One-Step Diffusion and Flow Distillation Through Implicit Generator Matching

Despite strong performances on many generative tasks, diffusion and flow matching models require a large number of sampling steps to generate high-quality images. This has motivated the community to develop effective methods to distill pre-trained models into more efficient models. In this paper, we present Implicit Generator Matching (IGM), a systematic approach to distill both pre-trained diffusion/flow matching models into one-step generator models, while maintaining almost the same sample generation ability as the original model, as well as being data-free with no need for training images. The key challenge is that the traditional diffusion/flow-matching loss is intractable to distill a teacher diffusion/flow model with an explicitly defined field into a student generator, whose field is defined implicitly. The main breakthrough, our Implicit Gradient Theorem, provides an exact and efficient gradient to directly optimize the student by aligning this implicit field with the teacher's. IGM shows strong empirical performance for one-step generators, setting new standards. On CIFAR10, our diffusion-based SIM achieves an FID score of 2.06, while flow-based FGM sets a flow-model record with a 3.08 FID. Scaling to text-to-image models, SIM distillation of PixArt-$\alpha$α yields a leading 6.42 aesthetic score, surpassing SDXL-TURBO (5.33), and FGM distillation of SD3 achieves a competitive 0.65 GenEval score against multi-step accelerators like Hyper-SD3 (0.63).