Resources

View source on GitHub

Adoption, Collaboration, and Usage

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

Models Distilled Using FastGen

• FastGen-AYF (distilled to 4-8 steps using AYF from FastGen)
• Qwen-Image-Flash (distilled to 4 steps using DMD2 from FastGen, NVIDIA ModelOpt, and NVIDIA AutoModel)
• Baseten FLUX.2 (distilled to 8 steps using DMD2)
• Reka Real-Time Video Generation (distilled to a real-time video generator using FastGen)
• CorrDiff (distilled to 1 step using ECT)
• cBottle Super-Resolution (distilled to 2 steps using ECT)

Projects Using FastGen

• Simplifying and Improving On-Policy Distillation for Few-Step Diffusion (built upon FastGen)
• Parametric Trajectory Distillation for Few-Step Video Generation (built upon FastGen)
• Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation (built upon FastGen)
• Parallel Decoding Distillation for Image and Video Generation (built upon FastGen)
• Transition Matching Distillation for Fast Video Generation (built upon FastGen)
• Mode Seeking meets Mean Seeking for Fast Long Video Generation (built upon FastGen)
• StreamingEffect: Real-Time Human-Centric Video Effect Generation (built upon FastGen)
• CARV: Variance Reduction for Expectations with Diffusion Teachers (used FastGen for the DMD2 experiments)

FastGen Collaborations

• FastH3 Omni (Minimax H3 Ref2VA distillation with FastVideo and Nuva Lab)
• FastH3 Preview (MiniMax H3 distillation with FastVideo and Nuva Lab)
• FlashDreams (optimized serving for FastGen-distilled autoregressive video models)
• NVIDIA Model Optimizer (ModelOpt) (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},
}