LatentBatch

Documentation

• Class name: LatentBatch
• Category: latent/batch
• Output node: False

The LatentBatch node is designed to merge two sets of latent samples into a single batch, potentially resizing one set to match the dimensions of the other before concatenation. This operation facilitates the combination of different latent representations for further processing or generation tasks.

Input types

Required

• samples1
  • The first set of latent samples to be merged. It plays a crucial role in determining the final shape of the merged batch.
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, torch.Tensor]
• samples2
  • The second set of latent samples to be merged. If its dimensions differ from the first set, it is resized to ensure compatibility before merging.
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, torch.Tensor]

Output types

• latent
  • Comfy dtype: LATENT
  • The merged set of latent samples, now combined into a single batch for further processing.
  • Python dtype: Tuple[Dict[str, torch.Tensor]]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class LatentBatch:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}

    RETURN_TYPES = ("LATENT",)
    FUNCTION = "batch"

    CATEGORY = "latent/batch"

    def batch(self, samples1, samples2):
        samples_out = samples1.copy()
        s1 = samples1["samples"]
        s2 = samples2["samples"]

        if s1.shape[1:] != s2.shape[1:]:
            s2 = comfy.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center")
        s = torch.cat((s1, s2), dim=0)
        samples_out["samples"] = s
        samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])
        return (samples_out,)