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convert nodes_sdupscale.py to V3 schema (#9943)

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Alexander Piskun 2025-09-27 00:12:38 +03:00 committed by GitHub
parent 7ea173c187
commit 80718908a9
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1 changed files with 35 additions and 19 deletions
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54
comfy_extras/nodes_sdupscale.py
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@ -1,23 +1,31 @@
from typing_extensions import override
import torch import torch
import comfy.utils import comfy.utils
from comfy_api.latest import ComfyExtension, io
class SD_4XUpscale_Conditioning: class SD_4XUpscale_Conditioning(io.ComfyNode):
@classmethod @classmethod
def INPUT_TYPES(s): def define_schema(cls):
return {"required": { "images": ("IMAGE",), return io.Schema(
"positive": ("CONDITIONING",), node_id="SD_4XUpscale_Conditioning",
"negative": ("CONDITIONING",), category="conditioning/upscale_diffusion",
"scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}), inputs=[
"noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), io.Image.Input("images"),
}} io.Conditioning.Input("positive"),
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") io.Conditioning.Input("negative"),
RETURN_NAMES = ("positive", "negative", "latent") 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"),
],
)
FUNCTION = "encode" @classmethod
def execute(cls, images, positive, negative, scale_ratio, noise_augmentation):
CATEGORY = "conditioning/upscale_diffusion"
def encode(self, images, positive, negative, scale_ratio, noise_augmentation):
width = max(1, round(images.shape[-2] * scale_ratio)) width = max(1, round(images.shape[-2] * scale_ratio))
height = max(1, round(images.shape[-3] * scale_ratio)) height = max(1, round(images.shape[-3] * scale_ratio))
@ -39,8 +47,16 @@ class SD_4XUpscale_Conditioning:
out_cn.append(n) out_cn.append(n)
latent = torch.zeros([images.shape[0], 4, height // 4, width // 4]) latent = torch.zeros([images.shape[0], 4, height // 4, width // 4])
return (out_cp, out_cn, {"samples":latent}) return io.NodeOutput(out_cp, out_cn, {"samples":latent})
NODE_CLASS_MAPPINGS = {
"SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning, class SdUpscaleExtension(ComfyExtension):
} @override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
SD_4XUpscale_Conditioning,
]
async def comfy_entrypoint() -> SdUpscaleExtension:
return SdUpscaleExtension()