diff --git a/v2/models/wan21/card.py b/v2/models/wan21/card.py
index 58f8f4416..ce03fbfed 100644
--- a/v2/models/wan21/card.py
+++ b/v2/models/wan21/card.py
@@ -182,7 +182,8 @@ def build_wan22_a14b_card(model_id: str = "wan2.2-t2v-a14b", *, checkpoint_root:
 # (``transformer_2``), and LTX-2 (``spatial_upsampler``) alike.
 _WAN21_SUBFOLDERS = {"transformer": "transformer", "transformer_2": "transformer_2",
                      "vae": "vae", "text_encoder": "text_encoder",
-                     "spatial_upsampler": "spatial_upsampler"}
+                     "spatial_upsampler": "spatial_upsampler",
+                     "audio_vae": "audio_vae", "vocoder": "vocoder"}   # LTX-2.3 T2VS audio branch
 
 
 def stamp_wan21_checkpoints(card: ModelCard, model_root: str) -> ModelCard:
diff --git a/v2/platform/backends/torch_adapters.py b/v2/platform/backends/torch_adapters.py
index 74794e312..6b050c2ea 100644
--- a/v2/platform/backends/torch_adapters.py
+++ b/v2/platform/backends/torch_adapters.py
@@ -363,3 +363,22 @@ def build_torch_upsampler(spec, instance, platform):
     stats_owner = getattr(vae_module, "decoder", None) or getattr(vae_module, "encoder", None) or vae_module
     from .torch_ltx2 import TorchLTX2Upsampler
     return TorchLTX2Upsampler(module, stats_owner, device=device, dtype=dtype)
+
+
+def build_torch_audio_vae(spec, instance, platform):
+    ckpt = _require_checkpoint(spec)
+    _ensure_fastvideo_runtime()
+    module = _load_component("AudioDecoderLoader", ckpt, _fastvideo_args(spec))   # LTX2AudioDecoder
+    device, dtype = _device(platform), _native_dtype(module)
+    voc = instance.component("vocoder")   # build/fetch the vocoder; TorchLTX2AudioVAE chains decoder->vocoder
+    from .torch_ltx2 import TorchLTX2AudioVAE
+    return TorchLTX2AudioVAE(module, voc, device=device, dtype=dtype)
+
+
+def build_torch_vocoder(spec, instance, platform):
+    ckpt = _require_checkpoint(spec)
+    _ensure_fastvideo_runtime()
+    module = _load_component("VocoderLoader", ckpt, _fastvideo_args(spec))        # LTX2Vocoder
+    device, dtype = _device(platform), _native_dtype(module)
+    from .torch_ltx2 import TorchLTX2Vocoder
+    return TorchLTX2Vocoder(module, device=device, dtype=dtype)
diff --git a/v2/platform/backends/torch_cuda.py b/v2/platform/backends/torch_cuda.py
index b365adf24..558021a8c 100644
--- a/v2/platform/backends/torch_cuda.py
+++ b/v2/platform/backends/torch_cuda.py
@@ -60,6 +60,16 @@ def _build_upsampler(spec, instance, platform):
     return build_torch_upsampler(spec, instance, platform)
 
 
+def _build_audio_vae(spec, instance, platform):
+    from .torch_adapters import build_torch_audio_vae
+    return build_torch_audio_vae(spec, instance, platform)
+
+
+def _build_vocoder(spec, instance, platform):
+    from .torch_adapters import build_torch_vocoder
+    return build_torch_vocoder(spec, instance, platform)
+
+
 def _flow_match_cuda(*args, **kwargs):
     from .torch_kernels import flow_match_step
     return flow_match_step(*args, **kwargs)
@@ -80,6 +90,10 @@ register_component("text_encoder", _build_text_encoder, device="cuda", available
                    source=f"{_ADAPTERS}:TorchT5Encoder")
 register_component("upsampler", _build_upsampler, device="cuda", available=_cuda_available,
                    source=f"{_ADAPTERS}:TorchLTX2Upsampler")
+register_component("audio_vae", _build_audio_vae, device="cuda", available=_cuda_available,
+                   source=f"{_ADAPTERS}:TorchLTX2AudioVAE")
+register_component("vocoder", _build_vocoder, device="cuda", available=_cuda_available,
+                   source=f"{_ADAPTERS}:TorchLTX2Vocoder")
 
 # --- solver ops: plain torch elementwise (no fused solver kernel exists in fastvideo-kernel) ---- #
 # arch="generic" (elementwise, arch-agnostic), workspace_bytes=0 (no static scratch).
diff --git a/v2/platform/backends/torch_ltx2.py b/v2/platform/backends/torch_ltx2.py
index 756ee86b3..da1556194 100644
--- a/v2/platform/backends/torch_ltx2.py
+++ b/v2/platform/backends/torch_ltx2.py
@@ -139,7 +139,10 @@ class TorchGemma:
         mask = toks.attention_mask.to(self.device)
         from fastvideo.forward_context import set_forward_context
         with set_forward_context(current_timestep=0, attn_metadata=None):
-            out = self.module(input_ids=ids, attention_mask=mask)
+            # output_hidden_states=True populates hidden_states=(audio_encoding,) — the SEPARATE audio
+            # connector projection (gemma.py:703); without it hidden_states is None and we'd wrongly
+            # fall back to the video embedding (different dim -> cross-attn shape mismatch).
+            out = self.module(input_ids=ids, attention_mask=mask, output_hidden_states=True)
         video = out.last_hidden_state if hasattr(out, "last_hidden_state") else out[0]
         hs = getattr(out, "hidden_states", None)
         audio = hs[0] if hs else video
diff --git a/v2/tests/test_torch_backend.py b/v2/tests/test_torch_backend.py
index dc9f4a379..0227b44ef 100644
--- a/v2/tests/test_torch_backend.py
+++ b/v2/tests/test_torch_backend.py
@@ -42,7 +42,8 @@ def _make_cuda_available(monkeypatch):
 # --------------------------------------------------------------------------- #
 def test_cuda_has_all_components_and_two_solver_ops():
     comps = sorted(r["kind"] for r in component_matrix() if r["device"] == "cuda")
-    assert comps == ["dit", "text_encoder", "upsampler", "vae"]   # + upsampler: LTX-2 spatial upsampler
+    # + upsampler (LTX-2 spatial), audio_vae + vocoder (LTX-2.3 T2VS audio branch)
+    assert comps == ["audio_vae", "dit", "text_encoder", "upsampler", "vae", "vocoder"]
     ops = sorted(r["op"] for r in kernel_matrix() if r["device"] == "cuda")
     assert ops == ["flow_match_step", "flow_sde_step"]