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alibaba-pai / Wan2.1-Fun-1.3B-Control
Model's Last Updated: December 11 2025
text-to-video
Introduction of Wan2.1-Fun-1.3B-Control
Model Details of Wan2.1-Fun-1.3B-Control
Wan-Fun
😊 Welcome!
目录
模型地址
V1.0:
| 名称 | 存储空间 | Hugging Face | Model Scope | 描述 |
|---|---|---|---|---|
| Wan2.1-Fun-1.3B-InP | 19.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-1.3B文图生视频权重，以多分辨率训练，支持首尾图预测。 |
| Wan2.1-Fun-14B-InP | 47.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-14B文图生视频权重，以多分辨率训练，支持首尾图预测。 |
| Wan2.1-Fun-1.3B-Control | 19.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-1.3B视频控制权重，支持不同的控制条件，如Canny、Depth、Pose、MLSD等，同时支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 |
| Wan2.1-Fun-14B-Control | 47.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-14B视频控制权重，支持不同的控制条件，如Canny、Depth、Pose、MLSD等，同时支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 |
视频作品
Wan2.1-Fun-14B-InP && Wan2.1-Fun-1.3B-InP
Wan2.1-Fun-14B-Control && Wan2.1-Fun-1.3B-Control
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
# Please use the hugginface link or modelscope link to download the model.
# CogVideoX-Fun
# https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-InP
# https://modelscope.cn/models/PAI/CogVideoX-Fun-V1.1-5b-InP
# Wan
# https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-InP
# https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP
2. 本地安装: 环境检查/下载/安装
a. 环境检查
我们已验证该库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/
│ ├── 📂 Wan2.1-Fun-14B-InP
│ └── 📂 Wan2.1-Fun-1.3B-InP/
├── 📂 Personalized_Model/
│ └── your trained trainformer model / your trained lora model (for UI load)
如何使用
1. 生成
a、显存节省方案
由于Wan2.1的参数非常大，我们需要考虑显存节省方案，以节省显存适应消费级显卡。我们给每个预测文件都提供了GPU_memory_mode，可以在model_cpu_offload，model_cpu_offload_and_qfloat8，sequential_cpu_offload中进行选择。该方案同样适用于CogVideoX-Fun的生成。
- model_cpu_offload代表整个模型在使用后会进入cpu，可以节省部分显存。
- model_cpu_offload_and_qfloat8代表整个模型在使用后会进入cpu，并且对transformer模型进行了float8的量化，可以节省更多的显存。
- sequential_cpu_offload代表模型的每一层在使用后会进入cpu，速度较慢，节省大量显存。
qfloat8会部分降低模型的性能，但可以节省更多的显存。如果显存足够，推荐使用model_cpu_offload。
b、通过comfyui
具体查看 ComfyUI README 。
c、运行python文件
- 步骤1：下载对应 权重 放入models文件夹。
-
步骤2：根据不同的权重与预测目标使用不同的文件进行预测。当前该库支持CogVideoX-Fun、Wan2.1和Wan2.1-Fun，在examples文件夹下用文件夹名以区分，不同模型支持的功能不同，请视具体情况予以区分。以CogVideoX-Fun为例。
-
文生视频：
- 使用examples/cogvideox_fun/predict_t2v.py文件中修改prompt、neg_prompt、guidance_scale和seed。
- 而后运行examples/cogvideox_fun/predict_t2v.py文件，等待生成结果，结果保存在samples/cogvideox-fun-videos文件夹中。
-
图生视频：
- 使用examples/cogvideox_fun/predict_i2v.py文件中修改validation_image_start、validation_image_end、prompt、neg_prompt、guidance_scale和seed。
- validation_image_start是视频的开始图片，validation_image_end是视频的结尾图片。
- 而后运行examples/cogvideox_fun/predict_i2v.py文件，等待生成结果，结果保存在samples/cogvideox-fun-videos_i2v文件夹中。
-
视频生视频：
- 使用examples/cogvideox_fun/predict_v2v.py文件中修改validation_video、validation_image_end、prompt、neg_prompt、guidance_scale和seed。
- validation_video是视频生视频的参考视频。您可以使用以下视频运行演示： 演示视频
- 而后运行examples/cogvideox_fun/predict_v2v.py文件，等待生成结果，结果保存在samples/cogvideox-fun-videos_v2v文件夹中。
-
普通控制生视频（Canny、Pose、Depth等）：
- 使用examples/cogvideox_fun/predict_v2v_control.py文件中修改control_video、validation_image_end、prompt、neg_prompt、guidance_scale和seed。
- control_video是控制生视频的控制视频，是使用Canny、Pose、Depth等算子提取后的视频。您可以使用以下视频运行演示： 演示视频
- 而后运行examples/cogvideox_fun/predict_v2v_control.py文件，等待生成结果，结果保存在samples/cogvideox-fun-videos_v2v_control文件夹中。
-
文生视频：
- 步骤3：如果想结合自己训练的其他backbone与Lora，则看情况修改examples/{model_name}/predict_t2v.py中的examples/{model_name}/predict_i2v.py和lora_path。
d、通过ui界面
webui支持文生视频、图生视频、视频生视频和普通控制生视频（Canny、Pose、Depth等）。当前该库支持CogVideoX-Fun、Wan2.1和Wan2.1-Fun，在examples文件夹下用文件夹名以区分，不同模型支持的功能不同，请视具体情况予以区分。以CogVideoX-Fun为例。
- 步骤1：下载对应 权重 放入models文件夹。
- 步骤2：运行examples/cogvideox_fun/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。
参考文献
- CogVideo: https://github.com/THUDM/CogVideo/
- EasyAnimate: https://github.com/aigc-apps/EasyAnimate
- Wan2.1: https://github.com/Wan-Video/Wan2.1/
许可证
本项目采用 Apache License (Version 2.0) .
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