add support for transformers==4.25.1

        def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):

            return self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)

        def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):

        def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):

            args = args[0:3] + ('/', ) + args[4:]  # resolved_archive_file; must set it to something to prevent what seems to be a bug

            return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)

        def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):

            # this file is always 404, prevent making request

            if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json':

                raise transformers.utils.hub.EntryNotFoundError

            try:

                return self.transformers_utils_hub_get_from_cache(url, *args, local_files_only=True, **kwargs)

                return original(url, *args, local_files_only=True, **kwargs)

            except Exception as e:

                return self.transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs)

                return original(url, *args, local_files_only=False, **kwargs)

        def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):

            return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)

        def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs):

            return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)

        def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):

            return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)

        self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_

        self.init_no_grad_normal = torch.nn.init._no_grad_normal_

        self.init_no_grad_uniform_ = torch.nn.init._no_grad_uniform_

        self.create_model_and_transforms = open_clip.create_model_and_transforms

        self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained

        self.transformers_utils_hub_get_from_cache = transformers.utils.hub.get_from_cache

        self.transformers_modeling_utils_load_pretrained_model = getattr(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', None)

        self.transformers_tokenization_utils_base_cached_file = getattr(transformers.tokenization_utils_base, 'cached_file', None)

        self.transformers_configuration_utils_cached_file = getattr(transformers.configuration_utils, 'cached_file', None)

        self.transformers_utils_hub_get_from_cache = getattr(transformers.utils.hub, 'get_from_cache', None)

        torch.nn.init.kaiming_uniform_ = do_nothing

        torch.nn.init._no_grad_normal_ = do_nothing

        torch.nn.init._no_grad_uniform_ = do_nothing

        open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained

        ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained

        if self.transformers_modeling_utils_load_pretrained_model is not None:

            transformers.modeling_utils.PreTrainedModel._load_pretrained_model = transformers_modeling_utils_load_pretrained_model

        if self.transformers_tokenization_utils_base_cached_file is not None:

            transformers.tokenization_utils_base.cached_file = transformers_tokenization_utils_base_cached_file

        if self.transformers_configuration_utils_cached_file is not None:

            transformers.configuration_utils.cached_file = transformers_configuration_utils_cached_file

        if self.transformers_utils_hub_get_from_cache is not None:

            transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache

    def __exit__(self, exc_type, exc_val, exc_tb):

        torch.nn.init._no_grad_uniform_ = self.init_no_grad_uniform_

        open_clip.create_model_and_transforms = self.create_model_and_transforms

        ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained

        if self.transformers_modeling_utils_load_pretrained_model is not None:

            transformers.modeling_utils.PreTrainedModel._load_pretrained_model = self.transformers_modeling_utils_load_pretrained_model

        if self.transformers_tokenization_utils_base_cached_file is not None:

            transformers.utils.hub.cached_file = self.transformers_tokenization_utils_base_cached_file

        if self.transformers_configuration_utils_cached_file is not None:

            transformers.utils.hub.cached_file = self.transformers_configuration_utils_cached_file

        if self.transformers_utils_hub_get_from_cache is not None:

            transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache

from ldm.util import instantiate_from_config

from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization

from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors

from modules.paths import models_path

from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting

    timer = Timer()

    try:

        with sd_disable_initialization.DisableInitialization():

            sd_model = instantiate_from_config(sd_config.model)

    except Exception as e:

        print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)

        sd_model = instantiate_from_config(sd_config.model)

    elapsed_create = timer.elapsed()