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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
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
Diffusion model - Wikipedia Diffusion models were introduced in 2015 as a method to train a model that can sample from a highly complex probability distribution They used techniques from non-equilibrium thermodynamics, especially diffusion [12] Consider, for example, how one might model the distribution of all naturally occurring photos Each image is a point in the space of all images, and the distribution of