4 papers
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…
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…
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…
Simulation-Free Training of Neural ODEs on Paired Data
Semin Kim, Jaehoon Yoo, Jinwoo Kim +3
In this work, we investigate a method for simulation-free training of Neural Ordinary Differential Equations (NODEs) for learning deterministic mappings between paired data. Despit…