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Text-to-video generation aims to produce a video based on a given prompt.Recently, several commercial video models have been able to generate plausiblevideos with minimal noise, excellent details, and high aesthetic scores.However, these models rely on large-scale, well-filtered, high-quality videosthat are not accessible to the community. Many existing research works, whichtrain models using the low-quality WebVid-10M dataset, struggle to generatehigh-quality videos because the models are optimized to fit WebVid-10M. In thiswork, we explore the training scheme of video models extended from StableDiffusion and investigate the feasibility of leveraging low-quality videos andsynthesized high-quality images to obtain a high-quality video model. We firstanalyze the connection between the spatial and temporal modules of video modelsand the distribution shift to low-quality videos. We observe that full trainingof all modules results in a stronger coupling between spatial and temporalmodules than only training temporal modules. Based on this stronger coupling,we shift the distribution to higher quality without motion degradation byfinetuning spatial modules with high-quality images, resulting in a generichigh-quality video model. Evaluations are conducted to demonstrate thesuperiority of the proposed method, particularly in picture quality, motion,and concept composition.

VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models