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