> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/fastgen/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/fastgen/_mcp/server.

# Resources

[View source on GitHub](https://github.com/NVlabs/FastGen/blob/7889625364c56fa02d2a54c1683c99a98bc891c3/README.md)

## Adoption, Collaboration, and Usage

FastGen is used across NVIDIA and the community to build fast generative models.

### Models Distilled Using FastGen

* **[FastGen-AYF](https://huggingface.co/nvidia/FastGen-AYF)** (distilled to 4-8 steps using AYF from FastGen)
* **[Qwen-Image-Flash](https://huggingface.co/nvidia/Qwen-Image-Flash)** (distilled to 4 steps using DMD2 from FastGen, [NVIDIA ModelOpt](https://github.com/NVIDIA/Model-Optimizer), and [NVIDIA AutoModel](https://github.com/NVIDIA-NeMo/Automodel))
* **[Baseten FLUX.2](https://www.baseten.co/blog/faster-image-generation-timestep-distillation-flux2/)** (distilled to 8 steps using DMD2)
* **[Reka Real-Time Video Generation](https://reka.ai/labs/research/real-time-video-generation)** (distilled to a real-time video generator using FastGen)
* **[CorrDiff](https://developer.nvidia.com/blog/accelerating-diffusion-models-with-an-open-plug-and-play-offering/)** (distilled to 1 step using ECT)
* **[cBottle Super-Resolution](https://github.com/NVlabs/cBottle/blob/main/src/cbottle/training/distill_super_resolution.py)** (distilled to 2 steps using ECT)

### Projects Using FastGen

* **[Simplifying and Improving On-Policy Distillation for Few-Step Diffusion](https://zjwfufu.github.io/SMD-Page/)** (built upon FastGen)
* **[Parametric Trajectory Distillation for Few-Step Video Generation](https://alan-lanfeng.github.io/PTD/)** (built upon FastGen)
* **[Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation](https://csy2077.github.io/dfd-project-page/)** (built upon FastGen)
* **[Parallel Decoding Distillation for Image and Video Generation](https://research.nvidia.com/labs/genair/pdd/)** (built upon FastGen)
* **[Transition Matching Distillation for Fast Video Generation](https://research.nvidia.com/labs/genair/tmd/)** (built upon FastGen)
* **[Mode Seeking meets Mean Seeking for Fast Long Video Generation](https://primecai.github.io/mmm/)** (built upon FastGen)
* **[StreamingEffect: Real-Time Human-Centric Video Effect Generation](https://github.com/showlab/StreamingEffect)** (built upon FastGen)
* **[CARV: Variance Reduction for Expectations with Diffusion Teachers](https://research.nvidia.com/labs/sil/projects/CARV/)** (used FastGen for the DMD2 experiments)

### FastGen Collaborations

* **[FastH3 Omni](https://haoailab.com/blogs/fastvideo-omniref-update/)** (Minimax H3 Ref2VA distillation with [FastVideo](https://github.com/hao-ai-lab/FastVideo) and Nuva Lab)
* **[FastH3 Preview](https://haoailab.com/blogs/fasth3-preview/)** (MiniMax H3 distillation with [FastVideo](https://github.com/hao-ai-lab/FastVideo) and Nuva Lab)
* **[FlashDreams](https://github.com/NVIDIA/flashdreams)** (optimized serving for FastGen-distilled autoregressive video models)
* **[NVIDIA Model Optimizer (ModelOpt)](https://github.com/NVIDIA/Model-Optimizer)** (integration of FastGen step-distillation methods)

## Reference

```
@misc{fastgen2026,
  title={NVIDIA FastGen: Fast Generation from Diffusion Models},
  author={Berner, Julius and Liu, Chao and Vahdat, Arash},
  url={https://github.com/NVlabs/FastGen},
  year={2026},
}
```