utils/model_utils.py

import hashlib
from io import BytesIO
from typing import Optional

import safetensors.torch
import torch


def model_hash(filename):
    """Old model hash used by stable-diffusion-webui"""
    try:
        with open(filename, "rb") as file:
            m = hashlib.sha256()

            file.seek(0x100000)
            m.update(file.read(0x10000))
            return m.hexdigest()[0:8]
    except FileNotFoundError:
        return "NOFILE"
    except IsADirectoryError:  # Linux?
        return "IsADirectory"
    except PermissionError:  # Windows
        return "IsADirectory"


def calculate_sha256(filename):
    """New model hash used by stable-diffusion-webui"""
    try:
        hash_sha256 = hashlib.sha256()
        blksize = 1024 * 1024

        with open(filename, "rb") as f:
            for chunk in iter(lambda: f.read(blksize), b""):
                hash_sha256.update(chunk)

        return hash_sha256.hexdigest()
    except FileNotFoundError:
        return "NOFILE"
    except IsADirectoryError:  # Linux?
        return "IsADirectory"
    except PermissionError:  # Windows
        return "IsADirectory"


def addnet_hash_legacy(b):
    """Old model hash used by sd-webui-additional-networks for .safetensors format files"""
    m = hashlib.sha256()

    b.seek(0x100000)
    m.update(b.read(0x10000))
    return m.hexdigest()[0:8]


def addnet_hash_safetensors(b):
    """New model hash used by sd-webui-additional-networks for .safetensors format files"""
    hash_sha256 = hashlib.sha256()
    blksize = 1024 * 1024

    b.seek(0)
    header = b.read(8)
    n = int.from_bytes(header, "little")

    offset = n + 8
    b.seek(offset)
    for chunk in iter(lambda: b.read(blksize), b""):
        hash_sha256.update(chunk)

    return hash_sha256.hexdigest()


def precalculate_safetensors_hashes(tensors, metadata):
    """Precalculate the model hashes needed by sd-webui-additional-networks to
    save time on indexing the model later."""

    # Because writing user metadata to the file can change the result of
    # sd_models.model_hash(), only retain the training metadata for purposes of
    # calculating the hash, as they are meant to be immutable
    metadata = {k: v for k, v in metadata.items() if k.startswith("ss_")}

    bytes = safetensors.torch.save(tensors, metadata)
    b = BytesIO(bytes)

    model_hash = addnet_hash_safetensors(b)
    legacy_hash = addnet_hash_legacy(b)
    return model_hash, legacy_hash


def dtype_to_str(dtype: torch.dtype) -> str:
    # get name of the dtype
    dtype_name = str(dtype).split(".")[-1]
    return dtype_name


def str_to_dtype(s: Optional[str], default_dtype: Optional[torch.dtype] = None) -> torch.dtype:
    """
    Convert a string to a torch.dtype

    Args:
        s: string representation of the dtype
        default_dtype: default dtype to return if s is None

    Returns:
        torch.dtype: the corresponding torch.dtype

    Raises:
        ValueError: if the dtype is not supported

    Examples:
        >>> str_to_dtype("float32")
        torch.float32
        >>> str_to_dtype("fp32")
        torch.float32
        >>> str_to_dtype("float16")
        torch.float16
        >>> str_to_dtype("fp16")
        torch.float16
        >>> str_to_dtype("bfloat16")
        torch.bfloat16
        >>> str_to_dtype("bf16")
        torch.bfloat16
        >>> str_to_dtype("fp8")
        torch.float8_e4m3fn
        >>> str_to_dtype("fp8_e4m3fn")
        torch.float8_e4m3fn
        >>> str_to_dtype("fp8_e4m3fnuz")
        torch.float8_e4m3fnuz
        >>> str_to_dtype("fp8_e5m2")
        torch.float8_e5m2
        >>> str_to_dtype("fp8_e5m2fnuz")
        torch.float8_e5m2fnuz
    """
    if s is None:
        return default_dtype
    if s in ["bf16", "bfloat16"]:
        return torch.bfloat16
    elif s in ["fp16", "float16"]:
        return torch.float16
    elif s in ["fp32", "float32", "float"]:
        return torch.float32
    elif s in ["fp8_e4m3fn", "e4m3fn", "float8_e4m3fn"]:
        return torch.float8_e4m3fn
    elif s in ["fp8_e4m3fnuz", "e4m3fnuz", "float8_e4m3fnuz"]:
        return torch.float8_e4m3fnuz
    elif s in ["fp8_e5m2", "e5m2", "float8_e5m2"]:
        return torch.float8_e5m2
    elif s in ["fp8_e5m2fnuz", "e5m2fnuz", "float8_e5m2fnuz"]:
        return torch.float8_e5m2fnuz
    elif s in ["fp8", "float8"]:
        return torch.float8_e4m3fn  # default fp8
    else:
        raise ValueError(f"Unsupported dtype: {s}")