Disable ipex autocast due to its bad perf

parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")

parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")

parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)

parser.add_argument("--use-ipex", action="store_true", help="use Intel XPU as torch device")

parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model")

parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")

parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)

from functools import lru_cache

import torch

from modules import errors, shared, xpu_specific

from modules import errors, shared

if sys.platform == "darwin":

    from modules import mac_specific

if shared.cmd_opts.use_ipex:

    from modules import xpu_specific

def has_xpu() -> bool:

    return shared.cmd_opts.use_ipex and xpu_specific.has_xpu

def has_mps() -> bool:

    if sys.platform != "darwin":

    if has_mps():

        return "mps"

    if xpu_specific.has_ipex:

    if has_xpu():

        return xpu_specific.get_xpu_device_string()

    return "cpu"

    if has_mps():

        mac_specific.torch_mps_gc()

    if has_xpu():

        xpu_specific.torch_xpu_gc()

def enable_tf32():

    if torch.cuda.is_available():

    if dtype == torch.float32 or shared.cmd_opts.precision == "full":

        return contextlib.nullcontext()

    if xpu_specific.has_xpu:

        return torch.autocast("xpu")

    return torch.autocast("cuda")

def without_autocast(disable=False):

    device_type = "xpu" if xpu_specific.has_xpu else "cuda"

    return torch.autocast(device_type, enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext()

    return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext()

class NansException(Exception):

import contextlib

from modules import shared

from modules.sd_hijack_utils import CondFunc

except Exception:

    pass

def check_for_xpu():

    if not has_ipex:

        return False

    return hasattr(torch, 'xpu') and torch.xpu.is_available()

def check_for_xpu():

    return has_ipex and hasattr(torch, 'xpu') and torch.xpu.is_available()

has_xpu = check_for_xpu()

def get_xpu_device_string():

    if shared.cmd_opts.device_id is not None:

        return f"xpu:{shared.cmd_opts.device_id}"

    return "xpu"

def return_null_context(*args, **kwargs): # pylint: disable=unused-argument

    return contextlib.nullcontext()

def torch_xpu_gc():

    with torch.xpu.device(get_xpu_device_string()):

        torch.xpu.empty_cache()

has_xpu = check_for_xpu()

if has_xpu:

    # W/A for https://github.com/intel/intel-extension-for-pytorch/issues/452: torch.Generator API doesn't support XPU device

    CondFunc('torch.Generator',

        lambda orig_func, device=None: torch.xpu.Generator(device),

        lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu")

        lambda orig_func, device=None: device is not None and device.type == "xpu")

    # W/A for some OPs that could not handle different input dtypes

    CondFunc('torch.nn.functional.layer_norm',

        lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:

        orig_func(input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs),

        lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:

        weight is not None and input.dtype != weight.data.dtype)

    CondFunc('torch.nn.modules.GroupNorm.forward',

        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),

        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)

    CondFunc('torch.nn.modules.linear.Linear.forward',

        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),

        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)

    CondFunc('torch.nn.modules.conv.Conv2d.forward',

        lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),

        lambda orig_func, self, input: input.dtype != self.weight.data.dtype)

@echo off

set PYTHON=

@REM The "Nuullll/intel-extension-for-pytorch" wheels were built from IPEX source for Intel Arc GPU: https://github.com/intel/intel-extension-for-pytorch/tree/xpu-main

@REM This is NOT an Intel official release so please use it at your own risk!!

@REM See https://github.com/Nuullll/intel-extension-for-pytorch/releases/tag/v2.0.110%2Bxpu-master%2Bdll-bundle for details.

@REM 

@REM Strengths (over official IPEX 2.0.110 windows release):

@REM   - AOT build (for Arc GPU only) to eliminate JIT compilation overhead: https://github.com/intel/intel-extension-for-pytorch/issues/399

@REM   - Bundles minimal oneAPI 2023.2 dependencies into the python wheels, so users don't need to install oneAPI for the whole system.

@REM   - Provides a compatible torchvision wheel: https://github.com/intel/intel-extension-for-pytorch/issues/465

@REM Limitation:

@REM   - Only works for python 3.10

set "TORCH_COMMAND=pip install https://github.com/Nuullll/intel-extension-for-pytorch/releases/download/v2.0.110%%2Bxpu-master%%2Bdll-bundle/torch-2.0.0a0+gite9ebda2-cp310-cp310-win_amd64.whl https://github.com/Nuullll/intel-extension-for-pytorch/releases/download/v2.0.110%%2Bxpu-master%%2Bdll-bundle/torchvision-0.15.2a0+fa99a53-cp310-cp310-win_amd64.whl https://github.com/Nuullll/intel-extension-for-pytorch/releases/download/v2.0.110%%2Bxpu-master%%2Bdll-bundle/intel_extension_for_pytorch-2.0.110+gitc6ea20b-cp310-cp310-win_amd64.whl"

set GIT=

set VENV_DIR=

set "COMMANDLINE_ARGS=--use-ipex --skip-torch-cuda-test --skip-version-check --opt-sdp-attention"

call webui.bat