Merge branch 'AUTOMATIC1111:master' into improved-hr-conflict-test

    "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",

    "\u{1f4c2}": "Open images output directory",

    "\u{1f4be}": "Save style",

    "\U0001F5D1": "Clear prompt",

    "\u{1f5d1}": "Clear prompt",

    "\u{1f4cb}": "Apply selected styles to current prompt",

    "\u{1f4d2}": "Paste available values into the field",

    "\u{1f3b4}": "Show extra networks",

    "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",

    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",

    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",

    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",

    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",

    "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",

    "Loopback": "Process an image, use it as an input, repeat.",

                if not is_installed("xformers"):

                    exit(0)

        elif platform.system() == "Linux":

            run_pip("install xformers==0.0.16rc425", "xformers")

            run_pip(f"install {xformers_package}", "xformers")

    if not is_installed("pyngrok") and ngrok:

        run_pip("install pyngrok", "ngrok")

import sys, os, shlex

import sys

import contextlib

import torch

from modules import errors

from packaging import version

if sys.platform == "darwin":

    from modules import mac_specific

# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.

# check `getattr` and try it for compatibility

def has_mps() -> bool:

    if not getattr(torch, 'has_mps', False):

        return False

    try:

        torch.zeros(1).to(torch.device("mps"))

        return True

    except Exception:

    if sys.platform != "darwin":

        return False

    else:

        return mac_specific.has_mps

def extract_device_id(args, name):

    for x in range(len(args)):

    message += " Use --disable-nan-check commandline argument to disable this check."

    raise NansException(message)

# MPS workaround for https://github.com/pytorch/pytorch/issues/79383

orig_tensor_to = torch.Tensor.to

def tensor_to_fix(self, *args, **kwargs):

    if self.device.type != 'mps' and \

       ((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \

       (isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')):

        self = self.contiguous()

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

# MPS workaround for https://github.com/pytorch/pytorch/issues/80800 

orig_layer_norm = torch.nn.functional.layer_norm

def layer_norm_fix(*args, **kwargs):

    if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps':

        args = list(args)

        args[0] = args[0].contiguous()

    return orig_layer_norm(*args, **kwargs)

# MPS workaround for https://github.com/pytorch/pytorch/issues/90532

orig_tensor_numpy = torch.Tensor.numpy

def numpy_fix(self, *args, **kwargs):

    if self.requires_grad:

        self = self.detach()

    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 output_dtype == torch.int64:

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

        elif cumsum_needs_bool_fix and output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16):

            return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64)

    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

        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"):

        cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))

        cumsum_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0))

        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) )

import filelock

from modules import shared

from modules.paths import data_path

    if sha256_value is not None:

        return sha256_value

    if shared.cmd_opts.no_hashing:

        return None

    print(f"Calculating sha256 for {filename}: ", end='')

    sha256_value = calculate_sha256(filename)

    print(f"{sha256_value}")

    def shorthash(self):

        sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}')

        return sha256[0:10]

        return sha256[0:10] if sha256 else None

def list_hypernetworks(path):