Summary of Pipefusion: Patch-level Pipeline Parallelism For Diffusion Transformers Inference, by Jiarui Fang and Jinzhe Pan and Jiannan Wang and Aoyu Li and Xibo Sun
PipeFusion: Patch-level Pipeline Parallelism for Diffusion Transformers Inference
by Jiarui Fang, Jinzhe Pan, Jiannan Wang, Aoyu Li, Xibo Sun
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Performance (cs.PF)
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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 PipeFusion is a parallel methodology designed to address the high latency issues associated with generating high-resolution images using diffusion transformers (DiTs) models. This approach partitions images into patches and distributes model layers across multiple GPUs, employing a patch-level pipeline parallel strategy to optimize communication and computation. By leveraging the similarity between inputs from successive diffusion steps, PipeFusion reuses stale feature maps to provide context for the current step, reducing communication costs compared to existing DiTs inference parallelism methods. The paper also showcases PipeFusion’s superior memory efficiency by distributing model parameters across multiple devices, making it more suitable for large-parameter-size DiTs like Flux.1. Experimental results demonstrate PipeFusion achieves state-of-the-art performance on 8xL40 PCIe GPUs for Pixart, Stable-Diffusion 3, and Flux.1. The paper also provides access to the source code on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make computers generate high-quality images quickly. The method is called PipeFusion, and it’s designed to solve a problem with current image generation techniques that take too long to produce high-resolution pictures. PipeFusion works by breaking the image into small pieces, processing each piece separately on multiple computer chips, and then combining the results. This approach saves time by not needing to send all the information between the computer chips. The researchers tested PipeFusion with different types of images and found it worked better than other methods for generating high-quality pictures quickly. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Inference
