From d429e11d5528f7a89b110110e5597d06ebdad9f5 Mon Sep 17 00:00:00 2001 From: Disty0 Date: Tue, 24 Mar 2026 17:06:57 +0300 Subject: [PATCH] Update OpenVINO to 2026 and Torch to 2.11 --- installer.py | 6 ++-- modules/intel/openvino/__init__.py | 44 +++++++----------------------- 2 files changed, 13 insertions(+), 37 deletions(-) diff --git a/installer.py b/installer.py index dde16945d..d16acc70d 100644 --- a/installer.py +++ b/installer.py @@ -700,12 +700,12 @@ def install_openvino(): #check_python(supported_minors=[10, 11, 12, 13], reason='OpenVINO backend requires a Python version between 3.10 and 3.13') if sys.platform == 'darwin': - torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.10.0 torchvision==0.25.0') + torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.11.0 torchvision==0.26.0') else: - torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.10.0+cpu torchvision==0.25.0 --index-url https://download.pytorch.org/whl/cpu') + torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.11.0+cpu torchvision==0.26.0 --index-url https://download.pytorch.org/whl/cpu') if not (args.skip_all or args.skip_requirements): - install(os.environ.get('OPENVINO_COMMAND', 'openvino==2025.4.1'), 'openvino') + install(os.environ.get('OPENVINO_COMMAND', 'openvino==2026.0.0'), 'openvino') ts('openvino', t_start) return torch_command diff --git a/modules/intel/openvino/__init__.py b/modules/intel/openvino/__init__.py index de8b88ef3..c8d008740 100644 --- a/modules/intel/openvino/__init__.py +++ b/modules/intel/openvino/__init__.py @@ -21,26 +21,6 @@ from modules import shared, devices, sd_models_utils from modules.logger import log -# importing openvino.runtime forces DeprecationWarning to "always" -# And Intel's own libs (NNCF) imports the deprecated module -# Don't allow openvino to override warning filters: -try: - import warnings - filterwarnings = warnings.filterwarnings - warnings.filterwarnings = lambda *args, **kwargs: None - import openvino.runtime # pylint: disable=unused-import - installer.torch_info.set(openvino=openvino.runtime.get_version()) - warnings.filterwarnings = filterwarnings -except Exception: - pass - -try: - # silence the pytorch version warning - import nncf - nncf.common.logging.logger.warn_bkc_version_mismatch = lambda *args, **kwargs: None -except Exception: - pass - # Set default params torch._dynamo.config.cache_size_limit = max(64, torch._dynamo.config.cache_size_limit) # pylint: disable=protected-access torch._dynamo.eval_frame.check_if_dynamo_supported = lambda: True # pylint: disable=protected-access @@ -212,9 +192,7 @@ def execute_cached(compiled_model, *args): def openvino_compile(gm: GraphModule, *example_inputs, model_hash_str: str = None, file_name=""): core = Core() - device = get_device() - global dont_use_quant if file_name is not None and os.path.isfile(file_name + ".xml") and os.path.isfile(file_name + ".bin"): om = core.read_model(file_name + ".xml") @@ -264,7 +242,6 @@ def openvino_compile(gm: GraphModule, *example_inputs, model_hash_str: str = Non if model_hash_str is not None: hints['CACHE_DIR'] = shared.opts.openvino_cache_path + '/blob' core.set_property(hints) - dont_use_quant = False compiled_model = core.compile_model(om, device) return compiled_model @@ -274,8 +251,6 @@ def openvino_compile_cached_model(cached_model_path, *example_inputs): core = Core() om = core.read_model(cached_model_path + ".xml") - global dont_use_quant - for idx, input_data in enumerate(example_inputs): om.inputs[idx].get_node().set_element_type(dtype_mapping[input_data.dtype]) om.inputs[idx].get_node().set_partial_shape(PartialShape(list(input_data.shape))) @@ -287,7 +262,6 @@ def openvino_compile_cached_model(cached_model_path, *example_inputs): elif shared.opts.openvino_accuracy == "accuracy": hints[ov_hints.execution_mode] = ov_hints.ExecutionMode.ACCURACY core.set_property(hints) - dont_use_quant = False compiled_model = core.compile_model(om, get_device()) return compiled_model @@ -413,10 +387,8 @@ def get_subgraph_type(tensor): @fake_tensor_unsupported def openvino_fx(subgraph, example_inputs, options=None): - global dont_use_quant global subgraph_type - dont_use_quant = False dont_use_faketensors = False executor_parameters = None inputs_reversed = False @@ -425,20 +397,24 @@ def openvino_fx(subgraph, example_inputs, options=None): subgraph_type = [] subgraph.apply(get_subgraph_type) + """ # SD 1.5 / SDXL VAE - if (subgraph_type[0] is torch.nn.modules.conv.Conv2d and + if ( + subgraph_type[0] is torch.nn.modules.conv.Conv2d and subgraph_type[1] is torch.nn.modules.conv.Conv2d and subgraph_type[2] is torch.nn.modules.normalization.GroupNorm and - subgraph_type[3] is torch.nn.modules.activation.SiLU): - + subgraph_type[3] is torch.nn.modules.activation.SiLU + ): pass + """ # SD 1.5 / SDXL Text Encoder - elif (subgraph_type[0] is torch.nn.modules.sparse.Embedding and + if ( + subgraph_type[0] is torch.nn.modules.sparse.Embedding and subgraph_type[1] is torch.nn.modules.sparse.Embedding and subgraph_type[2] is torch.nn.modules.normalization.LayerNorm and - subgraph_type[3] is torch.nn.modules.linear.Linear): - + subgraph_type[3] is torch.nn.modules.linear.Linear + ): dont_use_faketensors = True # Create a hash to be used for caching