SinDDM: A Single Image Denoising Diffusion Model
- URL: http://arxiv.org/abs/2211.16582v3
- Date: Tue, 6 Jun 2023 20:42:41 GMT
- Title: SinDDM: A Single Image Denoising Diffusion Model
- Authors: Vladimir Kulikov, Shahar Yadin, Matan Kleiner, Tomer Michaeli
- Abstract summary: We introduce a framework for training a Denoising diffusion model 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.
It is applicable in a wide array of tasks, including style transfer and harmonization.
- Score: 28.51951207066209
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling [64.09238330331195]
We propose a novel Multi-Modal Auto-Regressive (MMAR) probabilistic modeling framework.
Unlike discretization line of method, MMAR takes in continuous-valued image tokens to avoid information loss.
We show that MMAR demonstrates much more superior performance than other joint multi-modal models.
arXiv Detail & Related papers (2024-10-14T17:57:18Z) - Harnessing the Latent Diffusion Model for Training-Free Image Style Transfer [24.46409405016844]
Style transfer task is one of those challenges that transfers the visual attributes of a style image to another content image.
We propose a training-free style transfer algorithm, Style Tracking Reverse Diffusion Process (STRDP) for a pretrained Latent Diffusion Model (LDM)
Our algorithm employs Adaptive Instance Normalization (AdaIN) function in a distinct manner during the reverse diffusion process of an LDM.
arXiv Detail & Related papers (2024-10-02T09:28:21Z) - Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing [58.48890547818074]
We present a powerful modification of Contrastive Denoising Score (CUT) for latent diffusion models (LDM)
Our approach enables zero-shot imageto-image translation and neural field (NeRF) editing, achieving structural correspondence between the input and output.
arXiv Detail & Related papers (2023-11-30T15:06:10Z) - Improving Denoising Diffusion Probabilistic Models via Exploiting Shared
Representations [5.517338199249029]
SR-DDPM is a class of generative models that produce high-quality images by reversing a noisy diffusion process.
By exploiting the similarity between diverse data distributions, our method can scale to multiple tasks without compromising the image quality.
We evaluate our method on standard image datasets and show that it outperforms both unconditional and conditional DDPM in terms of FID and SSIM metrics.
arXiv Detail & Related papers (2023-11-27T22:30:26Z) - Denoising Diffusion Bridge Models [54.87947768074036]
Diffusion models are powerful generative models that map noise to data using processes.
For many applications such as image editing, the model input comes from a distribution that is not random noise.
In our work, we propose Denoising Diffusion Bridge Models (DDBMs)
arXiv Detail & Related papers (2023-09-29T03:24:24Z) - Stimulating the Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling [56.506240377714754]
We present a novel strategy called the Diffusion Model for Image Denoising (DMID)
Our strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained unconditional diffusion model.
Our DMID strategy achieves state-of-the-art performance on both distortion-based and perception-based metrics.
arXiv Detail & Related papers (2023-07-08T14:59:41Z) - Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance.
We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring.
Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
arXiv Detail & Related papers (2023-05-22T12:18:20Z) - Representation Learning with Diffusion Models [0.0]
Diffusion models (DMs) have achieved state-of-the-art results for image synthesis tasks as well as density estimation.
We introduce a framework for learning such representations with diffusion models (LRDM)
In particular, the DM and the representation encoder are trained jointly in order to learn rich representations specific to the generative denoising process.
arXiv Detail & Related papers (2022-10-20T07:26:47Z) - f-DM: A Multi-stage Diffusion Model via Progressive Signal
Transformation [56.04628143914542]
Diffusion models (DMs) have recently emerged as SoTA tools for generative modeling in various domains.
We propose f-DM, a generalized family of DMs which allows progressive signal transformation.
We apply f-DM in image generation tasks with a range of functions, including down-sampling, blurring, and learned transformations.
arXiv Detail & Related papers (2022-10-10T18:49:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.