From 4bd490727e156ff53107d53416d6b89be86f2a62 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 11 Jan 2023 18:54:04 +0300 Subject: [PATCH] fix for an error caused by skipping initialization, for realsies this time: TypeError: expected str, bytes or os.PathLike object, not NoneType --- modules/sd_disable_initialization.py | 71 +++++++++++++--------------- modules/sd_models.py | 1 + 2 files changed, 33 insertions(+), 39 deletions(-) diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py index 088ac24be..c72d8efca 100644 --- a/modules/sd_disable_initialization.py +++ b/modules/sd_disable_initialization.py @@ -20,6 +20,19 @@ class DisableInitialization: ``` """ + def __init__(self): + self.replaced = [] + + def replace(self, obj, field, func): + original = getattr(obj, field, None) + if original is None: + return None + + self.replaced.append((obj, field, original)) + setattr(obj, field, func) + + return original + def __enter__(self): def do_nothing(*args, **kwargs): pass @@ -37,11 +50,14 @@ class DisableInitialization: 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 + if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json': + return None try: - return original(url, *args, local_files_only=True, **kwargs) + res = original(url, *args, local_files_only=True, **kwargs) + if res is None: + res = original(url, *args, local_files_only=False, **kwargs) + return res except Exception as e: return original(url, *args, local_files_only=False, **kwargs) @@ -54,42 +70,19 @@ class DisableInitialization: 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_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 + self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing) + self.replace(torch.nn.init, '_no_grad_normal_', do_nothing) + self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing) + self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained) + self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained) + self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model) + self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file) + self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file) + self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache) def __exit__(self, exc_type, exc_val, exc_tb): - torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform - torch.nn.init._no_grad_normal_ = self.init_no_grad_normal - 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 + for obj, field, original in self.replaced: + setattr(obj, field, original) + + self.replaced.clear() diff --git a/modules/sd_models.py b/modules/sd_models.py index 084ba7fa1..c466f2735 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -334,6 +334,7 @@ def load_model(checkpoint_info=None): timer = Timer() sd_model = None + try: with sd_disable_initialization.DisableInitialization(): sd_model = instantiate_from_config(sd_config.model)