[Refactor] Migrate Ming-flash-omni-2.0 Image-gen examples with model_extras (#4835)

Signed-off-by: Yuanheng <jonathan.zhaoyh@gmail.com> Signed-off-by: Yuanheng Zhao <jonathan.zhaoyh@gmail.com>
2026-07-07 08:09:12 +00:00
1 parent d4a869fe5e
commit d365d3a69e
@@ -138,6 +138,15 @@ def parse_args() -> argparse.Namespace:
        required=True,
        help="Path(s) to input image file(s) (PNG, JPG, etc.). Can specify multiple images.",
    )
+    parser.add_argument(
+        "--deploy-config",
+        type=str,
+        default=None,
+        help=(
+            "Path to a deploy YAML. Required for multi-stage image-edit pipelines "
+            "whose deploy config is not auto-loaded."
+        ),
+    )
    parser.add_argument(
        "--prompt",
        type=str,
@@ -349,7 +358,12 @@ def parse_args() -> argparse.Namespace:
    parser.add_argument(
        "--enforce-eager",
        action="store_true",
-        help="Disable torch.compile and force eager execution.",
+        default=None,
+        help=(
+            "Disable torch.compile and force eager execution. Left unset (None) "
+            "so it is only forwarded when explicitly given; "
+            "otherwise the per-stage deploy YAML value wins."
+        ),
    )
    parser.add_argument(
        "--vae-use-slicing",
@@ -468,7 +482,7 @@ def main():
        }

    # Initialize Omni with appropriate pipeline
-    omni = Omni(
+    omni_kwargs: dict[str, Any] = dict(
        model=args.model,
        enable_layerwise_offload=args.enable_layerwise_offload,
        vae_use_slicing=args.vae_use_slicing,
@@ -476,11 +490,15 @@ def main():
        cache_backend=args.cache_backend,
        cache_config=cache_config,
        parallel_config=parallel_config,
-        enforce_eager=args.enforce_eager,
        enable_cpu_offload=args.enable_cpu_offload,
        enable_diffusion_pipeline_profiler=args.enable_diffusion_pipeline_profiler,
        profiler_config=args.profiler_config,
    )
+    if args.enforce_eager is not None:
+        omni_kwargs["enforce_eager"] = args.enforce_eager
+    if args.deploy_config:
+        omni_kwargs["deploy_config"] = args.deploy_config
+    omni = Omni(**omni_kwargs)
    model_class_name = get_model_class_name(omni)
    declared_extra_body_params = get_extra_body_params(model_class_name)
    print("Pipeline loaded")
@@ -90,14 +90,40 @@ Pass `--deploy-config /path/to/your_deploy.yaml` to any of the commands above to

 ### Image generation (text-to-image / img2img)

-The diffusion-stage image-generation path is currently only wired through
-the online OpenAI-compatible chat endpoint (`/v1/chat/completions` with
-`"modalities": ["image"]`). For payloads, optional knobs
-(`steps`/`cfg`/`seed`/`byte5_text`/`negative_prompt`), and the img2img
-reference-image flow, see the
-[image-gen section in the online-serving README](../../online_serving/ming_flash_omni/README.md#image-generation-text-to-image).
-`end2end.py` does not yet exercise the imagegen stage; that is tracked as
-follow-up work.
+Image generation is served through the standard task examples.
+The diffusion-stage knobs are declared centrally in `vllm_omni/model_extras/ming_flash_omni.py` and routed via `--extra-body`. With deploy yaml assigned properly, the model can run through the shared example scripts like any other diffusion model:
+
+Text-to-image (offline):
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+    --model Jonathan1909/Ming-flash-omni-2.0 \
+    --deploy-config vllm_omni/deploy/ming_flash_omni_image.yaml \
+    --prompt "Please draw a cute cat." \
+    --height 1024 \
+    --width 1024 \
+    --extra-body '{"steps": 30, "cfg": 2.0, "seed": 42}' \
+    --output ming_flash_omni_t2i.png
+```
+
+Image-to-image (offline):
+```bash
+# Reference image: figures/cases/person_gen_05.png from the upstream Ming repo
+# https://github.com/inclusionAI/Ming/blob/3954fcb880ff5e61ff128bcf7f1ec344d46a6fe3/examples/vllm_demo.py
+wget https://raw.githubusercontent.com/inclusionAI/Ming/3954fcb880ff5e61ff128bcf7f1ec344d46a6fe3/figures/cases/person_gen_05.png
+
+python examples/offline_inference/image_to_image/image_edit.py \
+    --model Jonathan1909/Ming-flash-omni-2.0 \
+    --deploy-config vllm_omni/deploy/ming_flash_omni_image.yaml \
+    --image person_gen_05.png \
+    --prompt "Put a pair of sunglasses on the person." \
+    --extra-args '{"steps": 30, "cfg": 2.0, "seed": 42}' \
+    --output ming_flash_omni_i2i.png
+```
+
+For the online path and the full knob list
+(`steps`/`cfg`/`seed`/`byte5_text`/`negative_prompt`/`height`/`width`), see the
+[image-generation section in the recipe](../../../recipes/inclusionAI/Ming-flash-omni-2.0.md#image-generation-text-to-image--img2img).

 ## Online serving

@@ -69,7 +69,16 @@ def parse_args() -> argparse.Namespace:
        "--stage-configs-path",
        type=str,
        default=None,
-        help="Path to a YAML file containing stage configurations for Omni.",
+        help="[Deprecated] Path to a legacy stage_args-format YAML. Prefer --deploy-config.",
+    )
+    parser.add_argument(
+        "--deploy-config",
+        type=str,
+        default=None,
+        help=(
+            "Path to a deploy YAML (new pipeline/stages format). Required for multi-stage "
+            "text-to-image pipelines whose deploy config is not auto-loaded."
+        ),
    )
    parser.add_argument("--prompt", default="a cup of coffee on the table", help="Text prompt for image generation.")
    parser.add_argument(
@@ -155,7 +164,12 @@ def parse_args() -> argparse.Namespace:
    parser.add_argument(
        "--enforce-eager",
        action="store_true",
-        help="Disable torch.compile and force eager execution.",
+        default=None,
+        help=(
+            "Disable torch.compile and force eager execution. Left unset (None) "
+            "so it is only forwarded when explicitly given; "
+            "otherwise the per-stage deploy YAML value wins."
+        ),
    )
    parser.add_argument(
        "--enable-cpu-offload",
@@ -221,8 +235,13 @@ def parse_args() -> argparse.Namespace:
    parser.add_argument(
        "--tensor-parallel-size",
        type=int,
-        default=1,
-        help="Number of GPUs used for tensor parallelism (TP) inside the DiT.",
+        default=None,
+        help=(
+            "Number of GPUs used for tensor parallelism (TP) inside the DiT. "
+            "Left unset so it is only forwarded when explicitly given; "
+            "otherwise the per-stage deploy YAML (or engine default of 1) wins. "
+            "Passing it always overrides the deploy YAML."
+        ),
    )
    parser.add_argument(
        "--enable-expert-parallel",
@@ -410,10 +429,8 @@ def main():
        "ring_degree": args.ring_degree,
        "ulysses_mode": args.ulysses_mode,
        "cfg_parallel_size": args.cfg_parallel_size,
-        "tensor_parallel_size": args.tensor_parallel_size,
        "vae_patch_parallel_size": args.vae_patch_parallel_size,
        "enable_expert_parallel": args.enable_expert_parallel,
-        "enforce_eager": args.enforce_eager,
        "enable_cpu_offload": args.enable_cpu_offload,
        "mode": "text-to-image",
        "log_stats": args.log_stats,
@@ -425,8 +442,14 @@ def main():
        **lora_args,
        **quant_kwargs,
    }
+    if args.tensor_parallel_size is not None:
+        omni_kwargs["tensor_parallel_size"] = args.tensor_parallel_size
+    if args.enforce_eager is not None:
+        omni_kwargs["enforce_eager"] = args.enforce_eager
    if args.stage_configs_path:
        omni_kwargs["stage_configs_path"] = args.stage_configs_path
+    if args.deploy_config:
+        omni_kwargs["deploy_config"] = args.deploy_config
    if use_nextstep:
        # NextStep-1.1 requires explicit pipeline class
        omni_kwargs["model_class_name"] = "NextStep11Pipeline"
@@ -451,8 +474,9 @@ def main():
    print(f"  Quantization: {args.quantization if args.quantization else 'None (BF16)'}")
    if ignored_layers:
        print(f"  Ignored layers: {ignored_layers}")
+    tp_display = args.tensor_parallel_size if args.tensor_parallel_size is not None else "deploy/default"
    print(
-        f"  Parallel configuration: tensor_parallel_size={args.tensor_parallel_size}, "
+        f"  Parallel configuration: tensor_parallel_size={tp_display}, "
        f"ulysses_degree={args.ulysses_degree}, ulysses_mode={args.ulysses_mode}, "
        f"ring_degree={args.ring_degree}, cfg_parallel_size={args.cfg_parallel_size}, "
        f"vae_patch_parallel_size={args.vae_patch_parallel_size}, "
@@ -49,22 +49,14 @@ python examples/online_serving/openai_chat_completion_client_for_multimodal_gene
    --modalities text
 ```

-Parameterized curl wrapper in this directory:

-```bash
-bash run_curl_multimodal_generation.sh text
-bash run_curl_multimodal_generation.sh use_image
-bash run_curl_multimodal_generation.sh use_audio
-bash run_curl_multimodal_generation.sh use_video
-bash run_curl_multimodal_generation.sh use_mixed_modalities
-bash run_curl_multimodal_generation.sh use_image_gen
-```
+## Image generation (text-to-image / img2img)

-## Image generation (text-to-image)
+Ming-flash-omni-2.0 also exposes an image-generation (diffusion) stage. Launch with the image deploy YAML, which adds an image-gen stage behind the thinker.

-Ming-flash-omni-2.0 also exposes an image-generation (diffusion) stage.
-Launch with the image deploy YAML, which adds an image-gen stage behind
-the thinker:
+The image-generation stage is a standard vLLM-Omni diffusion pipeline (`MingImagePipeline`); its request knobs are declared in `vllm_omni/model_extras/ming_flash_omni.py` and routed through `extra_body`, so they no longer need a bespoke `sampling_params_list` recipe (that form is still available for per-stage thinker sampling — see below).
+
+### Launch

 ```bash
 vllm serve Jonathan1909/Ming-flash-omni-2.0 --omni \
@@ -74,32 +66,58 @@ vllm serve Jonathan1909/Ming-flash-omni-2.0 --omni \
    --port 8091
 ```

-Then request image output by passing `"modalities": ["image"]`:
+
+### Text-to-image
+
+Request image output with `"modalities": ["image"]`:

 ```bash
 curl -s http://127.0.0.1:8091/v1/chat/completions \
-    -H "Content-Type: application/json" \
-    -d '{
-      "model": "Jonathan1909/Ming-flash-omni-2.0",
-      "messages": [{"role": "user", "content": "Please draw a cute cat."}],
-      "modalities": ["image"]
-    }' | jq -r '.choices[0].message.content[0].image_url.url | split(",")[1]' | base64 -d > ming_imagegen.png
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Jonathan1909/Ming-flash-omni-2.0",
+    "messages": [
+      {
+        "role": "user",
+        "content": "Please draw a cute cat."
+      }
+    ],
+    "modalities": ["image"]
+  }' \
+  | jq -r '.choices[0].message.content[0].image_url.url | split(",")[1]' \
+  | base64 -d > ming_imagegen.png
 ```

-### Optional knobs
+Pass generation knobs under a literal `extra_body` object (the OpenAI Python client's `extra_body=` kwarg produces the same request). Keys are filtered against the declared set and routed into every stage's `extra_args`:

-Pass image-gen overrides as flat keys on the diffusion-stage `sampling_params_list[1].extra_args`:
+```bash
+curl http://127.0.0.1:8091/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Jonathan1909/Ming-flash-omni-2.0",
+    "modalities": ["image"],
+    "messages": [
+      {
+        "role": "user",
+        "content": "Draw a poster."
+      }
+    ],
+    "extra_body": {
+      "steps": 6,
+      "cfg": 1.5,
+      "height": 512,
+      "width": 512,
+      "seed": 123,
+      "byte5_text": ["理解与生成统一"],
+      "negative_prompt": "ugly, blurry, distorted"
+    }
+  }' \
+  | jq -r '.choices[0].message.content[0].image_url.url | split(",")[1]' \
+  | base64 -d > ming_imagegen_extra_body.png
+```

-| Key | Default | Description |
-| --- | --- | --- |
-| `height` / `width` | from config (1024) | Output resolution (multiples of `vae_scale_factor * 2`, currently 16). |
-| `steps` | 30 | Number of FlowMatchEuler denoise steps. |
-| `cfg` | 2.0 | Classifier-free guidance scale. |
-| `seed` | 42 | Per-request RNG seed (deterministic when ByT5 is also seed-stable). |
-| `byte5_text` | (auto) | Override the glyph text for ByT5 enhancement; raw strings are auto-wrapped to Ming's `Text "...". ` format. |
-| `negative_prompt` | empty | Real CFG negative conditioning (set on **stage-0 thinker** `extra_args` so `expand_cfg_prompts` spawns the companion). |
-
-Example with all the knobs:
+NOTE: `extra_body` does **not** set the thinker's own sampling params. To tune the thinker (stage-0) sampling (`temperature` / `top_p` / `top_k` / `max_tokens`) or place knobs explicitly per stage, use `sampling_params_list` (`[thinker, imagegen]`).
+`negative_prompt` must sit on the **stage-0** entry to trigger the real-CFG companion; the imagegen knobs go on **stage-1**:

 ```bash
 curl http://127.0.0.1:8091/v1/chat/completions \
@@ -143,27 +161,48 @@ curl http://127.0.0.1:8091/v1/chat/completions \

 ### img2img (reference image)

-Add an `image_url` content part to the user message; the parser routes it
-into the diffusion stage as `extra[reference_image]`:
+Add an `image_url` content part (a base64 data URL) to the user message; it is routed into the DiT stage as `extra[reference_image]`. base64-encode a local file and stream it through `jq` via stdin - piping `base64 -> jq -> curl` so that avoids the shell `ARG_MAX` limit that inlining a large base64 string in `-d '…'` hits.

-```jsonc
-"messages": [{
-  "role": "user",
-  "content": [
-    {"type": "text", "text": "Change the background to a sandy beach at sunset."},
-    {"type": "image_url", "image_url": {"url": "data:image/png;base64,<base64>"}}
-  ]
-}]
+```bash
+# Reference image: figures/cases/person_gen_05.png from the upstream Ming repo
+# Check https://github.com/inclusionAI/Ming/blob/3954fcb880ff5e61ff128bcf7f1ec344d46a6fe3/examples/vllm_demo.py
+wget https://raw.githubusercontent.com/inclusionAI/Ming/3954fcb880ff5e61ff128bcf7f1ec344d46a6fe3/figures/cases/person_gen_05.png
+
+base64 -w0 person_gen_05.png \
+| jq -Rs --arg prompt "Put a pair of sunglasses on the person." '{
+    model: "Jonathan1909/Ming-flash-omni-2.0",
+    modalities: ["image"],
+    messages: [
+      {
+        role: "user",
+        content: [
+          { type: "text", text: $prompt },
+          { type: "image_url", image_url: { url: ("data:image/png;base64," + (. | rtrimstr("\n"))) } }
+        ]
+      }
+    ]
+  }' \
+| curl http://127.0.0.1:8091/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d @- \
+| jq -r '.choices[0].message.content[0].image_url.url | split(",")[1]' \
+| base64 -d > ming_img2img.png
 ```

-### GPU layout
+The reference image can also be a public URL (`"url": "https://…/photo.jpg"`) or the simplified `{"image": "<base64>"}` content-part form — see the [image-to-image request formats](../image_to_image/README.md#request-format).

-The shipped `ming_flash_omni_image.yaml` allocates the thinker on GPUs 0–3
-(TP=4) and the diffusion stage on GPU 4 (TP=1). Copy the YAML and edit
-`devices` per stage to relocate; with fewer GPUs available, drop
-the thinker TP to 2 and run the diffusion stage on a free card. Image-gen
-warmup takes roughly an extra 30–60 s on top of the thinker — set
-`--stage-init-timeout 1800` if the default 300 s is too tight.
+### Knobs (declared `extra_body` params)
+
+| Key | Default | Description |
+| --- | --- | --- |
+| `height` / `width` | 1024 | Output resolution (multiples of `vae_scale_factor * 2`, currently 16). |
+| `steps` | 30 | Number of FlowMatchEuler denoise steps. |
+| `cfg` | 2.0 | Classifier-free guidance scale. |
+| `seed` | 42 | Per-request RNG seed. |
+| `byte5_text` | (auto) | Glyph text for ByT5 enhancement; raw strings are auto-wrapped to Ming's `Text "…". ` format. Auto-extracted from quoted spans in the prompt when omitted. |
+| `negative_prompt` | (empty) | Real CFG negative conditioning (text-to-image only). |
+
+For the offline `text_to_image.py` / `image_edit.py` scripts and the full knob reference, see the [image-generation section in the recipe](../../../recipes/inclusionAI/Ming-flash-omni-2.0.md#image-generation-text-to-image--img2img).

 ## Modality control

@@ -1,169 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-# Server port
-PORT="${PORT:-8091}"
-# Default query type
-QUERY_TYPE="${1:-text}"
-
-# Validate query type
-if [[ ! "$QUERY_TYPE" =~ ^(text|use_audio|use_image|use_video|use_mixed_modalities|use_image_gen)$ ]]; then
-    echo "Error: Invalid query type '$QUERY_TYPE'"
-    echo "Usage: $0 [text|use_audio|use_image|use_video|use_mixed_modalities|use_image_gen]"
-    echo "  text: Text-only query"
-    echo "  use_audio: Audio + Text query"
-    echo "  use_image: Image + Text query"
-    echo "  use_video: Video + Text query"
-    echo "  use_mixed_modalities: Audio + Image + Video + Text query"
-    echo "  use_image_gen: Text-to-image (diffusion stage) — saves the generated PNG locally"
-    exit 1
-fi
-
-# Define URLs for assets
-MARY_HAD_LAMB_AUDIO_URL="https://vllm-public-assets.s3.us-west-2.amazonaws.com/multimodal_asset/mary_had_lamb.ogg"
-CHERRY_BLOSSOM_IMAGE_URL="https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg"
-SAMPLE_VIDEO_URL="https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/sample_demo_1.mp4"
-
-# Build user content based on query type
-case "$QUERY_TYPE" in
-  text)
-    user_content='[
-      {
-        "type": "text",
-        "text": "请详细介绍鹦鹉的生活习性。"
-      }
-    ]'
-    ;;
-  use_image)
-    user_content='[
-        {
-          "type": "image_url",
-          "image_url": {
-            "url": "'"$CHERRY_BLOSSOM_IMAGE_URL"'"
-          }
-        },
-        {
-          "type": "text",
-          "text": "Describe this image in detail."
-        }
-      ]'
-    ;;
-  use_audio)
-    user_content='[
-        {
-          "type": "audio_url",
-          "audio_url": {
-            "url": "'"$MARY_HAD_LAMB_AUDIO_URL"'"
-          }
-        },
-        {
-          "type": "text",
-          "text": "Please recognize the language of this speech and transcribe it. Format: oral."
-        }
-      ]'
-    ;;
-  use_video)
-    user_content='[
-        {
-          "type": "video_url",
-          "video_url": {
-            "url": "'"$SAMPLE_VIDEO_URL"'"
-          }
-        },
-        {
-          "type": "text",
-          "text": "Describe what is happening in this video."
-        }
-      ]'
-    ;;
-  use_mixed_modalities)
-    user_content='[
-        {
-          "type": "image_url",
-          "image_url": {
-            "url": "'"$CHERRY_BLOSSOM_IMAGE_URL"'"
-          }
-        },
-        {
-          "type": "audio_url",
-          "audio_url": {
-            "url": "'"$MARY_HAD_LAMB_AUDIO_URL"'"
-          }
-        },
-        {
-          "type": "text",
-          "text": "Describe the image, and recognize the language of this speech and transcribe it. Format: oral"
-        }
-      ]'
-    ;;
-esac
-
-echo "Running query type: $QUERY_TYPE"
-echo ""
-
-if [[ "$QUERY_TYPE" == "use_image_gen" ]]; then
-    # Image-gen branch: dual-stage diffusion endpoint, modalities=["image"].
-    # The shipped ming_flash_omni_image.yaml runs the thinker on cards 0-3
-    # (TP=4) and the diffusion stage on card 4 (TP=1). See the image-gen
-    # section in README.md for knobs and img2img.
-    out_path="${OUT_PATH:-/tmp/ming_imagegen.png}"
-    request_body=$(cat <<'EOF'
-{
-  "model": "Jonathan1909/Ming-flash-omni-2.0",
-  "modalities": ["image"],
-  "messages": [
-    {"role": "user", "content": "Draw a beautiful girl with short black hair and red dress."}
-  ]
-}
-EOF
-)
-    output=$(curl -sS --retry 3 --retry-delay 3 --retry-connrefused \
-        -X POST "http://localhost:${PORT}/v1/chat/completions" \
-        -H "Content-Type: application/json" \
-        -d "$request_body")
-    # Server returns base64-encoded PNG either as a data URL in
-    # ``choices[0].message.content[0].image_url.url`` or as a raw string.
-    b64=$(echo "$output" | jq -r '.choices[0].message.content
-        | if type == "array"
-            then (map(select(.image_url? != null)) | .[0].image_url.url // "")
-            elif type == "string" then . else "" end')
-    if [[ -z "$b64" ]]; then
-        echo "Error: no image returned. Raw response:"
-        echo "$output" | jq '.'
-        exit 1
-    fi
-    if [[ "$b64" == data:image* ]]; then b64="${b64#*,}"; fi
-    printf '%s' "$b64" | base64 --decode > "$out_path"
-    echo "Saved generated image: $out_path ($(stat -c%s "$out_path" 2>/dev/null || stat -f%z "$out_path") bytes)"
-    exit 0
-fi
-
-request_body=$(cat <<EOF
-{
-  "model": "Jonathan1909/Ming-flash-omni-2.0",
-  "modalities": ["text"],
-  "messages": [
-    {
-      "role": "system",
-      "content": [
-        {
-          "type": "text",
-          "text": "你是一个友好的AI助手。\n\ndetailed thinking off"
-        }
-      ]
-    },
-    {
-      "role": "user",
-      "content": $user_content
-    }
-  ]
-}
-EOF
-)
-
-output=$(curl -sS --retry 3 --retry-delay 3 --retry-connrefused \
-    -X POST http://localhost:${PORT}/v1/chat/completions \
-    -H "Content-Type: application/json" \
-    -d "$request_body")
-
-echo "Output of request: $(echo "$output" | jq '.choices[0].message.content')"
@@ -18,6 +18,7 @@ Use this recipe when you want a known-good starting point for serving

 - **Thinker only** — multimodal understanding with text output.
 - **Thinker + Talker (omni-speech)** — multimodal understanding with text and spoken output.
+- **Thinker + Imagegen (DiT)** — text-to-image / img2img
 - **Talker only (TTS)** — standalone text-to-speech via the OpenAI `/v1/audio/speech` endpoint.

 ## References
@@ -160,6 +161,129 @@ deltas appear at `choices[0].delta.content`.
 - Memory usage: size depends on output modalities and multimodal input; leave
  headroom for video frames and audio caches.

+## Image generation (text-to-image / img2img)
+
+Ming-flash-omni-2.0 also exposes an image-generation (diffusion) stage. Launch with the image deploy YAML, which adds an image-gen stage behind the thinker.
+
+The image-generation stage is a standard vLLM-Omni diffusion pipeline (`MingImagePipeline`); its request knobs are declared in `vllm_omni/model_extras/ming_flash_omni.py` and routed through `extra_body`, so they no longer need a bespoke `sampling_params_list` recipe (that form is still available for per-stage thinker sampling — see below).
+
+```bash
+vllm serve Jonathan1909/Ming-flash-omni-2.0 --omni \
+    --deploy-config vllm_omni/deploy/ming_flash_omni_image.yaml \
+    --stage-init-timeout 1800 \
+    --init-timeout 1800 \
+    --port 8091
+```
+
+With fewer GPUs, copy the YAML and drop the thinker to TP=2 with the DiT on a
+free card.
+
+### Online (text-to-image)
+
+Request image output with `"modalities": ["image"]`:
+
+```bash
+curl -s http://127.0.0.1:8091/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Jonathan1909/Ming-flash-omni-2.0",
+    "messages": [
+      {
+        "role": "user",
+        "content": "Please draw a cute cat."
+      }
+    ],
+    "modalities": ["image"]
+  }' \
+  | jq -r '.choices[0].message.content[0].image_url.url | split(",")[1]' \
+  | base64 -d > ming_imagegen.png
+```
+
+**Quick form — `extra_body`** (keys are filtered against the declared set and routed into every stage's `extra_args`). Convenient, but it does **not** let you set the thinker (stage-0) sampling params. Wrap the knobs in a literal `"extra_body"` object for raw curl; the OpenAI Python client's `extra_body=` kwarg produces the same request:
+
+```bash
+curl http://127.0.0.1:8091/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Jonathan1909/Ming-flash-omni-2.0",
+    "modalities": ["image"],
+    "messages": [
+      {
+        "role": "user",
+        "content": "Draw a poster."
+      }
+    ],
+    "extra_body": {
+      "steps": 6,
+      "cfg": 1.5,
+      "height": 512,
+      "width": 512,
+      "seed": 123,
+      "byte5_text": ["理解与生成统一"],
+      "negative_prompt": "ugly, blurry, distorted"
+    }
+  }' \
+  | jq -r '.choices[0].message.content[0].image_url.url | split(",")[1]' \
+  | base64 -d > ming_imagegen_extra_body.png
+```
+
+**Full control — `sampling_params_list`** (one entry per stage: `[thinker, imagegen]`).
+Use this when you need to tune the thinker's own sampling (`temperature` / `top_p` / `top_k` / `max_tokens`), or to place knobs explicitly per stage.
+Note `negative_prompt` must sit on the **stage-0 thinker** `extra_args`; the imagegen-stage knobs (`steps` / `cfg` / `height` / `width` / `seed` / `byte5_text`) go on the **stage-1** entry:
+
+```bash
+curl http://127.0.0.1:8091/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Jonathan1909/Ming-flash-omni-2.0",
+    "modalities": ["image"],
+    "sampling_params_list": [
+      {
+        "temperature": 0.4,
+        "top_p": 0.9,
+        "top_k": 1,
+        "max_tokens": 1,
+        "seed": 42,
+        "extra_args": {
+          "negative_prompt": "ugly, blurry, distorted"
+        }
+      },
+      {
+        "seed": 42,
+        "extra_args": {
+          "steps": 6,
+          "cfg": 1.5,
+          "height": 512,
+          "width": 512,
+          "seed": 123,
+          "byte5_text": ["理解与生成统一"]
+        }
+      }
+    ],
+    "messages": [
+      {
+        "role": "user",
+        "content": "Draw a poster."
+      }
+    ]
+  }' \
+  | jq -r '.choices[0].message.content[0].image_url.url | split(",")[1]' \
+  | base64 -d > ming_imagegen_knobs.png
+```
+
+### Knobs (declared `extra_body` params)
+
+| Key | Default | Description |
+| --- | --- | --- |
+| `height` / `width` | 1024 | Output resolution (multiples of `vae_scale_factor * 2`, currently 16). |
+| `steps` | 30 | Number of FlowMatchEuler denoise steps. |
+| `cfg` | 2.0 | Classifier-free guidance scale. |
+| `seed` | 42 | Per-request RNG seed. |
+| `byte5_text` | (auto) | Glyph text for ByT5 enhancement; raw strings are auto-wrapped to Ming's `Text "…". ` format. Auto-extracted from quoted spans in the prompt when omitted. |
+| `negative_prompt` | (empty) | Real CFG negative conditioning. Spawns a CFG-text companion via `expand_cfg_prompts`; **online / text-to-image only** (offline uses Ming's default zero-negative). |
+
+For img2img, add an `image_url` content part to the user message (online) or pass `--image` (offline); the reference image is routed into the DiT stage as `extra[reference_image]`.
+
 ### 1x H100 80GB — standalone TTS (talker only)

 The bundled `ming_flash_omni_tts.yaml` runs the talker on a single GPU and exposes the OpenAI `/v1/audio/speech` endpoint.
@@ -126,6 +126,102 @@ def test_magi_human_extra_registry_declares_request_and_response_params() -> Non
    assert should_init_extra_args_for_non_diffusion_stages("MagiHumanPipeline") is False


+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_ming_flash_omni_extra_registry_declares_request_and_response_params() -> None:
+    assert get_extra_body_params("MingImagePipeline") == frozenset(
+        {
+            "height",
+            "width",
+            "steps",
+            "cfg",
+            "seed",
+            "byte5_text",
+            "negative_prompt",
+        }
+    )
+    assert get_extra_output_params("MingImagePipeline") == frozenset()
+    assert should_init_extra_args_for_non_diffusion_stages("MingImagePipeline") is True
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_ming_flash_omni_declared_extra_args_route_into_sampling_params() -> None:
+    params = OmniDiffusionSamplingParams()
+    declared = get_extra_body_params("MingImagePipeline")
+    apply_declared_extra_args(
+        params,
+        declared,
+        {
+            "steps": 6,
+            "cfg": 1.5,
+            "byte5_text": ["理解与生成统一"],
+            "negative_prompt": "ugly, blurry",
+            "unknown": "ignored",
+        },
+    )
+    assert params.extra_args == {
+        "steps": 6,
+        "cfg": 1.5,
+        "byte5_text": ["理解与生成统一"],
+        "negative_prompt": "ugly, blurry",
+    }
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+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",
+        height=512,
+        width=768,
+    ) == {
+        "prompt": "Please draw a cute cat.",
+        "modalities": ["image"],
+        "mm_processor_kwargs": {
+            "modalities": ["image"],
+            "target_h": 512,
+            "target_w": 768,
+        },
+        "negative_prompt": "ugly, blurry",
+    }
+    # target_h/w are omitted when height/width are not supplied so the
+    # 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"],
+        "mm_processor_kwargs": {"modalities": ["image"]},
+    }
+
+
+@pytest.mark.core_model
+@pytest.mark.cpu
+def test_ming_flash_omni_image_to_image_prompt_builder() -> None:
+    dummy_image = Image.new("RGB", (64, 64))
+    result = build_image_to_image_prompt(
+        "MingImagePipeline",
+        prompt="Change the background to a sandy beach at sunset.",
+        negative_prompt=None,
+        input_image=dummy_image,
+        height=256,
+        width=256,
+    )
+    assert result["modalities"] == ["img2img"]
+    assert result["multi_modal_data"]["img2img"] is dummy_image
+    assert result["mm_processor_kwargs"] == {
+        "modalities": ["img2img"],
+        "target_h": 256,
+        "target_w": 256,
+    }
+    assert "negative_prompt" not in result
+
+
@pytest.mark.core_model
@pytest.mark.cpu
 def test_cosmos3_text_to_image_prompt_builder_selects_image_modality() -> None:
@@ -0,0 +1,66 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+from typing import Any
+
+from PIL import Image
+
+
+def build_text_to_image_prompt(
+    prompt: str,
+    negative_prompt: str | None,
+    height: int | None = None,
+    width: int | None = None,
+) -> dict[str, Any]:
+    text_prompt: dict[str, Any] = {
+        "prompt": prompt,
+        "modalities": ["image"],
+        "mm_processor_kwargs": {"modalities": ["image"]},
+    }
+    if height is not None:
+        text_prompt["mm_processor_kwargs"]["target_h"] = height
+    if width is not None:
+        text_prompt["mm_processor_kwargs"]["target_w"] = width
+    if negative_prompt is not None:
+        text_prompt["negative_prompt"] = negative_prompt
+    return text_prompt
+
+
+def build_image_to_image_prompt(
+    prompt: str,
+    negative_prompt: str | None,
+    input_image: Image.Image | list[Image.Image],
+    height: int | None = None,
+    width: int | None = None,
+) -> dict[str, Any]:
+    img_prompt: dict[str, Any] = {
+        "prompt": prompt,
+        "modalities": ["img2img"],
+        "multi_modal_data": {"img2img": input_image},
+        "mm_processor_kwargs": {"modalities": ["img2img"]},
+    }
+    if height is not None:
+        img_prompt["mm_processor_kwargs"]["target_h"] = height
+    if width is not None:
+        img_prompt["mm_processor_kwargs"]["target_w"] = width
+    if negative_prompt is not None:
+        img_prompt["negative_prompt"] = negative_prompt
+    return img_prompt
+
+
+# For Image-generation (diffusion-stage)
+MING_FLASH_OMNI_EXTRA_BODY_PARAMS = frozenset(
+    {
+        "height",
+        "width",
+        "steps",
+        "cfg",
+        "seed",
+        "byte5_text",
+        "negative_prompt",
+    }
+)
+MING_FLASH_OMNI_EXTRA_OUTPUT_PARAMS = frozenset()
+MING_FLASH_OMNI_INIT_EXTRA_ARGS_FOR_NON_DIFFUSION_STAGES = True
@@ -46,6 +46,17 @@ from vllm_omni.model_extras.mammothmodal2_preview import (
 from vllm_omni.model_extras.mammothmodal2_preview import (
    build_text_to_image_prompt as build_mammothmoda2_text_to_image_prompt,
 )
+from vllm_omni.model_extras.ming_flash_omni import (
+    MING_FLASH_OMNI_EXTRA_BODY_PARAMS,
+    MING_FLASH_OMNI_EXTRA_OUTPUT_PARAMS,
+    MING_FLASH_OMNI_INIT_EXTRA_ARGS_FOR_NON_DIFFUSION_STAGES,
+)
+from vllm_omni.model_extras.ming_flash_omni import (
+    build_image_to_image_prompt as build_ming_flash_omni_image_to_image_prompt,
+)
+from vllm_omni.model_extras.ming_flash_omni import (
+    build_text_to_image_prompt as build_ming_flash_omni_text_to_image_prompt,
+)
 from vllm_omni.model_extras.sensenova_u1 import (
    SENSENOVA_U1_EXTRA_BODY_PARAMS,
    SENSENOVA_U1_EXTRA_OUTPUT_PARAMS,
@@ -165,6 +176,13 @@ _EXTRA_SPECS: dict[str, dict[str, Any]] = {
        "init_extra_args_for_non_diffusion_stages": MAMMOTHMODA2_PREVIEW_INIT_EXTRA_ARGS_FOR_NON_DIFFUSION_STAGES,
        "text_to_image_prompt_builder": build_mammothmoda2_text_to_image_prompt,
    },
+    "MingImagePipeline": {
+        "extra_body_params": MING_FLASH_OMNI_EXTRA_BODY_PARAMS,
+        "extra_output_params": MING_FLASH_OMNI_EXTRA_OUTPUT_PARAMS,
+        "init_extra_args_for_non_diffusion_stages": MING_FLASH_OMNI_INIT_EXTRA_ARGS_FOR_NON_DIFFUSION_STAGES,
+        "text_to_image_prompt_builder": build_ming_flash_omni_text_to_image_prompt,
+        "image_to_image_prompt_builder": build_ming_flash_omni_image_to_image_prompt,
+    },
 }