import os, glob from collections import Counter from PIL import Image from math import isqrt, ceil from typing import List import logging import hashlib import torch from safetensors.torch import save_file, safe_open from insightface.app.common import Face from modules.images import FilenameGenerator, get_next_sequence_number from modules import shared, script_callbacks from scripts.reactor_globals import DEVICE, BASE_PATH, FACE_MODELS_PATH, IS_SDNEXT try: from modules.paths_internal import models_path except: try: from modules.paths import models_path except: model_path = os.path.abspath("models") MODELS_PATH = None def set_Device(value): global DEVICE DEVICE = value with open(os.path.join(BASE_PATH, "last_device.txt"), "w") as txt: txt.write(DEVICE) def get_Device(): global DEVICE return DEVICE def set_SDNEXT(): global IS_SDNEXT IS_SDNEXT = True def get_SDNEXT(): global IS_SDNEXT return IS_SDNEXT def make_grid(image_list: List): # Count the occurrences of each image size in the image_list size_counter = Counter(image.size for image in image_list) # Get the most common image size (size with the highest count) common_size = size_counter.most_common(1)[0][0] # Filter the image_list to include only images with the common size image_list = [image for image in image_list if image.size == common_size] # Get the dimensions (width and height) of the common size size = common_size # If there are more than one image in the image_list if len(image_list) > 1: num_images = len(image_list) # Calculate the number of rows and columns for the grid rows = isqrt(num_images) cols = ceil(num_images / rows) # Calculate the size of the square image square_size = (cols * size[0], rows * size[1]) # Create a new RGB image with the square size square_image = Image.new("RGB", square_size) # Paste each image onto the square image at the appropriate position for i, image in enumerate(image_list): row = i // cols col = i % cols square_image.paste(image, (col * size[0], row * size[1])) # Return the resulting square image return square_image # Return None if there are no images or only one image in the image_list return None def get_image_path(image, path, basename, seed=None, prompt=None, extension='png', p=None, suffix=""): namegen = FilenameGenerator(p, seed, prompt, image) save_to_dirs = shared.opts.save_to_dirs if save_to_dirs: dirname = namegen.apply(shared.opts.directories_filename_pattern or "[prompt_words]").lstrip(' ').rstrip('\\ /') path = os.path.join(path, dirname) os.makedirs(path, exist_ok=True) if seed is None: file_decoration = "" elif shared.opts.save_to_dirs: file_decoration = shared.opts.samples_filename_pattern or "[seed]" else: file_decoration = shared.opts.samples_filename_pattern or "[seed]-[prompt_spaces]" file_decoration = namegen.apply(file_decoration) + suffix add_number = shared.opts.save_images_add_number or file_decoration == '' if file_decoration != "" and add_number: file_decoration = f"-{file_decoration}" if add_number: basecount = get_next_sequence_number(path, basename) fullfn = None for i in range(500): fn = f"{basecount + i:05}" if basename == '' else f"{basename}-{basecount + i:04}" fullfn = os.path.join(path, f"{fn}{file_decoration}.{extension}") if not os.path.exists(fullfn): break else: fullfn = os.path.join(path, f"{file_decoration}.{extension}") pnginfo = {} params = script_callbacks.ImageSaveParams(image, p, fullfn, pnginfo) # script_callbacks.before_image_saved_callback(params) fullfn = params.filename fullfn_without_extension, extension = os.path.splitext(params.filename) if hasattr(os, 'statvfs'): max_name_len = os.statvfs(path).f_namemax fullfn_without_extension = fullfn_without_extension[:max_name_len - max(4, len(extension))] params.filename = fullfn_without_extension + extension fullfn = params.filename return fullfn def addLoggingLevel(levelName, levelNum, methodName=None): if not methodName: methodName = levelName.lower() def logForLevel(self, message, *args, **kwargs): if self.isEnabledFor(levelNum): self._log(levelNum, message, args, **kwargs) def logToRoot(message, *args, **kwargs): logging.log(levelNum, message, *args, **kwargs) logging.addLevelName(levelNum, levelName) setattr(logging, levelName, levelNum) setattr(logging.getLoggerClass(), methodName, logForLevel) setattr(logging, methodName, logToRoot) def get_image_md5hash(image: Image.Image): md5hash = hashlib.md5(image.tobytes()) return md5hash.hexdigest() def save_face_model(face: Face, filename: str) -> None: try: tensors = { "bbox": torch.tensor(face["bbox"]), "kps": torch.tensor(face["kps"]), "det_score": torch.tensor(face["det_score"]), "landmark_3d_68": torch.tensor(face["landmark_3d_68"]), "pose": torch.tensor(face["pose"]), "landmark_2d_106": torch.tensor(face["landmark_2d_106"]), "embedding": torch.tensor(face["embedding"]), "gender": torch.tensor(face["gender"]), "age": torch.tensor(face["age"]), } save_file(tensors, filename) # print(f"Face model has been saved to '{filename}'") except Exception as e: print(f"Error: {e}") def get_models(): global MODELS_PATH models_path_init = os.path.join(models_path, "insightface/*") models = glob.glob(models_path_init) models = [x for x in models if x.endswith(".onnx") or x.endswith(".pth")] models_names = [] for model in models: model_path = os.path.split(model) if MODELS_PATH is None: MODELS_PATH = model_path[0] model_name = model_path[1] models_names.append(model_name) return models_names def load_face_model(filename: str): face = {} model_path = os.path.join(FACE_MODELS_PATH, filename) with safe_open(model_path, framework="pt") as f: for k in f.keys(): face[k] = f.get_tensor(k).numpy() return Face(face) def get_facemodels(): models_path = os.path.join(FACE_MODELS_PATH, "*") models = glob.glob(models_path) models = [x for x in models if x.endswith(".safetensors")] return models def get_model_names(get_models): models = get_models() names = ["None"] for x in models: names.append(os.path.basename(x)) return names def get_images_from_folder(path: str): images_path = os.path.join(path, "*") images = glob.glob(images_path) return [Image.open(x) for x in images if x.endswith(('jpg', 'png', 'jpeg', 'webp', 'bmp'))] def get_images_from_list(imgs: List): return [Image.open(os.path.abspath(x.name)) for x in imgs]