DiffSynth: Latent In-Iteration Deflickering for Realistic Video Synthesis

Zhongjie Duan, Lizhou You, Chengyu Wang, Cen Chen, Ziheng Wu, Weining Qian, Jun Huang

Abstract: In recent years, diffusion models have emerged as a powerful approach in the field of image synthesis. However, applying these models directly to video synthesis presents challenges, often leading to noticeable flickering in the content. Although recently proposed zero-shot methods can alleviate flickering to some extent, generating coherent videos remains a struggle. In this paper, we propose DiffSynth, a novel approach that converts image synthesis pipelines into video synthesis pipelines. DiffSynth consists of two key components: a latent in-iteration deflickering framework and a video deflickering algorithm. The latent in-iteration deflickering framework applies video deflickering in the latent space of diffusion models, effectively preventing flicker accumulation in intermediate steps. Additionally, we introduce a video deflickering algorithm, named the patch blending algorithm, which remaps objects across different frames and blends them to enhance video consistency. One of the notable advantages of DiffSynth is its general applicability to various video synthesis tasks, including text-guided video stylization, fashion video synthesis, image-guided video stylization, video restoration, and 3D rendering. In the task of text-guided video stylization, we make it possible to synthesize high-quality videos without cherry-picking. The experimental results demonstrate the effectiveness of DiffSynth, and we further showcase its practical value on Alibaba e-commerce platform.