# Copyright (c) 2023, Teriks
#
# dgenerate is distributed under the following BSD 3-Clause License
#
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#
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os.path
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
_sdxl_refiner_uri_parser = _textprocessing.ConceptUriParser('SDXL Refiner',
['revision', 'variant', 'subfolder', 'dtype'])
_s_cascade_decoder_uri_parser = _textprocessing.ConceptUriParser('Stable Cascade decoder',
['revision', 'variant', 'subfolder', 'dtype'])
_torch_vae_uri_parser = _textprocessing.ConceptUriParser('VAE',
['model', 'revision', 'variant', 'subfolder', 'dtype'])
_flax_vae_uri_parser = _textprocessing.ConceptUriParser('VAE', ['model', 'revision', 'subfolder', 'dtype'])
_torch_control_net_uri_parser = _textprocessing.ConceptUriParser('ControlNet',
['scale', 'start', 'end', 'revision', 'variant',
'subfolder',
'dtype'])
_flax_control_net_uri_parser = _textprocessing.ConceptUriParser('ControlNet',
['scale', 'revision', 'subfolder', 'dtype',
'from_torch'])
_lora_uri_parser = _textprocessing.ConceptUriParser('LoRA', ['scale', 'revision', 'subfolder', 'weight-name'])
_textual_inversion_uri_parser = _textprocessing.ConceptUriParser('Textual Inversion',
['revision', 'subfolder', 'weight-name'])
_flax_unet_uri_parser = _textprocessing.ConceptUriParser('UNet',
['revision', 'subfolder', 'dtype'])
_torch_unet_uri_parser = _textprocessing.ConceptUriParser('UNet',
['revision', 'variant', 'subfolder', 'dtype'])
[docs]
class InvalidModelUriError(Exception):
"""
Thrown on model path syntax or logical usage error
"""
pass
[docs]
class InvalidSDXLRefinerUriError(InvalidModelUriError):
"""
Error in ``--sdxl-refiner`` uri
"""
pass
class InvalidSCascadeDecoderUriError(InvalidModelUriError):
"""
Error in ``--s-cascade-decoder`` uri
"""
pass
[docs]
class InvalidVaeUriError(InvalidModelUriError):
"""
Error in ``--vae`` uri
"""
pass
[docs]
class InvalidUNetUriError(InvalidModelUriError):
"""
Error in ``--unet`` uri
"""
pass
[docs]
class InvalidControlNetUriError(InvalidModelUriError):
"""
Error in ``--control-nets`` uri
"""
pass
[docs]
class InvalidLoRAUriError(InvalidModelUriError):
"""
Error in ``--loras`` uri
"""
pass
[docs]
class InvalidTextualInversionUriError(InvalidModelUriError):
"""
Error in ``--textual-inversions`` uri
"""
pass
[docs]
class FlaxControlNetUri:
"""
Representation of ``--control-nets`` uri when ``--model-type`` flax*
"""
@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 subfolder(self) -> _types.OptionalPath:
"""
Model repo subfolder
"""
return self._subfolder
@property
def dtype(self) -> typing.Optional[_enums.DataType]:
"""
Model dtype (precision)
"""
return self._dtype
@property
def scale(self) -> float:
"""
ControlNet guidance scale
"""
return self._scale
@property
def from_torch(self) -> bool:
"""
Load from a model format meant for torch?
"""
return self._from_torch
[docs]
def __init__(self,
model: str,
revision: _types.OptionalString = None,
subfolder: _types.OptionalPath = None,
dtype: typing.Union[_enums.DataType, str, None] = None,
scale: float = 1.0,
from_torch: bool = False):
"""
:param model: model path
:param revision: model revision (branch)
:param subfolder: model subfolder
:param dtype: data type (precision)
:param scale: control net scale value
:param from_torch: load from a model designed for torch?
:raises InvalidControlNetUriError: If the ``model`` path represents a singular file (not supported),
or if ``dtype`` is passed an invalid string
"""
if _hfutil.is_single_file_model_load(model):
raise InvalidControlNetUriError('Flax --control-nets do not support single file loads.')
self._model = model
self._revision = revision
self._subfolder = subfolder
try:
self._dtype = _enums.get_data_type_enum(dtype) if dtype else None
except ValueError:
raise InvalidControlNetUriError(
f'invalid dtype string, must be one of: {_textprocessing.oxford_comma(_enums.supported_data_type_strings(), "or")}')
self._scale = scale
self._from_torch = from_torch
[docs]
def load(self,
dtype_fallback: _enums.DataType = _enums.DataType.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False) -> tuple[diffusers.FlaxControlNetModel, typing.Any]:
"""
Load a :py:class:`diffusers.FlaxControlNetModel` from this URI.
:param dtype_fallback: Fallback datatype if ``dtype`` was not specified in the URI.
:param use_auth_token: Optional huggingface API auth token, used for downloading
restricted repos that your account has access to.
:param local_files_only: Avoid connecting to huggingface to download models and
only use cached models?
:raises ModelNotFoundError: If the model could not be found.
:return: tuple (:py:class:`diffusers.FlaxControlNetModel`, flax_control_net_params)
"""
try:
return self._load(dtype_fallback, use_auth_token, local_files_only)
except (huggingface_hub.utils.HFValidationError,
huggingface_hub.utils.HfHubHTTPError) as e:
raise _hfutil.ModelNotFoundError(e)
@_memoize(_cache._FLAX_CONTROL_NET_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("Flax ControlNet", key, hit[0]),
on_create=lambda key, new: _d_memoize.simple_cache_miss_debug("Flax ControlNet", key, new[0]))
def _load(self,
dtype_fallback: _enums.DataType = _enums.DataType.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False) -> tuple[diffusers.FlaxControlNetModel, typing.Any]:
estimated_memory_usage = _hfutil.estimate_model_memory_use(
repo_id=self.model,
revision=self.revision,
subfolder=self.subfolder,
flax=not self.from_torch,
use_auth_token=use_auth_token,
local_files_only=local_files_only
)
_cache.enforce_control_net_cache_constraints(
new_control_net_size=estimated_memory_usage)
flax_dtype = _enums.get_flax_dtype(
dtype_fallback if self.dtype is None else self.dtype)
new_net: diffusers.FlaxControlNetModel = \
diffusers.FlaxControlNetModel.from_pretrained(
self.model,
revision=self.revision,
subfolder=self.subfolder,
dtype=flax_dtype,
from_pt=self.from_torch,
token=use_auth_token,
local_files_only=local_files_only)
_messages.debug_log('Estimated Flax ControlNet Memory Use:',
_memory.bytes_best_human_unit(estimated_memory_usage))
_cache.controlnet_create_update_cache_info(controlnet=new_net[0],
estimated_size=estimated_memory_usage)
return new_net
[docs]
@staticmethod
def parse(uri: _types.Uri) -> 'FlaxControlNetUri':
"""
Parse a ``--model-type`` flax* ``--control-nets`` uri specification and return an object representing its constituents
:param uri: string with ``--control-nets`` uri syntax
:raise InvalidControlNetUriError:
:return: :py:class:`.FlaxControlNetPath`
"""
try:
r = _flax_control_net_uri_parser.parse(uri)
dtype = r.args.get('dtype')
scale = r.args.get('scale', 1.0)
from_torch = r.args.get('from_torch')
if from_torch is not None:
try:
from_torch = _types.parse_bool(from_torch)
except ValueError:
raise InvalidControlNetUriError(
f'Flax ControlNet from_torch must be undefined or boolean (true or false), received: {from_torch}')
supported_dtypes = _enums.supported_data_type_strings()
if dtype is not None and dtype not in supported_dtypes:
raise InvalidControlNetUriError(
f'Flax ControlNet "dtype" must be {", ".join(supported_dtypes)}, '
f'or left undefined, received: {dtype}')
try:
scale = float(scale)
except ValueError:
raise InvalidControlNetUriError(
f'Flax ControlNet scale must be a floating point number, received {scale}')
return FlaxControlNetUri(
model=r.concept,
revision=r.args.get('revision', None),
subfolder=r.args.get('subfolder', None),
scale=scale,
dtype=dtype,
from_torch=from_torch)
except _textprocessing.ConceptUriParseError as e:
raise InvalidControlNetUriError(e)
[docs]
class TorchControlNetUri:
"""
Representation of ``--control-nets`` uri when ``--model-type`` torch*
"""
@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) -> typing.Optional[_enums.DataType]:
"""
Model dtype (precision)
"""
return self._dtype
@property
def scale(self) -> float:
"""
ControlNet guidance scale
"""
return self._scale
@property
def start(self) -> float:
"""
ControlNet guidance start point, fraction of inference / timesteps.
"""
return self._start
@property
def end(self) -> float:
"""
ControlNet guidance end point, fraction of inference / timesteps.
"""
return self._end
[docs]
def __init__(self,
model: str,
revision: _types.OptionalString,
variant: _types.OptionalString,
subfolder: _types.OptionalPath,
dtype: typing.Union[_enums.DataType, str, None] = None,
scale: float = 1.0,
start: float = 0.0,
end: float = 1.0):
"""
: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)
:param scale: control net scale
:param start: control net guidance start value
:param end: control net guidance end value
:raises InvalidControlNetUriError: If ``dtype`` is passed an invalid data type string.
"""
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 InvalidControlNetUriError(
f'invalid dtype string, must be one of: {_textprocessing.oxford_comma(_enums.supported_data_type_strings(), "or")}')
self._scale = scale
self._start = start
self._end = end
[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) -> diffusers.ControlNetModel:
"""
Load a :py:class:`diffusers.ControlNetModel` from this URI.
:param dtype_fallback: Fallback datatype if ``dtype`` was not specified in the URI.
:param use_auth_token: Optional huggingface API auth token, used for downloading
restricted repos that your account has access to.
:param local_files_only: Avoid connecting to huggingface to download models and
only use cached models?
: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.ControlNetModel`
"""
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)
@_memoize(_cache._TORCH_CONTROL_NET_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 ControlNet", key, hit),
on_create=lambda key, new: _d_memoize.simple_cache_miss_debug("Torch ControlNet", 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) -> diffusers.ControlNetModel:
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.')
single_file_load_path = _hfutil.is_single_file_model_load(self.model)
torch_dtype = _enums.get_torch_dtype(
dtype_fallback if self.dtype is None else self.dtype)
if single_file_load_path:
estimated_memory_usage = _hfutil.estimate_model_memory_use(
repo_id=self.model,
revision=self.revision,
use_auth_token=use_auth_token,
local_files_only=local_files_only
)
_cache.enforce_control_net_cache_constraints(
new_control_net_size=estimated_memory_usage)
new_net: diffusers.ControlNetModel = \
diffusers.ControlNetModel.from_single_file(
self.model,
revision=self.revision,
torch_dtype=torch_dtype,
token=use_auth_token,
local_files_only=local_files_only)
else:
estimated_memory_usage = _hfutil.estimate_model_memory_use(
repo_id=self.model,
revision=self.revision,
variant=self.variant,
subfolder=self.subfolder,
use_auth_token=use_auth_token,
local_files_only=local_files_only
)
_cache.enforce_control_net_cache_constraints(
new_control_net_size=estimated_memory_usage)
new_net: diffusers.ControlNetModel = \
diffusers.ControlNetModel.from_pretrained(
self.model,
revision=self.revision,
variant=self.variant,
subfolder=self.subfolder,
torch_dtype=torch_dtype,
token=use_auth_token,
local_files_only=local_files_only)
_messages.debug_log('Estimated Torch ControlNet Memory Use:',
_memory.bytes_best_human_unit(estimated_memory_usage))
_cache.controlnet_create_update_cache_info(controlnet=new_net,
estimated_size=estimated_memory_usage)
return new_net
[docs]
@staticmethod
def parse(uri: _types.Uri) -> 'TorchControlNetUri':
"""
Parse a ``--model-type`` torch* ``--control-nets`` uri specification and return an object representing its constituents
:param uri: string with ``--control-nets`` uri syntax
:raise InvalidControlNetUriError:
:return: :py:class:`.TorchControlNetPath`
"""
try:
r = _torch_control_net_uri_parser.parse(uri)
dtype = r.args.get('dtype')
scale = r.args.get('scale', 1.0)
start = r.args.get('start', 0.0)
end = r.args.get('end', 1.0)
supported_dtypes = _enums.supported_data_type_strings()
if dtype is not None and dtype not in supported_dtypes:
raise InvalidControlNetUriError(
f'Torch ControlNet "dtype" must be {", ".join(supported_dtypes)}, '
f'or left undefined, received: {dtype}')
try:
scale = float(scale)
except ValueError:
raise InvalidControlNetUriError(
f'Torch ControlNet "scale" must be a floating point number, received: {scale}')
try:
start = float(start)
except ValueError:
raise InvalidControlNetUriError(
f'Torch ControlNet "start" must be a floating point number, received: {start}')
try:
end = float(end)
except ValueError:
raise InvalidControlNetUriError(
f'Torch ControlNet "end" must be a floating point number, received: {end}')
if start > end:
raise InvalidControlNetUriError(
f'Torch ControlNet "start" must be less than or equal to "end".')
return TorchControlNetUri(
model=r.concept,
revision=r.args.get('revision', None),
variant=r.args.get('variant', None),
subfolder=r.args.get('subfolder', None),
dtype=dtype,
scale=scale,
start=start,
end=end)
except _textprocessing.ConceptUriParseError as e:
raise InvalidControlNetUriError(e)
[docs]
class SDXLRefinerUri:
"""
Representation of ``--sdxl-refiner`` uri
"""
@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) -> typing.Optional[_enums.DataType]:
"""
Model dtype (precision)
"""
return self._dtype
[docs]
def __init__(self,
model: str,
revision: _types.OptionalString = None,
variant: _types.OptionalString = None,
subfolder: _types.OptionalPath = None,
dtype: typing.Union[_enums.DataType, str, None] = None):
"""
: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 InvalidSDXLRefinerUriError: If ``dtype`` is passed an invalid data type string.
"""
self._model = model
self._revision = revision
self._variant = variant
try:
self._dtype = _enums.get_data_type_enum(dtype) if dtype else None
except ValueError:
raise InvalidSDXLRefinerUriError(
f'invalid dtype string, must be one of: {_textprocessing.oxford_comma(_enums.supported_data_type_strings(), "or")}')
self._subfolder = subfolder
[docs]
@staticmethod
def parse(uri: _types.Uri) -> 'SDXLRefinerUri':
"""
Parse an ``--sdxl-refiner`` uri and return an object representing its constituents
:param uri: string with ``--sdxl-refiner`` uri syntax
:raise InvalidSDXLRefinerUriError:
:return: :py:class:`.SDXLRefinerUri`
"""
try:
r = _sdxl_refiner_uri_parser.parse(uri)
supported_dtypes = _enums.supported_data_type_strings()
dtype = r.args.get('dtype', None)
if dtype is not None and dtype not in supported_dtypes:
raise InvalidSDXLRefinerUriError(
f'Torch SDXL refiner "dtype" must be {", ".join(supported_dtypes)}, '
f'or left undefined, received: {dtype}')
return SDXLRefinerUri(
model=r.concept,
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 InvalidSDXLRefinerUriError(e)
class SCascadeDecoderUri:
"""
Representation of ``--s-cascade-decoder`` uri
"""
@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) -> typing.Optional[_enums.DataType]:
"""
Model dtype (precision)
"""
return self._dtype
def __init__(self,
model: str,
revision: _types.OptionalString = None,
variant: _types.OptionalString = None,
subfolder: _types.OptionalPath = None,
dtype: typing.Union[_enums.DataType, str, None] = None):
"""
: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)
"""
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 InvalidSCascadeDecoderUriError(
f'invalid dtype string, must be one of: '
f'{_textprocessing.oxford_comma(_enums.supported_data_type_strings(), "or")}')
@staticmethod
def parse(uri: _types.Uri) -> 'SCascadeDecoderUri':
"""
Parse an ``--s-cascade-decoder`` uri and return an object representing its constituents
:param uri: string with ``--s-cascade-decoder`` uri syntax
:return: :py:class:`.SDCascadeDecoderUri`
"""
try:
r = _s_cascade_decoder_uri_parser.parse(uri)
supported_dtypes = _enums.supported_data_type_strings()
dtype = r.args.get('dtype', None)
if dtype is not None and dtype not in supported_dtypes:
raise InvalidSCascadeDecoderUriError(
f'Torch Stable Cascade "dtype" must be {", ".join(supported_dtypes)}, '
f'or left undefined, received: {dtype}')
return SCascadeDecoderUri(
model=r.concept,
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 InvalidSCascadeDecoderUriError(e)
[docs]
class TorchVAEUri:
"""
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) -> typing.Optional[_enums.DataType]:
"""
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(TorchVAEUri._encoders.keys())
[docs]
def __init__(self,
encoder: str,
model: str,
revision: _types.OptionalString = None,
variant: _types.OptionalString = None,
subfolder: _types.OptionalString = None,
dtype: typing.Union[_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 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 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 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 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)
@_memoize(_cache._TORCH_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]
single_file_load_path = _hfutil.is_single_file_model_load(self.model)
if single_file_load_path:
estimated_memory_use = _hfutil.estimate_model_memory_use(
repo_id=self.model,
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(self.model,
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(self.model,
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=self.model,
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(
self.model,
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) -> 'TorchVAEUri':
"""
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 = _torch_vae_uri_parser.parse(uri)
model = r.args.get('model')
if model is None:
raise 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 InvalidVaeUriError(
f'Torch VAE "dtype" must be {", ".join(supported_dtypes)}, '
f'or left undefined, received: {dtype}')
return TorchVAEUri(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 InvalidVaeUriError(e)
[docs]
class TorchUNetUri:
"""
Representation of ``--unet`` uri when ``--model-type`` torch*
"""
@property
def model(self) -> str:
"""
Model path, huggingface slug, file path, or blob link
"""
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) -> typing.Optional[_enums.DataType]:
"""
Model dtype (precision)
"""
return self._dtype
[docs]
def __init__(self,
model: str,
revision: _types.OptionalString = None,
variant: _types.OptionalString = None,
subfolder: _types.OptionalString = None,
dtype: typing.Union[_enums.DataType, str, None] = None):
"""
: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 InvalidUNetUriError: If ``model`` points to a single file,
single file loads are not supported. Or if ``dtype`` is passed an
invalid data type string.
"""
if _hfutil.is_single_file_model_load(model):
raise InvalidUNetUriError(
'Loading a UNet from a single file is not supported.')
self._model = model
self._revision = revision
self._variant = variant
try:
self._dtype = _enums.get_data_type_enum(dtype) if dtype else None
except ValueError:
raise InvalidUNetUriError(
f'invalid dtype string, must be one of: {_textprocessing.oxford_comma(_enums.supported_data_type_strings(), "or")}')
self._subfolder = subfolder
[docs]
def load(self,
variant_fallback: _types.OptionalString = None,
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,
unet_class=diffusers.UNet2DConditionModel):
"""
Load a UNet of type :py:class:`diffusers.UNet2DConditionModel`
:param variant_fallback: If the URI does not specify a variant, use this variant.
: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?
:param unet_class: UNet class
:raises ModelNotFoundError: If the model could not be found.
:return: :py:class:`diffusers.UNet2DConditionModel`
"""
try:
return self._load(variant_fallback,
dtype_fallback,
use_auth_token,
local_files_only,
sequential_cpu_offload_member,
model_cpu_offload_member,
unet_class)
except (huggingface_hub.utils.HFValidationError,
huggingface_hub.utils.HfHubHTTPError) as e:
raise _hfutil.ModelNotFoundError(e)
@_memoize(_cache._TORCH_UNET_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 UNet", key, hit),
on_create=lambda key, new: _d_memoize.simple_cache_miss_debug("Torch UNet", key, new))
def _load(self,
variant_fallback: _types.OptionalString = None,
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,
unet_class=diffusers.UNet2DConditionModel):
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)
if self.variant is None:
variant = variant_fallback
else:
variant = self.variant
path = self.model
estimated_memory_use = _hfutil.estimate_model_memory_use(
repo_id=path,
revision=self.revision,
variant=variant,
subfolder=self.subfolder,
local_files_only=local_files_only,
use_auth_token=use_auth_token
)
_cache.enforce_unet_cache_constraints(new_unet_size=estimated_memory_use)
unet = unet_class.from_pretrained(
path,
revision=self.revision,
variant=variant,
torch_dtype=torch_dtype,
subfolder=self.subfolder,
token=use_auth_token,
local_files_only=local_files_only)
_messages.debug_log('Estimated Torch UNet Memory Use:',
_memory.bytes_best_human_unit(estimated_memory_use))
_cache.unet_create_update_cache_info(unet=unet,
estimated_size=estimated_memory_use)
return unet
[docs]
@staticmethod
def parse(uri: _types.Uri) -> 'TorchUNetUri':
"""
Parse a ``--model-type`` torch* ``--unet`` uri and return an object representing its constituents
:param uri: string with ``--unet`` uri syntax
:raise InvalidUNetUriError:
:return: :py:class:`.TorchUNetPath`
"""
try:
r = _torch_unet_uri_parser.parse(uri)
dtype = r.args.get('dtype')
supported_dtypes = _enums.supported_data_type_strings()
if dtype is not None and dtype not in supported_dtypes:
raise InvalidUNetUriError(
f'Torch UNet "dtype" must be {", ".join(supported_dtypes)}, '
f'or left undefined, received: {dtype}')
return TorchUNetUri(
model=r.concept,
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 InvalidUNetUriError(e)
[docs]
class FlaxVAEUri:
"""
Representation of ``--vae`` uri when ``--model-type`` flax*
"""
@property
def encoder(self) -> str:
"""
Encoder class name, "FlaxAutoencoderKL"
"""
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 subfolder(self) -> _types.OptionalPath:
"""
Model repo subfolder
"""
return self._subfolder
@property
def dtype(self) -> typing.Optional[_enums.DataType]:
"""
Model dtype (precision)
"""
return self._dtype
_encoders = {
'FlaxAutoencoderKL': diffusers.FlaxAutoencoderKL
}
[docs]
def __init__(self,
encoder: str,
model: str,
revision: _types.OptionalString,
subfolder: _types.OptionalPath,
dtype: typing.Optional[_enums.DataType]):
"""
:param encoder: Encoder class name
:param model: model path
:param revision: model revision (branch name)
:param subfolder: model subfolder
:param dtype: model data type (precision)
:raises InvalidVaeUriError: If ``encoder != 'FlaxAutoencoderKL'``, or if the ``model``
path is a single file and that is not supported, or if ``dtype`` is passed an
invalid string.
"""
if encoder not in self._encoders:
raise InvalidVaeUriError(
f'Unknown VAE flax encoder class {encoder}, '
f'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 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:
# in the future this will be supported?
if subfolder is not None:
raise InvalidVaeUriError('Single file VAE loads do not support the subfolder option.')
self._encoder = encoder
self._model = model
self._revision = revision
try:
self._dtype = _enums.get_data_type_enum(dtype) if dtype else None
except ValueError:
raise InvalidVaeUriError(
f'invalid dtype string, must be one of: {_textprocessing.oxford_comma(_enums.supported_data_type_strings(), "or")}')
self._subfolder = subfolder
[docs]
def load(self,
dtype_fallback: _enums.DataType = _enums.DataType.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False) -> tuple[diffusers.FlaxAutoencoderKL, typing.Any]:
"""
Load a :py:class:`diffusers.FlaxAutoencoderKL` VAE and its flax_params 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
:raises ModelNotFoundError: If the model could not be found.
:return: tuple (:py:class:`diffusers.FlaxAutoencoderKL`, flax_vae_params)
"""
try:
return self._load(dtype_fallback, use_auth_token, local_files_only)
except (huggingface_hub.utils.HFValidationError,
huggingface_hub.utils.HfHubHTTPError) as e:
raise _hfutil.ModelNotFoundError(e)
@_memoize(_cache._FLAX_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("Flax VAE", key, hit[0]),
on_create=lambda key, new: _d_memoize.simple_cache_miss_debug("Flax VAE", key, new[0]))
def _load(self,
dtype_fallback: _enums.DataType = _enums.DataType.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False) -> tuple[diffusers.FlaxAutoencoderKL, typing.Any]:
if self.dtype is None:
flax_dtype = _enums.get_flax_dtype(dtype_fallback)
else:
flax_dtype = _enums.get_flax_dtype(self.dtype)
encoder = self._encoders[self.encoder]
single_file_load_path = _hfutil.is_single_file_model_load(self.model)
if single_file_load_path:
estimated_memory_use = _hfutil.estimate_model_memory_use(
repo_id=self.model,
revision=self.revision,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
flax=True
)
_cache.enforce_vae_cache_constraints(new_vae_size=estimated_memory_use)
vae = encoder.from_single_file(
self.model,
revision=self.revision,
dtype=flax_dtype,
token=use_auth_token,
local_files_only=local_files_only)
else:
estimated_memory_use = _hfutil.estimate_model_memory_use(
repo_id=self.model,
revision=self.revision,
subfolder=self.subfolder,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
flax=True
)
_cache.enforce_vae_cache_constraints(new_vae_size=estimated_memory_use)
vae = encoder.from_pretrained(
self.model,
revision=self.revision,
dtype=flax_dtype,
subfolder=self.subfolder,
token=use_auth_token,
local_files_only=local_files_only)
_messages.debug_log('Estimated Flax VAE Memory Use:',
_memory.bytes_best_human_unit(estimated_memory_use))
_cache.vae_create_update_cache_info(vae=vae[0],
estimated_size=estimated_memory_use)
return vae
[docs]
@staticmethod
def parse(uri: _types.Uri) -> 'FlaxVAEUri':
"""
Parse a ``--model-type`` flax* ``--vae`` uri and return an object representing its constituents
:param uri: string with ``--vae`` uri syntax
:raise InvalidVaeUriError:
:return: :py:class:`.FlaxVAEUri`
"""
try:
r = _flax_vae_uri_parser.parse(uri)
model = r.args.get('model')
if model is None:
raise InvalidVaeUriError('model argument for flax 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 InvalidVaeUriError(
f'Flax VAE "dtype" must be {", ".join(supported_dtypes)}, '
f'or left undefined, received: {dtype}')
return FlaxVAEUri(encoder=r.concept,
model=model,
revision=r.args.get('revision', None),
dtype=_enums.get_flax_dtype(dtype),
subfolder=r.args.get('subfolder', None))
except _textprocessing.ConceptUriParseError as e:
raise InvalidVaeUriError(e)
[docs]
class FlaxUNetUri:
"""
Representation of ``--unet`` uri when ``--model-type`` flax*
"""
@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 subfolder(self) -> _types.OptionalPath:
"""
Model repo subfolder
"""
return self._subfolder
@property
def dtype(self) -> typing.Optional[_enums.DataType]:
"""
Model dtype (precision)
"""
return self._dtype
[docs]
def __init__(self,
model: str,
revision: _types.OptionalString,
subfolder: _types.OptionalPath,
dtype: typing.Optional[_enums.DataType]):
"""
:param model: model path
:param revision: model revision (branch name)
:param subfolder: model subfolder
:param dtype: model data type (precision)
:raises InvalidUNetUriError: If ``model`` points to a single file, single file loads are not supported, or
if ``dtype`` is passed an invalid string.
"""
single_file_load_path = _hfutil.is_single_file_model_load(model)
if single_file_load_path:
raise InvalidUNetUriError('Loading a UNet from a single file is not supported.')
self._model = model
self._revision = revision
try:
self._dtype = _enums.get_data_type_enum(dtype) if dtype else None
except ValueError:
raise InvalidUNetUriError(
f'invalid dtype string, must be one of: {_textprocessing.oxford_comma(_enums.supported_data_type_strings(), "or")}')
self._subfolder = subfolder
[docs]
def load(self,
dtype_fallback: _enums.DataType = _enums.DataType.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False) -> tuple[diffusers.FlaxUNet2DConditionModel, typing.Any]:
"""
Load a :py:class:`diffusers.FlaxUNet2DConditionModel` UNet and its flax_params 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
:raises ModelNotFoundError: If the model could not be found.
:return: tuple (:py:class:`diffusers.FlaxUNet2DConditionModel`, flax_unet_params)
"""
try:
return self._load(dtype_fallback, use_auth_token, local_files_only)
except (huggingface_hub.utils.HFValidationError,
huggingface_hub.utils.HfHubHTTPError) as e:
raise _hfutil.ModelNotFoundError(e)
@_memoize(_cache._FLAX_UNET_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("Flax UNet", key, hit[0]),
on_create=lambda key, new: _d_memoize.simple_cache_miss_debug("Flax UNet", key, new[0]))
def _load(self,
dtype_fallback: _enums.DataType = _enums.DataType.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False) -> tuple[diffusers.FlaxUNet2DConditionModel, typing.Any]:
if self.dtype is None:
flax_dtype = _enums.get_flax_dtype(dtype_fallback)
else:
flax_dtype = _enums.get_flax_dtype(self.dtype)
estimated_memory_use = _hfutil.estimate_model_memory_use(
repo_id=self.model,
revision=self.revision,
subfolder=self.subfolder,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
flax=True
)
_cache.enforce_unet_cache_constraints(new_unet_size=estimated_memory_use)
unet = diffusers.FlaxUNet2DConditionModel.from_pretrained(
self.model,
revision=self.revision,
dtype=flax_dtype,
subfolder=self.subfolder,
token=use_auth_token,
local_files_only=local_files_only)
_messages.debug_log('Estimated Flax UNet Memory Use:',
_memory.bytes_best_human_unit(estimated_memory_use))
_cache.unet_create_update_cache_info(unet=unet[0],
estimated_size=estimated_memory_use)
return unet
[docs]
@staticmethod
def parse(uri: _types.Uri) -> 'FlaxUNetUri':
"""
Parse a ``--model-type`` flax* ``--unet`` uri and return an object representing its constituents
:param uri: string with ``--unet`` uri syntax
:raise InvalidUNetUriError:
:return: :py:class:`.FlaxUNetUri`
"""
try:
r = _flax_unet_uri_parser.parse(uri)
dtype = r.args.get('dtype')
supported_dtypes = _enums.supported_data_type_strings()
if dtype is not None and dtype not in supported_dtypes:
raise InvalidUNetUriError(
f'Flax UNet "dtype" must be {", ".join(supported_dtypes)}, '
f'or left undefined, received: {dtype}')
return FlaxUNetUri(
model=r.concept,
revision=r.args.get('revision', None),
dtype=_enums.get_flax_dtype(dtype),
subfolder=r.args.get('subfolder', None))
except _textprocessing.ConceptUriParseError as e:
raise InvalidUNetUriError(e)
[docs]
class LoRAUri:
"""
Representation of a ``--loras`` uri
"""
@property
def model(self) -> str:
"""
Model path, huggingface slug, file path
"""
return self._model
@property
def revision(self) -> _types.OptionalString:
"""
Model repo revision
"""
return self._revision
@property
def subfolder(self) -> _types.OptionalPath:
"""
Model repo subfolder
"""
return self._subfolder
@property
def weight_name(self) -> _types.OptionalName:
"""
Model weight-name
"""
return self._weight_name
@property
def scale(self) -> float:
"""
LoRA scale
"""
return self._scale
[docs]
def __init__(self,
model: str,
revision: _types.OptionalString = None,
subfolder: _types.OptionalPath = None,
weight_name: _types.OptionalName = None,
scale: float = 1.0):
self._model = model
self._scale = scale
self._revision = revision
self._subfolder = subfolder
self._weight_name = weight_name
def __str__(self):
return f'{self.__class__.__name__}({str(_types.get_public_attributes(self))})'
def __repr__(self):
return str(self)
[docs]
def load_on_pipeline(self,
pipeline: diffusers.DiffusionPipeline,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False):
"""
Load LoRA weights on to a pipeline using this URI
:param pipeline: :py:class:`diffusers.DiffusionPipeline`
: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
:raises ModelNotFoundError: If the model could not be found.
"""
try:
self._load_on_pipeline(pipeline=pipeline,
use_auth_token=use_auth_token,
local_files_only=local_files_only)
except (huggingface_hub.utils.HFValidationError,
huggingface_hub.utils.HfHubHTTPError) as e:
raise _hfutil.ModelNotFoundError(e)
def _load_on_pipeline(self,
pipeline: diffusers.DiffusionPipeline,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False):
if hasattr(pipeline, 'load_lora_weights'):
debug_args = {k: v for k, v in locals().items() if k not in {'self', 'pipeline'}}
_messages.debug_log('pipeline.load_lora_weights('
+ str(_types.get_public_attributes(self) | debug_args) + ')')
load_path = self.model
if local_files_only:
# Temporary fix for diffusers bug
subfolder = self.subfolder if self.subfolder else ''
probable_path_1 = os.path.join(
subfolder, 'pytorch_lora_weights.safetensors' if
self.weight_name is None else self.weight_name)
probable_path_2 = os.path.join(
subfolder, 'pytorch_lora_weights.bin')
file_path = huggingface_hub.try_to_load_from_cache(self.model,
filename=probable_path_1,
revision=self.revision)
if not isinstance(file_path, str):
file_path = huggingface_hub.try_to_load_from_cache(self.model,
filename=probable_path_2,
revision=self.revision)
if not isinstance(file_path, str):
raise ModuleNotFoundError(
f'LoRA model "{self.model}" was not available in the local huggingface cache.')
load_path = os.path.dirname(file_path)
pipeline.load_lora_weights(load_path,
revision=self.revision,
subfolder=self.subfolder,
weight_name=self.weight_name,
local_files_only=local_files_only,
token=use_auth_token)
pipeline.fuse_lora(lora_scale=self.scale)
_messages.debug_log(f'Added LoRA: "{self}" to pipeline: "{pipeline.__class__.__name__}"')
[docs]
@staticmethod
def parse(uri: _types.Uri) -> 'LoRAUri':
"""
Parse a ``--loras`` uri and return an object representing its constituents
:param uri: string with ``--loras`` uri syntax
:raise InvalidLoRAUriError:
:return: :py:class:`.LoRAPath`
"""
try:
r = _lora_uri_parser.parse(uri)
return LoRAUri(model=r.concept,
scale=float(r.args.get('scale', 1.0)),
weight_name=r.args.get('weight-name', None),
revision=r.args.get('revision', None),
subfolder=r.args.get('subfolder', None))
except _textprocessing.ConceptUriParseError as e:
raise InvalidLoRAUriError(e)
[docs]
class TextualInversionUri:
"""
Representation of ``--textual-inversions`` uri
"""
@property
def model(self) -> str:
"""
Model path, huggingface slug, file path
"""
return self._model
@property
def revision(self) -> _types.OptionalString:
"""
Model repo revision
"""
return self._revision
@property
def subfolder(self) -> _types.OptionalPath:
"""
Model repo subfolder
"""
return self._subfolder
@property
def weight_name(self) -> _types.OptionalName:
"""
Model weight-name
"""
return self._weight_name
[docs]
def __init__(self,
model: str,
revision: _types.OptionalString = None,
subfolder: _types.OptionalPath = None,
weight_name: _types.OptionalName = None):
self._model = model
self._revision = revision
self._subfolder = subfolder
self._weight_name = weight_name
def __str__(self):
return f'{self.__class__.__name__}({str(_types.get_public_attributes(self))})'
def __repr__(self):
return str(self)
[docs]
def load_on_pipeline(self,
pipeline: diffusers.DiffusionPipeline,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False):
"""
Load Textual Inversion weights on to a pipeline using this URI
:param pipeline: :py:class:`diffusers.DiffusionPipeline`
: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
:raises ModelNotFoundError: If the model could not be found.
"""
try:
self._load_on_pipeline(pipeline=pipeline,
use_auth_token=use_auth_token,
local_files_only=local_files_only)
except (huggingface_hub.utils.HFValidationError,
huggingface_hub.utils.HfHubHTTPError) as e:
raise _hfutil.ModelNotFoundError(e)
def _load_on_pipeline(self,
pipeline: diffusers.DiffusionPipeline,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False):
if hasattr(pipeline, 'load_textual_inversion'):
debug_args = {k: v for k, v in locals().items() if k not in {'self', 'pipeline'}}
_messages.debug_log('pipeline.load_textual_inversion(' +
str(_types.get_public_attributes(self) | debug_args) + ')')
# this is tricky because there is stupidly a positional argument named 'token'
# as well as an accepted kwargs value with the key 'token'
old_token = os.environ.get('HF_TOKEN', None)
if use_auth_token is not None:
os.environ['HF_TOKEN'] = use_auth_token
try:
pipeline.load_textual_inversion(self.model,
revision=self.revision,
subfolder=self.subfolder,
weight_name=self.weight_name,
local_files_only=local_files_only)
finally:
if old_token is not None:
os.environ['HF_TOKEN'] = old_token
_messages.debug_log(f'Added Textual Inversion: "{self}" to pipeline: "{pipeline.__class__.__name__}"')
[docs]
@staticmethod
def parse(uri: _types.Uri) -> 'TextualInversionUri':
"""
Parse a ``--textual-inversions`` uri and return an object representing its constituents
:param uri: string with ``--textual-inversions`` uri syntax
:raise InvalidTextualInversionUriError:
:return: :py:class:`.TextualInversionPath`
"""
try:
r = _textual_inversion_uri_parser.parse(uri)
return TextualInversionUri(model=r.concept,
weight_name=r.args.get('weight-name', None),
revision=r.args.get('revision', None),
subfolder=r.args.get('subfolder', None))
except _textprocessing.ConceptUriParseError as e:
raise InvalidTextualInversionUriError(e)
__all__ = _types.module_all()