Add num_dataloader_workers arg to dreambooth script (#1107)

This is especially important for Windows users, who may have to set the
number of workers to 0.

docs/source/task_guides/dreambooth_lora.mdx

@@ -83,6 +83,7 @@ accelerate launch train_dreambooth.py \
   --output_dir=$OUTPUT_DIR \
   --train_text_encoder \
   --with_prior_preservation --prior_loss_weight=1.0 \
+  --num_dataloader_workers=1 \
   --instance_prompt="a photo of sks dog" \
   --class_prompt="a photo of dog" \
   --resolution=512 \
@@ -101,6 +102,8 @@ accelerate launch train_dreambooth.py \
   --max_train_steps=800
 ```
 
+If you are running this script on Windows, you may need to set the `--num_dataloader_workers` to 0.
+
 ## Inference with a single adapter
 
 To run inference with the fine-tuned model, first specify the base model with which the fine-tuned LoRA weights will be combined:

examples/lora_dreambooth/train_dreambooth.py

@@ -213,6 +213,10 @@ def parse_args(input_args=None):
         help="Bias type for Lora. Can be 'none', 'all' or 'lora_only', only used if use_lora and `train_text_encoder` are True",
     )
 
+    parser.add_argument(
+        "--num_dataloader_workers", type=int, default=1, help="Num of workers for the training dataloader."
+    )
+
     parser.add_argument(
         "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
     )
@@ -799,7 +803,7 @@ def main(args):
         batch_size=args.train_batch_size,
         shuffle=True,
         collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
-        num_workers=1,
+        num_workers=args.num_dataloader_workers,
     )
 
     # Scheduler and math around the number of training steps.