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import requests
from PIL import Image
from io import BytesIO
from fake_useragent import UserAgent as ua
import json
import modules.scripts as scripts
import gradio as gr
from modules import script_callbacks
import time
import threading
import urllib.request
import urllib.error
import os
from tqdm import tqdm
import re
from requests.exceptions import ConnectionError
import urllib.request
PLACEHOLDER = "<no select>"
def download_file(url, file_name):
# Maximum number of retries
max_retries = 5
# Delay between retries (in seconds)
retry_delay = 10
while True:
# Check if the file has already been partially downloaded
if os.path.exists(file_name):
# Get the size of the downloaded file
downloaded_size = os.path.getsize(file_name)
# Set the range of the request to start from the current
# size of the downloaded file
headers = {"Range": f"bytes={downloaded_size}-"}
else:
downloaded_size = 0
headers = {}
# Split filename from included path
tokens = re.split(re.escape('\\'), file_name)
file_name_display = tokens[-1]
# Initialize the progress bar
progress = tqdm(total=1000000000, unit="B", unit_scale=True,
desc=f"Downloading {file_name_display}",
initial=downloaded_size, leave=False)
# Open a local file to save the download
with open(file_name, "ab") as f:
while True:
try:
# Send a GET request to the URL and save the response to the local file
response = requests.get(url, headers=headers, stream=True)
# Get the total size of the file
total_size = int(response.headers.get("Content-Length", 0))
# Update the total size of the progress bar if the `Content-Length` header is present
if total_size == 0:
total_size = downloaded_size
progress.total = total_size
# Write the response to the local file and update the progress bar
for chunk in response.iter_content(chunk_size=1024):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
progress.update(len(chunk))
downloaded_size = os.path.getsize(file_name)
# Break out of the loop if the download is successful
break
except ConnectionError as e:
# Decrement the number of retries
max_retries -= 1
# If there are no more retries, raise the exception
if max_retries == 0:
raise e
# Wait for the specified delay before retrying
time.sleep(retry_delay)
# Close the progress bar
progress.close()
downloaded_size = os.path.getsize(file_name)
# Check if the download was successful
if downloaded_size >= total_size:
print(f"{file_name_display} successfully downloaded.")
break
else:
print(f"Error: File download failed. Retrying... {file_name_display}")
def make_new_folder(content_type, use_new_folder, model_name, lora_old):
if content_type == "Checkpoint":
folder = "models/Stable-diffusion"
new_folder = "models/Stable-diffusion/new"
elif content_type == "Hypernetwork":
folder = "models/hypernetworks"
new_folder = "models/hypernetworks/new"
elif content_type == "TextualInversion":
folder = "embeddings"
new_folder = "embeddings/new"
elif content_type == "AestheticGradient":
folder = "extensions/stable-diffusion-webui-aesthetic-gradients/aesthetic_embeddings"
new_folder = "extensions/stable-diffusion-webui-aesthetic-gradients/aesthetic_embeddings/new"
elif content_type == "VAE":
folder = "models/VAE"
new_folder = "models/VAE/new"
elif content_type == "LORA":
if lora_old:
folder = "extensions/sd-webui-additional-networks/models/lora"
new_folder = "extensions/sd-webui-additional-networks/models/lora/new"
else:
folder = "models/Lora"
new_folder = "models/Lora/new"
elif content_type == "LoCon":
if lora_old:
folder = "extensions/sd-webui-additional-networks/models/lora"
new_folder = "extensions/sd-webui-additional-networks/models/lora/new"
else:
folder = "models/Lora"
new_folder = "models/Lora/new"
if content_type == "TextualInversion" or content_type == "VAE" or \
content_type == "AestheticGradient":
if use_new_folder:
model_folder = new_folder
if not os.path.exists(new_folder):
os.makedirs(new_folder)
else:
model_folder = folder
if not os.path.exists(model_folder):
os.makedirs(model_folder)
else:
if use_new_folder:
model_folder = os.path.join(new_folder,model_name.replace(" ","_").replace("(","").replace(")","").replace("|","").replace(":","-"))
if not os.path.exists(new_folder):
os.makedirs(new_folder)
if not os.path.exists(model_folder):
os.makedirs(model_folder)
else:
model_folder = os.path.join(folder,model_name.replace(" ","_").replace("(","").replace(")","").replace("|","").replace(":","-"))
if not os.path.exists(model_folder):
os.makedirs(model_folder)
return model_folder
def download_file_thread(url, file_name, content_type, use_new_folder, model_name, lora_old):
model_folder = make_new_folder(content_type, use_new_folder, model_name, lora_old)
path_to_new_file = os.path.join(model_folder, file_name)
thread = threading.Thread(target=download_file, args=(url, path_to_new_file))
# Start the thread
thread.start()
def save_text_file(file_name, content_type, use_new_folder, trained_words, model_name, lora_old):
model_folder = make_new_folder(content_type, use_new_folder, model_name, lora_old)
path_to_new_file = os.path.join(model_folder, file_name.replace(".ckpt",".txt").replace(".safetensors",".txt").replace(".pt",".txt").replace(".yaml",".txt"))
if not os.path.exists(path_to_new_file):
with open(path_to_new_file, 'w') as f:
f.write(trained_words)
if os.path.getsize(path_to_new_file) == 0:
print("Current model doesn't have any trained tags")
else:
print("Trained tags saved as text file")
# Set the URL for the API endpoint
api_url = "https://civitai.com/api/v1/models?limit=50"
json_data = None
def api_to_data(content_type, sort_type, use_search_term, search_term=None):
if use_search_term and search_term:
search_term = search_term.replace(" ","%20")
return request_civit_api(f"{api_url}&types={content_type}&sort={sort_type}&query={search_term}")
else:
return request_civit_api(f"{api_url}&types={content_type}&sort={sort_type}")
def api_next_page(next_page_url=None):
global json_data
try: json_data['metadata']['nextPage']
except: return
if json_data['metadata']['nextPage'] is not None:
next_page_url = json_data['metadata']['nextPage']
if next_page_url is not None:
return request_civit_api(next_page_url)
def update_next_page(show_nsfw):
global json_data
json_data = api_next_page()
model_dict = {}
try: json_data['items']
except TypeError: return gr.Dropdown.update(choices=[], value=None)
if show_nsfw:
for item in json_data['items']:
model_dict[item['name']] = item['name']
else:
for item in json_data['items']:
temp_nsfw = item['nsfw']
if not temp_nsfw:
model_dict[item['name']] = item['name']
return gr.Dropdown.update(choices=[PLACEHOLDER] + [v for k, v in model_dict.items()], value=PLACEHOLDER), gr.Dropdown.update(choices=[], value=None)
def update_model_list(content_type, sort_type, use_search_term, search_term, show_nsfw):
global json_data
json_data = api_to_data(content_type, sort_type, use_search_term, search_term)
model_dict = {}
if show_nsfw:
for item in json_data['items']:
model_dict[item['name']] = item['name']
else:
for item in json_data['items']:
temp_nsfw = item['nsfw']
if not temp_nsfw:
model_dict[item['name']] = item['name']
return gr.Dropdown.update(choices=[PLACEHOLDER] + [v for k, v in model_dict.items()], value=PLACEHOLDER), gr.Dropdown.update(choices=[], value=None)
def update_model_versions(model_name=None):
if model_name is not None and model_name != PLACEHOLDER:
global json_data
versions_dict = {}
for item in json_data['items']:
if item['name'] == model_name:
for model in item['modelVersions']:
versions_dict[model['name']] = item["name"]
return gr.Dropdown.update(choices=[PLACEHOLDER] + [k + ' - ' + v for k, v in versions_dict.items()], value=PLACEHOLDER)
else:
return gr.Dropdown.update(choices=[], value=None)
def update_dl_url(model_name=None, model_version=None, model_filename=None):
if model_filename:
global json_data
dl_dict = {}
dl_url = None
model_version = model_version.replace(f' - {model_name}','').strip()
for item in json_data['items']:
if item['name'] == model_name:
for model in item['modelVersions']:
if model['name'] == model_version:
for file in model['files']:
if file['name'] == model_filename:
dl_url = file['downloadUrl']
return gr.Textbox.update(value=dl_url)
else:
return gr.Textbox.update(value=None)
def update_model_info(model_name=None, model_version=None):
if model_name and model_version and model_name != PLACEHOLDER and model_version != PLACEHOLDER:
model_version = model_version.replace(f' - {model_name}','').strip()
global json_data
output_html = ""
output_training = ""
img_html = ""
model_desc = ""
dl_dict = {}
for item in json_data['items']:
if item['name'] == model_name:
model_uploader = item['creator']['username']
if item['description']:
model_desc = item['description']
for model in item['modelVersions']:
if model['name'] == model_version:
if model['trainedWords']:
output_training = ", ".join(model['trainedWords'])
for file in model['files']:
dl_dict[file['name']] = file['downloadUrl']
model_url = model['downloadUrl']
#model_filename = model['files']['name']
img_html = '<HEAD><style>img { display: inline-block; }</style></HEAD><div class="column">'
for pic in model['images']:
img_html = img_html + f'<img src={pic["url"]} width=400px></img>'
img_html = img_html + '</div>'
output_html = f"<p><b>Model:</b> {model_name}<br><b>Version:</b> {model_version}<br><b>Uploaded by:</b> {model_uploader}<br><br><a href={model_url}><b>Download Here</b></a></p><br><br>{model_desc}<br><div align=center>{img_html}</div>"
return gr.HTML.update(value=output_html), gr.Textbox.update(value=output_training), gr.Dropdown.update(choices=[PLACEHOLDER] + [k for k, v in dl_dict.items()], value=PLACEHOLDER)
else:
return gr.HTML.update(value=None), gr.Textbox.update(value=None), gr.Dropdown.update(choices=[], value=None)
def request_civit_api(api_url=None):
# Make a GET request to the API
response = requests.get(api_url)
# Check the status code of the response
if response.status_code != 200:
print("Request failed with status code: {}".format(response.status_code))
exit()
data = json.loads(response.text)
return data
#from https://github.com/thetrebor/sd-civitai-browser/blob/add-download-images/scripts/civitai-api.py
def update_everything(list_models, list_versions, model_filename, dl_url):
(a, d, f) = update_model_info(list_models, list_versions)
dl_url = update_dl_url(list_models, list_versions, f['value'])
return (a, d, f, list_versions, list_models, dl_url)
def save_image_files(preview_image_html, model_filename, content_type, use_new_folder, list_models, lora_old):
print("Save Images Clicked")
model_folder = make_new_folder(content_type, use_new_folder, list_models, lora_old)
img_urls = re.findall(r'src=[\'"]?([^\'" >]+)', preview_image_html)
name = os.path.splitext(model_filename)[0]
assert(name != "<no select>"), "Please select a Model Filename to download"
current_directory = os.getcwd()
while os.path.basename(current_directory) != "stable-diffusion-webui":
current_directory = os.path.dirname(current_directory)
new_model_folder = os.path.join(current_directory, model_folder)
# new_model_folder = os.path.join(current_directory,list_models.replace(" ","_").replace("(","").replace(")","").replace("|","").replace(":","-"))
headers = {"User-Agent": str(ua.random)}
print(img_urls)
for i, img_url in enumerate(img_urls):
filename = f'{name}_{i}.png'
# img_url = img_url.replace("https", "http").replace("=","%3D")
print(f'Downloading {img_url} to {filename}')
try:
with requests.get(img_url, headers) as url:
with open(os.path.join(new_model_folder, filename), 'wb') as f:
with Image.open(BytesIO(url.content)) as save_me:
save_me.save(f)
print(f'Downloaded {img_url}')
# with urllib.request.urlretrieve(img_url, os.path.join(model_folder, filename)) as dl:
except urllib.error.URLError as e:
print(f'Error: {e.reason}')
finally:
print("Images downloaded.")
if os.path.exists(os.path.join(new_model_folder, f'{name}_0.png')):
with open(os.path.join(new_model_folder, f'{name}_0.png'), 'rb') as f_in:
with open(os.path.join(new_model_folder, f'{name}.png'), 'wb') as f_out:
f_out.write(f_in.read())
def on_ui_tabs():
with gr.Blocks() as civitai_interface:
with gr.Row():
with gr.Column(scale=2):
content_type = gr.Radio(label='Content type:', choices=["Checkpoint","Hypernetwork","TextualInversion","AestheticGradient", "VAE", "LORA", "LoCon"], value="Checkpoint", type="value")
with gr.Column(scale=2):
sort_type = gr.Radio(label='Sort List by:', choices=["Newest","Most Downloaded","Highest Rated","Most Liked"], value="Newest", type="value")
with gr.Column(scale=1):
show_nsfw = gr.Checkbox(label="Show NSFW", value=True)
with gr.Row():
use_search_term = gr.Checkbox(label="Search by term?", value=False)
search_term = gr.Textbox(label="Search Term", interactive=True, lines=1)
with gr.Row():
get_list_from_api = gr.Button(label="Get List", value="Get List")
get_next_page = gr.Button(value="Next Page")
with gr.Row():
list_models = gr.Dropdown(label="Model", choices=[], interactive=True, elem_id="quicksettings", value=None)
list_versions = gr.Dropdown(label="Version", choices=[], interactive=True, elem_id="quicksettings", value=None)
with gr.Row():
txt_list = ""
dummy = gr.Textbox(label='Trained Tags (if any)', value=f'{txt_list}', interactive=True, lines=1)
model_filename = gr.Dropdown(label="Model Filename", choices=[], interactive=True, value=None)
dl_url = gr.Textbox(label="Download Url", interactive=False, value=None)
with gr.Row():
update_info = gr.Button(value='1st - Get Model Info')
save_text = gr.Button(value="2nd - Save Trained Tags as Text")
save_images = gr.Button(value="3rd - Save Images")
download_model = gr.Button(value="4th - Download Model")
with gr.Row():
save_model_in_new = gr.Checkbox(label="Save Model to new folder", value=False)
old_lora = gr.Checkbox(label="Save LoRA to additional-networks", value=True)
with gr.Row():
preview_image_html = gr.HTML()
save_text.click(
fn=save_text_file,
inputs=[
model_filename,
content_type,
save_model_in_new,
dummy,
list_models,
old_lora,
],
outputs=[]
)
save_images.click(
fn=save_image_files,
inputs=[
preview_image_html,
model_filename,
content_type,
save_model_in_new,
list_models,
old_lora,
],
outputs=[]
)
download_model.click(
fn=download_file_thread,
inputs=[
dl_url,
model_filename,
content_type,
save_model_in_new,
list_models,
old_lora,
],
outputs=[]
)
get_list_from_api.click(
fn=update_model_list,
inputs=[
content_type,
sort_type,
use_search_term,
search_term,
show_nsfw,
],
outputs=[
list_models,
list_versions,
]
)
update_info.click(
fn=update_everything,
#fn=update_model_info,
inputs=[
list_models,
list_versions,
model_filename,
dl_url
],
outputs=[
preview_image_html,
dummy,
model_filename,
list_versions,
list_models,
dl_url
]
)
list_models.change(
fn=update_model_versions,
inputs=[
list_models,
],
outputs=[
list_versions,
]
)
list_versions.change(
fn=update_model_info,
inputs=[
list_models,
list_versions,
],
outputs=[
preview_image_html,
dummy,
model_filename,
]
)
model_filename.change(
fn=update_dl_url,
inputs=[list_models, list_versions, model_filename,],
outputs=[dl_url,]
)
get_next_page.click(
fn=update_next_page,
inputs=[
show_nsfw,
],
outputs=[
list_models,
list_versions,
]
)
return (civitai_interface, "CivitAi", "civitai_interface"),
script_callbacks.on_ui_tabs(on_ui_tabs)
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