11 papers
Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models
Jeongjae Lee, Jinho Chang, Jeongsol Kim +1
Reward-based fine-tuning steers a pretrained diffusion or flow-based generative model toward higher-reward samples while remaining close to the pretrained model. Although existing…
Gradient-Free Noise Optimization for Reward Alignment in Generative Models
Jeongsol Kim, Hongeun Kim, Jian Wang +1
Existing reward alignment methods for diffusion and flow models rely on multi-step stochastic trajectories, making them difficult to extend to deterministic generators. A natural a…
Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment
Bryan Sangwoo Kim, Jeongsol Kim, Jong Chul Ye
Modern single-image super-resolution (SISR) models deliver photo-realistic results at the scale factors on which they are trained, but collapse when asked to magnify far beyond tha…
Aligning Text to Image in Diffusion Models is Easier Than You Think
Jaa-Yeon Lee, Byunghee Cha, Jeongsol Kim +1
While recent advancements in generative modeling have significantly improved text-image alignment, some residual misalignment between text and image representations still remains.…
Generalizable Holographic Reconstruction via Amplitude-Only Diffusion Priors
Jeongsol Kim, Chanseok Lee, Jongin You +2
Phase retrieval in inline holography is a fundamental yet ill-posed inverse problem due to the nonlinear coupling between amplitude and phase in coherent imaging. We present a nove…
Diffusion models for inverse problems
Hyungjin Chung, Jeongsol Kim, Jong Chul Ye
Using diffusion priors to solve inverse problems in imaging have significantly matured over the years. In this chapter, we review the various different approaches that were propose…