What is CogVideoX
CogVideoX is the latest open-sourceAI video generationmodel launched by Zhipu AI, and it shares the same origin as Zhipu AI's commercial product " Qingying ". CogVideoX supports English prompts and can generate 6-second-long videos at 8 frames per second with a resolution of 720*480. Model inference requires 7.8-26GB of VRAM, and currently does not support quantized inference or multi-GPU inference. The project also includes a 3D Causal VAE component for video reconstruction, as well as a rich set of examples and tools, including CLI/WEB Demo, online experience, API interface examples, and fine-tuning guides.
CogVideoX's main features
- AI text-to-video: Supports generating video content from user-input text prompts.
- Low VRAM requirement: At INT8 precision, the inference VRAM requirement is only 7.8GB, and inference can even be completed with a 1080 Ti GPU.
- Customizable video parameters: Video length, frame rate, and resolution can be customized. Currently, it supports 6-second-long videos, 8 frames/second, and a resolution of 720*480.
- 3D Causal VAE technology: Uses 3D Causal VAE technology to achieve efficient reconstruction of video content.
- Inference and fine-tuning: The model supports basic inference to generate videos, and also provides fine-tuning capability to adapt to different needs.
CogVideoX's technical principles
- Text-to-video generation: CogVideoX uses deep learning models, especially Transformer-based architectures, to understand input text prompts and generate video content.
- 3D Causal VAE: CogVideoX adopts a 3D Causal Variational Autoencoder, a technology used for video reconstruction and compression, which can reconstruct videos almost losslessly and reduce storage and computational requirements.
- Expert Transformer: CogVideoX uses an Expert Transformer model, a special kind of Transformer that processes different tasks through multiple experts, such as handling spatial and temporal information, and controlling information flow.
- Encoder-decoder architecture: In the 3D VAE, the encoder converts the video into simplified codes, while the decoder reconstructs the video from these codes. The latent space regularizer ensures more accurate information transfer between encoding and decoding.
- Mixed-duration training: CogVideoX's training process adopts mixed-duration training, allowing the model to learn videos of different lengths and improving generalization ability.
- Multi-stage training: CogVideoX's training is divided into several stages, including low-resolution pre-training, high-resolution pre-training, and high-quality video fine-tuning, gradually improving the model's generation quality and details.
- Automatic and human evaluation: CogVideoX uses a combination of automatic evaluation and human evaluation to ensure that the quality of generated videos meets expectations.
CogVideoX's project addresses
- Zhipu Qingying experience：https://ai-bot.cn/chatglm-video/
- CogVideoX-2B model address：
- HuggingFace model library:https://huggingface.co/THUDM/CogVideoX-2b
- ModelScope community model library:https://modelscope.cn/models/ZhipuAI/CogVideoX-2b
- CogVideoX-5B model address：
- HuggingFace model library:https://huggingface.co/spaces/THUDM/CogVideoX-5B
- ModelScope community model library:https://modelscope.cn/models/ZhipuAI/CogVideoX-5b
- GitHub repository：https://github.com/THUDM/CogVideo
- arXiv technical paper：https://arxiv.org/pdf/2408.06072
Comparison of CogVideoX-2B and CogVideoX-5B parameters
CogVideoX's performance evaluation
To evaluate the quality of text-to-video generation, we used multiple metrics from VBench, such as human action, scene, dynamic degree, etc. We also used two additional video evaluation tools: Dynamic Quality in Devil and GPT4o-MT Score in Chrono-Magic, which focus on the dynamic characteristics of videos. As shown in the table below.
Application Scenarios of CogVideoX
- Creative Video Production: Provides tools for independent video creators and artists to quickly transform creative text descriptions into visual video content.
- Educational and Training Materials: Automatically generates educational videos to help explain complex concepts or demonstrate teaching scenarios.
- Advertising and Brand Promotion: Enterprises can use the CogVideoX model to generate video advertisements based on advertising copy, improving marketing effectiveness.
- Gaming and Entertainment Industry: Assists game developers in quickly generating in-game animations or story videos, enhancing the gaming experience.
- Film and Video Editing: Assists video editing work by generating specific scenes or special effects videos through text descriptions.
- Virtual Reality (VR) and Augmented Reality (AR): Generates immersive video content for VR and AR applications, enhancing user interactive experience.