diff --git a/assets/sdxl_conversion.png b/assets/sdxl_conversion.png
new file mode 100644
index 0000000..ba5168a
Binary files /dev/null and b/assets/sdxl_conversion.png differ
diff --git a/coreml_suite/converter.py b/coreml_suite/converter.py
index fd5edcf..875e6f1 100644
--- a/coreml_suite/converter.py
+++ b/coreml_suite/converter.py
@@ -52,7 +52,11 @@ def get_unet(model_type: ModelVersion, ref_pipe):
 
 
 def get_encoder_hidden_states_shape(ref_pipe, batch_size):
-    text_encoder = ref_pipe.text_encoder
+    text_encoder = (
+        ref_pipe.text_encoder_2
+        if hasattr(ref_pipe, "text_encoder_2")
+        else ref_pipe.text_encoder
+    )
 
     text_token_sequence_length = text_encoder.config.max_position_embeddings
     hidden_size = (text_encoder.config.hidden_size,)
@@ -166,11 +170,21 @@ def sdxl_inputs(sample_unet_inputs, ref_pipe):
     batch_size = sample_shape[0]
     h = sample_shape[2] * 8
     w = sample_shape[3] * 8
-    original_size = (h, w)  # output_resolution
-    crops_coords_top_left = (0, 0)  # topleft_crop_cond
-    target_size = (h, w)  # resolution_cond
+    original_size = (h, w)
+    crops_coords_top_left = (0, 0)
+
+    is_refiner = (
+        hasattr(ref_pipe.config, "requires_aesthetics_score")
+        and ref_pipe.config.requires_aesthetics_score
+    )
+
+    if is_refiner:
+        aesthetic_score = (6.0,)
+        time_ids_list = list(original_size + crops_coords_top_left + aesthetic_score)
+    else:
+        target_size = (h, w)
+        time_ids_list = list(original_size + crops_coords_top_left + target_size)
 
-    time_ids_list = list(original_size + crops_coords_top_left + target_size)
     time_ids = torch.tensor(time_ids_list).repeat(batch_size, 1).to(torch.int64)
     text_embeds_shape = (batch_size, ref_pipe.text_encoder_2.config.hidden_size)
 
diff --git a/coreml_suite/models.py b/coreml_suite/models.py
index c2905c4..05a506d 100644
--- a/coreml_suite/models.py
+++ b/coreml_suite/models.py
@@ -192,7 +192,7 @@ def is_sdxl_refiner(coreml_model):
     )
 
 
-def sdxl_model_function_wrapper(time_ids, text_embeds):
+def sdxl_model_function_wrapper(time_ids, text_embeds, refiner=False):
     def wrapper(model_function, params):
         x = params["input"]
         t = params["timestep"]
@@ -203,6 +203,10 @@ def sdxl_model_function_wrapper(time_ids, text_embeds):
         if context is None:
             return torch.zeros_like(x)
 
+        if refiner and context is not None:
+            # converted refiner accepts only g clip
+            c["c_crossattn"] = context[:, :, 768:]
+
         return model_function(x, t, **c, time_ids=time_ids, text_embeds=text_embeds)
 
     return wrapper
@@ -214,6 +218,9 @@ def add_sdxl_model_options(model_patcher, positive, negative):
     pos_dict = positive[0][1]
     neg_dict = negative[0][1]
 
+    pos_pooled = pos_dict["pooled_output"]
+    neg_pooled = neg_dict["pooled_output"]
+
     pos_time_ids = [
         pos_dict.get("height", 768),
         pos_dict.get("width", 768),
@@ -229,35 +236,33 @@ def add_sdxl_model_options(model_patcher, positive, negative):
     ]
 
     if model_patcher.model.diffusion_model.is_sdxl_base:
-        base_pos_time_ids = [
+        pos_time_ids += [
             pos_dict.get("target_height", 768),
             pos_dict.get("target_width", 768),
         ]
-        pos_time_ids += base_pos_time_ids
 
-        base_neg_time_ids = [
+        neg_time_ids += [
             neg_dict.get("target_height", 768),
             neg_dict.get("target_width", 768),
         ]
-        neg_time_ids += base_neg_time_ids
 
-    if model_patcher.model.diffusion_model.is_sdxl_refiner:
-        refiner_pos_time_ids = [
+    is_refiner = model_patcher.model.diffusion_model.is_sdxl_refiner
+    if is_refiner:
+        pos_time_ids += [
             pos_dict.get("aesthetic_score", 6),
         ]
-        pos_time_ids += refiner_pos_time_ids
 
-        refiner_neg_time_ids = [
+        neg_time_ids += [
             neg_dict.get("aesthetic_score", 2.5),
         ]
-        neg_time_ids += refiner_neg_time_ids
 
     time_ids = torch.tensor([pos_time_ids, neg_time_ids])
-
-    text_embeds = torch.cat((pos_dict["pooled_output"], neg_dict["pooled_output"]))
+    text_embeds = torch.cat((pos_pooled, neg_pooled))
 
     model_options = {
-        "model_function_wrapper": sdxl_model_function_wrapper(time_ids, text_embeds),
+        "model_function_wrapper": sdxl_model_function_wrapper(
+            time_ids, text_embeds, is_refiner
+        ),
     }
     mp.model_options |= model_options