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import math
import random
import gradio as gr
import modules.scripts as scripts
from modules import devices, deepbooru, images, processing, shared
from modules.processing import Processed
from modules.shared import opts, state
from PIL import Image
import copy
global pixmap
global xn
class Script(scripts.Script):
def __init__(self):
self.scalingW = 0
self.scalingH = 0
self.hr_denoise = 0
self.hr_steps = 0
self.scaler = ""
def title(self):
return "Alternate Init Noise"
def show(self, is_img2img):
if not is_img2img:
return scripts.AlwaysVisible
return False
def ui(self, is_img2img):
noise_types = [
"Plasma Noise",
"FBM Noise"
]
with gr.Accordion('Alternate Init Noise', open=False):
enabled = gr.Checkbox(label="Enabled", default=False)
noise_type = gr.Dropdown(label="Type", choices=[k for k in noise_types], type="index", value=next(iter(noise_types)))
# Plasma noise settings
turbulence = gr.Slider(minimum=0.05, maximum=10.0, step=0.05, label='Turbulence', value=4, elem_id=self.elem_id("turbulence"), visible=True, interactive=True)
# FBM noise settings
octaves = gr.Slider(minimum=1, maximum=32, step=1, label='Octaves', value=6, elem_id=self.elem_id("octaves"), visible=False, interactive=True)
smoothing = gr.Slider(minimum=1, maximum=100, step=1, label='Smoothing', value=1, elem_id=self.elem_id("smoothing"), visible=False, interactive=True)
octave_division = gr.Slider(minimum=1.0, maximum=10.0, step=0.01, label='Octave Division', value=2, elem_id=self.elem_id("octave_division"), visible=False, interactive=True)
grain = gr.Slider(minimum=0, maximum=256, step=1, label='Grain', value=0, elem_id=self.elem_id("grain"))
denoising = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.9, elem_id=self.elem_id("denoising"))
noise_mult = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Noise multiplier', value=1.0, elem_id=self.elem_id("noise_mult"))
with gr.Accordion('Color Adjustments', open=False):
with gr.Row():
val_min = gr.Slider(minimum=-1, maximum=255, step=1, value=-1, label="Brightness Min", elem_id=self.elem_id("plasma_val_min"))
val_max = gr.Slider(minimum=-1, maximum=255, step=1, value=-1, label="Brightness Max", elem_id=self.elem_id("plasma_val_max"))
with gr.Row():
red_min = gr.Slider(minimum=-1, maximum=255, step=1, value=-1, label="Red Min", elem_id=self.elem_id("plasma_red_min"))
red_max = gr.Slider(minimum=-1, maximum=255, step=1, value=-1, label="Red Max", elem_id=self.elem_id("plasma_red_max"))
with gr.Row():
grn_min = gr.Slider(minimum=-1, maximum=255, step=1, value=-1, label="Green Min", elem_id=self.elem_id("plasma_grn_min"))
grn_max = gr.Slider(minimum=-1, maximum=255, step=1, value=-1, label="Green Max", elem_id=self.elem_id("plasma_grn_max"))
with gr.Row():
blu_min = gr.Slider(minimum=-1, maximum=255, step=1, value=-1, label="Blue Min", elem_id=self.elem_id("plasma_blu_min"))
blu_max = gr.Slider(minimum=-1, maximum=255, step=1, value=-1, label="Blue Max", elem_id=self.elem_id("plasma_blu_max"))
contrast = gr.Slider(minimum=0, maximum=10, step=0.1, value=1, label="Contrast", elem_id=self.elem_id("noise_contrast"))
greyscale = gr.Checkbox(value=False, label="Greyscale", interactive=True, elem_id="noise_greyscale")
with gr.Row():
single_seed = gr.Checkbox(label="One seed for entire batch", info="speeds up noise generation for batch_size > 1, but noise seeds won't always match image seeds", default=False)
seed_choice = gr.Textbox(label="Seed override", value=-1, interactive=True, elem_id=self.elem_id("seed_choice"), visible=False)
def select_noise_type(noise_index):
return [gr.update(visible=noise_index == 0),
gr.update(visible=noise_index == 1),
gr.update(visible=noise_index == 1),
gr.update(visible=noise_index == 1)]
def show_seed_choice(seed_checked):
return gr.update(visible=seed_checked)
noise_type.change(
fn=select_noise_type,
inputs=noise_type,
outputs=[turbulence, octaves, smoothing, octave_division]
)
single_seed.change(
fn=show_seed_choice,
inputs=single_seed,
outputs=seed_choice
)
return [enabled, noise_type, turbulence, octaves, smoothing, octave_division, grain, denoising, noise_mult, val_min, val_max, red_min, red_max, grn_min, grn_max,
blu_min, blu_max, contrast, greyscale, single_seed, seed_choice]
def remap(self, v, low2, high2, contrast):
v = abs(v)
v = contrast * (v - 128) + 128
return int(low2 + v * (high2 - low2) / (255))
def create_plasma(self, p, seed, turbulence, grain, val_min, val_max, red_min, red_max, grn_min,
grn_max, blu_min, blu_max, contrast, greyscale):
global pixmap
global xn
xn = 0
w = p.width
h = p.height
random.seed(seed)
aw = copy.deepcopy(w)
ah = copy.deepcopy(h)
image = Image.new("RGB", (aw, ah))
if w >= h:
h = w
else:
w = h
# Clamp per channel and globally
clamp_v_min = val_min
clamp_v_max = val_max
clamp_r_min = red_min
clamp_r_max = red_max
clamp_g_min = grn_min
clamp_g_max = grn_max
clamp_b_min = blu_min
clamp_b_max = blu_max
# Handle value clamps
lv = 0
mv = 0
if clamp_v_min == -1:
lv = 0
else:
lv = clamp_v_min
if clamp_v_max == -1:
mv = 255
else:
mv = clamp_v_max
lr = 0
mr = 0
if clamp_r_min == -1:
lr = lv
else:
lr = clamp_r_min
if clamp_r_max == -1:
mr = mv
else:
mr = clamp_r_max
lg = 0
mg = 0
if clamp_g_min == -1:
lg = lv
else:
lg = clamp_g_min
if clamp_g_max == -1:
mg = mv
else:
mg = clamp_g_max
lb = 0
mb = 0
if clamp_b_min == -1:
lb = lv
else:
lb = clamp_b_min
if clamp_b_max == -1:
mb = mv
else:
mb = clamp_b_max
roughness = turbulence
def adjust(xa, ya, x, y, xb, yb):
global pixmap
if (pixmap[x][y] == 0):
d = math.fabs(xa - xb) + math.fabs(ya - yb)
v = (pixmap[xa][ya] + pixmap[xb][yb]) / 2.0 + (random.random() - 0.555) * d * roughness
c = int(math.fabs(v + (random.random() - 0.5) * grain))
if c < 0:
c = 0
elif c > 255:
c = 255
pixmap[x][y] = c
def subdivide(x1, y1, x2, y2):
global pixmap
if (not ((x2 - x1 < 2.0) and (y2 - y1 < 2.0))):
x = int((x1 + x2) / 2.0)
y = int((y1 + y2) / 2.0)
adjust(x1, y1, x, y1, x2, y1)
adjust(x2, y1, x2, y, x2, y2)
adjust(x1, y2, x, y2, x2, y2)
adjust(x1, y1, x1, y, x1, y2)
if (pixmap[x][y] == 0):
v = int((pixmap[x1][y1] + pixmap[x2][y1] + pixmap[x2][y2] + pixmap[x1][y2]) / 4.0)
pixmap[x][y] = v
subdivide(x1, y1, x, y)
subdivide(x, y1, x2, y)
subdivide(x, y, x2, y2)
subdivide(x1, y, x, y2)
pixmap = [[0 for i in range(h)] for j in range(w)]
pixmap[0][0] = int(random.random() * 255)
pixmap[w - 1][0] = int(random.random() * 255)
pixmap[w - 1][h - 1] = int(random.random() * 255)
pixmap[0][h - 1] = int(random.random() * 255)
subdivide(0, 0, w - 1, h - 1)
r = copy.deepcopy(pixmap)
if not greyscale:
pixmap = [[0 for i in range(h)] for j in range(w)]
pixmap[0][0] = int(random.random() * 255)
pixmap[w - 1][0] = int(random.random() * 255)
pixmap[w - 1][h - 1] = int(random.random() * 255)
pixmap[0][h - 1] = int(random.random() * 255)
subdivide(0, 0, w - 1, h - 1)
g = copy.deepcopy(pixmap)
pixmap = [[0 for i in range(h)] for j in range(w)]
pixmap[0][0] = int(random.random() * 255)
pixmap[w - 1][0] = int(random.random() * 255)
pixmap[w - 1][h - 1] = int(random.random() * 255)
pixmap[0][h - 1] = int(random.random() * 255)
subdivide(0, 0, w - 1, h - 1)
b = copy.deepcopy(pixmap)
for y in range(ah):
for x in range(aw):
if greyscale:
channel_r = self.remap(r[x][y], lr, mr, contrast)
final_pix = (channel_r, channel_r, channel_r)
else:
final_pix = (self.remap(r[x][y], lr, mr, contrast),
self.remap(g[x][y], lg, mg, contrast),
self.remap(b[x][y], lb, mb, contrast))
image.putpixel((x, y), final_pix)
return image
def createFBM(self, p, seed, octaves, smoothing, octave_division, grain, denoising, noise_mult, val_min, val_max, red_min, red_max, grn_min, grn_max,
blu_min, blu_max, contrast, greyscale):
random.seed(seed)
width = p.width
height = p.height
square = max(width, height)
max_octaves = 1
octave_pixel_size = 1
while True:
octave_pixel_size *= octave_division
if octave_pixel_size < square / octave_division:
max_octaves += 1
if max_octaves >= octaves:
break
else:
break
octaves = min(octaves, max_octaves)
mr = max(val_min, red_min, 0)
mg = max(val_min, grn_min, 0)
mb = max(val_min, blu_min, 0)
hv = 255
if val_max >= 0:
hv = val_max
hr = 255
if red_max >= 0:
hr = val_max
hg = 255
if grn_max >= 0:
hg = val_max
hb = 255
if blu_max >= 0:
hb = val_max
if grain > 0:
grain_image_r = [[0 for i in range(height)] for j in range(width)]
grain_image_g = [[0 for i in range(height)] for j in range(width)]
grain_image_b = [[0 for i in range(height)] for j in range(width)]
for y in range(height):
for x in range(width):
grain_image_r[x][y] = int((random.random() - 0.5) * grain)
if not greyscale:
grain_image_g[x][y] = int((random.random() - 0.5) * grain)
grain_image_b[x][y] = int((random.random() - 0.5) * grain)
final_image = Image.new("RGB", (width, height))
for o in range(octaves):
a = smoothing * pow(octave_division, octaves - o - 1)
s = int(square / a)
if s > square:
break
r = [[0 for i in range(s)] for j in range(s)]
g = [[0 for i in range(s)] for j in range(s)]
b = [[0 for i in range(s)] for j in range(s)]
octave_image = Image.new("RGB", (s, s))
for y in range(s):
for x in range(s):
r[x][y] = int(random.random() * 255)
if not greyscale:
g[x][y] = int(random.random() * 255)
b[x][y] = int(random.random() * 255)
for y in range(s):
for x in range(s):
octave_image.putpixel((x, y), (r[x][y], g[x][y], b[x][y]))
octave_image = octave_image.resize((square, square), Image.BILINEAR)
for y in range(height):
for x in range(width):
old_pix = final_image.getpixel((x, y))
new_pix = octave_image.getpixel((x, y))
amplitude = 1 / pow(2, o + 1)
new_pix = (int(new_pix[0] * amplitude), int(new_pix[1] * amplitude), int(new_pix[2] * amplitude))
if grain > 0 and o == octaves - 1:
if greyscale:
channel_r = self.remap(old_pix[0] + new_pix[0] + grain_image_r[x][y], mr, hr, contrast)
final_pix = (channel_r, channel_r, channel_r)
else:
final_pix = (self.remap(old_pix[0] + new_pix[0] + grain_image_r[x][y], mr, hr, contrast),
self.remap(old_pix[1] + new_pix[1] + grain_image_g[x][y], mg, hg, contrast),
self.remap(old_pix[2] + new_pix[2] + grain_image_b[x][y], mb, hb, contrast))
elif o == octaves - 1:
if greyscale:
channel_r = self.remap(old_pix[0] + new_pix[0], mr, hr, contrast)
final_pix = (channel_r, channel_r, channel_r)
else:
final_pix = (self.remap(old_pix[0] + new_pix[0], mr, hr, contrast),
self.remap(old_pix[1] + new_pix[1], mg, hg, contrast),
self.remap(old_pix[2] + new_pix[2], mb, hb, contrast))
else:
if greyscale:
channel_r = old_pix[0] + new_pix[0]
final_pix = (channel_r, channel_r, channel_r)
else:
final_pix = (old_pix[0] + new_pix[0],
old_pix[1] + new_pix[1],
old_pix[2] + new_pix[2])
final_image.putpixel((x, y), final_pix)
return final_image
def process(self, p, enabled, noise_type, turbulence, octaves, smoothing, octave_division, grain, denoising, noise_mult, val_min, val_max, red_min, red_max, grn_min, grn_max,
blu_min, blu_max, contrast, greyscale, single_seed, seed_choice):
if not enabled or "alt_hires" in p.extra_generation_params:
return None
if p.enable_hr:
self.hr_denoise = p.denoising_strength
self.hr_steps = p.hr_second_pass_steps
if self.hr_steps == 0:
self.hr_steps = p.steps
if p.hr_resize_x == 0 and p.hr_resize_y == 0:
self.scalingW = p.hr_scale
self.scalingH = p.hr_scale
else:
self.scalingW = p.hr_resize_x
self.scalingH = p.hr_resize_y
self.scaler = p.hr_upscaler
else:
self.scalingW = 0
# image size
p.__class__ = processing.StableDiffusionProcessingImg2Img
dummy = processing.StableDiffusionProcessingImg2Img()
for k, v in dummy.__dict__.items():
if hasattr(p, k):
continue
setattr(p, k, v)
p.extra_generation_params["Grain"] = grain
p.extra_generation_params["Alt denoising strength"] = denoising
p.extra_generation_params["Value Min"] = val_min
p.extra_generation_params["Value Max"] = val_max
p.extra_generation_params["Red Min"] = red_min
p.extra_generation_params["Red Max"] = red_max
p.extra_generation_params["Green Min"] = grn_min
p.extra_generation_params["Green Max"] = grn_max
p.extra_generation_params["Blue Min"] = blu_min
p.extra_generation_params["Blue Max"] = blu_max
p.initial_noise_multiplier = noise_mult
p.denoising_strength = float(denoising)
img_num = p.batch_size
if single_seed:
img_num = 1
p.init_images = []
if int(seed_choice) == -1 or not single_seed:
init_seed = p.all_seeds[0]
else:
init_seed = int(seed_choice)
for img in range(img_num):
real_seed = init_seed + img
if noise_type == 0:
# plasma noise
p.extra_generation_params["Alt noise type"] = "Plasma"
p.extra_generation_params["Turbulence"] = turbulence
image = self.create_plasma(p, real_seed, turbulence, grain, val_min, val_max, red_min, red_max, grn_min,
grn_max, blu_min, blu_max, contrast, greyscale)
if noise_type == 1:
# fbm noise
p.extra_generation_params["Alt noise type"] = "FBM"
p.extra_generation_params["Octaves"] = octaves
p.extra_generation_params["Smoothing"] = smoothing
image = self.createFBM(p, real_seed, octaves, smoothing, octave_division, grain, denoising, noise_mult, val_min, val_max, red_min, red_max, grn_min, grn_max,
blu_min, blu_max, contrast, greyscale)
p.init_images.append(image)
def postprocess(self, p, processed, enabled, noise_type, turbulence, octaves, smoothing, octave_division, grain, denoising, noise_mult, val_min, val_max, red_min, red_max, grn_min, grn_max,
blu_min, blu_max, contrast, greyscale, single_seed, seed_choice):
if not enabled or self.scalingW == 0 or "alt_hires" in p.extra_generation_params or not p.enable_hr:
return None
devices.torch_gc()
new_p = p
new_p.init_images = []
for i in range(len(processed.images)):
new_p.init_images.append(processed.images[i])
new_p.extra_generation_params["alt_hires"] = self.scalingW
new_p.width = int(new_p.width * self.scalingW)
new_p.height = int(new_p.height * self.scalingH)
new_p.denoising_strength = self.hr_denoise
if new_p.denoising_strength > 0:
new_p.steps = max(1, int(self.hr_steps / self.hr_denoise - 0.5))
else:
new_p.steps = 0
p.resize_mode = 3 if 'Latent' in self.scaler else 0
new_p.scripts = None
new_p = processing.process_images(new_p)
processed.images = new_p.images
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