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add training options

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This commit is contained in:
Vladimir Mandic
2023-05-21 09:44:36 -04:00
parent ea0780339a
commit d9647fd53e
3 changed files with 18 additions and 10 deletions
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cli/train/train.py
+16 -9
View File
@@ -82,7 +82,7 @@ def parse_args():
parser = argparse.ArgumentParser(description = 'Train')
group_main = parser.add_argument_group('Main')
group_main.add_argument('--type', type=str, choices=['embedding', 'lora', 'lycoris', 'dreambooth'], default=None, required=True, help='training type')
group_main.add_argument('--type', type=str, choices=['embedding', 'ti', 'lora', 'lyco', 'dreambooth', 'hypernetwork'], default=None, required=True, help='training type')
group_main.add_argument('--model', type=str, default='', required=False, help='base model to use for training, default: current loaded model')
group_main.add_argument('--name', type=str, default=None, required=True, help='output filename')
group_main.add_argument('--tag', type=str, default='person', required=False, help='primary tags, default: %(default)s')
@@ -97,11 +97,13 @@ def parse_args():
group_train.add_argument('--steps', type=int, default=2500, required=False, help='training steps, default: %(default)s')
group_train.add_argument('--batch', type=int, default=1, required=False, help='batch size, default: %(default)s')
group_train.add_argument('--lr', type=float, default=1e-04, required=False, help='model learning rate, default: %(default)s')
group_train.add_argument('--dim', type=int, default=40, required=False, help='network dimension or number of vectors, default: %(default)s')
group_train.add_argument('--dim', type=int, default=32, required=False, help='network dimension or number of vectors, default: %(default)s')
# lora params
group_train.add_argument('--repeats', type=int, default=10, required=False, help='number of repeats per image, default: %(default)s')
group_train.add_argument('--alpha', type=float, default=0, required=False, help='alpha for weights scaling, default: dim/2')
group_train.add_argument('--alpha', type=float, default=0, required=False, help='lora/lyco alpha for weights scaling, default: dim/2')
group_train.add_argument('--algo', type=str, default=None, choices=['locon', 'loha', 'lokr', 'ia3'], required=False, help='alternative lyco algoritm, default: %(default)s')
group_train.add_argument('--args', type=str, default=None, required=False, help='lora/lyco additional network arguments, default: %(default)s')
group_other = parser.add_argument_group('Other')
group_other.add_argument('--overwrite', default = False, action='store_true', help = "overwrite existing training, default: %(default)s")
@@ -239,13 +241,13 @@ def train_lora():
# lora imports
lora_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, 'modules', 'lora'))
sys.path.append(lora_path)
if args.type == 'lycoris':
if args.type == 'lyco':
lycoris_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir, 'modules', 'lycoris'))
sys.path.append(lycoris_path)
log.debug('importing lora lib')
import train_network
train_network.train(options.lora)
if args.type == 'lycoris':
if args.type == 'lyco':
log.debug('importing lycoris lib')
import importlib
_network_module = importlib.import_module(options.lora.network_module)
@@ -262,7 +264,7 @@ def prepare_options():
log.info('train using lora style training')
options.lora.output_dir = args.lora_dir
options.lora.in_json = os.path.join(args.process_dir, args.name + '.json')
if args.type == 'lycoris':
if args.type == 'lyco':
log.info('train using lycoris network')
options.lora.output_dir = args.lyco_dir
options.lora.network_module = 'lycoris.kohya'
@@ -273,10 +275,15 @@ def prepare_options():
options.lora.max_train_steps = args.steps
options.lora.network_dim = args.dim
options.lora.network_alpha = args.dim // 2 if args.alpha == 0 else args.alpha
options.lora.netwoork_args = []
if args.algo is not None:
options.lora.netwoork_args.append(f'algo={args.algo}')
if args.args is not None:
for net_arg in args.args:
options.lora.netwoork_args.append(net_arg)
options.lora.gradient_accumulation_steps = args.gradient
options.lora.learning_rate = args.lr
options.lora.train_batch_size = args.batch
options.lora.network_alpha = args.dim // 2 if args.alpha == 0 else args.alpha
options.lora.train_data_dir = args.process_dir
# embedding specific
options.embedding.embedding_name = args.name
@@ -322,7 +329,7 @@ def process_inputs():
concept = args.tag.split(',')[0].strip()
else:
concept = step
if args.type in ['lora', 'lycoris', 'dreambooth']:
if args.type in ['lora', 'lyco', 'dreambooth']:
folder = os.path.join(args.process_dir, str(args.repeats) + '_' + concept) # separate concepts per folder
if args.type in ['embedding']:
folder = os.path.join(args.process_dir) # everything into same folder
@@ -373,7 +380,7 @@ if __name__ == '__main__':
try:
if args.type == 'embedding':
train_embedding()
if args.type == 'lora' or args.type == 'lycoris' or args.type == 'dreambooth':
if args.type == 'lora' or args.type == 'lyco' or args.type == 'dreambooth':
train_lora()
except KeyboardInterrupt as e:
log.error('interrupt requested')
modules/cmd_args.py
+1
View File
@@ -107,6 +107,7 @@ def compatibility_args(opts, args):
opts.multiple_tqdm = False
opts.print_hypernet_extra = False
opts.dimensions_and_batch_together = True
opts.enable_pnginfo = True
args = parser.parse_args()
return args
modules/ui.py
+1 -1
View File
@@ -306,7 +306,7 @@ def create_sampler_and_steps_selection(choices, tabname):
chosen_sampler_name = modules.sd_samplers.samplers[0].name
sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=chosen_sampler_name if tabname == 'txt2img' else "Euler a", type="index")
steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
steps = gr.Slider(minimum=1, maximum=99, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
return steps, sampler_index