  In the rapidly evolving area of image synthesis, a serious challenge is the
presence of complex artifacts that compromise perceptual realism of synthetic
images. To alleviate artifacts and improve quality of synthetic images, we
fine-tune Vision-Language Model (VLM) as artifact classifier to automatically
identify and classify a wide range of artifacts and provide supervision for
further optimizing generative models. Specifically, we develop a comprehensive
artifact taxonomy and construct a dataset of synthetic images with artifact
annotations for fine-tuning VLM, named SynArtifact-1K. The fine-tuned VLM
exhibits superior ability of identifying artifacts and outperforms the baseline
by 25.66%. To our knowledge, this is the first time such end-to-end artifact
classification task and solution have been proposed. Finally, we leverage the
output of VLM as feedback to refine the generative model for alleviating
artifacts. Visualization results and user study demonstrate that the quality of
images synthesized by the refined diffusion model has been obviously improved.
