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EN
Wan2.1 Fun V1.1 1.3B Control
A text-to-video generation model supporting multi-resolution training with first and last frame prediction capability
Downloads 173
Release Time : 4/24/2025
Model Overview
This is a model that generates videos based on text and image inputs, supporting multiple resolutions and first/last frame prediction features.
Model Features
Multi-resolution support
The model supports video generation at various resolutions
First/last frame prediction
Can predict the first and last frames of videos based on inputs
Condition control
Supports multi-condition control (Canny/depth/pose etc.) for video generation
Model Capabilities
Text-to-video generation
Image-to-video generation
Multi-condition controlled video generation
Multi-resolution video generation
Use Cases
Creative content generation
Short video creation
Automatically generates creative short videos based on text descriptions
Generates video content matching the description
Advertisement production
Quickly generates product showcase videos
Automatically produces product demonstration videos
Education
Educational video generation
Automatically generates teaching videos based on textbook content
Produces visualized teaching materials
๐ Wan-Fun
๐ Welcome! This project is designed for text - to - video generation, offering various models and functionalities to create high - quality videos.
๐ Quick Start
1. Cloud Usage: AliyunDSW/Docker
a. Via Alibaba Cloud DSW
DSW offers free GPU hours. Users can apply once, and the free hours are valid for 3 months after application.
Alibaba Cloud provides free GPU hours on Freetier. Obtain them and use them in Alibaba Cloud PAI - DSW. You can start CogVideoX - Fun within 5 minutes.
b. Via ComfyUI
Our ComfyUI interface is as follows. For details, check ComfyUI README.
c. Via Docker
If you use Docker, make sure 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 run 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 to place the weights in the specified paths:
Via ComfyUI:
Place the models in 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:
| Name | Storage Space | Hugging Face | Model Scope | Description |
|---|---|---|---|---|
| Wan2.1-Fun-V1.1-1.3B-InP | 19.0 GB | ๐คLink | ๐Link | Weights for text - to - video generation of Wan2.1-Fun-V1.1-1.3B, trained at multiple resolutions, supporting first - and last - frame prediction. |
| Wan2.1-Fun-V1.1-14B-InP | 47.0 GB | ๐คLink | ๐Link | Weights for text - to - video generation of Wan2.1-Fun-V1.1-14B, trained at multiple resolutions, supporting first - and last - frame prediction. |
| Wan2.1-Fun-V1.1-1.3B-Control | 19.0 GB | ๐คLink | ๐Link | Video control weights of Wan2.1-Fun-V1.1-1.3B, supporting different control conditions such as Canny, Depth, Pose, MLSD, etc. It supports control with reference images + control conditions and trajectory control. Supports video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, and supports multi - language prediction. |
| Wan2.1-Fun-V1.1-14B-Control | 47.0 GB | ๐คLink | ๐Link | Video control weights of Wan2.1-Fun-V1.1-14B, supporting different control conditions such as Canny, Depth, Pose, MLSD, etc. It supports control with reference images + control conditions and trajectory control. Supports video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, and supports multi - language prediction. |
| Wan2.1-Fun-V1.1-1.3B-Control-Camera | 19.0 GB | ๐คLink | ๐Link | Camera lens control weights of Wan2.1-Fun-V1.1-1.3B. Supports video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, and supports multi - language prediction. |
| Wan2.1-Fun-V1.1-14B-Control | 47.0 GB | ๐คLink | ๐Link | Camera lens control weights of Wan2.1-Fun-V1.1-14B. Supports video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, and supports multi - language prediction. |
V1.0:
| Name | Storage Space | Hugging Face | Model Scope | Description |
|---|---|---|---|---|
| Wan2.1-Fun-1.3B-InP | 19.0 GB | ๐คLink | ๐Link | Weights for text - to - video generation of Wan2.1-Fun-1.3B, trained at multiple resolutions, supporting first - and last - frame prediction. |
| Wan2.1-Fun-14B-InP | 47.0 GB | ๐คLink | ๐Link | Weights for text - to - video generation of Wan2.1-Fun-14B, trained at multiple resolutions, supporting first - and last - frame prediction. |
| Wan2.1-Fun-1.3B-Control | 19.0 GB | ๐คLink | ๐Link | Video control weights of Wan2.1-Fun-1.3B, supporting different control conditions such as Canny, Depth, Pose, MLSD, etc., and trajectory control. Supports video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, and supports multi - language prediction. |
| Wan2.1-Fun-14B-Control | 47.0 GB | ๐คLink | ๐Link | Video control weights of Wan2.1-Fun-14B, supporting different control conditions such as Canny, Depth, Pose, MLSD, etc., and trajectory control. Supports video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, and supports 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
a. VRAM Saving Scheme
Since the parameters of Wan2.1 are very large, we need to consider VRAM saving schemes to 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 also applies to the generation of CogVideoX - Fun.
- model_cpu_offload means the entire model will be moved to the CPU after use, which can save some VRAM.
- model_cpu_offload_and_qfloat8 means the entire model will be moved to the CPU after use, and the transformer model is quantized to float8, which can save more VRAM.
- sequential_cpu_offload means each layer of the model will be moved to the CPU after use. It is slower but saves a large amount of VRAM.
qfloat8 will partially reduce the model's performance but save more VRAM. If the VRAM is sufficient, it is recommended to use model_cpu_offload.
b. Via ComfyUI
Check ComfyUI README for details.
c. Running Python Files
- Step 1: Download the corresponding [weights]
๐ License
The project is under the Apache - 2.0 license.
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