InvokeAI/invokeai/backend/model_management/util.py

119 lines
4.5 KiB
Python

# Copyright (c) 2023 The InvokeAI Development Team
"""Utilities used by the Model Manager"""
def lora_token_vector_length(checkpoint: dict) -> int:
"""
Given a checkpoint in memory, return the lora token vector length
:param checkpoint: The checkpoint
"""
def _handle_unet_key(key, tensor, checkpoint):
lora_token_vector_length = None
if "_attn2_to_k." not in key and "_attn2_to_v." not in key:
return lora_token_vector_length
# check lora/locon
if ".lora_up.weight" in key:
lora_token_vector_length = tensor.shape[0]
elif ".lora_down.weight" in key:
lora_token_vector_length = tensor.shape[1]
# check loha (don't worry about hada_t1/hada_t2 as it used only in 4d shapes)
elif ".hada_w1_b" in key or ".hada_w2_b" in key:
lora_token_vector_length = tensor.shape[1]
# check lokr (don't worry about lokr_t2 as it used only in 4d shapes)
elif ".lokr_" in key:
_lokr_key = key.split(".")[0]
if _lokr_key + ".lokr_w1" in checkpoint:
_lokr_w1 = checkpoint[_lokr_key + ".lokr_w1"]
elif _lokr_key + "lokr_w1_b" in checkpoint:
_lokr_w1 = checkpoint[_lokr_key + ".lokr_w1_b"]
else:
return lora_token_vector_length # unknown format
if _lokr_key + ".lokr_w2" in checkpoint:
_lokr_w2 = checkpoint[_lokr_key + ".lokr_w2"]
elif _lokr_key + "lokr_w2_b" in checkpoint:
_lokr_w2 = checkpoint[_lokr_key + ".lokr_w2_b"]
else:
return lora_token_vector_length # unknown format
lora_token_vector_length = _lokr_w1.shape[1] * _lokr_w2.shape[1]
elif ".diff" in key:
lora_token_vector_length = tensor.shape[1]
return lora_token_vector_length
def _handle_te_key(key, tensor, checkpoint):
lora_token_vector_length = None
if "text_model_encoder_layers_" not in key:
return lora_token_vector_length
# skip detect by mlp
if "_self_attn_" not in key:
return lora_token_vector_length
# check lora/locon
if ".lora_up.weight" in key:
lora_token_vector_length = tensor.shape[0]
elif ".lora_down.weight" in key:
lora_token_vector_length = tensor.shape[1]
# check loha (don't worry about hada_t1/hada_t2 as it used only in 4d shapes)
elif ".hada_w1_a" in key or ".hada_w2_a" in key:
lora_token_vector_length = tensor.shape[0]
elif ".hada_w1_b" in key or ".hada_w2_b" in key:
lora_token_vector_length = tensor.shape[1]
# check lokr (don't worry about lokr_t2 as it used only in 4d shapes)
elif ".lokr_" in key:
_lokr_key = key.split(".")[0]
if _lokr_key + ".lokr_w1" in checkpoint:
_lokr_w1 = checkpoint[_lokr_key + ".lokr_w1"]
elif _lokr_key + "lokr_w1_b" in checkpoint:
_lokr_w1 = checkpoint[_lokr_key + ".lokr_w1_b"]
else:
return lora_token_vector_length # unknown format
if _lokr_key + ".lokr_w2" in checkpoint:
_lokr_w2 = checkpoint[_lokr_key + ".lokr_w2"]
elif _lokr_key + "lokr_w2_b" in checkpoint:
_lokr_w2 = checkpoint[_lokr_key + ".lokr_w2_b"]
else:
return lora_token_vector_length # unknown format
lora_token_vector_length = _lokr_w1.shape[1] * _lokr_w2.shape[1]
elif ".diff" in key:
lora_token_vector_length = tensor.shape[1]
return lora_token_vector_length
lora_token_vector_length = None
lora_te1_length = None
lora_te2_length = None
for key, tensor in checkpoint.items():
if key.startswith("lora_unet_"):
lora_token_vector_length = _handle_unet_key(key, tensor, checkpoint)
elif key.startswith("lora_te_"):
lora_token_vector_length = _handle_te_key(key, tensor, checkpoint)
elif key.startswith("lora_te1_"):
lora_te1_length = _handle_te_key(key, tensor, checkpoint)
elif key.startswith("lora_te2_"):
lora_te2_length = _handle_te_key(key, tensor, checkpoint)
if lora_te1_length is not None and lora_te2_length is not None:
lora_token_vector_length = lora_te1_length + lora_te2_length
if lora_token_vector_length is not None:
break
return lora_token_vector_length