diff --git a/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py b/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py
index 05689b71fa047..c85e2c462b047 100644
--- a/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py
+++ b/examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py
@@ -38,7 +38,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import ProjectConfiguration, set_seed
 from braceexpand import braceexpand
-from huggingface_hub import create_repo
+from huggingface_hub import create_repo, upload_folder
 from packaging import version
 from peft import LoraConfig, get_peft_model, get_peft_model_state_dict
 from torch.utils.data import default_collate
@@ -847,7 +847,7 @@ def main(args):
             os.makedirs(args.output_dir, exist_ok=True)
 
         if args.push_to_hub:
-            create_repo(
+            repo_id = create_repo(
                 repo_id=args.hub_model_id or Path(args.output_dir).name,
                 exist_ok=True,
                 token=args.hub_token,
@@ -1366,6 +1366,14 @@ def compute_embeddings(prompt_batch, proportion_empty_prompts, text_encoder, tok
         lora_state_dict = get_peft_model_state_dict(unet, adapter_name="default")
         StableDiffusionPipeline.save_lora_weights(os.path.join(args.output_dir, "unet_lora"), lora_state_dict)
 
+        if args.push_to_hub:
+            upload_folder(
+                repo_id=repo_id,
+                folder_path=args.output_dir,
+                commit_message="End of training",
+                ignore_patterns=["step_*", "epoch_*"],
+            )
+
     accelerator.end_training()
 
 
diff --git a/examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py b/examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py
index 014a770fa0ba3..75671c18c5e09 100644
--- a/examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py
+++ b/examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py
@@ -39,7 +39,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import ProjectConfiguration, set_seed
 from braceexpand import braceexpand
-from huggingface_hub import create_repo
+from huggingface_hub import create_repo, upload_folder
 from packaging import version
 from peft import LoraConfig, get_peft_model, get_peft_model_state_dict
 from torch.utils.data import default_collate
@@ -842,7 +842,7 @@ def main(args):
             os.makedirs(args.output_dir, exist_ok=True)
 
         if args.push_to_hub:
-            create_repo(
+            repo_id = create_repo(
                 repo_id=args.hub_model_id or Path(args.output_dir).name,
                 exist_ok=True,
                 token=args.hub_token,
@@ -1424,6 +1424,14 @@ def compute_embeddings(
         lora_state_dict = get_peft_model_state_dict(unet, adapter_name="default")
         StableDiffusionXLPipeline.save_lora_weights(os.path.join(args.output_dir, "unet_lora"), lora_state_dict)
 
+        if args.push_to_hub:
+            upload_folder(
+                repo_id=repo_id,
+                folder_path=args.output_dir,
+                commit_message="End of training",
+                ignore_patterns=["step_*", "epoch_*"],
+            )
+
     accelerator.end_training()
 
 
diff --git a/examples/consistency_distillation/train_lcm_distill_sd_wds.py b/examples/consistency_distillation/train_lcm_distill_sd_wds.py
index 9e8df77aacbd3..b2085b7044ba8 100644
--- a/examples/consistency_distillation/train_lcm_distill_sd_wds.py
+++ b/examples/consistency_distillation/train_lcm_distill_sd_wds.py
@@ -38,7 +38,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import ProjectConfiguration, set_seed
 from braceexpand import braceexpand
-from huggingface_hub import create_repo
+from huggingface_hub import create_repo, upload_folder
 from packaging import version
 from torch.utils.data import default_collate
 from torchvision import transforms
@@ -835,7 +835,7 @@ def main(args):
             os.makedirs(args.output_dir, exist_ok=True)
 
         if args.push_to_hub:
-            create_repo(
+            repo_id = create_repo(
                 repo_id=args.hub_model_id or Path(args.output_dir).name,
                 exist_ok=True,
                 token=args.hub_token,
@@ -1354,6 +1354,14 @@ def compute_embeddings(prompt_batch, proportion_empty_prompts, text_encoder, tok
         target_unet = accelerator.unwrap_model(target_unet)
         target_unet.save_pretrained(os.path.join(args.output_dir, "unet_target"))
 
+        if args.push_to_hub:
+            upload_folder(
+                repo_id=repo_id,
+                folder_path=args.output_dir,
+                commit_message="End of training",
+                ignore_patterns=["step_*", "epoch_*"],
+            )
+
     accelerator.end_training()
 
 
diff --git a/examples/consistency_distillation/train_lcm_distill_sdxl_wds.py b/examples/consistency_distillation/train_lcm_distill_sdxl_wds.py
index da49649c918c8..ee86def673fae 100644
--- a/examples/consistency_distillation/train_lcm_distill_sdxl_wds.py
+++ b/examples/consistency_distillation/train_lcm_distill_sdxl_wds.py
@@ -39,7 +39,7 @@
 from accelerate.logging import get_logger
 from accelerate.utils import ProjectConfiguration, set_seed
 from braceexpand import braceexpand
-from huggingface_hub import create_repo
+from huggingface_hub import create_repo, upload_folder
 from packaging import version
 from torch.utils.data import default_collate
 from torchvision import transforms
@@ -875,7 +875,7 @@ def main(args):
             os.makedirs(args.output_dir, exist_ok=True)
 
         if args.push_to_hub:
-            create_repo(
+            repo_id = create_repo(
                 repo_id=args.hub_model_id or Path(args.output_dir).name,
                 exist_ok=True,
                 token=args.hub_token,
@@ -1457,6 +1457,14 @@ def compute_embeddings(
         target_unet = accelerator.unwrap_model(target_unet)
         target_unet.save_pretrained(os.path.join(args.output_dir, "unet_target"))
 
+        if args.push_to_hub:
+            upload_folder(
+                repo_id=repo_id,
+                folder_path=args.output_dir,
+                commit_message="End of training",
+                ignore_patterns=["step_*", "epoch_*"],
+            )
+
     accelerator.end_training()