Automatic1111 Stable Diffusion DreamBooth Guide: Optimal Classification Images Count Comparison Test

Sign up RunPod: https://bit.ly/RunPodIO. Our Discord : https://discord.gg/HbqgGaZVmr. New best training settings for DreamBooth training in Automatic1111 Web UI. If I have been of assistance to you and you would like to show your support for my work, please consider becoming a patron on 🥰 https://www.patreon.com/SECourses

Playlist of #StableDiffusion Tutorials, #Automatic1111 and Google Colab Guides, #DreamBooth, Textual Inversion / Embedding, LoRA, AI Upscaling, Pix2Pix, Img2Img: https://www.youtube.com/playlist?list=PL_pbwdIyffsmclLl0O144nQRnezKlNdx3

Easiest Way to Install & Run Stable Diffusion Web UI on PC by Using Open Source Automatic Installer: https://youtu.be/AZg6vzWHOTA

How to use Stable Diffusion V2.1 and Different Models in the Web UI - SD 1.5 vs 2.1 vs Anything V3: https://youtu.be/aAyvsX-EpG4

Zero To Hero Stable Diffusion DreamBooth Tutorial By Using Automatic1111 Web UI - Ultra Detailed: https://youtu.be/Bdl-jWR3Ukc

Sketches into Epic Art with 1 Click: A Guide to Stable Diffusion ControlNet in Automatic1111 Web UI: https://youtu.be/vhqqmkTBMlU

Ultimate RunPod Tutorial For Stable Diffusion - Automatic1111 - Data Transfers, Extensions, CivitAI: https://youtu.be/QN1vdGhjcRc

8 GB LoRA Training - Fix CUDA & xformers For DreamBooth and Textual Inversion in Automatic1111 SD UI: https://youtu.be/O01BrQwOd-Q

2400 Photo Of Man classification images: https://drive.google.com/file/d/1qBf8VyUbmPNalKqm076yOsQjE8BrcG7R/view

0:00 Introduction to Best Settings of DreamBooth training experiment 0:56 How to close initially started Web UI instance on RunPod Stable Diffusion template 2:20 Which RunPod machine you should pick for DreamBooth training and why 2:48 The used versions in this experiment such as Automatic1111 version, xformers version, DreamBooth version 4:20 Best DreamBooth settings for 0 classification images 4:45 How to continue DreamBooth training from a certain checkpoint 5:12 Used command line arguments for best DreamBooth training 5:20 Used extensions list for best DreamBooth training 5:45 Starting to set parameters for 0 classification images - equal to fine tuning 6:45 Used training dataset and what dataset features you need 7:45 Setting concepts tab of DreamBooth training 8:00 When you should use FileWords and why you should use for fine tuning and how to do fine tuning 10:15 Best training setup parameters for DreamBooth training when using classification images 11:28 How to calculate number of steps for each epoch 13:17 All trainings are completed 13:49 Comparison of sample and sanity sample images generated during training 13:55 Analysis of 0x classification samples 14:41 Analysis of 1x classification samples 15:14 Analysis of 2x classification samples 15:36 Analysis of 5x classification samples 16:12 Analysis of 10x classification samples 16:34 Analysis of 25x classification samples 16:45 Analysis of 50x classification samples 17:28 Analysis of 100x classification samples 17:49 Analysis of 100x classification samples 18:09 Comparing each checkpoint in all of the trained models 18:46 How to use x/y/z plot to check different training checkpoints 19:51 All grids are generated and how did i download them 20:40 Analysis of 0x classification x/y/z grid images 21:58 Analysis of 1x classification x/y/z grid images 23:10 Analysis of 2x classification x/y/z grid images 24:03 Analysis of 5x classification x/y/z grid images 25:00 Analysis of 10x classification x/y/z grid images 25:36 Analysis of 25x classification x/y/z grid images 26:15 Analysis of 50x classification x/y/z grid images 27:27 Analysis of 100x classification x/y/z grid images 28:02 Analysis of 100x classification x/y/z grid images 29:00 Summary of the experiment 29:40 Very important speech part

Text-Guided View Synthesis Our technique can synthesized images with specified viewpoints for a subject cat (left to right: top, bottom, side and back views). Note that the generated poses are different from the input poses, and the background changes in a realistic manner given a pose change. We also highlight the preservation of complex fur patterns on the subject cat's forehead.

Property Modification We show color modifications in the first row (using prompts a [color] [V] car''), and crosses between a specific dog and different animals in the second row (using prompts a cross of a [V] dog and a [target species]''). We highlight the fact that our method preserves unique visual features that give the subject its identity or essence, while performing the required property modification.

Accessorization Outfitting a dog with accessories. The identity of the subject is preserved and many different outfits or accessories can be applied to the dog given a prompt of type "a [V] dog wearing a police/chef/witch outfit''. We observe a realistic interaction between the subject dog and the outfits or accessories, as well as a large variety of possible options. ... https://www.youtube.com/watch?v=Tb4IYIYm4os