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The recent wave of AI-generated content has witnessed the great developmentand success of Text-to-Image (T2I) technologies. By contrast, Text-to-Video(T2V) still falls short of expectations though attracting increasing interests.Existing works either train from scratch or adapt large T2I model to videos,both of which are computation and resource expensive. In this work, we proposea Simple Diffusion Adapter (SimDA) that fine-tunes only 24M out of 1.1Bparameters of a strong T2I model, adapting it to video generation in aparameter-efficient way. In particular, we turn the T2I model for T2V bydesigning light-weight spatial and temporal adapters for transfer learning.Besides, we change the original spatial attention to the proposed Latent-ShiftAttention (LSA) for temporal consistency. With similar model architecture, wefurther train a video super-resolution model to generate high-definition(1024x1024) videos. In addition to T2V generation in the wild, SimDA could alsobe utilized in one-shot video editing with only 2 minutes tuning. Doing so, ourmethod could minimize the training effort with extremely few tunable parametersfor model adaptation.

SimDA: Simple Diffusion Adapter for Efficient Video Generation