diff --git a/src/diffusers/models/transformers/transformer_ideogram4.py b/src/diffusers/models/transformers/transformer_ideogram4.py
index 121118e3bd80..03cc6c84a051 100644
--- a/src/diffusers/models/transformers/transformer_ideogram4.py
+++ b/src/diffusers/models/transformers/transformer_ideogram4.py
@@ -70,8 +70,12 @@ def forward(self, position_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tenso
             raise ValueError(f"`position_ids` must have shape (B, L, 3), got {tuple(position_ids.shape)}.")
         batch_size, seq_len, _ = position_ids.shape
 
-        pos = position_ids.permute(2, 0, 1).to(dtype=torch.float32)
-        inv_freq = self.inv_freq.to(dtype=torch.float32)[None, None, :, None].expand(3, batch_size, -1, 1)
+        # Rotary frequencies are computed in float64: Ideogram4's image positions start at
+        # IMAGE_POSITION_OFFSET (65536), which float32 cannot represent distinctly once an ambient
+        # autocast runs the matmul/cos/sin in bfloat16, collapsing every image position to the same
+        # embedding. float64 is not downcast by autocast, matching the float64 rope path Flux uses.
+        pos = position_ids.permute(2, 0, 1).to(dtype=torch.float64)
+        inv_freq = self.inv_freq.to(dtype=torch.float64)[None, None, :, None].expand(3, batch_size, -1, 1)
         freqs = inv_freq @ pos.unsqueeze(2)
         freqs = freqs.transpose(2, 3)  # (3, B, L, inv_freq_size)
 
@@ -83,7 +87,7 @@ def forward(self, position_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tenso
             freqs_t[..., idx] = freqs[axis][..., idx]
 
         emb = torch.cat((freqs_t, freqs_t), dim=-1)
-        return emb.cos(), emb.sin()
+        return emb.cos().float(), emb.sin().float()
 
 
 class Ideogram4AttnProcessor:
diff --git a/tests/models/transformers/test_models_transformer_ideogram4.py b/tests/models/transformers/test_models_transformer_ideogram4.py
index 31592ada64bc..d8e7318d501d 100644
--- a/tests/models/transformers/test_models_transformer_ideogram4.py
+++ b/tests/models/transformers/test_models_transformer_ideogram4.py
@@ -21,6 +21,7 @@
     IMAGE_POSITION_OFFSET,
     LLM_TOKEN_INDICATOR,
     OUTPUT_IMAGE_INDICATOR,
+    Ideogram4MRoPE,
 )
 from diffusers.utils.torch_utils import randn_tensor
 
@@ -164,3 +165,20 @@ def test_gradient_checkpointing_is_applied(self):
 
 class TestIdeogram4TransformerAttention(Ideogram4TransformerTesterConfig, AttentionTesterMixin):
     """Attention processor tests for Ideogram 4 Transformer."""
+
+
+def test_ideogram4_mrope_is_autocast_invariant():
+    # Ideogram4's image positions start at IMAGE_POSITION_OFFSET (65536), so the rotary matmul must
+    # run in float32: under an ambient autocast it would otherwise execute in bfloat16 and round every
+    # image position to the same value, collapsing all spatial information (the decoded image goes flat).
+    rope = Ideogram4MRoPE(head_dim=256, base=5_000_000, mrope_section=(24, 20, 20)).to(torch_device)
+    position_ids = torch.tensor([[[0, 0, 0], [0, 0, 1], [0, 63, 63]]], device=torch_device) + IMAGE_POSITION_OFFSET
+
+    cos_ref, sin_ref = rope(position_ids)
+    with torch.autocast(device_type=torch.device(torch_device).type, dtype=torch.bfloat16):
+        cos_ac, sin_ac = rope(position_ids)
+
+    # Distinct image positions must keep distinct embeddings, identical to the float32 computation.
+    assert not torch.equal(cos_ac[0, 0], cos_ac[0, 1])
+    assert torch.equal(cos_ac, cos_ref)
+    assert torch.equal(sin_ac, sin_ref)