6e4813f8b4
Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com> Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
212 lines
16 KiB
Markdown
212 lines
16 KiB
Markdown
<div align="center">
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<img src=assets/logos/logo.svg width="30%"/>
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</div>
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<p align="center">
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| <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://haoailab.com/FastVideo/cookbook/"><b>Cookbook</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | <a href="https://github.com/hao-ai-lab/FastVideo/discussions/982" target="_blank"><b>Weekly Dev Meeting</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://github.com/hao-ai-lab/FastVideo/discussions/1097" target="_blank"> <b> WeChat </b> </a> |
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</p>
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**FastVideo is a unified post-training and real-time inference framework for accelerated video generation.**
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## NEWS
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- `2026/10/06`: FastH3 V2 now runs on a single consumer machine: NVIDIA RTX 5090, RTX 4090 and RTX PRO 6000 GPUs, DGX Spark and Apple Silicon. We also release [FastH3 Trim](https://huggingface.co/FastVideo/FastVideo-FastH3-Trim-8-Step-NVFP4), an experimental pruned model that is 4.2× smaller than base H3 and runs in as little as 8 GB of GPU memory. Get the [models](https://huggingface.co/collections/FastVideo/fastvideo-fasth3) and read the [Blog](https://haoailab.com/blogs/fasth3-rtx/).
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- `2026/10/06`: FastVideo now supports [Kandinsky 6](https://x.com/kandinskylab_ai/status/2107374635218055345) from Kandinsky Lab: text- and image-to-video with synchronized audio (base and 10-step distilled pi-Flow checkpoints) plus video super-resolution up to 4x. See the [Kandinsky 6 recipes](https://haoailab.com/FastVideo/cookbook/kandinsky6/).
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- `2026/09/15`: Release [FastH3 8-Step V2](https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2), an eight-forward data-free DMD2 checkpoint distilled from MiniMax-H3 with 80% Video Sparse Attention. Run it with `examples/inference/basic/basic_fasth3_8step.py` or the [FastH3 8-Step V2 recipe](https://haoailab.com/FastVideo/cookbook/minimax-h3/).
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- `2026/09/01`: FastH3 now runs locally on Apple Silicon through MLX and on NVIDIA DGX Spark through CUDA 13, including two-Spark inference. Follow the [FastH3 recipes](https://haoailab.com/FastVideo/cookbook/minimax-h3/) and read the [Blog](https://haoailab.com/blogs/fasth3-local/).
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- `2026/08/27`: [FastH3 Preview v1](https://haoailab.com/blogs/fasth3-preview/) is an open-weight 4-step sparse-distilled MiniMax-H3 model for synchronized video-and-audio generation, developed in collaboration with [Nuva Lab](https://nuvalab.ai/) and the [NVIDIA FastGen team](https://github.com/NVlabs/FastGen). Download the recommended [VSA / Data-Free weights](https://huggingface.co/FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree), or see the [full FastH3 collection](https://huggingface.co/collections/FastVideo/fastvideo-fasth3).
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- `2026/08/19`: FastVideo now supports MLX on Apple Silicon with [FastMetal-QAD](https://huggingface.co/collections/FastVideo/fastmetal), a family of 1.3B, 5B, and 14B models optimized for Mac. Follow the [MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/) and read the [Blog](https://haoailab.com/blogs/fastmetal/).
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- `2026/06/23`: Release FastWan-QAD: 5s of Video generated in 1.8s E2E. See the [FastWan-QAD models](https://huggingface.co/FastVideo/FastWan-QAD-FP8-1.3B), [Attn-QAT training guide](https://haoailab.com/FastVideo/training/attn_qat/), and [blog](https://haoailab.com/blogs/fastwan-qad/).
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- `2026/03/17`: Release demo: Into the Dreamverse: Vibe Directing in FastVideo, check out the [Blog](https://haoailab.com/blogs/dreamverse/).
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- `2026/03/13`: Release demo: Create a 5s 1080p Video in 4.5s with FastVideo on a Single GPU, check out the [Blog](https://haoailab.com/blogs/fastvideo_realtime_1080p/).
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- `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).
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- `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/).
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### More News
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- `2025/06/14`: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389).
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- `2025/04/24`: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released!
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- `2025/02/18`: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).
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## Key Features
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FastVideo has the following features:
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- End-to-end post-training support for bidirectional and autoregressive models:
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- Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs
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- Data preprocessing pipeline for video, image, and text data
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- Distribution Matching Distillation (DMD2) stepwise distillation.
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- Sparse attention with [Video Sparse Attention](https://arxiv.org/pdf/2505.13389)
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- [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achieve >50x denoising speedup
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- Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing.
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- Causal distillation through Self-Forcing
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- See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for the supported training workflows, and the [support matrix](https://hao-ai-lab.github.io/FastVideo/inference/support_matrix/) for supported models.
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- State-of-the-art performance optimizations for inference
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- Sequence Parallelism for distributed inference
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- Multiple state-of-the-art attention backends
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- User-friendly CLI and Python API
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- See this [page](https://hao-ai-lab.github.io/FastVideo/inference/optimizations/) for full list of supported optimizations.
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- Diverse hardware and OS support
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- Support H100, A100, 4090
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- Support Linux, Windows, MacOS
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- See this [page](https://hao-ai-lab.github.io/FastVideo/inference/support_matrix/) for full list of supported models, hardware assumptions, and optimization compatibility.
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- Realtime video generation & editing
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- [Dreamverse](apps/dreamverse/README.md): stream and "vibe direct" video in realtime ([live demo](https://dreamverse.fastvideo.org/)), deployable on local GPU, a self-hosted B200 server, Docker, or serverless Modal
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## Getting Started
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We recommend using [uv](https://docs.astral.sh/uv/) to create a clean environment. If you previously used Conda, switching to uv generally gives faster and more stable installs.
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```bash
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# Create and activate a new uv environment
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uv venv --python 3.12 --seed
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source .venv/bin/activate
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# Install FastVideo on NVIDIA CUDA 12
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UV_TORCH_BACKEND=cu126 uv pip install fastvideo
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```
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Use `UV_TORCH_BACKEND=cu130` on CUDA 13. Apple silicon users should follow the
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[MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/).
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> **On an Apple Silicon Mac?** Install with `uv pip install -e '.[mlx]'` from
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> a clone, then pick a recipe in the
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> [cookbook](https://haoailab.com/FastVideo/cookbook/). See the
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> [MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/).
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Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions.
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> **On an NVIDIA DGX Spark (GB10 / ARM64 + CUDA 13)?** There's no prebuilt ARM wheel for the FastVideo CUDA kernel, so it's an editable from-source install (`UV_TORCH_BACKEND=cu130 uv pip install -e .`, which compiles that kernel for you) rather than `UV_TORCH_BACKEND=cu130 uv pip install fastvideo`. A compatible prebuilt ARM64 FlashAttention wheel is available separately. Follow the [DGX Spark install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/spark/).
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### Install with an AI coding agent
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FastVideo is a monorepo with rich agent guidance (see [`AGENTS.md`](AGENTS.md)). If you use Claude Code, Cursor, or another coding agent, paste the prompt below — it detects your platform and follows the matching guide:
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```text
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Install FastVideo (https://github.com/hao-ai-lab/FastVideo) into a fresh uv virtual environment.
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1. Detect the platform: run `uname -m`, `nvidia-smi`, and `nvcc --version`.
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2. Read and follow the matching install guide exactly (in this repo, or at
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https://hao-ai-lab.github.io/FastVideo/getting_started/installation/):
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- NVIDIA GPU, x86_64 -> docs/getting_started/installation/gpu.md
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- NVIDIA DGX Spark / GB10, aarch64, CUDA 13 -> docs/getting_started/installation/spark.md
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- Apple Silicon, macOS -> docs/getting_started/installation/mlx.md
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3. Use uv for every step. If a command fails, debug it and tell me what you changed.
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4. Verify the result:
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python -c "import fastvideo, torch; print('cuda', torch.cuda.is_available())"
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fastvideo --help
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5. Report which platform you detected and any deviations you had to make.
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```
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## Sparse Distillation
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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/).
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See below for recipes and datasets:
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| Model | Sparse Distillation | Dataset |
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| ------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
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| [FastWan2.1-T2V-1.3B](https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P) | [FastVideo Synthetic Wan2.1 480P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k) |
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| [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free) | [FastVideo Synthetic Wan2.2 720P](https://huggingface.co/datasets/FastVideo/Wan2.2-Syn-121x704x1280_32k) |
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## Dreamverse — Realtime Video Generation & Editing
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[Dreamverse](apps/dreamverse/README.md) is FastVideo's realtime video generation
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and editing platform — "vibe directing" a video as it streams. It lives in the
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monorepo under [`apps/dreamverse/`](apps/dreamverse/) and ships its own backend
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(`dreamverse-server`) plus a web UI.
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Try the [live demo](https://dreamverse.fastvideo.org/), read the
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[blog](https://haoailab.com/blogs/dreamverse/), or run it yourself. Dreamverse
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deploys on a local GPU, a self-hosted B200 server over SSH, Docker, or
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serverless [Modal](apps/dreamverse/scripts/modal/README.md) — see the
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[Dreamverse README](apps/dreamverse/README.md).
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## Inference
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### Generating Your First Video
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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/attention/vsa/#installation). Create a file called `example.py` with the following code:
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```python
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import os
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from fastvideo import VideoGenerator
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def main():
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os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
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# Create a video generator with a pre-trained model
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generator = VideoGenerator.from_pretrained(
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"FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
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num_gpus=1, # Adjust based on your hardware
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)
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# Define a prompt for your video
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prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest."
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# Generate the video
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video = generator.generate_video(
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prompt,
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output_path="my_videos/", # Controls where videos are saved
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save_video=True
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)
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if __name__ == '__main__':
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main()
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```
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`num_gpus=1` runs the worker in-process (weights load once, no extra Python process). On Colab/Kaggle-style machines with ~16GB host RAM, keep `num_gpus=1`; free-tier system memory does not grow with extra T4s, so `num_gpus>1` is likely to OOM.
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Run the script with:
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```bash
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python example.py
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```
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For a more detailed guide, please see our [inference quick start](https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/).
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## More Guides
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- [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview/)
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- [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/)
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- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/contributing/overview/)
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## Awesome work using FastVideo or our research projects
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- [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.
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- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo.
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- [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.
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- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo.
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- [HY-WorldPlay](https://github.com/Tencent-Hunyuan/HY-WorldPlay): An action-conditioned world model model trained using FastVideo framework.
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- [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.
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- [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.
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- [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.
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## 🤝 Contributing
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We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview/).
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See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/899).
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## Acknowledgement
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We learned the design and reused code from the following projects: [Wan-Video](https://github.com/Wan-Video), [ThunderKittens](https://github.com/HazyResearch/ThunderKittens), [DMD2](https://github.com/tianweiy/DMD2), [diffusers](https://github.com/huggingface/diffusers), [xDiT](https://github.com/xdit-project/xDiT), [vLLM](https://github.com/vllm-project/vllm), [SGLang](https://github.com/sgl-project/sglang). We thank [MBZUAI](https://ifm.mbzuai.ac.ae/), [Anyscale](https://www.anyscale.com/), and [GMI Cloud](https://www.gmicloud.ai/) for their support throughout this project.
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## Citation
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If you find FastVideo useful, please consider citing our research work:
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```bibtex
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@article{zhang2025vsa,
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title={Vsa: Faster video diffusion with trainable sparse attention},
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author={Zhang, Peiyuan and Chen, Yongqi and Huang, Haofeng and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
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journal={arXiv preprint arXiv:2505.13389},
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year={2025}
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}
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@article{zhang2025fast,
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title={Fast video generation with sliding tile attention},
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author={Zhang, Peiyuan and Chen, Yongqi and Su, Runlong and Ding, Hangliang and Stoica, Ion and Liu, Zhengzhong and Zhang, Hao},
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journal={arXiv preprint arXiv:2502.04507},
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year={2025}
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}
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```
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