What is SeedEdit
SeedEdit is a general-purpose image editing model launched by the ByteDance Doubao Large Model Team. It edits images based on simple natural language instructions, including retouching, outfit changes, beautification, style transfer, and adding or removing elements in specified areas. The core advantage of SeedEdit is finding the optimal balance between maintaining the original image and generating a new image, achieving precise and high-quality editing results. As the first productized general-purpose image editing model in China, SeedEdit has made breakthroughs in versatility, controllability, and high-quality editing. It supports zero-shot learning and multi-turn editing operations, greatly simplifying the image editing workflow.
Main Features of SeedEdit
- Text-driven image editing: Users guide SeedEdit to perform image editing based on simple text prompts, such as changing the background, changing the style, local replacement, etc.
- Diverse editing capabilities: Supports multiple types of image editing, including local replacement, geometric transformation, relighting, style changes, etc.
- Zero-shot learning: SeedEdit performs stable image editing based on text prompts without additional samples.
- Multi-turn editing support: Supports users in performing continuous creative editing on the same image, enabling complex editing workflows.
- High-quality image output: Maintains high resolution and aesthetic quality during the editing process, ensuring that the edited image is natural and artistic.
- Versatility and controllability: SeedEdit achieves new breakthroughs in the versatility and controllability of image editing, accurately responding to vague editing instructions.
Technical Principles of SeedEdit
- Balancing reconstruction and regeneration: The core of SeedEdit is finding the optimal balance between preserving the original image (image reconstruction) and generating a new image (image regeneration).
- Text-to-image model (T2I): Treats the T2I model as a weak editing model, implements editing based on generating new images, and gradually aligns it into a strong editing model.
- Data generation and filtering strategy: Proposes an effective editing data generation and filtering strategy to gradually align the T2I model into a strong image editor.
- Causal diffusion model: Introduces a causal diffusion model for image-to-image generation, with two branches sharing parameters applied to input and output images/text respectively.
- Iterative alignment: Based on iterative data sampling and model optimization, gradually aligns the model, improving editing precision and image consistency.
- Precise interpretation of editing instructions: Designs a new editing architecture to accurately interpret editing instructions and generate images, improving the controllability and precision of editing.
Project Address of SeedEdit
- Project official website：team.doubao.com/en/special/seededit
- Technical paper：https://lf3-static.bytednsdoc.com/obj/eden-cn/lapzild-tss/ljhwZthlaukjlkulzlp/SeedEdit.pdf
- Online experience Demo：https://huggingface.co/spaces/ByteDance/SeedEdit-APP
Application Scenarios of SeedEdit
- Social media content creation: Users quickly edit personal photos or images for social media sharing, such as changing backgrounds, adjusting styles, etc.
- Advertising and marketing: Advertising designers quickly adjust advertising images to adapt to different marketing campaigns, such as changing product colors or scenes.
- E-commerce: E-commerce platforms provide tools that allow sellers and buyers to edit product images, such as changing clothing colors, simulating different lighting effects, etc.
- Artistic Creation: Artists and designers realize creative concepts, perform style transfer, or create unique works of art.
- News Media: Journalists and editors quickly adjust news images to better suit the reporting content or layout design.