refactor all control processors to support unload and offload

modules/control/proc/normalbae/__init__.py

@@ -1,7 +1,5 @@
 import os
 import types
-import warnings
-
 import cv2
 import numpy as np
 import torch
@@ -9,7 +7,8 @@ import torchvision.transforms as transforms
 from einops import rearrange
 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 .nets.NNET import NNET
 
@@ -25,7 +24,6 @@ def load_checkpoint(fpath, model):
             load_dict[k_] = v
         else:
             load_dict[k] = v
-
     model.load_state_dict(load_dict)
     return model
 
@@ -37,12 +35,10 @@ class NormalBaeDetector:
     @classmethod
     def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None):
         filename = filename or "scannet.pt"
-
         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)
-
         args = types.SimpleNamespace()
         args.mode = 'client'
         args.architecture = 'BN'
@@ -52,7 +48,6 @@ class NormalBaeDetector:
         model = NNET(args)
         model = load_checkpoint(model_path, model)
         model.eval()
-
         return cls(model)
 
     def to(self, device):
@@ -61,14 +56,7 @@ class NormalBaeDetector:
 
 
     def __call__(self, input_image, 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)
         device = next(iter(self.model.parameters())).device
         if not isinstance(input_image, np.ndarray):
             input_image = np.array(input_image, dtype=np.uint8)
@@ -89,19 +77,15 @@ class NormalBaeDetector:
         # d = torch.maximum(d, torch.ones_like(d) * 1e-5)
         # normal /= d
         normal = ((normal + 1) * 0.5).clip(0, 1)
-
         normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy()
         normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8)
-
         detected_map = normal_image
         detected_map = HWC3(detected_map)
-
         img = resize_image(input_image, image_resolution)
         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