Can anyone explain differences between sampling methods and their uses to me in simple terms, because all the info I've found so far is either very contradicting or complex and goes over my head

This page summarizes the projects mentioned and recommended in the original post on /r/StableDiffusion

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  • latent-diffusion

    High-Resolution Image Synthesis with Latent Diffusion Models

  • DDIM and PLMS were the original samplers. They were part of Latent Diffusion's repository. They stand for the papers that introduced them, Denoising Diffusion Implicit Models and Pseudo Numerical Methods for Diffusion Models on Manifolds.

  • k-diffusion

    Karras et al. (2022) diffusion models for PyTorch

  • Almost all other samplers come from work done by @RiversHaveWings or Katherine Crowson, which is mostly contained in her work at this repository. She is listed as the principal researcher at Stability AI. Her notes for those samplers are as follows:

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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