CogVideo & CogVideoX
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Experience the CogVideoX-5B model online at 🤗 Huggingface Space or 🤖 ModelScope Space
📚 View the paper and user guide
👋 Join our WeChat and Discord
📍 Visit QingYing and API Platform to experience larger-scale commercial video generation models.
Project Updates
- 🔥🔥 News:
2025/03/24: We have launched CogKit, a fine-tuning and inference framework for the CogView4 and CogVideoX series. This toolkit allows you to fully explore and utilize our multimodal generation models.
- 🔥 News:
2025/02/28: DDIM Inverse is now supported in CogVideoX-5B and CogVideoX1.5-5B. Check here.
- 🔥 News:
2025/01/08: We have updated the code for Lora fine-tuning based on the diffusers version model, which uses less GPU memory. For more details, please see here.
- 🔥 News:
2024/11/15: We released the CogVideoX1.5 model in the diffusers version. Only minor parameter adjustments are needed to continue using previous code.
- 🔥 News:
2024/11/08: We have released the CogVideoX1.5 model. CogVideoX1.5 is an upgraded version of the open-source model CogVideoX.
The CogVideoX1.5-5B series supports 10-second videos with higher resolution, and CogVideoX1.5-5B-I2V supports video generation at any resolution.
The SAT code has already been updated, while the diffusers version is still under adaptation. Download the SAT version code here.
- 🔥 News:
2024/10/13: A more cost-effective fine-tuning framework for CogVideoX-5B that works with a single
4090 GPU, cogvideox-factory, has been released. It supports
fine-tuning with multiple resolutions. Feel free to use it!
- 🔥 News:
2024/10/10: We have updated our technical report. Please
click here to view it. More training details and a demo have been added. To see
the demo, click here.- 🔥 News: 2024/10/09: We have publicly
released the technical documentation for CogVideoX
fine-tuning on Feishu, further increasing distribution flexibility. All examples in the public documentation can be
fully reproduced.
- 🔥 News:
2024/9/19: We have open-sourced the CogVideoX series image-to-video model CogVideoX-5B-I2V.
This model can take an image as a background input and generate a video combined with prompt words, offering greater
controllability. With this, the CogVideoX series models now support three tasks: text-to-video generation, video
continuation, and image-to-video generation. Welcome to try it online
at Experience.
- 🔥
2024/9/19: The Caption
model CogVLM2-Caption, used in the training process of
CogVideoX to convert video data into text descriptions, has been open-sourced. Welcome to download and use it.
- 🔥
2024/8/27: We have open-sourced a larger model in the CogVideoX series, CogVideoX-5B. We have
significantly optimized the model's inference performance, greatly lowering the inference threshold.
You can run CogVideoX-2B on older GPUs like GTX 1080TI, and CogVideoX-5B on desktop GPUs like RTX 3060. Please strictly
follow the requirements to update and install dependencies, and refer
to cli_demo for inference code. Additionally, the open-source license for
the CogVideoX-2B model has been changed to the Apache 2.0 License.
- 🔥
2024/8/6: We have open-sourced 3D Causal VAE, used for CogVideoX-2B, which can reconstruct videos with
almost no loss.
- 🔥
2024/8/6: We have open-sourced the first model of the CogVideoX series video generation models, **CogVideoX-2B
**.
- 🌱 Source:
2022/5/19: We have open-sourced the CogVideo video generation model (now you can see it in
the CogVideo branch). This is the first open-source large Transformer-based text-to-video generation model. You can
access the ICLR'23 paper for technical details.
Table of Contents
Jump to a specific section:
Quick Start
Prompt Optimization
Before running the model, please refer to this guide to see how we use large models like
GLM-4 (or other comparable products, such as GPT-4) to optimize the model. This is crucial because the model is trained
with long prompts, and a good prompt directly impacts the quality of the video generation.
SAT
Please make sure your Python version is between 3.10 and 3.12, inclusive of both 3.10 and 3.12.
Follow instructions in sat_demo: Contains the inference code and fine-tuning code of SAT weights. It is
recommended to improve based on the CogVideoX model structure. Innovative researchers use this code to better perform
rapid stacking and development.
Diffusers
Please make sure your Python version is between 3.10 and 3.12, inclusive of both 3.10 and 3.12.
pip install -r requirements.txt
Then follow diffusers_demo: A more detailed explanation of the inference code, mentioning the
significance of common parameters.
For more details on quantized inference, please refer
to diffusers-torchao. With Diffusers and TorchAO, quantized inference
is also possible leading to memory-efficient inference as well as speedup in some cases when compiled. A full list of
memory and time benchmarks with various settings on A100 and H100 has been published
at diffusers-torchao.
Gallery
CogVideoX-5B
CogVideoX-2B
To view the corresponding prompt words for the gallery, please click here
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.
| Model Name |
CogVideoX1.5-5B (Latest) |
CogVideoX1.5-5B-I2V (Latest) |
CogVideoX-2B |
CogVideoX-5B |
CogVideoX-5B-I2V |
| Release Date |
November 8, 2024 |
November 8, 2024 |
August 6, 2024 |
August 27, 2024 |
September 19, 2024 |
| Video Resolution |
1360 * 768 |
Min(W, H) = 768
768 ≤ Max(W, H) ≤ 1360
Max(W, H) % 16 = 0 |
720 * 480 |
| Number of Frames |
Should be 16N + 1 where N <= 10 (default 81) |
Should be 8N + 1 where N <= 6 (default 49) |
| Inference Precision |
BF16 (Recommended), FP16, FP32, FP8*, INT8, Not supported: INT4 |
FP16*(Recommended), BF16, FP32, FP8*, INT8, Not supported: INT4 |
BF16 (Recommended), FP16, FP32, FP8*, INT8, Not supported: INT4 |
Single GPU Memory Usage
|
SAT BF16: 76GB
diffusers BF16: from 10GB*
diffusers INT8(torchao): from 7GB* |
SAT FP16: 18GB
diffusers FP16: 4GB minimum*
diffusers INT8 (torchao): 3.6GB minimum* |
SAT BF16: 26GB
diffusers BF16 : 5GB minimum*
diffusers INT8 (torchao): 4.4GB minimum* |
| Multi-GPU Memory Usage |
BF16: 24GB* using diffusers
|
FP16: 10GB* using diffusers
|
BF16: 15GB* using diffusers
|
Inference Speed
(Step = 50, FP/BF16) |
Single A100: ~1000 seconds (5-second video)
Single H100: ~550 seconds (5-second video) |
Single A100: ~90 seconds
Single H100: ~45 seconds |
Single A100: ~180 seconds
Single H100: ~90 seconds |
| Prompt Language |
English* |
| Prompt Token Limit |
224 Tokens |
226 Tokens |
| Video Length |
5 seconds or 10 seconds |
6 seconds |
| Frame Rate |
16 frames / second |
8 frames / second |
| Position Encoding |
3d_rope_pos_embed |
3d_sincos_pos_embed |
3d_rope_pos_embed |
3d_rope_pos_embed + learnable_pos_embed |
| Download Link (Diffusers) |
🤗 HuggingFace
🤖 ModelScope
🟣 WiseModel |
🤗 HuggingFace
🤖 ModelScope
🟣 WiseModel |
🤗 HuggingFace
🤖 ModelScope
🟣 WiseModel |
🤗 HuggingFace
🤖 ModelScope
🟣 WiseModel |
🤗 HuggingFace
🤖 ModelScope
🟣 WiseModel |
| Download Link (SAT) |
🤗 HuggingFace
🤖 ModelScope
🟣 WiseModel |
SAT |
Data Explanation
- While testing using the diffusers library, all optimizations included in the diffusers library were enabled. This
scheme has not been tested for actual memory usage on devices outside of NVIDIA A100 / H100 architectures.
Generally, this scheme can be adapted to all NVIDIA Ampere architecture and above devices. If optimizations are
disabled, memory consumption will multiply, with peak memory usage being about 3 times the value in the table.
However, speed will increase by about 3-4 times. You can selectively disable some optimizations, including:
pipe.enable_sequential_cpu_offload()
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()
- For multi-GPU inference, the
enable_sequential_cpu_offload() optimization needs to be disabled.
- Using INT8 models will slow down inference, which is done to accommodate lower-memory GPUs while maintaining minimal
video quality loss, though inference speed will significantly decrease.
- The CogVideoX-2B model was trained in
FP16 precision, and all CogVideoX-5B models were trained in BF16 precision.
We recommend using the precision in which the model was trained for inference.
- PytorchAO and Optimum-quanto can be
used to quantize the text encoder, transformer, and VAE modules to reduce the memory requirements of CogVideoX. This
allows the model to run on free T4 Colabs or GPUs with smaller memory! Also, note that TorchAO quantization is fully
compatible with
torch.compile, which can significantly improve inference speed. FP8 precision must be used on
devices with NVIDIA H100 and above, requiring source installation of torch, torchao Python packages. CUDA 12.4 is recommended.
- The inference speed tests also used the above memory optimization scheme. Without memory optimization, inference speed
increases by about 10%. Only the
diffusers version of the model supports quantization.
- The model only supports English input; other languages can be translated into English for use via large model
refinement.
Friendly Links
We highly welcome contributions from the community and actively contribute to the open-source community. The following
works have already been adapted for CogVideoX, and we invite everyone to use them:
- LeMiCa: a diffusion model inference acceleration solution developed by China Unicom Data Science and Artificial Intelligence Research Institute. By leveraging cache-based techniques and global denoising path optimization, LeMiCa provides efficient inference support for CogVideoX, achieving nearly 2.5x lossless acceleration while maintaining visual consistency and quality.
- RIFLEx-CogVideoX:
RIFLEx extends the video with just one line of code:
freq[k-1]=(2np.pi)/(Ls). The framework not only supports training-free inference, but also offers models fine-tuned based on CogVideoX. By fine-tuning the model for just 1,000 steps on original-length videos, RIFLEx significantly enhances its length extrapolation capability.
- CogVideoX-Fun: CogVideoX-Fun is a modified pipeline based on the
CogVideoX architecture, supporting flexible resolutions and multiple launch methods.
- CogStudio: A separate repository for CogVideo's Gradio Web UI, which
supports more functional Web UIs.
- [Xorbits Inference](https://github.com/xorbit
一套开源的文生视频和图生视频模型系列:CogVideoX(2024 年)及其升级版 CogVideoX1.5,前身是 CogVideo(ICLR 2023)——首个基于大型 Transformer 的开源文生视频模型。