| | import sys |
| | import os |
| |
|
| | assert len(sys.argv) == 3, 'Args are wrong.' |
| |
|
| | input_path = sys.argv[1] |
| | output_path = sys.argv[2] |
| |
|
| | assert os.path.exists(input_path), 'Input model does not exist.' |
| | assert not os.path.exists(output_path), 'Output filename already exists.' |
| | assert os.path.exists(os.path.dirname(output_path)), 'Output path is not valid.' |
| |
|
| | import torch |
| | from share import * |
| | from cldm.model import create_model |
| |
|
| |
|
| | def get_node_name(name, parent_name): |
| | if len(name) <= len(parent_name): |
| | return False, '' |
| | p = name[:len(parent_name)] |
| | if p != parent_name: |
| | return False, '' |
| | return True, name[len(parent_name):] |
| |
|
| |
|
| | |
| | model = create_model(config_path='./models/cldm_fill50k.yaml') |
| |
|
| | pretrained_weights = torch.load(input_path) |
| | if 'state_dict' in pretrained_weights: |
| | pretrained_weights = pretrained_weights['state_dict'] |
| |
|
| | scratch_dict = model.state_dict() |
| |
|
| | target_dict = {} |
| | for k in scratch_dict.keys(): |
| | is_control, name = get_node_name(k, 'control_') |
| | if is_control: |
| | copy_k = 'model.diffusion_' + name |
| | else: |
| | copy_k = k |
| | if copy_k in pretrained_weights: |
| | target_dict[k] = pretrained_weights[copy_k].clone() |
| | else: |
| | target_dict[k] = scratch_dict[k].clone() |
| | print(f'These weights are newly added: {k}') |
| |
|
| | model.load_state_dict(target_dict, strict=True) |
| | torch.save(model.state_dict(), output_path) |
| | print('Done.') |
| |
|