Fix push_to_hub for dreambooth and textual_inversion (huggingface#748)

* Fix push_to_hub for dreambooth and textual_inversion

* Use repo.push_to_hub instead of push_to_hub

Author: YaYaB

examples/dreambooth/train_dreambooth.py

@@ -575,9 +575,7 @@ def collate_fn(examples):
         pipeline.save_pretrained(args.output_dir)
 
         if args.push_to_hub:
-            repo.push_to_hub(
-                args, pipeline, repo, commit_message="End of training", blocking=False, auto_lfs_prune=True
-            )
+            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
 
     accelerator.end_training()
 

examples/textual_inversion/textual_inversion.py

@@ -569,9 +569,7 @@ def main():
         save_progress(text_encoder, placeholder_token_id, accelerator, args)
 
         if args.push_to_hub:
-            repo.push_to_hub(
-                args, pipeline, repo, commit_message="End of training", blocking=False, auto_lfs_prune=True
-            )
+            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
 
     accelerator.end_training()
 

examples/unconditional_image_generation/train_unconditional.py

@@ -9,7 +9,7 @@
 from accelerate.logging import get_logger
 from datasets import load_dataset
 from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
-from diffusers.hub_utils import init_git_repo, push_to_hub
+from diffusers.hub_utils import init_git_repo
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import EMAModel
 from torchvision.transforms import (
@@ -190,7 +190,7 @@ def transforms(examples):
             if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
                 # save the model
                 if args.push_to_hub:
-                    push_to_hub(args, pipeline, repo, commit_message=f"Epoch {epoch}", blocking=False)
+                    repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=False)
                 else:
                     pipeline.save_pretrained(args.output_dir)
         accelerator.wait_for_everyone()