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[2511. 18822] DiP: Taming Diffusion Models in Pixel Space Diffusion models face a fundamental trade-off between generation quality and computational efficiency Latent Diffusion Models (LDMs) offer an efficient solution but suffer from potential information loss and non-end-to-end training In contrast, existing pixel space models bypass VAEs but are computationally prohibitive for high-resolution synthesis To resolve this dilemma, we propose DiP
Diffusion - Wikipedia The central idea of diffusion, however, is common to all of these: a substance or collection undergoing diffusion spreads out from a point or location at which there is a higher concentration of that substance or collection
Flow Matching and Diffusion Models — 2026 Version Diffusion and flow models are the cutting edge generative AI methods for images, videos, and many other data types This course offers a comprehensive introduction for students and researchers seeking a deeper understanding of these models Lectures will teach the core mathematical concepts necessary to understand diffusion models, including stochastic differential equations and the Fokker