mlsanityData to 7 Oct 2026

PromptArtisan: Multi-instruction Image Editing in Single Pass with Complete Attention Control

arXiv 2502.10258 ↗First posted 14 Feb 2025cs.CV cs.HCcited by 1

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Abstract (arXiv)

We present PromptArtisan, a groundbreaking approach to multi-instruction image editing that achieves remarkable results in a single pass, eliminating the need for time-consuming iterative refinement. Our method empowers users to provide multiple editing instructions, each associated with a specific mask within the image. This flexibility allows for complex edits involving mask intersections or overlaps, enabling the realization of intricate and nuanced image transformations. PromptArtisan leverages a pre-trained InstructPix2Pix model in conjunction with a novel Complete Attention Control Mechanism (CACM). This mechanism ensures precise adherence to user instructions, granting fine-grained control over the editing process. Furthermore, our approach is zero-shot, requiring no additional training, and boasts improved processing complexity compared to traditional iterative methods. By seamlessly integrating multi-instruction capabilities, single-pass efficiency, and complete attention control, PromptArtisan unlocks new possibilities for creative and efficient image editing workflows, catering to both novice and expert users alike.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 34 references that are in the map. First posted on arXiv 14 Feb 2025.

Builds on

29 of 34 references.

References · 29

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
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  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
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  10. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
  11. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
  12. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,172
17 more
  1. 2022Null-text Inversion for Editing Real Images using Guided Diffusion Modelscited by 1,168
  2. 2023Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generationcited by 950
  3. 2021Blended Diffusion for Text-driven Editing of Natural Imagescited by 924
  4. 2022eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoiserscited by 921
  5. 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 897
  6. 2023MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editingcited by 722
  7. 2022DiffEdit: Diffusion-based semantic image editing with mask guidancecited by 643
  8. 2023MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editingcited by 615
  9. 2023Zero-shot Image-to-Image Translationcited by 503
  10. 2022Blended Latent Diffusioncited by 430
  11. 2023Training-Free Layout Control with Cross-Attention Guidancecited by 312
  12. 2022SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Modelcited by 276
  13. 2023Guiding Instruction-based Image Editing via Multimodal Large Language Modelscited by 182
  14. 2023SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Modelscited by 174
  15. 2023InstructDiffusion: A Generalist Modeling Interface for Vision Taskscited by 162
  16. 2023Designing a Better Asymmetric VQGAN for StableDiffusioncited by 45
  17. 2023MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Pathcited by 18

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1 paper.