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EN
Wan2.1 Fun 1.3B Control
Wan2.1-Fun-1.3B is a text-to-video generation model that supports multi-resolution training and first/last frame prediction.
Downloads 22.19k
Release Time : 3/26/2025
Model Overview
This model specializes in generating high-quality videos from text, supporting multiple resolutions and first/last frame prediction, making it suitable for creative video generation scenarios.
Model Features
Multi-resolution support
Supports video generation at various resolutions including 512/768/1024
First/last frame prediction
Capable of predicting the first and last frames of videos to improve generation coherence
Multilingual input
Supports Chinese and English text input to accommodate users of different languages
Model Capabilities
Text-to-video generation
Multi-resolution video generation
First/last frame prediction
Chinese/English input support
Use Cases
Creative content generation
Short video creation
Automatically generates creative short videos based on text descriptions
Can generate coherent videos with 81 frames (16fps)
Advertisement production
Quickly generates product showcase videos
Supports multiple resolutions to meet different platform requirements
Educational content
Educational video generation
Automatically generates supporting videos based on textbook content
๐ Wan-Fun
Welcome to Wan-Fun, a powerful text-to-video generation project! It offers various models for high-quality video creation and provides multiple usage methods.
๐ Quick Start
1. Cloud Usage: AliyunDSW/Docker
a. Via Alibaba Cloud DSW
DSW offers free GPU usage time. Users can apply for it once, and the validity period is within 3 months after application.
Alibaba Cloud provides free GPU time on Freetier. Obtain it and use it 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/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. 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 to place the weights according to the specified path:
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-14B-InP
โ โโโ ๐ Wan2.1-Fun-1.3B-InP/
โโโ ๐ Personalized_Model/
โ โโโ your trained trainformer model / your trained lora model (for UI load)
โจ Features
Model Address
V1.0:
| 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 | Control weights for Wan2.1-Fun-1.3B video, supporting different control conditions such as Canny, Depth, Pose, MLSD, etc., and also supporting trajectory control. Supports video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, supporting multi-language prediction. |
| Wan2.1-Fun-14B-Control | 47.0 GB | ๐คLink | ๐Link | Control weights for Wan2.1-Fun-14B video, supporting different control conditions such as Canny, Depth, Pose, MLSD, etc., and also supporting trajectory control. Supports video prediction at multiple resolutions (512, 768, 1024), trained with 81 frames at 16 frames per second, supporting multi-language prediction. |
Video Works
Wan2.1-Fun-14B-InP && Wan2.1-Fun-1.3B-InP
Wan2.1-Fun-14B-Control && Wan2.1-Fun-1.3B-Control
๐ Documentation
How to Use
a. Video Generation
a. Memory Saving Scheme
Since the parameters of Wan2.1 are very large, we need to consider a memory saving scheme to save GPU 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_offload means the entire model will be transferred to the CPU after use, which can save some GPU memory.
- model_cpu_offload_and_qfloat8 means the entire model will be transferred to the CPU after use, and the transformer model is quantized to float8, which can save more GPU memory.
- sequential_cpu_offload means each layer of the model will be transferred to the CPU after use. It is slower but saves a large amount of GPU memory.
qfloat8 will partially reduce the performance of the model but can save more GPU memory. If the GPU memory is sufficient, it is recommended to use model_cpu_offload.
b. Via ComfyUI
For details, check ComfyUI README.
c. Run Python Files
- Step 1: Download the corresponding weights and place them in the models folder.
- Step 2: Use different files for prediction according to different weights and prediction targets. Currently, this library supports CogVideoX-Fun, Wan2.1, and Wan2.1-Fun, which are distinguished by folder names in the examples folder. Different models support different functions. Please make a distinction according to the specific situation. Take CogVideoX-Fun as an example.
- Text-to-Video:
- Modify prompt, neg_prompt, guidance_scale, and seed in the examples/cogvideox_fun/predict_t2v.py file.
- Then run the examples/cogvideox_fun/predict_t2v.py file and wait for the generation result. The result will be saved in the samples/cogvideox-fun-videos folder.
- Image-to-Video:
- Modify validation_image_start, validation_image_end, prompt, neg_prompt, guidance_scale, and seed in the examples/cogvideox_fun/predict_i2v.py file.
- validation_image_start is the start image of the video, and validation_image_end is the end image of the video.
- Then run the examples/cogvideox_fun/predict_i2v.py file and wait for the generation result. The result will be saved in the samples/cogvideox-fun-videos_i2v folder.
- Video-to-Video:
- Modify validation_video, validation_image_end, prompt, neg_prompt, guidance_scale, and seed in the examples/cogvideox_fun/predict_v2v.py file.
- validation_video is the reference video for video-to-video generation. You can use the following video for demonstration: Demo Video
- Then run the examples/cogvideox_fun/predict_v2v.py file and wait for the generation result. The result will be saved in the samples/cogvideox-fun-videos_v2v folder.
- Normal Control-to-Video (Canny, Pose, Depth, etc.):
- Modify control_video, validation_image_end, prompt, neg_prompt, guidance_scale, and seed in the examples/cogvideox_fun/predict_v2v_control.py file.
- control_video is the control video for control-to-video generation, which is a video extracted by operators such as Canny, Pose, Depth, etc. You can use the following video for demonstration: Demo Video
- Then run the examples/cogvideox_fun/predict_v2v_control.py file and wait for the generation result. The result will be saved in the samples/cogvideox-fun-videos_v2v_control folder.
- Text-to-Video:
- Step 3: If you want to combine other backbones and LoRA models you trained, modify the examples/{model_name}/predict_t2v.py and examples/{model_name}/predict_i2v.py and lora_path according to the situation.
d. Via UI Interface
The webui supports text-to-video, image-to-video, video-to-video, and normal control-to-video (Canny, Pose, Depth, etc.). Currently, this library supports CogVideoX-Fun, Wan2.1, and Wan2.1-Fun, which are distinguished by folder names in the examples folder. Different models support different functions. Please make a distinction according to the specific situation. Take CogVideoX-Fun as an example.
- Step 1: Download the corresponding weights and place them in the models folder.
- Step 2: Run the examples/cogvideox_fun/app.py file and enter the Gradio page.
- Step 3: Select the generation model according to the page, fill in prompt, neg_prompt, guidance_scale, and seed, etc., click Generate, and wait for the generation result. The result will be saved in the sample folder.
๐ License
This project uses the Apache License (Version 2.0).
๐ References
- CogVideo: https://github.com/THUDM/CogVideo/
- EasyAnimate: https://github.com/aigc-apps/EasyAnimate
- Wan2.1: https://github.com/Wan-Video/Wan2.1/
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