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V1.0:

Name	Storage Space	Hugging Face	Model Scope	Description
Wan2.1-Fun-1.3B-InP	19.0 GB	🤗Link	😄Link	Wan2.1-Fun-1.3B text-to-video weights, trained at multiple resolutions, supporting start and end frame prediction.
Wan2.1-Fun-14B-InP	47.0 GB	🤗Link	😄Link	Wan2.1-Fun-14B text-to-video weights, trained at multiple resolutions, supporting start and end frame prediction.
Wan2.1-Fun-1.3B-Control	19.0 GB	🤗Link	😄Link	Wan2.1-Fun-1.3B video control weights, supporting various control conditions such as Canny, Depth, Pose, MLSD, etc., and trajectory control. Supports multi-resolution (512, 768, 1024) video prediction at 81 frames, trained at 16 frames per second, with multilingual prediction support.
Wan2.1-Fun-14B-Control	47.0 GB	🤗Link	😄Link	Wan2.1-Fun-14B video control weights, supporting various control conditions such as Canny, Depth, Pose, MLSD, etc., and trajectory control. Supports multi-resolution (512, 768, 1024) video prediction at 81 frames, trained at 16 frames per second, with multilingual prediction support.

DSW has free GPU time, which can be applied once by a user and is valid for 3 months after applying.

Aliyun provide free GPU time in Freetier, get it and use in Aliyun PAI-DSW to start CogVideoX-Fun within 5min!

Our ComfyUI is as follows, please refer to ComfyUI README for details.

If you are using docker, please make sure that the graphics card driver and CUDA environment have been installed correctly in your machine.

Then execute the following commands in this way:

# 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

We have verified this repo execution on the following environment:

The detailed of Windows:

• OS: 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

The detailed of Linux:

• OS: 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 about 60GB available on disk (for saving weights), please check!

We'd better place the weights along the specified path:

📦 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)

Since Wan2.1 has a very large number of parameters, we need to consider memory optimization strategies to adapt to consumer-grade GPUs. We provide GPU_memory_mode for each prediction file, allowing you to choose between model_cpu_offload, model_cpu_offload_and_qfloat8, and sequential_cpu_offload. This solution is also applicable to CogVideoX-Fun generation.

• model_cpu_offload: The entire model is moved to the CPU after use, saving some GPU memory.
• model_cpu_offload_and_qfloat8: The entire model is moved to the CPU after use, and the transformer model is quantized to float8, saving more GPU memory.
• sequential_cpu_offload: Each layer of the model is moved to the CPU after use. It is slower but saves a significant amount of GPU memory.

qfloat8 may slightly reduce model performance but saves more GPU memory. If you have sufficient GPU memory, it is recommended to use model_cpu_offload.

For details, refer to ComfyUI README.

• Step 1: Download the corresponding weights and place them in the models folder.
• Step 2: Use different files for prediction based on the weights and prediction goals. This library currently supports CogVideoX-Fun, Wan2.1, and Wan2.1-Fun. Different models are distinguished by folder names under the examples folder, and their supported features vary. Use them accordingly. Below is an example using CogVideoX-Fun:
  • Text-to-Video:
    • Modify prompt, neg_prompt, guidance_scale, and seed in the file examples/cogvideox_fun/predict_t2v.py.
    • Run the file examples/cogvideox_fun/predict_t2v.py and wait for the results. The generated videos will be saved in the folder samples/cogvideox-fun-videos.
  • Image-to-Video:
    • Modify validation_image_start, validation_image_end, prompt, neg_prompt, guidance_scale, and seed in the file examples/cogvideox_fun/predict_i2v.py.
    • validation_image_start is the starting image of the video, and validation_image_end is the ending image of the video.
    • Run the file examples/cogvideox_fun/predict_i2v.py and wait for the results. The generated videos will be saved in the folder samples/cogvideox-fun-videos_i2v.
  • Video-to-Video:
    • Modify validation_video, validation_image_end, prompt, neg_prompt, guidance_scale, and seed in the file examples/cogvideox_fun/predict_v2v.py.
    • validation_video is the reference video for video-to-video generation. You can use the following demo video: Demo Video.
    • Run the file examples/cogvideox_fun/predict_v2v.py and wait for the results. The generated videos will be saved in the folder samples/cogvideox-fun-videos_v2v.
  • Controlled Video Generation (Canny, Pose, Depth, etc.):
    • Modify control_video, validation_image_end, prompt, neg_prompt, guidance_scale, and seed in the file examples/cogvideox_fun/predict_v2v_control.py.
    • control_video is the control video extracted using operators such as Canny, Pose, or Depth. You can use the following demo video: Demo Video.
    • Run the file examples/cogvideox_fun/predict_v2v_control.py and wait for the results. The generated videos will be saved in the folder samples/cogvideox-fun-videos_v2v_control.
• Step 3: If you want to integrate other backbones or Loras trained by yourself, modify lora_path and relevant paths in examples/{model_name}/predict_t2v.py or examples/{model_name}/predict_i2v.py as needed.

The web UI supports text-to-video, image-to-video, video-to-video, and controlled video generation (Canny, Pose, Depth, etc.). This library currently supports CogVideoX-Fun, Wan2.1, and Wan2.1-Fun. Different models are distinguished by folder names under the examples folder, and their supported features vary. Use them accordingly. Below is an example using CogVideoX-Fun:

• Step 1: Download the corresponding weights and place them in the models folder.
• Step 2: Run the file examples/cogvideox_fun/app.py to access the Gradio interface.
• Step 3: Select the generation model on the page, fill in prompt, neg_prompt, guidance_scale, and seed, click "Generate," and wait for the results. The generated videos will be saved in the sample folder.

• CogVideo: https://github.com/THUDM/CogVideo/
• EasyAnimate: https://github.com/aigc-apps/EasyAnimate
• Wan2.1: https://github.com/Wan-Video/Wan2.1/

This project is licensed under the Apache License (Version 2.0).