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("

Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8

") 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.mask_blur = mask_blur*4 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 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 if is_left else 0, expand_pixels + mask_blur if is_top else 0, mask.width - expand_pixels - mask_blur if is_right else res_w, mask.height - expand_pixels - mask_blur 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 * 2 if is_left else 0, expand_pixels + mask_blur * 2 if is_top else 0, mask.width - expand_pixels - mask_blur * 2 if is_right else res_w, mask.height - expand_pixels - mask_blur * 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