StableCascade_SuperResolutionControlnet

Documentation

Class name: StableCascade_SuperResolutionControlnet
Category: _for_testing/stable_cascade
Output node: False

This node is designed for generating super-resolution control inputs for a cascading image generation process, utilizing a VAE to encode images into latent representations that are then adjusted for different stages of image resolution enhancement.

Input types

Required

image
- The input image to be processed and encoded into latent representations for super-resolution enhancement.
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
vae
- A variational autoencoder (VAE) model used to encode the input image into a latent space, facilitating the super-resolution process.
- Comfy dtype: VAE
- Python dtype: torch.nn.Module

Output types

controlnet_input
- Comfy dtype: IMAGE
- The encoded latent representation of the input image, prepared for the control network of the super-resolution process.
- Python dtype: torch.Tensor
stage_c
- Comfy dtype: LATENT
- A latent representation tailored for the initial stage of the super-resolution process.
- Python dtype: Dict[str, torch.Tensor]
stage_b
- Comfy dtype: LATENT
- A latent representation tailored for the subsequent stage of the super-resolution process.
- Python dtype: Dict[str, torch.Tensor]

Usage tips

Infra type: GPU
Common nodes: unknown

Source code

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

    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "image": ("IMAGE",),
            "vae": ("VAE", ),
        }}
    RETURN_TYPES = ("IMAGE", "LATENT", "LATENT")
    RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b")
    FUNCTION = "generate"

    CATEGORY = "_for_testing/stable_cascade"

    def generate(self, image, vae):
        width = image.shape[-2]
        height = image.shape[-3]
        batch_size = image.shape[0]
        controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1)

        c_latent = torch.zeros([batch_size, 16, height // 16, width // 16])
        b_latent = torch.zeros([batch_size, 4, height // 2, width // 2])
        return (controlnet_input, {
            "samples": c_latent,
        }, {
            "samples": b_latent,
        })

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