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BrushEdit: All-In-One Image Inpainting and Editing

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arxiv 2412.10316 v3 pith:7CQ3N2NM submitted 2024-12-13 cs.CV cs.AI

Yaowei Li , Yuxuan Bian , Xuan Ju , Zhaoyang Zhang , Junhao Zhuang , Ying Shan , Yuexian Zou , Qiang Xu This is my paper · ORCID
classification cs.CVcs.AI
keywords editingimageinpaintingmodelsmllmsbrusheditframeworkfree-form
verification ladder T0 review T1 audit T2 compute T3 formal
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Image editing has advanced significantly with the development of diffusion models using both inversion-based and instruction-based methods. However, current inversion-based approaches struggle with big modifications (e.g., adding or removing objects) due to the structured nature of inversion noise, which hinders substantial changes. Meanwhile, instruction-based methods often constrain users to black-box operations, limiting direct interaction for specifying editing regions and intensity. To address these limitations, we propose BrushEdit, a novel inpainting-based instruction-guided image editing paradigm, which leverages multimodal large language models (MLLMs) and image inpainting models to enable autonomous, user-friendly, and interactive free-form instruction editing. Specifically, we devise a system enabling free-form instruction editing by integrating MLLMs and a dual-branch image inpainting model in an agent-cooperative framework to perform editing category classification, main object identification, mask acquisition, and editing area inpainting. Extensive experiments show that our framework effectively combines MLLMs and inpainting models, achieving superior performance across seven metrics including mask region preservation and editing effect coherence.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling

    cs.CV 2026-07 conditional novelty 6.0 of 10

    An MLLM-conditioned next-scale VAR decoder handles 15+ unified visual generation tasks with competitive quality and substantially lower latency than diffusion baselines.

  2. Towards Reliable Identification of Diffusion-based Image Manipulations

    cs.CV 2025-06 conditional novelty 6.0 of 10

    RADAR combines semantic and geometric vision features with contrastive learning to detect and localize diffusion-based image edits, outperforming prior methods on a new 28-model benchmark.

  3. Describe, Don't Dictate: Semantic Image Editing with Natural Language Intent

    cs.CV 2025-08 conditional novelty 5.0 of 10

    DescriptiveEdit turns semantic editing into reference-conditioned text-to-image generation, reporting state-of-the-art scores on the Emu Edit benchmark with a frozen backbone and about 75M trainable parameters.

  4. Instruction-based Image Editing: A Survey on Data, Models, Evaluation, and Applications

    cs.CV 2026-07 conditional novelty 4.0 of 10

    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.

  5. MIND-Edit: MLLM Insight-Driven Editing via Language-Vision Projection

    cs.CV 2025-05 reject novelty 4.0 of 10

    MIND-Edit combines instruction rewriting with MLLM-derived visual embeddings to guide diffusion-based image editing, but the reported numbers only partly support the claim of state-of-the-art performance.

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