Closed wzgrx closed 1 year ago
set bucketing to recursive.
set bucketing to recursive.
Okay, thank you. It would be great if there were basic default training settings.
set bucketing to recursive.
After reviewing the settings, the default setting is enabled, but there are still errors reported
Loading settings from /content/LoRA/config/config_file.toml... /content/LoRA/config/config_file prepare tokenizers Downloading (…)olve/main/vocab.json: 100% 961k/961k [00:00<00:00, 4.47MB/s] Downloading (…)olve/main/merges.txt: 100% 525k/525k [00:00<00:00, 820kB/s] Downloading (…)cial_tokens_map.json: 100% 389/389 [00:00<00:00, 2.57MB/s] Downloading (…)okenizer_config.json: 100% 905/905 [00:00<00:00, 6.25MB/s] update token length: 225 Training with captions. ╭───────────────────── Traceback (most recent call last) ──────────────────────╮ │ /content/kohya-trainer/sdxl_train_network.py:167 in │
│ │
│ 164 │ args = train_util.read_config_from_file(args, parser) │
│ 165 │ │
│ 166 │ trainer = SdxlNetworkTrainer() │
│ ❱ 167 │ trainer.train(args) │
│ 168 │
│ │
│ /content/kohya-trainer/train_network.py:177 in train │
│ │
│ 174 │ │ │ │ │ } │
│ 175 │ │ │ │
│ 176 │ │ │ blueprint = blueprint_generator.generate(user_config, args │
│ ❱ 177 │ │ │ train_dataset_group = config_util.generate_dataset_group_b │
│ 178 │ │ else: │
│ 179 │ │ │ # use arbitrary dataset class │
│ 180 │ │ │ train_dataset_group = train_util.load_arbitrary_dataset(ar │
│ │
│ /content/kohya-trainer/library/config_util.py:426 in │
│ generate_dataset_group_by_blueprint │
│ │
│ 423 │ dataset_klass = FineTuningDataset │
│ 424 │ │
│ 425 │ subsets = [subset_klass(asdict(subset_blueprint.params)) for sub │
│ ❱ 426 │ dataset = dataset_klass(subsets=subsets, asdict(dataset_blueprin │
│ 427 │ datasets.append(dataset) │
│ 428 │
│ 429 # print info │
│ │
│ /content/kohya-trainer/library/train_util.py:1381 in init │
│ │
│ 1378 │ │ │ │ with open(subset.metadata_file, "rt", encoding="utf-8 │
│ 1379 │ │ │ │ │ metadata = json.load(f) │
│ 1380 │ │ │ else: │
│ ❱ 1381 │ │ │ │ raise ValueError(f"no metadata / メタデータファイルが │
│ 1382 │ │ │ │
│ 1383 │ │ │ if len(metadata) < 1: │
│ 1384 │ │ │ │ print(f"ignore subset with '{subset.metadata_file}': │
╰──────────────────────────────────────────────────────────────────────────────╯
ValueError: no metadata / メタデータファイルがありません:
/content/LoRA/meta_lat.json
╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /usr/local/bin/accelerate:8 in │
│ │
│ 5 from accelerate.commands.accelerate_cli import main │
│ 6 if name == 'main': │
│ 7 │ sys.argv[0] = re.sub(r'(-script.pyw|.exe)?$', '', sys.argv[0]) │
│ ❱ 8 │ sys.exit(main()) │
│ 9 │
│ │
│ /usr/local/lib/python3.10/dist-packages/accelerate/commands/accelerate_cli.p │
│ y:45 in main │
│ │
│ 42 │ │ exit(1) │
│ 43 │ │
│ 44 │ # Run │
│ ❱ 45 │ args.func(args) │
│ 46 │
│ 47 │
│ 48 if name == "main": │
│ │
│ /usr/local/lib/python3.10/dist-packages/accelerate/commands/launch.py:918 in │
│ launch_command │
│ │
│ 915 │ elif defaults is not None and defaults.compute_environment == Comp │
│ 916 │ │ sagemaker_launcher(defaults, args) │
│ 917 │ else: │
│ ❱ 918 │ │ simple_launcher(args) │
│ 919 │
│ 920 │
│ 921 def main(): │
│ │
│ /usr/local/lib/python3.10/dist-packages/accelerate/commands/launch.py:580 in │
│ simple_launcher │
│ │
│ 577 │ process.wait() │
│ 578 │ if process.returncode != 0: │
│ 579 │ │ if not args.quiet: │
│ ❱ 580 │ │ │ raise subprocess.CalledProcessError(returncode=process.ret │
│ 581 │ │ else: │
│ 582 │ │ │ sys.exit(1) │
│ 583 │
╰──────────────────────────────────────────────────────────────────────────────╯
CalledProcessError: Command '['/usr/bin/python3', 'sdxl_train_network.py',
'--sample_prompts=/content/LoRA/config/sample_prompt.toml',
'--config_file=/content/LoRA/config/config_file.toml']' returned non-zero exit
status 1.