paper-with-me

홈 › Papers

Edit Everything: A Text-Guided Generative System for Images Editing

2023-04-27 · Defeng Xie, Ruichen Wang, Jian Ma, Chen Chen, Haonan Lu, Dong Yang, Fobo Shi, Xiaodong Lin

We introduce a new generative system called Edit Everything, which can take image and text inputs and produce image outputs. Edit Everything allows users to edit images using simple text instructions. Our system designs prompts to guide the visual module in generating requested images. Experiments demonstrate that Edit Everything facilitates the implementation of the visual aspects of Stable Diffusion with the use of Segment Anything model and CLIP. Our system is publicly available at https://github.com/DefengXie/Edit_Everything.

📄 PDF Abstract BibTeX arXiv:2304.14006

Code (1)

defengxie/edit_everything 공식 구현 pytorch

Methods 이 논문이 사용한 방법론

Diffusion Diffusion models generate samples by gradually removing noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound…
CLIP Contrastive Language-Image Pre-training (CLIP), consisting of a simplified version of ConVIRT trained from scratch, is an efficient method of image representation learning…

Similar Papers 제목 키워드 기반

SpotEdit: Evaluating Visually-Guided Image Editing Methods

2025-08-25 · Sara Ghazanfari, Wei-An Lin, Haitong Tian, Ersin Yumer arxiv

Visually-guided image editing, where edits are conditioned on both visual cues and textual prompts, has emerged as a powerful paradigm for fine-grained, controllable content generation. Although recent generative models …

Image Editing

RSEdit: Text-Guided Image Editing for Remote Sensing

2026-03-14 · Chen Zhenyuan, Zhang Zechuan, Zhang Feng arxiv

In this paper, we explore text-guided image editing in the remote sensing domain using generative modeling. We propose \rsedit, a collection of models from U-Net to DiT with various configurations. Specifically, we prese…

Image Editing

DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided Image Editing

2023-10-12 · Yueming Lyu, Kang Zhao, Bo Peng, Yue Jiang 외

Text-guided image editing faces significant challenges to training and inference flexibility. Much literature collects large amounts of annotated image-text pairs to train text-conditioned generative models from scratch,…

text-guided-image-editing

EditInspector: A Benchmark for Evaluation of Text-Guided Image Edits

2025-06-11 · Ron Yosef, Moran Yanuka, Yonatan Bitton, Dani Lischinski

Text-guided image editing, fueled by recent advancements in generative AI, is becoming increasingly widespread. This trend highlights the need for a comprehensive framework to verify text-guided edits and assess their qu…

Artifact DetectionCaption GenerationCommon Sense Reasoningtext-guided-image-editing

Exploring Text-Guided Single Image Editing for Remote Sensing Images

2024-05-09 · Fangzhou Han, Lingyu Si, Hongwei Dong, Lamei Zhang 외

Artificial intelligence generative content (AIGC) has significantly impacted image generation in the field of remote sensing. However, the equally important area of remote sensing image (RSI) editing has not received suf…

Image Generation