diff --git a/modules/onnx_impl/__init__.py b/modules/onnx_impl/__init__.py
index 73187c794..709917c5a 100644
--- a/modules/onnx_impl/__init__.py
+++ b/modules/onnx_impl/__init__.py
@@ -147,32 +147,13 @@ class VAE(TorchCompatibleModule):
         return self
 
 
-def preprocess_pipeline(p, refiner_enabled: bool):
+def preprocess_pipeline(p):
     from modules import shared, sd_models
 
     if "ONNX" not in shared.opts.diffusers_pipeline:
         shared.log.warning(f"Unsupported pipeline for 'olive-ai' compile backend: {shared.opts.diffusers_pipeline}. You should select one of the ONNX pipelines.")
         return shared.sd_model
 
-    if shared.opts.cuda_compile_backend == "olive-ai" and len(shared.opts.cuda_compile) != 1:
-        compile_height = p.height
-        compile_width = p.width
-        if (shared.compiled_model_state is None or
-        shared.compiled_model_state.height != compile_height
-        or shared.compiled_model_state.width != compile_width
-        or shared.compiled_model_state.batch_size != p.batch_size):
-            shared.log.info("Olive: Parameter change detected")
-            shared.log.info("Olive: Recompiling base model")
-            sd_models.unload_model_weights(op='model')
-            sd_models.reload_model_weights(op='model')
-            if refiner_enabled:
-                shared.log.info("Olive: Recompiling refiner")
-                sd_models.unload_model_weights(op='refiner')
-                sd_models.reload_model_weights(op='refiner')
-        shared.compiled_model_state.height = compile_height
-        shared.compiled_model_state.width = compile_width
-        shared.compiled_model_state.batch_size = p.batch_size
-
     if hasattr(shared.sd_model, "preprocess"):
         shared.sd_model = shared.sd_model.preprocess(p)
     if hasattr(shared.sd_refiner, "preprocess"):
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index afc75a7ed..bcf7fd3cd 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -357,8 +357,29 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
                 p.task_args['sag_scale'] = p.sag_scale
             else:
                 shared.log.warning(f'SAG incompatible scheduler: current={sd_model.scheduler.__class__.__name__} supported={supported}')
-        if sd_model.__class__.__name__ == "OnnxRawPipeline":
-            sd_model = preprocess_onnx_pipeline(p, is_refiner_enabled())
+
+        pipeline_name = sd_model.__class__.__name__
+        if shared.opts.cuda_compile_backend == "olive-ai" and pipeline_name.startswith("Onnx"):
+            compile_height = p.height
+            compile_width = p.width
+            if (shared.compiled_model_state is None or
+            shared.compiled_model_state.height != compile_height
+            or shared.compiled_model_state.width != compile_width
+            or shared.compiled_model_state.batch_size != p.batch_size):
+                shared.log.info("Olive: Parameter change detected")
+                shared.log.info("Olive: Recompiling base model")
+                sd_models.unload_model_weights(op='model')
+                sd_models.reload_model_weights(op='model')
+                if is_refiner_enabled():
+                    shared.log.info("Olive: Recompiling refiner")
+                    sd_models.unload_model_weights(op='refiner')
+                    sd_models.reload_model_weights(op='refiner')
+            shared.compiled_model_state.height = compile_height
+            shared.compiled_model_state.width = compile_width
+            shared.compiled_model_state.batch_size = p.batch_size
+            pipeline_name = shared.sd_model.__class__.__name__
+        if pipeline_name == "OnnxRawPipeline":
+            sd_model = preprocess_onnx_pipeline(p)
             nonlocal orig_pipeline
             orig_pipeline = sd_model # processed ONNX pipeline should not be replaced with original pipeline.
         return sd_model
@@ -468,7 +489,7 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
                 recompile_model(hires=True)
                 shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
                 if shared.sd_model.__class__.__name__ == "OnnxRawPipeline":
-                    shared.sd_model = preprocess_onnx_pipeline(p, is_refiner_enabled())
+                    shared.sd_model = preprocess_onnx_pipeline(p)
                 update_sampler(shared.sd_model, second_pass=True)
                 hires_args = set_pipeline_args(
                     model=shared.sd_model,