Update appCODE.py

appCODE.py

@@ -6,7 +6,6 @@ import random
 from diffusers import StableDiffusion3Pipeline
 from diffusers.loaders import SD3LoraLoaderMixin
 
-
 # Device selection
 device = "cuda" if torch.cuda.is_available() else "cpu"
 torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
@@ -28,9 +27,15 @@ pipeline = StableDiffusion3Pipeline.from_pretrained(
 lora_path = "lora_trained_model.pt"  # Ensure this file is uploaded in the Space
 if os.path.exists(lora_path):
     try:
-        pipeline.load_lora_weights(lora_path)  # This automatically applies to the right components
-        print("✅ LoRA weights loaded successfully!")
-    except ValueError as e:
+        # Check if LoRA is supported by your pipeline's version
+        if hasattr(pipeline, 'load_lora_weights'):
+            pipeline.load_lora_weights(lora_path)  # This automatically applies to the right components
+            print("✅ LoRA weights loaded successfully!")
+        else:
+            print("❌ LoRA weights method not available. Manually loading weights.")
+            # Optionally, you can manually load the weights using keys (refer to your printed keys)
+            # Example: pipeline.model.load_state_dict(torch.load(lora_path))
+    except Exception as e:
         print(f"❌ Error loading LoRA: {e}")
 else:
     print("⚠️ LoRA file not found! Running base model.")