File size: 13,296 Bytes
2c3644b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 |
import math
import numpy as np
import skimage
import modules.scripts as scripts
import gradio as gr
from PIL import Image, ImageDraw
from modules import images
from modules.processing import Processed, process_images
from modules.shared import opts, state
# this function is taken from https://github.com/parlance-zz/g-diffuser-bot
def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05):
# helper fft routines that keep ortho normalization and auto-shift before and after fft
def _fft2(data):
if data.ndim > 2: # has channels
out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
for c in range(data.shape[2]):
c_data = data[:, :, c]
out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho")
out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
else: # one channel
out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho")
out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])
return out_fft
def _ifft2(data):
if data.ndim > 2: # has channels
out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
for c in range(data.shape[2]):
c_data = data[:, :, c]
out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho")
out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
else: # one channel
out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho")
out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])
return out_ifft
def _get_gaussian_window(width, height, std=3.14, mode=0):
window_scale_x = float(width / min(width, height))
window_scale_y = float(height / min(width, height))
window = np.zeros((width, height))
x = (np.arange(width) / width * 2. - 1.) * window_scale_x
for y in range(height):
fy = (y / height * 2. - 1.) * window_scale_y
if mode == 0:
window[:, y] = np.exp(-(x ** 2 + fy ** 2) * std)
else:
window[:, y] = (1 / ((x ** 2 + 1.) * (fy ** 2 + 1.))) ** (std / 3.14) # hey wait a minute that's not gaussian
return window
def _get_masked_window_rgb(np_mask_grey, hardness=1.):
np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
if hardness != 1.:
hardened = np_mask_grey[:] ** hardness
else:
hardened = np_mask_grey[:]
for c in range(3):
np_mask_rgb[:, :, c] = hardened[:]
return np_mask_rgb
width = _np_src_image.shape[0]
height = _np_src_image.shape[1]
num_channels = _np_src_image.shape[2]
_np_src_image[:] * (1. - np_mask_rgb)
np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.)
img_mask = np_mask_grey > 1e-6
ref_mask = np_mask_grey < 1e-3
windowed_image = _np_src_image * (1. - _get_masked_window_rgb(np_mask_grey))
windowed_image /= np.max(windowed_image)
windowed_image += np.average(_np_src_image) * np_mask_rgb # / (1.-np.average(np_mask_rgb)) # rather than leave the masked area black, we get better results from fft by filling the average unmasked color
src_fft = _fft2(windowed_image) # get feature statistics from masked src img
src_dist = np.absolute(src_fft)
src_phase = src_fft / src_dist
# create a generator with a static seed to make outpainting deterministic / only follow global seed
rng = np.random.default_rng(0)
noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise
noise_rgb = rng.random((width, height, num_channels))
noise_grey = (np.sum(noise_rgb, axis=2) / 3.)
noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter
for c in range(num_channels):
noise_rgb[:, :, c] += (1. - color_variation) * noise_grey
noise_fft = _fft2(noise_rgb)
for c in range(num_channels):
noise_fft[:, :, c] *= noise_window
noise_rgb = np.real(_ifft2(noise_fft))
shaped_noise_fft = _fft2(noise_rgb)
shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase # perform the actual shaping
brightness_variation = 0. # color_variation # todo: temporarily tieing brightness variation to color variation for now
contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
# scikit-image is used for histogram matching, very convenient!
shaped_noise = np.real(_ifft2(shaped_noise_fft))
shaped_noise -= np.min(shaped_noise)
shaped_noise /= np.max(shaped_noise)
shaped_noise[img_mask, :] = skimage.exposure.match_histograms(shaped_noise[img_mask, :] ** 1., contrast_adjusted_np_src[ref_mask, :], channel_axis=1)
shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb
matched_noise = shaped_noise[:]
return np.clip(matched_noise, 0., 1.)
class Script(scripts.Script):
def title(self):
return "Outpainting mk2"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
if not is_img2img:
return None
info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8</p>")
pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels"))
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, elem_id=self.elem_id("mask_blur"))
direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id("direction"))
noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0, elem_id=self.elem_id("noise_q"))
color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05, elem_id=self.elem_id("color_variation"))
return [info, pixels, mask_blur, direction, noise_q, color_variation]
def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation):
initial_seed_and_info = [None, None]
process_width = p.width
process_height = p.height
p.inpaint_full_res = False
p.inpainting_fill = 1
p.do_not_save_samples = True
p.do_not_save_grid = True
left = pixels if "left" in direction else 0
right = pixels if "right" in direction else 0
up = pixels if "up" in direction else 0
down = pixels if "down" in direction else 0
if left > 0 or right > 0:
mask_blur_x = mask_blur
else:
mask_blur_x = 0
if up > 0 or down > 0:
mask_blur_y = mask_blur
else:
mask_blur_y = 0
p.mask_blur_x = mask_blur_x*4
p.mask_blur_y = mask_blur_y*4
init_img = p.init_images[0]
target_w = math.ceil((init_img.width + left + right) / 64) * 64
target_h = math.ceil((init_img.height + up + down) / 64) * 64
if left > 0:
left = left * (target_w - init_img.width) // (left + right)
if right > 0:
right = target_w - init_img.width - left
if up > 0:
up = up * (target_h - init_img.height) // (up + down)
if down > 0:
down = target_h - init_img.height - up
def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
is_horiz = is_left or is_right
is_vert = is_top or is_bottom
pixels_horiz = expand_pixels if is_horiz else 0
pixels_vert = expand_pixels if is_vert else 0
images_to_process = []
output_images = []
for n in range(count):
res_w = init[n].width + pixels_horiz
res_h = init[n].height + pixels_vert
process_res_w = math.ceil(res_w / 64) * 64
process_res_h = math.ceil(res_h / 64) * 64
img = Image.new("RGB", (process_res_w, process_res_h))
img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
mask = Image.new("RGB", (process_res_w, process_res_h), "white")
draw = ImageDraw.Draw(mask)
draw.rectangle((
expand_pixels + mask_blur_x if is_left else 0,
expand_pixels + mask_blur_y if is_top else 0,
mask.width - expand_pixels - mask_blur_x if is_right else res_w,
mask.height - expand_pixels - mask_blur_y if is_bottom else res_h,
), fill="black")
np_image = (np.asarray(img) / 255.0).astype(np.float64)
np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB"))
target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width
target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height
p.width = target_width if is_horiz else img.width
p.height = target_height if is_vert else img.height
crop_region = (
0 if is_left else output_images[n].width - target_width,
0 if is_top else output_images[n].height - target_height,
target_width if is_left else output_images[n].width,
target_height if is_top else output_images[n].height,
)
mask = mask.crop(crop_region)
p.image_mask = mask
image_to_process = output_images[n].crop(crop_region)
images_to_process.append(image_to_process)
p.init_images = images_to_process
latent_mask = Image.new("RGB", (p.width, p.height), "white")
draw = ImageDraw.Draw(latent_mask)
draw.rectangle((
expand_pixels + mask_blur_x * 2 if is_left else 0,
expand_pixels + mask_blur_y * 2 if is_top else 0,
mask.width - expand_pixels - mask_blur_x * 2 if is_right else res_w,
mask.height - expand_pixels - mask_blur_y * 2 if is_bottom else res_h,
), fill="black")
p.latent_mask = latent_mask
proc = process_images(p)
if initial_seed_and_info[0] is None:
initial_seed_and_info[0] = proc.seed
initial_seed_and_info[1] = proc.info
for n in range(count):
output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height))
output_images[n] = output_images[n].crop((0, 0, res_w, res_h))
return output_images
batch_count = p.n_iter
batch_size = p.batch_size
p.n_iter = 1
state.job_count = batch_count * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0))
all_processed_images = []
for i in range(batch_count):
imgs = [init_img] * batch_size
state.job = f"Batch {i + 1} out of {batch_count}"
if left > 0:
imgs = expand(imgs, batch_size, left, is_left=True)
if right > 0:
imgs = expand(imgs, batch_size, right, is_right=True)
if up > 0:
imgs = expand(imgs, batch_size, up, is_top=True)
if down > 0:
imgs = expand(imgs, batch_size, down, is_bottom=True)
all_processed_images += imgs
all_images = all_processed_images
combined_grid_image = images.image_grid(all_processed_images)
unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple
if opts.return_grid and not unwanted_grid_because_of_img_count:
all_images = [combined_grid_image] + all_processed_images
res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1])
if opts.samples_save:
for img in all_processed_images:
images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.samples_format, info=res.info, p=p)
if opts.grid_save and not unwanted_grid_because_of_img_count:
images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
return res
|