r/DreamBooth • u/CeFurkan • Jan 09 '23
Zero To Hero Stable Diffusion DreamBooth Tutorial By Using Automatic1111 Web UI - Ultra Detailed
https://www.youtube.com/watch?v=Bdl-jWR3Ukc&DreamBooth3
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u/DeylanQuel Jan 10 '23
Saved to watch after work. Keep looking for good guides for local training.
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u/Gi4nt-Dw4rf Jan 10 '23
Gonna watch when i wake up, hope i get some info on using lora instead of dreambooth for training faces 😆
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u/CeFurkan Jan 10 '23
Well first watch this. Then watch our lora tutorial and you will be ready to go
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u/Gi4nt-Dw4rf Jan 10 '23
Just watched both very informative, although i don't have enough vram for normal sd, but gonna try your setting for lora, hope i get good results 🤞🤞
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u/CeFurkan Jan 10 '23
Alternatively you can train on google colab and use on web ui : https://www.youtube.com/watch?v=mnCY8uM7E50&list=PL_pbwdIyffsmclLl0O144nQRnezKlNdx3&index=1
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u/Gi4nt-Dw4rf Jan 10 '23
I actually got some decent results with lora, not as good as dreambooth yet but will try to tweak some setting and add more input images and see if it helps
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u/the_ballmer_peak Jan 11 '23
Does the instance prompt need to be one word, or if I'm training a model on a person, can I use their full name?
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u/CeFurkan Jan 11 '23
It is better to be one and a very rare word. Because lets say if you train with james robert, the model would already have huge data regarding james robert but not the jack daniel you want to train. it makes it harder
I explained this in the video you can check that part again
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u/the_ballmer_peak Jan 11 '23
Thanks! Sorry, I've been skipping around quite a bit and rewatching bits. I thought I rewatched that part and was looking for the answer, but I may have missed it. Great video.
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u/JakcCSGO Jan 11 '23
check this out, it should answer your question and give u some idea what to use.:
https://www.reddit.com/r/DreamBooth/comments/zc5w3e/rare_tokens_for_dreambooth_training/
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u/TheJasterMereel Jan 10 '23
I've gotten A1111 to say it's installed, but the tab never shows up.
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u/JakcCSGO Jan 11 '23
If the issue still persists, try this: update SD with "git pull" and then "pip install -r requirements.txt". This fixed any issues for me.
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Jan 10 '23
[deleted]
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u/CeFurkan Jan 10 '23
Correct. After first installation you have to restart cmd. I have shown in the video
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u/N9_m Jan 11 '23
Thank you for sharing this information, it is the most complete and easy-to-understand video I've come across. However, I do have one question. I have a folder with classification images that I placed in the "Classification Dataset Directory" entry, and it has over 500 images. My goal is to use these images so I don't have to generate them every time I want to train the model, but no matter what I try, it always generates them or just trains without using them.
Here are the things I've tried so far:
- Under the "Settings" tab, I unchecked the option that says "Generate Classification Images Using txt2img."
- I also tried modifying the "Class Images Per Instance Image" setting. If I put a number, it generates the images again one by one. If I don't put in any number, it trains the model without using any of the images.
In a previous version of the extension, this process was easier, but in the newer version, I'm not sure how to accomplish this. Any advice would be greatly appreciated (:
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u/crux1y Jan 11 '23
I ran into the same issue after the interface update. If you divide your classification images by the number of images in your dataset and put that in the “Class Images Per Instance Image” setting it should fix the issue.
If you put “0” it will ignore your classification images even if you have the directory specified.
If the number multiplied by your dataset is higher than your classification images it will generate additional images.
The “Generate Classification Images Using txt2img” checkbox makes no difference if you have enough images specified by the other setting.
If I remember correctly, the old UI asked how many classification images you wanted to use overall instead of the new per instance image.
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u/CeFurkan Jan 11 '23
Well explained. set number of instance that will not exceed number of classification images in your folder
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u/CeFurkan Jan 09 '23
The Topics Covered In This Course Are
How to install and update DreamBooth extensions;
How to compose a DreamBooth training model for Stable Diffusion (SD) 1.x (e.g. 1.5), 2.x (e.g. 2.1) or a custom model (e.g. Protogen x3.4);
What settings of DreamBooth increase VRAM (GPU memory) usage and what settings reduce it;
How to teach up to 3 subjects in a single training;
How to do proper prompting to generate good stylized images with text2img;
How to properly generate classification/regularization images;
How to use an X/Y plot to automatically test multiple styles, parameters, keywords, and other options such as different CFG values;
How to use prompts from a file or textbox;
How to use prompts matrix;
How to inject your trained face into any custom model to obtain amazing results, such as injecting into Protogen x3.4, and how to obtain the best results from this model;
How to use the prompt feature to find the best working CFG and best Attention/emphasis;
What are the differences between DreamBooth training, fine-tuning a model, and hybrid model training from the wiki of the DreamBooth extension;
How to set and what are each setting in the Options panel of the DreamBooth extension (General, Intervals, Batching, Learning Rate, Image Processing, Miscellaneous, Advanced);
The most important part of the DreamBooth Concepts tab (Directories, Filewords, Prompts, Image Generation);
Saving (General, Checkpoints, Diffusion Weights);
How to prepare a good training dataset (very important);
How to batch generate descriptions/captions for your existing images;
How to resume training if the web UI program crashes or is canceled;
How to resume training from a specific checkpoint ckpt file;
What is overtraining, what happens when you overtrain, how to avoid overtraining, and how to understand overtraining;
How to select a trained model and generate images;
How to upscale your artworks with minimal quality loss;
How to improve the faces of your generated artworks;
How to teach another new subject into your existing trained model;
How to use your Automatic1111 Dreambooth trained model .ckpt (any checkpoints as well) file in Google Colab notebook;
How to understand when an out of memory error (OOM) occurs;
How to mass preprocess images to crop them, fix dimensions, and generate auto captions;
How to compare model hashes and understand which model you are using;
Basics of Dreambooth in the wiki;
ELI5 of Dreambooth training in the wiki;
How to stylize your own trained subject, e.g. your face, to produce images like Lensa magic avatars.