ComfyUI Node: LatentSelector

LatentSelector:

The LatentSelector node is designed to filter and select specific latent images from a batch based on user-defined indexes. This node is particularly useful when you have a collection of latent images and need to isolate certain ones for further processing or analysis. By specifying the indexes of the latent images you want to keep, you can streamline your workflow and focus on the most relevant data. This node enhances efficiency and precision in handling latent images, making it an essential tool for AI artists working with complex image datasets.

LatentSelector Input Parameters:

latent_image

This parameter represents the batch of latent images that you want to filter. It is a mapping of strings to torch tensors, where each tensor contains the latent representations of the images. The latent images are typically generated by previous nodes in your workflow and contain the encoded information of the images.

selected_indexes

This parameter specifies the indexes of the latent images you want to select from the batch. The indexes should be provided as a comma-separated string, and you can also specify ranges using a colon. For example, "1,3,5:7,9" will select the 1st, 3rd, 5th to 7th, and 9th latent images. This flexibility allows you to easily pick out specific images or ranges of images from the batch.

LatentSelector Output Parameters:

LATENT

The output is a filtered batch of latent images, containing only the selected images based on the provided indexes. If no valid indexes are provided, the output will be the original batch of latent images, effectively passing through the input without any changes. This ensures that you always have a valid output, even if the selection criteria are not met.

LatentSelector Usage Tips:

• Ensure that the indexes you provide in the selected_indexes parameter are within the range of the batch size of your latent images to avoid selecting non-existent images.
• Use the range selection feature (e.g., "5:10") to quickly select multiple consecutive latent images without listing each index individually.
• Combine individual indexes and ranges to create complex selection criteria, such as "1,3,5:7,9" for more precise filtering.

LatentSelector Common Errors and Solutions:

IndexError: list index out of range

• Explanation: This error occurs when the provided indexes in selected_indexes are out of the range of the latent image batch size.
• Solution: Verify that all indexes in the selected_indexes parameter are within the valid range of the latent image batch. Adjust the indexes accordingly to match the batch size.

ValueError: invalid literal for int() with base 10

• Explanation: This error occurs when the selected_indexes parameter contains non-numeric values or improperly formatted ranges.
• Solution: Ensure that the selected_indexes parameter contains only numeric values and properly formatted ranges. Remove any non-numeric characters or correct the formatting.

LatentSelector: selected no latents, passthrough

• Explanation: This message indicates that no valid latent images were selected based on the provided indexes, and the node is passing through the original batch.
• Solution: Check the selected_indexes parameter to ensure it contains valid indexes. Adjust the indexes to match the latent images you want to select.