Update app.py

app.py

@@ -41,11 +41,35 @@ if device == "cpu":
     pipe.enable_attention_slicing()
 
 # --- The Core Generation Function ---
+# def generate(prompt, quality, lora_choice):
+#     """
+#     Generates an image, dynamically loading the selected LoRA from the Hub.
+#     """
+#     # Unload any existing LoRA to reset to the base model
+#     pipe.unload_lora_weights()
+    
+#     lora_subfolder = AVAILABLE_LORAS.get(lora_choice)
+
+#     if lora_subfolder:
+#         print(f"✨ Downloading and applying LoRA: {lora_choice}")
+#         try:
+#             # Load LoRA directly from the Hugging Face Hub
+#             pipe.load_lora_weights(HF_REPO_ID, subfolder=lora_subfolder)
+#         except Exception as e:
+#             print(f"❌ Failed to load LoRA from Hub '{HF_REPO_ID}/{lora_subfolder}': {e}")
+#     else:
+#         print("🎨 Using base model (no LoRA selected)")
+
+#     steps = 25 if quality == "Fast" else 40
+#     guidance_scale = 7.5
+    
+#     print(f"🚀 Generating with prompt: '{prompt}'")
+#     with torch.no_grad():
+#         image = pipe(prompt, num_inference_steps=steps, guidance_scale=guidance_scale).images[0]
+        
+#     return image
 def generate(prompt, quality, lora_choice):
-    """
-    Generates an image, dynamically loading the selected LoRA from the Hub.
-    """
-    # Unload any existing LoRA to reset to the base model
+    # Reset to base model
     pipe.unload_lora_weights()
     
     lora_subfolder = AVAILABLE_LORAS.get(lora_choice)
@@ -53,8 +77,11 @@ def generate(prompt, quality, lora_choice):
     if lora_subfolder:
         print(f"✨ Downloading and applying LoRA: {lora_choice}")
         try:
-            # Load LoRA directly from the Hugging Face Hub
-            pipe.load_lora_weights(HF_REPO_ID, subfolder=lora_subfolder)
+            pipe.load_lora_weights(
+                HF_REPO_ID,
+                subfolder=lora_subfolder,
+                weight_name="adapter_model.safetensors"  # 👈 specify the file
+            )
         except Exception as e:
             print(f"❌ Failed to load LoRA from Hub '{HF_REPO_ID}/{lora_subfolder}': {e}")
     else:
@@ -69,6 +96,7 @@ def generate(prompt, quality, lora_choice):
         
     return image
 
+
 # --- Build the Gradio UI ---
 title = f"🎨 Stable Diffusion Gallery from {HF_REPO_ID}"
 description = "Select a trained LoRA model from your Hugging Face repository to apply its style. The first time you select a LoRA, it may take a moment to download."