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| #!/usr/bin/env python3 | |
| import argparse | |
| import fnmatch | |
| from safetensors.torch import load_file | |
| from diffusers import Kandinsky3UNet | |
| MAPPING = { | |
| "to_time_embed.1": "time_embedding.linear_1", | |
| "to_time_embed.3": "time_embedding.linear_2", | |
| "in_layer": "conv_in", | |
| "out_layer.0": "conv_norm_out", | |
| "out_layer.2": "conv_out", | |
| "down_samples": "down_blocks", | |
| "up_samples": "up_blocks", | |
| "projection_lin": "encoder_hid_proj.projection_linear", | |
| "projection_ln": "encoder_hid_proj.projection_norm", | |
| "feature_pooling": "add_time_condition", | |
| "to_query": "to_q", | |
| "to_key": "to_k", | |
| "to_value": "to_v", | |
| "output_layer": "to_out.0", | |
| "self_attention_block": "attentions.0", | |
| } | |
| DYNAMIC_MAP = { | |
| "resnet_attn_blocks.*.0": "resnets_in.*", | |
| "resnet_attn_blocks.*.1": ("attentions.*", 1), | |
| "resnet_attn_blocks.*.2": "resnets_out.*", | |
| } | |
| # MAPPING = {} | |
| def convert_state_dict(unet_state_dict): | |
| """ | |
| Convert the state dict of a U-Net model to match the key format expected by Kandinsky3UNet model. | |
| Args: | |
| unet_model (torch.nn.Module): The original U-Net model. | |
| unet_kandi3_model (torch.nn.Module): The Kandinsky3UNet model to match keys with. | |
| Returns: | |
| OrderedDict: The converted state dictionary. | |
| """ | |
| # Example of renaming logic (this will vary based on your model's architecture) | |
| converted_state_dict = {} | |
| for key in unet_state_dict: | |
| new_key = key | |
| for pattern, new_pattern in MAPPING.items(): | |
| new_key = new_key.replace(pattern, new_pattern) | |
| for dyn_pattern, dyn_new_pattern in DYNAMIC_MAP.items(): | |
| has_matched = False | |
| if fnmatch.fnmatch(new_key, f"*.{dyn_pattern}.*") and not has_matched: | |
| star = int(new_key.split(dyn_pattern.split(".")[0])[-1].split(".")[1]) | |
| if isinstance(dyn_new_pattern, tuple): | |
| new_star = star + dyn_new_pattern[-1] | |
| dyn_new_pattern = dyn_new_pattern[0] | |
| else: | |
| new_star = star | |
| pattern = dyn_pattern.replace("*", str(star)) | |
| new_pattern = dyn_new_pattern.replace("*", str(new_star)) | |
| new_key = new_key.replace(pattern, new_pattern) | |
| has_matched = True | |
| converted_state_dict[new_key] = unet_state_dict[key] | |
| return converted_state_dict | |
| def main(model_path, output_path): | |
| # Load your original U-Net model | |
| unet_state_dict = load_file(model_path) | |
| # Initialize your Kandinsky3UNet model | |
| config = {} | |
| # Convert the state dict | |
| converted_state_dict = convert_state_dict(unet_state_dict) | |
| unet = Kandinsky3UNet(config) | |
| unet.load_state_dict(converted_state_dict) | |
| unet.save_pretrained(output_path) | |
| print(f"Converted model saved to {output_path}") | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser(description="Convert U-Net PyTorch model to Kandinsky3UNet format") | |
| parser.add_argument("--model_path", type=str, required=True, help="Path to the original U-Net PyTorch model") | |
| parser.add_argument("--output_path", type=str, required=True, help="Path to save the converted model") | |
| args = parser.parse_args() | |
| main(args.model_path, args.output_path) | |