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.
| Comments: | Project page with code: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Graphics (cs.GR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2211.09800 [cs.CV] |
| (or arXiv:2211.09800v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2211.09800 arXiv-issued DOI via DataCite |
Submission history
From: Aleksander Holynski [view email]
[v1]
Thu, 17 Nov 2022 18:58:43 UTC (11,114 KB)
[v2]
Wed, 18 Jan 2023 17:31:52 UTC (11,114 KB)