47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
import torch
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import torch.nn.functional as F
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class Pixelize:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"pixel_size": ("INT", {
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"default": 8,
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"min": 2,
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"max": 128,
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"step": 1
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply_pixelize"
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CATEGORY = "postprocessing"
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def apply_pixelize(self, image: torch.Tensor, pixel_size: int):
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pixelized_image = self.pixelize_image(image, pixel_size)
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pixelized_image = torch.clamp(pixelized_image, 0, 1)
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return (pixelized_image,)
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def pixelize_image(self, image: torch.Tensor, pixel_size: int):
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batch_size, height, width, channels = image.shape
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new_height = height // pixel_size
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new_width = width // pixel_size
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image = image.permute(0, 3, 1, 2)
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image = F.avg_pool2d(image, kernel_size=pixel_size, stride=pixel_size)
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image = F.interpolate(image, size=(height, width), mode='nearest')
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image = image.permute(0, 2, 3, 1)
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return image
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NODE_CLASS_MAPPINGS = {
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"Pixelize": Pixelize,
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}
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