Results 1 to 10 of about 757,960 (318)
Q-Diffusion: Quantizing Diffusion Models
2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 The code is available at https://github.com/Xiuyu-Li/q ...
Xiuyu Li +7 more
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Radar-SR3: A Weather Radar Image Super-Resolution Generation Model Based on SR3
Atmosphere, 2023 To solve the problems of the current deep learning radar extrapolation model consuming many resources and the final prediction result lacking details, a weather radar image super-resolution weather model based on SR3 (super-resolution via image ...
Zhanpeng Shi +4 more
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Advances in Neural Information Processing Systems 35, 2022 Diffusion models are recent state-of-the-art methods for image generation and likelihood estimation. In this work, we generalize continuous-time diffusion models to arbitrary Riemannian manifolds and derive a variational framework for likelihood estimation.
Chin-Wei Huang +4 more
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Advances in Neural Information Processing Systems 35, 2022 Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial results. Our model is a natural extension of the standard image diffusion architecture, and it enables jointly ...
Jonathan Ho +5 more
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CoRR, 2022 Recently, Rissanen et al., (2022) have presented a new type of diffusion process for generative modeling based on heat dissipation, or blurring, as an alternative to isotropic Gaussian diffusion. Here, we show that blurring can equivalently be defined through a Gaussian diffusion process with non-isotropic noise.
Emiel Hoogeboom, Tim Salimans
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On the Generalization of Diffusion Model
CoRR, 2023 The diffusion probabilistic generative models are widely used to generate high-quality data. Though they can synthetic data that does not exist in the training set, the rationale behind such generalization is still unexplored. In this paper, we formally define the generalization of the generative model, which is measured by the mutual information ...
Mingyang Yi, Jiacheng Sun, Zhenguo Li
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Renormalizing Diffusion Models
CoRR, 2023 69+15 pages, 8 figures; v2: figure and references added, typos ...
Jordan Cotler, Semon Rezchikov
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CoRR, 2023 Diffusion models (DMs) have been adopted across diverse fields with its remarkable abilities in capturing intricate data distributions. In this paper, we propose a Fast Diffusion Model (FDM) to significantly speed up DMs from a stochastic optimization perspective for both faster training and sampling.
Zike Wu +3 more
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CoRR, 2023 The availability and accessibility of diffusion models (DMs) have significantly increased in recent years, making them a popular tool for analyzing and predicting the spread of information, behaviors, or phenomena through a population. Particularly, text-to-image diffusion models (e.g., DALLE 2 and Latent Diffusion Models (LDMs) have gained significant
Yugeng Liu +4 more
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CoRR, 2023 Diffusion models have shown remarkable performance on many generative tasks. Despite recent success, most diffusion models are restricted in that they only allow linear transformation of the data distribution. In contrast, broader family of transformations can potentially help train generative distributions more efficiently, simplifying the reverse ...
Bartosh, Grigory +2 more
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