284 lines
13 KiB
Python
284 lines
13 KiB
Python
import math
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import numpy as np
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import skimage
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import modules.scripts as scripts
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import gradio as gr
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from PIL import Image, ImageDraw
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from modules import images, processing, devices
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from modules.processing import Processed, process_images
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from modules.shared import opts, cmd_opts, state
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# this function is taken from https://github.com/parlance-zz/g-diffuser-bot
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def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05):
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# helper fft routines that keep ortho normalization and auto-shift before and after fft
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def _fft2(data):
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if data.ndim > 2: # has channels
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out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
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for c in range(data.shape[2]):
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c_data = data[:, :, c]
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out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho")
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out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c])
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else: # one channel
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out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
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out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho")
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out_fft[:, :] = np.fft.ifftshift(out_fft[:, :])
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return out_fft
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def _ifft2(data):
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if data.ndim > 2: # has channels
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out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128)
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for c in range(data.shape[2]):
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c_data = data[:, :, c]
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out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho")
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out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c])
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else: # one channel
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out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128)
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out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho")
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out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :])
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return out_ifft
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def _get_gaussian_window(width, height, std=3.14, mode=0):
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window_scale_x = float(width / min(width, height))
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window_scale_y = float(height / min(width, height))
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window = np.zeros((width, height))
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x = (np.arange(width) / width * 2. - 1.) * window_scale_x
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for y in range(height):
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fy = (y / height * 2. - 1.) * window_scale_y
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if mode == 0:
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window[:, y] = np.exp(-(x ** 2 + fy ** 2) * std)
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else:
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window[:, y] = (1 / ((x ** 2 + 1.) * (fy ** 2 + 1.))) ** (std / 3.14) # hey wait a minute that's not gaussian
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return window
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def _get_masked_window_rgb(np_mask_grey, hardness=1.):
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np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3))
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if hardness != 1.:
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hardened = np_mask_grey[:] ** hardness
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else:
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hardened = np_mask_grey[:]
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for c in range(3):
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np_mask_rgb[:, :, c] = hardened[:]
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return np_mask_rgb
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width = _np_src_image.shape[0]
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height = _np_src_image.shape[1]
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num_channels = _np_src_image.shape[2]
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np_src_image = _np_src_image[:] * (1. - np_mask_rgb)
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np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.)
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img_mask = np_mask_grey > 1e-6
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ref_mask = np_mask_grey < 1e-3
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windowed_image = _np_src_image * (1. - _get_masked_window_rgb(np_mask_grey))
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windowed_image /= np.max(windowed_image)
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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
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src_fft = _fft2(windowed_image) # get feature statistics from masked src img
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src_dist = np.absolute(src_fft)
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src_phase = src_fft / src_dist
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# create a generator with a static seed to make outpainting deterministic / only follow global seed
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rng = np.random.default_rng(0)
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noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise
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noise_rgb = rng.random((width, height, num_channels))
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noise_grey = (np.sum(noise_rgb, axis=2) / 3.)
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noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter
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for c in range(num_channels):
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noise_rgb[:, :, c] += (1. - color_variation) * noise_grey
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noise_fft = _fft2(noise_rgb)
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for c in range(num_channels):
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noise_fft[:, :, c] *= noise_window
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noise_rgb = np.real(_ifft2(noise_fft))
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shaped_noise_fft = _fft2(noise_rgb)
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shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase # perform the actual shaping
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brightness_variation = 0. # color_variation # todo: temporarily tieing brightness variation to color variation for now
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contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2.
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# scikit-image is used for histogram matching, very convenient!
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shaped_noise = np.real(_ifft2(shaped_noise_fft))
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shaped_noise -= np.min(shaped_noise)
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shaped_noise /= np.max(shaped_noise)
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shaped_noise[img_mask, :] = skimage.exposure.match_histograms(shaped_noise[img_mask, :] ** 1., contrast_adjusted_np_src[ref_mask, :], channel_axis=1)
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shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb
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matched_noise = shaped_noise[:]
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return np.clip(matched_noise, 0., 1.)
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class Script(scripts.Script):
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def title(self):
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return "Outpainting mk2"
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def show(self, is_img2img):
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return is_img2img
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def ui(self, is_img2img):
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if not is_img2img:
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return None
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info = gr.HTML("<p style=\"margin-bottom:0.75em\">Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8</p>")
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pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128)
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mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, visible=False)
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direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'])
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noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0)
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color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05)
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return [info, pixels, mask_blur, direction, noise_q, color_variation]
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def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation):
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initial_seed_and_info = [None, None]
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process_width = p.width
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process_height = p.height
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p.mask_blur = mask_blur*4
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p.inpaint_full_res = False
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p.inpainting_fill = 1
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p.do_not_save_samples = True
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p.do_not_save_grid = True
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left = pixels if "left" in direction else 0
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right = pixels if "right" in direction else 0
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up = pixels if "up" in direction else 0
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down = pixels if "down" in direction else 0
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init_img = p.init_images[0]
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target_w = math.ceil((init_img.width + left + right) / 64) * 64
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target_h = math.ceil((init_img.height + up + down) / 64) * 64
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if left > 0:
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left = left * (target_w - init_img.width) // (left + right)
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if right > 0:
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right = target_w - init_img.width - left
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if up > 0:
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up = up * (target_h - init_img.height) // (up + down)
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if down > 0:
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down = target_h - init_img.height - up
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def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):
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is_horiz = is_left or is_right
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is_vert = is_top or is_bottom
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pixels_horiz = expand_pixels if is_horiz else 0
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pixels_vert = expand_pixels if is_vert else 0
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images_to_process = []
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output_images = []
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for n in range(count):
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res_w = init[n].width + pixels_horiz
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res_h = init[n].height + pixels_vert
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process_res_w = math.ceil(res_w / 64) * 64
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process_res_h = math.ceil(res_h / 64) * 64
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img = Image.new("RGB", (process_res_w, process_res_h))
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img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))
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mask = Image.new("RGB", (process_res_w, process_res_h), "white")
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draw = ImageDraw.Draw(mask)
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draw.rectangle((
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expand_pixels + mask_blur if is_left else 0,
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expand_pixels + mask_blur if is_top else 0,
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mask.width - expand_pixels - mask_blur if is_right else res_w,
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mask.height - expand_pixels - mask_blur if is_bottom else res_h,
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), fill="black")
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np_image = (np.asarray(img) / 255.0).astype(np.float64)
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np_mask = (np.asarray(mask) / 255.0).astype(np.float64)
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noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)
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output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB"))
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target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width
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target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height
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p.width = target_width if is_horiz else img.width
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p.height = target_height if is_vert else img.height
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crop_region = (
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0 if is_left else output_images[n].width - target_width,
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0 if is_top else output_images[n].height - target_height,
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target_width if is_left else output_images[n].width,
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target_height if is_top else output_images[n].height,
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)
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mask = mask.crop(crop_region)
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p.image_mask = mask
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image_to_process = output_images[n].crop(crop_region)
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images_to_process.append(image_to_process)
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p.init_images = images_to_process
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latent_mask = Image.new("RGB", (p.width, p.height), "white")
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draw = ImageDraw.Draw(latent_mask)
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draw.rectangle((
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expand_pixels + mask_blur * 2 if is_left else 0,
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expand_pixels + mask_blur * 2 if is_top else 0,
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mask.width - expand_pixels - mask_blur * 2 if is_right else res_w,
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mask.height - expand_pixels - mask_blur * 2 if is_bottom else res_h,
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), fill="black")
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p.latent_mask = latent_mask
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proc = process_images(p)
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if initial_seed_and_info[0] is None:
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initial_seed_and_info[0] = proc.seed
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initial_seed_and_info[1] = proc.info
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for n in range(count):
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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))
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output_images[n] = output_images[n].crop((0, 0, res_w, res_h))
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return output_images
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batch_count = p.n_iter
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batch_size = p.batch_size
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p.n_iter = 1
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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))
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all_processed_images = []
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for i in range(batch_count):
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imgs = [init_img] * batch_size
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state.job = f"Batch {i + 1} out of {batch_count}"
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if left > 0:
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imgs = expand(imgs, batch_size, left, is_left=True)
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if right > 0:
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imgs = expand(imgs, batch_size, right, is_right=True)
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if up > 0:
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imgs = expand(imgs, batch_size, up, is_top=True)
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if down > 0:
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imgs = expand(imgs, batch_size, down, is_bottom=True)
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all_processed_images += imgs
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all_images = all_processed_images
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combined_grid_image = images.image_grid(all_processed_images)
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unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple
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if opts.return_grid and not unwanted_grid_because_of_img_count:
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all_images = [combined_grid_image] + all_processed_images
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res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1])
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if opts.samples_save:
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for img in all_processed_images:
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images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
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if opts.grid_save and not unwanted_grid_because_of_img_count:
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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)
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return res
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