作者生成自己是Parameter-Efficient Conditional Distillation,因为只更新了复制出来的encoder。 这里应该只用了new conditional data来训的蒸馏,所以作者论文里声称不需要原始的text-to-image data。 其实这里的条件控制是图像,有点类似于超分的任务。 图5.Algorithm 1 Conditional Diffusion Distillation (CDD). Thoughts ...
Breadcrumbs Conditional_Diffusion_MNIST / README.mdTop File metadata and controls Preview Code Blame 34 lines (26 loc) · 1.92 KB Raw Conditional Diffusion MNIST script.py is a minimal, self-contained implementation of a conditional diffusion model. It learns to generate MNIST digits, conditioned...
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During the training stage, the gradient of the conditional distribution is approximated by using a conditional diffusion model to predict the noise added to the original urban morphology. In the generation stage, the corresponding conditional distribution is parameterized based on the noise predict...
Enhanced Conditional Diffusion Models for Virtual Try-on Computer Vision Lab, DAMO Academy 1. Abstract Virtual try-on technology aims to improve the shopping experience by enabling customers and businesses to visualize how clothing items look when worn, using only digital images of the garments. Adva...
We consider a one-dimensional diffusion process conditioned by hitting times. We call this process a conditional diffusion process. We obtain the generator of the conditional diffusion process. We compare the original generator with that of the conditional diffusion process, and find a relation between...
In this work, we introduce DiffLinker, a conditional diffusion model that generates molecular linkers for a set of input fragments represented as a 3D atomic point cloud. First, our model generates the size of the prospective linker and then samples initial linker atom types and positions from ...
In recent years, diffusion-based generative modeling has become a highly effective way for conditional image synthesis, leading to exponential growth in the literature. However, the complexity of diffusion-based modeling, the wide range of image synthesis tasks, and the diversity of conditioning ...
因此,本文提出了一种离散条件扩散重排序(DCDR,Discrete Conditional Diffusion Reranking)框架,并且为了实现高效、稳健的推理,提出了一系列技术来实现 DCDR 在现实生活中的推荐系统中的部署。 简单介绍一下扩散模型 典型的扩散模型由两部分组成,正向过程和反向过程。
尽管CLDMs在图像生成任务中表现出色,但在图像恢复任务中,它们在低层次表示上建模退化图像与真实图像之间的关系时面临困难。 研究难点:图像恢复任务的目标是通过低层次表示精确恢复感知细节,而CLDMs在生成任务中擅长生成视觉上合理的输出,但在保持原始退化内容的保真度方面存在挑战。 相关工作:传统的图像恢复方法依赖于任务...