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# -*- coding: utf-8 -*-
import math
from .categories import NodeCategories
from .shared import *
from .dreamtypes import *
def _generate_noise(image: DreamImage, color_function, rng: random.Random, block_size, blur_amount,
density) -> DreamImage:
w = block_size[0]
h = block_size[1]
blur_radius = round(max(image.width, image.height) * blur_amount * 0.25)
if w <= (image.width // 128) or h <= (image.height // 128):
return image
max_placements = round(density * (image.width * image.height))
num = min(max_placements, round((image.width * image.height * 2) / (w * h)))
for i in range(num):
x = rng.randint(-w + 1, image.width - 1)
y = rng.randint(-h + 1, image.height - 1)
image.color_area(x, y, w, h, color_function(x + (w >> 1), y + (h >> 1)))
image = image.blur(blur_radius)
return _generate_noise(image, color_function, rng, (w >> 1, h >> 1), blur_amount, density)
class DreamNoiseFromPalette:
NODE_NAME = "Noise from Palette"
ICON = "🌫"
@classmethod
def INPUT_TYPES(cls):
return {
"required": SharedTypes.palette | {
"width": ("INT", {"default": 512, "min": 1, "max": 8192}),
"height": ("INT", {"default": 512, "min": 1, "max": 8192}),
"blur_amount": ("FLOAT", {"default": 0.3, "min": 0, "max": 1.0, "step": 0.05}),
"density": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.025}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})
},
}
CATEGORY = NodeCategories.IMAGE_GENERATE
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "result"
@classmethod
def IS_CHANGED(cls, *values):
return ALWAYS_CHANGED_FLAG
def result(self, palette: Tuple[RGBPalette], width, height, seed, blur_amount, density):
outputs = list()
rng = random.Random()
for p in palette:
seed += 1
color_iterator = p.random_iteration(seed)
image = DreamImage(pil_image=Image.new("RGB", (width, height), color=next(color_iterator)))
image = _generate_noise(image, lambda x, y: next(color_iterator), rng,
(image.width >> 1, image.height >> 1), blur_amount, density)
outputs.append(image)
return (DreamImage.join_to_tensor_data(outputs),)
class DreamNoiseFromAreaPalettes:
NODE_NAME = "Noise from Area Palettes"
@classmethod
def INPUT_TYPES(cls):
return {
"optional": {
"top_left_palette": (RGBPalette.ID,),
"top_center_palette": (RGBPalette.ID,),
"top_right_palette": (RGBPalette.ID,),
"center_left_palette": (RGBPalette.ID,),
"center_palette": (RGBPalette.ID,),
"center_right_palette": (RGBPalette.ID,),
"bottom_left_palette": (RGBPalette.ID,),
"bottom_center_palette": (RGBPalette.ID,),
"bottom_right_palette": (RGBPalette.ID,),
},
"required": {
"area_sharpness": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05}),
"width": ("INT", {"default": 512, "min": 1, "max": 8192}),
"height": ("INT", {"default": 512, "min": 1, "max": 8192}),
"blur_amount": ("FLOAT", {"default": 0.3, "min": 0, "max": 1.0, "step": 0.05}),
"density": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.025}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
},
}
CATEGORY = NodeCategories.IMAGE_GENERATE
ICON = "🌫"
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "result"
@classmethod
def IS_CHANGED(cls, *values):
return ALWAYS_CHANGED_FLAG
def _area_coordinates(self, width, height):
dx = width / 6
dy = height / 6
return {
"top_left_palette": (dx, dy),
"top_center_palette": (dx * 3, dy),
"top_right_palette": (dx * 5, dy),
"center_left_palette": (dx, dy * 3),
"center_palette": (dx * 3, dy * 3),
"center_right_palette": (dx * 5, dy * 3),
"bottom_left_palette": (dx * 1, dy * 5),
"bottom_center_palette": (dx * 3, dy * 5),
"bottom_right_palette": (dx * 5, dy * 5),
}
def _pick_random_area(self, active_coordinates, x, y, rng, area_sharpness):
def _dst(x1, y1, x2, y2):
a = x1 - x2
b = y1 - y2
return math.sqrt(a * a + b * b)
distances = list(map(lambda item: (item[0], _dst(item[1][0], item[1][1], x, y)), active_coordinates))
areas_by_weight = list(
map(lambda item: (math.pow((1.0 / max(1, item[1])), 0.5 + 4.5 * area_sharpness), item[0]), distances))
return pick_random_by_weight(areas_by_weight, rng)
def _setup_initial_colors(self, image: DreamImage, color_func):
w = image.width
h = image.height
wpart = round(w / 3)
hpart = round(h / 3)
for i in range(3):
for j in range(3):
image.color_area(wpart * i, hpart * j, w, h,
color_func(wpart * i + w // 2, hpart * j + h // 2))
def result(self, width, height, seed, blur_amount, density, area_sharpness, **palettes):
outputs = list()
rng = random.Random()
coordinates = self._area_coordinates(width, height)
active_palettes = list(filter(lambda pair: pair[1] is not None and len(pair[1]) > 0, palettes.items()))
active_coordinates = list(map(lambda item: (item[0], coordinates[item[0]]), active_palettes))
n = max(list(map(len, palettes.values())) + [0])
for b in range(n):
batch_palettes = dict(map(lambda item: (item[0], item[1][b].random_iteration(seed)), active_palettes))
def _color_func(x, y):
name = self._pick_random_area(active_coordinates, x, y, rng, area_sharpness)
rgb = batch_palettes[name]
return next(rgb)
image = DreamImage(pil_image=Image.new("RGB", (width, height)))
self._setup_initial_colors(image, _color_func)
image = _generate_noise(image, _color_func, rng, (round(image.width / 3), round(image.height / 3)),
blur_amount, density)
outputs.append(image)
if not outputs:
outputs.append(DreamImage(pil_image=Image.new("RGB", (width, height))))
return (DreamImage.join_to_tensor_data(outputs),)
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