diff --git a/modules/control/proc/sam2/__init__.py b/modules/control/proc/sam2/__init__.py
new file mode 100644
index 000000000..6c2138a9c
--- /dev/null
+++ b/modules/control/proc/sam2/__init__.py
@@ -0,0 +1,91 @@
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+from modules import devices
+from modules.shared import opts
+from modules.control.util import HWC3, resize_image
+
+
+class Sam2Detector:
+    def __init__(self, model, processor):
+        self.model = model
+        self.processor = processor
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_or_path="facebook/sam2.1-hiera-large", cache_dir=None, local_files_only=False):
+        from transformers import AutoProcessor, AutoModelForMaskGeneration
+        processor = AutoProcessor.from_pretrained(pretrained_model_or_path, cache_dir=cache_dir, local_files_only=local_files_only)
+        model = AutoModelForMaskGeneration.from_pretrained(pretrained_model_or_path, cache_dir=cache_dir, local_files_only=local_files_only, use_safetensors=True).to(devices.device).eval()
+        return cls(model, processor)
+
+    def _generate_grid_points(self, h, w, n_points_per_side=32):
+        ys = np.linspace(0, h, n_points_per_side + 2)[1:-1]
+        xs = np.linspace(0, w, n_points_per_side + 2)[1:-1]
+        points = np.array([[x, y] for y in ys for x in xs], dtype=np.float64)
+        labels = np.ones(len(points), dtype=np.int64)
+        return points, labels
+
+    def _colorize_masks(self, masks, h, w):
+        from numpy.random import default_rng
+        gen = default_rng(42)
+        canvas = np.zeros((h, w, 3), dtype=np.uint8)
+        if len(masks) == 0:
+            return canvas
+        sorted_masks = sorted(enumerate(masks), key=lambda x: x[1].sum(), reverse=True)
+        for _idx, mask in sorted_masks:
+            color = gen.integers(50, 256, size=3, dtype=np.uint8)
+            canvas[mask] = color
+        return canvas
+
+    def __call__(self, input_image, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs):
+        import torch.nn.functional as F
+        self.model.to(devices.device)
+        if not isinstance(input_image, np.ndarray):
+            input_image = np.array(input_image, dtype=np.uint8)
+        input_image = HWC3(input_image)
+        input_image = resize_image(input_image, detect_resolution)
+        h, w = input_image.shape[:2]
+        pil_image = Image.fromarray(input_image)
+        points, labels = self._generate_grid_points(h, w, n_points_per_side=16)
+        # Process grid points in batches to avoid OOM
+        all_masks = []
+        all_scores = []
+        batch_size = 64
+        for i in range(0, len(points), batch_size):
+            batch_pts = points[i:i + batch_size]
+            batch_lbl = labels[i:i + batch_size]
+            # SAM2 expects 4-level nesting: [image, object, point, coords]
+            pts_nested = [[[pt] for pt in batch_pts.tolist()]]
+            lbl_nested = [[[lb] for lb in batch_lbl.tolist()]]
+            inputs = self.processor(images=pil_image, input_points=pts_nested, input_labels=lbl_nested, return_tensors="pt")
+            inputs = {k: v.to(devices.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
+            with devices.inference_context():
+                outputs = self.model(**inputs)
+            # pred_masks: [1, N_objects, N_masks_per_obj, mask_h, mask_w] (low-res logits)
+            pred_masks = outputs.pred_masks[0]  # [N_objects, N_masks, mask_h, mask_w]
+            iou_scores = outputs.iou_scores[0] if hasattr(outputs, 'iou_scores') else torch.ones(pred_masks.shape[:2], device=pred_masks.device)
+            for obj_idx in range(pred_masks.shape[0]):
+                best = iou_scores[obj_idx].argmax()
+                # Upscale low-res mask logits to original image size
+                mask_logits = pred_masks[obj_idx, best].unsqueeze(0).unsqueeze(0).float()  # [1,1,mh,mw]
+                mask_upscaled = F.interpolate(mask_logits, size=(h, w), mode="bilinear", align_corners=False)
+                all_masks.append(mask_upscaled.squeeze().cpu().numpy() > 0.0)
+                all_scores.append(iou_scores[obj_idx, best].item())
+        if opts.control_move_processor:
+            self.model.to("cpu")
+        # Filter by IoU score
+        if len(all_masks) > 0:
+            scores = np.array(all_scores)
+            good = scores > 0.7
+            masks_np = [m for m, g in zip(all_masks, good) if g]
+        else:
+            masks_np = []
+        detected_map = self._colorize_masks(masks_np, h, w)
+        detected_map = HWC3(detected_map)
+        img = resize_image(input_image, image_resolution)
+        out_h, out_w = img.shape[:2]
+        detected_map = cv2.resize(detected_map, (out_w, out_h), interpolation=cv2.INTER_LINEAR)
+        if output_type == "pil":
+            detected_map = Image.fromarray(detected_map)
+        return detected_map