在此体系结构下,DiffusionCLIP微调过程中的梯度流如图3所示,与训练递归神经网络的过程类似。 一旦对扩散模型进行微调之后,任何来自预训练域的图像都可以被操作为与目标文本ytar对应的图像,如图4(a)所示。 Forward Diffusion and Generative Process 由于Eq. 3中的DDPM反向扩散过程是随机的,所以每次由相同的潜在特征产生...
近些年来无条件图像生成模型的发展,特别是近几年大火的基于diffusion的模型,使得无条件图像生成的结果可以直逼真实图像,因此作者期望在当前无条件图像生成模型的基础上进一步开发text-guided image generation的diffusion模型,并着重将其应用于image edit问题。 其实OpenAI在21年“Diffusion Models Beat GANs on Image Synthes...
个人理解:diffusion model的reverse process每一步扩散都是在一个正态分布的mean附近采样,而CLIP guidance在这个mean附近增加一个扰动,该扰动与 f(x)和g(c)点积的梯度 有关。 直观的motivation:一些利用CLIP将文本特征融合到diffusion model中的方法,通常是对diffusion model reverse process过程中加过噪声的图像进行特...
更大的模型:算法采用了Guided Diffusion方法中相同的Autoencoder结构,但是进一步扩大了通道数量,使得最终的网络参数数量达到了3.5 billion 文章: GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models Denoising Diffusion Probabilistic Models Improved denoising diffusion probabilistic ...
Diffusion models have recently gained significant traction due to their ability to generate high-fidelity and diverse images and videos conditioned on text prompts. In medicine, this application promises to address the critical challenge of data scarcity, a consequence of barriers in data sharing, stri...
Aside from offering state-of-the-art performance in medical image generation, denoising diffusion probabilistic models (DPM) can also serve as a representation learner to capture semantic information and potentially be used as an image representation for downstream tasks, e.g., segmentation. However,...
To fully leverage the image synthesis performance of diffusion models with the purpose of image manipulation, we require the deterministic process both in the forward and reverse direction with pretrained diffusion models for successful image manipulation. On the other hand, for the image translation ...
In recent years, denoising diffusion models have achieved remarkable success in generating pixel-level representations with semantic values for image generation modeling. In this study, we propose a novel end-to-end framework, called TGEDiff, focusing on medical image segmentation. TGEDiff fuses a te...
Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models O网页链接ChatPaper综述:该文章提出了针对文本导向图像编辑的问题,即虽然文本指导是用户友好的编辑界面,但往往无法确保用户所表达的精确概念。为解决这个问题,提出了Custom-Edit方法,通过定制扩散模型和一些参考图像,来优化定制化的文本导向编辑。同...
PromptableGameModels:Text-GuidedGameSimulationviaMaskedDiffusionModelsWILLIMENAPACE∗UniversityofTrentoItalyALIAKSANDRSIAROHINSnapInc.USASTÉPHANELATHUILIÈRELTCITélécomParisInstitutPolytechniquedeParisFrancePANOSACHLIOPTASSnapInc.USAVLADISLAVGOL