Summary of Progressive Knowledge Distillation Of Stable Diffusion Xl Using Layer Level Loss, by Yatharth Gupta et al.
Progressive Knowledge Distillation Of Stable Diffusion XL Using Layer Level Loss
by Yatharth Gupta, Vishnu V. Jaddipal, Harish Prabhala, Sayak Paul, Patrick Von Platen
First submitted to arxiv on: 5 Jan 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 Stable Diffusion XL (SDXL) is a leading open-source text-to-image model renowned for its versatility and exceptional image quality. However, to expand its reach and applicability, addressing the computational demands of SDXL models is crucial. This paper introduces two scaled-down variants, Segmind Stable Diffusion (SSD-1B) and Segmind-Vega, which reduce the model size while preserving generative quality through progressive removal using layer-level losses. The proposed methodology eliminates residual networks and transformer blocks from the U-Net structure of SDXL, resulting in significant reductions in parameters and latency. The compact models effectively emulate the original SDXL by capitalizing on transferred knowledge, achieving competitive results against larger multi-billion parameter SDXL models. This work showcases the efficacy of knowledge distillation coupled with layer-level losses in reducing model size while preserving high-quality generative capabilities, facilitating more accessible deployment in resource-constrained environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine creating images from text descriptions. This is what Stable Diffusion XL (SDXL) does best. But making it work on smaller computers or devices can be tricky. To solve this problem, the authors of this paper create two smaller versions of SDXL that still produce great results. They do this by removing some parts of the original model and using special techniques to keep the quality high. These smaller models can run on devices with fewer resources, making it possible for more people to use them. |
Keywords
» Artificial intelligence » Diffusion » Knowledge distillation » Transformer
