Add LoRA activation support and usage instructions

app.py

@@ -29,6 +29,17 @@ def initialize_models():
     if pipeline_manager is None:
         logger.info("Loading Stable Diffusion pipeline...")
         pipeline_manager = get_pipeline_manager()
+        
+        # Check if there's a LoRA model to load
+        from backend.config import get_lora_path
+        lora_path = get_lora_path()
+        if lora_path:
+            logger.info(f"LoRA model found: {lora_path}")
+            try:
+                pipeline_manager.load_lora(lora_path)
+                logger.info("✅ Custom LoRA loaded successfully!")
+            except Exception as e:
+                logger.warning(f"Failed to load LoRA: {e}")
 
 def enhance_image_simple(
     input_image: Image.Image,
@@ -123,6 +134,29 @@ with gr.Blocks(title="🏛️ Architecture AI Enhancer") as demo:
     Transform your architectural renders with AI-powered enhancement using Stable Diffusion 1.5
     """)
     
+    # Check for custom LoRA
+    from backend.config import get_lora_path
+    lora_path = get_lora_path()
+    if lora_path and lora_path.exists():
+        gr.Markdown(f"""
+        <div style="background: linear-gradient(90deg, #4CAF50 0%, #45a049 100%); padding: 10px; border-radius: 8px; margin: 10px 0;">
+            <p style="color: white; margin: 0; font-weight: bold;">
+                ✅ Custom LoRA Active: {lora_path.name}
+            </p>
+            <p style="color: rgba(255,255,255,0.9); margin: 5px 0 0 0; font-size: 0.9em;">
+                All enhancements will use your custom trained style
+            </p>
+        </div>
+        """)
+    else:
+        gr.Markdown("""
+        <div style="background: #f0f0f0; padding: 10px; border-radius: 8px; margin: 10px 0;">
+            <p style="color: #666; margin: 0;">
+                ℹ️ Using base Stable Diffusion 1.5 model. <a href="#" style="color: #2196F3;">Train a custom LoRA</a> for personalized style.
+            </p>
+        </div>
+        """)
+    
     with gr.Tabs():
         # Tab 1: Enhancement
         with gr.Tab("✨ Enhance Image"):
@@ -248,6 +282,84 @@ with gr.Blocks(title="🏛️ Architecture AI Enhancer") as demo:
             - **Alignment**: Input and target should show the same scene
             - **Target Quality**: Ensure targets represent your desired style accurately
             
+            ---
+            
+            ## 🎯 Using a Custom LoRA Model
+            
+            Once you've trained a LoRA model locally, here's how to use it:
+            
+            ### Option 1: Deploy to This Space (Recommended)
+            
+            1. **Upload your LoRA to Hugging Face Hub**:
+               ```bash
+               # Install huggingface_hub
+               pip install huggingface_hub
+               
+               # Upload your LoRA
+               from huggingface_hub import upload_file
+               upload_file(
+                   path_or_fileobj="models/lora/your_lora.safetensors",
+                   path_in_repo="your_lora.safetensors",
+                   repo_id="your-username/your-lora-repo",
+                   repo_type="model"
+               )
+               ```
+            
+            2. **Modify this Space to load your LoRA**:
+               - Fork this Space or create a duplicate
+               - Edit `backend/config.py`:
+                 ```python
+                 LORA_MODEL_NAME = "your_lora.safetensors"
+                 ```
+               - Add code in `app.py` to download from HF Hub:
+                 ```python
+                 from huggingface_hub import hf_hub_download
+                 
+                 lora_path = hf_hub_download(
+                     repo_id="your-username/your-lora-repo",
+                     filename="your_lora.safetensors",
+                     local_dir="models/lora"
+                 )
+                 ```
+            
+            3. **Restart the Space** - Your custom LoRA will be loaded automatically!
+            
+            ### Option 2: Use Locally
+            
+            1. **Place your LoRA file** in `models/lora/` directory
+            2. **Update config**: Set `LORA_MODEL_NAME` in `backend/config.py`
+            3. **Run backend**: `uvicorn main:app --reload`
+            4. The LoRA is automatically detected and loaded!
+            
+            ### How It Works
+            
+            When a LoRA is present:
+            - ✅ Pipeline automatically loads it on startup
+            - ✅ All enhancements use your custom style
+            - ✅ No additional configuration needed
+            - ✅ Can be combined with custom prompts for fine control
+            
+            ### Verify LoRA is Loaded
+            
+            Check the logs on startup:
+            ```
+            INFO: Loading Stable Diffusion pipeline...
+            INFO: LoRA model found: models/lora/your_lora.safetensors
+            INFO: ✅ Custom LoRA loaded successfully!
+            ```
+            
+            ### Example: Architecture Firm Custom Style
+            
+            ```python
+            # After training with your firm's rendering style
+            # Place: models/lora/firm_style.safetensors
+            
+            # In config.py:
+            LORA_MODEL_NAME = "firm_style.safetensors"
+            
+            # Now all enhancements will match your style!
+            ```
+            
             ---
             """)