Deploying to master from @ vladmandic/automatic@b1ea529c08 🚀

installer.py

@@ -375,10 +375,10 @@ def check_torch():
         os.environ.setdefault('NEOReadDebugKeys', '1')
         os.environ.setdefault('ClDeviceGlobalMemSizeAvailablePercent', '100')
         if "linux" in sys.platform:
-            torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.0.1a0 torchvision==0.15.2a0 intel_extension_for_pytorch==2.0.110+xpu -f https://developer.intel.com/ipex-whl-stable-xpu')
+            torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.0.1a0 torchvision==0.15.2a0 intel_extension_for_pytorch==2.0.110+xpu openvino==2023.1.0.dev20230728 -f https://developer.intel.com/ipex-whl-stable-xpu')
             os.environ.setdefault('TENSORFLOW_PACKAGE', 'tensorflow==2.13.0 intel-extension-for-tensorflow[gpu]')
         else:
-            torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.0.0a0 torchvision==0.15.1 intel_extension_for_pytorch==2.0.110+gitba7f6c1 -f https://developer.intel.com/ipex-whl-stable-xpu')
+            torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.0.0a0 torchvision==0.15.1 intel_extension_for_pytorch==2.0.110+gitba7f6c1 openvino==2023.1.0.dev20230728 -f https://developer.intel.com/ipex-whl-stable-xpu')
     else:
         machine = platform.machine()
         if sys.platform == 'darwin':
@@ -442,8 +442,6 @@ def check_torch():
         log.debug(f'Cannot install xformers package: {e}')
     if opts.get('cuda_compile_backend', '') == 'hidet':
         install('hidet', 'hidet')
-    if opts.get('cuda_compile_backend', '') == 'openvino_fx':
-        install('openvino==2023.1.0.dev20230728', 'openvino')
     if args.profile:
         print_profile(pr, 'Torch')
 

modules/ipex_specific/__init__.py

@@ -88,3 +88,7 @@ def ipex_init():
 
     ipex_hijacks()
     ipex_diffusers()
+    try:
+        from .openvino import openvino_fx
+    except Exception:
+        pass

modules/sd_hijack.py

@@ -188,8 +188,6 @@ class StableDiffusionModelHijack:
                 import logging
                 shared.log.info(f"Compiling pipeline={m.model.__class__.__name__} mode={opts.cuda_compile_backend}")
                 import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
-                if shared.opts.cuda_compile_backend == "openvino_fx":
-                    from modules.ipex_specific.openvino import openvino_fx
                 log_level = logging.WARNING if opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
                 if hasattr(torch, '_logging'):
                     torch._logging.set_logs(dynamo=log_level, aot=log_level, inductor=log_level) # pylint: disable=protected-access

modules/sd_models.py

@@ -745,7 +745,7 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
             sd_model.unet.to(memory_format=torch.channels_last)
 
         base_sent_to_cpu=False
-        if (shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none') or shared.opts.ipex_optimize:
+        if (shared.opts.cuda_compile or shared.opts.ipex_optimize) and torch.cuda.is_available():
             if op == 'refiner' and not sd_model.has_accelerate:
                 gpu_vram = memory_stats().get('gpu', {})
                 free_vram = gpu_vram.get('total', 0) - gpu_vram.get('used', 0)
@@ -775,11 +775,9 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
             except Exception as err:
                 shared.log.warning(f"IPEX Optimize not supported: {err}")
             try:
-                if shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none':
+                if shared.opts.cuda_compile:
                     shared.log.info(f"Compiling pipeline={sd_model.__class__.__name__} shape={8 * sd_model.unet.config.sample_size} mode={shared.opts.cuda_compile_backend}")
                     import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
-                    if shared.opts.cuda_compile_backend == "openvino_fx":
-                        from modules.ipex_specific.openvino import openvino_fx
                     log_level = logging.WARNING if shared.opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
                     if hasattr(torch, '_logging'):
                         torch._logging.set_logs(dynamo=log_level, aot=log_level, inductor=log_level) # pylint: disable=protected-access