I want to give a special thanks to Huggingface for their amazing work, and their continued support of my work. This repo would not be possible without them.
This is an active WIP repo that is not ready for others to use. And definitely not ready for non developers to use. I am making major breaking changes and pushing straight to master until I have it in a planned state. I have big changes planned for config files and the general structure. I may change how training works entirely. You are welcome to use but keep that in mind. If more people start to use it, I will follow better branch checkout standards, but for now this is my personal active experiment.
Report bugs as you find them, but not knowing how to train ML models, setup an environment, or use python is not a bug. I will make all of this more user-friendly eventually
I will make a better readme later.
Requirements:
- python >3.10
- Nvidia GPU with enough ram to do what you need
- python venv
- git
Linux:
git clone https://github.com/ostris/ai-toolkit.git
cd ai-toolkit
git submodule update --init --recursive
python3 -m venv venv
source venv/bin/activate
# .\venv\Scripts\activate on windows
# install torch first
pip3 install torch
pip3 install -r requirements.txtWindows:
git clone https://github.com/ostris/ai-toolkit.git
cd ai-toolkit
git submodule update --init --recursive
python -m venv venv
.\venv\Scripts\activate
pip install torch --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txtI have so many hodge podge scripts I am going to be moving over to this that I use in my ML work. But this is what is here so far.
A image generator that can take frompts from a config file or form a txt file and generate them to a
folder. I mainly needed this for an SDXL test I am doing but added some polish to it so it can be used
for generat batch image generation.
It all runs off a config file, which you can find an example of in config/examples/generate.example.yaml.
Mere info is in the comments in the example
It is based on the extractor in the LyCORIS tool, but adding some QOL features
and LoRA (lierla) support. It can do multiple types of extractions in one run.
It all runs off a config file, which you can find an example of in config/examples/extract.example.yml.
Just copy that file, into the config folder, and rename it to whatever_you_want.yml.
Then you can edit the file to your liking. and call it like so:
python3 run.py config/whatever_you_want.ymlYou can also put a full path to a config file, if you want to keep it somewhere else.
python3 run.py "/home/user/whatever_you_want.yml"More notes on how it works are available in the example config file itself. LoRA and LoCON both support extractions of 'fixed', 'threshold', 'ratio', 'quantile'. I'll update what these do and mean later. Most people used fixed, which is traditional fixed dimension extraction.
process is an array of different processes to run. You can add a few and mix and match. One LoRA, one LyCON, etc.
Change <lora:my_lora:4.6> to <lora:my_lora:1.0> or whatever you want with the same effect.
A tool for rescaling a LoRA's weights. Should would with LoCON as well, but I have not tested it.
It all runs off a config file, which you can find an example of in config/examples/mod_lora_scale.yml.
Just copy that file, into the config folder, and rename it to whatever_you_want.yml.
Then you can edit the file to your liking. and call it like so:
python3 run.py config/whatever_you_want.ymlYou can also put a full path to a config file, if you want to keep it somewhere else.
python3 run.py "/home/user/whatever_you_want.yml"More notes on how it works are available in the example config file itself. This is useful when making all LoRAs, as the ideal weight is rarely 1.0, but now you can fix that. For sliders, they can have weird scales form -2 to 2 or even -15 to 15. This will allow you to dile it in so they all have your desired scale
This is how I train most of the recent sliders I have on Civitai, you can check them out in my Civitai profile.
It is based off the work by p1atdev/LECO and rohitgandikota/erasing
But has been heavily modified to create sliders rather than erasing concepts. I have a lot more plans on this, but it is
very functional as is. It is also very easy to use. Just copy the example config file in config/examples/train_slider.example.yml
to the config folder and rename it to whatever_you_want.yml. Then you can edit the file to your liking. and call it like so:
python3 run.py config/whatever_you_want.ymlThere is a lot more information in that example file. You can even run the example as is without any modifications to see how it works. It will create a slider that turns all animals into dogs(neg) or cats(pos). Just run it like so:
python3 run.py config/examples/train_slider.example.ymlAnd you will be able to see how it works without configuring anything. No datasets are required for this method. I will post an better tutorial soon.
You can now make and share custom extensions. That run within this framework and have all the inbuilt tools
available to them. I will probably use this as the primary development method going
forward so I dont keep adding and adding more and more features to this base repo. I will likely migrate a lot
of the existing functionality as well to make everything modular. There is an example extension in the extensions
folder that shows how to make a model merger extension. All of the code is heavily documented which is hopefully
enough to get you started. To make an extension, just copy that example and replace all the things you need to.
It is located in the extensions folder. It is a fully finctional model merger that can merge as many models together
as you want. It is a good example of how to make an extension, but is also a pretty useful feature as well since most
mergers can only do one model at a time and this one will take as many as you want to feed it. There is an
example config file in there, just copy that to your config folder and rename it to whatever_you_want.yml.
and use it like any other config file.
This works, but is not ready for others to use and therefore does not have an example config. I am still working on it. I will update this when it is ready. I am adding a lot of features for criteria that I have used in my image enlargement work. A Critic (discriminator), content loss, style loss, and a few more. If you don't know, the VAE for stable diffusion (yes even the MSE one, and SDXL), are horrible at smaller faces and it holds SD back. I will fix this. I'll post more about this later with better examples later, but here is a quick test of a run through with various VAEs. Just went in and out. It is much worse on smaller faces than shown here.
- Add proper regs on sliders
- Add SDXL support (base model only for now)
- Add plain erasing
- Make Textual inversion network trainer (network that spits out TI embeddings)
- Huge memory rework and slider rework. Slider training is better thant ever with no more ram spikes. I also made it so all 4 parts of the slider algorythm run in one batch so they share gradient accumulation. This makes it much faster and more stable.
- Updated the example config to be something more practical and more updated to current methods. It is now a detail slide and shows how to train one without a subject. 512x512 slider training for 1.5 should work on 6GB gpu now. Will test soon to verify.
- Windows support bug fixes
- Extensions! Added functionality to make and share custom extensions for training, merging, whatever.
check out the example in the
extensionsfolder. Read more about that above. - Model Merging, provided via the example extension.
Another big refactor to make SD more modular.
Made batch image generation script
Major changes and update. New LoRA rescale tool, look above for details. Added better metadata so Automatic1111 knows what the base model is. Added some experiments and a ton of updates. This thing is still unstable at the moment, so hopefully there are not breaking changes.
Unfortunately, I am too lazy to write a proper changelog with all the changes.
I added SDXL training to sliders... but.. it does not work properly. The slider training relies on a model's ability to understand that an unconditional (negative prompt) means you do not want that concept in the output. SDXL does not understand this for whatever reason, which makes separating out concepts within the model hard. I am sure the community will find a way to fix this over time, but for now, it is not going to work properly. And if any of you are thinking "Could we maybe fix it by adding 1 or 2 more text encoders to the model as well as a few more entirely separate diffusion networks?" No. God no. It just needs a little training without every experimental new paper added to it. The KISS principal.
Added "anchors" to the slider trainer. This allows you to set a prompt that will be used as a regularizer. You can set the network multiplier to force spread consistency at high weights