Instructpix2pix: Learning to follow image editing instructions
Abstract
We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image. To obtain training data for this problem, we combine the knowledge of two large pretrained models—a language model (GPT-3) and a text-to-image model (Stable Diffusion)—to generate a large dataset of image editing examples. Our conditional diffusion model, InstructPix2Pix, is trained on our generated data, and generalizes to real images and user-written instructions at inference time. Since it performs edits in the forward pass and does not require per-example fine-tuning or inversion, our model edits images quickly, in a matter of seconds. We show compelling editing results for a diverse collection of input images and written instructions.
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Crossmodal attention mechanisms learn fine-grained dependencies between textual and visual tokens directly within the LLM's embedding space, enhancing semantic understanding for tasks like image editing.
“Crossmodal attention mechanisms learn fine-grained dependencies between textual and visual tokens directly within the LLM's embedding space, enhancing semantic understanding for tasks like image editing [570, 909, 102].”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [102]
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“A number of techniques have been developed on diffusion models to enable editing, personalization and inversion to token space (Gal et al., 2022a; Meng et al., 2021; Ruiz et al., 2022; Kawar et al., 2022; Brooks et al., 2022; Hertz et al., 2022; Mokady et al., 2022).”
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“Their powerful capability to adapt to complex data distributions has led diffusion models to achieve remarkable success across several domains including image synthesis [18, 39, 49, 54, 57, 58, 64, 75, 89], image editing [5, 19, 24, 29, 66, 87, 88], and video generation [4, 9, 21, 62, 65, 71].”
- Authors
- Brooks, T., Holynski, A., & Efros, A. A.
- Affiliations
- University of California, Berkeley (US)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 18392-18402
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076215
- Cited by
- 1,466
- References
- 12
- Citations by year
- 2026: 380 · 2025: 532 · 2024: 461 · 2023: 93
- FWCI
- 156.78
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Funders
- NSF
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- image editing, text-to-image editing, instruction following, conditional diffusion models, GPT-3, Stable Diffusion, synthetic training data generation, image-to-image translation
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