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 = "" 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 = '
' for pic in model['images']: img_html = img_html + f'' img_html = img_html + '
' output_html = f"

Model: {model_name}
Version: {model_version}
Uploaded by: {model_uploader}

Download Here



{model_desc}
{img_html}
" 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 != ""), "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)