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fix ruff format check

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This commit is contained in:
Lincoln Stein 2023-12-01 01:46:12 -05:00
parent 0719a46372
commit f95ce1870c
1 changed files with 12 additions and 9 deletions
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21
invokeai/backend/model_management/lora.py
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@ -195,7 +195,11 @@ class ModelPatcher:
def _get_ti_embedding(model_embeddings, ti):
# for SDXL models, select the embedding that matches the text encoder's dimensions
if ti.embedding_2 is not None:
return ti.embedding_2 if ti.embedding_2.shape[1] == model_embeddings.weight.data[0].shape[0] else ti.embedding
return (
ti.embedding_2
if ti.embedding_2.shape[1] == model_embeddings.weight.data[0].shape[0]
else ti.embedding
)
else:
return ti.embedding
@ -212,7 +216,6 @@ class ModelPatcher:
model_embeddings = text_encoder.get_input_embeddings()
for ti_name, ti in ti_list:
ti_tokens = []
for i in range(ti_embedding.shape[0]):
embedding = ti_embedding[i]
@ -282,7 +285,7 @@ class ModelPatcher:
class TextualInversionModel:
embedding: torch.Tensor # [n, 768]|[n, 1280]
embedding: torch.Tensor # [n, 768]|[n, 1280]
embedding_2: Optional[torch.Tensor] = None # [n, 768]|[n, 1280] - for SDXL models
@classmethod
@ -308,7 +311,7 @@ class TextualInversionModel:
if len(state_dict["string_to_param"]) > 1:
print(
f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first',
" token will be used."
" token will be used.",
)
result.embedding = next(iter(state_dict["string_to_param"].values()))
@ -319,8 +322,8 @@ class TextualInversionModel:
# v5(sdxl safetensors file)
elif "clip_g" in state_dict and "clip_l" in state_dict:
result.embedding = state_dict["clip_g"]
result.embedding_2 = state_dict["clip_l"]
result.embedding = state_dict["clip_g"]
result.embedding_2 = state_dict["clip_l"]
# v4(diffusers bin files)
else:
@ -332,7 +335,6 @@ class TextualInversionModel:
if not isinstance(result.embedding, torch.Tensor):
raise ValueError(f"Invalid embeddings file: {file_path.name}")
return result
@ -520,9 +522,10 @@ class ONNXModelPatcher:
# modify tokenizer
new_tokens_added = 0
for ti_name, ti in ti_list:
if ti.embedding_2 is not None:
ti_embedding = ti.embedding_2 if ti.embedding_2.shape[1] == orig_embeddings.shape[0] else ti.embedding
ti_embedding = (
ti.embedding_2 if ti.embedding_2.shape[1] == orig_embeddings.shape[0] else ti.embedding
)
else:
ti_embedding = ti.embedding