Continuous Adversarial MeanFlow Transfer
MeanFlow-Transfer (MF-T) unifies domain adaptation and sampling acceleration by mapping heterogeneous pretrained model outputs (DiT/ε, SiT/v, JiT/x, iMF/u) into a shared velocity representation for initialization Continuous Adversarial MeanFlow (CAMF) extends adversarial refinement from instantaneous velocities to MeanFlow's finite-interval average velocities, recovering fine details that regression averaging loses MF-T with CAMF matches or exceeds fine-tuned teacher models in FID and FDD at up
Analysis
TL;DR
- MeanFlow-Transfer (MF-T) unifies domain adaptation and sampling acceleration by mapping heterogeneous pretrained model outputs (DiT/ε, SiT/v, JiT/x, iMF/u) into a shared velocity representation for initialization
- Continuous Adversarial MeanFlow (CAMF) extends adversarial refinement from instantaneous velocities to MeanFlow's finite-interval average velocities, recovering fine details that regression averaging loses
- MF-T with CAMF matches or exceeds fine-tuned teacher models in FID and FDD at up to 125× fewer Neural Function Evaluations across five target domains
- CAMF improves MF-T's few-step FID by 29% on average, demonstrating significant quality gains at drastically reduced computational cost
- The framework addresses the lack of a common acceleration target across heterogeneous pretrained diffusion/flow parameterizations
Why It Matters
This work directly tackles two persistent bottlenecks in deploying generative models: the high sampling cost of pretrained diffusion/flow models and the difficulty of adapting them to new domains with limited data. By unifying adaptation and acceleration into a single training loop that works across heterogeneous model parameterizations, it removes a major barrier for practitioners who need to deploy fast, high-quality generators in resource-constrained settings.
Technical Details
- MeanFlow-Transfer (MF-T): Maps outputs from four different pretrained source models—DiT (ε-parameterization), SiT (v-parameterization), JiT (x-parameterization), and iMF (u-parameterization)—into a shared velocity representation, then initializes a MeanFlow generator from source weights and optimizes the MF objective on the target domain
- Continuous Adversarial MeanFlow (CAMF): A post-training stage that formulates adversarial refinement over finite-interval average velocities rather than instantaneous velocities; it contrasts changes in a learned potential between real and predicted interval endpoints, recovering high-frequency detail that MF regression averages away
- Theoretical consistency: CAMF reduces to the instantaneous adversarial criterion in the vanishing-interval limit, ensuring theoretical coherence with prior work
- Evaluation: Adapted four ImageNet-based source models to five target domains, measuring performance via FID and FDD metrics at varying Neural Function Evaluation (NFE) budgets
Industry Insight
- The unified velocity representation approach could become a standard bridge for transferring capabilities across the increasingly heterogeneous landscape of diffusion and flow-based generative models, reducing the need for model-specific adaptation pipelines
- The 125× NFE reduction with competitive or superior quality opens practical pathways for deploying high-fidelity generators on edge devices or in real-time applications where computational budget is severely constrained
- The extension of adversarial refinement to finite-interval velocities provides a generalizable framework that could be adapted to other flow-based generative modeling paradigms beyond MeanFlow
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