civitai_to_hf / utils.py
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import gradio as gr
from huggingface_hub import HfApi, HfFolder, hf_hub_download, snapshot_download
import os
from pathlib import Path
import shutil
import gc
import re
import urllib.parse
def get_token():
try:
token = HfFolder.get_token()
except Exception:
token = ""
return token
def set_token(token):
try:
HfFolder.save_token(token)
except Exception:
print(f"Error: Failed to save token.")
def get_user_agent():
return 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:127.0) Gecko/20100101 Firefox/127.0'
def is_repo_exists(repo_id: str, repo_type: str="model"):
hf_token = get_token()
api = HfApi(token=hf_token)
try:
if api.repo_exists(repo_id=repo_id, repo_type=repo_type, token=hf_token): return True
else: return False
except Exception as e:
print(f"Error: Failed to connect {repo_id} ({repo_type}). {e}")
return True # for safe
MODEL_TYPE_CLASS = {
"diffusers:StableDiffusionPipeline": "SD 1.5",
"diffusers:StableDiffusionXLPipeline": "SDXL",
"diffusers:FluxPipeline": "FLUX",
}
def get_model_type(repo_id: str):
hf_token = get_token()
api = HfApi(token=hf_token)
lora_filename = "pytorch_lora_weights.safetensors"
diffusers_filename = "model_index.json"
default = "SDXL"
try:
if api.file_exists(repo_id=repo_id, filename=lora_filename, token=hf_token): return "LoRA"
if not api.file_exists(repo_id=repo_id, filename=diffusers_filename, token=hf_token): return "None"
model = api.model_info(repo_id=repo_id, token=hf_token)
tags = model.tags
for tag in tags:
if tag in MODEL_TYPE_CLASS.keys(): return MODEL_TYPE_CLASS.get(tag, default)
except Exception:
return default
return default
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 is_repo_name(s):
return re.fullmatch(r'^[^/,\s\"\']+/[^/,\s\"\']+$', s)
def split_hf_url(url: str):
try:
s = list(re.findall(r'^(?:https?://huggingface.co/)(?:(datasets)/)?(.+?/.+?)/\w+?/.+?/(?:(.+)/)?(.+?.\w+)(?:\?download=true)?$', url)[0])
if len(s) < 4: return "", "", "", ""
repo_id = s[1]
repo_type = "dataset" if s[0] == "datasets" else "model"
subfolder = urllib.parse.unquote(s[2]) if s[2] else None
filename = urllib.parse.unquote(s[3])
return repo_id, filename, subfolder, repo_type
except Exception as e:
print(e)
def download_hf_file(directory, url, progress=gr.Progress(track_tqdm=True)):
hf_token = get_token()
repo_id, filename, subfolder, repo_type = split_hf_url(url)
try:
print(f"Downloading {url} to {directory}")
if subfolder is not None: path = hf_hub_download(repo_id=repo_id, filename=filename, subfolder=subfolder, repo_type=repo_type, local_dir=directory, token=hf_token)
else: path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type=repo_type, local_dir=directory, token=hf_token)
return path
except Exception as e:
print(f"Failed to download: {e}")
return None
def download_thing(directory, url, civitai_api_key="", progress=gr.Progress(track_tqdm=True)): # requires aria2, gdown
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", "")
if "/blob/" in url: url = url.replace("/blob/", "/resolve/")
download_hf_file(directory, url)
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("You need an API key to download Civitai models.")
else:
os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
def get_local_file_list(dir_path):
file_list = []
for file in Path(dir_path).glob("**/*.*"):
if file.is_file():
file_path = str(file)
file_list.append(file_path)
return file_list
def get_download_file(temp_dir, url, civitai_key, progress=gr.Progress(track_tqdm=True)):
if not "http" in url and is_repo_name(url) and not Path(url).exists():
print(f"Use HF Repo: {url}")
new_file = url
elif not "http" in url and Path(url).exists():
print(f"Use local file: {url}")
new_file = url
elif Path(f"{temp_dir}/{url.split('/')[-1]}").exists():
print(f"File to download alreday exists: {url}")
new_file = f"{temp_dir}/{url.split('/')[-1]}"
else:
print(f"Start downloading: {url}")
before = get_local_file_list(temp_dir)
try:
download_thing(temp_dir, url.strip(), civitai_key)
except Exception:
print(f"Download failed: {url}")
return ""
after = get_local_file_list(temp_dir)
new_file = list_sub(after, before)[0] if list_sub(after, before) else ""
if not new_file:
print(f"Download failed: {url}")
return ""
print(f"Download completed: {url}")
return new_file
# https://huggingface.co/docs/huggingface_hub/v0.25.1/en/package_reference/file_download#huggingface_hub.snapshot_download
def download_repo(repo_id, dir_path, progress=gr.Progress(track_tqdm=True)):
hf_token = get_token()
try:
snapshot_download(repo_id=repo_id, local_dir=dir_path, token=hf_token, allow_patterns=["*.safetensors", "*.bin"],
ignore_patterns=["*.fp16.*", "/*.safetensors", "/*.bin"], force_download=True)
return True
except Exception as e:
print(f"Error: Failed to download {repo_id}. {e}")
gr.Warning(f"Error: Failed to download {repo_id}. {e}")
return False
def upload_repo(new_repo_id, dir_path, is_private, progress=gr.Progress(track_tqdm=True)):
hf_token = get_token()
api = HfApi(token=hf_token)
try:
progress(0, desc="Start uploading...")
api.create_repo(repo_id=new_repo_id, token=hf_token, private=is_private, exist_ok=True)
for path in Path(dir_path).glob("*"):
if path.is_dir():
api.upload_folder(repo_id=new_repo_id, folder_path=str(path), path_in_repo=path.name, token=hf_token)
elif path.is_file():
api.upload_file(repo_id=new_repo_id, path_or_fileobj=str(path), path_in_repo=path.name, token=hf_token)
progress(1, desc="Uploaded.")
url = f"https://huggingface.co/{new_repo_id}"
except Exception as e:
print(f"Error: Failed to upload to {new_repo_id}. {e}")
return ""
return url