• [2025.11.27] We have released our paper on arXiv.

You can install the required environment using the provided requirements.txt file.

pip install -r requirements.txt

There are many open source video datasets, such as Openvid, VFHQ and Celebv-text. The video should be cut into a fixed number of frames (such as 17 or 25...), and the video data should be filtered based on aesthetic (use DOVER) and optical flow scores (refer to OpenSora data Processing).

You should organize your processed train data into a CSV file, as shown below:

video_path,text,num_frames,height,width,flow
./_JnC_Zj_P7s_22_0to190_extracted.mp4,scenery,17,720,1080,3.529723644
./_JnC_Zj_P7s_22_0to190_extracted.mp4,scenery,17,720,1080,4.014187813

You can use the provided ./train_scripts/train_i2v.sh script for training. The configuration file is located at: ./configs/mobilei2v_config/. Before training, download the weights for video-vae and qwen2-0.5B and replace the model path in the configuration file.

bash ./train_scripts/train_i2v.sh

You can use the provided ./test.sh script for inference. Provide a reference image or video (extract the first frame) to the asset/test.txt file and pass it to the --txt_file parameter.

CUDA_VISIBLE_DEVICES=0 python scripts/inference_i2v.py \
      --config=./configs/mobilei2v_config/MobileI2V_300M_img512.yaml \
      --save_path=humface_1126 \
      --model_path=./model/hybrid_371.pth \
      --txt_file=asset/test.txt \
      --flow_score=2.0 \

To achieve faster VAE decoder speeds, we replaced the LTX-Video decoder with the Turbo-VAED decoder.

Refer to the FVD evaluation script in vidm.

python scripts/evaluate_FVD.py -dir1 path/gts -dir2 path/videos -b 1 -r 32 -n 128 -ns 16 -i3d ./i3d_torchscript.pt

We designed the mobile UI and deployed the model, as shown in the video below:

Our MobileI2V codes are mainly built with SANA and LTX-Video. The data processing workflow is based on OpenSora. Thanks for all these great works.

If you find MobileI2V useful, please consider giving us a star 🌟 and citing it as follows:

@misc{MobileI2V,
      title={MobileI2V: Fast and High-Resolution Image-to-Video on Mobile Devices}, 
      author={Shuai Zhang and Bao Tang and Siyuan Yu and Yueting Zhu and Jingfeng Yao and Ya Zou and Shanglin Yuan and Li Yu and Wenyu Liu and Xinggang Wang},
      year={2025},
      eprint={2511.21475},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.21475}, 
}