To create a public link, set `share=True` in `launch()`.
Startup time: 16.5s (import gradio: 2.1s, import ldm: 1.2s, other imports: 1.8s, list extensions: 0.2s, setup codeformer: 0.2s, load scripts: 5.6s, load SD checkpoint: 3.4s, create ui: 1.9s).
Model dir set to: D:\AI\stable-diffusion-webui\models\dreambooth\MOIOHWXPRSON
Model dir set to: D:\AI\stable-diffusion-webui\models\dreambooth\MOIOHWXPRSON
Model dir set to: D:\AI\stable-diffusion-webui\models\dreambooth\MOIOHWXPRSON
Model dir set to: D:\AI\stable-diffusion-webui\models\dreambooth\MOIOHWXPRSON
Initializing dreambooth training...
The version of diffusers is less than or equal to 0.14.0. Performing monkey-patch...
Pre-processing images: moi: : 0it [00:00, ?it/s]
Nothing to generate.
Model dir set to: D:\AI\stable-diffusion-webui\models\dreambooth\MOIOHWXPRSON
Found 0 reg images.
Preparing dataset...
Init dataset!
Preparing Dataset (With Caching)
Caching latents...:   0%|                                                                        | 0/9 [00:00<?, ?it/s]Loading cached latents...
Bucket 0 (512, 512, 0) - Instance Images: 9 | Class Images: 0 | Max Examples/batch:  9
Caching latents...: 100%|████████████████████████████████████████████████████████████████| 9/9 [00:02<00:00,  2.79it/s]Saving cache!
Total Buckets 1 - Instance Images: 9 | Class Images: 0 | Max Examples/batch: 9
Caching latents...: 100%|████████████████████████████████████████████████████████████████| 9/9 [00:02<00:00,  2.79it/s]
Total images / batch: 9, total examples: 9
Caching latents...: 100%|████████████████████████████████████████████████████████████████| 9/9 [00:02<00:00,  3.13it/s]
Total dataset length (steps): 9
Initializing bucket counter!
  ***** Running training *****
  Num batches each epoch = 9
  Num Epochs = 150
  Batch Size Per Device = 1
  Gradient Accumulation steps = 1
  Total train batch size (w. parallel, distributed & accumulation) = 1
  Text Encoder Epochs: 150
  Total optimization steps = 1350
  Total training steps = 1350
  Resuming from checkpoint: False
  First resume epoch: 0
  First resume step: 0
  Lora: False, Optimizer: 8bit AdamW, Prec: fp16
  Gradient Checkpointing: True
  EMA: False
  UNET: True
  Freeze CLIP Normalization Layers: False
  LR: 2e-06
  V2: False
Steps:   0%|                                                                                  | 0/1350 [00:00<?, ?it/s]OOM Detected, reducing batch/grad size to 0/1.
Traceback (most recent call last):
  File "D:\AI\stable-diffusion-webui\extensions\sd_dreambooth_extension\dreambooth\memory.py", line 119, in decorator
    return function(batch_size, grad_size, prof, *args, **kwargs)
  File "D:\AI\stable-diffusion-webui\extensions\sd_dreambooth_extension\dreambooth\train_dreambooth.py", line 1328, in inner_loop
    accelerator.backward(loss)
  File "D:\AI\stable-diffusion-webui\venv\lib\site-packages\accelerate\accelerator.py", line 1634, in backward
    self.scaler.scale(loss).backward(**kwargs)
  File "D:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\_tensor.py", line 488, in backward
    torch.autograd.backward(
  File "D:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\autograd\__init__.py", line 197, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
  File "D:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\autograd\function.py", line 267, in apply
    return user_fn(self, *args)
  File "D:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\utils\checkpoint.py", line 157, in backward
    torch.autograd.backward(outputs_with_grad, args_with_grad)
  File "D:\AI\stable-diffusion-webui\venv\lib\site-packages\torch\autograd\__init__.py", line 197, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 8.00 GiB total capacity; 5.50 GiB already allocated; 0 bytes free; 7.12 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%|                                                                                  | 0/1350 [00:02<?, ?it/s]
Traceback (most recent call last):
  File "D:\AI\stable-diffusion-webui\extensions\sd_dreambooth_extension\dreambooth\ui_functions.py", line 724, in start_training
    result = main(class_gen_method=class_gen_method)
  File "D:\AI\stable-diffusion-webui\extensions\sd_dreambooth_extension\dreambooth\train_dreambooth.py", line 1462, in main
    return inner_loop()
  File "D:\AI\stable-diffusion-webui\extensions\sd_dreambooth_extension\dreambooth\memory.py", line 117, in decorator
    raise RuntimeError("No executable batch size found, reached zero.")
RuntimeError: No executable batch size found, reached zero.
Restored system models.
Duration: 00:00:09