mirror of https://github.com/vladmandic/automatic
276 lines
12 KiB
Python
Executable File
276 lines
12 KiB
Python
Executable File
#!/bin/env python
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"""
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Extract approximating LoRA by SVD from two SD models
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Based on: <https://github.com/kohya-ss/sd-scripts/blob/main/networks/train_network.py>
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Train LoRA with custom preprocessing, tagging and bucketing
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Disabled/broken:
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- `accelerate` with *dynamo* enabled
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- `xformers` due to *faketensors* requirement
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- `mem_eff_attn` due to *forwardfunc* mismatch
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- 'use_8bit_adam` due to *bitsandbyttes* CUDA errors
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"""
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import os
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import re
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import gc
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import sys
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import json
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import time
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import shutil
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import argparse
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import tempfile
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import torch
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import logging
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import importlib
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import transformers
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from pathlib import Path
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from modules.util import log, Map, get_memory
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import modules.process
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import modules.sdapi
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latents = importlib.import_module('modules.lora-latents')
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lora_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, 'modules', 'lora'))
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sys.path.append(lora_path)
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lycoris_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, 'modules', 'lycoris'))
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sys.path.append(lycoris_path)
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from train_network import train
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options = Map({
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"bucket_no_upscale": False,
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"bucket_reso_steps": 64,
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"cache_latents": True,
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"caption_dropout_every_n_epochs": None,
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"caption_dropout_rate": 0.0,
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"caption_extension": ".txt",
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"caption_extention": ".txt",
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"caption_tag_dropout_rate": 0.0,
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"clip_skip": None,
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"color_aug": False,
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"dataset_repeats": 1,
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"debug_dataset": False,
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"enable_bucket": False,
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"face_crop_aug_range": None,
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"flip_aug": False,
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"full_fp16": False,
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"gradient_accumulation_steps": 1,
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"gradient_checkpointing": False,
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"in_json": "",
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"keep_tokens": None,
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"learning_rate": 5e-05,
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"log_prefix": None,
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"logging_dir": None,
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"lr_scheduler_num_cycles": 1,
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"lr_scheduler_power": 1,
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"lr_scheduler": "cosine",
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"lr_warmup_steps": 0,
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"max_bucket_reso": 1024,
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"max_data_loader_n_workers": 8,
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"max_grad_norm": 0.0,
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"max_token_length": None,
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"max_train_epochs": None,
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"max_train_steps": 5000,
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"mem_eff_attn": False,
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"min_bucket_reso": 256,
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"mixed_precision": "fp16",
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"network_alpha": 1.0,
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"network_args": None,
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"network_dim": 16,
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"network_module": "networks.lora",
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"network_train_text_encoder_only": False,
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"network_train_unet_only": False,
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"network_weights": None,
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"no_metadata": False,
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"output_dir": "",
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"output_name": "",
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"persistent_data_loader_workers": False,
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"pretrained_model_name_or_path": "",
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"prior_loss_weight": 1.0,
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"random_crop": False,
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"reg_data_dir": None,
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"resolution": "512,512",
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"resume": None,
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"save_every_n_epochs": None,
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"save_last_n_epochs_state": None,
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"save_last_n_epochs": None,
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"save_model_as": "ckpt",
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"save_n_epoch_ratio": None,
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"save_precision": "fp16",
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"save_state": False,
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"seed": 42,
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"shuffle_caption": False,
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"text_encoder_lr": 5e-05,
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"train_batch_size": 1,
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"train_data_dir": "",
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"training_comment": "mood-magic",
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"unet_lr": 0.001,
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"use_8bit_adam": False,
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"v_parameterization": False,
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"v2": False,
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"vae": None,
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"xformers": False,
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})
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def mem_stats():
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gc.collect()
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if torch.cuda.is_available():
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with torch.no_grad():
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torch.cuda.empty_cache()
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with torch.cuda.device('cuda'):
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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mem = get_memory()
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log.info({ 'memory': { 'ram': mem.ram, 'gpu': mem.gpu } })
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description = 'train lora')
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parser.add_argument('--model', type=str, default=None, required=False, help='original model to use a base for training, default: active model')
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parser.add_argument('--input', '--dataset', type=str, default=None, required=True, help='input folder with training images')
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parser.add_argument('--output', '--lora', type=str, default=None, required=True, help='lora name')
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parser.add_argument('--tag', type=str, default=None, required=False, help='primary tag')
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parser.add_argument('--dir', type=str, default=None, required=False, help='folder containing lora checkpoints')
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parser.add_argument('--interim', type=int, default=0, help = 'save interim checkpoints after n epoch')
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parser.add_argument('--process', type=str, default='original', required=False, help='list of processing steps: original,face,body,blur,range,upscale,restore')
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parser.add_argument('--noprocess', default = False, action='store_true', help = 'skip processing and use existing input data')
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parser.add_argument('--notrain', default = False, action='store_true', help = 'just run processing and skip training')
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parser.add_argument('--nocaptions', default = False, action='store_true', help = 'skip creating captions and tags')
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parser.add_argument('--nolatents', default = False, action='store_true', help = 'skip generating vae latents')
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parser.add_argument('--offline', default = False, action='store_true', help = 'do not use webui server for processing')
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parser.add_argument('--shutdown', default = False, action='store_true', help = 'shutdown webui server')
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parser.add_argument('--gradient', type=int, default=1, required=False, help='gradient accumulation steps, default: %(default)s')
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parser.add_argument('--steps', type=int, default=4000, required=False, help='training steps, default: %(default)s')
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parser.add_argument('--dim', type=int, default=40, required=False, help='network dimension, default: %(default)s')
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parser.add_argument('--repeats', type=int, default=10, required=False, help='number of repeats per image, default: %(default)s')
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parser.add_argument('--alpha', type=float, default=0, required=False, help='alpha for weights scaling, default: half of dim')
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parser.add_argument('--batch', type=int, default=1, required=False, help='batch size, default: %(default)s')
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parser.add_argument('--lr', type=float, default=1e-04, required=False, help='model learning rate, default: %(default)s')
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parser.add_argument('--unetlr', type=float, default=1e-04, required=False, help='unet learning rate, default: %(default)s')
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parser.add_argument('--textlr', type=float, default=5e-05, required=False, help='text encoder learning rate, default: %(default)s')
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parser.add_argument('--dreambooth', default=False, action='store_true', help = "use dreambooth style training")
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parser.add_argument('--lycoris', default=False, action='store_true', help = "use lycoris style training")
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parser.add_argument('--debug', default=False, action='store_true', help = "enable debug logging")
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args = parser.parse_args()
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defaults = Map({ 'options': {}, 'flags': {} }) if args.offline else Map(modules.sdapi.options())
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if args.debug:
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log.setLevel(logging.DEBUG)
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log.debug({ 'debug': True })
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if args.model is None:
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args.model = defaults.options.get('sd_model_checkpoint', None)
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args.model = args.model.split(' [')[0] if args.model is not None else None
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if args.dir is None:
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args.dir = defaults.flags.get('lora_dir', None)
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if not os.path.isabs(args.model) and args.dir is not None and not os.path.exists(args.model):
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args.model = os.path.abspath(os.path.join(args.dir, os.pardir, 'Stable-diffusion', args.model))
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if args.dir is None:
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args.dir = os.path.join(args.input, 'lora')
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if not os.path.exists(args.model) or not os.path.isfile(args.model):
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log.error({ 'lora cannot find model': args.model })
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exit(1)
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if not os.path.exists(args.input) or not os.path.isdir(args.input):
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log.error({ 'lora cannot find training dir': args.input })
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exit(1)
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if not os.path.exists(args.dir) or not os.path.isdir(args.dir):
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log.error({ 'lora cannot find training dir': args.dir })
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exit(1)
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options.pretrained_model_name_or_path = args.model
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options.output_dir = args.dir
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options.output_name = args.output
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options.max_train_steps = args.steps
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options.network_dim = args.dim
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options.network_alpha = args.dim // 2 if args.alpha == 0 else args.alpha
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options.gradient_accumulation_steps = args.gradient
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options.save_every_n_epochs = args.interim if args.interim > 0 else None
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options.learning_rate = args.lr
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options.unet_lr = args.unetlr
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options.text_encoder_lr = args.textlr
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options.train_batch_size = args.batch
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options.network_alpha = args.alpha
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log.info({ 'train lora args': vars(options) })
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transformers.logging.set_verbosity_error()
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mem_stats()
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json_file = os.path.join(tempfile.gettempdir(), args.output, args.output + '.json')
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base = os.path.join(tempfile.gettempdir(), args.output)
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options.train_data_dir = base
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res = None
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if args.dreambooth:
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log.info({ 'using dreambooth style training': True })
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options.in_json = None
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else:
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options.in_json = json_file
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for root, _sub_dirs, folder in os.walk(args.input):
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files = [os.path.join(root, f) for f in folder]
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if not args.noprocess:
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# preprocess
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processing_options = args.process.split(',')
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processing_options = [opt.strip() for opt in re.split(',| ', args.process)]
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log.info({ 'processing steps': processing_options })
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if os.path.exists(json_file):
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os.remove(json_file)
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steps = [step for step in processing_options if step in ['face', 'body', 'original']]
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for step in steps:
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# processing_options = [step for step in processing_options if step not in ['face', 'body', 'original']].append(step)
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if step == 'face':
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opts = [step for step in processing_options if step not in ['body', 'original']]
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if step == 'body':
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opts = [step for step in processing_options if step not in ['face', 'original', 'upscale', 'restore']]
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if step == 'original':
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opts = [step for step in processing_options if step not in ['face', 'body', 'upscale', 'restore', 'blur', 'range']]
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log.info({ 'processing step': opts })
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concept = step
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if concept == 'original' and args.tag is not None:
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concept = args.tag.split(',')[0].strip()
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dir = os.path.join(base, str(args.repeats) + '_' + concept)
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if os.path.exists(dir):
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shutil.rmtree(dir, ignore_errors=True)
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Path(dir).mkdir(parents=True, exist_ok=True)
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for f in files:
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try:
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res, metadata = modules.process.process_file(f = f, dst = dir, preview = False, offline = args.offline, txt = args.dreambooth, tag = args.tag, opts = opts)
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if not args.dreambooth:
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with open(json_file, "w") as outfile:
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outfile.write(json.dumps(metadata, indent=2))
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except ValueError as e:
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exit(1)
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log.info({ 'processed step': step, 'outputs': res, 'inputs': len(files), 'metadata': json_file, 'path': dir })
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modules.process.unload_models()
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mem_stats()
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dirs = [os.path.join(base, dir) for dir in os.listdir(base) if os.path.isdir(os.path.join(base, dir))]
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log.info({ 'input datasets': dirs, 'metadata': json_file })
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if not args.nolatents and not args.dreambooth:
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# create latents
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for dir in dirs:
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latents.create_vae_latents(Map({ 'input': dir, 'json': json_file }))
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latents.unload_vae()
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mem_stats()
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else:
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log.info({ 'skip processing': len(files), 'metadata': json_file, 'path': dir })
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if args.shutdown:
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log.info({ 'server shutdown required': True })
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modules.sdapi.shutdown()
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time.sleep(1)
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if args.lycoris:
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log.info({ 'using lycoris network': True })
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options.network_module = 'lycoris.kohya'
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if not args.notrain:
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train(options)
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mem_stats()
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