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cs.CV2025
Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
Donghoon Ahn, Hyoungwon Cho, Jaewon Min +6
Recent studies have demonstrated that diffusion models are capable of generating high-quality samples, but their quality heavily depends on sampling guidance techniques, such as cl…
cs.CV2024
A Noise is Worth Diffusion Guidance
Donghoon Ahn, Jiwon Kang, Sanghyun Lee +9
Diffusion models excel in generating high-quality images. However, current diffusion models struggle to produce reliable images without guidance methods, such as classifier-free gu…
cs.CV2024
Diffusion Model for Dense Matching
Jisu Nam, Gyuseong Lee, Sunwoo Kim +4
The objective for establishing dense correspondence between paired images consists of two terms: a data term and a prior term. While conventional techniques focused on defining han…