CCA: Collaborative Competitive Agents for Image Editing
Tiankai Hang Shuyang Gu Dong Chen Xin Geng Baining Guo
This paper presents a novel generative model, Collaborative Competitive
Agents (CCA), which leverages the capabilities of multiple Large Language
Models (LLMs) based agents to execute complex tasks. Drawing inspiration from
Generative Adversarial Networks (GANs), the CCA system employs two equal-status
generator agents and a discriminator agent. The generators independently
process user instructions and generate results, while the discriminator
evaluates the outputs, and provides feedback for the generator agents to
further reflect and improve the generation results. Unlike the previous
generative model, our system can obtain the intermediate steps of generation.
This allows each generator agent to learn from other successful executions due
to its transparency, enabling a collaborative competition that enhances the
quality and robustness of the system's results. The primary focus of this study
is image editing, demonstrating the CCA's ability to handle intricate
instructions robustly. The paper's main contributions include the introduction
of a multi-agent-based generative model with controllable intermediate steps
and iterative optimization, a detailed examination of agent relationships, and
comprehensive experiments on image editing. Code is available at
\href{https://github.com/TiankaiHang/CCA}{https://github.com/TiankaiHang/CCA}.