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🚀 In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer

This project presents In-Context Edit, a novel approach for instruction-based image editing. It achieves state - of - the - art results using significantly less training data and parameters compared to prior SOTA methods.

🚀 Quick Start

This repository will contain the official implementation of ICEdit. You can access related resources through the following links:

• Arxiv
• Github
• Huggingface Demo
• Project Page

We present In-Context Edit, a novel approach that achieves state-of-the-art instruction-based editing using just 0.5% of the training data and 1% of the parameters required by prior SOTA methods. The first row illustrates a series of multi-turn edits, executed with high precision, while the second and third rows highlight diverse, visually impressive single-turn editing results from our method.

:open_book: For more visual results, go checkout our project page.

✨ Features

• Efficient Editing: Achieves state - of - the - art instruction - based editing with only 0.5% of the training data and 1% of the parameters required by prior SOTA methods.
• Multi - turn and Single - turn Editing: Supports both multi - turn and single - turn image editing with high precision.

📄 License

• License Type: Other
• License Name: no - commercial
• License Link: https://github.com/River-Zhang/ICEdit/blob/main/LICENSE

📦 Technical Details

Model Information

Tips

⚠️ Important Note

• If you encounter a failure case, please try again with a different seed!
• Our base model, FLUX, does not inherently support a wide range of styles, so a large portion of our dataset involves style transfer. As a result, the model may sometimes inexplicably change your artistic style.
• Our training dataset is mostly targeted at realistic images. For non - realistic images, such as anime or blurry pictures, the success rate of the editing drops and could potentially affect the final image quality.
• While the success rates for adding objects, modifying color attributes, applying style transfer, and changing backgrounds are high, the success rate for object removal is relatively lower due to the low quality of the OmniEdit removal dataset.

To Do List

• [x] Inference Code
• [ ] Inference - time Scaling with VLM
• [x] Pretrained Weights
• [ ] More Inference Demos
• [x] Gradio demo
• [ ] Comfy UI demo
• [ ] Training Code

The current model is the one used in the experiments in the paper, trained with only 4 A800 GPUs (total batch_size = 2 x 2 x 4 = 16). In the future, we will enhance the dataset, and do scale - up, finally release a more powerful model.