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add sdp cross-optimization

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This commit is contained in:
Vladimir Mandic
2023-03-06 16:52:48 -05:00
parent 5ec19418f1
commit 61aa82dcd0
7 changed files with 153 additions and 46 deletions
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cli/train-lora.py
+25 -12
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@@ -14,6 +14,7 @@ Disabled/broken:
"""
import os
import re
import gc
import sys
import json
@@ -127,12 +128,13 @@ def mem_stats():
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = 'train lora')
parser.add_argument('--model', type=str, default=None, required=True, help='original model to use a base for training')
parser.add_argument('--input', type=str, default=None, required=True, help='input folder with training images')
parser.add_argument('--output', type=str, default=None, required=True, help='lora name')
parser.add_argument('--model', type=str, default=None, required=False, help='original model to use a base for training, default: active model')
parser.add_argument('--input', '--dataset', type=str, default=None, required=True, help='input folder with training images')
parser.add_argument('--output', '--lora', type=str, default=None, required=True, help='lora name')
parser.add_argument('--tag', type=str, default=None, required=False, help='primary tag')
parser.add_argument('--dir', type=str, default=None, required=True, help='folder containing lora checkpoints')
parser.add_argument('--dir', type=str, default=None, required=False, help='folder containing lora checkpoints')
parser.add_argument('--interim', type=int, default=0, help = 'save interim checkpoints after n epoch')
parser.add_argument('--process', type=str, default='original', required=True, help='list of processing steps: original,face,body,blur,range,upscale,restore')
parser.add_argument('--noprocess', default = False, action='store_true', help = 'skip processing and use existing input data')
parser.add_argument('--notrain', default = False, action='store_true', help = 'just run processing and skip training')
parser.add_argument('--nocaptions', default = False, action='store_true', help = 'skip creating captions and tags')
@@ -152,19 +154,30 @@ if __name__ == '__main__':
parser.add_argument('--locon', default=False, action='store_true', help = "use locon style training")
parser.add_argument('--debug', default=False, action='store_true', help = "enable debug logging")
args = parser.parse_args()
defaults = Map({ 'options': {}, 'flags': {} }) if args.offline else Map(modules.sdapi.options())
if args.debug:
log.setLevel(logging.DEBUG)
log.debug({ 'debug': True })
if args.model is None:
args.model = defaults.options.get('sd_model_checkpoint', None)
args.model = args.model.split(' [')[0] if args.model is not None else None
if args.dir is None:
args.dir = defaults.flags.get('lora_dir', None)
if not os.path.isabs(args.model) and args.dir is not None and not os.path.exists(args.model):
args.model = os.path.abspath(os.path.join(args.dir, os.pardir, 'Stable-diffusion', args.model))
if args.dir is None:
args.dir = os.path.join(args.input, 'lora')
if not os.path.exists(args.model) or not os.path.isfile(args.model):
log.error({ 'lora cannot find model': args.model })
exit(1)
options.pretrained_model_name_or_path = args.model
if not os.path.exists(args.input) or not os.path.isdir(args.input):
log.error({ 'lora cannot find training dir': args.input })
exit(1)
if not os.path.exists(args.dir) or not os.path.isdir(args.dir):
log.error({ 'lora cannot find training dir': args.dir })
exit(1)
options.pretrained_model_name_or_path = args.model
options.output_dir = args.dir
options.output_name = args.output
options.max_train_steps = args.steps
@@ -200,9 +213,15 @@ if __name__ == '__main__':
if not args.noprocess:
# preprocess
processing_options = args.process.split(',')
processing_options = [opt.strip() for opt in re.split(',| ', args.process)]
log.info({ 'processing options': processing_options })
if os.path.exists(json_file):
os.remove(json_file)
for f in files:
try:
res, metadata = modules.process.process_file(f = f, dst = dir, preview = False, offline = args.offline, txt = args.dreambooth)
res, metadata = modules.process.process_file(f = f, dst = dir, preview = False, offline = args.offline, txt = args.dreambooth, tag = args.tag, opts = processing_options)
if not args.dreambooth:
with open(json_file, "w") as outfile:
outfile.write(json.dumps(metadata, indent=2))
@@ -210,12 +229,6 @@ if __name__ == '__main__':
exit(1)
modules.process.unload_models()
mem_stats()
if args.tag is not None:
for name, item in metadata.items():
item['caption'] = args.tag + ',' + item['caption']
item['tags'] = args.tag + ',' + item['tags']
with open(json_file, "w") as outfile:
outfile.write(json.dumps(metadata, indent=2))
if not args.nolatents and not args.dreambooth:
# create latents