Introduction to MuLan and the Multi-Object Generation Challenge
Explore MuLan's innovative agentic system design for text-to-image generation. Learn how MuLan breaks down complex prompts into manageable tasks, using a multi-step process with planning, progressive diffusion, and self-correction. Understand how this approach improves control, reliability, and compositional accuracy in generating multi-object images.
The problem space: Text-to-image generation
The one-shot process vs. an agentic architecture
In recent years, we’ve seen an explosion in the capabilities of text-to-image (T2I) models. These AI systems can take a simple text prompt and produce visually appealing, high-quality images in a single step. As the underlying models have improved, their ability to handle compositional requests has improved remarkably.
However, as agentic ...