下载命令(需要登录):huggingface-cli download --token hf_**stable-diffusion-v1-5/stable-diffusion-v1-5--local-dir D:/AI/checkpoints/stable-diffusion-v1-5 下载单个文件:huggingface-cli downloadstable-diffusion-v1-5/stable-diffusion-v1-5v1-5-pruned.ckpt--local-dir D:/AI/checkpoints/stable-...
Denoising Diffusion Step-aware Models 去噪扩散步长感知模型 论文链接 https://volctracer.com/w/AMsrUwWU 论文作者 Shuai Yang, Yukang Chen, Luozhou Wang, Shu Liu, Yingcong Chen 内容简介 本文提出了去噪扩散步长感知模型（DDSM），旨在解决去噪扩散概率模型（DDPMs）在数据生成过程中每一步都需要全网络计算...
换人换背景,5分钟学会,附赠AI秒扣图【stable diffusion】AI绘图step10:图生图蒙版应用,AI图片加人,AI换背景,AI秒扣图 3.2万 12 10:54 App 给我一张草稿,还你一幅美图!【stable diffusion】AI绘图step5: 认识和使用ControlNet插件,线稿秒变美图 4.2万 15 12:37 App 摆出你想要的造型!【stable diffusion...
Denoising Diffusion Step-aware Models论文下载 论文作者 Shuai Yang, Yukang Chen, Luozhou Wang, Shu Liu, Yingcong Chen 内容简介 本文提出了去噪扩散步长感知模型(DDSM),旨在解决去噪扩散概率模型(DDPMs)在数据生成过程中每一步都需要全网络计算导致的高计算开销问题。DDSM通过进化搜索确定每个生成步骤的重要性,并...
众所周知,step值能够影响画面的完成度(step越高,越会在后面的步数中倾向于渲染细节),cfg scale能够增加每个tag对画面整体的影响(cfg scale越高,tag权重和先后顺序的差异表现得越明显)。 下图是关于这两个参数的对照组: (一个大的xy图,但是分四次制作出来:) ...
论文介绍 One-step Diffusion with Distribution Matching Distillation 关注微信公众号: DeepGoAI 源码地址: https://tianweiy.github.io/dmd/ 论文地址: https://arxiv.org/abs/2311.18828 这篇论文介绍了一种新的图像生成方法,名为分布匹配蒸馏(DMD),将扩散模型转换为一步生成器,极大地加快了图像生成速度,同时...
A simplified process of making an insulated gate transistor entails forming the active regions in a single diffusion step. The method includes the steps of implanting and diffusing impurities of a first conductivity type (p for n-channel devices), implanting and diffusing a heavy dose of ...
DDSM: Denoising Diffusion Step-aware Models (ICLR 2024) Denoising Diffusion Step-aware Models Shuai Yang, Yukang Chen, Luozhou Wang, Shu Liu, Yingcong Chen [Paper] Our Strength 🚀 Achieve up to 76% reduction in computational costs for diffusion models without compromising on quality 🚀 Compat...
Diffusion models (DMs) have significantly advanced the development of real-world image super-resolution (Real-ISR), but the computational cost of multi-step diffusion models limits their application. One-step diffusion models generate high-quality images in a one sampling step, greatly reducing ...
We introduce DDSM, a novel framework optimizing diffusion models by dynamically adjusting neural network sizes per generative step, guided by evolutionary search. This method reduces computational load significantly—achieving up to 76% savings on tasks like ImageNet generation—without sacrificing generation...