mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-10-31 01:54:44 +00:00
73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
import os.path
|
|
from concurrent.futures import ProcessPoolExecutor
|
|
from multiprocessing import get_context
|
|
|
|
|
|
def _load_tf_and_return_tags(pil_image, threshold):
|
|
import deepdanbooru as dd
|
|
import tensorflow as tf
|
|
import numpy as np
|
|
|
|
this_folder = os.path.dirname(__file__)
|
|
model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
|
|
if not os.path.exists(os.path.join(model_path, 'project.json')):
|
|
# there is no point importing these every time
|
|
import zipfile
|
|
from basicsr.utils.download_util import load_file_from_url
|
|
load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
|
|
model_path)
|
|
with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
|
|
zip_ref.extractall(model_path)
|
|
os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
|
|
|
|
tags = dd.project.load_tags_from_project(model_path)
|
|
model = dd.project.load_model_from_project(
|
|
model_path, compile_model=True
|
|
)
|
|
|
|
width = model.input_shape[2]
|
|
height = model.input_shape[1]
|
|
image = np.array(pil_image)
|
|
image = tf.image.resize(
|
|
image,
|
|
size=(height, width),
|
|
method=tf.image.ResizeMethod.AREA,
|
|
preserve_aspect_ratio=True,
|
|
)
|
|
image = image.numpy() # EagerTensor to np.array
|
|
image = dd.image.transform_and_pad_image(image, width, height)
|
|
image = image / 255.0
|
|
image_shape = image.shape
|
|
image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
|
|
|
|
y = model.predict(image)[0]
|
|
|
|
result_dict = {}
|
|
|
|
for i, tag in enumerate(tags):
|
|
result_dict[tag] = y[i]
|
|
result_tags_out = []
|
|
result_tags_print = []
|
|
for tag in tags:
|
|
if result_dict[tag] >= threshold:
|
|
if tag.startswith("rating:"):
|
|
continue
|
|
result_tags_out.append(tag)
|
|
result_tags_print.append(f'{result_dict[tag]} {tag}')
|
|
|
|
print('\n'.join(sorted(result_tags_print, reverse=True)))
|
|
|
|
return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
|
|
|
|
|
|
def subprocess_init_no_cuda():
|
|
import os
|
|
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
|
|
|
|
|
|
def get_deepbooru_tags(pil_image, threshold=0.5):
|
|
context = get_context('spawn')
|
|
with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
|
|
f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
|
|
ret = f.result() # will rethrow any exceptions
|
|
return ret |