Spaces:
Runtime error
Runtime error
File size: 6,152 Bytes
2de3774 |
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 |
import datetime
import random
import time
from pathlib import Path
from contextlib import contextmanager
from typing import Optional
from urllib.parse import urlparse
from shared import path_manager, shared_cache
import json
def get_wildcard_files():
directories = ["wildcards", "wildcards_official"]
files = []
for directory in directories:
for file in Path(directory).rglob("*.txt"):
name = file.stem
if name not in files:
files.append(name)
onebutton = [
"onebuttonprompt",
"onebuttonsubject",
"onebuttonhumanoid",
"onebuttonmale",
"onebuttonfemale",
"onebuttonanimal",
"onebuttonobject",
"onebuttonlandscape",
"onebuttonconcept",
"onebuttonartist",
"onebutton1girl",
"onebutton1boy",
"onebuttonfurry",
]
both = files + onebutton
return both
def generate_temp_filename(folder="./outputs/", extension="png"):
current_time = datetime.datetime.now()
date_string = current_time.strftime("%Y-%m-%d")
time_string = current_time.strftime("%Y-%m-%d_%H-%M-%S")
random_number = random.randint(1000, 9999)
filename = f"{time_string}_{random_number}.{extension}"
result = Path(folder) / date_string / filename
return result.absolute()
def load_keywords(lora):
filename = Path(
path_manager.model_paths["cache_path"] / "loras" / Path(lora).name
).with_suffix(".txt")
try:
with open(filename, "r") as file:
data = file.read()
return data
except FileNotFoundError:
return " "
def _get_model_hashes(cache_path, not_found=None):
hashes = {
"AutoV1": "",
"AutoV2": "",
"SHA256": "",
"CRC32": "",
"BLAKE3": "",
"AutoV3": ""
}
filename = cache_path.with_suffix(".json")
if Path(filename).is_file():
try:
with open(filename) as f:
data = json.load(f)
except:
print(f"ERROR: model {cache_path} is missing json-file")
data = {}
if "files" not in data:
data = {"files": [{"hashes": {}}]}
hashes.update(data['files'][0]['hashes'])
return hashes
else:
if not_found:
return not_found
else:
return hashes
def get_checkpoint_hashes(model):
return _get_model_hashes(
Path(path_manager.model_paths["cache_path"] / "checkpoints" / Path(model).name)
)
def get_lora_hashes(model):
return _get_model_hashes(
Path(path_manager.model_paths["cache_path"] / "loras" / Path(model).name)
)
def _get_model_thumbnail(cache_path, not_found="html/warning.png"):
if cache_path in shared_cache:
return shared_cache[cache_path]
suffixes = [".jpeg", ".jpg", ".png", ".gif"]
for suffix in suffixes:
filename = cache_path.with_suffix(suffix)
if Path(filename).is_file():
shared_cache[cache_path] = str(filename)
return str(filename)
else:
return not_found
def get_model_thumbnail(model):
res = _get_model_thumbnail(
Path(path_manager.model_paths["cache_path"] / "checkpoints" / Path(model).name),
not_found=None
)
if res is not None:
return res
res = _get_model_thumbnail(
Path(path_manager.model_paths["cache_path"] / "loras" / Path(model).name),
not_found=None
)
if res is not None:
return str(res)
else:
return "html/warning.png"
def get_checkpoint_thumbnail(model):
if Path(model).suffix == ".merge":
not_found="html/merge.jpeg"
else:
not_found="html/warning.jpeg"
return _get_model_thumbnail(
Path(path_manager.model_paths["cache_path"] / "checkpoints" / Path(model).name),
not_found=not_found
)
def get_lora_thumbnail(model):
return _get_model_thumbnail(
Path(path_manager.model_paths["cache_path"] / "loras" / Path(model).name)
)
def get_model_path(model, folders):
for folder in folders:
filename = Path(folder) / model
if filename.exists():
return filename
return None
def get_checkpoint_path(model, folders=None):
if folders is None:
folders = [path_manager_model_paths["modelfile_path"]]
return get_model_path(model, folders)
def get_lora_path(model, folders=None):
if folders is None:
folders = [path_manager_model_paths["lorafile_path"]]
return get_model_path(model, folders)
def url_to_filename(url):
import string
keep = "-_.() %s%s" % (string.ascii_letters, string.digits)
url = url.replace(' ','_')
url = ''.join(c for c in url if c in keep)
return url
def load_file_from_url(
url: str,
*,
model_dir: str,
progress: bool = True,
file_name: Optional[str] = None,
) -> str:
"""Download a file from `url` into `model_dir`, using the file present if possible.
Returns the path to the downloaded file.
"""
Path(model_dir).mkdir(parents=True, exist_ok=True)
if not file_name:
parts = urlparse(url)
file_name = Path(parts.path).stem
for file in Path(model_dir).glob("**/*"):
if file.name == file_name:
cached_file = file
return str(cached_file)
cached_file = Path(model_dir) / file_name
if not cached_file.exists():
print(f'Downloading: "{url}" to {cached_file}\n')
from torch.hub import download_url_to_file
download_url_to_file(url, cached_file, progress=progress)
return str(cached_file)
class TimeIt:
def __init__(self, text=""):
self.text = text
def __enter__(self):
self.start = time.time()
return self
def __exit__(self, *args):
self.end = time.time()
self.interval = self.end - self.start
print(f"\033[91mTime taken: {self.interval:0.2f} seconds {self.text}\033[0m")
def remove_empty_str(items, default=None):
items = [x for x in items if x != ""]
if len(items) == 0 and default is not None:
return [default]
return items
|