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README.md

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 **[NEW!]** DistriFusion is accepted by CVPR 2024! Our code is publicly available!
 
 ![teaser](https://github.com/mit-han-lab/distrifuser/blob/main/assets/teaser.jpg)
-*We introduce DistriFusion, a training-free algorithm to harness multiple GPUs to accelerate diffusion model inference without sacrificing image quality. Naïve Patch (Overview (b)) suffers from the fragmentation issue due to the lack of patch interaction. The presented examples are generated with SDXL using a 50-step Euler sampler at 1280x1920 resolution, and latency is measured on A100 GPUs.*
+*We introduce DistriFusion, a training-free algorithm to harness multiple GPUs to accelerate diffusion model inference without sacrificing image quality. Naïve Patch (Overview (b)) suffers from the fragmentation issue due to the lack of patch interaction. The presented examples are generated with SDXL using a 50-step Euler sampler at 1280×1920 resolution, and latency is measured on A100 GPUs.*
 
 DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models</br>
 [Muyang Li](https://lmxyy.me/)\*, [Tianle Cai](https://www.tianle.website/)\*, [Jiaxin Cao](https://www.linkedin.com/in/jiaxin-cao-2166081b3/), [Qinsheng Zhang](https://qsh-zh.github.io), [Han Cai](https://han-cai.github.io), [Junjie Bai](https://www.linkedin.com/in/junjiebai/), [Yangqing Jia](https://daggerfs.com), [Ming-Yu Liu](https://mingyuliu.net), [Kai Li](https://www.cs.princeton.edu/~li/), and [Song Han](https://hanlab.mit.edu/songhan)</br>