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import sys |
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import os |
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assert len(sys.argv) == 3, 'Args are wrong.' |
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input_path = sys.argv[1] |
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output_path = sys.argv[2] |
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assert os.path.exists(input_path), 'Input model does not exist.' |
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assert not os.path.exists(output_path), 'Output filename already exists.' |
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assert os.path.exists(os.path.dirname(output_path)), 'Output path is not valid.' |
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import torch |
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from share import * |
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from cldm.model import create_model |
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def get_node_name(name, parent_name): |
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if len(name) <= len(parent_name): |
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return False, '' |
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p = name[:len(parent_name)] |
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if p != parent_name: |
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return False, '' |
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return True, name[len(parent_name):] |
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model = create_model(config_path='./models/cldm_v21.yaml') |
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pretrained_weights = torch.load(input_path) |
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if 'state_dict' in pretrained_weights: |
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pretrained_weights = pretrained_weights['state_dict'] |
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scratch_dict = model.state_dict() |
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target_dict = {} |
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for k in scratch_dict.keys(): |
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is_control, name = get_node_name(k, 'control_') |
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if is_control: |
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copy_k = 'model.diffusion_' + name |
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else: |
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copy_k = k |
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if copy_k in pretrained_weights: |
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target_dict[k] = pretrained_weights[copy_k].clone() |
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else: |
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target_dict[k] = scratch_dict[k].clone() |
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print(f'These weights are newly added: {k}') |
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model.load_state_dict(target_dict, strict=True) |
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torch.save(model.state_dict(), output_path) |
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print('Done.') |
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