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InstructPix2Pix: Learning to Follow Image Editing Instructions

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Tim Brooks* Aleksander Holynski* Alexei A. Efros
University of California, Berkeley

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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Figure 1: Given an image and an instruction for how to edit that image, our model performs the appropriate edit. Our model does not require full descriptions for the input or output image, and edits images in the forward pass without per-example inversion or fine-tuning.

1 Introduction

We present a method for teaching a generative model to follow human-written instructions for image editing. Since training data for this task is difficult to acquire at scale, we propose an approach for generating a paired dataset that combines multiple large models pretrained on different modalities: a large language model (GPT-3 1) and a text-to-image model (Stable Diffusion 2). These two models capture complementary knowledge about language and images that can be combined to create paired training data for a task spanning both modalities.

Using our generated paired data, we train a conditional diffusion model that, given an input image and a text instruction for how to edit it, generates the edited image. Our model directly performs the image edit in the forward pass, and does not require any additional example images, full descriptions of the input/output images, or per-example finetuning. Despite being trained entirely on synthetic examples (i.e., both generated written instructions and generated imagery), our model achieves zero-shot generalization to both arbitrary real images and natural human-written instructions. Our model enables intuitive image editing that can follow human instructions to perform a diverse collection of edits: replacing objects, changing the style of an image, changing the setting, the artistic medium, among others. Selected examples can be found in Figure 1.

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Figure 2: Our method consists of two parts: generating an image editing dataset, and training a diffusion model on that dataset. (a) We first use a finetuned GPT-3 to generate instructions and edited captions. (b) We then use StableDiffusion 52 in combination with Prompt-to-Prompt 17 to generate pairs of images from pairs of captions. We use this procedure to create a dataset (c) of over 450,000 training examples. (d) Finally, our InstructPix2Pix diffusion model is trained on our generated data to edit images from instructions. At inference time, our model generalizes to edit real images from human-written instructions.

2 Prior work

Composing large pretrained models

Recent work has shown that large pretrained models can be combined to solve multimodal tasks that no one model can perform alone, such as image captioning and visual question answering (tasks that require the knowledge of both a large language model and a text-image model). Techniques for combining pretrained models include joint finetuning on a new task 3 4 5 6, communication through prompting 7 8, composing probability distributions of energy-based models 9 10, guiding one model with feedback from another 11, and iterative optimization 12. Our method is similar to prior work in that it leverages the complementary abilities of two pretrained models—GPT-3 1) and Stable Diffusion 2 —but differs in that we use these models to generate paired multi-modal training data.

Diffusion-based generative models

Recent advances in diffusion models 13 have enabled state-of-the-art image synthesis 14 15 16 17 18 19 as well as generative models of other modalities such as video 20 21, audio 22, text 23 and network parameters 24. Recent text-to-image diffusion models 25 2 26 27 have shown to generate realistic images from arbitrary text captions.

Generative models for image editing

Image editing models traditionally targeted a single editing task such as style transfer 28 29 or translation between image domains 30 31 32 33 34. Numerous editing approaches invert 35 36 37 38 or encode 39 40 41 images into a latent space (e.g., StyleGAN 42 43) where they can be edited by manipulating latent vectors. Recent models have leveraged CLIP 44 embeddings to guide image editing using text 45 46 47 48 25 49 50 51. We compare with one of these methods, Text2Live 52, an editing method that optimizes for an additive image layer that maximizes a CLIP similarity objective.

Recent works have used pretrained text-to-image diffusion models for image editing 53 54 50 26 55. While some text-to-image models natively have the ability to edit images (e.g., DALLE-2 can create variations of images, inpaint regions, and manipulate the CLIP embedding 26), using these models for targeted editing is non-trivial, because in most cases they offer no guarantees that similar text prompts will yield similar images. Recent work by Hertz et al. 55 tackles this issue with Prompt-to-Prompt, a method for assimilating the generated images for similar text prompts, such that isolated edits can be made to a generated image. We use this method in generating training data. To edit non-generated (i.e., real) imagery, SDEdit 53 uses a pretrained model to noise and denoise an input image with a new target prompt. We compare with SDEdit as a baseline. Other recent works perform local inpainting given a caption and user-drawn mask 26 50, generate new images of a specific object or concept learned from a small collection of images 56 57, or perform editing by inverting (and fine-tuning) a single image, and subsequently regenerating with a new text description 54. In contrast to these approaches, our model takes only a single image and an instruction for how to edit that image (i.e., not a full description of any image), and performs the edit directly in the forward pass without need for a user-drawn mask, additional images, or per-example inversion or finetuning.

Learning to follow instructions

Our method differs from existing text-based image editing works 53 57 56 55 54 52 in that it enables editing from instructions that tell the model what action to perform, as opposed to text labels, captions or descriptions of input/output images. A key benefit of following editing instructions is that the user can just tell the model exactly what to do in natural written text. There is no need for the user to provide extra information, such as example images or descriptions of visual content that remains constant between the input and output images. Instructions are expressive, precise, and intuitive to write, allowing the user to easily isolate specific objects or visual attributes to change. Our goal to follow written image editing instructions is inspired by recent work teaching large language models to better follow human instructions for language tasks 58 59 60.

Training data generation with generative models

Deep models typically require large amounts of training data. Internet data collections are often suitable, but may not exist in the form necessary for supervision, e.g., paired data of particular modalities. As generative models continue to improve, there is growing interest in their use as a source of cheap and plentiful training data for downstream tasks 61 62 63 64 65 66. In this paper, we use two different off-the-shelf generative models (language, text-to-image) to produce training data for our editing model.

3 Method

We treat instruction-based image editing as a supervised learning problem: (1) first, we generate a paired training dataset of text editing instructions and images before/after the edit (Sec. 3.1, Fig. 2a-c), then (2) we train an image editing diffusion model on this generated dataset (Sec. 3.2, Fig 2d). Despite being trained with generated images and editing instructions, our model is able to generalize to editing real images using arbitrary human-written instructions. See Fig. 2 for an overview of our method.

3.1 Generating a Multi-modal Training Dataset

We combine the abilities of two large-scale pretrained models that operate on different modalities—a large language model 1 and a text-to-image model 2 —to generate a multi-modal training dataset containing text editing instructions and the corresponding images before and after the edit. In the following two sections, we describe in detail the two steps of this process. In Section 3.1.1, we describe the process of fine-tuning GPT-3 1 to generate a collection of text edits: given a prompt describing an image, produce a text instruction describing a change to be made and a prompt describing the image after that change (Figure 2a). Then, in Section 3.1.2, we describe the process of converting the two text prompts (i.e., before and after the edit) into a pair of corresponding images using a text-to-image model 2 (Figure 2b).

3.1.1 Generating Instructions and Paired Captions

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Table 1: We label a small text dataset, finetune GPT-3, and use that finetuned model to generate a large dataset of text triplets. As the input caption for both the labeled and generated examples, we use real image captions from LAION. Highlighted text is generated by GPT-3.

Footnotes

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