Update

Rename column name
Small change
2025-06-22 17:28:50 +00:00 · 2025-06-11 22:05:36 +00:00 · 2025-06-10 23:17:06 +00:00 · 2025-06-10 23:17:06 +00:00 · 2025-06-10 23:17:05 +00:00 · 2025-06-10 23:17:03 +00:00
310 changed files with 5363 additions and 12756 deletions
@@ -1,155 +0,0 @@
-env:
-  IMAGE_VERSION: "py3.12-latest"
-  BUILDKITE_CLEAN_CHECKOUT: true
-
-steps:
-  - label: "pre-commit"
-    command: ".buildkite/scripts/pre_commit.sh"
-    agents:
-      queue: "default"
-
-  - wait
-
-  - label: "Trigger Tests"
-    plugins:
-      - monorepo-diff#v1.4.0:
-          diff: "git diff --name-only $BUILDKITE_PULL_REQUEST_BASE_BRANCH...HEAD"
-          watch:
-            - path: 
-                - "fastvideo/v1/models/encoders/**"
-                - "fastvideo/v1/models/loader/**"
-                - "fastvideo/v1/tests/encoders/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Encoder Tests"
-                env:
-                  - TEST_TYPE=encoder
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/v1/models/vaes/**"
-                - "fastvideo/v1/models/loader/**"
-                - "fastvideo/v1/tests/vaes/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "VAE Tests"
-                env:
-                  - TEST_TYPE=vae
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/v1/models/dits/**"
-                - "fastvideo/v1/models/loader/**"
-                - "fastvideo/v1/tests/transformers/**"
-                - "fastvideo/v1/layers/**"
-                - "fastvideo/v1/attention/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Transformer Tests"
-                env:
-                  - TEST_TYPE=transformer
-                agents:
-                  queue: "default"
-            - path: 
-                - "fastvideo/v1/**/*.py"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 30m .buildkite/scripts/pr_test.sh"
-                label: "SSIM Tests"
-                env:
-                  - TEST_TYPE=ssim
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/v1/tests/lora/**"
-                - "fastvideo/v1/models/loader/**"
-                - "fastvideo/v1/tests/transformers/**"
-                - "fastvideo/v1/pipelines/**"
-                - "fastvideo/v1/layers/lora/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "LoRA Inference Tests"
-                env:
-                  - TEST_TYPE=inference_lora
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/v1/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Training Tests"
-                env:
-                  - TEST_TYPE=training
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/v1/**"
-                - "csrc/attn/vsa/**"
-                - "csrc/attn/tk/**"
-                - "csrc/attn/setup_vsa.py"
-                - "csrc/attn/config_vsa.py"
-                - "csrc/attn/vsa.cpp"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Training Tests VSA"
-                env:
-                  - TEST_TYPE=training_vsa
-                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/v1/**"
-                - "csrc/attn/st_attn/**"
-                - "csrc/attn/setup_sta.py"
-                - "csrc/attn/config_sta.py"
-                - "csrc/attn/st_attn.cpp"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Inference Tests STA"
-                env:
-                  - TEST_TYPE=inference_sta
-                agents:
-                  queue: "default"
-            - path:
-                - "csrc/attn/st_attn/**"
-                - "csrc/attn/setup_sta.py"
-                - "csrc/attn/config_sta.py"
-                - "csrc/attn/st_attn.cpp"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Precision Tests STA"
-                env:
-                  - TEST_TYPE=precision_sta
-                agents:
-                  queue: "default"
-            - path:
-                - "csrc/attn/vsa/**"
-                - "csrc/attn/tk/**"
-                - "csrc/attn/setup_vsa.py"
-                - "csrc/attn/config_vsa.py"
-                - "csrc/attn/vsa.cpp"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Precision Tests VSA"
-                env:
-                  - TEST_TYPE=precision_vsa
-                agents:
-                  queue: "default"
@@ -1,121 +0,0 @@
-#!/bin/bash
-set -uo pipefail
-
-log() {
-  echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
-}
-
-log "=== Starting Modal test execution ==="
-
-# Change to the project directory
-cd "$(dirname "$0")/../.."
-PROJECT_ROOT=$(pwd)
-log "Project root: $PROJECT_ROOT"
-
-# Install Modal if not available
-if ! python3 -m modal --version &> /dev/null; then
-    log "Modal not found, installing..."
-    python3 -m pip install modal
-    
-    # Verify installation
-    if ! python3 -m modal --version &> /dev/null; then
-        log "Error: Failed to install modal. Please install it manually."
-        exit 1
-    fi
-fi
-
-log "modal version: $(python3 -m modal --version)"
-
-# Set up Modal authentication using Buildkite secrets
-log "Setting up Modal authentication from Buildkite secrets..."
-MODAL_TOKEN_ID=$(buildkite-agent secret get modal_token_id)
-MODAL_TOKEN_SECRET=$(buildkite-agent secret get modal_token_secret)
-
-WANDB_API_KEY=$(buildkite-agent secret get wandb_api_key)
-
-WANDB_API_KEY=$(buildkite-agent secret get wandb_api_key)
-
-if [ -n "$MODAL_TOKEN_ID" ] && [ -n "$MODAL_TOKEN_SECRET" ]; then
-    log "Retrieved Modal credentials from Buildkite secrets"
-    python3 -m modal token set --token-id "$MODAL_TOKEN_ID" --token-secret "$MODAL_TOKEN_SECRET" --profile buildkite-ci --activate --verify
-    if [ $? -eq 0 ]; then
-        log "Modal authentication successful"
-    else
-        log "Error: Failed to set Modal credentials"
-        exit 1
-    fi
-else
-    log "Error: Could not retrieve Modal credentials from Buildkite secrets."
-    log "Please ensure 'modal_token_id' and 'modal_token_secret' secrets are set in Buildkite."
-    exit 1
-fi
-
-MODAL_TEST_FILE="fastvideo/v1/tests/modal/pr_test.py"
-
-if [ -z "${TEST_TYPE:-}" ]; then
-    log "Error: TEST_TYPE environment variable is not set"
-    exit 1
-fi
-log "Test type: $TEST_TYPE"
-
-MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUILDKITE_PULL_REQUEST=$BUILDKITE_PULL_REQUEST IMAGE_VERSION=$IMAGE_VERSION"
-
-case "$TEST_TYPE" in
-    "encoder")
-        log "Running encoder tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_encoder_tests"
-        ;;
-    "vae")
-        log "Running VAE tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_vae_tests"
-        ;;
-    "transformer")
-        log "Running transformer tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_transformer_tests"
-        ;;
-    "ssim")
-        log "Running SSIM tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_ssim_tests"
-        ;;
-    "training")
-        log "Running training tests..."
-        MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_training_tests"
-        ;;
-    "training_vsa")
-        log "Running training VSA tests..."
-        MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_training_tests_VSA"
-        ;;
-    "inference_sta")
-        log "Running inference STA tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_inference_tests_STA"
-        ;;
-    "precision_sta")
-        log "Running precision STA tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_precision_tests_STA"
-        ;;
-    "precision_vsa")
-        log "Running precision VSA tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_precision_tests_VSA"
-        ;;
-    "inference_lora")
-        log "Running LoRA tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_inference_lora_tests"
-        ;;
-    *)
-        log "Error: Unknown test type: $TEST_TYPE"
-        exit 1
-        ;;
-esac
-
-log "Executing: $MODAL_COMMAND"
-eval "$MODAL_COMMAND"
-TEST_EXIT_CODE=$?
-
-if [ $TEST_EXIT_CODE -eq 0 ]; then
-    log "Modal test completed successfully"
-else
-    log "Error: Modal test failed with exit code: $TEST_EXIT_CODE"
-fi
-
-log "=== Test execution completed with exit code: $TEST_EXIT_CODE ==="
-exit $TEST_EXIT_CODE
@@ -1,40 +0,0 @@
-#!/bin/bash
-set -uo pipefail
-
-log() {
-  echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
-}
-
-log "=== Starting pre-commit checks ==="
-
-cd "$(dirname "$0")/../.."
-PROJECT_ROOT=$(pwd)
-log "Project root: $PROJECT_ROOT"
-
-if ! python3 -m pre_commit --version &> /dev/null; then
-    log "pre-commit not found, installing..."
-    python3 -m pip install --user pre-commit==4.0.1
-    
-    if ! python3 -m pre_commit --version &> /dev/null; then
-        log "Error: Failed to install pre-commit."
-        exit 1
-    fi
-fi
-
-log "Pre-commit version: $(python3 -m pre_commit --version)"
-
-log "Installing/updating pre-commit hooks..."
-python3 -m pre_commit install --install-hooks
-
-log "Running pre-commit checks on all files..."
-python3 -m pre_commit run --all-files
-PRE_COMMIT_EXIT_CODE=$?
-
-if [ $PRE_COMMIT_EXIT_CODE -eq 0 ]; then
-    log "Pre-commit checks completed successfully"
-else
-    log "Error: Pre-commit checks failed with exit code: $PRE_COMMIT_EXIT_CODE"
-fi
-
-log "=== Pre-commit checks completed with exit code: $PRE_COMMIT_EXIT_CODE ==="
-exit $PRE_COMMIT_EXIT_CODE 
@@ -160,7 +160,8 @@ def execute_command(pod_id):
    setup_steps = [
        "tar -xzf /tmp/repo.tar.gz --no-same-owner -C /workspace/",
        f"cd /workspace/{repo_name}",
-        "source $HOME/.local/bin/env && source /opt/venv/bin/activate",
+        "source /opt/conda/etc/profile.d/conda.sh",
+        "conda activate fastvideo-dev",
        args.test_command
    ]
    
@@ -13,4 +13,4 @@
      ]
    }
  ]
-}
+}
@@ -12,11 +12,13 @@ on:
    paths:
      - "fastvideo/**/*.py"
      - ".github/workflows/pr-test.yml"
-      - "pyproject.toml"
-      - "docker/Dockerfile.python3.12"
-      - "csrc/**"
  workflow_dispatch:
    inputs:
+      custom_image:
+        description: "Custom image from this repository (default: fastvideo-dev:latest)"
+        required: false
+        default: "fastvideo-dev:latest"
+        type: string
      run_encoder_test:
        description: "Run encoder-test"
        required: false
@@ -37,41 +39,10 @@ on:
        required: false
        default: false
        type: boolean
-      run_training_test:
-        description: "Run training-test"
-        required: false
-        default: false
-        type: boolean
-      run_training_test_VSA:
-        description: "Run training-test-VSA"
-        required: false
-        default: false
-        type: boolean
-      run_inference_test_STA:
-        description: "Run inference-test-STA"
-        required: false
-        default: false
-        type: boolean
-      run_precision_test_STA:
-        description: "Run precision-test-STA"
-        required: false
-        default: false
-        type: boolean
-      run_precision_test_VSA:
-        description: "Run precision-test-VSA"
-        required: false
-        default: false
-        type: boolean
-      run_nightly_test:
-        description: "Run nightly-test"
-        required: false
-        default: false
-        type: boolean

 env:
  PYTHONUNBUFFERED: "1"

-
 concurrency:
  group: pr-test-${{ github.ref }}
  cancel-in-progress: true
@@ -88,72 +59,26 @@ jobs:
      encoder-test: ${{ steps.filter.outputs.encoder-test }}
      vae-test: ${{ steps.filter.outputs.vae-test }}
      transformer-test: ${{ steps.filter.outputs.transformer-test }}
-      training-test: ${{ steps.filter.outputs.training-test }}
-      training-test-VSA: ${{ steps.filter.outputs.training-test-VSA }}
-      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 }}
    steps:
      - uses: actions/checkout@v4
      - uses: dorny/paths-filter@v3
        id: filter
        with:
          filters: |
-            # Define reusable path patterns
-            common-paths: &common-paths
-              - 'pyproject.toml'
-              - 'docker/Dockerfile.python3.12'
-            sta-kernel-paths: &sta-kernel-paths
-              - 'csrc/attn/st_attn/**'
-              - 'csrc/attn/setup_sta.py'
-              - 'csrc/attn/config_sta.py'
-              - 'csrc/attn/st_attn.cpp'
-            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'
-            vsa-paths: &vsa-paths
-              - 'fastvideo/v1/**'
-              - *common-paths
-              - *vsa-kernel-paths
-            
-            # Actual tests
            encoder-test:
              - 'fastvideo/v1/models/encoders/**'
-              - 'fastvideo/v1/models/loader/**'
+              - 'fastvideo/v1/models/loaders/**'
              - 'fastvideo/v1/tests/encoders/**'
-              - *common-paths
            vae-test:
              - 'fastvideo/v1/models/vaes/**'
-              - 'fastvideo/v1/models/loader/**'
+              - 'fastvideo/v1/models/loaders/**'
              - 'fastvideo/v1/tests/vaes/**'
-              - *common-paths
            transformer-test:
              - 'fastvideo/v1/models/dits/**'
-              - 'fastvideo/v1/models/loader/**'
+              - 'fastvideo/v1/models/loaders/**'
              - 'fastvideo/v1/tests/transformers/**'
              - 'fastvideo/v1/layers/**'
              - 'fastvideo/v1/attention/**'
-              - *common-paths
-            training-test:
-              - 'fastvideo/v1/**'
-              - *common-paths
-            training-test-VSA:
-              - 'fastvideo/v1/**'
-              - *common-paths
-              - *vsa-kernel-paths
-            inference-test-STA:
-              - 'fastvideo/v1/**'
-              - *common-paths
-              - *sta-kernel-paths
-            precision-test-STA:
-              - *common-paths
-              - *sta-kernel-paths
-            precision-test-VSA:
-              - *common-paths
-              - *vsa-kernel-paths

  encoder-test:
    needs: change-filter
@@ -166,8 +91,8 @@ jobs:
      gpu_type: "NVIDIA A40"
      gpu_count: 1
      volume_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/encoders -s"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:latest' }}"
+      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/encoders -s"
      timeout_minutes: 30
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -184,8 +109,8 @@ jobs:
      gpu_type: "NVIDIA A40"
      gpu_count: 1
      volume_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/vaes -s"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:latest' }}"
+      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/vaes -s"
      timeout_minutes: 30
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -202,8 +127,8 @@ jobs:
      gpu_type: "NVIDIA L40S"
      gpu_count: 1
      volume_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/transformers -s"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:latest' }}"
+      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/transformers -s"
      timeout_minutes: 30
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -212,7 +137,8 @@ jobs:
  ssim-test:
    needs: change-filter
    if: >-
-      github.event_name != 'workflow_dispatch' || (github.event_name == 'workflow_dispatch' && github.event.inputs.run_ssim_test == 'true')
+      (github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || 
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_ssim_test == 'true')
    strategy:
      fail-fast: false
      matrix:
@@ -229,130 +155,14 @@ jobs:
      volume_size: 200
      disk_size: 200
      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:${{ matrix.python-version.tag }}"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/ssim -vs"
+      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/ssim -vs"
      timeout_minutes: 60
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-  
-  training-test:
-    needs: change-filter
-    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.training-test == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_training_test == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "training-test"
-      gpu_type: "NVIDIA A40"
-      gpu_count: 4
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/training/Vanilla -srP"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-      WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
-
-  training-test-VSA:
-    needs: change-filter
-    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.training-test-VSA == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_training_test_VSA == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "training-test-VSA"
-      gpu_type: "NVIDIA H100 NVL"
-      gpu_count: 2
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/training/VSA -srP"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-      WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
-
-  inference-test-STA:
-    needs: change-filter
-    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.inference-test-STA == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_inference_test_STA == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "inference-test-STA"
-      gpu_type: "NVIDIA H100 NVL"
-      gpu_count: 2
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/inference/STA -srP"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-
-  precision-test-STA:
-    needs: change-filter
-    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.precision-test-STA == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_precision_test_STA == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "precision-test-STA"
-      gpu_type: "NVIDIA H100 NVL"
-      gpu_count: 1
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && python csrc/attn/tests/test_sta.py"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-
-  precision-test-VSA:
-    needs: change-filter
-    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.precision-test-VSA == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_precision_test_VSA == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "precision-test-VSA"
-      gpu_type: "NVIDIA H100 NVL"
-      gpu_count: 1
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && python csrc/attn/tests/test_block_sparse.py"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-
-  nightly-test:
-    if: >-
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_nightly_test == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "nightly-test"
-      gpu_type: "NVIDIA A40"
-      gpu_count: 4
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/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
-    needs: [encoder-test, vae-test, transformer-test, ssim-test, training-test, training-test-VSA, inference-test-STA, precision-test-STA, precision-test-VSA] 
+    needs: [encoder-test, vae-test, transformer-test, ssim-test] # Add other jobs to this list as you create them
    if: ${{ always() && ((github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || github.event_name == 'workflow_dispatch') }}
    runs-on: ubuntu-latest
    steps:
@@ -369,7 +179,7 @@ jobs:

      - name: Cleanup all RunPod instances
        env:
-          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"]'
+          JOB_IDS: '["encoder-test", "vae-test", "transformer-test", "ssim-test-py3.10", "ssim-test-py3.11", "ssim-test-py3.12"]'
          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
@@ -43,8 +43,6 @@ on:
        required: true
      RUNPOD_PRIVATE_KEY:
        required: true
-      WANDB_API_KEY:
-        required: false

 jobs:
  run-test:
@@ -57,7 +55,7 @@ jobs:
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
-          python-version: "3.12"
+          python-version: "3.10"

      - name: Set up SSH key
        run: |
@@ -74,7 +72,6 @@ jobs:
          JOB_ID: ${{ inputs.job_id }}
          RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
          GITHUB_RUN_ID: ${{ github.run_id }}
-          WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
        timeout-minutes: ${{ inputs.timeout_minutes }}
        run: >-
          python .github/scripts/runpod_api.py 
@@ -5,7 +5,7 @@ on:
    branches:
      - main
    paths:
-      - "csrc/attn/setup_sta.py"
+      - "csrc/sliding_tile_attention/setup.py"
  workflow_dispatch:

 jobs:
@@ -23,13 +23,13 @@ jobs:
      - name: Check if version changed
        id: check-version
        run: |
-          cd csrc/attn
+          cd csrc/sliding_tile_attention
          # 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
@@ -136,21 +136,19 @@ jobs:

      - name: Build wheel
        run: |
-          export PYTHONPATH=$GITHUB_WORKSPACE:$PYTHONPATH
-          
          # We want setuptools >= 49.6.0 otherwise we can't compile the extension if system CUDA version is 11.7 and pytorch cuda version is 11.6
          # https://github.com/pytorch/pytorch/blob/664058fa83f1d8eede5d66418abff6e20bd76ca8/torch/utils/cpp_extension.py#L810
          # However this still fails so I'm using a newer version of setuptools
          pip install setuptools
          pip install ninja packaging wheel

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

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

          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 +163,7 @@ jobs:
        uses: actions/upload-artifact@v4
        with:
          name: ${{ env.wheel_name }}
-          path: csrc/attn/dist/*.whl
+          path: csrc/sliding_tile_attention/dist/*.whl
          retention-days: 90

  publish_package:
@@ -231,19 +229,17 @@ jobs:

      - name: Build source distribution
        run: |
-          export PYTHONPATH=$GITHUB_WORKSPACE:$PYTHONPATH
-          
          # We want setuptools >= 49.6.0 otherwise we can't compile the extension if system CUDA version is 11.7 and pytorch cuda version is 11.6
          # https://github.com/pytorch/pytorch/blob/664058fa83f1d8eede5d66418abff6e20bd76ca8/torch/utils/cpp_extension.py#L810
          # However this still fails so I'm using a newer version of setuptools
          pip install setuptools
          pip install ninja packaging wheel

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

      - name: Publish release distributions to PyPI
        uses: pypa/gh-action-pypi-publish@release/v1
        with:
-          packages-dir: csrc/attn/dist/
+          packages-dir: csrc/sliding_tile_attention/dist/
@@ -28,4 +28,4 @@ jobs:

      - name: Run Pytest
        run: |
-          pytest --ignore csrc/attn/test
+          pytest --ignore csrc/sliding_tile_attention/test
@@ -27,6 +27,7 @@ env
 **/build/
 **.pyc
 **.txt
+csrc/attn/tk/

 # Distribution / packaging
 build/
@@ -59,5 +60,3 @@ docs/source/inference/examples/

 # Static images
 !docs/source/_static/images/**/*.png
-!comfyui/assets/**/*.png
-!comfyui/assets/**/*.gif
@@ -1,3 +1,3 @@
-[submodule "csrc/attn/tk"]
-	path = csrc/attn/tk
+[submodule "csrc/sliding_tile_attention/tk"]
+	path = csrc/sliding_tile_attention/tk
 	url = https://github.com/HazyResearch/ThunderKittens.git
@@ -60,7 +60,7 @@ repos:
  rev: v1.15.0
  hooks:
  - id: mypy
-    args: [--python-version, '3.10', --follow-imports, "skip" ]
+    args: [--python-version, '3.10', --follow-imports, "skip", ]
    additional_dependencies: [types-cachetools, types-setuptools, types-PyYAML, types-requests]
 - repo: local
  hooks:
@@ -69,7 +69,7 @@ repos:
    entry: bash
    args:
      - -c
-      - 'git ls-files | grep -v "^fastvideo/v1/tests/ssim/" | grep -v "^fastvideo/v1/tests/inference/lora/L40S_reference_videos/" | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
+      - 'git ls-files | grep -v "^fastvideo/v1/tests/ssim/" | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
    language: system
    always_run: true
    pass_filenames: false
@@ -8,18 +8,13 @@ It features a clean, consistent API that works across popular video models, maki
 With FastVideo's optimizations, you can achieve more than 3x inference improvement compared to other systems.

 <p align="center">
-    | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start.html"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/FastVideo/FastHunyuan"  target="_blank"><b>FastHunyuan</b></a>  | 🤗 <a href="https://huggingface.co/FastVideo/FastMochi-diffusers" target="_blank"><b>FastMochi</b></a> | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ" target="_blank"> <b>Slack</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/FastVideo/FastHunyuan"  target="_blank"><b>FastHunyuan</b></a>  | 🤗 <a href="https://huggingface.co/FastVideo/FastMochi-diffusers" target="_blank"><b>FastMochi</b></a> | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-2zf6ru791-sRwI9lPIUJQq1mIeB_yjJg" target="_blank"> <b>Slack</b> </a> |
 </p>

 <div align="center">
 <img src=assets/perf.png width="90%"/>
 </div>

-## NEWS
- ```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!
- ```2025/02/18```: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).
-
 ## Key Features

 FastVideo has the following features:
@@ -96,7 +91,7 @@ For a more detailed guide, please see our [inference quick start](https://hao-ai

 ## Distillation and Finetuning
 - [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/training/distillation.html)
- [Finetuning Guide](https://hao-ai-lab.github.io/FastVideo/training/finetune.html)
+- [Finetuning Guide](https://hao-ai-lab.github.io/FastVideo/training/finetuning.html)

 ## 📑 Development Plan

@@ -116,7 +111,7 @@ For a more detailed guide, please see our [inference quick start](https://hao-ai

 ## 🤝 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/developer_guide/overview.html)

 ## Acknowledgement
 We learned and reused code from the following projects:
@@ -133,15 +128,6 @@ We thank MBZUAI and [Anyscale](https://www.anyscale.com/) for their support thro
 If you use FastVideo for your research, please cite our paper:

 ```bibtex
-@misc{zhang2025vsafastervideodiffusion,
-      title={VSA: Faster Video Diffusion with Trainable Sparse Attention}, 
-      author={Peiyuan Zhang and Haofeng Huang and Yongqi Chen and Will Lin and Zhengzhong Liu and Ion Stoica and Eric Xing and Hao Zhang},
-      year={2025},
-      eprint={2505.13389},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2505.13389}, 
-}
@misc{zhang2025fastvideogenerationsliding,
      title={Fast Video Generation with Sliding Tile Attention},
      author={Peiyuan Zhang and Yongqi Chen and Runlong Su and Hangliang Ding and Ion Stoica and Zhenghong Liu and Hao Zhang},
@@ -1,138 +0,0 @@
-# ComfyUI-FastVideo
-
-A custom node suite for ComfyUI that provides accelerated video generation using [FastVideo](https://github.com/hao-ai-labs/FastVideo). See the [blog post](https://hao-ai-lab.github.io/blogs/fastvideo/) about FastVideo V1 to learn more.
-
-## Multi-GPU Parallel Inference
-
-One of the key features ComfyUI-FastVideo brings to ComfyUI is its ability to distribute the generation workload across multiple GPUs, resulting in significantly faster inference times.
-
-![Wan2.1-I2V-14B-480P-Diffusers](./assets/wani2v.gif)
-
-Example of Wan2.1-I2V-14B-480P-Diffusers model running on 4 GPUs.
-## Features
-
- Generate high-quality videos from text prompts and images
- Configurable video parameters (prompt, resolution, frame count, FPS)
- Support for multiple GPUs with tensor and sequence parallelism
- Advanced configuration options for VAE, Text Encoder, and DIT components
- Interruption/cancellation support for long-running generations
-
-## Installation
-
-### Requirements
-
- [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
- CUDA-capable GPU(s) with sufficient VRAM
-
-### Install using ComfyUI Manager
-
-Coming soon!
-
-### Manual Installation
-
-#### Copy the FastVideo `comfyui` directory into your ComfyUI custom_nodes directory:
-
-```bash
-cp -r /path/to/FastVideo/comfyui /path/to/ComfyUI/custom_nodes/FastVideo
-```
-
-#### Install dependencies:
-
-Currently, the only dependency is `fastvideo`, which can be installed using pip.
-
-```bash
-pip install fastvideo
-```
-
-#### Install missing custom nodes:
-
-`ComfyUI-VideoHelperSuite`:
-
-```bash
-cd /path/to/ComfyUI/custom_nodes
-git clone https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite.git
-```
-
-If you're seeing `ImportError: libGL.so.1: cannot open shared object file: No such file or directory`,
-you may need to install ffmpeg
-
-```bash
-apt-get update && apt-get install ffmpeg
-```
-
-## Usage
-
-After installation, the following nodes will be available in the ComfyUI interface under the "fastvideo" category:
-
- **Video Generator**: The main node for generating videos from prompts
- **Inference Args**: Configure video generation parameters
- **VAE Config**
- **Text Encoder Config**
- **DIT Config**
- **Load Image Path**: Load images for potential conditioning
-
-You may have noticed many arguments on the nodes have 'auto' as the default value. This is because FastVideo will automatically detect the best values for these parameters based on the model and the hardware. However, you can also manually configure these parameters to get the best performance for your specific use case. We plan on releasing more optimized workflow files for different models and hardware configurations in the future.
-
-You can see what some of the default configurations are by looking at the FastVideo repo:
- [Wan2.1-I2V-14B-480P-Diffusers](https://github.com/hao-ai-lab/FastVideo/blob/main/fastvideo/v1/configs/wan_14B_i2v_480p_pipeline.json)
- [FastHunyuan-diffusers](https://github.com/hao-ai-lab/FastVideo/blob/main/fastvideo/v1/configs/fasthunyuan_t2v.json)
-
-### Node Configuration
-
-#### Video Generator
-
- **prompt**: Text description of the video to generate
- **output_path**: Directory where generated videos will be saved
- **num_gpus**: Number of GPUs to use for generation
- **model_path**: Path to the FastVideo model
- **embedded_cfg_scale**: Classifier-free guidance scale
- **sp_size**: Sequence parallelism size (usually should match num_gpus)
- **tp_size**: Tensor parallelism size (usually should match num_gpus)
- **precision**: Model precision (fp16 or bf16)
-
-`model_path takes either a model id from huggingface or a local path to a model. Models by default will be downloaded to ~/.cache/huggingface/hub/ and cached for subsequent runs.`
-
-#### Inference Args
-
- **height/width**: Resolution of the output video
- **num_frames**: Number of frames to generate
- **num_inference_steps**: Number of diffusion steps per frame
- **guidance_scale**: Classifier-free guidance scale
- **flow_shift**: Frame flow shift parameter
- **seed**: Random seed for reproducible generation
- **fps**: Frames per second of the output video
- **image_path**: Optional path to input image for conditioning (for i2v models)
-
-## Memory Management
-
-Models will remain loaded in GPU memory between runs when you only change inference arguments (such as prompt, resolution, frame count, FPS, guidance scale, etc.) or the prompt text. This allows for faster subsequent generations since the model doesn't need to be reloaded.
-
-However, if you need to change the following parameters, you will need to restart the ComfyUI server:
- **Number of GPUs** (`num_gpus`)
- **Model path** (`model_path`)
- **Tensor parallelism size** (`tp_size`)
- **Sequence parallelism size** (`sp_size`)
-
-These parameters affect the model's distribution across GPUs and require a complete reinitialization of the model pipeline.
-
-## Example workflows
-
-### Text to Video
-
-FastVideo-FastHunyuan-diffusers
-
-![FastVideo-FastHunyuan-diffusers](./assets/fasthunyuan.png)
-
- [FastHunyuan-diffusers.json](./examples/FastHunyuan-diffusers.json)
-
-### Image to Video
-
-Wan2.1-I2V-14B-480P-Diffusers
-
-![Wan2.1-I2V-14B-480P-Diffusers](./assets/wani2v.png)
-
- [Wan2.1-I2V-14B-480P-Diffusers.json](./examples/Wan2.1-I2V-14B-480P-Diffusers.json)
-
-## License
-
-This project is licensed under Apache 2.0.
@@ -1,5 +0,0 @@
-from .video_generator.nodes import (NODE_CLASS_MAPPINGS,
-                                    NODE_DISPLAY_NAME_MAPPINGS)
-
-WEB_DIRECTORY = "./web"
-__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']
@@ -1,645 +0,0 @@
-{
-  "id": "23a1f065-bbba-4a8f-b144-944e1318fcbf",
-  "revision": 0,
-  "last_node_id": 8,
-  "last_link_id": 7,
-  "nodes": [
-    {
-      "id": 4,
-      "type": "VAEConfig",
-      "pos": [
-        374.2159423828125,
-        554.85888671875
-      ],
-      "size": [
-        334.080078125,
-        322
-      ],
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "vae_config",
-          "type": "VAE_CONFIG",
-          "links": [
-            2
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VAEConfig"
-      },
-      "widgets_values": [
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999
-      ],
-      "auto_widget_states": {
-        "load_encoder": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": true
-        },
-        "load_decoder": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": true
-        },
-        "tile_sample_min_height": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 256
-        },
-        "tile_sample_min_width": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 256
-        },
-        "tile_sample_min_num_frames": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 16
-        },
-        "tile_sample_stride_height": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 192
-        },
-        "tile_sample_stride_width": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 192
-        },
-        "tile_sample_stride_num_frames": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 12
-        },
-        "blend_num_frames": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 0
-        },
-        "use_tiling": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": true
-        },
-        "use_temporal_tiling": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": true
-        },
-        "use_parallel_tiling": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": true
-        }
-      }
-    },
-    {
-      "id": 5,
-      "type": "TextEncoderConfig",
-      "pos": [
-        416.4937744140625,
-        953.6171875
-      ],
-      "size": [
-        270,
-        106
-      ],
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "text_encoder_config",
-          "type": "TEXT_ENCODER_CONFIG",
-          "links": [
-            7
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "TextEncoderConfig"
-      },
-      "widgets_values": [
-        -99999,
-        -99999,
-        -99999
-      ],
-      "auto_widget_states": {
-        "prefix": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": ""
-        },
-        "quant_config": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": ""
-        },
-        "lora_config": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": ""
-        }
-      }
-    },
-    {
-      "id": 6,
-      "type": "DITConfig",
-      "pos": [
-        415.1928405761719,
-        1154.1573486328125
-      ],
-      "size": [
-        270,
-        82
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "dit_config",
-          "type": "DIT_CONFIG",
-          "links": [
-            6
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "DITConfig"
-      },
-      "widgets_values": [
-        -99999,
-        -99999
-      ],
-      "auto_widget_states": {
-        "prefix": {
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-        },
-        "vae_precision": {
-          "isAuto": true,
-          "value": "fp16",
-          "cachedValue": "fp16"
-        },
-        "vae_tiling": {
-          "isAuto": true,
-          "value": true,
-          "cachedValue": true
-        },
-        "vae_sp": {
-          "isAuto": true,
-          "value": true,
-          "cachedValue": true
-        },
-        "text_encoder_precision": {
-          "isAuto": true,
-          "value": "fp16",
-          "cachedValue": "fp16"
-        },
-        "precision": {
-          "isAuto": true,
-          "value": "fp16",
-          "cachedValue": "fp16"
-        },
-        "use_cpu_offload": {
-          "isAuto": true,
-          "value": true,
-          "cachedValue": true
-        }
-      }
-    },
-    {
-      "id": 5,
-      "type": "TextEncoderConfig",
-      "pos": [
-        416.4937744140625,
-        953.6171875
-      ],
-      "size": [
-        270,
-        106
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "text_encoder_config",
-          "type": "TEXT_ENCODER_CONFIG",
-          "links": [
-            7
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "TextEncoderConfig"
-      },
-      "widgets_values": [
-        "",
-        "",
-        ""
-      ],
-      "auto_widget_states": {
-        "prefix": {
-          "isAuto": true,
-          "value": "",
-          "cachedValue": ""
-        },
-        "quant_config": {
-          "isAuto": true,
-          "value": "",
-          "cachedValue": ""
-        },
-        "lora_config": {
-          "isAuto": true,
-          "value": "",
-          "cachedValue": ""
-        }
-      }
-    },
-    {
-      "id": 6,
-      "type": "DITConfig",
-      "pos": [
-        415.1928405761719,
-        1154.1573486328125
-      ],
-      "size": [
-        270,
-        82
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "dit_config",
-          "type": "DIT_CONFIG",
-          "links": [
-            6
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "DITConfig"
-      },
-      "widgets_values": [
-        "",
-        ""
-      ],
-      "auto_widget_states": {
-        "prefix": {
-          "isAuto": true,
-          "value": "",
-          "cachedValue": ""
-        },
-        "quant_config": {
-          "isAuto": true,
-          "value": "",
-          "cachedValue": ""
-        }
-      }
-    }
-  ],
-  "links": [
-    [
-      1,
-      7,
-      0,
-      2,
-      0,
-      "STRING"
-    ],
-    [
-      2,
-      4,
-      0,
-      1,
-      1,
-      "VAE_CONFIG"
-    ],
-    [
-      3,
-      2,
-      0,
-      1,
-      0,
-      "INFERENCE_ARGS"
-    ],
-    [
-      4,
-      1,
-      0,
-      3,
-      2,
-      "STRING"
-    ],
-    [
-      5,
-      3,
-      0,
-      8,
-      0,
-      "IMAGE"
-    ],
-    [
-      6,
-      6,
-      0,
-      1,
-      3,
-      "DIT_CONFIG"
-    ],
-    [
-      7,
-      5,
-      0,
-      1,
-      2,
-      "TEXT_ENCODER_CONFIG"
-    ]
-  ],
-  "groups": [],
-  "config": {},
-  "extra": {
-    "ds": {
-      "scale": 0.8264462809917354,
-      "offset": [
-        646.7950212991898,
-        66.17259910028655
-      ]
-    },
-    "frontendVersion": "1.20.4",
-    "VHS_latentpreview": false,
-    "VHS_latentpreviewrate": 0,
-    "VHS_MetadataImage": true,
-    "VHS_KeepIntermediate": true
-  },
-  "version": 0.4
-}
@@ -1,31 +0,0 @@
-class DITConfig:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "optional": {
-                "prefix": ("STRING", {
-                    "default": ""
-                }),
-                "quant_config": ("STRING", {
-                    "default": ""
-                }),
-            }
-        }
-
-    @classmethod
-    def VALIDATE_INPUTS(cls, **kwargs):
-        return True
-
-    RETURN_TYPES = ("DIT_CONFIG", )
-    RETURN_NAMES = ("dit_config", )
-    FUNCTION = "set_args"
-    CATEGORY = "fastvideo"
-
-    def set_args(self, prefix, quant_config):
-        raw_args = {"prefix": prefix, "quant_config": quant_config}
-
-        # Filter out keys where value is -99999
-        args = {k: v for k, v in raw_args.items() if str(int(v)) != str(-99999)}
-
-        return (args, )
@@ -1,89 +0,0 @@
-class InferenceArgs:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "optional": {
-                "height": ("INT", {
-                    "default": 720
-                }),
-                "width": ("INT", {
-                    "default": 1280
-                }),
-                "num_frames": ("INT", {
-                    "default": 45
-                }),
-                "num_inference_steps": ("INT", {
-                    "default": 6
-                }),
-                "guidance_scale": ("FLOAT", {
-                    "default": 1.0
-                }),
-                "flow_shift": ("INT", {
-                    "default": 17
-                }),
-                "seed": ("INT", {
-                    "default": 1024
-                }),
-                "fps": ("INT", {
-                    "default": 24
-                }),
-                "image_path": ("STRING", {
-                    "default": "X://insert/path/here.mp4"
-                }),
-                "enable_teacache": ([True, False], {
-                    "default": False
-                }),
-            }
-        }
-
-    @classmethod
-    def VALIDATE_INPUTS(cls, **kwargs):
-        return True
-
-    RETURN_TYPES = ("INFERENCE_ARGS", )
-    RETURN_NAMES = ("inference_args", )
-    FUNCTION = "set_args"
-    CATEGORY = "fastvideo"
-
-    def set_args(
-        self,
-        height,
-        width,
-        num_frames,
-        num_inference_steps,
-        guidance_scale,
-        flow_shift,
-        seed,
-        fps,
-        image_path,
-        enable_teacache,
-    ):
-        raw_args = {
-            "height": height,
-            "width": width,
-            "num_frames": num_frames,
-            "num_inference_steps": num_inference_steps,
-            "guidance_scale": guidance_scale,
-            "flow_shift": flow_shift,
-            "seed": seed,
-            "fps": fps,
-            "image_path": image_path,
-            "enable_teacache": enable_teacache,
-        }
-
-        # Filter out keys where value is -99999, handling different types properly
-        args = {}
-        for k, v in raw_args.items():
-            try:
-                if isinstance(v, str):
-                    if v != "-99999":
-                        args[k] = v
-                elif v != -99999:
-                    # If it's not a string, compare directly
-                    args[k] = v
-            except (ValueError, TypeError):
-                # Include any value that causes an error in comparison
-                args[k] = v
-
-        return (args, )
@@ -1,103 +0,0 @@
-import hashlib
-import os
-
-import folder_paths
-import numpy as np
-import torch
-from PIL import Image, ImageOps, ImageSequence
-
-from .node_helpers import pillow
-
-
-class LoadImagePath:
-
-    @classmethod
-    def INPUT_TYPES(s):
-        input_dir = folder_paths.get_input_directory()
-        files = [
-            f for f in os.listdir(input_dir)
-            if os.path.isfile(os.path.join(input_dir, f))
-        ]
-        files = folder_paths.filter_files_content_types(files, ["image"])
-        return {
-            "required": {
-                "image": (sorted(files), {
-                    "image_upload": True
-                })
-            },
-        }
-
-    CATEGORY = "fastvideo"
-
-    RETURN_TYPES = ("STRING", "IMAGE", "MASK")
-    RETURN_NAMES = ("image_path", "IMAGE", "MASK")
-    FUNCTION = "load_image"
-
-    def load_image(self, image):
-        image_path = folder_paths.get_annotated_filepath(image)
-
-        img = pillow(Image.open, image_path)
-
-        output_images: list[torch.Tensor] = []
-        output_masks: list[torch.Tensor] = []
-        w, h = None, None
-
-        excluded_formats = ['MPO']
-
-        for i in ImageSequence.Iterator(img):
-            processed_image = pillow(ImageOps.exif_transpose, i)
-            if processed_image is None:
-                continue
-
-            if processed_image.mode == 'I':
-                processed_image = processed_image.point(lambda i: i * (1 / 255))
-            image = processed_image.convert("RGB")
-
-            if len(output_images) == 0:
-                w = image.size[0]
-                h = image.size[1]
-
-            if image.size[0] != w or image.size[1] != h:
-                continue
-
-            image = np.array(image).astype(np.float32) / 255.0
-            image = torch.from_numpy(image)[
-                None,
-            ]
-            if 'A' in processed_image.getbands():
-                mask = np.array(processed_image.getchannel('A')).astype(
-                    np.float32) / 255.0
-                mask = 1. - torch.from_numpy(mask)
-            elif processed_image.mode == 'P' and 'transparency' in processed_image.info:
-                mask = np.array(
-                    processed_image.convert('RGBA').getchannel('A')).astype(
-                        np.float32) / 255.0
-                mask = 1. - torch.from_numpy(mask)
-            else:
-                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
-            output_images.append(image)
-            output_masks.append(mask.unsqueeze(0))
-
-        if len(output_images) > 1 and img.format not in excluded_formats:
-            output_image = torch.cat(output_images, dim=0)
-            output_mask = torch.cat(output_masks, dim=0)
-        else:
-            output_image = output_images[0]
-            output_mask = output_masks[0]
-
-        return (image_path, output_image, output_mask)
-
-    @classmethod
-    def IS_CHANGED(s, image):
-        image_path = folder_paths.get_annotated_filepath(image)
-        m = hashlib.sha256()
-        with open(image_path, 'rb') as f:
-            m.update(f.read())
-        return m.digest().hex()
-
-    @classmethod
-    def VALIDATE_INPUTS(s, image):
-        if not folder_paths.exists_annotated_filepath(image):
-            return "Invalid image file: {}".format(image)
-
-        return True
@@ -1,68 +0,0 @@
-import hashlib
-from collections.abc import Callable
-from typing import Any, TypeVar
-
-import torch
-from comfy.cli_args import args
-from PIL import ImageFile, UnidentifiedImageError
-
-T = TypeVar('T')
-
-
-def conditioning_set_values(conditioning: list[Any],
-                            values: dict[str, Any] | None = None) -> list[Any]:
-    if values is None:
-        values = {}
-    c = []
-    for t in conditioning:
-        n = [t[0], t[1].copy()]
-        for k in values:
-            n[1][k] = values[k]
-        c.append(n)
-
-    return c
-
-
-def pillow(fn: Callable[[Any], T], arg: Any) -> T:
-    prev_value = None
-    try:
-        x = fn(arg)
-    except (OSError, UnidentifiedImageError, ValueError
-            ):  #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416
-        prev_value = ImageFile.LOAD_TRUNCATED_IMAGES
-        ImageFile.LOAD_TRUNCATED_IMAGES = True
-        x = fn(arg)
-    finally:
-        if prev_value is not None:
-            ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
-    return x
-
-
-def hasher() -> Callable[[], Any]:
-    hashfuncs = {
-        "md5": hashlib.md5,
-        "sha1": hashlib.sha1,
-        "sha256": hashlib.sha256,
-        "sha512": hashlib.sha512
-    }
-    return hashfuncs[args.default_hashing_function]
-
-
-def string_to_torch_dtype(string: str) -> torch.dtype | None:
-    if string == "fp32":
-        return torch.float32
-    if string == "fp16":
-        return torch.float16
-    if string == "bf16":
-        return torch.bfloat16
-    return None
-
-
-def image_alpha_fix(destination: torch.Tensor,
-                    source: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
-    if destination.shape[-1] < source.shape[-1]:
-        source = source[..., :destination.shape[-1]]
-    elif destination.shape[-1] > source.shape[-1]:
-        destination = torch.nn.functional.pad(destination, (0, 1))
-        destination[..., -1] = 1.0
-    return destination, source
@@ -1,24 +0,0 @@
-from .dit_config import DITConfig
-from .inference_args import InferenceArgs
-from .load_image import LoadImagePath
-from .text_encoder_config import TextEncoderConfig
-from .vae_config import VAEConfig
-from .video_generator import VideoGenerator
-
-NODE_CLASS_MAPPINGS = {
-    "VideoGenerator": VideoGenerator,
-    "InferenceArgs": InferenceArgs,
-    "VAEConfig": VAEConfig,
-    "TextEncoderConfig": TextEncoderConfig,
-    "DITConfig": DITConfig,
-    "LoadImagePath": LoadImagePath
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "VideoGenerator": "Video Generator",
-    "InferenceArgs": "Inference Args",
-    "VAEConfig": "VAE Config",
-    "TextEncoderConfig": "Text Encoder Config",
-    "DITConfig": "DIT Config",
-    "LoadImagePath": "Load Image Path"
-}
@@ -1,38 +0,0 @@
-class TextEncoderConfig:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "optional": {
-                "prefix": ("STRING", {
-                    "default": ""
-                }),
-                "quant_config": ("STRING", {
-                    "default": ""
-                }),
-                "lora_config": ("STRING", {
-                    "default": ""
-                }),
-            }
-        }
-
-    @classmethod
-    def VALIDATE_INPUTS(cls, **kwargs):
-        return True
-
-    RETURN_TYPES = ("TEXT_ENCODER_CONFIG", )
-    RETURN_NAMES = ("text_encoder_config", )
-    FUNCTION = "set_args"
-    CATEGORY = "fastvideo"
-
-    def set_args(self, prefix, quant_config, lora_config):
-        raw_args = {
-            "prefix": prefix,
-            "quant_config": quant_config,
-            "lora_config": lora_config
-        }
-
-        # Filter out keys where value is -99999
-        args = {k: v for k, v in raw_args.items() if str(int(v)) != str(-99999)}
-
-        return (args, )
@@ -1,88 +0,0 @@
-class VAEConfig:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "optional": {
-                "load_encoder": ([True, False], {
-                    "default": True
-                }),
-                "load_decoder": ([True, False], {
-                    "default": True
-                }),
-                "tile_sample_min_height": ("INT", {
-                    "default": 256
-                }),
-                "tile_sample_min_width": ("INT", {
-                    "default": 256
-                }),
-                "tile_sample_min_num_frames": ("INT", {
-                    "default": 16
-                }),
-                "tile_sample_stride_height": ("INT", {
-                    "default": 192
-                }),
-                "tile_sample_stride_width": ("INT", {
-                    "default": 192
-                }),
-                "tile_sample_stride_num_frames": ("INT", {
-                    "default": 12
-                }),
-                "blend_num_frames": ("INT", {
-                    "default": 0
-                }),
-                "use_tiling": ([True, False], {
-                    "default": True
-                }),
-                "use_temporal_tiling": ([True, False], {
-                    "default": True
-                }),
-                "use_parallel_tiling": ([True, False], {
-                    "default": True
-                }),
-            }
-        }
-
-    @classmethod
-    def VALIDATE_INPUTS(cls, **kwargs):
-        return True
-
-    RETURN_TYPES = ("VAE_CONFIG", )
-    RETURN_NAMES = ("vae_config", )
-    FUNCTION = "set_args"
-    CATEGORY = "fastvideo"
-
-    def set_args(
-        self,
-        load_encoder,
-        load_decoder,
-        tile_sample_min_height,
-        tile_sample_min_width,
-        tile_sample_min_num_frames,
-        tile_sample_stride_height,
-        tile_sample_stride_width,
-        tile_sample_stride_num_frames,
-        blend_num_frames,
-        use_tiling,
-        use_temporal_tiling,
-        use_parallel_tiling,
-    ):
-        raw_args = {
-            "load_encoder": load_encoder,
-            "load_decoder": load_decoder,
-            "tile_sample_min_height": tile_sample_min_height,
-            "tile_sample_min_width": tile_sample_min_width,
-            "tile_sample_min_num_frames": tile_sample_min_num_frames,
-            "tile_sample_stride_height": tile_sample_stride_height,
-            "tile_sample_stride_width": tile_sample_stride_width,
-            "tile_sample_stride_num_frames": tile_sample_stride_num_frames,
-            "blend_num_frames": blend_num_frames,
-            "use_tiling": use_tiling,
-            "use_temporal_tiling": use_temporal_tiling,
-            "use_parallel_tiling": use_parallel_tiling,
-        }
-
-        # Filter out any value explicitly set to -99999
-        args = {k: v for k, v in raw_args.items() if str(int(v)) != str(-99999)}
-
-        return (args, )
@@ -1,315 +0,0 @@
-from __future__ import annotations
-
-import glob
-import os
-import signal
-import sys
-import threading
-import time
-from typing import Any
-
-from comfy.model_management import processing_interrupted
-
-from fastvideo import PipelineConfig
-from fastvideo import VideoGenerator as FastVideoGenerator
-
-sys.path.insert(
-    0,
-    os.path.dirname(
-        os.path.dirname(
-            os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
-
-
-# Custom exception for interruption
-class GenerationInterruptedException(Exception):
-    pass
-
-
-# Custom exception for interruption that ComfyUI will recognize
-class GenerationCancelledException(Exception):
-
-    def __init__(self,
-                 message: str = "Generation was cancelled by user") -> None:
-        self.message = message
-        super().__init__(self.message)
-
-
-def update_config_from_args(config: Any, args_dict: dict[str, Any]) -> None:
-    """
-    Update configuration object from arguments dictionary.
-    
-    Args:
-        config: The configuration object to update
-        args_dict: Dictionary containing arguments
-    """
-    for key, value in args_dict.items():
-        if hasattr(config, key) and value is not None:
-            if key == "text_encoder_precisions" and isinstance(value, list):
-                setattr(config, key, tuple(value))
-            else:
-                setattr(config, key, value)
-
-
-class VideoGenerator:
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt": ("STRING", {
-                    "multiline":
-                    True,
-                    "default":
-                    "A ripe orange tumbles gently from a tree and lands on the head of a lounging capybara, "
-                    "who blinks slowly in response. The moment is quietly humorous and oddly serene, framed by "
-                    "lush green foliage and dappled sunlight. Mid-shot, warm and whimsical tones."
-                }),
-                "output_path": ("STRING", {
-                    "default": "/workspace/ComfyUI/outputs_video/"
-                }),
-                "num_gpus": ("INT", {
-                    "default": 2,
-                    "min": 1,
-                    "max": 16
-                }),
-                "model_path": ("STRING", {
-                    "default": "FastVideo/FastHunyuan-diffusers"
-                })
-            },
-            "optional": {
-                "inference_args": ("INFERENCE_ARGS", ),
-                "embedded_cfg_scale": ("FLOAT", {
-                    "default": 6.0
-                }),
-                "sp_size": ("INT", {
-                    "default": 2
-                }),
-                "tp_size": ("INT", {
-                    "default": 2
-                }),
-                "vae_config": ("VAE_CONFIG", ),
-                "vae_precision": (["fp16", "bf16"], {
-                    "default": "fp16"
-                }),
-                "vae_tiling": ([True, False], {
-                    "default": True
-                }),
-                "vae_sp": ([True, False], {
-                    "default": False
-                }),
-                "text_encoder_config": ("TEXT_ENCODER_CONFIG", ),
-                "text_encoder_precision": (["fp16", "bf16"], {
-                    "default": "fp16"
-                }),
-                "dit_config": ("DIT_CONFIG", ),
-                "precision": (["fp16", "bf16"], {
-                    "default": "fp16"
-                }),
-                "use_cpu_offload": ([True, False], {
-                    "default": False
-                }),
-            }
-        }
-
-    @classmethod
-    def VALIDATE_INPUTS(cls, **kwargs):
-        return True
-
-    RETURN_TYPES = ("STRING", )
-    RETURN_NAMES = ("video_path", )
-    FUNCTION = "launch_inference"
-    CATEGORY = "fastvideo"
-
-    generator: FastVideoGenerator | None = None
-    _interrupt_thread: threading.Thread | None = None
-    _generation_active: bool = False
-    _generation_interrupted: bool = False
-    _interrupt_event: threading.Event = threading.Event()
-    _generation_thread: threading.Thread | None = None
-    _generation_result: str | None = None
-    _generation_exception: Exception | None = None
-
-    def _monitor_for_interruption(self):
-        """Background thread that monitors for interruption requests"""
-        time.sleep(2)  # Give the generation thread time to send execute_forward
-
-        while self._generation_active and not self._interrupt_event.is_set():
-            if processing_interrupted():
-                print("Video generation interrupted by user")
-                self._generation_interrupted = True
-
-                # Try to send interrupt signal to worker processes
-                if self.generator is not None and hasattr(
-                        self.generator, 'executor'):
-                    try:
-                        # The MultiprocExecutor has a workers attribute
-                        if hasattr(self.generator.executor, 'workers'):
-                            for worker in self.generator.executor.workers:
-                                if worker.is_alive():
-                                    os.kill(worker.pid, signal.SIGINT)
-                            print("Interrupt signal sent to worker processes")
-                    except Exception as e:
-                        print(f"Error sending interrupt signal: {e}")
-
-                # Set the interrupt event to notify other threads
-                self._interrupt_event.set()
-                break
-            time.sleep(0.5)
-
-    def _run_generation(self, prompt: str, output_path: str,
-                        inference_args: dict[str, Any]) -> None:
-        """Thread function to run the generation"""
-        try:
-            if self.generator is not None:
-                self.generator.generate_video(prompt=prompt,
-                                              output_path=output_path,
-                                              **inference_args)
-                self._generation_result = os.path.join(output_path,
-                                                       f"{prompt[:100]}.mp4")
-            else:
-                raise RuntimeError("Generator is not initialized")
-        except Exception as e:
-            self._generation_exception = e
-            self._interrupt_event.set()
-
-    def load_output_video(self, output_dir):
-        video_extensions = ["*.mp4", "*.avi", "*.mov", "*.mkv"]
-        video_files = []
-
-        for ext in video_extensions:
-            video_files.extend(glob.glob(os.path.join(output_dir, ext)))
-
-        if not video_files:
-            print("No video files found in output directory: %s", output_dir)
-            return ""
-
-        video_files.sort()
-        return video_files[0]
-
-    def launch_inference(
-        self,
-        prompt,
-        output_path,
-        num_gpus,
-        model_path,
-        embedded_cfg_scale,
-        sp_size,
-        tp_size,
-        vae_precision,
-        vae_tiling,
-        vae_sp,
-        text_encoder_precision,
-        precision,
-        inference_args=None,
-        vae_config=None,
-        text_encoder_config=None,
-        dit_config=None,
-        use_cpu_offload=None,
-    ):
-        print('Running FastVideo inference')
-
-        # Reset interruption flag and event
-        self._generation_interrupted = False
-        self._interrupt_event.clear()
-        self._generation_result = None
-        self._generation_exception = None
-
-        # Load pipeline config from model path
-        pipeline_config = PipelineConfig.from_pretrained(model_path)
-        print('pipeline_config', pipeline_config)
-
-        # Update configs with provided config dictionaries
-        if dit_config is not None:
-            update_config_from_args(pipeline_config.dit_config, dit_config)
-
-        if vae_config is not None:
-            update_config_from_args(pipeline_config.vae_config, vae_config)
-
-        if text_encoder_config is not None:
-            update_config_from_args(pipeline_config.text_encoder_configs,
-                                    text_encoder_config)
-
-        # Update top-level pipeline config with remaining arguments
-        raw_pipeline_args = {}
-        if embedded_cfg_scale is not None:
-            raw_pipeline_args['embedded_cfg_scale'] = embedded_cfg_scale
-        if precision is not None:
-            raw_pipeline_args['precision'] = precision
-        if vae_precision is not None:
-            raw_pipeline_args['vae_precision'] = vae_precision
-        if vae_tiling is not None:
-            raw_pipeline_args['vae_tiling'] = vae_tiling
-        if vae_sp is not None:
-            raw_pipeline_args['vae_sp'] = vae_sp
-        if text_encoder_precision is not None:
-            raw_pipeline_args['text_encoder_precision'] = text_encoder_precision
-
-        # Filter out any value explicitly set to -99999 (auto values)
-        pipeline_args = {
-            k: v
-            for k, v in raw_pipeline_args.items() if str(int(v)) != str(-99999)
-        }
-
-        update_config_from_args(pipeline_config, pipeline_args)
-
-        raw_generation_args = {}
-        if num_gpus is not None:
-            raw_generation_args['num_gpus'] = num_gpus
-        if tp_size is not None:
-            raw_generation_args['tp_size'] = tp_size
-        if sp_size is not None:
-            raw_generation_args['sp_size'] = sp_size
-        if use_cpu_offload is not None:
-            raw_generation_args['use_cpu_offload'] = use_cpu_offload
-
-        generation_args = {
-            k: v
-            for k, v in raw_generation_args.items()
-            if str(int(v)) != str(-99999)
-        }
-
-        if self.generator is None:
-            print('generation_args', generation_args)
-            print('pipeline_config', pipeline_config)
-            self.generator = FastVideoGenerator.from_pretrained(
-                model_path=model_path,
-                **generation_args,
-                pipeline_config=pipeline_config)
-
-        print('inference_args', inference_args)
-
-        # Start a thread to run the generation
-        self._generation_thread = threading.Thread(target=self._run_generation,
-                                                   args=(prompt, output_path,
-                                                         inference_args),
-                                                   daemon=True)
-        self._generation_thread.start()
-
-        # Start a background thread to monitor for interruptions
-        self._generation_active = True
-        self._interrupt_thread = threading.Thread(
-            target=self._monitor_for_interruption, daemon=True)
-        self._interrupt_thread.start()
-
-        # Wait for either completion or interruption
-        while self._generation_thread.is_alive(
-        ) and not self._interrupt_event.is_set():
-            self._generation_thread.join(timeout=0.5)
-
-        self._generation_active = False
-        if self._interrupt_thread:
-            self._interrupt_thread.join(timeout=1.0)
-            self._interrupt_thread = None
-
-        if self._generation_interrupted:
-            print("Video generation was cancelled by user")
-            raise GenerationCancelledException()
-        elif self._generation_exception:
-            # Re-raise the exception from the generation thread
-            raise self._generation_exception
-        elif self._generation_result:
-            return (self._generation_result, )
-        else:
-            # This shouldn't happen, but just in case
-            print("Generation completed but no result was produced")
-            raise Exception("Generation failed to produce a result")
@@ -1,593 +0,0 @@
-import { app } from '../../../scripts/app.js'
-
-function chainCallback(object, property, callback) {
-    if (object == undefined) {
-        console.error("Tried to add callback to non-existent object");
-        return;
-    }
-    if (property in object && object[property]) {
-        const callback_orig = object[property];
-        object[property] = function () {
-            const r = callback_orig.apply(this, arguments);
-            return callback.apply(this, arguments) ?? r;
-        };
-    } else {
-        object[property] = callback;
-    }
-}
-
-function drawAutoAnnotated(ctx, node, widget_width, y, H) {
-    const litegraph_base = LiteGraph;
-    const show_text = app.canvas.ds.scale >= 0.5;
-    const margin = 15;
-
-    const autoTextWidth = 30;
-    const autoTextRightMargin = 5;
-
-    ctx.textAlign = 'left';
-    ctx.strokeStyle = litegraph_base.WIDGET_OUTLINE_COLOR;
-    ctx.fillStyle = litegraph_base.WIDGET_BGCOLOR;
-
-    ctx.beginPath();
-    if (show_text && ctx.roundRect) {
-        ctx.roundRect(margin, y, widget_width - margin * 2, H, [H * 0.5]);
-    } else {
-        ctx.rect(margin, y, widget_width - margin * 2, H);
-    }
-    ctx.fill();
-
-    if (show_text) {
-        if (!this.disabled) ctx.stroke();
-        const isAuto = this.isAuto === true;
-
-        ctx.save();
-        if (isAuto) {
-            ctx.fillStyle = litegraph_base.WIDGET_TEXT_COLOR;
-            ctx.strokeStyle = litegraph_base.WIDGET_TEXT_COLOR;
-        } else {
-            ctx.fillStyle = litegraph_base.WIDGET_SECONDARY_TEXT_COLOR;
-            ctx.strokeStyle = litegraph_base.WIDGET_SECONDARY_TEXT_COLOR;
-        }
-
-        // Position for the cog
-        const cogX = widget_width - autoTextRightMargin - autoTextWidth - 6;
-        const cogY = y + H * 0.5;
-        const cogRadius = 6;
-        const toothLength = 2;
-        const numTeeth = 8;
-        const holeRadius = 2; // Radius of the center hole
-
-        // Draw the cog
-        ctx.beginPath();
-        ctx.arc(cogX, cogY, cogRadius - toothLength, 0, Math.PI * 2);
-        ctx.fill();
-
-        // Draw the center hole (by clearing it)
-        ctx.beginPath();
-        ctx.arc(cogX, cogY, holeRadius, 0, Math.PI * 2);
-        ctx.fillStyle = litegraph_base.WIDGET_BGCOLOR;
-        ctx.fill();
-
-        // Reset fill style for the teeth
-        if (isAuto) {
-            ctx.fillStyle = litegraph_base.WIDGET_TEXT_COLOR;
-        } else {
-            ctx.fillStyle = litegraph_base.WIDGET_SECONDARY_TEXT_COLOR;
-        }
-
-        // Draw teeth
-        ctx.beginPath();
-        for (let i = 0; i < numTeeth; i++) {
-            const angle = (i / numTeeth) * Math.PI * 2;
-            const innerX = cogX + (cogRadius - toothLength) * Math.cos(angle);
-            const innerY = cogY + (cogRadius - toothLength) * Math.sin(angle);
-            const outerX = cogX + cogRadius * Math.cos(angle);
-            const outerY = cogY + cogRadius * Math.sin(angle);
-
-            ctx.moveTo(innerX, innerY);
-            ctx.lineTo(outerX, outerY);
-        }
-        ctx.lineWidth = 2;
-        ctx.stroke();
-
-        ctx.restore();
-
-        // Draw label
-        ctx.fillStyle = litegraph_base.WIDGET_SECONDARY_TEXT_COLOR;
-        const label = this.label || this.name;
-        if (label != null) {
-            ctx.fillText(label, margin * 2 + 5, y + H * 0.7);
-        }
-
-        // Draw value
-        ctx.textAlign = 'right';
-        const text = isAuto ? "auto" : this.displayValue();
-        ctx.fillStyle = isAuto ? litegraph_base.WIDGET_SECONDARY_TEXT_COLOR : litegraph_base.WIDGET_TEXT_COLOR;
-        ctx.fillText(text, widget_width - autoTextRightMargin - autoTextWidth - 15, y + H * 0.7);
-
-        // Draw increment/decrement buttons if not in AUTO mode and not a string widget
-        if (!isAuto && !this.disabled && this.config[0] !== "FVAUTOSTRING") {
-            // Draw decrement button (left triangle)
-            ctx.fillStyle = litegraph_base.WIDGET_TEXT_COLOR;
-            ctx.beginPath();
-            ctx.moveTo(margin + 16, y + 5);
-            ctx.lineTo(margin + 6, y + H * 0.5);
-            ctx.lineTo(margin + 16, y + H - 5);
-            ctx.fill();
-
-            // Draw increment button (right triangle)
-            ctx.beginPath();
-            ctx.moveTo(widget_width - margin - 16, y + 5);
-            ctx.lineTo(widget_width - margin - 6, y + H * 0.5);
-            ctx.lineTo(widget_width - margin - 16, y + H - 5);
-            ctx.fill();
-        }
-    }
-}
-
-function mouseAutoAnnotated(event, [x, y], node) {
-    const widget_width = node.size[0];
-    const margin = 15;
-    const H = 20; // Widget height
-
-    const autoTextWidth = 30;
-    const autoTextRightMargin = 5;
-
-    const cogRadius = 6;
-
-    if (this.isAuto) {
-        if (event.type === "pointerup" || event.type === "mouseup") {
-            const cogX = widget_width - autoTextRightMargin - autoTextWidth - 6;
-            const cogLeftEdge = cogX - cogRadius;
-            const cogRightEdge = cogX + cogRadius;
-
-            if (x > cogLeftEdge && x < cogRightEdge) {
-                this.isAuto = false;
-                this.value = this.cachedValue !== undefined ? this.cachedValue : (this.options.default || 0);
-
-                if (this.callback) {
-                    this.callback(this.value);
-                }
-                node.graph.setDirtyCanvas(true, false);
-            }
-        }
-
-        // Block ALL events in auto mode except cog clicks
-        event.preventDefault?.();
-        event.stopPropagation?.();
-        event.stopImmediatePropagation?.();
-        return true; // Always return true to indicate event was handled
-    }
-
-    // Determine if clicking on increment/decrement buttons
-    const delta = this.config[0] === "FVAUTOSTRING" ? 0 :
-        (x < 40 ? -1 : x > widget_width - 48 ? 1 : 0);
-
-    if (event.type === "pointerdown" || event.type === "mousedown") {
-        // ComfyUI appears to intercept pointerdown events, so this code path is never reached
-        console.log("pointerdown received (unexpected)");
-        return false;
-    } else if (event.type === "pointerup" || event.type === "mouseup") {
-        // Stop event propagation to prevent double handling
-        event.preventDefault?.();
-        event.stopPropagation?.();
-        event.stopImmediatePropagation?.();
-
-        const cogX = widget_width - autoTextRightMargin - autoTextWidth - 6;
-        const cogLeftEdge = widget_width - autoTextRightMargin - autoTextWidth - 6 - cogRadius;
-        const cogRightEdge = widget_width - autoTextRightMargin - autoTextWidth - 6 + cogRadius;
-
-        if (x > cogLeftEdge && x < cogRightEdge) {
-            this.isAuto = !this.isAuto;
-
-            if (this.isAuto) {
-                this.cachedValue = this.value;
-                this.value = -99999;
-            } else {
-                this.value = this.cachedValue !== undefined ? this.cachedValue : (this.options.default || 0);
-            }
-
-            if (this.callback) {
-                this.callback(this.value);
-            }
-
-            node.graph.setDirtyCanvas(true, false);
-            return true;
-        }
-
-        // If in auto mode and NOT clicking the cog, block all other interactions
-        if (this.isAuto) {
-            return true;
-        }
-
-        // Handle increment/decrement buttons if not in auto mode
-        if (delta !== 0 && !this.isAuto) {
-            if (this.config[0] === "FVAUTOCOMBO") {
-                const options = this.options.values || [];
-                if (options.length === 0) return true;
-
-                let currentIndex = -1;
-                for (let i = 0; i < options.length; i++) {
-                    const optValue = typeof options[i] === 'object' ? options[i].value : options[i];
-                    if (optValue == this.value || String(optValue) === String(this.value)) {
-                        currentIndex = i;
-                        break;
-                    }
-                }
-
-                if (currentIndex === -1) {
-                    currentIndex = 0;
-                }
-
-                let newIndex = currentIndex + delta;
-                if (newIndex < 0) {
-                    newIndex = options.length - 1;
-                } else if (newIndex >= options.length) {
-                    newIndex = 0;
-                }
-
-                const newOption = options[newIndex];
-                this.value = typeof newOption === 'object' ? newOption.value : newOption;
-
-                if (this.callback) {
-                    this.callback(this.value);
-                }
-
-                node.graph.setDirtyCanvas(true, false);
-                return true;
-            } else {
-                let v = parseFloat(this.value);
-                const increment = delta * 0.1 * (this.options.step || 1);
-
-                v += increment;
-
-                // Apply min/max constraints
-                if (this.options.min != null) {
-                    v = Math.max(this.options.min, v);
-                }
-                if (this.options.max != null) {
-                    v = Math.min(this.options.max, v);
-                }
-
-                // Round to precision or to integer
-                if (this.config[0] === "FVAUTOINT") {
-                    v = Math.round(v);
-                } else if (this.options.precision !== undefined) {
-                    const precision = Math.pow(10, this.options.precision);
-                    v = Math.round(v * precision) / precision;
-                }
-
-                this.value = v;
-
-                if (this.callback) {
-                    this.callback(this.value);
-                }
-
-                node.graph.setDirtyCanvas(true, false);
-                return true;
-            }
-        }
-
-        if (delta === 0 && !this.isAuto) {
-            if (this.config[0] === "FVAUTOCOMBO") {
-                const options = this.options.values || [];
-
-                // Create menu items
-                const menuItems = options.map(opt => {
-                    const value = typeof opt === 'object' ? opt.value : opt;
-                    const label = typeof opt === 'object' ? opt.label : opt.toString();
-
-                    return {
-                        content: label,
-                        callback: () => {
-                            this.value = value;
-
-                            if (this.callback) {
-                                this.callback(this.value);
-                            }
-
-                            node.graph.setDirtyCanvas(true, false);
-                        }
-                    };
-                });
-
-                new LiteGraph.ContextMenu(menuItems, {
-                    event: event,
-                    title: null,
-                    callback: null,
-                    extra: node
-                });
-
-                return true;
-            } else if (this.config[0] === "FVAUTOSTRING") {
-                const d_callback = (v) => {
-                    this.value = v;
-
-                    if (this.callback) {
-                        this.callback(this.value);
-                    }
-
-                    node.graph.setDirtyCanvas(true, false);
-                };
-
-                const dialog = app.canvas.prompt(
-                    'Value',
-                    this.value,
-                    d_callback,
-                    event
-                );
-
-                return true;
-            } else {
-                // For numeric widgets, show input dialog
-                const d_callback = (v) => {
-                    this.value = this.parseValue?.(v) ?? Number(v);
-
-                    // Apply min/max constraints
-                    if (this.options.min != null) {
-                        this.value = Math.max(this.options.min, this.value);
-                    }
-                    if (this.options.max != null) {
-                        this.value = Math.min(this.options.max, this.value);
-                    }
-
-                    // Round to precision or to integer
-                    if (this.config[0] === "FVAUTOINT") {
-                        this.value = Math.round(this.value);
-                    } else if (this.options.precision !== undefined) {
-                        const precision = Math.pow(10, this.options.precision);
-                        this.value = Math.round(this.value * precision) / precision;
-                    }
-
-                    if (this.callback) {
-                        this.callback(this.value);
-                    }
-
-                    node.graph.setDirtyCanvas(true, false);
-                };
-
-                const dialog = app.canvas.prompt(
-                    'Value',
-                    this.value,
-                    d_callback,
-                    event
-                );
-
-                return true;
-            }
-        }
-
-        return true;
-    }
-
-    return false;
-}
-
-function makeAutoAnnotated(widget, inputData) {
-    const original = {
-        callback: widget.callback,
-        type: widget.type,
-        value: widget.value
-    };
-
-    // Add AUTO properties to the widget
-    Object.assign(widget, {
-        type: "BOOLEAN",
-        draw: drawAutoAnnotated,
-        mouse: mouseAutoAnnotated,
-        onMouse: null, // Explicitly disable original onMouse handler
-
-        isAuto: true,
-        cachedValue: widget.value,
-        config: inputData,
-        options: Object.assign({}, inputData[1], widget.options),
-        original: original,  // Store original properties for reference
-
-        // Disable other potential mouse handlers with no-op functions
-        onClick: function () {
-            return false;
-        },
-        onPointerUp: function () {
-            return false;
-        },
-        onPointerDown: function () {
-            return false;
-        },
-        onMouseUp: function () {
-            return false;
-        },
-        onMouseDown: function () {
-            return false;
-        },
-
-        computeSize(width) {
-            return [width, 20];
-        },
-        displayValue: function () {
-            if (this.config[0] === "FVAUTOINT") {
-                return Math.round(this.value).toString();
-            }
-            if (this.config[0] === "FVAUTOCOMBO") {
-                return this.value;
-            }
-            if (this.config[0] === "FVAUTOSTRING") {
-                return this.value;
-            }
-            // For FLOAT values, check if it's actually an integer
-            if (Number.isInteger(this.value)) {
-                return this.value.toString();
-            }
-            return this.value.toFixed(this.options.precision || 2);
-        },
-        parseValue: function (v) {
-            if (this.config[0] === "FVAUTOSTRING") {
-                return v;
-            }
-            if (typeof v === "string") {
-                return parseFloat(v);
-            }
-            return v;
-        },
-        serializeValue: function () {
-            // Return special value for AUTO mode
-            return this.isAuto ? -99999 : this.value;
-        },
-        deserializeValue: function (data) {
-            if (data === -99999) {
-                this.isAuto = true;
-                this.value = -99999;
-            } else {
-                this.isAuto = false;
-                this.value = data;
-                this.cachedValue = data;
-            }
-        }
-    });
-
-    // Override callback to handle AUTO mode
-    widget.callback = function (v) {
-        if (this.isAuto) {
-            return; // Don't call the original callback in AUTO mode
-        }
-        const result = original.callback?.call(this, v);
-        return result;
-    };
-
-    // Override any potential click handlers
-    const originalOnClick = widget.onClick;
-    if (originalOnClick) {
-        widget.onClick = function (...args) {
-            if (this.isAuto) {
-                return false;
-            }
-            return originalOnClick.call(this, ...args);
-        };
-    }
-
-    return widget;
-}
-
-app.registerExtension({
-    name: "FastVideo.AutoWidgets",
-
-    async beforeRegisterNodeDef(nodeType, nodeData, app) {
-        if (nodeData?.name == "VideoGenerator" || nodeData?.name === "InferenceArgs" || nodeData?.name === "VAEConfig" ||
-            nodeData?.name === "TextEncoderConfig" || nodeData?.name === "DITConfig") {
-            // Add serialization support
-            chainCallback(nodeType.prototype, "onSerialize", function (info) {
-                if (!this.widgets) {
-                    return;
-                }
-                // Ensure widgets_values exists
-                if (!info.widgets_values) {
-                    info.widgets_values = {};
-                }
-
-                // Store AUTO widget states in a separate property
-                if (!info.auto_widget_states) {
-                    info.auto_widget_states = {};
-                }
-
-                // Handle AUTO widgets specially
-                for (const w of this.widgets) {
-                    if (w.type === "BOOLEAN" && w.isAuto !== undefined) {
-                        // Store the serialized value (for Python node)
-                        info.widgets_values[w.name] = w.serializeValue();
-
-                        // Store the full state (for UI restoration)
-                        info.auto_widget_states[w.name] = {
-                            isAuto: w.isAuto,
-                            value: w.value,
-                            cachedValue: w.cachedValue
-                        };
-                    }
-                }
-            });
-
-            // Add deserialization support
-            chainCallback(nodeType.prototype, "onConfigure", function (info) {
-                if (!this.widgets) {
-                    return;
-                }
-
-                // First, restore from widgets_values (for backward compatibility)
-                if (info.widgets_values && Array.isArray(info.widgets_values)) {
-                    for (let i = 0; i < this.widgets.length && i < info.widgets_values.length; i++) {
-                        const w = this.widgets[i];
-                        const value = info.widgets_values[i];
-
-                        if (w.type === "BOOLEAN" && w.isAuto !== undefined) {
-                            w.deserializeValue(value);
-                        }
-                    }
-                }
-
-                // Then, restore full state if available
-                if (info.auto_widget_states) {
-                    for (const w of this.widgets) {
-                        if (w.type === "BOOLEAN" && w.isAuto !== undefined && w.name in info.auto_widget_states) {
-                            const state = info.auto_widget_states[w.name];
-
-                            w.isAuto = state.isAuto;
-                            w.cachedValue = state.cachedValue;
-                            w.value = state.isAuto ? -99999 : state.value;
-
-                            w.callback?.(w.value);
-                        }
-                    }
-                }
-
-                // Force a redraw
-                this.graph?.setDirtyCanvas(true, true);
-            });
-
-
-            // Override addInput to handle AUTO widgets
-            chainCallback(nodeType.prototype, "onNodeCreated", function () {
-                // Convert any existing widgets to AUTO widgets if needed
-                let new_widgets = [];
-                const intWidgetNames = ["sp_size", "tp_size", "height", "width", "num_frames", "num_inference_steps", "flow_shift", "seed", "fps", "scale_factor",
-                    "tile_sample_min_height", "tile_sample_min_width", "tile_sample_min_num_frames", "tile_sample_stride_height", "tile_sample_stride_width",
-                    "tile_sample_stride_num_frames", "blend_num_frames"
-                ]
-                const floatWidgetNames = ["embedded_cfg_scale", "guidance_scale"]
-                const comboWidgetNames = ["vae_tiling", "vae_precision", "vae_sp", "text_encoder_precision", "precision",
-                    "load_encoder", "load_decoder", "use_tiling", "use_temporal_tiling", "use_parallel_tiling", "use_cpu_offload", "enable_teacache"
-                ]
-                const stringWidgetNames = ["prefix", "quant_config", "lora_config", "image_path"]
-
-                if (this.widgets) {
-                    for (let w of this.widgets) {
-                        if (intWidgetNames.includes(w.name)) {
-                            new_widgets.push(makeAutoAnnotated(w, ["FVAUTOINT", { "default": 0 }]));
-                        } else if (floatWidgetNames.includes(w.name)) {
-                            new_widgets.push(makeAutoAnnotated(w, ["FVAUTOFLOAT", { "default": 0 }]));
-                        } else if (comboWidgetNames.includes(w.name)) {
-                            new_widgets.push(makeAutoAnnotated(w, ["FVAUTOCOMBO", { "default": 0 }]));
-                        } else if (stringWidgetNames.includes(w.name)) {
-                            new_widgets.push(makeAutoAnnotated(w, ["FVAUTOSTRING", { "default": "" }]));
-                        } else {
-                            new_widgets.push(w);
-                        }
-                    }
-                    this.widgets = new_widgets;
-
-                    const autoWidgets = this.widgets.filter(w => w.type === "BOOLEAN" && w.isAuto !== undefined);
-                }
-
-                this.graph?.setDirtyCanvas(true, true);
-            });
-        }
-    },
-
-    async init() {
-        // Force a redraw of all nodes when the extension initializes
-        if (app.graph) {
-            setTimeout(() => {
-                app.graph.setDirtyCanvas(true, true);
-            }, 1000);
-        }
-    }
-});
-
-console.log("FastVideo.core.js loaded");
@@ -4,8 +4,9 @@


 ## Installation
-We test our code on Pytorch 2.5.0 and CUDA>=12.4. Currently we only support H100/H200, because ThunderKittens uses TMA but doesn't support Blackwell yet.
+We test our code on Pytorch 2.5.0 and CUDA>=12.4. Currently we only have implementation on H100.
 First, install C++20 for ThunderKittens:
+
 ```bash
 sudo apt update
 sudo apt install gcc-11 g++-11
@@ -15,27 +16,17 @@ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave
 sudo apt update
 sudo apt install clang-11
 ```
-
-## Environment Setup
-First, set up your CUDA environment:
+Install STA:
 ```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
 git submodule update --init --recursive
-```
-
-## Install Sliding Tile Attention (STA)
-```bash
-python setup_sta.py install
-```
-
-## Install Video Sparse Attention (VSA)
-```bash
-python setup_vsa.py install
+python setup.py install
 ```

 ## Usage
+
 ```python
 from st_attn import sliding_tile_attention
 # assuming video size (T, H, W) = (30, 48, 80), text tokens = 256 with padding. 
@@ -53,14 +44,8 @@ out = sliding_tile_attention(q, k, v, window_size, 0, False)

 ## Test
 ```bash
-python tests/test_sta.py # test STA
-python tests/test_block_sparse.py # test VSA
+python test/test_sta.py
 ```
-## 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. 
@@ -5,7 +5,6 @@ import matplotlib.pyplot as plt
 import numpy as np
 import torch
 from st_attn import sliding_tile_attention
-from triton.testing import do_bench


 def flops(batch, seqlen, nheads, headdim, causal, mode="fwd"):
@@ -14,16 +13,16 @@ def flops(batch, seqlen, nheads, headdim, causal, mode="fwd"):
    return f if mode == "fwd" else (2.5 * f if mode == "bwd" else 3.5 * f)


-def compute_TFLOPS(flops, ms):
-    flops = flops / 1e12
-    ms = ms / 1e3
-    return flops / ms
+def efficiency(flop, time):
+    flop = flop / 1e12
+    time = time / 1e6
+    return flop / time


 def benchmark_attention(configurations):
    results = {'fwd': defaultdict(list), 'bwd': defaultdict(list)}

-    for B, H, N, D, causal, dit_seq_shape, window_size in configurations:
+    for B, H, N, D, causal in configurations:
        print("=" * 60)
        print(f"Timing forward and backward pass for B={B}, H={H}, N={N}, D={D}, causal={causal}")

@@ -31,31 +30,38 @@ def benchmark_attention(configurations):
        k = torch.randn(B, H, N, D, dtype=torch.bfloat16, device='cuda', requires_grad=False).contiguous()
        v = torch.randn(B, H, N, D, dtype=torch.bfloat16, device='cuda', requires_grad=False).contiguous()

-        # grad_output = torch.randn_like(q, requires_grad=False).contiguous()
-        # qg = torch.zeros_like(q, requires_grad=False, dtype=torch.float).contiguous()
-        # kg = torch.zeros_like(k, requires_grad=False, dtype=torch.float).contiguous()
-        # vg = torch.zeros_like(v, requires_grad=False, dtype=torch.float).contiguous()
+        grad_output = torch.randn_like(q, requires_grad=False).contiguous()

+        qg = torch.zeros_like(q, requires_grad=False, dtype=torch.float).contiguous()
+        kg = torch.zeros_like(k, requires_grad=False, dtype=torch.float).contiguous()
+        vg = torch.zeros_like(v, requires_grad=False, dtype=torch.float).contiguous()

-        # # Warmup for forward pass
-        # for _ in range(10):
-        #     o = sliding_tile_attention(q, k, v, [[3, 6, 10]] * 24, 0, False, dit_seq_shape)
+        # Prepare for timing forward pass
+        start_events_fwd = [torch.cuda.Event(enable_timing=True) for _ in range(10)]
+        end_events_fwd = [torch.cuda.Event(enable_timing=True) for _ in range(10)]

-        # # Time the forward pass
-        # for i in range(10):
-        #     start_events_fwd[i].record()
-        #     o = sliding_tile_attention(q, k, v, [[3, 6, 10]] * 24, 0, False, dit_seq_shape)
-        #     end_events_fwd[i].record()
-        ms = do_bench(lambda: sliding_tile_attention(q, k, v, [window_size] * 24, 0, False, dit_seq_shape))
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()

-        # times_fwd = [s.elapsed_time(e) for s, e in zip(start_events_fwd, end_events_fwd)]
-        # time_us_fwd = np.mean(times_fwd) * 1000
+        # Warmup for forward pass
+        for _ in range(10):
+            o = sliding_tile_attention(q, k, v, [[3, 6, 10]] * 24, 0, False, '18x48x80')

-        tflops_fwd = compute_TFLOPS(flops(B, N, H, D, causal, 'fwd'), ms)
+        # Time the forward pass
+        for i in range(10):
+            start_events_fwd[i].record()
+            o = sliding_tile_attention(q, k, v, [[3, 6, 10]] * 24, 0, False, '18x48x80')
+            end_events_fwd[i].record()
+
+        torch.cuda.synchronize()
+        times_fwd = [s.elapsed_time(e) for s, e in zip(start_events_fwd, end_events_fwd)]
+        time_us_fwd = np.mean(times_fwd) * 1000
+
+        tflops_fwd = efficiency(flops(B, N, H, D, causal, 'fwd'), time_us_fwd)
        results['fwd'][(D, causal)].append((N, tflops_fwd))

-        print(f"Average time for forward pass (ms): {ms:.2f}")
-        print(f"Average TFLOPS: {tflops_fwd}")
+        print(f"Average time for forward pass in us: {time_us_fwd:.2f}")
+        print(f"Average efficiency for forward pass in TFLOPS: {tflops_fwd}")
        print("-" * 60)

        # torch.cuda.empty_cache()
@@ -79,14 +85,15 @@ def benchmark_attention(configurations):
        # times_bwd = [s.elapsed_time(e) for s, e in zip(start_events_bwd, end_events_bwd)]
        # time_us_bwd = np.mean(times_bwd) * 1000

-        # tflops_bwd = compute_TFLOPS(flops(B, N, H, D, causal, 'bwd'), ms)
+        # tflops_bwd = efficiency(flops(B, N, H, D, causal, 'bwd'), time_us_bwd)
        # results['bwd'][(D, causal)].append((N, tflops_bwd))

-        # print(f"Average time for backward pass(ms): {ms:.2f}")
-        # print(f"Average TFLOPS: {tflops_bwd}")
-        # print("=" * 60)
+        # print(f"Average time for backward pass in us: {time_us_bwd:.2f}")
+        # print(f"Average efficiency for backward pass in TFLOPS: {tflops_bwd}")
+        print("=" * 60)

        torch.cuda.empty_cache()
+        torch.cuda.synchronize()

    return results

@@ -117,10 +124,7 @@ def plot_results(results):

 # Example list of configurations to test
 configurations = [
-    (2, 24, 69120, 128, False, '18x48x80', [3, 6, 10]),
-    (2, 24, 69120, 128, True, '18x48x80', [3, 6, 10]),
-    (2, 24, 82944, 128, False, '36x48x48', [3, 3, 6]), # Stepvideo
-    (2, 24, 82944, 128, True, '36x48x48', [3, 3, 6]),
+    (2, 24, 69120, 128, False),
    # (16, 16, 768*16,  128, False),
    # (16, 16, 768*2,  128, False),
    # (16, 16, 768*4,  128, False),
@@ -4,17 +4,9 @@
 #include <cooperative_groups.h>
 #include <iostream>
 #include <stdio.h>
-#include <c10/cuda/CUDAGuard.h>
-
-// #define CLAMP(value, min, max) ((value) < (min) ? (min) : ((value) > (max) ? (max) : (value)))
-__device__ __forceinline__ int clamp_int(int value, int min, int max) {
-    return (value < min) ? min : ((value > max) ? max : value);
-}
-// #define ABS(x) ((x) < 0 ? -(x) : (x))
-__device__ __forceinline__ int abs_int(int value) {
-    return (value < 0) ? -value : value;
-}

+#define CLAMP(value, min, max) ((value) < (min) ? (min) : ((value) > (max) ? (max) : (value)))
+#define ABS(x) ((x) < 0 ? -(x) : (x))

 constexpr int CONSUMER_WARPGROUPS = (3); 
 constexpr int PRODUCER_WARPGROUPS = (1); 
@@ -125,16 +117,16 @@ void fwd_attend_ker(const __grid_constant__ fwd_globals<D> g) {
            int qt = seq_idx / 6 / (CH * CW);
            int qh = (seq_idx / 6) % (CH * CW) / CW;
            int qw = (seq_idx / 6) % CW;
-            qt = clamp_int(qt, DT, CT-DT-1);
-            qh = clamp_int(qh, DH, CH-DH-1);
-            qw = clamp_int(qw, DW, CW-DW-1);
+            qt = CLAMP(qt, DT, CT-DT-1);
+            qh = CLAMP(qh, DH, CH-DH-1);
+            qw = CLAMP(qw, DW, CW-DW-1);
            int count = 0;
            int j = 0;
            while (count < K::stages - 1) {
                int kt = j / 3 / (CH * CW);
                int kh = (j / 3) % (CH * CW) / CW;
                int kw = (j / 3) % CW;
-                bool mask = (abs_int(qt - kt) <= DT) && (abs_int(qh - kh) <= DH) && (abs_int(qw - kw) <= DW);
+                bool mask = (ABS(qt - kt) <= DT) && (ABS(qh - kh) <= DH) && (ABS(qw - kw) <= DW);
                if (mask){
                    coord<k_tile> kv_tile_idx = {blockIdx.z, kv_head_idx, j, 0};
                    tma::expect_bytes(k_smem_arrived[count], sizeof(k_tile));
@@ -175,15 +167,15 @@ void fwd_attend_ker(const __grid_constant__ fwd_globals<D> g) {
                int qt = seq_idx / 6 / (CH * CW);
                int qh = (seq_idx / 6) % (CH * CW) / CW;
                int qw = (seq_idx / 6) % CW;
-                qt = clamp_int(qt, DT, CT-DT-1);
-                qh = clamp_int(qh, DH, CH-DH-1);
-                qw = clamp_int(qw, DW, CW-DW-1);
-                int k_t_min = clamp_int(qt-DT, 0, CT-1);
-                int k_t_max = clamp_int(qt+DT, 0, CT-1);
-                int k_h_min = clamp_int(qh-DH, 0, CH-1);
-                int k_h_max = clamp_int(qh+DH, 0, CH-1);
-                int k_w_min = clamp_int(qw-DW, 0, CW-1);
-                int k_w_max = clamp_int(qw+DW, 0, CW-1);
+                qt = CLAMP(qt, DT, CT-DT-1);
+                qh = CLAMP(qh, DH, CH-DH-1);
+                qw = CLAMP(qw, DW, CW-DW-1);
+                int k_t_min = CLAMP(qt-DT, 0, CT-1);
+                int k_t_max = CLAMP(qt+DT, 0, CT-1);
+                int k_h_min = CLAMP(qh-DH, 0, CH-1);
+                int k_h_max = CLAMP(qh+DH, 0, CH-1);
+                int k_w_min = CLAMP(qw-DW, 0, CW-1);
+                int k_w_max = CLAMP(qw+DW, 0, CW-1);
                int count = 0;
                for (int kt = k_t_min; kt <= k_t_max; kt++) {
                    for (int kh = k_h_min; kh <= k_h_max; kh++) {
@@ -242,7 +234,7 @@ void fwd_attend_ker(const __grid_constant__ fwd_globals<D> g) {
            // the last three kv blocks are for text, we process them separately
            kv_iters = img_kv_blocks - 1;
        } else {
-            kv_iters = clamp_int(DT*2+1, 1, CT) * clamp_int(DH*2+1, 1, CH) * clamp_int(DW*2+1, 1, CW) * 3 - 1 ; 
+            kv_iters = CLAMP(DT*2+1, 1, CT) * CLAMP(DH*2+1, 1, CH) * CLAMP(DW*2+1, 1, CW) * 3 - 1 ; 
        }

        kittens::wait(qsmem_semaphore, 0);
@@ -423,9 +415,8 @@ sta_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o,
    float* l_ptr = reinterpret_cast<float*>(l_vec.data_ptr<float>());
    float* d_l   = reinterpret_cast<float*>(l_ptr);

-    //cudadevicesynchronize();
-    const c10::cuda::OptionalCUDAGuard device_guard(q.device());
-    const cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream(); 
+    cudaDeviceSynchronize();
+    auto stream = at::cuda::getCurrentCUDAStream().stream(); 


    if (head_dim == 128) {
@@ -451,8 +442,8 @@ sta_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o,

        globals g{qg_arg, kg_arg, vg_arg, lg_arg, og_arg, static_cast<int>(seq_len),  static_cast<int>(text_length), static_cast<int>(hr)};

-        constexpr int mem_size = kittens::MAX_SHARED_MEMORY;
-        int threads = NUM_WORKERS * kittens::WARP_THREADS;
+        auto mem_size = kittens::MAX_SHARED_MEMORY;
+        auto threads  = NUM_WORKERS * kittens::WARP_THREADS;
        if (has_text) {
            // TORCH_CHECK(seq_len % (CONSUMER_WARPGROUPS*kittens::TILE_DIM*4) == 0, "sequence length must be divisible by 192");
            dim3 grid_image(seq_len/(CONSUMER_WARPGROUPS*kittens::TILE_ROW_DIM<bf16>*4)-2, qo_heads, batch);
@@ -832,10 +823,10 @@ sta_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v, torch::Tensor o,

        }
        CHECK_CUDA_ERROR(cudaGetLastError());
-        // cudaStreamSynchronize(stream);
+        cudaStreamSynchronize(stream);
    }

    return o;
-    //cudadevicesynchronize();
+    cudaDeviceSynchronize();
 }

@@ -2,12 +2,11 @@ import torch
 import argparse
 from flash_attn.utils.benchmark import benchmark_forward
 from flash_attn import flash_attn_func
-from vsa import block_sparse_attention_fwd, block_sparse_attention_backward, BlockSparseAttentionFunction
-from vsa import BLOCK_M, BLOCK_N
+from st_attn import block_sparse_attention_fwd, block_sparse_attention_backward, BlockSparseAttentionFunction
+from st_attn import BLOCK_M, BLOCK_N

 import numpy as np
 import random
-import gc

 def set_seed(seed: int = 42):
    # Python random module
@@ -21,6 +20,15 @@ def set_seed(seed: int = 42):
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)  # if using multi-GPU

+def parse_arguments():
+    parser = argparse.ArgumentParser(description='Benchmark Block Sparse Attention')
+    parser.add_argument('--batch_size', type=int, default=4, help='Batch size')
+    parser.add_argument('--num_heads', type=int, default=6, help='Number of heads')
+    parser.add_argument('--head_dim', type=int, default=128, help='Head dimension')
+    parser.add_argument('--topk', type=int, default=64, help='Number of kv blocks each q block attends to')
+    parser.add_argument('--seq_lengths', type=int, nargs='+', default=[29120], help='Sequence lengths to benchmark')
+    parser.add_argument('--num_iterations', type=int, default=100, help='Number of test iterations to run')
+    return parser.parse_args()

@torch.no_grad
 def precision_metric(quant_o, fa2_o): 
@@ -127,7 +135,9 @@ def generate_block_sparse_pattern(bs, h, num_q_blocks, num_kv_blocks, k, device=
                
    return q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, block_sparse_mask

-def main(args):
+def main():
+    args = parse_arguments()
+
    set_seed(42)
    
    # Extract parameters
@@ -181,36 +191,23 @@ def main(args):
            block_mask_expanded = block_sparse_mask.unsqueeze(-1).unsqueeze(-2)  # [b, h, num_q_blocks, num_kv_blocks, 1, 1]
            block_mask_expanded = block_mask_expanded.expand(-1, -1, -1, -1, BLOCK_M, BLOCK_N)  # [b, h, num_q_blocks, num_kv_blocks, BLOCK_M, BLOCK_N]
            full_mask = block_mask_expanded.permute(0, 1, 2, 4, 3, 5).reshape(batch, head, seq_len, seq_len)
+            
+            q_sdpa = q.clone()
+            k_sdpa = k.clone()
+            v_sdpa = v.clone()

            q.requires_grad = True
            k.requires_grad = True
            v.requires_grad = True
-
-
-            # testing forward
-            o = BlockSparseAttentionFunction.apply(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num)
-            del q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num, block_sparse_mask, block_mask_expanded
-            grad_o = torch.randn_like(o)
-            o.backward(grad_o)
-            # clear memory
-            q_sdpa = q.detach().clone()
-            k_sdpa = k.detach().clone()
-            v_sdpa = v.detach().clone()
            q_sdpa.requires_grad = True
            k_sdpa.requires_grad = True
            v_sdpa.requires_grad = True
-            q.data = torch.empty(0, device=q.device)
-            k.data = torch.empty(0, device=k.device)
-            v.data = torch.empty(0, device=v.device)
-            torch.cuda.empty_cache()

+            # testing forward
+            o = BlockSparseAttentionFunction.apply(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num)
            o_sdpa = torch.nn.functional.scaled_dot_product_attention(q_sdpa, k_sdpa, v_sdpa, attn_mask=full_mask)

-
            sim, l1, rmse = precision_metric(o, o_sdpa)
-            assert sim > 0.9999, f"SSIM too low: {sim}"
-            assert l1 < 8e-5, f"l1 too large: {l1}"
-            assert rmse < 2e-5, f"RMSE too large: {rmse}"
            forward_metrics['sim'].append(sim)
            forward_metrics['l1'].append(l1)
            forward_metrics['rmse'].append(rmse)
@@ -218,72 +215,52 @@ def main(args):
            print(f"block_sparse_attention_fwd vs torch.nn.functional.scaled_dot_product_attention:\nsim: {sim}, l1: {l1}, rmse: {rmse}")

            # test backward
+            grad_o = torch.randn_like(o)
+            o.backward(grad_o)
            o_sdpa.backward(grad_o)

            sim, l1, rmse = precision_metric(q.grad, q_sdpa.grad)
-            # Error bounds collected on H100
-            assert sim > 0.9999, f"SSIM too low: {sim}"
-            assert l1 < 4e-3, f"l1 too large: {l1}"
-            assert rmse < 3e-4, f"RMSE too large: {rmse}"
            grad_q_metrics['sim'].append(sim)
            grad_q_metrics['l1'].append(l1)
            grad_q_metrics['rmse'].append(rmse)
            print(f"block_sparse_attention_bwd vs torch.nn.functional.scaled_dot_product_attention grad_q:\nsim: {sim}, l1: {l1}, rmse: {rmse}")        
            
            sim, l1, rmse = precision_metric(k.grad, k_sdpa.grad)
-            assert sim > 0.9999, f"SSIM too low: {sim}"
-            assert l1 < 4e-3, f"l1 too large: {l1}"
-            assert rmse < 2e-4, f"RMSE too large: {rmse}"
            grad_k_metrics['sim'].append(sim)
            grad_k_metrics['l1'].append(l1)
            grad_k_metrics['rmse'].append(rmse)
            print(f"block_sparse_attention_bwd vs torch.nn.functional.scaled_dot_product_attention grad_k:\nsim: {sim}, l1: {l1}, rmse: {rmse}")
            
            sim, l1, rmse = precision_metric(v.grad, v_sdpa.grad)
-            assert sim > 0.9999, f"SSIM too low: {sim}"
-            assert l1 < 1e-4, f"l1 too large: {l1}"
-            assert rmse < 2e-5, f"RMSE too large: {rmse}"
            grad_v_metrics['sim'].append(sim)
            grad_v_metrics['l1'].append(l1)
            grad_v_metrics['rmse'].append(rmse)
            print(f"block_sparse_attention_bwd vs torch.nn.functional.scaled_dot_product_attention grad_v:\nsim: {sim}, l1: {l1}, rmse: {rmse}")
-            
-            del o, o_sdpa, grad_o, q_sdpa, k_sdpa, v_sdpa
-            gc.collect()
-            torch.cuda.empty_cache()
-
+        
        # Print summary statistics if multiple iterations were run
        if num_iterations > 1:
            print("\n" + "="*50)
            print(f"Summary Statistics (over {num_iterations} iterations):")
            
            print("\nForward metrics:")
-            print(f"Similarity: mean={np.mean(forward_metrics['sim']):.6f}, std={np.std(forward_metrics['sim']):.6f}, min={np.min(forward_metrics['sim']):.6f}")
-            print(f"L1 error: mean={np.mean(forward_metrics['l1']):.6f}, std={np.std(forward_metrics['l1']):.6f}, max={np.max(forward_metrics['l1']):.6f}")
-            print(f"RMSE: mean={np.mean(forward_metrics['rmse']):.6f}, std={np.std(forward_metrics['rmse']):.6f}, max={np.max(forward_metrics['rmse']):.6f}")
+            print(f"Similarity: mean={np.mean(forward_metrics['sim']):.6f}, std={np.std(forward_metrics['sim']):.6f}")
+            print(f"L1 error: mean={np.mean(forward_metrics['l1']):.6f}, std={np.std(forward_metrics['l1']):.6f}")
+            print(f"RMSE: mean={np.mean(forward_metrics['rmse']):.6f}, std={np.std(forward_metrics['rmse']):.6f}")
            
            print("\nGradient Q metrics:")
-            print(f"Similarity: mean={np.mean(grad_q_metrics['sim']):.6f}, std={np.std(grad_q_metrics['sim']):.6f}, min={np.min(grad_q_metrics['sim']):.6f}")
-            print(f"L1 error: mean={np.mean(grad_q_metrics['l1']):.6f}, std={np.std(grad_q_metrics['l1']):.6f}, max={np.max(grad_q_metrics['l1']):.6f}")
-            print(f"RMSE: mean={np.mean(grad_q_metrics['rmse']):.6f}, std={np.std(grad_q_metrics['rmse']):.6f}, max={np.max(grad_q_metrics['rmse']):.6f}")
+            print(f"Similarity: mean={np.mean(grad_q_metrics['sim']):.6f}, std={np.std(grad_q_metrics['sim']):.6f}")
+            print(f"L1 error: mean={np.mean(grad_q_metrics['l1']):.6f}, std={np.std(grad_q_metrics['l1']):.6f}")
+            print(f"RMSE: mean={np.mean(grad_q_metrics['rmse']):.6f}, std={np.std(grad_q_metrics['rmse']):.6f}")
            
            print("\nGradient K metrics:")
-            print(f"Similarity: mean={np.mean(grad_k_metrics['sim']):.6f}, std={np.std(grad_k_metrics['sim']):.6f}, min={np.min(grad_k_metrics['sim']):.6f}")
-            print(f"L1 error: mean={np.mean(grad_k_metrics['l1']):.6f}, std={np.std(grad_k_metrics['l1']):.6f}, max={np.max(grad_k_metrics['l1']):.6f}")
-            print(f"RMSE: mean={np.mean(grad_k_metrics['rmse']):.6f}, std={np.std(grad_k_metrics['rmse']):.6f}, max={np.max(grad_k_metrics['rmse']):.6f}")
+            print(f"Similarity: mean={np.mean(grad_k_metrics['sim']):.6f}, std={np.std(grad_k_metrics['sim']):.6f}")
+            print(f"L1 error: mean={np.mean(grad_k_metrics['l1']):.6f}, std={np.std(grad_k_metrics['l1']):.6f}")
+            print(f"RMSE: mean={np.mean(grad_k_metrics['rmse']):.6f}, std={np.std(grad_k_metrics['rmse']):.6f}")
            
            print("\nGradient V metrics:")
-            print(f"Similarity: mean={np.mean(grad_v_metrics['sim']):.6f}, std={np.std(grad_v_metrics['sim']):.6f}, min={np.min(grad_v_metrics['sim']):.6f}")
-            print(f"L1 error: mean={np.mean(grad_v_metrics['l1']):.6f}, std={np.std(grad_v_metrics['l1']):.6f}, max={np.max(grad_v_metrics['l1']):.6f}")
-            print(f"RMSE: mean={np.mean(grad_v_metrics['rmse']):.6f}, std={np.std(grad_v_metrics['rmse']):.6f}, max={np.max(grad_v_metrics['rmse']):.6f}")
+            print(f"Similarity: mean={np.mean(grad_v_metrics['sim']):.6f}, std={np.std(grad_v_metrics['sim']):.6f}")
+            print(f"L1 error: mean={np.mean(grad_v_metrics['l1']):.6f}, std={np.std(grad_v_metrics['l1']):.6f}")
+            print(f"RMSE: mean={np.mean(grad_v_metrics['rmse']):.6f}, std={np.std(grad_v_metrics['rmse']):.6f}")

 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='Benchmark Block Sparse Attention')
-    parser.add_argument('--batch_size', type=int, default=4, help='Batch size')
-    parser.add_argument('--num_heads', type=int, default=6, help='Number of heads')
-    parser.add_argument('--head_dim', type=int, default=128, help='Head dimension')
-    parser.add_argument('--topk', type=int, default=64, help='Number of kv blocks each q block attends to')
-    parser.add_argument('--seq_lengths', type=int, nargs='+', default=[29120], help='Sequence lengths to benchmark')
-    parser.add_argument('--num_iterations', type=int, default=50, help='Number of test iterations to run')
-    args = parser.parse_args()
-    main(args)
+    main()
@@ -9,14 +9,14 @@ flex_attention = torch.compile(flex_attention, dynamic=False)


 def flex_test(Q, K, V, kernel_size):
-    mask = get_sliding_tile_attention_mask(kernel_size, (6, 8, 8), (18, 48, 80), 0, 'cuda', 0)
+    mask = get_sliding_tile_attention_mask(kernel_size, (6, 8, 8), (36, 48, 48), 39, 'cuda', 0)
    output = flex_attention(Q, K, V, block_mask=mask)

    return output


 def h100_fwd_kernel_test(Q, K, V, kernel_size):
-    o = sliding_tile_attention(Q, K, V, [kernel_size] * 24, 0, False, '18x48x80')
+    o = sliding_tile_attention(Q, K, V, [kernel_size] * 24, 39, False)
    return o


@@ -37,7 +37,7 @@ def check_correctness(b, h, n, d, causal, mean, std, num_iterations=50, error_mo
            'max_diff': 0
        },
    }
-    kernel_size_ls = [(3, 3, 5), (3, 1, 10)]
+    kernel_size_ls = [(6, 1, 6), (6, 6, 1)]
    from tqdm import tqdm
    for kernel_size in tqdm(kernel_size_ls):
        for _ in range(num_iterations):
@@ -74,14 +74,12 @@ def check_correctness(b, h, n, d, causal, mean, std, num_iterations=50, error_mo

 # Example usage
 b, h, d = 2, 24, 128
-n = 69120  # Sequence length
+n = 82944  # Sequence length
 causal = False
 mean = 1e-1
 std = 10

 # Run correctness check directly
 results = check_correctness(b, h, n, d, causal, mean, std, error_mode='output')
-assert results['TK vs FLEX']['avg_diff'] < 3e-6, f"Average difference: {results['TK vs FLEX']['avg_diff']} is too large"
-assert results['TK vs FLEX']['max_diff'] < 4e-2, f"Maximum difference: {results['TK vs FLEX']['max_diff']} is too large"
 print(f"Average difference: {results['TK vs FLEX']['avg_diff']}")
 print(f"Maximum difference: {results['TK vs FLEX']['max_diff']}")
@@ -2,7 +2,7 @@ import math

 import torch
 from torch.utils.checkpoint import detach_variable
-from typing import Tuple
+
 try:
    from vsa_cuda import block_sparse_fwd, block_sparse_bwd
 except ImportError:
@@ -12,116 +12,33 @@ except ImportError:

 BLOCK_M = 64
 BLOCK_N = 64
-
-def video_sparse_attn(q, k, v, topk, block_size, compress_attn_weight=None):
+def block_sparse_attention_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num):
    """
-    q: [batch_size, num_heads, seq_len, head_dim]
-    k: [batch_size, num_heads, seq_len, head_dim]
-    v: [batch_size, num_heads, seq_len, head_dim]
-    topk: int
-    block_size: int or tuple of 3 ints
-    video_shape: tuple of (T, H, W)
-    compress_attn_weight: [batch_size, num_heads, seq_len, head_dim]
-    select_attn_weight: [batch_size, num_heads, seq_len, head_dim]
-    
-    V1 of sparse attention. Include compress attn and sparse attn branch, use average pooling to compress. 
-    Assume q, k, v is flattened in this way: [batch_size, num_heads, T//block_size[0], H//block_size[1], W//block_size[2], block_size[0], block_size[1], block_size[2]]
+    block_sparse_mask: [bs, h, num_q_blocks, num_kv_blocks]. 
+        [*, *, i, j] = 1 means the i-th q block should attend to the j-th kv block.
    """
+    # assert all elements in q2k_block_sparse_num can be devisible by 2
+    o, lse = block_sparse_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num)
+    return o, lse

-    if isinstance(block_size, int):
-        block_size = (block_size, block_size, block_size)
+def block_sparse_attention_backward(q, k, v, o, l_vec, grad_output, k2q_block_sparse_index, k2q_block_sparse_num):
+    grad_q, grad_k, grad_v = block_sparse_bwd(q, k, v, o, l_vec, grad_output, k2q_block_sparse_index, k2q_block_sparse_num)
+    return grad_q, grad_k, grad_v

-    block_elements = block_size[0] * block_size[1] * block_size[2]
-    assert block_elements % 64 == 0 and block_elements >= 64
-    assert q.shape[2] % block_elements == 0
-    batch_size, num_heads, seq_len, head_dim = q.shape
-    # compress attn
-    q_compress = q.view(batch_size, num_heads, seq_len // block_elements,
-                        block_elements, head_dim).mean(dim=3)
-    k_compress = k.view(batch_size, num_heads, seq_len // block_elements,
-                        block_elements, head_dim).mean(dim=3)
-    v_compress = v.view(batch_size, num_heads, seq_len // block_elements,
-                        block_elements, head_dim).mean(dim=3)
+class BlockSparseAttentionFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num):
+        o, lse = block_sparse_attention_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num)
+        ctx.save_for_backward(q, k, v, o, lse, k2q_block_sparse_index, k2q_block_sparse_num)
+        return o

-    output_compress, block_attn_score = torch_attention(q_compress, k_compress,
-                                                        v_compress)
-
-    output_compress = output_compress.view(batch_size, num_heads,
-                                           seq_len // block_elements, 1,
-                                           head_dim)
-    output_compress = output_compress.repeat(1, 1, 1, block_elements,
-                                             1).view(batch_size, num_heads,
-                                                     seq_len, head_dim)
-
-    q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num = generate_topk_block_sparse_pattern(
-        block_attn_score, topk)
-
-    output_select = block_sparse_attn(q, k, v, q2k_block_sparse_index,
-                                      q2k_block_sparse_num,
-                                      k2q_block_sparse_index,
-                                      k2q_block_sparse_num)
-
-    if compress_attn_weight is not None:
-        final_output = output_compress * compress_attn_weight + output_select
-    else:
-        final_output = output_compress + output_select
-    return final_output
-
-def torch_attention(q, k, v) -> Tuple[torch.Tensor, torch.Tensor]:
-    QK = torch.matmul(q, k.transpose(-2, -1))
-    QK /= (q.size(-1)**0.5)
-
-    # Causal mask removed since causal is always false
-
-    QK = torch.nn.functional.softmax(QK, dim=-1)
-    output = torch.matmul(QK, v)
-    return output, QK
-
-def generate_topk_block_sparse_pattern(block_attn_score: torch.Tensor,
-                                       topk: int):
-    """
-    Generate a block sparse pattern where each q block attends to exactly topk kv blocks,
-    based on the provided attention scores.
-    
-    Args:
-        block_attn_score: [bs, h, num_q_blocks, num_kv_blocks]
-            Attention scores between query and key blocks
-        topk: int
-            Number of kv blocks each q block attends to
-        
-    Returns:
-        q2k_block_sparse_index: [bs, h, num_q_blocks, topk]
-            Contains the indices of kv blocks that each q block attends to.
-        q2k_block_sparse_num: [bs, h, num_q_blocks]
-            Contains the number of kv blocks that each q block attends to (all equal to topk).
-        k2q_block_sparse_index: [bs, h, num_kv_blocks, max_q_per_kv]
-            Contains the indices of q blocks that attend to each kv block.
-        k2q_block_sparse_num: [bs, h, num_kv_blocks]
-            Contains the number of q blocks that attend to each kv block.
-    """
-    device = block_attn_score.device
-    # Extract dimensions from block_attn_score
-    bs, h, num_q_blocks, num_kv_blocks = block_attn_score.shape
-
-    sorted_result = torch.sort(block_attn_score, dim=-1, descending=True)
-
-    sorted_indice = sorted_result.indices
-
-    q2k_block_sparse_index, _ = torch.sort(sorted_indice[:, :, :, :topk],
-                                           dim=-1)
-    q2k_block_sparse_index = q2k_block_sparse_index.to(dtype=torch.int32)
-    q2k_block_sparse_num = torch.full((bs, h, num_q_blocks),
-                                      topk,
-                                      device=device,
-                                      dtype=torch.int32)
-
-    block_map = topk_index_to_map(q2k_block_sparse_index,
-                                  num_kv_blocks,
-                                  transpose_map=True)
-    k2q_block_sparse_index, k2q_block_sparse_num = map_to_index(
-        block_map.transpose(2, 3))
-
-    return q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num
+    @staticmethod
+    def backward(ctx, grad_output):
+        q, k, v, o, lse, k2q_block_sparse_index, k2q_block_sparse_num = ctx.saved_tensors
+        grad_q, grad_k, grad_v = block_sparse_attention_backward(
+            q, k, v, o, lse, grad_output, k2q_block_sparse_index, k2q_block_sparse_num
+        )
+        return grad_q, grad_k, grad_v, None, None, None, None

@torch._dynamo.disable
 def block_sparse_attn(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num):
@@ -144,20 +61,6 @@ def block_sparse_attn(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q
        q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num
    )

-def block_sparse_attention_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num):
-    """
-    block_sparse_mask: [bs, h, num_q_blocks, num_kv_blocks]. 
-        [*, *, i, j] = 1 means the i-th q block should attend to the j-th kv block.
-    """
-    # assert all elements in q2k_block_sparse_num can be devisible by 2
-    o, lse = block_sparse_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num)
-    return o, lse
-
-def block_sparse_attention_backward(q, k, v, o, l_vec, grad_output, k2q_block_sparse_index, k2q_block_sparse_num):
-    grad_output = grad_output.contiguous()
-    grad_q, grad_k, grad_v = block_sparse_bwd(q, k, v, o, l_vec, grad_output, k2q_block_sparse_index, k2q_block_sparse_num)
-    return grad_q, grad_k, grad_v
-
 ## pytorch sdpa version of block sparse ##
 import triton
 import triton.language as tl
@@ -225,169 +128,6 @@ def index_to_mask(q2k_block_sparse_index, q2k_block_sparse_num, BLOCK_Q, BLOCK_K

    return mask

-@triton.jit
-def topk_index_to_map_kernel(
-    map_ptr,
-    index_ptr,
-    map_bs_stride,
-    map_h_stride,
-    map_q_stride,
-    map_kv_stride,
-    index_bs_stride,
-    index_h_stride,
-    index_q_stride,
-    index_kv_stride,
-    topk: tl.constexpr,
-):
-    b, h, q = tl.program_id(0), tl.program_id(1), tl.program_id(2)
-    index_ptr_base = index_ptr + b * index_bs_stride + h * index_h_stride + q * index_q_stride
-    map_ptr_base = map_ptr + b * map_bs_stride + h * map_h_stride + q * map_q_stride
-
-    for i in tl.static_range(topk):
-        index = tl.load(index_ptr_base + i * index_kv_stride)
-        tl.store(map_ptr_base + index * map_kv_stride, 1.0)
-
-@triton.jit
-def map_to_index_kernel(
-    map_ptr,
-    index_ptr,
-    index_num_ptr,
-    map_bs_stride,
-    map_h_stride,
-    map_q_stride,
-    map_kv_stride,
-    index_bs_stride,
-    index_h_stride,
-    index_q_stride,
-    index_kv_stride,
-    index_num_bs_stride,
-    index_num_h_stride,
-    index_num_q_stride,
-    num_kv_blocks: tl.constexpr,
-):
-    b, h, q = tl.program_id(0), tl.program_id(1), tl.program_id(2)
-    index_ptr_base = index_ptr + b * index_bs_stride + h * index_h_stride + q * index_q_stride
-    map_ptr_base = map_ptr + b * map_bs_stride + h * map_h_stride + q * map_q_stride
-
-    num = 0
-    for i in tl.static_range(num_kv_blocks):
-        map_entry = tl.load(map_ptr_base + i * map_kv_stride)
-        if map_entry:
-            tl.store(index_ptr_base + num * index_kv_stride, i)
-            num += 1
-
-    tl.store(
-        index_num_ptr + b * index_num_bs_stride + h * index_num_h_stride +
-        q * index_num_q_stride, num)
-
-def topk_index_to_map(index: torch.Tensor,
-                      num_kv_blocks: int,
-                      transpose_map: bool = False):
-    """
-    Convert topk indices to a map.
-    
-    Args:
-        index: [bs, h, num_q_blocks, topk]
-            The topk indices tensor.
-        num_kv_blocks: int
-            The number of key-value blocks in the block_map returned
-        transpose_map: bool
-            If True, the block_map will be transposed on the final two dimensions.
-    
-    Returns:
-        block_map: [bs, h, num_q_blocks, num_kv_blocks]
-            A binary map where 1 indicates that the q block attends to the kv block.
-    """
-    bs, h, num_q_blocks, topk = index.shape
-
-    if transpose_map is False:
-        block_map = torch.zeros((bs, h, num_q_blocks, num_kv_blocks),
-                                dtype=torch.bool,
-                                device=index.device)
-    else:
-        block_map = torch.zeros((bs, h, num_kv_blocks, num_q_blocks),
-                                dtype=torch.bool,
-                                device=index.device)
-        block_map = block_map.transpose(2, 3)
-
-    grid = (bs, h, num_q_blocks)
-    topk_index_to_map_kernel[grid](
-        block_map,
-        index,
-        block_map.stride(0),
-        block_map.stride(1),
-        block_map.stride(2),
-        block_map.stride(3),
-        index.stride(0),
-        index.stride(1),
-        index.stride(2),
-        index.stride(3),
-        topk=topk,
-    )
-
-    return block_map
-
-def map_to_index(block_map: torch.Tensor):
-    """
-    Convert a block map to indices and counts.
-    
-    Args:
-        block_map: [bs, h, num_q_blocks, num_kv_blocks]
-            The block map tensor.
-    
-    Returns:
-        index: [bs, h, num_q_blocks, num_kv_blocks]
-            The indices of the blocks.
-        index_num: [bs, h, num_q_blocks]
-            The number of blocks for each q block.
-    """
-    bs, h, num_q_blocks, num_kv_blocks = block_map.shape
-
-    index = torch.full((block_map.shape),
-                       -1,
-                       dtype=torch.int32,
-                       device=block_map.device)
-    index_num = torch.empty((bs, h, num_q_blocks),
-                            dtype=torch.int32,
-                            device=block_map.device)
-
-    grid = (bs, h, num_q_blocks)
-    map_to_index_kernel[grid](
-        block_map,
-        index,
-        index_num,
-        block_map.stride(0),
-        block_map.stride(1),
-        block_map.stride(2),
-        block_map.stride(3),
-        index.stride(0),
-        index.stride(1),
-        index.stride(2),
-        index.stride(3),
-        index_num.stride(0),
-        index_num.stride(1),
-        index_num.stride(2),
-        num_kv_blocks=num_kv_blocks,
-    )
-
-    return index, index_num
-
-class BlockSparseAttentionFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, q, k, v, q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num):
-        o, lse = block_sparse_attention_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse_num)
-        ctx.save_for_backward(q, k, v, o, lse, k2q_block_sparse_index, k2q_block_sparse_num)
-        return o
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        q, k, v, o, lse, k2q_block_sparse_index, k2q_block_sparse_num = ctx.saved_tensors
-        grad_q, grad_k, grad_v = block_sparse_attention_backward(
-            q, k, v, o, lse, grad_output, k2q_block_sparse_index, k2q_block_sparse_num
-        )
-        return grad_q, grad_k, grad_v, None, None, None, None
-
-
 class DummyOperator(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x):
@@ -467,4 +207,4 @@ class BlockSparseAttnTorch:

        o = DummyOperator.apply(output)
        o.register_hook(self.recompute)
-        return o
+        return o
@@ -3,8 +3,6 @@
 #include "kittens.cuh"
 #include <cooperative_groups.h>
 #include <iostream>
-#include <c10/cuda/CUDAGuard.h>
-

 using namespace kittens;
 namespace cg = cooperative_groups;
@@ -942,9 +940,8 @@ block_sparse_attention_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v
    float* l_ptr = reinterpret_cast<float*>(l_vec.data_ptr<float>());
    float* d_l   = reinterpret_cast<float*>(l_ptr);

-    //cudadevicesynchronize();
-    const c10::cuda::OptionalCUDAGuard device_guard(q.device());    
-    const cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream(); 
+    cudaDeviceSynchronize();
+    auto stream = at::cuda::getCurrentCUDAStream().stream(); 

    if (head_dim == 64) {
        using q_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::qo_height, fwd_attend_ker_tile_dims<64>::tile_width>;
@@ -969,7 +966,7 @@ block_sparse_attention_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v

        globals g{qg_arg, kg_arg, vg_arg, lg_arg, og_arg, static_cast<int>(seq_len), static_cast<int>(hr), static_cast<int>(max_kv_blocks_per_q), reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()), reinterpret_cast<int32_t*>(q2k_block_sparse_num.data_ptr())};

-        constexpr int mem_size = 54000;
+        auto mem_size = 54000;

        dim3 grid(seq_len/(64), qo_heads, batch);

@@ -982,7 +979,7 @@ block_sparse_attention_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v
        fwd_attend_ker<64><<<grid, (128), mem_size, stream>>>(g);

        CHECK_CUDA_ERROR(cudaGetLastError());
-        // cudaStreamSynchronize(stream);
+        cudaStreamSynchronize(stream);
    }

    if (head_dim == 128) {
@@ -1008,7 +1005,7 @@ block_sparse_attention_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v

        globals g{qg_arg, kg_arg, vg_arg, lg_arg, og_arg, static_cast<int>(seq_len), static_cast<int>(hr), static_cast<int>(max_kv_blocks_per_q), reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()), reinterpret_cast<int32_t*>(q2k_block_sparse_num.data_ptr())};

-        constexpr int mem_size = 54000;
+        auto mem_size = 54000;

        dim3 grid(seq_len/(64), qo_heads, batch);

@@ -1021,11 +1018,11 @@ block_sparse_attention_forward(torch::Tensor q, torch::Tensor k, torch::Tensor v
        fwd_attend_ker<128><<<grid, (128), mem_size, stream>>>(g);

        CHECK_CUDA_ERROR(cudaGetLastError());
-        // cudaStreamSynchronize(stream);
+        cudaStreamSynchronize(stream);
    }

    return {o, l_vec};
-    //cudadevicesynchronize();
+    cudaDeviceSynchronize();
 }

 std::vector<torch::Tensor> 
@@ -1135,14 +1132,13 @@ block_sparse_attention_backward(torch::Tensor q,
    float* d_kg = reinterpret_cast<float*>(kg_ptr);
    float* d_vg = reinterpret_cast<float*>(vg_ptr);

-    constexpr int mem_size = kittens::MAX_SHARED_MEMORY; 
-    int threads  = 4 * kittens::WARP_THREADS;
+    auto mem_size = kittens::MAX_SHARED_MEMORY; 
+    auto threads  = 4 * kittens::WARP_THREADS;

-    //cudadevicesynchronize();
-    const c10::cuda::OptionalCUDAGuard device_guard(q.device());    
-    const cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream(); 
+    cudaDeviceSynchronize();
+    auto stream = at::cuda::getCurrentCUDAStream().stream();

-    //  cudaStreamSynchronize(stream);
+    cudaStreamSynchronize(stream);

    // TORCH_CHECK(seq_len % (4*kittens::TILE_DIM*4) == 0, "sequence length must be divisible by 256");
    dim3 grid_bwd(seq_len/(4*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
@@ -1226,7 +1222,7 @@ block_sparse_attention_backward(torch::Tensor q,
        dim3 grid_bwd_2(seq_len/64, qo_heads, batch);
        threads = 128;

-        //cudadevicesynchronize();
+        cudaDeviceSynchronize();

        {
            cudaFuncSetAttribute(
@@ -1244,8 +1240,8 @@ block_sparse_attention_backward(torch::Tensor q,
        }

        // CHECK_CUDA_ERROR(cudaGetLastError());
-        // cudaStreamSynchronize(stream);
-        //cudadevicesynchronize();
+        cudaStreamSynchronize(stream);
+        cudaDeviceSynchronize();
        // const auto kernel_end = std::chrono::high_resolution_clock::now();
        // std::cout << "Kernel Time: " << std::chrono::duration_cast<std::chrono::microseconds>(kernel_end - start).count() << "us" << std::endl;
        // std::cout << "---" << std::endl;
@@ -1330,7 +1326,7 @@ block_sparse_attention_backward(torch::Tensor q,
        dim3 grid_bwd_2(seq_len/64, qo_heads, batch);
        threads = 128;

-        //cudadevicesynchronize();
+        cudaDeviceSynchronize();

        {
            cudaFuncSetAttribute(
@@ -1342,10 +1338,10 @@ block_sparse_attention_backward(torch::Tensor q,
            bwd_attend_ker<128><<<grid_bwd_2, threads, 113000, stream>>>(bwd_global); 
        }

-        // cudaStreamSynchronize(stream);
-        //cudadevicesynchronize();
+        cudaStreamSynchronize(stream);
+        cudaDeviceSynchronize();
    }

    return {qg, kg, vg};
-    //cudadevicesynchronize();
+    cudaDeviceSynchronize();
 }
@@ -1,9 +1,7 @@
-FROM nvidia/cuda:12.8.0-cudnn-devel-ubuntu22.04
+FROM nvidia/cuda:12.4.1-devel-ubuntu20.04

 ENV DEBIAN_FRONTEND=noninteractive

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

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

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

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

-# Install uv and source its environment
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
-    echo 'source $HOME/.local/bin/env' >> /root/.bashrc
+RUN conda create --name fastvideo-dev python=3.12.9 -y
+
+SHELL ["/bin/bash", "-c"]

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

-# Create and activate virtual environment with specific Python version and seed
-RUN source $HOME/.local/bin/env && \
-    uv venv --python 3.12 --seed /opt/venv && \
-    source /opt/venv/bin/activate && \
-    uv pip install --no-cache-dir --upgrade pip && \
-    uv pip install --no-cache-dir .[dev] && \
-    uv pip install --no-cache-dir flash-attn==2.8.0.post2 --no-build-isolation
+RUN conda run -n fastvideo-dev pip install --no-cache-dir --upgrade pip && \
+    conda run -n fastvideo-dev pip install --no-cache-dir .[dev] && \
+    conda run -n fastvideo-dev pip install --no-cache-dir flash-attn==2.7.4.post1 --no-build-isolation && \
+    conda clean -afy

 COPY . .

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

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

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

 EXPOSE 22
@@ -18,6 +18,7 @@ import os
 import re
 import sys
 from pathlib import Path
+from typing import Optional

 import requests

@@ -167,7 +168,8 @@ _cached_base: str = ""
 _cached_branch: str = ""


-def get_repo_base_and_branch(pr_number: str) -> tuple[str | None, str | None]:
+def get_repo_base_and_branch(
+        pr_number: str) -> tuple[Optional[str], Optional[str]]:
    global _cached_base, _cached_branch
    if _cached_base and _cached_branch:
        return _cached_base, _cached_branch
@@ -8,14 +8,12 @@ You can easily use the FastVideo Docker image as a custom container on [RunPod](

 Choose a GPU that supports CUDA 12.4

-Pick 1 or 2 L40S GPU(s)
-
 ![RunPod CUDA selection](../../_static/images/runpod_cuda.png)

 When creating your pod template, use this image:

 ```
-ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:py3.12-latest
+ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:latest
 ```

 Paste Container Start Command to support SSH ([RunPod Docs](https://docs.runpod.io/pods/configuration/use-ssh)):
@@ -5,6 +5,7 @@ import itertools
 import re
 from dataclasses import dataclass, field
 from pathlib import Path
+from typing import Optional

 ROOT_DIR = Path(__file__).parent.parent.parent.resolve()
 ROOT_DIR_RELATIVE = '../../../..'
@@ -88,7 +89,7 @@ class Example:
        generate() -> str: Generates the documentation content.
    """ # noqa: E501
    path: Path
-    category: str | None = None
+    category: Optional[str] = None
    main_file: Path = field(init=False)
    other_files: list[Path] = field(init=False)
    title: str = field(init=False)
@@ -117,4 +117,4 @@ If you're planning to contribute to FastVideo please see the following page:

 If you encounter any issues during installation, please open an issue on our [GitHub repository](https://github.com/hao-ai-lab/FastVideo).

-You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ) for additional support.
+You can also join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-2zf6ru791-sRwI9lPIUJQq1mIeB_yjJg) for additional support.
@@ -12,7 +12,7 @@ This guide explains how to implement a custom diffusion pipeline in FastVideo, l
 4. **Register Your Pipeline** - Make it discoverable by the framework
 5. **Configure Your Pipeline** - (Coming soon)

-Need help? Join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ).
+Need help? Join our [Slack community](https://join.slack.com/t/fastvideo/shared_invite/zt-2zf6ru791-sRwI9lPIUJQq1mIeB_yjJg).

 ## Step 1: Pipeline Modules

@@ -27,7 +27,7 @@ fastvideo generate --help
 ### Hardware Configuration

 - `--num-gpus {NUM_GPUS}`: Number of GPUs to use
- `--tp-size {TP_SIZE}`: Tensor parallelism size (only for the encoder, should not be larger than 1 if text encoder offload is enabled, as layerwise offload + prefetch is faster)
+- `--tp-size {TP_SIZE}`: Tensor parallelism size (Typically should match the number of GPUs)
 - `--sp-size {SP_SIZE}`: Sequence parallelism size (Typically should match the number of GPUs)

 #### Video Configuration
@@ -68,7 +68,7 @@ Example configuration file (config.json):
    "output_path": "outputs/",
    "num_gpus": 2,
    "sp_size": 2,
-    "tp_size": 1,
+    "tp_size": 2,
    "num_frames": 45,
    "height": 720,
    "width": 1280,
@@ -102,7 +102,7 @@ prompt: "A beautiful woman in a red dress walking down a street"
 output_path: "outputs/"
 num_gpus: 2
 sp_size: 2
-tp_size: 1
+tp_size: 2
 num_frames: 45
 height: 720
 width: 1280
@@ -121,4 +121,4 @@ If the generated video doesn't match your prompt:
 - Learn about using [Optimizations](#inference-optimizations)
 - See [Examples](../examples/examples_inference_index.md) for more usage scenarios
 - Join our [Community Discord](https://discord.gg/JA7cksDz86).
- Join our [Community Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ).
+- Join our [Community Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-2zf6ru791-sRwI9lPIUJQq1mIeB_yjJg).
@@ -1,7 +1,7 @@
 (sta-demo)=

 # 🔍 Demo
-This is is a demo for 2D STA with window size (6,6) operating on a (10, 10) image.
+There is a demo for 2D STA with window size (6,6) operating on a (10, 10) image.

 <div style="text-align: center;">
  <video controls width="800">
@@ -9,9 +9,3 @@ This is is a demo for 2D STA with window size (6,6) operating on a (10, 10) imag
    Your browser does not support the video tag.
  </video>
 </div>
-
-You can run STA using the following command:
-
-```bash
-bash scripts/inference/v1_inference_wan_STA.sh
-```
@@ -7,40 +7,70 @@ To save GPU memory, we precompute text embeddings and VAE latents to eliminate t
 We provide a sample dataset to help you get started. Download the source media using the following command:

 ```bash
-python scripts/huggingface/download_hf.py --repo_id=FastVideo/mini_i2v_dataset --local_dir=FastVideo/mini_i2v_dataset --repo_type=dataset
+python scripts/huggingface/download_hf.py --repo_id=FastVideo/Image-Vid-Finetune-Src --local_dir=data/Image-Vid-Finetune-Src --repo_type=dataset
 ```

-The folder `crush-smol_raw/` contains raw videos and captions for testing preprocessing, while `crush-smol_preprocessed/` contains latents prepared for testing training.
-
 To preprocess the dataset for fine-tuning or distillation, run:

 ```
-bash scripts/preprocess/v1_preprocess_wan_data_t2v # for wan
+bash scripts/preprocess/preprocess_mochi_data.sh # for mochi
+bash scripts/preprocess/preprocess_hunyuan_data.sh # for hunyuan
 ```

+The preprocessed dataset will be stored in `Image-Vid-Finetune-Mochi` or `Image-Vid-Finetune-HunYuan` correspondingly.
+
 ## Process your own dataset

-If you wish to create your own dataset for finetuning or distillation, please refer `mini_i2v_dataset/crush-smol_raw/` to structure you video dataset in the following format:
+If you wish to create your own dataset for finetuning or distillation, please structure you video dataset in the following format:

 ```
-path_to_your_dataset_folder/
-├── videos/
-│   ├── 0.mp4
+path_to_dataset_folder/
+├── media/
+│   ├── 0.jpg
 │   ├── 1.mp4
-├── videos.txt
-└── prompt.txt
+│   ├── 2.jpg
+├── video2caption.json
+└── merge.txt
 ```

-To geranate the `videos2caption.json` and `merge.txt`, run
+Format the JSON file as a list, where each item represents a media source:

-``` python
-python scripts/dataset_preparation/prepare_json_file.py --data_folder mini_i2v_dataset/crush-smol_raw/ --output your_output_folder
-```
-
-Adjust the `DATA_MERGE_PATH` and `OUTPUT_DIR` in `scripts/preprocess/v1_preprocess_****.sh` accordingly and run:
+For image media,

 ```
-bash scripts/preprocess/v1_preprocess_****.sh
+{
+    "path": "0.jpg",
+    "cap": ["captions"]
+}
+```
+
+For video media,
+
+```
+{
+    "path": "1.mp4",
+    "resolution": {
+      "width": 848,
+      "height": 480
+    },
+    "fps": 30.0,
+    "duration": 6.033333333333333,
+    "cap": [
+      "caption"
+    ]
+  }
+```
+
+Use a txt file (merge.txt) to contain the source folder for media and the JSON file for meta information:
+
+```
+path_to_media_source_foder,path_to_json_file
+```
+
+Adjust the `DATA_MERGE_PATH` and `OUTPUT_DIR` in `scripts/preprocess/preprocess_****_data.sh` accordingly and run:
+
+```
+bash scripts/preprocess/preprocess_****_data.sh
 ```

 The preprocessed data will be put into the `OUTPUT_DIR` and the `videos2caption.json` can be used in finetune and distill scripts.
@@ -16,13 +16,6 @@ bash scripts/finetune/finetune_mochi.sh # for mochi
 ```

 **Note that for finetuning, we did not tune the hyperparameters in the provided script.**
-## ⚡ Finetune with VSA
-Follow [data_preprocess.md](#v0-data-preprocess) to get parquet files for preproccessed latent, and then run:
-
-```bash
-bash scripts/finetune/finetune_v1_VSA.sh
-```
-
 ## ⚡ Lora Finetune

 Hunyuan supports Lora fine-tuning of videos up to 720p. Demos and prompts of Black-Myth-Wukong can be found in [here](https://huggingface.co/FastVideo/Hunyuan-Black-Myth-Wukong-lora-weight). You can download the Lora weight through:
@@ -10,7 +10,7 @@ def main():
    # attempt to identify the optimal arguments.
    generator = VideoGenerator.from_pretrained(
        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-        # FastVideo will automatically handle distributed setup
+        # if num_gpus > 1, FastVideo will automatically handle distributed setup
        num_gpus=2,
        use_fsdp_inference=True,
        use_cpu_offload=False
@@ -6,7 +6,7 @@ def main():
    # Initialize VideoGenerator with the Wan model
    generator = VideoGenerator.from_pretrained(
        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-        num_gpus=1,
+        num_gpus=2,
        lora_path="benjamin-paine/steamboat-willie-1.3b",
        lora_nickname="steamboat"
    )
@@ -16,7 +16,6 @@ def main():
        "num_frames": 81,
        "guidance_scale": 5.0,
        "num_inference_steps": 32,
-        "seed": 42,
    }
    # Generate video with LoRA style
    prompt = "steamboat willie style, golden era animation, close-up of a short fluffy monster  kneeling beside a melting red candle. the mood is one of wonder and curiosity,  as the monster gazes at the flame with wide eyes and open mouth. Its pose and expression  convey a sense of innocence and playfulness, as if it is exploring the world around it for the first time.  The use of warm colors and dramatic lighting further enhances the cozy atmosphere of the image."
@@ -30,17 +29,8 @@ def main():
        negative_prompt=negative_prompt,
        **kwargs
    )
-    del generator
-    
-    # Until FSDP resharding bug is fixed, multi-lora requires reloading the model
-    # see https://github.com/pytorch/pytorch/issues/157209
-    generator = VideoGenerator.from_pretrained(
-        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-        num_gpus=1,
-        lora_path="motimalu/wan-flat-color-1.3b-v2",
-        lora_nickname="flat_color"
-    )
-    # generator.set_lora_adapter(lora_nickname="flat_color", lora_path="motimalu/wan-flat-color-1.3b-v2")
+
+    generator.set_lora_adapter(lora_nickname="flat_color", lora_path="motimalu/wan-flat-color-1.3b-v2")
    prompt = "flat color, no lineart, blending, negative space, artist:[john kafka|ponsuke kaikai|hara id 21|yoneyama mai|fuzichoco],  1girl, sakura miko, pink hair, cowboy shot, white shirt, floral print, off shoulder, outdoors, cherry blossom, tree shade, wariza, looking up, falling petals, half-closed eyes, white sky, clouds,  live2d animation, upper body, high quality cinematic video of a woman sitting under a sakura tree. Dreamy and lonely, the camera close-ups on the face of the woman as she turns towards the viewer. The Camera is steady, This is a cowboy shot. The animation is smooth and fluid."
    negative_prompt = "bad quality video,色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
    video = generator.generate_video(
@@ -5,7 +5,7 @@ export FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN
 export MODEL_BASE=Wan-AI/Wan2.1-T2V-14B-Diffusers

 base_port=29503
-num_gpu=1
+num_gpu=$(nvidia-smi --query-gpu=gpu_name --format=csv,noheader | wc -l)
 gpu_ids=$(seq 0 $((num_gpu-1)))
 skip_time_steps=12

@@ -14,7 +14,7 @@ STA_mode="STA_searching"
 for i in $gpu_ids; do
    port=$((base_port+i))
    CUDA_VISIBLE_DEVICES=$i MASTER_PORT=$port python examples/inference/sta_mask_search/wan_example.py \
-        --prompt_path ./assets/prompt_${i}.txt \
+        --prompt_path ./assets/prompt_extend_${i}.txt \
        --output_path $output_path \
        --STA_mode $STA_mode &
    sleep 1
@@ -27,7 +27,7 @@ STA_mode="STA_tuning"
 for i in $gpu_ids; do
    port=$((base_port+i))
    CUDA_VISIBLE_DEVICES=$i MASTER_PORT=$port python examples/inference/sta_mask_search/wan_example.py \
-        --prompt_path ./assets/prompt_${i}.txt \
+        --prompt_path ./assets/prompt_extend_${i}.txt \
        --output_path $output_path \
        --STA_mode $STA_mode \
        --skip_time_steps $skip_time_steps &
@@ -1,10 +0,0 @@
-This directory contain e2e examples scripts for finetuning Wan2.1 I2V. 
-
-Execute the following commands from `FastVideo/` to run training:
-
- Download crush-smol dataset:
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/download_dataset.sh`
- Preprocess the videos and captions into latents:
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/preprocess_wan_data_i2v.sh`
- Edit the following file and run finetuning:
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.sh`
@@ -1,3 +0,0 @@
-#!/bin/bash
-
-python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -1,94 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
-DATA_DIR="data/crush-smol_processed_i2v_1_3b_inp/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
-NUM_GPUS=8
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_i2v_finetune"
-  --output_dir "$DATA_DIR/outputs/wan_i2v_finetune"
-  --max_train_steps 2000
-  --train_batch_size 4
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 4
-  --tp_size 4
-  --hsdp_replicate_dim 2
-  --hsdp_shard_dim 4
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 100
-  --validation_sampling_steps "40"
-  --validation_guidance_scale "1.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 2e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 2000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --allow_tf32
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.1
-  --multi_phased_distill_schedule "4000-1"
-  --not_apply_cfg_solver
-  --dit_precision "fp32"
-  --num_euler_timesteps 50
-  --ema_start_step 0
-  --enable_gradient_checkpointing_type "full"
-)
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/v1/training/wan_i2v_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,130 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=i2v
-#SBATCH --partition=main
-#SBATCH --nodes=4
-#SBATCH --ntasks=4
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=i2v_output/i2v_%j.out
-#SBATCH --error=i2v_output/i2v_%j.err
-#SBATCH --exclusive
-set -e -x
-
-# Environment Setup
-source ~/conda/miniconda/bin/activate
-conda activate will-fv
-
-# Basic Info
-export WANDB_MODE="online"
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29500
-export NODE_RANK=$SLURM_PROCID
-nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
-export MASTER_ADDR=${nodes[0]}
-export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
-export TOKENIZERS_PARALLELISM=false
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "NODE_RANK: $NODE_RANK"
-
-# Configs
-NUM_GPUS=8
-
-MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
-DATA_DIR="data/crush-smol_processed_i2v_1_3b_inp/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-
-# Training arguments
-training_args=(
-  --tracker_project_name wan_i2v_finetune
-  --output_dir="$DATA_DIR/outputs/wan_i2v_finetune_2n"
-  --max_train_steps=2000
-  --train_batch_size=2
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps=1
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size $NUM_GPUS
-  --tp_size $NUM_GPUS
-  --hsdp_replicate_dim $SLURM_JOB_NUM_NODES
-  --hsdp_shard_dim $NUM_GPUS
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 10
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 100
-  --validation_sampling_steps "40"
-  --validation_guidance_scale "1.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate=1e-5
-  --mixed_precision="bf16"
-  --checkpointing_steps=1000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --allow_tf32
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.1
-  --multi_phased_distill_schedule "4000-1"
-  --not_apply_cfg_solver
-  --dit_precision "fp32"
-  --num_euler_timesteps 50
-  --ema_start_step 0
-  --enable_gradient_checkpointing_type "full"
-)
-
-srun torchrun \
--nnodes $SLURM_JOB_NUM_NODES \
--nproc_per_node $NUM_GPUS \
--node_rank $SLURM_PROCID \
--rdzv_backend=c10d \
--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/v1/training/wan_i2v_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,26 +0,0 @@
-#!/bin/bash
-
-GPU_NUM=1 # 2,4,8
-MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
-MODEL_TYPE="wan"
-DATA_MERGE_PATH="data/crush-smol/merge.txt"
-OUTPUT_DIR="data/crush-smol_processed_i2v_1_3b_inp/"
-VALIDATION_PATH="examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
-
-torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/v1/pipelines/preprocess/v1_preprocess.py \
-    --model_path $MODEL_PATH \
-    --data_merge_path $DATA_MERGE_PATH \
-    --preprocess_video_batch_size 8 \
-    --max_height 480 \
-    --max_width 832 \
-    --num_frames 77 \
-    --dataloader_num_workers 0 \
-    --output_dir=$OUTPUT_DIR \
-    --model_type $MODEL_TYPE \
-    --train_fps 16 \
-    --validation_dataset_file $VALIDATION_PATH \
-    --samples_per_file 8 \
-    --flush_frequency 8 \
-    --video_length_tolerance_range 5 \
-    --preprocess_task "i2v" 
@@ -1,31 +0,0 @@
-{
-  "data": [
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-034.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-027.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-030.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    }
-  ]
-}
@@ -1,10 +0,0 @@
-This directory contain e2e examples scripts for finetuning Wan2.1 I2V. 
-
-Execute the following commands from `FastVideo/` to run training:
-
- Download crush-smol dataset:
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/download_dataset.sh`
- Preprocess the videos and captions into latents:
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/preprocess_wan_data_i2v.sh`
- Edit the following file and run finetuning:
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.sh`
@@ -1,3 +0,0 @@
-#!/bin/bash
-
-python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -1,94 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
-DATA_DIR="data/crush-smol_processed_i2v/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation.json"
-NUM_GPUS=8
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_i2v_finetune"
-  --output_dir "$DATA_DIR/outputs/wan_i2v_finetune"
-  --max_train_steps 2000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 8
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 8
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 100
-  --validation_sampling_steps "40"
-  --validation_guidance_scale "1.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 1000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --allow_tf32
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.1
-  --multi_phased_distill_schedule "4000-1"
-  --not_apply_cfg_solver
-  --dit_precision "fp32"
-  --num_euler_timesteps 50
-  --ema_start_step 0
-  --enable_gradient_checkpointing_type "full"
-)
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/v1/training/wan_i2v_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,130 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=i2v
-#SBATCH --partition=main
-#SBATCH --nodes=4
-#SBATCH --ntasks=4
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=i2v_output/i2v_%j.out
-#SBATCH --error=i2v_output/i2v_%j.err
-#SBATCH --exclusive
-set -e -x
-
-# Environment Setup
-source ~/conda/miniconda/bin/activate
-conda activate will-fv
-
-# Basic Info
-export WANDB_MODE="online"
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29500
-export NODE_RANK=$SLURM_PROCID
-nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
-export MASTER_ADDR=${nodes[0]}
-export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
-export TOKENIZERS_PARALLELISM=false
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "NODE_RANK: $NODE_RANK"
-
-# Configs
-NUM_GPUS=8
-
-MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
-DATA_DIR="data/crush-smol_processed_i2v/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation.json"
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-
-# Training arguments
-training_args=(
-  --tracker_project_name wan_i2v_finetune
-  --output_dir="$DATA_DIR/outputs/wan_i2v_finetune_2n"
-  --max_train_steps=2000
-  --train_batch_size=2
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps=1
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size $NUM_GPUS
-  --tp_size 1
-  --hsdp_replicate_dim $SLURM_JOB_NUM_NODES
-  --hsdp_shard_dim $NUM_GPUS
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 10
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 100
-  --validation_sampling_steps "40"
-  --validation_guidance_scale "1.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate=1e-5
-  --mixed_precision="bf16"
-  --checkpointing_steps=1000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --allow_tf32
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.1
-  --multi_phased_distill_schedule "4000-1"
-  --not_apply_cfg_solver
-  --dit_precision "fp32"
-  --num_euler_timesteps 50
-  --ema_start_step 0
-  --enable_gradient_checkpointing_type "full"
-)
-
-srun torchrun \
--nnodes $SLURM_JOB_NUM_NODES \
--nproc_per_node $NUM_GPUS \
--node_rank $SLURM_PROCID \
--rdzv_backend=c10d \
--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/v1/training/wan_i2v_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,26 +0,0 @@
-#!/bin/bash
-
-GPU_NUM=1 # 2,4,8
-MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
-MODEL_TYPE="wan"
-DATA_MERGE_PATH="data/crush-smol/merge.txt"
-OUTPUT_DIR="data/crush-smol_processed_i2v/"
-VALIDATION_PATH="examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation.json"
-
-torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/v1/pipelines/preprocess/v1_preprocess.py \
-    --model_path $MODEL_PATH \
-    --data_merge_path $DATA_MERGE_PATH \
-    --preprocess_video_batch_size 8 \
-    --max_height 480 \
-    --max_width 832 \
-    --num_frames 77 \
-    --dataloader_num_workers 0 \
-    --output_dir=$OUTPUT_DIR \
-    --model_type $MODEL_TYPE \
-    --train_fps 16 \
-    --validation_dataset_file $VALIDATION_PATH \
-    --samples_per_file 8 \
-    --flush_frequency 8 \
-    --video_length_tolerance_range 5 \
-    --preprocess_task "i2v" 
@@ -1,31 +0,0 @@
-{
-  "data": [
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-034.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-027.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-030.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    }
-  ]
-}
@@ -1,10 +0,0 @@
-This directory contain e2e examples scripts for finetuning Wan2.1 T2v. 
-
-Execute the following commands from `FastVideo/` to run training:
-
- Download crush-smol dataset:
-`bash examples/training/finetune/wan_t2v_1_3b/crush_smol/download_dataset.sh`
- Preprocess the videos and captions into latents:
-`bash examples/training/finetune/wan_t2v_1_3b/crush_smol/preprocess_wan_data_t2v.sh`
- Edit the following file and run finetuning:
-`bash examples/training/finetune/wan_t2v_1_3b/crush_smol/finetune_t2v.sh`
@@ -1,3 +0,0 @@
-#!/bin/bash
-
-python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -1,93 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="data/crush-smol_processed_t2v/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/training/finetune/wan_t2v_1_3b/crush_smol/validation.json"
-NUM_GPUS=4
-# export CUDA_VISIBLE_DEVICES=4,5
-
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_t2v_finetune"
-  --output_dir "outputs/wan_t2v_finetune"
-  --max_train_steps 5000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 8
-  --num_latent_t 8 
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS 
-  --sp_size $NUM_GPUS 
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim $NUM_GPUS
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path $DATA_DIR
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation 
-  --validation_dataset_file $VALIDATION_DATASET_FILE
-  --validation_steps 50
-  --validation_sampling_steps "50" 
-  --validation_guidance_scale "1.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 5e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 6000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --allow_tf32
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.1
-  --multi_phased_distill_schedule "4000-1"
-  --not_apply_cfg_solver
-  --dit_precision "fp32"
-  --num_euler_timesteps 50
-  --ema_start_step 0
-  --enable_gradient_checkpointing_type "full"
-)
-
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/v1/training/wan_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,127 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=t2v
-#SBATCH --partition=main
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=t2v_output/t2v_%j.out
-#SBATCH --error=t2v_output/t2v_%j.err
-#SBATCH --exclusive
-set -e -x
-
-# Environment Setup
-source ~/conda/miniconda/bin/activate
-conda activate will-fv
-
-# Basic Info
-export WANDB_MODE="online"
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29500
-export NODE_RANK=$SLURM_PROCID
-nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
-export MASTER_ADDR=${nodes[0]}
-export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
-export TOKENIZERS_PARALLELISM=false
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "NODE_RANK: $NODE_RANK"
-
-# Configs
-NUM_GPUS=8
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="data/crush-smol_processed_t2v/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/training/finetune/wan_t2v_1_3b/crush_smol/validation.json"
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name wan_t2v_finetune
-  --output_dir="outputs/wan_t2v_finetune"
-  --max_train_steps=1000
-  --train_batch_size=4
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps=1
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 4
-  --tp_size 1
-  --hsdp_replicate_dim 2
-  --hsdp_shard_dim 4
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 10
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 100
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "1.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate=5e-5
-  --mixed_precision="bf16"
-  --checkpointing_steps=500
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --allow_tf32
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.1
-  --multi_phased_distill_schedule "4000-1"
-  --not_apply_cfg_solver
-  --dit_precision "fp32"
-  --num_euler_timesteps 50
-  --ema_start_step 0
-  --enable_gradient_checkpointing_type "full"
-)
-
-srun torchrun \
--nnodes $SLURM_JOB_NUM_NODES \
--nproc_per_node $NUM_GPUS \
--node_rank $SLURM_PROCID \
--rdzv_backend=c10d \
--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/v1/training/wan_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,26 +0,0 @@
-#!/bin/bash
-
-GPU_NUM=1 # 2,4,8
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-MODEL_TYPE="wan"
-DATA_MERGE_PATH="data/crush-smol/merge.txt"
-OUTPUT_DIR="data/crush-smol_processed_t2v/"
-VALIDATION_PATH="examples/training/finetune/wan_t2v_1_3b/crush_smol/validation.json"
-
-torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/v1/pipelines/preprocess/v1_preprocess.py \
-    --model_path $MODEL_PATH \
-    --data_merge_path $DATA_MERGE_PATH \
-    --preprocess_video_batch_size 8 \
-    --max_height 480 \
-    --max_width 832 \
-    --num_frames 77 \
-    --dataloader_num_workers 0 \
-    --output_dir=$OUTPUT_DIR \
-    --model_type $MODEL_TYPE \
-    --train_fps 16 \
-    --validation_dataset_file $VALIDATION_PATH \
-    --samples_per_file 8 \
-    --flush_frequency 8 \
-    --video_length_tolerance_range 5 \
-    --preprocess_task "t2v" 
@@ -1,31 +0,0 @@
-{
-  "data": [
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 50,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 50,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 50,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    }
-  ]
-}
@@ -104,7 +104,13 @@ if __name__ == "__main__":
        default=None,
        help="The output directory where the model predictions and checkpoints will be written.",
    )
-
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
+    )

    args = parser.parse_args()
    main(args)
@@ -1,38 +1,37 @@
 import argparse
+import json
 import os

-from fastvideo import PipelineConfig
-from fastvideo.v1.configs.models.vaes import WanVAEConfig
-from fastvideo.v1.distributed import (
-    get_world_size, maybe_init_distributed_environment_and_model_parallel)
-from fastvideo.v1.fastvideo_args import FastVideoArgs
+import torch
+import torch.distributed as dist
+
 from fastvideo.v1.logger import init_logger
-from fastvideo.v1.pipelines.preprocess.preprocess_pipeline_i2v import (
-    PreprocessPipeline_I2V)
-from fastvideo.v1.pipelines.preprocess.preprocess_pipeline_t2v import (
-    PreprocessPipeline_T2V)
-from fastvideo.v1.utils import maybe_download_model
+from fastvideo.v1.utils import maybe_download_model, shallow_asdict
+from fastvideo.v1.distributed import maybe_init_distributed_environment_and_model_parallel, get_world_size
+from fastvideo.v1.fastvideo_args import FastVideoArgs
+from fastvideo.v1.configs.models.vaes import WanVAEConfig
+from fastvideo import PipelineConfig
+from fastvideo.v1.pipelines.preprocess.preprocess_pipeline_i2v import PreprocessPipeline_I2V
+from fastvideo.v1.pipelines.preprocess.preprocess_pipeline_t2v import PreprocessPipeline_T2V

 logger = init_logger(__name__)

-
-def main(args) -> None:
+def main(args):
    args.model_path = maybe_download_model(args.model_path)
-    maybe_init_distributed_environment_and_model_parallel(1, 1)
-    num_gpus = int(os.environ["WORLD_SIZE"])
-    assert num_gpus == 1, "Only support 1 GPU"
+    maybe_init_distributed_environment_and_model_parallel(args.tp_size, args.sp_size)
+
    pipeline_config = PipelineConfig.from_pretrained(args.model_path)
    kwargs = {
        "use_cpu_offload": False,
        "vae_precision": "fp32",
        "vae_config": WanVAEConfig(load_encoder=True, load_decoder=False),
    }
-    pipeline_config.update_config_from_dict(kwargs)
-    fastvideo_args = FastVideoArgs(
-        model_path=args.model_path,
-        num_gpus=get_world_size(),
-        pipeline_config=pipeline_config,
-    )
+    pipeline_config_args = shallow_asdict(pipeline_config)
+    pipeline_config_args.update(kwargs)
+    fastvideo_args = FastVideoArgs(model_path=args.model_path,
+                                   num_gpus=get_world_size(),
+                                   **pipeline_config_args,
+                                   )
    PreprocessPipeline = PreprocessPipeline_I2V if args.preprocess_task == "i2v" else PreprocessPipeline_T2V
    pipeline = PreprocessPipeline(args.model_path, fastvideo_args)
    pipeline.forward(batch=None, fastvideo_args=fastvideo_args, args=args)
@@ -44,14 +43,13 @@ if __name__ == "__main__":
    parser.add_argument("--model_path", type=str, default="data/mochi")
    parser.add_argument("--model_type", type=str, default="mochi")
    parser.add_argument("--data_merge_path", type=str, required=True)
-    parser.add_argument("--validation_dataset_file", type=str)
+    parser.add_argument("--validation_prompt_txt", type=str)
    parser.add_argument("--num_frames", type=int, default=163)
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=1,
-        help=
-        "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
+        help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
    )
    parser.add_argument(
        "--preprocess_video_batch_size",
@@ -59,42 +57,53 @@ if __name__ == "__main__":
        default=2,
        help="Batch size (per device) for the training dataloader.",
    )
-    parser.add_argument("--samples_per_file", type=int, default=64)
-    parser.add_argument("--flush_frequency",
-                        type=int,
-                        default=256,
-                        help="how often to save to parquet files")
-    parser.add_argument("--num_latent_t",
-                        type=int,
-                        default=28,
-                        help="Number of latent timesteps.")
+    parser.add_argument(
+        "--preprocess_text_batch_size",
+        type=int,
+        default=8,
+        help="Batch size (per device) for the training dataloader.",
+    )
+    parser.add_argument(
+        "--samples_per_file",
+        type=int,
+        default=64
+    )
+    parser.add_argument(
+        "--flush_frequency",
+        type=int,
+        default=256,
+        help="how often to save to parquet files"
+    )
+    parser.add_argument("--num_latent_t", type=int, default=28, help="Number of latent timesteps.")
    parser.add_argument("--max_height", type=int, default=480)
    parser.add_argument("--max_width", type=int, default=848)
    parser.add_argument("--video_length_tolerance_range", type=int, default=2.0)
    parser.add_argument("--group_frame", action="store_true")  # TODO
    parser.add_argument("--group_resolution", action="store_true")  # TODO
+    parser.add_argument("--dataset", default="t2v")
    parser.add_argument("--preprocess_task", type=str, default="t2v")
    parser.add_argument("--train_fps", type=int, default=30)
    parser.add_argument("--use_image_num", type=int, default=0)
    parser.add_argument("--text_max_length", type=int, default=256)
    parser.add_argument("--speed_factor", type=float, default=1.0)
    parser.add_argument("--drop_short_ratio", type=float, default=1.0)
-    parser.add_argument("--do_temporal_sample",
-                        default=False,
-                        action="store_true")
    # text encoder & vae & diffusion model
-    parser.add_argument("--text_encoder_name",
-                        type=str,
-                        default="google/t5-v1_1-xxl")
+    parser.add_argument("--text_encoder_name", type=str, default="google/t5-v1_1-xxl")
    parser.add_argument("--cache_dir", type=str, default="./cache_dir")
-    parser.add_argument("--training_cfg_rate", type=float, default=0.0)
+    parser.add_argument("--cfg", type=float, default=0.0)
    parser.add_argument(
        "--output_dir",
        type=str,
        default=None,
-        help=
-        "The output directory where the model predictions and checkpoints will be written.",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
    )

    args = parser.parse_args()
-    main(args)
+    main(args)
@@ -671,6 +671,13 @@ if __name__ == "__main__":
        help=("Whether training should be resumed from a previous lora checkpoint. Use a path saved by"
              ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'),
    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
+    )

    # optimizer & scheduler & Training
    parser.add_argument("--num_train_epochs", type=int, default=100)
@@ -693,6 +693,13 @@ if __name__ == "__main__":
        help=("Whether training should be resumed from a previous lora checkpoint. Use a path saved by"
              ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'),
    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
+    )

    # optimizer & scheduler & Training
    parser.add_argument("--num_train_epochs", type=int, default=100)
@@ -520,7 +520,13 @@ if __name__ == "__main__":
        help=("Whether training should be resumed from a previous lora checkpoint. Use a path saved by"
              ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'),
    )
-
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=("[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."),
+    )

    # optimizer & scheduler & Training
    parser.add_argument("--num_train_epochs", type=int, default=100)
@@ -62,7 +62,6 @@ SystemEnv = namedtuple(
 DEFAULT_CONDA_PATTERNS = {
    "torch",
    "numpy",
-    "mypy"
    "cudatoolkit",
    "soumith",
    "mkl",
@@ -81,6 +80,7 @@ DEFAULT_CONDA_PATTERNS = {
 DEFAULT_PIP_PATTERNS = {
    "torch",
    "numpy",
+    "mypy",
    "flake8",
    "triton",
    "optree",
@@ -2,18 +2,16 @@
 import json
 import os
 from collections import defaultdict
-from typing import Any
+from typing import Any, Dict, List, Optional, Tuple

 import numpy as np

-from fastvideo.v1.utils import dict_to_3d_list
-

 def configure_sta(mode: str = 'STA_searching',
                  layer_num: int = 40,
                  time_step_num: int = 50,
                  head_num: int = 40,
-                  **kwargs) -> list[list[list[Any]]]:
+                  **kwargs) -> List[List[List[Any]]]:
    """
    Configure Sliding Tile Attention (STA) parameters based on the specified mode.
    
@@ -53,22 +51,22 @@ def configure_sta(mode: str = 'STA_searching',

    if mode == 'STA_searching':
        # Get parameters with defaults
-        mask_candidates: list[str] | None = kwargs.get('mask_candidates')
+        mask_candidates: Optional[List[str]] = kwargs.get('mask_candidates')
        if mask_candidates is None:
            raise ValueError(
                "mask_candidates is required for STA_searching mode")
-        mask_selected: list[int] = kwargs.get('mask_selected',
+        mask_selected: List[int] = kwargs.get('mask_selected',
                                              list(range(len(mask_candidates))))

        # Parse selected masks
-        selected_masks: list[list[int]] = []
+        selected_masks: List[List[int]] = []
        for index in mask_selected:
            mask = mask_candidates[index]
            masks_list = [int(x) for x in mask.split(',')]
            selected_masks.append(masks_list)

        # Create 3D mask structure with fixed dimensions (t=50, l=60)
-        masks_3d: list[list[list[list[int]]]] = []
+        masks_3d: List[List[List[List[int]]]] = []
        for i in range(time_step_num):  # Fixed t dimension = 50
            row = []
            for j in range(layer_num):  # Fixed l dimension = 60
@@ -79,23 +77,25 @@ def configure_sta(mode: str = 'STA_searching',

    elif mode == 'STA_tuning':
        # Get required parameters
-        mask_search_files_path: str | None = kwargs.get(
+        mask_search_files_path: Optional[str] = kwargs.get(
            'mask_search_files_path')
        if not mask_search_files_path:
            raise ValueError(
                "mask_search_files_path is required for STA_tuning mode")

        # Get optional parameters with defaults
-        mask_candidates_tuning: list[str] | None = kwargs.get('mask_candidates')
+        mask_candidates_tuning: Optional[List[str]] = kwargs.get(
+            'mask_candidates')
        if mask_candidates_tuning is None:
            raise ValueError("mask_candidates is required for STA_tuning mode")
-        mask_selected_tuning: list[int] = kwargs.get(
+        mask_selected_tuning: List[int] = kwargs.get(
            'mask_selected', list(range(len(mask_candidates_tuning))))
-        skip_time_steps_tuning: int | None = kwargs.get('skip_time_steps')
-        save_dir_tuning: str | None = kwargs.get('save_dir', "mask_candidates")
+        skip_time_steps_tuning: Optional[int] = kwargs.get('skip_time_steps')
+        save_dir_tuning: Optional[str] = kwargs.get('save_dir',
+                                                    "mask_candidates")

        # Parse selected masks
-        selected_masks_tuning: list[list[int]] = []
+        selected_masks_tuning: List[List[int]] = []
        for index in mask_selected_tuning:
            mask = mask_candidates_tuning[index]
            masks_list = [int(x) for x in mask.split(',')]
@@ -106,7 +106,7 @@ def configure_sta(mode: str = 'STA_searching',
        averaged_results = average_head_losses(results, selected_masks_tuning)

        # Add full attention mask for specific cases
-        full_attention_mask_tuning: list[int] | None = kwargs.get(
+        full_attention_mask_tuning: Optional[List[int]] = kwargs.get(
            'full_attention_mask')
        if full_attention_mask_tuning is not None:
            selected_masks_tuning.append(full_attention_mask_tuning)
@@ -147,11 +147,11 @@ def configure_sta(mode: str = 'STA_searching',
        return mask_strategy_3d
    elif mode == 'STA_tuning_cfg':
        # Get required parameters for both positive and negative paths
-        mask_search_files_path_pos: str | None = kwargs.get(
+        mask_search_files_path_pos: Optional[str] = kwargs.get(
            'mask_search_files_path_pos')
-        mask_search_files_path_neg: str | None = kwargs.get(
+        mask_search_files_path_neg: Optional[str] = kwargs.get(
            'mask_search_files_path_neg')
-        save_dir_cfg: str | None = kwargs.get('save_dir')
+        save_dir_cfg: Optional[str] = kwargs.get('save_dir')

        if not mask_search_files_path_pos or not mask_search_files_path_neg or not save_dir_cfg:
            raise ValueError(
@@ -159,16 +159,16 @@ def configure_sta(mode: str = 'STA_searching',
            )

        # Get optional parameters with defaults
-        mask_candidates_cfg: list[str] | None = kwargs.get('mask_candidates')
+        mask_candidates_cfg: Optional[List[str]] = kwargs.get('mask_candidates')
        if mask_candidates_cfg is None:
            raise ValueError(
                "mask_candidates is required for STA_tuning_cfg mode")
-        mask_selected_cfg: list[int] = kwargs.get(
+        mask_selected_cfg: List[int] = kwargs.get(
            'mask_selected', list(range(len(mask_candidates_cfg))))
-        skip_time_steps_cfg: int | None = kwargs.get('skip_time_steps')
+        skip_time_steps_cfg: Optional[int] = kwargs.get('skip_time_steps')

        # Parse selected masks
-        selected_masks_cfg: list[list[int]] = []
+        selected_masks_cfg: List[List[int]] = []
        for index in mask_selected_cfg:
            mask = mask_candidates_cfg[index]
            masks_list = [int(x) for x in mask.split(',')]
@@ -185,7 +185,7 @@ def configure_sta(mode: str = 'STA_searching',
                                               selected_masks_cfg)

        # Add full attention mask for specific cases
-        full_attention_mask_cfg: list[int] | None = kwargs.get(
+        full_attention_mask_cfg: Optional[List[int]] = kwargs.get(
            'full_attention_mask')
        if full_attention_mask_cfg is not None:
            selected_masks_cfg.append(full_attention_mask_cfg)
@@ -225,7 +225,7 @@ def configure_sta(mode: str = 'STA_searching',

    else:  # STA_inference
        # Get parameters with defaults
-        load_path: str | None = kwargs.get(
+        load_path: Optional[str] = kwargs.get(
            'load_path', "mask_candidates/mask_strategy.json")
        if load_path is None:
            raise ValueError("load_path is required for STA_inference mode")
@@ -246,9 +246,9 @@ def configure_sta(mode: str = 'STA_searching',
 # Helper functions


-def read_specific_json_files(folder_path: str) -> list[dict[str, Any]]:
+def read_specific_json_files(folder_path: str) -> List[Dict[str, Any]]:
    """Read and parse JSON files containing mask search results."""
-    json_contents: list[dict[str, Any]] = []
+    json_contents: List[Dict[str, Any]] = []

    # List files only in the current directory (no walk)
    files = os.listdir(folder_path)
@@ -266,11 +266,11 @@ def read_specific_json_files(folder_path: str) -> list[dict[str, Any]]:


 def average_head_losses(
-        results: list[dict[str, Any]],
-        selected_masks: list[list[int]]) -> dict[str, dict[str, np.ndarray]]:
+        results: List[Dict[str, Any]],
+        selected_masks: List[List[int]]) -> Dict[str, Dict[str, np.ndarray]]:
    """Average losses across all prompts for each mask strategy."""
    # Initialize a dictionary to store the averaged results
-    averaged_losses: dict[str, dict[str, np.ndarray]] = {}
+    averaged_losses: Dict[str, Dict[str, np.ndarray]] = {}
    loss_type = 'L2_loss'
    # Get all loss types (e.g., 'L2_loss')
    averaged_losses[loss_type] = {}
@@ -292,14 +292,14 @@ def average_head_losses(


 def select_best_mask_strategy(
-        averaged_results: dict[str, dict[str, np.ndarray]],
-        selected_masks: list[list[int]],
+        averaged_results: Dict[str, Dict[str, np.ndarray]],
+        selected_masks: List[List[int]],
        skip_time_steps: int = 12,
        timesteps: int = 50,
        head_num: int = 40
-) -> tuple[dict[str, list[int]], float, dict[str, int]]:
+) -> Tuple[Dict[str, List[int]], float, Dict[str, int]]:
    """Select the best mask strategy for each head based on loss minimization."""
-    best_mask_strategy: dict[str, list[int]] = {}
+    best_mask_strategy: Dict[str, List[int]] = {}
    loss_type = 'L2_loss'
    # Get the shape of time steps and layers
    layers = len(averaged_results[loss_type][str(selected_masks[0])][0])
@@ -308,7 +308,7 @@ def select_best_mask_strategy(
    total_tokens = 0  # total number of masked tokens
    total_length = 0  # total sequence length

-    strategy_counts: dict[str, int] = {
+    strategy_counts: Dict[str, int] = {
        str(strategy): 0
        for strategy in selected_masks
    }
@@ -349,23 +349,38 @@ def select_best_mask_strategy(
    return best_mask_strategy, overall_sparsity, strategy_counts


+def dict_to_3d_list(mask_strategy: Optional[Dict[str, List[int]]],
+                    t_max: int = 50,
+                    l_max: int = 60,
+                    h_max: int = 24) -> List[List[List[Optional[List[int]]]]]:
+    result: List[List[List[Optional[List[int]]]]] = [[[
+        None for _ in range(h_max)
+    ] for _ in range(l_max)] for _ in range(t_max)]
+    if mask_strategy is None:
+        return result
+    for key, value in mask_strategy.items():
+        t, layer_idx, h = map(int, key.split('_'))
+        result[t][layer_idx][h] = value
+    return result
+
+
 def save_mask_search_results(
-        mask_search_final_result: list[dict[str, list[float]]],
+        mask_search_final_result: List[Dict[str, List[float]]],
        prompt: str,
-        mask_strategies: list[str],
-        output_dir: str = 'output/mask_search_result/') -> str | None:
+        mask_strategies: List[str],
+        output_dir: str = 'output/mask_search_result/') -> Optional[str]:
    if not mask_search_final_result:
        print("No mask search results to save")
        return None

    # Create result dictionary with defaultdict for nested lists
-    mask_search_dict: dict[str, dict[str, list[list[float]]]] = {
+    mask_search_dict: Dict[str, Dict[str, List[List[float]]]] = {
        "L2_loss": defaultdict(list),
        "L1_loss": defaultdict(list)
    }

    mask_selected = list(range(len(mask_strategies)))
-    selected_masks: list[list[int]] = []
+    selected_masks: List[List[int]] = []
    for index in mask_selected:
        mask = mask_strategies[index]
        masks_list = [int(x) for x in mask.split(',')]
@@ -375,7 +390,7 @@ def save_mask_search_results(
    for i, mask_strategy in enumerate(selected_masks):
        mask_strategy_str = str(mask_strategy)
        # Process L2 loss
-        step_results: list[list[float]] = []
+        step_results: List[List[float]] = []
        for step_data in mask_search_final_result:
            if isinstance(step_data, dict) and "L2_loss" in step_data:
                layer_losses = [float(loss) for loss in step_data["L2_loss"]]
@@ -10,8 +10,8 @@ from fastvideo.v1.attention.selector import get_attn_backend

 __all__ = [
    "DistributedAttention",
-    "LocalAttention",
    "DistributedAttention_VSA",
+    "LocalAttention",
    "AttentionBackend",
    "AttentionMetadata",
    "AttentionMetadataBuilder",
@@ -3,7 +3,8 @@

 from abc import ABC, abstractmethod
 from dataclasses import dataclass, fields
-from typing import TYPE_CHECKING, Any, Generic, Protocol, TypeVar
+from typing import (TYPE_CHECKING, Any, Dict, Generic, Optional, Protocol, Set,
+                    Type, TypeVar)

 if TYPE_CHECKING:
    from fastvideo.v1.fastvideo_args import FastVideoArgs
@@ -26,12 +27,12 @@ class AttentionBackend(ABC):

    @staticmethod
    @abstractmethod
-    def get_impl_cls() -> type["AttentionImpl"]:
+    def get_impl_cls() -> Type["AttentionImpl"]:
        raise NotImplementedError

    @staticmethod
    @abstractmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
        raise NotImplementedError

    # @staticmethod
@@ -45,7 +46,7 @@ class AttentionBackend(ABC):

    @staticmethod
    @abstractmethod
-    def get_builder_cls() -> type["AttentionMetadataBuilder"]:
+    def get_builder_cls() -> Type["AttentionMetadataBuilder"]:
        raise NotImplementedError


@@ -56,7 +57,8 @@ class AttentionMetadata:
    current_timestep: int

    def asdict_zerocopy(self,
-                        skip_fields: set[str] | None = None) -> dict[str, Any]:
+                        skip_fields: Optional[Set[str]] = None
+                        ) -> Dict[str, Any]:
        """Similar to dataclasses.asdict, but avoids deepcopying."""
        if skip_fields is None:
            skip_fields = set()
@@ -122,7 +124,7 @@ class AttentionImpl(ABC, Generic[T]):
        head_size: int,
        softmax_scale: float,
        causal: bool = False,
-        num_kv_heads: int | None = None,
+        num_kv_heads: Optional[int] = None,
        prefix: str = "",
        **extra_impl_args,
    ) -> None:
@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0

+from typing import List, Optional, Type
+
 import torch
 from flash_attn import flash_attn_func as flash_attn_2_func

@@ -26,7 +28,7 @@ class FlashAttentionBackend(AttentionBackend):
    accept_output_buffer: bool = True

    @staticmethod
-    def get_supported_head_sizes() -> list[int]:
+    def get_supported_head_sizes() -> List[int]:
        return [32, 64, 96, 128, 160, 192, 224, 256]

    @staticmethod
@@ -34,15 +36,15 @@ class FlashAttentionBackend(AttentionBackend):
        return "FLASH_ATTN"

    @staticmethod
-    def get_impl_cls() -> type["FlashAttentionImpl"]:
+    def get_impl_cls() -> Type["FlashAttentionImpl"]:
        return FlashAttentionImpl

    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
        raise NotImplementedError

    @staticmethod
-    def get_builder_cls() -> type["AttentionMetadataBuilder"]:
+    def get_builder_cls() -> Type["AttentionMetadataBuilder"]:
        raise NotImplementedError


@@ -54,7 +56,7 @@ class FlashAttentionImpl(AttentionImpl):
        head_size: int,
        causal: bool,
        softmax_scale: float,
-        num_kv_heads: int | None = None,
+        num_kv_heads: Optional[int] = None,
        prefix: str = "",
        **extra_impl_args,
    ) -> None:
@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
+from typing import List, Optional, Type

 import torch
 from sageattention import sageattn
@@ -17,7 +18,7 @@ class SageAttentionBackend(AttentionBackend):
    accept_output_buffer: bool = True

    @staticmethod
-    def get_supported_head_sizes() -> list[int]:
+    def get_supported_head_sizes() -> List[int]:
        return [32, 64, 96, 128, 160, 192, 224, 256]

    @staticmethod
@@ -25,7 +26,7 @@ class SageAttentionBackend(AttentionBackend):
        return "SAGE_ATTN"

    @staticmethod
-    def get_impl_cls() -> type["SageAttentionImpl"]:
+    def get_impl_cls() -> Type["SageAttentionImpl"]:
        return SageAttentionImpl

    # @staticmethod
@@ -41,7 +42,7 @@ class SageAttentionImpl(AttentionImpl):
        head_size: int,
        causal: bool,
        softmax_scale: float,
-        num_kv_heads: int | None = None,
+        num_kv_heads: Optional[int] = None,
        prefix: str = "",
        **extra_impl_args,
    ) -> None:
@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
+from typing import List, Optional, Type

 import torch

@@ -16,7 +17,7 @@ class SDPABackend(AttentionBackend):
    accept_output_buffer: bool = True

    @staticmethod
-    def get_supported_head_sizes() -> list[int]:
+    def get_supported_head_sizes() -> List[int]:
        return [32, 64, 96, 128, 160, 192, 224, 256]

    @staticmethod
@@ -24,7 +25,7 @@ class SDPABackend(AttentionBackend):
        return "SDPA"

    @staticmethod
-    def get_impl_cls() -> type["SDPAImpl"]:
+    def get_impl_cls() -> Type["SDPAImpl"]:
        return SDPAImpl

    # @staticmethod
@@ -40,7 +41,7 @@ class SDPAImpl(AttentionImpl):
        head_size: int,
        causal: bool,
        softmax_scale: float,
-        num_kv_heads: int | None = None,
+        num_kv_heads: Optional[int] = None,
        prefix: str = "",
        **extra_impl_args,
    ) -> None:
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 import json
 from dataclasses import dataclass
-from typing import Any
+from typing import Any, Dict, List, Optional, Type

 import torch
 from einops import rearrange
@@ -17,11 +17,33 @@ from fastvideo.v1.fastvideo_args import FastVideoArgs
 from fastvideo.v1.forward_context import ForwardContext, get_forward_context
 from fastvideo.v1.logger import init_logger
 from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.v1.utils import dict_to_3d_list

 logger = init_logger(__name__)


+# TODO(will-refactor): move this to a utils file
+def dict_to_3d_list(
+        mask_strategy: Dict[str,
+                            Any]) -> List[List[List[Optional[torch.Tensor]]]]:
+    indices = [tuple(map(int, key.split('_'))) for key in mask_strategy]
+
+    max_timesteps_idx = max(
+        timesteps_idx for timesteps_idx, layer_idx, head_idx in indices) + 1
+    max_layer_idx = max(layer_idx
+                        for timesteps_idx, layer_idx, head_idx in indices) + 1
+    max_head_idx = max(head_idx
+                       for timesteps_idx, layer_idx, head_idx in indices) + 1
+
+    result = [[[None for _ in range(max_head_idx)]
+               for _ in range(max_layer_idx)] for _ in range(max_timesteps_idx)]
+
+    for key, value in mask_strategy.items():
+        timesteps_idx, layer_idx, head_idx = map(int, key.split('_'))
+        result[timesteps_idx][layer_idx][head_idx] = value
+
+    return result
+
+
 class RangeDict(dict):

    def __getitem__(self, item: int) -> str:
@@ -40,7 +62,7 @@ class SlidingTileAttentionBackend(AttentionBackend):
    accept_output_buffer: bool = True

    @staticmethod
-    def get_supported_head_sizes() -> list[int]:
+    def get_supported_head_sizes() -> List[int]:
        # TODO(will-refactor): check this
        return [32, 64, 96, 128, 160, 192, 224, 256]

@@ -49,22 +71,22 @@ class SlidingTileAttentionBackend(AttentionBackend):
        return "SLIDING_TILE_ATTN"

    @staticmethod
-    def get_impl_cls() -> type["SlidingTileAttentionImpl"]:
+    def get_impl_cls() -> Type["SlidingTileAttentionImpl"]:
        return SlidingTileAttentionImpl

    @staticmethod
-    def get_metadata_cls() -> type["SlidingTileAttentionMetadata"]:
+    def get_metadata_cls() -> Type["SlidingTileAttentionMetadata"]:
        return SlidingTileAttentionMetadata

    @staticmethod
-    def get_builder_cls() -> type["SlidingTileAttentionMetadataBuilder"]:
+    def get_builder_cls() -> Type["SlidingTileAttentionMetadataBuilder"]:
        return SlidingTileAttentionMetadataBuilder


@dataclass
 class SlidingTileAttentionMetadata(AttentionMetadata):
    current_timestep: int
-    STA_param: list[list[
+    STA_param: List[List[
        Any]]  # each timestep with one metadata, shape [num_layers, num_heads]


@@ -98,7 +120,7 @@ class SlidingTileAttentionImpl(AttentionImpl):
        head_size: int,
        causal: bool,
        softmax_scale: float,
-        num_kv_heads: int | None = None,
+        num_kv_heads: Optional[int] = None,
        prefix: str = "",
        **extra_impl_args,
    ) -> None:
@@ -265,10 +287,16 @@ class SlidingTileAttentionImpl(AttentionImpl):
                    forward_batch.mask_search_final_result_pos[timestep].append(
                        layer_loss_save)
        else:
+            # windows = [
+            #     self.mask_strategy[timestep][layer_idx][head_idx + start_head]
+            #     for head_idx in range(head_num)
+            # ]
            windows = [
                STA_param[head_idx + start_head] for head_idx in range(head_num)
            ]
-
+            # if has_text is False:
+            #     from IPython import embed
+            #     embed()
            hidden_states = sliding_tile_attention(
                query, key, value, windows, text_length, has_text,
                self.dit_seq_shape_str).transpose(1, 2)
@@ -1,14 +1,18 @@
 # SPDX-License-Identifier: Apache-2.0
 import math
 from dataclasses import dataclass
+from typing import Any, List, Optional, Type, cast

 import torch
+import triton
+import triton.language as tl
 from einops import rearrange

 try:
-    from vsa import video_sparse_attn
-except ImportError:
-    video_sparse_attn = None
+    from vsa import block_sparse_attn
+except ImportError:  # noqa: E722
+    block_sparse_attn = None
+from typing import Tuple

 from fastvideo.v1.attention.backends.abstract import (AttentionBackend,
                                                      AttentionImpl,
@@ -27,7 +31,7 @@ class VideoSparseAttentionBackend(AttentionBackend):
    accept_output_buffer: bool = True

    @staticmethod
-    def get_supported_head_sizes() -> list[int]:
+    def get_supported_head_sizes() -> List[int]:
        return [64, 128]

    @staticmethod
@@ -35,22 +39,22 @@ class VideoSparseAttentionBackend(AttentionBackend):
        return "VIDEO_SPARSE_ATTN"

    @staticmethod
-    def get_impl_cls() -> type["VideoSparseAttentionImpl"]:
+    def get_impl_cls() -> Type["VideoSparseAttentionImpl"]:
        return VideoSparseAttentionImpl

    @staticmethod
-    def get_metadata_cls() -> type["VideoSparseAttentionMetadata"]:
+    def get_metadata_cls() -> Type["VideoSparseAttentionMetadata"]:
        return VideoSparseAttentionMetadata

    @staticmethod
-    def get_builder_cls() -> type["VideoSparseAttentionMetadataBuilder"]:
+    def get_builder_cls() -> Type["VideoSparseAttentionMetadataBuilder"]:
        return VideoSparseAttentionMetadataBuilder


@dataclass
 class VideoSparseAttentionMetadata(AttentionMetadata):
    current_timestep: int
-    dit_seq_shape: list[int]
+    dit_seq_shape: List[int]
    VSA_sparsity: float


@@ -71,18 +75,14 @@ class VideoSparseAttentionMetadataBuilder(AttentionMetadataBuilder):
        if forward_batch.latents is None:
            raise ValueError("latents cannot be None")

-        raw_latent_shape = forward_batch.raw_latent_shape
-        if raw_latent_shape is None:
-            raise ValueError("raw_latent_shape cannot be None")
-
-        patch_size = fastvideo_args.pipeline_config.dit_config.patch_size
+        raw_latent_shape = forward_batch.latents.shape
+        patch_size = fastvideo_args.dit_config.patch_size
        dit_seq_shape = [
            raw_latent_shape[2] // patch_size[0],
            raw_latent_shape[3] // patch_size[1],
            raw_latent_shape[4] // patch_size[2]
        ]
        VSA_sparsity = forward_batch.VSA_sparsity
-
        return VideoSparseAttentionMetadata(current_timestep=current_timestep,
                                            dit_seq_shape=dit_seq_shape,
                                            VSA_sparsity=VSA_sparsity)
@@ -96,7 +96,7 @@ class VideoSparseAttentionImpl(AttentionImpl):
        head_size: int,
        causal: bool,
        softmax_scale: float,
-        num_kv_heads: int | None = None,
+        num_kv_heads: Optional[int] = None,
        prefix: str = "",
        **extra_impl_args,
    ) -> None:
@@ -104,8 +104,8 @@ class VideoSparseAttentionImpl(AttentionImpl):
        sp_group = get_sp_group()
        self.sp_size = sp_group.world_size
        self.VSA_base_tile_size = [4, 4, 4]
-        self.dit_seq_shape: list[int]
-        self.full_window_size: list[int]
+        self.dit_seq_shape: List[int]
+        self.full_window_size: List[int]
        self.img_seq_length: int

    def tile(self, x: torch.Tensor) -> torch.Tensor:
@@ -177,16 +177,12 @@ class VideoSparseAttentionImpl(AttentionImpl):
        value = value.transpose(1, 2).contiguous()
        gate_compress = gate_compress.transpose(1, 2).contiguous()

-        VSA_sparsity = attn_metadata.VSA_sparsity
-
        cur_topk = math.ceil(
-            (1 - VSA_sparsity) *
+            (1 - attn_metadata.VSA_sparsity) *
            (self.img_seq_length / math.prod(self.VSA_base_tile_size)))

-        if video_sparse_attn is None:
-            raise NotImplementedError("video_sparse_attn is not installed")
-
-        hidden_states = video_sparse_attn(
+        # Cast to Any to bypass type checking for untyped function
+        hidden_states = cast(Any, sparse_attn_c_s_p)(
            query,
            key,
            value,
@@ -195,3 +191,267 @@ class VideoSparseAttentionImpl(AttentionImpl):
            compress_attn_weight=gate_compress).transpose(1, 2)

        return hidden_states
+
+
+def torch_attention(q, k, v) -> Tuple[torch.Tensor, torch.Tensor]:
+    QK = torch.matmul(q, k.transpose(-2, -1))
+    QK /= (q.size(-1)**0.5)
+
+    # Causal mask removed since causal is always false
+
+    QK = torch.nn.functional.softmax(QK, dim=-1)
+    output = torch.matmul(QK, v)
+    return output, QK
+
+
+def sparse_attn_c_s_p(q, k, v, topk, block_size, compress_attn_weight=None):
+    """
+    q: [batch_size, num_heads, seq_len, head_dim]
+    k: [batch_size, num_heads, seq_len, head_dim]
+    v: [batch_size, num_heads, seq_len, head_dim]
+    topk: int
+    block_size: int or tuple of 3 ints
+    video_shape: tuple of (T, H, W)
+    compress_attn_weight: [batch_size, num_heads, seq_len, head_dim]
+    select_attn_weight: [batch_size, num_heads, seq_len, head_dim]
+    
+    V1 of sparse attention. Include compress attn and sparse attn branch, use average pooling to compress. 
+    Assume q, k, v is flattened in this way: [batch_size, num_heads, T//block_size[0], H//block_size[1], W//block_size[2], block_size[0], block_size[1], block_size[2]]
+    """
+
+    if isinstance(block_size, int):
+        block_size = (block_size, block_size, block_size)
+
+    block_elements = block_size[0] * block_size[1] * block_size[2]
+    assert block_elements % 64 == 0 and block_elements >= 64
+    assert q.shape[2] % block_elements == 0
+    batch_size, num_heads, seq_len, head_dim = q.shape
+    # compress attn
+    q_compress = q.view(batch_size, num_heads, seq_len // block_elements,
+                        block_elements, head_dim).mean(dim=3)
+    k_compress = k.view(batch_size, num_heads, seq_len // block_elements,
+                        block_elements, head_dim).mean(dim=3)
+    v_compress = v.view(batch_size, num_heads, seq_len // block_elements,
+                        block_elements, head_dim).mean(dim=3)
+
+    output_compress, block_attn_score = torch_attention(q_compress, k_compress,
+                                                        v_compress)
+
+    output_compress = output_compress.view(batch_size, num_heads,
+                                           seq_len // block_elements, 1,
+                                           head_dim)
+    output_compress = output_compress.repeat(1, 1, 1, block_elements,
+                                             1).view(batch_size, num_heads,
+                                                     seq_len, head_dim)
+
+    q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num = generate_topk_block_sparse_pattern(
+        block_attn_score, topk)
+
+    output_select = block_sparse_attn(q, k, v, q2k_block_sparse_index,
+                                      q2k_block_sparse_num,
+                                      k2q_block_sparse_index,
+                                      k2q_block_sparse_num)
+
+    if compress_attn_weight is not None:
+        final_output = output_compress * compress_attn_weight + output_select
+    else:
+        final_output = output_compress + output_select
+    return final_output
+
+
+@triton.jit
+def topk_index_to_map_kernel(
+    map_ptr,
+    index_ptr,
+    map_bs_stride,
+    map_h_stride,
+    map_q_stride,
+    map_kv_stride,
+    index_bs_stride,
+    index_h_stride,
+    index_q_stride,
+    index_kv_stride,
+    topk: tl.constexpr,
+):
+    b, h, q = tl.program_id(0), tl.program_id(1), tl.program_id(2)
+    index_ptr_base = index_ptr + b * index_bs_stride + h * index_h_stride + q * index_q_stride
+    map_ptr_base = map_ptr + b * map_bs_stride + h * map_h_stride + q * map_q_stride
+
+    for i in tl.static_range(topk):
+        index = tl.load(index_ptr_base + i * index_kv_stride)
+        tl.store(map_ptr_base + index * map_kv_stride, 1.0)
+
+
+@triton.jit
+def map_to_index_kernel(
+    map_ptr,
+    index_ptr,
+    index_num_ptr,
+    map_bs_stride,
+    map_h_stride,
+    map_q_stride,
+    map_kv_stride,
+    index_bs_stride,
+    index_h_stride,
+    index_q_stride,
+    index_kv_stride,
+    index_num_bs_stride,
+    index_num_h_stride,
+    index_num_q_stride,
+    num_kv_blocks: tl.constexpr,
+):
+    b, h, q = tl.program_id(0), tl.program_id(1), tl.program_id(2)
+    index_ptr_base = index_ptr + b * index_bs_stride + h * index_h_stride + q * index_q_stride
+    map_ptr_base = map_ptr + b * map_bs_stride + h * map_h_stride + q * map_q_stride
+
+    num = 0
+    for i in tl.static_range(num_kv_blocks):
+        map_entry = tl.load(map_ptr_base + i * map_kv_stride)
+        if map_entry:
+            tl.store(index_ptr_base + num * index_kv_stride, i)
+            num += 1
+
+    tl.store(
+        index_num_ptr + b * index_num_bs_stride + h * index_num_h_stride +
+        q * index_num_q_stride, num)
+
+
+def topk_index_to_map(index: torch.Tensor,
+                      num_kv_blocks: int,
+                      transpose_map: bool = False):
+    """
+    Convert topk indices to a map.
+    
+    Args:
+        index: [bs, h, num_q_blocks, topk]
+            The topk indices tensor.
+        num_kv_blocks: int
+            The number of key-value blocks in the block_map returned
+        transpose_map: bool
+            If True, the block_map will be transposed on the final two dimensions.
+    
+    Returns:
+        block_map: [bs, h, num_q_blocks, num_kv_blocks]
+            A binary map where 1 indicates that the q block attends to the kv block.
+    """
+    bs, h, num_q_blocks, topk = index.shape
+
+    if transpose_map is False:
+        block_map = torch.zeros((bs, h, num_q_blocks, num_kv_blocks),
+                                dtype=torch.bool,
+                                device=index.device)
+    else:
+        block_map = torch.zeros((bs, h, num_kv_blocks, num_q_blocks),
+                                dtype=torch.bool,
+                                device=index.device)
+        block_map = block_map.transpose(2, 3)
+
+    grid = (bs, h, num_q_blocks)
+    topk_index_to_map_kernel[grid](
+        block_map,
+        index,
+        block_map.stride(0),
+        block_map.stride(1),
+        block_map.stride(2),
+        block_map.stride(3),
+        index.stride(0),
+        index.stride(1),
+        index.stride(2),
+        index.stride(3),
+        topk=topk,
+    )
+
+    return block_map
+
+
+def map_to_index(block_map: torch.Tensor):
+    """
+    Convert a block map to indices and counts.
+    
+    Args:
+        block_map: [bs, h, num_q_blocks, num_kv_blocks]
+            The block map tensor.
+    
+    Returns:
+        index: [bs, h, num_q_blocks, num_kv_blocks]
+            The indices of the blocks.
+        index_num: [bs, h, num_q_blocks]
+            The number of blocks for each q block.
+    """
+    bs, h, num_q_blocks, num_kv_blocks = block_map.shape
+
+    index = torch.full((block_map.shape),
+                       -1,
+                       dtype=torch.int32,
+                       device=block_map.device)
+    index_num = torch.empty((bs, h, num_q_blocks),
+                            dtype=torch.int32,
+                            device=block_map.device)
+
+    grid = (bs, h, num_q_blocks)
+    map_to_index_kernel[grid](
+        block_map,
+        index,
+        index_num,
+        block_map.stride(0),
+        block_map.stride(1),
+        block_map.stride(2),
+        block_map.stride(3),
+        index.stride(0),
+        index.stride(1),
+        index.stride(2),
+        index.stride(3),
+        index_num.stride(0),
+        index_num.stride(1),
+        index_num.stride(2),
+        num_kv_blocks=num_kv_blocks,
+    )
+
+    return index, index_num
+
+
+def generate_topk_block_sparse_pattern(block_attn_score: torch.Tensor,
+                                       topk: int):
+    """
+    Generate a block sparse pattern where each q block attends to exactly topk kv blocks,
+    based on the provided attention scores.
+    
+    Args:
+        block_attn_score: [bs, h, num_q_blocks, num_kv_blocks]
+            Attention scores between query and key blocks
+        topk: int
+            Number of kv blocks each q block attends to
+        
+    Returns:
+        q2k_block_sparse_index: [bs, h, num_q_blocks, topk]
+            Contains the indices of kv blocks that each q block attends to.
+        q2k_block_sparse_num: [bs, h, num_q_blocks]
+            Contains the number of kv blocks that each q block attends to (all equal to topk).
+        k2q_block_sparse_index: [bs, h, num_kv_blocks, max_q_per_kv]
+            Contains the indices of q blocks that attend to each kv block.
+        k2q_block_sparse_num: [bs, h, num_kv_blocks]
+            Contains the number of q blocks that attend to each kv block.
+    """
+    device = block_attn_score.device
+    # Extract dimensions from block_attn_score
+    bs, h, num_q_blocks, num_kv_blocks = block_attn_score.shape
+
+    sorted_result = torch.sort(block_attn_score, dim=-1, descending=True)
+
+    sorted_indice = sorted_result.indices
+
+    q2k_block_sparse_index, _ = torch.sort(sorted_indice[:, :, :, :topk],
+                                           dim=-1)
+    q2k_block_sparse_index = q2k_block_sparse_index.to(dtype=torch.int32)
+    q2k_block_sparse_num = torch.full((bs, h, num_q_blocks),
+                                      topk,
+                                      device=device,
+                                      dtype=torch.int32)
+
+    block_map = topk_index_to_map(q2k_block_sparse_index,
+                                  num_kv_blocks,
+                                  transpose_map=True)
+    k2q_block_sparse_index, k2q_block_sparse_num = map_to_index(
+        block_map.transpose(2, 3))
+
+    return q2k_block_sparse_index, q2k_block_sparse_num, k2q_block_sparse_index, k2q_block_sparse_num
@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0

+from typing import Optional, Tuple
+
 import torch
 import torch.nn as nn

@@ -10,7 +12,7 @@ from fastvideo.v1.distributed.communication_op import (
 from fastvideo.v1.distributed.parallel_state import (get_sp_parallel_rank,
                                                     get_sp_world_size)
 from fastvideo.v1.forward_context import ForwardContext, get_forward_context
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.v1.platforms import _Backend
 from fastvideo.v1.utils import get_compute_dtype


@@ -21,11 +23,11 @@ class DistributedAttention(nn.Module):
    def __init__(self,
                 num_heads: int,
                 head_size: int,
-                 num_kv_heads: int | None = None,
-                 softmax_scale: float | None = None,
+                 num_kv_heads: Optional[int] = None,
+                 softmax_scale: Optional[float] = None,
                 causal: bool = False,
-                 supported_attention_backends: tuple[AttentionBackendEnum, ...]
-                 | None = None,
+                 supported_attention_backends: Optional[Tuple[_Backend,
+                                                              ...]] = None,
                 prefix: str = "",
                 **extra_impl_args) -> None:
        super().__init__()
@@ -43,13 +45,13 @@ class DistributedAttention(nn.Module):
            dtype,
            supported_attention_backends=supported_attention_backends)
        impl_cls = attn_backend.get_impl_cls()
-        self.attn_impl = impl_cls(num_heads=num_heads,
-                                  head_size=head_size,
-                                  causal=causal,
-                                  softmax_scale=self.softmax_scale,
-                                  num_kv_heads=num_kv_heads,
-                                  prefix=f"{prefix}.impl",
-                                  **extra_impl_args)
+        self.impl = impl_cls(num_heads=num_heads,
+                             head_size=head_size,
+                             causal=causal,
+                             softmax_scale=self.softmax_scale,
+                             num_kv_heads=num_kv_heads,
+                             prefix=f"{prefix}.impl",
+                             **extra_impl_args)
        self.num_heads = num_heads
        self.head_size = head_size
        self.num_kv_heads = num_kv_heads
@@ -61,10 +63,10 @@ class DistributedAttention(nn.Module):
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
-        replicated_q: torch.Tensor | None = None,
-        replicated_k: torch.Tensor | None = None,
-        replicated_v: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        replicated_q: Optional[torch.Tensor] = None,
+        replicated_k: Optional[torch.Tensor] = None,
+        replicated_v: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
        """Forward pass for distributed attention.
        
        Args:
@@ -98,7 +100,7 @@ class DistributedAttention(nn.Module):
                                                    scatter_dim=2,
                                                    gather_dim=1)
        # Apply backend-specific preprocess_qkv
-        qkv = self.attn_impl.preprocess_qkv(qkv, ctx_attn_metadata)
+        qkv = self.impl.preprocess_qkv(qkv, ctx_attn_metadata)

        # Concatenate with replicated QKV if provided
        if replicated_q is not None:
@@ -114,7 +116,7 @@ class DistributedAttention(nn.Module):

        q, k, v = qkv.chunk(3, dim=0)

-        output = self.attn_impl.forward(q, k, v, ctx_attn_metadata)
+        output = self.impl.forward(q, k, v, ctx_attn_metadata)

        # Redistribute back if using sequence parallelism
        replicated_output = None
@@ -125,7 +127,7 @@ class DistributedAttention(nn.Module):
            replicated_output = sequence_model_parallel_all_gather(
                replicated_output.contiguous(), dim=2)
        # Apply backend-specific postprocess_output
-        output = self.attn_impl.postprocess_output(output, ctx_attn_metadata)
+        output = self.impl.postprocess_output(output, ctx_attn_metadata)

        output = sequence_model_parallel_all_to_all_4D(output,
                                                       scatter_dim=1,
@@ -142,11 +144,11 @@ class DistributedAttention_VSA(DistributedAttention):
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
-        replicated_q: torch.Tensor | None = None,
-        replicated_k: torch.Tensor | None = None,
-        replicated_v: torch.Tensor | None = None,
-        gate_compress: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        replicated_q: Optional[torch.Tensor] = None,
+        replicated_k: Optional[torch.Tensor] = None,
+        replicated_v: Optional[torch.Tensor] = None,
+        gate_compress: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
        """Forward pass for distributed attention.
        
        Args:
@@ -181,17 +183,18 @@ class DistributedAttention_VSA(DistributedAttention):
                                                     scatter_dim=2,
                                                     gather_dim=1)

-        qkvg = self.attn_impl.preprocess_qkv(qkvg, ctx_attn_metadata)
+        qkvg = self.impl.preprocess_qkv(
+            qkvg, ctx_attn_metadata)  # (yongqi) pass latent shape here?

        q, k, v, gate_compress = qkvg.chunk(4, dim=0)
-        output = self.attn_impl.forward(
-            q, k, v, gate_compress, ctx_attn_metadata)  # type: ignore[call-arg]
+        output = self.impl.forward(q, k, v, gate_compress,
+                                   ctx_attn_metadata)  # type: ignore[call-arg]

        # Redistribute back if using sequence parallelism
        replicated_output = None

        # Apply backend-specific postprocess_output
-        output = self.attn_impl.postprocess_output(output, ctx_attn_metadata)
+        output = self.impl.postprocess_output(output, ctx_attn_metadata)

        output = sequence_model_parallel_all_to_all_4D(output,
                                                       scatter_dim=1,
@@ -206,11 +209,11 @@ class LocalAttention(nn.Module):
    def __init__(self,
                 num_heads: int,
                 head_size: int,
-                 num_kv_heads: int | None = None,
-                 softmax_scale: float | None = None,
+                 num_kv_heads: Optional[int] = None,
+                 softmax_scale: Optional[float] = None,
                 causal: bool = False,
-                 supported_attention_backends: tuple[AttentionBackendEnum, ...]
-                 | None = None,
+                 supported_attention_backends: Optional[Tuple[_Backend,
+                                                              ...]] = None,
                 **extra_impl_args) -> None:
        super().__init__()
        if softmax_scale is None:
@@ -226,12 +229,12 @@ class LocalAttention(nn.Module):
            dtype,
            supported_attention_backends=supported_attention_backends)
        impl_cls = attn_backend.get_impl_cls()
-        self.attn_impl = impl_cls(num_heads=num_heads,
-                                  head_size=head_size,
-                                  softmax_scale=self.softmax_scale,
-                                  num_kv_heads=num_kv_heads,
-                                  causal=causal,
-                                  **extra_impl_args)
+        self.impl = impl_cls(num_heads=num_heads,
+                             head_size=head_size,
+                             softmax_scale=self.softmax_scale,
+                             num_kv_heads=num_kv_heads,
+                             causal=causal,
+                             **extra_impl_args)
        self.num_heads = num_heads
        self.head_size = head_size
        self.num_kv_heads = num_kv_heads
@@ -262,5 +265,5 @@ class LocalAttention(nn.Module):
        forward_context: ForwardContext = get_forward_context()
        ctx_attn_metadata = forward_context.attn_metadata

-        output = self.attn_impl.forward(q, k, v, ctx_attn_metadata)
+        output = self.impl.forward(q, k, v, ctx_attn_metadata)
        return output
@@ -2,23 +2,22 @@
 # Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/attention/selector.py

 import os
-from collections.abc import Generator
 from contextlib import contextmanager
 from functools import cache
-from typing import cast
+from typing import Generator, Optional, Tuple, Type, cast

 import torch

 import fastvideo.v1.envs as envs
 from fastvideo.v1.attention.backends.abstract import AttentionBackend
 from fastvideo.v1.logger import init_logger
-from fastvideo.v1.platforms import AttentionBackendEnum, current_platform
+from fastvideo.v1.platforms import _Backend, current_platform
 from fastvideo.v1.utils import STR_BACKEND_ENV_VAR, resolve_obj_by_qualname

 logger = init_logger(__name__)


-def backend_name_to_enum(backend_name: str) -> AttentionBackendEnum | None:
+def backend_name_to_enum(backend_name: str) -> Optional[_Backend]:
    """
    Convert a string backend name to a _Backend enum value.

@@ -28,11 +27,11 @@ def backend_name_to_enum(backend_name: str) -> AttentionBackendEnum | None:
            loaded.
    """
    assert backend_name is not None
-    return AttentionBackendEnum[backend_name] if backend_name in AttentionBackendEnum.__members__ else \
+    return _Backend[backend_name] if backend_name in _Backend.__members__ else \
          None


-def get_env_variable_attn_backend() -> AttentionBackendEnum | None:
+def get_env_variable_attn_backend() -> Optional[_Backend]:
    '''
    Get the backend override specified by the FastVideo attention
    backend environment variable, if one is specified.
@@ -54,11 +53,10 @@ def get_env_variable_attn_backend() -> AttentionBackendEnum | None:
 #
 # THIS SELECTION TAKES PRECEDENCE OVER THE
 # FASTVIDEO ATTENTION BACKEND ENVIRONMENT VARIABLE
-forced_attn_backend: AttentionBackendEnum | None = None
+forced_attn_backend: Optional[_Backend] = None


-def global_force_attn_backend(
-        attn_backend: AttentionBackendEnum | None) -> None:
+def global_force_attn_backend(attn_backend: Optional[_Backend]) -> None:
    '''
    Force all attention operations to use a specified backend.

@@ -73,7 +71,7 @@ def global_force_attn_backend(
    forced_attn_backend = attn_backend


-def get_global_forced_attn_backend() -> AttentionBackendEnum | None:
+def get_global_forced_attn_backend() -> Optional[_Backend]:
    '''
    Get the currently-forced choice of attention backend,
    or None if auto-selection is currently enabled.
@@ -84,9 +82,8 @@ def get_global_forced_attn_backend() -> AttentionBackendEnum | None:
 def get_attn_backend(
    head_size: int,
    dtype: torch.dtype,
-    supported_attention_backends: tuple[AttentionBackendEnum, ...]
-    | None = None,
-) -> type[AttentionBackend]:
+    supported_attention_backends: Optional[Tuple[_Backend, ...]] = None,
+) -> Type[AttentionBackend]:
    return _cached_get_attn_backend(head_size, dtype,
                                    supported_attention_backends)

@@ -95,9 +92,8 @@ def get_attn_backend(
 def _cached_get_attn_backend(
    head_size: int,
    dtype: torch.dtype,
-    supported_attention_backends: tuple[AttentionBackendEnum, ...]
-    | None = None,
-) -> type[AttentionBackend]:
+    supported_attention_backends: Optional[Tuple[_Backend, ...]] = None,
+) -> Type[AttentionBackend]:
    # Check whether a particular choice of backend was
    # previously forced.
    #
@@ -106,13 +102,13 @@ def _cached_get_attn_backend(
    if not supported_attention_backends:
        raise ValueError("supported_attention_backends is empty")
    selected_backend = None
-    backend_by_global_setting: AttentionBackendEnum | None = (
+    backend_by_global_setting: Optional[_Backend] = (
        get_global_forced_attn_backend())
    if backend_by_global_setting is not None:
        selected_backend = backend_by_global_setting
    else:
        # Check the environment variable and override if specified
-        backend_by_env_var: str | None = envs.FASTVIDEO_ATTENTION_BACKEND
+        backend_by_env_var: Optional[str] = envs.FASTVIDEO_ATTENTION_BACKEND
        if backend_by_env_var is not None:
            selected_backend = backend_name_to_enum(backend_by_env_var)

@@ -124,12 +120,12 @@ def _cached_get_attn_backend(
    if not attention_cls:
        raise ValueError(
            f"Invalid attention backend for {current_platform.device_name}")
-    return cast(type[AttentionBackend], resolve_obj_by_qualname(attention_cls))
+    return cast(Type[AttentionBackend], resolve_obj_by_qualname(attention_cls))


@contextmanager
 def global_force_attn_backend_context_manager(
-        attn_backend: AttentionBackendEnum) -> Generator[None, None, None]:
+        attn_backend: _Backend) -> Generator[None, None, None]:
    '''
    Globally force a FastVideo attention backend override within a
    context manager, reverting the global attention backend
@@ -4,7 +4,7 @@
  "use_cpu_offload": false,
  "disable_autocast": false,
  "precision": "bf16",
-  "vae_precision": "fp32",
+  "vae_precision": "fp16",
  "vae_tiling": true,
  "vae_sp": true,
  "vae_config": {
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
JerryZhou54	5ae1b1ca91	Update	2025-06-22 17:28:50 +00:00
JerryZhou54	7e6e37b6b1	Rename column name	2025-06-11 22:05:36 +00:00
JerryZhou54	64c9c780df	Small change	2025-06-10 23:17:06 +00:00
JerryZhou54	bd7b7078ba	Fix preprocess	2025-06-10 23:17:06 +00:00
Wei Zhou	3cde7934ab	Update preprocess_pipeline_i2v.py	2025-06-10 23:17:05 +00:00
JerryZhou54	c0147f611e	I2V Runnable	2025-06-10 23:17:03 +00:00
“BrianChen1129”	f6314572e6	Add Encoded first frame to processed dataset	2025-06-10 23:15:25 +00:00