Rename appCODE.py to app.py

appCODE.py → app.py

@@ -24,22 +24,21 @@ pipeline = StableDiffusion3Pipeline.from_pretrained(
 ).to(device)
 
 # Load the LoRA trained weights once at the start
-lora_path = "lora_trained_model.pt"  # Ensure this file is uploaded in the Space
+lora_path = "lora_trained_model.safetensors"  # Ensure this file is uploaded in the Space
 if os.path.exists(lora_path):
     try:
-        # 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))
+        SD3LoraLoaderMixin.load_lora_into_model(pipeline, lora_path)
+        print("✅ LoRA weights loaded successfully!")
     except Exception as e:
         print(f"❌ Error loading LoRA: {e}")
 else:
     print("⚠️ LoRA file not found! Running base model.")
 
+# Verify if LoRA is applied
+for name, param in pipeline.text_encoder.named_parameters():
+    if "lora" in name.lower():
+        print(f"LoRA applied to: {name}, requires_grad={param.requires_grad}")
+
 # Ensure GPU allocation in Hugging Face Spaces
 @spaces.GPU(duration=65)
 def generate_image(prompt: str, seed: int = None):
@@ -66,6 +65,3 @@ with gr.Blocks() as demo:
     output_image = gr.Image(label="Generated Image")
 
     generate_btn.click(generate_image, inputs=[prompt_input, seed_input], outputs=output_image)
-
-# Launch Gradio App
-demo.launch()