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Merge branch 'main' into Millu-patch-1

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Millun Atluri 2023-11-11 10:39:06 +11:00 committed by GitHub
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2 changed files with 13 additions and 4 deletions
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15
invokeai/backend/model_management/lora.py
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@ -166,6 +166,15 @@ class ModelPatcher:
init_tokens_count = None
new_tokens_added = None
# TODO: This is required since Transformers 4.32 see
# https://github.com/huggingface/transformers/pull/25088
# More information by NVIDIA:
# https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc
# This value might need to be changed in the future and take the GPUs model into account as there seem
# to be ideal values for different GPUS. This value is temporary!
# For references to the current discussion please see https://github.com/invoke-ai/InvokeAI/pull/4817
pad_to_multiple_of = 8
try:
# HACK: The CLIPTokenizer API does not include a way to remove tokens after calling add_tokens(...). As a
# workaround, we create a full copy of `tokenizer` so that its original behavior can be restored after
@ -175,7 +184,7 @@ class ModelPatcher:
# but a pickle roundtrip was found to be much faster (1 sec vs. 0.05 secs).
ti_tokenizer = pickle.loads(pickle.dumps(tokenizer))
ti_manager = TextualInversionManager(ti_tokenizer)
init_tokens_count = text_encoder.resize_token_embeddings(None).num_embeddings
init_tokens_count = text_encoder.resize_token_embeddings(None, pad_to_multiple_of).num_embeddings
def _get_trigger(ti_name, index):
trigger = ti_name
@ -190,7 +199,7 @@ class ModelPatcher:
new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
# modify text_encoder
text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added)
text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
model_embeddings = text_encoder.get_input_embeddings()
for ti_name, ti in ti_list:
@ -222,7 +231,7 @@ class ModelPatcher:
finally:
if init_tokens_count and new_tokens_added:
text_encoder.resize_token_embeddings(init_tokens_count)
text_encoder.resize_token_embeddings(init_tokens_count, pad_to_multiple_of)
@classmethod
@contextmanager

2
pyproject.toml
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@ -82,7 +82,7 @@ dependencies = [
"torchvision~=0.16",
"torchmetrics~=0.11.0",
"torchsde~=0.2.5",
"transformers~=4.31.0",
"transformers~=4.35.0",
"uvicorn[standard]~=0.21.1",
"windows-curses; sys_platform=='win32'",
]