additional hub arguments

examples/train_unconditional.py

@@ -74,7 +74,8 @@ def main(args):
         repo = init_git_repo(args, at_init=True)
 
     # Train!
-    world_size = torch.distributed.get_world_size() if args.local_rank != -1 else 1
+    is_distributed = torch.distributed.is_available() and torch.distributed.is_initialized()
+    world_size = torch.distributed.get_world_size() if is_distributed else 1
     total_train_batch_size = args.batch_size * args.gradient_accumulation_steps * world_size
     max_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_epochs
     logger.info("***** Running training *****")
@@ -120,17 +121,14 @@ def main(args):
                 pbar.set_postfix(loss=loss.detach().item(), lr=optimizer.param_groups[0]["lr"])
 
                 optimizer.step()
+        if is_distributed:
+            torch.distributed.barrier()
 
         # Generate a sample image for visual inspection
-        torch.distributed.barrier()
         if args.local_rank in [-1, 0]:
             model.eval()
             with torch.no_grad():
                 pipeline = DDPM(unet=unwrap_model(model), noise_scheduler=noise_scheduler)
-                if args.push_to_hub:
-                    push_to_hub(args, pipeline, repo, commit_message=f"Epoch {epoch}", blocking=False)
-                else:
-                    pipeline.save_pretrained(args.output_path)
 
                 generator = torch.manual_seed(0)
                 # run pipeline in inference (sample random noise and denoise)
@@ -143,9 +141,16 @@ def main(args):
             image_pil = PIL.Image.fromarray(image_processed[0])
 
             # save image
-                test_dir = os.path.join(args.output_path, "test_samples")
+            test_dir = os.path.join(args.output_dir, "test_samples")
             os.makedirs(test_dir, exist_ok=True)
             image_pil.save(f"{test_dir}/{epoch}.png")
+
+            # save the model
+            if args.push_to_hub:
+                push_to_hub(args, pipeline, repo, commit_message=f"Epoch {epoch}", blocking=False)
+            else:
+                pipeline.save_pretrained(args.output_dir)
+        if is_distributed:
             torch.distributed.barrier()
 
 
@@ -153,14 +158,18 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Simple example of a training script.")
     parser.add_argument("--local_rank", type=int, default=-1)
     parser.add_argument("--dataset", type=str, default="huggan/flowers-102-categories")
+    parser.add_argument("--output_dir", type=str, default="ddpm-model")
+    parser.add_argument("--overwrite_output_dir", action="store_true")
     parser.add_argument("--resolution", type=int, default=64)
-    parser.add_argument("--output_path", type=str, default="ddpm-model")
-    parser.add_argument("--batch_size", type=int, default=4)
+    parser.add_argument("--batch_size", type=int, default=16)
     parser.add_argument("--num_epochs", type=int, default=100)
     parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
     parser.add_argument("--lr", type=float, default=1e-4)
     parser.add_argument("--warmup_steps", type=int, default=500)
     parser.add_argument("--push_to_hub", action="store_true")
+    parser.add_argument("--hub_token", type=str, default=None)
+    parser.add_argument("--hub_model_id", type=str, default=None)
+    parser.add_argument("--hub_private_repo", action="store_true")
     parser.add_argument(
         "--mixed_precision",
         type=str,

src/diffusers/hub_utils.py

@@ -70,7 +70,7 @@ def init_git_repo(args, at_init: bool = False):
     repo.git_pull()
 
     # By default, ignore the checkpoint folders
-    if not os.path.exists(os.path.join(args.output_dir, ".gitignore")) and args.hub_strategy != "all_checkpoints":
+    if not os.path.exists(os.path.join(args.output_dir, ".gitignore")):
         with open(os.path.join(args.output_dir, ".gitignore"), "w", encoding="utf-8") as writer:
             writer.writelines(["checkpoint-*/"])