CAT pipeline

Lora Training

after adjusting the arguments in vanila_lora_train.py, run

accelerate launch vanila_lora_train.py

specific training and checkpoint saving

first set a directory to save your config, validation img and lora weight

you can use results dir already in the reposit

then for example

mkdir ./results/vanila_lora/pokemon_vanila_02012024
#create a config file for this training 
#set the output dir to the dir above
#set max_train_step and checkpointing_steps
#then for each checkpointing_steps, the program will save lora
#also validation prompts can be added
touch ./results/vanila_lora/pokemon_vanila_02012024/tuning_config.json
#then run train 
accelerate launch vanila_lora_train.py --tuning_config_path ./results/vanila_lora/pokemon_vanila_02012024/tuning_config.json
#change accelerate config to specify the devices to train

Dreambooth Training

just like lora, set saved dir and config file then run in python env where requirements are installed, for example

set (train_repeat + reg_repeat) * # of data images = max train step * # of processes

export CUDA_VISIBLE_DECIVES=2 && python dreambooth_train.py --tuning_config_path /data7/OnomaAi101/CAT/configs/dreambooth_tuning_config.json

for inference, adjust arguments of the following program and run

CAT Traing

the same as lora but use cat_tuning_config.json and set trigger word and cat factor

Inference

use test notebook by setting the environment and please set them neatly