diff --git a/src/diffusers/models/transformers/transformer_qwenimage.py b/src/diffusers/models/transformers/transformer_qwenimage.py
index 05379270c13b..c0fa031b9faf 100644
--- a/src/diffusers/models/transformers/transformer_qwenimage.py
+++ b/src/diffusers/models/transformers/transformer_qwenimage.py
@@ -180,7 +180,6 @@ def __init__(self, theta: int, axes_dim: List[int], scale_rope=False):
             ],
             dim=1,
         )
-        self.rope_cache = {}
 
         # DO NOT USING REGISTER BUFFER HERE, IT WILL CAUSE COMPLEX NUMBERS LOSE ITS IMAGINARY PART
         self.scale_rope = scale_rope
@@ -195,10 +194,20 @@ def rope_params(self, index, dim, theta=10000):
         freqs = torch.polar(torch.ones_like(freqs), freqs)
         return freqs
 
-    def forward(self, video_fhw, txt_seq_lens, device):
+    def forward(
+        self,
+        video_fhw: Union[Tuple[int, int, int], List[Tuple[int, int, int]]],
+        txt_seq_lens: List[int],
+        device: torch.device,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         """
-        Args: video_fhw: [frame, height, width] a list of 3 integers representing the shape of the video Args:
-        txt_length: [bs] a list of 1 integers representing the length of the text
+        Args:
+            video_fhw (`Tuple[int, int, int]` or `List[Tuple[int, int, int]]`):
+                A list of 3 integers [frame, height, width] representing the shape of the video.
+            txt_seq_lens (`List[int]`):
+                A list of integers of length batch_size representing the length of each text prompt.
+            device: (`torch.device`):
+                The device on which to perform the RoPE computation.
         """
         if self.pos_freqs.device != device:
             self.pos_freqs = self.pos_freqs.to(device)
@@ -213,14 +222,8 @@ def forward(self, video_fhw, txt_seq_lens, device):
         max_vid_index = 0
         for idx, fhw in enumerate(video_fhw):
             frame, height, width = fhw
-            rope_key = f"{idx}_{height}_{width}"
-
-            if not torch.compiler.is_compiling():
-                if rope_key not in self.rope_cache:
-                    self.rope_cache[rope_key] = self._compute_video_freqs(frame, height, width, idx)
-                video_freq = self.rope_cache[rope_key]
-            else:
-                video_freq = self._compute_video_freqs(frame, height, width, idx)
+            # RoPE frequencies are cached via a lru_cache decorator on _compute_video_freqs
+            video_freq = self._compute_video_freqs(frame, height, width, idx)
             video_freq = video_freq.to(device)
             vid_freqs.append(video_freq)
 
@@ -235,8 +238,8 @@ def forward(self, video_fhw, txt_seq_lens, device):
 
         return vid_freqs, txt_freqs
 
-    @functools.lru_cache(maxsize=None)
-    def _compute_video_freqs(self, frame, height, width, idx=0):
+    @functools.lru_cache(maxsize=128)
+    def _compute_video_freqs(self, frame: int, height: int, width: int, idx: int = 0) -> torch.Tensor:
         seq_lens = frame * height * width
         freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
         freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)