mlsanityData to 14 Oct 2026

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

arXiv 2304.14006 ↗First posted 27 Apr 2023cs.CVcited by 33

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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.

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

15 of 21 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  6. 2023Segment Anythingcited by 8,234
  7. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  8. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  9. 2022Classifier-Free Diffusion Guidancecited by 5,847
  10. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  11. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  12. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,152
3 more
  1. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,614
  2. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,387
  3. 2022Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmarkcited by 114

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 13; 2024: 16; 2025: 3; 2026: 1.

33 papers.

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  1. 2023Multimodal Foundation Models: From Specialists to General-Purpose Assistantscited by 247
  2. 2023SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimationcited by 117
  3. 2023Review of Large Vision Models and Visual Prompt Engineeringcited by 77
  4. 2024VRP-SAM: SAM with Visual Reference Promptcited by 75
  5. 2024EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Modelcited by 65
  6. 2023A Comprehensive Survey on Segment Anything Model for Vision and Beyondcited by 64
  7. 2023InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructionscited by 64
  8. 2023A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineeringcited by 45
  9. 2024Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Modelcited by 45
  10. 2023InstructVid2Vid: Controllable Video Editing with Natural Language Instructionscited by 44
  11. 2024Stable-Hair: Real-World Hair Transfer via Diffusion Modelcited by 35
  12. 2024Image Segmentation in Foundation Model Era: A Surveycited by 35
21 more
  1. 2024Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Datasetcited by 33
  2. 2023On the Robustness of Segment Anythingcited by 25
  3. 2023Segment Anything Model (SAM) for Radiation Oncologycited by 24
  4. 2024SAM as the Guide: Mastering Pseudo-Label Refinement in Semi-Supervised Referring Expression Segmentationcited by 17
  5. 2024Towards Generalizable Scene Change Detectioncited by 17
  6. 2024On Efficient Variants of Segment Anything Model: A Surveycited by 16
  7. 2025Hybrid Global-Local Representation with Augmented Spatial Guidance for Zero-Shot Referring Image Segmentationcited by 14
  8. 2023MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllerscited by 12
  9. 2023Prompt Space Optimizing Few-shot Reasoning Success with Large Language Modelscited by 11
  10. 2024Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Promptscited by 9
  11. 2024There is no SAMantics! Exploring SAM as a Backbone for Visual Understanding Taskscited by 7
  12. 2023Restore Anything Pipeline: Segment Anything Meets Image Restorationcited by 6
  13. 2024SAM-CP: Marrying SAM with Composable Prompts for Versatile Segmentationcited by 6
  14. 2024Click2Mask: Local Editing with Dynamic Mask Generationcited by 3
  15. 2024Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentationcited by 2
  16. 2023LCCo: Lending CLIP to Co-Segmentationcited by 1
  17. 2025Towards Fine-grained Interactive Segmentation in Images and Videoscited by 1
  18. 2025ViRefSAM: Visual Reference-Guided Segment Anything Model for Remote Sensing Segmentationcited by 1
  19. 2024Content-Aware Depth-Adaptive Image Restorationcited by 0
  20. 2024Cross-Modal Coordination Across a Diverse Set of Input Modalitiescited by 0
  21. 2026MakeupMirror: Improving Facial Attribute Preservation in Diffusion Models for Makeup Transfercited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Insert Anything: Image Insertion via In-Context Editing in DiT
  2. 2024FunEditor: Achieving Complex Image Edits via Function Aggregation with Diffusion Models
  3. 2026Editing on the Generative Manifold: A Theoretical and Empirical Study of General Diffusion-Based Image Editing Trade-offs
  4. 2025Image Editing As Programs with Diffusion Models
  5. 2024Towards Small Object Editing: A Benchmark Dataset and A Training-Free Approach
  6. 2023EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methods
  7. 2023PAIR-Diffusion: A Comprehensive Multimodal Object-Level Image Editor
  8. 2025IntrinsicEdit: Precise generative image manipulation in intrinsic space