refactor all control processors to support unload and offload

modules/control/proc/mlsd/__init__.py

@@ -1,12 +1,11 @@
 import os
-import warnings
-
 import cv2
 import numpy as np
 import torch
 from huggingface_hub import hf_hub_download
 from PIL import Image
-
+from modules import devices
+from modules.shared import opts
 from modules.control.util import HWC3, resize_image
 from .models.mbv2_mlsd_large import MobileV2_MLSD_Large
 from .utils import pred_lines
@@ -22,16 +21,13 @@ class MLSDdetector:
             filename = filename or "annotator/ckpts/mlsd_large_512_fp32.pth"
         else:
             filename = filename or "mlsd_large_512_fp32.pth"
-
         if os.path.isdir(pretrained_model_or_path):
             model_path = os.path.join(pretrained_model_or_path, filename)
         else:
             model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir)
-
         model = MobileV2_MLSD_Large()
         model.load_state_dict(torch.load(model_path), strict=True)
         model.eval()
-
         return cls(model)
 
     def to(self, device):
@@ -39,20 +35,11 @@ class MLSDdetector:
         return self
 
     def __call__(self, input_image, thr_v=0.1, thr_d=0.1, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs):
-        if "return_pil" in kwargs:
-            warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning)
-            output_type = "pil" if kwargs["return_pil"] else "np"
-        if type(output_type) is bool:
-            warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions")
-            if output_type:
-                output_type = "pil"
-
+        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)
-
         assert input_image.ndim == 3
         img = input_image
         img_output = np.zeros_like(img)
@@ -63,16 +50,13 @@ class MLSDdetector:
                 cv2.line(img_output, (x_start, y_start), (x_end, y_end), [255, 255, 255], 1)
         except Exception:
             pass
-
         detected_map = img_output[:, :, 0]
         detected_map = HWC3(detected_map)
-
         img = resize_image(input_image, image_resolution)
-        H, W, C = img.shape
-
+        H, W, _C = img.shape
         detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
-
         if output_type == "pil":
             detected_map = Image.fromarray(detected_map)
-
+        if opts.control_move_processor:
+            self.model.to('cpu')
         return detected_map