Given an input image and a language description for editing, our method can generate realistic and relevant images without the need for user-specified regions for editing. It performs local image editing while preserving the image context. Our method can also handle multiple-object and long-paragraph scenarios.

Abstract

Language has emerged as a natural interface for image editing. In this paper, we introduce a method for region-based image editing driven by textual prompts, without the need for user-provided masks or sketches. Specifically, our approach leverages an existing pre-trained text-to-image model and introduces a bounding box generator to find the edit regions that are aligned with the textual prompts. We show that this simple approach enables flexible editing that is compatible with current image generation models, and is able to handle complex prompts featuring multiple objects, complex sentences, or long paragraphs. We conduct an extensive user study to compare our method against state-of-the-art methods. Experiments demonstrate the competitive performance of our method in manipulating images with high fidelity and realism that align with the language descriptions provided.

Method

Framework figure. We first feed the input image into the self-supervised learning (SSL) model, e.g., DINO, to obtain the attention map and feature, which are used for anchor initialization. The region generation model initializes several region proposals (e.g., 3 proposals in this figure) around each anchor point, and learns to select the most suitable ones among them with the region generation network (RGN). The predicted region and the text descriptions are then fed into a pre-trained text-to-image model for image editing. We utilize the CLIP model for learning the score to measure the similarity between the given text description and the edited result, forming a training signal to learn our region generation model.

Video

Comparison

Our Region Generator + MaskGiT

Results Using Diverse Prompts

More Results

User Study

BibTeX

@article{lin2023text,
  title={Text-Driven Image Editing via Learnable Regions},
  author={Lin, Yuanze and Chen, Yi-Wen and Tsai, Yi-Hsuan and Jiang, Lu and Yang, Ming-Hsuan},
  journal={arXiv preprint arXiv:2311.16432},
  year={2023}
}