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InstructPix2Pix: Learning to Follow Image Editing Instructions

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arxiv 2211.09800 v2 pith:NTOX36GL submitted 2022-11-17 cs.CV cs.AIcs.CLcs.GRcs.LG

Tim Brooks , Aleksander Holynski , Alexei A. Efros This is my paper
classification cs.CVcs.AIcs.CLcs.GRcs.LG
keywords modelinstructionseditingimageimagesdatadiffusionedits
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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