ComfyUI/comfy_extras/v3/nodes_sdupscale.py
2025-07-23 14:55:53 -07:00

57 lines
1.9 KiB
Python

from __future__ import annotations
import torch
import comfy.utils
from comfy_api.v3 import io
class SD_4XUpscale_Conditioning(io.ComfyNodeV3):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="SD_4XUpscale_Conditioning_V3",
category="conditioning/upscale_diffusion",
inputs=[
io.Image.Input("images"),
io.Conditioning.Input("positive"),
io.Conditioning.Input("negative"),
io.Float.Input("scale_ratio", default=4.0, min=0.0, max=10.0, step=0.01),
io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001),
],
outputs=[
io.Conditioning.Output(display_name="positive"),
io.Conditioning.Output(display_name="negative"),
io.Latent.Output(display_name="latent"),
],
)
@classmethod
def execute(cls, 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 io.NodeOutput(out_cp, out_cn, {"samples":latent})
NODES_LIST = [SD_4XUpscale_Conditioning]