[Misc] Keep serving changes out of example refactor

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

examples/offline_inference/image_to_video/image_to_video.py

@@ -61,7 +61,9 @@ 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,
+    build_image_to_video_prompt as build_model_image_to_video_prompt,
+)
+from vllm_omni.model_extras import (
     get_extra_body_params,
     get_model_class_name,
 )
@@ -83,6 +85,22 @@ 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=(
@@ -522,10 +540,13 @@ 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

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

examples/offline_inference/text_to_image/text_to_image.py

@@ -21,7 +21,9 @@ 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,
+    build_text_to_image_prompt as build_model_text_to_image_prompt,
+)
+from vllm_omni.model_extras import (
     get_extra_body_params,
     get_model_class_name,
     should_init_extra_args_for_non_diffusion_stages,
@@ -56,6 +58,17 @@ 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 = []
@@ -505,9 +518,12 @@ 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

@@ -1,6 +1,9 @@
 # Image-To-Video
 
-This example demonstrates how to deploy the Wan2.2 image-to-video model for online video generation using vLLM-Omni.
+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.
 
 ## Start Server
 
@@ -20,9 +23,7 @@ bash run_server.sh
 
 The script allows overriding:
 - `MODEL` (default: `Wan-AI/Wan2.2-I2V-A14B-Diffusers`)
-- `PORT` (default: `8091`)
-- `BOUNDARY_RATIO` (default: `0.875`)
-- `FLOW_SHIFT` (default: `12.0`)
+- `PORT` (default: `8099`)
 - `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
-# Wan2.2 image-to-video server start script
+# Shared 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 Wan2.2 I2V server..."
+echo "Starting image-to-video server..."
 echo "Model: $MODEL"
 echo "Port: $PORT"
 echo "Cache backend: $CACHE_BACKEND"

examples/online_serving/text_to_image/README.md

@@ -1,6 +1,9 @@
 # Text-To-Image
 
-This example demonstrates how to deploy Qwen-Image model for online image generation service using vLLM-Omni.
+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.
 
 ## Start Server
 
@@ -226,8 +229,8 @@ 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   | 50      | Number of denoising steps      |
-| `true_cfg_scale`         | float | 4.0     | Qwen-Image CFG scale           |
+| `num_inference_steps`    | int   | model   | Number of denoising steps      |
+| `true_cfg_scale`         | float | model   | True CFG scale when supported  |
 | `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
 """
-Qwen-Image Gradio Demo for online serving.
+Shared text-to-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="Qwen-Image Demo") as demo:
-        gr.Markdown("# Qwen-Image Online Generation")
-        gr.Markdown("Generate images using Qwen-Image model")
+    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.Row():
             with gr.Column(scale=1):
@@ -115,7 +115,6 @@ 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,
                     )
@@ -173,7 +172,7 @@ def create_demo(server_url: str):
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Qwen-Image Gradio Demo")
+    parser = argparse.ArgumentParser(description="Shared text-to-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
 """
-Qwen-Image OpenAI-compatible image generation client.
+Shared OpenAI-compatible text-to-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: Qwen-Image CFG scale
+        true_cfg_scale: True CFG scale for models that support it
         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:
+    if negative_prompt is not None:
         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="Qwen-Image chat client")
+    parser = argparse.ArgumentParser(description="Shared text-to-image client")
     parser.add_argument("--prompt", "-p", default="a cup of coffee on the table", help="Text prompt")
-    parser.add_argument("--output", "-o", default="qwen_image_output.png", help="Output file")
+    parser.add_argument("--output", "-o", default="text_to_image_output.png", help="Output file")
     parser.add_argument("--server", "-s", default="http://localhost:8091", help="Server URL")
-    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("--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("--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
-# Qwen-Image online serving startup script
+# Shared text-to-image online serving startup script
 
 MODEL="${MODEL:-Qwen/Qwen-Image}"
 PORT="${PORT:-8091}"
 
-echo "Starting Qwen-Image server..."
+echo "Starting text-to-image server..."
 echo "Model: $MODEL"
 echo "Port: $PORT"
 

tests/examples/offline_inference/test_image_task_prompts.py

@@ -0,0 +1,47 @@
+# 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

@@ -3,6 +3,8 @@
 
 from __future__ import annotations
 
+from typing import Any
+
 import pytest
 from PIL import Image
 
@@ -135,67 +137,6 @@ 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"])
@@ -342,8 +283,11 @@ 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.",
-        negative_prompt="ugly, blurry",
+        {
+            "prompt": "Please draw a cute cat.",
+            "modalities": ["image"],
+            "negative_prompt": "ugly, blurry",
+        },
         height=512,
         width=768,
     ) == {
@@ -360,8 +304,7 @@ 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.",
-        negative_prompt=None,
+        {"prompt": "Draw a poster.", "modalities": ["image"]},
     ) == {
         "prompt": "Draw a poster.",
         "modalities": ["image"],
@@ -391,28 +334,6 @@ 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:
@@ -505,8 +426,11 @@ 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",
-        negative_prompt="blurry",
+        {
+            "prompt": "a cat",
+            "modalities": ["image"],
+            "negative_prompt": "blurry",
+        },
         height=512,
         width=768,
     ) == {
@@ -543,14 +467,17 @@ def test_bagel_image_to_image_prompt_builder() -> None:
 
 @pytest.mark.core_model
 @pytest.mark.cpu
-def test_unknown_pipeline_uses_default_text_to_image_prompt() -> None:
-    assert build_text_to_image_prompt(
+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",
-        prompt="a cat",
-        negative_prompt=None,
+            canonical_prompt,
             height=512,
             width=512,
-    ) == {"prompt": "a cat"}
+        )
+        is canonical_prompt
+    )
 
 
 @pytest.mark.core_model
@@ -569,12 +496,14 @@ def test_unknown_pipeline_uses_default_image_to_image_prompt() -> None:
     }
 
 
-def _build_vace_prompt(media_inputs: dict[str, object], *, num_frames: int = 5) -> dict:
+def _build_vace_prompt(media_inputs: dict[str, Any], *, num_frames: int = 5) -> dict:
     return build_image_to_video_prompt(
         "WanVACEPipeline",
-        prompt="a bird flying",
-        negative_prompt=None,
-        media_inputs=media_inputs,
+        {
+            "prompt": "a bird flying",
+            "modalities": ["video"],
+            "multi_modal_data": media_inputs,
+        },
         height=16,
         width=320,
         num_frames=num_frames,
@@ -597,7 +526,7 @@ def _build_vace_prompt(media_inputs: dict[str, object], *, num_frames: int = 5)
     ],
     ids=["i2v", "v2lf", "flf2v", "inpaint", "r2v"],
 )
-def test_vace_image_to_video_prompt_builder(media_inputs: dict[str, object]) -> None:
+def test_vace_image_to_video_prompt_builder(media_inputs: dict[str, Any]) -> None:
     result = _build_vace_prompt(media_inputs)
     mmd = result["multi_modal_data"]
     if "reference_images" in mmd:
@@ -616,7 +545,7 @@ def test_vace_image_to_video_prompt_builder(media_inputs: dict[str, object]) ->
         ({"control_image": Image.new("RGB", (320, 16))}, "Unsupported VACE media input"),
     ],
 )
-def test_vace_rejects_invalid_media_combinations(media_inputs: dict[str, object], message: str) -> None:
+def test_vace_rejects_invalid_media_combinations(media_inputs: dict[str, Any], message: str) -> None:
     with pytest.raises(ValueError, match=message):
         _build_vace_prompt(media_inputs)
 
@@ -664,8 +593,7 @@ def test_mammothmoda2_extra_registry_declares_request_and_response_params() -> N
 
     wrapper_prompt = build_text_to_image_prompt(
         "MammothModa2ForConditionalGeneration",
-        prompt="a cat",
-        negative_prompt=None,
+        {"prompt": "a cat", "modalities": ["image"]},
         height=256,
         width=256,
     )
@@ -679,8 +607,11 @@ 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",
-        negative_prompt="blurry",
+        {
+            "prompt": "a cat",
+            "modalities": ["image"],
+            "negative_prompt": "blurry",
+        },
         height=512,
         width=768,
     ) == {

vllm_omni/model_extras/cosmos3.py

@@ -3,32 +3,6 @@
 
 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

@@ -3,11 +3,6 @@
 
 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",
@@ -24,43 +19,3 @@ 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

@@ -28,21 +28,12 @@ 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,
@@ -147,18 +138,6 @@ 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,
@@ -175,20 +154,6 @@ 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,
@@ -208,12 +173,11 @@ _EXTRA_SPECS: dict[str, dict[str, Any]] = {
     "Cosmos3OmniDiffusersPipeline": {
         "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,
+        # The shared T2I example already supplies modalities=["image"].
     },
     "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,
@@ -229,8 +193,8 @@ _EXTRA_SPECS: dict[str, dict[str, Any]] = {
     },
     "LingBotVideoPipeline": {
         "extra_body_params": LINGBOT_VIDEO_EXTRA_BODY_PARAMS,
-        "text_to_image_prompt_builder": build_lingbot_text_to_image_prompt,
-        "image_to_video_prompt_builder": build_lingbot_image_to_video_prompt,
+        # Shared T2I/I2V envelopes select the output modality. LingBot's
+        # pipeline owns model-specific validation and normalization.
     },
     **{
         model_class_name: {
@@ -263,7 +227,7 @@ _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 adapters.
+# submodule, so both names must resolve to the same request builders.
 _EXTRA_SPECS["MammothModa2ForConditionalGeneration"] = _EXTRA_SPECS["MammothModa2DiTPipeline"]
 _EXTRA_SPECS["Mammothmoda2Model"] = _EXTRA_SPECS["MammothModa2DiTPipeline"]
 
@@ -305,18 +269,21 @@ 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: str,
-    negative_prompt: str | None,
+    prompt: dict[str, Any],
     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 = (
-        spec.get("text_to_image_prompt_builder", default_text_to_image_prompt)
-        if spec is not None
-        else default_text_to_image_prompt
+    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,
     )
-    return builder(prompt, negative_prompt, height, width)
 
 
 def build_image_to_image_prompt(
@@ -338,17 +305,24 @@ def build_image_to_image_prompt(
 
 def build_image_to_video_prompt(
     model_class_name: str | None,
-    prompt: str,
-    negative_prompt: str | None,
-    media_inputs: Mapping[str, Any],
+    prompt: dict[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 = (
-        spec.get("image_to_video_prompt_builder", default_image_to_video_prompt)
-        if spec is not None
-        else default_image_to_video_prompt
+    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,
     )
-    return builder(prompt, negative_prompt, media_inputs, height, width, num_frames)