Multi-Object Image Generation with MuLan Multimodal Agents

Traditional text-to-image generators often struggle to render multiple distinct objects accurately in a single scene. This course introduces you to MuLan, an innovative agentic framework that uses multimodal large language models to plan, execute, and refine complex image generation tasks. By reading through our structured explanations and analyzing real-world prompt workflows, you will understand how to orchestrate multi-step generation processes. You will learn to guide AI agents to position, scale, and detail multiple objects within a single cohesive image. What you'll learn: - Understand the core limitations of standard text-to-image models when handling multi-object scenes. - Explore the architecture of MuLan and how multimodal large language models act as planning agents. - Learn how to structure multi-step prompts that guide agents through layout planning and iterative refinement. - Analyze agentic workflows that decompose complex prompts into manageable spatial instructions. - Practice designing text prompts that control object relationships, positions, and attributes. - Discover modern concepts in AI agent feedback loops and interactive generation control. The course begins with foundational definitions of multimodal agents and spatial layouts, then guides you through the step-by-step logic of the MuLan framework using clear written examples and conceptual walk-throughs. This introductory course is designed for AI enthusiasts, prompt engineers, and digital creators who want to understand the next generation of agentic image creation, requiring no prior coding experience. Start reading today to master the principles of agent-driven image generation.