Diffusion Models without Classifier-free Guidance
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.
67b400719ff3ff79dae14701
Zhicong Tang
claimed_verified
2025-02-18T09:31:45.361Z
6372f265112fb535baf254c4
/avatars/9b821bc533175c7dded48cdb3a3e1a12.svg
tzco
user
tzco
67b400719ff3ff79dae14702
Jianmin Bao
67b400719ff3ff79dae14703
Dong Chen
67b400719ff3ff79dae14704
Baining Guo
Diffusion Models without Classifier-free Guidance
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.
Model-guidance improves diffusion model training by incorporating posterior probabilities, removing the need for Classifier-free guidance, and achieving faster inference and superior quality.
diffusion model Classifier-free guidance posterior probability Model-guidance ImageNet 256 FID