preprocessing text

[misc] Update Slack invite link (#786 )
Update WeChat Link
2025-09-06 05:56:13 +00:00 · 2025-09-05 12:16:18 -07:00 · 2025-09-05 11:40:47 -07:00 · 2025-09-05 01:23:01 -07:00 · 2025-09-04 17:51:27 -07:00 · 2025-09-03 20:47:33 -07:00
182 changed files with 8195 additions and 1090 deletions
@@ -117,11 +117,11 @@ steps:
                  queue: "default"
            - path:
                - "fastvideo/**"
-                - "csrc/attn/vsa/**"
-                - "csrc/attn/tk/**"
-                - "csrc/attn/setup_vsa.py"
-                - "csrc/attn/config_vsa.py"
-                - "csrc/attn/vsa.cpp"
+                - "csrc/attn/video_sparse_attn/**"
+                - "csrc/attn/video_sparse_attn/tk/**"
+                - "csrc/attn/video_sparse_attn/setup.py"
+                - "csrc/attn/video_sparse_attn/config_vsa.py"
+                - "csrc/attn/video_sparse_attn/vsa.cpp"
                - "pyproject.toml"
                - "docker/Dockerfile.python3.12"
              config:
@@ -133,10 +133,10 @@ steps:
                  queue: "default"
            - path:
                - "fastvideo/**"
-                - "csrc/attn/st_attn/**"
-                - "csrc/attn/setup_sta.py"
-                - "csrc/attn/config_sta.py"
-                - "csrc/attn/st_attn.cpp"
+                - "csrc/attn/sliding_tile_attn/**"
+                - "csrc/attn/sliding_tile_attn/setup.py"
+                - "csrc/attn/sliding_tile_attn/config_sta.py"
+                - "csrc/attn/sliding_tile_attn/st_attn.cpp"
                - "pyproject.toml"
                - "docker/Dockerfile.python3.12"
              config:
@@ -147,10 +147,10 @@ steps:
                agents:
                  queue: "default"
            - path:
-                - "csrc/attn/st_attn/**"
-                - "csrc/attn/setup_sta.py"
-                - "csrc/attn/config_sta.py"
-                - "csrc/attn/st_attn.cpp"
+                - "csrc/attn/sliding_tile_attn/**"
+                - "csrc/attn/sliding_tile_attn/setup.py"
+                - "csrc/attn/sliding_tile_attn/config_sta.py"
+                - "csrc/attn/sliding_tile_attn/st_attn.cpp"
                - "pyproject.toml"
                - "docker/Dockerfile.python3.12"
              config:
@@ -161,11 +161,12 @@ steps:
                agents:
                  queue: "default"
            - path:
-                - "csrc/attn/vsa/**"
-                - "csrc/attn/tk/**"
-                - "csrc/attn/setup_vsa.py"
-                - "csrc/attn/config_vsa.py"
-                - "csrc/attn/vsa.cpp"
+                - "csrc/attn/video_sparse_attn/**"
+                - "csrc/attn/video_sparse_attn/tk/**"
+                - "csrc/attn/tests/test_vsa.py"
+                - "csrc/attn/video_sparse_attn/setup.py"
+                - "csrc/attn/video_sparse_attn/config_vsa.py"
+                - "csrc/attn/video_sparse_attn/vsa.cpp"
                - "pyproject.toml"
                - "docker/Dockerfile.python3.12"
              config:
@@ -18,6 +18,12 @@ on:
        required: false
        default: false
        type: boolean
+      python_3_12_cuda_12_9:
+        description: 'Build Python 3.12 image Cuda 12.9'
+        required: false
+        default: false
+        type: boolean
+

 permissions:
  contents: read
@@ -49,4 +55,13 @@ jobs:
      python_version: '3.12'
      dockerfile_path: docker/Dockerfile.python3.12
      tag_suffix: py3.12
+    secrets: inherit
+
+  build-python-3-12-cuda-12-9:
+    if: ${{ github.event.inputs.python_3_12_cuda_12_9 == 'true' }}
+    uses: ./.github/workflows/build-image-template.yml
+    with:
+      python_version: '3.12'
+      dockerfile_path: docker/Dockerfile.python3.12.cuda12.9.1
+      tag_suffix: py3.12-cuda12.9.1
    secrets: inherit
@@ -104,16 +104,17 @@ jobs:
              - 'pyproject.toml'
              - 'docker/Dockerfile.python3.12'
            sta-kernel-paths: &sta-kernel-paths
-              - 'csrc/attn/st_attn/**'
-              - 'csrc/attn/setup_sta.py'
-              - 'csrc/attn/config_sta.py'
-              - 'csrc/attn/st_attn.cpp'
+              - 'csrc/attn/sliding_tile_attn/**'
+              - 'csrc/attn/sliding_tile_attn/tk/**'
+              - 'csrc/attn/sliding_tile_attn/setup.py'
+              - 'csrc/attn/sliding_tile_attn/config_sta.py'
+              - 'csrc/attn/sliding_tile_attn/st_attn.cpp'
            vsa-kernel-paths: &vsa-kernel-paths
-              - 'csrc/attn/vsa/**'
-              - 'csrc/attn/tk/**'
-              - 'csrc/attn/setup_vsa.py'
-              - 'csrc/attn/config_vsa.py'
-              - 'csrc/attn/vsa.cpp'
+              - 'csrc/attn/video_sparse_attn/**'
+              - 'csrc/attn/video_sparse_attn/tk/**'
+              - 'csrc/attn/video_sparse_attn/setup.py'
+              - 'csrc/attn/video_sparse_attn/config_vsa.py'
+              - 'csrc/attn/video_sparse_attn/vsa.cpp'
            vsa-paths: &vsa-paths
              - 'fastvideo/**'
              - *common-paths
@@ -234,7 +235,7 @@ jobs:
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-  
+
  training-test:
    needs: change-filter
    if: >-
@@ -372,4 +373,4 @@ jobs:
          JOB_IDS: '["encoder-test", "vae-test", "transformer-test", "ssim-test-py3.10", "ssim-test-py3.11", "ssim-test-py3.12", "training-test", "training-test-VSA", "inference-test-STA", "precision-test-STA", "precision-test-VSA"]'
          RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
          GITHUB_RUN_ID: ${{ github.run_id }}
-        run: python .github/scripts/runpod_cleanup.py
+        run: python .github/scripts/runpod_cleanup.py
@@ -5,7 +5,7 @@ on:
    branches:
      - main
    paths:
-      - "csrc/attn/setup_sta.py"
+      - "csrc/attn/sliding_tile_attn/setup.py"
  workflow_dispatch:

 jobs:
@@ -23,13 +23,13 @@ jobs:
      - name: Check if version changed
        id: check-version
        run: |
-          cd csrc/attn
+          cd csrc/attn/sliding_tile_attn
          # Get current commit's version
-          NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup_sta.py)
+          NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup.py)
          echo "New version: $NEW_VERSION"
          
          # Get previous version from git history
-          OLD_VERSION=$(git show HEAD~1:./setup_sta.py | grep -oP 'VERSION\s*=\s*"\K[^"]+' || echo "0.0.0")
+          OLD_VERSION=$(git show HEAD~1:./setup.py | grep -oP 'VERSION\s*=\s*"\K[^"]+' || echo "0.0.0")
          echo "Old version: $OLD_VERSION"
          
          if [ "$NEW_VERSION" != "$OLD_VERSION" ]; then
@@ -144,13 +144,13 @@ jobs:
          pip install setuptools
          pip install ninja packaging wheel

-          cd csrc/attn  # Move into the correct folder
+          cd csrc/attn/sliding_tile_attn  # Move into the correct folder
          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
-          python setup_sta.py bdist_wheel --dist-dir=dist
+          python setup.py bdist_wheel --dist-dir=dist

      - name: Rename wheel file
        run: |
-          cd csrc/attn
+          cd csrc/attn/sliding_tile_attn

          CUDA_SHORT_VERSION=$(echo ${{ matrix.cuda-version }} | cut -d. -f1,2 | sed 's/\.//g')
          TORCH_SHORT_VERSION=$(echo ${{ matrix.torch-version }} | cut -d. -f1,2)
@@ -165,7 +165,7 @@ jobs:
        uses: actions/upload-artifact@v4
        with:
          name: ${{ env.wheel_name }}
-          path: csrc/attn/dist/*.whl
+          path: csrc/attn/sliding_tile_attn/dist/*.whl
          retention-days: 90

  publish_package:
@@ -239,11 +239,11 @@ jobs:
          pip install setuptools
          pip install ninja packaging wheel

-          cd csrc/attn  # Move into the correct folder
+          cd csrc/attn/sliding_tile_attn  # Move into the correct folder
          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
-          python setup_sta.py sdist --dist-dir=dist
+          python setup.py sdist --dist-dir=dist

      - name: Publish release distributions to PyPI
        uses: pypa/gh-action-pypi-publish@release/v1
        with:
-          packages-dir: csrc/attn/dist/
+          packages-dir: csrc/attn/sliding_tile_attn/dist/
@@ -0,0 +1,257 @@
+name: Publish Video Sparse Attention Kernel to PyPI on Version Change
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - "csrc/attn/video_sparse_attn/setup.py"
+  workflow_dispatch:
+
+jobs:
+  check-version-change:
+    runs-on: ubuntu-latest
+    outputs:
+      version-changed: ${{ steps.check-version.outputs.changed }}
+      new-version: ${{ steps.check-version.outputs.new-version }}
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 2
+
+      - name: Check if version changed
+        id: check-version
+        run: |
+          cd csrc/attn/video_sparse_attn
+          # Get current commit's version
+          NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup.py)
+          echo "New version: $NEW_VERSION"
+          
+          # Get previous version from git history
+          OLD_VERSION=$(git show HEAD~1:./setup.py | grep -oP 'VERSION\s*=\s*"\K[^"]+' || echo "0.0.0")
+          echo "Old version: $OLD_VERSION"
+          
+          if [ "$NEW_VERSION" != "$OLD_VERSION" ]; then
+            echo "Version changed from $OLD_VERSION to $NEW_VERSION"
+            echo "changed=true" >> $GITHUB_OUTPUT
+            echo "new-version=$NEW_VERSION" >> $GITHUB_OUTPUT
+          else
+            echo "Version did not change"
+            echo "changed=false" >> $GITHUB_OUTPUT
+          fi
+
+  build_wheels:
+    name: Build Wheel
+    needs: check-version-change
+    if: ${{ needs.check-version-change.outputs.version-changed == 'true' || github.event_name == 'workflow_dispatch' }}
+    runs-on: ${{ matrix.os }}
+
+    strategy:
+      fail-fast: false
+      matrix:
+          # Using ubuntu-20.04 instead of 22.04 for more compatibility (glibc). Ideally we'd use the
+          # manylinux docker image, but I haven't figured out how to install CUDA on manylinux.
+          os: [ubuntu-22.04]
+          python-version: ['3.10', '3.11', '3.12', '3.13']
+          # For version reference https://pytorch.org/get-started/previous-versions/
+          torch-cuda:
+            - torch-version: '2.5.1'
+              cuda-version: '12.4.1'
+              torch-cuda-short: 'cu124'
+            - torch-version: '2.6.0'
+              cuda-version: '12.6.3'
+              torch-cuda-short: 'cu126'
+            - torch-version: '2.7.1'
+              cuda-version: '12.8.0'
+              torch-cuda-short: 'cu128'
+
+    steps:
+      - name: Free up disk space
+        run: |
+          echo "Initial disk space:"
+          df -h
+          
+          # Remove large directories
+          sudo rm -rf /usr/share/dotnet
+          sudo rm -rf /usr/local/lib/android
+          sudo rm -rf /opt/ghc
+          sudo rm -rf /usr/local/share/boost
+          sudo rm -rf /usr/share/swift
+          sudo rm -rf /usr/local/lib/node_modules
+          sudo rm -rf /usr/local/share/powershell
+          sudo rm -rf /usr/share/rust
+          sudo rm -rf /usr/local/.ghcup
+          
+          # Remove cached files
+          sudo rm -rf /var/lib/apt/lists/*
+          sudo rm -rf /var/cache/apt/archives/*
+          
+          echo "Disk space after cleanup:"
+          df -h
+
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install CUDA ${{ matrix.torch-cuda.cuda-version }}
+        uses: Jimver/cuda-toolkit@v0.2.21
+        id: cuda-toolkit
+        with:
+          cuda: ${{ matrix.torch-cuda.cuda-version }}
+          linux-local-args: '["--toolkit"]'
+          method: 'network'
+
+      - name: Install dependencies (GCC, Clang, CUDA Paths, Git)
+        run: |
+          sudo apt update
+          sudo apt install -y git patchelf gcc-11 g++-11 clang-11
+          sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
+
+          # Allow Git to Access Safe Directory
+          git config --global --add safe.directory /__w/FastVideo/FastVideo
+
+          # Set CUDA environment variables
+          export CUDA_HOME=/usr/local/cuda-${{ matrix.torch-cuda.cuda-version }}
+          export PATH=${CUDA_HOME}/bin:${PATH}
+          export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+          
+          # Verify installation
+          gcc --version
+          g++ --version
+          clang-11 --version
+          nvcc --version
+
+      - name: Install PyTorch ${{ matrix.torch-cuda.torch-version }}+cu${{ matrix.torch-cuda.cuda-version }}
+        run: |
+          pip install --upgrade pip
+          # With python 3.13 and torch 2.5.1, unless we update typing-extensions, we get error
+          # AttributeError: attribute '__default__' of 'typing.ParamSpec' objects is not writable
+          pip install typing-extensions==4.12.2
+          # We want to figure out the CUDA version to download pytorch
+          # e.g. we can have system CUDA version being 11.7 but if torch==1.12 then we need to download the wheel from cu116
+          # see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix
+          pip install --no-cache-dir torch==${{ matrix.torch-cuda.torch-version }} --index-url https://download.pytorch.org/whl/${{matrix.torch-cuda.torch-cuda-short}}
+          nvcc --version
+          python --version
+          python -c "import torch; print('PyTorch:', torch.__version__)"
+          python -c "import torch; print('CUDA:', torch.version.cuda)"
+          python -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)"
+
+      - name: Build wheel
+        run: |
+          export PYTHONPATH=$GITHUB_WORKSPACE:$PYTHONPATH
+          
+          # We want setuptools >= 49.6.0 otherwise we can't compile the extension if system CUDA version is 11.7 and pytorch cuda version is 11.6
+          # https://github.com/pytorch/pytorch/blob/664058fa83f1d8eede5d66418abff6e20bd76ca8/torch/utils/cpp_extension.py#L810
+          # However this still fails so I'm using a newer version of setuptools
+          pip install setuptools
+          pip install ninja packaging wheel
+
+          cd csrc/attn/video_sparse_attn  # Move into the correct folder
+          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
+          python setup.py bdist_wheel --dist-dir=dist
+
+      - name: Rename wheel file
+        run: |
+          cd csrc/attn/video_sparse_attn
+
+          CUDA_SHORT_VERSION=$(echo ${{ matrix.torch-cuda.cuda-version }} | cut -d. -f1,2 | sed 's/\.//g')
+          TORCH_SHORT_VERSION=$(echo ${{ matrix.torch-cuda.torch-version }} | cut -d. -f1,2)
+          # Get the correct version format
+          tmpname=cu${CUDA_SHORT_VERSION}torch${TORCH_SHORT_VERSION}
+          wheel_name=$(ls dist/*whl | xargs -n 1 basename | sed "s/-/+$tmpname-/2")
+          # Rename with version information
+          ls dist/*whl |xargs -I {} mv {} dist/${wheel_name}
+          echo "wheel_name=${wheel_name}" >> $GITHUB_ENV
+
+      - name: Upload wheel artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: ${{ env.wheel_name }}
+          path: csrc/attn/video_sparse_attn/dist/*.whl
+          retention-days: 90
+
+  publish_package:
+    name: Publish package
+    needs: [build_wheels, check-version-change]
+    if: ${{ needs.check-version-change.outputs.version-changed == 'true' || github.event_name == 'workflow_dispatch' }}
+    runs-on: ubuntu-22.04
+    permissions:
+      id-token: write  # Needed for OIDC Trusted Publishing
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+
+      - name: Install CUDA 12.4.1
+        uses: Jimver/cuda-toolkit@v0.2.21
+        id: cuda-toolkit
+        with:
+          cuda: 12.4.1
+          linux-local-args: '["--toolkit"]'
+          method: 'network'
+          sub-packages: '["nvcc"]'
+
+      - name: Install dependencies (GCC, Clang, CUDA Paths, Git)
+        run: |
+          sudo apt update
+          sudo apt install -y git patchelf gcc-11 g++-11 clang-11
+          sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
+
+          # Allow Git to Access Safe Directory
+          git config --global --add safe.directory /__w/FastVideo/FastVideo
+
+          # Set CUDA environment variables
+          export CUDA_HOME=/usr/local/cuda-12.4.1
+          export PATH=${CUDA_HOME}/bin:${PATH}
+          export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+          
+          # Verify installation
+          gcc --version
+          g++ --version
+          clang-11 --version
+          nvcc --version
+
+      - name: Install PyTorch 2.5.1+cu12.4.1
+        run: |
+          pip install --upgrade pip
+          # With python 3.13 and torch 2.5.1, unless we update typing-extensions, we get error
+          # AttributeError: attribute '__default__' of 'typing.ParamSpec' objects is not writable
+          pip install typing-extensions==4.12.2
+          # We want to figure out the CUDA version to download pytorch
+          # e.g. we can have system CUDA version being 11.7 but if torch==1.12 then we need to download the wheel from cu116
+          # see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix
+          export TORCH_CUDA_VERSION=124
+          pip install --no-cache-dir torch==2.5.1 --index-url https://download.pytorch.org/whl/cu${TORCH_CUDA_VERSION}
+          nvcc --version
+          python --version
+          python -c "import torch; print('PyTorch:', torch.__version__)"
+          python -c "import torch; print('CUDA:', torch.version.cuda)"
+          python -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)"
+
+      - name: Build source distribution
+        run: |
+          export PYTHONPATH=$GITHUB_WORKSPACE:$PYTHONPATH
+          
+          # We want setuptools >= 49.6.0 otherwise we can't compile the extension if system CUDA version is 11.7 and pytorch cuda version is 11.6
+          # https://github.com/pytorch/pytorch/blob/664058fa83f1d8eede5d66418abff6e20bd76ca8/torch/utils/cpp_extension.py#L810
+          # However this still fails so I'm using a newer version of setuptools
+          pip install setuptools
+          pip install ninja packaging wheel
+
+          cd csrc/attn/video_sparse_attn  # Move into the correct folder
+          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
+          python setup.py sdist --dist-dir=dist
+
+      - name: Publish release distributions to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages-dir: csrc/attn/video_sparse_attn/dist/
@@ -1,3 +1,7 @@
-[submodule "csrc/attn/tk"]
-	path = csrc/attn/tk
+[submodule "csrc/attn/video_sparse_attn/tk"]
+	path = csrc/attn/video_sparse_attn/tk
+	url = https://github.com/HazyResearch/ThunderKittens.git
+
+[submodule "csrc/attn/sliding_tile_attn/tk"]
+	path = csrc/attn/sliding_tile_attn/tk
 	url = https://github.com/HazyResearch/ThunderKittens.git
@@ -22,6 +22,7 @@ exclude: |
    examples/.*|
    .github/workflows/fastvideo-publish.yml|
    .github/workflows/sta-publish.yml|
+    .github/workflows/vsa-publish.yml|
    .github/workflows/build-image-template.yml|
    docs/source/inference/support_matrix.md
  )
@@ -1,13 +1,13 @@
 <div align="center">
-<img src=assets/logo.png width="30%"/>
+<img src=assets/logos/logo.svg width="30%"/>
 </div>

-**FastVideo is a unified framework for accelerated video generation.**
+**FastVideo is a unified post-training and inference framework for accelerated video generation.**

-FastVideo is an inference and post-training framework for diffusion models. It features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.
+FastVideo features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.

 <p align="center">
-    | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start.html"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers"  target="_blank"><b>FastWan2.1</b></a>  | 🤗 <a href="https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-Diffusers" target="_blank"><b>FastWan2.2</b></a> | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ" target="_blank"> <b>Slack</b> </a> |
+    | 🕹️ <a href="https://fastwan.fastvideo.org/"<b>Online Demo</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start.html"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/collections/FastVideo/fastwan-6886a305d9799c8cd1496408"  target="_blank"><b>FastWan</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3csdw1isz-Euq8_Q8~baewG8hxjXs2gQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/S7HLCSTh" target="_blank"> <b> WeChat </b> </a> |
 </p>

 <div align="center">
@@ -23,15 +23,19 @@ FastVideo is an inference and post-training framework for diffusion models. It f
 ## Key Features

 FastVideo has the following features:
+- End-to-end post-training support:
+  - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) for Wan2.1 and Wan2.2 to achineve >50x denoising speedup
+  - Data preprocessing pipeline for video data
+  - Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs
+  - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing, with near linear scaling to 64 GPUs
 - State-of-the-art performance optimizations for inference
+  - [Video Sparse Attention](https://arxiv.org/pdf/2505.13389)
  - [Sliding Tile Attention](https://arxiv.org/pdf/2502.04507)
  - [TeaCache](https://arxiv.org/pdf/2411.19108)
  - [Sage Attention](https://arxiv.org/abs/2410.02367)
- E2E post-training support
-  - Data preprocessing pipeline for video data.
-  - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) for Wan2.1 and Wan2.2 using [Video Sparse Attention](https://arxiv.org/pdf/2505.13389) and [Distribution Matching Distillation](https://tianweiy.github.io/dmd2/)
-  - Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs.
-  - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing, with near linear scaling to 64 GPUs.
+- Diverse hardware and OS support
+  - Support H100, A100, 4090
+  - Support Linux, Windows, MacOS

 ## Getting Started
 We recommend using an environment manager such as `Conda` to create a clean environment:
@@ -60,15 +64,18 @@ See below for recipes and datasets:

 ## Inference
 ### Generating Your First Video
-Here's a minimal example to generate a video using the default settings. Create a file called `example.py` with the following code:
+Here's a minimal example to generate a video using the default settings. Make sure VSA kernels are [installed](https://hao-ai-lab.github.io/FastVideo/video_sparse_attention/installation.html). Create a file called `example.py` with the following code:

 ```python
+import os
 from fastvideo import VideoGenerator

 def main():
+    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
+
    # Create a video generator with a pre-trained model
    generator = VideoGenerator.from_pretrained(
-        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+        "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
        num_gpus=1,  # Adjust based on your hardware
    )

@@ -105,18 +112,19 @@ For a more detailed guide, please see our [inference quick start](https://hao-ai
 <!-- - [Finetuning Guide](https://hao-ai-lab.github.io/FastVideo/training/finetune.html) -->

 ## 📑 Development Plan
-
 <!-- - More distillation methods -->
  <!-- - [ ] Add Distribution Matching Distillation -->
- More FastWan Models Coming Soon!
-  - [ ] Add FastWan2.1-T2V-14B
-  - [ ] Add FastWan2.2-T2V-14B
-  - [ ] Add FastWan2.2-I2V-14B
+More FastWan Models Coming Soon!
+- [ ] Add FastWan2.1-T2V-14B
+- [ ] Add FastWan2.2-T2V-14B
+- [ ] Add FastWan2.2-I2V-14B
 <!-- - Optimization features
 - Code updates -->
  <!-- - [ ] fp8 support -->
  <!-- - [ ] faster load model and save model support -->

+See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/468).
+
 ## 🤝 Contributing

 We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview.html)
@@ -132,10 +140,10 @@ We learned and reused code from the following projects:
 - [vLLM](https://github.com/vllm-project/vllm)
 - [SGLang](https://github.com/sgl-project/sglang)

-We thank MBZUAI and [Anyscale](https://www.anyscale.com/) for their support throughout this project.
+We thank [MBZUAI](https://ifm.mbzuai.ac.ae/), [Anyscale](https://www.anyscale.com/), and [GMI Cloud](https://www.gmicloud.ai/) for their support throughout this project.

 ## Citation
-If you use FastVideo for your research, please cite our work:
+If you find FastVideo useful, please considering citing our work:

 ```bibtex
@software{fastvideo2024,
@@ -146,22 +154,17 @@ If you use FastVideo for your research, please cite our work:
  year         = {2024},
 }

-@misc{zhang2025vsafastervideodiffusion,
-      title={VSA: Faster Video Diffusion with Trainable Sparse Attention}, 
-      author={Peiyuan Zhang and Haofeng Huang and Yongqi Chen and Will Lin and Zhengzhong Liu and Ion Stoica and Eric Xing and Hao Zhang},
-      year={2025},
-      eprint={2505.13389},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2505.13389}, 
+@article{zhang2025vsa,
+  title={VSA: Faster Video Diffusion with Trainable Sparse Attention},
+  author={Zhang, Peiyuan and Huang, Haofeng and Chen, Yongqi and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
+  journal={arXiv preprint arXiv:2505.13389},
+  year={2025}
 }
-@misc{zhang2025fastvideogenerationsliding,
-      title={Fast Video Generation with Sliding Tile Attention},
-      author={Peiyuan Zhang and Yongqi Chen and Runlong Su and Hangliang Ding and Ion Stoica and Zhenghong Liu and Hao Zhang},
-      year={2025},
-      eprint={2502.04507},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2502.04507},
+
+@article{zhang2025fast,
+  title={Fast video generation with sliding tile attention},
+  author={Zhang, Peiyuan and Chen, Yongqi and Su, Runlong and Ding, Hangliang and Stoica, Ion and Liu, Zhengzhong and Zhang, Hao},
+  journal={arXiv preprint arXiv:2502.04507},
+  year={2025}
 }
 ```
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@@ -25,9 +25,9 @@ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave
 sudo apt update
 sudo apt install clang-11
 ```
-(If you use CUDA12.4)
+(If you use CUDA12.8)
 ```bash
-export CUDA_HOME=/usr/local/cuda-12.4
+export CUDA_HOME=/usr/local/cuda-12.8
 export PATH=${CUDA_HOME}/bin:${PATH} 
 export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
 ```
@@ -84,7 +84,7 @@ out = sliding_tile_attention(q, k, v, window_size, 0, False)
 ### Test
 ```bash
 python tests/test_sta.py # test STA
-python tests/test_block_sparse.py # test VSA
+python tests/test_vsa.py # test VSA
 ```
 ### Benchmark
 ```bash
@@ -86,8 +86,6 @@ def benchmark_attention(configurations):
        # print(f"Average TFLOPS: {tflops_bwd}")
        # print("=" * 60)

-        torch.cuda.empty_cache()
-
    return results


@@ -1,4 +0,0 @@
-    off_hz = tl.program_id(2)
-    b = off_hz // H
-    h = off_hz % H
-    meta_base = ((b * H + h) * q_tiles + q_blk)
@@ -1,2 +1,2 @@
 recursive-include tk *
-include config.py
+include config_sta.py
@@ -0,0 +1,87 @@
+
+# Attention Kernel Used in FastVideo
+
+## Sliding Tile Attention (STA)
+We only support H100 for STA.
+
+### Installation
+```bash
+pip install st_attn
+``` 
+
+Install from source:
+
+```bash
+git submodule update --init --recursive
+python setup.py install
+```
+
+If you encounter error during installation, try below:
+Install C++20 for ThunderKittens:
+```bash
+sudo apt update
+sudo apt install gcc-11 g++-11
+
+sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
+
+sudo apt update
+sudo apt install clang-11
+```
+(If you use CUDA12.8)
+```bash
+export CUDA_HOME=/usr/local/cuda-12.8
+export PATH=${CUDA_HOME}/bin:${PATH} 
+export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+```
+
+###  Usage
+End-2-end inference with FastVideo:
+```bash
+bash scripts/inference/v1_inference_wan_STA.sh
+```
+
+If you want to use sliding tile attention in your custom model:
+```python
+from st_attn import sliding_tile_attention
+# assuming video size (T, H, W) = (30, 48, 80), text tokens = 256 with padding. 
+# q, k, v: [batch_size, num_heads, seq_length, head_dim], seq_length = T*H*W + 256
+# a tile is a cube of size (6, 8, 8)
+# window_size in tiles: [(window_t, window_h, window_w), (..)...]. For example, window size (3, 3, 3) means a query can attend to (3x6, 3x8, 3x8) = (18, 24, 24) tokens out of the total 30x48x80 video.
+# text_length: int ranging from 0 to 256
+# If your attention contains text token (Hunyuan)
+out = sliding_tile_attention(q, k, v, window_size, text_length)
+# If your attention does not contain text token (StepVideo)
+out = sliding_tile_attention(q, k, v, window_size, 0, False)
+```
+
+
+### Test
+```bash
+python ../tests/test_sta.py # test STA
+python ../tests/test_vsa.py # test VSA
+```
+### Benchmark
+```bash
+python ../benchmarks/bench_sta.py
+```
+
+
+### How Does STA Work?
+We give a demo for 2D STA with window size (6,6) operating on a (10, 10) image. 
+
+
+https://github.com/user-attachments/assets/f3b6dd79-7b43-4b60-a0fa-3d6495ec5747
+
+## Why is STA Fast?
+2D/3D Sliding Window Attention (SWA) creates many mixed blocks in the attention map. Even though mixed blocks have less output value,a mixed block is significantly slower than a dense block due to the GPU-unfriendly masking operation. 
+
+STA removes mixed blocks.
+
+
+<div align="center">
+<img src=../../../assets/sliding_tile_attn_map.png width="80%"/>
+</div>
+
+## Acknowledgement
+
+We learned or reuse code from FlexAtteniton, NATEN, and ThunderKittens.
@@ -1,7 +1,7 @@
 import os
 import subprocess

-from csrc.attn.config_sta import kernels, sources, target
+from config_sta import kernels, sources, target
 from setuptools import find_packages, setup
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension

@@ -9,7 +9,7 @@ target = target.lower()

 # Package metadata
 PACKAGE_NAME = "st_attn"
-VERSION = "0.0.4"
+VERSION = "0.0.6"
 AUTHOR = "Hao AI Lab"
 DESCRIPTION = "Sliding Tile Atteniton Kernel Used in FastVideo"
 URL = "https://github.com/hao-ai-lab/FastVideo/tree/main/csrc/sliding_tile_attention"
@@ -13,9 +13,9 @@ BLOCK_M = 64
 BLOCK_N = 64

 def pytorch_test(Q, K, V, block_sparse_mask, dO):
-    q_ = Q.clone().requires_grad_()
-    k_ = K.clone().requires_grad_()
-    v_ = V.clone().requires_grad_()
+    q_ = Q.clone().float().requires_grad_()
+    k_ = K.clone().float().requires_grad_()
+    v_ = V.clone().float().requires_grad_()

    QK = torch.matmul(q_, k_.transpose(-2, -1))
    QK /= (q_.size(-1) ** 0.5)
@@ -35,9 +35,9 @@ def pytorch_test(Q, K, V, block_sparse_mask, dO):


 def block_sparse_kernel_test(Q, K, V, block_sparse_mask, variable_block_sizes, non_pad_index, dO):
-    Q = Q.clone().requires_grad_()
-    K = K.clone().requires_grad_()
-    V = V.clone().requires_grad_()
+    Q = Q.detach().requires_grad_()
+    K = K.detach().requires_grad_()
+    V = V.detach().requires_grad_()
    
    q_padded = vsa_pad(Q, non_pad_index, variable_block_sizes.shape[0], BLOCK_M)
    k_padded = vsa_pad(K, non_pad_index, variable_block_sizes.shape[0], BLOCK_M)
@@ -60,11 +60,9 @@ def get_non_pad_index(

    return index_pad[index_mask]

-def generate_tensor(shape, mean, std, dtype, device):
+def generate_tensor(shape, dtype, device):
    tensor = torch.randn(shape, dtype=dtype, device=device)
-    magnitude = torch.norm(tensor, dim=-1, keepdim=True)
-    scaled_tensor = tensor * (torch.randn(magnitude.shape, dtype=dtype, device=device) * std + mean) / magnitude
-    return scaled_tensor.contiguous()
+    return tensor

 def generate_variable_block_sizes(num_blocks, min_size=32, max_size=64, device="cuda"):
    return torch.randint(min_size, max_size + 1, (num_blocks,), device=device, dtype=torch.int32)
@@ -75,7 +73,7 @@ def vsa_pad(x, non_pad_index, num_blocks, block_size):
    padded_x[:, :, non_pad_index, :] = x
    return padded_x

-def check_correctness(h, d, num_blocks, k, mean, std, num_iterations=20, error_mode='all'):
+def check_correctness(h, d, num_blocks, k,  num_iterations=20, error_mode='all'):
    results = {
        'gO': {'sum_diff': 0.0, 'sum_abs': 0.0, 'max_diff': 0.0},
        'gQ': {'sum_diff': 0.0, 'sum_abs': 0.0, 'max_diff': 0.0},
@@ -91,10 +89,10 @@ def check_correctness(h, d, num_blocks, k, mean, std, num_iterations=20, error_m
    block_mask = generate_block_sparse_mask_for_function(h, num_blocks, k, device)
    full_mask = create_full_mask_from_block_mask(block_mask, variable_block_sizes, device)
    for _ in range(num_iterations):
-        Q = generate_tensor((1, h, S, d), mean, std, torch.bfloat16, device)
-        K = generate_tensor((1, h, S, d), mean, std, torch.bfloat16, device)
-        V = generate_tensor((1, h, S, d), mean, std, torch.bfloat16, device)
-        dO = generate_tensor((1, h, S, d), mean, std, torch.bfloat16, device)
+        Q = generate_tensor((1, h, S, d),  torch.bfloat16, device)
+        K = generate_tensor((1, h, S, d), torch.bfloat16, device)
+        V = generate_tensor((1, h, S, d),  torch.bfloat16, device)
+        dO = generate_tensor((1, h, S, d), torch.bfloat16, device)

        # dO_padded = torch.zeros_like(dO_padded)
        # dO_padded[:, :, non_pad_index, :] = dO
@@ -107,7 +105,8 @@ def check_correctness(h, d, num_blocks, k, mean, std, num_iterations=20, error_m
                abs_diff = torch.abs(diff)
                results[name]['sum_diff'] += torch.sum(abs_diff).item()
                results[name]['sum_abs'] += torch.sum(torch.abs(pt)).item()
-                results[name]['max_diff'] = max(results[name]['max_diff'], torch.max(abs_diff).item())
+                rel_max_diff = torch.max(abs_diff) / torch.mean(torch.abs(pt))
+                results[name]['max_diff'] = max(results[name]['max_diff'], rel_max_diff.item())
        if torch.cuda.is_available():
            torch.cuda.empty_cache()

@@ -119,27 +118,27 @@ def check_correctness(h, d, num_blocks, k, mean, std, num_iterations=20, error_m

    return results

-def generate_error_graphs(h, d, mean, std, error_mode='all'):
+def generate_error_graphs(h, d, error_mode='all'):
    test_configs = [
        {"num_blocks": 16, "k": 2, "description": "Small sequence"},
        {"num_blocks": 32, "k": 4, "description": "Medium sequence"},
        {"num_blocks": 53, "k": 6, "description": "Large sequence"},
    ]
    
-    print(f"\nError Analysis for h={h}, d={d}, mean={mean}, std={std}, mode={error_mode}")
+    print(f"\nError Analysis for h={h}, d={d}, mode={error_mode}")
    print("=" * 150)
    print(f"{'Config':<20} {'Blocks':<8} {'K':<4} "
-          f"{'gQ Avg':<12} {'gQ Max':<12} "
-          f"{'gK Avg':<12} {'gK Max':<12} "
-          f"{'gV Avg':<12} {'gV Max':<12} "
-          f"{'gO Avg':<12} {'gO Max':<12}")
+          f"{'gQ Avg':<12} {'Rel gQ Max':<12} "
+          f"{'gK Avg':<12} {'Rel gK Max':<12} "
+          f"{'gV Avg':<12} {'Rel gV Max':<12} "
+          f"{'gO Avg':<12} {'Rel gO Max':<12}")
    print("-" * 150)

    for config in test_configs:
        num_blocks = config["num_blocks"]
        k = config["k"]
        description = config["description"]
-        results = check_correctness(h, d, num_blocks, k, mean, std, error_mode=error_mode)
+        results = check_correctness(h, d, num_blocks, k, error_mode=error_mode)
        print(f"{description:<20} {num_blocks:<8} {k:<4} "
              f"{results['gQ']['avg_diff']:<12.6e} {results['gQ']['max_diff']:<12.6e} "
              f"{results['gK']['avg_diff']:<12.6e} {results['gK']['max_diff']:<12.6e} "
@@ -150,10 +149,8 @@ def generate_error_graphs(h, d, mean, std, error_mode='all'):

 if __name__ == "__main__":
    h, d = 16, 128
-    mean = 0.0
-    std = 1
    print("Block Sparse Attention with Variable Block Sizes Analysis")
    print("=" * 60)
    for mode in ['backward']:
-        generate_error_graphs(h, d, mean, std, error_mode=mode)
+        generate_error_graphs(h, d, error_mode=mode)
    print("\nAnalysis completed for all modes.")
@@ -0,0 +1,2 @@
+recursive-include tk *
+include config_vsa.py
@@ -0,0 +1,61 @@
+
+
+# Attention Kernel Used in FastVideo
+
+## Video Sparse Attention (VSA)
+
+### Installation
+We support H100 (via TK) and any other GPU (via triton) for VSA.
+
+```bash
+pip install vsa
+``` 
+
+Install from source:
+
+```bash
+git submodule update --init --recursive
+python setup.py install
+```
+
+
+If you encounter error during installation, try below:
+Install C++20 for ThunderKittens:
+```bash
+sudo apt update
+sudo apt install gcc-11 g++-11
+
+sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
+
+sudo apt update
+sudo apt install clang-11
+```
+(If you use CUDA12.8)
+```bash
+export CUDA_HOME=/usr/local/cuda-12.8
+export PATH=${CUDA_HOME}/bin:${PATH} 
+export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+```
+
+### Verify if you have successfully installed
+
+```bash
+# test numerical
+python ../tests/test_vsa.py
+# (For H100) test speed
+python ../benchmarks/bench_vsa_hopper.py
+```
+
+bench_vsa_hopper.py should print something like this:
+
+```bash
+Using topk=76 kv blocks per q block (out of 768 total kv blocks)
+
+=== BLOCK SPARSE ATTENTION BENCHMARK ===
+Block Sparse Forward  - TFLOPS: 5622.26
+Block Sparse Backward - TFLOPS: 3865.68
+```
+
+## Acknowledgement
+
+We learned or reuse code from FlexAtteniton, NATEN, and ThunderKittens.
@@ -9,10 +9,10 @@ target = target.lower()

 # Package metadata
 PACKAGE_NAME = "vsa"
-VERSION = "0.0.1"
+VERSION = "0.0.3"
 AUTHOR = "Hao AI Lab"
 DESCRIPTION = "Video Sparse Attention Kernel Used in FastVideo"
-URL = "https://github.com/hao-ai-lab/FastVideo/tree/main/csrc/attn"
+URL = "https://github.com/hao-ai-lab/FastVideo/tree/main/csrc/attn/video_sparse_attn"

 # Set environment variables
 tk_root = os.getenv('THUNDERKITTENS_ROOT', os.path.abspath(os.path.join(os.getcwd(), 'tk/')))
@@ -51,19 +51,16 @@ for k in kernels:
        source_files.append(sources[k]['source_files'][target])
    cpp_flags.append(f'-DTK_COMPILE_{k.replace(" ", "_").upper()}')

-ext_modules = []
-import torch
-major, minor = torch.cuda.get_device_capability(0)
-if major == 9 and minor == 0:# check if H100
-    ext_modules = [
-        CUDAExtension('vsa_cuda',
-            sources=source_files,
-            extra_compile_args={
-                'cxx': cpp_flags,
-                'nvcc': cuda_flags
-            },
-            libraries=['cuda'])
-    ]
+
+ext_modules = [
+    CUDAExtension('vsa_cuda',
+        sources=source_files,
+        extra_compile_args={
+            'cxx': cpp_flags,
+            'nvcc': cuda_flags
+        },
+        libraries=['cuda'])
+]



@@ -1,12 +1,13 @@
 import torch
 from typing import Tuple
 block_sparse_attn=None
-
-try:
+import torch
+major, minor = torch.cuda.get_device_capability(0)
+if major == 9 and minor == 0:# check if H100
    from vsa_cuda import block_sparse_fwd, block_sparse_bwd
    from vsa.block_sparse_wrapper import block_sparse_attn_SM90
    block_sparse_attn = block_sparse_attn_SM90
-except ImportError:
+else:
    from vsa.block_sparse_wrapper import block_sparse_attn_triton
    block_sparse_fwd = None
    block_sparse_bwd = None
@@ -568,7 +568,6 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
        __syncthreads(); // wait for sd_smem shared memory write
        warpgroup::mm_AtB(qg_reg, ds_smem_t[0], k_smem[0]); //delat dQ = dSK
        warpgroup::mma_commit_group();
-        tma::store_async_wait();
        warpgroup::mma_async_wait();
        // store qg to shared memory
        warpgroup::store(qg_smem, qg_reg);
@@ -578,6 +577,7 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
        if (threadIdx.x / 32 == 0) {
            coord<qg_tile> tile_idx = {blockIdx.z, blockIdx.y, store_qg_block_index, 0};
            tma::store_add_async(g.qg, qg_smem, tile_idx);
+            tma::store_async_wait();
        }
    }

@@ -624,7 +624,6 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
        __syncthreads(); // wait for sd_smem shared memory write
        warpgroup::mm_AtB(qg_reg, ds_smem_t[0], k_smem[0]); //delat dQ = dSK
        warpgroup::mma_commit_group();
-        tma::store_async_wait();
        warpgroup::mma_async_wait();
        // store qg to shared memory
        warpgroup::store(qg_smem, qg_reg);
@@ -634,13 +633,14 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
        if (threadIdx.x / 32 == 0) {
            coord<qg_tile> tile_idx = {blockIdx.z, blockIdx.y, store_qg_block_index, 0};
            tma::store_add_async(g.qg, qg_smem, tile_idx);
+            tma::store_async_wait();
        }
    }

    // store kq and vq

    // ! the following two line seems unnecessary.
-    tma::store_async_wait(); // ensure qg is finished
+    // tma::store_async_wait(); // ensure qg is finished
    __syncthreads();

    warpgroup::store(kg_smem[0], kg_reg);
@@ -1174,4 +1174,4 @@ block_sparse_attention_backward(torch::Tensor q,

    return {qg, kg, vg};
    //cudadevicesynchronize();
-}
+}
@@ -0,0 +1,185 @@
+import torch
+try:
+    from vsa_cuda import block_sparse_fwd, block_sparse_bwd
+except ImportError:
+    block_sparse_fwd = None
+    block_sparse_bwd = None
+from vsa.block_sparse_attn_triton import triton_block_sparse_attn_forward, triton_block_sparse_attn_backward
+assert torch.__version__ >= "2.4.0", "VSA requires PyTorch 2.4.0 or higher"
+from vsa.index import map_to_index
+from typing import Tuple, Optional
+
+
+
+@torch.library.custom_op("vsa::block_sparse_attn_triton", mutates_args=(), device_types="cuda")
+def block_sparse_attn_triton(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+    block_map = block_map.int()
+    q2k_block_sparse_index, q2k_block_sparse_num = map_to_index(block_map)
+    o, M = triton_block_sparse_attn_forward(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, variable_block_sizes)
+    return o, M
+
+
+@torch.library.register_fake("vsa::block_sparse_attn_triton")
+def _block_sparse_attn_triton_fake(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+    o = torch.empty_like(q)
+    M = torch.empty((q.shape[0], q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
+    return o, M
+
+
+@torch.library.custom_op("vsa::block_sparse_attn_backward_triton", mutates_args=(), device_types="cuda")
+def block_sparse_attn_backward_triton(
+    grad_output_padded: torch.Tensor, 
+    q_padded: torch.Tensor, 
+    k_padded: torch.Tensor, 
+    v_padded: torch.Tensor, 
+    o_padded: torch.Tensor, 
+    M: torch.Tensor, 
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    grad_output_padded = grad_output_padded.contiguous()
+    q2k_block_sparse_index, q2k_block_sparse_num = map_to_index(block_map)
+    k2q_block_sparse_index, k2q_block_sparse_num = map_to_index(block_map.transpose(-1, -2))
+    dq, dk, dv = triton_block_sparse_attn_backward(grad_output_padded, q_padded, k_padded, v_padded, o_padded, M, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, variable_block_sizes)
+    return dq, dk, dv
+
+@torch.library.register_fake("vsa::block_sparse_attn_backward_triton")
+def _block_sparse_attn_backward_triton_fake(
+    grad_output_padded: torch.Tensor, 
+    q_padded: torch.Tensor, 
+    k_padded: torch.Tensor, 
+    v_padded: torch.Tensor, 
+    o_padded: torch.Tensor, 
+    M: torch.Tensor,
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    grad_output_padded = grad_output_padded.contiguous()
+    dq = torch.empty_like(grad_output_padded)
+    dk = torch.empty_like(grad_output_padded)
+    dv = torch.empty_like(grad_output_padded)
+    return dq, dk, dv
+
+
+def backward_triton(ctx, grad_output1, grad_output2):
+    q_padded, k_padded, v_padded, o_padded, M, block_map, variable_block_sizes = ctx.saved_tensors
+    dq, dk, dv = block_sparse_attn_backward_triton(grad_output1, q_padded, k_padded, v_padded, o_padded, M, block_map, variable_block_sizes)
+    return dq, dk, dv, None, None
+
+
+def setup_context_triton(ctx, inputs, output):
+    q_padded, k_padded, v_padded, block_map, variable_block_sizes = inputs
+    o_padded, M = output
+    ctx.save_for_backward(q_padded, k_padded, v_padded, o_padded, M, block_map, variable_block_sizes)
+    
+block_sparse_attn_triton.register_autograd(backward_triton, setup_context=setup_context_triton)
+
+
+major, minor = torch.cuda.get_device_capability(0)
+
+if major == 9 and minor == 0:# check if H100
+    @torch.library.custom_op("vsa::block_sparse_attn_SM90", mutates_args=(), device_types="cuda")
+    def block_sparse_attn_SM90(
+        q_padded: torch.Tensor,
+        k_padded: torch.Tensor, 
+        v_padded: torch.Tensor, 
+        block_map: torch.Tensor,
+        variable_block_sizes: torch.Tensor, 
+        )-> Tuple[torch.Tensor, torch.Tensor]:
+        q_padded = q_padded.contiguous()
+        k_padded = k_padded.contiguous()
+        v_padded = v_padded.contiguous()
+        q2k_block_sparse_index, q2k_block_sparse_num = map_to_index(block_map)
+        variable_block_sizes = variable_block_sizes.int()
+        o_padded, lse_padded = block_sparse_fwd(q_padded, k_padded, v_padded, q2k_block_sparse_index, q2k_block_sparse_num, variable_block_sizes)
+        return o_padded, lse_padded
+
+
+
+
+    @torch.library.register_fake("vsa::block_sparse_attn_SM90")
+    def _block_sparse_attn_SM90_fake(
+        q_padded: torch.Tensor,
+        k_padded: torch.Tensor, 
+        v_padded: torch.Tensor, 
+        block_map: torch.Tensor,
+        variable_block_sizes: torch.Tensor, 
+    ) -> Tuple[torch.Tensor,  torch.Tensor]:
+        q_padded, k_padded, v_padded = [x.contiguous() for x in (q_padded, k_padded, v_padded)]
+        B, H, S, D = q_padded.shape
+        o_padded = torch.empty_like(q_padded)
+        lse_padded = torch.empty((B, H, S, 1), device=q_padded.device, dtype=torch.float32)
+        return o_padded, lse_padded
+
+
+    @torch.library.custom_op("vsa::block_sparse_attn_backward_SM90", mutates_args=(), device_types="cuda")
+    def block_sparse_attn_backward_SM90(
+        grad_output_padded: torch.Tensor, 
+        q_padded: torch.Tensor, 
+        k_padded: torch.Tensor, 
+        v_padded: torch.Tensor, 
+        o_padded: torch.Tensor, 
+        lse_padded: torch.Tensor, 
+        block_map: torch.Tensor,
+        variable_block_sizes: torch.Tensor, 
+        )-> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        grad_output_padded = grad_output_padded.contiguous()
+        k2q_block_sparse_index, k2q_block_sparse_num = map_to_index(block_map.transpose(-1, -2))
+        grad_q_padded, grad_k_padded, grad_v_padded = block_sparse_bwd(
+            q_padded, k_padded, v_padded, o_padded, lse_padded, grad_output_padded, k2q_block_sparse_index, k2q_block_sparse_num, variable_block_sizes
+        )
+        grad_q_padded = grad_q_padded.to(grad_output_padded.dtype)
+        grad_k_padded = grad_k_padded.to(grad_output_padded.dtype)
+        grad_v_padded = grad_v_padded.to(grad_output_padded.dtype)
+        return grad_q_padded, grad_k_padded, grad_v_padded
+
+    @torch.library.register_fake("vsa::block_sparse_attn_backward_SM90")
+    def _block_sparse_attn_backward_SM90_fake(
+        grad_output_padded: torch.Tensor, 
+        q_padded: torch.Tensor, 
+        k_padded: torch.Tensor, 
+        v_padded: torch.Tensor, 
+        o_padded: torch.Tensor, 
+        lse_padded: torch.Tensor, 
+        block_map: torch.Tensor,
+        variable_block_sizes: torch.Tensor, 
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        torch._check(grad_output_padded.dtype == torch.bfloat16)
+        torch._check(lse_padded.dtype == torch.float32)
+        grad_output_padded = grad_output_padded.contiguous()
+        dq = torch.empty_like(grad_output_padded)
+        dk = torch.empty_like(grad_output_padded)
+        dv = torch.empty_like(grad_output_padded)
+        return dq, dk, dv
+
+
+    def backward_SM90(ctx, grad_output1, grad_output2):
+        q_padded, k_padded, v_padded, o_padded, lse_padded, block_map, variable_block_sizes= ctx.saved_tensors
+        dq, dk, dv = block_sparse_attn_backward_SM90(grad_output1, q_padded, k_padded, v_padded, o_padded, lse_padded, block_map, variable_block_sizes)
+        return dq, dk, dv, None, None
+
+    def setup_context_SM90(ctx, inputs, output):
+        q_padded, k_padded, v_padded, block_map, variable_block_sizes = inputs
+        o_padded, lse_padded = output
+        ctx.save_for_backward(q_padded, k_padded, v_padded, o_padded, lse_padded, block_map, variable_block_sizes)
+
+
+    block_sparse_attn_SM90.register_autograd(backward_SM90, setup_context=setup_context_SM90)
@@ -1,182 +0,0 @@
-import torch
-try:
-    from vsa_cuda import block_sparse_fwd, block_sparse_bwd
-except ImportError:
-    block_sparse_fwd = None
-    block_sparse_bwd = None
-from vsa.block_sparse_attn_triton import triton_block_sparse_attn_forward, triton_block_sparse_attn_backward
-assert torch.__version__ >= "2.4.0", "VSA requires PyTorch 2.4.0 or higher"
-from vsa.index import map_to_index
-from typing import Tuple, Optional
-
-
-
-@torch.library.custom_op("vsa::block_sparse_attn_SM90", mutates_args=(), device_types="cuda")
-def block_sparse_attn_SM90(
-    q_padded: torch.Tensor,
-    k_padded: torch.Tensor, 
-    v_padded: torch.Tensor, 
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor, 
-    )-> Tuple[torch.Tensor, torch.Tensor]:
-    q_padded = q_padded.contiguous()
-    k_padded = k_padded.contiguous()
-    v_padded = v_padded.contiguous()
-    q2k_block_sparse_index, q2k_block_sparse_num = map_to_index(block_map)
-    variable_block_sizes = variable_block_sizes.int()
-    o_padded, lse_padded = block_sparse_fwd(q_padded, k_padded, v_padded, q2k_block_sparse_index, q2k_block_sparse_num, variable_block_sizes)
-    return o_padded, lse_padded
-
-
-
-
-@torch.library.register_fake("vsa::block_sparse_attn_SM90")
-def _block_sparse_attn_SM90_fake(
-    q_padded: torch.Tensor,
-    k_padded: torch.Tensor, 
-    v_padded: torch.Tensor, 
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor, 
-) -> Tuple[torch.Tensor,  torch.Tensor]:
-    q_padded, k_padded, v_padded = [x.contiguous() for x in (q_padded, k_padded, v_padded)]
-    B, H, S, D = q_padded.shape
-    o_padded = torch.empty_like(q_padded)
-    lse_padded = torch.empty((B, H, S, 1), device=q_padded.device, dtype=torch.float32)
-    return o_padded, lse_padded
-
-
-@torch.library.custom_op("vsa::block_sparse_attn_backward_SM90", mutates_args=(), device_types="cuda")
-def block_sparse_attn_backward_SM90(
-    grad_output_padded: torch.Tensor, 
-    q_padded: torch.Tensor, 
-    k_padded: torch.Tensor, 
-    v_padded: torch.Tensor, 
-    o_padded: torch.Tensor, 
-    lse_padded: torch.Tensor, 
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor, 
-    )-> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    grad_output_padded = grad_output_padded.contiguous()
-    k2q_block_sparse_index, k2q_block_sparse_num = map_to_index(block_map.transpose(-1, -2))
-    grad_q_padded, grad_k_padded, grad_v_padded = block_sparse_bwd(
-        q_padded, k_padded, v_padded, o_padded, lse_padded, grad_output_padded, k2q_block_sparse_index, k2q_block_sparse_num, variable_block_sizes
-    )
-    grad_q_padded = grad_q_padded.to(grad_output_padded.dtype)
-    grad_k_padded = grad_k_padded.to(grad_output_padded.dtype)
-    grad_v_padded = grad_v_padded.to(grad_output_padded.dtype)
-    return grad_q_padded, grad_k_padded, grad_v_padded
-
-@torch.library.register_fake("vsa::block_sparse_attn_backward_SM90")
-def _block_sparse_attn_backward_SM90_fake(
-    grad_output_padded: torch.Tensor, 
-    q_padded: torch.Tensor, 
-    k_padded: torch.Tensor, 
-    v_padded: torch.Tensor, 
-    o_padded: torch.Tensor, 
-    lse_padded: torch.Tensor, 
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor, 
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
-    torch._check(grad_output_padded.dtype == torch.bfloat16)
-    torch._check(lse_padded.dtype == torch.float32)
-    grad_output_padded = grad_output_padded.contiguous()
-    dq = torch.empty_like(grad_output_padded)
-    dk = torch.empty_like(grad_output_padded)
-    dv = torch.empty_like(grad_output_padded)
-    return dq, dk, dv
-
-
-def backward_SM90(ctx, grad_output1, grad_output2):
-    q_padded, k_padded, v_padded, o_padded, lse_padded, block_map, variable_block_sizes= ctx.saved_tensors
-    dq, dk, dv = block_sparse_attn_backward_SM90(grad_output1, q_padded, k_padded, v_padded, o_padded, lse_padded, block_map, variable_block_sizes)
-    return dq, dk, dv, None, None
-
-def setup_context_SM90(ctx, inputs, output):
-    q_padded, k_padded, v_padded, block_map, variable_block_sizes = inputs
-    o_padded, lse_padded = output
-    ctx.save_for_backward(q_padded, k_padded, v_padded, o_padded, lse_padded, block_map, variable_block_sizes)
-
-
-block_sparse_attn_SM90.register_autograd(backward_SM90, setup_context=setup_context_SM90)
-
-
-@torch.library.custom_op("vsa::block_sparse_attn_triton", mutates_args=(), device_types="cuda")
-def block_sparse_attn_triton(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    q = q.contiguous()
-    k = k.contiguous()
-    v = v.contiguous()
-    block_map = block_map.int()
-    q2k_block_sparse_index, q2k_block_sparse_num = map_to_index(block_map)
-    o, M = triton_block_sparse_attn_forward(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, variable_block_sizes)
-    return o, M
-
-
-@torch.library.register_fake("vsa::block_sparse_attn_triton")
-def _block_sparse_attn_triton_fake(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    q = q.contiguous()
-    k = k.contiguous()
-    v = v.contiguous()
-    o = torch.empty_like(q)
-    M = torch.empty((q.shape[0], q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
-    return o, M
-
-
-@torch.library.custom_op("vsa::block_sparse_attn_backward_triton", mutates_args=(), device_types="cuda")
-def block_sparse_attn_backward_triton(
-    grad_output_padded: torch.Tensor, 
-    q_padded: torch.Tensor, 
-    k_padded: torch.Tensor, 
-    v_padded: torch.Tensor, 
-    o_padded: torch.Tensor, 
-    M: torch.Tensor, 
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    grad_output_padded = grad_output_padded.contiguous()
-    q2k_block_sparse_index, q2k_block_sparse_num = map_to_index(block_map)
-    k2q_block_sparse_index, k2q_block_sparse_num = map_to_index(block_map.transpose(-1, -2))
-    dq, dk, dv = triton_block_sparse_attn_backward(grad_output_padded, q_padded, k_padded, v_padded, o_padded, M, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, variable_block_sizes)
-    return dq, dk, dv
-
-@torch.library.register_fake("vsa::block_sparse_attn_backward_triton")
-def _block_sparse_attn_backward_triton_fake(
-    grad_output_padded: torch.Tensor, 
-    q_padded: torch.Tensor, 
-    k_padded: torch.Tensor, 
-    v_padded: torch.Tensor, 
-    o_padded: torch.Tensor, 
-    M: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    grad_output_padded = grad_output_padded.contiguous()
-    dq = torch.empty_like(grad_output_padded)
-    dk = torch.empty_like(grad_output_padded)
-    dv = torch.empty_like(grad_output_padded)
-    return dq, dk, dv
-
-
-def backward_triton(ctx, grad_output1, grad_output2):
-    q_padded, k_padded, v_padded, o_padded, M, block_map, variable_block_sizes = ctx.saved_tensors
-    dq, dk, dv = block_sparse_attn_backward_triton(grad_output1, q_padded, k_padded, v_padded, o_padded, M, block_map, variable_block_sizes)
-    return dq, dk, dv, None, None
-
-
-def setup_context_triton(ctx, inputs, output):
-    q_padded, k_padded, v_padded, block_map, variable_block_sizes = inputs
-    o_padded, M = output
-    ctx.save_for_backward(q_padded, k_padded, v_padded, o_padded, M, block_map, variable_block_sizes)
-    
-block_sparse_attn_triton.register_autograd(backward_triton, setup_context=setup_context_triton)
@@ -1,7 +1,9 @@
-FROM nvidia/cuda:12.4.1-devel-ubuntu20.04
+FROM nvidia/cuda:12.8.0-devel-ubuntu22.04

 ENV DEBIAN_FRONTEND=noninteractive

+SHELL ["/bin/bash", "-c"]
+
 WORKDIR /FastVideo

 RUN apt-get update && apt-get install -y --no-install-recommends \
@@ -9,17 +11,25 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    ca-certificates \
    openssh-server \
+    zsh \
+    vim \
+    curl \
+    gcc-11 \
+    g++-11 \
+    clang-11 \
    && rm -rf /var/lib/apt/lists/*

-RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
-    bash Miniconda3-latest-Linux-x86_64.sh -b -p /opt/conda && \
-    rm Miniconda3-latest-Linux-x86_64.sh
+# Set up C++20 compilers for ThunderKittens
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11

-ENV PATH=/opt/conda/bin:$PATH
+# Set CUDA environment variables
+ENV CUDA_HOME=/usr/local/cuda-12.8
+ENV PATH=${CUDA_HOME}/bin:${PATH}
+ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH

-RUN conda create --name fastvideo-dev python=3.10.0 -y
-
-SHELL ["/bin/bash", "-c"]
+# Install uv and source its environment
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
+    echo 'source $HOME/.local/bin/env' >> /root/.bashrc

 # Copy just the pyproject.toml first to leverage Docker cache
 COPY pyproject.toml ./
@@ -27,22 +37,36 @@ COPY pyproject.toml ./
 # Create a dummy README to satisfy the installation
 RUN echo "# Placeholder" > README.md

-RUN conda run -n fastvideo-dev pip install --no-cache-dir --upgrade pip && \
-    conda run -n fastvideo-dev pip install --no-cache-dir .[dev] && \
-    conda run -n fastvideo-dev pip install --no-cache-dir flash-attn==2.7.4.post1 --no-build-isolation && \
-    conda clean -afy
+# Create and activate virtual environment with specific Python version and seed
+RUN source $HOME/.local/bin/env && \
+    uv venv --python 3.10 --seed /opt/venv && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir --upgrade pip && \
+    uv pip install --no-cache-dir .[dev] && \
+    uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation

 COPY . .

-RUN conda run -n fastvideo-dev pip install --no-cache-dir -e .[dev]
+# Install dependencies using uv and set up shell configuration
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir -e .[dev] && \
+    git config --unset-all http.https://github.com/.extraheader || true && \
+    echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
+    echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile

-# Remove authentication headers
-RUN git config --unset-all http.https://github.com/.extraheader || true
+# Install STA (Sliding Tile Attention)
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn/sliding_tile_attn && \
+    git submodule update --init --recursive && \
+    python setup.py install

-# Set up automatic conda environment activation for all shells
-RUN echo 'source /opt/conda/etc/profile.d/conda.sh' >> /root/.bashrc && \
-    echo 'conda activate fastvideo-dev' >> /root/.bashrc && \
-    # Ensure .bashrc is sourced for SSH login shells
-    echo 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile
+# Install VSA 
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn/video_sparse_attn && \
+    git submodule update --init --recursive && \
+    python setup.py install

-EXPOSE 22
+EXPOSE 22
@@ -1,7 +1,9 @@
-FROM nvidia/cuda:12.4.1-devel-ubuntu20.04
+FROM nvidia/cuda:12.8.0-devel-ubuntu22.04

 ENV DEBIAN_FRONTEND=noninteractive

+SHELL ["/bin/bash", "-c"]
+
 WORKDIR /FastVideo

 RUN apt-get update && apt-get install -y --no-install-recommends \
@@ -9,17 +11,25 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    ca-certificates \
    openssh-server \
+    zsh \
+    vim \
+    curl \
+    gcc-11 \
+    g++-11 \
+    clang-11 \
    && rm -rf /var/lib/apt/lists/*

-RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
-    bash Miniconda3-latest-Linux-x86_64.sh -b -p /opt/conda && \
-    rm Miniconda3-latest-Linux-x86_64.sh
+# Set up C++20 compilers for ThunderKittens
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11

-ENV PATH=/opt/conda/bin:$PATH
+# Set CUDA environment variables
+ENV CUDA_HOME=/usr/local/cuda-12.8
+ENV PATH=${CUDA_HOME}/bin:${PATH}
+ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH

-RUN conda create --name fastvideo-dev python=3.11.11 -y
-
-SHELL ["/bin/bash", "-c"]
+# Install uv and source its environment
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
+    echo 'source $HOME/.local/bin/env' >> /root/.bashrc

 # Copy just the pyproject.toml first to leverage Docker cache
 COPY pyproject.toml ./
@@ -27,22 +37,36 @@ COPY pyproject.toml ./
 # Create a dummy README to satisfy the installation
 RUN echo "# Placeholder" > README.md

-RUN conda run -n fastvideo-dev pip install --no-cache-dir --upgrade pip && \
-    conda run -n fastvideo-dev pip install --no-cache-dir .[dev] && \
-    conda run -n fastvideo-dev pip install --no-cache-dir flash-attn==2.7.4.post1 --no-build-isolation && \
-    conda clean -afy
+# Create and activate virtual environment with specific Python version and seed
+RUN source $HOME/.local/bin/env && \
+    uv venv --python 3.11 --seed /opt/venv && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir --upgrade pip && \
+    uv pip install --no-cache-dir .[dev] && \
+    uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation

 COPY . .

-RUN conda run -n fastvideo-dev pip install --no-cache-dir -e .[dev]
+# Install dependencies using uv and set up shell configuration
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir -e .[dev] && \
+    git config --unset-all http.https://github.com/.extraheader || true && \
+    echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
+    echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile

-# Remove authentication headers
-RUN git config --unset-all http.https://github.com/.extraheader || true
+# Install STA (Sliding Tile Attention)
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn/sliding_tile_attn && \
+    git submodule update --init --recursive && \
+    python setup.py install

-# Set up automatic conda environment activation for all shells
-RUN echo 'source /opt/conda/etc/profile.d/conda.sh' >> /root/.bashrc && \
-    echo 'conda activate fastvideo-dev' >> /root/.bashrc && \
-    # Ensure .bashrc is sourced for SSH login shells
-    echo 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile
+# Install VSA 
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn/video_sparse_attn && \
+    git submodule update --init --recursive && \
+    python setup.py install

-EXPOSE 22
+EXPOSE 22
@@ -43,7 +43,7 @@ RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
    uv pip install --no-cache-dir --upgrade pip && \
    uv pip install --no-cache-dir .[dev] && \
-    uv pip install --no-cache-dir flash-attn==2.8.0.post2 --no-build-isolation
+    uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation

 COPY . .

@@ -58,15 +58,15 @@ RUN source $HOME/.local/bin/env && \
 # Install STA (Sliding Tile Attention)
 RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
-    cd csrc/attn && \
+    cd csrc/attn/sliding_tile_attn && \
    git submodule update --init --recursive && \
-    python setup_sta.py install
+    python setup.py install

 # Install VSA 
 RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
-    cd csrc/attn && \
+    cd csrc/attn/video_sparse_attn && \
    git submodule update --init --recursive && \
-    python setup_vsa.py install
+    python setup.py install

-EXPOSE 22
+EXPOSE 22
@@ -0,0 +1,72 @@
+FROM nvidia/cuda:12.9.1-cudnn-devel-ubuntu22.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+SHELL ["/bin/bash", "-c"]
+
+WORKDIR /FastVideo
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    wget \
+    git \
+    ca-certificates \
+    openssh-server \
+    zsh \
+    vim \
+    curl \
+    gcc-11 \
+    g++-11 \
+    clang-11 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set up C++20 compilers for ThunderKittens
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
+
+# Set CUDA environment variables
+ENV CUDA_HOME=/usr/local/cuda-12.9
+ENV PATH=${CUDA_HOME}/bin:${PATH}
+ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+
+# Install uv and source its environment
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
+    echo 'source $HOME/.local/bin/env' >> /root/.bashrc
+
+# Copy just the pyproject.toml first to leverage Docker cache
+COPY pyproject.toml ./
+
+# Create a dummy README to satisfy the installation
+RUN echo "# Placeholder" > README.md
+
+# Create and activate virtual environment with specific Python version and seed
+RUN source $HOME/.local/bin/env && \
+    uv venv --python 3.12 --seed /opt/venv && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir --upgrade pip && \
+    uv pip install --no-cache-dir .[dev] && \
+    uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation
+
+COPY . .
+
+# Install dependencies using uv and set up shell configuration
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir -e .[dev] && \
+    git config --unset-all http.https://github.com/.extraheader || true && \
+    echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
+    echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile
+
+# Install STA (Sliding Tile Attention)
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn/sliding_tile_attn && \
+    git submodule update --init --recursive && \
+    python setup.py install
+
+# Install VSA 
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn/video_sparse_attn && \
+    git submodule update --init --recursive && \
+    python setup.py install
+
+EXPOSE 22
@@ -96,8 +96,7 @@ copybutton_prompt_is_regexp = True
 #
 html_title = project
 html_theme = 'sphinx_book_theme'
-html_logo = '../../assets/logo.jpg'
-#html_favicon = 'assets/logos/vllm-logo-only-light.ico'
+html_logo = '../../assets/logos/icon_simple.svg'
 html_theme_options = {
    'path_to_docs': 'docs/source',
    'repository_url': 'https://github.com/hao-ai-lab/FastVideo/',
@@ -3,12 +3,12 @@

 If you prefer a containerized development environment or want to avoid managing dependencies manually, you can use our prebuilt Docker image:

-**Image:** [`ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:latest`](https://ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev)
+**Images:** [`ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:py3.12-latest`](https://ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev)

 ## Starting the container

 ```bash
-docker run --gpus all -it ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:latest
+docker run --gpus all -it ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:py3.12-latest
 ```

 This will:
@@ -6,7 +6,7 @@ You can easily use the FastVideo Docker image as a custom container on [RunPod](

 ## Creating a new pod

-Choose a GPU that supports CUDA 12.4
+Choose a GPU that supports CUDA 12.8

 Pick 1 or 2 L40S GPU(s)

@@ -22,10 +22,20 @@ source ~/.bashrc
 Create and activate a Conda environment for FastVideo:

 ```
-conda create -n fastvideo python=3.10 -y
+conda create -n fastvideo python=3.12 -y
 conda activate fastvideo
 ```

+Install `uv` (optional, but recommended):
+
+From instructions on [uv](https://astral.sh/uv/):
+
+```
+curl -LsSf https://astral.sh/uv/install.sh | sh
+# or 
+wget -qO- https://astral.sh/uv/install.sh | sh
+```
+
 Clone the FastVideo repository and go to the FastVideo directory:

 ```
@@ -36,10 +46,10 @@ git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
 Now you can install FastVideo and setup git hooks for running linting. By using `pre-commit`, the linters will run and have to pass before you'll be able to make a commit.

 ```bash
-pip install -e .[dev]
+uv pip install -e .[dev]

 # Can also install flash-attn (optional)
-pip install flash-attn==2.7.4.post1 --no-build-isolation 
+uv pip install flash-attn --no-build-isolation 

 # Linting, formatting and static type checking
 pre-commit install --hook-type pre-commit --hook-type commit-msg
@@ -50,3 +60,14 @@ pre-commit run --all-files
 # Unit tests
 pytest tests/
 ```
+
+If you are on a Hopper GPU, you should also install [FA3](https://github.com/Dao-AILab/flash-attention) for much better performance:
+
+```
+git clone https://github.com/Dao-AILab/flash-attention.git && cd flash-attention/hopper
+
+# make sure you have ninja installed
+uv pip install ninja
+
+python setup.py install
+```
@@ -6,8 +6,9 @@ We introduce a new finetuning strategy - **Sparse-distill**, which jointly integ

 We provide two distilled models:

- **[FastWan2.1-T2V-1.3B-Diffusers](https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers)**: 3-step inference, up to **20 FPS** on H100 GPU
- **[FastWan2.1-T2V-14B-480P-Diffusers](https://huggingface.co/FastVideo/FastWan2.1-T2V-14B-480P-Diffusers)**: 3-step inference, up to **50x speed up** at 480P, **70x speed up** at 720P for denoising loop
+- **[FastWan2.1-T2V-1.3B-Diffusers](https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers)**: 3-step inference, up to **16 FPS** on H100 GPU
+- **[FastWan2.1-T2V-14B-480P-Diffusers](https://huggingface.co/FastVideo/FastWan2.1-T2V-14B-480P-Diffusers)**: 3-step inference, up to **60x speed up** at 480P, **90x speed up** at 720P for denoising loop
+- **[FastWan2.2-TI2V-5B-FullAttn-Diffusers](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers)**: 3-step inference, up to **50x speed up** at 720P for denoising loop

 Both models are trained on **61×448×832** resolution but support generating videos with **any resolution** (1.3B  model mainly support 480P, 14B model support 480P and 720P, quality may degrade for different resolutions).

@@ -34,7 +35,7 @@ python scripts/huggingface/download_hf.py \

 ## 🚀 Training Scripts

-### 1.3B Model Sparse-Distill
+### Wan2.1 1.3B Model Sparse-Distill

 For the 1.3B model, we use **4 nodes with 32 H200 GPUs** (8 GPUs per node):

@@ -50,7 +51,7 @@ sbatch examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P/distill_dmd_VSA_t2v_1.3B.sl
 - VSA attention sparsity: 0.8
 - Training steps: 4000 (~12 hours)

-### 14B Model Sparse-Distill
+### Wan2.1 14B Model Sparse-Distill

 For the 14B model, we use **8 nodes with 64 H200 GPUs** (8 GPUs per node):

@@ -67,3 +68,20 @@ sbatch examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P/distill_dmd_VSA_t2v_14B.slu
 - VSA attention sparsity: 0.9
 - Training steps: 3000 (~52 hours)
 - HSDP shard dim: 8
+
+### Wan2.2 5B Model Sparse-Distill
+
+For the 5B model, we use **8 nodes with 64 H200 GPUs** (8 GPUs per node):
+
+```bash
+# Multi-node training (8 nodes, 64 GPUs total)
+sbatch examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/distill_dmd_t2v_5B.sh 
+```
+
+**Key Configuration:**
+- Global batch size: 64
+- Sequence parallel size: 1
+- Gradient accumulation steps: 1
+- Learning rate: 2e-5
+- Training steps: 3000 (~12 hours)
+- HSDP shard dim: 1
@@ -6,7 +6,7 @@ Instructions to install FastVideo for NVIDIA CUDA GPUs.

 - **OS: Linux or Windows WSL**
 - **Python: 3.10-3.12**
- **CUDA 12.4**
+- **CUDA 12.8**
 - **At least 1 NVIDIA GPU**

 ## Set up using Python
@@ -38,6 +38,7 @@ conda activate fastvideo

 :::{tip}
 We highly recommend using `uv` to install FastVideo. In our experience, `uv` speeds up installation by at least 3x.
+Note that you can also use `uv` to install FastVideo in a Conda environment.
 :::

 Or you can create a new Python environment using [uv](https://docs.astral.sh/uv/), a very fast Python environment manager. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following command:
@@ -60,7 +61,7 @@ uv pip install fastvideo
 Also optionally install flash-attn:

 ```bash
-pip install flash-attn==2.7.4.post1 --no-build-isolation
+pip install flash-attn --no-build-isolation
 ```

 ### Installation from Source
@@ -87,7 +88,7 @@ uv pip install -e .
 #### Flash Attention

 ```bash
-pip install flash-attn==2.7.4.post1 --no-build-isolation
+pip install flash-attn --no-build-isolation
 ```

 ## Set up using Docker
@@ -102,7 +103,7 @@ If you're planning to contribute to FastVideo please see the following page:
 ## Hardware Requirements

 ### For Basic Inference
- NVIDIA GPU with CUDA 12.4 support
+- NVIDIA GPU with CUDA 12.8 support

 ### For Lora Finetuning
 - 40GB GPU memory each for 2 GPUs with lora
@@ -39,6 +39,7 @@ conda activate fastvideo

 :::{tip}
 We highly recommend using `uv` to install FastVideo. In our experience, `uv` speeds up installation by at least 3x.
+Note that you can also use `uv` to install FastVideo in a Conda environment.
 :::

 Or you can create a new Python environment using [uv](https://docs.astral.sh/uv/), a very fast Python environment manager. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following command:
@@ -1,6 +1,6 @@
 # Welcome to FastVideo

-:::{figure} ../../assets/logo.png
+:::{figure} ../../assets/logos/logo.svg
 :align: center
 :alt: FastVideo
 :class: no-scaled-link
@@ -9,7 +9,7 @@

 :::{raw} html
 <p style="text-align:center">
-<strong>FastVideo is An unified inference and post-training framework for accelerated video generation.
+<strong>FastVideo is a unified inference and post-training framework for accelerated video generation.
 </strong>
 </p>

@@ -101,7 +101,6 @@ sliding_tile_attention/demo
 :maxdepth: 1

 video_sparse_attention/installation
-video_sparse_attention/demo
 :::

 :::{toctree}
@@ -5,7 +5,7 @@ This page contains step-by-step instructions to get you quickly started with vid
 ## Requirements
 - **OS**: Linux (Tested on Ubuntu 22.04+)
 - **Python**: 3.10-3.12
- **CUDA**: 12.4
+- **CUDA**: 12.8
 - **GPU**: At least one NVIDIA GPU

 ## Installation
@@ -6,6 +6,7 @@ The symbols used have the following meanings:

 - ✅ = Full compatibility
 - ❌ = No compatibility
+- ⭕ = Does not apply to this model

 ## Models x Optimization
 The `HuggingFace Model ID` can be directly pass to `from_pretrained()` methods and FastVideo will use the optimal default parameters when initializing and generating videos.
@@ -37,51 +38,94 @@ The `HuggingFace Model ID` can be directly pass to `from_pretrained()` methods a
  * TeaCache
  * Sliding Tile Attn
  * Sage Attn
+  * Video Sparse Attention (VSA)
+- * FastWan2.1 T2V 1.3B
+  * `FastVideo/FastWan2.1-T2V-1.3B-Diffusers`
+  * 480P
+  * ⭕
+  * ⭕
+  * ⭕
+  * ✅
+- * FastWan2.2 TI2V 5B Full Attn*
+  * `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers`
+  * 720P
+  * ⭕
+  * ⭕
+  * ⭕
+  * ✅
+- * Wan2.2 TI2V 5B 
+  * `Wan-AI/Wan2.2-TI2V-5B-Diffusers`
+  * 720P
+  * ⭕
+  * ⭕
+  * ✅
+  * ⭕
+- * Wan2.2 T2V A14B
+  * `Wan-AI/Wan2.2-T2V-A14B-Diffusers`
+  * 480P<br>720P
+  * ❌
+  * ❌
+  * ✅
+  * ⭕
+- * Wan2.2 I2V A14B
+  * `Wan-AI/Wan2.2-I2V-A14B-Diffusers`
+  * 480P<br>720P
+  * ❌
+  * ❌
+  * ✅
+  * ⭕
 - * HunyuanVideo
  * `hunyuanvideo-community/HunyuanVideo`
  * 720px1280p<br>544px960p
  * ❌
  * ✅
  * ✅
+  * ⭕
 - * FastHunyuan
  * `FastVideo/FastHunyuan-diffusers`
  * 720px1280p<br>544px960p
  * ❌
  * ✅
  * ✅
- * Wan T2V 1.3B
+  * ⭕
+- * Wan2.1 T2V 1.3B
  * `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`
  * 480P
  * ✅
  * ✅*
  * ✅
- * Wan T2V 14B
+  * ⭕
+- * Wan2.1 T2V 14B
  * `Wan-AI/Wan2.1-T2V-14B-Diffusers`
  * 480P, 720P
  * ✅
  * ✅*
  * ✅
- * Wan I2V 480P
+  * ⭕
+- * Wan2.1 I2V 480P
  * `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers`
  * 480P
  * ✅
  * ✅*
  * ✅
- * Wan I2V 720P
+  * ⭕
+- * Wan2.1 I2V 720P
  * `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers`
  * 720P
  * ✅
-  * ✅*
  * ✅
+  * ✅
+  * ⭕
 - * StepVideo T2V
  * `FastVideo/stepvideo-t2v-diffusers`
  * 768px768px204f<br>544px992px204f<br>544px992px136f
  * ❌
  * ❌
  * ✅
+  * ⭕
 :::

-**Note**: there are some known quality issues with Wan2.1 + Sliding Tile Attn. We are working on fixing this issue.
+**Note**: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.

 ## Special requirements

@@ -4,7 +4,7 @@
 You can install the Sliding Tile Attention package using

 ```
-pip install st_attn==0.0.4
+pip install st_attn
 ``` 

 # Building from Source
@@ -12,7 +12,6 @@ We test our code on Pytorch 2.5.0 and CUDA>=12.4. Currently we only have impleme
 First, install C++20 for ThunderKittens:

 ```bash
-cd csrc/sliding_tile_attention/
 sudo apt update
 sudo apt install gcc-11 g++-11

@@ -22,14 +21,20 @@ sudo apt update
 sudo apt install clang-11
 ```

-Install STA:
+Set up CUDA environment (if using CUDA 12.4):

 ```bash
 export CUDA_HOME=/usr/local/cuda-12.4
 export PATH=${CUDA_HOME}/bin:${PATH} 
 export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+```
+
+Install STA:
+
+```bash
+cd csrc/attn/sliding_tile_attn/
 git submodule update --init --recursive
-python setup_sta.py install
+python setup.py install
 ```

 # 🧪 Test
@@ -53,3 +58,9 @@ out = sliding_tile_attention(q, k, v, window_size, text_length)
 out = sliding_tile_attention(q, k, v, window_size, 0, False)

 ```
+
+# 🚀Inference
+
+```bash
+bash scripts/inference/v1_inference_wan_STA.sh
+```
@@ -1,3 +0,0 @@
-(vsa-demo)=
-
-# 🎬 Demo
@@ -4,8 +4,7 @@
 You can install the Video Sparse Attention package using

 ```bash
-git submodule update --init --recursive
-python setup_vsa.py install
+pip install vsa
 ```

 # Building from Source
@@ -23,10 +22,10 @@ sudo apt update
 sudo apt install clang-11
 ```

-Set up CUDA environment (if using CUDA 12.4):
+Set up CUDA environment (if using CUDA 12.8):

 ```bash
-export CUDA_HOME=/usr/local/cuda-12.4
+export CUDA_HOME=/usr/local/cuda-12.8
 export PATH=${CUDA_HOME}/bin:${PATH} 
 export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
 ```
@@ -34,9 +33,9 @@ export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
 Install VSA:

 ```bash
-cd csrc/attn/
+cd csrc/attn/video_sparse_attn/
 git submodule update --init --recursive
-python setup_vsa.py install
+python setup.py install
 ```

 # 🧪 Test
@@ -59,3 +58,9 @@ from vsa import video_sparse_attn
 output = video_sparse_attn(q, k, v, variable_block_sizes, topk, block_size, compress_attn_weight)

 ``` 
+
+# 🚀Inference
+
+```bash
+bash scripts/inference/v1_inference_wan_VSA.sh
+```
@@ -4,9 +4,7 @@ These are end-to-end example scripts for distilling Wan2.1 T2V 1.3B model using
 ### 0. Make sure you have installed VSA

 ```bash
-cd csrc/attn
-git submodule update --init --recursive
-python setup_vsa.py install
+pip install vsa
 ```

 ### 1. Download dataset:
@@ -86,7 +86,7 @@ validation_args=(
  --validation_dataset_file "$VALIDATION_DATASET_FILE"
  --validation_steps 200
  --validation_sampling_steps "3"
-  --validation_guidance_scale "1.0" # not used for dmd inference
+  --validation_guidance_scale "6.0" # not used for dmd inference
 )

 # Optimizer arguments
@@ -102,7 +102,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.0
  --dit_precision "fp32"
@@ -86,7 +86,7 @@ validation_args=(
  --validation_dataset_file "$VALIDATION_DATASET_FILE"
  --validation_steps 200
  --validation_sampling_steps "3"
-  --validation_guidance_scale "1.0" # not used for dmd inference
+  --validation_guidance_scale "6.0" # not used for dmd inference
 )

 # Optimizer arguments
@@ -102,7 +102,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.0
  --dit_precision "fp32"
@@ -86,7 +86,7 @@ validation_args=(
  --validation_dataset_file "$VALIDATION_DATASET_FILE"
  --validation_steps 200
  --validation_sampling_steps "3"
-  --validation_guidance_scale "1.0" # not used for dmd inference
+  --validation_guidance_scale "6.0" # not used for dmd inference
 )

 # Optimizer arguments
@@ -102,7 +102,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.0
  --dit_precision "fp32"
@@ -0,0 +1,11 @@
+# Wan2.2-5B Distill Example
+These are end-to-end example scripts for distilling Wan2.2 TI2V 5B model DMD+VSA methods.
+
+### 0. Make sure you have installed VSA
+
+```bash
+pip install vsa
+```
+
+### Data-free Distillation
+When `--simulate_generator_forward` is enabled, distillation becomes data-free by simulating intermediate steps through forward inference of the generator. This helps avoid training–inference mismatch. See Section 4.5 of [DMD2](https://arxiv.org/pdf/2405.14867) for details.
@@ -87,7 +87,7 @@ validation_args=(
  --validation_dataset_file "$VALIDATION_DIR"
  --validation_steps 200
  --validation_sampling_steps "3"
-  --validation_guidance_scale "1.0" # not used for dmd inference
+  --validation_guidance_scale "6.0" # not used for dmd inference
 )

 # Optimizer arguments
@@ -108,7 +108,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.0
  --dit_precision "fp32"
@@ -87,7 +87,7 @@ validation_args=(
  --validation_dataset_file "$VALIDATION_DIR"
  --validation_steps 200
  --validation_sampling_steps "3"
-  --validation_guidance_scale "1.0" # not used for dmd inference
+  --validation_guidance_scale "6.0" # not used for dmd inference
 )

 # Optimizer arguments
@@ -108,7 +108,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.0
  --dit_precision "fp32"
@@ -4,9 +4,17 @@ These are end-to-end example scripts for distilling Wan2.2 TI2V 5B model DMD+VSA
 ### 0. Make sure you have installed VSA

 ```bash
-cd csrc/attn
-git submodule update --init --recursive
-python setup_vsa.py install
+pip install vsa
 ```

-### TODO
+### 1. Download dataset:
+```bash
+bash examples/distill/Wan2.2-TI2V-5B-Diffusers/crush_smol/download_dataset.sh
+```
+
+### 2. Configure and run distillation:
+
+#### For DMD-only distillation:
+```bash
+bash examples/distill/Wan2.2-TI2V-5B-Diffusers/crush_smol/examples/distill/Wan2.2-TI2V-5B-Diffusers/crush_smol/
+```
@@ -63,7 +63,7 @@ validation_args=(
  --validation_dataset_file "$VALIDATION_DATASET_FILE"
  --validation_steps 200
  --validation_sampling_steps "3"
-  --validation_guidance_scale "1.0" # not used for dmd inference
+  --validation_guidance_scale "6.0" # not used for dmd inference
 )

 # Optimizer arguments
@@ -77,7 +77,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.0
  --dit_precision "fp32"
@@ -16,8 +16,7 @@ def main():
        dit_cpu_offload=False,
        vae_cpu_offload=False,
        text_encoder_cpu_offload=True,
-        # Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
-        pin_cpu_memory=False,
+        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
        # image_encoder_cpu_offload=False,
    )

@@ -17,7 +17,7 @@ def main():
        use_fsdp_inference=True,
        # Adjust these offload parameters if you have < 32GB of VRAM
        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
+        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
        dit_cpu_offload=False,
        vae_cpu_offload=False,
        VSA_sparsity=0.8,
@@ -0,0 +1,31 @@
+import os
+import time
+from fastvideo import VideoGenerator, SamplingParam
+
+OUTPUT_PATH = "video_samples_causal"
+def main():
+    # FastVideo will automatically use the optimal default arguments for the
+    # model.
+    # If a local path is provided, FastVideo will make a best effort
+    # attempt to identify the optimal arguments.
+    model_name = "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
+    generator = VideoGenerator.from_pretrained(
+        model_name,
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=True,
+        text_encoder_cpu_offload=False,
+        dit_cpu_offload=False,
+    )
+
+    sampling_param = SamplingParam.from_pretrained(model_name)
+
+    prompt = (
+        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
+        "wide with interest. The playful yet serene atmosphere is complemented by soft "
+        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
+    )
+    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, sampling_param=sampling_param)
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,48 @@
+from fastvideo import VideoGenerator
+
+# from fastvideo.configs.sample import SamplingParam
+
+OUTPUT_PATH = "video_samples_wan2_2_14B_t2v"
+def main():
+    # FastVideo will automatically use the optimal default arguments for the
+    # model.
+    # If a local path is provided, FastVideo will make a best effort
+    # attempt to identify the optimal arguments.
+    generator = VideoGenerator.from_pretrained(
+        "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True, # DiT need to be offloaded for MoE
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=True,
+        # Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
+        pin_cpu_memory=True,
+        # image_encoder_cpu_offload=False,
+    )
+
+    # sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
+    # sampling_param.num_frames = 45
+    # sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
+    # Generate videos with the same simple API, regardless of GPU count
+    prompt = (
+        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
+        "wide with interest. The playful yet serene atmosphere is complemented by soft "
+        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
+    )
+    _ = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, height=720, width=1280, num_frames=81)
+    # video = generator.generate_video(prompt, sampling_param=sampling_param, output_path="wan_t2v_videos/")
+
+    # Generate another video with a different prompt, without reloading the
+    # model!
+    prompt2 = (
+        "A majestic lion strides across the golden savanna, its powerful frame "
+        "glistening under the warm afternoon sun. The tall grass ripples gently in "
+        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
+        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
+        "cinematic.")
+    _ = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True, height=720, width=1280, num_frames=81)
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,30 @@
+from fastvideo import VideoGenerator
+
+# from fastvideo.configs.sample import SamplingParam
+
+OUTPUT_PATH = "video_samples_wan2_2_14B_i2v"
+def main():
+    # FastVideo will automatically use the optimal default arguments for the
+    # model.
+    # If a local path is provided, FastVideo will make a best effort
+    # attempt to identify the optimal arguments.
+    generator = VideoGenerator.from_pretrained(
+        "Wan-AI/Wan2.2-I2V-A14B-Diffusers",
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True, # DiT need to be offloaded for MoE
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=True,
+        # Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
+        pin_cpu_memory=True,
+        # image_encoder_cpu_offload=False,
+    )
+
+    prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+    image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
+
+    video = generator.generate_video(prompt, image_path=image_path, output_path=OUTPUT_PATH, save_video=True, height=832, width=480, num_frames=81)
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,41 @@
+from fastvideo import VideoGenerator
+
+OUTPUT_PATH = "video_samples_wan2_2_5B_ti2v"
+def main():
+    # FastVideo will automatically use the optimal default arguments for the
+    # model.
+    # If a local path is provided, FastVideo will make a best effort
+    # attempt to identify the optimal arguments.
+    model_name = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
+    generator = VideoGenerator.from_pretrained(
+        model_name,
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True,
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
+        # image_encoder_cpu_offload=False,
+    )
+
+    # I2V is triggered just by passing in an image_path argument
+    prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+    image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
+    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, image_path=image_path)
+
+    # Generate another video with a different prompt, without reloading the
+    # model!
+
+    # T2V mode
+    prompt2 = (
+        "A majestic lion strides across the golden savanna, its powerful frame "
+        "glistening under the warm afternoon sun. The tall grass ripples gently in "
+        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
+        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
+        "cinematic.")
+    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True)
+
+
+if __name__ == "__main__":
+    main()
@@ -1,59 +0,0 @@
-# FastVideo Gradio Demo
-
-This is a Gradio-based web interface for generating videos using the FastVideo framework. The demo allows users to create videos from text prompts with various customization options.
-
-## Overview
-
-The demo uses the FastVideo framework to generate videos based on text prompts. It provides a simple web interface built with Gradio that allows users to:
-
- Enter text prompts to generate videos
- Customize video parameters (dimensions, number of frames, etc.)
- Use negative prompts to guide the generation process
- Set or randomize seeds for reproducibility
-
---
-
-## Usage
-
-Run the demo with:
-
-```bash
-python examples/inference/gradio/gradio_demo.py
-```
-
-This will start a web server at `http://0.0.0.0:7860` where you can access the interface.
-
---
-
-## Model Initialization
-
-This demo initializes a `VideoGenerator` with the minimum required arguments for inference. Users can seamlessly adjust inference options between generations, including prompts, resolution, video length, or even the number of inference steps, *without ever needing to reload the model*.
-
-## Video Generation
-
-The core functionality is in the `generate_video` function, which:
-1. Processes user inputs
-2. Uses the FastVideo VideoGenerator from earlier to run inference (`generator.generate_video()`)
-3. Returns an output path that Gradio uses to display the generated video
-
-## Gradio Interface
-
-The interface is built with several components:
- A text input for the prompt
- A video display for the result
- Inference options in a collapsible accordion:
-  - Height and width sliders
-  - Number of frames slider
-  - Guidance scale slider
-  - Inference steps slider
-  - Negative prompt options
-  - Seed controls
-
-### Inference Options
-
- **Height/Width**: Control the resolution of the generated video
- **Number of Frames**: Set how many frames to generate
- **Guidance Scale**: Control how closely the generation follows the prompt
- **Inference Steps**: More steps can improve quality but take longer
- **Negative Prompt**: Specify what you don't want to see in the video
- **Seed**: Control randomness for reproducible results
@@ -1,169 +0,0 @@
-import argparse
-import os
-from copy import deepcopy
-
-import gradio as gr
-import torch
-
-from fastvideo import VideoGenerator
-from fastvideo.configs.sample.base import SamplingParam
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="FastVideo Gradio Demo")
-    parser.add_argument("--model_path",
-                        type=str,
-                        default="FastVideo/FastHunyuan-diffusers",
-                        help="Path to the model")
-    parser.add_argument("--num_gpus",
-                        type=int,
-                        default=1,
-                        help="Number of GPUs to use")
-    parser.add_argument("--output_path",
-                        type=str,
-                        default="outputs",
-                        help="Path to save generated videos")
-    parsed_args = parser.parse_args()
-
-    # args = FastVideoArgs(model_path="FastVideo/FastHunyuan-Diffusers", num_gpus=2)
-
-    generator = VideoGenerator.from_pretrained(
-        model_path=parsed_args.model_path, num_gpus=parsed_args.num_gpus)
-
-    default_params = SamplingParam.from_pretrained(parsed_args.model_path)
-
-    def generate_video(
-        prompt,
-        negative_prompt,
-        use_negative_prompt,
-        seed,
-        guidance_scale,
-        num_frames,
-        height,
-        width,
-        num_inference_steps,
-        randomize_seed=False,
-    ):
-        params = deepcopy(default_params)
-        params.prompt = prompt
-        params.negative_prompt = negative_prompt
-        params.seed = seed
-        params.guidance_scale = guidance_scale
-        params.num_frames = num_frames
-        params.height = height
-        params.width = width
-        params.num_inference_steps = num_inference_steps
-
-        if randomize_seed:
-            params.seed = torch.randint(0, 1000000, (1, )).item()
-
-        if not use_negative_prompt:
-            params.negative_prompt = None
-
-        generator.generate_video(prompt=prompt, sampling_param=params)
-
-        output_path = os.path.join(parsed_args.output_path,
-                                   f"{params.prompt[:100]}.mp4")
-
-        return output_path, params.seed
-
-    examples = [
-        "A hand enters the frame, pulling a sheet of plastic wrap over three balls of dough placed on a wooden surface. The plastic wrap is stretched to cover the dough more securely. The hand adjusts the wrap, ensuring that it is tight and smooth over the dough. The scene focuses on the hand’s movements as it secures the edges of the plastic wrap. No new objects appear, and the camera remains stationary, focusing on the action of covering the dough.",
-        "A vintage train snakes through the mountains, its plume of white steam rising dramatically against the jagged peaks. The cars glint in the late afternoon sun, their deep crimson and gold accents lending a touch of elegance. The tracks carve a precarious path along the cliffside, revealing glimpses of a roaring river far below. Inside, passengers peer out the large windows, their faces lit with awe as the landscape unfolds.",
-        "A crowded rooftop bar buzzes with energy, the city skyline twinkling like a field of stars in the background. Strings of fairy lights hang above, casting a warm, golden glow over the scene. Groups of people gather around high tables, their laughter blending with the soft rhythm of live jazz. The aroma of freshly mixed cocktails and charred appetizers wafts through the air, mingling with the cool night breeze.",
-    ]
-
-    with gr.Blocks() as demo:
-        gr.Markdown("# FastVideo Inference Demo")
-
-        with gr.Group():
-            with gr.Row():
-                prompt = gr.Text(
-                    label="Prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                    container=False,
-                )
-                run_button = gr.Button("Run", scale=0)
-            result = gr.Video(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced options", open=False):
-            with gr.Group():
-                with gr.Row():
-                    height = gr.Slider(
-                        label="Height",
-                        minimum=256,
-                        maximum=1024,
-                        step=32,
-                        value=default_params.height,
-                    )
-                    width = gr.Slider(label="Width",
-                                      minimum=256,
-                                      maximum=1024,
-                                      step=32,
-                                      value=default_params.width)
-
-                with gr.Row():
-                    num_frames = gr.Slider(
-                        label="Number of Frames",
-                        minimum=21,
-                        maximum=163,
-                        value=default_params.num_frames,
-                    )
-                    guidance_scale = gr.Slider(
-                        label="Guidance Scale",
-                        minimum=1,
-                        maximum=12,
-                        value=default_params.guidance_scale,
-                    )
-                    num_inference_steps = gr.Slider(
-                        label="Inference Steps",
-                        minimum=4,
-                        maximum=100,
-                        value=default_params.num_inference_steps,
-                    )
-
-                with gr.Row():
-                    use_negative_prompt = gr.Checkbox(
-                        label="Use negative prompt", value=False)
-                    negative_prompt = gr.Text(
-                        label="Negative prompt",
-                        max_lines=1,
-                        placeholder="Enter a negative prompt",
-                        visible=False,
-                    )
-
-                seed = gr.Slider(label="Seed",
-                                 minimum=0,
-                                 maximum=1000000,
-                                 step=1,
-                                 value=default_params.seed)
-                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                seed_output = gr.Number(label="Used Seed")
-
-        gr.Examples(examples=examples, inputs=prompt)
-
-        use_negative_prompt.change(
-            fn=lambda x: gr.update(visible=x),
-            inputs=use_negative_prompt,
-            outputs=default_params.negative_prompt,
-        )
-
-        run_button.click(
-            fn=generate_video,
-            inputs=[
-                prompt,
-                negative_prompt,
-                use_negative_prompt,
-                seed,
-                guidance_scale,
-                num_frames,
-                height,
-                width,
-                num_inference_steps,
-                randomize_seed,
-            ],
-            outputs=[result, seed_output],
-        )
-
-    demo.queue(max_size=20).launch(server_name="0.0.0.0", server_port=7860)
@@ -0,0 +1,730 @@
+import argparse
+import os
+import requests
+import base64
+import time
+
+import gradio as gr
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+MODEL_PATH_MAPPING = {
+    "FastWan2.1-T2V-1.3B": "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
+    "FastWan2.2-TI2V-5B-FullAttn": "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
+}
+
+
+class RayServeClient:
+    def __init__(self, backend_url: str):
+        self.backend_url = backend_url
+        self.session = requests.Session()
+    
+    def check_health(self) -> bool:
+        try:
+            response = self.session.get(f"{self.backend_url}/health", timeout=5)
+            return response.status_code == 200
+        except requests.exceptions.RequestException:
+            return False
+    
+    def generate_video(self, request_data: dict) -> dict:
+        start_time = time.time()
+        
+        try:
+            headers = {"Content-Type": "application/json"}
+            
+            response = self.session.post(
+                f"{self.backend_url}/generate_video",
+                json=request_data,
+                headers=headers,
+                timeout=300
+            )
+            
+            round_trip_time = time.time() - start_time
+            
+            if response.status_code == 200:
+                result = response.json()
+                backend_total = result.get("total_time", 0)
+                network_time = round_trip_time - backend_total
+                result["network_time"] = network_time
+                return result
+            else:
+                return {"success": False, "error_message": f"HTTP {response.status_code}: {response.text}"}
+                
+        except requests.exceptions.RequestException as e:
+            return {"success": False, "error_message": f"Request failed: {str(e)}"}
+
+
+def save_video_from_base64(video_data: str, output_dir: str, prompt: str) -> str:
+    if not video_data:
+        return None
+    
+    try:
+        if video_data.startswith('data:video/'):
+            video_data = video_data.split(',')[1]
+        
+        video_bytes = base64.b64decode(video_data)
+        
+        safe_prompt = prompt[:50].replace(' ', '_').replace('/', '_').replace('\\', '_')
+        video_filename = f"{safe_prompt}.mp4"
+        video_path = os.path.join(output_dir, video_filename)
+        
+        os.makedirs(output_dir, exist_ok=True)
+        
+        with open(video_path, 'wb') as f:
+            f.write(video_bytes)
+        
+        return video_path
+        
+    except Exception as e:
+        print(f"Failed to save video: {e}")
+        return None
+
+
+def create_timing_display(inference_time, encoding_time, network_time, total_time, stage_execution_times, num_frames):
+    dit_denoising_time = f"{stage_execution_times[5]:.2f}s" if len(stage_execution_times) > 5 else "N/A"
+    
+    timing_html = f"""
+    <div style="margin: 10px 0;">
+        <h3 style="text-align: center; margin-bottom: 10px;">⏱️ Timing Breakdown</h3>
+        <div style="display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; margin-bottom: 10px;">
+            <div class="timing-card timing-card-highlight">
+                <div style="font-size: 20px;">🚀</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">DiT Denoising</div>
+                <div style="font-size: 16px; color: #ffa200; font-weight: bold;">{dit_denoising_time}</div>
+            </div>
+            <div class="timing-card">
+                <div style="font-size: 20px;">🧠</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">E2E (w. vae/text encoder)</div>
+                <div style="font-size: 16px; color: #2563eb;">{inference_time:.2f}s</div>
+            </div>
+            <div class="timing-card">
+                <div style="font-size: 20px;">🎬</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Video Encoding</div>
+                <div style="font-size: 16px; color: #dc2626;">{encoding_time:.2f}s</div>
+            </div>
+            <div class="timing-card">
+                <div style="font-size: 20px;">🌐</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Network Transfer</div>
+                <div style="font-size: 16px; color: #059669;">{network_time:.2f}s</div>
+            </div>
+            <div class="timing-card">
+                <div style="font-size: 20px;">📊</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Total Processing</div>
+                <div style="font-size: 18px; color: #0277bd;">{total_time:.2f}s</div>
+            </div>
+        </div>"""
+    
+    if inference_time > 0:
+        fps = num_frames / inference_time
+        timing_html += f"""
+        <div class="performance-card" style="margin-top: 15px;">
+            <span style="font-weight: bold;">Generation Speed: </span>
+            <span style="font-size: 18px; color: #6366f1; font-weight: bold;">{fps:.1f} frames/second</span>
+        </div>"""
+    
+    return timing_html + "</div>"
+
+
+def load_example_prompts():
+    def contains_chinese(text):
+        return any('\u4e00' <= char <= '\u9fff' for char in text)
+    
+    def load_from_file(filepath):
+        prompts, labels = [], []
+        try:
+            with open(filepath, "r", encoding='utf-8') as f:
+                for line in f:
+                    line = line.strip()
+                    if line and not contains_chinese(line):
+                        label = line[:100] + "..." if len(line) > 100 else line
+                        labels.append(label)
+                        prompts.append(line)
+        except Exception as e:
+            print(f"Warning: Could not read {filepath}: {e}")
+        return prompts, labels
+    
+    examples, example_labels = load_from_file("prompts/prompts_final.txt")
+    
+    if not examples:
+        examples = ["A crowded rooftop bar buzzes with energy, the city skyline twinkling like a field of stars in the background."]
+        example_labels = ["Crowded rooftop bar at night"]
+    
+    return examples, example_labels
+
+
+def create_gradio_interface(backend_url: str, default_params: dict[str, SamplingParam]):
+    
+    client = RayServeClient(backend_url)
+    
+    def generate_video(
+        prompt, negative_prompt, use_negative_prompt, seed, guidance_scale,
+        num_frames, height, width, randomize_seed, model_selection, progress
+    ):
+        if not client.check_health():
+            return None, f"Backend is not available. Please check if Ray Serve is running at {backend_url}", ""
+        
+        # Validate dimensions
+        max_pixels = 720 * 1280
+        if height * width > max_pixels:
+            return None, f"Video dimensions too large. Maximum: 720x1280 pixels", ""
+        
+        if progress:
+            progress(0.1, desc="Checking backend health...")
+        
+        request_data = {
+            "prompt": prompt,
+            "negative_prompt": negative_prompt,
+            "use_negative_prompt": use_negative_prompt,
+            "seed": seed,
+            "guidance_scale": guidance_scale,
+            "num_frames": num_frames,
+            "height": height,
+            "width": width,
+            "randomize_seed": randomize_seed,
+            "return_frames": False,
+            "image_path": None,
+            "model_path": MODEL_PATH_MAPPING.get(model_selection, "FastVideo/FastWan2.1-T2V-1.3B-Diffusers")
+        }
+        
+        if progress:
+            progress(0.4, desc="Generating video...")
+        
+        response = client.generate_video(request_data)
+        
+        if progress:
+            progress(0.8, desc="Processing response...")
+        
+        if response.get("success", False):
+            video_data = response.get("video_data", "")
+            used_seed = response.get("seed", seed)
+            inference_time = response.get("inference_time", 0.0)
+            encoding_time = response.get("encoding_time", 0.0)
+            total_time = response.get("total_time", 0.0)
+            network_time = response.get("network_time", 0.0)
+            stage_execution_times = response.get("stage_execution_times", [])
+            
+            timing_details = create_timing_display(
+                inference_time, encoding_time, network_time, total_time, 
+                stage_execution_times, num_frames
+            )
+            
+            if video_data:
+                if progress:
+                    progress(0.9, desc="Saving video...")
+                
+                video_path = save_video_from_base64(video_data, "outputs", prompt)
+                
+                if progress:
+                    progress(1.0, desc="Generation complete!")
+                
+                if video_path and os.path.exists(video_path):
+                    return video_path, used_seed, timing_details
+                else:
+                    return None, "Failed to save video", ""
+            else:
+                return None, "No video data received from backend", ""
+        else:
+            error_msg = response.get("error_message", "Unknown error occurred")
+            return None, f"Generation failed: {error_msg}", ""
+    
+    examples, example_labels = load_example_prompts()
+    
+    theme = gr.themes.Base().set(
+        button_primary_background_fill="#2563eb",
+        button_primary_background_fill_hover="#1d4ed8",
+        button_primary_text_color="white",
+        slider_color="#2563eb",
+        checkbox_background_color_selected="#2563eb",
+    )
+    
+    def get_default_values(model_name):
+        model_path = MODEL_PATH_MAPPING.get(model_name)
+        if model_path and model_path in default_params:
+            params = default_params[model_path]
+            return {
+                'height': params.height,
+                'width': params.width,
+                'num_frames': params.num_frames,
+                'guidance_scale': params.guidance_scale,
+                'seed': params.seed,
+            }
+        
+        return {
+            'height': 448,
+            'width': 832,
+            'num_frames': 61,
+            'guidance_scale': 3.0,
+            'seed': 1024,
+        }
+    
+    initial_values = get_default_values("FastWan2.1-T2V-1.3B")
+    
+    with gr.Blocks(title="FastWan", theme=theme) as demo:
+        gr.Image("assets/logos/logo.svg", show_label=False, container=False, height=80)
+        gr.HTML("""
+        <div style="text-align: center; margin-bottom: 10px;">
+            <p style="font-size: 18px;"> Make Video Generation Go Blurrrrrrr </p>
+            <p style="font-size: 18px;"> <a href="https://github.com/hao-ai-lab/FastVideo/tree/main" target="_blank">Code</a> | <a href="https://hao-ai-lab.github.io/blogs/fastvideo_post_training/" target="_blank">Blog</a> | <a href="https://hao-ai-lab.github.io/FastVideo/" target="_blank">Docs</a>  </p>
+        </div>
+        """)
+        
+        with gr.Accordion("🎥 What Is FastVideo?", open=False):
+            gr.HTML("""
+            <div style="padding: 20px; line-height: 1.6;">
+                <p style="font-size: 16px; margin-bottom: 15px;">
+                    FastVideo is an inference and post-training framework for diffusion models. It features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.
+                </p>
+            </div>
+            """)
+        
+        with gr.Row():
+            model_selection = gr.Dropdown(
+                choices=list(MODEL_PATH_MAPPING.keys()),
+                value="FastWan2.1-T2V-1.3B",
+                label="Select Model",
+                interactive=True
+            )
+
+        with gr.Row():
+            example_dropdown = gr.Dropdown(
+                choices=example_labels,
+                label="Example Prompts",
+                value=None,
+                interactive=True,
+                allow_custom_value=False
+            )
+        
+        with gr.Row():
+            with gr.Column(scale=6):
+                prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=3,
+                    placeholder="Describe your scene...",
+                    container=False,
+                    lines=3,
+                    autofocus=True,
+                )
+            with gr.Column(scale=1, min_width=120, elem_classes="center-button"):
+                run_button = gr.Button("Run", variant="primary", size="lg")
+        
+        with gr.Row():
+            with gr.Column():
+                error_output = gr.Text(label="Error", visible=False)
+                timing_display = gr.Markdown(label="Timing Breakdown", visible=False)
+
+        with gr.Row(equal_height=True, elem_classes="main-content-row"):
+            with gr.Column(scale=1, elem_classes="advanced-options-column"):
+                with gr.Group():
+                    gr.HTML("<div style='margin: 0 0 15px 0; text-align: center; font-size: 16px;'>Advanced Options</div>")
+                    with gr.Row():
+                        height = gr.Number(
+                            label="Height",
+                            value=initial_values['height'],
+                            interactive=False,
+                            container=True
+                        )
+                        width = gr.Number(
+                            label="Width",
+                            value=initial_values['width'],
+                            interactive=False,
+                            container=True
+                        )
+                    
+                    with gr.Row():
+                        num_frames = gr.Number(
+                            label="Number of Frames",
+                            value=initial_values['num_frames'],
+                            interactive=False,
+                            container=True
+                        )
+                        guidance_scale = gr.Slider(
+                            label="Guidance Scale",
+                            minimum=1,
+                            maximum=12,
+                            value=initial_values['guidance_scale'],
+                        )
+                    
+                    with gr.Row():
+                        use_negative_prompt = gr.Checkbox(
+                            label="Use negative prompt", value=False)
+                        negative_prompt = gr.Text(
+                            label="Negative prompt",
+                            max_lines=3,
+                            lines=3,
+                            placeholder="Enter a negative prompt",
+                            visible=False,
+                        )
+
+                    seed = gr.Slider(
+                        label="Seed",
+                        minimum=0,
+                        maximum=1000000,
+                        step=1,
+                        value=initial_values['seed'],
+                    )
+                    randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
+                    seed_output = gr.Number(label="Used Seed")
+        
+            with gr.Column(scale=1, elem_classes="video-column"):
+                result = gr.Video(
+                    label="Generated Video", 
+                    show_label=True,
+                    height=466,
+                    width=600,
+                    container=True,
+                    elem_classes="video-component"
+                )
+        
+        gr.HTML("""
+        <style>
+        .center-button {
+            display: flex !important;
+            justify-content: center !important;
+            height: 100% !important;
+            padding-top: 1.4em !important;
+        }
+        
+        .gradio-container {
+            max-width: 1200px !important;
+            margin: 0 auto !important;
+        }
+        
+        .main {
+            max-width: 1200px !important;
+            margin: 0 auto !important;
+        }
+        
+        .gr-form, .gr-box, .gr-group {
+            max-width: 1200px !important;
+        }
+        
+        .gr-video {
+            max-width: 500px !important;
+            margin: 0 auto !important;
+        }
+        
+        .main-content-row {
+            display: flex !important;
+            align-items: flex-start !important;
+            min-height: 500px !important;
+            gap: 20px !important;
+        }
+        
+        .advanced-options-column,
+        .video-column {
+            display: flex !important;
+            flex-direction: column !important;
+            flex: 1 !important;
+            min-height: 400px !important;
+            align-items: stretch !important;
+        }
+        
+        .video-column > * {
+            margin-top: 0 !important;
+        }
+        
+        .video-column .gr-video,
+        .video-component {
+            margin-top: 0 !important;
+            padding-top: 0 !important;
+        }
+        
+        .video-column .gr-video .gr-form {
+            margin-top: 0 !important;
+        }
+        
+        .advanced-options-column .gr-group,
+        .video-column .gr-video {
+            margin-top: 0 !important;
+            vertical-align: top !important;
+        }
+        
+        .advanced-options-column > *:last-child,
+        .video-column > *:last-child {
+            flex-grow: 0 !important;
+        }
+        
+        @media (max-width: 1400px) {
+            .main-content-row {
+                min-height: 600px !important;
+            }
+            
+            .advanced-options-column,
+            .video-column {
+                min-height: 600px !important;
+            }
+        }
+        
+        @media (max-width: 1200px) {
+            .main-content-row {
+                flex-direction: column !important;
+                align-items: stretch !important;
+            }
+            
+            .advanced-options-column,
+            .video-column {
+                min-height: auto !important;
+                width: 100% !important;
+            }
+        }
+        
+        .timing-card {
+            background: var(--background-fill-secondary) !important;
+            border: 1px solid var(--border-color-primary) !important;
+            color: var(--body-text-color) !important;
+            padding: 10px;
+            border-radius: 8px;
+            text-align: center;
+            min-height: 80px;
+            display: flex;
+            flex-direction: column;
+            justify-content: center;
+        }
+        
+        .timing-card-highlight {
+            background: var(--background-fill-primary) !important;
+            border: 2px solid var(--color-accent) !important;
+        }
+        
+        .performance-card {
+            background: var(--background-fill-secondary) !important;
+            border: 1px solid var(--border-color-primary) !important;
+            color: var(--body-text-color) !important;
+            padding: 10px;
+            border-radius: 6px;
+            text-align: center;
+        }
+        
+        .gr-number input[readonly] {
+            background-color: var(--background-fill-secondary) !important;
+            border: 1px solid var(--border-color-primary) !important;
+            color: var(--body-text-color-subdued) !important;
+            cursor: default !important;
+            text-align: center !important;
+            font-weight: 500 !important;
+        }
+        </style>
+        """)
+        
+        def on_example_select(example_label):
+            if example_label and example_label in example_labels:
+                index = example_labels.index(example_label)
+                return examples[index]
+            return ""
+        
+        example_dropdown.change(
+            fn=on_example_select,
+            inputs=example_dropdown,
+            outputs=prompt,
+        )
+        
+        gr.HTML("""
+        <div style="text-align: center; margin-top: 10px; margin-bottom: 15px;">
+            <p style="font-size: 16px; margin: 0;">The compute for this demo is generously provided by <a href="https://www.gmicloud.ai/" target="_blank">GMI Cloud</a>. Note that this demo is meant to showcase FastWan's quality and that under a large number of requests, generation speed may be affected. We are also rate-limiting users to 3 requests per minute.</p>
+        </div>
+        """)
+        
+        use_negative_prompt.change(
+            fn=lambda x: gr.update(visible=x),
+            inputs=use_negative_prompt,
+            outputs=negative_prompt,
+        )
+        
+        def on_model_selection_change(selected_model):
+            if not selected_model:
+                selected_model = "FastWan2.1-T2V-1.3B"
+            
+            model_path = MODEL_PATH_MAPPING.get(selected_model)
+            
+            if model_path and model_path in default_params:
+                params = default_params[model_path]
+                return (
+                    gr.update(value=params.height),
+                    gr.update(value=params.width),
+                    gr.update(value=params.num_frames),
+                    gr.update(value=params.guidance_scale),
+                    gr.update(value=params.seed),
+                )
+            
+            return (
+                gr.update(value=448),
+                gr.update(value=832),
+                gr.update(value=61),
+                gr.update(value=3.0),
+                gr.update(value=1024),
+            )
+        
+        model_selection.change(
+            fn=on_model_selection_change,
+            inputs=model_selection,
+            outputs=[height, width, num_frames, guidance_scale, seed],
+        )
+        
+        def handle_generation(*args, progress=None, request: gr.Request = None):
+            model_selection, prompt, negative_prompt, use_negative_prompt, seed, guidance_scale, num_frames, height, width, randomize_seed = args
+            
+            result_path, seed_or_error, timing_details = generate_video(
+                prompt, negative_prompt, use_negative_prompt, seed, guidance_scale, 
+                num_frames, height, width, randomize_seed, model_selection, progress
+            )
+            
+            if result_path and os.path.exists(result_path):
+                return (
+                    result_path, 
+                    seed_or_error, 
+                    gr.update(visible=False),
+                    gr.update(visible=True, value=timing_details),
+                )
+            else:
+                return (
+                    None, 
+                    seed_or_error, 
+                    gr.update(visible=True, value=seed_or_error),
+                    gr.update(visible=False),
+                )
+        
+        run_button.click(
+            fn=handle_generation,
+            inputs=[
+                model_selection,
+                prompt,
+                negative_prompt,
+                use_negative_prompt,
+                seed,
+                guidance_scale,
+                num_frames,
+                height,
+                width,
+                randomize_seed,
+            ],
+            outputs=[result, seed_output, error_output, timing_display],
+            concurrency_limit=20,
+        )
+    
+    return demo
+
+
+def main():
+    parser = argparse.ArgumentParser(description="FastVideo Gradio Frontend")
+    parser.add_argument("--backend_url", type=str, default="http://localhost:8000",
+                        help="URL of the Ray Serve backend")
+    parser.add_argument("--t2v_model_paths", type=str,
+                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.1-T2V-14B-Diffusers",
+                        help="Comma separated list of paths to the T2V model(s)")
+    parser.add_argument("--host", type=str, default="0.0.0.0",
+                        help="Host to bind to")
+    parser.add_argument("--port", type=int, default=7860,
+                        help="Port to bind to")
+    
+    args = parser.parse_args()
+    
+    default_params = {}
+    model_paths = args.t2v_model_paths.split(",")
+    for model_path in model_paths:
+        default_params[model_path] = SamplingParam.from_pretrained(model_path)
+    
+    demo = create_gradio_interface(args.backend_url, default_params)
+    
+    print(f"Starting Gradio frontend at http://{args.host}:{args.port}")
+    print(f"Backend URL: {args.backend_url}")
+    print(f"T2V Models: {args.t2v_model_paths}")
+    
+    from fastapi import FastAPI, Request, HTTPException
+    from fastapi.responses import HTMLResponse, FileResponse
+    import uvicorn
+    
+    app = FastAPI()
+    
+    @app.get("/logo.svg")
+    def get_logo():
+        return FileResponse(
+            "assets/logos/logo.svg", 
+            media_type="image/svg+xml",
+            headers={
+                "Cache-Control": "public, max-age=3600",
+                "Access-Control-Allow-Origin": "*"
+            }
+        )
+    
+    @app.get("/favicon.ico")
+    def get_favicon():
+        favicon_path = "assets/logos/icon_simple.svg"
+        
+        if os.path.exists(favicon_path):
+            return FileResponse(
+                favicon_path, 
+                media_type="image/svg+xml",
+                headers={
+                    "Cache-Control": "public, max-age=3600",
+                    "Access-Control-Allow-Origin": "*"
+                }
+            )
+        else:
+            raise HTTPException(status_code=404, detail="Favicon not found")
+    
+    @app.get("/", response_class=HTMLResponse)
+    def index(request: Request):
+        base_url = str(request.base_url).rstrip('/')
+        return f"""
+        <!DOCTYPE html>
+        <html lang="en">
+        <head>
+            <meta charset="UTF-8" />
+            <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+            
+            <title>FastWan</title>
+            <meta name="title" content="FastWan">
+            <meta name="description" content="Make video generation go blurrrrrrr">
+            <meta name="keywords" content="FastVideo, video generation, AI, machine learning, FastWan">
+            
+            <meta property="og:type" content="website">
+            <meta property="og:url" content="{base_url}/">
+            <meta property="og:title" content="FastWan">
+            <meta property="og:description" content="Make video generation go blurrrrrrr">
+            <meta property="og:image" content="{base_url}/logo.svg">
+            <meta property="og:image:width" content="1200">
+            <meta property="og:image:height" content="630">
+            <meta property="og:site_name" content="FastWan">
+            
+            <meta property="twitter:card" content="summary_large_image">
+            <meta property="twitter:url" content="{base_url}/">
+            <meta property="twitter:title" content="FastWan">
+            <meta property="twitter:description" content="Make video generation go blurrrrrrr">
+            <meta property="twitter:image" content="{base_url}/logo.svg">
+            <link rel="icon" type="image/png" sizes="32x32" href="/favicon.ico">
+            <link rel="icon" type="image/png" sizes="16x16" href="/favicon.ico">
+            <link rel="apple-touch-icon" href="/favicon.ico">
+            <style>
+                body, html {{
+                    margin: 0;
+                    padding: 0;
+                    height: 100%;
+                    overflow: hidden;
+                }}
+                iframe {{
+                    width: 100%;
+                    height: 100vh;
+                    border: none;
+                }}
+            </style>
+        </head>
+        <body>
+            <iframe src="/gradio" width="100%" height="100%" style="border: none;"></iframe>
+        </body>
+        </html>
+        """
+    
+    app = gr.mount_gradio_app(
+        app, 
+        demo, 
+        path="/gradio",
+        allowed_paths=[os.path.abspath("outputs"), os.path.abspath("fastvideo-logos")]
+    )
+    
+    uvicorn.run(app, host=args.host, port=args.port)
+
+
+if __name__ == "__main__":
+    main() 
@@ -0,0 +1,379 @@
+import time
+import os
+import torch
+import base64
+import io
+from copy import deepcopy
+from typing import Dict, Any, Optional, List
+import signal
+import sys
+
+import ray
+from ray import serve
+from fastapi import FastAPI, Request, Response
+from pydantic import BaseModel
+import numpy as np
+from slowapi import Limiter, _rate_limit_exceeded_handler
+from slowapi.util import get_remote_address
+from slowapi.errors import RateLimitExceeded
+import imageio
+from ray.serve.handle import DeploymentHandle
+from prometheus_client import Counter, Histogram, generate_latest
+
+NUM_GPUS = 16
+DEFAULT_FPS = 16
+SEED_RANGE_MAX = 1_000_000
+SUPPORTED_MODELS = [
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers", 
+    "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
+]
+
+MODEL_CONFIGS = {
+    "1.3B": {
+        "num_cpus": 2,
+        "text_encoder_cpu_offload": False,
+        "dit_cpu_offload": False,
+        "vae_cpu_offload": False,
+        "VSA_sparsity": 0.8,
+    },
+    "14B": {
+        "num_cpus": 16,
+        "text_encoder_cpu_offload": True,
+        "dit_cpu_offload": True,
+        "vae_cpu_offload": False,
+        "VSA_sparsity": 0.9,
+    }
+}
+
+
+class VideoGenerationRequest(BaseModel):
+    prompt: str
+    negative_prompt: Optional[str] = None
+    use_negative_prompt: bool = False
+    seed: int = 42
+    guidance_scale: float = 7.5
+    num_frames: int = 21
+    height: int = 448
+    width: int = 832
+    randomize_seed: bool = False
+    return_frames: bool = False
+    model_path: Optional[str] = None
+
+
+class VideoGenerationResponse(BaseModel):
+    video_data: Optional[str] = None
+    seed: int
+    success: bool
+    error_message: Optional[str] = None
+    generation_time: Optional[float] = None
+    model_load_time: Optional[float] = None
+    inference_time: Optional[float] = None
+    encoding_time: Optional[float] = None
+    total_time: Optional[float] = None
+    stage_names: Optional[List[str]] = None
+    stage_execution_times: Optional[List[float]] = None
+
+
+def encode_video_to_base64(frames: List[np.ndarray], fps: int = DEFAULT_FPS) -> str:
+    if not frames:
+        return ""
+    
+    try:
+        buffer = io.BytesIO()
+        imageio.mimsave(buffer, frames, fps=fps, format="mp4")
+        buffer.seek(0)
+        
+        video_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
+        return f"data:video/mp4;base64,{video_base64}"
+        
+    except Exception as e:
+        print(f"Warning: Failed to encode video: {e}")
+        return ""
+
+
+def setup_model_environment(model_path: str) -> None:
+    if "fullattn" in model_path.lower():
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
+    else:
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
+    os.environ["FASTVIDEO_STAGE_LOGGING"] = "1"
+
+
+def process_generation_result(result: Any) -> tuple[List[np.ndarray], float, List[str], List[float]]:
+    frames = result if isinstance(result, list) else result.get("frames", [])
+    generation_time = result.get("generation_time", 0.0) if isinstance(result, dict) else 0.0
+    
+    logging_info = result.get("logging_info", None)
+    if logging_info:
+        stage_names = logging_info.get_execution_order()
+        stage_execution_times = [
+            logging_info.get_stage_info(stage_name).get("execution_time", 0.0) 
+            for stage_name in stage_names
+        ]
+    else:
+        stage_names = []
+        stage_execution_times = []
+    
+    return frames, generation_time, stage_names, stage_execution_times
+
+
+def prepare_sampling_params(video_request: VideoGenerationRequest, default_params: Any) -> Any:
+    params = deepcopy(default_params)
+    params.prompt = video_request.prompt
+    
+    if video_request.use_negative_prompt:
+        params.negative_prompt = video_request.negative_prompt
+
+    params.seed = (video_request.seed if not video_request.randomize_seed 
+                  else torch.randint(0, SEED_RANGE_MAX, (1,)).item())
+    params.randomize_seed = video_request.randomize_seed
+    params.guidance_scale = video_request.guidance_scale
+    params.num_frames = video_request.num_frames
+    params.height = video_request.height
+    params.width = video_request.width
+    params.save_video = False
+    params.return_frames = False
+    
+    return params
+
+
+class BaseModelDeployment:
+    def __init__(self, model_path: str, output_path: str = "outputs"):
+        self.model_path = model_path
+        self.output_path = output_path
+        self.generator = None
+        self.default_params = None
+
+        os.makedirs(self.output_path, exist_ok=True)
+        setup_model_environment(self.model_path)
+
+    def _initialize_generator(self, config: Dict[str, Any]) -> None:
+        from fastvideo.entrypoints.video_generator import VideoGenerator
+        from fastvideo.configs.sample.base import SamplingParam
+
+        print(f"Initializing model: {self.model_path}")
+        self.generator = VideoGenerator.from_pretrained(
+            model_path=self.model_path,
+            num_gpus=1,
+            use_fsdp_inference=True,
+            text_encoder_cpu_offload=config["text_encoder_cpu_offload"],
+            dit_cpu_offload=config["dit_cpu_offload"],
+            vae_cpu_offload=config["vae_cpu_offload"],
+            VSA_sparsity=config["VSA_sparsity"],
+            enable_stage_verification=False,
+        )
+        self.default_params = SamplingParam.from_pretrained(self.model_path)
+
+    def generate_video(self, video_request: VideoGenerationRequest) -> VideoGenerationResponse:
+        total_start_time = time.time()
+        
+        params = prepare_sampling_params(video_request, self.default_params)
+
+        inference_start_time = time.time()
+        result = self.generator.generate_video(
+            prompt=video_request.prompt,
+            sampling_param=params,
+            save_video=False,
+            return_frames=False,
+        )
+        inference_time = time.time() - inference_start_time
+
+        frames, generation_time, stage_names, stage_execution_times = process_generation_result(result)
+
+        encoding_start_time = time.time()
+        video_data = encode_video_to_base64(frames, fps=DEFAULT_FPS)
+        encoding_time = time.time() - encoding_start_time
+        
+        total_time = time.time() - total_start_time
+
+        return VideoGenerationResponse(
+            video_data=video_data,
+            seed=params.seed,
+            success=True,
+            generation_time=generation_time,
+            inference_time=inference_time,
+            encoding_time=encoding_time,
+            total_time=total_time,
+            stage_names=stage_names,
+            stage_execution_times=stage_execution_times,
+        )
+
+
+@serve.deployment(
+    ray_actor_options={"num_cpus": 2, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
+)
+class T2VModelDeployment(BaseModelDeployment):
+    def __init__(self, t2v_model_path: str, output_path: str = "outputs"):
+        super().__init__(t2v_model_path, output_path)
+        self._initialize_generator(MODEL_CONFIGS["1.3B"])
+        print("✅ T2V model initialized successfully")
+
+
+@serve.deployment(
+    ray_actor_options={"num_cpus": 16, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
+)
+class T2V14BModelDeployment(BaseModelDeployment):
+    def __init__(self, t2v_14b_model_path: str, output_path: str = "outputs"):
+        super().__init__(t2v_14b_model_path, output_path)
+        # Override environment for 14B model
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
+        self._initialize_generator(MODEL_CONFIGS["14B"])
+        print("✅ T2V 14B model initialized successfully")
+
+
+app = FastAPI()
+limiter = Limiter(key_func=get_remote_address)
+app.state.limiter = limiter
+app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)
+
+
+@serve.deployment(num_replicas=50, ray_actor_options={"num_cpus": 2})
+@serve.ingress(app)
+class FastVideoAPI:
+
+    def __init__(self, t2v_deployments: Dict[str, DeploymentHandle]):
+        self.t2v_deployments = t2v_deployments
+        
+        # Initialize Prometheus metrics
+        self.request_count = Counter('fastvideo_requests_total', 'Total FastVideo requests', ['model_type', 'status'])
+        self.request_duration = Histogram('fastvideo_request_duration_seconds', 'FastVideo request duration', ['model_type'])
+        self.video_generation_time = Histogram('fastvideo_video_generation_seconds', 'Video generation time', ['model_type'])
+    
+    def _get_model_name(self, model_path: Optional[str]) -> str:
+        return model_path.split('/')[-1] if model_path else "unknown"
+    
+    def _record_metrics(self, model_name: str, status: str, duration: float, response: Optional[VideoGenerationResponse] = None) -> None:
+        self.request_count.labels(model_type=model_name, status=status).inc()
+        self.request_duration.labels(model_type=model_name).observe(duration)
+        
+        if response and hasattr(response, 'generation_time') and response.generation_time:
+            self.video_generation_time.labels(model_type=model_name).observe(response.generation_time)
+    
+    @app.post("/generate_video", response_model=VideoGenerationResponse)
+    @limiter.limit("10/minute")
+    async def generate_video(self, request: Request, video_request: VideoGenerationRequest) -> VideoGenerationResponse:
+        """Route the request to the appropriate model deployment based on model_path."""
+        start_time = time.time()
+        model_name = self._get_model_name(video_request.model_path)
+        
+        try:
+            if video_request.model_path not in self.t2v_deployments:
+                raise ValueError(f"Model {video_request.model_path} not found")
+            
+            response_ref = self.t2v_deployments[video_request.model_path].generate_video.remote(video_request)
+            response = await response_ref
+            
+            self._record_metrics(model_name, "success", time.time() - start_time, response)
+            return response
+
+        except Exception as e:
+            self._record_metrics(model_name, "error", time.time() - start_time)
+            
+            return VideoGenerationResponse(
+                video_data=None,
+                seed=video_request.seed,
+                success=False,
+                error_message=str(e),
+                generation_time=0,
+                inference_time=0,
+                encoding_time=0,
+                total_time=0,
+            )
+    
+    @app.get("/health")
+    @limiter.limit("10/minute")
+    async def health_check(self, request: Request) -> Dict[str, str]:
+        return {"status": "healthy"}
+    
+    @app.get("/metrics")
+    async def metrics(self) -> Response:
+        return Response(generate_latest(), media_type="text/plain")
+
+
+def validate_configuration(model_paths: List[str], replicas: List[int]) -> None:
+    assert len(model_paths) == len(replicas), "Number of models and replicas must match"
+    assert sum(replicas) <= NUM_GPUS, f"Total replicas ({sum(replicas)}) must be <= {NUM_GPUS}"
+    
+    for model, replica_count in zip(model_paths, replicas):
+        assert model in SUPPORTED_MODELS, f"Model {model} not supported"
+        assert replica_count > 0, f"Replicas must be greater than 0"
+
+
+def start_ray_serve(
+    *,
+    t2v_model_paths: str,
+    t2v_model_replicas: str,
+    output_path: str = "outputs",
+    host: str = "0.0.0.0",
+    port: int = 8000,
+) -> None:
+    if not ray.is_initialized():
+        ray.init()
+
+    model_paths = t2v_model_paths.split(",")
+    replicas = [int(r) for r in t2v_model_replicas.split(",")]
+    validate_configuration(model_paths, replicas)
+
+    t2v_deps = {}
+    for model_path, replica_count in zip(model_paths, replicas):
+        t2v_dep = T2VModelDeployment.options(num_replicas=replica_count).bind(model_path, output_path)
+        t2v_deps[model_path] = t2v_dep
+
+    api = FastVideoAPI.bind(t2v_deps)
+    serve.run(api, route_prefix="/", name="fast_video")
+
+    print(f"Ray Serve backend started at http://{host}:{port}")
+    for model_path, replica_count in zip(model_paths, replicas):
+        print(f"T2V Model: {model_path} | Replicas: {replica_count}")
+    print(f"Health check: http://{host}:{port}/health")
+    print(f"Video generation endpoint: http://{host}:{port}/generate_video")
+
+
+def setup_signal_handlers() -> None:
+    signal.signal(signal.SIGINT, lambda *_: sys.exit(0))
+    signal.signal(signal.SIGTERM, lambda *_: sys.exit(0))
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="FastVideo Ray Serve Backend")
+    parser.add_argument("--t2v_model_paths",
+                        type=str,
+                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
+                        help="Comma separated list of paths to the T2V model(s)")
+    parser.add_argument("--t2v_model_replicas",
+                        type=str,
+                        default="4,4",
+                        help="Comma separated list of number of replicas for the T2V model(s)")
+    parser.add_argument("--output_path",
+                        type=str,
+                        default="outputs",
+                        help="Path to save generated videos")
+    parser.add_argument("--host",
+                        type=str,
+                        default="0.0.0.0",
+                        help="Host to bind to")
+    parser.add_argument("--port",
+                        type=int,
+                        default=8000,
+                        help="Port to bind to")
+    
+    args = parser.parse_args()
+
+    model_paths = args.t2v_model_paths.split(",")
+    replicas = [int(r) for r in args.t2v_model_replicas.split(",")]
+    validate_configuration(model_paths, replicas)
+    
+    start_ray_serve(
+        t2v_model_paths=args.t2v_model_paths,
+        t2v_model_replicas=args.t2v_model_replicas,
+        output_path=args.output_path,
+        host=args.host,
+        port=args.port,
+    )
+
+    setup_signal_handlers()
+    print("✅ FastVideo backend is running. Press Ctrl-C to stop.")
+    while True:
+        time.sleep(3600) 
@@ -0,0 +1,3 @@
+python examples/inference/gradio/start_ray_serve_app.py \
+--t2v_model_paths "FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers" \
+--t2v_model_replicas "4,4"
@@ -0,0 +1,257 @@
+"""
+Startup script for FastVideo with Ray Serve backend and Gradio frontend.
+This script starts both the backend and frontend services.
+"""
+
+import argparse
+import os
+import subprocess
+import sys
+import time
+import threading
+import signal
+import requests
+from pathlib import Path
+from typing import Dict, Any, Optional
+
+
+DEFAULT_BACKEND_HOST = "0.0.0.0"
+DEFAULT_BACKEND_PORT = 8000
+DEFAULT_FRONTEND_HOST = "0.0.0.0"
+DEFAULT_FRONTEND_PORT = 7860
+DEFAULT_OUTPUT_PATH = "outputs"
+DEFAULT_T2V_MODELS = "FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers"
+DEFAULT_T2V_REPLICAS = "4,4"
+
+HEALTH_CHECK_TIMEOUT = 5
+HEALTH_CHECK_MAX_RETRIES = 100
+HEALTH_CHECK_INTERVAL = 2
+PROCESS_SHUTDOWN_TIMEOUT = 5
+PROCESS_MONITOR_INTERVAL = 1
+
+PROJECT_ROOT = Path(__file__).parent.parent.parent.parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+
+class ServiceManager:
+    
+    def __init__(self, args: argparse.Namespace):
+        self.args = args
+        self.backend_process: Optional[subprocess.Popen] = None
+        self.frontend_process: Optional[subprocess.Popen] = None
+        self.backend_url = f"http://{args.backend_host}:{args.backend_port}"
+    
+    def check_backend_health(self, max_retries: int = HEALTH_CHECK_MAX_RETRIES) -> bool:
+        health_url = f"{self.backend_url}/health"
+        
+        for attempt in range(max_retries):
+            try:
+                response = requests.get(health_url, timeout=HEALTH_CHECK_TIMEOUT)
+                if response.status_code == 200:
+                    print(f"✅ Backend is healthy at {self.backend_url}")
+                    return True
+            except requests.exceptions.RequestException:
+                pass
+            
+            if attempt < max_retries - 1:
+                print(f"⏳ Waiting for backend to start... ({attempt + 1}/{max_retries})")
+                time.sleep(HEALTH_CHECK_INTERVAL)
+        
+        print(f"❌ Backend failed to start within {max_retries * HEALTH_CHECK_INTERVAL} seconds")
+        return False
+    
+    def _create_monitor_thread(self, process: subprocess.Popen, service_name: str) -> threading.Thread:
+        def monitor():
+            if process.stdout:
+                for line in process.stdout:
+                    print(f"[{service_name}] {line.rstrip()}")
+        
+        thread = threading.Thread(target=monitor, daemon=True)
+        thread.start()
+        return thread
+    
+    def _start_service(self, script_name: str, args_dict: Dict[str, Any], service_name: str) -> subprocess.Popen:
+        script_path = Path(__file__).parent / script_name
+        
+        cmd = [sys.executable, str(script_path)]
+        for key, value in args_dict.items():
+            cmd.extend([f"--{key}", str(value)])
+        
+        print(f"🚀 Starting {service_name}...")
+        print(f"Command: {' '.join(cmd)}")
+        
+        process = subprocess.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            universal_newlines=True,
+            bufsize=1
+        )
+        
+        self._create_monitor_thread(process, service_name.upper())
+        return process
+    
+    def start_backend(self) -> subprocess.Popen:
+        backend_args = {
+            "t2v_model_paths": self.args.t2v_model_paths,
+            "t2v_model_replicas": self.args.t2v_model_replicas,
+            "output_path": self.args.output_path,
+            "host": self.args.backend_host,
+            "port": self.args.backend_port
+        }
+        
+        self.backend_process = self._start_service("ray_serve_backend.py", backend_args, "backend")
+        return self.backend_process
+    
+    def start_frontend(self) -> subprocess.Popen:
+        frontend_args = {
+            "backend_url": self.backend_url,
+            "t2v_model_paths": self.args.t2v_model_paths,
+            "host": self.args.frontend_host,
+            "port": self.args.frontend_port
+        }
+        
+        self.frontend_process = self._start_service("gradio_frontend.py", frontend_args, "frontend")
+        return self.frontend_process
+    
+    def shutdown_services(self) -> None:
+        print("\n🛑 Shutting down services...")
+        
+        processes = []
+        if self.frontend_process:
+            self.frontend_process.terminate()
+            processes.append(("frontend", self.frontend_process))
+        
+        if self.backend_process:
+            self.backend_process.terminate()
+            processes.append(("backend", self.backend_process))
+        
+        for name, process in processes:
+            try:
+                process.wait(timeout=PROCESS_SHUTDOWN_TIMEOUT)
+                print(f"✅ {name.capitalize()} stopped gracefully")
+            except subprocess.TimeoutExpired:
+                print(f"⚠️  Force killing {name} process...")
+                process.kill()
+        
+        print("✅ All services stopped")
+    
+    def monitor_processes(self) -> None:
+        if not self.backend_process or not self.frontend_process:
+            print("❌ Processes not properly initialized")
+            return
+        
+        try:
+            while True:
+                if self.frontend_process.poll() is not None:
+                    print("❌ Frontend process died unexpectedly")
+                    break
+                
+                if self.backend_process.poll() is not None:
+                    print("❌ Backend process died unexpectedly")
+                    break
+                
+                time.sleep(PROCESS_MONITOR_INTERVAL)
+        
+        except KeyboardInterrupt:
+            pass
+        
+        self.shutdown_services()
+
+
+def setup_signal_handlers(service_manager: ServiceManager) -> None:
+    def signal_handler(signum: int, frame: Any) -> None:
+        service_manager.shutdown_services()
+        sys.exit(0)
+    
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+
+
+def print_startup_info(args: argparse.Namespace) -> None:
+    print("🎬 FastVideo Ray Serve App")
+    print("=" * 50)
+    print(f"T2V Models: {args.t2v_model_paths}")
+    print(f"T2V Model Replicas: {args.t2v_model_replicas}")
+    print(f"Output: {args.output_path}")
+    print(f"Backend: http://{args.backend_host}:{args.backend_port}")
+    print(f"Frontend: http://{args.frontend_host}:{args.frontend_port}")
+    print("=" * 50)
+
+
+def parse_arguments() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="FastVideo Ray Serve App")
+    
+    parser.add_argument("--t2v_model_paths", 
+                        type=str,
+                        default=DEFAULT_T2V_MODELS,
+                        help="Comma separated list of paths to the T2V model(s)")
+    parser.add_argument("--t2v_model_replicas", 
+                        type=str, 
+                        default=DEFAULT_T2V_REPLICAS,
+                        help="Comma separated list of number of replicas for the T2V model(s)")
+    parser.add_argument("--output_path", 
+                        type=str, 
+                        default=DEFAULT_OUTPUT_PATH,
+                        help="Path to save generated videos")
+    
+    parser.add_argument("--backend_host", 
+                        type=str, 
+                        default=DEFAULT_BACKEND_HOST,
+                        help="Backend host to bind to")
+    parser.add_argument("--backend_port", 
+                        type=int, 
+                        default=DEFAULT_BACKEND_PORT,
+                        help="Backend port to bind to")
+    
+    parser.add_argument("--frontend_host", 
+                        type=str, 
+                        default=DEFAULT_FRONTEND_HOST,
+                        help="Frontend host to bind to")
+    parser.add_argument("--frontend_port", 
+                        type=int, 
+                        default=DEFAULT_FRONTEND_PORT,
+                        help="Frontend port to bind to")
+    
+    parser.add_argument("--skip_backend_check", 
+                        action="store_true",
+                        help="Skip backend health check")
+    
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_arguments()
+    
+    os.makedirs(args.output_path, exist_ok=True)
+    print_startup_info(args)
+    
+    service_manager = ServiceManager(args)
+    setup_signal_handlers(service_manager)
+    
+    try:
+        service_manager.start_backend()
+        
+        if not args.skip_backend_check:
+            if not service_manager.check_backend_health():
+                print("❌ Backend failed to start. Terminating...")
+                service_manager.shutdown_services()
+                sys.exit(1)
+        
+        service_manager.start_frontend()
+        
+        print("\n🎉 Both services are starting up!")
+        print(f"📺 Frontend will be available at: http://{args.frontend_host}:{args.frontend_port}")
+        print(f"🔧 Backend API will be available at: http://{args.backend_host}:{args.backend_port}")
+        print("\nPress Ctrl+C to stop both services...")
+        
+        service_manager.monitor_processes()
+    
+    except Exception as e:
+        print(f"❌ Unexpected error: {e}")
+        service_manager.shutdown_services()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
@@ -10,7 +10,7 @@ def main():
        dit_cpu_offload=False,
        vae_cpu_offload=False,
        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
+        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
        lora_path="benjamin-paine/steamboat-willie-1.3b",
        lora_nickname="steamboat"
    )
@@ -18,7 +18,7 @@ def main():
        "height": 480,
        "width": 832,
        "num_frames": 81,
-        "guidance_scale": 5.0,
+        "guidance_scale": 6.0,
        "num_inference_steps": 32,
        "seed": 42,
    }
@@ -11,22 +11,23 @@ def main():
        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
        num_gpus=1,
        dit_cpu_offload=False,
-        vae_cpu_offload=False,
+        vae_cpu_offload=True,
        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        lora_path="checkpoints/wan_t2v_finetune_lora/checkpoint-1250/transformer",
+        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument" 
+        lora_path="checkpoints/wan_t2v_finetune_lora/checkpoint-160/transformer",
        lora_nickname="crush_smol"
    )
+    generator.unmerge_lora_weights()
    kwargs = {
        "height": 480,
        "width": 832,
        "num_frames": 77,
-        "guidance_scale": 5.0,
+        "guidance_scale": 6.0,
        "num_inference_steps": 50,
        "seed": 42,
    }
    # Generate video with LoRA style
-    prompt = "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table."
+    prompt = "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press."

    video = generator.generate_video(
        prompt,
@@ -34,6 +35,12 @@ def main():
        save_video=True,
        **kwargs
    )    
-
+    prompt = "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press."
+    video = generator.generate_video(
+        prompt,
+        output_path=OUTPUT_PATH,
+        save_video=True,
+        **kwargs
+    )
 if __name__ == "__main__":
    main()
@@ -14,7 +14,7 @@ def main():
        dit_cpu_offload=False,
        vae_cpu_offload=False,
        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
+        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
    )
    load_time = time.perf_counter() - start_time
    print(f"Model loading time: {load_time:.2f} seconds")
@@ -12,7 +12,7 @@ def main():
        dit_cpu_offload=False,
        vae_cpu_offload=False,
        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
+        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
    )
    load_time = time.perf_counter() - start_time
    print(f"Model loading time: {load_time:.2f} seconds")
@@ -54,7 +54,7 @@ validation_args=(
  --validation_dataset_file "$VALIDATION_DATASET_FILE"
  --validation_steps 100
  --validation_sampling_steps "40"
-  --validation_guidance_scale "1.0"
+  --validation_guidance_scale "6.0"
 )

 # Optimizer arguments
@@ -69,7 +69,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --multi_phased_distill_schedule "4000-1"
@@ -88,7 +88,7 @@ validation_args=(
  --validation_dataset_file "$VALIDATION_DATASET_FILE"
  --validation_steps 100
  --validation_sampling_steps "40"
-  --validation_guidance_scale "1.0"
+  --validation_guidance_scale "6.0"
 )

 # Optimizer arguments
@@ -103,7 +103,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --multi_phased_distill_schedule "4000-1"
@@ -101,7 +101,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --dit_precision "fp32"
@@ -7,9 +7,7 @@ These are e2e example scripts for finetuning Wan2.1 T2V with VSA to accelerate i
 ## Make sure you have installed VSA

 ```bash
-cd csrc/attn
-git submodule update --init --recursive
-python setup_vsa.py install
+pip install vsa
 ```

 ### Download the synthetic dataset:
@@ -101,7 +101,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --dit_precision "fp32"
@@ -54,7 +54,7 @@ validation_args=(
  --validation_dataset_file "$VALIDATION_DATASET_FILE"
  --validation_steps 100
  --validation_sampling_steps "40"
-  --validation_guidance_scale "1.0"
+  --validation_guidance_scale "6.0"
 )

 # Optimizer arguments
@@ -69,7 +69,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --multi_phased_distill_schedule "4000-1"
@@ -88,7 +88,7 @@ validation_args=(
  --validation_dataset_file "$VALIDATION_DATASET_FILE"
  --validation_steps 100
  --validation_sampling_steps "40"
-  --validation_guidance_scale "1.0"
+  --validation_guidance_scale "6.0"
 )

 # Optimizer arguments
@@ -103,7 +103,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --multi_phased_distill_schedule "4000-1"
@@ -54,7 +54,7 @@ validation_args=(
  --validation_preprocessed_path "$VALIDATION_DIR"
  --validation_steps 100
  --validation_sampling_steps "40"
-  --validation_guidance_scale "1.0"
+  --validation_guidance_scale "6.0"
 )

 # Optimizer arguments
@@ -69,7 +69,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --multi_phased_distill_schedule "4000-1"
@@ -0,0 +1,24 @@
+#!/bin/bash
+
+GPU_NUM=2 # 2,4,8
+MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
+DATASET_PATH="data/crush-smol/"
+OUTPUT_DIR="data/crush-smol_processed_i2v/"
+
+torchrun --nproc_per_node=$GPU_NUM \
+    -m fastvideo.pipelines.preprocess.v1_preprocessing_new \
+    --model_path $MODEL_PATH \
+    --mode preprocess \
+    --workload_type i2v \
+    --preprocess.dataset_type merged \
+    --preprocess.dataset_path $DATASET_PATH \
+    --preprocess.dataset_output_dir $OUTPUT_DIR \
+    --preprocess.preprocess_video_batch_size 2 \
+    --preprocess.dataloader_num_workers 0 \
+    --preprocess.max_height 480 \
+    --preprocess.max_width 832 \
+    --preprocess.num_frames 77 \
+    --preprocess.train_fps 16 \
+    --preprocess.samples_per_file 8 \
+    --preprocess.flush_frequency 8 \
+    --preprocess.video_length_tolerance_range 5
@@ -52,9 +52,9 @@ dataset_args=(
 validation_args=(
  --log_validation 
  --validation_dataset_file $VALIDATION_DATASET_FILE
-  --validation_steps 50
+  --validation_steps 200
  --validation_sampling_steps "50" 
-  --validation_guidance_scale "1.0"
+  --validation_guidance_scale "6.0"
 )

 # Optimizer arguments
@@ -69,7 +69,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --multi_phased_distill_schedule "4000-1"
@@ -78,6 +77,7 @@ miscellaneous_args=(
  --num_euler_timesteps 50
  --ema_start_step 0
  --enable_gradient_checkpointing_type "full"
+  # --resume_from_checkpoint "checkpoints/wan_t2v_finetune/checkpoint-2500"
 )

 torchrun \
@@ -85,14 +85,14 @@ validation_args=(
  --validation_dataset_file "$VALIDATION_DATASET_FILE"
  --validation_steps 100
  --validation_sampling_steps "50"
-  --validation_guidance_scale "1.0"
+  --validation_guidance_scale "6.0"
 )

 # Optimizer arguments
 optimizer_args=(
  --learning_rate 5e-5
  --mixed_precision "bf16"
-  --checkpointing_steps 500
+  --checkpointing_steps 400
  --weight_decay 1e-4
  --max_grad_norm 1.0
 )
@@ -100,7 +100,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --multi_phased_distill_schedule "4000-1"
@@ -7,7 +7,7 @@ export WANDB_MODE=online
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 DATA_DIR="data/crush-smol_processed_t2v/combined_parquet_dataset/"
 VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
-NUM_GPUS=2
+NUM_GPUS=1
 # export CUDA_VISIBLE_DEVICES=4,5


@@ -52,16 +52,16 @@ dataset_args=(
 validation_args=(
  --log_validation 
  --validation_dataset_file $VALIDATION_DATASET_FILE
-  --validation_steps 50
+  --validation_steps 200
  --validation_sampling_steps "50" 
-  --validation_guidance_scale "1.0"
+  --validation_guidance_scale "6.0"
 )

 # Optimizer arguments
 optimizer_args=(
  --learning_rate 5e-5
  --mixed_precision "bf16"
-  --checkpointing_steps 500
+  --checkpointing_steps 400
  --weight_decay 1e-4
  --max_grad_norm 1.0
 )
@@ -69,7 +69,6 @@ optimizer_args=(
 # Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
-  --allow_tf32
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --multi_phased_distill_schedule "4000-1"
@@ -77,6 +76,7 @@ miscellaneous_args=(
  --dit_precision "fp32"
  --num_euler_timesteps 50
  --ema_start_step 0
+  --resume_from_checkpoint "checkpoints/wan_t2v_finetune_lora/checkpoint-160"
 )

 torchrun \
@@ -0,0 +1,25 @@
+#!/bin/bash
+
+GPU_NUM=2 # 2,4,8
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATASET_PATH="data/crush-smol/"
+OUTPUT_DIR="data/crush-smol_processed_t2v/"
+
+torchrun --nproc_per_node=$GPU_NUM \
+    -m fastvideo.pipelines.preprocess.v1_preprocessing_new \
+    --model_path $MODEL_PATH \
+    --mode preprocess \
+    --workload_type t2v \
+    --preprocess.video_loader_type torchvision \
+    --preprocess.dataset_type merged \
+    --preprocess.dataset_path $DATASET_PATH \
+    --preprocess.dataset_output_dir $OUTPUT_DIR \
+    --preprocess.preprocess_video_batch_size 2 \
+    --preprocess.dataloader_num_workers 0 \
+    --preprocess.max_height 480 \
+    --preprocess.max_width 832 \
+    --preprocess.num_frames 77 \
+    --preprocess.train_fps 16 \
+    --preprocess.samples_per_file 8 \
+    --preprocess.flush_frequency 8 \
+    --preprocess.video_length_tolerance_range 5
@@ -1,9 +1,59 @@
 import dataclasses
+from enum import Enum
 from typing import Any, Optional

 from fastvideo.configs.utils import update_config_from_args
+from fastvideo.logger import init_logger
 from fastvideo.utils import FlexibleArgumentParser, StoreBoolean

+logger = init_logger(__name__)
+
+
+class DatasetType(str, Enum):
+    """
+    Enumeration for different dataset types.
+    """
+    HF = "hf"
+    MERGED = "merged"
+
+    @classmethod
+    def from_string(cls, value: str) -> "DatasetType":
+        """Convert string to DatasetType enum."""
+        try:
+            return cls(value.lower())
+        except ValueError:
+            raise ValueError(
+                f"Invalid dataset type: {value}. Must be one of: {', '.join([m.value for m in cls])}"
+            ) from None
+
+    @classmethod
+    def choices(cls) -> list[str]:
+        """Get all available choices as strings for argparse."""
+        return [dataset_type.value for dataset_type in cls]
+
+
+class VideoLoaderType(str, Enum):
+    """
+    Enumeration for different video loaders.
+    """
+    TORCHCODEC = "torchcodec"
+    TORCHVISION = "torchvision"
+
+    @classmethod
+    def from_string(cls, value: str) -> "VideoLoaderType":
+        """Convert string to VideoLoader enum."""
+        try:
+            return cls(value.lower())
+        except ValueError:
+            raise ValueError(
+                f"Invalid video loader: {value}. Must be one of: {', '.join([m.value for m in cls])}"
+            ) from None
+
+    @classmethod
+    def choices(cls) -> list[str]:
+        """Get all available choices as strings for argparse."""
+        return [video_loader.value for video_loader in cls]
+

@dataclasses.dataclass
 class PreprocessConfig:
@@ -12,6 +62,7 @@ class PreprocessConfig:
    # Model and dataset configuration
    model_path: str = ""
    dataset_path: str = ""
+    dataset_type: DatasetType = DatasetType.HF
    dataset_output_dir: str = "./output"

    # Dataloader configuration
@@ -23,6 +74,7 @@ class PreprocessConfig:
    flush_frequency: int = 256

    # Video processing parameters
+    video_loader_type: VideoLoaderType = VideoLoaderType.TORCHCODEC
    max_height: int = 480
    max_width: int = 848
    num_frames: int = 163
@@ -35,6 +87,9 @@ class PreprocessConfig:
    # Model configuration
    training_cfg_rate: float = 0.0

+    # framework configuration
+    seed: int = 42
+
    @staticmethod
    def add_cli_args(parser: FlexibleArgumentParser,
                     prefix: str = "preprocess") -> FlexibleArgumentParser:
@@ -52,6 +107,12 @@ class PreprocessConfig:
            type=str,
            default=PreprocessConfig.dataset_path,
            help="Path to the dataset directory for preprocessing")
+        preprocess_args.add_argument(
+            f"--{prefix_with_dot}dataset-type",
+            type=str,
+            choices=DatasetType.choices(),
+            default=PreprocessConfig.dataset_type.value,
+            help="Type of the dataset")
        preprocess_args.add_argument(
            f"--{prefix_with_dot}dataset-output-dir",
            type=str,
@@ -83,6 +144,12 @@ class PreprocessConfig:
                                     help="How often to save to parquet files")

        # Video processing parameters
+        preprocess_args.add_argument(
+            f"--{prefix_with_dot}video-loader-type",
+            type=str,
+            choices=VideoLoaderType.choices(),
+            default=PreprocessConfig.video_loader_type.value,
+            help="Type of the video loader")
        preprocess_args.add_argument(f"--{prefix_with_dot}max-height",
                                     type=int,
                                     default=PreprocessConfig.max_height,
@@ -123,6 +190,10 @@ class PreprocessConfig:
                                     type=float,
                                     default=PreprocessConfig.training_cfg_rate,
                                     help="Training CFG rate")
+        preprocess_args.add_argument(f"--{prefix_with_dot}seed",
+                                     type=int,
+                                     default=PreprocessConfig.seed,
+                                     help="Seed for random number generator")

        return parser

@@ -130,6 +201,14 @@ class PreprocessConfig:
    def from_kwargs(cls, kwargs: dict[str,
                                      Any]) -> Optional["PreprocessConfig"]:
        """Create PreprocessConfig from keyword arguments."""
+        if 'dataset_type' in kwargs and isinstance(kwargs['dataset_type'], str):
+            kwargs['dataset_type'] = DatasetType.from_string(
+                kwargs['dataset_type'])
+        if 'video_loader_type' in kwargs and isinstance(
+                kwargs['video_loader_type'], str):
+            kwargs['video_loader_type'] = VideoLoaderType.from_string(
+                kwargs['video_loader_type'])
+
        preprocess_config = cls()
        if not update_config_from_args(
                preprocess_config, kwargs, prefix="preprocess", pop_args=True):
@@ -92,11 +92,17 @@ class WanVideoArchConfig(DiTArchConfig):
    pos_embed_seq_len: int | None = None
    exclude_lora_layers: list[str] = field(default_factory=lambda: ["embedder"])

+    # Causal Wan
+    local_attn_size: int = -1  # Window size for temporal local attention (-1 indicates global attention)
+    sink_size: int = 0  # Size of the attention sink, we keep the first `sink_size` frames unchanged when rolling the KV cache
+    num_frames_per_block: int = 3
+    sliding_window_num_frames: int = 21
+
    def __post_init__(self):
        super().__post_init__()
        self.out_channels = self.out_channels or self.in_channels
        self.hidden_size = self.num_attention_heads * self.attention_head_dim
-        self.num_channels_latents = self.in_channels if self.added_kv_proj_dim is None else self.out_channels
+        self.num_channels_latents = self.out_channels


@dataclass
@@ -85,6 +85,9 @@ class PipelineConfig:
    # DMD parameters
    dmd_denoising_steps: list[int] | None = field(default=None)

+    # Wan2.2 TI2V parameters
+    ti2v_task: bool = False
+
    # Compilation
    # enable_torch_compile: bool = False

@@ -7,10 +7,14 @@ from collections.abc import Callable
 from fastvideo.configs.pipelines.base import PipelineConfig
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
-from fastvideo.configs.pipelines.wan import (FastWan2_1_T2V_480P_Config,
-                                             FastWan2_2_TI2V_5B_Config,
-                                             WanI2V480PConfig, WanI2V720PConfig,
-                                             WanT2V480PConfig, WanT2V720PConfig)
+
+# isort: off
+from fastvideo.configs.pipelines.wan import (
+    FastWan2_1_T2V_480P_Config, FastWan2_2_TI2V_5B_Config,
+    SelfForcingWanT2V480PConfig, Wan2_2_I2V_A14B_Config, Wan2_2_T2V_A14B_Config,
+    Wan2_2_TI2V_5B_Config, WanI2V480PConfig, WanI2V720PConfig, WanT2V480PConfig,
+    WanT2V720PConfig)
+# isort: on
 from fastvideo.logger import init_logger
 from fastvideo.utils import (maybe_download_model_index,
                             verify_model_config_and_directory)
@@ -31,7 +35,10 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": FastWan2_2_TI2V_5B_Config,
    "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VConfig,
    "FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers": WanT2V720PConfig,
-    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": WanT2V720PConfig
+    "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
+    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_Config,
+    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_Config,
+    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_Config,
    # Add other specific weight variants
 }

@@ -138,3 +138,13 @@ class Wan2_2_T2V_A14B_Config(WanT2V480PConfig):
@dataclass
 class Wan2_2_I2V_A14B_Config(WanT2V480PConfig):
    pass
+
+
+# =============================================
+# ============= Causal Self-Forcing =============
+# =============================================
+@dataclass
+class SelfForcingWanT2V480PConfig(WanT2V480PConfig):
+    is_causal: bool = True
+    dmd_denoising_steps: list[int] | None = field(
+        default_factory=lambda: [1000, 750, 500, 250])
@@ -20,7 +20,7 @@ class SamplingParam:

    # Text inputs
    prompt: str | list[str] | None = None
-    negative_prompt: str | None = None
+    negative_prompt: str = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
    prompt_path: str | None = None
    output_path: str = "outputs/"
    output_video_name: str | None = None
@@ -6,12 +6,21 @@ from typing import Any
 from fastvideo.configs.sample.hunyuan import (FastHunyuanSamplingParam,
                                              HunyuanSamplingParam)
 from fastvideo.configs.sample.stepvideo import StepVideoT2VSamplingParam
-from fastvideo.configs.sample.wan import (FastWanT2V480PConfig,
-                                          Wan2_2_TI2V_5B_SamplingParam,
-                                          WanI2V_14B_480P_SamplingParam,
-                                          WanI2V_14B_720P_SamplingParam,
-                                          WanT2V_1_3B_SamplingParam,
-                                          WanT2V_14B_SamplingParam)
+
+# isort: off
+from fastvideo.configs.sample.wan import (
+    FastWanT2V480PConfig,
+    Wan2_1_Fun_1_3B_InP_SamplingParam,
+    Wan2_2_I2V_A14B_SamplingParam,
+    Wan2_2_T2V_A14B_SamplingParam,
+    Wan2_2_TI2V_5B_SamplingParam,
+    WanI2V_14B_480P_SamplingParam,
+    WanI2V_14B_720P_SamplingParam,
+    WanT2V_1_3B_SamplingParam,
+    WanT2V_14B_SamplingParam,
+    SelfForcingWanT2V480PConfig,
+)
+# isort: on
 from fastvideo.logger import init_logger
 from fastvideo.utils import (maybe_download_model_index,
                             verify_model_config_and_directory)
@@ -21,18 +30,31 @@ logger = init_logger(__name__)
 SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
    "FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
    "hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParam,
+    "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
+
+    # Wan2.1
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V_1_3B_SamplingParam,
    "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V_14B_SamplingParam,
    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V_14B_480P_SamplingParam,
    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V_14B_720P_SamplingParam,
-    "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
-    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480PConfig,
+    "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers":
+    Wan2_1_Fun_1_3B_InP_SamplingParam,
+
+    # Wan2.2
    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
+    "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers":
+    Wan2_2_TI2V_5B_SamplingParam,
+    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_SamplingParam,
+    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_SamplingParam,
+
+    # FastWan2.1
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480PConfig,
+
+    # FastWan2.2
    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
-    # "Wan-AI/Wan2.2-T2V-A14B-Diffusers":
-    # Wan2_2_T2V_A14B_SamplingParam,
-    # "Wan-AI/Wan2.2-I2V-A14B-Diffusers":
-    # Wan2_2_I2V_A14B_SamplingParam,
+
+    # Causal Self-Forcing Wan2.1
+    "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
    # Add other specific weight variants
 }

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
RandNMR73	da5ca94091	preprocessing text	2025-09-06 05:56:13 +00:00
William Lin	d3ceb67e66	[misc] Update Slack invite link (#786 )	2025-09-05 12:16:18 -07:00
Zhang Peiyuan	7ac153a5ca	Update WeChat Link	2025-09-05 11:40:47 -07:00
William Lin	d1e7aa0abd	[CI] Add ssim test for causal inference (#784 )	2025-09-05 01:23:01 -07:00
William Lin	2d846c55a1	[misc] Improve text encoding stage (#774 )	2025-09-04 17:51:27 -07:00
Jinzhe Pan	b318063c0a	[Preprocess][Fix] video quality issue (#773 )	2025-09-03 20:47:33 -07:00
Jinzhe Pan	4aa307be55	[Preprocess][Feat] support torchvision to load video in new preprocessing (#761 )	2025-09-01 23:37:01 -07:00
William Lin	055e52e5ea	[misc] [VSA] [STA] fix tk_root in setup.py for VSA and STA (#772 )	2025-08-29 01:13:37 -07:00
William Lin	7d2069596b	[bugfix] [VSA] [STA] Fix MANIFEST.in for VSA and STA; Move tk into both directories (#771 )	2025-08-29 00:51:05 -07:00
William Lin	c45009c9a4	[bugfix] fix STA install setup.py import (#770 )	2025-08-28 23:02:53 -07:00
William LinandPeiyuan Zhang	b91020b407	[VSA] [STA] Fix directory structure for pypi publishing (#769 ) Co-authored-by: Peiyuan Zhang <a1286225768@gmail.com>	2025-08-28 22:34:03 -07:00
William Lin	2dcc5ea4f6	[chore] Release 0.1.6 (#768 )	2025-08-28 20:56:21 -07:00
Wei ZhouandSolitaryThinker	359151d9a0	[Feature] Add wan2.2 5b i2v (#760 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2025-08-28 18:15:59 -07:00
Wei ZhouandSolitaryThinker	ce67cd3729	[Feat] Support Self-Forcing's Causal Inference for Wan2.1 T2V 1.3B (#766 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2025-08-28 16:47:49 -07:00
Zhang Peiyuan	7c554e5da8	Update Community Link (#765 )	2025-08-27 16:12:47 -07:00
William Lin	663ea33ff1	[bugfix] Fix wrong HF model string for FastWan2.2 5B (#763 )	2025-08-26 22:05:40 -07:00
William Lin	3ef04f1654	[misc] [docs] Various fixes for logging and docs (#758 )	2025-08-23 21:13:50 -07:00
Jinzhe Pan	0eced76a41	[Feat][Preprocess] support multi-gpus (#753 )	2025-08-23 11:34:42 +08:00
Jinzhe Pan	3ab6470d1a	[Feat][Preprocess] support merged dataset (#752 )	2025-08-22 15:29:33 -07:00
Wenxuan Tan	989a03532c	Optionally use unmerged weights for inference (#745 )	2025-08-22 15:20:31 -07:00
William Lin	fa15369a02	[bugfix] Check that model_index.json module is in required_modules list before removing (#756 )	2025-08-22 14:36:44 -07:00
Zhang Peiyuan	78a9cb88d8	[Fix] fix seed in dmd denoising loop (#736 )	2025-08-21 18:06:16 -07:00
Peng Xiaoand肖鹏	a0bff12746	[bugfix] [dmd] Align backward simulation with dmd2 sample back (#744 ) Co-authored-by: 肖鹏 <xiaopeng1@aishi.ai>	2025-08-20 22:25:33 -07:00
William Lin	98f2af94e5	[bugfix] Missing Docker file for cuda12.9 (#750 )	2025-08-20 15:34:31 -07:00
William Lin	46f7b6d574	[Docker] add 12.9 docker image and also fix py3.10 and py3.11 dockerfile (#749 )	2025-08-20 15:31:15 -07:00
Jinzhe Pan	911a6a6a35	[Feat][Preprocessing] i2v preprocessing workflow (#737 )	2025-08-14 20:47:25 -07:00
Zhang Peiyuan	38c7949d5c	Update WeChat group link (#739 )	2025-08-14 15:03:35 -07:00
Jinzhe Pan	7e7a0dba9d	feat: preprocess validation dataset only when exist (#734 )	2025-08-12 02:16:31 -07:00
Zhang Peiyuan	f62e210ae6	Fix vsa backward gQ (#735 )	2025-08-11 21:43:13 -07:00
William Lin	6ceb4942a0	[bugfix] [dmd] Fix backward simulation and also naming in wan_i2v_dmd_pipeline (#731 )	2025-08-10 21:13:30 -07:00
William LinandRandNMR73	8cae5e4708	[feature] add Gradio live serving demo code (#727 ) Co-authored-by: RandNMR73 <notomatthew31@gmail.com>	2025-08-10 15:34:03 -07:00
William Lin	2a773fa34e	[bugfix] [distill] remove i2v validation schema import in distill (#728 )	2025-08-09 20:47:42 -07:00
Wenxuan Tan	5357f63327	Fix LoRA load from training checkpoint (#719 )	2025-08-09 20:46:00 -05:00
William Lin	60f61c8101	[bugfix] fix pyproject install and VSA precision test (#726 )	2025-08-08 18:45:03 -07:00
Jiali Chen	3d75ba8251	update version selection for VSA workflow (#725 )	2025-08-08 13:05:16 -07:00
Wenxuan Tan	6c6bcd914d	Remove all empty_cache (#713 )	2025-08-07 22:50:38 -07:00
Jiali Chen	f79b08de81	add cicd workflow for publishing VSA kernel (#723 )	2025-08-07 18:53:05 -07:00
Jinzhe Pan	f2bc037fff	[Fix] training pipeline pin_cpu_memory issue (#692 )	2025-08-07 02:31:20 -07:00
Jinzhe Pan	86604a684b	[3/3][Preprocess] add preprocessing workflows (#645 )	2025-08-07 01:49:07 -07:00
Zhang Peiyuan	47bd1e0178	[Misc] change installation logic of vsa (#721 )	2025-08-06 21:54:09 -07:00
Wei ZhouandSolitaryThinker	c41305ad18	[Feat] Add Wan2.2 14B MoE (#688 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2025-08-06 20:31:03 -07:00
Zhang Peiyuan	98ce9034f0	[Chore] Include our demo in the readme. (#720 )	2025-08-06 19:29:40 -07:00
William Lin	0ceff110da	[chore] Release 0.1.5 (#717 )	2025-08-06 13:07:52 -07:00
Yongqi Chen	1d018acb3e	[Feature]Add Data-free distillation readme (#710 )	2025-08-05 14:27:39 -04:00
Yongqi Chen	7d8cf38dbe	Fix typo (#709 )	2025-08-04 20:21:21 -07:00
Yongqi Chen	8d483fe4aa	[Bugfix] Fix neg_prompt bug when training from local cp (#708 )	2025-08-04 15:54:06 -07:00
Zhang Peiyuan	c1191250bf	Add WeChat group link (#707 )	2025-08-04 15:19:01 -07:00
Wenxuan Tan	4b7266349a	[misc] Remove allow_tf32 in scripts (#705 )	2025-08-04 15:37:56 -05:00
Yongqi Chen	22f9b7681f	[Feature]Update Wan2.2+DMD doc example (#706 )	2025-08-04 16:14:22 -04:00
Yongqi Chen	589d32cc39	[Feature] Update Readme and scripts (#703 )	2025-08-04 15:02:32 -04:00
Hao Zhang	89199837db	Update readme pre-release (#704 )	2025-08-04 11:54:31 -07:00