speed?

app.py

@@ -29,7 +29,7 @@ print("loading pipe")
 compiled = False
 from preprocess import Preprocessor
 preprocessor = Preprocessor()
-preprocessor.load("NormalBae")
+# preprocessor.load("NormalBae")
 # api = HfApi()
 
 import spaces

controlnet_aux_local/normalbae/__init__.py

@@ -1,31 +1,19 @@
 import os
 import types
 import warnings
-
-# import cv2
-import numpy as np
 import torch
 import torchvision.transforms as transforms
 from einops import rearrange
 from huggingface_hub import hf_hub_download
 from PIL import Image
+import numpy as np
 
 from ..util import HWC3, resize_image
 from .nets.NNET import NNET
 
-
-# load model
 def load_checkpoint(fpath, model):
     ckpt = torch.load(fpath, map_location='cpu')['model']
-
-    load_dict = {}
-    for k, v in ckpt.items():
-        if k.startswith('module.'):
-            k_ = k.replace('module.', '')
-            load_dict[k_] = v
-        else:
-            load_dict[k] = v
-
+    load_dict = {k.replace('module.', ''): v for k, v in ckpt.items()}
     model.load_state_dict(load_dict)
     return model
 
@@ -37,21 +25,10 @@ class NormalBaeDetector:
     @classmethod
     def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None, local_files_only=False):
         filename = filename or "scannet.pt"
+        model_path = os.path.join(pretrained_model_or_path, filename) if os.path.isdir(pretrained_model_or_path) else hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only)
 
-        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, local_files_only=local_files_only)
-
-        args = types.SimpleNamespace()
-        args.mode = 'client'
-        args.architecture = 'BN'
-        args.pretrained = 'scannet'
-        args.sampling_ratio = 0.4
-        args.importance_ratio = 0.7
-        model = NNET(args)
-        model = load_checkpoint(model_path, model)
-        model.eval()
+        args = types.SimpleNamespace(mode='client', architecture='BN', pretrained='scannet', sampling_ratio=0.4, importance_ratio=0.7)
+        model = load_checkpoint(model_path, NNET(args)).eval()
 
         return cls(model)
 
@@ -59,75 +36,24 @@ class NormalBaeDetector:
         self.model.to(device)
         return self
 
-    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"
-
-        device = next(iter(self.model.parameters())).device
-        if not isinstance(input_image, np.ndarray):
-            input_image = np.array(input_image, dtype=np.uint8)
+    @torch.no_grad()
+    def __call__(self, input_image, detect_resolution=512, output_type="pil", **kwargs):
+        if isinstance(output_type, bool) or "return_pil" in kwargs:
+            warnings.warn("Deprecated: Use output_type='pil' or 'np' instead of boolean values.", DeprecationWarning)
+            output_type = "pil" if (kwargs.get("return_pil", output_type) if isinstance(output_type, bool) else output_type) else "np"
 
+        device = next(self.model.parameters()).device
+        input_image = np.array(input_image, dtype=np.uint8) if not isinstance(input_image, np.ndarray) else input_image
         input_image = HWC3(input_image)
         input_image = resize_image(input_image, detect_resolution)
 
-        assert input_image.ndim == 3
-        image_normal = input_image
-        with torch.no_grad():
-            image_normal = torch.from_numpy(image_normal).float().to(device)
-            image_normal = image_normal / 255.0
-            image_normal = rearrange(image_normal, 'h w c -> 1 c h w')
-            image_normal = self.norm(image_normal)
-
-            normal = self.model(image_normal)
-            normal = normal[0][-1][:, :3]
-            normal = ((normal + 1) * 0.5).clip(0, 1)
+        image_normal = torch.from_numpy(input_image).float().to(device)
+        image_normal = self.norm(image_normal.permute(2, 0, 1).unsqueeze(0) / 255.0)
 
-            normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy()
-            normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8)
+        normal = self.model(image_normal)[0][-1][:, :3]
+        normal = ((normal + 1) * 0.5).clip(0, 1)
+        normal_image = (normal[0].permute(1, 2, 0).cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8)
 
-        # detected_map = normal_image
         detected_map = HWC3(normal_image)
 
-        # img = resize_image(input_image, image_resolution)
-        # H, W, C = input_image.shape
-
-        # detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
-
-        if output_type == "pil":
-            detected_map = Image.fromarray(detected_map)
-
-        return detected_map
-    
-    # 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"
-
-    #     device = next(iter(self.model.parameters())).device
-    #     input_image = resize_image(input_image, detect_resolution)
-
-    #     with torch.no_grad():
-    #         image_normal = torch.from_numpy(input_image).float().to(device)
-    #         image_normal = image_normal / 255.0
-    #         image_normal = rearrange(image_normal, 'h w c -> 1 c h w')
-    #         image_normal = self.norm(image_normal)
-    #         normal = self.model(image_normal)
-    #         normal = normal[0][-1][:, :3]
-    #         normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy()
-
-    #     detected_map = normal
-
-    #     if output_type == "pil":
-    #         detected_map = Image.fromarray((detected_map * 255.0).astype(np.uint8))
-
-    #     return detected_map
-    
+        return Image.fromarray(detected_map) if output_type == "pil" else detected_map

controlnet_aux_local/normalbae/__init__backup.py

@@ -0,0 +1,133 @@
+import os
+import types
+import warnings
+
+# import cv2
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+from einops import rearrange
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from ..util import HWC3, resize_image
+from .nets.NNET import NNET
+
+
+# load model
+def load_checkpoint(fpath, model):
+    ckpt = torch.load(fpath, map_location='cpu')['model']
+
+    load_dict = {}
+    for k, v in ckpt.items():
+        if k.startswith('module.'):
+            k_ = k.replace('module.', '')
+            load_dict[k_] = v
+        else:
+            load_dict[k] = v
+
+    model.load_state_dict(load_dict)
+    return model
+
+class NormalBaeDetector:
+    def __init__(self, model):
+        self.model = model
+        self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None, local_files_only=False):
+        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, local_files_only=local_files_only)
+
+        args = types.SimpleNamespace()
+        args.mode = 'client'
+        args.architecture = 'BN'
+        args.pretrained = 'scannet'
+        args.sampling_ratio = 0.4
+        args.importance_ratio = 0.7
+        model = NNET(args)
+        model = load_checkpoint(model_path, model)
+        model.eval()
+
+        return cls(model)
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+    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"
+
+        device = next(iter(self.model.parameters())).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
+        image_normal = input_image
+        with torch.no_grad():
+            image_normal = torch.from_numpy(image_normal).float().to(device)
+            image_normal = image_normal / 255.0
+            image_normal = rearrange(image_normal, 'h w c -> 1 c h w')
+            image_normal = self.norm(image_normal)
+
+            normal = self.model(image_normal)
+            normal = normal[0][-1][:, :3]
+            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(normal_image)
+
+        # img = resize_image(input_image, image_resolution)
+        # H, W, C = input_image.shape
+
+        # detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
+
+        if output_type == "pil":
+            detected_map = Image.fromarray(detected_map)
+
+        return detected_map
+    
+    # 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"
+
+    #     device = next(iter(self.model.parameters())).device
+    #     input_image = resize_image(input_image, detect_resolution)
+
+    #     with torch.no_grad():
+    #         image_normal = torch.from_numpy(input_image).float().to(device)
+    #         image_normal = image_normal / 255.0
+    #         image_normal = rearrange(image_normal, 'h w c -> 1 c h w')
+    #         image_normal = self.norm(image_normal)
+    #         normal = self.model(image_normal)
+    #         normal = normal[0][-1][:, :3]
+    #         normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy()
+
+    #     detected_map = normal
+
+    #     if output_type == "pil":
+    #         detected_map = Image.fromarray((detected_map * 255.0).astype(np.uint8))
+
+    #     return detected_map
+