5 papers
Adaptive Auxiliary Prompt Blending for Target-Faithful Diffusion Generation
Kwanyoung Lee, SeungJu Cha, Yebin Ahn +3
Diffusion-based text-to-image (T2I) models have made remarkable progress in generating photorealistic and semantically rich images. However, when the target concepts lie in low-den…
ADAPT: Attention Driven Adaptive Prompt Scheduling and InTerpolating Orthogonal Complements for Rare Concepts Generation
Kwanyoung Lee, Hyunwoo Oh, SeungJu Cha +2
Generating rare compositional concepts in text-to-image synthesis remains a challenge for diffusion models, particularly for attributes that are uncommon in the training data. Whil…
ScaleDiff: Higher-Resolution Image Synthesis via Efficient and Model-Agnostic Diffusion
Sungho Koh, SeungJu Cha, Hyunwoo Oh +2
Text-to-image diffusion models often exhibit degraded performance when generating images beyond their training resolution. Recent training-free methods can mitigate this limitation…
CatchPhrase: EXPrompt-Guided Encoder Adaptation for Audio-to-Image Generation
Hyunwoo Oh, SeungJu Cha, Kwanyoung Lee +2
We propose CatchPhrase, a novel audio-to-image generation framework designed to mitigate semantic misalignment between audio inputs and generated images. While recent advances in m…
VerbDiff: Text-Only Diffusion Models with Enhanced Interaction Awareness
SeungJu Cha, Kwanyoung Lee, Ye-Chan Kim +2
Recent large-scale text-to-image diffusion models generate photorealistic images but often struggle to accurately depict interactions between humans and objects due to their limite…