anyone else have a bug where the first time you start to train it wont see the regularization images
anyone else have a bug where the first time you start to train it wont see the regularization images ?
Ioh for some reason the copy folder data didnt do reg image folder
Ihow did you say I could speed up training by increasing batch size or batches?
Iive been sharing your tutorials in all the SD groups im in
Ican you just keep training a lora and it will get better? or can you overbake them like embeddings?
KI put 1500 images in the regularization folder and SD still try to create more. Later I found out that some of my images are not 512x512 so I resized them and everything work fine. Also don't forget to put "/" before your directory path if you are using Runpod (such as: /workspace/stable-diffusion-webui/test)
Ihe said you dont have to resize images to train a face lora
Ihigher resolution better
KOh is that so? Did he mean training images or regularization images?
KAll methods can be overcooked. And in my experience it usually is around 150-200 epochs
Ihow can people have so many epochs? i do 4k steps in 3 epochs is this wrong?
Ii feel like it shouldnt be taking me this long to train loras for not very good results
Ais there a way to set up regular Dreambooth training on an 8GB VRAM machine? I know there's hardware limitations but maybe there's a command line arg i dont know about
Awhat does your data and settings look like? params wise.
KIt depends on many factors like how many training images, their goal, parameters, etc. Dr.Furkan has a very detail and useful comparison video you may watch it here when you have free time https://www.youtube.com/watch?v=sRdtVanSRl4
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Iis this for kohya ss?
KHi Dr.Furkan I have watched most of your Dreambooth videos but I didn't see anything about Gradient Accumulation. Have you tested it? Youtuber Olivio Sarikas recommends setting the gradient accumulation to half of the total number of images. I found some links about it but don't really understand though
https://www.reddit.com/r/StableDiffusion/comments/z60uxw/sd_v2_768_embeddings_with_gradient_accumulation/
https://huggingface.co/docs/accelerate/usage_guides/gradient_accumulation
https://www.reddit.com/r/StableDiffusion/comments/z60uxw/sd_v2_768_embeddings_with_gradient_accumulation/
https://huggingface.co/docs/accelerate/usage_guides/gradient_accumulation
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CHi, can anybody help me? I just watched one of your videos (Realistic Photos By Kohya LoRA Stable Diffusion Training) and it says to just type man or woman to generate regularization images. When I type woman 90% of the images are nudes, however, I noticed that all your man images had clothes will this affect my outcome?
Fare you doing this on kohya ss? what is your repeating count?
Fi prefer to not use it if possible because i remember there once was a bug with it which prevented training
Prpg_v4 + controlnet + multi_frame video rendering
Pfrom your tutorial https://youtu.be/kmT-z2lqEPQ
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Pusing a custom LoRa and going for more photoreal
Pworking on a new render now to add more facial expression using two controlnets
RSo good
Pthank you. it's been a loooong day.
Pit'll be 6 hours till the render is done for the new video. fingers crossed for a touch of temporal coherence
THey had the same yesterday after watching the tutorial. Have you looked at the prompt of the generated images?
I saw only by chance later that there were words in my prompt that I did not enter
This was due to the extension Unprompted, which I once installed. This added keywords like nude and modelshoot
I saw only by chance later that there were words in my prompt that I did not enter
This was due to the extension Unprompted, which I once installed. This added keywords like nude and modelshoot
TEven after deactivation the prompt was added, so I had to restart automatic
Rwhat kind of progress are you making with temporal coherence, is this a holy grail, or do you see smoothness in the horizon? Just wondering if its readily available provided time is invested. But what is the timeframe, a couple of days, weeks? mad scientist dedication?
RJust wondering if worth getting into for myself
Fautomatic1111 released a new release that is good
PI'm getting okay results. Really hit and miss depending on how complex the generation is. I'm by no means an expert at this stuff so it's a lot of trial and error. As for when we'll achieve true temporal coherence? Could be tomorrow could be next year. But I'm sure there's people much smarter than me working on it right now. I'm trying to learn it because I'm setting goals for myself with stable diffusion. Rather than just endlessly generating images. I want to progress with the tech.
MI keep getting errors like,
CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 10.76 GiB total capacity; 4.29 GiB already allocated; 10.12 MiB free; 4.46 GiB reserved in total by PyTorch)
I have a 8gb 3070ti, is there any reason for me not to be able to use Konya correctly?
CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 10.76 GiB total capacity; 4.29 GiB already allocated; 10.12 MiB free; 4.46 GiB reserved in total by PyTorch)
I have a 8gb 3070ti, is there any reason for me not to be able to use Konya correctly?
Rgood to know. it's good to focus on things, to not get caught up in the noise of everything that is happening
PFor sure
PIs this the fabled update that supports torch2.0?
Fyep
Fit 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.