Add Face Realism and Perfection Style LoRAs with optimal scaling

- Add Canopus-LoRA-Flux-FaceRealism.safetensors with 1.1 scale
- Add perfection style v1.safetensors with 1.0 scale
- Implement get_optimal_lora_scale() function for per-LoRA optimization
- Apply optimal scaling during LoRA fusion for better performance

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

app.py

@@ -48,7 +48,9 @@ LORAS = {
     "None": None,
     "AntiBlur": "Shakker-Labs/FLUX.1-dev-LoRA-AntiBlur",
     "Add Details": "Shakker-Labs/FLUX.1-dev-LoRA-add-details",
-    "Ultra Realism": "https://huggingface.co/its-magick/merlin-test-loras/resolve/main/Canopus-LoRA-Flux-UltraRealism.safetensors"
+    "Ultra Realism": "https://huggingface.co/its-magick/merlin-test-loras/resolve/main/Canopus-LoRA-Flux-UltraRealism.safetensors",
+    "Face Realism": "https://huggingface.co/its-magick/merlin-test-loras/resolve/main/Canopus-LoRA-Flux-FaceRealism.safetensors",
+    "Perfection Style": "https://huggingface.co/its-magick/merlin-test-loras/resolve/main/perfection%20style%20v1.safetensors"
 }
 
 # Store loaded LoRA paths
@@ -84,6 +86,17 @@ def preload_loras():
     
     print(f"All {len(loaded_loras)} LoRAs downloaded and ready!")
 
+def get_optimal_lora_scale(lora_name):
+    """Return optimal LoRA scale based on LoRA type for better quality/speed balance"""
+    lora_scales = {
+        "AntiBlur": 0.8,  # Slightly lower for better balance
+        "Add Details": 1.2,  # Higher for more detail enhancement
+        "Ultra Realism": 0.9,  # Balanced for realism
+        "Face Realism": 1.1,  # Optimized for facial features
+        "Perfection Style": 1.0,  # Balanced for style enhancement
+    }
+    return lora_scales.get(lora_name, 1.0)
+
 # Download all LoRAs at startup
 preload_loras()
 
@@ -106,8 +119,12 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidan
         pipe.unload_lora_weights()
         
         if lora_selection != "None" and lora_selection in loaded_loras:
+            # Load with optimized scale for better performance
+            optimal_scale = get_optimal_lora_scale(lora_selection)
             pipe.load_lora_weights(loaded_loras[lora_selection])
-            print(f"Loaded LoRA: {lora_selection}")
+            # Apply optimal scaling during fusion
+            pipe.fuse_lora(lora_scale=optimal_scale)
+            print(f"Loaded LoRA: {lora_selection} with scale {optimal_scale}")
     except Exception as e:
         print(f"Failed to load LoRA {lora_selection}: {e}")