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SeedEdit: Align Image Re-Generation to Image Editing
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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 reconstruction, and generating a new image, i.e. image re-generation. To this end, we start from a weak generator (text-to-image model) that creates diverse pairs between such two directions and gradually align it into a strong image editor that well balances between the two tasks. SeedEdit can achieve more diverse and stable editing capability over prior image editing methods, enabling sequential revision over images generated by diffusion models.
Forward citations
Cited by 9 Pith papers
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GenSpace: Benchmarking Spatially-Aware Image Generation
GenSpace benchmarks spatial awareness in image generation with a 3D reconstruction-based evaluator, showing models struggle with allocentric relations and metric measurements.
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Understanding Generative AI Capabilities in Everyday Image Editing Tasks
On real Reddit photo-editing requests, human judges prefer human edits over AI edits 66% of the time, and AI editors can satisfactorily handle about 33% of requests.
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GPT-IMAGE-EDIT-1.5M: A Million-Scale, GPT-Generated Image Dataset
The paper introduces GPT-IMAGE-EDIT-1.5M, a 1.5-million-triplet image-editing dataset refined by GPT-4o, and shows that fine-tuning FluxKontext on it achieves state-of-the-art open-source scores on GEdit-EN and Complex-Edit.
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Image Editing As Programs with Diffusion Models
IEAP decomposes complex editing instructions into atomic operations executed sequentially on a diffusion transformer, and reports state-of-the-art results on MagicBrush and AnyEdit.
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Autoregressive Images Watermarking through Lexical Biasing: An Approach Resistant to Regeneration Attack
LBW embeds watermarks into autoregressive image token maps by biasing token sampling toward a secret green list and detects them with a z-test on green-token counts.
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DreamPoster: A Unified Framework for Image-Conditioned Generative Poster Design
DreamPoster fine-tunes Seedream3.0 with a deconstruction-recaptioning dataset pipeline and a three-stage curriculum to turn image-plus-text inputs into finished posters, reporting substantially higher usability than G...
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ByteMorph: Benchmarking Instruction-Guided Image Editing with Non-Rigid Motions
A released 6.4 million pair dataset and 613 sample benchmark for instruction-guided image editing of non-rigid motions, plus a Flux.1-dev based baseline that outperforms open-source methods on the new benchmark.
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Instruction-based Image Editing: A Survey on Data, Models, Evaluation, and Applications
A survey of instruction-based image editing plus a new 21-task benchmark, CDD-IIE, on which ten open models are scored by human experts.
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SeedEdit 3.0: Fast and High-Quality Generative Image Editing
SeedEdit 3.0 reports a 56.1% usability rate on internal real-image editing tests, beating SeedEdit 1.6, GPT-4o, and Gemini 2.0, with 8x faster inference after distillation and quantization.
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