Spaces:
Runtime error
Runtime error
File size: 11,543 Bytes
cd39c08 35b1cf8 a03d34d 35b1cf8 cd39c08 35b1cf8 cd39c08 26a5ac0 cd39c08 35b1cf8 cd39c08 35b1cf8 cd39c08 35b1cf8 cd39c08 35b1cf8 cd39c08 35b1cf8 cd39c08 64ae97f cd39c08 6322fa2 635b226 cd39c08 35b1cf8 cd39c08 35b1cf8 cd39c08 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 |
import spaces
import os
import gradio as gr
import json
import logging
logging.getLogger("diffusers").setLevel(logging.ERROR)
import diffusers
diffusers.utils.logging.set_verbosity(40)
import warnings
warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
from pathlib import Path
from huggingface_hub import HfApi
from env import (HF_TOKEN, hf_read_token, # to use only for private repos
CIVITAI_API_KEY, HF_LORA_PRIVATE_REPOS1, HF_LORA_PRIVATE_REPOS2, HF_LORA_ESSENTIAL_PRIVATE_REPO,
HF_VAE_PRIVATE_REPO, directory_models, directory_loras, directory_vaes,
download_model_list, download_lora_list, download_vae_list)
from modutils import (to_list, list_uniq, list_sub, get_lora_model_list, download_private_repo,
safe_float, escape_lora_basename, to_lora_key, to_lora_path, get_local_model_list,
get_private_lora_model_lists, get_valid_lora_name, get_valid_lora_path, get_valid_lora_wt,
get_lora_info, normalize_prompt_list, get_civitai_info, search_lora_on_civitai, MODEL_TYPE_DICT)
def download_things(directory, url, hf_token="", civitai_api_key=""):
url = url.strip()
if "drive.google.com" in url:
original_dir = os.getcwd()
os.chdir(directory)
os.system(f"gdown --fuzzy {url}")
os.chdir(original_dir)
elif "huggingface.co" in url:
url = url.replace("?download=true", "")
# url = urllib.parse.quote(url, safe=':/') # fix encoding
if "/blob/" in url:
url = url.replace("/blob/", "/resolve/")
user_header = f'"Authorization: Bearer {hf_token}"'
if hf_token:
os.system(f"aria2c --console-log-level=error --summary-interval=10 --header={user_header} -c -x 16 -k 1M -s 16 {url} -d {directory} -o {url.split('/')[-1]}")
else:
os.system(f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {directory} -o {url.split('/')[-1]}")
elif "civitai.com" in url:
if "?" in url:
url = url.split("?")[0]
if civitai_api_key:
url = url + f"?token={civitai_api_key}"
os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
else:
print("\033[91mYou need an API key to download Civitai models.\033[0m")
else:
os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
def get_model_list(directory_path):
model_list = []
valid_extensions = {'.ckpt' , '.pt', '.pth', '.safetensors', '.bin'}
for filename in os.listdir(directory_path):
if os.path.splitext(filename)[1] in valid_extensions:
name_without_extension = os.path.splitext(filename)[0]
file_path = os.path.join(directory_path, filename)
# model_list.append((name_without_extension, file_path))
model_list.append(file_path)
print('\033[34mFILE: ' + file_path + '\033[0m')
return model_list
# - **Download Models**
download_model = ", ".join(download_model_list)
# - **Download VAEs**
download_vae = ", ".join(download_vae_list)
# - **Download LoRAs**
download_lora = ", ".join(download_lora_list)
#download_private_repo(HF_LORA_ESSENTIAL_PRIVATE_REPO, directory_loras, True)
#download_private_repo(HF_VAE_PRIVATE_REPO, directory_vaes, False)
CIVITAI_API_KEY = os.environ.get("CIVITAI_API_KEY")
hf_token = os.environ.get("HF_TOKEN")
# Download stuffs
for url in [url.strip() for url in download_model.split(',')]:
if not os.path.exists(f"./models/{url.split('/')[-1]}"):
download_things(directory_models, url, hf_token, CIVITAI_API_KEY)
for url in [url.strip() for url in download_vae.split(',')]:
if not os.path.exists(f"./vaes/{url.split('/')[-1]}"):
download_things(directory_vaes, url, hf_token, CIVITAI_API_KEY)
for url in [url.strip() for url in download_lora.split(',')]:
if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
lora_model_list = get_lora_model_list()
vae_model_list = get_model_list(directory_vaes)
vae_model_list.insert(0, "None")
def is_repo_name(s):
import re
return re.fullmatch(r'^[^/]+?/[^/]+?$', s)
def get_t2i_model_info(repo_id: str):
api = HfApi(token=HF_TOKEN)
try:
if not is_repo_name(repo_id): return ""
model = api.model_info(repo_id=repo_id, timeout=5.0)
except Exception as e:
print(f"Error: Failed to get {repo_id}'s info.")
print(e)
return ""
if model.private or model.gated: return ""
tags = model.tags
info = []
url = f"https://huggingface.co/{repo_id}/"
if not 'diffusers' in tags: return ""
for k, v in MODEL_TYPE_DICT.items():
if k in tags: info.append(v)
if model.card_data and model.card_data.tags:
info.extend(list_sub(model.card_data.tags, ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']))
info.append(f"DLs: {model.downloads}")
info.append(f"likes: {model.likes}")
info.append(model.last_modified.strftime("lastmod: %Y-%m-%d"))
md = f"Model Info: {', '.join(info)}, [Model Repo]({url})"
return gr.update(value=md)
private_lora_dict = {"": ["", "", "", "", ""]}
try:
with open('lora_dict.json', encoding='utf-8') as f:
d = json.load(f)
for k, v in d.items():
private_lora_dict[escape_lora_basename(k)] = v
except Exception:
pass
private_lora_model_list = get_private_lora_model_lists()
loras_dict = {"None": ["", "", "", "", ""], "": ["", "", "", "", ""]} | private_lora_dict.copy()
loras_url_to_path_dict = {} # {"URL to download": "local filepath", ...}
civitai_lora_last_results = {} # {"URL to download": {search results}, ...}
all_lora_list = []
def get_all_lora_list():
global all_lora_list
loras = get_lora_model_list()
all_lora_list = loras.copy()
return loras
def get_all_lora_tupled_list():
global loras_dict
models = get_all_lora_list()
if not models: return []
tupled_list = []
for model in models:
#if not model: continue # to avoid GUI-related bug
basename = Path(model).stem
key = to_lora_key(model)
items = None
if key in loras_dict.keys():
items = loras_dict.get(key, None)
else:
items = get_civitai_info(model)
if items != None:
loras_dict[key] = items
name = basename
value = model
if items and items[2] != "":
if items[1] == "Pony":
name = f"{basename} (for {items[1]}🐴, {items[2]})"
else:
name = f"{basename} (for {items[1]}, {items[2]})"
tupled_list.append((name, value))
return tupled_list
def update_lora_dict(path: str):
global loras_dict
key = to_lora_key(path)
if key in loras_dict.keys(): return
items = get_civitai_info(path)
if items == None: return
loras_dict[key] = items
def download_lora(dl_urls: str):
global loras_url_to_path_dict
dl_path = ""
before = get_local_model_list(directory_loras)
urls = []
for url in [url.strip() for url in dl_urls.split(',')]:
local_path = f"{directory_loras}/{url.split('/')[-1]}"
if not Path(local_path).exists():
download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
urls.append(url)
after = get_local_model_list(directory_loras)
new_files = list_sub(after, before)
for i, file in enumerate(new_files):
path = Path(file)
if path.exists():
new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
path.resolve().rename(new_path.resolve())
loras_url_to_path_dict[urls[i]] = str(new_path)
update_lora_dict(str(new_path))
dl_path = str(new_path)
return dl_path
def copy_lora(path: str, new_path: str):
import shutil
if path == new_path: return new_path
cpath = Path(path)
npath = Path(new_path)
if cpath.exists():
try:
shutil.copy(str(cpath.resolve()), str(npath.resolve()))
except Exception:
return None
update_lora_dict(str(npath))
return new_path
else:
return None
def download_my_lora(dl_urls: str, lora):
path = download_lora(dl_urls)
if path: lora = path
choices = get_all_lora_tupled_list()
return gr.update(value=lora, choices=choices)
def apply_lora_prompt(lora_info: str):
if lora_info == "None": return ""
lora_tag = lora_info.replace("/",",")
lora_tags = lora_tag.split(",") if str(lora_info) != "None" else []
lora_prompts = normalize_prompt_list(lora_tags)
prompt = ", ".join(list_uniq(lora_prompts))
return prompt
def update_loras(prompt, lora, lora_wt):
on, label, tag, md = get_lora_info(lora)
choices = get_all_lora_tupled_list()
return gr.update(value=prompt), gr.update(value=lora, choices=choices), gr.update(value=lora_wt),\
gr.update(value=tag, label=label, visible=on), gr.update(value=md, visible=on)
def search_civitai_lora(query, base_model, sort="Highest Rated", period="AllTime", tag=""):
global civitai_lora_last_results
items = search_lora_on_civitai(query, base_model, 100, sort, period, tag)
if not items: return gr.update(choices=[("", "")], value="", visible=False),\
gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
civitai_lora_last_results = {}
choices = []
for item in items:
base_model_name = "Pony🐴" if item['base_model'] == "Pony" else item['base_model']
name = f"{item['name']} (for {base_model_name} / By: {item['creator']} / Tags: {', '.join(item['tags'])})"
value = item['dl_url']
choices.append((name, value))
civitai_lora_last_results[value] = item
if not choices: return gr.update(choices=[("", "")], value="", visible=False),\
gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
result = civitai_lora_last_results.get(choices[0][1], "None")
md = result['md'] if result else ""
return gr.update(choices=choices, value=choices[0][1], visible=True), gr.update(value=md, visible=True),\
gr.update(visible=True), gr.update(visible=True)
def select_civitai_lora(search_result):
if not "http" in search_result: return gr.update(value=""), gr.update(value="None", visible=True)
result = civitai_lora_last_results.get(search_result, "None")
md = result['md'] if result else ""
return gr.update(value=search_result), gr.update(value=md, visible=True)
def search_civitai_lora_json(query, base_model):
results = {}
items = search_lora_on_civitai(query, base_model)
if not items: return gr.update(value=results)
for item in items:
results[item['dl_url']] = item
return gr.update(value=results)
|