4 citations · 6 across the 15 of their papers we have counts for
5 papers · 1 filter
Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
Sihyun Yu, Sangkyung Kwak, Huiwon Jang +4
Recent studies have shown that the denoising process in (generative) diffusion models can induce meaningful (discriminative) representations inside the model, though the quality of…
DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing
June Suk Choi, Kyungmin Lee, Jongheon Jeong +3
Recent advances in diffusion models have introduced a new era of text-guided image manipulation, enabling users to create realistic edited images with simple textual prompts. Howev…
DreamFlow: High-Quality Text-to-3D Generation by Approximating Probability Flow
Kyungmin Lee, Kihyuk Sohn, Jinwoo Shin
Recent progress in text-to-3D generation has been achieved through the utilization of score distillation methods: they make use of the pre-trained text-to-image (T2I) diffusion mod…
Improving Diffusion Models for Authentic Virtual Try-on in the Wild
Yisol Choi, Sangkyung Kwak, Kyungmin Lee +2
This paper considers image-based virtual try-on, which renders an image of a person wearing a curated garment, given a pair of images depicting the person and the garment, respecti…
Direct Consistency Optimization for Robust Customization of Text-to-Image Diffusion Models
Kyungmin Lee, Sangkyung Kwak, Kihyuk Sohn +1
Text-to-image (T2I) diffusion models, when fine-tuned on a few personal images, can generate visuals with a high degree of consistency. However, such fine-tuned models are not robu…