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[Import] Don't force transformers to be installed (#5035)

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* [Import] Don't force transformers to be installed

* make style
This commit is contained in:
Patrick von Platen
2023-09-14 11:42:10 +02:00
parent 47f2d2c7be
commit 0c2f1ccc97
1 changed files with 5 additions and 5 deletions
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10
src/diffusers/loaders.py
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@@ -41,7 +41,7 @@ from .utils.import_utils import BACKENDS_MAPPING
if is_transformers_available():
from transformers import CLIPTextModel, CLIPTextModelWithProjection, PreTrainedModel, PreTrainedTokenizer
from transformers import CLIPTextModel, CLIPTextModelWithProjection
if is_accelerate_available():
from accelerate import init_empty_weights
@@ -627,7 +627,7 @@ class TextualInversionLoaderMixin:
Load textual inversion tokens and embeddings to the tokenizer and text encoder.
"""
def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"):
def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"): # noqa: F821
r"""
Processes prompts that include a special token corresponding to a multi-vector textual inversion embedding to
be replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual
@@ -654,7 +654,7 @@ class TextualInversionLoaderMixin:
return prompts
def _maybe_convert_prompt(self, prompt: str, tokenizer: "PreTrainedTokenizer"):
def _maybe_convert_prompt(self, prompt: str, tokenizer: "PreTrainedTokenizer"): # noqa: F821
r"""
Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds
to a multi-vector textual inversion embedding, this function will process the prompt so that the special token
@@ -688,8 +688,8 @@ class TextualInversionLoaderMixin:
self,
pretrained_model_name_or_path: Union[str, List[str], Dict[str, torch.Tensor], List[Dict[str, torch.Tensor]]],
token: Optional[Union[str, List[str]]] = None,
tokenizer: Optional[PreTrainedTokenizer] = None,
text_encoder: Optional[PreTrainedModel] = None,
tokenizer: Optional["PreTrainedTokenizer"] = None, # noqa: F821
text_encoder: Optional["PreTrainedModel"] = None, # noqa: F821
**kwargs,
):
r"""