The Uncanny Valley: A Comprehensive Analysis of Diffusion Models
Karam Ghanem Danilo Bzdok
Through Diffusion Models (DMs), we have made significant advances in
generating high-quality images. Our exploration of these models delves deeply
into their core operational principles by systematically investigating key
aspects across various DM architectures: i) noise schedules, ii) samplers, and
iii) guidance. Our comprehensive examination of these models sheds light on
their hidden fundamental mechanisms, revealing the concealed foundational
elements that are essential for their effectiveness. Our analyses emphasize the
hidden key factors that determine model performance, offering insights that
contribute to the advancement of DMs. Past findings show that the configuration
of noise schedules, samplers, and guidance is vital to the quality of generated
images; however, models reach a stable level of quality across different
configurations at a remarkably similar point, revealing that the decisive
factors for optimal performance predominantly reside in the diffusion process
dynamics and the structural design of the model's network, rather than the
specifics of configuration details. Our comparative analysis reveals that
Denoising Diffusion Probabilistic Model (DDPM)-based diffusion dynamics
consistently outperform the Noise Conditioned Score Network (NCSN)-based ones,
not only when evaluated in their original forms but also when continuous
through Stochastic Differential Equation (SDE)-based implementations.