[Misc] Apply shared prompt builder review feedback

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 (
-    adapt_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,
 )
@@ -542,7 +544,7 @@ def main():
         negative_prompt=negative_prompt,
         media_inputs=media_inputs,
     )
-    prompt_dict = adapt_image_to_video_prompt(
+    prompt_dict = build_model_image_to_video_prompt(
         model_class_name=model_class_name,
         prompt=prompt_dict,
         height=height,

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 (
-    adapt_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,
@@ -519,7 +521,7 @@ def main():
         prompt=args.prompt,
         negative_prompt=args.negative_prompt,
     )
-    prompt_dict = adapt_text_to_image_prompt(
+    prompt_dict = build_model_text_to_image_prompt(
         model_class_name=model_class_name,
         prompt=prompt_dict,
         height=args.height,

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 shared 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 client sends the canonical Videos API
+request; the server side owns model-specific prompt processing.
 
 ## 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_curl_image_to_video.sh

@@ -1,14 +1,24 @@
 #!/bin/bash
-# Wan2.2 image-to-video curl example using the async video job API.
+# Shared image-to-video curl example using the async video job API.
 
 set -euo pipefail
 
 INPUT_IMAGE="${INPUT_IMAGE:-../../offline_inference/image_to_video/qwen-bear.png}"
 BASE_URL="${BASE_URL:-http://localhost:8099}"
-OUTPUT_PATH="${OUTPUT_PATH:-wan22_i2v_output.mp4}"
+OUTPUT_PATH="${OUTPUT_PATH:-image_to_video_output.mp4}"
+PROMPT="${PROMPT:-A bear playing with yarn, smooth motion}"
 NEGATIVE_PROMPT="${NEGATIVE_PROMPT:-}"
 SAMPLE_SOLVER="${SAMPLE_SOLVER:-}"
 POLL_INTERVAL="${POLL_INTERVAL:-2}"
+VIDEO_SECONDS="${VIDEO_SECONDS:-}"
+SIZE="${SIZE:-}"
+FPS="${FPS:-}"
+NUM_INFERENCE_STEPS="${NUM_INFERENCE_STEPS:-}"
+GUIDANCE_SCALE="${GUIDANCE_SCALE:-}"
+GUIDANCE_SCALE_2="${GUIDANCE_SCALE_2:-}"
+BOUNDARY_RATIO="${BOUNDARY_RATIO:-}"
+FLOW_SHIFT="${FLOW_SHIFT:-}"
+SEED="${SEED:-}"
 
 if [ ! -f "$INPUT_IMAGE" ]; then
     echo "Input image not found: $INPUT_IMAGE"
@@ -18,19 +28,28 @@ fi
 create_cmd=(
   curl -sS -X POST "${BASE_URL}/v1/videos"
   -H "Accept: application/json"
-  -F "prompt=A bear playing with yarn, smooth motion"
+  -F "prompt=${PROMPT}"
   -F "input_reference=@${INPUT_IMAGE}"
-  -F "seconds=2"
-  -F "size=832x480"
-  -F "fps=16"
-  -F "num_inference_steps=40"
-  -F "guidance_scale=1.0"
-  -F "guidance_scale_2=1.0"
-  -F "boundary_ratio=0.875"
-  -F "flow_shift=12.0"
-  -F "seed=42"
 )
 
+append_optional_field() {
+  local name="$1"
+  local value="$2"
+  if [ -n "${value}" ]; then
+    create_cmd+=(-F "${name}=${value}")
+  fi
+}
+
+append_optional_field "seconds" "${VIDEO_SECONDS}"
+append_optional_field "size" "${SIZE}"
+append_optional_field "fps" "${FPS}"
+append_optional_field "num_inference_steps" "${NUM_INFERENCE_STEPS}"
+append_optional_field "guidance_scale" "${GUIDANCE_SCALE}"
+append_optional_field "guidance_scale_2" "${GUIDANCE_SCALE_2}"
+append_optional_field "boundary_ratio" "${BOUNDARY_RATIO}"
+append_optional_field "flow_shift" "${FLOW_SHIFT}"
+append_optional_field "seed" "${SEED}"
+
 if [ -n "${NEGATIVE_PROMPT}" ]; then
   create_cmd+=(-F "negative_prompt=${NEGATIVE_PROMPT}")
 fi

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 shared example demonstrates online text-to-image generation with vLLM-Omni.
+The startup script defaults to Qwen-Image, but `MODEL` can select any supported
+text-to-image model. Clients send the canonical Images API request; the server
+side owns model-specific prompt processing.
 
 ## 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_curl_text_to_image.sh

@@ -1,10 +1,21 @@
 #!/bin/bash
-# Qwen-Image text-to-image curl example
+# Shared text-to-image curl example
 
-curl -X POST http://localhost:8091/v1/images/generations \
+set -euo pipefail
+
+BASE_URL="${BASE_URL:-http://localhost:8091}"
+PROMPT="${PROMPT:-a dragon laying over the spine of the Green Mountains of Vermont}"
+SIZE="${SIZE:-}"
+SEED="${SEED:-}"
+OUTPUT_PATH="${OUTPUT_PATH:-text_to_image_output.png}"
+
+curl -sS -X POST "${BASE_URL}/v1/images/generations" \
   -H "Content-Type: application/json" \
-  -d '{
-    "prompt": "a dragon laying over the spine of the Green Mountains of Vermont",
-    "size": "1024x1024",
-    "seed": 42
-  }' | jq -r '.data[0].b64_json' | base64 -d > dragon.png
+  -d "$(jq -n --arg prompt "${PROMPT}" --arg size "${SIZE}" --arg seed "${SEED}" \
+    '{prompt: $prompt}
+     + (if $size == "" then {} else {size: $size} end)
+     + (if $seed == "" then {} else {seed: ($seed | tonumber)} end)')" \
+  | jq -r '.data[0].b64_json' \
+  | base64 -d > "${OUTPUT_PATH}"
+
+echo "Saved image to ${OUTPUT_PATH}"

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/entrypoints/openai_api/test_video_server.py

@@ -433,7 +433,7 @@ def test_i2v_video_generation_form(test_client, mocker: MockerFixture):
     assert input_image.size == (48, 32)
 
 
-def test_i2v_online_applies_model_specific_prompt_adapter() -> None:
+def test_i2v_online_applies_model_specific_prompt_builder() -> None:
     engine = FakeAsyncOmni()
     engine.model_class_name = "WanVACEPipeline"
     handler = OmniOpenAIServingVideo.for_diffusion(
@@ -451,7 +451,7 @@ def test_i2v_online_applies_model_specific_prompt_adapter() -> None:
                 height=16,
                 num_frames=5,
             ),
-            "vace-online-adapter",
+            "vace-online-builder",
             reference_image=ReferenceImage(image),
         )
     )

tests/model_extras/test_model_extras.py

@@ -11,9 +11,9 @@ from PIL import Image
 from vllm_omni.diffusion.utils.param_utils import apply_declared_extra_args
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.model_extras import (
-    adapt_image_to_video_prompt,
-    adapt_text_to_image_prompt,
     build_image_to_image_prompt,
+    build_image_to_video_prompt,
+    build_text_to_image_prompt,
     build_x_to_text_prompt,
     get_extra_body_params,
     get_extra_output_params,
@@ -281,7 +281,7 @@ def test_ming_flash_omni_declared_extra_args_route_into_sampling_params() -> Non
 @pytest.mark.core_model
 @pytest.mark.cpu
 def test_ming_flash_omni_text_to_image_prompt_builder() -> None:
-    assert adapt_text_to_image_prompt(
+    assert build_text_to_image_prompt(
         "MingImagePipeline",
         {
             "prompt": "Please draw a cute cat.",
@@ -302,7 +302,7 @@ def test_ming_flash_omni_text_to_image_prompt_builder() -> None:
     }
     # target_h/w are omitted when height/width are not supplied so the
     # pipeline's 1024x1024 default still applies.
-    assert adapt_text_to_image_prompt(
+    assert build_text_to_image_prompt(
         "MingImagePipeline",
         {"prompt": "Draw a poster.", "modalities": ["image"]},
     ) == {
@@ -424,7 +424,7 @@ def test_unknown_pipeline_has_empty_extra_registry() -> None:
 @pytest.mark.core_model
 @pytest.mark.cpu
 def test_bagel_text_to_image_prompt_builder() -> None:
-    assert adapt_text_to_image_prompt(
+    assert build_text_to_image_prompt(
         "BagelPipeline",
         {
             "prompt": "a cat",
@@ -470,7 +470,7 @@ def test_bagel_image_to_image_prompt_builder() -> None:
 def test_unknown_pipeline_preserves_canonical_text_to_image_prompt() -> None:
     canonical_prompt = {"prompt": "a cat", "modalities": ["image"]}
     assert (
-        adapt_text_to_image_prompt(
+        build_text_to_image_prompt(
             "UnknownPipeline",
             canonical_prompt,
             height=512,
@@ -497,7 +497,7 @@ 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:
-    return adapt_image_to_video_prompt(
+    return build_image_to_video_prompt(
         "WanVACEPipeline",
         {
             "prompt": "a bird flying",
@@ -591,7 +591,7 @@ def test_mammothmoda2_extra_registry_declares_request_and_response_params() -> N
         assert get_extra_output_params(model_class_name) == frozenset()
         assert should_init_extra_args_for_non_diffusion_stages(model_class_name) is True
 
-    wrapper_prompt = adapt_text_to_image_prompt(
+    wrapper_prompt = build_text_to_image_prompt(
         "MammothModa2ForConditionalGeneration",
         {"prompt": "a cat", "modalities": ["image"]},
         height=256,
@@ -605,7 +605,7 @@ def test_mammothmoda2_extra_registry_declares_request_and_response_params() -> N
 def test_mammothmoda2_text_to_image_prompt_builder() -> None:
     # Image dims are converted to the AR grid (width/16 x height/16); the negative
     # prompt is ignored (MammothModa2 t2i uses CFG, not an explicit negative path).
-    assert adapt_text_to_image_prompt(
+    assert build_text_to_image_prompt(
         "MammothModa2DiTPipeline",
         {
             "prompt": "a cat",

vllm_omni/entrypoints/openai/serving_video.py

@@ -30,7 +30,7 @@ from vllm_omni.entrypoints.openai.stage_params import (
 from vllm_omni.entrypoints.openai.utils import get_stage_type, parse_lora_request
 from vllm_omni.entrypoints.openai.video_api_utils import _encode_video_bytes, encode_video_base64
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams, OmniTextPrompt
-from vllm_omni.model_extras import adapt_image_to_video_prompt
+from vllm_omni.model_extras import build_image_to_video_prompt
 from vllm_omni.outputs.output_metadata import (
     DiffusionMetadataMapping,
     DiffusionMultimodalOutput,
@@ -259,7 +259,7 @@ class OmniOpenAIServingVideo:
         model_class_name = getattr(self._get_diffusion_od_config(), "model_class_name", None)
         prompt = cast(
             OmniTextPrompt,
-            adapt_image_to_video_prompt(
+            build_image_to_video_prompt(
                 model_class_name=model_class_name,
                 prompt=prompt,
                 height=gen_params.height,

vllm_omni/model_extras/__init__.py

@@ -2,9 +2,9 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from vllm_omni.model_extras.registry import (
-    adapt_image_to_video_prompt,
-    adapt_text_to_image_prompt,
     build_image_to_image_prompt,
+    build_image_to_video_prompt,
+    build_text_to_image_prompt,
     build_x_to_text_prompt,
     get_extra_body_params,
     get_extra_output_params,
@@ -14,9 +14,9 @@ from vllm_omni.model_extras.registry import (
 )
 
 __all__ = [
-    "adapt_image_to_video_prompt",
-    "adapt_text_to_image_prompt",
     "build_image_to_image_prompt",
+    "build_image_to_video_prompt",
+    "build_text_to_image_prompt",
     "build_x_to_text_prompt",
     "get_extra_body_params",
     "get_extra_output_params",

vllm_omni/model_extras/registry.py

@@ -227,7 +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"]
 
@@ -267,13 +267,13 @@ def should_init_extra_args_for_non_diffusion_stages(model_class_name: str | None
     return bool(spec and spec.get("init_extra_args_for_non_diffusion_stages", False))
 
 
-def adapt_text_to_image_prompt(
+def build_text_to_image_prompt(
     model_class_name: str | None,
     prompt: dict[str, Any],
     height: int | None = None,
     width: int | None = None,
 ) -> dict[str, Any]:
-    """Apply only model-specific changes to an example-owned T2I prompt."""
+    """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:
@@ -303,14 +303,14 @@ def build_image_to_image_prompt(
     return builder(prompt, negative_prompt, input_image, height, width)
 
 
-def adapt_image_to_video_prompt(
+def build_image_to_video_prompt(
     model_class_name: str | None,
     prompt: dict[str, Any],
     height: int | None = None,
     width: int | None = None,
     num_frames: int | None = None,
 ) -> dict[str, Any]:
-    """Apply only model-specific changes to an example-owned I2V prompt."""
+    """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: