Foundation.Finetune(): Mastering Diffusion Models for Medical AI

Zahra TehraniNasab

Keywords: Foundation Models, Fine Tuning, Medical Image Generation, Parameter Efficient Finetuning

Abstract:

Foundation models like Stable Diffusion have revolutionized image generation through their rich, general-purpose representations learned from massive datasets. However, adapting these models to specialized domains such as medical imaging presents unique challenges due to their billion-parameter scale and computational requirements. This comprehensive tutorial explores various fine-tuning paradigms for diffusion models, with a particular focus on medical image generation applications. We systematically examine both full fine-tuning approaches and parameter-efficient fine-tuning (PEFT) methods, including LoRA, DoRA, BitFit, and component-specific strategies targeting the U-Net, VAE, and text encoder components of Stable Diffusion. Through detailed implementation guides, performance comparisons, and practical recommendations, we demonstrate how different fine-tuning strategies can be effectively applied to domain-specific image generation tasks while managing computational constraints. Our analysis reveals that while full U-Net fine-tuning achieves superior performance, parameter-efficient methods like DoRA and LoRA can approach comparable quality with significantly reduced computational overhead, making them practical alternatives for resource-constrained environments.
Link to the blog: https://tehraninasab.github.io/blog/2025/fine-tuning-foundation-models-for-medical-image-analysis/
Link to github: https://github.com/tehraninasab/PixelUPressure

Submission Number: 11