diff --git a/python/sglang/multimodal_gen/configs/pipeline_configs/sana.py b/python/sglang/multimodal_gen/configs/pipeline_configs/sana.py
index 234bc0066..e564947d5 100644
--- a/python/sglang/multimodal_gen/configs/pipeline_configs/sana.py
+++ b/python/sglang/multimodal_gen/configs/pipeline_configs/sana.py
@@ -52,7 +52,7 @@ class SanaPipelineConfig(SpatialImagePipelineConfig):
     # DC-AE does not support tiling or SP VAE decode yet.
     vae_tiling: bool = False
     vae_sp: bool = False
-    vae_precision: str = "bf16"
+    vae_precision: str = "fp32"
 
     dit_config: DiTConfig = field(default_factory=SanaConfig)
     vae_config: VAEConfig = field(default_factory=SanaVAEConfig)
diff --git a/python/sglang/multimodal_gen/runtime/layers/attention/turbo_layer.py b/python/sglang/multimodal_gen/runtime/layers/attention/turbo_layer.py
index 0172c649b..c40948d68 100644
--- a/python/sglang/multimodal_gen/runtime/layers/attention/turbo_layer.py
+++ b/python/sglang/multimodal_gen/runtime/layers/attention/turbo_layer.py
@@ -12,21 +12,23 @@ from torch.nn import Module
 from sglang.multimodal_gen.runtime.layers.attention.backends.attention_backend import (
     AttentionImpl,
 )
-from sglang.multimodal_gen.runtime.layers.attention.backends.sparse_linear_attn import (
-    SageSparseLinearAttentionBackend,
-    SparseLinearAttentionBackend,
-)
 from sglang.multimodal_gen.runtime.layers.attention.selector import get_attn_backend
 from sglang.multimodal_gen.runtime.managers.forward_context import (
     ForwardContext,
     get_forward_context,
 )
 from sglang.multimodal_gen.runtime.platforms.interface import AttentionBackendEnum
+from sglang.multimodal_gen.runtime.server_args import get_global_server_args
 from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 from sglang.multimodal_gen.utils import get_compute_dtype
 
 logger = init_logger(__name__)
 
+_TURBO_WAN_SPARSE_BACKENDS = {
+    AttentionBackendEnum.SLA_ATTN,
+    AttentionBackendEnum.SAGE_SLA_ATTN,
+}
+
 
 def post_all2all(local_seq_2_local_head, seq_world_size):
     def post_func(input):
@@ -74,6 +76,52 @@ def single_all_to_all(input, local_seq_2_local_head, group, async_op=False):
     return res
 
 
+def _attention_backend_from_name(
+    backend_name: str | None,
+) -> AttentionBackendEnum | None:
+    if backend_name is None:
+        return None
+    try:
+        return AttentionBackendEnum[backend_name.upper()]
+    except KeyError:
+        return None
+
+
+def _resolve_turbo_wan_sparse_backend(
+    attention_type: str,
+    requested_attention_backend: str | None = None,
+    supported_attention_backends: set[AttentionBackendEnum] | None = None,
+) -> tuple[AttentionBackendEnum, str | None]:
+    available_backends = _TURBO_WAN_SPARSE_BACKENDS
+    if supported_attention_backends is not None:
+        available_backends = _TURBO_WAN_SPARSE_BACKENDS & supported_attention_backends
+        if not available_backends:
+            available_backends = _TURBO_WAN_SPARSE_BACKENDS
+
+    preferred_backend = (
+        AttentionBackendEnum.SAGE_SLA_ATTN
+        if attention_type == "sagesla"
+        else AttentionBackendEnum.SLA_ATTN
+    )
+    if preferred_backend not in available_backends:
+        preferred_backend = sorted(available_backends, key=lambda b: b.name)[0]
+
+    requested_backend = _attention_backend_from_name(requested_attention_backend)
+
+    if requested_backend in available_backends:
+        return requested_backend, None
+    if requested_attention_backend is None:
+        return preferred_backend, None
+
+    return (
+        preferred_backend,
+        "TurboWan only supports `sla_attn` or `sage_sla_attn`; "
+        f"got attention_backend={requested_attention_backend!r}. "
+        f"Using `{preferred_backend.name.lower()}` from "
+        f"attention_type={attention_type!r}.",
+    )
+
+
 def async_a2a_communicate(
     a2a_inputs: Union[torch.Tensor, List[torch.Tensor]],
     cp_size: int,
@@ -237,21 +285,24 @@ class MinimalA2AAttnOp(DistributedAttention):
         prefix: str = "",
     ):
         dtype = get_compute_dtype()
+        try:
+            requested_attention_backend = get_global_server_args().attention_backend
+        except ValueError:
+            requested_attention_backend = None
+        selected_attention_backend, warning_message = _resolve_turbo_wan_sparse_backend(
+            attention_type,
+            requested_attention_backend,
+            supported_attention_backends,
+        )
+        if warning_message is not None:
+            logger.warning_once(warning_message)
+
         attn_backend = get_attn_backend(
-            head_size, dtype, supported_attention_backends=supported_attention_backends
+            head_size,
+            dtype,
+            supported_attention_backends={selected_attention_backend},
+            selected_attention_backend=selected_attention_backend,
         )
-        # Maintained for compatibility purposes; can be removed when CI allows setting Attention_backend or when TurboWan supports FA.
-        if attn_backend not in (
-            SparseLinearAttentionBackend,
-            SageSparseLinearAttentionBackend,
-        ):
-            logger.warning_once(
-                "TurboWan now only supports `sla_attn` or `sage_sla_attn` and has been automatically set to attention_type. Please set --attention-backend to `sla_attn` or `sage_sla_attn`."
-            )
-            if attention_type == "sagesla":
-                attn_backend = SageSparseLinearAttentionBackend
-            else:
-                attn_backend = SparseLinearAttentionBackend
         impl_cls: Type[AttentionImpl] = attn_backend.get_impl_cls()
         local_attn = impl_cls(
             num_heads=num_heads,