Empty Latent Image (Big Batch) 🎭🅐🅓

Documentation

Class name: ADE_EmptyLatentImageLarge
Category: Animate Diff 🎭🅐🅓/extras
Output node: False

The ADE_EmptyLatentImageLarge node is designed to initialize a large latent image tensor with zeros. This tensor serves as a blank canvas for further generative processes, allowing for the creation and manipulation of images at a latent level.

Input types

Required

width
- Specifies the width of the latent image to be generated. It determines the horizontal dimension of the resulting tensor.
- Comfy dtype: INT
- Python dtype: int
height
- Determines the height of the latent image. It affects the vertical dimension of the resulting tensor.
- Comfy dtype: INT
- Python dtype: int
batch_size
- Controls the number of latent images to generate in a single batch. It influences the first dimension of the resulting tensor, allowing for batch processing of multiple images.
- Comfy dtype: INT
- Python dtype: int

Output types

latent
- Comfy dtype: LATENT
- The output is a tensor representing a batch of blank latent images. Each image is initialized with zeros, ready for subsequent generative modifications.
- Python dtype: torch.Tensor

Usage tips

Infra type: GPU
Common nodes:
- BatchPromptScheduleLatentInput
- KSampler

Source code

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

    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "width": ("INT", {"default": 512, "min": 64, "max": comfy_nodes.MAX_RESOLUTION, "step": 8}),
                              "height": ("INT", {"default": 512, "min": 64, "max": comfy_nodes.MAX_RESOLUTION, "step": 8}),
                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 262144})}}
    RETURN_TYPES = ("LATENT",)
    FUNCTION = "generate"

    CATEGORY = "Animate Diff 🎭🅐🅓/extras"

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

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