5 papers
Gradient Preconditioning for Efficient and Reliable Reward-Guided Generation
Jisung Hwang, Minhyuk Sung
We propose a gradient preconditioning method that makes reward-guided generation with one-step generative models both efficient and reliable. Test-time noise optimization can unloc…
Demystifying Transition Matching: When and Why It Can Beat Flow Matching
Jaihoon Kim, Rajarshi Saha, Minhyuk Sung +1
Flow Matching (FM) underpins many state-of-the-art generative models, yet recent results indicate that Transition Matching (TM) can achieve higher quality with fewer sampling steps…
Inference-Time Scaling for Flow Models via Stochastic Generation and Rollover Budget Forcing
Jaihoon Kim, Taehoon Yoon, Jisung Hwang +1
We propose an inference-time scaling approach for pretrained flow models. Recently, inference-time scaling has gained significant attention in LLMs and diffusion models, improving…
Moment- and Power-Spectrum-Based Gaussianity Regularization for Text-to-Image Models
Jisung Hwang, Jaihoon Kim, Minhyuk Sung
We propose a novel regularization loss that enforces standard Gaussianity, encouraging samples to align with a standard Gaussian distribution. This facilitates a range of downstrea…
MemBench: Memorized Image Trigger Prompt Dataset for Diffusion Models
Chunsan Hong, Tae-Hyun Oh, Minhyuk Sung
Diffusion models have achieved remarkable success in Text-to-Image generation tasks, leading to the development of many commercial models. However, recent studies have reported tha…