diff --git a/src/diffusers/models/transformers/transformer_kandinsky.py b/src/diffusers/models/transformers/transformer_kandinsky.py
index 8d2bae11cb..680b456df3 100644
--- a/src/diffusers/models/transformers/transformer_kandinsky.py
+++ b/src/diffusers/models/transformers/transformer_kandinsky.py
@@ -174,14 +174,6 @@ def nablaT_v2(
     )
 
 
-def sdpa(q, k, v):
-    query = q.transpose(1, 2).contiguous()
-    key = k.transpose(1, 2).contiguous()
-    value = v.transpose(1, 2).contiguous()
-    out = F.scaled_dot_product_attention(query, key, value).transpose(1, 2).contiguous()
-    return out
-
-
 @torch.autocast(device_type="cuda", dtype=torch.float32)
 def apply_scale_shift_norm(norm, x, scale, shift):
     return (norm(x) * (scale + 1.0) + shift).to(torch.bfloat16)
@@ -355,7 +347,12 @@ class Kandinsky5SDPAAttentionProcessor(nn.Module):
         **kwargs,
     ):
         # Process attention with the given query, key, value tensors
-        out = sdpa(q=query, k=key, v=value).flatten(-2, -1)
+        
+        query = query.transpose(1, 2).contiguous()
+        key = key.transpose(1, 2).contiguous()
+        value = value.transpose(1, 2).contiguous()
+        out = F.scaled_dot_product_attention(query, key, value).transpose(1, 2).contiguous().flatten(-2, -1)
+
         return out
 
 
diff --git a/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py b/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
index 3e61ae0bf2..bdf7e41df9 100644
--- a/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
+++ b/src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py
@@ -260,7 +260,7 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
 
         return sparse_params
 
-    def _get_qwen_prompt_embeds(
+    def _encode_prompt_qwen(
         self,
         prompt: Union[str, List[str]],
         device: Optional[torch.device] = None,
@@ -314,7 +314,7 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
     
         return embeds.to(dtype), cu_seqlens
 
-    def _get_clip_prompt_embeds(
+    def _encode_prompt_clip(
         self,
         prompt: Union[str, List[str]],
         device: Optional[torch.device] = None,