add example to docs

.buildkite/pipeline.yml

@@ -1,138 +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"
-              config:
-                command: "timeout 30m .buildkite/scripts/pr_test.sh"
-                label: "SSIM Tests"
-                env:
-                  - TEST_TYPE=ssim
-                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"

.buildkite/scripts/pr_test.sh

@@ -1,117 +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"
-        ;;
-    *)
-        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

.buildkite/scripts/pre_commit.sh

@@ -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 

.github/scripts/runpod_api.py

@@ -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
     ]
     

.github/workflows/pr-test.yml

@@ -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
@@ -42,36 +44,10 @@ on:
         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
@@ -89,71 +65,28 @@ jobs:
       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 +99,8 @@ 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: ${{ secrets.RUNPOD_API_KEY }}
@@ -184,8 +117,8 @@ jobs:
       gpu_type: "NVIDIA A40"
       gpu_count: 1
       volume_size: 100
-      image: "ghcr.io/${{ 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: ${{ secrets.RUNPOD_API_KEY }}
@@ -202,8 +135,8 @@ jobs:
       gpu_type: "NVIDIA L40S"
       gpu_count: 1
       volume_size: 100
-      image: "ghcr.io/${{ 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: ${{ secrets.RUNPOD_API_KEY }}
@@ -212,7 +145,8 @@ jobs:
   ssim-test:
     needs: change-filter
     if: >-
-      github.event_name != 'workflow_dispatch' || (github.event_name == 'workflow_dispatch' && github.event.inputs.run_ssim_test == 'true')
+      (github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || 
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_ssim_test == 'true')
     strategy:
       fail-fast: false
       matrix:
@@ -229,7 +163,7 @@ jobs:
       volume_size: 200
       disk_size: 200
       image: "ghcr.io/${{ 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: ${{ secrets.RUNPOD_API_KEY }}
@@ -238,7 +172,7 @@ jobs:
   training-test:
     needs: change-filter
     if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.training-test == 'true') || 
+      (github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || 
       (github.event_name == 'workflow_dispatch' && github.event.inputs.run_training_test == 'true')
     uses: ./.github/workflows/runpod-test.yml
     with:
@@ -247,112 +181,15 @@ jobs:
       gpu_count: 4
       volume_size: 100
       disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/training/Vanilla -srP"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:latest' }}"
+      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/training -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 +206,7 @@ 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

.github/workflows/runpod-test.yml

@@ -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 

.gitignore

@@ -59,5 +59,3 @@ docs/source/inference/examples/
 
 # Static images
 !docs/source/_static/images/**/*.png
-!comfyui/assets/**/*.png
-!comfyui/assets/**/*.gif

README.md

@@ -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},

comfyui/README.md

@@ -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.

comfyui/__init__.py

@@ -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']

comfyui/examples/FastHunyuan-diffusers.json

@@ -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": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": ""
-        },
-        "quant_config": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": ""
-        }
-      }
-    },
-    {
-      "id": 1,
-      "type": "VideoGenerator",
-      "pos": [
-        818.804931640625,
-        348.9299621582031
-      ],
-      "size": [
-        400,
-        436
-      ],
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "inference_args",
-          "shape": 7,
-          "type": "INFERENCE_ARGS",
-          "link": 3
-        },
-        {
-          "name": "vae_config",
-          "shape": 7,
-          "type": "VAE_CONFIG",
-          "link": 2
-        },
-        {
-          "name": "text_encoder_config",
-          "shape": 7,
-          "type": "TEXT_ENCODER_CONFIG",
-          "link": 7
-        },
-        {
-          "name": "dit_config",
-          "shape": 7,
-          "type": "DIT_CONFIG",
-          "link": 6
-        }
-      ],
-      "outputs": [
-        {
-          "name": "video_path",
-          "type": "STRING",
-          "links": [
-            4
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VideoGenerator"
-      },
-      "widgets_values": [
-        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest. The playful yet serene atmosphere is complemented by soft natural light filtering through the petals. Mid-shot, warm and cheerful tones.",
-        "/workspace/ComfyUI/outputs_video/",
-        2,
-        "FastVideo/FastHunyuan-diffusers",
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999,
-        -99999
-      ],
-      "auto_widget_states": {
-        "embedded_cfg_scale": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 6
-        },
-        "sp_size": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 2
-        },
-        "tp_size": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 2
-        },
-        "vae_precision": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": "fp16"
-        },
-        "vae_tiling": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": true
-        },
-        "vae_sp": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": true
-        },
-        "text_encoder_precision": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": "fp16"
-        },
-        "precision": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": "fp16"
-        },
-        "use_cpu_offload": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": true
-        }
-      }
-    },
-    {
-      "id": 3,
-      "type": "VHS_LoadVideoPath",
-      "pos": [
-        1350.136962890625,
-        331.20361328125
-      ],
-      "size": [
-        231.8896484375,
-        286
-      ],
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "meta_batch",
-          "shape": 7,
-          "type": "VHS_BatchManager",
-          "link": null
-        },
-        {
-          "name": "vae",
-          "shape": 7,
-          "type": "VAE",
-          "link": null
-        },
-        {
-          "name": "video",
-          "type": "STRING",
-          "widget": {
-            "name": "video"
-          },
-          "link": 4
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            5
-          ]
-        },
-        {
-          "name": "frame_count",
-          "type": "INT",
-          "links": null
-        },
-        {
-          "name": "audio",
-          "type": "AUDIO",
-          "links": null
-        },
-        {
-          "name": "video_info",
-          "type": "VHS_VIDEOINFO",
-          "links": null
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VHS_LoadVideoPath"
-      },
-      "widgets_values": {
-        "video": "",
-        "force_rate": 0,
-        "custom_width": 0,
-        "custom_height": 0,
-        "frame_load_cap": 0,
-        "skip_first_frames": 0,
-        "select_every_nth": 1,
-        "format": "Wan",
-        "videopreview": {
-          "hidden": false,
-          "paused": false,
-          "params": {
-            "filename": "",
-            "type": "path",
-            "format": "video/",
-            "force_rate": 0,
-            "custom_width": 0,
-            "custom_height": 0,
-            "frame_load_cap": 0,
-            "skip_first_frames": 0,
-            "select_every_nth": 1
-          }
-        }
-      }
-    },
-    {
-      "id": 2,
-      "type": "InferenceArgs",
-      "pos": [
-        411.46307373046875,
-        178.18182373046875
-      ],
-      "size": [
-        278.73828125,
-        298
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "inference_args",
-          "type": "INFERENCE_ARGS",
-          "links": [
-            3
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "InferenceArgs"
-      },
-      "widgets_values": [
-        720,
-        1280,
-        45,
-        6,
-        -99999,
-        -99999,
-        1025,
-        "fixed",
-        24,
-        -99999,
-        -99999
-      ],
-      "auto_widget_states": {
-        "height": {
-          "isAuto": false,
-          "value": 720,
-          "cachedValue": 720
-        },
-        "width": {
-          "isAuto": false,
-          "value": 1280,
-          "cachedValue": 1280
-        },
-        "num_frames": {
-          "isAuto": false,
-          "value": 45,
-          "cachedValue": 45
-        },
-        "num_inference_steps": {
-          "isAuto": false,
-          "value": 6,
-          "cachedValue": 6
-        },
-        "guidance_scale": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 1
-        },
-        "flow_shift": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 17
-        },
-        "seed": {
-          "isAuto": false,
-          "value": 1025,
-          "cachedValue": 1024
-        },
-        "fps": {
-          "isAuto": false,
-          "value": 24,
-          "cachedValue": 24
-        },
-        "image_path": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": "X://insert/path/here.mp4"
-        },
-        "enable_teacache": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": true
-        }
-      }
-    },
-    {
-      "id": 8,
-      "type": "VHS_VideoCombine",
-      "pos": [
-        1668.3499755859375,
-        328.22625732421875
-      ],
-      "size": [
-        507.507080078125,
-        622.2227172851562
-      ],
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 5
-        },
-        {
-          "name": "audio",
-          "shape": 7,
-          "type": "AUDIO",
-          "link": null
-        },
-        {
-          "name": "meta_batch",
-          "shape": 7,
-          "type": "VHS_BatchManager",
-          "link": null
-        },
-        {
-          "name": "vae",
-          "shape": 7,
-          "type": "VAE",
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "name": "Filenames",
-          "type": "VHS_FILENAMES",
-          "links": null
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VHS_VideoCombine"
-      },
-      "widgets_values": {
-        "frame_rate": 24,
-        "loop_count": 0,
-        "filename_prefix": "",
-        "format": "video/h264-mp4",
-        "pix_fmt": "yuv420p",
-        "crf": 19,
-        "save_metadata": true,
-        "trim_to_audio": false,
-        "pingpong": false,
-        "save_output": false,
-        "videopreview": {
-          "hidden": false,
-          "paused": false,
-          "params": {
-            "filename": "._00003.mp4",
-            "subfolder": "",
-            "type": "temp",
-            "format": "video/h264-mp4",
-            "frame_rate": 24,
-            "workflow": "._00003.png",
-            "fullpath": "/workspace/ComfyUI/temp/._00003.mp4"
-          }
-        }
-      }
-    }
-  ],
-  "links": [
-    [
-      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": 0.9090909090909091,
-      "offset": [
-        112.86678372727341,
-        -71.45635903989245
-      ]
-    },
-    "frontendVersion": "1.20.4",
-    "VHS_latentpreview": false,
-    "VHS_latentpreviewrate": 0,
-    "VHS_MetadataImage": true,
-    "VHS_KeepIntermediate": true
-  },
-  "version": 0.4
-}

comfyui/examples/Wan2.1-I2V-14B-480P-Diffusers.json

@@ -1,697 +0,0 @@
-{
-  "id": "23a1f065-bbba-4a8f-b144-944e1318fcbf",
-  "revision": 0,
-  "last_node_id": 8,
-  "last_link_id": 7,
-  "nodes": [
-    {
-      "id": 7,
-      "type": "LoadImagePath",
-      "pos": [
-        33.15385437011719,
-        191.2037353515625
-      ],
-      "size": [
-        270,
-        334
-      ],
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "image_path",
-          "type": "STRING",
-          "links": [
-            1
-          ]
-        },
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": null
-        },
-        {
-          "name": "MASK",
-          "type": "MASK",
-          "links": null
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LoadImagePath"
-      },
-      "widgets_values": [
-        "woman.jpg",
-        "image"
-      ]
-    },
-    {
-      "id": 3,
-      "type": "VHS_LoadVideoPath",
-      "pos": [
-        1350.136962890625,
-        331.20361328125
-      ],
-      "size": [
-        231.8896484375,
-        286
-      ],
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "meta_batch",
-          "shape": 7,
-          "type": "VHS_BatchManager",
-          "link": null
-        },
-        {
-          "name": "vae",
-          "shape": 7,
-          "type": "VAE",
-          "link": null
-        },
-        {
-          "name": "video",
-          "type": "STRING",
-          "widget": {
-            "name": "video"
-          },
-          "link": 4
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            5
-          ]
-        },
-        {
-          "name": "frame_count",
-          "type": "INT",
-          "links": null
-        },
-        {
-          "name": "audio",
-          "type": "AUDIO",
-          "links": null
-        },
-        {
-          "name": "video_info",
-          "type": "VHS_VIDEOINFO",
-          "links": null
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VHS_LoadVideoPath"
-      },
-      "widgets_values": {
-        "video": "",
-        "force_rate": 0,
-        "custom_width": 0,
-        "custom_height": 0,
-        "frame_load_cap": 0,
-        "skip_first_frames": 0,
-        "select_every_nth": 1,
-        "format": "Wan",
-        "videopreview": {
-          "hidden": false,
-          "paused": false,
-          "params": {
-            "filename": "",
-            "type": "path",
-            "format": "video/",
-            "force_rate": 0,
-            "custom_width": 0,
-            "custom_height": 0,
-            "frame_load_cap": 0,
-            "skip_first_frames": 0,
-            "select_every_nth": 1
-          }
-        }
-      }
-    },
-    {
-      "id": 8,
-      "type": "VHS_VideoCombine",
-      "pos": [
-        1668.3499755859375,
-        328.22625732421875
-      ],
-      "size": [
-        214.7587890625,
-        334
-      ],
-      "flags": {},
-      "order": 7,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 5
-        },
-        {
-          "name": "audio",
-          "shape": 7,
-          "type": "AUDIO",
-          "link": null
-        },
-        {
-          "name": "meta_batch",
-          "shape": 7,
-          "type": "VHS_BatchManager",
-          "link": null
-        },
-        {
-          "name": "vae",
-          "shape": 7,
-          "type": "VAE",
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "name": "Filenames",
-          "type": "VHS_FILENAMES",
-          "links": null
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VHS_VideoCombine"
-      },
-      "widgets_values": {
-        "frame_rate": 24,
-        "loop_count": 0,
-        "filename_prefix": "",
-        "format": "video/h264-mp4",
-        "pix_fmt": "yuv420p",
-        "crf": 19,
-        "save_metadata": true,
-        "trim_to_audio": false,
-        "pingpong": false,
-        "save_output": false,
-        "videopreview": {
-          "hidden": false,
-          "paused": false,
-          "params": {}
-        }
-      }
-    },
-    {
-      "id": 4,
-      "type": "VAEConfig",
-      "pos": [
-        374.2159423828125,
-        554.85888671875
-      ],
-      "size": [
-        334.080078125,
-        322
-      ],
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "vae_config",
-          "type": "VAE_CONFIG",
-          "links": [
-            2
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VAEConfig"
-      },
-      "widgets_values": [
-        true,
-        true,
-        256,
-        256,
-        16,
-        192,
-        192,
-        12,
-        0,
-        true,
-        true,
-        true
-      ],
-      "auto_widget_states": {
-        "load_encoder": {
-          "isAuto": true,
-          "value": true,
-          "cachedValue": true
-        },
-        "load_decoder": {
-          "isAuto": true,
-          "value": true,
-          "cachedValue": true
-        },
-        "tile_sample_min_height": {
-          "isAuto": true,
-          "value": 256,
-          "cachedValue": 256
-        },
-        "tile_sample_min_width": {
-          "isAuto": true,
-          "value": 256,
-          "cachedValue": 256
-        },
-        "tile_sample_min_num_frames": {
-          "isAuto": true,
-          "value": 16,
-          "cachedValue": 16
-        },
-        "tile_sample_stride_height": {
-          "isAuto": true,
-          "value": 192,
-          "cachedValue": 192
-        },
-        "tile_sample_stride_width": {
-          "isAuto": true,
-          "value": 192,
-          "cachedValue": 192
-        },
-        "tile_sample_stride_num_frames": {
-          "isAuto": true,
-          "value": 12,
-          "cachedValue": 12
-        },
-        "blend_num_frames": {
-          "isAuto": true,
-          "value": 0,
-          "cachedValue": 0
-        },
-        "use_tiling": {
-          "isAuto": true,
-          "value": true,
-          "cachedValue": true
-        },
-        "use_temporal_tiling": {
-          "isAuto": true,
-          "value": true,
-          "cachedValue": true
-        },
-        "use_parallel_tiling": {
-          "isAuto": true,
-          "value": true,
-          "cachedValue": true
-        }
-      }
-    },
-    {
-      "id": 2,
-      "type": "InferenceArgs",
-      "pos": [
-        411.46307373046875,
-        178.18182373046875
-      ],
-      "size": [
-        278.73828125,
-        298
-      ],
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "image_path",
-          "shape": 7,
-          "type": "STRING",
-          "widget": {
-            "name": "image_path"
-          },
-          "link": 1
-        }
-      ],
-      "outputs": [
-        {
-          "name": "inference_args",
-          "type": "INFERENCE_ARGS",
-          "links": [
-            3
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "InferenceArgs"
-      },
-      "widgets_values": [
-        832,
-        480,
-        45,
-        20,
-        1,
-        17,
-        1024,
-        "fixed",
-        24,
-        "X://insert/path/here.mp4",
-        true
-      ],
-      "auto_widget_states": {
-        "height": {
-          "isAuto": false,
-          "value": 832,
-          "cachedValue": 720
-        },
-        "width": {
-          "isAuto": false,
-          "value": 480,
-          "cachedValue": 1280
-        },
-        "num_frames": {
-          "isAuto": false,
-          "value": 45,
-          "cachedValue": 45
-        },
-        "num_inference_steps": {
-          "isAuto": false,
-          "value": 20,
-          "cachedValue": 6
-        },
-        "guidance_scale": {
-          "isAuto": true,
-          "value": 1,
-          "cachedValue": 1
-        },
-        "flow_shift": {
-          "isAuto": true,
-          "value": 17,
-          "cachedValue": 17
-        },
-        "seed": {
-          "isAuto": false,
-          "value": 1024,
-          "cachedValue": 1024
-        },
-        "fps": {
-          "isAuto": false,
-          "value": 24,
-          "cachedValue": 24
-        },
-        "image_path": {
-          "isAuto": true,
-          "value": "X://insert/path/here.mp4",
-          "cachedValue": "X://insert/path/here.mp4"
-        },
-        "enable_teacache": {
-          "isAuto": true,
-          "value": true,
-          "cachedValue": true
-        }
-      }
-    },
-    {
-      "id": 1,
-      "type": "VideoGenerator",
-      "pos": [
-        818.804931640625,
-        348.9299621582031
-      ],
-      "size": [
-        400,
-        436
-      ],
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "inference_args",
-          "shape": 7,
-          "type": "INFERENCE_ARGS",
-          "link": 3
-        },
-        {
-          "name": "vae_config",
-          "shape": 7,
-          "type": "VAE_CONFIG",
-          "link": 2
-        },
-        {
-          "name": "text_encoder_config",
-          "shape": 7,
-          "type": "TEXT_ENCODER_CONFIG",
-          "link": 7
-        },
-        {
-          "name": "dit_config",
-          "shape": 7,
-          "type": "DIT_CONFIG",
-          "link": 6
-        }
-      ],
-      "outputs": [
-        {
-          "name": "video_path",
-          "type": "STRING",
-          "links": [
-            4
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VideoGenerator"
-      },
-      "widgets_values": [
-        "A woman crying from laughter.",
-        "/workspace/ComfyUI/outputs_video/",
-        4,
-        "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
-        6,
-        2,
-        2,
-        "fp16",
-        true,
-        true,
-        "fp16",
-        "fp16",
-        true
-      ],
-      "auto_widget_states": {
-        "embedded_cfg_scale": {
-          "isAuto": true,
-          "value": 6,
-          "cachedValue": 6
-        },
-        "sp_size": {
-          "isAuto": true,
-          "value": 2,
-          "cachedValue": 2
-        },
-        "tp_size": {
-          "isAuto": true,
-          "value": 2,
-          "cachedValue": 2
-        },
-        "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
-}

comfyui/video_generator/dit_config.py

@@ -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, )

comfyui/video_generator/inference_args.py

@@ -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, )

comfyui/video_generator/load_image.py

@@ -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

comfyui/video_generator/node_helpers.py

@@ -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

comfyui/video_generator/nodes.py

@@ -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"
-}

comfyui/video_generator/text_encoder_config.py

@@ -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, )

comfyui/video_generator/vae_config.py

@@ -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, )

comfyui/video_generator/video_generator.py

@@ -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")

comfyui/web/js/FastVideo.core.js

@@ -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");

csrc/attn/README.md

@@ -4,7 +4,7 @@
 
 
 ## 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
@@ -53,14 +53,8 @@ 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. 

csrc/attn/bench/bench_sta.py

@@ -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),

csrc/attn/st_attn/st_attn_h100.cu

@@ -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();
 }
 

csrc/attn/test/test_block_sparse.py

@@ -7,7 +7,6 @@ from vsa 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()

csrc/attn/test/test_sta.py

@@ -81,7 +81,5 @@ 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']}")

csrc/attn/vsa/block_sparse_h100.cu

@@ -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();
 }

docker/Dockerfile.python3.12

@@ -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

docs/source/conf.py

@@ -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

docs/source/contributing/developer_env/runpod.md

@@ -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)):

docs/source/design/overview.md

@@ -288,7 +288,7 @@ Sequence parallelism splits sequences across devices:
 
 ```python
 # Distributed attention for long sequences
-from fastvideo.v1.attention import DistributedAttention
+from fastvideo.v1.layers.attention import DistributedAttention
 
 self.attn = DistributedAttention(
     num_heads=num_heads,

docs/source/generate_examples.py

@@ -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)

docs/source/getting_started/installation.md

@@ -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.

docs/source/inference/add_pipeline.md

@@ -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
 
@@ -96,8 +96,8 @@ Replace standard attention with FastVideo's optimized attention:
 
 ```python
 # Local attention patterns
-from fastvideo.v1.attention import LocalAttention
-from fastvideo.v1.attention.backends.abstract import _Backend
+from fastvideo.v1.layers.attention import LocalAttention
+from fastvideo.v1.layers.attention.backends.abstract import _Backend
 self.attn = LocalAttention(
     num_heads=num_heads,
     head_size=head_dim,
@@ -108,7 +108,7 @@ self.attn = LocalAttention(
 )
 
 # Distributed attention for long sequences
-from fastvideo.v1.attention import DistributedAttention
+from fastvideo.v1.layers.attention import DistributedAttention
 self.attn = DistributedAttention(
     num_heads=num_heads,
     head_size=head_dim,

docs/source/inference/cli.md

@@ -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

docs/source/inference/inference_quick_start.md

@@ -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).

docs/source/sliding_tile_attention/demo.md

@@ -13,5 +13,6 @@ This is is a demo for 2D STA with window size (6,6) operating on a (10, 10) imag
 You can run STA using the following command:
 
 ```bash
-bash scripts/inference/v1_inference_wan_STA.sh
+huggingface-cli download hunyuanvideo-community/HunyuanVideo --local-dir data/hunyuan
+bash scripts/inference/inference_hunyuan_STA.sh
 ```

docs/source/training/data_preprocess.md

@@ -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.

docs/source/training/finetune.md

@@ -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:

examples/inference/basic/basic.py

@@ -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

examples/inference/sta_mask_search/inference_wan_sta.sh

@@ -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 &

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/README.md

@@ -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`

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/download_dataset.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"

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/finetune_i2v.sh

@@ -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="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
-)
-
-# 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[@]}"

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/finetune_i2v.slurm

@@ -1,131 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=i2v
-#SBATCH --partition=main
-#SBATCH --qos=hao
-#SBATCH --nodes=4
-#SBATCH --ntasks=4
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --nodelist=fs-mbz-gpu-[100-850]
-#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
-)
-
-# 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[@]}"

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/preprocess_wan_data_i2v.sh

@@ -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" 

examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json

@@ -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
-    }
-  ]
-}

examples/training/finetune/wan_i2v_14b_480p/crush_smol/README.md

@@ -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`

examples/training/finetune/wan_i2v_14b_480p/crush_smol/download_dataset.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"

examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.sh

@@ -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-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
-)
-
-# 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[@]}"

examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.slurm

@@ -1,131 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=i2v
-#SBATCH --partition=main
-#SBATCH --qos=hao
-#SBATCH --nodes=4
-#SBATCH --ntasks=4
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --nodelist=fs-mbz-gpu-[100-850]
-#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
-)
-
-# 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[@]}"

examples/training/finetune/wan_i2v_14b_480p/crush_smol/preprocess_wan_data_i2v.sh

@@ -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" 

examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation.json

@@ -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
-    }
-  ]
-}

examples/training/finetune/wan_t2v_1_3b/crush_smol/README.md

@@ -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`

examples/training/finetune/wan_t2v_1_3b/crush_smol/download_dataset.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"

examples/training/finetune/wan_t2v_1_3b/crush_smol/finetune_t2v.sh

@@ -1,92 +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
-)
-
-# 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[@]}"

examples/training/finetune/wan_t2v_1_3b/crush_smol/finetune_t2v.slurm

@@ -1,128 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=t2v
-#SBATCH --partition=main
-#SBATCH --qos=hao
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --nodelist=fs-mbz-gpu-[100-850]
-#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
-)
-
-# 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[@]}"

examples/training/finetune/wan_t2v_1_3b/crush_smol/preprocess_wan_data_t2v.sh

@@ -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" 

examples/training/finetune/wan_t2v_1_3b/crush_smol/validation.json

@@ -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
-    }
-  ]
-}

fastvideo/v1/STA_configuration.py

@@ -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"]]

fastvideo/v1/attention/__init__.py

@@ -1,20 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-from fastvideo.v1.attention.backends.abstract import (AttentionBackend,
-                                                      AttentionMetadata,
-                                                      AttentionMetadataBuilder)
-from fastvideo.v1.attention.layer import (DistributedAttention,
-                                          DistributedAttention_VSA,
-                                          LocalAttention)
-from fastvideo.v1.attention.selector import get_attn_backend
-
-__all__ = [
-    "DistributedAttention",
-    "LocalAttention",
-    "DistributedAttention_VSA",
-    "AttentionBackend",
-    "AttentionMetadata",
-    "AttentionMetadataBuilder",
-    # "AttentionState",
-    "get_attn_backend",
-]

fastvideo/v1/configs/models/base.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field, fields
-from typing import Any
+from typing import Any, Dict
 
 from fastvideo.v1.logger import init_logger
 
@@ -12,9 +12,7 @@ logger = init_logger(__name__)
 # 3. Any field in ArchConfig is fixed upon initialization, and should be hidden away from users
 @dataclass
 class ArchConfig:
-    stacked_params_mapping: list[tuple[str, str, str]] = field(
-        default_factory=list
-    )  # mapping from huggingface weight names to custom names
+    pass
 
 
 @dataclass
@@ -44,7 +42,7 @@ class ModelConfig:
         self.__dict__.update(state)
 
     # This should be used only when loading from transformers/diffusers
-    def update_model_arch(self, source_model_dict: dict[str, Any]) -> None:
+    def update_model_arch(self, source_model_dict: Dict[str, Any]) -> None:
         arch_config = self.arch_config
         valid_fields = {f.name for f in fields(arch_config)}
 
@@ -58,7 +56,7 @@ class ModelConfig:
         if hasattr(arch_config, "__post_init__"):
             arch_config.__post_init__()
 
-    def update_model_config(self, source_model_dict: dict[str, Any]) -> None:
+    def update_model_config(self, source_model_dict: Dict[str, Any]) -> None:
         assert "arch_config" not in source_model_dict, "Source model config shouldn't contain arch_config."
 
         valid_fields = {f.name for f in fields(self)}

fastvideo/v1/configs/models/dits/base.py

@@ -1,10 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
-from typing import Any
+from typing import Any, List, Optional, Tuple
 
 from fastvideo.v1.configs.models.base import ArchConfig, ModelConfig
 from fastvideo.v1.layers.quantization import QuantizationConfig
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.v1.platforms import _Backend
 
 
 @dataclass
@@ -12,17 +12,18 @@ class DiTArchConfig(ArchConfig):
     _fsdp_shard_conditions: list = field(default_factory=list)
     _compile_conditions: list = field(default_factory=list)
     _param_names_mapping: dict = field(default_factory=dict)
-    _reverse_param_names_mapping: dict = field(default_factory=dict)
     _lora_param_names_mapping: dict = field(default_factory=dict)
-    _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (
-        AttentionBackendEnum.SLIDING_TILE_ATTN, AttentionBackendEnum.SAGE_ATTN,
-        AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA,
-        AttentionBackendEnum.VIDEO_SPARSE_ATTN)
+    _supported_attention_backends: Tuple[_Backend,
+                                         ...] = (_Backend.SLIDING_TILE_ATTN,
+                                                 _Backend.SAGE_ATTN,
+                                                 _Backend.FLASH_ATTN,
+                                                 _Backend.TORCH_SDPA,
+                                                 _Backend.VIDEO_SPARSE_ATTN)
 
     hidden_size: int = 0
     num_attention_heads: int = 0
     num_channels_latents: int = 0
-    exclude_lora_layers: list[str] = field(default_factory=list)
+    exclude_lora_layers: List[str] = field(default_factory=list)
 
     def __post_init__(self) -> None:
         if not self._compile_conditions:
@@ -35,7 +36,7 @@ class DiTConfig(ModelConfig):
 
     # FastVideoDiT-specific parameters
     prefix: str = ""
-    quant_config: QuantizationConfig | None = None
+    quant_config: Optional[QuantizationConfig] = None
 
     @staticmethod
     def add_cli_args(parser: Any, prefix: str = "dit-config") -> Any:

fastvideo/v1/configs/models/dits/hunyuanvideo.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
+from typing import List, Optional, Tuple
 
 import torch
 
@@ -146,9 +147,6 @@ class HunyuanVideoArchConfig(DiTArchConfig):
             r"final_layer.linear.\1",
         })
 
-    # Reverse mapping for saving checkpoints: training -> diffusers
-    _reverse_param_names_mapping: dict = field(default_factory=lambda: {})
-
     patch_size: int = 2
     patch_size_t: int = 1
     in_channels: int = 16
@@ -159,14 +157,14 @@ class HunyuanVideoArchConfig(DiTArchConfig):
     num_layers: int = 20
     num_single_layers: int = 40
     num_refiner_layers: int = 2
-    rope_axes_dim: tuple[int, int, int] = (16, 56, 56)
+    rope_axes_dim: Tuple[int, int, int] = (16, 56, 56)
     guidance_embeds: bool = False
-    dtype: torch.dtype | None = None
+    dtype: Optional[torch.dtype] = None
     text_embed_dim: int = 4096
     pooled_projection_dim: int = 768
     rope_theta: int = 256
     qk_norm: str = "rms_norm"
-    exclude_lora_layers: list[str] = field(
+    exclude_lora_layers: List[str] = field(
         default_factory=lambda: ["img_in", "txt_in", "time_in", "vector_in"])
 
     def __post_init__(self):

fastvideo/v1/configs/models/dits/stepvideo.py

@@ -1,14 +1,17 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
+from typing import List, Optional, Tuple, Union
 
 from fastvideo.v1.configs.models.dits.base import DiTArchConfig, DiTConfig
 
 
+def is_blocks(n: str, m) -> bool:
+    return "blocks" in n and str.isdigit(n.split(".")[-1])
+
+
 @dataclass
 class StepVideoArchConfig(DiTArchConfig):
-    _fsdp_shard_conditions: list = field(
-        default_factory=lambda:
-        [lambda n, m: "transformer_blocks" in n and n.split(".")[-1].isdigit()])
+    _fsdp_shard_conditions: list = field(default_factory=lambda: [is_blocks])
 
     _param_names_mapping: dict = field(
         default_factory=lambda: {
@@ -38,18 +41,18 @@ class StepVideoArchConfig(DiTArchConfig):
     num_attention_heads: int = 48
     attention_head_dim: int = 128
     in_channels: int = 64
-    out_channels: int | None = 64
+    out_channels: Optional[int] = 64
     num_layers: int = 48
     dropout: float = 0.0
     patch_size: int = 1
     norm_type: str = "ada_norm_single"
     norm_elementwise_affine: bool = False
     norm_eps: float = 1e-6
-    caption_channels: int | list[int] | tuple[int, ...] | None = field(
+    caption_channels: Optional[Union[int, List[int], Tuple[int, ...]]] = field(
         default_factory=lambda: [6144, 1024])
-    attention_type: str | None = "torch"
-    use_additional_conditions: bool | None = False
-    exclude_lora_layers: list[str] = field(default_factory=lambda: [])
+    attention_type: Optional[str] = "torch"
+    use_additional_conditions: Optional[bool] = False
+    exclude_lora_layers: List[str] = field(default_factory=lambda: [])
 
     def __post_init__(self):
         self.hidden_size = self.num_attention_heads * self.attention_head_dim

fastvideo/v1/configs/models/dits/wanvideo.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
+from typing import List, Optional, Tuple
 
 from fastvideo.v1.configs.models.dits.base import DiTArchConfig, DiTConfig
 
@@ -48,13 +49,9 @@ class WanVideoArchConfig(DiTArchConfig):
             r"blocks.\1.ffn.fc_in.\2",
             r"^blocks\.(\d+)\.ffn\.net\.2\.(.*)$":
             r"blocks.\1.ffn.fc_out.\2",
-            r"^blocks\.(\d+)\.norm2\.(.*)$":
+            r"blocks\.(\d+)\.norm2\.(.*)$":
             r"blocks.\1.self_attn_residual_norm.norm.\2",
         })
-
-    # Reverse mapping for saving checkpoints: training -> diffusers
-    _reverse_param_names_mapping: dict = field(default_factory=lambda: {})
-
     # Some LoRA adapters use the original official layer names instead of hf layer names,
     # so apply this before the param_names_mapping
     _lora_param_names_mapping: dict = field(
@@ -73,7 +70,7 @@ class WanVideoArchConfig(DiTArchConfig):
             r"^blocks\.(\d+)\.ffn\.2\.(.*)$": r"blocks.\1.ffn.fc_out.\2",
         })
 
-    patch_size: tuple[int, int, int] = (1, 2, 2)
+    patch_size: Tuple[int, int, int] = (1, 2, 2)
     text_len = 512
     num_attention_heads: int = 40
     attention_head_dim: int = 128
@@ -86,10 +83,10 @@ class WanVideoArchConfig(DiTArchConfig):
     cross_attn_norm: bool = True
     qk_norm: str = "rms_norm_across_heads"
     eps: float = 1e-6
-    image_dim: int | None = None
-    added_kv_proj_dim: int | None = None
+    image_dim: Optional[int] = None
+    added_kv_proj_dim: Optional[int] = None
     rope_max_seq_len: int = 1024
-    exclude_lora_layers: list[str] = field(default_factory=lambda: ["embedder"])
+    exclude_lora_layers: List[str] = field(default_factory=lambda: ["embedder"])
 
     def __post_init__(self):
         super().__post_init__()

fastvideo/v1/configs/models/encoders/base.py

@@ -1,19 +1,19 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
-from typing import Any
+from typing import Any, Dict, List, Optional, Tuple
 
 import torch
 
 from fastvideo.v1.configs.models.base import ArchConfig, ModelConfig
 from fastvideo.v1.layers.quantization import QuantizationConfig
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.v1.platforms import _Backend
 
 
 @dataclass
 class EncoderArchConfig(ArchConfig):
-    architectures: list[str] = field(default_factory=lambda: [])
-    _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (
-        AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA)
+    architectures: List[str] = field(default_factory=lambda: [])
+    _supported_attention_backends: Tuple[_Backend, ...] = (_Backend.FLASH_ATTN,
+                                                           _Backend.TORCH_SDPA)
     output_hidden_states: bool = False
     use_return_dict: bool = True
 
@@ -32,11 +32,8 @@ class TextEncoderArchConfig(EncoderArchConfig):
     output_past: bool = True
     scalable_attention: bool = True
     tie_word_embeddings: bool = False
-    stacked_params_mapping: list[tuple[str, str, str]] = field(
-        default_factory=list
-    )  # mapping from huggingface weight names to custom names
-    tokenizer_kwargs: dict[str, Any] = field(default_factory=dict)
-    _fsdp_shard_conditions: list = field(default_factory=lambda: [])
+
+    tokenizer_kwargs: Dict[str, Any] = field(default_factory=dict)
 
     def __post_init__(self) -> None:
         self.tokenizer_kwargs = {
@@ -53,11 +50,11 @@ class ImageEncoderArchConfig(EncoderArchConfig):
 
 @dataclass
 class BaseEncoderOutput:
-    last_hidden_state: torch.FloatTensor | None = None
-    pooler_output: torch.FloatTensor | None = None
-    hidden_states: tuple[torch.FloatTensor, ...] | None = None
-    attentions: tuple[torch.FloatTensor, ...] | None = None
-    attention_mask: torch.Tensor | None = None
+    last_hidden_state: Optional[torch.FloatTensor] = None
+    pooler_output: Optional[torch.FloatTensor] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
+    attention_mask: Optional[torch.Tensor] = None
 
 
 @dataclass
@@ -65,8 +62,8 @@ class EncoderConfig(ModelConfig):
     arch_config: ArchConfig = field(default_factory=EncoderArchConfig)
 
     prefix: str = ""
-    quant_config: QuantizationConfig | None = None
-    lora_config: Any | None = None
+    quant_config: Optional[QuantizationConfig] = None
+    lora_config: Optional[Any] = None
 
 
 @dataclass

fastvideo/v1/configs/models/encoders/clip.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
+from typing import Optional
 
 from fastvideo.v1.configs.models.encoders.base import (ImageEncoderArchConfig,
                                                        ImageEncoderConfig,
@@ -7,14 +8,6 @@ from fastvideo.v1.configs.models.encoders.base import (ImageEncoderArchConfig,
                                                        TextEncoderConfig)
 
 
-def _is_transformer_layer(n: str, m) -> bool:
-    return "layers" in n and str.isdigit(n.split(".")[-1])
-
-
-def _is_embeddings(n: str, m) -> bool:
-    return n.endswith("embeddings")
-
-
 @dataclass
 class CLIPTextArchConfig(TextEncoderArchConfig):
     vocab_size: int = 49408
@@ -34,15 +27,6 @@ class CLIPTextArchConfig(TextEncoderArchConfig):
     bos_token_id: int = 49406
     eos_token_id: int = 49407
     text_len: int = 77
-    stacked_params_mapping: list[tuple[str, str,
-                                       str]] = field(default_factory=lambda: [
-                                           # (param_name, shard_name, shard_id)
-                                           ("qkv_proj", "q_proj", "q"),
-                                           ("qkv_proj", "k_proj", "k"),
-                                           ("qkv_proj", "v_proj", "v"),
-                                       ])
-    _fsdp_shard_conditions: list = field(
-        default_factory=lambda: [_is_transformer_layer, _is_embeddings])
 
 
 @dataclass
@@ -61,13 +45,6 @@ class CLIPVisionArchConfig(ImageEncoderArchConfig):
     attention_dropout: float = 0.0
     initializer_range: float = 0.02
     initializer_factor: float = 1.0
-    stacked_params_mapping: list[tuple[str, str,
-                                       str]] = field(default_factory=lambda: [
-                                           # (param_name, shard_name, shard_id)
-                                           ("qkv_proj", "q_proj", "q"),
-                                           ("qkv_proj", "k_proj", "k"),
-                                           ("qkv_proj", "v_proj", "v"),
-                                       ])
 
 
 @dataclass
@@ -75,8 +52,8 @@ class CLIPTextConfig(TextEncoderConfig):
     arch_config: TextEncoderArchConfig = field(
         default_factory=CLIPTextArchConfig)
 
-    num_hidden_layers_override: int | None = None
-    require_post_norm: bool | None = None
+    num_hidden_layers_override: Optional[int] = None
+    require_post_norm: Optional[bool] = None
     prefix: str = "clip"
 
 
@@ -85,6 +62,6 @@ class CLIPVisionConfig(ImageEncoderConfig):
     arch_config: ImageEncoderArchConfig = field(
         default_factory=CLIPVisionArchConfig)
 
-    num_hidden_layers_override: int | None = None
-    require_post_norm: bool | None = None
+    num_hidden_layers_override: Optional[int] = None
+    require_post_norm: Optional[bool] = None
     prefix: str = "clip"

fastvideo/v1/configs/models/encoders/llama.py

@@ -1,22 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
+from typing import Optional
 
 from fastvideo.v1.configs.models.encoders.base import (TextEncoderArchConfig,
                                                        TextEncoderConfig)
 
 
-def _is_transformer_layer(n: str, m) -> bool:
-    return "layers" in n and str.isdigit(n.split(".")[-1])
-
-
-def _is_embeddings(n: str, m) -> bool:
-    return n.endswith("embed_tokens")
-
-
-def _is_final_norm(n: str, m) -> bool:
-    return n.endswith("norm")
-
-
 @dataclass
 class LlamaArchConfig(TextEncoderArchConfig):
     vocab_size: int = 32000
@@ -24,7 +13,7 @@ class LlamaArchConfig(TextEncoderArchConfig):
     intermediate_size: int = 11008
     num_hidden_layers: int = 32
     num_attention_heads: int = 32
-    num_key_value_heads: int | None = None
+    num_key_value_heads: Optional[int] = None
     hidden_act: str = "silu"
     max_position_embeddings: int = 2048
     initializer_range: float = 0.02
@@ -36,25 +25,13 @@ class LlamaArchConfig(TextEncoderArchConfig):
     pretraining_tp: int = 1
     tie_word_embeddings: bool = False
     rope_theta: float = 10000.0
-    rope_scaling: float | None = None
+    rope_scaling: Optional[float] = None
     attention_bias: bool = False
     attention_dropout: float = 0.0
     mlp_bias: bool = False
-    head_dim: int | None = None
+    head_dim: Optional[int] = None
     hidden_state_skip_layer: int = 2
     text_len: int = 256
-    stacked_params_mapping: list[tuple[str, str, str]] = field(
-        default_factory=lambda: [
-            # (param_name, shard_name, shard_id)
-            (".qkv_proj", ".q_proj", "q"),
-            (".qkv_proj", ".k_proj", "k"),
-            (".qkv_proj", ".v_proj", "v"),
-            (".gate_up_proj", ".gate_proj", 0),  # type: ignore
-            (".gate_up_proj", ".up_proj", 1),  # type: ignore
-        ])
-    _fsdp_shard_conditions: list = field(
-        default_factory=lambda:
-        [_is_transformer_layer, _is_embeddings, _is_final_norm])
 
 
 @dataclass

fastvideo/v1/configs/models/encoders/t5.py

@@ -1,22 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
+from typing import Optional
 
 from fastvideo.v1.configs.models.encoders.base import (TextEncoderArchConfig,
                                                        TextEncoderConfig)
 
 
-def _is_transformer_layer(n: str, m) -> bool:
-    return "block" in n and str.isdigit(n.split(".")[-1])
-
-
-def _is_embeddings(n: str, m) -> bool:
-    return n.endswith("shared")
-
-
-def _is_final_layernorm(n: str, m) -> bool:
-    return n.endswith("final_layer_norm")
-
-
 @dataclass
 class T5ArchConfig(TextEncoderArchConfig):
     vocab_size: int = 32128
@@ -24,7 +13,7 @@ class T5ArchConfig(TextEncoderArchConfig):
     d_kv: int = 64
     d_ff: int = 2048
     num_layers: int = 6
-    num_decoder_layers: int | None = None
+    num_decoder_layers: Optional[int] = None
     num_heads: int = 8
     relative_attention_num_buckets: int = 32
     relative_attention_max_distance: int = 128
@@ -40,16 +29,6 @@ class T5ArchConfig(TextEncoderArchConfig):
     eos_token_id: int = 1
     classifier_dropout: float = 0.0
     text_len: int = 512
-    stacked_params_mapping: list[tuple[str, str,
-                                       str]] = field(default_factory=lambda: [
-                                           # (param_name, shard_name, shard_id)
-                                           (".qkv_proj", ".q", "q"),
-                                           (".qkv_proj", ".k", "k"),
-                                           (".qkv_proj", ".v", "v"),
-                                       ])
-    _fsdp_shard_conditions: list = field(
-        default_factory=lambda:
-        [_is_transformer_layer, _is_embeddings, _is_final_layernorm])
 
     # Referenced from https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/configuration_t5.py
     def __post_init__(self):

fastvideo/v1/configs/models/vaes/base.py

@@ -2,7 +2,7 @@
 import argparse
 import dataclasses
 from dataclasses import dataclass, field
-from typing import Any
+from typing import Any, Union
 
 import torch
 
@@ -12,7 +12,7 @@ from fastvideo.v1.utils import StoreBoolean
 
 @dataclass
 class VAEArchConfig(ArchConfig):
-    scaling_factor: float | torch.Tensor = 0
+    scaling_factor: Union[float, torch.tensor] = 0
 
     temporal_compression_ratio: int = 4
     spatial_compression_ratio: int = 8

fastvideo/v1/configs/models/vaes/hunyuanvae.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
+from typing import Tuple
 
 from fastvideo.v1.configs.models.vaes.base import VAEArchConfig, VAEConfig
 
@@ -9,19 +10,19 @@ class HunyuanVAEArchConfig(VAEArchConfig):
     in_channels: int = 3
     out_channels: int = 3
     latent_channels: int = 16
-    down_block_types: tuple[str, ...] = (
+    down_block_types: Tuple[str, ...] = (
         "HunyuanVideoDownBlock3D",
         "HunyuanVideoDownBlock3D",
         "HunyuanVideoDownBlock3D",
         "HunyuanVideoDownBlock3D",
     )
-    up_block_types: tuple[str, ...] = (
+    up_block_types: Tuple[str, ...] = (
         "HunyuanVideoUpBlock3D",
         "HunyuanVideoUpBlock3D",
         "HunyuanVideoUpBlock3D",
         "HunyuanVideoUpBlock3D",
     )
-    block_out_channels: tuple[int, ...] = (128, 256, 512, 512)
+    block_out_channels: Tuple[int, ...] = (128, 256, 512, 512)
     layers_per_block: int = 2
     act_fn: str = "silu"
     norm_num_groups: int = 32

fastvideo/v1/configs/models/vaes/wanvae.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
+from typing import Tuple
 
 import torch
 
@@ -10,12 +11,12 @@ from fastvideo.v1.configs.models.vaes.base import VAEArchConfig, VAEConfig
 class WanVAEArchConfig(VAEArchConfig):
     base_dim: int = 96
     z_dim: int = 16
-    dim_mult: tuple[int, ...] = (1, 2, 4, 4)
+    dim_mult: Tuple[int, ...] = (1, 2, 4, 4)
     num_res_blocks: int = 2
-    attn_scales: tuple[float, ...] = ()
-    temperal_downsample: tuple[bool, ...] = (False, True, True)
+    attn_scales: Tuple[float, ...] = ()
+    temperal_downsample: Tuple[bool, ...] = (False, True, True)
     dropout: float = 0.0
-    latents_mean: tuple[float, ...] = (
+    latents_mean: Tuple[float, ...] = (
         -0.7571,
         -0.7089,
         -0.9113,
@@ -33,7 +34,7 @@ class WanVAEArchConfig(VAEArchConfig):
         0.2503,
         -0.2921,
     )
-    latents_std: tuple[float, ...] = (
+    latents_std: Tuple[float, ...] = (
         2.8184,
         1.4541,
         2.3275,

fastvideo/v1/configs/pipelines/base.py

@@ -1,9 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 import json
-from collections.abc import Callable
 from dataclasses import asdict, dataclass, field, fields
-from enum import Enum
-from typing import Any, cast
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast
 
 import torch
 
@@ -18,15 +16,6 @@ from fastvideo.v1.utils import (FlexibleArgumentParser, StoreBoolean,
 logger = init_logger(__name__)
 
 
-class STA_Mode(str, Enum):
-    """STA (Sliding Tile Attention) modes."""
-    STA_INFERENCE = "STA_inference"
-    STA_SEARCHING = "STA_searching"
-    STA_TUNING = "STA_tuning"
-    STA_TUNING_CFG = "STA_tuning_cfg"
-    NONE = None
-
-
 def preprocess_text(prompt: str) -> str:
     return prompt
 
@@ -40,11 +29,11 @@ def postprocess_text(output: BaseEncoderOutput) -> torch.tensor:
 class PipelineConfig:
     """Base configuration for all pipeline architectures."""
     model_path: str = ""
-    pipeline_config_path: str | None = None
+    pipeline_config_path: Optional[str] = None
 
     # Video generation parameters
     embedded_cfg_scale: float = 6.0
-    flow_shift: float | None = None
+    flow_shift: Optional[float] = None
     disable_autocast: bool = False
 
     # Model configuration
@@ -53,7 +42,7 @@ class PipelineConfig:
 
     # VAE configuration
     vae_config: VAEConfig = field(default_factory=VAEConfig)
-    vae_precision: str = "fp32"
+    vae_precision: str = "fp16"
     vae_tiling: bool = True
     vae_sp: bool = True
 
@@ -63,30 +52,31 @@ class PipelineConfig:
 
     # Text encoder configuration
     DEFAULT_TEXT_ENCODER_PRECISIONS = ("fp16", )
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(
+    text_encoder_configs: Tuple[EncoderConfig, ...] = field(
         default_factory=lambda: (EncoderConfig(), ))
-    text_encoder_precisions: tuple[str, ...] = field(
+    text_encoder_precisions: Tuple[str, ...] = field(
         default_factory=lambda: ("fp16", ))
-    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(
+    preprocess_text_funcs: Tuple[Callable[[str], str], ...] = field(
         default_factory=lambda: (preprocess_text, ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.tensor],
+    postprocess_text_funcs: Tuple[Callable[[BaseEncoderOutput], torch.tensor],
                                   ...] = field(default_factory=lambda:
                                                (postprocess_text, ))
 
     # LoRA parameters
-    lora_path: str | None = None
-    lora_nickname: str | None = "default"  # for swapping adapters in the pipeline
-    lora_target_names: list[
-        str] | None = None  # can restrict list of layers to adapt, e.g. ["q_proj"]
+    lora_path: Optional[str] = None
+    lora_nickname: Optional[
+        str] = "default"  # for swapping adapters in the pipeline
+    lora_target_names: Optional[List[
+        str]] = None  # can restrict list of layers to adapt, e.g. ["q_proj"]
 
     # StepVideo specific parameters
-    pos_magic: str | None = None
-    neg_magic: str | None = None
-    timesteps_scale: bool | None = None
+    pos_magic: Optional[str] = None
+    neg_magic: Optional[str] = None
+    timesteps_scale: Optional[bool] = None
 
     # STA (Sliding Tile Attention) parameters
-    mask_strategy_file_path: str | None = None
-    STA_mode: STA_Mode = STA_Mode.STA_INFERENCE
+    mask_strategy_file_path: Optional[str] = None
+    STA_mode: Optional[str] = None
     skip_time_steps: int = 15
 
     # Compilation
@@ -217,7 +207,7 @@ class PipelineConfig:
         return parser
 
     def update_config_from_dict(self,
-                                args: dict[str, Any],
+                                args: Dict[str, Any],
                                 prefix: str = "") -> None:
         prefix_with_dot = f"{prefix}." if (prefix.strip() != "") else ""
         update_config_from_args(self, args, prefix, pop_args=True)
@@ -243,7 +233,7 @@ class PipelineConfig:
 
     @classmethod
     def from_kwargs(cls,
-                    kwargs: dict[str, Any],
+                    kwargs: Dict[str, Any],
                     config_cli_prefix: str = "") -> "PipelineConfig":
         """
         Load PipelineConfig from kwargs Dictionary.
@@ -255,11 +245,11 @@ class PipelineConfig:
 
         prefix_with_dot = f"{config_cli_prefix}." if (config_cli_prefix.strip()
                                                       != "") else ""
-        model_path: str | None = kwargs.get(prefix_with_dot + 'model_path',
-                                            None) or kwargs.get('model_path')
-        pipeline_config_or_path: str | PipelineConfig | dict[
-            str, Any] | None = kwargs.get(prefix_with_dot + 'pipeline_config',
-                                          None) or kwargs.get('pipeline_config')
+        model_path: Optional[str] = kwargs.get(prefix_with_dot + 'model_path',
+                                               None) or kwargs.get('model_path')
+        pipeline_config_or_path: Optional[Union[str, PipelineConfig, Dict[
+            str, Any]]] = kwargs.get(prefix_with_dot + 'pipeline_config',
+                                     None) or kwargs.get('pipeline_config')
         if model_path is None:
             raise ValueError("model_path is required in kwargs")
 
@@ -344,7 +334,7 @@ class PipelineConfig:
             input_pipeline_dict = json.load(f)
         self.update_pipeline_config(input_pipeline_dict)
 
-    def update_pipeline_config(self, source_pipeline_dict: dict[str,
+    def update_pipeline_config(self, source_pipeline_dict: Dict[str,
                                                                 Any]) -> None:
         for f in fields(self):
             key = f.name
@@ -360,9 +350,8 @@ class PipelineConfig:
                     assert len(current_value) == len(
                         new_value
                     ), "Users shouldn't delete or add text encoder config objects in your json"
-                    for target_config, source_config in zip(current_value,
-                                                            new_value,
-                                                            strict=True):
+                    for target_config, source_config in zip(
+                            current_value, new_value):
                         target_config.update_model_config(source_config)
                 else:
                     setattr(self, key, new_value)

fastvideo/v1/configs/pipelines/hunyuan.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
-from collections.abc import Callable
 from dataclasses import dataclass, field
-from typing import TypedDict
+from typing import Callable, Tuple, TypedDict
 
 import torch
 
@@ -40,7 +39,7 @@ def llama_preprocess_text(prompt: str) -> str:
 def llama_postprocess_text(outputs: BaseEncoderOutput) -> torch.tensor:
     hidden_state_skip_layer = 2
     assert outputs.hidden_states is not None
-    hidden_states: tuple[torch.Tensor, ...] = outputs.hidden_states
+    hidden_states: Tuple[torch.Tensor, ...] = outputs.hidden_states
     last_hidden_state: torch.tensor = hidden_states[-(hidden_state_skip_layer +
                                                       1)]
     crop_start = prompt_template_video.get("crop_start", -1)
@@ -71,11 +70,11 @@ class HunyuanConfig(PipelineConfig):
     flow_shift: int = 7
 
     # Text encoding stage
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(
+    text_encoder_configs: Tuple[EncoderConfig, ...] = field(
         default_factory=lambda: (LlamaConfig(), CLIPTextConfig()))
-    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(
+    preprocess_text_funcs: Tuple[Callable[[str], str], ...] = field(
         default_factory=lambda: (llama_preprocess_text, clip_preprocess_text))
-    postprocess_text_funcs: tuple[
+    postprocess_text_funcs: Tuple[
         Callable[[BaseEncoderOutput], torch.tensor],
         ...] = field(default_factory=lambda:
                      (llama_postprocess_text, clip_postprocess_text))
@@ -83,7 +82,7 @@ class HunyuanConfig(PipelineConfig):
     # Precision for each component
     dit_precision: str = "bf16"
     vae_precision: str = "fp16"
-    text_encoder_precisions: tuple[str, ...] = field(
+    text_encoder_precisions: Tuple[str, ...] = field(
         default_factory=lambda: ("fp16", "fp16"))
 
     def __post_init__(self):

fastvideo/v1/configs/pipelines/registry.py

@@ -2,7 +2,7 @@
 """Registry for pipeline weight-specific configurations."""
 
 import os
-from collections.abc import Callable
+from typing import Callable, Dict, Optional, Type
 
 from fastvideo.v1.configs.pipelines.base import PipelineConfig
 from fastvideo.v1.configs.pipelines.hunyuan import (FastHunyuanConfig,
@@ -19,11 +19,10 @@ from fastvideo.v1.utils import (maybe_download_model_index,
 logger = init_logger(__name__)
 
 # Registry maps specific model weights to their config classes
-PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
+PIPE_NAME_TO_CONFIG: Dict[str, Type[PipelineConfig]] = {
     "FastVideo/FastHunyuan-diffusers": FastHunyuanConfig,
     "hunyuanvideo-community/HunyuanVideo": HunyuanConfig,
     "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V480PConfig,
-    "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers": WanI2V480PConfig,
     "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V480PConfig,
     "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V720PConfig,
     "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V720PConfig,
@@ -32,7 +31,7 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
 }
 
 # For determining pipeline type from model ID
-PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
+PIPELINE_DETECTOR: Dict[str, Callable[[str], bool]] = {
     "hunyuan": lambda id: "hunyuan" in id.lower(),
     "wanpipeline": lambda id: "wanpipeline" in id.lower(),
     "wanimagetovideo": lambda id: "wanimagetovideo" in id.lower(),
@@ -41,7 +40,7 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
 }
 
 # Fallback configs when exact match isn't found but architecture is detected
-PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
+PIPELINE_FALLBACK_CONFIG: Dict[str, Type[PipelineConfig]] = {
     "hunyuan":
     HunyuanConfig,  # Base Hunyuan config as fallback for any Hunyuan variant
     "wanpipeline":
@@ -53,7 +52,7 @@ PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
 
 
 def get_pipeline_config_cls_from_name(
-        pipeline_name_or_path: str) -> type[PipelineConfig]:
+        pipeline_name_or_path: str) -> Type[PipelineConfig]:
     """Get the appropriate configuration class for a given pipeline name or path.
 
     This function implements a multi-step lookup process to find the most suitable
@@ -82,7 +81,7 @@ def get_pipeline_config_cls_from_name(
         - Warning messages are logged when falling back to less specific configurations
     """
 
-    pipeline_config_cls: type[PipelineConfig] | None = None
+    pipeline_config_cls: Optional[Type[PipelineConfig]] = None
 
     # First try exact match for specific weights
     if pipeline_name_or_path in PIPE_NAME_TO_CONFIG:

fastvideo/v1/configs/pipelines/wan.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
-from collections.abc import Callable
 from dataclasses import dataclass, field
+from typing import Callable, Tuple
 
 import torch
 
@@ -17,7 +17,7 @@ def t5_postprocess_text(outputs: BaseEncoderOutput) -> torch.tensor:
     hidden_state: torch.tensor = outputs.last_hidden_state
     seq_lens = mask.gt(0).sum(dim=1).long()
     assert torch.isnan(hidden_state).sum() == 0
-    prompt_embeds = [u[:v] for u, v in zip(hidden_state, seq_lens, strict=True)]
+    prompt_embeds = [u[:v] for u, v in zip(hidden_state, seq_lens)]
     prompt_embeds_tensor: torch.tensor = torch.stack([
         torch.cat([u, u.new_zeros(512 - u.size(0), u.size(1))])
         for u in prompt_embeds
@@ -42,16 +42,16 @@ class WanT2V480PConfig(PipelineConfig):
     flow_shift: int = 3
 
     # Text encoding stage
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(
+    text_encoder_configs: Tuple[EncoderConfig, ...] = field(
         default_factory=lambda: (T5Config(), ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.tensor],
+    postprocess_text_funcs: Tuple[Callable[[BaseEncoderOutput], torch.tensor],
                                   ...] = field(default_factory=lambda:
                                                (t5_postprocess_text, ))
 
     # Precision for each component
     precision: str = "bf16"
-    vae_precision: str = "fp32"
-    text_encoder_precisions: tuple[str, ...] = field(
+    vae_precision: str = "fp16"
+    text_encoder_precisions: Tuple[str, ...] = field(
         default_factory=lambda: ("fp32", ))
 
     # WanConfig-specific added parameters

fastvideo/v1/configs/sample/base.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass
-from typing import Any
+from typing import Any, Dict, List, Optional, Union
 
 from fastvideo.v1.logger import init_logger
 
@@ -16,12 +16,12 @@ class SamplingParam:
     data_type: str = "video"
 
     # Image inputs
-    image_path: str | None = None
+    image_path: Optional[str] = None
 
     # Text inputs
-    prompt: str | list[str] | None = None
-    negative_prompt: str | None = None
-    prompt_path: str | None = None
+    prompt: Optional[Union[str, List[str]]] = None
+    negative_prompt: Optional[str] = None
+    prompt_path: Optional[str] = None
     output_path: str = "outputs/"
 
     # Batch info
@@ -54,7 +54,7 @@ class SamplingParam:
         if self.prompt_path and not self.prompt_path.endswith(".txt"):
             raise ValueError("prompt_path must be a txt file")
 
-    def update(self, source_dict: dict[str, Any]) -> None:
+    def update(self, source_dict: Dict[str, Any]) -> None:
         for key, value in source_dict.items():
             if hasattr(self, key):
                 setattr(self, key, value)

fastvideo/v1/configs/sample/registry.py

@@ -1,13 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 import os
-from collections.abc import Callable
-from typing import Any
+from typing import Any, Callable, Dict, Optional
 
 from fastvideo.v1.configs.sample.hunyuan import (FastHunyuanSamplingParam,
                                                  HunyuanSamplingParam)
 from fastvideo.v1.configs.sample.stepvideo import StepVideoT2VSamplingParam
-from fastvideo.v1.configs.sample.wan import (Wan2_1_Fun_1_3B_InP_SamplingParam,
-                                             WanI2V_14B_480P_SamplingParam,
+from fastvideo.v1.configs.sample.wan import (WanI2V_14B_480P_SamplingParam,
                                              WanI2V_14B_720P_SamplingParam,
                                              WanT2V_1_3B_SamplingParam,
                                              WanT2V_14B_SamplingParam)
@@ -17,21 +15,19 @@ from fastvideo.v1.utils import (maybe_download_model_index,
 
 logger = init_logger(__name__)
 # Registry maps specific model weights to their config classes
-SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
+SAMPLING_PARAM_REGISTRY: Dict[str, Any] = {
     "FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
     "hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParam,
     "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V_1_3B_SamplingParam,
     "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V_14B_SamplingParam,
     "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V_14B_480P_SamplingParam,
     "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V_14B_720P_SamplingParam,
-    "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers":
-    Wan2_1_Fun_1_3B_InP_SamplingParam,
     "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
     # Add other specific weight variants
 }
 
 # For determining pipeline type from model ID
-SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
+SAMPLING_PARAM_DETECTOR: Dict[str, Callable[[str], bool]] = {
     "hunyuan": lambda id: "hunyuan" in id.lower(),
     "wanpipeline": lambda id: "wanpipeline" in id.lower(),
     "wanimagetovideo": lambda id: "wanimagetovideo" in id.lower(),
@@ -40,7 +36,7 @@ SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
 }
 
 # Fallback configs when exact match isn't found but architecture is detected
-SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
+SAMPLING_FALLBACK_PARAM: Dict[str, Any] = {
     "hunyuan":
     HunyuanSamplingParam,  # Base Hunyuan config as fallback for any Hunyuan variant
     "wanpipeline":
@@ -51,7 +47,8 @@ SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
 }
 
 
-def get_sampling_param_cls_for_name(pipeline_name_or_path: str) -> Any | None:
+def get_sampling_param_cls_for_name(
+        pipeline_name_or_path: str) -> Optional[Any]:
     """Get the appropriate sampling param for specific pretrained weights."""
 
     if os.path.exists(pipeline_name_or_path):

fastvideo/v1/configs/sample/wan.py

@@ -94,20 +94,3 @@ class WanI2V_14B_720P_SamplingParam(WanT2V_14B_SamplingParam):
                 -5784.54975374, 5449.50911966, -1811.16591783, 256.27178429,
                 -13.02252404
             ]))
-
-
-# =============================================
-# ============= Wan2.1 Fun Models =============
-# =============================================
-
-
-@dataclass
-class Wan2_1_Fun_1_3B_InP_SamplingParam(SamplingParam):
-    """Sampling parameters for Wan2.1 Fun 1.3B InP model."""
-    height: int = 480
-    width: int = 832
-    num_frames: int = 81
-    fps: int = 16
-    negative_prompt: str | None = "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"
-    guidance_scale: float = 6.0
-    num_inference_steps: int = 50

fastvideo/v1/configs/utils.py

@@ -1,8 +1,8 @@
-from typing import Any
+from typing import Any, Dict
 
 
 def update_config_from_args(config: Any,
-                            args_dict: dict[str, Any],
+                            args_dict: Dict[str, Any],
                             prefix: str = "",
                             pop_args: bool = False) -> None:
     """

fastvideo/v1/configs/wan_1.3B_t2v_pipeline.json

@@ -4,7 +4,7 @@
   "use_cpu_offload": true,
   "disable_autocast": false,
   "precision": "bf16",
-  "vae_precision": "fp32",
+  "vae_precision": "fp16",
   "vae_tiling": false,
   "vae_sp": false,
   "vae_config": {

fastvideo/v1/configs/wan_14B_i2v_480p_pipeline.json

@@ -4,7 +4,7 @@
   "use_cpu_offload": true,
   "disable_autocast": false,
   "precision": "bf16",
-  "vae_precision": "fp32",
+  "vae_precision": "fp16",
   "vae_tiling": false,
   "vae_sp": false,
   "vae_config": {