Summary of Rb-modulation: Training-free Personalization Of Diffusion Models Using Stochastic Optimal Control, by Litu Rout et al.
RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control
by Litu Rout, Yujia Chen, Nataniel Ruiz, Abhishek Kumar, Constantine Caramanis, Sanjay Shakkottai, Wen-Sheng Chu
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 We propose Reference-Based Modulation (RB-Modulation), a novel plug-and-play solution for training-free personalization of diffusion models. Existing approaches struggle with style extraction from reference images without additional text descriptions, unwanted content leakage, and effective composition of style and content. RB-Modulation builds on a stochastic optimal controller that encodes desired attributes through a terminal cost. The resulting drift overcomes these difficulties, ensuring high fidelity to the reference style and adherence to the given text prompt. A cross-attention-based feature aggregation scheme decouples content and style from the reference image, enabling precise extraction and control in a training-free manner. Our framework demonstrates empirical evidence and theoretical justification for seamless composition of content and style. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make artificial intelligence models produce better results without needing lots of training data. They call this method “Reference-Based Modulation” or RB-Modulation. The problem they solve is that current methods have trouble copying the style of an image (like making a picture look like it was taken by another photographer) without using any extra information. Their new approach uses a special kind of controller to make sure the model gets the right details and doesn’t accidentally copy unwanted parts. This makes their method more reliable and accurate. |
Keywords
» Artificial intelligence » Cross attention » Diffusion » Prompt
