3 papers
cs.LG2026
FlowBind: Efficient Any-to-Any Generation with Bidirectional Flows
Yeonwoo Cha, Semin Kim, Jinhyeon Kwon +1
Any-to-any generation seeks to translate between arbitrary subsets of modalities, enabling flexible cross-modal synthesis. Despite recent success, existing flow-based approaches ar…
cs.CV2026
Training-Free Refinement of Flow Matching with Divergence-based Sampling
Yeonwoo Cha, Jaehoon Yoo, Semin Kim +3
Flow-based models learn a target distribution by modeling a marginal velocity field, defined as the average of sample-wise velocities connecting each sample from a simple prior to…
cs.LG2025
Reward-Agnostic Prompt Optimization for Text-to-Image Diffusion Models
Semin Kim, Yeonwoo Cha, Jaehoon Yoo +1
We investigate a general approach for improving user prompts in text-to-image (T2I) diffusion models by finding prompts that maximize a reward function specified at test-time. Alth…