Introduction to MuLan and the Multi-Object Generation Challenge
Understand MuLan's agentic design that improves text-to-image generation by dividing complex prompts into manageable single-object tasks. Learn how its architecture uses LLM planning, progressive diffusion, and VLM feedback for enhanced control, self-correction, and accuracy in creating detailed 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 ...