Thanks to the powerful language comprehension capabilities of Large Language Models (LLMs), existing instruction-based image editing methods have introduced Multimodal Large Language Models (MLLMs) to promote information exchange between instructions and images, ensuring the controllability and flexibility of image editing. However, these frameworks often build a multi-instruction dataset to train the model to handle multiple editing tasks, which is not only time-consuming and labor-intensive but also fails to achieve satisfactory results. In this paper, we present TalkPhoto, a versatile training-free image editing framework that facilitates precise image manipulation through conversational interaction. We instruct the open-source LLM with a specially designed prompt template to analyze user needs after receiving instructions and hierarchically invoke existing advanced editing methods, all without additional training. Moreover, we implement a plug-and-play and efficient invocation of image editing methods, allowing complex and unseen editing tasks to be integrated into the current framework, achieving stable and high-quality editing results. Extensive experiments demonstrate that our method not only provides more accurate invocation with fewer token consumption but also achieves higher editing quality across various image editing tasks.

翻译：得益于大型语言模型（LLM）强大的语言理解能力，现有的基于指令的图像编辑方法引入了多模态大型语言模型（MLLM），以促进指令与图像之间的信息交换，从而确保图像编辑的可控性和灵活性。然而，这些框架通常需要构建多指令数据集来训练模型以处理多种编辑任务，这不仅耗时耗力，而且往往难以取得令人满意的效果。本文提出TalkPhoto，一种多功能免训练图像编辑框架，通过对话交互实现精确的图像操控。我们使用专门设计的提示模板指导开源LLM，使其在接收指令后分析用户需求，并分层调用现有的先进编辑方法，整个过程无需额外训练。此外，我们实现了图像编辑方法的即插即用式高效调用，使得复杂且未经预见的编辑任务能够整合到当前框架中，从而获得稳定且高质量的编辑结果。大量实验表明，我们的方法不仅能够以更少的token消耗提供更准确的调用，而且在多种图像编辑任务上均实现了更高的编辑质量。