6 papers · 1 filter
Unconditional Priors Matter! Improving Conditional Generation of Fine-Tuned Diffusion Models
Prin Phunyaphibarn, Phillip Y. Lee, Jaihoon Kim +1
Classifier-Free Guidance (CFG) is a fundamental technique in training conditional diffusion models. The common practice for CFG-based training is to use a single network to learn b…
MatLat: Material Latent Space for PBR Texture Generation
Kyeongmin Yeo, Yunhong Min, Jaihoon Kim +1
We propose a generative framework for producing high-quality PBR textures on a given 3D mesh. As large-scale PBR texture datasets are scarce, our approach focuses on effectively le…
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…
StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces
Kyeongmin Yeo, Jaihoon Kim, Minhyuk Sung
We propose a zero-shot method for generating images in arbitrary spaces (e.g., a sphere for 360° panoramas and a mesh surface for texture) using a pretrained image diffusion model…
SyncTweedies: A General Generative Framework Based on Synchronized Diffusions
Jaihoon Kim, Juil Koo, Kyeongmin Yeo +1
We introduce a general framework for generating diverse visual content, including ambiguous images, panorama images, mesh textures, and Gaussian splat textures, by synchronizing mu…