diff --git a/optimum/exporters/onnx/utils.py b/optimum/exporters/onnx/utils.py
index c1bee9a4da..24a809a977 100644
--- a/optimum/exporters/onnx/utils.py
+++ b/optimum/exporters/onnx/utils.py
@@ -100,17 +100,17 @@ def _get_submodels_for_export_stable_diffusion(
     """
     Returns the components of a Stable Diffusion model.
     """
-    from diffusers import StableDiffusionXLPipeline
+    from diffusers import StableDiffusionXLImg2ImgPipeline
 
     models_for_export = {}
-    if isinstance(pipeline, StableDiffusionXLPipeline):
+    if isinstance(pipeline, StableDiffusionXLImg2ImgPipeline):
         projection_dim = pipeline.text_encoder_2.config.projection_dim
     else:
         projection_dim = pipeline.text_encoder.config.projection_dim
 
     # Text encoder
     if pipeline.text_encoder is not None:
-        if isinstance(pipeline, StableDiffusionXLPipeline):
+        if isinstance(pipeline, StableDiffusionXLImg2ImgPipeline):
             pipeline.text_encoder.config.output_hidden_states = True
         models_for_export["text_encoder"] = pipeline.text_encoder
 
@@ -118,6 +118,9 @@ def _get_submodels_for_export_stable_diffusion(
     # PyTorch does not support the ONNX export of torch.nn.functional.scaled_dot_product_attention
     pipeline.unet.set_attn_processor(AttnProcessor())
     pipeline.unet.config.text_encoder_projection_dim = projection_dim
+    # The U-NET time_ids inputs shapes depends on the value of `requires_aesthetics_score`
+    # https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py#L571
+    pipeline.unet.config.requires_aesthetics_score = getattr(pipeline.config, "requires_aesthetics_score", False)
     models_for_export["unet"] = pipeline.unet
 
     # VAE Encoder https://github.com/huggingface/diffusers/blob/v0.11.1/src/diffusers/models/vae.py#L565
diff --git a/optimum/exporters/tasks.py b/optimum/exporters/tasks.py
index 2f3c432968..2f1654bbbd 100644
--- a/optimum/exporters/tasks.py
+++ b/optimum/exporters/tasks.py
@@ -171,7 +171,7 @@ class TasksManager:
             "audio-xvector": "AutoModelForAudioXVector",
             "image-to-text": "AutoModelForVision2Seq",
             "stable-diffusion": "StableDiffusionPipeline",
-            "stable-diffusion-xl": "StableDiffusionXLPipeline",
+            "stable-diffusion-xl": "StableDiffusionXLImg2ImgPipeline",
             "zero-shot-image-classification": "AutoModelForZeroShotImageClassification",
             "zero-shot-object-detection": "AutoModelForZeroShotObjectDetection",
         }
diff --git a/optimum/utils/input_generators.py b/optimum/utils/input_generators.py
index d88f21fd2b..d062a29d7e 100644
--- a/optimum/utils/input_generators.py
+++ b/optimum/utils/input_generators.py
@@ -622,6 +622,7 @@ def __init__(
         self.task = task
         self.vocab_size = normalized_config.vocab_size
         self.text_encoder_projection_dim = normalized_config.text_encoder_projection_dim
+        self.time_ids = 5 if normalized_config.requires_aesthetics_score else 6
         if random_batch_size_range:
             low, high = random_batch_size_range
             self.batch_size = random.randint(low, high)
@@ -634,7 +635,7 @@ def generate(self, input_name: str, framework: str = "pt"):
         if input_name == "timestep":
             return self.random_int_tensor(shape, max_value=self.vocab_size, framework=framework)
 
-        shape.append(self.text_encoder_projection_dim if input_name == "text_embeds" else 6)
+        shape.append(self.text_encoder_projection_dim if input_name == "text_embeds" else self.time_ids)
         return self.random_float_tensor(shape, max_value=self.vocab_size, framework=framework)