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Comprehensive Guide to 160+ Best Stable Diffusion 1.5 Custom Models - Comparison and Download Script

Offered By: Software Engineering Courses - SE Courses via YouTube

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Stable Diffusion Courses Artificial Intelligence Courses Deep Learning Courses Computer Vision Courses Prompt Engineering Courses Image Generation Courses Machine Learning Models Courses

Course Description

Overview

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Explore a comprehensive tutorial on downloading and comparing over 160 top Stable Diffusion 1.5 custom models. Learn how to use a one-click script for efficient model acquisition, analyze X/Y/Z comparison grids, and understand the performance differences between various models. Gain insights into prompt variations, resolution capabilities, and the distinctions between overfit and generalized models. Discover techniques for managing downloads across different platforms and evaluate the disk space requirements for these extensive model collections.

Syllabus

Introduction to the very best Stable Diffusion 1.5 custom models hosted on CivitAI
How to 1 click download all of the 161+ SD 1.5 based models on to your computer
How to change the default download location for the models
How to start download progress of all SD 1.5 models
What to do if errors occurs during the download
How all of the downloaded models looks like in the folder
The disk space requirement of all of the models
How to disable download of the unwanted models
How to download models on RunPod - follow same strategy for all other platforms
How to download over 1 gigapixel X/Y/Z comparison images for all SD 1.5 models
Why I did test photo of man, picture of a man and image of a man prompts
How to open X/Y/Z grid files
Starting to analyze photo of a man X/Y/Z grid for 161 SD 1.5 very best models
Overfit vs generalized model comparison
What difference photo vs image vs picture prompt makes
Image of a man prompt results
Comparison of 1024x1024 pixel resolution capability of Stable Diffusion 1.5 based models
What happens when you generate images in a higher resolution
The models that can handle 1024x1024 pixel resolution output
What I am going to do as a next step


Taught by

Software Engineering Courses - SE Courses

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