Need help installed but get this error when trying to train

[BotLifeGaming]

Been at this all day

===================================BUG REPORT===================================
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CUDA SETUP: Loading binary C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\venv\lib\site-packages\bitsandbytes\libbitsandbytes_cuda116.dll...
use 8-bit AdamW optimizer | {}
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\train_db.py:486 in │
│ │
│ 483 │ args = parser.parse_args() │
│ 484 │ args = train_util.read_config_from_file(args, parser) │
│ 485 │ │
│ ❱ 486 │ train(args) │
│ 487 │
│ │
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\train_db.py:206 in train │
│ │
│ 203 │ │
│ 204 │ # acceleratorがなんかよろしくやってくれるらしい │
│ 205 │ if train_text_encoder: │
│ ❱ 206 │ │ unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prep │
│ 207 │ │ │ unet, text_encoder, optimizer, train_dataloader, lr_scheduler │
│ 208 │ │ ) │
│ 209 │ else: │
│ │
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\venv\lib\site-packages\accelerate\accele │
│ rator.py:1143 in prepare │
│ │
│ 1140 │ │ elif self.distributed_type == DistributedType.MEGATRON_LM: │
│ 1141 │ │ │ result = self._prepare_megatron_lm(*args) │
│ 1142 │ │ else: │
│ ❱ 1143 │ │ │ result = tuple( │
│ 1144 │ │ │ │ self._prepare_one(obj, first_pass=True, device_placement=d) for obj, d i │
│ 1145 │ │ │ ) │
│ 1146 │ │ │ result = tuple(self._prepare_one(obj, device_placement=d) for obj, d in zip( │
│ │
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\venv\lib\site-packages\accelerate\accele │
│ rator.py:1144 in │
│ │
│ 1141 │ │ │ result = self._prepare_megatron_lm(*args) │
│ 1142 │ │ else: │
│ 1143 │ │ │ result = tuple( │
│ ❱ 1144 │ │ │ │ self._prepare_one(obj, first_pass=True, device_placement=d) for obj, d i │
│ 1145 │ │ │ ) │
│ 1146 │ │ │ result = tuple(self._prepare_one(obj, device_placement=d) for obj, d in zip( │
│ 1147 │
│ │
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\venv\lib\site-packages\accelerate\accele │
│ rator.py:995 in _prepare_one │
│ │
│ 992 │ │ │ if isinstance(obj, torch.utils.data.DataLoader): │
│ 993 │ │ │ │ return self.prepare_data_loader(obj, device_placement=device_placement) │
│ 994 │ │ │ elif isinstance(obj, torch.nn.Module): │
│ ❱ 995 │ │ │ │ return self.prepare_model(obj, device_placement=device_placement) │
│ 996 │ │ │ elif isinstance(obj, torch.optim.Optimizer): │
│ 997 │ │ │ │ optimizer = self.prepare_optimizer(obj, device_placement=device_placemen │
│ 998 │ │ │ │ return optimizer │
│ │
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\venv\lib\site-packages\accelerate\accele │
│ rator.py:1290 in prepare_model │
│ │
│ 1287 │ │ if self.state.dynamo_plugin.backend != DynamoBackend.NO: │
│ 1288 │ │ │ if not is_torch_version(">=", "2.0"): │
│ 1289 │ │ │ │ raise ValueError("Using torch.compile requires PyTorch 2.0 or higher." │
│ ❱ 1290 │ │ │ model = torch.compile(model, **self.state.dynamo_plugin.to_kwargs()) │
│ 1291 │ │ return model │
│ 1292 │ │
│ 1293 │ def prepare_deepspeed(self, *args): │
│ │
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\venv\lib\site-packages\torch_init.py │
│ :1441 in compile │
│ │
│ 1438 │ │ mode = "default" │
│ 1439 │ if backend == "inductor": │
│ 1440 │ │ backend = _TorchCompileInductorWrapper(mode, options, dynamic) │
│ ❱ 1441 │ return torch._dynamo.optimize(backend=backend, nopython=fullgraph, dynamic=dynamic, │
│ 1442 │
│ 1443 │
│ 1444 def _register_device_module(device_type, module): │
│ │
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\venv\lib\site-packages\torch_dynamo\eva │
│ l_frame.py:413 in optimize │
│ │
│ 410 │ │ def toy_example(a, b): │
│ 411 │ │ │ ... │
│ 412 │ """ │
│ ❱ 413 │ check_if_dynamo_supported() │
│ 414 │ # Note: The hooks object could be global instead of passed around, however that wo │
│ 415 │ # for a confusing API usage and plumbing story wherein we nest multiple .optimize ca │
│ 416 │ # There is some prior art around this, w/r/t nesting backend calls are enforced to b │
│ │
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\venv\lib\site-packages\torch_dynamo\eva │
│ l_frame.py:375 in check_if_dynamo_supported │
│ │
│ 372 │
│ 373 def check_if_dynamo_supported(): │
│ 374 │ if sys.platform == "win32": │
│ ❱ 375 │ │ raise RuntimeError("Windows not yet supported for torch.compile") │
│ 376 │ if sys.version_info >= (3, 11): │
│ 377 │ │ raise RuntimeError("Python 3.11+ not yet supported for torch.compile") │
│ 378 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
RuntimeError: Windows not yet supported for torch.compile
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\AppData\Local\Programs\Python\Python310\lib\runpy.py:196 in │
│ _run_module_as_main │
│ │
│ 193 │ main_globals = sys.modules["main"].dict │
│ 194 │ if alter_argv: │
│ 195 │ │ sys.argv[0] = mod_spec.origin │
│ ❱ 196 │ return _run_code(code, main_globals, None, │
│ 197 │ │ │ │ │ "main", mod_spec) │
│ 198 │
│ 199 def run_module(mod_name, init_globals=None, │
│ │
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\AppData\Local\Programs\Python\Python310\lib\runpy.py:86 in │
│ _run_code │
│ │
│ 83 │ │ │ │ │ loader = loader, │
│ 84 │ │ │ │ │ package = pkg_name, │
│ 85 │ │ │ │ │ spec = mod_spec) │
│ ❱ 86 │ exec(code, run_globals) │
│ 87 │ return run_globals │
│ 88 │
│ 89 def _run_module_code(code, init_globals=None, │
│ │
│ in :7 │
│ │
│ 4 from accelerate.commands.accelerate_cli import main │
│ 5 if name == 'main': │
│ 6 │ sys.argv[0] = re.sub(r'(-script.pyw|.exe)?$', '', sys.argv[0]) │
│ ❱ 7 │ sys.exit(main()) │
│ 8 │
│ │
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\venv\lib\site-packages\accelerate\comman │
│ ds\accelerate_cli.py:45 in main │
│ │
│ 42 │ │ exit(1) │
│ 43 │ │
│ 44 │ # Run │
│ ❱ 45 │ args.func(args) │
│ 46 │
│ 47 │
│ 48 if name == "main": │
│ │
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\venv\lib\site-packages\accelerate\comman │
│ ds\launch.py:918 in launch_command │
│ │
│ 915 │ elif defaults is not None and defaults.compute_environment == ComputeEnvironment.AMA │
│ 916 │ │ sagemaker_launcher(defaults, args) │
│ 917 │ else: │
│ ❱ 918 │ │ simple_launcher(args) │
│ 919 │
│ 920 │
│ 921 def main(): │
│ │
│ C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\venv\lib\site-packages\accelerate\comman │
│ ds\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.returncode, cmd=cmd) │
│ 581 │ │ else: │
│ 582 │ │ │ sys.exit(1) │
│ 583 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
CalledProcessError: Command '['C:\Users\GamingPC.DESKTOP-UE5QGUL\Desktop\Lora\kohya_ss\venv\Scripts\python.exe','train_db.py', '--enable_bucket', '--pretrained_model_name_or_path=C:/Users/GamingPC.DESKTOP-UE5QGUL/Desktop/SUPER SD
2.0/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned.ckpt',
'--train_data_dir=C:/Users/GamingPC.DESKTOP-UE5QGUL/Desktop/Lora/Lora_training_data/jkabak/image',
'--resolution=512,512', '--output_dir=C:/Users/GamingPC.DESKTOP-UE5QGUL/Desktop/Lora/Lora_training_data/jkabak/model',
'--logging_dir=C:/Users/GamingPC.DESKTOP-UE5QGUL/Desktop/Lora/Lora_training_data/jkabak/log',
'--save_model_as=safetensors', '--output_name=last', '--max_data_loader_n_workers=0', '--learning_rate=1e-05',
'--lr_scheduler=cosine', '--lr_warmup_steps=60', '--train_batch_size=1', '--max_train_steps=600',
'--save_every_n_epochs=1', '--mixed_precision=fp16', '--save_precision=fp16', '--cache_latents',
'--optimizer_type=AdamW8bit', '--max_data_loader_n_workers=0', '--bucket_reso_steps=64', '--xformers',
'--bucket_no_upscale']' returned non-zero exit status 1.

[J-Kazusa]

+1

[JetsprintRacer]

I got similar error, when tried to train SDXL LoRA, using a checkpoint from Civitai. Fixed this by downloading an SDXL base model.

[ronbere]

I got similar error, when tried to train SDXL LoRA, using a checkpoint from Civitai. Fixed this by downloading an SDXL base model.

same error for me