arXiv:2602.20360v2 Announce Type: replace Abstract: Flow-based generative methods offer a simple and effective framework for high-fidelity generation, yet pretrained flow models are rarely used in their vanilla conditional form: in image generation, samples without guidance often appear diffuse and lack fine-grained detail. Existing guidance techniques such as classifier-free guidance (CFG) improve fidelity but reduce sample diversity. We introduce Momentum Guidance (MG), a guidance method that improves sample quality by extrapolating the current velocity away from an exponential moving averag
The continuous evolution of AI models demands new techniques to enhance output quality without sacrificing diversity, addressing limitations of current guidance methods like CFG.
Improved guidance methods like Momentum Guidance can significantly enhance the fidelity and diversity of generative AI outputs, making these models more practically useful across various applications.
The ability to generate high-quality images with better detail and diversity from flow models without extensive retraining, potentially broadening their application scope.
- · AI developers
- · Generative AI platforms
- · Creative industries
- · Researchers in machine learning
- · Prior methods with poor diversity
- · Generative models struggling with detail
Generative AI models, specifically flow-based ones, will produce more visually appealing and varied outputs.
This improvement could lead to increased adoption of advanced generative AI in sectors like design, media, and digital content creation.
The enhanced realism and diversity of generated content might accelerate debates around synthetic media authenticity and ethical use.
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Read at arXiv cs.LG