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Empty Latent Image

Documentation

  • Class name: EmptyLatentImage
  • Category: latent
  • Output node: False

The EmptyLatentImage node is designed to generate a blank latent space representation with specified dimensions and batch size. This node serves as a foundational step in generating or manipulating images in latent space, providing a starting point for further image synthesis or modification processes.

Input types

Required

  • width
    • Specifies the width of the latent image to be generated. This parameter directly influences the spatial dimensions of the resulting latent representation.
    • Comfy dtype: INT
    • Python dtype: int
  • height
    • Determines the height of the latent image to be generated. This parameter is crucial for defining the spatial dimensions of the latent space representation.
    • Comfy dtype: INT
    • Python dtype: int
  • batch_size
    • Controls the number of latent images to be generated in a single batch. This allows for the generation of multiple latent representations simultaneously, facilitating batch processing.
    • Comfy dtype: INT
    • Python dtype: int

Output types

  • latent
    • Comfy dtype: LATENT
    • The output is a tensor representing a batch of blank latent images, serving as a base for further image generation or manipulation in latent space.
    • Python dtype: torch.Tensor

Usage tips

Source code

class EmptyLatentImage:
    def __init__(self):
        self.device = comfy.model_management.intermediate_device()

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
                              "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "generate"

    CATEGORY = "latent"

    def generate(self, width, height, batch_size=1):
        latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device)
        return ({"samples":latent}, )