Shortcutting Pre-trained Flow Matching Diffusion Models is Almost Free Lunch
This work addresses the high computational cost of adapting large-scale diffusion models for efficient sampling, offering a nearly free solution for researchers and practitioners in generative AI.
The paper tackles the problem of efficiently converting pre-trained flow matching diffusion models into few-step samplers without retraining, achieving this through a novel velocity field self-distillation method that trains in less than one A100 day for a 3-step model and enables few-shot distillation with state-of-the-art performance.
We present an ultra-efficient post-training method for shortcutting large-scale pre-trained flow matching diffusion models into efficient few-step samplers, enabled by novel velocity field self-distillation. While shortcutting in flow matching, originally introduced by shortcut models, offers flexible trajectory-skipping capabilities, it requires a specialized step-size embedding incompatible with existing models unless retraining from scratch$\unicode{x2013}$a process nearly as costly as pretraining itself. Our key contribution is thus imparting a more aggressive shortcut mechanism to standard flow matching models (e.g., Flux), leveraging a unique distillation principle that obviates the need for step-size embedding. Working on the velocity field rather than sample space and learning rapidly from self-guided distillation in an online manner, our approach trains efficiently, e.g., producing a 3-step Flux less than one A100 day. Beyond distillation, our method can be incorporated into the pretraining stage itself, yielding models that inherently learn efficient, few-step flows without compromising quality. This capability also enables, to our knowledge, the first few-shot distillation method (e.g., 10 text-image pairs) for dozen-billion-parameter diffusion models, delivering state-of-the-art performance at almost free cost.