CogVideoX-5B

CogVideoX-5B is a large-scale, open-source video generation model developed by Zhipu AI. It is a 5-billion parameter variant of the CogVideoX architecture, designed to generate high-quality videos with strong temporal consistency and semantic alignment. The model serves as a higher-capacity successor to the initial 2B version, offering improved visual texture and better adherence to complex text prompts.

The model utilizes a 3D Causal Variational Autoencoder (VAE) to compress video data into a low-dimensional latent space, which reduces computational overhead and helps prevent flickering in generated sequences. Its generation backbone is a Diffusion Transformer (DiT) that incorporates an "expert" transformer design and 3D full attention to accurately capture motion and spatial dependencies over time.

CogVideoX-5B is capable of producing videos at a resolution of 720x480 pixels, typically spanning 5 to 10 seconds in duration. To increase accessibility, the model supports advanced quantization methods such as INT8 and FP8, allowing it to run on consumer-grade hardware with as little as 10GB of VRAM. It is released alongside a toolchain that includes image-to-video (I2V) capabilities and specialized video captioning models.