Controllable image generation using diffusion models

Abstract

This research investigates advanced control mechanisms for diffusion-based image generation models, focusing on developing a comprehensive pipeline for highly controllable outputs. The study evaluates four key control approaches: prompt engineering, ControlNet guidance, Low-Rank Adaptation (LoRA), and inpainting techniques. Through systematic experimentation with the Flux.dev diffusion model, the research demonstrates significant improvements in character consistency, pose control, and targeted image refinement. The findings reveal that while prompt engineering provides effective stylistic guidance, it faces limitations in spatial relationship control and character consistency. LoRA training offers a resource-efficient alternative to full model fine-tuning, though with slightly reduced accuracy. ControlNet integration proves particularly effective for structural and pose guidance, with different pre-processors showing distinct strengths in various applications. The research culminates in a unified pipeline that combines these mechanisms, demonstrating practical applications in content creation, professional photography, and e-commerce. The study's outcomes provide valuable insights for implementing controllable image generation in real-world applications while identifying areas for future technical enhancement and research directions.