LyAhn
e41eccf1a9
- ImageAspectSize: reads input image dimensions and outputs width/height scaled to a target longest-side, snapped to multiples of 8, with a flip toggle for portrait/landscape rotation - MultiSwitch: any-type switch node with dynamic slot pairs (JS-driven add/remove), colour-coded active/inactive sides, and clean labelling
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
import torch
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class ImageAspectSize:
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TITLE = "Image Aspect Size"
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CATEGORY = "JezzWTF/image"
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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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"target_size": ("INT", {
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"default": 1024,
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"min": 64,
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"max": 8192,
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"step": 8,
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"tooltip": "Longest side in pixels. The other dimension is calculated to preserve aspect ratio, snapped to multiples of 8.",
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}),
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"flip": ("BOOLEAN", {
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"default": False,
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"label_on": "Flipped (portrait↔landscape)",
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"label_off": "Normal",
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"tooltip": "Swap width and height before scaling — useful for rotating orientation without changing the image.",
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}),
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},
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}
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RETURN_TYPES = ("IMAGE", "INT", "INT")
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RETURN_NAMES = ("IMAGE", "WIDTH", "HEIGHT")
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FUNCTION = "calculate"
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def calculate(self, image: torch.Tensor, target_size: int, flip: bool) -> tuple[torch.Tensor, int, int]:
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_, H, W, _ = image.shape
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if flip:
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W, H = H, W
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scale = target_size / max(W, H)
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width = round(W * scale / 8) * 8
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height = round(H * scale / 8) * 8
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return (image, width, height)
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