Summary of Efficient Pruning Of Text-to-image Models: Insights From Pruning Stable Diffusion, by Samarth N Ramesh et al.
Efficient Pruning of Text-to-Image Models: Insights from Pruning Stable Diffusion
by Samarth N Ramesh, Zhixue Zhao
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 The paper presents a pioneering study on post-training pruning of Stable Diffusion 2, a powerful text-to-image model. The authors tackle the challenge of compressing this large model to make it feasible for deployment on resource-constrained devices. They focus on pruning techniques specifically designed for multi-modal generation models, such as Stable Diffusion 2, which combines text and image processing. The study compares different pruning strategies for the entire model, its textual component, or its image generation component separately, in various levels of sparsity. The results show that simple magnitude pruning outperforms more advanced techniques in this context. The authors also find that Stable Diffusion 2 can be pruned to 38.5% sparsity with minimal quality loss, achieving a significant reduction in model size. They propose an optimal pruning configuration and discuss the implications of their findings on model compression, interoperability, and bias identification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to make big text-to-image models smaller so they can be used on devices that don’t have as much power. The authors are trying to figure out how to compress this kind of model without losing its ability to generate good images from text prompts. They tested different methods and found that one simple way works better than more complicated approaches. They also discovered that the model can be shrunk a lot (38.5%) without hurting image quality. This is important because it could make these models useful for everyday use, not just super powerful computers. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Model compression » Multi modal » Pruning
