Continuous Adversarial MeanFlow Transfer
MeanFlow-Transfer and CAMF achieve fast domain adaptation with limited data, improving FID by 29%.
Key Findings
Methodology
This study introduces MeanFlow-Transfer (MF-T) and Continuous Adversarial MeanFlow (CAMF). MF-T maps heterogeneous source outputs into a shared velocity representation, initializes an MF generator, and optimizes an MF objective on the target domain. CAMF extends continuous adversarial flow models from instantaneous to finite-interval average velocities, recovering details averaged away by MF regression.
Key Results
- MF-T and CAMF adapt four ImageNet-based source models to five target domains, matching or exceeding fine-tuned teacher models in FID and FDD with up to 125× fewer Neural Function Evaluations.
- CAMF improves MF-T's few-step FID by 29% on average.
- Experiments demonstrate efficient domain adaptation and acceleration across multiple datasets with MF-T and CAMF combined.
Significance
This research achieves efficient domain adaptation of fast generators under limited data, addressing the costly multi-step sampling issue of pretrained generators. By unifying adaptation and acceleration in a single training loop, the method applies to a wide range of pretrained models, significantly reducing Neural Function Evaluations and enhancing generation quality. This offers new possibilities for rapid deployment of GANs in practical applications.
Technical Contribution
Technical contributions include the introduction of MeanFlow-Transfer and CAMF methods, solving the unified acceleration and adaptation problem for heterogeneous source models. By mapping heterogeneous source outputs to a shared velocity representation, the method achieves adaptation and acceleration in a single loop. CAMF recovers details through adversarial learning, reducing the averaging problem of MF regression.
Novelty
This study is the first to extend continuous adversarial flow models to MeanFlow's finite-interval average velocities, proposing a unified adaptation and acceleration framework, addressing the inconsistent acceleration targets of heterogeneous pretrained generators.
Limitations
- In extreme data scarcity scenarios, the model may not fully adapt to the target domain.
- The method may require additional tuning for specific domain transfers.
Future Work
Future work can explore applications on more datasets and complex models, further optimizing adaptation and acceleration efficiency, and investigating applications in other generative tasks.
AI Executive Summary
Training fast generators on new domains with limited data is challenging. Existing methods fail to address the costly multi-step sampling issue when adapting pretrained diffusion or flow models. To tackle this, researchers propose MeanFlow-Transfer and Continuous Adversarial MeanFlow methods. MeanFlow-Transfer maps heterogeneous source outputs into a shared velocity representation, initializing an MF generator and optimizing an MF objective on the target domain, achieving adaptation and acceleration in a single training loop. Continuous Adversarial MeanFlow recovers details through adversarial learning, reducing the averaging problem of MF regression.
Experimental results show that the method adapts four ImageNet-based source models to five target domains, matching or exceeding fine-tuned teacher models in FID and FDD with up to 125× fewer Neural Function Evaluations. CAMF improves MF-T's few-step FID by 29% on average, demonstrating efficient domain adaptation and acceleration across multiple datasets.
However, the method may not fully adapt to the target domain in extreme data scarcity scenarios and may require additional tuning for specific domain transfers. Future work can explore applications on more datasets and complex models, further optimizing adaptation and acceleration efficiency, and investigating applications in other generative tasks.
Deep Analysis
Background
In recent years, GANs and flow models have made significant advances in image generation. However, fast adaptation to new domains under limited data remains a challenge. Traditional methods often require multi-step sampling, leading to high computational costs. Researchers have explored the combination of acceleration and adaptation to improve generation efficiency.
Core Problem
The core problem is how to quickly adapt to new domains under limited data. Existing methods fail to address the costly multi-step sampling issue when adapting pretrained diffusion or flow models. Additionally, the inconsistent acceleration targets of heterogeneous pretrained generators increase the complexity of adaptation.
Innovation
The study proposes MeanFlow-Transfer and Continuous Adversarial MeanFlow methods. The former solves the inconsistent acceleration target problem by mapping heterogeneous source outputs to a shared velocity representation. The latter recovers details through adversarial learning, reducing the averaging problem of MF regression.
Methodology
- �� MeanFlow-Transfer maps heterogeneous source outputs to a shared velocity representation.
- �� Initializes an MF generator and optimizes an MF objective on the target domain.
- �� Continuous Adversarial MeanFlow recovers details through adversarial learning.
- �� Reduces the averaging problem of MF regression through contrastive learning.
Experiments
The experiments adapt four ImageNet-based source models to five target domains. FID and FDD metrics are used to evaluate generation quality, comparing performance at different steps. Results demonstrate efficient domain adaptation and acceleration across multiple datasets.
Results
Experiments demonstrate efficient domain adaptation and acceleration across multiple datasets with MF-T and CAMF combined. They match or exceed fine-tuned teacher models in FID and FDD with up to 125× fewer Neural Function Evaluations.
Applications
The method can be used in scenarios requiring fast high-quality image generation, such as real-time image generation, augmented reality, and virtual reality. By reducing computational costs, it enhances the feasibility of GANs in practical applications.
Limitations & Outlook
In extreme data scarcity scenarios, the model may not fully adapt to the target domain. Additionally, the method may require additional tuning for specific domain transfers. Future work can explore applications on more datasets and complex models.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking a meal. You have a recipe, but only limited ingredients. You need to quickly adjust the recipe to fit these ingredients while ensuring the dish tastes the same. MeanFlow-Transfer is like a universal recipe adjuster, allowing you to quickly adapt to different ingredient combinations, enabling you to make a delicious dish in limited time. Continuous Adversarial MeanFlow is like a flavor master, adding details and flavors to your dish in the final step, making it even more delicious.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a game where you need to build a super cool castle with the materials you have in limited time. MeanFlow-Transfer is like a magical toolbox that helps you quickly adapt to different materials, allowing you to build an awesome castle in no time. Continuous Adversarial MeanFlow is like a wizard that adds details to your castle in the final step, making it look even more amazing! Isn't that cool?
Glossary
Generative Adversarial Network (GAN)
A deep learning model consisting of a generator and a discriminator, used to generate realistic data.
Used for image generation tasks to enhance generation quality.
Domain Adaptation
The process of transferring a model from a source domain to a target domain.
Achieving fast adaptation under limited data conditions.
Flow Model
A model used for generating data by learning the flow of data distribution to generate new samples.
Used for image generation tasks to enhance generation efficiency.
FID
A metric used to evaluate the quality of generated images, with lower values indicating higher quality.
Used to compare the performance of different generative models.
Adversarial Learning
A training strategy that enhances model performance through adversarial interactions between a generator and a discriminator.
Used to enhance the details and quality of generated images.
Open Questions Unanswered questions from this research
- 1 How to achieve efficient domain adaptation in extreme data scarcity scenarios? Current methods perform poorly with insufficient data, requiring new strategies to enhance adaptation capabilities.
- 2 How does the method perform on more datasets and complex models? Further research is needed to verify the method's generalizability.
Applications
Immediate Applications
Real-time Image Generation
The method can be used for real-time high-quality image generation, such as in augmented reality and virtual reality applications. By reducing computational costs, it enhances generation efficiency.
Long-term Vision
Automated Design
In the future, the method could be used in automated design, enhancing design efficiency and creativity by rapidly generating high-quality images.
Abstract
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--$ε$, $x$, $v$, 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 ($ε$), 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.