[CI][Accuracy] Add Qwen-Image-2512 Qwen-Image-Edit-2511 pixel accuracy tests (#3502)

Signed-off-by: david6666666 <530634352@qq.com>

.buildkite/test-nightly.yml

@@ -660,87 +660,6 @@ steps:
                       path: /mnt/hf-cache
                       type: DirectoryOrCreate
 
-      - label: ":full_moon: Diffusion X2I(&A&T) · GEBench Accuracy Test"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/e2e/accuracy/test_gebench_h100_smoke.py --run-level full_model --gebench-model Qwen/Qwen-Image-2512 --accuracy-judge-model QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ --accuracy-gpu 0 --gebench-port 8093 --accuracy-workers 1
-          - buildkite-agent artifact upload "tests/e2e/accuracy/artifacts/gebench_qwen-image-2512/summary*.json"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · GEdit-Bench Accuracy Test"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/e2e/accuracy/test_gedit_bench_h100_smoke.py --run-level full_model --gedit-model Qwen/Qwen-Image-Edit --accuracy-judge-model QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ --accuracy-gpu 0 --gedit-port 8093 --gedit-samples-per-group 20 --accuracy-workers 1
-          - buildkite-agent artifact upload "tests/e2e/accuracy/artifacts/gedit_scores_qwen-image-edit/qwen-image-edit_all_all_vie_score_*.csv"
-          - buildkite-agent artifact upload "tests/e2e/accuracy/artifacts/gedit_scores_qwen-image-edit/qwen-image-edit_all_all_summary_*.json"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: VLLM_HTTP_TIMEOUT_KEEP_ALIVE
-                        value: "120"
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
       - label: ":full_moon: Diffusion X2I(&A&T) · Accuracy Test"
         timeout_in_minutes: 180
         commands:

tests/e2e/accuracy/helpers.py

@@ -127,6 +127,74 @@ def assert_image_sequence_similarity(
         )
 
 
+def assert_images_pixel_close(
+    *,
+    model_name: str,
+    vllm_image: Image.Image,
+    diffusers_image: Image.Image,
+    mean_threshold: float,
+    p99_threshold: float,
+    compare_mode: str = "RGB",
+) -> None:
+    """Assert full-image pixel closeness between online serving and diffusers output.
+
+    Match the threshold style used by diffusion parallelism tests: convert both
+    images to float32 RGB tensors in the [0, 1] range, then compare mean and p99
+    absolute channel differences.
+
+    Accuracy improves as both values move lower:
+    - ``mean_abs_diff`` tracks global average drift across the whole image.
+    - ``p99_abs_diff`` tracks tail drift while ignoring the noisiest 1% of
+      channel samples.
+
+    The printed mismatch ratios are diagnostics only. ``pixel_ratio`` counts
+    pixels where any channel exceeds a tolerance, while ``channel_ratio`` counts
+    individual channel samples above that tolerance.
+    """
+    assert vllm_image.size == diffusers_image.size, (
+        f"Online and diffusers output sizes mismatch: online={vllm_image.size}, diffusers={diffusers_image.size}"
+    )
+
+    vllm_array = np.asarray(vllm_image.convert(compare_mode), dtype=np.float32) / 255.0
+    diffusers_array = np.asarray(diffusers_image.convert(compare_mode), dtype=np.float32) / 255.0
+    channel_abs_diff = np.abs(vllm_array - diffusers_array)
+    mean_abs_diff = float(channel_abs_diff.mean())
+    p99_abs_diff = float(np.quantile(channel_abs_diff, 0.99))
+    percentiles = {
+        percentile: float(np.quantile(channel_abs_diff, percentile / 100.0)) for percentile in (50, 90, 95, 99, 99.9)
+    }
+
+    print(f"{model_name} pixel metrics:")
+    print(
+        f"  mean_abs_diff: value={mean_abs_diff:.6e}, threshold<={mean_threshold:.6e}, "
+        "range=[0, 1], lower_is_better=True"
+    )
+    print(
+        f"  p99_abs_diff: value={p99_abs_diff:.6e}, threshold<={p99_threshold:.6e}, range=[0, 1], lower_is_better=True"
+    )
+    print("  abs_diff_percentiles:")
+    for percentile, value in percentiles.items():
+        print(f"    p{percentile}: value={value:.6e}, range=[0, 1], lower_is_better=True")
+    print("  mismatch_ratios_by_channel_threshold:")
+    for tolerance in (0, 1, 2, 4, 8, 16, 32, 64, 128):
+        normalized_tolerance = tolerance / 255.0
+        pixel_mismatch = np.any(channel_abs_diff > normalized_tolerance, axis=-1)
+        pixel_mismatch_ratio = float(np.count_nonzero(pixel_mismatch) / pixel_mismatch.size)
+        channel_mismatch_ratio = float(
+            np.count_nonzero(channel_abs_diff > normalized_tolerance) / channel_abs_diff.size
+        )
+        print(
+            f"    threshold>{normalized_tolerance:.6e} ({tolerance}/255): pixel_ratio={pixel_mismatch_ratio:.8f}, "
+            f"channel_ratio={channel_mismatch_ratio:.8f}"
+        )
+
+    assert mean_abs_diff <= mean_threshold and p99_abs_diff <= p99_threshold, (
+        f"Image diff exceeded threshold for {model_name}: mean_abs_diff={mean_abs_diff:.6e}, "
+        f"p99_abs_diff={p99_abs_diff:.6e} (thresholds: mean<={mean_threshold:.6e}, "
+        f"p99<={p99_threshold:.6e})"
+    )
+
+
 def compute_image_ssim_psnr(
     *,
     prediction: Image.Image,

tests/e2e/accuracy/test_qwen_image.py

@@ -12,7 +12,7 @@ import torch
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from PIL import Image
 
-from tests.e2e.accuracy.helpers import assert_similarity, model_output_dir
+from tests.e2e.accuracy.helpers import assert_images_pixel_close, assert_similarity, model_output_dir
 from tests.helpers.env import run_post_test_cleanup, run_pre_test_cleanup
 from tests.helpers.mark import hardware_test
 from tests.helpers.runtime import OmniServer
@@ -32,11 +32,38 @@ SEED = 42
 SSIM_THRESHOLD = 0.97
 PSNR_THRESHOLD = 30.0
 
+MODEL_2512_ID = "Qwen/Qwen-Image-2512"
+MODEL_2512_ENV_VAR = "QWEN_IMAGE_2512_MODEL"
+PROMPT_2512 = (
+    "A 20-year-old East Asian girl with delicate, charming features and large, bright brown eyes—expressive and "
+    "lively, with a cheerful or subtly smiling expression. Her naturally wavy long hair is either loose or tied in "
+    "twin ponytails. She has fair skin and light makeup accentuating her youthful freshness. She wears a modern, "
+    "cute dress or relaxed outfit in bright, soft colors—lightweight fabric, minimalist cut. She stands indoors at "
+    "an anime convention, surrounded by banners, posters, or stalls. Lighting is typical indoor illumination—no "
+    "staged lighting—and the image resembles a casual iPhone snapshot: unpretentious composition, yet brimming "
+    "with vivid, fresh, youthful charm."
+)
+NEGATIVE_PROMPT_2512 = (
+    "低分辨率，低画质，肢体畸形，手指畸形，画面过饱和，蜡像感，人脸无细节，过度光滑，画面具有AI感。"
+    "构图混乱。文字模糊，扭曲。"
+)
+WIDTH_2512 = 1664
+HEIGHT_2512 = 928
+NUM_INFERENCE_STEPS_2512 = 50
+TRUE_CFG_SCALE_2512 = 4.0
+SEED_2512 = 42
+MEAN_ABS_DIFF_THRESHOLD_2512 = 3e-2
+P99_ABS_DIFF_THRESHOLD_2512 = 4e-1
+
 
 def _model_name() -> str:
     return os.environ.get(MODEL_ENV_VAR, MODEL_ID)
 
 
+def _model_2512_name() -> str:
+    return os.environ.get(MODEL_2512_ENV_VAR, MODEL_2512_ID)
+
+
 def _local_files_only(model: str) -> bool:
     return Path(model).exists()
 
@@ -102,6 +129,67 @@ def _run_diffusers_qwen_image(*, model: str, output_path: Path) -> Image.Image:
         run_post_test_cleanup()
 
 
+def _run_vllm_omni_qwen_image_2512(*, model: str, output_path: Path) -> Image.Image:
+    server_args = ["--num-gpus", "1", "--stage-init-timeout", "300", "--init-timeout", "900"]
+    with OmniServer(model, server_args, use_omni=True) as omni_server:
+        response = requests.post(
+            f"http://{omni_server.host}:{omni_server.port}/v1/images/generations",
+            json={
+                "model": omni_server.model,
+                "prompt": PROMPT_2512,
+                "size": f"{WIDTH_2512}x{HEIGHT_2512}",
+                "n": 1,
+                "response_format": "b64_json",
+                "negative_prompt": NEGATIVE_PROMPT_2512,
+                "num_inference_steps": NUM_INFERENCE_STEPS_2512,
+                "true_cfg_scale": TRUE_CFG_SCALE_2512,
+                "seed": SEED_2512,
+            },
+            timeout=1200,
+        )
+        response.raise_for_status()
+        payload = response.json()
+        assert len(payload["data"]) == 1
+        image_bytes = base64.b64decode(payload["data"][0]["b64_json"])
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        image.load()
+        image.save(output_path)
+        return image
+
+
+def _run_diffusers_qwen_image_2512(*, model: str, output_path: Path) -> Image.Image:
+    run_pre_test_cleanup()
+    pipe: DiffusionPipeline | None = None
+    try:
+        pipe = DiffusionPipeline.from_pretrained(
+            model,
+            torch_dtype=torch.bfloat16,
+            trust_remote_code=True,
+            local_files_only=_local_files_only(model),
+        ).to("cuda")
+        generator = torch.Generator(device="cuda").manual_seed(SEED_2512)
+        result = pipe(  # pyright: ignore[reportCallIssue]
+            prompt=PROMPT_2512,
+            negative_prompt=NEGATIVE_PROMPT_2512,
+            width=WIDTH_2512,
+            height=HEIGHT_2512,
+            num_inference_steps=NUM_INFERENCE_STEPS_2512,
+            true_cfg_scale=TRUE_CFG_SCALE_2512,
+            generator=generator,
+        )
+        output_image = result.images[0].convert("RGB")
+        output_image.save(output_path)
+        return output_image
+    finally:
+        if pipe is not None and hasattr(pipe, "maybe_free_model_hooks"):
+            pipe.maybe_free_model_hooks()
+        del pipe
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.accelerator.empty_cache()
+        run_post_test_cleanup()
+
+
 @pytest.mark.benchmark
 @hardware_test(res={"cuda": "H100"}, num_cards=1)
 def test_qwen_image_matches_diffusers(accuracy_artifact_root: Path) -> None:
@@ -120,3 +208,27 @@ def test_qwen_image_matches_diffusers(accuracy_artifact_root: Path) -> None:
         ssim_threshold=SSIM_THRESHOLD,
         psnr_threshold=PSNR_THRESHOLD,
     )
+
+
+@pytest.mark.benchmark
+@hardware_test(res={"cuda": "H100"}, num_cards=1)
+def test_qwen_image_2512_matches_diffusers_pixelwise(accuracy_artifact_root: Path) -> None:
+    model = _model_2512_name()
+    output_dir = model_output_dir(accuracy_artifact_root, MODEL_2512_ID)
+    vllm_output_path = output_dir / "vllm_omni_2512.png"
+    diffusers_output_path = output_dir / "diffusers_2512.png"
+
+    vllm_output = _run_vllm_omni_qwen_image_2512(model=model, output_path=vllm_output_path)
+    diffusers_output = _run_diffusers_qwen_image_2512(model=model, output_path=diffusers_output_path)
+
+    print(f"{MODEL_2512_ID} generated images:")
+    print(f"  vllm_omni: {vllm_output_path}")
+    print(f"  diffusers: {diffusers_output_path}")
+
+    assert_images_pixel_close(
+        model_name=MODEL_2512_ID,
+        vllm_image=vllm_output,
+        diffusers_image=diffusers_output,
+        mean_threshold=MEAN_ABS_DIFF_THRESHOLD_2512,
+        p99_threshold=P99_ABS_DIFF_THRESHOLD_2512,
+    )

tests/e2e/accuracy/test_qwen_image_edit.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import gc
+import os
 from pathlib import Path
 
 import pytest
@@ -10,7 +11,7 @@ from diffusers import QwenImageEditPipeline, QwenImageEditPlusPipeline
 from PIL import Image
 
 from benchmarks.accuracy.common import decode_base64_image, pil_to_png_bytes
-from tests.e2e.accuracy.helpers import assert_similarity, model_output_dir
+from tests.e2e.accuracy.helpers import assert_images_pixel_close, assert_similarity, model_output_dir
 from tests.helpers.env import run_post_test_cleanup, run_pre_test_cleanup
 from tests.helpers.mark import hardware_test
 from tests.helpers.runtime import OmniServer
@@ -28,12 +29,43 @@ SEED = 42
 SSIM_THRESHOLD = 0.94
 PSNR_THRESHOLD = 28.0
 
+EDIT_2511_MODEL = "Qwen/Qwen-Image-Edit-2511"
+EDIT_2511_MODEL_ENV_VAR = "QWEN_IMAGE_EDIT_2511_MODEL"
+EDIT_2511_WIDTH = 1190
+EDIT_2511_HEIGHT = 2032
+EDIT_2511_NUM_INFERENCE_STEPS = 40
+EDIT_2511_TRUE_CFG_SCALE = 4.0
+EDIT_2511_GUIDANCE_SCALE = 1.0
+EDIT_2511_SEED = 42
+EDIT_2511_MEAN_ABS_DIFF_THRESHOLD = 3e-2
+EDIT_2511_P99_ABS_DIFF_THRESHOLD = 4e-1
+EDIT_2511_NEGATIVE_PROMPT = " "
+EDIT_2511_PROMPT = (
+    "将第二张图中人脸/猫狗脸转换为3D卡通形象，质量要求：毛发纹理自然电影级色彩校准，注意保留原图的外貌、"
+    "肤色、发型特征，仅替换第一张财神爷/猫狗财神的用绿框框住的头部区域，耳朵样式和原图的3D卡通形象一致， "
+    "（帽子紧贴头顶，未贴合部分可调整帽子摆放角度达到完全贴合）， 头部与身体过渡必须自然，无拼接痕迹，"
+    "无额外元素出现在头部四周，纯白背景，不要出现绿色圆圈"
+)
+EDIT_2511_ASSET_DIR = Path(__file__).resolve().parent / "assets"
+EDIT_2511_IMAGE_PATHS = [
+    EDIT_2511_ASSET_DIR / "qwen_image_edit_2511_test1.png",
+    EDIT_2511_ASSET_DIR / "qwen_image_edit_2511_test2.png",
+]
+
 PROMPT_SINGLE_IMAGE = "The input is a 2D cartoon bear mascot. Restyle it into a painterly oil artwork with warm colors while preserving the main structure."
 PROMPT_MULTIPLE_IMAGE = "Put the cartoon bear mascot and the furry rabbit into one coherent scene with a painterly oil artwork style and consistent lighting."
 NEGATIVE_PROMPT = "low quality, blurry, artifacts, distortion"
 SERVER_ARGS = ["--num-gpus", "1", "--stage-init-timeout", "300", "--init-timeout", "900"]
 
 
+def _edit_2511_model_name() -> str:
+    return os.environ.get(EDIT_2511_MODEL_ENV_VAR, EDIT_2511_MODEL)
+
+
+def _local_files_only(model: str) -> bool:
+    return Path(model).exists()
+
+
 def _run_vllm_omni_image_edit(
     *,
     omni_server: OmniServer,
@@ -79,17 +111,15 @@ def _run_diffusers_image_edit(
 ) -> Image.Image:
     run_pre_test_cleanup()
     pipe: QwenImageEditPipeline | QwenImageEditPlusPipeline | None = None
-    device = torch.device("cuda:0")
-    torch.cuda.set_device(device)
     try:
         images = input_images[0] if len(input_images) == 1 else input_images
         pipe = pipeline_class.from_pretrained(
             model,
             torch_dtype=torch.bfloat16,
             trust_remote_code=True,
-        ).to(device)
+        ).to("cuda")
         pipe.set_progress_bar_config(disable=False)
-        generator = torch.Generator(device=device).manual_seed(SEED)
+        generator = torch.Generator(device="cuda").manual_seed(SEED)
         result = pipe(  # pyright: ignore[reportCallIssue]
             prompt=prompt,
             image=images,
@@ -113,6 +143,88 @@ def _run_diffusers_image_edit(
         run_post_test_cleanup()
 
 
+def _load_edit_2511_input_images() -> list[Image.Image]:
+    return [Image.open(path).convert("RGB") for path in EDIT_2511_IMAGE_PATHS]
+
+
+def _run_vllm_omni_image_edit_2511(
+    *,
+    model: str,
+    input_images: list[Image.Image],
+    output_path: Path,
+) -> Image.Image:
+    with OmniServer(model=model, serve_args=SERVER_ARGS) as server:
+        response = requests.post(
+            f"http://{server.host}:{server.port}/v1/images/edits",
+            data={
+                "model": server.model,
+                "prompt": EDIT_2511_PROMPT,
+                "size": f"{EDIT_2511_WIDTH}x{EDIT_2511_HEIGHT}",
+                "n": 1,
+                "response_format": "b64_json",
+                "negative_prompt": EDIT_2511_NEGATIVE_PROMPT,
+                "num_inference_steps": EDIT_2511_NUM_INFERENCE_STEPS,
+                "true_cfg_scale": EDIT_2511_TRUE_CFG_SCALE,
+                "guidance_scale": EDIT_2511_GUIDANCE_SCALE,
+                "seed": EDIT_2511_SEED,
+            },
+            files=[
+                ("image", (f"image_{index}.png", pil_to_png_bytes(image), "image/png"))
+                for index, image in enumerate(input_images)
+            ],
+            timeout=1200,
+        )
+        response.raise_for_status()
+        payload = response.json()
+        assert len(payload["data"]) == 1
+        image = decode_base64_image(payload["data"][0]["b64_json"])
+        image.load()
+        image.save(output_path)
+        return image
+
+
+def _run_diffusers_image_edit_2511(
+    *,
+    model: str,
+    input_images: list[Image.Image],
+    output_path: Path,
+) -> Image.Image:
+    run_pre_test_cleanup()
+    pipe: QwenImageEditPlusPipeline | None = None
+    try:
+        pipe = QwenImageEditPlusPipeline.from_pretrained(
+            model,
+            torch_dtype=torch.bfloat16,
+            trust_remote_code=True,
+            local_files_only=_local_files_only(model),
+        ).to("cuda")
+        pipe.set_progress_bar_config(disable=False)
+        generator = torch.Generator(device="cuda").manual_seed(EDIT_2511_SEED)
+        result = pipe(  # pyright: ignore[reportCallIssue]
+            image=input_images,
+            prompt=EDIT_2511_PROMPT,
+            generator=generator,
+            true_cfg_scale=EDIT_2511_TRUE_CFG_SCALE,
+            negative_prompt=EDIT_2511_NEGATIVE_PROMPT,
+            num_inference_steps=EDIT_2511_NUM_INFERENCE_STEPS,
+            guidance_scale=EDIT_2511_GUIDANCE_SCALE,
+            num_images_per_prompt=1,
+            width=EDIT_2511_WIDTH,
+            height=EDIT_2511_HEIGHT,
+        )
+        output_image = result.images[0].convert("RGB")  # pyright: ignore[reportAttributeAccessIssue]
+        output_image.save(output_path)
+        return output_image
+    finally:
+        if pipe is not None and hasattr(pipe, "maybe_free_model_hooks"):
+            pipe.maybe_free_model_hooks()
+        del pipe
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.accelerator.empty_cache()
+        run_post_test_cleanup()
+
+
 def _vllm_omni_output_single_image(
     accuracy_artifact_root: Path,
     qwen_bear_image: Image.Image,
@@ -226,3 +338,43 @@ def test_qwen_image_edit_multiple_matches_diffusers(
         ssim_threshold=SSIM_THRESHOLD,
         psnr_threshold=PSNR_THRESHOLD,
     )
+
+
+@pytest.mark.benchmark
+@hardware_test(res={"cuda": "H100"}, num_cards=1)
+def test_qwen_image_edit_2511_matches_diffusers_pixelwise(accuracy_artifact_root: Path) -> None:
+    model = _edit_2511_model_name()
+    output_dir = model_output_dir(accuracy_artifact_root, EDIT_2511_MODEL)
+    input_images = _load_edit_2511_input_images()
+    input_paths: list[Path] = []
+    for index, image in enumerate(input_images, start=1):
+        input_path = output_dir / f"edit_2511_input_{index}.png"
+        image.save(input_path)
+        input_paths.append(input_path)
+    vllm_output_path = output_dir / "vllm_omni_edit_2511.png"
+    diffusers_output_path = output_dir / "diffusers_edit_2511.png"
+
+    vllm_image = _run_vllm_omni_image_edit_2511(
+        model=model,
+        input_images=input_images,
+        output_path=vllm_output_path,
+    )
+    diffusers_image = _run_diffusers_image_edit_2511(
+        model=model,
+        input_images=input_images,
+        output_path=diffusers_output_path,
+    )
+
+    print(f"{EDIT_2511_MODEL} generated images:")
+    for index, input_path in enumerate(input_paths, start=1):
+        print(f"  input_{index}: {input_path}")
+    print(f"  vllm_omni: {vllm_output_path}")
+    print(f"  diffusers: {diffusers_output_path}")
+
+    assert_images_pixel_close(
+        model_name=EDIT_2511_MODEL,
+        vllm_image=vllm_image,
+        diffusers_image=diffusers_image,
+        mean_threshold=EDIT_2511_MEAN_ABS_DIFF_THRESHOLD,
+        p99_threshold=EDIT_2511_P99_ABS_DIFF_THRESHOLD,
+    )