InstructPix2Pix: Learning to Follow Image Editing Instructions
This enables quick and user-friendly image editing for general users, though it is incremental as it builds on existing large models.
The paper tackles the problem of editing images based on human-written instructions by training a conditional diffusion model called InstructPix2Pix on a dataset generated using GPT-3 and Stable Diffusion, achieving edits in seconds without per-example fine-tuning.
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.