  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.
