GetLatentsFromBatchIndexed

Documentation

• Class name: GetLatentsFromBatchIndexed
• Category: KJNodes
• Output node: False

This node is designed to select and return specific latents from a given batch based on provided indices. It facilitates the extraction of a subset of latents for further processing or analysis, making it a crucial component for operations requiring targeted manipulation or inspection of latent spaces.

Input types

Required

• latents
  • The 'latents' parameter represents the batch of latents from which specific items will be selected. It is crucial for determining the scope of latents available for selection.
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, torch.Tensor]
• indexes
  • The 'indexes' parameter specifies the indices of the latents to be selected from the batch. It plays a key role in identifying which latents are to be extracted and processed.
  • Comfy dtype: STRING
  • Python dtype: str

Output types

• latent
  • Comfy dtype: LATENT
  • The output is a modified version of the input latent batch, containing only the latents at the specified indices.
  • Python dtype: Tuple[Dict[str, torch.Tensor]]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class GetLatentsFromBatchIndexed:

    RETURN_TYPES = ("LATENT",)
    FUNCTION = "indexedlatentsfrombatch"
    CATEGORY = "KJNodes"
    DESCRIPTION = """
Selects and returns the latents at the specified indices as an latent batch.
"""

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                 "latents": ("LATENT",),
                 "indexes": ("STRING", {"default": "0, 1, 2", "multiline": True}),
        },
    } 

    def indexedlatentsfrombatch(self, latents, indexes):

        samples = latents.copy()
        latent_samples = samples["samples"] 

        # Parse the indexes string into a list of integers
        index_list = [int(index.strip()) for index in indexes.split(',')]

        # Convert list of indices to a PyTorch tensor
        indices_tensor = torch.tensor(index_list, dtype=torch.long)

        # Select the latents at the specified indices
        chosen_latents = latent_samples[indices_tensor]

        samples["samples"] = chosen_latents
        return (samples,)