diff --git a/comfy/ldm/twinflow/model.py b/comfy/ldm/twinflow/model.py
index a99bcd06e..94ec1e3f3 100644
--- a/comfy/ldm/twinflow/model.py
+++ b/comfy/ldm/twinflow/model.py
@@ -516,6 +516,41 @@ class TwinFlowZImageTransformer(nn.Module):
         self.dim = dim
         self.n_heads = n_heads
 
+    def _compute_twinflow_adaln(self, t: torch.Tensor, x_dtype: torch.dtype, transformer_options={}):
+        """
+        Compute TwinFlow adaLN input.
+
+        If `target_timestep` is provided in transformer options, apply the
+        TwinFlow delta-time conditioning:
+          t_emb + t_embedder_2((target - t) * time_scale) * abs(target - t)
+        otherwise fallback to the baseline additive embedding.
+        """
+        t_emb = self.t_embedder(t * self.time_scale, dtype=x_dtype)
+        target_timestep = transformer_options.get("target_timestep", None)
+        if target_timestep is None:
+            t_emb_2 = self.t_embedder_2(t * self.time_scale, dtype=x_dtype)
+            return t_emb + t_emb_2
+
+        target_t = target_timestep.to(device=t.device, dtype=t.dtype)
+        if target_t.ndim == 0:
+            target_t = target_t.expand_as(t)
+
+        # If values look scaled (roughly sigma/timestep in [0..1000]), normalize.
+        t_abs_max = float(t.detach().abs().max().item()) if t.numel() else 0.0
+        tt_abs_max = float(target_t.detach().abs().max().item()) if target_t.numel() else 0.0
+        scaled_domain = (max(t_abs_max, tt_abs_max) > 2.0) and (self.time_scale > 2.0)
+        if scaled_domain:
+            t_norm = t / self.time_scale
+            tt_norm = target_t / self.time_scale
+        else:
+            t_norm = t
+            tt_norm = target_t
+
+        delta_abs = (t_norm - tt_norm).abs().unsqueeze(1).to(t_emb.dtype)
+        diff_in = (tt_norm - t_norm) * self.time_scale
+        t_emb_2 = self.t_embedder_2(diff_in, dtype=x_dtype)
+        return t_emb + t_emb_2 * delta_abs
+
     def unpatchify(
         self,
         x: torch.Tensor,
@@ -651,10 +686,8 @@ class TwinFlowZImageTransformer(nn.Module):
     ):
         t = 1.0 - timesteps
 
+        adaln_input = self._compute_twinflow_adaln(t, x.dtype, transformer_options=transformer_options)
         t_emb = self.t_embedder(t * self.time_scale, dtype=x.dtype)
-        t_emb_2 = self.t_embedder_2(t * self.time_scale, dtype=x.dtype)
-
-        adaln_input = t_emb + t_emb_2
 
         cap_feats = context
         cap_mask = attention_mask