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This node organizes a list of latent images and their corresponding conditioning data by their resolution. It groups together items that share the same height and width, creating separate batches for each unique resolution. This process is useful for preparing data for efficient training, as it allows models to process multiple items of the same size together.

Inputs

Note: The number of items in the latents list must exactly match the number of items in the conditioning list. If the counts do not match, the node raises an error. Each latent dictionary can contain a batch of samples, and the corresponding conditioning list must contain a matching number of conditioning items for that batch. Latent samples may have a shape of (B, C, H, W) for images or (B, T, C, H, W) for videos; the node groups them by height and width only.

Outputs

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