D-OPSD: On‑Policy Self‑Distillation Lets Few‑Step Diffusion Models Learn While Running

D-OPSD presents the first online self‑distillation framework for step‑distilled diffusion models, allowing them to continuously fine‑tune with only image‑text pairs, retain their fast few‑step sampling, and acquire new concepts, styles, or domain preferences without reward models.

LoRAdiffusion modelsfew-step sampling

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D-OPSD: On‑Policy Self‑Distillation Lets Few‑Step Diffusion Models Learn While Running