diff --git a/modules/prompt_parser_diffusers.py b/modules/prompt_parser_diffusers.py
index 10c1bc3db..fd50ce14e 100644
--- a/modules/prompt_parser_diffusers.py
+++ b/modules/prompt_parser_diffusers.py
@@ -1,11 +1,16 @@
 import torch
 import modules.shared as shared
 from compel import Compel, ReturnedEmbeddingsType
+import diffusers
+import typing
 
-def compel_encode_prompt(pipeline, prompt, negative_prompt, prompt_2=None, negative_prompt_2=None, refiner=False):
-    if "XL" not in pipeline.__class__.__name__:
-      print(f"Compel parser is not configured for: {pipeline.__class__.__name__}")
-      return None, None, None, None
+def compel_encode_prompt(pipeline: typing.Any, *args, **kwargs):
+   compel_encode_fn = COMPEL_ENCODE_FN_DICT.get(type(pipeline), None)
+   if compel_encode_fn is None:
+      raise TypeError(f"Compel encoding not yet supported for {type(pipeline).__name__}.")
+   return compel_encode_fn(pipeline, *args, **kwargs)
+
+def compel_encode_prompt_sdxl(pipeline: diffusers.StableDiffusionXLPipeline, prompt: str, negative_prompt: str, prompt_2: typing.Optional[str]=None, negative_prompt_2: typing.Optional[str]=None, refiner=False):
     compel_te1 = Compel(
       tokenizer=pipeline.tokenizer,
       text_encoder=pipeline.text_encoder,
@@ -19,7 +24,7 @@ def compel_encode_prompt(pipeline, prompt, negative_prompt, prompt_2=None, negat
       returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
       requires_pooled=True,
       )
-    if not refiner:
+    if refiner is None:
       positive_te1 = compel_te1(prompt)
       positive_te2, pooled = compel_te2(prompt_2)
       positive = torch.cat((positive_te1, positive_te2), dim=-1)
@@ -27,10 +32,12 @@ def compel_encode_prompt(pipeline, prompt, negative_prompt, prompt_2=None, negat
       negative_te1 = compel_te1(negative_prompt)
       negative_te2, negative_pooled = compel_te2(negative_prompt_2)
       negative = torch.cat((negative_te1, negative_te2), dim=-1)
-    if refiner:
+    else:
       positive, pooled = compel_te2(prompt)
       negative, negative_pooled = compel_te2(negative_prompt)
     
     
     [prompt_embed, negative_embed] = compel_te2.pad_conditioning_tensors_to_same_length([positive, negative])
-    return prompt_embed, pooled, negative_embed, negative_pooled
\ No newline at end of file
+    return prompt_embed, pooled, negative_embed, negative_pooled
+
+COMPEL_ENCODE_FN_DICT = {diffusers.StableDiffusionXLPipeline: compel_encode_prompt_sdxl}
\ No newline at end of file