ComfyUI Node: Depth Pro

Depth Pro:

DepthPro is a sophisticated node designed to estimate depth from images, providing a powerful tool for AI artists who wish to incorporate depth information into their creative projects. This node leverages advanced neural network models to infer depth, transforming 2D images into rich, three-dimensional representations. The primary benefit of using DepthPro is its ability to accurately predict depth, which can be crucial for applications such as 3D modeling, augmented reality, and virtual reality. By converting images into depth maps, DepthPro enables artists to add a new dimension to their work, enhancing realism and depth perception. The node is designed to be user-friendly, making it accessible even to those without a deep technical background, while still offering the precision and reliability needed for professional-grade projects.

Depth Pro Input Parameters:

depth_pro_model

The depth_pro_model parameter is essential as it specifies the model used for depth estimation. This parameter requires a pre-trained DepthPro model, which contains the necessary weights and configurations to perform depth inference. The model is responsible for processing the input image and generating the corresponding depth map. It is crucial to ensure that the model is compatible with the node to achieve accurate results. There are no specific minimum, maximum, or default values for this parameter, but it must be a valid DepthPro model.

image

The image parameter is the input image for which the depth estimation is to be performed. This parameter accepts an image in a format that the node can process, typically a tensor representation of the image. The quality and resolution of the input image can significantly impact the accuracy of the depth estimation. While there are no strict constraints on the image size, higher resolution images may provide more detailed depth maps. It is important to ensure that the image is pre-processed correctly to match the model's input requirements.

Depth Pro Output Parameters:

metric_depth

The metric_depth output parameter provides the estimated depth map of the input image. This depth map is a representation of the distance of each pixel in the image from the camera, expressed in metric units. The depth map can be used to create 3D models or enhance visual effects in digital art. The output is typically a tensor that can be further processed or visualized to suit the artist's needs.

focal_list

The focal_list output parameter is a list of focal lengths in pixels, corresponding to the input images. This list provides information about the camera's focal length used during the depth estimation process, which can be useful for understanding the scale and perspective of the depth map. The focal lengths are derived from the model's predictions and can vary depending on the input image characteristics.

focal_avg

The focal_avg output parameter is the average focal length in pixels, calculated from the focal_list. This value gives a single representative focal length for the entire batch of input images, providing a simplified overview of the camera settings used during depth estimation. The average focal length can be useful for standardizing depth maps across different images or for further analysis.

Depth Pro Usage Tips:

• Ensure that the input image is pre-processed to match the model's input requirements, such as normalization and resizing, to improve depth estimation accuracy.
• Use high-resolution images to obtain more detailed depth maps, which can enhance the realism of 3D models or visual effects.
• Experiment with different DepthPro models to find the one that best suits your specific artistic needs and provides the most accurate depth predictions.

Depth Pro Common Errors and Solutions:

Model Compatibility Error

• Explanation: This error occurs when the provided depth_pro_model is not compatible with the node, possibly due to incorrect model architecture or missing weights.
• Solution: Ensure that you are using a pre-trained DepthPro model that is compatible with the node. Verify that the model's architecture and weights are correctly loaded and match the node's requirements.

Image Format Error

• Explanation: This error arises when the input image is not in a format that the node can process, such as an incorrect tensor shape or data type.
• Solution: Check that the input image is correctly formatted as a tensor with the appropriate dimensions and data type. Pre-process the image to ensure it meets the model's input specifications, such as normalization and channel ordering.