Search papers, labs, and topics across Lattice.
This paper introduces MeanFlow-Transfer, a novel approach that enables the adaptation of heterogeneous pretrained diffusion and flow models to new domains while addressing the limitations of costly multi-step sampling and acceleration methods. By mapping outputs into a shared velocity representation and optimizing a MeanFlow objective, the method unifies adaptation and acceleration in a single training loop. The proposed Continuous Adversarial MeanFlow further enhances the quality of generated outputs by recovering fine details lost in average velocity predictions, achieving significant improvements in performance metrics with drastically fewer Neural Function Evaluations.
Adapting four ImageNet-based models to new domains, the authors achieve up to 125x fewer evaluations while improving output quality by 29% on average.
Training fast generators on new domains with limited data remains challenging for two reasons. First, adapting a pretrained diffusion or flow model to a new domain leaves its costly multi-step sampling unaddressed, and existing acceleration methods are tied to the source parameterization--$\epsilon$, $x$, $v$, or $u$--leaving heterogeneous pretrained models with no common acceleration target. Second, while adversarial refinement is proven effective for few-step quality, it is formulated only for instantaneous-velocity flows, not for the finite-interval average velocities that MeanFlow (MF) models predict. We address both problems. We propose MeanFlow-Transfer, which maps heterogeneous source outputs into a shared velocity representation, uses it to initialize an MF generator from the source weights, and optimizes an MF objective on the target domain. This unifies adaptation and acceleration in a single training loop across a broad range of pretrained models. We then introduce Continuous Adversarial MeanFlow, a post-training stage that extends continuous adversarial flow models from instantaneous velocities to MF's finite-interval average velocities. CAMF contrasts changes in a learned potential between real and predicted interval endpoints, recovering fine detail that MF regression averages away, and reduces to the instantaneous criterion in the vanishing-interval limit. Adapting four ImageNet-based source models--DiT ($\epsilon$), SiT ($v$), JiT ($x$), iMF ($u$)--to five target domains, MF-T with CAMF matches or exceeds the fine-tuned teacher in FID and FDD at up to $125\times$ fewer Neural Function Evaluations (NFEs), while CAMF improves MF-T's few-step FID by $29\%$ on average.
