On tuning consistent annealed sampling for denoising score matching
Score-based generative models provide state-of-the-art quality for image and audio synthesis. Sampling from these models is performed iteratively, typically employing a discretized series of noise levels and a predefined scheme. In this note, we first overview three common sampling schemes for models trained with denoising score matching. Next, we focus on one of them, consistent annealed sampling, and study its hyper-parameter boundaries. We then highlight a possible formulation of such hyper-parameter that explicitly considers those boundaries and facilitates tuning when using few or a variable number of steps. Finally, we highlight some connections of the formulation with other sampling schemes.
Code (0)
등록된 구현이 없습니다.
Tasks
Audio SynthesisDenoisingSimilar Papers 제목 키워드 기반
Adversarial score matching and improved sampling for image generation
Denoising Score Matching with Annealed Langevin Sampling (DSM-ALS) has recently found success in generative modeling. The approach works by first training a neural network to estimate the score of a distribution, and the…
DenoisingImage GenerationAnnealed Denoising score matching: learning Energy based model in high-dimensional spaces
Energy based models outputs unmormalized log-probability values given datasamples. Such a estimation is essential in a variety of application problems suchas sample generation, denoising, sample restoration, out…
DenoisingImage InpaintingOutlier DetectionHeavy-tailed denoising score matching
Score-based model research in the last few years has produced state of the art generative models by employing Gaussian denoising score-matching (DSM). However, the Gaussian noise assumption has several high-dimensional l…
DenoisingTheoretical guidelines for annealed Langevin dynamics in compositional simulation-based inference
Compositional score-based approaches to simulation-based inference (SBI) approximate the posterior over a shared parameter given $n$ independent observations by aggregating individually learned posterior scores: currentl…
Time-Annealed Perturbation Sampling: Diverse Generation for Diffusion Language Models
Diffusion language models (Diffusion-LMs) introduce an explicit temporal dimension into text generation, yet how this structure can be leveraged to control generation diversity for exploring multiple valid semantic or re…
Image GenerationText Generation