# 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'])
_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'])
[docs]
class InvalidModelUriError(Exception):
"""
Thrown on model path syntax or logical usage error
"""
pass
[docs]
class InvalidSDXLRefinerUriError(InvalidModelUriError):
"""
Error in ``--sdxl-refiner`` uri
"""
pass
[docs]
class InvalidVaeUriError(InvalidModelUriError):
"""
Error in ``--vae`` 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*
"""
model: str
"""
Model path, huggingface slug
"""
revision: _types.OptionalString
"""
Model repo revision
"""
subfolder: _types.OptionalPath
"""
Model repo subfolder
"""
dtype: typing.Optional[_enums.DataTypes]
"""
Model dtype (precision)
"""
scale: float
"""
ControlNet guidance scale
"""
from_torch: bool
"""
Load from a model format meant for torch?
"""
[docs]
def __init__(self,
model: str,
revision: _types.OptionalString = None,
subfolder: _types.OptionalPath = None,
dtype: typing.Union[_enums.DataTypes, str, None] = None,
scale: float = 1.0,
from_torch: bool = False):
self.model = model
self.revision = revision
self.subfolder = subfolder
self.dtype = _enums.get_data_type_enum(dtype) if dtype else None
self.scale = scale
self.from_torch = from_torch
[docs]
def load(self,
dtype_fallback: _enums.DataTypes = _enums.DataTypes.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False) -> typing.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?
: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.DataTypes = _enums.DataTypes.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False) -> typing.Tuple[diffusers.FlaxControlNetModel, typing.Any]:
single_file_load_path = _hfutil.is_single_file_model_load(self.model)
if single_file_load_path:
raise NotImplementedError('Flax --control-nets do not support single file loads from disk.')
else:
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,
use_auth_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_concept_uri(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.ConceptPathParseError as e:
raise InvalidControlNetUriError(e)
[docs]
class TorchControlNetUri:
"""
Representation of ``--control-nets`` uri when ``--model-type`` torch*
"""
model: str
"""
Model path, huggingface slug
"""
revision: _types.OptionalString
"""
Model repo revision
"""
variant: _types.OptionalString
"""
Model repo revision
"""
subfolder: _types.OptionalPath
"""
Model repo subfolder
"""
dtype: typing.Optional[_enums.DataTypes]
"""
Model dtype (precision)
"""
scale: float
"""
ControlNet guidance scale
"""
start: float
"""
ControlNet guidance start point, fraction of inference / timesteps.
"""
end: float
"""
ControlNet guidance end point, fraction of inference / timesteps.
"""
[docs]
def __init__(self,
model: str,
revision: _types.OptionalString,
variant: _types.OptionalString,
subfolder: _types.OptionalPath,
dtype: typing.Union[_enums.DataTypes, str, None] = None,
scale: float = 1.0,
start: float = 0.0,
end: float = 1.0):
self.model = model
self.revision = revision
self.variant = variant
self.subfolder = subfolder
self.dtype = _enums.get_data_type_enum(dtype) if dtype else None
self.scale = scale
self.start = start
self.end = end
[docs]
def load(self,
dtype_fallback: _enums.DataTypes = _enums.DataTypes.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: 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?
:return: :py:class:`diffusers.ControlNetModel`
"""
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._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.DataTypes = _enums.DataTypes.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False) -> diffusers.ControlNetModel:
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,
use_auth_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,
use_auth_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_concept_uri(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.ConceptPathParseError as e:
raise InvalidControlNetUriError(e)
[docs]
class SDXLRefinerUri:
"""
Representation of ``--sdxl-refiner`` uri
"""
model: str
"""
Model path, huggingface slug
"""
revision: _types.OptionalString
"""
Model repo revision
"""
variant: _types.OptionalString
"""
Model repo revision
"""
subfolder: _types.OptionalPath
"""
Model repo subfolder
"""
dtype: typing.Optional[_enums.DataTypes]
"""
Model dtype (precision)
"""
[docs]
def __init__(self,
model: str,
revision: _types.OptionalString = None,
variant: _types.OptionalString = None,
subfolder: _types.OptionalPath = None,
dtype: typing.Union[_enums.DataTypes, str, None] = None):
self.model = model
self.revision = revision
self.variant = variant
self.dtype = _enums.get_data_type_enum(dtype) if dtype else None
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:`.SDXLRefinerPath`
"""
try:
r = _sdxl_refiner_uri_parser.parse_concept_uri(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.ConceptPathParseError as e:
raise InvalidSDXLRefinerUriError(e)
[docs]
class TorchVAEUri:
"""
Representation of ``--vae`` uri when ``--model-type`` torch*
"""
encoder: str
"""
Encoder class name such as "AutoencoderKL"
"""
model: str
"""
Model path, huggingface slug, file path, or blob link
"""
revision: _types.OptionalString
"""
Model repo revision
"""
variant: _types.OptionalString
"""
Model repo revision
"""
subfolder: _types.OptionalPath
"""
Model repo subfolder
"""
dtype: typing.Optional[_enums.DataTypes]
"""
Model dtype (precision)
"""
[docs]
def __init__(self,
encoder: str,
model: str,
revision: _types.OptionalString = None,
variant: _types.OptionalString = None,
subfolder: _types.OptionalString = None,
dtype: typing.Union[_enums.DataTypes, str, None] = None):
self.encoder = encoder
self.model = model
self.revision = revision
self.variant = variant
self.dtype = _enums.get_data_type_enum(dtype) if dtype else None
self.subfolder = subfolder
[docs]
def load(self,
dtype_fallback: _enums.DataTypes = _enums.DataTypes.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only=False) -> typing.Union[diffusers.AutoencoderKL,
diffusers.AsymmetricAutoencoderKL,
diffusers.AutoencoderTiny]:
"""
Load a VAE of type :py:class:`diffusers.AutoencoderKL`, :py:class:`diffusers.AsymmetricAutoencoderKL`,
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
:return: :py:class:`diffusers.AutoencoderKL`, :py:class:`diffusers.AsymmetricAutoencoderKL`,
or :py:class:`diffusers.AutoencoderTiny`
"""
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._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.DataTypes = _enums.DataTypes.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only=False) -> typing.Union[diffusers.AutoencoderKL,
diffusers.AsymmetricAutoencoderKL,
diffusers.AutoencoderTiny]:
if self.dtype is None:
torch_dtype = _enums.get_torch_dtype(dtype_fallback)
else:
torch_dtype = _enums.get_torch_dtype(self.dtype)
encoder_name = self.encoder
if encoder_name == 'AutoencoderKL':
encoder = diffusers.AutoencoderKL
elif encoder_name == 'AsymmetricAutoencoderKL':
encoder = diffusers.AsymmetricAutoencoderKL
elif encoder_name == 'AutoencoderTiny':
encoder = diffusers.AutoencoderTiny
else:
raise InvalidVaeUriError(f'Unknown VAE encoder class {encoder_name}')
path = self.model
can_single_file_load = hasattr(encoder, 'from_single_file')
single_file_load_path = _hfutil.is_single_file_model_load(path)
if single_file_load_path and not can_single_file_load:
raise NotImplementedError(f'{encoder_name} 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 self.subfolder is not None:
raise NotImplementedError('Single file VAE loads do not support the subfolder option.')
estimated_memory_use = _hfutil.estimate_model_memory_use(
repo_id=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(path,
revision=self.revision,
local_files_only=local_files_only) \
.to(dtype=torch_dtype, non_blocking=False)
else:
vae = encoder.from_single_file(path,
revision=self.revision,
torch_dtype=torch_dtype,
local_files_only=local_files_only)
else:
estimated_memory_use = _hfutil.estimate_model_memory_use(
repo_id=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(path,
revision=self.revision,
variant=self.variant,
torch_dtype=torch_dtype,
subfolder=self.subfolder,
use_auth_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_concept_uri(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.ConceptPathParseError as e:
raise InvalidVaeUriError(e)
[docs]
class FlaxVAEUri:
"""
Representation of ``--vae`` uri when ``--model-type`` flax*
"""
encoder: str
"""
Encoder class name such as "FlaxAutoencoderKL"
"""
model: str
"""
Model path, huggingface slug, file path, or blob link
"""
revision: _types.OptionalString
"""
Model repo revision
"""
subfolder: _types.OptionalPath
"""
Model repo subfolder
"""
dtype: typing.Optional[_enums.DataTypes]
"""
Model dtype (precision)
"""
[docs]
def __init__(self,
encoder: str,
model: str,
revision: _types.OptionalString,
subfolder: _types.OptionalPath,
dtype: typing.Optional[_enums.DataTypes]):
self.encoder = encoder
self.model = model
self.revision = revision
self.dtype = dtype
self.subfolder = subfolder
[docs]
def load(self,
dtype_fallback: _enums.DataTypes = _enums.DataTypes.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only=False) -> typing.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
: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.DataTypes = _enums.DataTypes.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only=False) -> typing.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_name = self.encoder
if encoder_name == 'FlaxAutoencoderKL':
encoder = diffusers.FlaxAutoencoderKL
else:
raise InvalidVaeUriError(f'Unknown VAE flax encoder class {encoder_name}')
path = self.model
can_single_file_load = hasattr(encoder, 'from_single_file')
single_file_load_path = _hfutil.is_single_file_model_load(path)
if single_file_load_path and not can_single_file_load:
raise NotImplementedError(f'{encoder_name} 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 self.subfolder is not None:
raise NotImplementedError('Single file VAE loads do not support the subfolder option.')
estimated_memory_use = _hfutil.estimate_model_memory_use(
repo_id=path,
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(path,
revision=self.revision,
dtype=flax_dtype,
use_auth_token=use_auth_token,
local_files_only=local_files_only)
else:
estimated_memory_use = _hfutil.estimate_model_memory_use(
repo_id=path,
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(path,
revision=self.revision,
dtype=flax_dtype,
subfolder=self.subfolder,
use_auth_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:`.FlaxVAEPath`
"""
try:
r = _flax_vae_uri_parser.parse_concept_uri(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.ConceptPathParseError as e:
raise InvalidVaeUriError(e)
[docs]
class LoRAUri:
"""
Representation of a ``--loras`` uri
"""
model: str
"""
Model path, huggingface slug, file path
"""
revision: _types.OptionalString
"""
Model repo revision
"""
subfolder: _types.OptionalPath
"""
Model repo subfolder
"""
weight_name: _types.OptionalName
"""
Model weight-name
"""
scale: float
"""
LoRA 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=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
"""
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=False):
extra_args = {k: v for k, v in locals().items() if k not in {'self', 'pipeline'}}
if hasattr(pipeline, 'load_lora_weights'):
_messages.debug_log('pipeline.load_lora_weights('
+ str(_types.get_public_attributes(self) | extra_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 RuntimeError(
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,
**extra_args)
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_concept_uri(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.ConceptPathParseError as e:
raise InvalidLoRAUriError(e)
[docs]
class TextualInversionUri:
"""
Representation of ``--textual-inversions`` uri
"""
model: str
"""
Model path, huggingface slug, file path
"""
revision: _types.OptionalString
"""
Model repo revision
"""
subfolder: _types.OptionalPath
"""
Model repo subfolder
"""
weight_name: _types.OptionalName
"""
Model 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=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
"""
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=False):
extra_args = {k: v for k, v in locals().items() if k not in {'self', 'pipeline'}}
if hasattr(pipeline, 'load_textual_inversion'):
_messages.debug_log('pipeline.load_textual_inversion(' +
str(_types.get_public_attributes(self) | extra_args) + ')')
pipeline.load_textual_inversion(self.model,
revision=self.revision,
subfolder=self.subfolder,
weight_name=self.weight_name,
**extra_args)
_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_concept_uri(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.ConceptPathParseError as e:
raise InvalidTextualInversionUriError(e)
__all__ = _types.module_all()