A Two-Stage System for Layout-Controlled Image Generation using Large Language Models and Diffusion Models

The introduction of a two-stage system for layout-controlled image generation marks a significant advancement in the field of AI-driven image synthesis. By utilizing Large Language Models (LLMs) to create structured layouts followed by a layout-conditioned diffusion model for image synthesis, the system addresses previous limitations in controlling object counts and spatial arrangements. This innovative approach has led to a remarkable increase in object recall from 57.2% to 99.9%, demonstrating the effectiveness of task decomposition in spatial planning. Additionally, the comparison between two conditioning methods, ControlNet and GLIGEN, reveals important trade-offs: while ControlNet maintains text-based stylistic control, it is prone to object hallucination, whereas GLIGEN offers superior layout fidelity but sacrifices some prompt-based controllability. This end-to-end system not only enhances the precision of image generation but also opens new avenues for applications in various d…