fix: create_repo call.

app.py

@@ -11,9 +11,9 @@ This Space lets you convert KerasCV Stable Diffusion weights to a format compati
 
 * The Space downloads a couple of pre-trained weights and runs a dummy inference. Depending, on the machine type, the enture process can take anywhere between 2 - 5 minutes.
 * Only Stable Diffusion (v1) is supported as of now. In particular this checkpoint: [`"CompVis/stable-diffusion-v1-4"`](https://huggingface.co/CompVis/stable-diffusion-v1-4).
-* Only the text encoder and the UNet parameters converted since only these two elements are generally fine-tuned.
+* Only the text encoder and UNet parameters are converted since only these two elements are generally fine-tuned.
 * [This Colab Notebook](https://colab.research.google.com/drive/1RYY077IQbAJldg8FkK8HSEpNILKHEwLb?usp=sharing) was used to develop the conversion utilities initially.
-* You can choose not to provide `text_encoder_weights` and `unet_weights` in case you don't have any fine-tuned weights. In that case, the original parameters of the respective models (text encoder and UNet) from KerasCV will be used.
+* You can choose NOT to provide `text_encoder_weights` and `unet_weights` in case you don't have any fine-tuned weights. In that case, the original parameters of the respective models (text encoder and UNet) from KerasCV will be used.
 * You can provide only `text_encoder_weights` or `unet_weights` or both.
 * When providing the weights' links, ensure they're directly downloadable. Internally, the Space uses [`tf.keras.utils.get_file()`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/get_file) to retrieve the weights locally. 
 * If you don't provide `your_hf_token` the converted pipeline won't be pushed. 

hub_utils/repo.py

@@ -13,9 +13,9 @@ def push_to_hub(hf_token: str, push_dir: str, repo_prefix: None) -> str:
                 if repo_prefix == ""
                 else f"{user}/{repo_prefix}-{push_dir}"
             )
-            _ = create_repo(repo_id=repo_id, token=hf_token)
+            _ = create_repo(repo_id=repo_id, token=hf_token, exist_ok=True)
             url = hf_api.upload_folder(
-                folder_path=push_dir, repo_id=repo_id, exist_ok=True
+                folder_path=push_dir, repo_id=repo_id
             )
             return f"Model successfully pushed: [{url}]({url})"
     except Exception as e: