论文标题
sinddm：单个图像降级扩散模型
SinDDM: A Single Image Denoising Diffusion Model
论文作者
论文摘要
去核扩散模型（DDMS）导致图像生成，编辑和恢复的性能突飞猛进。但是，现有的DDM使用非常大的数据集进行培训。在这里，我们介绍了一个在单个图像上训练DDM的框架。我们创建的方法是通过使用多尺度扩散过程来了解训练图像的内部统计信息。为了驱动反向扩散过程，我们使用了完全跨局的轻质denoiser，该磁化剂均以噪声水平和比例为条件。该体系结构允许以粗到1的方式生成任意维度的样本。正如我们所说明的那样，sinddm生成了多种高质量的样本，并且适用于各种任务，包括样式转移和协调。此外，它很容易受到外部监督的指导。特别是，我们使用预训练的剪辑模型从单个图像中展示了文本引导的生成。
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.
