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