Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution images with text conditions, there are still several open problems to be solved, which limits the further improvement of image fidelity and text relevancy. In this paper, we propose ERNIE-ViLG 2.0, a large-scale Chinese text-to-image diffusion model, to progressively upgrade the quality of generated images by: (1) incorporating fine-grained textual and visual knowledge of key elements in the scene, and (2) utilizing different denoising experts at different denoising stages. With the proposed mechanisms, ERNIE-ViLG 2.0 not only achieves a new state-of-the-art on MS-COCO with zero-shot FID score of 6.75, but also significantly outperforms recent models in terms of image fidelity and image-text alignment, with side-by-side human evaluation on the bilingual prompt set ViLG-300.

翻译：近年来扩散模型的进展彻底革新了文本到图像生成的流行技术。尽管现有方法能够生成具有文本条件的光照真实感高分辨率图像，但仍存在若干待解决的开放性问题，这限制了图像保真度和文本相关性的进一步提升。本文提出大规模中文文本到图像扩散模型ERNIE-ViLG 2.0，通过以下方式逐步提升生成图像质量：（1）融入场景关键要素的细粒度文本与视觉知识；（2）在不同去噪阶段采用不同的去噪专家。借助所提出的机制，ERNIE-ViLG 2.0不仅在MS-COCO上以6.75的零样本FID得分取得新最优结果，还在图像保真度和图文对齐方面显著优于近期模型，经双语提示集ViLG-300上的人工对比评估验证。