OpenVINO fix resolution change

modules/intel/openvino/__init__.py

@@ -11,16 +11,10 @@
 
 class ModelState:
     def __init__(self):
-        self.recompile = 1
-        self.device = "GPU"
         self.height = 512
         self.width = 512
         self.batch_size = 1
-        self.mode = 0
-        self.partition_id = 0
-        self.model_hash = ""
-
-model_state = ModelState()
+        self.first_pass = True
 
 @register_backend
 @fake_tensor_unsupported
@@ -31,8 +25,6 @@ def openvino_fx(subgraph, example_inputs):
         if os.getenv("OPENVINO_TORCH_MODEL_CACHING") != "0":
             os.environ.setdefault('OPENVINO_TORCH_MODEL_CACHING', "1")
             model_hash_str = sha256(subgraph.code.encode('utf-8')).hexdigest()
-            model_hash_str_file = model_hash_str + str(model_state.partition_id)
-            model_state.partition_id = model_state.partition_id + 1
             executor_parameters = {"model_hash_str": model_hash_str}
 
         example_inputs.reverse()
@@ -48,9 +40,17 @@ def openvino_fx(subgraph, example_inputs):
         else:
             os.environ.setdefault('OPENVINO_TORCH_BACKEND_DEVICE', device)
 
-        file_name = get_cached_file_name(*example_inputs, model_hash_str=model_hash_str_file, device=device, cache_root=cache_root)
-
-        if file_name is not None and os.path.isfile(file_name + ".xml") and os.path.isfile(file_name + ".bin"):
+        #Cache saving keeps increasing the partition id
+        #This loop check if non 0 partition id caches exist
+        #Takes 0.002 seconds when nothing is found
+        use_cached_file = False
+        for i in range(100):
+            file_name = get_cached_file_name(*example_inputs, model_hash_str=str(model_hash_str + str(i)), device=device, cache_root=cache_root)
+            if file_name is not None and os.path.isfile(file_name + ".xml") and os.path.isfile(file_name + ".bin"):
+                use_cached_file = True
+                break
+
+        if use_cached_file:
             om = core.read_model(file_name + ".xml")
 
             dtype_mapping = {

modules/processing_diffusers.py

@@ -192,6 +192,31 @@ def set_pipeline_args(model, prompts: list, negative_prompts: list, prompts_2: t
         shared.log.debug(f'Diffuser pipeline: {pipeline.__class__.__name__} task={sd_models.get_diffusers_task(model)} set={clean}')
         return args
 
+    def recompile_model(hires=False):
+        if shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none':
+            if shared.opts.cuda_compile_backend == "openvino_fx":
+                compile_height = p.height if not hires else p.hr_upscale_to_y
+                compile_width = p.width if not hires else p.hr_upscale_to_x
+                if (not hasattr(shared.sd_model, "compiled_model_state") or (not shared.sd_model.compiled_model_state.first_pass
+                and (shared.sd_model.compiled_model_state.height != compile_height or shared.sd_model.compiled_model_state.width != compile_width
+                or shared.sd_model.compiled_model_state.batch_size != p.batch_size))):
+                    shared.log.info("OpenVINO: Resolution change detected")
+                    shared.log.info("OpenVINO: Recompiling base model")
+                    sd_models.unload_model_weights(op='model')
+                    sd_models.reload_model_weights(op='model')
+                    if is_refiner_enabled:
+                        shared.log.info("OpenVINO: Recompiling refiner")
+                        sd_models.unload_model_weights(op='refiner')
+                        sd_models.reload_model_weights(op='refiner')
+                shared.sd_model.compiled_model_state.height = compile_height
+                shared.sd_model.compiled_model_state.width = compile_width
+                shared.sd_model.compiled_model_state.batch_size = p.batch_size
+                shared.sd_model.compiled_model_state.first_pass = False
+            else:
+                pass #Can be implemented for normal compile or TensorRT
+        else:
+            pass #Do nothing if compile is disabled
+
     is_karras_compatible = shared.sd_model.__class__.__init__.__annotations__.get("scheduler", None) == diffusers.schedulers.scheduling_utils.KarrasDiffusionSchedulers
     use_sampler = p.sampler_name if not p.is_hr_pass else p.latent_sampler
     if (not hasattr(shared.sd_model.scheduler, 'name')) or (shared.sd_model.scheduler.name != use_sampler) and (use_sampler != 'Default') and is_karras_compatible:
@@ -229,6 +254,8 @@ def set_pipeline_args(model, prompts: list, negative_prompts: list, prompts_2: t
         unload_diffusers_lora()
         return results
 
+    recompile_model()
+
     if shared.opts.diffusers_move_base and not shared.sd_model.has_accelerate:
         shared.sd_model.to(devices.device)
 
@@ -262,6 +289,7 @@ def set_pipeline_args(model, prompts: list, negative_prompts: list, prompts_2: t
     # optional hires pass
     if p.is_hr_pass:
         p.init_hr()
+        recompile_model(hires=True)
         if p.width != p.hr_upscale_to_x or p.height != p.hr_upscale_to_y:
             if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_highres_fix and hasattr(shared.sd_model, 'vae'):
                 save_intermediate(latents=output.images, suffix="-before-hires")

modules/sd_models.py

@@ -807,9 +807,10 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
                     import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
                     if shared.opts.cuda_compile_backend == "openvino_fx":
                         torch._dynamo.reset()
-                        from modules.intel.openvino import openvino_fx, openvino_clear_caches, model_state # pylint: disable=unused-import
+                        from modules.intel.openvino import openvino_fx, openvino_clear_caches, ModelState # pylint: disable=unused-import
                         openvino_clear_caches()
-                        model_state.partition_id = 0
+                        sd_model.compiled_model_state = ModelState()
+                        sd_model.compiled_model_state.first_pass = True if not shared.opts.cuda_compile_precompile else False
                     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