# Copyright (c) 2023, Teriks
#
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import os.path
import diffusers
import huggingface_hub
import dgenerate.messages as _messages
import dgenerate.pipelinewrapper.hfutil as _hfutil
import dgenerate.textprocessing as _textprocessing
import dgenerate.types as _types
from dgenerate.pipelinewrapper.uris import exceptions as _exceptions
_lora_uri_parser = _textprocessing.ConceptUriParser('LoRA', ['scale', 'revision', 'subfolder', 'weight-name'])
[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)
except Exception as e:
raise _exceptions.LoRAUriLoadError(
f'error loading lora "{self.model}": {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) + ')')
model_path = _hfutil.download_non_hf_model(self.model)
if local_files_only and not os.path.exists(model_path):
# 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.')
model_path = os.path.dirname(file_path)
try:
pipeline.load_lora_weights(model_path,
revision=self.revision,
subfolder=self.subfolder,
weight_name=self.weight_name,
local_files_only=local_files_only,
use_safetensors=True,
token=use_auth_token)
except EnvironmentError:
# brute force, try for .bin files
pipeline.load_lora_weights(model_path,
revision=self.revision,
subfolder=self.subfolder,
weight_name=self.weight_name,
local_files_only=local_files_only,
token=use_auth_token)
if hasattr(pipeline, 'fuse_lora'):
pipeline.fuse_lora(lora_scale=self.scale)
elif self.scale != 1.0:
_messages.log('lora scale argument not supported, ignored.',
level=_messages.WARNING)
_messages.debug_log(f'Added LoRA: "{self}" to pipeline: "{pipeline.__class__.__name__}"')
else:
raise RuntimeError(f'Pipeline: {pipeline.__class__.__name__} '
f'does not support loading LoRAs.')
[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 _exceptions.InvalidLoRAUriError(e)