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Running
on
Zero
Running
on
Zero
import torch | |
import comfy.utils | |
class SD_4XUpscale_Conditioning: | |
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 = comfy.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, | |
} | |