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---
# this is in yaml format. You can use json if you prefer
# I like both but yaml is easier to read and write
# plus it has comments which is nice for documentation
job: extract # tells the runner what to do
config:
# the name will be used to create a folder in the output folder
# it will also replace any [name] token in the rest of this config
name: name_of_your_model
# can be hugging face model, a .ckpt, or a .safetensors
base_model: "/path/to/base/model.safetensors"
# can be hugging face model, a .ckpt, or a .safetensors
extract_model: "/path/to/model/to/extract/trained.safetensors"
# we will create folder here with name above so. This will create /path/to/output/folder/name_of_your_model
output_folder: "/path/to/output/folder"
is_v2: false
dtype: fp16 # saved dtype
device: cpu # cpu, cuda:0, etc
# processes can be chained like this to run multiple in a row
# they must all use same models above, but great for testing different
# sizes and typed of extractions. It is much faster as we already have the models loaded
process:
# process 1
- type: locon # locon or lora (locon is lycoris)
filename: "[name]_64_32.safetensors" # will be put in output folder
dtype: fp16
mode: fixed
linear: 64
conv: 32
# process 2
- type: locon
output_path: "/absolute/path/for/this/output.safetensors" # can be absolute
mode: ratio
linear: 0.2
conv: 0.2
# process 3
- type: locon
filename: "[name]_ratio_02.safetensors"
mode: quantile
linear: 0.5
conv: 0.5
# process 4
- 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 # lora dim or rank
# no conv for lora
# process 5
- type: lora
filename: "[name]_q05.safetensors"
mode: quantile
linear: 0.5
# you can put any information you want here, and it will be saved in the model
# the below is an example. I recommend doing trigger words at a minimum
# in the metadata. The software will include this plus some other information
meta:
name: "[name]" # [name] gets replaced with the name above
description: A short description of your model
trigger_words:
- put
- trigger
- words
- here
version: '0.1'
creator:
name: Your Name
email: [email protected]
website: https://yourwebsite.com
any: All meta data above is arbitrary, it can be whatever you want.
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