diff --git a/MindIE/MultiModal/Flux.1-DEV/FLUX1dev/layers/attention_processor.py b/MindIE/MultiModal/Flux.1-DEV/FLUX1dev/layers/attention_processor.py
index cad77988f9f5afcfa8cfb871a967531cfca34bac..4fb6370b5dd9ec4c56acebb185cc71198f51a6e0 100644
--- a/MindIE/MultiModal/Flux.1-DEV/FLUX1dev/layers/attention_processor.py
+++ b/MindIE/MultiModal/Flux.1-DEV/FLUX1dev/layers/attention_processor.py
@@ -30,8 +30,10 @@ from ..utils import get_local_rank, get_world_size
 logger = logging.get_logger(__name__)
 
 
-def apply_rotary_emb_mindspeed(x, freqs_cis):
+def apply_rotary_emb_mindiesd(x, freqs_cis):
     cos, sin = freqs_cis
+    cos = cos[None, None]
+    sin = sin[None, None]
     cos, sin = cos.to(x.device), sin.to(x.device)
 
     return rotary_position_embedding(x, cos, sin, rotated_mode="rotated_interleaved", head_first=False, fused=True)
@@ -374,8 +376,8 @@ class FluxSingleAttnProcessor2_0:
 
         # Apply RoPE if needed
         if image_rotary_emb is not None:
-            query = apply_rotary_emb_mindspeed(query, image_rotary_emb)
-            key = apply_rotary_emb_mindspeed(key, image_rotary_emb)
+            query = apply_rotary_emb_mindiesd(query, image_rotary_emb)
+            key = apply_rotary_emb_mindiesd(key, image_rotary_emb)
 
         # the output of sdp = (batch, num_heads, seq_len, head_dim)
         hidden_states = apply_fa(query, key, value, attention_mask)
@@ -465,8 +467,8 @@ class FluxAttnProcessor2_0:
         value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
 
         if image_rotary_emb is not None:
-            query = apply_rotary_emb_mindspeed(query, image_rotary_emb)
-            key = apply_rotary_emb_mindspeed(key, image_rotary_emb)
+            query = apply_rotary_emb_mindiesd(query, image_rotary_emb)
+            key = apply_rotary_emb_mindiesd(key, image_rotary_emb)
 
         
         hidden_states = apply_fa(query, key, value, attention_mask)