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Diffusion Models without Classifier-free Guidance

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arxiv 2502.12154 v1 pith:P6XL4SEV submitted 2025-02-17 cs.CV cs.AIcs.LG

Zhicong Tang , Jianmin Bao , Dong Chen , Baining Guo This is my paper
classification cs.CVcs.AIcs.LG
keywords modelsdiffusionclassifier-freeguidancetrainingacceleratesachieveaddresses
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper presents Model-guidance (MG), a novel objective for training diffusion model that addresses and removes of the commonly used Classifier-free guidance (CFG). Our innovative approach transcends the standard modeling of solely data distribution to incorporating the posterior probability of conditions. The proposed technique originates from the idea of CFG and is easy yet effective, making it a plug-and-play module for existing models. Our method significantly accelerates the training process, doubles the inference speed, and achieve exceptional quality that parallel and even surpass concurrent diffusion models with CFG. Extensive experiments demonstrate the effectiveness, efficiency, scalability on different models and datasets. Finally, we establish state-of-the-art performance on ImageNet 256 benchmarks with an FID of 1.34. Our code is available at https://github.com/tzco/Diffusion-wo-CFG.

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Cited by 5 Pith papers

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