CogVideoX-Fun

😊 Welcome!

Table of Contents
Introduction
Quick Start
Video Result
How to use
Model zoo
TODO List
Reference
License

Introduction

CogVideoX-Fun is a modified pipeline based on the CogVideoX structure, designed to provide more flexibility in generation. It can be used to create AI images and videos, as well as to train baseline models and Lora models for Diffusion Transformer. We support predictions directly from the already trained CogVideoX-Fun model, allowing the generation of videos at different resolutions, approximately 6 seconds long with 8 fps (1 to 49 frames). Users can also train their own baseline models and Lora models to achieve certain style transformations.

We will support quick pull-ups from different platforms, refer to Quick Start.

What's New:

Use reward backpropagation to train Lora and optimize the video, aligning it better with human preferences, detailes in here. A new version of the control model supports various conditions (e.g., Canny, Depth, Pose, MLSD, etc.). [2024.11.21]
CogVideoX-Fun Control is now supported in diffusers. Thanks to a-r-r-o-w who contributed the support in this PR. Check out the docs to know more. [ 2024.10.16 ]
Retrain the i2v model and add noise to increase the motion amplitude of the video. Upload the control model training code and control model. [ 2024.09.29 ]
Create code! Now supporting Windows and Linux. Supports 2b and 5b models. Supports video generation at any resolution from 256x256x49 to 1024x1024x49. [ 2024.09.18 ]

Function：

Data Preprocessing
Train DiT
Video Generation

Our UI interface is as follows:

Quick Start

1. Cloud usage: AliyunDSW/Docker

a. From AliyunDSW

DSW has free GPU time, which can be applied once by a user and is valid for 3 months after applying.

Aliyun provide free GPU time in Freetier, get it and use in Aliyun PAI-DSW to start CogVideoX-Fun within 5min!

b. From ComfyUI

Our ComfyUI is as follows, please refer to ComfyUI README for details.

c. From docker

If you are using docker, please make sure that the graphics card driver and CUDA environment have been installed correctly in your machine.

Then execute the following commands in this way:

# pull image
docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun

# enter image
docker run -it -p 7860:7860 --network host --gpus all --security-opt seccomp:unconfined --shm-size 200g mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun

# clone code
git clone https://github.com/aigc-apps/CogVideoX-Fun.git

# enter CogVideoX-Fun's dir
cd CogVideoX-Fun

# download weights
mkdir models/Diffusion_Transformer
mkdir models/Personalized_Model

wget https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/Diffusion_Transformer/CogVideoX-Fun-V1.1-2b-InP.tar.gz -O models/Diffusion_Transformer/CogVideoX-Fun-V1.1-2b-InP.tar.gz

cd models/Diffusion_Transformer/
tar -xvf CogVideoX-Fun-V1.1-2b-InP.tar.gz
cd ../../

2. Local install: Environment Check/Downloading/Installation

a. Environment Check

We have verified CogVideoX-Fun execution on the following environment:

The detailed of Windows:

OS: Windows 10
python: python3.10 & python3.11
pytorch: torch2.2.0
CUDA: 11.8 & 12.1
CUDNN: 8+
GPU： Nvidia-3060 12G & Nvidia-3090 24G

The detailed of Linux:

OS: Ubuntu 20.04, CentOS
python: python3.10 & python3.11
pytorch: torch2.2.0
CUDA: 11.8 & 12.1
CUDNN: 8+
GPU：Nvidia-V100 16G & Nvidia-A10 24G & Nvidia-A100 40G & Nvidia-A100 80G

We need about 60GB available on disk (for saving weights), please check!

b. Weights

We'd better place the weights along the specified path:

📦 models/
├── 📂 Diffusion_Transformer/
│   ├── 📂 CogVideoX-Fun-V1.1-2b-InP/
│   └── 📂 CogVideoX-Fun-V1.1-5b-InP/
├── 📂 Personalized_Model/
│   └── your trained trainformer model / your trained lora model (for UI load)

Video Result

The results displayed are all based on image.

CogVideoX-Fun-V1.1-5B

Resolution-1024

Resolution-768

Resolution-512

CogVideoX-Fun-V1.1-5B with Reward Backpropagation

Prompt	CogVideoX-Fun-V1.1-5B	CogVideoX-Fun-V1.1-5B HPSv2.1 Reward LoRA	CogVideoX-Fun-V1.1-5B MPS Reward LoRA
Pig with wings flying above a diamond mountain
A dog runs through a field while a cat climbs a tree

CogVideoX-Fun-V1.1-5B-Control


A young woman with beautiful clear eyes and blonde hair, wearing white clothes and twisting her body, with the camera focused on her face. High quality, masterpiece, best quality, high resolution, ultra-fine, dreamlike.	A young woman with beautiful clear eyes and blonde hair, wearing white clothes and twisting her body, with the camera focused on her face. High quality, masterpiece, best quality, high resolution, ultra-fine, dreamlike.	A young bear.

CogVideoX-Fun-V1.1-5B-Pose

Resolution-512	Resolution-768	Resolution-1024

CogVideoX-Fun-V1.1-2B

Resolution-768

CogVideoX-Fun-V1.1-2B-Pose

Resolution-512	Resolution-768	Resolution-1024

How to use

1. Inference

a. Using Python Code

Step 1: Download the corresponding weights and place them in the models folder.
Step 2: Modify prompt, neg_prompt, guidance_scale, and seed in the predict_t2v.py file.
Step 3: Run the predict_t2v.py file, wait for the generated results, and save the results in the samples/cogvideox-fun-videos-t2v folder.
Step 4: If you want to combine other backbones you have trained with Lora, modify the predict_t2v.py and Lora_path in predict_t2v.py depending on the situation.

b. Using webui

Step 1: Download the corresponding weights and place them in the models folder.
Step 2: Run the app.py file to enter the graph page.
Step 3: Select the generated model based on the page, fill in prompt, neg_prompt, guidance_scale, and seed, click on generate, wait for the generated result, and save the result in the samples folder.

c. From ComfyUI

Please refer to ComfyUI README for details.

2. Model Training

A complete CogVideoX-Fun training pipeline should include data preprocessing, and Video DiT training.

a. data preprocessing

We have provided a simple demo of training the Lora model through image data, which can be found in the wiki for details.

A complete data preprocessing link for long video segmentation, cleaning, and description can refer to README in the video captions section.

If you want to train a text to image and video generation model. You need to arrange the dataset in this format.

📦 project/
├── 📂 datasets/
│   ├── 📂 internal_datasets/
│       ├── 📂 train/
│       │   ├── 📄 00000001.mp4
│       │   ├── 📄 00000002.jpg
│       │   └── 📄 .....
│       └── 📄 json_of_internal_datasets.json

The json_of_internal_datasets.json is a standard JSON file. The file_path in the json can to be set as relative path, as shown in below:

[
    {
      "file_path": "train/00000001.mp4",
      "text": "A group of young men in suits and sunglasses are walking down a city street.",
      "type": "video"
    },
    {
      "file_path": "train/00000002.jpg",
      "text": "A group of young men in suits and sunglasses are walking down a city street.",
      "type": "image"
    },
    .....
]

You can also set the path as absolute path as follow:

[
    {
      "file_path": "/mnt/data/videos/00000001.mp4",
      "text": "A group of young men in suits and sunglasses are walking down a city street.",
      "type": "video"
    },
    {
      "file_path": "/mnt/data/train/00000001.jpg",
      "text": "A group of young men in suits and sunglasses are walking down a city street.",
      "type": "image"
    },
    .....
]

b. Video DiT training

If the data format is relative path during data preprocessing, please set scripts/train.sh as follow.

export DATASET_NAME="datasets/internal_datasets/"
export DATASET_META_NAME="datasets/internal_datasets/json_of_internal_datasets.json"

If the data format is absolute path during data preprocessing, please set scripts/train.sh as follow.

export DATASET_NAME=""
export DATASET_META_NAME="/mnt/data/json_of_internal_datasets.json"

Then, we run scripts/train.sh.

sh scripts/train.sh

For details on setting some parameters, please refer to Readme Train, Readme Lora and Readme Control.

Model zoo

V1.5:

Name	Storage Space	Hugging Face	Model Scope	Description
CogVideoX-Fun-V1.5-5b-InP	20.0 GB	🤗Link	😄Link	Our official graph-generated video model is capable of predicting videos at multiple resolutions (512, 768, 1024) and has been trained on 85 frames at a rate of 8 frames per second.

V1.1:

Name	Storage Space	Hugging Face	Model Scope	Description
CogVideoX-Fun-V1.1-2b-InP	13.0 GB	🤗Link	😄Link	Our official graph-generated video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second.
CogVideoX-Fun-V1.1-5b-InP	20.0 GB	🤗Link	😄Link	Our official graph-generated video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second. Noise has been added to the reference image, and the amplitude of motion is greater compared to V1.0.
CogVideoX-Fun-V1.1-2b-Pose	13.0 GB	🤗Link	😄Link	Our official pose-control video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second.
CogVideoX-Fun-V1.1-2b-Control	13.0 GB	🤗Link	😄Link	Our official control video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second. Supporting various control conditions such as Canny, Depth, Pose, MLSD, etc.
CogVideoX-Fun-V1.1-5b-Pose	20.0 GB	🤗Link	😄Link	Our official pose-control video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second.
CogVideoX-Fun-V1.1-5b-Control	20.0 GB	🤗Link	😄Link	Our official control video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second. Supporting various control conditions such as Canny, Depth, Pose, MLSD, etc.
CogVideoX-Fun-V1.1-Reward-LoRAs	-	🤗Link	😄Link	The official reward backpropagation technology model optimizes the videos generated by CogVideoX-Fun-V1.1 to better match human preferences. ｜

V1.0:

Name	Storage Space	Hugging Face	Model Scope	Description
CogVideoX-Fun-2b-InP	13.0 GB	🤗Link	😄Link	Our official graph-generated video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second.
CogVideoX-Fun-5b-InP	20.0 GB	🤗Link	😄Link	Our official graph-generated video model is capable of predicting videos at multiple resolutions (512, 768, 1024, 1280) and has been trained on 49 frames at a rate of 8 frames per second.

TODO List

Support Chinese.

Reference

CogVideo: https://github.com/THUDM/CogVideo/
EasyAnimate: https://github.com/aigc-apps/EasyAnimate

License

This project is licensed under the Apache License (Version 2.0).

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) is released under the CogVideoX LICENSE.