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 def download_things(directory, url, hf_token="", civitai_api_key=""): 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", "") # url = urllib.parse.quote(url, safe=':/') # fix encoding if "/blob/" in url: url = url.replace("/blob/", "/resolve/") user_header = f'"Authorization: Bearer {hf_token}"' 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 {url.split('/')[-1]}") 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 {url.split('/')[-1]}") 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("\033[91mYou need an API key to download Civitai models.\033[0m") else: os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}") 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): for url in [url.strip() for url in link_url.split(',')]: if not os.path.exists(f"./loras/{url.split('/')[-1]}"): download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY) 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 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 ), 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, token: str): api = HfApi(token=token) api.restart_space(repo_id=repo_id, factory_reboot=factory_reboot) 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"""
{int(percentage)}%
""" def html_template_message(msg): return f"""
{msg}
"""