Added info, config, etc for lora extracotr and slider trainer

README.md

@@ -1,60 +1,106 @@
 # AI Toolkit by Ostris
 
-WIP for now, but will be a collection of tools for AI tools as I need them.
+## IMPORTANT NOTE - READ THIS
+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.
 
 ## Installation
 
-I will try to update this to be more beginner-friendly, but for now I am assuming
+Requirements:
-a general understanding of python, pip, pytorch, and using virtual environments:
+- python >3.10
+- Nvidia GPU with enough ram to do what you need
+- python venv
+- git
+
+
 
 Linux:
-
 ```bash
+git clone https://github.com/ostris/ai-toolkit.git
+cd ai-toolkit
 git submodule update --init --recursive
-pythion3 -m venv venv
+python3 -m venv venv
 source venv/bin/activate
-pip install -r requirements.txt
+# or source venv/Scripts/activate on windows
-cd requirements/sd-scripts
+pip3 install -r requirements.txt
-pip install --no-deps -e .
-cd ../..
 ```
 
-Windows:
+---
-
-```bash
-git submodule update --init --recursive
-pythion3 -m venv venv
-venv\Scripts\activate
-pip install -r requirements.txt
-cd requirements/sd-scripts
-pip install --no-deps -e .
-cd ../..
-```
 
 ## Current Tools
 
-### LyCORIS extractor
+I 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.
 
-It is similar to the [LyCORIS](https://github.com/KohakuBlueleaf/LyCORIS) tool, but adding some QOL features.
+### LoRA (lierla), LoCON (LyCORIS) extractor
-It all runs off a config file, which you can find an example of in  `config/examples/locon_config.example.json`.
+
-Just copy that file, into the `config` folder, and rename it to `whatever_you_want.json`.
+It is based on the extractor in the [LyCORIS](https://github.com/KohakuBlueleaf/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:
 
 ```bash
-python3 run.py "whatever_you_want"
+python3 run.py config/whatever_you_want.yml
 ```
 
 You can also put a full path to a config file, if you want to keep it somewhere else.
 
 ```bash
-python3 run.py "/home/user/whatever_you_want.json"
+python3 run.py "/home/user/whatever_you_want.yml"
 ```
 
-File name is auto generated and dumped into the `output` folder. You can put whatever meta you want in the
+More notes on how it works are available in the example config file itself. LoRA and LoCON both support
-`meta` section of the config file, and it will be added to the metadata of the output file. I just have
+extractions of 'fixed', 'threshold', 'ratio', 'quantile'. I'll update what these do and mean later.
-some recommended fields in the example file. The script will add some other useful metadata as well.
+Most people used fixed, which is traditional fixed dimension extraction.
 
-process is an array or different processes to run on the conversion to test. You will normally just need one though.
+`process` is an array of different processes to run. You can add a few and mix and match. One LoRA, one LyCON, etc.
 
-Will update this later.
+
+### LoRA Slider Trainer
+
+This is how I train most of the recent sliders I have on Civitai, you can check them out in my [Civitai profile](https://civitai.com/user/Ostris/models).
+It is based off the work by [p1atdev/LECO](https://github.com/p1atdev/LECO) and [rohitgandikota/erasing](https://github.com/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:
+
+```bash
+python3 run.py config/whatever_you_want.yml
+```
+
+There 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:
+
+```bash
+python3 run.py config/examples/train_slider.example.yml
+```
+
+And 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. 
+
+---
+
+## WIP Tools
+
+
+### VAE (Variational Auto Encoder) Trainer
+
+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.
+
+<img src="https://raw.githubusercontent.com/ostris/ai-toolkit/main/assets/VAE_test1.jpg" width="768" height="auto"> 
 

config/examples/extract.example.yml

@@ -47,7 +47,8 @@ config:
   - type: lora  # traditional lora extraction (lierla) with linear layers only
     filename: "[name]_4.safetensors"
     mode: fixed  # fixed, ratio, quantile supported for lora as well
-    linear: 4
+    linear: 4 # lora dim or rank
+    # no conv for lora
 
   # process 5
   - type: lora

config/examples/train_slider.example.yml

@@ -33,7 +33,7 @@ config:
         # how many steps to train. More is not always better. I rarely go over 1000
         steps: 500
         # I have had good results with 4e-4 to 1e-4 at 500 steps
-        lr: 2e-4
+        lr: 1e-4
         # train the unet. I recommend leaving this true
         train_unet: true
         # train the text encoder. I don't recommend this unless you have a special use case
@@ -70,7 +70,7 @@ config:
       # saving config
       save:
         dtype: float16 # precision to save. I recommend float16
-        save_every: 100 # save every this many steps
+        save_every: 50 # save every this many steps
 
       # sampling config
       sample:
@@ -90,7 +90,7 @@ config:
         #  --n [string]  # negative prompt, will inherit sample.neg if not set
 
         # Only 75 tokens allowed currently
-        prompts:
+        prompts:  # our example is an animal slider, neg: dog, pos: cat
           - "a golden retriever --m -5"
           - "a golden retriever --m -3"
           - "a golden retriever --m 3"
@@ -99,6 +99,10 @@ config:
           - "calico cat --m -3"
           - "calico cat --m 3"
           - "calico cat --m 5"
+          - "an elephant --m -5"
+          - "an elephant --m -3"
+          - "an elephant --m 3"
+          - "an elephant --m 5"
         # negative prompt used on all prompts above as default if they don't have one
         neg: "cartoon, fake, drawing, illustration, cgi, animated, anime, monochrome"
         # seed for sampling. 42 is the answer for everything