load_video

.buildkite/pipeline.yml

@@ -1,138 +1,66 @@
 env:
   IMAGE_VERSION: "py3.12-latest"
-  BUILDKITE_CLEAN_CHECKOUT: true
 
 steps:
-  - label: "pre-commit"
+  - block: "Start Build"
-    command: ".buildkite/scripts/pre_commit.sh"
+    blocked_state: "running"
-    agents:
+    prompt: "Approve build?"
-      queue: "default"
-
-  - wait
 
   - label: "Trigger Tests"
+    command: |
+      echo "Current working directory: $(pwd)"
+      echo "Current branch:"
+      git branch --show-current
+      echo "Full diff:"
+      git diff --name-only $BUILDKITE_PULL_REQUEST_BASE_BRANCH...HEAD
     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/models/loaders/**"
                 - "fastvideo/v1/tests/encoders/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
               config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
                 label: "Encoder Tests"
                 env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
                   - TEST_TYPE=encoder
                 agents:
                   queue: "default"
             - path:
                 - "fastvideo/v1/models/vaes/**"
-                - "fastvideo/v1/models/loader/**"
+                - "fastvideo/v1/models/loaders/**"
                 - "fastvideo/v1/tests/vaes/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
               config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
                 label: "VAE Tests"
                 env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
                   - TEST_TYPE=vae
                 agents:
                   queue: "default"
             - path:
                 - "fastvideo/v1/models/dits/**"
-                - "fastvideo/v1/models/loader/**"
+                - "fastvideo/v1/models/loaders/**"
                 - "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"
+                command: "timeout 30m .buildkite/scripts/pr_test.sh"
                 label: "Transformer Tests"
                 env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
                   - TEST_TYPE=transformer
                 agents:
                   queue: "default"
-            - path: 
+            - path: "fastvideo/v1/**/*.py"
-                - "fastvideo/v1/**/*.py"
               config:
-                command: "timeout 30m .buildkite/scripts/pr_test.sh"
+                command: "timeout 60m .buildkite/scripts/pr_test.sh"
                 label: "SSIM Tests"
                 env:
+                  - BUILDKITE_CLEAN_CHECKOUT=true
                   - 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

@@ -31,10 +31,6 @@ 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
@@ -58,44 +54,22 @@ if [ -z "${TEST_TYPE:-}" ]; then
 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"
+        MODAL_COMMAND="python3 -m modal run $MODAL_TEST_FILE::run_encoder_tests"
         ;;
     "vae")
         log "Running VAE tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_vae_tests"
+        MODAL_COMMAND="python3 -m modal run $MODAL_TEST_FILE::run_vae_tests"
         ;;
     "transformer")
         log "Running transformer tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_transformer_tests"
+        MODAL_COMMAND="python3 -m modal run $MODAL_TEST_FILE::run_transformer_tests"
         ;;
     "ssim")
         log "Running SSIM tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_ssim_tests"
+        MODAL_COMMAND="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"

.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/workflows/pr-test.yml

@@ -14,9 +14,13 @@ on:
       - ".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:py3.12-latest)"
+        required: false
+        default: "fastvideo-dev:py3.12-latest"
+        type: string
       run_encoder_test:
         description: "Run encoder-test"
         required: false
@@ -52,16 +56,6 @@ on:
         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
@@ -71,7 +65,6 @@ on:
 env:
   PYTHONUNBUFFERED: "1"
 
-
 concurrency:
   group: pr-test-${{ github.ref }}
   cancel-in-progress: true
@@ -91,69 +84,44 @@ jobs:
       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
+              - 'pyproject.toml'
+              - 'docker/Dockerfile.python3.12'
             vae-test:
               - 'fastvideo/v1/models/vaes/**'
-              - 'fastvideo/v1/models/loader/**'
+              - 'fastvideo/v1/models/loaders/**'
               - 'fastvideo/v1/tests/vaes/**'
-              - *common-paths
+              - 'pyproject.toml'
+              - 'docker/Dockerfile.python3.12'
             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
+              - 'pyproject.toml'
+              - 'docker/Dockerfile.python3.12'
             training-test:
               - 'fastvideo/v1/**'
-              - *common-paths
+              - 'pyproject.toml'
+              - 'docker/Dockerfile.python3.12'
             training-test-VSA:
               - 'fastvideo/v1/**'
-              - *common-paths
+              - 'pyproject.toml'
-              - *vsa-kernel-paths
+              - 'docker/Dockerfile.python3.12'
             inference-test-STA:
               - 'fastvideo/v1/**'
-              - *common-paths
+              - 'pyproject.toml'
-              - *sta-kernel-paths
+              - 'docker/Dockerfile.python3.12'
-            precision-test-STA:
-              - *common-paths
-              - *sta-kernel-paths
-            precision-test-VSA:
-              - *common-paths
-              - *vsa-kernel-paths
 
   encoder-test:
     needs: change-filter
@@ -166,7 +134,7 @@ jobs:
       gpu_type: "NVIDIA A40"
       gpu_count: 1
       volume_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:py3.12-latest' }}"
       test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/encoders -s"
       timeout_minutes: 30
     secrets:
@@ -184,7 +152,7 @@ jobs:
       gpu_type: "NVIDIA A40"
       gpu_count: 1
       volume_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:py3.12-latest' }}"
       test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/vaes -s"
       timeout_minutes: 30
     secrets:
@@ -202,7 +170,7 @@ jobs:
       gpu_type: "NVIDIA L40S"
       gpu_count: 1
       volume_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:py3.12-latest' }}"
       test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/transformers -s"
       timeout_minutes: 30
     secrets:
@@ -212,7 +180,8 @@ jobs:
   ssim-test:
     needs: change-filter
     if: >-
-      github.event_name != 'workflow_dispatch' || (github.event_name == 'workflow_dispatch' && github.event.inputs.run_ssim_test == 'true')
+      (github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || 
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_ssim_test == 'true')
     strategy:
       fail-fast: false
       matrix:
@@ -238,7 +207,7 @@ jobs:
   training-test:
     needs: change-filter
     if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.training-test == 'true') || 
+      (github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || 
       (github.event_name == 'workflow_dispatch' && github.event.inputs.run_training_test == 'true')
     uses: ./.github/workflows/runpod-test.yml
     with:
@@ -247,7 +216,7 @@ jobs:
       gpu_count: 4
       volume_size: 100
       disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:py3.12-latest' }}"
       test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/training/Vanilla -srP"
       timeout_minutes: 30
     secrets:
@@ -258,16 +227,16 @@ jobs:
   training-test-VSA:
     needs: change-filter
     if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.training-test-VSA == 'true') || 
+      (github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || 
       (github.event_name == 'workflow_dispatch' && github.event.inputs.run_training_test_VSA == 'true')
     uses: ./.github/workflows/runpod-test.yml
     with:
       job_id: "training-test-VSA"
       gpu_type: "NVIDIA H100 NVL"
-      gpu_count: 2
+      gpu_count: 1
       volume_size: 100
       disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:py3.12-latest' }}"
       test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/training/VSA -srP"
       timeout_minutes: 30
     secrets:
@@ -278,60 +247,22 @@ jobs:
   inference-test-STA:
     needs: change-filter
     if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.inference-test-STA == 'true') || 
+      (github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || 
       (github.event_name == 'workflow_dispatch' && github.event.inputs.run_inference_test_STA == 'true')
     uses: ./.github/workflows/runpod-test.yml
     with:
       job_id: "inference-test-STA"
       gpu_type: "NVIDIA H100 NVL"
-      gpu_count: 2
+      gpu_count: 1
       volume_size: 100
       disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || '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: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-
   nightly-test:
     if: >-
       (github.event_name == 'workflow_dispatch' && github.event.inputs.run_nightly_test == 'true')
@@ -342,7 +273,7 @@ jobs:
       gpu_count: 4
       volume_size: 100
       disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || '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:
@@ -351,8 +282,7 @@ jobs:
       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] # 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] 
     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 +299,7 @@ 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

.gitignore

@@ -59,8 +59,3 @@ docs/source/inference/examples/
 
 # Static images
 !docs/source/_static/images/**/*.png
-
-# Local scripts (keep local but don't track in git)
-local_scripts/
-!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:
@@ -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,
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-        554.85888671875
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-        334.080078125,
-        322
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-      "flags": {},
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-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
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-          "type": "VAE_CONFIG",
-          "links": [
-            2
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VAEConfig"
-      },
-      "widgets_values": [
-        -99999,
-        -99999,
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-          "value": -99999,
-          "cachedValue": true
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-        "load_decoder": {
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-          "cachedValue": true
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-          "value": -99999,
-          "cachedValue": 256
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-        "tile_sample_min_width": {
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-          "value": -99999,
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-        "tile_sample_stride_num_frames": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 12
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-        "blend_num_frames": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": 0
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-        "use_tiling": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": true
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-        "use_temporal_tiling": {
-          "isAuto": true,
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-        953.6171875
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-          "name": "text_encoder_config",
-          "type": "TEXT_ENCODER_CONFIG",
-          "links": [
-            7
-          ]
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-        -99999,
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-        -99999
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-          "value": -99999,
-          "cachedValue": ""
-        },
-        "quant_config": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": ""
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-        "lora_config": {
-          "isAuto": true,
-          "value": -99999,
-          "cachedValue": ""
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-    },
-    {
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-        415.1928405761719,
-        1154.1573486328125
-      ],
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-          "link": 2
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-          "type": "STRING",
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-            4
-          ]
-        }
-      ],
-      "properties": {
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-      },
-      "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,
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-        -99999,
-        -99999,
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-    [
-      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 test/test_sta.py
-python tests/test_block_sparse.py # test VSA
 ```
-## Benchmark
-```bash
-python benchmarks/bench_sta.py
-```
-
 
 ## How Does STA Work?
 We give a demo for 2D STA with window size (6,6) operating on a (10, 10) image. 

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):
+def efficiency(flop, time):
-    flops = flops / 1e12
+    flop = flop / 1e12
-    ms = ms / 1e3
+    time = time / 1e6
-    return flops / ms
+    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()
+        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()
 
+        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
+        # Prepare for timing forward pass
-        # for _ in range(10):
+        start_events_fwd = [torch.cuda.Event(enable_timing=True) for _ in range(10)]
-        #     o = sliding_tile_attention(q, k, v, [[3, 6, 10]] * 24, 0, False, dit_seq_shape)
+        end_events_fwd = [torch.cuda.Event(enable_timing=True) for _ in range(10)]
 
-        # # Time the forward pass
+        torch.cuda.empty_cache()
-        # for i in range(10):
+        torch.cuda.synchronize()
-        #     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))
 
-        # times_fwd = [s.elapsed_time(e) for s, e in zip(start_events_fwd, end_events_fwd)]
+        # Warmup for forward pass
-        # time_us_fwd = np.mean(times_fwd) * 1000
+        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 time for forward pass in us: {time_us_fwd:.2f}")
-        print(f"Average TFLOPS: {tflops_fwd}")
+        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 time for backward pass in us: {time_us_bwd:.2f}")
-        # print(f"Average TFLOPS: {tflops_bwd}")
+        # print(f"Average efficiency for backward pass in TFLOPS: {tflops_bwd}")
-        # print("=" * 60)
+        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, False),
-    (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]),
     # (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);
+            qt = CLAMP(qt, DT, CT-DT-1);
-            qh = clamp_int(qh, DH, CH-DH-1);
+            qh = CLAMP(qh, DH, CH-DH-1);
-            qw = clamp_int(qw, DW, CW-DW-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);
+                qt = CLAMP(qt, DT, CT-DT-1);
-                qh = clamp_int(qh, DH, CH-DH-1);
+                qh = CLAMP(qh, DH, CH-DH-1);
-                qw = clamp_int(qw, DW, CW-DW-1);
+                qw = CLAMP(qw, DW, CW-DW-1);
-                int k_t_min = clamp_int(qt-DT, 0, CT-1);
+                int k_t_min = CLAMP(qt-DT, 0, CT-1);
-                int k_t_max = clamp_int(qt+DT, 0, CT-1);
+                int k_t_max = CLAMP(qt+DT, 0, CT-1);
-                int k_h_min = clamp_int(qh-DH, 0, CH-1);
+                int k_h_min = CLAMP(qh-DH, 0, CH-1);
-                int k_h_max = clamp_int(qh+DH, 0, CH-1);
+                int k_h_max = CLAMP(qh+DH, 0, CH-1);
-                int k_w_min = clamp_int(qw-DW, 0, CW-1);
+                int k_w_min = CLAMP(qw-DW, 0, CW-1);
-                int k_w_max = clamp_int(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();
+    cudaDeviceSynchronize();
-    const c10::cuda::OptionalCUDAGuard device_guard(q.device());
+    auto stream = at::cuda::getCurrentCUDAStream().stream(); 
-    const cudaStream_t 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;
+        auto mem_size = kittens::MAX_SHARED_MEMORY;
-        int threads = NUM_WORKERS * kittens::WARP_THREADS;
+        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"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}, max={np.max(forward_metrics['l1']):.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}, max={np.max(forward_metrics['rmse']):.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"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}, max={np.max(grad_q_metrics['l1']):.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}, max={np.max(grad_q_metrics['rmse']):.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"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}, max={np.max(grad_k_metrics['l1']):.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}, max={np.max(grad_k_metrics['rmse']):.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"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}, max={np.max(grad_v_metrics['l1']):.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}, max={np.max(grad_v_metrics['rmse']):.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')
+    main()
-    parser.add_argument('--batch_size', type=int, default=2, help='Batch size')
-    parser.add_argument('--num_heads', type=int, default=12, help='Number of heads')
-    parser.add_argument('--head_dim', type=int, default=64, help='Head dimension')
-    parser.add_argument('--topk', type=int, default=32, 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')
-    args = parser.parse_args()
-    main(args)

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();
+    cudaDeviceSynchronize();
-    const c10::cuda::OptionalCUDAGuard device_guard(q.device());    
+    auto stream = at::cuda::getCurrentCUDAStream().stream(); 
-    const cudaStream_t 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; 
+    auto mem_size = kittens::MAX_SHARED_MEMORY; 
-    int threads  = 4 * kittens::WARP_THREADS;
+    auto threads  = 4 * kittens::WARP_THREADS;
 
-    //cudadevicesynchronize();
+    cudaDeviceSynchronize();
-    const c10::cuda::OptionalCUDAGuard device_guard(q.device());    
+    auto stream = at::cuda::getCurrentCUDAStream().stream();
-    const cudaStream_t 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);
+        cudaStreamSynchronize(stream);
-        //cudadevicesynchronize();
+        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);
+        cudaStreamSynchronize(stream);
-        //cudadevicesynchronize();
+        cudaDeviceSynchronize();
     }
 
     return {qg, kg, vg};
-    //cudadevicesynchronize();
+    cudaDeviceSynchronize();
 }

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/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
 

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).

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/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

@@ -1,94 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
-DATA_DIR="data/crush-smol_processed_i2v_1_3b_inp/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
-NUM_GPUS=8
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_i2v_finetune"
-  --wandb_run_name "wan_i2v_finetune"
-  --output_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 1
-  --tp_size 1
-  --hsdp_replicate_dim 8
-  --hsdp_shard_dim 1
-)
-
-# 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 "50"
-  --validation_guidance_scale "1.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 2e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 2000
-  --weight_decay 0.1
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --allow_tf32
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --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

@@ -1,13 +1,9 @@
-#!/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"
+VALIDATION_DIR="data/crush-smol_processed_i2v/validation_parquet_dataset/"
 NUM_GPUS=8
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]
@@ -16,7 +12,7 @@ NUM_GPUS=8
 training_args=(
   --tracker_project_name "wan_i2v_finetune"
   --output_dir "$DATA_DIR/outputs/wan_i2v_finetune"
-  --max_train_steps 2000
+  --max_train_steps 5000
   --train_batch_size 1
   --train_sp_batch_size 1
   --gradient_accumulation_steps 1
@@ -30,15 +26,15 @@ training_args=(
 parallel_args=(
   --num_gpus $NUM_GPUS
   --sp_size 8
-  --tp_size 1
+  --tp_size 8
   --hsdp_replicate_dim 1
   --hsdp_shard_dim 8
 )
 
 # Model arguments
 model_args=(
-  --model_path $MODEL_PATH
+  --model_path "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
-  --pretrained_model_name_or_path $MODEL_PATH
+  --pretrained_model_name_or_path "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
 )
 
 # Dataset arguments
@@ -50,7 +46,7 @@ dataset_args=(
 # Validation arguments
 validation_args=(
   --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_preprocessed_path "$VALIDATION_DIR"
   --validation_steps 100
   --validation_sampling_steps "40"
   --validation_guidance_scale "1.0"
@@ -60,7 +56,7 @@ validation_args=(
 optimizer_args=(
   --learning_rate 1e-5
   --mixed_precision "bf16"
-  --checkpointing_steps 1000
+  --checkpointing_steps 6000
   --weight_decay 1e-4
   --max_grad_norm 1.0
 )
@@ -70,13 +66,12 @@ miscellaneous_args=(
   --inference_mode False
   --allow_tf32
   --checkpoints_total_limit 3
-  --training_cfg_rate 0.1
+  --cfg 0.0
   --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

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

@@ -1,5 +1,5 @@
 #!/bin/bash
-#SBATCH --job-name=i2v
+#SBATCH --job-name=FV_2N_14B
 #SBATCH --partition=main
 #SBATCH --qos=hao
 #SBATCH --nodes=4
@@ -7,10 +7,10 @@
 #SBATCH --ntasks-per-node=1
 #SBATCH --gres=gpu:8
 #SBATCH --cpus-per-task=128
-#SBATCH --nodelist=fs-mbz-gpu-[100-850]
+#SBATCH --nodelist=fs-mbz-gpu-[400-550]
 #SBATCH --mem=1440G
-#SBATCH --output=i2v_output/i2v_%j.out
+#SBATCH --output=4n_i2v/4n_i2v_%j.out
-#SBATCH --error=i2v_output/i2v_%j.err
+#SBATCH --error=4n_i2v/4n_i2v_%j.err
 #SBATCH --exclusive
 set -e -x
 
@@ -30,9 +30,7 @@ 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_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-export WANDB_MODE=online
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
 
 echo "MASTER_ADDR: $MASTER_ADDR"
 echo "NODE_RANK: $NODE_RANK"
@@ -40,92 +38,60 @@ echo "NODE_RANK: $NODE_RANK"
 # Configs
 NUM_GPUS=8
 
-MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
+export WANDB_BASE_URL="https://api.wandb.ai"
-DATA_DIR="data/crush-smol_processed_i2v/combined_parquet_dataset/"
+export WANDB_MODE=online
-VALIDATION_DATASET_FILE="examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation.json"
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+DATA_DIR=data/crush-smol_processed_i2v/combined_parquet_dataset
+VALIDATION_DIR=data/crush-smol_processed_i2v/validation_parquet_dataset
+NUM_GPUS=8
 # 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:$MASTER_PORT" \
-    fastvideo/v1/training/wan_i2v_training_pipeline.py \
+    fastvideo/v1/training/wan_i2v_training_pipeline.py\
-    "${parallel_args[@]}" \
+    --model_path Wan-AI/Wan2.1-I2V-14B-480P-Diffusers \
-    "${model_args[@]}" \
+    --inference_mode False\
-    "${dataset_args[@]}" \
+    --pretrained_model_name_or_path Wan-AI/Wan2.1-I2V-14B-480P-Diffusers \
-    "${training_args[@]}" \
+    --cache_dir "/home/ray/.cache"\
-    "${optimizer_args[@]}" \
+    --data_path "$DATA_DIR"\
-    "${validation_args[@]}" \
+    --validation_preprocessed_path "$VALIDATION_DIR"\
-    "${miscellaneous_args[@]}"
+    --train_batch_size=1\
+    --num_latent_t 16 \
+    --num_gpus $NUM_GPUS \
+    --sp_size $NUM_GPUS \
+    --tp_size $NUM_GPUS \
+    --hsdp_replicate_dim $SLURM_JOB_NUM_NODES \
+    --hsdp_shard_dim $NUM_GPUS \
+    --train_sp_batch_size 1\
+    --dataloader_num_workers 10\
+    --gradient_accumulation_steps=2\
+    --max_train_steps=10000 \
+    --learning_rate=5e-5\
+    --mixed_precision="bf16"\
+    --checkpointing_steps=11000 \
+    --validation_steps 100\
+    --validation_sampling_steps "40" \
+    --log_validation \
+    --checkpoints_total_limit 3\
+    --allow_tf32\
+    --ema_start_step 0\
+    --cfg 0.0\
+    --output_dir="$DATA_DIR/outputs/wan_i2v_finetune_2n"\
+    --tracker_project_name wan_i2v_finetune \
+    --num_height 480 \
+    --num_width 832 \
+    --num_frames 77 \
+    --validation_guidance_scale "1.0" \
+    --num_euler_timesteps 50 \
+    --multi_phased_distill_schedule "4000-1" \
+    --weight_decay 1e-4 \
+    --not_apply_cfg_solver \
+    --dit_precision "fp32" \
+    --max_grad_norm 1.0

examples/training/finetune/wan_i2v_14b_480p/crush_smol/v1_preprocess_wan_data_i2v.sh

@@ -1,5 +1,4 @@
-#!/bin/bash
+# export WANDB_MODE="offline"
-
 GPU_NUM=1 # 2,4,8
 MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
 MODEL_TYPE="wan"
@@ -22,5 +21,4 @@ torchrun --nproc_per_node=$GPU_NUM \
     --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

@@ -26,24 +26,6 @@
       "height": 480,
       "width": 832,
       "num_frames": 77
-    },
-    {
-      "caption": "The video shows a close-up of an orange being flattened as if it were under a hydraulic press, with the press moving down and compressing the fruit until it is completely flattened.",
-      "image_path": null,
-      "video_path": "validation_dataset/EJqsC21GSBY-Scene-059.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 98
-    },
-    {
-      "caption": "The video shows a stack of colorful sponges being flattened as if they were under a hydraulic press. The sponges, which are pink, white, blue, and green, are compressed into a smaller size, demonstrating the press's power. The background features a green wall with a yellow and red sign, adding context to the setting.",
-      "image_path": null,
-      "video_path": "validation_dataset/EJqsC21GSBY-Scene-013.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 148
     }
   ]
 }

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

@@ -1,13 +1,10 @@
-#!/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"
+VALIDATION_DIR="data/crush-smol_processed_t2v/validation_parquet_dataset/"
 NUM_GPUS=4
 # export CUDA_VISIBLE_DEVICES=4,5
 
@@ -19,7 +16,7 @@ training_args=(
   --max_train_steps 5000
   --train_batch_size 1
   --train_sp_batch_size 1
-  --gradient_accumulation_steps 8
+  --gradient_accumulation_steps 1
   --num_latent_t 8 
   --num_height 480
   --num_width 832
@@ -28,11 +25,11 @@ training_args=(
 
 # Parallel arguments
 parallel_args=(
-  --num_gpus $NUM_GPUS 
+  --num_gpus $NUM_GPUS \
-  --sp_size $NUM_GPUS 
+  --sp_size $NUM_GPUS \
-  --tp_size 1
+  --tp_size $NUM_GPUS \
-  --hsdp_replicate_dim 1
+  --hsdp_replicate_dim 1 \
-  --hsdp_shard_dim $NUM_GPUS
+  --hsdp_shard_dim $NUM_GPUS \
 )
 
 # Model arguments
@@ -50,18 +47,18 @@ dataset_args=(
 # Validation arguments
 validation_args=(
   --log_validation 
-  --validation_dataset_file $VALIDATION_DATASET_FILE
+  --validation_preprocessed_path $VALIDATION_DIR
-  --validation_steps 50
+  --validation_steps 100
   --validation_sampling_steps "50" 
   --validation_guidance_scale "1.0"
 )
 
 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 5e-5
+  --learning_rate 1e-5
   --mixed_precision "bf16"
   --checkpointing_steps 6000
-  --weight_decay 1e-4
+  --weight_decay 0.01
   --max_grad_norm 1.0
 )
 
@@ -70,13 +67,12 @@ miscellaneous_args=(
   --inference_mode False
   --allow_tf32
   --checkpoints_total_limit 3
-  --training_cfg_rate 0.1
+  --cfg 0.0
   --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 \

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

@@ -1,5 +1,5 @@
 #!/bin/bash
-#SBATCH --job-name=t2v
+#SBATCH --job-name=FV_2N_14B
 #SBATCH --partition=main
 #SBATCH --qos=hao
 #SBATCH --nodes=1
@@ -7,10 +7,10 @@
 #SBATCH --ntasks-per-node=1
 #SBATCH --gres=gpu:8
 #SBATCH --cpus-per-task=128
-#SBATCH --nodelist=fs-mbz-gpu-[100-850]
+#SBATCH --nodelist=fs-mbz-gpu-[400-550]
 #SBATCH --mem=1440G
-#SBATCH --output=t2v_output/t2v_%j.out
+#SBATCH --output=4n_i2v/4n_i2v_%j.out
-#SBATCH --error=t2v_output/t2v_%j.err
+#SBATCH --error=4n_i2v/4n_i2v_%j.err
 #SBATCH --exclusive
 set -e -x
 
@@ -30,99 +30,69 @@ 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_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-export WANDB_MODE=online
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
 
 echo "MASTER_ADDR: $MASTER_ADDR"
 echo "NODE_RANK: $NODE_RANK"
 
 # Configs
 NUM_GPUS=8
+
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+# 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"
+VALIDATION_DIR="data/crush-smol_processed_t2v/validation_parquet_dataset/"
+NUM_GPUS=8
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]
 
-# Training arguments
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-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:$MASTER_PORT" \
-    fastvideo/v1/training/wan_training_pipeline.py \
+    fastvideo/v1/training/wan_training_pipeline.py\
-    "${parallel_args[@]}" \
+    --model_path $MODEL_PATH \
-    "${model_args[@]}" \
+    --inference_mode False\
-    "${dataset_args[@]}" \
+    --pretrained_model_name_or_path $MODEL_PATH \
-    "${training_args[@]}" \
+    --cache_dir "/home/ray/.cache"\
-    "${optimizer_args[@]}" \
+    --data_path "$DATA_DIR"\
-    "${validation_args[@]}" \
+    --validation_preprocessed_path "$VALIDATION_DIR"\
-    "${miscellaneous_args[@]}"
+    --train_batch_size=1\
+    --num_latent_t 8 \
+    --num_gpus $NUM_GPUS \
+    --sp_size $NUM_GPUS \
+    --tp_size $NUM_GPUS \
+    --hsdp_replicate_dim $SLURM_JOB_NUM_NODES \
+    --hsdp_shard_dim $NUM_GPUS \
+    --train_sp_batch_size 1\
+    --dataloader_num_workers 10\
+    --gradient_accumulation_steps=1\
+    --max_train_steps=10000 \
+    --learning_rate=5e-5\
+    --mixed_precision="bf16"\
+    --checkpointing_steps=11000 \
+    --validation_steps 100\
+    --validation_sampling_steps "40" \
+    --log_validation \
+    --checkpoints_total_limit 3\
+    --allow_tf32\
+    --ema_start_step 0\
+    --cfg 0.0\
+    --output_dir="$DATA_DIR/outputs/wan_i2v_finetune_2n"\
+    --tracker_project_name wan_i2v_finetune \
+    --num_height 480 \
+    --num_width 832 \
+    --num_frames 77 \
+    --validation_guidance_scale "1.0" \
+    --num_euler_timesteps 50 \
+    --multi_phased_distill_schedule "4000-1" \
+    --weight_decay 1e-4 \
+    --not_apply_cfg_solver \
+    --dit_precision "fp32" \
+    --max_grad_norm 1.0

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

@@ -0,0 +1,13 @@
+{
+  "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": "examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation_dataset/yYcK4nANZz4-Scene-034.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    }
+  ]
+}

examples/training/finetune/wan_t2v_1_3b/crush_smol/v1_preprocess_wan_data_t2v.sh

@@ -1,5 +1,4 @@
-#!/bin/bash
+# export WANDB_MODE="offline"
-
 GPU_NUM=1 # 2,4,8
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 MODEL_TYPE="wan"
@@ -22,5 +21,4 @@ torchrun --nproc_per_node=$GPU_NUM \
     --validation_dataset_file $VALIDATION_PATH \
     --samples_per_file 8 \
     --flush_frequency 8 \
-    --video_length_tolerance_range 5 \
     --preprocess_task "t2v" 

fastvideo/v1/STA_configuration.py

@@ -2,7 +2,7 @@
 import json
 import os
 from collections import defaultdict
-from typing import Any
+from typing import Any, Dict, List, Optional, Tuple
 
 import numpy as np
 
@@ -13,7 +13,7 @@ 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 +53,22 @@ 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 +79,25 @@ 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')
+        skip_time_steps_tuning: Optional[int] = kwargs.get('skip_time_steps')
-        save_dir_tuning: str | None = kwargs.get('save_dir', "mask_candidates")
+        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 +108,7 @@ 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 +149,11 @@ 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 +161,16 @@ 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 +187,7 @@ 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 +227,7 @@ 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 +248,9 @@ 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 +268,11 @@ def read_specific_json_files(folder_path: str) -> list[dict[str, Any]]:
 
 
 def average_head_losses(
-        results: list[dict[str, Any]],
+        results: List[Dict[str, Any]],
-        selected_masks: list[list[int]]) -> dict[str, dict[str, np.ndarray]]:
+        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 +294,14 @@ def average_head_losses(
 
 
 def select_best_mask_strategy(
-        averaged_results: dict[str, dict[str, np.ndarray]],
+        averaged_results: Dict[str, Dict[str, np.ndarray]],
-        selected_masks: list[list[int]],
+        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 +310,7 @@ 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
     }
@@ -350,22 +352,22 @@ def select_best_mask_strategy(
 
 
 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],
+        mask_strategies: List[str],
-        output_dir: str = 'output/mask_search_result/') -> str | None:
+        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 +377,7 @@ 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/backends/abstract.py

@@ -3,7 +3,8 @@
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, fields
-from typing import TYPE_CHECKING, Any, Generic, Protocol, TypeVar
+from typing import (TYPE_CHECKING, Any, Dict, Generic, Optional, Protocol, Set,
+                    Type, TypeVar)
 
 if TYPE_CHECKING:
     from fastvideo.v1.fastvideo_args import FastVideoArgs
@@ -26,12 +27,12 @@ class AttentionBackend(ABC):
 
     @staticmethod
     @abstractmethod
-    def get_impl_cls() -> type["AttentionImpl"]:
+    def get_impl_cls() -> Type["AttentionImpl"]:
         raise NotImplementedError
 
     @staticmethod
     @abstractmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
         raise NotImplementedError
 
     # @staticmethod
@@ -45,7 +46,7 @@ class AttentionBackend(ABC):
 
     @staticmethod
     @abstractmethod
-    def get_builder_cls() -> type["AttentionMetadataBuilder"]:
+    def get_builder_cls() -> Type["AttentionMetadataBuilder"]:
         raise NotImplementedError
 
 
@@ -56,7 +57,8 @@ class AttentionMetadata:
     current_timestep: int
 
     def asdict_zerocopy(self,
-                        skip_fields: set[str] | None = None) -> dict[str, Any]:
+                        skip_fields: Optional[Set[str]] = None
+                        ) -> Dict[str, Any]:
         """Similar to dataclasses.asdict, but avoids deepcopying."""
         if skip_fields is None:
             skip_fields = set()
@@ -122,7 +124,7 @@ class AttentionImpl(ABC, Generic[T]):
         head_size: int,
         softmax_scale: float,
         causal: bool = False,
-        num_kv_heads: int | None = None,
+        num_kv_heads: Optional[int] = None,
         prefix: str = "",
         **extra_impl_args,
     ) -> None:

fastvideo/v1/attention/backends/flash_attn.py

@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
+from typing import List, Optional, Type
+
 import torch
 from flash_attn import flash_attn_func as flash_attn_2_func
 
@@ -26,7 +28,7 @@ class FlashAttentionBackend(AttentionBackend):
     accept_output_buffer: bool = True
 
     @staticmethod
-    def get_supported_head_sizes() -> list[int]:
+    def get_supported_head_sizes() -> List[int]:
         return [32, 64, 96, 128, 160, 192, 224, 256]
 
     @staticmethod
@@ -34,15 +36,15 @@ class FlashAttentionBackend(AttentionBackend):
         return "FLASH_ATTN"
 
     @staticmethod
-    def get_impl_cls() -> type["FlashAttentionImpl"]:
+    def get_impl_cls() -> Type["FlashAttentionImpl"]:
         return FlashAttentionImpl
 
     @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
+    def get_metadata_cls() -> Type["AttentionMetadata"]:
         raise NotImplementedError
 
     @staticmethod
-    def get_builder_cls() -> type["AttentionMetadataBuilder"]:
+    def get_builder_cls() -> Type["AttentionMetadataBuilder"]:
         raise NotImplementedError
 
 
@@ -54,7 +56,7 @@ class FlashAttentionImpl(AttentionImpl):
         head_size: int,
         causal: bool,
         softmax_scale: float,
-        num_kv_heads: int | None = None,
+        num_kv_heads: Optional[int] = None,
         prefix: str = "",
         **extra_impl_args,
     ) -> None:

fastvideo/v1/attention/backends/sage_attn.py

@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
+from typing import List, Optional, Type
 
 import torch
 from sageattention import sageattn
@@ -17,7 +18,7 @@ class SageAttentionBackend(AttentionBackend):
     accept_output_buffer: bool = True
 
     @staticmethod
-    def get_supported_head_sizes() -> list[int]:
+    def get_supported_head_sizes() -> List[int]:
         return [32, 64, 96, 128, 160, 192, 224, 256]
 
     @staticmethod
@@ -25,7 +26,7 @@ class SageAttentionBackend(AttentionBackend):
         return "SAGE_ATTN"
 
     @staticmethod
-    def get_impl_cls() -> type["SageAttentionImpl"]:
+    def get_impl_cls() -> Type["SageAttentionImpl"]:
         return SageAttentionImpl
 
     # @staticmethod
@@ -41,7 +42,7 @@ class SageAttentionImpl(AttentionImpl):
         head_size: int,
         causal: bool,
         softmax_scale: float,
-        num_kv_heads: int | None = None,
+        num_kv_heads: Optional[int] = None,
         prefix: str = "",
         **extra_impl_args,
     ) -> None:

fastvideo/v1/attention/backends/sdpa.py

@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
+from typing import List, Optional, Type
 
 import torch
 
@@ -16,7 +17,7 @@ class SDPABackend(AttentionBackend):
     accept_output_buffer: bool = True
 
     @staticmethod
-    def get_supported_head_sizes() -> list[int]:
+    def get_supported_head_sizes() -> List[int]:
         return [32, 64, 96, 128, 160, 192, 224, 256]
 
     @staticmethod
@@ -24,7 +25,7 @@ class SDPABackend(AttentionBackend):
         return "SDPA"
 
     @staticmethod
-    def get_impl_cls() -> type["SDPAImpl"]:
+    def get_impl_cls() -> Type["SDPAImpl"]:
         return SDPAImpl
 
     # @staticmethod
@@ -40,7 +41,7 @@ class SDPAImpl(AttentionImpl):
         head_size: int,
         causal: bool,
         softmax_scale: float,
-        num_kv_heads: int | None = None,
+        num_kv_heads: Optional[int] = None,
         prefix: str = "",
         **extra_impl_args,
     ) -> None:

fastvideo/v1/attention/backends/sliding_tile_attn.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 import json
 from dataclasses import dataclass
-from typing import Any
+from typing import Any, List, Optional, Type
 
 import torch
 from einops import rearrange
@@ -40,7 +40,7 @@ class SlidingTileAttentionBackend(AttentionBackend):
     accept_output_buffer: bool = True
 
     @staticmethod
-    def get_supported_head_sizes() -> list[int]:
+    def get_supported_head_sizes() -> List[int]:
         # TODO(will-refactor): check this
         return [32, 64, 96, 128, 160, 192, 224, 256]
 
@@ -49,22 +49,22 @@ class SlidingTileAttentionBackend(AttentionBackend):
         return "SLIDING_TILE_ATTN"
 
     @staticmethod
-    def get_impl_cls() -> type["SlidingTileAttentionImpl"]:
+    def get_impl_cls() -> Type["SlidingTileAttentionImpl"]:
         return SlidingTileAttentionImpl
 
     @staticmethod
-    def get_metadata_cls() -> type["SlidingTileAttentionMetadata"]:
+    def get_metadata_cls() -> Type["SlidingTileAttentionMetadata"]:
         return SlidingTileAttentionMetadata
 
     @staticmethod
-    def get_builder_cls() -> type["SlidingTileAttentionMetadataBuilder"]:
+    def get_builder_cls() -> Type["SlidingTileAttentionMetadataBuilder"]:
         return SlidingTileAttentionMetadataBuilder
 
 
 @dataclass
 class SlidingTileAttentionMetadata(AttentionMetadata):
     current_timestep: int
-    STA_param: list[list[
+    STA_param: List[List[
         Any]]  # each timestep with one metadata, shape [num_layers, num_heads]
 
 
@@ -98,7 +98,7 @@ class SlidingTileAttentionImpl(AttentionImpl):
         head_size: int,
         causal: bool,
         softmax_scale: float,
-        num_kv_heads: int | None = None,
+        num_kv_heads: Optional[int] = None,
         prefix: str = "",
         **extra_impl_args,
     ) -> None:

fastvideo/v1/attention/backends/video_sparse_attn.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 import math
 from dataclasses import dataclass
+from typing import List, Optional, Type
 
 import torch
 from einops import rearrange
@@ -27,7 +28,7 @@ class VideoSparseAttentionBackend(AttentionBackend):
     accept_output_buffer: bool = True
 
     @staticmethod
-    def get_supported_head_sizes() -> list[int]:
+    def get_supported_head_sizes() -> List[int]:
         return [64, 128]
 
     @staticmethod
@@ -35,22 +36,22 @@ class VideoSparseAttentionBackend(AttentionBackend):
         return "VIDEO_SPARSE_ATTN"
 
     @staticmethod
-    def get_impl_cls() -> type["VideoSparseAttentionImpl"]:
+    def get_impl_cls() -> Type["VideoSparseAttentionImpl"]:
         return VideoSparseAttentionImpl
 
     @staticmethod
-    def get_metadata_cls() -> type["VideoSparseAttentionMetadata"]:
+    def get_metadata_cls() -> Type["VideoSparseAttentionMetadata"]:
         return VideoSparseAttentionMetadata
 
     @staticmethod
-    def get_builder_cls() -> type["VideoSparseAttentionMetadataBuilder"]:
+    def get_builder_cls() -> Type["VideoSparseAttentionMetadataBuilder"]:
         return VideoSparseAttentionMetadataBuilder
 
 
 @dataclass
 class VideoSparseAttentionMetadata(AttentionMetadata):
     current_timestep: int
-    dit_seq_shape: list[int]
+    dit_seq_shape: List[int]
     VSA_sparsity: float
 
 
@@ -96,7 +97,7 @@ class VideoSparseAttentionImpl(AttentionImpl):
         head_size: int,
         causal: bool,
         softmax_scale: float,
-        num_kv_heads: int | None = None,
+        num_kv_heads: Optional[int] = None,
         prefix: str = "",
         **extra_impl_args,
     ) -> None:
@@ -104,8 +105,8 @@ class VideoSparseAttentionImpl(AttentionImpl):
         sp_group = get_sp_group()
         self.sp_size = sp_group.world_size
         self.VSA_base_tile_size = [4, 4, 4]
-        self.dit_seq_shape: list[int]
+        self.dit_seq_shape: List[int]
-        self.full_window_size: list[int]
+        self.full_window_size: List[int]
         self.img_seq_length: int
 
     def tile(self, x: torch.Tensor) -> torch.Tensor:

fastvideo/v1/attention/layer.py

@@ -1,5 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
+from typing import Optional, Tuple
+
 import torch
 import torch.nn as nn
 
@@ -21,11 +23,11 @@ class DistributedAttention(nn.Module):
     def __init__(self,
                  num_heads: int,
                  head_size: int,
-                 num_kv_heads: int | None = None,
+                 num_kv_heads: Optional[int] = None,
-                 softmax_scale: float | None = None,
+                 softmax_scale: Optional[float] = None,
                  causal: bool = False,
-                 supported_attention_backends: tuple[AttentionBackendEnum, ...]
+                 supported_attention_backends: Optional[Tuple[
-                 | None = None,
+                     AttentionBackendEnum, ...]] = None,
                  prefix: str = "",
                  **extra_impl_args) -> None:
         super().__init__()
@@ -61,10 +63,10 @@ class DistributedAttention(nn.Module):
         q: torch.Tensor,
         k: torch.Tensor,
         v: torch.Tensor,
-        replicated_q: torch.Tensor | None = None,
+        replicated_q: Optional[torch.Tensor] = None,
-        replicated_k: torch.Tensor | None = None,
+        replicated_k: Optional[torch.Tensor] = None,
-        replicated_v: torch.Tensor | None = None,
+        replicated_v: Optional[torch.Tensor] = None,
-    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         """Forward pass for distributed attention.
         
         Args:
@@ -142,11 +144,11 @@ class DistributedAttention_VSA(DistributedAttention):
         q: torch.Tensor,
         k: torch.Tensor,
         v: torch.Tensor,
-        replicated_q: torch.Tensor | None = None,
+        replicated_q: Optional[torch.Tensor] = None,
-        replicated_k: torch.Tensor | None = None,
+        replicated_k: Optional[torch.Tensor] = None,
-        replicated_v: torch.Tensor | None = None,
+        replicated_v: Optional[torch.Tensor] = None,
-        gate_compress: torch.Tensor | None = None,
+        gate_compress: Optional[torch.Tensor] = None,
-    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
         """Forward pass for distributed attention.
         
         Args:
@@ -206,11 +208,11 @@ class LocalAttention(nn.Module):
     def __init__(self,
                  num_heads: int,
                  head_size: int,
-                 num_kv_heads: int | None = None,
+                 num_kv_heads: Optional[int] = None,
-                 softmax_scale: float | None = None,
+                 softmax_scale: Optional[float] = None,
                  causal: bool = False,
-                 supported_attention_backends: tuple[AttentionBackendEnum, ...]
+                 supported_attention_backends: Optional[Tuple[
-                 | None = None,
+                     AttentionBackendEnum, ...]] = None,
                  **extra_impl_args) -> None:
         super().__init__()
         if softmax_scale is None:

fastvideo/v1/attention/selector.py

@@ -2,10 +2,9 @@
 # Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/attention/selector.py
 
 import os
-from collections.abc import Generator
 from contextlib import contextmanager
 from functools import cache
-from typing import cast
+from typing import Generator, Optional, Tuple, Type, cast
 
 import torch
 
@@ -18,7 +17,7 @@ from fastvideo.v1.utils import STR_BACKEND_ENV_VAR, resolve_obj_by_qualname
 logger = init_logger(__name__)
 
 
-def backend_name_to_enum(backend_name: str) -> AttentionBackendEnum | None:
+def backend_name_to_enum(backend_name: str) -> Optional[AttentionBackendEnum]:
     """
     Convert a string backend name to a _Backend enum value.
 
@@ -32,7 +31,7 @@ def backend_name_to_enum(backend_name: str) -> AttentionBackendEnum | None:
           None
 
 
-def get_env_variable_attn_backend() -> AttentionBackendEnum | None:
+def get_env_variable_attn_backend() -> Optional[AttentionBackendEnum]:
     '''
     Get the backend override specified by the FastVideo attention
     backend environment variable, if one is specified.
@@ -54,11 +53,11 @@ def get_env_variable_attn_backend() -> AttentionBackendEnum | None:
 #
 # THIS SELECTION TAKES PRECEDENCE OVER THE
 # FASTVIDEO ATTENTION BACKEND ENVIRONMENT VARIABLE
-forced_attn_backend: AttentionBackendEnum | None = None
+forced_attn_backend: Optional[AttentionBackendEnum] = None
 
 
 def global_force_attn_backend(
-        attn_backend: AttentionBackendEnum | None) -> None:
+        attn_backend: Optional[AttentionBackendEnum]) -> None:
     '''
     Force all attention operations to use a specified backend.
 
@@ -73,7 +72,7 @@ def global_force_attn_backend(
     forced_attn_backend = attn_backend
 
 
-def get_global_forced_attn_backend() -> AttentionBackendEnum | None:
+def get_global_forced_attn_backend() -> Optional[AttentionBackendEnum]:
     '''
     Get the currently-forced choice of attention backend,
     or None if auto-selection is currently enabled.
@@ -84,9 +83,9 @@ def get_global_forced_attn_backend() -> AttentionBackendEnum | None:
 def get_attn_backend(
     head_size: int,
     dtype: torch.dtype,
-    supported_attention_backends: tuple[AttentionBackendEnum, ...]
+    supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
-    | None = None,
+                                                 ...]] = None,
-) -> type[AttentionBackend]:
+) -> Type[AttentionBackend]:
     return _cached_get_attn_backend(head_size, dtype,
                                     supported_attention_backends)
 
@@ -95,9 +94,9 @@ def get_attn_backend(
 def _cached_get_attn_backend(
     head_size: int,
     dtype: torch.dtype,
-    supported_attention_backends: tuple[AttentionBackendEnum, ...]
+    supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
-    | None = None,
+                                                 ...]] = None,
-) -> type[AttentionBackend]:
+) -> Type[AttentionBackend]:
     # Check whether a particular choice of backend was
     # previously forced.
     #
@@ -106,13 +105,13 @@ def _cached_get_attn_backend(
     if not supported_attention_backends:
         raise ValueError("supported_attention_backends is empty")
     selected_backend = None
-    backend_by_global_setting: AttentionBackendEnum | None = (
+    backend_by_global_setting: Optional[AttentionBackendEnum] = (
         get_global_forced_attn_backend())
     if backend_by_global_setting is not None:
         selected_backend = backend_by_global_setting
     else:
         # Check the environment variable and override if specified
-        backend_by_env_var: str | None = envs.FASTVIDEO_ATTENTION_BACKEND
+        backend_by_env_var: Optional[str] = envs.FASTVIDEO_ATTENTION_BACKEND
         if backend_by_env_var is not None:
             selected_backend = backend_name_to_enum(backend_by_env_var)
 
@@ -124,7 +123,7 @@ def _cached_get_attn_backend(
     if not attention_cls:
         raise ValueError(
             f"Invalid attention backend for {current_platform.device_name}")
-    return cast(type[AttentionBackend], resolve_obj_by_qualname(attention_cls))
+    return cast(Type[AttentionBackend], resolve_obj_by_qualname(attention_cls))
 
 
 @contextmanager

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(
+    pass
-        default_factory=list
-    )  # mapping from huggingface weight names to custom names
 
 
 @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

@@ -1,6 +1,6 @@
 # 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
@@ -12,9 +12,8 @@ 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, ...] = (
+    _supported_attention_backends: Tuple[AttentionBackendEnum, ...] = (
         AttentionBackendEnum.SLIDING_TILE_ATTN, AttentionBackendEnum.SAGE_ATTN,
         AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA,
         AttentionBackendEnum.VIDEO_SPARSE_ATTN)
@@ -22,7 +21,7 @@ class DiTArchConfig(ArchConfig):
     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 +34,7 @@ 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(
+    _fsdp_shard_conditions: list = field(default_factory=lambda: [is_blocks])
-        default_factory=lambda:
-        [lambda n, m: "transformer_blocks" in n and n.split(".")[-1].isdigit()])
 
     _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"
+    attention_type: Optional[str] = "torch"
-    use_additional_conditions: bool | None = False
+    use_additional_conditions: Optional[bool] = False
-    exclude_lora_layers: list[str] = field(default_factory=lambda: [])
+    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
+    image_dim: Optional[int] = None
-    added_kv_proj_dim: int | None = 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

@@ -1,6 +1,6 @@
 # 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
 
@@ -11,8 +11,8 @@ from fastvideo.v1.platforms import AttentionBackendEnum
 
 @dataclass
 class EncoderArchConfig(ArchConfig):
-    architectures: list[str] = field(default_factory=lambda: [])
+    architectures: List[str] = field(default_factory=lambda: [])
-    _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (
+    _supported_attention_backends: Tuple[AttentionBackendEnum, ...] = (
         AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.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
+    tokenizer_kwargs: Dict[str, Any] = field(default_factory=dict)
-    )  # 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: [])
 
     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
+    last_hidden_state: Optional[torch.FloatTensor] = None
-    pooler_output: torch.FloatTensor | None = None
+    pooler_output: Optional[torch.FloatTensor] = None
-    hidden_states: tuple[torch.FloatTensor, ...] | None = None
+    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
-    attentions: tuple[torch.FloatTensor, ...] | None = None
+    attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
-    attention_mask: torch.Tensor | None = 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
+    quant_config: Optional[QuantizationConfig] = None
-    lora_config: Any | None = 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
+    num_hidden_layers_override: Optional[int] = None
-    require_post_norm: bool | None = 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
+    num_hidden_layers_override: Optional[int] = None
-    require_post_norm: bool | None = 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, ...] = ()
+    attn_scales: Tuple[float, ...] = ()
-    temperal_downsample: tuple[bool, ...] = (False, True, True)
+    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 +1,8 @@
 # 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
 
@@ -40,11 +39,11 @@ 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 +52,7 @@ 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
 
@@ -63,29 +62,30 @@ 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_path: Optional[str] = None
-    lora_nickname: str | None = "default"  # for swapping adapters in the pipeline
+    lora_nickname: Optional[
-    lora_target_names: list[
+        str] = "default"  # for swapping adapters in the pipeline
-        str] | None = None  # can restrict list of layers to adapt, e.g. ["q_proj"]
+    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
+    pos_magic: Optional[str] = None
-    neg_magic: str | None = None
+    neg_magic: Optional[str] = None
-    timesteps_scale: bool | None = None
+    timesteps_scale: Optional[bool] = None
 
     # STA (Sliding Tile Attention) parameters
-    mask_strategy_file_path: str | None = None
+    mask_strategy_file_path: Optional[str] = None
     STA_mode: STA_Mode = STA_Mode.STA_INFERENCE
     skip_time_steps: int = 15
 
@@ -217,7 +217,7 @@ 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 +243,7 @@ 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 +255,11 @@ 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',
+        model_path: Optional[str] = kwargs.get(prefix_with_dot + 'model_path',
-                                            None) or kwargs.get('model_path')
+                                               None) or kwargs.get('model_path')
-        pipeline_config_or_path: str | PipelineConfig | dict[
+        pipeline_config_or_path: Optional[Union[str, PipelineConfig, Dict[
-            str, Any] | None = kwargs.get(prefix_with_dot + 'pipeline_config',
+            str, Any]]] = kwargs.get(prefix_with_dot + 'pipeline_config',
-                                          None) or kwargs.get('pipeline_config')
+                                     None) or kwargs.get('pipeline_config')
         if model_path is None:
             raise ValueError("model_path is required in kwargs")
 
@@ -344,7 +344,7 @@ 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 +360,8 @@ 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,
+                    for target_config, source_config in zip(
-                                                            new_value,
+                            current_value, new_value):
-                                                            strict=True):
                         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"
+    vae_precision: str = "fp16"
-    text_encoder_precisions: tuple[str, ...] = field(
+    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
+    prompt: Optional[Union[str, List[str]]] = None
-    negative_prompt: str | None = None
+    negative_prompt: Optional[str] = None
-    prompt_path: str | None = 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

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 import os
-from collections.abc import Callable
+from typing import Any, Callable, Dict, Optional
-from typing import Any
 
 from fastvideo.v1.configs.sample.hunyuan import (FastHunyuanSamplingParam,
                                                  HunyuanSamplingParam)
@@ -9,28 +8,26 @@ from fastvideo.v1.configs.sample.stepvideo import StepVideoT2VSamplingParam
 from fastvideo.v1.configs.sample.wan import (WanI2V_14B_480P_SamplingParam,
                                              WanI2V_14B_720P_SamplingParam,
                                              WanT2V_1_3B_SamplingParam,
-                                             WanT2V_14B_SamplingParam,
+                                             WanT2V_14B_SamplingParam)
-                                             Wan2_1_Fun_1_3B_InP_SamplingParam)
 from fastvideo.v1.logger import init_logger
 from fastvideo.v1.utils import (maybe_download_model_index,
                                 verify_model_config_and_directory)
 
 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(),
@@ -39,7 +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":
@@ -50,7 +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": {

fastvideo/v1/dataset/__init__.py

@@ -12,10 +12,7 @@ from fastvideo.v1.dataset.validation_dataset import ValidationDataset
 
 
 def getdataset(args) -> VideoCaptionMergedDataset:
-    if args.do_temporal_sample:
+    temporal_sample = TemporalRandomCrop(args.num_frames)  # 16 x
-        temporal_sample = TemporalRandomCrop(args.num_frames)  # 16 x
-    else:
-        temporal_sample = None
     norm_fun = Lambda(lambda x: 2.0 * x - 1.0)
     resize_topcrop = [
         CenterCropResizeVideo((args.max_height, args.max_width), top_crop=True),

fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py

@@ -11,7 +11,7 @@ from fastvideo.v1.dataset.parquet_dataset_iterable_style import (
     build_parquet_iterable_style_dataloader)
 from fastvideo.v1.distributed import get_world_rank
 from fastvideo.v1.distributed.parallel_state import (
-    cleanup_dist_env_and_memory, get_local_torch_device,
+    cleanup_dist_env_and_memory, get_torch_device,
     maybe_init_distributed_environment_and_model_parallel)
 from fastvideo.v1.logger import init_logger
 
@@ -128,8 +128,8 @@ def main() -> None:
 
         # Verify all sums match
         if all(
-                abs(a - b) < 1e-6 for a, b in zip(
+                abs(a - b) < 1e-6
-                    first_pass_sums, second_pass_sums, strict=True)):
+                for a, b in zip(first_pass_sums, second_pass_sums)):
             logger.info(
                 "All latent sums match between passes - resume verification successful!"
             )
@@ -148,8 +148,8 @@ def main() -> None:
                 break
 
             # Move data to device
-            latents = latents.to(get_local_torch_device())
+            latents = latents.to(get_torch_device())
-            embeddings = embeddings.to(get_local_torch_device())
+            embeddings = embeddings.to(get_torch_device())
 
             # Calculate actual batch size
             batch_size = latents.size(0)

fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_map_style.py

@@ -8,12 +8,11 @@ import torch
 import torch.distributed as dist
 import torch.distributed.checkpoint as dist_cp
 
-from fastvideo.v1.dataset.dataloader.schema import pyarrow_schema_t2v
 from fastvideo.v1.dataset.parquet_dataset_map_style import (
     build_parquet_map_style_dataloader)
 from fastvideo.v1.distributed import get_world_rank
 from fastvideo.v1.distributed.parallel_state import (
-    cleanup_dist_env_and_memory, get_local_torch_device,
+    cleanup_dist_env_and_memory, get_torch_device,
     maybe_init_distributed_environment_and_model_parallel)
 from fastvideo.v1.logger import init_logger
 
@@ -68,18 +67,14 @@ def main() -> None:
 
     # Create DataLoader with proper settings
     dataset, dataloader = build_parquet_map_style_dataloader(
-        args.path,
+        args.path, args.batch_size, args.num_data_workers)
-        args.batch_size,
-        parquet_schema=pyarrow_schema_t2v,
-        num_data_workers=args.num_data_workers)
     logger.info("Initialized dataloader with %d batches", len(dataloader))
 
     if args.verify_resume:
         # First pass - record latent sums
         first_pass_sums = []
-        for i, batch in enumerate(dataloader):
+        for i, (latents, embeddings, masks,
-            latents = batch['vae_latent']
+                caption_text) in enumerate(dataloader):
-            embeddings = batch['text_embedding']
             latent_sum = latents.sum().item()
             first_pass_sums.append(latent_sum)
             logger.info("Batch %d latent sum: %f", i, latent_sum)
@@ -105,18 +100,14 @@ def main() -> None:
 
         # Recreate dataloader and load state
         dataset, dataloader = build_parquet_map_style_dataloader(
-            args.path,
+            args.path, args.batch_size, args.num_data_workers)
-            args.batch_size,
-            parquet_schema=pyarrow_schema_t2v,
-            num_data_workers=args.num_data_workers)
         load_states = {"dataloader": dataloader}
         dist_cp.load(load_states, checkpoint_id=checkpoint_dir.as_posix())
         logger.info("Rank %d: Loaded dataloader state from %s",
                     get_world_rank(), checkpoint_dir)
 
-        for i, batch in enumerate(dataloader):
+        for i, (latents, embeddings, masks,
-            latents = batch['vae_latent']
+                caption_text) in enumerate(dataloader):
-            embeddings = batch['text_embedding']
             latent_sum = latents.sum().item()
             first_pass_sums.append(latent_sum)
             logger.info("Batch %d latent sum: %f",
@@ -125,16 +116,11 @@ def main() -> None:
                 break
 
         dataset, dataloader = build_parquet_map_style_dataloader(
-            args.path,
+            args.path, args.batch_size, args.num_data_workers)
-            args.batch_size,
-            parquet_schema=pyarrow_schema_t2v,
-            num_data_workers=args.num_data_workers)
 
         # Second pass - verify latent sums match
         second_pass_sums = []
-        for i, batch in enumerate(dataloader):
+        for i, (latents, embeddings, masks) in enumerate(dataloader):
-            latents = batch['vae_latent']
-            embeddings = batch['text_embedding']
             latent_sum = latents.sum().item()
             second_pass_sums.append(latent_sum)
             logger.info("Batch %d latent sum: %f (should match first pass: %f)",
@@ -144,8 +130,8 @@ def main() -> None:
 
         # Verify all sums match
         if all(
-                abs(a - b) < 1e-6 for a, b in zip(
+                abs(a - b) < 1e-6
-                    first_pass_sums, second_pass_sums, strict=True)):
+                for a, b in zip(first_pass_sums, second_pass_sums)):
             logger.info(
                 "All latent sums match between passes - resume verification successful!"
             )
@@ -158,15 +144,14 @@ def main() -> None:
     total_samples = 0
     total_batches = 0
     for _ in range(args.num_epoch):
-        for i, batch in enumerate(dataloader):
+        for i, (latents, embeddings, masks,
-            latents = batch['vae_latent']
+                caption_text) in enumerate(dataloader):
-            embeddings = batch['text_embedding']
             if i >= args.num_batches_per_epoch:
                 break
 
             # Move data to device
-            latents = latents.to(get_local_torch_device())
+            latents = latents.to(get_torch_device())
-            embeddings = embeddings.to(get_local_torch_device())
+            embeddings = embeddings.to(get_torch_device())
 
             # Calculate actual batch size
             batch_size = latents.size(0)

fastvideo/v1/dataset/latent_datasets.py

@@ -76,7 +76,7 @@ def latent_collate_function(batch):
     # latent_attn_mask: # b t h w
     # text_attn_mask: b 1 l
     # needs to check if the latent/prompt' size and apply padding & attn mask
-    latents, prompt_embeds, prompt_attention_masks = zip(*batch, strict=True)
+    latents, prompt_embeds, prompt_attention_masks = zip(*batch)
     # calculate max shape
     max_t = max([latent.shape[1] for latent in latents])
     max_h = max([latent.shape[2] for latent in latents])

fastvideo/v1/dataset/parquet_dataset_iterable_style.py

@@ -1,17 +1,19 @@
 import os
 import pickle
 import random
+from typing import Dict, List, Tuple
 
 import numpy as np
 import pyarrow as pa
 import pyarrow.parquet as pq
+import torch
 import tqdm
 from torch.utils.data import IterableDataset, get_worker_info
 from torchdata.stateful_dataloader import StatefulDataLoader
 
 from fastvideo.v1.dataset.utils import collate_latents_embs_masks
-from fastvideo.v1.distributed import (get_sp_world_size, get_world_group,
+from fastvideo.v1.distributed import (get_sp_world_size, get_world_rank,
-                                      get_world_rank, get_world_size)
+                                      get_world_size)
 from fastvideo.v1.logger import init_logger
 
 logger = init_logger(__name__)
@@ -137,7 +139,7 @@ def shard_parquet_files_across_sp_groups_and_workers(
     num_sp_groups: int,
     num_workers: int,
     seed: int = 42,
-) -> tuple[list[list[str]], list[int], list[dict[str, int]]]:
+) -> Tuple[List[List[str]], List[int], List[Dict[str, int]]]:
     """
     Shard parquet files across SP groups and workers in a balanced way.
     
@@ -156,123 +158,94 @@ def shard_parquet_files_across_sp_groups_and_workers(
     # Check if sharding plan already exists
     sharding_info_dir = os.path.join(
         path, f"sharding_info_{num_sp_groups}_sp_groups_{num_workers}_workers")
-
+    if os.path.exists(sharding_info_dir):
-    # Only rank 0 handles cache checking and file scanning
+        logger.info("Sharding plan already exists")
-    if get_world_rank() == 0:
+        logger.info("Loading sharding plan from %s", sharding_info_dir)
-        cache_loaded = False
+        try:
-        shard_parquet_files = None
-        shard_total_samples = None
-        shard_parquet_lengths = None
-
-        # First try to load existing sharding plan
-        if os.path.exists(sharding_info_dir):
-            logger.info("Loading sharding plan from %s", sharding_info_dir)
-            try:
-                with open(
-                        os.path.join(sharding_info_dir,
-                                     "shard_parquet_files.pkl"), "rb") as f:
-                    shard_parquet_files = pickle.load(f)
-                with open(
-                        os.path.join(sharding_info_dir,
-                                     "shard_total_samples.pkl"), "rb") as f:
-                    shard_total_samples = pickle.load(f)
-                with open(
-                        os.path.join(sharding_info_dir,
-                                     "shard_parquet_lengths.pkl"), "rb") as f:
-                    shard_parquet_lengths = pickle.load(f)
-                cache_loaded = True
-                logger.info("Successfully loaded sharding plan")
-            except Exception as e:
-                logger.error("Error loading sharding plan: %s", str(e))
-                logger.info("Falling back to creating new sharding plan")
-                cache_loaded = False
-
-        # If cache not loaded (either doesn't exist or failed to load), create sharding plan
-        if not cache_loaded:
-            logger.info("Creating new sharding plan")
-            logger.info("Scanning for parquet files in %s", path)
-
-            # Find all parquet files
-            parquet_files = []
-
-            for root, _, files in os.walk(path):
-                for file in files:
-                    if file.endswith('.parquet'):
-                        parquet_files.append(os.path.join(root, file))
-
-            if not parquet_files:
-                raise ValueError("No parquet files found in %s", path)
-
-            # Calculate file lengths efficiently using a single pass
-            logger.info("Calculating file lengths...")
-            lengths = []
-            for file in tqdm.tqdm(parquet_files, desc="Reading parquet files"):
-                lengths.append(pq.ParquetFile(file).metadata.num_rows)
-
-            total_samples = sum(lengths)
-            logger.info("Found %d files with %d total samples",
-                        len(parquet_files), total_samples)
-
-            # Sort files by length for better balancing
-            sorted_indices = np.argsort(lengths)
-            sorted_files = [parquet_files[i] for i in sorted_indices]
-            sorted_lengths = [lengths[i] for i in sorted_indices]
-
-            # Create shards
-            num_shards = num_sp_groups * num_workers
-            shard_parquet_files = [[] for _ in range(num_shards)]
-            shard_total_samples = [0] * num_shards
-            shard_parquet_lengths = [{} for _ in range(num_shards)]
-
-            # Distribute files to shards using a greedy approach
-            logger.info("Distributing files to shards...")
-            for file, length in zip(reversed(sorted_files),
-                                    reversed(sorted_lengths),
-                                    strict=True):
-                # Find shard with minimum current length
-                target_shard = np.argmin(shard_total_samples)
-                shard_parquet_files[target_shard].append(file)
-                shard_total_samples[target_shard] += length
-                shard_parquet_lengths[target_shard][file] = length
-            #randomize each shard
-            for shard in shard_parquet_files:
-                random.seed(seed)
-                random.shuffle(shard)
-
-            # Save the sharding plan
-            os.makedirs(sharding_info_dir, exist_ok=True)
             with open(
                     os.path.join(sharding_info_dir, "shard_parquet_files.pkl"),
-                    "wb") as f:
+                    "rb") as f:
-                pickle.dump(shard_parquet_files, f)
+                shard_parquet_files = pickle.load(f)
             with open(
                     os.path.join(sharding_info_dir, "shard_total_samples.pkl"),
-                    "wb") as f:
+                    "rb") as f:
-                pickle.dump(shard_total_samples, f)
+                shard_total_samples = pickle.load(f)
             with open(
                     os.path.join(sharding_info_dir,
-                                 "shard_parquet_lengths.pkl"), "wb") as f:
+                                 "shard_parquet_lengths.pkl"), "rb") as f:
-                pickle.dump(shard_parquet_lengths, f)
+                shard_parquet_lengths = pickle.load(f)
-            logger.info("Saved sharding info to %s", sharding_info_dir)
+            return shard_parquet_files, shard_total_samples, shard_parquet_lengths
+        except Exception as e:
+            logger.error("Error loading sharding plan: %s", str(e))
+            logger.info("Falling back to creating new sharding plan")
 
-    # Wait for rank 0 to finish creating/loading sharding plan
+    if get_world_rank() == 0:
-    world_group = get_world_group()
+        logger.info("Scanning for parquet files in %s", path)
-    world_group.barrier()
 
-    # Now all ranks load the sharding plan (it should exist and be valid now)
+        # Find all parquet files
-    logger.info("Loading sharding plan from %s after barrier",
+        parquet_files = []
-                sharding_info_dir)
-    with open(os.path.join(sharding_info_dir, "shard_parquet_files.pkl"),
-              "rb") as f:
-        shard_parquet_files = pickle.load(f)
-    with open(os.path.join(sharding_info_dir, "shard_total_samples.pkl"),
-              "rb") as f:
-        shard_total_samples = pickle.load(f)
-    with open(os.path.join(sharding_info_dir, "shard_parquet_lengths.pkl"),
-              "rb") as f:
-        shard_parquet_lengths = pickle.load(f)
 
-    return shard_parquet_files, shard_total_samples, shard_parquet_lengths
+        for root, _, files in os.walk(path):
+            for file in files:
+                if file.endswith('.parquet'):
+                    parquet_files.append(os.path.join(root, file))
+
+        if not parquet_files:
+            raise ValueError("No parquet files found in %s", path)
+
+        # Calculate file lengths efficiently using a single pass
+        logger.info("Calculating file lengths...")
+        lengths = []
+        for file in tqdm.tqdm(parquet_files, desc="Reading parquet files"):
+            lengths.append(pq.ParquetFile(file).metadata.num_rows)
+
+        total_samples = sum(lengths)
+        logger.info("Found %d files with %d total samples", len(parquet_files),
+                    total_samples)
+
+        # Sort files by length for better balancing
+        sorted_indices = np.argsort(lengths)
+        sorted_files = [parquet_files[i] for i in sorted_indices]
+        sorted_lengths = [lengths[i] for i in sorted_indices]
+
+        # Create shards
+        num_shards = num_sp_groups * num_workers
+        shard_parquet_files = [[] for _ in range(num_shards)]
+        shard_total_samples = [0] * num_shards
+        shard_parquet_lengths = [{} for _ in range(num_shards)]
+
+        # Distribute files to shards using a greedy approach
+        logger.info("Distributing files to shards...")
+        for file, length in zip(reversed(sorted_files),
+                                reversed(sorted_lengths)):
+            # Find shard with minimum current length
+            target_shard = np.argmin(shard_total_samples)
+            shard_parquet_files[target_shard].append(file)
+            shard_total_samples[target_shard] += length
+            shard_parquet_lengths[target_shard][file] = length
+        #randomize each shard
+        for shard in shard_parquet_files:
+            random.seed(seed)
+            random.shuffle(shard)
+
+        save_dir = os.path.join(
+            path,
+            f"sharding_info_{num_sp_groups}_sp_groups_{num_workers}_workers")
+        os.makedirs(save_dir, exist_ok=True)
+        with open(os.path.join(save_dir, "shard_parquet_files.pkl"), "wb") as f:
+            pickle.dump(shard_parquet_files, f)
+        with open(os.path.join(save_dir, "shard_total_samples.pkl"), "wb") as f:
+            pickle.dump(shard_total_samples, f)
+        with open(os.path.join(save_dir, "shard_parquet_lengths.pkl"),
+                  "wb") as f:
+            pickle.dump(shard_parquet_lengths, f)
+        logger.info("Saved sharding info to %s", save_dir)
+
+    # wait for all ranks to finish
+    torch.distributed.barrier()
+    # recursive call
+    return shard_parquet_files_across_sp_groups_and_workers(
+        path, num_sp_groups, num_workers, seed)
 
 
 def build_parquet_iterable_style_dataloader(
@@ -284,7 +257,7 @@ def build_parquet_iterable_style_dataloader(
     text_padding_length: int = 512,
     seed: int = 42,
     read_batch_size: int = 32
-) -> tuple[LatentsParquetIterStyleDataset, StatefulDataLoader]:
+) -> Tuple[LatentsParquetIterStyleDataset, StatefulDataLoader]:
     """Build a dataloader for the LatentsParquetIterStyleDataset."""
     dataset = LatentsParquetIterStyleDataset(
         path=path,

fastvideo/v1/dataset/parquet_dataset_map_style.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 import os
 import pickle
-from typing import Any
+from typing import Any, Dict, List, Tuple
 
 import pyarrow as pa
 import pyarrow.parquet as pq
@@ -13,14 +13,14 @@ from torch.utils.data import Dataset, Sampler
 from torchdata.stateful_dataloader import StatefulDataLoader
 
 from fastvideo.v1.dataset.utils import collate_rows_from_parquet_schema
-from fastvideo.v1.distributed import (get_sp_world_size, get_world_group,
+from fastvideo.v1.distributed import (get_sp_world_size, get_world_rank,
-                                      get_world_rank, get_world_size)
+                                      get_world_size)
 from fastvideo.v1.logger import init_logger
 
 logger = init_logger(__name__)
 
 
-class DP_SP_BatchSampler(Sampler[list[int]]):
+class DP_SP_BatchSampler(Sampler[List[int]]):
     """
     A simple sequential batch sampler that yields batches of indices.
     """
@@ -97,68 +97,49 @@ def get_parquet_files_and_length(path: str):
     cache_dir = os.path.join(path, "map_style_cache")
     cache_file = os.path.join(cache_dir, "file_info.pkl")
 
-    # Only rank 0 checks for cache and scans files if needed
+    if os.path.exists(cache_file):
+        logger.info("Loading cached file info from %s", cache_file)
+        try:
+            with open(cache_file, "rb") as f:
+                file_names_sorted, lengths_sorted = pickle.load(f)
+            return file_names_sorted, lengths_sorted
+        except Exception as e:
+            logger.error("Error loading cached file info: %s", str(e))
+            logger.info("Falling back to scanning files")
+
+    # If no cache exists or loading failed, scan files
     if get_world_rank() == 0:
-        cache_loaded = False
+        lengths = []
-        file_names_sorted = None
+        file_names = []
-        lengths_sorted = None
+        for root, _, files in os.walk(path):
+            for file in sorted(files):
+                if file.endswith('.parquet'):
+                    file_path = os.path.join(root, file)
+                    file_names.append(file_path)
+        for file_path in tqdm.tqdm(file_names,
+                                   desc="Reading parquet files to get lengths"):
+            num_rows = pq.ParquetFile(file_path).metadata.num_rows
+            lengths.append(num_rows)
+        # sort according to file name to ensure all rank has the same order (in case os.walk is not sorted)
+        file_names_sorted, lengths_sorted = zip(
+            *sorted(zip(file_names, lengths), key=lambda x: x[0]))
+        assert len(
+            file_names_sorted) != 0, "No parquet files found in the dataset"
 
-        # First try to load existing cache
+        os.makedirs(cache_dir, exist_ok=True)
-        if os.path.exists(cache_file):
+        with open(cache_file, "wb") as f:
-            logger.info("Loading cached file info from %s", cache_file)
+            pickle.dump((file_names_sorted, lengths_sorted), f)
-            try:
+        logger.info("Saved file info to %s", cache_file)
-                with open(cache_file, "rb") as f:
-                    file_names_sorted, lengths_sorted = pickle.load(f)
-                cache_loaded = True
-                logger.info("Successfully loaded cached file info")
-            except Exception as e:
-                logger.error("Error loading cached file info: %s", str(e))
-                logger.info("Falling back to scanning files")
-                cache_loaded = False
 
-        # If cache not loaded (either doesn't exist or failed to load), scan files
+    # Wait for rank 0 to finish saving
-        if not cache_loaded:
+    if get_world_size() > 1:
-            logger.info("Scanning parquet files to get lengths")
+        torch.distributed.barrier()
-            lengths = []
-            file_names = []
-            for root, _, files in os.walk(path):
-                for file in sorted(files):
-                    if file.endswith('.parquet'):
-                        file_path = os.path.join(root, file)
-                        file_names.append(file_path)
-            for file_path in tqdm.tqdm(
-                    file_names, desc="Reading parquet files to get lengths"):
-                num_rows = pq.ParquetFile(file_path).metadata.num_rows
-                lengths.append(num_rows)
-            # sort according to file name to ensure all rank has the same order
-            file_names_sorted, lengths_sorted = zip(*sorted(zip(file_names,
-                                                                lengths,
-                                                                strict=True),
-                                                            key=lambda x: x[0]),
-                                                    strict=True)
-            assert len(
-                file_names_sorted) != 0, "No parquet files found in the dataset"
 
-            # Save the cache
+    return get_parquet_files_and_length(path)
-            os.makedirs(cache_dir, exist_ok=True)
-            with open(cache_file, "wb") as f:
-                pickle.dump((file_names_sorted, lengths_sorted), f)
-            logger.info("Saved file info to %s", cache_file)
-
-    # Wait for rank 0 to finish creating/loading cache
-    world_group = get_world_group()
-    world_group.barrier()
-
-    # Now all ranks load the cache (it should exist and be valid now)
-    logger.info("Loading cached file info from %s after barrier", cache_file)
-    with open(cache_file, "rb") as f:
-        file_names_sorted, lengths_sorted = pickle.load(f)
-
-    return file_names_sorted, lengths_sorted
 
 
-def read_row_from_parquet_file(parquet_files: list[str], global_row_idx: int,
+def read_row_from_parquet_file(parquet_files: List[str], global_row_idx: int,
-                               lengths: list[int]) -> dict[str, Any]:
+                               lengths: List[int]) -> Dict[str, Any]:
     '''
     Read a row from a parquet file.
     Args:
@@ -169,27 +150,17 @@ def read_row_from_parquet_file(parquet_files: list[str], global_row_idx: int,
     '''
     # find the parquet file and local row index
     cumulative = 0
-    file_index = 0
-    local_row_idx = 0
-
     for file_index in range(len(lengths)):
         if cumulative + lengths[file_index] > global_row_idx:
             local_row_idx = global_row_idx - cumulative
             break
         cumulative += lengths[file_index]
-    else:
-        # If we reach here, global_row_idx is out of bounds
-        raise IndexError(
-            f"global_row_idx {global_row_idx} is out of bounds for dataset")
 
     parquet_file = pq.ParquetFile(parquet_files[file_index])
 
     # Calculate the row group to read into memory and the local idx
     # This way we can avoid reading in the entire parquet file
     cumulative = 0
-    row_group_index = 0
-    local_index = 0
-
     for i in range(parquet_file.num_row_groups):
         num_rows = parquet_file.metadata.row_group(i).num_rows
         if cumulative + num_rows > local_row_idx:
@@ -197,11 +168,6 @@ def read_row_from_parquet_file(parquet_files: list[str], global_row_idx: int,
             local_index = local_row_idx - cumulative
             break
         cumulative += num_rows
-    else:
-        # If we reach here, local_row_idx is out of bounds for this parquet file
-        raise IndexError(
-            f"local_row_idx {local_row_idx} is out of bounds for parquet file {parquet_files[file_index]}"
-        )
 
     row_group = parquet_file.read_row_group(row_group_index).to_pydict()
     row_dict = {k: v[local_index] for k, v in row_group.items()}
@@ -219,6 +185,9 @@ class LatentsParquetMapStyleDataset(Dataset):
     Note: 
     Using parquet for map style dataset is not efficient, we mainly keep it for backward compatibility and debugging.
     """
+    # Modify this in the future if we want to add more keys, for example, in image to video.
+    keys = [("vae_latent", "latent"), "text_embedding", "clip_feature",
+            "first_frame_latent", "pil_image"]
 
     def __init__(
         self,
@@ -235,6 +204,10 @@ class LatentsParquetMapStyleDataset(Dataset):
         self.path = path
         self.cfg_rate = cfg_rate
         self.parquet_schema = parquet_schema
+        if cfg_rate > 0.0:
+            raise ValueError(
+                "cfg_rate > 0.0 is not supported for now because it will trigger bug when num_data_workers > 0"
+            )
         logger.info("Initializing LatentsParquetMapStyleDataset with path: %s",
                     path)
         self.parquet_files, self.lengths = get_parquet_files_and_length(path)
@@ -270,8 +243,7 @@ class LatentsParquetMapStyleDataset(Dataset):
 
         batch = collate_rows_from_parquet_schema([row_dict],
                                                  self.parquet_schema,
-                                                 self.text_padding_length,
+                                                 self.text_padding_length)
-                                                 cfg_rate=0.0)
         negative_prompt = batch['info_list'][0]['prompt']
         negative_prompt_embedding = batch['text_embedding']
         negative_prompt_attention_mask = batch['text_attention_mask']
@@ -284,7 +256,7 @@ class LatentsParquetMapStyleDataset(Dataset):
         return negative_prompt_embedding, negative_prompt_attention_mask, negative_prompt
 
     # PyTorch calls this ONLY because the batch_sampler yields a list
-    def __getitems__(self, indices: list[int]) -> dict[str, Any]:
+    def __getitems__(self, indices: List[int]) -> Dict[str, Any]:
         """
         Batch fetch using read_row_from_parquet_file for each index.
         """
@@ -293,10 +265,11 @@ class LatentsParquetMapStyleDataset(Dataset):
             for idx in indices
         ]
 
-        batch = collate_rows_from_parquet_schema(rows,
+        # all_latents, all_embs, all_masks, caption_text, all_extra_latents, all_infos = collate_latents_embs_masks(
-                                                 self.parquet_schema,
+        #     rows, self.text_padding_length, self.keys)
-                                                 self.text_padding_length,
+        # return all_latents, all_embs, all_masks, caption_text, all_extra_latents, all_infos
-                                                 cfg_rate=self.cfg_rate)
+        batch = collate_rows_from_parquet_schema(rows, self.parquet_schema,
+                                                 self.text_padding_length)
         return batch
 
     def __len__(self):
@@ -319,7 +292,7 @@ def build_parquet_map_style_dataloader(
         drop_last=True,
         drop_first_row=False,
         text_padding_length=512,
-        seed=42) -> tuple[LatentsParquetMapStyleDataset, StatefulDataLoader]:
+        seed=42) -> Tuple[LatentsParquetMapStyleDataset, StatefulDataLoader]:
     dataset = LatentsParquetMapStyleDataset(
         path,
         batch_size,

fastvideo/v1/dataset/preprocessing_datasets.py

@@ -7,7 +7,7 @@ from abc import ABC, abstractmethod
 from collections import Counter
 from dataclasses import dataclass
 from os.path import join as opj
-from typing import Any
+from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
 import torch
@@ -31,21 +31,21 @@ class PreprocessBatch:
     """
     # Raw metadata
     path: str
-    cap: str | list[str]
+    cap: Union[str, List[str]]
-    resolution: dict | None = None
+    resolution: Optional[Dict] = None
-    fps: float | None = None
+    fps: Optional[float] = None
-    duration: float | None = None
+    duration: Optional[float] = None
 
     # Processed metadata
-    num_frames: int | None = None
+    num_frames: Optional[int] = None
-    sample_frame_index: list[int] | None = None
+    sample_frame_index: Optional[List[int]] = None
-    sample_num_frames: int | None = None
+    sample_num_frames: Optional[int] = None
 
     # Processed data
-    pixel_values: torch.Tensor | None = None
+    pixel_values: Optional[torch.Tensor] = None
-    text: str | None = None
+    text: Optional[str] = None
-    input_ids: torch.Tensor | None = None
+    input_ids: Optional[torch.Tensor] = None
-    cond_mask: torch.Tensor | None = None
+    cond_mask: Optional[torch.Tensor] = None
 
     @property
     def is_video(self) -> bool:
@@ -281,12 +281,10 @@ class FrameSamplingStage(DatasetFilterStage):
                                   frame_interval).astype(int)
 
         # Temporal crop if too long
-        if len(frame_indices) > self.num_frames:
+        if len(frame_indices
-            if temporal_sample_fn is not None:
+               ) > self.num_frames and temporal_sample_fn is not None:
-                begin_index, end_index = temporal_sample_fn(len(frame_indices))
+            begin_index, end_index = temporal_sample_fn(len(frame_indices))
-                frame_indices = frame_indices[begin_index:end_index]
+            frame_indices = frame_indices[begin_index:end_index]
-            else:
-                frame_indices = frame_indices[:self.num_frames]
 
         batch.sample_frame_index = frame_indices.tolist()
         batch.sample_num_frames = len(frame_indices)
@@ -413,29 +411,6 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
                                 torch.distributed.checkpoint.stateful.Stateful):
     """
     Merged dataset for video and caption data with stage-based processing.
-    Assumes that data_merge_path is a txt file with the following format:
-    <folder_path>,<json_file_path>
-
-        The folder should contain videos.
-
-        The json file should be a list of dictionaries with the following format:
-        [
-        {
-            "path": "1gGQy4nxyUo-Scene-016.mp4",
-            "resolution": {
-            "width": 1920,
-            "height": 1080
-            },
-            "size": 2439112,
-            "fps": 25.0,
-            "duration": 6.88,
-            "num_frames": 172,
-            "cap": [
-            "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open."
-            ]
-        },
-        ...
-        ]
     
     This dataset processes video and image data through a series of stages:
     - Data validation
@@ -486,32 +461,32 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
         self.text_encoding_stage = TextEncodingStage(
             tokenizer=tokenizer,
             text_max_length=args.text_max_length,
-            cfg_rate=args.training_cfg_rate)
+            cfg_rate=args.cfg)
 
-    def _load_raw_data(self) -> list[dict]:
+    def _load_raw_data(self) -> List[Dict]:
         """Load raw data from JSON files."""
+        all_data = []
+
         # Read folder-annotation pairs
         with open(self.data_merge_path) as f:
             folder_anno_pairs = [
                 line.strip().split(",") for line in f if line.strip()
             ]
-        assert len(
-            folder_anno_pairs) == 1, "Only support one folder-annotation pair"
-        assert len(folder_anno_pairs[0]
-                   ) == 2, "Folder-annotation pair should have two elements"
-        folder, annotation_file = folder_anno_pairs[0]
 
-        data_items: list[dict] = []
+        # Process each folder-annotation pair
-        with open(annotation_file) as f:
+        for folder, annotation_file in folder_anno_pairs:
-            data_items = json.load(f)
+            with open(annotation_file) as f:
+                data_items = json.load(f)
 
-        # Update paths with folder prefix
+            # Update paths with folder prefix
-        for item in data_items:
+            for item in data_items:
-            item["path"] = opj(folder, item["path"])
+                item["path"] = opj(folder, item["path"])
 
-        return data_items
+            all_data.extend(data_items)
 
-    def _process_metadata(self) -> list[PreprocessBatch]:
+        return all_data[self.start_idx:]
+
+    def _process_metadata(self) -> List[PreprocessBatch]:
         """Process the raw metadata through all filtering stages."""
         raw_data = self._load_raw_data()
         processed_batches = []
@@ -522,7 +497,7 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
             "resolution_failed": 0,
             "frame_sampling_failed": 0
         }
-        sample_num_frames: list[int] = []
+        sample_num_frames: List[int] = []
 
         for item in raw_data:
             batch = PreprocessBatch(path=item["path"],
@@ -548,7 +523,7 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
         return processed_batches
 
     def _apply_filter_stages(self, batch: PreprocessBatch,
-                             filter_counts: dict[str, int]) -> bool:
+                             filter_counts: Dict[str, int]) -> bool:
         """Apply all filter stages and update counters. Returns True if batch should be kept."""
         if not self.validation_stage.should_keep(batch):
             filter_counts["validation_failed"] += 1
@@ -564,8 +539,8 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
 
         return True
 
-    def _log_filtering_stats(self, filter_counts: dict[str, int],
+    def _log_filtering_stats(self, filter_counts: Dict[str, int],
-                             sample_num_frames: list[int], before_count: int,
+                             sample_num_frames: List[int], before_count: int,
                              after_count: int):
         """Log filtering statistics."""
         logger.info(
@@ -584,7 +559,7 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
     def __len__(self):
         return len(self.processed_batches)
 
-    def _get_item(self, idx: int) -> dict:
+    def _get_item(self, idx: int) -> Dict:
         """Get a single processed data item."""
         batch = self.processed_batches[idx]
 
@@ -608,10 +583,10 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
 
         return result
 
-    def state_dict(self) -> dict[str, Any]:
+    def state_dict(self) -> Dict[str, Any]:
         """Return state dict for checkpointing."""
         return {"processed_batches": self.processed_batches}
 
-    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
         """Load state dict from checkpoint."""
         self.processed_batches = state_dict["processed_batches"]