7 papers
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,…
Unlearning the Unpromptable: Prompt-free Instance Unlearning in Diffusion Models
Kyungryeol Lee, Kyeonghyun Lee, Seongmin Hong +2
Machine unlearning aims to remove specific outputs from trained models, often at the concept level, such as forgetting all occurrences of a particular celebrity or filtering conten…
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…
Continual Multiple Instance Learning with Enhanced Localization for Histopathological Whole Slide Image Analysis
Byung Hyun Lee, Wongi Jeong, Woojae Han +2
Multiple instance learning (MIL) significantly reduced annotation costs via bag-level weak labels for large-scale images, such as histopathological whole slide images (WSIs). Howev…
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…
Efficient Personalization of Quantized Diffusion Model without Backpropagation
Hoigi Seo, Wongi Jeong, Kyungryeol Lee +1
Diffusion models have shown remarkable performance in image synthesis, but they demand extensive computational and memory resources for training, fine-tuning and inference. Althoug…