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Improve speed of applying TI embeddings #5422

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Jan 5, 2024
Merged

Improve speed of applying TI embeddings #5422

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9 changes: 7 additions & 2 deletions invokeai/backend/model_management/lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
from transformers import CLIPTextModel, CLIPTokenizer

from invokeai.app.shared.models import FreeUConfig
from invokeai.backend.model_management.model_load_optimizations import skip_torch_weight_init

from .models.lora import LoRAModel

Expand Down Expand Up @@ -211,8 +212,12 @@ def _get_ti_embedding(model_embeddings, ti):
for i in range(ti_embedding.shape[0]):
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, pad_to_multiple_of)
# Modify text_encoder.
# resize_token_embeddings(...) constructs a new torch.nn.Embedding internally. Initializing the weights of
# this embedding is slow and unnecessary, so we wrap this step in skip_torch_weight_init() to save some
# time.
with skip_torch_weight_init():
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:
Expand Down