diff --git a/modules/control/proc/marigold_normals/__init__.py b/modules/control/proc/marigold_normals/__init__.py
new file mode 100644
index 000000000..c4af14f64
--- /dev/null
+++ b/modules/control/proc/marigold_normals/__init__.py
@@ -0,0 +1,39 @@
+import torch
+import numpy as np
+from PIL import Image
+from modules import devices
+from modules.shared import opts
+
+
+class MarigoldNormalsDetector:
+    def __init__(self, model):
+        self.model = model
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_or_path="prs-eth/marigold-normals-v1-1", cache_dir=None, **load_config):
+        from diffusers import MarigoldNormalsPipeline
+        # Load in float32 to avoid NaN from SD.Next global fp16 precision settings
+        model = MarigoldNormalsPipeline.from_pretrained(pretrained_model_or_path, torch_dtype=torch.float32, cache_dir=cache_dir, **load_config)
+        return cls(model)
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+    def __call__(self, input_image, denoising_steps=4, ensemble_size=4, processing_res=768, match_input_res=True, output_type=None):
+        if isinstance(input_image, np.ndarray):
+            input_image = Image.fromarray(input_image)
+        self.model.to(device=devices.device)
+        res = self.model(
+            input_image,
+            num_inference_steps=denoising_steps,
+            ensemble_size=ensemble_size,
+            processing_resolution=processing_res,
+            match_input_resolution=match_input_res,
+            batch_size=1,
+            output_type="pt",
+        )
+        normal_images = self.model.image_processor.visualize_normals(res.prediction)
+        if opts.control_move_processor:
+            self.model.to("cpu")
+        return normal_images[0]