bytedance-research/Phantom

Phantom: Subject-Consistent Video Generation via Cross-Modal Alignment

Lijie Liu^*, Tianxiang Ma^*, Bingchuan Li^*, Zhuowei Chen^*, Jiawei Liu, Gen Li, Siyu Zhou, Qian He, Xinglong Wu
^*Equal contribution,^†Project lead
Intelligent Creation Team, ByteDance

🔥 Latest News!

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.
Apr 20, 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.

📑 Todo List

Inference codes and Checkpoint of Phantom-Wan 1.3B
Checkpoint of Phantom-Wan 14B
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.

⚡️ Quickstart

Installation

Clone the repo:

git clone https://github.com/Phantom-video/Phantom.git
cd Phantom

Install dependencies:

# Ensure torch >= 2.4.0
pip install -r requirements.txt

Model Download

First you need to download the 1.3B original model of Wan2.1. Download Wan2.1-1.3B using huggingface-cli:

pip install "huggingface_hub[cli]"
huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B --local-dir ./Wan2.1-T2V-1.3B

Then download the Phantom-Wan-1.3B model:

huggingface-cli download xxx --local-dir ./Phantom-Wan-1.3B

Run Subject-to-Video Generation

Single-GPU inference

python generate.py --task s2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --phantom_ckpt ./Phantom-Wan-1.3B/Phantom-Wan-1.3B.pth  --ref_image "examples/ref1.png,examples/ref2.png" --prompt "暖阳漫过草地，扎着双马尾、头戴绿色蝴蝶结、身穿浅绿色连衣裙的小女孩蹲在盛开的雏菊旁。她身旁一只棕白相间的狗狗吐着舌头，毛茸茸尾巴欢快摇晃。小女孩笑着举起黄红配色、带有蓝色按钮的玩具相机，将和狗狗的欢乐瞬间定格。" --base_seed 42

Multi-GPU inference using FSDP + xDiT USP

pip install "xfuser>=0.4.1"
torchrun --nproc_per_node=8 generate.py --task s2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --phantom_ckpt ./Phantom-Wan-1.3B/Phantom-Wan-1.3B.pth  --ref_image "examples/ref3.png,examples/ref4.png" --dit_fsdp --t5_fsdp --ulysses_size 4 --ring_size 2 --prompt "夕阳下，一位有着小麦色肌肤、留着乌黑长发的女人穿上有着大朵立体花朵装饰、肩袖处带有飘逸纱带的红色纱裙，漫步在金色的海滩上，海风轻拂她的长发，画面唯美动人。" --base_seed 42

💡Note:

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:

🆚 Comparative Results

Identity Preserving Video Generation.
Single Reference Subject-to-Video Generation.
Multi-Reference Subject-to-Video Generation.

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.

BibTeX

@article{liu2025phantom,
  title={Phantom: Subject-Consistent Video Generation via Cross-Modal Alignment},
  author={Liu, Lijie and Ma, Tianxaing and Li, Bingchuan and Chen, Zhuowei and Liu, Jiawei and He, Qian and Wu, Xinglong},
  journal={arXiv preprint arXiv:2502.11079},
  year={2025}
}