Authors: Zhicong Tang, Jianmin Bao, Dong Chen, Baining Guo
Abstract: 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.
Source: http://arxiv.org/abs/2502.12154v1
