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alibaba-pai / CogVideoX-Fun-V1.1-5b-Control
Model's Last Updated: December 11 2025
Introduction of CogVideoX-Fun-V1.1-5b-Control
Model Details of CogVideoX-Fun-V1.1-5b-Control
CogVideoX-Fun
😊 Welcome!
English | 简体中文
目录
简介
CogVideoX-Fun是一个基于CogVideoX结构修改后的的pipeline，是一个生成条件更自由的CogVideoX，可用于生成AI图片与视频、训练Diffusion Transformer的基线模型与Lora模型，我们支持从已经训练好的CogVideoX-Fun模型直接进行预测，生成不同分辨率，6秒左右、fps8的视频（1 ~ 49帧），也支持用户训练自己的基线模型与Lora模型，进行一定的风格变换。
我们会逐渐支持从不同平台快速启动，请参阅 快速启动 。
新特性：
- 重新训练i2v模型，添加Noise，使得视频的运动幅度更大。上传控制模型训练代码与Control模型。[ 2024.09.29 ]
- 创建代码！现在支持 Windows 和 Linux。支持2b与5b最大256x256x49到1024x1024x49的任意分辨率的视频生成。[ 2024.09.18 ]
功能概览：
我们的ui界面如下:
快速启动
1. 云使用: AliyunDSW/Docker
a. 通过阿里云 DSW
DSW 有免费 GPU 时间，用户可申请一次，申请后3个月内有效。
阿里云在 Freetier 提供免费GPU时间，获取并在阿里云PAI-DSW中使用，5分钟内即可启动CogVideoX-Fun。
b. 通过ComfyUI
我们的ComfyUI界面如下，具体查看
ComfyUI README
。
c. 通过docker
使用docker的情况下，请保证机器中已经正确安装显卡驱动与CUDA环境，然后以此执行以下命令：
# 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. 本地安装: 环境检查/下载/安装
a. 环境检查
我们已验证CogVideoX-Fun可在以下环境中执行：
Windows 的详细信息：
- 操作系统 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
Linux 的详细信息：
- 操作系统 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
我们需要大约 60GB 的可用磁盘空间，请检查！
b. 权重放置
我们最好将 权重 按照指定路径进行放置：
📦 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)
视频作品
所展示的结果都是图生视频获得。
CogVideoX-Fun-5B
Resolution-1024
Resolution-768
Resolution-512
CogVideoX-Fun-2B
Resolution-768
如何使用
1. 生成
a. 视频生成
i、运行python文件
- 步骤1：下载对应 权重 放入models文件夹。
- 步骤2：在predict_t2v.py文件中修改prompt、neg_prompt、guidance_scale和seed。
- 步骤3：运行predict_t2v.py文件，等待生成结果，结果保存在samples/cogvideox-fun-videos-t2v文件夹中。
- 步骤4：如果想结合自己训练的其他backbone与Lora，则看情况修改predict_t2v.py中的predict_t2v.py和lora_path。
ii、通过ui界面
- 步骤1：下载对应 权重 放入models文件夹。
- 步骤2：运行app.py文件，进入gradio页面。
- 步骤3：根据页面选择生成模型，填入prompt、neg_prompt、guidance_scale和seed等，点击生成，等待生成结果，结果保存在sample文件夹中。
iii、通过comfyui
具体查看 ComfyUI README 。
2. 模型训练
一个完整的CogVideoX-Fun训练链路应该包括数据预处理和Video DiT训练。
a.数据预处理
我们给出了一个简单的demo通过图片数据训练lora模型，详情可以查看[wiki](https://github.com/aigc-apps/CogVideoX-Fun/wiki/Training-Lora)。一个完整的长视频切分、清洗、描述的数据预处理链路可以参考video caption部分的 README 进行。
如果期望训练一个文生图视频的生成模型，您需要以这种格式排列数据集。
📦 project/
├── 📂 datasets/
│ ├── 📂 internal_datasets/
│ ├── 📂 train/
│ │ ├── 📄 00000001.mp4
│ │ ├── 📄 00000002.jpg
│ │ └── 📄 .....
│ └── 📄 json_of_internal_datasets.json
json_of_internal_datasets.json是一个标准的json文件。json中的file_path可以被设置为相对路径，如下所示：
[
{
"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"
},
.....
]
你也可以将路径设置为绝对路径：
[
{
"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训练
如果数据预处理时，数据的格式为相对路径，则进入scripts/train.sh进行如下设置。
export DATASET_NAME="datasets/internal_datasets/"
export DATASET_META_NAME="datasets/internal_datasets/json_of_internal_datasets.json"
...
train_data_format="normal"
如果数据的格式为绝对路径，则进入scripts/train.sh进行如下设置。
export DATASET_NAME=""
export DATASET_META_NAME="/mnt/data/json_of_internal_datasets.json"
最后运行scripts/train.sh。
sh scripts/train.sh
关于一些参数的设置细节，可以查看 Readme Train 与 Readme Lora
模型地址
V1.1:
| 名称 | 存储空间 | Hugging Face | Model Scope | 描述 |
|---|---|---|---|---|
| CogVideoX-Fun-V1.1-2b-InP.tar.gz | 解压前 9.7 GB / 解压后 13.0 GB | 🤗Link | 😄Link | 官方的图生视频权重。添加了Noise，运动幅度相比于V1.0更大。支持多分辨率（512，768，1024，1280）的视频预测，以49帧、每秒8帧进行训练 |
| CogVideoX-Fun-V1.1-5b-InP.tar.gz | 解压前 16.0GB / 解压后 20.0 GB | 🤗Link | 😄Link | 官方的图生视频权重。添加了Noise，运动幅度相比于V1.0更大。支持多分辨率（512，768，1024，1280）的视频预测，以49帧、每秒8帧进行训练 |
| CogVideoX-Fun-V1.1-2b-Pose.tar.gz | 解压前 9.7 GB / 解压后 13.0 GB | 🤗Link | 😄Link | 官方的姿态控制生视频权重。支持多分辨率（512，768，1024，1280）的视频预测，以49帧、每秒8帧进行训练 |
| CogVideoX-Fun-V1.1-5b-Pose.tar.gz | 解压前 16.0GB / 解压后 20.0 GB | 🤗Link | 😄Link | 官方的姿态控制生视频权重。支持多分辨率（512，768，1024，1280）的视频预测，以49帧、每秒8帧进行训练 |
V1.0:
| 名称 | 存储空间 | Hugging Face | Model Scope | 描述 |
|---|---|---|---|---|
| CogVideoX-Fun-2b-InP.tar.gz | 解压前 9.7 GB / 解压后 13.0 GB | 🤗Link | 😄Link | 官方的图生视频权重。支持多分辨率（512，768，1024，1280）的视频预测，以49帧、每秒8帧进行训练 |
| CogVideoX-Fun-5b-InP.tar.gz | 解压前 16.0GB / 解压后 20.0 GB | 🤗Link | 😄Link | 官方的图生视频权重。支持多分辨率（512，768，1024，1280）的视频预测，以49帧、每秒8帧进行训练 |
未来计划
- 支持中文。
参考文献
- CogVideo: https://github.com/THUDM/CogVideo/
- EasyAnimate: https://github.com/aigc-apps/EasyAnimate
许可证
本项目采用 Apache License (Version 2.0) .
CogVideoX-2B 模型 (包括其对应的Transformers模块，VAE模块) 根据 Apache 2.0 协议 许可证发布。
CogVideoX-5B 模型（Transformer 模块）在 CogVideoX许可证 下发布.
Runs of alibaba-pai CogVideoX-Fun-V1.1-5b-Control on huggingface.co
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More Information About CogVideoX-Fun-V1.1-5b-Control huggingface.co Model
Url of CogVideoX-Fun-V1.1-5b-Control
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