ComfyUI Node: LeReS Depth Map (enable boost for leres++)

LeReS Depth Map (enable boost for leres++):

The LeReS-DepthMapPreprocessor node is designed to generate depth maps from input images using the LeReS (Learning-based Reconstructive Estimation of Surface) model. This node is particularly useful for AI artists who want to add depth information to their images, which can be used for various applications such as 3D rendering, augmented reality, and more. The LeReS model is known for its accuracy and ability to handle complex scenes, making it a valuable tool for enhancing the visual depth of your projects. Additionally, the node offers an optional boost mode, which leverages the enhanced capabilities of the LeReS++ model for even more detailed depth estimation.

LeReS Depth Map (enable boost for leres++) Input Parameters:

rm_nearest

This parameter controls the removal of the nearest objects in the depth map. It is a float value that ranges from 0.0 to 100, with a default value of 0.0. Increasing this value will remove objects that are closer to the camera, which can be useful for focusing on background elements or reducing clutter in the depth map.

rm_background

This parameter controls the removal of background objects in the depth map. It is a float value that ranges from 0.0 to 100, with a default value of 0.0. Increasing this value will remove objects that are farther from the camera, which can help in isolating foreground elements or reducing background noise in the depth map.

boost

This parameter enables or disables the boost mode, which uses the enhanced capabilities of the LeReS++ model for more detailed depth estimation. It can be set to either "enable" or "disable," with the default value being "disable." Enabling boost mode can provide more accurate and detailed depth maps, but may require more computational resources.

LeReS Depth Map (enable boost for leres++) Output Parameters:

IMAGE

The output of this node is an image that represents the depth map of the input image. The depth map is a single-channel image where the intensity of each pixel corresponds to the distance of that point from the camera. Brighter pixels represent points that are closer to the camera, while darker pixels represent points that are farther away. This depth information can be used in various applications such as 3D modeling, augmented reality, and more.

LeReS Depth Map (enable boost for leres++) Usage Tips:

• To focus on background elements, increase the rm_nearest parameter to remove closer objects from the depth map.
• To isolate foreground elements, increase the rm_background parameter to remove farther objects from the depth map.
• Enable the boost parameter for more detailed and accurate depth maps, especially in complex scenes, but be aware that this may require more computational resources.

LeReS Depth Map (enable boost for leres++) Common Errors and Solutions:

"Model loading failed"

• Explanation: This error occurs when the LeReS model fails to load, possibly due to missing files or incorrect paths.
• Solution: Ensure that the model files are correctly placed in the expected directories and that the paths are correctly specified.

"Invalid input image"

• Explanation: This error occurs when the input image is not in a valid format or is corrupted.
• Solution: Verify that the input image is in a supported format (e.g., JPEG, PNG) and is not corrupted. Try using a different image to see if the issue persists.

"Insufficient memory"

• Explanation: This error occurs when there is not enough memory available to process the image, especially when boost mode is enabled.
• Solution: Try reducing the resolution of the input image or disabling the boost mode to reduce memory usage. Ensure that your system has sufficient memory available for processing.

"Parameter out of range"

• Explanation: This error occurs when one of the input parameters is set to a value outside its allowed range.
• Solution: Check the input parameters and ensure that they are within the specified ranges. For example, rm_nearest and rm_background should be between 0.0 and 100.