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cs.CV2026
DualHeads: Reward-tilted few-step diffusion made simple
Yuanzhi Zhu, Eleftherios Tsonis, Simo Alami Chehboune +3
Distilled diffusion models generate high-quality images in a few sampling steps, but aligning them with task requirements and human preferences is difficult. Methods developed for…
cs.CV2025
Soft-Di[M]O: Improving One-Step Discrete Image Generation with Soft Embeddings
Yuanzhi Zhu, Xi Wang, Stéphane Lathuilière +1
One-step generators distilled from Masked Diffusion Models (MDMs) compress multiple sampling steps into a single forward pass, enabling efficient text and image synthesis. However,…
cs.CV2025
Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentation
Yasser Benigmim, Subhankar Roy, Khalid Oublal +4
The rise of Artificial Intelligence as a Service (AIaaS) democratizes access to pre-trained models via Application Programming Interfaces (APIs), but also raises a fundamental ques…