| import torch | |
| import sys | |
| import re | |
| import safetensors | |
| sys.path.append(".") | |
| from causalvideovae.model import CausalVAEModel | |
| origin_ckpt_path = "/remote-home1/clh/models/sd2_1/vae-ft-mse-840000-ema-pruned.ckpt" | |
| config_path = "/remote-home1/clh/models/sd2_1/config.json" | |
| output_path = "/remote-home1/clh/models/norm3d_vae_pretrained_weight" | |
| init_method = "tail" | |
| model = CausalVAEModel.from_config(config_path) | |
| if origin_ckpt_path.endswith('ckpt'): | |
| ckpt = torch.load(origin_ckpt_path, map_location="cpu")['state_dict'] | |
| elif origin_ckpt_path.endswith('safetensors'): | |
| ckpt = {} | |
| with safetensors.safe_open(origin_ckpt_path, framework="pt") as file: | |
| for k in file.keys(): | |
| ckpt[k] = file.get_tensor(k) | |
| print("key", k) | |
| for name, module in model.named_modules(): | |
| if "loss" in name: | |
| continue | |
| if isinstance(module, torch.nn.Conv3d): | |
| in_channels = module.in_channels | |
| out_channels = module.out_channels | |
| kernel_size = module.kernel_size | |
| old_name = re.sub(".conv$", "", name) | |
| if old_name + ".weight" not in ckpt: | |
| print(old_name + ".weight", "not found") | |
| continue | |
| if init_method == "tail": | |
| shape_2d = ckpt[old_name + ".weight"].shape | |
| new_weight = torch.zeros(*shape_2d) | |
| new_weight = new_weight.unsqueeze(2).repeat(1, 1, kernel_size[0], 1, 1) | |
| middle_idx = kernel_size[0] // 2 | |
| new_weight[:, :, -1, :, :] = ckpt[old_name + ".weight"] | |
| new_bias = ckpt[old_name + ".bias"] | |
| elif init_method == "avg": | |
| new_weight = ckpt[old_name + ".weight"].unsqueeze(2) | |
| new_weight = new_weight.repeat(1, 1, kernel_size[0], 1, 1) / kernel_size[0] | |
| new_bias = ckpt[old_name + ".bias"] | |
| assert new_weight.shape == module.weight.shape | |
| module.weight.data = new_weight.cpu().float() | |
| module.bias.data = new_bias.cpu().float() | |
| elif isinstance(module, torch.nn.GroupNorm): | |
| old_name = name | |
| if old_name + ".weight" not in ckpt: | |
| print(old_name + ".weight", "not found") | |
| continue | |
| new_weight = ckpt[old_name + ".weight"] | |
| new_bias = ckpt[old_name + ".bias"] | |
| module.weight.data = new_weight.cpu().float() | |
| module.bias.data = new_bias.cpu().float() | |
| elif isinstance(module, torch.nn.Conv2d): | |
| in_channels = module.in_channels | |
| out_channels = module.out_channels | |
| kernel_size = module.kernel_size | |
| old_name = name | |
| if old_name + ".weight" not in ckpt: | |
| print(old_name + ".weight", "not found") | |
| continue | |
| new_weight = ckpt[old_name + ".weight"] | |
| new_bias = ckpt[old_name + ".bias"] | |
| assert new_weight.shape == module.weight.shape | |
| module.weight.data = new_weight.cpu().float() | |
| module.bias.data = new_bias.cpu().float() | |
| model.save_pretrained(output_path) |