作者期待通过蒸馏更先进的模型和引入变化的指导尺度,进一步提升模型性能和灵活性。 图1展现了一下和目前SOTA方法的对比: 基线稳定扩散(Stable Diffusion, SD):生成每张图像需要约250毫秒。扩散匹配蒸馏(Diffusion Matching Distillation, DMD):生成每张图像仅需约90毫秒。强调了DMD技术生成图像的速度比SD快得多,同时还在...
作者期待通过蒸馏更先进的模型和引入变化的指导尺度,进一步提升模型性能和灵活性。 图1展现了一下和目前SOTA方法的对比: 基线稳定扩散(Stable Diffusion, SD):生成每张图像需要约250毫秒。扩散匹配蒸馏(Diffusion Matching Distillation, DMD):生成每张图像仅需约90毫秒。强调了DMD技术生成图像的速度比SD快得多,同时还在...
在Diffusion步数蒸馏的相关工作里,大家更加喜欢用分布匹配的概念,经典的工作比如SDS, VSD,Diff-Instruct,DMD,SiD, eta,而传统的像feature蒸馏那样去蒸馏output的做法逐渐被大家所抛弃,比如Progressive Distillation。 那么这里有两个问题,1.直接学习output有什么问题?2. 什么是分布匹配,为什么它可以? 首先思考第一个问题...
Distribution Matching Distillation (DMD): DMD核心将预训练的扩散去噪器(diffusion denoiser)转化为快速的“一步”(one-step)图像生成器,同时保持生成图像的高质量。 预训练模型及一步模型生成器: 预训练基础模型(Pretrained base model): 假设存在一个已经预训练好的扩散模型 base,该模型能够将高斯噪声样本逐步去噪,...
Generative equilibrium transformers are disclosed. Disclosed embodiments provide a simple and effective technique that can distill a multi-step diffusion process into a single-step generative model using solely noise/image pairs.ZHENGYANG GENGASHWINI POLKEJEREMY KOLTERBAHARE AZARIIVAN BATALOVFILIPE CONDESSA...
网络单步扩散;一次扩散 网络释义
几篇论文实现代码:《One-Step Diffusion Distillation through Score Implicit Matching》(NeurIPS 2024) GitHub: github.com/maple-research-lab/SIM 《AutoPSV: Automated Process-Supervised Verifier》(Neu...
To address this, we propose TSD-SR, a novel distillation framework specifically designed for real-world image super-resolution, aiming to construct an efficient and effective one-step model. We first introduce the Target Score Distillation, which leverages the priors of diffusion models and real ...
Official repo of our paper "SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions" - IDKiro/sdxs
& Rayner, Jeremy (2008) `Third Generation Policy Diffusion Studies and the Analysis of Policy Mixes: Two Steps Forward and One Step Back? Journal of Comparative Policy Analysis: Research and Practice 10, (4): 385-402Third Generation Policy Diffusion Studies and the Analysis of Policy Mixes: ...