diff --git a/src/diffusers/models/transformers/transformer_ltx2.py b/src/diffusers/models/transformers/transformer_ltx2.py
index 465408d94693..0ba202f92741 100644
--- a/src/diffusers/models/transformers/transformer_ltx2.py
+++ b/src/diffusers/models/transformers/transformer_ltx2.py
@@ -586,7 +586,11 @@ def get_mod_params(
         scale_shift_table: torch.Tensor, temb: torch.Tensor, batch_size: int
     ) -> tuple[torch.Tensor, ...]:
         num_ada_params = scale_shift_table.shape[0]
-        ada_values = scale_shift_table[None, None].to(temb.device) + temb.reshape(
+        # Cast to temb's dtype at the use site (matching the original implementation):
+        # checkpoints store the scale_shift tables in fp32 alongside bf16 weights, so
+        # without the cast the fp32 tables promote the modulated hidden states and the
+        # following linear layers fail on mixed dtypes.
+        ada_values = scale_shift_table[None, None].to(device=temb.device, dtype=temb.dtype) + temb.reshape(
             batch_size, temb.shape[1], num_ada_params, -1
         )
         ada_params = ada_values.unbind(dim=2)
@@ -1620,14 +1624,19 @@ def forward(
                 )
 
         # 6. Output layers (including unpatchification)
-        scale_shift_values = self.scale_shift_table[None, None] + embedded_timestep[:, :, None]
+        scale_shift_values = (
+            self.scale_shift_table[None, None].to(embedded_timestep.dtype) + embedded_timestep[:, :, None]
+        )
         shift, scale = scale_shift_values[:, :, 0], scale_shift_values[:, :, 1]
 
         hidden_states = self.norm_out(hidden_states)
         hidden_states = hidden_states * (1 + scale) + shift
         output = self.proj_out(hidden_states)
 
-        audio_scale_shift_values = self.audio_scale_shift_table[None, None] + audio_embedded_timestep[:, :, None]
+        audio_scale_shift_values = (
+            self.audio_scale_shift_table[None, None].to(audio_embedded_timestep.dtype)
+            + audio_embedded_timestep[:, :, None]
+        )
         audio_shift, audio_scale = audio_scale_shift_values[:, :, 0], audio_scale_shift_values[:, :, 1]
 
         audio_hidden_states = self.audio_norm_out(audio_hidden_states)
diff --git a/src/diffusers/pipelines/ltx2/connectors.py b/src/diffusers/pipelines/ltx2/connectors.py
index 8a00a0c6b452..cedfb219c937 100644
--- a/src/diffusers/pipelines/ltx2/connectors.py
+++ b/src/diffusers/pipelines/ltx2/connectors.py
@@ -302,13 +302,19 @@ def forward(
             if binary_attn_mask.ndim == 4:
                 binary_attn_mask = binary_attn_mask.squeeze(1).squeeze(1)  # [B, 1, 1, L] --> [B, L]
 
-            # Replace padding positions with learned registers using vectorized masking
-            mask = binary_attn_mask.unsqueeze(-1)  # [B, L, 1]
+            # Move the valid tokens to the front in their original order and fill the tail
+            # with registers indexed by absolute position, matching the original LTX
+            # implementation (`_replace_padded_with_learnable_registers`). A stable argsort
+            # of the inverted mask gathers valid tokens first while preserving their order.
+            order = torch.argsort(1 - binary_attn_mask, dim=1, stable=True)  # [B, L]
+            front_aligned = torch.gather(
+                hidden_states, 1, order.unsqueeze(-1).expand(-1, -1, hidden_states.shape[-1])
+            )
+            num_valid = binary_attn_mask.sum(dim=1, keepdim=True)  # [B, 1]
+            positions = torch.arange(seq_len, device=hidden_states.device).unsqueeze(0)  # [1, L]
+            front_mask = (positions < num_valid).unsqueeze(-1)  # [B, L, 1]
             registers_expanded = registers.unsqueeze(0).expand(batch_size, -1, -1)  # [B, L, D]
-            hidden_states = mask * hidden_states + (1 - mask) * registers_expanded
-
-            # Flip sequence: embeddings move to front, registers to back (from left padding layout)
-            hidden_states = torch.flip(hidden_states, dims=[1])
+            hidden_states = torch.where(front_mask, front_aligned, registers_expanded.to(hidden_states.dtype))
 
             # Overwrite attention_mask with an all-zeros mask if using registers.
             attention_mask = torch.zeros_like(attention_mask)
diff --git a/tests/models/transformers/test_models_transformer_ltx2.py b/tests/models/transformers/test_models_transformer_ltx2.py
index e0e858bb6916..42202303e4f3 100644
--- a/tests/models/transformers/test_models_transformer_ltx2.py
+++ b/tests/models/transformers/test_models_transformer_ltx2.py
@@ -115,6 +115,31 @@ def get_dummy_inputs(self) -> dict[str, torch.Tensor]:
 class TestLTX2Transformer(LTX2TransformerTesterConfig, ModelTesterMixin):
     """Core model tests for LTX2 Video Transformer."""
 
+    def test_fp32_scale_shift_tables_match_uniform_dtype(self):
+        # Published LTX-2 checkpoints store the AdaLN scale_shift tables in fp32
+        # alongside bf16 weights. The tables are cast to the activation dtype at
+        # the use site (as in the original implementation), so a natively loaded
+        # mixed-dtype model must run and produce the same outputs as one whose
+        # tables were flattened to the weight dtype at load time.
+        torch.manual_seed(0)
+        model = self.model_class(**self.get_init_dict()).to(torch.bfloat16).to(torch_device).eval()
+        inputs = {
+            key: value.to(torch.bfloat16) if isinstance(value, torch.Tensor) and value.is_floating_point() else value
+            for key, value in self.get_dummy_inputs().items()
+        }
+
+        with torch.no_grad():
+            reference = model(**inputs)
+
+        for name, param in model.named_parameters():
+            if "scale_shift_table" in name:
+                param.data = param.data.float()
+        with torch.no_grad():
+            mixed = model(**inputs)
+
+        assert torch.equal(reference[0], mixed[0])
+        assert torch.equal(reference[1], mixed[1])
+
 
 class TestLTX2TransformerMemory(LTX2TransformerTesterConfig, MemoryTesterMixin):
     """Memory optimization tests for LTX2 Video Transformer."""