Evolution of Diffusion Models: From Birth to Enhanced Efficiency and Controllability
賴杰昕博士Sony AI, Tokyo, Japan 13:30 – 15:00 鴻經館 M107
Abstract：
Diffusion models, pioneers in Generative AI, have significantly propelled the creation of synthetic images, audio, 3D objects/scenes, and proteins. Beyond their role in generation, these models have found practical applications in tasks like media content editing/restoration, as well as in diverse domains such as robotics learning.
In this talk, we'll explore the origins of diffusion models, gaining insights into their mechanisms as differential equations (DE) solving (Song et al. ICLR 2020). With this, we introduced, FP-Diffusion (Lai et al. ICML 2023), improves the diffusion model by aligning it with its underlying mathematical structure, the Fokker-Planck (FP) Equation. Additionally, the link between diffusion models and DE solving reveals limitations associated with the slow sampling speed of thousand-step generation. Motivated by this, we'll introduce the Consistency Trajectory Model (CTM) (Kim & Lai et al. ICLR 2024), an innovative method enabling one-step diffusion model generation while preserving high fidelity and diversity. If time permits, we'll delve into controllable generation using pre-trained diffusion models, showcasing their utility in tasks such as media restoration and user-specified applications, and exploring their practical applications in industrial business.
20240222演講_ 賴杰昕博士 (Sony AI, Tokyo, Japan)