Skip to main content
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

ParameterDescriptionData TypeRequiredRange
latentsList of latent dicts to bucket by resolution.LATENTYesN/A
conditioningList of conditioning lists (must match latents length).CONDITIONINGYesN/A
Note: The number of items in the latents list must exactly match the number of items in the conditioning list. 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.

Outputs

Output NameDescriptionData Type
latentsList of batched latent dicts, one per resolution bucket.LATENT
conditioningList of condition lists, one per resolution bucket.CONDITIONING
This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! Edit on GitHub

Source fingerprint (SHA-256): 20a0794e5a2c88ac60bb729b60840c5a632a115196de285b764effaf43ab73e0