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cs.CV2026
Salvation Lies Within: Eliciting Inherent Style Transfer in Step-Distilled Diffusion Models
Shengyin Sun, Yiming Li, Yingzhao Lian +7
Adapting step-distilled text-to-image (T2I) models through post-training incurs additional computational costs and affects native few-step generation behavior. This motivates a com…
cs.CV2026
Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget
Guoxuan Chen, Chufeng Xiao, Haoran Yang +30
We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turbo variants. It delivers compet…