Summary of Remix-dit: Mixing Diffusion Transformers For Multi-expert Denoising, by Gongfan Fang et al.
Remix-DiT: Mixing Diffusion Transformers for Multi-Expert Denoising
by Gongfan Fang, Xinyin Ma, Xinchao Wang
First submitted to arxiv on: 7 Dec 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 Transformer-based diffusion models have revolutionized generative tasks, but they often require large transformer models, resulting in significant training and inference overhead. To address this limitation, we introduce Remix-DiT, a novel method designed to enhance output quality at a low cost. Remix-DiT involves crafting multiple denoising experts for different timesteps, without requiring the expensive training of N independent models. This approach uses K basis models (where K < N) and learnable mixing coefficients to adaptively craft expert models. The design offers two key advantages: first, it maintains the same architecture as a plain model, making the overall model efficient; second, it allocates model capacity across timesteps, improving generation quality. Experiments on the ImageNet dataset demonstrate that Remix-DiT achieves promising results compared to standard diffusion transformers and other multiple-expert methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a special machine that can create new images or music by combining small pieces of information. But this machine is very big and takes a long time to work. We’ve found a way to make it smaller and faster by giving it many “experts” who help it decide what to do at each step. This helps the machine create better results without needing to be so big. We tested our idea on a large dataset of images and it worked well, even beating some other similar machines. |
Keywords
» Artificial intelligence » Diffusion » Inference » Transformer
