models/util.py

import os
import torch

from omegaconf import OmegaConf
from ldm.util import instantiate_from_config


def get_state_dict(d):
 return d.get('state_dict', d)


def load_state_dict(ckpt_path, location='cpu'):
 _, extension = os.path.splitext(ckpt_path)
 if extension.lower() == ".safetensors":
 import safetensors.torch
 state_dict = safetensors.torch.load_file(ckpt_path, device=location)
 else:
 state_dict = get_state_dict(torch.load(ckpt_path, map_location=torch.device(location)))
 state_dict = get_state_dict(state_dict)
 print(f'Loaded state_dict from [{ckpt_path}]')
 return state_dict


def load_ckpt(model, state_dict, strict=True, input_channel_copy_indices=None):
 input_channel_key = "local_adapter.feature_extractor.pre_extractor.0.weight"
 model_state_dict = model.state_dict()
 if input_channel_key in model_state_dict and input_channel_key in state_dict:
 model_shape = model_state_dict[input_channel_key].shape
 shape = state_dict[input_channel_key].shape
 if model_shape != shape:
 if input_channel_copy_indices is None:
 state_dict[input_channel_key] = state_dict[input_channel_key][:, :model_shape[1], :, :]
 else:
 cout, cin, h, w = model_shape
 weight = state_dict[input_channel_key].view(cout, -1, cin//3, h, w)
 weight = weight[:, input_channel_copy_indices].view(cout, cin, h, w)
 state_dict[input_channel_key] = weight
 model.load_state_dict(state_dict, strict=strict)


def create_model(config_path):
 config = OmegaConf.load(config_path)
 model = instantiate_from_config(config.model).cpu()
 print(f'Loaded model config from [{config_path}]')
 return model