May 27, 2025: 🎉 We have released the Phantom-Wan-14B model, a more powerful Subject-to-Video generation model.
Apr 23, 2025: 😊 Thanks to ComfyUI-WanVideoWrapper for adapting ComfyUI to Phantom-Wan-1.3B. Everyone is welcome to use it!
Apr 21, 2025: 👋 Phantom-Wan is coming! We adapted the Phantom framework into the Wan2.1 video generation model. The inference codes and checkpoint have been released.
Apr 10, 2025: We have updated the full version of the Phantom paper, which now includes more detailed descriptions of the model architecture and dataset pipeline.
Feb 16, 2025: We proposed a novel subject-consistent video generation model, Phantom, and have released the report publicly. For more video demos, please visit the project page.
📑 Todo List
Inference codes and Checkpoint of Phantom-Wan-1.3B
Checkpoint of Phantom-Wan-14B
Checkpoint of Phantom-Wan-14B Pro
Open source Phantom-Data
Training codes of Phantom-Wan
📖 Overview
Phantom is a unified video generation framework for single and multi-subject references, built on existing text-to-video and image-to-video architectures. It achieves cross-modal alignment using text-image-video triplet data by redesigning the joint text-image injection model. Additionally, it emphasizes subject consistency in human generation while enhancing ID-preserving video generation.
First you need to download the 1.3B original model of Wan2.1, since our Phantom-Wan model relies on the Wan2.1 VAE and Text Encoder model. Download Wan2.1-1.3B using huggingface-cli:
Changing --ref_image can achieve single reference Subject-to-Video generation or multi-reference Subject-to-Video generation. The number of reference images should be within 4.
To achieve the best generation results, we recommend that you describe the visual content of the reference image as accurately as possible when writing --prompt. For example, "examples/ref1.png" can be described as "a toy camera in yellow and red with blue buttons".
When the generated video is unsatisfactory, the most straightforward solution is to try changing the --base_seed and modifying the description in the --prompt.
For inferencing examples, please refer to "infer.sh". You will get the following generated results:
The currently released Phantom-Wan-14B model was trained on 480P data but can also be applied to generating videos at 720P and higher resolutions, though the results may be less stable. We plan to release a version further trained on 720P data in the future.
The Phantom-Wan-14B model was trained on 24fps data, but it can also generate 16fps videos, similar to the native Wan2.1. However, the quality may experience a slight decline.
For more inference examples, please refer to "infer.sh". You will get the following generated results:
GIF 1
GIF 2
GIF 3
The GIF videos are compressed.
Acknowledgements
We would like to express our gratitude to the SEED team for their support. Special thanks to Lu Jiang, Haoyuan Guo, Zhibei Ma, and Sen Wang for their assistance with the model and data. In addition, we are also very grateful to Siying Chen, Qingyang Li, and Wei Han for their help with the evaluation.
If you find this project useful for your research, please consider citing our paper.
BibTeX
bibtex
1@article{liu2025phantom,
2 title={Phantom: Subject-consistent video generation via cross-modal alignment},
3 author={Liu, Lijie and Ma, Tianxiang and Li, Bingchuan and Chen, Zhuowei and Liu, Jiawei and Li, Gen and Zhou, Siyu and He, Qian and Wu, Xinglong},
4 journal={arXiv preprint arXiv:2502.11079},
5 year={2025}
6}
📧 Contact
If you have any comments or questions regarding this open-source project, please open a new issue or contact Tianxiang Ma.
