밤에 쓰는 편지 밤에 쓰는 편지

https://arxiv.org/pdf/2202.00512https://github.com/google-research/google-research/tree/master/diffusion_distillationAbstractDiffusion models have recently shown great promise for generative modeling, outperforming GANs on perceptual quality and autoregressive models at density estimation. A remaining downside is their slow sampling time: generating high quality samples takes many hundreds or th..

https://arxiv.org/pdf/2010.02502(Oct 2022 ICLR 2021)https://github.com/ermongroup/ddimAbstractDenoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps in order to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more effici..

https://arxiv.org/pdf/2112.10752AbstractBy decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically opera..

https://arxiv.org/pdf/2105.05233AbstractWe show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient m..

https://arxiv.org/pdf/2102.09672AbstractDenoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive loglikelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process al..