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from typing import TYPE_CHECKING
from toolkit.config_modules import NetworkConfig
from toolkit.lora_special import LoRASpecialNetwork
from safetensors.torch import load_file
if TYPE_CHECKING:
from toolkit.stable_diffusion_model import StableDiffusion
def load_assistant_lora_from_path(adapter_path, sd: 'StableDiffusion') -> LoRASpecialNetwork:
if not sd.is_flux:
raise ValueError("Only Flux models can load assistant adapters currently.")
pipe = sd.pipeline
print(f"Loading assistant adapter from {adapter_path}")
adapter_name = adapter_path.split("/")[-1].split(".")[0]
lora_state_dict = load_file(adapter_path)
linear_dim = int(lora_state_dict['transformer.single_transformer_blocks.0.attn.to_k.lora_A.weight'].shape[0])
# linear_alpha = int(lora_state_dict['lora_transformer_single_transformer_blocks_0_attn_to_k.alpha'].item())
linear_alpha = linear_dim
transformer_only = 'transformer.proj_out.alpha' not in lora_state_dict
# get dim and scale
network_config = NetworkConfig(
linear=linear_dim,
linear_alpha=linear_alpha,
transformer_only=transformer_only,
)
network = LoRASpecialNetwork(
text_encoder=pipe.text_encoder,
unet=pipe.transformer,
lora_dim=network_config.linear,
multiplier=1.0,
alpha=network_config.linear_alpha,
train_unet=True,
train_text_encoder=False,
is_flux=True,
network_config=network_config,
network_type=network_config.type,
transformer_only=network_config.transformer_only,
is_assistant_adapter=True
)
network.apply_to(
pipe.text_encoder,
pipe.transformer,
apply_text_encoder=False,
apply_unet=True
)
network.force_to(sd.device_torch, dtype=sd.torch_dtype)
network.eval()
network._update_torch_multiplier()
network.load_weights(lora_state_dict)
network.is_active = True
return network
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