Merge branch 'dev' into master
This commit is contained in:
bmaltais
GitHub
commit
fa92a5c1e3
@ -25,7 +25,7 @@ from library.common_gui import (
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gradio_config,
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gradio_source_model,
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set_legacy_8bitadam,
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update_optimizer,
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my_data,
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)
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from library.tensorboard_gui import (
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gradio_tensorboard,
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@ -214,7 +214,7 @@ def open_configuration(
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my_data = json.load(f)
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print('Loading config...')
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# Update values to fix deprecated use_8bit_adam checkbox and set appropriate optimizer if it is set to True
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my_data = update_optimizer(my_data)
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my_data = my_data(my_data)
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else:
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file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
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my_data = {}
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@ -19,7 +19,7 @@ from library.common_gui import (
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color_aug_changed,
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run_cmd_training,
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set_legacy_8bitadam,
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update_optimizer,
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my_data,
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)
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from library.tensorboard_gui import (
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gradio_tensorboard,
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@ -217,7 +217,7 @@ def open_config_file(
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my_data_db = json.load(f)
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print('Loading config...')
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# Update values to fix deprecated use_8bit_adam checkbox and set appropriate optimizer if it is set to True
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my_data = update_optimizer(my_data)
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my_data = my_data(my_data)
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else:
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file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
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my_data_db = {}
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@ -10,10 +10,14 @@ save_style_symbol = '\U0001f4be' # 💾
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document_symbol = '\U0001F4C4' # 📄
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def update_optimizer(my_data):
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def my_data(my_data):
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if my_data.get('use_8bit_adam', False):
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my_data['optimizer'] = 'AdamW8bit'
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my_data['use_8bit_adam'] = False
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if my_data.get('model_list', 'custom') == []:
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print('Old config with empty model list. Setting to custom...')
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my_data['model_list'] = 'custom'
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return my_data
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@ -25,7 +25,7 @@ def caption_images(
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return
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print(f'GIT captioning files in {train_data_dir}...')
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run_cmd = f'{PYTHON} "finetune/make_captions.py"'
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run_cmd = f'{PYTHON} "finetune/make_captions_by_git.py"'
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if not model_id == '':
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run_cmd += f' --model_id="{model_id}"'
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run_cmd += f' --batch_size="{int(batch_size)}"'
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@ -25,7 +25,7 @@ from library.common_gui import (
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gradio_source_model,
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run_cmd_training,
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set_legacy_8bitadam,
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update_optimizer,
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my_data,
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)
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from library.dreambooth_folder_creation_gui import (
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gradio_dreambooth_folder_creation_tab,
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@ -239,7 +239,7 @@ def open_configuration(
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my_data = json.load(f)
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print('Loading config...')
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# Update values to fix deprecated use_8bit_adam checkbox and set appropriate optimizer if it is set to True
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my_data = update_optimizer(my_data)
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my_data = my_data(my_data)
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else:
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file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
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my_data = {}
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@ -25,7 +25,7 @@ from library.common_gui import (
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gradio_config,
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gradio_source_model,
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set_legacy_8bitadam,
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update_optimizer,
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my_data,
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)
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from library.tensorboard_gui import (
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gradio_tensorboard,
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@ -226,7 +226,7 @@ def open_configuration(
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my_data_db = json.load(f)
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print('Loading config...')
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# Update values to fix deprecated use_8bit_adam checkbox and set appropriate optimizer if it is set to True
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my_data = update_optimizer(my_data)
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my_data = my_data(my_data)
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else:
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file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
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my_data_db = {}
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106
tools/extract_locon.py
Normal file
106
tools/extract_locon.py
Normal file
@ -0,0 +1,106 @@
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#
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# From: https://raw.githubusercontent.com/KohakuBlueleaf/LoCon/main/extract_locon.py
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#
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import argparse
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"base_model", help="The model which use it to train the dreambooth model",
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default='', type=str
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)
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parser.add_argument(
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"db_model", help="the dreambooth model you want to extract the locon",
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default='', type=str
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)
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parser.add_argument(
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"output_name", help="the output model",
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default='./out.pt', type=str
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)
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parser.add_argument(
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"--is_v2", help="Your base/db model is sd v2 or not",
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default=False, action="store_true"
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)
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parser.add_argument(
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"--device", help="Which device you want to use to extract the locon",
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default='cpu', type=str
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)
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parser.add_argument(
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"--mode",
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help=(
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'extraction mode, can be "fixed", "threshold", "ratio", "percentile". '
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'If not "fixed", network_dim and conv_dim will be ignored'
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),
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default='fixed', type=str
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)
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parser.add_argument(
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"--linear_dim", help="network dim for linear layer in fixed mode",
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default=1, type=int
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)
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parser.add_argument(
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"--conv_dim", help="network dim for conv layer in fixed mode",
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default=1, type=int
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)
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parser.add_argument(
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"--linear_threshold", help="singular value threshold for linear layer in threshold mode",
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default=0., type=float
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)
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parser.add_argument(
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"--conv_threshold", help="singular value threshold for conv layer in threshold mode",
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default=0., type=float
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)
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parser.add_argument(
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"--linear_ratio", help="singular ratio for linear layer in ratio mode",
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default=0., type=float
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)
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parser.add_argument(
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"--conv_ratio", help="singular ratio for conv layer in ratio mode",
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default=0., type=float
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)
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parser.add_argument(
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"--linear_percentile", help="singular value percentile for linear layer percentile mode",
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default=1., type=float
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)
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parser.add_argument(
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"--conv_percentile", help="singular value percentile for conv layer percentile mode",
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default=1., type=float
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)
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return parser.parse_args()
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ARGS = get_args()
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from locon.utils import extract_diff
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from locon.kohya_model_utils import load_models_from_stable_diffusion_checkpoint
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import torch
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def main():
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args = ARGS
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base = load_models_from_stable_diffusion_checkpoint(args.is_v2, args.base_model)
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db = load_models_from_stable_diffusion_checkpoint(args.is_v2, args.db_model)
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linear_mode_param = {
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'fixed': args.linear_dim,
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'threshold': args.linear_threshold,
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'ratio': args.linear_ratio,
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'percentile': args.linear_percentile,
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}[args.mode]
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conv_mode_param = {
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'fixed': args.conv_dim,
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'threshold': args.conv_threshold,
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'ratio': args.conv_ratio,
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'percentile': args.conv_percentile,
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}[args.mode]
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state_dict = extract_diff(
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base, db,
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args.mode,
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linear_mode_param, conv_mode_param,
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args.device
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)
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torch.save(state_dict, args.output_name)
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if __name__ == '__main__':
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main()
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339
tools/resize_lora.py
Normal file
339
tools/resize_lora.py
Normal file
@ -0,0 +1,339 @@
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#
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# File from: https://raw.githubusercontent.com/mgz-dev/sd-scripts/main/networks/resize_lora.py
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#
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# Convert LoRA to different rank approximation (should only be used to go to lower rank)
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# This code is based off the extract_lora_from_models.py file which is based on https://github.com/cloneofsimo/lora/blob/develop/lora_diffusion/cli_svd.py
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# Thanks to cloneofsimo and kohya
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import argparse
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import torch
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from safetensors.torch import load_file, save_file, safe_open
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from tqdm import tqdm
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from library import train_util, model_util
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import numpy as np
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MIN_SV = 1e-6
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def load_state_dict(file_name, dtype):
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if model_util.is_safetensors(file_name):
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sd = load_file(file_name)
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with safe_open(file_name, framework="pt") as f:
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metadata = f.metadata()
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else:
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sd = torch.load(file_name, map_location='cpu')
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metadata = None
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for key in list(sd.keys()):
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if type(sd[key]) == torch.Tensor:
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sd[key] = sd[key].to(dtype)
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return sd, metadata
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def save_to_file(file_name, model, state_dict, dtype, metadata):
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if dtype is not None:
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for key in list(state_dict.keys()):
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if type(state_dict[key]) == torch.Tensor:
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state_dict[key] = state_dict[key].to(dtype)
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if model_util.is_safetensors(file_name):
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save_file(model, file_name, metadata)
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else:
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torch.save(model, file_name)
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def index_sv_cumulative(S, target):
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original_sum = float(torch.sum(S))
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cumulative_sums = torch.cumsum(S, dim=0)/original_sum
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index = int(torch.searchsorted(cumulative_sums, target)) + 1
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if index >= len(S):
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index = len(S) - 1
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return index
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def index_sv_fro(S, target):
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S_squared = S.pow(2)
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s_fro_sq = float(torch.sum(S_squared))
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sum_S_squared = torch.cumsum(S_squared, dim=0)/s_fro_sq
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index = int(torch.searchsorted(sum_S_squared, target**2)) + 1
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if index >= len(S):
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index = len(S) - 1
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return index
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# Modified from Kohaku-blueleaf's extract/merge functions
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def extract_conv(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
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out_size, in_size, kernel_size, _ = weight.size()
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U, S, Vh = torch.linalg.svd(weight.reshape(out_size, -1).to(device))
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param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
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lora_rank = param_dict["new_rank"]
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U = U[:, :lora_rank]
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S = S[:lora_rank]
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U = U @ torch.diag(S)
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Vh = Vh[:lora_rank, :]
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param_dict["lora_down"] = Vh.reshape(lora_rank, in_size, kernel_size, kernel_size).cpu()
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param_dict["lora_up"] = U.reshape(out_size, lora_rank, 1, 1).cpu()
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del U, S, Vh, weight
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return param_dict
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def extract_linear(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
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out_size, in_size = weight.size()
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U, S, Vh = torch.linalg.svd(weight.to(device))
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param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
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lora_rank = param_dict["new_rank"]
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U = U[:, :lora_rank]
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S = S[:lora_rank]
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U = U @ torch.diag(S)
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Vh = Vh[:lora_rank, :]
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param_dict["lora_down"] = Vh.reshape(lora_rank, in_size).cpu()
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param_dict["lora_up"] = U.reshape(out_size, lora_rank).cpu()
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del U, S, Vh, weight
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return param_dict
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def merge_conv(lora_down, lora_up, device):
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in_rank, in_size, kernel_size, k_ = lora_down.shape
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out_size, out_rank, _, _ = lora_up.shape
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assert in_rank == out_rank and kernel_size == k_, f"rank {in_rank} {out_rank} or kernel {kernel_size} {k_} mismatch"
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lora_down = lora_down.to(device)
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lora_up = lora_up.to(device)
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merged = lora_up.reshape(out_size, -1) @ lora_down.reshape(in_rank, -1)
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weight = merged.reshape(out_size, in_size, kernel_size, kernel_size)
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del lora_up, lora_down
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return weight
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|
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|
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def merge_linear(lora_down, lora_up, device):
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in_rank, in_size = lora_down.shape
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out_size, out_rank = lora_up.shape
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assert in_rank == out_rank, f"rank {in_rank} {out_rank} mismatch"
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|
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lora_down = lora_down.to(device)
|
||||
lora_up = lora_up.to(device)
|
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|
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weight = lora_up @ lora_down
|
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del lora_up, lora_down
|
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return weight
|
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|
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|
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def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
|
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param_dict = {}
|
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|
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if dynamic_method=="sv_ratio":
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# Calculate new dim and alpha based off ratio
|
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max_sv = S[0]
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min_sv = max_sv/dynamic_param
|
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new_rank = max(torch.sum(S > min_sv).item(),1)
|
||||
new_alpha = float(scale*new_rank)
|
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|
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elif dynamic_method=="sv_cumulative":
|
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# Calculate new dim and alpha based off cumulative sum
|
||||
new_rank = index_sv_cumulative(S, dynamic_param)
|
||||
new_rank = max(new_rank, 1)
|
||||
new_alpha = float(scale*new_rank)
|
||||
|
||||
elif dynamic_method=="sv_fro":
|
||||
# Calculate new dim and alpha based off sqrt sum of squares
|
||||
new_rank = index_sv_fro(S, dynamic_param)
|
||||
new_rank = min(max(new_rank, 1), len(S)-1)
|
||||
new_alpha = float(scale*new_rank)
|
||||
else:
|
||||
new_rank = rank
|
||||
new_alpha = float(scale*new_rank)
|
||||
|
||||
|
||||
if S[0] <= MIN_SV: # Zero matrix, set dim to 1
|
||||
new_rank = 1
|
||||
new_alpha = float(scale*new_rank)
|
||||
elif new_rank > rank: # cap max rank at rank
|
||||
new_rank = rank
|
||||
new_alpha = float(scale*new_rank)
|
||||
|
||||
|
||||
# Calculate resize info
|
||||
s_sum = torch.sum(torch.abs(S))
|
||||
s_rank = torch.sum(torch.abs(S[:new_rank]))
|
||||
|
||||
S_squared = S.pow(2)
|
||||
s_fro = torch.sqrt(torch.sum(S_squared))
|
||||
s_red_fro = torch.sqrt(torch.sum(S_squared[:new_rank]))
|
||||
fro_percent = float(s_red_fro/s_fro)
|
||||
|
||||
param_dict["new_rank"] = new_rank
|
||||
param_dict["new_alpha"] = new_alpha
|
||||
param_dict["sum_retained"] = (s_rank)/s_sum
|
||||
param_dict["fro_retained"] = fro_percent
|
||||
param_dict["max_ratio"] = S[0]/S[new_rank]
|
||||
|
||||
return param_dict
|
||||
|
||||
|
||||
def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dynamic_param, verbose):
|
||||
network_alpha = None
|
||||
network_dim = None
|
||||
verbose_str = "\n"
|
||||
fro_list = []
|
||||
|
||||
# Extract loaded lora dim and alpha
|
||||
for key, value in lora_sd.items():
|
||||
if network_alpha is None and 'alpha' in key:
|
||||
network_alpha = value
|
||||
if network_dim is None and 'lora_down' in key and len(value.size()) == 2:
|
||||
network_dim = value.size()[0]
|
||||
if network_alpha is not None and network_dim is not None:
|
||||
break
|
||||
if network_alpha is None:
|
||||
network_alpha = network_dim
|
||||
|
||||
scale = network_alpha/network_dim
|
||||
|
||||
if dynamic_method:
|
||||
print(f"Dynamically determining new alphas and dims based off {dynamic_method}: {dynamic_param}, max rank is {new_rank}")
|
||||
|
||||
lora_down_weight = None
|
||||
lora_up_weight = None
|
||||
|
||||
o_lora_sd = lora_sd.copy()
|
||||
block_down_name = None
|
||||
block_up_name = None
|
||||
|
||||
with torch.no_grad():
|
||||
for key, value in tqdm(lora_sd.items()):
|
||||
if 'lora_down' in key:
|
||||
block_down_name = key.split(".")[0]
|
||||
lora_down_weight = value
|
||||
if 'lora_up' in key:
|
||||
block_up_name = key.split(".")[0]
|
||||
lora_up_weight = value
|
||||
|
||||
weights_loaded = (lora_down_weight is not None and lora_up_weight is not None)
|
||||
|
||||
if (block_down_name == block_up_name) and weights_loaded:
|
||||
|
||||
conv2d = (len(lora_down_weight.size()) == 4)
|
||||
|
||||
if conv2d:
|
||||
full_weight_matrix = merge_conv(lora_down_weight, lora_up_weight, device)
|
||||
param_dict = extract_conv(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
|
||||
else:
|
||||
full_weight_matrix = merge_linear(lora_down_weight, lora_up_weight, device)
|
||||
param_dict = extract_linear(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
|
||||
|
||||
if verbose:
|
||||
max_ratio = param_dict['max_ratio']
|
||||
sum_retained = param_dict['sum_retained']
|
||||
fro_retained = param_dict['fro_retained']
|
||||
if not np.isnan(fro_retained):
|
||||
fro_list.append(float(fro_retained))
|
||||
|
||||
verbose_str+=f"{block_down_name:75} | "
|
||||
verbose_str+=f"sum(S) retained: {sum_retained:.1%}, fro retained: {fro_retained:.1%}, max(S) ratio: {max_ratio:0.1f}"
|
||||
|
||||
if verbose and dynamic_method:
|
||||
verbose_str+=f", dynamic | dim: {param_dict['new_rank']}, alpha: {param_dict['new_alpha']}\n"
|
||||
else:
|
||||
verbose_str+=f"\n"
|
||||
|
||||
new_alpha = param_dict['new_alpha']
|
||||
o_lora_sd[block_down_name + "." + "lora_down.weight"] = param_dict["lora_down"].to(save_dtype).contiguous()
|
||||
o_lora_sd[block_up_name + "." + "lora_up.weight"] = param_dict["lora_up"].to(save_dtype).contiguous()
|
||||
o_lora_sd[block_up_name + "." "alpha"] = torch.tensor(param_dict['new_alpha']).to(save_dtype)
|
||||
|
||||
block_down_name = None
|
||||
block_up_name = None
|
||||
lora_down_weight = None
|
||||
lora_up_weight = None
|
||||
weights_loaded = False
|
||||
del param_dict
|
||||
|
||||
if verbose:
|
||||
print(verbose_str)
|
||||
|
||||
print(f"Average Frobenius norm retention: {np.mean(fro_list):.2%} | std: {np.std(fro_list):0.3f}")
|
||||
print("resizing complete")
|
||||
return o_lora_sd, network_dim, new_alpha
|
||||
|
||||
|
||||
def resize(args):
|
||||
|
||||
def str_to_dtype(p):
|
||||
if p == 'float':
|
||||
return torch.float
|
||||
if p == 'fp16':
|
||||
return torch.float16
|
||||
if p == 'bf16':
|
||||
return torch.bfloat16
|
||||
return None
|
||||
|
||||
if args.dynamic_method and not args.dynamic_param:
|
||||
raise Exception("If using dynamic_method, then dynamic_param is required")
|
||||
|
||||
merge_dtype = str_to_dtype('float') # matmul method above only seems to work in float32
|
||||
save_dtype = str_to_dtype(args.save_precision)
|
||||
if save_dtype is None:
|
||||
save_dtype = merge_dtype
|
||||
|
||||
print("loading Model...")
|
||||
lora_sd, metadata = load_state_dict(args.model, merge_dtype)
|
||||
|
||||
print("Resizing Lora...")
|
||||
state_dict, old_dim, new_alpha = resize_lora_model(lora_sd, args.new_rank, save_dtype, args.device, args.dynamic_method, args.dynamic_param, args.verbose)
|
||||
|
||||
# update metadata
|
||||
if metadata is None:
|
||||
metadata = {}
|
||||
|
||||
comment = metadata.get("ss_training_comment", "")
|
||||
|
||||
if not args.dynamic_method:
|
||||
metadata["ss_training_comment"] = f"dimension is resized from {old_dim} to {args.new_rank}; {comment}"
|
||||
metadata["ss_network_dim"] = str(args.new_rank)
|
||||
metadata["ss_network_alpha"] = str(new_alpha)
|
||||
else:
|
||||
metadata["ss_training_comment"] = f"Dynamic resize with {args.dynamic_method}: {args.dynamic_param} from {old_dim}; {comment}"
|
||||
metadata["ss_network_dim"] = 'Dynamic'
|
||||
metadata["ss_network_alpha"] = 'Dynamic'
|
||||
|
||||
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
|
||||
metadata["sshs_model_hash"] = model_hash
|
||||
metadata["sshs_legacy_hash"] = legacy_hash
|
||||
|
||||
print(f"saving model to: {args.save_to}")
|
||||
save_to_file(args.save_to, state_dict, state_dict, save_dtype, metadata)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
parser.add_argument("--save_precision", type=str, default=None,
|
||||
choices=[None, "float", "fp16", "bf16"], help="precision in saving, float if omitted / 保存時の精度、未指定時はfloat")
|
||||
parser.add_argument("--new_rank", type=int, default=4,
|
||||
help="Specify rank of output LoRA / 出力するLoRAのrank (dim)")
|
||||
parser.add_argument("--save_to", type=str, default=None,
|
||||
help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors")
|
||||
parser.add_argument("--model", type=str, default=None,
|
||||
help="LoRA model to resize at to new rank: ckpt or safetensors file / 読み込むLoRAモデル、ckptまたはsafetensors")
|
||||
parser.add_argument("--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う")
|
||||
parser.add_argument("--verbose", action="store_true",
|
||||
help="Display verbose resizing information / rank変更時の詳細情報を出力する")
|
||||
parser.add_argument("--dynamic_method", type=str, default=None, choices=[None, "sv_ratio", "sv_fro", "sv_cumulative"],
|
||||
help="Specify dynamic resizing method, --new_rank is used as a hard limit for max rank")
|
||||
parser.add_argument("--dynamic_param", type=float, default=None,
|
||||
help="Specify target for dynamic reduction")
|
||||
|
||||
|
||||
args = parser.parse_args()
|
||||
resize(args)
|
||||
Loading…
Reference in New Issue
Block a user