add from_onnx for image models

Author: sbalandi

tools/who_what_benchmark/whowhatbench/model_loaders.py

@@ -272,9 +272,10 @@ def load_text2image_model(
         if 'adapters' in kwargs and kwargs['adapters'] is not None:
             raise ValueError("Adapters are not supported for OVPipelineForText2Image.")
 
+        model_kwargs = {"from_onnx": kwargs.get('from_onnx', False), "safety_checker": None}
         try:
             model = TEXT2IMAGEPipeline.from_pretrained(
-                model_id, device=device, ov_config=ov_config, safety_checker=None,
+                model_id, device=device, ov_config=ov_config, **model_kwargs
             )
         except ValueError:
             model = TEXT2IMAGEPipeline.from_pretrained(
@@ -283,7 +284,7 @@ def load_text2image_model(
                 use_cache=True,
                 device=device,
                 ov_config=ov_config,
-                safety_checker=None,
+                **model_kwargs
             )
 
     return model
@@ -412,7 +413,7 @@ def load_image2image_genai_pipeline(model_dir, device="CPU", ov_config=None):
 
 
 def load_imagetext2image_model(
-    model_id, device="CPU", ov_config=None, use_hf=False, use_genai=False
+    model_id, device="CPU", ov_config=None, use_hf=False, use_genai=False, **kwargs
 ):
     if use_hf:
         from diffusers import AutoPipelineForImage2Image
@@ -426,9 +427,10 @@ def load_imagetext2image_model(
     else:
         logger.info("Using Optimum API")
         from optimum.intel.openvino import OVPipelineForImage2Image
+        model_kwargs = {"from_onnx": kwargs.get('from_onnx', False), "safety_checker": None}
         try:
             model = OVPipelineForImage2Image.from_pretrained(
-                model_id, device=device, ov_config=ov_config, safety_checker=None,
+                model_id, device=device, ov_config=ov_config, **model_kwargs
             )
         except ValueError:
             model = OVPipelineForImage2Image.from_pretrained(
@@ -437,7 +439,7 @@ def load_imagetext2image_model(
                 use_cache=True,
                 device=device,
                 ov_config=ov_config,
-                safety_checker=None,
+                **model_kwargs
             )
     return model
 
@@ -457,7 +459,7 @@ def load_inpainting_genai_pipeline(model_dir, device="CPU", ov_config=None):
 
 
 def load_inpainting_model(
-    model_id, device="CPU", ov_config=None, use_hf=False, use_genai=False
+    model_id, device="CPU", ov_config=None, use_hf=False, use_genai=False, **kwargs
 ):
     if use_hf:
         from diffusers import AutoPipelineForInpainting
@@ -471,9 +473,10 @@ def load_inpainting_model(
     else:
         logger.info("Using Optimum API")
         from optimum.intel.openvino import OVPipelineForInpainting
+        model_kwargs = {"from_onnx": kwargs.get('from_onnx', False), "safety_checker": None}
         try:
             model = OVPipelineForInpainting.from_pretrained(
-                model_id, device=device, ov_config=ov_config, safety_checker=None,
+                model_id, device=device, ov_config=ov_config, **model_kwargs
             )
         except ValueError as e:
             logger.error("Failed to load inpaiting pipeline. Details:\n", e)
@@ -483,7 +486,7 @@ def load_inpainting_model(
                 use_cache=True,
                 device=device,
                 ov_config=ov_config,
-                safety_checker=None,
+                **model_kwargs
             )
     return model
 
@@ -631,9 +634,9 @@ def load_model(
     elif model_type == "visual-text":
         return load_visual_text_model(model_id, device, ov_options, use_hf, use_genai, **kwargs)
     elif model_type == "image-to-image":
-        return load_imagetext2image_model(model_id, device, ov_options, use_hf, use_genai)
+        return load_imagetext2image_model(model_id, device, ov_options, use_hf, use_genai, **kwargs)
     elif model_type == "image-inpainting":
-        return load_inpainting_model(model_id, device, ov_options, use_hf, use_genai)
+        return load_inpainting_model(model_id, device, ov_options, use_hf, use_genai, **kwargs)
     elif model_type == "text-embedding":
         return load_embedding_model(model_id, device, ov_options, use_hf, use_genai, **kwargs)
     elif model_type == "text-reranking":