---
base_model:
- Wan-AI/Wan2.1-T2V-14B
license: apache-2.0
pipeline_tag: text-to-video
library_name: diffusers
---
Wan-Alpha
Video Generation with Stable Transparency via Shiftable RGB-A Distribution Learner
[](https://arxiv.org/pdf/2509.24979)
[](https://donghaotian123.github.io/Wan-Alpha/)
[](https://huggingface.co/htdong/Wan-Alpha)
[](https://huggingface.co/htdong/Wan-Alpha_ComfyUI)
[](https://huggingface.co/htdong/Wan-Alpha-v2.0)
>Qualitative results of video generation using **Wan-Alpha-v2.0**. Our model successfully generates various scenes with accurate and clearly rendered transparency. Notably, it can synthesize diverse semi-transparent objects, glowing effects, and fine-grained details such as hair.
---
### ๐ฅ News
* **[2025.12.16]** Released Wan-Alpha v2.0, the Wan2.1-14B-T2Vโadapted weights and inference code are now open-sourced.
* **[2025.12.16]** We update our paper on [arXiv](https://arxiv.org/pdf/2509.24979).
* **[2025.09.30]** Our technical report is available on [arXiv](https://arxiv.org/pdf/2509.24979).
* **[2025.09.30]** Released Wan-Alpha v1.0, the Wan2.1-14B-T2Vโadapted weights and inference code are now open-sourced.
---
### ๐ To-Do List
- [x] **Paper**: Available on [arXiv](https://arxiv.org/pdf/2509.24979).
- [x] **Inference Code**: Released inference pipeline for Wan-Alpha v1.0 and v2.0.
- [x] **Model Weights**: Released checkpoints for Wan-Alpha v1.0 and v2.0.
- [ ] **Image-to-Video**: Release Wan-Alpha-I2V model weights.
- [ ] **Dataset**: Open-source the VAE and T2V training dataset.
- [ ] **Training Code (VAE&T2V)**: Release training scripts for the VAE and text-to-RGBA video generation.
### ๐ Showcase
##### Text-to-Video Generation with Alpha Channel
| Prompt | Preview Video | Alpha Video |
| :---: | :---: | :---: |
| "The background of this video is transparent. It features a beige, woven rattan hanging chair with soft seat and back cushions. Realistic style. Medium shot." | 
| 
|
##### For more results, please visit [Our Website](https://donghaotian123.github.io/Wan-Alpha/)
### ๐ Quick Start
##### 1. Environment Setup
```bash
# Clone the project repository
git clone https://github.com/WeChatCV/Wan-Alpha.git
cd Wan-Alpha
# Create and activate Conda environment
conda create -n Wan-Alpha python=3.11 -y
conda activate Wan-Alpha
# Install dependencies
pip install -r requirements.txt
```
##### 2. Model Download
Download [Wan2.1-T2V-14B](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B)
Download [Lightx2v-T2V-14B](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Lightx2v/lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank64_bf16.safetensors)
Download [Wan-Alpha VAE](https://huggingface.co/htdong/Wan-Alpha-v2.0)
### ๐งช Usage
You can test our model through:
```bash
torchrun --nproc_per_node=8 --master_port=29501 generate_dora_lightx2v_mask.py --size 832*480\
--ckpt_dir "path/to/your/Wan-2.1/Wan2.1-T2V-14B" \
--dit_fsdp --t5_fsdp --ulysses_size 8 \
--vae_lora_checkpoint "path/to/your/decoder.bin" \
--lora_path "path/to/your/t2v.safetensors" \
--lightx2v_path "path/to/your/lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank64_bf16.safetensors" \
--sample_guide_scale 1.0 \
--frame_num 81 \
--sample_steps 4 \
--lora_ratio 1.0 \
--lora_prefix "" \
--alpha_shift_mean 0.05 \
--cache_path_mask "path/to/your/gauss_mask" \
--prompt_file ./data/prompt.txt \
--output_dir ./output
```
You can specify the weights of `Wan2.1-T2V-14B` with `--ckpt_dir`, `LightX2V-T2V-14B` with `--lightx2v_path`, `Wan-Alpha-VAE` with `--vae_lora_checkpoint`, and `Wan-Alpha-T2V` with `--lora_path`. Finally, you can find the rendered RGBA videos with a checkerboard background and PNG frames at `--output_dir`.
You can use `gen_gaussian_mask.py` to generate a Gaussian mask from an existing alpha video. Alternatively, you can directly create a Gaussian ellipse video, which can be either static or dynamic (e.g., moving from left to right). Note that alpha_shift_mean is a fixed parameter.
**Prompt Writing Tip:** You need to specify that the background of the video is transparent, the visual style, the shot type (such as close-up, medium shot, wide shot, or extreme close-up), and a description of the main subject. Prompts support both Chinese and English input.
```bash
# An example of prompt.
This video has a transparent background. Close-up shot. A colorful parrot flying. Realistic style.
```
### ๐จ Official ComfyUI Version
Coming soon...
### ๐ค Acknowledgements
This project is built upon the following excellent open-source projects:
* [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) (training/inference framework)
* [Wan2.1](https://github.com/Wan-Video/Wan2.1) (base video generation model)
* [LightX2V](https://github.com/ModelTC/LightX2V) (inference acceleration)
* [WanVideo_comfy](https://huggingface.co/Kijai/WanVideo_comfy) (inference acceleration)
We sincerely thank the authors and contributors of these projects.
### โ Citation
If you find our work helpful for your research, please consider citing our paper:
```bibtex
@misc{dong2025wanalpha,
title={Video Generation with Stable Transparency via Shiftable RGB-A Distribution Learner},
author={Haotian Dong and Wenjing Wang and Chen Li and Jing Lyu and Di Lin},
year={2025},
eprint={2509.24979},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2509.24979},
}
```
### ๐ฌ Contact Us
If you have any questions or suggestions, feel free to reach out via [GitHub Issues](https://github.com/WeChatCV/Wan-Alpha/issues) . We look forward to your feedback!