Environment

For the relevant data construction scripts, model training and inference scripts, please refer to X2Edit.

Prepare the environment, install the required libraries:

$ git clone https://github.com/OPPO-Mente-Lab/X2Edit.git
$ cd X2Edit
$ conda create --name X2Edit python==3.11
$ conda activate X2Edit
$ pip install -r requirements.txt

Inference

We provides inference scripts for editing images with resolutions of 1024 and 512. In addition, we can choose the base model of X2Edit, including FLUX.1-Krea, FLUX.1-dev, FLUX.1-schnell, PixelWave, shuttle-3-diffusion, and choose the LoRA for integration with MoE-LoRA including Turbo-Alpha, AntiBlur, Midjourney-Mix2, Super-Realism, Chatgpt-Ghibli. Choose the model you like and download it. For the MoE-LoRA, we will open source a unified checkpoint that can be used for both 512 and 1024 resolutions.

Before executing the script, download Qwen3-8B to select the task type for the input instruction, base model(FLUX.1-Krea, FLUX.1-dev, FLUX.1-schnell, shuttle-3-diffusion), MLLM and Alignet. All scripts follow analogous command patterns. Simply replace the script filename while maintaining consistent parameter configurations.

$ python infer.py --device cuda --pixel 1024 --num_experts 12 --base_path BASE_PATH --qwen_path QWEN_PATH --lora_path LORA_PATH --extra_lora_path EXTRA_LORA_PATH

device: The device used for inference. default: cuda
pixel: The resolution of the input image, , you can choose from [512, 1024]. default: 1024
num_experts: The number of expert in MoE. default: 12
base_path: The path of base model.
qwen_path: The path of model used to select the task type for the input instruction. We use Qwen3-8B here.
lora_path: The path of MoE-LoRA in X2Edit.
extra_lora_path: The path of extra LoRA for plug-and-play. default: None.

Citation

🌟 If you find our work helpful, please consider citing our paper and leaving valuable stars

@misc{ma2025x2editrevisitingarbitraryinstructionimage,
      title={X2Edit: Revisiting Arbitrary-Instruction Image Editing through Self-Constructed Data and Task-Aware Representation Learning}, 
      author={Jian Ma and Xujie Zhu and Zihao Pan and Qirong Peng and Xu Guo and Chen Chen and Haonan Lu},
      year={2025},
      eprint={2508.07607},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.07607}, 
}