Folder 40_FHDMNSX manchester united player portrait: 8 images found
Folder 40_FHDMNSX manchester united player portrait: 320 steps
max_train_steps = 3840
stop_text_encoder_training = 0
lr_warmup_steps = 384
accelerate launch --num_cpu_threads_per_process=2 "train_network.py" --enable_bucket --pretrained_model_name_or_path="C:/SD/stable-diffusion-webui/models/Stable-diffusion/realisticVisionV20_v20.safetensors" --train_data_dir="C:/SD/training/Kohya video/img" --reg_data_dir="C:/SD/training/Kohya video/reg" --resolution=512,512 --output_dir="C:/SD/training/Kohya video/model" --logging_dir="C:/SD/training/Kohya video/log" --network_alpha="1" --training_comment="FHDMNSX is the instance prompt and manchester united player portrait is the class promptso use FHDMNSX manchester united player portrait to be able to use this model" --save_model_as=safetensors --network_module=networks.lora --text_encoder_lr=5e-5 --unet_lr=0.0001 --network_dim=128 --output_name="test1" --lr_scheduler_num_cycles="12" --learning_rate="0.0001" --lr_scheduler="cosine" --lr_warmup_steps="384" --train_batch_size="1" --max_train_steps="3840" --save_every_n_epochs="1" --mixed_precision="fp16" --save_precision="fp16" --seed="1234" --cache_latents --optimizer_type="AdamW8bit" --max_data_loader_n_workers="0" --bucket_reso_steps=64 --xformers --bucket_no_upscale
prepare tokenizer
Use DreamBooth method.
prepare images.
found directory C:\SD\training\Kohya video\img\40_FHDMNSX manchester united player portrait contains 8 image files
found directory C:\SD\training\Kohya video\reg\1_manchester united player portrait contains 323 image files
320 train images with repeating.
323 reg images.
some of reg images are not used / 正則化画像の数が多いので、一部使用されない正則化画像があります
[Dataset 0]
  batch_size: 1
  resolution: (512, 512)
  enable_bucket: True
  min_bucket_reso: 256
  max_bucket_reso: 1024
  bucket_reso_steps: 64
  bucket_no_upscale: True

  [Subset 0 of Dataset 0]
    image_dir: "C:\SD\training\Kohya video\img\40_FHDMNSX manchester united player portrait"
    image_count: 8
    num_repeats: 40
    shuffle_caption: False
    keep_tokens: 0
    caption_dropout_rate: 0.0
    caption_dropout_every_n_epoches: 0
    caption_tag_dropout_rate: 0.0
    color_aug: False
    flip_aug: False
    face_crop_aug_range: None
    random_crop: False
    token_warmup_min: 1,
    token_warmup_step: 0,
    is_reg: False
    class_tokens: FHDMNSX manchester united player portrait
    caption_extension: .caption

  [Subset 1 of Dataset 0]
    image_dir: "C:\SD\training\Kohya video\reg\1_manchester united player portrait"
    image_count: 323
    num_repeats: 1
    shuffle_caption: False
    keep_tokens: 0
    caption_dropout_rate: 0.0
    caption_dropout_every_n_epoches: 0
    caption_tag_dropout_rate: 0.0
    color_aug: False
    flip_aug: False
    face_crop_aug_range: None
    random_crop: False
    token_warmup_min: 1,
    token_warmup_step: 0,
    is_reg: True
    class_tokens: manchester united player portrait
    caption_extension: .caption


[Dataset 0]
loading image sizes.
100%|███████████████████████████████████████████████████████████████████████████████| 328/328 [00:01<00:00, 237.91it/s]
make buckets
min_bucket_reso and max_bucket_reso are ignored if bucket_no_upscale is set, because bucket reso is defined by image size automatically / bucket_no_upscaleが指定された場合は、bucketの解像度は画像サイズから自動計算されるため、min_bucket_resoとmax_bucket_resoは無視 されます
number of images (including repeats) / 各bucketの画像枚数（繰り返し回数を含む）
bucket 0: resolution (512, 512), count: 640
mean ar error (without repeats): 0.0
prepare accelerator
Using accelerator 0.15.0 or above.
loading model for process 0/1
load StableDiffusion checkpoint
loading u-net: <All keys matched successfully>
loading vae: <All keys matched successfully>
Downloading pytorch_model.bin: 100%|██████████████████████████████████████████████| 1.71G/1.71G [02:39<00:00, 10.7MB/s]
C:\kohya_ss\venv\lib\site-packages\huggingface_hub\file_download.py:133: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\Users\Kashyap\.cache\huggingface\hub. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.
To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development
  warnings.warn(message)
loading text encoder: <All keys matched successfully>
Replace CrossAttention.forward to use xformers
[Dataset 0]
caching latents.
  0%|                                                                                          | 0/328 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "C:\kohya_ss\train_network.py", line 773, in <module>
    train(args)
  File "C:\kohya_ss\train_network.py", line 175, in train
    train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)  File "C:\kohya_ss\library\train_util.py", line 1391, in cache_latents
    dataset.cache_latents(vae, vae_batch_size, cache_to_disk, is_main_process)
  File "C:\kohya_ss\library\train_util.py", line 805, in cache_latents
    latents = vae.encode(img_tensors).latent_dist.sample().to("cpu")
  File "C:\kohya_ss\venv\lib\site-packages\diffusers\models\vae.py", line 566, in encode
    h = self.encoder(x)
  File "C:\kohya_ss\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\kohya_ss\venv\lib\site-packages\diffusers\models\vae.py", line 130, in forward
    sample = self.conv_in(sample)
  File "C:\kohya_ss\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:\kohya_ss\venv\lib\site-packages\torch\nn\modules\conv.py", line 457, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "C:\kohya_ss\venv\lib\site-packages\torch\nn\modules\conv.py", line 453, in _conv_forward
    return F.conv2d(input, weight, bias, self.stride,
RuntimeError: "slow_conv2d_cpu" not implemented for 'Half'
Traceback (most recent call last):
  File "C:\Users\Kashyap\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "C:\Users\Kashyap\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "C:\kohya_ss\venv\Scripts\accelerate.exe\__main__.py", line 7, in <module>
  File "C:\kohya_ss\venv\lib\site-packages\accelerate\commands\accelerate_cli.py", line 45, in main
    args.func(args)
  File "C:\kohya_ss\venv\lib\site-packages\accelerate\commands\launch.py", line 1104, in launch_command
    simple_launcher(args)
  File "C:\kohya_ss\venv\lib\site-packages\accelerate\commands\launch.py", line 567, in simple_launcher
    raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError: Command '['C:\\kohya_ss\\venv\\Scripts\\python.exe', 'train_network.py', '--enable_bucket', '--pretrained_model_name_or_path=C:/SD/stable-diffusion-webui/models/Stable-diffusion/realisticVisionV20_v20.safetensors', '--train_data_dir=C:/SD/training/Kohya video/img', '--reg_data_dir=C:/SD/training/Kohya video/reg', '--resolution=512,512', '--output_dir=C:/SD/training/Kohya video/model', '--logging_dir=C:/SD/training/Kohya video/log', '--network_alpha=1', '--training_comment=FHDMNSX is the instance prompt and manchester united player portrait is the class promptso use FHDMNSX manchester united player portrait to be able to use this model', '--save_model_as=safetensors', '--network_module=networks.lora', '--text_encoder_lr=5e-5', '--unet_lr=0.0001', '--network_dim=128', '--output_name=test1', '--lr_scheduler_num_cycles=12', '--learning_rate=0.0001', '--lr_scheduler=cosine', '--lr_warmup_steps=384', '--train_batch_size=1', '--max_train_steps=3840', '--save_every_n_epochs=1', '--mixed_precision=fp16', '--save_precision=fp16', '--seed=1234', '--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.