SuperEdit - An image editing method launched by ByteDance and other organizations.

What is SuperEdit?

SuperEdit is a command-guided image editing method jointly developed by ByteDance's Intelligent Creation Team and the Computer Vision Research Center at the University of Central Florida. It improves the accuracy and effectiveness of image editing by optimizing supervisory signals. SuperEdit corrects editing commands, aligning them more accurately with the original and edited image pairs, and introduces contrastive supervisory signals to further optimize model training. SuperEdit does not require an additional Visual Language Model (VLM) or pre-training tasks; it relies solely on high-quality supervisory signals and achieves significant performance improvements across multiple benchmarks.

SuperEdit's main functions

• High-precision image editingIt enables precise image editing based on natural language instructions, supporting various editing tasks such as global, local, and style-based editing.
• High-efficiency trainingAchieve high performance with limited training data and a small model size, reducing training costs.
• Preserve original image qualityWhen performing editing tasks, preserve the structure and details of the original image to the greatest extent possible, and avoid unnecessary modifications.

SuperEdit's technical principles

• Generative properties of diffusion modelsThe method uses the generation attributes of the diffusion model at different inference stages to guide the correction of editing instructions. The diffusion model focuses on global layout in the early stage, local object attributes in the middle stage, and image details in the later stage, with style changes throughout the entire sampling process.
• Editing command correctionBased on the input original image and the edited image pair, a Visual Language Model (VLM) is generated to describe the differences between the two. A unified correction guideline is defined to ensure that the generated editing instructions more accurately reflect the actual changes between the original and edited images.
• Comparison of monitoring signals: Introducing a comparative supervision signal. By generating erroneous editing instructions, positive and negative sample pairs are created, and the model is trained using a triplet loss function to better distinguish between correct and erroneous editing instructions.
• High-efficiency training strategiesSuperEdit uses a small amount of high-quality editing data during training, avoiding the computational burden of large-scale datasets. Based on optimized supervision signals, it achieves performance comparable to or even better than complex architectures with limited data and a smaller model size.
• Model architecture: Based on the InstructPix2Pix framework, a pre-trained diffusion model (such as Stable Diffusion) is used as the base model, and fine-tuning is performed based on correction editing instructions and contrastive supervision signals.

SuperEdit's project address

• Project official website:https://liming-ai.github.io/SuperEdit/
• GitHub repository:https://github.com/bytedance/SuperEdit
• HuggingFace model library:https://huggingface.co/datasets/limingcv/SuperEdit
• arXiv technical paper:https://arxiv.org/pdf/2505.02370

Application scenarios of SuperEdit

• Content creation and designUsed in advertising design, social media image creation, etc., to quickly generate images that match a specific style or theme, thereby enhancing the appeal of the content.
• Film and EntertainmentIn film and television special effects production and character design, it enables rapid adjustment of scene and character appearance, accelerating the film and television production process.
• Game developmentQuickly edit game characters and scenes, generate concept art, and improve game development efficiency.
• Education and TrainingTo create teaching materials and virtual laboratory images to assist teaching and research and enhance learning outcomes.
• Medical and HealthProcessing medical images and producing health education materials to support medical teaching and health promotion.