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57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
from __future__ import annotations
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import torch
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import comfy.utils
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from comfy_api.v3 import io
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class SD_4XUpscale_Conditioning(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="SD_4XUpscale_Conditioning_V3",
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category="conditioning/upscale_diffusion",
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inputs=[
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io.Image.Input("images"),
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io.Conditioning.Input("positive"),
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io.Conditioning.Input("negative"),
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io.Float.Input("scale_ratio", default=4.0, min=0.0, max=10.0, step=0.01),
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io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001),
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],
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outputs=[
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io.Conditioning.Output(display_name="positive"),
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io.Conditioning.Output(display_name="negative"),
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io.Latent.Output(display_name="latent"),
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],
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)
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@classmethod
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def execute(cls, images, positive, negative, scale_ratio, noise_augmentation):
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width = max(1, round(images.shape[-2] * scale_ratio))
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height = max(1, round(images.shape[-3] * scale_ratio))
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pixels = comfy.utils.common_upscale(
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(images.movedim(-1,1) * 2.0) - 1.0, width // 4, height // 4, "bilinear", "center"
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)
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out_cp = []
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out_cn = []
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for t in positive:
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n = [t[0], t[1].copy()]
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n[1]['concat_image'] = pixels
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n[1]['noise_augmentation'] = noise_augmentation
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out_cp.append(n)
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for t in negative:
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n = [t[0], t[1].copy()]
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n[1]['concat_image'] = pixels
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n[1]['noise_augmentation'] = noise_augmentation
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out_cn.append(n)
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latent = torch.zeros([images.shape[0], 4, height // 4, width // 4])
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return io.NodeOutput(out_cp, out_cn, {"samples":latent})
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NODES_LIST = [SD_4XUpscale_Conditioning]
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