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EN
Wan2.1 Fun V1.1 1.3B Control Camera
A text-to-video model supporting multi-resolution training with first and last frame prediction capabilities
Downloads 54
Release Time : 4/24/2025
Model Overview
This is a text-to-video generation model that supports multi-resolution training and features first and last frame prediction, capable of generating high-quality video content from text or images.
Model Features
Multi-resolution support
Supports training and generation at various resolutions to meet different application needs
First and last frame prediction
Features first and last frame prediction for generating more coherent video content
Multi-language input
Supports both English and Chinese input to accommodate users of different languages
Model Capabilities
Text-to-video
Image-to-video
Multi-resolution video generation
First and last frame prediction
Use Cases
Content creation
Short video generation
Automatically generates short video content based on text descriptions
Can produce high-quality, coherent short video clips
Advertisement production
Quickly generates product promotional videos
Can create professional-grade advertisement videos based on product descriptions
Education
Educational video generation
Automatically generates demonstration videos based on teaching content
Can produce clear and vivid instructional demonstration videos
๐ Wan-Fun
๐ Welcome! This project is a text - to - video solution that enables users to generate high - quality videos from text inputs. It offers various model versions with different capabilities and can be easily used through multiple methods.
๐ Quick Start
1. Cloud Usage: AliyunDSW/Docker
a. Through Alibaba Cloud DSW
DSW provides free GPU hours. Users can apply for it once, and it will be valid for 3 months after application.
Alibaba Cloud offers free GPU hours on Freetier. Obtain them and use them in Alibaba Cloud PAI - DSW. You can start CogVideoX - Fun within 5 minutes.
b. Through ComfyUI
Our ComfyUI interface is as follows. For details, check ComfyUI README.
c. Through Docker
If you use Docker, make sure that the graphics card driver and CUDA environment are correctly installed on your machine, and then execute the following commands in sequence:
# 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/VideoX-Fun.git
# enter VideoX-Fun's dir
cd VideoX-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-V1.1-14B-InP
# https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP
2. Local Installation: Environment Check/Download/Installation
a. Environment Check
We have verified that this library can be executed in the following environments:
Details for Windows:
- Operating System: 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
Details for Linux:
- Operating System: 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 approximately 60GB of available disk space. Please check!
b. Weight Placement
It is recommended that we place the weights according to the specified paths:
Through ComfyUI:
Put the models into the weight folder ComfyUI/models/Fun_Models/ of ComfyUI:
๐ฆ ComfyUI/
โโโ ๐ models/
โ โโโ ๐ Fun_Models/
โ โโโ ๐ CogVideoX-Fun-V1.1-2b-InP/
โ โโโ ๐ CogVideoX-Fun-V1.1-5b-InP/
โ โโโ ๐ Wan2.1-Fun-V1.1-14B-InP
โ โโโ ๐ Wan2.1-Fun-V1.1-1.3B-InP/
When running your own Python files or UI interface:
๐ฆ models/
โโโ ๐ Diffusion_Transformer/
โ โโโ ๐ CogVideoX-Fun-V1.1-2b-InP/
โ โโโ ๐ CogVideoX-Fun-V1.1-5b-InP/
โ โโโ ๐ Wan2.1-Fun-V1.1-14B-InP
โ โโโ ๐ Wan2.1-Fun-V1.1-1.3B-InP/
โโโ ๐ Personalized_Model/
โ โโโ your trained trainformer model / your trained lora model (for UI load)
โจ Features
Model Address
V1.1:
| Property | Storage Space | Hugging Face | Model Scope | Details |
|---|---|---|---|---|
| Wan2.1-Fun-V1.1-1.3B-InP | 19.0 GB | ๐คLink | ๐Link | The text - to - video weights of Wan2.1-Fun-V1.1-1.3B, trained at multiple resolutions, support the prediction of the first and last frames. |
| Wan2.1-Fun-V1.1-14B-InP | 47.0 GB | ๐คLink | ๐Link | The text - to - video weights of Wan2.1-Fun-V1.1-14B, trained at multiple resolutions, support the prediction of the first and last frames. |
| Wan2.1-Fun-V1.1-1.3B-Control | 19.0 GB | ๐คLink | ๐Link | The video control weights of Wan2.1-Fun-V1.1-1.3B support different control conditions, such as Canny, Depth, Pose, MLSD, etc. Support control with reference images + control conditions and trajectory control. Support video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, and support multi - language prediction. |
| Wan2.1-Fun-V1.1-14B-Control | 47.0 GB | ๐คLink | ๐Link | The video control weights of Wan2.1-Fun-V1.1-14B support different control conditions, such as Canny, Depth, Pose, MLSD, etc. Support control with reference images + control conditions and trajectory control. Support video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, and support multi - language prediction. |
| Wan2.1-Fun-V1.1-1.3B-Control-Camera | 19.0 GB | ๐คLink | ๐Link | The camera lens control weights of Wan2.1-Fun-V1.1-1.3B. Support video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, and support multi - language prediction. |
| Wan2.1-Fun-V1.1-14B-Control | 47.0 GB | ๐คLink | ๐Link | The camera lens control weights of Wan2.1-Fun-V1.1-14B. Support video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, and support multi - language prediction. |
V1.0:
| Property | Storage Space | Hugging Face | Model Scope | Details |
|---|---|---|---|---|
| Wan2.1-Fun-1.3B-InP | 19.0 GB | ๐คLink | ๐Link | The text - to - video weights of Wan2.1-Fun-1.3B, trained at multiple resolutions, support the prediction of the first and last frames. |
| Wan2.1-Fun-14B-InP | 47.0 GB | ๐คLink | ๐Link | The text - to - video weights of Wan2.1-Fun-14B, trained at multiple resolutions, support the prediction of the first and last frames. |
| Wan2.1-Fun-1.3B-Control | 19.0 GB | ๐คLink | ๐Link | The video control weights of Wan2.1-Fun-1.3B support different control conditions, such as Canny, Depth, Pose, MLSD, etc., and also support trajectory control. Support video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, and support multi - language prediction. |
| Wan2.1-Fun-14B-Control | 47.0 GB | ๐คLink | ๐Link | The video control weights of Wan2.1-Fun-14B support different control conditions, such as Canny, Depth, Pose, MLSD, etc., and also support trajectory control. Support video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, and support multi - language prediction. |
Video Works
Wan2.1-Fun-V1.1-14B-InP && Wan2.1-Fun-V1.1-1.3B-InP
Wan2.1-Fun-V1.1-14B-Control && Wan2.1-Fun-V1.1-1.3B-Control
Generic Control Video + Reference Image:
| Reference Image | Control Video | Wan2.1-Fun-V1.1-14B-Control | Wan2.1-Fun-V1.1-1.3B-Control |
|
|
|||
Generic Control Video (Canny, Pose, Depth, etc.) and Trajectory Control:
Wan2.1-Fun-V1.1-14B-Control-Camera && Wan2.1-Fun-V1.1-1.3B-Control-Camera
| Pan Up | Pan Left | Pan Right |
| Pan Down | Pan Up + Pan Left | Pan Up + Pan Right |
๐ Documentation
How to Use
1. Generation
a. Video Memory Saving Scheme
Since the parameters of Wan2.1 are very large, we need to consider a video memory saving scheme to save video memory and adapt to consumer - grade graphics cards. We provide GPU_memory_mode for each prediction file, which can be selected from model_cpu_offload, model_cpu_offload_and_qfloat8, and sequential_cpu_offload. This scheme is also applicable to the generation of CogVideoX - Fun.
model_cpu_offloadmeans that the entire model will be transferred to the CPU after use, which can save some video memory.model_cpu_offload_and_qfloat8means that the entire model will be transferred to the CPU after use, and the Transformer model is quantized to float8, which can save more video memory.sequential_cpu_offloadmeans that each layer of the model will be transferred to the CPU after use. It is slower but saves a large amount of video memory.
qfloat8 will partially reduce the performance of the model but can save more video memory. If the video memory is sufficient, it is recommended to use model_cpu_offload.
b. Through ComfyUI
For details, check ComfyUI README.
c. Running Python Files
- Step 1: Download the corresponding [weights]
๐ License
The project is licensed under the Apache - 2.0 license.
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