MachinePainting
c864dcf0f7
Fixes: - Frequency Separate: locked to Photoshop Apply Image formula (Scale 2, Offset 128, Subtract), removed mode/epsilon - Frequency Combine: locked to Linear Light blend, added opacity slider, removed mode/epsilon Updates: - Blur Pro: removed distance and iterations params, motion blur uses radius, box blur single pass - Inpaint Mask Pro: removed SEGS support, inpaint_mask now required
84 lines
2.3 KiB
Python
84 lines
2.3 KiB
Python
import torch
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import numpy as np
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class FrequencySeparate:
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"""
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Splits an image into high and low frequency layers using the Photoshop
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Apply Image method: Scale 2, Offset 128, Subtract.
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Feed the original image and a blurred version (from Blur Pro or any blur node).
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The high frequency output is a neutral gray layer containing texture/detail only.
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"""
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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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"original": ("IMAGE",),
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"low_frequency": ("IMAGE",),
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},
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}
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RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE")
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RETURN_NAMES = ("thru", "high_frequency", "low_frequency")
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FUNCTION = "separate"
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CATEGORY = "MachinePaintingNodes/Filter"
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def separate(self, original, low_frequency):
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orig = original.detach().clone()
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low = low_frequency.detach().clone()
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# Photoshop Apply Image: Scale 2, Offset 128, Subtract
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# high = (original - low) / 2 + 0.5
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high = (orig - low) / 2.0 + 0.5
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high = torch.clamp(high, 0.0, 1.0)
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return (original, high, low_frequency)
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class FrequencyCombine:
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"""
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Recombines high and low frequency layers using Linear Light blend.
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result = low + 2 * (high - 0.5)
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Opacity controls how much texture (high frequency) is applied.
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At 0: just the low frequency (smooth). At 1: full reconstruction.
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"""
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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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"high_frequency": ("IMAGE",),
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"low_frequency": ("IMAGE",),
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},
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"optional": {
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"opacity": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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"display": "slider"
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}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "combine"
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CATEGORY = "MachinePaintingNodes/Filter"
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def combine(self, high_frequency, low_frequency, opacity=1.0):
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low = low_frequency.detach().clone()
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# Linear Light: result = low + 2 * (high - 0.5)
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# With opacity: result = low + 2 * (high - 0.5) * opacity
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result = low + 2.0 * (high_frequency - 0.5) * opacity
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result = torch.clamp(result, 0.0, 1.0)
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return (result,)
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