it was already supporting
it was already supporting
Fi already have shown multiple times
Fbut now out of box
PYeah. I followed one of your tutorials that had torch2.0. I got it set up and then a git pull reverted me right back. Lol. Didn't have the heart to go through that again. I'll update in the morning when my render is done.
Fah i see what you mean. i dont do automatic updates D:
Fwould you guys be interested in deep voice cloning?
TI'm pulling my hair out here, where does the calculation come from that you see in the text file video????
https://youtu.be/mnCY8uM7E50?t=1153
https://youtu.be/mnCY8uM7E50?t=1153
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Fit is totally based on experience of users
Fbut the best way is
TI tried to get ChatGPT to explain dreambooth_training.py to me.
So far this worked well.
Then I asked what values I should put here, starting from 15 images of mine:
lr_warmup_steps, num_class_images, sample_batch_size, max_train_steps,save_interval
After analyzing all the code, I thought she should know on what bases the calculation is based. But apparently it doesn't
According to the calculation in the video it should be 15*12=180. But ChatGPT says 15...
Furthermore she says:
The number of class images --num_class_images should be set to 12, because this is the number of training images you have for your class.
The --sample_batch_size depends on how much GPU memory you have available. A batch size of 4 is relatively small and should be fine for most GPUs, but it depends on your specific configuration.
The --max_train_steps should be at least large enough to go through all the training frames once. Since you have 12 training frames and use a batch size of 1, you should set at least 12 steps, e.g. --max_train_steps=12.
The --save_interval depends on how often you want to save the model weights. A good rule is to save every few epochs. Since you only have 12 training frames, you might not want to train too many epochs to avoid overfitting. For example, if you want to train 3 epochs, you can use --max_train_steps=36 and --save_interval=12 to save the model after each epoch.
So far this worked well.
Then I asked what values I should put here, starting from 15 images of mine:
lr_warmup_steps, num_class_images, sample_batch_size, max_train_steps,save_interval
After analyzing all the code, I thought she should know on what bases the calculation is based. But apparently it doesn't
According to the calculation in the video it should be 15*12=180. But ChatGPT says 15...
Furthermore she says:
The number of class images --num_class_images should be set to 12, because this is the number of training images you have for your class.
The --sample_batch_size depends on how much GPU memory you have available. A batch size of 4 is relatively small and should be fine for most GPUs, but it depends on your specific configuration.
The --max_train_steps should be at least large enough to go through all the training frames once. Since you have 12 training frames and use a batch size of 1, you should set at least 12 steps, e.g. --max_train_steps=12.
The --save_interval depends on how often you want to save the model weights. A good rule is to save every few epochs. Since you only have 12 training frames, you might not want to train too many epochs to avoid overfitting. For example, if you want to train 3 epochs, you can use --max_train_steps=36 and --save_interval=12 to save the model after each epoch.
Fhaving checkpoints and comparing them
Fwhat gpu do you have?
TTrying the Google Colab currently.
TOn my local machines a RTX2080 Ti and on my Laptop RTX3070 8GB. I searching for a good eGPU
TOn Colab its the T4 - I used the link from the video
P3070 8gb is pretty good for local training. Just make sure you have a good cooling pad for the laptop.
TI can use Dreambooth on the RTX2080 - I tested it, but my results all where bad
Not because of the machine but because of my knowledge
Not because of the machine but because of my knowledge
PWhat are you trying to train? Yourself?
TYes at first myself and my son. And afterwards some Web UI
Fwell you have much better options
Fwith rtx 2080 because you can easily save checkpoints
Fand compare them
Fhave you installed dreambooth?
Fi can quickly install dreambooth on your machine
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#Kohya SS web GUI DreamBooth #LoRA training full tutorial. You don't need technical knowledge to follow this tutorial. In this tutorial I have explained how to generate professional photo studio quality portrait / self images for free with Stable Diffusion training.
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TYes I made that. But all of them are pretty bad. So I was wondering if it is because of my settings in the Colab.
Fye these settings also working great
PFor sure
F256 network size works even better
Fare you interested in learning fundementals?
TThe Kohya SS is running currently under my RTX2080. Im looking the video on my TV
But besides I wanted to start a training in the colab. So I can compare the LoRA and things I made with "standard" Dreambooth..
But besides I wanted to start a training in the colab. So I can compare the LoRA and things I made with "standard" Dreambooth..
TYes sure - I watched nearly all your last Videos. The
Fok with colab
Fhow much gdrive you have?
Tthing with the epochs and steps and number of images in both Videos is confusing me hahahaha
Tcurrently only the free space - I need to get a credit card first to use Colab Pro
Fye it is hard to understand
Fi explain many fundementals in this video actually
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Playlist of Stable Diffusion Tutorials, Automatic1111 and Google Colab ...
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Falso this video will help you greatly for x/y/z plot check point comparison logic
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TYesterday I made a training on the Colab, based on your Video with a friend of mine. But all of the results are really really uncanny
Fthe training dataset and number of epochs makes huge difference
TYour videos and results are great. The realistic portraits from Kohya you made are excellent.
Fi will show hopefully a way to
Fsort them with similarity
Fso you can generate thousands of images
Fand let AI the sort best ones for you
Fworking on it
Ii have ~20 images, so i have repeats on 30 for those and 1 for the regularization images.