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I get an error message that LORA cannot run because of a missing paging file, even though the required operating environment should be in place. #57
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According to the log there is ann issue with some of the installation packeges:
Best is probably to follow the folowing instructions: Open a new powershell window and run:
Then redo the kohya_ss installation. |
Thank you for replying. When uninstalled(venv) PS E:\dreambooth0k\sd-scripts> pip freeze > uninstall.txt When reinstalling
(venv) PS E:\dreambooth0k\sd-scripts> pip list
Package Version
---------- -------
pip 22.2.2
setuptools 63.2.0
wheel 0.38.4
Logs run after uninstall & reinstall
(venv) PS E:\dreambooth0k\sd-scripts> accelerate launch --num_cpu_threads_per_process=6 "train_network.py" --enable_bucket --pretrained_model_name_or_path="E:\train\dream\koyadream1\motomodel\model.safetensors" --train_data_dir="E:\train\dream\2\img" --reg_data_dir="E:\train\dream\2\reg" --resolution=512,512 --output_dir="E:\train\dream\2\model" --use_8bit_adam --xformers --logging_dir="E:\train\dream\2\log" --save_model_as=safetensors --network_module=networks.lora --text_encoder_lr=5e-5 --unet_lr=1e-3 --network_dim=4 --output_name="last" --learning_rate="1e-5" --lr_scheduler="cosine" --lr_warmup_steps="68" --train_batch_size="1" --max_train_steps="680" --save_every_n_epochs="1" --mixed_precision="fp16" --save_precision="fp16" --seed="1234" --cache_latents --xformers --use_8bit_adam
prepare tokenizer
Use DreamBooth method.
prepare train images.
found directory 40_asd 1girl contains 17 image files
680 train images with repeating.
prepare reg images.
found directory 1_1girl contains 1000 image files
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
loading image sizes.
100%|███████████████████████████████████████████████████████████████████████████████| 697/697 [00:01<00:00, 353.34it/s]
make buckets
number of images (including repeats) / 各bucketの画像枚数(繰り返し回数を含む)
bucket 0: resolution (256, 832), count: 0
bucket 1: resolution (256, 896), count: 0
bucket 2: resolution (256, 960), count: 0
bucket 3: resolution (256, 1024), count: 0
bucket 4: resolution (320, 704), count: 0
bucket 5: resolution (320, 768), count: 0
bucket 6: resolution (384, 640), count: 0
bucket 7: resolution (448, 576), count: 0
bucket 8: resolution (512, 512), count: 1360
bucket 9: resolution (576, 448), count: 0
bucket 10: resolution (640, 384), count: 0
bucket 11: resolution (704, 320), count: 0
bucket 12: resolution (768, 320), count: 0
bucket 13: resolution (832, 256), count: 0
bucket 14: resolution (896, 256), count: 0
bucket 15: resolution (960, 256), count: 0
bucket 16: resolution (1024, 256), count: 0
mean ar error (without repeats): 0.0
prepare accelerator
Using accelerator 0.15.0 or above.
load StableDiffusion checkpoint
loading u-net:
loading vae:
Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.7.self_attn.q_proj.weight', 'vision_model.encoder.layers.21.layer_norm2.weight', 'vision_model.encoder.layers.16.layer_norm2.bias', 'vision_model.encoder.layers.3.layer_norm1.bias', 'vision_model.encoder.layers.9.mlp.fc1.weight', 'vision_model.encoder.layers.16.self_attn.k_proj.bias', 'vision_model.encoder.layers.9.mlp.fc1.bias', 'vision_model.encoder.layers.22.mlp.fc1.weight', 'vision_model.encoder.layers.21.mlp.fc2.weight', 'vision_model.encoder.layers.13.self_attn.out_proj.bias', 'vision_model.encoder.layers.17.self_attn.out_proj.weight', 'vision_model.encoder.layers.3.mlp.fc2.weight', 'vision_model.encoder.layers.22.self_attn.q_proj.weight', 'vision_model.encoder.layers.4.mlp.fc2.bias', 'vision_model.encoder.layers.6.self_attn.q_proj.bias', 'vision_model.encoder.layers.20.self_attn.out_proj.weight', 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- This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
loading text encoder:
Replace CrossAttention.forward to use xformers
caching latents.
100%|████████████████████████████████████████████████████████████████████████████████| 697/697 [01:01<00:00, 11.37it/s]
import network module: networks.lora
create LoRA for Text Encoder: 72 modules.
create LoRA for U-Net: 192 modules.
enable LoRA for text encoder
enable LoRA for U-Net
prepare optimizer, data loader etc.
|
Have you tried increasing you windows paging system? How much RAM do you have. Less than 32GB of system memory you will require a large paging file because training take a lot of system RAM and GPU VRAM |
Thank you very much for your support. I was able to get the program to work by setting up the paging file and restarting. It turns out I had set it too low, to 16MB in order to free up space on the C drive, which was not ideal. I increased the paging file to 9048MB and was able to run the program at the lightest setting. I am still a little concerned that the GPU is almost 100%. Is it really possible to run this program with 8GB of VRAM? I also got a bit carried away and tried a slightly heavier setting (network_dim value from 4 to 128) and this did not work. I am wondering if I need to increase the paging file more, or if it is not possible on my machine in the first place. I will experiment to find the optimal settings. Again, thank you for your support. It is much appreciated. |
I have an RTX3060 with 12GB of VRAM and LORA is giving me an error that I don't have enough paging files and I can't learn.
Am I doing something wrong?
I would appreciate it if you could let me know.
●What we have already done
Reinstalled venv and pip install
Clean install
Change from multiple monitors to just one
Turned off chrome running at the same time and changed it to LORA only.
update
●Result
None of those things worked and I keep getting the same error.
●Situation
When I checked the task manager, 12GB of VRAM is being used 100% at the time of loading image sizes.
Then, the moment the console says training start, the VRAM usage goes to 0% and the following error message is displayed.
Operating Environment
Edition Windows 11 Home version 22H2
Processor 12th generation Intel(R) Core(TM) i5-12400F 2.50 GHz
RAM 64.0 GB (63.8 GB available)
System type 64-bit OS, x64-based processor
Graphics board is RTX3060 VRAM 12GB.
Due to the use of RAM disks, actual usable RAM is 53 GB.
Console executed: powershell
python version: 3.10.6
Commit hash: 785c4d8
error message
List of packages installed in the virtual environment:.
I am a person who does not know anything about program.
I would appreciate it if you could let me know if there is any other necessary information I need to provide in order to solve the problem.
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