Prompt Tuning Inversion for Text-Driven Image Editing Using Diffusion Models

TL;DR

This paper introduces Prompt Tuning Inversion, a fast and accurate method for text-driven image editing with diffusion models that balances editability and fidelity, outperforming existing techniques.

Contribution

The paper proposes a novel inversion technique that encodes input images into learnable embeddings, enabling high-quality, user-friendly image editing guided solely by text prompts.

Findings

Outperforms state-of-the-art baselines in ImageNet editing tasks

Achieves a good balance between editability and image fidelity

Enables precise color and object modifications using only text prompts

Abstract

Recently large-scale language-image models (e.g., text-guided diffusion models) have considerably improved the image generation capabilities to generate photorealistic images in various domains. Based on this success, current image editing methods use texts to achieve intuitive and versatile modification of images. To edit a real image using diffusion models, one must first invert the image to a noisy latent from which an edited image is sampled with a target text prompt. However, most methods lack one of the following: user-friendliness (e.g., additional masks or precise descriptions of the input image are required), generalization to larger domains, or high fidelity to the input image. In this paper, we design an accurate and quick inversion technique, Prompt Tuning Inversion, for text-driven image editing. Specifically, our proposed editing method consists of a reconstruction stage and an editing stage. In the first stage, we encode the information of the input image into a learnable conditional embedding via Prompt Tuning Inversion. In the second stage, we apply classifier-free guidance to sample the edited image, where the conditional embedding is calculated by linearly interpolating between the target embedding and the optimized one obtained in the first stage. This technique ensures a superior trade-off between editability and high fidelity to the input image of our method. For example, we can change the color of a specific object while preserving its original shape and background under the guidance of only a target text prompt. Extensive experiments on ImageNet demonstrate the superior editing performance of our method compared to the state-of-the-art baselines.