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**FastVideo is a unified post-training and real-time inference framework for accelerated video generation.** ## NEWS - `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/). - `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/). - `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/). - `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/). - `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). - `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/). - `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/). - `2026/03/17`: Release demo: Into the Dreamverse: Vibe Directing in FastVideo, check out the [Blog](https://haoailab.com/blogs/dreamverse/). - `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/). - `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/). ### More News - `2025/06/14`: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389). - `2025/04/24`: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released! - `2025/02/18`: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/). ## Key Features FastVideo has the following features: - End-to-end post-training support for bidirectional and autoregressive models: - Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs - Data preprocessing pipeline for video, image, and text data - Distribution Matching Distillation (DMD2) stepwise distillation. - Sparse attention with [Video Sparse Attention](https://arxiv.org/pdf/2505.13389) - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achieve >50x denoising speedup - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing. - Causal distillation through Self-Forcing - 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. - State-of-the-art performance optimizations for inference - Sequence Parallelism for distributed inference - Multiple state-of-the-art attention backends - User-friendly CLI and Python API - See this [page](https://hao-ai-lab.github.io/FastVideo/inference/optimizations/) for full list of supported optimizations. - Diverse hardware and OS support - Support H100, A100, 4090 - Support Linux, Windows, MacOS - See this [page](https://hao-ai-lab.github.io/FastVideo/inference/support_matrix/) for full list of supported models, hardware assumptions, and optimization compatibility. - Realtime video generation & editing - [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 ## Getting Started 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. ```bash # Create and activate a new uv environment uv venv --python 3.12 --seed source .venv/bin/activate # Install FastVideo on NVIDIA CUDA 12 UV_TORCH_BACKEND=cu126 uv pip install fastvideo ``` Use `UV_TORCH_BACKEND=cu130` on CUDA 13. Apple silicon users should follow the [MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/). > **On an Apple Silicon Mac?** Install with `uv pip install -e '.[mlx]'` from > a clone, then pick a recipe in the > [cookbook](https://haoailab.com/FastVideo/cookbook/). See the > [MLX install guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/mlx/). Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions. > **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/). ### Install with an AI coding agent 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: ```text Install FastVideo (https://github.com/hao-ai-lab/FastVideo) into a fresh uv virtual environment. 1. Detect the platform: run `uname -m`, `nvidia-smi`, and `nvcc --version`. 2. Read and follow the matching install guide exactly (in this repo, or at https://hao-ai-lab.github.io/FastVideo/getting_started/installation/): - NVIDIA GPU, x86_64 -> docs/getting_started/installation/gpu.md - NVIDIA DGX Spark / GB10, aarch64, CUDA 13 -> docs/getting_started/installation/spark.md - Apple Silicon, macOS -> docs/getting_started/installation/mlx.md 3. Use uv for every step. If a command fails, debug it and tell me what you changed. 4. Verify the result: python -c "import fastvideo, torch; print('cuda', torch.cuda.is_available())" fastvideo --help 5. Report which platform you detected and any deviations you had to make. ``` ## 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/). See below for recipes and datasets: | Model | Sparse Distillation | Dataset | | ------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | | [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) | | [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) | ## Dreamverse — Realtime Video Generation & Editing [Dreamverse](apps/dreamverse/README.md) is FastVideo's realtime video generation and editing platform — "vibe directing" a video as it streams. It lives in the monorepo under [`apps/dreamverse/`](apps/dreamverse/) and ships its own backend (`dreamverse-server`) plus a web UI. Try the [live demo](https://dreamverse.fastvideo.org/), read the [blog](https://haoailab.com/blogs/dreamverse/), or run it yourself. Dreamverse deploys on a local GPU, a self-hosted B200 server over SSH, Docker, or serverless [Modal](apps/dreamverse/scripts/modal/README.md) — see the [Dreamverse README](apps/dreamverse/README.md). ## 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/attention/vsa/#installation). Create a file called `example.py` with the following code: ```python import os from fastvideo import VideoGenerator def main(): os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN" # Create a video generator with a pre-trained model generator = VideoGenerator.from_pretrained( "FastVideo/FastWan2.1-T2V-1.3B-Diffusers", num_gpus=1, # Adjust based on your hardware ) # Define a prompt for your video prompt = "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest." # Generate the video video = generator.generate_video( prompt, output_path="my_videos/", # Controls where videos are saved save_video=True ) if __name__ == '__main__': main() ``` `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. Run the script with: ```bash 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/). ## More Guides - [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview/) - [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/) - [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/contributing/overview/) ## Awesome work using FastVideo or our research projects - [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. - [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo. - [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. - [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo. - [HY-WorldPlay](https://github.com/Tencent-Hunyuan/HY-WorldPlay): An action-conditioned world model model trained using FastVideo framework. - [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. - [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. - [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. ## 🤝 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). ## Acknowledgement 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. ## Citation If you find FastVideo useful, please consider citing our research work: ```bibtex @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}, journal={arXiv preprint arXiv:2505.13389}, year={2025} } @article{zhang2025fast, title={Fast video generation with sliding tile attention}, author={Zhang, Peiyuan and Chen, Yongqi and Su, Runlong and Ding, Hangliang and Stoica, Ion and Liu, Zhengzhong and Zhang, Hao}, journal={arXiv preprint arXiv:2502.04507}, year={2025} } ```