From d5a22d3951bc4244d538900a20eaaa3cfe0585c4 Mon Sep 17 00:00:00 2001
From: vladmandic <mandic00@live.com>
Date: Thu, 12 Mar 2026 14:16:50 +0100
Subject: [PATCH] merge: modules/control/proc/stablenormal/__init__.py

---
 modules/control/proc/stablenormal/__init__.py | 52 +++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 modules/control/proc/stablenormal/__init__.py

diff --git a/modules/control/proc/stablenormal/__init__.py b/modules/control/proc/stablenormal/__init__.py
new file mode 100644
index 000000000..0c5e1d7d8
--- /dev/null
+++ b/modules/control/proc/stablenormal/__init__.py
@@ -0,0 +1,52 @@
+import os
+import torch
+import numpy as np
+from PIL import Image
+from modules import devices
+from modules.shared import opts
+
+
+class StableNormalDetector:
+    def __init__(self, model):
+        self.model = model
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_or_path="Stable-X/StableNormal", cache_dir=None, local_files_only=False):
+        hub_dir = os.path.join(cache_dir, 'torch_hub') if cache_dir else None
+        old_hub_dir = torch.hub.get_dir()
+        if hub_dir:
+            os.makedirs(hub_dir, exist_ok=True)
+            torch.hub.set_dir(hub_dir)
+        try:
+            # StableNormal's custom pipeline imports from the old diffusers path
+            import diffusers.models
+            if not hasattr(diffusers.models, 'controlnet'):
+                import diffusers.models.controlnets.controlnet as _cn_compat
+                diffusers.models.controlnet = _cn_compat
+                import sys
+                sys.modules['diffusers.models.controlnet'] = _cn_compat
+            model = torch.hub.load("Stable-X/StableNormal", "StableNormal_turbo", trust_repo=True)
+        finally:
+            torch.hub.set_dir(old_hub_dir)
+        model = model.to(devices.device)
+        return cls(model)
+
+    def __call__(self, image, output_type="pil", **kwargs):
+        self.model.to(devices.device)
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
+        with devices.inference_context():
+            normals = self.model(image)
+        if opts.control_move_processor:
+            self.model.to("cpu")
+        if isinstance(normals, torch.Tensor):
+            normals = normals.squeeze(0).permute(1, 2, 0).cpu().numpy()
+            normals = ((normals + 1.0) * 0.5 * 255.0).clip(0, 255).astype(np.uint8)
+        elif isinstance(normals, np.ndarray):
+            if normals.max() <= 1.0:
+                normals = ((normals + 1.0) * 0.5 * 255.0).clip(0, 255).astype(np.uint8)
+        if output_type == "pil":
+            if isinstance(normals, Image.Image):
+                return normals
+            normals = Image.fromarray(normals)
+        return normals