compact code following @limiteinductive suggestion https://github.com…

…/finegrain-ai/refiners/pull/263\#discussion_r1488512476

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py

@@ -76,20 +76,11 @@ def compute_clip_text_embedding(self, text: str | list[str], negative_text: str
             negative_text: The negative prompt to compute the CLIP text embedding of.
                 If not provided, the negative prompt is assumed to be empty (i.e., `""`).
         """
+        text = [text] if isinstance(text, str) else text
+        negative_text = [negative_text] if isinstance(negative_text, str) else negative_text
+        assert len(text) == len(negative_text), "The length of the text list and negative_text should be the same"
         conditional_embedding = self.clip_text_encoder(text)
-        if text == negative_text:
-            negative_embedding = conditional_embedding
-        else:
-            if isinstance(text, list) and isinstance(negative_text, list):
-                assert len(text) == len(
-                    negative_text
-                ), "The length of the text list and negative_text should be the same"
-
-            if isinstance(negative_text, str) and isinstance(text, list):
-                negative_text = [negative_text] * len(text)
-
-            negative_embedding = self.clip_text_encoder(negative_text)
-
+        negative_embedding = self.clip_text_encoder(negative_text) if negative_text else conditional_embedding
         return cat((negative_embedding, conditional_embedding))
 
     def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None: