StableCascade_EmptyLatentImage

Documentation

• Class name: StableCascade_EmptyLatentImage
• Category: latent/stable_cascade
• Output node: False

This node is designed to generate empty latent images with specified dimensions and compression settings. It primarily serves in the initialization or preparation stages of image generation processes, where creating a blank canvas or base for further modifications is required.

Input types

Required

• width
  • Specifies the width of the generated latent image. It influences the spatial dimensions of the output latent representation.
  • Comfy dtype: INT
  • Python dtype: int
• height
  • Determines the height of the generated latent image, affecting the vertical dimension of the output latent representation.
  • Comfy dtype: INT
  • Python dtype: int
• compression
  • Controls the compression level applied to the latent image dimensions, directly impacting the resolution and size of the output.
  • Comfy dtype: INT
  • Python dtype: int
• batch_size
  • Indicates the number of latent images to generate in a single batch, allowing for batch processing of multiple images.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• stage_c
  • Comfy dtype: LATENT
  • Represents the 'C' stage latent output of the generation process, typically a lower-resolution base for further image refinement.
  • Python dtype: torch.Tensor
• stage_b
  • Comfy dtype: LATENT
  • Denotes the 'B' stage latent output, offering a slightly higher resolution latent base for subsequent image development stages.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class StableCascade_EmptyLatentImage:
    def __init__(self, device="cpu"):
        self.device = device

    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}),
            "height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}),
            "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
            "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})
        }}
    RETURN_TYPES = ("LATENT", "LATENT")
    RETURN_NAMES = ("stage_c", "stage_b")
    FUNCTION = "generate"

    CATEGORY = "latent/stable_cascade"

    def generate(self, width, height, compression, batch_size=1):
        c_latent = torch.zeros([batch_size, 16, height // compression, width // compression])
        b_latent = torch.zeros([batch_size, 4, height // 4, width // 4])
        return ({
            "samples": c_latent,
        }, {
            "samples": b_latent,
        })