CogVideoX: video generator — what it does and what you need to run it

Here we have CogVideoX — an open-source video generator that creates videos from text descriptions. The developer, Zhipu AI, positions it as a tool for turning text prompts into video clips. The model is also mentioned in connection with image-to-video generation, although the primary task in the fact sheet is listed as text-to-video.

What it does

The authors describe CogVideoX as a system for generating video from text and images. In the repository, the project is tagged with text-to-video and image-to-video, indicating two modes of operation. Key terms also include llm and sora, which suggests that language models form the foundation, and the technology itself belongs to the class of solutions similar to Sora. The fact sheet does not provide details on maximum video duration or supported resolutions.

What you need to run it

The code is written in Python and distributed under the Apache-2.0 license. The model weights have a different license — other. You will need an NVIDIA GPU with CUDA support. As described by the author, verbatim: devices with NVIDIA H100 and above, with the torch and torchao packages required to be installed from source. The recommended CUDA version is 12.4. The largest weight file takes up 9.2 GB, and the total size of all files is 20 GB. The model is integrated into the diffusers library. Running on your own hardware is possible, but the hardware requirements are quite specific.

Who it is for

The tool is aimed at developers and researchers who have access to powerful server accelerators. The open source code and the presence of a GitHub repository make it possible to study the architecture and fine-tune the model for your own tasks. Judging by the stated requirements, CogVideoX is not intended for consumer use on consumer-grade GPUs. Those looking for a cloud service without the need to set up an environment should also consider other solutions — here you will need to manually build dependencies and work with Python code.

The model continues to evolve: the latest code change is dated November 2025, and the latest release is marked as v1.0. This indicates that the project is in an active support phase, but has not yet accumulated a large number of intermediate releases.