DSG(Guidance with Spherical Gaussian Constraint for Conditional Diffusion) 将guidance 的计算看作是在球面高斯(Spherical Gaussian)约束下使得 guided-loss 最小的优化问题,并且计算出对应的闭式解(closed-form solution)。 这种方法能够避免(减少)流形偏离的现象,从而能够在采样过程中使用 large guidance step size,...
keywords: conditional generation, training-free, zero-shot, unsupervised, diffusion models, inverse problems, image inpainting, image restoration, image editing 图生图 这一章介绍的方法都是在给定一张参考图像的条件下进行采样生成。在这种情况下,通常是希望模型能够生成与参考图像相似的图片,或者对参考图像进行...
Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram SynthesisConditional video diffusion models (CDM) have shown promising results for video synthesis, potentially enabling the generation of realistic echocardiograms to address the problem of data scarcity. However,...
FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model Jiwen Yu1 Yinhuai Wang1 Chen Zhao2† Bernard Ghanem2 Jian Zhang1† 1 Peking University Shenzhen Graduate School 2 King Abdullah University of Science and Technology (KAUST) {yujiwen, yinhuai}@stu.pku.edu...
This approach enables zero-shot conditional generation for universal control formats, which appears to offer a free lunch in diffusion guidance. In this paper, we aim to develop a deeper understanding of training-free guidance, as well as overcome its limitations. We of...
In this work, we introduce DIAG, a training-free Diffusion-based In-distribution Anomaly Generation pipeline for data augmentation. Unlike conventional image generation techniques, we implement a human-in-the-loop pipeline, where domain experts provide multimodal guidance to the model through text ...
TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition Shilin Lu1 Yanzhu Liu2 Adams Wai-Kin Kong1 1School of Computer Science and Engineering, Nanyang Technological University, Singapore 2Institute for Infocomm Research (I2R) & Centre for Frontier AI ...
[ICCV 2023] Official PyTorch implementation for the paper "FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model" - vvictoryuki/FreeDoM
Conditional Denoising Diffusion Implicit Model for Speech Enhancement Recently, denoising diffusion probabilistic models (DDPMs) have been effective in speech enhancement. However, existing models largely follow the original ... C Yang,X Yu,S Huang - 《International Journal of Speech Technology》 被引...
本文使用了Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction的方法,从中间的隐变量x_t作为起点,而不是从纯噪声开始,从而加速算法。 x_t = \alpha_t y + \sigma_t \epsilon,\epsilon \sim N(0,I). ...