# ClickRemoval: An Interactive Open-Source Tool for Object Removal in Diffusion Models

arXiv: https://arxiv.org/abs/2605.14461 · HTML: https://mlsanity.com/paper/2605.14461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 14 May 2026.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 9 of 9)

- [SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model](https://mlsanity.com/paper/2212.05034/) (2022)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion](https://mlsanity.com/paper/2403.06976/) (2024)
- [A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting](https://mlsanity.com/paper/2312.03594/) (2023)
- [Pico-Banana-400K: A Large-Scale Dataset for Text-Guided Image Editing](https://mlsanity.com/paper/2510.19808/) (2025)
- [Entropy Rectifying Guidance for Diffusion and Flow Models](https://mlsanity.com/paper/2504.13987/) (2025)
- [Repurposing Stable Diffusion Attention for Training-Free Unsupervised Interactive Segmentation](https://mlsanity.com/paper/2411.10411/) (2024)
- [PixelHacker: Image Inpainting with Structural and Semantic Consistency](https://mlsanity.com/paper/2504.20438/) (2025)
- [Object Remover Performance Evaluation Methods using Class-wise Object Removal Images](https://mlsanity.com/paper/2404.11104/) (2024)

## Built on by (most cited first, 1 of 1)

- [Zero-Shot Object Removal via Attention Masking, Latent Anchoring, and Refinement](https://mlsanity.com/paper/2609.28342/) (2026)

## Abstract (arXiv)

Existing object removal tools often rely on manual masks or text prompts, making precise removal difficult for non-expert users in complex scenes and often leading to incomplete removal or unnatural background completion. To address this issue, we present ClickRemoval, an open-source interactive object removal tool built on pretrained Stable Diffusion models and driven solely by user clicks. Without additional training, hand-drawn masks, or text descriptions, ClickRemoval localizes target objects and restores the background through self-attention modulation during denoising. Experiments show that ClickRemoval achieves competitive results across quantitative metrics and user studies. We release a complete software package at https://github.com/zld-make/ClickRemoval under the Apache-2.0 license.