MachinePainting
3a574494eb
None of the nodes showed description text in ComfyUI because ComfyUI reads obj_class.DESCRIPTION when it builds node_info - it does not read the class docstring, which is what these nodes had. Every registered node now carries a one-line DESCRIPTION, kept to 29-67 characters so it fits the limited space in ComfyUI Manager and the node search results. Image Transform Pro's four-sentence description is shortened to match. Descriptions are derived from each node's existing docstring, or from the README for the five classes that had no docstring (ImageBlendPro, ColorMatchBlend and the three Boolean switch nodes). README also updated for the recent changes: the New Nodes table now lists the real "Rotate & Scale" category, the XY placement direction is documented on both nodes (positive X right, positive Y up), and the v2.2.0 changelog records the placement convention, the node outputs and the MP_ImageTransform internal name.
88 lines
2.5 KiB
Python
88 lines
2.5 KiB
Python
import torch
|
|
import numpy as np
|
|
|
|
|
|
class FrequencySeparate:
|
|
"""
|
|
Splits an image into high and low frequency layers using the Photoshop
|
|
Apply Image method: Scale 2, Offset 128, Subtract.
|
|
|
|
Feed the original image and a blurred version (from Blur Pro or any blur node).
|
|
The high frequency output is a neutral gray layer containing texture/detail only.
|
|
"""
|
|
|
|
DESCRIPTION = "Split an image into high and low frequency layers."
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"original": ("IMAGE",),
|
|
"low_frequency": ("IMAGE",),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE")
|
|
RETURN_NAMES = ("thru", "high_frequency", "low_frequency")
|
|
FUNCTION = "separate"
|
|
CATEGORY = "MachinePaintingNodes/Filter"
|
|
|
|
def separate(self, original, low_frequency):
|
|
orig = original.detach().clone()
|
|
low = low_frequency.detach().clone()
|
|
|
|
# Photoshop Apply Image: Scale 2, Offset 128, Subtract
|
|
# high = (original - low) / 2 + 0.5
|
|
high = (orig - low) / 2.0 + 0.5
|
|
|
|
high = torch.clamp(high, 0.0, 1.0)
|
|
|
|
return (original, high, low_frequency)
|
|
|
|
|
|
class FrequencyCombine:
|
|
"""
|
|
Recombines high and low frequency layers using Linear Light blend.
|
|
|
|
result = low + 2 * (high - 0.5)
|
|
|
|
Opacity controls how much texture (high frequency) is applied.
|
|
At 0: just the low frequency (smooth). At 1: full reconstruction.
|
|
"""
|
|
|
|
DESCRIPTION = "Recombine high and low frequency layers with Linear Light."
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"high_frequency": ("IMAGE",),
|
|
"low_frequency": ("IMAGE",),
|
|
},
|
|
"optional": {
|
|
"opacity": ("FLOAT", {
|
|
"default": 1.0,
|
|
"min": 0.0,
|
|
"max": 1.0,
|
|
"step": 0.01,
|
|
"display": "slider"
|
|
}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE",)
|
|
RETURN_NAMES = ("image",)
|
|
FUNCTION = "combine"
|
|
CATEGORY = "MachinePaintingNodes/Filter"
|
|
|
|
def combine(self, high_frequency, low_frequency, opacity=1.0):
|
|
low = low_frequency.detach().clone()
|
|
|
|
# Linear Light: result = low + 2 * (high - 0.5)
|
|
# With opacity: result = low + 2 * (high - 0.5) * opacity
|
|
result = low + 2.0 * (high_frequency - 0.5) * opacity
|
|
|
|
result = torch.clamp(result, 0.0, 1.0)
|
|
|
|
return (result,)
|