Summary of Hollowed Net For On-device Personalization Of Text-to-image Diffusion Models, by Wonguk Cho et al.
Hollowed Net for On-Device Personalization of Text-to-Image Diffusion Models
by Wonguk Cho, Seokeon Choi, Debasmit Das, Matthias Reisser, Taesup Kim, Sungrack Yun, Fatih Porikli
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); 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 an efficient LoRA-based personalization approach for on-device subject-driven generation of text-to-image diffusion models. The proposed method, called Hollowed Net, modifies the architecture of a U-Net to temporarily remove a fraction of its deep layers, creating a hollowed structure that directly addresses memory constraints and reduces GPU memory requirements during training. This approach enables inference without additional memory overhead by transferring the personalized model back into the original U-Net. The results demonstrate that Hollowed Net not only reduces training memory but also maintains or improves personalization performance compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for computers to create custom images from text prompts, a technology called text-to-image diffusion models. To make this work on devices like smartphones, the researchers developed a new way to personalize these models using a technique called LoRA. The approach is called Hollowed Net and it works by temporarily removing some parts of the model’s architecture, which saves memory and makes it faster to train. This means that computers can create custom images without needing as much memory or processing power. The results show that this method not only saves memory but also creates better personalized images. |
Keywords
» Artificial intelligence » Diffusion » Inference » Lora
