InstructPix2Pix is a method for editing images based on human instructions. The model uses a conditional diffusion model trained on a large dataset generated by combining a language model (GPT-3) and a text-to-image model (Stable Diffusion). This dataset contains text instructions and corresponding images before and after the edit. The model is trained to perform the image edit directly in the forward pass without requiring per-example fine-tuning or inversion, allowing it to edit images quickly. The model can handle a variety of edits, including replacing objects, changing the style, and modifying the setting. It generalizes well to real images and user-written instructions, achieving zero-shot generalization. The model is compared with other methods like SDEdit and Text2Live, showing superior performance in terms of image consistency and edit quality. The model also demonstrates the ability to handle complex edits, such as changing the medium of an image or transforming it into a different artistic style. However, the model has limitations, including difficulties with spatial reasoning and object counting, as well as potential biases inherited from the training data. The model's performance is also affected by the size and quality of the training data, with larger and higher-quality datasets leading to better results. Overall, InstructPix2Pix provides a powerful tool for intuitive image editing based on human instructions.InstructPix2Pix is a method for editing images based on human instructions. The model uses a conditional diffusion model trained on a large dataset generated by combining a language model (GPT-3) and a text-to-image model (Stable Diffusion). This dataset contains text instructions and corresponding images before and after the edit. The model is trained to perform the image edit directly in the forward pass without requiring per-example fine-tuning or inversion, allowing it to edit images quickly. The model can handle a variety of edits, including replacing objects, changing the style, and modifying the setting. It generalizes well to real images and user-written instructions, achieving zero-shot generalization. The model is compared with other methods like SDEdit and Text2Live, showing superior performance in terms of image consistency and edit quality. The model also demonstrates the ability to handle complex edits, such as changing the medium of an image or transforming it into a different artistic style. However, the model has limitations, including difficulties with spatial reasoning and object counting, as well as potential biases inherited from the training data. The model's performance is also affected by the size and quality of the training data, with larger and higher-quality datasets leading to better results. Overall, InstructPix2Pix provides a powerful tool for intuitive image editing based on human instructions.