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

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arxiv 2304.14006 v1 pith:XPP5TLMW submitted 2023-04-27 cs.CV

Defeng Xie , Ruichen Wang , Jian Ma , Chen Chen , Haonan Lu , Dong Yang , Fobo Shi , Xiaodong Lin This is my paper · ORCID
classification cs.CV
keywords editeverythingsystemimagesgenerativeimagetextvisual
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

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

  1. ViRefSAM: Visual Reference-Guided Segment Anything Model for Remote Sensing Segmentation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A few-shot segmentation framework that injects reference-image prototypes into SAM's decoder and image encoder, eliminating per-image manual prompts and improving remote sensing segmentation accuracy.

  2. Towards Fine-grained Interactive Segmentation in Images and Videos

    cs.CV 2025-02 conditional novelty 6.0 of 10

    SAM2Refiner adds localization, prompt-retargeting and mask-refinement modules to SAM2, and reports state-of-the-art fine-grained segmentation on four image and two video benchmarks.

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