votepurchase-crash / modutils.py
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import spaces
import json
import gradio as gr
from huggingface_hub import HfApi
import os
from pathlib import Path
from env import (
HF_LORA_PRIVATE_REPOS1,
HF_LORA_PRIVATE_REPOS2,
HF_MODEL_USER_EX,
HF_MODEL_USER_LIKES,
directory_loras,
hf_read_token,
hf_token,
CIVITAI_API_KEY,
)
def get_user_agent():
return 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:127.0) Gecko/20100101 Firefox/127.0'
def to_list(s):
return [x.strip() for x in s.split(",") if not s == ""]
def list_uniq(l):
return sorted(set(l), key=l.index)
def list_sub(a, b):
return [e for e in a if e not in b]
def get_local_model_list(dir_path):
model_list = []
valid_extensions = ('.ckpt', '.pt', '.pth', '.safetensors', '.bin')
for file in Path(dir_path).glob("*"):
if file.suffix in valid_extensions:
file_path = str(Path(f"{dir_path}/{file.name}"))
model_list.append(file_path)
return model_list
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 escape_lora_basename(basename: str):
return basename.replace(".", "_").replace(" ", "_").replace(",", "")
def to_lora_key(path: str):
return escape_lora_basename(Path(path).stem)
def to_lora_path(key: str):
if Path(key).is_file(): return key
path = Path(f"{directory_loras}/{escape_lora_basename(key)}.safetensors")
return str(path)
def safe_float(input):
output = 1.0
try:
output = float(input)
except Exception:
output = 1.0
return output
def save_gallery_images(images, progress=gr.Progress(track_tqdm=True)):
from datetime import datetime, timezone, timedelta
progress(0, desc="Updating gallery...")
dt_now = datetime.now(timezone(timedelta(hours=9)))
basename = dt_now.strftime('%Y%m%d_%H%M%S_')
i = 1
if not images: return images
output_images = []
output_paths = []
for image in images:
filename = basename + str(i) + ".png"
i += 1
oldpath = Path(image[0])
newpath = oldpath
try:
if oldpath.exists():
newpath = oldpath.resolve().rename(Path(filename).resolve())
except Exception:
pass
finally:
output_paths.append(str(newpath))
output_images.append((str(newpath), str(filename)))
progress(1, desc="Gallery updated.")
return gr.update(value=output_images), gr.update(value=output_paths), gr.update(visible=True)
def download_private_repo(repo_id, dir_path, is_replace):
from huggingface_hub import snapshot_download
if not hf_read_token: return
try:
snapshot_download(repo_id=repo_id, local_dir=dir_path, allow_patterns=['*.ckpt', '*.pt', '*.pth', '*.safetensors', '*.bin'], use_auth_token=hf_read_token)
except Exception as e:
print(f"Error: Failed to download {repo_id}. ")
return
if is_replace:
for file in Path(dir_path).glob("*"):
if file.exists() and "." in file.stem or " " in file.stem and file.suffix in ['.ckpt', '.pt', '.pth', '.safetensors', '.bin']:
newpath = Path(f'{file.parent.name}/{escape_lora_basename(file.stem)}{file.suffix}')
file.resolve().rename(newpath.resolve())
private_model_path_repo_dict = {} # {"local filepath": "huggingface repo_id", ...}
def get_private_model_list(repo_id, dir_path):
global private_model_path_repo_dict
api = HfApi()
if not hf_read_token: return []
try:
files = api.list_repo_files(repo_id, token=hf_read_token)
except Exception as e:
print(f"Error: Failed to list {repo_id}. ")
return []
model_list = []
for file in files:
path = Path(f"{dir_path}/{file}")
if path.suffix in ['.ckpt', '.pt', '.pth', '.safetensors', '.bin']:
model_list.append(str(path))
for model in model_list:
private_model_path_repo_dict[model] = repo_id
return model_list
def download_private_file(repo_id, path, is_replace):
from huggingface_hub import hf_hub_download
file = Path(path)
newpath = Path(f'{file.parent.name}/{escape_lora_basename(file.stem)}{file.suffix}') if is_replace else file
if not hf_read_token or newpath.exists(): return
filename = file.name
dirname = file.parent.name
try:
hf_hub_download(repo_id=repo_id, filename=filename, local_dir=dirname, use_auth_token=hf_read_token)
except Exception as e:
print(f"Error: Failed to download {filename}. ")
return
if is_replace:
file.resolve().rename(newpath.resolve())
def download_private_file_from_somewhere(path, is_replace):
if not path in private_model_path_repo_dict.keys(): return
repo_id = private_model_path_repo_dict.get(path, None)
download_private_file(repo_id, path, is_replace)
model_id_list = []
def get_model_id_list():
global model_id_list
if len(model_id_list) != 0: return model_id_list
api = HfApi()
model_ids = []
try:
models_likes = []
for author in HF_MODEL_USER_LIKES:
models_likes.extend(api.list_models(author=author, cardData=True, sort="likes"))
models_ex = []
for author in HF_MODEL_USER_EX:
models_ex = api.list_models(author=author, cardData=True, sort="last_modified")
except Exception as e:
print(f"Error: Failed to list {author}'s models. ")
return model_ids
for model in models_likes:
model_ids.append(model.id) if not model.private else ""
anime_models = []
real_models = []
for model in models_ex:
if not model.private:
anime_models.append(model.id) if 'anime' in model.tags else real_models.append(model.id)
model_ids.extend(anime_models)
model_ids.extend(real_models)
model_id_list = model_ids.copy()
return model_ids
model_id_list = get_model_id_list()
def get_t2i_model_info(repo_id: str):
api = HfApi()
try:
if " " in repo_id or not api.repo_exists(repo_id): return ""
model = api.model_info(repo_id=repo_id)
except Exception as e:
print(f"Error: Failed to get {repo_id}'s info. ")
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 ""
if 'diffusers:StableDiffusionXLPipeline' in tags:
info.append("SDXL")
elif 'diffusers:StableDiffusionPipeline' in tags:
info.append("SD1.5")
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)
def get_tupled_model_list(model_list):
if not model_list: return []
tupled_list = []
for repo_id in model_list:
api = HfApi()
try:
if not api.repo_exists(repo_id): continue
model = api.model_info(repo_id=repo_id)
except Exception as e:
continue
if model.private or model.gated: continue
tags = model.tags
info = []
if not 'diffusers' in tags: continue
if 'diffusers:StableDiffusionXLPipeline' in tags:
info.append("SDXL")
elif 'diffusers:StableDiffusionPipeline' in tags:
info.append("SD1.5")
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']))
if "pony" in info:
info.remove("pony")
name = f"{repo_id} (Pony🐴, {', '.join(info)})"
else:
name = f"{repo_id} ({', '.join(info)})"
tupled_list.append((name, repo_id))
return tupled_list
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
loras_dict = {"None": ["", "", "", "", ""], "": ["", "", "", "", ""]} | private_lora_dict.copy()
civitai_not_exists_list = []
loras_url_to_path_dict = {} # {"URL to download": "local filepath", ...}
civitai_lora_last_results = {} # {"URL to download": {search results}, ...}
all_lora_list = []
private_lora_model_list = []
def get_private_lora_model_lists():
global private_lora_model_list
if len(private_lora_model_list) != 0: return private_lora_model_list
models1 = []
models2 = []
for repo in HF_LORA_PRIVATE_REPOS1:
models1.extend(get_private_model_list(repo, directory_loras))
for repo in HF_LORA_PRIVATE_REPOS2:
models2.extend(get_private_model_list(repo, directory_loras))
models = list_uniq(models1 + sorted(models2))
private_lora_model_list = models.copy()
return models
private_lora_model_list = get_private_lora_model_lists()
def get_civitai_info(path):
global civitai_not_exists_list
import requests
from urllib3.util import Retry
from requests.adapters import HTTPAdapter
if path in set(civitai_not_exists_list): return ["", "", "", "", ""]
if not Path(path).exists(): return None
user_agent = get_user_agent()
headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
base_url = 'https://civitai.com/api/v1/model-versions/by-hash/'
params = {}
session = requests.Session()
retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
session.mount("https://", HTTPAdapter(max_retries=retries))
import hashlib
with open(path, 'rb') as file:
file_data = file.read()
hash_sha256 = hashlib.sha256(file_data).hexdigest()
url = base_url + hash_sha256
try:
r = session.get(url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
except Exception as e:
return ["", "", "", "", ""]
if not r.ok: return None
json = r.json()
if not 'baseModel' in json:
civitai_not_exists_list.append(path)
return ["", "", "", "", ""]
items = []
items.append(" / ".join(json['trainedWords']))
items.append(json['baseModel'])
items.append(json['model']['name'])
items.append(f"https://civitai.com/models/{json['modelId']}")
items.append(json['images'][0]['url'])
return items
def get_lora_model_list():
loras = list_uniq(get_private_lora_model_lists() + get_local_model_list(directory_loras))
loras.insert(0, "None")
loras.insert(0, "")
return loras
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):
global loras_dict
key = escape_lora_basename(Path(path).stem)
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)
i = 0
for file in 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)
i += 1
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, lora1: str, lora2: str, lora3: str, lora4: str, lora5: str):
path = download_lora(dl_urls)
if path:
if not lora1 or lora1 == "None":
lora1 = path
elif not lora2 or lora2 == "None":
lora2 = path
elif not lora3 or lora3 == "None":
lora3 = path
elif not lora4 or lora4 == "None":
lora4 = path
elif not lora5 or lora5 == "None":
lora5 = path
choices = get_all_lora_tupled_list()
return gr.update(value=lora1, choices=choices), gr.update(value=lora2, choices=choices), gr.update(value=lora3, choices=choices),\
gr.update(value=lora4, choices=choices), gr.update(value=lora5, choices=choices)
def get_valid_lora_name(query: str):
path = "None"
if not query or query == "None": return "None"
if to_lora_key(query) in loras_dict.keys(): return query
if query in loras_url_to_path_dict.keys():
path = loras_url_to_path_dict[query]
else:
path = to_lora_path(query.strip().split('/')[-1])
if Path(path).exists():
return path
elif "http" in query:
dl_file = download_lora(query)
if dl_file and Path(dl_file).exists(): return dl_file
else:
dl_file = find_similar_lora(query)
if dl_file and Path(dl_file).exists(): return dl_file
return "None"
def get_valid_lora_path(query: str):
path = None
if not query or query == "None": return None
if to_lora_key(query) in loras_dict.keys(): return query
if Path(path).exists():
return path
else:
return None
def get_valid_lora_wt(prompt: str, lora_path: str, lora_wt: float):
import re
wt = lora_wt
result = re.findall(f'<lora:{to_lora_key(lora_path)}:(.+?)>', prompt)
if not result: return wt
wt = safe_float(result[0][0])
return wt
def set_prompt_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
import re
if not "Classic" in str(prompt_syntax): return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
lora1 = get_valid_lora_name(lora1)
lora2 = get_valid_lora_name(lora2)
lora3 = get_valid_lora_name(lora3)
lora4 = get_valid_lora_name(lora4)
lora5 = get_valid_lora_name(lora5)
if not "<lora" in prompt: return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
lora1_wt = get_valid_lora_wt(prompt, lora1, lora1_wt)
lora2_wt = get_valid_lora_wt(prompt, lora2, lora2_wt)
lora3_wt = get_valid_lora_wt(prompt, lora3, lora3_wt)
lora4_wt = get_valid_lora_wt(prompt, lora4, lora4_wt)
lora5_wt = get_valid_lora_wt(prompt, lora5, lora5_wt)
on1, label1, tag1, md1 = get_lora_info(lora1)
on2, label2, tag2, md2 = get_lora_info(lora2)
on3, label3, tag3, md3 = get_lora_info(lora3)
on4, label4, tag4, md4 = get_lora_info(lora4)
on5, label5, tag5, md5 = get_lora_info(lora5)
lora_paths = [lora1, lora2, lora3, lora4, lora5]
prompts = prompt.split(",") if prompt else []
for p in prompts:
p = str(p).strip()
if "<lora" in p:
result = re.findall(r'<lora:(.+?):(.+?)>', p)
if not result: continue
key = result[0][0]
wt = result[0][1]
path = to_lora_path(key)
if not key in loras_dict.keys() or not path:
path = get_valid_lora_name(path)
if not path or path == "None": continue
if path in lora_paths:
continue
elif not on1:
lora1 = path
lora_paths = [lora1, lora2, lora3, lora4, lora5]
lora1_wt = safe_float(wt)
on1 = True
elif not on2:
lora2 = path
lora_paths = [lora1, lora2, lora3, lora4, lora5]
lora2_wt = safe_float(wt)
on2 = True
elif not on3:
lora3 = path
lora_paths = [lora1, lora2, lora3, lora4, lora5]
lora3_wt = safe_float(wt)
on3 = True
elif not on4:
lora4 = path
lora_paths = [lora1, lora2, lora3, lora4, lora5]
lora4_wt = safe_float(wt)
on4, label4, tag4, md4 = get_lora_info(lora4)
elif not on5:
lora5 = path
lora_paths = [lora1, lora2, lora3, lora4, lora5]
lora5_wt = safe_float(wt)
on5 = True
return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
def get_lora_info(lora_path: str):
is_valid = False
tag = ""
label = ""
md = "None"
if not lora_path or lora_path == "None":
print("LoRA file not found.")
return is_valid, label, tag, md
path = Path(lora_path)
new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
if not to_lora_key(str(new_path)) in loras_dict.keys() and str(path) not in set(get_all_lora_list()):
print("LoRA file is not registered.")
return tag, label, tag, md
if not new_path.exists():
download_private_file_from_somewhere(str(path), True)
basename = new_path.stem
label = f'Name: {basename}'
items = loras_dict.get(basename, None)
if items == None:
items = get_civitai_info(str(new_path))
if items != None:
loras_dict[basename] = items
if items and items[2] != "":
tag = items[0]
label = f'Name: {basename}'
if items[1] == "Pony":
label = f'Name: {basename} (for Pony🐴)'
if items[4]:
md = f'<img src="{items[4]}" alt="thumbnail" width="150" height="240"><br>[LoRA Model URL]({items[3]})'
elif items[3]:
md = f'[LoRA Model URL]({items[3]})'
is_valid = True
return is_valid, label, tag, md
def normalize_prompt_list(tags: list[str]):
prompts = []
for tag in tags:
tag = str(tag).strip()
if tag:
prompts.append(tag)
return prompts
def apply_lora_prompt(prompt: str = "", lora_info: str = ""):
if lora_info == "None": return gr.update(value=prompt)
tags = prompt.split(",") if prompt else []
prompts = normalize_prompt_list(tags)
lora_tag = lora_info.replace("/",",")
lora_tags = lora_tag.split(",") if str(lora_info) != "None" else []
lora_prompts = normalize_prompt_list(lora_tags)
empty = [""]
prompt = ", ".join(list_uniq(prompts + lora_prompts) + empty)
return gr.update(value=prompt)
def update_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
import re
on1, label1, tag1, md1 = get_lora_info(lora1)
on2, label2, tag2, md2 = get_lora_info(lora2)
on3, label3, tag3, md3 = get_lora_info(lora3)
on4, label4, tag4, md4 = get_lora_info(lora4)
on5, label5, tag5, md5 = get_lora_info(lora5)
lora_paths = [lora1, lora2, lora3, lora4, lora5]
output_prompt = prompt
if "Classic" in str(prompt_syntax):
prompts = prompt.split(",") if prompt else []
output_prompts = []
for p in prompts:
p = str(p).strip()
if "<lora" in p:
result = re.findall(r'<lora:(.+?):(.+?)>', p)
if not result: continue
key = result[0][0]
wt = result[0][1]
path = to_lora_path(key)
if not key in loras_dict.keys() or not path: continue
if path in lora_paths:
output_prompts.append(f"<lora:{to_lora_key(path)}:{safe_float(wt):.2f}>")
elif p:
output_prompts.append(p)
lora_prompts = []
if on1: lora_prompts.append(f"<lora:{to_lora_key(lora1)}:{lora1_wt:.2f}>")
if on2: lora_prompts.append(f"<lora:{to_lora_key(lora2)}:{lora2_wt:.2f}>")
if on3: lora_prompts.append(f"<lora:{to_lora_key(lora3)}:{lora3_wt:.2f}>")
if on4: lora_prompts.append(f"<lora:{to_lora_key(lora4)}:{lora4_wt:.2f}>")
if on5: lora_prompts.append(f"<lora:{to_lora_key(lora5)}:{lora5_wt:.2f}>")
output_prompt = ", ".join(list_uniq(output_prompts + lora_prompts + [""]))
choices = get_all_lora_tupled_list()
return gr.update(value=output_prompt), gr.update(value=lora1, choices=choices), gr.update(value=lora1_wt),\
gr.update(value=tag1, label=label1, visible=on1), gr.update(visible=on1), gr.update(value=md1, visible=on1),\
gr.update(value=lora2, choices=choices), gr.update(value=lora2_wt),\
gr.update(value=tag2, label=label2, visible=on2), gr.update(visible=on2), gr.update(value=md2, visible=on2),\
gr.update(value=lora3, choices=choices), gr.update(value=lora3_wt),\
gr.update(value=tag3, label=label3, visible=on3), gr.update(visible=on3), gr.update(value=md3, visible=on3),\
gr.update(value=lora4, choices=choices), gr.update(value=lora4_wt),\
gr.update(value=tag4, label=label4, visible=on4), gr.update(visible=on4), gr.update(value=md4, visible=on4),\
gr.update(value=lora5, choices=choices), gr.update(value=lora5_wt),\
gr.update(value=tag5, label=label5, visible=on5), gr.update(visible=on5), gr.update(value=md5, visible=on5)
def get_my_lora(link_url):
from pathlib import Path
before = get_local_model_list(directory_loras)
for url in [url.strip() for url in link_url.split(',')]:
if not Path(f"{directory_loras}/{url.split('/')[-1]}").exists():
download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
after = get_local_model_list(directory_loras)
new_files = list_sub(after, before)
for file in 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())
update_lora_dict(str(new_path))
new_lora_model_list = get_lora_model_list()
new_lora_tupled_list = get_all_lora_tupled_list()
return gr.update(
choices=new_lora_tupled_list, value=new_lora_model_list[-1]
), gr.update(
choices=new_lora_tupled_list
), gr.update(
choices=new_lora_tupled_list
), gr.update(
choices=new_lora_tupled_list
), gr.update(
choices=new_lora_tupled_list
)
def upload_file_lora(files, progress=gr.Progress(track_tqdm=True)):
progress(0, desc="Uploading...")
file_paths = [file.name for file in files]
progress(1, desc="Uploaded.")
return gr.update(value=file_paths, visible=True), gr.update(visible=True)
def move_file_lora(filepaths):
import shutil
for file in filepaths:
path = Path(shutil.move(Path(file).resolve(), Path(f"./{directory_loras}").resolve()))
newpath = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
path.resolve().rename(newpath.resolve())
update_lora_dict(str(newpath))
new_lora_model_list = get_lora_model_list()
new_lora_tupled_list = get_all_lora_tupled_list()
return gr.update(
choices=new_lora_tupled_list, value=new_lora_model_list[-1]
), gr.update(
choices=new_lora_tupled_list
), gr.update(
choices=new_lora_tupled_list
), gr.update(
choices=new_lora_tupled_list
), gr.update(
choices=new_lora_tupled_list
)
def get_civitai_info(path):
global civitai_not_exists_list
global loras_url_to_path_dict
import requests
from requests.adapters import HTTPAdapter
from urllib3.util import Retry
default = ["", "", "", "", ""]
if path in set(civitai_not_exists_list): return default
if not Path(path).exists(): return None
user_agent = get_user_agent()
headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
base_url = 'https://civitai.com/api/v1/model-versions/by-hash/'
params = {}
session = requests.Session()
retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
session.mount("https://", HTTPAdapter(max_retries=retries))
import hashlib
with open(path, 'rb') as file:
file_data = file.read()
hash_sha256 = hashlib.sha256(file_data).hexdigest()
url = base_url + hash_sha256
try:
r = session.get(url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
except Exception as e:
print(e)
return default
else:
if not r.ok: return None
json = r.json()
if 'baseModel' not in json:
civitai_not_exists_list.append(path)
return default
items = []
items.append(" / ".join(json['trainedWords'])) # The words (prompts) used to trigger the model
items.append(json['baseModel']) # Base model (SDXL1.0, Pony, ...)
items.append(json['model']['name']) # The name of the model version
items.append(f"https://civitai.com/models/{json['modelId']}") # The repo url for the model
items.append(json['images'][0]['url']) # The url for a sample image
loras_url_to_path_dict[path] = json['downloadUrl'] # The download url to get the model file for this specific version
return items
def search_lora_on_civitai(query: str, allow_model: list[str] = ["Pony", "SDXL 1.0"], limit: int = 100):
import requests
from requests.adapters import HTTPAdapter
from urllib3.util import Retry
if not query: return None
user_agent = get_user_agent()
headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
base_url = 'https://civitai.com/api/v1/models'
params = {'query': query, 'types': ['LORA'], 'sort': 'Highest Rated', 'period': 'AllTime',
'nsfw': 'true', 'supportsGeneration ': 'true', 'limit': limit}
session = requests.Session()
retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
session.mount("https://", HTTPAdapter(max_retries=retries))
try:
r = session.get(base_url, params=params, headers=headers, stream=True, timeout=(3.0, 30))
except Exception as e:
print(e)
return None
else:
if not r.ok: return None
json = r.json()
if 'items' not in json: return None
items = []
for j in json['items']:
for model in j['modelVersions']:
item = {}
if model['baseModel'] not in set(allow_model): continue
item['name'] = j['name']
item['creator'] = j['creator']['username']
item['tags'] = j['tags']
item['model_name'] = model['name']
item['base_model'] = model['baseModel']
item['dl_url'] = model['downloadUrl']
item['md'] = f'<img src="{model["images"][0]["url"]}" alt="thumbnail" width="150" height="240"><br>[LoRA Model URL](https://civitai.com/models/{j["id"]})'
items.append(item)
return items
def search_civitai_lora(query, base_model):
global civitai_lora_last_results
items = search_lora_on_civitai(query, base_model)
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 find_similar_lora(q: str):
from rapidfuzz.process import extractOne
from rapidfuzz.utils import default_process
query = to_lora_key(q)
print(f"Finding <lora:{query}:...>...")
keys = list(private_lora_dict.keys())
values = [x[2] for x in list(private_lora_dict.values())]
s = default_process(query)
e1 = extractOne(s, keys + values, processor=default_process, score_cutoff=80.0)
key = ""
if e1:
e = e1[0]
if e in set(keys): key = e
elif e in set(values): key = keys[values.index(e)]
if key:
path = to_lora_path(key)
new_path = to_lora_path(query)
if not Path(path).exists():
if not Path(new_path).exists(): download_private_file_from_somewhere(path, True)
if Path(path).exists() and copy_lora(path, new_path): return new_path
print(f"Finding <lora:{query}:...> on Civitai...")
civitai_query = Path(query).stem if Path(query).is_file() else query
civitai_query = civitai_query.replace("_", " ").replace("-", " ")
base_model = ["Pony", "SDXL 1.0"]
items = search_lora_on_civitai(civitai_query, base_model, 1)
if items:
item = items[0]
path = download_lora(item['dl_url'])
new_path = query if Path(query).is_file() else to_lora_path(query)
if path and copy_lora(path, new_path): return new_path
return None
def change_interface_mode(mode: str):
if mode == "Fast":
return gr.update(open=False), gr.update(visible=True), gr.update(open=False), gr.update(open=False),\
gr.update(visible=True), gr.update(open=False), gr.update(visible=True), gr.update(open=False),\
gr.update(visible=True), gr.update(value="Fast")
elif mode == "Simple": # t2i mode
return gr.update(open=True), gr.update(visible=True), gr.update(open=False), gr.update(open=False),\
gr.update(visible=True), gr.update(open=False), gr.update(visible=False), gr.update(open=True),\
gr.update(visible=False), gr.update(value="Standard")
elif mode == "LoRA": # t2i LoRA mode
return gr.update(open=True), gr.update(visible=True), gr.update(open=True), gr.update(open=False),\
gr.update(visible=True), gr.update(open=True), gr.update(visible=True), gr.update(open=False),\
gr.update(visible=False), gr.update(value="Standard")
else: # Standard
return gr.update(open=False), gr.update(visible=True), gr.update(open=False), gr.update(open=False),\
gr.update(visible=True), gr.update(open=False), gr.update(visible=True), gr.update(open=False),\
gr.update(visible=True), gr.update(value="Standard")
quality_prompt_list = [
{
"name": "None",
"prompt": "",
"negative_prompt": "lowres",
},
{
"name": "Animagine Common",
"prompt": "anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres",
"negative_prompt": "lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
},
{
"name": "Pony Anime Common",
"prompt": "source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres",
"negative_prompt": "source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends",
},
{
"name": "Pony Common",
"prompt": "source_anime, score_9, score_8_up, score_7_up",
"negative_prompt": "source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends",
},
{
"name": "Animagine Standard v3.0",
"prompt": "masterpiece, best quality",
"negative_prompt": "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name",
},
{
"name": "Animagine Standard v3.1",
"prompt": "masterpiece, best quality, very aesthetic, absurdres",
"negative_prompt": "lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
},
{
"name": "Animagine Light v3.1",
"prompt": "(masterpiece), best quality, very aesthetic, perfect face",
"negative_prompt": "(low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn",
},
{
"name": "Animagine Heavy v3.1",
"prompt": "(masterpiece), (best quality), (ultra-detailed), very aesthetic, illustration, disheveled hair, perfect composition, moist skin, intricate details",
"negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair, extra digit, fewer digits, cropped, worst quality, low quality, very displeasing",
},
]
style_list = [
{
"name": "None",
"prompt": "",
"negative_prompt": "",
},
{
"name": "Cinematic",
"prompt": "cinematic still, emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
"negative_prompt": "cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
},
{
"name": "Photographic",
"prompt": "cinematic photo, 35mm photograph, film, bokeh, professional, 4k, highly detailed",
"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
},
{
"name": "Anime",
"prompt": "anime artwork, anime style, vibrant, studio anime, highly detailed",
"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
},
{
"name": "Manga",
"prompt": "manga style, vibrant, high-energy, detailed, iconic, Japanese comic style",
"negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
},
{
"name": "Digital Art",
"prompt": "concept art, digital artwork, illustrative, painterly, matte painting, highly detailed",
"negative_prompt": "photo, photorealistic, realism, ugly",
},
{
"name": "Pixel art",
"prompt": "pixel-art, low-res, blocky, pixel art style, 8-bit graphics",
"negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
},
{
"name": "Fantasy art",
"prompt": "ethereal fantasy concept art, magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
"negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
},
{
"name": "Neonpunk",
"prompt": "neonpunk style, cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
"negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
},
{
"name": "3D Model",
"prompt": "professional 3d model, octane render, highly detailed, volumetric, dramatic lighting",
"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
},
]
optimization_list = {
"None": [28, 7., 'Euler a', False, 'None', 1.],
"Default": [28, 7., 'Euler a', False, 'None', 1.],
"SPO": [28, 7., 'Euler a', True, 'loras/spo_sdxl_10ep_4k-data_lora_diffusers.safetensors', 1.],
"DPO": [28, 7., 'Euler a', True, 'loras/sdxl-DPO-LoRA.safetensors', 1.],
"DPO Turbo": [8, 2.5, 'LCM', True, 'loras/sd_xl_dpo_turbo_lora_v1-128dim.safetensors', 1.],
"SDXL Turbo": [8, 2.5, 'LCM', True, 'loras/sd_xl_turbo_lora_v1.safetensors', 1.],
"Hyper-SDXL 12step": [12, 5., 'TCD', True, 'loras/Hyper-SDXL-12steps-CFG-lora.safetensors', 1.],
"Hyper-SDXL 8step": [8, 5., 'TCD', True, 'loras/Hyper-SDXL-8steps-CFG-lora.safetensors', 1.],
"Hyper-SDXL 4step": [4, 0, 'TCD', True, 'loras/Hyper-SDXL-4steps-lora.safetensors', 1.],
"Hyper-SDXL 2step": [2, 0, 'TCD', True, 'loras/Hyper-SDXL-2steps-lora.safetensors', 1.],
"Hyper-SDXL 1step": [1, 0, 'TCD', True, 'loras/Hyper-SDXL-1steps-lora.safetensors', 1.],
"PCM 16step": [16, 4., 'Euler a trailing', True, 'loras/pcm_sdxl_normalcfg_16step_converted.safetensors', 1.],
"PCM 8step": [8, 4., 'Euler a trailing', True, 'loras/pcm_sdxl_normalcfg_8step_converted.safetensors', 1.],
"PCM 4step": [4, 2., 'Euler a trailing', True, 'loras/pcm_sdxl_smallcfg_4step_converted.safetensors', 1.],
"PCM 2step": [2, 1., 'Euler a trailing', True, 'loras/pcm_sdxl_smallcfg_2step_converted.safetensors', 1.],
}
def set_optimization(opt, steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora_gui, lora_scale_gui):
if not opt in list(optimization_list.keys()): opt = "None"
def_steps_gui = 28
def_cfg_gui = 7.
steps = optimization_list.get(opt, "None")[0]
cfg = optimization_list.get(opt, "None")[1]
sampler = optimization_list.get(opt, "None")[2]
clip_skip = optimization_list.get(opt, "None")[3]
lora = optimization_list.get(opt, "None")[4]
lora_scale = optimization_list.get(opt, "None")[5]
if opt == "None":
steps = max(steps_gui, def_steps_gui)
cfg = max(cfg_gui, def_cfg_gui)
clip_skip = clip_skip_gui
elif opt == "SPO" or opt == "DPO":
steps = max(steps_gui, def_steps_gui)
cfg = max(cfg_gui, def_cfg_gui)
return gr.update(value=steps), gr.update(value=cfg), gr.update(value=sampler),\
gr.update(value=clip_skip), gr.update(value=lora), gr.update(value=lora_scale),
# [sampler_gui, steps_gui, cfg_gui, clip_skip_gui, img_width_gui, img_height_gui, optimization_gui]
preset_sampler_setting = {
"None": ["Euler a", 28, 7., True, 1024, 1024, "None"],
"Anime 3:4 Fast": ["LCM", 8, 2.5, True, 896, 1152, "DPO Turbo"],
"Anime 3:4 Standard": ["Euler a", 28, 7., True, 896, 1152, "None"],
"Anime 3:4 Heavy": ["Euler a", 40, 7., True, 896, 1152, "None"],
"Anime 1:1 Fast": ["LCM", 8, 2.5, True, 1024, 1024, "DPO Turbo"],
"Anime 1:1 Standard": ["Euler a", 28, 7., True, 1024, 1024, "None"],
"Anime 1:1 Heavy": ["Euler a", 40, 7., True, 1024, 1024, "None"],
"Photo 3:4 Fast": ["LCM", 8, 2.5, False, 896, 1152, "DPO Turbo"],
"Photo 3:4 Standard": ["DPM++ 2M Karras", 28, 7., False, 896, 1152, "None"],
"Photo 3:4 Heavy": ["DPM++ 2M Karras", 40, 7., False, 896, 1152, "None"],
"Photo 1:1 Fast": ["LCM", 8, 2.5, False, 1024, 1024, "DPO Turbo"],
"Photo 1:1 Standard": ["DPM++ 2M Karras", 28, 7., False, 1024, 1024, "None"],
"Photo 1:1 Heavy": ["DPM++ 2M Karras", 40, 7., False, 1024, 1024, "None"],
}
def set_sampler_settings(sampler_setting):
if not sampler_setting in list(preset_sampler_setting.keys()) or sampler_setting == "None":
return gr.update(value="Euler a"), gr.update(value=28), gr.update(value=7.), gr.update(value=True),\
gr.update(value=1024), gr.update(value=1024), gr.update(value="None")
v = preset_sampler_setting.get(sampler_setting, ["Euler a", 28, 7., True, 1024, 1024])
# sampler, steps, cfg, clip_skip, width, height, optimization
return gr.update(value=v[0]), gr.update(value=v[1]), gr.update(value=v[2]), gr.update(value=v[3]),\
gr.update(value=v[4]), gr.update(value=v[5]), gr.update(value=v[6])
preset_styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
preset_quality = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in quality_prompt_list}
def process_style_prompt(prompt: str, neg_prompt: str, styles_key: str = "None", quality_key: str = "None", type: str = "Auto"):
def to_list(s):
return [x.strip() for x in s.split(",") if not s == ""]
def list_sub(a, b):
return [e for e in a if e not in b]
def list_uniq(l):
return sorted(set(l), key=l.index)
animagine_ps = to_list("anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres")
animagine_nps = to_list("lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
pony_ps = to_list("source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres")
pony_nps = to_list("source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends")
prompts = to_list(prompt)
neg_prompts = to_list(neg_prompt)
all_styles_ps = []
all_styles_nps = []
for d in style_list:
all_styles_ps.extend(to_list(str(d.get("prompt", ""))))
all_styles_nps.extend(to_list(str(d.get("negative_prompt", ""))))
all_quality_ps = []
all_quality_nps = []
for d in quality_prompt_list:
all_quality_ps.extend(to_list(str(d.get("prompt", ""))))
all_quality_nps.extend(to_list(str(d.get("negative_prompt", ""))))
quality_ps = to_list(preset_quality[quality_key][0])
quality_nps = to_list(preset_quality[quality_key][1])
styles_ps = to_list(preset_styles[styles_key][0])
styles_nps = to_list(preset_styles[styles_key][1])
prompts = list_sub(prompts, animagine_ps + pony_ps + all_styles_ps + all_quality_ps)
neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps + all_styles_nps + all_quality_nps)
last_empty_p = [""] if not prompts and type != "None" and type != "Auto" and styles_key != "None" and quality_key != "None" else []
last_empty_np = [""] if not neg_prompts and type != "None" and type != "Auto" and styles_key != "None" and quality_key != "None" else []
if type == "Animagine":
prompts = prompts + animagine_ps
neg_prompts = neg_prompts + animagine_nps
elif type == "Pony":
prompts = prompts + pony_ps
neg_prompts = neg_prompts + pony_nps
prompts = prompts + styles_ps + quality_ps
neg_prompts = neg_prompts + styles_nps + quality_nps
prompt = ", ".join(list_uniq(prompts) + last_empty_p)
neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np)
return gr.update(value=prompt), gr.update(value=neg_prompt), gr.update(value=type)
def set_quick_presets(genre:str = "None", type:str = "Auto", speed:str = "None", aspect:str = "None"):
quality = "None"
style = "None"
sampler = "None"
opt = "None"
if genre == "Anime":
if type != "None" and type != "Auto": style = "Anime"
if aspect == "1:1":
if speed == "Heavy":
sampler = "Anime 1:1 Heavy"
elif speed == "Fast":
sampler = "Anime 1:1 Fast"
else:
sampler = "Anime 1:1 Standard"
elif aspect == "3:4":
if speed == "Heavy":
sampler = "Anime 3:4 Heavy"
elif speed == "Fast":
sampler = "Anime 3:4 Fast"
else:
sampler = "Anime 3:4 Standard"
if type == "Pony":
quality = "Pony Anime Common"
elif type == "Animagine":
quality = "Animagine Common"
else:
quality = "None"
elif genre == "Photo":
if type != "None" and type != "Auto": style = "Photographic"
if aspect == "1:1":
if speed == "Heavy":
sampler = "Photo 1:1 Heavy"
elif speed == "Fast":
sampler = "Photo 1:1 Fast"
else:
sampler = "Photo 1:1 Standard"
elif aspect == "3:4":
if speed == "Heavy":
sampler = "Photo 3:4 Heavy"
elif speed == "Fast":
sampler = "Photo 3:4 Fast"
else:
sampler = "Photo 3:4 Standard"
if type == "Pony":
quality = "Pony Common"
else:
quality = "None"
if speed == "Fast":
opt = "DPO Turbo"
if genre == "Anime" and type != "Pony" and type != "Auto": quality = "Animagine Light v3.1"
return gr.update(value=quality), gr.update(value=style), gr.update(value=sampler), gr.update(value=opt), gr.update(value=type)
textual_inversion_dict = {}
try:
with open('textual_inversion_dict.json', encoding='utf-8') as f:
textual_inversion_dict = json.load(f)
except Exception:
pass
textual_inversion_file_token_list = []
def get_tupled_embed_list(embed_list):
global textual_inversion_file_list
tupled_list = []
for file in embed_list:
token = textual_inversion_dict.get(Path(file).name, [Path(file).stem.replace(",",""), False])[0]
tupled_list.append((token, file))
textual_inversion_file_token_list.append(token)
return tupled_list
def set_textual_inversion_prompt(textual_inversion_gui, prompt_gui, neg_prompt_gui, prompt_syntax_gui):
ti_tags = list(textual_inversion_dict.values()) + textual_inversion_file_token_list
tags = prompt_gui.split(",") if prompt_gui else []
prompts = []
for tag in tags:
tag = str(tag).strip()
if tag and not tag in ti_tags:
prompts.append(tag)
ntags = neg_prompt_gui.split(",") if neg_prompt_gui else []
neg_prompts = []
for tag in ntags:
tag = str(tag).strip()
if tag and not tag in ti_tags:
neg_prompts.append(tag)
ti_prompts = []
ti_neg_prompts = []
for ti in textual_inversion_gui:
tokens = textual_inversion_dict.get(Path(ti).name, [Path(ti).stem.replace(",",""), False])
is_positive = tokens[1] == True or "positive" in Path(ti).parent.name
if is_positive: # positive prompt
ti_prompts.append(tokens[0])
else: # negative prompt (default)
ti_neg_prompts.append(tokens[0])
empty = [""]
prompt = ", ".join(prompts + ti_prompts + empty)
neg_prompt = ", ".join(neg_prompts + ti_neg_prompts + empty)
return gr.update(value=prompt), gr.update(value=neg_prompt),
def get_model_pipeline(repo_id: str):
from huggingface_hub import HfApi
api = HfApi()
default = "StableDiffusionPipeline"
try:
if " " in repo_id or not api.repo_exists(repo_id): return default
model = api.model_info(repo_id=repo_id)
except Exception as e:
return default
if model.private or model.gated: return default
tags = model.tags
if not 'diffusers' in tags: return default
if 'diffusers:StableDiffusionXLPipeline' in tags:
return "StableDiffusionXLPipeline"
elif 'diffusers:StableDiffusionPipeline' in tags:
return "StableDiffusionPipeline"
else:
return default