4 papers · 1 filter
Which Tokens to Merge? Diffusion Dynamics for Efficient Image Generation
SeungJu Cha, Ye-Chan Kim, HyunGee Kim +2
Token merging has emerged as an effective training-free strategy for accelerating diffusion models by reducing redundant token computation while minimizing generation degradation.…
Beyond Pairwise Repulsion: Collective Diversity via Determinant Maximization in Diffusion Sampling
Kwanyoung Lee, SeungJu Cha, Yebin Ahn +2
Strong guidance in text-to-image (T2I) diffusion models concentrates sampling toward high-density modes of the conditional distribution, collapsing independently sampled noise vect…
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…