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Magicremover: Tuning-free Text-guided Image inpainting with Diffusion Models

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arxiv 2310.02848 v1 pith:W4OLGVXO submitted 2023-10-04 cs.CV

Siyuan Yang , Lu Zhang , Liqian Ma , Yu Liu , JingJing Fu , You He This is my paper · ORCID
classification cs.CV
keywords magicremoverimageinpaintingmodelsdiffusioncontentproposesampling
verification ladder T0 review T1 audit T2 compute T3 formal
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Image inpainting aims to fill in the missing pixels with visually coherent and semantically plausible content. Despite the great progress brought from deep generative models, this task still suffers from i. the difficulties in large-scale realistic data collection and costly model training; and ii. the intrinsic limitations in the traditionally user-defined binary masks on objects with unclear boundaries or transparent texture. In this paper, we propose MagicRemover, a tuning-free method that leverages the powerful diffusion models for text-guided image inpainting. We introduce an attention guidance strategy to constrain the sampling process of diffusion models, enabling the erasing of instructed areas and the restoration of occluded content. We further propose a classifier optimization algorithm to facilitate the denoising stability within less sampling steps. Extensive comparisons are conducted among our MagicRemover and state-of-the-art methods including quantitative evaluation and user study, demonstrating the significant improvement of MagicRemover on high-quality image inpainting. We will release our code at https://github.com/exisas/Magicremover.

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Cited by 4 Pith papers

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

  1. BrushEdit: All-In-One Image Inpainting and Editing

    cs.CV 2024-12 conditional novelty 6.0 of 10

    BrushEdit couples a multimodal language model and an object detector with a single arbitrary-mask inpainting model to turn free-form text instructions into interactive, multi-turn image edits.

  2. Pinco: Position-induced Consistent Adapter for Diffusion Transformer in Foreground-conditioned Inpainting

    cs.CV 2024-12 conditional novelty 6.0 of 10

    Pinco is a plug-and-play adapter that enables diffusion transformers to inpaint backgrounds around a provided foreground object, preserving its shape via self-attention injection and a positional anchor.

  3. Exploring the latent space of diffusion models directly through singular value decomposition

    cs.CV 2025-02 reject novelty 5.0 of 10

    The authors report that singular value decomposition of diffusion latent codes reveals stable, order-mobile attribute directions and propose Attribute Vector Integration, a per-pair MLP-based editor that transfers tex...

  4. EditScout: Locating Forged Regions from Diffusion-based Edited Images with Multimodal LLM

    cs.CV 2024-12 conditional novelty 5.0 of 10

    A multimodal LLM with a SAM-based mask decoder localizes diffusion-edited regions better than traditional forensic methods on MagicBrush, CocoGLIDE, and a new BrushNet dataset.

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