SinDDM: A Multi-Scale Denoising Diffusion Model for a Single Image

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Image and Video Processing

SinDDM: A Multi-Scale Denoising Diffusion Model for a Single Image

SinDDM: A Single Image Denoising Diffusion Model

We introduce a framework for training a denoising diffusion model (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, and is applicable in a wide array of tasks, including style transfer and harmonization.

Furthermore, it can be easily guided by external supervision.

We demonstrate text-guided generation from a single image using a pre-trained clip model.

Authors

Vladimir Kulikov, Shahar Yadin, Matan Kleiner, Tomer Michaeli

Related Topics

Introduction

We propose a hierarchical denoising diffusion model (ddm) that can be trained on a single natural image.

At the core of our method is a fully-convolutional denoiser, which we train on various scales of the image, each corrupted by various levels of noise.

We take the denoiser to be relatively small so that it only captures the statistics of the fine details within each scale.

At test time, we use this denoiser in a coarse-to-fine manner, which allows generating diverse random samples of arbitrary dimensions.

As illustrated in fig.

Result

We present a new single image generative model that combines the power and flexibility of deep learning models with the multi-scale structure of singan.

We demonstrate text-guided image generation, where we controlled the contents and style of the samples.

Read the Paper

Read Paper

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Keep Up With Latest Trending Papers. Computer Science, AI and Machine Learning and more.Subscribe

Added to collectionTop 100 Trending AI & Computer Science Papers

Image and Video Processing

SinDDM: A Multi-Scale Denoising Diffusion Model for a Single Image

SinDDM: A Single Image Denoising Diffusion Model

We introduce a framework for training a denoising diffusion model (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, and is applicable in a wide array of tasks, including style transfer and harmonization.

Furthermore, it can be easily guided by external supervision.

We demonstrate text-guided generation from a single image using a pre-trained clip model.

Authors

Vladimir Kulikov, Shahar Yadin, Matan Kleiner, Tomer Michaeli

Related Topics

Introduction

We propose a hierarchical denoising diffusion model (ddm) that can be trained on a single natural image.

At the core of our method is a fully-convolutional denoiser, which we train on various scales of the image, each corrupted by various levels of noise.

We take the denoiser to be relatively small so that it only captures the statistics of the fine details within each scale.

At test time, we use this denoiser in a coarse-to-fine manner, which allows generating diverse random samples of arbitrary dimensions.

As illustrated in fig.

Result

We present a new single image generative model that combines the power and flexibility of deep learning models with the multi-scale structure of singan.

We demonstrate text-guided image generation, where we controlled the contents and style of the samples.

Read the Paper

Read Paper

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