import os
import re
import gradio as gr
from constants import (
DIFFUSERS_FORMAT_LORAS,
CIVITAI_API_KEY,
HF_TOKEN,
MODEL_TYPE_CLASS,
DIRECTORY_LORAS,
)
from huggingface_hub import HfApi
from diffusers import DiffusionPipeline
from huggingface_hub import model_info as model_info_data
from diffusers.pipelines.pipeline_loading_utils import variant_compatible_siblings
from pathlib import PosixPath
from unidecode import unidecode
import urllib.parse
import copy
import requests
from requests.adapters import HTTPAdapter
from urllib3.util import Retry
USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:127.0) Gecko/20100101 Firefox/127.0'
def request_json_data(url):
model_version_id = url.split('/')[-1]
if "?modelVersionId=" in model_version_id:
match = re.search(r'modelVersionId=(\d+)', url)
model_version_id = match.group(1)
endpoint_url = f"https://civitai.com/api/v1/model-versions/{model_version_id}"
params = {}
headers = {'User-Agent': USER_AGENT, 'content-type': 'application/json'}
session = requests.Session()
retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
session.mount("https://", HTTPAdapter(max_retries=retries))
try:
result = session.get(endpoint_url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
result.raise_for_status()
json_data = result.json()
return json_data if json_data else None
except Exception as e:
print(f"Error: {e}")
return None
class ModelInformation:
def __init__(self, json_data):
self.model_version_id = json_data.get("id", "")
self.model_id = json_data.get("modelId", "")
self.download_url = json_data.get("downloadUrl", "")
self.model_url = f"https://civitai.com/models/{self.model_id}?modelVersionId={self.model_version_id}"
self.filename_url = next(
(v.get("name", "") for v in json_data.get("files", []) if str(self.model_version_id) in v.get("downloadUrl", "")), ""
)
self.filename_url = self.filename_url if self.filename_url else ""
self.description = json_data.get("description", "")
if self.description is None: self.description = ""
self.model_name = json_data.get("model", {}).get("name", "")
self.model_type = json_data.get("model", {}).get("type", "")
self.nsfw = json_data.get("model", {}).get("nsfw", False)
self.poi = json_data.get("model", {}).get("poi", False)
self.images = [img.get("url", "") for img in json_data.get("images", [])]
self.example_prompt = json_data.get("trainedWords", [""])[0] if json_data.get("trainedWords") else ""
self.original_json = copy.deepcopy(json_data)
def retrieve_model_info(url):
json_data = request_json_data(url)
if not json_data:
return None
model_descriptor = ModelInformation(json_data)
return model_descriptor
def download_things(directory, url, hf_token="", civitai_api_key="", romanize=False):
url = url.strip()
downloaded_file_path = None
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}"'
filename = unidecode(url.split('/')[-1]) if romanize else url.split('/')[-1]
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 {filename}")
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 {filename}")
downloaded_file_path = os.path.join(directory, filename)
elif "civitai.com" in url:
if not civitai_api_key:
print("\033[91mYou need an API key to download Civitai models.\033[0m")
model_profile = retrieve_model_info(url)
if model_profile.download_url and model_profile.filename_url:
url = model_profile.download_url
filename = unidecode(model_profile.filename_url) if romanize else model_profile.filename_url
else:
if "?" in url:
url = url.split("?")[0]
filename = ""
url_dl = url + f"?token={civitai_api_key}"
print(f"Filename: {filename}")
param_filename = ""
if filename:
param_filename = f"-o '{filename}'"
aria2_command = (
f'aria2c --console-log-level=error --summary-interval=10 -c -x 16 '
f'-k 1M -s 16 -d "{directory}" {param_filename} "{url_dl}"'
)
os.system(aria2_command)
if param_filename and os.path.exists(os.path.join(directory, filename)):
downloaded_file_path = os.path.join(directory, filename)
# # PLAN B
# # Follow the redirect to get the actual download URL
# curl_command = (
# f'curl -L -sI --connect-timeout 5 --max-time 5 '
# f'-H "Content-Type: application/json" '
# f'-H "Authorization: Bearer {civitai_api_key}" "{url}"'
# )
# headers = os.popen(curl_command).read()
# # Look for the redirected "Location" URL
# location_match = re.search(r'location: (.+)', headers, re.IGNORECASE)
# if location_match:
# redirect_url = location_match.group(1).strip()
# # Extract the filename from the redirect URL's "Content-Disposition"
# filename_match = re.search(r'filename%3D%22(.+?)%22', redirect_url)
# if filename_match:
# encoded_filename = filename_match.group(1)
# # Decode the URL-encoded filename
# decoded_filename = urllib.parse.unquote(encoded_filename)
# filename = unidecode(decoded_filename) if romanize else decoded_filename
# print(f"Filename: {filename}")
# aria2_command = (
# f'aria2c --console-log-level=error --summary-interval=10 -c -x 16 '
# f'-k 1M -s 16 -d "{directory}" -o "{filename}" "{redirect_url}"'
# )
# return_code = os.system(aria2_command)
# # if return_code != 0:
# # raise RuntimeError(f"Failed to download file: {filename}. Error code: {return_code}")
# downloaded_file_path = os.path.join(directory, filename)
# if not os.path.exists(downloaded_file_path):
# downloaded_file_path = None
# if not downloaded_file_path:
# # Old method
# if "?" in url:
# url = url.split("?")[0]
# 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:
os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
return downloaded_file_path
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
def extract_parameters(input_string):
parameters = {}
input_string = input_string.replace("\n", "")
if "Negative prompt:" not in input_string:
if "Steps:" in input_string:
input_string = input_string.replace("Steps:", "Negative prompt: Steps:")
else:
print("Invalid metadata")
parameters["prompt"] = input_string
return parameters
parm = input_string.split("Negative prompt:")
parameters["prompt"] = parm[0].strip()
if "Steps:" not in parm[1]:
print("Steps not detected")
parameters["neg_prompt"] = parm[1].strip()
return parameters
parm = parm[1].split("Steps:")
parameters["neg_prompt"] = parm[0].strip()
input_string = "Steps:" + parm[1]
# Extracting Steps
steps_match = re.search(r'Steps: (\d+)', input_string)
if steps_match:
parameters['Steps'] = int(steps_match.group(1))
# Extracting Size
size_match = re.search(r'Size: (\d+x\d+)', input_string)
if size_match:
parameters['Size'] = size_match.group(1)
width, height = map(int, parameters['Size'].split('x'))
parameters['width'] = width
parameters['height'] = height
# Extracting other parameters
other_parameters = re.findall(r'(\w+): (.*?)(?=, \w+|$)', input_string)
for param in other_parameters:
parameters[param[0]] = param[1].strip('"')
return parameters
def get_my_lora(link_url, romanize):
l_name = ""
for url in [url.strip() for url in link_url.split(',')]:
if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
l_name = download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY, romanize)
new_lora_model_list = get_model_list(DIRECTORY_LORAS)
new_lora_model_list.insert(0, "None")
new_lora_model_list = new_lora_model_list + DIFFUSERS_FORMAT_LORAS
msg_lora = "Downloaded"
if l_name:
msg_lora += f": {l_name}"
print(msg_lora)
return gr.update(
choices=new_lora_model_list
), gr.update(
choices=new_lora_model_list
), gr.update(
choices=new_lora_model_list
), gr.update(
choices=new_lora_model_list
), gr.update(
choices=new_lora_model_list
), gr.update(
value=msg_lora
)
def info_html(json_data, title, subtitle):
return f"""
{title}
Details
{subtitle}
"""
def get_model_type(repo_id: str):
api = HfApi(token=os.environ.get("HF_TOKEN")) # if use private or gated model
default = "SD 1.5"
try:
model = api.model_info(repo_id=repo_id, timeout=5.0)
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 restart_space(repo_id: str, factory_reboot: bool):
api = HfApi(token=os.environ.get("HF_TOKEN"))
try:
runtime = api.get_space_runtime(repo_id=repo_id)
if runtime.stage == "RUNNING":
api.restart_space(repo_id=repo_id, factory_reboot=factory_reboot)
print(f"Restarting space: {repo_id}")
else:
print(f"Space {repo_id} is in stage: {runtime.stage}")
except Exception as e:
print(e)
def extract_exif_data(image):
if image is None: return ""
try:
metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment']
for key in metadata_keys:
if key in image.info:
return image.info[key]
return str(image.info)
except Exception as e:
return f"Error extracting metadata: {str(e)}"
def create_mask_now(img, invert):
import numpy as np
import time
time.sleep(0.5)
transparent_image = img["layers"][0]
# Extract the alpha channel
alpha_channel = np.array(transparent_image)[:, :, 3]
# Create a binary mask by thresholding the alpha channel
binary_mask = alpha_channel > 1
if invert:
print("Invert")
# Invert the binary mask so that the drawn shape is white and the rest is black
binary_mask = np.invert(binary_mask)
# Convert the binary mask to a 3-channel RGB mask
rgb_mask = np.stack((binary_mask,) * 3, axis=-1)
# Convert the mask to uint8
rgb_mask = rgb_mask.astype(np.uint8) * 255
return img["background"], rgb_mask
def download_diffuser_repo(repo_name: str, model_type: str, revision: str = "main", token=True):
variant = None
if token is True and not os.environ.get("HF_TOKEN"):
token = None
if model_type == "SDXL":
info = model_info_data(
repo_name,
token=token,
revision=revision,
timeout=5.0,
)
filenames = {sibling.rfilename for sibling in info.siblings}
model_filenames, variant_filenames = variant_compatible_siblings(
filenames, variant="fp16"
)
if len(variant_filenames):
variant = "fp16"
cached_folder = DiffusionPipeline.download(
pretrained_model_name=repo_name,
force_download=False,
token=token,
revision=revision,
# mirror="https://hf-mirror.com",
variant=variant,
use_safetensors=True,
trust_remote_code=False,
timeout=5.0,
)
if isinstance(cached_folder, PosixPath):
cached_folder = cached_folder.as_posix()
# Task model
# from huggingface_hub import hf_hub_download
# hf_hub_download(
# task_model,
# filename="diffusion_pytorch_model.safetensors", # fix fp16 variant
# )
return cached_folder
def progress_step_bar(step, total):
# Calculate the percentage for the progress bar width
percentage = min(100, ((step / total) * 100))
return f"""
"""
def html_template_message(msg):
return f"""
"""