Summary of Block-wise Lora: Revisiting Fine-grained Lora For Effective Personalization and Stylization in Text-to-image Generation, by Likun Li et al.
Block-wise LoRA: Revisiting Fine-grained LoRA for Effective Personalization and Stylization in Text-to-Image Generation
by Likun Li, Haoqi Zeng, Changpeng Yang, Haozhe Jia, Di Xu
First submitted to arxiv on: 12 Mar 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 The paper addresses the challenge of personalizing text-to-image (T2I) models to new concepts introduced by users while maintaining expected styles. Recent parameter-efficient fine-tuning (PEFT) approaches have advanced this field, but existing methods struggle with effective personalization and stylization. To overcome this limitation, the authors propose block-wise Low-Rank Adaptation (LoRA), a novel approach that performs fine-grained fine-tuning for different blocks of Style Diffusion (SD). This method enables the generation of images faithful to input prompts, target identity, and desired style. The paper presents extensive experiments demonstrating the effectiveness of LoRA in achieving high-quality T2I generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers create pictures from text descriptions by making them better at understanding new ideas introduced by users. Right now, computer models are good at generating images that look like they were painted by a certain artist or have a specific style. But it’s hard to make them adapt to new ideas and still keep the same style. The authors of this paper propose a new way to improve these models so they can better understand what users want and generate images that match their descriptions. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Lora » Low rank adaptation » Parameter efficient
