Refactor ode trajectory generation

Add env variable to enforce running flash attn

.buildkite/pipeline.yml

@@ -104,18 +104,6 @@ steps:
                   - TEST_TYPE=distillation_dmd
                 agents:
                   queue: "default"
-            - path:
-                - "fastvideo/training/*self_forcing_distillation_pipeline.py"
-                - "fastvideo/tests/training/self-forcing/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Self-Forcing Tests"
-                env:
-                  - TEST_TYPE=self_forcing
-                agents:
-                  queue: "default"
             - path:
                 - "fastvideo/**"
                 - "pyproject.toml"
@@ -222,14 +210,3 @@ steps:
                   - TEST_TYPE=unit_test
                 agents:
                   queue: "default"
-            - path:
-                - "scripts/lora_extraction/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 90m .buildkite/scripts/pr_test.sh"
-                label: "LoRA Extraction Tests"
-                env:
-                  - TEST_TYPE=lora_extraction
-                agents:
-                  queue: "default"

.buildkite/scripts/pr_test.sh

@@ -31,9 +31,9 @@ log "Setting up Modal authentication from Buildkite secrets..."
 MODAL_TOKEN_ID=$(buildkite-agent secret get modal_token_id)
 MODAL_TOKEN_SECRET=$(buildkite-agent secret get modal_token_secret)
 
-# Retrieve other secrets
 WANDB_API_KEY=$(buildkite-agent secret get wandb_api_key)
-HF_API_KEY=$(buildkite-agent secret get hf_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"
@@ -63,19 +63,19 @@ MODAL_ENV="BUILDKITE_REPO=$BUILDKITE_REPO BUILDKITE_COMMIT=$BUILDKITE_COMMIT BUI
 case "$TEST_TYPE" in
     "encoder")
         log "Running encoder tests..."
-        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_encoder_tests"
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_encoder_tests"
         ;;
     "vae")
         log "Running VAE tests..."
-        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_vae_tests"
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_vae_tests"
         ;;
     "transformer")
         log "Running transformer tests..."
-        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_transformer_tests"
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_transformer_tests"
         ;;
     "ssim")
         log "Running SSIM tests..."
-        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_ssim_tests"
+        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_ssim_tests"
         ;;
     "training")
         log "Running training tests..."
@@ -110,10 +110,6 @@ case "$TEST_TYPE" in
         MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_distill_dmd_tests"
         ;;
         # run_inference_tests_vmoba
-    "self_forcing")
-        log "Running self-forcing tests..."
-        MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_self_forcing_tests"
-        ;;
     "inference_vmoba")
         log "Running V-MoBA inference tests..."
         MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_inference_tests_vmoba"
@@ -126,10 +122,6 @@ case "$TEST_TYPE" in
         log "Running unit tests..."
         MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_unit_test"
         ;;
-    "lora_extraction")
-        log "Running LoRA extraction tests..."
-        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_lora_extraction_tests"
-        ;;
     *)
         log "Error: Unknown test type: $TEST_TYPE"
         exit 1

.github/workflows/docs.yml

@@ -1,65 +1,82 @@
-name: Deploy Documentation
+# Sample workflow for building and deploying a Hugo site to GitHub Pages
+name: Deploy FastVideo Docs to Pages
 
 on:
+  # Runs on pushes targeting the default branch
   push:
-    branches: [ main ]
+    branches:
+      - main
     paths:
-      - 'docs/**'
-      - 'mkdocs.yml'
-      - 'requirements-mkdocs.txt'
-      - '.github/workflows/docs.yml'
+      - "docs/**/*.md"
+      - "fastvideo/examples/**/*.py"
   pull_request:
-    branches: [ main ]
+    branches: 
+      - main
+    types: [opened, ready_for_review, synchronize, reopened]
     paths:
-      - 'docs/**'
-      - 'mkdocs.yml'
-      - 'requirements-mkdocs.txt'
-      - '.github/workflows/docs.yml'
+      - "docs/**/*.md"
+      - "fastvideo/examples/**/*.py"
 
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
 permissions:
   contents: read
   pages: write
   id-token: write
 
+# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
+# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
 concurrency:
   group: "pages"
   cancel-in-progress: false
 
+# Default to bash
+defaults:
+  run:
+    shell: bash
+
 jobs:
+  pre-commit:
+    uses: ./.github/workflows/pre-commit.yml
+
+  # Build job
   build:
     runs-on: ubuntu-latest
+    needs: pre-commit
     steps:
       - name: Checkout
         uses: actions/checkout@v4
-
-      - name: Setup Python
+      - name: Setup Pages
+        id: pages
+        uses: actions/configure-pages@v5
+      - name: Set up Python
         uses: actions/setup-python@v5
         with:
-          python-version: '3.12'
-
+          python-version: "3.10"
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
-          pip install -r requirements-mkdocs.txt
-
-      - name: Setup Pages
-        uses: actions/configure-pages@v4
-
-      - name: Build documentation
-        run: mkdocs build
-
+          cd docs
+          pip install -r requirements-docs.txt
+      - name: Build docs
+        run: |
+          cd docs
+          make clean
+          make html
       - name: Upload artifact
         uses: actions/upload-pages-artifact@v3
         with:
-          path: ./site
+          path: ./docs/build/html
 
+  # Deployment job
   deploy:
     environment:
       name: github-pages
       url: ${{ steps.deployment.outputs.page_url }}
+    if: ${{ github.event_name == 'push' }}
     runs-on: ubuntu-latest
     needs: build
-    if: github.ref == 'refs/heads/main'
     steps:
       - name: Deploy to GitHub Pages
         id: deployment

.github/workflows/pr-test.yml

@@ -352,7 +352,7 @@ jobs:
       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/dataset/ -vs && pytest ./fastvideo/workflow/ -vs && pytest ./fastvideo/entrypoints/ -vs"
+      test_command: "uv pip install -e .[test] && pytest ./fastvideo/dataset/ -vs && pytest ./fastvideo/workflow/ -vs"
       timeout_minutes: 30
     secrets:
       RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}

.gitignore

@@ -14,8 +14,6 @@ wandb/
 *.pt
 cache_dir/
 wandb/
-venv/
-.venv/
 runs/
 samples/
 *validation/
@@ -39,13 +37,12 @@ dist/
 eggs/
 .eggs/
 
-# MkDocs documentation
-site/
-docs/getting_started/examples/
-docs/inference/examples/
-docs/training/examples/
-docs/distillation/examples/
-!requirements-mkdocs.txt
+# Sphinx documentation
+docs/_build/
+docs/source/getting_started/examples/
+docs/source/inference/examples/
+docs/source/training/examples/
+docs/source/distillation/examples/
 
 # VSCode
 .vscode/
@@ -64,7 +61,7 @@ docs/distillation/examples/
 !fastvideo/tests/ssim/reference_videos/**/*.mp4
 
 # Static images
-!docs/assets/images/**/*.png
+!docs/source/_static/images/**/*.png
 !comfyui/assets/**/*.png
 !comfyui/assets/**/*.gif
 

.pre-commit-config.yaml

@@ -10,9 +10,11 @@ exclude: |
     demo/.*|
     predict\.py|
     scripts/.*|
-    prompts/.*|
     fastvideo/data_preprocess/.*|
     fastvideo/dataset/.*|
+    fastvideo/distill/.*|
+    fastvideo/distill\.py|
+    fastvideo/distill_adv\.py|
     fastvideo/models/.*|
     fastvideo/sample/.*|
     fastvideo/train\.py|
@@ -42,10 +44,10 @@ repos:
   - id: codespell
     additional_dependencies: ['tomli']
     args: ['--toml', 'pyproject.toml']
-# - repo: https://github.com/PyCQA/isort
-#   rev: 6.0.1
-#   hooks:
-#   - id: isort
+- repo: https://github.com/PyCQA/isort
+  rev: 6.0.1
+  hooks:
+  - id: isort
 - repo: https://github.com/jackdewinter/pymarkdown
   rev: v0.9.30
   hooks:

README.md

@@ -1,12 +1,13 @@
 <div align="center">
 <img src=assets/logos/logo.svg width="30%"/>
 </div>
+
 **FastVideo is a unified post-training and inference framework for accelerated video generation.**
 
 FastVideo features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.
 
 <p align="center">
-    | 🕹️ <a href="https://fastwan.fastvideo.org/"<b>Online Demo</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/collections/FastVideo/fastwan-6886a305d9799c8cd1496408"  target="_blank"><b>FastWan</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/c7g1qdD" target="_blank"> <b> WeChat </b> </a> |
+    | 🕹️ <a href="https://fastwan.fastvideo.org/"<b>Online Demo</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start.html"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/collections/FastVideo/fastwan-6886a305d9799c8cd1496408"  target="_blank"><b>FastWan</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3csdw1isz-Euq8_Q8~baewG8hxjXs2gQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/S7HLCSTh" target="_blank"> <b> WeChat </b> </a> |
 </p>
 
 <div align="center">
@@ -14,8 +15,7 @@ FastVideo features an end-to-end unified pipeline for accelerating diffusion mod
 </div>
 
 ## NEWS
-- ```2025/11/19```: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py)
-- ```2025/08/04```: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
+- ```2025/08/04```: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd.html) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
 - ```2025/06/14```: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389)
 - ```2025/04/24```: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released!
 - ```2025/02/18```: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).
@@ -49,10 +49,10 @@ conda activate fastvideo
 pip install fastvideo
 ```
 
-Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions.
+Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation.html) for more detailed installation instructions.
 
 ## Sparse Distillation
-For our sparse distillation techniques, please see our [distillation docs](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/) and check out our [blog](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
+For our sparse distillation techniques, please see our [distillation docs](https://hao-ai-lab.github.io/FastVideo/distillation/dmd.html) and check out our [blog](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
 
 See below for recipes and datasets:
 
@@ -64,7 +64,7 @@ See below for recipes and datasets:
 
 ## Inference
 ### Generating Your First Video
-Here's a minimal example to generate a video using the default settings. Make sure VSA kernels are [installed](https://hao-ai-lab.github.io/FastVideo/video_sparse_attention/installation/). Create a file called `example.py` with the following code:
+Here's a minimal example to generate a video using the default settings. Make sure VSA kernels are [installed](https://hao-ai-lab.github.io/FastVideo/video_sparse_attention/installation.html). Create a file called `example.py` with the following code:
 
 ```python
 import os
@@ -100,32 +100,35 @@ Run the script with:
 python example.py
 ```
 
-For a more detailed guide, please see our [inference quick start](https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/).
+For a more detailed guide, please see our [inference quick start](https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start.html).
 
 ### Other docs:
 
-- [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview/)
-- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/)
+- [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview.html)
+- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation.html)
 
 ## Distillation and Finetuning
-- [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/)
+- [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd.html)
 <!-- - [Finetuning Guide](https://hao-ai-lab.github.io/FastVideo/training/finetune.html) -->
 
-## Awesome work using FastVideo or our research projects
+## 📑 Development Plan
+<!-- - More distillation methods -->
+  <!-- - [ ] Add Distribution Matching Distillation -->
+More FastWan Models Coming Soon!
+- [ ] Add FastWan2.1-T2V-14B
+- [ ] Add FastWan2.2-T2V-14B
+- [ ] Add FastWan2.2-I2V-14B
+<!-- - Optimization features
+- Code updates -->
+  <!-- - [ ] fp8 support -->
+  <!-- - [ ] faster load model and save model support -->
 
-- [SGLang](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen): SGLang's diffusion inference functionality is based  on a fork of FastVideo on Sept. 24, 2025. [![Star](https://img.shields.io/github/stars/sgl-project/sglang.svg?style=social&label=Star)](https://github.com/sgl-project/sglang)
-
-- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A  unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/XueZeyue/DanceGRPO.svg?style=social&label=Star)](https://github.com/XueZeyue/DanceGRPO)
-- [SRPO](https://github.com/Tencent-Hunyuan/SRPO): A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/SRPO.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/SRPO)
-- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/Vchitect/DCM.svg?style=social&label=Star)](https://github.com/Vchitect/DCM)
-- [Hunyuan Video 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5): A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention. [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/HunyuanVideo-1.5.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5)
-- [Kandinsky-5.0](https://github.com/kandinskylab/kandinsky-5): A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch. [![Star](https://img.shields.io/github/stars/kandinskylab/kandinsky-5.svg?style=social&label=Star)](https://github.com/kandinskylab/kandinsky-5)
-- [LongCat Video](https://github.com/meituan-longcat/LongCat-Video): A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention. [![Star](https://img.shields.io/github/stars/meituan-longcat/LongCat-Video.svg?style=social&label=Star)](https://github.com/meituan-longcat/LongCat-Video)
+See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/468).
 
 ## 🤝 Contributing
 
-We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview/).
-See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/899).
+We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview.html)
+
 ## Acknowledgement
 We learned and reused code from the following projects:
 - [Wan-Video](https://github.com/Wan-Video)
@@ -152,8 +155,8 @@ If you find FastVideo useful, please considering citing our work:
 }
 
 @article{zhang2025vsa,
-  title={Vsa: Faster video diffusion with trainable sparse attention},
-  author={Zhang, Peiyuan and Chen, Yongqi and Huang, Haofeng and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
+  title={VSA: Faster Video Diffusion with Trainable Sparse Attention},
+  author={Zhang, Peiyuan and Huang, Haofeng and Chen, Yongqi and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
   journal={arXiv preprint arXiv:2505.13389},
   year={2025}
 }

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benchmarks/fvd/README.md

@@ -1,103 +0,0 @@
-# FVD (Fréchet Video Distance) Benchmark
-
-Evaluate generated video quality using FVD with the I3D feature extractor.
-
-## Quick Start
-
-**Run the benchmark:**
-
-```bash
-bash benchmarks/scripts/run.sh
-```
-
-That's it! The script auto-installs dependencies and runs the benchmark.
-
-**To customize:** Edit `benchmarks/fvd/run_fvd.py` to change:
-- Video paths (`real_dir`, `gen_dir`)
-- Number of videos, frames, sampling strategy
-- Device, batch size, caching, etc.
-
-## Advanced Usage (CLI)
-
-For more control without editing Python files, use the CLI.
-
-**First-time setup** (one-time per pod/environment):
-
-```bash
-bash benchmarks/scripts/setup_fvd.sh
-```
-
-Then run any configuration you want:
-
-```bash
-# Custom configuration
-python -m benchmarks.fvd.cli \
-    --real-path data/real/ \
-    --gen-path outputs/gen/ \
-    --num-videos 1024 \
-    --num-frames 32 \
-    --clip-strategy random \
-    --batch-size 32 \
-    --seed 42
-```
-
-**Standard protocols:**
-
-```bash
-# Use predefined protocols
-python -m benchmarks.fvd.cli \
-    --real-path data/real/ \
-    --gen-path outputs/gen/ \
-    --protocol fvd2048_16f  # or fvd2048_128f, quick_test, etc.
-```
-
-**Feature caching** (speed up repeated evaluations):
-
-```bash
-python -m benchmarks.fvd.cli \
-    --real-path data/real/ \
-    --gen-path outputs/gen/ \
-    --protocol fvd2048_16f \
-    --cache-real-features cache/real  # Directory path (will save/load cache/real/real_features.pkl)
-```
-
-Run `python -m benchmarks.fvd.cli --help` for all options.
-
-## Available Protocols
-
-- `fvd2048_16f` - Standard (2048 videos, 16 frames)
-- `fvd2048_128f` - Long videos (128 frames)
-- `fvd2048_128f_subsample8` - Subsampled long videos
-- `quick_test` - Fast testing (10 videos)
-
-## Configuration Options
-
-Key options in `FVDConfig`:
-
-```python
-num_videos=2048,              # Videos to evaluate
-num_frames_per_clip=16,       # Frames per clip
-clip_strategy='beginning',    # beginning|random|uniform|middle|sliding
-frame_stride=1,               # Frame subsampling
-batch_size=32,                # GPU batch size
-device='cuda',                # cuda|cpu
-cache_real_features=None,     # Cache path for speed
-seed=42,                      # Reproducibility
-```
-
-## Programmatic Usage
-
-```python
-from benchmarks.fvd import compute_fvd_with_config, FVDConfig
-
-config = FVDConfig.fvd2048_16f()  # or custom config
-results = compute_fvd_with_config('data/real/', 'outputs/gen/', config)
-print(f"FVD: {results['fvd']:.2f}")
-```
-
-## Notes
-
-- I3D model auto-downloads from Hugging Face on first run
-- Requires minimum 10 frames per clip
-- Supports both video files (.mp4, .avi, etc.) and frame directories
-- `--cache-real-features` expects a **directory path** (e.g., `cache/real`), it will automatically create/load `real_features.pkl` inside that directory

benchmarks/fvd/__init__.py

@@ -1,35 +0,0 @@
-"""
-FastVideo Frechet Video Distance (FVD) Benchmark Module.
-    >>> from fastvideo.benchmarks.fvd import compute_fvd_with_config, FVDConfig
-    >>> config = FVDConfig.fvd2048_16f()  # Standard protocol
-    >>> results = compute_fvd_with_config('data/real/', 'outputs/gen/', config)
-    >>> print(f"FVD: {results['fvd']:.2f}")
-"""
-
-from .fvd import (
-    compute_fvd,
-    compute_fvd_with_config,
-    compute_frechet_distance,
-    compute_statistics,
-    FVDConfig,
-)
-from .i3d_model import I3DFeatureExtractor
-from .video_utils import (
-    load_video_auto,
-    sample_clips_from_video,
-    load_video_clips_streaming,
-    ClipSamplingStrategy,
-)
-
-__all__ = [
-    'compute_fvd',
-    'compute_fvd_with_config',
-    'compute_frechet_distance',
-    'compute_statistics',
-    'FVDConfig',
-    'I3DFeatureExtractor',
-    'load_video_auto',
-    'sample_clips_from_video',
-    'load_video_clips_streaming',
-    'ClipSamplingStrategy',
-]

benchmarks/fvd/cli.py

@@ -1,185 +0,0 @@
-import argparse
-import json
-import sys
-from pathlib import Path
-
-from .fvd import compute_fvd_with_config, FVDConfig
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description='Compute Fréchet Video Distance (FVD)',
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-        epilog="""
-Examples:
-  # Standard FVD2048_16f protocol
-  python -m fastvideo.benchmarks.fvd.cli \\
-      --real-path data/real/ \\
-      --gen-path outputs/gen/ \\
-      --protocol fvd2048_16f
-
-  # Custom configuration
-  python -m fastvideo.benchmarks.fvd.cli \\
-      --real-path data/real/ \\
-      --gen-path outputs/gen/ \\
-      --num-videos 1024 \\
-      --num-frames 32 \\
-      --clip-strategy random \\
-      --frame-stride 2
-        """)
-
-    # Required arguments
-    parser.add_argument('--real-path',
-                        type=str,
-                        required=True,
-                        help='Path to real videos directory')
-    parser.add_argument('--gen-path',
-                        type=str,
-                        required=True,
-                        help='Path to generated videos directory')
-
-    # Reproducibility
-    parser.add_argument(
-        '--seed',
-        type=int,
-        default=None,
-        help='Random seed for reproducibility (np.random, random, torch)')
-
-    # Protocol presets
-    parser.add_argument('--protocol',
-                        type=str,
-                        default=None,
-                        choices=[
-                            'fvd2048_16f', 'fvd2048_128f',
-                            'fvd2048_128f_subsample8', 'quick_test'
-                        ],
-                        help='Use standard protocol (overrides other settings)')
-
-    # Video selection
-    parser.add_argument('--num-videos',
-                        type=int,
-                        default=2048,
-                        help='Number of videos to use (default: 2048)')
-
-    # Clip sampling
-    parser.add_argument('--num-frames',
-                        type=int,
-                        default=16,
-                        help='Number of frames per clip (default: 16)')
-    parser.add_argument('--num-clips',
-                        type=int,
-                        default=1,
-                        help='Number of clips per video (default: 1)')
-    parser.add_argument(
-        '--clip-strategy',
-        type=str,
-        default='beginning',
-        choices=['beginning', 'random', 'uniform', 'middle', 'sliding', 'all'],
-        help='Clip sampling strategy (default: beginning)')
-    parser.add_argument(
-        '--frame-stride',
-        type=int,
-        default=1,
-        help='Frame stride for FPS subsampling (default: 1, no subsampling)')
-    parser.add_argument('--temporal-stride',
-                        type=int,
-                        default=1,
-                        help='Temporal stride for sliding window (default: 1)')
-
-    # Data processing
-    parser.add_argument('--no-frame-dirs',
-                        action='store_true',
-                        help='Disable frame directory support')
-
-    # Computation
-    parser.add_argument('--batch-size',
-                        type=int,
-                        default=32,
-                        help='Batch size for feature extraction (default: 32)')
-    parser.add_argument('--device',
-                        type=str,
-                        default='cuda',
-                        choices=['cuda', 'cpu'],
-                        help='Device to use (default: cuda)')
-
-    # Caching
-    parser.add_argument('--cache-real-features',
-                        type=str,
-                        default=None,
-                        help='Path to cache real video features')
-    parser.add_argument('--i3d-model-path',
-                        type=str,
-                        default=None,
-                        help='Custom cache path for I3D model')
-
-    # Output
-    parser.add_argument('--output',
-                        type=str,
-                        default='fvd_results.json',
-                        help='Output JSON file (default: fvd_results.json)')
-    parser.add_argument('--quiet',
-                        action='store_true',
-                        help='Suppress progress output')
-
-    args = parser.parse_args()
-
-    # Create config
-    if args.protocol:
-        protocol_map = {
-            'fvd2048_16f': FVDConfig.fvd2048_16f,
-            'fvd2048_128f': FVDConfig.fvd2048_128f,
-            'fvd2048_128f_subsample8': FVDConfig.fvd2048_128f_subsample8,
-            'quick_test': FVDConfig.quick_test,
-        }
-        config = protocol_map[args.protocol]()
-
-        # Override device and caching from args
-        config.device = args.device
-        config.cache_real_features = args.cache_real_features
-        config.i3d_model_path = args.i3d_model_path
-        config.batch_size = args.batch_size
-        config.seed = args.seed
-    else:
-        # Custom config from args
-        config = FVDConfig(num_videos=args.num_videos,
-                           num_frames_per_clip=args.num_frames,
-                           num_clips_per_video=args.num_clips,
-                           clip_strategy=args.clip_strategy,
-                           frame_stride=args.frame_stride,
-                           temporal_stride=args.temporal_stride,
-                           support_frame_dirs=not args.no_frame_dirs,
-                           batch_size=args.batch_size,
-                           device=args.device,
-                           cache_real_features=args.cache_real_features,
-                           i3d_model_path=args.i3d_model_path,
-                           seed=args.seed)
-
-    # Compute FVD
-    try:
-        results = compute_fvd_with_config(real_videos=args.real_path,
-                                          gen_videos=args.gen_path,
-                                          config=config,
-                                          verbose=not args.quiet)
-
-        # Save results
-        output_path = Path(args.output)
-        output_path.parent.mkdir(parents=True, exist_ok=True)
-
-        with open(output_path, 'w') as f:
-            json.dump(results, f, indent=2)
-
-        print(f"\nResults saved to {output_path}")
-        print(f"FVD: {results['fvd']:.2f}")
-        print(f"Protocol: {results['protocol']}")
-
-        return 0
-
-    except Exception as e:
-        print(f"Error: {e}", file=sys.stderr)
-        import traceback
-        traceback.print_exc()
-        return 1
-
-
-if __name__ == '__main__':
-    sys.exit(main())

benchmarks/fvd/fvd.py

@@ -1,447 +0,0 @@
-import numpy as np
-import scipy.linalg
-import torch
-from pathlib import Path
-from collections.abc import Iterator
-import pickle
-from dataclasses import dataclass, field
-
-from .i3d_model import I3DFeatureExtractor
-from .video_utils import ClipSamplingStrategy, load_video_clips_streaming
-
-
-def compute_statistics(features: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
-    """Compute mean and covariance."""
-    mu = np.mean(features, axis=0)
-    sigma = np.cov(features, rowvar=False)
-    return mu, sigma
-
-
-def compute_frechet_distance(mu1: np.ndarray,
-                             sigma1: np.ndarray,
-                             mu2: np.ndarray,
-                             sigma2: np.ndarray,
-                             eps: float = 1e-6) -> float:
-    """
-    Compute Fréchet distance between two Gaussians.
-    """
-    sigma1 = sigma1 + eps * np.eye(sigma1.shape[0])
-    sigma2 = sigma2 + eps * np.eye(sigma2.shape[0])
-
-    diff = mu1 - mu2
-    mean_distance = np.sum(diff**2)
-
-    trace_sum = np.trace(sigma1 + sigma2)
-
-    covmean = scipy.linalg.sqrtm(sigma1 @ sigma2)
-
-    if np.iscomplexobj(covmean):
-        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
-            print(
-                f"Warning: Imaginary component: {np.max(np.abs(covmean.imag))}")
-        covmean = covmean.real
-
-    trace_product = np.trace(covmean)
-
-    fvd = mean_distance + trace_sum - 2 * trace_product
-
-    return float(fvd)
-
-
-@dataclass
-class FVDConfig:
-    # default configuration for FVD computation:
-
-    # Video selection
-    num_videos: int = 2048
-
-    # Clip sampling
-    num_frames_per_clip: int = 16
-    num_clips_per_video: int = 1
-    clip_strategy: str | ClipSamplingStrategy = 'beginning'
-
-    # Temporal subsampling
-    frame_stride: int = 1  # 1=no subsampling, 2=every 2nd, 8=every 8th
-    temporal_stride: int = 1  # For sliding window clips
-
-    # Data processing
-    video_extensions: list[str] = field(
-        default_factory=lambda: ['.mp4', '.avi', '.mov', '.mkv'])
-    support_frame_dirs: bool = True
-
-    # Computation
-    batch_size: int = 32
-    device: str = 'cuda'
-
-    use_streaming: bool = True
-    resize_before_extraction: bool = True
-
-    # Caching
-    cache_real_features: str | None = None
-    i3d_model_path: str | None = None
-
-    # Reproducibility
-    seed: int | None = None
-
-    @classmethod
-    def fvd2048_16f(cls) -> 'FVDConfig':
-        """
-        Standard FVD protocol: 2048 videos, 16 frames, beginning clip.
-        
-        most common FVD configuration used in papers
-        """
-        return cls(num_videos=2048,
-                   num_frames_per_clip=16,
-                   clip_strategy='beginning',
-                   use_streaming=True)
-
-    @classmethod
-    def fvd2048_128f(cls) -> 'FVDConfig':
-        """Long video protocol: 2048 videos, 128 frames."""
-        return cls(num_videos=2048,
-                   num_frames_per_clip=128,
-                   clip_strategy='beginning',
-                   use_streaming=True)
-
-    @classmethod
-    def fvd2048_128f_subsample8(cls) -> 'FVDConfig':
-        """
-        Long video with FPS subsampling: 2048 videos, 128 frames (every 8th).
-        Used for very long videos - samples every 8th frame
-        """
-        return cls(num_videos=2048,
-                   num_frames_per_clip=16,
-                   frame_stride=8,
-                   clip_strategy='beginning',
-                   use_streaming=True)
-
-    @classmethod
-    def quick_test(cls) -> 'FVDConfig':
-        """Quick test config: 100 videos, 16 frames."""
-        return cls(num_videos=100,
-                   num_frames_per_clip=16,
-                   clip_strategy='beginning')
-
-    def to_dict(self) -> dict:
-        """Export config to dict for logging"""
-        return {
-            'num_videos': self.num_videos,
-            'num_frames_per_clip': self.num_frames_per_clip,
-            'num_clips_per_video': self.num_clips_per_video,
-            'clip_strategy': str(self.clip_strategy),
-            'frame_stride': self.frame_stride,
-            'temporal_stride': self.temporal_stride,
-            'batch_size': self.batch_size,
-            'device': self.device,
-            'seed': self.seed,
-            'use_streaming': self.use_streaming,
-        }
-
-    def __str__(self) -> str:
-        """Human-readable protocol name"""
-        desc = f"FVD{self.num_videos}_{self.num_frames_per_clip}f"
-        if self.frame_stride > 1:
-            desc += f"_subsample{self.frame_stride}"
-        if self.num_clips_per_video > 1:
-            desc += f"_{self.num_clips_per_video}clips"
-        if self.clip_strategy != 'beginning':
-            desc += f"_{self.clip_strategy}"
-        return desc
-
-
-def extract_features_streaming(video_generator: Iterator[torch.Tensor],
-                               extractor: I3DFeatureExtractor,
-                               batch_size: int = 32,
-                               max_clips: int | None = None,
-                               verbose: bool = True) -> np.ndarray:
-    """
-    Extract features from a video clip generator using streaming.
-
-    Args:
-        video_generator: Iterator yielding clips [T, C, H, W]
-        extractor: I3D feature extractor
-        batch_size: Batch size for processing
-        max_clips: Maximum clips to process (for validation)
-        verbose: Show progress
-    
-    Returns:
-        features: [N, 400] numpy array
-    """
-    all_features = []
-    batch = []
-    clip_count = 0
-
-    if verbose:
-        print(f"Extracting features with batch_size={batch_size}...")
-
-    for clip_count, clip in enumerate(video_generator):
-        batch.append(clip)
-
-        # Process batch when full
-        if len(batch) == batch_size:
-            batch_tensor = torch.stack(batch).to(extractor.device)
-            features = extractor.extract_features(batch_tensor,
-                                                  batch_size=batch_size,
-                                                  verbose=False)
-            all_features.append(features.cpu().numpy())
-
-            batch = []  # Clear batch
-
-            if verbose and clip_count % (batch_size * 10) == 0:
-                print(f"Processed {clip_count} clips...")
-
-        # Stop if we've reached max_clips
-        if max_clips is not None and clip_count >= max_clips:
-            break
-
-    # Process remaining clips
-    if len(batch) > 0:
-        batch_tensor = torch.stack(batch).to(extractor.device)
-        features = extractor.extract_features(batch_tensor,
-                                              batch_size=len(batch),
-                                              verbose=False)
-        all_features.append(features.cpu().numpy())
-
-    if len(all_features) == 0:
-        raise RuntimeError("No features extracted - check video loading")
-
-    features = np.concatenate(all_features, axis=0)
-
-    if verbose:
-        print(f"Extracted {len(features)} feature vectors")
-
-    return features
-
-
-def load_or_compute_features(videos: str | Path | torch.Tensor,
-                             extractor: I3DFeatureExtractor,
-                             config: FVDConfig,
-                             cache_path: str | None = None,
-                             cache_name: str = "real_features") -> np.ndarray:
-    """Load features from cache or compute (with streaming support)"""
-
-    if cache_path is not None:
-        cache_file = Path(cache_path) / f"{cache_name}.pkl"
-        if cache_file.exists():
-            print(f"Loading cached features from {cache_file}")
-            with open(cache_file, 'rb') as f:
-                features = pickle.load(f)
-
-            # Validate and limit based on config
-            max_features = config.num_videos * config.num_clips_per_video
-            if len(features) < max_features:
-                print(
-                    f"WARNING: Cache has {len(features)} features but need {max_features}"
-                )
-                print("Recomputing features...")
-            elif len(features) > max_features:
-                features = features[:max_features]
-                return features
-            else:
-                return features
-
-    # Compute features
-    if isinstance(videos, str | Path):
-        target_size = (224, 224) if config.resize_before_extraction else None
-
-        video_generator = load_video_clips_streaming(
-            videos,
-            num_frames=config.num_frames_per_clip,
-            max_videos=config.num_videos,
-            clip_strategy=config.clip_strategy,
-            frame_stride=config.frame_stride,
-            num_clips_per_video=config.num_clips_per_video,
-            video_extensions=config.video_extensions,
-            support_frame_dirs=config.support_frame_dirs,
-            target_size=target_size,
-            verbose=True)
-
-        max_clips = config.num_videos * config.num_clips_per_video
-        features = extract_features_streaming(video_generator,
-                                              extractor,
-                                              batch_size=config.batch_size,
-                                              max_clips=max_clips,
-                                              verbose=True)
-
-    else:
-        # Already a tensor
-        print(f"Extracting features from {len(videos)} video tensors...")
-        features = extractor.extract_features(videos,
-                                              batch_size=config.batch_size,
-                                              verbose=True)
-        features = features.numpy()
-
-    # Validate feature count
-    expected_count = config.num_videos * config.num_clips_per_video
-    if len(features) < expected_count:
-        raise ValueError(
-            f"ERROR: Only extracted {len(features)} features, but need {expected_count}!\n"
-            f"Found fewer videos than expected. Check your video directory.")
-    elif len(features) > expected_count:
-        print(f"Truncating {len(features)} features to {expected_count}")
-        features = features[:expected_count]
-
-    # Cache features if requested
-    if cache_path is not None:
-        cache_dir = Path(cache_path)
-        cache_dir.mkdir(parents=True, exist_ok=True)
-        cache_file = cache_dir / f"{cache_name}.pkl"
-        print(f"Caching features to {cache_file}")
-        with open(cache_file, 'wb') as f:
-            pickle.dump(features, f)
-
-    return features
-
-
-def compute_fvd(real_videos: str | Path | torch.Tensor,
-                gen_videos: str | Path | torch.Tensor,
-                num_frames: int = 16,
-                batch_size: int = 32,
-                device: str = 'cuda',
-                num_videos: int | None = 2048,
-                cache_real_features: str | None = None,
-                i3d_model_path: str | None = None,
-                seed: int | None = None,
-                verbose: bool = True) -> float:
-    """
-    Compute Fréchet Video Distance (FVD)
-    
-    For advanced control, use compute_fvd_with_config() instead.
-    
-    Args:
-        real_videos: Path to real videos or tensor [N, T, C, H, W]
-        gen_videos: Path to generated videos or tensor [N, T, C, H, W]
-        num_frames: Frames per video (default: 16)
-        batch_size: Batch size (default: 32)
-        device: 'cuda' or 'cpu' (default: 'cuda')
-        num_videos: Max videos (default: 2048)
-        cache_real_features: Cache path for real features
-        i3d_model_path: Custom I3D model cache path
-        seed: Random seed for reproducibility
-        verbose: Print progress
-    
-    Returns:
-        FVD score (float). Lower is better.
-    """
-    num_videos = num_videos if num_videos is not None else 2048
-
-    config = FVDConfig(
-        num_videos=num_videos,
-        num_frames_per_clip=num_frames,
-        batch_size=batch_size,
-        device=device,
-        cache_real_features=cache_real_features,
-        i3d_model_path=i3d_model_path,
-        seed=seed,
-    )
-
-    result = compute_fvd_with_config(real_videos, gen_videos, config, verbose)
-    return result['fvd']
-
-
-def compute_fvd_with_config(real_videos: str | Path | torch.Tensor,
-                            gen_videos: str | Path | torch.Tensor,
-                            config: FVDConfig,
-                            verbose: bool = True) -> dict:
-    """
-    Compute FVD using a standardized configuration.
-    
-    This is the recommended way to compute FVD for reproducibility.
-    
-    Args:
-        real_videos: Path or tensors
-        gen_videos: Path or tensors
-        config: FVDConfig specifying protocol
-        verbose: Print progress
-    
-    Returns:
-        results: Dictionary with:
-            - 'fvd': FVD score (float)
-            - 'protocol': Protocol name (str)
-            - 'config': Configuration dict
-    
-    Example:
-        >>> config = FVDConfig.fvd2048_16f()
-        >>> results = compute_fvd_with_config('data/real/', 'outputs/gen/', config)
-        >>> print(f"FVD: {results['fvd']:.2f}")
-        >>> print(f"Protocol: {results['protocol']}")  # "FVD2048_16f"
-    """
-    # Seed for reproducibility
-    if config.seed is not None:
-        import random as _rnd
-        _rnd.seed(config.seed)
-        np.random.seed(config.seed)
-        torch.manual_seed(config.seed)
-        if torch.cuda.is_available():
-            torch.cuda.manual_seed_all(config.seed)
-
-    if verbose:
-        print("=" * 70)
-        print(f"Computing FVD with protocol: {config}")
-        print("=" * 70)
-        print("\nConfiguration:")
-        for key, value in config.to_dict().items():
-            print(f"  {key}: {value}")
-        print()
-
-    # Initialize I3D
-    if verbose:
-        print(f"\nInitializing I3D model on {config.device}...")
-
-    extractor = I3DFeatureExtractor(device=config.device,
-                                    cache_dir=config.i3d_model_path)
-
-    # Extract features
-    if verbose:
-        print(f"\n{'='*70}")
-        print("Extracting REAL video features...")
-        print(f"{'='*70}")
-
-    real_features = load_or_compute_features(
-        videos=real_videos,
-        extractor=extractor,
-        config=config,
-        cache_path=config.cache_real_features,
-        cache_name="real_features")
-
-    if verbose:
-        print(f"\n{'='*70}")
-        print("Extracting GENERATED video features...")
-        print(f"{'='*70}")
-
-    gen_features = load_or_compute_features(videos=gen_videos,
-                                            extractor=extractor,
-                                            config=config,
-                                            cache_path=None,
-                                            cache_name="gen_features")
-
-    if verbose:
-        print(f"\nReal videos/clips: {len(real_features)}")
-        print(f"Generated videos/clips: {len(gen_features)}")
-        print(f"\n{'='*70}")
-        print("Computing statistics...")
-        print(f"{'='*70}")
-
-    mu_real, sigma_real = compute_statistics(real_features)
-    mu_gen, sigma_gen = compute_statistics(gen_features)
-
-    if verbose:
-        print(f"\n{'='*70}")
-        print("Computing Fréchet distance...")
-        print(f"{'='*70}")
-
-    fvd = compute_frechet_distance(mu_real, sigma_real, mu_gen, sigma_gen)
-
-    if verbose:
-        print(f"\n{'='*70}")
-        print(f"FVD Score: {fvd:.4f}")
-        print(f"Protocol: {config}")
-        print(f"{'='*70}\n")
-
-    results = {
-        'fvd': fvd,
-        'protocol': str(config),
-        'config': config.to_dict(),
-    }
-
-    return results

benchmarks/fvd/i3d_model.py

@@ -1,142 +0,0 @@
-"""I3D Feature Extractor for FVD Computation"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from pathlib import Path
-from huggingface_hub import hf_hub_download
-from tqdm import tqdm
-from contextlib import suppress
-
-
-class I3DFeatureExtractor(nn.Module):
-    """
-    I3D feature extractor for FVD computation.
-    Extracts 400-dimensional features from videos using I3D model
-    trained on Kinetics-400.
-    """
-
-    REPO_ID = 'flateon/FVD-I3D-torchscript'
-    MODEL_FILENAME = 'i3d_torchscript.pt'
-
-    def __init__(self,
-                 device: str = 'cuda',
-                 cache_dir: str | Path | None = None):
-        super().__init__()
-
-        self.device_str = device
-        if device == 'cuda' and not torch.cuda.is_available():
-            print(
-                "Warning: CUDA requested but not available – falling back to CPU"
-            )
-            self.device = torch.device('cpu')
-        else:
-            self.device = torch.device(device)
-
-        self.cache_dir: str | None
-        if cache_dir is not None:
-            self.cache_dir = str(Path(cache_dir).resolve())
-        else:
-            self.cache_dir = None  # Use HF default cache
-
-        self.model = self._load_model()
-        self.model.eval()
-
-        with suppress(Exception):
-            self.model.to(self.device)
-
-    def _load_model(self) -> torch.nn.Module:
-        """Download and load I3D TorchScript model from Hugging Face Hub."""
-        print(f"Loading I3D model from Hugging Face Hub ({self.REPO_ID})...")
-
-        try:
-            # Download model from Hugging Face Hub
-            model_path = hf_hub_download(repo_id=self.REPO_ID,
-                                         filename=self.MODEL_FILENAME,
-                                         cache_dir=self.cache_dir)
-
-            # Load directly to chosen device
-            model = torch.jit.load(model_path, map_location=self.device)
-            print("I3D model loaded successfully")
-            return model
-
-        except Exception as e:
-            raise RuntimeError(
-                f"Failed to load I3D model from Hugging Face Hub. Error: {e}\n"
-                f"Ensure you have internet connection and huggingface_hub installed:\n"
-                f"pip install huggingface_hub") from e
-
-    def preprocess(self, videos: torch.Tensor) -> torch.Tensor:
-        """
-        Preprocess videos for I3D.
-        
-        Args:
-            videos: [B, T, C, H, W], values in [0, 255]
-        
-        Returns:
-            Preprocessed videos [B, C, T, 224, 224] (normalized and resized)
-        """
-        B, T, C, H, W = videos.shape
-
-        if T < 10:
-            raise ValueError(f"I3D requires at least 10 frames, got {T}")
-
-        # Normalize to [0, 1] if needed
-        if videos.max() > 1.0:
-            videos = videos / 255.0
-
-        # Resize to 224x224 if needed
-        if H != 224 or W != 224:
-            videos = videos.reshape(B * T, C, H, W)
-            videos = F.interpolate(videos,
-                                   size=(224, 224),
-                                   mode='bilinear',
-                                   align_corners=False)
-            videos = videos.reshape(B, T, C, 224, 224)
-
-        # Convert to [B, C, T, H, W] format
-        videos = videos.permute(0, 2, 1, 3, 4).contiguous()
-
-        return videos
-
-    @torch.no_grad()
-    def extract_features(self,
-                         videos: torch.Tensor,
-                         batch_size: int = 32,
-                         verbose: bool = True) -> torch.Tensor:
-        """
-        Extract I3D features
-        
-        Args:
-            videos: [N, T, C, H, W], values in [0, 255]
-            batch_size: Batch size for processing
-            verbose: Show progress bar
-        
-        Returns:
-            Features [N, 400]
-        """
-        N = len(videos)
-        all_features = []
-
-        iterator = range(0, N, batch_size)
-        if verbose:
-            iterator = tqdm(iterator, desc="Extracting I3D features")
-
-        for i in iterator:
-            batch = videos[i:i + batch_size].to(self.device)
-            batch = self.preprocess(batch)  # Now returns [B, C, T, H, W]
-
-            # Use the HF model without rescale/resize (we handle it in preprocess)
-            features = self.model(batch,
-                                  rescale=False,
-                                  resize=False,
-                                  return_features=True)
-
-            all_features.append(features.cpu())
-
-        return torch.cat(all_features, dim=0)
-
-    def __call__(self,
-                 videos: torch.Tensor,
-                 batch_size: int = 32) -> torch.Tensor:
-        return self.extract_features(videos, batch_size=batch_size)

benchmarks/fvd/run_fvd.py

@@ -1,34 +0,0 @@
-import sys
-from pathlib import Path
-from benchmarks.fvd.fvd import FVDConfig, compute_fvd_with_config
-
-root_dir = Path(__file__).parent.parent.parent
-sys.path.insert(0, str(root_dir))
-
-
-def main() -> None:
-    # Get script directory
-    script_dir = Path(__file__).parent.resolve()
-
-    clip_strategy = 'beginning'  # Options: 'uniform', 'random', 'beginning', 'end', 'all'
-    cfg = FVDConfig(
-        num_videos=650,
-        num_frames_per_clip=16,
-        num_clips_per_video=1,
-        clip_strategy=clip_strategy,
-        frame_stride=1,
-        batch_size=32,
-        device='cuda',
-        seed=42,
-        cache_real_features=str(script_dir / f'fvd-cache/{clip_strategy}'),
-    )
-
-    real_dir = "benchmarks/data/real_videos"
-    gen_dir = "benchmarks/data/generated_videos"
-
-    results = compute_fvd_with_config(real_dir, gen_dir, cfg, verbose=True)
-    print(f"FVD = {results['fvd']:.2f}")
-
-
-if __name__ == '__main__':
-    main()

benchmarks/fvd/validate_fvd.py

@@ -1,97 +0,0 @@
-#!/usr/bin/env python3
-import sys
-from pathlib import Path
-import shutil
-import random
-from fvd import compute_fvd_with_config, FVDConfig
-
-script_path = Path(__file__).resolve()
-fastvideo_root = script_path.parent.parent.parent
-sys.path.insert(0, str(fastvideo_root))
-
-
-def split_videos(video_dir: Path, n_per_subset: int = 128, seed: int = 42):
-    subset_a = video_dir.parent / 'bair_full_subset_A'
-    subset_b = video_dir.parent / 'bair_full_subset_B'
-
-    if subset_a.exists():
-        shutil.rmtree(subset_a)
-    if subset_b.exists():
-        shutil.rmtree(subset_b)
-
-    subset_a.mkdir(parents=True)
-    subset_b.mkdir(parents=True)
-
-    videos = sorted(video_dir.glob('*.mp4'))
-
-    random.seed(seed)
-    shuffled = list(videos)
-    random.shuffle(shuffled)
-
-    needed = n_per_subset * 2
-    if len(shuffled) > needed:
-        shuffled = shuffled[:needed]
-
-    mid = len(shuffled) // 2
-
-    print(f"\nSplitting {len(shuffled)} BAIR FULL videos:")
-    print(f"  Subset A: {mid} videos")
-    print(f"  Subset B: {len(shuffled) - mid} videos")
-
-    for v in shuffled[:mid]:
-        shutil.copy2(v, subset_a / v.name)
-
-    for v in shuffled[mid:]:
-        shutil.copy2(v, subset_b / v.name)
-
-    return subset_a, subset_b, mid
-
-
-def validate_fvd(subset_a: Path, subset_b: Path, num_videos: int):
-    config = FVDConfig(num_videos=num_videos,
-                       num_frames_per_clip=16,
-                       clip_strategy='beginning',
-                       batch_size=8,
-                       device='cuda',
-                       seed=42)
-
-    print("\n" + "=" * 70)
-    print("TEST 1: Identity Test")
-    print("=" * 70)
-
-    result1 = compute_fvd_with_config(real_videos=str(subset_a),
-                                      gen_videos=str(subset_a),
-                                      config=config,
-                                      verbose=False)
-    fvd_identity = result1['fvd']
-    print(f"\nIdentity FVD: {fvd_identity:.2f}")
-
-    print("\n" + "=" * 70)
-    print("TEST 2: Real vs Real")
-    print("=" * 70)
-
-    result2 = compute_fvd_with_config(real_videos=str(subset_a),
-                                      gen_videos=str(subset_b),
-                                      config=config,
-                                      verbose=False)
-    fvd_real = result2['fvd']
-    print(f"\nReal vs Real FVD: {fvd_real:.2f}")
-
-    print("\n" + "=" * 70)
-    print("RESULTS")
-    print("=" * 70)
-    print(f"Identity:        {fvd_identity:.2f}")
-    print(f"Real vs Real:    {fvd_real:.2f}")
-
-
-def main() -> None:
-    bair_dir = Path('benchmarks/data/bair_full_videos')
-
-    subset_a, subset_b, count = split_videos(bair_dir,
-                                             n_per_subset=128,
-                                             seed=42)
-    validate_fvd(subset_a, subset_b, count)
-
-
-if __name__ == '__main__':
-    main()

benchmarks/fvd/video_utils.py

@@ -1,490 +0,0 @@
-import torch
-import cv2
-import numpy as np
-from pathlib import Path
-from collections.abc import Iterator
-from tqdm import tqdm
-from enum import Enum
-
-
-class ClipSamplingStrategy(Enum):
-    """Clip sampling strategies for FVD evaluation."""
-    BEGINNING = 'beginning'  # Take first N frames (most common)
-    RANDOM = 'random'  # Random N consecutive frames
-    UNIFORM = 'uniform'  # Uniformly spaced frames across video
-    MIDDLE = 'middle'  # Middle N frames
-    SLIDING = 'sliding'  # Multiple sliding windows
-    ALL = 'all'  # All possible clips
-
-
-def _load_video_cv2(video_path: str | Path,
-                    num_frames: int | None = 16,
-                    sample_strategy: str = 'uniform') -> torch.Tensor:
-    """
-    Load video from video file using OpenCV.
-    
-    Args:
-        video_path: Path to video file (MP4, AVI, MOV, MKV)
-        num_frames: Number of frames to extract
-        sample_strategy: 'uniform' or 'random'
-    
-    Returns:
-        video: [T, C, H, W]
-    """
-    video_path = str(video_path)
-    cap = cv2.VideoCapture(video_path)
-
-    if not cap.isOpened():
-        raise RuntimeError(f"Cannot open video: {video_path}")
-
-    frames = []
-    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
-    if num_frames is None:
-        # Read all available frames
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            frames.append(frame)
-
-        cap.release()
-        if len(frames) == 0:
-            raise RuntimeError(f"Video has 0 frames: {video_path}")
-
-        frames = np.stack(frames)  # [T, H, W, C]
-        frames = torch.from_numpy(frames).permute(0, 3, 1,
-                                                  2).float()  # [T, C, H, W]
-        return frames
-
-    if total_frames == 0:
-        raise RuntimeError(f"Video has 0 frames: {video_path}")
-
-    # Determine frame indices for sampling
-    if total_frames < num_frames:
-        frame_indices = list(range(
-            total_frames)) + [total_frames - 1] * (num_frames - total_frames)
-    elif sample_strategy == 'uniform':
-        frame_indices = np.linspace(0, total_frames - 1, num_frames,
-                                    dtype=int).tolist()
-    elif sample_strategy == 'random':
-        frame_indices = sorted(
-            np.random.choice(total_frames, num_frames, replace=False))
-    else:
-        raise ValueError(f"Unknown sample_strategy: {sample_strategy}")
-
-    # Extract frames
-    for idx in frame_indices:
-        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
-        ret, frame = cap.read()
-
-        if not ret:
-            if len(frames) > 0:
-                frames.append(frames[-1].copy())
-            else:
-                h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-                frames.append(np.zeros((h, w, 3), dtype=np.uint8))
-            continue
-
-        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        frames.append(frame)
-
-    cap.release()
-
-    frames = np.stack(frames)  # [T, H, W, C]
-    frames = torch.from_numpy(frames).permute(0, 3, 1,
-                                              2).float()  # [T, C, H, W]
-
-    return frames
-
-
-def _load_video_from_frames(
-        frame_dir: str | Path,
-        num_frames: int | None = 16,
-        sample_strategy: str = 'uniform',
-        frame_extensions: list[str] | None = None) -> torch.Tensor:
-    """
-    Load video from directory of frame images.
-    
-    Args:
-        frame_dir: Directory containing frames
-        num_frames: Number of frames to sample
-        sample_strategy: 'uniform' or 'random'
-        frame_extensions: Image file extensions to look for
-    
-    Returns:
-        video: [T, C, H, W]
-    """
-    if frame_extensions is None:
-        frame_extensions = ['.jpg', '.png', '.jpeg', '.bmp']
-
-    frame_dir = Path(frame_dir)
-
-    if not frame_dir.exists():
-        raise FileNotFoundError(f"Frame directory not found: {frame_dir}")
-
-    # Find all frames
-    frame_files: list[Path] = []
-    for ext in frame_extensions:
-        frame_files.extend(frame_dir.glob(f"*{ext}"))
-
-    if len(frame_files) == 0:
-        raise ValueError(
-            f"No frames found in {frame_dir} with extensions {frame_extensions}"
-        )
-
-    frame_files = sorted(frame_files, key=lambda x: x.name)
-    total_frames = len(frame_files)
-
-    # Determine frame indices
-    if num_frames is None:
-        frame_indices = list(range(total_frames))
-    else:
-        if total_frames < num_frames:
-            frame_indices = list(range(total_frames)) + [total_frames - 1] * (
-                num_frames - total_frames)
-        elif sample_strategy == 'uniform':
-            frame_indices = np.linspace(0,
-                                        total_frames - 1,
-                                        num_frames,
-                                        dtype=int).tolist()
-        elif sample_strategy == 'random':
-            frame_indices = sorted(
-                np.random.choice(total_frames, num_frames, replace=False))
-        else:
-            raise ValueError(f"Unknown sample_strategy: {sample_strategy}")
-
-    # Load frames
-    frames = []
-    for idx in frame_indices:
-        frame_path = frame_files[idx]
-        frame = cv2.imread(str(frame_path))
-
-        if frame is None:
-            raise RuntimeError(f"Failed to load frame: {frame_path}")
-
-        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        frames.append(frame)
-
-    # Stack and convert to tensor
-    frames = np.stack(frames)  # [T, H, W, C]
-    frames = torch.from_numpy(frames).permute(0, 3, 1,
-                                              2).float()  # [T, C, H, W]
-
-    return frames
-
-
-def _detect_video_format(path: str | Path) -> str:
-    """
-    Detect if path is a video file or frame directory.
-    
-    Returns:
-        'video_file', 'frame_directory', or 'unknown'
-    """
-    path = Path(path)
-
-    if path.is_file():
-        return 'video_file'
-    elif path.is_dir():
-        # Check if contains image files
-        image_extensions = ['.jpg', '.jpeg', '.png', '.bmp']
-        for ext in image_extensions:
-            if list(path.glob(f"*{ext}")):
-                return 'frame_directory'
-        return 'unknown'
-    else:
-        raise ValueError(f"Path does not exist: {path}")
-
-
-def load_video_auto(video_path: str | Path,
-                    num_frames: int | None = 16,
-                    sample_strategy: str = 'uniform') -> torch.Tensor:
-    """
-    Automatically detect format and load video.
-    
-    Supports:
-    - Video files (MP4, AVI, MOV, MKV)
-    - Frame directories (JPG, PNG)
-    
-    Args:
-        video_path: Path to video file or frame directory
-        num_frames: Number of frames to extract
-        sample_strategy: 'uniform' or 'random'
-    
-    Returns:
-        video: [T, C, H, W]
-    """
-    format_type = _detect_video_format(video_path)
-
-    if format_type == 'video_file':
-        return _load_video_cv2(video_path, num_frames, sample_strategy)
-    elif format_type == 'frame_directory':
-        return _load_video_from_frames(video_path, num_frames, sample_strategy)
-    else:
-        raise ValueError(f"Unknown video format at {video_path}")
-
-
-def sample_clips_from_video(
-        video: torch.Tensor,
-        num_frames_per_clip: int = 16,
-        num_clips: int = 1,
-        strategy: str | ClipSamplingStrategy = ClipSamplingStrategy.BEGINNING,
-        frame_stride: int = 1,
-        temporal_stride: int = 1) -> list[torch.Tensor]:
-    """
-    Sample clips from a video with various strategies.
-        
-    Args:
-        video: [T, C, H, W] full video
-        num_frames_per_clip: Frames per clip
-        num_clips: Number of clips to extract
-        strategy: ClipSamplingStrategy or string ('beginning', 'random', etc.)
-        frame_stride: Skip frames (FPS control: 1=all, 2=every 2nd, 8=every 8th)
-        temporal_stride: Stride between clips for sliding window
-    
-    Returns:
-        List of clips, each [num_frames_per_clip, C, H, W]
-    
-    Examples:
-        >>> # Beginning clip (most common for FVD)
-        >>> clips = sample_clips_from_video(video, 16, strategy='beginning')
-        
-        >>> # Multiple random clips
-        >>> clips = sample_clips_from_video(video, 16, num_clips=4, strategy='random')
-        
-        >>> # Subsample FPS by 2x (every 2nd frame)
-        >>> clips = sample_clips_from_video(video, 16, frame_stride=2)
-        
-        >>> # Sliding window with overlap
-        >>> clips = sample_clips_from_video(video, 16, strategy='sliding', temporal_stride=8)
-    """
-    # Convert string to enum if needed
-    if isinstance(strategy, str):
-        strategy = ClipSamplingStrategy(strategy)
-
-    T, C, H, W = video.shape
-
-    # Apply frame stride (FPS subsampling)
-    if frame_stride > 1:
-        video = video[::frame_stride]
-        T = len(video)
-
-    effective_clip_length = num_frames_per_clip
-
-    # Handle videos shorter than clip length
-    if effective_clip_length > T:
-        pad_length = effective_clip_length - T
-        last_frame = video[-1:].repeat(pad_length, 1, 1, 1)
-        video = torch.cat([video, last_frame], dim=0)
-        T = len(video)
-
-    clips = []
-
-    if strategy == ClipSamplingStrategy.BEGINNING:
-        # Take first clip (most common for FVD evaluation)
-        clip = video[:effective_clip_length]
-        clips.append(clip)
-
-    elif strategy == ClipSamplingStrategy.MIDDLE:
-        # Take middle clip
-        start = (T - effective_clip_length) // 2
-        clip = video[start:start + effective_clip_length]
-        clips.append(clip)
-
-    elif strategy == ClipSamplingStrategy.RANDOM:
-        # Sample N random clips
-        for _ in range(num_clips):
-            if effective_clip_length == T:
-                start = 0
-            else:
-                start = np.random.randint(0, T - effective_clip_length + 1)
-            clip = video[start:start + effective_clip_length]
-            clips.append(clip)
-
-    elif strategy == ClipSamplingStrategy.UNIFORM:
-        # Uniformly spaced clips
-        if num_clips == 1:
-            # Single clip from middle
-            start = (T - effective_clip_length) // 2
-            clip = video[start:start + effective_clip_length]
-            clips.append(clip)
-        else:
-            # Multiple uniformly spaced clips
-            step = (T - effective_clip_length) / (num_clips -
-                                                  1) if num_clips > 1 else 0
-            for i in range(num_clips):
-                start = int(i * step)
-                start = min(start, T - effective_clip_length)
-                clip = video[start:start + effective_clip_length]
-                clips.append(clip)
-
-    elif strategy == ClipSamplingStrategy.SLIDING:
-        # Sliding window with stride
-        for start in range(0, T - effective_clip_length + 1, temporal_stride):
-            clip = video[start:start + effective_clip_length]
-            clips.append(clip)
-            if len(clips) >= num_clips:
-                break
-
-    elif strategy == ClipSamplingStrategy.ALL:
-        # All possible clips (overlapping)
-        for start in range(T - effective_clip_length + 1):
-            clip = video[start:start + effective_clip_length]
-            clips.append(clip)
-
-    else:
-        raise ValueError(f"Unknown strategy: {strategy}")
-
-    return clips
-
-
-def load_video_clips_streaming(directory: str | Path,
-                               num_frames: int = 16,
-                               max_videos: int | None = None,
-                               clip_strategy: str
-                               | ClipSamplingStrategy = 'beginning',
-                               frame_stride: int = 1,
-                               num_clips_per_video: int = 1,
-                               video_extensions: list[str] | None = None,
-                               support_frame_dirs: bool = True,
-                               target_size: tuple[int, int] | None = (224, 224),
-                               verbose: bool = True) -> Iterator[torch.Tensor]:
-    """
-    This generator yields clips one-by-one instead of loading all videos into RAM.
-    Perfect for large datasets where memory is limited.
-    
-    Args:
-        directory: Path to directory with videos
-        num_frames: Frames per clip
-        max_videos: Max videos to load
-        clip_strategy: 'beginning', 'random', 'uniform', etc.
-        frame_stride: Frame skip (1=all, 2=every 2nd, 8=every 8th)
-        num_clips_per_video: Number of clips per video
-        video_extensions: Video file extensions
-        support_frame_dirs: Also load frame directories
-        target_size: Resize clips to (H, W). If None, keep original size.
-        verbose: Show progress
-    
-    Yields:
-        clip: [T, C, H, W] individual clips
-    
-    Example:
-        >>> for clip in load_video_clips_streaming('data/videos/', num_frames=16):
-        >>>     features = model.extract_features(clip.unsqueeze(0))
-        >>>     # Process one clip at a time - low memory usage!
-    """
-    if video_extensions is None:
-        video_extensions = ['.mp4', '.avi', '.mov', '.mkv']
-
-    directory = Path(directory)
-
-    if not directory.exists():
-        raise FileNotFoundError(f"Directory not found: {directory}")
-
-    # Find video paths
-    video_paths: list[Path] = []
-
-    # Find video files
-    for ext in video_extensions:
-        video_paths.extend(directory.glob(f"**/*{ext}"))
-
-    # Find frame directories if enabled
-    if support_frame_dirs:
-        for subdir in directory.iterdir():
-            if subdir.is_dir():
-                # Check if it contains frames
-                image_extensions = ['.jpg', '.jpeg', '.png', '.bmp']
-                for ext in image_extensions:
-                    if list(subdir.glob(f"*{ext}")):
-                        video_paths.append(subdir)
-                        break
-
-    if len(video_paths) == 0:
-        raise ValueError(f"No videos found in {directory}")
-
-    video_paths = sorted(video_paths)
-
-    if max_videos is not None:
-        video_paths = video_paths[:max_videos]
-
-    if verbose:
-        print(f"Found {len(video_paths)} videos in {directory}")
-        if num_clips_per_video > 1:
-            print(f"Extracting {num_clips_per_video} clips per video...")
-        if frame_stride > 1:
-            print(f"Subsampling frames with stride {frame_stride}...")
-        if target_size:
-            print(f"Resizing clips to {target_size}...")
-
-    # Track statistics
-    failed_count = 0
-    total_clips = 0
-
-    iterator = tqdm(video_paths,
-                    desc="Loading videos") if verbose else video_paths
-
-    for video_path in iterator:
-        try:
-            # Load full video
-            video = load_video_auto(video_path,
-                                    num_frames=None,
-                                    sample_strategy='uniform')
-
-            # Sample clips from video
-            clips = sample_clips_from_video(video,
-                                            num_frames_per_clip=num_frames,
-                                            num_clips=num_clips_per_video,
-                                            strategy=clip_strategy,
-                                            frame_stride=frame_stride)
-
-            if target_size is not None:
-                resized_clips = []
-                for clip in clips:
-                    T, C, H, W = clip.shape
-                    if target_size != (H, W):
-                        # Resize to target size
-                        clip = clip.contiguous(
-                        )  # Fix non-contiguous tensors first
-                        clip_flat = clip.view(T * C, H,
-                                              W).unsqueeze(0)  # [1, T*C, H, W]
-                        clip_resized = torch.nn.functional.interpolate(
-                            clip_flat,
-                            size=target_size,
-                            mode='bilinear',
-                            align_corners=False)
-                        clip = clip_resized.squeeze(0).view(
-                            T, C, target_size[0],
-                            target_size[1])  # Back to [T, C, H, W]
-                    resized_clips.append(clip)
-                clips = resized_clips
-
-            # Yield clips one by one
-            for clip in clips:
-                yield clip
-                total_clips += 1
-
-            # Free memory
-            del video, clips
-
-        except Exception as e:
-            failed_count += 1
-            if verbose:
-                print(f"\nWarning: Failed to load {video_path}: {e}")
-            continue
-
-    # Validate
-    if total_clips == 0:
-        raise RuntimeError(f"Failed to load any videos from {directory}")
-
-    failure_rate = failed_count / len(video_paths)
-    if failure_rate > 0.1:  # More than 10% failed
-        print(
-            f"\nWARNING: {failure_rate:.1%} of videos failed to load ({failed_count}/{len(video_paths)})"
-        )
-
-    if verbose:
-        print(
-            f"\nSuccessfully loaded {total_clips} clips from {len(video_paths) - failed_count} videos"
-        )

benchmarks/scripts/run.sh

@@ -1,7 +0,0 @@
-#!/bin/bash
-
-# 1. Install missing dependency
-pip install -q opencv-python-headless
-
-# 2. Run FVD script
-python benchmarks/fvd/run_fvd.py

benchmarks/scripts/setup_fvd.sh

@@ -1,4 +0,0 @@
-#!/bin/bash
-
-# 1. Install missing dependency
-pip install -q opencv-python-headless

csrc/attn/sliding_tile_attn/README.md

@@ -72,15 +72,6 @@ We give a demo for 2D STA with window size (6,6) operating on a (10, 10) image.
 
 https://github.com/user-attachments/assets/f3b6dd79-7b43-4b60-a0fa-3d6495ec5747
 
-
-## STA Configuration Logic
-Here is a diagram of how the window is configured and passed through the FastVideo pipeline:
-
-<div align="center">
-<img src="../../../docs/assets/images/STA_configuration.png" width="80%"/>
-</div>
-
-
 ## Why is STA Fast?
 2D/3D Sliding Window Attention (SWA) creates many mixed blocks in the attention map. Even though mixed blocks have less output value,a mixed block is significantly slower than a dense block due to the GPU-unfriendly masking operation. 
 

csrc/attn/tests/test_vsa.py

@@ -34,16 +34,16 @@ def pytorch_test(Q, K, V, block_sparse_mask, dO):
     )
 
 
-def block_sparse_kernel_test(Q, K, V, block_sparse_mask, variable_block_sizes, q_non_pad_index, kv_non_pad_index, q_num_blocks, kv_num_blocks, dO):
+def block_sparse_kernel_test(Q, K, V, block_sparse_mask, variable_block_sizes, non_pad_index, dO):
     Q = Q.detach().requires_grad_()
     K = K.detach().requires_grad_()
     V = V.detach().requires_grad_()
     
-    q_padded = vsa_pad(Q, q_non_pad_index, q_num_blocks, BLOCK_M)
-    k_padded = vsa_pad(K, kv_non_pad_index, kv_num_blocks, BLOCK_M)
-    v_padded = vsa_pad(V, kv_non_pad_index, kv_num_blocks, BLOCK_M)
+    q_padded = vsa_pad(Q, non_pad_index, variable_block_sizes.shape[0], BLOCK_M)
+    k_padded = vsa_pad(K, non_pad_index, variable_block_sizes.shape[0], BLOCK_M)
+    v_padded = vsa_pad(V, non_pad_index, variable_block_sizes.shape[0], BLOCK_M)
     output, _= block_sparse_attn(q_padded, k_padded, v_padded, block_sparse_mask, variable_block_sizes)
-    output = output[:, :, q_non_pad_index, :]
+    output = output[:, :, non_pad_index, :]
     output.backward(dO)
     return output, Q.grad, K.grad, V.grad
 
@@ -64,7 +64,7 @@ def generate_tensor(shape, dtype, device):
     tensor = torch.randn(shape, dtype=dtype, device=device)
     return tensor
 
-def generate_variable_block_sizes(num_blocks, min_size=16, max_size=64, device="cuda"):
+def generate_variable_block_sizes(num_blocks, min_size=32, max_size=64, device="cuda"):
     return torch.randint(min_size, max_size + 1, (num_blocks,), device=device, dtype=torch.int32)
 
 
@@ -86,21 +86,19 @@ def check_correctness(h, d, num_blocks, k,  num_iterations=20, error_mode='all')
     S = int(variable_block_sizes.sum().item())
     padded_S = num_blocks * BLOCK_M
     non_pad_index = get_non_pad_index(variable_block_sizes, num_blocks, BLOCK_M)
-    block_mask = generate_block_sparse_mask_for_function(h, num_blocks, num_blocks, k, device)
-    full_mask = create_full_mask_from_block_mask(block_mask, variable_block_sizes, variable_block_sizes, device)
+    block_mask = generate_block_sparse_mask_for_function(h, num_blocks, k, device)
+    full_mask = create_full_mask_from_block_mask(block_mask, variable_block_sizes, device)
     for _ in range(num_iterations):
         Q = generate_tensor((1, h, S, d),  torch.bfloat16, device)
         K = generate_tensor((1, h, S, d), torch.bfloat16, device)
         V = generate_tensor((1, h, S, d),  torch.bfloat16, device)
         dO = generate_tensor((1, h, S, d), torch.bfloat16, device)
-        
-        # print(Q.shape, K.shape, V.shape, dO.shape)
 
         # dO_padded = torch.zeros_like(dO_padded)
         # dO_padded[:, :, non_pad_index, :] = dO
 
         pt_o, pt_qg, pt_kg, pt_vg = pytorch_test(Q, K, V, full_mask, dO)
-        bs_o, bs_qg, bs_kg, bs_vg = block_sparse_kernel_test(Q, K, V, block_mask.unsqueeze(0), variable_block_sizes, non_pad_index, non_pad_index, num_blocks, num_blocks, dO)
+        bs_o, bs_qg, bs_kg, bs_vg = block_sparse_kernel_test(Q, K, V, block_mask.unsqueeze(0), variable_block_sizes,non_pad_index, dO)
         for name, (pt, bs) in zip(['gQ', 'gK', 'gV', 'gO'], [(pt_qg, bs_qg), (pt_kg, bs_kg), (pt_vg, bs_vg), (pt_o, bs_o)]):
             if bs is not None:
                 diff = pt - bs
@@ -120,60 +118,6 @@ def check_correctness(h, d, num_blocks, k,  num_iterations=20, error_mode='all')
 
     return results
 
-def check_correctness_qkdiff(h, d, num_q_blocks, num_kv_blocks, k, num_iterations=20, error_mode='all'):
-    results = {
-        'gO': {'sum_diff': 0.0, 'sum_abs': 0.0, 'max_diff': 0.0},
-        'gQ': {'sum_diff': 0.0, 'sum_abs': 0.0, 'max_diff': 0.0},
-        'gK': {'sum_diff': 0.0, 'sum_abs': 0.0, 'max_diff': 0.0},
-        'gV': {'sum_diff': 0.0, 'sum_abs': 0.0, 'max_diff': 0.0},
-    }
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    q_variable_block_sizes = generate_variable_block_sizes(num_q_blocks, device=device)
-    kv_variable_block_sizes = generate_variable_block_sizes(num_kv_blocks, device=device)
-    
-    S_q = int(q_variable_block_sizes.sum().item())
-    S_kv = int(kv_variable_block_sizes.sum().item())
-    
-    q_non_pad_index = get_non_pad_index(q_variable_block_sizes, num_q_blocks, BLOCK_M)
-    kv_non_pad_index = get_non_pad_index(kv_variable_block_sizes, num_kv_blocks, BLOCK_M)
-    
-    block_mask = generate_block_sparse_mask_for_function(h, num_q_blocks, num_kv_blocks, k, device)
-    full_mask = create_full_mask_from_block_mask(block_mask, q_variable_block_sizes, kv_variable_block_sizes, device)
-    
-    for _ in range(num_iterations):
-        Q = generate_tensor((1, h, S_q, d),  torch.bfloat16, device)
-        K = generate_tensor((1, h, S_kv, d), torch.bfloat16, device)
-        V = generate_tensor((1, h, S_kv, d),  torch.bfloat16, device)
-        dO = generate_tensor((1, h, S_q, d), torch.bfloat16, device)
-        
-        # print(Q.shape, K.shape, V.shape, dO.shape)
-
-        pt_o, pt_qg, pt_kg, pt_vg = pytorch_test(Q, K, V, full_mask, dO)
-        bs_o, bs_qg, bs_kg, bs_vg = block_sparse_kernel_test(Q, K, V, block_mask.unsqueeze(0), kv_variable_block_sizes, q_non_pad_index, kv_non_pad_index, num_q_blocks, num_kv_blocks, dO)
-        
-        for name, (pt, bs) in zip(['gQ', 'gK', 'gV', 'gO'], [(pt_qg, bs_qg), (pt_kg, bs_kg), (pt_vg, bs_vg), (pt_o, bs_o)]):
-            if bs is not None:
-                diff = pt - bs
-                abs_diff = torch.abs(diff)
-                results[name]['sum_diff'] += torch.sum(abs_diff).item()
-                results[name]['sum_abs'] += torch.sum(torch.abs(pt)).item()
-                rel_max_diff = torch.max(abs_diff) / torch.mean(torch.abs(pt))
-                results[name]['max_diff'] = max(results[name]['max_diff'], rel_max_diff.item())
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-
-    total_elements_q = h * S_q * d * num_iterations
-    total_elements_kv = h * S_kv * d * num_iterations
-    
-    for name, data in results.items():
-        total_elements = total_elements_q if name in ['gQ', 'gO'] else total_elements_kv
-        avg_diff = data['sum_diff'] / total_elements
-        max_diff = data['max_diff']
-        results[name] = {'avg_diff': avg_diff, 'max_diff': max_diff}
-
-    return results
-
 def generate_error_graphs(h, d, error_mode='all'):
     test_configs = [
         {"num_blocks": 16, "k": 2, "description": "Small sequence"},
@@ -203,43 +147,10 @@ def generate_error_graphs(h, d, error_mode='all'):
 
     print("-" * 150)
 
-def generate_error_graphs_qkdiff(h, d, error_mode='all'):
-    test_configs = [
-        {"num_q_blocks": 16, "num_kv_blocks": 32, "k": 2, "description": "Small Q, Med KV"},
-        {"num_q_blocks": 32, "num_kv_blocks": 16, "k": 4, "description": "Med Q, Small KV"},
-        {"num_q_blocks": 53, "num_kv_blocks": 32, "k": 6, "description": "Large Q, Med KV"},
-        {"num_q_blocks": 16, "num_kv_blocks": 48, "k": 2, "description": "Small Q, Large KV"},
-        {"num_q_blocks": 48, "num_kv_blocks": 16, "k": 2, "description": "Large Q, Small KV"},
-    ]
-    
-    print(f"\nError Analysis (QK Diff) for h={h}, d={d}, mode={error_mode}")
-    print("=" * 150)
-    print(f"{'Config':<20} {'Q Blks':<8} {'KV Blks':<8} {'K':<4} "
-          f"{'gQ Avg':<12} {'Rel gQ Max':<12} "
-          f"{'gK Avg':<12} {'Rel gK Max':<12} "
-          f"{'gV Avg':<12} {'Rel gV Max':<12} "
-          f"{'gO Avg':<12} {'Rel gO Max':<12}")
-    print("-" * 150)
-
-    for config in test_configs:
-        num_q_blocks = config["num_q_blocks"]
-        num_kv_blocks = config["num_kv_blocks"]
-        k = config["k"]
-        description = config["description"]
-        results = check_correctness_qkdiff(h, d, num_q_blocks, num_kv_blocks, k, error_mode=error_mode)
-        print(f"{description:<20} {num_q_blocks:<8} {num_kv_blocks:<8} {k:<4} "
-              f"{results['gQ']['avg_diff']:<12.6e} {results['gQ']['max_diff']:<12.6e} "
-              f"{results['gK']['avg_diff']:<12.6e} {results['gK']['max_diff']:<12.6e} "
-              f"{results['gV']['avg_diff']:<12.6e} {results['gV']['max_diff']:<12.6e} "
-              f"{results['gO']['avg_diff']:<12.6e} {results['gO']['max_diff']:<12.6e}")
-
-    print("-" * 150)
-
 if __name__ == "__main__":
     h, d = 16, 128
     print("Block Sparse Attention with Variable Block Sizes Analysis")
     print("=" * 60)
     for mode in ['backward']:
         generate_error_graphs(h, d, error_mode=mode)
-        generate_error_graphs_qkdiff(h, d, error_mode=mode)
-    print("\nAnalysis completed for all modes.")
+    print("\nAnalysis completed for all modes.")

csrc/attn/tests/test_vsa_forward.py

@@ -1,236 +0,0 @@
-import os
-import sys
-from typing import Tuple
-
-import torch
-
-# Make sure we can import from the project root (`vsa`, `tests.utils`, etc.)
-CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
-PROJECT_ROOT = os.path.dirname(CURRENT_DIR)
-if PROJECT_ROOT not in sys.path:
-    sys.path.append(PROJECT_ROOT)
-if CURRENT_DIR not in sys.path:
-    sys.path.append(CURRENT_DIR)
-
-from tests.utils import (
-    generate_block_sparse_mask_for_function,
-    create_full_mask_from_block_mask,
-)
-from vsa import block_sparse_attn, BLOCK_M
-import test_vsa as ref  # reuse helper functions from backward test
-
-
-def pytorch_forward(
-    Q: torch.Tensor,
-    K: torch.Tensor,
-    V: torch.Tensor,
-    block_sparse_mask: torch.Tensor,
-) -> torch.Tensor:
-    """
-    Dense PyTorch reference forward:
-    - Q: [1, h, S_q, d]
-    - K,V: [1, h, S_kv, d]
-    - block_sparse_mask: [h, S_q, S_kv] bool
-    """
-    q = Q.clone().float()
-    k = K.clone().float()
-    v = V.clone().float()
-
-    attn = torch.matmul(q, k.transpose(-2, -1))  # [1, h, S_q, S_kv]
-    attn = attn / (q.size(-1) ** 0.5)
-    attn = attn.masked_fill(~block_sparse_mask.unsqueeze(0), float("-inf"))
-    attn = torch.nn.functional.softmax(attn, dim=-1)
-    out = torch.matmul(attn, v)  # [1, h, S_q, d]
-    return out.to(torch.bfloat16)
-
-
-def block_sparse_forward_test(
-    Q: torch.Tensor,
-    K: torch.Tensor,
-    V: torch.Tensor,
-    block_sparse_mask: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-    q_non_pad_index: torch.Tensor,
-    kv_non_pad_index: torch.Tensor,
-    q_num_blocks: int,
-    kv_num_blocks: int,
-) -> torch.Tensor:
-    """
-    Forward-only wrapper around `block_sparse_attn`, mirroring `block_sparse_kernel_test`
-    but without any backward / grad logic.
-    """
-    Q = Q.detach()
-    K = K.detach()
-    V = V.detach()
-
-    q_padded = ref.vsa_pad(Q, q_non_pad_index, q_num_blocks, BLOCK_M)
-    k_padded = ref.vsa_pad(K, kv_non_pad_index, kv_num_blocks, BLOCK_M)
-    v_padded = ref.vsa_pad(V, kv_non_pad_index, kv_num_blocks, BLOCK_M)
-
-    out_padded, _ = block_sparse_attn(
-        q_padded, k_padded, v_padded, block_sparse_mask, variable_block_sizes
-    )
-    # Remove padding on the query side
-    out = out_padded[:, :, q_non_pad_index, :]
-    return out
-
-
-def run_forward_equal_qk(
-    h: int = 16,
-    d: int = 128,
-    num_blocks: int = 16,
-    k: int = 2,
-    num_iterations: int = 5,
-) -> Tuple[float, float]:
-    """
-    Forward-only correctness test for the case S_q == S_kv.
-    Mirrors `check_correctness` but only compares forward outputs.
-    """
-    assert torch.cuda.is_available(), "VSA kernels require CUDA"
-    device = "cuda"
-
-    variable_block_sizes = ref.generate_variable_block_sizes(
-        num_blocks, device=device
-    )
-    S = int(variable_block_sizes.sum().item())
-    non_pad_index = ref.get_non_pad_index(
-        variable_block_sizes, num_blocks, BLOCK_M
-    )
-
-    block_mask = generate_block_sparse_mask_for_function(
-        h, num_blocks, num_blocks, k, device
-    )
-    full_mask = create_full_mask_from_block_mask(
-        block_mask, variable_block_sizes, variable_block_sizes, device
-    )
-    print(f"[qkequal] h: {h}, d: {d}, num_blocks: {num_blocks}, k: {k}")
-    print(f"[qkequal] variable_block_sizes: {variable_block_sizes}, non_pad_index: {non_pad_index.shape}, block_mask: {block_mask.shape}, full_mask: {full_mask.shape}")
-    sum_diff = 0.0
-    sum_abs = 0.0
-    max_rel_diff = 0.0
-
-    for i in range(num_iterations):
-        Q = ref.generate_tensor((1, h, S, d), torch.bfloat16, device)
-        K = ref.generate_tensor((1, h, S, d), torch.bfloat16, device)
-        V = ref.generate_tensor((1, h, S, d), torch.bfloat16, device)
-
-        if i == 0: print(f"[qkequal] Q: {Q.shape}, K: {K.shape}, V: {V.shape}, full_mask: {full_mask.shape}")
-        if i == 0: print(f"[qkequal] block_mask: {block_mask.shape}")
-
-        pt_o = pytorch_forward(Q, K, V, full_mask)
-        bs_o = block_sparse_forward_test(
-            Q,
-            K,
-            V,
-            block_mask.unsqueeze(0),
-            variable_block_sizes,
-            non_pad_index,
-            non_pad_index,
-            num_blocks,
-            num_blocks,
-        )
-
-        diff = (pt_o - bs_o).abs()
-        sum_diff += diff.sum().item()
-        sum_abs += pt_o.abs().sum().item()
-        rel_max = diff.max() / (pt_o.abs().mean() + 1e-6)
-        max_rel_diff = max(max_rel_diff, rel_max.item())
-
-    total_elems = h * S * d * num_iterations
-    avg_abs_err = sum_diff / total_elems
-    return avg_abs_err, max_rel_diff
-
-
-def run_forward_qk_diff(
-    h: int = 16,
-    d: int = 128,
-    num_q_blocks: int = 16,
-    num_kv_blocks: int = 32,
-    k: int = 2,
-    num_iterations: int = 5,
-) -> Tuple[float, float]:
-    """
-    Forward-only correctness test for the case S_q != S_kv.
-
-    NOTE:
-      - The Triton backend supports different Q/KV logical lengths via padding.
-      - The SM90 (H100) CUDA backend currently assumes the same number of blocks
-        for Q and KV, so we skip this test there.
-    """
-    assert torch.cuda.is_available(), "VSA kernels require CUDA"
-
-    device = "cuda"
-
-    q_variable_block_sizes = ref.generate_variable_block_sizes(
-        num_q_blocks, device=device
-    )
-    kv_variable_block_sizes = ref.generate_variable_block_sizes(
-        num_kv_blocks, device=device
-    )
-
-    S_q = int(q_variable_block_sizes.sum().item())
-    S_kv = int(kv_variable_block_sizes.sum().item())
-
-    q_non_pad_index = ref.get_non_pad_index(
-        q_variable_block_sizes, num_q_blocks, BLOCK_M
-    )
-    kv_non_pad_index = ref.get_non_pad_index(
-        kv_variable_block_sizes, num_kv_blocks, BLOCK_M
-    )
-
-    block_mask = generate_block_sparse_mask_for_function(
-        h, num_q_blocks, num_kv_blocks, k, device
-    )
-    full_mask = create_full_mask_from_block_mask(
-        block_mask, q_variable_block_sizes, kv_variable_block_sizes, device
-    )
-
-    sum_diff = 0.0
-    sum_abs = 0.0
-    max_rel_diff = 0.0
-
-    for _ in range(num_iterations):
-        Q = ref.generate_tensor((1, h, S_q, d), torch.bfloat16, device)
-        K = ref.generate_tensor((1, h, S_kv, d), torch.bfloat16, device)
-        V = ref.generate_tensor((1, h, S_kv, d), torch.bfloat16, device)
-
-        pt_o = pytorch_forward(Q, K, V, full_mask)
-        bs_o = block_sparse_forward_test(
-            Q,
-            K,
-            V,
-            block_mask.unsqueeze(0),
-            kv_variable_block_sizes,
-            q_non_pad_index,
-            kv_non_pad_index,
-            num_q_blocks,
-            num_kv_blocks,
-        )
-
-        diff = (pt_o - bs_o).abs()
-        sum_diff += diff.sum().item()
-        sum_abs += pt_o.abs().sum().item()
-        rel_max = diff.max() / (pt_o.abs().mean() + 1e-6)
-        max_rel_diff = max(max_rel_diff, rel_max.item())
-
-    total_elems = h * S_q * d * num_iterations
-    avg_abs_err = sum_diff / total_elems
-    return avg_abs_err, max_rel_diff
-
-
-if __name__ == "__main__":
-    h, d = 16, 128
-    print("Forward Block Sparse Attention Check (QK Equal)")
-    print("=" * 80)
-    avg_err_eq, max_rel_eq = run_forward_equal_qk(h, d, num_blocks=32, k=2)
-    print(f"QK equal: avg |ΔO| = {avg_err_eq:.6e}, max rel ΔO = {max_rel_eq:.6e}")
-
-    print("\nForward Block Sparse Attention Check (QK Different)")
-    print("=" * 80)
-    avg_err_diff, max_rel_diff = run_forward_qk_diff(
-        h, d, num_q_blocks=32, num_kv_blocks=48, k=2
-    )
-    print(
-        f"QK diff:  avg |ΔO| = {avg_err_diff:.6e}, max rel ΔO = {max_rel_diff:.6e}"
-    )
-

csrc/attn/tests/utils.py

@@ -1,60 +1,54 @@
 import torch
 
-def generate_block_sparse_mask_for_function(h, num_q_blocks, num_kv_blocks, k, device="cuda"):
+def generate_block_sparse_mask_for_function(h, num_blocks, k, device="cuda"):
     """
-    Generate block sparse mask of shape [h, num_q_blocks, num_kv_blocks].
+    Generate block sparse mask of shape [h, num_blocks, num_blocks].
     
     Args:
         h: number of heads
-        num_q_blocks: number of query blocks
-        num_kv_blocks: number of key/value blocks
+        num_blocks: number of blocks
         k: number of kv blocks each q block attends to
         device: device to create tensors on
 
     Returns:
-        block_sparse_mask: [h, num_q_blocks, num_kv_blocks] bool tensor
+        block_sparse_mask: [h, num_blocks, num_blocks] bool tensor
     """
-    k = min(k, num_kv_blocks)
-    scores = torch.rand(h, num_q_blocks, num_kv_blocks, device=device)
+    k = min(k, num_blocks)
+    scores = torch.rand(h, num_blocks, num_blocks, device=device)
     _, indices = torch.topk(scores, k, dim=-1)
-    block_sparse_mask = torch.zeros(h, num_q_blocks, num_kv_blocks, dtype=torch.bool, device=device)
+    block_sparse_mask = torch.zeros(h, num_blocks, num_blocks, dtype=torch.bool, device=device)
 
     block_sparse_mask = block_sparse_mask.scatter_(2, indices, 1).bool()
     return block_sparse_mask
 
 
-def create_full_mask_from_block_mask(block_sparse_mask, q_variable_block_sizes, 
-                                     kv_variable_block_sizes, device="cuda"):
+def create_full_mask_from_block_mask(block_sparse_mask, variable_block_sizes, device="cuda"):
     """
     Convert block-level sparse mask to full attention mask.
     
     Args:
-        block_sparse_mask: [h, num_q_blocks, num_kv_blocks] bool tensor
-        q_variable_block_sizes: [num_q_blocks] tensor
-        kv_variable_block_sizes: [num_kv_blocks] tensor
+        block_sparse_mask: [h, num_blocks, num_blocks] bool tensor
+        variable_block_sizes: [num_blocks] tensor
         device: device to create tensors on
 
     Returns:
-        full_mask: [h, S_q, S_kv] bool tensor where S = total sequence length
+        full_mask: [h, S, S] bool tensor where S = total sequence length
     """
-    h, num_q_blocks, num_kv_blocks = block_sparse_mask.shape
-    total_q_seq_len = q_variable_block_sizes.sum().item()
-    total_kv_seq_len = kv_variable_block_sizes.sum().item()
-    
-    q_cumsum = torch.cat([torch.tensor([0], device=device), q_variable_block_sizes.cumsum(dim=0)[:-1]])
-    kv_cumsum = torch.cat([torch.tensor([0], device=device), kv_variable_block_sizes.cumsum(dim=0)[:-1]])
+    h, num_blocks, _ = block_sparse_mask.shape
+    total_seq_len = variable_block_sizes.sum().item()
+    cumsum = torch.cat([torch.tensor([0], device=device), variable_block_sizes.cumsum(dim=0)[:-1]])
 
-    full_mask = torch.zeros(h, total_q_seq_len, total_kv_seq_len, dtype=torch.bool, device=device)
+    full_mask = torch.zeros(h, total_seq_len, total_seq_len, dtype=torch.bool, device=device)
 
     for head in range(h):
-        for q_block in range(num_q_blocks):
-            q_start = q_cumsum[q_block]
-            q_end = q_start + q_variable_block_sizes[q_block]
+        for q_block in range(num_blocks):
+            q_start = cumsum[q_block]
+            q_end = q_start + variable_block_sizes[q_block]
 
-            for kv_block in range(num_kv_blocks):
+            for kv_block in range(num_blocks):
                 if block_sparse_mask[head, q_block, kv_block]:
-                    kv_start = kv_cumsum[kv_block]
-                    kv_end = kv_start + kv_variable_block_sizes[kv_block]
+                    kv_start = cumsum[kv_block]
+                    kv_end = kv_start + variable_block_sizes[kv_block]
                     full_mask[head, q_start:q_end, kv_start:kv_end] = True
 
     return full_mask

csrc/attn/video_sparse_attn/vsa/block_sparse_attn_triton.py

@@ -250,9 +250,8 @@ def _attn_bwd_dq(dq, q, K, V,  #
     
     
     for blk_idx in range(kv_blocks*2):
-        kv_idx = tl.load(kv_ptr + blk_idx//2).to(tl.int32)
-        block_size = tl.load(variable_block_sizes + kv_idx) -  (blk_idx % 2) * step_n
-        block_sparse_offset = (kv_idx*2 + blk_idx%2) * step_n * stride_tok
+        block_sparse_offset = (tl.load(kv_ptr + blk_idx//2).to(tl.int32)*2 + blk_idx%2) *step_n * stride_tok
+        block_size = tl.load(variable_block_sizes + blk_idx//2) - (blk_idx%2) * step_n
         kT = tl.load(kT_ptrs + block_sparse_offset)
         vT = tl.load(vT_ptrs + block_sparse_offset)
         qk = tl.dot(q, kT)

csrc/attn/video_sparse_attn/vsa/block_sparse_h100.cu

@@ -672,32 +672,23 @@ block_sparse_attention_forward(
     torch::Tensor v, 
     torch::Tensor q2k_block_sparse_index, 
     torch::Tensor q2k_block_sparse_num, 
-    torch::Tensor kv_block_size
+    torch::Tensor block_size
 )
 {
     CHECK_INPUT(q);
     CHECK_INPUT(k);
     CHECK_INPUT(v);
 
-    // q shape: (batch, qo_heads, q_seq_len, head_dim)
-    // k shape: (batch, kv_heads, kv_seq_len, head_dim)
-    // v shape: (batch, kv_heads, kv_seq_len, head_dim)
-    // q2k_block_sparse_index shape: (batch, qo_heads, num_q_blocks, max_kv_blocks_per_q)
-    // q2k_block_sparse_num shape: (batch, qo_heads, num_q_blocks)
-    // kv_block_size shape: (num_kv_blocks) This does not need other dimensions because across all batch/heads the padding is the same.
-    
     auto batch    = q.size(0);
-    auto q_seq_len  = q.size(2);
-    auto kv_seq_len = k.size(2);
+    auto seq_len  = q.size(2); 
     auto head_dim = q.size(3); 
     auto qo_heads = q.size(1);
     auto kv_heads = k.size(1);
     auto max_kv_blocks_per_q = q2k_block_sparse_index.size(3);
-    auto num_q_blocks = q2k_block_sparse_index.size(2);
-    auto num_kv_blocks = kv_block_size.size(0);
+    auto num_q_blocks = block_size.size(0);
     TORCH_CHECK(batch==1, "Batch size dim will be removed in the future, please set batch to 1");
-    TORCH_CHECK(num_q_blocks * BLOCK_M == q_seq_len, "This kernel supports variable q block size, but it assumes the input sequence is properly padded.");
-    TORCH_CHECK(num_kv_blocks * BLOCK_M == kv_seq_len, "This kernel supports variable kv block size, but it assumes the input sequence is properly padded.");
+    TORCH_CHECK(num_q_blocks * 64 == seq_len, "This kernel supports variable block size, but it assumes the input sequence is properly padded.");
+    TORCH_CHECK(num_q_blocks == q2k_block_sparse_index.size(2), "Number of Q blocks does not match between q2k_block_sparse_index and block_size");
     // check to see that these dimensions match for all inputs
     TORCH_CHECK(q.size(0) == batch, "Q batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(k.size(0) == batch, "K batch dimension - idx 0 - must match for all inputs");
@@ -705,8 +696,11 @@ block_sparse_attention_forward(
     TORCH_CHECK(q2k_block_sparse_index.size(0) == batch, "q2k_block_sparse_index batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(q2k_block_sparse_num.size(0) == batch, "q2k_block_sparse_num batch dimension - idx 0 - must match for all inputs");
 
-    TORCH_CHECK(v.size(2) == kv_seq_len, "V sequence length dimension - idx 2 - must match K inputs");
-    TORCH_CHECK(q2k_block_sparse_num.size(2) == num_q_blocks, "q2k_block_sparse_num idx 2 - must match num_q_blocks");
+    TORCH_CHECK(q.size(2) == seq_len, "Q sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(k.size(2) == seq_len, "K sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(v.size(2) == seq_len, "V sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(q2k_block_sparse_index.size(2) == seq_len / BLOCK_M, "q2k_block_sparse_index idx 2 - must match seq_len / BLOCK_M");
+    TORCH_CHECK(q2k_block_sparse_num.size(2) == seq_len / BLOCK_M, "q2k_block_sparse_num idx 2 - must match seq_len / BLOCK_M");
 
 
     TORCH_CHECK(q.size(3) == head_dim, "Q head dimension - idx 3 - must match for all non-vector inputs");
@@ -733,12 +727,12 @@ block_sparse_attention_forward(
     // for the returned outputs
     torch::Tensor o     = torch::empty({static_cast<const uint>(batch), 
                                         static_cast<const uint>(qo_heads), 
-                                        static_cast<const uint>(q_seq_len), 
+                                        static_cast<const uint>(seq_len), 
                                         static_cast<const uint>(head_dim)}, v.options());
     
     torch::Tensor l_vec = torch::empty({static_cast<const uint>(batch), 
                                         static_cast<const uint>(qo_heads), 
-                                        static_cast<const uint>(q_seq_len), 
+                                        static_cast<const uint>(seq_len), 
                                         static_cast<const uint>(1)}, 
                                         torch::TensorOptions().dtype(torch::kFloat).device(q.device()).memory_format(at::MemoryFormat::Contiguous));
         
@@ -768,11 +762,11 @@ block_sparse_attention_forward(
 
         using globals      = fwd_globals<64>;
 
-        q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(q_seq_len)};
-        o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
+        q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(seq_len)};
+        o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
 
         globals g{
             qg_arg, 
@@ -780,17 +774,17 @@ block_sparse_attention_forward(
             vg_arg, 
             lg_arg, 
             og_arg, 
-            static_cast<int>(q_seq_len), 
+            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()),
-            reinterpret_cast<int32_t*>(kv_block_size.data_ptr())
+            reinterpret_cast<int32_t*>(block_size.data_ptr())
         };
 
         constexpr int mem_size = 54000;
 
-        dim3 grid(q_seq_len/(BLOCK_M), qo_heads, batch);
+        dim3 grid(seq_len/(64), qo_heads, batch);
 
         cudaFuncSetAttribute(
             fwd_attend_ker<64>,
@@ -819,11 +813,11 @@ block_sparse_attention_forward(
 
         using globals      = fwd_globals<128>;
 
-        q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(q_seq_len)};
-        o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
+        q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(seq_len)};
+        o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
 
         globals g{
             qg_arg, 
@@ -831,17 +825,17 @@ block_sparse_attention_forward(
             vg_arg, 
             lg_arg, 
             og_arg, 
-            static_cast<int>(q_seq_len), 
+            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()),
-            reinterpret_cast<int32_t*>(kv_block_size.data_ptr())
+            reinterpret_cast<int32_t*>(block_size.data_ptr())
         };
 
         constexpr int mem_size = 54000;
 
-        dim3 grid(q_seq_len/(BLOCK_M), qo_heads, batch);
+        dim3 grid(seq_len/(64), qo_heads, batch);
 
         cudaFuncSetAttribute(
             fwd_attend_ker<128>,
@@ -868,7 +862,7 @@ block_sparse_attention_backward(torch::Tensor q,
                    torch::Tensor og,
                    torch::Tensor k2q_block_sparse_index,
                    torch::Tensor k2q_block_sparse_num,
-                   torch::Tensor kv_block_size)
+                   torch::Tensor block_size)
 {
     CHECK_INPUT(q);
     CHECK_INPUT(k);
@@ -877,23 +871,11 @@ block_sparse_attention_backward(torch::Tensor q,
     CHECK_INPUT(o);
     CHECK_INPUT(og);
 
-    // q: [batch, qo_heads, q_seq_len, head_dim]
-    // k: [batch, kv_heads, kv_seq_len, head_dim]
-    // v: [batch, kv_heads, kv_seq_len, head_dim]
-    // o: [batch, qo_heads, q_seq_len, head_dim]
-    // l_vec: [batch, qo_heads, q_seq_len, 1]
-    // og: [batch, qo_heads, q_seq_len, head_dim]
-    // k2q_block_sparse_index: [batch, kv_heads, num_kv_blocks, max_num_q_blocks]
-    // k2q_block_sparse_num: [batch, kv_heads, num_kv_blocks]
-    // kv_block_size: [num_kv_blocks]
-
     auto batch    = q.size(0);
-    auto q_seq_len  = q.size(2);
-    auto kv_seq_len = k.size(2);
+    auto seq_len  = q.size(2);
     auto head_dim = q.size(3);
     auto max_q_blocks_per_kv = k2q_block_sparse_index.size(3);
-    auto num_kv_blocks = kv_block_size.size(0);
-    TORCH_CHECK(k2q_block_sparse_index.size(2) == num_kv_blocks, "k2q_block_sparse_index.size(2) must match num_kv_blocks (kv_block_size.size(0))");
+    TORCH_CHECK(k2q_block_sparse_index.size(2) == block_size.size(0), "k2q_block_sparse_index.size(2) must match block_size.size(0)");
     // check to see that these dimensions match for all inputs
     TORCH_CHECK(q.size(0)     == batch, "Q  batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(k.size(0)     == batch, "K  batch dimension - idx 0 - must match for all inputs");
@@ -904,18 +886,23 @@ block_sparse_attention_backward(torch::Tensor q,
     TORCH_CHECK(k2q_block_sparse_index.size(0) == batch, "k2q_block_sparse_index batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(k2q_block_sparse_num.size(0) == batch, "k2q_block_sparse_num batch dimension - idx 0 - must match for all inputs");
 
-    TORCH_CHECK(v.size(2)     == kv_seq_len, "V  sequence length dimension - idx 2 - must match K sequence length");
-    TORCH_CHECK(l_vec.size(2) == q_seq_len, "L  sequence length dimension - idx 2 - must match Q sequence length");
-    TORCH_CHECK(o.size(2)     == q_seq_len, "O  sequence length dimension - idx 2 - must match Q sequence length");
-    TORCH_CHECK(og.size(2)    == q_seq_len, "OG sequence length dimension - idx 2 - must match Q sequence length");
-    TORCH_CHECK(k2q_block_sparse_index.size(2) == num_kv_blocks, "k2q_block_sparse_index idx 2 - must match num_kv_blocks (kv_block_size.size(0))");
-    TORCH_CHECK(k2q_block_sparse_num.size(2) == num_kv_blocks, "k2q_block_sparse_num idx 2 - must match num_kv_blocks (kv_block_size.size(0))");
+
+    TORCH_CHECK(q.size(2)     == seq_len, "Q  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(k.size(2)     == seq_len, "K  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(v.size(2)     == seq_len, "V  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(l_vec.size(2) == seq_len, "L  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(o.size(2)     == seq_len, "O  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(og.size(2)    == seq_len, "OG sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(k2q_block_sparse_index.size(2) == seq_len / BLOCK_N, "k2q_block_sparse_index idx 2 - must match seq_len / BLOCK_N");
+    TORCH_CHECK(k2q_block_sparse_num.size(2) == seq_len / BLOCK_N, "k2q_block_sparse_num idx 2 - must match seq_len / BLOCK_N");
 
     TORCH_CHECK(q.size(3)  == head_dim, "Q  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(k.size(3)  == head_dim, "K  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(v.size(3)  == head_dim, "V  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(o.size(3)  == head_dim, "O  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(og.size(3) == head_dim, "OG head dimension - idx 3 - must match for all non-vector inputs");
+    
+
 
     auto qo_heads = q.size(1);
     auto kv_heads = k.size(1);
@@ -942,20 +929,20 @@ block_sparse_attention_backward(torch::Tensor q,
 
     torch::Tensor qg = torch::zeros({static_cast<const uint>(batch), 
                                      static_cast<const uint>(qo_heads), 
-                                     static_cast<const uint>(q_seq_len), 
+                                     static_cast<const uint>(seq_len), 
                                      static_cast<const uint>(head_dim)},   l_vec.options());
     torch::Tensor kg = torch::zeros({static_cast<const uint>(batch), 
                                      static_cast<const uint>(kv_heads), 
-                                     static_cast<const uint>(kv_seq_len), 
+                                     static_cast<const uint>(seq_len), 
                                      static_cast<const uint>(head_dim)},   l_vec.options());
     torch::Tensor vg = torch::zeros({static_cast<const uint>(batch), 
                                      static_cast<const uint>(kv_heads), 
-                                     static_cast<const uint>(kv_seq_len), 
+                                     static_cast<const uint>(seq_len), 
                                      static_cast<const uint>(head_dim)},   l_vec.options());
     
     torch::Tensor d_vec = torch::empty({static_cast<const uint>(batch), 
                                         static_cast<const uint>(qo_heads), 
-                                        static_cast<const uint>(q_seq_len), 
+                                        static_cast<const uint>(seq_len), 
                                         static_cast<const uint>(1)},       l_vec.options());
 
     float*         qg_ptr = qg.data_ptr<float>();
@@ -984,7 +971,7 @@ block_sparse_attention_backward(torch::Tensor q,
     //  cudaStreamSynchronize(stream);
 
     // TORCH_CHECK(seq_len % (4*kittens::TILE_DIM*4) == 0, "sequence length must be divisible by 256");
-    dim3 grid_bwd(q_seq_len/(PREP_NUM_WARPS*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
+    dim3 grid_bwd(seq_len/(PREP_NUM_WARPS*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
 
     if (head_dim == 64)  {
         using og_tile = st_bf<4*16, 64>;
@@ -997,9 +984,9 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_prep_globals = bwd_prep_globals<64>;
 
-        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
 
         bwd_prep_globals bwd_g{prep_og_arg, prep_o_arg, prep_d_arg};
 
@@ -1036,15 +1023,15 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_global_args = bwd_globals<64>;
 
-        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
-        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
 
         bwd_global_args bwd_global{bwd_q_arg, 
                         bwd_k_arg, 
@@ -1055,14 +1042,14 @@ block_sparse_attention_backward(torch::Tensor q,
                         bwd_vg_arg, 
                         bwd_l_arg, 
                         bwd_d_arg, 
-                        static_cast<int>(kv_seq_len), // N is not used in the kernel
+                        static_cast<int>(seq_len), 
                         static_cast<int>(hr),
                         static_cast<int>(max_q_blocks_per_kv),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_index.data_ptr()),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_num.data_ptr()),
-                        reinterpret_cast<int32_t*>(kv_block_size.data_ptr())};
+                        reinterpret_cast<int32_t*>(block_size.data_ptr())};
 
-        dim3 grid_bwd_2(kv_seq_len/BLOCK_N, qo_heads, batch);
+        dim3 grid_bwd_2(seq_len/64, qo_heads, batch);
         threads = 128;
 
         //cudadevicesynchronize();
@@ -1101,9 +1088,9 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_prep_globals = bwd_prep_globals<128>;
 
-        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
 
         bwd_prep_globals bwd_g{prep_og_arg, prep_o_arg, prep_d_arg};
 
@@ -1140,15 +1127,15 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_global_args = bwd_globals<128>;
 
-        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
-        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
 
         bwd_global_args bwd_global{bwd_q_arg, 
                         bwd_k_arg, 
@@ -1159,14 +1146,14 @@ block_sparse_attention_backward(torch::Tensor q,
                         bwd_vg_arg, 
                         bwd_l_arg, 
                         bwd_d_arg, 
-                        static_cast<int>(kv_seq_len), // N is not used in the kernel
+                        static_cast<int>(seq_len), 
                         static_cast<int>(hr),
                         static_cast<int>(max_q_blocks_per_kv),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_index.data_ptr()),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_num.data_ptr()),
-                        reinterpret_cast<int32_t*>(kv_block_size.data_ptr())};
+                        reinterpret_cast<int32_t*>(block_size.data_ptr())};
 
-        dim3 grid_bwd_2(kv_seq_len/BLOCK_N, qo_heads, batch);
+        dim3 grid_bwd_2(seq_len/64, qo_heads, batch);
         threads = 128;
 
         //cudadevicesynchronize();

docker/Dockerfile.python3.10

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.8.0-cudnn-devel-ubuntu22.04
+FROM nvidia/cuda:12.8.0-devel-ubuntu22.04
 
 ENV DEBIAN_FRONTEND=noninteractive
 
@@ -43,7 +43,7 @@ RUN source $HOME/.local/bin/env && \
     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 https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3%2Bcu128torch2.9-cp310-cp310-linux_x86_64.whl
+    uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation
 
 COPY . .
 

docker/Dockerfile.python3.11

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.8.0-cudnn-devel-ubuntu22.04
+FROM nvidia/cuda:12.8.0-devel-ubuntu22.04
 
 ENV DEBIAN_FRONTEND=noninteractive
 
@@ -43,7 +43,7 @@ RUN source $HOME/.local/bin/env && \
     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 https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3%2Bcu128torch2.9-cp311-cp311-linux_x86_64.whl
+    uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation
 
 COPY . .
 

docker/Dockerfile.python3.12

@@ -43,7 +43,7 @@ RUN source $HOME/.local/bin/env && \
     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 https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3%2Bcu128torch2.9-cp312-cp312-linux_x86_64.whl
+    uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation
 
 COPY . .
 

docs/Makefile

@@ -0,0 +1,26 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+clean:
+	@$(SPHINXBUILD) -M clean "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	rm -rf "$(SOURCEDIR)/getting_started/examples"
+	rm -rf "$(SOURCEDIR)/inference/examples"
+	rm -rf "$(SOURCEDIR)/training/examples"

docs/README.md

@@ -1,39 +1,20 @@
-# FastVideo Documentation
+# FastVideo documents
 
-This directory contains the FastVideo documentation built with MkDocs.
-
-## Build the docs locally
+## Build the docs
 
 ```bash
-# Install dependencies
-pip install -r docs/requirements-mkdocs.txt
+# Install dependencies.
+pip install -r requirements-docs.txt
 
-# Serve docs with live reload (recommended for development)
-mkdocs serve
-
-# Or build static site
-mkdocs build
+# Build the docs.
+make clean
+make html
 ```
 
-## View the docs
-
-### Development server (with live reload)
+## Open the docs with your browser
 
 ```bash
-mkdocs serve
+python -m http.server -d build/html/
 ```
 
-Then open your browser to: http://127.0.0.1:8000
-
-### Static build
-
-```bash
-mkdocs build
-python -m http.server -d site/
-```
-
-Then open your browser to: http://localhost:8000
-
-## Automatic Deployment
-
-Documentation is automatically built and deployed to GitHub Pages when changes are pushed to the `main` branch via the `.github/workflows/docs.yml` workflow.
+Launch your browser and open localhost:8000.

docs/api/fastvideo.md

@@ -1,248 +0,0 @@
-# FastVideo API Reference
-
-This page contains the complete API reference for the FastVideo library.
-
-## fastvideo
-
-### Modules
-
-| Name | Description |
-|------|-------------|
-| [attention](#fastvideoattention) | Attention mechanisms and backends for video generation |
-| [configs](#fastvideoconfigs) | Configuration classes for pipelines, models, and sampling |
-| [distributed](#fastvideodistributed) | Distributed execution and communication utilities |
-| [entrypoints](#fastvideoentrypoints) | Main API entry points for video generation |
-| [models](#fastvideomodels) | Model implementations (transformers, VAEs, schedulers) |
-| [pipelines](#fastvideopipelines) | Core pipeline classes for video diffusion |
-| [training](#fastvideotraining) | Training utilities and helpers |
-| [workflow](#fastvideoworkflow) | Workflow management and orchestration |
-| [dataset](#fastvideodataset) | Dataset handling and preprocessing |
-| [layers](#fastvideolayers) | Custom neural network layers |
-| [platforms](#fastvideoplatforms) | Platform-specific implementations |
-| [utils](#fastvideoutils) | Utility functions and helpers |
-| [worker](#fastvideoworker) | Execution workers for video generation |
-
-## fastvideo.attention
-
-::: fastvideo.attention
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      show_submodules: true
-      heading_level: 3
-
-## fastvideo.configs
-
-::: fastvideo.configs
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      show_submodules: true
-      heading_level: 3
-
-### Submodules
-
-#### fastvideo.configs.pipelines
-
-::: fastvideo.configs.pipelines
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      heading_level: 4
-
-#### fastvideo.configs.models
-
-::: fastvideo.configs.models
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      heading_level: 4
-
-#### fastvideo.configs.sample
-
-::: fastvideo.configs.sample
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      heading_level: 4
-
-## fastvideo.distributed
-
-::: fastvideo.distributed
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      show_submodules: true
-      heading_level: 3
-
-## fastvideo.entrypoints
-
-::: fastvideo.entrypoints
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      show_submodules: true
-      heading_level: 3
-
-## fastvideo.models
-
-::: fastvideo.models
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      show_submodules: true
-      heading_level: 3
-
-### Submodules
-
-#### fastvideo.models.registry
-
-::: fastvideo.models.registry
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      heading_level: 4
-
-#### fastvideo.models.loader
-
-::: fastvideo.models.loader
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      heading_level: 4
-
-## fastvideo.pipelines
-
-::: fastvideo.pipelines
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      show_submodules: true
-      heading_level: 3
-
-### Submodules
-
-#### fastvideo.pipelines.composed_pipeline_base
-
-::: fastvideo.pipelines.composed_pipeline_base
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      heading_level: 4
-
-#### fastvideo.pipelines.lora_pipeline
-
-::: fastvideo.pipelines.lora_pipeline
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      heading_level: 4
-
-#### fastvideo.pipelines.pipeline_batch_info
-
-::: fastvideo.pipelines.pipeline_batch_info
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      heading_level: 4
-
-#### fastvideo.pipelines.pipeline_registry
-
-::: fastvideo.pipelines.pipeline_registry
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      heading_level: 4
-
-#### fastvideo.pipelines.stages
-
-::: fastvideo.pipelines.stages
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      heading_level: 4
-
-## fastvideo.training
-
-::: fastvideo.training
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      show_submodules: true
-      heading_level: 3
-
-## fastvideo.workflow
-
-::: fastvideo.workflow
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      show_submodules: true
-      heading_level: 3
-
-## fastvideo.dataset
-
-::: fastvideo.dataset
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      show_submodules: true
-      heading_level: 3
-
-## fastvideo.layers
-
-::: fastvideo.layers
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      show_submodules: true
-      heading_level: 3
-
-## fastvideo.platforms
-
-::: fastvideo.platforms
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      show_submodules: true
-      heading_level: 3
-
-## fastvideo.utils
-
-::: fastvideo.utils
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      heading_level: 3
-
-## fastvideo.worker
-
-::: fastvideo.worker
-    options:
-      show_source: true
-      show_root_heading: true
-      show_root_toc_entry: true
-      show_submodules: true
-      heading_level: 3

docs/api/summary.md

@@ -1,27 +0,0 @@
-# API Summary
-
-This page provides a quick overview of the main FastVideo API components.
-
-## Video Generator
-
-::: fastvideo.VideoGenerator
-    options:
-      show_root_heading: false
-      show_source: false
-      heading_level: 3
-
-## Initialization Configuration
-
-::: fastvideo.PipelineConfig
-    options:
-      show_root_heading: false
-      show_source: false
-      heading_level: 3
-
-## Sampling Configuration
-
-::: fastvideo.SamplingParam
-    options:
-      show_root_heading: false
-      show_source: false
-      heading_level: 3

docs/assets/custom.css

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docs/contributing/profiling.md

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-# Profiling FastVideo
-
-!!! warning
-    Profiling is only intended for FastVideo developers and maintainers to understand the proportion of time spent in different parts of the codebase. **FastVideo end-users should never turn on profiling** as it will significantly slow down the inference.
-
-## Profiling with PyTorch
-
-FastVideo exposes a process-wide torch profiler that you can enable via environment variables. Set `FASTVIDEO_TORCH_PROFILER_DIR` to an absolute directory path to start collecting traces, and specify the regions you want recorded with `FASTVIDEO_TORCH_PROFILE_REGIONS`:
-
-```bash
-FASTVIDEO_TORCH_PROFILER_DIR=/mnt/traces/fastvideo \
-FASTVIDEO_TORCH_PROFILE_REGIONS="profiler_region_model_loading,profiler_region_training_step"
-```
-
-All profiled regions must be registered in `fastvideo.profiler`; the current list includes:
-
-- `profiler_region_model_loading` — pipeline/module loading
-- `profiler_region_inference_pre_denoising`
-- `profiler_region_inference_denoising`
-- `profiler_region_inference_post_denoising`
-- `profiler_region_training_checkpoint_saving`
-- `profiler_region_training_dit`
-- `profiler_region_training_validation`
-- `profiler_region_training_epoch`
-- `profiler_region_training_step`
-- `profiler_region_training_backward`
-- `profiler_region_training_optimizer`
-- `profiler_region_distillation_teacher_forward`
-- `profiler_region_distillation_student_forward`
-- `profiler_region_distillation_loss`
-- `profiler_region_distillation_update`
-
-While profiling is enabled, FastVideo records additional annotations:
-
-- `fastvideo.region::<name>` spans are emitted when entering a region.
-- `fastvideo.profiler.enable_collection` / `fastvideo.profiler.disable_collection` events mark when torch profiler collection is toggled on or off.
-
-Only one profiler instance is created per process; subsequent pipelines reuse the same controller. If you set `FASTVIDEO_TORCH_PROFILE_REGIONS` incorrectly (e.g. misspelled name), FastVideo logs a warning and ignores that entry.
-
-Additional knobs:
-
-- `FASTVIDEO_TORCH_PROFILER_RECORD_SHAPES`
-- `FASTVIDEO_TORCH_PROFILER_WITH_PROFILE_MEMORY`
-- `FASTVIDEO_TORCH_PROFILER_WITH_STACK`
-- `FASTVIDEO_TORCH_PROFILER_WITH_FLOPS`
-
-Traces can be visualized using <https://ui.perfetto.dev/>.
-
-### Best Practices
-
-- Keep the profiled step count small; traces can be large and slow down job shutdown while the profiler flushes data.
-- After profiling, clean up trace directories to avoid filling disks.
-- When adding new regions, register them in `fastvideo.profiler` and wrap the corresponding code block with `with self.profiler_controller.region("your_region"):` or the `@profile_region` decorator.

docs/contributing/testing.md

@@ -1,129 +0,0 @@
-# Testing in FastVideo
-
-This guide explains how to add and run tests in FastVideo. The testing suite is divided into several categories to ensure correctness across components, training workflows, and inference quality.
-
-## Test Types
-
-* **Unit Tests**: Located in `fastvideo/tests/dataset`, `fastvideo/tests/entrypoints`, and `fastvideo/tests/workflow`. These test individual functions and classes.
-* **Component Tests**: Located in `fastvideo/tests/encoders`, `fastvideo/tests/transformers`, and `fastvideo/tests/vaes`. These verify the loading and basic functionality of model components.
-* **SSIM Tests**: Located in `fastvideo/tests/ssim`. These are regression tests that compare generated videos against reference videos using the Structural Similarity Index Measure (SSIM) to detect quality degradation.
-* **Training Tests**: Located in `fastvideo/tests/training`. These validate training loops, loss calculations, and specific training techniques like LoRA, Distillation, and VSA.
-* **Inference Tests**: Located in `fastvideo/tests/inference`. These test specialized inference pipelines and optimizations (e.g., STA, V-MoBA).
-
-For now, we will focus on **SSIM Tests**.
-
-## SSIM Tests
-
-SSIM tests are located in `fastvideo/tests/ssim`. These tests generate videos using specific models and parameters, and compare them against reference videos to ensure that changes in the codebase do not degrade generation quality or alter the output unexpectedly.
-
-!!! note
-    If you are adding an SSIM test, this serves as a safeguard. Any future code changes that break or cause errors with the specific arguments and configurations you defined will trigger a failure. Therefore, it is important to include multiple settings and arguments that cover the core features of your new pipeline to ensure robust regression testing.
-
-### Directory Structure
-
-```
-fastvideo/tests/ssim/
-├── <GPU>_reference_videos/   # Reference videos organized by GPU type (e.g., L40S_reference_videos)
-│   ├── <Model_Name>/
-│   │   ├── <Backend>/        # e.g., FLASH_ATTN, TORCH_SDPA
-│   │   │   └── <Video_File>
-├── test_causal_similarity.py
-├── test_inference_similarity.py
-├── update_reference_videos.sh
-└── ...
-```
-
-### Adding a New SSIM Test
-
-To add a new SSIM test, follow these steps:
-
-1. **Create or Update a Test File**: You can add a new test function to an existing file (like `test_inference_similarity.py`) or create a new one if testing a distinct category of models.
-
-2. **Define Model Parameters**: Define the configuration for the model you want to test. This includes model path, dimensions, inference steps, and other generation parameters. **Note:** Consider using lower `num_inference_steps` or reduced resolution (e.g., 480p instead of 720p) to keep test execution time reasonable, provided it doesn't compromise the test's ability to detect regression.
-
-   ```python
-   MY_MODEL_PARAMS = {
-       "num_gpus": 1,
-       "model_path": "organization/model-name",
-       "height": 480,
-       "width": 832,
-       "num_frames": 45,
-       "num_inference_steps": 20,
-       # ... other parameters
-   }
-   ```
-
-3. **Implement the Test Function**:
-   * Use `pytest.mark.parametrize` to run the test with different prompts, backends, and models.
-   * Set the attention backend environment variable.
-   * Initialize the `VideoGenerator`.
-   * Generate the video.
-   * Compare the generated video with the reference video using `compute_video_ssim_torchvision`.
-
-   Example structure:
-
-   ```python
-   @pytest.mark.parametrize("prompt", TEST_PROMPTS)
-   @pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN"])
-   def test_my_model_similarity(prompt, ATTENTION_BACKEND):
-       # Setup output directories
-       # ...
-
-       # Initialize Generator
-       generator = VideoGenerator.from_pretrained(...)
-       generator.generate_video(prompt, ...)
-
-       # Compare with Reference
-       ssim_values = compute_video_ssim_torchvision(reference_path, generated_path, use_ms_ssim=True)
-        assert ssim_values[0] >= 0.98  # Threshold
-    ```
-
-4. **Reference Videos**:
-   * When running the test for the first time (or when updating the reference), the test will fail because the reference video is missing. The generated video will be saved in `fastvideo/tests/ssim/generated_videos`.
-   * Inspect the generated video to ensure it meets quality expectations.
-   * Move the generated video to the appropriate reference folder: `fastvideo/tests/ssim/<GPU>_reference_videos/<Model>/<Backend>/`.
-   * You can use the helper script `update_reference_videos.sh` to automate copying videos from `generated_videos` to `L40S_reference_videos`. Note: Check the script to ensure paths match your environment (it defaults to `L40S_reference_videos`).
-
-### Running Tests Locally
-
-To run the SSIM tests locally:
-
-```bash
-pytest fastvideo/tests/ssim/ -vs
-```
-
-Ensure you have the necessary GPUs available as defined in your test parameters.
-
-## Modal Workflow
-
-FastVideo uses [Modal](https://modal.com/) for running tests in a CI environment. The workflow scripts are located in `fastvideo/tests/modal/`.
-
-### `pr_test.py`
-
-The main entry point for CI tests is `fastvideo/tests/modal/pr_test.py`. This script defines Modal functions that execute the pytest suites on specific hardware (e.g., L40S, H100).
-
-### Updating Modal Configuration
-
-If you add a new test that requires:
-* **Different GPU Hardware**: You may need to change the `@app.function(gpu=...)` decorator.
-* **Longer Execution Time**: Increase the `timeout` parameter.
-* **New Environment Variables/Secrets**: Add them to `secrets=[...]` or the image environment. For example, if your model is gated on Hugging Face, ensure `HF_API_KEY` is passed.
-
-For SSIM tests, the `run_ssim_tests` function in `pr_test.py` currently runs:
-
-```python
-@app.function(gpu="L40S:2", image=image, timeout=2700, secrets=[modal.Secret.from_dict({"HF_API_KEY": os.environ.get("HF_API_KEY", "")})])
-def run_ssim_tests():
-    run_test("hf auth login --token $HF_API_KEY && pytest ./fastvideo/tests/ssim -vs")
-```
-
-If your new test file is inside `fastvideo/tests/ssim`, it will automatically be picked up by this command. However, ensure that the `gpu="L40S:2"` configuration is sufficient for your model. If your model requires more GPUs (e.g., 4 or 8), you might need to create a separate Modal function or update the existing one.
-
-### Workflow Scripts
-
-The shell script that triggers these tests in the CI pipeline is located at `.buildkite/scripts/pr_test.sh`. If you add a new test category (e.g., a new folder outside of `ssim`), you will need to:
-1. Add a new function in `fastvideo/tests/modal/pr_test.py`.
-2. Add a new case in `.buildkite/scripts/pr_test.sh` to handle the new test type.
-
-!!! note
-    If you are a maintainer, you'll need to finally manually update the workflow script in Buildkite. Otherwise, a maintainer will help you update.

docs/examples/examples_index.md

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

docs/getting_started/installation.md

@@ -1,41 +0,0 @@
-
-# 🔧 Installation
-
-FastVideo supports the following hardware platforms:
-
-- [NVIDIA CUDA](installation/gpu.md)
-- [Apple silicon](installation/mps.md)
-
-## Quick Installation
-
-### Using pip
-
-```bash
-pip install fastvideo
-```
-
-### Using conda
-
-```bash
-conda install -c conda-forge fastvideo
-```
-
-### From source
-
-```bash
-git clone https://github.com/hao-ai-lab/FastVideo.git
-cd FastVideo
-pip install -e .
-```
-
-## Hardware Requirements
-
-- **NVIDIA GPUs**: CUDA 11.8+ with compute capability 7.0+
-- **Apple Silicon**: macOS 12.0+ with M1/M2/M3 chips
-- **CPU**: x86_64 architecture (for CPU-only inference)
-
-## Next Steps
-
-- [Quick Start Guide](quick_start.md) - Get started with your first video generation
-- [Configuration](../inference/configuration.md) - Learn about configuration options
-- [Examples](../inference/examples/) - Explore example scripts and notebooks

docs/getting_started/quick_start.md

@@ -1,55 +0,0 @@
-# 🚀 Quick Start
-
-Get up and running with FastVideo in minutes!
-
-## Installation
-
-First, install FastVideo:
-
-```bash
-pip install fastvideo
-```
-
-## Basic Usage
-
-### Text-to-Video Generation
-
-```python
-from fastvideo import FastVideoPipeline
-
-# Initialize the pipeline
-pipe = FastVideoPipeline.from_pretrained("wan2.1-t2v-1.3B")
-
-# Generate a video
-prompt = "A cat playing with a ball of yarn"
-video = pipe(prompt, num_frames=16, height=512, width=512)
-
-# Save the video
-video.save("output.mp4")
-```
-
-### Image-to-Video Generation
-
-```python
-from fastvideo import FastVideoPipeline
-from PIL import Image
-
-# Load an image
-image = Image.open("input.jpg")
-
-# Initialize the pipeline
-pipe = FastVideoPipeline.from_pretrained("wan2.1-i2v-14B-480p")
-
-# Generate a video from the image
-video = pipe(image, num_frames=16, height=480, width=480)
-
-# Save the video
-video.save("output.mp4")
-```
-
-## Next Steps
-
-- [Installation Guide](installation.md) - Detailed installation instructions
-- [Configuration](../inference/configuration.md) - Learn about configuration options
-- [Examples](../inference/examples/) - Explore more examples
-- [Optimizations](../inference/optimizations.md) - Performance optimization tips

docs/getting_started/v1_api.md

@@ -1,42 +0,0 @@
-# V1 API
-
-FastVideo's V1 API provides a streamlined interface for video generation tasks with powerful customization options. This page documents the primary components of the API.
-
-## Video Generator
-
-This class will be the primary Python API for generating videos and images.
-
-::: fastvideo.entrypoints.video_generator.VideoGenerator
-    options:
-      show_root_heading: true
-      show_source: false
-      members:
-        - from_pretrained
-      heading_level: 3
-
-`VideoGenerator.from_pretrained()` should be the primary way of creating a new video generator.
-
-## Configuring FastVideo
-
-The following two classes `PipelineConfig` and `SamplingParam` are used to configure initialization and sampling parameters, respectively.
-
-### PipelineConfig
-
-::: fastvideo.configs.pipelines.base.PipelineConfig
-    options:
-      show_root_heading: true
-      show_source: false
-      members:
-        - from_pretrained
-        - dump_to_json
-      heading_level: 4
-
-### SamplingParam
-
-::: fastvideo.configs.sample.base.SamplingParam
-    options:
-      show_root_heading: true
-      show_source: false
-      members:
-        - from_pretrained
-      heading_level: 4

docs/inference/support_matrix.md

@@ -1,66 +0,0 @@
-# Compatibility Matrix
-
-The table below shows every supported model and optimizations supported for them.
-
-The symbols used have the following meanings:
-
-- ✅ = Full compatibility
-- ❌ = No compatibility
-- ⭕ = Does not apply to this model
-
-## Models x Optimization
-
-The `HuggingFace Model ID` can be directly pass to `from_pretrained()` methods and FastVideo will use the optimal default parameters when initializing and generating videos.
-
-<style>
-  /* Target tables in this section */
-  #models-x-optimization + p + table {
-    display: block;
-    overflow-x: auto;
-    width: 100%;
-    font-size: 0.85rem;
-  }
-  
-  #models-x-optimization + p + table td,
-  #models-x-optimization + p + table th {
-    text-align: center;
-    white-space: nowrap;
-    padding: 0.5em;
-  }
-  
-  /* First two columns can wrap */
-  #models-x-optimization + p + table td:nth-child(1),
-  #models-x-optimization + p + table td:nth-child(2) {
-    white-space: normal;
-    min-width: 120px;
-  }
-  
-  #models-x-optimization + p + table td:nth-child(2) code {
-    font-size: 0.75rem;
-  }
-</style>
-
-| Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn | Sage Attn | VSA |
-|------------|---------------------|-------------|----------|-------------------|-----------|-----|
-| FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ✅ |
-| FastWan2.2 TI2V 5B Full Attn* | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ |
-| Wan2.2 TI2V 5B | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 720P | ⭕ | ⭕ | ✅ | ⭕ |
-| Wan2.2 T2V A14B | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ |
-| Wan2.2 I2V A14B | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ |
-| HunyuanVideo | `hunyuanvideo-community/HunyuanVideo` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ |
-| FastHunyuan | `FastVideo/FastHunyuan-diffusers` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ |
-| Wan2.1 T2V 1.3B | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ |
-| Wan2.1 T2V 14B | `Wan-AI/Wan2.1-T2V-14B-Diffusers` | 480P, 720P | ✅ | ✅* | ✅ | ⭕ |
-| Wan2.1 I2V 480P | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ |
-| Wan2.1 I2V 720P | `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` | 720P | ✅ | ✅ | ✅ | ⭕ |
-| StepVideo T2V | `FastVideo/stepvideo-t2v-diffusers` | 768px768px204f<br>544px992px204f<br>544px992px136f | ❌ | ❌ | ✅ | ⭕ |
-
-**Note**: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.
-
-## Special requirements
-
-### StepVideo T2V
-- The self-attention in text-encoder (step_llm) only supports CUDA capabilities sm_80 sm_86 and sm_90
-
-### Sliding Tile Attention
-- Currently only Hopper GPUs (H100s) are supported.

docs/make.bat

@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

docs/requirements-docs.txt

@@ -0,0 +1,15 @@
+sphinx==7.4.7
+sphinx-argparse==0.5.2
+sphinx-autodoc2==0.5.0
+sphinx-book-theme==1.1.4
+sphinx-copybutton==0.5.2
+sphinx-design==0.6.1
+sphinx-togglebutton==0.3.2
+myst-parser==3.0.1
+msgspec
+commonmark # Required by sphinx-argparse when using :markdownhelp:
+
+# packages to install to build the documentation
+cachetools
+# -f https://download.pytorch.org/whl/cpu
+torch

docs/seed_parameter_behavior.md

@@ -0,0 +1,51 @@
+# Seed Parameter Behavior in vLLM
+
+## Overview
+
+The `seed` parameter in vLLM is used to control the random states for various random number generators. This parameter can affect the behavior of random operations in user code, especially when working with models in vLLM.
+
+## Default Behavior
+
+By default, the `seed` parameter is set to `None`. When the `seed` parameter is `None`, the global random states for `random`, `np.random`, and `torch.manual_seed` are not set. This means that the random operations will behave as expected, without any fixed random states.
+
+## Specifying a Seed
+
+If a specific seed value is provided, the global random states for `random`, `np.random`, and `torch.manual_seed` will be set accordingly. This can be useful for reproducibility, as it ensures that the random operations produce the same results across multiple runs.
+
+## Example Usage
+
+### Without Specifying a Seed
+
+```python
+import random
+from vllm import LLM
+
+# Initialize a vLLM model without specifying a seed
+model = LLM(model="Qwen/Qwen2.5-0.5B-Instruct")
+
+# Try generating random numbers
+print(random.randint(0, 100))  # Outputs different numbers across runs
+```
+
+### Specifying a Seed
+
+```python
+import random
+from vllm import LLM
+
+# Initialize a vLLM model with a specific seed
+model = LLM(model="Qwen/Qwen2.5-0.5B-Instruct", seed=42)
+
+# Try generating random numbers
+print(random.randint(0, 100))  # Outputs the same number across runs
+```
+
+## Important Notes
+
+- If the `seed` parameter is not specified, the behavior of global random states remains unaffected.
+- If a specific seed value is provided, the global random states for `random`, `np.random`, and `torch.manual_seed` will be set to that value.
+- This behavior can be useful for reproducibility but may lead to non-intuitive behavior if the user is not explicitly aware of it.
+
+## Conclusion
+
+Understanding the behavior of the `seed` parameter in vLLM is crucial for ensuring the expected behavior of random operations in your code. By default, the `seed` parameter is set to `None`, which means that the global random states are not affected. However, specifying a seed value can help achieve reproducibility in your experiments.

docs/source/_static/custom.css

@@ -0,0 +1,8 @@
+.vertical-table-header th.head:not(.stub) {
+    writing-mode: sideways-lr;
+    white-space: nowrap;
+    max-width: 0;
+    p {
+       margin: 0;
+    }
+}

docs/source/_templates/sections/header.html

@@ -0,0 +1,39 @@
+<style>
+  .notification-bar {
+    width: 100vw;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    font-size: 16px;
+    padding: 0 6px 0 6px;
+  }
+  .notification-bar p {
+    margin: 0;
+  }
+  .notification-bar a {
+    font-weight: bold;
+    text-decoration: none;
+  }
+
+  /* Light mode styles (default) */
+  .notification-bar {
+    background-color: #fff3cd;
+    color: #856404;
+  }
+  .notification-bar a {
+    color: #d97706;
+  }
+
+  /* Dark mode styles */
+  html[data-theme=dark] .notification-bar {
+    background-color: #333;
+    color: #ddd;
+  }
+  html[data-theme=dark] .notification-bar a {
+    color: #ffa500; /* Brighter color for visibility */
+  }
+</style>
+
+<!-- <div class="notification-bar">
+  <p>You are viewing the latest developer preview docs. <a href="https://docs.vllm.ai/en/stable/">Click here</a> to view docs for the latest stable release.</p>
+</div> -->

docs/source/api/summary.md

@@ -0,0 +1,19 @@
+# Summary
+
+## Video Generator
+
+```{autodoc2-summary}
+    fastvideo.VideoGenerator
+```
+
+## Initialization Configuration
+
+```{autodoc2-summary}
+    fastvideo.configs.pipelines.PipelineConfig
+```
+
+## Sampling Configuration
+
+```{autodoc2-summary}
+    fastvideo.configs.sample.SamplingParam
+```

docs/source/autodoc2_docstring_parser.py

@@ -0,0 +1,22 @@
+# type: ignore
+# SPDX-License-Identifier: Apache-2.0
+from docutils import nodes
+from myst_parser.parsers.sphinx_ import MystParser
+from sphinx.ext.napoleon import docstring
+
+
+class NapoleonParser(MystParser):
+
+    def parse(self, input_string: str, document: nodes.document) -> None:
+        # Get the Sphinx configuration
+        config = document.settings.env.config
+
+        parsed_content = str(
+            docstring.GoogleDocstring(
+                str(docstring.NumpyDocstring(input_string, config)),
+                config,
+            ))
+        return super().parse(parsed_content, document)
+
+
+Parser = NapoleonParser

docs/source/conf.py

@@ -0,0 +1,275 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+
+import datetime
+import logging
+import os
+import re
+import sys
+from pathlib import Path
+
+import requests
+
+logger = logging.getLogger(__name__)
+REPO_ROOT = Path(__file__).resolve().parent.parent.parent
+print(os.path.abspath(REPO_ROOT))
+sys.path.append(os.path.abspath(REPO_ROOT))
+
+# -- Project information -----------------------------------------------------
+
+project = 'FastVideo'
+copyright = f'{datetime.datetime.now().year}, FastVideo Team'
+author = 'the FastVideo Team'
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "sphinx.ext.napoleon",
+    "sphinx.ext.linkcode",
+    "sphinx.ext.intersphinx",
+    "sphinx_copybutton",
+    "autodoc2",
+    "myst_parser",
+    "sphinxarg.ext",
+    "sphinx_design",
+    "sphinx_togglebutton",
+]
+myst_enable_extensions = [
+    "colon_fence",
+    "fieldlist",
+]
+autodoc2_packages = [
+    {
+        "path": "../../fastvideo",
+        "exclude_dirs": ["__pycache__", "third_party"],
+    },
+]
+autodoc2_output_dir = "api"
+autodoc2_render_plugin = "myst"
+autodoc2_hidden_objects = ["dunder", "private", "inherited"]
+autodoc2_docstring_parser_regexes = [
+    (".*", "docs.source.autodoc2_docstring_parser"),
+]
+autodoc2_sort_names = True
+autodoc2_index_template = None
+autodoc2_skip_module_regexes = [
+    "fastvideo.dataset",
+    "fastvideo.distill",
+    "fastvideo.data_preprocess",
+    "fastvideo.models",
+    "fastvideo.sample",
+    "fastvideo.utils",
+    "fastvideo.distill_adv",
+    "fastvideo.train",
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns: list[str] = ["**/*.template.md", "**/*.inc.md"]
+
+# Exclude the prompt "$" when copying code
+copybutton_prompt_text = r"\$ "
+copybutton_prompt_is_regexp = True
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_title = project
+html_theme = 'sphinx_book_theme'
+html_logo = '../../assets/logos/icon_simple.svg'
+html_theme_options = {
+    'path_to_docs': 'docs/source',
+    'repository_url': 'https://github.com/hao-ai-lab/FastVideo/',
+    'use_repository_button': True,
+    'use_edit_page_button': True,
+    # Prevents the full API being added to the left sidebar of every page.
+    # Reduces build time by 2.5x and reduces build size from ~225MB to ~95MB.
+    'collapse_navbar': True,
+    # Makes API visible in the right sidebar on API reference pages.
+    'show_toc_level': 3,
+}
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+html_js_files = ["custom.js"]
+html_css_files = ["custom.css"]
+
+myst_url_schemes = {
+    'http': None,
+    'https': None,
+    'mailto': None,
+    'ftp': None,
+    "gh-issue": {
+        "url":
+        "https://github.com/hao-ai-lab/FastVideo/issues/{{path}}#{{fragment}}",
+        "title": "Issue #{{path}}",
+        "classes": ["github"],
+    },
+    "gh-pr": {
+        "url":
+        "https://github.com/hao-ai-lab/FastVideo/pull/{{path}}#{{fragment}}",
+        "title": "Pull Request #{{path}}",
+        "classes": ["github"],
+    },
+    "gh-dir": {
+        "url": "https://github.com/hao-ai-lab/FastVideo/tree/main/{{path}}",
+        "title": "{{path}}",
+        "classes": ["github"],
+    },
+    "gh-file": {
+        "url": "https://github.com/hao-ai-lab/FastVideo/blob/main/{{path}}",
+        "title": "{{path}}",
+        "classes": ["github"],
+    },
+}
+
+# see https://docs.readthedocs.io/en/stable/reference/environment-variables.html # noqa
+READTHEDOCS_VERSION_TYPE = os.environ.get('READTHEDOCS_VERSION_TYPE')
+if READTHEDOCS_VERSION_TYPE == "tag":
+    # remove the warning banner if the version is a tagged release
+    header_file = os.path.join(os.path.dirname(__file__),
+                               "_templates/sections/header.html")
+    # The file might be removed already if the build is triggered multiple times
+    # (readthedocs build both HTML and PDF versions separately)
+    if os.path.exists(header_file):
+        os.remove(header_file)
+
+
+# Generate additional rst documentation here.
+def setup(app):
+    from docs.source.generate_examples import generate_examples
+    generate_examples()
+
+
+_cached_base: str = ""
+_cached_branch: str = ""
+
+
+def get_repo_base_and_branch(pr_number: str) -> tuple[str | None, str | None]:
+    global _cached_base, _cached_branch
+    if _cached_base and _cached_branch:
+        return _cached_base, _cached_branch
+
+    url = f"https://api.github.com/repos/hao-ai-lab/FastVideo/pulls/{pr_number}"
+    response = requests.get(url)
+    if response.status_code == 200:
+        data = response.json()
+        _cached_base = data['head']['repo']['full_name']
+        _cached_branch = data['head']['ref']
+        return _cached_base, _cached_branch
+    else:
+        logger.error("Failed to fetch PR details: %s", response)
+        return None, None
+
+
+def linkcode_resolve(domain, info):
+    if domain != 'py':
+        return None
+    if not info['module']:
+        return None
+
+    # Get path from module name
+    file = Path(f"{info['module'].replace('.', '/')}.py")
+    path = REPO_ROOT / file
+    if not path.exists():
+        path = REPO_ROOT / file.with_suffix("") / "__init__.py"
+    if not path.exists():
+        return None
+
+    # Get the line number of the object
+    with open(path) as f:
+        lines = f.readlines()
+    name = info['fullname'].split(".")[-1]
+    pattern = fr"^( {{4}})*((def|class) )?{name}\b.*"
+    for lineno, line in enumerate(lines, 1):
+        if not line or line.startswith("#"):
+            continue
+        if re.match(pattern, line):
+            break
+
+    # If the line number is not found, return None
+    if lineno == len(lines):
+        return None
+
+    # If the line number is found, create the URL
+    filename = path.relative_to(REPO_ROOT)
+    if "checkouts" in path.parts:
+        # a PR build on readthedocs
+        pr_number = REPO_ROOT.name
+        base, branch = get_repo_base_and_branch(pr_number)
+        if base and branch:
+            return f"https://github.com/{base}/blob/{branch}/{filename}#L{lineno}"
+    # Otherwise, link to the source file on the main branch
+    return f"https://github.com/hao-ai-lab/FastVideo/blob/main/{filename}#L{lineno}"
+
+
+# Mock out external dependencies here, otherwise the autodoc pages may be blank.
+autodoc_mock_imports = [
+    "blake3",
+    "compressed_tensors",
+    "cpuinfo",
+    "cv2",
+    "torch",
+    "huggingface_hub",
+    "torchvision",
+    "transformers",
+    "psutil",
+    "prometheus_client",
+    "sentencepiece",
+    "vllm._C",
+    "PIL",
+    "numpy",
+    'triton',
+    "tqdm",
+    "tensorizer",
+    "pynvml",
+    "outlines",
+    "xgrammar",
+    "librosa",
+    "soundfile",
+    "gguf",
+    "lark",
+    "decord",
+]
+
+for mock_target in autodoc_mock_imports:
+    if mock_target in sys.modules:
+        logger.info(
+            "Potentially problematic mock target (%s) found; "
+            "autodoc_mock_imports cannot mock modules that have already "
+            "been loaded into sys.modules when the sphinx build starts.",
+            mock_target)
+
+intersphinx_mapping = {
+    "python": ("https://docs.python.org/3", None),
+    "typing_extensions":
+    ("https://typing-extensions.readthedocs.io/en/latest", None),
+    "aiohttp": ("https://docs.aiohttp.org/en/stable", None),
+    "pillow": ("https://pillow.readthedocs.io/en/stable", None),
+    "numpy": ("https://numpy.org/doc/stable", None),
+    "torch": ("https://pytorch.org/docs/stable", None),
+    "psutil": ("https://psutil.readthedocs.io/en/stable", None),
+}
+
+navigation_with_keys = False

docs/source/contributing/developer_env/docker.md

@@ -1,4 +1,4 @@
-
+(docker)=
 # 🐳 Using the FastVideo Docker Image
 
 If you prefer a containerized development environment or want to avoid managing dependencies manually, you can use our prebuilt Docker image:

docs/source/contributing/developer_env/index.md

@@ -3,3 +3,11 @@
 # 🧰 Developer Environment
 
 Accelerate your FastVideo development workflow by leveraging Docker images and cloud GPUs for efficient experimentation and reproducible environments.
+
+:::{toctree}
+:caption: Contents
+:maxdepth: 1
+
+docker
+runpod
+:::

docs/source/contributing/developer_env/runpod.md

@@ -1,3 +1,4 @@
+(runpod)=
 
 # 📦 Developing FastVideo on RunPod
 
@@ -9,7 +10,7 @@ Choose a GPU that supports CUDA 12.8
 
 Pick 1 or 2 L40S GPU(s)
 
-![RunPod CUDA selection](../../assets/images/runpod_cuda.png)
+![RunPod CUDA selection](../../_static/images/runpod_cuda.png)
 
 When creating your pod template, use this image:
 
@@ -23,11 +24,11 @@ Paste Container Start Command to support SSH ([RunPod Docs](https://docs.runpod.
 bash -c "apt update;DEBIAN_FRONTEND=noninteractive apt-get install openssh-server -y;mkdir -p ~/.ssh;cd $_;chmod 700 ~/.ssh;echo \"$PUBLIC_KEY\" >> authorized_keys;chmod 700 authorized_keys;service ssh start;sleep infinity"
 ```
 
-![RunPod template configuration](../../assets/images/runpod_template.png)
+![RunPod template configuration](../../_static/images/runpod_template.png)
 
 After deploying, the pod will take a few minutes to pull the image and start the SSH service.
 
-![RunPod ssh](../../assets/images/runpod_ssh.png)
+![RunPod ssh](../../_static/images/runpod_ssh.png)
 
 ## Working with the pod
 

docs/source/contributing/overview.md

@@ -1,3 +1,4 @@
+(developer-overview)=
 
 # 🛠️ Contributing to FastVideo
 
@@ -70,7 +71,3 @@ uv pip install ninja
 
 python setup.py install
 ```
-
-## Testing
-
-Please refer to the [Testing Guide](testing.md) for more information on how to add and run tests in FastVideo.

docs/source/design/overview.md

@@ -29,6 +29,7 @@ FastVideo separates model components from execution logic with these principles:
 - **Custom Attention Backends**: Components can support and use different Attention implementations
 - **Pipeline Abstraction**: Consistent interface across diffusion models
 
+(design-fastvideo-args)=
 ## FastVideoArgs
 
 The `FastVideoArgs` class in `fastvideo/fastvideo_args.py` serves as the central configuration system for FastVideo. It contains all parameters needed to control model loading, inference configuration, performance optimization settings, and more.
@@ -60,6 +61,7 @@ with set_current_fastvideo_args(fastvideo_args):
     result = generate_video()
 ```
 
+(design-pipeline-system)=
 ## Pipeline System
 
 ### `ComposedPipelineBase`
@@ -106,8 +108,7 @@ def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> Forward
     return batch
 ```
 
-![Pipeline execution and data flow](../assets/images/pipeline.png)
-
+(design-forwardbatch)=
 ### ForwardBatch
 
 Defined in `fastvideo/pipelines/pipeline_batch_info.py`, `ForwardBatch` encapsulates the data payload passed between pipeline stages. It typically holds:
@@ -119,10 +120,12 @@ Defined in `fastvideo/pipelines/pipeline_batch_info.py`, `ForwardBatch` encapsul
 
 This structure facilitates clear state transitions between stages.
 
+(design-model-components)=
 ## Model Components
 
 The `fastvideo/models/` directory contains implementations of the core neural network models used in video diffusion:
 
+(design-transformer-models)=
 ### Transformer Models
 
 Transformer networks perform the actual denoising during diffusion:
@@ -149,6 +152,7 @@ def forward(
     return noise_pred  # Predicted noise residual
 ```
 
+(design-vae-variational-auto-encoder)=
 ### VAE (Variational Auto-Encoder)
 
 VAEs handle conversion between pixel space and latent space:
@@ -166,6 +170,7 @@ FastVideo's VAE implementations include:
 - Optional tiling for large frames
 - Distributed weight support
 
+(design-text-and-image-encoders)=
 ### Text and Image Encoders
 
 Encoders process conditioning inputs into embeddings:
@@ -183,6 +188,7 @@ FastVideo implements optimizations such as:
 - Caching for common prompts
 - Precision-tuned computation
 
+(design-schedulers)=
 ### Schedulers
 
 Schedulers manage the diffusion sampling process:
@@ -210,10 +216,7 @@ def step(
     return prev_sample
 ```
 
-This diagram shows how models are discovered, validated, and loaded across entrypoints, executors, pipelines, and model loaders.
-
-![Model loading flow](../assets/images/load_models.png)
-
+(design-optimized-attention)=
 ## Optimized Attention
 
 The `fastvideo/attention/` directory contains optimized attention implementations crucial for efficient video diffusion:
@@ -237,17 +240,17 @@ self.attn = LocalAttention(
 # export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
 ```
 
-![Attention backend selector design](../assets/images/attention_backend.png)
-
 ### Attention Patterns
 Supports various patterns with memory optimization techniques:
 - **Cross/Self/Temporal/Global-Local Attention**
 - Chunking, progressive computation, optimized masking
 
+(design-distributed-processing)=
 ## Distributed Processing
 
 The `fastvideo/distributed/` directory contains implementations for distributed model execution:
 
+(design-tensor-parallelism)=
 ### Tensor Parallelism
 
 Tensor parallelism splits model weights across devices:
@@ -304,6 +307,7 @@ Efficient communication primitives minimize distributed overhead:
 - **Tensor-Parallel AllReduce**: Combines partial results
 - **Distributed Synchronization**: Coordinates execution
 
+(design-forwardcontext)=
 ## Forward Context Management
 
 ### ForwardContext
@@ -326,6 +330,7 @@ with set_forward_context(current_timestep, attn_metadata, fastvideo_args):
     output = model(inputs)
 ```
 
+(design-executor-and-worker-abstractions)=
 ## Executor and Worker System
 
 The `fastvideo/worker/` directory contains the distributed execution framework:
@@ -352,6 +357,7 @@ Each GPU worker:
 
 This design allows FastVideo to efficiently utilize multiple GPUs while providing a simple, unified interface for model execution.
 
+(design-platforms)=
 ## Platforms
 
 The `fastvideo/platforms/` directory provides hardware platform abstractions that enable FastVideo to run efficiently on different hardware configurations:
@@ -382,6 +388,7 @@ else:
 
 The platform system is designed to be extensible for future hardware targets.
 
+(design-logger)=
 ## Logger
 See [PR](https://github.com/hao-ai-lab/FastVideo/pull/356)
 

docs/source/distillation/data_preprocess.md

@@ -1,3 +1,4 @@
+(v0-data-preprocess)=
 
 # 🧱 Data Preprocess for Distillation
 

docs/source/distillation/dmd.md

@@ -13,7 +13,7 @@ We provide two distilled models:
 Both models are trained on **61×448×832** resolution but support generating videos with **any resolution** (1.3B  model mainly support 480P, 14B model support 480P and 720P, quality may degrade for different resolutions).
 
 ## ⚙️ Inference
-First install [VSA](https://hao-ai-lab.github.io/FastVideo/video_sparse_attention/installation). Set `MODEL_BASE` to your own model path and run:
+First install [VSA](https://hao-ai-lab.github.io/FastVideo/video_sparse_attention/installation.html). Set `MODEL_BASE` to your own model path and run:
 
 ```bash
 bash scripts/inference/v1_inference_wan_dmd.sh

docs/source/generate_examples.py

@@ -6,11 +6,10 @@ import re
 from dataclasses import dataclass, field
 from pathlib import Path
 
-ROOT_DIR = Path(__file__).parent.parent.resolve()
-ROOT_DIR_RELATIVE = '../..'
+ROOT_DIR = Path(__file__).parent.parent.parent.resolve()
+ROOT_DIR_RELATIVE = '../../../..'
 EXAMPLE_DIR = ROOT_DIR / "examples"
-EXAMPLE_DOC_DIR = ROOT_DIR / "docs/getting_started/examples"
-GITHUB_REPO = "hao-ai-lab/FastVideo"  # Update this to your repo
+EXAMPLE_DOC_DIR = ROOT_DIR / "docs/source/getting_started/examples"
 
 
 def fix_case(text: str) -> str:
@@ -72,16 +71,9 @@ class Index:
 
     def generate(self) -> str:
         content = f"# {self.title}\n\n{self.description}\n\n"
-        if self.caption:
-            content += f"## {self.caption}\n\n"
-        # Generate a simple list of links for MkDocs
-        for doc in self.documents:
-            # Convert document path to proper link
-            doc_link = doc.replace("\\", "/")
-            # Get just the filename for the link text
-            doc_title = fix_case(Path(doc).stem.replace("_", " ").title())
-            content += f"- [{doc_title}]({doc_link}.md)\n"
-        content += "\n"
+        content += ":::{toctree}\n"
+        content += f":caption: {self.caption}\n:maxdepth: {self.maxdepth}\n"
+        content += "\n".join(self.documents) + "\n:::\n"
         return content
 
 
@@ -150,66 +142,30 @@ class Example:
         return fix_case(self.path.stem.replace("_", " ").title())
 
     def generate(self) -> str:
-        # Create GitHub link to source
-        github_path = str(self.path.relative_to(ROOT_DIR)).replace("\\", "/")
-        github_url = f"https://github.com/{GITHUB_REPO}/blob/main/{github_path}"
-        content = f"**Source:** [{github_path}]({github_url})\n\n"
+        # Convert the path to a relative path from __file__
+        make_relative = lambda path: ROOT_DIR_RELATIVE / path.relative_to(
+            ROOT_DIR)
 
-        # Add title for code files
-        if self.main_file.suffix != ".md":
+        content = f"Source <gh-file:{self.path.relative_to(ROOT_DIR)}>.\n\n"
+        include = "include" if self.main_file.suffix == ".md" else \
+            "literalinclude"
+        if include == "literalinclude":
             content += f"# {self.title}\n\n"
-
-        # Include main file content
-        if self.main_file.suffix == ".md":
-            # For markdown files, include the content directly
-            with open(self.main_file, encoding='utf-8') as f:
-                content += f.read() + "\n\n"
-        else:
-            # For code files, use code blocks
-            language = self.main_file.suffix[1:] if self.main_file.suffix else ""
-            with open(self.main_file, encoding='utf-8') as f:
-                file_content = f.read()
-            content += f"```{language}\n{file_content}\n```\n\n"
+        content += f":::{{{include}}} {make_relative(self.main_file)}\n"  # type: ignore[no-untyped-call]
+        if include == "literalinclude":
+            content += f":language: {self.main_file.suffix[1:]}\n"
+        content += ":::\n\n"
 
         if not self.other_files:
             return content
 
-        content += "## Additional Files\n\n"
-        # Define binary/non-text file extensions to skip
-        binary_extensions = {
-            '.mp4', '.avi', '.mov', '.mkv', '.gif', '.jpg', '.jpeg', '.png',
-            '.webp', '.bmp', '.pdf', '.zip', '.tar', '.gz', '.mp3', '.wav'
-        }
-
+        content += "## Example materials\n\n"
         for file in sorted(self.other_files):
-            # Skip binary files
-            if file.suffix.lower() in binary_extensions:
-                continue
-
-            file_rel_path = file.relative_to(self.path)
-            # Use collapsible admonition syntax for MkDocs
-            content += f"??? note \"{file_rel_path}\"\n\n"
-
-            try:
-                if file.suffix == ".md":
-                    # Include markdown content with indentation
-                    with open(file, encoding='utf-8') as f:
-                        for line in f:
-                            content += f"    {line}"
-                else:
-                    # Include code with proper formatting
-                    language = file.suffix[1:] if file.suffix else ""
-                    with open(file, encoding='utf-8') as f:
-                        file_content = f.read()
-                    # Indent the code block for the admonition
-                    content += f"    ```{language}\n"
-                    for line in file_content.split('\n'):
-                        content += f"    {line}\n"
-                    content += "    ```\n"
-                content += "\n"
-            except UnicodeDecodeError:
-                # Skip files that can't be decoded as UTF-8
-                continue
+            include = "include" if file.suffix == ".md" else "literalinclude"
+            content += f":::{{admonition}} {file.relative_to(self.path)}\n"
+            content += ":class: dropdown\n\n"
+            content += f":::{{{include}}} {make_relative(file)}\n:::\n"  # type: ignore[no-untyped-call]
+            content += ":::\n\n"
 
         return content
 
@@ -239,7 +195,7 @@ class NestedStructure:
 
 def create_category_indices() -> dict[str, Index]:
     """Create category indices with their respective configurations."""
-    main_index_dir = ROOT_DIR / "docs/examples"
+    main_index_dir = ROOT_DIR / "docs/source/examples"
     if not main_index_dir.exists():
         main_index_dir.mkdir(parents=True)
 
@@ -247,16 +203,17 @@ def create_category_indices() -> dict[str, Index]:
         "inference":
         Index(
             path=ROOT_DIR /
-            "docs/inference/examples/examples_inference_index.md",
+            "docs/source/inference/examples/examples_inference_index.md",
             title="🚀 Examples",
             description=
-            "Inference examples demonstrate how to use FastVideo inference. We recommend starting with [basic.md](basic.md).",
+            "Inference examples demonstrate how to use FastVideo inference. We recommend starting with <project:basic.md>.",
             caption="Examples",
             maxdepth=1,
         ),
         "training":
         Index(
-            path=ROOT_DIR / "docs/training/examples/examples_training_index.md",
+            path=ROOT_DIR /
+            "docs/source/training/examples/examples_training_index.md",
             title="🚀 Examples",
             description=
             "Training examples demonstrate how to use FastVideo training.",
@@ -266,7 +223,7 @@ def create_category_indices() -> dict[str, Index]:
         "distillation":
         Index(
             path=ROOT_DIR /
-            "docs/distillation/examples/examples_distillation_index.md",
+            "docs/source/distillation/examples/examples_distillation_index.md",
             title="🚀 Examples",
             description=
             "Distillation examples demonstrate how to use FastVideo distillation.",
@@ -289,21 +246,9 @@ def find_examples(category_indices: dict[str, Index],
     examples = []
     glob_patterns = ["*.py", "*.md", "*.sh"]
 
-    # Map category names to actual directory names
-    category_dir_mapping = {
-        "distillation": "distill",  # examples/distill/ -> distillation category
-    }
-
     # Find categorised examples
     for category in category_indices:
-        # Use mapped directory name if available, otherwise use category name
-        dir_name = category_dir_mapping.get(category, category)
-        category_dir = EXAMPLE_DIR / dir_name
-
-        # Skip if directory doesn't exist
-        if not category_dir.exists():
-            continue
-
+        category_dir = EXAMPLE_DIR / category
         globs = [category_dir.glob(pattern) for pattern in glob_patterns]
         for path in itertools.chain(*globs):
             examples.append(Example(path, category))
@@ -334,18 +279,11 @@ def create_nested_structures(
                                                 dict[str,
                                                      NestedStructure]]]] = {}
 
-    # Map category names to actual directory names
-    category_dir_mapping = {
-        "distillation": "distill",
-    }
-
     for example in examples:
         if example.category not in ["training", "distillation"]:
             continue
 
-        # Use mapped directory name if available
-        dir_name = category_dir_mapping.get(example.category, example.category)
-        category_dir = EXAMPLE_DIR / dir_name
+        category_dir = EXAMPLE_DIR / example.category
         relative_path = example.path.relative_to(category_dir)
         path_parts = relative_path.parts
 
@@ -477,7 +415,7 @@ def generate_nested_examples(nested_structures: dict[str, dict[str, dict[
             category_index.documents.append(method)
 
 
-def generate_examples(generate_main_index: bool = False) -> None:
+def generate_examples(generate_main_index=False):
     """
     Generate example documentation.
     
@@ -491,14 +429,12 @@ def generate_examples(generate_main_index: bool = False) -> None:
     # Create the main examples index only if requested
     examples_index = None
     if generate_main_index:
-        main_index_dir = ROOT_DIR / "docs/examples"
+        main_index_dir = ROOT_DIR / "docs/source/examples"
         examples_index = Index(
             path=main_index_dir / "examples_index.md",
             title="💡 Examples",
             description=
-            "A collection of examples demonstrating usage of FastVideo.\n\n"
-            f"All documented examples are autogenerated using [generate_examples.py](https://github.com/{GITHUB_REPO}/blob/main/docs/generate_examples.py) "
-            f"from examples found in the [examples](https://github.com/{GITHUB_REPO}/tree/main/examples) directory.",
+            "A collection of examples demonstrating usage of FastVideo.\nAll documented examples are autogenerated using <gh-file:docs/source/generate_examples.py> from examples found in <gh-file:examples>.",
             caption="Examples",
             maxdepth=2)
 
@@ -535,19 +471,3 @@ def generate_examples(generate_main_index: bool = False) -> None:
     if generate_main_index and examples_index:
         with open(examples_index.path, "w+") as f:
             f.write(examples_index.generate())
-
-
-def on_pre_build_hook(config, **kwargs):
-    """
-    MkDocs hook to generate examples before building the documentation.
-    This function is called automatically by the mkdocs-simple-hooks plugin.
-    """
-    print("Generating example documentation...")
-    generate_examples(generate_main_index=True)
-    print("Example documentation generated successfully!")
-
-
-if __name__ == "__main__":
-    print("Generating example documentation...")
-    generate_examples(generate_main_index=True)
-    print("Example documentation generated successfully!")

docs/source/getting_started/installation.md

@@ -0,0 +1,18 @@
+(installation-index)=
+
+# 🔧 Installation
+
+FastVideo supports the following hardware platforms:
+
+:::{toctree}
+:maxdepth: 1
+:hidden:
+
+installation/gpu
+installation/mps
+:::
+
+- <project:installation/gpu.md>
+  - NVIDIA CUDA
+- <project:installation/mps.md>
+  - Apple silicon

docs/source/getting_started/installation/gpu.md

@@ -30,8 +30,17 @@ conda create -n fastvideo python=3.12 -y
 conda activate fastvideo
 ```
 
+:::{note}
+[PyTorch has deprecated the conda release channel](https://github.com/pytorch/pytorch/issues/138506). If you use `conda`, please only use it to create Python environment rather than installing packages.
+:::
+
 #### uv
 
+:::{tip}
+We highly recommend using `uv` to install FastVideo. In our experience, `uv` speeds up installation by at least 3x.
+Note that you can also use `uv` to install FastVideo in a Conda environment.
+:::
+
 Or you can create a new Python environment using [uv](https://docs.astral.sh/uv/), a very fast Python environment manager. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following command:
 
 ```console

docs/source/getting_started/installation/mps.md

@@ -31,8 +31,17 @@ conda create -n fastvideo python=3.12.4 -y
 conda activate fastvideo
 ```
 
+:::{note}
+[PyTorch has deprecated the conda release channel](https://github.com/pytorch/pytorch/issues/138506). If you use `conda`, please only use it to create Python environment rather than installing packages.
+:::
+
 #### uv
 
+:::{tip}
+We highly recommend using `uv` to install FastVideo. In our experience, `uv` speeds up installation by at least 3x.
+Note that you can also use `uv` to install FastVideo in a Conda environment.
+:::
+
 Or you can create a new Python environment using [uv](https://docs.astral.sh/uv/), a very fast Python environment manager. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following command:
 
 ```console

docs/source/getting_started/v1_api.md

@@ -0,0 +1,83 @@
+# V1 API
+
+FastVideo's V1 API provides a streamlined interface for video generation tasks with powerful customization options. This page documents the primary components of the API.
+
+## Video Generator
+
+This class will be the primary Python API for generating videos and images.
+
+```{autodoc2-summary}
+    fastvideo.VideoGenerator
+```
+
+`````{py:class} VideoGenerator(fastvideo_args: fastvideo.fastvideo_args.FastVideoArgs, executor_class: type[fastvideo.worker.executor.Executor], log_stats: bool)
+:canonical: fastvideo.entrypoints.video_generator.VideoGenerator
+
+```{autodoc2-docstring} fastvideo.entrypoints.video_generator.VideoGenerator
+:parser: docs.source.autodoc2_docstring_parser
+```
+
+`VideoGenerator.from_pretrained()` should be the primary way of creating a new video generator.
+
+````{py:method} from_pretrained(model_path: str, device: typing.Optional[str] = None, torch_dtype: typing.Optional[torch.dtype] = None, pipeline_config: typing.Optional[typing.Union[str | fastvideo.configs.pipelines.PipelineConfig]] = None, **kwargs) -> fastvideo.entrypoints.video_generator.VideoGenerator
+:canonical: fastvideo.entrypoints.video_generator.VideoGenerator.from_pretrained
+:classmethod:
+
+```{autodoc2-docstring} fastvideo.entrypoints.video_generator.VideoGenerator.from_pretrained
+:parser: docs.source.autodoc2_docstring_parser
+```
+
+
+## Configuring FastVideo
+
+The follow two classes `PipelineConfig` and `SamplingParam` are used to configure initialization and sampling parameters, respectively.
+
+### PipelineConfig
+```{autodoc2-summary}
+    fastvideo.PipelineConfig
+```
+
+`````{py:class} PipelineConfig
+:canonical: fastvideo.configs.pipelines.base.PipelineConfig
+
+```{autodoc2-docstring} fastvideo.configs.pipelines.base.PipelineConfig
+:parser: docs.source.autodoc2_docstring_parser
+```
+
+````{py:method} from_pretrained(model_path: str) -> fastvideo.configs.pipelines.base.PipelineConfig
+:canonical: fastvideo.configs.pipelines.base.PipelineConfig.from_pretrained
+:classmethod:
+
+```{autodoc2-docstring} fastvideo.configs.pipelines.base.PipelineConfig.from_pretrained
+:parser: docs.source.autodoc2_docstring_parser
+```
+
+
+````{py:method} dump_to_json(file_path: str)
+:canonical: fastvideo.configs.pipelines.base.PipelineConfig.dump_to_json
+
+```{autodoc2-docstring} fastvideo.configs.pipelines.base.PipelineConfig.dump_to_json
+:parser: docs.source.autodoc2_docstring_parser
+```
+
+
+### SamplingParam
+
+```{autodoc2-summary}
+    fastvideo.SamplingParam
+```
+
+`````{py:class} SamplingParam
+:canonical: fastvideo.configs.sample.base.SamplingParam
+
+```{autodoc2-docstring} fastvideo.configs.sample.base.SamplingParam
+:parser: docs.source.autodoc2_docstring_parser
+```
+
+````{py:method} from_pretrained(model_path: str) -> fastvideo.configs.sample.base.SamplingParam
+:canonical: fastvideo.configs.sample.base.SamplingParam.from_pretrained
+:classmethod:
+
+```{autodoc2-docstring} fastvideo.configs.sample.base.SamplingParam.from_pretrained
+:parser: docs.source.autodoc2_docstring_parser
+```

docs/source/index.md

@@ -1,52 +1,131 @@
 # Welcome to FastVideo
 
-<div style="text-align: center;">
-  <img src="assets/logos/logo.svg" alt="FastVideo" style="width: 60%;" />
-</div>
+:::{figure} ../../assets/logos/logo.svg
+:align: center
+:alt: FastVideo
+:class: no-scaled-link
+:width: 60%
+:::
 
-<div style="text-align: center;">
-<strong>FastVideo is a unified inference and post-training framework for accelerated video generation.</strong>
-</div>
+:::{raw} html
+<p style="text-align:center">
+<strong>FastVideo is a unified inference and post-training framework for accelerated video generation.
+</strong>
+</p>
 
-<div style="text-align: center;">
+<p style="text-align:center">
 <script async defer src="https://buttons.github.io/buttons.js"></script>
 <a class="github-button" href="https://github.com/hao-ai-lab/FastVideo/" data-show-count="true" data-size="large" aria-label="Star">Star</a>
 <a class="github-button" href="https://github.com/hao-ai-lab/FastVideo/subscription" data-icon="octicon-eye" data-size="large" aria-label="Watch">Watch</a>
 <a class="github-button" href="https://github.com/hao-ai-lab/FastVideo/fork" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork">Fork</a>
-</div>
+</p>
+:::
 
 FastVideo is an inference and post-training framework for diffusion models. It features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.
 
 <div style="text-align: center;">
-  <img src="assets/images/fastwan.png" style="width: 100%;"/>
+  <img src=_static/images/fastwan.png width="100%"/>
 </div>
 
 ## Key Features
 
 FastVideo has the following features:
-
 - State-of-the-art performance optimizations for inference
   - [Sliding Tile Attention](https://arxiv.org/pdf/2502.04507)
   - [TeaCache](https://arxiv.org/pdf/2411.19108)
   - [Sage Attention](https://arxiv.org/abs/2410.02367)
 - E2E post-training support
-  - Data preprocessing pipeline for video data
+  - Data preprocessing pipeline for video data.
   - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) for Wan2.1 and Wan2.2 using [Video Sparse Attention](https://arxiv.org/pdf/2505.13389) and [Distribution Matching Distillation](https://tianweiy.github.io/dmd2/)
   - Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs.
   - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing, with near linear scaling to 64 GPUs.
 
 ## Documentation
 
-Welcome to FastVideo! This documentation will help you get started with our unified inference and post-training framework for accelerated video generation.
+% How to start using FastVideo?
 
-Use the navigation menu on the left to explore different sections:
+:::{toctree}
+:caption: Getting Started
+:maxdepth: 1
 
-- **Getting Started**: Installation and quick start guides
-- **Inference**: Learn how to use FastVideo for video generation
-- **Training**: Data preprocessing and fine-tuning workflows
-- **Distillation**: Post-training optimization techniques
-- **Sliding Tile Attention**: Advanced attention mechanisms
-- **Video Sparse Attention**: Efficient attention for video models
-- **Design**: Framework architecture and design principles
-- **Developer Guide**: Contributing and development setup
-- **API Reference**: Complete API documentation
+getting_started/installation
+<!-- getting_started/v1_api -->
+:::
+
+:::{toctree}
+:caption: Inference
+:maxdepth: 1
+
+inference/inference_quick_start
+inference/examples/examples_inference_index
+inference/configuration
+inference/optimizations
+inference/comfyui
+inference/support_matrix
+inference/cli
+inference/add_pipeline
+:::
+
+:::{toctree}
+:caption: Training
+:maxdepth: 1
+
+training/examples/examples_training_index
+training/data_preprocess
+<!-- training/finetune -->
+:::
+
+:::{toctree}
+:caption: Distillation
+:maxdepth: 1
+
+distillation/examples/examples_distillation_index
+distillation/data_preprocess
+distillation/dmd
+:::
+
+% What is STA Kernel?
+
+:::{toctree}
+:caption: Sliding Tile Attention
+:maxdepth: 1
+
+sliding_tile_attention/installation
+sliding_tile_attention/demo
+:::
+
+% What is VSA Kernel?
+
+:::{toctree}
+:caption: Video Sparse Attention
+:maxdepth: 1
+
+video_sparse_attention/installation
+:::
+
+:::{toctree}
+:caption: Design
+:maxdepth: 1
+design/overview
+:::
+
+:::{toctree}
+:caption: Developer Guide
+:maxdepth: 2
+
+contributing/overview
+contributing/developer_env/index
+:::
+
+:::{toctree}
+:caption: API Reference
+:maxdepth: 2
+
+<!-- api/summary -->
+api/fastvideo/fastvideo
+:::
+
+## Indices and tables
+
+- {ref}`genindex`
+- {ref}`modindex`

docs/source/inference/add_pipeline.md

@@ -1,3 +1,4 @@
+(add-pipeline)=
 
 # 🏗️ Adding a New Pipeline
 

docs/source/inference/configuration.md

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

docs/source/inference/optimizations.md

@@ -1,37 +1,32 @@
-
+(inference-optimizations)=
 # Optimizations
 
 This page describes the various options for speeding up generation times in FastVideo.
 
 ## Table of Contents
-
 - Optimized Attention Backends
-
   - [Flash Attention](#optimizations-flash)
   - [Sliding Tile Attention](#optimizations-sta)
   - [Sage Attention](#optimizations-sage)
-  - [Sage Attention 3](#optimizations-sage3)
 
 - Caching Techniques
   - [TeaCache](#optimizations-teacache)
 
+(optimizations-backends)=
 ## Attention Backends
 
 ### Available Backends
-
 - Torch SDPA: `FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA`
 - Flash Attention 2 and 3: `FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN`
 - Sliding Tile Attention: `FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN`
 - Video Sparse Attention: `FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN`
 - Sage Attention: `FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN`
-- Sage Attention 3: `FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN_THREE`
 
 ### Configuring Backends
 
 There are two ways to configure the attention backend in FastVideo.
 
 #### 1. In Python
-
 In python, set the `FASTVIDEO_ATTENTION_BACKEND` environment variable before instantiating `VideoGenerator` like this:
 
 ```python
@@ -39,13 +34,13 @@ os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLIDING_TILE_ATTN"
 ```
 
 #### 2. In CLI
-
 You can also set the environment variable on the command line:
 
 ```bash
 FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN python example.py
 ```
 
+(optimizations-flash)=
 ### Flash Attention
 
 **`FLASH_ATTN`**
@@ -62,12 +57,16 @@ And if using a Hopper+ GPU (ie H100), installing [Flash Attention 3](https://git
 git clone https://github.com/Dao-AILab/flash-attention.git && cd flash-attention
 
 cd hopper
-pip install ninja
+pip install ninja 
 python setup.py install
 ```
 
-### Sliding Tile Attention
+:::{note}
+FastVideo will automatically detect and use `FA3` if it is installed when using `FLASH_ATTN` backend.
+:::
 
+(optimizations-sta)=
+### Sliding Tile Attention
 **`SLIDING_TILE_ATTN`**
 
 ```bash
@@ -76,8 +75,8 @@ pip install st_attn==0.0.4
 
 Please see [this page](#sta-installation) for more installation instructions.
 
+(optimizations-vsa)=
 ### Video Sparse Attention
-
 **`VIDEO_SPARSE_ATTN`**
 
 ```bash
@@ -87,40 +86,20 @@ python setup_vsa.py install
 
 Please see [this page](#vsa-installation) for more installation instructions.
 
+(optimizations-sage)=
 ### Sage Attention
-
 **`SAGE_ATTN`**
 
 To use [SageAttention](https://github.com/thu-ml/SageAttention) 2.1.1, please compile from source:
 
 ```bash
 git clone https://github.com/thu-ml/SageAttention.git
-cd sageattention
+cd sageattention 
 python setup.py install  # or pip install -e .
 ```
 
-### Sage Attention 3
-
-**`SAGE_ATTN_THREE`**
-
-[SageAttention 3](https://huggingface.co/jt-zhang/SageAttention3) is an advanced attention mechanism that leverages FP4 quantization and Blackwell GPU Tensor Cores for significant performance improvements.
-
-#### Hardware Requirements
-
-- RTX5090
-
-#### Installation
-
-Note that Sage Attention 3 requires `python>=3.13`, `torch>=2.8.0`, `CUDA >=12.8`. If you are using `uv` and using `torch==2.8.0` make sure that `sentencepiece==0.2.1` in the pyproject.toml file.
-
-To use Sage Attention 3 in FastVideo, first get access to the SageAttention3 code, then move `sageattn/` and `setup.py` to the directory `fastvideo/attention/backends`, then install from using:
-
-```bash
-python setup.py install
-```
-
+(optimizations-teacache)=
 ## Teacache
-
 TeaCache is an optimization technique supported in FastVideo that can significantly speed up video generation by skipping redundant calculations across diffusion steps. This guide explains how to enable and configure TeaCache for optimal performance in FastVideo.
 
 ### What is TeaCache?

docs/source/inference/support_matrix.md

@@ -0,0 +1,136 @@
+(support-matrix)=
+# Compatibility Matrix
+The table below shows every supported model and optimizations supported for them.
+
+The symbols used have the following meanings:
+
+- ✅ = Full compatibility
+- ❌ = No compatibility
+- ⭕ = Does not apply to this model
+
+## Models x Optimization
+The `HuggingFace Model ID` can be directly pass to `from_pretrained()` methods and FastVideo will use the optimal default parameters when initializing and generating videos.
+
+:::{raw} html
+<style>
+  /* Make smaller to try to improve readability  */
+  td {
+    font-size: 0.9rem;
+    text-align: center;
+  }
+
+  th {
+    text-align: center;
+    font-size: 0.9rem;
+  }
+</style>
+:::
+
+:::{list-table}
+:header-rows: 1
+:stub-columns: 3
+:widths: auto
+:class: vertical-table-header
+
+- * Model Name
+  * HuggingFace Model ID
+  * Resolutions
+  * TeaCache
+  * Sliding Tile Attn
+  * Sage Attn
+  * Video Sparse Attention (VSA)
+- * FastWan2.1 T2V 1.3B
+  * `FastVideo/FastWan2.1-T2V-1.3B-Diffusers`
+  * 480P
+  * ⭕
+  * ⭕
+  * ⭕
+  * ✅
+- * FastWan2.2 TI2V 5B Full Attn*
+  * `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers`
+  * 720P
+  * ⭕
+  * ⭕
+  * ⭕
+  * ✅
+- * Wan2.2 TI2V 5B 
+  * `Wan-AI/Wan2.2-TI2V-5B-Diffusers`
+  * 720P
+  * ⭕
+  * ⭕
+  * ✅
+  * ⭕
+- * Wan2.2 T2V A14B
+  * `Wan-AI/Wan2.2-T2V-A14B-Diffusers`
+  * 480P<br>720P
+  * ❌
+  * ❌
+  * ✅
+  * ⭕
+- * Wan2.2 I2V A14B
+  * `Wan-AI/Wan2.2-I2V-A14B-Diffusers`
+  * 480P<br>720P
+  * ❌
+  * ❌
+  * ✅
+  * ⭕
+- * HunyuanVideo
+  * `hunyuanvideo-community/HunyuanVideo`
+  * 720px1280p<br>544px960p
+  * ❌
+  * ✅
+  * ✅
+  * ⭕
+- * FastHunyuan
+  * `FastVideo/FastHunyuan-diffusers`
+  * 720px1280p<br>544px960p
+  * ❌
+  * ✅
+  * ✅
+  * ⭕
+- * Wan2.1 T2V 1.3B
+  * `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`
+  * 480P
+  * ✅
+  * ✅*
+  * ✅
+  * ⭕
+- * Wan2.1 T2V 14B
+  * `Wan-AI/Wan2.1-T2V-14B-Diffusers`
+  * 480P, 720P
+  * ✅
+  * ✅*
+  * ✅
+  * ⭕
+- * Wan2.1 I2V 480P
+  * `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers`
+  * 480P
+  * ✅
+  * ✅*
+  * ✅
+  * ⭕
+- * Wan2.1 I2V 720P
+  * `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers`
+  * 720P
+  * ✅
+  * ✅
+  * ✅
+  * ⭕
+- * StepVideo T2V
+  * `FastVideo/stepvideo-t2v-diffusers`
+  * 768px768px204f<br>544px992px204f<br>544px992px136f
+  * ❌
+  * ❌
+  * ✅
+  * ⭕
+:::
+
+**Note**: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.
+
+## Special requirements
+
+### StepVideo T2V
+- The self-attention in text-encoder (step_llm) only supports CUDA capabilities sm_80 sm_86 and sm_90
+
+### Sliding Tile Attention
+- Currently only Hopper GPUs (H100s) are supported.

docs/source/sliding_tile_attention/demo.md

@@ -1,3 +1,4 @@
+(sta-demo)=
 
 # 🔍 Demo
 This is is a demo for 2D STA with window size (6,6) operating on a (10, 10) image.

docs/source/sliding_tile_attention/installation.md

@@ -1,3 +1,4 @@
+(sta-installation)=
 
 # 🔧 Installation
 You can install the Sliding Tile Attention package using

docs/source/training/data_preprocess.md

@@ -1,3 +1,4 @@
+(v0-data-preprocess)=
 
 # 🧱 Data Preprocess
 

docs/source/training/finetune.md

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

docs/source/video_sparse_attention/installation.md

@@ -1,3 +1,4 @@
+(vsa-installation)=
 
 # 🔧 Installation
 You can install the Video Sparse Attention package using

examples/datasets/crush_smol/download_dataset.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"

examples/datasets/crush_smol/validation.json

@@ -0,0 +1,76 @@
+{
+  "data": [
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
+      "image_path": null,
+      "video_path": "validation_dataset/yYcK4nANZz4-Scene-034.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
+      "image_path": null,
+      "video_path": "validation_dataset/yYcK4nANZz4-Scene-027.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
+      "image_path": null,
+      "video_path": "validation_dataset/yYcK4nANZz4-Scene-030.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.",
+      "image_path": null,
+      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-016.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "The video shows a green and orange object being flattened as if it were under a hydraulic press, with the press moving down and compressing the object.",
+      "image_path": null,
+      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-056.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "The video shows a cylindrical object with a cityscape image being flattened as if it were under a hydraulic press. The object is placed on a metal platform, and a large, striped cylinder presses down on it, causing it to collapse and release a liquid inside. The background features a green wall with a yellow and red warning sign.",
+      "image_path": null,
+      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-059.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "The video shows a close-up of an orange being flattened as if it were under a hydraulic press, with the press moving down and compressing the fruit until it is completely flattened.",
+      "image_path": null,
+      "video_path": "validation_dataset/EJqsC21GSBY-Scene-059.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A colorful puzzle ball is being crushed by a large metal cylinder, which flattens the objects as if they were under a hydraulic press.",
+      "image_path": null,
+      "video_path": "validation_dataset/GBSfpTcKegk-Scene-003.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    }
+  ]
+}

examples/datasets/crush_smol/validation_single.json

@@ -0,0 +1,13 @@
+{
+  "data": [
+    {
+      "caption": "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.",
+      "image_path": null,
+      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-016.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    }
+  ]
+}

examples/distill/SFWan2.1-T2V/distill_dmd_t2v_1.3B.sh

@@ -16,29 +16,30 @@ 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=29503
+export MASTER_PORT=29501
 export TOKENIZERS_PARALLELISM=false
-export WANDB_API_KEY=your_wandb_api_key
+export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
 export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
+export WANDB_MODE=offline
 export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
 
 # Configs
-NUM_GPUS=1
+NUM_GPUS=4
 
 # Model paths for Self-Forcing DMD distillation:
 GENERATOR_MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
 REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-14B-Diffusers"  # Teacher model
 FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Critic model
 
-DATA_DIR=your_data_dir
-VALIDATION_DATASET_FILE=your_validation_data_dir
+DATA_DIR="data/mixkit-64_processed/Node_0_GPU_1_File_1/combined_parquet_dataset"
+VALIDATION_DATASET_FILE="data/mixkit-64_processed/validation.json"
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]
 
+# Training arguments
 training_args=(
-  --tracker_project_name SFwan_t2v_distill_self_forcing_dmd  
-  --output_dir your_output_dir
+  --tracker_project_name SFwan_t2v_distill_self_forcing_dmd  # Updated for self-forcing DMD
+  --output_dir "/mnt/sharefs/users/hao.zhang/SFwan_t2v_finetune"
   --max_train_steps 4000
   --train_batch_size 1
   --train_sp_batch_size 1
@@ -46,15 +47,16 @@ training_args=(
   --num_latent_t 21
   --num_height 480
   --num_width 832
+  --num_frames 81  # Must be divisible by num_frame_per_block (81 % 3 = 0 ✓)
   --enable_gradient_checkpointing_type "full"
   --log_visualization
   --simulate_generator_forward
-  --num_frames 81
   --num_frame_per_block 3  # Frame generation block size for self-forcing
   --enable_gradient_masking
   --gradient_mask_last_n_frames 21
 )
 
+# Parallel arguments
 parallel_args=(
   --num_gpus $NUM_GPUS # 64
   --sp_size 1
@@ -63,26 +65,31 @@ parallel_args=(
   --hsdp_shard_dim $NUM_GPUS
 )
 
+# Model arguments
 model_args=(
   --model_path $GENERATOR_MODEL_PATH  # TODO: check if you can remove this in this script
   --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
+  --generator_model_path $GENERATOR_MODEL_PATH
   --real_score_model_path $REAL_SCORE_MODEL_PATH
   --fake_score_model_path $FAKE_SCORE_MODEL_PATH
 )
 
+# Dataset arguments
 dataset_args=(
   --data_path "$DATA_DIR"
   --dataloader_num_workers 4
 )
 
+# Validation arguments
 validation_args=(
   --log_validation
   --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
+  --validation_steps 100
   --validation_sampling_steps "4"
   --validation_guidance_scale "6.0" # not used for dmd inference
 )
 
+# Optimizer arguments
 optimizer_args=(
   --learning_rate 1e-5
   --mixed_precision "bf16"
@@ -93,6 +100,7 @@ optimizer_args=(
   --max_grad_norm 1.0
 )
 
+# Miscellaneous arguments
 miscellaneous_args=(
   --inference_mode False
   --checkpoints_total_limit 3
@@ -103,9 +111,10 @@ miscellaneous_args=(
   --use_ema True
   --ema_decay 0.99
   --ema_start_step 100
-  --init_weights_from_safetensors your_ode_init_weights_path
+  --init_weights_from_safetensors "/mnt/weka/home/hao.zhang/wl/Self-Forcing/diffusers_ode_init/model.safetensors"
 )
 
+# Self-forcing DMD arguments
 dmd_args=(
   --dmd_denoising_steps '1000,750,500,250'
   --min_timestep_ratio 0.02
@@ -117,11 +126,13 @@ dmd_args=(
   --warp_denoising_step
 )
 
+# Self-forcing specific arguments
 self_forcing_args=(
   --independent_first_frame False  # Whether to treat first frame independently
   --same_step_across_blocks True  # Whether to use same denoising step across all blocks
   --last_step_only False  # Whether to only use the last denoising step
   --context_noise 0  # Amount of noise to add during context caching (0 = no noise)
+  --validate_cache_structure False  # Set to True for debugging KV cache issues
 )
 
 torchrun \
@@ -137,4 +148,4 @@ torchrun \
     "${validation_args[@]}" \
     "${miscellaneous_args[@]}" \
     "${dmd_args[@]}" \
-    "${self_forcing_args[@]}"
+    "${self_forcing_args[@]}"

examples/distill/SFWan2.1-T2V/download_dataset.sh

@@ -1,3 +1,3 @@
 #!/bin/bash
 
-python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
+python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"

examples/distill/SFWan2.1-T2V/preprocess_data.sh

@@ -21,4 +21,4 @@ torchrun --nproc_per_node=$GPU_NUM \
     --samples_per_file 8 \
     --flush_frequency 8 \
     --video_length_tolerance_range 5 \
-    --preprocess_task "t2v" 
+    --preprocess_task "t2v" 

examples/distill/SFWan2.2-A14B/distill_dmd.sh

@@ -1,157 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=t2v
-#SBATCH --partition=main
-#SBATCH --nodes=4
-#SBATCH --ntasks=4
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=dmd_t2v_output/t2v_%j.out
-#SBATCH --error=dmd_t2v_output/t2v_%j.err
-#SBATCH --exclusive
-
-# Basic Info
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-export NCCL_DEBUG_SUBSYS=INIT,NET
-# 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 TOKENIZERS_PARALLELISM=false
-export WANDB_API_KEY="2f25ad37933894dbf0966c838c0b8494987f9f2f"
-# export WANDB_API_KEY='your_wandb_api_key_here'
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-
-# Configs
-NUM_GPUS=8
-
-# Model paths for Self-Forcing DMD distillation with Wan2.2:
-# GENERATOR_MODEL_PATH="Wan-AI/Wan2.2-T2V-A14B-Diffusers"  # Updated to Wan2.2
-# REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.2-T2V-A14B-Diffusers" # Teacher model
-# FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.2-T2V-A14B-Diffusers" # Critic model
-GENERATOR_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Updated to Wan2.2
-REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Teacher model
-FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers" # Critic model
-
-# DATA_DIR="data/test-text-preprocessing/Node_0_GPU_1_File_1/combined_parquet_dataset/"
-DATA_DIR="/mnt/weka/home/hao.zhang/matthew/FastVideo/data/test-text-preprocessing"
-# DATA_DIR=data/crush-smol_processed_t2v/combined_parquet_dataset
-# DATA_DIR="/mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn-upload/latents_i2v/train/"
-# VALIDATION_DATASET_FILE="data/crush-smol-single_processed_t2v/validation.json"
-VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
-
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-training_args=(
-  --tracker_project_name SFwan2.2_t2v_distill_self_forcing_dmd  # Updated for Wan2.2
-  --output_dir "/mnt/sharefs/users/hao.zhang/SFwan2.2_t2v_finetune"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --max_train_steps 4000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 448  # Updated to match Wan2.2 config
-  --num_width 832   # Updated to match Wan2.2 config
-  --enable_gradient_checkpointing_type "full"
-  --simulate_generator_forward
-  # --log_visualization
-  --num_frames 81
-  --num_frame_per_block 3  # Frame generation block size for self-forcing
-  --enable_gradient_masking
-  --gradient_mask_last_n_frames 21
-  # --init_weights_from_safetensors /mnt/sharefs/users/hao.zhang/wl/models/sf_ode_init_wan22_checkpoints/high/3k/
-  # --init_weights_from_safetensors_2 /mnt/sharefs/users/hao.zhang/wl/models/sf_ode_init_wan22_checkpoints/low/3k/
-)
-
-parallel_args=(
-  --num_gpus 32 # 64
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1 # 64
-  --hsdp_shard_dim 32
-)
-
-model_args=(
-  --model_path $GENERATOR_MODEL_PATH
-  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
-)
-
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 4
-)
-
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 20
-  --validation_sampling_steps "4"
-  --validation_guidance_scale "6.0" # not used for dmd inference
-)
-
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 500
-  --weight_only_checkpointing_steps 500
-  --weight_decay 0.01
-  --betas '0.0,0.999'
-  --max_grad_norm 1.0
-)
-
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --flow_shift 5
-  --seed 1000
-  --use_ema True
-  --ema_decay 0.99
-  --ema_start_step 100
-)
-
-dmd_args=(
-  --dmd_denoising_steps '1000,750,500,250'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --dfake_gen_update_ratio 5
-  --real_score_guidance_scale 3.0
-  --fake_score_learning_rate 8e-6
-  --fake_score_betas '0.0,0.999'
-  --warp_denoising_step
-)
-
-self_forcing_args=(
-  --independent_first_frame False  # Whether to treat first frame independently
-  --same_step_across_blocks True  # Whether to use same denoising step across all blocks
-  --last_step_only False  # Whether to only use the last denoising step
-  --context_noise 0  # Amount of noise to add during context caching (0 = no noise)
-)
-
-srun torchrun \
---nnodes $SLURM_JOB_NUM_NODES \
---nproc_per_node $NUM_GPUS \
---node_rank $SLURM_PROCID \
---rdzv_backend=c10d \
---rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/training/wan_self_forcing_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}" \
-    "${self_forcing_args[@]}"