diff --git a/timm/layers/pos_embed_sincos.py b/timm/layers/pos_embed_sincos.py
index ac4d3fc97a..ddf60140f1 100644
--- a/timm/layers/pos_embed_sincos.py
+++ b/timm/layers/pos_embed_sincos.py
@@ -85,13 +85,9 @@ def swap_shape_xy(seq: List[int]) -> List[int]:
     return [seq[1], seq[0]] + list(seq[2:])
 
 
-def build_fourier_pos_embed(
+def _build_fourier_pos_embed(
         feat_shape: List[int],
-        bands: Optional[torch.Tensor] = None,
-        num_bands: int = 64,
-        max_res: int = 224,
-        temperature: float = 10000.,
-        linear_bands: bool = False,
+        bands: torch.Tensor,
         include_grid: bool = False,
         in_pixels: bool = True,
         ref_feat_shape: Optional[List[int]] = None,
@@ -100,46 +96,10 @@ def build_fourier_pos_embed(
         device: Optional[torch.device] = None,
         dtype: torch.dtype = torch.float32,
 ) -> List[torch.Tensor]:
-    """
-
-    Args:
-        feat_shape: Feature shape for embedding.
-        bands: Pre-calculated frequency bands.
-        num_bands: Number of frequency bands (determines output dim).
-        max_res: Maximum resolution for pixel based freq.
-        temperature: Temperature for non-pixel freq.
-        linear_bands: Linear band spacing for pixel based freq.
-        include_grid: Include the spatial grid in output.
-        in_pixels: Output in pixel freq.
-        ref_feat_shape: Reference feature shape for resize / fine-tune.
-        grid_offset: Constant offset to add to grid for non-pixel freq.
-        grid_indexing: Indexing mode for meshgrid ('ij' or 'xy')
-        dtype: Output dtype.
-        device: Output device.
-
-    Returns:
-
-    """
-    if bands is None:
-        if in_pixels:
-            bands = pixel_freq_bands(
-                num_bands,
-                float(max_res),
-                linear_bands=linear_bands,
-                device=device,
-            )
-        else:
-            bands = freq_bands(
-                num_bands,
-                temperature=temperature,
-                step=1,
-                device=device,
-            )
-    else:
-        if device is None:
-            device = bands.device
-        if dtype is None:
-            dtype = bands.dtype
+    if device is None:
+        device = bands.device
+    if dtype is None:
+        dtype = bands.dtype
 
     if grid_indexing == 'xy':
         feat_shape = swap_shape_xy(feat_shape)
@@ -170,6 +130,92 @@ def build_fourier_pos_embed(
     return out
 
 
+def _compute_bands(
+        bands: Optional[torch.Tensor],
+        num_bands: int,
+        max_res: int,
+        temperature: float,
+        linear_bands: bool,
+        in_pixels: bool,
+        device: Optional[torch.device],
+        dtype: torch.dtype,
+) -> torch.Tensor:
+    if bands is None:
+        if in_pixels:
+            bands = pixel_freq_bands(
+                num_bands,
+                float(max_res),
+                linear_bands=linear_bands,
+                device=device,
+            )
+        else:
+            bands = freq_bands(
+                num_bands,
+                temperature=temperature,
+                step=1,
+                device=device,
+            )
+    return bands
+
+
+def build_fourier_pos_embed(
+        feat_shape: List[int],
+        bands: Optional[torch.Tensor] = None,
+        num_bands: int = 64,
+        max_res: int = 224,
+        temperature: float = 10000.,
+        linear_bands: bool = False,
+        include_grid: bool = False,
+        in_pixels: bool = True,
+        ref_feat_shape: Optional[List[int]] = None,
+        grid_offset: float = 0.,
+        grid_indexing: str = 'ij',
+        device: Optional[torch.device] = None,
+        dtype: torch.dtype = torch.float32,
+) -> List[torch.Tensor]:
+    """
+
+    Args:
+        feat_shape: Feature shape for embedding.
+        bands: Pre-calculated frequency bands.
+        num_bands: Number of frequency bands (determines output dim).
+        max_res: Maximum resolution for pixel based freq.
+        temperature: Temperature for non-pixel freq.
+        linear_bands: Linear band spacing for pixel based freq.
+        include_grid: Include the spatial grid in output.
+        in_pixels: Output in pixel freq.
+        ref_feat_shape: Reference feature shape for resize / fine-tune.
+        grid_offset: Constant offset to add to grid for non-pixel freq.
+        grid_indexing: Indexing mode for meshgrid ('ij' or 'xy')
+        dtype: Output dtype.
+        device: Output device.
+
+    Returns:
+
+    """
+    bands = _compute_bands(
+        bands=bands,
+        num_bands=num_bands,
+        max_res=max_res,
+        temperature=temperature,
+        linear_bands=linear_bands,
+        in_pixels=in_pixels,
+        device=device,
+        dtype=dtype,
+    )
+    return _build_fourier_pos_embed(
+        feat_shape,
+        bands,
+        include_grid,
+        in_pixels,
+        ref_feat_shape,
+        grid_offset,
+        grid_indexing,
+        device,
+        dtype,
+    )
+
+
 class FourierEmbed(nn.Module):
 
     def __init__(
@@ -206,7 +252,7 @@ def init_non_persistent_buffers(self) -> None:
     def forward(self, x):
         B, C = x.shape[:2]
         feat_shape = x.shape[2:]
-        emb = build_fourier_pos_embed(
+        emb = _build_fourier_pos_embed(
             feat_shape,
             self.bands,
             include_grid=self.concat_grid,
@@ -336,6 +382,35 @@ def apply_keep_indices_nlc(
     return pos_embed.gather(-2, keep_indices)
 
 
+def _build_rotary_pos_embed(
+        feat_shape: List[int],
+        bands: torch.Tensor,
+        in_pixels: bool = True,
+        ref_feat_shape: Optional[List[int]] = None,
+        grid_offset: float = 0.,
+        grid_indexing: str = 'ij',
+        device: Optional[torch.device] = None,
+        dtype: torch.dtype = torch.float32,
+):
+    sin_emb, cos_emb = _build_fourier_pos_embed(
+        feat_shape,
+        bands=bands,
+        in_pixels=in_pixels,
+        ref_feat_shape=ref_feat_shape,
+        grid_offset=grid_offset,
+        grid_indexing=grid_indexing,
+        device=device,
+        dtype=dtype,
+    )
+    num_spatial_dim = 1
+    # this would be much nicer as a .numel() call to torch.Size(), but torchscript sucks
+    for x in feat_shape:
+        num_spatial_dim *= x
+    sin_emb = sin_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1)
+    cos_emb = cos_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1)
+    return sin_emb, cos_emb
+
+
 def build_rotary_pos_embed(
         feat_shape: List[int],
         bands: Optional[torch.Tensor] = None,
@@ -369,27 +444,26 @@ def build_rotary_pos_embed(
     Returns:
 
     """
-    sin_emb, cos_emb = build_fourier_pos_embed(
-        feat_shape,
+    bands = _compute_bands(
         bands=bands,
         num_bands=dim // 4,
         max_res=max_res,
         temperature=temperature,
         linear_bands=linear_bands,
         in_pixels=in_pixels,
-        ref_feat_shape=ref_feat_shape,
-        grid_offset=grid_offset,
-        grid_indexing=grid_indexing,
         device=device,
         dtype=dtype,
     )
-    num_spatial_dim = 1
-    # this would be much nicer as a .numel() call to torch.Size(), but torchscript sucks
-    for x in feat_shape:
-        num_spatial_dim *= x
-    sin_emb = sin_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1)
-    cos_emb = cos_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1)
-    return sin_emb, cos_emb
+    return _build_rotary_pos_embed(
+        feat_shape,
+        bands,
+        in_pixels,
+        ref_feat_shape,
+        grid_offset,
+        grid_indexing,
+        device,
+        dtype,
+    )
 
 
 class RotaryEmbedding(nn.Module):
@@ -480,12 +554,10 @@ def _compute_bands(self, device=None, dtype=None):
         return bands.to(device=device, dtype=dtype)
 
     def _get_pos_embed_values(self, feat_shape: List[int], device=None, dtype=torch.float32):
-        emb_sin, emb_cos = build_rotary_pos_embed(
+        bands = self._compute_bands(device, dtype)
+        emb_sin, emb_cos = _build_rotary_pos_embed(
             feat_shape=feat_shape,
-            dim=self.dim,
-            max_res=self.max_res,
-            temperature=self.temperature,
-            linear_bands=self.linear_bands,
+            bands=bands,
             in_pixels=self.in_pixels,
             ref_feat_shape=self.ref_feat_shape,
             grid_offset=self.grid_offset,
@@ -514,7 +586,7 @@ def update_feat_shape(self, feat_shape: List[int]):
     def get_embed(self, shape: Optional[List[int]] = None):
         if shape is not None and self.bands is not None:
             # rebuild embeddings every call, use if target shape changes
-            return build_rotary_pos_embed(
+            return _build_rotary_pos_embed(
                 shape,
                 self.bands,
                 in_pixels=self.in_pixels,
@@ -614,12 +686,10 @@ def _compute_bands(self, device=None, dtype=None):
         return bands.to(device=device, dtype=dtype)
 
     def _get_pos_embed_values(self, feat_shape: List[int], device=None, dtype=torch.float32):
-        embeds = build_rotary_pos_embed(
+        bands = self._compute_bands(device, dtype)
+        embeds = _build_rotary_pos_embed(
             feat_shape=feat_shape,
-            dim=self.dim,
-            max_res=self.max_res,
-            temperature=self.temperature,
-            linear_bands=self.linear_bands,
+            bands=bands,
             in_pixels=self.in_pixels,
             ref_feat_shape=self.ref_feat_shape,
             grid_offset=self.grid_offset,
@@ -647,7 +717,7 @@ def update_feat_shape(self, feat_shape: List[int]):
     def get_embed(self, shape: Optional[List[int]] = None):
         if shape is not None and self.bands is not None:
             # rebuild embeddings from cached bands every call, use if target shape changes
-            embeds = build_rotary_pos_embed(
+            embeds = _build_rotary_pos_embed(
                 shape,
                 self.bands,
                 in_pixels=self.in_pixels,
@@ -691,7 +761,7 @@ def get_batch_embeds(
         max_w = max(w for h, w in shapes)
 
         # Generate embeddings for max size ONCE
-        sin_emb, cos_emb = build_rotary_pos_embed(
+        sin_emb, cos_emb = _build_rotary_pos_embed(
             feat_shape=(max_h, max_w),
             bands=self.bands,
             in_pixels=self.in_pixels,