InstructPix2Pix: Learning to Follow Image Editing Instructions
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a novel approach to image editing using generative models that can follow human instructions, significantly advancing the capabilities of image editing technologies. The combination of large pretrained models to create a robust training dataset is a noteworthy contribution that could influence future research and applications in multimodal machine learning.
Methodology
The methodology presented in this paper is innovative as it combines two large pretrained models, GPT-3 and Stable Diffusion, to generate a dataset for training a conditional diffusion model for image editing based on human-written instructions. The approach of generating paired training data from these models is a significant contribution, as it allows for zero-shot generalization to real images and user-written instructions. The model's ability to perform edits directly in the forward pass without requiring additional fine-tuning or inversion is a notable advancement in the field of image editing.
Results & evaluation
The experimental evaluation is robust, showcasing a diverse collection of editing tasks and comparing the proposed method against existing techniques such as SDEdit and Text2Live. The authors provide qualitative and quantitative comparisons, demonstrating the effectiveness of their method in preserving image consistency while achieving desired edits. The use of metrics like cosine similarity of CLIP embeddings adds rigor to the evaluation, although the paper could benefit from more extensive ablation studies to further validate the impact of various components of the model.
Reproducibility
The paper provides sufficient details on the methodology, including the training process and the generation of the dataset. However, it lacks a comprehensive description of the implementation, which may hinder reproducibility. The authors mention that additional training details are provided in the supplemental material, but access to this material is necessary for full reproducibility.
Limitations
The model is limited by the quality of the generated dataset and may inherit biases from the pretrained models used. It struggles with spatial reasoning and counting objects, which are common challenges in image editing tasks. Additionally, the reliance on synthetic data may not fully capture the complexity of real-world scenarios, potentially affecting the model's performance in practice.
Broader impact
The proposed method has significant implications for the field of image editing and generative models. By enabling intuitive and precise edits based on natural language instructions, it opens up new avenues for creative applications in art, design, and content creation. The ability to perform complex edits quickly and without extensive user input could democratize access to advanced image editing tools, making them more accessible to non-experts. This paper presents a novel approach to image editing using generative models that can follow human instructions, significantly advancing the capabilities of image editing technologies. The combination of large pretrained models to create a robust training dataset is a noteworthy contribution that could influence future research and applications in multimodal machine learning.
Curator note
Brooks et al., UC Berkeley; text-guided image editing; enabled fine-grained image control
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