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- W4310556944 abstract "Denoising diffusion models (DDMs) have led to staggering performance leaps in image generation, editing and restoration. However, existing DDMs use very large datasets for training. Here, we introduce a framework for training a DDM on a single image. Our method, which we coin SinDDM, learns the internal statistics of the training image by using a multi-scale diffusion process. To drive the reverse diffusion process, we use a fully-convolutional light-weight denoiser, which is conditioned on both the noise level and the scale. This architecture allows generating samples of arbitrary dimensions, in a coarse-to-fine manner. As we illustrate, SinDDM generates diverse high-quality samples, and is applicable in a wide array of tasks, including style transfer and harmonization. Furthermore, it can be easily guided by external supervision. Particularly, we demonstrate text-guided generation from a single image using a pre-trained CLIP model." @default.
- W4310556944 created "2022-12-12" @default.
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- W4310556944 date "2022-11-29" @default.
- W4310556944 modified "2023-09-23" @default.
- W4310556944 title "SinDDM: A Single Image Denoising Diffusion Model" @default.
- W4310556944 doi "https://doi.org/10.48550/arxiv.2211.16582" @default.
- W4310556944 hasPublicationYear "2022" @default.
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