UserWarning: TypedStorage is deprecated

I try to train LORA based on XL and when I start the training it always gives me the same error (I have tried several different configurations). I have a 12gb NVIDIA 3060 and 32GB of ram on the PC

Thanks in advance!

08:04:47-745672 INFO Start training LoRA Standard ...
08:04:47-746674 INFO Checking for duplicate image filenames in training data directory...
08:04:47-747674 INFO Valid image folder names found in: C:/Users/Usuario/Documents/IA/Kohya/Supercute\img
08:04:47-749676 INFO Valid image folder names found in: C:/Users/Usuario/Documents/IA/Kohya/Supercute\reg
08:04:47-750678 INFO Folder 40_supercute style: 10 images found
08:04:47-751678 INFO Folder 40_supercute style: 400 steps
08:04:47-752678 WARNING Regularisation images are used... Will double the number of steps required...
08:04:47-753679 INFO Total steps: 400
08:04:47-754680 INFO Train batch size: 5
08:04:47-754680 INFO Gradient accumulation steps: 1
08:04:47-755682 INFO Epoch: 10
08:04:47-756682 INFO Regulatization factor: 2
08:04:47-757684 INFO max_train_steps (400 / 5 / 1 * 10 * 2) = 1600
08:04:47-758684 INFO stop_text_encoder_training = 0
08:04:47-759685 INFO lr_warmup_steps = 0
08:04:47-760686 INFO Saving training config to C:/Users/Usuario/Documents/IA/Kohya/Supercute\model\XL_supercute_style_20230813-080447.json...
08:04:47-763690 INFO accelerate launch --num_cpu_threads_per_process=2 "./sdxl_train_network.py" --enable_bucket --min_bucket_reso=256 --max_bucket_reso=2048 --pretrained_model_name_or_path="C:/Users/Usuario/Documents/IA/SD
XL/stable-diffusion-webui/models/Stable-diffusion/sd_xl_base_1.0.safetensors" --train_data_dir="C:/Users/Usuario/Documents/IA/Kohya/Supercute\img"
--reg_data_dir="C:/Users/Usuario/Documents/IA/Kohya/Supercute\reg" --resolution="1024,1024" --output_dir="C:/Users/Usuario/Documents/IA/Kohya/Supercute\model"
--logging_dir="C:/Users/Usuario/Documents/IA/Kohya/Supercute\log" --network_alpha="1" --save_model_as=safetensors --network_module=networks.lora --text_encoder_lr=0.0009 --unet_lr=0.0009 --network_dim=256
--output_name="XL_supercute_style" --lr_scheduler_num_cycles="10" --no_half_vae --learning_rate="0.0009" --lr_scheduler="constant" --train_batch_size="5" --max_train_steps="1600" --save_every_n_epochs="1"
--mixed_precision="bf16" --save_precision="bf16" --caption_extension=".txt" --cache_latents --cache_latents_to_disk --optimizer_type="Adafactor" --optimizer_args scale_parameter=False relative_step=False
warmup_init=False --max_data_loader_n_workers="0" --bucket_reso_steps=64 --gradient_checkpointing --xformers --bucket_no_upscale --noise_offset=0.0
A matching Triton is not available, some optimizations will not be enabled.
Error caught was: No module named 'triton'
prepare tokenizers
Using DreamBooth method.
prepare images.
found directory C:\Users\Usuario\Documents\IA\Kohya\Supercute\img\40_supercute style contains 10 image files
found directory C:\Users\Usuario\Documents\IA\Kohya\Supercute\reg\1_style contains 1000 image files
No caption file found for 1000 images. Training will continue without captions for these images. If class token exists, it will be used. / 1000枚の画像にキャプションファイルが見つかりませんでした。これらの画像についてはキャプションなしで学習を続行します。class tokenが存在する場合はそれを使います。
C:\Users\Usuario\Documents\IA\Kohya\Supercute\reg\1_style\style_0001.jpg
C:\Users\Usuario\Documents\IA\Kohya\Supercute\reg\1_style\style_0002.jpg
C:\Users\Usuario\Documents\IA\Kohya\Supercute\reg\1_style\style_0003.jpg
C:\Users\Usuario\Documents\IA\Kohya\Supercute\reg\1_style\style_0004.jpg
C:\Users\Usuario\Documents\IA\Kohya\Supercute\reg\1_style\style_0005.jpg
C:\Users\Usuario\Documents\IA\Kohya\Supercute\reg\1_style\style_0006.jpg... and 995 more
400 train images with repeating.
1000 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: 5
resolution: (1024, 1024)
enable_bucket: True
min_bucket_reso: 256
max_bucket_reso: 2048
bucket_reso_steps: 64
bucket_no_upscale: True

[Subset 0 of Dataset 0]
image_dir: "C:\Users\Usuario\Documents\IA\Kohya\Supercute\img\40_supercute style"
image_count: 10
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: supercute style
caption_extension: .txt

[Subset 1 of Dataset 0]
image_dir: "C:\Users\Usuario\Documents\IA\Kohya\Supercute\reg\1_style"
image_count: 1000
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: style
caption_extension: .txt

[Dataset 0]
loading image sizes.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 410/410 [00:00<00:00, 1357.17it/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 (1024, 1024), count: 800
mean ar error (without repeats): 0.0
Warning: SDXL has been trained with noise_offset=0.0357 / SDXLはnoise_offset=0.0357で学習されています
noise_offset is set to 0.0 / noise_offsetが0.0に設定されました
preparing accelerator
loading model for process 0/1
load StableDiffusion checkpoint: C:/Users/Usuario/Documents/IA/SD XL/stable-diffusion-webui/models/Stable-diffusion/sd_xl_base_1.0.safetensors
building U-Net
loading U-Net from checkpoint
U-Net:
building text encoders
loading text encoders from checkpoint
text encoder 1:
text encoder 2:
building VAE
loading VAE from checkpoint
VAE:
Enable xformers for U-Net
import network module: networks.lora
[Dataset 0]
caching latents.
checking cache validity...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 410/410 [00:00<00:00, 420.47it/s]
caching latents...
0it [00:00, ?it/s]
create LoRA network. base dim (rank): 256, alpha: 1.0
neuron dropout: p=None, rank dropout: p=None, module dropout: p=None
create LoRA for Text Encoder 1:
create LoRA for Text Encoder 2:
create LoRA for Text Encoder: 264 modules.
create LoRA for U-Net: 722 modules.
enable LoRA for text encoder
enable LoRA for U-Net
prepare optimizer, data loader etc.
use Adafactor optimizer | {'scale_parameter': False, 'relative_step': False, 'warmup_init': False}
because max_grad_norm is set, clip_grad_norm is enabled. consider set to 0 / max_grad_normが設定されているためclip_grad_normが有効になります。0に設定して無効にしたほうがいいかもしれません
constant_with_warmup will be good / スケジューラはconstant_with_warmupが良いかもしれません
running training / 学習開始
num train images * repeats / 学習画像の数×繰り返し回数: 400
num reg images / 正則化画像の数: 1000
num batches per epoch / 1epochのバッチ数: 160
num epochs / epoch数: 10
batch size per device / バッチサイズ: 5
gradient accumulation steps / 勾配を合計するステップ数 = 1
total optimization steps / 学習ステップ数: 1600
steps: 0%| | 0/1600 [00:00

Start with the warning location shown in the log, venv\lib\site-packages\xformers\ops\fmha\flash.py:339, and reproduce the SDXL LoRA training configuration if possible. Check whether the warning comes from this project's code or the installed xformers dependency; done should mean identifying its source and documenting or resolving the supported fix.

Written by the indexing model from the issue text.