wip

[bigfix] [distillation] Fix DMD inference pipeline noise initialization shape (#921 )
2025-12-12 06:30:08 +00:00 · 2025-12-10 01:59:07 +00:00 · 2025-12-08 13:00:48 -08:00 · 2025-12-08 00:44:53 -08:00 · 2025-12-07 21:48:32 -08:00 · 2025-12-06 18:20:04 -08:00
356 changed files with 22751 additions and 3384 deletions
@@ -104,6 +104,18 @@ steps:
                  - TEST_TYPE=distillation_dmd
                agents:
                  queue: "default"
+            - path:
+                - "fastvideo/training/*self_forcing_distillation_pipeline.py"
+                - "fastvideo/tests/training/self-forcing/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: "Self-Forcing Tests"
+                env:
+                  - TEST_TYPE=self_forcing
+                agents:
+                  queue: "default"
            - path:
                - "fastvideo/**"
                - "pyproject.toml"
@@ -117,11 +129,11 @@ 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 +145,10 @@ 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 +159,10 @@ 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:
@@ -161,12 +173,12 @@ steps:
                agents:
                  queue: "default"
            - path:
-                - "csrc/attn/vsa/**"
-                - "csrc/attn/tk/**"
+                - "csrc/attn/video_sparse_attn/**"
+                - "csrc/attn/video_sparse_attn/tk/**"
                - "csrc/attn/tests/test_vsa.py"
-                - "csrc/attn/setup_vsa.py"
-                - "csrc/attn/config_vsa.py"
-                - "csrc/attn/vsa.cpp"
+                - "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:
@@ -176,3 +188,37 @@ steps:
                  - TEST_TYPE=precision_vsa
                agents:
                  queue: "default"
+            - path:
+                - "csrc/attn/vmoba_attn/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: "Precision Tests VMoBA"
+                env:
+                  - TEST_TYPE=precision_vmoba
+                agents:
+                  queue: "default"
+            - path:
+                - "csrc/attn/vmoba_attn/vmoba/**"
+                - "fastvideo/attention/backends/vmoba.py"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: "Inference Tests VMoBA"
+                env:  
+                  - TEST_TYPE=inference_vmoba
+                agents:
+                  queue: "default"
+            - path:
+                - "fastvideo/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                label: "Unit Tests"
+                env:
+                  - TEST_TYPE=unit_test
+                agents:
+                  queue: "default"
@@ -31,9 +31,9 @@ log "Setting up Modal authentication from Buildkite secrets..."
 MODAL_TOKEN_ID=$(buildkite-agent secret get modal_token_id)
 MODAL_TOKEN_SECRET=$(buildkite-agent secret get modal_token_secret)

+# Retrieve other secrets
 WANDB_API_KEY=$(buildkite-agent secret get wandb_api_key)
-
-WANDB_API_KEY=$(buildkite-agent secret get wandb_api_key)
+HF_API_KEY=$(buildkite-agent secret get hf_api_key)

 if [ -n "$MODAL_TOKEN_ID" ] && [ -n "$MODAL_TOKEN_SECRET" ]; then
    log "Retrieved Modal credentials from Buildkite secrets"
@@ -63,19 +63,19 @@ MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUI
 case "$TEST_TYPE" in
    "encoder")
        log "Running encoder tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_encoder_tests"
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_encoder_tests"
        ;;
    "vae")
        log "Running VAE tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_vae_tests"
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_vae_tests"
        ;;
    "transformer")
        log "Running transformer tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_transformer_tests"
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_transformer_tests"
        ;;
    "ssim")
        log "Running SSIM tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_ssim_tests"
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_ssim_tests"
        ;;
    "training")
        log "Running training tests..."
@@ -109,6 +109,23 @@ case "$TEST_TYPE" in
        log "Running distillation DMD tests..."
        MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_distill_dmd_tests"
        ;;
+        # run_inference_tests_vmoba
+    "self_forcing")
+        log "Running self-forcing tests..."
+        MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_self_forcing_tests"
+        ;;
+    "inference_vmoba")
+        log "Running V-MoBA inference tests..."
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_inference_tests_vmoba"
+        ;;
+    "precision_vmoba")
+        log "Running V-MoBA precision tests..."
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_precision_tests_vmoba"
+        ;;
+    "unit_test")
+        log "Running unit tests..."
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_unit_test"
+        ;;
    *)
        log "Error: Unknown test type: $TEST_TYPE"
        exit 1
@@ -1,82 +1,65 @@
-# Sample workflow for building and deploying a Hugo site to GitHub Pages
-name: Deploy FastVideo Docs to Pages
+name: Deploy Documentation

 on:
-  # Runs on pushes targeting the default branch
  push:
-    branches:
-      - main
+    branches: [ main ]
    paths:
-      - "docs/**/*.md"
-      - "fastvideo/examples/**/*.py"
+      - 'docs/**'
+      - 'mkdocs.yml'
+      - 'requirements-mkdocs.txt'
+      - '.github/workflows/docs.yml'
  pull_request:
-    branches: 
-      - main
-    types: [opened, ready_for_review, synchronize, reopened]
+    branches: [ main ]
    paths:
-      - "docs/**/*.md"
-      - "fastvideo/examples/**/*.py"
+      - 'docs/**'
+      - 'mkdocs.yml'
+      - 'requirements-mkdocs.txt'
+      - '.github/workflows/docs.yml'

-  # Allows you to run this workflow manually from the Actions tab
-  workflow_dispatch:
-
-# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
 permissions:
  contents: read
  pages: write
  id-token: write

-# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
-# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
 concurrency:
  group: "pages"
  cancel-in-progress: false

-# Default to bash
-defaults:
-  run:
-    shell: bash
-
 jobs:
-  pre-commit:
-    uses: ./.github/workflows/pre-commit.yml
-
-  # Build job
  build:
    runs-on: ubuntu-latest
-    needs: pre-commit
    steps:
      - name: Checkout
        uses: actions/checkout@v4
-      - name: Setup Pages
-        id: pages
-        uses: actions/configure-pages@v5
-      - name: Set up Python
+
+      - name: Setup Python
        uses: actions/setup-python@v5
        with:
-          python-version: "3.10"
+          python-version: '3.12'
+
      - name: Install dependencies
        run: |
-          cd docs
-          pip install -r requirements-docs.txt
-      - name: Build docs
-        run: |
-          cd docs
-          make clean
-          make html
+          python -m pip install --upgrade pip
+          pip install -r requirements-mkdocs.txt
+
+      - name: Setup Pages
+        uses: actions/configure-pages@v4
+
+      - name: Build documentation
+        run: mkdocs build
+
      - name: Upload artifact
        uses: actions/upload-pages-artifact@v3
        with:
-          path: ./docs/build/html
+          path: ./site

-  # Deployment job
  deploy:
    environment:
      name: github-pages
      url: ${{ steps.deployment.outputs.page_url }}
-    if: ${{ github.event_name == 'push' }}
    runs-on: ubuntu-latest
    needs: build
+    if: github.ref == 'refs/heads/main'
    steps:
      - name: Deploy to GitHub Pages
        id: deployment
@@ -62,8 +62,8 @@ on:
        required: false
        default: false
        type: boolean
-      run_nightly_test:
-        description: "Run nightly-test"
+      run_unit_test:
+        description: "Run unit-test"
        required: false
        default: false
        type: boolean
@@ -93,6 +93,7 @@ jobs:
      inference-test-STA: ${{ steps.filter.outputs.inference-test-STA }}
      precision-test-STA: ${{ steps.filter.outputs.precision-test-STA }}
      precision-test-VSA: ${{ steps.filter.outputs.precision-test-VSA }}
+      unit-test: ${{ steps.filter.outputs.unit-test }}
    steps:
      - uses: actions/checkout@v4
      - uses: dorny/paths-filter@v3
@@ -102,18 +103,21 @@ jobs:
            # Define reusable path patterns
            common-paths: &common-paths
              - 'pyproject.toml'
+              - 'docker/Dockerfile.python3.10'
+              - 'docker/Dockerfile.python3.11'
              - '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
@@ -154,6 +158,9 @@ jobs:
            precision-test-VSA:
              - *common-paths
              - *vsa-kernel-paths
+            unit-test:
+              - 'fastvideo/**'
+              - *common-paths

  encoder-test:
    needs: change-filter
@@ -234,7 +241,7 @@ jobs:
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-  
+
  training-test:
    needs: change-filter
    if: >-
@@ -332,23 +339,42 @@ jobs:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}

-  nightly-test:
+  unit-test:
+    needs: change-filter
    if: >-
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_nightly_test == 'true')
+      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.unit-test == 'true') || 
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_unit_test == 'true')
    uses: ./.github/workflows/runpod-test.yml
    with:
-      job_id: "nightly-test"
-      gpu_type: "NVIDIA A40"
-      gpu_count: 4
+      job_id: "unit-test"
+      gpu_type: "NVIDIA L40S"
+      gpu_count: 1
      volume_size: 100
      disk_size: 100
      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/tests/nightly/test_e2e_overfit_single_sample.py -vs"
+      test_command: "uv pip install -e .[test] && pytest ./fastvideo/dataset/ -vs && pytest ./fastvideo/workflow/ -vs && pytest ./fastvideo/entrypoints/ -vs"
      timeout_minutes: 30
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-      WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
+
+  # nightly-test:
+  #   if: >-
+  #     (github.event_name == 'workflow_dispatch' && github.event.inputs.run_nightly_test == 'true')
+  #   uses: ./.github/workflows/runpod-test.yml
+  #   with:
+  #     job_id: "nightly-test"
+  #     gpu_type: "NVIDIA A40"
+  #     gpu_count: 4
+  #     volume_size: 100
+  #     disk_size: 100
+  #     image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+  #     test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/tests/nightly/test_e2e_overfit_single_sample.py -vs"
+  #     timeout_minutes: 30
+  #   secrets:
+  #     RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
+  #     RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
+  #     WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}

  runpod-cleanup:
    # Add other jobs to this list as you create them
@@ -372,4 +398,4 @@ 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/
@@ -5,7 +5,7 @@ on:
    branches:
      - main
    paths:
-      - "csrc/attn/setup_vsa.py"
+      - "csrc/attn/video_sparse_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/video_sparse_attn
          # Get current commit's version
-          NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup_vsa.py)
+          NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup.py)
          echo "New version: $NEW_VERSION"
          
          # Get previous version from git history
-          OLD_VERSION=$(git show HEAD~1:./setup_vsa.py | grep -oP 'VERSION\s*=\s*"\K[^"]+' || echo "0.0.0")
+          OLD_VERSION=$(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
@@ -152,13 +152,13 @@ jobs:
          pip install setuptools
          pip install ninja packaging wheel

-          cd csrc/attn  # Move into the correct folder
+          cd csrc/attn/video_sparse_attn  # Move into the correct folder
          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
-          python setup_vsa.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/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)
@@ -173,7 +173,7 @@ jobs:
        uses: actions/upload-artifact@v4
        with:
          name: ${{ env.wheel_name }}
-          path: csrc/attn/dist/*.whl
+          path: csrc/attn/video_sparse_attn/dist/*.whl
          retention-days: 90

  publish_package:
@@ -247,11 +247,11 @@ jobs:
          pip install setuptools
          pip install ninja packaging wheel

-          cd csrc/attn  # Move into the correct folder
+          cd csrc/attn/video_sparse_attn  # Move into the correct folder
          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
-          python setup_vsa.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/video_sparse_attn/dist/
@@ -14,6 +14,8 @@ wandb/
 *.pt
 cache_dir/
 wandb/
+venv/
+.venv/
 runs/
 samples/
 *validation/
@@ -37,12 +39,13 @@ dist/
 eggs/
 .eggs/

-# Sphinx documentation
-docs/_build/
-docs/source/getting_started/examples/
-docs/source/inference/examples/
-docs/source/training/examples/
-docs/source/distillation/examples/
+# MkDocs documentation
+site/
+docs/getting_started/examples/
+docs/inference/examples/
+docs/training/examples/
+docs/distillation/examples/
+!requirements-mkdocs.txt

 # VSCode
 .vscode/
@@ -61,6 +64,9 @@ docs/source/distillation/examples/
 !fastvideo/tests/ssim/reference_videos/**/*.mp4

 # Static images
-!docs/source/_static/images/**/*.png
+!docs/assets/images/**/*.png
 !comfyui/assets/**/*.png
 !comfyui/assets/**/*.gif
+
+dmd_t2v_output/
+preprocess_output_text/
@@ -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
@@ -10,11 +10,9 @@ exclude: |
    demo/.*|
    predict\.py|
    scripts/.*|
+    prompts/.*|
    fastvideo/data_preprocess/.*|
    fastvideo/dataset/.*|
-    fastvideo/distill/.*|
-    fastvideo/distill\.py|
-    fastvideo/distill_adv\.py|
    fastvideo/models/.*|
    fastvideo/sample/.*|
    fastvideo/train\.py|
@@ -44,10 +42,10 @@ repos:
  - id: codespell
    additional_dependencies: ['tomli']
    args: ['--toml', 'pyproject.toml']
- repo: https://github.com/PyCQA/isort
-  rev: 6.0.1
-  hooks:
-  - id: isort
+# - repo: https://github.com/PyCQA/isort
+#   rev: 6.0.1
+#   hooks:
+#   - id: isort
 - repo: https://github.com/jackdewinter/pymarkdown
  rev: v0.9.30
  hooks:
@@ -1,13 +1,12 @@
 <div align="center">
 <img src=assets/logos/logo.svg width="30%"/>
 </div>
-
 **FastVideo is a unified post-training and inference framework for accelerated video generation.**

 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://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-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/rG0QpZdw" target="_blank"> <b> WeChat </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/"><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-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/TM8JyJCd" target="_blank"> <b> WeChat </b> </a> |
 </p>

 <div align="center">
@@ -15,6 +14,7 @@ FastVideo features an end-to-end unified pipeline for accelerating diffusion mod
 </div>

 ## NEWS
+- ```2025/11/19```: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py)
 - ```2025/08/04```: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd.html) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
 - ```2025/06/14```: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389)
 - ```2025/04/24```: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released!
@@ -49,10 +49,10 @@ conda activate fastvideo
 pip install fastvideo
 ```

-Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation.html) for more detailed installation instructions.
+Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions.

 ## Sparse Distillation
-For our sparse distillation techniques, please see our [distillation docs](https://hao-ai-lab.github.io/FastVideo/distillation/dmd.html) and check out our [blog](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
+For our sparse distillation techniques, please see our [distillation docs](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/) and check out our [blog](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).

 See below for recipes and datasets:

@@ -64,7 +64,7 @@ See below for recipes and datasets:

 ## Inference
 ### Generating Your First Video
-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:
+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/). Create a file called `example.py` with the following code:

 ```python
 import os
@@ -100,35 +100,32 @@ Run the script with:
 python example.py
 ```

-For a more detailed guide, please see our [inference quick start](https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start.html).
+For a more detailed guide, please see our [inference quick start](https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/).

 ### Other docs:

- [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview.html)
- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation.html)
+- [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview/)
+- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/)

 ## Distillation and Finetuning
- [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd.html)
+- [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/)
 <!-- - [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
-<!-- - Optimization features
- Code updates -->
-  <!-- - [ ] fp8 support -->
-  <!-- - [ ] faster load model and save model support -->
+## Awesome work using FastVideo or our research projects

-See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/468).
+- [SGLang](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen): SGLang's diffusion inference functionality is based  on a fork of FastVideo on Sept. 24, 2025. [![Star](https://img.shields.io/github/stars/sgl-project/sglang.svg?style=social&label=Star)](https://github.com/sgl-project/sglang)
+
+- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A  unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/XueZeyue/DanceGRPO.svg?style=social&label=Star)](https://github.com/XueZeyue/DanceGRPO)
+- [SRPO](https://github.com/Tencent-Hunyuan/SRPO): A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/SRPO.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/SRPO)
+- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/Vchitect/DCM.svg?style=social&label=Star)](https://github.com/Vchitect/DCM)
+- [Hunyuan Video 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5): A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention. [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/HunyuanVideo-1.5.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5)
+- [Kandinsky-5.0](https://github.com/kandinskylab/kandinsky-5): A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch. [![Star](https://img.shields.io/github/stars/kandinskylab/kandinsky-5.svg?style=social&label=Star)](https://github.com/kandinskylab/kandinsky-5)
+- [LongCat Video](https://github.com/meituan-longcat/LongCat-Video): A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention. [![Star](https://img.shields.io/github/stars/meituan-longcat/LongCat-Video.svg?style=social&label=Star)](https://github.com/meituan-longcat/LongCat-Video)

 ## 🤝 Contributing

-We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview.html)
-
+We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview/).
+See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/468).
 ## Acknowledgement
 We learned and reused code from the following projects:
 - [Wan-Video](https://github.com/Wan-Video)
@@ -155,8 +152,8 @@ If you find FastVideo useful, please considering citing our work:
 }

@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},
+  title={Vsa: Faster video diffusion with trainable sparse attention},
+  author={Zhang, Peiyuan and Chen, Yongqi and Huang, Haofeng and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
  journal={arXiv preprint arXiv:2505.13389},
  year={2025}
 }
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@@ -0,0 +1,6 @@
+<svg width="160" height="93" viewBox="0 0 160 93" fill="none" xmlns="http://www.w3.org/2000/svg">
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+</svg>
@@ -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
 ```
@@ -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 +1,96 @@
+
+# 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
+
+
+## STA Configuration Logic
+Here is a diagram of how the window is configured and passed through the FastVideo pipeline:
+
+<div align="center">
+<img src="../../../docs/assets/images/STA_configuration.png" width="80%"/>
+</div>
+
+
+## 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"
@@ -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/')))
@@ -247,11 +247,11 @@ def _attn_bwd_dq(dq, q, K, V,  #

    kv_blocks = tl.load(q2k_num  + meta_base)                 # int32
    kv_ptr    = q2k_index + meta_base * max_kv_blks           # ptr to list
+    block_size = tl.load(variable_block_sizes + q_blk)
    
    
    for blk_idx in range(kv_blocks*2):
        block_sparse_offset = (tl.load(kv_ptr + blk_idx//2).to(tl.int32)*2 + blk_idx%2) *step_n * stride_tok
-        block_size = tl.load(variable_block_sizes + blk_idx//2) - (blk_idx%2) * step_n
        kT = tl.load(kT_ptrs + block_sparse_offset)
        vT = tl.load(vT_ptrs + block_sparse_offset)
        qk = tl.dot(q, kT)
@@ -0,0 +1,32 @@
+# Attention Kernel Used in FastVideo
+
+## VMoBA: Mixture-of-Block Attention for Video Diffusion Models (VMoBA)
+
+### Installation
+Please ensure that you have installed FlashAttention version **2.7.1 or higher**, as some interfaces have changed in recent releases.
+
+### Usage
+
+You can use `moba_attn_varlen` in the following ways:
+
+**Install from source:**
+```bash
+python setup.py install
+```
+
+**Import after installation:**
+```python
+from vmoba import moba_attn_varlen
+```
+
+**Or import directly from the project root:**
+```python
+from csrc.attn.vmoba_attn.vmoba import moba_attn_varlen
+```
+
+### Verify if you have successfully installed
+
+```bash
+python csrc/attn/vmoba_attn/vmoba/vmoba.py
+```
+
@@ -0,0 +1,26 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from setuptools import find_packages, setup
+
+PACKAGE_NAME = "vmoba"
+VERSION = "0.0.0"
+AUTHOR = "JianzongWu"
+DESCRIPTION = "VMoBA: Mixture-of-Block Attention for Video Diffusion Models"
+URL = "https://github.com/KwaiVGI/VMoBA"
+
+setup(
+    name=PACKAGE_NAME,
+    version=VERSION,
+    author=AUTHOR,
+    description=DESCRIPTION,
+    url=URL,
+    packages=find_packages(),
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: Apache Software License",
+    ],
+    python_requires='>=3.12',
+    install_requires=[
+        "flash-attn >= 2.7.1",
+    ]
+)
@@ -0,0 +1,97 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+import pytest
+import random
+from csrc.attn.vmoba_attn.vmoba  import moba_attn_varlen
+
+def generate_test_data(batch_size, total_seqlen, num_heads, head_dim, dtype, device="cuda"):
+    """
+    Generates random data for testing the variable-length attention function.
+    """
+    torch.manual_seed(42)
+    random.seed(42)
+    torch.cuda.manual_seed_all(42)
+
+    # Generate sequence lengths for each item in the batch
+    if batch_size > 1:
+        # Ensure sequence lengths are reasonably distributed
+        avg_seqlen = total_seqlen // batch_size
+        seqlens = [random.randint(avg_seqlen // 2, avg_seqlen + avg_seqlen // 2) for _ in range(batch_size - 1)]
+        remaining_len = total_seqlen - sum(seqlens)
+        if remaining_len > 0:
+            seqlens.append(remaining_len)
+        else: # Adjust if sum exceeds total_seqlen
+            seqlens.append(avg_seqlen)
+            current_sum = sum(seqlens)
+            seqlens[-1] -= (current_sum - total_seqlen)
+        # Ensure all lengths are positive
+        seqlens = [max(1, s) for s in seqlens]
+        # Final adjustment to match total_seqlen
+        seqlens[-1] += total_seqlen - sum(seqlens)
+
+    else:
+        seqlens = [total_seqlen]
+
+    cu_seqlens = torch.tensor([0] + list(torch.cumsum(torch.tensor(seqlens), 0)), device=device, dtype=torch.int32)
+    max_seqlen = max(seqlens) if seqlens else 0
+
+    q = torch.randn((total_seqlen, num_heads, head_dim), dtype=dtype, device=device, requires_grad=False)
+    k = torch.randn((total_seqlen, num_heads, head_dim), dtype=dtype, device=device, requires_grad=False)
+    v = torch.randn((total_seqlen, num_heads, head_dim), dtype=dtype, device=device, requires_grad=False)
+
+    return q, k, v, cu_seqlens, max_seqlen
+
+
+@pytest.mark.parametrize("batch_size", [1, 2])
+@pytest.mark.parametrize("total_seqlen", [512, 1024])
+@pytest.mark.parametrize("num_heads", [8])
+@pytest.mark.parametrize("head_dim", [64])
+@pytest.mark.parametrize("moba_chunk_size", [64])
+@pytest.mark.parametrize("moba_topk", [2, 4])
+@pytest.mark.parametrize("select_mode", ["topk", "threshold"])
+@pytest.mark.parametrize("threshold_type", ["query_head", "head_global", "overall"])
+@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16])
+def test_moba_attn_varlen_forward(
+    batch_size, total_seqlen, num_heads, head_dim, moba_chunk_size, moba_topk, select_mode, threshold_type, dtype
+):
+    """
+    Tests the forward pass of moba_attn_varlen for basic correctness.
+    It checks output shape, dtype, and for the presence of NaNs/Infs.
+    """
+    if dtype == torch.float32:
+        pytest.skip("float32 is not supported in flash attention")
+
+    q, k, v, cu_seqlens, max_seqlen = generate_test_data(
+        batch_size, total_seqlen, num_heads, head_dim, dtype
+    )
+
+    # Ensure chunk size is not larger than the smallest sequence length
+    min_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).min().item()
+    if moba_chunk_size > min_seqlen:
+        pytest.skip("moba_chunk_size is larger than the minimum sequence length in the batch")
+
+    try:
+        output = moba_attn_varlen(
+            q=q,
+            k=k,
+            v=v,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            moba_chunk_size=moba_chunk_size,
+            moba_topk=moba_topk,
+            select_mode=select_mode,
+            threshold_type=threshold_type,
+            simsum_threshold=0.5, # A reasonable default for threshold mode
+        )
+    except Exception as e:
+        pytest.fail(f"moba_attn_varlen forward pass failed with exception: {e}")
+
+    # 1. Check output shape
+    assert output.shape == q.shape, f"Expected output shape {q.shape}, but got {output.shape}"
+
+    # 2. Check output dtype
+    assert output.dtype == q.dtype, f"Expected output dtype {q.dtype}, but got {output.dtype}"
+
+    # 3. Check for NaNs or Infs in the output
+    assert torch.all(torch.isfinite(output)), "Output contains NaN or Inf values"
@@ -0,0 +1,2 @@
+# SPDX-License-Identifier: Apache-2.0
+from .vmoba import moba_attn_varlen, process_moba_input, process_moba_output
@@ -0,0 +1,868 @@
+# SPDX-License-Identifier: Apache-2.0
+# Adapt from https://github.com/KwaiVGI/VMoBA/blob/main/src/vmoba.py
+
+import random
+import time
+import os
+import torch
+from typing import Tuple
+try:
+    from flash_attn import flash_attn_varlen_func  # Use the new flash attention function
+    from flash_attn.flash_attn_interface import _flash_attn_varlen_forward, _flash_attn_varlen_backward
+except ImportError:
+    def _unsupported(*args, **kwargs):
+        raise ImportError("flash-attn is not installed. Please install it, e.g., `pip install flash-attn`.")
+    _flash_attn_varlen_forward = _unsupported
+    _flash_attn_varlen_backward = _unsupported
+    flash_attn_varlen_func = _unsupported
+    
+from functools import lru_cache
+from einops import rearrange
+
+
+@lru_cache(maxsize=16)
+def calc_chunks(cu_seqlen, moba_chunk_size):
+    """
+    Calculate chunk boundaries.
+
+    For vision tasks we include all chunks (even the last one which might be shorter)
+    so that every chunk can be selected.
+    """
+    batch_sizes = cu_seqlen[1:] - cu_seqlen[:-1]
+    batch_num_chunk = (batch_sizes + (moba_chunk_size - 1)) // moba_chunk_size
+    cu_num_chunk = torch.ones(
+        batch_num_chunk.numel() + 1,
+        device=cu_seqlen.device,
+        dtype=batch_num_chunk.dtype,
+    )
+    cu_num_chunk[1:] = batch_num_chunk.cumsum(dim=0)
+    num_chunk = cu_num_chunk[-1]
+    chunk_sizes = torch.full(
+        (num_chunk + 1,), moba_chunk_size, dtype=torch.int32, device=cu_seqlen.device
+    )
+    chunk_sizes[0] = 0
+    batch_last_chunk_size = batch_sizes - (batch_num_chunk - 1) * moba_chunk_size
+    chunk_sizes[cu_num_chunk[1:]] = batch_last_chunk_size
+    cu_chunk = chunk_sizes.cumsum(dim=-1, dtype=torch.int32)
+    chunk_to_batch = torch.zeros(
+        (num_chunk,), dtype=torch.int32, device=cu_seqlen.device
+    )
+    chunk_to_batch[cu_num_chunk[1:-1]] = 1
+    chunk_to_batch = chunk_to_batch.cumsum(dim=0, dtype=torch.int32)
+
+    # Do not filter out any chunk
+    filtered_chunk_indices = torch.arange(
+        num_chunk, device=cu_seqlen.device, dtype=torch.int32
+    )
+    num_filtered_chunk = num_chunk
+
+    return cu_chunk, filtered_chunk_indices, num_filtered_chunk, chunk_to_batch
+
+
+# --- Threshold Selection Helper Functions ---
+
+def _select_threshold_query_head(
+    gate: torch.Tensor,
+    valid_gate_mask: torch.Tensor,
+    gate_self_chunk_mask: torch.Tensor,
+    simsum_threshold: float
+) -> torch.Tensor:
+    """
+    Selects chunks for each <query, head> pair based on threshold.
+    Normalization and sorting happen along the chunk dimension (dim=0).
+    """
+    C, H, S = gate.shape
+    eps = 1e-6
+
+    # LSE‐style normalization per <head, query> (across chunks)
+    gate_masked = torch.where(valid_gate_mask, gate, -torch.inf) # Use -inf for max
+    gate_min_val = torch.where(valid_gate_mask, gate, torch.inf) # Use +inf for min
+
+    row_min = gate_min_val.amin(dim=0)        # (H, S)
+    row_max = gate_masked.amax(dim=0)         # (H, S)
+    denom = row_max - row_min
+    denom = torch.where(denom <= eps, torch.ones_like(denom), denom) # avoid divide‑by‑zero
+
+    gate_norm = (gate - row_min.unsqueeze(0)) / denom.unsqueeze(0)
+    gate_norm = torch.where(valid_gate_mask, gate_norm, 0.0) # (C, H, S)
+
+    # 1) pull out the self‐chunk’s normalized weight for each <head,seq>
+    self_norm = (gate_norm * gate_self_chunk_mask).sum(dim=0)  # (H, S)
+
+    # 2) compute how much more normalized weight we need beyond self
+    total_norm_sum = gate_norm.sum(dim=0) # (H, S)
+    remain_ratio = simsum_threshold - self_norm / (total_norm_sum + eps)  # (H, S)
+    remain_ratio = torch.clamp(remain_ratio, min=0.0) # if already ≥ thresh, no extra needed
+
+    # 3) zero out the self‐chunk in a copy, so we only sort “others”
+    others_norm = gate_norm.clone()
+    others_norm[gate_self_chunk_mask] = 0.0
+
+    # 4) sort the other chunks by descending norm, per <head,seq>
+    sorted_norm, sorted_idx = torch.sort(others_norm, descending=True, dim=0) # (C, H, S)
+
+    # 5) cumulative‑sum the sorted norms per <head,seq>
+    cumsum_others = sorted_norm.cumsum(dim=0) # (C, H, S)
+
+    # 6) for each <head,seq>, find the smallest k where cumsum_ratio ≥ remain_ratio
+    ratio = cumsum_others / (total_norm_sum.unsqueeze(0) + eps) # (C, H, S)
+    cond = ratio >= remain_ratio.unsqueeze(0) # (C, H, S) boolean mask
+    any_cond = cond.any(dim=0) # (H, S)
+    # Find the index of the first True value along dim 0. If none, use C-1.
+    cutoff = torch.where(any_cond, cond.float().argmax(dim=0), torch.full_like(any_cond, fill_value=C - 1)) # (H, S)
+
+    # 7) build a mask in sorted order up to that cutoff
+    idx_range = torch.arange(C, device=gate.device).view(-1, 1, 1) # (C, 1, 1)
+    sorted_mask = idx_range <= cutoff.unsqueeze(0) # (C, H, S)
+
+    # 8) scatter it back to original chunk order
+    others_mask = torch.zeros_like(gate, dtype=torch.bool)
+    others_mask.scatter_(0, sorted_idx, sorted_mask)
+
+    # 9) finally, include every self‐chunk plus all selected others
+    final_gate_mask = valid_gate_mask & (others_mask | gate_self_chunk_mask)
+
+    return final_gate_mask
+
+
+def _select_threshold_block(
+    gate: torch.Tensor,
+    valid_gate_mask: torch.Tensor,
+    gate_self_chunk_mask: torch.Tensor,
+    simsum_threshold: float
+) -> torch.Tensor:
+    """
+    Selects <query, head> pairs for each block based on threshold.
+    Normalization and sorting happen across the head and sequence dimensions (dim=1, 2).
+    """
+    C, H, S = gate.shape
+    HS = H * S
+    eps = 1e-6
+
+    # LSE‐style normalization per block (across heads and queries)
+    gate_masked = torch.where(valid_gate_mask, gate, -torch.inf) # Use -inf for max
+    gate_min_val = torch.where(valid_gate_mask, gate, torch.inf) # Use +inf for min
+
+    block_max = gate_masked.amax(dim=(1, 2), keepdim=True) # (C, 1, 1)
+    block_min = gate_min_val.amin(dim=(1, 2), keepdim=True) # (C, 1, 1)
+    block_denom = block_max - block_min
+    block_denom = torch.where(block_denom <= eps, torch.ones_like(block_denom), block_denom) # (C, 1, 1)
+
+    gate_norm = (gate - block_min) / block_denom # (C, H, S)
+    gate_norm = torch.where(valid_gate_mask, gate_norm, 0.0) # (C, H, S)
+
+    # 1) identify normalized weights of entries that *are* self-chunks (from query perspective)
+    self_norm_entries = gate_norm * gate_self_chunk_mask # (C, H, S)
+    # Sum these weights *per block*
+    self_norm_sum_per_block = self_norm_entries.sum(dim=(1, 2)) # (C,)
+
+    # 2) compute how much more normalized weight each block needs beyond its self-chunk contributions
+    total_norm_sum_per_block = gate_norm.sum(dim=(1, 2)) # (C,)
+    remain_ratio = simsum_threshold - self_norm_sum_per_block / (total_norm_sum_per_block + eps) # (C,)
+    remain_ratio = torch.clamp(remain_ratio, min=0.0) # (C,)
+
+    # 3) zero out the self‐chunk entries in a copy, so we only sort “others”
+    others_norm = gate_norm.clone()
+    others_norm[gate_self_chunk_mask] = 0.0 # Zero out self entries
+
+    # 4) sort the other <head, seq> pairs by descending norm, per block
+    others_flat = others_norm.contiguous().view(C, HS) # (C, H*S)
+    sorted_others_flat, sorted_indices_flat = torch.sort(others_flat, dim=1, descending=True) # (C, H*S)
+
+    # 5) cumulative‑sum the sorted norms per block
+    cumsum_others_flat = sorted_others_flat.cumsum(dim=1) # (C, H*S)
+
+    # 6) for each block, find the smallest k where cumsum_ratio ≥ remain_ratio
+    ratio_flat = cumsum_others_flat / (total_norm_sum_per_block.unsqueeze(1) + eps) # (C, H*S)
+    cond_flat = ratio_flat >= remain_ratio.unsqueeze(1) # (C, H*S) boolean mask
+    any_cond = cond_flat.any(dim=1) # (C,)
+    # Find the index of the first True value along dim 1. If none, use HS-1.
+    cutoff_flat = torch.where(any_cond, cond_flat.float().argmax(dim=1), torch.full_like(any_cond, fill_value=HS - 1)) # (C,)
+
+    # 7) build a mask in sorted order up to that cutoff per block
+    idx_range_flat = torch.arange(HS, device=gate.device).unsqueeze(0) # (1, H*S)
+    sorted_mask_flat = idx_range_flat <= cutoff_flat.unsqueeze(1) # (C, H*S)
+
+    # 8) scatter it back to original <head, seq> order per block
+    others_mask_flat = torch.zeros_like(others_flat, dtype=torch.bool) # (C, H*S)
+    others_mask_flat.scatter_(1, sorted_indices_flat, sorted_mask_flat)
+    others_mask = others_mask_flat.view(C, H, S) # (C, H, S)
+
+    # 9) finally, include every self‐chunk entry plus all selected others
+    final_gate_mask = valid_gate_mask & (others_mask | gate_self_chunk_mask)
+
+    return final_gate_mask
+
+
+def _select_threshold_overall(
+    gate: torch.Tensor,
+    valid_gate_mask: torch.Tensor,
+    gate_self_chunk_mask: torch.Tensor,
+    simsum_threshold: float
+) -> torch.Tensor:
+    """
+    Selects <chunk, query, head> triplets globally based on threshold.
+    Normalization and sorting happen across all valid entries.
+    """
+    C, H, S = gate.shape
+    CHS = C * H * S
+    eps = 1e-6
+
+    # LSE‐style normalization globally across all valid entries
+    gate_masked = torch.where(valid_gate_mask, gate, -torch.inf) # Use -inf for max
+    gate_min_val = torch.where(valid_gate_mask, gate, torch.inf) # Use +inf for min
+
+    overall_max = gate_masked.max() # scalar
+    overall_min = gate_min_val.min() # scalar
+    overall_denom = overall_max - overall_min
+    overall_denom = torch.where(overall_denom <= eps, torch.tensor(1.0, device=gate.device, dtype=gate.dtype), overall_denom)
+
+    gate_norm = (gate - overall_min) / overall_denom # (C, H, S)
+    gate_norm = torch.where(valid_gate_mask, gate_norm, 0.0) # (C, H, S)
+
+    # 1) identify normalized weights of entries that *are* self-chunks
+    self_norm_entries = gate_norm * gate_self_chunk_mask # (C, H, S)
+    # Sum these weights globally
+    self_norm_sum_overall = self_norm_entries.sum() # scalar
+
+    # 2) compute how much more normalized weight is needed globally beyond self-chunk contributions
+    total_norm_sum_overall = gate_norm.sum() # scalar
+    remain_ratio = simsum_threshold - self_norm_sum_overall / (total_norm_sum_overall + eps) # scalar
+    remain_ratio = torch.clamp(remain_ratio, min=0.0) # scalar
+
+    # 3) zero out the self‐chunk entries in a copy, so we only sort “others”
+    others_norm = gate_norm.clone()
+    others_norm[gate_self_chunk_mask] = 0.0 # Zero out self entries
+
+    # 4) sort all other entries by descending norm, globally
+    others_flat = others_norm.flatten() # (C*H*S,)
+    valid_others_mask_flat = valid_gate_mask.flatten() & ~gate_self_chunk_mask.flatten() # Mask for valid, non-self entries
+    
+    # Only sort the valid 'other' entries
+    valid_others_indices = torch.where(valid_others_mask_flat)[0]
+    valid_others_values = others_flat[valid_others_indices]
+
+    sorted_others_values, sort_perm = torch.sort(valid_others_values, descending=True) # (N_valid_others,)
+    sorted_original_indices = valid_others_indices[sort_perm] # Original indices in C*H*S space, sorted by value
+
+    # 5) cumulative‑sum the sorted valid 'other' norms globally
+    cumsum_others_values = sorted_others_values.cumsum(dim=0) # (N_valid_others,)
+
+    # 6) find the smallest k where cumsum_ratio ≥ remain_ratio globally
+    ratio_values = cumsum_others_values / (total_norm_sum_overall + eps) # (N_valid_others,)
+    cond_values = ratio_values >= remain_ratio # (N_valid_others,) boolean mask
+    any_cond = cond_values.any() # scalar
+    
+    # Find the index of the first True value in the *sorted* list. If none, use all valid others.
+    cutoff_idx_in_sorted = torch.where(
+        any_cond, 
+        cond_values.float().argmax(dim=0), 
+        torch.tensor(len(sorted_others_values) - 1, device=gate.device, dtype=torch.long)
+    )
+
+    # 7) build a mask selecting the top-k others based on the cutoff
+    # Select the original indices corresponding to the top entries in the sorted list
+    selected_other_indices = sorted_original_indices[:cutoff_idx_in_sorted + 1]
+
+    # 8) create the mask in the original flat shape
+    others_mask_flat = torch.zeros_like(others_flat, dtype=torch.bool) # (C*H*S,)
+    if selected_other_indices.numel() > 0: # Check if any 'other' indices were selected
+        others_mask_flat[selected_other_indices] = True
+    others_mask = others_mask_flat.view(C, H, S) # (C, H, S)
+
+    # 9) finally, include every self‐chunk entry plus all selected others
+    final_gate_mask = valid_gate_mask & (others_mask | gate_self_chunk_mask)
+
+    return final_gate_mask
+
+
+def _select_threshold_head_global(
+    gate: torch.Tensor,
+    valid_gate_mask: torch.Tensor,
+    gate_self_chunk_mask: torch.Tensor,
+    simsum_threshold: float
+) -> torch.Tensor:
+    """
+    Selects <chunk, query> globally for each head based on threshold.
+    """
+    C, H, S = gate.shape
+    eps = 1e-6
+
+    # 1) LSE‐style normalization per head (across chunks and sequence dims)
+    gate_masked = torch.where(valid_gate_mask, gate, -torch.inf)
+    gate_min_val = torch.where(valid_gate_mask, gate, torch.inf)
+
+    max_per_head = gate_masked.amax(dim=(0, 2), keepdim=True)  # (1, H, 1)
+    min_per_head = gate_min_val.amin(dim=(0, 2), keepdim=True)  # (1, H, 1)
+    denom = max_per_head - min_per_head
+    denom = torch.where(denom <= eps, torch.ones_like(denom), denom)
+
+    gate_norm = (gate - min_per_head) / denom
+    gate_norm = torch.where(valid_gate_mask, gate_norm, 0.0)  # (C, H, S)
+
+    # 2) sum normalized self‐chunk contributions per head
+    self_norm_sum = (gate_norm * gate_self_chunk_mask).sum(dim=(0, 2))  # (H,)
+
+    # 3) total normalized sum per head
+    total_norm_sum = gate_norm.sum(dim=(0, 2))  # (H,)
+
+    # 4) how much more normalized weight needed per head
+    remain_ratio = simsum_threshold - self_norm_sum / (total_norm_sum + eps)  # (H,)
+    remain_ratio = torch.clamp(remain_ratio, min=0.0)
+
+    # 5) zero out self‐chunk entries to focus on "others"
+    others_norm = gate_norm.clone()
+    others_norm[gate_self_chunk_mask] = 0.0  # (C, H, S)
+
+    # 6) flatten chunk and sequence dims, per head
+    CS = C * S
+    others_flat = others_norm.permute(1, 0, 2).reshape(H, CS)  # (H, C*S)
+    valid_flat  = (valid_gate_mask & ~gate_self_chunk_mask) \
+                   .permute(1, 0, 2).reshape(H, CS)           # (H, C*S)
+
+    # 7) vectorized selection of “others” per head
+    masked_flat = torch.where(valid_flat, others_flat, torch.zeros_like(others_flat))
+    sorted_vals, sorted_idx = torch.sort(masked_flat, dim=1, descending=True)  # (H, C*S)
+
+    cumsum_vals = sorted_vals.cumsum(dim=1)                                    # (H, C*S)
+    ratio_vals  = cumsum_vals / (total_norm_sum.unsqueeze(1) + eps)            # (H, C*S)
+    cond        = ratio_vals >= remain_ratio.unsqueeze(1)                      # (H, C*S)
+
+    has_cutoff = cond.any(dim=1)                                               # (H,)
+    default    = torch.full((H,), CS - 1, device=gate.device, dtype=torch.long)
+    cutoff     = torch.where(has_cutoff, cond.float().argmax(dim=1), default)  # (H,)
+
+    idx_range   = torch.arange(CS, device=gate.device).unsqueeze(0)            # (1, C*S)
+    sorted_mask = idx_range <= cutoff.unsqueeze(1)                             # (H, C*S)
+
+    selected_flat = torch.zeros_like(valid_flat)                               # (H, C*S)
+    selected_flat.scatter_(1, sorted_idx, sorted_mask)                         # (H, C*S)
+
+    # 8) reshape selection mask back to (C, H, S)
+    others_mask = selected_flat.reshape(H, C, S).permute(1, 0, 2)               # (C, H, S)
+
+    # 9) include self‐chunks plus selected others, and obey valid mask
+    final_gate_mask = valid_gate_mask & (gate_self_chunk_mask | others_mask)
+
+    return final_gate_mask
+
+
+class MixedAttention(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        q,
+        k,
+        v,
+        self_attn_cu_seqlen,
+        moba_q,
+        moba_kv,
+        moba_cu_seqlen_q,
+        moba_cu_seqlen_kv,
+        max_seqlen,
+        moba_chunk_size,
+        moba_q_sh_indices,
+    ):
+        ctx.max_seqlen = max_seqlen
+        ctx.moba_chunk_size = moba_chunk_size
+        ctx.softmax_scale = softmax_scale = q.shape[-1] ** (-0.5)
+
+        # Non-causal self-attention branch
+        # return out, softmax_lse, S_dmask, rng_state
+        self_attn_out_sh, self_attn_lse_hs, _, _ = _flash_attn_varlen_forward(
+            q=q,
+            k=k,
+            v=v,
+            cu_seqlens_q=self_attn_cu_seqlen,
+            cu_seqlens_k=self_attn_cu_seqlen,
+            max_seqlen_q=max_seqlen,
+            max_seqlen_k=max_seqlen,
+            softmax_scale=softmax_scale,
+            causal=False,
+            dropout_p=0.0,
+        )
+        # MOBA attention branch (non-causal)
+        moba_attn_out, moba_attn_lse_hs, _, _ = _flash_attn_varlen_forward(
+            q=moba_q,
+            k=moba_kv[:, 0],
+            v=moba_kv[:, 1],
+            cu_seqlens_q=moba_cu_seqlen_q,
+            cu_seqlens_k=moba_cu_seqlen_kv,
+            max_seqlen_q=max_seqlen,
+            max_seqlen_k=moba_chunk_size,
+            softmax_scale=softmax_scale,
+            causal=False,
+            dropout_p=0.0,
+        )
+
+        self_attn_lse_sh = self_attn_lse_hs.t().contiguous()
+        moba_attn_lse = moba_attn_lse_hs.t().contiguous()
+
+        output = torch.zeros((q.shape[0], q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
+        output_2d = output.view(-1, q.shape[2])
+
+        max_lse_1d = self_attn_lse_sh.view(-1)
+        max_lse_1d = max_lse_1d.index_reduce(
+            0, moba_q_sh_indices, moba_attn_lse.view(-1), "amax"
+        )
+        self_attn_lse_sh = self_attn_lse_sh - max_lse_1d.view_as(self_attn_lse_sh)
+        moba_attn_lse = (
+            moba_attn_lse.view(-1)
+            .sub(max_lse_1d.index_select(0, moba_q_sh_indices))
+            .reshape_as(moba_attn_lse)
+        )
+
+        mixed_attn_se_sh = self_attn_lse_sh.exp()
+        moba_attn_se = moba_attn_lse.exp()
+
+        mixed_attn_se_sh.view(-1).index_add_(
+            0, moba_q_sh_indices, moba_attn_se.view(-1)
+        )
+        mixed_attn_lse_sh = mixed_attn_se_sh.log()
+
+        # Combine self-attention output
+        factor = (self_attn_lse_sh - mixed_attn_lse_sh).exp()  # [S, H]
+        self_attn_out_sh = self_attn_out_sh * factor.unsqueeze(-1)
+        output_2d += self_attn_out_sh.reshape_as(output_2d)
+
+        # Combine MOBA attention output
+        mixed_attn_lse = (
+            mixed_attn_lse_sh.view(-1)
+            .index_select(0, moba_q_sh_indices)
+            .view_as(moba_attn_lse)
+        )
+        factor = (moba_attn_lse - mixed_attn_lse).exp()  # [S, H]
+        moba_attn_out = moba_attn_out * factor.unsqueeze(-1)
+        raw_attn_out = moba_attn_out.view(-1, moba_attn_out.shape[-1])
+        output_2d.index_add_(0, moba_q_sh_indices, raw_attn_out)
+        output = output.to(q.dtype)
+        mixed_attn_lse_sh = mixed_attn_lse_sh + max_lse_1d.view_as(mixed_attn_se_sh)
+        ctx.save_for_backward(
+            output,
+            mixed_attn_lse_sh,
+            q,
+            k,
+            v,
+            self_attn_cu_seqlen,
+            moba_q,
+            moba_kv,
+            moba_cu_seqlen_q,
+            moba_cu_seqlen_kv,
+            moba_q_sh_indices,
+        )
+
+        return output
+
+    @staticmethod
+    def backward(ctx, d_output):
+
+        max_seqlen = ctx.max_seqlen
+        moba_chunk_size = ctx.moba_chunk_size
+        softmax_scale = ctx.softmax_scale
+
+        (
+            output,
+            mixed_attn_vlse_sh,
+            q,
+            k,
+            v,
+            self_attn_cu_seqlen,
+            moba_q,
+            moba_kv,
+            moba_cu_seqlen_q,
+            moba_cu_seqlen_kv,
+            moba_q_sh_indices,
+        ) = ctx.saved_tensors
+
+        d_output = d_output.contiguous()
+
+        dq = torch.empty_like(q)
+        dk = torch.empty_like(k)
+        dv = torch.empty_like(v)
+        _ = _flash_attn_varlen_backward(
+            dout=d_output,
+            q=q,
+            k=k,
+            v=v,
+            out=output,
+            softmax_lse=mixed_attn_vlse_sh.t().contiguous(),
+            dq=dq,
+            dk=dk,
+            dv=dv,
+            cu_seqlens_q=self_attn_cu_seqlen,
+            cu_seqlens_k=self_attn_cu_seqlen,
+            max_seqlen_q=max_seqlen,
+            max_seqlen_k=max_seqlen,
+            softmax_scale=softmax_scale,
+            causal=False,
+            dropout_p=0.0,
+            softcap=0.0,
+            alibi_slopes=None,
+            deterministic=True,
+            window_size_left=-1,
+            window_size_right=-1
+        )
+
+        headdim = q.shape[-1]
+        d_moba_output = (
+            d_output.view(-1, headdim).index_select(0, moba_q_sh_indices).unsqueeze(1)
+        )
+        moba_output = (
+            output.view(-1, headdim).index_select(0, moba_q_sh_indices).unsqueeze(1)
+        )
+
+        mixed_attn_vlse = (
+            mixed_attn_vlse_sh.view(-1).index_select(0, moba_q_sh_indices).view(1, -1)
+        )
+
+        dmq = torch.empty_like(moba_q)
+        dmkv = torch.empty_like(moba_kv)
+        _ = _flash_attn_varlen_backward(
+            dout=d_moba_output,
+            q=moba_q,
+            k=moba_kv[:, 0],
+            v=moba_kv[:, 1],
+            out=moba_output,
+            softmax_lse=mixed_attn_vlse,
+            dq=dmq,
+            dk=dmkv[:,0],
+            dv=dmkv[:,1],
+            cu_seqlens_q=moba_cu_seqlen_q,
+            cu_seqlens_k=moba_cu_seqlen_kv,
+            max_seqlen_q=max_seqlen,
+            max_seqlen_k=moba_chunk_size,
+            softmax_scale=softmax_scale,
+            causal=False,
+            dropout_p=0.0,
+            softcap=0.0,
+            alibi_slopes=None,
+            deterministic=True,
+            window_size_left=-1,
+            window_size_right=-1
+        )
+
+        return dq, dk, dv, None, dmq, dmkv, None, None, None, None, None
+
+
+def moba_attn_varlen(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    cu_seqlens: torch.Tensor,
+    max_seqlen: int,
+    moba_chunk_size: int,
+    moba_topk: int,
+    select_mode: str = 'threshold', # "topk" or "threshold"
+    simsum_threshold: float = 0.25,
+    threshold_type: str = 'query_head',
+) -> torch.Tensor:
+    """
+    Accelerated MOBA attention for vision tasks with proper LSE normalization.
+
+    This version:
+      - Splits KV into chunks.
+      - For each query head, selects the top-k relevant KV chunks (including the self chunk)
+        by amplifying the diagonal (self-chunk) logits.
+      - Aggregates the attention outputs from the selected chunks using a log-sum-exp
+        reduction so that attending to each query over the selected chunks is equivalent
+        to the original algorithm.
+    """
+    # Stack keys and values.
+    kv = torch.stack((k, v), dim=1)
+    seqlen, num_head, head_dim = q.shape
+
+    # Compute chunk boundaries.
+    cu_chunk, filtered_chunk_indices, num_filtered_chunk, chunk_to_batch = calc_chunks(
+        cu_seqlens, moba_chunk_size
+    )
+
+    self_attn_cu_seqlen = cu_chunk
+
+    # Update top-k selection to include the self chunk.
+    moba_topk = min(moba_topk, num_filtered_chunk)
+
+    # --- Build filtered KV from chunks ---
+    chunk_starts = cu_chunk[filtered_chunk_indices]          # [num_filtered_chunk]
+    chunk_ends = cu_chunk[filtered_chunk_indices + 1]          # [num_filtered_chunk]
+    chunk_lengths = chunk_ends - chunk_starts                  # [num_filtered_chunk]
+    max_chunk_len = int(chunk_lengths.max().item())
+
+    range_tensor = torch.arange(max_chunk_len, device=kv.device, dtype=chunk_starts.dtype).unsqueeze(0)
+    indices = chunk_starts.unsqueeze(1) + range_tensor
+    indices = torch.clamp(indices, max=kv.shape[0] - 1)
+    valid_mask = range_tensor < chunk_lengths.unsqueeze(1)
+    gathered = kv[indices.view(-1)].view(num_filtered_chunk, max_chunk_len, *kv.shape[1:])
+    gathered = gathered * valid_mask.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).type_as(gathered)
+
+    # Compute key_gate_weight over valid tokens.
+    key_values = gathered[:, :, 0].float()  # [num_filtered_chunk, max_chunk_len, num_head, head_dim]
+    valid_mask_exp = valid_mask.unsqueeze(-1).unsqueeze(-1)
+    key_sum = (key_values * valid_mask_exp).sum(dim=1)
+    divisor = valid_mask.sum(dim=1).unsqueeze(-1).unsqueeze(-1)
+    key_gate_weight = key_sum / divisor  # [num_filtered_chunk, num_head, head_dim]
+
+    # Compute gate logits between key_gate_weight and queries.
+    q_float = q.float()
+    # gate = torch.einsum("nhd,shd->nhs", key_gate_weight, q_float)  # [num_filtered_chunk, num_head, seqlen]
+    gate = torch.bmm(key_gate_weight.permute(1, 0, 2), q_float.permute(1, 0, 2).transpose(1, 2)).permute(1, 0, 2)
+
+    # Amplify the diagonal (self chunk) contributions.
+    gate_seq_idx = torch.arange(seqlen, device=q.device, dtype=torch.int32).unsqueeze(0).expand(num_filtered_chunk, seqlen)
+    chunk_start = cu_chunk[filtered_chunk_indices]  # [num_filtered_chunk]
+    chunk_end = cu_chunk[filtered_chunk_indices + 1]  # [num_filtered_chunk]
+    gate_self_chunk_mask = ((gate_seq_idx >= chunk_start.unsqueeze(1)) &
+                            (gate_seq_idx < chunk_end.unsqueeze(1))).unsqueeze(1).expand(-1, num_head, -1)
+    amplification_factor = 1e9  # Example factor; adjust as needed.
+    origin_gate = gate.clone()
+    gate = gate.clone()
+    if select_mode == "topk":
+        gate[gate_self_chunk_mask] += amplification_factor
+
+    # Exclude positions that are outside the valid batch boundaries.
+    batch_starts = cu_seqlens[chunk_to_batch[filtered_chunk_indices]]
+    batch_ends = cu_seqlens[chunk_to_batch[filtered_chunk_indices] + 1]
+    gate_batch_start_mask = gate_seq_idx < batch_starts.unsqueeze(1)
+    gate_batch_end_mask = gate_seq_idx >= batch_ends.unsqueeze(1)
+    gate_inf_mask = gate_batch_start_mask | gate_batch_end_mask
+    gate.masked_fill_(gate_inf_mask.unsqueeze(1), -float("inf"))
+
+    if select_mode == 'topk':
+        # We amplify self‐chunk in gate already, so self entries will rank highest.
+        valid_gate_mask = gate != -float("inf")
+        if threshold_type == 'query_head':
+            # === per‐<head,seq> top-k across chunks (original behavior) ===
+            # gate: (C, H, S)
+            _, gate_topk_idx = torch.topk(gate, k=moba_topk, dim=0, largest=True, sorted=False)
+            gate_idx_mask = torch.zeros_like(gate, dtype=torch.bool)
+            gate_idx_mask.scatter_(0, gate_topk_idx, True)
+            gate_mask = valid_gate_mask & gate_idx_mask
+        elif threshold_type == 'overall':
+            # === global top-k across all (chunk, head, seq) entries ===
+            C, H, S = gate.shape
+            flat_gate = gate.flatten()
+            flat_mask = valid_gate_mask.flatten()
+            flat_gate_masked = torch.where(flat_mask, flat_gate, -float("inf"))
+            # pick topk global entries
+            vals, idx = torch.topk(flat_gate_masked, k=moba_topk * H * S, largest=True, sorted=False)
+            others_mask_flat = torch.zeros_like(flat_mask, dtype=torch.bool)
+            others_mask_flat[idx] = True
+            gate_mask = (valid_gate_mask.flatten() & others_mask_flat).view(gate.shape)
+        elif threshold_type == 'head_global':
+            # per-head top-k across all chunks and sequence positions
+            C, H, S = gate.shape
+            CS = C * S
+            flat_gate  = gate.permute(1, 0, 2).reshape(H, CS)
+            flat_valid = valid_gate_mask.permute(1, 0, 2).reshape(H, CS)
+            flat_gate_masked = torch.where(flat_valid, flat_gate, torch.full_like(flat_gate, -float('inf')))
+            # pick top-k indices per head
+            _, topk_idx = torch.topk(flat_gate_masked, k=moba_topk * S, dim=1, largest=True, sorted=False)
+            gate_idx_flat = torch.zeros_like(flat_valid, dtype=torch.bool)
+            gate_idx_flat.scatter_(1, topk_idx, True)
+            gate_mask = gate_idx_flat.reshape(H, C, S).permute(1, 0, 2)
+        else:
+            raise ValueError(
+                f"Invalid threshold_type for topk: {threshold_type}. "
+                "Choose 'query_head', 'block', or 'overall'."
+            )
+    elif select_mode == 'threshold':
+        # Delegate to the specific thresholding function
+        valid_gate_mask = gate != -float("inf")  # (num_chunk, num_head, seqlen)
+        if threshold_type == 'query_head':
+            gate_mask = _select_threshold_query_head(gate, valid_gate_mask, gate_self_chunk_mask, simsum_threshold)
+        elif threshold_type == 'block':
+            gate_mask = _select_threshold_block(gate, valid_gate_mask, gate_self_chunk_mask, simsum_threshold)
+        elif threshold_type == 'overall':
+            gate_mask = _select_threshold_overall(gate, valid_gate_mask, gate_self_chunk_mask, simsum_threshold)
+        elif threshold_type == 'head_global':
+            gate_mask = _select_threshold_head_global(gate, valid_gate_mask, gate_self_chunk_mask, simsum_threshold)
+        else:
+            raise ValueError(f"Invalid threshold_type: {threshold_type}. Choose 'query_head', 'block', or 'overall'.")
+    else:
+        raise ValueError(f"Invalid select_mode: {select_mode}. Choose 'topk' or 'threshold'.")
+    
+    # eliminate self_chunk in MoBA branch
+    gate_mask = gate_mask & ~gate_self_chunk_mask
+    # if gate_mask is all false, perform flash_attn instead
+    if gate_mask.sum() == 0:
+        return flash_attn_varlen_func(
+            q, k, v, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen, causal=False
+        )
+
+    # Determine which query positions are selected.
+    # nonzero_indices has shape [N, 3] where each row is [chunk_index, head_index, seq_index].
+    moba_q_indices = gate_mask.reshape(gate_mask.shape[0], -1).nonzero(as_tuple=True)[-1] # [(h s k)]
+    moba_q_sh_indices = (moba_q_indices % seqlen) * num_head + (moba_q_indices // seqlen)
+    moba_q = rearrange(q, "s h d -> (h s) d").index_select(0, moba_q_indices).unsqueeze(1)
+
+    # Build cumulative sequence lengths for the selected queries.
+    moba_seqlen_q = gate_mask.sum(dim=-1).flatten()
+    q_zero_mask = moba_seqlen_q == 0
+    valid_expert_mask = ~q_zero_mask
+    if q_zero_mask.sum() > 0:
+        moba_seqlen_q = moba_seqlen_q[valid_expert_mask]
+    moba_cu_seqlen_q = torch.cat(
+        (
+            torch.tensor([0], device=q.device, dtype=moba_seqlen_q.dtype),
+            moba_seqlen_q.cumsum(dim=0),
+        ),
+        dim=0,
+    ).to(torch.int32)
+
+    # Rearrange gathered KV for the MOBA branch.
+    experts_tensor = rearrange(gathered, "nc cl two h d -> (nc h) cl two d")
+    valid_expert_lengths = chunk_lengths.unsqueeze(1).expand(num_filtered_chunk, num_head).reshape(-1).to(torch.int32)
+    if q_zero_mask.sum() > 0:
+        experts_tensor = experts_tensor[valid_expert_mask]
+        valid_expert_lengths = valid_expert_lengths[valid_expert_mask]
+
+    seq_range = torch.arange(experts_tensor.shape[1], device=experts_tensor.device).unsqueeze(0)
+    mask = seq_range < valid_expert_lengths.unsqueeze(1)
+    moba_kv = experts_tensor[mask]  # Shape: ((nc h cl_valid) two d)
+    moba_kv = moba_kv.unsqueeze(2)   # Shape: ((nc h cl_valid) two 1 d)
+
+    moba_cu_seqlen_kv = torch.cat(
+        [torch.zeros(1, device=experts_tensor.device, dtype=torch.int32),
+         valid_expert_lengths.cumsum(dim=0)],
+        dim=0,
+    ).to(torch.int32)
+
+    assert (
+        moba_cu_seqlen_kv.shape == moba_cu_seqlen_q.shape
+    ), f"Mismatch between moba_cu_seqlen_kv.shape and moba_cu_seqlen_q.shape: {moba_cu_seqlen_kv.shape} vs {moba_cu_seqlen_q.shape}"
+
+    return MixedAttention.apply(
+        q,
+        k,
+        v,
+        self_attn_cu_seqlen,
+        moba_q,
+        moba_kv,
+        moba_cu_seqlen_q,
+        moba_cu_seqlen_kv,
+        max_seqlen,
+        moba_chunk_size,
+        moba_q_sh_indices,
+    )
+
+
+def process_moba_input(
+    x,
+    patch_resolution,
+    chunk_size,
+):
+    """
+    Process inputs for the attention function.
+
+    Args:
+        x (torch.Tensor): Input tensor with shape [batch_size, num_patches, num_heads, head_dim].
+        patch_resolution (tuple): Tuple containing the patch resolution (t, h, w).
+        chunk_size (int): Size of the chunk. (maybe tuple or int, according to chunk type)
+
+    Returns:
+        torch.Tensor: Processed input tensor.
+    """
+    if isinstance(chunk_size, float) or isinstance(chunk_size, int):
+        moba_chunk_size = int(chunk_size * patch_resolution[1] * patch_resolution[2])
+    else:
+        assert isinstance(chunk_size, (Tuple, list)), f"chunk_size should be a tuple, list, or int, now it is: {type(chunk_size)}"
+        if len(chunk_size) == 2:
+            assert patch_resolution[1] % chunk_size[0] == 0 and patch_resolution[2] % chunk_size[1] == 0, f"spatial patch_resolution {patch_resolution[1:]} should be divisible by 2d chunk_size {chunk_size}"
+            nch, ncw = patch_resolution[1] // chunk_size[0], patch_resolution[2] // chunk_size[1]
+            x = rearrange(x, "b (t nch ch ncw cw) n d -> b (nch ncw t ch cw) n d", t=patch_resolution[0], nch=nch, ncw=ncw, ch=chunk_size[0], cw=chunk_size[1])
+            moba_chunk_size = patch_resolution[0] * chunk_size[0] * chunk_size[1]
+        elif len(chunk_size) == 3:
+            assert patch_resolution[0] % chunk_size[0] == 0 and patch_resolution[1] % chunk_size[1] == 0 and patch_resolution[2] % chunk_size[2] == 0, f"patch_resolution {patch_resolution} should be divisible by 3d chunk_size {chunk_size}"
+            nct, nch, ncw = patch_resolution[0] // chunk_size[0], patch_resolution[1] // chunk_size[1], patch_resolution[2] // chunk_size[2]
+            x = rearrange(x, "b (nct ct nch ch ncw cw) n d -> b (nct nch ncw ct ch cw) n d", nct=nct, nch=nch, ncw=ncw, ct=chunk_size[0], ch=chunk_size[1], cw=chunk_size[2])
+            moba_chunk_size = chunk_size[0] * chunk_size[1] * chunk_size[2]
+        else:
+            raise ValueError(f"chunk_size should be a int, or a tuple of length 2 or 3, now it is: {len(chunk_size)}")
+        
+    return x, moba_chunk_size
+
+
+def process_moba_output(
+    x,
+    patch_resolution,
+    chunk_size,
+):
+    if isinstance(chunk_size, float) or isinstance(chunk_size, int):
+        pass
+    elif len(chunk_size) == 2:
+        x = rearrange(x, "b (nch ncw t ch cw) n d -> b (t nch ch ncw cw) n d", nch=patch_resolution[1] // chunk_size[0], ncw=patch_resolution[2] // chunk_size[1], t=patch_resolution[0], ch=chunk_size[0], cw=chunk_size[1])
+    elif len(chunk_size) == 3:
+        x = rearrange(x, "b (nct nch ncw ct ch cw) n d -> b (nct ct nch ch ncw cw) n d", nct=patch_resolution[0] // chunk_size[0], nch=patch_resolution[1] // chunk_size[1], ncw=patch_resolution[2] // chunk_size[2], ct=chunk_size[0], ch=chunk_size[1], cw=chunk_size[2])
+
+    return x
+
+
+# TEST
+def generate_data(batch_size, seqlen, num_head, head_dim, dtype):
+    random.seed(0)
+    torch.manual_seed(0)
+    torch.cuda.manual_seed(0)
+    device = torch.cuda.current_device()
+
+    q = torch.randn((batch_size, seqlen, num_head, head_dim), requires_grad=True).to(dtype=dtype, device='cuda')
+    k = torch.randn((batch_size, seqlen, num_head, head_dim), requires_grad=True).to(dtype=dtype, device='cuda')
+    v = torch.randn((batch_size, seqlen, num_head, head_dim), requires_grad=True).to(dtype=dtype, device='cuda')
+    print(f"q.shape: {q.shape}, k.shape: {k.shape}, v.shape: {v.shape}")
+    cu_seqlens = torch.arange(0, q.shape[0] * q.shape[1] + 1, q.shape[1], dtype=torch.int32, device='cuda')
+    max_seqlen = q.shape[1]
+    q = rearrange(q, "b s ... -> (b s) ...")
+    k = rearrange(k, "b s ... -> (b s) ...")
+    v = rearrange(v, "b s ... -> (b s) ...")
+
+    return q, k, v, cu_seqlens, max_seqlen
+
+
+def test_attn_varlen_moba_speed(batch, head, seqlen, head_dim, moba_chunk_size, moba_topk, dtype=torch.bfloat16, select_mode='threshold', simsum_threshold=0.25, threshold_type='query_head'):
+    """Speed test comparing flash_attn vs moba_attention"""
+    # Get data
+    q, k, v, cu_seqlen, max_seqlen = generate_data(batch, seqlen, head, head_dim, dtype)
+    print(f"batch:{batch} head:{head} seqlen:{seqlen} chunk:{moba_chunk_size} topk:{moba_topk} select_mode: {select_mode} simsum_threshold:{simsum_threshold}")
+    vo_grad = torch.randn_like(q)
+    
+    # Warmup
+    warmup_iters = 3
+    perf_test_iters = 10
+
+    # Warmup
+    for _ in range(warmup_iters):
+        o = flash_attn_varlen_func(q, k, v, cu_seqlen, cu_seqlen, max_seqlen, max_seqlen, causal=False)
+        torch.autograd.backward(o, vo_grad)
+    
+    torch.cuda.synchronize()
+    start_flash = time.perf_counter()
+    for _ in range(perf_test_iters):
+        o = flash_attn_varlen_func(q, k, v, cu_seqlen, cu_seqlen, max_seqlen, max_seqlen, causal=False)
+        torch.autograd.backward(o, vo_grad)
+        
+    torch.cuda.synchronize()
+    time_flash = (time.perf_counter() - start_flash) / perf_test_iters * 1000
+
+    # Warmup
+    for _ in range(warmup_iters):
+        om = moba_attn_varlen(q, k, v, cu_seqlen, max_seqlen, moba_chunk_size=moba_chunk_size, moba_topk=moba_topk, select_mode=select_mode, simsum_threshold=simsum_threshold, threshold_type=threshold_type)
+        torch.autograd.backward(om, vo_grad)
+
+    torch.cuda.synchronize()
+    start_moba = time.perf_counter()
+    for _ in range(perf_test_iters):
+        om = moba_attn_varlen(q, k, v, cu_seqlen, max_seqlen, moba_chunk_size=moba_chunk_size, moba_topk=moba_topk, select_mode=select_mode, simsum_threshold=simsum_threshold, threshold_type=threshold_type)
+        torch.autograd.backward(om, vo_grad)
+    
+    torch.cuda.synchronize()
+    time_moba = (time.perf_counter() - start_moba) / perf_test_iters * 1000
+    
+    print(f"Flash: {time_flash:.2f}ms, MoBA: {time_moba:.2f}ms")
+    print(f"Speedup:  {time_flash / time_moba:.2f}x")
+
+
+
+if __name__ == "__main__":
+    """
+    CUDA_VISIBLE_DEVICES=1 \
+    python -u csrc/attn/vmoba_attn/vmoba/vmoba.py
+    """
+    test_attn_varlen_moba_speed(batch=1, head=12, seqlen=32760, head_dim=128, moba_chunk_size=32760 // 3 // 6 // 4, moba_topk=3, select_mode='threshold', simsum_threshold=0.3, threshold_type='query_head')
@@ -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
@@ -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
@@ -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
@@ -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
@@ -1,26 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS    ?=
-SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = source
-BUILDDIR      = build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-clean:
-	@$(SPHINXBUILD) -M clean "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-	rm -rf "$(SOURCEDIR)/getting_started/examples"
-	rm -rf "$(SOURCEDIR)/inference/examples"
-	rm -rf "$(SOURCEDIR)/training/examples"
@@ -1,20 +1,39 @@
-# FastVideo documents
+# FastVideo Documentation

-## Build the docs
+This directory contains the FastVideo documentation built with MkDocs.
+
+## Build the docs locally

 ```bash
-# Install dependencies.
-pip install -r requirements-docs.txt
+# Install dependencies
+pip install -r docs/requirements-mkdocs.txt

-# Build the docs.
-make clean
-make html
+# Serve docs with live reload (recommended for development)
+mkdocs serve
+
+# Or build static site
+mkdocs build
 ```

-## Open the docs with your browser
+## View the docs
+
+### Development server (with live reload)

 ```bash
-python -m http.server -d build/html/
+mkdocs serve
 ```

-Launch your browser and open localhost:8000.
+Then open your browser to: http://127.0.0.1:8000
+
+### Static build
+
+```bash
+mkdocs build
+python -m http.server -d site/
+```
+
+Then open your browser to: http://localhost:8000
+
+## Automatic Deployment
+
+Documentation is automatically built and deployed to GitHub Pages when changes are pushed to the `main` branch via the `.github/workflows/docs.yml` workflow.
@@ -0,0 +1,248 @@
+# FastVideo API Reference
+
+This page contains the complete API reference for the FastVideo library.
+
+## fastvideo
+
+### Modules
+
+| Name | Description |
+|------|-------------|
+| [attention](#fastvideoattention) | Attention mechanisms and backends for video generation |
+| [configs](#fastvideoconfigs) | Configuration classes for pipelines, models, and sampling |
+| [distributed](#fastvideodistributed) | Distributed execution and communication utilities |
+| [entrypoints](#fastvideoentrypoints) | Main API entry points for video generation |
+| [models](#fastvideomodels) | Model implementations (transformers, VAEs, schedulers) |
+| [pipelines](#fastvideopipelines) | Core pipeline classes for video diffusion |
+| [training](#fastvideotraining) | Training utilities and helpers |
+| [workflow](#fastvideoworkflow) | Workflow management and orchestration |
+| [dataset](#fastvideodataset) | Dataset handling and preprocessing |
+| [layers](#fastvideolayers) | Custom neural network layers |
+| [platforms](#fastvideoplatforms) | Platform-specific implementations |
+| [utils](#fastvideoutils) | Utility functions and helpers |
+| [worker](#fastvideoworker) | Execution workers for video generation |
+
+## fastvideo.attention
+
+::: fastvideo.attention
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      show_submodules: true
+      heading_level: 3
+
+## fastvideo.configs
+
+::: fastvideo.configs
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      show_submodules: true
+      heading_level: 3
+
+### Submodules
+
+#### fastvideo.configs.pipelines
+
+::: fastvideo.configs.pipelines
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      heading_level: 4
+
+#### fastvideo.configs.models
+
+::: fastvideo.configs.models
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      heading_level: 4
+
+#### fastvideo.configs.sample
+
+::: fastvideo.configs.sample
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      heading_level: 4
+
+## fastvideo.distributed
+
+::: fastvideo.distributed
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      show_submodules: true
+      heading_level: 3
+
+## fastvideo.entrypoints
+
+::: fastvideo.entrypoints
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      show_submodules: true
+      heading_level: 3
+
+## fastvideo.models
+
+::: fastvideo.models
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      show_submodules: true
+      heading_level: 3
+
+### Submodules
+
+#### fastvideo.models.registry
+
+::: fastvideo.models.registry
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      heading_level: 4
+
+#### fastvideo.models.loader
+
+::: fastvideo.models.loader
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      heading_level: 4
+
+## fastvideo.pipelines
+
+::: fastvideo.pipelines
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      show_submodules: true
+      heading_level: 3
+
+### Submodules
+
+#### fastvideo.pipelines.composed_pipeline_base
+
+::: fastvideo.pipelines.composed_pipeline_base
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      heading_level: 4
+
+#### fastvideo.pipelines.lora_pipeline
+
+::: fastvideo.pipelines.lora_pipeline
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      heading_level: 4
+
+#### fastvideo.pipelines.pipeline_batch_info
+
+::: fastvideo.pipelines.pipeline_batch_info
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      heading_level: 4
+
+#### fastvideo.pipelines.pipeline_registry
+
+::: fastvideo.pipelines.pipeline_registry
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      heading_level: 4
+
+#### fastvideo.pipelines.stages
+
+::: fastvideo.pipelines.stages
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      heading_level: 4
+
+## fastvideo.training
+
+::: fastvideo.training
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      show_submodules: true
+      heading_level: 3
+
+## fastvideo.workflow
+
+::: fastvideo.workflow
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      show_submodules: true
+      heading_level: 3
+
+## fastvideo.dataset
+
+::: fastvideo.dataset
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      show_submodules: true
+      heading_level: 3
+
+## fastvideo.layers
+
+::: fastvideo.layers
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      show_submodules: true
+      heading_level: 3
+
+## fastvideo.platforms
+
+::: fastvideo.platforms
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      show_submodules: true
+      heading_level: 3
+
+## fastvideo.utils
+
+::: fastvideo.utils
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      heading_level: 3
+
+## fastvideo.worker
+
+::: fastvideo.worker
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      show_submodules: true
+      heading_level: 3
@@ -0,0 +1,27 @@
+# API Summary
+
+This page provides a quick overview of the main FastVideo API components.
+
+## Video Generator
+
+::: fastvideo.VideoGenerator
+    options:
+      show_root_heading: false
+      show_source: false
+      heading_level: 3
+
+## Initialization Configuration
+
+::: fastvideo.PipelineConfig
+    options:
+      show_root_heading: false
+      show_source: false
+      heading_level: 3
+
+## Sampling Configuration
+
+::: fastvideo.SamplingParam
+    options:
+      show_root_heading: false
+      show_source: false
+      heading_level: 3
@@ -0,0 +1,41 @@
+.vertical-table-header th.head:not(.stub) {
+    writing-mode: sideways-lr;
+    white-space: nowrap;
+    max-width: 0;
+    p {
+       margin: 0;
+    }
+}
+
+/* Image sizing classes */
+.image-small {
+    max-width: 200px;
+    height: auto;
+}
+
+.image-medium {
+    max-width: 400px;
+    height: auto;
+}
+
+.image-large {
+    max-width: 600px;
+    height: auto;
+}
+
+.image-full {
+    max-width: 100%;
+    height: auto;
+}
+
+/* Responsive images */
+img {
+    max-width: 100%;
+    height: auto;
+}
+
+/* Center images */
+.image-center {
+    display: block;
+    margin: 0 auto;
+}
@@ -0,0 +1,6 @@
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+<path d="M8.12646 55.5451L14.065 100.999L15.2527 100.999L9.31417 55.5451L8.12646 55.5451Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.18771"/>
+<path d="M1 55.5451L6.93853 100.999L7.53239 100.999L1.59385 55.5451L1 55.5451Z" fill="#356CFF" stroke="#356CFF" stroke-width="0.593853"/>
+<path d="M15.2524 48.2724L30.0988 1H33.6619L18.8156 48.2724H15.2524Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.18771"/>
+<path d="M8.12646 48.2724L22.9728 1H24.1605L9.31417 48.2724H8.12646Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.18771"/>
+<path d="M1 48.2724L15.8463 1H16.4402L1.59385 48.2724H1Z" fill="#356CFF" stroke="#356CFF" stroke-width="0.593853"/>
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+</svg>
@@ -1,4 +1,4 @@
-(docker)=
+
 # 🐳 Using the FastVideo Docker Image

 If you prefer a containerized development environment or want to avoid managing dependencies manually, you can use our prebuilt Docker image:
@@ -3,11 +3,3 @@
 # 🧰 Developer Environment

 Accelerate your FastVideo development workflow by leveraging Docker images and cloud GPUs for efficient experimentation and reproducible environments.
-
-:::{toctree}
-:caption: Contents
-:maxdepth: 1
-
-docker
-runpod
-:::
@@ -1,4 +1,3 @@
-(runpod)=

 # 📦 Developing FastVideo on RunPod

@@ -10,7 +9,7 @@ Choose a GPU that supports CUDA 12.8

 Pick 1 or 2 L40S GPU(s)

-![RunPod CUDA selection](../../_static/images/runpod_cuda.png)
+![RunPod CUDA selection](../../assets/images/runpod_cuda.png)

 When creating your pod template, use this image:

@@ -24,11 +23,11 @@ Paste Container Start Command to support SSH ([RunPod Docs](https://docs.runpod.
 bash -c "apt update;DEBIAN_FRONTEND=noninteractive apt-get install openssh-server -y;mkdir -p ~/.ssh;cd $_;chmod 700 ~/.ssh;echo \"$PUBLIC_KEY\" >> authorized_keys;chmod 700 authorized_keys;service ssh start;sleep infinity"
 ```

-![RunPod template configuration](../../_static/images/runpod_template.png)
+![RunPod template configuration](../../assets/images/runpod_template.png)

 After deploying, the pod will take a few minutes to pull the image and start the SSH service.

-![RunPod ssh](../../_static/images/runpod_ssh.png)
+![RunPod ssh](../../assets/images/runpod_ssh.png)

 ## Working with the pod

@@ -1,4 +1,3 @@
-(developer-overview)=

 # 🛠️ Contributing to FastVideo

@@ -71,3 +70,7 @@ uv pip install ninja

 python setup.py install
 ```
+
+## Testing
+
+Please refer to the [Testing Guide](testing.md) for more information on how to add and run tests in FastVideo.
@@ -0,0 +1,53 @@
+# Profiling FastVideo
+
+!!! warning
+    Profiling is only intended for FastVideo developers and maintainers to understand the proportion of time spent in different parts of the codebase. **FastVideo end-users should never turn on profiling** as it will significantly slow down the inference.
+
+## Profiling with PyTorch
+
+FastVideo exposes a process-wide torch profiler that you can enable via environment variables. Set `FASTVIDEO_TORCH_PROFILER_DIR` to an absolute directory path to start collecting traces, and specify the regions you want recorded with `FASTVIDEO_TORCH_PROFILE_REGIONS`:
+
+```bash
+FASTVIDEO_TORCH_PROFILER_DIR=/mnt/traces/fastvideo \
+FASTVIDEO_TORCH_PROFILE_REGIONS="profiler_region_model_loading,profiler_region_training_step"
+```
+
+All profiled regions must be registered in `fastvideo.profiler`; the current list includes:
+
+- `profiler_region_model_loading` — pipeline/module loading
+- `profiler_region_inference_pre_denoising`
+- `profiler_region_inference_denoising`
+- `profiler_region_inference_post_denoising`
+- `profiler_region_training_checkpoint_saving`
+- `profiler_region_training_dit`
+- `profiler_region_training_validation`
+- `profiler_region_training_epoch`
+- `profiler_region_training_step`
+- `profiler_region_training_backward`
+- `profiler_region_training_optimizer`
+- `profiler_region_distillation_teacher_forward`
+- `profiler_region_distillation_student_forward`
+- `profiler_region_distillation_loss`
+- `profiler_region_distillation_update`
+
+While profiling is enabled, FastVideo records additional annotations:
+
+- `fastvideo.region::<name>` spans are emitted when entering a region.
+- `fastvideo.profiler.enable_collection` / `fastvideo.profiler.disable_collection` events mark when torch profiler collection is toggled on or off.
+
+Only one profiler instance is created per process; subsequent pipelines reuse the same controller. If you set `FASTVIDEO_TORCH_PROFILE_REGIONS` incorrectly (e.g. misspelled name), FastVideo logs a warning and ignores that entry.
+
+Additional knobs:
+
+- `FASTVIDEO_TORCH_PROFILER_RECORD_SHAPES`
+- `FASTVIDEO_TORCH_PROFILER_WITH_PROFILE_MEMORY`
+- `FASTVIDEO_TORCH_PROFILER_WITH_STACK`
+- `FASTVIDEO_TORCH_PROFILER_WITH_FLOPS`
+
+Traces can be visualized using <https://ui.perfetto.dev/>.
+
+### Best Practices
+
+- Keep the profiled step count small; traces can be large and slow down job shutdown while the profiler flushes data.
+- After profiling, clean up trace directories to avoid filling disks.
+- When adding new regions, register them in `fastvideo.profiler` and wrap the corresponding code block with `with self.profiler_controller.region("your_region"):` or the `@profile_region` decorator.
@@ -0,0 +1,129 @@
+# Testing in FastVideo
+
+This guide explains how to add and run tests in FastVideo. The testing suite is divided into several categories to ensure correctness across components, training workflows, and inference quality.
+
+## Test Types
+
+* **Unit Tests**: Located in `fastvideo/tests/dataset`, `fastvideo/tests/entrypoints`, and `fastvideo/tests/workflow`. These test individual functions and classes.
+* **Component Tests**: Located in `fastvideo/tests/encoders`, `fastvideo/tests/transformers`, and `fastvideo/tests/vaes`. These verify the loading and basic functionality of model components.
+* **SSIM Tests**: Located in `fastvideo/tests/ssim`. These are regression tests that compare generated videos against reference videos using the Structural Similarity Index Measure (SSIM) to detect quality degradation.
+* **Training Tests**: Located in `fastvideo/tests/training`. These validate training loops, loss calculations, and specific training techniques like LoRA, Distillation, and VSA.
+* **Inference Tests**: Located in `fastvideo/tests/inference`. These test specialized inference pipelines and optimizations (e.g., STA, V-MoBA).
+
+For now, we will focus on **SSIM Tests**.
+
+## SSIM Tests
+
+SSIM tests are located in `fastvideo/tests/ssim`. These tests generate videos using specific models and parameters, and compare them against reference videos to ensure that changes in the codebase do not degrade generation quality or alter the output unexpectedly.
+
+!!! note
+    If you are adding an SSIM test, this serves as a safeguard. Any future code changes that break or cause errors with the specific arguments and configurations you defined will trigger a failure. Therefore, it is important to include multiple settings and arguments that cover the core features of your new pipeline to ensure robust regression testing.
+
+### Directory Structure
+
+```
+fastvideo/tests/ssim/
+├── <GPU>_reference_videos/   # Reference videos organized by GPU type (e.g., L40S_reference_videos)
+│   ├── <Model_Name>/
+│   │   ├── <Backend>/        # e.g., FLASH_ATTN, TORCH_SDPA
+│   │   │   └── <Video_File>
+├── test_causal_similarity.py
+├── test_inference_similarity.py
+├── update_reference_videos.sh
+└── ...
+```
+
+### Adding a New SSIM Test
+
+To add a new SSIM test, follow these steps:
+
+1. **Create or Update a Test File**: You can add a new test function to an existing file (like `test_inference_similarity.py`) or create a new one if testing a distinct category of models.
+
+2. **Define Model Parameters**: Define the configuration for the model you want to test. This includes model path, dimensions, inference steps, and other generation parameters. **Note:** Consider using lower `num_inference_steps` or reduced resolution (e.g., 480p instead of 720p) to keep test execution time reasonable, provided it doesn't compromise the test's ability to detect regression.
+
+   ```python
+   MY_MODEL_PARAMS = {
+       "num_gpus": 1,
+       "model_path": "organization/model-name",
+       "height": 480,
+       "width": 832,
+       "num_frames": 45,
+       "num_inference_steps": 20,
+       # ... other parameters
+   }
+   ```
+
+3. **Implement the Test Function**:
+   * Use `pytest.mark.parametrize` to run the test with different prompts, backends, and models.
+   * Set the attention backend environment variable.
+   * Initialize the `VideoGenerator`.
+   * Generate the video.
+   * Compare the generated video with the reference video using `compute_video_ssim_torchvision`.
+
+   Example structure:
+
+   ```python
+   @pytest.mark.parametrize("prompt", TEST_PROMPTS)
+   @pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN"])
+   def test_my_model_similarity(prompt, ATTENTION_BACKEND):
+       # Setup output directories
+       # ...
+
+       # Initialize Generator
+       generator = VideoGenerator.from_pretrained(...)
+       generator.generate_video(prompt, ...)
+
+       # Compare with Reference
+       ssim_values = compute_video_ssim_torchvision(reference_path, generated_path, use_ms_ssim=True)
+        assert ssim_values[0] >= 0.98  # Threshold
+    ```
+
+4. **Reference Videos**:
+   * When running the test for the first time (or when updating the reference), the test will fail because the reference video is missing. The generated video will be saved in `fastvideo/tests/ssim/generated_videos`.
+   * Inspect the generated video to ensure it meets quality expectations.
+   * Move the generated video to the appropriate reference folder: `fastvideo/tests/ssim/<GPU>_reference_videos/<Model>/<Backend>/`.
+   * You can use the helper script `update_reference_videos.sh` to automate copying videos from `generated_videos` to `L40S_reference_videos`. Note: Check the script to ensure paths match your environment (it defaults to `L40S_reference_videos`).
+
+### Running Tests Locally
+
+To run the SSIM tests locally:
+
+```bash
+pytest fastvideo/tests/ssim/ -vs
+```
+
+Ensure you have the necessary GPUs available as defined in your test parameters.
+
+## Modal Workflow
+
+FastVideo uses [Modal](https://modal.com/) for running tests in a CI environment. The workflow scripts are located in `fastvideo/tests/modal/`.
+
+### `pr_test.py`
+
+The main entry point for CI tests is `fastvideo/tests/modal/pr_test.py`. This script defines Modal functions that execute the pytest suites on specific hardware (e.g., L40S, H100).
+
+### Updating Modal Configuration
+
+If you add a new test that requires:
+* **Different GPU Hardware**: You may need to change the `@app.function(gpu=...)` decorator.
+* **Longer Execution Time**: Increase the `timeout` parameter.
+* **New Environment Variables/Secrets**: Add them to `secrets=[...]` or the image environment. For example, if your model is gated on Hugging Face, ensure `HF_API_KEY` is passed.
+
+For SSIM tests, the `run_ssim_tests` function in `pr_test.py` currently runs:
+
+```python
+@app.function(gpu="L40S:2", image=image, timeout=2700, secrets=[modal.Secret.from_dict({"HF_API_KEY": os.environ.get("HF_API_KEY", "")})])
+def run_ssim_tests():
+    run_test("hf auth login --token $HF_API_KEY && pytest ./fastvideo/tests/ssim -vs")
+```
+
+If your new test file is inside `fastvideo/tests/ssim`, it will automatically be picked up by this command. However, ensure that the `gpu="L40S:2"` configuration is sufficient for your model. If your model requires more GPUs (e.g., 4 or 8), you might need to create a separate Modal function or update the existing one.
+
+### Workflow Scripts
+
+The shell script that triggers these tests in the CI pipeline is located at `.buildkite/scripts/pr_test.sh`. If you add a new test category (e.g., a new folder outside of `ssim`), you will need to:
+1. Add a new function in `fastvideo/tests/modal/pr_test.py`.
+2. Add a new case in `.buildkite/scripts/pr_test.sh` to handle the new test type.
+
+!!! note
+    If you are a maintainer, you'll need to finally manually update the workflow script in Buildkite. Otherwise, a maintainer will help you update.
@@ -29,7 +29,6 @@ FastVideo separates model components from execution logic with these principles:
 - **Custom Attention Backends**: Components can support and use different Attention implementations
 - **Pipeline Abstraction**: Consistent interface across diffusion models

-(design-fastvideo-args)=
 ## FastVideoArgs

 The `FastVideoArgs` class in `fastvideo/fastvideo_args.py` serves as the central configuration system for FastVideo. It contains all parameters needed to control model loading, inference configuration, performance optimization settings, and more.
@@ -61,7 +60,6 @@ with set_current_fastvideo_args(fastvideo_args):
    result = generate_video()
 ```

-(design-pipeline-system)=
 ## Pipeline System

 ### `ComposedPipelineBase`
@@ -108,7 +106,8 @@ def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> Forward
    return batch
 ```

-(design-forwardbatch)=
+![Pipeline execution and data flow](../assets/images/pipeline.png)
+
 ### ForwardBatch

 Defined in `fastvideo/pipelines/pipeline_batch_info.py`, `ForwardBatch` encapsulates the data payload passed between pipeline stages. It typically holds:
@@ -120,12 +119,10 @@ Defined in `fastvideo/pipelines/pipeline_batch_info.py`, `ForwardBatch` encapsul

 This structure facilitates clear state transitions between stages.

-(design-model-components)=
 ## Model Components

 The `fastvideo/models/` directory contains implementations of the core neural network models used in video diffusion:

-(design-transformer-models)=
 ### Transformer Models

 Transformer networks perform the actual denoising during diffusion:
@@ -152,7 +149,6 @@ def forward(
    return noise_pred  # Predicted noise residual
 ```

-(design-vae-variational-auto-encoder)=
 ### VAE (Variational Auto-Encoder)

 VAEs handle conversion between pixel space and latent space:
@@ -170,7 +166,6 @@ FastVideo's VAE implementations include:
 - Optional tiling for large frames
 - Distributed weight support

-(design-text-and-image-encoders)=
 ### Text and Image Encoders

 Encoders process conditioning inputs into embeddings:
@@ -188,7 +183,6 @@ FastVideo implements optimizations such as:
 - Caching for common prompts
 - Precision-tuned computation

-(design-schedulers)=
 ### Schedulers

 Schedulers manage the diffusion sampling process:
@@ -216,7 +210,10 @@ def step(
    return prev_sample
 ```

-(design-optimized-attention)=
+This diagram shows how models are discovered, validated, and loaded across entrypoints, executors, pipelines, and model loaders.
+
+![Model loading flow](../assets/images/load_models.png)
+
 ## Optimized Attention

 The `fastvideo/attention/` directory contains optimized attention implementations crucial for efficient video diffusion:
@@ -240,17 +237,17 @@ self.attn = LocalAttention(
 # export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
 ```

+![Attention backend selector design](../assets/images/attention_backend.png)
+
 ### Attention Patterns
 Supports various patterns with memory optimization techniques:
 - **Cross/Self/Temporal/Global-Local Attention**
 - Chunking, progressive computation, optimized masking

-(design-distributed-processing)=
 ## Distributed Processing

 The `fastvideo/distributed/` directory contains implementations for distributed model execution:

-(design-tensor-parallelism)=
 ### Tensor Parallelism

 Tensor parallelism splits model weights across devices:
@@ -307,7 +304,6 @@ Efficient communication primitives minimize distributed overhead:
 - **Tensor-Parallel AllReduce**: Combines partial results
 - **Distributed Synchronization**: Coordinates execution

-(design-forwardcontext)=
 ## Forward Context Management

 ### ForwardContext
@@ -330,7 +326,6 @@ with set_forward_context(current_timestep, attn_metadata, fastvideo_args):
    output = model(inputs)
 ```

-(design-executor-and-worker-abstractions)=
 ## Executor and Worker System

 The `fastvideo/worker/` directory contains the distributed execution framework:
@@ -357,7 +352,6 @@ Each GPU worker:

 This design allows FastVideo to efficiently utilize multiple GPUs while providing a simple, unified interface for model execution.

-(design-platforms)=
 ## Platforms

 The `fastvideo/platforms/` directory provides hardware platform abstractions that enable FastVideo to run efficiently on different hardware configurations:
@@ -388,7 +382,6 @@ else:

 The platform system is designed to be extensible for future hardware targets.

-(design-logger)=
 ## Logger
 See [PR](https://github.com/hao-ai-lab/FastVideo/pull/356)

@@ -1,4 +1,3 @@
-(v0-data-preprocess)=

 # 🧱 Data Preprocess for Distillation

@@ -0,0 +1,12 @@
+# 💡 Examples
+
+A collection of examples demonstrating usage of FastVideo.
+
+All documented examples are autogenerated using [generate_examples.py](https://github.com/hao-ai-lab/FastVideo/blob/main/docs/generate_examples.py) from examples found in the [examples](https://github.com/hao-ai-lab/FastVideo/tree/main/examples) directory.
+
+## Examples
+
+- [Examples Distillation Index](distillation/examples/examples_distillation_index.md)
+- [Examples Training Index](training/examples/examples_training_index.md)
+- [Examples Inference Index](inference/examples/examples_inference_index.md)
+
@@ -6,10 +6,11 @@ import re
 from dataclasses import dataclass, field
 from pathlib import Path

-ROOT_DIR = Path(__file__).parent.parent.parent.resolve()
-ROOT_DIR_RELATIVE = '../../../..'
+ROOT_DIR = Path(__file__).parent.parent.resolve()
+ROOT_DIR_RELATIVE = '../..'
 EXAMPLE_DIR = ROOT_DIR / "examples"
-EXAMPLE_DOC_DIR = ROOT_DIR / "docs/source/getting_started/examples"
+EXAMPLE_DOC_DIR = ROOT_DIR / "docs/getting_started/examples"
+GITHUB_REPO = "hao-ai-lab/FastVideo"  # Update this to your repo


 def fix_case(text: str) -> str:
@@ -71,9 +72,16 @@ class Index:

    def generate(self) -> str:
        content = f"# {self.title}\n\n{self.description}\n\n"
-        content += ":::{toctree}\n"
-        content += f":caption: {self.caption}\n:maxdepth: {self.maxdepth}\n"
-        content += "\n".join(self.documents) + "\n:::\n"
+        if self.caption:
+            content += f"## {self.caption}\n\n"
+        # Generate a simple list of links for MkDocs
+        for doc in self.documents:
+            # Convert document path to proper link
+            doc_link = doc.replace("\\", "/")
+            # Get just the filename for the link text
+            doc_title = fix_case(Path(doc).stem.replace("_", " ").title())
+            content += f"- [{doc_title}]({doc_link}.md)\n"
+        content += "\n"
        return content


@@ -142,30 +150,66 @@ class Example:
        return fix_case(self.path.stem.replace("_", " ").title())

    def generate(self) -> str:
-        # Convert the path to a relative path from __file__
-        make_relative = lambda path: ROOT_DIR_RELATIVE / path.relative_to(
-            ROOT_DIR)
+        # Create GitHub link to source
+        github_path = str(self.path.relative_to(ROOT_DIR)).replace("\\", "/")
+        github_url = f"https://github.com/{GITHUB_REPO}/blob/main/{github_path}"
+        content = f"**Source:** [{github_path}]({github_url})\n\n"

-        content = f"Source <gh-file:{self.path.relative_to(ROOT_DIR)}>.\n\n"
-        include = "include" if self.main_file.suffix == ".md" else \
-            "literalinclude"
-        if include == "literalinclude":
+        # Add title for code files
+        if self.main_file.suffix != ".md":
            content += f"# {self.title}\n\n"
-        content += f":::{{{include}}} {make_relative(self.main_file)}\n"  # type: ignore[no-untyped-call]
-        if include == "literalinclude":
-            content += f":language: {self.main_file.suffix[1:]}\n"
-        content += ":::\n\n"
+
+        # Include main file content
+        if self.main_file.suffix == ".md":
+            # For markdown files, include the content directly
+            with open(self.main_file, encoding='utf-8') as f:
+                content += f.read() + "\n\n"
+        else:
+            # For code files, use code blocks
+            language = self.main_file.suffix[1:] if self.main_file.suffix else ""
+            with open(self.main_file, encoding='utf-8') as f:
+                file_content = f.read()
+            content += f"```{language}\n{file_content}\n```\n\n"

        if not self.other_files:
            return content

-        content += "## Example materials\n\n"
+        content += "## Additional Files\n\n"
+        # Define binary/non-text file extensions to skip
+        binary_extensions = {
+            '.mp4', '.avi', '.mov', '.mkv', '.gif', '.jpg', '.jpeg', '.png',
+            '.webp', '.bmp', '.pdf', '.zip', '.tar', '.gz', '.mp3', '.wav'
+        }
+
        for file in sorted(self.other_files):
-            include = "include" if file.suffix == ".md" else "literalinclude"
-            content += f":::{{admonition}} {file.relative_to(self.path)}\n"
-            content += ":class: dropdown\n\n"
-            content += f":::{{{include}}} {make_relative(file)}\n:::\n"  # type: ignore[no-untyped-call]
-            content += ":::\n\n"
+            # Skip binary files
+            if file.suffix.lower() in binary_extensions:
+                continue
+
+            file_rel_path = file.relative_to(self.path)
+            # Use collapsible admonition syntax for MkDocs
+            content += f"??? note \"{file_rel_path}\"\n\n"
+
+            try:
+                if file.suffix == ".md":
+                    # Include markdown content with indentation
+                    with open(file, encoding='utf-8') as f:
+                        for line in f:
+                            content += f"    {line}"
+                else:
+                    # Include code with proper formatting
+                    language = file.suffix[1:] if file.suffix else ""
+                    with open(file, encoding='utf-8') as f:
+                        file_content = f.read()
+                    # Indent the code block for the admonition
+                    content += f"    ```{language}\n"
+                    for line in file_content.split('\n'):
+                        content += f"    {line}\n"
+                    content += "    ```\n"
+                content += "\n"
+            except UnicodeDecodeError:
+                # Skip files that can't be decoded as UTF-8
+                continue

        return content

@@ -195,7 +239,7 @@ class NestedStructure:

 def create_category_indices() -> dict[str, Index]:
    """Create category indices with their respective configurations."""
-    main_index_dir = ROOT_DIR / "docs/source/examples"
+    main_index_dir = ROOT_DIR / "docs/examples"
    if not main_index_dir.exists():
        main_index_dir.mkdir(parents=True)

@@ -203,17 +247,16 @@ def create_category_indices() -> dict[str, Index]:
        "inference":
        Index(
            path=ROOT_DIR /
-            "docs/source/inference/examples/examples_inference_index.md",
+            "docs/inference/examples/examples_inference_index.md",
            title="🚀 Examples",
            description=
-            "Inference examples demonstrate how to use FastVideo inference. We recommend starting with <project:basic.md>.",
+            "Inference examples demonstrate how to use FastVideo inference. We recommend starting with [basic.md](basic.md).",
            caption="Examples",
            maxdepth=1,
        ),
        "training":
        Index(
-            path=ROOT_DIR /
-            "docs/source/training/examples/examples_training_index.md",
+            path=ROOT_DIR / "docs/training/examples/examples_training_index.md",
            title="🚀 Examples",
            description=
            "Training examples demonstrate how to use FastVideo training.",
@@ -223,7 +266,7 @@ def create_category_indices() -> dict[str, Index]:
        "distillation":
        Index(
            path=ROOT_DIR /
-            "docs/source/distillation/examples/examples_distillation_index.md",
+            "docs/distillation/examples/examples_distillation_index.md",
            title="🚀 Examples",
            description=
            "Distillation examples demonstrate how to use FastVideo distillation.",
@@ -246,9 +289,21 @@ def find_examples(category_indices: dict[str, Index],
    examples = []
    glob_patterns = ["*.py", "*.md", "*.sh"]

+    # Map category names to actual directory names
+    category_dir_mapping = {
+        "distillation": "distill",  # examples/distill/ -> distillation category
+    }
+
    # Find categorised examples
    for category in category_indices:
-        category_dir = EXAMPLE_DIR / category
+        # Use mapped directory name if available, otherwise use category name
+        dir_name = category_dir_mapping.get(category, category)
+        category_dir = EXAMPLE_DIR / dir_name
+
+        # Skip if directory doesn't exist
+        if not category_dir.exists():
+            continue
+
        globs = [category_dir.glob(pattern) for pattern in glob_patterns]
        for path in itertools.chain(*globs):
            examples.append(Example(path, category))
@@ -279,11 +334,18 @@ def create_nested_structures(
                                                dict[str,
                                                     NestedStructure]]]] = {}

+    # Map category names to actual directory names
+    category_dir_mapping = {
+        "distillation": "distill",
+    }
+
    for example in examples:
        if example.category not in ["training", "distillation"]:
            continue

-        category_dir = EXAMPLE_DIR / example.category
+        # Use mapped directory name if available
+        dir_name = category_dir_mapping.get(example.category, example.category)
+        category_dir = EXAMPLE_DIR / dir_name
        relative_path = example.path.relative_to(category_dir)
        path_parts = relative_path.parts

@@ -415,7 +477,7 @@ def generate_nested_examples(nested_structures: dict[str, dict[str, dict[
            category_index.documents.append(method)


-def generate_examples(generate_main_index=False):
+def generate_examples(generate_main_index: bool = False) -> None:
    """
    Generate example documentation.
    
@@ -429,12 +491,14 @@ def generate_examples(generate_main_index=False):
    # Create the main examples index only if requested
    examples_index = None
    if generate_main_index:
-        main_index_dir = ROOT_DIR / "docs/source/examples"
+        main_index_dir = ROOT_DIR / "docs/examples"
        examples_index = Index(
            path=main_index_dir / "examples_index.md",
            title="💡 Examples",
            description=
-            "A collection of examples demonstrating usage of FastVideo.\nAll documented examples are autogenerated using <gh-file:docs/source/generate_examples.py> from examples found in <gh-file:examples>.",
+            "A collection of examples demonstrating usage of FastVideo.\n\n"
+            f"All documented examples are autogenerated using [generate_examples.py](https://github.com/{GITHUB_REPO}/blob/main/docs/generate_examples.py) "
+            f"from examples found in the [examples](https://github.com/{GITHUB_REPO}/tree/main/examples) directory.",
            caption="Examples",
            maxdepth=2)

@@ -471,3 +535,19 @@ def generate_examples(generate_main_index=False):
    if generate_main_index and examples_index:
        with open(examples_index.path, "w+") as f:
            f.write(examples_index.generate())
+
+
+def on_pre_build_hook(config, **kwargs):
+    """
+    MkDocs hook to generate examples before building the documentation.
+    This function is called automatically by the mkdocs-simple-hooks plugin.
+    """
+    print("Generating example documentation...")
+    generate_examples(generate_main_index=True)
+    print("Example documentation generated successfully!")
+
+
+if __name__ == "__main__":
+    print("Generating example documentation...")
+    generate_examples(generate_main_index=True)
+    print("Example documentation generated successfully!")
@@ -0,0 +1,41 @@
+
+# 🔧 Installation
+
+FastVideo supports the following hardware platforms:
+
+- [NVIDIA CUDA](installation/gpu.md)
+- [Apple silicon](installation/mps.md)
+
+## Quick Installation
+
+### Using pip
+
+```bash
+pip install fastvideo
+```
+
+### Using conda
+
+```bash
+conda install -c conda-forge fastvideo
+```
+
+### From source
+
+```bash
+git clone https://github.com/hao-ai-lab/FastVideo.git
+cd FastVideo
+pip install -e .
+```
+
+## Hardware Requirements
+
+- **NVIDIA GPUs**: CUDA 11.8+ with compute capability 7.0+
+- **Apple Silicon**: macOS 12.0+ with M1/M2/M3 chips
+- **CPU**: x86_64 architecture (for CPU-only inference)
+
+## Next Steps
+
+- [Quick Start Guide](quick_start.md) - Get started with your first video generation
+- [Configuration](../inference/configuration.md) - Learn about configuration options
+- [Examples](../inference/examples/) - Explore example scripts and notebooks
@@ -30,17 +30,8 @@ conda create -n fastvideo python=3.12 -y
 conda activate fastvideo
 ```

-:::{note}
-[PyTorch has deprecated the conda release channel](https://github.com/pytorch/pytorch/issues/138506). If you use `conda`, please only use it to create Python environment rather than installing packages.
-:::
-
 #### uv

-:::{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:

 ```console
@@ -31,17 +31,8 @@ conda create -n fastvideo python=3.12.4 -y
 conda activate fastvideo
 ```

-:::{note}
-[PyTorch has deprecated the conda release channel](https://github.com/pytorch/pytorch/issues/138506). If you use `conda`, please only use it to create Python environment rather than installing packages.
-:::
-
 #### uv

-:::{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:

 ```console
@@ -0,0 +1,55 @@
+# 🚀 Quick Start
+
+Get up and running with FastVideo in minutes!
+
+## Installation
+
+First, install FastVideo:
+
+```bash
+pip install fastvideo
+```
+
+## Basic Usage
+
+### Text-to-Video Generation
+
+```python
+from fastvideo import FastVideoPipeline
+
+# Initialize the pipeline
+pipe = FastVideoPipeline.from_pretrained("wan2.1-t2v-1.3B")
+
+# Generate a video
+prompt = "A cat playing with a ball of yarn"
+video = pipe(prompt, num_frames=16, height=512, width=512)
+
+# Save the video
+video.save("output.mp4")
+```
+
+### Image-to-Video Generation
+
+```python
+from fastvideo import FastVideoPipeline
+from PIL import Image
+
+# Load an image
+image = Image.open("input.jpg")
+
+# Initialize the pipeline
+pipe = FastVideoPipeline.from_pretrained("wan2.1-i2v-14B-480p")
+
+# Generate a video from the image
+video = pipe(image, num_frames=16, height=480, width=480)
+
+# Save the video
+video.save("output.mp4")
+```
+
+## Next Steps
+
+- [Installation Guide](installation.md) - Detailed installation instructions
+- [Configuration](../inference/configuration.md) - Learn about configuration options
+- [Examples](../inference/examples/) - Explore more examples
+- [Optimizations](../inference/optimizations.md) - Performance optimization tips
@@ -0,0 +1,42 @@
+# V1 API
+
+FastVideo's V1 API provides a streamlined interface for video generation tasks with powerful customization options. This page documents the primary components of the API.
+
+## Video Generator
+
+This class will be the primary Python API for generating videos and images.
+
+::: fastvideo.entrypoints.video_generator.VideoGenerator
+    options:
+      show_root_heading: true
+      show_source: false
+      members:
+        - from_pretrained
+      heading_level: 3
+
+`VideoGenerator.from_pretrained()` should be the primary way of creating a new video generator.
+
+## Configuring FastVideo
+
+The following two classes `PipelineConfig` and `SamplingParam` are used to configure initialization and sampling parameters, respectively.
+
+### PipelineConfig
+
+::: fastvideo.configs.pipelines.base.PipelineConfig
+    options:
+      show_root_heading: true
+      show_source: false
+      members:
+        - from_pretrained
+        - dump_to_json
+      heading_level: 4
+
+### SamplingParam
+
+::: fastvideo.configs.sample.base.SamplingParam
+    options:
+      show_root_heading: true
+      show_source: false
+      members:
+        - from_pretrained
+      heading_level: 4
@@ -1,131 +1,52 @@
 # Welcome to FastVideo

-:::{figure} ../../assets/logos/logo.svg
-:align: center
-:alt: FastVideo
-:class: no-scaled-link
-:width: 60%
-:::
+<div style="text-align: center;">
+  <img src="assets/logos/logo.svg" alt="FastVideo" style="width: 60%;" />
+</div>

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

-<p style="text-align:center">
+<div style="text-align: center;">
 <script async defer src="https://buttons.github.io/buttons.js"></script>
 <a class="github-button" href="https://github.com/hao-ai-lab/FastVideo/" data-show-count="true" data-size="large" aria-label="Star">Star</a>
 <a class="github-button" href="https://github.com/hao-ai-lab/FastVideo/subscription" data-icon="octicon-eye" data-size="large" aria-label="Watch">Watch</a>
 <a class="github-button" href="https://github.com/hao-ai-lab/FastVideo/fork" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork">Fork</a>
-</p>
-:::
+</div>

 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.

 <div style="text-align: center;">
-  <img src=_static/images/fastwan.png width="100%"/>
+  <img src="assets/images/fastwan.png" style="width: 100%;"/>
 </div>

 ## Key Features

 FastVideo has the following features:
+
 - State-of-the-art performance optimizations for inference
  - [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.
+  - 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.

 ## Documentation

-% How to start using FastVideo?
+Welcome to FastVideo! This documentation will help you get started with our unified inference and post-training framework for accelerated video generation.

-:::{toctree}
-:caption: Getting Started
-:maxdepth: 1
+Use the navigation menu on the left to explore different sections:

-getting_started/installation
-<!-- getting_started/v1_api -->
-:::
-
-:::{toctree}
-:caption: Inference
-:maxdepth: 1
-
-inference/inference_quick_start
-inference/examples/examples_inference_index
-inference/configuration
-inference/optimizations
-inference/comfyui
-inference/support_matrix
-inference/cli
-inference/add_pipeline
-:::
-
-:::{toctree}
-:caption: Training
-:maxdepth: 1
-
-training/examples/examples_training_index
-training/data_preprocess
-<!-- training/finetune -->
-:::
-
-:::{toctree}
-:caption: Distillation
-:maxdepth: 1
-
-distillation/examples/examples_distillation_index
-distillation/data_preprocess
-distillation/dmd
-:::
-
-% What is STA Kernel?
-
-:::{toctree}
-:caption: Sliding Tile Attention
-:maxdepth: 1
-
-sliding_tile_attention/installation
-sliding_tile_attention/demo
-:::
-
-% What is VSA Kernel?
-
-:::{toctree}
-:caption: Video Sparse Attention
-:maxdepth: 1
-
-video_sparse_attention/installation
-:::
-
-:::{toctree}
-:caption: Design
-:maxdepth: 1
-design/overview
-:::
-
-:::{toctree}
-:caption: Developer Guide
-:maxdepth: 2
-
-contributing/overview
-contributing/developer_env/index
-:::
-
-:::{toctree}
-:caption: API Reference
-:maxdepth: 2
-
-<!-- api/summary -->
-api/fastvideo/fastvideo
-:::
-
-## Indices and tables
-
- {ref}`genindex`
- {ref}`modindex`
+- **Getting Started**: Installation and quick start guides
+- **Inference**: Learn how to use FastVideo for video generation
+- **Training**: Data preprocessing and fine-tuning workflows
+- **Distillation**: Post-training optimization techniques
+- **Sliding Tile Attention**: Advanced attention mechanisms
+- **Video Sparse Attention**: Efficient attention for video models
+- **Design**: Framework architecture and design principles
+- **Developer Guide**: Contributing and development setup
+- **API Reference**: Complete API documentation
@@ -1,4 +1,3 @@
-(add-pipeline)=

 # 🏗️ Adding a New Pipeline

@@ -1,4 +1,4 @@
-(inference-configuration)=
+
 # Configuration

 ## Multi-GPU Setup
@@ -1,32 +1,37 @@
-(inference-optimizations)=
+
 # Optimizations

 This page describes the various options for speeding up generation times in FastVideo.

 ## Table of Contents
+
 - Optimized Attention Backends
+
  - [Flash Attention](#optimizations-flash)
  - [Sliding Tile Attention](#optimizations-sta)
  - [Sage Attention](#optimizations-sage)
+  - [Sage Attention 3](#optimizations-sage3)

 - Caching Techniques
  - [TeaCache](#optimizations-teacache)

-(optimizations-backends)=
 ## Attention Backends

 ### Available Backends
+
 - Torch SDPA: `FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA`
 - Flash Attention 2 and 3: `FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN`
 - Sliding Tile Attention: `FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN`
 - Video Sparse Attention: `FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN`
 - Sage Attention: `FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN`
+- Sage Attention 3: `FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN_THREE`

 ### Configuring Backends

 There are two ways to configure the attention backend in FastVideo.

 #### 1. In Python
+
 In python, set the `FASTVIDEO_ATTENTION_BACKEND` environment variable before instantiating `VideoGenerator` like this:

 ```python
@@ -34,13 +39,13 @@ os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLIDING_TILE_ATTN"
 ```

 #### 2. In CLI
+
 You can also set the environment variable on the command line:

 ```bash
 FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN python example.py
 ```

-(optimizations-flash)=
 ### Flash Attention

 **`FLASH_ATTN`**
@@ -57,16 +62,12 @@ And if using a Hopper+ GPU (ie H100), installing [Flash Attention 3](https://git
 git clone https://github.com/Dao-AILab/flash-attention.git && cd flash-attention

 cd hopper
-pip install ninja 
+pip install ninja
 python setup.py install
 ```

-:::{note}
-FastVideo will automatically detect and use `FA3` if it is installed when using `FLASH_ATTN` backend.
-:::
-
-(optimizations-sta)=
 ### Sliding Tile Attention
+
 **`SLIDING_TILE_ATTN`**

 ```bash
@@ -75,8 +76,8 @@ pip install st_attn==0.0.4

 Please see [this page](#sta-installation) for more installation instructions.

-(optimizations-vsa)=
 ### Video Sparse Attention
+
 **`VIDEO_SPARSE_ATTN`**

 ```bash
@@ -86,20 +87,40 @@ python setup_vsa.py install

 Please see [this page](#vsa-installation) for more installation instructions.

-(optimizations-sage)=
 ### Sage Attention
+
 **`SAGE_ATTN`**

 To use [SageAttention](https://github.com/thu-ml/SageAttention) 2.1.1, please compile from source:

 ```bash
 git clone https://github.com/thu-ml/SageAttention.git
-cd sageattention 
+cd sageattention
 python setup.py install  # or pip install -e .
 ```

-(optimizations-teacache)=
+### Sage Attention 3
+
+**`SAGE_ATTN_THREE`**
+
+[SageAttention 3](https://huggingface.co/jt-zhang/SageAttention3) is an advanced attention mechanism that leverages FP4 quantization and Blackwell GPU Tensor Cores for significant performance improvements.
+
+#### Hardware Requirements
+
+- RTX5090
+
+#### Installation
+
+Note that Sage Attention 3 requires `python>=3.13`, `torch>=2.8.0`, `CUDA >=12.8`. If you are using `uv` and using `torch==2.8.0` make sure that `sentencepiece==0.2.1` in the pyproject.toml file.
+
+To use Sage Attention 3 in FastVideo, first get access to the SageAttention3 code, then move `sageattn/` and `setup.py` to the directory `fastvideo/attention/backends`, then install from using:
+
+```bash
+python setup.py install
+```
+
 ## Teacache
+
 TeaCache is an optimization technique supported in FastVideo that can significantly speed up video generation by skipping redundant calculations across diffusion steps. This guide explains how to enable and configure TeaCache for optimal performance in FastVideo.

 ### What is TeaCache?
@@ -0,0 +1,66 @@
+# Compatibility Matrix
+
+The table below shows every supported model and optimizations supported for them.
+
+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.
+
+<style>
+  /* Target tables in this section */
+  #models-x-optimization + p + table {
+    display: block;
+    overflow-x: auto;
+    width: 100%;
+    font-size: 0.85rem;
+  }
+  
+  #models-x-optimization + p + table td,
+  #models-x-optimization + p + table th {
+    text-align: center;
+    white-space: nowrap;
+    padding: 0.5em;
+  }
+  
+  /* First two columns can wrap */
+  #models-x-optimization + p + table td:nth-child(1),
+  #models-x-optimization + p + table td:nth-child(2) {
+    white-space: normal;
+    min-width: 120px;
+  }
+  
+  #models-x-optimization + p + table td:nth-child(2) code {
+    font-size: 0.75rem;
+  }
+</style>
+
+| Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn | Sage Attn | 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 | ❌ | ✅ | ✅ | ⭕ |
+| Wan2.1 T2V 1.3B | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ |
+| Wan2.1 T2V 14B | `Wan-AI/Wan2.1-T2V-14B-Diffusers` | 480P, 720P | ✅ | ✅* | ✅ | ⭕ |
+| Wan2.1 I2V 480P | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ |
+| 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**: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.
+
+## Special requirements
+
+### StepVideo T2V
+- The self-attention in text-encoder (step_llm) only supports CUDA capabilities sm_80 sm_86 and sm_90
+
+### Sliding Tile Attention
+- Currently only Hopper GPUs (H100s) are supported.
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=source
-set BUILDDIR=build
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.https://www.sphinx-doc.org/
-	exit /b 1
-)
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd
@@ -1,15 +0,0 @@
-sphinx==7.4.7
-sphinx-argparse==0.5.2
-sphinx-autodoc2==0.5.0
-sphinx-book-theme==1.1.4
-sphinx-copybutton==0.5.2
-sphinx-design==0.6.1
-sphinx-togglebutton==0.3.2
-myst-parser==3.0.1
-msgspec
-commonmark # Required by sphinx-argparse when using :markdownhelp:
-
-# packages to install to build the documentation
-cachetools
-# -f https://download.pytorch.org/whl/cpu
-torch
@@ -1,51 +0,0 @@
-# Seed Parameter Behavior in vLLM
-
-## Overview
-
-The `seed` parameter in vLLM is used to control the random states for various random number generators. This parameter can affect the behavior of random operations in user code, especially when working with models in vLLM.
-
-## Default Behavior
-
-By default, the `seed` parameter is set to `None`. When the `seed` parameter is `None`, the global random states for `random`, `np.random`, and `torch.manual_seed` are not set. This means that the random operations will behave as expected, without any fixed random states.
-
-## Specifying a Seed
-
-If a specific seed value is provided, the global random states for `random`, `np.random`, and `torch.manual_seed` will be set accordingly. This can be useful for reproducibility, as it ensures that the random operations produce the same results across multiple runs.
-
-## Example Usage
-
-### Without Specifying a Seed
-
-```python
-import random
-from vllm import LLM
-
-# Initialize a vLLM model without specifying a seed
-model = LLM(model="Qwen/Qwen2.5-0.5B-Instruct")
-
-# Try generating random numbers
-print(random.randint(0, 100))  # Outputs different numbers across runs
-```
-
-### Specifying a Seed
-
-```python
-import random
-from vllm import LLM
-
-# Initialize a vLLM model with a specific seed
-model = LLM(model="Qwen/Qwen2.5-0.5B-Instruct", seed=42)
-
-# Try generating random numbers
-print(random.randint(0, 100))  # Outputs the same number across runs
-```
-
-## Important Notes
-
- If the `seed` parameter is not specified, the behavior of global random states remains unaffected.
- If a specific seed value is provided, the global random states for `random`, `np.random`, and `torch.manual_seed` will be set to that value.
- This behavior can be useful for reproducibility but may lead to non-intuitive behavior if the user is not explicitly aware of it.
-
-## Conclusion
-
-Understanding the behavior of the `seed` parameter in vLLM is crucial for ensuring the expected behavior of random operations in your code. By default, the `seed` parameter is set to `None`, which means that the global random states are not affected. However, specifying a seed value can help achieve reproducibility in your experiments.
@@ -1,4 +1,3 @@
-(sta-demo)=

 # 🔍 Demo
 This is is a demo for 2D STA with window size (6,6) operating on a (10, 10) image.
@@ -1,10 +1,9 @@
-(sta-installation)=

 # 🔧 Installation
 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 +11,6 @@ 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 +20,20 @@ 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
@@ -1,8 +0,0 @@
-.vertical-table-header th.head:not(.stub) {
-    writing-mode: sideways-lr;
-    white-space: nowrap;
-    max-width: 0;
-    p {
-       margin: 0;
-    }
-}
@@ -1,39 +0,0 @@
-<style>
-  .notification-bar {
-    width: 100vw;
-    display: flex;
-    justify-content: center;
-    align-items: center;
-    font-size: 16px;
-    padding: 0 6px 0 6px;
-  }
-  .notification-bar p {
-    margin: 0;
-  }
-  .notification-bar a {
-    font-weight: bold;
-    text-decoration: none;
-  }
-
-  /* Light mode styles (default) */
-  .notification-bar {
-    background-color: #fff3cd;
-    color: #856404;
-  }
-  .notification-bar a {
-    color: #d97706;
-  }
-
-  /* Dark mode styles */
-  html[data-theme=dark] .notification-bar {
-    background-color: #333;
-    color: #ddd;
-  }
-  html[data-theme=dark] .notification-bar a {
-    color: #ffa500; /* Brighter color for visibility */
-  }
-</style>
-
-<!-- <div class="notification-bar">
-  <p>You are viewing the latest developer preview docs. <a href="https://docs.vllm.ai/en/stable/">Click here</a> to view docs for the latest stable release.</p>
-</div> -->
@@ -1,19 +0,0 @@
-# Summary
-
-## Video Generator
-
-```{autodoc2-summary}
-    fastvideo.VideoGenerator
-```
-
-## Initialization Configuration
-
-```{autodoc2-summary}
-    fastvideo.configs.pipelines.PipelineConfig
-```
-
-## Sampling Configuration
-
-```{autodoc2-summary}
-    fastvideo.configs.sample.SamplingParam
-```
@@ -1,22 +0,0 @@
-# type: ignore
-# SPDX-License-Identifier: Apache-2.0
-from docutils import nodes
-from myst_parser.parsers.sphinx_ import MystParser
-from sphinx.ext.napoleon import docstring
-
-
-class NapoleonParser(MystParser):
-
-    def parse(self, input_string: str, document: nodes.document) -> None:
-        # Get the Sphinx configuration
-        config = document.settings.env.config
-
-        parsed_content = str(
-            docstring.GoogleDocstring(
-                str(docstring.NumpyDocstring(input_string, config)),
-                config,
-            ))
-        return super().parse(parsed_content, document)
-
-
-Parser = NapoleonParser
@@ -1,275 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-
-import datetime
-import logging
-import os
-import re
-import sys
-from pathlib import Path
-
-import requests
-
-logger = logging.getLogger(__name__)
-REPO_ROOT = Path(__file__).resolve().parent.parent.parent
-print(os.path.abspath(REPO_ROOT))
-sys.path.append(os.path.abspath(REPO_ROOT))
-
-# -- Project information -----------------------------------------------------
-
-project = 'FastVideo'
-copyright = f'{datetime.datetime.now().year}, FastVideo Team'
-author = 'the FastVideo Team'
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    "sphinx.ext.napoleon",
-    "sphinx.ext.linkcode",
-    "sphinx.ext.intersphinx",
-    "sphinx_copybutton",
-    "autodoc2",
-    "myst_parser",
-    "sphinxarg.ext",
-    "sphinx_design",
-    "sphinx_togglebutton",
-]
-myst_enable_extensions = [
-    "colon_fence",
-    "fieldlist",
-]
-autodoc2_packages = [
-    {
-        "path": "../../fastvideo",
-        "exclude_dirs": ["__pycache__", "third_party"],
-    },
-]
-autodoc2_output_dir = "api"
-autodoc2_render_plugin = "myst"
-autodoc2_hidden_objects = ["dunder", "private", "inherited"]
-autodoc2_docstring_parser_regexes = [
-    (".*", "docs.source.autodoc2_docstring_parser"),
-]
-autodoc2_sort_names = True
-autodoc2_index_template = None
-autodoc2_skip_module_regexes = [
-    "fastvideo.dataset",
-    "fastvideo.distill",
-    "fastvideo.data_preprocess",
-    "fastvideo.models",
-    "fastvideo.sample",
-    "fastvideo.utils",
-    "fastvideo.distill_adv",
-    "fastvideo.train",
-]
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns: list[str] = ["**/*.template.md", "**/*.inc.md"]
-
-# Exclude the prompt "$" when copying code
-copybutton_prompt_text = r"\$ "
-copybutton_prompt_is_regexp = True
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-#
-html_title = project
-html_theme = 'sphinx_book_theme'
-html_logo = '../../assets/logos/icon_simple.svg'
-html_theme_options = {
-    'path_to_docs': 'docs/source',
-    'repository_url': 'https://github.com/hao-ai-lab/FastVideo/',
-    'use_repository_button': True,
-    'use_edit_page_button': True,
-    # Prevents the full API being added to the left sidebar of every page.
-    # Reduces build time by 2.5x and reduces build size from ~225MB to ~95MB.
-    'collapse_navbar': True,
-    # Makes API visible in the right sidebar on API reference pages.
-    'show_toc_level': 3,
-}
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_static"]
-html_js_files = ["custom.js"]
-html_css_files = ["custom.css"]
-
-myst_url_schemes = {
-    'http': None,
-    'https': None,
-    'mailto': None,
-    'ftp': None,
-    "gh-issue": {
-        "url":
-        "https://github.com/hao-ai-lab/FastVideo/issues/{{path}}#{{fragment}}",
-        "title": "Issue #{{path}}",
-        "classes": ["github"],
-    },
-    "gh-pr": {
-        "url":
-        "https://github.com/hao-ai-lab/FastVideo/pull/{{path}}#{{fragment}}",
-        "title": "Pull Request #{{path}}",
-        "classes": ["github"],
-    },
-    "gh-dir": {
-        "url": "https://github.com/hao-ai-lab/FastVideo/tree/main/{{path}}",
-        "title": "{{path}}",
-        "classes": ["github"],
-    },
-    "gh-file": {
-        "url": "https://github.com/hao-ai-lab/FastVideo/blob/main/{{path}}",
-        "title": "{{path}}",
-        "classes": ["github"],
-    },
-}
-
-# see https://docs.readthedocs.io/en/stable/reference/environment-variables.html # noqa
-READTHEDOCS_VERSION_TYPE = os.environ.get('READTHEDOCS_VERSION_TYPE')
-if READTHEDOCS_VERSION_TYPE == "tag":
-    # remove the warning banner if the version is a tagged release
-    header_file = os.path.join(os.path.dirname(__file__),
-                               "_templates/sections/header.html")
-    # The file might be removed already if the build is triggered multiple times
-    # (readthedocs build both HTML and PDF versions separately)
-    if os.path.exists(header_file):
-        os.remove(header_file)
-
-
-# Generate additional rst documentation here.
-def setup(app):
-    from docs.source.generate_examples import generate_examples
-    generate_examples()
-
-
-_cached_base: str = ""
-_cached_branch: str = ""
-
-
-def get_repo_base_and_branch(pr_number: str) -> tuple[str | None, str | None]:
-    global _cached_base, _cached_branch
-    if _cached_base and _cached_branch:
-        return _cached_base, _cached_branch
-
-    url = f"https://api.github.com/repos/hao-ai-lab/FastVideo/pulls/{pr_number}"
-    response = requests.get(url)
-    if response.status_code == 200:
-        data = response.json()
-        _cached_base = data['head']['repo']['full_name']
-        _cached_branch = data['head']['ref']
-        return _cached_base, _cached_branch
-    else:
-        logger.error("Failed to fetch PR details: %s", response)
-        return None, None
-
-
-def linkcode_resolve(domain, info):
-    if domain != 'py':
-        return None
-    if not info['module']:
-        return None
-
-    # Get path from module name
-    file = Path(f"{info['module'].replace('.', '/')}.py")
-    path = REPO_ROOT / file
-    if not path.exists():
-        path = REPO_ROOT / file.with_suffix("") / "__init__.py"
-    if not path.exists():
-        return None
-
-    # Get the line number of the object
-    with open(path) as f:
-        lines = f.readlines()
-    name = info['fullname'].split(".")[-1]
-    pattern = fr"^( {{4}})*((def|class) )?{name}\b.*"
-    for lineno, line in enumerate(lines, 1):
-        if not line or line.startswith("#"):
-            continue
-        if re.match(pattern, line):
-            break
-
-    # If the line number is not found, return None
-    if lineno == len(lines):
-        return None
-
-    # If the line number is found, create the URL
-    filename = path.relative_to(REPO_ROOT)
-    if "checkouts" in path.parts:
-        # a PR build on readthedocs
-        pr_number = REPO_ROOT.name
-        base, branch = get_repo_base_and_branch(pr_number)
-        if base and branch:
-            return f"https://github.com/{base}/blob/{branch}/{filename}#L{lineno}"
-    # Otherwise, link to the source file on the main branch
-    return f"https://github.com/hao-ai-lab/FastVideo/blob/main/{filename}#L{lineno}"
-
-
-# Mock out external dependencies here, otherwise the autodoc pages may be blank.
-autodoc_mock_imports = [
-    "blake3",
-    "compressed_tensors",
-    "cpuinfo",
-    "cv2",
-    "torch",
-    "huggingface_hub",
-    "torchvision",
-    "transformers",
-    "psutil",
-    "prometheus_client",
-    "sentencepiece",
-    "vllm._C",
-    "PIL",
-    "numpy",
-    'triton',
-    "tqdm",
-    "tensorizer",
-    "pynvml",
-    "outlines",
-    "xgrammar",
-    "librosa",
-    "soundfile",
-    "gguf",
-    "lark",
-    "decord",
-]
-
-for mock_target in autodoc_mock_imports:
-    if mock_target in sys.modules:
-        logger.info(
-            "Potentially problematic mock target (%s) found; "
-            "autodoc_mock_imports cannot mock modules that have already "
-            "been loaded into sys.modules when the sphinx build starts.",
-            mock_target)
-
-intersphinx_mapping = {
-    "python": ("https://docs.python.org/3", None),
-    "typing_extensions":
-    ("https://typing-extensions.readthedocs.io/en/latest", None),
-    "aiohttp": ("https://docs.aiohttp.org/en/stable", None),
-    "pillow": ("https://pillow.readthedocs.io/en/stable", None),
-    "numpy": ("https://numpy.org/doc/stable", None),
-    "torch": ("https://pytorch.org/docs/stable", None),
-    "psutil": ("https://psutil.readthedocs.io/en/stable", None),
-}
-
-navigation_with_keys = False
@@ -1,18 +0,0 @@
-(installation-index)=
-
-# 🔧 Installation
-
-FastVideo supports the following hardware platforms:
-
-:::{toctree}
-:maxdepth: 1
-:hidden:
-
-installation/gpu
-installation/mps
-:::
-
- <project:installation/gpu.md>
-  - NVIDIA CUDA
- <project:installation/mps.md>
-  - Apple silicon
@@ -1,83 +0,0 @@
-# V1 API
-
-FastVideo's V1 API provides a streamlined interface for video generation tasks with powerful customization options. This page documents the primary components of the API.
-
-## Video Generator
-
-This class will be the primary Python API for generating videos and images.
-
-```{autodoc2-summary}
-    fastvideo.VideoGenerator
-```
-
-`````{py:class} VideoGenerator(fastvideo_args: fastvideo.fastvideo_args.FastVideoArgs, executor_class: type[fastvideo.worker.executor.Executor], log_stats: bool)
-:canonical: fastvideo.entrypoints.video_generator.VideoGenerator
-
-```{autodoc2-docstring} fastvideo.entrypoints.video_generator.VideoGenerator
-:parser: docs.source.autodoc2_docstring_parser
-```
-
-`VideoGenerator.from_pretrained()` should be the primary way of creating a new video generator.
-
-````{py:method} from_pretrained(model_path: str, device: typing.Optional[str] = None, torch_dtype: typing.Optional[torch.dtype] = None, pipeline_config: typing.Optional[typing.Union[str | fastvideo.configs.pipelines.PipelineConfig]] = None, **kwargs) -> fastvideo.entrypoints.video_generator.VideoGenerator
-:canonical: fastvideo.entrypoints.video_generator.VideoGenerator.from_pretrained
-:classmethod:
-
-```{autodoc2-docstring} fastvideo.entrypoints.video_generator.VideoGenerator.from_pretrained
-:parser: docs.source.autodoc2_docstring_parser
-```
-
-
-## Configuring FastVideo
-
-The follow two classes `PipelineConfig` and `SamplingParam` are used to configure initialization and sampling parameters, respectively.
-
-### PipelineConfig
-```{autodoc2-summary}
-    fastvideo.PipelineConfig
-```
-
-`````{py:class} PipelineConfig
-:canonical: fastvideo.configs.pipelines.base.PipelineConfig
-
-```{autodoc2-docstring} fastvideo.configs.pipelines.base.PipelineConfig
-:parser: docs.source.autodoc2_docstring_parser
-```
-
-````{py:method} from_pretrained(model_path: str) -> fastvideo.configs.pipelines.base.PipelineConfig
-:canonical: fastvideo.configs.pipelines.base.PipelineConfig.from_pretrained
-:classmethod:
-
-```{autodoc2-docstring} fastvideo.configs.pipelines.base.PipelineConfig.from_pretrained
-:parser: docs.source.autodoc2_docstring_parser
-```
-
-
-````{py:method} dump_to_json(file_path: str)
-:canonical: fastvideo.configs.pipelines.base.PipelineConfig.dump_to_json
-
-```{autodoc2-docstring} fastvideo.configs.pipelines.base.PipelineConfig.dump_to_json
-:parser: docs.source.autodoc2_docstring_parser
-```
-
-
-### SamplingParam
-
-```{autodoc2-summary}
-    fastvideo.SamplingParam
-```
-
-`````{py:class} SamplingParam
-:canonical: fastvideo.configs.sample.base.SamplingParam
-
-```{autodoc2-docstring} fastvideo.configs.sample.base.SamplingParam
-:parser: docs.source.autodoc2_docstring_parser
-```
-
-````{py:method} from_pretrained(model_path: str) -> fastvideo.configs.sample.base.SamplingParam
-:canonical: fastvideo.configs.sample.base.SamplingParam.from_pretrained
-:classmethod:
-
-```{autodoc2-docstring} fastvideo.configs.sample.base.SamplingParam.from_pretrained
-:parser: docs.source.autodoc2_docstring_parser
-```
@@ -1,136 +0,0 @@
-(support-matrix)=
-# Compatibility Matrix
-The table below shows every supported model and optimizations supported for them.
-
-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.
-
-:::{raw} html
-<style>
-  /* Make smaller to try to improve readability  */
-  td {
-    font-size: 0.9rem;
-    text-align: center;
-  }
-
-  th {
-    text-align: center;
-    font-size: 0.9rem;
-  }
-</style>
-:::
-
-:::{list-table}
-:header-rows: 1
-:stub-columns: 3
-:widths: auto
-:class: vertical-table-header
-
- * Model Name
-  * HuggingFace Model ID
-  * Resolutions
-  * 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
-  * ❌
-  * ✅
-  * ✅
-  * ⭕
- * Wan2.1 T2V 1.3B
-  * `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`
-  * 480P
-  * ✅
-  * ✅*
-  * ✅
-  * ⭕
- * Wan2.1 T2V 14B
-  * `Wan-AI/Wan2.1-T2V-14B-Diffusers`
-  * 480P, 720P
-  * ✅
-  * ✅*
-  * ✅
-  * ⭕
- * Wan2.1 I2V 480P
-  * `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers`
-  * 480P
-  * ✅
-  * ✅*
-  * ✅
-  * ⭕
- * 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**: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.
-
-## Special requirements
-
-### StepVideo T2V
- The self-attention in text-encoder (step_llm) only supports CUDA capabilities sm_80 sm_86 and sm_90
-
-### Sliding Tile Attention
- Currently only Hopper GPUs (H100s) are supported.
@@ -1,4 +1,3 @@
-(v0-data-preprocess)=

 # 🧱 Data Preprocess

@@ -1,4 +1,4 @@
-(v0-finetune)=
+
 # 🧠 Finetune
 ## ⚡ Full Finetune
 Ensure your data is prepared and preprocessed in the format specified in [data_preprocess.md](#v0-data-preprocess). For convenience, we also provide a mochi preprocessed Black Myth Wukong data that can be downloaded directly:
@@ -1,11 +1,9 @@
-(vsa-installation)=

 # 🔧 Installation
 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
@@ -34,9 +32,9 @@ 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
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
SolitaryThinker	e9d95b1c10	wip	2025-12-12 06:30:08 +00:00
SolitaryThinker	8b55e9706c	wip	2025-12-10 01:59:07 +00:00
William Lin	3ff640b2e6	[bigfix] [distillation] Fix DMD inference pipeline noise initialization shape (#921 )	2025-12-08 13:00:48 -08:00
William Lin	c722429ab5	[docs] fix testing.md visibility (#920 )	2025-12-08 00:44:53 -08:00
KyleShaoandKyleS1016	e04a192de6	[feat]: add COSMOS 2.5 DiT implementation (#897 ) Co-authored-by: KyleS1016 <kyle.s@gmicloud.ai>	2025-12-07 21:48:32 -08:00
William Lin	c9ca6d1298	[docs] add docs for ssim testing (#918 )	2025-12-06 18:20:04 -08:00
Wenxuan TanandSolitaryThinker	754292c419	Use assert_close in tests (#429 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2025-12-06 18:18:25 -08:00
Qi Jia	0082bc66fc	fix: correct mp backend GPU assignment on multi-GPU systems (#912 )	2025-11-30 23:00:22 -08:00
Ohm-Rishabh	8b1937422e	[feat] training mfu calculation scripts (#871 )	2025-11-27 16:54:17 -08:00
Eitan Turok gemini-code-assist[bot]William Lin	fb6cbf23e6	Fix the docs (#905 ) Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: William Lin <SolitaryThinker@users.noreply.github.com>	2025-11-27 00:37:03 -08:00
Mihir Jagtap	c8fdd5ed7b	[docs] modified the .github/workflows/docs.yml file to include path filtering (#906 )	2025-11-26 17:34:21 -08:00
Loay Rashid	1c19a6a00c	[Bugfix] Minor bugfixes (#889 )	2025-11-26 17:20:45 -08:00
William Lin	d44409c704	[CI] fix VSA training CI (#900 )	2025-11-24 17:47:59 -08:00
Zhang Peiyuan	5d1c7852b7	+ Awesome work using FastVideo or our research projects (#898 )	2025-11-23 22:22:27 -08:00
Wenxuan Tan	77a211d006	[misc] Update wechat link (#893 )	2025-11-20 19:59:05 -08:00
Wei Zhou	bef8169bb1	[Feat] [I2V] resize all image sizes to below 480*832 (#890 )	2025-11-20 00:08:36 -08:00
William Lin	681f1583f9	[readme] update link to inference code (#887 )	2025-11-19 13:24:13 -08:00
Wei Zhou RandNMR73 SolitaryThinker	e3b4564d5a	[feat] Add inference for MoE SF (#880 ) Co-authored-by: RandNMR73 <notomatthew31@gmail.com> Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2025-11-19 13:16:24 -08:00
Shao Duan	c0d03fc43d	[bugfix] [lora] [CI] Fix LoRA alpha scaling factor & Fix LoRA Inference CI (#870 )	2025-11-19 01:02:01 -08:00
Wei Zhou	404ee8538e	[Bugfix] [DMD Distillation] Each rank should have its own timestep sampled (#885 )	2025-11-18 14:03:25 -08:00
Shao Duan	e57ac59462	Fix mp worker busy loop to handle all string RPC methods (#881 )	2025-11-16 13:26:44 -08:00
Mihir Jagtap	8c55fdaf7e	[docs] add favicon (#878 )	2025-11-15 13:44:16 -08:00
Y-aang	c30779184f	fix: incorrect dv in vsa Triton kernel causing test_vsa error (#879 )	2025-11-14 22:00:39 -08:00
William Lin	9d188c0b6c	[misc] update wechat and slack invite links (#875 )	2025-11-12 23:03:56 -08:00
Mihir Jagtap	9dd7c54221	[docs] Update Home Readme.md with fixed links (#873 )	2025-11-12 13:32:44 -08:00
William Lin	62b95d8287	[feat] prepare for wan2.2 SF (#861 )	2025-11-04 18:06:48 -08:00
Kaiqin Kong	fdf21702f5	[Docs] add diagrams to docs (#863 )	2025-11-04 16:29:07 -08:00
Ohm-Rishabh	2972fc9449	Improve FSDP loading with size-based filtering (#853 )	2025-11-04 15:31:07 -08:00
Mihir Jagtap	8f5712629f	[docs] port to mkdocs (#855 )	2025-11-04 14:31:56 -08:00
Kevin Lin	436c701b9f	[bugfix] Add Cosmos2 sampling params to registry (#862 )	2025-11-02 00:09:17 -07:00
Kevin Lin	543fea88e3	[Feature] Add Cosmos2 i2v pipeline (#837 )	2025-10-30 20:03:57 -07:00
Kaiqin Kong	bdec816b31	move STA_configuration.py to fastvideo/attention/backends (#856 )	2025-10-29 13:54:13 -07:00
William Lin	2cd2e57d2e	[ci] fix causal ssim test (#848 )	2025-10-26 19:33:07 -07:00
William Linandainsley	9370234294	[feat] Add gradio local inference demo (#847 ) Co-authored-by: ainsley <jzhang2765@wisc.edu>	2025-10-26 07:01:33 -07:00
Jinzhe Pan	50da62e722	[bugfix] always force spawn instead of fork (#852 )	2025-10-23 16:36:50 -07:00
William Lin	4f3e8751db	[bugfix] [misc] Use training_state_checkpointing_steps in scripts/ (#846 )	2025-10-19 20:20:53 -07:00
Jinzhe PanandXingyu Long	f4c58894d9	[Feat] add ray support (#838 ) Co-authored-by: Xingyu Long <xingyulong97@gmail.com>	2025-10-16 23:17:54 -07:00
Ohm-Rishabh	01c94ef385	[feat] unified trainer logging (#841 )	2025-10-16 23:16:16 -07:00
Zhang Peiyuan	2415226d25	Update WeChat Link	2025-10-13 21:02:46 -07:00
Jiali Chen	404314d00f	[Feature]Add video-to-video (V2V) pipeline (#829 )	2025-10-12 21:53:05 -07:00
zyang6andkiritorl	87489f0872	Add wan2.1 functionality support for Ascend NPU platform (#810 ) Co-authored-by: kiritorl <1021709528@qq.com>	2025-10-09 16:25:08 -07:00
Zhang Peiyuan	9ce7c8039e	Update Wechat link	2025-10-06 15:01:19 -07:00
William Lin	e1e25e95f9	[feature] Add torch profiler (#827 )	2025-10-06 07:59:46 -07:00
William Lin	490bde90e1	[bugfix] Allow overriding dit checkpoint for inference and Lower VSA LR in example scripts (#831 )	2025-10-05 01:44:49 -07:00
RandNMR73 SolitaryThinker gemini-code-assist[bot]	dc7596b973	[self-forcing][8/n] Self-Forcing For Wan2.2-A14B + torch.compile training and distillation support (#818 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2025-10-02 15:01:45 -07:00
William Lin	335afa4457	[bugfix] Use training_state_checkpointing_steps instead of checkpointing_steps (#821 )	2025-09-28 15:22:43 -07:00
Yongqi Chen	3f77a6805a	[Feature]Update count trainable param for FSDP2 (#820 )	2025-09-28 15:22:04 -07:00
RandNMR73	13d0aae706	Add Sage Attention 3 Backend (#815 )	2025-09-24 15:11:38 -07:00
William Lin	404cbf4f3c	[self-forcing] [6/n] Add Ode Init training (#811 )	2025-09-22 17:58:19 -07:00
William Lin	958ffec844	[bugfix] Update learning rates for sparse distillation recipe (#812 )	2025-09-22 12:07:03 -07:00
Wei Zhou RandNMR73 SolitaryThinker	31f000d1cc	[self-forcing] [5/n] Add Self-Forcing distillation pipeline (#808 ) Co-authored-by: RandNMR73 <notomatthew31@gmail.com> Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2025-09-20 19:32:10 -07:00
Yongqi Chen	cd32b3e02f	Update example files and readme (#809 )	2025-09-20 18:15:59 -07:00
Zhang Peiyuan	bf27908095	Update WeChat Link	2025-09-20 14:16:20 -07:00
William Lin	c5f9ea53b2	[self-forcing] [4/n] Preprocessing for collecting ODE trajectory (#788 )	2025-09-15 17:54:42 -07:00
William Lin	d32a7184da	[bugfix] Wan2.2 Boundary ratio (#804 )	2025-09-15 11:17:35 -07:00
Wenxuan Tanandgemini-code-assist[bot]	2930abe456	[Bugfix] Fix VMoba requirements (#802 ) Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2025-09-14 18:28:52 -07:00
William Lin	b93ef4289d	[bugfix] Fix empty PipelineConfigs for Wan2.2 A14B (#800 )	2025-09-13 17:31:38 -07:00
William Lin RandNMR73 JerryZhou54 kevin314	401bdbd316	[self-forcing] [3/n] Text embed only preprocessing (#797 ) Co-authored-by: RandNMR73 <notomatthew31@gmail.com> Co-authored-by: JerryZhou54 <zhouw.jerry2017@outlook.com> Co-authored-by: kevin314 <kevin.lin.cs1@gmail.com>	2025-09-13 14:03:53 -07:00
William Lin	1048d79cf8	[bugfix] pin gradio version and set current_vsa_sparsity in TrainingPipeline (#798 )	2025-09-11 17:04:47 -07:00
Zhaobin Chu zbchu2 William Lin	1e8406162d	[bugfix] Fix delta calculation (#796 ) Co-authored-by: zbchu2 <zbchu2@iflytek.com> Co-authored-by: William Lin <SolitaryThinker@users.noreply.github.com>	2025-09-11 16:31:23 -07:00
William Lin	03edd35c83	[preprocessing] [self-forcing] [2/n] Improve preprocessing and add ode trajectory dataset schema (#794 )	2025-09-10 17:33:57 -07:00
William LinandRandNMR73	ac11127397	[Self-forcing] [1/n] Handle extra dim in time embedding and add timestep warping (#792 ) Co-authored-by: RandNMR73 <notomatthew31@gmail.com>	2025-09-09 02:52:02 -07:00
Eric LiangandEricLiang	e028dcc7c0	[Backend][Vmoba] Add implementation of VMoba (#778 ) Co-authored-by: EricLiang <https://github.com/EricLina>	2025-09-08 23:53:25 -07:00
Wenxuan Tanandgemini-code-assist[bot]	076f45c1ee	[Feature] Support Lora for DMD (#755 ) Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2025-09-08 14:18:21 -07:00
Zhaobin Chu zbchu2 Wenxuan Tan	85eb7265db	fix: lora_B init zeros (#781 ) Co-authored-by: zbchu2 <zbchu2@iflytek.com> Co-authored-by: Wenxuan Tan <wenxuan.tan@wisc.edu>	2025-09-05 22:56:52 -07:00
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