SeedEdit 3.0: Fast and High-Quality Generative Image Editing
We introduce SeedEdit 3.0, in companion with our T2I model Seedream 3.0, which significantly improves over our previous SeedEdit versions in both aspects of edit instruction following and image content (e.g., ID/IP) preservation on real image inputs. Additional to model upgrading with T2I, in this report, we present several key improvements. First, we develop an enhanced data curation pipeline with a meta-info paradigm and meta-info embedding strategy that help mix images from multiple data sources. This allows us to scale editing data effectively, and meta information is helpfult to connect VLM with diffusion model more closely. Second, we introduce a joint learning pipeline for computing a diffusion loss and reward losses. Finally, we evaluate SeedEdit 3.0 on our testing benchmarks, for real/synthetic image editing, where it achieves a best trade-off between multiple aspects, yielding a high usability rate of 56.1%, compared to SeedEdit 1.6 (38.4%), GPT4o (37.1%) and Gemini 2.0 (30.3%).
Code (0)
등록된 구현이 없습니다.
Tasks
Instruction FollowingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SeedEdit: Align Image Re-Generation to Image Editing
We introduce SeedEdit, a diffusion model that is able to revise a given image with any text prompt. In our perspective, the key to such a task is to obtain an optimal balance between maintaining the original image, i.e. …
Image ReconstructionDreamPoster: A Unified Framework for Image-Conditioned Generative Poster Design
We present DreamPoster, a Text-to-Image generation framework that intelligently synthesizes high-quality posters from user-provided images and text prompts while maintaining content fidelity and supporting flexible resol…
Text-to-Image GenerationUnderstanding Generative AI Capabilities in Everyday Image Editing Tasks
Generative AI (GenAI) holds significant promise for automating everyday image editing tasks, especially following the recent release of GPT-4o on March 25, 2025. However, what subjects do people most often want edited? W…
AnyRefill: A Unified, Data-Efficient Framework for Left-Prompt-Guided Vision Tasks
In this paper, we present a novel Left-Prompt-Guided (LPG) paradigm to address a diverse range of reference-based vision tasks. Inspired by the human creative process, we reformulate these tasks using a left-right stitch…
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
Reinforcement learning (RL) has improved guided image generation with diffusion models by directly optimizing rewards that capture image quality, aesthetics, and instruction following capabilities. However, the resulting…
Image GenerationInstruction Followingreinforcement-learningReinforcement Learning+3