Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling

no code implementations • 19 Apr 2024 • Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth

To address these limitations, we introduce Neural Flow Diffusion Models (NFDM), a novel framework that enhances diffusion models by supporting a broader range of forward processes beyond the fixed linear Gaussian.

TEncDM: Understanding the Properties of Diffusion Model in the Space of Language Model Encodings

no code implementations • 29 Feb 2024 • Alexander Shabalin, Viacheslav Meshchaninov, Tingir Badmaev, Dmitry Molchanov, Grigory Bartosh, Sergey Markov, Dmitry Vetrov

Drawing inspiration from the success of diffusion models in various domains, numerous research papers proposed methods for adapting them to text data.

Decoder Denoising +3

Neural Diffusion Models

no code implementations • 12 Oct 2023 • Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth

In this paper, we present Neural Diffusion Models (NDMs), a generalization of conventional diffusion models that enables defining and learning time-dependent non-linear transformations of data.

Image Generation

Star-Shaped Denoising Diffusion Probabilistic Models

1 code implementation • NeurIPS 2023 • Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Viktor Ohanesian, Aibek Alanov, Dmitry Vetrov

In the case of Gaussian distributions, SS-DDPM is equivalent to DDPM.

Denoising