[CI/Build] Clarify model-specific example filename

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

.claude/skills/precheck-pr/SKILL.md

@@ -1,6 +1,6 @@
 ---
 name: precheck-pr
-description: Self-check your branch before creating a PR — catch dead code, verify accuracy/perf claims, validate PR title format, and confirm merge readiness. Use when the user says "precheck", "self review", "pre-submit check", or "check my PR before I open it." Never posts to GitHub.
+description: Self-check your branch before creating a PR — catch dead code, prevent new model-specific Python examples, verify accuracy/perf claims, validate PR title format, and confirm merge readiness. Use when the user says "precheck", "self review", "pre-submit check", or "check my PR before I open it." Never posts to GitHub.
 ---
 
 # PR Pre-Check
@@ -65,6 +65,8 @@ Ask: "Quick mode or full mode?" Then walk the checklist for the detected PR type
 
 **Also run the Code-Quality sweep on every PR**, regardless of type or mode: the five diff-scoped checks in [references/code-quality.md](references/code-quality.md) — kwargs fragility, broad-except swallow, `Any`/wrong type hints, hot-path `.clone()`/`deepcopy`, and event-loop blocking — plus the advisory conventions (log level, structured logging, synchronization, cleanup, dependencies, naming) in [checklists.md](references/checklists.md). These count only lines the PR *adds* — the pre-existing backlog across the repo is out of scope.
 
+**Also run the Examples-Policy check on every PR** using [references/examples-policy.md](references/examples-policy.md). Inspect only Python paths introduced by the diff. Treat a new model-, checkpoint-, vendor-, or family-specific Python example as blocking; do not report pre-existing example debt.
+
 ### Step 5: Print Report
 
 ```
@@ -77,6 +79,7 @@ Pre-check report for <branch>
   ─────────────────  ──────
   PR title format    ✓
   Code quality       ⚠ 1 broad except, 2 Any hints
+  Examples policy    ✗ new model-specific Python example
   PR desc integrity  ✓
   Registry/config    ✓
   Dead code          ⚠ 2 warnings

.claude/skills/precheck-pr/references/checklists.md

@@ -47,6 +47,19 @@ Diff-scoped sweep for five fragility patterns that are pervasive in the existing
 
 Roll the ⚠/✗ counts into the report as a single **Code quality** dimension row.
 
+---
+
+## All PRs: Examples Policy (run on every PR, quick & full)
+
+Inspect Python files introduced under `examples/` using
+[examples-policy.md](examples-policy.md). This is a diff-scoped ratchet: existing
+model-specific examples may be edited or removed without triggering the check.
+
+- [ ] **No new model-specific Python examples:** the diff adds, copies, or renames no Python file whose path or behavior is specific to one model, checkpoint, vendor, or model family. ✗ for any violation. Route inference through a shared task/protocol entrypoint, model request behavior through `vllm_omni/model_extras`, and commands through task docs or `recipes/`.
+- [ ] **New generic example code is justified:** any new task-neutral Python file under `examples/` is an actual user-facing entrypoint rather than reusable production logic, a benchmark, an app, a setup tool, or a reproducer. ⚠ when the placement is questionable.
+
+Roll the result into the report as a separate **Examples policy** dimension.
+
 ### Conventions (eyeball — no grep)
 
 Judgment calls that don't grep cleanly — apply them while reading the diff, not as a mechanical check:

.claude/skills/precheck-pr/references/examples-policy.md

@@ -0,0 +1,68 @@
+# Examples Python Policy
+
+Prevent new model-specific Python examples without forcing cleanup of the
+existing backlog. Run this check on every PR in quick and full modes.
+
+## Scope
+
+Set the merge base as in the main workflow, then list Python paths introduced
+by the PR, including additions, copies, and renames:
+
+```bash
+BASE="$(git merge-base HEAD origin/main)"
+git diff --name-status --diff-filter=ACR --find-renames --find-copies \
+  "${BASE}"...HEAD -- 'examples/**/*.py'
+```
+
+Inspect destination paths for copies and renames. Existing model-specific files
+that are only modified or deleted are grandfathered and must not be reported.
+
+## Blocking classification
+
+Mark ✗ when a newly introduced Python file does any of the following:
+
+- Names or scopes a path to one model, checkpoint, vendor, or model family.
+- Hard-codes a model identifier, model-specific prompt, request shape, output
+  conversion, or launch configuration behind an otherwise generic filename.
+- Duplicates an existing task/protocol runner to demonstrate one model.
+- Adds a per-model helper below a generic task hub such as `text_to_speech/`.
+
+Do not infer safety from a generic filename alone. For example, a file named
+`text_to_audio.py` is still model-specific if it only implements one model's
+contract.
+
+## Acceptable additions
+
+A new Python example may pass when it is a genuinely model-neutral,
+user-facing task or protocol entrypoint. It must accept model selection through
+configuration and keep model-specific behavior out of the script.
+
+Use ⚠ when the file is model-neutral but reusable logic appears misplaced under
+`examples/`. Route code by purpose:
+
+| Code purpose | Preferred location |
+|---|---|
+| Model prompt, defaults, request/output adaptation | `vllm_omni/model_extras/` or production model modules |
+| Runnable model command and validation evidence | Task documentation or `recipes/` |
+| UI or interactive application | `apps/` |
+| Benchmark or evaluator | `benchmarks/` |
+| Regression reproducer | `tests/` |
+| Download, conversion, or setup utility | `tools/` |
+
+If a model cannot use an existing shared runner, report the missing generic
+capability. Do not solve the gap by adding a model-specific Python example.
+
+## Report format
+
+Add one row to the pre-check report:
+
+```text
+Examples policy    ✓ no new Python example paths
+Examples policy    ⚠ generic helper belongs in tools/
+Examples policy    ✗ examples/offline_inference/new_model/end2end.py is model-specific
+Examples policy    ✗ examples/offline_inference/x_to_y_model_name.py is model-specific
+```
+
+For a blocker, name the path, the model-specific behavior, and the appropriate
+shared runner or destination. Do not propose deleting unrelated existing
+examples as part of the PR.

.claude/skills/review-pr/SKILL.md

@@ -97,6 +97,7 @@ If docs and live code disagree, verify the code/tests and report the drift.
 | [test-quality-evaluation.md](references/checks/test-quality-evaluation.md) | Tests change, are absent for risky code, or may not exercise production behavior. |
 | [tests-docs-checklist.md](references/checks/tests-docs-checklist.md) | Coverage, CI markers, examples, user docs, or PR evidence need review. |
 | [verification.md](references/checks/verification.md) | Hardware, a server, or a runnable affected path is available for active verification. |
+| [examples-policy.md](../precheck-pr/references/examples-policy.md) | The PR adds, copies, or renames Python under `examples/`; apply the canonical policy shared with `precheck-pr`. |
 
 ### Delivery and reviewer coordination
 
@@ -152,6 +153,13 @@ contract they protect or use only the applicable evidence checks.
 Apply every category in [general-checks.md](references/process/general-checks.md) before
 lower-priority comments.
 
+If the diff census contains an added, copied, or renamed Python path under
+`examples/`, read and apply the canonical
+[examples policy](../precheck-pr/references/examples-policy.md). Treat a new
+model-specific Python example as blocking. Do not flag model-specific example
+debt that the PR only modifies or removes, and do not run the rest of the
+author-oriented `precheck-pr` workflow.
+
 For each changed value or behavior, trace:
 
 ```text

examples/offline_inference/image_to_video/image_to_video.py

@@ -61,9 +61,7 @@ from vllm_omni.diffusion.utils.param_utils import apply_declared_extra_args
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.model_extras import (
-    build_image_to_video_prompt as build_model_image_to_video_prompt,
-)
-from vllm_omni.model_extras import (
+    build_image_to_video_prompt,
     get_extra_body_params,
     get_model_class_name,
 )
@@ -85,22 +83,6 @@ def parse_json_object(value: str, flag_name: str = "argument") -> dict[str, Any]
 parse_profiler_config = functools.partial(parse_json_object, flag_name="--profiler-config")
 
 
-def build_image_to_video_prompt(
-    prompt: str,
-    negative_prompt: str | None,
-    media_inputs: dict[str, Any],
-) -> dict[str, Any]:
-    """Build the canonical request envelope for the shared I2V example."""
-    result: dict[str, Any] = {
-        "prompt": prompt,
-        "modalities": ["video"],
-        "multi_modal_data": media_inputs,
-    }
-    if negative_prompt is not None:
-        result["negative_prompt"] = negative_prompt
-    return result
-
-
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
         description=(
@@ -540,13 +522,10 @@ def main():
         # Preserve the historical empty-prompt behavior for non-LTX examples.
         negative_prompt = ""
     prompt_dict = build_image_to_video_prompt(
+        model_class_name=model_class_name,
         prompt=args.prompt,
         negative_prompt=negative_prompt,
         media_inputs=media_inputs,
-    )
-    prompt_dict = build_model_image_to_video_prompt(
-        model_class_name=model_class_name,
-        prompt=prompt_dict,
         height=height,
         width=width,
         num_frames=num_frames,

examples/offline_inference/text_to_image/README.md

@@ -57,9 +57,8 @@ python examples/offline_inference/text_to_image/text_to_image.py \
   --output lingbot_t2i.png
 ```
 
-The shared T2I entry point selects the `image` output modality. The LingBot
-pipeline validates that T2I requests produce one frame and do not include an
-input image.
+The LingBot prompt builder selects the `image` output modality and requests one
+frame. Passing an input image is not supported in LingBot T2I mode.
 
 ## Quick Start
 

examples/offline_inference/text_to_image/text_to_image.py

@@ -21,9 +21,7 @@ from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.lora.request import LoRARequest
 from vllm_omni.lora.utils import stable_lora_int_id
 from vllm_omni.model_extras import (
-    build_text_to_image_prompt as build_model_text_to_image_prompt,
-)
-from vllm_omni.model_extras import (
+    build_text_to_image_prompt,
     get_extra_body_params,
     get_model_class_name,
     should_init_extra_args_for_non_diffusion_stages,
@@ -58,17 +56,6 @@ def parse_json_object(value: str, flag_name: str = "argument") -> dict[str, Any]
 parse_profiler_config = functools.partial(parse_json_object, flag_name="--profiler-config")
 
 
-def build_text_to_image_prompt(prompt: str, negative_prompt: str | None) -> dict[str, Any]:
-    """Build the canonical request envelope for the shared T2I example."""
-    result: dict[str, Any] = {
-        "prompt": prompt,
-        "modalities": ["image"],
-    }
-    if negative_prompt is not None:
-        result["negative_prompt"] = negative_prompt
-    return result
-
-
 def _normalize_images_for_save(images: list[Any]) -> list[Any]:
     """Convert NumPy diffusion outputs to PIL images before saving."""
     normalized = []
@@ -518,12 +505,9 @@ def main():
     generation_start = time.perf_counter()
 
     prompt_dict = build_text_to_image_prompt(
+        model_class_name=model_class_name,
         prompt=args.prompt,
         negative_prompt=args.negative_prompt,
-    )
-    prompt_dict = build_model_text_to_image_prompt(
-        model_class_name=model_class_name,
-        prompt=prompt_dict,
         height=args.height,
         width=args.width,
     )

examples/online_serving/image_to_video/README.md

@@ -1,9 +1,6 @@
 # Image-To-Video
 
-This example demonstrates online image-to-video generation with vLLM-Omni.
-The startup script defaults to Wan2.2, but `MODEL` can select any supported
-image-to-video model. The existing curl helper remains a concrete Wan2.2
-request through the standard Videos API.
+This example demonstrates how to deploy the Wan2.2 image-to-video model for online video generation using vLLM-Omni.
 
 ## Start Server
 
@@ -23,7 +20,9 @@ bash run_server.sh
 
 The script allows overriding:
 - `MODEL` (default: `Wan-AI/Wan2.2-I2V-A14B-Diffusers`)
-- `PORT` (default: `8099`)
+- `PORT` (default: `8091`)
+- `BOUNDARY_RATIO` (default: `0.875`)
+- `FLOW_SHIFT` (default: `12.0`)
 - `CACHE_BACKEND` (default: `none`)
 - `ENABLE_CACHE_DIT_SUMMARY` (default: `0`)
 

examples/online_serving/image_to_video/run_server.sh

@@ -1,12 +1,12 @@
 #!/bin/bash
-# Shared image-to-video server start script
+# Wan2.2 image-to-video server start script
 
 MODEL="${MODEL:-Wan-AI/Wan2.2-I2V-A14B-Diffusers}"
 PORT="${PORT:-8099}"
 CACHE_BACKEND="${CACHE_BACKEND:-none}"
 ENABLE_CACHE_DIT_SUMMARY="${ENABLE_CACHE_DIT_SUMMARY:-0}"
 
-echo "Starting image-to-video server..."
+echo "Starting Wan2.2 I2V server..."
 echo "Model: $MODEL"
 echo "Port: $PORT"
 echo "Cache backend: $CACHE_BACKEND"

examples/online_serving/text_to_image/README.md

@@ -1,9 +1,6 @@
 # Text-To-Image
 
-This example demonstrates online text-to-image generation with vLLM-Omni. The
-startup script and Python clients default to Qwen-Image, but `MODEL` can select
-any supported text-to-image model. The existing curl helper remains a concrete
-Qwen-Image request through the standard Images API.
+This example demonstrates how to deploy Qwen-Image model for online image generation service using vLLM-Omni.
 
 ## Start Server
 
@@ -229,8 +226,8 @@ count, use `size` and `n` rather than `height`, `width`, or
 | `height`                 | int   | None    | Image height in pixels         |
 | `width`                  | int   | None    | Image width in pixels          |
 | `size`                   | str   | None    | Image size (e.g., "1024x1024") |
-| `num_inference_steps`    | int   | model   | Number of denoising steps      |
-| `true_cfg_scale`         | float | model   | True CFG scale when supported  |
+| `num_inference_steps`    | int   | 50      | Number of denoising steps      |
+| `true_cfg_scale`         | float | 4.0     | Qwen-Image CFG scale           |
 | `seed`                   | int   | None    | Random seed (reproducible)     |
 | `negative_prompt`        | str   | None    | Negative prompt                |
 | `num_outputs_per_prompt` | int   | 1       | Number of images to generate   |

examples/online_serving/text_to_image/gradio_demo.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Shared text-to-image Gradio demo for online serving.
+Qwen-Image Gradio Demo for online serving.
 
 Usage:
     python gradio_demo.py [--server http://localhost:8091] [--port 7860]
@@ -77,9 +77,9 @@ def generate_image(
 def create_demo(server_url: str):
     """Create Gradio demo interface."""
 
-    with gr.Blocks(title="Text-to-Image Demo") as demo:
-        gr.Markdown("# Text-to-Image Online Generation")
-        gr.Markdown("Generate images using the model configured on the server")
+    with gr.Blocks(title="Qwen-Image Demo") as demo:
+        gr.Markdown("# Qwen-Image Online Generation")
+        gr.Markdown("Generate images using Qwen-Image model")
 
         with gr.Row():
             with gr.Column(scale=1):
@@ -115,6 +115,7 @@ def create_demo(server_url: str):
                         label="Inference Steps",
                         minimum=10,
                         maximum=100,
+                        # Default steps aligned with run_curl_text_to_image.sh to 100
                         value=100,
                         step=5,
                     )
@@ -172,7 +173,7 @@ def create_demo(server_url: str):
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Shared text-to-image Gradio demo")
+    parser = argparse.ArgumentParser(description="Qwen-Image Gradio Demo")
     parser.add_argument("--server", default="http://localhost:8091", help="Server URL")
     parser.add_argument("--port", type=int, default=7860, help="Gradio port")
     parser.add_argument("--share", action="store_true", help="Create public link")

examples/online_serving/text_to_image/openai_chat_client.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Shared OpenAI-compatible text-to-image generation client.
+Qwen-Image OpenAI-compatible image generation client.
 
 Usage:
     python openai_chat_client.py --prompt "A beautiful landscape" --output output.png
@@ -39,7 +39,7 @@ def generate_image(
         height: Image height in pixels
         width: Image width in pixels
         steps: Number of diffusion steps
-        true_cfg_scale: True CFG scale for models that support it
+        true_cfg_scale: Qwen-Image CFG scale
         seed: Random seed
         negative_prompt: Negative prompt
         num_outputs_per_prompt: Number of images to generate
@@ -70,7 +70,7 @@ def generate_image(
         payload["num_inference_steps"] = steps
     if true_cfg_scale is not None:
         payload["true_cfg_scale"] = true_cfg_scale
-    if negative_prompt is not None:
+    if negative_prompt:
         payload["negative_prompt"] = negative_prompt
     if seed is not None:
         payload["seed"] = seed
@@ -115,15 +115,15 @@ def generate_image(
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Shared text-to-image client")
+    parser = argparse.ArgumentParser(description="Qwen-Image chat client")
     parser.add_argument("--prompt", "-p", default="a cup of coffee on the table", help="Text prompt")
-    parser.add_argument("--output", "-o", default="text_to_image_output.png", help="Output file")
+    parser.add_argument("--output", "-o", default="qwen_image_output.png", help="Output file")
     parser.add_argument("--server", "-s", default="http://localhost:8091", help="Server URL")
-    parser.add_argument("--height", type=int, help="Image height; defaults to the model setting")
-    parser.add_argument("--width", type=int, help="Image width; defaults to the model setting")
-    parser.add_argument("--steps", type=int, help="Inference steps; defaults to the model setting")
-    parser.add_argument("--cfg-scale", type=float, help="True CFG scale when supported")
-    parser.add_argument("--seed", type=int, help="Random seed")
+    parser.add_argument("--height", type=int, default=1024, help="Image height")
+    parser.add_argument("--width", type=int, default=1024, help="Image width")
+    parser.add_argument("--steps", type=int, default=50, help="Inference steps")
+    parser.add_argument("--cfg-scale", type=float, default=4.0, help="True CFG scale")
+    parser.add_argument("--seed", type=int, default=0, help="Random seed")
     parser.add_argument("--negative", help="Negative prompt")
 
     parser.add_argument("--lora-path", default=None, help="Server-local LoRA adapter folder (PEFT format)")

examples/online_serving/text_to_image/run_server.sh

@@ -1,10 +1,10 @@
 #!/bin/bash
-# Shared text-to-image online serving startup script
+# Qwen-Image online serving startup script
 
 MODEL="${MODEL:-Qwen/Qwen-Image}"
 PORT="${PORT:-8091}"
 
-echo "Starting text-to-image server..."
+echo "Starting Qwen-Image server..."
 echo "Model: $MODEL"
 echo "Port: $PORT"
 

tests/config/test_config_factory.py

@@ -2206,24 +2206,6 @@ class TestBaseConfigInheritance:
 class TestPlatformOverrides:
     """Test platform-specific deploy config overrides."""
 
-    def test_qwen3_omni_musa_clears_parent_quantization_for_audio_stages(self):
-        pipeline = resolve_pipeline_config(
-            "qwen3_omni_moe",
-            Q3_OMNI_ALL_STAGES_HF_CONFIG,
-        )
-        assert isinstance(pipeline, PipelineConfig)
-        deploy_path = Path(get_deploy_config_path("qwen3_omni_moe.yaml"))
-
-        base_stages = merge_pipeline_deploy(pipeline, load_deploy_config(deploy_path))
-        assert "hf_overrides" not in base_stages[1].yaml_engine_args
-        assert "hf_overrides" not in base_stages[2].yaml_engine_args
-
-        musa = _apply_platform_overrides(load_deploy_config(deploy_path), platform="musa")
-        musa_stages = merge_pipeline_deploy(pipeline, musa)
-        expected = {"quantization_config": None}
-        assert musa_stages[1].yaml_engine_args["hf_overrides"] == expected
-        assert musa_stages[2].yaml_engine_args["hf_overrides"] == expected
-
     def test_qwen3_tts_rocm_disables_code2wav_outer_cudagraph(self):
         deploy_path = Path(get_deploy_config_path("qwen3_tts.yaml"))
 

tests/examples/offline_inference/test_image_task_prompts.py

@@ -1,47 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-from __future__ import annotations
-
-import pytest
-from PIL import Image
-
-from examples.offline_inference.image_to_video.image_to_video import build_image_to_video_prompt
-from examples.offline_inference.text_to_image.text_to_image import build_text_to_image_prompt
-
-pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
-
-
-@pytest.mark.parametrize(
-    ("negative_prompt", "expected_negative_prompt"),
-    [(None, None), ("", ""), ("blurry", "blurry")],
-)
-def test_text_to_image_builds_canonical_prompt(
-    negative_prompt: str | None,
-    expected_negative_prompt: str | None,
-) -> None:
-    result = build_text_to_image_prompt("a red fox", negative_prompt)
-
-    assert result["prompt"] == "a red fox"
-    assert result["modalities"] == ["image"]
-    if expected_negative_prompt is None:
-        assert "negative_prompt" not in result
-    else:
-        assert result["negative_prompt"] == expected_negative_prompt
-
-
-def test_image_to_video_builds_canonical_prompt() -> None:
-    image = Image.new("RGB", (32, 16), "red")
-
-    result = build_image_to_video_prompt(
-        prompt="the fox turns toward the camera",
-        negative_prompt="flicker",
-        media_inputs={"image": image},
-    )
-
-    assert result == {
-        "prompt": "the fox turns toward the camera",
-        "modalities": ["video"],
-        "multi_modal_data": {"image": image},
-        "negative_prompt": "flicker",
-    }

tests/model_extras/test_model_extras.py

@@ -3,8 +3,6 @@
 
 from __future__ import annotations
 
-from typing import Any
-
 import pytest
 from PIL import Image
 
@@ -137,6 +135,67 @@ def test_lingbot_extra_registry_declares_request_params() -> None:
     assert should_init_extra_args_for_non_diffusion_stages("LingBotVideoPipeline") is False
 
 
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_lingbot_text_to_image_prompt_builder_preserves_empty_negative_prompt() -> None:
+    assert build_text_to_image_prompt(
+        "LingBotVideoPipeline",
+        prompt="a red fox in fresh snow",
+        negative_prompt="",
+        height=192,
+        width=320,
+    ) == {
+        "prompt": "a red fox in fresh snow",
+        "modalities": ["image"],
+        "negative_prompt": "",
+    }
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_lingbot_image_to_video_prompt_builder() -> None:
+    image = Image.new("RGB", (320, 192), "red")
+    result = build_image_to_video_prompt(
+        "LingBotVideoPipeline",
+        prompt="the fox looks toward the camera",
+        negative_prompt=None,
+        media_inputs={"image": image},
+        height=192,
+        width=320,
+        num_frames=9,
+    )
+    assert result == {
+        "prompt": "the fox looks toward the camera",
+        "modalities": ["video"],
+        "multi_modal_data": {"image": image},
+        "num_frames": 9,
+    }
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+@pytest.mark.parametrize(
+    "media_inputs",
+    [
+        {},
+        {"image": "input.png"},
+        {"image": [Image.new("RGB", (16, 16))]},
+        {"image": Image.new("RGB", (16, 16)), "video": b"video"},
+    ],
+    ids=["missing", "path", "multiple", "extra-modality"],
+)
+def test_lingbot_image_to_video_prompt_builder_rejects_invalid_media(
+    media_inputs: dict[str, object],
+) -> None:
+    with pytest.raises(ValueError, match="exactly one PIL image"):
+        build_image_to_video_prompt(
+            "LingBotVideoPipeline",
+            prompt="move",
+            negative_prompt=None,
+            media_inputs=media_inputs,
+        )
+
+
 @pytest.mark.core_model
 @pytest.mark.cpu
 @pytest.mark.parametrize("pipeline_name", ["Cosmos3OmniDiffusersPipeline", "Cosmos3OmniPipeline"])
@@ -283,11 +342,8 @@ def test_ming_flash_omni_declared_extra_args_route_into_sampling_params() -> Non
 def test_ming_flash_omni_text_to_image_prompt_builder() -> None:
     assert build_text_to_image_prompt(
         "MingImagePipeline",
-        {
-            "prompt": "Please draw a cute cat.",
-            "modalities": ["image"],
-            "negative_prompt": "ugly, blurry",
-        },
+        prompt="Please draw a cute cat.",
+        negative_prompt="ugly, blurry",
         height=512,
         width=768,
     ) == {
@@ -304,7 +360,8 @@ def test_ming_flash_omni_text_to_image_prompt_builder() -> None:
     # pipeline's 1024x1024 default still applies.
     assert build_text_to_image_prompt(
         "MingImagePipeline",
-        {"prompt": "Draw a poster.", "modalities": ["image"]},
+        prompt="Draw a poster.",
+        negative_prompt=None,
     ) == {
         "prompt": "Draw a poster.",
         "modalities": ["image"],
@@ -334,6 +391,28 @@ def test_ming_flash_omni_image_to_image_prompt_builder() -> None:
     assert "negative_prompt" not in result
 
 
+@pytest.mark.core_model
+@pytest.mark.cpu
+@pytest.mark.parametrize("pipeline_name", ["Cosmos3OmniDiffusersPipeline", "Cosmos3OmniPipeline"])
+def test_cosmos3_text_to_image_prompt_builder_selects_image_modality(pipeline_name: str) -> None:
+    assert build_text_to_image_prompt(
+        pipeline_name,
+        prompt="a red sports car at golden hour",
+        negative_prompt="blurry, distorted",
+        height=1024,
+        width=1024,
+    ) == {
+        "prompt": "a red sports car at golden hour",
+        "modalities": ["image"],
+        "negative_prompt": "blurry, distorted",
+    }
+    assert build_text_to_image_prompt(
+        pipeline_name,
+        prompt="a red sports car",
+        negative_prompt=None,
+    ) == {"prompt": "a red sports car", "modalities": ["image"]}
+
+
 @pytest.mark.core_model
 @pytest.mark.cpu
 def test_audiox_extra_registry_declares_request_and_response_params() -> None:
@@ -426,11 +505,8 @@ def test_unknown_pipeline_has_empty_extra_registry() -> None:
 def test_bagel_text_to_image_prompt_builder() -> None:
     assert build_text_to_image_prompt(
         "BagelPipeline",
-        {
-            "prompt": "a cat",
-            "modalities": ["image"],
-            "negative_prompt": "blurry",
-        },
+        prompt="a cat",
+        negative_prompt="blurry",
         height=512,
         width=768,
     ) == {
@@ -467,17 +543,14 @@ def test_bagel_image_to_image_prompt_builder() -> None:
 
 @pytest.mark.core_model
 @pytest.mark.cpu
-def test_unknown_pipeline_preserves_canonical_text_to_image_prompt() -> None:
-    canonical_prompt = {"prompt": "a cat", "modalities": ["image"]}
-    assert (
-        build_text_to_image_prompt(
-            "UnknownPipeline",
-            canonical_prompt,
-            height=512,
-            width=512,
-        )
-        is canonical_prompt
-    )
+def test_unknown_pipeline_uses_default_text_to_image_prompt() -> None:
+    assert build_text_to_image_prompt(
+        "UnknownPipeline",
+        prompt="a cat",
+        negative_prompt=None,
+        height=512,
+        width=512,
+    ) == {"prompt": "a cat"}
 
 
 @pytest.mark.core_model
@@ -496,14 +569,12 @@ def test_unknown_pipeline_uses_default_image_to_image_prompt() -> None:
     }
 
 
-def _build_vace_prompt(media_inputs: dict[str, Any], *, num_frames: int = 5) -> dict:
+def _build_vace_prompt(media_inputs: dict[str, object], *, num_frames: int = 5) -> dict:
     return build_image_to_video_prompt(
         "WanVACEPipeline",
-        {
-            "prompt": "a bird flying",
-            "modalities": ["video"],
-            "multi_modal_data": media_inputs,
-        },
+        prompt="a bird flying",
+        negative_prompt=None,
+        media_inputs=media_inputs,
         height=16,
         width=320,
         num_frames=num_frames,
@@ -526,7 +597,7 @@ def _build_vace_prompt(media_inputs: dict[str, Any], *, num_frames: int = 5) ->
     ],
     ids=["i2v", "v2lf", "flf2v", "inpaint", "r2v"],
 )
-def test_vace_image_to_video_prompt_builder(media_inputs: dict[str, Any]) -> None:
+def test_vace_image_to_video_prompt_builder(media_inputs: dict[str, object]) -> None:
     result = _build_vace_prompt(media_inputs)
     mmd = result["multi_modal_data"]
     if "reference_images" in mmd:
@@ -545,7 +616,7 @@ def test_vace_image_to_video_prompt_builder(media_inputs: dict[str, Any]) -> Non
         ({"control_image": Image.new("RGB", (320, 16))}, "Unsupported VACE media input"),
     ],
 )
-def test_vace_rejects_invalid_media_combinations(media_inputs: dict[str, Any], message: str) -> None:
+def test_vace_rejects_invalid_media_combinations(media_inputs: dict[str, object], message: str) -> None:
     with pytest.raises(ValueError, match=message):
         _build_vace_prompt(media_inputs)
 
@@ -593,7 +664,8 @@ def test_mammothmoda2_extra_registry_declares_request_and_response_params() -> N
 
     wrapper_prompt = build_text_to_image_prompt(
         "MammothModa2ForConditionalGeneration",
-        {"prompt": "a cat", "modalities": ["image"]},
+        prompt="a cat",
+        negative_prompt=None,
         height=256,
         width=256,
     )
@@ -607,11 +679,8 @@ def test_mammothmoda2_text_to_image_prompt_builder() -> None:
     # prompt is ignored (MammothModa2 t2i uses CFG, not an explicit negative path).
     assert build_text_to_image_prompt(
         "MammothModa2DiTPipeline",
-        {
-            "prompt": "a cat",
-            "modalities": ["image"],
-            "negative_prompt": "blurry",
-        },
+        prompt="a cat",
+        negative_prompt="blurry",
         height=512,
         width=768,
     ) == {

tests/worker/test_omni_gpu_model_runner.py

@@ -3,88 +3,13 @@ from types import SimpleNamespace
 
 import pytest
 import torch
-from vllm.v1.cudagraph_dispatcher import CUDAGraphMode
 
-from vllm_omni.worker.gpu_model_runner import (
-    OmniGPUModelRunner,
-    _filter_mrope_kwargs_for_model,
-)
+from vllm_omni.worker.gpu_model_runner import OmniGPUModelRunner, _filter_mrope_kwargs_for_model
 from vllm_omni.worker.omni_connector_model_runner_mixin import OmniConnectorModelRunnerMixin
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
 
-def _runner_for_talker_graph_init(
-    *,
-    talker_mtp_graph_safe: bool | None,
-    has_separate_talker: bool = True,
-    model_stage: str = "talker",
-) -> OmniGPUModelRunner:
-    runner = object.__new__(OmniGPUModelRunner)
-    runner.model = SimpleNamespace(
-        talker=object() if has_separate_talker else None,
-        talker_mtp=object(),
-        model_stage=model_stage,
-    )
-    if talker_mtp_graph_safe is not None:
-        runner.model.talker_mtp_graph_safe = talker_mtp_graph_safe
-    runner.model_config = SimpleNamespace(hf_text_config=SimpleNamespace(hidden_size=4))
-    runner.compilation_config = SimpleNamespace(
-        cudagraph_mode=CUDAGraphMode.FULL,
-        max_cudagraph_capture_size=1,
-    )
-    runner.vllm_config = object()
-    runner.max_num_reqs = 1
-    runner.dtype = torch.float32
-    runner._make_buffer = lambda *args, **kwargs: SimpleNamespace(args=args, kwargs=kwargs)
-    return runner
-
-
-def test_talker_mtp_skips_graph_when_model_declares_unsafe(monkeypatch):
-    runner = _runner_for_talker_graph_init(talker_mtp_graph_safe=False)
-    talker_mtp = runner.model.talker_mtp
-    monkeypatch.setattr(
-        "vllm_omni.worker.gpu_model_runner.current_omni_platform.get_graph_wrapper_cls",
-        lambda: pytest.fail("graph wrapper must not be selected"),
-    )
-
-    OmniGPUModelRunner._init_talker_mtp(runner)
-
-    assert runner.talker_mtp is talker_mtp
-
-
-@pytest.mark.parametrize("model_stage", ["thinker", "code2wav"])
-def test_non_talker_stage_does_not_use_talker_mtp_graph(monkeypatch, model_stage: str):
-    runner = _runner_for_talker_graph_init(
-        talker_mtp_graph_safe=None,
-        has_separate_talker=False,
-        model_stage=model_stage,
-    )
-    talker_mtp = runner.model.talker_mtp
-    monkeypatch.setattr(
-        "vllm_omni.worker.gpu_model_runner.current_omni_platform.get_graph_wrapper_cls",
-        lambda: pytest.fail("graph wrapper must not be selected"),
-    )
-
-    OmniGPUModelRunner._init_talker_mtp(runner)
-
-    assert runner.talker_mtp is talker_mtp
-
-
-@pytest.mark.parametrize("talker_mtp_graph_safe", [None, True])
-def test_talker_mtp_uses_graph_for_legacy_or_explicit_safe_model(monkeypatch, talker_mtp_graph_safe):
-    runner = _runner_for_talker_graph_init(talker_mtp_graph_safe=talker_mtp_graph_safe)
-    wrapped = object()
-    monkeypatch.setattr(
-        "vllm_omni.worker.gpu_model_runner.current_omni_platform.get_graph_wrapper_cls",
-        lambda: lambda *args, **kwargs: wrapped,
-    )
-
-    OmniGPUModelRunner._init_talker_mtp(runner)
-
-    assert runner.talker_mtp is wrapped
-
-
 class DummyBuffer:
     """A minimal buffer wrapper that exposes the `.gpu` attribute."""
 

vllm_omni/deploy/qwen3_omni_moe.yaml

@@ -69,18 +69,6 @@ stages:
       repetition_penalty: 1.1
 
 platforms:
-  # The supported Qwen3-Omni ModelOpt checkpoint quantizes the thinker only. Clear the
-  # checkpoint-level metadata for the BF16 audio stages on the verified MUSA
-  # profile before vLLM performs its normal quantization auto-detection.
-  musa:
-    stages:
-      - stage_id: 1
-        hf_overrides:
-          quantization_config: null
-      - stage_id: 2
-        hf_overrides:
-          quantization_config: null
-
   npu:
     stages:
       - stage_id: 0

vllm_omni/model_executor/models/qwen3_omni/qwen3_omni.py

@@ -152,9 +152,6 @@ class Qwen3OmniMoeForConditionalGeneration(
                 dtype=torch.long,
             )
         elif self.model_stage == "talker":
-            # The outer wrapper exposes talker_mtp for every stage, but only
-            # the talker stage owns the module that the method invokes.
-            self.talker_mtp_graph_safe = current_omni_platform.supports_talker_mtp_graph_capture()
             multimodal_config.skip_mm_profiling = True
             self.has_preprocess = True
             self.has_postprocess = True

vllm_omni/model_extras/cosmos3.py

@@ -3,6 +3,32 @@
 
 from __future__ import annotations
 
+from typing import Any
+
+
+def build_text_to_image_prompt(
+    prompt: str,
+    negative_prompt: str | None,
+    height: int | None = None,
+    width: int | None = None,
+) -> dict[str, Any]:
+    """Build a Cosmos3 T2I prompt by selecting image output modality.
+
+    Cosmos3 uses the same pipeline class for T2I and video modes. The OpenAI
+    image endpoint selects T2I by adding ``modalities=["image"]``. ``height``
+    and ``width`` are accepted for registry compatibility; sizing travels
+    through sampling params instead of the prompt payload.
+    """
+    del height, width
+    text_prompt: dict[str, Any] = {
+        "prompt": prompt,
+        "modalities": ["image"],
+    }
+    if negative_prompt is not None:
+        text_prompt["negative_prompt"] = negative_prompt
+    return text_prompt
+
+
 COSMOS3_EXTRA_BODY_PARAMS = frozenset(
     {
         "flow_shift",

vllm_omni/model_extras/lingbot_video.py

@@ -3,6 +3,11 @@
 
 from __future__ import annotations
 
+from collections.abc import Mapping
+from typing import Any
+
+from PIL import Image
+
 LINGBOT_VIDEO_EXTRA_BODY_PARAMS = frozenset(
     {
         "batch_cfg",
@@ -19,3 +24,43 @@ LINGBOT_VIDEO_EXTRA_BODY_PARAMS = frozenset(
         "t_thresh",
     }
 )
+
+
+def build_text_to_image_prompt(
+    prompt: str,
+    negative_prompt: str | None,
+    height: int | None = None,
+    width: int | None = None,
+) -> dict[str, Any]:
+    del height, width
+    result: dict[str, Any] = {
+        "prompt": prompt,
+        "modalities": ["image"],
+    }
+    if negative_prompt is not None:
+        result["negative_prompt"] = negative_prompt
+    return result
+
+
+def build_image_to_video_prompt(
+    prompt: str,
+    negative_prompt: str | None,
+    media_inputs: Mapping[str, Any],
+    height: int | None = None,
+    width: int | None = None,
+    num_frames: int | None = None,
+) -> dict[str, Any]:
+    del height, width
+    if set(media_inputs) != {"image"} or not isinstance(media_inputs["image"], Image.Image):
+        raise ValueError("LingBot text-image-to-video requires exactly one PIL image input.")
+
+    result: dict[str, Any] = {
+        "prompt": prompt,
+        "modalities": ["video"],
+        "multi_modal_data": {"image": media_inputs["image"]},
+    }
+    if num_frames is not None:
+        result["num_frames"] = num_frames
+    if negative_prompt is not None:
+        result["negative_prompt"] = negative_prompt
+    return result

vllm_omni/model_extras/registry.py

@@ -28,12 +28,21 @@ from vllm_omni.model_extras.cosmos3 import (
     COSMOS3_EXTRA_BODY_PARAMS,
     COSMOS3_EXTRA_OUTPUT_PARAMS,
 )
+from vllm_omni.model_extras.cosmos3 import (
+    build_text_to_image_prompt as build_cosmos3_text_to_image_prompt,
+)
 from vllm_omni.model_extras.helios import (
     HELIOS_EXTRA_BODY_PARAMS,
     HELIOS_EXTRA_OUTPUT_PARAMS,
 )
 from vllm_omni.model_extras.hunyuan_image3 import build_x_to_text_prompt as build_hunyuan_x_to_text_prompt
 from vllm_omni.model_extras.lingbot_video import LINGBOT_VIDEO_EXTRA_BODY_PARAMS
+from vllm_omni.model_extras.lingbot_video import (
+    build_image_to_video_prompt as build_lingbot_image_to_video_prompt,
+)
+from vllm_omni.model_extras.lingbot_video import (
+    build_text_to_image_prompt as build_lingbot_text_to_image_prompt,
+)
 from vllm_omni.model_extras.ltx2 import LTX_EXTRA_BODY_PARAMS, LTX_EXTRA_OUTPUT_PARAMS
 from vllm_omni.model_extras.magi_human import (
     MAGI_HUMAN_EXTRA_BODY_PARAMS,
@@ -138,6 +147,18 @@ def build_x_to_text_prompt(
     return builder(model, prompt, has_image)
 
 
+def default_text_to_image_prompt(
+    prompt: str,
+    negative_prompt: str | None,
+    height: int | None = None,
+    width: int | None = None,
+) -> dict[str, Any]:
+    result: dict[str, Any] = {"prompt": prompt}
+    if negative_prompt is not None:
+        result["negative_prompt"] = negative_prompt
+    return result
+
+
 def default_image_to_image_prompt(
     prompt: str,
     negative_prompt: str | None,
@@ -154,6 +175,20 @@ def default_image_to_image_prompt(
     return result
 
 
+def default_image_to_video_prompt(
+    prompt: str,
+    negative_prompt: str | None,
+    media_inputs: Mapping[str, Any],
+    height: int | None = None,
+    width: int | None = None,
+    num_frames: int | None = None,
+) -> dict[str, Any]:
+    del height, width, num_frames
+    if set(media_inputs) != {"image"} or not isinstance(media_inputs["image"], Image.Image):
+        raise ValueError("This model only supports a single --image input in the shared image-to-video example.")
+    return default_image_to_image_prompt(prompt, negative_prompt, media_inputs["image"])
+
+
 _EXTRA_SPECS: dict[str, dict[str, Any]] = {
     "AudioXPipeline": {
         "extra_body_params": AUDIOX_EXTRA_BODY_PARAMS,
@@ -173,11 +208,12 @@ _EXTRA_SPECS: dict[str, dict[str, Any]] = {
     "Cosmos3OmniDiffusersPipeline": {
         "extra_body_params": COSMOS3_EXTRA_BODY_PARAMS,
         "extra_output_params": COSMOS3_EXTRA_OUTPUT_PARAMS,
-        # The shared T2I example already supplies modalities=["image"].
+        "text_to_image_prompt_builder": build_cosmos3_text_to_image_prompt,
     },
     "Cosmos3OmniPipeline": {
         "extra_body_params": COSMOS3_EXTRA_BODY_PARAMS,
         "extra_output_params": COSMOS3_EXTRA_OUTPUT_PARAMS,
+        "text_to_image_prompt_builder": build_cosmos3_text_to_image_prompt,
     },
     "MagiHumanPipeline": {
         "extra_body_params": MAGI_HUMAN_EXTRA_BODY_PARAMS,
@@ -193,8 +229,8 @@ _EXTRA_SPECS: dict[str, dict[str, Any]] = {
     },
     "LingBotVideoPipeline": {
         "extra_body_params": LINGBOT_VIDEO_EXTRA_BODY_PARAMS,
-        # Shared T2I/I2V envelopes select the output modality. LingBot's
-        # pipeline owns model-specific validation and normalization.
+        "text_to_image_prompt_builder": build_lingbot_text_to_image_prompt,
+        "image_to_video_prompt_builder": build_lingbot_image_to_video_prompt,
     },
     **{
         model_class_name: {
@@ -227,7 +263,7 @@ _EXTRA_SPECS: dict[str, dict[str, Any]] = {
 }
 
 # Multi-stage discovery reports the top-level wrapper rather than its DiT
-# submodule, so both names must resolve to the same request builders.
+# submodule, so both names must resolve to the same request adapters.
 _EXTRA_SPECS["MammothModa2ForConditionalGeneration"] = _EXTRA_SPECS["MammothModa2DiTPipeline"]
 _EXTRA_SPECS["Mammothmoda2Model"] = _EXTRA_SPECS["MammothModa2DiTPipeline"]
 
@@ -269,21 +305,18 @@ def should_init_extra_args_for_non_diffusion_stages(model_class_name: str | None
 
 def build_text_to_image_prompt(
     model_class_name: str | None,
-    prompt: dict[str, Any],
+    prompt: str,
+    negative_prompt: str | None,
     height: int | None = None,
     width: int | None = None,
 ) -> dict[str, Any]:
-    """Build a model-specific T2I prompt from an example-owned envelope."""
     spec = _get_spec(model_class_name)
-    builder: TextToImagePromptBuilder | None = spec.get("text_to_image_prompt_builder") if spec else None
-    if builder is None:
-        return prompt
-    return builder(
-        prompt=str(prompt["prompt"]),
-        negative_prompt=prompt.get("negative_prompt"),
-        height=height,
-        width=width,
+    builder: TextToImagePromptBuilder = (
+        spec.get("text_to_image_prompt_builder", default_text_to_image_prompt)
+        if spec is not None
+        else default_text_to_image_prompt
     )
+    return builder(prompt, negative_prompt, height, width)
 
 
 def build_image_to_image_prompt(
@@ -305,24 +338,17 @@ def build_image_to_image_prompt(
 
 def build_image_to_video_prompt(
     model_class_name: str | None,
-    prompt: dict[str, Any],
+    prompt: str,
+    negative_prompt: str | None,
+    media_inputs: Mapping[str, Any],
     height: int | None = None,
     width: int | None = None,
     num_frames: int | None = None,
 ) -> dict[str, Any]:
-    """Build a model-specific I2V prompt from an example-owned envelope."""
     spec = _get_spec(model_class_name)
-    builder: ImageToVideoPromptBuilder | None = spec.get("image_to_video_prompt_builder") if spec else None
-    if builder is None:
-        return prompt
-    media_inputs = prompt.get("multi_modal_data") or {}
-    if not isinstance(media_inputs, Mapping):
-        raise TypeError("Canonical I2V prompt multi_modal_data must be a mapping.")
-    return builder(
-        prompt=str(prompt["prompt"]),
-        negative_prompt=prompt.get("negative_prompt"),
-        media_inputs=media_inputs,
-        height=height,
-        width=width,
-        num_frames=num_frames,
+    builder: ImageToVideoPromptBuilder = (
+        spec.get("image_to_video_prompt_builder", default_image_to_video_prompt)
+        if spec is not None
+        else default_image_to_video_prompt
     )
+    return builder(prompt, negative_prompt, media_inputs, height, width, num_frames)

vllm_omni/platforms/interface.py

@@ -155,11 +155,6 @@ class OmniPlatform(Platform):
         """Check if the platform supports torch.compile with inductor backend."""
         raise NotImplementedError
 
-    @classmethod
-    def supports_talker_mtp_graph_capture(cls) -> bool:
-        """Whether a model may capture its dedicated talker MTP graph."""
-        return True
-
     @classmethod
     def has_flash_attn_package(cls) -> bool:
         """Check if a Flash Attention package is available and usable on this platform."""

vllm_omni/platforms/musa/platform.py

@@ -115,11 +115,6 @@ class MUSAOmniPlatform(OmniPlatform, MUSAPlatformBase):
         """MUSA supports torch.compile with inductor backend."""
         return True
 
-    @classmethod
-    def supports_talker_mtp_graph_capture(cls) -> bool:
-        """MUSA keeps Qwen3 talker MTP outside its dedicated FULL graph."""
-        return False
-
     @classmethod
     def supports_float64(cls) -> bool:
         """MUSA does not support float64 yet."""

vllm_omni/worker/gpu_model_runner.py

@@ -206,12 +206,8 @@ class OmniGPUModelRunner(GPUModelRunner):
         cudagraph_mode = self.compilation_config.cudagraph_mode
         assert cudagraph_mode is not None
         has_separate_talker = getattr(self.model, "talker", None) is not None
-        talker_mtp_graph_safe = getattr(self.model, "talker_mtp_graph_safe", None)
-        # Preserve the legacy separate-talker default only when the model has
-        # not declared graph safety. An explicit False must disable wrapping.
-        if talker_mtp_graph_safe is None:
-            talker_mtp_graph_safe = has_separate_talker
-        if cudagraph_mode.has_full_cudagraphs() and talker_mtp_graph_safe:
+        talker_mtp_graph_safe = getattr(self.model, "talker_mtp_graph_safe", False)
+        if cudagraph_mode.has_full_cudagraphs() and (has_separate_talker or talker_mtp_graph_safe):
             graph_wrapper_cls = current_omni_platform.get_graph_wrapper_cls()
             self.talker_mtp = graph_wrapper_cls(talker_mtp, self.vllm_config, runtime_mode=CUDAGraphMode.FULL)
         # TTS exposes mtp_hidden_size; Omni uses hf_text_config.hidden_size.