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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]
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