
+//   3. pickle object: sys_info metadata, ignored by this reader
+//   4. pickle object: state_dict metadata, parsed by pickle_io.cpp
+//   5. pickle object: serialized storage key list, skipped here
+//   6. raw storage data payloads
+//      - PyTorch writes storages after the pickles, ordered by storage key
+//      - each storage has an 8-byte legacy storage header followed by raw bytes
+static constexpr size_t LEGACY_STORAGE_HEADER_SIZE = 8;
+
+static void set_error(std::string* error, const std::string& message) {
+    if (error != nullptr) {
+        *error = message;
+    }
+}
+
+static std::string bytes_to_hex(const std::vector<uint8_t>& bytes) {
+    static const char* hex = "0123456789ABCDEF";
+    std::string result;
+    result.reserve(bytes.size() * 3);
+    for (size_t i = 0; i < bytes.size(); ++i) {
+        if (i > 0) {
+            result.push_back('-');
+        }
+        result.push_back(hex[(bytes[i] >> 4) & 0x0F]);
+        result.push_back(hex[bytes[i] & 0x0F]);
+    }
+    return result;
+}
+
+static bool is_probably_tar_file(const std::vector<uint8_t>& header) {
+    return header.size() >= 262 &&
+           header[257] == 'u' &&
+           header[258] == 's' &&
+           header[259] == 't' &&
+           header[260] == 'a' &&
+           header[261] == 'r';
+}
+
+static std::string torch_legacy_diagnostics(const std::string& file_path, const std::vector<uint8_t>& buffer) {
+    if (!ends_with(file_path, ".pt") && !ends_with(file_path, ".pth")) {
+        return "";
+    }
+    if (buffer.empty()) {
+        return "unsupported PyTorch file '" + file_path + "': empty file";
+    }
+
+    size_t short_len = std::min<size_t>(buffer.size(), 32);
+    std::vector<uint8_t> short_header(buffer.begin(), buffer.begin() + short_len);
+    const bool raw_pickle = buffer[0] == 0x80;
+    const bool tar_file   = is_probably_tar_file(buffer);
+
+    std::string message = "unsupported PyTorch file '" + file_path + "': first bytes " +
+                          bytes_to_hex(short_header) +
+                          ", raw_pickle=" + (raw_pickle ? "true" : "false") +
+                          ", tar=" + (tar_file ? "true" : "false");
+    if (raw_pickle) {
+        message += "; raw pickle did not match the restricted state_dict layouts currently supported";
+    } else if (tar_file) {
+        message += "; legacy tar PyTorch checkpoints are not supported yet";
+    }
+    return message;
+}
+
+bool read_torch_legacy_file(const std::string& file_path,
+                            std::vector<TensorStorage>& tensor_storages,
+                            std::string* error) {
+    std::ifstream file(file_path, std::ios::binary);
+    if (!file.is_open()) {
+        set_error(error, "failed to open '" + file_path + "'");
+        return false;
+    }
+
+    file.seekg(0, file.end);
+    size_t file_size = (size_t)file.tellg();
+    file.seekg(0, file.beg);
+    if (file_size == 0) {
+        set_error(error, "empty file '" + file_path + "'");
+        return false;
+    }
+
+    std::vector<uint8_t> buffer(file_size);
+    file.read((char*)buffer.data(), file_size);
+    if (!file) {
+        set_error(error, "failed to read '" + file_path + "'");
+        return false;
+    }
+
+    auto finalize_tensor_offsets = [&](size_t storage_data_offset,
+                                       const std::unordered_map<std::string, uint64_t>& legacy_storage_map) -> bool {
+        if (storage_data_offset > file_size) {
+            return false;
+        }
+
+        std::vector<std::string> storage_keys;
+        storage_keys.reserve(legacy_storage_map.size());
+        for (const auto& [storage_key, _] : legacy_storage_map) {
+            storage_keys.push_back(storage_key);
+        }
+        std::sort(storage_keys.begin(), storage_keys.end());
+
+        std::unordered_map<std::string, uint64_t> storage_offsets;
+        uint64_t current_offset = storage_data_offset;
+        for (const auto& storage_key : storage_keys) {
+            auto it = legacy_storage_map.find(storage_key);
+            if (it == legacy_storage_map.end()) {
+                return false;
+            }
+            if (current_offset + LEGACY_STORAGE_HEADER_SIZE + it->second > file_size) {
+                return false;
+            }
+            storage_offsets[storage_key] = current_offset + LEGACY_STORAGE_HEADER_SIZE;
+            current_offset += LEGACY_STORAGE_HEADER_SIZE + it->second;
+        }
+
+        for (auto& tensor_storage : tensor_storages) {
+            if (tensor_storage.storage_key.empty()) {
+                continue;
+            }
+
+            auto it_offset = storage_offsets.find(tensor_storage.storage_key);
+            auto it_size   = legacy_storage_map.find(tensor_storage.storage_key);
+            if (it_offset == storage_offsets.end() || it_size == legacy_storage_map.end()) {
+                return false;
+            }
+
+            uint64_t base_offset    = it_offset->second;
+            uint64_t storage_nbytes = it_size->second;
+            uint64_t tensor_nbytes  = tensor_storage.nbytes_to_read();
+            if (tensor_storage.offset + tensor_nbytes > storage_nbytes) {
+                return false;
+            }
+
+            tensor_storage.offset = base_offset + tensor_storage.offset;
+            tensor_storage.storage_key.clear();
+        }
+
+        return true;
+    };
+
+    auto parse_state_dict_at = [&](size_t state_dict_offset, size_t state_dict_size, size_t* storage_data_offset) -> bool {
+        tensor_storages.clear();
+        std::unordered_map<std::string, uint64_t> legacy_storage_map;
+        if (!parse_torch_state_dict_pickle(buffer.data() + state_dict_offset,
+                                           state_dict_size,
+                                           tensor_storages,
+                                           legacy_storage_map,
+                                           error)) {
+            return false;
+        }
+
+        size_t offset_after_state_dict = state_dict_offset + state_dict_size;
+        size_t storage_keys_size       = 0;
+        if (!skip_pickle_object(buffer.data() + offset_after_state_dict,
+                                buffer.size() - offset_after_state_dict,
+                                &storage_keys_size)) {
+            return false;
+        }
+
+        *storage_data_offset = offset_after_state_dict + storage_keys_size;
+        return finalize_tensor_offsets(*storage_data_offset, legacy_storage_map);
+    };
+
+    size_t object_size_1 = 0;
+    size_t offset        = 0;
+
+    if (skip_pickle_object(buffer.data(), buffer.size(), &object_size_1) &&
+        pickle_object_is_torch_magic_number(buffer.data(), object_size_1)) {
+        offset += object_size_1;
+
+        size_t object_size_2 = 0;
+        if (!skip_pickle_object(buffer.data() + offset, buffer.size() - offset, &object_size_2)) {
+            set_error(error, torch_legacy_diagnostics(file_path, buffer));
+            return false;
+        }
+        uint32_t protocol_version = 0;
+        if (!parse_pickle_uint32_object(buffer.data() + offset, object_size_2, &protocol_version) || protocol_version != 1001) {
+            set_error(error, torch_legacy_diagnostics(file_path, buffer));
+            return false;
+        }
+        offset += object_size_2;
+
+        size_t object_size_3 = 0;
+        if (!skip_pickle_object(buffer.data() + offset, buffer.size() - offset, &object_size_3)) {
+            set_error(error, torch_legacy_diagnostics(file_path, buffer));
+            return false;
+        }
+        offset += object_size_3;
+
+        size_t state_dict_size = 0;
+        if (!skip_pickle_object(buffer.data() + offset, buffer.size() - offset, &state_dict_size)) {
+            set_error(error, torch_legacy_diagnostics(file_path, buffer));
+            return false;
+        }
+
+        size_t storage_data_offset = 0;
+        if (parse_state_dict_at(offset, state_dict_size, &storage_data_offset)) {
+            return true;
+        }
+
+        if (error != nullptr && error->empty()) {
+            set_error(error, torch_legacy_diagnostics(file_path, buffer));
+        }
+        return false;
+    }
+
+    size_t state_dict_size = 0;
+    if (skip_pickle_object(buffer.data(), buffer.size(), &state_dict_size)) {
+        size_t storage_data_offset = 0;
+        if (parse_state_dict_at(0, state_dict_size, &storage_data_offset)) {
+            return true;
+        }
+    }
+
+    if (error != nullptr && error->empty()) {
+        set_error(error, torch_legacy_diagnostics(file_path, buffer));
+    }
+    return false;
+}
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
leejet	dfb2390dd4	refactor: extract Wan VAE implementation (#1614 )	2026-06-07 01:33:49 +08:00
leejet	cfbc19d186	refactor: unify model config detection (#1613 )	2026-06-07 01:05:12 +08:00
leejet	b9254dda0d	feat: add ideogram4 support (#1609 )	2026-06-06 16:34:16 +08:00
fszontagh	0648f4426b	perf: ratchet streaming budget so plan stops re-merging every step (#1611 )	2026-06-06 16:32:03 +08:00
YOSHIDA Keiji	74f513d512	fix: Suppress spurious error output for --help (#1607 ) (#1608 ) Signed-off-by: kei-g <km.8k6ce+github@gmail.com>	2026-06-06 16:23:44 +08:00
fszontagh	064001b524	perf: allocate CPU-offloaded params from runtime device pinned host buffer (#1601 )	2026-06-06 16:22:18 +08:00
leejet	1f9ee88e09	fix: zero Wan2.2 TI2V timesteps for fixed frames (#1604 )	2026-06-03 23:32:31 +08:00
fszontagh	a7f2e03da4	perf: keep chunk-K residency engaged with runtime LoRA (#1598 )	2026-06-03 23:12:00 +08:00
stduhpfandleejet	4513e3fda9	refactor: img-cond->img_uncond (#1594 ) * refactor: img-cond->img_uncond * align APG and CFG++ with img-uncond CFG * set default img_cfg to 1.f --------- Co-authored-by: leejet <leejet714@gmail.com>	2026-06-03 22:57:42 +08:00
leejet	2d40a8b2ad	feat: make Wan2.2 5B FLF2V work (#1110 )	2026-06-02 23:16:09 +08:00
leejet	9c7f9a20b3	chore: embed server web UI in Docker images (#1597 )	2026-06-02 22:46:25 +08:00
fszontagh	ed74577c40	feat: `--stream-layers` for streaming weights from CPU during generation (#1576 )	2026-06-02 22:35:28 +08:00
RapidMark	7948df8ac1	fix(cmake): build HIP backend with PIC so the static-lib PIE link succeeds (#1593 )	2026-06-02 00:07:48 +08:00
Wagner Brunaandleejet	02f06370a7	refactor: call CPU backend functions dynamically (#1591 ) Co-authored-by: leejet <leejet714@gmail.com>	2026-06-01 23:41:21 +08:00
stduhpfandleejet	f8935d6f25	feat: support img-cfg for edit models (#929 ) Co-authored-by: leejet <leejet714@gmail.com>	2026-06-01 22:54:25 +08:00
stduhpf	be65ac7511	feat: add support for APG (adaptive projected guidance) + unconditionnal SLG (#593 )	2026-06-01 00:55:49 +08:00
leejet	20901f6d8e	fix: remove kv padding from flash attention wrapper (#1453 )	2026-05-31 23:23:19 +08:00
leejet	0982807139	feat: add PiD support (#1585 )	2026-05-31 22:38:39 +08:00
leejet	d2797b8667	fix: correct Gemma3 rope settings and vram limit propagation (#1583 )	2026-05-30 22:23:49 +08:00
leejet	d3b2cb047e	fix: split tokens before normalization (#1582 )	2026-05-30 18:38:46 +08:00
akleine	b4ba55d8d7	fix: prevent crash in case of a mem alloc error and graceful exit (#1566 )	2026-05-30 18:34:07 +08:00
Wagner Bruna	b54bd83a3f	fix: explicitly exclude f8, f64 and i64 tensors from mmap (#1575 )	2026-05-30 18:31:08 +08:00
Wagner Bruna	0e4ee04488	fix: correct tae for models that use the flux2 vae (#1571 )	2026-05-28 09:13:16 +08:00
leejet	29ab511fc7	fix: resolve LLM norm tensor names by architecture (#1570 )	2026-05-28 00:36:16 +08:00
leejet	55c2aed52c	refactor: simplify diffusion model runner params (#1569 )	2026-05-28 00:12:35 +08:00
leejet	8eded497e5	fix: preserve frontend tooling in ROCm CI build (#1568 )	2026-05-27 21:26:16 +08:00
leejet	92dc7268fc	feat: add microsoft lens support (#1560 )	2026-05-27 01:04:17 +08:00
schirikandSerge F. Chirik	07b2b18e70	fix: skip permission denied errors in recursive_directory_iterator (#1564 ) Co-authored-by: Serge F. Chirik <s.chirik@timbel.info>	2026-05-27 00:56:16 +08:00
leejet	1ceb5bd9df	fix: package ROCm BLAS runtime in Windows artifacts (#1562 )	2026-05-26 00:57:37 +08:00
leejet	202c6154a2	fix: use flux flow prediction for LTXAV (#1561 )	2026-05-26 00:23:39 +08:00
stduhpfandleejet	a397e03488	feat: add Longcat-Image / Longcat-Image-Edit support (#1053 ) Co-authored-by: leejet <leejet714@gmail.com>	2026-05-24 02:02:02 +08:00
leejet	72e512a0cc	fix: make macOS binaries use relocatable rpaths (#1552 )	2026-05-23 12:27:06 +08:00
leejet	0baf721215	feat: add LTX temporal latent upscaler support (#1551 )	2026-05-23 01:35:13 +08:00
leejet	645e6e9089	feat: add LTX rational latent upscaler (#1549 )	2026-05-23 00:28:15 +08:00
stduhpf	cbf92191c3	fix: strip trailing latent channels for preview decode (#1548 )	2026-05-23 00:26:40 +08:00
stduhpf	8cf55a3b3b	fix: load TAESD preview-only model correctly (#1547 )	2026-05-23 00:22:35 +08:00
leejet	3a8788cb7d	refactor: unify extra argument parsing (#1540 )	2026-05-22 01:00:03 +08:00
leejet	449165caf5	feat: stream LTX VAE temporal tile decoding (#1539 )	2026-05-22 00:25:04 +08:00
stduhpfandleejet	adaa599a3b	Feat: Temporal tile custom size with overlap (#1510 ) * Temporal tile size + overlap * add --extra-tiling-args support --------- Co-authored-by: leejet <leejet714@gmail.com>	2026-05-21 23:44:12 +08:00
leejet	2e3514625a	perf: run LTX audio VAE decode in one ggml graph (#1538 )	2026-05-21 22:43:14 +08:00
stduhpf	47d8198b69	feat: add taeltx2_3_wide support (#1535 )	2026-05-21 22:34:12 +08:00
leejet	ef92a0027e	feat: add graph cut markers for LTXAV transformer (#1534 )	2026-05-20 23:22:10 +08:00
leejet	b3374e6a71	feat: add LTX spatial latent upscale hires support (#1533 )	2026-05-20 22:27:09 +08:00
stduhpf	bdd937f29a	feat: add taeltx2/taeltx2.3 support (#1531 )	2026-05-20 22:14:05 +08:00
stduhpf	c51ec7cad9	fix: always load runtimle lora params on runtime backend (#1532 )	2026-05-20 22:13:15 +08:00
leejet	5b0267e941	fix: avoid Vulkan f16 repeat in LTX audio VAE (#1528 )	2026-05-19 23:15:26 +08:00
leejet	0045a72b96	fix: trigger ci for docker image changes (#1527 )	2026-05-19 22:05:03 +08:00
leejet	99bd062546	fix: update sycl docker image to oneapi 2025.3 (#1526 )	2026-05-19 21:59:15 +08:00
leejet	9d8c9e4279	fix: build web UI for Windows ROCm server releases (#1525 )	2026-05-19 21:53:48 +08:00
George Sofianos	caa823a8c0	ci: add RDNA1 + RDNA2 targets for ROCm 7.13 (#1511 )	2026-05-19 01:38:02 +08:00
leejet	22c8c40b0d	sync: update ggml (#1520 )	2026-05-19 01:30:11 +08:00
leejet	b706d682ad	fix: restore singleton dims for LLM outputs (#1518 )	2026-05-18 23:47:10 +08:00
leejet	b758b7de13	fix: only enable TAE after successful load (#1517 )	2026-05-18 23:32:03 +08:00
Wagner Bruna	f683c88a28	feat: make negative max_vram control the amount of spare vram (#1503 )	2026-05-18 23:00:06 +08:00
Christoph	21fd4e6788	ci: add CUDA Docker image support for NVIDIA Spark GB10 (#1512 )	2026-05-18 22:52:01 +08:00
leejet	830804262b	docs: update news	2026-05-18 00:24:29 +08:00
leejet	82e03ef137	ci: add inactive pr clean up workflow	2026-05-18 00:09:45 +08:00
leejet	baf7eda1e4	refactor: minify vocab files (#1509 )	2026-05-17 23:06:58 +08:00
Wagner Bruna	e7eb92fd84	feat: add Gradient Estimation sampler (#1484 )	2026-05-17 22:54:28 +08:00
leejet	50134e51dd	refactor: split guidance composition (#1506 )	2026-05-17 20:20:16 +08:00
leejet	e43b24cf48	feat: add ltx2.3 flf2v support (#1505 )	2026-05-17 18:40:14 +08:00
stduhpf	06accf2b39	feat: add ltxav latent2rgb projection matrix (#1502 )	2026-05-17 17:52:05 +08:00
stduhpfandleejet	cde20d5ef0	fix: handle stereo format in sd_audio (#1489 ) Co-authored-by: leejet <leejet714@gmail.com>	2026-05-17 16:55:39 +08:00
leejet	67dda3f897	feat: add ltx2.3 support (#1463 ) * add GemmaTokenizer * add basic ltx2.3 support * change vocab file encoding * fix ci * fix ubuntu build * add temporal tiling support * add ltx audio support * update ggml submodule url * fix generate_video * add i2v support * minify bundled Gemma tokenizer vocab sources * pass video fps into temporal rope embeddings * fix av_ca_timestep_scale_multiplier * add LTX2Scheduler support * update docs * fix ci	2026-05-17 16:46:20 +08:00
Mario Limonciello	3b4d26f3d9	ci: update ROCm builds for Windows and Linux to use ROCm 7.13 (#1504 )	2026-05-17 16:32:19 +08:00
Taylor	bd17f53b73	docs: update zit example to 8 steps (#1294 )	2026-05-16 21:32:03 +08:00
leejet	d7ecbe1d01	fix: avoid repeated T5 EOS tokens in Anima prompt weights (#1501 )	2026-05-16 21:22:46 +08:00
leejetandStéphane du Hamel	36330724bd	feat: add module backend assignment support (#1500 ) Co-authored-by: Stéphane du Hamel <stephduh@live.fr>	2026-05-16 20:27:06 +08:00
Mario Limonciello	0c1ca170ca	ci: update ROCm Windows builds (#1282 )	2026-05-16 20:25:38 +08:00
Mario Limonciello	839f6a94d2	ci: switch over ROCm builds to artifacts both for stable and preview releases (#1281 )	2026-05-16 20:23:26 +08:00
leejet	38b14adb67	feat: auto-detect max VRAM budget with --max-vram -1 (#1498 )	2026-05-16 16:14:25 +08:00
Wagner Bruna	fd1a2794f3	refactor: unify Euler, Euler Ancestral and DDIM implementations (#1474 )	2026-05-16 16:13:28 +08:00
cphlipot	db08b84607	fix: Fix broken GCC 16 build (enforce C11/C++17 compile ) (#1478 )	2026-05-16 16:10:16 +08:00
Wagner Bruna	686856edca	chore: do not report the fake VAE "allocation" as an error (#1494 )	2026-05-16 16:08:31 +08:00
leejet	0b8296915c	docs: add .github/pull_request_template.md	2026-05-15 01:16:21 +08:00
leejet	381e0df50f	docs: add CONTRIBUTING.md	2026-05-15 01:09:45 +08:00
leejet	0665a7f8bf	feat: add hidream o1 image support (#1485 )	2026-05-15 00:40:21 +08:00
Craig Andrews	eeac950b44	fix: Use PkgConfig for WebP and WebM (#1400 )	2026-05-15 00:31:10 +08:00
Wagner Bruna Piotr Wilkin Junmo Kim leejet	57ff2eb0f4	feat: support for memory-mapping model weights (#1414 ) Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com> Co-authored-by: Junmo Kim <me@junmo.kim> Co-authored-by: leejet <leejet714@gmail.com>	2026-05-15 00:30:03 +08:00
Daniele	9d683417cb	feat: add Euler CFG++ and Euler-A CFG++ samplers (#1354 )	2026-05-15 00:29:04 +08:00
l8bloom	60477fd50f	docs: add new go bindings for stable-diffusion.cpp (#1480 )	2026-05-14 23:59:06 +08:00
cphlipot	6ee0684d74	feat: display server url with "http://" prefix. (#1486 )	2026-05-14 23:57:22 +08:00
leejet	90e87bc846	feat: add max-vram based segmented param offload (#1476 )	2026-05-06 21:56:02 +08:00
Wagner Bruna	586b6f1481	feat: adapt res samplers for flow models for eta > 0 (#1436 )	2026-05-06 21:49:06 +08:00
fszontagh	9097ce5211	fix: skip empty MultiLoraAdapter when no LoRAs target a model (#1469 )	2026-05-06 21:45:47 +08:00
leejet	3d6064b37e	perf: speed up tensor_to_sd_image conversion (#1466 )	2026-04-30 01:13:56 +08:00
Wagner Bruna Stéphane du Hamel Cyberhan123 leejet	b8079e253d	feat: transition from compile-time to runtime backend discovery (#1448 ) Co-authored-by: Stéphane du Hamel <stephduh@live.fr> Co-authored-by: Cyberhan123 <255542417@qq.com> Co-authored-by: leejet <leejet714@gmail.com>	2026-04-29 23:26:57 +08:00
Wagner Bruna	331cfa5387	fix: release VAE compute buffer after tiled encoding (#1465 )	2026-04-29 22:25:30 +08:00
Douglas Griffith	a81677f59c	docs: performance tips markup (#1460 )	2026-04-27 22:55:30 +08:00
leejet	f40a707d0f	feat: add sdcpp-specific generation metadata to image outputs (#1462 )	2026-04-27 22:43:13 +08:00
akleine	970c4a3312	chore: replace some NULL with nullptr + use "%zu" for printing some size_t data (#1457 )	2026-04-27 22:42:57 +08:00
leejet	b8bdffc199	feat: add more built-in highres upscalers (#1456 )	2026-04-23 22:17:58 +08:00
leejet	c97702e105	feat: add sd-webui style Hires. fix support (#1451 )	2026-04-22 23:51:09 +08:00
leejet	44cca3d626	feat: support safetensors export in convert mode (#1444 )	2026-04-20 00:22:11 +08:00
leejet	0a7ae07f94	feat: add restricted torch legacy checkpoint loading (#1443 )	2026-04-19 23:09:43 +08:00
leejet	66143340b6	refactor: move model file IO into dedicated module (#1442 )	2026-04-19 17:52:56 +08:00
Wagner Bruna	7023fc4cfb	fix: correct image to image DDIM and TCD (#1410 )	2026-04-19 17:51:28 +08:00