OpenVINO update to torch 2.2.0

CHANGELOG.md

@@ -19,6 +19,8 @@
       *settings -> diffusers*  
     - allow manual install of specific onnx flavor  
       *settings -> onnx*  
+  - **OpenVINO**  
+    - update to `torch 2.2.0`  
   - add `--theme` cli param to force theme on startup  
 - **wiki**:
   - updated benchmark notes for IPEX and Olive  

installer.py

@@ -495,10 +495,10 @@ def check_torch():
         install('onnxruntime-openvino', 'onnxruntime-openvino', ignore=True)
     elif allow_openvino and args.use_openvino:
         log.info('Using OpenVINO')
-        torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cpu')
+        torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.2.0 torchvision==0.17.0 --index-url https://download.pytorch.org/whl/cpu')
         install(os.environ.get('OPENVINO_PACKAGE', 'openvino==2023.3.0'), 'openvino')
         install('onnxruntime-openvino', 'onnxruntime-openvino', ignore=True) # TODO openvino: numpy version conflicts with tensorflow and doesn't support Python 3.11
-        install('nncf==2.8.0', 'nncf')
+        install('nncf==2.8.1', 'nncf')
         os.environ.setdefault('PYTORCH_TRACING_MODE', 'TORCHFX')
         os.environ.setdefault('NEOReadDebugKeys', '1')
         os.environ.setdefault('ClDeviceGlobalMemSizeAvailablePercent', '100')

modules/sd_hijack_optimizations.py

@@ -568,8 +568,8 @@ def find_dynamic_attention_v1_slice_sizes(query_shape, query_element_size, slice
 class DynamicAttnProcessorV1:
     r"""
     dynamically slices attention queries based on query size and slice rate in GB
-    saves VRAM similar to Sub-Quad on Original backend and it is compatible with HyperTile
     slicing will not get triggered if the query size is smaller than the slice rate to gain performance
+    based on AttnProcessor V1
     """
 
     def __call__(self, attn, hidden_states: torch.FloatTensor,