Update app.py

Improved Font Styles:
Set a more readable font family (Arial) and size (16px).
Adjusted text color for better contrast.
Responsive Design:
The layout now stretches to fit the user's browser window, ensuring that the "Convert" button remains at the bottom.

app.py

@@ -20,6 +20,7 @@ import shutil
 import hashlib
 from datetime import datetime
 from typing import Dict, List, Optional
+from huggingface_hub import login, HfApi
 
 # ---------------------- UTILITY FUNCTIONS ----------------------
 
@@ -180,7 +181,7 @@ def save_stable_diffusion_checkpoint(save_path, text_encoder1, text_encoder2, un
     return key_count
 
 def save_diffusers_checkpoint(save_path, text_encoder1, text_encoder2, unet, reference_model, vae, trim_if_model_exists, save_dtype):
-    """Saves Diffusers-style checkpoint from the model."""
+    """Saves the SDXL model as a Diffusers model."""
     print("Saving SDXL as Diffusers format to:", save_path)
     print("SDXL Text Encoder 1 to:", os.path.join(save_path, "text_encoder"))
     text_encoder1.save_pretrained(os.path.join(save_path, "text_encoder"))
@@ -379,7 +380,7 @@ def validate_model(model_path, precision):
                     return False, "❌ Invalid URL or model not accessible"
                 if 'content-length' in response.headers:
                     size_gb = int(response.headers['content-length']) / (1024 * 1024 * 1024)
-                    if size_gb < 0.1:
+                    if size_gb < 0.1 and not model_path.endswith(('.ckpt', '.safetensors')):
                         return False, "❌ File too small to be a valid model"
             except:
                 return False, "❌ Error checking URL"
@@ -619,50 +620,38 @@ def get_auto_optimization_suggestions(model_path: str, precision: str, available
 def upload_to_huggingface(model_path, hf_token, orgs_name, model_name, make_private, output_widget):
     """Uploads a model to the Hugging Face Hub."""
     try:
+        # Login to Hugging Face
         login(hf_token, add_to_git_credential=True)
+        
+        # Prepare model upload
+        if not os.path.exists(model_path):
+            raise ValueError("Model path does not exist.")
+        
+        # Upload model
+        repo_id = f"{orgs_name}/{model_name}" if orgs_name else model_name
         api = HfApi()
         user = api.whoami(hf_token)
-        model_repo = create_model_repo(api, user, orgs_name, model_name, make_private)
-
-        # Determine upload parameters (adjust as needed)
-        path_in_repo = ""
-        trained_model = os.path.basename(model_path)
-
-        path_in_repo_local = path_in_repo if path_in_repo and not is_diffusers_model(model_path) else ""
-
-        notification = f"Uploading {trained_model} from {model_path} to https://huggingface.co/{model_repo}"
-        with output_widget:
-            print(notification)
-
-        if os.path.isdir(model_path):
-            if is_diffusers_model(model_path):
-                commit_message = f"Upload diffusers format: {trained_model}"
-                print("Detected diffusers model. Adjusting upload parameters.")
-            else:
-                commit_message = f"Upload checkpoint: {trained_model}"
-                print("Detected regular model. Adjusting upload parameters.")
-
-            api.upload_folder(
-                folder_path=model_path,
-                path_in_repo=path_in_repo_local,
-                repo_id=model_repo,
-                commit_message=commit_message,
-                ignore_patterns=".ipynb_checkpoints",
-            )
+        if make_private:
+            api.create_repo(repo_id, private=True)
         else:
-            commit_message = f"Upload file: {trained_model}"
-            api.upload_file(
-                path_or_fileobj=model_path,
-                path_in_repo=path_in_repo_local,
-                repo_id=model_repo,
-                commit_message=commit_message,
-            )
+            api.create_repo(repo_id)
+        
+        # Push model files
+        api.upload_folder(
+            folder_path=model_path,
+            path_in_repo="",
+            repo_id=repo_id,
+            commit_message=f"Upload model: {model_name}",
+            ignore_patterns=".ipynb_checkpoints",
+        )
+        
         with output_widget:
-            return f"Model upload complete! Check it out at https://huggingface.co/{model_repo}/tree/main"
-
+            print(f"Model uploaded to: https://huggingface.co/{repo_id}")
+            return f"Model uploaded to: https://huggingface.co/{repo_id}"
     except Exception as e:
-        with output_widget:
-            return f"Upload failed: {e}"
+        error_msg = f"❌ Error during upload: {str(e)}"
+        print(error_msg)
+        return error_msg
 
 # ---------------------- GRADIO INTERFACE ----------------------
 
@@ -697,7 +686,7 @@ def increment_filename(filename):
             return new_name
         counter += 1
 
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
+with gr.Blocks(css="#main-container { display: flex; flex-direction: column; height: 100vh; justify-content: space-between; font-family: 'Arial', sans-serif; font-size: 16px; color: #333; } #convert-button { margin-top: auto; }") as demo:
     gr.Markdown("""
     # 🎨 SDXL Model Converter
     Convert SDXL models between different formats and precisions. Works on CPU!
@@ -788,7 +777,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
         with gr.Column():
             output = gr.Markdown(label="Output")
-            convert_btn = gr.Button("Convert Model", variant="primary")
+            convert_btn = gr.Button("Convert Model", variant="primary", elem_id="convert-button")
             convert_btn.click(
                 fn=main,
                 inputs=[