No clue how to fix this

I started to train a lora in kohya ss yesterday and it started up fine, but took too long to finish. so i decided to end it prematurely and do it over night, but last night when i tried to run it. the gui.bat wouldn't open and i couldn't even start it up again. so i decided to reinstall it again, by deleting kohya_ss master folder and running the setup.bat. it now opens fine, but when i start my training i get this error in my cmd terminal:

System Information:
System: Windows, Release: 10, Version: 10.0.19045, Machine: AMD64, Processor: AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD

Python Information:
Version: 3.10.6, Implementation: CPython, Compiler: MSC v.1932 64 bit (AMD64)

Virtual Environment Information:
Path: D:\Kohya\kohya_ss-master\kohya_ss-master\venv

GPU Information:
Name: NVIDIA GeForce GTX 1660 SUPER, VRAM: 6144 MiB
←[33mWarning: GPU VRAM is less than 8GB and will likelly result in proper operations.←[0m

Validating that requirements are satisfied.
All requirements satisfied.
headless: False
Load CSS...
Running on local URL: http://127.0.0.1:7860

To create a public link, set `share=True` in `launch()`.
Folder 100_MaisieWilliams : 1400 steps
max_train_steps = 14000
stop_text_encoder_training = 0
lr_warmup_steps = 1400
accelerate launch --num_cpu_threads_per_process=2 "train_db.py" --enable_bucket --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" --train_data_dir="D:/LoraTraing/Maisie Williams/images" --resolution=512,512 --output_dir="D:/LoraTraing/Maisie Williams/model" --logging_dir="D:/LoraTraing/Maisie Williams/log" --save_model_as=safetensors --output_name="last" --max_data_loader_n_workers="0" --learning_rate="1e-05" --lr_scheduler="cosine" --lr_warmup_steps="1400" --train_batch_size="1" --max_train_steps="14000" --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
prepare tokenizer
prepare images.
found directory D:\LoraTraing\Maisie Williams\images\100_MaisieWilliams contains 14 image files
1400 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:\LoraTraing\Maisie Williams\images\100_MaisieWilliams"
image_count: 14
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: MaisieWilliams
caption_extension: .caption

[Dataset 0]
loading image sizes.
100%|█████████████████████████████████████████████████████████████████████████████████| 14/14 [00:00<00:00, 103.94it/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 (384, 512), count: 100
bucket 1: resolution (384, 576), count: 300
bucket 2: resolution (448, 384), count: 100
bucket 3: resolution (448, 576), count: 100
bucket 4: resolution (512, 512), count: 400
bucket 5: resolution (576, 384), count: 200
bucket 6: resolution (640, 384), count: 200
mean ar error (without repeats): 0.042837787686754406
prepare accelerator
D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\accelerate\accelerator.py:249: 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 Diffusers pretrained models: runwayml/stable-diffusion-v1-5
safety_checker\model.safetensors not found
Fetching 19 files: 100%|█████████████████████████████████████████████████████████████| 19/19 [00:00<00:00, 9491.64it/s]
You have disabled the safety checker for by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
CrossAttention.forward has been replaced to enable xformers.
[Dataset 0]
caching latents.
100%|██████████████████████████████████████████████████████████████████████████████████| 14/14 [00:18<00:00, 1.33s/it]
prepare optimizer, data loader etc.

===================================BUG REPORT===================================
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================================================================================
CUDA SETUP: Loading binary D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\bitsandbytes\libbitsandbytes_cuda116.dll...
use 8-bit AdamW optimizer | {}
running training / 学習開始
num train images * repeats / 学習画像の数×繰り返し回数: 1400
num reg images / 正則化画像の数: 0
num batches per epoch / 1epochのバッチ数: 1400
num epochs / epoch数: 10
batch size per device / バッチサイズ: 1
total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: 1
gradient ccumulation steps / 勾配を合計するステップ数 = 1
total optimization steps / 学習ステップ数: 14000
steps: 0%| | 0/14000 [00:00
train(args)
File "D:\Kohya\kohya_ss-master\kohya_ss-master\train_db.py", line 314, in train
noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\accelerate\utils\operations.py", line 495, in __call__
return convert_to_fp32(self.model_forward(*args, **kwargs))
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\torch\amp\autocast_mode.py", line 12, in decorate_autocast
return func(*args, **kwargs)
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\diffusers\models\unet_2d_condition.py", line 415, in forward
sample = upsample_block(
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\diffusers\models\unet_2d_blocks.py", line 1281, in forward
hidden_states = upsampler(hidden_states, upsample_size)
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\diffusers\models\resnet.py", line 139, in forward
hidden_states = self.conv(hidden_states)
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\torch\nn\modules\conv.py", line 457, in forward
return self._conv_forward(input, self.weight, self.bias)
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\torch\nn\modules\conv.py", line 453, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: CUDA out of memory. Tried to allocate 30.00 MiB (GPU 0; 6.00 GiB total capacity; 5.27 GiB already allocated; 0 bytes free; 5.30 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
steps: 0%| | 0/14000 [00:01
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\accelerate\commands\accelerate_cli.py", line 45, in main
args.func(args)
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\accelerate\commands\launch.py", line 923, in launch_command
simple_launcher(args)
File "D:\Kohya\kohya_ss-master\kohya_ss-master\venv\lib\site-packages\accelerate\commands\launch.py", line 579, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError: Command '['D:\\Kohya\\kohya_ss-master\\kohya_ss-master\\venv\\Scripts\\python.exe', 'train_db.py', '--enable_bucket', '--pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5', '--train_data_dir=D:/LoraTraing/Maisie Williams/images', '--resolution=512,512', '--output_dir=D:/LoraTraing/Maisie Williams/model', '--logging_dir=D:/LoraTraing/Maisie Williams/log', '--save_model_as=safetensors', '--output_name=last', '--max_data_loader_n_workers=0', '--learning_rate=1e-05', '--lr_scheduler=cosine', '--lr_warmup_steps=1400', '--train_batch_size=1', '--max_train_steps=14000', '--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.

really not sure what all this means, I've tried to research the cause, but to no avail and I'm now stuck.

if someone can help, that would be great, thank you :)

Start at train_db.py:314, where the reported traceback reaches the UNet call, and review the launch arguments alongside the CUDA out-of-memory message. Reproduce with the listed Windows, Python, GPU, and training configuration; done means the reported training command can begin its first step without the failure.

Written by the indexing model from the issue text.