# Copyright (c) 2023, Teriks
#
# dgenerate is distributed under the following BSD 3-Clause License
#
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in
# the documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import typing
import diffusers
import huggingface_hub
import dgenerate.memoize as _d_memoize
import dgenerate.memory as _memory
import dgenerate.messages as _messages
import dgenerate.pipelinewrapper.cache as _cache
import dgenerate.pipelinewrapper.enums as _enums
import dgenerate.pipelinewrapper.hfutil as _hfutil
import dgenerate.textprocessing as _textprocessing
import dgenerate.types as _types
from dgenerate.memoize import memoize as _memoize
from dgenerate.pipelinewrapper.uris import exceptions as _exceptions
_vae_uri_parser = _textprocessing.ConceptUriParser(
'VAE', ['model', 'revision', 'variant', 'subfolder', 'dtype'])
[docs]
class VAEUri:
"""
Representation of ``--vae`` uri when ``--model-type`` torch*
"""
@property
def encoder(self) -> str:
"""
Encoder class name such as "AutoencoderKL"
"""
return self._encoder
@property
def model(self) -> str:
"""
Model path, huggingface slug
"""
return self._model
@property
def revision(self) -> _types.OptionalString:
"""
Model repo revision
"""
return self._revision
@property
def variant(self) -> _types.OptionalString:
"""
Model repo revision
"""
return self._variant
@property
def subfolder(self) -> _types.OptionalPath:
"""
Model repo subfolder
"""
return self._subfolder
@property
def dtype(self) -> _enums.DataType | None:
"""
Model dtype (precision)
"""
return self._dtype
_encoders = {
'AutoencoderKL': diffusers.AutoencoderKL,
'AsymmetricAutoencoderKL': diffusers.AsymmetricAutoencoderKL,
'AutoencoderTiny': diffusers.AutoencoderTiny,
'ConsistencyDecoderVAE': diffusers.ConsistencyDecoderVAE
}
[docs]
@staticmethod
def supported_encoder_names() -> list[str]:
return list(VAEUri._encoders.keys())
[docs]
def __init__(self,
encoder: str,
model: str,
revision: _types.OptionalString = None,
variant: _types.OptionalString = None,
subfolder: _types.OptionalString = None,
dtype: _enums.DataType | str | None = None):
"""
:param encoder: encoder class name, for example ``AutoencoderKL``
:param model: model path
:param revision: model revision (branch name)
:param variant: model variant, for example ``fp16``
:param subfolder: model subfolder
:param dtype: model data type (precision)
:raises InvalidVaeUriError: If ``dtype`` is passed an invalid data type string, or if
``model`` points to a single file and the specified ``encoder`` class name does not
support loading from a single file.
"""
if encoder not in self._encoders:
raise _exceptions.InvalidVaeUriError(
f'Unknown VAE encoder class {encoder}, must be one of: {_textprocessing.oxford_comma(self._encoders.keys(), "or")}')
can_single_file_load = hasattr(self._encoders[encoder], 'from_single_file')
single_file_load_path = _hfutil.is_single_file_model_load(model)
if single_file_load_path and not can_single_file_load:
raise _exceptions.InvalidVaeUriError(f'{encoder} is not capable of loading from a single file, '
f'must be loaded from a huggingface repository slug or folder on disk.')
if single_file_load_path:
if subfolder is not None:
raise _exceptions.InvalidVaeUriError('Single file VAE loads do not support the subfolder option.')
self._encoder = encoder
self._model = model
self._revision = revision
self._variant = variant
self._subfolder = subfolder
try:
self._dtype = _enums.get_data_type_enum(dtype) if dtype else None
except ValueError:
raise _exceptions.InvalidVaeUriError(
f'invalid dtype string, must be one of: {_textprocessing.oxford_comma(_enums.supported_data_type_strings(), "or")}')
[docs]
def load(self,
dtype_fallback: _enums.DataType = _enums.DataType.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False,
sequential_cpu_offload_member: bool = False,
model_cpu_offload_member: bool = False) -> \
typing.Union[
diffusers.AutoencoderKL,
diffusers.AsymmetricAutoencoderKL,
diffusers.AutoencoderTiny,
diffusers.ConsistencyDecoderVAE]:
"""
Load a VAE of type :py:class:`diffusers.AutoencoderKL`, :py:class:`diffusers.AsymmetricAutoencoderKL`,
:py:class:`diffusers.AutoencoderKLTemporalDecoder`, or :py:class:`diffusers.AutoencoderTiny` from this URI
:param dtype_fallback: If the URI does not specify a dtype, use this dtype.
:param use_auth_token: optional huggingface auth token.
:param local_files_only: avoid downloading files and only look for cached files
when the model path is a huggingface slug or blob link
:param sequential_cpu_offload_member: This model will be attached to
a pipeline which will have sequential cpu offload enabled?
:param model_cpu_offload_member: This model will be attached to a pipeline
which will have model cpu offload enabled?
:raises ModelNotFoundError: If the model could not be found.
:return: :py:class:`diffusers.AutoencoderKL`, :py:class:`diffusers.AsymmetricAutoencoderKL`,
:py:class:`diffusers.AutoencoderKLTemporalDecoder`, or :py:class:`diffusers.AutoencoderTiny`
"""
try:
return self._load(dtype_fallback,
use_auth_token,
local_files_only,
sequential_cpu_offload_member,
model_cpu_offload_member)
except (huggingface_hub.utils.HFValidationError,
huggingface_hub.utils.HfHubHTTPError) as e:
raise _hfutil.ModelNotFoundError(e)
except Exception as e:
raise _exceptions.VAEUriLoadError(
f'error loading vae "{self.model}": {e}')
@_memoize(_cache._VAE_CACHE,
exceptions={'local_files_only'},
hasher=lambda args: _d_memoize.args_cache_key(args, {'self': _d_memoize.struct_hasher}),
on_hit=lambda key, hit: _d_memoize.simple_cache_hit_debug("Torch VAE", key, hit),
on_create=lambda key, new: _d_memoize.simple_cache_miss_debug("Torch VAE", key, new))
def _load(self,
dtype_fallback: _enums.DataType = _enums.DataType.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False,
sequential_cpu_offload_member: bool = False,
model_cpu_offload_member: bool = False) -> \
typing.Union[
diffusers.AutoencoderKL,
diffusers.AsymmetricAutoencoderKL,
diffusers.AutoencoderTiny,
diffusers.ConsistencyDecoderVAE]:
if sequential_cpu_offload_member and model_cpu_offload_member:
# these are used for cache differentiation only
raise ValueError('sequential_cpu_offload_member and model_cpu_offload_member cannot both be True.')
if self.dtype is None:
torch_dtype = _enums.get_torch_dtype(dtype_fallback)
else:
torch_dtype = _enums.get_torch_dtype(self.dtype)
encoder = self._encoders[self.encoder]
model_path = _hfutil.download_non_hf_model(self.model)
single_file_load_path = _hfutil.is_single_file_model_load(model_path)
if single_file_load_path:
estimated_memory_use = _hfutil.estimate_model_memory_use(
repo_id=model_path,
revision=self.revision,
local_files_only=local_files_only,
use_auth_token=use_auth_token
)
_cache.enforce_vae_cache_constraints(new_vae_size=estimated_memory_use)
if encoder is diffusers.AutoencoderKL:
# There is a bug in their cast
vae = encoder.from_single_file(model_path,
token=use_auth_token,
revision=self.revision,
local_files_only=local_files_only) \
.to(dtype=torch_dtype, non_blocking=False)
else:
vae = encoder.from_single_file(model_path,
token=use_auth_token,
revision=self.revision,
torch_dtype=torch_dtype,
local_files_only=local_files_only)
else:
estimated_memory_use = _hfutil.estimate_model_memory_use(
repo_id=model_path,
revision=self.revision,
variant=self.variant,
subfolder=self.subfolder,
local_files_only=local_files_only,
use_auth_token=use_auth_token
)
_cache.enforce_vae_cache_constraints(new_vae_size=estimated_memory_use)
vae = encoder.from_pretrained(
model_path,
revision=self.revision,
variant=self.variant,
torch_dtype=torch_dtype,
subfolder=self.subfolder,
token=use_auth_token,
local_files_only=local_files_only)
_messages.debug_log('Estimated Torch VAE Memory Use:',
_memory.bytes_best_human_unit(estimated_memory_use))
_cache.vae_create_update_cache_info(vae=vae,
estimated_size=estimated_memory_use)
return vae
[docs]
@staticmethod
def parse(uri: _types.Uri) -> 'VAEUri':
"""
Parse a ``--model-type`` torch* ``--vae`` uri and return an object representing its constituents
:param uri: string with ``--vae`` uri syntax
:raise InvalidVaeUriError:
:return: :py:class:`.TorchVAEPath`
"""
try:
r = _vae_uri_parser.parse(uri)
model = r.args.get('model')
if model is None:
raise _exceptions.InvalidVaeUriError('model argument for torch VAE specification must be defined.')
dtype = r.args.get('dtype')
supported_dtypes = _enums.supported_data_type_strings()
if dtype is not None and dtype not in supported_dtypes:
raise _exceptions.InvalidVaeUriError(
f'Torch VAE "dtype" must be {", ".join(supported_dtypes)}, '
f'or left undefined, received: {dtype}')
return VAEUri(encoder=r.concept,
model=model,
revision=r.args.get('revision', None),
variant=r.args.get('variant', None),
dtype=dtype,
subfolder=r.args.get('subfolder', None))
except _textprocessing.ConceptUriParseError as e:
raise _exceptions.InvalidVaeUriError(e)