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| import os | |
| import os.path as osp | |
| import cv2 | |
| import numpy as np | |
| import numpy.random as npr | |
| import torch | |
| import torch.nn.functional as F | |
| import torchvision.transforms as tvtrans | |
| import PIL.Image | |
| from tqdm import tqdm | |
| from PIL import Image | |
| import copy | |
| import json | |
| from collections import OrderedDict | |
| ####### | |
| # css # | |
| ####### | |
| css_empty = "" | |
| css_version_4_11_0 = """ | |
| #customized_imbox { | |
| min-height: 450px; | |
| max-height: 450px; | |
| } | |
| #customized_imbox>div[data-testid="image"] { | |
| min-height: 450px; | |
| } | |
| #customized_imbox>div[data-testid="image"]>span[data-testid="source-select"] { | |
| max-height: 0px; | |
| } | |
| #customized_imbox>div[data-testid="image"]>span[data-testid="source-select"]>button { | |
| max-height: 0px; | |
| } | |
| #customized_imbox>div[data-testid="image"]>div.upload-container>div.image-frame>img { | |
| position: absolute; | |
| top: 50%; | |
| left: 50%; | |
| transform: translateX(-50%) translateY(-50%); | |
| width: unset; | |
| height: unset; | |
| max-height: 450px; | |
| } | |
| #customized_imbox>div.unpadded_box { | |
| min-height: 450px; | |
| } | |
| #myinst { | |
| font-size: 0.8rem; | |
| margin: 0rem; | |
| color: #6B7280; | |
| } | |
| #maskinst { | |
| text-align: justify; | |
| min-width: 1200px; | |
| } | |
| #maskinst>img { | |
| min-width:399px; | |
| max-width:450px; | |
| vertical-align: top; | |
| display: inline-block; | |
| } | |
| #maskinst:after { | |
| content: ""; | |
| width: 100%; | |
| display: inline-block; | |
| } | |
| """ | |
| ########## | |
| # helper # | |
| ########## | |
| def highlight_print(info): | |
| print('') | |
| print(''.join(['#']*(len(info)+4))) | |
| print('# '+info+' #') | |
| print(''.join(['#']*(len(info)+4))) | |
| print('') | |
| def auto_dropdown(name, choices_od, value): | |
| import gradio as gr | |
| option_list = [pi for pi in choices_od.keys()] | |
| return gr.Dropdown(label=name, choices=option_list, value=value) | |
| def load_sd_from_file(target): | |
| if osp.splitext(target)[-1] == '.ckpt': | |
| sd = torch.load(target, map_location='cpu')['state_dict'] | |
| elif osp.splitext(target)[-1] == '.pth': | |
| sd = torch.load(target, map_location='cpu') | |
| elif osp.splitext(target)[-1] == '.safetensors': | |
| from safetensors.torch import load_file as stload | |
| sd = OrderedDict(stload(target, device='cpu')) | |
| else: | |
| assert False, "File type must be .ckpt or .pth or .safetensors" | |
| return sd | |
| def torch_to_numpy(x): | |
| return x.detach().to('cpu').numpy() | |
| if __name__ == '__main__': | |
| pass | |