[Feat] LTX Standard Two-Stage Pipeline (#5500)

Signed-off-by: mglyn <1203789601@qq.com>

.buildkite/cuda/test-nightly.yml

@@ -456,7 +456,7 @@ steps:
         timeout_in_minutes: 90
         commands:
           - pytest -s -v tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and cuda" --run-level "full_model"
-          - pytest -s -v tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_ltx2_expansion.py -m "full_model and cuda" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_ltx.py -m "full_model and cuda" --run-level "full_model"
         mirror_hardwares: h100_2
 
       - label: ":full_moon: Diffusion X2V · Wan2.2 Accuracy Test"
@@ -477,10 +477,16 @@ steps:
         commands:
           - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_t2v/test_hunyuanvideo15_t2v_video_similarity.py -m full_model --run-level full_model
           - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m full_model --run-level full_model
-          - pytest -s -v 'tests/e2e/accuracy/ltx/test_ltx_official_similarity.py::test_ltx_one_stage_matches_official[ltx2_3_i2v]' -m full_model --run-level full_model
           - pytest -s -v tests/e2e/accuracy/test_video_streaming_output_similarity.py -m full_model --run-level full_model
         mirror_hardwares: h100_2
 
+      - label: ":full_moon: Diffusion X2V · LTX Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - pytest -s -v 'tests/e2e/accuracy/ltx/test_ltx_official_similarity.py::test_ltx_matches_official[ltx2_3_i2v]' -m full_model --run-level full_model
+          - pytest -s -v 'tests/e2e/accuracy/ltx/test_ltx_official_similarity.py::test_ltx_matches_official[ltx23_two_stage_layer_fused_i2v]' -m full_model --run-level full_model
+        mirror_hardwares: h100_1
+
       - label: ":full_moon: Diffusion X2V · Perf Test"
         key: nightly-diffusion-x2v-performance
         timeout_in_minutes: 180

docs/models/supported_models.md

@@ -42,7 +42,8 @@ th {
 | `Wan22VACEPipeline` | Wan2.1-VACE | `Wan-AI/Wan2.1-VACE-1.3B-diffusers`, `Wan-AI/Wan2.1-VACE-14B-diffusers` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `Wan22VACEPipeline` | Wan2.2-VACE | `Pyros13/Wan2.2-VACE-Fun-A14B-Diffusers` | ✅︎ |   |   |   | — |
 | `LTX2Pipeline` | LTX-2 / LTX-2.3 one-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` | ✅︎ | ✅︎ | | | — |
-| `LTX2DistilledPipeline` | LTX-2 distilled two-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled` | ✅︎ | ✅︎ | | | — |
+| `LTX2TwoStagePipeline` | LTX-2 / LTX-2.3 ordinary two-stage T2V and I2V | `Lightricks/LTX-2`, `diffusers/LTX-2.3-Diffusers` + matching Lightricks LoRA and upsampler | ✅︎ | ✅︎ | | | — |
+| `LTX2DistilledPipeline` | LTX-2 / LTX-2.3 full-distilled two-stage T2V and I2V | `rootonchair/LTX-2-19b-distilled`, `diffusers/LTX-2.3-Distilled-Diffusers` + `Lightricks/LTX-2.3` upsampler | ✅︎ | ✅︎ | | | — |
 | `LingBotVideoPipeline` | LingBot-Video dense and MoE T2I, T2V, TI2V | `robbyant/lingbot-video-dense-1.3b`, `robbyant/lingbot-video-moe-30b-a3b` | ✅︎ | | | | — |
 | `MiniMaxH3Pipeline` | MiniMax H3 T2VA, first/last-keyframe FL2VA, and mixed-reference Ref2VA | `MiniMaxAI/MiniMax-H3` | ✅︎ | ✅︎ | | | [Published](https://recipes.vllm.ai/MiniMaxAI/MiniMax-H3) |
 | `DreamZeroPipeline` | DreamZero-DROID | `GEAR-Dreams/DreamZero-DROID` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |

docs/user_guide/quantization/fp8.md

@@ -80,12 +80,14 @@ warmup_quack_fp8([(14040, 2048, 6144), (14040, 2048, 2048)])
 
 ## Model Type Support
 
-### Diffusion Model (Qwen-Image, Wan2.2)
+### Diffusion Models
 
 | Model | HF models | Online | Pre-calibrated | Recommendation | `ignored_layers` | Text-Encoder quantization |
 |-------|-----------|:-------:|:------:|----------------|------------------|------------------|
 | Qwen-Image | `Qwen/Qwen-Image`, `Qwen/Qwen-Image-2512` | Yes | Yes | Skip sensitive image-stream MLPs when quality regresses | `img_mlp` | |
 | Wan2.2 | Wan2.2 diffusion pipelines | Not validated | Not validated | Validate against BF16 before documenting as supported | TBD | |
+| LTX-2 | `Lightricks/LTX-2`, `rootonchair/LTX-2-19b-distilled` | Yes | Not validated | Transformer only; use dynamic phase LoRA for ordinary two-stage | None | |
+| LTX-2.3 | `diffusers/LTX-2.3-Diffusers`, `diffusers/LTX-2.3-Distilled-Diffusers` | Yes | Not validated | Transformer only; use dynamic phase LoRA for ordinary two-stage | None | |
 | Z-Image | `Tongyi-MAI/Z-Image-Turbo` | Yes | Yes | All layers | None | ✅︎ |
 | FLUX.1 | `black-forest-labs/FLUX.1-dev`, `black-forest-labs/FLUX.1-schnell` | Yes | Yes | All layers | None | |
 | FLUX.2-dev | `black-forest-labs/FLUX.2-dev` | Yes | Not validated | All layers | None | ✅︎ |

recipes/LTX/LTX-2.md

@@ -4,11 +4,20 @@
 
 ## Pipelines
 
-| Model | Checkpoint | Task | `--model-class-name` | Batching |
-|---|---|---|---|---|
-| LTX-2 | `Lightricks/LTX-2` | One-stage T2V/I2V | `LTX2Pipeline` | Yes |
-| LTX-2 distilled | `rootonchair/LTX-2-19b-distilled` | Two-stage T2V/I2V | `LTX2DistilledPipeline` | No |
-| LTX-2.3 | `diffusers/LTX-2.3-Diffusers` | One-stage T2V/I2V | `LTX2Pipeline` | Yes |
+| `--model-class-name` | Task | Required checkpoint repositories |
+|---|---|---|
+| `LTX2Pipeline` | LTX-2 one-stage T2V/I2V | `Lightricks/LTX-2` |
+| `LTX2TwoStagePipeline` | LTX-2 ordinary two-stage T2V/I2V | `Lightricks/LTX-2` |
+| `LTX2DistilledPipeline` | LTX-2 full-distilled two-stage T2V/I2V | `rootonchair/LTX-2-19b-distilled` |
+| `LTX2Pipeline` | LTX-2.3 one-stage T2V/I2V | `diffusers/LTX-2.3-Diffusers` |
+| `LTX2TwoStagePipeline` | LTX-2.3 ordinary two-stage T2V/I2V | `diffusers/LTX-2.3-Diffusers`<br>`Lightricks/LTX-2.3` |
+| `LTX2DistilledPipeline` | LTX-2.3 full-distilled two-stage T2V/I2V | `diffusers/LTX-2.3-Distilled-Diffusers`<br>`Lightricks/LTX-2.3` |
+
+Repositories in the table are download units. A full pipeline repository
+contains the Transformer, text encoder, connectors, VAEs, vocoder, scheduler,
+and tokenizer; an additional repository supplies LoRA or upsampler sidecars.
+The runtime first searches the model root for sidecars under their official
+filenames, then downloads them from the matching Lightricks Hub repository.
 
 `LTX2Pipeline` is the unified one-stage entry. Checkpoint metadata selects the
 LTX-2 or LTX-2.3 profile; omitting an image selects T2V, while one initial
@@ -16,10 +25,10 @@ image selects I2V. Both one-stage repositories declare this class, so
 `--model-class-name` is optional. LTX-2.3 requires the Diffusers checkpoint;
 the raw `Lightricks/LTX-2.3` safetensors repository is not directly loadable.
 
-`LTX2DistilledPipeline` is the unified distilled entry: omit `image` for T2V
-or provide one initial image for I2V. As in the official LTX pipelines,
-distilled mode is selected explicitly rather than inferred from the checkpoint
-name or metadata; always pass `--model-class-name LTX2DistilledPipeline`.
+`LTX2TwoStagePipeline` samples the regular model at half resolution, upsamples,
+then refines with the distilled LoRA. `LTX2DistilledPipeline` uses a fully
+merged distilled Transformer in both stages. Both entries support T2V and I2V;
+select their class explicitly.
 
 ## API Migration
 
@@ -68,24 +77,28 @@ The model recipe also supplies the default negative prompt. Top-level
 API defaults `num_frames` to `1`, so set it explicitly; the offline LTX scripts
 default to `121`.
 
-## Distilled Two-Stage Defaults
+## Two-Stage Defaults
 
-| Parameter | Value |
-|---|---:|
-| Final width × height | 1536 × 1024 |
-| Stage 1 width × height | 768 × 512 |
-| Frames / frame rate | 121 / 24 |
-| Stage 1 / Stage 2 denoise steps | 8 / 3 |
-| Guidance | Fixed positive-only |
+| Parameter | Ordinary | Full-distilled |
+|---|---:|---:|
+| Final width × height | 1536 × 1024 | 1536 × 1024 |
+| Stage 1 width × height | 768 × 512 | 768 × 512 |
+| Frames / frame rate | 121 / 24 | 121 / 24 |
+| Stage 1 / Stage 2 steps | 40 (LTX-2) or 30 (LTX-2.3) / 3 | 8 / 3 |
+| Guidance | Stage 1 guided; Stage 2 positive-only | Fixed positive-only |
 
-The dimensions passed to the API are the final output dimensions and must be
-divisible by 64. Both one- and two-stage requests require `num_frames = 8k+1`.
-The distilled recipe fixes both sigma schedules; `num_inference_steps` must be
-`8` when supplied.
+API dimensions are final dimensions and must be divisible by 64. All LTX
+requests require `num_frames = 8k+1`. Ordinary Stage 1 uses the LTX-2 or
+LTX-2.3 one-stage defaults shown above. Distilled schedules are fixed, so
+`num_inference_steps`, when supplied, must be `8`. Both entries reject custom
+sigmas and input latents.
+
+Ordinary two-stage uses layer-fused LoRA for an unquantized BF16 Transformer
+and automatically switches to dynamic LoRA when quantization is enabled.
 
 ## Serving
 
-Start either checkpoint:
+Start either one-stage checkpoint:
 
 ```bash
 vllm serve Lightricks/LTX-2 --omni --stage-init-timeout 600
@@ -95,11 +108,19 @@ vllm serve Lightricks/LTX-2 --omni --stage-init-timeout 600
 vllm serve diffusers/LTX-2.3-Diffusers --omni --stage-init-timeout 600
 ```
 
-Start the distilled checkpoint with its two-stage entry:
+Start a two-stage checkpoint with its explicit class:
 
 ```bash
 vllm serve rootonchair/LTX-2-19b-distilled --omni \
   --model-class-name LTX2DistilledPipeline --stage-init-timeout 600
+# LTX-2.3 full-distilled; the v1.1 x2 upsampler is resolved when absent
+vllm serve diffusers/LTX-2.3-Distilled-Diffusers --omni \
+  --model-class-name LTX2DistilledPipeline --stage-init-timeout 600
+# Ordinary LTX-2.3 two-stage
+vllm serve diffusers/LTX-2.3-Diffusers --omni \
+  --model-class-name LTX2TwoStagePipeline \
+  --enable-layerwise-offload \
+  --stage-init-timeout 600
 ```
 
 The same server handles T2V and I2V. A T2V request using the selected recipe's
@@ -135,8 +156,9 @@ together with `input_reference`.
 
 ## Guidance
 
-LTX supports independent video/audio CFG, spatio-temporal guidance (STG),
-cross-modality guidance, and rescaling:
+One-stage and ordinary Stage 1 support independent video/audio CFG,
+spatio-temporal guidance (STG), cross-modality guidance, and rescaling.
+Distilled stages and ordinary Stage 2 are fixed positive-only.
 
 | Parameter | Default | Effect | Alias |
 |---|---:|---|---|
@@ -172,9 +194,9 @@ complete guidance plan in parallel. Despite the legacy `cfg` name, it covers
 text CFG, STG, and cross-modality guidance passes; guidance rescaling is
 applied after all pass predictions are gathered.
 
-The default one-stage recipe has four Transformer passes per denoise step:
-`cond`, `uncond`, `ptb` (STG), and `mod` (cross-modality). The useful balanced
-configurations are therefore:
+The default one-stage recipe and ordinary Stage 1 have four Transformer passes
+per denoise step: `cond`, `uncond`, `ptb` (STG), and `mod`
+(cross-modality). The useful balanced configurations are therefore:
 
 | `--cfg-parallel-size` | Passes per rank | Guidance-slot utilization | Notes |
 |---:|---:|---:|---|
@@ -195,9 +217,8 @@ vllm serve diffusers/LTX-2.3-Diffusers --omni \
 ```
 
 The total device count is the product of `cfg_parallel_size` and the other
-configured parallel dimensions. Distilled pipelines use positive-only
-guidance, so `cfg_parallel_size > 1` does not provide guidance speedup for
-them.
+configured parallel dimensions. Positive-only distilled phases do not benefit
+from `cfg_parallel_size > 1`.
 
 ### Python API
 
@@ -242,22 +263,22 @@ noted below.
 | `image` | image or batch, `None` | Direct value wins over request images; no image selects T2V. I2V accepts one image per prompt, and a batch cannot mix T2V/I2V. |
 | `prompt` | string or list, `None` | Positive-text fallback; request prompts win. Mutually exclusive with `prompt_embeds`. |
 | `negative_prompt` | string or list, `None` | Fallback after request values and before the recipe default; mutually exclusive with negative embeddings. |
-| `height` | `int`, `None` | Request → direct value → recipe default; divisible by 32 one-stage or 64 distilled. |
+| `height` | `int`, `None` | Request → direct value → recipe default; divisible by 32 one-stage or 64 two-stage. |
 | `width` | `int`, `None` | Same precedence and alignment as `height`. |
 | `num_frames` | `int`, `None` | Request → direct value → recipe default; must be `8k+1` and also determines audio duration with `frame_rate`. |
 | `frame_rate` | `float`, `None` | Request `frame_rate` → request `fps` → direct value → recipe default. |
-| `num_inference_steps` | `int`, `None` | Request → direct value → recipe default; minimum 2 one-stage, fixed at 8 for distilled Stage 1. Custom one-stage `sigmas` determine actual steps. |
+| `num_inference_steps` | `int`, `None` | Request → direct value → recipe default; minimum 2 one-stage and ordinary Stage 1, fixed at 8 for distilled Stage 1. Custom one-stage `sigmas` determine actual steps. |
 | `sigmas` | list of float, `None` | One-stage only. Request values win; every request in a fused batch must use the same schedule. |
 | `timesteps` | list of int, `None` | Compatibility slot; LTX accepts only `None`. Use `sigmas`. |
-| `guidance_scale` | `float`, `None` | One-stage common video/audio CFG fallback; an explicit request value wins. Distilled two-stage uses fixed positive-only guidance. |
+| `guidance_scale` | `float`, `None` | One-stage and ordinary Stage 1 common video/audio CFG fallback; an explicit request value wins. Distilled phases use fixed positive-only guidance. |
 | `guidance_rescale` | `float`, `None` | Accepts only `None` or `0.0`; use the modality rescale fields. |
 | `noise_scale` | `float`, `0.0` | Compatibility slot; LTX accepts only `0.0`. |
 | `num_videos_per_prompt` | `int`, `1` | Output-count fallback; positive request `num_outputs_per_prompt` wins. |
 | `generator` | generator or list, `None` | Explicit RNG; otherwise request generators/seeds are collated. Lists must match the effective output batch. |
-| `latents` | tensor, `None` | Request tensors win. One-stage accepts packed `[B, S, C]` and validated unpacked video layouts. |
-| `audio_latents` | tensor, `None` | One-stage initial audio latents; request tensors win and are collated. |
+| `latents` | tensor, `None` | One-stage only. Request tensors win; packed `[B, S, C]` and validated unpacked video layouts are accepted. |
+| `audio_latents` | tensor, `None` | One-stage only. Request tensors win and are collated. |
 | `prompt_embeds` | tensor, `None` | Precomputed positive conditioning; requires `prompt_attention_mask` and cannot accompany `prompt`. |
-| `negative_prompt_embeds` | tensor, `None` | One-stage precomputed negative conditioning; requires its mask and cannot accompany `negative_prompt`. |
+| `negative_prompt_embeds` | tensor, `None` | One-stage and ordinary Stage 1 conditioning; requires its mask and cannot accompany `negative_prompt`. |
 | `prompt_attention_mask` | tensor, `None` | Mask for positive embeddings; request `prompt_attention_mask` or `attention_mask` wins. |
 | `negative_prompt_attention_mask` | tensor, `None` | Mask for negative embeddings; request negative mask fields win. |
 | `decode_timestep` | float or list, `0.0` | Video-VAE decode timestep; request value wins. Lists may match 1, prompt batch, or output batch. |
@@ -273,13 +294,13 @@ request prompt payload; LTX guidance fields live in sampling `extra_args`.
 
 ### Recipe-Specific Request Capabilities
 
-| Override | One-stage | Distilled two-stage |
-|---|---|---|
-| Guidance | Supported | Fixed positive-only |
-| Negative prompt/embeddings | Supported | Rejected |
-| `num_inference_steps` | Supported | Fixed at 8 for Stage 1; Stage 2 uses 3 |
-| Custom `sigmas` | Supported | Rejected; both phases use fixed schedules |
-| Video/audio latents | Supported | Rejected |
+| Override | One-stage | Ordinary two-stage | Distilled two-stage |
+|---|---|---|---|
+| Guidance | Supported | Stage 1 only; Stage 2 is positive-only | Fixed positive-only |
+| Negative prompt/embeddings | Supported | Supported by Stage 1 | Rejected |
+| `num_inference_steps` | Supported | Controls Stage 1; Stage 2 uses 3 | Fixed at 8 for Stage 1; Stage 2 uses 3 |
+| Custom `sigmas` | Supported | Rejected | Rejected; both phases use fixed schedules |
+| Video/audio latents | Supported | Rejected | Rejected |
 
 These capability checks apply equally to direct `forward` keywords and
 values in `OmniDiffusionSamplingParams`; unsupported values fail instead of
@@ -309,6 +330,10 @@ bundled offline CLI do not currently expose `sigmas`.
   `--max-num-seqs 1`.
 - `--cfg-parallel-size` shards the complete LTX guidance plan, including STG,
   modality guidance, and rescale-compatible prediction gathering.
+- Sequence parallelism may pad audio latents. Pure Ulysses masks the padding;
+  Ring cannot, so audio length must be SP-divisible. Use `ring_degree=1` or a
+  divisible request shape.
+- Cache-DiT is one-stage only; multi-stage configurations are rejected.
 
 ## Operational Notes
 
@@ -323,14 +348,15 @@ bundled offline CLI do not currently expose `sigmas`.
   request parameter.
 - For benchmarks, use `tests/dfx/perf/tests/test_ltx2_vllm_omni.json` with
   `tests/dfx/perf/scripts/run_diffusion_benchmark.py`.
-- Current two-stage support is limited to the distilled checkpoint; official
-  LTX two-stage/HQ execution remains out of scope.
+- Ordinary and full-distilled two-stage T2V/I2V are supported; HQ execution
+  remains out of scope.
 
 ## References
 
 - <https://huggingface.co/Lightricks/LTX-2>
 - <https://huggingface.co/Lightricks/LTX-2.3>
 - <https://huggingface.co/diffusers/LTX-2.3-Diffusers>
+- <https://huggingface.co/diffusers/LTX-2.3-Distilled-Diffusers>
 - [Online video generation](../../docs/user_guide/examples/online_serving/text_to_video.md)
 - [Diffusion execution modes](../../docs/user_guide/diffusion/execution_modes.md)
 - [T2V offline example](../../examples/offline_inference/text_to_video/text_to_video.md)

tests/diffusion/models/ltx2/test_ltx2_latents.py

@@ -23,7 +23,7 @@ def _make_pipeline(pipeline_cls, sequence_parallel_size: int = 1):
     return pipeline
 
 
-def test_prepare_video_latents_samples_directly_in_packed_token_space():
+def test_prepare_video_latents_matches_official_values_and_token_major_layout():
     pipeline = _make_pipeline(LTX2Pipeline)
     pipeline.vae_spatial_compression_ratio = 8
     pipeline.vae_temporal_compression_ratio = 8
@@ -44,6 +44,7 @@ def test_prepare_video_latents_samples_directly_in_packed_token_space():
     )
 
     torch.testing.assert_close(actual, expected, rtol=0, atol=0)
+    assert actual.stride()[1:] == (1, actual.shape[1])
 
 
 def test_prepare_audio_latents_samples_directly_in_packed_token_space():
@@ -81,6 +82,42 @@ def test_prepare_audio_latents_pads_generated_dummy_length_for_sp():
     assert original_num_frames == 1
     assert padded_num_frames == 2
     assert latents.shape == (1, 2, 128)
+    torch.testing.assert_close(latents[:, 1:], torch.zeros_like(latents[:, 1:]))
+
+
+def test_prepare_audio_latents_request_rng_is_invariant_to_sp_padding():
+    pipeline_sp1 = _make_pipeline(LTX2Pipeline, sequence_parallel_size=1)
+    pipeline_sp4 = _make_pipeline(LTX2Pipeline, sequence_parallel_size=4)
+    generator_sp1 = torch.Generator().manual_seed(42)
+    generator_sp4 = torch.Generator().manual_seed(42)
+
+    latents_sp1, _, _ = pipeline_sp1.prepare_audio_latents(
+        batch_size=1,
+        num_channels_latents=2,
+        num_mel_bins=8,
+        audio_latent_length=3,
+        dtype=torch.float32,
+        device=torch.device("cpu"),
+        generator=generator_sp1,
+    )
+    latents_sp4, _, _ = pipeline_sp4.prepare_audio_latents(
+        batch_size=1,
+        num_channels_latents=2,
+        num_mel_bins=8,
+        audio_latent_length=3,
+        dtype=torch.float32,
+        device=torch.device("cpu"),
+        generator=generator_sp4,
+    )
+
+    torch.testing.assert_close(latents_sp4[:, :3], latents_sp1, rtol=0, atol=0)
+    torch.testing.assert_close(latents_sp4[:, 3:], torch.zeros_like(latents_sp4[:, 3:]))
+    torch.testing.assert_close(
+        torch.randn(8, generator=generator_sp4),
+        torch.randn(8, generator=generator_sp1),
+        rtol=0,
+        atol=0,
+    )
 
 
 def test_prepare_audio_latents_pads_packed_sequence_dim_for_provided_latents():
@@ -137,7 +174,9 @@ def test_prepare_audio_latents_accepts_already_padded_4d_latents_for_sp():
     assert original_num_frames == 10
     assert padded_num_frames == 12
     assert padded.shape == (1, 12, 8)
-    torch.testing.assert_close(padded, latent_ops.pack_audio_latents(latents))
+    packed = latent_ops.pack_audio_latents(latents)
+    torch.testing.assert_close(padded[:, :10], packed[:, :10])
+    torch.testing.assert_close(padded[:, 10:], torch.zeros_like(padded[:, 10:]))
 
 
 def test_resolve_audio_latent_length_preserves_legacy_4d_shape_inference():

tests/diffusion/models/ltx2/test_ltx2_parallel.py

@@ -12,6 +12,19 @@ import torch
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
 
+def test_velocity_from_x0_uses_official_host_scalar_sigma(mocker):
+    from vllm_omni.diffusion.models.ltx2.ltx2_guidance import velocity_from_x0
+
+    sigma = mocker.MagicMock()
+    sigma.to.return_value.item.return_value = 0.5
+
+    actual = velocity_from_x0(torch.tensor([1.0]), torch.tensor([0.0]), sigma)
+
+    sigma.to.assert_called_once_with(torch.float32)
+    sigma.to.return_value.item.assert_called_once_with()
+    torch.testing.assert_close(actual, torch.tensor([2.0]))
+
+
 class TestCFGParallelHelpers:
     """Test LTX-2.3 CFG helper math without loading model weights."""
 
@@ -199,6 +212,7 @@ class TestCFGParallelHelpers:
             ),
             attention_kwargs=None,
             audio_scheduler=SimpleNamespace(sigmas=torch.stack([audio_sigma])),
+            original_audio_num_frames=state.audio.shape[1],
         )
 
         actual_video, actual_audio = executor.predict_parallel_guidance(

tests/diffusion/models/ltx2/test_ltx2_phase_adapter.py

@@ -0,0 +1,483 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Unit tests for the fixed LTX refinement-phase adapter."""
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+from safetensors.torch import save_file
+
+from vllm_omni.diffusion.models.ltx2 import ltx2_phase_adapter
+from vllm_omni.diffusion.models.ltx2.ltx2_adapter_parser import (
+    AdapterTarget,
+    parse_ltx_adapter,
+)
+from vllm_omni.diffusion.models.ltx2.ltx2_components import LTX23_TWO_STAGE_COMPONENT_PROFILE
+from vllm_omni.diffusion.models.ltx2.ltx2_phase_adapter import LTXPhaseAdapterRuntime
+from vllm_omni.diffusion.models.ltx2.ltx2_recipes import (
+    LTX2_TWO_STAGE_RECIPE,
+    LTX_DISTILLED_ADAPTER_SLOT,
+)
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+def _target(
+    lora_a,
+    lora_b,
+    *,
+    source_module="proj",
+    module="proj",
+    target_slice=None,
+):
+    return AdapterTarget(
+        source_module=source_module,
+        module=module,
+        slice=target_slice,
+        a_key="a",
+        b_key="b",
+        a_shape=tuple(lora_a.shape),
+        b_shape=tuple(lora_b.shape),
+    )
+
+
+def _wrap(
+    layer,
+    lora_a,
+    lora_b,
+    *,
+    source_module="proj",
+    module="proj",
+    target_slice=None,
+    layer_fused=False,
+):
+    target = _target(
+        lora_a,
+        lora_b,
+        source_module=source_module,
+        module=module,
+        target_slice=target_slice,
+    )
+    wrapper = ltx2_phase_adapter._PhaseAdapterLinear(
+        layer,
+        [target],
+        adapter_dtype=lora_a.dtype,
+        layer_fused=layer_fused,
+    )
+    wrapper.load_target(target, lora_a, lora_b)
+    wrapper.set_enabled(True)
+    return wrapper
+
+
+class _LoRAAttention(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.to_qkv = torch.nn.Identity()
+
+
+class _LoRATransformer(torch.nn.Module):
+    stacked_params_mapping = ((".attn1.to_qkv", ".attn1.to_q", "q"),)
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.proj_in = torch.nn.Linear(4, 6, bias=False)
+        block = torch.nn.Module()
+        block.attn1 = _LoRAAttention()
+        self.transformer_blocks = torch.nn.ModuleList([block])
+
+
+def test_ltx_adapter_parser_maps_official_and_packed_module_names(tmp_path):
+    adapter_path = tmp_path / "distilled.safetensors"
+    save_file(
+        {
+            "diffusion_model.text_embedding_projection.aggregate_embed.lora_A.weight": torch.randn(2, 4),
+            "diffusion_model.patchify_proj.lora_A.weight": torch.randn(2, 4),
+            "diffusion_model.patchify_proj.lora_B.weight": torch.randn(6, 2),
+            "diffusion_model.transformer_blocks.0.attn1.to_q.lora_A.weight": torch.randn(2, 4),
+            "diffusion_model.transformer_blocks.0.attn1.to_q.lora_B.weight": torch.randn(4, 2),
+        },
+        adapter_path,
+    )
+
+    manifest = parse_ltx_adapter(_LoRATransformer(), str(adapter_path))
+    assert {(target.source_module, target.module, target.slice) for target in manifest.targets} == {
+        ("proj_in", "proj_in", None),
+        ("transformer_blocks.0.attn1.to_q", "transformer_blocks.0.attn1.to_qkv", "q"),
+    }
+
+
+def test_ltx_adapter_parser_rejects_unpaired_b_tensor(tmp_path):
+    adapter_path = tmp_path / "distilled.safetensors"
+    save_file(
+        {"diffusion_model.patchify_proj.lora_B.weight": torch.randn(6, 2)},
+        adapter_path,
+    )
+
+    with pytest.raises(ValueError, match="Missing paired LoRA A tensors"):
+        parse_ltx_adapter(_LoRATransformer(), str(adapter_path))
+
+
+def _pipeline(*, dtype, quantization_config):
+    return SimpleNamespace(
+        pipeline_recipe=LTX2_TWO_STAGE_RECIPE,
+        component_profile=LTX23_TWO_STAGE_COMPONENT_PROFILE,
+        transformer=object(),
+        od_config=SimpleNamespace(
+            model="unused",
+            lora_path=None,
+            dtype=dtype,
+            quantization_config=quantization_config,
+        ),
+    )
+
+
+@pytest.mark.parametrize(
+    ("dtype", "quantization_config", "layer_fused"),
+    [
+        (torch.float32, object(), False),
+        (torch.bfloat16, None, True),
+    ],
+)
+def test_ltx_phase_adapter_selects_execution_from_quantization(
+    monkeypatch,
+    dtype,
+    quantization_config,
+    layer_fused,
+):
+    pipeline = _pipeline(dtype=dtype, quantization_config=quantization_config)
+    manifest = object()
+    captured = {}
+
+    def resolve_artifact(*_args, **kwargs):
+        assert not kwargs
+        return "adapter"
+
+    def parse(transformer, path):
+        assert transformer is pipeline.transformer
+        assert path == "adapter"
+        return manifest
+
+    class Runtime:
+        def __init__(self, transformer, parsed_manifest, **kwargs):
+            captured.update(transformer=transformer, manifest=parsed_manifest, **kwargs)
+
+    monkeypatch.setattr(ltx2_phase_adapter, "resolve_ltx_artifact", resolve_artifact)
+    monkeypatch.setattr(ltx2_phase_adapter, "parse_ltx_adapter", parse)
+    monkeypatch.setattr(ltx2_phase_adapter, "LTXPhaseAdapterRuntime", Runtime)
+
+    runtime = ltx2_phase_adapter.build_ltx_phase_adapter(pipeline)
+
+    assert isinstance(runtime, Runtime)
+    assert captured == {
+        "transformer": pipeline.transformer,
+        "manifest": manifest,
+        "dtype": dtype,
+        "layer_fused": layer_fused,
+    }
+
+
+def test_ltx_phase_adapter_rejects_non_bfloat16_layer_fused(monkeypatch):
+    pipeline = _pipeline(dtype=torch.float16, quantization_config=None)
+    monkeypatch.setattr(ltx2_phase_adapter, "resolve_ltx_artifact", lambda *_args, **_kwargs: "adapter")
+
+    with pytest.raises(ValueError, match="bfloat16"):
+        ltx2_phase_adapter.build_ltx_phase_adapter(pipeline)
+
+
+def test_ltx_phase_adapter_keeps_one_transformer_and_switches_a_fixed_slot(tmp_path):
+    class TinyTransformer(torch.nn.Module):
+        stacked_params_mapping = ()
+
+        def __init__(self) -> None:
+            super().__init__()
+            self.proj = torch.nn.Linear(2, 2, bias=False)
+
+    adapter_path = tmp_path / "distilled.safetensors"
+    save_file(
+        {
+            "diffusion_model.proj.lora_A.weight": torch.tensor([[1.0, 2.0]]),
+            "diffusion_model.proj.lora_B.weight": torch.tensor([[3.0], [4.0]]),
+        },
+        adapter_path,
+    )
+    transformer = TinyTransformer()
+    runtime = LTXPhaseAdapterRuntime(
+        transformer,
+        parse_ltx_adapter(transformer, str(adapter_path)),
+        dtype=torch.float32,
+    )
+
+    assert runtime.base_parameter_name("proj.weight") == "proj.base_layer.weight"
+    assert {name for name, _ in transformer.named_parameters()} == {"proj.base_layer.weight"}
+    with torch.no_grad():
+        transformer.proj.base_layer.weight.copy_(torch.eye(2))
+    runtime.finalize()
+
+    x = torch.tensor([[1.0, 1.0]])
+    runtime.activate(None)
+    torch.testing.assert_close(transformer.proj(x), torch.tensor([[1.0, 1.0]]))
+    runtime.activate(LTX_DISTILLED_ADAPTER_SLOT)
+    torch.testing.assert_close(transformer.proj(x), torch.tensor([[10.0, 13.0]]))
+
+
+def test_ltx_layer_fused_adapter_matches_official_bf16_weight_fusion():
+    layer = torch.nn.Linear(4, 3, bias=True, dtype=torch.bfloat16)
+    base_weight = torch.tensor(
+        [
+            [0.5, -0.25, 0.125, 0.75],
+            [-0.375, 0.625, 0.25, -0.5],
+            [0.875, -0.125, -0.75, 0.375],
+        ],
+        dtype=torch.bfloat16,
+    )
+    lora_a = torch.tensor(
+        [
+            [0.1015625, -0.203125, 0.3046875, -0.40625],
+            [0.5078125, -0.609375, 0.7109375, -0.8125],
+        ],
+        dtype=torch.bfloat16,
+    )
+    lora_b = torch.tensor(
+        [[0.9140625, -0.1171875], [-0.21875, 0.3203125], [0.421875, -0.5234375]],
+        dtype=torch.bfloat16,
+    )
+    with torch.no_grad():
+        layer.weight.copy_(base_weight)
+        layer.bias.copy_(torch.tensor([0.125, -0.25, 0.5], dtype=torch.bfloat16))
+    wrapper = _wrap(layer, lora_a, lora_b, layer_fused=True)
+
+    expected_weight = torch.matmul(lora_b, lora_a)
+    expected_weight.add_(base_weight)
+    x = torch.tensor([[0.6015625, -0.703125, 0.8046875, -0.90625]], dtype=torch.bfloat16)
+    expected = torch.nn.functional.linear(x, expected_weight, layer.bias)
+
+    assert torch.equal(wrapper(x), expected)
+    assert torch.equal(layer.weight, base_weight)
+
+
+def test_ltx_layer_fused_row_parallel_uses_one_collective(monkeypatch):
+    from vllm.model_executor.layers.linear import UnquantizedLinearMethod
+
+    class FakeRow(torch.nn.Module):
+        input_size = 4
+        output_size = 2
+        input_size_per_partition = 2
+        input_is_parallel = True
+        tp_rank = 0
+        tp_size = 2
+        reduce_results = True
+        skip_bias_add = False
+        return_bias = False
+
+        def __init__(self):
+            super().__init__()
+            self.weight = torch.nn.Parameter(torch.eye(2, dtype=torch.bfloat16))
+            self.bias = torch.nn.Parameter(torch.tensor([0.25, -0.5], dtype=torch.bfloat16))
+            self.quant_method = UnquantizedLinearMethod()
+
+    monkeypatch.setattr(ltx2_phase_adapter, "RowParallelLinear", FakeRow)
+    reductions = []
+
+    def all_reduce(value):
+        reductions.append(value.clone())
+        return value + 1
+
+    monkeypatch.setattr(ltx2_phase_adapter, "tensor_model_parallel_all_reduce", all_reduce)
+    layer = FakeRow()
+    lora_a = torch.tensor([[0.5, -0.25, 0.75, -0.125]], dtype=torch.bfloat16)
+    lora_b = torch.tensor([[0.25], [-0.5]], dtype=torch.bfloat16)
+    wrapper = _wrap(layer, lora_a, lora_b, layer_fused=True)
+
+    expected_weight = torch.matmul(lora_b, lora_a[:, :2])
+    expected_weight.add_(layer.weight)
+    x = torch.tensor([[1.0, 2.0]], dtype=torch.bfloat16)
+    expected_local = torch.nn.functional.linear(x, expected_weight, layer.bias)
+
+    actual = wrapper(x)
+    assert len(reductions) == 1
+    assert torch.equal(reductions[0], expected_local)
+    assert torch.equal(actual, expected_local + 1)
+
+
+def test_ltx_layer_fused_column_parallel_uses_local_weight_shard(monkeypatch):
+    from vllm.model_executor.layers.linear import UnquantizedLinearMethod
+
+    class FakeColumn(torch.nn.Module):
+        input_size = 3
+        output_size = 4
+        output_size_per_partition = 2
+        tp_rank = 1
+        tp_size = 2
+        gather_output = False
+        skip_bias_add = False
+        return_bias = False
+
+        def __init__(self):
+            super().__init__()
+            self.weight = torch.nn.Parameter(
+                torch.tensor([[0.5, -0.25, 0.125], [-0.375, 0.625, 0.25]], dtype=torch.bfloat16)
+            )
+            self.bias = torch.nn.Parameter(torch.tensor([0.25, -0.5], dtype=torch.bfloat16))
+            self.quant_method = UnquantizedLinearMethod()
+
+    monkeypatch.setattr(ltx2_phase_adapter, "ColumnParallelLinear", FakeColumn)
+    layer = FakeColumn()
+    base_weight = layer.weight.detach().clone()
+    lora_a = torch.tensor([[0.5, -0.25, 0.75]], dtype=torch.bfloat16)
+    lora_b = torch.tensor([[0.25], [-0.5], [0.75], [-1.0]], dtype=torch.bfloat16)
+    wrapper = _wrap(layer, lora_a, lora_b, layer_fused=True)
+
+    expected_weight = torch.matmul(lora_b[2:], lora_a)
+    expected_weight.add_(base_weight)
+    x = torch.tensor([[1.0, -0.5, 0.25]], dtype=torch.bfloat16)
+    expected = torch.nn.functional.linear(x, expected_weight, layer.bias)
+
+    assert torch.equal(wrapper(x), expected)
+    assert torch.equal(layer.weight, base_weight)
+
+
+def test_ltx_layer_fused_packed_qkv_updates_only_the_target_slice(monkeypatch):
+    from vllm.model_executor.layers.linear import UnquantizedLinearMethod
+
+    class FakeColumn(torch.nn.Module):
+        pass
+
+    class FakeQKV(FakeColumn):
+        input_size = 4
+        head_size = 2
+        v_head_size = 2
+        total_num_heads = 2
+        total_num_kv_heads = 2
+        num_heads = 1
+        num_kv_heads = 1
+        num_kv_head_replicas = 1
+        tp_rank = 1
+        tp_size = 2
+        gather_output = False
+        skip_bias_add = True
+        return_bias = True
+
+        def __init__(self):
+            super().__init__()
+            self.weight = torch.nn.Parameter(
+                torch.arange(24, dtype=torch.float32).reshape(6, 4).to(torch.bfloat16) / 16
+            )
+            self.bias = torch.nn.Parameter(torch.arange(6, dtype=torch.float32).to(torch.bfloat16) / 8)
+            self.quant_method = UnquantizedLinearMethod()
+
+    monkeypatch.setattr(ltx2_phase_adapter, "ColumnParallelLinear", FakeColumn)
+    monkeypatch.setattr(ltx2_phase_adapter, "QKVParallelLinear", FakeQKV)
+    layer = FakeQKV()
+    base_weight = layer.weight.detach().clone()
+    lora_a = torch.tensor([[0.5, -0.25, 0.75, -0.125]], dtype=torch.bfloat16)
+    lora_b = torch.tensor([[0.25], [-0.5], [0.75], [-1.0]], dtype=torch.bfloat16)
+    wrapper = _wrap(
+        layer,
+        lora_a,
+        lora_b,
+        source_module="to_k",
+        module="to_qkv",
+        target_slice="k",
+        layer_fused=True,
+    )
+
+    expected_weight = base_weight.clone()
+    expected_k = torch.matmul(lora_b[2:], lora_a)
+    expected_k.add_(base_weight[2:4])
+    expected_weight[2:4].copy_(expected_k)
+    x = torch.tensor([[1.0, -0.5, 0.25, -0.125]], dtype=torch.bfloat16)
+    expected_output = torch.nn.functional.linear(x, expected_weight)
+
+    actual_output, returned_bias = wrapper(x)
+    assert torch.equal(actual_output, expected_output)
+    assert returned_bias is layer.bias
+    assert torch.equal(layer.weight, base_weight)
+
+
+@pytest.mark.parametrize(
+    ("layer_factory", "target_slice", "expected_a", "expected_b", "expected_output"),
+    [
+        (
+            lambda: type(
+                "FakeColumn",
+                (torch.nn.Module,),
+                {
+                    "input_size": 4,
+                    "output_size": 4,
+                    "output_size_per_partition": 2,
+                    "tp_rank": 1,
+                    "gather_output": False,
+                    "forward": lambda self, x: x.new_zeros((*x.shape[:-1], 2)),
+                },
+            )(),
+            None,
+            torch.tensor([[1.0, 10.0, 100.0, 1000.0]]),
+            torch.tensor([[1.0], [2.0], [3.0], [4.0]]),
+            torch.tensor([[12963.0, 17284.0]]),
+        ),
+        (
+            lambda: type(
+                "FakeRow",
+                (torch.nn.Module,),
+                {
+                    "input_size": 4,
+                    "output_size": 2,
+                    "input_size_per_partition": 2,
+                    "tp_rank": 1,
+                    "reduce_results": False,
+                    "forward": lambda self, x: x.new_zeros((*x.shape[:-1], 2)),
+                },
+            )(),
+            None,
+            torch.tensor([[1.0, 10.0, 100.0, 1000.0]]),
+            torch.tensor([[2.0], [3.0]]),
+            torch.tensor([[8600.0, 12900.0]]),
+        ),
+        (
+            lambda: type(
+                "FakeQKV",
+                (torch.nn.Module,),
+                {
+                    "input_size": 4,
+                    "head_size": 2,
+                    "v_head_size": 2,
+                    "total_num_heads": 2,
+                    "total_num_kv_heads": 2,
+                    "num_heads": 1,
+                    "num_kv_heads": 1,
+                    "num_kv_head_replicas": 1,
+                    "tp_rank": 1,
+                    "forward": lambda self, x: x.new_zeros((*x.shape[:-1], 6)),
+                },
+            )(),
+            "k",
+            torch.tensor([[1.0, 10.0, 100.0, 1000.0]]),
+            torch.tensor([[1.0], [2.0], [3.0], [4.0]]),
+            torch.tensor([[0.0, 0.0, 12963.0, 17284.0, 0.0, 0.0]]),
+        ),
+    ],
+)
+def test_ltx_phase_adapter_uses_local_tp_lora_slices(
+    monkeypatch,
+    layer_factory,
+    target_slice,
+    expected_a,
+    expected_b,
+    expected_output,
+):
+    layer = layer_factory()
+    layer.register_buffer("device_anchor", torch.empty(0))
+    layer_type = type(layer)
+    if target_slice == "k":
+        monkeypatch.setattr(ltx2_phase_adapter, "QKVParallelLinear", layer_type)
+    elif "Column" in layer_type.__name__:
+        monkeypatch.setattr(ltx2_phase_adapter, "ColumnParallelLinear", layer_type)
+    else:
+        monkeypatch.setattr(ltx2_phase_adapter, "RowParallelLinear", layer_type)
+
+    wrapper = _wrap(layer, expected_a, expected_b, target_slice=target_slice)
+    torch.testing.assert_close(wrapper(torch.tensor([[1.0, 2.0, 3.0, 4.0]])), expected_output)
+    expected_local_a_width = getattr(layer, "input_size_per_partition", layer.input_size)
+    assert wrapper.pieces[0].lora_a.shape[-1] == expected_local_a_width

tests/diffusion/models/ltx2/test_ltx2_pipeline.py

@@ -13,13 +13,21 @@ import pytest
 import torch
 
 from vllm_omni.diffusion.data import DiffusionOutput
-from vllm_omni.diffusion.models.ltx2 import ltx2_components
+from vllm_omni.diffusion.models.ltx2 import (
+    ltx2_components,
+    ltx2_transformer,
+)
 from vllm_omni.diffusion.models.ltx2.ltx2_components import (
     LTX2_COMPONENT_PROFILE,
     LTX2_DISTILLED_COMPONENT_PROFILE,
+    LTX2_TWO_STAGE_COMPONENT_PROFILE,
     LTX23_COMPONENT_PROFILE,
+    LTX23_DISTILLED_COMPONENT_PROFILE,
+    LTX23_TWO_STAGE_COMPONENT_PROFILE,
     _install_connector_attention,
     detect_ltx_model_version,
+    resolve_ltx_artifact,
+    resolve_ltx_component_profile,
 )
 from vllm_omni.diffusion.models.ltx2.ltx2_conditioning import LTXI2VConditioningMixin
 from vllm_omni.diffusion.models.ltx2.ltx2_denoise import (
@@ -33,6 +41,7 @@ from vllm_omni.diffusion.models.ltx2.ltx2_guidance import (
     LTXGuidancePlan,
     LTXGuidanceSpec,
     LTXModalityGuidance,
+    _repeat_batch,
     build_perturbation_kwargs,
     combine_guided_x0,
 )
@@ -40,18 +49,29 @@ from vllm_omni.diffusion.models.ltx2.ltx2_latents import LTXAVState
 from vllm_omni.diffusion.models.ltx2.ltx2_recipes import (
     LTX2_DISTILLED_TWO_STAGE_RECIPE,
     LTX2_ONE_STAGE_RECIPE,
+    LTX2_TWO_STAGE_RECIPE,
+    LTX23_DISTILLED_TWO_STAGE_RECIPE,
     LTX23_ONE_STAGE_RECIPE,
+    LTX23_TWO_STAGE_RECIPE,
     LTX_DEFAULT_NEGATIVE_PROMPT,
     LTX_POSITIVE_ONLY_RECIPE,
+    resolve_ltx_pipeline_recipe,
+)
+from vllm_omni.diffusion.models.ltx2.ltx2_request import (
+    LTXRequestInputs,
+    validate_ltx_checkpoint,
+    validate_pipeline_request,
 )
-from vllm_omni.diffusion.models.ltx2.ltx2_request import LTXRequestInputs, validate_pipeline_request
 from vllm_omni.diffusion.models.ltx2.ltx2_runtime import LTXRuntime
 from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import (
     LTX2I2VDMD2Pipeline,
     LTX2Pipeline,
     LTX2T2VDMD2Pipeline,
 )
-from vllm_omni.diffusion.models.ltx2.pipeline_ltx2_two_stage import LTX2DistilledPipeline
+from vllm_omni.diffusion.models.ltx2.pipeline_ltx2_two_stage import (
+    LTX2DistilledPipeline,
+    LTX2TwoStagePipeline,
+)
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
@@ -104,10 +124,22 @@ def test_ltx_public_entries_share_runtime_and_keep_recipe_boundaries():
     )
 
     assert issubclass(LTX2Pipeline, LTXRuntime)
+    assert issubclass(LTX2TwoStagePipeline, LTXRuntime)
     assert issubclass(LTX2DistilledPipeline, LTXRuntime)
     assert LTX2Pipeline.component_profile is LTX2_COMPONENT_PROFILE
     assert LTX2Pipeline.pipeline_recipe is LTX2_ONE_STAGE_RECIPE
+    assert LTX2TwoStagePipeline.component_profile is LTX2_TWO_STAGE_COMPONENT_PROFILE
+    assert LTX2TwoStagePipeline.pipeline_recipe is LTX2_TWO_STAGE_RECIPE
     assert LTX2DistilledPipeline.component_profile is LTX2_DISTILLED_COMPONENT_PROFILE
+    for pipeline_cls, profile in (
+        (LTX2Pipeline, LTX2_COMPONENT_PROFILE),
+        (LTX2TwoStagePipeline, LTX2_TWO_STAGE_COMPONENT_PROFILE),
+        (LTX2DistilledPipeline, LTX2_DISTILLED_COMPONENT_PROFILE),
+    ):
+        assert pipeline_cls._dit_modules == list(profile.dit_modules)
+        assert pipeline_cls._encoder_modules == list(profile.encoder_modules)
+        assert pipeline_cls._vae_modules == list(profile.vae_modules)
+        assert pipeline_cls._resident_modules == list(profile.resident_modules)
     assert LTX2_COMPONENT_PROFILE.video_vae_cls is DistributedAutoencoderKLLTX2Video
     assert LTX23_COMPONENT_PROFILE.video_vae_cls is DistributedAutoencoderKLLTX2Video
 
@@ -123,6 +155,72 @@ def test_ltx_checkpoint_version_detection_uses_metadata(tmp_path):
     assert detect_ltx_model_version(str(tmp_path)) == "2"
 
 
+def test_ltx_artifact_falls_back_to_unpinned_hub_download(tmp_path, monkeypatch):
+    filename = "ltx-sidecar.safetensors"
+    calls = []
+
+    def fake_download(**kwargs):
+        calls.append(kwargs)
+        return "/cache/ltx-sidecar.safetensors"
+
+    monkeypatch.setattr(ltx2_components, "hf_hub_download", fake_download)
+
+    assert (
+        resolve_ltx_artifact(str(tmp_path / "model"), "Lightricks/test", filename) == "/cache/ltx-sidecar.safetensors"
+    )
+    assert calls == [{"repo_id": "Lightricks/test", "filename": filename}]
+
+
+def test_ltx_artifact_prefers_model_root(tmp_path, monkeypatch):
+    filename = "ltx-sidecar.safetensors"
+    expected = tmp_path / filename
+    expected.write_bytes(b"sidecar")
+    monkeypatch.setattr(ltx2_components, "hf_hub_download", lambda **_kwargs: pytest.fail("unexpected Hub lookup"))
+
+    assert resolve_ltx_artifact(str(tmp_path), "Lightricks/test", filename) == str(expected)
+
+
+@pytest.mark.parametrize(
+    ("expected_kind", "scheduler_config"),
+    [
+        ("regular", {"use_dynamic_shifting": True, "shift_terminal": 0.1}),
+        ("distilled", {"use_dynamic_shifting": False, "shift_terminal": None}),
+    ],
+)
+def test_ltx_checkpoint_validation_accepts_matching_scheduler_metadata(expected_kind, scheduler_config):
+    validate_ltx_checkpoint(
+        scheduler_config,
+        expected_kind=expected_kind,
+        pipeline_name="TestLTXPipeline",
+    )
+
+
+@pytest.mark.parametrize(
+    ("expected_kind", "scheduler_config", "error"),
+    [
+        ("regular", {"use_dynamic_shifting": False, "shift_terminal": None}, "regular non-distilled"),
+        ("distilled", {"use_dynamic_shifting": True, "shift_terminal": 0.1}, "merged distilled"),
+    ],
+)
+def test_ltx_checkpoint_validation_rejects_mismatched_scheduler_metadata(expected_kind, scheduler_config, error):
+    with pytest.raises(ValueError, match=error):
+        validate_ltx_checkpoint(
+            scheduler_config,
+            expected_kind=expected_kind,
+            pipeline_name="TestLTXPipeline",
+        )
+
+
+def test_ltx_rejects_advanced_uaa_before_component_initialization():
+    od_config = SimpleNamespace(
+        model="unused",
+        parallel_config=SimpleNamespace(ulysses_mode="advanced_uaa"),
+    )
+
+    with pytest.raises(ValueError, match="does not support ulysses_mode='advanced_uaa'"):
+        LTX2Pipeline(od_config=od_config)
+
+
 def test_ltx23_checkpoint_selects_version_specific_one_stage_profile(tmp_path, monkeypatch):
     from vllm_omni.diffusion.models.ltx2 import ltx2_runtime
 
@@ -144,16 +242,104 @@ def test_ltx23_checkpoint_selects_version_specific_one_stage_profile(tmp_path, m
     assert pipe.reports_stage_durations
 
 
-def test_ltx_one_stage_entry_exposes_batch_image_and_distributed_decode_contracts():
+def test_ltx_multistage_rejects_cache_dit_before_component_initialization(tmp_path, monkeypatch):
+    from vllm_omni.diffusion.models.ltx2 import ltx2_runtime
+
+    (tmp_path / "model_index.json").write_text(json.dumps({"vocoder": ["ltx2", "LTX2Vocoder"]}))
+    initialized = False
+
+    def stub_components(*_args, **_kwargs):
+        nonlocal initialized
+        initialized = True
+
+    monkeypatch.setattr(ltx2_runtime, "initialize_pipeline_components", stub_components)
+
+    with pytest.raises(ValueError, match="only one-stage LTX recipes"):
+        LTX2TwoStagePipeline(
+            od_config=SimpleNamespace(
+                model=str(tmp_path),
+                cache_backend="cache_dit",
+            )
+        )
+
+    assert not initialized
+
+
+def test_ltx23_checkpoint_selects_version_specific_two_stage_profiles(tmp_path, monkeypatch):
+    from vllm_omni.diffusion.models.ltx2 import ltx2_runtime
+
+    (tmp_path / "model_index.json").write_text(json.dumps({"vocoder": ["ltx2", "LTX2VocoderWithBWE"]}))
+
+    def stub_components(pipe, od_config):
+        pipe.od_config = od_config
+        pipe.vae_spatial_compression_ratio = 32
+
+    monkeypatch.setattr(ltx2_runtime, "initialize_pipeline_components", stub_components)
+    monkeypatch.setattr(LTXRuntime, "setup_diffusion_pipeline_profiler", lambda *_args, **_kwargs: None)
+    monkeypatch.setattr(ltx2_runtime, "build_ltx_phase_adapter", lambda *_args: SimpleNamespace())
+    pipe = LTX2TwoStagePipeline(
+        od_config=SimpleNamespace(
+            model=str(tmp_path),
+            enable_diffusion_pipeline_profiler=False,
+            lora_path=None,
+            model_config={},
+        )
+    )
+
+    assert pipe.model_version == "2.3"
+    assert pipe.component_profile is LTX23_TWO_STAGE_COMPONENT_PROFILE
+    assert pipe.pipeline_recipe is LTX23_TWO_STAGE_RECIPE
+
+
+def test_ltx23_checkpoint_selects_full_distilled_profile_and_recipe(tmp_path, monkeypatch):
+    from vllm_omni.diffusion.models.ltx2 import ltx2_runtime
+
+    (tmp_path / "model_index.json").write_text(json.dumps({"vocoder": ["ltx2", "LTX2VocoderWithBWE"]}))
+
+    def stub_components(pipe, od_config):
+        pipe.od_config = od_config
+        pipe.vae_spatial_compression_ratio = 32
+
+    monkeypatch.setattr(ltx2_runtime, "initialize_pipeline_components", stub_components)
+    monkeypatch.setattr(LTXRuntime, "setup_diffusion_pipeline_profiler", lambda *_args, **_kwargs: None)
+
+    pipe = LTX2DistilledPipeline(
+        od_config=SimpleNamespace(
+            model=str(tmp_path),
+            enable_diffusion_pipeline_profiler=False,
+        )
+    )
+
+    assert pipe.model_version == "2.3"
+    assert pipe.component_profile is LTX23_DISTILLED_COMPONENT_PROFILE
+    assert pipe.pipeline_recipe is LTX23_DISTILLED_TWO_STAGE_RECIPE
+    assert pipe._phase_adapter is None
+    assert pipe.preserve_sp_padded_audio_duration
+    assert pipe.reports_stage_durations
+
+
+def test_ltx_entries_expose_batch_image_and_distributed_decode_contracts():
     assert LTX2Pipeline.supports_request_batch
     assert LTX2Pipeline.support_image_input
     assert LTX2Pipeline.unified_text_image_entry
     assert LTX2Pipeline.distributed_video_decode
+    assert not LTX2TwoStagePipeline.supports_request_batch
+    assert LTX2TwoStagePipeline.support_image_input
+    assert LTX2TwoStagePipeline.unified_text_image_entry
     assert not LTX2DistilledPipeline.supports_request_batch
     assert LTX2DistilledPipeline.support_image_input
     assert LTX2DistilledPipeline.unified_text_image_entry
 
 
+def test_ltx_two_stage_resolves_request_embedded_images():
+    pipe = object.__new__(LTX2TwoStagePipeline)
+    request_inputs = SimpleNamespace(latents=None)
+    image = torch.zeros(3, 8, 8)
+    req = SimpleNamespace(prompts=[{"prompt": "image conditioned", "multi_modal_data": {"image": image}}])
+
+    assert pipe._resolve_request_image(req, None, request_inputs) is image
+
+
 def test_ltx_dmd2_entries_retain_their_task_boundaries():
     assert not LTX2T2VDMD2Pipeline.support_image_input
     assert LTX2I2VDMD2Pipeline.support_image_input
@@ -330,6 +516,15 @@ def test_ltx_guidance_uses_official_close_to_disabled_semantics():
     assert LTXGuidancePlan.build(LTXGuidanceSpec(video=guidance)).names == ("cond",)
 
 
+def test_ltx_positive_only_guidance_preserves_token_major_layout():
+    tensor = torch.arange(24).view(1, 3, 8).transpose(1, 2).contiguous().transpose(1, 2)
+
+    repeated = _repeat_batch(tensor, 1)
+
+    assert repeated is tensor
+    assert repeated.stride() == tensor.stride()
+
+
 def test_ltx_guidance_rescale_is_invariant_to_request_batching():
     cond = torch.tensor(
         [
@@ -380,6 +575,32 @@ def test_ltx_guidance_rescale_handles_zero_variance_prediction():
     torch.testing.assert_close(actual, torch.zeros_like(cond))
 
 
+def test_ltx_audio_guidance_rescale_ignores_sp_padding_tokens():
+    logical_cond = torch.tensor([[[1.0, 2.0], [3.0, 5.0]]])
+    logical_uncond = torch.tensor([[[0.5, -1.0], [2.0, 1.0]]])
+    guidance = LTXModalityGuidance(cfg_scale=3.0, rescale_scale=1.0)
+    expected = combine_guided_x0(
+        cond=logical_cond,
+        uncond_text=logical_uncond,
+        uncond_perturbed=0.0,
+        uncond_modality=0.0,
+        guidance=guidance,
+    )
+    padded_cond = torch.cat([logical_cond, torch.tensor([[[1000.0, -1000.0]]])], dim=1)
+    padded_uncond = torch.cat([logical_uncond, torch.tensor([[[-500.0, 500.0]]])], dim=1)
+
+    actual = combine_guided_x0(
+        cond=padded_cond,
+        uncond_text=padded_uncond,
+        uncond_perturbed=0.0,
+        uncond_modality=0.0,
+        guidance=guidance,
+        rescale_token_count=2,
+    )
+
+    torch.testing.assert_close(actual[:, :2], expected)
+
+
 def test_ltx_guidance_builds_per_sample_stg_and_modality_masks():
     plan = LTXGuidancePlan.build(LTX23_ONE_STAGE_RECIPE.request_guidance)
     perturbations = build_perturbation_kwargs(plan, batch_size=2, reference=torch.ones(8, 1, 1))
@@ -547,11 +768,19 @@ def test_ltx_connector_attention_dispatches_through_omni_kernel(has_learned_regi
 
 def test_ltx2_distilled_pipeline_shares_recipe_runtime():
     assert issubclass(LTX2DistilledPipeline, LTXI2VConditioningMixin)
-    assert issubclass(LTX2DistilledPipeline, LTX2Pipeline)
+    assert not issubclass(LTX2DistilledPipeline, LTX2Pipeline)
     assert LTX2DistilledPipeline._run_recipe is LTXRuntime._run_recipe
     assert "_forward_request" not in LTX2DistilledPipeline.__dict__
 
 
+def test_ltx_two_stage_entry_is_a_thin_pipeline_variant():
+    assert "_run_recipe" not in LTX2TwoStagePipeline.__dict__
+    assert "_forward_request" not in LTX2TwoStagePipeline.__dict__
+    assert "load_weights" not in LTX2TwoStagePipeline.__dict__
+    assert issubclass(LTX2TwoStagePipeline, LTXI2VConditioningMixin)
+    assert not issubclass(LTX2TwoStagePipeline, LTX2Pipeline)
+
+
 def test_ltx_variants_share_denoise_loop_and_i2v_conditioning():
     assert "_denoise_loop" not in LTX2Pipeline.__dict__
     assert issubclass(LTX2Pipeline, LTXI2VConditioningMixin)
@@ -594,7 +823,8 @@ def test_denoise_executor_owns_progress_and_interrupt():
     torch.testing.assert_close(state.audio, torch.tensor(11.0))
 
 
-def test_ltx2_distilled_two_stage_executes_declarative_phase_plan():
+@pytest.mark.parametrize("pipeline_cls", [LTX2TwoStagePipeline, LTX2DistilledPipeline])
+def test_ltx_two_stage_executes_declarative_i2v_phase_plan(pipeline_cls):
     request_inputs = LTXRequestInputs(
         prompt="prompt",
         negative_prompt="",
@@ -671,12 +901,15 @@ def test_ltx2_distilled_two_stage_executes_declarative_phase_plan():
             return latents.repeat_interleave(2, dim=-2).repeat_interleave(2, dim=-1)
 
     prompt_context_sentinel = prompt_context
-    pipeline = object.__new__(LTX2DistilledPipeline)
+    pipeline = object.__new__(pipeline_cls)
     torch.nn.Module.__init__(pipeline)
     pipeline.device = torch.device("cpu")
     pipeline.vae_spatial_compression_ratio = 32
     pipeline.vae_temporal_compression_ratio = 8
     pipeline.latent_upsampler = FakeUpsampler()
+    activated_slots = []
+    if pipeline_cls is LTX2TwoStagePipeline:
+        pipeline._phase_adapter = SimpleNamespace(activate=activated_slots.append)
     object.__setattr__(pipeline, "_resolve_request_inputs", resolve_request_inputs)
     object.__setattr__(pipeline, "run_phase", run_phase)
     object.__setattr__(pipeline, "decode_phase", decode_phase)
@@ -686,14 +919,18 @@ def test_ltx2_distilled_two_stage_executes_declarative_phase_plan():
 
     assert len(phase_calls) == 2
     assert phase_calls[1][2] is prompt_context_sentinel
+    if pipeline_cls is LTX2TwoStagePipeline:
+        assert activated_slots == [None, "ltx_distilled"]
     torch.testing.assert_close(output.output[0], torch.full((1, 128, 1, 2, 2), 3.0))
-    torch.testing.assert_close(output.output[1], torch.full((1, 8, 1, 2), 4.0))
+    expected_audio = 2.0 if pipeline_cls is LTX2TwoStagePipeline else 4.0
+    torch.testing.assert_close(output.output[1], torch.full((1, 8, 1, 2), expected_audio))
 
 
-def test_ltx2_distilled_stage2_reapplies_final_resolution_i2v_conditioning():
+@pytest.mark.parametrize("pipeline_cls", [LTX2TwoStagePipeline, LTX2DistilledPipeline])
+def test_ltx_two_stage_stage2_reapplies_final_resolution_i2v_conditioning(pipeline_cls):
     from vllm_omni.diffusion.models.ltx2 import ltx2_latents
 
-    pipeline = object.__new__(LTX2DistilledPipeline)
+    pipeline = object.__new__(pipeline_cls)
     torch.nn.Module.__init__(pipeline)
     pipeline.vae_spatial_compression_ratio = 32
     pipeline.vae_temporal_compression_ratio = 8
@@ -750,6 +987,18 @@ def test_ltx2_distilled_two_stage_recipe_is_fixed_positive_only():
     assert (LTX2_DISTILLED_TWO_STAGE_RECIPE.height, LTX2_DISTILLED_TWO_STAGE_RECIPE.width) == (1024, 1536)
 
 
+def test_ltx23_full_distilled_route_uses_merged_weights_without_lora():
+    assert resolve_ltx_component_profile("distilled_two_stage", "2.3") is LTX23_DISTILLED_COMPONENT_PROFILE
+    assert resolve_ltx_pipeline_recipe("distilled_two_stage", "2.3") is LTX23_DISTILLED_TWO_STAGE_RECIPE
+    assert LTX23_DISTILLED_TWO_STAGE_RECIPE is LTX2_DISTILLED_TWO_STAGE_RECIPE
+    assert all(phase.adapter_slot is None for phase in LTX23_DISTILLED_TWO_STAGE_RECIPE.phases)
+    assert LTX23_DISTILLED_COMPONENT_PROFILE.distilled_lora_filename is None
+    assert LTX23_DISTILLED_COMPONENT_PROFILE.vocoder_cls is LTX23_COMPONENT_PROFILE.vocoder_cls
+    assert LTX23_DISTILLED_COMPONENT_PROFILE.latent_upsampler_filename == (
+        "ltx-2.3-spatial-upscaler-x2-1.1.safetensors"
+    )
+
+
 @pytest.mark.parametrize(
     ("request_inputs", "error"),
     [
@@ -958,6 +1207,98 @@ def test_ltx_custom_sigmas_bypass_scheduler_shifting():
     torch.testing.assert_close(timesteps, expected[:-1] * scheduler.config.num_train_timesteps)
 
 
+@pytest.mark.parametrize(
+    ("recipe", "steps", "stg_block"),
+    [
+        (LTX2_TWO_STAGE_RECIPE, 40, 29),
+        (LTX23_TWO_STAGE_RECIPE, 30, 28),
+    ],
+)
+def test_ltx_official_two_stage_recipes_only_vary_by_model_defaults(recipe, steps, stg_block):
+    stage1, stage2 = recipe.phases
+    assert (recipe.width, recipe.height) == (1536, 1024)
+    assert recipe.num_inference_steps == steps
+    assert stage1.spatial_downscale == 2
+    assert stage1.adapter_slot is None
+    assert stage1.guidance.video.stg_blocks == (stg_block,)
+    assert stage1.guidance.audio.stg_blocks == (stg_block,)
+    assert stage2.input_transform == "spatial_upsample"
+    assert stage2.adapter_slot == "ltx_distilled"
+    assert stage2.guidance == LTXGuidanceSpec.positive_only()
+    assert stage2.num_inference_steps == 3
+    assert stage2.sigmas == (0.909375, 0.725, 0.421875, 0.0)
+    # Official Stage 2 refines video only; its audio result is discarded.
+    assert (recipe.video_output_phase, recipe.audio_output_phase) == (1, 0)
+
+
+def test_ltx_two_stage_entries_select_the_official_adapter_slots():
+    phase_switches = []
+    ordinary_pipeline = object.__new__(LTX2TwoStagePipeline)
+    ordinary_pipeline._phase_adapter = SimpleNamespace(activate=phase_switches.append)
+    for phase in LTX2_TWO_STAGE_RECIPE.phases:
+        ordinary_pipeline._enter_phase(phase)
+    assert phase_switches == [None, "ltx_distilled"]
+
+    distilled_pipeline = object.__new__(LTX2DistilledPipeline)
+    for phase in LTX2_DISTILLED_TWO_STAGE_RECIPE.phases:
+        distilled_pipeline._enter_phase(phase)
+
+
+def test_ltx23_gate_projection_declares_its_global_column_parallel_layout(monkeypatch):
+    class FakeColumn(torch.nn.Module):
+        def __init__(
+            self,
+            input_size,
+            output_size,
+            *,
+            bias,
+            gather_output,
+            return_bias,
+            quant_config,
+            prefix,
+        ):
+            super().__init__()
+            self.input_size = input_size
+            self.output_size = output_size
+            self.bias_enabled = bias
+            self.gather_output = gather_output
+            self.return_bias = return_bias
+            self.quant_config = quant_config
+            self.prefix = prefix
+
+    class FakeRow(torch.nn.Module):
+        def __init__(self, *args, **kwargs):
+            super().__init__()
+
+    class FakeAttention(torch.nn.Module):
+        def __init__(self, **kwargs):
+            super().__init__()
+
+    monkeypatch.setattr(ltx2_transformer, "ColumnParallelLinear", FakeColumn)
+    monkeypatch.setattr(ltx2_transformer, "RowParallelLinear", FakeRow)
+    monkeypatch.setattr(ltx2_transformer, "_LTX2ParallelAttention", FakeAttention)
+    monkeypatch.setattr(ltx2_transformer, "get_tensor_model_parallel_world_size", lambda: 2)
+
+    attention = ltx2_transformer.LTX2Attention(
+        query_dim=16,
+        heads=4,
+        dim_head=4,
+        qk_norm="rms_norm",
+        apply_gated_attention=True,
+        pack_qkv=False,
+        prefix="transformer_blocks.0.attn1",
+    )
+
+    gate = attention.to_gate_logits
+    assert isinstance(gate, FakeColumn)
+    assert gate.input_size == 16
+    assert gate.output_size == 4
+    assert gate.gather_output is False
+    assert gate.return_bias is False
+    assert gate.quant_config is None
+    assert gate.prefix == "transformer_blocks.0.attn1.to_gate_logits"
+
+
 class TestLTXRequestParsing:
     def test_request_resolves_official_and_explicit_negative_prompts_per_sample(self):
         from vllm_omni.diffusion.request import OmniDiffusionRequest
@@ -1432,6 +1773,7 @@ class TestPipelineComponents:
 
     def test_distilled_profile_keeps_upsampler_in_managed_resident_components(self):
         assert LTX2_DISTILLED_COMPONENT_PROFILE.resident_modules == ("vocoder", "latent_upsampler")
+        assert LTX23_DISTILLED_COMPONENT_PROFILE.resident_modules == ("vocoder", "latent_upsampler")
 
 
 class TestLTX23DecodeConditioning:
@@ -1477,6 +1819,11 @@ class TestRegistryIntegration:
         from vllm_omni.diffusion.registry import _DIFFUSION_MODELS
 
         assert _DIFFUSION_MODELS["LTX2Pipeline"] == ("ltx2", "pipeline_ltx2", "LTX2Pipeline")
+        assert _DIFFUSION_MODELS["LTX2TwoStagePipeline"] == (
+            "ltx2",
+            "pipeline_ltx2_two_stage",
+            "LTX2TwoStagePipeline",
+        )
         assert _DIFFUSION_MODELS["LTX2DistilledPipeline"] == (
             "ltx2",
             "pipeline_ltx2_two_stage",
@@ -1488,6 +1835,9 @@ class TestRegistryIntegration:
             "LTX23ImageToVideoPipeline",
             "LTX2TwoStagesPipeline",
             "LTX2ImageToVideoTwoStagesPipeline",
+            "LTX2ImageToVideoTwoStagePipeline",
+            "LTX23TwoStagePipeline",
+            "LTX23ImageToVideoTwoStagePipeline",
         }
         assert removed_entries.isdisjoint(_DIFFUSION_MODELS)
 
@@ -1497,6 +1847,7 @@ class TestRegistryIntegration:
 
         expected = [
             "LTX2Pipeline",
+            "LTX2TwoStagePipeline",
             "LTX2DistilledPipeline",
             "LTX2T2VDMD2Pipeline",
             "LTX2I2VDMD2Pipeline",
@@ -1509,6 +1860,7 @@ class TestRegistryIntegration:
         "model_class_name",
         [
             "LTX2Pipeline",
+            "LTX2TwoStagePipeline",
             "LTX2DistilledPipeline",
             "LTX2T2VDMD2Pipeline",
             "LTX2I2VDMD2Pipeline",

tests/diffusion/models/ltx2/test_ltx2_transformer.py

@@ -1,13 +1,20 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from types import SimpleNamespace
+
 import pytest
+import torch
 from torch import nn
 
+from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata
 from vllm_omni.diffusion.cache.cachedit import CacheDiTAdapterConfig
-from vllm_omni.diffusion.models.ltx2.ltx2_transformer import LTX2VideoTransformer3DModel, _make_rms_norm
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+from vllm_omni.diffusion.models.ltx2.ltx2_transformer import (
+    LTX2AudioVideoAttnProcessor,
+    LTX2VideoTransformer3DModel,
+    _LTX2ParallelAttention,
+    _make_rms_norm,
+)
 
 pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
 
@@ -66,3 +73,83 @@ def test_ltx_sp_plan_shards_video_and_audio_timesteps_together(rope_type):
     assert video_timestep.expected_dims == audio_timestep.expected_dims == 2
     assert not video_timestep.split_output
     assert not audio_timestep.split_output
+
+
+def test_ltx_sp_keeps_cross_attention_key_padding_mask(monkeypatch):
+    monkeypatch.setattr(LTX2AudioVideoAttnProcessor, "_is_sp_enabled", staticmethod(lambda: True))
+    processor = LTX2AudioVideoAttnProcessor()
+    hidden_states = torch.zeros(1, 3, 4)
+    encoder_hidden_states = torch.zeros(1, 4, 4)
+    key_padding_mask = torch.tensor([[True, True, True, False]])
+
+    actual = processor._prepare_attention_mask(
+        SimpleNamespace(),
+        hidden_states,
+        encoder_hidden_states,
+        key_padding_mask,
+        batch_size=1,
+        sequence_length=4,
+    )
+
+    torch.testing.assert_close(actual, key_padding_mask[:, None, None, :])
+
+
+def _fake_ltx_parallel_attention():
+    calls = []
+
+    class Backend:
+        @staticmethod
+        def get_name():
+            return "FLASH_ATTN"
+
+        @staticmethod
+        def supports_attention_mask():
+            return True
+
+    def run(name):
+        def forward(query, _key, _value, _metadata):
+            calls.append(name)
+            return torch.zeros_like(query)
+
+        return forward
+
+    layer = object.__new__(_LTX2ParallelAttention)
+    nn.Module.__init__(layer)
+    layer.attn_backend = Backend
+    layer.attention = SimpleNamespace(forward=run("native"))
+    layer.sdpa_fallback = SimpleNamespace(forward=run("sdpa"))
+    layer.backend_pref = "FLASH_ATTN"
+    return layer, calls
+
+
+@pytest.mark.parametrize(("query_length", "key_length"), [(4, 4), (3, 4)])
+def test_ltx_masked_cross_attention_uses_sdpa(query_length, key_length):
+    layer, calls = _fake_ltx_parallel_attention()
+    query = torch.zeros(1, query_length, 2, 4, dtype=torch.bfloat16)
+    key = value = torch.zeros(1, key_length, 2, 4, dtype=torch.bfloat16)
+    key_padding_mask = torch.tensor([[True] * (key_length - 1) + [False]])
+    metadata = AttentionMetadata(attn_mask=key_padding_mask[:, None, None, :])
+
+    layer._run_local_attention(query, key, value, metadata)
+
+    assert calls == ["sdpa"]
+
+
+def test_ltx_masked_self_attention_uses_sdpa():
+    layer, calls = _fake_ltx_parallel_attention()
+    query = key = value = torch.zeros(1, 4, 2, 4, dtype=torch.bfloat16)
+    key_padding_mask = torch.tensor([[True, True, True, False]])
+    metadata = AttentionMetadata(attn_mask=key_padding_mask[:, None, None, :])
+
+    layer._run_local_attention(query, key, value, metadata)
+
+    assert calls == ["sdpa"]
+
+
+def test_ltx_unmasked_cross_attention_keeps_native_backend():
+    layer, calls = _fake_ltx_parallel_attention()
+    query = key = value = torch.zeros(1, 4, 2, 4, dtype=torch.bfloat16)
+
+    layer._run_local_attention(query, key, value, None)
+
+    assert calls == ["native"]

tests/e2e/accuracy/ltx/run_ltx_reference.py

@@ -20,6 +20,11 @@ from PIL import Image
 def _parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser()
     parser.add_argument("--backend", choices=("official", "omni"), required=True)
+    parser.add_argument(
+        "--pipeline-kind",
+        choices=("one_stage", "distilled", "two_stage"),
+        default="one_stage",
+    )
     parser.add_argument("--request", type=Path, required=True)
     parser.add_argument("--output-dir", type=Path, required=True)
     parser.add_argument("--model")
@@ -27,6 +32,8 @@ def _parse_args() -> argparse.Namespace:
     parser.add_argument("--official-root", type=Path)
     parser.add_argument("--checkpoint", type=Path)
     parser.add_argument("--gemma-root", type=Path)
+    parser.add_argument("--spatial-upsampler", type=Path)
+    parser.add_argument("--distilled-lora", type=Path)
     parser.add_argument("--enable-layerwise-offload", action="store_true")
     return parser.parse_args()
 
@@ -91,9 +98,13 @@ def _configure_official_sdpa(pipeline: Any) -> None:
         attention=attention,
         masked_attention=attention,
     )
-    owners_and_attributes = (
-        (pipeline.stage, "_transformer_builder"),
+    owners_and_attributes = [
         (pipeline.prompt_encoder, "_embeddings_processor_builder"),
+    ]
+    owners_and_attributes.extend(
+        (stage, "_transformer_builder")
+        for name in ("stage", "stage_1", "stage_2")
+        if (stage := getattr(pipeline, name, None)) is not None
     )
     for owner, attribute in owners_and_attributes:
         builder = getattr(owner, attribute)
@@ -104,6 +115,12 @@ def _configure_official_sdpa(pipeline: Any) -> None:
         )
 
 
+def _require_path(path: Path | None, description: str) -> str:
+    if path is None or not path.is_file():
+        raise ValueError(f"Official {description} is required and must exist: {path}")
+    return str(path)
+
+
 @torch.inference_mode()
 def _run_official(args: argparse.Namespace, request: dict[str, Any]) -> None:
     if args.official_root is None or args.checkpoint is None or args.gemma_root is None:
@@ -113,16 +130,54 @@ def _run_official(args: argparse.Namespace, request: dict[str, Any]) -> None:
     torch.backends.cuda.enable_cudnn_sdp(False)
 
     from ltx_core.components.guiders import MultiModalGuiderParams
+    from ltx_core.loader import LTXV_LORA_COMFY_RENAMING_MAP, LoraPathStrengthAndSDOps
+    from ltx_pipelines.distilled import DistilledPipeline
     from ltx_pipelines.ti2vid_one_stage import TI2VidOneStagePipeline
+    from ltx_pipelines.ti2vid_two_stages import TI2VidTwoStagesPipeline
     from ltx_pipelines.utils.args import ImageConditioningInput
     from ltx_pipelines.utils.types import OffloadMode
 
-    pipeline = TI2VidOneStagePipeline(
-        checkpoint_path=str(args.checkpoint),
-        gemma_root=str(args.gemma_root),
-        loras=(),
-        offload_mode=OffloadMode.CPU if args.enable_layerwise_offload else OffloadMode.NONE,
-    )
+    if request.get("pipeline_kind", "one_stage") != args.pipeline_kind:
+        raise ValueError(
+            f"Request pipeline kind {request.get('pipeline_kind')!r} does not match {args.pipeline_kind!r}"
+        )
+    checkpoint = _require_path(args.checkpoint, "checkpoint")
+    gemma_root = str(args.gemma_root)
+    if not Path(gemma_root).is_dir():
+        raise ValueError(f"Official Gemma root is required and must be a directory: {gemma_root}")
+    offload_mode = OffloadMode.CPU if args.enable_layerwise_offload else OffloadMode.NONE
+    pipeline: Any
+    if args.pipeline_kind == "one_stage":
+        pipeline = TI2VidOneStagePipeline(
+            checkpoint_path=checkpoint,
+            gemma_root=gemma_root,
+            loras=(),
+            offload_mode=offload_mode,
+        )
+    elif args.pipeline_kind == "distilled":
+        pipeline = DistilledPipeline(
+            distilled_checkpoint_path=checkpoint,
+            spatial_upsampler_path=_require_path(args.spatial_upsampler, "spatial upsampler"),
+            gemma_root=gemma_root,
+            loras=(),
+            offload_mode=offload_mode,
+        )
+    else:
+        distilled_lora = [
+            LoraPathStrengthAndSDOps(
+                path=_require_path(args.distilled_lora, "distilled LoRA"),
+                strength=1.0,
+                sd_ops=LTXV_LORA_COMFY_RENAMING_MAP,
+            )
+        ]
+        pipeline = TI2VidTwoStagesPipeline(
+            checkpoint_path=checkpoint,
+            distilled_lora=distilled_lora,
+            spatial_upsampler_path=_require_path(args.spatial_upsampler, "spatial upsampler"),
+            gemma_root=gemma_root,
+            loras=(),
+            offload_mode=offload_mode,
+        )
     _configure_official_sdpa(pipeline)
     image_path = request.get("image")
     images = (
@@ -140,34 +195,45 @@ def _run_official(args: argparse.Namespace, request: dict[str, Any]) -> None:
             )
         ]
     )
-    video, audio = pipeline(
-        prompt=request["prompt"],
-        negative_prompt=request["negative_prompt"],
-        seed=request["seed"],
-        height=request["height"],
-        width=request["width"],
-        num_frames=request["num_frames"],
-        frame_rate=request["fps"],
-        num_inference_steps=request["num_inference_steps"],
-        video_guider_params=MultiModalGuiderParams(
-            cfg_scale=request["video_cfg_scale"],
-            stg_scale=request["video_stg_scale"],
-            rescale_scale=request["video_rescale_scale"],
-            modality_scale=request["video_modality_scale"],
-            skip_step=0,
-            stg_blocks=request["video_stg_blocks"],
-        ),
-        audio_guider_params=MultiModalGuiderParams(
-            cfg_scale=request["audio_cfg_scale"],
-            stg_scale=request["audio_stg_scale"],
-            rescale_scale=request["audio_rescale_scale"],
-            modality_scale=request["audio_modality_scale"],
-            skip_step=0,
-            stg_blocks=request["audio_stg_blocks"],
-        ),
-        images=images,
-        max_batch_size=4,
-    )
+    if args.pipeline_kind == "distilled":
+        video, audio = pipeline(
+            prompt=request["prompt"],
+            seed=request["seed"],
+            height=request["height"],
+            width=request["width"],
+            num_frames=request["num_frames"],
+            frame_rate=request["fps"],
+            images=images,
+        )
+    else:
+        video, audio = pipeline(
+            prompt=request["prompt"],
+            negative_prompt=request["negative_prompt"],
+            seed=request["seed"],
+            height=request["height"],
+            width=request["width"],
+            num_frames=request["num_frames"],
+            frame_rate=request["fps"],
+            num_inference_steps=request["num_inference_steps"],
+            video_guider_params=MultiModalGuiderParams(
+                cfg_scale=request["video_cfg_scale"],
+                stg_scale=request["video_stg_scale"],
+                rescale_scale=request["video_rescale_scale"],
+                modality_scale=request["video_modality_scale"],
+                skip_step=0,
+                stg_blocks=request["video_stg_blocks"],
+            ),
+            audio_guider_params=MultiModalGuiderParams(
+                cfg_scale=request["audio_cfg_scale"],
+                stg_scale=request["audio_stg_scale"],
+                rescale_scale=request["audio_rescale_scale"],
+                modality_scale=request["audio_modality_scale"],
+                skip_step=0,
+                stg_blocks=request["audio_stg_blocks"],
+            ),
+            images=images,
+            max_batch_size=4,
+        )
     video_tensor = torch.cat([chunk.detach().cpu() for chunk in video], dim=0)
     _save_outputs(
         args.output_dir,
@@ -179,6 +245,7 @@ def _run_official(args: argparse.Namespace, request: dict[str, Any]) -> None:
             "attention_backend": "torch_sdpa",
             "official_revision": os.environ.get("VLLM_TEST_LTX_OFFICIAL_REVISION"),
             "checkpoint": str(args.checkpoint),
+            "pipeline_kind": args.pipeline_kind,
         },
     )
 
@@ -253,6 +320,10 @@ def _canonical_video(video: Any) -> torch.Tensor:
 def _run_omni(args: argparse.Namespace, request: dict[str, Any]) -> None:
     if args.model is None or args.model_class_name is None:
         raise ValueError("Omni backend requires --model and --model-class-name")
+    if request.get("pipeline_kind", "one_stage") != args.pipeline_kind:
+        raise ValueError(
+            f"Request pipeline kind {request.get('pipeline_kind')!r} does not match {args.pipeline_kind!r}"
+        )
 
     attention_config = {"default": {"backend": "TORCH_SDPA"}}
 
@@ -273,7 +344,8 @@ def _run_omni(args: argparse.Namespace, request: dict[str, Any]) -> None:
         parallel_config=DiffusionParallelConfig(),
     )
     try:
-        model_class_name = get_model_class_name(omni)
+        detected_model_class_name = get_model_class_name(omni)
+        model_class_name = args.model_class_name
         sampling_params = OmniDiffusionSamplingParams(
             height=request["height"],
             width=request["width"],
@@ -289,10 +361,9 @@ def _run_omni(args: argparse.Namespace, request: dict[str, Any]) -> None:
             key: value for key, value in request.items() if key.startswith("video_") or key.startswith("audio_")
         }
         apply_declared_extra_args(sampling_params, get_extra_body_params(model_class_name), guidance)
-        prompt: dict[str, Any] = {
-            "prompt": request["prompt"],
-            "negative_prompt": request["negative_prompt"],
-        }
+        prompt: dict[str, Any] = {"prompt": request["prompt"]}
+        if request.get("negative_prompt"):
+            prompt["negative_prompt"] = request["negative_prompt"]
         image_path = request.get("image")
         if image_path is not None:
             with Image.open(str(image_path)) as source_image:
@@ -312,6 +383,8 @@ def _run_omni(args: argparse.Namespace, request: dict[str, Any]) -> None:
                 "attention_backend": "torch_sdpa",
                 "model": args.model,
                 "model_class_name": model_class_name,
+                "model_config_class_name": detected_model_class_name,
+                "pipeline_kind": args.pipeline_kind,
             },
         )
     finally:

tests/e2e/accuracy/ltx/test_ltx_official_similarity.py

@@ -3,10 +3,9 @@
 
 """E2E accuracy guard against a pinned Lightricks LTX pipeline revision.
 
-The comparison runs both runtimes through PyTorch SDPA and uses
-``max_batch_size=4`` in the official reference to match Omni's fused guidance
-batch. Video and audio guidance use the official non-HQ one-stage defaults;
-only the generation shape and step count are reduced for CI runtime.
+The original reduced one-stage guards remain unchanged. Four complementary
+default-shape cases cover both model versions, both two-stage weight families,
+and T2V/I2V without expanding every combination into a duplicate golden.
 """
 
 from __future__ import annotations
@@ -17,8 +16,9 @@ import shutil
 import subprocess
 import sys
 import time
-from dataclasses import dataclass
+from dataclasses import asdict, dataclass, replace
 from pathlib import Path
+from typing import Literal
 
 import numpy as np
 import pytest
@@ -53,74 +53,260 @@ NEGATIVE_PROMPT = (
 
 # Both runtimes use PyTorch SDPA with the current Torch dispatch defaults.
 ATTENTION_BACKEND = "torch_sdpa"
-VIDEO_SSIM_MEAN_THRESHOLD = 0.95
-VIDEO_SSIM_MIN_THRESHOLD = 0.90
-VIDEO_PSNR_MEAN_THRESHOLD = 30.0
-AUDIO_RELATIVE_L2_THRESHOLD = 0.2
-AUDIO_COSINE_THRESHOLD = 0.95
+
+
+@dataclass(frozen=True)
+class LTXArtifact:
+    repo_id: str
+    filename: str
+    revision: str
+    env: str
+    repo_type: Literal["model", "dataset"] = "model"
+
+
+@dataclass(frozen=True)
+class LTXAccuracyThresholds:
+    video_ssim_mean: float
+    video_ssim_min: float
+    video_psnr_mean_db: float
+    # Zero disables gating for waveform-sensitive audio that is not expected
+    # to align with the official output.
+    audio_relative_l2: float
+    audio_cosine_similarity: float
+
+
+STRICT_THRESHOLDS = LTXAccuracyThresholds(
+    video_ssim_mean=0.95,
+    video_ssim_min=0.90,
+    video_psnr_mean_db=30.0,
+    audio_relative_l2=0.20,
+    audio_cosine_similarity=0.95,
+)
 
 
 @dataclass(frozen=True)
 class LTXAccuracyCase:
     name: str
+    pipeline_kind: Literal["one_stage", "distilled", "two_stage"]
     model_id: str
     model_revision: str
     model_env: str
     model_class_name: str
-    checkpoint_repo: str
-    checkpoint_filename: str
-    checkpoint_revision: str
-    checkpoint_env: str
-    stg_block: int
+    checkpoint: LTXArtifact
+    width: int
+    height: int
+    num_frames: int
+    num_inference_steps: int
+    seed: int
+    stg_block: int | None
+    thresholds: LTXAccuracyThresholds
     prompt: str = PROMPT
-    image_repo: str | None = None
-    image_filename: str | None = None
-    image_revision: str | None = None
+    gemma_model_id: str | None = None
+    gemma_model_revision: str | None = None
+    gemma_model_env: str | None = None
+    image: LTXArtifact | None = None
+    spatial_upsampler: LTXArtifact | None = None
+    distilled_lora: LTXArtifact | None = None
+    # Official uses block streaming; Omni uses layerwise offload. Both preserve
+    # bf16 arithmetic while keeping full-resolution two-stage cases on one H100.
+    enable_layerwise_offload: bool = False
 
 
-CASES = (
+LTX2_REVISION = "47da56e2ad66ce4125a9922b4a8826bf407f9d0a"
+LTX23_REVISION = "4229404625088d21c4f112eb640fb04a0900ee25"
+LTX2_CHECKPOINT = LTXArtifact(
+    repo_id="Lightricks/LTX-2",
+    filename="ltx-2-19b-dev.safetensors",
+    revision=LTX2_REVISION,
+    env="VLLM_TEST_LTX2_OFFICIAL_CHECKPOINT",
+)
+LTX2_DISTILLED_CHECKPOINT = replace(
+    LTX2_CHECKPOINT,
+    filename="ltx-2-19b-distilled.safetensors",
+    env="VLLM_TEST_LTX2_DISTILLED_OFFICIAL_CHECKPOINT",
+)
+LTX2_UPSAMPLER = replace(
+    LTX2_CHECKPOINT,
+    filename="ltx-2-spatial-upscaler-x2-1.0.safetensors",
+    env="VLLM_TEST_LTX2_UPSAMPLER",
+)
+LTX2_DISTILLED_LORA = replace(
+    LTX2_CHECKPOINT,
+    filename="ltx-2-19b-distilled-lora-384.safetensors",
+    env="VLLM_TEST_LTX2_DISTILLED_LORA",
+)
+LTX23_CHECKPOINT = LTXArtifact(
+    repo_id="Lightricks/LTX-2.3",
+    filename="ltx-2.3-22b-dev.safetensors",
+    revision=LTX23_REVISION,
+    env="VLLM_TEST_LTX23_OFFICIAL_CHECKPOINT",
+)
+LTX23_DISTILLED_CHECKPOINT = replace(
+    LTX23_CHECKPOINT,
+    # The pinned Diffusers checkpoint contains the original merged distilled
+    # Transformer. Keep the reference Transformer on the same version; the
+    # spatial upsampler is versioned independently and remains on 1.1 below.
+    filename="ltx-2.3-22b-distilled.safetensors",
+    env="VLLM_TEST_LTX23_DISTILLED_OFFICIAL_CHECKPOINT",
+)
+LTX23_UPSAMPLER = replace(
+    LTX23_CHECKPOINT,
+    filename="ltx-2.3-spatial-upscaler-x2-1.1.safetensors",
+    env="VLLM_TEST_LTX23_UPSAMPLER",
+)
+LTX23_DISTILLED_LORA = replace(
+    LTX23_CHECKPOINT,
+    filename="ltx-2.3-22b-distilled-lora-384-1.1.safetensors",
+    env="VLLM_TEST_LTX23_DISTILLED_LORA",
+)
+I2V_IMAGE = LTXArtifact(
+    repo_id="huggingface/documentation-images",
+    filename="diffusers/svd/rocket.png",
+    revision="645d8364f0c7a101180b364811b5a11a362e4010",
+    env="VLLM_TEST_LTX_I2V_IMAGE",
+    repo_type="dataset",
+)
+
+
+LEGACY_CASES = (
     LTXAccuracyCase(
         name="ltx2",
+        pipeline_kind="one_stage",
         model_id="Lightricks/LTX-2",
-        model_revision="47da56e2ad66ce4125a9922b4a8826bf407f9d0a",
+        model_revision=LTX2_REVISION,
         model_env="VLLM_TEST_LTX2_MODEL",
         model_class_name="LTX2Pipeline",
-        checkpoint_repo="Lightricks/LTX-2",
-        checkpoint_filename="ltx-2-19b-dev.safetensors",
-        checkpoint_revision="47da56e2ad66ce4125a9922b4a8826bf407f9d0a",
-        checkpoint_env="VLLM_TEST_LTX2_OFFICIAL_CHECKPOINT",
+        checkpoint=LTX2_CHECKPOINT,
+        width=512,
+        height=384,
+        num_frames=25,
+        num_inference_steps=20,
+        seed=42,
         stg_block=29,
+        thresholds=STRICT_THRESHOLDS,
     ),
     LTXAccuracyCase(
         name="ltx2_3",
+        pipeline_kind="one_stage",
         model_id="diffusers/LTX-2.3-Diffusers",
         model_revision="8eee8edcf067e838b843f926ec4d4cc9b2be1aaf",
         model_env="VLLM_TEST_LTX23_MODEL",
         model_class_name="LTX2Pipeline",
-        checkpoint_repo="Lightricks/LTX-2.3",
-        checkpoint_filename="ltx-2.3-22b-dev.safetensors",
-        checkpoint_revision="4229404625088d21c4f112eb640fb04a0900ee25",
-        checkpoint_env="VLLM_TEST_LTX23_OFFICIAL_CHECKPOINT",
+        checkpoint=LTX23_CHECKPOINT,
+        width=512,
+        height=384,
+        num_frames=25,
+        num_inference_steps=20,
+        seed=42,
         stg_block=28,
+        thresholds=STRICT_THRESHOLDS,
     ),
     LTXAccuracyCase(
         name="ltx2_3_i2v",
+        pipeline_kind="one_stage",
         model_id="diffusers/LTX-2.3-Diffusers",
         model_revision="8eee8edcf067e838b843f926ec4d4cc9b2be1aaf",
         model_env="VLLM_TEST_LTX23_MODEL",
         model_class_name="LTX2Pipeline",
-        checkpoint_repo="Lightricks/LTX-2.3",
-        checkpoint_filename="ltx-2.3-22b-dev.safetensors",
-        checkpoint_revision="4229404625088d21c4f112eb640fb04a0900ee25",
-        checkpoint_env="VLLM_TEST_LTX23_OFFICIAL_CHECKPOINT",
+        checkpoint=LTX23_CHECKPOINT,
+        width=512,
+        height=384,
+        num_frames=25,
+        num_inference_steps=20,
+        seed=42,
         stg_block=28,
-        image_repo="huggingface/documentation-images",
-        image_filename="diffusers/svd/rocket.png",
-        image_revision="645d8364f0c7a101180b364811b5a11a362e4010",
+        thresholds=STRICT_THRESHOLDS,
+        image=I2V_IMAGE,
     ),
 )
 
 
+TWO_STAGE_CASES = (
+    LTXAccuracyCase(
+        name="ltx2_distilled_t2v",
+        pipeline_kind="distilled",
+        model_id="rootonchair/LTX-2-19b-distilled",
+        model_revision="388e2846f54aae51687498ffb6b27c7c2c9ce9e5",
+        model_env="VLLM_TEST_LTX2_DISTILLED_MODEL",
+        model_class_name="LTX2DistilledPipeline",
+        checkpoint=LTX2_DISTILLED_CHECKPOINT,
+        spatial_upsampler=LTX2_UPSAMPLER,
+        width=1536,
+        height=1024,
+        num_frames=121,
+        num_inference_steps=8,
+        seed=10,
+        stg_block=None,
+        gemma_model_id="Lightricks/LTX-2",
+        gemma_model_revision=LTX2_REVISION,
+        gemma_model_env="VLLM_TEST_LTX2_MODEL",
+        thresholds=STRICT_THRESHOLDS,
+    ),
+    LTXAccuracyCase(
+        name="ltx23_distilled_i2v",
+        pipeline_kind="distilled",
+        model_id="diffusers/LTX-2.3-Distilled-Diffusers",
+        model_revision="432e0d3c2d1769aaa4d295f9243f7062bf6b47ee",
+        model_env="VLLM_TEST_LTX23_DISTILLED_MODEL",
+        model_class_name="LTX2DistilledPipeline",
+        checkpoint=LTX23_DISTILLED_CHECKPOINT,
+        spatial_upsampler=LTX23_UPSAMPLER,
+        width=1536,
+        height=1024,
+        num_frames=121,
+        num_inference_steps=8,
+        seed=10,
+        stg_block=None,
+        gemma_model_id="diffusers/LTX-2.3-Diffusers",
+        gemma_model_revision="8eee8edcf067e838b843f926ec4d4cc9b2be1aaf",
+        gemma_model_env="VLLM_TEST_LTX23_MODEL",
+        thresholds=STRICT_THRESHOLDS,
+        image=I2V_IMAGE,
+    ),
+    LTXAccuracyCase(
+        name="ltx2_two_stage_layer_fused_t2v",
+        pipeline_kind="two_stage",
+        model_id="Lightricks/LTX-2",
+        model_revision=LTX2_REVISION,
+        model_env="VLLM_TEST_LTX2_MODEL",
+        model_class_name="LTX2TwoStagePipeline",
+        checkpoint=LTX2_CHECKPOINT,
+        spatial_upsampler=LTX2_UPSAMPLER,
+        distilled_lora=LTX2_DISTILLED_LORA,
+        width=1536,
+        height=1024,
+        num_frames=121,
+        num_inference_steps=40,
+        seed=10,
+        stg_block=29,
+        enable_layerwise_offload=True,
+        thresholds=STRICT_THRESHOLDS,
+    ),
+    LTXAccuracyCase(
+        name="ltx23_two_stage_layer_fused_i2v",
+        pipeline_kind="two_stage",
+        model_id="diffusers/LTX-2.3-Diffusers",
+        model_revision="8eee8edcf067e838b843f926ec4d4cc9b2be1aaf",
+        model_env="VLLM_TEST_LTX23_MODEL",
+        model_class_name="LTX2TwoStagePipeline",
+        checkpoint=LTX23_CHECKPOINT,
+        spatial_upsampler=LTX23_UPSAMPLER,
+        distilled_lora=LTX23_DISTILLED_LORA,
+        width=1536,
+        height=1024,
+        num_frames=121,
+        num_inference_steps=30,
+        seed=10,
+        stg_block=28,
+        enable_layerwise_offload=True,
+        thresholds=STRICT_THRESHOLDS,
+        image=I2V_IMAGE,
+    ),
+)
+
+CASES = (*LEGACY_CASES, *TWO_STAGE_CASES)
+
+
 def _run(command: list[str], *, env: dict[str, str], timeout: int = 1800) -> None:
     start = time.perf_counter()
     subprocess.run(command, env=env, timeout=timeout, check=True)
@@ -209,6 +395,7 @@ def _resolve_model(case: LTXAccuracyCase) -> Path:
                 "model_index.json",
                 "audio_vae/*",
                 "connectors/*",
+                "latent_upsampler/*",
                 "processor/*",
                 "scheduler/*",
                 "text_encoder/config.json",
@@ -223,74 +410,82 @@ def _resolve_model(case: LTXAccuracyCase) -> Path:
     )
 
 
-def _resolve_gemma_root(model: Path) -> Path:
+def _resolve_gemma_root(case: LTXAccuracyCase, model: Path) -> Path:
     configured_root = os.environ.get("VLLM_TEST_LTX_GEMMA_ROOT")
     if configured_root:
         root = Path(configured_root)
         assert root.is_dir(), f"Gemma root not found: {root}"
         return root
+    if case.gemma_model_id is not None:
+        configured_model = os.environ.get(case.gemma_model_env or "")
+        if configured_model and Path(configured_model).is_dir():
+            return Path(configured_model)
+        return Path(
+            snapshot_download(
+                repo_id=case.gemma_model_id,
+                revision=case.gemma_model_revision,
+                allow_patterns=[
+                    "processor/*",
+                    "text_encoder/*",
+                    "tokenizer/*",
+                ],
+            )
+        )
     return model
 
 
-def _resolve_checkpoint(case: LTXAccuracyCase, model: Path) -> Path:
-    configured_checkpoint = os.environ.get(case.checkpoint_env)
-    if configured_checkpoint:
-        checkpoint = Path(configured_checkpoint)
-        assert checkpoint.is_file(), f"Official checkpoint not found: {checkpoint}"
-        return checkpoint
-    model_checkpoint = model / case.checkpoint_filename
-    if model_checkpoint.is_file():
-        return model_checkpoint
+def _resolve_artifact(artifact: LTXArtifact, model: Path | None = None) -> Path:
+    configured_path = os.environ.get(artifact.env)
+    if configured_path:
+        path = Path(configured_path)
+        assert path.is_file(), f"Configured LTX artifact not found: {path}"
+        return path
+    if model is not None:
+        model_path = model / artifact.filename
+        if model_path.is_file():
+            return model_path
     return Path(
         hf_hub_download(
-            repo_id=case.checkpoint_repo,
-            filename=case.checkpoint_filename,
-            revision=case.checkpoint_revision,
+            repo_id=artifact.repo_id,
+            repo_type=None if artifact.repo_type == "model" else artifact.repo_type,
+            filename=artifact.filename,
+            revision=artifact.revision,
         )
     )
 
 
 def _resolve_image(case: LTXAccuracyCase) -> Path | None:
-    if case.image_filename is None:
+    if case.image is None:
         return None
-    if case.image_repo is None or case.image_revision is None:
-        raise ValueError(f"Incomplete image source for LTX accuracy case {case.name!r}.")
-    configured_image = os.environ.get("VLLM_TEST_LTX_I2V_IMAGE")
-    if configured_image:
-        image = Path(configured_image)
-        assert image.is_file(), f"LTX I2V conditioning image not found: {image}"
-        return image
-    return Path(
-        hf_hub_download(
-            repo_id=case.image_repo,
-            repo_type="dataset",
-            filename=case.image_filename,
-            revision=case.image_revision,
-        )
-    )
+    return _resolve_artifact(case.image)
 
 
 def _request(case: LTXAccuracyCase, image: Path | None) -> dict[str, object]:
     request: dict[str, object] = {
+        "pipeline_kind": case.pipeline_kind,
         "prompt": case.prompt,
-        "negative_prompt": NEGATIVE_PROMPT,
-        "width": 512,
-        "height": 384,
-        "num_frames": 25,
+        "negative_prompt": "" if case.pipeline_kind == "distilled" else NEGATIVE_PROMPT,
+        "width": case.width,
+        "height": case.height,
+        "num_frames": case.num_frames,
         "fps": 24,
-        "num_inference_steps": 20,
-        "seed": 42,
-        "video_cfg_scale": 3.0,
-        "audio_cfg_scale": 7.0,
-        "video_stg_scale": 1.0,
-        "audio_stg_scale": 1.0,
-        "video_modality_scale": 3.0,
-        "audio_modality_scale": 3.0,
-        "video_rescale_scale": 0.7,
-        "audio_rescale_scale": 0.7,
-        "video_stg_blocks": [case.stg_block],
-        "audio_stg_blocks": [case.stg_block],
+        "num_inference_steps": case.num_inference_steps,
+        "seed": case.seed,
     }
+    if case.pipeline_kind != "distilled":
+        assert case.stg_block is not None
+        request.update(
+            video_cfg_scale=3.0,
+            audio_cfg_scale=7.0,
+            video_stg_scale=1.0,
+            audio_stg_scale=1.0,
+            video_modality_scale=3.0,
+            audio_modality_scale=3.0,
+            video_rescale_scale=0.7,
+            audio_rescale_scale=0.7,
+            video_stg_blocks=[case.stg_block],
+            audio_stg_blocks=[case.stg_block],
+        )
     if image is not None:
         request["image"] = str(image.resolve())
     return request
@@ -299,20 +494,30 @@ def _request(case: LTXAccuracyCase, image: Path | None) -> dict[str, object]:
 def _video_metrics(reference: np.ndarray, prediction: np.ndarray) -> dict[str, float]:
     assert reference.shape == prediction.shape
     assert reference.ndim == 4 and reference.shape[-1] == 3
+    device = torch.device("cpu")
+    ssim = StructuralSimilarityIndexMeasure(data_range=1.0).to(device)
+    psnr = PeakSignalNoiseRatio(data_range=1.0).to(device)
     ssim_scores: list[float] = []
     psnr_scores: list[float] = []
-    for reference_frame, prediction_frame in zip(reference, prediction, strict=True):
-        reference_tensor = torch.from_numpy(reference_frame).permute(2, 0, 1).unsqueeze(0)
-        prediction_tensor = torch.from_numpy(prediction_frame).permute(2, 0, 1).unsqueeze(0)
-        ssim_scores.append(float(StructuralSimilarityIndexMeasure(data_range=1.0)(prediction_tensor, reference_tensor)))
-        psnr_scores.append(float(PeakSignalNoiseRatio(data_range=1.0)(prediction_tensor, reference_tensor)))
-    difference = np.abs(reference.astype(np.float64) - prediction.astype(np.float64))
+    max_abs = 0.0
+    absolute_error_sum = 0.0
+    with torch.inference_mode():
+        for reference_frame, prediction_frame in zip(reference, prediction, strict=True):
+            reference_tensor = torch.from_numpy(reference_frame).permute(2, 0, 1).unsqueeze(0).to(device)
+            prediction_tensor = torch.from_numpy(prediction_frame).permute(2, 0, 1).unsqueeze(0).to(device)
+            ssim_scores.append(float(ssim(prediction_tensor, reference_tensor)))
+            psnr_scores.append(float(psnr(prediction_tensor, reference_tensor)))
+            ssim.reset()
+            psnr.reset()
+            difference = np.abs(reference_frame.astype(np.float64) - prediction_frame.astype(np.float64))
+            max_abs = max(max_abs, float(difference.max()))
+            absolute_error_sum += float(difference.sum())
     return {
         "ssim_mean": float(np.mean(ssim_scores)),
         "ssim_min": float(np.min(ssim_scores)),
         "psnr_mean_db": float(np.mean(psnr_scores)),
-        "max_abs": float(difference.max()),
-        "mean_abs": float(difference.mean()),
+        "max_abs": max_abs,
+        "mean_abs": absolute_error_sum / reference.size,
     }
 
 
@@ -343,13 +548,19 @@ def _audio_metrics(reference: np.ndarray, prediction: np.ndarray) -> dict[str, f
 @pytest.mark.benchmark
 @pytest.mark.diffusion
 @hardware_test(res={"cuda": "H100"}, num_cards=1)
-def test_ltx_one_stage_matches_official(case: LTXAccuracyCase, accuracy_artifact_root: Path) -> None:
-    """Compare official and Omni raw AV outputs from the same E2E request."""
+def test_ltx_matches_official(case: LTXAccuracyCase, accuracy_artifact_root: Path) -> None:
+    """Compare official and Omni raw AV outputs from the same single-H100 request."""
+    configured_artifact_root = os.environ.get("VLLM_TEST_LTX_ARTIFACT_ROOT")
+    if configured_artifact_root:
+        accuracy_artifact_root = Path(configured_artifact_root)
+        accuracy_artifact_root.mkdir(parents=True, exist_ok=True)
     output_root = reset_artifact_dir(accuracy_artifact_root / "ltx_official" / case.name)
     official_root, official_revision = _official_source(accuracy_artifact_root / "ltx_official")
     model = _resolve_model(case)
-    gemma_root = _resolve_gemma_root(model)
-    checkpoint = _resolve_checkpoint(case, model)
+    gemma_root = _resolve_gemma_root(case, model)
+    checkpoint = _resolve_artifact(case.checkpoint, model)
+    spatial_upsampler = _resolve_artifact(case.spatial_upsampler, model) if case.spatial_upsampler is not None else None
+    distilled_lora = _resolve_artifact(case.distilled_lora, model) if case.distilled_lora is not None else None
     image = _resolve_image(case)
     request_path = output_root / "request.json"
     request_path.write_text(json.dumps(_request(case, image), indent=2) + "\n")
@@ -359,8 +570,13 @@ def test_ltx_one_stage_matches_official(case: LTXAccuracyCase, accuracy_artifact
         str(runner),
         "--request",
         str(request_path),
+        "--pipeline-kind",
+        case.pipeline_kind,
     ]
-    if os.environ.get("VLLM_TEST_LTX_ENABLE_LAYERWISE_OFFLOAD", "").lower() in {"1", "true", "yes", "on"}:
+    enable_layerwise_offload = case.enable_layerwise_offload or os.environ.get(
+        "VLLM_TEST_LTX_ENABLE_LAYERWISE_OFFLOAD", ""
+    ).lower() in {"1", "true", "yes", "on"}
+    if enable_layerwise_offload:
         runner_args.append("--enable-layerwise-offload")
     env = os.environ.copy()
     env["VLLM_TEST_LTX_OFFICIAL_REVISION"] = official_revision
@@ -372,10 +588,9 @@ def test_ltx_one_stage_matches_official(case: LTXAccuracyCase, accuracy_artifact
     )
 
     official_output = output_root / "official"
-    _run(
-        _official_runner_prefix()
-        + runner_args
-        + [
+    official_args = _official_runner_prefix() + runner_args
+    official_args.extend(
+        [
             "--backend",
             "official",
             "--output-dir",
@@ -386,12 +601,16 @@ def test_ltx_one_stage_matches_official(case: LTXAccuracyCase, accuracy_artifact
             str(checkpoint),
             "--gemma-root",
             str(gemma_root),
-        ],
-        env=env,
+        ]
     )
+    if spatial_upsampler is not None:
+        official_args.extend(["--spatial-upsampler", str(spatial_upsampler)])
+    if distilled_lora is not None:
+        official_args.extend(["--distilled-lora", str(distilled_lora)])
+    _run(official_args, env=env)
 
     omni_output = output_root / "omni"
-    _run(
+    omni_args = (
         [sys.executable]
         + runner_args
         + [
@@ -403,9 +622,9 @@ def test_ltx_one_stage_matches_official(case: LTXAccuracyCase, accuracy_artifact
             str(model),
             "--model-class-name",
             case.model_class_name,
-        ],
-        env=env,
+        ]
     )
+    _run(omni_args, env=env)
 
     official_metadata = json.loads((official_output / "metadata.json").read_text())
     omni_metadata = json.loads((omni_output / "metadata.json").read_text())
@@ -423,18 +642,27 @@ def test_ltx_one_stage_matches_official(case: LTXAccuracyCase, accuracy_artifact
     result = {
         "case": case.name,
         "task": "i2v" if image is not None else "t2v",
+        "pipeline_kind": case.pipeline_kind,
+        "model_class_name": case.model_class_name,
         "attention_backend": ATTENTION_BACKEND,
         "official_revision": official_revision,
         "model_revision": case.model_revision,
-        "checkpoint_revision": case.checkpoint_revision,
+        "checkpoint_revision": case.checkpoint.revision,
+        "spatial_upsampler_revision": (case.spatial_upsampler.revision if case.spatial_upsampler is not None else None),
+        "distilled_lora_revision": case.distilled_lora.revision if case.distilled_lora is not None else None,
+        "enable_layerwise_offload": enable_layerwise_offload,
+        "thresholds": asdict(case.thresholds),
+        "request": _request(case, image),
         "video": video_metrics,
         "audio": audio_metrics,
     }
     (output_root / "metrics.json").write_text(json.dumps(result, indent=2) + "\n")
     print(json.dumps(result, indent=2))
 
-    assert video_metrics["ssim_mean"] >= VIDEO_SSIM_MEAN_THRESHOLD
-    assert video_metrics["ssim_min"] >= VIDEO_SSIM_MIN_THRESHOLD
-    assert video_metrics["psnr_mean_db"] >= VIDEO_PSNR_MEAN_THRESHOLD
-    assert audio_metrics["relative_l2"] <= AUDIO_RELATIVE_L2_THRESHOLD
-    assert audio_metrics["cosine_similarity"] >= AUDIO_COSINE_THRESHOLD
+    assert video_metrics["ssim_mean"] >= case.thresholds.video_ssim_mean
+    assert video_metrics["ssim_min"] >= case.thresholds.video_ssim_min
+    assert video_metrics["psnr_mean_db"] >= case.thresholds.video_psnr_mean_db
+    if case.thresholds.audio_relative_l2 > 0:
+        assert audio_metrics["relative_l2"] <= case.thresholds.audio_relative_l2
+    if case.thresholds.audio_cosine_similarity > 0:
+        assert audio_metrics["cosine_similarity"] >= case.thresholds.audio_cosine_similarity

tests/e2e/online_serving/test_ltx.py

@@ -0,0 +1,109 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Online-serving smoke tests for the public LTX-2 pipeline entries."""
+
+import os
+
+import pytest
+
+from tests.helpers.mark import hardware_marks
+from tests.helpers.media import generate_synthetic_image
+from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler
+
+pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
+
+DISTILLED_MODEL = os.getenv("VLLM_TEST_LTX23_DISTILLED_MODEL", "diffusers/LTX-2.3-Distilled-Diffusers")
+BASE_MODEL = os.getenv("VLLM_TEST_LTX23_MODEL", "diffusers/LTX-2.3-Diffusers")
+PROMPT = "A serene lake at sunset with mountains in the background."
+SINGLE_CARD_MARKS = hardware_marks(res={"cuda": "H100"})
+HSDP_MARKS = hardware_marks(res={"cuda": "H100"}, num_cards=2)
+
+
+def _server(
+    model: str,
+    model_class_name: str,
+    *extra_args: str,
+) -> OmniServerParams:
+    return OmniServerParams(
+        model=model,
+        server_args=[
+            "--enforce-eager",
+            *extra_args,
+            "--model-class-name",
+            model_class_name,
+        ],
+    )
+
+
+def _cases():
+    return [
+        pytest.param(
+            _server(BASE_MODEL, "LTX2Pipeline"),
+            3,
+            id="one_stage",
+            marks=SINGLE_CARD_MARKS,
+        ),
+        pytest.param(
+            _server(DISTILLED_MODEL, "LTX2DistilledPipeline"),
+            8,
+            id="distilled",
+            marks=SINGLE_CARD_MARKS,
+        ),
+        pytest.param(
+            _server(
+                DISTILLED_MODEL,
+                "LTX2DistilledPipeline",
+                "--use-hsdp",
+                "--hsdp-shard-size",
+                "2",
+            ),
+            8,
+            id="distilled_hsdp2",
+            marks=HSDP_MARKS,
+        ),
+        pytest.param(
+            _server(
+                BASE_MODEL,
+                "LTX2TwoStagePipeline",
+                "--enable-layerwise-offload",
+            ),
+            3,
+            id="two_stage",
+            marks=SINGLE_CARD_MARKS,
+        ),
+    ]
+
+
+@pytest.mark.parametrize(
+    ("omni_server", "num_inference_steps"),
+    _cases(),
+    indirect=["omni_server"],
+)
+def test_ltx_pipeline_entries(
+    omni_server: OmniServer,
+    num_inference_steps: int,
+    openai_client: OpenAIClientHandler,
+    subtests,
+):
+    for task in ("t2v", "i2v"):
+        with subtests.test(task=task):
+            form_data = {
+                "prompt": PROMPT,
+                "model": omni_server.model,
+                "height": 128,
+                "width": 128,
+                "num_frames": 9,
+                "fps": 8,
+                "num_inference_steps": num_inference_steps,
+                "seed": 42,
+            }
+            request_config = {
+                "model": omni_server.model,
+                "form_data": form_data,
+            }
+            if task == "i2v":
+                request_config["image_reference"] = (
+                    f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}"
+                )
+
+            openai_client.send_video_diffusion_request(request_config)

tests/e2e/online_serving/test_ltx2_expansion.py

@@ -1,91 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-Expansion tests for LTX-2 two-stage pipelines.
-
-Coverage:
-- HSDP with LTX2DistilledPipeline (text-to-video and image-to-video)
-"""
-
-import os
-
-import pytest
-
-from tests.helpers.mark import hardware_marks
-from tests.helpers.media import generate_synthetic_image
-from tests.helpers.runtime import OmniServer, OmniServerParams, OpenAIClientHandler
-
-pytestmark = [pytest.mark.diffusion, pytest.mark.full_model]
-
-MODEL = os.getenv("VLLM_TEST_LTX2_MODEL", "rootonchair/LTX-2-19b-distilled")
-PROMPT = "A serene lake at sunset with mountains in the background."
-PARALLEL_MARKS = hardware_marks(res={"cuda": "H100"}, num_cards=2)
-HSDP_ARGS = ["--use-hsdp", "--hsdp-shard-size", "2"]
-
-
-def _cases():
-    cases = []
-
-    cases.append(
-        pytest.param(
-            OmniServerParams(
-                model=MODEL,
-                server_args=[
-                    *HSDP_ARGS,
-                    "--model-class-name",
-                    "LTX2DistilledPipeline",
-                ],
-            ),
-            False,
-            id="t2v_hsdp",
-            marks=PARALLEL_MARKS,
-        )
-    )
-
-    cases.append(
-        pytest.param(
-            OmniServerParams(
-                model=MODEL,
-                server_args=[
-                    *HSDP_ARGS,
-                    "--model-class-name",
-                    "LTX2DistilledPipeline",
-                ],
-            ),
-            True,
-            id="i2v_hsdp",
-            marks=PARALLEL_MARKS,
-        )
-    )
-
-    return cases
-
-
-@pytest.mark.parametrize(("omni_server", "is_i2v"), _cases(), indirect=["omni_server"])
-def test_ltx2_two_stage_hsdp(
-    omni_server: OmniServer,
-    is_i2v: bool,
-    openai_client: OpenAIClientHandler,
-):
-    # Keep CI small while exercising the fixed 8+3-step distilled recipe.
-    # Height and width describe the final output; Stage 1 runs at half size.
-    form_data = {
-        "prompt": PROMPT,
-        "model": omni_server.model,
-        "height": 128,
-        "width": 128,
-        "num_frames": 9,
-        "fps": 8,
-        "num_inference_steps": 8,
-        "seed": 42,
-    }
-
-    request_config = {
-        "model": omni_server.model,
-        "form_data": form_data,
-    }
-
-    if is_i2v:
-        request_config["image_reference"] = f"data:image/jpeg;base64,{generate_synthetic_image(512, 512)['base64']}"
-
-    openai_client.send_video_diffusion_request(request_config)

tests/model_extras/test_model_extras.py

@@ -233,10 +233,13 @@ def test_ltx_extra_registry_declares_official_guidance_params() -> None:
         }
     )
 
-    assert get_extra_body_params("LTX2Pipeline") == expected
-    assert get_extra_output_params("LTX2Pipeline") == frozenset()
-    assert get_extra_body_params("LTX2DistilledPipeline") == expected
-    assert get_extra_output_params("LTX2DistilledPipeline") == frozenset()
+    for pipeline_name in (
+        "LTX2Pipeline",
+        "LTX2TwoStagePipeline",
+        "LTX2DistilledPipeline",
+    ):
+        assert get_extra_body_params(pipeline_name) == expected
+        assert get_extra_output_params(pipeline_name) == frozenset()
 
 
 @pytest.mark.core_model

tests/model_tests/diffusion/test_alignment.py

@@ -27,6 +27,7 @@ EXCLUDED_MODELS = [
     "OvisImagePipeline",
     "WanPipeline",
     "WanVACEPipeline",
+    "LTX2TwoStagePipeline",
     "LTX2DistilledPipeline",
     "LTX2T2VDMD2Pipeline",
     "LTX2I2VDMD2Pipeline",

vllm_omni/diffusion/models/ltx2/__init__.py

@@ -12,12 +12,16 @@ from vllm_omni.diffusion.models.ltx2.pipeline_ltx2 import (
     LTX2Pipeline,
     LTX2T2VDMD2Pipeline,
 )
-from vllm_omni.diffusion.models.ltx2.pipeline_ltx2_two_stage import LTX2DistilledPipeline
+from vllm_omni.diffusion.models.ltx2.pipeline_ltx2_two_stage import (
+    LTX2DistilledPipeline,
+    LTX2TwoStagePipeline,
+)
 
 __all__ = [
     "LTX2Pipeline",
     "LTX2T2VDMD2Pipeline",
     "LTX2I2VDMD2Pipeline",
+    "LTX2TwoStagePipeline",
     "LTX2DistilledPipeline",
     "get_ltx2_post_process_func",
     "load_transformer_config",

vllm_omni/diffusion/models/ltx2/ltx2_adapter_parser.py

@@ -0,0 +1,156 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Parse official LTX distilled adapters without materializing their tensors.
+
+The official LTX adapters are raw safetensors files rather than PEFT
+checkpoints.  This module is deliberately LTX-specific: it translates the
+official names into the locally constructed Diffusers transformer names and
+describes the resulting adapter targets.  Applying those targets is the job
+of :mod:`ltx2_phase_adapter`.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Iterator
+from dataclasses import dataclass
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+from safetensors import safe_open
+
+_IGNORED_OFFICIAL_LORA_MODULES = {"text_embedding_projection.aggregate_embed"}
+_OFFICIAL_TO_DIFFUSERS_PREFIXES = (
+    ("audio_prompt_adaln_single.", "audio_prompt_adaln."),
+    ("prompt_adaln_single.", "prompt_adaln."),
+    ("audio_adaln_single.", "audio_time_embed."),
+    ("adaln_single.", "time_embed."),
+    ("audio_patchify_proj", "audio_proj_in"),
+    ("patchify_proj", "proj_in"),
+    ("av_ca_video_scale_shift_adaln_single.", "av_cross_attn_video_scale_shift."),
+    ("av_ca_audio_scale_shift_adaln_single.", "av_cross_attn_audio_scale_shift."),
+    ("av_ca_a2v_gate_adaln_single.", "av_cross_attn_video_a2v_gate."),
+    ("av_ca_v2a_gate_adaln_single.", "av_cross_attn_audio_v2a_gate."),
+)
+
+
+@dataclass(frozen=True)
+class AdapterTarget:
+    """One A/B pair mapped onto a local transformer linear layer."""
+
+    # Keep the original Diffusers module name for diagnostics after LTX packs
+    # Q/K/V into the runtime module path.
+    source_module: str
+    module: str
+    slice: str | None
+    a_key: str
+    b_key: str
+    a_shape: tuple[int, ...]
+    b_shape: tuple[int, ...]
+
+
+@dataclass(frozen=True)
+class AdapterManifest:
+    """Tensor-free description of the fixed refinement adapter."""
+
+    path: str
+    targets: tuple[AdapterTarget, ...]
+
+
+def _to_diffusers_module_name(name: str) -> str:
+    for official_prefix, diffusers_prefix in _OFFICIAL_TO_DIFFUSERS_PREFIXES:
+        if name.startswith(official_prefix):
+            return diffusers_prefix + name[len(official_prefix) :]
+    return name
+
+
+def _resolve_lora_target(transformer: nn.Module, module_name: str) -> tuple[str, str | int | None] | None:
+    modules = dict(transformer.named_modules())
+    if module_name in modules:
+        return module_name, None
+    for packed_suffix, sub_suffix, shard_id in getattr(transformer, "stacked_params_mapping", ()):
+        if sub_suffix in module_name:
+            packed_name = module_name.replace(sub_suffix, packed_suffix)
+            if packed_name in modules:
+                return packed_name, shard_id
+    return None
+
+
+def parse_ltx_adapter(transformer: nn.Module, path: str) -> AdapterManifest:
+    """Translate one official raw LTX adapter into local Transformer targets."""
+    path = str(Path(path))
+    targets: list[AdapterTarget] = []
+    unresolved: list[str] = []
+    seen: set[tuple[str, str | int | None]] = set()
+
+    with safe_open(path, framework="pt", device="cpu") as handle:
+        tensor_names = set(handle.keys())
+        official_name_set = {
+            tensor_name.removeprefix("diffusion_model.").rsplit(".lora_", 1)[0]
+            for tensor_name in tensor_names
+            if tensor_name.endswith(".lora_A.weight")
+        }
+        official_b_names = {
+            tensor_name.removeprefix("diffusion_model.").rsplit(".lora_", 1)[0]
+            for tensor_name in tensor_names
+            if tensor_name.endswith(".lora_B.weight")
+        }
+        missing_a = sorted(official_b_names - official_name_set - _IGNORED_OFFICIAL_LORA_MODULES)
+        if missing_a:
+            raise ValueError(f"Missing paired LoRA A tensors for {missing_a} in {path}.")
+
+        official_names = sorted(official_name_set)
+        for official_name in official_names:
+            if official_name in _IGNORED_OFFICIAL_LORA_MODULES:
+                continue
+            key_prefix = f"diffusion_model.{official_name}"
+            key_a = f"{key_prefix}.lora_A.weight"
+            key_b = f"{key_prefix}.lora_B.weight"
+            if key_b not in tensor_names:
+                raise ValueError(f"Missing paired LoRA tensor {key_b!r} in {path}.")
+
+            a_shape = tuple(handle.get_slice(key_a).get_shape())
+            b_shape = tuple(handle.get_slice(key_b).get_shape())
+            if len(a_shape) != 2 or len(b_shape) != 2 or b_shape[1] != a_shape[0]:
+                raise ValueError(f"Invalid LoRA pair for {official_name}: A={a_shape}, B={b_shape}.")
+
+            source_module = _to_diffusers_module_name(official_name)
+            resolved = _resolve_lora_target(transformer, source_module)
+            if resolved is None:
+                unresolved.append(official_name)
+                continue
+            module, shard_id = resolved
+            target_id = (module, shard_id)
+            if target_id in seen:
+                raise ValueError(f"Official LTX LoRA contains duplicate target {module!r}, slice {shard_id!r}.")
+            seen.add(target_id)
+            targets.append(
+                AdapterTarget(
+                    source_module=source_module,
+                    module=module,
+                    slice=shard_id,
+                    a_key=key_a,
+                    b_key=key_b,
+                    a_shape=a_shape,
+                    b_shape=b_shape,
+                )
+            )
+
+    if unresolved:
+        raise ValueError(f"Official LTX LoRA contains unmapped modules: {unresolved}.")
+    if not targets:
+        raise ValueError(f"No Transformer LoRA tensors were found in {path}.")
+    return AdapterManifest(path=path, targets=tuple(targets))
+
+
+def iter_adapter_tensors(manifest: AdapterManifest) -> Iterator[tuple[AdapterTarget, torch.Tensor, torch.Tensor]]:
+    """Stream adapter pairs in manifest order without a full checkpoint copy."""
+
+    with safe_open(manifest.path, framework="pt", device="cpu") as handle:
+        for target in manifest.targets:
+            yield (
+                target,
+                handle.get_tensor(target.a_key),
+                handle.get_tensor(target.b_key),
+            )

vllm_omni/diffusion/models/ltx2/ltx2_components.py

@@ -9,7 +9,8 @@ import inspect
 import json
 import logging
 import os
-from dataclasses import dataclass
+from dataclasses import dataclass, replace
+from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
 import torch
@@ -19,6 +20,8 @@ from diffusers.pipelines.ltx2.latent_upsampler import LTX2LatentUpsamplerModel
 from diffusers.pipelines.ltx2.vocoder import LTX2Vocoder
 from diffusers.video_processor import VideoProcessor
 from huggingface_hub import hf_hub_download
+from safetensors import safe_open
+from safetensors.torch import load_file
 from transformers import AutoTokenizer, Gemma3ForConditionalGeneration
 
 from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata
@@ -32,6 +35,7 @@ from vllm_omni.diffusion.offloader.module_collector import ModuleDiscovery
 if TYPE_CHECKING:
     from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
 
+from .ltx2_request import LTXCheckpointKind, validate_ltx_checkpoint
 from .ltx2_transformer import (
     LTX2VideoTransformer3DModel,
     apply_interleaved_rotary_emb,
@@ -70,6 +74,9 @@ class LTXComponentProfile:
     video_vae_cls: type = AutoencoderKLLTX2Video
     vocoder_cls: type = LTX2Vocoder
     vocoder_fallback_cls: type | None = None
+    artifact_repo_id: str | None = None
+    latent_upsampler_filename: str | None = None
+    distilled_lora_filename: str | None = None
 
 
 LTX2_COMPONENT_PROFILE = LTXComponentProfile(
@@ -101,16 +108,96 @@ LTX2_DISTILLED_COMPONENT_PROFILE = LTXComponentProfile(
     video_vae_cls=DistributedAutoencoderKLLTX2Video,
 )
 
+LTX23_DISTILLED_COMPONENT_PROFILE = replace(
+    LTX23_COMPONENT_PROFILE,
+    name="ltx2_3_distilled",
+    resident_modules=(*LTX23_COMPONENT_PROFILE.resident_modules, "latent_upsampler"),
+    artifact_repo_id="Lightricks/LTX-2.3",
+    latent_upsampler_filename="ltx-2.3-spatial-upscaler-x2-1.1.safetensors",
+)
+
+LTX2_TWO_STAGE_COMPONENT_PROFILE = replace(
+    LTX2_COMPONENT_PROFILE,
+    name="ltx2_two_stage",
+    resident_modules=(*LTX2_COMPONENT_PROFILE.resident_modules, "latent_upsampler"),
+    artifact_repo_id="Lightricks/LTX-2",
+    latent_upsampler_filename="ltx-2-spatial-upscaler-x2-1.0.safetensors",
+    distilled_lora_filename="ltx-2-19b-distilled-lora-384.safetensors",
+)
+
+LTX23_TWO_STAGE_COMPONENT_PROFILE = replace(
+    LTX23_COMPONENT_PROFILE,
+    name="ltx2_3_two_stage",
+    resident_modules=(*LTX23_COMPONENT_PROFILE.resident_modules, "latent_upsampler"),
+    artifact_repo_id="Lightricks/LTX-2.3",
+    latent_upsampler_filename="ltx-2.3-spatial-upscaler-x2-1.1.safetensors",
+    distilled_lora_filename="ltx-2.3-22b-distilled-lora-384-1.1.safetensors",
+)
+
 
 _COMPONENT_PROFILES: dict[tuple[str, str], LTXComponentProfile] = {
     ("one_stage", "2"): LTX2_COMPONENT_PROFILE,
     ("one_stage", "2.3"): LTX23_COMPONENT_PROFILE,
+    ("two_stage", "2"): LTX2_TWO_STAGE_COMPONENT_PROFILE,
+    ("two_stage", "2.3"): LTX23_TWO_STAGE_COMPONENT_PROFILE,
     ("distilled_two_stage", "2"): LTX2_DISTILLED_COMPONENT_PROFILE,
+    ("distilled_two_stage", "2.3"): LTX23_DISTILLED_COMPONENT_PROFILE,
     ("dmd2", "2"): LTX2_COMPONENT_PROFILE,
     ("dmd2", "2.3"): LTX23_COMPONENT_PROFILE,
 }
 
 
+def resolve_ltx_artifact(
+    model: str,
+    repo_id: str,
+    filename: str,
+) -> str:
+    """Resolve an official LTX sidecar from the model root or its Hub repository."""
+    candidate = Path(model) / filename
+    if candidate.is_file():
+        return str(candidate)
+
+    try:
+        return hf_hub_download(repo_id=repo_id, filename=filename)
+    except Exception as exc:
+        raise FileNotFoundError(
+            f"Unable to resolve LTX artifact {filename!r}. Searched {candidate}; "
+            f"place the file in the model root or make {repo_id} available."
+        ) from exc
+
+
+def _load_ltx_latent_upsampler_single_file(path: str, dtype: torch.dtype) -> LTX2LatentUpsamplerModel:
+    """Load an official single-file upsampler into the Diffusers module."""
+    with safe_open(path, framework="pt", device="cpu") as handle:
+        metadata = handle.metadata() or {}
+    raw_config = json.loads(metadata.get("config", "{}"))
+    config = {
+        "in_channels": raw_config.get("in_channels", 128),
+        "mid_channels": raw_config.get("mid_channels", 1024),
+        "num_blocks_per_stage": raw_config.get("num_blocks_per_stage", 4),
+        "dims": raw_config.get("dims", 3),
+        "spatial_upsample": raw_config.get("spatial_upsample", True),
+        "temporal_upsample": raw_config.get("temporal_upsample", False),
+        "rational_spatial_scale": raw_config.get("spatial_scale", 2.0),
+        "use_rational_resampler": raw_config.get("rational_resampler", False),
+    }
+    with torch.device("cpu"):
+        upsampler = LTX2LatentUpsamplerModel(**config).to(dtype=dtype)
+
+    state_dict = load_file(path, device="cpu")
+    if "upsampler.0.weight" in state_dict and hasattr(upsampler.upsampler, "conv"):
+        state_dict["upsampler.conv.weight"] = state_dict.pop("upsampler.0.weight")
+        state_dict["upsampler.conv.bias"] = state_dict.pop("upsampler.0.bias")
+    missing, unexpected = upsampler.load_state_dict(state_dict, strict=False)
+    unresolved_missing = set(missing) - {"upsampler.blur_down.kernel"}
+    if unresolved_missing or unexpected:
+        raise ValueError(
+            f"Invalid LTX latent upsampler {path}: missing={sorted(unresolved_missing)}, "
+            f"unexpected={sorted(unexpected)}."
+        )
+    return upsampler
+
+
 def resolve_ltx_component_profile(pipeline_kind: str, model_version: str) -> LTXComponentProfile:
     """Resolve component construction independently from execution recipes."""
     try:
@@ -392,16 +479,34 @@ def initialize_pipeline_components(pipeline: Any, od_config: Any) -> None:
         )
 
     if "latent_upsampler" in profile.resident_modules:
-        # BlurDownsample constructs an integer kernel that must be initialized
-        # on CPU; component placement is handled uniformly after construction.
-        with torch.device("cpu"):
-            pipeline.latent_upsampler = _load_component(
-                LTX2LatentUpsamplerModel,
+        upsampler_config = os.path.join(model, "latent_upsampler", "config.json")
+        if os.path.isfile(upsampler_config) or not local_files_only:
+            try:
+                pipeline.latent_upsampler = _load_component(
+                    LTX2LatentUpsamplerModel,
+                    model,
+                    "latent_upsampler",
+                    local_files_only=local_files_only,
+                    dtype=dtype,
+                )
+            except (OSError, ValueError):
+                if profile.latent_upsampler_filename is None or profile.artifact_repo_id is None:
+                    raise
+                upsampler_path = resolve_ltx_artifact(
+                    model,
+                    profile.artifact_repo_id,
+                    profile.latent_upsampler_filename,
+                )
+                pipeline.latent_upsampler = _load_ltx_latent_upsampler_single_file(upsampler_path, dtype)
+        else:
+            if profile.latent_upsampler_filename is None or profile.artifact_repo_id is None:
+                raise FileNotFoundError(f"LTX latent upsampler component not found under {model}.")
+            upsampler_path = resolve_ltx_artifact(
                 model,
-                "latent_upsampler",
-                local_files_only=local_files_only,
-                dtype=dtype,
+                profile.artifact_repo_id,
+                profile.latent_upsampler_filename,
             )
+            pipeline.latent_upsampler = _load_ltx_latent_upsampler_single_file(upsampler_path, dtype)
 
     transformer_config = load_transformer_config(model, "transformer", local_files_only)
     quant_config = getattr(od_config, "quantization_config", None)
@@ -412,6 +517,16 @@ def initialize_pipeline_components(pipeline: Any, od_config: Any) -> None:
         subfolder="scheduler",
         local_files_only=local_files_only,
     )
+    expected_checkpoint_kind: LTXCheckpointKind | None = None
+    if pipeline.pipeline_kind == "two_stage":
+        expected_checkpoint_kind = "regular"
+    elif pipeline.pipeline_kind == "distilled_two_stage":
+        expected_checkpoint_kind = "distilled"
+    validate_ltx_checkpoint(
+        pipeline.scheduler.config,
+        expected_kind=expected_checkpoint_kind,
+        pipeline_name=type(pipeline).__name__,
+    )
 
     pipeline.vae_spatial_compression_ratio = pipeline.vae.spatial_compression_ratio
     pipeline.vae_temporal_compression_ratio = pipeline.vae.temporal_compression_ratio

vllm_omni/diffusion/models/ltx2/ltx2_denoise.py

@@ -62,13 +62,14 @@ class LTXDenoiseContext:
     audio_latents: torch.Tensor
     video_coords: torch.Tensor
     audio_coords: torch.Tensor
+    audio_attention_mask: torch.Tensor | None = None
     conditioning_mask: torch.Tensor | None = None
     conditioning_mask_for_model: torch.Tensor | None = None
 
 
 @dataclass
 class LTXPhaseResult:
-    """Denoised, unpacked AV latents and the context used to produce them."""
+    """Denoised AV latents and the context used to produce them."""
 
     forward_context: LTXForwardContext
     video: torch.Tensor
@@ -284,7 +285,8 @@ def prepare_rope_coords_stage(
     latents: torch.Tensor,
     audio_latents: torch.Tensor,
 ) -> tuple[torch.Tensor, torch.Tensor]:
-    video_coords = pipeline.transformer.rope.prepare_video_coords(
+    transformer = pipeline.transformer
+    video_coords = transformer.rope.prepare_video_coords(
         latents.shape[0],
         forward_ctx.latent_num_frames,
         forward_ctx.latent_height,
@@ -292,7 +294,7 @@ def prepare_rope_coords_stage(
         latents.device,
         fps=forward_ctx.request_inputs.frame_rate,
     )
-    audio_coords = pipeline.transformer.audio_rope.prepare_audio_coords(
+    audio_coords = transformer.audio_rope.prepare_audio_coords(
         audio_latents.shape[0],
         forward_ctx.padded_audio_num_frames,
         audio_latents.device,
@@ -331,6 +333,7 @@ def build_transformer_kwargs(
         # with a learned register, making all output context tokens valid.
         "encoder_attention_mask": None,
         "audio_encoder_attention_mask": None,
+        "audio_attention_mask": denoise_ctx.audio_attention_mask,
         "num_frames": forward_ctx.latent_num_frames,
         "height": forward_ctx.latent_height,
         "width": forward_ctx.latent_width,
@@ -442,11 +445,25 @@ class LTXPhaseExecutor:
             video_audio_step_adapter=video_audio_step_adapter,
         )
         video_coords, audio_coords = prepare_rope_coords_stage(pipeline, forward_ctx, latents, audio_latents)
+        ring_degree = getattr(pipeline.od_config.parallel_config, "ring_degree", 1) or 1
+        if padded_audio_num_frames > original_audio_num_frames and ring_degree > 1:
+            raise ValueError(
+                "LTX audio padding requires an attention mask, which Ring sequence parallelism does not support. "
+                "Use Ulysses-only SP or choose a request whose audio latent length is divisible by the SP size."
+            )
         denoise_ctx = LTXDenoiseContext(
             latents=latents,
             audio_latents=audio_latents,
             video_coords=video_coords,
             audio_coords=audio_coords,
+            audio_attention_mask=(
+                torch.arange(padded_audio_num_frames, device=audio_latents.device)
+                .lt(original_audio_num_frames)
+                .unsqueeze(0)
+                .expand(audio_latents.shape[0], -1)
+                if padded_audio_num_frames > original_audio_num_frames
+                else None
+            ),
             conditioning_mask=conditioning_mask,
         )
         denoise_ctx = pipeline._prepare_denoise_context_for_guidance(forward_ctx, denoise_ctx)

vllm_omni/diffusion/models/ltx2/ltx2_guidance.py

@@ -126,7 +126,10 @@ def x0_from_velocity(sample: torch.Tensor, velocity: torch.Tensor, sigma: torch.
 
 def velocity_from_x0(sample: torch.Tensor, x0: torch.Tensor, sigma: torch.Tensor) -> torch.Tensor:
     """Convert x0 back to velocity using the official fp32 arithmetic order."""
-    sigma = sigma.to(torch.float32)
+    # Official LTX converts the scalar scheduler sigma to a host value before
+    # division. A CUDA scalar tensor selects different kernel arithmetic and
+    # can cross bf16 rounding boundaries late in the denoise trajectory.
+    sigma = sigma.to(torch.float32).item()
     return ((sample.to(torch.float32) - x0.to(torch.float32)) / sigma).to(sample.dtype)
 
 
@@ -148,6 +151,7 @@ def combine_guided_x0(
     uncond_perturbed: torch.Tensor | float,
     uncond_modality: torch.Tensor | float,
     guidance: LTXModalityGuidance,
+    rescale_token_count: int | None = None,
 ) -> torch.Tensor:
     dtype = cond.dtype
     cond = cond.float()
@@ -163,9 +167,18 @@ def combine_guided_x0(
         # uses its full-tensor standard deviation. In Omni, dimension 0 may
         # instead contain independently scheduled requests. Reduce each item
         # separately so its result cannot depend on unrelated co-batched work.
-        reduce_dims = tuple(range(1, pred.ndim))
-        cond_std = cond.std(dim=reduce_dims, keepdim=True)
-        pred_std = pred.std(dim=reduce_dims, keepdim=True)
+        stats_cond = cond
+        stats_pred = pred
+        if rescale_token_count is not None:
+            if cond.ndim < 2 or not 0 < rescale_token_count <= cond.shape[1]:
+                raise ValueError(
+                    f"Guidance rescale token count must be in [1, {cond.shape[1]}], got {rescale_token_count}."
+                )
+            stats_cond = cond[:, :rescale_token_count]
+            stats_pred = pred[:, :rescale_token_count]
+        reduce_dims = tuple(range(1, stats_pred.ndim))
+        cond_std = stats_cond.std(dim=reduce_dims, keepdim=True)
+        pred_std = stats_pred.std(dim=reduce_dims, keepdim=True)
         eps = torch.finfo(pred.dtype).eps
         factor = cond_std / pred_std.clamp_min(eps)
         factor = torch.where(pred_std > eps, factor, torch.ones_like(factor))
@@ -175,6 +188,8 @@ def combine_guided_x0(
 
 
 def _repeat_batch(tensor: torch.Tensor, repeats: int) -> torch.Tensor:
+    if repeats == 1:
+        return tensor
     return tensor.repeat((repeats,) + (1,) * (tensor.ndim - 1))
 
 
@@ -278,6 +293,11 @@ class LTXGuidanceExecutor:
         if model_pass_count > 1:
             denoise_ctx.video_coords = _repeat_batch(denoise_ctx.video_coords, model_pass_count)
             denoise_ctx.audio_coords = _repeat_batch(denoise_ctx.audio_coords, model_pass_count)
+            if denoise_ctx.audio_attention_mask is not None:
+                denoise_ctx.audio_attention_mask = _repeat_batch(
+                    denoise_ctx.audio_attention_mask,
+                    model_pass_count,
+                )
         return denoise_ctx
 
     @staticmethod
@@ -322,6 +342,7 @@ class LTXGuidanceExecutor:
         guidance: LTXModalityGuidance,
         *,
         model_sigma: torch.Tensor | None = None,
+        rescale_token_count: int | None = None,
     ) -> torch.Tensor:
         model_sigma = sigma if model_sigma is None else model_sigma
         # Official guidance reduces contiguous BSC tensors. The fused
@@ -334,6 +355,7 @@ class LTXGuidanceExecutor:
             uncond_perturbed=x0.get("ptb", 0.0),
             uncond_modality=x0.get("mod", 0.0),
             guidance=guidance,
+            rescale_token_count=rescale_token_count,
         )
         return velocity_from_x0(sample, guided, sigma)
 
@@ -434,6 +456,7 @@ class LTXGuidanceExecutor:
                 audio_splits,
                 forward_ctx.audio_scheduler.sigmas[index],
                 plan.spec.audio,
+                rescale_token_count=forward_ctx.original_audio_num_frames,
             ),
         )
 
@@ -509,6 +532,7 @@ class LTXGuidanceExecutor:
                 audio_splits,
                 forward_ctx.audio_scheduler.sigmas[index],
                 plan.spec.audio,
+                rescale_token_count=forward_ctx.original_audio_num_frames,
             ),
         )
 

vllm_omni/diffusion/models/ltx2/ltx2_latents.py

@@ -117,7 +117,9 @@ def create_noised_state(
         device=latents.device,
         dtype=latents.dtype,
     )
-    return noise_scale * noise + (1 - noise_scale) * latents
+    if isinstance(noise_scale, torch.Tensor):
+        noise_scale = noise_scale.float()
+    return torch.lerp(latents.float(), noise.float(), noise_scale).to(latents.dtype)
 
 
 def pack_audio_latents(latents: torch.Tensor) -> torch.Tensor:
@@ -135,6 +137,16 @@ def unpad_audio_latents(latents: torch.Tensor, num_frames: int) -> torch.Tensor:
     return latents[:, :num_frames]
 
 
+def clear_audio_padding(latents: torch.Tensor, num_frames: int) -> torch.Tensor:
+    """Keep Omni's SP-only audio padding outside the sampler state."""
+    if not 0 < num_frames <= latents.shape[1]:
+        raise ValueError(f"Audio frame count must be in [1, {latents.shape[1]}], got {num_frames}.")
+    if num_frames == latents.shape[1]:
+        return latents
+    padding = latents.new_zeros(latents.shape[0], latents.shape[1] - num_frames, latents.shape[2])
+    return torch.cat([latents[:, :num_frames], padding], dim=1)
+
+
 def get_sp_padded_audio_latent_length(audio_latent_length: int, sp_size: int) -> int:
     if sp_size > 1:
         audio_latent_length += (sp_size - (audio_latent_length % sp_size)) % sp_size
@@ -205,7 +217,12 @@ def prepare_video_latents(
             f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
             f" size of {batch_size}. Make sure the batch size matches the length of the generators."
         )
-    return randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+    latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+    # Official LTX samples the 5D video latent first and then patchifies it,
+    # leaving token-major storage behind the [B, tokens, channels] view.  The
+    # logical values are unchanged, but preserving that layout is important:
+    # CUDA selects a different bf16 GEMM path for a contiguous token tensor.
+    return latents.transpose(1, 2).contiguous().transpose(1, 2)
 
 
 def prepare_audio_latents(
@@ -226,6 +243,16 @@ def prepare_audio_latents(
     sp_size = getattr(pipeline.od_config.parallel_config, "sequence_parallel_size", 1) or 1
     padded_latent_length = get_sp_padded_audio_latent_length(original_latent_length, int(sp_size))
 
+    def pad_logical_latents(logical_latents: torch.Tensor) -> torch.Tensor:
+        if padded_latent_length == original_latent_length:
+            return logical_latents
+        padding = logical_latents.new_zeros(
+            logical_latents.shape[0],
+            padded_latent_length - original_latent_length,
+            logical_latents.shape[2],
+        )
+        return torch.cat([logical_latents, padding], dim=1)
+
     if latents is not None:
         if latents.ndim == 4:
             latents = pack_audio_latents(latents)
@@ -237,29 +264,24 @@ def prepare_audio_latents(
                 pipeline.audio_vae.latents_mean,
                 pipeline.audio_vae.latents_std,
             )
-        latents = create_noised_state(latents, noise_scale, generator)
 
         if latents.shape[1] not in {original_latent_length, padded_latent_length}:
             raise ValueError(
                 "Provided `audio_latents` has incompatible audio frame count "
                 f"{latents.shape[1]}; expected {original_latent_length} or {padded_latent_length}."
             )
-        if latents.shape[1] == original_latent_length and padded_latent_length > original_latent_length:
-            padding = torch.zeros(
-                latents.shape[0],
-                padded_latent_length - original_latent_length,
-                latents.shape[2],
-                dtype=latents.dtype,
-                device=latents.device,
-            )
-            latents = torch.cat([latents, padding], dim=1)
+        # Padding is an Omni implementation detail, not part of the official
+        # audio state. Draw request RNG only for logical tokens so later phases
+        # remain seed-invariant across SP degrees, then append deterministic 0s.
+        latents = create_noised_state(latents[:, :original_latent_length], noise_scale, generator)
+        latents = pad_logical_latents(latents)
         return latents.to(device=device, dtype=dtype), original_latent_length, padded_latent_length
 
-    shape = (batch_size, padded_latent_length, num_channels_latents * latent_mel_bins)
+    shape = (batch_size, original_latent_length, num_channels_latents * latent_mel_bins)
     if isinstance(generator, list) and len(generator) != batch_size:
         raise ValueError(
             f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
             f" size of {batch_size}. Make sure the batch size matches the length of the generators."
         )
-    latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+    latents = pad_logical_latents(randn_tensor(shape, generator=generator, device=device, dtype=dtype))
     return latents, original_latent_length, padded_latent_length

vllm_omni/diffusion/models/ltx2/ltx2_phase_adapter.py

@@ -0,0 +1,430 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Construction and execution of a fixed LTX refinement-phase adapter.
+
+Checkpoint key parsing remains in :mod:`ltx2_adapter_parser`; this module
+resolves the declared artifact and makes the existing Transformer linears
+adapter-capable while retaining their quantization and tensor-parallel paths.
+"""
+
+from __future__ import annotations
+
+from collections import defaultdict
+from collections.abc import Iterable
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import vllm.envs as envs
+from vllm.distributed import (
+    split_tensor_along_last_dim,
+    tensor_model_parallel_all_reduce,
+)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.batch_invariant import linear_batch_invariant
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+    UnquantizedLinearMethod,
+)
+from vllm.model_executor.layers.utils import dispatch_unquantized_gemm
+from vllm.platforms import current_platform
+
+from .ltx2_adapter_parser import AdapterManifest, AdapterTarget, iter_adapter_tensors, parse_ltx_adapter
+from .ltx2_components import resolve_ltx_artifact
+from .ltx2_recipes import LTX_DISTILLED_ADAPTER_SLOT
+
+logger = init_logger(__name__)
+
+
+@dataclass(frozen=True)
+class _AdapterLayout:
+    """Rank-local storage and execution layout for one LoRA pair."""
+
+    kind: str
+    input_size: int
+    output_size: int
+    a_input_start: int
+    a_input_size: int
+    b_output_start: int
+    b_output_size: int
+    output_start: int
+    reduce_results: bool = False
+
+
+def _module_device(module: nn.Module) -> torch.device:
+    for tensor in (*module.parameters(), *module.buffers()):
+        if tensor.device.type != "meta":
+            return tensor.device
+    raise RuntimeError(f"Cannot materialize an LTX phase adapter for meta module {module._get_name()}.")
+
+
+def _build_layout(layer: nn.Module, target: AdapterTarget) -> _AdapterLayout:
+    if isinstance(layer, QKVParallelLinear):
+        if target.slice not in {"q", "k", "v"}:
+            raise ValueError(
+                f"LTX packed QKV target {target.module!r} requires a q/k/v adapter slice, got {target.slice!r}."
+            )
+        q_local = layer.num_heads * layer.head_size
+        k_local = layer.num_kv_heads * layer.head_size
+        v_local = layer.num_kv_heads * layer.v_head_size
+        if target.slice == "q":
+            output_size = layer.total_num_heads * layer.head_size
+            b_output_start = layer.tp_rank * q_local
+            b_output_size = q_local
+            output_start = 0
+        elif target.slice == "k":
+            output_size = layer.total_num_kv_heads * layer.head_size
+            shard_rank = layer.tp_rank // layer.num_kv_head_replicas
+            b_output_start = shard_rank * k_local
+            b_output_size = k_local
+            output_start = q_local
+        else:
+            output_size = layer.total_num_kv_heads * layer.v_head_size
+            shard_rank = layer.tp_rank // layer.num_kv_head_replicas
+            b_output_start = shard_rank * v_local
+            b_output_size = v_local
+            output_start = q_local + k_local
+        return _AdapterLayout(
+            kind="qkv",
+            input_size=layer.input_size,
+            output_size=output_size,
+            a_input_start=0,
+            a_input_size=layer.input_size,
+            b_output_start=b_output_start,
+            b_output_size=b_output_size,
+            output_start=output_start,
+        )
+
+    if isinstance(layer, ColumnParallelLinear):
+        if layer.gather_output:
+            raise ValueError(
+                f"LTX phase adapter does not support gathered ColumnParallelLinear target {target.module!r}."
+            )
+        return _AdapterLayout(
+            kind="column",
+            input_size=layer.input_size,
+            output_size=layer.output_size,
+            a_input_start=0,
+            a_input_size=layer.input_size,
+            b_output_start=layer.tp_rank * layer.output_size_per_partition,
+            b_output_size=layer.output_size_per_partition,
+            output_start=0,
+        )
+
+    if isinstance(layer, RowParallelLinear):
+        return _AdapterLayout(
+            kind="row",
+            input_size=layer.input_size,
+            output_size=layer.output_size,
+            a_input_start=layer.tp_rank * layer.input_size_per_partition,
+            a_input_size=layer.input_size_per_partition,
+            b_output_start=0,
+            b_output_size=layer.output_size,
+            output_start=0,
+            reduce_results=layer.reduce_results,
+        )
+
+    if isinstance(layer, nn.Linear):
+        return _AdapterLayout(
+            kind="replicated",
+            input_size=layer.in_features,
+            output_size=layer.out_features,
+            a_input_start=0,
+            a_input_size=layer.in_features,
+            b_output_start=0,
+            b_output_size=layer.out_features,
+            output_start=0,
+        )
+
+    raise TypeError(f"LTX phase adapter target {target.module!r} has unsupported layer type {type(layer).__name__}.")
+
+
+class _AdapterPiece(nn.Module):
+    """One rank-local A/B pair stored as non-persistent buffers."""
+
+    def __init__(self, target: AdapterTarget, layout: _AdapterLayout) -> None:
+        super().__init__()
+        self.target = target
+        self.layout = layout
+        # The fixed graph is installed before base checkpoint loading.  The
+        # empty buffers are replaced only after the base layer's quant/TP layout
+        # has settled and before offload/compile snapshots are taken.
+        self.register_buffer("lora_a", torch.empty(0), persistent=False)
+        self.register_buffer("lora_b", torch.empty(0), persistent=False)
+
+    def load(self, lora_a: torch.Tensor, lora_b: torch.Tensor, *, device: torch.device, dtype: torch.dtype) -> None:
+        if tuple(lora_a.shape) != self.target.a_shape or tuple(lora_b.shape) != self.target.b_shape:
+            raise ValueError(
+                f"LTX adapter source shape changed for {self.target.source_module!r}: "
+                f"A={tuple(lora_a.shape)}, B={tuple(lora_b.shape)}."
+            )
+        if lora_a.ndim != 2 or lora_b.ndim != 2:
+            raise ValueError(f"LTX adapter target {self.target.source_module!r} must use rank-2 A/B tensors.")
+        rank = self.target.a_shape[0]
+        if lora_a.shape[1] != self.layout.input_size or lora_b.shape[0] != self.layout.output_size:
+            raise ValueError(
+                f"LTX adapter shape does not match {self.target.module!r}: "
+                f"expected A=({rank}, {self.layout.input_size}), "
+                f"B=({self.layout.output_size}, {rank}); "
+                f"got A={tuple(lora_a.shape)}, B={tuple(lora_b.shape)}."
+            )
+
+        local_a = lora_a.narrow(1, self.layout.a_input_start, self.layout.a_input_size)
+        local_b = lora_b.narrow(0, self.layout.b_output_start, self.layout.b_output_size)
+        self.lora_a = local_a.to(device=device, dtype=dtype)
+        self.lora_b = local_b.to(device=device, dtype=dtype)
+
+    def delta(self, input_: torch.Tensor) -> torch.Tensor:
+        if self.lora_a.numel() == 0 or self.lora_b.numel() == 0:
+            raise RuntimeError(f"LTX phase adapter target {self.target.module!r} was not materialized.")
+        if self.layout.kind == "row" and input_.shape[-1] != self.layout.a_input_size:
+            input_ = input_.narrow(-1, self.layout.a_input_start, self.layout.a_input_size)
+        input_ = input_.to(dtype=self.lora_a.dtype)
+        delta = F.linear(F.linear(input_, self.lora_a), self.lora_b)
+        if self.layout.kind == "row" and self.layout.reduce_results:
+            delta = tensor_model_parallel_all_reduce(delta)
+        return delta
+
+    def weight_delta(self) -> torch.Tensor:
+        """Materialize the rank-local BF16 weight delta used by official fusion."""
+        if self.lora_a.numel() == 0 or self.lora_b.numel() == 0:
+            raise RuntimeError(f"LTX phase adapter target {self.target.module!r} was not materialized.")
+        return torch.matmul(self.lora_b, self.lora_a)
+
+
+def _apply_unquantized_gemm_with_weight(
+    layer: nn.Module,
+    input_: torch.Tensor,
+    weight: torch.Tensor,
+    bias: torch.Tensor | None,
+) -> torch.Tensor:
+    if envs.VLLM_BATCH_INVARIANT and current_platform.is_cuda_alike():
+        return linear_batch_invariant(input_, weight, bias)
+    return dispatch_unquantized_gemm()(layer, input_, weight, bias)
+
+
+def _forward_with_weight(
+    layer: nn.Module,
+    input_: torch.Tensor,
+    weight: torch.Tensor,
+) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor | None]:
+    """Run an unquantized base layer with an ephemeral fused weight."""
+    if isinstance(layer, nn.Linear):
+        return F.linear(input_, weight, layer.bias)
+
+    quant_method = getattr(layer, "quant_method", None)
+    if not isinstance(quant_method, UnquantizedLinearMethod):
+        raise ValueError("LTX layer-fused phase LoRA requires unquantized Transformer linears.")
+
+    if isinstance(layer, RowParallelLinear):
+        if layer.input_is_parallel:
+            input_parallel = input_
+        else:
+            input_parallel = split_tensor_along_last_dim(input_, num_partitions=layer.tp_size)[
+                layer.tp_rank
+            ].contiguous()
+        bias = None if layer.tp_rank > 0 or layer.skip_bias_add else layer.bias
+        output_parallel = _apply_unquantized_gemm_with_weight(layer, input_parallel, weight, bias)
+        if layer.reduce_results and layer.tp_size > 1:
+            output = tensor_model_parallel_all_reduce(output_parallel)
+        else:
+            output = output_parallel
+    elif isinstance(layer, ColumnParallelLinear):
+        bias = layer.bias if not layer.skip_bias_add else None
+        output = _apply_unquantized_gemm_with_weight(layer, input_, weight, bias)
+    else:
+        raise TypeError(f"Unsupported LTX layer-fused linear type {type(layer).__name__}.")
+
+    if not layer.return_bias:
+        return output
+    return output, layer.bias if layer.skip_bias_add else None
+
+
+class _PhaseAdapterLinear(nn.Module):
+    """Delegate the base linear unchanged and add an enabled adapter slot."""
+
+    def __init__(
+        self,
+        base_layer: nn.Module,
+        targets: Iterable[AdapterTarget],
+        *,
+        adapter_dtype: torch.dtype,
+        layer_fused: bool = False,
+    ) -> None:
+        super().__init__()
+        self.base_layer = base_layer
+        self.adapter_dtype = adapter_dtype
+        if layer_fused and not (
+            isinstance(base_layer, nn.Linear)
+            or isinstance(getattr(base_layer, "quant_method", None), UnquantizedLinearMethod)
+        ):
+            raise ValueError("LTX layer-fused phase LoRA requires unquantized Transformer linears.")
+        self.layer_fused = layer_fused
+        pieces = [_AdapterPiece(target, _build_layout(base_layer, target)) for target in targets]
+        self.pieces = nn.ModuleList(pieces)
+        self._pieces_by_target = {piece.target: piece for piece in pieces}
+        self._enabled = False
+
+    def load_target(self, target: AdapterTarget, lora_a: torch.Tensor, lora_b: torch.Tensor) -> None:
+        try:
+            piece = self._pieces_by_target[target]
+        except KeyError as exc:
+            raise ValueError(f"Adapter target {target.module!r} is not installed on this layer.") from exc
+        piece.load(lora_a, lora_b, device=_module_device(self.base_layer), dtype=self.adapter_dtype)
+
+    def set_enabled(self, enabled: bool) -> None:
+        self._enabled = enabled
+
+    def _add_adapter_delta(self, input_: torch.Tensor, output: torch.Tensor) -> torch.Tensor:
+        if not self._enabled:
+            return output
+        if output.requires_grad:
+            output = output.clone()
+        for piece in self.pieces:
+            delta = piece.delta(input_).to(dtype=output.dtype)
+            start = piece.layout.output_start
+            output[..., start : start + piece.layout.b_output_size].add_(delta)
+        return output
+
+    @torch.no_grad()
+    def _fused_weight(self) -> torch.Tensor:
+        base_weight = self.base_layer.weight
+        fused_weight = base_weight.clone()
+        for piece in self.pieces:
+            start = piece.layout.output_start
+            base_slice = base_weight.narrow(0, start, piece.layout.b_output_size)
+            delta = piece.weight_delta()
+            if delta.shape != base_slice.shape:
+                raise ValueError(
+                    f"LTX layer-fused delta shape {tuple(delta.shape)} does not match base weight slice "
+                    f"{tuple(base_slice.shape)} for {piece.target.module!r}."
+                )
+            # Match official fusion exactly: materialize B@A in the adapter
+            # dtype, then add the base tensor in-place before the main GEMM.
+            delta.add_(base_slice)
+            fused_weight.narrow(0, start, piece.layout.b_output_size).copy_(delta)
+        return fused_weight
+
+    def forward(self, input_: torch.Tensor):
+        if not self._enabled:
+            return self.base_layer(input_)
+        if self.layer_fused:
+            return _forward_with_weight(self.base_layer, input_, self._fused_weight())
+        output = self.base_layer(input_)
+        if isinstance(output, tuple):
+            return (self._add_adapter_delta(input_, output[0]), *output[1:])
+        return self._add_adapter_delta(input_, output)
+
+
+class LTXPhaseAdapterRuntime:
+    """Install and operate one fixed phase adapter on an existing transformer."""
+
+    def __init__(
+        self,
+        transformer: nn.Module,
+        manifest: AdapterManifest,
+        *,
+        dtype: torch.dtype,
+        layer_fused: bool = False,
+    ) -> None:
+        self.transformer = transformer
+        self.manifest = manifest
+        self._wrappers: dict[str, _PhaseAdapterLinear] = {}
+        self._target_names: tuple[str, ...] = ()
+        self._materialized = False
+        grouped: dict[str, list[AdapterTarget]] = defaultdict(list)
+        for target in manifest.targets:
+            grouped[target.module].append(target)
+
+        original_layers = {name: self.transformer.get_submodule(name) for name in grouped}
+        for name, targets in grouped.items():
+            wrapper = _PhaseAdapterLinear(
+                original_layers[name],
+                targets,
+                adapter_dtype=dtype,
+                layer_fused=layer_fused,
+            )
+            parent_name, _, child_name = name.rpartition(".")
+            parent = self.transformer.get_submodule(parent_name) if parent_name else self.transformer
+            if child_name not in parent._modules:
+                raise ValueError(f"LTX phase adapter target {name!r} is no longer a direct module child.")
+            parent._modules[child_name] = wrapper
+            self._wrappers[name] = wrapper
+
+        self._target_names = tuple(sorted(self._wrappers, key=len, reverse=True))
+        # LTX's custom loader receives the original checkpoint names.  Keep the
+        # adaptation local to that loader rather than changing AutoWeightsLoader.
+        self.transformer._phase_adapter_parameter_name = self.base_parameter_name
+        logger.info(
+            "Installed %d LTX %s phase-adapter linears",
+            len(self._wrappers),
+            "layer-fused" if layer_fused else "dynamic",
+        )
+
+    def base_parameter_name(self, name: str) -> str:
+        for target_name in self._target_names:
+            prefix = f"{target_name}."
+            if name.startswith(prefix):
+                return f"{target_name}.base_layer.{name[len(prefix) :]}"
+        return name
+
+    def finalize(self) -> None:
+        """Stream rank-local A/B buffers after base weight processing completes."""
+        if self._materialized:
+            return
+        for target, lora_a, lora_b in iter_adapter_tensors(self.manifest):
+            self._wrappers[target.module].load_target(target, lora_a, lora_b)
+        self._materialized = True
+        logger.info("Materialized %d LTX phase-adapter tensor pairs", len(self.manifest.targets))
+
+    def activate(self, adapter_slot: str | None) -> None:
+        if adapter_slot is not None:
+            if adapter_slot != LTX_DISTILLED_ADAPTER_SLOT:
+                raise ValueError(f"Unknown LTX phase adapter slot {adapter_slot!r}.")
+            if not self._materialized:
+                raise RuntimeError("LTX phase adapter data must be materialized before activation.")
+        for wrapper in self._wrappers.values():
+            wrapper.set_enabled(adapter_slot is not None)
+
+
+def build_ltx_phase_adapter(pipeline: Any) -> LTXPhaseAdapterRuntime | None:
+    """Build the fixed adapter required by a multi-phase LTX recipe."""
+    adapter_slots = {phase.adapter_slot for phase in pipeline.pipeline_recipe.phases if phase.adapter_slot is not None}
+    if not adapter_slots:
+        return None
+    if adapter_slots != {LTX_DISTILLED_ADAPTER_SLOT}:
+        raise ValueError(f"Unsupported LTX phase adapter slots: {sorted(adapter_slots)}.")
+
+    profile = pipeline.component_profile
+    if profile.distilled_lora_filename is None or profile.artifact_repo_id is None:
+        raise ValueError(f"{profile.name} does not declare the required distilled adapter artifact.")
+    if getattr(pipeline.od_config, "lora_path", None) is not None:
+        raise ValueError(
+            f"{pipeline.__class__.__name__} reserves LoRA execution for its phase adapter; "
+            "request or static LoRA composition is not supported yet."
+        )
+
+    quantization_config = getattr(pipeline.od_config, "quantization_config", None)
+    layer_fused = quantization_config is None
+    adapter_path = resolve_ltx_artifact(
+        pipeline.od_config.model,
+        profile.artifact_repo_id,
+        profile.distilled_lora_filename,
+    )
+
+    if layer_fused and pipeline.od_config.dtype is not torch.bfloat16:
+        raise ValueError("LTX layer-fused phase LoRA requires a bfloat16 pipeline dtype.")
+
+    manifest = parse_ltx_adapter(pipeline.transformer, adapter_path)
+    runtime = LTXPhaseAdapterRuntime(
+        pipeline.transformer,
+        manifest,
+        dtype=pipeline.od_config.dtype,
+        layer_fused=layer_fused,
+    )
+    return runtime

vllm_omni/diffusion/models/ltx2/ltx2_recipes.py

@@ -24,6 +24,7 @@ LTX_DEFAULT_NEGATIVE_PROMPT = (
 
 LTX_DISTILLED_SIGMAS = (1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0)
 LTX_STAGE_2_DISTILLED_SIGMAS = (0.909375, 0.725, 0.421875, 0.0)
+LTX_DISTILLED_ADAPTER_SLOT = "ltx_distilled"
 
 
 @dataclass(frozen=True)
@@ -36,6 +37,7 @@ class LTXPhaseRecipe:
     sigmas: tuple[float, ...] | None = None
     noise_scale: float = 0.0
     input_transform: Literal["initial", "spatial_upsample"] = "initial"
+    adapter_slot: str | None = None
     allow_guidance_override: bool = True
     use_official_sigma_schedule: bool = True
 
@@ -157,11 +159,59 @@ LTX2_DISTILLED_TWO_STAGE_RECIPE = LTXPipelineRecipe(
     fixed_num_inference_steps=True,
 )
 
+# LTX-2.3 full-distilled uses the same official fixed 8 + 3 sigma schedules
+# and request contract. Its distinct component profile selects the 22B merged
+# distilled Transformer, BWE vocoder, and matching spatial upsampler; no
+# distilled LoRA is loaded for either phase.
+LTX23_DISTILLED_TWO_STAGE_RECIPE = LTX2_DISTILLED_TWO_STAGE_RECIPE
+
+
+def _official_two_stage_recipe(one_stage_recipe: LTXPipelineRecipe) -> LTXPipelineRecipe:
+    return LTXPipelineRecipe(
+        height=one_stage_recipe.height * 2,
+        width=one_stage_recipe.width * 2,
+        num_frames=one_stage_recipe.num_frames,
+        frame_rate=one_stage_recipe.frame_rate,
+        num_inference_steps=one_stage_recipe.num_inference_steps,
+        negative_prompt=one_stage_recipe.negative_prompt,
+        phases=(
+            LTXPhaseRecipe(
+                name="generate_lowres",
+                guidance=one_stage_recipe.request_guidance,
+                spatial_downscale=2,
+                noise_scale=1.0,
+            ),
+            LTXPhaseRecipe(
+                name="refine",
+                guidance=LTXGuidanceSpec.positive_only(),
+                sigmas=LTX_STAGE_2_DISTILLED_SIGMAS,
+                noise_scale=LTX_STAGE_2_DISTILLED_SIGMAS[0],
+                input_transform="spatial_upsample",
+                adapter_slot=LTX_DISTILLED_ADAPTER_SLOT,
+                allow_guidance_override=False,
+                use_official_sigma_schedule=False,
+            ),
+        ),
+        video_output_phase=1,
+        # The official second stage refines video only and deliberately
+        # discards its audio result. Decode the full-context Stage-1 audio.
+        audio_output_phase=0,
+        allow_request_sigmas=False,
+        allow_request_latents=False,
+    )
+
+
+LTX2_TWO_STAGE_RECIPE = _official_two_stage_recipe(LTX2_ONE_STAGE_RECIPE)
+LTX23_TWO_STAGE_RECIPE = _official_two_stage_recipe(LTX23_ONE_STAGE_RECIPE)
+
 
 _PIPELINE_RECIPES: dict[tuple[str, str], LTXPipelineRecipe] = {
     ("one_stage", "2"): LTX2_ONE_STAGE_RECIPE,
     ("one_stage", "2.3"): LTX23_ONE_STAGE_RECIPE,
+    ("two_stage", "2"): LTX2_TWO_STAGE_RECIPE,
+    ("two_stage", "2.3"): LTX23_TWO_STAGE_RECIPE,
     ("distilled_two_stage", "2"): LTX2_DISTILLED_TWO_STAGE_RECIPE,
+    ("distilled_two_stage", "2.3"): LTX23_DISTILLED_TWO_STAGE_RECIPE,
     ("dmd2", "2"): LTX_POSITIVE_ONLY_RECIPE,
     ("dmd2", "2.3"): LTX_POSITIVE_ONLY_RECIPE,
 }

vllm_omni/diffusion/models/ltx2/ltx2_request.py

@@ -5,8 +5,9 @@
 
 from __future__ import annotations
 
+from collections.abc import Mapping
 from dataclasses import dataclass, replace
-from typing import Any
+from typing import Any, Literal
 
 import torch
 
@@ -49,6 +50,34 @@ class LTXRequestInputs:
         return self.guidance.video.cfg_scale
 
 
+LTXCheckpointKind = Literal["distilled", "regular"]
+
+
+def validate_ltx_checkpoint(
+    scheduler_config: Mapping[str, Any],
+    *,
+    expected_kind: LTXCheckpointKind | None,
+    pipeline_name: str,
+) -> None:
+    """Ensure scheduler metadata matches the checkpoint required by a profile."""
+    if expected_kind is None:
+        return
+
+    if expected_kind == "distilled":
+        if scheduler_config.get("use_dynamic_shifting", True) or scheduler_config.get("shift_terminal") is not None:
+            raise ValueError(
+                f"{pipeline_name} requires a merged distilled LTX2 checkpoint; "
+                "the loaded scheduler metadata describes a non-distilled checkpoint."
+            )
+        return
+
+    if not scheduler_config.get("use_dynamic_shifting", False) or scheduler_config.get("shift_terminal") is None:
+        raise ValueError(
+            f"{pipeline_name} requires a regular non-distilled LTX checkpoint; "
+            "the loaded scheduler metadata describes a distilled checkpoint."
+        )
+
+
 def validate_pipeline_request(
     request_inputs: LTXRequestInputs,
     *,

vllm_omni/diffusion/models/ltx2/ltx2_runtime.py

@@ -47,6 +47,7 @@ from .ltx2_guidance import (
     LTXGuidanceExecutor,
     LTXGuidancePlan,
 )
+from .ltx2_phase_adapter import LTXPhaseAdapterRuntime, build_ltx_phase_adapter
 from .ltx2_recipes import (
     LTXPhaseRecipe,
     LTXPipelineRecipe,
@@ -137,9 +138,21 @@ class LTXRuntime(
 
     def __init__(self, *, od_config: OmniDiffusionConfig, prefix: str = "") -> None:
         del prefix
+        parallel_config = getattr(od_config, "parallel_config", None)
+        if getattr(parallel_config, "ulysses_mode", "strict") == "advanced_uaa":
+            raise ValueError(
+                f"{self.__class__.__name__} does not support ulysses_mode='advanced_uaa'. "
+                "Use the default ulysses_mode='strict' for LTX sequence parallelism."
+            )
         self.model_version = detect_ltx_model_version(od_config.model)
         self.component_profile = resolve_ltx_component_profile(self.pipeline_kind, self.model_version)
         self.pipeline_recipe = resolve_ltx_pipeline_recipe(self.pipeline_kind, self.model_version)
+        if getattr(od_config, "cache_backend", "none") == "cache_dit" and len(self.pipeline_recipe.phases) > 1:
+            raise ValueError(
+                f"{self.__class__.__name__} does not support cache_backend='cache_dit'. "
+                "Cache-DiT currently supports only one-stage LTX recipes; multi-stage recipes require "
+                "phase-aware cache enable and refresh."
+            )
         self._dit_modules = list(self.component_profile.dit_modules)
         self._encoder_modules = list(self.component_profile.encoder_modules)
         self._vae_modules = list(self.component_profile.vae_modules)
@@ -150,6 +163,7 @@ class LTXRuntime(
         super().__init__()
         self._guidance_plan = LTXGuidancePlan.build(self.pipeline_recipe.request_guidance)
         initialize_pipeline_components(self, od_config)
+        self._phase_adapter: LTXPhaseAdapterRuntime | None = build_ltx_phase_adapter(self)
         self.setup_diffusion_pipeline_profiler(
             enable_diffusion_pipeline_profiler=self.od_config.enable_diffusion_pipeline_profiler
         )
@@ -205,6 +219,8 @@ class LTXRuntime(
             max_sequence_length=max_sequence_length,
         )
         image = self._resolve_request_image(req, image, request_inputs)
+        if image is not None and not self.support_image_input:
+            raise ValueError(f"{self.__class__.__name__} does not support `image` input.")
         request_sigmas = self._resolve_request_sigmas(req, sigmas)
         validate_pipeline_request(
             request_inputs,
@@ -214,6 +230,13 @@ class LTXRuntime(
             pipeline_name=self.__class__.__name__,
             request_sigmas=request_sigmas,
         )
+        phase_adapter = getattr(self, "_phase_adapter", None)
+        if phase_adapter is not None and any(
+            getattr(sampling, "lora_request", None) is not None for sampling in req.sampling_params_list
+        ):
+            raise ValueError(
+                f"{self.__class__.__name__} cannot compose a request LoRA with its internal phase adapter."
+            )
         return self._run_recipe(req, request_inputs, request_sigmas=request_sigmas, image=image)
 
     def _run_recipe(
@@ -258,8 +281,20 @@ class LTXRuntime(
         return self.decode_phase(output_phase)
 
     def _enter_phase(self, phase: LTXPhaseRecipe) -> None:
-        """Hook for a future phase-weight strategy."""
         self._active_phase_name = phase.name
+        phase_adapter = getattr(self, "_phase_adapter", None)
+        if phase_adapter is None:
+            if phase.adapter_slot is not None:
+                raise RuntimeError(f"LTX phase {phase.name!r} requires adapter slot {phase.adapter_slot!r}.")
+            return
+        phase_adapter.activate(phase.adapter_slot)
+
+    def eval(self):
+        result = super().eval()
+        phase_adapter = getattr(self, "_phase_adapter", None)
+        if phase_adapter is not None:
+            phase_adapter.finalize()
+        return result
 
     def prepare_latents(
         self,
@@ -692,6 +727,7 @@ class LTXRuntime(
             noise_pred_audio,
             timestep,
         )
+        audio = latent_ops.clear_audio_padding(audio, forward_ctx.original_audio_num_frames)
         return latent_ops.LTXAVState(video=video, audio=audio)
 
     def _unpack_and_denormalize_stage(

vllm_omni/diffusion/models/ltx2/ltx2_transformer.py

@@ -355,6 +355,19 @@ def to_ltx_padding_mask(attention_mask: torch.Tensor) -> torch.Tensor:
     return attention_mask
 
 
+class _LTX2ParallelAttention(Attention):
+    """Preserve LTX SP collectives while applying model-specific padding masks."""
+
+    def _run_local_attention(self, query, key, value, attn_metadata):
+        attention_mask = attn_metadata.attn_mask if attn_metadata is not None else None
+        if attention_mask is not None:
+            # LTX masks describe K/V padding. Keep this semantic local to LTX:
+            # Flash varlen applies a 2D mask to both Q and K, while SP uses a
+            # broadcast 4D key mask that only SDPA consumes correctly.
+            return self.sdpa_fallback.forward(query, key, value, attn_metadata)
+        return super()._run_local_attention(query, key, value, attn_metadata)
+
+
 class LTX2AudioVideoAttnProcessor:
     r"""
     Processor for implementing attention (SDPA is used by default if you're using PyTorch 2.0) for the LTX-2.0 model.
@@ -396,11 +409,10 @@ class LTX2AudioVideoAttnProcessor:
             return None
 
         if self._is_sp_enabled():
-            # In SP, Ulysses expects a 2D padding mask that matches query length.
-            # For cross-attention, encoder sequence length != query length, so drop the mask.
-            if encoder_hidden_states is not None and encoder_hidden_states.shape[1] != hidden_states.shape[1]:
-                return None
-            return to_ltx_padding_mask(attention_mask)
+            # Keep the full key mask replicated while strict Ulysses reshards
+            # Q/K/V. The broadcast Q dimension also works when cross-attention
+            # has different Q and K lengths, without changing generic Ulysses.
+            return to_ltx_padding_mask(attention_mask)[:, None, None, :]
 
         attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
         attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
@@ -696,7 +708,7 @@ class LTX2Attention(torch.nn.Module):
                 torch.nn.Dropout(dropout) if dropout > 0 else torch.nn.Identity(),
             ]
         )
-        self.attn = Attention(
+        self.attn = _LTX2ParallelAttention(
             num_heads=self.query_num_heads,
             head_size=dim_head,
             num_kv_heads=self.kv_num_heads,
@@ -709,7 +721,15 @@ class LTX2Attention(torch.nn.Module):
         # LTX-2.3: per-head gated attention
         # leave unquantized for this linear
         if apply_gated_attention:
-            self.to_gate_logits = nn.Linear(query_dim, self.query_num_heads, bias=True)
+            self.to_gate_logits = ColumnParallelLinear(
+                query_dim,
+                heads,
+                bias=True,
+                gather_output=False,
+                return_bias=False,
+                quant_config=None,
+                prefix=f"{prefix}.to_gate_logits" if prefix else "to_gate_logits",
+            )
         else:
             self.to_gate_logits = None
 
@@ -1498,6 +1518,14 @@ class LTX2VideoTransformer3DModel(nn.Module):
     _layerwise_offload_blocks_attrs = ["transformer_blocks"]
     _hsdp_shard_conditions = [is_transformer_block_module]
     _sp_plan: dict[str, Any] | None = None
+    stacked_params_mapping = (
+        (".attn1.to_qkv", ".attn1.to_q", "q"),
+        (".attn1.to_qkv", ".attn1.to_k", "k"),
+        (".attn1.to_qkv", ".attn1.to_v", "v"),
+        (".audio_attn1.to_qkv", ".audio_attn1.to_q", "q"),
+        (".audio_attn1.to_qkv", ".audio_attn1.to_k", "k"),
+        (".audio_attn1.to_qkv", ".audio_attn1.to_v", "v"),
+    )
     packed_modules_mapping = {
         "to_qkv": ["to_q", "to_k", "to_v"],
     }
@@ -1828,6 +1856,7 @@ class LTX2VideoTransformer3DModel(nn.Module):
         audio_sigma: torch.Tensor | None = None,
         encoder_attention_mask: torch.Tensor | None = None,
         audio_encoder_attention_mask: torch.Tensor | None = None,
+        audio_attention_mask: torch.Tensor | None = None,
         num_frames: int | None = None,
         height: int | None = None,
         width: int | None = None,
@@ -1861,6 +1890,8 @@ class LTX2VideoTransformer3DModel(nn.Module):
                 Optional multiplicative text attention mask of shape `(batch_size, text_seq_len)`.
             audio_encoder_attention_mask (`torch.Tensor`, *optional*):
                 Optional multiplicative text attention mask of shape `(batch_size, text_seq_len)` for audio modeling.
+            audio_attention_mask (`torch.Tensor`, *optional*):
+                Optional audio-token key padding mask shared by audio self-attention and audio-to-video attention.
             num_frames (`int`, *optional*):
                 The number of latent video frames. Used if calculating the video coordinates for RoPE.
             height (`int`, *optional*):
@@ -2044,6 +2075,8 @@ class LTX2VideoTransformer3DModel(nn.Module):
                 "ca_audio_rotary_emb": audio_cross_attn_rotary_emb,
                 "encoder_attention_mask": encoder_attention_mask,
                 "audio_encoder_attention_mask": audio_encoder_attention_mask,
+                "audio_self_attention_mask": audio_attention_mask,
+                "a2v_cross_attention_mask": audio_attention_mask,
                 "video_self_attention_perturbation_mask": perturbation_mask_for("video_self_attention", block_idx),
                 "audio_self_attention_perturbation_mask": perturbation_mask_for("audio_self_attention", block_idx),
                 "a2v_cross_attention_perturbation_mask": perturbation_mask_for("a2v_cross_attention", block_idx),
@@ -2097,16 +2130,8 @@ class LTX2VideoTransformer3DModel(nn.Module):
         Returns:
             Set of parameter names that were successfully loaded.
         """
-        stacked_params_mapping = [
-            (".attn1.to_qkv", ".attn1.to_q", "q"),
-            (".attn1.to_qkv", ".attn1.to_k", "k"),
-            (".attn1.to_qkv", ".attn1.to_v", "v"),
-            (".audio_attn1.to_qkv", ".audio_attn1.to_q", "q"),
-            (".audio_attn1.to_qkv", ".audio_attn1.to_k", "k"),
-            (".audio_attn1.to_qkv", ".audio_attn1.to_v", "v"),
-        ]
-
         params_dict = dict(self.named_parameters())
+        phase_adapter_parameter_name = getattr(self, "_phase_adapter_parameter_name", None)
         tp_size = get_tensor_model_parallel_world_size()
         tp_rank = get_tensor_model_parallel_rank() if tp_size > 1 else 0
         loaded_params: set[str] = set()
@@ -2127,10 +2152,12 @@ class LTX2VideoTransformer3DModel(nn.Module):
             return weight
 
         for name, loaded_weight in weights:
-            for param_name, weight_name, shard_id in stacked_params_mapping:
+            for param_name, weight_name, shard_id in self.stacked_params_mapping:
                 if weight_name not in name:
                     continue
                 packed_name = name.replace(weight_name, param_name)
+                if callable(phase_adapter_parameter_name):
+                    packed_name = phase_adapter_parameter_name(packed_name)
                 if packed_name not in params_dict:
                     continue
                 name = packed_name
@@ -2139,6 +2166,8 @@ class LTX2VideoTransformer3DModel(nn.Module):
                 weight_loader(param, loaded_weight, shard_id)
                 break
             else:
+                if callable(phase_adapter_parameter_name):
+                    name = phase_adapter_parameter_name(name)
                 if name not in params_dict:
                     logger.warning(
                         "Skipping transformer weight %s -- not found in model "

vllm_omni/diffusion/models/ltx2/pipeline_ltx2_two_stage.py

@@ -1,27 +1,48 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-"""Distilled two-stage entry points for the LTX model family."""
+"""Two-stage entry points for the LTX model family."""
 
-from vllm_omni.diffusion.data import OmniDiffusionConfig
+from typing import ClassVar
 
-from .ltx2_components import LTX2_DISTILLED_COMPONENT_PROFILE
+from .ltx2_components import (
+    LTX2_DISTILLED_COMPONENT_PROFILE,
+    LTX2_TWO_STAGE_COMPONENT_PROFILE,
+)
 from .ltx2_components import (
     get_ltx2_post_process_func as get_ltx2_post_process_func,  # noqa: F401
 )
-from .ltx2_recipes import LTX2_DISTILLED_TWO_STAGE_RECIPE
-from .pipeline_ltx2 import LTX2Pipeline
+from .ltx2_conditioning import LTXI2VConditioningMixin
+from .ltx2_recipes import (
+    LTX2_DISTILLED_TWO_STAGE_RECIPE,
+    LTX2_TWO_STAGE_RECIPE,
+)
+from .ltx2_runtime import LTXRuntime
 
 
-class LTX2DistilledPipeline(LTX2Pipeline):
-    """Unified LTX-2 distilled two-stage T2V/I2V entry."""
+class _LTX2TwoStageBase(LTXI2VConditioningMixin, LTXRuntime):
+    """Static discovery contract shared by the two-stage public entries."""
 
-    pipeline_kind = "distilled_two_stage"
-    component_profile = LTX2_DISTILLED_COMPONENT_PROFILE
-    pipeline_recipe = LTX2_DISTILLED_TWO_STAGE_RECIPE
+    _dit_modules: ClassVar[list[str]] = list(LTX2_TWO_STAGE_COMPONENT_PROFILE.dit_modules)
+    _encoder_modules: ClassVar[list[str]] = list(LTX2_TWO_STAGE_COMPONENT_PROFILE.encoder_modules)
+    _vae_modules: ClassVar[list[str]] = list(LTX2_TWO_STAGE_COMPONENT_PROFILE.vae_modules)
+    _resident_modules: ClassVar[list[str]] = list(LTX2_TWO_STAGE_COMPONENT_PROFILE.resident_modules)
     supports_request_batch = False
     support_image_input = True
     unified_text_image_entry = True
 
-    def __init__(self, *, od_config: OmniDiffusionConfig, prefix: str = ""):
-        super().__init__(od_config=od_config, prefix=prefix)
+
+class LTX2TwoStagePipeline(_LTX2TwoStageBase):
+    """Regular checkpoint with low-resolution generation and LoRA refinement."""
+
+    pipeline_kind = "two_stage"
+    component_profile = LTX2_TWO_STAGE_COMPONENT_PROFILE
+    pipeline_recipe = LTX2_TWO_STAGE_RECIPE
+
+
+class LTX2DistilledPipeline(_LTX2TwoStageBase):
+    """Unified LTX-2/LTX-2.3 full-distilled two-stage T2V/I2V entry."""
+
+    pipeline_kind = "distilled_two_stage"
+    component_profile = LTX2_DISTILLED_COMPONENT_PROFILE
+    pipeline_recipe = LTX2_DISTILLED_TWO_STAGE_RECIPE

vllm_omni/diffusion/registry.py

@@ -71,6 +71,11 @@ _DIFFUSION_MODELS = {
         "pipeline_ltx2",
         "LTX2Pipeline",
     ),
+    "LTX2TwoStagePipeline": (
+        "ltx2",
+        "pipeline_ltx2_two_stage",
+        "LTX2TwoStagePipeline",
+    ),
     "LTX2DistilledPipeline": (
         "ltx2",
         "pipeline_ltx2_two_stage",
@@ -516,6 +521,7 @@ _DIFFUSION_POST_PROCESS_FUNCS = {
     "WanPipeline": "get_wan22_post_process_func",
     "WanVACEPipeline": "get_wan22_vace_post_process_func",
     "LTX2Pipeline": "get_ltx2_post_process_func",
+    "LTX2TwoStagePipeline": "get_ltx2_post_process_func",
     "LTX2DistilledPipeline": "get_ltx2_post_process_func",
     "LTX2T2VDMD2Pipeline": "get_ltx2_post_process_func",
     "LTX2I2VDMD2Pipeline": "get_ltx2_post_process_func",

vllm_omni/model_extras/registry.py

@@ -205,6 +205,7 @@ _EXTRA_SPECS: dict[str, dict[str, Any]] = {
         }
         for model_class_name in (
             "LTX2Pipeline",
+            "LTX2TwoStagePipeline",
             "LTX2DistilledPipeline",
         )
     },