Move functions to common_gui
Add model name support
This commit is contained in:
bmaltais
parent
fdb4508a62
commit
dc5afbb057
@ -101,6 +101,9 @@ Once you have created the LoRA network you can generate images via auto1111 by i
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## Change history
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* 2023/01/10 (v20.1):
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- Add support for `--output_name` to trainers
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- Refactor code for easier maintenance
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* 2023/01/10 (v20.0):
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- Update code base to match latest kohys_ss code upgrade in https://github.com/kohya-ss/sd-scripts
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* 2023/01/09 (v19.4.3):
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@ -18,6 +18,8 @@ from library.common_gui import (
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get_any_file_path,
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get_saveasfile_path,
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color_aug_changed,
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save_inference_file,
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set_pretrained_model_name_or_path_input,
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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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@ -102,45 +104,6 @@ def save_configuration(
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'save_as',
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]
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}
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# variables = {
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# 'pretrained_model_name_or_path': pretrained_model_name_or_path,
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# 'v2': v2,
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# 'v_parameterization': v_parameterization,
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# 'logging_dir': logging_dir,
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# 'train_data_dir': train_data_dir,
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# 'reg_data_dir': reg_data_dir,
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# 'output_dir': output_dir,
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# 'max_resolution': max_resolution,
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# 'learning_rate': learning_rate,
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# 'lr_scheduler': lr_scheduler,
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# 'lr_warmup': lr_warmup,
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# 'train_batch_size': train_batch_size,
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# 'epoch': epoch,
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# 'save_every_n_epochs': save_every_n_epochs,
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# 'mixed_precision': mixed_precision,
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# 'save_precision': save_precision,
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# 'seed': seed,
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# 'num_cpu_threads_per_process': num_cpu_threads_per_process,
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# 'cache_latent': cache_latent,
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# 'caption_extention': caption_extention,
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# 'enable_bucket': enable_bucket,
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# 'gradient_checkpointing': gradient_checkpointing,
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# 'full_fp16': full_fp16,
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# 'no_token_padding': no_token_padding,
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# 'stop_text_encoder_training': stop_text_encoder_training,
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# 'use_8bit_adam': use_8bit_adam,
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# 'xformers': xformers,
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# 'save_model_as': save_model_as,
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# 'shuffle_caption': shuffle_caption,
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# 'save_state': save_state,
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# 'resume': resume,
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# 'prior_loss_weight': prior_loss_weight,
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# 'color_aug': color_aug,
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# 'flip_aug': flip_aug,
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# 'clip_skip': clip_skip,
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# 'vae': vae,
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# 'output_name': output_name,
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# }
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# Save the data to the selected file
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with open(file_path, 'w') as file:
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@ -194,71 +157,24 @@ def open_configuration(
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original_file_path = file_path
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file_path = get_file_path(file_path)
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# print(file_path)
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if not file_path == '' and not file_path == None:
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# load variables from JSON file
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with open(file_path, 'r') as f:
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my_data = json.load(f)
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my_data_db = json.load(f)
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print("Loading config...")
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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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my_data_db = {}
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values = [file_path]
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for key, value in parameters:
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# Set the value in the dictionary to the corresponding value in `my_data`, or the default value if not found
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if not key in ['file_path']:
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values.append(my_data.get(key, value))
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# print(values)
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values.append(my_data_db.get(key, value))
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return tuple(values)
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# Return the values of the variables as a dictionary
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# return (
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# file_path,
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# my_data.get(
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# 'pretrained_model_name_or_path', pretrained_model_name_or_path
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# ),
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# my_data.get('v2', v2),
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# my_data.get('v_parameterization', v_parameterization),
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# my_data.get('logging_dir', logging_dir),
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# my_data.get('train_data_dir', train_data_dir),
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# my_data.get('reg_data_dir', reg_data_dir),
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# my_data.get('output_dir', output_dir),
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# my_data.get('max_resolution', max_resolution),
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# my_data.get('learning_rate', learning_rate),
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# my_data.get('lr_scheduler', lr_scheduler),
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# my_data.get('lr_warmup', lr_warmup),
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# my_data.get('train_batch_size', train_batch_size),
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# my_data.get('epoch', epoch),
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# my_data.get('save_every_n_epochs', save_every_n_epochs),
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# my_data.get('mixed_precision', mixed_precision),
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# my_data.get('save_precision', save_precision),
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# my_data.get('seed', seed),
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# my_data.get(
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# 'num_cpu_threads_per_process', num_cpu_threads_per_process
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# ),
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# my_data.get('cache_latent', cache_latent),
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# my_data.get('caption_extention', caption_extention),
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# my_data.get('enable_bucket', enable_bucket),
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# my_data.get('gradient_checkpointing', gradient_checkpointing),
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# my_data.get('full_fp16', full_fp16),
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# my_data.get('no_token_padding', no_token_padding),
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# my_data.get('stop_text_encoder_training', stop_text_encoder_training),
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# my_data.get('use_8bit_adam', use_8bit_adam),
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# my_data.get('xformers', xformers),
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# my_data.get('save_model_as', save_model_as),
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# my_data.get('shuffle_caption', shuffle_caption),
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# my_data.get('save_state', save_state),
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# my_data.get('resume', resume),
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# my_data.get('prior_loss_weight', prior_loss_weight),
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# my_data.get('color_aug', color_aug),
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# my_data.get('flip_aug', flip_aug),
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# my_data.get('clip_skip', clip_skip),
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# my_data.get('vae', vae),
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# my_data.get('output_name', output_name),
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# )
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def train_model(
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pretrained_model_name_or_path,
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v2,
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@ -298,29 +214,6 @@ def train_model(
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vae,
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output_name,
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):
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def save_inference_file(output_dir, v2, v_parameterization, output_name):
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# List all files in the directory
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files = os.listdir(output_dir)
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# Iterate over the list of files
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for file in files:
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# Check if the file starts with the value of save_inference_file
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if file.startswith(output_name):
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# Copy the v2-inference-v.yaml file to the current file, with a .yaml extension
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if v2 and v_parameterization:
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print(f'Saving v2-inference-v.yaml as {output_dir}/{file}.yaml')
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shutil.copy(
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f'./v2_inference/v2-inference-v.yaml',
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f'{output_dir}/{file}.yaml',
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)
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elif v2:
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print(f'Saving v2-inference.yaml as {output_dir}/{file}.yaml')
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shutil.copy(
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f'./v2_inference/v2-inference.yaml',
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f'{output_dir}/{file}.yaml',
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)
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if pretrained_model_name_or_path == '':
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msgbox('Source model information is missing')
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return
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@ -487,57 +380,6 @@ def train_model(
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save_inference_file(output_dir, v2, v_parameterization, output_name)
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def set_pretrained_model_name_or_path_input(value, v2, v_parameterization):
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# define a list of substrings to search for
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substrings_v2 = [
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'stabilityai/stable-diffusion-2-1-base',
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'stabilityai/stable-diffusion-2-base',
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]
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# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
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if str(value) in substrings_v2:
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print('SD v2 model detected. Setting --v2 parameter')
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v2 = True
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v_parameterization = False
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return value, v2, v_parameterization
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# define a list of substrings to search for v-objective
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substrings_v_parameterization = [
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'stabilityai/stable-diffusion-2-1',
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'stabilityai/stable-diffusion-2',
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]
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# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v_parameterization list
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if str(value) in substrings_v_parameterization:
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print(
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'SD v2 v_parameterization detected. Setting --v2 parameter and --v_parameterization'
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)
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v2 = True
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v_parameterization = True
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return value, v2, v_parameterization
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# define a list of substrings to v1.x
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substrings_v1_model = [
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'CompVis/stable-diffusion-v1-4',
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'runwayml/stable-diffusion-v1-5',
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]
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if str(value) in substrings_v1_model:
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v2 = False
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v_parameterization = False
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return value, v2, v_parameterization
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if value == 'custom':
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value = ''
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v2 = False
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v_parameterization = False
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return value, v2, v_parameterization
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def UI(username, password):
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css = ''
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@ -593,11 +435,6 @@ def dreambooth_tab(
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placeholder="type the configuration file path or use the 'Open' button above to select it...",
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interactive=True,
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)
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# config_file_name.change(
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# remove_doublequote,
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# inputs=[config_file_name],
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# outputs=[config_file_name],
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# )
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with gr.Tab('Source model'):
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# Define the input elements
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with gr.Row():
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681
fine_tune.py
681
fine_tune.py
@ -16,321 +16,456 @@ import library.train_util as train_util
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def collate_fn(examples):
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return examples[0]
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return examples[0]
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def train(args):
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train_util.verify_training_args(args)
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train_util.prepare_dataset_args(args, True)
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train_util.verify_training_args(args)
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train_util.prepare_dataset_args(args, True)
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cache_latents = args.cache_latents
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cache_latents = args.cache_latents
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if args.seed is not None:
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set_seed(args.seed) # 乱数系列を初期化する
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if args.seed is not None:
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set_seed(args.seed) # 乱数系列を初期化する
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tokenizer = train_util.load_tokenizer(args)
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tokenizer = train_util.load_tokenizer(args)
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train_dataset = train_util.FineTuningDataset(args.in_json, args.train_batch_size, args.train_data_dir,
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tokenizer, args.max_token_length, args.shuffle_caption, args.keep_tokens,
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args.resolution, args.enable_bucket, args.min_bucket_reso, args.max_bucket_reso,
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args.flip_aug, args.color_aug, args.face_crop_aug_range, args.random_crop,
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args.dataset_repeats, args.debug_dataset)
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train_dataset.make_buckets()
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train_dataset = train_util.FineTuningDataset(
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args.in_json,
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args.train_batch_size,
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args.train_data_dir,
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tokenizer,
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args.max_token_length,
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args.shuffle_caption,
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args.keep_tokens,
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args.resolution,
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args.enable_bucket,
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args.min_bucket_reso,
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args.max_bucket_reso,
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args.flip_aug,
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args.color_aug,
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args.face_crop_aug_range,
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args.random_crop,
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args.dataset_repeats,
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args.debug_dataset,
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)
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train_dataset.make_buckets()
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if args.debug_dataset:
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train_util.debug_dataset(train_dataset)
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return
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if len(train_dataset) == 0:
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print("No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。")
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return
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if args.debug_dataset:
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train_util.debug_dataset(train_dataset)
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return
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if len(train_dataset) == 0:
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print(
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'No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。'
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)
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return
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# acceleratorを準備する
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print("prepare accelerator")
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accelerator, unwrap_model = train_util.prepare_accelerator(args)
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# acceleratorを準備する
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print('prepare accelerator')
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accelerator, unwrap_model = train_util.prepare_accelerator(args)
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# mixed precisionに対応した型を用意しておき適宜castする
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weight_dtype, save_dtype = train_util.prepare_dtype(args)
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# mixed precisionに対応した型を用意しておき適宜castする
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weight_dtype, save_dtype = train_util.prepare_dtype(args)
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# モデルを読み込む
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text_encoder, vae, unet, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype)
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# モデルを読み込む
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(
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text_encoder,
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vae,
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unet,
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load_stable_diffusion_format,
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) = train_util.load_target_model(args, weight_dtype)
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# verify load/save model formats
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if load_stable_diffusion_format:
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src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
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src_diffusers_model_path = None
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else:
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src_stable_diffusion_ckpt = None
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src_diffusers_model_path = args.pretrained_model_name_or_path
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if args.save_model_as is None:
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save_stable_diffusion_format = load_stable_diffusion_format
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use_safetensors = args.use_safetensors
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else:
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save_stable_diffusion_format = args.save_model_as.lower() == 'ckpt' or args.save_model_as.lower() == 'safetensors'
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use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower())
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# Diffusers版のxformers使用フラグを設定する関数
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def set_diffusers_xformers_flag(model, valid):
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# model.set_use_memory_efficient_attention_xformers(valid) # 次のリリースでなくなりそう
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# pipeが自動で再帰的にset_use_memory_efficient_attention_xformersを探すんだって(;´Д｀)
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# U-Netだけ使う時にはどうすればいいのか……仕方ないからコピって使うか
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# 0.10.2でなんか巻き戻って個別に指定するようになった(;^ω^)
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# Recursively walk through all the children.
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# Any children which exposes the set_use_memory_efficient_attention_xformers method
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# gets the message
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def fn_recursive_set_mem_eff(module: torch.nn.Module):
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if hasattr(module, "set_use_memory_efficient_attention_xformers"):
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module.set_use_memory_efficient_attention_xformers(valid)
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for child in module.children():
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fn_recursive_set_mem_eff(child)
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fn_recursive_set_mem_eff(model)
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# モデルに xformers とか memory efficient attention を組み込む
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if args.diffusers_xformers:
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print("Use xformers by Diffusers")
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set_diffusers_xformers_flag(unet, True)
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else:
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# Windows版のxformersはfloatで学習できないのでxformersを使わない設定も可能にしておく必要がある
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print("Disable Diffusers' xformers")
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set_diffusers_xformers_flag(unet, False)
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train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
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# 学習を準備する
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if cache_latents:
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vae.to(accelerator.device, dtype=weight_dtype)
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vae.requires_grad_(False)
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vae.eval()
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with torch.no_grad():
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train_dataset.cache_latents(vae)
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vae.to("cpu")
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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gc.collect()
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# 学習を準備する：モデルを適切な状態にする
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training_models = []
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if args.gradient_checkpointing:
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unet.enable_gradient_checkpointing()
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training_models.append(unet)
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if args.train_text_encoder:
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print("enable text encoder training")
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if args.gradient_checkpointing:
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text_encoder.gradient_checkpointing_enable()
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training_models.append(text_encoder)
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else:
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text_encoder.to(accelerator.device, dtype=weight_dtype)
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text_encoder.requires_grad_(False) # text encoderは学習しない
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if args.gradient_checkpointing:
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text_encoder.gradient_checkpointing_enable()
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text_encoder.train() # required for gradient_checkpointing
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# verify load/save model formats
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if load_stable_diffusion_format:
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src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
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src_diffusers_model_path = None
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else:
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text_encoder.eval()
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src_stable_diffusion_ckpt = None
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src_diffusers_model_path = args.pretrained_model_name_or_path
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if not cache_latents:
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vae.requires_grad_(False)
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vae.eval()
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vae.to(accelerator.device, dtype=weight_dtype)
|
||||
if args.save_model_as is None:
|
||||
save_stable_diffusion_format = load_stable_diffusion_format
|
||||
use_safetensors = args.use_safetensors
|
||||
else:
|
||||
save_stable_diffusion_format = (
|
||||
args.save_model_as.lower() == 'ckpt'
|
||||
or args.save_model_as.lower() == 'safetensors'
|
||||
)
|
||||
use_safetensors = args.use_safetensors or (
|
||||
'safetensors' in args.save_model_as.lower()
|
||||
)
|
||||
|
||||
for m in training_models:
|
||||
m.requires_grad_(True)
|
||||
params = []
|
||||
for m in training_models:
|
||||
params.extend(m.parameters())
|
||||
params_to_optimize = params
|
||||
# Diffusers版のxformers使用フラグを設定する関数
|
||||
def set_diffusers_xformers_flag(model, valid):
|
||||
# model.set_use_memory_efficient_attention_xformers(valid) # 次のリリースでなくなりそう
|
||||
# pipeが自動で再帰的にset_use_memory_efficient_attention_xformersを探すんだって(;´Д｀)
|
||||
# U-Netだけ使う時にはどうすればいいのか……仕方ないからコピって使うか
|
||||
# 0.10.2でなんか巻き戻って個別に指定するようになった(;^ω^)
|
||||
|
||||
# 学習に必要なクラスを準備する
|
||||
print("prepare optimizer, data loader etc.")
|
||||
# Recursively walk through all the children.
|
||||
# Any children which exposes the set_use_memory_efficient_attention_xformers method
|
||||
# gets the message
|
||||
def fn_recursive_set_mem_eff(module: torch.nn.Module):
|
||||
if hasattr(module, 'set_use_memory_efficient_attention_xformers'):
|
||||
module.set_use_memory_efficient_attention_xformers(valid)
|
||||
|
||||
# 8-bit Adamを使う
|
||||
if args.use_8bit_adam:
|
||||
try:
|
||||
import bitsandbytes as bnb
|
||||
except ImportError:
|
||||
raise ImportError("No bitsand bytes / bitsandbytesがインストールされていないようです")
|
||||
print("use 8-bit Adam optimizer")
|
||||
optimizer_class = bnb.optim.AdamW8bit
|
||||
else:
|
||||
optimizer_class = torch.optim.AdamW
|
||||
for child in module.children():
|
||||
fn_recursive_set_mem_eff(child)
|
||||
|
||||
# betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略
|
||||
optimizer = optimizer_class(params_to_optimize, lr=args.learning_rate)
|
||||
fn_recursive_set_mem_eff(model)
|
||||
|
||||
# dataloaderを準備する
|
||||
# DataLoaderのプロセス数：0はメインプロセスになる
|
||||
n_workers = min(8, os.cpu_count() - 1) # cpu_count-1 ただし最大8
|
||||
train_dataloader = torch.utils.data.DataLoader(
|
||||
train_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn, num_workers=n_workers)
|
||||
# モデルに xformers とか memory efficient attention を組み込む
|
||||
if args.diffusers_xformers:
|
||||
print('Use xformers by Diffusers')
|
||||
set_diffusers_xformers_flag(unet, True)
|
||||
else:
|
||||
# Windows版のxformersはfloatで学習できないのでxformersを使わない設定も可能にしておく必要がある
|
||||
print("Disable Diffusers' xformers")
|
||||
set_diffusers_xformers_flag(unet, False)
|
||||
train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
|
||||
|
||||
# lr schedulerを用意する
|
||||
lr_scheduler = diffusers.optimization.get_scheduler(
|
||||
args.lr_scheduler, optimizer, num_warmup_steps=args.lr_warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps)
|
||||
|
||||
# 実験的機能：勾配も含めたfp16学習を行う モデル全体をfp16にする
|
||||
if args.full_fp16:
|
||||
assert args.mixed_precision == "fp16", "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
|
||||
print("enable full fp16 training.")
|
||||
unet.to(weight_dtype)
|
||||
text_encoder.to(weight_dtype)
|
||||
|
||||
# acceleratorがなんかよろしくやってくれるらしい
|
||||
if args.train_text_encoder:
|
||||
unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
unet, text_encoder, optimizer, train_dataloader, lr_scheduler)
|
||||
else:
|
||||
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
|
||||
|
||||
# 実験的機能：勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
|
||||
if args.full_fp16:
|
||||
train_util.patch_accelerator_for_fp16_training(accelerator)
|
||||
|
||||
# resumeする
|
||||
if args.resume is not None:
|
||||
print(f"resume training from state: {args.resume}")
|
||||
accelerator.load_state(args.resume)
|
||||
|
||||
# epoch数を計算する
|
||||
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
||||
num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
||||
|
||||
# 学習する
|
||||
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
||||
print("running training / 学習開始")
|
||||
print(f" num examples / サンプル数: {train_dataset.num_train_images}")
|
||||
print(f" num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
|
||||
print(f" num epochs / epoch数: {num_train_epochs}")
|
||||
print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
|
||||
print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
|
||||
print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
|
||||
print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
|
||||
|
||||
progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
|
||||
global_step = 0
|
||||
|
||||
noise_scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear",
|
||||
num_train_timesteps=1000, clip_sample=False)
|
||||
|
||||
if accelerator.is_main_process:
|
||||
accelerator.init_trackers("finetuning")
|
||||
|
||||
for epoch in range(num_train_epochs):
|
||||
print(f"epoch {epoch+1}/{num_train_epochs}")
|
||||
for m in training_models:
|
||||
m.train()
|
||||
|
||||
loss_total = 0
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
with accelerator.accumulate(training_models[0]): # 複数モデルに対応していない模様だがとりあえずこうしておく
|
||||
# 学習を準備する
|
||||
if cache_latents:
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
with torch.no_grad():
|
||||
if "latents" in batch and batch["latents"] is not None:
|
||||
latents = batch["latents"].to(accelerator.device)
|
||||
else:
|
||||
# latentに変換
|
||||
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
|
||||
latents = latents * 0.18215
|
||||
b_size = latents.shape[0]
|
||||
train_dataset.cache_latents(vae)
|
||||
vae.to('cpu')
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
gc.collect()
|
||||
|
||||
with torch.set_grad_enabled(args.train_text_encoder):
|
||||
# Get the text embedding for conditioning
|
||||
input_ids = batch["input_ids"].to(accelerator.device)
|
||||
encoder_hidden_states = train_util.get_hidden_states(
|
||||
args, input_ids, tokenizer, text_encoder, None if not args.full_fp16 else weight_dtype)
|
||||
# 学習を準備する：モデルを適切な状態にする
|
||||
training_models = []
|
||||
if args.gradient_checkpointing:
|
||||
unet.enable_gradient_checkpointing()
|
||||
training_models.append(unet)
|
||||
|
||||
# Sample noise that we'll add to the latents
|
||||
noise = torch.randn_like(latents, device=latents.device)
|
||||
|
||||
# Sample a random timestep for each image
|
||||
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
|
||||
timesteps = timesteps.long()
|
||||
|
||||
# Add noise to the latents according to the noise magnitude at each timestep
|
||||
# (this is the forward diffusion process)
|
||||
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
||||
|
||||
# Predict the noise residual
|
||||
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
||||
|
||||
if args.v_parameterization:
|
||||
# v-parameterization training
|
||||
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
||||
if args.train_text_encoder:
|
||||
print('enable text encoder training')
|
||||
if args.gradient_checkpointing:
|
||||
text_encoder.gradient_checkpointing_enable()
|
||||
training_models.append(text_encoder)
|
||||
else:
|
||||
text_encoder.to(accelerator.device, dtype=weight_dtype)
|
||||
text_encoder.requires_grad_(False) # text encoderは学習しない
|
||||
if args.gradient_checkpointing:
|
||||
text_encoder.gradient_checkpointing_enable()
|
||||
text_encoder.train() # required for gradient_checkpointing
|
||||
else:
|
||||
target = noise
|
||||
text_encoder.eval()
|
||||
|
||||
loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="mean")
|
||||
if not cache_latents:
|
||||
vae.requires_grad_(False)
|
||||
vae.eval()
|
||||
vae.to(accelerator.device, dtype=weight_dtype)
|
||||
|
||||
accelerator.backward(loss)
|
||||
if accelerator.sync_gradients:
|
||||
params_to_clip = []
|
||||
for m in training_models:
|
||||
params_to_clip.extend(m.parameters())
|
||||
accelerator.clip_grad_norm_(params_to_clip, 1.0) # args.max_grad_norm)
|
||||
for m in training_models:
|
||||
m.requires_grad_(True)
|
||||
params = []
|
||||
for m in training_models:
|
||||
params.extend(m.parameters())
|
||||
params_to_optimize = params
|
||||
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
# 学習に必要なクラスを準備する
|
||||
print('prepare optimizer, data loader etc.')
|
||||
|
||||
# Checks if the accelerator has performed an optimization step behind the scenes
|
||||
if accelerator.sync_gradients:
|
||||
progress_bar.update(1)
|
||||
global_step += 1
|
||||
# 8-bit Adamを使う
|
||||
if args.use_8bit_adam:
|
||||
try:
|
||||
import bitsandbytes as bnb
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
'No bitsand bytes / bitsandbytesがインストールされていないようです'
|
||||
)
|
||||
print('use 8-bit Adam optimizer')
|
||||
optimizer_class = bnb.optim.AdamW8bit
|
||||
else:
|
||||
optimizer_class = torch.optim.AdamW
|
||||
|
||||
current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず
|
||||
if args.logging_dir is not None:
|
||||
logs = {"loss": current_loss, "lr": lr_scheduler.get_last_lr()[0]}
|
||||
accelerator.log(logs, step=global_step)
|
||||
# betaやweight decayはdiffusers DreamBoothもDreamBooth SDもデフォルト値のようなのでオプションはとりあえず省略
|
||||
optimizer = optimizer_class(params_to_optimize, lr=args.learning_rate)
|
||||
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / (step+1)
|
||||
logs = {"loss": avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
# dataloaderを準備する
|
||||
# DataLoaderのプロセス数：0はメインプロセスになる
|
||||
n_workers = min(8, os.cpu_count() - 1) # cpu_count-1 ただし最大8
|
||||
train_dataloader = torch.utils.data.DataLoader(
|
||||
train_dataset,
|
||||
batch_size=1,
|
||||
shuffle=False,
|
||||
collate_fn=collate_fn,
|
||||
num_workers=n_workers,
|
||||
)
|
||||
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
# lr schedulerを用意する
|
||||
lr_scheduler = diffusers.optimization.get_scheduler(
|
||||
args.lr_scheduler,
|
||||
optimizer,
|
||||
num_warmup_steps=args.lr_warmup_steps,
|
||||
num_training_steps=args.max_train_steps
|
||||
* args.gradient_accumulation_steps,
|
||||
)
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {"epoch_loss": loss_total / len(train_dataloader)}
|
||||
accelerator.log(logs, step=epoch+1)
|
||||
# 実験的機能：勾配も含めたfp16学習を行う モデル全体をfp16にする
|
||||
if args.full_fp16:
|
||||
assert (
|
||||
args.mixed_precision == 'fp16'
|
||||
), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
|
||||
print('enable full fp16 training.')
|
||||
unet.to(weight_dtype)
|
||||
text_encoder.to(weight_dtype)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
# acceleratorがなんかよろしくやってくれるらしい
|
||||
if args.train_text_encoder:
|
||||
(
|
||||
unet,
|
||||
text_encoder,
|
||||
optimizer,
|
||||
train_dataloader,
|
||||
lr_scheduler,
|
||||
) = accelerator.prepare(
|
||||
unet, text_encoder, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
else:
|
||||
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
||||
unet, optimizer, train_dataloader, lr_scheduler
|
||||
)
|
||||
|
||||
if args.save_every_n_epochs is not None:
|
||||
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
|
||||
train_util.save_sd_model_on_epoch_end(args, accelerator, src_path, save_stable_diffusion_format, use_safetensors,
|
||||
save_dtype, epoch, num_train_epochs, global_step, unwrap_model(text_encoder), unwrap_model(unet), vae)
|
||||
# 実験的機能：勾配も含めたfp16学習を行う PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
|
||||
if args.full_fp16:
|
||||
train_util.patch_accelerator_for_fp16_training(accelerator)
|
||||
|
||||
is_main_process = accelerator.is_main_process
|
||||
if is_main_process:
|
||||
unet = unwrap_model(unet)
|
||||
text_encoder = unwrap_model(text_encoder)
|
||||
# resumeする
|
||||
if args.resume is not None:
|
||||
print(f'resume training from state: {args.resume}')
|
||||
accelerator.load_state(args.resume)
|
||||
|
||||
accelerator.end_training()
|
||||
# epoch数を計算する
|
||||
num_update_steps_per_epoch = math.ceil(
|
||||
len(train_dataloader) / args.gradient_accumulation_steps
|
||||
)
|
||||
num_train_epochs = math.ceil(
|
||||
args.max_train_steps / num_update_steps_per_epoch
|
||||
)
|
||||
|
||||
if args.save_state:
|
||||
train_util.save_state_on_train_end(args, accelerator)
|
||||
# 学習する
|
||||
total_batch_size = (
|
||||
args.train_batch_size
|
||||
* accelerator.num_processes
|
||||
* args.gradient_accumulation_steps
|
||||
)
|
||||
print('running training / 学習開始')
|
||||
print(f' num examples / サンプル数: {train_dataset.num_train_images}')
|
||||
print(f' num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}')
|
||||
print(f' num epochs / epoch数: {num_train_epochs}')
|
||||
print(f' batch size per device / バッチサイズ: {args.train_batch_size}')
|
||||
print(
|
||||
f' total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}'
|
||||
)
|
||||
print(
|
||||
f' gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}'
|
||||
)
|
||||
print(f' total optimization steps / 学習ステップ数: {args.max_train_steps}')
|
||||
|
||||
del accelerator # この後メモリを使うのでこれは消す
|
||||
progress_bar = tqdm(
|
||||
range(args.max_train_steps),
|
||||
smoothing=0,
|
||||
disable=not accelerator.is_local_main_process,
|
||||
desc='steps',
|
||||
)
|
||||
global_step = 0
|
||||
|
||||
if is_main_process:
|
||||
src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
|
||||
train_util.save_sd_model_on_train_end(args, src_path, save_stable_diffusion_format, use_safetensors,
|
||||
save_dtype, epoch, global_step, text_encoder, unet, vae)
|
||||
print("model saved.")
|
||||
noise_scheduler = DDPMScheduler(
|
||||
beta_start=0.00085,
|
||||
beta_end=0.012,
|
||||
beta_schedule='scaled_linear',
|
||||
num_train_timesteps=1000,
|
||||
clip_sample=False,
|
||||
)
|
||||
|
||||
if accelerator.is_main_process:
|
||||
accelerator.init_trackers('finetuning')
|
||||
|
||||
for epoch in range(num_train_epochs):
|
||||
print(f'epoch {epoch+1}/{num_train_epochs}')
|
||||
for m in training_models:
|
||||
m.train()
|
||||
|
||||
loss_total = 0
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
with accelerator.accumulate(
|
||||
training_models[0]
|
||||
): # 複数モデルに対応していない模様だがとりあえずこうしておく
|
||||
with torch.no_grad():
|
||||
if 'latents' in batch and batch['latents'] is not None:
|
||||
latents = batch['latents'].to(accelerator.device)
|
||||
else:
|
||||
# latentに変換
|
||||
latents = vae.encode(
|
||||
batch['images'].to(dtype=weight_dtype)
|
||||
).latent_dist.sample()
|
||||
latents = latents * 0.18215
|
||||
b_size = latents.shape[0]
|
||||
|
||||
with torch.set_grad_enabled(args.train_text_encoder):
|
||||
# Get the text embedding for conditioning
|
||||
input_ids = batch['input_ids'].to(accelerator.device)
|
||||
encoder_hidden_states = train_util.get_hidden_states(
|
||||
args,
|
||||
input_ids,
|
||||
tokenizer,
|
||||
text_encoder,
|
||||
None if not args.full_fp16 else weight_dtype,
|
||||
)
|
||||
|
||||
# Sample noise that we'll add to the latents
|
||||
noise = torch.randn_like(latents, device=latents.device)
|
||||
|
||||
# Sample a random timestep for each image
|
||||
timesteps = torch.randint(
|
||||
0,
|
||||
noise_scheduler.config.num_train_timesteps,
|
||||
(b_size,),
|
||||
device=latents.device,
|
||||
)
|
||||
timesteps = timesteps.long()
|
||||
|
||||
# Add noise to the latents according to the noise magnitude at each timestep
|
||||
# (this is the forward diffusion process)
|
||||
noisy_latents = noise_scheduler.add_noise(
|
||||
latents, noise, timesteps
|
||||
)
|
||||
|
||||
# Predict the noise residual
|
||||
noise_pred = unet(
|
||||
noisy_latents, timesteps, encoder_hidden_states
|
||||
).sample
|
||||
|
||||
if args.v_parameterization:
|
||||
# v-parameterization training
|
||||
target = noise_scheduler.get_velocity(
|
||||
latents, noise, timesteps
|
||||
)
|
||||
else:
|
||||
target = noise
|
||||
|
||||
loss = torch.nn.functional.mse_loss(
|
||||
noise_pred.float(), target.float(), reduction='mean'
|
||||
)
|
||||
|
||||
accelerator.backward(loss)
|
||||
if accelerator.sync_gradients:
|
||||
params_to_clip = []
|
||||
for m in training_models:
|
||||
params_to_clip.extend(m.parameters())
|
||||
accelerator.clip_grad_norm_(
|
||||
params_to_clip, 1.0
|
||||
) # args.max_grad_norm)
|
||||
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
optimizer.zero_grad(set_to_none=True)
|
||||
|
||||
# Checks if the accelerator has performed an optimization step behind the scenes
|
||||
if accelerator.sync_gradients:
|
||||
progress_bar.update(1)
|
||||
global_step += 1
|
||||
|
||||
current_loss = loss.detach().item() # 平均なのでbatch sizeは関係ないはず
|
||||
if args.logging_dir is not None:
|
||||
logs = {
|
||||
'loss': current_loss,
|
||||
'lr': lr_scheduler.get_last_lr()[0],
|
||||
}
|
||||
accelerator.log(logs, step=global_step)
|
||||
|
||||
loss_total += current_loss
|
||||
avr_loss = loss_total / (step + 1)
|
||||
logs = {'loss': avr_loss} # , "lr": lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
|
||||
if args.logging_dir is not None:
|
||||
logs = {'epoch_loss': loss_total / len(train_dataloader)}
|
||||
accelerator.log(logs, step=epoch + 1)
|
||||
|
||||
accelerator.wait_for_everyone()
|
||||
|
||||
if args.save_every_n_epochs is not None:
|
||||
src_path = (
|
||||
src_stable_diffusion_ckpt
|
||||
if save_stable_diffusion_format
|
||||
else src_diffusers_model_path
|
||||
)
|
||||
train_util.save_sd_model_on_epoch_end(
|
||||
args,
|
||||
accelerator,
|
||||
src_path,
|
||||
save_stable_diffusion_format,
|
||||
use_safetensors,
|
||||
save_dtype,
|
||||
epoch,
|
||||
num_train_epochs,
|
||||
global_step,
|
||||
unwrap_model(text_encoder),
|
||||
unwrap_model(unet),
|
||||
vae,
|
||||
)
|
||||
|
||||
is_main_process = accelerator.is_main_process
|
||||
if is_main_process:
|
||||
unet = unwrap_model(unet)
|
||||
text_encoder = unwrap_model(text_encoder)
|
||||
|
||||
accelerator.end_training()
|
||||
|
||||
if args.save_state:
|
||||
train_util.save_state_on_train_end(args, accelerator)
|
||||
|
||||
del accelerator # この後メモリを使うのでこれは消す
|
||||
|
||||
if is_main_process:
|
||||
src_path = (
|
||||
src_stable_diffusion_ckpt
|
||||
if save_stable_diffusion_format
|
||||
else src_diffusers_model_path
|
||||
)
|
||||
train_util.save_sd_model_on_train_end(
|
||||
args,
|
||||
src_path,
|
||||
save_stable_diffusion_format,
|
||||
use_safetensors,
|
||||
save_dtype,
|
||||
epoch,
|
||||
global_step,
|
||||
text_encoder,
|
||||
unet,
|
||||
vae,
|
||||
)
|
||||
print('model saved.')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
train_util.add_sd_models_arguments(parser)
|
||||
train_util.add_dataset_arguments(parser, False, True)
|
||||
train_util.add_training_arguments(parser, False)
|
||||
train_util.add_sd_saving_arguments(parser)
|
||||
train_util.add_sd_models_arguments(parser)
|
||||
train_util.add_dataset_arguments(parser, False, True)
|
||||
train_util.add_training_arguments(parser, False)
|
||||
train_util.add_sd_saving_arguments(parser)
|
||||
|
||||
parser.add_argument("--diffusers_xformers", action='store_true',
|
||||
help='use xformers by diffusers / Diffusersでxformersを使用する')
|
||||
parser.add_argument("--train_text_encoder", action="store_true", help="train text encoder / text encoderも学習する")
|
||||
parser.add_argument(
|
||||
'--diffusers_xformers',
|
||||
action='store_true',
|
||||
help='use xformers by diffusers / Diffusersでxformersを使用する',
|
||||
)
|
||||
parser.add_argument(
|
||||
'--train_text_encoder',
|
||||
action='store_true',
|
||||
help='train text encoder / text encoderも学習する',
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
train(args)
|
||||
args = parser.parse_args()
|
||||
train(args)
|
||||
|
||||
203
finetune_gui.py
203
finetune_gui.py
@ -11,6 +11,8 @@ from library.common_gui import (
|
||||
get_file_path,
|
||||
get_any_file_path,
|
||||
get_saveasfile_path,
|
||||
save_inference_file,
|
||||
set_pretrained_model_name_or_path_input,
|
||||
)
|
||||
from library.utilities import utilities_tab
|
||||
|
||||
@ -63,7 +65,11 @@ def save_configuration(
|
||||
gradient_accumulation_steps,
|
||||
mem_eff_attn,
|
||||
shuffle_caption,
|
||||
output_name,
|
||||
):
|
||||
# Get list of function parameters and values
|
||||
parameters = list(locals().items())
|
||||
|
||||
original_file_path = file_path
|
||||
|
||||
save_as_bool = True if save_as.get('label') == 'True' else False
|
||||
@ -83,51 +89,18 @@ def save_configuration(
|
||||
|
||||
# Return the values of the variables as a dictionary
|
||||
variables = {
|
||||
'pretrained_model_name_or_path': pretrained_model_name_or_path,
|
||||
'v2': v2,
|
||||
'v_parameterization': v_parameterization,
|
||||
'train_dir': train_dir,
|
||||
'image_folder': image_folder,
|
||||
'output_dir': output_dir,
|
||||
'logging_dir': logging_dir,
|
||||
'max_resolution': max_resolution,
|
||||
'min_bucket_reso': min_bucket_reso,
|
||||
'max_bucket_reso': max_bucket_reso,
|
||||
'batch_size': batch_size,
|
||||
'flip_aug': flip_aug,
|
||||
'caption_metadata_filename': caption_metadata_filename,
|
||||
'latent_metadata_filename': latent_metadata_filename,
|
||||
'full_path': full_path,
|
||||
'learning_rate': learning_rate,
|
||||
'lr_scheduler': lr_scheduler,
|
||||
'lr_warmup': lr_warmup,
|
||||
'dataset_repeats': dataset_repeats,
|
||||
'train_batch_size': train_batch_size,
|
||||
'epoch': epoch,
|
||||
'save_every_n_epochs': save_every_n_epochs,
|
||||
'mixed_precision': mixed_precision,
|
||||
'save_precision': save_precision,
|
||||
'seed': seed,
|
||||
'num_cpu_threads_per_process': num_cpu_threads_per_process,
|
||||
'train_text_encoder': train_text_encoder,
|
||||
'create_buckets': create_buckets,
|
||||
'create_caption': create_caption,
|
||||
'save_model_as': save_model_as,
|
||||
'caption_extension': caption_extension,
|
||||
'use_8bit_adam': use_8bit_adam,
|
||||
'xformers': xformers,
|
||||
'clip_skip': clip_skip,
|
||||
'save_state': save_state,
|
||||
'resume': resume,
|
||||
'gradient_checkpointing': gradient_checkpointing,
|
||||
'gradient_accumulation_steps': gradient_accumulation_steps,
|
||||
'mem_eff_attn': mem_eff_attn,
|
||||
'shuffle_caption': shuffle_caption,
|
||||
name: value
|
||||
for name, value in parameters # locals().items()
|
||||
if name
|
||||
not in [
|
||||
'file_path',
|
||||
'save_as',
|
||||
]
|
||||
}
|
||||
|
||||
# Save the data to the selected file
|
||||
with open(file_path, 'w') as file:
|
||||
json.dump(variables, file)
|
||||
json.dump(variables, file, indent=2)
|
||||
|
||||
return file_path
|
||||
|
||||
@ -174,7 +147,11 @@ def open_config_file(
|
||||
gradient_accumulation_steps,
|
||||
mem_eff_attn,
|
||||
shuffle_caption,
|
||||
output_name,
|
||||
):
|
||||
# Get list of function parameters and values
|
||||
parameters = list(locals().items())
|
||||
|
||||
original_file_path = file_path
|
||||
file_path = get_file_path(file_path)
|
||||
|
||||
@ -182,59 +159,18 @@ def open_config_file(
|
||||
print(f'Loading config file {file_path}')
|
||||
# load variables from JSON file
|
||||
with open(file_path, 'r') as f:
|
||||
my_data = json.load(f)
|
||||
my_data_ft = json.load(f)
|
||||
else:
|
||||
file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
|
||||
my_data = {}
|
||||
|
||||
# Return the values of the variables as a dictionary
|
||||
return (
|
||||
file_path,
|
||||
my_data.get(
|
||||
'pretrained_model_name_or_path', pretrained_model_name_or_path
|
||||
),
|
||||
my_data.get('v2', v2),
|
||||
my_data.get('v_parameterization', v_parameterization),
|
||||
my_data.get('train_dir', train_dir),
|
||||
my_data.get('image_folder', image_folder),
|
||||
my_data.get('output_dir', output_dir),
|
||||
my_data.get('logging_dir', logging_dir),
|
||||
my_data.get('max_resolution', max_resolution),
|
||||
my_data.get('min_bucket_reso', min_bucket_reso),
|
||||
my_data.get('max_bucket_reso', max_bucket_reso),
|
||||
my_data.get('batch_size', batch_size),
|
||||
my_data.get('flip_aug', flip_aug),
|
||||
my_data.get('caption_metadata_filename', caption_metadata_filename),
|
||||
my_data.get('latent_metadata_filename', latent_metadata_filename),
|
||||
my_data.get('full_path', full_path),
|
||||
my_data.get('learning_rate', learning_rate),
|
||||
my_data.get('lr_scheduler', lr_scheduler),
|
||||
my_data.get('lr_warmup', lr_warmup),
|
||||
my_data.get('dataset_repeats', dataset_repeats),
|
||||
my_data.get('train_batch_size', train_batch_size),
|
||||
my_data.get('epoch', epoch),
|
||||
my_data.get('save_every_n_epochs', save_every_n_epochs),
|
||||
my_data.get('mixed_precision', mixed_precision),
|
||||
my_data.get('save_precision', save_precision),
|
||||
my_data.get('seed', seed),
|
||||
my_data.get(
|
||||
'num_cpu_threads_per_process', num_cpu_threads_per_process
|
||||
),
|
||||
my_data.get('train_text_encoder', train_text_encoder),
|
||||
my_data.get('create_buckets', create_buckets),
|
||||
my_data.get('create_caption', create_caption),
|
||||
my_data.get('save_model_as', save_model_as),
|
||||
my_data.get('caption_extension', caption_extension),
|
||||
my_data.get('use_8bit_adam', use_8bit_adam),
|
||||
my_data.get('xformers', xformers),
|
||||
my_data.get('clip_skip', clip_skip),
|
||||
my_data.get('save_state', save_state),
|
||||
my_data.get('resume', resume),
|
||||
my_data.get('gradient_checkpointing', gradient_checkpointing),
|
||||
my_data.get('gradient_accumulation_steps', gradient_accumulation_steps),
|
||||
my_data.get('mem_eff_attn', mem_eff_attn),
|
||||
my_data.get('shuffle_caption', shuffle_caption),
|
||||
)
|
||||
my_data_ft = {}
|
||||
|
||||
values = [file_path]
|
||||
for key, value in parameters:
|
||||
# Set the value in the dictionary to the corresponding value in `my_data_ft`, or the default value if not found
|
||||
if not key in ['file_path']:
|
||||
values.append(my_data_ft.get(key, value))
|
||||
# print(values)
|
||||
return tuple(values)
|
||||
|
||||
|
||||
def train_model(
|
||||
@ -278,22 +214,8 @@ def train_model(
|
||||
gradient_accumulation_steps,
|
||||
mem_eff_attn,
|
||||
shuffle_caption,
|
||||
output_name,
|
||||
):
|
||||
def save_inference_file(output_dir, v2, v_parameterization):
|
||||
# Copy inference model for v2 if required
|
||||
if v2 and v_parameterization:
|
||||
print(f'Saving v2-inference-v.yaml as {output_dir}/last.yaml')
|
||||
shutil.copy(
|
||||
f'./v2_inference/v2-inference-v.yaml',
|
||||
f'{output_dir}/last.yaml',
|
||||
)
|
||||
elif v2:
|
||||
print(f'Saving v2-inference.yaml as {output_dir}/last.yaml')
|
||||
shutil.copy(
|
||||
f'./v2_inference/v2-inference.yaml',
|
||||
f'{output_dir}/last.yaml',
|
||||
)
|
||||
|
||||
# create caption json file
|
||||
if generate_caption_database:
|
||||
if not os.path.exists(train_dir):
|
||||
@ -407,68 +329,19 @@ def train_model(
|
||||
run_cmd += ' --save_state'
|
||||
if not resume == '':
|
||||
run_cmd += f' --resume={resume}'
|
||||
if not output_name == '':
|
||||
run_cmd += f' --output_name="{output_name}"'
|
||||
|
||||
print(run_cmd)
|
||||
# Run the command
|
||||
subprocess.run(run_cmd)
|
||||
|
||||
# check if output_dir/last is a folder... therefore it is a diffuser model
|
||||
last_dir = pathlib.Path(f'{output_dir}/last')
|
||||
last_dir = pathlib.Path(f'{output_dir}/{output_name}')
|
||||
|
||||
if not last_dir.is_dir():
|
||||
# Copy inference model for v2 if required
|
||||
save_inference_file(output_dir, v2, v_parameterization)
|
||||
|
||||
|
||||
def set_pretrained_model_name_or_path_input(value, v2, v_parameterization):
|
||||
# define a list of substrings to search for
|
||||
substrings_v2 = [
|
||||
'stabilityai/stable-diffusion-2-1-base',
|
||||
'stabilityai/stable-diffusion-2-base',
|
||||
]
|
||||
|
||||
# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
|
||||
if str(value) in substrings_v2:
|
||||
print('SD v2 model detected. Setting --v2 parameter')
|
||||
v2 = True
|
||||
v_parameterization = False
|
||||
|
||||
return value, v2, v_parameterization
|
||||
|
||||
# define a list of substrings to search for v-objective
|
||||
substrings_v_parameterization = [
|
||||
'stabilityai/stable-diffusion-2-1',
|
||||
'stabilityai/stable-diffusion-2',
|
||||
]
|
||||
|
||||
# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v_parameterization list
|
||||
if str(value) in substrings_v_parameterization:
|
||||
print(
|
||||
'SD v2 v_parameterization detected. Setting --v2 parameter and --v_parameterization'
|
||||
)
|
||||
v2 = True
|
||||
v_parameterization = True
|
||||
|
||||
return value, v2, v_parameterization
|
||||
|
||||
# define a list of substrings to v1.x
|
||||
substrings_v1_model = [
|
||||
'CompVis/stable-diffusion-v1-4',
|
||||
'runwayml/stable-diffusion-v1-5',
|
||||
]
|
||||
|
||||
if str(value) in substrings_v1_model:
|
||||
v2 = False
|
||||
v_parameterization = False
|
||||
|
||||
return value, v2, v_parameterization
|
||||
|
||||
if value == 'custom':
|
||||
value = ''
|
||||
v2 = False
|
||||
v_parameterization = False
|
||||
|
||||
return value, v2, v_parameterization
|
||||
save_inference_file(output_dir, v2, v_parameterization, output_name)
|
||||
|
||||
|
||||
def remove_doublequote(file_path):
|
||||
@ -610,7 +483,7 @@ def finetune_tab():
|
||||
)
|
||||
with gr.Row():
|
||||
output_dir_input = gr.Textbox(
|
||||
label='Output folder',
|
||||
label='Model output folder',
|
||||
placeholder='folder where the model will be saved',
|
||||
)
|
||||
output_dir_input_folder = gr.Button(
|
||||
@ -630,6 +503,13 @@ def finetune_tab():
|
||||
logging_dir_input_folder.click(
|
||||
get_folder_path, outputs=logging_dir_input
|
||||
)
|
||||
with gr.Row():
|
||||
output_name = gr.Textbox(
|
||||
label='Model output name',
|
||||
placeholder='Name of the model to output',
|
||||
value='last',
|
||||
interactive=True,
|
||||
)
|
||||
train_dir_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[train_dir_input],
|
||||
@ -814,6 +694,7 @@ def finetune_tab():
|
||||
gradient_accumulation_steps,
|
||||
mem_eff_attn,
|
||||
shuffle_caption,
|
||||
output_name,
|
||||
]
|
||||
|
||||
button_run.click(train_model, inputs=settings_list)
|
||||
|
||||
@ -2,6 +2,7 @@ from tkinter import filedialog, Tk
|
||||
import os
|
||||
import gradio as gr
|
||||
from easygui import msgbox
|
||||
import shutil
|
||||
|
||||
def get_dir_and_file(file_path):
|
||||
dir_path, file_name = os.path.split(file_path)
|
||||
@ -183,4 +184,81 @@ def color_aug_changed(color_aug):
|
||||
msgbox('Disabling "Cache latent" because "Color augmentation" has been selected...')
|
||||
return gr.Checkbox.update(value=False, interactive=False)
|
||||
else:
|
||||
return gr.Checkbox.update(value=True, interactive=True)
|
||||
return gr.Checkbox.update(value=True, interactive=True)
|
||||
|
||||
def save_inference_file(output_dir, v2, v_parameterization, output_name):
|
||||
# List all files in the directory
|
||||
files = os.listdir(output_dir)
|
||||
|
||||
# Iterate over the list of files
|
||||
for file in files:
|
||||
# Check if the file starts with the value of output_name
|
||||
if file.startswith(output_name):
|
||||
# Check if it is a file or a directory
|
||||
if os.path.isfile(os.path.join(output_dir, file)):
|
||||
# Split the file name and extension
|
||||
file_name, ext = os.path.splitext(file)
|
||||
|
||||
# Copy the v2-inference-v.yaml file to the current file, with a .yaml extension
|
||||
if v2 and v_parameterization:
|
||||
print(f'Saving v2-inference-v.yaml as {output_dir}/{file_name}.yaml')
|
||||
shutil.copy(
|
||||
f'./v2_inference/v2-inference-v.yaml',
|
||||
f'{output_dir}/{file_name}.yaml',
|
||||
)
|
||||
elif v2:
|
||||
print(f'Saving v2-inference.yaml as {output_dir}/{file_name}.yaml')
|
||||
shutil.copy(
|
||||
f'./v2_inference/v2-inference.yaml',
|
||||
f'{output_dir}/{file_name}.yaml',
|
||||
)
|
||||
|
||||
def set_pretrained_model_name_or_path_input(value, v2, v_parameterization):
|
||||
# define a list of substrings to search for
|
||||
substrings_v2 = [
|
||||
'stabilityai/stable-diffusion-2-1-base',
|
||||
'stabilityai/stable-diffusion-2-base',
|
||||
]
|
||||
|
||||
# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
|
||||
if str(value) in substrings_v2:
|
||||
print('SD v2 model detected. Setting --v2 parameter')
|
||||
v2 = True
|
||||
v_parameterization = False
|
||||
|
||||
return value, v2, v_parameterization
|
||||
|
||||
# define a list of substrings to search for v-objective
|
||||
substrings_v_parameterization = [
|
||||
'stabilityai/stable-diffusion-2-1',
|
||||
'stabilityai/stable-diffusion-2',
|
||||
]
|
||||
|
||||
# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v_parameterization list
|
||||
if str(value) in substrings_v_parameterization:
|
||||
print(
|
||||
'SD v2 v_parameterization detected. Setting --v2 parameter and --v_parameterization'
|
||||
)
|
||||
v2 = True
|
||||
v_parameterization = True
|
||||
|
||||
return value, v2, v_parameterization
|
||||
|
||||
# define a list of substrings to v1.x
|
||||
substrings_v1_model = [
|
||||
'CompVis/stable-diffusion-v1-4',
|
||||
'runwayml/stable-diffusion-v1-5',
|
||||
]
|
||||
|
||||
if str(value) in substrings_v1_model:
|
||||
v2 = False
|
||||
v_parameterization = False
|
||||
|
||||
return value, v2, v_parameterization
|
||||
|
||||
if value == 'custom':
|
||||
value = ''
|
||||
v2 = False
|
||||
v_parameterization = False
|
||||
|
||||
return value, v2, v_parameterization
|
||||
272
lora_gui.py
272
lora_gui.py
@ -18,6 +18,8 @@ from library.common_gui import (
|
||||
get_any_file_path,
|
||||
get_saveasfile_path,
|
||||
color_aug_changed,
|
||||
save_inference_file,
|
||||
set_pretrained_model_name_or_path_input,
|
||||
)
|
||||
from library.dreambooth_folder_creation_gui import (
|
||||
gradio_dreambooth_folder_creation_tab,
|
||||
@ -76,8 +78,11 @@ def save_configuration(
|
||||
clip_skip,
|
||||
gradient_accumulation_steps,
|
||||
mem_eff_attn,
|
||||
# vae,
|
||||
output_name,
|
||||
):
|
||||
# Get list of function parameters and values
|
||||
parameters = list(locals().items())
|
||||
|
||||
original_file_path = file_path
|
||||
|
||||
save_as_bool = True if save_as.get('label') == 'True' else False
|
||||
@ -97,85 +102,51 @@ def save_configuration(
|
||||
|
||||
# Return the values of the variables as a dictionary
|
||||
variables = {
|
||||
'pretrained_model_name_or_path': pretrained_model_name_or_path,
|
||||
'v2': v2,
|
||||
'v_parameterization': v_parameterization,
|
||||
'logging_dir': logging_dir,
|
||||
'train_data_dir': train_data_dir,
|
||||
'reg_data_dir': reg_data_dir,
|
||||
'output_dir': output_dir,
|
||||
'max_resolution': max_resolution,
|
||||
'lr_scheduler': lr_scheduler,
|
||||
'lr_warmup': lr_warmup,
|
||||
'train_batch_size': train_batch_size,
|
||||
'epoch': epoch,
|
||||
'save_every_n_epochs': save_every_n_epochs,
|
||||
'mixed_precision': mixed_precision,
|
||||
'save_precision': save_precision,
|
||||
'seed': seed,
|
||||
'num_cpu_threads_per_process': num_cpu_threads_per_process,
|
||||
'cache_latent': cache_latent,
|
||||
'caption_extention': caption_extention,
|
||||
'enable_bucket': enable_bucket,
|
||||
'gradient_checkpointing': gradient_checkpointing,
|
||||
'full_fp16': full_fp16,
|
||||
'no_token_padding': no_token_padding,
|
||||
'stop_text_encoder_training': stop_text_encoder_training,
|
||||
'use_8bit_adam': use_8bit_adam,
|
||||
'xformers': xformers,
|
||||
'save_model_as': save_model_as,
|
||||
'shuffle_caption': shuffle_caption,
|
||||
'save_state': save_state,
|
||||
'resume': resume,
|
||||
'prior_loss_weight': prior_loss_weight,
|
||||
'text_encoder_lr': text_encoder_lr,
|
||||
'unet_lr': unet_lr,
|
||||
'network_dim': network_dim,
|
||||
'lora_network_weights': lora_network_weights,
|
||||
'color_aug': color_aug,
|
||||
'flip_aug': flip_aug,
|
||||
'clip_skip': clip_skip,
|
||||
'gradient_accumulation_steps': gradient_accumulation_steps,
|
||||
'mem_eff_attn': mem_eff_attn,
|
||||
# 'vae': vae,
|
||||
name: value
|
||||
for name, value in parameters # locals().items()
|
||||
if name
|
||||
not in [
|
||||
'file_path',
|
||||
'save_as',
|
||||
]
|
||||
}
|
||||
|
||||
# Save the data to the selected file
|
||||
with open(file_path, 'w') as file:
|
||||
json.dump(variables, file)
|
||||
json.dump(variables, file, indent=2)
|
||||
|
||||
return file_path
|
||||
|
||||
|
||||
def open_configuration(
|
||||
file_path,
|
||||
pretrained_model_name_or_path,
|
||||
v2,
|
||||
v_parameterization,
|
||||
logging_dir,
|
||||
train_data_dir,
|
||||
reg_data_dir,
|
||||
output_dir,
|
||||
max_resolution,
|
||||
lr_scheduler,
|
||||
lr_warmup,
|
||||
train_batch_size,
|
||||
epoch,
|
||||
save_every_n_epochs,
|
||||
mixed_precision,
|
||||
save_precision,
|
||||
seed,
|
||||
num_cpu_threads_per_process,
|
||||
cache_latent,
|
||||
caption_extention,
|
||||
enable_bucket,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing,
|
||||
full_fp16,
|
||||
no_token_padding,
|
||||
stop_text_encoder_training,
|
||||
use_8bit_adam,
|
||||
xformers,
|
||||
save_model_as,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
save_model_as_dropdown,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
@ -189,70 +160,29 @@ def open_configuration(
|
||||
clip_skip,
|
||||
gradient_accumulation_steps,
|
||||
mem_eff_attn,
|
||||
# vae,
|
||||
output_name,
|
||||
):
|
||||
# Get list of function parameters and values
|
||||
parameters = list(locals().items())
|
||||
|
||||
original_file_path = file_path
|
||||
file_path = get_file_path(file_path)
|
||||
# print(file_path)
|
||||
|
||||
if not file_path == '' and not file_path == None:
|
||||
# load variables from JSON file
|
||||
with open(file_path, 'r') as f:
|
||||
my_data = json.load(f)
|
||||
my_data_lora = json.load(f)
|
||||
print("Loading config...")
|
||||
else:
|
||||
file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
|
||||
my_data = {}
|
||||
|
||||
# Return the values of the variables as a dictionary
|
||||
return (
|
||||
file_path,
|
||||
my_data.get(
|
||||
'pretrained_model_name_or_path', pretrained_model_name_or_path
|
||||
),
|
||||
my_data.get('v2', v2),
|
||||
my_data.get('v_parameterization', v_parameterization),
|
||||
my_data.get('logging_dir', logging_dir),
|
||||
my_data.get('train_data_dir', train_data_dir),
|
||||
my_data.get('reg_data_dir', reg_data_dir),
|
||||
my_data.get('output_dir', output_dir),
|
||||
my_data.get('max_resolution', max_resolution),
|
||||
my_data.get('lr_scheduler', lr_scheduler),
|
||||
my_data.get('lr_warmup', lr_warmup),
|
||||
my_data.get('train_batch_size', train_batch_size),
|
||||
my_data.get('epoch', epoch),
|
||||
my_data.get('save_every_n_epochs', save_every_n_epochs),
|
||||
my_data.get('mixed_precision', mixed_precision),
|
||||
my_data.get('save_precision', save_precision),
|
||||
my_data.get('seed', seed),
|
||||
my_data.get(
|
||||
'num_cpu_threads_per_process', num_cpu_threads_per_process
|
||||
),
|
||||
my_data.get('cache_latent', cache_latent),
|
||||
my_data.get('caption_extention', caption_extention),
|
||||
my_data.get('enable_bucket', enable_bucket),
|
||||
my_data.get('gradient_checkpointing', gradient_checkpointing),
|
||||
my_data.get('full_fp16', full_fp16),
|
||||
my_data.get('no_token_padding', no_token_padding),
|
||||
my_data.get('stop_text_encoder_training', stop_text_encoder_training),
|
||||
my_data.get('use_8bit_adam', use_8bit_adam),
|
||||
my_data.get('xformers', xformers),
|
||||
my_data.get('save_model_as', save_model_as),
|
||||
my_data.get('shuffle_caption', shuffle_caption),
|
||||
my_data.get('save_state', save_state),
|
||||
my_data.get('resume', resume),
|
||||
my_data.get('prior_loss_weight', prior_loss_weight),
|
||||
my_data.get('text_encoder_lr', text_encoder_lr),
|
||||
my_data.get('unet_lr', unet_lr),
|
||||
my_data.get('network_dim', network_dim),
|
||||
my_data.get('lora_network_weights', lora_network_weights),
|
||||
my_data.get('color_aug', color_aug),
|
||||
my_data.get('flip_aug', flip_aug),
|
||||
my_data.get('clip_skip', clip_skip),
|
||||
my_data.get('gradient_accumulation_steps', gradient_accumulation_steps),
|
||||
my_data.get('mem_eff_attn', mem_eff_attn),
|
||||
# my_data.get('vae', vae),
|
||||
)
|
||||
my_data_lora = {}
|
||||
|
||||
values = [file_path]
|
||||
for key, value in parameters:
|
||||
# Set the value in the dictionary to the corresponding value in `my_data`, or the default value if not found
|
||||
if not key in ['file_path']:
|
||||
values.append(my_data_lora.get(key, value))
|
||||
return tuple(values)
|
||||
|
||||
|
||||
def train_model(
|
||||
@ -296,23 +226,8 @@ def train_model(
|
||||
clip_skip,
|
||||
gradient_accumulation_steps,
|
||||
mem_eff_attn,
|
||||
# vae,
|
||||
output_name,
|
||||
):
|
||||
def save_inference_file(output_dir, v2, v_parameterization):
|
||||
# Copy inference model for v2 if required
|
||||
if v2 and v_parameterization:
|
||||
print(f'Saving v2-inference-v.yaml as {output_dir}/last.yaml')
|
||||
shutil.copy(
|
||||
f'./v2_inference/v2-inference-v.yaml',
|
||||
f'{output_dir}/last.yaml',
|
||||
)
|
||||
elif v2:
|
||||
print(f'Saving v2-inference.yaml as {output_dir}/last.yaml')
|
||||
shutil.copy(
|
||||
f'./v2_inference/v2-inference.yaml',
|
||||
f'{output_dir}/last.yaml',
|
||||
)
|
||||
|
||||
if pretrained_model_name_or_path == '':
|
||||
msgbox('Source model information is missing')
|
||||
return
|
||||
@ -379,17 +294,6 @@ def train_model(
|
||||
# Print the result
|
||||
print(f'Folder {folder}: {steps} steps')
|
||||
|
||||
# Print the result
|
||||
# print(f"{total_steps} total steps")
|
||||
|
||||
# if reg_data_dir == '':
|
||||
# reg_factor = 1
|
||||
# else:
|
||||
# print(
|
||||
# 'Regularisation images are used... Will double the number of steps required...'
|
||||
# )
|
||||
# reg_factor = 2
|
||||
|
||||
# calculate max_train_steps
|
||||
max_train_steps = int(
|
||||
math.ceil(
|
||||
@ -496,68 +400,19 @@ def train_model(
|
||||
run_cmd += f' --gradient_accumulation_steps={int(gradient_accumulation_steps)}'
|
||||
# if not vae == '':
|
||||
# run_cmd += f' --vae="{vae}"'
|
||||
if not output_name == '':
|
||||
run_cmd += f' --output_name="{output_name}"'
|
||||
|
||||
print(run_cmd)
|
||||
# Run the command
|
||||
subprocess.run(run_cmd)
|
||||
|
||||
# check if output_dir/last is a folder... therefore it is a diffuser model
|
||||
last_dir = pathlib.Path(f'{output_dir}/last')
|
||||
last_dir = pathlib.Path(f'{output_dir}/{output_name}')
|
||||
|
||||
if not last_dir.is_dir():
|
||||
# Copy inference model for v2 if required
|
||||
save_inference_file(output_dir, v2, v_parameterization)
|
||||
|
||||
|
||||
def set_pretrained_model_name_or_path_input(value, v2, v_parameterization):
|
||||
# define a list of substrings to search for
|
||||
substrings_v2 = [
|
||||
'stabilityai/stable-diffusion-2-1-base',
|
||||
'stabilityai/stable-diffusion-2-base',
|
||||
]
|
||||
|
||||
# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
|
||||
if str(value) in substrings_v2:
|
||||
print('SD v2 model detected. Setting --v2 parameter')
|
||||
v2 = True
|
||||
v_parameterization = False
|
||||
|
||||
return value, v2, v_parameterization
|
||||
|
||||
# define a list of substrings to search for v-objective
|
||||
substrings_v_parameterization = [
|
||||
'stabilityai/stable-diffusion-2-1',
|
||||
'stabilityai/stable-diffusion-2',
|
||||
]
|
||||
|
||||
# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v_parameterization list
|
||||
if str(value) in substrings_v_parameterization:
|
||||
print(
|
||||
'SD v2 v_parameterization detected. Setting --v2 parameter and --v_parameterization'
|
||||
)
|
||||
v2 = True
|
||||
v_parameterization = True
|
||||
|
||||
return value, v2, v_parameterization
|
||||
|
||||
# define a list of substrings to v1.x
|
||||
substrings_v1_model = [
|
||||
'CompVis/stable-diffusion-v1-4',
|
||||
'runwayml/stable-diffusion-v1-5',
|
||||
]
|
||||
|
||||
if str(value) in substrings_v1_model:
|
||||
v2 = False
|
||||
v_parameterization = False
|
||||
|
||||
return value, v2, v_parameterization
|
||||
|
||||
if value == 'custom':
|
||||
value = ''
|
||||
v2 = False
|
||||
v_parameterization = False
|
||||
|
||||
return value, v2, v_parameterization
|
||||
save_inference_file(output_dir, v2, v_parameterization, output_name)
|
||||
|
||||
|
||||
def UI(username, password):
|
||||
@ -731,6 +586,13 @@ def lora_tab(
|
||||
logging_dir_input_folder.click(
|
||||
get_folder_path, outputs=logging_dir_input
|
||||
)
|
||||
with gr.Row():
|
||||
output_name = gr.Textbox(
|
||||
label='Model output name',
|
||||
placeholder='Name of the model to output',
|
||||
value='last',
|
||||
interactive=True,
|
||||
)
|
||||
train_data_dir_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[train_data_dir_input],
|
||||
@ -766,7 +628,6 @@ def lora_tab(
|
||||
outputs=lora_network_weights,
|
||||
)
|
||||
with gr.Row():
|
||||
# learning_rate_input = gr.Textbox(label='Learning rate', value=1e-4, visible=False)
|
||||
lr_scheduler_input = gr.Dropdown(
|
||||
label='LR Scheduler',
|
||||
choices=[
|
||||
@ -941,7 +802,6 @@ def lora_tab(
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
# learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
@ -974,7 +834,7 @@ def lora_tab(
|
||||
clip_skip,
|
||||
gradient_accumulation_steps,
|
||||
mem_eff_attn,
|
||||
# vae,
|
||||
output_name,
|
||||
]
|
||||
|
||||
button_open_config.click(
|
||||
|
||||
Loading…
Reference in New Issue
Block a user