Delete scripts

scripts/to_safetensors.py

@@ -1,143 +0,0 @@
-import argparse
-from pathlib import Path
-from typing import Dict
-import safetensors.torch
-import torch
-import json
-import shutil
-
-
-def load_text_encoder(index_path: Path) -> Dict:
-    with open(index_path, "r") as f:
-        index: Dict = json.load(f)
-
-    loaded_tensors = {}
-    for part_file in set(index.get("weight_map", {}).values()):
-        tensors = safetensors.torch.load_file(
-            index_path.parent / part_file, device="cpu"
-        )
-        for tensor_name in tensors:
-            loaded_tensors[tensor_name] = tensors[tensor_name]
-
-    return loaded_tensors
-
-
-def convert_unet(unet: Dict, add_prefix=True) -> Dict:
-    if add_prefix:
-        return {"model.diffusion_model." + key: value for key, value in unet.items()}
-    return unet
-
-
-def convert_vae(vae_path: Path, add_prefix=True) -> Dict:
-    state_dict = torch.load(vae_path / "autoencoder.pth", weights_only=True)
-    stats_path = vae_path / "per_channel_statistics.json"
-    if stats_path.exists():
-        with open(stats_path, "r") as f:
-            data = json.load(f)
-        transposed_data = list(zip(*data["data"]))
-        data_dict = {
-            f"{'vae.' if add_prefix else ''}per_channel_statistics.{col}": torch.tensor(
-                vals
-            )
-            for col, vals in zip(data["columns"], transposed_data)
-        }
-    else:
-        data_dict = {}
-
-    result = {
-        ("vae." if add_prefix else "") + key: value for key, value in state_dict.items()
-    }
-    result.update(data_dict)
-    return result
-
-
-def convert_encoder(encoder: Dict) -> Dict:
-    return {
-        "text_encoders.t5xxl.transformer." + key: value
-        for key, value in encoder.items()
-    }
-
-
-def save_config(config_src: str, config_dst: str):
-    shutil.copy(config_src, config_dst)
-
-
-def load_vae_config(vae_path: Path) -> str:
-    config_path = vae_path / "config.json"
-    if not config_path.exists():
-        raise FileNotFoundError(f"VAE config file {config_path} not found.")
-    return str(config_path)
-
-
-def main(
-    unet_path: str,
-    vae_path: str,
-    out_path: str,
-    mode: str,
-    unet_config_path: str = None,
-    scheduler_config_path: str = None,
-) -> None:
-    unet = convert_unet(
-        torch.load(unet_path, weights_only=True), add_prefix=(mode == "single")
-    )
-
-    # Load VAE from directory and config
-    vae = convert_vae(Path(vae_path), add_prefix=(mode == "single"))
-    vae_config_path = load_vae_config(Path(vae_path))
-
-    if mode == "single":
-        result = {**unet, **vae}
-        safetensors.torch.save_file(result, out_path)
-    elif mode == "separate":
-        # Create directories for unet, vae, and scheduler
-        unet_dir = Path(out_path) / "unet"
-        vae_dir = Path(out_path) / "vae"
-        scheduler_dir = Path(out_path) / "scheduler"
-
-        unet_dir.mkdir(parents=True, exist_ok=True)
-        vae_dir.mkdir(parents=True, exist_ok=True)
-        scheduler_dir.mkdir(parents=True, exist_ok=True)
-
-        # Save unet and vae safetensors with the name diffusion_pytorch_model.safetensors
-        safetensors.torch.save_file(
-            unet, unet_dir / "unet_diffusion_pytorch_model.safetensors"
-        )
-        safetensors.torch.save_file(
-            vae, vae_dir / "vae_diffusion_pytorch_model.safetensors"
-        )
-
-        # Save config files for unet, vae, and scheduler
-        if unet_config_path:
-            save_config(unet_config_path, unet_dir / "config.json")
-        if vae_config_path:
-            save_config(vae_config_path, vae_dir / "config.json")
-        if scheduler_config_path:
-            save_config(scheduler_config_path, scheduler_dir / "scheduler_config.json")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--unet_path", "-u", type=str, default="unet/ema-002.pt")
-    parser.add_argument("--vae_path", "-v", type=str, default="vae/")
-    parser.add_argument("--out_path", "-o", type=str, default="xora.safetensors")
-    parser.add_argument(
-        "--mode",
-        "-m",
-        type=str,
-        choices=["single", "separate"],
-        default="single",
-        help="Choose 'single' for the original behavior, 'separate' to save unet and vae separately.",
-    )
-    parser.add_argument(
-        "--unet_config_path",
-        type=str,
-        help="Path to the UNet config file (for separate mode)",
-    )
-    parser.add_argument(
-        "--scheduler_config_path",
-        type=str,
-        help="Path to the Scheduler config file (for separate mode)",
-    )
-
-    args = parser.parse_args()
-    main(**args.__dict__)