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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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118 lines
4.2 KiB
Python
118 lines
4.2 KiB
Python
# 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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import re
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# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
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TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
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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 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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allowed_zip_names = ["archive/data.pkl", "archive/version"]
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allowed_zip_names_re = re.compile(r"^archive/data/\d+$")
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def check_zip_filenames(filename, names):
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for name in names:
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if name in allowed_zip_names:
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continue
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if allowed_zip_names_re.match(name):
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continue
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raise Exception(f"bad file inside {filename}: {name}")
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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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check_zip_filenames(filename, z.namelist())
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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 pickle.UnpicklingError:
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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"-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
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print(f"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
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return None
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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.\n\n", 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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