Summary of Asyncdiff: Parallelizing Diffusion Models by Asynchronous Denoising, By Zigeng Chen et al.
AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising
by Zigeng Chen, Xinyin Ma, Gongfan Fang, Zhenxiong Tan, Xinchao Wang
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AsyncDiff acceleration scheme enables model parallelism across multiple devices, dividing the noise prediction model into components and processing them asynchronously on separate devices. This approach significantly reduces inference latency while maintaining generative quality. For example, it achieves a 2.7x speedup for Stable Diffusion v2.1 with negligible degradation, or a 4.0x speedup with only a slight reduction in CLIP Score, on four NVIDIA A5000 GPUs. AsyncDiff can also be applied to video diffusion models with promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AsyncDiff is a new way to make diffusion models run faster. It breaks the model into smaller pieces and lets each piece work separately on different computers or devices. This makes it much quicker than before, without sacrificing how well it generates pictures. For example, it can make Stable Diffusion v2.1 go 2.7 times faster with almost no difference in quality, or even 4 times faster with a tiny bit of a drop-off. |
Keywords
» Artificial intelligence » Diffusion » Inference
