Merge pull request #14046 from hidenorly/AddFP32FallbackSupportOnSdVaeApprox

import logging

import torch

from torch import Tensor

import platform

from modules.sd_hijack_utils import CondFunc

from packaging import version

    return cumsum_func(input, *args, **kwargs)

# MPS workaround for https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046

def interpolate_with_fp32_fallback(orig_func, *args, **kwargs) -> Tensor:

    try:

        return orig_func(*args, **kwargs)

    except RuntimeError as e:

        if "not implemented for" in str(e) and "Half" in str(e):

            input_tensor = args[0]

            return orig_func(input_tensor.to(torch.float32), *args[1:], **kwargs).to(input_tensor.dtype)

        else:

            print(f"An unexpected RuntimeError occurred: {str(e)}")

if has_mps:

    if platform.mac_ver()[0].startswith("13.2."):

        # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)

        # MPS workaround for https://github.com/pytorch/pytorch/issues/96113

        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda _, input, *args, **kwargs: len(args) == 4 and input.device.type == 'mps')

        # MPS workaround for https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046

        CondFunc('torch.nn.functional.interpolate', interpolate_with_fp32_fallback, None)

        # MPS workaround for https://github.com/pytorch/pytorch/issues/92311

        if platform.processor() == 'i386':

            for funcName in ['torch.argmax', 'torch.Tensor.argmax']: