  Diffusion models have emerged as the de facto paradigm for video generation.
However, their reliance on web-scale data of varied quality often yields
results that are visually unappealing and misaligned with the textual prompts.
To tackle this problem, we propose InstructVideo to instruct text-to-video
diffusion models with human feedback by reward fine-tuning. InstructVideo has
two key ingredients: 1) To ameliorate the cost of reward fine-tuning induced by
generating through the full DDIM sampling chain, we recast reward fine-tuning
as editing. By leveraging the diffusion process to corrupt a sampled video,
InstructVideo requires only partial inference of the DDIM sampling chain,
reducing fine-tuning cost while improving fine-tuning efficiency. 2) To
mitigate the absence of a dedicated video reward model for human preferences,
we repurpose established image reward models, e.g., HPSv2. To this end, we
propose Segmental Video Reward, a mechanism to provide reward signals based on
segmental sparse sampling, and Temporally Attenuated Reward, a method that
mitigates temporal modeling degradation during fine-tuning. Extensive
experiments, both qualitative and quantitative, validate the practicality and
efficacy of using image reward models in InstructVideo, significantly enhancing
the visual quality of generated videos without compromising generalization
capabilities. Code and models will be made publicly available.
