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Diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model

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Lirui Zhao, Tianshuo Yang, Wenqi Shao, Yuxin Zhang, Yu Qiao, Ping Luo, Kaipeng Zhang, Rongrong Ji

Computer Vision and Pattern Recognition · Submitted 24 Jul 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Extensive experiments demonstrate that Diffree excels in adding new objects with a high success rate while maintaining background consistency, spatial appropriateness, and object relevance and quality.

how it is stated · asserted 1.00 read the record →

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arXiv ID2407.16982 (version 1)
Pith IDVIYP4M5C, Pith's permanent record of this paper and its review
CitationKaipeng Zhang, Lirui Zhao, Ping Luo, Rongrong Ji, Tianshuo Yang, Wenqi Shao, Yu Qiao, Yuxin Zhang. "Diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model." arXiv:2407.16982 (2024). https://pith.science/paper/VIYP4M5C
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,305 input tokens.

What the abstract itself states, as probabilities: claims first 0.11 · states limitation 0.09 · human participants 0.04 · controlled comparison 0.17 · randomized assignment 0.01 · states quantitative result 0.69 · compares with prior results 0.58 · mentions code or data availability 0.05.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This paper addresses an important problem of object addition for images with only text guidance. It is challenging because the new object must be integrated seamlessly into the image with consistent visual context, such as lighting, texture, and spatial location. While existing text-guided image inpainting methods can add objects, they either fail to preserve the background consistency or involve cumbersome human intervention in specifying bounding boxes or user-scribbled masks. To tackle this challenge, we introduce Diffree, a Text-to-Image (T2I) model that facilitates text-guided object addition with only text control. To this end, we curate OABench, an exquisite synthetic dataset by removing objects with advanced image inpainting techniques. OABench comprises 74K real-world tuples of an original image, an inpainted image with the object removed, an object mask, and object descriptions. Trained on OABench using the Stable Diffusion model with an additional mask prediction module, Diffree uniquely predicts the position of the new object and achieves object addition with guidance from only text. Extensive experiments demonstrate that Diffree excels in adding new objects with a high success rate while maintaining background consistency, spatial appropriateness, and object relevance and quality.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper addresses an important problem of object addition for images with only text guidance. background 1.00 0.19 0.04 asserted 1.00
s1 It is challenging because the new object must be integrated seamlessly into the image with consistent visual context, such as lighting, texture, and spatial location. background 0.98 0.06 0.02 asserted 1.00
s2 While existing text-guided image inpainting methods can add objects, they either fail to preserve the background consistency or involve cumbersome human intervention in specifying bounding boxes or user-scribbled masks. gap 0.81 0.04 0.02 asserted 1.00
s3 To tackle this challenge, we introduce Diffree, a Text-to-Image (T2I) model that facilitates text-guided object addition with only text control. contribution 0.97 0.88 0.09 asserted 1.00
s4 To this end, we curate OABench, an exquisite synthetic dataset by removing objects with advanced image inpainting techniques. method 0.80 0.95 0.02 asserted 1.00
s5 OABench comprises 74K real-world tuples of an original image, an inpainted image with the object removed, an object mask, and object descriptions. method 0.83 0.91 0.03 asserted 1.00
s6 Trained on OABench using the Stable Diffusion model with an additional mask prediction module, Diffree uniquely predicts the position of the new object and achieves object addition with guidance from only text. method 0.95 0.92 0.16 asserted 1.00
s7 Extensive experiments demonstrate that Diffree excels in adding new objects with a high success rate while maintaining background consistency, spatial appropriateness, and object relevance and quality. result 1.00 0.95 0.88 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.98.

What was examined, and what was not

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not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This paper addresses an important problem of object addition for images with only text guidance. It is challenging because the new object must be integrated seamlessly into the image with consistent visual context, such as lighting, texture, and spatial location. While existing text-guided image inpainting methods can add objects, they either fail to preserve the background consistency or involve cumbersome human intervention in specifying bounding boxes or user-scribbled masks. To tackle this challenge, we introduce Diffree, a Text-to-Image (T2I) model that facilitates text-guided object addition with only text control. To this end, we curate OABench, an exquisite synthetic dataset by removing objects with advanced image inpainting techniques. OABench comprises 74K real-world tuples of an original image, an inpainted image with the object removed, an object mask, and object descriptions. Trained on OABench using the Stable Diffusion model with an additional mask prediction module, Diffree uniquely predicts the position of the new object and achieves object addition with guidance from only text. Extensive experiments demonstrate that Diffree excels in adding new objects with a high success rate while maintaining background consistency, spatial appropriateness, and object relevance and quality.

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How the literature uses it

4 arXiv papers cite this in 8 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 2 sentences without a confident label

Uses its method or tool 1 paper
  • We use Grounded-SAM [ref] for instance detection and segmentation, Geowizard [ref] for depth and normal estimation, Clipaway [ref] for background inpainting and object removal, and Diffree [ref] to generate objects and masks from text descriptions.

    PhysLayer: Language-Guided Layered Animation with Depth-Aware Physics · Implementation Details · 1.00

Uses its data or values 1 paper
Compares with it 1 paper
Cites it as background 1 paper

What it cites · 5 sentences without a confident label

Uses the method or tool of 1 paper
  • Stable Diffusion [ref] apply them in the latent space of powerful pre-trained variational autoencoder [ref] , including encoder $ℰ$ and decoder $𝒟$, to reduce computing resources while maintaining quality and flexibility.

    Auto-Encoding Variational Bayes · Diffusion Model · 0.86

Compares with 1 paper
Cites as background 8 papers
  • learns to generate data samples by iteratively applying denoising autoencoders that estimate the score function [ref] of a given data distribution [ref] .

    Score-Based Generative Modeling through Stochastic Differential Equations · Diffusion Model · 0.96

  • InstructPix2Pix [ref] combines two large pretrained models, a large language model [ref] and a T2I model [ref] , to generate a dataset for training a diffusion model to follow written image editing text prompts.

    Language Models are Few-Shot Learners · Related Work · 0.89

  • Mask-Guided Image Inpainting Mask-guided image inpainting methods [ref] alter the image in specific areas under addition conditions (, text), while maintaining the background in its original state.

    AnyDoor: Zero-shot Object-level Image Customization · Related Work · 0.97

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