Unify-Agent: A Unified Multimodal Agent for World-Grounded Image Synthesis
Hi there, little explorer!
Imagine you have a super-duper robot friend who loves to draw pictures. But sometimes, if you ask it to draw a "purple dinosaur eating a taco on the moon," it might draw a silly one that doesn't look quite right.
This new AI friend, called Unify-Agent, is like a smarter robot artist! Instead of just guessing, it first thinks really hard, like a detective. It looks up information in its giant brain, like "What does a taco look like?" or "Is there really a purple dinosaur?"
Then, it uses all that knowledge to draw you a much better, more real-looking picture! It's like it learns about the world before it draws, making its art super cool and accurate. Yay for smart drawing robots!
Unify-Agent integrates agent-based modeling with multimodal understanding to enhance image synthesis through reasoning, searching, and generation processes grounded in external knowledge. (18 upvotes on HuggingFace)
Published on Mar 31
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Abstract
Unify-Agent integrates agent-based modeling with multimodal understanding to enhance image synthesis through reasoning, searching, and generation processes grounded in external knowledge.
AI-generated summary
Unified multimodal models provide a natural and promising architecture for understanding diverse and complex real-world knowledge while generating high-quality images. However, they still rely primarily on frozen parametric knowledge, which makes them struggle with real-world image generation involving long-tail and knowledge-intensive concepts. Inspired by the broad success of agents on real-world tasks, we explore agentic modeling to address this limitation. Specifically, we present Unify-Agent, a unified multimodal agent for world-grounded image synthesis, which reframes image generation as an agentic pipeline consisting of prompt understanding, multimodal evidence searching, grounded recaptioning, and final synthesis. To train our model, we construct a tailored multimodal data pipeline and curate 143K high-quality agent trajectories for world-grounded image synthesis, enabling effective supervision over the full agentic generation process. We further introduce FactIP, a benchmark covering 12 categories of culturally significant and long-tail factual concepts that explicitly requires external knowledge grounding. Extensive experiments show that our proposed Unify-Agent substantially improves over its base unified model across diverse benchmarks and real world generation tasks, while approaching the world knowledge capabilities of the strongest closed-source models. As an early exploration of agent-based modeling for world-grounded image synthesis, our work highlights the value of tightly coupling reasoning, searching, and generation for reliable open-world agentic image synthesis.
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