Parallel Sampling of Diffusion Models, Shihet al., NeurIPS 2023 SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis, Podellet al., ICLR 2024 Prerequisites Python3 NVIDIA GPU + CUDA >= 12.0 and corresponding CuDNN
Densify Sampling Network Diffusion Interpolation With Barriers Empirical Bayesian Kriging GA Layer To Contour GA Layer To Grid GA Layer To Points Gaussian Geostatistical Simulations (for conditional simulation) Moving Window Kriging Increased processing speed is most noticeable in complex models ...
Quicksort is an example of a divide-and-conquer algorithm for ordering data. • The regular sampling parallel sort algorithm improves efficiency and scalability for distributed computing, still borrowing from the quicksort method. • Manager–worker workflow has one process, the manager, controlling...
For example, diffusion model sampling (from diffusers). Example: import torch from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler model_id = "stabilityai/stable-diffusion-2-1" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.scheduler = ...
35 proposed a constructive algorithm for a diffusion-area FJSP, incorporating iterative sampling and simulated annealing (SA), which demonstrated effectiveness on real-world instances. Knopp et al.36 introduced a new DG and a greedy randomized adaptive search procedure (GRASP) metaheuristic combined ...
Pocket2Mol: efficient molecular sampling based on 3D protein pockets. Preprint at https://arxiv.org/abs/2205.07249 (2022). Francoeur, P. G. et al. Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. J. Chem. Inf. Model. 60, 4200...
Recent Vector-Quantized image models have overcome this limitation of image resolution but are prohibitively slow and unidirectional as they generate tokens via element-wise autoregressive sampling from the prior. By contrast, in this paper we propose a novel discrete diffusion probabilistic model prior ...
ranging from Smoothed-Particle Hydrodynamics (SPH) to Molecular Dynamics (MD), Discrete Element Methods (DEM), Vortex Methods, stencil codes (finite differences), and high-dimensional Monte Carlo sampling (CMA-ES), comparing it to the current state of the art and to existing software frameworks....
This sampling data is constantly collated and processed using Bayesian techniques to yeild uncertainty-controlled kMC/Markov models of complex atomistic target systems, with minimal end-user involvement. Recent developements allow for the autonomous construction and convergence of arbitrarily complex diffusion...
synergistically combined with various traditional k-space PI models, generating learning-based priors to produce hig-fidelity reconstructions. Experimental re-sults on datasets with varying sampling patterns and ac-celeration factors demonstrate that WKGM can attain state-of-the-art reconstruction results ...