docs: restore quantization overview page

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>

.buildkite/cuda/test-nightly.yml

@@ -427,6 +427,12 @@ steps:
           - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2v_matches_diffusers' -m full_model --run-level full_model
         mirror_hardwares: h100_2
 
+      - label: ":full_moon: Diffusion X2V · MiniMax H3 T2VA Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - pytest -s -v tests/e2e/accuracy/minimax_h3/test_minimax_h3_t2va_similarity.py -m full_model --run-level full_model
+        mirror_hardwares: h100_4
+
       - label: ":full_moon: Diffusion X2V · Other Accuracy Test"
         timeout_in_minutes: 180
         commands:
@@ -477,12 +483,18 @@ steps:
           - pytest -sv tests/diffusion/offloader -m "full_model and cuda"
         mirror_hardwares: l4_1
 
-      - label: ":full_moon: Diffusion · Quantization Test with H100"
+      - label: ":full_moon: Diffusion · Quantization Test with H100 · Single-GPU"
         timeout_in_minutes: 60
         commands:
           - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and H100' --run-level "full_model"
         mirror_hardwares: h100_1
 
+      - label: ":full_moon: Diffusion · MiniMax H3 FP8 Quality Test with H100 · 2-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/models/minimax_h3/test_minimax_h3_quantization_quality.py -m 'full_model and cuda and H100' --run-level "full_model"
+        mirror_hardwares: h100_2
+
       - label: ":full_moon: Diffusion · Quantization Test with L4"
         timeout_in_minutes: 60
         commands:

.claude/skills/add-diffusion-model/SKILL.md

@@ -309,7 +309,7 @@ For Omni or custom models, create:
 
 Required updates:
 1. `docs/user_guide/diffusion/parallelism/overview.md` — parallelism support overview/table
-2. `docs/user_guide/diffusion/cpu_offload_diffusion.md` — if CPU offload supported (add to supported models table)
+2. `docs/user_guide/diffusion/cpu_offload.md` — if CPU offload supported (add to supported models table)
 3. `docs/user_guide/diffusion/cache_acceleration/teacache.md` — if TeaCache supported
 4. `docs/user_guide/diffusion/cache_acceleration/cache_dit.md` — if Cache-DiT supported
 5. Offline example docs under `examples/offline_inference/<name>/` (`README.md` or category-specific `.md`)

.github/CODEOWNERS

@@ -16,15 +16,15 @@
 /.claude/ @hsliuustc0106
 
 # Tests
-/tests/ @yenuo26
+/tests/ @yenuo26 @NickCao
 
 # CI
-/.buildkite/ @yenuo26 @congw729
+/.buildkite/ @yenuo26 @congw729 @NickCao
 
 # Engine and model_executor
 /vllm_omni/core/ @tzhouam @yinpeiqi @fake0fan @Sy0307
 /vllm_omni/engine/ @tzhouam @yinpeiqi @fake0fan @Sy0307
-/vllm_omni/entrypoints/ @tzhouam @yinpeiqi @fake0fan @alex-jw-brooks
+/vllm_omni/entrypoints/ @tzhouam @yinpeiqi @fake0fan @alex-jw-brooks @NickCao
 /vllm_omni/model_executor/ @tzhouam @gcanlin
 /vllm_omni/worker/ @tzhouam @gcanlin
 
@@ -37,7 +37,7 @@
 
 # Omni and TTS support
 /vllm_omni/transformers_utils/ @tzhouam @yuanheng-zhao
-/vllm_omni/model_executor/models/ @linyueqian @ZeldaHuang @princepride @yuanheng-zhao @amy-why-3459 @Bounty-hunter @Sy0307 @gcanlin
+/vllm_omni/model_executor/models/ @linyueqian @ZeldaHuang @princepride @yuanheng-zhao @amy-why-3459 @Bounty-hunter @Sy0307 @gcanlin @NickCao
 
 # Benchmarks
 /benchmarks/accuracy/ @david6666666

README.md

@@ -16,14 +16,10 @@ Easy, fast, and cheap omni-modality model serving for everyone
 ---
 
 *Latest News* 🔥
+- [2026/08] We released [0.26.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.26.0) - aligned with the vLLM 0.26 release line, featuring [MiniMax H3](recipes/MiniMaxAI/MiniMax-H3.md) joint video/audio generation, an experimental full-duplex realtime runtime for [MiniCPM-o 4.5](recipes/OpenBMB/MiniCPM-o-4_5.md), distributed layerwise diffusion offload, and broader model, hardware, streaming, TTS, and quantization support.
 - [2026/07] We released [0.24.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.24.0) - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support.
-- [2026/06] We released [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) - an **omnimodal world-model** release aligned with vLLM 0.22, featuring [Nvidia Cosmos3](recipes/cosmos3/Cosmos3-Nano.md)/DreamZero world model support, expanded quantization coverage across Blackwell/NPU/XPU, TTS production improvements, new models including MiniCPM-o 4.5, MOSS-TTS, and Lance, plus RL integration with [VeRL-Omni](https://github.com/verl-project/verl-omni).
+- [2026/06] Starting with [0.14.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.14.0), vLLM-Omni publishes a stable release aligned with every even-numbered upstream vLLM minor version. [0.16.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.16.0), [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0), [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0), and [0.22.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.22.0) continued this cadence, expanding omni and world-model support with [NVIDIA Cosmos3](recipes/cosmos3/Cosmos3-Nano.md) and DreamZero, adding models such as MiniCPM-o 4.5, MOSS-TTS, and Lance, and advancing TTS, diffusion, distributed execution, quantization, RL integration through [VeRL-Omni](https://github.com/verl-project/verl-omni), and CUDA/ROCm/MUSA/NPU/XPU coverage.
-- [2026/05] We released [0.20.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.20.0) - refreshes the serving/runtime stack for large-scale omni workloads, and improves diffusion model performance, quantization, and hardware readiness across CUDA, ROCm, MUSA, NPU, and XPU backends.
-- [2026/03] We released [0.18.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.18.0) - strengthens the core runtime through a large entrypoint refactor and scheduler/runtime cleanups, expands unified quantization and diffusion execution, broadens multimodal model coverage, and improves production readiness across audio, omni, image, video, RL, and multi-platform deployments.
 - [2026/03] Check out our first public [project deepdive](https://youtu.be/sgwNfsNnR9I) at the vLLM Hong Kong Meetup!
-- [2026/03] **[vllm-omni-skills](https://github.com/hsliuustc0106/vllm-omni-skills)** is a community-driven collection of AI assistant skills that help developers work with vLLM-Omni more effectively. These skills can be used with popular agentic AI coding assistants like **Cursor IDE**, **Claude**, **Codex**, and more.
-- [2026/02] We released [0.16.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.16.0) - A major alignment + capability release that rebases onto **upstream vLLM v0.16.0** and significantly expands performance, distributed execution, and production readiness across **Qwen3-Omni / Qwen3-TTS**, **Bagel**, **MiMo-Audio**, **GLM-Image** and the **Diffusion (DiT) image/video stack**—while also improving platform coverage (CUDA / ROCm / NPU / XPU), CI quality, and documentation.
-- [2026/02] We released [0.14.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.14.0) - This is the first **stable release** of vLLM-Omni that expands Omni’s diffusion / image-video generation and audio / TTS stack, improves distributed execution and memory efficiency, and broadens platform/backend coverage (GPU/ROCm/NPU/XPU). It also brings meaningful upgrades to serving APIs, profiling & benchmarking, and overall stability. Please check our latest [paper](https://arxiv.org/abs/2602.02204) for architecture design and performance results.
 - [2025/11] vLLM community officially released [vllm-project/vllm-omni](https://github.com/vllm-project/vllm-omni) in order to support omni-modality models serving.
 
 ---
@@ -55,12 +51,13 @@ vLLM-Omni is flexible and easy to use with:
 - Tensor, pipeline, data and expert parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
+- Full-duplex realtime serving with streaming audio input and output (experimental)
 
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
 
-- **Omni-modality models** (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
+- **Omni-modality models** (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
 - **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
-- **Diffusion models** — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX)
+- **Diffusion models** — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
 - **Robot-policy and action models** (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
 
 ## Getting Started

apps/ComfyUI-vLLM-Omni/README.md

@@ -47,7 +47,7 @@ If no, check your shell running the ComfyUI process. There may be some error mes
 This extension offers the following nodes based on the output modalities (at **ComfyUI sidebar -> Node Library**):
 
 - **Generate Image** for text-to-image and image-to-image tasks
-- **Generate Video** for text-to-video and image-to-video tasks
+- **Generate Video** for text-to-video, first-frame/image-to-video, and reference-conditioned video
 - **Multimodality Understanding** for multimodality-to-text and multimodality-to-audio tasks
 - **TTS** and **TTS Voice Clone** for TTS tasks
 
@@ -105,7 +105,7 @@ You can configure per-stage sampling parameters for multi-stage models.
 > [!TIP]
 > The node automatically choose text-to-image or image-to-image API endpoints depending on whether you connect an image input or not.
 
-### Text-to-video and image-to-video generation (e.g., Wan)
+### Text-to-video and image-to-video generation (e.g., Wan, MiniMax-H3)
 
 (Also available at **ComfyUI sidebar->Template->vLLM-Omni->vLLM-Omni Video Generation**)
 
@@ -117,7 +117,11 @@ You can configure per-stage sampling parameters for multi-stage models.
 </p>
 
 > [!TIP]
-> The node automatically choose text-to-video or image-to-video API endpoints depending on whether you connect an image input or not.
+> Connect a **frame** image for first-frame / image-to-video (e.g., Wan I2V, MiniMax-H3 FL2VA).
+>
+> For reference-conditioned generation (MiniMax-H3 Ref2VA), connect a **Video References** node instead.
+>
+> Do not use `frame` and `references` together. Task routing is automatic from which inputs you connect.
 
 ### TTS (e.g., Qwen TTS series)
 

apps/ComfyUI-vLLM-Omni/__init__.py

@@ -15,11 +15,13 @@ from .comfyui_vllm_omni.nodes import (
     VLLMOmniDiffusionSampling,
     VLLMOmniGenerateImage,
     VLLMOmniGenerateVideo,
+    VLLMOmniMiniMaxH3Params,
     VLLMOmniQwenTTSParams,
     VLLMOmniRemoteLoRA,
     VLLMOmniSamplingParamsList,
     VLLMOmniTTS,
     VLLMOmniUnderstanding,
+    VLLMOmniVideoReferences,
     VLLMOmniVoiceClone,
     VLLMOmniWanParams,
 )
@@ -32,6 +34,7 @@ NODE_CLASS_MAPPINGS = {
     "VLLMOmniUnderstanding": VLLMOmniUnderstanding,
     "VLLMOmniTTS": VLLMOmniTTS,
     "VLLMOmniVoiceClone": VLLMOmniVoiceClone,
+    "VLLMOmniVideoReferences": VLLMOmniVideoReferences,
     # === Params ===
     "VLLMOmniARSampling": VLLMOmniARSampling,
     "VLLMOmniDiffusionSampling": VLLMOmniDiffusionSampling,
@@ -39,6 +42,7 @@ NODE_CLASS_MAPPINGS = {
     "VLLMOmniRemoteLoRA": VLLMOmniRemoteLoRA,
     "VLLMOmniQwenTTSParams": VLLMOmniQwenTTSParams,
     "VLLMOmniWanParams": VLLMOmniWanParams,
+    "VLLMOmniMiniMaxH3Params": VLLMOmniMiniMaxH3Params,
 }
 
 # A dictionary that contains the friendly/humanly readable titles for the nodes
@@ -49,6 +53,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "VLLMOmniUnderstanding": "Multimodality Understanding",
     "VLLMOmniTTS": "TTS (Text to Speech)",
     "VLLMOmniVoiceClone": "TTS Voice Cloning",
+    "VLLMOmniVideoReferences": "Video References",
     # === Params ===
     "VLLMOmniARSampling": "AR Sampling Params",
     "VLLMOmniDiffusionSampling": "Diffusion Sampling Params",
@@ -56,6 +61,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "VLLMOmniRemoteLoRA": "LoRA",
     "VLLMOmniQwenTTSParams": "Qwen TTS Params",
     "VLLMOmniWanParams": "Wan Video Params",
+    "VLLMOmniMiniMaxH3Params": "MiniMax-H3 Video Params",
 }
 
 WEB_DIRECTORY = "./web"

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/nodes.py

@@ -10,7 +10,9 @@ from .utils.types import (
     AudioFormat,
     AutoregressionSamplingParams,
     DiffusionSamplingParams,
+    MiniMaxH3ModelSpecificParams,
     QwenTTSModelSpecificParams,
+    VideoReferences,
     WanModelSpecificParams,
 )
 from .utils.validators import (
@@ -163,7 +165,8 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
                 "num_frames": ("INT", {"default": 41, "min": 1}),
             },
             "optional": {
-                "image": ("IMAGE",),
+                "frame": ("IMAGE",),
+                "references": ("VIDEO_REFERENCES",),
                 "sampling_params": ("SAMPLING_PARAMS",),
                 "lora": ("REMOTE_LORA",),
                 "model_params": ("VIDEO_PARAMS",),
@@ -174,6 +177,15 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
     RETURN_NAMES = ("video",)
     FUNCTION = "generate"
 
+    @classmethod
+    def VALIDATE_INPUTS(cls, url, model, frame=None, references=None, **_kwargs) -> str | Literal[True]:
+        base = super().VALIDATE_INPUTS(url, model)
+        if base is not True:
+            return base
+        if frame is not None and references is not None:
+            return "Provide only one of frame or references, not both."
+        return True
+
     async def generate(
         self,
         url: str,
@@ -184,7 +196,8 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
         fps: int,
         num_frames: int,
         negative_prompt: str | None = None,
-        image: torch.Tensor | None = None,
+        frame: torch.Tensor | None = None,
+        references: dict | None = None,
         sampling_params: dict | list[dict] | None = None,
         model_params: dict | None = None,
         lora: dict | None = None,
@@ -207,14 +220,16 @@ class VLLMOmniGenerateVideo(_VLLMOmniGenerateBase):
 
         if sampling_params is not None:
             sampling_params.pop("type", None)  # internal fields
-        if model_params is not None:
+        # model_params["type"] is also an internal field, but is used in api_client
-            model_params.pop("type", None)  # internal fields
+        # if model_params is not None:
+        #     model_params.pop("type", None)  # internal fields
 
         client = VLLMOmniClient(url)
         output = await client.generate_video(
             model=model,
             prompt=prompt,
-            image=image,  # image present => i2v, absent => t2v
+            frame=frame,  # frame present => fl2va / Wan I2V
+            references=references,
             width=width,
             height=height,
             num_frames=num_frames,
@@ -734,3 +749,78 @@ class VLLMOmniWanParams:
 
     def get_params(self, **kwargs):
         return (WanModelSpecificParams(kwargs),)
+
+
+class VLLMOmniMiniMaxH3Params:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio_flow_shift": (
+                    "FLOAT",
+                    {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1},
+                ),
+                "flow_shift": (
+                    "FLOAT",
+                    {"default": 12.0, "min": 0.0, "max": 100.0, "step": 0.1},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("VIDEO_PARAMS",)
+    RETURN_NAMES = ("MiniMax-H3 video params",)
+    FUNCTION = "get_params"
+    CATEGORY = "vLLM-Omni/Video Params"
+
+    def get_params(self, **kwargs):
+        params = MiniMaxH3ModelSpecificParams(kwargs)
+        params["type"] = "minimax_h3"
+        return (params,)
+
+
+class VLLMOmniVideoReferences:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {},
+            "optional": {
+                "image_1": ("IMAGE",),
+                "image_2": ("IMAGE",),
+                "audio_1": ("AUDIO",),
+                "audio_2": ("AUDIO",),
+                "video_1": ("VIDEO",),
+                "video_2": ("VIDEO",),
+            },
+        }
+
+    RETURN_TYPES = ("VIDEO_REFERENCES",)
+    RETURN_NAMES = ("references",)
+    FUNCTION = "get_references"
+    CATEGORY = "vLLM-Omni"
+
+    def get_references(
+        self,
+        image_1: torch.Tensor | None = None,
+        image_2: torch.Tensor | None = None,
+        audio_1: AudioInput | None = None,
+        audio_2: AudioInput | None = None,
+        video_1: VideoInput | None = None,
+        video_2: VideoInput | None = None,
+        **kwargs,
+    ):
+        if kwargs:
+            logger.info("Uncaught kwargs: %s", kwargs)
+        refs = VideoReferences()
+        if image_1 is not None:
+            refs["image_1"] = image_1
+        if image_2 is not None:
+            refs["image_2"] = image_2
+        if audio_1 is not None:
+            refs["audio_1"] = audio_1
+        if audio_2 is not None:
+            refs["audio_2"] = audio_2
+        if video_1 is not None:
+            refs["video_1"] = video_1
+        if video_2 is not None:
+            refs["video_2"] = video_2
+        return (refs,)

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/api_client.py

@@ -24,6 +24,7 @@ from .format import (
     image_tensor_to_base64,
     image_tensor_to_png_bytes,
     video_to_base64,
+    video_to_bytes,
 )
 from .logger import get_logger, pretty_printer
 from .models import lookup_model_spec
@@ -63,7 +64,11 @@ async def url_bytes(session: aiohttp.ClientSession, url: str, verb: str = "get",
 
 class VLLMOmniClient:
     def __init__(
-        self, base_url: str, timeout: float | None = None, poll_interval: float = 5.0, max_poll_duration: float = 60 * 5
+        self,
+        base_url: str,
+        timeout: float | None = None,
+        poll_interval: float = 5.0,
+        max_poll_duration: float = 60 * 30,
     ):
         self.base_url = base_url
         self.timeout = aiohttp.ClientTimeout(total=timeout)
@@ -261,12 +266,17 @@ class VLLMOmniClient:
         num_frames: int,
         fps: int,
         negative_prompt: str | None = None,
-        image: torch.Tensor | None = None,
+        frame: torch.Tensor | None = None,
+        references: dict | None = None,
         sampling_params: dict | None = None,
         model_params: dict | None = None,
         lora: dict | None = None,
-        **extra_body,
+        **extra_params,
     ) -> VideoInput:
+        if frame is not None and references is not None:
+            raise ValueError("Provide only one of frame or references, not both.")
+
+        # === regular payload fields ===
         form = aiohttp.FormData()
         form.add_field("model", model)
         form.add_field("prompt", prompt)
@@ -279,23 +289,91 @@ class VLLMOmniClient:
         if sampling_params is not None:
             for k, v in sampling_params.items():
                 form.add_field(k, str(v))
-        if model_params is not None:
-            for k, v in model_params.items():
-                form.add_field(k, str(v))
         if lora is not None:
             form.add_field("lora", json.dumps(lora, ensure_ascii=False))
-        if extra_body:
-            form.add_field("extra_body", json.dumps(extra_body, ensure_ascii=False))
 
-        if image is not None:
+        # === multimodal inputs (first-last-frames, references, etc.) ===
+        input_reference_image: torch.Tensor | None = None
+        audio_reference: AudioInput | None = None
+        reference_videos: list[VideoInput] = []
+        video_task: str | None = None
+
+        if frame is not None:
+            input_reference_image = frame
+            video_task = "fl2va"
+        elif references is not None:
+            images = [references[k] for k in ("image_1", "image_2") if k in references and references[k] is not None]
+            audios = [references[k] for k in ("audio_1", "audio_2") if k in references and references[k] is not None]
+            videos = [references[k] for k in ("video_1", "video_2") if k in references and references[k] is not None]
+
+            if not images and not audios and not videos:
+                raise ValueError("references is empty; connect at least one image/audio/video.")
+
+            if videos:
+                if images or audios:
+                    raise ValueError(
+                        "Video references cannot be combined with image or audio references. "
+                        "Connect only video_1/video_2 for multi-video Ref2VA."
+                    )
+                reference_videos = videos
+                video_task = "ref2va"
+            elif len(images) == 1 and len(audios) == 1:
+                input_reference_image = images[0]
+                audio_reference = audios[0]
+                video_task = "ref2va"
+            else:
+                raise ValueError(
+                    "Invalid references combination. Supported modes: "
+                    "(1) one or more videos only, or (2) exactly one image and one audio."
+                )
+        else:
+            video_task = "t2va"
+
+        if input_reference_image is not None:
             image_filename = "image.png"  # Required for multipart form
             form.add_field(
                 "input_reference",
-                image_tensor_to_png_bytes(image, image_filename),
+                image_tensor_to_png_bytes(input_reference_image, image_filename),
                 filename=image_filename,
                 content_type="image/png",
             )
 
+        if audio_reference is not None:
+            form.add_field(
+                "audio_reference",
+                json.dumps({"audio_url": audio_to_base64(audio_reference)}, ensure_ascii=False),
+            )
+
+        for idx, video in enumerate(reference_videos, start=1):
+            video_filename = f"reference_{idx}.mp4"
+            form.add_field(
+                "input_references",
+                video_to_bytes(video, video_filename),
+                filename=video_filename,
+                content_type="video/mp4",
+            )
+
+        # === model specific params. Either use a specialized builder, or add flattened fields as-is ===
+        if model_params is not None:
+            model_params = dict(model_params)
+            model_params.pop("type", None)
+
+        spec, _ = lookup_model_spec(model)
+        params_builder = spec.get("params_builder") if spec else None
+        if params_builder is not None:
+            form_fields = params_builder(
+                model_params or {},
+                extra_params={**extra_params, "task": video_task},
+            )
+            for k, v in form_fields.items():
+                form.add_field(k, v if isinstance(v, str) else str(v))
+        else:
+            if model_params is not None:
+                for k, v in model_params.items():
+                    form.add_field(k, str(v))
+            if extra_params:
+                form.add_field("extra_params", json.dumps(extra_params, ensure_ascii=False))
+
         async with aiohttp.ClientSession(timeout=self.timeout) as session:
             # Start the video generation job
             url = f"{self.base_url}/videos"

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/models.py

@@ -1,7 +1,12 @@
+import json
 import re
+from typing import Any
 
+from .logger import get_logger
 from .types import Modality, ModelMode, Spec
 
+logger = get_logger(__name__)
+
 
 def _bagel_payload_preprocessor(payload: dict) -> dict:
     try:
@@ -29,6 +34,30 @@ def _qwen25_payload_preprocessor(payload: dict) -> dict:
     return payload
 
 
+def _minimaxh3_params_builder(
+    model_params: dict[str, Any],
+    *,
+    extra_params: dict[str, Any],
+) -> dict[str, Any]:
+    """Build multipart form fields for MiniMax-H3 from model_params + routed task."""
+    params = dict(model_params)
+    params.pop("type", None)
+    form_fields: dict[str, Any] = {}
+    merged_extra_params: dict[str, Any] = dict(extra_params or {})
+
+    if "flow_shift" in params:
+        form_fields["flow_shift"] = params.pop("flow_shift")
+    for key in ("audio_flow_shift",):
+        if key in params:
+            merged_extra_params[key] = params.pop(key)
+    if params:
+        logger.warning("Unused MiniMax-H3 model params ignored: %s", sorted(params))
+
+    if merged_extra_params:
+        form_fields["extra_params"] = json.dumps(merged_extra_params, ensure_ascii=False)
+    return form_fields
+
+
 _MODEL_PIPELINE_SPECS: dict[str, Spec] = {
     r"BAGEL-7B-MoT": {
         "stages": [
@@ -71,6 +100,21 @@ _MODEL_PIPELINE_SPECS: dict[str, Spec] = {
             }
         ],
     },
+    r"MiniMax-H3(/FL2VA|/Ref2VA)?": {
+        "stages": ["diffusion"],
+        "modes": [
+            {
+                "mode": ModelMode.VIDEO_GENERATION,
+                "input_modalities": [
+                    Modality.TEXT,
+                    Modality.IMAGE,
+                    Modality.VIDEO,
+                    Modality.AUDIO,
+                ],
+            }
+        ],
+        "params_builder": _minimaxh3_params_builder,
+    },
 }
 # Convert dict keys to regex patterns
 MODEL_PIPELINE_SPECS: dict[re.Pattern, Spec] = {}
@@ -80,12 +124,10 @@ del _MODEL_PIPELINE_SPECS
 
 
 def lookup_model_spec(model: str) -> tuple[Spec | None, str | None]:
-    try:
+    normalized = model.rstrip("/").lstrip("/")
-        last_component = model.rstrip("/").rsplit("/", 1)[-1]
+    lookup_key = normalized.split("/", 1)[-1]
-    except IndexError:
-        last_component = model
     for pattern, spec in MODEL_PIPELINE_SPECS.items():
-        if pattern.search(last_component):
+        if pattern.search(lookup_key):
             return spec, pattern.pattern
     return None, None
 
@@ -101,6 +143,9 @@ if __name__ == "__main__":
         "Qwen/Qwen3-Omni-30B-A3B-Instruct",
         "Custom/Path/UnknownModel-Instruct",
         "Not/Matching/Anything",
+        "/path/to/MiniMax-H3/FL2VA",
+        "/path/to/MiniMax-H3/Ref2VA",
+        "MiniMaxAI/MiniMax-H3",
     ]
 
     test_payload = {"messages": [{"role": "user", "content": "prompt"}]}
@@ -112,7 +157,13 @@ if __name__ == "__main__":
             if preprocessor := spec.get("payload_preprocessor"):
                 result = preprocessor(test_payload)
                 print(f"✓ {path:<40} → {result}")
+            elif params_builder := spec.get("params_builder"):
+                result = params_builder(
+                    {"audio_flow_shift": 3.0, "flow_shift": 12.0, "type": "minimax_h3"},
+                    extra_params={"task": "t2va"},
+                )
+                print(f"✓ {path:<40} → {result}")
             else:
-                print(f"✓ {path:<40} → No preprocessor")
+                print(f"✓ {path:<40} → No preprocessor/params_builder")
         else:
             print(f"✗ {path:<40} → No match")

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/types.py

@@ -23,6 +23,14 @@ class WanModelSpecificParams(dict):
     pass
 
 
+class MiniMaxH3ModelSpecificParams(dict):
+    pass
+
+
+class VideoReferences(dict):
+    pass
+
+
 class ModelMode(Enum):
     IMAGE_GENERATION = auto()
     VIDEO_GENERATION = auto()
@@ -43,9 +51,11 @@ class ModelModeSpec(TypedDict):
 
 
 PayloadPreprocessor: TypeAlias = Callable[[dict[str, Any]], dict[str, Any]]
+ParamsBuilder: TypeAlias = Callable[..., dict[str, Any]]
 
 
 class Spec(TypedDict):
     stages: list[Literal["diffusion", "autoregression"]]
     modes: list[ModelModeSpec]
     payload_preprocessor: NotRequired[PayloadPreprocessor]
+    params_builder: NotRequired[ParamsBuilder]

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni Video Generation.json

@@ -1,513 +1 @@
-{
+{"id":"3aea1d08-7de5-4997-ba71-b2f66ad72a21","revision":0,"last_node_id":24,"last_link_id":35,"nodes":[{"id":13,"type":"SaveVideo","pos":[1315.729153459605,-137.92038948394355],"size":[374,313.7692307692308],"flags":{},"order":9,"mode":0,"inputs":[{"localized_name":"video","name":"video","type":"VIDEO","link":32},{"localized_name":"filename_prefix","name":"filename_prefix","type":"STRING","widget":{"name":"filename_prefix"},"link":null},{"localized_name":"format","name":"format","type":"COMBO","widget":{"name":"format"},"link":null},{"localized_name":"codec","name":"codec","type":"COMFY_DYNAMICCOMBO_V3","widget":{"name":"codec"},"link":null}],"outputs":[{"localized_name":"video","name":"video","type":"VIDEO","links":null}],"properties":{},"widgets_values":["video/ComfyUI","auto","auto"]},{"id":10,"type":"MarkdownNote","pos":[183.74636797766794,-571.2529158046766],"size":[240.3326478922474,136.3505820791881],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[],"title":"Note: Task and Input","properties":{},"widgets_values":["vLLM-Omni nodes are categorized based on the output modality. The \"Generate Video\" node supports both *text-to-video*, *frame-to-video*, and *reference-to-video* generation. The node will create corresponding payload depending on whether an input image is present or not."],"color":"#432","bgcolor":"#000"},{"id":4,"type":"LoadImage","pos":[452.07189605801125,-569.7242536425791],"size":[270,314],"flags":{},"order":1,"mode":0,"inputs":[{"localized_name":"image","name":"image","type":"COMBO","widget":{"name":"image"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"IMAGEUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","links":[29]},{"localized_name":"MASK","name":"MASK","type":"MASK","links":null}],"properties":{"Node name for S&R":"LoadImage"},"widgets_values":["rap.png","image"]},{"id":23,"type":"LoadImage","pos":[9.206110246057657,-7.6955291509170785],"size":[270,314],"flags":{},"order":2,"mode":0,"inputs":[{"localized_name":"image","name":"image","type":"COMBO","widget":{"name":"image"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"IMAGEUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","links":[34]},{"localized_name":"MASK","name":"MASK","type":"MASK","links":null}],"properties":{"Node name for S&R":"LoadImage"},"widgets_values":["white-cat.jpg","image"]},{"id":22,"type":"MarkdownNote","pos":[-9.783113411416666,-148.8790300038605],"size":[297.3012007349086,88],"flags":{},"order":3,"mode":0,"inputs":[],"outputs":[],"title":"Note: Reference to video (r2v)","properties":{},"widgets_values":["For r2v tasks, connect this Video Reference block to the main Generate Video block. Then disconnect the \"frame\" input above."],"color":"#432","bgcolor":"#000"},{"id":24,"type":"LoadAudio","pos":[317.9929742385805,-141.44821500065095],"size":[282.798828125,136],"flags":{},"order":4,"mode":0,"inputs":[{"localized_name":"audio","name":"audio","type":"COMBO","widget":{"name":"audio"},"link":null},{"localized_name":"audioUI","name":"audioUI","type":"AUDIO_UI","widget":{"name":"audioUI"},"link":null},{"localized_name":"choose file to upload","name":"upload","type":"AUDIOUPLOAD","widget":{"name":"upload"},"link":null}],"outputs":[{"localized_name":"AUDIO","name":"AUDIO","type":"AUDIO","links":[35]}],"properties":{"Node name for S&R":"LoadAudio"},"widgets_values":["nothing-beats-a-jet2-holiday.mp3",null,""]},{"id":21,"type":"VLLMOmniVideoReferences","pos":[376.6129172897595,105.41557277556292],"size":[156.9380859375,126],"flags":{},"order":7,"mode":0,"inputs":[{"localized_name":"image_1","name":"image_1","shape":7,"type":"IMAGE","link":34},{"localized_name":"image_2","name":"image_2","shape":7,"type":"IMAGE","link":null},{"localized_name":"audio_1","name":"audio_1","shape":7,"type":"AUDIO","link":35},{"localized_name":"audio_2","name":"audio_2","shape":7,"type":"AUDIO","link":null},{"localized_name":"video_1","name":"video_1","shape":7,"type":"VIDEO","link":null},{"localized_name":"video_2","name":"video_2","shape":7,"type":"VIDEO","link":null}],"outputs":[{"localized_name":"references","name":"references","type":"VIDEO_REFERENCES","links":null}],"properties":{"Node name for S&R":"VLLMOmniVideoReferences"},"widgets_values":[]},{"id":8,"type":"VLLMOmniDiffusionSampling","pos":[83.24620208113915,422.08543477542605],"size":[284.205078125,226],"flags":{},"order":5,"mode":0,"inputs":[{"localized_name":"n","name":"n","type":"INT","widget":{"name":"n"},"link":null},{"localized_name":"num_inference_steps","name":"num_inference_steps","type":"INT","widget":{"name":"num_inference_steps"},"link":null},{"localized_name":"guidance_scale","name":"guidance_scale","type":"FLOAT","widget":{"name":"guidance_scale"},"link":null},{"localized_name":"true_cfg_scale","name":"true_cfg_scale","type":"FLOAT","widget":{"name":"true_cfg_scLine truncated
-    "id": "3aea1d08-7de5-4997-ba71-b2f66ad72a21",
-    "revision": 0,
-    "last_node_id": 14,
-    "last_link_id": 28,
-    "nodes": [
-        {
-            "id": 10,
-            "type": "MarkdownNote",
-            "pos": [
-                227.99306819575278,
-                -231.24143306069843
-            ],
-            "size": [
-                240.3326478922474,
-                136.3505820791881
-            ],
-            "flags": {},
-            "order": 0,
-            "mode": 0,
-            "inputs": [],
-            "outputs": [],
-            "title": "Note: Task and Input",
-            "properties": {},
-            "widgets_values": [
-                "vLLM-Omni nodes are categorized based on the output modality. The \"Generate Video\" node supports both text-to-video generation and image-to-video generation. The node will create corresponding payload depending on whether an input image is present or not."
-            ],
-            "color": "#432",
-            "bgcolor": "#000"
-        },
-        {
-            "id": 4,
-            "type": "LoadImage",
-            "pos": [
-                496.31859627609606,
-                -229.71277089860084
-            ],
-            "size": [
-                270,
-                314
-            ],
-            "flags": {},
-            "order": 1,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "image",
-                    "name": "image",
-                    "type": "COMBO",
-                    "widget": {
-                        "name": "image"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "choose file to upload",
-                    "name": "upload",
-                    "type": "IMAGEUPLOAD",
-                    "widget": {
-                        "name": "upload"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [
-                {
-                    "localized_name": "IMAGE",
-                    "name": "IMAGE",
-                    "type": "IMAGE",
-                    "links": [
-                        25
-                    ]
-                },
-                {
-                    "localized_name": "MASK",
-                    "name": "MASK",
-                    "type": "MASK",
-                    "links": null
-                }
-            ],
-            "properties": {
-                "Node name for S&R": "LoadImage"
-            },
-            "widgets_values": [
-                "cute-cat.jpg",
-                "image"
-            ]
-        },
-        {
-            "id": 8,
-            "type": "VLLMOmniDiffusionSampling",
-            "pos": [
-                465.5140218220927,
-                148.80072646828987
-            ],
-            "size": [
-                284.205078125,
-                226
-            ],
-            "flags": {},
-            "order": 2,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "n",
-                    "name": "n",
-                    "type": "INT",
-                    "widget": {
-                        "name": "n"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "num_inference_steps",
-                    "name": "num_inference_steps",
-                    "type": "INT",
-                    "widget": {
-                        "name": "num_inference_steps"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "guidance_scale",
-                    "name": "guidance_scale",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "guidance_scale"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "true_cfg_scale",
-                    "name": "true_cfg_scale",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "true_cfg_scale"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "vae_use_slicing",
-                    "name": "vae_use_slicing",
-                    "type": "BOOLEAN",
-                    "widget": {
-                        "name": "vae_use_slicing"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "vae_use_tiling",
-                    "name": "vae_use_tiling",
-                    "type": "BOOLEAN",
-                    "widget": {
-                        "name": "vae_use_tiling"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "seed",
-                    "name": "seed",
-                    "type": "INT",
-                    "widget": {
-                        "name": "seed"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [
-                {
-                    "localized_name": "diffusion sampling params",
-                    "name": "diffusion sampling params",
-                    "type": "SAMPLING_PARAMS",
-                    "links": [
-                        27
-                    ]
-                }
-            ],
-            "properties": {
-                "Node name for S&R": "VLLMOmniDiffusionSampling"
-            },
-            "widgets_values": [
-                1,
-                50,
-                1,
-                1,
-                false,
-                false,
-                1992,
-                "randomize"
-            ]
-        },
-        {
-            "id": 14,
-            "type": "VLLMOmniWanParams",
-            "pos": [
-                518.6005521845747,
-                428.4455663719664
-            ],
-            "size": [
-                270,
-                106
-            ],
-            "flags": {},
-            "order": 3,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "guidance_scale_2",
-                    "name": "guidance_scale_2",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "guidance_scale_2"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "boundary_ratio",
-                    "name": "boundary_ratio",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "boundary_ratio"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "flow_shift",
-                    "name": "flow_shift",
-                    "type": "FLOAT",
-                    "widget": {
-                        "name": "flow_shift"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [
-                {
-                    "localized_name": "Wan video params",
-                    "name": "Wan video params",
-                    "type": "VIDEO_PARAMS",
-                    "links": [
-                        28
-                    ]
-                }
-            ],
-            "properties": {
-                "Node name for S&R": "VLLMOmniWanParams"
-            },
-            "widgets_values": [
-                4,
-                0.875,
-                5
-            ]
-        },
-        {
-            "id": 12,
-            "type": "VLLMOmniGenerateVideo",
-            "pos": [
-                827.2566336087867,
-                -73.77449831827545
-            ],
-            "size": [
-                400,
-                346
-            ],
-            "flags": {},
-            "order": 4,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "image",
-                    "name": "image",
-                    "shape": 7,
-                    "type": "IMAGE",
-                    "link": 25
-                },
-                {
-                    "localized_name": "sampling_params",
-                    "name": "sampling_params",
-                    "shape": 7,
-                    "type": "SAMPLING_PARAMS",
-                    "link": 27
-                },
-                {
-                    "localized_name": "lora",
-                    "name": "lora",
-                    "shape": 7,
-                    "type": "REMOTE_LORA",
-                    "link": null
-                },
-                {
-                    "localized_name": "model_params",
-                    "name": "model_params",
-                    "shape": 7,
-                    "type": "VIDEO_PARAMS",
-                    "link": 28
-                },
-                {
-                    "localized_name": "url",
-                    "name": "url",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "url"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "model",
-                    "name": "model",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "model"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "prompt",
-                    "name": "prompt",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "prompt"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "negative_prompt",
-                    "name": "negative_prompt",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "negative_prompt"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "width",
-                    "name": "width",
-                    "type": "INT",
-                    "widget": {
-                        "name": "width"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "height",
-                    "name": "height",
-                    "type": "INT",
-                    "widget": {
-                        "name": "height"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "fps",
-                    "name": "fps",
-                    "type": "INT",
-                    "widget": {
-                        "name": "fps"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "num_frames",
-                    "name": "num_frames",
-                    "type": "INT",
-                    "widget": {
-                        "name": "num_frames"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [
-                {
-                    "localized_name": "video",
-                    "name": "video",
-                    "type": "VIDEO",
-                    "links": [
-                        26
-                    ]
-                }
-            ],
-            "properties": {
-                "Node name for S&R": "VLLMOmniGenerateVideo"
-            },
-            "widgets_values": [
-                "http://localhost:8000/v1",
-                "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
-                "Make the cat in the attached video yawning. Eye closed and mouth wide open. Keep the original realistic style.",
-                "Cartoonish",
-                900,
-                1200,
-                12,
-                41
-            ]
-        },
-        {
-            "id": 13,
-            "type": "SaveVideo",
-            "pos": [
-                1257.5441369501657,
-                -44.12956394419863
-            ],
-            "size": [
-                374,
-                372.34896826553796
-            ],
-            "flags": {},
-            "order": 5,
-            "mode": 0,
-            "inputs": [
-                {
-                    "localized_name": "video",
-                    "name": "video",
-                    "type": "VIDEO",
-                    "link": 26
-                },
-                {
-                    "localized_name": "filename_prefix",
-                    "name": "filename_prefix",
-                    "type": "STRING",
-                    "widget": {
-                        "name": "filename_prefix"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "format",
-                    "name": "format",
-                    "type": "COMBO",
-                    "widget": {
-                        "name": "format"
-                    },
-                    "link": null
-                },
-                {
-                    "localized_name": "codec",
-                    "name": "codec",
-                    "type": "COMBO",
-                    "widget": {
-                        "name": "codec"
-                    },
-                    "link": null
-                }
-            ],
-            "outputs": [],
-            "properties": {},
-            "widgets_values": [
-                "video/ComfyUI",
-                "auto",
-                "auto"
-            ]
-        }
-    ],
-    "links": [
-        [
-            25,
-            4,
-            0,
-            12,
-            0,
-            "IMAGE"
-        ],
-        [
-            26,
-            12,
-            0,
-            13,
-            0,
-            "VIDEO"
-        ],
-        [
-            27,
-            8,
-            0,
-            12,
-            1,
-            "SAMPLING_PARAMS"
-        ],
-        [
-            28,
-            14,
-            0,
-            12,
-            3,
-            "VIDEO_PARAMS"
-        ]
-    ],
-    "groups": [
-        {
-            "id": 1,
-            "title": "Input",
-            "bounding": [
-                213.1706010087147,
-                -313.20095750667554,
-                560.8246471437027,
-                407.4246532472182
-            ],
-            "color": "#3f789e",
-            "font_size": 24,
-            "flags": {}
-        }
-    ],
-    "config": {},
-    "extra": {
-        "workflowRendererVersion": "LG",
-        "ds": {
-            "scale": 1.1469075819486894,
-            "offset": [
-                101.7632950847089,
-                492.29121889347806
-            ]
-        }
-    },
-    "version": 0.4
-}

docs/.nav.yml

@@ -68,13 +68,12 @@ nav:
         - Sequence Parallel: user_guide/diffusion/parallelism/sequence_parallel.md
         - Tensor Parallel: user_guide/diffusion/parallelism/tensor_parallel.md
         - VAE Parallelism: user_guide/diffusion/parallelism/vae_parallelism.md
-      - CPU Offloading: user_guide/diffusion/cpu_offload_diffusion.md
+      - CPU Offloading: user_guide/diffusion/cpu_offload.md
       - LoRA: user_guide/diffusion/lora.md
       - Custom Pipeline: features/custom_pipeline.md
-      - Request-Level Batching: user_guide/diffusion/request_batching.md
+      - Execution Modes: user_guide/diffusion/execution_modes.md
-      - Step Execution: user_guide/diffusion/step_execution.md
     - Quantization:
-      - Overview: user_guide/quantization/overview.md
+      - Quantization: user_guide/quantization/overview.md
       - Online Quantization: user_guide/quantization/online.md
       - Quantized KV Cache: user_guide/quantization/quantized_kvcache.md
       - FP8 W8A8: user_guide/quantization/fp8.md
@@ -102,31 +101,49 @@ nav:
   - CI: contributing/ci
   - Design Documents:
     - design/index.md
-    - design/architecture_overview.md
+    - Architecture Documents:
+      - Architecture Overview: design/architecture_overview.md
     - Feature Design:
-      - design/feature/disaggregated_inference.md
+      - Runtime and Stage Execution:
-      - design/feature/ray_based_execution.md
+        - Disaggregated Inference: design/feature/disaggregated_inference.md
-      - design/feature/omni_connectors/
+        - Async Chunk: design/feature/async_chunk.md
-      - design/feature/prefix_caching.md
+        - Async Diffusion Output: design/feature/async_diffusion_output.md
-      - design/feature/cfg_parallel.md
+        - Async Omni Output Materialization: design/feature/omni_async_output_materialization.md
-      - design/feature/expert_parallel.md
+        - Automatic Prefix Caching: design/feature/prefix_caching.md
-      - design/feature/sequence_parallel.md
+      - Communication:
-      - design/feature/tensor_parallel.md
+        - OmniConnector Implementations:
-      - design/feature/vae_parallel.md
+          - Mooncake Store Connector: design/feature/omni_connectors/mooncake_store_connector.md
-      - design/feature/hsdp.md
+          - Mooncake Transfer Engine Connector: design/feature/omni_connectors/mooncake_transfer_engine_connector.md
-      - design/feature/cache_dit.md
+          - Mori Transfer Engine Connector: design/feature/omni_connectors/mori_transfer_engine_connector.md
-      - design/feature/teacache.md
+          - Shared Memory Connector: design/feature/omni_connectors/shared_memory_connector.md
-      - design/feature/skip_softmax.md
+          - Yuanrong Store Connector: design/feature/omni_connectors/yuanrong_connector.md
-      - design/feature/async_chunk.md
+          - Yuanrong Transfer Engine Connector: design/feature/omni_connectors/yuanrong_transfer_engine_connector.md
-      - design/feature/vae_parallel.md
+      - Diffusion Acceleration:
-      - design/feature/diffusion_step_execution.md
+        - Parallelism:
-      - design/feature/diffusion_request_level_batching.md
+          - CFG-Parallel: design/feature/cfg_parallel.md
-      - design/feature/diffusion_continuous_batching.md
+          - Expert Parallel: design/feature/expert_parallel.md
+          - Hybrid Sharded Data Parallel (HSDP): design/feature/hsdp.md
+          - Pipeline Parallel: design/feature/pipeline_parallel.md
+          - Sequence Parallel: design/feature/sequence_parallel.md
+          - Tensor Parallel: design/feature/tensor_parallel.md
+          - VAE Patch Parallelism: design/feature/vae_parallel.md
+        - Attention Backends:
+          - Skip-Softmax: design/feature/skip_softmax.md
+        - Quantization: design/feature/quantization.md
+        - Cache-DiT: design/feature/cache_dit.md
+        - TeaCache: design/feature/teacache.md
+        - Diffusion Continuous Batching: design/feature/diffusion_continuous_batching.md
+        - Distributed Layerwise Offload: design/feature/distributed_layerwise_offload.md
+    - Infrastructure and Performance:
+      - Prometheus Metrics: design/metrics.md
+      - Speech Generation Performance Optimizations: design/qwen3_omni_tts_performance_optimization.md
     - Module Design:
-      - design/module/ar_module.md
+      - Runtime Modules:
-      - design/module/dit_module.md
+        - AR Module: design/module/ar_module.md
-      - design/module/entrypoint_module.md
+        - DIT Module: design/module/dit_module.md
-      - design/module/async_omni_architecture.md
+      - Orchestration:
+        - Entrypoint Module: design/module/entrypoint_module.md
+        - AsyncOmni Architecture: design/module/async_omni_architecture.md
   - Docs Guide: contributing/DOCS_GUIDE.md
 - API Reference:
   - api/README.md

docs/README.md

@@ -52,12 +52,13 @@ vLLM-Omni is flexible and easy to use with:
 - Tensor, pipeline, data and expert parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
+- Full-duplex realtime serving with streaming audio input and output (experimental)
 
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:
 
-- Omni-modality models (e.g. Qwen3-Omni, Cosmos3, HunyuanImage, BAGEL)
+- Omni-modality models (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
 - TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
-- Diffusion models — image, video, and audio generation (e.g. Qwen-Image, Wan2.2, FLUX)
+- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
 - Robot-policy and action models (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
 
 For more information, checkout the following:

docs/community/governance.md

@@ -40,6 +40,7 @@ Committers have write access and merge rights. They typically have deep expertis
 - [@Isotr0py](https://github.com/Isotr0py): Diffusion and Quantization
 - [@linyueqian](https://github.com/linyueqian): TTS and Omni Support
 - [@lishunyang12](https://github.com/lishunyang12): Quantization and Configuration
+- [@NickCao](https://github.com/NickCao): CI, Tests, Entrypoint, and TTS Model Support
 - [@princepride](https://github.com/princepride): Diffusion and Omni Support
 - [@RuixiangMa](https://github.com/RuixiangMa): Diffusion models, parallel, cache, and docs
 - [@SamitHuang](https://github.com/SamitHuang): RL, Diffusion, and cache

docs/configuration/README.md

@@ -2,7 +2,7 @@
 
 This section lists the most common options for running vLLM-Omni.
 
-For options within a vLLM Engine. Please refer to [vLLM Configuration](https://docs.vllm.ai/en/v0.16.0/configuration/index.html)
+For options within a vLLM Engine, please refer to the [vLLM 0.26 configuration guide](https://docs.vllm.ai/en/v0.26.0/configuration/index.html).
 
 Currently, the main options are maintained by stage configs for each model.
 

docs/configuration/stage_configs.md

@@ -387,8 +387,8 @@ that support request-level batching with `step_execution` disabled.
 
 Use this together with `max_num_seqs > 1` for bursty serving traffic. `0`
 disables admission waiting and preserves the lowest first-request latency.
-For diffusion request-level batching tuning, see
+For diffusion execution and batching tuning, see
-[Request-Level Batching](../user_guide/diffusion/request_batching.md).
+[Diffusion Execution Modes](../user_guide/diffusion/execution_modes.md).
 
 Default: `0.0`
 

docs/contributing/ci/test_examples/l4_functionality_tests.inc.md

@@ -1,6 +1,6 @@
 **Scope**
 
-For diffusion models, the L4 functionality test covers all or common *diffusion features* that are supported by this model, including several [parallelism acceleration methods](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/parallelism_acceleration/), [CPU offloading](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/cpu_offload_diffusion/), [TeaCache](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/teacache/) and [Cache-DiT](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/cache_dit_acceleration/) cache backends, [quantization methods](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/quantization/overview/).
+For diffusion models, the L4 functionality test covers all or common *diffusion features* that are supported by this model, including several [parallelism acceleration methods](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/parallelism_acceleration/), [CPU offloading](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/cpu_offload/), [TeaCache](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/teacache/) and [Cache-DiT](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion/cache_dit_acceleration/) cache backends, [quantization methods](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/quantization/overview/).
 
 **Test Case Design**
 

docs/contributing/ci/test_examples/l4_performance_tests.inc.md

@@ -44,9 +44,11 @@ Pass **`--test-config-file`** to run one JSON file as-is, or omit it for the bul
             "ignore_eos": true,
             "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
             "baseline": {
-                "mean_ttft_ms": [500, 800],
+                "H100": {
-                "mean_audio_ttfp_ms": [2000, 3500],
+                    "mean_ttft_ms": [500, 800],
-                "mean_audio_rtf": [0.25, 0.35]
+                    "mean_audio_ttfp_ms": [2000, 3500],
+                    "mean_audio_rtf": [0.25, 0.35]
+                }
             }
         }
     ]
@@ -142,10 +144,6 @@ pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py \
   --test-config-file tests/dfx/perf/tests/test_bagel_vllm_omni.json
 pytest -s -v tests/dfx/perf/scripts/run_benchmark.py \
   --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json
-
-# Optional baseline assertion (default off)
-pytest -sv tests/dfx/perf/scripts/run_diffusion_benchmark.py --assert-baseline \
-  --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
 ```
 
 See also [Markers for Tests](#markers-for-tests) for registered hardware markers (`H100`, `L4`, `cuda`, `distributed_cuda`, …).
@@ -202,4 +200,4 @@ You can add any benchmark running parameters you need here. For all optional par
 | num_prompts     | int / array | Yes      | 10,[10, 20, 30] | Number of requests. Supports single values or arrays. If a single value is used, it will be automatically expanded to match the number of qps or max_concurrency, e.g., [10,10,10]. If an array is used, its length must match the number of qps or max_concurrency. |
 | request_rate    | float / array | No  | 0.5, [0.5, 1, inf] | Queries per second. Supports single values or arrays. If a single value is used, it will be automatically expanded to match the number of num_prompts, e.g., [1,1,1]. If an array is used, its length must match the number of num_prompts.                          |
 | max_concurrency | int / array | No       | 1, [1, 2, 3]    | Maximum concurrent in-flight requests. Same array / expansion rules as `request_rate` (mutually exclusive with QPS mode).                                                                                                                                                                                                             |
-| baseline        | object      | No       | see above       | Optional per-metric thresholds; keys must match benchmark output fields. Scalar, list (per sweep step), or object (keyed by concurrency or QPS string).  
+| baseline        | object      | No       | see below       | Optional thresholds. Prefer hardware-nested form: `{"H100": {"throughput_qps": …}, "A3": {…}}` (selected via runtime hardware label). Legacy flat `{metric: threshold}` still works. Per metric: scalar, list (sweep step), or object keyed by concurrency / QPS.

docs/contributing/model/adding_diffusion_model.md

@@ -755,7 +755,8 @@ omni = Omni(model="your-model", ulysses_degree=2, ring_degree=2)
 
 ### Step Execution
 
-See detailed design guide: [How to add step execution support](../../design/feature/diffusion_step_execution.md)
+See the detailed
+[Diffusion Continuous Batching design guide](../../design/feature/diffusion_continuous_batching.md).
 
 Use this only when your pipeline can be split into stable request-scoped and
 step-scoped phases. The reference implementation is
@@ -769,9 +770,8 @@ step-scoped phases. The reference implementation is
 Do not enable `step_execution=True` until those four methods are implemented
 and validated against the request-level path.
 
-If you want the pipeline to work with the experimental batched step-wise path
+The same design guide covers the experimental batched step-wise path used when
-(`max_num_seqs > 1`), also see:
+`max_num_seqs > 1`.
-[Continuous Batching for Step-Wise Diffusion](../../design/feature/diffusion_continuous_batching.md).
 
 If you expose this in example scripts or recipes, keep it opt-in. Surface
 runtime features like `step_execution` as optional flags instead of silently
@@ -824,7 +824,7 @@ omni = Omni(model="your-model",
 
 ### CPU Offload
 
-See detailed guide: [CPU Offloading for Diffusion Models](../../user_guide/diffusion/cpu_offload_diffusion.md)
+See detailed guide: [CPU Offloading for Diffusion Models](../../user_guide/diffusion/cpu_offload.md)
 
 vLLM-Omni provides two offloading strategies to reduce GPU memory usage:
 

docs/design/feature/diffusion_continuous_batching.md

@@ -1,165 +1,232 @@
-# Continuous Batching for Step-Wise Diffusion
+# Diffusion Continuous Batching
 
-!!! warning "Experimental Feature"
+This document describes the unified diffusion execution architecture, including
-    This feature is experimental. It currently applies only to native
+request execution, request-level batching, step execution, continuous batching,
-    diffusion pipelines running with `step_execution=True`.
+and output delivery. For user-facing configuration and CLI examples, see
-
+[Diffusion Execution Modes](../../user_guide/diffusion/execution_modes.md).
-This document describes the batching extension built on top of
-[Diffusion Step Execution](diffusion_step_execution.md). The base
-step-execution contract is unchanged. The batching work is mainly in the
-scheduler and runner layers.
-
-## Why It Helps
-
-Step-wise execution breaks a long denoise loop into scheduler-visible units.
-That gives the runtime a place to admit other compatible requests between
-steps instead of waiting for an entire request to finish.
-
-This matters most in low-MFU or bursty serving scenarios:
-
-- one request's denoise step may not fully saturate the GPU
-- several compatible requests can share the same denoise forward pass
-- throughput and device utilization can improve without changing request-local
-  scheduler state
-
-This is **not** a guaranteed single-request latency win. The main benefit is
-usually higher utilization and better throughput when the workload contains
-multiple in-flight compatible requests.
 
 ## Overview
 
-With continuous batching enabled:
+`DiffusionEngine` selects an execution mode from `step_execution` and uses
+`max_num_seqs` as that mode's scheduler capacity:
 
-- the scheduler may keep multiple compatible requests active at the same time
+| Configuration | Engine mode | Scheduler | Execution |
-- the runner packs request-local step state into one `InputBatch`
+|---|---|---|---|
-- `denoise_step()` runs on that batch
+| `step_execution=False`, `max_num_seqs=1` | `REQUEST_BATCH` | `RequestScheduler` | One complete request-level `forward()` |
-- `step_scheduler()` and `post_decode()` still run per request
+| `step_execution=False`, `max_num_seqs>1` | `REQUEST_BATCH` | `RequestScheduler` | One fused `forward()` over compatible requests |
+| `step_execution=True`, `max_num_seqs=1` | `STEP_BATCH` | `StepScheduler` | One request advanced one denoise step per scheduler tick |
+| `step_execution=True`, `max_num_seqs>1` | `STEP_BATCH` | `StepScheduler` | Compatible requests advanced together in step waves |
 
-The current implementation is conservative:
+Serial request execution is handled inside `REQUEST_BATCH`; it is not a
+separate engine mode. Similarly, continuous batching is the multi-request
+configuration of `STEP_BATCH`, not an independent execution mode.
 
-- only compatible requests are batched together
+The engine performs the following initialization:
-- per-request progress and completion remain independent
 
-Here, "continuous batching" means the step-wise path enabled by
+1. Resolve model-specific pre- and post-processing hooks.
-`step_execution=True`. Request-mode `DiffusionRequestBatch` is static
+2. Select `REQUEST_BATCH` or `STEP_BATCH`.
-request-level batching for one full pipeline `forward()` call; it does not
+3. Construct the configured `DiffusionExecutor`.
-admit or remove requests between denoise steps.
+4. Construct `RequestScheduler` or `StepScheduler`, unless a scheduler was
+   explicitly injected.
+5. Bind `execute_batch` or `execute_step`.
 
-## Enablement
+`DiffusionEngine.make_engine()` owns startup warmup after engine construction.
+This keeps construction, backend selection, and warmup failure cleanup in one
+lifecycle.
 
-Use `--step-execution` as the feature gate, then increase `--max-num-seqs`
+## Unified Output Stream
-above `1` if you want batching:
 
-```bash
+All asynchronous diffusion requests use the same per-request output stream,
-vllm serve Qwen/Qwen-Image --omni \
+regardless of whether the caller exposes streaming results to the user.
-  --port 8091 \
+
-  --step-execution \
+```python
-  --max-num-seqs 8
+async for output in engine.step_streaming(request):
+    ...
 ```
 
-`--max-num-seqs 1` keeps the step-wise path without enabling batching.
+When a request is admitted, the engine creates an
+`asyncio.Queue[DiffusionOutput]` keyed by its request ID. The busy loop sends
+both intermediate chunks and final results through that queue:
 
-For a reproducible replay flow using the bundled serving benchmark, see the
+```text
-Qwen-Image replay commands in
+request
-[`benchmarks/diffusion/README.md`](gh-file:benchmarks/diffusion/README.md)
+  -> scheduler.add_request()
-and
+  -> scheduler.schedule()
-[`benchmarks/diffusion/performance_dashboard/qwen_image_serving_performance.md`](gh-file:benchmarks/diffusion/performance_dashboard/qwen_image_serving_performance.md).
+  -> execute_batch() or execute_step()
+  -> scheduler.update_from_output()
+  -> per-request output queue
+  -> step_streaming()
+```
 
-## Scheduler
+Streaming callers forward every yielded output. Non-streaming callers consume
+the same generator to completion and return only its final output. This avoids
+maintaining separate future-based and queue-based result paths.
 
-The scheduler derives its batch capacity from `max_num_seqs` through
+`DiffusionEngine.step()` and
-`max_num_running_reqs`.
+`async_add_req_and_wait_for_response()` remain deprecated compatibility
+wrappers. New integrations should consume `step_streaming()` or
+`async_add_req_and_stream_response()`.
 
-Batch admission is gated by
+### Completion and Cancellation
-[`StepBatchSamplingParamsKey`](gh-file:vllm_omni/diffusion/sched/interface.py),
-which is built from shape-sensitive and CFG-sensitive sampling fields. This is
-the core correctness rule for batching: requests are only co-batched when they
-share the same denoise tensor contract.
 
-There are three important details:
+Output delivery and scheduler lifecycle are related but separate:
 
-- `num_inference_steps` is not part of the key, so requests with different
+- A terminal scheduler result always finalizes request state.
-  total step counts can still share a batch
+- A disconnected or cancelled consumer removes only its delivery queue.
-- requests also do not need to be at the same current denoise progress; active
+- The scheduler may still finish and clean up the request after its queue has
-  requests can continue batching even when their current step indices diverge
+  been removed.
-- admission is still FIFO, so an incompatible request at the head of the
+- Engine shutdown sends an error output to all remaining streams.
-  waiting queue blocks later compatible requests
 
-Today that compatibility rule is still shape-sensitive. `height`, `width`,
+This separation prevents cancelled consumers from leaving active request IDs in
-`num_frames`, and CFG-related fields remain part of the key, so different
+the scheduler.
-resolutions or incompatible guidance settings do **not** co-batch yet. The
-key also covers LoRA identity (`lora_int_id`, `lora_scale`), so requests
-targeting different adapters or scales run in separate batches and the
-worker can activate exactly one adapter per step.
 
-The scheduler batching unit is one logical `OmniDiffusionRequest`. In the
+## Request-Batch Execution
-step-wise path, runtime tensor batching is represented as `StepInputBatch`. For
-request-mode prompt semantics, see
-[Request-Level Batching](../../user_guide/diffusion/request_batching.md).
 
-## Runner
+Request-batch execution runs the complete diffusion pipeline for each scheduler
+wave. Each `OmniDiffusionRequest` remains an independent logical request with
+its own prompt, sampling parameters, seed, request ID, output, error, and abort
+state.
 
-The runner keeps persistent per-request execution state in
+With `max_num_seqs=1`, `execute_batch()` handles the serial request path. With a
-[`StepRequestState`](gh-file:vllm_omni/diffusion/worker/utils.py),
+larger value, a pipeline can opt into fused execution by declaring:
-while the scheduler owns a separate lightweight
-[`SchedulerRequestState`](gh-file:vllm_omni/diffusion/sched/interface.py)
-for queueing and lifecycle tracking.
 
-For each step, the runner builds an
+```python
-[`InputBatch`](gh-file:vllm_omni/diffusion/worker/input_batch.py) from the
+supports_request_batch = True
-active request states:
+```
 
-- prompt embeddings and masks are normalized and padded
+Its `forward()` method must accept `DiffusionRequestBatch` and return one
-- dynamic tensors such as `latents` and `timesteps` are gathered each step
+`DiffusionOutput` per request. The runner validates the result count and maps
-- buffers are reused when batch composition stays the same
+outputs back to their original request IDs with `BatchRunnerOutput`.
 
-The step-wise batched path is:
+Current in-tree request-batch implementations include the Flux, LTX-2, SD3, and
+Qwen-Image pipelines. Treat the pipeline capability flag in the source as
+authoritative because support continues to expand.
 
-1. Run `prepare_encode()` for newly admitted requests.
+### Request-Batch Data Flow
+
+- `DiffusionSchedulerOutput` carries scheduled request IDs and payloads.
+- `DiffusionRequestBatch` provides the pipeline-facing static batch.
+- `BatchRunnerOutput` carries per-request results back to the engine.
+
+Request-local setup, such as seed handling and output/error mapping, remains
+per request. Cache refresh, homogeneous LoRA activation, and
+`pipeline.forward(req_batch)` can be shared by the batch.
+
+### Compatibility and Admission
+
+`RequestBatchSamplingParamsKey` contains fields that affect the tensor contract,
+including shape, guidance, output count, and LoRA identity. Only compatible
+requests can share a fused forward call.
+
+Admission is FIFO and conservative. An incompatible request at the front of the
+waiting queue can prevent later compatible requests from joining the current
+wave.
+
+`request_batch_max_wait_ms` optionally delays the first schedule operation of a
+new wave so bursty arrivals can accumulate. It applies only when:
+
+- the pipeline supports request batching;
+- step execution is disabled;
+- no requests are currently running; and
+- the configured wait is greater than zero.
+
+The wait ends when the queue reaches capacity, stabilizes briefly, reaches its
+deadline, or the engine stops.
+
+## Step-Batch Execution
+
+Step execution exposes denoising progress to the scheduler. A supporting
+pipeline implements four stateful operations:
+
+| Operation | Responsibility |
+|---|---|
+| `prepare_encode(state)` | Validate input, encode prompts, initialize latents and timesteps, and create request-local scheduler state |
+| `denoise_step(input_batch, *, states=...)` | Run one denoise forward for the scheduler-provided request states |
+| `step_scheduler(state, noise_pred)` | Update latents and advance request progress |
+| `post_decode(state)` | Decode and postprocess a completed request or output boundary |
+
+Persistent request state lives in `StepRequestState`. Pipeline-specific fields
+that do not belong in the shared contract should be stored in `state.extra`.
+Queueing and lifecycle metadata remain in the scheduler's request state.
+
+Current native pipelines that explicitly enable step execution include
+Qwen-Image, HunyuanImage3, and Helios. Step execution alone does not imply
+continuous-batching support: Qwen-Image accepts batched step states.
+HunyuanImage3 accepts batched step states only when its resolved self-attention
+backend is `TORCH_SDPA`; otherwise it rejects groups larger than one request.
+Configure `DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA` or
+`diffusion_attention_config.default.backend=TORCH_SDPA` when
+`max_num_seqs>1`. See the
+[HunyuanImage-3.0 recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Tencent/HunyuanImage-3.0-Instruct.md)
+for its validated configuration. Helios supports only a single active step
+request and must use `max_num_seqs=1`.
+
+### Continuous Batching
+
+When `max_num_seqs>1`, `StepScheduler` can keep multiple compatible requests
+active. The runner gathers their state into `InputBatch`, performs one batched
+denoise forward, then applies scheduler updates per request:
+
+1. Receive transferred KV payloads, then run `prepare_encode()` for newly
+   admitted requests.
 2. Build or refresh `InputBatch`.
-3. Run one batched `denoise_step(input_batch)`.
+3. Run one batched `denoise_step(input_batch, states=states)`.
-4. Slice the batched `noise_pred` back per request.
+4. Slice noise predictions back to each request.
-5. Run per-request `step_scheduler()`.
+5. Run each request's `step_scheduler()`.
-6. Run `post_decode()` only for requests that finished denoising.
+6. Run `post_decode()` at a chunk boundary or request completion.
-7. Scatter updated latents back into persistent request state with
+7. Scatter updated latents back into persistent request state.
-   [`scatter_latents()`](gh-file:vllm_omni/diffusion/worker/input_batch.py).
 
-This keeps the shared work limited to the denoise forward pass while preserving
+`StepBatchSamplingParamsKey` protects the batched tensor contract. Requests can
-request-local scheduler state and outputs.
+have different total step counts and current step indices, but shape-sensitive,
+CFG-sensitive, and LoRA fields must be compatible. FIFO head-of-line blocking
+also applies here.
 
-## Engine
+Chunk-capable pipelines may emit intermediate results. Final-only step
+pipelines emit when the request finishes even if `streaming_output=True`.
 
-[`DiffusionEngine`](gh-file:vllm_omni/diffusion/diffusion_engine.py) provides
+## Model Author Guidelines
-the background loop and async add-request path needed for multiple requests to
-accumulate in the scheduler.
 
-When `step_execution=True`, the engine routes work through the step-wise
+When converting a request-level pipeline:
-executor path. The continuous batching behavior is defined by scheduler-side
-compatibility gating and runner-side `StepInputBatch` packing.
 
-## Current Limitations
+- Reuse the same helpers as `forward()` to avoid behavior drift.
+- Copy request-scoped scheduler state into `state.scheduler`.
+- Advance `state.step_index` only in `step_scheduler()`.
+- Keep model forward work in `denoise_step()`.
+- Keep latent mutation in `step_scheduler()`.
+- Keep final decoding equivalent to the tail of `forward()`.
+- Store masks, condition latents, and other request-local tensors in the
+  request state rather than on the shared pipeline.
 
-- Experimental feature; use `max_num_seqs=1` for the conservative single-request
+Before setting `supports_step_execution = True`, validate output parity for the
-  step path.
+same seed and sampling parameters, scheduler isolation across concurrent
-- Only native pipelines that already support `step_execution=True`.
+requests, abort cleanup, progress reporting, and all supported CFG paths.
-- Only homogeneous batches keyed by `StepBatchSamplingParamsKey` are supported.
-- `cache_backend`, KV transfer, and other request-mode extras are not wired
-  into the batched step-wise path yet.
-- Future work can relax the current same-shape restriction with richer
-  heterogeneous batching policies such as bucketing or padded execution for
-  different resolutions.
 
-## Related Files
+Before setting `supports_request_batch = True`, validate the batch input
+contract, one-output-per-request result shape, request identity and error
+mapping, seeded concurrency, LoRA compatibility, and tensor IPC.
 
-- Scheduler base:
+## Limitations
-  [`vllm_omni/diffusion/sched/base_scheduler.py`](gh-file:vllm_omni/diffusion/sched/base_scheduler.py)
+
-- Scheduler interface:
+- Request batching requires an explicit pipeline capability when
-  [`vllm_omni/diffusion/sched/interface.py`](gh-file:vllm_omni/diffusion/sched/interface.py)
+  `max_num_seqs>1`.
-- Step scheduler:
+- Step execution requires the four-method stateful pipeline contract.
-  [`vllm_omni/diffusion/sched/step_scheduler.py`](gh-file:vllm_omni/diffusion/sched/step_scheduler.py)
+- Both batching paths currently require homogeneous compatibility keys.
-- Runner:
+- FIFO scheduling can reduce batching opportunities.
-  [`vllm_omni/diffusion/worker/diffusion_model_runner.py`](gh-file:vllm_omni/diffusion/worker/diffusion_model_runner.py)
+- `request_batch_max_wait_ms` trades first-request latency for burst
-- Input batch:
+  coalescing.
-  [`vllm_omni/diffusion/worker/input_batch.py`](gh-file:vllm_omni/diffusion/worker/input_batch.py)
+- All diffusion cache backends are currently unsupported in step mode.
-- Tests:
+- KV transfer is supported for newly admitted step requests; some other
-  [`tests/diffusion/test_diffusion_scheduler.py`](gh-file:tests/diffusion/test_diffusion_scheduler.py)
+  request-mode extras remain unsupported.
+- Step continuous batching remains experimental; use `max_num_seqs=1` for the
+  conservative step path.
+
+## Related Implementation
+
+- Engine: [`vllm_omni/diffusion/diffusion_engine.py`](gh-file:vllm_omni/diffusion/diffusion_engine.py)
+- Model contract: [`vllm_omni/diffusion/models/interface.py`](gh-file:vllm_omni/diffusion/models/interface.py)
+- Scheduler interface: [`vllm_omni/diffusion/sched/interface.py`](gh-file:vllm_omni/diffusion/sched/interface.py)
+- Request scheduler: [`vllm_omni/diffusion/sched/request_scheduler.py`](gh-file:vllm_omni/diffusion/sched/request_scheduler.py)
+- Step scheduler: [`vllm_omni/diffusion/sched/step_scheduler.py`](gh-file:vllm_omni/diffusion/sched/step_scheduler.py)
+- Runner: [`vllm_omni/diffusion/worker/diffusion_model_runner.py`](gh-file:vllm_omni/diffusion/worker/diffusion_model_runner.py)
+- Request batch: [`vllm_omni/diffusion/worker/request_batch.py`](gh-file:vllm_omni/diffusion/worker/request_batch.py)
+- Step input batch: [`vllm_omni/diffusion/worker/input_batch.py`](gh-file:vllm_omni/diffusion/worker/input_batch.py)

docs/design/feature/diffusion_request_level_batching.md

@@ -1,175 +0,0 @@
-# Request-Level Batching for Diffusion
-
-This document describes the request-mode batching path for diffusion pipelines.
-For end-user enablement and tuning, see
-[Request-Level Batching](../../user_guide/diffusion/request_batching.md).
-
-This is separate from
-[Continuous Batching for Step-Wise Diffusion](diffusion_continuous_batching.md).
-Request-level batching runs one full pipeline `forward()` over a static batch of
-compatible requests. Step-wise continuous batching admits work between denoise
-steps when `step_execution=True`.
-
-## Why It Helps
-
-The request-level design avoids coupling several logical requests to one request
-object. This keeps request identity, abort/error handling, and per-request
-metadata unambiguous while still allowing one fused pipeline forward pass for
-bursty or concurrent traffic.
-
-## Overview
-
-With request-level batching enabled:
-
-- each `OmniDiffusionRequest` contains one `prompt` and one `request_id`
-- the scheduler groups compatible waiting requests into one scheduler wave
-- `DiffusionRequestBatch` wraps the scheduled requests for pipeline `forward()`
-- batch-capable pipelines return `list[DiffusionOutput]`, one output per
-  request
-- `BatchRunnerOutput` maps each result back to its original `request_id`
-
-Pipelines opt in with `supports_request_batch = True` and a `forward()` method
-that accepts `DiffusionRequestBatch` and returns `list[DiffusionOutput]`.
-Pipelines that do not opt in keep the existing per-request execution path.
-
-## Enablement
-
-Request-level batching is the request-mode path, so `step_execution` must remain
-disabled. Increase `max_num_seqs` above `1` to let the scheduler keep multiple
-compatible requests active:
-
-```bash
-vllm serve Qwen/Qwen-Image --omni \
-  --port 8091 \
-  --max-num-seqs 4
-```
-
-For bursty online ingress, `request_batch_max_wait_ms` can add a bounded
-admission wait before the first `schedule()` of a scheduler wave:
-
-```bash
-vllm serve Qwen/Qwen-Image --omni \
-  --port 8091 \
-  --max-num-seqs 4 \
-  --request-batch-max-wait-ms 20
-```
-
-`request_batch_max_wait_ms=0` disables this wait and is the default.
-
-## Request Contract
-
-`OmniDiffusionRequest` represents one logical request. It owns one prompt,
-sampling parameters, request id, and request-local metadata. Runtime batches are
-formed by the scheduler and represented separately from the request payload.
-
-Runtime batching is represented by:
-
-- [`DiffusionSchedulerOutput`](gh-file:vllm_omni/diffusion/sched/interface.py)
-  for scheduled request ids and request payloads
-- [`DiffusionRequestBatch`](gh-file:vllm_omni/diffusion/worker/request_batch.py)
-  for the pipeline-facing request batch
-- [`BatchRunnerOutput`](gh-file:vllm_omni/diffusion/worker/utils.py) for
-  per-request results
-
-`DiffusionRequestBatch` intentionally exposes compatibility properties such as
-`prompts`, `sampling_params`, `request_id`, and `kv_sender_info` so migrated
-pipelines can stay close to upstream code while using a batch-aware contract.
-
-## Scheduler
-
-The scheduler derives its capacity from `max_num_seqs` through
-`max_num_running_reqs`. It exposes waiting/running queue counters so the engine
-can decide whether admission wait is useful before scheduling a new wave.
-
-Batch compatibility is controlled by
-[`RequestBatchSamplingParamsKey`](gh-file:vllm_omni/diffusion/sched/interface.py). The key
-contains shape-sensitive and guidance-sensitive fields, including output count
-and LoRA identity. Requests with incompatible shapes, CFG settings, output
-counts, LoRA adapters, or LoRA scales are kept in separate batches.
-
-Admission is conservative:
-
-- the scheduler only batches compatible requests
-- FIFO ordering is preserved
-- an incompatible request at the head of the waiting queue blocks later
-  compatible requests
-
-## Engine
-
-[`DiffusionEngine`](gh-file:vllm_omni/diffusion/diffusion_engine.py) resolves
-request-batch capability during initialization from the configured pipeline
-class, including custom pipeline classes.
-
-The capability check uses the pipeline class attribute
-`supports_request_batch = True`. Pipelines that set this attribute must implement
-a request-batch-compatible `forward()` contract and return one
-`DiffusionOutput` per request; the runner validates that return shape at runtime.
-
-When the selected pipeline is batch-capable and `step_execution=False`, request
-mode routes scheduler waves through the batch executor path. Otherwise it keeps
-the per-request executor path.
-
-The optional admission wait runs only when:
-
-- request batching is supported
-- `step_execution=False`
-- `request_batch_max_wait_ms > 0`
-- no requests are currently running
-
-The wait exits early when the waiting queue reaches capacity, when the queue is
-stable for a short window, when the deadline expires, or when the engine stops.
-
-## Executor And Runner
-
-The executor exposes two request-mode entries:
-
-- `execute_request`: one worker call per scheduled request
-- `execute_batch`: one worker call for the whole `DiffusionSchedulerOutput`
-
-On the batch path, the worker builds a `DiffusionRequestBatch` and runs the
-pipeline once. Request-local setup remains per request:
-
-- KV transfer metadata
-- random generator and seed handling
-- request output/error/abort mapping
-
-Shared batch setup happens once per batch when possible:
-
-- cache refresh
-- LoRA activation for the homogeneous adapter key
-- pipeline `forward(req_batch)`
-
-Large tensor IPC still uses the shared-memory packing path. The packer traverses
-both normal `RunnerOutput.result` wrappers and nested batch results so batched
-outputs do not fall back to pickle IPC for tensor payloads.
-
-## Current Limitations
-
-- Only pipelines that declare the request-batch contract use fused batch
-  execution.
-- Batches are homogeneous under `RequestBatchSamplingParamsKey`; heterogeneous resolution or
-  incompatible guidance settings do not co-batch yet.
-- FIFO scheduling can reduce batching opportunities when an incompatible
-  request is at the front of the queue.
-- `request_batch_max_wait_ms` improves burst coalescing but can add latency to
-  the first request in a scheduler wave. Keep it small for latency-sensitive
-  serving.
-- Step-wise continuous batching is documented separately and only applies when
-  `step_execution=True`.
-
-## Related Files
-
-- Request object and request batch:
-  [`vllm_omni/diffusion/request.py`](gh-file:vllm_omni/diffusion/request.py)
-- Scheduler interface:
-  [`vllm_omni/diffusion/sched/interface.py`](gh-file:vllm_omni/diffusion/sched/interface.py)
-- Scheduler base:
-  [`vllm_omni/diffusion/sched/base_scheduler.py`](gh-file:vllm_omni/diffusion/sched/base_scheduler.py)
-- Engine:
-  [`vllm_omni/diffusion/diffusion_engine.py`](gh-file:vllm_omni/diffusion/diffusion_engine.py)
-- Worker runner:
-  [`vllm_omni/diffusion/worker/diffusion_model_runner.py`](gh-file:vllm_omni/diffusion/worker/diffusion_model_runner.py)
-- Executor interface:
-  [`vllm_omni/diffusion/executor/abstract.py`](gh-file:vllm_omni/diffusion/executor/abstract.py)
-- Tests:
-  [`tests/diffusion/test_diffusion_engine.py`](gh-file:tests/diffusion/test_diffusion_engine.py)

docs/design/feature/diffusion_step_execution.md

@@ -1,131 +0,0 @@
-# Adding Step Execution Support for Diffusion Pipelines
-
-This guide documents vLLM-Omni's stepwise diffusion contract for model authors
-and contributors implementing `step_execution=True` support for a diffusion
-pipeline.
-
-For end-user enablement, supported models, and current limitations, see
-[Step Execution](../../user_guide/diffusion/step_execution.md).
-
-This document describes the base step-execution contract only. For the
-experimental batching policy layered on top of the step-wise path, see
-[Continuous Batching for Step-Wise Diffusion](diffusion_continuous_batching.md).
-
-## Current Support Scope
-
-`step_execution` is **not** a generic diffusion toggle. It only works for
-pipelines that implement the segmented stateful contract in
-[`vllm_omni/diffusion/models/interface.py`](gh-file:vllm_omni/diffusion/models/interface.py).
-
-This page is intentionally author-facing. Treat runtime enablement
-(`step_execution=True` in Python or `--step-execution` in serving) as an
-opt-in user knob layered on top of the implementation contract below.
-
-Current in-tree support:
-
-| Pipeline | Example models | Step execution |
-|----------|----------------|----------------|
-| `QwenImagePipeline` | `Qwen/Qwen-Image`, `Qwen/Qwen-Image-2512` | Yes |
-| All other diffusion pipelines | `QwenImageEditPipeline`, `QwenImageEditPlusPipeline`, `QwenImageLayeredPipeline`, GLM-Image, Wan, Flux, etc. | No |
-
-Current engine/runtime limitations:
-
-- Continuous batching with `max_num_seqs > 1` is experimental and documented in
-  [Continuous Batching for Step-Wise Diffusion](diffusion_continuous_batching.md).
-  Keep `max_num_seqs=1` for the conservative single-request step path.
-- `cache_backend` is not supported in step mode.
-- Request-mode extras such as KV transfer are not wired into step mode yet.
-- Unsupported pipelines now fail early during model loading instead of failing on the first request.
-
-## Execution Contract
-
-Step mode is driven by four pipeline methods plus the shared mutable request
-state object:
-
-- `prepare_encode(state)`: one-time request preparation.
-- `denoise_step(state)`: compute the noise prediction for the current step.
-- `step_scheduler(state, noise_pred)`: mutate latents and advance step state.
-- `post_decode(state)`: decode the final output after denoising is complete.
-
-The state lives in
-[`vllm_omni/diffusion/worker/utils.py`](gh-file:vllm_omni/diffusion/worker/utils.py)
-as `StepRequestState`. Store request-scoped tensors there, or use
-`state.extra` for model-specific fields that do not justify extending the core
-dataclass.
-
-The worker-side step loop lives in
-[`vllm_omni/diffusion/worker/diffusion_model_runner.py`](gh-file:vllm_omni/diffusion/worker/diffusion_model_runner.py):
-
-1. `prepare_encode()` runs once for a new request.
-2. `denoise_step()` runs every scheduler tick.
-3. `step_scheduler()` mutates `state.latents` and advances `state.step_index`.
-4. `post_decode()` runs exactly once after `state.denoise_completed` becomes true.
-
-## Recommended Split
-
-When converting an existing request-level `forward()` pipeline, keep the split
-strict and mechanical:
-
-| Request-level phase | Stepwise method | What belongs there |
-|---------------------|-----------------|--------------------|
-| Input validation, prompt encoding, latent init, timestep prep, per-request scheduler creation | `prepare_encode()` | Anything that should happen once per request |
-| Transformer forward / noise prediction | `denoise_step()` | Pure denoise computation for the current timestep |
-| `scheduler.step(...)` and `step_index += 1` | `step_scheduler()` | Only latent/state mutation for one step |
-| VAE decode / postprocess | `post_decode()` | Final decode only |
-
-Keep the stepwise path reusing the same helpers as the request-level path
-whenever possible. Reimplementing the denoise loop from scratch is the easiest
-way to introduce behavioral drift.
-
-## Qwen-Image Reference
-
-[`pipeline_qwen_image.py`](gh-file:vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image.py)
-is the reference implementation and is split correctly for the current
-contract:
-
-- `prepare_encode()` reuses `_prepare_generation_context()` so prompt encoding,
-  latent init, timestep creation, CFG setup, and shape bookkeeping stay aligned
-  with `forward()`.
-- `prepare_encode()` deep-copies `self.scheduler` **after**
-  `prepare_timesteps()` so request-specific scheduler state is isolated.
-- `denoise_step()` reuses `_build_denoise_kwargs()` plus
-  `predict_noise_maybe_with_cfg()`, so sequential CFG, CFG-parallel, and
-  non-CFG behavior stay identical to the request-level path.
-- `step_scheduler()` only calls
-  `scheduler_step_maybe_with_cfg(..., per_request_scheduler=state.scheduler)`
-  and increments `state.step_index`.
-- `post_decode()` reuses `_decode_latents()`, so the final image decode matches
-  the normal `forward()` path.
-
-That decomposition is the target pattern for future models.
-
-## Rules For New Pipelines
-
-- Do not keep request-scoped scheduler state on `self.scheduler`. Copy it into
-  `state.scheduler` during `prepare_encode()`.
-- Do not mutate `state.step_index` inside `denoise_step()`. Only
-  `step_scheduler()` should advance the step.
-- Do not decode partial outputs in `denoise_step()` or `step_scheduler()`.
-- If the request-level pipeline has condition latents, masks, or edit-specific
-  tensors, store them in `state` or `state.extra`, not in global pipeline
-  attributes.
-- Preserve CFG behavior by sharing the same helper path used by `forward()`.
-- Keep `post_decode()` equivalent to the tail of `forward()`.
-
-## Validation Checklist
-
-Before marking a pipeline as `supports_step_execution = True`, verify:
-
-- Stepwise output matches request-level output for the same seed and sampling params.
-- Per-request scheduler state is isolated across concurrent requests.
-- Abort during denoise does not leak cached state.
-- `step_index` reported by `RunnerOutput` matches the scheduler progress.
-- CFG-parallel and non-CFG paths both work if the request-level pipeline supports them.
-
-## Related Files
-
-- Contract: [`vllm_omni/diffusion/models/interface.py`](gh-file:vllm_omni/diffusion/models/interface.py)
-- State: [`vllm_omni/diffusion/worker/utils.py`](gh-file:vllm_omni/diffusion/worker/utils.py)
-- Runner loop: [`vllm_omni/diffusion/worker/diffusion_model_runner.py`](gh-file:vllm_omni/diffusion/worker/diffusion_model_runner.py)
-- Scheduler transport: [`vllm_omni/diffusion/sched/interface.py`](gh-file:vllm_omni/diffusion/sched/interface.py)
-- Reference pipeline: [`vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image.py`](gh-file:vllm_omni/diffusion/models/qwen_image/pipeline_qwen_image.py)

docs/design/feature/distributed_layerwise_offload.md

@@ -0,0 +1,156 @@
+# Distributed Layerwise Offload
+
+This document describes distributed layerwise offload (DLO) for diffusion
+models. DLO keeps only a small number of DiT blocks on the accelerator and
+streams the remaining blocks from host memory. The distributed backend can
+either shard those host-side weights across an existing parallel group or keep
+the standard loader's rank-local weights and avoid an additional collective.
+
+For user-facing commands, see the
+[distributed layerwise offloading guide](../../user_guide/diffusion/cpu_offload.md)
+and the [Cosmos3 recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/cosmos3/Cosmos3-DistOffload.md).
+
+## Status
+
+DLO is implemented for multi-device diffusion execution. The default
+AllGather path is the primary path for DP and SP deployments. The
+`--dlo-no-use-allgather` path is a rank-local compatibility mode: it is useful
+for standard-loader sharding, workstation bring-up, and systems where an
+additional DLO collective is undesirable, but it does not reduce host weight
+storage across ranks.
+
+The compatibility matrix below describes the current implementation. The
+unit-level guards are covered, but not every parallelism combination has a
+full model-and-hardware end-to-end test.
+
+## Design
+
+### DLO consumes the existing parallel topology
+
+DLO does not create a new DP, TP, or SP topology. It reads the configured
+`DiffusionParallelConfig` and attaches offload hooks to the DiT blocks after the
+standard distributed groups have been initialized.
+
+The DLO weight-sharding group is selected as follows:
+
+1. Use the existing DP group when `data_parallel_size > 1`.
+2. When DP is one and SP is greater than one, use the SP group.
+3. Otherwise, run rank-locally without a DLO process group.
+
+TP is deliberately not used as DLO's AllGather group. HSDP has its own
+parameter-sharding lifecycle and is not allowed to be sharded a second time by
+DLO's AllGather path.
+
+### AllGather path
+
+With the default `dlo_use_allgather=True`, each rank stores approximately
+`1 / group_size` of each streamable block in pinned host memory. The next
+block's shard is copied to a device buffer and reconstructed with
+`all_gather_into_tensor` on a communication stream while the current block is
+executing.
+
+```text
+Compute:    [Block N]             [Block N+1]          [Block N+2]
+H2D:                      [shard N+1]           [shard N+2]
+AllGather:                [full N+1]             [full N+2]
+Buffers:    [current slot]       [prefetch slot]       [current slot]
+```
+
+![DLO double-buffer prefetch pipeline](../figures/dlo/dlo_pipeline.gif)
+
+The backend uses two shared device buffers, so accelerator weight residency is
+bounded by the largest streamed blocks rather than the complete model.
+
+When DP is greater than one, the engine can process one request per DP rank in
+the same denoising wave. Because AllGather is a collective, all participating
+requests must take the same execution path at every denoising step.
+
+### Rank-local path without DLO AllGather
+
+With `--dlo-no-use-allgather`, DLO forces its internal offload shard size to
+one. The regular model loader remains responsible for preparing each rank's
+weights, including TP-local tensors or HSDP-managed parameters. DLO then
+streams those rank-local tensors block by block using H2D copies only.
+
+This mode means:
+
+- DP still provides independent replicas, but DLO does not shard weights
+  across DP ranks.
+- SP still performs its normal activation/attention collectives, but DLO does
+  not shard weights across SP ranks.
+- TP/HSDP/SP collectives, if configured, are not disabled by this flag; only
+  DLO's additional weight AllGather is disabled.
+- Pure DP deployments keep a full host-side model copy per rank, subject to
+  shared OS page-cache behavior.
+- The scheduler does not require a synchronized DP request wave for DLO.
+
+This path is intentionally not implemented by reusing the AllGather mmap
+loader. It relies on the standard loader so model-specific TP/HSDP transforms
+remain intact.
+
+## Parallelism compatibility
+
+| Parallelism | DLO + AllGather | DLO without AllGather |
+|---|---|---|
+| **DP** | Supported primary path. DLO shards host weights across the DP group and can run DP multi-concurrency. | Supported rank-local path. DP replicas remain independent; no DLO weight sharding or cross-DP DLO collective. |
+| **SP** | Supported in the implementation. With DP=1, DLO uses the SP group for host-weight sharding; SP still shards sequence/activation work. | SP remains active, but DLO keeps standard-loader rank-local weights and adds no SP weight collective. |
+| **TP > 1** | Unsupported in the DLO mmap path. TP-aware loader callbacks are bypassed, so the backend rejects this configuration when it enters that path. | Standard loading is retained and TP-local tensors can be streamed. This is the intended compatibility path, but it still needs broader model and hardware validation. |
+| **HSDP** | Rejected. HSDP has already sharded parameters, so DLO AllGather would double-shard them. | Accepted by configuration. HSDP owns parameter sharding and its own gathers; DLO only stages rank-local parameters. End-to-end coverage is limited. |
+
+### Combined dimensions
+
+- **DP + SP:** DLO uses the DP group for weight sharding when DP is greater
+  than one; SP continues to use its own sequence-parallel group. If DP is one,
+  the SP group becomes DLO's sharding group in AllGather mode.
+- **DP + TP/SP without AllGather:** standard model loading defines the
+  rank-local tensor layout. DLO adds no cross-DP, cross-TP, or cross-SP weight
+  collective.
+- **HSDP + SP:** the general parallel configuration permits HSDP over SP, but
+  DLO must use `--dlo-no-use-allgather`. HSDP remains responsible for weight
+  materialization and synchronization.
+- **HSDP + DP or TP:** rejected independently by the diffusion parallel
+  configuration.
+
+## Request and loading constraints
+
+AllGather DP multi-concurrency requires:
+
+- explicit `num_inference_steps`;
+- the same `num_inference_steps` for all requests in a wave; and
+- identical request arguments that affect the collective execution path.
+
+The no-AllGather path does not impose these DLO-specific synchronized-wave
+requirements.
+
+The mmap loader is used only by the supported DLO+AllGather path when the
+model has a compatible checkpoint layout. Online quantization is incompatible
+with that sharded mmap path; use `--dlo-no-use-allgather` or disable online
+quantization. Models that do not meet the mmap requirements use the regular
+loader path.
+
+## Validation coverage
+
+Current source-level validation includes:
+
+- HSDP + DLO + AllGather rejection;
+- HSDP + DLO without AllGather acceptance at configuration level;
+- TP rejection in the DLO+AllGather mmap path;
+- resident-layer requests requiring no-AllGather;
+- DP request-wave validation for denoising-step compatibility;
+- sharding, double-buffer, AllGather-size, and heterogeneous-block regression
+  tests.
+
+The highest-value missing coverage is end-to-end numerical comparison against
+ordinary layerwise offload for DP+SP, TP+no-AllGather, and HSDP+SP+no-AllGather
+on the target CUDA/NCCL or CANN/HCCL hardware.
+
+## Recommendations
+
+- Use **DP + DLO AllGather** for the supported throughput and host-memory
+  scaling path.
+- Use **SP + DLO AllGather** for long-sequence workloads when DP concurrency is
+  not the goal.
+- Use **no-AllGather** to bring up TP or workstation PCIe configurations, with
+  the expectation of higher host-memory use and lower validation confidence.
+- Prefer **HSDP alone** for production HSDP deployments until the combined
+  HSDP + DLO no-AllGather path has broader end-to-end coverage.

docs/design/feature/omni_async_output_materialization.md

@@ -0,0 +1,386 @@
+# Async Omni Output Materialization
+
+## Table of Contents
+
+1. [Overview](#overview)
+2. [Performance](#performance)
+3. [Architecture](#architecture)
+4. [Enabling the Feature](#enabling-the-feature)
+5. [Compatibility and Fallbacks](#compatibility-and-fallbacks)
+6. [Related Files](#related-files)
+
+## Overview
+
+Async Omni output materialization moves CPU-side construction of
+`OmniModelRunnerOutput` out of the autoregressive (AR) decode critical path.
+It complements [Async Chunk](async_chunk.md): async chunk pipelines partial
+outputs between stages, while async output materialization prevents the
+construction of those partial outputs from blocking the next decode step.
+
+An AR stage must return sampled token IDs quickly because the scheduler needs
+them for the next decode step. However, an Omni stage can also produce hidden
+states, multimodal tensors, streaming inter-stage payloads, and connector
+metadata. Building these payloads can require device-to-host (D2H) copies,
+tensor slicing, flattening, and Python object construction.
+
+Without this feature, all of that work runs inline in `sample_tokens()`:
+
+```text
+sample tokens
+  -> update decode state
+  -> copy hidden and multimodal outputs to CPU
+  -> build per-request payloads
+  -> build the streaming wire payload
+  -> collect connector signals
+  -> return sampled tokens
+  -> launch the next decode step
+```
+
+With async output materialization, only work required by the next decode step
+stays on the critical path:
+
+```text
+sample tokens
+  -> update decode state
+  -> snapshot output state and start asynchronous D2H copies
+  -> register sampled tokens
+  -> launch the next decode step
+
+background output path
+  -> wait for the payload snapshot
+  -> build per-request payloads
+  -> build the streaming wire payload
+  -> collect connector signals
+  -> construct OmniModelRunnerOutput
+```
+
+This allows payload construction for step `N` to overlap with GPU decode work
+for step `N + 1`. It does not change model computation or generated output.
+
+!!! note "Platform and validation scope"
+    The performance and correctness validation in this document covers CUDA
+    and ROCm. This is a validation scope, not a runtime platform guard: XPU
+    inherits `GPUARModelRunner`, and MUSA selects `GPUARWorker`, so those
+    backends may enter the asynchronous path when its other guards pass. XPU
+    and MUSA have not been validated for this optimization. Ascend NPU uses a
+    separate `NPUARModelRunner` path that still constructs
+    `OmniModelRunnerOutput` and drains connector output synchronously before
+    wrapping the result as an asynchronous runner output.
+
+<p align="center">
+  <img
+    alt="Decode step gap before and after async output materialization"
+    src="../../../source/architecture/qwen3-omni-async-output-step-gap.svg"
+    width="100%"
+  >
+</p>
+
+<p align="center">
+  <em>
+    Figure 1: Moving payload construction off the decode path reduces the
+    observed gap between consecutive Talker steps from about 2.8 ms to 41 µs.
+    Adapted from the
+    <a href="https://vllm.ai/blog/2026-07-01-qwen3-omni-optimization">
+      Qwen3-Omni optimization blog
+    </a>.
+  </em>
+</p>
+
+For Qwen3-Omni, the feature is enabled for both AR stages:
+
+- **Thinker**: Defers hidden-state and multimodal payload materialization.
+- **Talker**: Runs its decode-state postprocess eagerly, then defers codec
+  payload materialization. Because Code2Wav consumes codec codes rather than
+  Talker hidden states, the Talker also skips unnecessary hidden-state D2H.
+- **Code2Wav**: Does not use this path because it is a generation stage rather
+  than an AR stage.
+
+Qwen3-TTS uses the same mechanism in its AR Talker stage. The Talker updates
+the decode state it needs for the next step before returning, then constructs
+the codec payload for Code2Wav in the background.
+
+## Performance
+
+In the controlled Qwen3-Omni optimization sweep, enabling async output
+materialization on top of CUDA Graph and async chunk produced the following
+results at concurrency 64:
+
+| Configuration | Request throughput | Mean audio TTFP | Mean audio RTF |
+|---|---:|---:|---:|
+| CUDA Graph + async chunk | 9.3 req/s | 655 ms | 0.63 |
+| + async output materialization | 11.3 req/s | 631 ms | 0.47 |
+| Change | **+22%** | **-4%** | **-25%** |
+
+The sweep used `Qwen/Qwen3-Omni-30B-A3B-Instruct`, 640 Seed-TTS English
+prompts, and one replica each for Thinker, Talker, and Code2Wav. Async output
+materialization recovers throughput by removing CPU payload construction from
+the decode critical path without regressing time to first audio packet (TTFP).
+See the
+[Qwen3-Omni optimization blog](https://vllm.ai/blog/2026-07-01-qwen3-omni-optimization)
+for the complete benchmark methodology and results.
+
+The implementation was also validated against the synchronous safe-copy path.
+The text and audio output hashes matched, confirming that overlap does not
+change the generated result. See
+[PR #4476](https://github.com/vllm-project/vllm-omni/pull/4476) for the
+correctness and profiling details.
+
+## Architecture
+
+### Output Lifecycle
+
+`OmniAsyncGPUModelRunnerOutput` extends vLLM's
+`AsyncGPUModelRunnerOutput`. It preserves the existing asynchronous
+sampled-token feedback while adding a background builder for the complete Omni
+output.
+
+The output lifecycle is:
+
+1. `GPUARModelRunner.sample_tokens()` samples tokens and performs the
+   bookkeeping required by the next decode step.
+2. The runner snapshots step-local metadata such as request IDs, token spans,
+   scheduler output, and query start locations.
+3. CUDA payload tensors are cloned before reusable model or CUDA Graph buffers
+   can be overwritten.
+4. The cloned payload is copied to pinned CPU memory on a dedicated CUDA
+   stream. A CUDA event records when the copy is ready.
+5. `OmniAsyncGPUModelRunnerOutput` starts a background thread that waits for
+   the payload event and builds `OmniModelRunnerOutput`.
+6. The AR runner immediately registers the sampled-token CPU copy with the
+   input batch and returns, allowing async scheduling to advance.
+7. When the engine calls `get_output()`, it joins the background builder,
+   propagates any builder exception, and finalizes sampled tokens and logprobs
+   through the upstream output implementation.
+
+```text
+GPU / decode path                         Background output path
+
+forward + sample
+      |
+      +-- clone output tensors
+      +-- enqueue D2H copy --------------> wait for D2H event
+      +-- register sampled tokens               |
+      +-- return async output                    +-- slice per request
+      |                                         +-- build multimodal payloads
+next decode step                                +-- partition streaming payloads
+                                                +-- build the wire payload
+                                                +-- drain connector signals
+                                                +-- build OmniModelRunnerOutput
+```
+
+### Safe State Snapshots
+
+The next scheduler step can mutate runner state while the background builder is
+still active. Most step-local builder inputs are therefore detached snapshots.
+The snapshotted state includes:
+
+- scheduler token counts and speculative-token metadata
+- request IDs and request-to-batch-index mappings
+- sampled token IDs, logprobs, and prompt logprobs
+- query start locations and scheduled-token spans
+- hidden states and multimodal output tensors
+- KV-connector and encoder-cache outputs captured for the step
+
+CUDA tensor cloning is required because CUDA Graph and model output buffers can
+be reused by the next forward pass. The dedicated copy stream and pinned host
+buffers make the D2H transfer asynchronous; the snapshot retains its cloned
+CUDA sources until the transfer event completes.
+
+### Live Runner State and Ownership
+
+The complete builder input is not a detached snapshot. Connector output state
+remains runner-owned: `get_omni_connector_output()` drains live, per-cycle
+connector signals after the streaming payload is built. Correctness therefore
+depends on that builder being the only consumer that drains connector signals
+for its output cycle. Receive-side connector state written by the background
+receiver is coordinated by the connector mixin's `_lock`.
+`OmniAsyncGPUModelRunnerOutput.get_output()` joins the builder and is the
+completion and exception boundary before the materialized output is consumed.
+
+The shared output builder also contains a full-payload accumulation branch
+that reads `self.requests`. That branch is not reachable when async Omni output
+materialization is enabled: this feature requires `async_chunk`, while
+`should_accumulate_full_payload_output()` returns `False` whenever
+`async_chunk` is enabled. The feature path partitions each step's payload into
+streaming inter-stage and client handoffs instead.
+
+### Output Builder
+
+The background builder performs the work that previously ran inline:
+
+- resolves which requests need downstream payloads
+- converts hidden and multimodal tensors into per-request CPU payloads
+- applies model-specific payload processing
+- partitions per-step payloads into inter-stage and client streams
+- creates the tensor-only `multimodal_outputs` wire payload
+- drains live connector readiness signals after building the streaming payload
+- constructs the final `OmniModelRunnerOutput`
+
+Input-side connector operations remain synchronous with model execution. In
+particular, receiving inputs and flushing previously completed connector
+outputs are not deferred because they affect scheduler-visible request state.
+
+### Qwen3-Omni Stage Behavior
+
+| Stage | Async output behavior | Reason |
+|---|---|---|
+| Thinker | Snapshots hidden states and multimodal outputs; builds the downstream payload in the background | The Talker needs the payload, but the next Thinker decode step only needs sampled-token feedback |
+| Talker | Runs lightweight postprocess eagerly; snapshots codec outputs; omits hidden states from the downstream payload | `hidden_states.last` is needed by the next Talker step, while Code2Wav only needs codec codes |
+| Code2Wav | Uses the normal generation-stage output path | Code2Wav is not executed by `GPUARModelRunner` |
+
+The Qwen3-TTS Talker follows the same pattern as the Qwen3-Omni Talker:
+postprocess runs eagerly because the next decode step needs its updated state,
+while codec payload construction runs asynchronously. Its Code2Wav stage also
+uses the normal generation-stage path.
+
+## Enabling the Feature
+
+There is no separate `async_omni_output` command-line or YAML option. The
+feature is selected automatically for model stages that opt in when the runtime
+conditions are safe. On the validated CUDA and ROCm configurations, using the
+bundled deployment profile is enough to turn it on for the supported
+Qwen3-Omni and Qwen3-TTS pipelines. The selection logic has no CUDA/ROCm-only
+guard; see [Compatibility and Fallbacks](#compatibility-and-fallbacks) for the
+other platform paths. The same profile does not enable background Omni
+materialization on Ascend NPU.
+
+### Example 1: Qwen3-Omni
+
+Start the full Qwen3-Omni pipeline:
+
+```bash
+vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct \
+  --omni \
+  --port 8091
+```
+
+The model registry automatically loads
+`vllm_omni/deploy/qwen3_omni_moe.yaml`. Its relevant settings are:
+
+```yaml
+async_chunk: true
+
+stages:
+  - stage_id: 0  # Thinker
+    enable_prefix_caching: false
+    # async_scheduling defaults to true for AR stages
+
+  - stage_id: 1  # Talker
+    enable_prefix_caching: false
+    # async_scheduling defaults to true for AR stages
+
+  - stage_id: 2  # Code2Wav
+    enable_prefix_caching: false
+    async_scheduling: false
+```
+
+With this profile, async output materialization activates automatically for:
+
+- **Stage 0, Thinker**: Defers hidden-state and multimodal payload
+  construction.
+- **Stage 1, Talker**: Defers codec payload construction after eagerly
+  updating its decode state.
+
+Stage 2 does not use this feature because Code2Wav is not an AR stage.
+
+### Example 2: Qwen3-TTS
+
+Start Qwen3-TTS with any checkpoint that uses the Qwen3-TTS Talker pipeline.
+For example:
+
+```bash
+vllm serve Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
+  --omni \
+  --port 8091
+```
+
+The model registry automatically loads `vllm_omni/deploy/qwen3_tts.yaml`. Its
+relevant settings are:
+
+```yaml
+async_chunk: true
+
+stages:
+  - stage_id: 0  # Talker
+    async_scheduling: true
+    enable_prefix_caching: false
+
+  - stage_id: 1  # Code2Wav
+    enable_prefix_caching: false
+```
+
+Async output materialization activates only for Stage 0, the AR Talker. The
+Talker keeps the state required by its next decode step on the GPU, skips an
+unnecessary hidden-state D2H copy, and builds the codec payload for Code2Wav in
+the background. Stage 1 uses the generation-stage output path.
+
+The same behavior applies to the Base and VoiceDesign Qwen3-TTS checkpoints
+because they use the same Talker implementation.
+
+!!! warning
+    Do not pass `--no-async-chunk` or enable prefix caching when you want this
+    optimization. Either change causes the runner to fall back to synchronous
+    output construction. Enabling async chunk alone cannot activate the feature
+    for a model that has not opted into async Omni output.
+
+!!! note "Prefix cache compatibility"
+    Async Omni output materialization and Omni prefix caching cannot currently
+    run together. `_should_use_async_omni_output()` returns `False` whenever
+    `self.omni_prefix_cache` is present, so prefix caching selects synchronous
+    output materialization. Supporting both features requires snapshotting or
+    otherwise synchronizing prefix-cache merge and update state before the
+    background output path can consume it safely.
+
+## Compatibility and Fallbacks
+
+`GPUARModelRunner` uses the async output path only when all of the following
+runtime conditions hold:
+
+| Requirement | Reason |
+|---|---|
+| AR async scheduling is enabled | The optimization relies on the scheduler advancing while the prior output is materialized |
+| `async_chunk` is enabled | The feature targets incremental downstream Omni payloads |
+| The model stage opts in with `use_async_omni_output` | Models must declare that their output lifecycle is safe to defer |
+| Omni prefix cache is disabled | Prefix-cache merge and update ordering currently requires synchronous materialization |
+| Speculative decoding is disabled | Speculative output state is not included in this deferred path |
+| Routed-expert output is disabled | Routed-expert extraction currently requires the synchronous path |
+| Postprocess is absent or explicitly runs eagerly | State needed by the next decode step must be updated before the runner returns |
+
+These checks are evaluated per stage on runners based on `GPUARModelRunner`.
+An unsupported combination does not prevent serving; it only falls back to
+synchronous output construction for that stage. CUDA and ROCm are the validated
+platforms. XPU inherits `GPUARModelRunner`, and MUSA selects `GPUARWorker`, so
+they may select the async path when these checks pass, but this optimization is
+not yet validated on either backend.
+
+Ascend NPU does not use this selection path. `NPUARModelRunner.sample_tokens()`
+fully constructs `OmniModelRunnerOutput`, calls
+`get_omni_connector_output()`, and only then creates the asynchronous wrapper,
+so Omni payload materialization remains synchronous.
+
+!!! note
+    `use_async_omni_output`,
+    `eager_omni_postprocess_before_async_output`, and
+    `omni_pooler_payload_include_hidden` are model implementation contracts,
+    not user-facing configuration fields.
+
+## Related Files
+
+- `vllm_omni/worker/gpu_ar_model_runner.py`: Async output object, safe tensor
+  snapshots, compatibility guards, and deferred Omni output builder.
+- `vllm_omni/platforms/npu/worker/npu_ar_model_runner.py`: Separate Ascend NPU
+  runner, where Omni output materialization remains synchronous.
+- `vllm_omni/model_executor/models/qwen3_omni/qwen3_omni.py`: Thinker and
+  Talker opt-in behavior.
+- `vllm_omni/deploy/qwen3_omni_moe.yaml`: Default Qwen3-Omni deployment
+  settings.
+- `vllm_omni/model_executor/models/qwen3_tts/qwen3_tts_talker.py`: Qwen3-TTS
+  Talker opt-in and eager postprocess behavior.
+- `vllm_omni/deploy/qwen3_tts.yaml`: Default Qwen3-TTS deployment settings.
+- `tests/worker/test_gpu_ar_model_runner.py`: Snapshot, guard, connector
+  ordering, and background error-propagation tests.
+- [Async Chunk](async_chunk.md): Inter-stage chunking and scheduling design.
+- [Qwen3-Omni optimization blog](https://vllm.ai/blog/2026-07-01-qwen3-omni-optimization):
+  Optimization context and controlled performance results.
+- [PR #4476](https://github.com/vllm-project/vllm-omni/pull/4476): Feature
+  implementation, validation, and review history.

docs/design/feature/quantization.md

@@ -0,0 +1,249 @@
+# Quantization
+
+This document describes the quantization architecture in vLLM-Omni and the
+extension points for contributors. For user-facing configuration, supported
+hardware, and method-specific instructions, see the
+[quantization user guide](../../user_guide/quantization/overview.md).
+
+## Goals and scope
+
+The design has four goals:
+
+1. Provide one `quantization_config` entry point for diffusion-only,
+   multi-stage omni/TTS, and multi-stage diffusion models.
+2. Reuse vLLM's quantization registry and `QuantizationConfig` contract where
+   a vLLM backend already provides the required behavior.
+3. Allow different components of one pipeline to use different quantization
+   configurations.
+4. Preserve the distinction between online quantization of a BF16/FP16
+   checkpoint and loading a pre-quantized checkpoint with serialized scales.
+
+This document covers configuration resolution, component routing, model
+integration, backend extension, and validation. Kernel implementations and
+user-facing support matrices are outside its scope.
+
+## Architecture
+
+The quantization path is split across the following layers:
+
+| Layer | Responsibility | Source |
+| --- | --- | --- |
+| Public API | Exposes `build_quant_config`, component routing, and backend registration | `vllm_omni/quantization/__init__.py` |
+| Factory | Normalizes method names, selects Omni overrides or vLLM registry backends, detects ModelOpt configs, and reconciles checkpoint metadata | `vllm_omni/quantization/factory.py` |
+| Component router | Resolves a quantization config from a layer prefix using longest-prefix matching | `vllm_omni/quantization/component_config.py` |
+| Method configs | Implement `QuantizationConfig` and select the platform-specific linear method | `vllm_omni/quantization/*_config.py` |
+| Diffusion configuration | Stores the canonical runtime config and propagates checkpoint configuration | `vllm_omni/diffusion/data.py` |
+| Worker and model integration | Passes the resolved config into vLLM's model configuration and model layers | `vllm_omni/diffusion/worker/diffusion_worker.py` and `vllm_omni/diffusion/models/` |
+
+The runtime contract remains vLLM's `QuantizationConfig`:
+
+```text
+user/CLI or model config
+          |
+          v
+OmniDiffusionConfig.quantization_config
+          |
+          +--> build_quant_config()
+          |        |
+          |        +--> Omni override
+          |        +--> vLLM registry backend
+          |        +--> ComponentQuantizationConfig
+          |
+          +--> checkpoint metadata reconciliation
+          |
+          +--> diffusion worker / vLLM config
+          |
+          +--> model layers call get_quant_method(layer, prefix)
+```
+
+The model layer receives either a method implementation or `None`. Returning
+`None` leaves that layer unquantized.
+
+## Configuration resolution
+
+`build_quant_config()` accepts the following forms:
+
+| Input | Resolution |
+| --- | --- |
+| `None` or `"none"` | Disable quantization. |
+| Method string, such as `"fp8"` | Resolve an Omni override first, then the vLLM quantization registry. |
+| Flat dictionary with `method` or `quant_method` | Normalize the method and construct its `QuantizationConfig`. |
+| Per-component dictionary | Construct a `ComponentQuantizationConfig`. |
+| Existing `QuantizationConfig` | Pass through without rebuilding it. |
+
+Method aliases are normalized case-insensitively, with `-` and `_` treated as
+equivalent. `auto-round` and `auto_round` use the Omni INC/AutoRound adapter.
+ModelOpt checkpoint metadata is detected from its method, producer metadata, or
+`quant_algo`, then mapped to the corresponding vLLM ModelOpt configuration.
+
+The factory uses Omni-specific overrides for methods whose diffusion behavior
+or platform dispatch is not provided by the vLLM registry. The current override
+set includes INT8, BitsAndBytes, MXFP8, MXFP4, MXFP4 dual-scale, and INC/
+AutoRound. Other methods, such as FP8, GGUF, and ModelOpt, are resolved through
+vLLM's registry when their configuration is compatible.
+
+## Per-component routing
+
+Multi-stage models do not necessarily want one quantization policy for every
+module. A component map expresses that policy without requiring each pipeline
+to construct separate vLLM configurations:
+
+```python
+quantization_config = {
+    "transformer": {"method": "fp8"},
+    "vae": None,
+    "default": None,
+}
+```
+
+`ComponentQuantizationConfig` applies the following rules:
+
+1. Component keys are interpreted as runtime layer-prefixes.
+2. The longest matching prefix wins.
+3. `None` disables quantization for the matching component.
+4. `default`, when present, handles prefixes with no explicit match.
+5. A matching child config delegates to its normal
+   `get_quant_method(layer, prefix)` implementation.
+
+Prefix matching happens after the model's weight-name mapping. New model
+integrations must verify the actual runtime prefixes, especially when a
+`WeightsMapper` changes checkpoint names. A prefix mismatch can silently route
+a layer to the default configuration, so component-routing tests should cover
+both positive matches and fall-through behavior.
+
+## Online and pre-quantized checkpoints
+
+The same method name can represent two different loading paths:
+
+- **Online**: the source checkpoint contains BF16/FP16 weights; the method
+  creates quantized weights or scales while the model is loaded.
+- **Pre-quantized**: the checkpoint contains quantized weights and the metadata
+  required by the method; the loader consumes those serialized tensors.
+
+Method configuration owns the mode flag, for example
+`is_checkpoint_int8_serialized` or `is_checkpoint_mxfp8_serialized`. The
+configuration's `get_quant_method()` selects the corresponding linear method.
+
+For pipelines with transformer-specific `config.json` files, such as cascade
+models, `resolve_quant_config_from_disk()` reconciles the active configuration
+with each transformer's checkpoint metadata. Its invariants are:
+
+- If no active configuration exists, valid checkpoint metadata can be used for
+  auto-detection.
+- A method mismatch is an error rather than an implicit conversion.
+- Serialized-checkpoint flags rebuild an online configuration into the
+  matching offline configuration.
+- Checkpoint-specific `ignored_layers` are preserved by rebuilding the active
+  configuration when they differ.
+- AutoRound MXFP8 metadata (`data_type="mx_fp"`) selects its offline path.
+
+This prevents a model from interpreting serialized weights with an online
+loader or silently applying the wrong method to one transformer in a
+multi-transformer pipeline.
+
+## Model and layer integration
+
+Diffusion model constructors receive the resolved configuration and pass it to
+quantizable vLLM layers. Each method configuration is responsible for:
+
+- identifying supported layers in `get_quant_method()`;
+- creating the correct parameter shapes and loaders;
+- processing weights after loading when conversion is required; and
+- applying the platform-specific quantized operation at runtime.
+
+Model code must keep precision-sensitive or unsupported modules unquantized.
+The shared `safe_quant_config()` helper is used by model integrations for
+normalization and modulation layers where a generic FP8 configuration would be
+unsafe. Text encoders, audio/vision encoders, VAEs, schedulers, and tokenizers
+are not implicitly quantized just because the main transformer is quantized;
+they require explicit component routing and method support.
+
+Weight quantization and quantized KV cache are separate paths. The former is
+resolved through `quantization_config`; runtime attention/KV-cache quantization
+is configured by the diffusion attention configuration and must be validated
+independently.
+
+## Platform and parallelism boundaries
+
+Platform checks belong in the method implementation rather than in the common
+factory. This keeps configuration parsing portable while allowing CUDA, NPU,
+and XPU methods to select different kernels or reject unsupported execution at
+model construction time. The user guide is the source of truth for the current
+hardware support matrix.
+
+Quantization methods must also respect the weight layout produced by the
+configured parallelism strategy:
+
+- Tensor-parallel layers may receive partitioned weight shapes and loaders.
+- Sequence-parallel execution changes activation movement but does not by
+  itself change the quantization configuration.
+- Data-parallel replicas build their own method state; quantization metadata
+  must be available consistently on every rank.
+- `WeightsMapper` transformations must be applied before matching ignored-layer
+  lists or component prefixes.
+
+Distributed layerwise offload has an additional boundary: its sharded mmap
+AllGather path is not compatible with online quantization because the path
+expects already materialized checkpoint weights. Use the rank-local
+`--dlo-no-use-allgather` path or a compatible pre-quantized checkpoint when
+combining these features. See the [DLO design document](distributed_layerwise_offload.md)
+for the full compatibility matrix.
+
+## Adding a quantization backend
+
+Contributors should follow this sequence:
+
+1. Check whether an existing vLLM backend can provide the required weight
+   format and kernel behavior. Prefer reuse over a duplicate Omni backend.
+2. If Omni-specific behavior is required, implement a `QuantizationConfig` and
+   its `QuantizeMethodBase`/linear method in `vllm_omni/quantization/`.
+3. Implement `get_name()`, supported activation dtypes, minimum capability,
+   checkpoint parsing, ignored-layer handling, and `get_quant_method()`.
+4. Add a lazy factory override in `factory.py`. Keep optional platform
+   dependencies out of package import paths and normalize aliases to one
+   canonical method name.
+5. If the method is pre-quantized, define the checkpoint metadata and the
+   serialized loading path before adding an online path.
+6. Add model-specific component routing only when the model contains stages
+   that must use different policies.
+7. Document user-visible configuration in the quantization user guide and keep
+   implementation rationale here.
+
+The public factory registration hook,
+`register_quantization_override(method, builder)`, is intended for integrations
+that need to register an Omni-compatible builder without changing the vLLM
+registry.
+
+## Validation
+
+Validation should cover the complete path, not only whether a config object can
+be instantiated:
+
+| Area | Required checks |
+| --- | --- |
+| Factory | Strings, aliases, flat dictionaries, invalid inputs, passthrough, and component maps |
+| Method config | Online/offline selection, ignored layers, platform guards, and weight loading |
+| Checkpoint metadata | Auto-detection, method mismatch errors, serialized flags, and per-transformer metadata |
+| Model integration | Correct quantization scope, unquantized components, and parallel weight layouts |
+| Numerical quality | Same-seed BF16 versus quantized output comparison at representative resolutions and step counts |
+| Performance | Peak device memory, generation latency, and any backend-specific throughput metric |
+
+The unit tests live under `tests/diffusion/quantization/`. The trajectory
+similarity tool in
+`vllm_omni/quantization/tools/compare_diffusion_trajectory_similarity.py`
+provides a repeatable quality and memory comparison for diffusion models.
+Method support and feature combinations remain model- and hardware-dependent;
+passing factory tests alone does not establish production support.
+
+## Design invariants
+
+- `quantization_config` is the single runtime source of truth after config
+  normalization.
+- A checkpoint method mismatch fails loudly instead of risking weight
+  corruption.
+- Components without an explicit supported quantization path remain in their
+  original precision.
+- Optional backend dependencies are imported lazily.
+- Method implementations own platform-specific capability checks and kernels.
+- User-facing support claims require model, hardware, numerical, and memory
+  validation in addition to configuration-level tests.

docs/design/feature/ray_based_execution.md

@@ -1,59 +0,0 @@
-# Distributed utils
-
-This directory (vllm_omni/distributed/ray_utils) contains utilities for distributed execution in vllm-omni, supporting both **Ray** and **Multiprocessing** backends.
-## 1. Installation
-```bash
-pip install "ray[default]"
-```
-## 2. Ray Utils
-
-The `ray_utils` module provides helper functions for managing Ray clusters and actors, which is essential for:
-*   **Multi-node deployment**: Running pipeline stages across different physical machines.
-*   **Resource management**: Efficient GPU/CPU allocation.
-
-### 2.1 Basic Usage
-
-To use the Ray backend, specify `worker_backend="ray"` when initializing the engine.
-
-**Command Line Example:**
-```bash
-vllm serve Qwen/Qwen2.5-Omni-7B \
-  --omni \
-  --port 8091 \
-  --worker-backend ray \
-  --ray-address auto
-```
-
-### 2.2 Cluster Setup
-
-**Step 1: Start Head Node**
-Run this on your primary machine:
-```bash
-ray start --head --port=6399
-```
-
-**Step 2: Connect Worker Nodes**
-Run this on each worker machine:
-```bash
-ray start --address=<HEAD_NODE_IP>:6399
-```
-
-> **Tip**: For a complete cluster setup script, refer to the vLLM example:
-> [run_cluster.sh](https://github.com/vllm-project/vllm/blob/main/examples/online_serving/run_cluster.sh)
-
-### 2.3 Distributed Connector Support
-
-When running on Ray, the system automatically adapts its communication strategy:
-
-*   **Cross-Node**: Recommended to use `MooncakeTransferEngineConnector` (RDMA, fastest) or `MooncakeStoreConnector` (TCP fallback).
-*   **Same-Node**: Can still use `SharedMemoryConnector` for efficiency, or Ray's native object store (plasma).
-*   **SHM payload routing**: `SharedMemoryConnector` always stores payloads in `/dev/shm`.
-
-### 2.4 Internal Helpers
-
-*   **`initialize_ray_cluster`**: Connects to an existing Ray cluster or starts a local one.
-
-## 3. Troubleshooting
-
-*   **Connection Issues**: Ensure the Ray head node is accessible and ports (default 6399 in this example) are open.
-*   **Version Mismatch**: Ensure all nodes run the same version of Ray and Python.

docs/design/index.md

@@ -1,6 +1,8 @@
 # Design Documents
 
-This section contains design documents and architecture specifications for vLLM-Omni.
+This section contains design documents and architecture specifications for
+vLLM-Omni. The sidebar groups documents by the system concern they describe,
+while the source paths remain stable so existing links continue to work.
 
 ## Architecture Documents
 
@@ -8,18 +10,63 @@ This section contains design documents and architecture specifications for vLLM-
 
 ## Feature Design Documents
 
-- [Disaggregated Inference](feature/disaggregated_inference.md)
+### Runtime and stage execution
-- [Ray-based Execution](feature/ray_based_execution.md)
-- [Adding Step Execution Support for Diffusion Pipelines](feature/diffusion_step_execution.md)
-- [Request-Level Batching for Diffusion](feature/diffusion_request_level_batching.md)
-- [Continuous Batching for Step-Wise Diffusion](feature/diffusion_continuous_batching.md)
 
-## Infrastructure Design Documents
+- [Disaggregated Inference](feature/disaggregated_inference.md)
+- [Async Chunk](feature/async_chunk.md)
+- [Async Diffusion Output](feature/async_diffusion_output.md)
+- [Async Omni Output Materialization](feature/omni_async_output_materialization.md)
+- [Automatic Prefix Caching in Omni Models](feature/prefix_caching.md)
+
+### Communication
+
+#### OmniConnector implementations
+
+- [Mooncake Store Connector](feature/omni_connectors/mooncake_store_connector.md)
+- [Mooncake Transfer Engine Connector](feature/omni_connectors/mooncake_transfer_engine_connector.md)
+- [Mori Transfer Engine Connector](feature/omni_connectors/mori_transfer_engine_connector.md)
+- [Shared Memory Connector](feature/omni_connectors/shared_memory_connector.md)
+- [Yuanrong Store Connector](feature/omni_connectors/yuanrong_connector.md)
+- [Yuanrong Transfer Engine Connector](feature/omni_connectors/yuanrong_transfer_engine_connector.md)
+
+### Diffusion acceleration
+
+#### Parallelism
+
+- [CFG-Parallel](feature/cfg_parallel.md)
+- [Expert Parallel](feature/expert_parallel.md)
+- [Hybrid Sharded Data Parallel (HSDP)](feature/hsdp.md)
+- [Pipeline Parallel](feature/pipeline_parallel.md)
+- [Sequence Parallel](feature/sequence_parallel.md)
+- [Tensor Parallel](feature/tensor_parallel.md)
+- [VAE Patch Parallelism](feature/vae_parallel.md)
+
+#### Attention Backends
+
+- [Skip-Softmax](feature/skip_softmax.md)
+
+#### Quantization
+
+- [Quantization](feature/quantization.md)
+
+- [Cache-DiT](feature/cache_dit.md)
+- [TeaCache](feature/teacache.md)
+- [Diffusion Continuous Batching](feature/diffusion_continuous_batching.md)
+- [Distributed Layerwise Offload](feature/distributed_layerwise_offload.md)
+
+## Infrastructure and Performance
 
 - [Prometheus Metrics](metrics.md)
+- [Speech Generation Performance Optimizations](qwen3_omni_tts_performance_optimization.md)
 
 ## Module Design Documents
 
+### Runtime Modules
+
 - [AR Module](module/ar_module.md)
 - [DIT Module](module/dit_module.md)
+
+### Orchestration
+
 - [Entrypoint Module](module/entrypoint_module.md)
+- [AsyncOmni Architecture (Qwen3-Omni Example)](module/async_omni_architecture.md)

docs/design/module/dit_module.md

@@ -252,8 +252,7 @@ while True:
   batch of compatible independent requests for one full pipeline forward when
   the pipeline supports request-level batching; the step-wise path can also
   admit or remove compatible requests between denoise steps. See
-  [Request-Level Batching](../feature/diffusion_request_level_batching.md) and
+  [Diffusion Continuous Batching](../feature/diffusion_continuous_batching.md).
-  [Continuous Batching for Step-Wise Diffusion](../feature/diffusion_continuous_batching.md).
 
 ---
 

docs/getting_started/installation/README.md

@@ -1,5 +1,8 @@
 # Installation
 
+!!! important
+    vLLM-Omni is released against the matching upstream vLLM major/minor version. vLLM-Omni 0.26.x requires vLLM 0.26.x; the stable v0.26.0 instructions pin vLLM 0.26.0.
+
 vLLM-Omni supports the following hardware platforms:
 
 - [GPU](gpu.md)

docs/getting_started/installation/gpu/cuda.inc.md

@@ -5,7 +5,7 @@
 # --8<-- [end:requirements]
 # --8<-- [start:set-up-using-python]
 
-vLLM-Omni depends vLLM. So please follow instructions below mainly for vLLM.
+vLLM-Omni depends on the matching major/minor release of vLLM. The vLLM-Omni 0.26.x release line uses vLLM 0.26.x.
 
 !!! note
     PyTorch installed via `conda` will statically link `NCCL` library, which can cause issues when vLLM tries to use `NCCL`. See <gh-issue:8420> for more details.
@@ -124,7 +124,7 @@ If you want to specify the base vLLM version:
 ```bash
 DOCKER_BUILDKIT=1 docker build \
   -f docker/Dockerfile.cuda \
-  --build-arg BASE_IMAGE=vllm/vllm-openai:v0.22.1 \
+  --build-arg BASE_IMAGE=vllm/vllm-openai:v0.26.0 \
   -t vllm-omni-cuda .
 ```
 

docs/getting_started/installation/npu/npu.inc.md

@@ -53,16 +53,20 @@ We are keeping [issue #886](https://github.com/vllm-project/vllm-omni/issues/886
 You can also build vLLM-Omni from the latest main branch if you want to use the latest features or bug fixes. (But sometimes it will break for a while. You can check [issue #886](https://github.com/vllm-project/vllm-omni/issues/886) for the status of the latest commit of vLLM-Omni main branch on NPU.)
 
 ```bash
-# Pin vLLM version to 0.18.0
+# Pin vLLM and vLLM-Ascend to the v0.26 release line
-git clone -b v0.18.0 https://github.com/vllm-project/vllm.git
+git clone -b v0.26.0 https://github.com/vllm-project/vllm.git
+cd vllm
 VLLM_TARGET_DEVICE=empty pip install -v -e .
+cd ..
 
-git clone -b v0.18.0rc1 https://github.com/vllm-project/vllm-ascend.git
+git clone -b releases/v0.26.0rc https://github.com/vllm-project/vllm-ascend.git
+cd vllm-ascend
 pip install -v -e .
+cd ..
 
 # Install vLLM-Omni from the latest main branch
 git clone https://github.com/vllm-project/vllm-omni.git
-cd /vllm-workspace/vllm-omni
+cd vllm-omni
 pip install -v -e . --no-build-isolation
 # or VLLM_OMNI_TARGET_DEVICE=npu pip install -v -e .
 export VLLM_WORKER_MULTIPROC_METHOD=spawn

docs/getting_started/quickstart.md

@@ -87,9 +87,8 @@ if __name__ == "__main__":
 
 !!! info
 
-    For diffusion request-level batching controls such as `max_num_seqs` and
+    For diffusion request batching, step execution, and streaming controls, see
-    `request_batch_max_wait_ms`, see
+    [Diffusion Execution Modes](../user_guide/diffusion/execution_modes.md).
-    [Request-Level Batching](../user_guide/diffusion/request_batching.md).
 
 For more usages, please refer to [offline inference](../user_guide/examples/offline_inference/qwen2_5_omni.md)
 

docs/models/supported_models.md

@@ -44,7 +44,7 @@ th {
 | `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` | ✅︎ | ✅︎ | | |
 | `LingBotVideoPipeline` | LingBot-Video dense and MoE T2V | `robbyant/lingbot-video-dense-1.3b`, `robbyant/lingbot-video-moe-30b-a3b` | ✅︎ | | | |
-| `MiniMaxH3Pipeline` | MiniMax H3 T2VA, FL2VA, and Ref2VA | `MiniMaxAI/MiniMax-H3` | ✅︎ | | | |
+| `MiniMaxH3Pipeline` | MiniMax H3 T2VA, first/last-keyframe FL2VA, and mixed-reference Ref2VA | `MiniMaxAI/MiniMax-H3` | ✅︎ | | | |
 | `DreamZeroPipeline` | DreamZero-DROID | `GEAR-Dreams/DreamZero-DROID` | ✅︎ | ✅︎ | ✅︎ | ✅︎ |
 | `HeliosPipeline`, `HeliosPyramidPipeline` | Helios | `BestWishYsh/Helios-Base`, `BestWishYsh/Helios-Mid`, `BestWishYsh/Helios-Distilled` | ✅︎ | ✅︎ | ✅︎ | |
 | `MagiHumanPipeline` | MagiHuman | `SII-GAIR/daVinci-MagiHuman-Base-1080p` | ✅︎ | ✅︎ | | |

docs/source/architecture/qwen3-omni-async-output-step-gap.svg

@@ -0,0 +1,150 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="1024" height="600" viewBox="0 0 1024 600" role="img" aria-labelledby="title desc">
+  <title id="title">Qwen3-Omni Talker step gap before and after asynchronous output materialization</title>
+  <desc id="desc">Before the optimization, synchronous D2H copy, per-request payload construction, streaming payload handoff, and connector-signal drain separate consecutive Talker forward and sample steps by about 2.8 milliseconds. After the optimization, that Omni output work runs on a background builder while the next Talker step executes, leaving a boundary of about 41 microseconds for snapshot, D2H enqueue, state update, and sampled-token feedback.</desc>
+  <defs>
+    <marker id="time-arrow" viewBox="0 0 10 10" refX="8" refY="5" markerWidth="7" markerHeight="7" orient="auto">
+      <path d="M 0 0 L 10 5 L 0 10 z" fill="#64748b"/>
+    </marker>
+    <marker id="red-end" viewBox="0 0 10 10" refX="8" refY="5" markerWidth="5" markerHeight="5" orient="auto">
+      <path d="M 0 0 L 10 5 L 0 10 z" fill="#b91c1c"/>
+    </marker>
+    <marker id="red-start" viewBox="0 0 10 10" refX="2" refY="5" markerWidth="5" markerHeight="5" orient="auto">
+      <path d="M 10 0 L 0 5 L 10 10 z" fill="#b91c1c"/>
+    </marker>
+    <marker id="blue-end" viewBox="0 0 10 10" refX="8" refY="5" markerWidth="6" markerHeight="6" orient="auto">
+      <path d="M 0 0 L 10 5 L 0 10 z" fill="#2563eb"/>
+    </marker>
+    <style>
+      .canvas { fill: #ffffff; stroke: #e5e7eb; }
+      .title { font: 700 22px system-ui, -apple-system, Segoe UI, sans-serif; fill: #111827; }
+      .subtitle { font: 500 13px system-ui, -apple-system, Segoe UI, sans-serif; fill: #64748b; }
+      .phase { font: 700 16px system-ui, -apple-system, Segoe UI, sans-serif; fill: #1f2937; }
+      .phase-sub { font: 500 12px system-ui, -apple-system, Segoe UI, sans-serif; fill: #64748b; }
+      .lane { font: 600 12px system-ui, -apple-system, Segoe UI, sans-serif; fill: #374151; }
+      .block-title { font: 700 10px system-ui, -apple-system, Segoe UI, sans-serif; letter-spacing: .25px; fill: #ffffff; }
+      .block-op { font: 500 9.5px system-ui, -apple-system, Segoe UI, sans-serif; fill: #ffffff; }
+      .axis { font: 500 11px system-ui, -apple-system, Segoe UI, sans-serif; fill: #94a3b8; }
+      .measure { font: 700 12px system-ui, -apple-system, Segoe UI, sans-serif; fill: #b91c1c; }
+      .inset-title { font: 700 11px system-ui, -apple-system, Segoe UI, sans-serif; fill: #475569; }
+      .inset-step { font: 600 10px system-ui, -apple-system, Segoe UI, sans-serif; fill: #ffffff; }
+      .note-title { font: 700 12px system-ui, -apple-system, Segoe UI, sans-serif; fill: #1e3a8a; }
+      .note-text { font: 500 10.5px system-ui, -apple-system, Segoe UI, sans-serif; fill: #475569; }
+      .stat-cap { font: 600 11px system-ui, -apple-system, Segoe UI, sans-serif; fill: #64748b; }
+      .stat-big { font: 800 16px system-ui, -apple-system, Segoe UI, sans-serif; fill: #111827; }
+      .stat-sub { font: 500 11px system-ui, -apple-system, Segoe UI, sans-serif; fill: #64748b; }
+      .before-card { fill: #eef4ff; stroke: #c7dbf6; }
+      .after-card { fill: #eafaf0; stroke: #bfe7cf; }
+      .talker { fill: #16a34a; }
+      .sync-output { fill: #ea580c; }
+      .async-output { fill: #2563eb; }
+      .zoom-card { fill: #fff7f7; stroke: #f3caca; }
+      .note-card { fill: #eff6ff; stroke: #bfdbfe; }
+      .stat-blue { fill: #eef4ff; stroke: #c7dbf6; }
+      .stat-green { fill: #eafaf0; stroke: #bfe7cf; }
+      .stat-gray { fill: #f4f5f7; stroke: #e2e5ea; }
+      .guide { stroke: #e2e8f0; stroke-width: 1; }
+      .time-line { stroke: #64748b; stroke-width: 1.3; fill: none; marker-end: url(#time-arrow); }
+      .measure-line { stroke: #b91c1c; stroke-width: 1.4; marker-start: url(#red-start); marker-end: url(#red-end); }
+      .zoom-lead { stroke: #dc2626; stroke-width: 1.15; stroke-dasharray: 4 3; fill: none; }
+      .handoff { stroke: #2563eb; stroke-width: 1.3; stroke-dasharray: 3 2; fill: none; marker-end: url(#blue-end); }
+    </style>
+  </defs>
+
+  <rect x="14" y="14" width="996" height="572" rx="14" class="canvas"/>
+
+  <text x="44" y="52" class="title">Async output: what moves off the Talker decode path</text>
+  <text x="44" y="74" class="subtitle">Qwen3-Omni Talker stage &#183; consecutive decode steps in a fixed profiler window</text>
+
+  <!-- ===================== BEFORE ===================== -->
+  <rect x="44" y="94" width="144" height="110" rx="10" class="before-card"/>
+  <text x="60" y="120" class="phase">Before</text>
+  <text x="60" y="139" class="phase-sub">Omni output built</text>
+  <text x="60" y="156" class="phase-sub">synchronously on the</text>
+  <text x="60" y="173" class="phase-sub">AR critical path</text>
+
+  <text x="206" y="122" class="lane">AR path</text>
+  <line x1="300" y1="88" x2="300" y2="210" class="guide"/>
+
+  <rect x="300" y="104" width="190" height="54" rx="5" class="talker"/>
+  <text x="395" y="124" text-anchor="middle" class="block-title">TALKER STAGE &#183; STEP N</text>
+  <text x="395" y="145" text-anchor="middle" class="block-op">forward &#8594; sample</text>
+
+  <rect x="490" y="104" width="250" height="54" rx="5" class="sync-output"/>
+  <text x="615" y="120" text-anchor="middle" class="block-title">SYNC OMNI OUTPUT &#183; STEP N</text>
+  <text x="615" y="138" text-anchor="middle" class="block-op">D2H &#8594; per-request slice / payload build</text>
+  <text x="615" y="152" text-anchor="middle" class="block-op">streaming handoff &#8594; connector-signal drain</text>
+
+  <rect x="740" y="104" width="220" height="54" rx="5" class="talker"/>
+  <text x="850" y="124" text-anchor="middle" class="block-title">TALKER STAGE &#183; STEP N+1</text>
+  <text x="850" y="145" text-anchor="middle" class="block-op">forward &#8594; sample</text>
+
+  <line x1="490" y1="174" x2="740" y2="174" class="measure-line"/>
+  <text x="615" y="194" text-anchor="middle" class="measure">~2.8 ms GPU gap: output work is inline</text>
+
+  <!-- ===================== AFTER ===================== -->
+  <rect x="44" y="218" width="144" height="140" rx="10" class="after-card"/>
+  <text x="60" y="244" class="phase">After</text>
+  <text x="60" y="263" class="phase-sub">Only step feedback</text>
+  <text x="60" y="280" class="phase-sub">stays inline; payload</text>
+  <text x="60" y="297" class="phase-sub">work runs in the</text>
+  <text x="60" y="314" class="phase-sub">background</text>
+
+  <text x="206" y="254" class="lane">Talker GPU</text>
+  <text x="206" y="326" class="lane">Output builder</text>
+  <line x1="300" y1="212" x2="300" y2="370" class="guide"/>
+
+  <rect x="300" y="226" width="235" height="54" rx="5" class="talker"/>
+  <text x="418" y="246" text-anchor="middle" class="block-title">TALKER STAGE &#183; STEP N</text>
+  <text x="418" y="267" text-anchor="middle" class="block-op">forward &#8594; sample</text>
+
+  <rect x="540" y="226" width="235" height="54" rx="5" class="talker"/>
+  <text x="658" y="246" text-anchor="middle" class="block-title">TALKER STAGE &#183; STEP N+1</text>
+  <text x="658" y="267" text-anchor="middle" class="block-op">forward &#8594; sample</text>
+
+  <path d="M 535 280 L 548 298" class="handoff"/>
+  <rect x="540" y="298" width="360" height="58" rx="5" class="async-output"/>
+  <text x="720" y="315" text-anchor="middle" class="block-title">BACKGROUND OMNI OUTPUT &#183; STEP N</text>
+  <text x="720" y="334" text-anchor="middle" class="block-op">wait for D2H &#8594; per-request slice / payload build</text>
+  <text x="720" y="349" text-anchor="middle" class="block-op">streaming handoff &#8594; connector-signal drain</text>
+
+  <line x1="300" y1="366" x2="930" y2="366" class="time-line"/>
+  <text x="922" y="382" text-anchor="end" class="axis">time</text>
+
+  <!-- leads from the real 41 us boundary to the magnified inset -->
+  <line x1="535" y1="280" x2="485" y2="424" class="zoom-lead"/>
+  <line x1="540" y1="280" x2="525" y2="424" class="zoom-lead"/>
+
+  <!-- ===================== 41 µs BOUNDARY ===================== -->
+  <rect x="300" y="386" width="400" height="92" rx="10" class="zoom-card"/>
+  <text x="316" y="404" class="inset-title">Zoom &#183; Talker step boundary (magnified)</text>
+
+  <rect x="320" y="416" width="165" height="26" rx="4" class="talker"/>
+  <rect x="525" y="416" width="155" height="26" rx="4" class="talker"/>
+  <text x="403" y="433" text-anchor="middle" class="inset-step">Talker step N</text>
+  <text x="603" y="433" text-anchor="middle" class="inset-step">Talker step N+1</text>
+
+  <line x1="485" y1="452" x2="525" y2="452" class="measure-line"/>
+  <text x="505" y="469" text-anchor="middle" class="measure">~41 &#181;s</text>
+
+  <rect x="720" y="386" width="240" height="92" rx="10" class="note-card"/>
+  <text x="736" y="405" class="note-title">Inline at the ~41 &#181;s boundary</text>
+  <text x="736" y="424" class="note-text">&#8226; eager Talker state update</text>
+  <text x="736" y="441" class="note-text">&#8226; output snapshot + D2H enqueue</text>
+  <text x="736" y="458" class="note-text">&#8226; sampled-token feedback</text>
+
+  <!-- ===================== SUMMARY ===================== -->
+  <rect x="44" y="494" width="296" height="70" rx="9" class="stat-blue"/>
+  <text x="60" y="514" class="stat-cap">Step-to-step gap</text>
+  <text x="60" y="538" class="stat-big">2.8 ms &#8594; 41 &#181;s</text>
+  <text x="60" y="555" class="stat-sub">~68&#215; tighter</text>
+
+  <rect x="356" y="494" width="296" height="70" rx="9" class="stat-green"/>
+  <text x="372" y="514" class="stat-cap">Talker GPU</text>
+  <text x="372" y="538" class="stat-big">next step starts immediately</text>
+  <text x="372" y="555" class="stat-sub">decode workers stay busy</text>
+
+  <rect x="668" y="494" width="312" height="70" rx="9" class="stat-gray"/>
+  <text x="684" y="514" class="stat-cap">Background builder</text>
+  <text x="684" y="538" class="stat-big">materializes Omni output</text>
+  <text x="684" y="555" class="stat-sub">payload work overlaps the next Talker step</text>
+</svg>

docs/user_guide/diffusion/cpu_offload.md

@@ -184,10 +184,15 @@ class Flux2Transformer2DModel(nn.Module):
 ### How It Works
 
 Distributed layerwise offloading extends single-GPU layerwise offloading to
-multi-device deployments.  Each DP rank stores only **1/dp_size** of the model
+multi-device deployments. In the default mode, each rank stores only
-weights in host memory; full layer weights are reconstructed at runtime via
+**1/dp_size** of the model weights in host memory; full layer weights are
-**AllGather** on a dedicated communication stream, overlapped with computation
+reconstructed at runtime via **AllGather** on a dedicated communication stream,
-via a fixed double-buffer scheme.
+overlapped with computation via a fixed double-buffer scheme. The
+`--dlo-no-use-allgather` mode instead streams standard-loader rank-local
+weights independently and does not shard weights across ranks.
+
+For the implementation design and compatibility matrix, see
+[Distributed Layerwise Offload](../../design/feature/distributed_layerwise_offload.md).
 
 **Key features:**
 - **Weight sharding + AllGather**: each rank stores 1/dp_size of weights,
@@ -219,7 +224,7 @@ vllm serve /path/to/model --omni \
   --enable-distributed-layerwise-offload \
   --data-parallel-size 4
 
-# Without AllGather (each rank loads full weights, no sharding)
+# Without DLO AllGather (each rank streams standard-loader rank-local weights)
 vllm serve /path/to/model --omni \
   --enable-distributed-layerwise-offload \
   --data-parallel-size 4 \
@@ -247,9 +252,9 @@ m = Omni(
 | Flag | Description | Default |
 |------|-------------|---------|
 | `--enable-distributed-layerwise-offload` | Enable DLO | `false` |
-| `--data-parallel-size N` | Number of DP ranks for weight sharding | `1` |
+| `--data-parallel-size N` | Number of DP ranks; DLO weight-sharding group when AllGather is enabled | `1` |
 | `--dlo-use-allgather` | Shard + AllGather (saves CPU, requires concurrent requests) | `true` |
-| `--dlo-no-use-allgather` | Full weights per rank (no sharding, no AllGather) | `false` |
+| `--dlo-no-use-allgather` | Use standard-loader rank-local weights; no DLO sharding or AllGather | `false` |
 
 ### How model weights are loaded (mmap path)
 
@@ -303,9 +308,15 @@ request while AllGather synchronizes only weight shards (request-independent).
 
 ### Limitations
 - Online quantization (FP8) is incompatible with mmap loading — falls back to
-  regular `load_weights()` automatically
+  regular loading only when the model is outside the DLO+AllGather mmap path;
-- Tensor Parallel is not supported (DLO uses DP-based sharding)
+  online quantization with DLO+AllGather is rejected. Use
-- HSDP + AllGather is rejected (would double-shard weights)
+  `--dlo-no-use-allgather` or disable online quantization.
+- Tensor Parallel > 1 is rejected in the DLO+AllGather mmap path because TP-aware
+  weight-loader callbacks are bypassed. The no-AllGather path retains the
+  standard TP loader and is experimental.
+- HSDP + AllGather is rejected because it would double-shard parameters. HSDP
+  with no-AllGather is accepted at configuration level, but has limited
+  end-to-end validation.
 - `num_inference_steps=None` is not allowed in DP multi-concurrency mode
 
 **Module Discovery**
@@ -338,7 +349,7 @@ OffloadBackend (base class)
 ├── LayerWiseOffloadBackend → uses LayerwiseOffloadHook
 │                          (single-GPU, full weights on host)
 └── DistributedLayerwiseOffloadBackend → uses DistributedLayerwiseOffloadHook
-                                         (multi-GPU, 1/dp_size sharded weights + AllGather)
+                                         (multi-GPU, sharded AllGather or rank-local mode)
 ```
 
 Factory function `get_offload_backend()` selects the appropriate backend based on
@@ -369,4 +380,10 @@ reasoner/generator components inside the model forward pass.
 **Notes:**
 - Model-Level Offloading is expected to be supported by all common diffusion models (DiT and encoders) naturally
 - Layerwise Offloading requires DiT class to define `_layerwise_offload_blocks_attrs` pointing to transformer blocks
-- Distributed Layerwise Offloading works with any model that supports Layerwise Offloading — no additional model changes required.  See [Cosmos3 DistOffload recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/cosmos3/Cosmos3-DistOffload.md) for usage examples.
+- Distributed Layerwise Offloading reuses the layerwise block topology, but the
+  model and parallelism combination still needs validation. AllGather mode
+  additionally requires a compatible checkpoint/mmap layout; no-AllGather mode
+  retains the standard loader's rank-local tensors. See the
+  [DLO design](../../design/feature/distributed_layerwise_offload.md) and
+  [Cosmos3 DistOffload recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/cosmos3/Cosmos3-DistOffload.md)
+  for usage examples.

docs/user_guide/diffusion/execution_modes.md

@@ -0,0 +1,252 @@
+# Diffusion Execution Modes
+
+vLLM-Omni supports complete-request execution and step-wise diffusion
+execution. Both modes use the same asynchronous engine output stream; the
+configuration controls how work is scheduled and whether intermediate outputs
+are exposed.
+
+## Choose a Mode
+
+| Goal | CLI configuration |
+|---|---|
+| Serial request execution | `--max-num-seqs 1` |
+| Fused request-level batching | `--max-num-seqs N` |
+| Single-request step execution | `--step-execution --max-num-seqs 1` |
+| Step-wise continuous batching | `--step-execution --max-num-seqs N` |
+| Chunked diffusion output | `--diffusion-streaming-output` |
+
+`N` must be greater than `1` to allow batching. The selected pipeline must
+support request-level batching or batched-step execution as appropriate;
+single-request step support alone is not sufficient for step-wise continuous
+batching.
+
+## Request Execution
+
+Request execution is the default when `--step-execution` is omitted. The
+pipeline performs a complete `forward()` for each scheduler wave.
+
+### Serial Requests
+
+Use the conservative serial path with any request-mode pipeline:
+
+```bash
+vllm serve MODEL --omni \
+  --port 8091 \
+  --max-num-seqs 1
+```
+
+### Request-Level Batching
+
+Set `max_num_seqs` above one to combine compatible independent requests into a
+single pipeline forward:
+
+```bash
+vllm serve Qwen/Qwen-Image --omni \
+  --port 8091 \
+  --max-num-seqs 4
+```
+
+For bursty traffic, add a small admission window:
+
+```bash
+vllm serve Qwen/Qwen-Image --omni \
+  --port 8091 \
+  --max-num-seqs 4 \
+  --request-batch-max-wait-ms 20
+```
+
+`--request-batch-max-wait-ms 0` is the default. A nonzero value can improve
+batch formation but adds up to that much latency before a new scheduler wave.
+It has no effect in step mode.
+
+Each prompt remains a separate logical request. Do not submit a top-level list
+as one packed prompt. Concurrent serving requests are batched internally when
+their shapes, guidance settings, output counts, and LoRA settings are
+compatible.
+
+Only pipelines that declare request-batch support accept
+`max_num_seqs>1` in request mode. Unsupported pipelines fail during engine
+initialization; use `max_num_seqs=1` instead.
+
+## Step Execution
+
+Step execution lets the scheduler advance and abort requests between denoise
+steps:
+
+```bash
+vllm serve Qwen/Qwen-Image --omni \
+  --port 8091 \
+  --step-execution \
+  --max-num-seqs 1
+```
+
+Set a larger capacity to allow compatible requests to share denoise waves:
+
+```bash
+vllm serve Qwen/Qwen-Image --omni \
+  --port 8091 \
+  --step-execution \
+  --max-num-seqs 8
+```
+
+Step continuous batching is experimental. Start with `max_num_seqs=1` when
+validating a model or debugging correctness, then increase it for
+multi-request throughput.
+
+Step execution is capability-based, not a generic switch for every diffusion
+model. Qwen-Image supports step-wise continuous batching. HunyuanImage3 also
+supports it, but only when its resolved self-attention backend is `TORCH_SDPA`;
+set `DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA` or configure
+`diffusion_attention_config.default.backend=TORCH_SDPA` before using
+`--max-num-seqs >1`. See the
+[HunyuanImage-3.0 recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Tencent/HunyuanImage-3.0-Instruct.md)
+for its validated configuration. Helios supports single-request step execution only: use
+`--step-execution --max-num-seqs 1` for Helios. Consult the selected pipeline's
+documentation and source for the latest support status.
+
+## Streaming Output
+
+Use `--diffusion-streaming-output` for a pipeline that can produce intermediate
+diffusion outputs:
+
+```bash
+vllm serve BestWishYsh/Helios-Distilled --omni \
+  --port 8000 \
+  --diffusion-streaming-output
+```
+
+Streaming output requires step execution. If
+`--diffusion-streaming-output` is set without `--step-execution`, the engine
+enables step execution automatically. Model initialization fails if the
+pipeline does not implement step execution.
+
+Chunk-capable pipelines emit intermediate and final outputs through the same
+request stream. Final-only step pipelines emit only the final result even when
+streaming output is enabled.
+
+The normal non-streaming serving path uses the same internal stream but drains
+it and returns only the final output. Users do not need to enable
+`--diffusion-streaming-output` to benefit from the unified engine lifecycle.
+
+## Send Requests
+
+Execution-mode flags configure the server. They do not change the
+OpenAI-compatible client request format. For example, after starting any of
+the non-streaming configurations above:
+
+```bash
+curl -X POST http://localhost:8091/v1/images/generations \
+  -H "Content-Type: application/json" \
+  -d '{
+    "prompt": "a cat sitting on a windowsill",
+    "size": "1024x1024",
+    "num_inference_steps": 50,
+    "seed": 42
+  }' | jq -r '.data[0].b64_json' | base64 -d > cat.png
+```
+
+To create a request-level batch, send multiple independent requests
+concurrently. Do not put several prompts into one `prompt` field:
+
+```bash
+for prompt in "a red fox" "a blue bird" "a green frog"; do
+  curl -s -X POST http://localhost:8091/v1/images/generations \
+    -H "Content-Type: application/json" \
+    -d "{\"prompt\":\"${prompt}\",\"size\":\"1024x1024\",\"seed\":42}" \
+    > /dev/null &
+done
+wait
+```
+
+The scheduler may batch these requests when their sampling parameters are
+compatible. See the
+[Image Generation API](../../serving/image_generation_api.md) for response
+formats and additional client examples. Streaming models can use
+model-specific streaming endpoints documented by their serving guide.
+
+## Python API
+
+The Python arguments mirror the CLI flags:
+
+```python
+from vllm_omni import Omni
+
+omni = Omni(
+    model="Qwen/Qwen-Image",
+    max_num_seqs=4,
+    request_batch_max_wait_ms=20.0,
+)
+
+outputs = omni.generate(
+    [
+        "a cup of coffee on a table",
+        "a toy dinosaur on a sandy beach",
+        "a fox waking up in bed and yawning",
+    ]
+)
+```
+
+Each list item passed to `Omni.generate()` becomes an independent logical
+request that the scheduler may batch with compatible items.
+
+For step execution:
+
+```python
+from vllm_omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+omni = Omni(
+    model="Qwen/Qwen-Image",
+    step_execution=True,
+    max_num_seqs=1,
+)
+
+outputs = omni.generate(
+    "A cat sitting on a windowsill",
+    OmniDiffusionSamplingParams(num_inference_steps=50),
+)
+```
+
+## Stage Configuration
+
+Use the equivalent engine arguments in a deployment YAML:
+
+```yaml
+stage_args:
+  - stage_id: 0
+    stage_type: diffusion
+    engine_args:
+      step_execution: false
+      max_num_seqs: 4
+      request_batch_max_wait_ms: 20
+```
+
+For step execution, set `step_execution: true` and remove
+`request_batch_max_wait_ms`.
+
+## CLI Reference
+
+| Flag | Default | Effect |
+|---|---:|---|
+| `--step-execution` | disabled | Select step-wise scheduling |
+| `--max-num-seqs` | `1` for diffusion stages | Set request- or step-scheduler capacity |
+| `--request-batch-max-wait-ms` | `0` | Wait for burst coalescing in request mode |
+| `--diffusion-streaming-output` | disabled | Expose supported intermediate diffusion outputs and require step execution |
+
+## Limitations and Troubleshooting
+
+- Requests batch only when their compatibility-sensitive parameters match.
+- Different LoRA adapters or scales run in separate batches.
+- FIFO scheduling can cause an incompatible request to block later compatible
+  requests.
+- All diffusion cache backends are unsupported in step mode. KV transfer is
+  supported for newly admitted step requests; some other request-mode extras
+  remain unsupported.
+- If request-mode startup reports that the pipeline does not support batching,
+  use `--max-num-seqs 1`.
+- If step-mode startup mentions `prepare_encode()`, `denoise_step()`,
+  `step_scheduler()`, or `post_decode()`, the pipeline does not implement the
+  required step contract.
+
+For implementation details and model-author guidance, see
+[Diffusion Continuous Batching](../../design/feature/diffusion_continuous_batching.md).

docs/user_guide/diffusion/request_batching.md

@@ -1,119 +0,0 @@
-# Request-Level Batching
-
-Request-level batching lets diffusion serving combine multiple compatible
-logical requests into one pipeline `forward()` call. Each prompt remains a
-separate request with its own `request_id`, sampling parameters, seed, output,
-error, and abort state. The scheduler decides which requests can run together.
-
-!!! warning "Prompt List Semantics"
-
-    Diffusion request-level batching does not support a top-level packed
-    list-prompt request. Submit multiple prompts as independent requests and let
-    the scheduler batch compatible in-flight requests. Multimodal payloads stay
-    inside a single prompt dict, for example
-    `{"prompt": "...", "multi_modal_data": {"image": image}}`.
-
-## Enablement
-
-Request-level execution is the default diffusion request mode when
-`step_execution=False`. `max_num_seqs` controls the maximum number of compatible
-requests in one scheduler wave:
-
-- `max_num_seqs=1` is the serial request path.
-- `max_num_seqs>1` allows fused request-level batching for pipelines that
-  declare request-batch support.
-
-Increase `max_num_seqs` above `1` to allow the request scheduler to keep more
-than one compatible request active:
-
-```bash
-vllm serve Qwen/Qwen-Image --omni \
-  --port 8091 \
-  --max-num-seqs 4
-```
-
-For bursty online traffic, you can also set a small admission wait window. This
-lets the engine wait briefly before the first `schedule()` of a new wave so
-nearby compatible requests can arrive and share the same fused forward pass:
-
-```bash
-vllm serve Qwen/Qwen-Image --omni \
-  --port 8091 \
-  --max-num-seqs 4 \
-  --request-batch-max-wait-ms 20
-```
-
-`--request-batch-max-wait-ms 0` is the default and disables admission waiting,
-so there is no added wait latency.
-
-For deploy YAMLs, configure the diffusion stage engine args:
-
-```yaml
-stage_args:
-  - stage_id: 0
-    stage_type: diffusion
-    engine_args:
-      max_num_seqs: 4
-      request_batch_max_wait_ms: 20
-```
-
-## Compatibility
-
-Only pipelines that declare request-batch support can use the fused
-multi-request `pipeline.forward(batch)` path. Pipelines that do not support this
-contract can still use request-level execution with `max_num_seqs=1`, which is
-the serial request path. Setting `max_num_seqs>1` for a pipeline without
-request-batch support fails early during engine initialization.
-
-The scheduler batches only compatible requests. Compatibility is based on
-shape-sensitive and guidance-sensitive sampling fields, including resolution,
-frame count, CFG settings, output count, and LoRA identity. Requests with
-different LoRA adapters or scales are kept in separate batches so the worker
-activates one adapter per batch.
-
-Request-level batching applies when `step_execution=False`. For the separate
-step-wise runtime, see [Step Execution](step_execution.md).
-
-## Tuning
-
-- `max_num_seqs` caps the number of active compatible requests in one scheduler
-  wave. `1` means serial request execution; values above `1` enable fused
-  request-level batching when the pipeline supports it.
-- `request_batch_max_wait_ms` is an upper bound on extra admission wait before a
-  new wave starts. Keep it small for latency-sensitive serving; values such as
-  `10` to `50` ms are a practical starting range for bursty HTTP ingress.
-- `0` disables admission waiting and preserves the lowest first-request latency.
-- FIFO ordering is conservative: an incompatible request at the front of the
-  waiting queue can block later compatible requests from joining the current
-  batch.
-
-## Python API
-
-When constructing `Omni`, pass the same engine arguments:
-
-```python
-from vllm_omni.entrypoints.omni import Omni
-
-omni = Omni(
-    model="Qwen/Qwen-Image",
-    max_num_seqs=4,
-    request_batch_max_wait_ms=20.0,
-)
-
-outputs = omni.generate(
-    [
-        "a cup of coffee on a table",
-        "a toy dinosaur on a sandy beach",
-        "a fox waking up in bed and yawning",
-    ]
-)
-```
-
-`Omni.generate([...])` submits each list item as its own logical diffusion
-request. The runtime may batch those requests internally when their sampling
-parameters are compatible.
-
-## For Contributors
-
-For implementation details and model-author guidance, see
-[Request-Level Batching for Diffusion](../../design/feature/diffusion_request_level_batching.md).

docs/user_guide/diffusion/step_execution.md

@@ -1,100 +0,0 @@
-# Step Execution
-
-Step execution is an opt-in diffusion execution mode enabled with
-`step_execution=True` when constructing `Omni`.
-
-It is not a generic diffusion toggle for every pipeline. Only pipelines that
-implement the stepwise contract support it today.
-
-## Quick Start
-
-### Python API
-
-```python
-from vllm_omni import Omni
-from vllm_omni.inputs.data import OmniDiffusionSamplingParams
-
-omni = Omni(
-    model="Qwen/Qwen-Image",
-    step_execution=True,
-)
-
-outputs = omni.generate(
-    "A cat sitting on a windowsill",
-    OmniDiffusionSamplingParams(
-        num_inference_steps=50,
-    ),
-)
-```
-
-### Serving
-
-```bash
-vllm serve Qwen/Qwen-Image --omni \
-  --port 8091 \
-  --step-execution \
-  --max-num-seqs 8
-```
-
-For serving, `--step-execution` enables the step-wise runtime. Continuous
-batching only becomes relevant when `--max-num-seqs > 1`.
-
-`max_num_seqs` controls the step scheduler capacity:
-
-- `max_num_seqs=1` runs one request through the step-wise state machine.
-- `max_num_seqs>1` allows compatible active requests to share step waves.
-
-## Supported Pipelines
-
-| Pipeline | Example models | Step execution |
-|----------|----------------|----------------|
-| `QwenImagePipeline` | `Qwen/Qwen-Image`, `Qwen/Qwen-Image-2512` | Yes |
-| All other diffusion pipelines | `QwenImageEditPipeline`, `QwenImageEditPlusPipeline`, `QwenImageLayeredPipeline`, GLM-Image, Wan, Flux, etc. | No |
-
-!!! warning "Experimental continuous batching"
-    When `--step-execution` is enabled and `max_num_seqs > 1` is configured,
-    the step-wise path can batch
-    compatible requests together. This is experimental. Requests with
-    incompatible sampling parameters are intentionally kept in separate batches,
-    and `max_num_seqs=1` remains the conservative single-request step path.
-
-## Current Limitations
-
-- Continuous batching under `step_execution` is experimental and only batches
-  compatible requests.
-- `cache_backend` is not supported together with step execution.
-- Unsupported pipelines fail early during model loading.
-- Request-mode extras such as KV transfer are not wired into step mode yet.
-- LoRA is supported in step mode, but each batch must use a single adapter:
-  requests with different `lora_request` or `lora_scale` are scheduled into
-  separate batches.
-
-## When To Use It
-
-Use step execution only when you specifically need the pipeline to run through
-its stepwise request state machine. For normal diffusion inference, leave it
-disabled unless your workflow depends on this mode.
-
-For Qwen-Image online serving, the usual progression is:
-
-- start with `--step-execution --max-num-seqs 1` if you only need the step-wise path
-- increase `--max-num-seqs` after that if you want the experimental compatible-request batching behavior
-
-If you are looking for general diffusion speedups, see
-[Diffusion Features Overview](../diffusion_features.md).
-
-## Troubleshooting
-
-If model loading fails with a message mentioning `prepare_encode()`,
-`denoise_step()`, `step_scheduler()`, and `post_decode()`, the selected
-pipeline does not support step execution.
-
-## For Model Authors
-
-If you want to add step execution support to a new diffusion pipeline, see the
-implementation guide:
-[Diffusion Step Execution Design](../../design/feature/diffusion_step_execution.md).
-
-If you also want that pipeline to participate in the experimental batched
-step-wise path, see:
-[Continuous Batching for Step-Wise Diffusion](../../design/feature/diffusion_continuous_batching.md).

docs/user_guide/diffusion_features.md

@@ -59,7 +59,7 @@ Memory optimization methods help reduce GPU memory usage, enabling inference on
 
 | Method | Description | Best For |
 |--------|-------------|----------|
-| **[CPU Offload](diffusion/cpu_offload_diffusion.md)** | Offloads model components to CPU memory | Limited VRAM, large models on consumer GPUs |
+| **[CPU Offload](diffusion/cpu_offload.md)** | Offloads model components to CPU memory | Limited VRAM, large models on consumer GPUs |
 | **[Quantization](quantization/overview.md)** | Reduces transformer stages from BF16 to FP8/INT8/etc. | Limited VRAM, minimal accuracy loss    |
 | **[VAE Parallelism](diffusion/parallelism/vae_parallelism.md)** | Distributes VAE decode work across GPUs | High-resolution generation with reduced VAE memory peak |
 
@@ -80,12 +80,11 @@ denoise steps.
 
 | Method | Description | Best For |
 |--------|-------------|----------|
-| **[Request-Level Batching](diffusion/request_batching.md)** | Scheduler batches compatible independent diffusion requests into one pipeline forward pass | Bursty online serving and multi-request throughput |
+| **[Diffusion Execution Modes](diffusion/execution_modes.md)** | Configures serial requests, request batching, step execution, continuous batching, and streaming output | Matching latency, throughput, cancellation, and output-delivery requirements |
-| **[Step Execution](diffusion/step_execution.md)** | Per-step denoise execution with mid-request abort support | Request cancellation between denoise steps, fine-grained execution control |
 
-**Note:** Request-level batching is available for pipelines that declare the
+**Note:** Request-level batching and step execution are capability-based.
-request-batch forward contract. Step execution is currently supported by
+Consult the execution guide and the selected pipeline's documentation for
-QwenImagePipeline only. See [Supported Models](#supported-models) for details.
+current support.
 
 ### Quantization Methods
 
@@ -105,8 +104,8 @@ The following tables show which models support each feature:
 
 > Notes:
 
-> 1. CPU Offload has two methods: Module-wise (default for models with DiT + text encoder) and Layerwise. The tables below show **Layerwise support** only. Split models like Cosmos3 (no separate text encoder) swap their reasoner/generator components for module-wise offload; see the [CPU Offload Guide](diffusion/cpu_offload_diffusion.md).
+> 1. CPU Offload has two methods: Module-wise (default for models with DiT + text encoder) and Layerwise. The tables below show **Layerwise support** only. Split models like Cosmos3 (no separate text encoder) swap their reasoner/generator components for module-wise offload; see the [CPU Offload Guide](diffusion/cpu_offload.md).
-> 2. The **💾Quantization** column is collapsed for readability. See [Quantization Overview](quantization/overview.md) for per-method and per-model support details.
+> 2. The **💾Quantization** column is collapsed for readability. See [Quantization](quantization/overview.md) for per-method and per-model support details.
 
 ### ImageGen
 
@@ -160,7 +159,7 @@ The following tables show which models support each feature:
 | **HunyuanVideo-1.5 T2V I2V** |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
 | **DreamID-Omni**             |     ❌     |     ❌      |           ❌           |       ✅        |         ❌         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Cosmos3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
-
+| **MiniMax-H3**               |     ❌     |     ✅      |           ✅           |       ❌        |       ✅ (DiT/TE)  |         ❌         |   ✅    |             ✅             |       ✅ (tile)       |      ✅ (DiT)      |        ❌         |
 
 **Frame Interpolation Support**
 
@@ -206,7 +205,7 @@ The following tables show which models support each feature:
     3. CPU Offloading (Layerwise) and CPU Offloading (Module-wise) are not compatible.
     4. CPU Offloading (Layerwise) supports single-card for now.
     5. Using FP8-Quant as an example of qunatization methods.
-    6. Step Execution is not compatible with cache backends (TeaCache, Cache-DiT). LoRA is supported, but each scheduled batch must use a single adapter (requests with different `lora_request` or `lora_scale` are kept in separate batches).
+    6. Step Execution is not compatible with any diffusion cache backend. LoRA is supported, but each scheduled batch must use a single adapter (requests with different `lora_request` or `lora_scale` are kept in separate batches).
 
 
 ## Multi-Thread Weight Loading
@@ -275,9 +274,9 @@ Measured on NVIDIA H800:
 
 **Memory Optimization:**
 
-- **[CPU Offload Guide](diffusion/cpu_offload_diffusion.md)** - Offload model components to CPU, reduce GPU memory usage
+- **[CPU Offload Guide](diffusion/cpu_offload.md)** - Offload model components to CPU, reduce GPU memory usage
 - **[VAE Parallelism Guide](diffusion/parallelism/vae_parallelism.md)** - Distribute VAE decode work across GPUs for high-resolution images and videos
-- **[Quantization Overview](quantization/overview.md)** - Overview of quantization methods for diffusion, multi-stage omni/TTS, and multi-stage diffusion models
+- **[Quantization](quantization/overview.md)** - Quantization methods for diffusion, multi-stage omni/TTS, and multi-stage diffusion models
 
 **Extensions:**
 
@@ -286,7 +285,7 @@ Measured on NVIDIA H800:
 
 **Execution Modes:**
 
-- **[Step Execution Guide](diffusion/step_execution.md)** - Per-step denoise execution with mid-request abort support
+- **[Diffusion Execution Modes](diffusion/execution_modes.md)** - Configure request batching, step execution, continuous batching, and streaming output
 
 **Startup Optimization:**
 

docs/user_guide/examples/offline_inference/text_to_image.md

@@ -184,7 +184,7 @@ if __name__ == "__main__":
 !!! info
 
     For diffusion request-level batching controls such as `max_num_seqs`, see
-    [Request-Level Batching](../../diffusion/request_batching.md).
+    [Diffusion Execution Modes](../../diffusion/execution_modes.md).
 
 ### Negative Prompts
 

docs/user_guide/quantization/fp8.md

@@ -93,6 +93,7 @@ warmup_quack_fp8([(14040, 2048, 6144), (14040, 2048, 2048)])
 | HunyuanImage-3.0 | `tencent/HunyuanImage-3.0`, `tencent/HunyuanImage-3.0-Instruct` | Yes | Yes | All layers; use the Hunyuan stage config for multi-stage runs | None | |
 | HunyuanVideo-1.5 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v`, `720p_t2v`, `480p_i2v` | Yes | Yes | All layers | None | |
 | Cosmos3 | `nvidia/Cosmos3-Nano`, `nvidia/Cosmos3-Super` | Yes | Not validated | All layers | None | |
+| MiniMax H3 | `MiniMaxAI/MiniMax-H3` | Yes | Not validated | DiT linears except patch, timestep, and final projections; not compatible with layerwise offload | None | |
 
 ### Multi-Stage Omni/TTS Model (Qwen3-Omni, Qwen3-TTS)
 

docs/user_guide/quantization/overview.md

@@ -5,6 +5,9 @@ path. The same configuration entrypoint is used across diffusion-only models,
 multi-stage omni/TTS models, and multi-stage diffusion models, but each model
 type has a different quantization scope.
 
+For the internal architecture and backend extension points, see the
+[quantization design document](../../design/feature/quantization.md).
+
 ## Quantization Modes
 
 | Mode | Guide | Description | Methods |

examples/offline_inference/personaplex/personaplex_offline.py

@@ -0,0 +1,77 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Offline PersonaPlex inference through the vllm-omni fullduplex backend.
+
+This is the runnable end-to-end proof for the integration: it drives a user WAV
+through :class:`PersonaPlexEngine` + :class:`PersonaPlexSession` (our wrappers
+around the Moshi/Mimi lockstep loop) and writes the agent's audio + inner-monologue
+text. It mirrors ``python -m moshi.offline`` but goes through our ``FrameStepper``
+seam, so a green run validates that the session/adapter plumbing is faithful.
+
+Prereqs: HF access to ``nvidia/personaplex-7b-v1`` (``export HF_TOKEN=...``). The
+stack is moshi-free (native temporal + depformer + streaming Mimi via
+``transformers``); decoding is greedy.
+
+Example:
+    python examples/offline_inference/personaplex/personaplex_offline.py \\
+        --input-wav input_assistant.wav --output-wav out.wav \\
+        --voice-prompt NATF2.pt --persona "You are a wise and friendly teacher."
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+
+import numpy as np
+
+from vllm_omni.experimental.fullduplex.personaplex import (
+    PersonaPlexConfig,
+    PersonaPlexEngine,
+    PersonaPlexSession,
+)
+from vllm_omni.experimental.fullduplex.personaplex.config import DEFAULT_PERSONA
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--input-wav", required=True, help="User-side input WAV")
+    parser.add_argument("--output-wav", required=True, help="Agent audio output WAV")
+    parser.add_argument("--output-text", default=None, help="Optional JSON for agent text")
+    parser.add_argument("--voice-prompt", default="NATF2.pt", help="Voice clone (bundled .pt or .wav)")
+    parser.add_argument("--persona", default=DEFAULT_PERSONA, help="System role text")
+    parser.add_argument("--hf-repo", default="nvidia/personaplex-7b-v1")
+    parser.add_argument("--device", default="cuda")
+    parser.add_argument("--cpu-offload", action="store_true")
+    args = parser.parse_args()
+
+    import sphn  # local import: only the runnable driver needs the audio I/O dep
+
+    config = PersonaPlexConfig(
+        hf_repo=args.hf_repo,
+        voice_prompt=args.voice_prompt,
+        persona=args.persona,
+        device=args.device,
+        cpu_offload=args.cpu_offload,
+    )
+
+    engine = PersonaPlexEngine(config).load()
+    session = PersonaPlexSession(engine, config)
+    session.open()
+
+    # Load exactly like the reference (moshi.lm.load_audio): native read then
+    # explicit resample, so greedy runs stay bit-faithful to `python -m moshi.offline`.
+    user_pcm, src_sr = sphn.read(args.input_wav)
+    user_pcm = sphn.resample(user_pcm, src_sample_rate=src_sr, dst_sample_rate=config.sample_rate)
+    audio, text = session.run(np.asarray(user_pcm[0], dtype=np.float32))
+
+    sphn.write_wav(args.output_wav, audio, config.sample_rate)
+    print(f"wrote {audio.shape[0] / config.sample_rate:.2f}s of agent audio to {args.output_wav}")
+    print(f"agent text: {text!r}")
+    if args.output_text:
+        with open(args.output_text, "w") as f:
+            json.dump({"text": text}, f, ensure_ascii=False)
+
+
+if __name__ == "__main__":
+    main()

examples/offline_inference/text_to_image/README.md

@@ -260,7 +260,7 @@ if __name__ == "__main__":
     For diffusion pipelines, the input list is sliced into single-item requests
     before feeding into the diffusion pipeline. For request-level batching
     controls such as `max_num_seqs`, see
-    [Request-Level Batching](../../../docs/user_guide/diffusion/request_batching.md).
+    [Diffusion Execution Modes](../../../docs/user_guide/diffusion/execution_modes.md).
 
 ### Negative Prompts
 

examples/online_serving/personaplex/README.md

@@ -0,0 +1,84 @@
+# PersonaPlex full-duplex online serving
+
+Serve [`nvidia/personaplex-7b-v1`](https://huggingface.co/nvidia/personaplex-7b-v1)
+(a Moshi-based full-duplex speech-to-speech model) with the native vLLM-Omni engine.
+
+The server hosts the **official PersonaPlex web client** at `/` (auto-downloaded from
+the model repo) and implements its WebSocket protocol at `/api/chat`, so you talk to the
+model live in the browser — mic in, agent speech + inner-monologue text out.
+
+> Experimental. Requires a GPU and Hugging Face access to the gated repo
+> (`HF_TOKEN` with access to `nvidia/personaplex-7b-v1`).
+
+## Start the server
+
+```bash
+HF_TOKEN=... CUDA_VISIBLE_DEVICES=0 bash run_server.sh
+# or directly:
+HF_TOKEN=... CUDA_VISIBLE_DEVICES=0 python -m \
+    vllm_omni.experimental.fullduplex.personaplex.serving.server --port 8124
+```
+
+## Talk to it
+
+- **Browser (recommended):** open `http://localhost:8124/` and allow the microphone.
+  Use headphones so the agent does not hear itself.
+- **Headless:** stream a 24 kHz mono WAV and save the reply:
+
+  ```bash
+  python duplex_client.py --url ws://localhost:8124/api/chat --input user.wav --out reply.wav
+  ```
+
+## Unified Realtime API
+
+The default `vllm_omni/deploy/personaplex.yaml` also enables main's engine-owned
+full-duplex control plane:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python -m vllm_omni.entrypoints.cli.main serve \
+  /path/to/personaplex-7b-v1 \
+  --omni \
+  --stage-configs-path vllm_omni/deploy/personaplex.yaml
+```
+
+Validate the actual `/v1/realtime?duplex=1` scheduler path with paced 24 kHz
+PCM, two concurrent sessions, overflow admission, per-session slot recycling,
+and non-silent output:
+
+```bash
+python tests/e2e/online_serving/personaplex_realtime_duplex.py \
+  --model /path/to/personaplex-7b-v1 \
+  --input-wav /path/to/speech.wav \
+  --output-dir /tmp/personaplex-realtime-duplex
+```
+
+This is distinct from the standalone `/api/chat` compatibility server above.
+The unified endpoint advertises `supports_barge_in=false`: overlapping speech
+is native model behavior, but destructive output interruption and model-state
+rewind have not been validated for PersonaPlex.
+
+## Protocol (`/api/chat`)
+
+Binary WebSocket messages, first byte is a tag (identical to `moshi.server`):
+
+| Direction | Tag | Payload |
+| --- | --- | --- |
+| server → client | `\x00` | ready handshake (after the system-prompt prefill) |
+| client → server | `\x01` | Opus-encoded mic audio (24 kHz) |
+| server → client | `\x01` | Opus-encoded agent audio |
+| server → client | `\x02` | UTF-8 inner-monologue text |
+
+Connect with query params `text_prompt` (persona) and `voice_prompt` (voice file).
+A raw-PCM endpoint (`/v1/audio/duplex`, JSON `open` + float32 frames) is also available
+for clients that do not want an Opus dependency.
+
+## Notes
+
+- **Run the client near the server.** Real-time 80 ms audio is sensitive to network
+  latency/jitter; over a high-latency remote link playback can stutter regardless of
+  engine speed. On localhost it is smooth.
+- Greedy decoding by default. One conversation per server by default (a second
+  concurrent connection is rejected with WS close 1013 until the first ends);
+  pass `--batch-size N` to host N concurrent conversations on one engine
+  (elastic batching; connections beyond N are rejected until a slot frees).
+- Full runbook: [`recipes/NVIDIA/PersonaPlex.md`](../../../recipes/NVIDIA/PersonaPlex.md).

examples/online_serving/personaplex/duplex_client.py

@@ -0,0 +1,114 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Headless client for the PersonaPlex duplex server (official Moshi protocol).
+
+Streams a 24 kHz mono WAV to ``/api/chat`` exactly as the browser client does
+(Opus over WebSocket, tags ``\\x00`` ready / ``\\x01`` audio / ``\\x02`` text),
+collects the agent's reply audio + inner-monologue text, and writes a WAV.
+
+The browser UI at ``http://<host>:<port>/`` is the primary demo; this script is
+for headless testing / scripting.
+
+    python duplex_client.py --url ws://localhost:8124/api/chat \
+        --input user.wav --out reply.wav
+
+Deps: ``websockets``, ``sphn``, ``soundfile``, ``numpy``.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+
+import numpy as np
+import soundfile as sf
+import sphn
+
+SR = 24000  # PersonaPlex / Mimi codec rate
+
+
+async def converse(url: str, pcm: np.ndarray) -> tuple[np.ndarray, str]:
+    import websockets
+
+    reader = sphn.OpusStreamReader(SR)  # decode agent audio
+    writer = sphn.OpusStreamWriter(SR)  # encode our mic audio
+    text: list[str] = []
+    ready = asyncio.Event()
+    last = {"t": 0.0}
+
+    async with websockets.connect(url, max_size=None) as ws:
+
+        async def receive() -> None:
+            loop = asyncio.get_event_loop()
+            while True:
+                try:
+                    msg = await ws.recv()
+                except Exception:
+                    return
+                if not isinstance(msg, (bytes, bytearray)) or not msg:
+                    continue
+                tag, payload = msg[0], msg[1:]
+                if tag == 0:
+                    ready.set()
+                elif tag == 1:
+                    reader.append_bytes(bytes(payload))
+                    last["t"] = loop.time()
+                elif tag == 2:
+                    text.append(payload.decode("utf8", errors="replace"))
+
+        recv_task = asyncio.create_task(receive())
+        await asyncio.wait_for(ready.wait(), timeout=120)  # waits for system-prompt prefill
+
+        frame = 1920  # 80 ms
+        for i in range(0, len(pcm), frame):
+            chunk = pcm[i : i + frame]
+            if len(chunk) < frame:
+                chunk = np.concatenate([chunk, np.zeros(frame - len(chunk), dtype=np.float32)])
+            writer.append_pcm(chunk)
+            data = writer.read_bytes()
+            if data:
+                await ws.send(b"\x01" + data)
+            await asyncio.sleep(frame / SR)  # pace at real time
+
+        loop = asyncio.get_event_loop()
+        last["t"] = loop.time()
+        while loop.time() - last["t"] < 2.5:  # drain until the agent stops talking
+            await asyncio.sleep(0.1)
+        recv_task.cancel()
+
+    chunks: list[np.ndarray] = []
+    while True:
+        out = reader.read_pcm()
+        if out is None or out.shape[-1] == 0:
+            break
+        chunks.append(np.asarray(out, dtype=np.float32))
+    audio = np.concatenate(chunks) if chunks else np.zeros(0, dtype=np.float32)
+    return audio, "".join(text)
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--url", default="ws://localhost:8124/api/chat")
+    ap.add_argument("--input", required=True, help="24 kHz mono WAV of the user turn")
+    ap.add_argument("--out", default="personaplex_reply.wav")
+    ap.add_argument("--tail", type=float, default=10.0, help="trailing silence (s) for the agent reply")
+    ap.add_argument("--persona", default="")
+    ap.add_argument("--voice", default="NATF2.pt")
+    args = ap.parse_args()
+
+    wav, sr = sf.read(args.input, dtype="float32")
+    if wav.ndim > 1:
+        wav = wav.mean(axis=1)
+    if sr != SR:
+        raise SystemExit(f"input must be {SR} Hz mono; got {sr} Hz. Resample it first.")
+    wav = np.concatenate([wav, np.zeros(int(args.tail * SR), dtype=np.float32)])
+
+    url = f"{args.url}?text_prompt={args.persona}&voice_prompt={args.voice}"
+    audio, text = asyncio.run(converse(url, wav))
+    sf.write(args.out, audio if audio.size else np.zeros(SR, dtype=np.float32), SR)
+    print(f"agent reply: {audio.size / SR:.2f}s -> {args.out}")
+    print(f"inner-monologue: {text.strip()!r}")
+
+
+if __name__ == "__main__":
+    main()

examples/online_serving/personaplex/run_server.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+# PersonaPlex full-duplex online serving.
+#
+# Serves the OFFICIAL PersonaPlex web client at / (auto-downloaded from
+# nvidia/personaplex-7b-v1) and the Moshi WebSocket protocol at /api/chat,
+# backed by the native vLLM-Omni engine. Requires HF access to the gated repo.
+
+HOST="${HOST:-0.0.0.0}"
+PORT="${PORT:-8124}"
+
+echo "Starting PersonaPlex duplex server on ${HOST}:${PORT}..."
+echo "Open http://localhost:${PORT}/ for the official web client (run it near the server)."
+
+python -m vllm_omni.experimental.fullduplex.personaplex.serving.server \
+    --host "$HOST" --port "$PORT"

mkdocs.yml

@@ -71,6 +71,14 @@ plugins:
   - search
   - autorefs
   - awesome-nav
+  - redirects:
+      redirect_maps:
+        user_guide/diffusion/request_batching.md: user_guide/diffusion/execution_modes.md
+        user_guide/diffusion/step_execution.md: user_guide/diffusion/execution_modes.md
+        user_guide/diffusion/cpu_offload_diffusion.md: user_guide/diffusion/cpu_offload.md
+        user_guide/quantization/index.md: user_guide/quantization/overview.md
+        design/feature/diffusion_request_level_batching.md: design/feature/diffusion_continuous_batching.md
+        design/feature/diffusion_step_execution.md: design/feature/diffusion_continuous_batching.md
   - glightbox
   - git-revision-date-localized:
       # exclude files

pyproject.toml

@@ -70,8 +70,7 @@ dev = [
     "FlagEmbedding==1.4.0", # for hunyuan_image3 tests
     "kernels==0.14.1", # for accuracy tests comparison with diffusers output (0.15.0 incompatible with diffusers 0.38.0)
     "s3tokenizer==0.3.0", # for tts test
-    "step-audio2==1.0.0", # Step-Audio2 Token2Wav (flashcosyvoice / hyperpyyaml)
+    "step-audio2==1.0.0", # Step-Audio2 / MiniCPM-o 4.5 Token2Wav (flashcosyvoice / hyperpyyaml)
-    "stepaudio2-minicpmo", # for MiniCPM-o 4.5 talker vocoder (stepaudio2-minicpmo Token2wav)
 ]
 
 demo = [
@@ -109,6 +108,7 @@ docs = [
     "mkdocstrings-python",
     "mkdocs-gen-files",
     "mkdocs-awesome-nav",
+    "mkdocs-redirects",
     "mkdocs-glightbox",
     "mkdocs-git-revision-date-localized-plugin",
     "mkdocs-minify-plugin",

recipes/LTX/LTX-2.md

@@ -332,6 +332,6 @@ bundled offline CLI do not currently expose `sigmas`.
 - <https://huggingface.co/Lightricks/LTX-2.3>
 - <https://huggingface.co/diffusers/LTX-2.3-Diffusers>
 - [Online video generation](../../docs/user_guide/examples/online_serving/text_to_video.md)
-- [Request batching](../../docs/user_guide/diffusion/request_batching.md)
+- [Diffusion execution modes](../../docs/user_guide/diffusion/execution_modes.md)
 - [T2V offline example](../../examples/offline_inference/text_to_video/text_to_video.md)
 - [I2V offline example](../../examples/offline_inference/image_to_video/README.md)

recipes/MiniMaxAI/MiniMax-H3-5090.md

@@ -0,0 +1,61 @@
+# MiniMax-H3 on RTX 5090
+
+This recipe uses BF16 weights, tiled VAE decode, tensor parallelism where a
+second GPU is available, and distributed layerwise offload (DLO). It is a
+memory-first serving configuration; lower resident counts reduce HBM use and
+increase CPU-to-GPU transfer time.
+
+## Capacity requirements
+
+| Resource | One RTX 5090 | Two RTX 5090s |
+| --- | ---: | ---: |
+| GPU HBM | 32 GiB | 32 GiB per GPU |
+| Checkpoint storage | 135 GiB per partition | 135 GiB per partition |
+| Available system RAM | 200 GiB minimum | 200 GiB minimum |
+| Recommended system RAM | 384 GiB | 384 GiB |
+
+`FL2VA` and `Ref2VA` are separate 135 GiB checkpoint partitions. Start one
+server at a time. DLO keeps rank-local weights in pinned host memory; increasing
+`--dlo-resident-layers` improves latency but does **not** reduce host RAM in the
+current implementation because resident layers retain pinned CPU master copies.
+
+> **Modular H3:** after #5720 lands, preserve this recipe's one-partition
+> behavior with `--task-type fl2va` or `--task-type ref2va`.
+
+## One RTX 5090: 1344x768, 5 seconds
+
+Use 12 resident DiT layers. A 50-step B300 allocation test with this exact
+single-rank topology peaked at 26.50 GiB; re-measure peak HBM on the target
+card before increasing the resident count.
+
+```bash
+CUDA_VISIBLE_DEVICES=0 vllm serve /path/to/MiniMax-H3/FL2VA \
+  --omni --trust-remote-code --host 0.0.0.0 --port 8000 \
+  --num-gpus 1 --tensor-parallel-size 1 --text-encoder-tp-size 1 \
+  --usp 1 --ring 1 --vae-patch-parallel-size 1 \
+  --vae-parallel-mode tile --vae-use-tiling \
+  --enable-distributed-layerwise-offload --dlo-no-use-allgather \
+  --dlo-resident-layers 12 --enforce-eager \
+  --diffusion-attention-backend CUDNN_ATTN
+```
+
+## Two RTX 5090s: 1344x768, 5 seconds
+
+Use TP2 and 20 resident DiT layers. The two-rank B300 capacity run peaked at
+27,726 MiB per rank for this shape and 50 steps. This is a memory/correctness
+proxy, not a consumer-GPU latency claim.
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1 vllm serve /path/to/MiniMax-H3/FL2VA \
+  --omni --trust-remote-code --host 0.0.0.0 --port 8000 \
+  --num-gpus 2 --tensor-parallel-size 2 --text-encoder-tp-size 2 \
+  --usp 1 --ring 1 --vae-patch-parallel-size 2 \
+  --vae-parallel-mode tile --vae-use-tiling \
+  --enable-distributed-layerwise-offload --dlo-no-use-allgather \
+  --dlo-resident-layers 20 --enforce-eager \
+  --diffusion-attention-backend CUDNN_ATTN
+```
+
+For Ref2VA, stop the FL2VA server and restart the same command with
+`/path/to/MiniMax-H3/Ref2VA`. Ref2VA reference video count and prompt length
+can increase activation memory; begin with one request at a time.

recipes/MiniMaxAI/MiniMax-H3-MUSA.md

@@ -0,0 +1,147 @@
+# MiniMax H3 on Moore Threads MUSA
+
+> Joint video and audio generation on Moore Threads GPUs
+
+## Summary
+
+- Vendor: MiniMaxAI
+- Model: [`MiniMaxAI/MiniMax-H3`](https://huggingface.co/MiniMaxAI/MiniMax-H3)
+- Tasks: T2VA, FL2VA, and Ref2VA
+- Mode: OpenAI-compatible `/v1/videos` HTTP serving
+- Hardware: 4x Moore Threads MTT S5000 for the validated Ref2VA profile
+- Maintainer: Community
+
+This recipe adapts [MiniMax-H3.md](MiniMax-H3.md) for MUSA environments.
+MiniMax H3 has two independently served checkpoint partitions:
+
+- `FL2VA` serves text-to-video+audio (`t2va`) and
+  first-frame-to-video+audio (`fl2va`).
+- `Ref2VA` serves image/audio or video-reference generation (`ref2va`).
+
+## Prerequisites
+
+### Checkpoint
+
+Only download the partition needed by the server. Downloading the whole model
+repository also retrieves duplicate shared components and the original
+top-level pipeline layout.
+
+For T2VA and FL2VA:
+
+```bash
+python -m pip install modelscope
+export MODEL_ROOT=/path/to/MiniMax-H3
+modelscope download MiniMax/MiniMax-H3 \
+  --local_dir "${MODEL_ROOT}" \
+  --max-workers 16 \
+  --include 'FL2VA/**'
+```
+
+For Ref2VA, download `Ref2VA/**` instead.
+
+### Environment
+
+Install a compatible PyTorch/torch-musa, torchada, MATE, and vLLM-MUSA
+stack before installing vLLM-Omni. Importing vLLM-Omni should report that the
+`musa` vLLM platform plugin is active.
+
+Install vLLM-Omni from a checkout containing MiniMax H3 support:
+
+```bash
+python -m pip install -e .
+```
+
+MiniMax H3 reference inputs and MP4 output require `soundfile`, `ffmpeg`, and
+`ffprobe`. The Python dependency is installed by vLLM-Omni; install the two
+executables with the operating system package manager and verify them:
+
+```bash
+python -c 'import soundfile; print(soundfile.__version__)'
+ffmpeg -version
+ffprobe -version
+```
+
+When torchaudio cannot load TorchCodec, vLLM-Omni automatically falls back to
+soundfile. Formats that libsndfile cannot read are demuxed through ffmpeg.
+
+## Start a server
+
+One server loads one checkpoint partition. Set `MODEL` to `FL2VA` for T2VA
+and FL2VA, or to `Ref2VA` for Ref2VA.
+
+The validated Ref2VA configuration uses tensor parallelism across four MTT
+S5000 GPUs and offloads inactive model components to CPU:
+
+```bash
+export MODEL="${MODEL_ROOT}/FL2VA"
+export PORT=8091
+
+MUSA_VISIBLE_DEVICES=0,1,2,3 \
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+VLLM_OMNI_VIDEO_SYNC_TIMEOUT=1800 \
+vllm serve "${MODEL}" \
+  --omni \
+  --host 0.0.0.0 \
+  --port "${PORT}" \
+  --trust-remote-code \
+  --num-gpus 4 \
+  --tensor-parallel-size 4 \
+  --vae-patch-parallel-size 4 \
+  --enable-cpu-offload \
+  --vae-use-tiling \
+  --diffusion-attention-backend FLASH_ATTN
+```
+
+On MUSA, `FLASH_ATTN` selects the MATE/FlashAttention-3 implementation. Do
+not install the CUDA-only `fa4` optional dependency.
+
+For request examples and parameter definitions, see
+[MiniMax-H3.md](MiniMax-H3.md#http-api-examples).
+
+## MUSA validation
+
+The following weight-independent gates have been validated on one MTT S5000:
+
+- packed variable-length attention selects MATE FlashAttention-3 and returns
+  finite BF16 output;
+- MiniMax H3 RoPE casts CPU FP64 position metadata to FP32 before device
+  arithmetic and returns finite output;
+- the video-VAE seeded RNG context is deterministic on MUSA and restores both
+  CPU and MUSA RNG states;
+- the soundfile fallback preserves stereo channel layout, FP32 samples, and
+  the native sample rate when torchaudio/TorchCodec is unavailable.
+
+Ref2VA image+audio generation was also validated end to end on one MTT S5000
+with CPU offload and VAE tiling. The smoke used a 448x256 reference image, a
+four-second 32 kHz stereo audio reference, two denoising steps, and seed 42.
+The synchronous HTTP request completed in 50.6 seconds and returned an MP4
+with the following decoded properties:
+
+- 107 video frames at 448x256 and 24 FPS;
+- non-static video (`pixel_std=13.07`, `temporal_delta=0.49`);
+- finite stereo audio at 32 kHz (`audio_rms=0.0458`).
+
+Official Ref2VA video-reference serving was validated with the expanded
+MiniMax prompt at 1344x768, 5 seconds, seed 0, and 50 steps. A true TP4 run on
+four MTT S5000 GPUs returned HTTP 200 in 638.6 seconds and produced 124 H.264
+frames at 24 FPS with AAC stereo audio at 32 kHz.
+
+Keep `--vae-use-tiling` enabled for this serving profile.
+
+## Known limitations
+
+- Keep Ring Attention at degree 1. The current Ring path does not preserve
+  MiniMax H3 packed padding boundaries.
+- H3 is CFG-distilled, so `--cfg-parallel-size` must remain 1.
+- Each server process loads only one checkpoint partition.
+- H3 currently executes one generation request per diffusion batch.
+- FP8 quantization has not been enabled for MiniMax H3.
+- MP3, M4A, MP4, and reference-video audio fallback requires `ffmpeg` on
+  `PATH`; WAV inputs can be read directly through soundfile.
+
+## Additional resources
+
+- [MiniMax-H3.md](MiniMax-H3.md) — full model and API guide
+- [MiniMax-H3-NPU.md](MiniMax-H3-NPU.md) — Ascend NPU deployment guide
+- [Supported models](../../docs/models/supported_models.md)
+- [Video API](../../docs/serving/videos_api.md)

recipes/MiniMaxAI/MiniMax-H3-NPU.md

@@ -1,7 +1,7 @@
 # MiniMax H3 on Ascend NPU
 
-> Joint video and audio generation with text, first-frame, image/audio, or
+> Joint video and audio generation with text, first/last keyframes, and mixed
-> multi-video conditions — Ascend NPU deployment guide
+> image/video/audio references — Ascend NPU deployment guide
 
 ## Summary
 
@@ -149,9 +149,9 @@ throughput guarantee.
 - H3 currently executes one generation request per diffusion batch.
 - The first regional-compile request is a warmup and should not be included
   in steady-state performance measurements.
-- Image+audio Ref2VA accepts exactly one image and one audio reference.
+- The official H3 input matrix and media limits are documented in the [GPU
-- Video Ref2VA accepts one or more video files, but not an additional
+  recipe](MiniMax-H3.md#official-input-matrix-and-limits); this NPU path uses
-  standalone audio reference.
+  the same HTTP request contract.
 - VAE patch parallelism requires size 1 or the full DiT group size and
   supports the H3 native `tile` mode only.
 

recipes/MiniMaxAI/MiniMax-H3.md

@@ -1,7 +1,7 @@
 # MiniMax H3
 
-> Joint video and audio generation with text, first-frame, image/audio, or
+> Joint video and audio generation with text, first/last keyframes, and
-> multi-video conditions
+> mixed image/video/audio references
 
 ## Summary
 
@@ -16,8 +16,8 @@ checkpoint has two independently served partitions:
 
 - `FL2VA`: text-to-video+audio (`t2va`) and first-frame-to-video+audio
   (`fl2va`)
-- `Ref2VA`: image+audio or one-or-more-video reference-to-video+audio
+- `Ref2VA`: up to 9 images, 3 videos, and 3 audio references in supported
-  (`ref2va`)
+  image/video/audio combinations (`ref2va`; audio-only is rejected)
 
 The generated MP4 contains H.264 video and synchronized stereo audio.
 
@@ -33,8 +33,10 @@ export MODEL_ROOT=/path/to/MiniMax-H3
 hf download MiniMaxAI/MiniMax-H3 --local-dir "${MODEL_ROOT}"
 ```
 
-Install vLLM-Omni from the checkout containing MiniMax H3 support. On
+Install vLLM-Omni from the checkout containing MiniMax H3 support. The
-Blackwell, install the optional FlashAttention-4 dependency:
+two-GPU RTX 5090/4090 profiles use cuDNN attention and do not need
+FlashAttention-4. Install the optional dependency only for the four-GPU
+B300/GB200 `FLASH_ATTN` profile:
 
 ```bash
 uv venv
@@ -50,6 +52,26 @@ reference-video preparation and MP4 output.
 One server loads one checkpoint partition. Set `MODEL` to `FL2VA` for T2VA
 and FL2VA requests, or to `Ref2VA` for either Ref2VA request.
 
+### Memory and storage requirements
+
+Treat GPU HBM, host RAM, and checkpoint storage as separate requirements. Each
+H3 checkpoint partition (`FL2VA` or `Ref2VA`) contains about **134 GiB** of
+BF16 safetensors (about **135 GiB** on disk). Keeping both partitions locally
+therefore needs roughly **270 GiB** of model storage, although the examples
+serve only one partition at a time.
+
+CPU offload and distributed layerwise offload reduce GPU residency; they do
+not make the model weights disappear. With `--dlo-no-use-allgather`, each
+worker retains its standard-loader rank-local weights in host memory, including
+pinned CPU buffers used for H2D streaming. Use at least **200 GiB available
+system RAM** before starting the two-GPU recipe; a **384 GiB host is
+recommended** to leave room for the OS, CUDA/PyTorch allocations, request
+inputs, and filesystem cache. Do not run the FL2VA and Ref2VA servers at the
+same time on a host sized for this minimum.
+
+The consumer-GPU profiles below are HBM budgets only. They still require the
+host-RAM budget above.
+
 ### Single GPU: accuracy and memory first
 
 The single-GPU configuration uses model-level CPU offload.
@@ -77,6 +99,98 @@ Use a GPU with enough memory for the active H3 component and enough system RAM
 for the offloaded components. CPU offload reduces GPU memory pressure but adds
 PCIe/NVLink transfer latency.
 
+### Two 24/32 GB GPUs: TP2 distributed layerwise offload
+
+For two PCIe consumer GPUs, combine TP2 with distributed layerwise offload
+(DLO). The standard loader first creates the rank-local TP shard. DLO keeps
+that shard in pinned host memory and streams the 30 tail DiT blocks through a
+shared two-buffer window without a DP AllGather. The first 20 DiT blocks are
+copied to the GPUs once per denoise stage, reused by every sampling step, and
+released before VAE decode so the decoder can reuse their HBM.
+
+```bash
+export MODEL="${MODEL_ROOT}/FL2VA"
+export PORT=8091
+
+CUDA_VISIBLE_DEVICES=0,1 \
+VLLM_WORKER_MULTIPROC_METHOD=spawn \
+VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400 \
+vllm serve "${MODEL}" \
+  --omni \
+  --host 0.0.0.0 \
+  --port "${PORT}" \
+  --trust-remote-code \
+  --num-gpus 2 \
+  --tensor-parallel-size 2 \
+  --usp 1 \
+  --ring 1 \
+  --text-encoder-tp-size 2 \
+  --vae-patch-parallel-size 2 \
+  --vae-parallel-mode tile \
+  --vae-use-tiling \
+  --enable-distributed-layerwise-offload \
+  --dlo-no-use-allgather \
+  --dlo-resident-layers 20 \
+  --enforce-eager \
+  --diffusion-attention-backend CUDNN_ATTN
+```
+
+Use the profile that matches the per-GPU memory capacity:
+
+| Profile | GPUs | Starting shape | Resident DiT blocks | Attention | Execution | Status |
+|---|---:|---:|---:|---|---|---|
+| `rtx5090` | 2 x 32 GB | 1344x768 | 20 | cuDNN attention | eager | Target-hardware validated |
+| `rtx4090` | 2 x 24 GB | 1024x576 | 12 | cuDNN attention | eager | Capacity-proxy starting point |
+
+This topology uses all available parallel capacity: TP2 shards both the DiT
+and text encoder, `--dlo-no-use-allgather` streams each rank's local TP shard
+without reconstructing full blocks, and VAE patch parallelism splits tiled
+decode across both GPUs. cuDNN attention is selected explicitly for the RTX
+consumer path; the server stays eager to avoid an unqualified compile path.
+
+The resident count changes placement and transfer frequency only; it does not
+quantize or change the BF16/FP32 denoise math. Re-measure peak memory before
+increasing it on a different request shape.
+
+### RTX 5090 target-hardware validation
+
+At vLLM-Omni commit `ae6577ea`, one full 50-step T2VA request completed on
+2 x RTX 5090 without OOM:
+
+| Shape | Frames | Client E2E | Sampled peak/GPU | Output validation |
+|---:|---:|---:|---:|---|
+| 1344x768 | 124 at 24 FPS | 8 min 38 s | approximately 22.6 GiB | H.264 video + 32 kHz stereo AAC; full `ffmpeg` decode passed |
+
+This is a single end-to-end validation run, not a warmed multi-run latency
+benchmark. The sampled `nvidia-smi` peak is also not a CUDA allocator
+high-water mark. The environment used vLLM 0.26.0, vLLM-Omni
+`0.26.1.dev14+gae6577ea`, and PyTorch 2.11.0+cu130. The
+[run record](https://github.com/lishunyang12/vllm-omni-rankings/blob/dcd06d7e83cb069842535918c0169ee9f3f29ba0/scripts/%E5%BE%AE%E4%BF%A1%E5%9B%BE%E7%89%87_20260805000034_86_237.png)
+captures the environment, output contract, elapsed time, and sampled peak.
+
+Before the target run, both profiles were exercised on two B300 ranks as an
+allocation and correctness proxy. At 1344x768, 124 frames, and 50 steps, the
+20-layer profile peaked at 27,726 MiB per rank. At 1024x576, the 12-layer
+profile peaked at 18,888 MiB per rank in a 5-step capacity run. The resident
+and fully streamed placements produced identical decoded video-frame and audio
+hashes for the same shape, step count, prompt, and seed. The B300 result does
+not establish RTX 4090 PCIe latency; treat the 4090 profile as a conservative
+starting point until it is measured on that GPU.
+
+To run T2VA, FL2VA, image+audio Ref2VA, and two-video Ref2VA in order, validate
+every MP4's H.264/AAC streams, and retain live server and GPU-memory logs:
+
+```bash
+RUN_ROOT=/path/to/run-root \
+MODEL_ROOT=/path/to/MiniMax-H3 \
+GPU_IDS=0,1 \
+PROFILE=rtx5090 \
+bash examples/offline_inference/minimax_h3/run_h3_2gpu_all_tasks.sh
+```
+
+The script selects 20 resident layers for `PROFILE=rtx5090` and 12 for
+`PROFILE=rtx4090`; `DLO_RESIDENT_LAYERS=N` overrides either default.
+
 ### Four GPUs: measured best-practice throughput
 
 The best validated four-GPU configuration on four NVIDIA B300 GPUs is:
@@ -85,7 +199,8 @@ The best validated four-GPU configuration on four NVIDIA B300 GPUs is:
 - Ulysses sequence parallelism degree 4;
 - native tiled VAE patch parallelism degree 4;
 - regional `torch.compile` for the repeated DiT blocks;
-- FlashAttention, with Ring and TP left at 1.
+- CuTe FlashAttention-4 through the `FLASH_ATTN` backend, with Ring and TP
+  left at 1.
 
 ```bash
 export MODEL="${MODEL_ROOT}/FL2VA"
@@ -109,6 +224,9 @@ vllm serve "${MODEL}" \
   --diffusion-attention-backend FLASH_ATTN
 ```
 
+On Blackwell, `FLASH_ATTN` selects FA4. Confirm the server log contains
+`Using CuTe FlashAttention-4 on Blackwell` before recording measurements.
+
 Do not add `--enforce-eager` to this performance configuration. The first
 request includes regional compilation; warm the server once before measuring
 steady-state latency. H3 is CFG-distilled, so `--cfg-parallel-size` must remain
@@ -164,6 +282,42 @@ command with:
 export MODEL="${MODEL_ROOT}/Ref2VA"
 ```
 
+### Online FP8 quantization
+
+MiniMax H3 supports load-time FP8 quantization of the DiT. The checkpoint
+remains BF16 on disk; vLLM-Omni quantizes eligible weights while loading and
+uses dynamic activation scaling during inference. By default, attention and
+MLP linears in the token refiner and main DiT blocks, the condition
+projection, and all AdaLN projections use FP8. Patch, timestep, and final
+projections remain FP32; the text encoder and VAEs are unchanged.
+
+Add this option to an existing H3 server command:
+
+```bash
+--quantization fp8
+```
+
+Use `ignored_layers` to keep any otherwise eligible linear in BF16. H3
+resolves the `transformer` component before constructing the DiT, so names do
+not start with `transformer.`. Entries are exact runtime linear prefixes; a
+parent name such as `blocks.0.attn` does not exclude its children.
+
+Eligible names are the `attn.qkv_proj`, `attn.out_proj`, `mlp.fc1`, and
+`mlp.fc2` children under `token_refiner.blocks.<0-1>` or `blocks.<0-49>`.
+The other eligible names are `condition_proj`,
+`blocks.<0-49>.adaln_proj.linear`, and `final_layer.adaln_proj.linear`.
+For example, keep the first main block's attention projections in BF16 with:
+
+```bash
+--diffusion-quantization-config \
+  '{"transformer":{"method":"fp8","ignored_layers":["blocks.0.attn.qkv_proj","blocks.0.attn.out_proj"]}}'
+```
+
+The structured option replaces `--quantization fp8`. Online FP8 is currently
+incompatible with H3 layerwise offload because the offload path produces a
+weight stride rejected by the Cutlass FP8 kernel. Use resident FP8 with tensor
+parallelism and VAE tiling instead.
+
 ## HTTP API examples
 
 The following requests use the synchronous endpoint so the returned body can
@@ -189,6 +343,7 @@ curl -sS -X POST "${API_URL}" \
   -F 'prompt=In a snowy blue-purple forest, Ori carefully walks past a sleeping giant; footsteps crunch in the snow while the creature breathes and softly snorts.' \
   -F 'width=1344' \
   -F 'height=768' \
+  -F 'aspect_ratio=16:9' \
   -F 'fps=24' \
   -F 'num_inference_steps=50' \
   -F 'flow_shift=12' \
@@ -217,11 +372,31 @@ curl -sS -X POST "${API_URL}" \
   -o fl2va.mp4
 ```
 
-### 3. Ref2VA: image and audio reference
+Use the same `FL2VA` partition for the official tail-keyframe forms. A single
+image with `frame_indices=[-1]` conditions the last frame; two ordered images
+with `frame_indices=[0,-1]` condition the first and last frames:
 
-Run this request against the `Ref2VA` partition. `audio_reference` accepts an
+```bash
-HTTP(S) URL or a `data:` URL. In one terminal, expose the local reference
+export LAST_FRAME=/path/to/fl2va_last_frame.png
-assets to the serving host:
+export FIRST_FRAME=/path/to/fl2va_first_frame.png
+
+curl -sS -X POST "${API_URL}" \
+  -F 'prompt=The subject moves naturally from the first image to the last image.' \
+  -F 'num_inference_steps=50' \
+  -F 'flow_shift=12' \
+  -F 'seed=2102' \
+  -F 'extra_params={"task":"fl2va","duration":8.7,"frame_indices":[0,-1],"audio_flow_shift":3.0}' \
+  -F "input_references=@${FIRST_FRAME};type=image/png" \
+  -F "input_references=@${LAST_FRAME};type=image/png" \
+  -o fl2va_first_last.mp4
+```
+
+### 3. Ref2VA: image-only, image/audio, or mixed references
+
+Run these requests against the `Ref2VA` partition. Image-only Ref2VA omits
+`audio_reference`; adding one or more audio references is optional. The typed
+fields accept one object or an ordered JSON list. `audio_reference` accepts an
+HTTP(S) URL or a `data:` URL.
 
 ```bash
 python -m http.server 8092 \
@@ -229,7 +404,24 @@ python -m http.server 8092 \
   --directory /path/to/reference_assets
 ```
 
-Then submit the image and audio request from another terminal:
+Then submit an image-only request from another terminal:
+
+```bash
+export REF_IMAGE=/path/to/reference_assets/ref2va_image.png
+
+curl -sS -X POST "${API_URL}" \
+  -F 'prompt=A white cat sits on a beige couch and slowly looks toward the camera.' \
+  -F 'aspect_ratio=adaptive' \
+  -F 'short_edge=768' \
+  -F 'num_inference_steps=50' \
+  -F 'flow_shift=12' \
+  -F 'seed=3100' \
+  -F 'extra_params={"task":"ref2va","duration":8.0,"audio_flow_shift":3.0}' \
+  -F "input_reference=@${REF_IMAGE};type=image/png" \
+  -o ref2va_image_only.mp4
+```
+
+An image-plus-audio request is:
 
 ```bash
 export REF_IMAGE=/path/to/reference_assets/ref2va_image.png
@@ -252,7 +444,7 @@ curl -sS -X POST "${API_URL}" \
 The requested duration should cover the complete audio. If `duration` is
 shorter, the reference soundtrack is truncated to the generated clip.
 
-### 4. Ref2VA: two video references
+### 4. Ref2VA: video, separate audio, and mixed references
 
 Run this request against the `Ref2VA` partition. Repeat the
 `input_references` multipart field once per source video. H3 consumes the
@@ -277,10 +469,40 @@ curl -sS -X POST "${API_URL}" \
   -o ref2va_video_video.mp4
 ```
 
-The server stores uploaded multi-video references only for the lifetime of the
+The server stores uploaded references only for the lifetime of the request and
-request and deletes the temporary files after generation. Video Ref2VA uses
+deletes temporary files after generation. A video may use its embedded
-the source-video soundtracks and does not accept a separate
+soundtrack, a separate `audio_reference`, or both. To send a mixed multipart
-`audio_reference`.
+request, repeat `input_references` for each image, video, or audio file; the
+server classifies them by MIME type and preserves the per-type order.
+
+Reference videos must be MP4/MOV with H.264/H.265 video, optional AAC/MP3
+audio, 2–15 seconds each, and at most 15 seconds combined. They may still be
+longer than the generated clip. Use `start_time_seconds` to select a
+synchronized segment; for multiple typed video references, pass one value per
+video in `extra_params.start_time_seconds`.
+
+Reference images accept JPG/JPEG, PNG, WEBP, HEIC, or HEIF up to 30 MiB. Standalone
+audio references accept WAV or MP3 up to 15 MiB, with 2–15 seconds per file and
+at most 15 seconds combined.
+
+## Official input matrix and limits
+
+| Task | Supported references | Limits |
+|------|----------------------|--------|
+| T2VA | text only | prompt must be non-empty |
+| FL2VA | first image, last image, or ordered first+last images | at most 2 images; `frame_indices` is `[0]`, `[-1]`, or `[0,-1]` |
+| Ref2VA | image-only, image+image, image+video, video+audio, and mixed image/video/audio | images ≤9, videos ≤3, audios ≤3, total references ≤12; audio requires a visual reference |
+
+The H3 output contract is 4–15 seconds at 24 FPS, stereo 32 kHz audio, and a
+32-pixel canvas multiple. T2VA requires one named output ratio from `21:9`,
+`16:9`, `4:3`, `1:1`, `3:4`, or `9:16`. FL2VA always follows the first input
+image's ratio and ignores a generic `aspect_ratio` override. Ref2VA defaults to
+`16:9`; `adaptive` and SGLang's `auto` spelling are accepted aliases for that
+default. `short_edge` controls the 768-pixel canvas and must be `768`.
+`num_outputs_per_prompt` accepts 1–10 and derives each output seed as
+`seed + output_index`. The asynchronous endpoint returns all
+outputs; the synchronous raw-MP4 endpoint returns the first output when more
+than one is requested.
 
 ## Key parameters
 
@@ -293,7 +515,15 @@ the source-video soundtracks and does not accept a separate
 | `flow_shift` | `12` | Video sigma shift |
 | `audio_flow_shift` | `3` | Audio sigma shift, passed in `extra_params` |
 | `seed` | Task-specific | Use a fixed value for reproducibility |
-| `width`, `height` | Multiples of 32 | Aspect ratio must be between 1:4 and 4:1 |
+| `aspect_ratio` | Task-specific | T2VA requires a named ratio; FL2VA follows the input image; Ref2VA defaults to `16:9` |
+| `short_edge` | `768` | H3 shape policy requires exactly 768 when `width`/`height` are omitted |
+| `num_outputs_per_prompt` | `1` | 1–10; async API returns every output |
+| `start_time_seconds` | `0` | Reference-video segment start; use a list in `extra_params` for multiple videos |
+| `width`, `height` | Multiples of 32 | Output aspect ratio must be between 1:4 and 4:1 |
+
+## ComfyUI Frontend
+
+Users can also use a ComfyUI frontend to interact with a hosted MiniMax-H3 service. The ComfyUI frontend can run in a separate environment or machine. Refer to [vLLM-Omni ComfyUI Integration](../../docs/features/comfyui.md) for details.
 
 ## Validated four-GPU evidence
 
@@ -310,12 +540,21 @@ throughput guarantee. Multi-video Ref2VA is much slower because the two
 reference videos expand both the Qwen3-VL vision sequence and the packed DiT
 attention sequence.
 
+## Validated FP8 evidence
+
+With eager DiT/text-encoder TP2 and VAE tiling, the 384x672, 107-frame,
+10-step quality case measured LPIPS 0.1156 (limit 0.20), PSNR 23.6316 dB,
+audio spectral cosine 0.9589 (minimum 0.80), and audio RMS ratio 0.9342. The
+resident per-GPU peak was 68.52 GiB for BF16 and 53.51 GiB for FP8, a 22%
+reduction.
+
 ## Known limitations
 
 - Each server process loads only one checkpoint partition.
 - H3 currently executes one generation request per diffusion batch.
 - The first regional-compile request is a warmup and should not be included in
   steady-state performance measurements.
+- Online FP8 is not compatible with layerwise offload.
 - Image+audio Ref2VA accepts exactly one image and one audio reference.
 - Video Ref2VA accepts one or more video files, but not an additional standalone
   audio reference.

recipes/NVIDIA/PersonaPlex.md

@@ -0,0 +1,174 @@
+# PersonaPlex
+
+> Live full-duplex speech-to-speech (talk to the model in your browser, it listens
+> while it speaks) with persona and voice control, on the native vLLM-Omni engine.
+
+## Summary
+
+- Vendor: NVIDIA
+- Model: [`nvidia/personaplex-7b-v1`](https://huggingface.co/nvidia/personaplex-7b-v1)
+  (gated; a Moshi finetune)
+- Task: Full-duplex speech-to-speech. 24 kHz mic audio in, agent speech +
+  inner-monologue text out, in 80 ms lockstep. Persona (role text) and voice
+  (zero-shot clone) are set per session.
+- Mode: Live duplex WebSocket server with the official browser client, single or
+  multi-session (`--batch-size`), plus an offline WAV-in / WAV-out example
+- Maintainer: [`@linyueqian`](https://github.com/linyueqian)
+
+## When to use this recipe
+
+Use this recipe to run `nvidia/personaplex-7b-v1` as a live voice agent on one GPU.
+The integration is **moshi-free**: the temporal transformer, depformer, and
+streaming Mimi codec are all native vLLM-Omni modules (Mimi runs on
+`transformers.MimiModel`), so no vendored fork is installed. Decoding is greedy,
+matching the reference implementation frame for frame on the golden replays used
+as the acceptance gate.
+
+PersonaPlex is the first Moshi-class (pure-lockstep) model in the experimental
+full-duplex framework (`vllm_omni/experimental/fullduplex/`), and the first with
+elastic multi-session batching: one engine hosts `--batch-size` concurrent
+conversations, callers join or leave on any 80 ms tick without disturbing live
+slots (greedy isolation is bit-exact).
+
+## References
+
+- Serving package:
+  [`vllm_omni/experimental/fullduplex/personaplex/`](../../vllm_omni/experimental/fullduplex/personaplex/)
+- Native model modules (temporal / depformer / streaming Mimi):
+  [`vllm_omni/model_executor/models/personaplex/`](../../vllm_omni/model_executor/models/personaplex/)
+- Online example (server + headless client):
+  [`examples/online_serving/personaplex/`](../../examples/online_serving/personaplex/)
+- Offline example:
+  [`examples/offline_inference/personaplex/personaplex_offline.py`](../../examples/offline_inference/personaplex/personaplex_offline.py)
+- Staged offline pipeline (talker -> Mimi code2wav, async-chunk streaming):
+  [`vllm_omni/deploy/personaplex.yaml`](../../vllm_omni/deploy/personaplex.yaml)
+- Integration PR: [vllm-project/vllm-omni#4771](https://github.com/vllm-project/vllm-omni/pull/4771)
+- Framework context: [RFC #3745](https://github.com/vllm-project/vllm-omni/issues/3745)
+  (duplex adapter patterns), [#1335](https://github.com/vllm-project/vllm-omni/issues/1335)
+  (full-duplex target), [PR #3907](https://github.com/vllm-project/vllm-omni/pull/3907)
+  (fullduplex core contracts this adapter conforms to)
+- Upstream model card:
+  [`nvidia/personaplex-7b-v1`](https://huggingface.co/nvidia/personaplex-7b-v1)
+
+## Why it is different from MiniCPM-o / JoyVL duplex
+
+| | MiniCPM-o 4.5 (#3907) | JoyVL | **PersonaPlex (this)** |
+|---|---|---|---|
+| Cadence | 1 s chunk groups | ~1 fps frames | **80 ms lockstep (12.5 Hz)** |
+| Turn control | learned `⟨listen⟩`/`⟨speak⟩` | `</silence>`/`</response>` | **none, pure lockstep** |
+| Per step | variable-length token group | text decision | **1 user frame in -> 1 agent frame + 1 text token** |
+| Barge-in | at chunk boundary | n/a | **native (model always hears the user)** |
+| Session state | chunk-group KV | per-tick HTTP | **persistent ring KV + streaming Mimi state** |
+
+This is the lockstep ("parallel-frame joint") shape of the duplex adapter
+patterns: the adapter declares `DuplexCapability.continuous = True`, and
+`core.DuplexRuntime` runs ONE eternal response that consumes input frames as they
+arrive and drains on close. The flag is a small, default-off lifecycle mode, so
+turn-based adapters (JoyVL, MiniCPM-o) are unaffected.
+
+## Architecture (Moshi RQ-Transformer, all native)
+
+- **Mimi codec** 24 kHz, 12.5 Hz, 1920 samples/frame, 8 active codebooks
+  (card 2048). Streaming encode + decode via `transformers.MimiModel` with the
+  PersonaPlex reference checkpoint weights.
+- **Temporal transformer** (Helium backbone) 4096-d / 32 layers / 32 heads,
+  sliding window 3000 frames (a 4-minute rolling context) held in a ring KV cache
+  with per-slot recycle.
+- **Depformer** 1024-d / 6 layers / 16 heads, autoregressive over 16 codebooks
+  per frame (8 vocoded), reset each frame.
+- Per-frame token column `[B, 17, 1]`: row 0 = inner-monologue text,
+  rows 1-8 = agent audio, rows 9-16 = user audio.
+- Persona + voice are injected once at session open through the same lockstep
+  step: voice clone forces the agent stream from reference-audio Mimi codes;
+  persona forces the text stream from `<system> ... <system>` tokens.
+
+## Hardware Support
+
+Verified on GPU (Hopper-class). The serving path is plain PyTorch eager plus the
+native modules, so other CUDA GPUs with enough memory are expected to work.
+
+## GPU
+
+### 1x 141 GB Hopper-class GPU (H20-class, verified)
+
+#### Environment
+
+- OS: Linux
+- Python: 3.10+ (CI covers 3.11 / 3.12)
+- vLLM-Omni: PR #4771 branch or current `main` once merged
+- Extra dependency for the live server: `pip install sphn` (Opus framing;
+  `aiohttp` already ships with vLLM)
+- `export HF_TOKEN=...` with access to the gated
+  `nvidia/personaplex-7b-v1` repo (accept the license on the model page)
+
+First run auto-downloads from the model repo: `model.safetensors`,
+`tokenizer_spm_32k_3.model`, `voices.tgz`, and the Mimi reference checkpoint;
+`kyutai/mimi` is fetched via `transformers` for the codec module graph.
+
+#### Command
+
+Live duplex server (official browser client served at `/`):
+
+```bash
+HF_TOKEN=... CUDA_VISIBLE_DEVICES=0 \
+python -m vllm_omni.experimental.fullduplex.personaplex.serving.server \
+    --port 8124 --voice NATF2.pt
+```
+
+Multi-session on the same GPU (elastic batching, callers join/leave any tick):
+
+```bash
+HF_TOKEN=... CUDA_VISIBLE_DEVICES=0 \
+python -m vllm_omni.experimental.fullduplex.personaplex.serving.server \
+    --port 8124 --batch-size 4
+```
+
+Then open `http://localhost:8124/` and allow the microphone (use headphones so
+the agent does not hear itself). Headless alternative:
+
+```bash
+python examples/online_serving/personaplex/duplex_client.py \
+    --url ws://localhost:8124/api/chat --input user.wav --out reply.wav
+```
+
+Offline WAV-in / WAV-out (no server):
+
+```bash
+python examples/offline_inference/personaplex/personaplex_offline.py \
+    --input-wav question.wav --output-wav reply.wav --output-text reply.json \
+    --voice-prompt NATF2.pt --persona "You are a wise and friendly teacher."
+```
+
+#### Verification
+
+```bash
+# GPU-free contract tests (stubbed FrameStepper)
+pytest tests/e2e/features/fullduplex/test_personaplex_adapter.py \
+       tests/e2e/features/fullduplex/test_personaplex_session.py \
+       tests/e2e/features/fullduplex/test_personaplex_batched.py -q
+
+# GPU e2e: batched isolation + slot recycle (coherence check needs a real
+# question WAV via PPLEX_QUESTION_WAV)
+pytest tests/e2e/features/fullduplex/test_personaplex_elastic.py -q
+```
+
+A green offline run producing a coherent spoken reply (and inner-monologue text
+in `reply.json`) is the quickest end-to-end check.
+
+#### Notes
+
+- Realtime budget is 80 ms/frame. Verified eager per-tick latency at
+  `--batch-size 4` is ~70-74 ms on this hardware class, i.e. all four
+  conversations stay realtime; single-session has comfortable headroom.
+- Decoding is greedy only (temperature/top-k knobs are intentionally not
+  exposed): greedy is what the parity gates pin against the reference
+  implementation, and sampling amplifies sub-bit numeric drift into divergence.
+- With `--batch-size N`, connections beyond N are rejected until a slot frees;
+  a recycled slot is bit-exact with a fresh engine for the same inputs.
+- Voice prompts: bundled `voices.tgz` names (`NATF*`/`NATM*` natural,
+  `VARF*`/`VARM*` varied) or a path to your own `.pt`/`.wav`.
+- Run the browser client near the server. The 80 ms cadence is sensitive to
+  network jitter; over a high-latency link playback can stutter regardless of
+  engine speed. On localhost it is smooth.
+- Sliding window is 3000 frames (~4 min). The ring KV recycles beyond that;
+  very long sessions keep running with a rolling context.

recipes/README.md

@@ -46,6 +46,7 @@ recipes/
 | [`inclusionAI/Ming-omni-tts.md`](./inclusionAI/Ming-omni-tts.md) | Offline + online dense Ming TTS/audio generation | 1x H100 80GB / 1x AMD MI300X (ROCm 7.2) |
 | [`IndexTeam/IndexTTS-2.md`](./IndexTeam/IndexTTS-2.md) | Online serving for voice-cloned TTS with optional emotion control | 1x L4 24GB or larger CUDA GPU |
 | [`MiniMaxAI/MiniMax-H3.md`](./MiniMaxAI/MiniMax-H3.md) | T2VA, FL2VA, image+audio Ref2VA, and multi-video Ref2VA serving | 1x GPU with CPU offload / 4x B300 |
+| [`MiniMaxAI/MiniMax-H3-MUSA.md`](./MiniMaxAI/MiniMax-H3-MUSA.md) | MiniMax H3 T2VA, FL2VA, and Ref2VA on MUSA | 4x MTT S5000 (TP4 Ref2VA profile) |
 | [`krea/Krea-2.md`](./krea/Krea-2.md) | Text-to-image (Turbo + Raw), offline + online, with LoRA | 1x H100 80GB |
 | [`LTX/LTX-2.md`](./LTX/LTX-2.md) | LTX-2/LTX-2.3 text-to-video and image-to-video with synchronized audio | H200 141GB / 96GB-class GPU |
 | [`MammothModa2/MammothModa2.md`](./MammothModa2/MammothModa2.md) | Preview and Dev text-to-image (AR → DiT); Dev text/image understanding | Preview: 1x L40S 48GB / 1x ≥40GB GPU; Dev: 1x NVIDIA GPU with sufficient cache headroom |

recipes/Tencent/HunyuanImage-3.0-Instruct.md

@@ -164,10 +164,11 @@ curl -s http://localhost:8091/v1/chat/completions \
 PR [#2495](https://github.com/vllm-project/vllm-omni/pull/2495) adds
 performance CI configs for the same DiT-only settings. The CI step is
 currently opt-in (gated by `RUN_HUNYUAN_IMAGE3_PERF=1`) with `soft_fail`
-enabled, intended for initial data collection. Performance assertions are
+enabled, intended for initial data collection. Perf runners no longer
-skipped (`skip-performance-assertion: true`); the baseline values in the
+compare metrics against JSON baselines at assert time; the `baseline`
-JSON configs are reference-only and will be promoted to regression gates
+blocks in the configs are reference-only and are copied into result JSON
-once enough nightly data has been collected.
+for reporting. Regression gates can be reintroduced once enough nightly
+data has been collected.
 
 The user-facing equivalent is to launch one of the CLI commands above and
 generate 1024x1024 images with 50 denoising steps.

recipes/cosmos3/Cosmos3-DistOffload.md

@@ -56,10 +56,14 @@ vllm serve /path/to/Cosmos3-Super \
   --data-parallel-size 4
 ```
 
-### Without AllGather (full weights per rank, no sharding)
+### Without DLO AllGather (rank-local weights, no DLO sharding)
 
-Use `--dlo-no-use-allgather` when you want each rank to load full weights independently —
+Use `--dlo-no-use-allgather` when you want each rank to stream the standard
-no AllGather synchronization, no concurrent request requirement, but N× host memory.
+loader's rank-local weights independently — no DLO AllGather synchronization,
+no concurrent request requirement, but no DLO weight sharding and higher host
+memory use. In pure DP, this is equivalent to independent full-weight
+layerwise-offload replicas; with TP or HSDP, each rank retains the standard
+loader's local tensor layout.
 
 ```bash
 vllm serve /path/to/Cosmos3-Nano \
@@ -154,9 +158,9 @@ wait
 | Flag | Description | Default |
 |------|-------------|---------|
 | `--enable-distributed-layerwise-offload` | Enable DLO with H2D + AllGather overlap | `false` |
-| `--data-parallel-size N` | Number of DP ranks (weight sharding + concurrent requests) | `1` |
+| `--data-parallel-size N` | Number of DP ranks; DLO weight sharding + concurrent requests when AllGather is enabled | `1` |
 | `--dlo-use-allgather` | Use shard + AllGather for weight reconstruction (recommended) | `true` |
-| `--dlo-no-use-allgather` | Each rank loads full weights independently (no sharding, no AllGather) | `false` |
+| `--dlo-no-use-allgather` | Stream standard-loader rank-local weights independently (no DLO sharding, no DLO AllGather) | `false` |
 | `--usp N` | Use SP instead of DP for weight sharding (long sequences) | `1` |
 
 ## Mode comparison
@@ -166,7 +170,7 @@ wait
 | DLO + AllGather (DP4) | `--enable-distributed-layerwise-offload --data-parallel-size 4` | 1/4 model | 2 blocks | **3.3× HSDP** | Max throughput, short sequences |
 | DLO + AllGather (DP2) | `--enable-distributed-layerwise-offload --data-parallel-size 2` | 1/2 model | 2 blocks | 2× HSDP | Balanced throughput + memory |
 | DLO + AllGather (SP4) | `--enable-distributed-layerwise-offload --usp 4` | 1/4 model | 2 blocks | 1× (single req) | Long sequences (720p+) |
-| DLO no-AllGather | `--enable-distributed-layerwise-offload --dlo-no-use-allgather` | Full model | 2 blocks | 0.4× HSDP | No AllGather overhead, high CPU |
+| DLO no-AllGather | `--enable-distributed-layerwise-offload --dlo-no-use-allgather` | Standard-loader rank-local weights | 2 blocks | 0.4× HSDP | No DLO AllGather overhead, high CPU |
 | HSDP | `--use-hsdp --hsdp-shard-size 4` | 0 | 1/4 model | 1× (baseline) | Weights fit in HBM |
 
 ## Memory expectations
@@ -185,7 +189,11 @@ wait
 
 - `--dlo-use-allgather` (default) requires all concurrent requests to have the same
   `num_inference_steps` (AllGather is a collective that needs all ranks synchronized)
-- Online quantization (FP8) is incompatible with mmap loading — falls back to regular
+- Online quantization (FP8) is incompatible with the DLO+AllGather mmap path;
-  `load_weights()` automatically
+  use `--dlo-no-use-allgather` or disable online quantization
-- Tensor Parallel is not supported (DLO uses DP-based sharding, a different dimension)
+- Tensor Parallel > 1 is rejected in the DLO+AllGather mmap path. TP with
-- HSDP + AllGather is rejected (would double-shard weights)
+  `--dlo-no-use-allgather` retains the standard TP loader and should be treated
+  as an experimental compatibility path
+- HSDP + AllGather is rejected (would double-shard weights). HSDP with
+  `--dlo-no-use-allgather` is accepted at configuration level, but is not yet a
+  broadly validated production combination

recipes/cosmos3/Cosmos3-Nano.md

@@ -423,6 +423,156 @@ ffprobe -v error -show_entries stream=codec_type,nb_frames,width,height cosmos3_
   once in `vllm_omni/model_extras/cosmos3.py` and forwarded through `--extra-body`;
   unknown keys for the model are dropped.
 
+## ROCm
+
+### 1x MI350X (Online serving)
+
+#### Environment
+
+- OS: Ubuntu 22.04+
+- Python: 3.12+
+- Driver / runtime: ROCm, HIP 7.2, **gfx942 or gfx950** (validated on gfx950 / MI350X,
+  which exposes 252 GiB of HBM to PyTorch)
+- PyTorch: 2.11.0 (ROCm build)
+- vLLM version: match the repository requirements from your current checkout
+- vLLM-Omni version or commit: use the commit you are deploying from
+
+No ROCm-specific patch or flag is needed: Cosmos3 runs on the stock code path.
+
+#### Command
+
+The NVIDIA command works unchanged. Guardrails need the gated
+`nvidia/Cosmos-1.0-Guardrail` repo, so the quick path is to disable them (you are
+then responsible for license compliance):
+
+```bash
+vllm serve nvidia/Cosmos3-Nano \
+  --omni \
+  --no-guardrails \
+  --host 0.0.0.0 --port 8000 \
+  --init-timeout 1800
+```
+
+For 720p video, add `--vae-use-tiling`: it cuts peak VRAM by ~68% for ~13%
+latency, which is the difference between needing a 120 GiB card and a 40 GiB one.
+`--quantization fp8` and `--enable-layerwise-offload` are also supported; see the
+Notes for the measured cost of each. For extra GPUs use `--ulysses-degree N` or
+`--tensor-parallel-size N` (not validated on ROCm yet).
+
+#### Verification
+
+Use the same `curl` requests as the CUDA section above. The ROCm images do not
+ship `ffprobe`, so the CUDA section's `ffprobe` line does not work here. `imageio`
+and `pyav` are already installed and report the same thing:
+
+```bash
+python -c "from PIL import Image; im=Image.open('cosmos3_t2i.png'); print(im.size, im.mode)"
+python -c "import imageio.v3 as iio; print(iio.improps('cosmos3_t2v.mp4', plugin='pyav').shape)"
+```
+
+Expect `(1024, 1024) RGB` and `(189, 720, 1280, 3)` for the requests above.
+Re-running the same request with the same seed reproduces a byte-identical mp4,
+so an md5 comparison across two runs also works as a smoke check.
+
+#### Notes
+
+- **Warm up once per output shape before timing anything.** The first generation
+  of a shape pays a one-time cost that is cached on disk and reused by later
+  processes in the same container: a ~250 s aiter attention-kernel JIT build plus
+  a ~390 s build of the VAE decode path *for that shape*, so warming 1024² images
+  does nothing for 189-frame video. At 720p / 189 frames / 35 steps this is
+  **540 s cold vs 161 s warm, with byte-identical output**.
+- **Measured on 1x MI350X (bf16, guardrails off, warm):** T2I 1024² @ 50 steps
+  **~2.7 s**; T2V 1280×720 / 189 frames @ 35 steps **~161 s**, of which ~92% is
+  the DiT denoise loop and ~4% VAE decode, so optimization effort belongs in the
+  denoise loop. The optional flags act on different terms of the memory bill and
+  are therefore complementary rather than redundant:
+
+  | Flag | Latency | Peak reserved | Peak allocated | Acts on |
+  |---|---|---|---|---|
+  | *(none)* | 161 s | 120 GiB | 95 GiB | — |
+  | `--vae-use-tiling` | 183 s (+13.5%) | **38 GiB** (−68%) | **36 GiB** (−62%) | decode activations |
+  | `--enable-layerwise-offload` | 162 s (+0.8%) | 84 GiB (−30%) | 69 GiB (−27%) | weights |
+  | `--quantization fp8` | 150 s (−6.6%) | 107 GiB (−11%) | 82 GiB (−14%) | weights (online, no calibration) |
+
+  Reserved is the caching-allocator high water mark and runs ~2–25 GiB above
+  allocated; allocated is the figure to compare against another platform.
+- **`--vae-use-tiling` is by far the biggest lever for video.** Of the 95 GiB
+  allocated by default only ~35 GiB is weights and fixed overhead; the remaining
+  **60 GiB is activations from decoding all 189 frames in one piece**, which is
+  why fp8 and offload cannot touch it and why tiling, which leaves under 5 GiB of
+  them, wins so much. `--vae-use-slicing` is a no-op for a single video request:
+  peak memory is bit-identical to the default.
+- **`--enable-layerwise-offload` is close to free on this part:** per-block DiT
+  compute is long enough to hide the weight transfer, so on a card this size it
+  buys headroom for concurrency or higher resolution rather than the
+  speed-for-memory trade it is on small cards.
+- **Attention backend:** the numbers above are aiter FlashAttention, not SDPA.
+  ROCm picks aiter when it is installed and the encoded device capability
+  satisfies `90 < major * 10 + minor < 100` (gfx942 and gfx950); otherwise it
+  falls back to `TORCH_SDPA` and says so only at debug level, which is easy to
+  miss. If you are reporting numbers, assert the choice instead of trusting the
+  default: `RocmOmniPlatform.get_diffusion_attn_backend_cls(None, 128)` returns
+  the resolved path.
+- **Determinism is per-configuration:** same seed and same flags reproduce a
+  byte-identical mp4 across processes, but different configurations do not match
+  each other — including offload, which nominally only relocates weights, so do
+  not use it as a bit-exact regression baseline. Quality was not evaluated for
+  any configuration.
+
+### 1x MI350X (Offline generation)
+
+#### Environment
+
+Same as the online serving section above.
+
+#### Command
+
+The standard task examples run unchanged:
+
+```bash
+# Text-to-image
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model nvidia/Cosmos3-Nano \
+  --prompt "A photorealistic red sports car at golden hour, cinematic lighting." \
+  --negative-prompt "blurry, distorted, low quality" \
+  --height 1024 --width 1024 --num-inference-steps 50 --guidance-scale 7.0 \
+  --extra-body '{"flow_shift": 3.0, "guardrails": false}' \
+  --output cosmos3_t2i.png
+
+# Text-to-video
+python examples/offline_inference/text_to_video/text_to_video.py \
+  --model nvidia/Cosmos3-Nano \
+  --prompt "A robot arm is cleaning a plate in the kitchen." \
+  --negative-prompt "blurry, distorted, low quality" \
+  --height 720 --width 1280 --num-frames 189 --fps 24 \
+  --num-inference-steps 35 --guidance-scale 6.0 --flow-shift 10.0 \
+  --extra-body '{"max_sequence_length": 4096, "guardrails": false,
+                 "use_resolution_template": false, "use_duration_template": false}' \
+  --output cosmos3_t2v.mp4
+```
+
+Both scripts print `Total generation time` and `Worker peak GPU memory
+(reserved)`, which is where the latency and reserved figures in the Notes above
+come from. Add `--vae-use-tiling`, `--quantization fp8` or
+`--enable-layerwise-offload` to reproduce the other rows. The allocated column
+needs `VLLM_LOGGING_LEVEL=DEBUG`, which enables a per-request line reporting
+reserved, allocated and pool overhead together.
+
+#### Verification
+
+Same as the online serving section above.
+
+#### Notes
+
+- Run each command twice and read the second run; the first pays the one-time
+  compilation described above. If you run in a container, keep the container
+  alive between runs (`docker run --rm` throws the JIT cache away and you pay
+  the ~250 s kernel build every time).
+- Latency and memory figures are identical to the online serving section — they
+  were taken from this path, because a single process has no client/server
+  timing ambiguity.
+
 ## NPU
 
 ### 1x Ascend 910B / 910C (Atlas A2 / A3) — Online serving

tests/config/test_omni_config.py

@@ -81,6 +81,21 @@ def _from_pipeline_key(
     )
 
 
+def test_duplex_session_capacity_propagates_to_every_structured_stage() -> None:
+    omni_config = _from_pipeline_key("personaplex")
+
+    assert [stage.model_config.duplex_max_sessions for stage in omni_config.stage_configs] == [2, 2]
+
+
+def test_non_duplex_deploy_keeps_model_session_capacity_at_one(tmp_path: Path) -> None:
+    deploy_path = tmp_path / "personaplex-turn.yaml"
+    deploy_path.write_text("session_mode: turn\n", encoding="utf-8")
+
+    omni_config = _from_pipeline_key("personaplex", deploy_config_path=str(deploy_path))
+
+    assert [stage.model_config.duplex_max_sessions for stage in omni_config.stage_configs] == [1, 1]
+
+
 @pytest.mark.parametrize("model_type", sorted(OMNI_PIPELINES))
 def test_vllm_omni_config_from_pipeline_config_matches_merge_pipeline_deploy(model_type: str):
     pipeline = _resolve_pipeline_or_skip(model_type)
@@ -105,6 +120,7 @@ def test_vllm_omni_config_from_pipeline_config_matches_merge_pipeline_deploy(mod
         assert omni_stage.runtime_config.num_replicas == legacy_stage.yaml_runtime.get("num_replicas", 1)
 
         engine_args = legacy_stage.yaml_engine_args
+        assert omni_stage.model_config.duplex_max_sessions == engine_args.get("duplex_max_sessions", 1)
         assert omni_stage.model_config.enforce_eager == engine_args.get("enforce_eager", False)
         assert omni_stage.load_config.load_format == engine_args.get("load_format", "auto")
         assert omni_stage.load_config.tokenizer_mode == engine_args.get("tokenizer_mode", "auto")
@@ -379,6 +395,7 @@ def test_runtime_config_fields_match_rfc_runtime_scope():
 def test_sub_config_fields_match_rfc_scopes():
     assert {f.name for f in fields(OmniStageModelConfig)} == {
         "active_stream_window",
+        "duplex_max_sessions",
         "enable_sleep_mode",
         "default_sampling_params",
         "subtalker_sampling_params",

tests/core/sched/test_omni_ar_scheduler_logprobs.py

@@ -3,7 +3,8 @@
 from __future__ import annotations
 
 from collections import defaultdict
-from types import SimpleNamespace
+from collections.abc import Callable
+from types import MethodType, SimpleNamespace
 
 import numpy as np
 import pytest
@@ -12,9 +13,22 @@ from vllm.v1.outputs import LogprobsLists
 from vllm.v1.request import RequestStatus
 
 from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler, _slice_sampled_logprobs
+from vllm_omni.core.sched.omni_scheduler_mixin import OmniSchedulerMixin
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
+# Shared update_from_output helpers moved onto OmniSchedulerMixin; bind them on
+# the lightweight stub so direct method calls still exercise the real paths.
+_MIXIN_UPDATE_HELPERS = (
+    "_remove_stopped_requests_from_queues",
+    "_handle_failed_kv_load_outputs",
+    "_aggregate_kv_connector_stats",
+    "_publish_kv_cache_events",
+    "_attach_finished_request_sets",
+    "_attach_scheduler_stats",
+    "_capture_omni_connector_output",
+)
+
 
 def _logprobs(
     token_ids: list[list[int]],
@@ -105,18 +119,14 @@ class _RequestQueue(list):
                 self.remove(request)
 
 
-def test_mid_step_stop_trims_logprob_rows_with_token_ids() -> None:
+def _make_scheduler_stub(requests: list[_Request]) -> SimpleNamespace:
-    """Spec decode can sample tokens past a stop; the trailing tokens are
+    """Build a minimal stub for OmniARScheduler.update_from_output()."""
-    trimmed by _update_request_with_output, and the emitted logprob rows must
-    be trimmed with them (upstream slices logprobs after the trim)."""
-    request = _Request("req")
-    request.num_computed_tokens = 8
     scheduler = SimpleNamespace(
         perf_metrics=None,
         connector=None,
         chunk_transfer_adapter=None,
-        requests={request.request_id: request},
+        requests={request.request_id: request for request in requests},
-        running=[request],
+        running=list(requests),
         waiting=_RequestQueue(),
         skipped_waiting=_RequestQueue(),
         structured_output_manager=SimpleNamespace(should_advance=lambda _request: False),
@@ -129,7 +139,42 @@ def test_mid_step_stop_trims_logprob_rows_with_token_ids() -> None:
         finished_req_ids_dict=defaultdict(set),
         kv_cache_manager=SimpleNamespace(take_events=lambda: None),
         kv_event_publisher=SimpleNamespace(publish=lambda _events: None),
+        recompute_kv_load_failures=False,
     )
+    for name in _MIXIN_UPDATE_HELPERS:
+        setattr(scheduler, name, MethodType(getattr(OmniSchedulerMixin, name), scheduler))
+    scheduler._cleanup_kv_tracking = MethodType(OmniARScheduler._cleanup_kv_tracking, scheduler)
+    scheduler.make_spec_decoding_stats = lambda *args, **kwargs: None
+    scheduler.make_stats = lambda *args, **kwargs: None
+    return scheduler
+
+
+def _bind_request_lifecycle(
+    scheduler: SimpleNamespace,
+    *,
+    update_request: Callable,
+    handle_stopped: Callable | None = None,
+) -> None:
+    def free_request(request):
+        scheduler.requests.pop(request.request_id, None)
+        scheduler.finished_req_ids.add(request.request_id)
+        scheduler.finished_req_ids_dict[request.client_index].add(request.request_id)
+        # vLLM 0.26 contract: (kv_xfer_params, ec_xfer_params)
+        return None, None
+
+    scheduler._update_request_with_output = update_request
+    scheduler._process_kv_transfer_trigger = lambda _request, _tokens: False
+    scheduler._handle_stopped_request = handle_stopped or (lambda _request: True)
+    scheduler._free_request = free_request
+
+
+def test_mid_step_stop_trims_logprob_rows_with_token_ids() -> None:
+    """Spec decode can sample tokens past a stop; the trailing tokens are
+    trimmed by _update_request_with_output, and the emitted logprob rows must
+    be trimmed with them (upstream slices logprobs after the trim)."""
+    request = _Request("req")
+    request.num_computed_tokens = 8
+    scheduler = _make_scheduler_stub([request])
 
     def update_request_trimming(req, token_ids):
         # Mirror vllm Scheduler._update_request_with_output: the first sampled
@@ -139,19 +184,7 @@ def test_mid_step_stop_trims_logprob_rows_with_token_ids() -> None:
         req.status = RequestStatus.FINISHED_STOPPED
         return token_ids, True
 
-    def free_request(req):
+    _bind_request_lifecycle(scheduler, update_request=update_request_trimming)
-        scheduler.requests.pop(req.request_id, None)
-        scheduler.finished_req_ids.add(req.request_id)
-        scheduler.finished_req_ids_dict[req.client_index].add(req.request_id)
-        return None, None
-
-    scheduler._update_request_with_output = update_request_trimming
-    scheduler._process_kv_transfer_trigger = lambda _request, _tokens: False
-    scheduler._handle_stopped_request = lambda _request: True
-    scheduler._free_request = free_request
-    scheduler.make_spec_decoding_stats = lambda *args, **kwargs: None
-    scheduler.make_stats = lambda *args, **kwargs: None
-    scheduler._capture_omni_connector_output = lambda _output: None
 
     scheduler_output = SimpleNamespace(
         num_scheduled_tokens={"req": 3},
@@ -190,45 +223,14 @@ def test_invalid_logprobs_finish_only_the_affected_scheduler_request() -> None:
     """A bad runner response must not bring down unrelated batch requests."""
     bad = _Request("bad")
     good = _Request("good")
-    scheduler = SimpleNamespace(
+    scheduler = _make_scheduler_stub([bad, good])
-        perf_metrics=None,
-        connector=None,
-        chunk_transfer_adapter=None,
-        requests={bad.request_id: bad, good.request_id: good},
-        running=[bad, good],
-        waiting=_RequestQueue(),
-        skipped_waiting=_RequestQueue(),
-        structured_output_manager=SimpleNamespace(should_advance=lambda _request: False),
-        transfer_triggered_requests=set(),
-        active_kv_transfers=set(),
-        pending_stop_after_extraction=set(),
-        waiting_for_transfer_free=set(),
-        _new_prompt_len_snapshot={},
-        finished_req_ids=set(),
-        finished_req_ids_dict=defaultdict(set),
-        kv_cache_manager=SimpleNamespace(take_events=lambda: None),
-        kv_event_publisher=SimpleNamespace(publish=lambda _events: None),
-    )
     update_calls: list[str] = []
 
     def update_request(request, token_ids):
         update_calls.append(request.request_id)
         return token_ids, False
 
-    def free_request(request):
+    _bind_request_lifecycle(scheduler, update_request=update_request)
-        scheduler.requests.pop(request.request_id, None)
-        scheduler.finished_req_ids.add(request.request_id)
-        scheduler.finished_req_ids_dict[request.client_index].add(request.request_id)
-        # vLLM 0.26 contract: (kv_xfer_params, ec_xfer_params)
-        return None, None
-
-    scheduler._update_request_with_output = update_request
-    scheduler._process_kv_transfer_trigger = lambda _request, _tokens: False
-    scheduler._handle_stopped_request = lambda _request: True
-    scheduler._free_request = free_request
-    scheduler.make_spec_decoding_stats = lambda *args, **kwargs: None
-    scheduler.make_stats = lambda *args, **kwargs: None
-    scheduler._capture_omni_connector_output = lambda _output: None
 
     scheduler_output = SimpleNamespace(
         num_scheduled_tokens={"bad": 1, "good": 1},

tests/core/sched/test_omni_generation_scheduler_update_session.py

@@ -110,6 +110,9 @@ def test_resumable_generation_stop_marks_segment_boundary() -> None:
     session.status = RequestStatus.RUNNING
     session.resumable = True
     session.num_computed_tokens = len(session.prompt_token_ids)
+    # schedule() has counted this step's token as in flight; update_from_output()
+    # must settle it before handling the segment boundary.
+    session.num_in_flight_tokens = 1
 
     sched = MagicMock()
     sched.requests = {session.request_id: session}
@@ -118,12 +121,20 @@ def test_resumable_generation_stop_marks_segment_boundary() -> None:
         is_done_receiving_chunks=lambda _request_id: True,
         segment_finished_requests={session.request_id},
     )
-    sched._handle_stopped_request.return_value = False
+
+    def rearm_resumable_request(request: Request) -> bool:
+        request.status = RequestStatus.WAITING
+        return False
+
+    sched._handle_stopped_request.side_effect = rearm_resumable_request
     sched.running = [session]
     sched.waiting = MagicMock()
     sched.skipped_waiting = MagicMock()
     sched.structured_output_manager.should_advance.return_value = False
-    sched._pending_finish_reqs = []
+    # Async scheduling can observe the segment boundary in the next schedule()
+    # before this model output is applied and defer the same request for finish.
+    # update_from_output() must not re-arm the resumable request twice.
+    sched._pending_finish_reqs = [session]
     sched.recompute_kv_load_failures = False
     sched.connector = None
     sched.kv_cache_manager.take_events.return_value = None
@@ -155,6 +166,8 @@ def test_resumable_generation_stop_marks_segment_boundary() -> None:
     output = outputs[session.client_index].outputs[0]
     assert output.finish_reason is not None
     assert output.is_segment_finished is True
+    sched._handle_stopped_request.assert_called_once_with(session)
+    assert sched._pending_finish_reqs == []
 
 
 def test_async_chunk_resumable_stop_rearms_connector_polling() -> None:

tests/core/sched/test_omni_scheduler_mixin_shared.py

@@ -0,0 +1,90 @@
+from collections import defaultdict
+from types import SimpleNamespace
+
+import pytest
+from vllm.v1.engine import FinishReason
+
+from vllm_omni.core.sched.omni_scheduler_mixin import OmniSchedulerMixin
+from vllm_omni.core.sched.output import OmniChunkRecvHandle
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+class _Scheduler(OmniSchedulerMixin):
+    pass
+
+
+def test_schedule_lifecycle_helpers_process_and_restore_both_input_paths():
+    calls = []
+    scheduler = _Scheduler()
+    scheduler.waiting = ["waiting"]
+    scheduler.running = ["running"]
+    scheduler.requests = {"request": object()}
+    scheduler._consume_pending_connector_output = lambda mode: calls.append(("consume", mode))
+    scheduler._process_pending_input_timeouts = lambda: calls.append(("timeouts",))
+    scheduler.chunk_transfer_adapter = SimpleNamespace(
+        process_pending_chunks=lambda waiting, running, scheduler_requests: calls.append(
+            ("process", waiting, running, scheduler_requests)
+        ),
+        restore_queues=lambda waiting, running, scheduler_requests: calls.append(
+            ("restore-chunks", waiting, running, scheduler_requests)
+        ),
+    )
+    scheduler.input_coordinator = SimpleNamespace(
+        restore_queues=lambda waiting: calls.append(("restore-full", waiting))
+    )
+
+    scheduler._process_pending_omni_inputs("ar")
+    scheduler._restore_omni_wait_queues()
+
+    assert calls == [
+        ("consume", "ar"),
+        ("timeouts",),
+        ("process", scheduler.waiting, scheduler.running, scheduler.requests),
+        ("restore-chunks", scheduler.waiting, scheduler.running, scheduler.requests),
+        ("restore-full", scheduler.waiting),
+    ]
+
+
+@pytest.mark.parametrize(
+    ("synthesize_abort_outputs", "expected_finish_reason"),
+    [(False, None), (True, FinishReason.ABORT)],
+)
+def test_finished_request_attachment_keeps_ar_abort_policy_explicit(
+    synthesize_abort_outputs,
+    expected_finish_reason,
+):
+    scheduler = _Scheduler()
+    scheduler.finished_req_ids_dict = defaultdict(set, {2: {"req-finished"}})
+    outputs = {}
+
+    scheduler._attach_finished_request_sets(
+        outputs,
+        synthesize_abort_outputs=synthesize_abort_outputs,
+    )
+
+    assert outputs[2].finished_requests == {"req-finished"}
+    if expected_finish_reason is None:
+        assert outputs[2].outputs == []
+    else:
+        assert outputs[2].outputs[0].finish_reason == expected_finish_reason
+    assert scheduler.finished_req_ids_dict == {}
+
+
+def test_chunk_receive_handle_carries_minimal_registration_fields():
+    handle = OmniChunkRecvHandle(request_id="req", external_req_id="external")
+    assert handle.request_id == "req"
+    assert handle.external_req_id == "external"
+
+
+def test_output_helper_preserves_required_nan_counter_default():
+    scheduler = _Scheduler()
+    request = SimpleNamespace(
+        request_id="req-output",
+        trace_headers=None,
+        take_events=lambda: [],
+    )
+
+    output = scheduler._make_omni_engine_output(request, new_token_ids=[])
+
+    assert output.num_nans_in_logits == 0

tests/core/test_prefix_cache.py

@@ -1,3 +1,6 @@
+import math
+from typing import NamedTuple
+
 import pytest
 import torch
 
@@ -317,6 +320,103 @@ def test_deferred_multimodal_cache_write_commits_on_completion():
     assert torch.equal(rows[8:16], expected)
 
 
+def test_discard_deferred_mm_outputs_drops_pending_chunks():
+    """discard_deferred_mm_outputs is the abort path: a request that
+    leaves without a cache commit must not leak its staged chunks into
+    the cache if commit_deferred_mm_outputs is later called for it."""
+    cache = get_omni_pcache()
+    mm_key = "codes.audio"
+    block_table = torch.tensor(
+        [
+            [2, 3, 4, 5],
+            [6, 7, 8, 9],
+        ],
+        dtype=torch.long,
+    )
+    input_batch = MockInputBatch(
+        num_computed_tokens_cpu=torch.tensor([4, 0], dtype=torch.long),
+        block_table=block_table,
+    )
+
+    cache.update_omni_tensor_prefix_cache(
+        hidden_states=None,
+        multimodal_outputs={mm_key: torch.arange(8, dtype=torch.long).reshape(4, 2)},
+        num_tokens_unpadded=4,
+        slot_mapping=torch.arange(8, 12),
+        skip_mm_cache_keys={mm_key},
+    )
+
+    chunk = torch.arange(8, dtype=torch.long).reshape(4, 2)
+    cache.stage_deferred_mm_outputs(
+        query_start_loc=torch.tensor([0, 4], dtype=torch.long),
+        input_batch=input_batch,
+        multimodal_outputs={mm_key: chunk},
+        num_scheduled_tokens={"req1": 4, "req2": 0},
+        deferred_mm_cache_keys={mm_key},
+    )
+    assert "req1" in cache._deferred_mm_outputs
+
+    cache.discard_deferred_mm_outputs("req1")
+    assert "req1" not in cache._deferred_mm_outputs
+
+    # A later commit call for the now-discarded req_id must be a no-op,
+    # not a resurrection of the dropped chunk.
+    cache.commit_deferred_mm_outputs({"req1"}, input_batch)
+    rows = cache.mm_outputs_cache[mm_key].view(-1, 2)
+    assert torch.all(rows[8:12] == 0)
+
+
+def test_deferred_mm_outputs_request_id_reused_after_discard():
+    """A request ID reused after discard_deferred_mm_outputs must start
+    clean: the new occupant's committed data must not be mixed with the
+    previous (discarded) occupant's staged chunks."""
+    cache = get_omni_pcache()
+    mm_key = "codes.audio"
+    block_table = torch.tensor(
+        [
+            [2, 3, 4, 5],
+            [6, 7, 8, 9],
+        ],
+        dtype=torch.long,
+    )
+    input_batch = MockInputBatch(
+        num_computed_tokens_cpu=torch.tensor([4, 0], dtype=torch.long),
+        block_table=block_table,
+    )
+
+    cache.update_omni_tensor_prefix_cache(
+        hidden_states=None,
+        multimodal_outputs={mm_key: torch.arange(8, dtype=torch.long).reshape(4, 2)},
+        num_tokens_unpadded=4,
+        slot_mapping=torch.arange(8, 12),
+        skip_mm_cache_keys={mm_key},
+    )
+
+    stale_chunk = torch.full((4, 2), -1, dtype=torch.long)
+    cache.stage_deferred_mm_outputs(
+        query_start_loc=torch.tensor([0, 4], dtype=torch.long),
+        input_batch=input_batch,
+        multimodal_outputs={mm_key: stale_chunk},
+        num_scheduled_tokens={"req1": 4, "req2": 0},
+        deferred_mm_cache_keys={mm_key},
+    )
+    cache.discard_deferred_mm_outputs("req1")
+
+    # "req1" is reused by a brand-new request with different data.
+    fresh_chunk = torch.arange(8, dtype=torch.long).reshape(4, 2)
+    cache.stage_deferred_mm_outputs(
+        query_start_loc=torch.tensor([0, 4], dtype=torch.long),
+        input_batch=input_batch,
+        multimodal_outputs={mm_key: fresh_chunk},
+        num_scheduled_tokens={"req1": 4, "req2": 0},
+        deferred_mm_cache_keys={mm_key},
+    )
+    cache.commit_deferred_mm_outputs({"req1"}, input_batch)
+
+    rows = cache.mm_outputs_cache[mm_key].view(-1, 2)
+    assert torch.equal(rows[8:12], fresh_chunk)
+
+
 def test_deferred_multimodal_cache_can_be_merged_on_full_block_hit():
     cache = get_omni_pcache()
     mm_key = "codes.audio"
@@ -599,3 +699,219 @@ def test_get_merged_multimodal_outputs(feat_dims, num_tokens_padded, mocker):
 
             assert req2_merged_mm_outputs.shape == torch.Size([num_new_toks_req2, curr_feat_dim])
             assert torch.all(req2_merged_mm_outputs == req2_new_mm_outputs)
+
+
+@pytest.mark.parametrize(
+    "passthrough_shape",
+    [(5,), (5, 3)],
+    ids=["1d_token_metadata", "2d_feature_tensor"],
+)
+def test_get_merged_multimodal_outputs_slices_passthrough_tensor_per_request(passthrough_shape, mocker):
+    """A passthrough key that is itself a real tensor (not the string/list
+    passthrough already covered above) must be sliced per request through
+    to_payload_element's tensor branch, under the same mixed cache
+    hit/miss + uneven scheduled-length batch as test_get_merged_multimodal_outputs.
+
+    Covers both a 1D token-aligned metadata tensor (e.g. ref_code_len,
+    codec_streaming -- shape (total_tokens,)) and a 2D per-token feature
+    tensor (shape (total_tokens, feat_dim)), since W2's batching could
+    easily special-case one and silently mishandle the other.
+    """
+    feat_dims = {"foo": 100, "bar": 100}
+    cache = get_omni_pcache_with_mm_tensors(feat_dims, seq_len=DEFAULT_SEQ_LEN)
+
+    orig_num_tokens_unpadded = 8
+    slot_offset = 8
+    orig_slot_mapping = torch.arange(slot_offset, slot_offset + orig_num_tokens_unpadded)
+    feature_dims = {key: val.shape[-1] for key, val in cache.mm_outputs_cache.items()}
+    orig_mm_outputs = {
+        key: torch.rand((orig_num_tokens_unpadded, feature_dims[key]), dtype=DTYPE) for key in cache.mm_cache_keys
+    }
+
+    cache.update_omni_tensor_prefix_cache(
+        hidden_states=None,
+        multimodal_outputs=orig_mm_outputs,
+        num_tokens_unpadded=orig_num_tokens_unpadded,
+        slot_mapping=orig_slot_mapping,
+        num_tokens_padded=None,
+    )
+
+    num_new_toks_req1 = 3
+    num_new_toks_req2 = 2
+    cache.add_prefix_cached_new_req_id("req1")
+
+    num_scheduled_tokens = {
+        "req1": num_new_toks_req1,
+        "req2": num_new_toks_req2,
+    }
+
+    new_mm_outputs = {}
+    for mm_key in cache.mm_cache_keys:
+        new_mm_outputs[mm_key] = torch.rand(
+            (num_new_toks_req1 + num_new_toks_req2, feature_dims[mm_key]),
+            dtype=DTYPE,
+        )
+    total_scheduled_tokens = num_new_toks_req1 + num_new_toks_req2
+    assert passthrough_shape[0] == total_scheduled_tokens
+    passthrough_tensor = torch.arange(int(torch.prod(torch.tensor(passthrough_shape))), dtype=DTYPE).reshape(
+        passthrough_shape
+    )
+    new_mm_outputs["passthrough_tensor"] = passthrough_tensor
+
+    input_batch = MockInputBatch(num_computed_tokens_cpu=torch.Tensor([orig_num_tokens_unpadded, 0]))
+
+    mocker.patch(
+        "vllm_omni.core.prefix_cache.OmniTensorPrefixCache._get_cached_block_ids",
+        new=fake_get_cached_block_ids,
+    )
+    merged_mm_outputs = cache.get_merged_multimodal_states(
+        query_start_loc=[0, num_new_toks_req1],
+        input_batch=input_batch,
+        multimodal_outputs=new_mm_outputs,
+        num_scheduled_tokens=num_scheduled_tokens,
+    )
+
+    assert "passthrough_tensor" not in cache.mm_cache_keys
+
+    passthrough_out = merged_mm_outputs["passthrough_tensor"]
+    # req1 (cache hit) gets tokens [0:3), req2 (cache miss) gets tokens [3:5) --
+    # a passthrough tensor is sliced by scheduled position, not merged with
+    # any cached history (unlike the mm_cache_keys tensors above).
+    assert torch.equal(passthrough_out["req1"], passthrough_tensor[0:num_new_toks_req1])
+    assert torch.equal(passthrough_out["req2"], passthrough_tensor[num_new_toks_req1:total_scheduled_tokens])
+
+
+class MergeHarness(NamedTuple):
+    """A primed cache plus the arguments get_merged_multimodal_states needs."""
+
+    cache: OmniTensorPrefixCache
+    input_batch: MockInputBatch
+    num_scheduled_tokens: dict[str, int]
+    query_start_loc: list[int]
+    total_scheduled_tokens: int
+    new_mm_outputs: dict[str, object]
+
+
+def build_mixed_hit_miss_harness(mocker, num_new_toks_req1: int = 3, num_new_toks_req2: int = 2) -> MergeHarness:
+    """Two requests over one cache key: req1 hits 8 cached tokens, req2 misses,
+    and their scheduled lengths differ -- the same batch shape as
+    test_get_merged_multimodal_outputs, which is what makes a per-request
+    boundary error observable. Callers add their passthrough keys to
+    ``new_mm_outputs`` before calling get_merged_multimodal_states.
+    """
+    cache = get_omni_pcache_with_mm_tensors({"foo": 100}, seq_len=DEFAULT_SEQ_LEN)
+
+    orig_num_tokens_unpadded = 8
+    feature_dims = {key: val.shape[-1] for key, val in cache.mm_outputs_cache.items()}
+    cache.update_omni_tensor_prefix_cache(
+        hidden_states=None,
+        multimodal_outputs={
+            key: torch.rand((orig_num_tokens_unpadded, feature_dims[key]), dtype=DTYPE) for key in cache.mm_cache_keys
+        },
+        num_tokens_unpadded=orig_num_tokens_unpadded,
+        slot_mapping=torch.arange(8, 8 + orig_num_tokens_unpadded),
+        num_tokens_padded=None,
+    )
+    cache.add_prefix_cached_new_req_id("req1")
+
+    total_scheduled_tokens = num_new_toks_req1 + num_new_toks_req2
+    mocker.patch(
+        "vllm_omni.core.prefix_cache.OmniTensorPrefixCache._get_cached_block_ids",
+        new=fake_get_cached_block_ids,
+    )
+    return MergeHarness(
+        cache=cache,
+        input_batch=MockInputBatch(num_computed_tokens_cpu=torch.Tensor([orig_num_tokens_unpadded, 0])),
+        num_scheduled_tokens={"req1": num_new_toks_req1, "req2": num_new_toks_req2},
+        query_start_loc=[0, num_new_toks_req1],
+        total_scheduled_tokens=total_scheduled_tokens,
+        new_mm_outputs={
+            key: torch.rand((total_scheduled_tokens, feature_dims[key]), dtype=DTYPE) for key in cache.mm_cache_keys
+        },
+    )
+
+
+def test_get_merged_multimodal_outputs_passes_per_request_tensor_list_through(mocker):
+    """The passthrough shape qwen3-tts actually emits: a nested ``codes``/``meta``
+    dict whose ``codes.ref`` holds one tensor per request
+    (qwen3_tts_talker.make_omni_output), carried through to_payload_element's
+    pass_lists_through branch beside 1D int metadata that IS sliced per request.
+
+    Each request must receive the list *whole*. Per-request indexing happens
+    later, in the runner's _unwrap_lists; a W2 that split the list here would
+    make that second index select a different request's reference codes. The
+    existing list coverage passes a list of strings, which cannot catch a
+    tensor-specific regression, and no other test covers an int dtype.
+    """
+    harness = build_mixed_hit_miss_harness(mocker)
+
+    ref_codes = [torch.tensor([11, 12], dtype=torch.long), torch.tensor([21], dtype=torch.long)]
+    ref_code_len = torch.arange(harness.total_scheduled_tokens, dtype=torch.int32)
+    harness.new_mm_outputs["codes"] = {"ref": ref_codes}
+    harness.new_mm_outputs["meta"] = {"ref_code_len": ref_code_len}
+
+    merged = harness.cache.get_merged_multimodal_states(
+        query_start_loc=harness.query_start_loc,
+        input_batch=harness.input_batch,
+        multimodal_outputs=harness.new_mm_outputs,
+        num_scheduled_tokens=harness.num_scheduled_tokens,
+    )
+
+    assert "codes" not in harness.cache.mm_cache_keys
+    for req_id in ("req1", "req2"):
+        req_refs = merged["codes"][req_id]["ref"]
+        assert len(req_refs) == len(ref_codes)
+        for got, want in zip(req_refs, ref_codes):
+            assert torch.equal(got, want)
+            assert got.dtype == torch.long
+
+    # Per-request clones: the branch exists so one request's downstream mutation
+    # cannot reach another request's payload or the source list.
+    merged["codes"]["req1"]["ref"][0] += 100
+    assert torch.equal(merged["codes"]["req2"]["ref"][0], torch.tensor([11, 12], dtype=torch.long))
+    assert torch.equal(ref_codes[0], torch.tensor([11, 12], dtype=torch.long))
+
+    # Token-aligned 1D metadata is still split per request, unlike the list.
+    req1_len = harness.num_scheduled_tokens["req1"]
+    assert torch.equal(merged["meta"]["req1"]["ref_code_len"], ref_code_len[:req1_len])
+    assert torch.equal(merged["meta"]["req2"]["ref_code_len"], ref_code_len[req1_len:])
+    assert merged["meta"]["req1"]["ref_code_len"].dtype == torch.int32
+
+
+@pytest.mark.parametrize(
+    "passthrough_shape",
+    [(3, 4), (1, 4), (7,)],
+    ids=["shorter_2d", "single_row_2d", "longer_1d"],
+)
+def test_get_merged_multimodal_outputs_broadcasts_non_token_aligned_passthrough(passthrough_shape, mocker):
+    """A passthrough tensor whose first dimension is not the scheduled-token
+    count is request-invariant: to_payload_element hands every request the whole
+    tensor, cloned, rather than slicing it.
+
+    W2 batching most plausibly breaks by assuming every passthrough tensor is
+    token-aligned; that assumption would slice these and give each request a
+    truncated view, or an empty one once start exceeds the first dimension.
+    """
+    harness = build_mixed_hit_miss_harness(mocker)
+    assert passthrough_shape[0] != harness.total_scheduled_tokens
+
+    passthrough = torch.arange(math.prod(passthrough_shape), dtype=DTYPE).reshape(passthrough_shape)
+    expected = passthrough.clone()
+    harness.new_mm_outputs["speaker_embedding"] = passthrough
+
+    merged = harness.cache.get_merged_multimodal_states(
+        query_start_loc=harness.query_start_loc,
+        input_batch=harness.input_batch,
+        multimodal_outputs=harness.new_mm_outputs,
+        num_scheduled_tokens=harness.num_scheduled_tokens,
+    )
+
+    assert "speaker_embedding" not in harness.cache.mm_cache_keys
+    out = merged["speaker_embedding"]
+    for req_id in ("req1", "req2"):
+        assert out[req_id].shape == passthrough_shape
+        assert torch.equal(out[req_id], expected)
+
+    out["req1"] += 100
+    assert torch.equal(out["req2"], expected)
+    assert torch.equal(passthrough, expected)

tests/core/test_prefix_cache_async_write.py

@@ -0,0 +1,119 @@
+import pytest
+import torch
+
+from vllm_omni.core.prefix_cache import OmniTensorPrefixCache
+
+# `cpu` is the lane selector, not a hardware claim: the only Buildkite steps that
+# reach tests/ select `core_model and cpu`, and they run on GPU hardware (l4_1).
+# Marking this `cuda` collects it nowhere. Real hardware need is the skipif below.
+pytestmark = [
+    pytest.mark.core_model,
+    pytest.mark.cpu,
+    pytest.mark.skipif(not torch.cuda.is_available(), reason="requires a CUDA device"),
+]
+
+NUM_BLOCKS = 10
+BLOCK_SIZE = 4
+HIDDEN_SIZE = 2
+DTYPE = torch.float32
+
+
+def get_omni_pcache() -> OmniTensorPrefixCache:
+    """Build an OmniTensorPrefixCache, but don't init mm tensors."""
+    return OmniTensorPrefixCache(
+        num_blocks=NUM_BLOCKS,
+        block_size=BLOCK_SIZE,
+        hidden_size=HIDDEN_SIZE,
+        hs_dtype=DTYPE,
+    )
+
+
+def test_drain_ready_async_writes_is_a_noop_before_the_event_is_ready(monkeypatch):
+    """drain_ready_async_writes() must not touch the cache while the
+    scheduled D2H's event has not completed, and must consume it exactly
+    once after it has.
+
+    Real GPU timing (e.g. via torch.cuda._sleep to stall the copy stream)
+    was tried and found unreliable on this box: cross-stream wait_stream/
+    wait_event did not reliably delay event.query() from reporting ready
+    immediately, likely a virtualization/driver quirk of this cloud GPU.
+    Forcing the event's query() result directly tests the branching logic
+    in drain_ready_async_writes/_consume_pending_write deterministically,
+    independent of real CUDA scheduling behavior.
+    """
+    cache = get_omni_pcache()
+    mm_key = "codes.audio"
+    cache.maybe_init_missing_mm_cache_keys({mm_key: torch.zeros(4, HIDDEN_SIZE)}, seq_len=4)
+
+    num_tokens = 4
+    slot_mapping_gpu = torch.arange(8, 8 + num_tokens, dtype=torch.int64, device="cuda")
+    mm_gpu = {
+        mm_key: torch.arange(num_tokens * HIDDEN_SIZE, dtype=torch.float32, device="cuda").reshape(
+            num_tokens, HIDDEN_SIZE
+        )
+    }
+
+    cache.schedule_async_write(
+        hidden_states_gpu=None,
+        multimodal_outputs_gpu=mm_gpu,
+        slot_mapping_gpu=slot_mapping_gpu,
+        num_tokens_unpadded=num_tokens,
+        num_tokens_padded=num_tokens,
+    )
+
+    pending_event = cache._pending_write.event
+    rows = cache.mm_outputs_cache[mm_key].view(-1, HIDDEN_SIZE)
+
+    monkeypatch.setattr(pending_event, "query", lambda: False)
+    assert cache.drain_ready_async_writes() == 0
+    assert torch.all(rows[8 : 8 + num_tokens] == 0)
+    assert cache._pending_write is not None
+
+    monkeypatch.setattr(pending_event, "query", lambda: True)
+    assert cache.drain_ready_async_writes() == 1
+    assert torch.equal(rows[8 : 8 + num_tokens], mm_gpu[mm_key].cpu())
+    assert cache._pending_write is None
+
+    # Nothing pending anymore: a second drain is a no-op, not a re-scatter.
+    assert cache.drain_ready_async_writes() == 0
+
+
+def test_schedule_async_write_consumes_previous_pending_write_first():
+    """Scheduling step N+1's write must first consume (scatter) step N's
+    pending write, even if the caller never explicitly called
+    drain_ready_async_writes() in between -- otherwise step N's data would
+    be silently dropped when overwritten by step N+1's pending write."""
+    cache = get_omni_pcache()
+    mm_key = "codes.audio"
+    cache.maybe_init_missing_mm_cache_keys({mm_key: torch.zeros(4, HIDDEN_SIZE)}, seq_len=4)
+
+    num_tokens = 4
+    first_slots = torch.arange(8, 8 + num_tokens, dtype=torch.int64, device="cuda")
+    first_gpu = {mm_key: torch.full((num_tokens, HIDDEN_SIZE), 1.0, device="cuda")}
+    second_slots = torch.arange(12, 12 + num_tokens, dtype=torch.int64, device="cuda")
+    second_gpu = {mm_key: torch.full((num_tokens, HIDDEN_SIZE), 2.0, device="cuda")}
+
+    cache.schedule_async_write(
+        hidden_states_gpu=None,
+        multimodal_outputs_gpu=first_gpu,
+        slot_mapping_gpu=first_slots,
+        num_tokens_unpadded=num_tokens,
+        num_tokens_padded=num_tokens,
+    )
+    torch.accelerator.synchronize()
+
+    # This call's internal _consume_pending_write() must flush the FIRST
+    # write before the second one is scheduled; the caller never drained.
+    cache.schedule_async_write(
+        hidden_states_gpu=None,
+        multimodal_outputs_gpu=second_gpu,
+        slot_mapping_gpu=second_slots,
+        num_tokens_unpadded=num_tokens,
+        num_tokens_padded=num_tokens,
+    )
+    torch.accelerator.synchronize()
+    cache.drain_ready_async_writes()
+
+    rows = cache.mm_outputs_cache[mm_key].view(-1, HIDDEN_SIZE)
+    assert torch.all(rows[8:12] == 1.0)
+    assert torch.all(rows[12:16] == 2.0)

tests/dfx/conftest.py

@@ -1,16 +1,15 @@
+import copy
 import json
 import os
 import re
 import subprocess
 import threading
-import time
 from datetime import datetime
 from pathlib import Path
 from typing import Any
 
 import pytest
 
-from tests.dfx.reliability.helpers import list_remote_process_pids_by_pattern, post_chat_completions_raw
 from tests.helpers.mark import hardware_marks
 from tests.helpers.runtime import OmniServerParams
 from tests.helpers.stage_config import modify_stage_config
@@ -119,32 +118,6 @@ def create_unique_server_pytest_params(
     ]
 
 
-def create_benchmark_pytest_params(
-    benchmark_configs: list[dict[str, Any]],
-    server_to_benchmark_mapping: dict[str, dict],
-) -> list[Any]:
-    """Like :func:`create_benchmark_indices`, but wrap each index in ``pytest.param`` with JSON marks."""
-    marks_by_name = _marks_by_test_name(benchmark_configs)
-    params: list[Any] = []
-    seen: set[str] = set()
-    for config in benchmark_configs:
-        test_name = config["test_name"]
-        if test_name in seen:
-            continue
-        seen.add(test_name)
-        params_list = get_benchmark_params_for_server(test_name, server_to_benchmark_mapping)
-        id_suffixes = _unique_benchmark_param_id_suffixes(params_list)
-        for idx, id_suffix in enumerate(id_suffixes):
-            params.append(
-                pytest.param(
-                    (test_name, idx),
-                    marks=marks_by_name.get(test_name, []),
-                    id=f"{test_name}-{id_suffix}",
-                )
-            )
-    return params
-
-
 def create_paired_benchmark_pytest_params(
     server_entries: list[tuple[Any, str]],
     params_by_test_name: dict[str, list[Any]],
@@ -357,10 +330,6 @@ def supports_video_generation(model_name: str) -> bool:
     return any(key in lower for key in ("wan", "video", "i2v", "t2v"))
 
 
-def supports_chat_generation(model_name: str) -> bool:
-    return not supports_video_generation(model_name)
-
-
 def parse_stage_devices(stage_config_path: str) -> str:
     text = Path(stage_config_path).read_text(encoding="utf-8")
     raw_devices: list[str] = re.findall(r"^\s*devices:\s*\"?([0-9,\s]+)\"?\s*$", text, flags=re.MULTILINE)
@@ -389,48 +358,6 @@ def assert_fault_exception(exc: Exception, error_keywords: tuple[str, ...]) -> N
     assert any(key in text for key in error_keywords), f"unexpected error under fault injection: {exc}"
 
 
-def wait_chat_request_ready(host: str, port: int, model: str, timeout_sec: int = 180) -> None:
-    """Poll a minimal chat request until success."""
-    deadline = time.time() + timeout_sec
-    payload = json.dumps(
-        {
-            "model": model,
-            "messages": [{"role": "user", "content": "Say hello in one short sentence."}],
-            "stream": False,
-            "modalities": ["text"],
-        }
-    )
-    last_error: str | None = None
-    while time.time() < deadline:
-        try:
-            status, body = post_chat_completions_raw(host, port, payload)
-            if status == 200:
-                return
-            last_error = f"http={status} body={body[:200]!r}"
-        except Exception as exc:  # noqa: BLE001
-            last_error = str(exc)
-        time.sleep(2)
-    raise TimeoutError(f"runtime-teardown warmup request did not succeed within {timeout_sec}s: {last_error}")
-
-
-def assert_no_extra_worker_processes(
-    baseline_pids: set[int],
-    worker_pattern: str,
-    timeout_sec: int = 60,
-) -> None:
-    """Ensure no extra worker PIDs remain after teardown."""
-    deadline = time.time() + timeout_sec
-    while time.time() < deadline:
-        current = set(list_remote_process_pids_by_pattern(worker_pattern))
-        extra = current - baseline_pids
-        if not extra:
-            return
-        time.sleep(2)
-    current = set(list_remote_process_pids_by_pattern(worker_pattern))
-    extra = sorted(current - baseline_pids)
-    assert not extra, f"orphan worker processes remain after container teardown: {extra}"
-
-
 def create_test_parameter_mapping(configs: list[dict[str, Any]]) -> dict[str, dict]:
     mapping = {}
     for config in configs:
@@ -637,57 +564,6 @@ def extract_configs_resource_label(configs: list[dict[str, Any]]) -> str:
     return get_runtime_resource_label()
 
 
-def resolve_baseline_value(
-    baseline_raw: Any,
-    *,
-    sweep_index: int | None,
-    max_concurrency: Any = None,
-    request_rate: Any = None,
-) -> Any:
-    """Pick the baseline threshold for this sweep step."""
-    if baseline_raw is None:
-        return 100000
-    if isinstance(baseline_raw, dict):
-        if max_concurrency is not None:
-            for key in (max_concurrency, str(max_concurrency)):
-                if key in baseline_raw:
-                    return baseline_raw[key]
-        if request_rate is not None:
-            for key in (request_rate, str(request_rate)):
-                if key in baseline_raw:
-                    return baseline_raw[key]
-        raise KeyError(
-            f"baseline dict has no key for max_concurrency={max_concurrency!r} "
-            f"or request_rate={request_rate!r}; keys={list(baseline_raw.keys())!r}"
-        )
-    if isinstance(baseline_raw, (list, tuple)):
-        if sweep_index is None:
-            raise ValueError("list baseline requires sweep_index")
-        if not (0 <= sweep_index < len(baseline_raw)):
-            raise IndexError(f"baseline list len={len(baseline_raw)} has no index {sweep_index}")
-        return baseline_raw[sweep_index]
-    return baseline_raw
-
-
-def _baseline_thresholds_for_step(
-    baseline_data: dict[str, Any],
-    *,
-    sweep_index: int | None = None,
-    max_concurrency: Any = None,
-    request_rate: Any = None,
-) -> dict[str, Any]:
-    """Resolve baseline config to one threshold per metric for this iteration."""
-    return {
-        metric_name: resolve_baseline_value(
-            baseline_raw,
-            sweep_index=sweep_index,
-            max_concurrency=max_concurrency,
-            request_rate=request_rate,
-        )
-        for metric_name, baseline_raw in baseline_data.items()
-    }
-
-
 def run_benchmark(
     args: list[str],
     test_name: str,
@@ -696,18 +572,17 @@ def run_benchmark(
     num_prompt: int,
     *,
     baseline_config: dict[str, Any] | None = None,
-    sweep_index: int | None = None,
-    request_rate: Any | None = None,
-    max_concurrency: Any | None = None,
     random_input_len: Any | None = None,
     random_output_len: Any | None = None,
     resource_label: str | None = None,
 ) -> dict[str, Any]:
     """Run one ``vllm bench serve --omni`` iteration and return parsed metrics.
 
-    After ``vllm bench`` writes the JSON, ``result["baseline"]`` holds the resolved per-metric thresholds
+    After ``vllm bench`` writes the JSON, ``result["baseline"]`` stores the raw
-    (when ``baseline_config`` is provided). If the benchmark exits without writing a result file,
+    ``baseline`` object from the test config (when ``baseline_config`` is provided),
-    ``result_omni_template.json`` is used as a fallback.
+    including any hardware-nested map (e.g. ``{"H100": {...}}``). Downstream report
+    tooling can select a hardware bucket when needed. If the benchmark exits without
+    writing a result file, ``result_omni_template.json`` is used as a fallback.
     """
     current_dt = datetime.now().strftime("%Y%m%d-%H%M%S")
     ri = _safe_filename_token(random_input_len)
@@ -774,12 +649,8 @@ def run_benchmark(
             result = json.load(f)
 
     if baseline_config:
-        result["baseline"] = _baseline_thresholds_for_step(
+        # Persist the JSON baseline as-is (no hardware / sweep resolution).
-            baseline_config,
+        result["baseline"] = copy.deepcopy(baseline_config)
-            sweep_index=sweep_index,
-            request_rate=request_rate,
-            max_concurrency=max_concurrency,
-        )
     else:
         result["baseline"] = {}
     if random_input_len is not None:
@@ -800,12 +671,3 @@ def pytest_addoption(parser: pytest.Parser) -> None:
         default=None,
         help=("Path to benchmark config JSON. Example: --test-config-file tests/dfx/perf/tests/test_tts.json"),
     )
-    parser.addoption(
-        "--assert-baseline",
-        action="store_true",
-        default=False,
-        help=(
-            "When set, omni/diffusion perf runners compare metrics against the baseline block in the JSON config "
-            "(default: off)."
-        ),
-    )

tests/dfx/perf/scripts/run_benchmark.py

@@ -15,15 +15,10 @@ from tests.dfx.conftest import (
     get_runtime_resource_label,
     is_diffusion_perf_config,
     load_benchmark_configs,
-    resolve_baseline_value,
     run_benchmark,
 )
 from tests.helpers.runtime import OmniServer
 
-# Compare metrics to each test JSON ``baseline`` block only when pytest is run with ``--assert-baseline``
-# (registered in ``tests/dfx/conftest.py``; default: off). ``run_benchmark`` and ``_resolve_baseline_value`` are
-# defined in the same module.
-#
 # Optional JSON field ``mark`` is applied as pytest marks via
 # ``create_paired_omni_benchmark_pytest_params`` (e.g. ``"mark": [{"hardware_marks":
 # {"res": {"cuda": "H100"}, "num_cards": 2}}, "full_model", "omni"]``).
@@ -162,36 +157,8 @@ def benchmark_params(request):
     }
 
 
-def assert_result(
+def assert_result(result, num_prompt) -> None:
-    result,
-    params,
-    num_prompt,
-    *,
-    assert_baseline: bool,
-    sweep_index: int | None = None,
-    max_concurrency: Any | None = None,
-    request_rate: Any | None = None,
-) -> None:
     assert result["completed"] == num_prompt, "Request failures exist"
-    if not assert_baseline:
-        return
-    baseline_data = params.get("baseline", {})
-    for metric_name, baseline_raw in baseline_data.items():
-        current_value = result[metric_name]
-        baseline_value = resolve_baseline_value(
-            baseline_raw,
-            sweep_index=sweep_index,
-            max_concurrency=max_concurrency,
-            request_rate=request_rate,
-        )
-        if "throughput" in metric_name:
-            if current_value <= baseline_value:
-                print(
-                    f"ERROR: Throughput test results were below baseline: {metric_name}: {current_value} > {baseline_value}"
-                )
-        else:
-            if current_value >= baseline_value:
-                print(f"ERROR: Test results exceeded baseline: {metric_name}: {current_value} < {baseline_value}")
 
 
 @pytest.mark.benchmark
@@ -200,7 +167,7 @@ def assert_result(
     paired_benchmark_params,
     indirect=["omni_server", "benchmark_params"],
 )
-def test_performance_benchmark(omni_server, benchmark_params, request):
+def test_performance_benchmark(omni_server, benchmark_params):
     test_name = benchmark_params["test_name"]
     params = benchmark_params["params"]
     dataset_name = params.get("dataset_name", "")
@@ -212,7 +179,6 @@ def test_performance_benchmark(omni_server, benchmark_params, request):
     print(f"Running benchmark for model: {model}")
     print(f"Benchmark parameters: {benchmark_params}")
 
-    assert_baseline = request.config.getoption("--assert-baseline", default=False)
     resource_label = get_runtime_resource_label()
 
     def to_list(value, default=None):
@@ -271,7 +237,7 @@ def test_performance_benchmark(omni_server, benchmark_params, request):
         break
 
     # QPS / request-rate sweep
-    for i, (qps, num_prompt) in enumerate(zip(qps_list, num_prompt_list)):
+    for qps, num_prompt in zip(qps_list, num_prompt_list):
         args = args + ["--request-rate", str(qps), "--num-prompts", str(num_prompt)]
         result = run_benchmark(
             args=args,
@@ -280,24 +246,14 @@ def test_performance_benchmark(omni_server, benchmark_params, request):
             dataset_name=dataset_name,
             num_prompt=num_prompt,
             baseline_config=params.get("baseline"),
-            sweep_index=i,
-            request_rate=qps,
-            max_concurrency=None,
             random_input_len=params.get("random_input_len"),
             random_output_len=params.get("random_output_len"),
             resource_label=resource_label,
         )
-        assert_result(
+        assert_result(result, num_prompt)
-            result,
-            params,
-            num_prompt,
-            assert_baseline=assert_baseline,
-            sweep_index=i,
-            request_rate=qps,
-        )
 
     # concurrency test
-    for i, (concurrency, num_prompt) in enumerate(zip(max_concurrency_list, num_prompt_list)):
+    for concurrency, num_prompt in zip(max_concurrency_list, num_prompt_list):
         args = args + ["--max-concurrency", str(concurrency), "--num-prompts", str(num_prompt), "--request-rate", "inf"]
         result = run_benchmark(
             args=args,
@@ -306,18 +262,8 @@ def test_performance_benchmark(omni_server, benchmark_params, request):
             dataset_name=dataset_name,
             num_prompt=num_prompt,
             baseline_config=params.get("baseline"),
-            sweep_index=i,
-            request_rate=None,
-            max_concurrency=concurrency,
             random_input_len=params.get("random_input_len"),
             random_output_len=params.get("random_output_len"),
             resource_label=resource_label,
         )
-        assert_result(
+        assert_result(result, num_prompt)
-            result,
-            params,
-            num_prompt,
-            assert_baseline=assert_baseline,
-            sweep_index=i,
-            max_concurrency=concurrency,
-        )

tests/dfx/perf/scripts/run_diffusion_benchmark.py

@@ -13,8 +13,6 @@ A config JSON file may be passed via --test-config-file. If omitted, every ``*.j
   pytest run_diffusion_benchmark.py -m "diffusion"
   pytest run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
 
-Optional: ``--assert-baseline`` compares metrics to the ``baseline`` block in each benchmark entry (default: off).
-
 Optional JSON field ``mark`` is applied as pytest marks on that case via
 ``pytest.param`` (e.g. ``"mark": [{"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 1}}, "full_model", "diffusion"]``).
 
@@ -25,6 +23,7 @@ of all runs from cases in that file). Bulk load without ``--test-config-file`` u
 the same per-file aggregation; ``-m`` only selects which cases run.
 """
 
+import copy
 import json
 import os
 import socket
@@ -274,11 +273,6 @@ def _write_result_record(record: dict[str, Any]) -> Path:
     return target
 
 
-def _append_to_aggregated_file(record: dict[str, Any]) -> None:
-    """Backward-compatible wrapper; prefer :func:`_write_result_record`."""
-    _write_result_record(record)
-
-
 _server_lock = threading.Lock()
 
 # ---------------------------------------------------------------------------
@@ -678,7 +672,7 @@ def run_benchmark(
     with tempfile.NamedTemporaryFile(mode="w", suffix=".json", prefix="diffusion_bench_tmp_", delete=False) as tmp:
         tmp_result_file = Path(tmp.name)
 
-    exclude_keys = {"baseline", "dataset", "task", "name", "skip-performance-assertion"}
+    exclude_keys = {"baseline", "dataset", "task", "name"}
 
     cmd = [
         sys.executable,
@@ -772,12 +766,17 @@ def run_benchmark(
     completed = metrics.get("completed_requests", metrics.get("completed", 0))
     failed = metrics.get("failed_requests", metrics.get("failed", 0))
 
+    # Persist the JSON baseline as-is (no hardware / sweep resolution).
+    baseline = copy.deepcopy(params.get("baseline") or {})
+    metrics["baseline"] = baseline
+
     record: dict[str, Any] = {
         "test_name": test_name,
         "endpoint": endpoint,
         "timestamp": timestamp,
         "server_params": server_cfg.get("server_params"),
         "benchmark_params": params,
+        "baseline": baseline,
         "result": metrics,
         "log_file": str(log_file),
         "Model": model,
@@ -827,70 +826,14 @@ def run_benchmark(
 # ---------------------------------------------------------------------------
 
 
-def _resolve_baseline_value(
-    baseline_raw: Any,
-    *,
-    sweep_index: int | None,
-    max_concurrency: Any = None,
-    request_rate: Any = None,
-) -> Any:
-    """Pick the baseline threshold for this sweep step."""
-    if baseline_raw is None:
-        return 100000
-    if isinstance(baseline_raw, dict):
-        if max_concurrency is not None:
-            for key in (max_concurrency, str(max_concurrency)):
-                if key in baseline_raw:
-                    return baseline_raw[key]
-        if request_rate is not None:
-            for key in (request_rate, str(request_rate)):
-                if key in baseline_raw:
-                    return baseline_raw[key]
-        raise KeyError(
-            f"baseline dict has no key for max_concurrency={max_concurrency!r} "
-            f"or request_rate={request_rate!r}; keys={list(baseline_raw.keys())!r}"
-        )
-    if isinstance(baseline_raw, (list, tuple)):
-        if sweep_index is None:
-            raise ValueError("sweep_index is required when baseline is a list or tuple")
-        return baseline_raw[sweep_index]
-    return baseline_raw
-
-
 def assert_result(
     result: dict[str, Any],
-    params: dict[str, Any],
     num_prompts: int,
-    *,
-    sweep_index: int | None = None,
-    max_concurrency: Any = None,
-    request_rate: Any = None,
-    assert_baseline: bool = True,
 ) -> None:
-    """Assert that benchmark metrics satisfy the configured baselines."""
+    """Assert that the expected number of requests completed."""
     completed = result.get("completed_requests", result.get("completed", 0))
     assert completed == num_prompts, f"Expected {num_prompts} completed requests, got {completed}"
 
-    if not assert_baseline:
-        return
-    if params.get("skip-performance-assertion", False):
-        print("Skipping performance assertions.")
-        return
-
-    for metric, baseline_raw in params.get("baseline", {}).items():
-        current = result.get(metric)
-        assert current is not None, f"Metric '{metric}' not found in result: {list(result.keys())}"
-        threshold = _resolve_baseline_value(
-            baseline_raw,
-            sweep_index=sweep_index,
-            max_concurrency=max_concurrency,
-            request_rate=request_rate,
-        )
-        if "throughput" in metric:
-            assert current >= threshold, f"{metric}: {current:.4f} < baseline {threshold}"
-        else:
-            assert current <= threshold, f"{metric}: {current:.4f} > baseline {threshold}"
-
 
 def _default_benchmark_endpoint_for_task(task: str) -> str:
     """Return the default client-side benchmark endpoint for a diffusion task."""
@@ -919,7 +862,6 @@ def _build_run_params(
     params: dict[str, Any],
     *,
     num_prompts: int,
-    sweep_index: int | None = None,
     request_rate: Any | None = None,
     max_concurrency: Any | None = None,
 ) -> dict[str, Any]:
@@ -931,16 +873,6 @@ def _build_run_params(
         run_params["request-rate"] = request_rate
     if max_concurrency is not None:
         run_params["max-concurrency"] = max_concurrency
-    if "baseline" in params:
-        run_params["baseline"] = {
-            metric: _resolve_baseline_value(
-                baseline_raw,
-                sweep_index=sweep_index,
-                max_concurrency=max_concurrency,
-                request_rate=request_rate,
-            )
-            for metric, baseline_raw in params["baseline"].items()
-        }
     return run_params
 
 
@@ -963,36 +895,28 @@ def _iter_sweep_runs(params: dict[str, Any]) -> list[dict[str, Any]]:
 
     sweep_runs: list[dict[str, Any]] = []
 
-    for i, (request_rate, num_prompts) in enumerate(zip(request_rate_list, num_prompt_list)):
+    for request_rate, num_prompts in zip(request_rate_list, num_prompt_list):
         sweep_runs.append(
             {
                 "params": _build_run_params(
                     params,
                     request_rate=request_rate,
                     num_prompts=num_prompts,
-                    sweep_index=i,
                 ),
                 "num_prompts": num_prompts,
-                "sweep_index": i,
-                "request_rate": request_rate,
-                "max_concurrency": None,
             }
         )
 
-    for i, (max_concurrency, num_prompts) in enumerate(zip(max_concurrency_list, num_prompt_list)):
+    for max_concurrency, num_prompts in zip(max_concurrency_list, num_prompt_list):
         sweep_runs.append(
             {
                 "params": _build_run_params(
                     params,
                     max_concurrency=max_concurrency,
                     num_prompts=num_prompts,
-                    sweep_index=i,
                     request_rate="inf",
                 ),
                 "num_prompts": num_prompts,
-                "sweep_index": i,
-                "request_rate": None,
-                "max_concurrency": max_concurrency,
             }
         )
 
@@ -1005,9 +929,6 @@ def _iter_sweep_runs(params: dict[str, Any]) -> list[dict[str, Any]]:
                     num_prompts=default_num_prompts,
                 ),
                 "num_prompts": default_num_prompts,
-                "sweep_index": None,
-                "request_rate": None,
-                "max_concurrency": None,
             }
         )
 
@@ -1023,14 +944,12 @@ def _iter_sweep_runs(params: dict[str, Any]) -> list[dict[str, Any]]:
     paired_benchmark_params,
     indirect=["diffusion_server", "benchmark_params"],
 )
-def test_diffusion_performance_benchmark(diffusion_server, benchmark_params, request):
+def test_diffusion_performance_benchmark(diffusion_server, benchmark_params):
     """Run the diffusion performance benchmark and verify request completion.
 
     One server is started per unique parallel configuration (module scope).
     For each server, all benchmark parameter sets defined in the config JSON
     are executed sequentially; metrics are recorded to the aggregated result file.
-
-    Pass ``--assert-baseline`` to compare ``throughput_qps``, ``latency_mean``, etc. to the JSON ``baseline`` block.
     """
     test_name = benchmark_params["test_name"]
     params = benchmark_params["params"]
@@ -1073,14 +992,4 @@ def test_diffusion_performance_benchmark(diffusion_server, benchmark_params, req
             print(f"\n  Aggregated results: {_aggregated_result_file_for_source(str(source))}")
         print("=" * 60)
 
-        assert_baseline = request.config.getoption("--assert-baseline", default=False)
+        assert_result(result, sweep_run["num_prompts"])
-
-        assert_result(
-            result,
-            params,
-            sweep_run["num_prompts"],
-            sweep_index=sweep_run["sweep_index"],
-            max_concurrency=sweep_run["max_concurrency"],
-            request_rate=sweep_run["request_rate"],
-            assert_baseline=assert_baseline,
-        )

tests/dfx/perf/tests/test_bagel_vllm_omni.json

@@ -40,10 +40,12 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.495,
+          "H100": {
-          "latency_mean": 2.0238,
+            "throughput_qps": 0.495,
-          "peak_memory_mb_max": 29393.4286,
+            "latency_mean": 2.0238,
-          "peak_memory_mb_mean": 29392.7143
+            "peak_memory_mb_max": 29393.4286,
+            "peak_memory_mb_mean": 29392.7143
+          }
         }
       }
     ]
@@ -90,10 +92,12 @@
         "enable-negative-prompt": true,
         "num-input-images": 1,
         "baseline": {
-          "throughput_qps": 0.3451,
+          "H100": {
-          "latency_mean": 2.9031,
+            "throughput_qps": 0.3451,
-          "peak_memory_mb_max": 30480.2857,
+            "latency_mean": 2.9031,
-          "peak_memory_mb_mean": 30479.2857
+            "peak_memory_mb_max": 30480.2857,
+            "peak_memory_mb_mean": 30479.2857
+          }
         }
       }
     ]
@@ -139,10 +143,12 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.5048,
+          "H100": {
-          "latency_mean": 1.987,
+            "throughput_qps": 0.5048,
-          "peak_memory_mb_max": 29380.0,
+            "latency_mean": 1.987,
-          "peak_memory_mb_mean": 29380.0
+            "peak_memory_mb_max": 29380.0,
+            "peak_memory_mb_mean": 29380.0
+          }
         }
       }
     ]
@@ -189,10 +195,12 @@
         "enable-negative-prompt": true,
         "num-input-images": 1,
         "baseline": {
-          "throughput_qps": 0.149,
+          "H100": {
-          "latency_mean": 6.7157,
+            "throughput_qps": 0.149,
-          "peak_memory_mb_max": 30574.0,
+            "latency_mean": 6.7157,
-          "peak_memory_mb_mean": 30574.0
+            "peak_memory_mb_max": 30574.0,
+            "peak_memory_mb_mean": 30574.0
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_boogu_image_edit_vllm_omni.json

@@ -24,10 +24,12 @@
                 "warmup-num-inference-steps": 28,
                 "enable-negative-prompt": true,
                 "baseline": {
-                    "throughput_qps": {"1": 0.0303, "2": 0.0302, "4": 0.0302},
+                    "H100": {
-                    "latency_mean": {"1": 32.6, "2": 62.2, "4": 111.7},
+                        "throughput_qps": {"1": 0.0303, "2": 0.0302, "4": 0.0302},
-                    "peak_memory_mb_max": 40500,
+                        "latency_mean": {"1": 32.6, "2": 62.2, "4": 111.7},
-                    "peak_memory_mb_mean": 40500
+                        "peak_memory_mb_max": 40500,
+                        "peak_memory_mb_mean": 40500
+                    }
                 }
             }
         ]

tests/dfx/perf/tests/test_boogu_image_vllm_omni.json

@@ -24,10 +24,12 @@
                 "warmup-num-inference-steps": 28,
                 "enable-negative-prompt": true,
                 "baseline": {
-                    "throughput_qps": {"1": 0.0549, "2": 0.0547, "4": 0.0549},
+                    "H100": {
-                    "latency_mean": {"1": 18.0, "2": 34.3, "4": 61.4},
+                        "throughput_qps": {"1": 0.0549, "2": 0.0547, "4": 0.0549},
-                    "peak_memory_mb_max": 40500,
+                        "latency_mean": {"1": 18.0, "2": 34.3, "4": 61.4},
-                    "peak_memory_mb_mean": 40500
+                        "peak_memory_mb_max": 40500,
+                        "peak_memory_mb_mean": 40500
+                    }
                 }
             }
         ]

tests/dfx/perf/tests/test_cosmos3_vllm_omni.json

@@ -48,9 +48,11 @@
           "guardrails": false
         },
         "baseline": {
-          "throughput_qps": 1.107,
+          "H100": {
-          "latency_mean": 0.8929,
+            "throughput_qps": 1.107,
-          "peak_memory_mb_mean": 80000.0
+            "latency_mean": 0.8929,
+            "peak_memory_mb_mean": 80000.0
+          }
         }
       }
     ]
@@ -115,9 +117,11 @@
           "use_duration_template": false
         },
         "baseline": {
-          "throughput_qps": 0.027,
+          "H100": {
-          "latency_mean": 36.6824,
+            "throughput_qps": 0.027,
-          "peak_memory_mb_mean": 30155.4
+            "latency_mean": 36.6824,
+            "peak_memory_mb_mean": 30155.4
+          }
         }
       }
     ]
@@ -181,9 +185,11 @@
           "guardrails": false
         },
         "baseline": {
-          "throughput_qps": 0.027,
+          "H100": {
-          "latency_mean": 36.968,
+            "throughput_qps": 0.027,
-          "peak_memory_mb_mean": 30443.6
+            "latency_mean": 36.968,
+            "peak_memory_mb_mean": 30443.6
+          }
         }
       }
     ]
@@ -251,9 +257,11 @@
           "condition_video_keep": "first"
         },
         "baseline": {
-          "throughput_qps": 0.027,
+          "H100": {
-          "latency_mean": 37.2117,
+            "throughput_qps": 0.027,
-          "peak_memory_mb_mean": 30135.6
+            "latency_mean": 37.2117,
+            "peak_memory_mb_mean": 30135.6
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_higgs_audio_v3.json

@@ -46,12 +46,14 @@
         "seed_tts_locale": "en",
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            223.3584
+            "median_audio_ttfp_ms": [
-          ],
+              223.3584
-          "median_audio_rtf": [
+            ],
-            0.4393
+            "median_audio_rtf": [
-          ]
+              0.4393
+            ]
+          }
         }
       },
       {
@@ -70,15 +72,17 @@
         "seed_tts_locale": "en",
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            276.575
+            "median_audio_ttfp_ms": [
-          ],
+              276.575
-          "median_audio_rtf": [
+            ],
-            0.5073
+            "median_audio_rtf": [
-          ],
+              0.5073
-          "audio_throughput": [
+            ],
-            27.3728
+            "audio_throughput": [
-          ]
+              27.3728
+            ]
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_hunyuan_image3_it2i.json

@@ -133,13 +133,14 @@
         "bot-task": "think_recaption",
         "max-concurrency": 16,
         "num-prompts": 96,
-        "skip-performance-assertion": true,
         "baseline": {
-          "throughput_qps": 0.1874,
+          "H100": {
-          "latency_p99": 101.852,
+            "throughput_qps": 0.1874,
-          "latency_mean": 79.0385,
+            "latency_p99": 101.852,
-          "peak_memory_mb_max": 70000,
+            "latency_mean": 79.0385,
-          "peak_memory_mb_mean": 70000
+            "peak_memory_mb_max": 70000,
+            "peak_memory_mb_mean": 70000
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_hunyuan_image_tp2_cfgp2.json

@@ -37,12 +37,13 @@
         "num-inference-steps": 8,
         "num-prompts": 10,
         "max-concurrency": 1,
-        "skip-performance-assertion": true,
         "baseline": {
-          "throughput_qps": 0.21,
+          "H100": {
-          "latency_p99": 4.7469,
+            "throughput_qps": 0.21,
-          "peak_memory_mb_max": 101100,
+            "latency_p99": 4.7469,
-          "peak_memory_mb_mean": 101100
+            "peak_memory_mb_max": 101100,
+            "peak_memory_mb_mean": 101100
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_hunyuan_image_tp2_sp2.json

@@ -37,12 +37,13 @@
         "num-inference-steps": 8,
         "num-prompts": 10,
         "max-concurrency": 1,
-        "skip-performance-assertion": true,
         "baseline": {
-          "throughput_qps": 0.20,
+          "H100": {
-          "latency_p99": 5.1025,
+            "throughput_qps": 0.20,
-          "peak_memory_mb_max": 97402,
+            "latency_p99": 5.1025,
-          "peak_memory_mb_mean": 97402
+            "peak_memory_mb_max": 97402,
+            "peak_memory_mb_mean": 97402
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_hunyuan_image_tp4.json

@@ -36,13 +36,14 @@
         "num-inference-steps": 8,
         "num-prompts": 10,
         "max-concurrency": 1,
-        "skip-performance-assertion": true,
         "baseline": {
-          "throughput_qps": 0.213,
+          "H100": {
-          "latency_mean": 4.6953,
+            "throughput_qps": 0.213,
-          "latency_p99": 4.8601,
+            "latency_mean": 4.6953,
-          "peak_memory_mb_max": 56912.0,
+            "latency_p99": 4.8601,
-          "peak_memory_mb_mean": 56912.0
+            "peak_memory_mb_max": 56912.0,
+            "peak_memory_mb_mean": 56912.0
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_lingbot_video_vllm_omni.json

@@ -26,8 +26,7 @@
                 "warmup-requests": 1,
                 "warmup-concurrency": 1,
                 "warmup-num-inference-steps": 2,
-                "enable-negative-prompt": true,
+                "enable-negative-prompt": true
-                "skip-performance-assertion": true
             }
         ]
     }

tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json

@@ -44,27 +44,29 @@
         "ignore_eos": true,
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            96.4061,
+            "mean_ttft_ms": [
-            140.8147,
+              96.4061,
-            271.9025,
+              140.8147,
-            362.2916,
+              271.9025,
-            507.776
+              362.2916,
-          ],
+              507.776
-          "mean_e2el_ms": [
+            ],
-            18507.4582,
+            "mean_e2el_ms": [
-            28365.4956,
+              18507.4582,
-            31907.9613,
+              28365.4956,
-            48161.5829,
+              31907.9613,
-            72630.8601
+              48161.5829,
-          ],
+              72630.8601
-          "mean_audio_rtf": [
+            ],
-            0.1175,
+            "mean_audio_rtf": [
-            0.18,
+              0.1175,
-            0.2359,
+              0.18,
-            0.3278,
+              0.2359,
-            0.6296
+              0.3278,
-          ]
+              0.6296
+            ]
+          }
         }
       },
       {
@@ -91,15 +93,17 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            354.0951
+            "mean_ttft_ms": [
-          ],
+              354.0951
-          "mean_e2el_ms": [
+            ],
-            3952.5615
+            "mean_e2el_ms": [
-          ],
+              3952.5615
-          "mean_audio_rtf": [
+            ],
-            0.117
+            "mean_audio_rtf": [
-          ]
+              0.117
+            ]
+          }
         }
       },
       {
@@ -128,15 +132,17 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            259.6317
+            "mean_ttft_ms": [
-          ],
+              259.6317
-          "mean_e2el_ms": [
+            ],
-            4253.3009
+            "mean_e2el_ms": [
-          ],
+              4253.3009
-          "mean_audio_rtf": [
+            ],
-            0.1448
+            "mean_audio_rtf": [
-          ]
+              0.1448
+            ]
+          }
         }
       },
       {
@@ -167,15 +173,17 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            457.4453
+            "mean_ttft_ms": [
-          ],
+              457.4453
-          "mean_e2el_ms": [
+            ],
-            5912.5623
+            "mean_e2el_ms": [
-          ],
+              5912.5623
-          "mean_audio_rtf": [
+            ],
-            0.1967
+            "mean_audio_rtf": [
-          ]
+              0.1967
+            ]
+          }
         }
       },
       {
@@ -206,20 +214,22 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            90.8033,
+            "mean_ttft_ms": [
-            202.1779,
+              90.8033,
-            326.5864,
+              202.1779,
-            544.8914,
+              326.5864,
-            961.3929
+              544.8914,
-          ],
+              961.3929
-          "mean_e2el_ms": [
+            ],
-            4517.3411,
+            "mean_e2el_ms": [
-            7857.9711,
+              4517.3411,
-            9861.1687,
+              7857.9711,
-            13283.3363,
+              9861.1687,
-            19541.0736
+              13283.3363,
-          ]
+              19541.0736
+            ]
+          }
         }
       },
       {
@@ -251,12 +261,14 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            318.048
+            "mean_ttft_ms": [
-          ],
+              318.048
-          "mean_e2el_ms": [
+            ],
-            807.6114
+            "mean_e2el_ms": [
-          ]
+              807.6114
+            ]
+          }
         }
       },
       {
@@ -290,12 +302,14 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            186.168
+            "mean_ttft_ms": [
-          ],
+              186.168
-          "mean_e2el_ms": [
+            ],
-            726.1137
+            "mean_e2el_ms": [
-          ]
+              726.1137
+            ]
+          }
         }
       },
       {
@@ -331,12 +345,14 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            375.5282
+            "mean_ttft_ms": [
-          ],
+              375.5282
-          "mean_e2el_ms": [
+            ],
-            1006.1663
+            "mean_e2el_ms": [
-          ]
+              1006.1663
+            ]
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_qwen3_omni_multi_replicas.json

@@ -35,10 +35,12 @@
         "ignore_eos": true,
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-            "mean_ttft_ms": [130.5751, 278.6469, 324.7249, 911.6085],
+            "H100": {
-            "mean_tpot_ms": [7.9854, 11.1209, 16.1507, 20.7485],
+                "mean_ttft_ms": [130.5751, 278.6469, 324.7249, 911.6085],
-            "mean_audio_ttfp_ms": [486.486, 954.1791, 1289.6514, 2196.8275],
+                "mean_tpot_ms": [7.9854, 11.1209, 16.1507, 20.7485],
-            "mean_audio_rtf": [0.1896, 0.3962, 0.4098, 0.7322]
+                "mean_audio_ttfp_ms": [486.486, 954.1791, 1289.6514, 2196.8275],
+                "mean_audio_rtf": [0.1896, 0.3962, 0.4098, 0.7322]
+            }
         }
       },
       {
@@ -61,9 +63,11 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-            "mean_ttft_ms": [340.5122],
+            "H100": {
-            "mean_audio_ttfp_ms": [463.0752],
+                "mean_ttft_ms": [340.5122],
-            "mean_audio_rtf": [0.1153]
+                "mean_audio_ttfp_ms": [463.0752],
+                "mean_audio_rtf": [0.1153]
+            }
         }
       },
       {
@@ -88,9 +92,11 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-            "mean_ttft_ms": [236.926],
+            "H100": {
-            "mean_audio_ttfp_ms": [395.2182],
+                "mean_ttft_ms": [236.926],
-            "mean_audio_rtf": [0.1259]
+                "mean_audio_ttfp_ms": [395.2182],
+                "mean_audio_rtf": [0.1259]
+            }
         }
       },
       {
@@ -117,9 +123,11 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-            "mean_ttft_ms": [470.6126],
+            "H100": {
-            "mean_audio_ttfp_ms": [665.5474],
+                "mean_ttft_ms": [470.6126],
-            "mean_audio_rtf": [0.166]
+                "mean_audio_ttfp_ms": [665.5474],
+                "mean_audio_rtf": [0.166]
+            }
         }
       }
     ]

tests/dfx/perf/tests/test_qwen3_omni_no_async_chunk.json

@@ -44,27 +44,29 @@
         "ignore_eos": true,
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            115.8348,
+            "mean_ttft_ms": [
-            173.1823,
+              115.8348,
-            446.672,
+              173.1823,
-            464.5985,
+              446.672,
-            3420.0634
+              464.5985,
-          ],
+              3420.0634
-          "mean_e2el_ms": [
+            ],
-            25070.9523,
+            "mean_e2el_ms": [
-            31992.0646,
+              25070.9523,
-            37619.7669,
+              31992.0646,
-            50393.8257,
+              37619.7669,
-            136619.2743
+              50393.8257,
-          ],
+              136619.2743
-          "mean_audio_rtf": [
+            ],
-            0.169,
+            "mean_audio_rtf": [
-            0.2111,
+              0.169,
-            0.2475,
+              0.2111,
-            0.3551,
+              0.2475,
-            0.9266
+              0.3551,
-          ]
+              0.9266
+            ]
+          }
         }
       },
       {
@@ -91,15 +93,17 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            343.1377
+            "mean_ttft_ms": [
-          ],
+              343.1377
-          "mean_e2el_ms": [
+            ],
-            4811.1123
+            "mean_e2el_ms": [
-          ],
+              4811.1123
-          "mean_audio_rtf": [
+            ],
-            0.1463
+            "mean_audio_rtf": [
-          ]
+              0.1463
+            ]
+          }
         }
       },
       {
@@ -128,15 +132,17 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            245.5446
+            "mean_ttft_ms": [
-          ],
+              245.5446
-          "mean_e2el_ms": [
+            ],
-            5019.1256
+            "mean_e2el_ms": [
-          ],
+              5019.1256
-          "mean_audio_rtf": [
+            ],
-            0.1713
+            "mean_audio_rtf": [
-          ]
+              0.1713
+            ]
+          }
         }
       },
       {
@@ -167,15 +173,17 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            506.1407
+            "mean_ttft_ms": [
-          ],
+              506.1407
-          "mean_e2el_ms": [
+            ],
-            6814.9656
+            "mean_e2el_ms": [
-          ],
+              6814.9656
-          "mean_audio_rtf": [
+            ],
-            0.2213
+            "mean_audio_rtf": [
-          ]
+              0.2213
+            ]
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_qwen3_omni_vllm_text.json

@@ -51,20 +51,22 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            69.8081,
+            "mean_ttft_ms": [
-            173.0651,
+              69.8081,
-            271.8108,
+              173.0651,
-            413.4079,
+              271.8108,
-            497.2546
+              413.4079,
-          ],
+              497.2546
-          "mean_e2el_ms": [
+            ],
-            4720.8225,
+            "mean_e2el_ms": [
-            7937.2532,
+              4720.8225,
-            9962.3566,
+              7937.2532,
-            13724.5479,
+              9962.3566,
-            18324.091
+              13724.5479,
-          ]
+              18324.091
+            ]
+          }
         }
       },
       {
@@ -97,12 +99,14 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            25.9477
+            "mean_ttft_ms": [
-          ],
+              25.9477
-          "mean_e2el_ms": [
+            ],
-            517.3155
+            "mean_e2el_ms": [
-          ]
+              517.3155
+            ]
+          }
         }
       },
       {
@@ -137,12 +141,14 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            26.7056
+            "mean_ttft_ms": [
-          ],
+              26.7056
-          "mean_e2el_ms": [
+            ],
-            551.308
+            "mean_e2el_ms": [
-          ]
+              551.308
+            ]
+          }
         }
       },
       {
@@ -179,12 +185,14 @@
         },
         "percentile-metrics": "ttft,tpot,itl,e2el",
         "baseline": {
-          "mean_ttft_ms": [
+          "H100": {
-            27.6906
+            "mean_ttft_ms": [
-          ],
+              27.6906
-          "mean_e2el_ms": [
+            ],
-            596.5284
+            "mean_e2el_ms": [
-          ]
+              596.5284
+            ]
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_qwen_image_edit_2511_vllm_omni.json

@@ -34,10 +34,12 @@
         "num-input-images": 2,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.0721,
+          "H100": {
-          "latency_mean": 13.8398,
+            "throughput_qps": 0.0721,
-          "peak_memory_mb_max": 57610.0,
+            "latency_mean": 13.8398,
-          "peak_memory_mb_mean": 57610.0
+            "peak_memory_mb_max": 57610.0,
+            "peak_memory_mb_mean": 57610.0
+          }
         }
       },
       {
@@ -52,10 +54,12 @@
         "num-input-images": 2,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.0186,
+          "H100": {
-          "latency_mean": 53.6942,
+            "throughput_qps": 0.0186,
-          "peak_memory_mb_max": 67790.0,
+            "latency_mean": 53.6942,
-          "peak_memory_mb_mean": 67790.0
+            "peak_memory_mb_max": 67790.0,
+            "peak_memory_mb_mean": 67790.0
+          }
         }
       }
     ]
@@ -100,10 +104,12 @@
         "num-input-images": 2,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.0538,
+          "H100": {
-          "latency_mean": 18.5591,
+            "throughput_qps": 0.0538,
-          "peak_memory_mb_max": 56449.4286,
+            "latency_mean": 18.5591,
-          "peak_memory_mb_mean": 56449.4286
+            "peak_memory_mb_max": 56449.4286,
+            "peak_memory_mb_mean": 56449.4286
+          }
         }
       }
     ]
@@ -157,10 +163,12 @@
         "num-input-images": 2,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.2776,
+          "H100": {
-          "latency_mean": 3.5938,
+            "throughput_qps": 0.2776,
-          "peak_memory_mb_max": 58113.7143,
+            "latency_mean": 3.5938,
-          "peak_memory_mb_mean": 58075.1429
+            "peak_memory_mb_max": 58113.7143,
+            "peak_memory_mb_mean": 58075.1429
+          }
         }
       },
       {
@@ -175,10 +183,12 @@
         "num-input-images": 2,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.1051,
+          "H100": {
-          "latency_mean": 9.4918,
+            "throughput_qps": 0.1051,
-          "peak_memory_mb_max": 68107.4286,
+            "latency_mean": 9.4918,
-          "peak_memory_mb_mean": 68107.4286
+            "peak_memory_mb_max": 68107.4286,
+            "peak_memory_mb_mean": 68107.4286
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_qwen_image_edit_vllm_omni.json

@@ -33,10 +33,12 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.1129,
+          "H100": {
-          "latency_mean": 8.8352,
+            "throughput_qps": 0.1129,
-          "peak_memory_mb_max": 58572.0,
+            "latency_mean": 8.8352,
-          "peak_memory_mb_mean": 58572.0
+            "peak_memory_mb_max": 58572.0,
+            "peak_memory_mb_mean": 58572.0
+          }
         }
       },
       {
@@ -50,10 +52,12 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.024,
+          "H100": {
-          "latency_mean": 41.5686,
+            "throughput_qps": 0.024,
-          "peak_memory_mb_max": 66368.0,
+            "latency_mean": 41.5686,
-          "peak_memory_mb_mean": 66368.0
+            "peak_memory_mb_max": 66368.0,
+            "peak_memory_mb_mean": 66368.0
+          }
         }
       }
     ]
@@ -96,10 +100,12 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.0672,
+          "H100": {
-          "latency_mean": 14.8517,
+            "throughput_qps": 0.0672,
-          "peak_memory_mb_max": 57076.0,
+            "latency_mean": 14.8517,
-          "peak_memory_mb_mean": 57076.0
+            "peak_memory_mb_max": 57076.0,
+            "peak_memory_mb_mean": 57076.0
+          }
         }
       }
     ]
@@ -152,10 +158,12 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.2879,
+          "H100": {
-          "latency_mean": 3.4701,
+            "throughput_qps": 0.2879,
-          "peak_memory_mb_max": 58758.0,
+            "latency_mean": 3.4701,
-          "peak_memory_mb_mean": 58758.0
+            "peak_memory_mb_max": 58758.0,
+            "peak_memory_mb_mean": 58758.0
+          }
         }
       },
       {
@@ -169,10 +177,12 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.1113,
+          "H100": {
-          "latency_mean": 8.9656,
+            "throughput_qps": 0.1113,
-          "peak_memory_mb_max": 67424.0,
+            "latency_mean": 8.9656,
-          "peak_memory_mb_mean": 67424.0
+            "peak_memory_mb_max": 67424.0,
+            "peak_memory_mb_mean": 67424.0
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_qwen_image_layered_vllm_omni.json

@@ -33,10 +33,12 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.0703,
+          "H100": {
-          "latency_mean": 14.208,
+            "throughput_qps": 0.0703,
-          "peak_memory_mb_max": 63225.1429,
+            "latency_mean": 14.208,
-          "peak_memory_mb_mean": 63225.1429
+            "peak_memory_mb_max": 63225.1429,
+            "peak_memory_mb_mean": 63225.1429
+          }
         }
       },
       {
@@ -50,10 +52,12 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.0417,
+          "H100": {
-          "latency_mean": 23.914,
+            "throughput_qps": 0.0417,
-          "peak_memory_mb_max": 63225.1429,
+            "latency_mean": 23.914,
-          "peak_memory_mb_mean": 63225.1429
+            "peak_memory_mb_max": 63225.1429,
+            "peak_memory_mb_mean": 63225.1429
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_qwen_image_vllm_omni.json

@@ -33,9 +33,11 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.4153,
+          "H100": {
-          "latency_mean": 2.4065,
+            "throughput_qps": 0.4153,
-          "peak_memory_mb_mean": 55121.4286
+            "latency_mean": 2.4065,
+            "peak_memory_mb_mean": 55121.4286
+          }
         }
       },
       {
@@ -49,9 +51,11 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.0423,
+          "H100": {
-          "latency_mean": 23.5991,
+            "throughput_qps": 0.0423,
-          "peak_memory_mb_mean": 65118.0
+            "latency_mean": 23.5991,
+            "peak_memory_mb_mean": 65118.0
+          }
         }
       }
     ]
@@ -91,9 +95,11 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.4175,
+          "H100": {
-          "latency_mean": 2.3953,
+            "throughput_qps": 0.4175,
-          "peak_memory_mb_mean": 55088.5714
+            "latency_mean": 2.3953,
+            "peak_memory_mb_mean": 55088.5714
+          }
         }
       },
       {
@@ -107,9 +113,11 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.0423,
+          "H100": {
-          "latency_mean": 23.5768,
+            "throughput_qps": 0.0423,
-          "peak_memory_mb_mean": 66012.2857
+            "latency_mean": 23.5768,
+            "peak_memory_mb_mean": 66012.2857
+          }
         }
       }
     ]
@@ -152,9 +160,11 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.1214,
+          "H100": {
-          "latency_mean": 8.2211,
+            "throughput_qps": 0.1214,
-          "peak_memory_mb_mean": 54501.1429
+            "latency_mean": 8.2211,
+            "peak_memory_mb_mean": 54501.1429
+          }
         }
       }
     ]
@@ -207,9 +217,11 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.559,
+          "H100": {
-          "latency_mean": 1.794,
+            "throughput_qps": 0.559,
-          "peak_memory_mb_mean": 55290.5714
+            "latency_mean": 1.794,
+            "peak_memory_mb_mean": 55290.5714
+          }
         }
       },
       {
@@ -223,9 +235,11 @@
         "max-concurrency": 1,
         "enable-negative-prompt": true,
         "baseline": {
-          "throughput_qps": 0.1918,
+          "H100": {
-          "latency_mean": 5.2022,
+            "throughput_qps": 0.1918,
-          "peak_memory_mb_mean": 64838.8571
+            "latency_mean": 5.2022,
+            "peak_memory_mb_mean": 64838.8571
+          }
         }
       }
     ]
@@ -269,9 +283,11 @@
         "warmup-num-inference-steps": 20,
         "enable-negative-prompt": true,
         "baseline": {
-            "throughput_qps": [0.4125, 0.6714, 0.6674, 0.8159],
+            "H100": {
-            "latency_mean": [2.4215, 2.992, 5.9293, 9.656],
+                "throughput_qps": [0.4125, 0.6714, 0.6674, 0.8159],
-            "peak_memory_mb_mean": [55147.4286, 55148.0, 55150.4643, 55152.3821]
+                "latency_mean": [2.4215, 2.992, 5.9293, 9.656],
+                "peak_memory_mb_mean": [55147.4286, 55148.0, 55150.4643, 55152.3821]
+            }
         }
       }
     ]

tests/dfx/perf/tests/test_tts.json

@@ -39,12 +39,14 @@
         "seed_tts_locale": "en",
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            141.2791
+            "median_audio_ttfp_ms": [
-          ],
+              141.2791
-          "median_audio_rtf": [
+            ],
-            0.1591
+            "median_audio_rtf": [
-          ]
+              0.1591
+            ]
+          }
         }
       },
       {
@@ -67,21 +69,23 @@
         "seed_tts_locale": "en",
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            226.747,
+            "median_audio_ttfp_ms": [
-            647.1272,
+              226.747,
-            4777.2714
+              647.1272,
-          ],
+              4777.2714
-          "median_audio_rtf": [
+            ],
-            0.3313,
+            "median_audio_rtf": [
-            0.3642,
+              0.3313,
-            1.2282
+              0.3642,
-          ],
+              1.2282
-          "audio_throughput": [
+            ],
-            24.2296,
+            "audio_throughput": [
-            40.3552,
+              24.2296,
-            43.2572
+              40.3552,
-          ]
+              43.2572
+            ]
+          }
         }
       },
       {
@@ -102,9 +106,11 @@
         "seed_tts_wer_eval": true,
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "mean_audio_rtf": [
+          "H100": {
-            0.45
+            "mean_audio_rtf": [
-          ]
+              0.45
+            ]
+          }
         }
       }
     ]
@@ -153,12 +159,14 @@
         },
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            46.8434
+            "median_audio_ttfp_ms": [
-          ],
+              46.8434
-          "median_audio_rtf": [
+            ],
-            0.1367
+            "median_audio_rtf": [
-          ]
+              0.1367
+            ]
+          }
         }
       },
       {
@@ -186,21 +194,23 @@
         },
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            75.3341,
+            "median_audio_ttfp_ms": [
-            713.7727,
+              75.3341,
-            5941.7179
+              713.7727,
-          ],
+              5941.7179
-          "median_audio_rtf": [
+            ],
-            0.1859,
+            "median_audio_rtf": [
-            0.3148,
+              0.1859,
-            1.1662
+              0.3148,
-          ],
+              1.1662
-          "audio_throughput": [
+            ],
-            38.3858,
+            "audio_throughput": [
-            55.3523,
+              38.3858,
-            68.7032
+              55.3523,
-          ]
+              68.7032
+            ]
+          }
         }
       },
       {
@@ -226,9 +236,11 @@
         "seed_tts_wer_eval": true,
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "mean_audio_rtf": [
+          "H100": {
-            0.35
+            "mean_audio_rtf": [
-          ]
+              0.35
+            ]
+          }
         }
       },
       {
@@ -252,15 +264,17 @@
         },
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            56.746
+            "median_audio_ttfp_ms": [
-          ],
+              56.746
-          "median_audio_rtf": [
+            ],
-            0.1571
+            "median_audio_rtf": [
-          ],
+              0.1571
-          "audio_throughput": [
+            ],
-            11.1226
+            "audio_throughput": [
-          ]
+              11.1226
+            ]
+          }
         }
       },
       {
@@ -283,12 +297,14 @@
         },
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            46.3479
+            "median_audio_ttfp_ms": [
-          ],
+              46.3479
-          "median_audio_rtf": [
+            ],
-            0.1352
+            "median_audio_rtf": [
-          ]
+              0.1352
+            ]
+          }
         }
       },
       {
@@ -315,21 +331,23 @@
         },
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            75.5967,
+            "median_audio_ttfp_ms": [
-            587.5877,
+              75.5967,
-            4960.3726
+              587.5877,
-          ],
+              4960.3726
-          "median_audio_rtf": [
+            ],
-            0.1871,
+            "median_audio_rtf": [
-            0.3143,
+              0.1871,
-            1.1703
+              0.3143,
-          ],
+              1.1703
-          "audio_throughput": [
+            ],
-            38.9378,
+            "audio_throughput": [
-            55.1846,
+              38.9378,
-            71.5708
+              55.1846,
-          ]
+              71.5708
+            ]
+          }
         }
       },
       {
@@ -352,15 +370,17 @@
         },
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            55.5029
+            "median_audio_ttfp_ms": [
-          ],
+              55.5029
-          "median_audio_rtf": [
+            ],
-            0.1508
+            "median_audio_rtf": [
-          ],
+              0.1508
-          "audio_throughput": [
+            ],
-            9.4885
+            "audio_throughput": [
-          ]
+              9.4885
+            ]
+          }
         }
       }
     ]
@@ -402,12 +422,14 @@
         },
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            82.1886
+            "median_audio_ttfp_ms": [
-          ],
+              82.1886
-          "median_audio_rtf": [
+            ],
-            0.1241
+            "median_audio_rtf": [
-          ]
+              0.1241
+            ]
+          }
         }
       },
       {
@@ -429,15 +451,17 @@
         },
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            160.9168
+            "median_audio_ttfp_ms": [
-          ],
+              160.9168
-          "median_audio_rtf": [
+            ],
-            0.1907
+            "median_audio_rtf": [
-          ],
+              0.1907
-          "audio_throughput": [
+            ],
-            39.2332
+            "audio_throughput": [
-          ]
+              39.2332
+            ]
+          }
         }
       },
       {
@@ -459,15 +483,17 @@
         },
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            100.1265
+            "median_audio_ttfp_ms": [
-          ],
+              100.1265
-          "median_audio_rtf": [
+            ],
-            0.1437
+            "median_audio_rtf": [
-          ],
+              0.1437
-          "audio_throughput": [
+            ],
-            11.7605
+            "audio_throughput": [
-          ]
+              11.7605
+            ]
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_voxcpm2.json

@@ -37,12 +37,14 @@
         "seed_tts_locale": "en",
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            137.645
+            "median_audio_ttfp_ms": [
-          ],
+              137.645
-          "median_audio_rtf": [
+            ],
-            0.0497
+            "median_audio_rtf": [
-          ]
+              0.0497
+            ]
+          }
         }
       },
       {
@@ -61,15 +63,17 @@
         "seed_tts_locale": "en",
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            475.3357
+            "median_audio_ttfp_ms": [
-          ],
+              475.3357
-          "median_audio_rtf": [
+            ],
-            0.1243
+            "median_audio_rtf": [
-          ],
+              0.1243
-          "audio_throughput": [
+            ],
-            36.6358
+            "audio_throughput": [
-          ]
+              36.6358
+            ]
+          }
         }
       },
       {
@@ -90,9 +94,11 @@
         "seed_tts_wer_eval": true,
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "mean_audio_rtf": [
+          "H100": {
-            0.45
+            "mean_audio_rtf": [
-          ]
+              0.45
+            ]
+          }
         }
       },
       {
@@ -114,12 +120,14 @@
         },
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            79.6927
+            "median_audio_ttfp_ms": [
-          ],
+              79.6927
-          "median_audio_rtf": [
+            ],
-            0.0423
+            "median_audio_rtf": [
-          ]
+              0.0423
+            ]
+          }
         }
       },
       {
@@ -141,15 +149,17 @@
         },
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            262.6212
+            "median_audio_ttfp_ms": [
-          ],
+              262.6212
-          "median_audio_rtf": [
+            ],
-            0.0753
+            "median_audio_rtf": [
-          ],
+              0.0753
-          "audio_throughput": [
+            ],
-            103.5843
+            "audio_throughput": [
-          ]
+              103.5843
+            ]
+          }
         }
       },
       {
@@ -171,15 +181,17 @@
         },
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "median_audio_ttfp_ms": [
+          "H100": {
-            80.1095
+            "median_audio_ttfp_ms": [
-          ],
+              80.1095
-          "median_audio_rtf": [
+            ],
-            0.0479
+            "median_audio_rtf": [
-          ],
+              0.0479
-          "audio_throughput": [
+            ],
-            17.2918
+            "audio_throughput": [
-          ]
+              17.2918
+            ]
+          }
         }
       },
       {
@@ -203,9 +215,11 @@
         "seed_tts_wer_eval": true,
         "percentile-metrics": "ttft,e2el,audio_rtf,audio_ttfp,audio_duration",
         "baseline": {
-          "mean_audio_rtf": [
+          "H100": {
-            0.35
+            "mean_audio_rtf": [
-          ]
+              0.35
+            ]
+          }
         }
       }
     ]

tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json

@@ -42,9 +42,11 @@
           }
         ],
         "baseline": {
-          "throughput_qps": 0.0434,
+          "H100": {
-          "latency_mean": 23.0654,
+            "throughput_qps": 0.0434,
-          "peak_memory_mb_mean": 76360.0
+            "latency_mean": 23.0654,
+            "peak_memory_mb_mean": 76360.0
+          }
         }
       }
     ]
@@ -97,9 +99,11 @@
           }
         ],
         "baseline": {
-          "throughput_qps": 0.0606,
+          "H100": {
-          "latency_mean": 16.4928,
+            "throughput_qps": 0.0606,
-          "peak_memory_mb_mean": 47164.8
+            "latency_mean": 16.4928,
+            "peak_memory_mb_mean": 47164.8
+          }
         }
       },
       {
@@ -122,9 +126,11 @@
           }
         ],
         "baseline": {
-          "throughput_qps": 0.0124,
+          "H100": {
-          "latency_mean": 80.7784,
+            "throughput_qps": 0.0124,
-          "peak_memory_mb_mean": 55374.3429
+            "latency_mean": 80.7784,
+            "peak_memory_mb_mean": 55374.3429
+          }
         }
       }
     ]

tests/dfx/reliability/invalid_param_test/test_invalid_server_control.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Omni sleep/wakeup JSON validation (live ``Qwen/Qwen-Image`` process)."""
+"""Omni sleep/wakeup JSON validation and level-2 wake contract (live ``Qwen/Qwen-Image``)."""
 
 from __future__ import annotations
 
@@ -15,9 +15,17 @@ pytestmark = [pytest.mark.slow, pytest.mark.diffusion, pytest.mark.omni]
 
 _SKIP_ISSUE_3649 = pytest.mark.skip(reason="https://github.com/vllm-project/vllm-omni/issues/3649")
 
+# One module-scoped server for this file. ``--enable-sleep-mode`` is required by the
+# level-2 wake contract test; invalid-JSON cases only hit request validation and are
+# unaffected. Do not mix ``omni_server`` (module) with ``omni_server_function`` here:
+# ``iter_omni_server`` holds ``omni_fixture_lock`` across the module yield, so a
+# function-scoped second server would deadlock / contend for the GPU.
 _QWEN_IMAGE = [
     pytest.param(
-        OmniServerParams(model="Qwen/Qwen-Image"),
+        OmniServerParams(
+            model="Qwen/Qwen-Image",
+            server_args=["--enable-sleep-mode"],
+        ),
         id="qwen_image",
         marks=hardware_marks(res={"cuda": "H100"}),
     ),
@@ -93,3 +101,37 @@ def test_omni_sleep_wakeup_invalid_requests(
         openai_client.send_omni_sleep_http_request(cfg)
     else:
         openai_client.send_omni_wakeup_http_request(cfg)
+
+
+# Keep this after the invalid-JSON cases: sleep(level=2) discards weights and the
+# shared module server is not usable afterward; module teardown then stops it.
+@pytest.mark.parametrize("omni_server", _QWEN_IMAGE, indirect=True)
+def test_wakeup_after_level2_sleep_fails(
+    omni_server: OmniServer,
+    openai_client: OpenAIClientHandler,
+) -> None:
+    """Regression for #4473 Repro A via HTTP: wake after sleep(level=2) → 501.
+
+    Moved from ``tests/entrypoints/test_omni_sleep_mode.py`` (BAGEL cold start).
+    Live serve surfaces ``NotImplementedError`` as OpenAI-style JSON with code 501.
+    """
+    assert omni_server is not None
+    sleep_resps = openai_client.send_omni_sleep_http_request(
+        {
+            "json": {"stage_ids": [0], "level": 2},
+            "timeout": 180,
+        }
+    )
+    sleep_resp = sleep_resps[0]
+    assert sleep_resp.status_code == 200, sleep_resp
+    assert sleep_resp.json_body is not None
+    assert sleep_resp.json_body.get("status") == "SUCCESS", sleep_resp.json_body
+
+    openai_client.send_omni_wakeup_http_request(
+        {
+            "json": {"stage_ids": [0]},
+            "timeout": 120,
+            "err_code": 501,
+            "err_message": ("sleep(level=2)", "not yet implemented"),
+        }
+    )

tests/diffusion/attention/test_cudnn_attn.py

@@ -0,0 +1,64 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from contextlib import nullcontext
+
+import pytest
+import torch
+
+from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata
+from vllm_omni.diffusion.attention.backends.cudnn_attn import CuDNNAttentionImpl
+
+pytestmark = [pytest.mark.diffusion, pytest.mark.cpu, pytest.mark.core_model]
+
+
+def test_cudnn_slices_valid_kv_prefix_without_padding_mask(monkeypatch):
+    observed = {}
+
+    def fake_sdpa(query, key, value, **kwargs):
+        observed.update(query=query, key=key, value=value, kwargs=kwargs)
+        return query
+
+    monkeypatch.setattr(
+        "vllm_omni.diffusion.attention.backends.cudnn_attn.sdpa_kernel",
+        lambda _backends: nullcontext(),
+    )
+    monkeypatch.setattr(torch.nn.functional, "scaled_dot_product_attention", fake_sdpa)
+    impl = CuDNNAttentionImpl(
+        num_heads=2,
+        head_size=4,
+        softmax_scale=0.5,
+    )
+    query = torch.randn(1, 8, 2, 4)
+    key = torch.randn_like(query)
+    value = torch.randn_like(query)
+
+    output = impl.forward_cuda(
+        query,
+        key,
+        value,
+        AttentionMetadata(extra={"valid_kv_length": 5}),
+    )
+
+    assert output.shape == query.shape
+    assert observed["query"].shape == (1, 2, 8, 4)
+    assert observed["key"].shape == (1, 2, 5, 4)
+    assert observed["value"].shape == (1, 2, 5, 4)
+    assert observed["kwargs"]["attn_mask"] is None
+
+
+def test_cudnn_rejects_invalid_valid_kv_length():
+    impl = CuDNNAttentionImpl(
+        num_heads=2,
+        head_size=4,
+        softmax_scale=0.5,
+    )
+    query = torch.randn(1, 8, 2, 4)
+
+    with pytest.raises(ValueError, match="valid_kv_length"):
+        impl.forward_cuda(
+            query,
+            query,
+            query,
+            AttentionMetadata(extra={"valid_kv_length": 9}),
+        )

tests/diffusion/layers/test_norm.py

@@ -2,6 +2,9 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Unit tests for LayerNorm and RMSNorm custom ops in diffusion layers."""
 
+import sys
+import types
+
 import pytest
 import torch
 from pytest_mock import MockerFixture
@@ -299,6 +302,44 @@ def test_rmsnorm_numerical_correctness():
     torch.testing.assert_close(out, expected, atol=1e-5, rtol=1e-5)
 
 
+def test_rmsnorm_dtype_matches_parameter_and_fp32_accumulation_contract():
+    """dtype controls gamma storage; reduced-precision inputs still use fp32 accumulation."""
+    from vllm_omni.diffusion.layers.norm import RMSNorm
+
+    hidden_size = 64
+    eps = 1e-6
+    norm = RMSNorm(hidden_size, eps=eps, dtype=torch.bfloat16)
+    x = torch.randn(2, 4, hidden_size, dtype=torch.bfloat16)
+
+    assert norm.weight.dtype == torch.bfloat16
+
+    x_fp32 = x.float()
+    expected = (x_fp32 * torch.rsqrt(x_fp32.square().mean(-1, keepdim=True) + eps) * norm.weight.float()).to(x.dtype)
+
+    torch.testing.assert_close(norm.forward_native(x), expected, atol=0, rtol=0)
+
+
+def test_rmsnorm_npu_receives_gamma_with_configured_dtype(monkeypatch: pytest.MonkeyPatch):
+    """The NPU fused op receives the bf16 checkpoint gamma used by H3."""
+    from vllm_omni.diffusion.layers.norm import RMSNorm
+
+    captured: dict[str, torch.Tensor] = {}
+
+    def npu_rms_norm(x: torch.Tensor, gamma: torch.Tensor, epsilon: float):
+        captured["gamma"] = gamma
+        captured["epsilon"] = torch.tensor(epsilon)
+        return (x,)
+
+    monkeypatch.setitem(sys.modules, "torch_npu", types.SimpleNamespace(npu_rms_norm=npu_rms_norm))
+    norm = RMSNorm(64, eps=1e-5, dtype=torch.bfloat16)
+    x = torch.randn(2, 4, 64, dtype=torch.bfloat16)
+
+    assert norm.forward_npu(x) is x
+    assert captured["gamma"] is norm.weight
+    assert captured["gamma"].dtype == torch.bfloat16
+    assert captured["epsilon"].item() == pytest.approx(1e-5)
+
+
 # ── RMSNorm compile-path regression tests ──
 
 

tests/diffusion/layers/test_rope_broadcast.py

@@ -10,10 +10,13 @@ forward_hip, and apply_rotary_emb_mindiesd paths.
 
 from __future__ import annotations
 
+import sys
+import types
+
 import pytest
 import torch
 
-from vllm_omni.diffusion.layers.rope import RotaryEmbedding
+from vllm_omni.diffusion.layers.rope import RotaryEmbedding, apply_rotary_emb_mindiesd
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
@@ -234,3 +237,33 @@ class TestRotaryEmbeddingNativeShapeRegression:
         output = rope.forward_native(x, cos, sin)
         assert output.shape == x.shape
         assert not torch.isnan(output).any()
+
+
+def test_mindie_full_dim_rope_keeps_h3_cos_sin_layout(monkeypatch: pytest.MonkeyPatch) -> None:
+    """H3 passes complete rotary dims to MindIE instead of the CUDA half-dim layout."""
+    captured: dict[str, object] = {}
+
+    def rotary_position_embedding(x, cos, sin, **kwargs):
+        captured.update(x=x, cos=cos, sin=sin, kwargs=kwargs)
+        return x
+
+    monkeypatch.setitem(
+        sys.modules,
+        "mindiesd",
+        types.SimpleNamespace(rotary_position_embedding=rotary_position_embedding),
+    )
+    x = torch.randn(11, 3, 96, dtype=torch.bfloat16)
+    cos = torch.randn(11, 96, dtype=torch.bfloat16)
+    sin = torch.randn(11, 96, dtype=torch.bfloat16)
+
+    actual = apply_rotary_emb_mindiesd(x, cos, sin, interleaved=False, half_head_dim=False)
+
+    assert actual is x
+    assert captured["x"] is x
+    assert captured["cos"] is cos
+    assert captured["sin"] is sin
+    assert captured["kwargs"] == {
+        "rotated_mode": "rotated_half",
+        "head_first": False,
+        "fused": True,
+    }

tests/diffusion/models/minimax_h3/test_minimax_h3_contract.py

@@ -30,6 +30,13 @@ def test_pipeline_import_registry_and_component_discovery():
     assert MiniMaxH3Pipeline._vae_modules == ["video_vae", "audio_vae"]
 
 
+def test_dlo_offload_plan_includes_token_refiner():
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    assert MiniMaxH3Pipeline._offload_plan.offload_submodules == {"token_refiner": "blocks"}
+    assert MiniMaxH3Pipeline._offload_plan.resident_dit_paths == frozenset({"transformer"})
+
+
 def test_joint_postprocess_is_multiprocessing_picklable():
     from vllm_omni.diffusion.models.minimax_h3 import (
         get_minimax_h3_post_process_func,
@@ -87,6 +94,87 @@ def test_shifted_sigma_schedule_matches_reference_values():
     )
 
 
+def test_cudnn_packed_attention_uses_python_length_without_padding_mask():
+    from vllm_omni.diffusion.models.minimax_h3.minimax_h3_transformer import (
+        MiniMaxH3Attention,
+    )
+
+    class FakeBackend:
+        supports_prefix_kv_slicing = True
+
+    class FakeAttention(torch.nn.Module):
+        attn_backend = FakeBackend
+
+        def __init__(self):
+            super().__init__()
+            self.metadata = None
+
+        def forward(self, query, key, value, metadata):
+            self.metadata = metadata
+            return query
+
+    attention = object.__new__(MiniMaxH3Attention)
+    torch.nn.Module.__init__(attention)
+    attention.attention = FakeAttention()
+    # Model-side Ulysses shards rows before Attention gathers them again.
+    # The Python packed_total must therefore remain global even though q is
+    # local here.
+    q = torch.randn(4, 2, 4)
+    cu_seqlens = torch.tensor([0, 5, 8], dtype=torch.int32)
+
+    output = attention._run_packed_attention(
+        q,
+        q,
+        q,
+        cu_seqlens=cu_seqlens,
+        max_seqlen=5,
+        packed_total=8,
+    )
+
+    assert output.shape == q.shape
+    assert attention.attention.metadata.attn_mask is None
+    assert attention.attention.metadata.extra["valid_kv_length"] == 5
+
+
+def test_packed_attention_keeps_padding_mask_for_other_backends():
+    from vllm_omni.diffusion.models.minimax_h3.minimax_h3_transformer import (
+        MiniMaxH3Attention,
+    )
+
+    class FakeBackend:
+        supports_prefix_kv_slicing = False
+
+    class FakeAttention(torch.nn.Module):
+        attn_backend = FakeBackend
+
+        def __init__(self):
+            super().__init__()
+            self.metadata = None
+
+        def forward(self, query, key, value, metadata):
+            self.metadata = metadata
+            return query
+
+    attention = object.__new__(MiniMaxH3Attention)
+    torch.nn.Module.__init__(attention)
+    attention.attention = FakeAttention()
+    q = torch.randn(8, 2, 4)
+
+    attention._run_packed_attention(
+        q,
+        q,
+        q,
+        cu_seqlens=torch.tensor([0, 5, 8], dtype=torch.int32),
+        max_seqlen=5,
+        packed_total=8,
+    )
+
+    assert torch.equal(
+        attention.attention.metadata.attn_mask,
+        torch.tensor([[True, True, True, True, True, False, False, False]]),
+    )
+
+
 def test_reference_image_resize_contract():
     from PIL import Image
 
@@ -102,6 +190,52 @@ def test_reference_image_resize_contract():
         _reference_image_shape(Image.new("RGB", (100, 501)))
 
 
+def test_fl2va_supports_first_last_and_explicit_frame_index_contracts():
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
+        _resolve_fl2va_keyframe_indices,
+    )
+
+    assert _resolve_fl2va_keyframe_indices({}, 1) == [0]
+    assert _resolve_fl2va_keyframe_indices({}, 2) == [0, -1]
+    assert _resolve_fl2va_keyframe_indices({"frame_index": -1}, 1) == [-1]
+    assert _resolve_fl2va_keyframe_indices({"frame_indices": [0, -1]}, 2) == [0, -1]
+    with pytest.raises(ValueError, match="frame_indices"):
+        _resolve_fl2va_keyframe_indices({"frame_indices": [0, 1]}, 2)
+
+
+def test_minimax_h3_uses_the_official_output_canvas_policy():
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
+        _resolve_output_canvas,
+    )
+
+    assert _resolve_output_canvas(21 / 9, 768) == (672, 1536)
+    assert _resolve_output_canvas(16 / 9, 768) == (768, 1344)
+    assert _resolve_output_canvas(9 / 16, 768) == (1344, 768)
+    with pytest.raises(ValueError, match="short_edge"):
+        _resolve_output_canvas(16 / 9, 720)
+
+
+def test_minimax_h3_accepts_sglang_auto_aspect_ratio_alias():
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    sampling = SimpleNamespace(
+        fps=24,
+        num_frames=1,
+        height=None,
+        width=None,
+        extra_args={"duration": 5.0, "aspect_ratio": "auto"},
+    )
+    height, width, *_ = pipeline._resolve_shape("ref2va", sampling, None)
+    assert (height, width) == (768, 1344)
+
+
+def test_minimax_h3_advertises_the_official_ref2va_image_limit():
+    from vllm_omni.diffusion.model_metadata import get_diffusion_model_metadata
+
+    assert get_diffusion_model_metadata("MiniMaxH3Pipeline").max_multimodal_image_inputs == 9
+
+
 def test_encoder_forward_uses_hook_compatible_encode_entrypoint():
     from vllm_omni.diffusion.models.minimax_h3.encoder import (
         MiniMaxH3Qwen3VLEncoder,
@@ -247,6 +381,127 @@ def test_layerwise_offload_releases_text_encoder():
     pipeline.text_encoder.offload_to_cpu.assert_called_once_with()
 
 
+def test_distributed_layerwise_offload_releases_text_encoder():
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    pipeline.od_config = SimpleNamespace(
+        enable_cpu_offload=False,
+        enable_layerwise_offload=False,
+        enable_distributed_layerwise_offload=True,
+    )
+    pipeline.text_encoder = Mock()
+    expected = torch.ones(2, 3)
+    pipeline.text_encoder.encode_ids.return_value = expected
+
+    actual = pipeline._encode_text_hidden(torch.tensor([1, 2]), {})
+
+    assert actual is expected
+    pipeline.text_encoder.load_to_device.assert_called_once_with()
+    pipeline.text_encoder.offload_to_cpu.assert_called_once_with()
+
+
+def test_distributed_layerwise_offload_stages_vae_component():
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    pipeline.od_config = SimpleNamespace(
+        enable_layerwise_offload=False,
+        enable_distributed_layerwise_offload=True,
+    )
+    component = Mock()
+
+    with pipeline._component_on_device(component):
+        component.load_to_device.assert_called_once_with()
+        component.offload_to_cpu.assert_not_called()
+
+    component.offload_to_cpu.assert_called_once_with()
+
+
+def test_distributed_layerwise_resident_blocks_are_stage_scoped():
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    controller = Mock()
+    pipeline._dlo_residency_controller = controller
+
+    with pipeline._resident_dit_layers_on_device():
+        controller.load_resident_layers.assert_called_once_with()
+        controller.offload_resident_layers.assert_not_called()
+
+    controller.offload_resident_layers.assert_called_once_with()
+
+
+def test_distributed_layerwise_resident_blocks_release_on_failure():
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    torch.nn.Module.__init__(pipeline)
+    controller = Mock()
+    pipeline._dlo_residency_controller = controller
+
+    with pytest.raises(RuntimeError, match="denoise failed"):
+        with pipeline._resident_dit_layers_on_device():
+            raise RuntimeError("denoise failed")
+
+    controller.load_resident_layers.assert_called_once_with()
+    controller.offload_resident_layers.assert_called_once_with()
+
+
+def test_encoder_layerwise_offload_keeps_tp_blocks_rank_local(monkeypatch):
+    from vllm_omni.diffusion.models.minimax_h3.encoder import (
+        MiniMaxH3Qwen3VLEncoder,
+    )
+
+    class Stack(torch.nn.Module):
+        def __init__(self, count):
+            super().__init__()
+            self.blocks = torch.nn.ModuleList([torch.nn.Linear(2, 2) for _ in range(count)])
+
+    class TextStack(torch.nn.Module):
+        def __init__(self, count):
+            super().__init__()
+            self.layers = torch.nn.ModuleList([torch.nn.Linear(2, 2) for _ in range(count)])
+
+    hooks = []
+
+    def fake_apply(block, next_block, device, stream, pin_memory):
+        hook = SimpleNamespace(
+            block=block,
+            next_block=next_block,
+            device=device,
+            stream=stream,
+            pin_memory=pin_memory,
+            _prev_hook=None,
+            offload_layer=Mock(),
+        )
+        hooks.append(hook)
+        return hook
+
+    monkeypatch.setattr(
+        "vllm_omni.diffusion.offloader.layerwise_backend.apply_block_hook",
+        fake_apply,
+    )
+    monkeypatch.setattr(
+        "vllm_omni.platforms.current_omni_platform.Stream",
+        Mock(return_value="copy-stream"),
+    )
+    encoder = object.__new__(MiniMaxH3Qwen3VLEncoder)
+    torch.nn.Module.__init__(encoder)
+    encoder.device_target = torch.device("cpu")
+    encoder.vision = Stack(2)
+    encoder.text_model = TextStack(3)
+
+    encoder.enable_omni_layerwise_offload(pin_memory=False)
+
+    assert len(hooks) == 5
+    assert all(hook._prev_hook is not None for hook in hooks)
+    assert all(hook.device == torch.device("cpu") for hook in hooks)
+
+
 def test_video_vae_keeps_reference_fp32_weights(monkeypatch):
     from vllm_omni.diffusion.models.minimax_h3 import vae as vae_module
 
@@ -278,6 +533,82 @@ def test_video_vae_keeps_reference_fp32_weights(monkeypatch):
     assert next(video_vae.parameters()).dtype == torch.float32
 
 
+def test_video_vae_can_load_on_cpu_for_staged_gpu_residency(monkeypatch):
+    from vllm_omni.diffusion.models.minimax_h3 import vae as vae_module
+
+    class FakeRemote(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.model = torch.nn.Linear(1, 1)
+
+    monkeypatch.setattr(
+        vae_module,
+        "_load_component_config",
+        lambda _path: {
+            "latent_channels": 1,
+            "latents_mean": [0.0],
+            "latents_std": [1.0],
+        },
+    )
+    monkeypatch.setattr(
+        vae_module,
+        "_load_remote_component",
+        lambda _path, _config: FakeRemote(),
+    )
+
+    video_vae = vae_module.MiniMaxH3VideoVAE(
+        "unused",
+        device=torch.device("meta"),
+        load_device=torch.device("cpu"),
+    )
+
+    assert next(video_vae.parameters()).device.type == "cpu"
+    assert video_vae._device_target.type == "meta"
+
+
+def test_video_vae_uses_snapshot_stager_for_accelerator_target(monkeypatch):
+    from vllm_omni.diffusion.models.minimax_h3 import vae as vae_module
+
+    class FakeRemote(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.model = torch.nn.Linear(1, 1)
+
+    stager = Mock()
+    stager_cls = Mock(return_value=stager)
+    monkeypatch.setattr(
+        vae_module,
+        "_load_component_config",
+        lambda _path: {
+            "latent_channels": 1,
+            "latents_mean": [0.0],
+            "latents_std": [1.0],
+        },
+    )
+    monkeypatch.setattr(
+        vae_module,
+        "_load_remote_component",
+        lambda _path, _config: FakeRemote(),
+    )
+    monkeypatch.setattr(vae_module, "PinnedModuleStager", stager_cls)
+
+    video_vae = vae_module.MiniMaxH3VideoVAE(
+        "unused",
+        device=torch.device("cuda"),
+        load_device=torch.device("cpu"),
+    )
+    video_vae.load_to_device()
+    video_vae.offload_to_cpu()
+
+    stager_cls.assert_called_once_with(
+        video_vae.remote,
+        torch.device("cuda"),
+        pin_memory=True,
+    )
+    stager.load.assert_called_once_with()
+    stager.offload.assert_called_once_with()
+
+
 def test_video_vae_encode_uses_configured_parallel_tiling():
     from vllm_omni.diffusion.models.minimax_h3.vae import (
         MiniMaxH3VideoVAE,
@@ -373,3 +704,360 @@ def test_distributed_video_vae_encodes_references_sequentially(monkeypatch):
         torch.tensor([[1.0, 1.0], [2.0, 2.0]]),
     )
     assert shapes == [(1, 2, 3), (2, 2, 3)]
+
+
+@pytest.mark.parametrize(
+    ("case", "extra", "image_count", "expected"),
+    [
+        ("F1", {"frame_index": -1}, 1, [-1]),
+        ("F2", {"frame_indices": [0, -1]}, 2, [0, -1]),
+    ],
+)
+def test_f1_f2_official_fl2va_keyframe_matrix(case, extra, image_count, expected):
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
+        _resolve_fl2va_keyframe_indices,
+    )
+
+    assert case in {"F1", "F2"}
+    assert _resolve_fl2va_keyframe_indices(extra, image_count) == expected
+
+
+@pytest.mark.parametrize(
+    ("case", "counts"),
+    [
+        ("R1", (3, 0, 0)),
+        ("R2", (1, 0, 0)),
+        ("R3", (1, 1, 0)),
+        ("R4", (0, 1, 1)),
+        ("R5", (1, 1, 1)),
+        ("R6", (1, 0, 2)),
+    ],
+)
+def test_r1_r6_ref2va_reference_count_matrix(case, counts):
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
+        _validate_ref2va_reference_counts,
+    )
+
+    assert case.startswith("R")
+    _validate_ref2va_reference_counts(*counts)
+
+
+def test_ref2va_reference_count_validation_preserves_client_error_metadata():
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
+        _validate_ref2va_reference_counts,
+    )
+    from vllm_omni.errors import OmniClientError
+
+    with pytest.raises(OmniClientError, match="at least one image or video"):
+        _validate_ref2va_reference_counts(0, 0, 0)
+
+
+@pytest.mark.parametrize(
+    ("case", "start_time", "expected_duration"),
+    [
+        ("R7", None, 10.0),
+        ("R8", 4.0, 6.0),
+    ],
+)
+def test_r7_r8_ref2va_video_segment_matrix(monkeypatch, tmp_path, case, start_time, expected_duration):
+    from vllm_omni.diffusion.models.minimax_h3 import reference_video as reference_video_module
+
+    source = tmp_path / f"{case}.mp4"
+    source.touch()
+    metadata = {
+        "width": 1280,
+        "height": 720,
+        "fps": 24.0,
+        "frame_count": 240,
+        "duration": 10.0,
+        "format_names": ("mp4",),
+        "video_codec": "h264",
+        "audio_codecs": (),
+        "file_size": 1024,
+    }
+    transcode_calls = []
+    monkeypatch.setattr(reference_video_module, "_probe_video", lambda _path: metadata)
+    monkeypatch.setattr(
+        reference_video_module,
+        "_transcode_reference_video",
+        lambda source, **kwargs: transcode_calls.append((source, kwargs)) or "prepared.mp4",
+    )
+
+    prepared = reference_video_module.prepare_reference_videos(
+        [str(source)],
+        target_frame_count=209,
+        workdir=str(tmp_path / "work"),
+        start_time_seconds=start_time,
+    )
+
+    assert prepared[0]["duration_seconds"] == pytest.approx(expected_duration)
+    assert prepared[0]["start_time_seconds"] == pytest.approx(start_time or 0.0)
+    assert transcode_calls[0][1]["duration_seconds"] == pytest.approx(expected_duration)
+
+
+def test_ref2va_two_video_recipe_tolerates_container_rounding(monkeypatch, tmp_path):
+    from vllm_omni.diffusion.models.minimax_h3 import reference_video as reference_video_module
+
+    first = tmp_path / "first.mp4"
+    second = tmp_path / "second.mp4"
+    first.touch()
+    second.touch()
+    metadata = {
+        str(first): {
+            "width": 1280,
+            "height": 720,
+            "fps": 24.0,
+            "frame_count": 241,
+            "duration": 10.041667,
+            "format_names": ("mp4",),
+            "video_codec": "h264",
+            "audio_codecs": (),
+            "file_size": 1024,
+        },
+        str(second): {
+            "width": 1280,
+            "height": 720,
+            "fps": 24.0,
+            "frame_count": 120,
+            "duration": 4.966667,
+            "format_names": ("mp4",),
+            "video_codec": "h264",
+            "audio_codecs": (),
+            "file_size": 1024,
+        },
+    }
+    transcode_calls = []
+    monkeypatch.setattr(reference_video_module, "_probe_video", lambda path: metadata[str(path)])
+    monkeypatch.setattr(
+        reference_video_module,
+        "_transcode_reference_video",
+        lambda source, **kwargs: transcode_calls.append((source, kwargs)) or f"{source}.prepared.mp4",
+    )
+
+    prepared = reference_video_module.prepare_reference_videos(
+        [str(first), str(second)],
+        target_frame_count=124,
+        workdir=str(tmp_path / "work"),
+    )
+
+    assert [item["duration_seconds"] for item in prepared] == pytest.approx([10.041667, 4.958333])
+    assert sum(item["duration_seconds"] for item in prepared) == pytest.approx(15.0)
+    assert transcode_calls[1][1]["duration_seconds"] == pytest.approx(4.958333)
+
+
+def test_ref2va_two_video_recipe_rejects_real_duration_overflow(monkeypatch, tmp_path):
+    from vllm_omni.diffusion.models.minimax_h3 import reference_video as reference_video_module
+    from vllm_omni.errors import OmniClientError
+
+    first = tmp_path / "first.mp4"
+    second = tmp_path / "second.mp4"
+    first.touch()
+    second.touch()
+    metadata = {
+        str(first): {
+            "width": 1280,
+            "height": 720,
+            "fps": 24.0,
+            "frame_count": 240,
+            "duration": 10.0,
+            "format_names": ("mp4",),
+            "video_codec": "h264",
+            "audio_codecs": (),
+            "file_size": 1024,
+        },
+        str(second): {
+            "width": 1280,
+            "height": 720,
+            "fps": 24.0,
+            "frame_count": 120,
+            "duration": 5.02,
+            "format_names": ("mp4",),
+            "video_codec": "h264",
+            "audio_codecs": (),
+            "file_size": 1024,
+        },
+    }
+    monkeypatch.setattr(reference_video_module, "_probe_video", lambda path: metadata[str(path)])
+    monkeypatch.setattr(reference_video_module, "_transcode_reference_video", lambda source, **kwargs: "prepared.mp4")
+
+    with pytest.raises(OmniClientError, match="15 seconds"):
+        reference_video_module.prepare_reference_videos(
+            [str(first), str(second)],
+            target_frame_count=124,
+            workdir=str(tmp_path / "work"),
+        )
+
+
+@pytest.mark.parametrize("duration", [4.0, 15.0])
+def test_g2_output_duration_accepts_official_boundaries(duration):
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    sampling = SimpleNamespace(
+        fps=24,
+        num_frames=1,
+        height=None,
+        width=None,
+        extra_args={"duration": duration},
+    )
+
+    height, width, *_ = pipeline._resolve_shape("ref2va", sampling, None)
+    assert height > 0 and width > 0
+
+
+@pytest.mark.parametrize("duration", [3.99, 15.01, float("nan"), "not-a-duration"])
+def test_g2_output_duration_rejects_out_of_contract_values(duration):
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    sampling = SimpleNamespace(
+        fps=24,
+        num_frames=1,
+        height=None,
+        width=None,
+        extra_args={"duration": duration},
+    )
+    with pytest.raises(ValueError, match="duration"):
+        pipeline._resolve_shape("ref2va", sampling, None)
+
+
+def test_g1_fanout_uses_incrementing_output_seeds():
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
+        _minimax_h3_output_seeds,
+        _resolve_minimax_h3_num_outputs,
+    )
+
+    assert _resolve_minimax_h3_num_outputs(3) == 3
+    assert _minimax_h3_output_seeds(100, 3) == [100, 101, 102]
+    with pytest.raises(ValueError, match="num_outputs_per_prompt"):
+        _resolve_minimax_h3_num_outputs(11)
+
+
+def test_g3_task_specific_aspect_ratio_policy():
+    from PIL import Image
+
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
+        _resolve_minimax_h3_aspect_ratio,
+    )
+
+    image = Image.new("RGB", (1280, 720))
+    assert _resolve_minimax_h3_aspect_ratio("fl2va", "9:16", image) == pytest.approx(16 / 9)
+    assert _resolve_minimax_h3_aspect_ratio("ref2va", None, None) == pytest.approx(16 / 9)
+    assert _resolve_minimax_h3_aspect_ratio("ref2va", "auto", None) == pytest.approx(16 / 9)
+    with pytest.raises(ValueError, match="requires an explicit"):
+        _resolve_minimax_h3_aspect_ratio("t2va", None, None)
+    with pytest.raises(ValueError, match="one of"):
+        _resolve_minimax_h3_aspect_ratio("t2va", "2:1", None)
+
+
+def test_g3_t2va_shape_requires_a_named_ratio_and_fl2va_ignores_override():
+    from PIL import Image
+
+    from vllm_omni.diffusion.models.minimax_h3 import MiniMaxH3Pipeline
+
+    pipeline = object.__new__(MiniMaxH3Pipeline)
+    t2va_sampling = SimpleNamespace(
+        fps=24,
+        num_frames=1,
+        height=None,
+        width=None,
+        extra_args={"duration": 5.0},
+    )
+    with pytest.raises(ValueError, match="requires an explicit aspect_ratio"):
+        pipeline._resolve_shape("t2va", t2va_sampling, None)
+
+    fl2va_sampling = SimpleNamespace(
+        fps=24,
+        num_frames=1,
+        height=None,
+        width=None,
+        extra_args={"duration": 5.0, "aspect_ratio": "9:16"},
+    )
+    height, width, *_ = pipeline._resolve_shape("fl2va", fl2va_sampling, Image.new("RGB", (1280, 720)))
+    assert (height, width) == (768, 1344)
+
+
+def test_g4_reference_image_boundaries_and_aspect_ratio():
+    from PIL import Image
+
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
+        _validate_reference_image,
+    )
+
+    _validate_reference_image(Image.new("RGB", (256, 256)))
+    _validate_reference_image(Image.new("RGB", (5760, 2304)))
+    with pytest.raises(ValueError, match="dimensions"):
+        _validate_reference_image(Image.new("RGB", (255, 255)))
+    with pytest.raises(ValueError, match="aspect ratio"):
+        _validate_reference_image(Image.new("RGB", (256, 641)))
+
+
+def test_g4_reference_image_file_format_and_size_contract(tmp_path):
+    from PIL import Image
+
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
+        _load_image,
+    )
+
+    valid_path = tmp_path / "reference.png"
+    Image.new("RGB", (256, 256)).save(valid_path)
+    assert _load_image(valid_path).size == (256, 256)
+
+    invalid_path = tmp_path / "reference.bmp"
+    Image.new("RGB", (256, 256)).save(invalid_path)
+    with pytest.raises(ValueError, match="must use"):
+        _load_image(invalid_path)
+
+
+def test_g4_standalone_audio_duration_and_total_duration_contract():
+    from vllm_omni.diffusion.models.minimax_h3.reference_video import (
+        validate_reference_audio_waveforms,
+    )
+
+    sample_rate = 16000
+    validate_reference_audio_waveforms(
+        [
+            (torch.zeros(1, 2 * sample_rate), sample_rate),
+            (torch.zeros(1, 13 * sample_rate), sample_rate),
+        ]
+    )
+    with pytest.raises(ValueError, match="duration"):
+        validate_reference_audio_waveforms([(torch.zeros(1, int(1.9 * sample_rate)), sample_rate)])
+    with pytest.raises(ValueError, match="at most 15 seconds in total"):
+        validate_reference_audio_waveforms(
+            [
+                (torch.zeros(1, 8 * sample_rate), sample_rate),
+                (torch.zeros(1, 8 * sample_rate), sample_rate),
+            ]
+        )
+
+
+@pytest.mark.parametrize(
+    ("field", "value", "message"),
+    [
+        ("fps", 23.0, "FPS"),
+        ("duration", 1.0, "duration"),
+        ("format_names", ("avi",), "container"),
+        ("video_codec", "vp9", "H.264"),
+        ("audio_codecs", ("opus",), "AAC"),
+        ("file_size", 50 * 1024 * 1024 + 1, "size"),
+    ],
+)
+def test_g4_reference_video_metadata_validation(field, value, message, tmp_path):
+    from vllm_omni.diffusion.models.minimax_h3.reference_video import (
+        _validate_reference_video_metadata,
+    )
+
+    metadata = {
+        "width": 1280,
+        "height": 720,
+        "fps": 24.0,
+        "duration": 10.0,
+        "format_names": ("mp4",),
+        "video_codec": "h264",
+        "audio_codecs": ("aac",),
+        "file_size": 1024,
+    }
+    metadata[field] = value
+    with pytest.raises(ValueError, match=message):
+        _validate_reference_video_metadata(metadata, index=0, source=str(tmp_path / "reference.mp4"))

tests/diffusion/models/minimax_h3/test_minimax_h3_e2e.py

@@ -16,6 +16,17 @@ pytestmark = [
 MODEL_ENV = "VLLM_TEST_MINIMAX_H3_FL2VA_MODEL"
 
 
+def _assert_joint_output(outputs):
+    assert len(outputs) == 1
+    frames = np.asarray(outputs[0].images[0])
+    assert frames.shape == (39, 256, 448, 3)
+    multimodal = outputs[0].multimodal_output
+    assert multimodal is not None
+    assert np.asarray(multimodal["audio"]).shape[1] == 2
+    assert multimodal["audio_sample_rate"] == 32000
+    assert multimodal["fps"] == 24
+
+
 @pytest.mark.skipif(
     not os.environ.get(MODEL_ENV),
     reason=f"set {MODEL_ENV} to an authorized FL2VA checkpoint path",
@@ -50,6 +61,8 @@ def test_minimax_h3_t2va_ulysses8_smoke():
                 output_type="np",
                 extra_args={
                     "task": "t2va",
+                    "duration": 4.0,
+                    "aspect_ratio": "16:9",
                     "flow_shift": 12.0,
                     "audio_flow_shift": 3.0,
                 },
@@ -61,9 +74,61 @@ def test_minimax_h3_t2va_ulysses8_smoke():
 
     assert len(outputs) == 1
     frames = np.asarray(outputs[0].images[0])
-    assert frames.shape == (39, 256, 448, 3)
+    assert frames.shape == (107, 256, 448, 3)
     multimodal = outputs[0].multimodal_output
     assert multimodal is not None
     assert np.asarray(multimodal["audio"]).shape[1] == 2
     assert multimodal["audio_sample_rate"] == 32000
     assert multimodal["fps"] == 24
+
+
+@pytest.mark.skipif(
+    not os.environ.get(MODEL_ENV),
+    reason=f"set {MODEL_ENV} to an authorized FL2VA checkpoint path",
+)
+def test_minimax_h3_t2va_fp8_single_gpu_smoke():
+    import torch
+
+    from vllm_omni.diffusion.data import DiffusionParallelConfig
+    from vllm_omni.entrypoints.omni import Omni
+    from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+    if torch.accelerator.device_count() < 1:
+        pytest.skip("MiniMax H3 FP8 smoke requires a CUDA device")
+
+    engine = Omni(
+        model=os.environ[MODEL_ENV],
+        quantization_config={
+            "transformer": {"method": "fp8"},
+            "text_encoder": None,
+            "video_vae": None,
+            "audio_vae": None,
+        },
+        parallel_config=DiffusionParallelConfig(ulysses_degree=1),
+        trust_remote_code=True,
+        enable_cpu_offload=True,
+        enforce_eager=True,
+    )
+    try:
+        outputs = engine.generate(
+            "A quiet cinematic night scene with matching ambient sound.",
+            OmniDiffusionSamplingParams(
+                height=256,
+                width=448,
+                num_frames=29,
+                fps=24,
+                num_inference_steps=2,
+                seed=42,
+                output_type="np",
+                extra_args={
+                    "task": "t2va",
+                    "flow_shift": 12.0,
+                    "audio_flow_shift": 3.0,
+                },
+            ),
+            use_tqdm=False,
+        )
+    finally:
+        engine.close()
+
+    _assert_joint_output(outputs)

tests/diffusion/models/minimax_h3/test_minimax_h3_packing.py

@@ -144,3 +144,20 @@ def test_condition_noise_is_seeded_and_keeps_clean_anchor_at_timestep_one():
         noise_aug=0.25,
     )
     torch.testing.assert_close(first, second)
+
+
+def test_condition_noise_accepts_a_reference_video_longer_than_the_target():
+    from vllm_omni.diffusion.models.minimax_h3.condition_noise import (
+        minimax_h3_imgvid_cond_noise_aug_rows,
+    )
+
+    rows = torch.zeros(4 * 2 * 2, 96)
+    result = minimax_h3_imgvid_cond_noise_aug_rows(
+        rows,
+        condition_shapes=[(4, 4, 4)],
+        target_latent_t=2,
+        imgvid_cond_num_frames=1,
+        seed=7,
+        noise_aug=0.5,
+    )
+    assert result.shape == rows.shape

tests/diffusion/models/minimax_h3/test_minimax_h3_parallel.py

@@ -61,6 +61,33 @@ def test_packed_attention_is_a_regional_compile_boundary():
     assert getattr(MiniMaxH3Attention._run_packed_attention, "_torchdynamo_disable", False)
 
 
+def test_h3_fused_rope_matches_reference_and_preserves_unrotated_dims():
+    from vllm_omni.diffusion.layers.rope import RotaryEmbedding
+    from vllm_omni.diffusion.models.minimax_h3.minimax_h3_transformer import (
+        MiniMaxH3Attention,
+    )
+
+    attention = object.__new__(MiniMaxH3Attention)
+    nn.Module.__init__(attention)
+    attention.rope = RotaryEmbedding(is_neox_style=True, half_head_dim=False)
+    attention.rope._forward_method = attention.rope.forward_native
+
+    x = torch.randn(11, 3, 128, dtype=torch.bfloat16)
+    freqs_half = torch.randn(11, 48)
+    freqs = torch.cat((freqs_half, freqs_half), dim=-1)
+    actual = attention._apply_rope(x, freqs)
+
+    cos = torch.cos(freqs).to(x.dtype).unsqueeze(1)
+    sin = torch.sin(freqs).to(x.dtype).unsqueeze(1)
+    x_rot = x[..., :96]
+    x1, x2 = x_rot.chunk(2, dim=-1)
+    expected_rot = x_rot * cos + torch.cat((-x2, x1), dim=-1) * sin
+    expected = torch.cat((expected_rot, x[..., 96:]), dim=-1)
+
+    torch.testing.assert_close(actual, expected, atol=0, rtol=0)
+    torch.testing.assert_close(actual[..., 96:], x[..., 96:], atol=0, rtol=0)
+
+
 @pytest.mark.parametrize(
     ("tp_size", "message"),
     [

tests/diffusion/models/minimax_h3/test_minimax_h3_quantization.py

@@ -0,0 +1,213 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+import pytest
+import torch
+import torch.nn as nn
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
+
+
+class _FakeLinear(nn.Module):
+    def __init__(
+        self,
+        *args,
+        bias=False,
+        params_dtype=None,
+        quant_config=None,
+        prefix="",
+        total_num_heads=None,
+        total_num_kv_heads=None,
+        **kwargs,
+    ):
+        del args, kwargs
+        super().__init__()
+        self.prefix = prefix
+        self.quant_config = quant_config
+        self.weight = nn.Parameter(torch.empty(1, dtype=params_dtype))
+        if bias:
+            self.bias = nn.Parameter(torch.empty(1, dtype=params_dtype))
+        else:
+            self.register_parameter("bias", None)
+        self.num_heads = total_num_heads
+        self.num_kv_heads = total_num_kv_heads
+
+
+class _FakeAttention(nn.Module):
+    def __init__(self, **kwargs):
+        del kwargs
+        super().__init__()
+
+
+def _small_od_config():
+    arch = {
+        "num_layers": 1,
+        "token_refiner_num_layers": 1,
+        "hidden_size": 8,
+        "num_attention_heads": 2,
+        "attention_head_dim": 4,
+        "ffn_hidden_size": 16,
+        "latents_dim": 2,
+        "audio_latents_dim": 2,
+        "patch_size": (1, 2, 2),
+        "text_dim": 6,
+        "timestep_input_dim": 4,
+        "time_embed_hidden_size": 8,
+        "time_embed_dim": 4,
+        "adaln_out_features": 18 * 8,
+        "final_adaln_out_features": 2 * 8,
+        "rope_inv_freq_len": 2,
+    }
+    return SimpleNamespace(
+        tf_model_config=arch,
+        parallel_config=SimpleNamespace(ulysses_degree=1),
+    )
+
+
+def test_fp8_scope_and_prefix_propagation(monkeypatch):
+    from vllm.model_executor.layers.linear import LinearBase, UnquantizedLinearMethod
+    from vllm.model_executor.layers.quantization.fp8 import Fp8Config
+    from vllm.model_executor.layers.quantization.utils.quant_utils import (
+        is_layer_skipped,
+    )
+
+    from vllm_omni.diffusion.models.minimax_h3 import minimax_h3_transformer as h3
+
+    monkeypatch.setattr(h3, "ColumnParallelLinear", _FakeLinear)
+    monkeypatch.setattr(h3, "MergedColumnParallelLinear", _FakeLinear)
+    monkeypatch.setattr(h3, "QKVParallelLinear", _FakeLinear)
+    monkeypatch.setattr(h3, "RowParallelLinear", _FakeLinear)
+    monkeypatch.setattr(h3, "Attention", _FakeAttention)
+    monkeypatch.setattr(h3, "get_tensor_model_parallel_world_size", lambda: 1)
+
+    ignored_layers = {
+        "token_refiner.blocks.0.mlp.fc2",
+        "blocks.0.attn.qkv_proj",
+        "condition_proj",
+        "blocks.0.adaln_proj.linear",
+        "final_layer.adaln_proj.linear",
+    }
+    fp8_config = Fp8Config(ignored_layers=sorted(ignored_layers))
+    model = h3.MiniMaxH3DiTModel(
+        _small_od_config(),
+        quant_config=fp8_config,
+    )
+    linears = {module.prefix: module.quant_config for module in model.modules() if isinstance(module, _FakeLinear)}
+
+    quantized = {
+        "token_refiner.blocks.0.attn.qkv_proj",
+        "token_refiner.blocks.0.attn.out_proj",
+        "token_refiner.blocks.0.mlp.fc1",
+        "token_refiner.blocks.0.mlp.fc2",
+        "blocks.0.attn.qkv_proj",
+        "blocks.0.attn.out_proj",
+        "blocks.0.mlp.fc1",
+        "blocks.0.mlp.fc2",
+        "condition_proj",
+        "blocks.0.adaln_proj.linear",
+        "final_layer.adaln_proj.linear",
+    }
+    full_precision = {
+        "video_patch_proj",
+        "audio_patch_proj",
+        "time_embedder.proj_in",
+        "time_embedder.proj_out",
+        "final_layer.video_out",
+        "final_layer.audio_out",
+    }
+
+    assert quantized <= linears.keys()
+    assert full_precision <= linears.keys()
+    assert all(linears[prefix] is fp8_config for prefix in quantized)
+    assert all(linears[prefix] is None for prefix in full_precision)
+    assert {prefix for prefix in quantized if is_layer_skipped(prefix, fp8_config.ignored_layers)} == ignored_layers
+    linear = Mock(spec=LinearBase)
+    assert all(
+        isinstance(
+            fp8_config.get_quant_method(linear, prefix),
+            UnquantizedLinearMethod,
+        )
+        for prefix in ignored_layers
+    )
+
+
+class _WeightTarget(nn.Module):
+    def __init__(self, loader):
+        super().__init__()
+        self.weight = nn.Parameter(torch.empty(1))
+        self.weight.weight_loader = loader
+
+
+def test_model_load_weights_transforms_before_calling_vllm_loader():
+    from vllm_omni.diffusion.models.minimax_h3.minimax_h3_transformer import (
+        MiniMaxH3DiTArchConfig,
+        MiniMaxH3DiTModel,
+    )
+
+    qkv_calls = []
+    fc1_calls = []
+
+    def qkv_loader(param, loaded_weight):
+        del param
+        qkv_calls.append(loaded_weight.clone())
+
+    def fc1_loader(param, loaded_weight, shard_id):
+        del param
+        fc1_calls.append((shard_id, loaded_weight.clone()))
+
+    model = object.__new__(MiniMaxH3DiTModel)
+    nn.Module.__init__(model)
+    model.arch = MiniMaxH3DiTArchConfig(
+        hidden_size=1,
+        num_attention_heads=2,
+        attention_head_dim=1,
+        ffn_hidden_size=2,
+    )
+    model.blocks = nn.ModuleList([nn.Module()])
+    model.blocks[0].attn = nn.Module()
+    model.blocks[0].attn.qkv_proj = _WeightTarget(qkv_loader)
+    model.blocks[0].mlp = nn.Module()
+    model.blocks[0].mlp.fc1 = _WeightTarget(fc1_loader)
+
+    qkv = torch.arange(6, dtype=torch.float32).reshape(6, 1)
+    fc1 = torch.arange(4, dtype=torch.float32).reshape(4, 1)
+    loaded = model.load_weights(
+        [
+            ("blocks.0.attn.qkv_proj.weight", qkv),
+            ("blocks.0.mlp.fc1.weight", fc1),
+        ]
+    )
+
+    assert loaded == {
+        "blocks.0.attn.qkv_proj.weight",
+        "blocks.0.mlp.fc1.weight",
+    }
+    assert qkv_calls[0][:, 0].tolist() == [0, 3, 1, 4, 2, 5]
+    assert [(shard_id, tensor[:, 0].tolist()) for shard_id, tensor in fc1_calls] == [
+        (0, [0, 1]),
+        (1, [2, 3]),
+    ]
+
+
+def test_pipeline_resolves_transformer_component_quant_config():
+    from vllm_omni.diffusion.models.minimax_h3.pipeline_minimax_h3 import (
+        _resolve_component_quant_config,
+    )
+    from vllm_omni.quantization import build_quant_config
+
+    ignored_layers = ["blocks.0.attn.qkv_proj"]
+    component_config = build_quant_config(
+        {
+            "transformer": {
+                "method": "fp8",
+                "ignored_layers": ignored_layers,
+            }
+        }
+    )
+    transformer_config = component_config.resolve("transformer")
+
+    assert transformer_config.ignored_layers == ignored_layers
+    assert _resolve_component_quant_config(component_config, "transformer") is transformer_config
+    assert _resolve_component_quant_config(transformer_config, "transformer") is transformer_config