Aligned Diffusion: Building Controllable, Robust, and Scalable Generative AI

Dr. Vishnu Lokhande

Date : 22/8/2025

Abstract:

Over the past few years, diffusion-based generative AI has transformed how we create images and multimodal content, yet alignment of these models with human goals remains poorly understood. Unlike language models, where failures are obvious, misalignment in diffusion models is subtle outputs are high-dimensional, stochastic, and judged by imperfect metrics rather than direct human feedback. Early work has focused mainly on filtering unsafe content, but the deeper challenges lie in enabling precise user control, ensuring robustness under real-world variability, and scaling models to new tasks and data without breaking alignment. This talk outlines a research agenda centered on three thrusts. Controllability: engineering fine-grained, object- and attribute-level steering mechanisms that go beyond coarse prompt editing. Robustness: designing alignment strategies resilient to domain shift, adversarial manipulation, and confounding data sources. Scalability: building continual learning frameworks that allow diffusion models to adapt and integrate heterogeneous data without catastrophic forgetting. Medical imaging provides a compelling proving ground: generating full 3D scans from limited input or allowing radiologists to explore “what-if” scenarios could revolutionize diagnosis and treatment planning, but only if models are trustworthy and adaptable. By tackling controllability, robustness, and scalability together, this work aims to turn diffusion-based generative AI into reliable, human-aligned systems for both biomedical and creative domains.