Merge branch 'master' of https://github.com/AUTOMATIC1111/stable-diffusion-webui into upstream-master
ssysm
3 years ago
commit
6fdad291bd
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import os.path
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from concurrent.futures import ProcessPoolExecutor
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from multiprocessing import get_context
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def _load_tf_and_return_tags(pil_image, threshold):
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import deepdanbooru as dd
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import tensorflow as tf
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import numpy as np
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this_folder = os.path.dirname(__file__)
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model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
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if not os.path.exists(os.path.join(model_path, 'project.json')):
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# there is no point importing these every time
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import zipfile
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from basicsr.utils.download_util import load_file_from_url
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load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
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model_path)
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with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
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zip_ref.extractall(model_path)
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os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
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tags = dd.project.load_tags_from_project(model_path)
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model = dd.project.load_model_from_project(
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model_path, compile_model=True
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)
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width = model.input_shape[2]
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height = model.input_shape[1]
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image = np.array(pil_image)
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image = tf.image.resize(
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image,
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size=(height, width),
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method=tf.image.ResizeMethod.AREA,
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preserve_aspect_ratio=True,
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)
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image = image.numpy() # EagerTensor to np.array
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image = dd.image.transform_and_pad_image(image, width, height)
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image = image / 255.0
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image_shape = image.shape
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image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
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y = model.predict(image)[0]
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result_dict = {}
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for i, tag in enumerate(tags):
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result_dict[tag] = y[i]
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result_tags_out = []
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result_tags_print = []
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for tag in tags:
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if result_dict[tag] >= threshold:
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if tag.startswith("rating:"):
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continue
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result_tags_out.append(tag)
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result_tags_print.append(f'{result_dict[tag]} {tag}')
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print('\n'.join(sorted(result_tags_print, reverse=True)))
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return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
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def subprocess_init_no_cuda():
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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def get_deepbooru_tags(pil_image, threshold=0.5):
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context = get_context('spawn')
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with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
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f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
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ret = f.result() # will rethrow any exceptions
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return ret
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@ -0,0 +1,89 @@
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# this code is adapted from the script contributed by anon from /h/
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import io
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import pickle
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import collections
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import sys
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import traceback
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import torch
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import numpy
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import _codecs
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import zipfile
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def encode(*args):
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out = _codecs.encode(*args)
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return out
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class RestrictedUnpickler(pickle.Unpickler):
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def persistent_load(self, saved_id):
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assert saved_id[0] == 'storage'
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return torch.storage._TypedStorage()
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def find_class(self, module, name):
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if module == 'collections' and name == 'OrderedDict':
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return getattr(collections, name)
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if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
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return getattr(torch._utils, name)
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if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:
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return getattr(torch, name)
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if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
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return getattr(torch.nn.modules.container, name)
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if module == 'numpy.core.multiarray' and name == 'scalar':
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return numpy.core.multiarray.scalar
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if module == 'numpy' and name == 'dtype':
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return numpy.dtype
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if module == '_codecs' and name == 'encode':
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return encode
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if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':
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import pytorch_lightning.callbacks
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return pytorch_lightning.callbacks.model_checkpoint
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if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint':
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import pytorch_lightning.callbacks.model_checkpoint
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return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint
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if module == "__builtin__" and name == 'set':
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return set
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# Forbid everything else.
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raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
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def check_pt(filename):
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try:
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# new pytorch format is a zip file
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with zipfile.ZipFile(filename) as z:
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with z.open('archive/data.pkl') as file:
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unpickler = RestrictedUnpickler(file)
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unpickler.load()
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except zipfile.BadZipfile:
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# if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
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with open(filename, "rb") as file:
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unpickler = RestrictedUnpickler(file)
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for i in range(5):
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unpickler.load()
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def load(filename, *args, **kwargs):
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from modules import shared
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try:
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if not shared.cmd_opts.disable_safe_unpickle:
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check_pt(filename)
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except Exception:
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print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
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print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr)
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return None
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return unsafe_torch_load(filename, *args, **kwargs)
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unsafe_torch_load = torch.load
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torch.load = load
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