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

arXiv: https://arxiv.org/abs/2304.14006 · HTML: https://mlsanity.com/paper/2304.14006/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark](https://mlsanity.com/paper/2202.06767/) (2022)

## Built on by (most cited first, 30 of 33)

- [Multimodal Foundation Models: From Specialists to General-Purpose Assistants](https://mlsanity.com/paper/2309.10020/) (2023)
- [SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation](https://mlsanity.com/paper/2311.15707/) (2023)
- [Review of Large Vision Models and Visual Prompt Engineering](https://mlsanity.com/paper/2307.00855/) (2023)
- [VRP-SAM: SAM with Visual Reference Prompt](https://mlsanity.com/paper/2402.17726/) (2024)
- [EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model](https://mlsanity.com/paper/2406.20076/) (2024)
- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructions](https://mlsanity.com/paper/2305.18047/) (2023)
- [A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering](https://mlsanity.com/paper/2306.06211/) (2023)
- [Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model](https://mlsanity.com/paper/2403.07764/) (2024)
- [InstructVid2Vid: Controllable Video Editing with Natural Language Instructions](https://mlsanity.com/paper/2305.12328/) (2023)
- [Stable-Hair: Real-World Hair Transfer via Diffusion Model](https://mlsanity.com/paper/2407.14078/) (2024)
- [Image Segmentation in Foundation Model Era: A Survey](https://mlsanity.com/paper/2408.12957/) (2024)
- [Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset](https://mlsanity.com/paper/2406.06039/) (2024)
- [On the Robustness of Segment Anything](https://mlsanity.com/paper/2305.16220/) (2023)
- [Segment Anything Model (SAM) for Radiation Oncology](https://mlsanity.com/paper/2306.11730/) (2023)
- [SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentation](https://mlsanity.com/paper/2406.01451/) (2024)
- [Towards Generalizable Scene Change Detection](https://mlsanity.com/paper/2409.06214/) (2024)
- [On Efficient Variants of Segment Anything Model: A Survey](https://mlsanity.com/paper/2410.04960/) (2024)
- [Hybrid Global-Local Representation with Augmented Spatial Guidance for Zero-Shot Referring Image Segmentation](https://mlsanity.com/paper/2504.00356/) (2025)
- [MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllers](https://mlsanity.com/paper/2309.04372/) (2023)
- [Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models](https://mlsanity.com/paper/2306.03799/) (2023)
- [Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts](https://mlsanity.com/paper/2407.02075/) (2024)
- [There is no SAMantics! Exploring SAM as a Backbone for Visual Understanding Tasks](https://mlsanity.com/paper/2411.15288/) (2024)
- [Restore Anything Pipeline: Segment Anything Meets Image Restoration](https://mlsanity.com/paper/2305.13093/) (2023)
- [SAM-CP: Marrying SAM with Composable Prompts for Versatile Segmentation](https://mlsanity.com/paper/2407.16682/) (2024)
- [Click2Mask: Local Editing with Dynamic Mask Generation](https://mlsanity.com/paper/2409.08272/) (2024)
- [Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation](https://mlsanity.com/paper/2403.10780/) (2024)
- [LCCo: Lending CLIP to Co-Segmentation](https://mlsanity.com/paper/2308.11506/) (2023)
- [Towards Fine-grained Interactive Segmentation in Images and Videos](https://mlsanity.com/paper/2502.09660/) (2025)
- [ViRefSAM: Visual Reference-Guided Segment Anything Model for Remote Sensing Segmentation](https://mlsanity.com/paper/2507.02294/) (2025)

## Abstract (arXiv)

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.