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cs.CV2026
Setting-Matched and Semantics-Scaled Benchmarking of One-Step Generative Models Against Multistep Diffusion and Flow Models
Advaith Ravishankar, Serena Liu, Mingyang Wang +11
State-of-the-art text-to-image models produce high-quality images, but inference remains expensive as generation requires several sequential ODE or denoising steps. Native one-step…
cs.CV2025
DynASyn: Multi-Subject Personalization Enabling Dynamic Action Synthesis
Yongjin Choi, Chanhun Park, Seung Jun Baek
Recent advances in text-to-image diffusion models spurred research on personalization, i.e., a customized image synthesis, of subjects within reference images. Although existing pe…