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DiverseVAR: Balancing Diversity and Quality of Next-Scale Visual Autoregressive Models
Mingue Park, Prin Phunyaphibarn, Phillip Y. Lee +1
We introduce DiverseVAR, a framework that enhances the diversity of text-conditioned visual autoregressive models (VAR) at test time without requiring retraining, fine-tuning, or s…
Unconditional Priors Matter! Improving Conditional Generation of Fine-Tuned Diffusion Models
Prin Phunyaphibarn, Phillip Y. Lee, Jaihoon Kim +1
Classifier-Free Guidance (CFG) is a fundamental technique in training conditional diffusion models. The common practice for CFG-based training is to use a single network to learn b…
GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation
Phillip Y. Lee, Taehoon Yoon, Minhyuk Sung
We introduce GrounDiT, a novel training-free spatial grounding technique for text-to-image generation using Diffusion Transformers (DiT). Spatial grounding with bounding boxes has…
ReGround: Improving Textual and Spatial Grounding at No Cost
Phillip Y. Lee, Minhyuk Sung
When an image generation process is guided by both a text prompt and spatial cues, such as a set of bounding boxes, do these elements work in harmony, or does one dominate the othe…