我们的研究从扩散变换器(Diffusion Transformer,简称DiT)[32]开始。我们选择这种基于Transformer的DDM有几个原因:(i)不同于其他基于UNet的DDM [11, 33],基于Transformer的架构可以与其他基于Transformer的自监督学习基线(例如,[7, 21])进行更公平的比较;(ii)DiT在编码器和解码器之间有更清晰的区分,而UNet的编码器...
DDM可以操作两种类型的输入空间。一个是原始像素空间,其中原始图像x0直接用作z0。另一种选择是在由标记器产生的潜在空间上构建DDM。将图像x0映射到其潜在的z0=f(x0)中。 Deconstructing Denoising Diffusion Models Reorienting DDM for Self-supervised Learning 虽然DDM在概念上是DAE的一种形式,但它最初是为了生成...
To alleviate this problem we learn a prior over scene geometry and color, using a denoising diffusion model (DDM). Our DDM is trained on RGBD patches of the synthetic Hypersim dataset and can be used to predict the gradient of the logarithm of a joint probability distribution of color and ...
Denoising diffusion models (DDMs) have emerged as a powerful class of generative models. A forward diffusion process slowly perturbs the data, while a deep model learns to gradually denoise. Synthesis amounts to solving a differential equation (DE) defined by the learnt model. Solving the DE re...
In this study, we examine the representation learning abilities of Denoising Diffusion Models (DDM) that were originally purposed for image generation. Our philosophy is to deconstruct a DDM, gradually transforming it into a classical Denoising Autoencoder (DAE). This deconstructive procedure allows us...
This paper proposes a novel method, Environmental Perception based on Denoising Diffusion Models(EP-DDM), for ship trajectory prediction in multi-ship interaction scenarios, utilizing denoising diffusion models to enhance environmental perception. EP-DDM is based on an encoder-decoder architecture but ...
Similar to our proposed method, diffusion models offer the advantage of denoising MRI scans without the need for paired noisy and noise-free data. However, their drawbacks include computational complexity, which can result in time-consuming processes. Despite these challenges, diffusion models have dem...
As denoising diffusion models evolved, additional prediction objectives have been introduced alongside the noise prediction objective, such as the 𝐯v-objective and the 𝐬(0)s(0)-objective. Essentially, training a DDM is about training an ANN so that it can accurately predict the desired ...
# Github repo(oversea) pip install git+https://github.com/lvyufeng/denoising-diffusion-mindspore # From OpenI repo(in China) pip install git+https://openi.pcl.ac.cn/lvyufeng/denoising-diffusion-mindspore Usage from ddm import Unet, GaussianDiffusion, value_and_grad from ddm.ops import randn ...
Official pytorch implementation of the paper: "SinDDM: A Single Image Denoising Diffusion Model" - fallenshock/SinDDM