totoro_extras/nodes_sdupscale.py

import torch
import totoro.utils

class SD_4XUpscale_Conditioning:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "images": ("IMAGE",),
                              "positive": ("CONDITIONING",),
                              "negative": ("CONDITIONING",),
                              "scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                              "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                             }}
    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
    RETURN_NAMES = ("positive", "negative", "latent")

    FUNCTION = "encode"

    CATEGORY = "conditioning/upscale_diffusion"

    def encode(self, images, positive, negative, scale_ratio, noise_augmentation):
        width = max(1, round(images.shape[-2] * scale_ratio))
        height = max(1, round(images.shape[-3] * scale_ratio))

        pixels = totoro.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width // 4, height // 4, "bilinear", "center")

        out_cp = []
        out_cn = []

        for t in positive:
            n = [t[0], t[1].copy()]
            n[1]['concat_image'] = pixels
            n[1]['noise_augmentation'] = noise_augmentation
            out_cp.append(n)

        for t in negative:
            n = [t[0], t[1].copy()]
            n[1]['concat_image'] = pixels
            n[1]['noise_augmentation'] = noise_augmentation
            out_cn.append(n)

        latent = torch.zeros([images.shape[0], 4, height // 4, width // 4])
        return (out_cp, out_cn, {"samples":latent})

NODE_CLASS_MAPPINGS = {
    "SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning,
}