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1、什么是Stable Diffusion？Stable Diffusion是一种文本生成图像的AI模型。它通过对互联网上的数百万个图像和文本描述对应上的训练，因此，该模型理解哪些文本描述与哪些图像相关联，具备很高的图像生成能力。因此，如果您输入“一张猫坐在建筑物顶部的照片(A Photo of a cat sitting on top of a building)”的...
This means that there are really lots of ways to use Stable Diffusion: you can download it and run it on your own computer, set up your own model using Leap AI, or use something like NightCafe to access the API. But the two simplest options—and the ones I'll walk you through here...
5. 条件控制(Conditioning) 上述对Stable Diffusion的理解还不完整。怎么通过文本提示来控制出图呢? 这就需要Conditioning,其目的是引导noise predictor,以便预测的噪声从图像中减掉后能得出我们想要的东西。 5.1 文本条件(text-to-image) 下图是文本提示(text prompt)被处理并喂给noise predictor的过程。Tokenizer先把提...
This repository contains a conversion tool, some examples, and instructions on how to set up Stable Diffusion with ONNX models. This was mainly intended for use with AMD GPUs but should work just as well with other DirectML devices (e.g. Intel Arc). I'd be very interested to hear of ...
DreamStudio grants users access to the latest version of Stable Diffusion models and allows them to generate an image in up to 15 seconds. DreamStudio user interface. Image source: DreamStudio. When writing this tutorial, new users receive 100 free credits to try out DreamStudio, which is eno...
官方论文:https://stable-diffusion-art.com/how-stable-diffusion-work/ 首先我们的文本包含正面提示词和负面提示词,通过text coder文本编码器(一般是CLIP模型)转换成固定长度的、易于电脑计算的矢量数据 ,然后与初始化噪声图像一起带入Unet模型网络进行多个采样迭代次数的去噪处理,去噪后的图像数据再通过VAE模型进行解...
How to Convert Stable Diffusion AI Video to Video Free 1. ControlNet-M2M script 2. Mov2mov extension 3. Temporal Kit Alternatives to Stable Diffusion AI 1. Deep Dream 2. Neural Style Transfer 3. CycleGAN Wrapping Up What Is Stable Diffusion AI ...
Related: How to Run Stable Diffusion Locally With a GUI on WindowsDon't be intimidated by the fact that Stable Diffusion currently runs in a command-line interface (CLI). Getting it up and running is pretty straight-forward. If you can double-click an executable and type in a box, you ...
Stable Diffusion所使用的是一个4x64x64的潜空间,相较于图像的像素空间【译注:512x512的图像】而言小了48倍。所有我们上一章节所描述的前向扩散与逆向扩散的过程都实际是在这个空间中完成的。 训练的过程也是一样的,训练流程中生成的并不是噪声图像,而是潜空间中的张量(即一个4x64x64的噪声图)。训练中像潜空...