CogVideoX

About

What CogVideoX is

CogVideoX-2B 📍 Visit <a href="https://chatglm.cn/video?lang=en?fr=osmcogvideo"QingYing</a and <a href="https://open.bigmodel.cn/?utmcampaign=open&channeltrackkey=OWTVNma9"API Platform</a to experience commercial video generation models. Demo Show .video-container { display: flex; flex-wrap: wrap; justify-content: space-around; } .video-item { width: 45%; margin-bottom: 20px; transition: transform 0.3s; } .video-item:hover { transform: scale(1.1); } .caption { text-align: center; margin-top: 10px; font-size: 11px; } Model Introduction CogVideoX is an open-source version of the video generation model originating from QingYing. The table below displays the list of video generation models we currently offer, along with their foundational information. Data Explanation + When testing using the diffusers library, all optimizations provided by the diffusers library were enabled. This solution has not been tested for actual VRAM/memory usage on devices other than NVIDIA A100 / H100. Generally, this solution can be adapted to all devices with NVIDIA Ampere architecture and above. If the optimizations are disabled, VRAM usage will increase significantly, with peak VRAM usage being about 3 times higher than the table shows. However, speed will increase by 3-4 times. You can selectively disable some optimizations, including: + When performing multi-GPU inference, the enablemodelcpuoffload() optimization needs to be disabled. + Using INT8 models will reduce inference speed. This is to ensure that GPUs with lower VRAM can perform inference normally while maintaining minimal video quality loss, though inference speed will decrease significantly. + The 2B model is trained with FP16 precision, and the 5B model is trained with BF16 precision. We recommend using the precision the model was trained with for inference. + PytorchAO and Optimum-quanto can be used to quantize the text encoder, Transformer, and VAE modules to reduce CogVideoX's memory requirements. This makes it possible to run the model on a free T4 Colab or GPUs with smaller VRAM! It is also worth noting that TorchAO quantization is fully compatible with torch.compile, which can significantly improve inference speed. F

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How video models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys cogvideox for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (cogvideox below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/videos/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"cogvideox","prompt":"a drone shot over a forest"}'

Details

Languages, data & research

Papers

Licence

Commercial licence needed

Sources

Weights & code

Explore

More video models