Merge pull request #6402 from brkirch/work-with-nightly-local-builds

    return orig_tensor_numpy(self, *args, **kwargs)

# MPS workaround for https://github.com/pytorch/pytorch/issues/89784

orig_cumsum = torch.cumsum

orig_Tensor_cumsum = torch.Tensor.cumsum

def cumsum_fix(input, cumsum_func, *args, **kwargs):

    if input.device.type == 'mps':

        output_dtype = kwargs.get('dtype', input.dtype)

        if any(output_dtype == broken_dtype for broken_dtype in [torch.bool, torch.int8, torch.int16, torch.int64]):

            return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)

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

if has_mps():

    if version.parse(torch.__version__) < version.parse("1.13"):

        # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working

if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):

        torch.Tensor.to = tensor_to_fix

        torch.nn.functional.layer_norm = layer_norm_fix

        torch.Tensor.numpy = numpy_fix

    elif version.parse(torch.__version__) > version.parse("1.13.1"):

        if not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.Tensor([1,1]).to(torch.device("mps")).cumsum(0, dtype=torch.int16)):

            torch.cumsum = lambda input, *args, **kwargs: ( cumsum_fix(input, orig_cumsum, *args, **kwargs) )

            torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) )

        orig_narrow = torch.narrow

        torch.narrow = lambda *args, **kwargs: ( orig_narrow(*args, **kwargs).clone() )

import time

import importlib

import signal

import threading

import re

from fastapi import FastAPI

from fastapi.middleware.cors import CORSMiddleware

from fastapi.middleware.gzip import GZipMiddleware

from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call

from modules.paths import script_path

import torch

# Truncate version number of nightly/local build of PyTorch to not cause exceptions with CodeFormer or Safetensors

if ".dev" in torch.__version__ or "+git" in torch.__version__:

    torch.__version__ = re.search(r'[\d.]+', torch.__version__).group(0)

from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir

import modules.codeformer_model as codeformer

import modules.extras