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import os |
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import cv2 |
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import yaml |
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import copy |
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import pygame |
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import numpy as np |
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from PIL import Image |
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from fontTools.ttLib import TTFont |
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import torch |
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import torchvision.transforms as transforms |
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def save_args_to_yaml(args, output_file): |
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args_dict = vars(args) |
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with open(output_file, 'w') as yaml_file: |
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yaml.dump(args_dict, yaml_file, default_flow_style=False) |
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def save_single_image(save_dir, image): |
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save_path = f"{save_dir}/out_single.png" |
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image.save(save_path) |
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def save_image_with_content_style(save_dir, image, content_image_pil, content_image_path, style_image_path, resolution): |
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new_image = Image.new('RGB', (resolution*3, resolution)) |
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if content_image_pil is not None: |
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content_image = content_image_pil |
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else: |
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content_image = Image.open(content_image_path).convert("RGB").resize((resolution, resolution), Image.BILINEAR) |
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style_image = Image.open(style_image_path).convert("RGB").resize((resolution, resolution), Image.BILINEAR) |
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new_image.paste(content_image, (0, 0)) |
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new_image.paste(style_image, (resolution, 0)) |
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new_image.paste(image, (resolution*2, 0)) |
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save_path = f"{save_dir}/out_with_cs.jpg" |
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new_image.save(save_path) |
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def x0_from_epsilon(scheduler, noise_pred, x_t, timesteps): |
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"""Return the x_0 from epsilon |
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""" |
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batch_size = noise_pred.shape[0] |
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for i in range(batch_size): |
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noise_pred_i = noise_pred[i] |
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noise_pred_i = noise_pred_i[None, :] |
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t = timesteps[i] |
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x_t_i = x_t[i] |
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x_t_i = x_t_i[None, :] |
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pred_original_sample_i = scheduler.step( |
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model_output=noise_pred_i, |
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timestep=t, |
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sample=x_t_i, |
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generator=None, |
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return_dict=True, |
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).pred_original_sample |
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if i == 0: |
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pred_original_sample = pred_original_sample_i |
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else: |
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pred_original_sample = torch.cat((pred_original_sample, pred_original_sample_i), dim=0) |
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return pred_original_sample |
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def reNormalize_img(pred_original_sample): |
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pred_original_sample = (pred_original_sample / 2 + 0.5).clamp(0, 1) |
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return pred_original_sample |
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def normalize_mean_std(image): |
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transforms_norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) |
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image = transforms_norm(image) |
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return image |
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def is_char_in_font(font_path, char): |
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TTFont_font = TTFont(font_path) |
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cmap = TTFont_font['cmap'] |
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for subtable in cmap.tables: |
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if ord(char) in subtable.cmap: |
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return True |
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return False |
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def load_ttf(ttf_path, fsize=128): |
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pygame.init() |
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font = pygame.freetype.Font(ttf_path, size=fsize) |
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return font |
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def ttf2im(font, char, fsize=128): |
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try: |
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surface, _ = font.render(char) |
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except: |
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print("No glyph for char {}".format(char)) |
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return |
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bg = np.full((fsize, fsize), 255) |
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imo = pygame.surfarray.pixels_alpha(surface).transpose(1, 0) |
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imo = 255 - np.array(Image.fromarray(imo)) |
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im = copy.deepcopy(bg) |
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h, w = imo.shape[:2] |
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if h > fsize: |
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h, w = fsize, round(w*fsize/h) |
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imo = cv2.resize(imo, (w, h)) |
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if w > fsize: |
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h, w = round(h*fsize/w), fsize |
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imo = cv2.resize(imo, (w, h)) |
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x, y = round((fsize-w)/2), round((fsize-h)/2) |
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im[y:h+y, x:x+w] = imo |
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pil_im = Image.fromarray(im.astype('uint8')).convert('RGB') |
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return pil_im |
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