Papers › CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers
CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers
Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, Jie Tang
Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-image (DALL-E and CogView) generation. Its application to video generation is still facing many challenges: The potential huge computation cost makes the training from scratch unaffordable; The scarcity and weak relevance of text-video datasets hinder the model understanding complex movement semantics. In this work, we present 9B-parameter transformer CogVideo, trained by inheriting a pretrained text-to-image model, CogView2. We also propose multi-frame-rate hierarchical training strategy to better align text and video clips. As (probably) the first open-source large-scale pretrained text-to-video model, CogVideo outperforms all publicly available models at a large margin in machine and human evaluations.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Generation | UCF-101 | CogVideo (128x128, class-conditional) | FVD16 | 305 | #22 of 48 | Archive leaderboard | report |
| Video Generation | UCF-101 | CogVideo (128x128, class-conditional) | Inception Score | 51.11 | #22 of 48 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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