ComfyUI Node: ISNet Segment

ISNet Segment:

The ISNetSegment node is designed to perform image segmentation using the ISNet model, which is particularly useful for AI artists looking to isolate specific parts of an image. This node leverages deep learning techniques to generate precise masks that can be used to separate foreground elements from the background, enabling more refined and creative image manipulations. By utilizing this node, you can automate the process of segmenting images, saving time and effort while achieving high-quality results. The node is capable of handling multiple images at once and can operate in different device modes to optimize performance based on your hardware setup.

ISNet Segment Input Parameters:

images

This parameter expects a tensor of images that you want to segment. The images should be in a format compatible with PyTorch tensors. The quality and resolution of the input images can significantly impact the segmentation results.

threshold

This parameter sets the threshold value for the segmentation process. It determines the sensitivity of the mask generation, with a range from 0 to 1. A lower threshold might result in more areas being included in the mask, while a higher threshold will make the mask more selective. The default value is 0.5, which provides a balanced approach for most use cases.

device_mode

This optional parameter allows you to specify the device on which the segmentation will be performed. The available options are "AUTO", "Prefer GPU", and "CPU". "AUTO" will automatically select the best available device, "Prefer GPU" will prioritize using a GPU if available, and "CPU" will force the operation to run on the CPU. The default setting is "AUTO".

enabled

This optional boolean parameter enables or disables the segmentation process. If set to False, the node will return the original images with zero masks. This can be useful for debugging or when you want to temporarily bypass the segmentation. The default value is True.

isnet_model

This optional parameter allows you to provide a pre-loaded ISNet model. If not provided, the node will attempt to load a model from the available checkpoints or download a default model if none are found. This can be useful if you have a custom-trained model or want to avoid the overhead of loading the model each time.

ISNet Segment Output Parameters:

segmented

This output parameter returns the segmented images as a tensor. Each image in the tensor will have the background removed or altered based on the generated mask, allowing for further creative processing or direct use in your projects.

mask

This output parameter provides the masks generated during the segmentation process. The masks are returned as a tensor and can be used to understand which parts of the image were identified as foreground. These masks can be further refined or used in combination with other image processing techniques.

ISNet Segment Usage Tips:

• Ensure your input images are of high quality and properly pre-processed to achieve the best segmentation results.
• Experiment with the threshold parameter to find the optimal value for your specific images and desired level of detail.
• Utilize the device_mode parameter to optimize performance based on your hardware setup, especially if you have access to a GPU.
• If you have a custom-trained ISNet model, use the isnet_model parameter to load it directly and bypass the default model loading process.

ISNet Segment Common Errors and Solutions:

Model override <model_override> not found. Use <model_name> instead.

• Explanation: The specified model override was not found in the available checkpoints.
• Solution: Ensure the model name is correct and exists in the specified directory. If not, use the default model name provided.

No ISNet model checkpoints found.

• Explanation: The node could not find any ISNet model checkpoints in the specified directory.
• Solution: Verify that the model checkpoints are correctly placed in the directory or allow the node to download the default model.

CUDA out of memory.

• Explanation: The GPU does not have enough memory to perform the segmentation.
• Solution: Reduce the batch size of the input images or switch to CPU mode by setting the device_mode parameter to "CPU".

Invalid image tensor format.

• Explanation: The input images are not in a compatible tensor format.
• Solution: Ensure that the input images are properly converted to PyTorch tensors before passing them to the node.