SuperEdit – An image editing method introduced by institutions such as ByteDance

What is SuperEdit?

SuperEdit is an instruction-guided image editing method jointly developed by ByteDance’s Intelligent Creation Team and the Center for Research in Computer Vision at the University of Central Florida. It enhances editing accuracy and quality by optimizing supervision signals. SuperEdit aligns edited images more precisely with the original image based on corrected editing instructions and introduces contrastive supervision to further optimize model training. Unlike many other methods, SuperEdit does not require additional vision-language models (VLMs) or pretraining tasks—it relies solely on high-quality supervision signals and achieves significant performance improvements across multiple benchmarks.

Key Features of SuperEdit

• High-Precision Image Editing: Edits images based on natural language instructions with high accuracy. Supports global, local, and style-specific editing tasks.

• Efficient Training: Achieves high performance using limited training data and a relatively small model size, lowering the cost of training.

• Preservation of Original Image Quality: Maintains the structure and details of the original image as much as possible during editing, avoiding unnecessary changes.

Technical Principles of SuperEdit

• Generative Properties of Diffusion Models: Utilizes the generative behavior of diffusion models at different inference stages—early stages focus on global layout, middle stages on object attributes, and late stages on fine details, while style changes persist throughout.

• Editing Instruction Correction: Generates accurate editing instructions by comparing the original and edited images using a vision-language model (VLM). A unified correction guide ensures the instructions reflect the actual changes.

• Contrastive Supervision Signal: Introduces contrastive learning by generating incorrect instructions to form positive and negative pairs. A triplet loss function trains the model to distinguish between accurate and inaccurate instructions.

• Efficient Training Strategy: Uses a small amount of high-quality editing data, avoiding the computational burden of large-scale datasets. Optimized supervision signals enable SuperEdit to match or exceed the performance of more complex models.

• Model Architecture: Built on the InstructPix2Pix framework and fine-tuned on top of pretrained diffusion models (e.g., Stable Diffusion), incorporating corrected instructions and contrastive supervision.

Project Links for SuperEdit

• Official Website: https://liming-ai.github.io/SuperEdit/

• GitHub Repository: https://github.com/bytedance/SuperEdit

• HuggingFace Dataset: https://huggingface.co/datasets/limingcv/SuperEdit

• arXiv Paper: https://arxiv.org/pdf/2505.02370

Application Scenarios for SuperEdit

• Content Creation and Design: Ideal for advertising and social media image creation, enabling quick generation of visuals in specific styles or themes to boost content appeal.

• Film and Entertainment: Useful in visual effects production and character design, allowing for fast scene and appearance adjustments to speed up the production process.

• Game Development: Efficiently edit game characters and environments, generate concept art, and improve development productivity.

• Education and Training: Create educational materials and virtual lab visuals to support teaching and research, enhancing learning experiences.

• Medical and Health: Edit medical imagery and produce health education content, supporting medical training and public health promotion.