fix: gray QR preprocessing + boost cn_weights for scannability

handler.py

@@ -22,6 +22,7 @@ import logging
 import time
 from typing import Any
 
+import numpy as np
 import torch
 from diffusers import (
     ControlNetModel,
@@ -42,25 +43,27 @@ logger = logging.getLogger(__name__)
 # Categories with <35% accept rate get 3 passes instead of 2.
 
 CATEGORY_PARAMS = {
-    # High-texture cluster (cn_weight=1.80, 2 passes)
-    "food":       {"cn_weight": 1.80, "cfg": 7.5, "steps": 50, "passes": 2},
-    "luxury":     {"cn_weight": 1.80, "cfg": 7.5, "steps": 50, "passes": 2},
-    "wedding":    {"cn_weight": 1.80, "cfg": 7.5, "steps": 50, "passes": 2},
-    "sports":     {"cn_weight": 1.80, "cfg": 7.5, "steps": 50, "passes": 2},
-    "restaurant": {"cn_weight": 1.80, "cfg": 7.5, "steps": 50, "passes": 2},
-    "retail":     {"cn_weight": 1.80, "cfg": 7.5, "steps": 50, "passes": 2},
-    # Geometric cluster (cn_weight=1.38, 2-3 passes)
-    "architecture": {"cn_weight": 1.38, "cfg": 7.5, "steps": 40, "passes": 3},
-    "nature":       {"cn_weight": 1.38, "cfg": 7.5, "steps": 40, "passes": 2},
-    "social":       {"cn_weight": 1.38, "cfg": 7.5, "steps": 40, "passes": 3},
-    "seasonal":     {"cn_weight": 1.59, "cfg": 7.5, "steps": 40, "passes": 3},
-    "tech":         {"cn_weight": 1.38, "cfg": 7.5, "steps": 40, "passes": 2},
-    "world_wonders": {"cn_weight": 1.38, "cfg": 7.5, "steps": 40, "passes": 2},
-    "medieval":     {"cn_weight": 1.38, "cfg": 7.5, "steps": 40, "passes": 2},
-    "professional": {"cn_weight": 1.80, "cfg": 7.5, "steps": 50, "passes": 2},
-    "real_estate":  {"cn_weight": 1.80, "cfg": 7.5, "steps": 50, "passes": 2},
+    # High-texture cluster (2 passes)
+    # Boosted cn_weight from 1.80 → 2.15 to compensate for 0.05→0.85 guidance window
+    "food":       {"cn_weight": 2.15, "cfg": 7.5, "steps": 50, "passes": 2},
+    "luxury":     {"cn_weight": 2.15, "cfg": 7.5, "steps": 50, "passes": 2},
+    "wedding":    {"cn_weight": 2.15, "cfg": 7.5, "steps": 50, "passes": 2},
+    "sports":     {"cn_weight": 2.15, "cfg": 7.5, "steps": 50, "passes": 2},
+    "restaurant": {"cn_weight": 2.15, "cfg": 7.5, "steps": 50, "passes": 2},
+    "retail":     {"cn_weight": 2.15, "cfg": 7.5, "steps": 50, "passes": 2},
+    # Geometric cluster (2-3 passes)
+    # Boosted cn_weight from 1.38 → 1.85
+    "architecture": {"cn_weight": 1.85, "cfg": 7.5, "steps": 40, "passes": 3},
+    "nature":       {"cn_weight": 1.85, "cfg": 7.5, "steps": 40, "passes": 2},
+    "social":       {"cn_weight": 1.85, "cfg": 7.5, "steps": 40, "passes": 3},
+    "seasonal":     {"cn_weight": 2.00, "cfg": 7.5, "steps": 40, "passes": 3},
+    "tech":         {"cn_weight": 1.85, "cfg": 7.5, "steps": 40, "passes": 2},
+    "world_wonders": {"cn_weight": 1.85, "cfg": 7.5, "steps": 40, "passes": 2},
+    "medieval":     {"cn_weight": 1.85, "cfg": 7.5, "steps": 40, "passes": 2},
+    "professional": {"cn_weight": 2.15, "cfg": 7.5, "steps": 50, "passes": 2},
+    "real_estate":  {"cn_weight": 2.15, "cfg": 7.5, "steps": 50, "passes": 2},
     # Default fallback
-    "default":    {"cn_weight": 1.50, "cfg": 7.5, "steps": 40, "passes": 2},
+    "default":    {"cn_weight": 2.00, "cfg": 7.5, "steps": 40, "passes": 2},
 }
 
 
@@ -175,6 +178,16 @@ class EndpointHandler:
         except Exception as e:
             return {"error": f"Failed to decode qr_code_image: {e}"}
 
+        # CRITICAL: Preprocess QR — ensure gray background (#808080)
+        # Monster v2 ControlNet was trained on gray-background QR codes.
+        # White background gives wrong contrast signals and breaks scannability.
+        qr_array = np.array(qr_image)
+        white_mask = np.all(qr_array > 200, axis=2)
+        if np.sum(white_mask) > 0:
+            logger.info("Converting white QR background to gray (#808080)")
+            qr_array[white_mask] = [128, 128, 128]
+            qr_image = Image.fromarray(qr_array)
+
         # Resolve parameters
         category = inputs.get("category", "default")
         params = CATEGORY_PARAMS.get(category, CATEGORY_PARAMS["default"])
@@ -220,9 +233,9 @@ class EndpointHandler:
 
         # ---- Pass 2: img2img + ControlNet (QR FORCE pass) ----
         if num_passes >= 2:
-            p2_cn = cn_weight + 0.4
-            p2_cfg = 10.0
-            p2_strength = 0.35
+            p2_cn = cn_weight + 0.6
+            p2_cfg = 12.0
+            p2_strength = 0.45
             p2_steps = 30
 
             logger.info(
@@ -246,9 +259,9 @@ class EndpointHandler:
 
         # ---- Pass 3: img2img + ControlNet (RESCUE pass) ----
         if num_passes >= 3:
-            p3_cn = cn_weight + 0.8
-            p3_cfg = 13.0
-            p3_strength = 0.45
+            p3_cn = cn_weight + 1.0
+            p3_cfg = 14.0
+            p3_strength = 0.55
             p3_steps = 25
 
             logger.info(