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Skip weight initialization when resizing text encoder token embeddings to accomodate new TI embeddings. This saves time.

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Ryan Dick 2024-01-05 14:53:00 -05:00 committed by Kent Keirsey
parent 8e17e29a5c
commit f7f697849c
1 changed files with 7 additions and 2 deletions
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7
invokeai/backend/model_management/lora.py
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@ -13,6 +13,7 @@ from safetensors.torch import load_file
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
@ -211,7 +212,11 @@ class ModelPatcher:
for i in range(ti_embedding.shape[0]):
new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
# modify text_encoder
# 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()