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Fix push_to_hub for dreambooth and textual_inversion #748

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Merged
patil-suraj merged 2 commits into from
Oct 7, 2022
Merged

Fix push_to_hub for dreambooth and textual_inversion #748

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895795d
Fix push_to_hub for dreambooth and textual_inversion
YaYaB Oct 6, 2022
c34a766
Use repo.push_to_hub instead of push_to_hub
YaYaB Oct 7, 2022
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4 changes: 1 addition & 3 deletions examples/dreambooth/train_dreambooth.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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()

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4 changes: 1 addition & 3 deletions examples/textual_inversion/textual_inversion.py
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Expand Up @@ -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()

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4 changes: 2 additions & 2 deletions examples/unconditional_image_generation/train_unconditional.py
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Expand Up @@ -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 (
Expand Down Expand Up @@ -185,7 +185,7 @@ 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()
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