CogVideo & CogVideoX

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Experience the CogVideoX-5B model online at 🤗 Huggingface Space or 🤖 ModelScope Space

📚 View the paper and user guide

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📍 Visit QingYing and API Platform to experience larger-scale commercial video generation models.

Project Updates

• 🔥🔥 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
  • SAT
  • Diffusers
• CogVideoX-2B Video Works
• Introduction to the CogVideoX Model
• Full Project Structure
  • Inference
  • SAT
  • Tools
• Introduction to CogVideo(ICLR’23) Model
• Citations
• Model License

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
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:

• 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: A powerful and comprehensive distributed inference
  framework, allowing you to easily deploy your own models or the latest cutting-edge open-source models with just one
  click.
• ComfyUI-CogVideoXWrapper Use the ComfyUI framework to integrate
  CogVideoX into your workflow.
• VideoSys: VideoSys provides a user-friendly, high-performance
  infrastructure for video generation, with full pipeline support and continuous integration of the latest models and
  techniques.
• AutoDL Space: A one-click deployment Huggingface
  Space image provided by community members.
• Interior Design Fine-Tuning Model:
  is a fine-tuned model based on CogVideoX, specifically designed for interior design.
• xDiT: xDiT is a scalable inference engine for Diffusion Transformers (DiTs)
  on multiple GPU Clusters. xDiT supports real-time image and video generations services.
  cogvideox-factory: A cost-effective
  fine-tuning framework for CogVideoX, compatible with the diffusers version model. Supports more resolutions, and
  fine-tuning CogVideoX-5B can be done with a single 4090 GPU.
• CogVideoX-Interpolation: A pipeline based on the modified CogVideoX
  structure, aimed at providing greater flexibility for keyframe interpolation generation.
• DiffSynth-Studio: DiffSynth Studio is a diffusion engine. It has
  restructured the architecture, including text encoders, UNet, VAE, etc., enhancing computational performance while
  maintaining compatibility with open-source community models. The framework has been adapted for CogVideoX.
• CogVideoX-Controlnet: A simple ControlNet module code that includes the CogVideoX model.
• VideoTuna: VideoTuna is the first repo that integrates multiple AI video generation models for text-to-video, image-to-video, text-to-image generation.

Project Structure

This open-source repository will guide developers to quickly get started with the basic usage and fine-tuning examples
of the CogVideoX open-source model.

Quick Start with Colab

Here provide three projects that can be run directly on free Colab T4 instances:

• CogVideoX-5B-T2V-Colab.ipynb:
  CogVideoX-5B Text-to-Video Colab code.
• CogVideoX-5B-T2V-Int8-Colab.ipynb:
  CogVideoX-5B Quantized Text-to-Video Inference Colab code, which takes about 30 minutes per run.
• CogVideoX-5B-I2V-Colab.ipynb:
  CogVideoX-5B Image-to-Video Colab code.
• CogVideoX-5B-V2V-Colab.ipynb:
  CogVideoX-5B Video-to-Video Colab code.

Inference

• dcli_demo: A more detailed inference code explanation, including the significance of
  common parameters. All of this is covered here.
• cli_demo_quantization:
  Quantized model inference code that can run on devices with lower memory. You can also modify this code to support
  running CogVideoX models in FP8 precision.
• diffusers_vae_demo: Code for running VAE inference separately.
• space demo: The same GUI code as used in the Huggingface Space, with frame
  interpolation and super-resolution tools integrated.

• convert_demo: How to convert user input into long-form input suitable for CogVideoX.
  Since CogVideoX is trained on long texts, we need to transform the input text distribution to match the training data
  using an LLM. The script defaults to using GLM-4, but it can be replaced with GPT, Gemini, or any other large language
  model.
• gradio_web_demo: A simple Gradio web application demonstrating how to use the
  CogVideoX-2B / 5B model to generate videos. Similar to our Huggingface Space, you can use this script to run a simple
  web application for video generation.

finetune

• finetune_demo: Fine-tuning scheme and details of the diffusers version of the CogVideoX model.

sat

• 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.

Tools

This folder contains some tools for model conversion / caption generation, etc.

• convert_weight_sat2hf: Converts SAT model weights to Huggingface model weights.
• caption_demo: Caption tool, a model that understands videos and outputs descriptions in
  text.
• export_sat_lora_weight: SAT fine-tuning model export tool, exports the SAT Lora
  Adapter in diffusers format.
• load_cogvideox_lora: Tool code for loading the diffusers version of fine-tuned Lora
  Adapter.
• llm_flux_cogvideox: Automatically generate videos using an
  open-source local large language model + Flux + CogVideoX.
• parallel_inference_xdit:
  Supported by xDiT, parallelize the
  video generation process on multiple GPUs.

CogVideo(ICLR’23)

The official repo for the
paper: CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers
is on the CogVideo branch

CogVideo is able to generate relatively high-frame-rate videos.
A 4-second clip of 32 frames is shown below.

The demo for CogVideo is at https://models.aminer.cn/cogvideo, where you can get
hands-on practice on text-to-video generation. The original input is in Chinese.

Citation

🌟 If you find our work helpful, please leave us a star and cite our paper.

@article{yang2024cogvideox,
  title={CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer},
  author={Yang, Zhuoyi and Teng, Jiayan and Zheng, Wendi and Ding, Ming and Huang, Shiyu and Xu, Jiazheng and Yang, Yuanming and Hong, Wenyi and Zhang, Xiaohan and Feng, Guanyu and others},
  journal={arXiv preprint arXiv:2408.06072},
  year={2024}
}
@article{hong2022cogvideo,
  title={CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers},
  author={Hong, Wenyi and Ding, Ming and Zheng, Wendi and Liu, Xinghan and Tang, Jie},
  journal={arXiv preprint arXiv:2205.15868},
  year={2022}
}

We welcome your contributions! You can click here for more information.

Model-License

The code in this repository is released under the Apache 2.0 License.

The CogVideoX-2B model (including its corresponding Transformers module and VAE module) is released under
the Apache 2.0 License.

The CogVideoX-5B model (Transformers module, include I2V and T2V) is released under
the CogVideoX LICENSE.