Update app.py

app.py

@@ -120,11 +120,10 @@ def infer(
     prompt,
     negative_prompt,
     style,
-    # Removed general img2img reference as we are specializing
-    input_image_pose, # New: for ControlNet OpenPose
-    pose_strength,    # New: strength for ControlNet
-    input_image_face, # New: for IP-Adapter Face
-    face_fidelity,    # New: fidelity/strength for IP-Adapter
+    input_image_pose,
+    pose_strength,
+    input_image_face,
+    face_fidelity,
     seed,
     randomize_seed,
     width,
@@ -138,41 +137,35 @@ def infer(
     prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
     generator = torch.Generator().manual_seed(seed)
 
-    # --- NEW: Prepare ControlNet and IP-Adapter inputs ---
     controlnet_images = []
     controlnet_conditioning_scales = []
     controlnet_models_to_use = []
-    ip_adapter_image_embeddings = None # Will store the face embeddings
-
+    
     # Process Pose Reference
     if input_image_pose:
-        # Preprocess the image to get the OpenPose skeleton
         processed_pose_image = openpose_detector(input_image_pose)
         controlnet_images.append(processed_pose_image)
         controlnet_conditioning_scales.append(pose_strength)
         controlnet_models_to_use.append(controlnet_openpose)
 
     # Process Face Reference (IP-Adapter)
-    if input_image_face and pipe.has_lora_weights("ip_adapter"): # Check if IP-Adapter was loaded successfully
-        # For IP-Adapter FaceID, the pipeline itself usually handles embedding extraction
-        # You just pass the image directly.
-        # The scale is set before the call.
+    # CORRECTED LINE HERE: Use the 'ip_adapter_loaded' flag
+    if input_image_face and ip_adapter_loaded: # Use the flag to check if IP-Adapter loaded successfully
         pipe.set_ip_adapter_scale(face_fidelity)
-        # ip_adapter_image_embeddings = pipe.encode_ip_adapter_image(input_image_face) # If you need to manually encode
-        # Often, you just pass the image to the main call directly if IP-Adapter is loaded.
-
-    # --- END NEW INPUT PREPARATION ---
+    else:
+        # If no face input or IP-Adapter failed to load, ensure scale is reset or not applied
+        # This check is for the general 'lora_scale' attribute which IP-Adapter uses
+        if hasattr(pipe, 'lora_scale') and pipe.lora_scale is not None:
+             pipe.set_ip_adapter_scale(0.0) # Reset scale to 0.0
 
-    # Adjusting the pipe call to use ControlNet and IP-Adapter
-    # Note: If no reference images are provided, it will fall back to text-to-image.
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
-        image=controlnet_images if controlnet_images else None, # Pass processed pose image(s) if available
+        image=controlnet_images if controlnet_images else None,
         controlnet_conditioning_scale=controlnet_conditioning_scales if controlnet_conditioning_scales else None,
         controlnet=controlnet_models_to_use if controlnet_models_to_use else None,
-        ip_adapter_image=input_image_face if input_image_face else None, # Pass the raw face image for IP-Adapter
-        # ip_adapter_image_embeds=ip_adapter_image_embeddings, # Use this if you pre-encode
+        # Only pass ip_adapter_image if there's an input_image_face AND the IP-Adapter was successfully loaded
+        ip_adapter_image=input_image_face if input_image_face and ip_adapter_loaded else None,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
         width=width,