TalkPhoto: A Versatile Training-Free Conversational Assistant for Intelligent Image Editing
This work addresses the need for flexible and efficient image editing tools for users by offering a training-free solution that integrates complex tasks, though it is incremental as it builds on existing methods without new model training.
The paper tackles the problem of time-consuming and labor-intensive training for instruction-based image editing by introducing TalkPhoto, a training-free conversational assistant that hierarchically invokes existing editing methods, achieving higher editing quality and more accurate invocation with fewer tokens.
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.