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| import torch | |
| from safetensors.torch import load_file, save_file | |
| from collections import OrderedDict | |
| model_path = "/home/jaret/Dev/models/hf/PixArt-Sigma-XL-2-1024_tiny/transformer/diffusion_pytorch_model_orig.safetensors" | |
| output_path = "/home/jaret/Dev/models/hf/PixArt-Sigma-XL-2-1024_tiny/transformer/diffusion_pytorch_model.safetensors" | |
| state_dict = load_file(model_path) | |
| meta = OrderedDict() | |
| meta["format"] = "pt" | |
| new_state_dict = {} | |
| # Move non-blocks over | |
| for key, value in state_dict.items(): | |
| if not key.startswith("transformer_blocks."): | |
| new_state_dict[key] = value | |
| block_names = ['transformer_blocks.{idx}.attn1.to_k.bias', 'transformer_blocks.{idx}.attn1.to_k.weight', | |
| 'transformer_blocks.{idx}.attn1.to_out.0.bias', 'transformer_blocks.{idx}.attn1.to_out.0.weight', | |
| 'transformer_blocks.{idx}.attn1.to_q.bias', 'transformer_blocks.{idx}.attn1.to_q.weight', | |
| 'transformer_blocks.{idx}.attn1.to_v.bias', 'transformer_blocks.{idx}.attn1.to_v.weight', | |
| 'transformer_blocks.{idx}.attn2.to_k.bias', 'transformer_blocks.{idx}.attn2.to_k.weight', | |
| 'transformer_blocks.{idx}.attn2.to_out.0.bias', 'transformer_blocks.{idx}.attn2.to_out.0.weight', | |
| 'transformer_blocks.{idx}.attn2.to_q.bias', 'transformer_blocks.{idx}.attn2.to_q.weight', | |
| 'transformer_blocks.{idx}.attn2.to_v.bias', 'transformer_blocks.{idx}.attn2.to_v.weight', | |
| 'transformer_blocks.{idx}.ff.net.0.proj.bias', 'transformer_blocks.{idx}.ff.net.0.proj.weight', | |
| 'transformer_blocks.{idx}.ff.net.2.bias', 'transformer_blocks.{idx}.ff.net.2.weight', | |
| 'transformer_blocks.{idx}.scale_shift_table'] | |
| # New block idx 0, 1, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 27 | |
| current_idx = 0 | |
| for i in range(28): | |
| if i not in [0, 1, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 27]: | |
| # todo merge in with previous block | |
| for name in block_names: | |
| try: | |
| new_state_dict_key = name.format(idx=current_idx - 1) | |
| old_state_dict_key = name.format(idx=i) | |
| new_state_dict[new_state_dict_key] = (new_state_dict[new_state_dict_key] * 0.5) + (state_dict[old_state_dict_key] * 0.5) | |
| except KeyError: | |
| raise KeyError(f"KeyError: {name.format(idx=current_idx)}") | |
| else: | |
| for name in block_names: | |
| new_state_dict[name.format(idx=current_idx)] = state_dict[name.format(idx=i)] | |
| current_idx += 1 | |
| # make sure they are all fp16 and on cpu | |
| for key, value in new_state_dict.items(): | |
| new_state_dict[key] = value.to(torch.float16).cpu() | |
| # save the new state dict | |
| save_file(new_state_dict, output_path, metadata=meta) | |
| new_param_count = sum([v.numel() for v in new_state_dict.values()]) | |
| old_param_count = sum([v.numel() for v in state_dict.values()]) | |
| print(f"Old param count: {old_param_count:,}") | |
| print(f"New param count: {new_param_count:,}") |