标题： 编辑器：文本到图像扩散模型的有效且可解释的提示反演 显示英文标题

标题： EDITOR: Effective and Interpretable Prompt Inversion for Text-to-Image Diffusion Models

摘要： 文-图生成模型（例如，Stable Diffusion）取得了显著进展，能够基于文本描述创建高质量且逼真的图像。 提示反演任务旨在识别用于生成特定作品的文本提示，在数据归因、模型溯源和水印验证等应用中具有重要意义。 近期研究引入了一种延迟投影方案，以优化代表词汇空间的提示，但语义流畅性和效率方面的挑战仍然存在。 先进的图像描述模型或视觉大语言模型可以生成高度可解释的提示，但它们往往缺乏图像相似性。 本文提出了一种针对文-图扩散模型的提示反演技术\sys ，包括使用预训练的图像描述模型初始化嵌入，通过潜在空间逆向工程进行精炼，并利用嵌入到文本模型将其转换为文本。 我们在广泛使用的数据集（如MS COCO、LAION和Flickr）上的实验表明，我们的方法在图像相似性、文本对齐、提示可解释性和泛化能力方面优于现有方法。 我们进一步展示了所生成提示在跨概念图像合成、概念操作、进化多概念生成和无监督分割等任务中的应用。 显示英文摘要

摘要： Text-to-image generation models~(e.g., Stable Diffusion) have achieved significant advancements, enabling the creation of high-quality and realistic images based on textual descriptions. Prompt inversion, the task of identifying the textual prompt used to generate a specific artifact, holds significant potential for applications including data attribution, model provenance, and watermarking validation. Recent studies introduced a delayed projection scheme to optimize for prompts representative of the vocabulary space, though challenges in semantic fluency and efficiency remain. Advanced image captioning models or visual large language models can generate highly interpretable prompts, but they often lack in image similarity. In this paper, we propose a prompt inversion technique called \sys for text-to-image diffusion models, which includes initializing embeddings using a pre-trained image captioning model, refining them through reverse-engineering in the latent space, and converting them to texts using an embedding-to-text model. Our experiments on the widely-used datasets, such as MS COCO, LAION, and Flickr, show that our method outperforms existing methods in terms of image similarity, textual alignment, prompt interpretability and generalizability. We further illustrate the application of our generated prompts in tasks such as cross-concept image synthesis, concept manipulation, evolutionary multi-concept generation and unsupervised segmentation.