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

• URL: http://arxiv.org/abs/2310.02848v1
• Date: Wed, 4 Oct 2023 14:34:11 GMT
• Title: Magicremover: Tuning-free Text-guided Image inpainting with Diffusion Models
• Authors: Siyuan Yang, Lu Zhang, Liqian Ma, Yu Liu, JingJing Fu and You He
• Abstract summary: 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.
• Score: 24.690863845885367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Modification Takes Courage: Seamless Image Stitching via Reference-Driven Inpainting [0.17975553762582286]
  Current image stitching methods produce noticeable seams in challenging scenarios such as uneven hue and large parallax. We propose the Reference-Driven Inpainting Stitcher (RDIStitcher) to reformulate the image fusion and rectangling as a reference-based inpainting model. We present the Multimodal Large Language Models (MLLMs)-based metrics, offering a new perspective on evaluating stitched image quality.
  arXiv  Detail & Related papers  (2024-11-15T16:05:01Z)
• MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling [64.09238330331195]
  We propose a novel Multi-Modal Auto-Regressive (MMAR) probabilistic modeling framework. Unlike discretization line of method, MMAR takes in continuous-valued image tokens to avoid information loss. We show that MMAR demonstrates much more superior performance than other joint multi-modal models.
  arXiv  Detail & Related papers  (2024-10-14T17:57:18Z)
• ZePo: Zero-Shot Portrait Stylization with Faster Sampling [61.14140480095604]
  This paper presents an inversion-free portrait stylization framework based on diffusion models that accomplishes content and style feature fusion in merely four sampling steps. We propose a feature merging strategy to amalgamate redundant features in Consistency Features, thereby reducing the computational load of attention control.
  arXiv  Detail & Related papers  (2024-08-10T08:53:41Z)
• DiffUHaul: A Training-Free Method for Object Dragging in Images [78.93531472479202]
  We propose a training-free method, dubbed DiffUHaul, for the object dragging task. We first apply attention masking in each denoising step to make the generation more disentangled across different objects. In the early denoising steps, we interpolate the attention features between source and target images to smoothly fuse new layouts with the original appearance.
  arXiv  Detail & Related papers  (2024-06-03T17:59:53Z)
• Paint by Inpaint: Learning to Add Image Objects by Removing Them First [8.399234415641319]
  We train a diffusion model to inverse the inpainting process, effectively adding objects into images. We provide detailed descriptions of the removed objects and a Large Language Model to convert these descriptions into diverse, natural-language instructions.
  arXiv  Detail & Related papers  (2024-04-28T15:07:53Z)
• BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion [61.90969199199739]
  BrushNet is a novel plug-and-play dual-branch model engineered to embed pixel-level masked image features into any pre-trained DM. BrushNet's superior performance over existing models across seven key metrics, including image quality, mask region preservation, and textual coherence.
  arXiv  Detail & Related papers  (2024-03-11T17:59:31Z)
• MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learning [59.988458964353754]
  Text-to-image diffusion models allow seamless generation of personalized images from scant reference photos. Existing approaches perturb user images in imperceptible way to render them "unlearnable" from malicious uses. We propose MetaCloak, which solves the bi-level poisoning problem with a meta-learning framework.
  arXiv  Detail & Related papers  (2023-11-22T03:31:31Z)
• SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting [26.309834304515544]
  We introduce a challenging image restoration task, referred to as SuperInpaint. This task aims to reconstruct missing regions in low-resolution images and generate completed images with arbitrarily higher resolutions. We propose the detail-enhanced attentional implicit representation that can achieve SuperInpaint with a single model.
  arXiv  Detail & Related papers  (2023-07-26T20:28:58Z)
• Inst-Inpaint: Instructing to Remove Objects with Diffusion Models [18.30057229657246]
  In this work, we are interested in an image inpainting algorithm that estimates which object to be removed based on natural language input and removes it, simultaneously. We present a novel inpainting framework, Inst-Inpaint, that can remove objects from images based on the instructions given as text prompts.
  arXiv  Detail & Related papers  (2023-04-06T17:29:50Z)
• High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling [122.06593036862611]
  Existing image inpainting methods often produce artifacts when dealing with large holes in real applications. We propose an iterative inpainting method with a feedback mechanism. Experiments show that our method significantly outperforms existing methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2020-05-24T13:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.