Getting Errors while using Model Training

[IsmetAcar77]

Hi, I'm new here. I used to trains model in kohya in february to march but after some time I left the SD. Recently I need to train a model and Tried to use Kohya but I got error sayin: ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\train_network.py:974 in │ │ │ │ 971 │ args = train_util.read_config_from_file(args, parser) │ │ 972 │ │ │ 973 │ trainer = NetworkTrainer() │ │ ❱ 974 │ trainer.train(args) │ │ 975 │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\train_network.py:250 in train │ │ │ │ 247 │ │ │ vae.requiresgrad(False) │ │ 248 │ │ │ vae.eval() │ │ 249 │ │ │ with torch.no_grad(): │ │ ❱ 250 │ │ │ │ train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_l │ │ 251 │ │ │ vae.to("cpu") │ │ 252 │ │ │ if torch.cuda.is_available(): │ │ 253 │ │ │ │ torch.cuda.empty_cache() │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\library\train_util.py:1730 in cache_latents │ │ │ │ 1727 │ def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_process= │ │ 1728 │ │ for i, dataset in enumerate(self.datasets): │ │ 1729 │ │ │ print(f"[Dataset {i}]") │ │ ❱ 1730 │ │ │ dataset.cache_latents(vae, vae_batch_size, cache_to_disk, is_main_process) │ │ 1731 │ │ │ 1732 │ def is_latent_cacheable(self) -> bool: │ │ 1733 │ │ return all([dataset.is_latent_cacheable() for dataset in self.datasets]) │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\library\train_util.py:913 in cache_latents │ │ │ │ 910 │ │ │ img_tensors = torch.stack(images, dim=0) │ │ 911 │ │ │ img_tensors = img_tensors.to(device=vae.device, dtype=vae.dtype) │ │ 912 │ │ │ │ │ ❱ 913 │ │ │ latents = vae.encode(img_tensors).latent_dist.sample().to("cpu") │ │ 914 │ │ │ │ │ 915 │ │ │ for info, latent in zip(batch, latents): │ │ 916 │ │ │ │ # check NaN │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\venv\lib\site-packages\diffusers\utils\accelerate_uti │ │ ls.py:46 in wrapper │ │ │ │ 43 │ def wrapper(self, *args, kwargs): │ │ 44 │ │ if hasattr(self, "_hf_hook") and hasattr(self._hf_hook, "pre_forward"): │ │ 45 │ │ │ self._hf_hook.pre_forward(self) │ │ ❱ 46 │ │ return method(self, *args, *kwargs) │ │ 47 │ │ │ 48 │ return wrapper │ │ 49 │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\venv\lib\site-packages\diffusers\models\autoencoder_k │ │ l.py:164 in encode │ │ │ │ 161 │ │ if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > │ │ 162 │ │ │ return self.tiled_encode(x, return_dict=return_dict) │ │ 163 │ │ │ │ ❱ 164 │ │ h = self.encoder(x) │ │ 165 │ │ moments = self.quant_conv(h) │ │ 166 │ │ posterior = DiagonalGaussianDistribution(moments) │ │ 167 │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\venv\lib\site-packages\torch\nn\modules\module.py:113 │ │ 0 in _call_impl │ │ │ │ 1127 │ │ # this function, and just call forward. │ │ 1128 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks o │ │ 1129 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ │ ❱ 1130 │ │ │ return forward_call(input, kwargs) │ │ 1131 │ │ # Do not call functions when jit is used │ │ 1132 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ │ 1133 │ │ if self._backward_hooks or _global_backward_hooks: │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\venv\lib\site-packages\diffusers\models\vae.py:142 in │ │ forward │ │ │ │ 139 │ │ │ │ sample = down_block(sample) │ │ 140 │ │ │ │ │ 141 │ │ │ # middle │ │ ❱ 142 │ │ │ sample = self.mid_block(sample) │ │ 143 │ │ │ │ 144 │ │ # post-process │ │ 145 │ │ sample = self.conv_norm_out(sample) │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\venv\lib\site-packages\torch\nn\modules\module.py:113 │ │ 0 in _call_impl │ │ │ │ 1127 │ │ # this function, and just call forward. │ │ 1128 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks o │ │ 1129 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ │ ❱ 1130 │ │ │ return forward_call(*input, *kwargs) │ │ 1131 │ │ # Do not call functions when jit is used │ │ 1132 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ │ 1133 │ │ if self._backward_hooks or _global_backward_hooks: │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\venv\lib\site-packages\diffusers\models\unet_2d_block │ │ s.py:472 in forward │ │ │ │ 469 │ │ hidden_states = self.resnets[0](hidden_states, temb) │ │ 470 │ │ for attn, resnet in zip(self.attentions, self.resnets[1:]): │ │ 471 │ │ │ if attn is not None: │ │ ❱ 472 │ │ │ │ hidden_states = attn(hidden_states, temb=temb) │ │ 473 │ │ │ hidden_states = resnet(hidden_states, temb) │ │ 474 │ │ │ │ 475 │ │ return hidden_states │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\venv\lib\site-packages\torch\nn\modules\module.py:113 │ │ 0 in _call_impl │ │ │ │ 1127 │ │ # this function, and just call forward. │ │ 1128 │ │ if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks o │ │ 1129 │ │ │ │ or _global_forward_hooks or _global_forward_pre_hooks): │ │ ❱ 1130 │ │ │ return forward_call(input, kwargs) │ │ 1131 │ │ # Do not call functions when jit is used │ │ 1132 │ │ full_backward_hooks, non_full_backward_hooks = [], [] │ │ 1133 │ │ if self._backward_hooks or _global_backward_hooks: │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\venv\lib\site-packages\diffusers\models\attention_pro │ │ cessor.py:320 in forward │ │ │ │ 317 │ │ # The Attention class can call different attention processors / attention func │ │ 318 │ │ # here we simply pass along all tensors to the selected processor class │ │ 319 │ │ # For standard processors that are defined here, `cross_attention_kwargs` is e │ │ ❱ 320 │ │ return self.processor( │ │ 321 │ │ │ self, │ │ 322 │ │ │ hidden_states, │ │ 323 │ │ │ encoder_hidden_states=encoder_hidden_states, │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\venv\lib\site-packages\diffusers\models\attention_pro │ │ cessor.py:1045 in call │ │ │ │ 1042 │ │ key = attn.head_to_batch_dim(key).contiguous() │ │ 1043 │ │ value = attn.head_to_batch_dim(value).contiguous() │ │ 1044 │ │ │ │ ❱ 1045 │ │ hidden_states = xformers.ops.memory_efficient_attention( │ │ 1046 │ │ │ query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=att │ │ 1047 │ │ ) │ │ 1048 │ │ hidden_states = hidden_states.to(query.dtype) │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ TypeError: memory_efficient_attention() got an unexpected keyword argument 'scale' ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ C:\Users\Ismet\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\Ismet\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\Ismet\Documents\SD\Kohya\kohya_ss\venv\lib\site-packages\accelerate\commands\accelerate │ │ _cli.py:45 in main │ │ │ │ 42 │ │ exit(1) │ │ 43 │ │ │ 44 │ # Run │ │ ❱ 45 │ args.func(args) │ │ 46 │ │ 47 │ │ 48 if name == "main": │ │ │ │ C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\venv\lib\site-packages\accelerate\commands\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\Ismet\Documents\SD\Kohya\kohya_ss\venv\lib\site-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.returncode, cmd=cmd) │ │ 581 │ │ else: │ │ 582 │ │ │ sys.exit(1) │ │ 583 │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ CalledProcessError: Command '['C:\Users\Ismet\Documents\SD\Kohya\kohya_ss\venv\Scripts\python.exe', './train_network.py', '--pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5', '--train_data_dir=C:/Users/Ismet/Documents/SD/LORA/Athena/Athena_512/Athena Lora/image', '--resolution=512,512', '--output_dir=C:/Users/Ismet/Documents/SD/LORA/Athena/Athena_512/Athena Lora/model', '--logging_dir=C:/Users/Ismet/Documents/SD/LORA/Athena/Athena_512/Athena Lora/log', '--network_alpha=128', '--save_model_as=safetensors', '--network_module=networks.lora', '--text_encoder_lr=5e-05', '--unet_lr=0.0001', '--network_dim=128', '--output_name=Athena', '--lr_scheduler_num_cycles=1', '--no_half_vae', '--learning_rate=0.0001', '--lr_scheduler=constant', '--train_batch_size=1', '--max_train_steps=24450', '--save_every_n_epochs=1', '--mixed_precision=fp16', '--save_precision=fp16', '--seed=1234', '--caption_extension=.txt', '--cache_latents', '--optimizer_type=AdamW8bit', '--max_data_loader_n_workers=1', '--clip_skip=2', '--bucket_reso_steps=64', '--mem_eff_attn', '--gradient_checkpointing', '--xformers', '--bucket_no_upscale']' returned non-zero exit status 1.

Note that: I'm not a software engineer this is just a hobby for me