feat: copy docs

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+# Documentation
+- Class name: Inference_Core_MiDaS-DepthMapPreprocessor
+- Category: ControlNet Preprocessors/Normal and Depth Estimators
+- Output node: False
+- Repo Ref: https://github.com/comfyanonymous/ComfyUI
+
+MiDaS Depth Map Preprocessor节点旨在利用MiDaS模型将输入图像转换为深度图。这一过程增强了图像中的深度感知，有助于3D建模、增强现实等多种应用，通过提供详细的深度估计来实现这一目标。
+
+# Input types
+## Required
+- image
+    - image参数是用于生成深度图的输入图像，作为深度估计的主要数据来源。
+    - Comfy dtype: IMAGE
+    - Python dtype: torch.Tensor
+## Optional
+- a
+    - a参数影响深度图中法线的计算，从而影响输出中深度和纹理的感知。
+    - Comfy dtype: FLOAT
+    - Python dtype: float
+- bg_threshold
+    - bg_threshold参数设置深度图中背景分离的阈值，通过过滤背景噪声来增强对前景元素的关注。
+    - Comfy dtype: FLOAT
+    - Python dtype: float
+- resolution
+    - resolution参数指定输出深度图的分辨率，影响生成的深度图的细节水平和尺寸。
+    - Comfy dtype: INT
+    - Python dtype: int
+
+# Output types
+- image
+    - 输出是一个深度图图像，提供输入图像的像素级深度估计。
+    - Comfy dtype: IMAGE
+    - Python dtype: torch.Tensor
+
+
+## Usage tips
+- Infra type: `GPU`
+- Common nodes: unknown
+
+
+## Source code
+```python
+class MIDAS_Depth_Map_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return create_node_input_types(
+            a =  ("FLOAT", {"default": np.pi * 2.0, "min": 0.0, "max": np.pi * 5.0, "step": 0.05}),
+            bg_threshold = ("FLOAT", {"default": 0.1, "min": 0, "max": 1, "step": 0.05})
+        )
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
+
+    def execute(self, image, a, bg_threshold, resolution=512, **kwargs):
+        from controlnet_aux.midas import MidasDetector
+
+        # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_depth2image.py
+        model = MidasDetector.from_pretrained().to(model_management.get_torch_device())
+        out = common_annotator_call(model, image, resolution=resolution, a=a, bg_th=bg_threshold)
+        del model
+        return (out, )
+
+```