RB-Modulation: Training-Free Stylization using Reference-Based Modulation
Litu Rout Yujia Chen Nataniel Ruiz Abhishek Kumar Constantine Caramanis Sanjay Shakkottai Wen-Sheng Chu
We propose Reference-Based Modulation (RB-Modulation), a new plug-and-play solution for training-free personalization of diffusion models.
Existing training-free approaches exhibit difficulties in (a) style extraction from reference images in the absence of additional style or content text descriptions, (b) unwanted content leakage from reference style images, and (c) effective composition of style and content. 
RB-Modulation is built on a novel stochastic optimal controller where a style descriptor encodes the desired attributes through a terminal cost. 
The resulting drift not only overcomes the difficulties above, but also ensures high fidelity to the reference style and adheres to the given text prompt. 
We also introduce a cross-attention-based feature aggregation scheme that allows RB-Modulation to decouple content and style from the reference image.
With theoretical justification and empirical evidence, our test-time optimization framework demonstrates precise extraction and control of *content* and *style* in a training-free manner. 
Further, our method allows a seamless composition of content and style, which marks a departure from the dependency on external adapters or ControlNets. See project page:  https://rb-modulation.github.io/ for code and further details.
Inverse Problems, Generative Modeling, Diffusion Models, Posterior Sampling, Optimal Control, Test-time Optimization
We propose Reference-Based Modulation (RB-Modulation), a new plug-and-play test-time optimization method for training-free personalization (stylization and content-style composition) of diffusion models using stochastic optimal control.