Adding Depth Anything v2 to ControlNet Integrated (#1519)

* Update install.py

adding install of depth anything v2

* Add files via upload

adding depth anything v2 preprocessor

* Update preprocessor_compiled.py

adding preprocessor

* Update preprocessor.py

adding preprocessor functions

extensions-builtin/forge_legacy_preprocessors/annotator/depth_anything_v2.py

@@ -0,0 +1,78 @@
+import os
+import torch
+import cv2
+import numpy as np
+import torch.nn.functional as F
+from torchvision.transforms import Compose
+from safetensors.torch import load_file
+
+from depth_anything_v2.dpt import DepthAnythingV2
+from depth_anything_v2.util.transform import Resize, NormalizeImage, PrepareForNet
+from .util import load_model
+from .annotator_path import models_path
+
+transform = Compose(
+    [
+        Resize(
+            width=518,
+            height=518,
+            resize_target=False,
+            keep_aspect_ratio=True,
+            ensure_multiple_of=14,
+            resize_method="lower_bound",
+            image_interpolation_method=cv2.INTER_CUBIC,
+        ),
+        NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        PrepareForNet(),
+    ]
+)
+
+class DepthAnythingV2Detector:
+    """https://github.com/MackinationsAi/Upgraded-Depth-Anything-V2"""
+
+    model_dir = os.path.join(models_path, "depth_anything_v2")
+
+    def __init__(self, device: torch.device):
+        self.device = device
+        self.model = (
+            DepthAnythingV2(
+                encoder="vitl",
+                features=256,
+                out_channels=[256, 512, 1024, 1024],
+            )
+            .to(device)
+            .eval()
+        )
+        remote_url = os.environ.get(
+            "CONTROLNET_DEPTH_ANYTHING_V2_MODEL_URL",
+            "https://huggingface.co/MackinationsAi/Depth-Anything-V2_Safetensors/resolve/main/depth_anything_v2_vitl.safetensors",
+        )
+        model_path = load_model(
+            "depth_anything_v2_vitl.safetensors", remote_url=remote_url, model_dir=self.model_dir
+        )
+        self.model.load_state_dict(load_file(model_path))
+
+    def __call__(self, image: np.ndarray, colored: bool = True) -> np.ndarray:
+        self.model.to(self.device)
+        h, w = image.shape[:2]
+
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
+        image = transform({"image": image})["image"]
+        image = torch.from_numpy(image).unsqueeze(0).to(self.device)
+        @torch.no_grad()
+        def predict_depth(model, image):
+            return model(image)
+        depth = predict_depth(self.model, image)
+        depth = F.interpolate(
+            depth[None], (h, w), mode="bilinear", align_corners=False
+        )[0, 0]
+        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+        depth = depth.cpu().numpy().astype(np.uint8)
+        if colored:
+            depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1]
+            return depth_color
+        else:
+            return depth
+
+    def unload_model(self):
+        self.model.to("cpu")