extensions/sd-webui-reactor/scripts/reactor_helpers.py

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]