Add mobile_sam with controlnet_aux (#3000)

* Add mobile_sam with controlnet_aux for CNXL_Union

annotator/mobile_sam/__init__.py

@@ -0,0 +1,49 @@
+from __future__ import print_function
+
+import os
+import numpy as np
+from PIL import Image
+from typing import Union
+
+from modules import devices
+from annotator.util import load_model
+from annotator.annotator_path import models_path
+
+from controlnet_aux import SamDetector
+from controlnet_aux.segment_anything import sam_model_registry, SamAutomaticMaskGenerator
+
+class SamDetector_Aux(SamDetector):
+
+    model_dir = os.path.join(models_path, "mobile_sam")
+
+    def __init__(self, mask_generator: SamAutomaticMaskGenerator, sam):
+        super().__init__(mask_generator)
+        self.device = devices.device
+        self.model = sam.to(self.device).eval()
+
+    @classmethod
+    def from_pretrained(cls):
+        """
+        Possible model_type : vit_h, vit_l, vit_b, vit_t
+        download weights from https://huggingface.co/dhkim2810/MobileSAM
+        """
+        remote_url = os.environ.get(
+            "CONTROLNET_MOBILE_SAM_MODEL_URL",
+            "https://huggingface.co/dhkim2810/MobileSAM/resolve/main/mobile_sam.pt",
+        )
+        model_path = load_model(
+            "mobile_sam.pt", remote_url=remote_url, model_dir=cls.model_dir
+        )
+
+        sam = sam_model_registry["vit_t"](checkpoint=model_path)
+
+        cls.model = sam.to(devices.device).eval()
+
+        mask_generator = SamAutomaticMaskGenerator(cls.model)
+
+        return cls(mask_generator, sam)
+
+    def __call__(self, input_image: Union[np.ndarray, Image.Image]=None, detect_resolution=512, image_resolution=512, output_type="cv2", **kwargs) -> np.ndarray:
+        self.model.to(self.device)
+        image = super().__call__(input_image=input_image, detect_resolution=detect_resolution, image_resolution=image_resolution, output_type=output_type, **kwargs)
+        return np.array(image).astype(np.uint8)

requirements.txt

@@ -9,3 +9,4 @@ matplotlib
 facexlib
 timm<=0.9.5
 pydantic<=1.10.17
+controlnet_aux

scripts/preprocessor/__init__.py

@@ -5,4 +5,5 @@ from .lama_inpaint import *
 from .ip_adapter_auto import *
 from .normal_dsine import *
 from .model_free_preprocessors import *
-from .legacy.legacy_preprocessors import *
+from .legacy.legacy_preprocessors import *
+from .mobile_sam import *

scripts/preprocessor/mobile_sam.py

@@ -0,0 +1,25 @@
+from annotator.mobile_sam import SamDetector_Aux
+from scripts.supported_preprocessor import Preprocessor
+
+class PreprocessorMobileSam(Preprocessor):
+    def __init__(self):
+        super().__init__(name="mobile_sam")
+        self.tags = ["Segmentation"]
+        self.model = None
+
+    def __call__(
+        self,
+        input_image,
+        resolution,
+        slider_1=None,
+        slider_2=None,
+        slider_3=None,
+        **kwargs
+    ):
+        if self.model is None:
+            self.model = SamDetector_Aux.from_pretrained()
+
+        result = self.model(input_image, detect_resolution=resolution, image_resolution=resolution, output_type="cv2")
+        return result
+    
+Preprocessor.add_supported_preprocessor(PreprocessorMobileSam())