Remove basicsr's need

v11/hed_v11/__init__.py

@@ -59,7 +59,7 @@ def __init__(self):
         remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth"
         modelpath = os.path.join(annotator_ckpts_path, "ControlNetHED.pth")
         if not os.path.exists(modelpath):
-            from basicsr.utils.download_util import load_file_from_url
+            from comfy_controlnet_preprocessors.util import load_file_from_url
             load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path)
         self.netNetwork = ControlNetHED_Apache2().float().to(model_management.get_torch_device()).eval()
         self.netNetwork.load_state_dict(torch.load(modelpath))

v11/normalbae/__init__.py

@@ -12,14 +12,14 @@
 from comfy_controlnet_preprocessors.util import annotator_ckpts_path
 import torchvision.transforms as transforms
 import model_management
+from comfy_controlnet_preprocessors.util import load_file_from_url
 
 
 class NormalBaeDetector:
     def __init__(self):
         remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/scannet.pt"
         modelpath = os.path.join(annotator_ckpts_path, "scannet.pt")
         if not os.path.exists(modelpath):
-            from basicsr.utils.download_util import load_file_from_url
             load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path)
         args = types.SimpleNamespace()
         args.mode = 'client'

v11/openpose_v11/__init__.py

@@ -14,6 +14,7 @@
 from .hand import Hand
 from .face import Face
 from comfy_controlnet_preprocessors import annotator_ckpts_path
+from comfy_controlnet_preprocessors.util import load_file_from_url
 
 
 body_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/body_pose_model.pth"
@@ -48,15 +49,12 @@ def __init__(self):
         face_modelpath = os.path.join(annotator_ckpts_path, "facenet.pth")
 
         if not os.path.exists(body_modelpath):
-            from basicsr.utils.download_util import load_file_from_url
             load_file_from_url(body_model_path, model_dir=annotator_ckpts_path)
 
         if not os.path.exists(hand_modelpath):
-            from basicsr.utils.download_util import load_file_from_url
             load_file_from_url(hand_model_path, model_dir=annotator_ckpts_path)
 
         if not os.path.exists(face_modelpath):
-            from basicsr.utils.download_util import load_file_from_url
             load_file_from_url(face_model_path, model_dir=annotator_ckpts_path)
 
         self.body_estimation = Body(body_modelpath)

v11/pidinet_v11/__init__.py

@@ -6,15 +6,14 @@
 import numpy as np
 from einops import rearrange
 from .model import pidinet
-from comfy_controlnet_preprocessors.util import annotator_ckpts_path, safe_step
+from comfy_controlnet_preprocessors.util import annotator_ckpts_path, safe_step, load_file_from_url
 
 
 class PidiNetDetector:
     def __init__(self):
         remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/table5_pidinet.pth"
         modelpath = os.path.join(annotator_ckpts_path, "table5_pidinet.pth")
         if not os.path.exists(modelpath):
-            from basicsr.utils.download_util import load_file_from_url
             load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path)
         self.netNetwork = pidinet()
         self.netNetwork.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(modelpath)['state_dict'].items()})

v11/pidinet_v11/model.py

@@ -10,7 +10,35 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from basicsr.utils import img2tensor
+
+def img2tensor(imgs, bgr2rgb=True, float32=True):
+    """Numpy array to tensor.
+
+    Args:
+        imgs (list[ndarray] | ndarray): Input images.
+        bgr2rgb (bool): Whether to change bgr to rgb.
+        float32 (bool): Whether to change to float32.
+
+    Returns:
+        list[tensor] | tensor: Tensor images. If returned results only have
+            one element, just return tensor.
+    """
+
+    def _totensor(img, bgr2rgb, float32):
+        if img.shape[2] == 3 and bgr2rgb:
+            if img.dtype == 'float64':
+                img = img.astype('float32')
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = torch.from_numpy(img.transpose(2, 0, 1))
+        if float32:
+            img = img.float()
+        return img
+
+    if isinstance(imgs, list):
+        return [_totensor(img, bgr2rgb, float32) for img in imgs]
+    else:
+        return _totensor(imgs, bgr2rgb, float32)
+
 
 nets = {
     'baseline': {

v11/zoe/__init__.py

@@ -9,15 +9,13 @@
 from einops import rearrange
 from .zoedepth.models.zoedepth.zoedepth_v1 import ZoeDepth
 from .zoedepth.utils.config import get_config
-from comfy_controlnet_preprocessors.util import annotator_ckpts_path
-
+from comfy_controlnet_preprocessors.util import annotator_ckpts_path, load_file_from_url
 
 class ZoeDetector:
     def __init__(self):
         remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ZoeD_M12_N.pt"
         modelpath = os.path.join(annotator_ckpts_path, "ZoeD_M12_N.pt")
         if not os.path.exists(modelpath):
-            from basicsr.utils.download_util import load_file_from_url
             load_file_from_url(remote_model_path, model_dir=annotator_ckpts_path)
         conf = get_config("zoedepth", "infer")
         model = ZoeDepth.build_from_config(conf)