8 papers
Triadic Dynamics Aware Diffusion Posterior Sampling for Inverse Problems: Optimizing Guidance and Stochasticity Schedules
Junseo Bang, Dong Ju Mun, Hoigi Seo +2
Generative posterior sampling using diffusion models has emerged as a dominant paradigm for solving inverse problems in imaging, which usually consists of three main components: da…
Erasing Thousands of Concepts: Towards Scalable and Practical Concept Erasure for Text-to-Image Diffusion Models
Hoigi Seo, Byung Hyun Lee, Jaehyun Cho +2
Large-scale text-to-image (T2I) diffusion models deliver remarkable visual fidelity but pose safety risks due to their capacity to reproduce undesirable content, such as copyrighte…
Training-free, Perceptually Consistent Low-Resolution Previews with High-Resolution Image for Efficient Workflows of Diffusion Models
Wongi Jeong, Hoigi Seo, Se Young Chun
Image generative models have become indispensable tools to yield exquisite high-resolution (HR) images for everyone, ranging from general users to professional designers. However,…
Training-free Mixed-Resolution Latent Upsampling for Spatially Accelerated Diffusion Transformers
Wongi Jeong, Kyungryeol Lee, Hoigi Seo +1
Diffusion transformers (DiTs) offer excellent scalability for high-fidelity generation, but their computational overhead poses a great challenge for practical deployment. Existing…
On Epistemic Uncertainty of Visual Tokens for Object Hallucinations in Large Vision-Language Models
Hoigi Seo, Dong Un Kang, Hyunjin Cho +2
Large vision-language models (LVLMs), which integrate a vision encoder (VE) with a large language model, have achieved remarkable success across various tasks. However, there are s…
Geometrical Properties of Text Token Embeddings for Strong Semantic Binding in Text-to-Image Generation
Hoigi Seo, Junseo Bang, Haechang Lee +3
Text-to-image (T2I) models often suffer from text-image misalignment in complex scenes involving multiple objects and attributes. Semantic binding has attempted to associate the ge…