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45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
import torch
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class Mosaic:
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interpolation_methods = ['nearest', 'bilinear', 'bicubic', 'area', 'nearest-exact']
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@classmethod
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def INPUT_TYPES(s):
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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": 4,
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"min": 1,
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"max": 512,
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"step": 1
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}),
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"interpolation_method": (s.interpolation_methods, {"default": s.interpolation_methods[0]}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "mosaic"
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CATEGORY = "image"
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def mosaic(self, image, interpolation_method, pixel_size):
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samples = image.movedim(-1,1)
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starting_width = samples.shape[3]
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starting_height = samples.shape[2]
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#downsample dimensions
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dowsample_width = int(starting_width / pixel_size) | 1
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dowsample_height = int(starting_height / pixel_size) | 1
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downsampled_image = torch.nn.functional.interpolate(samples, size=(dowsample_height, dowsample_width), mode=interpolation_method)
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output_image = torch.nn.functional.interpolate(downsampled_image, size=(starting_height, starting_width), mode='nearest')
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output = output_image.movedim(1,-1)
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return (output,)
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NODE_CLASS_MAPPINGS = {
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"Mosaic": Mosaic
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} |