Summary of Layer- and Timestep-adaptive Differentiable Token Compression Ratios For Efficient Diffusion Transformers, by Haoran You et al.
Layer- and Timestep-Adaptive Differentiable Token Compression Ratios for Efficient Diffusion Transformers
by Haoran You, Connelly Barnes, Yuqian Zhou, Yan Kang, Zhenbang Du, Wei Zhou, Lingzhi Zhang, Yotam Nitzan, Xiaoyang Liu, Zhe Lin, Eli Shechtman, Sohrab Amirghodsi, Yingyan Celine Lin
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel diffusion transformer architecture, dubbed DiffRatio-MoD, is introduced for efficient image generation. The existing Diffusion Transformers (DiTs) achieve state-of-the-art image generation quality but suffer from high latency and memory inefficiency, making them challenging to deploy on resource-constrained devices. The proposed framework addresses this efficiency bottleneck by dynamically routing computation across layers and timesteps based on the importance of each image token. This is achieved through a token-level routing scheme, where each DiT layer includes a router that predicts token importance scores; a layer-wise differentiable ratio mechanism, which learns varying compression ratios for redundant layers; and a timestep-wise differentiable ratio mechanism, which adapts to the noise level at each denoising step. The resulting model achieves superior trade-offs between generation quality and efficiency compared to prior works on both text-to-image and inpainting tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want a computer to create an image from scratch! Usually, this is very difficult because computers need lots of memory and time to do it. But what if we could make the computer work smarter, not harder? That’s exactly what scientists did in this new study. They created a special kind of computer program called a “diffusion transformer” that can generate images more efficiently than before. This means it can create high-quality images faster and using less memory! The program does this by prioritizing the most important parts of the image, so it doesn’t waste time on things that don’t matter. This new technology could be really useful for creating fake images or even filling in missing pieces of real ones! |
Keywords
» Artificial intelligence » Diffusion » Image generation » Token » Transformer
