Error Training Lora - Githubissues

daniellumertz commented 1 year ago

Hello people I am having a trouble training a Lora like error 192 https://github.com/bmaltais/kohya_ss/issues/192 , but the solution there : "Alternatively, just replace library/train_util.py with kohya's new version https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py"

Didnt do nothing here, by the way I just git pull so it wouldnt work....

The error message is :


Traceback (most recent call last):
  File "C:\Users\DSL\Documents\_Documentos\_Imagens\SD\koya\kohya_ss\train_network.py", line 507, in <module>
    train(args)
  File "C:\Users\DSL\Documents\_Documentos\_Imagens\SD\koya\kohya_ss\train_network.py", line 150, in train
    optimizer_name, optimizer_args, optimizer = train_util.get_optimizer(args, trainable_params)
  File "C:\Users\DSL\Documents\_Documentos\_Imagens\SD\koya\kohya_ss\library\train_util.py", line 1536, in get_optimizer    assert optimizer_type is None or optimizer_type == "", "both option use_8bit_adam and optimizer_type are specified / use_8bit_adamとoptimizer_typeの両方のオプションが指定されています"
AssertionError: both option use_8bit_adam and optimizer_type are specified / use_8bit_adamとoptimizer_typeの両方のオプションが指定されています
Traceback (most recent call last):
  File "C:\Users\DSL\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\DSL\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "C:\Users\DSL\Documents\_Documentos\_Imagens\SD\koya\kohya_ss\venv\Scripts\accelerate.exe\__main__.py", line 7, in <module>
  File "C:\Users\DSL\Documents\_Documentos\_Imagens\SD\koya\kohya_ss\venv\lib\site-packages\accelerate\commands\accelerate_cli.py", line 45, in main
    args.func(args)
  File "C:\Users\DSL\Documents\_Documentos\_Imagens\SD\koya\kohya_ss\venv\lib\site-packages\accelerate\commands\launch.py", line 1104, in launch_command
    simple_launcher(args)
  File "C:\Users\DSL\Documents\_Documentos\_Imagens\SD\koya\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:\\Users\\DSL\\Documents\\_Documentos\\_Imagens\\SD\\koya\\kohya_ss\\venv\\Scripts\\python.exe', 'train_network.py', '--enable_bucket', '--pretrained_model_name_or_path=C:/Users/DSL/Documents/_Documentos/_Imagens/SD/stable-diffusion-webui/models/Stable-diffusion/cheeseDaddys_35.safetensors', '--train_data_dir=C:\\Users\\DSL\\Desktop\\mjwall\\image', '--resolution=512,512', '--output_dir=C:\\Users\\DSL\\Desktop\\mjwall\\model', '--logging_dir=C:\\Users\\DSL\\Desktop\\mjwall\\log', '--network_alpha=1', '--save_model_as=safetensors', '--network_module=networks.lora', '--text_encoder_lr=5e-5', '--unet_lr=0.0001', '--network_dim=8', '--output_name=last', '--lr_scheduler_num_cycles=1', '--learning_rate=0.0001', '--lr_scheduler=cosine', '--lr_warmup_steps=200', '--train_batch_size=1', '--max_train_steps=2000', '--save_every_n_epochs=1', '--mixed_precision=fp16', '--save_precision=fp16', '--seed=1234', '--cache_latents', '--optimizer_type=AdamW', '--bucket_reso_steps=64', '--xformers', '--use_8bit_adam', '--bucket_no_upscale']' returned non-zero exit status 1.

what solved to me is turning off 8bit_adam

florinbarbisch commented 1 year ago

Got the same error. Turning off 8bit_adam worked too.

GreaseBal commented 1 year ago

how do i turn 8bit_adam off?

florinbarbisch commented 1 year ago

Under training parameters and then advanced is the checkbox you are looking for.

yugi778899 commented 1 year ago

@florinbarbisch YOU ARE MY HERO

ProducingIt commented 1 year ago

Under training parameters and then advanced is the checkbox you are looking for.

I tried to look there, but the checkbox for 8bit isn't there for me in the GUI. What's weird is that I know where it's supposed to be as I have been watching tutorials, but it seems that I missing that option that others see. Could possibly be because of an update? Is there another way one could turn it off?

hunny1389 commented 1 year ago

Same... I too couldn't find it

aphaits commented 1 year ago

Traceback (most recent call last): File "F:\StableDiffusion\kohya_ss\train_network.py", line 783, in train(args) File "F:\StableDiffusion\kohya_ss\train_network.py", line 253, in train unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( File "F:\StableDiffusion\kohya_ss\venv\lib\site-packages\accelerate\accelerator.py", line 1122, in prepare result = tuple( File "F:\StableDiffusion\kohya_ss\venv\lib\site-packages\accelerate\accelerator.py", line 1123, in self._prepare_one(obj, first_pass=True, device_placement=d) for obj, d in zip(args, device_placement) File "F:\StableDiffusion\kohya_ss\venv\lib\site-packages\accelerate\accelerator.py", line 977, in _prepare_one return self.prepare_model(obj, device_placement=device_placement) File "F:\StableDiffusion\kohya_ss\venv\lib\site-packages\accelerate\accelerator.py", line 1265, in prepare_model raise ValueError("Using torch.compile requires PyTorch 2.0 or higher.") ValueError: Using torch.compile requires PyTorch 2.0 or higher. Traceback (most recent call last): File "C:\Users\Aphaits\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\Aphaits\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 86, in _run_code exec(code, run_globals) File "F:\StableDiffusion\kohya_ss\venv\Scripts\accelerate.exe__main__.py", line 7, in File "F:\StableDiffusion\kohya_ss\venv\lib\site-packages\accelerate\commands\accelerate_cli.py", line 45, in main args.func(args) File "F:\StableDiffusion\kohya_ss\venv\lib\site-packages\accelerate\commands\launch.py", line 923, in launch_command simple_launcher(args) File "F:\StableDiffusion\kohya_ss\venv\lib\site-packages\accelerate\commands\launch.py", line 579, in simple_launcher raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd) subprocess.CalledProcessError: Command '['F:\StableDiffusion\kohya_ss\venv\Scripts\python.exe', 'train_network.py', '--enable_bucket', '--pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5', '--train_data_dir=F:/StableDiffusion/Training/Gadgetronics/gadgetronics_keyword/image', '--resolution=512,512', '--output_dir=F:/StableDiffusion/Training/Gadgetronics/gadgetronics_keyword/model', '--logging_dir=F:/StableDiffusion/Training/Gadgetronics/gadgetronics_keyword/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=gadgetronics_keyword', '--lr_scheduler_num_cycles=1', '--learning_rate=0.0001', '--lr_scheduler=constant', '--train_batch_size=1', '--max_train_steps=11100', '--save_every_n_epochs=1', '--mixed_precision=fp16', '--save_precision=fp16', '--seed=1234', '--caption_extension=.txt', '--cache_latents', '--optimizer_type=AdamW', '--max_data_loader_n_workers=1', '--clip_skip=2', '--bucket_reso_steps=64', '--xformers', '--bucket_no_upscale']' returned non-zero exit status 1.

same issue here, changing AdamW8bit to AdamW same results

ZachariBarnes commented 1 year ago

Having this error also, All the optimizers seem to fail with the error: ValueError: Using torch.compile requires PyTorch 2.0 or higher.

wojwilk commented 1 year ago

It seems like i have simmilar issue.

Ive setup freshly Stable Diffusion and Lora and I'm stuck with this error:

←[1;33m=============================================================
Modules installed outside the virtual environment were found.
This can cause issues. Please review the installed modules.

You can uninstall all local modules with:

←[1;34mdeactivate
pip freeze > uninstall.txt
pip uninstall -y -r uninstall.txt
←[1;33m=============================================================←[0m

11:26:23-202822 INFO     nVidia toolkit detected
11:26:25-387345 INFO     Torch 2.0.1+cu118
11:26:25-437329 INFO     Torch backend: nVidia CUDA 11.8 cuDNN 8700
11:26:25-440027 INFO     Torch detected GPU: NVIDIA GeForce RTX 4070 VRAM 12281 Arch (8, 9) Cores 46
11:26:25-443028 INFO     Verifying requirements
11:26:28-508309 INFO     headless: False
11:26:28-513320 INFO     Load CSS...
Running on local URL:  http://127.0.0.1:7860

To create a public link, set `share=True` in `launch()`.
11:26:38-685997 INFO     Loading config...
11:28:06-006947 INFO     Start training LoRA Standard ...
11:28:06-008938 INFO     Folder 100_Device: 39 images found
11:28:06-009938 INFO     Folder 100_Device: 3900 steps
11:28:06-011939 INFO     Total steps: 3900
11:28:06-012941 INFO     Train batch size: 1
11:28:06-014949 INFO     Gradient accumulation steps: 1
11:28:06-015949 INFO     Epoch: 1
11:28:06-016949 INFO     Regulatization factor: 1
11:28:06-017940 INFO     max_train_steps (3900 / 1 / 1 * 1 * 1) = 3900
11:28:06-020940 INFO     stop_text_encoder_training = 0
11:28:06-021941 INFO     lr_warmup_steps = 0
11:28:06-023951 INFO     accelerate launch --num_cpu_threads_per_process=1 "train_network.py" --enable_bucket
                         --pretrained_model_name_or_path="D:/PROJEKTY/SD/stable-diffusion-webui/models/Stable-diffusion/
                         realisticVisionV20_v13.safetensors"
                         --train_data_dir="D:/PROJEKTY/SD/Lora_training_data/Hard_surface/Device/image"
                         --resolution=512,512 --output_dir="D:/PROJEKTY/SD/Lora_training_data/Hard_surface/Device/model"
                         --logging_dir="D:/PROJEKTY/SD/Lora_training_data/Hard_surface/Device/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="HardSurface_device"
                         --lr_scheduler_num_cycles="1" --learning_rate="0.0001" --lr_scheduler="constant"
                         --train_batch_size="1" --max_train_steps="3900" --save_every_n_epochs="1"
                         --mixed_precision="fp16" --save_precision="fp16" --caption_extension=".txt" --cache_latents
                         --optimizer_type="Lion" --max_data_loader_n_workers="0" --clip_skip=2 --bucket_reso_steps=64
                         --xformers --bucket_no_upscale
A matching Triton is not available, some optimizations will not be enabled.
Error caught was: No module named 'triton'
prepare tokenizer
Using DreamBooth method.
prepare images.
found directory D:\PROJEKTY\SD\Lora_training_data\Hard_surface\Device\image\100_Device contains 39 image files
3900 train images with repeating.
0 reg images.
no regularization images / 正則化画像が見つかりませんでした
[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: "D:\PROJEKTY\SD\Lora_training_data\Hard_surface\Device\image\100_Device"
    image_count: 39
    num_repeats: 100
    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: Device
    caption_extension: .txt

[Dataset 0]
loading image sizes.
100%|████████████████████████████████████████████████████████████████████████████████| 39/39 [00:00<00:00, 2051.34it/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: 3900
mean ar error (without repeats): 0.0
preparing accelerator
D:\PROJEKTY\SD\kohya_ss\venv\lib\site-packages\accelerate\accelerator.py:258: FutureWarning: `logging_dir` is deprecated and will be removed in version 0.18.0 of 🤗 Accelerate. Use `project_dir` instead.
  warnings.warn(
Using accelerator 0.15.0 or above.
loading model for process 0/1
load StableDiffusion checkpoint: D:/PROJEKTY/SD/stable-diffusion-webui/models/Stable-diffusion/realisticVisionV20_v13.safetensors
D:\PROJEKTY\SD\kohya_ss\venv\lib\site-packages\safetensors\torch.py:98: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()
  with safe_open(filename, framework="pt", device=device) as f:
loading u-net: <All keys matched successfully>
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ D:\PROJEKTY\SD\kohya_ss\train_network.py:864 in <module>                                         │
│                                                                                                  │
│   861 │   args = parser.parse_args()                                                             │
│   862 │   args = train_util.read_config_from_file(args, parser)                                  │
│   863 │                                                                                          │
│ ❱ 864 │   train(args)                                                                            │
│   865                                                                                            │
│                                                                                                  │
│ D:\PROJEKTY\SD\kohya_ss\train_network.py:160 in train                                            │
│                                                                                                  │
│   157 │   weight_dtype, save_dtype = train_util.prepare_dtype(args)                              │
│   158 │                                                                                          │
│   159 │   # モデルを読み込む                                                                     │
│ ❱ 160 │   text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accele   │
│   161 │                                                                                          │
│   162 │   # モデルに xformers とか memory efficient attention を組み込む                         │
│   163 │   train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)                │
│                                                                                                  │
│ D:\PROJEKTY\SD\kohya_ss\library\train_util.py:3061 in load_target_model                          │
│                                                                                                  │
│   3058 │   │   if pi == accelerator.state.local_process_index:                                   │
│   3059 │   │   │   print(f"loading model for process {accelerator.state.local_process_index}/{a  │
│   3060 │   │   │                                                                                 │
│ ❱ 3061 │   │   │   text_encoder, vae, unet, load_stable_diffusion_format = _load_target_model(   │
│   3062 │   │   │   │   args, weight_dtype, accelerator.device if args.lowram else "cpu"          │
│   3063 │   │   │   )                                                                             │
│   3064                                                                                           │
│                                                                                                  │
│ D:\PROJEKTY\SD\kohya_ss\library\train_util.py:3027 in _load_target_model                         │
│                                                                                                  │
│   3024 │   load_stable_diffusion_format = os.path.isfile(name_or_path)  # determine SD or Diffu  │
│   3025 │   if load_stable_diffusion_format:                                                      │
│   3026 │   │   print(f"load StableDiffusion checkpoint: {name_or_path}")                         │
│ ❱ 3027 │   │   text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoin  │
│   3028 │   else:                                                                                 │
│   3029 │   │   # Diffusers model is loaded to CPU                                                │
│   3030 │   │   print(f"load Diffusers pretrained models: {name_or_path}")                        │
│                                                                                                  │
│ D:\PROJEKTY\SD\kohya_ss\library\model_util.py:868 in                                             │
│ load_models_from_stable_diffusion_checkpoint                                                     │
│                                                                                                  │
│    865 │                                                                                         │
│    866 │   # Convert the VAE model.                                                              │
│    867 │   vae_config = create_vae_diffusers_config()                                            │
│ ❱  868 │   converted_vae_checkpoint = convert_ldm_vae_checkpoint(state_dict, vae_config)         │
│    869 │                                                                                         │
│    870 │   vae = AutoencoderKL(**vae_config).to(device)                                          │
│    871 │   info = vae.load_state_dict(converted_vae_checkpoint)                                  │
│                                                                                                  │
│ D:\PROJEKTY\SD\kohya_ss\library\model_util.py:384 in convert_ldm_vae_checkpoint                  │
│                                                                                                  │
│    381 │                                                                                         │
│    382 │   new_checkpoint = {}                                                                   │
│    383 │                                                                                         │
│ ❱  384 │   new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]   │
│    385 │   new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]       │
│    386 │   new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"  │
│    387 │   new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]     │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
KeyError: 'encoder.conv_in.weight'
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ C:\Users\Wilq\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\Wilq\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 <module>: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                                                                                              │
│                                                                                                  │
│ D:\PROJEKTY\SD\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__":                                                                  │
│                                                                                                  │
│ D:\PROJEKTY\SD\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():                                                                                │
│                                                                                                  │
│ D:\PROJEKTY\SD\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 '['D:\\PROJEKTY\\SD\\kohya_ss\\venv\\Scripts\\python.exe', 'train_network.py',
'--enable_bucket',
'--pretrained_model_name_or_path=D:/PROJEKTY/SD/stable-diffusion-webui/models/Stable-diffusion/realisticVisionV20_v13.sa
fetensors', '--train_data_dir=D:/PROJEKTY/SD/Lora_training_data/Hard_surface/Device/image', '--resolution=512,512',
'--output_dir=D:/PROJEKTY/SD/Lora_training_data/Hard_surface/Device/model',
'--logging_dir=D:/PROJEKTY/SD/Lora_training_data/Hard_surface/Device/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=HardSurface_device', '--lr_scheduler_num_cycles=1', '--learning_rate=0.0001',
'--lr_scheduler=constant', '--train_batch_size=1', '--max_train_steps=3900', '--save_every_n_epochs=1',
'--mixed_precision=fp16', '--save_precision=fp16', '--caption_extension=.txt', '--cache_latents',
'--optimizer_type=Lion', '--max_data_loader_n_workers=0', '--clip_skip=2', '--bucket_reso_steps=64', '--xformers',
'--bucket_no_upscale']' returned non-zero exit status 1.

kohya-ss commented 1 year ago

It appears that the model you specified does not include VAE. Could you please try another model and see if you get the same error?

If the error does not appear in the other model, it may work if you merge the VAE into that model with some model merger.

sebastianf777 commented 1 year ago

for me, this is the case I didn't know that the model should have to include VAE, thanks!

lastsummerx commented 1 year ago

I specify vae using "--vae", but same error occurs.

Traceback (most recent call last): File "/root/autodl-tmp/lora-scripts-minimal/./sd-scripts/train_network.py", line 873, in <module> train(args) File "/root/autodl-tmp/lora-scripts-minimal/./sd-scripts/train_network.py", line 168, in train text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accelerator) File "/root/autodl-tmp/lora-scripts-minimal/sd-scripts/library/train_util.py", line 3149, in load_target_model text_encoder, vae, unet, load_stable_diffusion_format = _load_target_model( File "/root/autodl-tmp/lora-scripts-minimal/sd-scripts/library/train_util.py", line 3115, in _load_target_model text_encoder, vae, unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, name_or_path, device) File "/root/autodl-tmp/lora-scripts-minimal/sd-scripts/library/model_util.py", line 871, in load_models_from_stable_diffusion_checkpoint info = vae.load_state_dict(converted_vae_checkpoint) File "/root/autodl-tmp/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for AutoencoderKL: Missing key(s) in state_dict: "encoder.mid_block.attentions.0.to_q.weight", "encoder.mid_block.attentions.0.to_q.bias", "encoder.mid_block.attentions.0.to_k.weight", "encoder.mid_block.attentions.0.to_k.bias", "encoder.mid_block.attentions.0.to_v.weight", "encoder.mid_block.attentions.0.to_v.bias", "encoder.mid_block.attentions.0.to_out.0.weight", "encoder.mid_block.attentions.0.to_out.0.bias", "decoder.mid_block.attentions.0.to_q.weight", "decoder.mid_block.attentions.0.to_q.bias", "decoder.mid_block.attentions.0.to_k.weight", "decoder.mid_block.attentions.0.to_k.bias", "decoder.mid_block.attentions.0.to_v.weight", "decoder.mid_block.attentions.0.to_v.bias", "decoder.mid_block.attentions.0.to_out.0.weight", "decoder.mid_block.attentions.0.to_out.0.bias". Unexpected key(s) in state_dict: "encoder.mid_block.attentions.0.query.weight", "encoder.mid_block.attentions.0.query.bias", "encoder.mid_block.attentions.0.key.weight", "encoder.mid_block.attentions.0.key.bias", "encoder.mid_block.attentions.0.value.weight", "encoder.mid_block.attentions.0.value.bias", "encoder.mid_block.attentions.0.proj_attn.weight", "encoder.mid_block.attentions.0.proj_attn.bias", "decoder.mid_block.attentions.0.query.weight", "decoder.mid_block.attentions.0.query.bias", "decoder.mid_block.attentions.0.key.weight", "decoder.mid_block.attentions.0.key.bias", "decoder.mid_block.attentions.0.value.weight", "decoder.mid_block.attentions.0.value.bias", "decoder.mid_block.attentions.0.proj_attn.weight", "decoder.mid_block.attentions.0.proj_attn.bias". Traceback (most recent call last): File "/root/autodl-tmp/lora-scripts-minimal/venv/bin/accelerate", line 8, in <module> sys.exit(main()) File "/root/autodl-tmp/stable-diffusion-webui/venv/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 45, in main args.func(args) File "/root/autodl-tmp/stable-diffusion-webui/venv/lib/python3.10/site-packages/accelerate/commands/launch.py", line 923, in launch_command simple_launcher(args) File "/root/autodl-tmp/stable-diffusion-webui/venv/lib/python3.10/site-packages/accelerate/commands/launch.py", line 579, in simple_launcher raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd) subprocess.CalledProcessError: Command '['/root/autodl-tmp/stable-diffusion-webui/venv/bin/python', './sd-scripts/train_network.py', '--vae=/root/autodl-tmp/stable-diffusion-webui/models/VAE/anime.vae.pt

kohya-ss / sd-scripts

Error Training Lora #230