DDIM

Why DDIM Hallucinates More Than DDPM: A Theoretical Analysis of Reverse Dynamics

Announce Type: replace Abstract: We theoretically study the hallucination phenomena in two canonical diffusion samplers: the stochastic Denoising Diffusion Probabilistic Model (DDPM) and the deterministic Denoising Diffusion Implicit Model (DDIM). We analyze the reverse ODE (DDIM) and SDE (DDPM) for a Gaussian mixture target, proving that after a critical time $\tau$, (a) DDIM can become stuck on the segment connecting the two nearest modes and (b) DDPM *stochasticity* helps it become...

'Sgen i ddim mynediad at fy hoff leoedd ers bod mewn cadair olwyn'

Dyn 'ddim yn rasio ar unrhyw adeg' cyn gwrthdrawiad laddodd tad i ddau

Inverting the Generation Process of Denoising Diffusion Implicit Models: Empirical Evaluation and a Novel Method

arXiv:2606.03111v1 Announce Type: new Abstract: This paper studies the problem of inverting the DDIM image generation process to recover latent variables, particularly the initial noise map, from a generated image. Existing methods often struggle with accuracy in this task. We propose a novel hybrid approach that combines direct inversion via gradient descent for the first step, followed by a fixed-point method for subsequent steps.

Pam nad yw menyw gyflymaf Cymru yn cael rhedeg yng Ngemau'r Gymanwlad?

Hannah Brier ydi'r fenyw gyflymaf erioed yn hanes athletau Cymru, ond dydy hi ddim wedi cael ei dewis ar gyfer Gemau'r Gymanwlad.

Y salon sy'n rhoi hyder i gleifion canser

Y cerddor a'r cynhyrchydd Robin Evans wedi marw yn 72 oed

Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms

arXiv:2503.07154v3 Announce Type: replace Abstract: Generative pre-training is often framed through a false dichotomy between autoregressive models for discrete signals and diffusion models for continuous signals. We argue that the dichotomy is false because it conflates model family, data representation, training objective, and inference procedure. Autoregression is an inference procedure that expands a sequence through normalized conditional draws, while diffusion is a refinement procedure...

Ai trefn debyg i'r Alpau yw'r ateb i broblemau parcio Eryri?

MENO: MeanFlow-Enhanced Neural Operators for Dynamical Systems

arXiv:2604.06881v2 Announce Type: replace-cross Abstract: Neural operators have emerged as powerful surrogates for dynamical systems due to their grid-invariant properties and computational efficiency. However, Fourier-based variants inherently truncate high-frequency components in spectral space, resulting in the loss of small-scale structures and degraded prediction quality at high resolutions when trained on low-resolution data.