zai-org/cogvideo

text and image to video generation: CogVideoX (2024) and CogVideo (ICLR 2023)

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Summary Information

Updated 59 minutes ago
Added to GitGenius on September 4th, 2026
Created on May 29th, 2022
Open Issues & Pull Requests: 118 (+0)
GitHub issues: Enabled
Number of forks: 1,341
Total Stargazers: 13,003 (+0)
Total Subscribers: 139 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 7.1 hours
Mean response time: 3.1 days
90th percentile: 2.2 days
Tracked items: 486

How this project is maintained

99% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Three people close 71% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 108
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 515 days
Stale 30+ days: 107
Stale 90+ days: 103

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 1
Events in 7 days: 1

Top labels

  • good first issue (7)
  • sat (7)
  • enhancement (5)
  • help wanted (3)
  • duplicate (1)

Most active issues this week

Detailed Description

CogVideo is a text and image to video generation framework that implements both CogVideoX and the earlier CogVideo model from ICLR 2023.

The project addresses the problem of generating videos from text prompts and images by providing implementations of two distinct models. CogVideoX represents the newer approach to this task, while CogVideo offers an established baseline from published research. The framework allows developers to work with both models within a unified codebase, supporting different scales and capabilities for video synthesis.

Developers should choose this tool if they need to generate videos from text descriptions or images and want access to multiple model variants. The project suits research applications, prototyping, and integration into systems that require video generation capabilities. The framework provides online demonstration spaces through hosted platforms, allowing evaluation before local deployment. For those seeking production-scale commercial video generation, the project documentation points toward larger models available through dedicated API platforms.

The project maintains active engagement across multiple communication channels including Discord and WeChat communities. Documentation is provided in multiple languages to serve a global audience. The codebase includes references to academic papers and detailed user guides, indicating ongoing documentation of the models' capabilities and usage patterns.