i2v validation

Small change
Fix preprocess
2025-06-11 21:35:10 -07:00 · 2025-06-11 13:29:11 -07:00 · 2025-06-11 13:29:11 -07:00 · 2025-06-11 13:29:11 -07:00 · 2025-06-11 13:29:11 -07:00 · 2025-06-11 13:29:10 -07:00
200 changed files with 3646 additions and 7613 deletions
@@ -1,148 +0,0 @@
-env:
-  IMAGE_VERSION: "py3.12-latest"
-
-steps:
-  - label: "pre-commit"
-    command: ".buildkite/scripts/pre_commit.sh"
-    agents:
-      queue: "default"
-    env:
-      - BUILDKITE_CLEAN_CHECKOUT=true
-
-  - wait
-
-  - label: "Trigger Tests"
-    plugins:
-      - monorepo-diff#v1.4.0:
-          diff: "git diff --name-only $BUILDKITE_PULL_REQUEST_BASE_BRANCH...HEAD"
-          watch:
-            - path: 
-                - "fastvideo/v1/models/encoders/**"
-                - "fastvideo/v1/models/loader/**"
-                - "fastvideo/v1/tests/encoders/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 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/tests/vaes/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                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/tests/transformers/**"
-                - "fastvideo/v1/layers/**"
-                - "fastvideo/v1/attention/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 30m .buildkite/scripts/pr_test.sh"
-                label: "Transformer Tests"
-                env:
-                  - BUILDKITE_CLEAN_CHECKOUT=true
-                  - TEST_TYPE=transformer
-                agents:
-                  queue: "default"
-            - path: 
-                - "fastvideo/v1/**/*.py"
-              config:
-                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 30m .buildkite/scripts/pr_test.sh"
-                label: "Training Tests"
-                env:
-                  - BUILDKITE_CLEAN_CHECKOUT=true
-                  - 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 30m .buildkite/scripts/pr_test.sh"
-                label: "Training Tests VSA"
-                env:
-                  - BUILDKITE_CLEAN_CHECKOUT=true
-                  - 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 30m .buildkite/scripts/pr_test.sh"
-                label: "Inference Tests STA"
-                env:
-                  - BUILDKITE_CLEAN_CHECKOUT=true
-                  - 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 30m .buildkite/scripts/pr_test.sh"
-                label: "Precision Tests STA"
-                env:
-                  - BUILDKITE_CLEAN_CHECKOUT=true
-                  - 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 30m .buildkite/scripts/pr_test.sh"
-                label: "Precision Tests VSA"
-                env:
-                  - BUILDKITE_CLEAN_CHECKOUT=true
-                  - TEST_TYPE=precision_vsa
-                agents:
-                  queue: "default"
@@ -1,117 +0,0 @@
-#!/bin/bash
-set -uo pipefail
-
-log() {
-  echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
-}
-
-log "=== Starting Modal test execution ==="
-
-# Change to the project directory
-cd "$(dirname "$0")/../.."
-PROJECT_ROOT=$(pwd)
-log "Project root: $PROJECT_ROOT"
-
-# Install Modal if not available
-if ! python3 -m modal --version &> /dev/null; then
-    log "Modal not found, installing..."
-    python3 -m pip install modal
-    
-    # Verify installation
-    if ! python3 -m modal --version &> /dev/null; then
-        log "Error: Failed to install modal. Please install it manually."
-        exit 1
-    fi
-fi
-
-log "modal version: $(python3 -m modal --version)"
-
-# Set up Modal authentication using Buildkite secrets
-log "Setting up Modal authentication from Buildkite secrets..."
-MODAL_TOKEN_ID=$(buildkite-agent secret get modal_token_id)
-MODAL_TOKEN_SECRET=$(buildkite-agent secret get modal_token_secret)
-
-WANDB_API_KEY=$(buildkite-agent secret get wandb_api_key)
-
-WANDB_API_KEY=$(buildkite-agent secret get wandb_api_key)
-
-if [ -n "$MODAL_TOKEN_ID" ] && [ -n "$MODAL_TOKEN_SECRET" ]; then
-    log "Retrieved Modal credentials from Buildkite secrets"
-    python3 -m modal token set --token-id "$MODAL_TOKEN_ID" --token-secret "$MODAL_TOKEN_SECRET" --profile buildkite-ci --activate --verify
-    if [ $? -eq 0 ]; then
-        log "Modal authentication successful"
-    else
-        log "Error: Failed to set Modal credentials"
-        exit 1
-    fi
-else
-    log "Error: Could not retrieve Modal credentials from Buildkite secrets."
-    log "Please ensure 'modal_token_id' and 'modal_token_secret' secrets are set in Buildkite."
-    exit 1
-fi
-
-MODAL_TEST_FILE="fastvideo/v1/tests/modal/pr_test.py"
-
-if [ -z "${TEST_TYPE:-}" ]; then
-    log "Error: TEST_TYPE environment variable is not set"
-    exit 1
-fi
-log "Test type: $TEST_TYPE"
-
-MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT IMAGE_VERSION=$IMAGE_VERSION"
-
-case "$TEST_TYPE" in
-    "encoder")
-        log "Running encoder tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_encoder_tests"
-        ;;
-    "vae")
-        log "Running VAE tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_vae_tests"
-        ;;
-    "transformer")
-        log "Running transformer tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_transformer_tests"
-        ;;
-    "ssim")
-        log "Running SSIM tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_ssim_tests"
-        ;;
-    "training")
-        log "Running training tests..."
-        MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_training_tests"
-        ;;
-    "training_vsa")
-        log "Running training VSA tests..."
-        MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_training_tests_VSA"
-        ;;
-    "inference_sta")
-        log "Running inference STA tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_inference_tests_STA"
-        ;;
-    "precision_sta")
-        log "Running precision STA tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_precision_tests_STA"
-        ;;
-    "precision_vsa")
-        log "Running precision VSA tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_precision_tests_VSA"
-        ;;
-    *)
-        log "Error: Unknown test type: $TEST_TYPE"
-        exit 1
-        ;;
-esac
-
-log "Executing: $MODAL_COMMAND"
-eval "$MODAL_COMMAND"
-TEST_EXIT_CODE=$?
-
-if [ $TEST_EXIT_CODE -eq 0 ]; then
-    log "Modal test completed successfully"
-else
-    log "Error: Modal test failed with exit code: $TEST_EXIT_CODE"
-fi
-
-log "=== Test execution completed with exit code: $TEST_EXIT_CODE ==="
-exit $TEST_EXIT_CODE
@@ -1,40 +0,0 @@
-#!/bin/bash
-set -uo pipefail
-
-log() {
-  echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1"
-}
-
-log "=== Starting pre-commit checks ==="
-
-cd "$(dirname "$0")/../.."
-PROJECT_ROOT=$(pwd)
-log "Project root: $PROJECT_ROOT"
-
-if ! python3 -m pre_commit --version &> /dev/null; then
-    log "pre-commit not found, installing..."
-    python3 -m pip install --user pre-commit==4.0.1
-    
-    if ! python3 -m pre_commit --version &> /dev/null; then
-        log "Error: Failed to install pre-commit."
-        exit 1
-    fi
-fi
-
-log "Pre-commit version: $(python3 -m pre_commit --version)"
-
-log "Installing/updating pre-commit hooks..."
-python3 -m pre_commit install --install-hooks
-
-log "Running pre-commit checks on all files..."
-python3 -m pre_commit run --all-files
-PRE_COMMIT_EXIT_CODE=$?
-
-if [ $PRE_COMMIT_EXIT_CODE -eq 0 ]; then
-    log "Pre-commit checks completed successfully"
-else
-    log "Error: Pre-commit checks failed with exit code: $PRE_COMMIT_EXIT_CODE"
-fi
-
-log "=== Pre-commit checks completed with exit code: $PRE_COMMIT_EXIT_CODE ==="
-exit $PRE_COMMIT_EXIT_CODE 
@@ -160,7 +160,8 @@ def execute_command(pod_id):
    setup_steps = [
        "tar -xzf /tmp/repo.tar.gz --no-same-owner -C /workspace/",
        f"cd /workspace/{repo_name}",
-        "source $HOME/.local/bin/env && source /opt/venv/bin/activate",
+        "source /opt/conda/etc/profile.d/conda.sh",
+        "conda activate fastvideo-dev",
        args.test_command
    ]
    
@@ -12,11 +12,13 @@ on:
    paths:
      - "fastvideo/**/*.py"
      - ".github/workflows/pr-test.yml"
-      - "pyproject.toml"
-      - "docker/Dockerfile.python3.12"
-      - "csrc/**"
  workflow_dispatch:
    inputs:
+      custom_image:
+        description: "Custom image from this repository (default: fastvideo-dev:latest)"
+        required: false
+        default: "fastvideo-dev:latest"
+        type: string
      run_encoder_test:
        description: "Run encoder-test"
        required: false
@@ -37,41 +39,10 @@ on:
        required: false
        default: false
        type: boolean
-      run_training_test:
-        description: "Run training-test"
-        required: false
-        default: false
-        type: boolean
-      run_training_test_VSA:
-        description: "Run training-test-VSA"
-        required: false
-        default: false
-        type: boolean
-      run_inference_test_STA:
-        description: "Run inference-test-STA"
-        required: false
-        default: false
-        type: boolean
-      run_precision_test_STA:
-        description: "Run precision-test-STA"
-        required: false
-        default: false
-        type: boolean
-      run_precision_test_VSA:
-        description: "Run precision-test-VSA"
-        required: false
-        default: false
-        type: boolean
-      run_nightly_test:
-        description: "Run nightly-test"
-        required: false
-        default: false
-        type: boolean

 env:
  PYTHONUNBUFFERED: "1"

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

  encoder-test:
    needs: change-filter
@@ -166,8 +91,8 @@ jobs:
      gpu_type: "NVIDIA A40"
      gpu_count: 1
      volume_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/encoders -s"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:latest' }}"
+      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/encoders -s"
      timeout_minutes: 30
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -184,8 +109,8 @@ jobs:
      gpu_type: "NVIDIA A40"
      gpu_count: 1
      volume_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/vaes -s"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:latest' }}"
+      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/vaes -s"
      timeout_minutes: 30
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -202,8 +127,8 @@ jobs:
      gpu_type: "NVIDIA L40S"
      gpu_count: 1
      volume_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/transformers -s"
+      image: "ghcr.io/${{ github.repository }}/${{ github.event.inputs.custom_image || 'fastvideo-dev:latest' }}"
+      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/transformers -s"
      timeout_minutes: 30
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
@@ -212,7 +137,8 @@ jobs:
  ssim-test:
    needs: change-filter
    if: >-
-      github.event_name != 'workflow_dispatch' || (github.event_name == 'workflow_dispatch' && github.event.inputs.run_ssim_test == 'true')
+      (github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || 
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_ssim_test == 'true')
    strategy:
      fail-fast: false
      matrix:
@@ -229,130 +155,14 @@ jobs:
      volume_size: 200
      disk_size: 200
      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:${{ matrix.python-version.tag }}"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/ssim -vs"
+      test_command: "pip install -e .[test] && pytest ./fastvideo/v1/tests/ssim -vs"
      timeout_minutes: 60
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-  
-  training-test:
-    needs: change-filter
-    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.training-test == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_training_test == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "training-test"
-      gpu_type: "NVIDIA A40"
-      gpu_count: 4
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/training/Vanilla -srP"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-      WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
-
-  training-test-VSA:
-    needs: change-filter
-    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.training-test-VSA == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_training_test_VSA == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "training-test-VSA"
-      gpu_type: "NVIDIA H100 NVL"
-      gpu_count: 1
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/training/VSA -srP"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-      WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
-
-  inference-test-STA:
-    needs: change-filter
-    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.inference-test-STA == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_inference_test_STA == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "inference-test-STA"
-      gpu_type: "NVIDIA H100 NVL"
-      gpu_count: 1
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/v1/tests/inference/STA -srP"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-
-  precision-test-STA:
-    needs: change-filter
-    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.precision-test-STA == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_precision_test_STA == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "precision-test-STA"
-      gpu_type: "NVIDIA H100 NVL"
-      gpu_count: 1
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && python csrc/attn/tests/test_sta.py"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-
-  precision-test-VSA:
-    needs: change-filter
-    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.precision-test-VSA == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_precision_test_VSA == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "precision-test-VSA"
-      gpu_type: "NVIDIA H100 NVL"
-      gpu_count: 1
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && python csrc/attn/tests/test_block_sparse.py"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-
-  nightly-test:
-    if: >-
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_nightly_test == 'true')
-    uses: ./.github/workflows/runpod-test.yml
-    with:
-      job_id: "nightly-test"
-      gpu_type: "NVIDIA A40"
-      gpu_count: 4
-      volume_size: 100
-      disk_size: 100
-      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/v1/tests/nightly/test_e2e_overfit_single_sample.py -vs"
-      timeout_minutes: 30
-    secrets:
-      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-      WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}

  runpod-cleanup:
-    # Add other jobs to this list as you create them
-    needs: [encoder-test, vae-test, transformer-test, ssim-test, training-test, training-test-VSA, inference-test-STA, precision-test-STA, precision-test-VSA] 
+    needs: [encoder-test, vae-test, transformer-test, ssim-test] # Add other jobs to this list as you create them
    if: ${{ always() && ((github.event_name != 'workflow_dispatch' && github.event.pull_request.draft == false) || github.event_name == 'workflow_dispatch') }}
    runs-on: ubuntu-latest
    steps:
@@ -369,7 +179,7 @@ jobs:

      - name: Cleanup all RunPod instances
        env:
-          JOB_IDS: '["encoder-test", "vae-test", "transformer-test", "ssim-test-py3.10", "ssim-test-py3.11", "ssim-test-py3.12", "training-test", "training-test-VSA", "inference-test-STA", "precision-test-STA", "precision-test-VSA"]'
+          JOB_IDS: '["encoder-test", "vae-test", "transformer-test", "ssim-test-py3.10", "ssim-test-py3.11", "ssim-test-py3.12"]'
          RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
          GITHUB_RUN_ID: ${{ github.run_id }}
        run: python .github/scripts/runpod_cleanup.py
@@ -43,8 +43,6 @@ on:
        required: true
      RUNPOD_PRIVATE_KEY:
        required: true
-      WANDB_API_KEY:
-        required: false

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

      - name: Set up SSH key
        run: |
@@ -74,7 +72,6 @@ jobs:
          JOB_ID: ${{ inputs.job_id }}
          RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
          GITHUB_RUN_ID: ${{ github.run_id }}
-          WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
        timeout-minutes: ${{ inputs.timeout_minutes }}
        run: >-
          python .github/scripts/runpod_api.py 
@@ -91,7 +91,7 @@ For a more detailed guide, please see our [inference quick start](https://hao-ai

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

 ## 📑 Development Plan

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

 ## 🤝 Contributing

-We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview.html)
+We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/developer_guide/overview.html)

 ## Acknowledgement
 We learned and reused code from the following projects:
@@ -4,17 +4,8 @@


 ## Installation
-We test our code on Pytorch 2.5.0 and CUDA>=12.4. Currently we only support H100/H200, because ThunderKittens uses TMA but doesn't support Blackwell yet.
+We test our code on Pytorch 2.5.0 and CUDA>=12.4. Currently we only have implementation on H100.
 First, install C++20 for ThunderKittens:
-```bash
-sudo apt update
-sudo apt install gcc-11 g++-11
-
-sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
-
-sudo apt update
-sudo apt install clang-11
-```

 ## Environment Setup
 First, set up your CUDA environment:
@@ -53,14 +44,8 @@ out = sliding_tile_attention(q, k, v, window_size, 0, False)

 ## Test
 ```bash
-python tests/test_sta.py # test STA
-python tests/test_block_sparse.py # test VSA
+python test/test_sta.py
 ```
-## Benchmark
-```bash
-python benchmarks/bench_sta.py
-```
-

 ## How Does STA Work?
 We give a demo for 2D STA with window size (6,6) operating on a (10, 10) image. 
@@ -5,7 +5,6 @@ import matplotlib.pyplot as plt
 import numpy as np
 import torch
 from st_attn import sliding_tile_attention
-from triton.testing import do_bench


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


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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

    return results

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

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

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

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

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

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


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

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

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

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

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

@@ -7,7 +7,6 @@ from vsa import BLOCK_M, BLOCK_N

 import numpy as np
 import random
-import gc

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

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

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

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

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

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

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

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

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

 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='Benchmark Block Sparse Attention')
-    parser.add_argument('--batch_size', type=int, default=4, help='Batch size')
-    parser.add_argument('--num_heads', type=int, default=6, help='Number of heads')
-    parser.add_argument('--head_dim', type=int, default=128, help='Head dimension')
-    parser.add_argument('--topk', type=int, default=64, help='Number of kv blocks each q block attends to')
-    parser.add_argument('--seq_lengths', type=int, nargs='+', default=[29120], help='Sequence lengths to benchmark')
-    parser.add_argument('--num_iterations', type=int, default=50, help='Number of test iterations to run')
-    args = parser.parse_args()
-    main(args)
+    main()
@@ -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']}")
@@ -154,7 +154,6 @@ def block_sparse_attention_fwd(q, k, v, q2k_block_sparse_index, q2k_block_sparse
    return o, lse

 def block_sparse_attention_backward(q, k, v, o, l_vec, grad_output, k2q_block_sparse_index, k2q_block_sparse_num):
-    grad_output = grad_output.contiguous()
    grad_q, grad_k, grad_v = block_sparse_bwd(q, k, v, o, l_vec, grad_output, k2q_block_sparse_index, k2q_block_sparse_num)
    return grad_q, grad_k, grad_v

@@ -3,8 +3,6 @@
 #include "kittens.cuh"
 #include <cooperative_groups.h>
 #include <iostream>
-#include <c10/cuda/CUDAGuard.h>
-

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 ENV DEBIAN_FRONTEND=noninteractive

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

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

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

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

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

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

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

 COPY . .

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

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

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

 EXPOSE 22
@@ -1,7 +1,7 @@
 (sta-demo)=

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

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

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

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

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

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

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

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

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

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

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

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

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

 Hunyuan supports Lora fine-tuning of videos up to 720p. Demos and prompts of Black-Myth-Wukong can be found in [here](https://huggingface.co/FastVideo/Hunyuan-Black-Myth-Wukong-lora-weight). You can download the Lora weight through:
@@ -10,7 +10,7 @@ def main():
    # attempt to identify the optimal arguments.
    generator = VideoGenerator.from_pretrained(
        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-        # FastVideo will automatically handle distributed setup
+        # if num_gpus > 1, FastVideo will automatically handle distributed setup
        num_gpus=2,
        use_fsdp_inference=True,
        use_cpu_offload=False
@@ -5,7 +5,7 @@ export FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN
 export MODEL_BASE=Wan-AI/Wan2.1-T2V-14B-Diffusers

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

@@ -14,7 +14,7 @@ STA_mode="STA_searching"
 for i in $gpu_ids; do
    port=$((base_port+i))
    CUDA_VISIBLE_DEVICES=$i MASTER_PORT=$port python examples/inference/sta_mask_search/wan_example.py \
-        --prompt_path ./assets/prompt_${i}.txt \
+        --prompt_path ./assets/prompt_extend_${i}.txt \
        --output_path $output_path \
        --STA_mode $STA_mode &
    sleep 1
@@ -27,7 +27,7 @@ STA_mode="STA_tuning"
 for i in $gpu_ids; do
    port=$((base_port+i))
    CUDA_VISIBLE_DEVICES=$i MASTER_PORT=$port python examples/inference/sta_mask_search/wan_example.py \
-        --prompt_path ./assets/prompt_${i}.txt \
+        --prompt_path ./assets/prompt_extend_${i}.txt \
        --output_path $output_path \
        --STA_mode $STA_mode \
        --skip_time_steps $skip_time_steps &
@@ -1,10 +0,0 @@
-This directory contain e2e examples scripts for finetuning Wan2.1 I2V. 
-
-Execute the following commands from `FastVideo/` to run training:
-
- Download crush-smol dataset:
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/download_dataset.sh`
- Preprocess the videos and captions into latents:
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/preprocess_wan_data_i2v.sh`
- Edit the following file and run finetuning:
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.sh`
@@ -1,3 +0,0 @@
-#!/bin/bash
-
-python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -1,91 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
-DATA_DIR="data/crush-smol_processed_i2v/combined_parquet_dataset/"
-VALIDATION_DIR="data/crush-smol_processed_i2v/validation_parquet_dataset/"
-NUM_GPUS=8
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_i2v_finetune"
-  --output_dir "$DATA_DIR/outputs/wan_i2v_finetune"
-  --max_train_steps 2000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 8
-  --tp_size 8
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 8
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_preprocessed_path "$VALIDATION_DIR"
-  --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
-)
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/v1/training/wan_i2v_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,130 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=i2v
-#SBATCH --partition=main
-#SBATCH --qos=hao
-#SBATCH --nodes=4
-#SBATCH --ntasks=4
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --nodelist=fs-mbz-gpu-[100-850]
-#SBATCH --mem=1440G
-#SBATCH --output=i2v_output/i2v_%j.out
-#SBATCH --error=i2v_output/i2v_%j.err
-#SBATCH --exclusive
-set -e -x
-
-# Environment Setup
-source ~/conda/miniconda/bin/activate
-conda activate will-fv
-
-# Basic Info
-export WANDB_MODE="online"
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29500
-export NODE_RANK=$SLURM_PROCID
-nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
-export MASTER_ADDR=${nodes[0]}
-export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
-export TOKENIZERS_PARALLELISM=false
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "NODE_RANK: $NODE_RANK"
-
-# Configs
-NUM_GPUS=8
-
-MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
-DATA_DIR="data/crush-smol_processed_i2v/combined_parquet_dataset/"
-VALIDATION_DIR="data/crush-smol_processed_i2v/validation_parquet_dataset/"
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-
-# Training arguments
-training_args=(
-  --tracker_project_name wan_i2v_finetune
-  --output_dir="$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_preprocessed_path "$VALIDATION_DIR"
-  --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
-)
-
-srun torchrun \
--nnodes $SLURM_JOB_NUM_NODES \
--nproc_per_node $NUM_GPUS \
--node_rank $SLURM_PROCID \
--rdzv_backend=c10d \
--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/v1/training/wan_i2v_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-GPU_NUM=1 # 2,4,8
-MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
-MODEL_TYPE="wan"
-DATA_MERGE_PATH="data/crush-smol/merge.txt"
-OUTPUT_DIR="data/crush-smol_processed_i2v/"
-VALIDATION_PATH="examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation.json"
-
-torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/v1/pipelines/preprocess/v1_preprocess.py \
-    --model_path $MODEL_PATH \
-    --data_merge_path $DATA_MERGE_PATH \
-    --preprocess_video_batch_size 8 \
-    --max_height 480 \
-    --max_width 832 \
-    --num_frames 77 \
-    --dataloader_num_workers 0 \
-    --output_dir=$OUTPUT_DIR \
-    --model_type $MODEL_TYPE \
-    --train_fps 16 \
-    --validation_dataset_file $VALIDATION_PATH \
-    --samples_per_file 8 \
-    --flush_frequency 8 \
-    --preprocess_task "i2v" 
@@ -3,8 +3,8 @@
    {
      "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,
+      "video_path": "examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation_dataset/yYcK4nANZz4-Scene-034.mp4",
+      "num_inference_steps": 50,
      "height": 480,
      "width": 832,
      "num_frames": 77
@@ -12,8 +12,8 @@
    {
      "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,
+      "video_path": "examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation_dataset/yYcK4nANZz4-Scene-027.mp4",
+      "num_inference_steps": 50,
      "height": 480,
      "width": 832,
      "num_frames": 77
@@ -21,8 +21,8 @@
    {
      "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,
+      "video_path": "examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation_dataset/yYcK4nANZz4-Scene-030.mp4",
+      "num_inference_steps": 50,
      "height": 480,
      "width": 832,
      "num_frames": 77
@@ -1,10 +0,0 @@
-This directory contain e2e examples scripts for finetuning Wan2.1 T2v. 
-
-Execute the following commands from `FastVideo/` to run training:
-
- Download crush-smol dataset:
-`bash examples/training/finetune/wan_t2v_1_3b/crush_smol/download_dataset.sh`
- Preprocess the videos and captions into latents:
-`bash examples/training/finetune/wan_t2v_1_3b/crush_smol/preprocess_wan_data_t2v.sh`
- Edit the following file and run finetuning:
-`bash examples/training/finetune/wan_t2v_1_3b/crush_smol/finetune_t2v.sh`
@@ -1,3 +0,0 @@
-#!/bin/bash
-
-python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -1,90 +0,0 @@
-#!/bin/bash
-
-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_DIR="data/crush-smol_processed_t2v/validation_parquet_dataset/"
-NUM_GPUS=4
-# export CUDA_VISIBLE_DEVICES=4,5
-
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_t2v_finetune"
-  --output_dir "outputs/wan_t2v_finetune"
-  --max_train_steps 5000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 8
-  --num_latent_t 8 
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS 
-  --sp_size $NUM_GPUS 
-  --tp_size $NUM_GPUS
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim $NUM_GPUS
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path $DATA_DIR
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation 
-  --validation_preprocessed_path $VALIDATION_DIR
-  --validation_steps 50
-  --validation_sampling_steps "50" 
-  --validation_guidance_scale "1.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 5e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 6000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --allow_tf32
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.1
-  --multi_phased_distill_schedule "4000-1"
-  --not_apply_cfg_solver
-  --dit_precision "fp32"
-  --num_euler_timesteps 50
-  --ema_start_step 0
-)
-
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/v1/training/wan_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,127 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=t2v
-#SBATCH --partition=main
-#SBATCH --qos=hao
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --nodelist=fs-mbz-gpu-[100-850]
-#SBATCH --mem=1440G
-#SBATCH --output=t2v_output/t2v_%j.out
-#SBATCH --error=t2v_output/t2v_%j.err
-#SBATCH --exclusive
-set -e -x
-
-# Environment Setup
-source ~/conda/miniconda/bin/activate
-conda activate will-fv
-
-# Basic Info
-export WANDB_MODE="online"
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29500
-export NODE_RANK=$SLURM_PROCID
-nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
-export MASTER_ADDR=${nodes[0]}
-export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
-export TOKENIZERS_PARALLELISM=false
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "NODE_RANK: $NODE_RANK"
-
-# Configs
-NUM_GPUS=8
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="data/crush-smol_processed_t2v/combined_parquet_dataset/"
-VALIDATION_DIR="data/crush-smol_processed_t2v/validation_parquet_dataset/"
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name wan_t2v_finetune
-  --output_dir="outputs/wan_t2v_finetune"
-  --max_train_steps=1000
-  --train_batch_size=4
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps=1
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 4
-  --tp_size 4
-  --hsdp_replicate_dim 2
-  --hsdp_shard_dim 4
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 10
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_preprocessed_path "$VALIDATION_DIR"
-  --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
-)
-
-srun torchrun \
--nnodes $SLURM_JOB_NUM_NODES \
--nproc_per_node $NUM_GPUS \
--node_rank $SLURM_PROCID \
--rdzv_backend=c10d \
--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/v1/training/wan_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-GPU_NUM=1 # 2,4,8
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-MODEL_TYPE="wan"
-DATA_MERGE_PATH="data/crush-smol/merge.txt"
-OUTPUT_DIR="data/crush-smol_processed_t2v/"
-VALIDATION_PATH="examples/training/finetune/wan_t2v_1_3b/crush_smol/validation.json"
-
-torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/v1/pipelines/preprocess/v1_preprocess.py \
-    --model_path $MODEL_PATH \
-    --data_merge_path $DATA_MERGE_PATH \
-    --preprocess_video_batch_size 8 \
-    --max_height 480 \
-    --max_width 832 \
-    --num_frames 77 \
-    --dataloader_num_workers 0 \
-    --output_dir=$OUTPUT_DIR \
-    --model_type $MODEL_TYPE \
-    --train_fps 16 \
-    --validation_dataset_file $VALIDATION_PATH \
-    --samples_per_file 8 \
-    --flush_frequency 8 \
-    --preprocess_task "t2v" 
@@ -1,31 +0,0 @@
-{
-  "data": [
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 50,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 50,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 50,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    }
-  ]
-}
@@ -1,38 +1,37 @@
 import argparse
+import json
 import os

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

 logger = init_logger(__name__)

-
-def main(args) -> None:
+def main(args):
    args.model_path = maybe_download_model(args.model_path)
    maybe_init_distributed_environment_and_model_parallel(1, 1)
-    num_gpus = int(os.environ["WORLD_SIZE"])
-    assert num_gpus == 1, "Only support 1 GPU"
+
    pipeline_config = PipelineConfig.from_pretrained(args.model_path)
    kwargs = {
        "use_cpu_offload": False,
        "vae_precision": "fp32",
        "vae_config": WanVAEConfig(load_encoder=True, load_decoder=False),
    }
-    pipeline_config.update_config_from_dict(kwargs)
-    fastvideo_args = FastVideoArgs(
-        model_path=args.model_path,
-        num_gpus=get_world_size(),
-        pipeline_config=pipeline_config,
-    )
+    pipeline_config_args = shallow_asdict(pipeline_config)
+    pipeline_config_args.update(kwargs)
+    fastvideo_args = FastVideoArgs(model_path=args.model_path,
+                                   num_gpus=get_world_size(),
+                                   **pipeline_config_args,
+                                   )
    PreprocessPipeline = PreprocessPipeline_I2V if args.preprocess_task == "i2v" else PreprocessPipeline_T2V
    pipeline = PreprocessPipeline(args.model_path, fastvideo_args)
    pipeline.forward(batch=None, fastvideo_args=fastvideo_args, args=args)
@@ -50,8 +49,7 @@ if __name__ == "__main__":
        "--dataloader_num_workers",
        type=int,
        default=1,
-        help=
-        "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
+        help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
    )
    parser.add_argument(
        "--preprocess_video_batch_size",
@@ -59,20 +57,30 @@ if __name__ == "__main__":
        default=2,
        help="Batch size (per device) for the training dataloader.",
    )
-    parser.add_argument("--samples_per_file", type=int, default=64)
-    parser.add_argument("--flush_frequency",
-                        type=int,
-                        default=256,
-                        help="how often to save to parquet files")
-    parser.add_argument("--num_latent_t",
-                        type=int,
-                        default=28,
-                        help="Number of latent timesteps.")
+    parser.add_argument(
+        "--preprocess_text_batch_size",
+        type=int,
+        default=8,
+        help="Batch size (per device) for the training dataloader.",
+    )
+    parser.add_argument(
+        "--samples_per_file",
+        type=int,
+        default=64
+    )
+    parser.add_argument(
+        "--flush_frequency",
+        type=int,
+        default=256,
+        help="how often to save to parquet files"
+    )
+    parser.add_argument("--num_latent_t", type=int, default=28, help="Number of latent timesteps.")
    parser.add_argument("--max_height", type=int, default=480)
    parser.add_argument("--max_width", type=int, default=848)
    parser.add_argument("--video_length_tolerance_range", type=int, default=2.0)
    parser.add_argument("--group_frame", action="store_true")  # TODO
    parser.add_argument("--group_resolution", action="store_true")  # TODO
+    parser.add_argument("--dataset", default="t2v")
    parser.add_argument("--preprocess_task", type=str, default="t2v")
    parser.add_argument("--train_fps", type=int, default=30)
    parser.add_argument("--use_image_num", type=int, default=0)
@@ -80,18 +88,15 @@ if __name__ == "__main__":
    parser.add_argument("--speed_factor", type=float, default=1.0)
    parser.add_argument("--drop_short_ratio", type=float, default=1.0)
    # text encoder & vae & diffusion model
-    parser.add_argument("--text_encoder_name",
-                        type=str,
-                        default="google/t5-v1_1-xxl")
+    parser.add_argument("--text_encoder_name", type=str, default="google/t5-v1_1-xxl")
    parser.add_argument("--cache_dir", type=str, default="./cache_dir")
-    parser.add_argument("--training_cfg_rate", type=float, default=0.0)
+    parser.add_argument("--cfg", type=float, default=0.0)
    parser.add_argument(
        "--output_dir",
        type=str,
        default=None,
-        help=
-        "The output directory where the model predictions and checkpoints will be written.",
+        help="The output directory where the model predictions and checkpoints will be written.",
    )

    args = parser.parse_args()
-    main(args)
+    main(args)
@@ -6,8 +6,6 @@ from typing import Any, Dict, List, Optional, Tuple

 import numpy as np

-from fastvideo.v1.utils import dict_to_3d_list
-

 def configure_sta(mode: str = 'STA_searching',
                  layer_num: int = 40,
@@ -351,6 +349,21 @@ def select_best_mask_strategy(
    return best_mask_strategy, overall_sparsity, strategy_counts


+def dict_to_3d_list(mask_strategy: Optional[Dict[str, List[int]]],
+                    t_max: int = 50,
+                    l_max: int = 60,
+                    h_max: int = 24) -> List[List[List[Optional[List[int]]]]]:
+    result: List[List[List[Optional[List[int]]]]] = [[[
+        None for _ in range(h_max)
+    ] for _ in range(l_max)] for _ in range(t_max)]
+    if mask_strategy is None:
+        return result
+    for key, value in mask_strategy.items():
+        t, layer_idx, h = map(int, key.split('_'))
+        result[t][layer_idx][h] = value
+    return result
+
+
 def save_mask_search_results(
        mask_search_final_result: List[Dict[str, List[float]]],
        prompt: str,
@@ -10,8 +10,8 @@ from fastvideo.v1.attention.selector import get_attn_backend

 __all__ = [
    "DistributedAttention",
-    "LocalAttention",
    "DistributedAttention_VSA",
+    "LocalAttention",
    "AttentionBackend",
    "AttentionMetadata",
    "AttentionMetadataBuilder",
@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 import json
 from dataclasses import dataclass
-from typing import Any, List, Optional, Type
+from typing import Any, Dict, List, Optional, Type

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

 logger = init_logger(__name__)


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

    def __getitem__(self, item: int) -> str:
@@ -5,11 +5,7 @@ from typing import List, Optional, Type

 import torch
 from einops import rearrange
-
-try:
-    from vsa import video_sparse_attn
-except ImportError:
-    video_sparse_attn = None
+from vsa import video_sparse_attn

 from fastvideo.v1.attention.backends.abstract import (AttentionBackend,
                                                      AttentionImpl,
@@ -72,18 +68,14 @@ class VideoSparseAttentionMetadataBuilder(AttentionMetadataBuilder):
        if forward_batch.latents is None:
            raise ValueError("latents cannot be None")

-        raw_latent_shape = forward_batch.raw_latent_shape
-        if raw_latent_shape is None:
-            raise ValueError("raw_latent_shape cannot be None")
-
-        patch_size = fastvideo_args.pipeline_config.dit_config.patch_size
+        raw_latent_shape = forward_batch.latents.shape
+        patch_size = fastvideo_args.dit_config.patch_size
        dit_seq_shape = [
            raw_latent_shape[2] // patch_size[0],
            raw_latent_shape[3] // patch_size[1],
            raw_latent_shape[4] // patch_size[2]
        ]
        VSA_sparsity = forward_batch.VSA_sparsity
-
        return VideoSparseAttentionMetadata(current_timestep=current_timestep,
                                            dit_seq_shape=dit_seq_shape,
                                            VSA_sparsity=VSA_sparsity)
@@ -178,15 +170,10 @@ class VideoSparseAttentionImpl(AttentionImpl):
        value = value.transpose(1, 2).contiguous()
        gate_compress = gate_compress.transpose(1, 2).contiguous()

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

-        if video_sparse_attn is None:
-            raise NotImplementedError("video_sparse_attn is not installed")
-
        hidden_states = video_sparse_attn(
            query,
            key,
@@ -12,7 +12,7 @@ from fastvideo.v1.distributed.communication_op import (
 from fastvideo.v1.distributed.parallel_state import (get_sp_parallel_rank,
                                                     get_sp_world_size)
 from fastvideo.v1.forward_context import ForwardContext, get_forward_context
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.v1.platforms import _Backend
 from fastvideo.v1.utils import get_compute_dtype


@@ -26,8 +26,8 @@ class DistributedAttention(nn.Module):
                 num_kv_heads: Optional[int] = None,
                 softmax_scale: Optional[float] = None,
                 causal: bool = False,
-                 supported_attention_backends: Optional[Tuple[
-                     AttentionBackendEnum, ...]] = None,
+                 supported_attention_backends: Optional[Tuple[_Backend,
+                                                              ...]] = None,
                 prefix: str = "",
                 **extra_impl_args) -> None:
        super().__init__()
@@ -45,13 +45,13 @@ class DistributedAttention(nn.Module):
            dtype,
            supported_attention_backends=supported_attention_backends)
        impl_cls = attn_backend.get_impl_cls()
-        self.attn_impl = impl_cls(num_heads=num_heads,
-                                  head_size=head_size,
-                                  causal=causal,
-                                  softmax_scale=self.softmax_scale,
-                                  num_kv_heads=num_kv_heads,
-                                  prefix=f"{prefix}.impl",
-                                  **extra_impl_args)
+        self.impl = impl_cls(num_heads=num_heads,
+                             head_size=head_size,
+                             causal=causal,
+                             softmax_scale=self.softmax_scale,
+                             num_kv_heads=num_kv_heads,
+                             prefix=f"{prefix}.impl",
+                             **extra_impl_args)
        self.num_heads = num_heads
        self.head_size = head_size
        self.num_kv_heads = num_kv_heads
@@ -100,7 +100,7 @@ class DistributedAttention(nn.Module):
                                                    scatter_dim=2,
                                                    gather_dim=1)
        # Apply backend-specific preprocess_qkv
-        qkv = self.attn_impl.preprocess_qkv(qkv, ctx_attn_metadata)
+        qkv = self.impl.preprocess_qkv(qkv, ctx_attn_metadata)

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

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

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

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

        output = sequence_model_parallel_all_to_all_4D(output,
                                                       scatter_dim=1,
@@ -183,17 +183,18 @@ class DistributedAttention_VSA(DistributedAttention):
                                                     scatter_dim=2,
                                                     gather_dim=1)

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

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

        # Redistribute back if using sequence parallelism
        replicated_output = None

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

        output = sequence_model_parallel_all_to_all_4D(output,
                                                       scatter_dim=1,
@@ -211,8 +212,8 @@ class LocalAttention(nn.Module):
                 num_kv_heads: Optional[int] = None,
                 softmax_scale: Optional[float] = None,
                 causal: bool = False,
-                 supported_attention_backends: Optional[Tuple[
-                     AttentionBackendEnum, ...]] = None,
+                 supported_attention_backends: Optional[Tuple[_Backend,
+                                                              ...]] = None,
                 **extra_impl_args) -> None:
        super().__init__()
        if softmax_scale is None:
@@ -228,12 +229,12 @@ class LocalAttention(nn.Module):
            dtype,
            supported_attention_backends=supported_attention_backends)
        impl_cls = attn_backend.get_impl_cls()
-        self.attn_impl = impl_cls(num_heads=num_heads,
-                                  head_size=head_size,
-                                  softmax_scale=self.softmax_scale,
-                                  num_kv_heads=num_kv_heads,
-                                  causal=causal,
-                                  **extra_impl_args)
+        self.impl = impl_cls(num_heads=num_heads,
+                             head_size=head_size,
+                             softmax_scale=self.softmax_scale,
+                             num_kv_heads=num_kv_heads,
+                             causal=causal,
+                             **extra_impl_args)
        self.num_heads = num_heads
        self.head_size = head_size
        self.num_kv_heads = num_kv_heads
@@ -264,5 +265,5 @@ class LocalAttention(nn.Module):
        forward_context: ForwardContext = get_forward_context()
        ctx_attn_metadata = forward_context.attn_metadata

-        output = self.attn_impl.forward(q, k, v, ctx_attn_metadata)
+        output = self.impl.forward(q, k, v, ctx_attn_metadata)
        return output
@@ -11,13 +11,13 @@ import torch
 import fastvideo.v1.envs as envs
 from fastvideo.v1.attention.backends.abstract import AttentionBackend
 from fastvideo.v1.logger import init_logger
-from fastvideo.v1.platforms import AttentionBackendEnum, current_platform
+from fastvideo.v1.platforms import _Backend, current_platform
 from fastvideo.v1.utils import STR_BACKEND_ENV_VAR, resolve_obj_by_qualname

 logger = init_logger(__name__)


-def backend_name_to_enum(backend_name: str) -> Optional[AttentionBackendEnum]:
+def backend_name_to_enum(backend_name: str) -> Optional[_Backend]:
    """
    Convert a string backend name to a _Backend enum value.

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


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


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

@@ -72,7 +71,7 @@ def global_force_attn_backend(
    forced_attn_backend = attn_backend


-def get_global_forced_attn_backend() -> Optional[AttentionBackendEnum]:
+def get_global_forced_attn_backend() -> Optional[_Backend]:
    '''
    Get the currently-forced choice of attention backend,
    or None if auto-selection is currently enabled.
@@ -83,8 +82,7 @@ def get_global_forced_attn_backend() -> Optional[AttentionBackendEnum]:
 def get_attn_backend(
    head_size: int,
    dtype: torch.dtype,
-    supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
-                                                 ...]] = None,
+    supported_attention_backends: Optional[Tuple[_Backend, ...]] = None,
 ) -> Type[AttentionBackend]:
    return _cached_get_attn_backend(head_size, dtype,
                                    supported_attention_backends)
@@ -94,8 +92,7 @@ def get_attn_backend(
 def _cached_get_attn_backend(
    head_size: int,
    dtype: torch.dtype,
-    supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
-                                                 ...]] = None,
+    supported_attention_backends: Optional[Tuple[_Backend, ...]] = None,
 ) -> Type[AttentionBackend]:
    # Check whether a particular choice of backend was
    # previously forced.
@@ -105,7 +102,7 @@ def _cached_get_attn_backend(
    if not supported_attention_backends:
        raise ValueError("supported_attention_backends is empty")
    selected_backend = None
-    backend_by_global_setting: Optional[AttentionBackendEnum] = (
+    backend_by_global_setting: Optional[_Backend] = (
        get_global_forced_attn_backend())
    if backend_by_global_setting is not None:
        selected_backend = backend_by_global_setting
@@ -128,7 +125,7 @@ def _cached_get_attn_backend(

@contextmanager
 def global_force_attn_backend_context_manager(
-        attn_backend: AttentionBackendEnum) -> Generator[None, None, None]:
+        attn_backend: _Backend) -> Generator[None, None, None]:
    '''
    Globally force a FastVideo attention backend override within a
    context manager, reverting the global attention backend
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field, fields
-from typing import Any, Dict, List, Tuple
+from typing import Any, Dict

 from fastvideo.v1.logger import init_logger

@@ -12,9 +12,7 @@ logger = init_logger(__name__)
 # 3. Any field in ArchConfig is fixed upon initialization, and should be hidden away from users
@dataclass
 class ArchConfig:
-    stacked_params_mapping: List[Tuple[str, str, str]] = field(
-        default_factory=list
-    )  # mapping from huggingface weight names to custom names
+    pass


@dataclass
@@ -4,7 +4,7 @@ from typing import Any, List, Optional, Tuple

 from fastvideo.v1.configs.models.base import ArchConfig, ModelConfig
 from fastvideo.v1.layers.quantization import QuantizationConfig
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.v1.platforms import _Backend


@dataclass
@@ -12,12 +12,13 @@ class DiTArchConfig(ArchConfig):
    _fsdp_shard_conditions: list = field(default_factory=list)
    _compile_conditions: list = field(default_factory=list)
    _param_names_mapping: dict = field(default_factory=dict)
-    _reverse_param_names_mapping: dict = field(default_factory=dict)
    _lora_param_names_mapping: dict = field(default_factory=dict)
-    _supported_attention_backends: Tuple[AttentionBackendEnum, ...] = (
-        AttentionBackendEnum.SLIDING_TILE_ATTN, AttentionBackendEnum.SAGE_ATTN,
-        AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA,
-        AttentionBackendEnum.VIDEO_SPARSE_ATTN)
+    _supported_attention_backends: Tuple[_Backend,
+                                         ...] = (_Backend.SLIDING_TILE_ATTN,
+                                                 _Backend.SAGE_ATTN,
+                                                 _Backend.FLASH_ATTN,
+                                                 _Backend.TORCH_SDPA,
+                                                 _Backend.VIDEO_SPARSE_ATTN)

    hidden_size: int = 0
    num_attention_heads: int = 0
@@ -147,9 +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
@@ -5,11 +5,13 @@ from typing import List, Optional, Tuple, Union
 from fastvideo.v1.configs.models.dits.base import DiTArchConfig, DiTConfig


+def is_blocks(n: str, m) -> bool:
+    return "blocks" in n and str.isdigit(n.split(".")[-1])
+
+
@dataclass
 class StepVideoArchConfig(DiTArchConfig):
-    _fsdp_shard_conditions: list = field(
-        default_factory=lambda:
-        [lambda n, m: "transformer_blocks" in n and n.split(".")[-1].isdigit()])
+    _fsdp_shard_conditions: list = field(default_factory=lambda: [is_blocks])

    _param_names_mapping: dict = field(
        default_factory=lambda: {
@@ -49,13 +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(
@@ -6,14 +6,14 @@ import torch

 from fastvideo.v1.configs.models.base import ArchConfig, ModelConfig
 from fastvideo.v1.layers.quantization import QuantizationConfig
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.v1.platforms import _Backend


@dataclass
 class EncoderArchConfig(ArchConfig):
    architectures: List[str] = field(default_factory=lambda: [])
-    _supported_attention_backends: Tuple[AttentionBackendEnum, ...] = (
-        AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA)
+    _supported_attention_backends: Tuple[_Backend, ...] = (_Backend.FLASH_ATTN,
+                                                           _Backend.TORCH_SDPA)
    output_hidden_states: bool = False
    use_return_dict: bool = True

@@ -32,11 +32,8 @@ class TextEncoderArchConfig(EncoderArchConfig):
    output_past: bool = True
    scalable_attention: bool = True
    tie_word_embeddings: bool = False
-    stacked_params_mapping: List[Tuple[str, str, str]] = field(
-        default_factory=list
-    )  # mapping from huggingface weight names to custom names
+
    tokenizer_kwargs: Dict[str, Any] = field(default_factory=dict)
-    _fsdp_shard_conditions: list = field(default_factory=lambda: [])

    def __post_init__(self) -> None:
        self.tokenizer_kwargs = {
@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
-from typing import List, Optional, Tuple
+from typing import Optional

 from fastvideo.v1.configs.models.encoders.base import (ImageEncoderArchConfig,
                                                       ImageEncoderConfig,
@@ -8,14 +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
@@ -35,15 +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
@@ -1,23 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
-from typing import List, Optional, Tuple
+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
@@ -44,18 +32,6 @@ class LlamaArchConfig(TextEncoderArchConfig):
    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
@@ -1,23 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 from dataclasses import dataclass, field
-from typing import List, Optional, Tuple
+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
@@ -41,16 +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):
@@ -1,6 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-import argparse
-import dataclasses
 from dataclasses import dataclass, field
 from typing import Any, Union

@@ -131,12 +129,3 @@ class VAEConfig(ModelConfig):
        )

        return parser
-
-    @classmethod
-    def from_cli_args(cls, args: argparse.Namespace) -> "VAEConfig":
-        kwargs = {}
-        for attr in dataclasses.fields(cls):
-            value = getattr(args, attr.name, None)
-            if value is not None:
-                kwargs[attr.name] = value
-        return cls(**kwargs)
@@ -3,7 +3,7 @@ from fastvideo.v1.configs.pipelines.base import (PipelineConfig,
 from fastvideo.v1.configs.pipelines.hunyuan import (FastHunyuanConfig,
                                                    HunyuanConfig)
 from fastvideo.v1.configs.pipelines.registry import (
-    get_pipeline_config_cls_from_name)
+    get_pipeline_config_cls_for_name)
 from fastvideo.v1.configs.pipelines.stepvideo import StepVideoT2VConfig
 from fastvideo.v1.configs.pipelines.wan import (WanI2V480PConfig,
                                                WanI2V720PConfig,
@@ -14,5 +14,5 @@ __all__ = [
    "HunyuanConfig", "FastHunyuanConfig", "PipelineConfig",
    "SlidingTileAttnConfig", "WanT2V480PConfig", "WanI2V480PConfig",
    "WanT2V720PConfig", "WanI2V720PConfig", "StepVideoT2VConfig",
-    "get_pipeline_config_cls_from_name"
+    "get_pipeline_config_cls_for_name"
 ]
@@ -1,31 +1,19 @@
 # SPDX-License-Identifier: Apache-2.0
 import json
 from dataclasses import asdict, dataclass, field, fields
-from enum import Enum
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast
+from typing import Any, Callable, Dict, Optional, Tuple, cast

 import torch

 from fastvideo.v1.configs.models import (DiTConfig, EncoderConfig, ModelConfig,
                                         VAEConfig)
 from fastvideo.v1.configs.models.encoders import BaseEncoderOutput
-from fastvideo.v1.configs.utils import update_config_from_args
 from fastvideo.v1.logger import init_logger
-from fastvideo.v1.utils import (FlexibleArgumentParser, StoreBoolean,
-                                shallow_asdict)
+from fastvideo.v1.utils import shallow_asdict

 logger = init_logger(__name__)


-class STA_Mode(str, Enum):
-    """STA (Sliding Tile Attention) modes."""
-    STA_INFERENCE = "STA_inference"
-    STA_SEARCHING = "STA_searching"
-    STA_TUNING = "STA_tuning"
-    STA_TUNING_CFG = "STA_tuning_cfg"
-    NONE = None
-
-
 def preprocess_text(prompt: str) -> str:
    return prompt

@@ -34,282 +22,59 @@ def postprocess_text(output: BaseEncoderOutput) -> torch.tensor:
    raise NotImplementedError


-# config for a single pipeline
@dataclass
 class PipelineConfig:
    """Base configuration for all pipeline architectures."""
-    model_path: str = ""
-    pipeline_config_path: Optional[str] = None
-
    # Video generation parameters
    embedded_cfg_scale: float = 6.0
    flow_shift: Optional[float] = None
    disable_autocast: bool = False

    # Model configuration
-    dit_config: DiTConfig = field(default_factory=DiTConfig)
-    dit_precision: str = "bf16"
+    precision: str = "bf16"

    # VAE configuration
-    vae_config: VAEConfig = field(default_factory=VAEConfig)
    vae_precision: str = "fp16"
    vae_tiling: bool = True
    vae_sp: bool = True
+    vae_config: VAEConfig = field(default_factory=VAEConfig)

-    # Image encoder configuration
-    image_encoder_config: EncoderConfig = field(default_factory=EncoderConfig)
-    image_encoder_precision: str = "fp32"
+    # DiT configuration
+    dit_config: DiTConfig = field(default_factory=DiTConfig)

    # Text encoder configuration
-    DEFAULT_TEXT_ENCODER_PRECISIONS = ("fp16", )
-    text_encoder_configs: Tuple[EncoderConfig, ...] = field(
-        default_factory=lambda: (EncoderConfig(), ))
    text_encoder_precisions: Tuple[str, ...] = field(
        default_factory=lambda: ("fp16", ))
+    text_encoder_configs: Tuple[EncoderConfig, ...] = field(
+        default_factory=lambda: (EncoderConfig(), ))
    preprocess_text_funcs: Tuple[Callable[[str], str], ...] = field(
        default_factory=lambda: (preprocess_text, ))
    postprocess_text_funcs: Tuple[Callable[[BaseEncoderOutput], torch.tensor],
                                  ...] = field(default_factory=lambda:
                                               (postprocess_text, ))

-    # LoRA parameters
-    lora_path: Optional[str] = None
-    lora_nickname: Optional[
-        str] = "default"  # for swapping adapters in the pipeline
-    lora_target_names: Optional[List[
-        str]] = None  # can restrict list of layers to adapt, e.g. ["q_proj"]
-
-    # StepVideo specific parameters
-    pos_magic: Optional[str] = None
-    neg_magic: Optional[str] = None
-    timesteps_scale: Optional[bool] = None
-
-    # STA (Sliding Tile Attention) parameters
+    # STA (Spatial-Temporal Attention) parameters
    mask_strategy_file_path: Optional[str] = None
-    STA_mode: STA_Mode = STA_Mode.STA_INFERENCE
+    STA_mode: str = "STA_inference"
    skip_time_steps: int = 15

    # Compilation
-    # enable_torch_compile: bool = False
-
-    @staticmethod
-    def add_cli_args(parser: FlexibleArgumentParser,
-                     prefix: str = "") -> FlexibleArgumentParser:
-        prefix_with_dot = f"{prefix}." if (prefix.strip() != "") else ""
-
-        # model_path will be conflicting with the model_path in FastVideoArgs,
-        # so we add it separately if prefix is not empty
-        if prefix_with_dot != "":
-            parser.add_argument(
-                f"--{prefix_with_dot}model-path",
-                type=str,
-                dest=f"{prefix_with_dot.replace('-', '_')}model_path",
-                default=PipelineConfig.model_path,
-                help="Path to the pretrained model",
-            )
-
-        parser.add_argument(
-            f"--{prefix_with_dot}pipeline-config-path",
-            type=str,
-            dest=f"{prefix_with_dot.replace('-', '_')}pipeline_config_path",
-            default=PipelineConfig.pipeline_config_path,
-            help="Path to the pipeline config",
-        )
-        parser.add_argument(
-            f"--{prefix_with_dot}embedded-cfg-scale",
-            type=float,
-            dest=f"{prefix_with_dot.replace('-', '_')}embedded_cfg_scale",
-            default=PipelineConfig.embedded_cfg_scale,
-            help="Embedded CFG scale",
-        )
-        parser.add_argument(
-            f"--{prefix_with_dot}flow-shift",
-            type=float,
-            dest=f"{prefix_with_dot.replace('-', '_')}flow_shift",
-            default=PipelineConfig.flow_shift,
-            help="Flow shift parameter",
-        )
-
-        # DiT configuration
-        parser.add_argument(
-            f"--{prefix_with_dot}dit-precision",
-            type=str,
-            dest=f"{prefix_with_dot.replace('-', '_')}dit_precision",
-            default=PipelineConfig.dit_precision,
-            choices=["fp32", "fp16", "bf16"],
-            help="Precision for the DiT model",
-        )
-
-        # VAE configuration
-        parser.add_argument(
-            f"--{prefix_with_dot}vae-precision",
-            type=str,
-            dest=f"{prefix_with_dot.replace('-', '_')}vae_precision",
-            default=PipelineConfig.vae_precision,
-            choices=["fp32", "fp16", "bf16"],
-            help="Precision for VAE",
-        )
-        parser.add_argument(
-            f"--{prefix_with_dot}vae-tiling",
-            action=StoreBoolean,
-            dest=f"{prefix_with_dot.replace('-', '_')}vae_tiling",
-            default=PipelineConfig.vae_tiling,
-            help="Enable VAE tiling",
-        )
-        parser.add_argument(
-            f"--{prefix_with_dot}vae-sp",
-            action=StoreBoolean,
-            dest=f"{prefix_with_dot.replace('-', '_')}vae_sp",
-            help="Enable VAE spatial parallelism",
-        )
-
-        # Text encoder configuration
-        parser.add_argument(
-            f"--{prefix_with_dot}text-encoder-precisions",
-            nargs="+",
-            type=str,
-            dest=f"{prefix_with_dot.replace('-', '_')}text_encoder_precisions",
-            default=PipelineConfig.DEFAULT_TEXT_ENCODER_PRECISIONS,
-            choices=["fp32", "fp16", "bf16"],
-            help="Precision for each text encoder",
-        )
-
-        # Image encoder configuration
-        parser.add_argument(
-            f"--{prefix_with_dot}image-encoder-precision",
-            type=str,
-            dest=f"{prefix_with_dot.replace('-', '_')}image_encoder_precision",
-            default=PipelineConfig.image_encoder_precision,
-            choices=["fp32", "fp16", "bf16"],
-            help="Precision for image encoder",
-        )
-        parser.add_argument(
-            f"--{prefix_with_dot}pos_magic",
-            type=str,
-            dest=f"{prefix_with_dot.replace('-', '_')}pos_magic",
-            default=PipelineConfig.pos_magic,
-            help="Positive magic prompt for sampling, used in stepvideo",
-        )
-        parser.add_argument(
-            f"--{prefix_with_dot}neg_magic",
-            type=str,
-            dest=f"{prefix_with_dot.replace('-', '_')}neg_magic",
-            default=PipelineConfig.neg_magic,
-            help="Negative magic prompt for sampling, used in stepvideo",
-        )
-        parser.add_argument(
-            f"--{prefix_with_dot}timesteps_scale",
-            type=bool,
-            dest=f"{prefix_with_dot.replace('-', '_')}timesteps_scale",
-            default=PipelineConfig.timesteps_scale,
-            help=
-            "Bool for applying scheduler scale in set_timesteps, used in stepvideo",
-        )
-
-        # Add VAE configuration arguments
-        from fastvideo.v1.configs.models.vaes.base import VAEConfig
-        VAEConfig.add_cli_args(parser, prefix=f"{prefix_with_dot}vae-config")
-
-        # Add DiT configuration arguments
-        from fastvideo.v1.configs.models.dits.base import DiTConfig
-        DiTConfig.add_cli_args(parser, prefix=f"{prefix_with_dot}dit-config")
-
-        return parser
-
-    def update_config_from_dict(self,
-                                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)
-        update_config_from_args(self.vae_config,
-                                args,
-                                f"{prefix_with_dot}vae_config",
-                                pop_args=True)
-        update_config_from_args(self.dit_config,
-                                args,
-                                f"{prefix_with_dot}dit_config",
-                                pop_args=True)
+    enable_torch_compile: bool = False

    @classmethod
    def from_pretrained(cls, model_path: str) -> "PipelineConfig":
-        """
-        use the pipeline class setting from model_path to match the pipeline config
-        """
        from fastvideo.v1.configs.pipelines.registry import (
-            get_pipeline_config_cls_from_name)
-        pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)
-
-        return cast(PipelineConfig, pipeline_config_cls(model_path=model_path))
-
-    @classmethod
-    def from_kwargs(cls,
-                    kwargs: Dict[str, Any],
-                    config_cli_prefix: str = "") -> "PipelineConfig":
-        """
-        Load PipelineConfig from kwargs Dictionary.
-        kwargs: dictionary of kwargs
-        config_cli_prefix: prefix of CLI arguments for this PipelineConfig instance
-        """
-        from fastvideo.v1.configs.pipelines.registry import (
-            get_pipeline_config_cls_from_name)
-
-        prefix_with_dot = f"{config_cli_prefix}." if (config_cli_prefix.strip()
-                                                      != "") else ""
-        model_path: Optional[str] = kwargs.get(prefix_with_dot + 'model_path',
-                                               None) or kwargs.get('model_path')
-        pipeline_config_or_path: Optional[Union[str, PipelineConfig, Dict[
-            str, Any]]] = kwargs.get(prefix_with_dot + 'pipeline_config',
-                                     None) or kwargs.get('pipeline_config')
-        if model_path is None:
-            raise ValueError("model_path is required in kwargs")
-
-        # 1. Get the pipeline config class from the registry
-        pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)
-
-        # 2. Instantiate PipelineConfig
-        if pipeline_config_cls is None:
+            get_pipeline_config_cls_for_name)
+        pipeline_config_cls = get_pipeline_config_cls_for_name(model_path)
+        if pipeline_config_cls is not None:
+            pipeline_config = pipeline_config_cls()
+        else:
            logger.warning(
-                "Couldn't find pipeline config for %s. Using the default pipeline config.",
+                "Couldn't find an optimal sampling param for %s. Using the default sampling param.",
                model_path)
            pipeline_config = cls()
-        else:
-            pipeline_config = pipeline_config_cls()

-        # 3. Load PipelineConfig from a json file or a PipelineConfig object if provided
-        if isinstance(pipeline_config_or_path, str):
-            pipeline_config.load_from_json(pipeline_config_or_path)
-            kwargs[prefix_with_dot +
-                   'pipeline_config_path'] = pipeline_config_or_path
-        elif isinstance(pipeline_config_or_path, PipelineConfig):
-            pipeline_config = pipeline_config_or_path
-        elif isinstance(pipeline_config_or_path, dict):
-            pipeline_config.update_pipeline_config(pipeline_config_or_path)
-
-        # 4. Update PipelineConfig from CLI arguments if provided
-        kwargs[prefix_with_dot + 'model_path'] = model_path
-        pipeline_config.update_config_from_dict(kwargs, config_cli_prefix)
-        return pipeline_config
-
-    def check_pipeline_config(self) -> None:
-        if self.vae_sp and not self.vae_tiling:
-            raise ValueError(
-                "Currently enabling vae_sp requires enabling vae_tiling, please set --vae-tiling to True."
-            )
-
-        if len(self.text_encoder_configs) != len(self.text_encoder_precisions):
-            raise ValueError(
-                f"Length of text encoder configs ({len(self.text_encoder_configs)}) must be equal to length of text encoder precisions ({len(self.text_encoder_precisions)})"
-            )
-
-        if len(self.text_encoder_configs) != len(self.preprocess_text_funcs):
-            raise ValueError(
-                f"Length of text encoder configs ({len(self.text_encoder_configs)}) must be equal to length of text preprocessing functions ({len(self.preprocess_text_funcs)})"
-            )
-
-        if len(self.preprocess_text_funcs) != len(self.postprocess_text_funcs):
-            raise ValueError(
-                f"Length of text postprocess functions ({len(self.postprocess_text_funcs)}) must be equal to length of text preprocessing functions ({len(self.preprocess_text_funcs)})"
-            )
+        return cast(PipelineConfig, pipeline_config)

    def dump_to_json(self, file_path: str):
        output_dict = shallow_asdict(self)
@@ -80,7 +80,7 @@ class HunyuanConfig(PipelineConfig):
                     (llama_postprocess_text, clip_postprocess_text))

    # Precision for each component
-    dit_precision: str = "bf16"
+    precision: str = "bf16"
    vae_precision: str = "fp16"
    text_encoder_precisions: Tuple[str, ...] = field(
        default_factory=lambda: ("fp16", "fp16"))
@@ -19,7 +19,7 @@ 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]] = {
+WEIGHT_CONFIG_REGISTRY: Dict[str, Type[PipelineConfig]] = {
    "FastVideo/FastHunyuan-diffusers": FastHunyuanConfig,
    "hunyuanvideo-community/HunyuanVideo": HunyuanConfig,
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V480PConfig,
@@ -51,74 +51,37 @@ PIPELINE_FALLBACK_CONFIG: Dict[str, Type[PipelineConfig]] = {
 }


-def get_pipeline_config_cls_from_name(
-        pipeline_name_or_path: str) -> Type[PipelineConfig]:
-    """Get the appropriate configuration class for a given pipeline name or path.
+def get_pipeline_config_cls_for_name(
+        pipeline_name_or_path: str) -> Optional[type[PipelineConfig]]:
+    """Get the appropriate config class for specific pretrained weights."""

-    This function implements a multi-step lookup process to find the most suitable
-    configuration class for a given pipeline. It follows this order:
-    1. Exact match in the PIPE_NAME_TO_CONFIG
-    2. Partial match in the PIPE_NAME_TO_CONFIG
-    3. Fallback to class name in the model_index.json
-    4. else raise an error
+    if os.path.exists(pipeline_name_or_path):
+        config = verify_model_config_and_directory(pipeline_name_or_path)
+        logger.warning(
+            "FastVideo may not correctly identify the optimal config for this model, as the local directory may have been renamed."
+        )
+    else:
+        config = maybe_download_model_index(pipeline_name_or_path)

-    Args:
-        pipeline_name_or_path (str): The name or path of the pipeline. This can be:
-            - A registered model ID (e.g., "FastVideo/FastHunyuan-diffusers")
-            - A local path to a model directory
-            - A model ID that will be downloaded
-
-    Returns:
-        Type[PipelineConfig]: The configuration class that best matches the pipeline.
-            This will be one of:
-            - A specific weight configuration class if an exact match is found
-            - A fallback configuration class based on the pipeline architecture
-            - The base PipelineConfig class if no matches are found
-
-    Note:
-        - For local paths, the function will verify the model configuration
-        - For remote models, it will attempt to download the model index
-        - Warning messages are logged when falling back to less specific configurations
-    """
-
-    pipeline_config_cls: Optional[Type[PipelineConfig]] = None
+    pipeline_name = config["_class_name"]

    # First try exact match for specific weights
-    if pipeline_name_or_path in PIPE_NAME_TO_CONFIG:
-        pipeline_config_cls = PIPE_NAME_TO_CONFIG[pipeline_name_or_path]
+    if pipeline_name_or_path in WEIGHT_CONFIG_REGISTRY:
+        return WEIGHT_CONFIG_REGISTRY[pipeline_name_or_path]

    # Try partial matches (for local paths that might include the weight ID)
-    for registered_id, config_class in PIPE_NAME_TO_CONFIG.items():
+    for registered_id, config_class in WEIGHT_CONFIG_REGISTRY.items():
        if registered_id in pipeline_name_or_path:
-            pipeline_config_cls = config_class
-            break
+            return config_class

    # If no match, try to use the fallback config
-    if pipeline_config_cls is None:
-        if os.path.exists(pipeline_name_or_path):
-            config = verify_model_config_and_directory(pipeline_name_or_path)
-        else:
-            config = maybe_download_model_index(pipeline_name_or_path)
-        logger.warning(
-            "Trying to use the config from the model_index.json. FastVideo may not correctly identify the optimal config for this model in this situation."
-        )
+    fallback_config = None
+    # Try to determine pipeline architecture for fallback
+    for pipeline_type, detector in PIPELINE_DETECTOR.items():
+        if detector(pipeline_name.lower()):
+            fallback_config = PIPELINE_FALLBACK_CONFIG.get(pipeline_type)
+            break

-        pipeline_name = config["_class_name"]
-        # Try to determine pipeline architecture for fallback
-        for pipeline_type, detector in PIPELINE_DETECTOR.items():
-            if detector(pipeline_name.lower()):
-                pipeline_config_cls = PIPELINE_FALLBACK_CONFIG.get(
-                    pipeline_type)
-                break
-
-        if pipeline_config_cls is not None:
-            logger.warning(
-                "No match found for pipeline %s, using fallback config %s.",
-                pipeline_name_or_path, pipeline_config_cls)
-
-    if pipeline_config_cls is None:
-        raise ValueError(
-            f"No match found for pipeline {pipeline_name_or_path}, please check the pipeline name or path."
-        )
-
-    return pipeline_config_cls
+    logger.warning("No match found for pipeline %s, using fallback config %s.",
+                   pipeline_name_or_path, fallback_config)
+    return fallback_config
@@ -39,6 +39,7 @@ class SamplingParam:
    num_inference_steps: int = 50
    guidance_scale: float = 1.0
    guidance_rescale: float = 0.0
+    VSA_sparsity: float = 0.0

    # TeaCache parameters
    enable_teacache: bool = False
@@ -184,6 +185,12 @@ class SamplingParam:
            default=SamplingParam.image_path,
            help="Path to input image for image-to-video generation",
        )
+        parser.add_argument(
+            "--VSA-sparsity",
+            type=float,
+            default=SamplingParam.VSA_sparsity,
+            help="VSA attention sparsity",
+        )
        return parser


@@ -1,45 +0,0 @@
-from typing import Any, Dict
-
-
-def update_config_from_args(config: Any,
-                            args_dict: Dict[str, Any],
-                            prefix: str = "",
-                            pop_args: bool = False) -> None:
-    """
-    Update configuration object from arguments dictionary.
-    
-    Args:
-        config: The configuration object to update
-        args_dict: Dictionary containing arguments
-        prefix: Prefix for the configuration parameters in the args_dict.
-               If None, assumes direct attribute mapping without prefix.
-    """
-    # Handle top-level attributes (no prefix)
-    args_not_to_remove = [
-        'model_path',
-    ]
-    args_to_remove = []
-    if prefix.strip() == "":
-        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)
-                if pop_args:
-                    args_to_remove.append(key)
-    else:
-        # Handle nested attributes with prefix
-        prefix_with_dot = f"{prefix}."
-        for key, value in args_dict.items():
-            if key.startswith(prefix_with_dot) and value is not None:
-                attr_name = key[len(prefix_with_dot):]
-                if hasattr(config, attr_name):
-                    setattr(config, attr_name, value)
-                if pop_args:
-                    args_to_remove.append(key)
-
-    if pop_args:
-        for key in args_to_remove:
-            if key not in args_not_to_remove:
-                args_dict.pop(key)
@@ -1,17 +1,19 @@
-# SPDX-License-Identifier: Apache-2.0
+import os
+
 from torchvision import transforms
 from torchvision.transforms import Lambda
+from transformers import AutoTokenizer

-from fastvideo.v1.dataset.parquet_dataset_map_style import (
-    build_parquet_map_style_dataloader)
-from fastvideo.v1.dataset.preprocessing_datasets import (
-    VideoCaptionMergedDataset)
+from fastvideo.v1.dataset.t2v_datasets import T2V_dataset
 from fastvideo.v1.dataset.transform import (CenterCropResizeVideo, Normalize255,
                                            TemporalRandomCrop)
-from fastvideo.v1.dataset.validation_dataset import ValidationDataset
+
+from .parquet_dataset_map_style import build_parquet_map_style_dataloader
+
+__all__ = ["build_parquet_map_style_dataloader"]


-def getdataset(args) -> VideoCaptionMergedDataset:
+def getdataset(args, start_idx=0) -> T2V_dataset:
    temporal_sample = TemporalRandomCrop(args.num_frames)  # 16 x
    norm_fun = Lambda(lambda x: 2.0 * x - 1.0)
    resize_topcrop = [
@@ -29,14 +31,15 @@ def getdataset(args) -> VideoCaptionMergedDataset:
        *resize_topcrop,
        norm_fun,
    ])
-    return VideoCaptionMergedDataset(data_merge_path=args.data_merge_path,
-                                     args=args,
-                                     transform=transform,
-                                     temporal_sample=temporal_sample,
-                                     transform_topcrop=transform_topcrop)
+    tokenizer_path = os.path.join(args.model_path, "tokenizer")
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
+                                              cache_dir=args.cache_dir)
+    if args.dataset == "t2v":
+        return T2V_dataset(args,
+                           transform=transform,
+                           temporal_sample=temporal_sample,
+                           tokenizer=tokenizer,
+                           transform_topcrop=transform_topcrop,
+                           start_idx=start_idx)

-
-__all__ = [
-    "build_parquet_map_style_dataloader", "ValidationDataset",
-    "VideoCaptionMergedDataset"
-]
+    raise NotImplementedError(args.dataset)
@@ -1,185 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import argparse
-import os
-import pathlib
-import time
-
-import torch.distributed as dist
-import torch.distributed.checkpoint as dist_cp
-
-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,
-    maybe_init_distributed_environment_and_model_parallel)
-from fastvideo.v1.logger import init_logger
-
-logger = init_logger(__name__)
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        description="Benchmark parquet iterable style dataset loading speed")
-    parser.add_argument(
-        "--path",
-        type=str,
-        help="Path to parquet dataset",
-    )
-    parser.add_argument("--batch_size",
-                        type=int,
-                        default=4,
-                        help="Batch size for DataLoader")
-    parser.add_argument("--num_data_workers",
-                        type=int,
-                        help="Number of DataLoader workers")
-    parser.add_argument("--num_epoch",
-                        type=int,
-                        default=2,
-                        help="Number of epoches to benchmark")
-    parser.add_argument("--verify_resume",
-                        action="store_true",
-                        help="Verify resume")
-    parser.add_argument(
-        "--num_batches_per_epoch",
-        type=int,
-        default=1000,
-        help="Number of batches to benchmark",
-    )
-    parser.add_argument('--checkpoint_path',
-                        type=str,
-                        default='dataloader_checkpoint',
-                        help='Path to save/load checkpoint')
-    '''
-    example launch command:
-    torchrun --nproc_per_node=1 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 2 --num_epoch 2 --num_batches_per_epoch 2 --verify_resume
-    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 5 --verify_resume
-    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_iterable_style.py --path /mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn/latents/ --batch_size 2 --num_data_workers 4 --num_epoch 2 --num_batches_per_epoch 100 
-    '''
-    args = parser.parse_args()
-    world_size = int(os.environ.get("WORLD_SIZE", 1))
-    maybe_init_distributed_environment_and_model_parallel(
-        tp_size=(world_size + 1) // 2, sp_size=(world_size + 1) // 2)
-    logger.info("Initialized distributed environment with world_size=%d",
-                world_size)
-
-    # Create DataLoader with proper settings
-    dataset, dataloader = build_parquet_iterable_style_dataloader(
-        args.path, args.batch_size, args.num_data_workers)
-    logger.info("Initialized dataloader")
-
-    if args.verify_resume:
-        # First pass - record latent sums
-        first_pass_sums = []
-        for i, (latents, embeddings, masks,
-                caption_text) in enumerate(dataloader):
-            latent_sum = latents.sum().item()
-            first_pass_sums.append(latent_sum)
-            logger.info("Batch %d latent sum: %f", i, latent_sum)
-            if i >= args.num_batches_per_epoch - 1:
-                break
-
-        # Save dataloader state using distributed checkpoint
-        checkpoint_dir = pathlib.Path(args.checkpoint_path)
-        logger.info("Rank %d: Saving dataloader state to %s", get_world_rank(),
-                    checkpoint_dir)
-        states = {"dataloader": dataloader}
-
-        begin_time = time.monotonic()
-        dist_cp.save(states, checkpoint_id=checkpoint_dir.as_posix())
-        end_time = time.monotonic()
-
-        logger.info("Rank %d: Saved checkpoint in %.2f seconds",
-                    get_world_rank(), end_time - begin_time)
-
-        # Make sure all processes wait for checkpoint to be saved
-        if world_size > 1:
-            dist.barrier()
-
-        # Recreate dataloader and load state
-        dataset, dataloader = build_parquet_iterable_style_dataloader(
-            args.path, args.batch_size, 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)
-
-        # Second pass - verify latent sums match
-        for i, (latents, embeddings, masks) in enumerate(dataloader):
-            latent_sum = latents.sum().item()
-            first_pass_sums.append(latent_sum)
-            logger.info("Batch %d latent sum: %f",
-                        i + args.num_batches_per_epoch, latent_sum)
-            if i >= args.num_batches_per_epoch - 1:
-                break
-
-        dataset, dataloader = build_parquet_iterable_style_dataloader(
-            args.path, args.batch_size, args.num_data_workers)
-        # Second pass - verify latent sums match
-        second_pass_sums = []
-        for i, (latents, embeddings, masks,
-                caption_text) in enumerate(dataloader):
-            latent_sum = latents.sum().item()
-            second_pass_sums.append(latent_sum)
-            logger.info("Batch %d latent sum: %f (should match first pass: %f)",
-                        i, latent_sum, first_pass_sums[i])
-            if i >= args.num_batches_per_epoch * 2 - 1:
-                break
-
-        # Verify all sums match
-        if all(
-                abs(a - b) < 1e-6
-                for a, b in zip(first_pass_sums, second_pass_sums)):
-            logger.info(
-                "All latent sums match between passes - resume verification successful!"
-            )
-        else:
-            raise ValueError(
-                "Latent sums do not match between passes - resume verification failed!"
-            )
-
-    start_time = time.time()
-    total_samples = 0
-    total_batches = 0
-    for _ in range(args.num_epoch):
-        for i, (latents, embeddings, masks,
-                caption_text) in enumerate(dataloader):
-            if i >= args.num_batches_per_epoch:
-                break
-
-            # Move data to device
-            latents = latents.to(get_local_torch_device())
-            embeddings = embeddings.to(get_local_torch_device())
-
-            # Calculate actual batch size
-            batch_size = latents.size(0)
-            total_samples += batch_size
-            total_batches += 1
-
-            # Print progress only from rank 0
-            if get_world_rank() == 0 and (i + 1) % 10 == 0:
-                elapsed = time.time() - start_time
-                samples_per_sec = total_samples / elapsed
-                logger.info("Batch %d/%d, Speed: %.2f samples/sec", i + 1,
-                            args.num_batches_per_epoch, samples_per_sec)
-
-    # Final statistics
-    if world_size > 1:
-        dist.barrier()
-
-    if get_world_rank() == 0:
-        elapsed = time.time() - start_time
-        samples_per_sec = total_samples / elapsed
-
-        logger.info("\nBenchmark Results:")
-        logger.info("Total time: %.2f seconds", elapsed)
-        logger.info("Total samples: %d", total_samples)
-        logger.info("Average speed: %.2f samples/sec", samples_per_sec)
-        logger.info("Time per batch: %.2f ms", elapsed / total_batches * 1000)
-
-
-if __name__ == "__main__":
-    try:
-        main()
-    finally:
-        cleanup_dist_env_and_memory()
@@ -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

@@ -55,9 +54,9 @@ def main() -> None:
                        help='Path to save/load checkpoint')
    '''
    example launch command:
-    torchrun --nproc_per_node=1 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 4 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 3 --verify_resume
-    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 5 --verify_resume
-    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/benchmarks/benchmark_parquet_dataset_map_style.py --path /mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn/latents/ --batch_size 2 --num_data_workers 4 --num_epoch 2 --num_batches_per_epoch 100 
+    torchrun --nproc_per_node=1 --master_port=12358 fastvideo/v1/dataset/parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 4 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 5
+    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 4 --num_data_workers 1 --num_epoch 2 --num_batches_per_epoch 5
+    torchrun --nproc_per_node=8 --master_port=12358 fastvideo/v1/dataset/parquet_dataset_map_style.py --path data/crush-smol/latents/combined_parquet_dataset --batch_size 2 --num_data_workers 4 --num_epoch 2 --num_batches_per_epoch 2 --verify_resume
    '''
    args = parser.parse_args()
    world_size = int(os.environ.get("WORLD_SIZE", 1))
@@ -67,22 +66,14 @@ def main() -> None:
                world_size)

    # Create DataLoader with proper settings
-    dataset, dataloader = build_parquet_map_style_dataloader(
-        args.path,
-        args.batch_size,
-        parquet_schema=pyarrow_schema_t2v,
-        num_data_workers=args.num_data_workers)
+    dataloader = build_parquet_map_style_dataloader(args.path, args.batch_size,
+                                                    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):
-            latents = batch['vae_latent']
-            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)
+        for i, (latents, embeddings, masks,
+                data_indices) in enumerate(dataloader):
+            logger.info("Batch %d data_indices: %s", i, data_indices)
            if i >= args.num_batches_per_epoch - 1:
                break

@@ -103,70 +94,47 @@ def main() -> None:
        if world_size > 1:
            dist.barrier()

-        # Recreate dataloader and load state
-        dataset, dataloader = build_parquet_map_style_dataloader(
-            args.path,
-            args.batch_size,
-            parquet_schema=pyarrow_schema_t2v,
-            num_data_workers=args.num_data_workers)
+        dataloader = build_parquet_map_style_dataloader(args.path,
+                                                        args.batch_size,
+                                                        args.num_data_workers)
+        # Load dataloader state using distributed checkpoint
+        logger.info("Rank %d: Loading dataloader state from %s",
+                    get_world_rank(), checkpoint_dir)
        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):
-            latents = batch['vae_latent']
-            embeddings = batch['text_embedding']
-            latent_sum = latents.sum().item()
-            first_pass_sums.append(latent_sum)
-            logger.info("Batch %d latent sum: %f",
-                        i + args.num_batches_per_epoch, latent_sum)
+        for i, (latents, embeddings, masks,
+                data_indices) in enumerate(dataloader):
+            logger.info("Batch %d data_indices: %s", i, data_indices)
            if i >= args.num_batches_per_epoch - 1:
                break

-        dataset, dataloader = build_parquet_map_style_dataloader(
-            args.path,
-            args.batch_size,
-            parquet_schema=pyarrow_schema_t2v,
-            num_data_workers=args.num_data_workers)
+        logger.info("Restart from the beginning")

-        # Second pass - verify latent sums match
-        second_pass_sums = []
-        for i, batch 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)",
-                        i, latent_sum, first_pass_sums[i])
+        dataloader = build_parquet_map_style_dataloader(args.path,
+                                                        args.batch_size,
+                                                        args.num_data_workers)
+
+        for i, (latents, embeddings, masks,
+                data_indices) in enumerate(dataloader):
+            logger.info("Batch %d data_indices: %s", i, data_indices)
            if i >= args.num_batches_per_epoch * 2 - 1:
                break

-        # Verify all sums match
-        if all(
-                abs(a - b) < 1e-6
-                for a, b in zip(first_pass_sums, second_pass_sums)):
-            logger.info(
-                "All latent sums match between passes - resume verification successful!"
-            )
-        else:
-            raise ValueError(
-                "Latent sums do not match between passes - resume verification failed!"
-            )
-
    start_time = time.time()
    total_samples = 0
    total_batches = 0
    for _ in range(args.num_epoch):
-        for i, batch in enumerate(dataloader):
-            latents = batch['vae_latent']
-            embeddings = batch['text_embedding']
+        for i, (latents, embeddings, masks,
+                data_indices) in enumerate(dataloader):
            if i >= args.num_batches_per_epoch:
                break

            # Move data to device
-            latents = latents.to(get_local_torch_device())
-            embeddings = embeddings.to(get_local_torch_device())
+            latents = latents.to(get_torch_device())
+            embeddings = embeddings.to(get_torch_device())

            # Calculate actual batch size
            batch_size = latents.size(0)
@@ -26,47 +26,18 @@ pyarrow_schema_i2v = pa.schema([
    pa.field("text_embedding_shape", pa.list_(pa.int64())),
    # e.g., 'bfloat16' or 'float32'
    pa.field("text_embedding_dtype", pa.string()),
+    pa.field("text_attention_mask_bytes", pa.binary()),
+    # e.g., [SeqLen]
+    pa.field("text_attention_mask_shape", pa.list_(pa.int64())),
+    # e.g., 'bool' or 'int8'
+    pa.field("text_attention_mask_dtype", pa.string()),
    #I2V
    pa.field("clip_feature_bytes", pa.binary()),
    pa.field("clip_feature_shape", pa.list_(pa.int64())),
    pa.field("clip_feature_dtype", pa.string()),
-    pa.field("first_frame_latent_bytes", pa.binary()),
-    pa.field("first_frame_latent_shape", pa.list_(pa.int64())),
-    pa.field("first_frame_latent_dtype", pa.string()),
-    # I2V Validation
-    pa.field("pil_image_bytes", pa.binary()),
-    pa.field("pil_image_shape", pa.list_(pa.int64())),
-    pa.field("pil_image_dtype", pa.string()),
-    # --- Metadata ---
-    pa.field("file_name", pa.string()),
-    pa.field("caption", pa.string()),
-    pa.field("media_type", pa.string()),  # 'image' or 'video'
-    pa.field("width", pa.int64()),
-    pa.field("height", pa.int64()),
-    # -- Video-specific (can be null/default for images) ---
-    # Number of frames processed (e.g., 1 for image, N for video)
-    pa.field("num_frames", pa.int64()),
-    pa.field("duration_sec", pa.float64()),
-    pa.field("fps", pa.float64()),
-])
-
-pyarrow_schema_i2v_validation = pa.schema([
-    pa.field("id", pa.string()),
-    # --- Text encoder output tensor ---
-    # Tensors are stored as raw bytes with shape and dtype info for loading
-    pa.field("text_embedding_bytes", pa.binary()),
-    # e.g., [SeqLen, Dim]
-    pa.field("text_embedding_shape", pa.list_(pa.int64())),
-    # e.g., 'bfloat16' or 'float32'
-    pa.field("text_embedding_dtype", pa.string()),
-    #I2V
-    pa.field("clip_feature_bytes", pa.binary()),
-    pa.field("clip_feature_shape", pa.list_(pa.int64())),
-    pa.field("clip_feature_dtype", pa.string()),
-    # I2V Validation
-    pa.field("pil_image_bytes", pa.binary()),
-    pa.field("pil_image_shape", pa.list_(pa.int64())),
-    pa.field("pil_image_dtype", pa.string()),
+    pa.field("encoded_first_frame_bytes", pa.binary()),
+    pa.field("encoded_first_frame_shape", pa.list_(pa.int64())),
+    pa.field("encoded_first_frame_dtype", pa.string()),
    # --- Metadata ---
    pa.field("file_name", pa.string()),
    pa.field("caption", pa.string()),
@@ -96,6 +67,11 @@ pyarrow_schema_t2v = pa.schema([
    pa.field("text_embedding_shape", pa.list_(pa.int64())),
    # e.g., 'bfloat16' or 'float32'
    pa.field("text_embedding_dtype", pa.string()),
+    pa.field("text_attention_mask_bytes", pa.binary()),
+    # e.g., [SeqLen]
+    pa.field("text_attention_mask_shape", pa.list_(pa.int64())),
+    # e.g., 'bool' or 'int8'
+    pa.field("text_attention_mask_dtype", pa.string()),
    # --- Metadata ---
    pa.field("file_name", pa.string()),
    pa.field("caption", pa.string()),
@@ -107,26 +83,4 @@ pyarrow_schema_t2v = pa.schema([
    pa.field("num_frames", pa.int64()),
    pa.field("duration_sec", pa.float64()),
    pa.field("fps", pa.float64()),
-])
-
-pyarrow_schema_t2v_validation = pa.schema([
-    pa.field("id", pa.string()),
-    # --- Text encoder output tensor ---
-    # Tensors are stored as raw bytes with shape and dtype info for loading
-    pa.field("text_embedding_bytes", pa.binary()),
-    # e.g., [SeqLen, Dim]
-    pa.field("text_embedding_shape", pa.list_(pa.int64())),
-    # e.g., 'bfloat16' or 'float32'
-    pa.field("text_embedding_dtype", pa.string()),
-    # --- Metadata ---
-    pa.field("file_name", pa.string()),
-    pa.field("caption", pa.string()),
-    pa.field("media_type", pa.string()),  # 'image' or 'video'
-    pa.field("width", pa.int64()),
-    pa.field("height", pa.int64()),
-    # -- Video-specific (can be null/default for images) ---
-    # Number of frames processed (e.g., 1 for image, N for video)
-    pa.field("num_frames", pa.int64()),
-    pa.field("duration_sec", pa.float64()),
-    pa.field("fps", pa.float64()),
-])
+])
@@ -0,0 +1,137 @@
+# SPDX-License-Identifier: Apache-2.0
+import argparse
+import json
+import os
+import time
+from multiprocessing import Pool, cpu_count
+from pathlib import Path
+
+import torchvision
+from tqdm import tqdm
+
+
+def get_video_info(video_path):
+    """Get video information using torchvision."""
+    # Read video tensor (T, C, H, W)
+    video_tensor, _, info = torchvision.io.read_video(str(video_path),
+                                                      output_format="TCHW",
+                                                      pts_unit="sec")
+
+    num_frames = video_tensor.shape[0]
+    height = video_tensor.shape[2]
+    width = video_tensor.shape[3]
+    fps = info.get("video_fps", 0)
+    duration = num_frames / fps if fps > 0 else 0
+
+    # Extract name
+    _, _, videos_dir, video_name = str(video_path).split("/")
+
+    return {
+        "path": str(video_name),
+        "resolution": {
+            "width": width,
+            "height": height
+        },
+        "size": os.path.getsize(video_path),
+        "fps": fps,
+        "duration": duration,
+        "num_frames": num_frames
+    }
+
+
+def prepare_dataset_json(folder_path,
+                         output_name="videos2caption.json",
+                         num_workers=None) -> None:
+    """Prepare dataset information from a folder containing videos and prompt.txt."""
+    folder_path = Path(folder_path)
+
+    # Read prompt file
+    prompt_file = folder_path / "prompt.txt"
+    if not prompt_file.exists():
+        raise FileNotFoundError(f"prompt.txt not found in {folder_path}")
+
+    with open(prompt_file) as f:
+        prompts = [line.strip() for line in f.readlines() if line.strip()]
+
+    # Read videos file
+    videos_file = folder_path / "videos.txt"
+    if not videos_file.exists():
+        raise FileNotFoundError(f"videos.txt not found in {folder_path}")
+
+    with open(videos_file) as f:
+        video_paths = [line.strip() for line in f.readlines() if line.strip()]
+
+    if len(prompts) != len(video_paths):
+        raise ValueError(
+            f"Number of prompts ({len(prompts)}) does not match number of videos ({len(video_paths)})"
+        )
+
+    # Prepare arguments for multiprocessing
+    process_args = [folder_path / video_path for video_path in video_paths]
+
+    # Determine number of workers
+    if num_workers is None:
+        num_workers = max(1, cpu_count() - 1)  # Leave one CPU free
+
+    # Process videos in parallel
+    start_time = time.time()
+    with Pool(num_workers) as pool:
+        results = list(
+            tqdm(pool.imap(get_video_info, process_args),
+                 total=len(process_args),
+                 desc="Processing videos",
+                 unit="video"))
+
+    # Combine results with prompts
+    dataset_info = []
+    for result, prompt in zip(results, prompts):
+        result["cap"] = [prompt]
+        dataset_info.append(result)
+
+    # Calculate total processing time
+    total_time = time.time() - start_time
+    total_videos = len(dataset_info)
+    avg_time_per_video = total_time / total_videos if total_videos > 0 else 0
+
+    print("\nProcessing completed:")
+    print(f"Total videos processed: {total_videos}")
+    print(f"Total time: {total_time:.2f} seconds")
+    print(f"Average time per video: {avg_time_per_video:.2f} seconds")
+
+    # Save to JSON file
+    output_file = folder_path / output_name
+    with open(output_file, 'w') as f:
+        json.dump(dataset_info, f, indent=2)
+
+    # Create merge.txt
+    merge_file = folder_path / "merge.txt"
+    with open(merge_file, 'w') as f:
+        f.write(f"{folder_path}/videos,{output_file}\n")
+
+    print(f"Dataset information saved to {output_file}")
+    print(f"Merge file created at {merge_file}")
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description='Prepare video dataset information in JSON format')
+    parser.add_argument(
+        '--folder',
+        type=str,
+        required=True,
+        help='Path to the folder containing videos and prompt.txt')
+    parser.add_argument(
+        '--output',
+        type=str,
+        default='videos2caption.json',
+        help='Name of the output JSON file (default: videos2caption.json)')
+    parser.add_argument('--workers',
+                        type=int,
+                        default=32,
+                        help='Number of worker processes (default: 16)')
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    prepare_dataset_json(args.folder, args.output, args.workers)
@@ -1,278 +0,0 @@
-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_rank,
-                                      get_world_size)
-from fastvideo.v1.logger import init_logger
-
-logger = init_logger(__name__)
-
-
-class BatchIterator:
-    # TODO: Implement state_dict and load_state_dict to support resume.
-    def __init__(self, files, batch_size, text_padding_length, keys,
-                 worker_num_samples, read_batch_size):
-        self.files = files
-        self.batch_size = batch_size
-        self.text_padding_length = text_padding_length
-        self.keys = keys
-        self.worker_num_samples = worker_num_samples
-        self.processed_samples = 0
-        self.buffer = []
-        self.read_batch_size = read_batch_size
-
-    def __iter__(self):
-        for file in self.files:
-            if self.processed_samples >= self.worker_num_samples:
-                return
-
-            reader = pq.ParquetFile(file)
-            for batch in reader.iter_batches(batch_size=self.read_batch_size):
-                if self.processed_samples >= self.worker_num_samples:
-                    return
-
-                self.buffer.extend(batch.to_pylist())
-
-                while len(self.buffer) >= self.batch_size:
-                    if self.processed_samples >= self.worker_num_samples:
-                        return
-
-                    batch_to_process = self.buffer[:self.batch_size]
-                    self.buffer = self.buffer[self.batch_size:]
-
-                    all_latents, all_embs, all_masks, caption_text = collate_latents_embs_masks(
-                        batch_to_process, self.text_padding_length, self.keys)
-                    self.processed_samples += self.batch_size
-                    yield all_latents, all_embs, all_masks, caption_text
-
-
-class LatentsParquetIterStyleDataset(IterableDataset):
-    """Efficient loader for video-text data from a directory of Parquet files."""
-
-    # Modify this in the future if we want to add more keys, for example, in image to video.
-    keys = [("vae_latent", "latent"), ("text_embedding")]
-
-    def __init__(self,
-                 path: str,
-                 batch_size: int = 1024,
-                 cfg_rate: float = 0.1,
-                 num_workers: int = 1,
-                 drop_last: bool = True,
-                 text_padding_length: int = 512,
-                 seed: int = 42,
-                 read_batch_size: int = 32,
-                 parquet_schema: pa.Schema = None):
-        super().__init__()
-        self.path = str(path)
-        self.batch_size = batch_size
-        self.parquet_schema = parquet_schema
-        self.cfg_rate = cfg_rate
-        self.text_padding_length = text_padding_length
-        self.seed = seed
-        self.read_batch_size = read_batch_size
-        # Get distributed training info
-        self.global_rank = get_world_rank()
-        self.world_size = get_world_size()
-        self.sp_world_size = get_sp_world_size()
-        self.num_sp_groups = self.world_size // self.sp_world_size
-        num_workers = 1 if num_workers == 0 else num_workers
-        # Get sharding info
-        shard_parquet_files, shard_total_samples, shard_parquet_lengths = shard_parquet_files_across_sp_groups_and_workers(
-            self.path, self.num_sp_groups, num_workers, seed)
-
-        if drop_last:
-            self.worker_num_samples = min(
-                shard_total_samples) // batch_size * batch_size
-            # Assign files to current rank's SP group
-            ith_sp_group = self.global_rank // self.sp_world_size
-            self.sp_group_parquet_files = shard_parquet_files[ith_sp_group::self
-                                                              .num_sp_groups]
-            self.sp_group_parquet_lengths = shard_parquet_lengths[
-                ith_sp_group::self.num_sp_groups]
-            self.sp_group_num_samples = shard_total_samples[ith_sp_group::self.
-                                                            num_sp_groups]
-            logger.info(
-                "In total %d parquet files, %d samples, after sharding we retain %d samples due to drop_last",
-                sum([len(shard) for shard in shard_parquet_files]),
-                sum(shard_total_samples),
-                self.worker_num_samples * self.num_sp_groups * num_workers)
-        else:
-            raise ValueError("drop_last must be True")
-        logger.info("Each dataloader worker will load %d samples",
-                    self.worker_num_samples)
-
-    def __iter__(self):
-        worker_info = get_worker_info()
-        worker_id = worker_info.id if worker_info is not None else 1
-
-        worker_files = self.sp_group_parquet_files[worker_id]
-
-        batch_iterator = BatchIterator(
-            files=worker_files,
-            batch_size=self.batch_size,
-            text_padding_length=self.text_padding_length,
-            keys=self.keys,
-            worker_num_samples=self.worker_num_samples,
-            read_batch_size=self.read_batch_size)  # type: ignore
-
-        yield from batch_iterator
-
-        if batch_iterator.processed_samples != self.worker_num_samples:
-            raise ValueError(
-                "Rank %d, Worker %d: Not enough samples to process, this should not happen",
-                self.global_rank, worker_id)
-
-
-def shard_parquet_files_across_sp_groups_and_workers(
-    path: str,
-    num_sp_groups: int,
-    num_workers: int,
-    seed: int = 42,
-) -> Tuple[List[List[str]], List[int], List[Dict[str, int]]]:
-    """
-    Shard parquet files across SP groups and workers in a balanced way.
-    
-    Args:
-        path: Directory containing parquet files
-        num_sp_groups: Number of SP groups to shard across
-        num_workers: Number of workers per SP group
-        seed: Random seed for shuffling
-        
-    Returns:
-        Tuple containing:
-        - List of lists of parquet files for each shard
-        - List of total samples per shard
-        - List of dictionaries mapping file paths to their lengths
-    """
-    # 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):
-        logger.info("Sharding plan already exists")
-        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)
-            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")
-
-    if get_world_rank() == 0:
-        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)):
-            # 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(
-    path: str,
-    batch_size: int,
-    num_data_workers: int,
-    cfg_rate: float = 0.0,
-    drop_last: bool = True,
-    text_padding_length: int = 512,
-    seed: int = 42,
-    read_batch_size: int = 32
-) -> Tuple[LatentsParquetIterStyleDataset, StatefulDataLoader]:
-    """Build a dataloader for the LatentsParquetIterStyleDataset."""
-    dataset = LatentsParquetIterStyleDataset(
-        path=path,
-        batch_size=batch_size,
-        cfg_rate=cfg_rate,
-        num_workers=num_data_workers,
-        drop_last=drop_last,
-        text_padding_length=text_padding_length,
-        seed=seed,
-        read_batch_size=read_batch_size)
-
-    loader = StatefulDataLoader(
-        dataset,
-        batch_size=1,
-        num_workers=num_data_workers,
-        pin_memory=True,
-    )
-    return dataset, loader
@@ -1,18 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 import os
-import pickle
 from typing import Any, Dict, List, Tuple

-import pyarrow as pa
+import numpy as np
 import pyarrow.parquet as pq
 # Torch in general
 import torch
-import tqdm
 # Dataset
 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_rank,
                                      get_world_size)
 from fastvideo.v1.logger import init_logger
@@ -33,7 +30,6 @@ class DP_SP_BatchSampler(Sampler[List[int]]):
        sp_world_size: int,
        global_rank: int,
        drop_last: bool = True,
-        drop_first_row: bool = False,
        seed: int = 0,
    ):
        self.batch_size = batch_size
@@ -49,11 +45,6 @@ class DP_SP_BatchSampler(Sampler[List[int]]):
        # Create a random permutation of all indices
        global_indices = torch.randperm(self.dataset_size, generator=rng)

-        if drop_first_row:
-            # drop 0 in global_indices
-            global_indices = global_indices[global_indices != 0]
-            self.dataset_size = self.dataset_size - 1
-
        if self.drop_last:
            # For drop_last=True, we:
            # 1. Ensure total samples is divisible by (batch_size * num_sp_groups)
@@ -65,22 +56,19 @@ class DP_SP_BatchSampler(Sampler[List[int]]):
                                            self.num_sp_groups *
                                            self.batch_size]
        else:
-            if self.dataset_size % (self.num_sp_groups * self.batch_size) != 0:
-                # add more indices to make it divisible by (batch_size * num_sp_groups)
-                padding_size = self.num_sp_groups * self.batch_size - (
-                    self.dataset_size % (self.num_sp_groups * self.batch_size))
-                logger.info("Padding the dataset from %d to %d",
-                            self.dataset_size, self.dataset_size + padding_size)
-                global_indices = torch.cat(
-                    [global_indices, global_indices[:padding_size]])
+            # add more indices to make it divisible by (batch_size * num_sp_groups)
+            padding_size = self.num_sp_groups * self.batch_size - (
+                self.dataset_size % (self.num_sp_groups * self.batch_size))
+            global_indices = torch.cat(
+                [global_indices, global_indices[:padding_size]])

        # shard the indices to each sp group
        ith_sp_group = self.global_rank // self.sp_world_size
        sp_group_local_indices = global_indices[ith_sp_group::self.
                                                num_sp_groups]
+
        self.sp_group_local_indices = sp_group_local_indices
-        logger.info("Dataset size for each sp group: %d",
-                    len(sp_group_local_indices))
+        logger.info("sp_group_local_indices: %d", len(sp_group_local_indices))

    def __iter__(self):
        indices = self.sp_group_local_indices
@@ -93,49 +81,20 @@ class DP_SP_BatchSampler(Sampler[List[int]]):


 def get_parquet_files_and_length(path: str):
-    # Check if cached info exists
-    cache_dir = os.path.join(path, "map_style_cache")
-    cache_file = os.path.join(cache_dir, "file_info.pkl")
-
-    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:
-        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 (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"
-
-        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 saving
-    if get_world_size() > 1:
-        torch.distributed.barrier()
-
-    return get_parquet_files_and_length(path)
+    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)
+                num_rows = pq.ParquetFile(file_path).metadata.num_rows
+                lengths.append(num_rows)
+                file_names.append(file_path)
+    # 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"
+    return file_names_sorted, lengths_sorted


 def read_row_from_parquet_file(parquet_files: List[str], global_row_idx: int,
@@ -185,27 +144,39 @@ 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", "text_embedding"]

    def __init__(
        self,
        path: str,
        batch_size: int,
-        parquet_schema: pa.Schema,
        cfg_rate: float = 0.0,
        seed: int = 42,
        drop_last: bool = True,
-        drop_first_row: bool = False,
        text_padding_length: int = 512,
    ):
        super().__init__()
        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)
        self.batch = batch_size
        self.text_padding_length = text_padding_length
+        self._cols = [
+            "vae_latent_bytes",
+            "vae_latent_shape",
+            "text_embedding_bytes",
+            "text_embedding_shape",
+            "text_embedding_dtype",
+            "height",
+            "width",
+        ]
        self.sampler = DP_SP_BatchSampler(
            batch_size=batch_size,
            dataset_size=sum(self.lengths),
@@ -213,14 +184,27 @@ class LatentsParquetMapStyleDataset(Dataset):
            sp_world_size=get_sp_world_size(),
            global_rank=get_world_rank(),
            drop_last=drop_last,
-            drop_first_row=drop_first_row,
            seed=seed,
        )
        logger.info("Dataset initialized with %d parquet files and %d rows",
                    len(self.parquet_files), sum(self.lengths))

-    def get_validation_negative_prompt(
-            self) -> tuple[torch.Tensor, torch.Tensor, str]:
+    def _get_torch_tensors_from_row_dict(
+            self, row_dict: Dict[str, Any]) -> Dict[str, torch.Tensor]:
+        """
+        Get the latents and prompts from a row dictionary.
+        """
+        return_dict = {}
+        for key in self.keys:
+            shape = row_dict[f"{key}_shape"]
+            bytes = row_dict[f"{key}_bytes"]
+            # TODO (peiyuan): read precision
+            data = np.frombuffer(bytes, dtype=np.float32).reshape(shape).copy()
+            data = torch.from_numpy(data)
+            return_dict[key] = data
+        return return_dict
+
+    def get_validation_negative_prompt(self) -> tuple[Any, Any, Any, Any]:
        """
        Get the negative prompt for validation. 
        This method ensures the negative prompt is loaded and cached properly.
@@ -234,23 +218,39 @@ class LatentsParquetMapStyleDataset(Dataset):
        row_dict = read_row_from_parquet_file([file_path], row_idx,
                                              [self.lengths[0]])

-        batch = collate_rows_from_parquet_schema([row_dict],
-                                                 self.parquet_schema,
-                                                 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']
-        if len(negative_prompt_embedding.shape) == 2:
-            negative_prompt_embedding = negative_prompt_embedding.unsqueeze(0)
-        if len(negative_prompt_attention_mask.shape) == 1:
-            negative_prompt_attention_mask = negative_prompt_attention_mask.unsqueeze(
-                0).unsqueeze(0)
+        # Get tensors using the existing helper method
+        data = self._get_torch_tensors_from_row_dict(row_dict)
+        emb = data["text_embedding"]

-        return negative_prompt_embedding, negative_prompt_attention_mask, negative_prompt
+        # Pad the embedding and get mask
+        padded_emb, mask = self._pad(emb, self.text_padding_length)
+
+        # Pin memory for faster transfer to GPU
+        padded_emb = padded_emb
+        mask = mask
+
+        return None, padded_emb, mask, None
+
+    def _pad(self, t: torch.Tensor, padding_length: int) -> torch.Tensor:
+        """
+        Pad or crop an embedding [L, D] to exactly padding_length tokens.
+        Return:
+        - [L, D] tensor in pinned CPU memory
+        - [L] attention mask in pinned CPU memory
+        """
+        L, D = t.shape
+        if padding_length > L:  # pad
+            pad = torch.zeros(padding_length - L,
+                              D,
+                              dtype=t.dtype,
+                              device=t.device)
+            return torch.cat([t, pad], 0), torch.cat(
+                [torch.ones(L), torch.zeros(padding_length - L)], 0)
+        else:  # crop
+            return t[:padding_length], torch.ones(padding_length)

    # 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]):
        """
        Batch fetch using read_row_from_parquet_file for each index.
        """
@@ -259,11 +259,31 @@ class LatentsParquetMapStyleDataset(Dataset):
            for idx in indices
        ]

-        batch = collate_rows_from_parquet_schema(rows,
-                                                 self.parquet_schema,
-                                                 self.text_padding_length,
-                                                 cfg_rate=self.cfg_rate)
-        return batch
+        # Initialize tensors to hold padded embeddings and masks
+        all_latents = []
+        all_embs = []
+        all_masks = []
+
+        # Process each row individually
+        for i, row in enumerate(rows):
+            # Get tensors from row
+            data = self._get_torch_tensors_from_row_dict(row)
+            print(data)
+            import pdb; pdb.set_trace()
+            latents, emb = data["vae_latent"], data["text_embedding"]
+
+            padded_emb, mask = self._pad(emb, self.text_padding_length)
+            # Store in batch tensors
+            all_latents.append(latents)
+            all_embs.append(padded_emb)
+            all_masks.append(mask)
+
+        # Pin memory for faster transfer to GPU
+        all_latents = torch.stack(all_latents)
+        all_embs = torch.stack(all_embs)
+        all_masks = torch.stack(all_masks)
+
+        return all_latents, all_embs, all_masks, indices

    def __len__(self):
        return sum(self.lengths)
@@ -280,10 +300,8 @@ def build_parquet_map_style_dataloader(
        path,
        batch_size,
        num_data_workers,
-        parquet_schema,
        cfg_rate=0.0,
        drop_last=True,
-        drop_first_row=False,
        text_padding_length=512,
        seed=42) -> Tuple[LatentsParquetMapStyleDataset, StatefulDataLoader]:
    dataset = LatentsParquetMapStyleDataset(
@@ -291,9 +309,7 @@ def build_parquet_map_style_dataloader(
        batch_size,
        cfg_rate=cfg_rate,
        drop_last=drop_last,
-        drop_first_row=drop_first_row,
        text_padding_length=text_padding_length,
-        parquet_schema=parquet_schema,
        seed=seed)

    loader = StatefulDataLoader(
@@ -1,615 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import json
-import math
-import os
-import random
-from abc import ABC, abstractmethod
-from collections import Counter
-from dataclasses import dataclass
-from os.path import join as opj
-from typing import Any, Dict, List, Optional, Union
-
-import numpy as np
-import torch
-import torchvision
-from einops import rearrange
-from PIL import Image
-from transformers import AutoTokenizer
-
-from fastvideo.v1.logger import init_logger
-
-logger = init_logger(__name__)
-
-
-@dataclass
-class PreprocessBatch:
-    """
-    Batch information for dataset processing stages.
-    
-    This class holds all the information about a video-caption or image-caption pair
-    as it moves through the processing pipeline. Fields are populated by different stages.
-    """
-    # Raw metadata
-    path: str
-    cap: Union[str, List[str]]
-    resolution: Optional[Dict] = None
-    fps: Optional[float] = None
-    duration: Optional[float] = None
-
-    # Processed metadata
-    num_frames: Optional[int] = None
-    sample_frame_index: Optional[List[int]] = None
-    sample_num_frames: Optional[int] = None
-
-    # Processed data
-    pixel_values: Optional[torch.Tensor] = None
-    text: Optional[str] = None
-    input_ids: Optional[torch.Tensor] = None
-    cond_mask: Optional[torch.Tensor] = None
-
-    @property
-    def is_video(self) -> bool:
-        """Check if this is a video item."""
-        return self.path.endswith(".mp4")
-
-    @property
-    def is_image(self) -> bool:
-        """Check if this is an image item."""
-        return self.path.endswith(".jpg")
-
-
-class DatasetStage(ABC):
-    """
-    Abstract base class for dataset processing stages.
-    
-    Similar to PipelineStage but designed for dataset preprocessing operations.
-    """
-
-    @abstractmethod
-    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
-        """
-        Process the dataset batch.
-        
-        Args:
-            batch: Dataset batch to process
-            **kwargs: Additional processing parameters
-            
-        Returns:
-            Processed batch
-        """
-        raise NotImplementedError
-
-
-class DatasetFilterStage(ABC):
-    """
-    Abstract base class for dataset filtering stages.
-    
-    These stages can filter out items during metadata processing.
-    """
-
-    @abstractmethod
-    def should_keep(self, batch: PreprocessBatch, **kwargs) -> bool:
-        """
-        Check if batch should be kept.
-        
-        Args:
-            batch: Dataset batch to check
-            **kwargs: Additional parameters
-            
-        Returns:
-            True if batch should be kept, False otherwise
-        """
-        raise NotImplementedError
-
-    @abstractmethod
-    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
-        """
-        Process the dataset batch (for non-filtering operations).
-        
-        Args:
-            batch: Dataset batch to process
-            **kwargs: Additional processing parameters
-            
-        Returns:
-            Processed batch
-        """
-        raise NotImplementedError
-
-
-class DataValidationStage(DatasetFilterStage):
-    """Stage for validating data items."""
-
-    def should_keep(self, batch: PreprocessBatch, **kwargs) -> bool:
-        """
-        Validate data item.
-        
-        Args:
-            batch: Dataset batch to validate
-            
-        Returns:
-            True if valid, False if invalid
-        """
-        # Check for caption
-        if batch.cap is None:
-            return False
-
-        if batch.is_video:
-            # Validate video-specific fields
-            if batch.duration is None or batch.fps is None:
-                return False
-        elif not batch.is_image:
-            return False
-
-        return True
-
-    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
-        """Process does nothing for validation - filtering is handled by should_keep."""
-        return batch
-
-
-class ResolutionFilterStage(DatasetFilterStage):
-    """Stage for filtering data items based on resolution constraints."""
-
-    def __init__(self,
-                 max_h_div_w_ratio: float = 17 / 16,
-                 min_h_div_w_ratio: float = 8 / 16,
-                 max_height: int = 1024,
-                 max_width: int = 1024):
-        self.max_h_div_w_ratio = max_h_div_w_ratio
-        self.min_h_div_w_ratio = min_h_div_w_ratio
-        self.max_height = max_height
-        self.max_width = max_width
-
-    def should_keep(self, batch: PreprocessBatch, **kwargs) -> bool:
-        """
-        Check if data item passes resolution filtering.
-        
-        Args:
-            batch: Dataset batch with resolution information
-            
-        Returns:
-            True if passes filter, False otherwise
-        """
-        # Only apply to videos
-        if not batch.is_video:
-            return True
-
-        if batch.resolution is None:
-            return False
-
-        height = batch.resolution.get("height", None)
-        width = batch.resolution.get("width", None)
-        if height is None or width is None:
-            return False
-
-        # Check aspect ratio
-        aspect = self.max_height / self.max_width
-        hw_aspect_thr = 1.5
-
-        return self.filter_resolution(
-            height,
-            width,
-            max_h_div_w_ratio=hw_aspect_thr * aspect,
-            min_h_div_w_ratio=1 / hw_aspect_thr * aspect,
-        )
-
-    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
-        """Process does nothing for resolution filtering - filtering is handled by should_keep."""
-        return batch
-
-    def filter_resolution(self, h: int, w: int, max_h_div_w_ratio: float,
-                          min_h_div_w_ratio: float) -> bool:
-        """Filter based on height/width ratio."""
-        return h / w <= max_h_div_w_ratio and h / w >= min_h_div_w_ratio
-
-
-class FrameSamplingStage(DatasetFilterStage):
-    """Stage for temporal frame sampling and indexing."""
-
-    def __init__(self,
-                 num_frames: int,
-                 train_fps: int,
-                 speed_factor: int = 1,
-                 video_length_tolerance_range: float = 5.0,
-                 drop_short_ratio: float = 0.0):
-        self.num_frames = num_frames
-        self.train_fps = train_fps
-        self.speed_factor = speed_factor
-        self.video_length_tolerance_range = video_length_tolerance_range
-        self.drop_short_ratio = drop_short_ratio
-
-    def should_keep(self, batch: PreprocessBatch, **kwargs) -> bool:
-        """
-        Check if video should be kept based on length constraints.
-        
-        Args:
-            batch: Dataset batch
-            
-        Returns:
-            True if should be kept, False otherwise
-        """
-        if batch.is_image:
-            return True
-
-        if batch.duration is None or batch.fps is None:
-            return False
-
-        num_frames = math.ceil(batch.fps * batch.duration)
-
-        # Check if video is too long
-        if (num_frames / batch.fps > self.video_length_tolerance_range *
-            (self.num_frames / self.train_fps * self.speed_factor)):
-            return False
-
-        # Resample frame indices to check length
-        frame_interval = batch.fps / self.train_fps
-        start_frame_idx = 0
-        frame_indices = np.arange(start_frame_idx, num_frames,
-                                  frame_interval).astype(int)
-
-        # Filter short videos
-        return not (len(frame_indices) < self.num_frames
-                    and random.random() < self.drop_short_ratio)
-
-    def process(self,
-                batch: PreprocessBatch,
-                temporal_sample_fn=None,
-                **kwargs) -> PreprocessBatch:
-        """
-        Process frame sampling for video data items.
-        
-        Args:
-            batch: Dataset batch
-            temporal_sample_fn: Function for temporal sampling
-            
-        Returns:
-            Updated batch with frame sampling info
-        """
-        if batch.is_image:
-            # For images, just add sample info
-            batch.sample_frame_index = [0]
-            batch.sample_num_frames = 1
-            return batch
-
-        assert batch.duration is not None and batch.fps is not None
-        batch.num_frames = math.ceil(batch.fps * batch.duration)
-
-        # Resample frame indices
-        frame_interval = batch.fps / self.train_fps
-        start_frame_idx = 0
-        frame_indices = np.arange(start_frame_idx, batch.num_frames,
-                                  frame_interval).astype(int)
-
-        # Temporal crop if too long
-        if len(frame_indices
-               ) > self.num_frames and temporal_sample_fn is not None:
-            begin_index, end_index = temporal_sample_fn(len(frame_indices))
-            frame_indices = frame_indices[begin_index:end_index]
-
-        batch.sample_frame_index = frame_indices.tolist()
-        batch.sample_num_frames = len(frame_indices)
-
-        return batch
-
-
-class VideoTransformStage(DatasetStage):
-    """Stage for video data transformation."""
-
-    def __init__(self, transform) -> None:
-        self.transform = transform
-
-    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
-        """
-        Transform video data.
-        
-        Args:
-            batch: Dataset batch with video information
-            
-        Returns:
-            Batch with transformed video tensor
-        """
-        if not batch.is_video:
-            return batch
-
-        assert os.path.exists(batch.path), f"file {batch.path} do not exist!"
-        assert batch.sample_frame_index is not None, "Frame indices must be set before transformation"
-
-        torchvision_video, _, metadata = torchvision.io.read_video(
-            batch.path, output_format="TCHW")
-        video = torchvision_video[batch.sample_frame_index]
-        if self.transform is not None:
-            video = self.transform(video)
-        video = rearrange(video, "t c h w -> c t h w")
-        video = video.to(torch.uint8)
-
-        h, w = video.shape[-2:]
-        assert (
-            h / w <= 17 / 16 and h / w >= 8 / 16
-        ), f"Only videos with a ratio (h/w) less than 17/16 and more than 8/16 are supported. But video ({batch.path}) found ratio is {round(h / w, 2)} with the shape of {video.shape}"
-
-        video = video.float() / 127.5 - 1.0
-        batch.pixel_values = video
-        return batch
-
-
-class ImageTransformStage(DatasetStage):
-    """Stage for image data transformation."""
-
-    def __init__(self, transform, transform_topcrop) -> None:
-        self.transform = transform
-        self.transform_topcrop = transform_topcrop
-
-    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
-        """
-        Transform image data.
-        
-        Args:
-            batch: Dataset batch with image information
-            
-        Returns:
-            Batch with transformed image tensor
-        """
-        if not batch.is_image:
-            return batch
-
-        image = Image.open(batch.path).convert("RGB")
-        image = torch.from_numpy(np.array(image))
-        image = rearrange(image, "h w c -> c h w").unsqueeze(0)
-
-        if self.transform_topcrop is not None:
-            image = self.transform_topcrop(image)
-        elif self.transform is not None:
-            image = self.transform(image)
-
-        image = image.transpose(0, 1)  # [1 C H W] -> [C 1 H W]
-        image = image.float() / 127.5 - 1.0
-        batch.pixel_values = image
-        return batch
-
-
-class TextEncodingStage(DatasetStage):
-    """Stage for text tokenization and encoding."""
-
-    def __init__(self, tokenizer, text_max_length: int, cfg_rate: float = 0.0):
-        self.tokenizer = tokenizer
-        self.text_max_length = text_max_length
-        self.cfg_rate = cfg_rate
-
-    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
-        """
-        Process text data.
-        
-        Args:
-            batch: Dataset batch with caption information
-            
-        Returns:
-            Batch with encoded text information
-        """
-        text = batch.cap
-        if not isinstance(text, list):
-            text = [text]
-        text = [random.choice(text)]
-
-        text = text[0] if random.random() > self.cfg_rate else ""
-        text_tokens_and_mask = self.tokenizer(
-            text,
-            max_length=self.text_max_length,
-            padding="max_length",
-            truncation=True,
-            return_attention_mask=True,
-            add_special_tokens=True,
-            return_tensors="pt",
-        )
-
-        batch.text = text
-        batch.input_ids = text_tokens_and_mask["input_ids"]
-        batch.cond_mask = text_tokens_and_mask["attention_mask"]
-        return batch
-
-
-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
-    - Resolution filtering  
-    - Frame sampling
-    - Transformation
-    - Text encoding
-    """
-
-    def __init__(self,
-                 data_merge_path: str,
-                 args,
-                 transform,
-                 temporal_sample,
-                 transform_topcrop,
-                 start_idx: int = 0):
-        self.data_merge_path = data_merge_path
-        self.start_idx = start_idx
-        self.args = args
-        self.temporal_sample = temporal_sample
-
-        # Initialize tokenizer
-        tokenizer_path = os.path.join(args.model_path, "tokenizer")
-        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
-                                                  cache_dir=args.cache_dir)
-
-        # Initialize processing stages
-        self._init_stages(args, transform, transform_topcrop, tokenizer)
-
-        # Process metadata
-        self.processed_batches = self._process_metadata()
-
-    def _init_stages(self, args, transform, transform_topcrop,
-                     tokenizer) -> None:
-        """Initialize all processing stages."""
-        self.validation_stage = DataValidationStage()
-        self.resolution_filter_stage = ResolutionFilterStage(
-            max_height=args.max_height, max_width=args.max_width)
-        self.frame_sampling_stage = FrameSamplingStage(
-            num_frames=args.num_frames,
-            train_fps=args.train_fps,
-            speed_factor=args.speed_factor,
-            video_length_tolerance_range=args.video_length_tolerance_range,
-            drop_short_ratio=args.drop_short_ratio)
-        self.video_transform_stage = VideoTransformStage(transform)
-        self.image_transform_stage = ImageTransformStage(
-            transform, transform_topcrop)
-        self.text_encoding_stage = TextEncodingStage(
-            tokenizer=tokenizer,
-            text_max_length=args.text_max_length,
-            cfg_rate=args.training_cfg_rate)
-
-    def _load_raw_data(self) -> List[Dict]:
-        """Load raw data from JSON files."""
-        # 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] = []
-        with open(annotation_file) as f:
-            data_items = json.load(f)
-
-        # Update paths with folder prefix
-        for item in data_items:
-            item["path"] = opj(folder, item["path"])
-
-        return data_items
-
-    def _process_metadata(self) -> List[PreprocessBatch]:
-        """Process the raw metadata through all filtering stages."""
-        raw_data = self._load_raw_data()
-        processed_batches = []
-
-        # Initialize counters
-        filter_counts = {
-            "validation_failed": 0,
-            "resolution_failed": 0,
-            "frame_sampling_failed": 0
-        }
-        sample_num_frames: List[int] = []
-
-        for item in raw_data:
-            batch = PreprocessBatch(path=item["path"],
-                                    cap=item["cap"],
-                                    resolution=item.get("resolution"),
-                                    fps=item.get("fps"),
-                                    duration=item.get("duration"))
-
-            # Apply filtering stages
-            if not self._apply_filter_stages(batch, filter_counts):
-                continue
-
-            # Apply frame sampling processing
-            batch = self.frame_sampling_stage.process(
-                batch, temporal_sample_fn=self.temporal_sample)
-
-            processed_batches.append(batch)
-            assert batch.sample_num_frames is not None
-            sample_num_frames.append(batch.sample_num_frames)
-
-        self._log_filtering_stats(filter_counts, sample_num_frames,
-                                  len(raw_data), len(processed_batches))
-        return processed_batches
-
-    def _apply_filter_stages(self, batch: PreprocessBatch,
-                             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
-            return False
-
-        if not self.resolution_filter_stage.should_keep(batch):
-            filter_counts["resolution_failed"] += 1
-            return False
-
-        if not self.frame_sampling_stage.should_keep(batch):
-            filter_counts["frame_sampling_failed"] += 1
-            return False
-
-        return True
-
-    def _log_filtering_stats(self, filter_counts: Dict[str, int],
-                             sample_num_frames: List[int], before_count: int,
-                             after_count: int):
-        """Log filtering statistics."""
-        logger.info(
-            "validation_failed: %d, resolution_failed: %d, frame_sampling_failed: %d, "
-            "Counter(sample_num_frames): %s, before filter: %d, after filter: %d",
-            filter_counts['validation_failed'],
-            filter_counts['resolution_failed'],
-            filter_counts['frame_sampling_failed'], Counter(sample_num_frames),
-            before_count, after_count)
-
-    def __iter__(self):
-        """Iterate through processed data items."""
-        for idx in range(len(self.processed_batches)):
-            yield self._get_item(idx)
-
-    def __len__(self):
-        return len(self.processed_batches)
-
-    def _get_item(self, idx: int) -> Dict:
-        """Get a single processed data item."""
-        batch = self.processed_batches[idx]
-
-        # Apply transformation stages
-        batch = self.video_transform_stage.process(batch)
-        batch = self.image_transform_stage.process(batch)
-        batch = self.text_encoding_stage.process(batch)
-
-        # Build result dictionary
-        result = {
-            "pixel_values": batch.pixel_values,
-            "text": batch.text,
-            "input_ids": batch.input_ids,
-            "cond_mask": batch.cond_mask,
-            "path": batch.path,
-        }
-
-        # Add video-specific fields
-        if batch.is_video:
-            result.update({"fps": batch.fps, "duration": batch.duration})
-
-        return result
-
-    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:
-        """Load state dict from checkpoint."""
-        self.processed_batches = state_dict["processed_batches"]
@@ -0,0 +1,352 @@
+# SPDX-License-Identifier: Apache-2.0
+import json
+import math
+import os
+import random
+from collections import Counter
+from os.path import join as opj
+
+import numpy as np
+import torch
+import torchvision
+from einops import rearrange
+from PIL import Image
+from torch.utils.data import Dataset
+
+from fastvideo.utils.dataset_utils import DecordInit
+from fastvideo.utils.logging_ import main_print
+
+
+class SingletonMeta(type):
+    _instances: dict[type, 'SingletonMeta'] = {}
+
+    def __call__(cls, *args, **kwargs):
+        if cls not in cls._instances:
+            instance = super().__call__(*args, **kwargs)
+            cls._instances[cls] = instance
+        return cls._instances[cls]
+
+
+class DataSetProg(metaclass=SingletonMeta):
+
+    def __init__(self) -> None:
+        self.cap_list: list[dict] = []
+        self.elements: list[int] = []
+        self.num_workers = 1
+        self.n_elements = 0
+        self.worker_elements: dict[int, list[int]] = {}
+        self.n_used_elements: dict[int, int] = {}
+
+    def set_cap_list(self, num_workers, cap_list, n_elements) -> None:
+        self.num_workers = num_workers
+        self.cap_list = cap_list
+        self.n_elements = n_elements
+        self.elements = list(range(n_elements))
+        random.shuffle(self.elements)
+        print(f"n_elements: {len(self.elements)}", flush=True)
+
+        for i in range(self.num_workers):
+            self.n_used_elements[i] = 0
+            per_worker = int(
+                math.ceil(len(self.elements) / float(self.num_workers)))
+            start = i * per_worker
+            end = min(start + per_worker, len(self.elements))
+            self.worker_elements[i] = self.elements[start:end]
+
+    def get_item(self, work_info) -> int:
+        worker_id = 0 if work_info is None else work_info.id
+
+        idx = self.worker_elements[worker_id][
+            self.n_used_elements[worker_id] %
+            len(self.worker_elements[worker_id])]
+        self.n_used_elements[worker_id] += 1
+        return idx
+
+
+dataset_prog = DataSetProg()
+
+
+def filter_resolution(h: int,
+                      w: int,
+                      max_h_div_w_ratio: float = 17 / 16,
+                      min_h_div_w_ratio: float = 8 / 16) -> bool:
+    return h / w <= max_h_div_w_ratio and h / w >= min_h_div_w_ratio
+
+
+class T2V_dataset(Dataset):
+
+    def __init__(self,
+                 args,
+                 transform,
+                 temporal_sample,
+                 tokenizer,
+                 transform_topcrop,
+                 start_idx=0) -> None:
+        self.start_idx = start_idx
+        self.data = args.data_merge_path
+        self.num_frames = args.num_frames
+        self.train_fps = args.train_fps
+        self.use_image_num = args.use_image_num
+        self.transform = transform
+        self.transform_topcrop = transform_topcrop
+        self.temporal_sample = temporal_sample
+        self.tokenizer = tokenizer
+        self.text_max_length = args.text_max_length
+        self.cfg = args.cfg
+        self.speed_factor = args.speed_factor
+        self.max_height = args.max_height
+        self.max_width = args.max_width
+        self.drop_short_ratio = args.drop_short_ratio
+        assert self.speed_factor >= 1
+        self.v_decoder = DecordInit()
+        self.video_length_tolerance_range = args.video_length_tolerance_range
+        self.support_Chinese = True
+        if "mt5" not in args.text_encoder_name:
+            self.support_Chinese = False
+
+        cap_list = self.get_cap_list()
+
+        assert len(cap_list) > 0
+        cap_list, self.sample_num_frames = self.define_frame_index(cap_list)
+        self.lengths = self.sample_num_frames
+
+        n_elements = len(cap_list)
+        dataset_prog.set_cap_list(args.dataloader_num_workers, cap_list,
+                                  n_elements)
+
+        print(f"video length: {len(dataset_prog.cap_list)}", flush=True)
+
+    def set_checkpoint(self, n_used_elements):
+        for i in range(len(dataset_prog.n_used_elements)):
+            dataset_prog.n_used_elements[i] = n_used_elements
+
+    def __len__(self):
+        return dataset_prog.n_elements
+
+    def __getitem__(self, idx):
+
+        data = self.get_data(idx)
+        return data
+
+    def get_data(self, idx) -> dict:
+        path = dataset_prog.cap_list[idx]["path"]
+        if path.endswith(".mp4"):
+            return self.get_video(idx)
+        else:
+            return self.get_image(idx)
+
+    def get_video(self, idx) -> dict:
+        video_path = dataset_prog.cap_list[idx]["path"]
+        assert os.path.exists(video_path), f"file {video_path} do not exist!"
+        frame_indices = dataset_prog.cap_list[idx]["sample_frame_index"]
+
+        torchvision_video, _, metadata = torchvision.io.read_video(
+            video_path, output_format="TCHW")
+        video = torchvision_video[frame_indices]
+        video = self.transform(video)
+        video = rearrange(video, "t c h w -> c t h w")
+        video = video.to(torch.uint8)
+        assert video.dtype == torch.uint8
+
+        h, w = video.shape[-2:]
+        assert (
+            h / w <= 17 / 16 and h / w >= 8 / 16
+        ), f"Only videos with a ratio (h/w) less than 17/16 and more than 8/16 are supported. But video ({video_path}) found ratio is {round(h / w, 2)} with the shape of {video.shape}"
+
+        video = video.float() / 127.5 - 1.0
+
+        text = dataset_prog.cap_list[idx]["cap"]
+        if not isinstance(text, list):
+            text = [text]
+        text = [random.choice(text)]
+
+        text = text[0] if random.random() > self.cfg else ""
+        text_tokens_and_mask = self.tokenizer(
+            text,
+            max_length=self.text_max_length,
+            padding="max_length",
+            truncation=True,
+            return_attention_mask=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+        input_ids = text_tokens_and_mask["input_ids"]
+        cond_mask = text_tokens_and_mask["attention_mask"]
+        return dict(pixel_values=video,
+                    text=text,
+                    input_ids=input_ids,
+                    cond_mask=cond_mask,
+                    path=video_path,
+                    fps=dataset_prog.cap_list[idx]["fps"],
+                    duration=dataset_prog.cap_list[idx]["duration"])
+
+    def get_image(self, idx) -> dict:
+        image_data = dataset_prog.cap_list[
+            idx]  # [{'path': path, 'cap': cap}, ...]
+
+        image = Image.open(image_data["path"]).convert("RGB")  # [h, w, c]
+        image = torch.from_numpy(np.array(image))  # [h, w, c]
+        image = rearrange(image, "h w c -> c h w").unsqueeze(0)  #  [1 c h w]
+        # for i in image:
+        #     h, w = i.shape[-2:]
+        #     assert h / w <= 17 / 16 and h / w >= 8 / 16, f'Only image with a ratio (h/w) less than 17/16 and more than 8/16 are supported. But found ratio is {round(h / w, 2)} with the shape of {i.shape}'
+
+        image = (self.transform_topcrop(image) if "human_images"
+                 in image_data["path"] else self.transform(image)
+                 )  #  [1 C H W] -> num_img [1 C H W]
+        image = image.transpose(0, 1)  # [1 C H W] -> [C 1 H W]
+
+        image = image.float() / 127.5 - 1.0
+
+        caps: list[str] = (image_data["cap"] if isinstance(
+            image_data["cap"], list) else [image_data["cap"]])
+        caps = [random.choice(caps)]
+        text = caps
+        input_ids, cond_mask = [], []
+        single_text = text[0] if random.random() > self.cfg else ""
+        text_tokens_and_mask = self.tokenizer(
+            single_text,
+            max_length=self.text_max_length,
+            padding="max_length",
+            truncation=True,
+            return_attention_mask=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+        input_ids = text_tokens_and_mask["input_ids"]  # 1, l
+        cond_mask = text_tokens_and_mask["attention_mask"]  # 1, l
+        return dict(
+            pixel_values=image,
+            text=text,
+            input_ids=input_ids,
+            cond_mask=cond_mask,
+            path=image_data["path"],
+        )
+
+    def define_frame_index(self, cap_list) -> tuple[list[dict], list[int]]:
+        new_cap_list = []
+        sample_num_frames = []
+        cnt_too_long = 0
+        cnt_too_short = 0
+        cnt_no_cap = 0
+        cnt_no_resolution = 0
+        cnt_resolution_mismatch = 0
+        cnt_movie = 0
+        cnt_img = 0
+        for i in cap_list:
+            path = i["path"]
+            cap = i.get("cap", None)
+            # ======no caption=====
+            if cap is None:
+                cnt_no_cap += 1
+                continue
+            if path.endswith(".mp4"):
+                # ======no fps and duration=====
+                duration = i.get("duration", None)
+                fps = i.get("fps", None)
+                if fps is None or duration is None:
+                    continue
+
+                # ======resolution mismatch=====
+                resolution = i.get("resolution", None)
+                if resolution is None:
+                    cnt_no_resolution += 1
+                    continue
+                else:
+                    if (resolution.get("height", None) is None
+                            or resolution.get("width", None) is None):
+                        cnt_no_resolution += 1
+                        continue
+                    height, width = i["resolution"]["height"], i["resolution"][
+                        "width"]
+                    aspect = self.max_height / self.max_width
+                    hw_aspect_thr = 1.5
+                    is_pick = filter_resolution(
+                        height,
+                        width,
+                        max_h_div_w_ratio=hw_aspect_thr * aspect,
+                        min_h_div_w_ratio=1 / hw_aspect_thr * aspect,
+                    )
+                    if not is_pick:
+                        print("resolution mismatch")
+                        cnt_resolution_mismatch += 1
+                        continue
+
+                # if path == 'finetrainers/3dgs-dissolve/videos/1.mp4':
+                #     from IPython import embed; embed()
+                i["num_frames"] = math.ceil(fps * duration)
+                # max 5.0 and min 1.0 are just thresholds to filter some videos which have suitable duration.
+                if i["num_frames"] / fps > self.video_length_tolerance_range * (
+                        self.num_frames / self.train_fps * self.speed_factor
+                ):  # too long video is not suitable for this training stage (self.num_frames)
+                    cnt_too_long += 1
+                    continue
+
+                # resample in case high fps, such as 50/60/90/144 -> train_fps(e.g, 24)
+                frame_interval = fps / self.train_fps
+                start_frame_idx = 0
+                frame_indices = np.arange(start_frame_idx, i["num_frames"],
+                                          frame_interval).astype(int)
+
+                # comment out it to enable dynamic frames training
+                if (len(frame_indices) < self.num_frames
+                        and random.random() < self.drop_short_ratio):
+                    cnt_too_short += 1
+                    continue
+
+                #  too long video will be temporal-crop randomly
+                if len(frame_indices) > self.num_frames:
+                    begin_index, end_index = self.temporal_sample(
+                        len(frame_indices))
+                    frame_indices = frame_indices[begin_index:end_index]
+                    # frame_indices = frame_indices[:self.num_frames]  # head crop
+                i["sample_frame_index"] = frame_indices.tolist()
+                new_cap_list.append(i)
+                i["sample_num_frames"] = len(
+                    i["sample_frame_index"]
+                )  # will use in dataloader(group sampler)
+                sample_num_frames.append(i["sample_num_frames"])
+            elif path.endswith(".jpg"):  # image
+                cnt_img += 1
+                new_cap_list.append(i)
+                i["sample_num_frames"] = 1
+                sample_num_frames.append(i["sample_num_frames"])
+            else:
+                raise NameError(
+                    f"Unknown file extension {path.split('.')[-1]}, only support .mp4 for video and .jpg for image"
+                )
+        # import ipdb;ipdb.set_trace()
+        main_print(
+            f"no_cap: {cnt_no_cap}, too_long: {cnt_too_long}, too_short: {cnt_too_short}, "
+            f"no_resolution: {cnt_no_resolution}, resolution_mismatch: {cnt_resolution_mismatch}, "
+            f"Counter(sample_num_frames): {Counter(sample_num_frames)}, cnt_movie: {cnt_movie}, cnt_img: {cnt_img}, "
+            f"before filter: {len(cap_list)}, after filter: {len(new_cap_list)}"
+        )
+        return new_cap_list, sample_num_frames
+
+    def decord_read(self, path, frame_indices) -> torch.Tensor:
+        decord_vr = self.v_decoder(path)
+        video_data = decord_vr.get_batch(frame_indices).asnumpy()
+        video_data = torch.from_numpy(video_data)
+        video_data = video_data.permute(0, 3, 1, 2)  # (T, H, W, C) -> (T C H W)
+        return video_data
+
+    def read_jsons(self, data) -> list[dict]:
+        cap_lists = []
+        with open(data) as f:
+            folder_anno = [
+                i.strip().split(",") for i in f.readlines()
+                if len(i.strip()) > 0
+            ]
+        print(folder_anno)
+        for folder, anno in folder_anno:
+            with open(anno) as f:
+                sub_list = json.load(f)
+            for i in range(len(sub_list)):
+                sub_list[i]["path"] = opj(folder, sub_list[i]["path"])
+            cap_lists += sub_list
+        return cap_lists
+
+    def get_cap_list(self) -> list:
+        cap_lists = self.read_jsons(self.data)[self.start_idx:]
+        return cap_lists
@@ -1,220 +0,0 @@
-import random
-from typing import Any, Dict, List, cast
-
-import numpy as np
-import torch
-
-
-def pad(t: torch.Tensor, padding_length: int) -> torch.Tensor:
-    """
-    Pad or crop an embedding [L, D] to exactly padding_length tokens.
-    Return:
-    - [L, D] tensor in pinned CPU memory
-    - [L] attention mask in pinned CPU memory
-    """
-    L, D = t.shape
-    if padding_length > L:  # pad
-        pad = torch.zeros(padding_length - L, D, dtype=t.dtype, device=t.device)
-        return torch.cat([t, pad], 0), torch.cat(
-            [torch.ones(L), torch.zeros(padding_length - L)], 0)
-    else:  # crop
-        return t[:padding_length], torch.ones(padding_length)
-
-
-def get_torch_tensors_from_row_dict(row_dict, keys, cfg_rate) -> Dict[str, Any]:
-    """
-    Get the latents and prompts from a row dictionary.
-    """
-    return_dict = {}
-    for key in keys:
-        shape, bytes = None, None
-        if isinstance(key, tuple):
-            for k in key:
-                try:
-                    shape = row_dict[f"{k}_shape"]
-                    bytes = row_dict[f"{k}_bytes"]
-                except KeyError:
-                    continue
-            key = key[0]
-            if shape is None or bytes is None:
-                raise ValueError(f"Key {key} not found in row_dict")
-        else:
-            shape = row_dict[f"{key}_shape"]
-            bytes = row_dict[f"{key}_bytes"]
-
-        # TODO (peiyuan): read precision
-        if key == 'text_embedding' and random.random() < cfg_rate:
-            data = np.zeros((512, 4096), dtype=np.float32)
-        else:
-            data = np.frombuffer(bytes, dtype=np.float32).reshape(shape).copy()
-        data = torch.from_numpy(data)
-        if len(data.shape) == 3:
-            B, L, D = data.shape
-            assert B == 1, "Batch size must be 1"
-            data = data.squeeze(0)
-        return_dict[key] = data
-    return return_dict
-
-
-def collate_latents_embs_masks(
-        batch_to_process,
-        text_padding_length,
-        keys,
-        cfg_rate=0.0
-) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, List[str]]:
-    # Initialize tensors to hold padded embeddings and masks
-    all_latents = []
-    all_embs = []
-    all_masks = []
-    caption_text = []
-    # Process each row individually
-    for i, row in enumerate(batch_to_process):
-        # Get tensors from row
-        data = get_torch_tensors_from_row_dict(row, keys, cfg_rate)
-        latents, emb = data["vae_latent"], data["text_embedding"]
-
-        padded_emb, mask = pad(emb, text_padding_length)
-        # Store in batch tensors
-        all_latents.append(latents)
-        all_embs.append(padded_emb)
-        all_masks.append(mask)
-        # TODO(py): remove this once we fix preprocess
-        try:
-            caption_text.append(row["prompt"])
-        except KeyError:
-            caption_text.append(row["caption"])
-
-    # Pin memory for faster transfer to GPU
-    all_latents = torch.stack(all_latents)
-    all_embs = torch.stack(all_embs)
-    all_masks = torch.stack(all_masks)
-
-    return all_latents, all_embs, all_masks, caption_text
-
-
-def collate_rows_from_parquet_schema(rows,
-                                     parquet_schema,
-                                     text_padding_length,
-                                     cfg_rate=0.0) -> Dict[str, Any]:
-    """
-    Collate rows from parquet files based on the provided schema.
-    Dynamically processes tensor fields based on schema and returns batched data.
-    
-    Args:
-        rows: List of row dictionaries from parquet files
-        parquet_schema: PyArrow schema defining the structure of the data
-    
-    Returns:
-        Dict containing batched tensors and metadata
-    """
-    if not rows:
-        return cast(Dict[str, Any], {})
-
-    # Initialize containers for different data types
-    batch_data: Dict[str, Any] = {}
-
-    # Get tensor and metadata field names from schema (fields ending with '_bytes')
-    tensor_fields = []
-    metadata_fields = []
-    for field in parquet_schema.names:
-        if field.endswith('_bytes'):
-            shape_field = field.replace('_bytes', '_shape')
-            dtype_field = field.replace('_bytes', '_dtype')
-            tensor_name = field.replace('_bytes', '')
-            tensor_fields.append(tensor_name)
-            assert shape_field in parquet_schema.names, f"Shape field {shape_field} not found in schema for field {field}. Currently we only support *_bytes fields for tensors."
-            assert dtype_field in parquet_schema.names, f"Dtype field {dtype_field} not found in schema for field {field}. Currently we only support *_bytes fields for tensors."
-        elif not field.endswith('_shape') and not field.endswith('_dtype'):
-            # Only add actual metadata fields, not the shape/dtype helper fields
-            metadata_fields.append(field)
-
-    # Process each tensor field
-    for tensor_name in tensor_fields:
-        tensor_list = []
-
-        for row in rows:
-            # Get tensor data from row using the existing helper function pattern
-            shape_key = f"{tensor_name}_shape"
-            bytes_key = f"{tensor_name}_bytes"
-
-            if shape_key in row and bytes_key in row:
-                shape = row[shape_key]
-                bytes_data = row[bytes_key]
-
-                if len(bytes_data) == 0:
-                    tensor = torch.zeros(0, dtype=torch.bfloat16)
-                else:
-                    # Convert bytes to tensor using float32 as default
-                    if tensor_name == 'text_embedding' and random.random(
-                    ) < cfg_rate:
-                        data = np.zeros((512, 4096), dtype=np.float32)
-                    else:
-                        data = np.frombuffer(
-                            bytes_data, dtype=np.float32).reshape(shape).copy()
-                    tensor = torch.from_numpy(data)
-                    # if len(data.shape) == 3:
-                    #     B, L, D = tensor.shape
-                    #     assert B == 1, "Batch size must be 1"
-                    #     tensor = tensor.squeeze(0)
-
-                tensor_list.append(tensor)
-            else:
-                # Handle missing tensor data
-                tensor_list.append(torch.zeros(0, dtype=torch.bfloat16))
-
-        # Stack tensors with special handling for text embeddings
-        if tensor_name == 'text_embedding':
-            # Handle text embeddings with padding
-            padded_tensors = []
-            attention_masks = []
-
-            for tensor in tensor_list:
-                if tensor.numel() > 0:
-                    padded_tensor, mask = pad(tensor, text_padding_length)
-                    padded_tensors.append(padded_tensor)
-                    attention_masks.append(mask)
-                else:
-                    # Handle empty embeddings - assume default embedding dimension
-                    padded_tensors.append(
-                        torch.zeros(text_padding_length,
-                                    768,
-                                    dtype=torch.bfloat16))
-                    attention_masks.append(torch.zeros(text_padding_length))
-
-            batch_data[tensor_name] = torch.stack(padded_tensors)
-            batch_data['text_attention_mask'] = torch.stack(attention_masks)
-        else:
-            # Stack all tensors to preserve batch consistency
-            # Don't filter out None or empty tensors as this breaks batch sizing
-            try:
-                batch_data[tensor_name] = torch.stack(tensor_list)
-            except ValueError as e:
-                shapes = [
-                    t.shape
-                    if t is not None and hasattr(t, 'shape') else 'None/Invalid'
-                    for t in tensor_list
-                ]
-                raise ValueError(
-                    f"Failed to stack tensors for field '{tensor_name}'. "
-                    f"Tensor shapes: {shapes}. "
-                    f"All tensors in a batch must have compatible shapes. "
-                    f"Original error: {e}") from e
-
-    # Process metadata fields into info_list
-    info_list = []
-    for row in rows:
-        info = {}
-        for field in metadata_fields:
-            info[field] = row.get(field, "")
-
-        # Add prompt field for backward compatibility
-        info["prompt"] = info.get("caption", "")
-        info_list.append(info)
-
-    batch_data['info_list'] = info_list
-
-    # Add caption_text for backward compatibility
-    if info_list and 'caption' in info_list[0]:
-        batch_data['caption_text'] = [info['caption'] for info in info_list]
-
-    return batch_data
@@ -1,103 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# adapted from: https://github.com/a-r-r-o-w/finetrainers/blob/main/finetrainers/data/dataset.py
-import os
-import pathlib
-
-import datasets
-import torch
-
-from fastvideo.v1.logger import init_logger
-from fastvideo.v1.models.vision_utils import load_image, load_video
-
-logger = init_logger(__name__)
-
-
-class ValidationDataset(torch.utils.data.IterableDataset):
-
-    def __init__(self, filename: str):
-        super().__init__()
-
-        self.filename = pathlib.Path(filename)
-        # get directory of filename
-        self.dir = os.path.abspath(self.filename.parent)
-
-        if not self.filename.exists():
-            raise FileNotFoundError(
-                f"File {self.filename.as_posix()} does not exist")
-
-        if self.filename.suffix == ".csv":
-            data = datasets.load_dataset("csv",
-                                         data_files=self.filename.as_posix(),
-                                         split="train")
-        elif self.filename.suffix == ".json":
-            data = datasets.load_dataset("json",
-                                         data_files=self.filename.as_posix(),
-                                         split="train",
-                                         field="data")
-        elif self.filename.suffix == ".parquet":
-            data = datasets.load_dataset("parquet",
-                                         data_files=self.filename.as_posix(),
-                                         split="train")
-        elif self.filename.suffix == ".arrow":
-            data = datasets.load_dataset("arrow",
-                                         data_files=self.filename.as_posix(),
-                                         split="train")
-        else:
-            _SUPPORTED_FILE_FORMATS = [".csv", ".json", ".parquet", ".arrow"]
-            raise ValueError(
-                f"Unsupported file format {self.filename.suffix} for validation dataset. Supported formats are: {_SUPPORTED_FILE_FORMATS}"
-            )
-
-        self._data = data.to_iterable_dataset()
-
-    def __iter__(self):
-        for sample in self._data:
-            # For consistency reasons, we mandate that "caption" is always present in the validation dataset.
-            # However, since the model specifications use "prompt", we create an alias here.
-            sample["prompt"] = sample["caption"]
-
-            # Load image or video if the path is provided
-            # TODO(aryan): need to handle custom columns here for control conditions
-            sample["image"] = None
-            sample["video"] = None
-
-            if sample.get("image_path", None) is not None:
-                image_path = sample["image_path"]
-                image_path = os.path.join(self.dir, image_path)
-                if not pathlib.Path(image_path).is_file(
-                ) and not image_path.startswith("http"):
-                    logger.warning("Image file %s does not exist.", image_path)
-                else:
-                    sample["image"] = load_image(image_path)
-
-            if sample.get("video_path", None) is not None:
-                video_path = sample["video_path"]
-                video_path = os.path.join(self.dir, video_path)
-                if not pathlib.Path(video_path).is_file(
-                ) and not video_path.startswith("http"):
-                    logger.warning("Video file %s does not exist.", video_path)
-                else:
-                    sample["video"] = load_video(video_path)
-
-            if sample.get("control_image_path", None) is not None:
-                control_image_path = sample["control_image_path"]
-                control_image_path = os.path.join(self.dir, control_image_path)
-                if not pathlib.Path(control_image_path).is_file(
-                ) and not control_image_path.startswith("http"):
-                    logger.warning("Control Image file %s does not exist.",
-                                   control_image_path)
-                else:
-                    sample["control_image"] = load_image(control_image_path)
-
-            if sample.get("control_video_path", None) is not None:
-                control_video_path = sample["control_video_path"]
-                control_video_path = os.path.join(self.dir, control_video_path)
-                if not pathlib.Path(control_video_path).is_file(
-                ) and not control_video_path.startswith("http"):
-                    logger.warning("Control Video file %s does not exist.",
-                                   control_video_path)
-                else:
-                    sample["control_video"] = load_video(control_video_path)
-
-            sample = {k: v for k, v in sample.items() if v is not None}
-            yield sample
@@ -3,10 +3,10 @@
 from fastvideo.v1.distributed.communication_op import *
 from fastvideo.v1.distributed.parallel_state import (
    cleanup_dist_env_and_memory, get_dp_group, get_dp_rank, get_dp_world_size,
-    get_local_torch_device, get_sp_group, get_sp_parallel_rank,
-    get_sp_world_size, get_tp_group, get_tp_rank, get_tp_world_size,
-    get_world_group, get_world_rank, get_world_size,
-    init_distributed_environment, initialize_model_parallel,
+    get_sp_group, get_sp_parallel_rank, get_sp_world_size, get_torch_device,
+    get_tp_group, get_tp_rank, get_tp_world_size, get_world_group,
+    get_world_rank, get_world_size, init_distributed_environment,
+    initialize_model_parallel,
    maybe_init_distributed_environment_and_model_parallel,
    model_parallel_is_initialized)
 from fastvideo.v1.distributed.utils import *
@@ -40,5 +40,5 @@ __all__ = [
    "get_tp_world_size",

    # Get torch device
-    "get_local_torch_device",
+    "get_torch_device",
 ]
@@ -36,7 +36,6 @@ from unittest.mock import patch

 import torch
 import torch.distributed
-import torch.distributed as dist
 from torch.distributed import Backend, ProcessGroup, ReduceOp

 import fastvideo.v1.envs as envs
@@ -693,7 +692,6 @@ class GroupCoordinator:


 _WORLD: Optional[GroupCoordinator] = None
-_NODE: Optional[GroupCoordinator] = None


 def get_world_group() -> GroupCoordinator:
@@ -701,11 +699,6 @@ def get_world_group() -> GroupCoordinator:
    return _WORLD


-def get_node_group() -> GroupCoordinator:
-    assert _NODE is not None, ("node group is not initialized")
-    return _NODE
-
-
 def init_world_group(ranks: List[int], local_rank: int,
                     backend: str) -> GroupCoordinator:
    return GroupCoordinator(
@@ -717,18 +710,6 @@ def init_world_group(ranks: List[int], local_rank: int,
    )


-def init_node_group(local_rank: int, backend: str):
-    cpu_group = get_world_group().cpu_group
-    node_ranks = same_node_ranks(cpu_group)
-    node_size = len(node_ranks)
-    all_node_ranks = [
-        list(range(i * node_size, (i + 1) * node_size))
-        for i in range(dist.get_world_size() // node_size)
-    ]
-    global _NODE
-    _NODE = init_model_parallel_group(all_node_ranks, local_rank, backend)
-
-
 def init_model_parallel_group(
    group_ranks: List[List[int]],
    local_rank: int,
@@ -801,8 +782,6 @@ def init_distributed_environment(
    else:
        assert _WORLD.world_size == torch.distributed.get_world_size(), (
            "world group already initialized with a different world size")
-    # Init a group for each node
-    init_node_group(local_rank, backend)


 _SP: Optional[GroupCoordinator] = None
@@ -925,7 +904,7 @@ def get_dp_rank() -> int:
    return get_dp_group().rank_in_group


-def get_local_torch_device() -> torch.device:
+def get_torch_device() -> torch.device:
    """Return the torch device for the current rank."""
    return torch.device(f"cuda:{envs.LOCAL_RANK}")

@@ -1042,22 +1021,17 @@ def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
            "torch._C._host_emptyCache() only available in Pytorch >=2.5")


-def same_node_ranks(pg: Union[ProcessGroup, StatelessProcessGroup],
-                    source_rank: int = 0) -> List[int]:
+def in_the_same_node_as(pg: Union[ProcessGroup, StatelessProcessGroup],
+                        source_rank: int = 0) -> List[bool]:
    """
-    This is a collective operation that returns ranks that are in the same node
+    This is a collective operation that returns if each rank is in the same node
    as the source rank. It tests if processes are attached to the same
    memory system (shared access to shared memory).
-    Args:
-        pg: the global process group to test
-        source_rank: the rank to test against
-    Returns:
-        A list of ranks that are in the same node as the source rank.
    """
    if isinstance(pg, ProcessGroup):
        assert torch.distributed.get_backend(
            pg) != torch.distributed.Backend.NCCL, (
-                "same_node_ranks should be tested with a non-NCCL group.")
+                "in_the_same_node_as should be tested with a non-NCCL group.")
        # local rank inside the group
        rank = torch.distributed.get_rank(group=pg)
        world_size = torch.distributed.get_world_size(group=pg)
@@ -1129,7 +1103,7 @@ def same_node_ranks(pg: Union[ProcessGroup, StatelessProcessGroup],
            rank_data = pg.broadcast_obj(is_in_the_same_node, src=i)
            aggregated_data += rank_data

-    return [i for i, x in enumerate(aggregated_data.tolist()) if x == 1]
+    return [x == 1 for x in aggregated_data.tolist()]


 def initialize_tensor_parallel_group(
@@ -4,9 +4,9 @@
 import argparse
 import dataclasses
 import os
-from typing import List, cast
+from typing import Any, Dict, List, Optional, cast

-from fastvideo import VideoGenerator
+from fastvideo import PipelineConfig, VideoGenerator
 from fastvideo.v1.configs.sample.base import SamplingParam
 from fastvideo.v1.entrypoints.cli.cli_types import CLISubcommand
 from fastvideo.v1.entrypoints.cli.utils import RaiseNotImplementedAction
@@ -37,6 +37,8 @@ class GenerateSubcommand(CLISubcommand):
    def cmd(self, args: argparse.Namespace) -> None:
        excluded_args = ['subparser', 'config', 'dispatch_function']

+        FastVideoArgs.from_cli_args(args)
+
        provided_args = {}
        for k, v in vars(args).items():
            if (k not in excluded_args and v is not None
@@ -64,19 +66,27 @@ class GenerateSubcommand(CLISubcommand):

        init_args = {
            k: v
-            for k, v in merged_args.items()
-            if k not in self.generation_arg_names
+            for k, v in merged_args.items() if k in self.init_arg_names
        }
        generation_args = {
            k: v
            for k, v in merged_args.items() if k in self.generation_arg_names
        }

+        pipeline_config = PipelineConfig.from_pretrained(
+            merged_args['model_path'])
+
+        update_config_from_args(pipeline_config.dit_config, merged_args,
+                                "dit_config")
+        update_config_from_args(pipeline_config.vae_config, merged_args,
+                                "vae_config")
+        update_config_from_args(pipeline_config, merged_args)
+
        model_path = init_args.pop('model_path')
        prompt = generation_args.pop('prompt')

-        generator = VideoGenerator.from_pretrained(model_path=model_path,
-                                                   **init_args)
+        generator = VideoGenerator.from_pretrained(
+            model_path=model_path, **init_args, pipeline_config=pipeline_config)

        generator.generate_video(prompt=prompt, **generation_args)

@@ -122,3 +132,34 @@ class GenerateSubcommand(CLISubcommand):

 def cmd_init() -> List[CLISubcommand]:
    return [GenerateSubcommand()]
+
+
+def update_config_from_args(config: Any,
+                            args_dict: Dict[str, Any],
+                            prefix: Optional[str] = None) -> None:
+    """
+    Update configuration object from arguments dictionary.
+    
+    Args:
+        config: The configuration object to update
+        args_dict: Dictionary containing arguments
+        prefix: Prefix for the configuration parameters in the args_dict.
+               If None, assumes direct attribute mapping without prefix.
+    """
+    # Handle top-level attributes (no prefix)
+    if prefix is None:
+        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)
+        return
+
+    # Handle nested attributes with prefix
+    prefix_with_dot = f"{prefix}."
+    for key, value in args_dict.items():
+        if key.startswith(prefix_with_dot) and value is not None:
+            attr_name = key[len(prefix_with_dot):]
+            if hasattr(config, attr_name):
+                setattr(config, attr_name, value)
@@ -18,6 +18,8 @@ import torch
 import torchvision
 from einops import rearrange

+from fastvideo.v1.configs.pipelines import (PipelineConfig,
+                                            get_pipeline_config_cls_for_name)
 from fastvideo.v1.configs.sample import SamplingParam
 from fastvideo.v1.fastvideo_args import FastVideoArgs
 from fastvideo.v1.logger import init_logger
@@ -53,6 +55,9 @@ class VideoGenerator:
                        model_path: str,
                        device: Optional[str] = None,
                        torch_dtype: Optional[torch.dtype] = None,
+                        pipeline_config: Optional[
+                            Union[str
+                                  | PipelineConfig]] = None,
                        **kwargs) -> "VideoGenerator":
        """
        Create a video generator from a pretrained model.
@@ -61,17 +66,35 @@ class VideoGenerator:
            model_path: Path or identifier for the pretrained model
            device: Device to load the model on (e.g., "cuda", "cuda:0", "cpu")
            torch_dtype: Data type for model weights (e.g., torch.float16)
-            pipeline_config: Pipeline config to use for inference
-            **kwargs: Additional arguments to customize model loading, set any FastVideoArgs or PipelineConfig attributes here.
+            **kwargs: Additional arguments to customize model loading
                
        Returns:
            The created video generator

        Priority level: Default pipeline config < User's pipeline config < User's kwargs
        """
-        # If users also provide some kwargs, it will override the FastVideoArgs and PipelineConfig.
-        kwargs['model_path'] = model_path
-        fastvideo_args = FastVideoArgs.from_kwargs(kwargs)
+        config = None
+        # 1. If users provide a pipeline config, it will override the default pipeline config
+        if isinstance(pipeline_config, PipelineConfig):
+            config = pipeline_config
+        else:
+            config_cls = get_pipeline_config_cls_for_name(model_path)
+            if config_cls is not None:
+                config = config_cls()
+                if isinstance(pipeline_config, str):
+                    config.load_from_json(pipeline_config)
+
+        # 2. If users also provide some kwargs, it will override the pipeline config.
+        # The user kwargs shouldn't contain model config parameters!
+        if config is None:
+            logger.warning("No config found for model %s, using default config",
+                           model_path)
+            config_args = kwargs
+        else:
+            config_args = shallow_asdict(config)
+            config_args.update(kwargs)
+
+        fastvideo_args = FastVideoArgs(model_path=model_path, **config_args)

        return cls.from_fastvideo_args(fastvideo_args)

@@ -127,17 +150,16 @@ class VideoGenerator:
        """
        # Create a copy of inference args to avoid modifying the original
        fastvideo_args = self.fastvideo_args
-        pipeline_config = fastvideo_args.pipeline_config

        # Validate inputs
        if not isinstance(prompt, str):
            raise TypeError(
                f"`prompt` must be a string, but got {type(prompt)}")
        prompt = prompt.strip()
+
        if sampling_param is None:
            sampling_param = SamplingParam.from_pretrained(
                fastvideo_args.model_path)
-
        kwargs["prompt"] = prompt
        sampling_param.update(kwargs)

@@ -154,10 +176,10 @@ class VideoGenerator:
                f"height={sampling_param.height}, width={sampling_param.width}, "
                f"num_frames={sampling_param.num_frames}")

-        temporal_scale_factor = pipeline_config.vae_config.arch_config.temporal_compression_ratio
+        temporal_scale_factor = fastvideo_args.vae_config.arch_config.temporal_compression_ratio
        num_frames = sampling_param.num_frames
        num_gpus = fastvideo_args.num_gpus
-        use_temporal_scaling_frames = pipeline_config.vae_config.use_temporal_scaling_frames
+        use_temporal_scaling_frames = fastvideo_args.vae_config.use_temporal_scaling_frames

        # Adjust number of frames based on number of GPUs
        if use_temporal_scaling_frames:
@@ -216,18 +238,18 @@ class VideoGenerator:
       num_videos_per_prompt: {sampling_param.num_videos_per_prompt}
              guidance_scale: {sampling_param.guidance_scale}
                    n_tokens: {n_tokens}
-                  flow_shift: {fastvideo_args.pipeline_config.flow_shift}
-     embedded_guidance_scale: {fastvideo_args.pipeline_config.embedded_cfg_scale}
+                  flow_shift: {fastvideo_args.flow_shift}
+     embedded_guidance_scale: {fastvideo_args.embedded_cfg_scale}
                  save_video: {sampling_param.save_video}
                  output_path: {sampling_param.output_path}
        """ # type: ignore[attr-defined]
        logger.info(debug_str)
+
        # Prepare batch
        batch = ForwardBatch(
            **shallow_asdict(sampling_param),
            eta=0.0,
            n_tokens=n_tokens,
-            VSA_sparsity=fastvideo_args.VSA_sparsity,
            extra={},
        )

@@ -6,32 +6,26 @@ import argparse
 import dataclasses
 from contextlib import contextmanager
 from dataclasses import field
-from typing import Any, Dict, List, Optional
+from typing import Any, Callable, List, Optional, Tuple

-from fastvideo.v1.configs.pipelines.base import PipelineConfig, STA_Mode
+from fastvideo.v1.configs.models import DiTConfig, EncoderConfig, VAEConfig
 from fastvideo.v1.logger import init_logger
 from fastvideo.v1.utils import FlexibleArgumentParser, StoreBoolean

 logger = init_logger(__name__)


-def clean_cli_args(args: argparse.Namespace) -> Dict[str, Any]:
-    """
-    Clean the arguments by removing the ones that not explicitly provided by the user.
-    """
-    provided_args = {}
-    for k, v in vars(args).items():
-        if (v is not None and hasattr(args, '_provided')
-                and k in args._provided):
-            provided_args[k] = v
+def preprocess_text(prompt: str) -> str:
+    return prompt

-    return provided_args
+
+def postprocess_text(output: Any) -> Any:
+    raise NotImplementedError


-# args for fastvideo framework
@dataclasses.dataclass
 class FastVideoArgs:
-    # Model and path configuration (for convenience)
+    # Model and path configuration
    model_path: str

    # Cache strategy
@@ -54,30 +48,66 @@ class FastVideoArgs:
    hsdp_shard_dim: int = -1
    dist_timeout: Optional[int] = None  # timeout for torch.distributed

-    pipeline_config: PipelineConfig = field(default_factory=PipelineConfig)
+    # Video generation parameters
+    embedded_cfg_scale: float = 6.0
+    flow_shift: Optional[float] = None

    output_type: str = "pil"

-    use_cpu_offload: bool = True  # For DiT
+    # DiT configuration
+    dit_config: DiTConfig = field(default_factory=DiTConfig)
+    precision: str = "bf16"
+    use_cpu_offload: bool = True
    use_fsdp_inference: bool = True
-    text_encoder_offload: bool = True
-    pin_cpu_memory: bool = True

-    # STA (Sliding Tile Attention) parameters
-    mask_strategy_file_path: Optional[str] = None
-    STA_mode: STA_Mode = STA_Mode.STA_INFERENCE
+    # VAE configuration
+    vae_precision: str = "fp16"
+    vae_tiling: bool = True  # Might change in between forward passes
+    vae_sp: bool = False  # Might change in between forward passes
+    # vae_scale_factor: Optional[int] = None # Deprecated
+    vae_config: VAEConfig = field(default_factory=VAEConfig)
+
+    # Image encoder configuration
+    image_encoder_precision: str = "fp32"
+    image_encoder_config: EncoderConfig = field(default_factory=EncoderConfig)
+
+    # Text encoder configuration
+    DEFAULT_TEXT_ENCODER_PRECISIONS = (
+        "fp16",
+        # "fp16",
+    )
+    text_encoder_precisions: Tuple[str, ...] = field(
+        default_factory=lambda: FastVideoArgs.DEFAULT_TEXT_ENCODER_PRECISIONS)
+    text_encoder_configs: Tuple[EncoderConfig, ...] = field(
+        default_factory=lambda: (EncoderConfig(), ))
+    preprocess_text_funcs: Tuple[Callable[[str], str], ...] = field(
+        default_factory=lambda: (preprocess_text, ))
+    postprocess_text_funcs: Tuple[Callable[[Any], Any], ...] = field(
+        default_factory=lambda: (postprocess_text, ))
+
+    # STA parameters
+    STA_mode: Optional[str] = None
    skip_time_steps: int = 15
+    # LoRA parameters
+    lora_path: Optional[str] = None
+    lora_nickname: Optional[
+        str] = "default"  # for swapping adapters in the pipeline
+    lora_target_names: Optional[List[
+        str]] = None  # can restrict list of layers to adapt, e.g. ["q_proj"]

-    # Compilation
+    # STA parameters
+    mask_strategy_file_path: Optional[str] = None
    enable_torch_compile: bool = False

    disable_autocast: bool = False

-    # VSA parameters
-    VSA_sparsity: float = 0.0  # inference/validation sparsity
+    # StepVideo specific parameters
+    pos_magic: Optional[str] = None
+    neg_magic: Optional[str] = None
+    timesteps_scale: Optional[bool] = None

-    # Stage verification
-    enable_stage_verification: bool = True
+    # Logging
+    log_level: str = "info"

    @property
    def training_mode(self) -> bool:
@@ -95,6 +125,11 @@ class FastVideoArgs:
            help=
            "The path of the model weights. This can be a local folder or a Hugging Face repo ID.",
        )
+        parser.add_argument(
+            "--dit-weight",
+            type=str,
+            help="Path to the DiT model weights",
+        )
        parser.add_argument(
            "--model-dir",
            type=str,
@@ -140,12 +175,14 @@ class FastVideoArgs:
            help="The number of GPUs to use.",
        )
        parser.add_argument(
+            "--tensor-parallel-size",
            "--tp-size",
            type=int,
            default=FastVideoArgs.tp_size,
            help="The tensor parallelism size.",
        )
        parser.add_argument(
+            "--sequence-parallel-size",
            "--sp-size",
            type=int,
            default=FastVideoArgs.sp_size,
@@ -170,7 +207,19 @@ class FastVideoArgs:
            help="Set timeout for torch.distributed initialization.",
        )

-        # Output type
+        parser.add_argument(
+            "--embedded-cfg-scale",
+            type=float,
+            default=FastVideoArgs.embedded_cfg_scale,
+            help="Embedded CFG scale",
+        )
+        parser.add_argument(
+            "--flow-shift",
+            "--shift",
+            type=float,
+            default=FastVideoArgs.flow_shift,
+            help="Flow shift parameter",
+        )
        parser.add_argument(
            "--output-type",
            type=str,
@@ -179,14 +228,62 @@ class FastVideoArgs:
            help="Output type for the generated video",
        )

-        # STA (Sliding Tile Attention) parameters
+        parser.add_argument(
+            "--precision",
+            type=str,
+            default=FastVideoArgs.precision,
+            choices=["fp32", "fp16", "bf16"],
+            help="Precision for the model",
+        )
+
+        # VAE configuration
+        parser.add_argument(
+            "--vae-precision",
+            type=str,
+            default=FastVideoArgs.vae_precision,
+            choices=["fp32", "fp16", "bf16"],
+            help="Precision for VAE",
+        )
+        parser.add_argument(
+            "--vae-tiling",
+            action=StoreBoolean,
+            default=FastVideoArgs.vae_tiling,
+            help="Enable VAE tiling",
+        )
+        parser.add_argument(
+            "--vae-sp",
+            action=StoreBoolean,
+            help="Enable VAE spatial parallelism",
+        )
+
+        parser.add_argument(
+            "--text-encoder-precisions",
+            nargs="+",
+            type=str,
+            default=FastVideoArgs.DEFAULT_TEXT_ENCODER_PRECISIONS,
+            choices=["fp32", "fp16", "bf16"],
+            help="Precision for each text encoder",
+        )
+
+        # Image encoder config
+        parser.add_argument(
+            "--image-encoder-precision",
+            type=str,
+            default=FastVideoArgs.image_encoder_precision,
+            choices=["fp32", "fp16", "bf16"],
+            help="Precision for image encoder",
+        )
+
+        # STA parameters
        parser.add_argument(
            "--STA-mode",
            type=str,
-            default=FastVideoArgs.STA_mode.value,
-            choices=[mode.value for mode in STA_Mode],
-            help=
-            "STA mode contains STA_inference, STA_searching, STA_tuning, STA_tuning_cfg, None",
+            default=FastVideoArgs.STA_mode,
+            choices=[
+                "STA_inference", "STA_searching", "STA_tuning",
+                "STA_tuning_cfg", None
+            ],
+            help="STA mode",
        )
        parser.add_argument(
            "--skip-time-steps",
@@ -210,7 +307,7 @@ class FastVideoArgs:
            "--use-cpu-offload",
            action=StoreBoolean,
            help=
-            "Use CPU offload for DiT inference. Enable if run out of memory with FSDP.",
+            "Use CPU offload for model inference. Enable if run out of memory with FSDP.",
        )
        parser.add_argument(
            "--use-fsdp-inference",
@@ -218,19 +315,7 @@ class FastVideoArgs:
            help=
            "Use FSDP for inference by sharding the model weights. Latency is very low due to prefetch--enable if run out of memory.",
        )
-        parser.add_argument(
-            "--text-encoder-cpu-offload",
-            action=StoreBoolean,
-            help=
-            "Use CPU offload for text encoder. Enable if run out of memory.",
-        )
-        parser.add_argument(
-            "--pin-cpu-memory",
-            action=StoreBoolean,
-            help=
-            "Pin memory for CPU offload. Only added as a temp workaround if it throws \"CUDA error: invalid argument\". "
-            "Should be enabled in almost all cases",
-        )
+
        parser.add_argument(
            "--disable-autocast",
            action=StoreBoolean,
@@ -238,50 +323,69 @@ class FastVideoArgs:
            "Disable autocast for denoising loop and vae decoding in pipeline sampling",
        )

-        # VSA parameters
        parser.add_argument(
-            "--VSA-sparsity",
-            type=float,
-            default=FastVideoArgs.VSA_sparsity,
-            help="Validation sparsity for VSA",
+            "--pos_magic",
+            type=str,
+            default=FastVideoArgs.pos_magic,
+            help="Positive magic prompt for sampling",
+        )
+        parser.add_argument(
+            "--neg_magic",
+            type=str,
+            default=FastVideoArgs.neg_magic,
+            help="Negative magic prompt for sampling",
+        )
+        parser.add_argument(
+            "--timesteps_scale",
+            type=bool,
+            default=FastVideoArgs.timesteps_scale,
+            help="Bool for applying scheduler scale in set_timesteps",
        )

-        # Stage verification
+        # Logging
        parser.add_argument(
-            "--enable-stage-verification",
-            action=StoreBoolean,
-            default=FastVideoArgs.enable_stage_verification,
-            help="Enable input/output verification for pipeline stages",
+            "--log-level",
+            type=str,
+            default=FastVideoArgs.log_level,
+            help="The logging level of all loggers.",
        )

-        # Add pipeline configuration arguments
-        PipelineConfig.add_cli_args(parser)
+        # Add VAE configuration arguments
+        from fastvideo.v1.configs.models.vaes.base import VAEConfig
+        VAEConfig.add_cli_args(parser)
+
+        # Add DiT configuration arguments
+        from fastvideo.v1.configs.models.dits.base import DiTConfig
+        DiTConfig.add_cli_args(parser)

        return parser

    @classmethod
    def from_cli_args(cls, args: argparse.Namespace) -> "FastVideoArgs":
-        provided_args = clean_cli_args(args)
+        args.tp_size = args.tensor_parallel_size
+        args.sp_size = args.sequence_parallel_size
+        args.flow_shift = getattr(args, "shift", args.flow_shift)
+
        # Get all fields from the dataclass
        attrs = [attr.name for attr in dataclasses.fields(cls)]

        # Create a dictionary of attribute values, with defaults for missing attributes
        kwargs = {}
        for attr in attrs:
-            if attr == 'pipeline_config':
-                pipeline_config = PipelineConfig.from_kwargs(provided_args)
-                kwargs[attr] = pipeline_config
+            # Handle renamed attributes or those with multiple CLI names
+            if attr == 'tp_size' and hasattr(args, 'tensor_parallel_size'):
+                kwargs[attr] = args.tensor_parallel_size
+            elif attr == 'sp_size' and hasattr(args, 'sequence_parallel_size'):
+                kwargs[attr] = args.sequence_parallel_size
+            elif attr == 'flow_shift' and hasattr(args, 'shift'):
+                kwargs[attr] = args.shift
            # Use getattr with default value from the dataclass for potentially missing attributes
            else:
                default_value = getattr(cls, attr, None)
                value = getattr(args, attr, default_value)
-                kwargs[attr] = value  # type: ignore
+                if value is not None:
+                    kwargs[attr] = value

-        return cls(**kwargs)  # type: ignore
-
-    @classmethod
-    def from_kwargs(cls, kwargs: Dict[str, Any]) -> "FastVideoArgs":
-        kwargs['pipeline_config'] = PipelineConfig.from_kwargs(kwargs)
        return cls(**kwargs)

    def check_fastvideo_args(self) -> None:
@@ -310,17 +414,33 @@ class FastVideoArgs:
                f"tp_size ({self.tp_size}) must be equal to sp_size ({self.sp_size})"
            )

+        # Validate VAE spatial parallelism with VAE tiling
+        if self.vae_sp and not self.vae_tiling:
+            raise ValueError(
+                "Currently enabling vae_sp requires enabling vae_tiling, please set --vae-tiling to True."
+            )
+
+        if len(self.text_encoder_configs) != len(self.text_encoder_precisions):
+            raise ValueError(
+                f"Length of text encoder configs ({len(self.text_encoder_configs)}) must be equal to length of text encoder precisions ({len(self.text_encoder_precisions)})"
+            )
+
+        if len(self.text_encoder_configs) != len(self.preprocess_text_funcs):
+            raise ValueError(
+                f"Length of text encoder configs ({len(self.text_encoder_configs)}) must be equal to length of text preprocessing functions ({len(self.preprocess_text_funcs)})"
+            )
+
+        if len(self.preprocess_text_funcs) != len(self.postprocess_text_funcs):
+            raise ValueError(
+                f"Length of text postprocess functions ({len(self.postprocess_text_funcs)}) must be equal to length of text preprocessing functions ({len(self.preprocess_text_funcs)})"
+            )
+
        if self.enable_torch_compile and self.num_gpus > 1:
            logger.warning(
                "Currently torch compile does not work with multi-gpu. Setting enable_torch_compile to False"
            )
            self.enable_torch_compile = False

-        if self.pipeline_config is None:
-            raise ValueError("pipeline_config is not set in FastVideoArgs")
-
-        self.pipeline_config.check_pipeline_config()
-

 _current_fastvideo_args = None

@@ -394,22 +514,22 @@ class TrainingArgs(FastVideoArgs):
    # text encoder & vae & diffusion model
    pretrained_model_name_or_path: str = ""
    dit_model_name_or_path: str = ""
+    cache_dir: str = ""

    # diffusion setting
    ema_decay: float = 0.0
    ema_start_step: int = 0
-    training_cfg_rate: float = 0.0
+    cfg: float = 0.0
    precondition_outputs: bool = False

    # validation & logs
    validation_dataset_file: str = ""
-    validation_preprocessed_path: str = ""
+    validation_path: str = ""
    validation_sampling_steps: str = ""
    validation_guidance_scale: str = ""
    validation_steps: float = 0.0
    log_validation: bool = False
    tracker_project_name: str = ""
-    wandb_run_name: str = ""
    seed: Optional[int] = None

    # output
@@ -457,29 +577,27 @@ class TrainingArgs(FastVideoArgs):
    # master_weight_type
    master_weight_type: str = ""

-    # VSA training decay parameters
-    VSA_decay_rate: float = 0.01  # decay rate -> 0.02
-    VSA_decay_interval_steps: int = 1  # decay interval steps -> 50
-
    @classmethod
    def from_cli_args(cls, args: argparse.Namespace) -> "TrainingArgs":
-        provided_args = clean_cli_args(args)
        # Get all fields from the dataclass
        attrs = [attr.name for attr in dataclasses.fields(cls)]
-        logger.info(provided_args)
+
        # Create a dictionary of attribute values, with defaults for missing attributes
        kwargs = {}
        for attr in attrs:
-            if attr == 'pipeline_config':
-                pipeline_config = PipelineConfig.from_kwargs(provided_args)
-                kwargs[attr] = pipeline_config
-            # Use getattr with default value from the dataclass for potentially missing attributes
+            # Handle renamed attributes or those with multiple CLI names
+            if attr == 'tp_size' and hasattr(args, 'tensor_parallel_size'):
+                kwargs[attr] = args.tensor_parallel_size
+            elif attr == 'sp_size' and hasattr(args, 'sequence_parallel_size'):
+                kwargs[attr] = args.sequence_parallel_size
+            elif attr == 'flow_shift' and hasattr(args, 'shift'):
+                kwargs[attr] = args.shift
            else:
                default_value = getattr(cls, attr, None)
-                value = getattr(args, attr, default_value)
-                kwargs[attr] = value  # type: ignore
+                if getattr(args, attr, default_value) is not None:
+                    kwargs[attr] = getattr(args, attr, default_value)

-        return cls(**kwargs)  # type: ignore
+        return cls(**kwargs)

    @staticmethod
    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
@@ -542,7 +660,7 @@ class TrainingArgs(FastVideoArgs):
                            type=int,
                            default=0,
                            help="Step to start EMA")
-        parser.add_argument("--training-cfg-rate",
+        parser.add_argument("--cfg",
                            type=float,
                            help="Classifier-free guidance scale")
        parser.add_argument(
@@ -553,10 +671,10 @@ class TrainingArgs(FastVideoArgs):
        # Validation and logging
        parser.add_argument("--validation-dataset-file",
                            type=str,
-                            help="Path to unprocessed validation dataset")
-        parser.add_argument("--validation-preprocessed-path",
+                            help="File containing validation dataset")
+        parser.add_argument("--validation-path",
                            type=str,
-                            help="Path to processed validation dataset")
+                            help="Path to validation dataset")
        parser.add_argument("--validation-sampling-steps",
                            type=str,
                            help="Validation sampling steps")
@@ -572,9 +690,6 @@ class TrainingArgs(FastVideoArgs):
        parser.add_argument("--tracker-project-name",
                            type=str,
                            help="Project name for tracking")
-        parser.add_argument("--wandb-run-name",
-                            type=str,
-                            help="Run name for wandb")
        parser.add_argument("--seed",
                            type=int,
                            default=42,
@@ -713,16 +828,4 @@ class TrainingArgs(FastVideoArgs):
                            type=str,
                            help="Master weight type")

-        # VSA parameters for training with dense to sparse adaption
-        parser.add_argument(
-            "--VSA-decay-rate",  # decay rate, how much sparsity you want to decay each step
-            type=float,
-            default=TrainingArgs.VSA_decay_rate,
-            help="VSA decay rate")
-        parser.add_argument(
-            "--VSA-decay-interval-steps",  # how many steps for training with current sparsity
-            type=int,
-            default=TrainingArgs.VSA_decay_interval_steps,
-            help="VSA decay interval steps")
-
        return parser
@@ -5,16 +5,15 @@ import time
 from collections import defaultdict
 from contextlib import contextmanager
 from dataclasses import dataclass
-from typing import TYPE_CHECKING, Optional
+from typing import Optional

 import torch

+# if TYPE_CHECKING:
+from fastvideo.v1.attention import AttentionMetadata
 from fastvideo.v1.fastvideo_args import FastVideoArgs
 from fastvideo.v1.logger import init_logger
-
-if TYPE_CHECKING:
-    from fastvideo.v1.attention import AttentionMetadata
-    from fastvideo.v1.pipelines import ForwardBatch
+from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch

 logger = init_logger(__name__)

@@ -37,13 +36,13 @@ class ForwardContext:
    # attn_layers: Dict[str, Any]
    # TODO: extend to support per-layer dynamic forward context
    attn_metadata: "AttentionMetadata"  # set dynamically for each forward pass
-    forward_batch: Optional["ForwardBatch"] = None
+    forward_batch: Optional[ForwardBatch] = None


-_forward_context: Optional["ForwardContext"] = None
+_forward_context: Optional[ForwardContext] = None


-def get_forward_context() -> "ForwardContext":
+def get_forward_context() -> ForwardContext:
    """Get the current forward context."""
    assert _forward_context is not None, (
        "Forward context is not set. "
@@ -55,7 +54,7 @@ def get_forward_context() -> "ForwardContext":
@contextmanager
 def set_forward_context(current_timestep,
                        attn_metadata,
-                        forward_batch: Optional["ForwardBatch"] = None,
+                        forward_batch: Optional[ForwardBatch] = None,
                        fastvideo_args: Optional[FastVideoArgs] = None):
    """A context manager that stores the current forward context,
    can be attention metadata, etc.
@@ -5,8 +5,6 @@ from typing import Optional, Tuple, Union

 import torch
 import torch.nn as nn
-import torch.nn.functional as F
-from torch.distributed.tensor import DTensor

 from fastvideo.v1.layers.custom_op import CustomOp

@@ -71,12 +69,7 @@ class RMSNorm(CustomOp):
        x = x * torch.rsqrt(variance + self.variance_epsilon)
        x = x.to(orig_dtype)
        if self.has_weight:
-            # TODO(wenxuan): When using CPU offload, FSDP has a bug that doesn't unwrap DTensor in final_layer_norm.
-            # Report this
-            if isinstance(self.weight, DTensor):
-                x = x * self.weight.to(x.device).full_tensor()
-            else:
-                x = x * self.weight
+            x = x * self.weight
        if residual is None:
            return x
        else:
@@ -102,22 +95,6 @@ class ScaleResidual(nn.Module):
        return residual + x * gate


-# adapted from Diffusers: https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py
-# NOTE(will): Needed to match behavior of diffusers and wan2.1 even while using
-# FSDP's MixedPrecisionPolicy
-class FP32LayerNorm(nn.LayerNorm):
-
-    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
-        origin_dtype = inputs.dtype
-        return F.layer_norm(
-            inputs.float(),
-            self.normalized_shape,
-            self.weight.float() if self.weight is not None else None,
-            self.bias.float() if self.bias is not None else None,
-            self.eps,
-        ).to(origin_dtype)
-
-
 class ScaleResidualLayerNormScaleShift(nn.Module):
    """
    Fused operation that combines:
@@ -135,7 +112,6 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
        eps: float = 1e-6,
        elementwise_affine: bool = False,
        dtype: torch.dtype = torch.float32,
-        compute_dtype: torch.dtype | None = None,
        prefix: str = "",
    ):
        super().__init__()
@@ -145,15 +121,10 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
                                eps=eps,
                                dtype=dtype)
        elif norm_type == "layer":
-            if compute_dtype == torch.float32:
-                self.norm = FP32LayerNorm(hidden_size,
-                                          elementwise_affine=elementwise_affine,
-                                          eps=eps)
-            else:
-                self.norm = nn.LayerNorm(hidden_size,
-                                         elementwise_affine=elementwise_affine,
-                                         eps=eps,
-                                         dtype=dtype)
+            self.norm = nn.LayerNorm(hidden_size,
+                                     elementwise_affine=elementwise_affine,
+                                     eps=eps,
+                                     dtype=dtype)
        else:
            raise NotImplementedError(f"Norm type {norm_type} not implemented")

@@ -192,25 +163,18 @@ class LayerNormScaleShift(nn.Module):
        eps: float = 1e-6,
        elementwise_affine: bool = False,
        dtype: torch.dtype = torch.float32,
-        compute_dtype: torch.dtype | None = None,
        prefix: str = "",
    ):
        super().__init__()
-        self.compute_dtype = compute_dtype
        if norm_type == "rms":
            self.norm = RMSNorm(hidden_size,
                                has_weight=elementwise_affine,
                                eps=eps)
        elif norm_type == "layer":
-            if self.compute_dtype == torch.float32:
-                self.norm = FP32LayerNorm(hidden_size,
-                                          elementwise_affine=elementwise_affine,
-                                          eps=eps)
-            else:
-                self.norm = nn.LayerNorm(hidden_size,
-                                         elementwise_affine=elementwise_affine,
-                                         eps=eps,
-                                         dtype=dtype)
+            self.norm = nn.LayerNorm(hidden_size,
+                                     elementwise_affine=elementwise_affine,
+                                     eps=eps,
+                                     dtype=dtype)
        else:
            raise NotImplementedError(f"Norm type {norm_type} not implemented")

@@ -218,7 +182,4 @@ class LayerNormScaleShift(nn.Module):
                scale: torch.Tensor) -> torch.Tensor:
        """Apply ln followed by scale and shift in a single fused operation."""
        normalized = self.norm(x)
-        if self.compute_dtype == torch.float32:
-            return (normalized.float() * (1.0 + scale) + shift).to(x.dtype)
-        else:
-            return normalized * (1.0 + scale) + shift
+        return normalized * (1.0 + scale) + shift
@@ -114,7 +114,7 @@ def _info(logger: Logger,

    if (main_process_only and is_main_process) or (local_main_process_only
                                                   and is_local_main_process):
-        logger.log(logging.INFO, msg, *args, stacklevel=2, **kwargs)
+        logger.log(logging.INFO, msg, *args, **kwargs)

    global _warned_local_main_process, _warned_main_process

@@ -134,7 +134,7 @@ def _info(logger: Logger,
        _warned_main_process = True

    if not main_process_only and not local_main_process_only:
-        logger.log(logging.INFO, msg, *args, stacklevel=2, **kwargs)
+        logger.log(logging.INFO, msg, *args, **kwargs)


 class _FastvideoLogger(Logger):
@@ -6,7 +6,7 @@ import torch
 from torch import nn

 from fastvideo.v1.configs.models import DiTConfig
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.v1.platforms import _Backend


 # TODO
@@ -14,13 +14,12 @@ class BaseDiT(nn.Module, ABC):
    _fsdp_shard_conditions: list = []
    _compile_conditions: list = []
    _param_names_mapping: dict
-    _reverse_param_names_mapping: dict
    hidden_size: int
    num_attention_heads: int
    num_channels_latents: int
    # always supports torch_sdpa
    _supported_attention_backends: Tuple[
-        AttentionBackendEnum, ...] = DiTConfig()._supported_attention_backends
+        _Backend, ...] = DiTConfig()._supported_attention_backends

    def __init_subclass__(cls) -> None:
        required_class_attrs = [
@@ -66,7 +65,7 @@ class BaseDiT(nn.Module, ABC):
                )

    @property
-    def supported_attention_backends(self) -> Tuple[AttentionBackendEnum, ...]:
+    def supported_attention_backends(self) -> Tuple[_Backend, ...]:
        return self._supported_attention_backends


@@ -79,7 +78,6 @@ class CachableDiT(BaseDiT):
    # These are required class attributes that should be overridden by concrete implementations
    _fsdp_shard_conditions = []
    _param_names_mapping = {}
-    _reverse_param_names_mapping = {}
    _lora_param_names_mapping: dict = {}
    # Ensure these instance attributes are properly defined in subclasses
    hidden_size: int
@@ -87,7 +85,7 @@ class CachableDiT(BaseDiT):
    num_channels_latents: int
    # always supports torch_sdpa
    _supported_attention_backends: Tuple[
-        AttentionBackendEnum, ...] = DiTConfig()._supported_attention_backends
+        _Backend, ...] = DiTConfig()._supported_attention_backends

    def __init__(self, config: DiTConfig, **kwargs) -> None:
        super().__init__(config, **kwargs)
@@ -23,7 +23,7 @@ from fastvideo.v1.layers.visual_embedding import (ModulateProjection,
                                                  unpatchify)
 from fastvideo.v1.models.dits.base import CachableDiT
 from fastvideo.v1.models.utils import modulate
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.v1.platforms import _Backend


 class HunyuanRMSNorm(nn.Module):
@@ -96,8 +96,7 @@ class MMDoubleStreamBlock(nn.Module):
        num_attention_heads: int,
        mlp_ratio: float,
        dtype: Optional[torch.dtype] = None,
-        supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
-                                                     ...]] = None,
+        supported_attention_backends: Optional[Tuple[_Backend, ...]] = None,
        prefix: str = "",
    ):
        super().__init__()
@@ -304,8 +303,7 @@ class MMSingleStreamBlock(nn.Module):
        num_attention_heads: int,
        mlp_ratio: float = 4.0,
        dtype: Optional[torch.dtype] = None,
-        supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
-                                                     ...]] = None,
+        supported_attention_backends: Optional[Tuple[_Backend, ...]] = None,
        prefix: str = "",
    ):
        super().__init__()
@@ -442,8 +440,6 @@ class HunyuanVideoTransformer3DModel(CachableDiT):
    _supported_attention_backends = HunyuanVideoConfig(
    )._supported_attention_backends
    _param_names_mapping = HunyuanVideoConfig()._param_names_mapping
-    _reverse_param_names_mapping = HunyuanVideoConfig(
-    )._reverse_param_names_mapping
    _lora_param_names_mapping = HunyuanVideoConfig()._lora_param_names_mapping

    def __init__(self, config: HunyuanVideoConfig, hf_config: dict[str, Any]):
@@ -880,8 +876,8 @@ class IndividualTokenRefinerBlock(nn.Module):
            num_heads=num_attention_heads,
            head_size=hidden_size // num_attention_heads,
            # TODO: remove hardcode; remove STA
-            supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
-                                          AttentionBackendEnum.TORCH_SDPA),
+            supported_attention_backends=(_Backend.FLASH_ATTN,
+                                          _Backend.TORCH_SDPA),
        )

    def forward(self, x, c):
@@ -26,7 +26,7 @@ from fastvideo.v1.layers.rotary_embedding import (_apply_rotary_emb,
                                                  get_rotary_pos_embed)
 from fastvideo.v1.layers.visual_embedding import TimestepEmbedder
 from fastvideo.v1.models.dits.base import BaseDiT
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.v1.platforms import _Backend


 class PatchEmbed2D(nn.Module):
@@ -139,17 +139,16 @@ class StepVideoRMSNorm(nn.Module):

 class SelfAttention(nn.Module):

-    def __init__(
-        self,
-        hidden_dim,
-        head_dim,
-        rope_split: Tuple[int, int, int] = (64, 32, 32),
-        bias: bool = False,
-        with_rope: bool = True,
-        with_qk_norm: bool = True,
-        attn_type: str = "torch",
-        supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
-                                      AttentionBackendEnum.TORCH_SDPA)):
+    def __init__(self,
+                 hidden_dim,
+                 head_dim,
+                 rope_split: Tuple[int, int, int] = (64, 32, 32),
+                 bias: bool = False,
+                 with_rope: bool = True,
+                 with_qk_norm: bool = True,
+                 attn_type: str = "torch",
+                 supported_attention_backends=(_Backend.FLASH_ATTN,
+                                               _Backend.TORCH_SDPA)):
        super().__init__()
        self.head_dim = head_dim
        self.hidden_dim = hidden_dim
@@ -258,8 +257,7 @@ class CrossAttention(nn.Module):
        head_dim,
        bias=False,
        with_qk_norm=True,
-        supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
-                                      AttentionBackendEnum.TORCH_SDPA)
+        supported_attention_backends=(_Backend.FLASH_ATTN, _Backend.TORCH_SDPA)
    ) -> None:
        super().__init__()
        self.head_dim = head_dim
@@ -455,10 +453,11 @@ class StepVideoTransformerBlock(nn.Module):

 class StepVideoModel(BaseDiT):
    # (Optional) Keep the same attribute for compatibility with splitting, etc.
-    _fsdp_shard_conditions = StepVideoConfig()._fsdp_shard_conditions
+    _fsdp_shard_conditions = [
+        lambda n, m: "transformer_blocks" in n and n.split(".")[-1].isdigit(),
+        # lambda n, m: "pos_embed" in n  # If needed for the patch embedding.
+    ]
    _param_names_mapping = StepVideoConfig()._param_names_mapping
-    _reverse_param_names_mapping = StepVideoConfig(
-    )._reverse_param_names_mapping
    _lora_param_names_mapping = StepVideoConfig()._lora_param_names_mapping
    _supported_attention_backends = StepVideoConfig(
    )._supported_attention_backends
@@ -14,8 +14,8 @@ from fastvideo.v1.configs.models.dits import WanVideoConfig
 from fastvideo.v1.configs.sample.wan import WanTeaCacheParams
 from fastvideo.v1.distributed.parallel_state import get_sp_world_size
 from fastvideo.v1.forward_context import get_forward_context
-from fastvideo.v1.layers.layernorm import (FP32LayerNorm, LayerNormScaleShift,
-                                           RMSNorm, ScaleResidual,
+from fastvideo.v1.layers.layernorm import (LayerNormScaleShift, RMSNorm,
+                                           ScaleResidual,
                                           ScaleResidualLayerNormScaleShift)
 from fastvideo.v1.layers.linear import ReplicatedLinear
 # from torch.nn import RMSNorm
@@ -26,7 +26,7 @@ from fastvideo.v1.layers.rotary_embedding import (_apply_rotary_emb,
 from fastvideo.v1.layers.visual_embedding import (ModulateProjection,
                                                  PatchEmbed, TimestepEmbedder)
 from fastvideo.v1.models.dits.base import CachableDiT
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.v1.platforms import _Backend


 class WanImageEmbedding(torch.nn.Module):
@@ -34,9 +34,9 @@ class WanImageEmbedding(torch.nn.Module):
    def __init__(self, in_features: int, out_features: int):
        super().__init__()

-        self.norm1 = FP32LayerNorm(in_features)
+        self.norm1 = nn.LayerNorm(in_features)
        self.ff = MLP(in_features, in_features, out_features, act_type="gelu")
-        self.norm2 = FP32LayerNorm(out_features)
+        self.norm2 = nn.LayerNorm(out_features)

    def forward(self,
                encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
@@ -125,8 +125,8 @@ class WanSelfAttention(nn.Module):
            dropout_rate=0,
            softmax_scale=None,
            causal=False,
-            supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
-                                          AttentionBackendEnum.TORCH_SDPA))
+            supported_attention_backends=(_Backend.FLASH_ATTN,
+                                          _Backend.TORCH_SDPA))

    def forward(self, x: torch.Tensor, context: torch.Tensor,
                context_lens: int):
@@ -174,8 +174,7 @@ class WanI2VCrossAttention(WanSelfAttention):
        window_size=(-1, -1),
        qk_norm=True,
        eps=1e-6,
-        supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
-                                                     ...]] = None
+        supported_attention_backends: Optional[Tuple[_Backend, ...]] = None
    ) -> None:
        super().__init__(dim, num_heads, window_size, qk_norm, eps,
                         supported_attention_backends)
@@ -217,22 +216,21 @@ class WanI2VCrossAttention(WanSelfAttention):

 class WanTransformerBlock(nn.Module):

-    def __init__(
-            self,
-            dim: int,
-            ffn_dim: int,
-            num_heads: int,
-            qk_norm: str = "rms_norm_across_heads",
-            cross_attn_norm: bool = False,
-            eps: float = 1e-6,
-            added_kv_proj_dim: Optional[int] = None,
-            supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
-                                                         ...]] = None,
-            prefix: str = ""):
+    def __init__(self,
+                 dim: int,
+                 ffn_dim: int,
+                 num_heads: int,
+                 qk_norm: str = "rms_norm_across_heads",
+                 cross_attn_norm: bool = False,
+                 eps: float = 1e-6,
+                 added_kv_proj_dim: Optional[int] = None,
+                 supported_attention_backends: Optional[Tuple[_Backend,
+                                                              ...]] = None,
+                 prefix: str = ""):
        super().__init__()

        # 1. Self-attention
-        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
+        self.norm1 = nn.LayerNorm(dim, eps, elementwise_affine=False)
        self.to_q = ReplicatedLinear(dim, dim, bias=True)
        self.to_k = ReplicatedLinear(dim, dim, bias=True)
        self.to_v = ReplicatedLinear(dim, dim, bias=True)
@@ -263,8 +261,7 @@ class WanTransformerBlock(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=True,
-            dtype=torch.float32,
-            compute_dtype=torch.float32)
+            dtype=torch.float32)

        # 2. Cross-attention
        if added_kv_proj_dim is not None:
@@ -284,8 +281,7 @@ class WanTransformerBlock(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=False,
-            dtype=torch.float32,
-            compute_dtype=torch.float32)
+            dtype=torch.float32)

        # 3. Feed-forward
        self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
@@ -362,22 +358,21 @@ class WanTransformerBlock(nn.Module):

 class WanTransformerBlock_VSA(nn.Module):

-    def __init__(
-            self,
-            dim: int,
-            ffn_dim: int,
-            num_heads: int,
-            qk_norm: str = "rms_norm_across_heads",
-            cross_attn_norm: bool = False,
-            eps: float = 1e-6,
-            added_kv_proj_dim: Optional[int] = None,
-            supported_attention_backends: Optional[Tuple[AttentionBackendEnum,
-                                                         ...]] = None,
-            prefix: str = ""):
+    def __init__(self,
+                 dim: int,
+                 ffn_dim: int,
+                 num_heads: int,
+                 qk_norm: str = "rms_norm_across_heads",
+                 cross_attn_norm: bool = False,
+                 eps: float = 1e-6,
+                 added_kv_proj_dim: Optional[int] = None,
+                 supported_attention_backends: Optional[Tuple[_Backend,
+                                                              ...]] = None,
+                 prefix: str = ""):
        super().__init__()

        # 1. Self-attention
-        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
+        self.norm1 = nn.LayerNorm(dim, eps, elementwise_affine=False)
        self.to_q = ReplicatedLinear(dim, dim, bias=True)
        self.to_k = ReplicatedLinear(dim, dim, bias=True)
        self.to_v = ReplicatedLinear(dim, dim, bias=True)
@@ -409,8 +404,7 @@ class WanTransformerBlock_VSA(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=True,
-            dtype=torch.float32,
-            compute_dtype=torch.float32)
+            dtype=torch.float32)

        # 2. Cross-attention
        if added_kv_proj_dim is not None:
@@ -430,8 +424,7 @@ class WanTransformerBlock_VSA(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=False,
-            dtype=torch.float32,
-            compute_dtype=torch.float32)
+            dtype=torch.float32)

        # 3. Feed-forward
        self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
@@ -518,7 +511,6 @@ class WanTransformer3DModel(CachableDiT):
    _supported_attention_backends = WanVideoConfig(
    )._supported_attention_backends
    _param_names_mapping = WanVideoConfig()._param_names_mapping
-    _reverse_param_names_mapping = WanVideoConfig()._reverse_param_names_mapping
    _lora_param_names_mapping = WanVideoConfig()._lora_param_names_mapping

    def __init__(self, config: WanVideoConfig, hf_config: dict[str,
@@ -569,8 +561,7 @@ class WanTransformer3DModel(CachableDiT):
                                            norm_type="layer",
                                            eps=config.eps,
                                            elementwise_affine=False,
-                                            dtype=torch.float32,
-                                            compute_dtype=torch.float32)
+                                            dtype=torch.float32)
        self.proj_out = nn.Linear(
            inner_dim, config.out_channels * math.prod(config.patch_size))
        self.scale_shift_table = nn.Parameter(
@@ -578,17 +569,6 @@ class WanTransformer3DModel(CachableDiT):

        self.gradient_checkpointing = False

-        # For type checking
-        self.previous_e0_even = None
-        self.previous_e0_odd = None
-        self.previous_residual_even = None
-        self.previous_residual_odd = None
-        self.is_even = True
-        self.should_calc_even = True
-        self.should_calc_odd = True
-        self.accumulated_rel_l1_distance_even = 0
-        self.accumulated_rel_l1_distance_odd = 0
-        self.cnt = 0
        self.__post_init__()

    def forward(self,
@@ -638,6 +618,8 @@ class WanTransformer3DModel(CachableDiT):

        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
            timestep, encoder_hidden_states, encoder_hidden_states_image)
+        if encoder_hidden_states.dim() == 2:
+            encoder_hidden_states = encoder_hidden_states.unsqueeze(0)
        timestep_proj = timestep_proj.unflatten(1, (6, -1))

        if encoder_hidden_states_image is not None:
@@ -675,7 +657,7 @@ class WanTransformer3DModel(CachableDiT):
        # 5. Output norm, projection & unpatchify
        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2,
                                                                          dim=1)
-        hidden_states = self.norm_out(hidden_states, shift, scale)
+        hidden_states = self.norm_out(hidden_states.float(), shift, scale)
        hidden_states = self.proj_out(hidden_states)

        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 from abc import ABC, abstractmethod
-from dataclasses import field
-from typing import List, Optional, Tuple
+from typing import Optional, Tuple

 import torch
 from torch import nn
@@ -9,22 +8,16 @@ from torch import nn
 from fastvideo.v1.configs.models.encoders import (BaseEncoderOutput,
                                                  ImageEncoderConfig,
                                                  TextEncoderConfig)
-from fastvideo.v1.platforms import AttentionBackendEnum
+from fastvideo.v1.platforms import _Backend


 class TextEncoder(nn.Module, ABC):
-    _fsdp_shard_conditions: list = field(default_factory=lambda: [])
-    _stacked_params_mapping: List[Tuple[str, str,
-                                        str]] = field(default_factory=list)
    _supported_attention_backends: Tuple[
-        AttentionBackendEnum,
-        ...] = TextEncoderConfig()._supported_attention_backends
+        _Backend, ...] = TextEncoderConfig()._supported_attention_backends

    def __init__(self, config: TextEncoderConfig) -> None:
        super().__init__()
        self.config = config
-        self._fsdp_shard_conditions = config._fsdp_shard_conditions
-        self._stacked_params_mapping = config.arch_config.stacked_params_mapping
        if not self.supported_attention_backends:
            raise ValueError(
                f"Subclass {self.__class__.__name__} must define _supported_attention_backends"
@@ -41,14 +34,13 @@ class TextEncoder(nn.Module, ABC):
        pass

    @property
-    def supported_attention_backends(self) -> Tuple[AttentionBackendEnum, ...]:
+    def supported_attention_backends(self) -> Tuple[_Backend, ...]:
        return self._supported_attention_backends


 class ImageEncoder(nn.Module, ABC):
    _supported_attention_backends: Tuple[
-        AttentionBackendEnum,
-        ...] = ImageEncoderConfig()._supported_attention_backends
+        _Backend, ...] = ImageEncoderConfig()._supported_attention_backends

    def __init__(self, config: ImageEncoderConfig) -> None:
        super().__init__()
@@ -64,5 +56,5 @@ class ImageEncoder(nn.Module, ABC):
        pass

    @property
-    def supported_attention_backends(self) -> Tuple[AttentionBackendEnum, ...]:
+    def supported_attention_backends(self) -> Tuple[_Backend, ...]:
        return self._supported_attention_backends
@@ -596,7 +596,12 @@ class CLIPVisionModel(ImageEncoder):
    #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
-
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
        params_dict = dict(self.named_parameters())
        loaded_params: Set[str] = set()
        layer_count = len(self.vision_model.encoder.layers)
@@ -615,8 +620,7 @@ class CLIPVisionModel(ImageEncoder):
                if layer_idx >= layer_count:
                    continue

-            for (param_name, weight_name,
-                 shard_id) in self.config.arch_config.stacked_params_mapping:
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
@@ -369,7 +369,14 @@ class LlamaModel(TextEncoder):

    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
-
+        stacked_params_mapping = [
+            # (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),
+            (".gate_up_proj", ".up_proj", 1),
+        ]
        params_dict = dict(self.named_parameters())
        loaded_params: Set[str] = set()
        for name, loaded_weight in weights:
@@ -399,7 +406,7 @@ class LlamaModel(TextEncoder):
                    continue
                else:
                    name = kv_scale_name
-            for param_name, weight_name, shard_id in self.config.arch_config.stacked_params_mapping:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
@@ -494,7 +494,7 @@ class T5Stack(nn.Module):
                attention_mask=attention_mask,
                attn_metadata=attn_metadata,
            )
-        hidden_states = self.final_layer_norm.forward(hidden_states)
+        hidden_states = self.final_layer_norm.forward_native(hidden_states)
        return hidden_states


@@ -631,13 +631,19 @@ class UMT5EncoderModel(TextEncoder):

    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q", "q"),
+            (".qkv_proj", ".k", "k"),
+            (".qkv_proj", ".v", "v"),
+        ]
        params_dict = dict(self.named_parameters())
        loaded_params: Set[str] = set()
        for name, loaded_weight in weights:
            loaded = False
            if "decoder" in name or "lm_head" in name:
                continue
-            for param_name, weight_name, shard_id in self.config.arch_config.stacked_params_mapping:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
@@ -81,7 +81,10 @@ def get_hf_config(
    return config


-def get_diffusers_config(model: str, ) -> Dict[str, Any]:
+def get_diffusers_config(
+    model: str,
+    fastvideo_args: Optional[dict] = None,
+) -> Dict[str, Any]:
    """Gets a configuration for the given diffusers model.
    
    Args:
@@ -102,8 +105,7 @@ def get_diffusers_config(model: str, ) -> Dict[str, Any]:
                # Load the config directly from the file
                with open(config_file) as f:
                    config_dict: Dict[str, Any] = json.load(f)
-                if "_diffusers_version" in config_dict:
-                    config_dict.pop("_diffusers_version")
+
                # TODO(will): apply any overrides from inference args
                return config_dict
            except Exception as e:
@@ -10,20 +10,16 @@ from copy import deepcopy
 from typing import Any, Generator, Iterable, List, Optional, Tuple, cast

 import torch
-import torch.distributed as dist
 import torch.nn as nn
 from safetensors.torch import load_file as safetensors_load_file
 from transformers import AutoImageProcessor, AutoTokenizer
 from transformers.utils import SAFE_WEIGHTS_INDEX_NAME

-from fastvideo.v1.configs.models import EncoderConfig
-from fastvideo.v1.distributed import get_local_torch_device
-from fastvideo.v1.fastvideo_args import FastVideoArgs
+from fastvideo.v1.distributed import get_torch_device
+from fastvideo.v1.fastvideo_args import FastVideoArgs, TrainingArgs
 from fastvideo.v1.logger import init_logger
 from fastvideo.v1.models.hf_transformer_utils import get_diffusers_config
-from fastvideo.v1.models.loader.fsdp_load import (init_device_mesh,
-                                                  maybe_load_fsdp_model,
-                                                  shard_model)
+from fastvideo.v1.models.loader.fsdp_load import maybe_load_fsdp_model
 from fastvideo.v1.models.loader.utils import set_default_torch_dtype
 from fastvideo.v1.models.loader.weight_utils import (
    filter_duplicate_safetensors_files, filter_files_not_needed_for_inference,
@@ -49,7 +45,7 @@ class ComponentLoader(ABC):
        Args:
            model_path: Path to the component model
            architecture: Architecture of the component model
-            fastvideo_args: FastVideoArgs
+            fastvideo_args: Inference arguments
            
        Returns:
            The loaded component
@@ -166,19 +162,16 @@ class TextEncoderLoader(ComponentLoader):
        return hf_folder, hf_weights_files, use_safetensors

    def _get_weights_iterator(
-            self,
-            source: "Source",
-            to_cpu: bool = True
+            self, source: "Source"
    ) -> Generator[Tuple[str, torch.Tensor], None, None]:
        """Get an iterator for the model weights based on the load format."""
        hf_folder, hf_weights_files, use_safetensors = self._prepare_weights(
            source.model_or_path, source.fall_back_to_pt,
            source.allow_patterns_overrides)
        if use_safetensors:
-            weights_iterator = safetensors_weights_iterator(
-                hf_weights_files, to_cpu)
+            weights_iterator = safetensors_weights_iterator(hf_weights_files)
        else:
-            weights_iterator = pt_weights_iterator(hf_weights_files, to_cpu)
+            weights_iterator = pt_weights_iterator(hf_weights_files)

        if self.counter_before_loading_weights == 0.0:
            self.counter_before_loading_weights = time.perf_counter()
@@ -187,27 +180,25 @@ class TextEncoderLoader(ComponentLoader):
                for (name, tensor) in weights_iterator)

    def _get_all_weights(
-            self,
-            model_config: Any,
-            model: nn.Module,
-            model_path: str,
-            to_cpu: bool = True
+        self,
+        model_config: Any,
+        model: nn.Module,
    ) -> Generator[Tuple[str, torch.Tensor], None, None]:
        primary_weights = TextEncoderLoader.Source(
-            model_path,
+            model_config.model,
            prefix="",
            fall_back_to_pt=getattr(model, "fall_back_to_pt_during_load", True),
            allow_patterns_overrides=getattr(model, "allow_patterns_overrides",
                                             None),
        )
-        yield from self._get_weights_iterator(primary_weights, to_cpu)
+        yield from self._get_weights_iterator(primary_weights)

        secondary_weights = cast(
            Iterable[TextEncoderLoader.Source],
            getattr(model, "secondary_weights", ()),
        )
        for source in secondary_weights:
-            yield from self._get_weights_iterator(source, to_cpu)
+            yield from self._get_weights_iterator(source)

    def load(self, model_path: str, architecture: str,
             fastvideo_args: FastVideoArgs):
@@ -218,7 +209,8 @@ class TextEncoderLoader(ComponentLoader):
        #     revision=fastvideo_args.revision,
        #     model_override_args=None,
        # )
-        model_config = get_diffusers_config(model=model_path)
+        with open(os.path.join(model_path, "config.json")) as f:
+            model_config = json.load(f)
        model_config.pop("_name_or_path", None)
        model_config.pop("transformers_version", None)
        model_config.pop("model_type", None)
@@ -228,34 +220,24 @@ class TextEncoderLoader(ComponentLoader):

        # @TODO(Wei): Better way to handle this?
        try:
-            encoder_config = fastvideo_args.pipeline_config.text_encoder_configs[
-                0]
+            encoder_config = fastvideo_args.text_encoder_configs[0]
            encoder_config.update_model_arch(model_config)
-            encoder_precision = fastvideo_args.pipeline_config.text_encoder_precisions[
-                0]
+            encoder_precision = fastvideo_args.text_encoder_precisions[0]
        except Exception:
-            encoder_config = fastvideo_args.pipeline_config.text_encoder_configs[
-                1]
+            encoder_config = fastvideo_args.text_encoder_configs[1]
            encoder_config.update_model_arch(model_config)
-            encoder_precision = fastvideo_args.pipeline_config.text_encoder_precisions[
-                1]
+            encoder_precision = fastvideo_args.text_encoder_precisions[1]

-        target_device = get_local_torch_device()
+        target_device = get_torch_device()
        # TODO(will): add support for other dtypes
        return self.load_model(model_path, encoder_config, target_device,
-                               fastvideo_args, encoder_precision)
+                               encoder_precision)

    def load_model(self,
                   model_path: str,
-                   model_config: EncoderConfig,
+                   model_config,
                   target_device: torch.device,
-                   fastvideo_args: FastVideoArgs,
                   dtype: str = "fp16"):
-        use_cpu_offload = fastvideo_args.text_encoder_offload and len(
-            getattr(model_config, "_fsdp_shard_conditions", [])) > 0
-
-        if fastvideo_args.text_encoder_offload:
-            target_device = torch.device("cpu")
        with set_default_torch_dtype(PRECISION_TO_TYPE[dtype]):
            with target_device:
                architectures = getattr(model_config, "architectures", [])
@@ -263,27 +245,14 @@ class TextEncoderLoader(ComponentLoader):
                model = model_cls(model_config)

            weights_to_load = {name for name, _ in model.named_parameters()}
+            model_config.model = model_path
            loaded_weights = model.load_weights(
-                self._get_all_weights(model_config, model, model_path,
-                                      use_cpu_offload))
+                self._get_all_weights(model_config, model))
            self.counter_after_loading_weights = time.perf_counter()
            logger.info(
                "Loading weights took %.2f seconds",
                self.counter_after_loading_weights -
                self.counter_before_loading_weights)
-
-            if use_cpu_offload:
-                mesh = init_device_mesh(
-                    "cuda",
-                    mesh_shape=(1, dist.get_world_size()),
-                    mesh_dim_names=("offload", "replicate"),
-                )
-                shard_model(model,
-                            cpu_offload=True,
-                            reshard_after_forward=True,
-                            mesh=mesh["offload"],
-                            fsdp_shard_conditions=model._fsdp_shard_conditions,
-                            pin_cpu_memory=fastvideo_args.pin_cpu_memory)
            # We only enable strict check for non-quantized models
            # that have loaded weights tracking currently.
            # if loaded_weights is not None:
@@ -292,6 +261,7 @@ class TextEncoderLoader(ComponentLoader):
                raise ValueError("Following weights were not initialized from "
                                 f"checkpoint: {weights_not_loaded}")

+        # TODO(will): add support for training/finetune
        return model.eval()


@@ -314,14 +284,13 @@ class ImageEncoderLoader(TextEncoderLoader):
        model_config.pop("model_type", None)
        logger.info("HF Model config: %s", model_config)

-        encoder_config = fastvideo_args.pipeline_config.image_encoder_config
+        encoder_config = fastvideo_args.image_encoder_config
        encoder_config.update_model_arch(model_config)

-        target_device = get_local_torch_device()
+        target_device = get_torch_device()
        # TODO(will): add support for other dtypes
-        return self.load_model(
-            model_path, encoder_config, target_device, fastvideo_args,
-            fastvideo_args.pipeline_config.image_encoder_precision)
+        return self.load_model(model_path, encoder_config, target_device,
+                               fastvideo_args.image_encoder_precision)


 class ImageProcessorLoader(ComponentLoader):
@@ -363,17 +332,18 @@ class VAELoader(ComponentLoader):
    def load(self, model_path: str, architecture: str,
             fastvideo_args: FastVideoArgs):
        """Load the VAE based on the model path, architecture, and inference args."""
+        # TODO(will): move this to a constants file
        config = get_diffusers_config(model=model_path)
+
        class_name = config.pop("_class_name")
        assert class_name is not None, "Model config does not contain a _class_name attribute. Only diffusers format is supported."
+        config.pop("_diffusers_version")

-        vae_config = fastvideo_args.pipeline_config.vae_config
+        vae_config = fastvideo_args.vae_config
        vae_config.update_model_arch(config)
+        vae_cls, _ = ModelRegistry.resolve_model_cls(class_name)

-        with set_default_torch_dtype(PRECISION_TO_TYPE[
-                fastvideo_args.pipeline_config.vae_precision]):
-            vae_cls, _ = ModelRegistry.resolve_model_cls(class_name)
-            vae = vae_cls(vae_config).to(get_local_torch_device())
+        vae = vae_cls(vae_config).to(get_torch_device())

        # Find all safetensors files
        safetensors_list = glob.glob(
@@ -385,8 +355,10 @@ class VAELoader(ComponentLoader):
        loaded = safetensors_load_file(safetensors_list[0])
        vae.load_state_dict(
            loaded, strict=False)  # We might only load encoder or decoder
+        dtype = PRECISION_TO_TYPE[fastvideo_args.vae_precision]
+        vae = vae.eval().to(dtype)

-        return vae.eval()
+        return vae


 class TransformerLoader(ComponentLoader):
@@ -402,9 +374,10 @@ class TransformerLoader(ComponentLoader):
            raise ValueError(
                "Model config does not contain a _class_name attribute. "
                "Only diffusers format is supported.")
+        config.pop("_diffusers_version")

        # Config from Diffusers supersedes fastvideo's model config
-        dit_config = fastvideo_args.pipeline_config.dit_config
+        dit_config = fastvideo_args.dit_config
        dit_config.update_model_arch(config)

        model_cls, _ = ModelRegistry.resolve_model_cls(cls_name)
@@ -418,8 +391,14 @@ class TransformerLoader(ComponentLoader):
        logger.info("Loading model from %s safetensors files in %s",
                    len(safetensors_list), model_path)

-        default_dtype = PRECISION_TO_TYPE[
-            fastvideo_args.pipeline_config.dit_precision]
+        # initialize_sequence_parallel_group(fastvideo_args.sp_size)
+        if fastvideo_args.training_mode:
+            assert isinstance(
+                fastvideo_args, TrainingArgs
+            ), "fastvideo_args must be a TrainingArgs object when training_mode is True"
+            default_dtype = PRECISION_TO_TYPE[fastvideo_args.master_weight_type]
+        else:
+            default_dtype = PRECISION_TO_TYPE[fastvideo_args.precision]

        # Load the model using FSDP loader
        logger.info("Loading model from %s, default_dtype: %s", cls_name,
@@ -432,7 +411,7 @@ class TransformerLoader(ComponentLoader):
                "hf_config": hf_config
            },
            weight_dir_list=safetensors_list,
-            device=get_local_torch_device(),
+            device=get_torch_device(),
            hsdp_replicate_dim=fastvideo_args.hsdp_replicate_dim,
            hsdp_shard_dim=fastvideo_args.hsdp_shard_dim,
            cpu_offload=fastvideo_args.use_cpu_offload,
@@ -483,15 +462,15 @@ class SchedulerLoader(ComponentLoader):

        class_name = config.pop("_class_name")
        assert class_name is not None, "Model config does not contain a _class_name attribute. Only diffusers format is supported."
+        config.pop("_diffusers_version")

        scheduler_cls, _ = ModelRegistry.resolve_model_cls(class_name)

        scheduler = scheduler_cls(**config)
-        if fastvideo_args.pipeline_config.flow_shift is not None:
-            scheduler.set_shift(fastvideo_args.pipeline_config.flow_shift)
-        if fastvideo_args.pipeline_config.timesteps_scale is not None:
-            scheduler.set_timesteps_scale(
-                fastvideo_args.pipeline_config.timesteps_scale)
+        if fastvideo_args.flow_shift is not None:
+            scheduler.set_shift(fastvideo_args.flow_shift)
+        if fastvideo_args.timesteps_scale is not None:
+            scheduler.set_timesteps_scale(fastvideo_args.timesteps_scale)
        return scheduler


@@ -550,7 +529,7 @@ class PipelineComponentLoader:
            component_model_path: Path to the component model
            transformers_or_diffusers: Whether the module is from transformers or diffusers
            architecture: Architecture of the component model
-            pipeline_args: Inference arguments
+            fastvideo_args: Inference arguments
            
        Returns:
            The loaded module
@@ -69,7 +69,6 @@ def maybe_load_fsdp_model(
    fsdp_inference: bool = False,
    output_dtype: Optional[torch.dtype] = None,
    training_mode: bool = True,
-    pin_cpu_memory: bool = True,
 ) -> torch.nn.Module:
    """
    Load the model with FSDP if is training, else load the model without FSDP.
@@ -102,12 +101,9 @@ def maybe_load_fsdp_model(
                cpu_offload=cpu_offload,
                reshard_after_forward=True,
                mp_policy=mp_policy,
-                mesh=device_mesh,
-                fsdp_shard_conditions=model._fsdp_shard_conditions,
-                pin_cpu_memory=pin_cpu_memory)
+                mesh=device_mesh)

-    weight_iterator = safetensors_weights_iterator(
-        weight_dir_list, to_cpu=cpu_offload, async_broadcast=not cpu_offload)
+    weight_iterator = safetensors_weights_iterator(weight_dir_list)
    param_names_mapping_fn = get_param_names_mapping(model._param_names_mapping)
    load_model_from_full_model_state_dict(
        model,
@@ -130,13 +126,12 @@ def maybe_load_fsdp_model(

 def shard_model(
    model,
+    *,
    cpu_offload: bool,
    reshard_after_forward: bool = True,
-    mp_policy: Optional[MixedPrecisionPolicy] = MixedPrecisionPolicy(),  # noqa
+    mp_policy: Optional[MixedPrecisionPolicy] = None,
+    dp_mesh: Optional[DeviceMesh] = None,
    mesh: Optional[DeviceMesh] = None,
-    fsdp_shard_conditions: Optional[List[Callable[[str, nn.Module],
-                                                  bool]]] = None,
-    pin_cpu_memory: bool = True,
 ) -> None:
    """
    Utility to shard a model with FSDP using the PyTorch Distributed fully_shard API.
@@ -155,28 +150,19 @@ def shard_model(
        reshard_after_forward (bool): Whether to reshard parameters and buffers after
            the forward pass. Setting this to True corresponds to the FULL_SHARD sharding strategy
            from FSDP1, while setting it to False corresponds to the SHARD_GRAD_OP sharding strategy.
-        mesh (Optional[DeviceMesh]): Device mesh to use for FSDP sharding under multiple parallelism.
+        dp_mesh (Optional[DeviceMesh]): Device mesh to use for FSDP sharding under multiple parallelism.
            Default to None.
-        fsdp_shard_conditions (Optional[List[Callable[[str, nn.Module], bool]]]): A list of functions to determine
-            which modules to shard with FSDP. 

    Raises:
        ValueError: If no layer modules were sharded, indicating that no shard_condition was triggered.
    """
-    if fsdp_shard_conditions is None or len(fsdp_shard_conditions) == 0:
-        logger.warning(
-            "The FSDP shard condition list is empty or None. No modules will be sharded in %s",
-            type(model).__name__)
-        return
-
    fsdp_kwargs = {
        "reshard_after_forward": reshard_after_forward,
        "mesh": mesh,
        "mp_policy": mp_policy,
    }
    if cpu_offload:
-        fsdp_kwargs["offload_policy"] = CPUOffloadPolicy(
-            pin_memory=pin_cpu_memory)
+        fsdp_kwargs["offload_policy"] = CPUOffloadPolicy()

    # iterating in reverse to start with
    # lowest-level modules first
@@ -186,7 +172,7 @@ def shard_model(
    for n, m in reversed(list(model.named_modules())):
        if any([
                shard_condition(n, m)
-                for shard_condition in fsdp_shard_conditions
+                for shard_condition in model._fsdp_shard_conditions
        ]):
            fully_shard(m, **fsdp_kwargs)
            num_layers_sharded += 1
@@ -195,6 +181,7 @@ def shard_model(
        raise ValueError(
            "No layer modules were sharded. Please check if shard conditions are working as expected."
        )
+
    # Finally shard the entire model to account for any stragglers
    fully_shard(model, **fsdp_kwargs)

@@ -235,17 +222,10 @@ def load_model_from_full_model_state_dict(
    used_keys = set()
    sharded_sd = {}
    to_merge_params: DefaultDict[str, Dict[Any, Any]] = defaultdict(dict)
-    reverse_param_names_mapping = {}
-    assert param_names_mapping is not None
-
-    # iterate over all the weights to sync broadcast before use
-    full_sd_iterator = list(full_sd_iterator)  # type: ignore
    for source_param_name, full_tensor in full_sd_iterator:
+        assert param_names_mapping is not None
        target_param_name, merge_index, num_params_to_merge = param_names_mapping(
            source_param_name)
-        reverse_param_names_mapping[target_param_name] = (source_param_name,
-                                                          merge_index,
-                                                          num_params_to_merge)
        used_keys.add(target_param_name)
        if merge_index is not None:
            to_merge_params[target_param_name][merge_index] = full_tensor
@@ -280,7 +260,6 @@ def load_model_from_full_model_state_dict(
                sharded_tensor = sharded_tensor.cpu()
        sharded_sd[target_param_name] = nn.Parameter(sharded_tensor)

-    model._reverse_param_names_mapping = reverse_param_names_mapping
    unused_keys = set(meta_sd.keys()) - used_keys
    if unused_keys:
        logger.warning("Found new parameters in meta state dict: %s",
@@ -11,11 +11,9 @@ from typing import Generator, List, Optional, Tuple, Union
 import filelock
 import huggingface_hub.constants
 import torch
-import torch.distributed as dist
 from safetensors.torch import safe_open
 from tqdm.auto import tqdm

-from fastvideo.v1.distributed.parallel_state import get_node_group
 from fastvideo.v1.logger import init_logger

 logger = init_logger(__name__)
@@ -120,77 +118,36 @@ _BAR_FORMAT = "{desc}: {percentage:3.0f}% Completed | {n_fmt}/{total_fmt} [{elap


 def safetensors_weights_iterator(
-    hf_weights_files: List[str],
-    to_cpu: bool = False,
-    async_broadcast: bool = False
+    hf_weights_files: List[str]
 ) -> Generator[Tuple[str, torch.Tensor], None, None]:
-    """Iterate over the weights in the model safetensor files.
-    Args:
-        hf_weights_files: List of safetensor files to load.
-        to_cpu: Whether to load the weights to CPU. If False, will load to the GPU device bound to the current process.
-        async_broadcast: Whether to overlap loading from disk and broadcasting to other ranks. If True,
-            must iterate over all the weights before use. Only use if to_cpu is False.
-    """
-    local_rank = get_node_group().rank
-    device = f"cuda:{local_rank}" if not to_cpu else "cpu"
-    enable_tqdm = not torch.distributed.is_initialized() or get_node_group(
-    ).rank == 0
-    assert not (async_broadcast
-                and to_cpu), "Cannot broadcast weights when loading to CPU"
-
-    handles = []
+    """Iterate over the weights in the model safetensor files."""
+    enable_tqdm = not torch.distributed.is_initialized(
+    ) or torch.distributed.get_rank() == 0
    for st_file in tqdm(
            hf_weights_files,
            desc="Loading safetensors checkpoint shards",
            disable=not enable_tqdm,
            bar_format=_BAR_FORMAT,
    ):
-        with safe_open(st_file, framework="pt", device=device) as f:
+        with safe_open(st_file, framework="pt") as f:
            for name in f.keys():  # noqa: SIM118
-                if to_cpu:
-                    param = f.get_tensor(name)
-                else:
-                    if local_rank == 0:
-                        param = f.get_tensor(name)
-                    else:
-                        shape = f.get_slice(name).get_shape()
-                        param = torch.empty(shape, device=device)
-                    # broadcast to local ranks
-                    # TODO(Wenxuan): scatter instead of broadcast
-                    if get_node_group().world_size > 1:
-                        group = get_node_group().device_group
-                        if async_broadcast:
-                            handle = dist.broadcast(param,
-                                                    src=dist.get_global_rank(
-                                                        group, 0),
-                                                    async_op=True)
-                            handles.append(handle)
-                        else:
-                            dist.broadcast(param,
-                                           src=dist.get_global_rank(group, 0))
+                param = f.get_tensor(name)
                yield name, param

-        if async_broadcast:
-            for handle in handles:
-                handle.wait()
-

 def pt_weights_iterator(
-    hf_weights_files: List[str],
-    to_cpu: bool = True  # default to CPU for text encoder
+    hf_weights_files: List[str]
 ) -> Generator[Tuple[str, torch.Tensor], None, None]:
    """Iterate over the weights in the model bin/pt files."""
-    local_rank = get_node_group().rank
-    device = f"cuda:{local_rank}" if not to_cpu else "cpu"
-    enable_tqdm = not torch.distributed.is_initialized() or get_node_group(
-    ).rank == 0
+    enable_tqdm = not torch.distributed.is_initialized(
+    ) or torch.distributed.get_rank() == 0
    for bin_file in tqdm(
            hf_weights_files,
            desc="Loading pt checkpoint shards",
            disable=not enable_tqdm,
            bar_format=_BAR_FORMAT,
    ):
-        state = torch.load(bin_file, map_location=device, weights_only=True)
+        state = torch.load(bin_file, map_location="cpu", weights_only=True)
        yield from state.items()
        del state

@@ -51,6 +51,11 @@ def auto_attributes(init_func):
    return wrapper


+def set_random_seed(seed: int) -> None:
+    from fastvideo.v1.platforms import current_platform
+    current_platform.seed_everything(seed)
+
+
 def set_weight_attrs(
    weight: torch.Tensor,
    weight_attrs: Optional[Dict[str, Any]],
@@ -1,11 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0

 import os
-import tempfile
 from typing import Callable, List, Optional, Tuple, Union
-from urllib.parse import unquote, urlparse

-import imageio
 import numpy as np
 import PIL.Image
 import PIL.ImageOps
@@ -89,7 +86,6 @@ def normalize(
    return 2.0 * images - 1.0


-# adapted from diffusers.utils import load_image
 def load_image(
    image: Union[str, PIL.Image.Image],
    convert_method: Optional[Callable[[PIL.Image.Image],
@@ -135,85 +131,6 @@ def load_image(
    return image


-# adapted from diffusers.utils import load_video
-def load_video(
-    video: str,
-    convert_method: Optional[Callable[[List[PIL.Image.Image]],
-                                      List[PIL.Image.Image]]] = None,
-) -> List[PIL.Image.Image]:
-    """
-    Loads `video` to a list of PIL Image.
-    Args:
-        video (`str`):
-            A URL or Path to a video to convert to a list of PIL Image format.
-        convert_method (Callable[[List[PIL.Image.Image]], List[PIL.Image.Image]], *optional*):
-            A conversion method to apply to the video after loading it. When set to `None` the images will be converted
-            to "RGB".
-    Returns:
-        `List[PIL.Image.Image]`:
-            The video as a list of PIL images.
-    """
-    is_url = video.startswith("http://") or video.startswith("https://")
-    is_file = os.path.isfile(video)
-    was_tempfile_created = False
-
-    if not (is_url or is_file):
-        raise ValueError(
-            f"Incorrect path or URL. URLs must start with `http://` or `https://`, and {video} is not a valid path."
-        )
-
-    if is_url:
-        response = requests.get(video, stream=True)
-        if response.status_code != 200:
-            raise ValueError(
-                f"Failed to download video. Status code: {response.status_code}"
-            )
-
-        parsed_url = urlparse(video)
-        file_name = os.path.basename(unquote(parsed_url.path))
-
-        suffix = os.path.splitext(file_name)[1] or ".mp4"
-        with tempfile.NamedTemporaryFile(suffix=suffix,
-                                         delete=False) as temp_file:
-            video_path = temp_file.name
-            video_data = response.iter_content(chunk_size=8192)
-            for chunk in video_data:
-                temp_file.write(chunk)
-
-        video = video_path
-
-    pil_images = []
-    if video.endswith(".gif"):
-        gif = PIL.Image.open(video)
-        try:
-            while True:
-                pil_images.append(gif.copy())
-                gif.seek(gif.tell() + 1)
-        except EOFError:
-            pass
-
-    else:
-        try:
-            imageio.plugins.ffmpeg.get_exe()
-        except AttributeError:
-            raise AttributeError(
-                "`Unable to find an ffmpeg installation on your machine. Please install via `pip install imageio-ffmpeg"
-            ) from None
-
-        with imageio.get_reader(video) as reader:
-            # Read all frames
-            for frame in reader:
-                pil_images.append(PIL.Image.fromarray(frame))
-
-    if was_tempfile_created:
-        os.remove(video_path)
-
-    if convert_method is not None:
-        pil_images = convert_method(pil_images)
-
-    return pil_images
-
-
 def get_default_height_width(
    image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],
    vae_scale_factor: int,
@@ -11,8 +11,7 @@ from fastvideo.v1.fastvideo_args import FastVideoArgs
 from fastvideo.v1.logger import init_logger
 from fastvideo.v1.pipelines.composed_pipeline_base import ComposedPipelineBase
 from fastvideo.v1.pipelines.lora_pipeline import LoRAPipeline
-from fastvideo.v1.pipelines.pipeline_batch_info import (ForwardBatch,
-                                                        TrainingBatch)
+from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch
 from fastvideo.v1.pipelines.pipeline_registry import PipelineRegistry
 from fastvideo.v1.utils import (maybe_download_model,
                                verify_model_config_and_directory)
@@ -50,7 +49,7 @@ def build_pipeline(fastvideo_args: FastVideoArgs) -> PipelineWithLoRA:
        pipeline_architecture)

    # instantiate the pipeline
-    pipeline = pipeline_cls(model_path, fastvideo_args)
+    pipeline = pipeline_cls(model_path, fastvideo_args, config)
    logger.info("Pipeline instantiated")

    # pipeline is now initialized and ready to use
@@ -64,5 +63,4 @@ __all__ = [
    "PipelineRegistry",
    "ForwardBatch",
    "LoRAPipeline",
-    "TrainingBatch",
 ]
@@ -8,11 +8,13 @@ This module defines the base class for pipelines that are composed of multiple s
 import argparse
 import os
 from abc import ABC, abstractmethod
+from copy import deepcopy
 from typing import Any, Dict, List, Optional, Union, cast

 import torch

-from fastvideo.v1.configs.pipelines import PipelineConfig
+from fastvideo.v1.configs.pipelines import (PipelineConfig,
+                                            get_pipeline_config_cls_for_name)
 from fastvideo.v1.distributed import (
    maybe_init_distributed_environment_and_model_parallel)
 from fastvideo.v1.fastvideo_args import FastVideoArgs, TrainingArgs
@@ -20,7 +22,7 @@ from fastvideo.v1.logger import init_logger
 from fastvideo.v1.models.loader.component_loader import PipelineComponentLoader
 from fastvideo.v1.pipelines.pipeline_batch_info import ForwardBatch
 from fastvideo.v1.pipelines.stages import PipelineStage
-from fastvideo.v1.utils import (maybe_download_model,
+from fastvideo.v1.utils import (maybe_download_model, shallow_asdict,
                                verify_model_config_and_directory)

 logger = init_logger(__name__)
@@ -44,16 +46,24 @@ class ComposedPipelineBase(ABC):
    # TODO(will): args should support both inference args and training args
    def __init__(self,
                 model_path: str,
-                 fastvideo_args: Union[FastVideoArgs, TrainingArgs],
+                 fastvideo_args: FastVideoArgs,
+                 config: Optional[Dict[str, Any]] = None,
                 required_config_modules: Optional[List[str]] = None,
                 loaded_modules: Optional[Dict[str, torch.nn.Module]] = None):
        """
        Initialize the pipeline. After __init__, the pipeline should be ready to
        use. The pipeline should be stateless and not hold any batch state.
        """
-        self.fastvideo_args = fastvideo_args

-        self.model_path: str = model_path
+        if fastvideo_args.training_mode:
+            assert isinstance(fastvideo_args, TrainingArgs)
+            self.training_args = fastvideo_args
+            assert self.training_args is not None
+        else:
+            self.fastvideo_args = fastvideo_args
+            assert self.fastvideo_args is not None
+
+        self.model_path = model_path
        self._stages: List[PipelineStage] = []
        self._stage_name_mapping: Dict[str, PipelineStage] = {}

@@ -64,6 +74,13 @@ class ComposedPipelineBase(ABC):
            raise NotImplementedError(
                "Subclass must set _required_config_modules")

+        if config is None:
+            # Load configuration
+            logger.info("Loading pipeline configuration...")
+            self.config = self._load_config(model_path)
+        else:
+            self.config = config
+
        maybe_init_distributed_environment_and_model_parallel(
            fastvideo_args.tp_size, fastvideo_args.sp_size)

@@ -72,8 +89,6 @@ class ComposedPipelineBase(ABC):
        self.modules = self.load_modules(fastvideo_args, loaded_modules)

        if fastvideo_args.training_mode:
-            assert isinstance(fastvideo_args, TrainingArgs)
-            self.training_args = fastvideo_args
            assert self.training_args is not None
            if self.training_args.log_validation:
                self.initialize_validation_pipeline(self.training_args)
@@ -112,16 +127,39 @@ class ComposedPipelineBase(ABC):
        loaded_modules: Optional[Dict[str, torch.nn.Module]] = None,
        If provided, loaded_modules will be used instead of loading from config/pretrained weights.
        """
-        if args is None or args.inference_mode:
+        config = None
+        # 1. If users provide a pipeline config, it will override the default pipeline config
+        if isinstance(pipeline_config, PipelineConfig):
+            config = pipeline_config
+        else:
+            config_cls = get_pipeline_config_cls_for_name(model_path)
+            if config_cls is not None:
+                config = config_cls()
+                if isinstance(pipeline_config, str):
+                    config.load_from_json(pipeline_config)

-            kwargs['model_path'] = model_path
-            fastvideo_args = FastVideoArgs.from_kwargs(kwargs)
+        # 2. If users also provide some kwargs, it will override the pipeline config.
+        # The user kwargs shouldn't contain model config parameters!
+        if config is None:
+            logger.warning("No config found for model %s, using default config",
+                           model_path)
+            config_args = kwargs
+        else:
+            config_args = shallow_asdict(config)
+            config_args.update(kwargs)
+
+        if args is None or args.inference_mode:
+            fastvideo_args = FastVideoArgs(model_path=model_path, **config_args)
+
+            fastvideo_args.model_path = model_path
+            for key, value in config_args.items():
+                setattr(fastvideo_args, key, value)
        else:
            assert args is not None, "args must be provided for training mode"
            fastvideo_args = TrainingArgs.from_cli_args(args)
            # TODO(will): fix this so that its not so ugly
            fastvideo_args.model_path = model_path
-            for key, value in kwargs.items():
+            for key, value in config_args.items():
                setattr(fastvideo_args, key, value)

            fastvideo_args.use_cpu_offload = False
@@ -132,7 +170,7 @@ class ComposedPipelineBase(ABC):
            # use FSDP2's MixedPrecisionPolicy to set the precision for the
            # fwd, bwd, and other operations' precision.
            # fastvideo_args.precision = fastvideo_args.master_weight_type
-            assert fastvideo_args.pipeline_config.dit_precision == 'fp32', 'only fp32 is supported for training'
+            assert fastvideo_args.master_weight_type == 'fp32', 'only fp32 is supported for training'
            # assert fastvideo_args.precision == 'fp32', 'only fp32 is supported for training'

        logger.info("fastvideo_args in from_pretrained: %s", fastvideo_args)
@@ -212,21 +250,20 @@ class ComposedPipelineBase(ABC):
        loaded_modules: Optional[Dict[str, torch.nn.Module]] = None, 
        If provided, loaded_modules will be used instead of loading from config/pretrained weights.
        """
-
-        model_index = self._load_config(self.model_path)
-        logger.info("Loading pipeline modules from config: %s", model_index)
+        logger.info("Loading pipeline modules from config: %s", self.config)
+        modules_config = deepcopy(self.config)

        # remove keys that are not pipeline modules
-        model_index.pop("_class_name")
-        model_index.pop("_diffusers_version")
+        modules_config.pop("_class_name")
+        modules_config.pop("_diffusers_version")

        # some sanity checks
        assert len(
-            model_index
+            modules_config
        ) > 1, "model_index.json must contain at least one pipeline module"

        for module_name in self.required_config_modules:
-            if module_name not in model_index:
+            if module_name not in modules_config:
                raise ValueError(
                    f"model_index.json must contain a {module_name} module")

@@ -236,7 +273,7 @@ class ComposedPipelineBase(ABC):

        modules = {}
        for module_name, (transformers_or_diffusers,
-                          architecture) in model_index.items():
+                          architecture) in modules_config.items():
            if module_name not in required_modules:
                logger.info("Skipping module %s", module_name)
                continue
@@ -298,7 +335,3 @@ class ComposedPipelineBase(ABC):

        # Return the output
        return batch
-
-    def train(self) -> None:
-        raise NotImplementedError(
-            "if training_mode is True, the pipeline must implement this method")
@@ -36,17 +36,16 @@ class LoRAPipeline(ComposedPipelineBase):
            "transformer"].config.arch_config.exclude_lora_layers

        self.convert_to_lora_layers()
-        if self.fastvideo_args.pipeline_config.lora_path is not None:
+        if self.fastvideo_args.lora_path is not None:
            self.set_lora_adapter(
-                self.fastvideo_args.pipeline_config.
-                lora_nickname,  # type: ignore
-                self.fastvideo_args.pipeline_config.lora_path)
+                self.fastvideo_args.lora_nickname,  # type: ignore
+                self.fastvideo_args.lora_path)

    def is_target_layer(self, module_name: str) -> bool:
-        if self.fastvideo_args.pipeline_config.lora_target_names is None:
+        if self.fastvideo_args.lora_target_names is None:
            return True
-        return any(target_name in module_name for target_name in
-                   self.fastvideo_args.pipeline_config.lora_target_names)
+        return any(target_name in module_name
+                   for target_name in self.fastvideo_args.lora_target_names)

    def convert_to_lora_layers(self) -> None:
        """
@@ -11,10 +11,9 @@ import pprint
 from dataclasses import asdict, dataclass, field
 from typing import Any, Dict, List, Optional, Union

-import PIL.Image
 import torch
+import PIL.Image

-from fastvideo.v1.attention import AttentionMetadata
 from fastvideo.v1.configs.sample.teacache import (TeaCacheParams,
                                                  WanTeaCacheParams)

@@ -38,8 +37,8 @@ class ForwardBatch:
    # Image inputs
    image_path: Optional[str] = None
    image_embeds: List[torch.Tensor] = field(default_factory=list)
-    pil_image: Optional[PIL.Image.Image] = None
    preprocessed_image: Optional[torch.Tensor] = None
+    pil_image: Optional[PIL.Image.Image] = None

    # Text inputs
    prompt: Optional[Union[str, List[str]]] = None
@@ -71,7 +70,6 @@ class ForwardBatch:

    # Latent tensors
    latents: Optional[torch.Tensor] = None
-    raw_latent_shape: Optional[torch.Tensor] = None
    noise_pred: Optional[torch.Tensor] = None
    image_latent: Optional[torch.Tensor] = None

@@ -140,37 +138,3 @@ class ForwardBatch:

    def __str__(self):
        return pprint.pformat(asdict(self), indent=2, width=120)
-
-
-@dataclass
-class TrainingBatch:
-    current_timestep: int = 0
-    current_vsa_sparsity: float = 0.0
-
-    # Dataloader batch outputs
-    latents: Optional[torch.Tensor] = None
-    encoder_hidden_states: Optional[torch.Tensor] = None
-    encoder_attention_mask: Optional[torch.Tensor] = None
-    # i2v
-    preprocessed_image: Optional[torch.Tensor] = None
-    image_embeds: Optional[torch.Tensor] = None
-    image_latents: Optional[torch.Tensor] = None
-    infos: Optional[List[Dict[str, Any]]] = None
-
-    # Transformer inputs
-    noisy_model_input: Optional[torch.Tensor] = None
-    timesteps: Optional[torch.Tensor] = None
-    sigmas: Optional[torch.Tensor] = None
-    noise: Optional[torch.Tensor] = None
-
-    attn_metadata: Optional[AttentionMetadata] = None
-
-    # input kwargs
-    input_kwargs: Optional[Dict[str, Any]] = None
-
-    # Training loss
-    loss: torch.Tensor | None = None
-
-    # Training outputs
-    total_loss: float | None = None
-    grad_norm: float | None = None
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
SolitaryThinker	e26b389f37	i2v validation	2025-06-11 21:35:10 -07:00
JerryZhou54	86dc4c4bb3	Small change	2025-06-11 13:29:11 -07:00
JerryZhou54	41d0400832	Fix preprocess	2025-06-11 13:29:11 -07:00
Wei Zhou	e97aa17d33	Update preprocess_pipeline_i2v.py	2025-06-11 13:29:11 -07:00
JerryZhou54	0a40b79b36	I2V Runnable	2025-06-11 13:29:11 -07:00
“BrianChen1129”	f8c69045d6	Add Encoded first frame to processed dataset	2025-06-11 13:29:10 -07:00