fix: formatting.

app.py

@@ -30,10 +30,16 @@ def run(hf_token, text_encoder_weights, unet_weights, repo_prefix):
     pipeline = run_conversion(text_encoder_weights, unet_weights)
     output_path = "kerascv_sd_diffusers_pipeline"
     pipeline.save_pretrained(output_path)
+
+    weight_paths = []
+    if text_encoder_weights is not None:
+        weight_paths.append(text_encoder_weights)
+    if unet_weights is not None:
+        weight_paths.append(unet_weights)
     save_model_card(
         base_model=PRETRAINED_CKPT,
         repo_folder=output_path,
-        weight_paths=[text_encoder_weights, unet_weights],
+        weight_paths=weight_paths,
     )
     push_str = push_to_hub(hf_token, output_path, repo_prefix)
     return push_str

hub_utils/readme.py

@@ -1,8 +1,9 @@
 import os
+from typing import List
 
 
 # Copied from https://github.com/huggingface/diffusers/blob/31be42209ddfdb69d9640a777b32e9b5c6259bf0/examples/text_to_image/train_text_to_image_lora.py#L55
-def save_model_card(base_model=str, repo_folder=None, weight_paths=None):
+def save_model_card(base_model=str, repo_folder=None, weight_paths: List = None):
     yaml = f"""
 ---
 license: creativeml-openrail-m
@@ -22,8 +23,8 @@ The pipeline contained in this repository was created using [this Space](https:/
 
 """
 
-    if weight_paths is not None:
-        model_card += "Following weight paths (KerasCV) were used: {weight_paths}"
+    if len(weight_paths) > 0:
+        model_card += f"Following weight paths (KerasCV) were used \n: {weight_paths}"
 
     with open(os.path.join(repo_folder, "README.md"), "w") as f:
         f.write(yaml + model_card)

hub_utils/repo.py

@@ -14,9 +14,7 @@ def push_to_hub(hf_token: str, push_dir: str, repo_prefix: None) -> str:
                 else f"{user}/{repo_prefix}-{push_dir}"
             )
             _ = 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
-            )
+            url = hf_api.upload_folder(folder_path=push_dir, repo_id=repo_id)
             return f"Model successfully pushed: [{url}]({url})"
     except Exception as e:
         return f"{e}"