ComfyUI Node: Latent Batch

Latent Batch:

The Latent Batch node is designed to combine two sets of latent samples into a single batch. This is particularly useful in scenarios where you need to merge different latent representations for further processing or analysis. By concatenating the samples along the batch dimension, this node ensures that the combined output maintains the integrity and structure of the original latent data. The primary goal of this node is to facilitate the seamless integration of multiple latent samples, making it easier to handle and manipulate large sets of latent data in your AI art projects.

Latent Batch Input Parameters:

samples1

samples1 is the first set of latent samples that you want to combine. This parameter expects a latent data type, which typically contains multi-dimensional arrays representing the latent space of your data. The shape and structure of these samples are crucial as they need to be compatible with the second set of samples for successful concatenation.

samples2

samples2 is the second set of latent samples to be combined with samples1. Similar to samples1, this parameter also expects a latent data type. If the dimensions of samples2 do not match those of samples1, the node will automatically upscale samples2 to ensure compatibility, using bilinear interpolation and center alignment.

Latent Batch Output Parameters:

LATENT

The output parameter is a single combined set of latent samples, labeled as LATENT. This output retains the structure of the input samples but concatenates them along the batch dimension. The resulting batch includes all the samples from both samples1 and samples2, along with an updated batch_index that reflects the new combined batch.

Latent Batch Usage Tips:

• Ensure that the dimensions of samples1 and samples2 are compatible or can be made compatible through upscaling to avoid errors during concatenation.
• Use this node when you need to merge different latent representations for tasks such as batch processing, data augmentation, or model training.
• If you encounter issues with mismatched dimensions, consider preprocessing your latent samples to have similar shapes before using this node.

Latent Batch Common Errors and Solutions:

ValueError: "At least one input latent must be provided."

• Explanation: This error occurs when neither samples1 nor samples2 is provided as input to the node.
• Solution: Ensure that you provide at least one set of latent samples for the node to process. Both samples1 and samples2 should be valid latent data types.

DimensionMismatchError: "Input dimensions do not match and cannot be upscaled."

• Explanation: This error occurs when the dimensions of samples1 and samples2 are incompatible and cannot be upscaled to match each other.
• Solution: Check the dimensions of your input samples and ensure they are either the same or can be made compatible through upscaling. You may need to preprocess your data to achieve this.

KeyError: "Missing 'samples' key in input data."

• Explanation: This error occurs when the input data does not contain the required samples key.
• Solution: Verify that your input data is correctly formatted and includes the samples key with the appropriate latent data.