[Model] Add JoyAI-Image-Edit pipeline support (#4112)

Signed-off-by: nagisa-kun <1434936049@qq.com>
Signed-off-by: nagisa <1434936049@qq.com>
Signed-off-by: hurukawa <61525444+nagisa-kunhah@users.noreply.github.com>

.buildkite/amd/scripts/prewarm_cosmos3_aiter.py

@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+"""Prebuild the BF16 AITER MHA modules used by Cosmos3 on ROCm."""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import torch
+
+from vllm_omni.diffusion.attention.backends.utils import fa
+from vllm_omni.platforms import current_omni_platform
+
+ATTENTION_VARIANTS = (
+    (True, "mha_fwd_bf16_nbias_mask_nlse_ndropout_nqscale"),
+    (False, "mha_fwd_bf16_nbias_nmask_nlse_ndropout_nqscale"),
+)
+
+
+def main() -> None:
+    if not current_omni_platform.is_rocm():
+        raise RuntimeError("The Cosmos3 AITER prewarm must run on ROCm")
+    if fa.flash_attn_func is None:
+        raise RuntimeError("ROCm FlashAttention/AITER is unavailable")
+
+    cache_dir_value = os.environ.get("AITER_JIT_DIR")
+    if not cache_dir_value:
+        raise RuntimeError("AITER_JIT_DIR must identify the job-local cache")
+    cache_dir = Path(cache_dir_value)
+
+    device = torch.device(current_omni_platform.device_type)
+    dtype = torch.bfloat16
+    batch_size = 1
+    sequence_length = 64
+    num_heads = 8
+    head_dim = 64
+
+    torch.manual_seed(42)
+    query = torch.randn(
+        batch_size,
+        sequence_length,
+        num_heads,
+        head_dim,
+        device=device,
+        dtype=dtype,
+    )
+    key = torch.randn_like(query)
+    value = torch.randn_like(query)
+
+    for causal, expected_module in ATTENTION_VARIANTS:
+        output = fa.flash_attn_func(
+            query,
+            key,
+            value,
+            dropout_p=0.0,
+            softmax_scale=head_dim**-0.5,
+            causal=causal,
+        )
+        if isinstance(output, tuple):
+            output = output[0]
+        current_omni_platform.synchronize()
+
+        if output.shape != query.shape:
+            raise RuntimeError(f"Unexpected AITER output shape: {output.shape}")
+        if not torch.isfinite(output).all():
+            raise RuntimeError("AITER prewarm produced non-finite output")
+
+        module_path = cache_dir / f"{expected_module}.so"
+        if not module_path.is_file():
+            raise RuntimeError(f"Expected AITER module was not built: {module_path}")
+        print(f"Prebuilt Cosmos3 AITER module: {module_path}", flush=True)
+
+
+if __name__ == "__main__":
+    main()

.buildkite/amd/test-amd-merge.yml

@@ -37,24 +37,22 @@ steps:
       mirror_hardwares: [amdproduction]
       grade: Blocking
       timeout_in_minutes: 45
+      # Buildkite's status-based rule cannot prove every exit 134 is a GPU hang.
+      # It retries 134 once; the Kubernetes job retry starts a fresh pod.
+      # Assertions (1), timeouts (124), and OOMs (137) remain non-retryable.
+      retry:
+        automatic:
+          - exit_status: 134
+            limit: 1
       commands:
         - export VLLM_ROCM_USE_AITER=0
         # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
         # that is not supported on CPU
         # Keep multi-GPU tests out of this one-GPU pool even when their module
         # also carries the cpu marker for unit tests.
+        # Keep π0.5 CPU units out of these shards: CUDA READY/MERGE already run
+        # the same suite mandatorily, and AMD covers it non-blocking in nightly.
         - "pytest -sv tests/diffusion -m 'core_model and cpu and not (cards_2 or cards_3 or cards_4 or cards_5 or cards_6 or cards_7 or cards_8)' --ignore=tests/diffusion/cache/test_teacache_extractors.py --ignore=tests/diffusion/models/pi05/test_pi05_units.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
-    # This module takes about 22 minutes on MI300 and was consistently assigned to shard 4,
-    # pushing that shard to the 45-minute timeout. Keep its module-scoped fixture in one lane.
-    - label: "Simple · Pi0.5 CPU Test"
-      agent_pool: mi300_1
-      depends_on: amd-build
-      mirror_hardwares: [amdproduction]
-      grade: Blocking
-      timeout_in_minutes: 45
-      commands:
-        - export VLLM_ROCM_USE_AITER=0
-        - "pytest -sv tests/diffusion/models/pi05/test_pi05_units.py -m 'core_model and cpu'"
 
     - label: "Diffusion · AuK VAE GPU Test"
       agent_pool: mi300_1
@@ -91,6 +89,25 @@ steps:
 
 - group: ":card_index_dividers: Diffusion Test"
   steps:
+    # AR paged attention is real GPU work, including cold AITER compilation.
+    # Keep its complete numerical matrix out of the CPU-marked Simple shards.
+    - label: "ROCm · AR Diffusion Paged Attention GPU Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      timeout_in_minutes: 20
+      commands:
+        - export VLLM_ROCM_USE_AITER=1
+        - export VLLM_OMNI_AR_FA_REQUIRED=1
+        - export AITER_JIT_DIR="/tmp/vllm-omni-ar-aiter-$$BUILDKITE_JOB_ID"
+        - export TORCH_EXTENSIONS_DIR="/tmp/vllm-omni-ar-torch-extensions-$$BUILDKITE_JOB_ID"
+        - mkdir -p "$$AITER_JIT_DIR" "$$TORCH_EXTENSIONS_DIR"
+        - >-
+          timeout --signal=TERM --kill-after=2m 15m
+          pytest -sv tests/diffusion/ar_diffusion/test_paged_attention.py
+          -m 'core_model and rocm and MI325 and cards_1' --run-level core_model
+
     - label: "Diffusion · LoRA Test"
       agent_pool: mi300_1
       depends_on: amd-build
@@ -109,6 +126,7 @@ steps:
       commands:
         - export VLLM_LOGGING_LEVEL=DEBUG
         - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - export DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA
         - pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
         - >-
           pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py

.buildkite/amd/test-amd-nightly.yml

@@ -82,3 +82,89 @@ steps:
               limit: 1
         commands:
           - pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "slow" --run-level "core_model"
+
+  # π0.5 CPU units are mandatory on CUDA READY/MERGE. On AMD they exercise the
+  # ROCm container/import path with CPU tensors only, so keep them off the
+  # blocking READY/MERGE critical path and retain non-blocking nightly coverage.
+  - group: ":robot_face: Pi0.5 CPU Nightly"
+    steps:
+      - label: "Simple · Pi0.5 CPU Test"
+        agent_pool: mi300_1
+        mirror_hardwares: [amdproduction]
+        grade: NonBlocking
+        timeout_in_minutes: 60
+        commands:
+          - export VLLM_ROCM_USE_AITER=0
+          - "pytest -sv tests/diffusion/models/pi05/test_pi05_units.py -m 'core_model and cpu'"
+
+  - group: ":full_moon: LingBot-Video MI300 Nightly"
+    steps:
+      - label: "LingBot-Video Dense CFG-off Smoke"
+        agent_pool: mi300_1
+        mirror_hardwares: [amdproduction]
+        grade: NonBlocking
+        timeout_in_minutes: 90
+        artifact_paths:
+          - artifacts/rocm-lingbot-nightly/**/*
+        commands:
+          - pytest --collect-only -q tests/e2e/online_serving/test_lingbot_video_expansion.py::test_cfg_off -m "full_model and diffusion and rocm and MI325 and cards_1" --run-level full_model
+          - pytest -s -v tests/e2e/online_serving/test_lingbot_video_expansion.py::test_cfg_off -m "full_model and diffusion and rocm and MI325 and cards_1" --run-level full_model --junitxml=/tmp/lingbot-pytest.xml
+          - |
+            artifact_dir="$${BUILDKITE_BUILD_CHECKOUT_PATH:?}/artifacts/rocm-lingbot-nightly"
+            mkdir -p "$$artifact_dir"
+            cp -v /tmp/lingbot-pytest.xml "$$artifact_dir/"
+
+  - group: ":full_moon: Cosmos3 MI300 Nightly"
+    steps:
+      - label: "Cosmos3 T2I Function"
+        agent_pool: mi300_1
+        mirror_hardwares: [amdproduction]
+        grade: NonBlocking
+        timeout_in_minutes: 90
+        artifact_paths:
+          - artifacts/rocm-cosmos3-nightly/**/*
+        commands:
+          # Cosmos3 uses both masked and unmasked BF16 AITER MHA modules. Build
+          # them before server startup, then reuse the same job-local cache
+          # during the smoke test.
+          - export VLLM_ROCM_USE_AITER=1
+          - export AITER_JIT_DIR="/tmp/vllm-omni-cosmos3-aiter-$$BUILDKITE_JOB_ID"
+          - export TORCH_EXTENSIONS_DIR="/tmp/vllm-omni-cosmos3-torch-extensions-$$BUILDKITE_JOB_ID"
+          - mkdir -p "$$AITER_JIT_DIR" "$$TORCH_EXTENSIONS_DIR"
+          - pytest --collect-only -q tests/e2e/online_serving/test_cosmos3.py::test_text_to_image_001 -m "core_model and rocm and MI325 and cards_1" --run-level full_model
+          - >-
+            timeout --signal=TERM --kill-after=2m 55m
+            python .buildkite/amd/scripts/prewarm_cosmos3_aiter.py
+          - >-
+            timeout --signal=TERM --kill-after=2m 25m
+            pytest -s -v
+            tests/e2e/online_serving/test_cosmos3.py::test_text_to_image_001
+            -m "core_model and rocm and MI325 and cards_1"
+            --run-level full_model
+            --junitxml=/tmp/cosmos3-pytest.xml
+          - |
+            artifact_dir="$${BUILDKITE_BUILD_CHECKOUT_PATH:?}/artifacts/rocm-cosmos3-nightly"
+            mkdir -p "$$artifact_dir"
+            cp -v /tmp/cosmos3-pytest.xml "$$artifact_dir/"
+
+  - group: ":full_moon: HunyuanImage3 MI300 Nightly"
+    steps:
+      - label: "HunyuanImage3 Offline Pixel Accuracy"
+        agent_pool: mi300_4
+        mirror_hardwares: [amdproduction]
+        grade: NonBlocking
+        timeout_in_minutes: 180
+        env:
+          HUNYUAN_IMAGE3_MODEL: "tencent/HunyuanImage-3.0-Instruct"
+          HUNYUAN_IMAGE3_DEVICES: "0,1,2,3"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        artifact_paths:
+          - artifacts/rocm-hunyuanimage3/**/*
+        commands:
+          - pytest --collect-only -q tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py::test_hunyuan_image3_pixel_accuracy_offline -m "full_model and rocm and MI325 and cards_4" --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py::test_hunyuan_image3_pixel_accuracy_offline -m "full_model and rocm and MI325 and cards_4" --run-level full_model --junitxml=/tmp/hunyuanimage3-pytest.xml
+          - |
+            artifact_dir="$${BUILDKITE_BUILD_CHECKOUT_PATH:?}/artifacts/rocm-hunyuanimage3"
+            mkdir -p "$$artifact_dir"
+            cp -v tests/e2e/accuracy/artifacts/HunyuanImage-3_0-Instruct/*.png "$$artifact_dir/"
+            cp -v /tmp/hunyuanimage3-pytest.xml "$$artifact_dir/"

.buildkite/amd/test-amd-ready.yml

@@ -37,24 +37,22 @@ steps:
       mirror_hardwares: [amdproduction]
       grade: Blocking
       timeout_in_minutes: 45
+      # Buildkite's status-based rule cannot prove every exit 134 is a GPU hang.
+      # It retries 134 once; the Kubernetes job retry starts a fresh pod.
+      # Assertions (1), timeouts (124), and OOMs (137) remain non-retryable.
+      retry:
+        automatic:
+          - exit_status: 134
+            limit: 1
       commands:
         - export VLLM_ROCM_USE_AITER=0
         # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
         # that is not supported on CPU
         # Keep multi-GPU tests out of this one-GPU pool even when their module
         # also carries the cpu marker for unit tests.
+        # Keep π0.5 CPU units out of these shards: CUDA READY/MERGE already run
+        # the same suite mandatorily, and AMD covers it non-blocking in nightly.
         - "pytest -sv tests/diffusion -m 'core_model and cpu and not (cards_2 or cards_3 or cards_4 or cards_5 or cards_6 or cards_7 or cards_8)' --ignore=tests/diffusion/cache/test_teacache_extractors.py --ignore=tests/diffusion/models/pi05/test_pi05_units.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB"
-    # This module takes about 22 minutes on MI300 and was consistently assigned to shard 4,
-    # pushing that shard to the 45-minute timeout. Keep its module-scoped fixture in one lane.
-    - label: "Simple · Pi0.5 CPU Test"
-      agent_pool: mi300_1
-      depends_on: amd-build
-      mirror_hardwares: [amdproduction]
-      grade: Blocking
-      timeout_in_minutes: 45
-      commands:
-        - export VLLM_ROCM_USE_AITER=0
-        - "pytest -sv tests/diffusion/models/pi05/test_pi05_units.py -m 'core_model and cpu'"
 
     - label: "Diffusion · AuK VAE GPU Test"
       agent_pool: mi300_1
@@ -128,6 +126,34 @@ steps:
           tests/diffusion/attention/
           -m 'core_model and rocm and MI325 and cards_1'
           --run-level "core_model"
+        # MammothModa2 exercises the same two AITER MHA modules built by the
+        # preceding suite. Keep its default-backend run in this job so a cold
+        # worker compiles each module once instead of timing out the generic
+        # model job.
+        - >-
+          timeout --signal=TERM --kill-after=2m 10m
+          pytest -s -v -n 1
+          tests/diffusion/models/mammoth_moda2/test_dit_attention_cuda.py
+          --run-level "core_model"
+
+    # AR paged attention is real GPU work, including cold AITER compilation.
+    # Keep its complete numerical matrix out of the CPU-marked Simple shards.
+    - label: "ROCm · AR Diffusion Paged Attention GPU Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      timeout_in_minutes: 20
+      commands:
+        - export VLLM_ROCM_USE_AITER=1
+        - export VLLM_OMNI_AR_FA_REQUIRED=1
+        - export AITER_JIT_DIR="/tmp/vllm-omni-ar-aiter-$$BUILDKITE_JOB_ID"
+        - export TORCH_EXTENSIONS_DIR="/tmp/vllm-omni-ar-torch-extensions-$$BUILDKITE_JOB_ID"
+        - mkdir -p "$$AITER_JIT_DIR" "$$TORCH_EXTENSIONS_DIR"
+        - >-
+          timeout --signal=TERM --kill-after=2m 15m
+          pytest -sv tests/diffusion/ar_diffusion/test_paged_attention.py
+          -m 'core_model and rocm and MI325 and cards_1' --run-level core_model
 
     - label: "Diffusion · Batch Test"
       agent_pool: mi300_1
@@ -197,9 +223,17 @@ steps:
         - >-
           timeout 15m pytest -sv tests/diffusion/models/
           --ignore=tests/diffusion/models/auk/test_auk_vae_fastpath.py
+          --ignore=tests/diffusion/models/mammoth_moda2/test_dit_attention_cuda.py
           -m 'core_model and cuda and not
           (cards_2 or cards_3 or cards_4 or cards_5 or cards_6 or cards_7 or cards_8)'
           --run-level "core_model"
+        # Keep the model-specific numerical coverage blocking without paying
+        # the default ROCm backend's cold AITER compilation cost in this lane.
+        - >-
+          timeout 10m env DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA
+          pytest -sv
+          tests/diffusion/models/mammoth_moda2/test_dit_attention_cuda.py
+          --run-level "core_model"
 
     - label: "Diffusion · Wan22 Test"
       agent_pool: mi300_1
@@ -226,7 +260,7 @@ steps:
 #     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
 #     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model
 
-- label: "ROCm · Engine & Model Executor GPU Coverage (R2-01)"
+- label: "ROCm · Model Executor GPU Coverage (R2-01)"
   agent_pool: mi300_1
   depends_on: amd-build
   mirror_hardwares: [amdproduction]
@@ -239,18 +273,17 @@ steps:
     # artifacts relative to its checkout directory.
     - export R2_01_ARTIFACT_DIR="$$BUILDKITE_BUILD_CHECKOUT_PATH/artifacts/rocm-r2-01"
     - mkdir -p "$$R2_01_ARTIFACT_DIR"
+    # The dedicated Engine Test below owns the only selected tests/engine case;
+    # do not duplicate its model startup in this time-bounded coverage lane.
     # The CUDA ready job also covers tests/worker/test_batched_omni_output.py.
-    # Keep the initial ROCm burn-in scoped to the requested directories.
+    # Keep the initial ROCm burn-in scoped to tests/model_executor/.
     # Verbose pytest output and JUnit retain node IDs without a second
     # collection-only pass over the same GPU-backed test tree.
-    # The CUDA/L4-only CosyVoice3 ragged-flow parity test cold-JITs multiple
-    # AITER kernels on ROCm and is covered by the CUDA ready lane.
     - >-
       pytest -s -v -ra
-      tests/engine/ tests/model_executor/
-      -m 'core_model and cuda and not
+      tests/model_executor/
+      -m 'core_model and cuda and not nvidia_only and not
       (cards_2 or cards_3 or cards_4 or cards_5 or cards_6 or cards_7 or cards_8)'
-      -k 'not test_code2wav_streaming_batch_matches_ragged_flow_numerics'
       --run-level "core_model"
       --durations=0
       --junitxml="$$R2_01_ARTIFACT_DIR/pytest.xml"

.buildkite/common/ci_source_file_dependencies.yml

@@ -115,6 +115,10 @@ x-qwen-image: &qwen_image
   - vllm_omni/diffusion/models/dmd2/
   - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py
 
+x-joy-image: &joy_image
+  - vllm_omni/diffusion/models/joy_image/
+  - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py
+
 x-hunyuan-image3: &hunyuan_image3
   - vllm_omni/diffusion/models/hunyuan_image3/
   - vllm_omni/model_executor/models/hunyuan_image3/
@@ -149,6 +153,10 @@ x-lingbot-video: &lingbot_video
 x-lingbot-world: &lingbot_world
   - vllm_omni/diffusion/models/lingbot_world/
 
+x-kandinsky6: &kandinsky6
+  - vllm_omni/diffusion/models/kandinsky6/
+  - vllm_omni/model_extras/kandinsky6.py
+
 
 source_file_dependencies:
   # Hardware presets and shared test helpers. Not a job key — do not attach this
@@ -383,6 +391,12 @@ source_file_dependencies:
     - tests/e2e/accuracy/test_qwen_image.py
     - tests/e2e/accuracy/test_diffusers_backend_similarity.py
 
+  diffusion_joy_image_accuracy:
+    - *joy_image
+    - tests/diffusion/models/joy_image/
+    - tests/e2e/accuracy/test_joy_image_edit.py
+    - recipes/JD/JoyAI-Image-Edit.md
+
   diffusion_qwen_image_perf:
     - *qwen_image
     - tests/dfx/perf/scripts/run_diffusion_benchmark.py
@@ -491,6 +505,11 @@ source_file_dependencies:
     - tests/dfx/perf/scripts/run_benchmark.py
     - tests/dfx/perf/tests/test_lingbot_video_vllm_omni.json
 
+  diffusion_kandinsky6_function:
+    - *kandinsky6
+    - tests/e2e/offline_inference/test_kandinsky6.py
+    - tests/e2e/online_serving/test_kandinsky6.py
+
 
   # Invalid-param jobs: model business code only, plus the reliability scripts.
   reliability_invalid_param_h100:

.buildkite/common/scripts/upload_pipeline.py

@@ -102,6 +102,7 @@ CI_SOURCE_FILE_DEPENDENCIES_PATH = ROOT / ".buildkite/common/ci_source_file_depe
 SOURCE_FILTER_FALLBACK_KEY = "source_filter_fallback"
 CUDA_HF_TOKEN_ENV = "VLLM_CI_HF_TOKEN"
 CUDA_HF_TOKEN_EXPORT = f'if [ -n "$${{{CUDA_HF_TOKEN_ENV}:-}}" ]; then export HF_TOKEN="$${{{CUDA_HF_TOKEN_ENV}}}"; fi'
+CUDA_RUNTIME_CHECK = "python3 .buildkite/cuda/scripts/check_vllm_runtime.py"
 
 # Bootstrap Buildkite ``if`` expressions.
 # ``*_MAIN_IF``: main + env schedule. ``*_LABEL_IF``: PR label (and/or composed with MAIN).
@@ -620,11 +621,11 @@ def _expand_mirror_hardwares(step: dict[str, Any]) -> dict[str, Any] | None:
     if any(preset_name == chip or preset_name.startswith(f"{chip}_") for chip in _cuda_mirror_chips()):
         commands = merged.get("commands")
         if isinstance(commands, list):
-            merged["commands"] = [CUDA_HF_TOKEN_EXPORT, *commands]
+            merged["commands"] = [CUDA_HF_TOKEN_EXPORT, CUDA_RUNTIME_CHECK, *commands]
         elif commands is None:
-            merged["commands"] = [CUDA_HF_TOKEN_EXPORT]
+            merged["commands"] = [CUDA_HF_TOKEN_EXPORT, CUDA_RUNTIME_CHECK]
         else:
-            merged["commands"] = [CUDA_HF_TOKEN_EXPORT, commands]
+            merged["commands"] = [CUDA_HF_TOKEN_EXPORT, CUDA_RUNTIME_CHECK, commands]
     # Preset retry (K8S_RETRY on l4_*) must not clobber a step that opted out.
     if "retry" in step:
         merged["retry"] = step["retry"]

.buildkite/cuda/scripts/check_vllm_runtime.py

@@ -0,0 +1,38 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+"""Verify the pinned vLLM and compiled CUDA imports on a GPU CI worker."""
+
+from __future__ import annotations
+
+import importlib
+import re
+from pathlib import Path
+
+
+def main() -> None:
+    dockerfile = Path(__file__).resolve().parents[3] / "docker" / "Dockerfile.ci"
+    match = re.search(
+        r"^ARG VLLM_VERSION=([0-9]+\.[0-9]+\.[0-9]+)$",
+        dockerfile.read_text(),
+        re.MULTILINE,
+    )
+    if match is None:
+        raise RuntimeError(f"Missing vLLM release version in {dockerfile}")
+    expected_version = match.group(1)
+
+    import torch
+    import vllm
+
+    assert vllm.__version__ == expected_version, vllm.__version__
+    assert torch.cuda.is_available(), "vLLM runtime check requires a GPU worker"
+    for module in ("flashinfer", "vllm._C_stable_libtorch"):
+        importlib.import_module(module)
+    print(
+        f"Verified vLLM CUDA runtime: vllm={vllm.__version__} "
+        f"expected={expected_version} torch={torch.__version__} "
+        f"cuda={torch.version.cuda} device={torch.cuda.get_device_name()}"
+    )
+
+
+if __name__ == "__main__":
+    main()

.buildkite/cuda/test-merge.yml

@@ -38,6 +38,17 @@ steps:
   - group: ":card_index_dividers: Diffusion Test"
     depends_on: upload-merge-pipeline
     steps:
+      - label: "Diffusion · AR Paged Attention GPU Test"
+        timeout_in_minutes: 20
+        commands:
+          - export VLLM_OMNI_AR_FA_REQUIRED=1
+          - >-
+            timeout 15m pytest -sv
+            tests/diffusion/ar_diffusion/test_paged_attention.py
+            -m 'core_model and cuda and L4 and cards_1'
+            --run-level "core_model"
+        mirror_hardwares: l4_1
+
       - label: "Diffusion · LoRA Test"
         timeout_in_minutes: 30
         commands:
@@ -178,6 +189,15 @@ steps:
             '
         mirror_hardwares: h100_1
 
+      - label: "Diffusion · Kandinsky6 Test"
+        source_file_dependencies: diffusion_kandinsky6_function
+        commands:
+          - |
+            timeout 40m bash -c "
+              pytest -s -v tests/e2e/offline_inference/test_kandinsky6.py tests/e2e/online_serving/test_kandinsky6.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            "
+        mirror_hardwares: h100_1
+
       - label: "Diffusion · Wan22 Test"
         source_file_dependencies: diffusion_wan22_function
         commands:

.buildkite/cuda/test-nightly.yml

@@ -48,6 +48,8 @@ steps:
       - label: ":full_moon: Omni · Doc Test with H100 · 2-GPU"
         source_file_dependencies: omni_qwen3_omni_doc
         timeout_in_minutes: 90
+        artifact_paths:
+          - audio_*.wav
         commands:
           - pytest -s -v tests/examples/ -m "full_model and omni and H100 and B200 and cards_2" --run-level "full_model"
 
@@ -156,6 +158,11 @@ steps:
       - label: ":full_moon: TTS · Function Test with L4"
         source_file_dependencies: tts_qwen3_tts_function
         timeout_in_minutes: 120
+        env:
+          VLLM_OMNI_FAILED_SPEECH_AUDIO_DIR: .
+        artifact_paths:
+          - failed_speech_*.wav
+          - failed_speech_*.json
         commands:
           - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
           - export VLLM_USE_DEEP_GEMM="0"
@@ -261,6 +268,12 @@ steps:
           - pytest -s -v tests/e2e/accuracy/test_qwen_image.py -m "H100 and B200 and cards_1" --run-level full_model
           - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2i_matches_diffusers' -m "full_model and H100 and B200 and cards_1" --run-level full_model
 
+      - label: ":full_moon: JoyAI-Image-Edit · Accuracy Test"
+        source_file_dependencies: diffusion_joy_image_accuracy
+        timeout_in_minutes: 180
+        commands:
+          - pytest -s -v tests/e2e/accuracy/test_joy_image_edit.py -m "full_model and diffusion and H100 and B200 and cards_1" --run-level full_model
+
       - label: ":full_moon: HunyuanImage3-DIT · Accuracy Test"
         source_file_dependencies: diffusion_hunyuan_image3_accuracy
         timeout_in_minutes: 180

.buildkite/cuda/test-ready.yml

@@ -105,7 +105,7 @@ steps:
 
       - label: "Diffusion · Other Test"
         commands:
-          - timeout 15m pytest -sv tests/diffusion/*.py tests/diffusion/ar_diffusion -m 'core_model and cuda' --run-level "core_model"
+          - timeout 15m pytest -sv tests/diffusion/*.py tests/diffusion/ar_diffusion tests/diffusion/layers/test_adalayernorm_*.py -m 'core_model and cuda' --run-level "core_model"
         mirror_hardwares: l4_1
 
   - group: ":card_index_dividers: Tiny Model Tests [Single GPU]"
@@ -122,7 +122,7 @@ steps:
     commands:
       - |
         timeout 15m bash -c "
-                  pytest -sv tests/engine/ tests/model_executor/ tests/worker/test_batched_omni_output.py tests/worker_v2/ -m 'core_model and cuda' --run-level 'core_model'
+                  pytest -sv tests/engine/ tests/model_executor/ tests/worker/test_batched_omni_output.py tests/worker/test_gpu_generation_async_output.py tests/worker_v2/ -m 'core_model and cuda' --run-level 'core_model'
         "
     mirror_hardwares: l4_1
 

.buildkite/intel/pipeline-intel.yml

@@ -10,7 +10,7 @@ steps:
           DOCKER_BUILDKIT: "1"
           # Buildkite will automatically replace this with the actual commit hash
           VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}"
-          VLLM_VERSION: "v0.30.0"
+          VLLM_VERSION: "v0.31.0"
         priority: 100
         timeout_in_minutes: 60
         soft_fail: false

apps/ComfyUI-vLLM-Omni/README.md

@@ -271,10 +271,10 @@ The [WF-05 template](example_workflows/vLLM-Omni%20MiniMax-H3%20Latent%20Mask%20
 Connect a **Latent Mask Editing** node to **Generate Video → latent_edit** to edit a source clip instead of generating from scratch. It uploads the source media and serializes the video/audio noise masks the MiniMax H3 API accepts:
 
 - `source_video` / `source_audio` — the media to edit.
-- `video_mask` — a ComfyUI mask image; `0` preserves a region, `1` regenerates it, fractional values blend. A 2D mask `[H, W]` is applied to every frame; a 3D mask `[T, H, W]` is treated as a temporal mask (one slice per frame) for continuation or extension. It is resized to the video latent grid.
+- `video_mask` — a ComfyUI mask image; `0` preserves a region, `1` regenerates it, fractional values blend. A 2D mask `[H, W]` is applied to every frame; a 3D mask `[T, H, W]` is treated as a temporal mask (one slice per frame) for continuation or extension. The client area-downsamples it by 16 to keep the upload small, and the server resizes it to the video latent grid.
 - `audio_mask` — a scalar in `[0, 1]`; `0` keeps the source audio, `1` regenerates it, fractional values blend.
 
-A non-trivial mask requires its matching source, and a source without a mask is rejected. This node only forwards inputs to the server; the served model must declare latent-mask editing support (MiniMax-H3).
+The node requires at least one mask; the server then enforces the cross-field rules (a source requires a matching mask, and a non-trivial mask requires its source). This node only forwards inputs to the server; the served model must declare latent-mask editing support (MiniMax-H3).
 
 ### TTS (e.g., Qwen TTS series)
 

apps/ComfyUI-vLLM-Omni/__init__.py

@@ -36,7 +36,6 @@ from .comfyui_vllm_omni.nodes import (
 
 # A dictionary that contains all nodes you want to export with their names
 NODE_CLASS_MAPPINGS = {
-    "VLLMOmniMiniMaxH3TemporalMask": VLLMOmniMiniMaxH3TemporalMask,
     # === Generation ===
     "VLLMOmniGenerateImage": VLLMOmniGenerateImage,
     "VLLMOmniGenerateVideo": VLLMOmniGenerateVideo,
@@ -46,6 +45,7 @@ NODE_CLASS_MAPPINGS = {
     "VLLMOmniVoiceClone": VLLMOmniVoiceClone,
     "VLLMOmniVideoReferences": VLLMOmniVideoReferences,
     "VLLMOmniLatentMaskEditing": VLLMOmniLatentMaskEditing,
+    "VLLMOmniMiniMaxH3TemporalMask": VLLMOmniMiniMaxH3TemporalMask,
     # === Params ===
     "VLLMOmniARSampling": VLLMOmniARSampling,
     "VLLMOmniDiffusionSampling": VLLMOmniDiffusionSampling,
@@ -59,7 +59,6 @@ NODE_CLASS_MAPPINGS = {
 
 # A dictionary that contains the friendly/humanly readable titles for the nodes
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "VLLMOmniMiniMaxH3TemporalMask": "MiniMax-H3 Temporal Mask",
     # === Generation ===
     "VLLMOmniGenerateImage": "Generate Image",
     "VLLMOmniGenerateVideo": "Generate Video",
@@ -69,6 +68,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "VLLMOmniVoiceClone": "TTS Voice Cloning",
     "VLLMOmniVideoReferences": "Video References",
     "VLLMOmniLatentMaskEditing": "Latent Mask Editing",
+    "VLLMOmniMiniMaxH3TemporalMask": "MiniMax-H3 Temporal Mask",
     # === Params ===
     "VLLMOmniARSampling": "AR Sampling Params",
     "VLLMOmniDiffusionSampling": "Diffusion Sampling Params",

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/nodes.py

@@ -8,7 +8,7 @@ import torch
 from comfy_api.input import AudioInput, VideoInput
 
 from .utils.api_client import VLLMOmniClient
-from .utils.latent_mask import _align_frame_count, _video_latent_t
+from .utils.latent_mask import _align_frame_count
 from .utils.logger import get_logger
 from .utils.models import lookup_model_spec
 from .utils.types import (
@@ -1133,13 +1133,13 @@ class VLLMOmniMiniMaxH3TemporalMask:
         else:
             raise ValueError(f"Unknown temporal mask mode: {mode}")
         available = min(frames, math.floor(boundary * 24 + 1e-8))
+        # Snap the preserved prefix to whole VAE clips (5 + 17n frames) so every
+        # latent it maps to is fully preserved.
         prefix = 0 if available < 5 else 5 + 17 * ((available - 5) // 17)
-        preserved = _video_latent_t(prefix) if prefix else 0
-        total = _video_latent_t(frames)
-        mask = torch.ones(total, 1, 1)
-        mask[:preserved] = 0
+        # One slice per output frame; the server pools frames to the latent grid.
+        mask = torch.ones(frames, 1, 1)
+        mask[:prefix] = 0
         indices = torch.arange(frames, device=images.device)
         source_indices = (indices * (source_fps / 24)).floor().long().clamp(max=images.shape[0] - 1)
         preview_images = images.index_select(0, source_indices)
-        preview_mask = mask.index_select(0, torch.arange(frames) * total // frames)
-        return mask, 24.0, preview_images, preview_mask
+        return mask, 24.0, preview_images, mask

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/api_client.py

@@ -30,7 +30,7 @@ from .format import (
     video_to_base64,
     video_to_bytes,
 )
-from .latent_mask import scalar_mask_to_json, video_mask_to_grid_json
+from .latent_mask import scalar_mask_to_json, video_mask_to_json
 from .logger import get_logger, pretty_printer
 from .models import lookup_model_spec
 from .types import (
@@ -388,6 +388,15 @@ class VLLMOmniClient:
                 content_type="image/png",
             )
 
+        if len(keyframe_images) == 2:
+            for image_filename, image in keyframe_images:
+                form.add_field(
+                    "input_references",
+                    image_tensor_to_png_bytes(image, image_filename),
+                    filename=image_filename,
+                    content_type="image/png",
+                )
+
         # === latent-mask editing (MiniMax H3) ===
         if latent_edit is not None:
             source_video = latent_edit.get("source_video")
@@ -398,21 +407,16 @@ class VLLMOmniClient:
             if video_mask is None and audio_mask is None:
                 raise ValueError("Latent-mask editing requires at least one mask.")
 
-            video_mask_trivial = video_mask is None or bool((video_mask == 1.0).all().item())
-            audio_mask_trivial = audio_mask is None or audio_mask == 1.0
-            if not video_mask_trivial and source_video is None:
-                raise ValueError("A non-trivial video mask requires a source video.")
-            if not audio_mask_trivial and source_audio is None and source_video is None:
-                raise ValueError("A non-trivial audio mask requires a source audio or a source video with audio.")
-
             if source_video is not None:
                 form.add_field(
                     "source_video",
-                    video_to_bytes(source_video, "source.mp4"),
+                    video_to_bytes(source_video),
                     filename="source.mp4",
                     content_type="video/mp4",
                 )
             if source_audio is not None:
+                # The filename extension selects the audio codec (mp3), so it is
+                # meaningful even though aiohttp uses the explicit filename below.
                 form.add_field(
                     "source_audio",
                     audio_to_bytes(source_audio, "source_audio.mp3"),
@@ -420,7 +424,7 @@ class VLLMOmniClient:
                     content_type="audio/mpeg",
                 )
             if video_mask is not None:
-                mask_json = video_mask_to_grid_json(video_mask, width=width, height=height, num_frames=num_frames)
+                mask_json = video_mask_to_json(video_mask)
                 form.add_field(
                     "video_noise_mask",
                     mask_json.encode("utf-8"),
@@ -435,15 +439,6 @@ class VLLMOmniClient:
                     content_type="application/json",
                 )
 
-        if len(keyframe_images) == 2:
-            for image_filename, image in keyframe_images:
-                form.add_field(
-                    "input_references",
-                    image_tensor_to_png_bytes(image, image_filename),
-                    filename=image_filename,
-                    content_type="image/png",
-                )
-
         # === model specific params. Either use a specialized builder, or add flattened fields as-is ===
         if model_params is not None:
             model_params = dict(model_params)

apps/ComfyUI-vLLM-Omni/comfyui_vllm_omni/utils/latent_mask.py

@@ -1,11 +1,25 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
+"""Client-side serialization of MiniMax-H3 latent-edit masks.
+
+A video mask is sent in frame space — a 2D spatial ``[H, W]`` (applied to every
+frame) or a 3D ``[T, H, W]`` (one slice per frame) — and the server resolves it
+to the latent grid. An audio mask is a scalar applied to all time steps.
+"""
+
 import json
+import math
 
 import torch
 import torch.nn.functional as F
 
+# The server rejects mask JSON above 8 MiB, so the spatial axes are
+# area-downsampled by the VAE spatial stride before upload. This depends only on
+# the stride, not on the H3 shape lattice, which the server still owns.
+_VAE_SPATIAL_STRIDE = 16
+_MASK_DECIMALS = 4
+
 
 def scalar_mask_to_json(value: float) -> str:
     if not 0.0 <= value <= 1.0:
@@ -13,6 +27,27 @@ def scalar_mask_to_json(value: float) -> str:
     return str(value)
 
 
+def video_mask_to_json(mask: torch.Tensor) -> str:
+    """Serialize a frame-space video mask to JSON for the ``video_noise_mask`` field.
+
+    ``mask`` is a 2D spatial mask or a 3D frame-space mask. Its spatial axes are
+    area-downsampled by the VAE stride; the server resizes the result to the
+    latent grid, so the client neither floors the canvas nor maps frames to
+    latents.
+    """
+    if mask.ndim not in (2, 3):
+        raise ValueError(f"expected a 2D or 3D mask tensor, got {mask.ndim}D")
+    frames = mask.unsqueeze(0) if mask.ndim == 2 else mask
+    size = tuple(max(1, math.ceil(dim / _VAE_SPATIAL_STRIDE)) for dim in frames.shape[1:])
+    frames = F.interpolate(frames.unsqueeze(1).float(), size=size, mode="area").squeeze(1)
+    if mask.ndim == 2:
+        frames = frames.squeeze(0)
+    # Round in float64 so tolist() emits short decimals rather than float32 noise.
+    return json.dumps(frames.double().round(decimals=_MASK_DECIMALS).tolist(), separators=(",", ":"))
+
+
+# The temporal-mask node snaps its preserve boundary to the H3 frame lattice;
+# this mirrors the server shape planner.
 def _align_frame_count(frame_count: int) -> int:
     if frame_count <= 0:
         return 1
@@ -20,32 +55,3 @@ def _align_frame_count(frame_count: int) -> int:
     while current % 17 != 5:
         current += 1
     return current
-
-
-def _video_latent_t(frame_count: int) -> int:
-    if frame_count <= 5:
-        return 2
-    return ((int(frame_count) - 5) // 17) * 5 + 2
-
-
-def video_mask_to_grid(mask: torch.Tensor, *, width: int, height: int, num_frames: int) -> torch.Tensor:
-    tv = _video_latent_t(_align_frame_count(num_frames))
-    height = int(height) // 32 * 32
-    width = int(width) // 32 * 32
-    gh, gw = height // 16, width // 16
-
-    if mask.ndim == 2:
-        mask = mask.unsqueeze(0)
-    elif mask.ndim != 3:
-        raise ValueError(f"expected a 2D or 3D mask tensor, got {mask.ndim}D")
-
-    grid = F.interpolate(mask.unsqueeze(1).float(), size=(gh, gw), mode="area").squeeze(1)
-    if grid.shape[0] == 1:
-        grid = grid.expand(tv, gh, gw)
-    elif grid.shape[0] != tv:
-        grid = F.interpolate(grid.unsqueeze(0).unsqueeze(0), size=(tv, gh, gw), mode="nearest").squeeze(0).squeeze(0)
-    return grid
-
-
-def video_mask_to_grid_json(mask: torch.Tensor, *, width: int, height: int, num_frames: int) -> str:
-    return json.dumps(video_mask_to_grid(mask, width=width, height=height, num_frames=num_frames).tolist())

apps/ComfyUI-vLLM-Omni/docs/wf05-h3-latent-editing.md

@@ -12,6 +12,6 @@ Edit a source video with MiniMax-H3 using spatial or temporal masks. The workflo
 Open **vLLM-Omni MiniMax-H3 Latent Mask Editing.json** from the example workflows and set the Generate Video URL/model for the case you want to run.
 
 1. Upload your source video in Load Video.
-2. Adjust the case's mask. For removal and inpainting, set Mask Canvas to the video dimensions, Mask Rectangle to the region size, and Combine Masks x/y to its position. For continuation and extension, set MiniMax-H3 Temporal Mask's mode and duration; continuation also uses `preserve_fraction`. Match the duration in Generate Video, and use a duration longer than the source for extension. Mask values are `0` to preserve and `1` to regenerate.
+2. Adjust the case's mask. For removal and inpainting, set Mask Canvas to the video dimensions, Mask Rectangle to the region size, and Combine Masks x/y to its position, or replace that chain with Load Image (as Mask) for an arbitrary-shape mask. For continuation and extension, set MiniMax-H3 Temporal Mask's mode and duration; continuation also uses `preserve_fraction`. Match the duration in Generate Video, and use a duration longer than the source for extension. Mask values are `0` to preserve and `1` to regenerate.
 3. Adjust the prompt to describe the desired content. Set the output dimensions and duration in Generate Video. If editing audio, update the sound description and audio mask (`0` preserves, `1` regenerates the whole clip).
 4. Select the case's Mask Preview Save Video node and choose **Execute to selected output nodes** to preview the mask as a red overlay without inference, or select its Result Save Video node to generate the edited video. Running the entire workflow generates all four cases.

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni Latent Mask Editing - Image Mask.json

@@ -1,499 +0,0 @@
-{
-  "id": "latent-mask-editing-7380",
-  "revision": 0,
-  "last_node_id": 7,
-  "last_link_id": 5,
-  "nodes": [
-    {
-      "id": 1,
-      "type": "MarkdownNote",
-      "pos": [
-        40,
-        20
-      ],
-      "size": [
-        420,
-        210
-      ],
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [],
-      "title": "Latent-mask editing server prerequisite",
-      "properties": {},
-      "widgets_values": [
-        "Requires a vLLM-Omni server that declares latent-mask editing support (MiniMax-H3, EDIT-01). The source clip and video/audio noise masks are uploaded to /v1/videos; 0 preserves, 1 regenerates, fractional blends."
-      ],
-      "color": "#432",
-      "bgcolor": "#000"
-    },
-    {
-      "id": 2,
-      "type": "LoadVideo",
-      "pos": [
-        40,
-        280
-      ],
-      "size": [
-        420,
-        120
-      ],
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "file",
-          "name": "file",
-          "type": "COMBO",
-          "widget": {
-            "name": "file"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "video",
-          "name": "video",
-          "type": "VIDEO",
-          "links": [
-            1
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LoadVideo"
-      },
-      "widgets_values": [
-        ""
-      ]
-    },
-    {
-      "id": 3,
-      "type": "LoadImageMask",
-      "pos": [
-        40,
-        460
-      ],
-      "size": [
-        420,
-        160
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "image",
-          "type": "STRING",
-          "widget": {
-            "name": "image"
-          },
-          "link": null
-        },
-        {
-          "name": "channel",
-          "type": "STRING",
-          "widget": {
-            "name": "channel"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "mask",
-          "name": "mask",
-          "type": "MASK",
-          "links": [
-            2
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LoadImageMask"
-      },
-      "widgets_values": [
-        "",
-        "red"
-      ]
-    },
-    {
-      "id": 4,
-      "type": "VLLMOmniLatentMaskEditing",
-      "pos": [
-        560,
-        260
-      ],
-      "size": [
-        420,
-        300
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "source_video",
-          "name": "source_video",
-          "type": "VIDEO",
-          "link": 1,
-          "shape": 7
-        },
-        {
-          "localized_name": "source_audio",
-          "name": "source_audio",
-          "type": "AUDIO",
-          "link": null
-        },
-        {
-          "localized_name": "video_mask",
-          "name": "video_mask",
-          "type": "MASK",
-          "link": 2,
-          "shape": 7
-        },
-        {
-          "localized_name": "audio_mask",
-          "name": "audio_mask",
-          "type": "FLOAT",
-          "widget": {
-            "name": "audio_mask"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "latent_edit",
-          "name": "latent_edit",
-          "type": "LATENT_MASK_EDITING",
-          "links": [
-            3
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VLLMOmniLatentMaskEditing"
-      },
-      "widgets_values": [
-        0.5
-      ]
-    },
-    {
-      "id": 5,
-      "type": "VLLMOmniMiniMaxH3Params",
-      "pos": [
-        40,
-        680
-      ],
-      "size": [
-        420,
-        140
-      ],
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "audio_flow_shift",
-          "name": "audio_flow_shift",
-          "type": "FLOAT",
-          "widget": {
-            "name": "audio_flow_shift"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "flow_shift",
-          "name": "flow_shift",
-          "type": "FLOAT",
-          "widget": {
-            "name": "flow_shift"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "video params",
-          "name": "video params",
-          "type": "VIDEO_PARAMS",
-          "links": [
-            4
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VLLMOmniMiniMaxH3Params"
-      },
-      "widgets_values": [
-        3.0,
-        12.0
-      ]
-    },
-    {
-      "id": 6,
-      "type": "VLLMOmniGenerateVideo",
-      "pos": [
-        1080,
-        200
-      ],
-      "size": [
-        540,
-        680
-      ],
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "frame",
-          "name": "frame",
-          "type": "IMAGE",
-          "link": null,
-          "shape": 7
-        },
-        {
-          "localized_name": "references",
-          "name": "references",
-          "type": "VIDEO_REFERENCES",
-          "link": null,
-          "shape": 7
-        },
-        {
-          "localized_name": "sampling_params",
-          "name": "sampling_params",
-          "type": "SAMPLING_PARAMS",
-          "link": null
-        },
-        {
-          "localized_name": "lora",
-          "name": "lora",
-          "type": "REMOTE_LORA",
-          "link": null
-        },
-        {
-          "localized_name": "model_params",
-          "name": "model_params",
-          "type": "VIDEO_PARAMS",
-          "link": 4,
-          "shape": 7
-        },
-        {
-          "localized_name": "fast_h3",
-          "name": "fast_h3",
-          "type": "FASTH3_DEPLOYMENT",
-          "link": null
-        },
-        {
-          "localized_name": "latent_edit",
-          "name": "latent_edit",
-          "type": "LATENT_MASK_EDITING",
-          "link": 3,
-          "shape": 7
-        },
-        {
-          "localized_name": "url",
-          "name": "url",
-          "type": "STRING",
-          "widget": {
-            "name": "url"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "model",
-          "name": "model",
-          "type": "STRING",
-          "widget": {
-            "name": "model"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "prompt",
-          "name": "prompt",
-          "type": "STRING",
-          "widget": {
-            "name": "prompt"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "negative_prompt",
-          "name": "negative_prompt",
-          "type": "STRING",
-          "widget": {
-            "name": "negative_prompt"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "width",
-          "name": "width",
-          "type": "INT",
-          "widget": {
-            "name": "width"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "height",
-          "name": "height",
-          "type": "INT",
-          "widget": {
-            "name": "height"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "fps",
-          "name": "fps",
-          "type": "INT",
-          "widget": {
-            "name": "fps"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "duration",
-          "name": "duration",
-          "type": "FLOAT",
-          "widget": {
-            "name": "duration"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "video",
-          "name": "video",
-          "type": "VIDEO",
-          "links": [
-            5
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VLLMOmniGenerateVideo"
-      },
-      "widgets_values": [
-        "http://127.0.0.1:8000/v1",
-        "MiniMaxAI/MiniMax-H3",
-        "Partially restyle the clip and soundtrack",
-        "",
-        160,
-        120,
-        24,
-        4.458
-      ]
-    },
-    {
-      "id": 7,
-      "type": "SaveVideo",
-      "pos": [
-        1700,
-        300
-      ],
-      "size": [
-        420,
-        160
-      ],
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "video",
-          "name": "video",
-          "type": "VIDEO",
-          "link": 5,
-          "shape": 7
-        },
-        {
-          "localized_name": "filename_prefix",
-          "name": "filename_prefix",
-          "type": "STRING",
-          "widget": {
-            "name": "filename_prefix"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "format",
-          "name": "format",
-          "type": "COMFY_DYNAMICCOMBO_V3",
-          "widget": {
-            "name": "format"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "video",
-          "name": "video",
-          "type": "VIDEO",
-          "links": []
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "SaveVideo"
-      },
-      "widgets_values": [
-        "vllm-omni/latent-edit",
-        "mp4"
-      ]
-    }
-  ],
-  "links": [
-    [
-      1,
-      2,
-      0,
-      4,
-      0,
-      "VIDEO"
-    ],
-    [
-      2,
-      3,
-      0,
-      4,
-      2,
-      "MASK"
-    ],
-    [
-      3,
-      4,
-      0,
-      6,
-      6,
-      "LATENT_MASK_EDITING"
-    ],
-    [
-      4,
-      5,
-      0,
-      6,
-      4,
-      "VIDEO_PARAMS"
-    ],
-    [
-      5,
-      6,
-      0,
-      7,
-      0,
-      "VIDEO"
-    ]
-  ],
-  "groups": [],
-  "config": {},
-  "extra": {},
-  "version": 0.4
-}

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni Latent Mask Editing - Temporal Mask.json

@@ -1,560 +0,0 @@
-{
-  "last_node_id": 9,
-  "last_link_id": 7,
-  "nodes": [
-    {
-      "id": 2,
-      "type": "LoadVideo",
-      "pos": [
-        40,
-        100
-      ],
-      "size": [
-        420,
-        120
-      ],
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "file",
-          "type": "STRING",
-          "widget": {
-            "name": "file"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "name": "video",
-          "type": "VIDEO",
-          "links": [
-            1
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LoadVideo"
-      },
-      "widgets_values": [
-        ""
-      ]
-    },
-    {
-      "id": 3,
-      "type": "SolidMask",
-      "pos": [
-        40,
-        300
-      ],
-      "size": [
-        420,
-        160
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "value",
-          "type": "FLOAT",
-          "widget": {
-            "name": "value"
-          },
-          "link": null
-        },
-        {
-          "name": "width",
-          "type": "INT",
-          "widget": {
-            "name": "width"
-          },
-          "link": null
-        },
-        {
-          "name": "height",
-          "type": "INT",
-          "widget": {
-            "name": "height"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "name": "mask",
-          "type": "MASK",
-          "links": [
-            2
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "SolidMask"
-      },
-      "widgets_values": [
-        0.0,
-        160,
-        120
-      ]
-    },
-    {
-      "id": 4,
-      "type": "SolidMask",
-      "pos": [
-        40,
-        500
-      ],
-      "size": [
-        420,
-        160
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "value",
-          "type": "FLOAT",
-          "widget": {
-            "name": "value"
-          },
-          "link": null
-        },
-        {
-          "name": "width",
-          "type": "INT",
-          "widget": {
-            "name": "width"
-          },
-          "link": null
-        },
-        {
-          "name": "height",
-          "type": "INT",
-          "widget": {
-            "name": "height"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "name": "mask",
-          "type": "MASK",
-          "links": [
-            3
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "SolidMask"
-      },
-      "widgets_values": [
-        1.0,
-        160,
-        120
-      ]
-    },
-    {
-      "id": 5,
-      "type": "BatchMasksNode",
-      "pos": [
-        250,
-        380
-      ],
-      "size": [
-        200,
-        100
-      ],
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "masks.mask0",
-          "type": "MASK",
-          "link": 2,
-          "slot_index": 0
-        },
-        {
-          "name": "masks.mask1",
-          "type": "MASK",
-          "link": 3,
-          "slot_index": 1
-        }
-      ],
-      "outputs": [
-        {
-          "name": "MASK",
-          "type": "MASK",
-          "links": [
-            4
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "BatchMasksNode"
-      },
-      "widgets_values": []
-    },
-    {
-      "id": 6,
-      "type": "VLLMOmniLatentMaskEditing",
-      "pos": [
-        450,
-        180
-      ],
-      "size": [
-        400,
-        120
-      ],
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "source_video",
-          "type": "VIDEO",
-          "link": 1
-        },
-        {
-          "name": "source_audio",
-          "type": "AUDIO",
-          "link": null
-        },
-        {
-          "name": "video_mask",
-          "type": "MASK",
-          "link": 4
-        },
-        {
-          "name": "audio_mask",
-          "type": "FLOAT",
-          "widget": {
-            "name": "audio_mask"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "name": "latent_edit",
-          "type": "LATENT_MASK_EDITING",
-          "links": [
-            5
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VLLMOmniLatentMaskEditing"
-      },
-      "widgets_values": [
-        -1.0
-      ]
-    },
-    {
-      "id": 7,
-      "type": "VLLMOmniMiniMaxH3Params",
-      "pos": [
-        40,
-        700
-      ],
-      "size": [
-        420,
-        160
-      ],
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "audio_flow_shift",
-          "type": "FLOAT",
-          "widget": {
-            "name": "audio_flow_shift"
-          },
-          "link": null
-        },
-        {
-          "name": "flow_shift",
-          "type": "FLOAT",
-          "widget": {
-            "name": "flow_shift"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "name": "video params",
-          "type": "VIDEO_PARAMS",
-          "links": [
-            6
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VLLMOmniMiniMaxH3Params"
-      },
-      "widgets_values": [
-        3.0,
-        12.0
-      ]
-    },
-    {
-      "id": 8,
-      "type": "VLLMOmniGenerateVideo",
-      "pos": [
-        700,
-        180
-      ],
-      "size": [
-        450,
-        380
-      ],
-      "flags": {},
-      "order": 7,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "frame",
-          "type": "IMAGE",
-          "link": null
-        },
-        {
-          "name": "references",
-          "type": "VIDEO_REFERENCES",
-          "link": null
-        },
-        {
-          "name": "sampling_params",
-          "type": "SAMPLING_PARAMS",
-          "link": null
-        },
-        {
-          "name": "lora",
-          "type": "LORA",
-          "link": null
-        },
-        {
-          "name": "model_params",
-          "type": "VIDEO_PARAMS",
-          "link": 6
-        },
-        {
-          "name": "fast_h3",
-          "type": "FAST_H3",
-          "link": null
-        },
-        {
-          "name": "latent_edit",
-          "type": "LATENT_MASK_EDITING",
-          "link": 5
-        },
-        {
-          "name": "url",
-          "type": "STRING",
-          "widget": {
-            "name": "url"
-          },
-          "link": null
-        },
-        {
-          "name": "model",
-          "type": "STRING",
-          "widget": {
-            "name": "model"
-          },
-          "link": null
-        },
-        {
-          "name": "prompt",
-          "type": "STRING",
-          "widget": {
-            "name": "prompt"
-          },
-          "link": null
-        },
-        {
-          "name": "negative_prompt",
-          "type": "STRING",
-          "widget": {
-            "name": "negative_prompt"
-          },
-          "link": null
-        },
-        {
-          "name": "width",
-          "type": "INT",
-          "widget": {
-            "name": "width"
-          },
-          "link": null
-        },
-        {
-          "name": "height",
-          "type": "INT",
-          "widget": {
-            "name": "height"
-          },
-          "link": null
-        },
-        {
-          "name": "fps",
-          "type": "INT",
-          "widget": {
-            "name": "fps"
-          },
-          "link": null
-        },
-        {
-          "name": "duration",
-          "type": "FLOAT",
-          "widget": {
-            "name": "duration"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "name": "video",
-          "type": "VIDEO",
-          "links": [
-            7
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VLLMOmniGenerateVideo"
-      },
-      "widgets_values": [
-        "http://127.0.0.1:8000/v1",
-        "MiniMaxAI/MiniMax-H3",
-        "temporal mask: regenerate the ending",
-        "",
-        160,
-        120,
-        24,
-        4.458
-      ]
-    },
-    {
-      "id": 9,
-      "type": "SaveVideo",
-      "pos": [
-        900,
-        180
-      ],
-      "size": [
-        420,
-        200
-      ],
-      "flags": {},
-      "order": 8,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "video",
-          "type": "VIDEO",
-          "link": 7
-        },
-        {
-          "name": "filename_prefix",
-          "type": "STRING",
-          "widget": {
-            "name": "filename_prefix"
-          },
-          "link": null
-        },
-        {
-          "name": "format",
-          "type": "STRING",
-          "widget": {
-            "name": "format"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "name": "video",
-          "type": "VIDEO",
-          "links": []
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "SaveVideo"
-      },
-      "widgets_values": [
-        "temporal_mask",
-        "mp4"
-      ]
-    }
-  ],
-  "links": [
-    [
-      1,
-      2,
-      0,
-      6,
-      0,
-      "VIDEO"
-    ],
-    [
-      2,
-      3,
-      0,
-      5,
-      0,
-      "MASK"
-    ],
-    [
-      3,
-      4,
-      0,
-      5,
-      1,
-      "MASK"
-    ],
-    [
-      4,
-      5,
-      0,
-      6,
-      2,
-      "MASK"
-    ],
-    [
-      5,
-      6,
-      0,
-      8,
-      6,
-      "LATENT_MASK_EDITING"
-    ],
-    [
-      6,
-      7,
-      0,
-      8,
-      4,
-      "VIDEO_PARAMS"
-    ],
-    [
-      7,
-      8,
-      0,
-      9,
-      0,
-      "VIDEO"
-    ]
-  ],
-  "groups": [],
-  "config": {},
-  "extra": {},
-  "version": 0.4
-}

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni Latent Mask Editing.json

@@ -1,511 +0,0 @@
-{
-  "id": "latent-mask-editing-7380",
-  "revision": 0,
-  "last_node_id": 7,
-  "last_link_id": 5,
-  "nodes": [
-    {
-      "id": 1,
-      "type": "MarkdownNote",
-      "pos": [
-        40,
-        20
-      ],
-      "size": [
-        420,
-        210
-      ],
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [],
-      "title": "Latent-mask editing server prerequisite",
-      "properties": {},
-      "widgets_values": [
-        "Requires a vLLM-Omni server that declares latent-mask editing support (MiniMax-H3, EDIT-01). The source clip and video/audio noise masks are uploaded to /v1/videos; 0 preserves, 1 regenerates, fractional blends."
-      ],
-      "color": "#432",
-      "bgcolor": "#000"
-    },
-    {
-      "id": 2,
-      "type": "LoadVideo",
-      "pos": [
-        40,
-        280
-      ],
-      "size": [
-        420,
-        120
-      ],
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "file",
-          "name": "file",
-          "type": "COMBO",
-          "widget": {
-            "name": "file"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "video",
-          "name": "video",
-          "type": "VIDEO",
-          "links": [
-            1
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LoadVideo"
-      },
-      "widgets_values": [
-        ""
-      ]
-    },
-    {
-      "id": 3,
-      "type": "SolidMask",
-      "pos": [
-        40,
-        460
-      ],
-      "size": [
-        420,
-        160
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "value",
-          "name": "value",
-          "type": "FLOAT",
-          "widget": {
-            "name": "value"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "width",
-          "name": "width",
-          "type": "INT",
-          "widget": {
-            "name": "width"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "height",
-          "name": "height",
-          "type": "INT",
-          "widget": {
-            "name": "height"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "mask",
-          "name": "mask",
-          "type": "MASK",
-          "links": [
-            2
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "SolidMask"
-      },
-      "widgets_values": [
-        1.0,
-        160,
-        120
-      ]
-    },
-    {
-      "id": 4,
-      "type": "VLLMOmniLatentMaskEditing",
-      "pos": [
-        560,
-        260
-      ],
-      "size": [
-        420,
-        300
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "source_video",
-          "name": "source_video",
-          "type": "VIDEO",
-          "link": 1,
-          "shape": 7
-        },
-        {
-          "localized_name": "source_audio",
-          "name": "source_audio",
-          "type": "AUDIO",
-          "link": null
-        },
-        {
-          "localized_name": "video_mask",
-          "name": "video_mask",
-          "type": "MASK",
-          "link": 2,
-          "shape": 7
-        },
-        {
-          "localized_name": "audio_mask",
-          "name": "audio_mask",
-          "type": "FLOAT",
-          "widget": {
-            "name": "audio_mask"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "latent_edit",
-          "name": "latent_edit",
-          "type": "LATENT_MASK_EDITING",
-          "links": [
-            3
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VLLMOmniLatentMaskEditing"
-      },
-      "widgets_values": [
-        0.5
-      ]
-    },
-    {
-      "id": 5,
-      "type": "VLLMOmniMiniMaxH3Params",
-      "pos": [
-        40,
-        680
-      ],
-      "size": [
-        420,
-        140
-      ],
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "audio_flow_shift",
-          "name": "audio_flow_shift",
-          "type": "FLOAT",
-          "widget": {
-            "name": "audio_flow_shift"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "flow_shift",
-          "name": "flow_shift",
-          "type": "FLOAT",
-          "widget": {
-            "name": "flow_shift"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "video params",
-          "name": "video params",
-          "type": "VIDEO_PARAMS",
-          "links": [
-            4
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VLLMOmniMiniMaxH3Params"
-      },
-      "widgets_values": [
-        3.0,
-        12.0
-      ]
-    },
-    {
-      "id": 6,
-      "type": "VLLMOmniGenerateVideo",
-      "pos": [
-        1080,
-        200
-      ],
-      "size": [
-        540,
-        680
-      ],
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "frame",
-          "name": "frame",
-          "type": "IMAGE",
-          "link": null,
-          "shape": 7
-        },
-        {
-          "localized_name": "references",
-          "name": "references",
-          "type": "VIDEO_REFERENCES",
-          "link": null,
-          "shape": 7
-        },
-        {
-          "localized_name": "sampling_params",
-          "name": "sampling_params",
-          "type": "SAMPLING_PARAMS",
-          "link": null
-        },
-        {
-          "localized_name": "lora",
-          "name": "lora",
-          "type": "REMOTE_LORA",
-          "link": null
-        },
-        {
-          "localized_name": "model_params",
-          "name": "model_params",
-          "type": "VIDEO_PARAMS",
-          "link": 4,
-          "shape": 7
-        },
-        {
-          "localized_name": "fast_h3",
-          "name": "fast_h3",
-          "type": "FASTH3_DEPLOYMENT",
-          "link": null
-        },
-        {
-          "localized_name": "latent_edit",
-          "name": "latent_edit",
-          "type": "LATENT_MASK_EDITING",
-          "link": 3,
-          "shape": 7
-        },
-        {
-          "localized_name": "url",
-          "name": "url",
-          "type": "STRING",
-          "widget": {
-            "name": "url"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "model",
-          "name": "model",
-          "type": "STRING",
-          "widget": {
-            "name": "model"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "prompt",
-          "name": "prompt",
-          "type": "STRING",
-          "widget": {
-            "name": "prompt"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "negative_prompt",
-          "name": "negative_prompt",
-          "type": "STRING",
-          "widget": {
-            "name": "negative_prompt"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "width",
-          "name": "width",
-          "type": "INT",
-          "widget": {
-            "name": "width"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "height",
-          "name": "height",
-          "type": "INT",
-          "widget": {
-            "name": "height"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "fps",
-          "name": "fps",
-          "type": "INT",
-          "widget": {
-            "name": "fps"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "duration",
-          "name": "duration",
-          "type": "FLOAT",
-          "widget": {
-            "name": "duration"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "video",
-          "name": "video",
-          "type": "VIDEO",
-          "links": [
-            5
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VLLMOmniGenerateVideo"
-      },
-      "widgets_values": [
-        "http://127.0.0.1:8000/v1",
-        "MiniMaxAI/MiniMax-H3",
-        "Partially restyle the clip and soundtrack",
-        "",
-        160,
-        120,
-        24,
-        4.458
-      ]
-    },
-    {
-      "id": 7,
-      "type": "SaveVideo",
-      "pos": [
-        1700,
-        300
-      ],
-      "size": [
-        420,
-        160
-      ],
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [
-        {
-          "localized_name": "video",
-          "name": "video",
-          "type": "VIDEO",
-          "link": 5,
-          "shape": 7
-        },
-        {
-          "localized_name": "filename_prefix",
-          "name": "filename_prefix",
-          "type": "STRING",
-          "widget": {
-            "name": "filename_prefix"
-          },
-          "link": null
-        },
-        {
-          "localized_name": "format",
-          "name": "format",
-          "type": "COMFY_DYNAMICCOMBO_V3",
-          "widget": {
-            "name": "format"
-          },
-          "link": null
-        }
-      ],
-      "outputs": [
-        {
-          "localized_name": "video",
-          "name": "video",
-          "type": "VIDEO",
-          "links": []
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "SaveVideo"
-      },
-      "widgets_values": [
-        "vllm-omni/latent-edit",
-        "mp4"
-      ]
-    }
-  ],
-  "links": [
-    [
-      1,
-      2,
-      0,
-      4,
-      0,
-      "VIDEO"
-    ],
-    [
-      2,
-      3,
-      0,
-      4,
-      2,
-      "MASK"
-    ],
-    [
-      3,
-      4,
-      0,
-      6,
-      6,
-      "LATENT_MASK_EDITING"
-    ],
-    [
-      4,
-      5,
-      0,
-      6,
-      4,
-      "VIDEO_PARAMS"
-    ],
-    [
-      5,
-      6,
-      0,
-      7,
-      0,
-      "VIDEO"
-    ]
-  ],
-  "groups": [],
-  "config": {},
-  "extra": {},
-  "version": 0.4
-}

apps/ComfyUI-vLLM-Omni/example_workflows/vLLM-Omni MiniMax-H3 Latent Mask Editing.json

@@ -2293,10 +2293,10 @@
       "title": "Usage Guide",
       "properties": {},
       "widgets_values": [
-        "Use a compatible MiniMax-H3 latent-edit server. Select a source video and set the server URL/model in each Generate Video node.\n\nSelect one Save Preview or Save Video output, then use Execute to selected output nodes. Running the whole workflow submits all four cases.\n\nMask: 0 preserves source latents; 1 regenerates. Audio mask applies to the whole clip, not individual sounds. Preview overlays are silent and do not run inference."
+        "Use a compatible MiniMax-H3 latent-edit server. Select a source video and set the server URL/model in each Generate Video node.\n\nSelect one Save Preview or Save Video output, then use Execute to selected output nodes. Running the whole workflow submits all four cases.\n\nMask: 0 preserves source latents; 1 regenerates. For an arbitrary-shape spatial mask, replace a case's Mask Rectangle chain with Load Image (as Mask). Audio mask applies to the whole clip, not individual sounds. Preview overlays are silent and do not run inference."
       ],
       "widgets_values_named": {
-        "text": "Use a compatible MiniMax-H3 latent-edit server. Select a source video and set the server URL/model in each Generate Video node.\n\nSelect one Save Preview or Save Video output, then use Execute to selected output nodes. Running the whole workflow submits all four cases.\n\nMask: 0 preserves source latents; 1 regenerates. Audio mask applies to the whole clip, not individual sounds. Preview overlays are silent and do not run inference."
+        "text": "Use a compatible MiniMax-H3 latent-edit server. Select a source video and set the server URL/model in each Generate Video node.\n\nSelect one Save Preview or Save Video output, then use Execute to selected output nodes. Running the whole workflow submits all four cases.\n\nMask: 0 preserves source latents; 1 regenerates. For an arbitrary-shape spatial mask, replace a case's Mask Rectangle chain with Load Image (as Mask). Audio mask applies to the whole clip, not individual sounds. Preview overlays are silent and do not run inference."
       },
       "color": "#432",
       "bgcolor": "#000"
@@ -2823,7 +2823,7 @@
         "Node name for S&R": "VLLMOmniGenerateVideo"
       },
       "widgets_values": [
-        "http://127.0.0.1:8001/v1",
+        "http://127.0.0.1:8000/v1",
         "MiniMaxAI/MiniMax-H3",
         "integrated_multimodal_description: The masked area is a flat, seamless continuation of the surrounding wall, matching its color, texture, and lighting. The wall surface remains consistent throughout the video.\n\noverall_soundscape: \n\nnon_diegetic_music: ",
         "",
@@ -2833,7 +2833,7 @@
         5
       ],
       "widgets_values_named": {
-        "url": "http://127.0.0.1:8001/v1",
+        "url": "http://127.0.0.1:8000/v1",
         "model": "MiniMaxAI/MiniMax-H3",
         "prompt": "integrated_multimodal_description: The masked area is a flat, seamless continuation of the surrounding wall, matching its color, texture, and lighting. The wall surface remains consistent throughout the video.\n\noverall_soundscape: \n\nnon_diegetic_music: ",
         "negative_prompt": "",
@@ -3677,7 +3677,7 @@
         "Node name for S&R": "VLLMOmniGenerateVideo"
       },
       "widgets_values": [
-        "http://127.0.0.1:8001/v1",
+        "http://127.0.0.1:8000/v1",
         "MiniMaxAI/MiniMax-H3",
         "integrated_multimodal_description: A small orange cat rests curled up in the masked area, gently swaying its tail from side to side. Its appearance matches the surrounding anime style, lighting, and shadows.\n\noverall_soundscape:\n\nnon_diegetic_music: ",
         "",
@@ -3687,7 +3687,7 @@
         5
       ],
       "widgets_values_named": {
-        "url": "http://127.0.0.1:8001/v1",
+        "url": "http://127.0.0.1:8000/v1",
         "model": "MiniMaxAI/MiniMax-H3",
         "prompt": "integrated_multimodal_description: A small orange cat rests curled up in the masked area, gently swaying its tail from side to side. Its appearance matches the surrounding anime style, lighting, and shadows.\n\noverall_soundscape:\n\nnon_diegetic_music: ",
         "negative_prompt": "",
@@ -3916,7 +3916,7 @@
         "Node name for S&R": "VLLMOmniGenerateVideo"
       },
       "widgets_values": [
-        "http://127.0.0.1:8001/v1",
+        "http://127.0.0.1:8000/v1",
         "MiniMaxAI/MiniMax-H3",
         "integrated_multimodal_description: The sky turns completely overcast, with the sun hidden and no direct sunlight, as torrential rain pours over the school entrance in dense visible streaks and splashes on the wet pavement. Distant lightning flashes in the sky behind the school buildings.\n\noverall_soundscape: Loud, steady rainfall, splashing water, and gusts of wind.\n\nnon_diegetic_music: None.",
         "",
@@ -3926,7 +3926,7 @@
         5
       ],
       "widgets_values_named": {
-        "url": "http://127.0.0.1:8001/v1",
+        "url": "http://127.0.0.1:8000/v1",
         "model": "MiniMaxAI/MiniMax-H3",
         "prompt": "integrated_multimodal_description: The sky turns completely overcast, with the sun hidden and no direct sunlight, as torrential rain pours over the school entrance in dense visible streaks and splashes on the wet pavement. Distant lightning flashes in the sky behind the school buildings.\n\noverall_soundscape: Loud, steady rainfall, splashing water, and gusts of wind.\n\nnon_diegetic_music: None.",
         "negative_prompt": "",
@@ -4363,7 +4363,7 @@
         "Node name for S&R": "VLLMOmniGenerateVideo"
       },
       "widgets_values": [
-        "http://127.0.0.1:8001/v1",
+        "http://127.0.0.1:8000/v1",
         "MiniMaxAI/MiniMax-H3",
         "integrated_multimodal_description: Cut to the same school on a summer night beneath a deep blue sky dotted with stars. A firework rises from behind the school buildings, leaving a glowing trail; the camera slowly tilts upward to follow it into the sky, ending as it bursts into a large bloom of colorful sparks.\n\noverall_soundscape: Soft summer insect sounds, followed by the firework rising with a whistle and bursting with a distant boom and crackling sparks.\n\nnon_diegetic_music: N/A",
         "",
@@ -4373,7 +4373,7 @@
         10
       ],
       "widgets_values_named": {
-        "url": "http://127.0.0.1:8001/v1",
+        "url": "http://127.0.0.1:8000/v1",
         "model": "MiniMaxAI/MiniMax-H3",
         "prompt": "integrated_multimodal_description: Cut to the same school on a summer night beneath a deep blue sky dotted with stars. A firework rises from behind the school buildings, leaving a glowing trail; the camera slowly tilts upward to follow it into the sky, ending as it bursts into a large bloom of colorful sparks.\n\noverall_soundscape: Soft summer insect sounds, followed by the firework rising with a whistle and bursting with a distant boom and crackling sparks.\n\nnon_diegetic_music: N/A",
         "negative_prompt": "",

benchmarks/diffusion/bench_wan_vae_decode.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Benchmark and exactness check for the Wan VAE decoder fast path.
 
 Loads a diffusers Wan VAE (by default the Cosmos3 one), decodes seeded latents
@@ -30,10 +30,13 @@ return the full video on rank 0 only, so equality and PSNR are computed there.
 from __future__ import annotations
 
 import argparse
+import json
 import math
 import os
+import statistics
 import time
 from collections.abc import Iterable
+from unittest.mock import patch
 
 import torch
 import torch.distributed as dist
@@ -82,6 +85,10 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--profile", action="store_true", help="Print a torch.profiler CUDA kernel table")
     parser.add_argument("--profile-rows", type=int, default=25)
     parser.add_argument("--save-output", default=None, help="Save the `off` decode output to this .pt path")
+    parser.add_argument(
+        "--first-frame-ablation", action="store_true", help="Compare channels_last with/without first-frame Conv2d"
+    )
+    parser.add_argument("--latents", help="For ablation: saved VAE-input tensor (.pt), after latent mean/std scaling")
     parser.add_argument(
         "--vae-patch-parallel-size",
         type=int,
@@ -254,8 +261,99 @@ def run_level(
     return output.detach(), stats
 
 
+@torch.inference_mode()
+def run_first_frame_ablation(args: argparse.Namespace, dtype: torch.dtype, device: torch.device) -> None:
+    """Same-model ABBA comparison; disable only the first-frame helper in the reference arm."""
+    from vllm_omni.diffusion.distributed.autoencoders.wan_vae_fastpath import forwards
+
+    if args.iters < 2 or args.warmup < 0:
+        raise ValueError("Ablation requires --iters >= 2 and --warmup >= 0")
+    vae = load_vae(args, dtype, device, parallel=False)
+    report = install_wan_vae_fastpath(vae, level="channels_last")
+    if not report.installed:
+        raise RuntimeError(f"channels_last installation failed: {report.reason}")
+    latents = make_latents(vae, args, dtype, device)
+    if args.latents:
+        saved = torch.load(args.latents, map_location="cpu", weights_only=True)
+        saved = saved[:, :, : latents.shape[2]]
+        if saved.shape != latents.shape:
+            raise ValueError(f"Saved latent shape {saved.shape} does not match {latents.shape}")
+        latents = saved.to(device=device, dtype=dtype).contiguous()
+    helpers = {"baseline": lambda conv, x, bias: None, "candidate": forwards._first_frame_conv2d}
+    optimized_calls = 0
+
+    def counted(conv, x, bias):
+        nonlocal optimized_calls
+        result = helpers["candidate"](conv, x, bias)
+        optimized_calls += result is not None
+        return result
+
+    with patch.object(forwards, "_first_frame_conv2d", counted):
+        decode(vae, latents)
+        sync()
+    if optimized_calls == 0:
+        raise RuntimeError("The selected workload did not exercise first-frame Conv2d")
+    measurements = []
+    outputs: dict[str, list[torch.Tensor]] = {arm: [] for arm in helpers}
+    for _ in range(args.warmup):
+        for helper in helpers.values():
+            with patch.object(forwards, "_first_frame_conv2d", helper):
+                decode(vae, latents)
+                sync()
+    for iteration in range(args.iters):
+        # Alternating AB and BA pairs give ABBA order without dropping odd runs.
+        arms = ("baseline", "candidate") if iteration % 2 == 0 else ("candidate", "baseline")
+        for arm in arms:
+            with patch.object(forwards, "_first_frame_conv2d", helpers[arm]):
+                sync()
+                torch.accelerator.reset_peak_memory_stats()
+                start = time.perf_counter()
+                output = decode(vae, latents)
+                sync()
+                elapsed = (time.perf_counter() - start) * 1000
+                peak = torch.accelerator.max_memory_allocated()
+            measurements.append({"arm": arm, "wall_ms": elapsed, "peak_allocated_bytes": peak})
+            if len(outputs[arm]) < 2:
+                outputs[arm].append(output.cpu())
+            del output
+    baseline, candidate = outputs["baseline"][0], outputs["candidate"][0]
+    difference = candidate.float() - baseline.float()
+    stats = {}
+    for arm in helpers:
+        times = [row["wall_ms"] for row in measurements if row["arm"] == arm]
+        stats[arm] = {
+            "median_ms": statistics.median(times),
+            "mean_ms": statistics.mean(times),
+            "std_ms": statistics.pstdev(times),
+            "min_ms": min(times),
+            "max_ms": max(times),
+        }
+    result = {
+        "scope": "VAE decode; same-head channels_last first-frame Conv2d ablation",
+        "args": vars(args),
+        "torch": torch.__version__,
+        "cuda": torch.version.cuda,
+        "baseline": stats["baseline"],
+        "candidate": stats["candidate"],
+        "latency_reduction": 1 - stats["candidate"]["median_ms"] / stats["baseline"]["median_ms"],
+        "optimized_calls_per_decode": optimized_calls,
+        "outputs_finite": bool(torch.isfinite(baseline).all() and torch.isfinite(candidate).all()),
+        "self_repeat_equal": {arm: torch.equal(*values) for arm, values in outputs.items()},
+        "candidate_vs_baseline": {
+            "torch_equal": torch.equal(candidate, baseline),
+            "max_abs": difference.abs().max().item(),
+            "relative_l2": (difference.norm() / baseline.float().norm().clamp_min(1e-30)).item(),
+            "psnr_db": psnr(candidate, baseline),
+        },
+        "measurements": measurements,
+    }
+    print("FIRST_FRAME_ABLATION " + json.dumps(result))
+
+
 def main() -> None:
     args = parse_args()
+    if args.latents and not args.first_frame_ablation:
+        raise SystemExit("--latents requires --first-frame-ablation")
     if not torch.cuda.is_available():
         raise SystemExit("CUDA device required")
     rank, world_size = init_distributed()
@@ -267,6 +365,12 @@ def main() -> None:
         )
     device = torch.device("cuda", torch.accelerator.current_device_index()) if world_size > 1 else torch.device("cuda")
     dtype = DTYPES[args.dtype]
+    if args.first_frame_ablation:
+        if world_size != 1 or parallel:
+            raise SystemExit("--first-frame-ablation requires one GPU")
+        with torch.backends.cudnn.flags(benchmark=False, allow_tf32=False):
+            run_first_frame_ablation(args, dtype, device)
+        return
     levels: Iterable[str] = [level.strip() for level in args.fast_path.split(",") if level.strip()]
     for level in levels:
         if level not in VAE_FAST_PATH_LEVELS:

benchmarks/diffusion/benchmark_mammoth_moda2_qk_norm_rope.py

@@ -0,0 +1,262 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+"""Benchmark MammothModa2's Q/K RMSNorm + adjacent-pair real RoPE.
+
+Example:
+
+    CUDA_VISIBLE_DEVICES=0 python \
+      benchmarks/diffusion/benchmark_mammoth_moda2_qk_norm_rope.py \
+      --tokens 512 4173 8346 --warmup 50 --iters 200
+"""
+
+from __future__ import annotations
+
+import argparse
+import importlib.metadata
+import json
+import os
+import statistics
+from collections.abc import Callable
+
+import torch
+
+from vllm_omni.diffusion.layers.fused_qk_norm_rope import (
+    _fused_cuda_supported,
+    fused_qk_norm_rope,
+)
+from vllm_omni.diffusion.layers.norm import RMSNorm
+from vllm_omni.diffusion.models.mammoth_moda2.rope_real import (
+    apply_real_rotary_emb,
+)
+
+_HEAD_DIM = 120
+_Q_HEADS = 21
+_KV_HEADS = 7
+_EPS = 1e-5
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--tokens", nargs="+", type=int, default=[512, 4173, 8346])
+    parser.add_argument("--warmup", type=int, default=50)
+    parser.add_argument("--iters", type=int, default=200)
+    parser.add_argument("--rounds", type=int, default=3)
+    parser.add_argument("--attention-sequences", nargs="*", type=int, default=[])
+    return parser.parse_args()
+
+
+def _version(distribution: str) -> str | None:
+    try:
+        return importlib.metadata.version(distribution)
+    except importlib.metadata.PackageNotFoundError:
+        return None
+
+
+def _measure(
+    fn: Callable[[], object],
+    warmup: int,
+    iters: int,
+) -> dict[str, float]:
+    for _ in range(warmup):
+        fn()
+    torch.accelerator.synchronize()
+
+    torch.accelerator.reset_peak_memory_stats()
+    baseline_bytes = torch.accelerator.memory_allocated()
+    samples = []
+    for _ in range(iters):
+        start = torch.cuda.Event(enable_timing=True)
+        end = torch.cuda.Event(enable_timing=True)
+        start.record()
+        fn()
+        end.record()
+        end.synchronize()
+        samples.append(float(start.elapsed_time(end)))
+
+    ordered = sorted(samples)
+    return {
+        "median_ms": statistics.median(ordered),
+        "mean_ms": statistics.mean(ordered),
+        "stddev_ms": statistics.pstdev(ordered),
+        "min_ms": ordered[0],
+        "max_ms": ordered[-1],
+        "p90_ms": ordered[min(len(ordered) - 1, int(len(ordered) * 0.9))],
+        "extra_peak_mib": (torch.accelerator.max_memory_allocated() - baseline_bytes) / 2**20,
+    }
+
+
+@torch.inference_mode()
+def _run_shape(tokens: int, warmup: int, iters: int, *, fused_first: bool = False) -> dict[str, object]:
+    dtype = torch.bfloat16
+    device = torch.device("cuda")
+    torch.manual_seed(42)
+    query = torch.randn(tokens, _Q_HEADS, _HEAD_DIM, device=device, dtype=dtype)
+    key = torch.randn(tokens, _KV_HEADS, _HEAD_DIM, device=device, dtype=dtype)
+    norm_q = RMSNorm(_HEAD_DIM, eps=_EPS).to(device=device, dtype=dtype)
+    norm_k = RMSNorm(_HEAD_DIM, eps=_EPS).to(device=device, dtype=dtype)
+    norm_q.weight.normal_(mean=1.0, std=0.2)
+    norm_k.weight.normal_(mean=1.0, std=0.2)
+    angles = torch.rand(tokens, _HEAD_DIM // 2, device=device) * (2 * torch.pi)
+    cos = angles.cos().repeat_interleave(2, dim=-1).to(dtype)
+    sin = angles.sin().repeat_interleave(2, dim=-1).to(dtype)
+
+    def native() -> tuple[torch.Tensor, torch.Tensor]:
+        q_out = apply_real_rotary_emb(norm_q(query).unsqueeze(0), cos.unsqueeze(0), sin.unsqueeze(0)).squeeze(0)
+        k_out = apply_real_rotary_emb(norm_k(key).unsqueeze(0), cos.unsqueeze(0), sin.unsqueeze(0)).squeeze(0)
+        return q_out, k_out
+
+    def fused() -> tuple[torch.Tensor, torch.Tensor]:
+        rope_table = torch.cat((cos[..., 0::2], sin[..., 0::2]), dim=-1)
+        return fused_qk_norm_rope(
+            query,
+            key,
+            norm_q.weight,
+            norm_k.weight,
+            rope_table,
+            _EPS,
+            head_dim=_HEAD_DIM,
+            rotary_dim=_HEAD_DIM,
+            interleaved=True,
+        )
+
+    expected_q, expected_k = native()
+    actual_q, actual_k = fused()
+    errors = {
+        "q_max_abs": (actual_q.float() - expected_q.float()).abs().max().item(),
+        "q_mean_abs": (actual_q.float() - expected_q.float()).abs().mean().item(),
+        "k_max_abs": (actual_k.float() - expected_k.float()).abs().max().item(),
+        "k_mean_abs": (actual_k.float() - expected_k.float()).abs().mean().item(),
+    }
+    torch.testing.assert_close(actual_q, expected_q, atol=0.0625, rtol=0.02)
+    torch.testing.assert_close(actual_k, expected_k, atol=0.0625, rtol=0.02)
+
+    functions = {"native": native, "fused": fused}
+    order = ("fused", "native") if fused_first else ("native", "fused")
+    measured = {name: _measure(functions[name], warmup, iters) for name in order}
+    native_stats, fused_stats = measured["native"], measured["fused"]
+    return {
+        "tokens": tokens,
+        "native": native_stats,
+        "fused_including_rope_pack": fused_stats,
+        "median_speedup": native_stats["median_ms"] / fused_stats["median_ms"],
+        "errors": errors,
+    }
+
+
+@torch.inference_mode()
+def _run_attention(seq: int, warmup: int, iters: int, *, fused_first: bool = False) -> dict[str, object]:
+    from vllm_omni.diffusion.models.mammoth_moda2.mammothmoda2_dit_model import TransformerBlock
+
+    torch.manual_seed(42)
+    block = (
+        TransformerBlock(
+            2520,
+            _Q_HEADS,
+            _KV_HEADS,
+            multiple_of=256,
+            ffn_dim_multiplier=1.0,
+            norm_eps=_EPS,
+            rope_repeats_pairs=True,
+        )
+        .to(device="cuda", dtype=torch.bfloat16)
+        .eval()
+    )
+    block.attn.norm_q.weight.normal_(1.0, 0.2)
+    block.attn.norm_k.weight.normal_(1.0, 0.2)
+    hidden = torch.randn(2, seq, 2520, device="cuda", dtype=torch.bfloat16)
+    mask = torch.ones(2, seq, device="cuda", dtype=torch.bool)
+    mask[0, seq - min(300, seq // 2) :] = False
+    mask[1, seq - min(17, seq // 2) :] = False
+    angles = torch.rand(2, seq, _HEAD_DIM // 2, device="cuda") * (2 * torch.pi)
+    # The production producer supplies FP32 tables; each example has its own positions.
+    rotary = angles.cos().repeat_interleave(2, dim=-1), angles.sin().repeat_interleave(2, dim=-1)
+    env = "VLLM_OMNI_FUSED_QK_NORM_ROPE_MIN_TOKENS"
+    previous = os.environ.get(env)
+
+    def run():
+        return block.attn(hidden, hidden, attention_mask=mask, image_rotary_emb=rotary)
+
+    try:
+        outputs = {}
+        timings = {}
+        order = ("fused", "native") if fused_first else ("native", "fused")
+        for name in order:
+            os.environ[env] = "0" if name == "fused" else str(2 * seq + 1)
+            outputs[name] = run()
+            timings[name] = _measure(run, warmup, iters)
+        diff = (outputs["native"].float() - outputs["fused"].float()).abs()[mask]
+        max_diff, mean_diff = diff.max().item(), diff.mean().item()
+        assert max_diff < 2e-2 and mean_diff < 1e-3, (max_diff, mean_diff)
+        assert torch.count_nonzero(outputs["fused"][~mask]) == 0
+        return {
+            "sequence": seq,
+            "batch": 2,
+            "backend": block.attn.omni_attn.attn_backend.__name__,
+            "native": timings["native"],
+            "fused": timings["fused"],
+            "median_speedup": timings["native"]["median_ms"] / timings["fused"]["median_ms"],
+            "max_abs_diff": max_diff,
+            "mean_abs_diff": mean_diff,
+        }
+    finally:
+        if previous is None:
+            os.environ.pop(env, None)
+        else:
+            os.environ[env] = previous
+
+
+def main() -> None:
+    args = _parse_args()
+    if (
+        args.rounds < 1
+        or args.iters < 1
+        or args.warmup < 0
+        or any(value < 1 for value in [*args.tokens, *args.attention_sequences])
+    ):
+        raise ValueError("rounds, iters and sequence lengths must be positive; warmup must be nonnegative")
+    if not torch.cuda.is_available():
+        raise RuntimeError("This benchmark requires CUDA")
+
+    probe_q = torch.empty(1, _Q_HEADS, _HEAD_DIM, device="cuda", dtype=torch.bfloat16)
+    probe_k = torch.empty(1, _KV_HEADS, _HEAD_DIM, device="cuda", dtype=torch.bfloat16)
+    if not _fused_cuda_supported(probe_q, probe_k, _HEAD_DIM, _HEAD_DIM, interleaved=True):
+        raise RuntimeError("The fused CUDA QK norm/RoPE path is unavailable")
+
+    result = {
+        "environment": {
+            "gpu": torch.cuda.get_device_name(),
+            "torch": torch.__version__,
+            "cuda": torch.version.cuda,
+            "triton": _version("triton"),
+            "vllm": _version("vllm"),
+            "vllm_omni": _version("vllm-omni"),
+        },
+        "config": {
+            "dtype": "bfloat16",
+            "q_heads": _Q_HEADS,
+            "kv_heads": _KV_HEADS,
+            "head_dim": _HEAD_DIM,
+            "warmup": args.warmup,
+            "iters": args.iters,
+            "rounds": args.rounds,
+            "inference_mode": True,
+            "alternating_order": True,
+            "norm_weights": "normal(mean=1.0, std=0.2)",
+        },
+        "results": [
+            {"round": round_id, **_run_shape(tokens, args.warmup, args.iters, fused_first=bool(round_id % 2))}
+            for round_id in range(args.rounds)
+            for tokens in args.tokens
+        ],
+        "attention_results": [
+            {"round": round_id, **_run_attention(seq, args.warmup, args.iters, fused_first=bool(round_id % 2))}
+            for round_id in range(args.rounds)
+            for seq in args.attention_sequences
+        ],
+    }
+    print(json.dumps(result, indent=2))
+
+
+if __name__ == "__main__":
+    main()

benchmarks/diffusion/performance_dashboard/mammoth_moda2_serving_performance.md

@@ -0,0 +1,209 @@
+# MammothModa2-Preview Serving Performance Dashboard
+
+This document describes how to deploy and benchmark **bytedance-research/MammothModa2-Preview** (T2I) using vLLM-Omni. It includes service startup configuration, benchmark methodology, dataset settings, and performance results.
+
+MammothModa2 is a two-stage AR→DiT text-to-image pipeline: stage 0 is an autoregressive LLM that encodes the prompt into a visual-token grid, and stage 1 is a generation-LLM DiT that renders the final image (`final_output_type: image`). Unlike single-stage diffusion pipelines, `/v1/images/generations` requests traverse both stages, and the DiT stage ran with `max_num_seqs: 1` in the then-default deploy config, so concurrent requests serialized on the DiT stage while the AR stage batched (request-level DiT batching landed later in #7476).
+
+> **Legacy-topology baseline**: all numbers in this dashboard were measured on the legacy topology, where the MammothModa2 DiT stage runs as an LLM-generation stage (`stage_type: llm`) built from `VllmConfig`, served through the LLM-typed image-stage compatibility path. #7134 migrates this pipeline to the shared diffusion runtime (DiT registered as a standard `DIFFUSION` stage built from `OmniDiffusionConfig`). These results should be treated as the pre-migration baseline and are expected to be rerun after #7134 lands; they are not directly comparable with post-migration numbers.
+
+---
+
+# 1. Overview
+
+This document covers:
+
+* Service launch configuration
+* Benchmark scripts and usage
+* Dataset and workload settings
+* Performance measurement results
+* Reproducibility guidelines
+
+---
+
+# 2. Test Environment
+
+| Component | Specification |
+|------------|----------------|
+| GPU | NVIDIA A800-SXM4-80GB (1x) |
+| CPU | 18 cores |
+| Model weights | `bytedance-research/MammothModa2-Preview` |
+| Deploy config | `vllm_omni/deploy/mammoth_moda2.yaml` (bundled) |
+| Stage 0 (AR) | `gpu_memory_utilization: 0.5`, `enforce_eager: true`, `max_num_seqs: 100` |
+| Stage 1 (DiT) | `gpu_memory_utilization: 0.3`, `enforce_eager: true`, `max_num_seqs: 1` |
+| Sampling params (per request) | `text_guidance_scale: 9.0`, `cfg_range: [0.0, 1.0]`, `num_inference_steps: 50` sent via `--extra-body` / `--num-inference-steps` |
+
+---
+
+# 3. Service Launch Configuration
+
+## 3.1 Basic Serving Command
+
+```bash
+vllm-omni serve bytedance-research/MammothModa2-Preview --omni \
+    --port 8091 \
+    --trust-remote-code
+```
+
+When serving from a local weights directory (no `model_index.json`), pass the bundled deploy config explicitly so the `mammoth_moda2` pipeline is selected:
+
+```bash
+vllm-omni serve /path/to/MammothModa2-Preview --omni \
+    --port 8091 \
+    --trust-remote-code \
+    --deploy-config vllm_omni/deploy/mammoth_moda2.yaml
+```
+
+## 3.2 Key Parameters
+
+| Parameter | Description |
+|-----------|-------------|
+| `--deploy-config` | Path to the deploy YAML; defaults to the bundled `mammoth_moda2.yaml` for the HF checkpoint |
+| `size` (request) | `WxH`, e.g. `1024x1024`; routed to the AR grid and the DiT `target_w/target_h` |
+| `num_inference_steps` (request) | DiT sampling steps (default 50) |
+| `guidance_scale` (request) | Text guidance scale (default 9.0) |
+
+Note: the benchmark script's `--warmup-num-inference-steps` shortcut is not usable for this pipeline — a reduced step count truncates the AR stage's visual-token output and crashes the DiT stage. Use `--warmup-requests 0` and warm up with a real request instead.
+
+---
+
+# 4. Benchmark Script
+
+## 4.1 Benchmark Entry
+
+```bash
+python benchmarks/diffusion/diffusion_benchmark_serving.py \
+    --base-url http://localhost:8091 \
+    --model bytedance-research/MammothModa2-Preview \
+    --endpoint /v1/images/generations \
+    --task t2i \
+    --dataset random \
+    --num-prompts 8 \
+    --width <W> --height <H> \
+    --num-inference-steps <S> \
+    --extra-body '{"text_guidance_scale": 9.0, "cfg_range": [0.0, 1.0]}' \
+    --max-concurrency <C> \
+    --warmup-requests 0 \
+    --seed 142 \
+    --output-file <OUT>.json
+```
+
+## 4.2 Dataset
+
+`random` synthetic T2I prompts, fixed seed 142, 8 prompts per configuration (4 for 2048x2048 due to per-request latency).
+
+---
+
+# 5. Performance Results
+
+All requests succeeded (0 failures across the sweep). Latency is end-to-end per request (AR + DiT + VAE decode + base64 response).
+
+> Note: these are legacy-topology baseline results (see the callout in the overview); rerun after the #7134 diffusion-runtime migration before comparing across topologies.
+
+## 5.1 Latency
+
+| Resolution | Steps | Concurrency | Mean (s) | P50 (s) | P95 (s) | P99 (s) |
+|------------|-------|-------------|----------|---------|---------|---------|
+| 512x512    | 20    | 1           | 21.8     | 21.7    | 22.1    | 22.2    |
+| 512x512    | 20    | 2           | 24.8     | 25.2    | 25.6    | 25.8    |
+| 512x512    | 20    | 4           | 28.5     | 29.2    | 31.1    | 31.8    |
+| 1024x1024  | 50    | 1           | 96.3     | 95.8    | 99.8    | 100.8   |
+| 1024x1024  | 50    | 2           | 121.5    | 125.4   | 127.9   | 128.5   |
+| 1024x1024  | 50    | 4           | 150.9    | 157.6   | 178.6   | 186.3   |
+| 2048x2048  | 50    | 1           | 271.9    | 271.6   | 273.6   | 273.8   |
+
+## 5.2 Throughput
+
+| Resolution | Steps | Concurrency | Throughput (img/s) |
+|------------|-------|-------------|--------------------|
+| 512x512    | 20    | 1           | 0.0459             |
+| 512x512    | 20    | 2           | 0.0798             |
+| 512x512    | 20    | 4           | 0.1329             |
+| 1024x1024  | 50    | 1           | 0.0104             |
+| 1024x1024  | 50    | 2           | 0.0162             |
+| 1024x1024  | 50    | 4           | 0.0238             |
+| 2048x2048  | 50    | 1           | 0.0037             |
+
+## 5.3 Stage Breakdown (concurrency 1, from server logs)
+
+Per-stage wall time from the server's `[OmniTiming]` records across the §5.1/§5.4/§5.5 sweeps (n = 18/9/5 per row; at concurrency 1 stage wall time equals `stage_gen_time_ms` in the engine stats table):
+
+| Resolution | Steps | AR stage (s) | DiT stage (s) | E2E (s) | AR share | AR output tokens | AR ms/token |
+|------------|-------|--------------|---------------|---------|----------|------------------|-------------|
+| 512x512    | 20    | 20.3         | 1.7           | 22.0    | 92%      | 1,057            | 19.2        |
+| 1024x1024  | 50    | 78.9         | 17.2          | 96.1    | 82%      | 4,161            | 19.0        |
+| 2048x2048  | 50    | 156.3        | 114.8         | 271.2   | 58%      | 8,154            | 19.2        |
+
+Stage 0 is the AR prompt encoder (TTFT 82 ms at 1024x1024); stage 1 is the DiT renderer. Three observations:
+
+1. **AR per-token cost is constant (~19 ms/token) across resolutions**, so AR time is fully predictable: the AR stage decodes a fixed visual grid whose length scales with resolution (1,057 → 4,161 → 8,154 tokens), giving `AR ≈ tokens × 19 ms`. This is also why §5.4's step sweep is sub-linear — the AR token count is step-invariant.
+2. **DiT time scales super-linearly with pixels**: a 16x pixel increase (512 → 2048) multiplies DiT time by 67 (1.7 → 114.8 s).
+3. **The dominant stage flips with resolution**: AR is 92% of E2E at 512x512 (DiT optimization is nearly pointless there) but 58% at 2048x2048, where both stages matter.
+
+At concurrency > 1, per-stage wall times additionally include intra-stage queueing (each stage serializes at `max_num_seqs: 1`), so the c2/c4 rows in §5.1 cannot be decomposed additively from this table.
+
+## 5.4 Inference-Step Sensitivity (1024x1024 / 512x512, concurrency 1)
+
+Steps 25 at 1024x1024 (half the default 50) and steps 25 at 512x512, same protocol as §5.1:
+
+| Resolution | Steps | Concurrency | Mean (s) | P50 (s) | P95 (s) | P99 (s) |
+|------------|-------|-------------|----------|---------|---------|---------|
+| 1024x1024  | 25    | 1           | 85.2     | 85.3    | 87.8    | 88.7    |
+| 1024x1024  | 25    | 4           | 113.7    | 115.8   | 126.1   | 129.4   |
+| 512x512    | 25    | 1           | 21.9     | 21.8    | 22.2    | 22.3    |
+
+Halving DiT steps does not halve end-to-end latency (96.3 → 85.2 s at 1024): the AR stage emits a fixed count of visual tokens (4,161 at 1024x1024) regardless of step count, so its ~79 s contribution (§5.3) is step-invariant; only the DiT sampling time scales with steps. At 512x512, steps 20 → 25 adds ~0.1 s per step (21.8 → 21.9 s).
+
+## 5.5 Prompt-Length Sensitivity (long vs short prompts)
+
+A ~430-word long prompt (repeated via `--random-request-config '[{"weight":1,"prompt":"..."}]'`) against the default short synthetic prompt, same seed and settings:
+
+| Resolution | Steps | Concurrency | Prompt | Mean (s) | P99 (s) | Throughput (img/s) |
+|------------|-------|-------------|--------|----------|---------|---------------------|
+| 1024x1024  | 50    | 1           | short  | 96.3     | 100.8   | 0.0104              |
+| 1024x1024  | 50    | 1           | long   | 93.6     | 94.4    | 0.0107              |
+| 1024x1024  | 50    | 4           | short  | 150.9    | 186.3   | 0.0238              |
+| 1024x1024  | 50    | 4           | long   | 141.7    | 174.2   | 0.0255              |
+| 512x512    | 20    | 1           | short  | 21.8     | 22.2    | 0.0459              |
+| 512x512    | 20    | 1           | long   | 22.2     | 22.5    | 0.0450              |
+
+Prompt length has no material effect on latency (the long-prompt rows are within run-to-run variance and even slightly faster with tighter P99 tails). This confirms the AR stage's cost is dominated by decoding the fixed 4,161-token visual grid, not by prompt encoding: the KV prefill of a few hundred extra text tokens is negligible against 4,161 decode steps.
+
+## 5.6 Peak GPU Memory (per-stage process, nvidia-smi polling at 1 Hz)
+
+Sampled during the §5.4/§5.5 sweeps (stage processes identified by PID; both stages share the single A800):
+
+| Configuration (worst case) | stage0_ar (MB) | stage1_dit (MB) |
+|----------------------------|----------------|-----------------|
+| 1024x1024, 50 steps, c4    | 39,290         | 11,104          |
+| all other configs          | 39,194         | 11,102–11,104   |
+
+Memory is essentially flat across the sweep: the AR stage grows by only ~96 MB at concurrency 4 and the DiT stage by 2 MB, because both stages pre-allocate from `gpu_memory_utilization` budgets (0.5 / 0.3) at startup. Combined steady-state usage ≈ 50.4 GiB of the 80 GiB device.
+
+## 5.7 DiT-Stage Component Attribution (pipeline profiler)
+
+With `enable_diffusion_pipeline_profiler: true` in the stage-1 deploy config (`MammothModa2DiTPipeline` targets: `gen_transformer.forward`, `gen_vae.decode`, `gen_image_condition_refiner.forward`), three warm 1024x1024 / 50-step requests (profiler adds a `synchronize` per call, so timings are indicative, not throughput-grade):
+
+| Component | Per request (s) | Share of DiT pipeline |
+|-----------|-----------------|-----------------------|
+| `gen_transformer.forward` (100 calls: 50 steps x 2 passes) | 14.01–14.07 | 98.6% |
+| `gen_vae.decode` (1 call) | 0.148 | 1.0% |
+| `gen_image_condition_refiner.forward` (1 call) | 0.005 | 0.03% |
+| whole `MammothModa2DiTPipeline.forward` | 14.18–14.27 | — |
+
+The DiT latency is ~99% transformer sampling; VAE decode costs only ~0.15 s per image at 1024x1024 and is not a bottleneck. Per-step transformer time is ~140 ms (two forward passes per step: ~2.8 s nominal x CFG), consistent with §5.3's 17.2 s image-TTFT figure once base64 encoding and stage handoff are included.
+
+## 5.8 Endpoint Fix Verification
+
+Before the serving fix, `/v1/images/generations` returned HTTP 503 ("No diffusion stage found in multi-stage pipeline") because the pipeline's image-output stage is a generation-LLM DiT rather than a `diffusion`-typed stage. After the fix all requests return HTTP 200 with a valid base64 image (verified across every sweep configuration above).
+
+---
+
+# 6. Reproducibility
+
+1. Start the server as in §3.1 and wait for `Application startup complete`.
+2. Warm up with one real request (any size) — do not use reduced-step warmups.
+3. Run the sweep commands from §4.1 for each row in §5.
+4. Server-side per-stage timings are printed in the engine stats table (`stage_gen_time_ms`, `output_unit_count`) at `--log-stats` level info.
+5. New rows use the same seed (`--seed 142`), `--num-prompts 8`, and `--warmup-requests 0` protocol as §4.1; when saving results with `--output-file`, name them `<res>_s<steps>_c<conc>[_long].json` to keep runs comparable (steps-25, long-prompt, peak-memory and profiler runs).
+6. To reproduce §5.6 peak-memory numbers, poll `nvidia-smi --query-gpu=memory.used --format=csv -l 1` per stage PID while a sweep is in flight (e.g. `nvidia-smi pmon` / `--query-compute-apps`).
+7. To reproduce §5.7, copy the bundled deploy config, set `enable_diffusion_pipeline_profiler: true` on the stage-1 entry, restart, and grep `[DiffusionPipelineProfiler]` lines from the server log; timings include a `torch.cuda.synchronize` per call.

docker/Dockerfile.ci

@@ -1,10 +1,7 @@
-# Target vLLM version: v0.30.0 (tag @ ced6857afa0e).
-# Use the matching release image and its coherent CUDA/PyTorch/FlashInfer
-# runtime. Do not reinstall a commit wheel or repair dependencies on top of
-# an older base image at a release boundary. For a future unreleased target,
-# restore an explicit wheel pin and validate its complete runtime stack.
+# Use the official release image with its matching CUDA/PyTorch stack.
+ARG VLLM_VERSION=0.31.0
 ARG VLLM_BASE_IMAGE=vllm/vllm-openai
-ARG VLLM_BASE_TAG=v0.30.0
+ARG VLLM_BASE_TAG=v${VLLM_VERSION}
 
 # We use registry-based layer caching for the dependencies,
 # where the tag is determined based on the hash of the contents
@@ -12,6 +9,7 @@ ARG VLLM_BASE_TAG=v0.30.0
 # everything except the actual project dir, since that should not
 # change much across branches.
 FROM ${VLLM_BASE_IMAGE}:${VLLM_BASE_TAG} AS deps
+ARG VLLM_VERSION
 # Keep APP_DIR outside /workspace so k8s Buildkite workspace mounts cannot
 # shadow the editable install target (#5364). Docker WORKDIR creates /opt if
 # needed.
@@ -36,12 +34,17 @@ RUN apt-get update && \
 # for CI
 COPY pyproject.toml setup.py ./
 COPY requirements/ requirements/
-ENV SETUPTOOLS_SCM_PRETEND_VERSION=0.0.0
+# Scope the override to Omni; dependency source builds need their own versions.
+ENV VLLM_OMNI_VERSION_OVERRIDE=0.0.0
 # Editable install: deps + project metadata are cached in this layer, while
 # the package itself is linked to APP_DIR (populated later by COPY --link).
 # Do NOT use a non-editable install here — that copies an empty stub into
 # site-packages and breaks ``import vllm_omni.entrypoints``.
 RUN uv pip install --system -e ".[dev]"
+# Fail the image build if dependency resolution replaced the selected vLLM.
+# The CPU builder has no NVIDIA driver; CUDA imports are checked at the start
+# of each GPU test job by .buildkite/cuda/scripts/check_vllm_runtime.py.
+RUN python3 -c "import vllm; assert vllm.__version__ == '${VLLM_VERSION}', vllm.__version__"
 
 # The final stage just copies the working dir on top of the
 # dependencies image. This uses --link so that we can skip

docker/Dockerfile.ci.dockerignore

@@ -1,6 +1,6 @@
 # NOTE: this is largely taken from vLLM
 
-# Exclude .git for CI builds — version is set via SETUPTOOLS_SCM_PRETEND_VERSION
+# Exclude .git for CI builds — version is set via VLLM_OMNI_VERSION_OVERRIDE
 .git
 .venv
 /build

docker/Dockerfile.cuda

@@ -1,4 +1,4 @@
-ARG BASE_IMAGE=vllm/vllm-openai:v0.30.0
+ARG BASE_IMAGE=vllm/vllm-openai:v0.31.0
 FROM ${BASE_IMAGE}
 
 ARG COMMON_WORKDIR=/app

docker/Dockerfile.npu

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_VERSION=v0.30.0
+ARG VLLM_ASCEND_VERSION=v0.31.0
 ARG VLLM_ASCEND_DEVICE_SUFFIX=
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_VERSION}${VLLM_ASCEND_DEVICE_SUFFIX}
 

docker/Dockerfile.npu.ci

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_VERSION=v0.30.0
+ARG VLLM_ASCEND_VERSION=v0.31.0
 ARG VLLM_ASCEND_DEVICE_SUFFIX=
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_VERSION}${VLLM_ASCEND_DEVICE_SUFFIX}
 

docker/Dockerfile.rocm

@@ -1,7 +1,7 @@
 # Keep the ROCm runtime aligned with the vLLM release targeted by
 # docker/Dockerfile.ci. A stale base image can build successfully while failing
 # at runtime when vLLM-Omni imports APIs added by the newer vLLM release.
-ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.30.0
+ARG BASE_IMAGE=vllm/vllm-openai-rocm:v0.31.0
 
 # Keep the dependency-heavy portion independent from the source tree. The
 # registry cache is keyed on these inputs, while the final source copy can be
@@ -12,11 +12,11 @@ FROM ${BASE_IMAGE} AS deps
 # BASE_IMAGE pins the default prebuilt runtime when USE_NIGHTLY_BUILD=0.
 # VLLM_VERSION_OR_COMMIT_HASH is consumed only by the opt-in source rebuild when
 # USE_NIGHTLY_BUILD=1. Keep both aligned with docker/Dockerfile.ci's
-# VLLM_BASE_TAG. This is enforced by tests/buildkite/test_rocm_dockerfile.py. An
+# release version. This is enforced by tests/buildkite/test_rocm_dockerfile.py. An
 # intentional custom source pin must update that regression in the same change,
 # and should return to the stable release as soon as one is available.
 ARG USE_NIGHTLY_BUILD=0
-ARG VLLM_VERSION_OR_COMMIT_HASH=v0.30.0
+ARG VLLM_VERSION_OR_COMMIT_HASH=v0.31.0
 ARG ARG_PYTORCH_ROCM_ARCH
 ENV PYTORCH_ROCM_ARCH=${ARG_PYTORCH_ROCM_ARCH:-${PYTORCH_ROCM_ARCH}}
 
@@ -75,7 +75,8 @@ WORKDIR ${COMMON_WORKDIR}/vllm-omni
 RUN mkdir -p vllm_omni && touch vllm_omni/__init__.py
 COPY pyproject.toml setup.py ./
 COPY requirements/ requirements/
-ENV SETUPTOOLS_SCM_PRETEND_VERSION=0.0.0
+# Scope the override to Omni; dependency source builds need their own versions.
+ENV VLLM_OMNI_VERSION_OVERRIDE=0.0.0
 
 RUN uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" \
         --no-cache-dir -e ".[dev]" --no-build-isolation && \

docker/Dockerfile.xpu

@@ -1,4 +1,4 @@
-# Target vLLM version: v0.30.0. Keep VLLM_VERSION aligned with the target vLLM
+# Target vLLM version: v0.31.0. Keep VLLM_VERSION aligned with the target vLLM
 # release on every rebase.
 #
 # This file layers vLLM-Omni on top of the published upstream XPU image, which
@@ -18,7 +18,7 @@
 #
 # .buildkite/intel/scripts/run-xpu-test.sh automates that pull-then-build fallback.
 ARG VLLM_BASE_IMAGE=vllm/vllm-openai-xpu
-ARG VLLM_VERSION=v0.30.0
+ARG VLLM_VERSION=v0.31.0
 ARG VLLM_BASE=${VLLM_BASE_IMAGE}:${VLLM_VERSION}
 
 FROM ${VLLM_BASE} AS vllm-omni

docs/configuration/README.md

@@ -2,7 +2,7 @@
 
 This section lists the most common options for running vLLM-Omni.
 
-For options within a vLLM Engine, please refer to the [vLLM 0.30 configuration guide](https://docs.vllm.ai/en/v0.30.0/configuration/index.html).
+For options within a vLLM Engine, please refer to the [vLLM 0.31 configuration guide](https://docs.vllm.ai/en/v0.31.0/configuration/index.html).
 
 Each model defines fixed topology in a registered `PipelineConfig` and runtime
 overrides in a deploy YAML.

docs/configuration/environment_variables.md

@@ -130,7 +130,7 @@ settings.
 
 | Name | Type and default | Applies to and read time | Precedence and invalid values | Lifecycle |
 | --- | --- | --- | --- | --- |
-| `VLLM_OMNI_FUSED_QK_NORM_ROPE_MIN_TOKENS` | Non-negative integer; unset or empty means each consumer's own default (Boogu-Image: `2048`) | Token gate of the shared `fused_qk_norm_rope` op: a rotary table is packed for the fused kernel only when it spans at least this many positions (`B*S`); read at forward time | Environment-only. `0` = always fuse; a very large value disables the fused path. Any other value raises `ValueError` on the first forward. The crossover is host-dependent; re-benchmark before overriding. | Experimental performance control |
+| `VLLM_OMNI_FUSED_QK_NORM_ROPE_MIN_TOKENS` | Non-negative integer; unset or empty means each consumer's own default (Boogu-Image: `2048`; MammothModa2: `0`) | Token gate of the shared `fused_qk_norm_rope` op: a rotary table is packed for the fused kernel only when it spans at least this many positions (`B*S`); read at forward time | Environment-only. `0` = always fuse; a very large value disables the fused path. Any other value raises `ValueError` on the first forward. The crossover is host-dependent; re-benchmark before overriding. | Experimental performance control |
 | `VLLM_OMNI_SKIP_NVFP4_NAN_CLAMP` | Boolean truthy spellings: `1`, `true`, `yes`, `on`; default false | ModelOpt NVFP4 compatibility patch; read when `vllm_omni.patch` imports | Environment-only escape hatch. Any other value means false. Set only to diagnose the upstream NaN-scale issue. | Diagnostic and temporary |
 | `VLLM_OMNI_USE_QUACK_FP8` | Boolean truthy spellings: `1`, `true`, `yes`, `on`; unset means hardware auto-detection | FP8 scaled matrix multiplication; evaluated when quack capability is selected | A set value overrides auto-detection. Any non-truthy value forces quack off. If quack cannot load, vLLM-Omni warns and falls back to FlashInfer. | Experimental performance control |
 
@@ -186,7 +186,7 @@ their keys only.
 ## Inherited vLLM variables
 
 vLLM-Omni also reads variables through its aligned vLLM dependency. Refer to
-the [vLLM 0.30 environment-variable reference](https://docs.vllm.ai/en/v0.30.0/configuration/env_vars.html)
+the [vLLM 0.31 environment-variable reference](https://docs.vllm.ai/en/v0.31.0/configuration/env_vars.html)
 for their definitions. This includes vLLM launch, cache, logging, plugin, ROCm,
 XPU, ModelScope, and FlashInfer workspace settings.
 

docs/configuration/stage_configs.md

@@ -35,7 +35,7 @@ Common `StagePipelineConfig` fields include:
 | `final_output` / `final_output_type` | Whether the stage produces a user-visible output and its modality. |
 | `owns_tokenizer` | Whether this stage owns the pipeline tokenizer. |
 | `model_arch`, `hf_config_name` | Stage-specific model architecture and nested HF config selector. |
-| `engine_output_type` | Runtime output representation such as `text`, `latent`, or `audio`. |
+| `engine_output_type` | Canonical runtime output type: `text`, `image`, `audio`, `latent`, or `token_ids`. |
 | `custom_process_input_func` | Processor applied to this stage's incoming payload. |
 | `custom_process_next_stage_input_func` | Processor used for full-payload handoff to the next stage. |
 | `async_chunk_process_next_stage_input_func` | Processor used for async chunk handoff. |
@@ -45,6 +45,50 @@ To add or change topology, define and register a new pipeline variant. Use
 deploy YAML only for runtime placement, resource sizing, connectors, and other
 deployment overrides.
 
+### Engine output types
+
+`engine_output_type` is parsed when the LLM stage's output processor is built
+during service initialization. Invalid values fail startup before requests
+are accepted; engine replicas may already have been initialized at that point
+and are cleaned up on failure.
+
+Use the exact lowercase names listed above. Combinations such as `text+audio`
+or `text,audio` remain supported, with every component canonical. Omitted or
+`None` values retain the default behavior (`TEXT` in the output processor).
+Empty strings, whitespace, case variants, and aliases are rejected.
+
+For existing custom pipelines or direct callers, replace old aliases explicitly:
+
+| Old value | Canonical value |
+| ------- | ------- |
+| `speech`, `wav`, `waveform` | `audio` |
+| `images`, `pixel_values`, `pixels` | `image` |
+| `latents` | `latent` |
+| `tokens` | `text` for text output, or `token_ids` for discrete IDs |
+
+`token_ids` is a separate type, not a `text` alias. It describes discrete
+vocabulary or codebook indices, distinct from continuous latent representations.
+Existing models using `latent` retain their historical payload contracts.
+GLM-Image's AR stage keeps `engine_output_type="token_ids"`; generated IDs still
+travel through `cumulative_token_ids`, and source-image IDs through
+`ids.prior_image`.
+
+Bare primary tensors and generic producer keys (`model_outputs` or `hidden`)
+use the declared type as their default output key, including `token_ids`.
+The normalizer does not inspect or convert their dtype. Distinct auxiliary
+outputs must use explicit field names: `model_outputs` and `hidden` both map
+to the same default key and must not represent two independent outputs.
+GLM-Image disables auxiliary hidden output for its default GPU execution.
+Some runner paths still attach it; that field is then named `token_ids` even
+though it contains hidden states. The GLM-Image bridge ignores this auxiliary
+field and reads generated IDs from `cumulative_token_ids` instead.
+
+Token-ID tensors use the existing dimension-0 accumulation default, subject to
+registered per-key overrides, and remain available after DELTA emissions.
+This is separate from the existing accumulation of `cumulative_token_ids`.
+`final_output_type` and Diffusion's `output_type` are separate settings and are
+unchanged by these rules.
+
 ## Deploy configuration schema
 
 The new deploy schema lives under `vllm_omni/deploy/` and is paired with a frozen `PipelineConfig` registered by the model's `pipeline.py`. Each deploy YAML has these top-level fields:
@@ -78,7 +122,44 @@ explicit CLI or stage override, or the downstream vLLM engine default.
 Note: for the diffusion path, an omitted `distributed_executor_backend` selects
 `uni` on a single GPU (in-process worker, no MessageQueue / `/dev/shm` output
 segments) and `mp` when `num_gpus > 1`. Set `mp` explicitly to keep a worker
-subprocess on one GPU. `ray` / `external_launcher` are not fully supported yet.
+subprocess on one GPU. Set `ray` for Ray-managed single- or multi-node
+diffusion workers. `external_launcher` is not supported yet.
+
+The diffusion `ray` backend runs one worker actor per GPU and supports workers
+spanning Ray nodes. Enable it in the deploy YAML:
+
+```yaml
+distributed_executor_backend: ray
+```
+
+The executor reuses Ray when already initialized; otherwise it calls
+`ray.init()`, which handles cluster discovery or local startup and respects
+`RAY_ADDRESS`. There is no diffusion-specific address setting. Ray must be
+installed on every node.
+
+Ray diffusion workers receive the driver's `VLLM_*`, `OMNI_*`, `DIFFUSION_*`,
+`NCCL_*`, `TORCH_NCCL_*`, `UCX_*`, and Hugging Face environment settings,
+plus `PYTHONPATH`, `CUDA_LAUNCH_BLOCKING`, and `OMP_NUM_THREADS`.
+Explicit stage `runtime.env` entries are also forwarded and take precedence.
+Device visibility, rank/rendezvous settings, worker host addresses, and `RAY_*`
+variables are excluded so each worker retains its own assignment and identity.
+
+For multiple nodes, start the cluster before launching Omni. For example,
+with head IP `10.0.0.1`:
+
+```bash
+# On the head node:
+ray start --head --node-ip-address=10.0.0.1 --port=6379
+
+# On each additional worker node:
+ray start --address=10.0.0.1:6379
+
+# On the host launching Omni (MODEL and my_deploy.yaml are placeholders):
+RAY_ADDRESS=10.0.0.1:6379 vllm serve MODEL --omni --deploy-config my_deploy.yaml
+```
+
+Omni creates actors on the resources of that cluster; it does not start Ray
+on additional machines.
 
 ### Stage-level runner selection
 
@@ -517,8 +598,9 @@ the intended GPU, and warm the complete pipeline before measuring performance.
 
 ## MOSS-TTS Local 1.5 with Model Runner V2
 
-`OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5` can opt into CUDA MRV2 using
-the shared runtime introduced for Qwen3-TTS:
+`OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5` defaults to CUDA MRV2 using
+the shared runtime introduced for Qwen3-TTS. To select the C64 profile without
+MPS explicitly:
 
 ```bash
 vllm serve OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5 --omni \
@@ -526,7 +608,7 @@ vllm serve OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5 --omni \
 ```
 
 This profile inherits the batching, codec graph buckets and 1-frame/15-frame
-chunk geometry from `moss_tts_local.yaml`, and selects V2 for both the Local
+chunk geometry from `moss_tts_local_v1.yaml`, and selects V2 for both the Local
 Talker and codec stages. The Local depth predictor exposes the MRV2 `mtp`
 capabilities while retaining its V1 `talker_mtp` implementation, sampling
 defaults and explicit request-seed handling. Codec chunks use the native data
@@ -550,8 +632,12 @@ guarantee that 128 maximum-length prompts fit simultaneously. See the
 [MOSS recipe](gh-file:recipes/OpenMOSS/MOSS-TTS.md#local-15-mrv2-and-slot-attention)
 for activation, backend comparisons, memory requirements and benchmark commands.
 
-Omitting `--deploy-config`, or selecting `moss_tts_local.yaml`, retains V1.
-NPU, XPU, ROCm and MUSA overrides also retain V1. This profile does not enable
+Omitting `--deploy-config` selects the C128 system profile on CUDA GPUs with
+at least 140 GiB memory and MPS available. Smaller GPUs or a failed memory
+query use C64 with utilization-based memory budgets; missing MPS selects
+C64 without MPS. Selecting `moss_tts_local.yaml` explicitly uses C64 with MPS.
+Use `moss_tts_local_v1.yaml` for the previous V1 deployment.
+NPU, XPU, ROCm and MUSA overrides retain V1. These profiles do not enable
 MRV2 for MOSS Delay, Realtime or Nano. Local 1.5 outputs 48 kHz stereo audio;
 set `VLLM_OMNI_BENCH_AUDIO_SAMPLE_RATE=48000` and
 `VLLM_OMNI_BENCH_AUDIO_CHANNELS=2` when benchmarking raw PCM.
@@ -559,5 +645,5 @@ set `VLLM_OMNI_BENCH_AUDIO_SAMPLE_RATE=48000` and
 Event-driven orchestration remains independently selectable with
 `VLLM_OMNI_EVENT_DRIVEN_ORCH=0` or `1`. Keep the runner and deployment identical
 when comparing these modes. Model-runner selection does not change the
-orchestration default or enable experimental reference encoding, chunk ramps,
-generation-output draining or MPS.
+orchestration default. MPS, codec dispatch and graph settings belong to the
+deployment profile; the automatic CUDA default selects MPS when available.

docs/design/feature/attention_execution_contract_poc.md

@@ -131,3 +131,24 @@ and real-kernel schema/fake checks pass. The full suite must be rerun with the
 vLLM version required by upstream. NPU/ROCm device numerics and compilation remain
 unvalidated. Pre-commit passes with the CI hook skips. Upstream PyTorch/CUTLASS
 warnings remain.
+
+## TRTLLM dense execution
+
+`TRTLLM_ATTN` declares `SUPPORTED` / `CUSTOM_OP` for noncausal dense BF16 on
+B200/GB200 (SM100) and B300/GB300 (SM103), head dimension 128, and equal Q/K/V
+head counts, without parallel, paged-KV, piecewise, or HSDP boundaries. Pre-construction, SAGE, skip-softmax, packed,
+and other unverified paths remain `UNMIGRATED`. Resolution and dispatch share
+metadata validation; workspace mutation is explicit in the custom-op schema.
+
+CPU contract tests cover fullgraph replay and schema/fake consistency using a
+substituted dispatcher. Real-kernel validation requires Blackwell with FlashInfer:
+
+```bash
+python -m pytest tests/diffusion/attention/test_trtllm_attn.py \
+  -k dense_contract_fullgraph_matches_sdpa -q -rs
+```
+
+These hardware tests compare eager/compiled output with FP32 SDPA and check
+schema/fake agreement. The TRTLLM and contract suite passed on Blackwell before the final architecture
+restriction was added: 61 passed, no skips (19 dependency deprecation warnings).
+The SM100/SM103 restriction is additionally covered by CPU contract tests.

docs/design/feature/sequence_parallel.md

@@ -281,6 +281,10 @@ NOTE: be careful to test adequately when refactoring classes that take this styl
 | `expected_dims` | int \| None | Expected tensor rank for validation (optional) |
 | `split_output` | bool | `False`: shard **input** params; `True`: shard **output** tensors |
 | `auto_pad` | bool | Auto-pad if sequence not divisible by world_size (default: `False`) |
+| `clone_shard` | bool | Give multi-rank shards their own contiguous storage (default: `False`); single-rank inputs are unchanged |
+
+With `clone_shard=True`, memory savings require callers to drop their references to the full tensor.
+Other references to the same storage also keep the full allocation alive.
 
 **SequenceParallelOutput Parameters:**
 

docs/design/fullduplex.md

@@ -220,6 +220,7 @@ vllm_omni/
 │       │                            DUPLEX_REALTIME_CAPABILITIES, duplex_response_format
 │       ├── events.py                re-export shim (DuplexEvent = RealtimeEvent) + the runner's
 │       │                            epoch-filter sets DOMAIN_TERMINAL_EVENTS / MODEL_OUTPUT_EVENTS
+│       ├── delivery.py              DuplexOutputBuffer (bounded output shared with the session handle)
 │       ├── realtime_events.py       RealtimeProjectionState: internal event -> typed events
 │       ├── messages.py              queue envelopes (Open/Close/Resume/Touch/Command/Result/Event), DuplexSessionError
 │       ├── config.py                DuplexSessionConfig, DuplexCapabilities, ResponseCreateOptions
@@ -284,7 +285,8 @@ command  handle.submit(DuplexCommand)
          -> engine.submit_command_async -> DuplexSessionCommandMessage on the request queue [one-way]
          -> DuplexSessionManager.dispatch: unknown_session / input_backpressure checks, then runner mailbox
 output   DuplexOrchestrator._intercept_stage_output -> runner.on_stage_output -> mailbox -> typed events
-         -> output_sink (DuplexSessionEventMessage) -> DuplexOmni._route_engine_message -> handle.events()
+         -> session's bounded output buffer -> handle.events(); closure and errors without a live session
+            use output_sink (DuplexSessionEventMessage) -> DuplexOmni._route_engine_message
 detach   DuplexOmni.detach_session(expected_lease_generation) -> touch(DETACH): engine-owned disconnect
          grace, refused for a lease newer than the one the caller held; expiry -> SessionExpired
 resume   DuplexOmni.resume_session(expected_lease_generation) -> lease CAS keyed by the control id (a replay
@@ -303,7 +305,9 @@ reap     DuplexSessionManager.reaper_loop: idle TTL / disconnect grace expiry, c
 
 ### Concurrency and ordering model of the runner
 
-Everything below runs on the orchestrator asyncio loop; there is no lock.
+The runner's session state is owned by the orchestrator asyncio loop and
+needs no lock. The output buffer shared with the caller thread uses a lock
+for queue updates and the final validity check before delivery.
 
 - **Single writer per session.** `DuplexEngineSession` is mutated only by its
   runner. Inputs reach the runner through one mailbox in this order: client
@@ -332,13 +336,15 @@ Everything below runs on the orchestrator asyncio loop; there is no lock.
   on one loop in program order, nothing can be emitted for an old epoch after
   its terminal event; `runner.emit()` still drops a terminal carrying a stale
   epoch and stamps `epoch` on every event for clients that filter
-  defensively. This is a statement about order, not about latency: the engine
-  output queue and each `DuplexSessionHandle` outbox are unbounded and FIFO,
-  so a cancellation is delivered behind whatever audio was already emitted for
-  the response it cancels. A client that must stop quickly cancels its own
-  playback on `response.done` / `audio.cancelled` rather than waiting for the
-  stream to drain. Bounding those buffers and letting an accepted invalidation
-  skip undelivered media is left to the follow-up RFC.
+  defensively. In this experimental follow-up, public output goes directly to
+  one bounded buffer shared by the engine and `DuplexSessionHandle`. Accepted
+  cancellation removes queued `AudioDelta` events for that response through
+  the cancelled epoch. Text increments, audio/transcript completion markers,
+  other responses and normal completion retain FIFO order. The consumer also
+  checks an event already taken from the buffer: WebSocket delivery repeats
+  that check under the connection lock, before sequencing and journaling.
+  Already sequenced replay entries are unchanged; already delivered audio
+  still requires the client to stop its own playback.
 - **Backpressure before the mailbox.** `DuplexSessionManager.dispatch`
   checks `max_pending_input_bytes_per_session` and reserves a pending turn for
   `Commit` before the put; a rejected command is answered with
@@ -349,6 +355,46 @@ Everything below runs on the orchestrator asyncio loop; there is no lock.
   `ResponseDone.stage_metrics` and `metadata.vllm_omni.stage_metrics` on the
   wire keep their meaning.
 
+### Experimental output limits
+
+`max_pending_output_bytes_per_session` defaults to 2 MiB and
+`max_pending_output_events_per_session` to 512. The byte limit counts compact
+Realtime JSON, including base64 audio, not decoded PCM or Python object
+overhead. Audio is already base64-encoded by the producer; counting its length
+avoids serializing or decoding that large field again. Neither default has
+been tuned from a performance study.
+
+Errors and response endings have an additional 64 KiB / eight-event reserve
+and retain FIFO order. One final session-closure notification has an independent
+slot, so an exhausted reserve cannot prevent closure or break delivery to
+other sessions. If that reserve also fills, an error or response-ending
+notification may be omitted; the final session-closure notification still
+has its independent slot. Oversized close details (over 4 KiB with generated identity)
+are replaced by a compact reason, preserving terminal type and identity.
+Late writes after closure are ignored; a locally ended iterator is not reopened
+by a late terminal, and a failed open closes the abandoned handle. Events other
+than errors are not forwarded once their session's buffer has been removed.
+One event held by the consumer is outside the queued budget.
+
+The overflow policy fails the active response without committing its partial
+text to history and closes only the affected session. If overflow occurs while
+a completion batch is being delivered, its not-yet-queued `ResponseDone` is
+converted to failure with its identity and output preserved; any history entry
+for that response is removed. An ending already accepted by the buffer is not
+replaced or followed by a second ending. After overflow, newly
+emitted ordinary events are suppressed, including text/audio completion,
+content-part and output-item completion markers. Previously queued non-audio
+events retain FIFO order; error and failed response-ending notifications may
+use the reserve before the independent closure notification. This differs from
+accepted cancellation, which removes only matching audio. See the
+[public error and closure fields](../serving/realtime_duplex_api.md#output-limits-and-slow-consumers).
+Response-only recovery is not implemented. These limits do not bound the runner's raw stage
+output mailbox, replay journal, or client playback queues. The shared buffer
+requires the current same-process, separate-thread engine arrangement; it is
+not a cross-process delivery protocol. Direct handle iteration and WebSocket
+delivery share buffer validity, but end-to-end client behavior still needs
+independent real-model verification.
+
 ## Orchestrator seams
 
 `OrchestratorBase` keeps the generic machinery (request handler skeleton,

docs/design/minicpm_o45_mrv2_performance.md

@@ -12,11 +12,25 @@ require new end-to-end performance measurements after the mainline integration.
 Previously reported numbers do not describe this revised codec backend.
 
 The generic MRv2 profile and the opt-in native V1 duplex H200 profile enable
-TF32 matmuls within Code2Wav forward/capture only; the previous process policy
-is restored afterwards. cuDNN's TF32 policy is independent. TF32 changes rounding.
+ordinary TF32 for CFM DiT GEMMs within Code2Wav forward/capture only
+(`torch.backends.cuda.matmul.allow_tf32` on dense QKV/MLP, Triton
+`input_precision="tf32"` on tiled attention). This is not compensated TF32x3.
+The previous process matmul policy is restored afterwards. cuDNN's TF32 policy
+is independent. HiFT stays IEEE FP32. TF32 changes rounding.
 Duplex keeps the mainline V1 session path. Turn results do not establish duplex
 performance or interruption correctness.
 
 The shared asynchronous output snapshot and batched Talker preprocessing also
 apply to V1 when async chunking/scheduling is enabled. Default V1 behavior must
 therefore be included in end-to-end regression validation.
+
+CFM graph execution defaults to the fused DiT body on CUDA when TF32 is
+allowed. NVIDIA SM80+ uses tiled TF32 attention; older CUDA devices use the
+fused body's SDPA fallback. Unsupported DiT layouts or non-FP32 attention
+caches retain the original body. Other platforms retain their existing default.
+Set `cfm_fused_body: false` in connector `extra` to restore the original body.
+Disabling TF32 with `token2wav_allow_tf32: false` or
+`MINICPMO_CODE2WAV_TF32=off` also disables the default fusion choice; an explicit
+`cfm_fused_body: true` overrides that choice and may use TF32 attention.
+Slot pooling and row-offset merging remain opt-in. Fusion changes floating-point
+rounding and does not promise identical waveforms.

docs/design/minicpmo_encoder_cuda_graph.md

@@ -0,0 +1,61 @@
+# MiniCPM-o 4.5 input encoder CUDA graphs
+
+The vision transformer and stateless audio encoder reuse vLLM's encoder graph
+manager for exact input shapes. Each encoder retains at most four graphs by
+default, and captures on the second call with the same shape and CUDA stream.
+Startup profiling can consume slots. Graphs are never evicted: once the cache
+is full, other shapes run eagerly for the lifetime of the model.
+
+Use `--hf-overrides` to configure both encoders:
+
+```json
+{
+  "encoder_cuda_graph": true,
+  "encoder_cuda_graph_max_graphs": 4,
+  "encoder_cuda_graph_min_capture_calls": 2,
+  "encoder_cuda_graph_min_free_bytes": 1073741824,
+  "encoder_cuda_graph_share_pools": true
+}
+```
+
+`max_graphs` is a nonnegative integer per encoder; zero disables capture.
+`min_capture_calls` is an integer of at least two. Increasing it reduces capture
+of short-lived shapes. Admission history is bounded to four times `max_graphs`,
+so an evicted history entry must accumulate its calls again. Increasing the
+graph cap can improve coverage but retains more GPU memory. Existing graphs
+are not replaced, avoiding repeated capture costs when traffic changes.
+
+By default, graphs in the same encoder on the same device and replay stream
+share a memory pool and capture stream. Different encoders and replay streams
+remain isolated. Graph inputs and output buffers are allocated outside capture;
+only transient capture allocations share the pool. Returned outputs remain
+cloned. A late capture waits for earlier replay work and output clones before
+reusing the pool. Set `encoder_cuda_graph_share_pools` to `false` for an A/B
+comparison with a separate pool and capture stream per graph.
+
+The graph count is **not a byte limit**. Sharing pools reuses intermediate
+storage but does not eliminate per-shape static input and output buffers.
+Before a new capture, the adapter checks device-free memory against
+`min_free_bytes`, a nonnegative integer (default: 1 GiB; zero disables the check).
+Below this floor the call runs eagerly, and a later call may try admission
+again. Existing graphs can still replay. This check is conservative about
+reusable allocator memory and does not predict capture allocations, reserve
+memory, or guarantee that capture fits. Size the floor for the deployment;
+disable encoder graphs when their additional memory cannot be accommodated.
+
+Each adapter exposes `get_cumulative_stats()`: graph hits, total misses, hit
+rate, graph count, and separate ineligible, capacity, warmup and memory misses.
+These include calls rejected before the upstream manager, so a full cache's
+lost coverage is visible. Vision uses `vpm._encoder_graph`; audio uses
+`_audio_encoder_graph` on the model. The counters are per adapter, not global.
+
+Capture failures propagate the original exception and make that adapter
+unusable. Subsequent calls raise without retrying capture or running CUDA eager
+work. Restart the worker, optionally with encoder graphs disabled. This is a
+fatal contract, not automatic recovery: a failed CUDA capture can leave the
+device context unusable. `capture_failures` records this terminal state.
+
+`--enforce-eager` always disables these graphs. CPU, grad/autocast, stateful
+streaming audio and the other model-specific eager fallbacks remain unchanged.
+The adapter clones graph outputs through upstream postprocessing so retained
+embeddings survive later replays.

docs/design/module/ar_runtime.md

@@ -47,6 +47,29 @@ adapter, not injected through unrelated scheduler state.
 
 **Rule:** Workers and model runners MUST NOT implement cross-stage routing.
 
+## Token-only output payloads
+
+An AR stage whose downstream decoder consumes sampled tokens rather than
+hidden states can set `omni_pooler_payload_include_hidden = False`. Hidden
+states remain available for logits and sampling; prefill conditioning and
+other required multimodal outputs still cross the stage boundary.
+
+A chunk processor that needs IDs on steps without a tensor payload declares
+`requires_token_updates = True`. The scheduler then enqueues nonempty sampled
+IDs while preserving final-stage exclusions and the processor's EOF handling.
+Processors without this declaration retain their tensor-payload trigger.
+
+When hidden and multimodal payloads are absent, the shared runner skips empty
+per-request payload construction. Prefix-cache merges and pending model
+postprocess retain the normal path. This policy is independent of whether
+output materialization runs inline or in the background.
+
+Inline construction consumes query offsets, scheduled-token counts and
+scheduler metadata before the next execution can reuse them. Background
+construction snapshots those inputs. Both paths reuse the request IDs already
+copied by upstream bookkeeping; connector collection stays on the main thread
+because it can run a tensor-parallel collective.
+
 ## Safe-change guide
 
 Test request lifecycle, abort, cache state, and every affected worker execution

docs/design/module/diffusion/diffusion_runtime.md

@@ -36,6 +36,7 @@ validation_paths:
   - tests/diffusion/test_diffusion_worker.py
   - tests/diffusion/test_multiproc_engine_concurrency.py
   - tests/diffusion/test_uniproc_executor.py
+  - tests/diffusion/test_ray_executor.py
   - tests/diffusion/test_result_pump.py
   - tests/diffusion/test_async_output_worker.py
   - tests/diffusion/test_diffusion_ipc.py
@@ -96,8 +97,10 @@ The executor then chooses how the worker runs:
   RPC.
 - **`mp`** (default when `num_gpus > 1`, or when set explicitly):
   `MultiprocDiffusionExecutor` starts one `WorkerProc` per device.
+- **`ray`** (explicit): `RayDiffusionExecutor` starts one GPU actor per worker
+  across one or more Ray nodes.
 
-Either way, each worker owns its device, distributed state,
+In all backends, each worker owns its device, distributed state,
 `DiffusionWorker`, `DiffusionModelRunner`, and pipeline instance.
 
 ```mermaid
@@ -108,7 +111,7 @@ flowchart TB
         stageProc["StageDiffusionProc<br/>stage transport and lifecycle"]
         engine["DiffusionEngine<br/>admission and control loop"]
         scheduler["RequestScheduler or StepScheduler<br/>request state and policy"]
-        executor["DiffusionExecutor<br/>uni or mp backend"]
+        executor["DiffusionExecutor<br/>uni, mp, or ray backend"]
         streams["Per-request output streams"]
 
         stageProc --> engine
@@ -132,6 +135,8 @@ flowchart TB
     client -.->|"inline: direct call"| engine
     executor -->|"mp: broadcast / result queues"| workerProc
     executor -.->|"uni: in-process call"| worker
+    executor -->|"ray: actor RPC"| rayActor["RayDiffusionWorkerWrapper"]
+    rayActor --> worker
     streams -->|"process-backed"| stageProc
     stageProc -->|"ZMQ results"| client
     streams -.->|"inline"| client
@@ -154,6 +159,7 @@ flowchart TB
 | `DiffusionExecutor` | Execution backend contract, worker RPC, health, shutdown | Admission and request-state transitions |
 | `UniProcDiffusionExecutor` | Single in-process worker for `num_gpus == 1`; no IPC or async output pump | Multi-GPU execution |
 | `MultiprocDiffusionExecutor` | Worker processes, shared-memory message queues, result dispatch, worker monitoring | In-process single-GPU path |
+| `RayDiffusionExecutor` | GPU actors, placement groups, remote RPC, actor monitoring | Local worker subprocesses and shared-memory IPC |
 | `WorkerProc` | One `mp` worker process's IPC loop and reply rules | Scheduling policy; unused by `uni` |
 | `DiffusionWorker` | Device/distributed setup, LoRA activation, profiling, sleep/wake, runner delegation | Request admission |
 | `DiffusionModelRunner` | Pipeline loading, request-local model state, cache/compile setup, request or step execution | Queueing and cross-stage routing |
@@ -219,12 +225,12 @@ uses fused `execute_model_batch()` and requires the pipeline to declare
 request-batch support.
 
 **Distributed layerwise offload with AllGather (DLO DP concurrency)** is a
-separate multiproc dispatch path. It activates when
+separate dispatch path supported by `mp` and `ray`. It activates when
 `data_parallel_size > 1`, `enable_distributed_layerwise_offload` is set, and
 `dlo_use_allgather` is true. The engine then sets `dp_concurrent = True` and
 raises `scheduler.max_num_running_reqs` to `dp_size`, overriding the
 `max_num_seqs` value from `initialize()`. The scheduler still emits a
-multi-request `DiffusionSchedulerOutput`; the multiproc executor routes that
+multi-request `DiffusionSchedulerOutput`; these executors route that
 wave through `execute_request()` instead of fused `execute_model_batch()`.
 
 Optional admission coalescing can fill a `dp_size` wave: when
@@ -233,7 +239,7 @@ the first schedule of a wave (under `dp_concurrent`, the stable window is
 `min(0.3s, wait/2)`). With the default `request_batch_max_wait_ms == 0`,
 there is no wait.
 
-Before dispatch, `MultiprocDiffusionExecutor.execute_request()` rejects waves
+Before dispatch, both backends reject waves
 whose requests differ in sampling-parameter compatibility or `extra_args`
 (AllGather requires every DP rank to follow the same forward schedule). Each
 worker rank then picks one envelope from the wave:
@@ -297,8 +303,9 @@ head-of-line blocking.
 | --- | --- | --- |
 | `uni` | Default for `num_gpus == 1`, or set explicitly | One in-process worker |
 | `mp` | Default for `num_gpus > 1`, or set explicitly | One worker process per device |
+| `ray` | Set explicitly | One GPU actor per worker across Ray nodes |
 
-Ray and external-launcher diffusion backends are not implemented. A custom
+The external-launcher diffusion backend is not implemented. A custom
 `DiffusionExecutor` subclass or import path is also accepted.
 
 ### Uniproc backend
@@ -337,11 +344,27 @@ result path and does not start those pumps.
 See [Async diffusion output](../../feature/async_diffusion_output.md) for the
 multiproc request-mode timeline.
 
+### Ray backend
+
+`RayDiffusionExecutor` reuses initialized Ray or calls `ray.init()` (respecting
+`RAY_ADDRESS`). It reuses the current placement group or reserves one GPU
+bundle per worker with `PACK` placement. Ray owns GPU placement; PyTorch
+owns distributed collectives through a shared TCP rendezvous. Global ranks
+are grouped by host, while each actor uses local device 0.
+
+Request batches, step execution, and DLO DP waves use actor RPCs with
+rank-aware replies. Returned tensors move to CPU before Ray serialization;
+this backend does not use the multiproc async output pumps. Driver inference
+settings and explicit stage `runtime.env` are forwarded, excluding worker
+identity and device assignment. See [Stage configuration](../../../configuration/stage_configs.md)
+for cluster setup and environment details.
+
 ## Worker and runner boundary
 
 On the `mp` path, `WorkerProc` receives messages and calls methods through
 `WorkerWrapperBase`. On the `uni` path, the executor owns that wrapper
-directly. In both cases, `DiffusionWorker` handles the parts tied to a device:
+directly. Ray actors also construct `WorkerWrapperBase`. In all cases,
+`DiffusionWorker` handles the parts tied to a device:
 distributed initialization, model-runner construction, LoRA activation,
 profiling, and memory sleep/wake. It delegates actual model work to
 `DiffusionModelRunner`.
@@ -399,6 +422,10 @@ streams with an error, closes scheduler state, and shuts down the executor.
   async-output futures as failed, then clears them. Completed async outputs
   cached for later `wait_output_ready()` calls are dropped with the executor
   object.
+- **`ray`**: the executor gives actors up to five seconds to shut down, then
+  kills them and removes placement groups it owns. Actor death is monitored
+  even while idle; actor errors and RPC timeouts fail the group and notify the
+  stage. The stage client allows subprocess cleanup before signal fallback.
 - **`uni`**: the executor shuts down the in-process worker, drops the final
   model reference, and empties the accelerator cache so a later engine can reuse
   the device.
@@ -407,7 +434,7 @@ streams with an error, closes scheduler state, and shuts down the executor.
 
     Do not continue using a worker group after a fatal collective timeout or
     unexpected worker exit. Distributed state may be incomplete. The
-    multiprocess executor deliberately fails closed so the stage can restart.
+    `mp` and `ray` executors deliberately fail closed so the stage can restart.
     The uniproc executor fails closed when the accelerator context itself is
     poisoned.
 
@@ -417,10 +444,11 @@ streams with an error, closes scheduler state, and shuts down the executor.
   `diffusion_model_runner_cls` configuration still wins.
 - Tests and custom engine integrations may inject a `BaseScheduler` subclass.
   `SchedulerInterface` remains only as a deprecated compatibility name.
-- `distributed_executor_backend` may be `"uni"`, `"mp"`, a custom `DiffusionExecutor`
+- `distributed_executor_backend` may be `"uni"`, `"mp"`, `"ray"`, a custom `DiffusionExecutor`
   subclass, or an import path. A backend must preserve scheduler output,
   request identity, health, and cleanup contracts. Use `"mp"` when you need
-  process isolation or multi-GPU; `"uni"` is the single-GPU default.
+  local process isolation or multi-GPU, or `"ray"` for workers across nodes;
+  `"uni"` is the single-GPU default.
 - Worker extensions go through `WorkerWrapperBase`. They add worker methods;
   they do not become a second scheduler or request lifecycle.
 
@@ -466,7 +494,7 @@ Test the smallest affected slice, then cover its neighboring boundary:
 | --- | --- |
 | Admission or state transitions | `test_diffusion_scheduler.py`, including duplicate IDs, compatibility, abort, and missing output |
 | Engine loop or output delivery | `test_diffusion_engine.py`, `test_diffusion_engine_cleanup.py`, `test_diffusion_engine_rpc_routing.py` |
-| Executor or IPC | `test_multiproc_engine_concurrency.py`, `test_uniproc_executor.py`, `test_result_pump.py`, `test_diffusion_ipc.py`, `test_async_output_worker.py` |
+| Executor or IPC | `test_multiproc_engine_concurrency.py`, `test_uniproc_executor.py`, `test_ray_executor.py`, `test_result_pump.py`, `test_diffusion_ipc.py`, `test_async_output_worker.py` |
 | Worker or runner | `test_diffusion_worker.py`, `test_diffusion_model_runner.py` |
 | Stage boundary | `test_stage_diffusion_proc.py`, `test_inline_stage_diffusion_client.py` |
 | Warmup or streaming | `test_diffusion_engine_dummy_run.py`, `test_diffusion_streaming_output.py` |

docs/getting_started/installation/README.md

@@ -1,7 +1,7 @@
 # Installation
 
 !!! important
-    vLLM-Omni is released against the matching upstream vLLM major/minor version. The vLLM-Omni 0.30.x release line uses vLLM 0.30.x; the source-install instructions pin vLLM 0.30.0.
+    vLLM-Omni is released against the matching upstream vLLM major/minor version. This source checkout targets vLLM 0.31.0. The published vLLM-Omni 0.30.0 wheel instructions use vLLM 0.30.0; choose the installation path that matches your Omni revision.
 
 vLLM-Omni supports the following hardware platforms:
 

docs/getting_started/installation/gpu/cuda.inc.md

@@ -1,11 +1,13 @@
 # --8<-- [start:requirements]
 
-- GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)
+- GPU: compute capability 7.5 or higher (e.g., T4, RTX20xx, A100, L4, H100, etc.)
 
 # --8<-- [end:requirements]
 # --8<-- [start:set-up-using-python]
 
-vLLM-Omni depends on the matching major/minor release of vLLM. The vLLM-Omni 0.30.x release line uses vLLM 0.30.x.
+vLLM-Omni depends on the matching major/minor release of vLLM. This source
+checkout targets vLLM 0.31.0. The pre-built Omni wheel instructions below install
+the published 0.30.0 release with its matching vLLM dependency.
 
 !!! note
     PyTorch installed via `conda` will statically link `NCCL` library, which can cause issues when vLLM tries to use `NCCL`. See <gh-issue:8420> for more details.
@@ -41,13 +43,17 @@ uv pip install 'vllm-omni[demo]==0.30.0'
 # --8<-- [start:build-wheel-from-source]
 
 #### Installation of vLLM
-If you do not need to modify source code of vLLM, you can directly install the stable 0.30.0 release version of the library
+If you do not need to modify source code of vLLM, install the final 0.31.0
+release wheel from its commit-pinned index:
 
 ```bash
-uv pip install vllm==0.30.0 --torch-backend=auto
+uv pip install vllm==0.31.0 --torch-backend=auto \
+  --extra-index-url https://wheels.vllm.ai/db9527a46873454610df6dbedf79a36d6bf1a7f6
 ```
 
-The 0.30.0 release of vLLM ships CUDA 13.0-compatible binaries by default. If you need a different CUDA variant or want to reuse an existing PyTorch installation, build vLLM from source instead.
+The default 0.31.0 wheel contains CUDA 12.9 binaries. If you need a different
+CUDA variant or want to reuse an existing PyTorch installation, build vLLM
+from source instead.
 
 #### Installation of vLLM-Omni
 Since vllm-omni is rapidly evolving, it's recommended to install it from source
@@ -68,16 +74,16 @@ If you want to check, modify or debug with source code of vLLM, install the libr
 ```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
-git checkout v0.30.0
+git checkout v0.31.0
 ```
 Set up environment variables to get pre-built wheels. If there are internet problems, just download the whl file manually. And set `VLLM_PRECOMPILED_WHEEL_LOCATION` as your local absolute path of whl file.
 ```bash
-#For CUDA 13.0 (the default for v0.30.0; the wheel filename has no `+cu130` suffix)
-export VLLM_PRECOMPILED_WHEEL_LOCATION=https://github.com/vllm-project/vllm/releases/download/v0.30.0/vllm-0.30.0-cp38-abi3-manylinux_2_28_x86_64.whl
+# Default CUDA 12.9 wheel for v0.31.0 on x86_64
+export VLLM_PRECOMPILED_WHEEL_LOCATION=https://wheels.vllm.ai/db9527a46873454610df6dbedf79a36d6bf1a7f6/vllm-0.31.0-cp38-abi3-manylinux_2_28_x86_64.whl
 ```
 Install vllm with command below (If you have no existing PyTorch).
 ```bash
-uv pip install --editable .
+VLLM_USE_PRECOMPILED=1 uv pip install --editable . --torch-backend=auto
 ```
 Install vllm with command below (If you already have PyTorch).
 ```bash
@@ -128,12 +134,13 @@ docker run --runtime nvidia --gpus 2 \
 DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.cuda -t vllm-omni-cuda .
 ```
 
-If you want to specify the base vLLM version:
+The source build defaults to `vllm/vllm-openai:v0.31.0`. To select that
+upstream release image explicitly:
 
 ```bash
 DOCKER_BUILDKIT=1 docker build \
   -f docker/Dockerfile.cuda \
-  --build-arg BASE_IMAGE=vllm/vllm-openai:v0.30.0 \
+  --build-arg BASE_IMAGE=vllm/vllm-openai:v0.31.0 \
   -t vllm-omni-cuda .
 ```
 

docs/getting_started/installation/gpu/rocm.inc.md

@@ -10,8 +10,9 @@
 
 For ROCm, vLLM-Omni currently recommends the setup steps through Docker Images.
 
-vLLM-Omni depends on the matching major/minor release of vLLM. The vLLM-Omni
-0.30.x release line uses vLLM 0.30.x.
+vLLM-Omni depends on the matching major/minor release of vLLM. This source
+checkout targets vLLM 0.31.0. The pre-built Omni wheel instructions below install
+the published 0.30.0 release with its matching vLLM dependency.
 
 The Dockerfile's `BASE_IMAGE` pin applies only to Docker builds. The
 `vllm-omni` package does not install vLLM as a dependency, so non-Docker source
@@ -49,13 +50,14 @@ uv pip install onnxruntime-rocm
 
 #### Installation of vLLM
 
-If you do not need to modify source code of vLLM, you can directly install the stable 0.30.0 release version of the library
+If you do not need to modify source code of vLLM, install the final 0.31.0
+release wheel for ROCm 7.2.3:
 
 ```bash
-uv pip install vllm==0.30.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.30.0/rocm723
+uv pip install vllm==0.31.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.31.0/rocm723
 ```
 
-The pre-built 0.30.0 vLLM wheel targets ROCm 7.2.3. If you need a different ROCm stack or want to reuse an existing PyTorch installation, build vLLM from source instead.
+The pre-built 0.31.0 vLLM wheel targets ROCm 7.2.3. If you need a different ROCm stack or want to reuse an existing PyTorch installation, build vLLM from source instead.
 
 #### Installation of vLLM-Omni
 
@@ -75,7 +77,7 @@ If you want to check, modify or debug with source code of vLLM, install the libr
 ```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
-git checkout v0.30.0
+git checkout v0.31.0
 python3 -m pip install -r requirements/rocm.txt
 python3 setup.py develop
 ```
@@ -87,7 +89,7 @@ python3 setup.py develop
 #### Build docker image
 
 The source build defaults to the published upstream base image
-`vllm/vllm-openai-rocm:v0.30.0`, aligned with the vLLM release used by CI.
+`vllm/vllm-openai-rocm:v0.31.0`, aligned with this source checkout.
 Older or custom bases must provide the vLLM APIs checked by the Dockerfile's
 image-build canary. This upstream base is distinct from the prebuilt
 `vllm/vllm-omni-rocm` images discussed below; their published-tag availability
@@ -102,7 +104,7 @@ To select the upstream ROCm base explicitly, pass `BASE_IMAGE`:
 ```bash
 DOCKER_BUILDKIT=1 docker build \
   -f docker/Dockerfile.rocm \
-  --build-arg BASE_IMAGE=vllm/vllm-openai-rocm:v0.30.0 \
+  --build-arg BASE_IMAGE=vllm/vllm-openai-rocm:v0.31.0 \
   -t vllm-omni-rocm .
 ```
 

docs/getting_started/installation/gpu/xpu.inc.md

@@ -1,8 +1,11 @@
+<!-- markdownlint-disable MD001 MD025 -->
+
 # --8<-- [start:requirements]
 
 - GPU: Validated on Intel® Arc™ B-Series.
 
 # --8<-- [end:requirements]
+
 # --8<-- [start:set-up-using-python]
 
 vLLM-Omni currently recommends using the Docker image setup steps below.
@@ -20,12 +23,15 @@ vLLM-Omni currently recommends using the Docker image setup steps below.
 #### Build docker image
 
 ```bash
-DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.xpu -t vllm-omni-xpu --shm-size=4g .
+export VLLM_VERSION=v0.31.0
+DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.xpu -t vllm-omni-xpu --shm-size=4g \
+  --build-arg VLLM_VERSION="$VLLM_VERSION" .
 ```
 
 This layers vLLM-Omni on top of the published `vllm/vllm-openai-xpu:<VLLM_VERSION>`
-base image (v0.30.0 by default), which Docker pulls automatically. To target a different vLLM release,
-pass `--build-arg VLLM_VERSION=<tag>`. If that tag has not been published yet, build
+base image, which Docker pulls automatically. The Dockerfile defaults to
+v0.31.0. To target a different vLLM release, pass
+`--build-arg VLLM_VERSION=<tag>`. If that tag has not been published yet, build
 the base from upstream's own Dockerfile first and point the build at it:
 
 ```bash
@@ -40,7 +46,7 @@ DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.xpu -t vllm-omni-xpu --shm-s
 
 ##### Launch with OpenAI API Server
 
-```
+```bash
 docker run -it -d --shm-size 10g \
   --name {container_name} \
   --net=host \

docs/getting_started/installation/npu/npu.inc.md

@@ -10,25 +10,25 @@ The recommended way to use vLLM-Omni on NPU is through the vLLM-Ascend pre-built
 === "A2"
 
     ```bash
-    export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.30.0
+    export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.31.0
     ```
 
 === "A3"
 
     ```bash
-    export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.30.0-a3
+    export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.31.0-a3
     ```
 
 === "A5"
 
     ```bash
-    export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.30.0-a5
+    export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.31.0-a5
     ```
 
 === "310P (Experimental)"
 
     ```bash
-    export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.30.0-310p
+    export IMAGE=quay.io/atlas-ci/vllm-ascend:v0.31.0-310p
     ```
 
 ```bash
@@ -71,10 +71,14 @@ We are keeping [issue #886](https://github.com/vllm-project/vllm-omni/issues/886
 
 # --8<-- [start:installation-main]
 
-You can also install vLLM-Omni from the latest main branch if you want to use the latest features or bug fixes. Use the aligned vLLM-Ascend v0.30.0 image described above, and check [issue #886](https://github.com/vllm-project/vllm-omni/issues/886) for the status of the latest vLLM-Omni commit on NPU.
+You can also install vLLM-Omni from source if you want to use the latest features
+or bug fixes. This source checkout targets vLLM 0.31.0 and needs a matching
+vLLM-Ascend runtime. The v0.30.0 images above are for the published Omni v0.30.0
+release. Check [issue #886](https://github.com/vllm-project/vllm-omni/issues/886)
+for aligned versions and NPU validation before selecting a development image.
 
 ```bash
-# Inside the vLLM-Ascend v0.30.0 container, install vLLM-Omni from main
+# Inside a container with matching vLLM and vLLM-Ascend, install Omni from source
 cd /vllm-workspace
 git clone https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni

docs/getting_started/quickstart.md

@@ -19,17 +19,20 @@ uv venv --python 3.12 --seed
 source .venv/bin/activate
 
 # On CUDA
-uv pip install vllm==0.30.0 --torch-backend=auto
+uv pip install vllm==0.31.0 --torch-backend=auto \
+  --extra-index-url https://wheels.vllm.ai/db9527a46873454610df6dbedf79a36d6bf1a7f6
 
 # On ROCm
-uv pip install vllm==0.30.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.30.0/rocm723
+uv pip install vllm==0.31.0+rocm723 --extra-index-url https://wheels.vllm.ai/rocm/0.31.0/rocm723
 
 git clone https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
 uv pip install -e .
 ```
 
-For additional installation methods — please see the [installation guide](installation/README.md).
+These commands install vLLM 0.31.0 for this source checkout. The CUDA index is
+pinned to the final release commit. For published vLLM-Omni wheels and additional
+installation methods, see the [installation guide](installation/README.md).
 
 !!! note
     It is important to install the same major & minor version of vLLM and vLLM Omni, otherwise things may not work as expected. If the versions are misaligned, you will see a warning when you import vLLM Omni.

docs/models/supported_models.md

@@ -29,9 +29,11 @@ th {
 | `QwenImagePipeline` | Qwen-Image | `Qwen/Qwen-Image` | ✅︎ | | | ✅︎ | [Published](https://recipes.vllm.ai/Qwen/Qwen-Image) |
 | `QwenImagePipeline` | Qwen-Image-2512 | `Qwen/Qwen-Image-2512` | ✅︎ | | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen-Image-2512.md) |
 | `QwenImageEditPipeline` | Qwen-Image-Edit | `Qwen/Qwen-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `JoyImageEditPipeline` | JoyAI-Image-Edit | `jdopensource/JoyAI-Image-Edit-Diffusers` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/JD/JoyAI-Image-Edit.md) |
 | `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2509 | `Qwen/Qwen-Image-Edit-2509` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `QwenImageLayeredPipeline` | Qwen-Image-Layered | `Qwen/Qwen-Image-Layered` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `QwenImageEditPlusPipeline` | Qwen-Image-Edit-2511 | `Qwen/Qwen-Image-Edit-2511` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
+| `QwenImage21Pipeline` | Qwen-Image-2.1 (T2I + image-conditioned generation, up to 4 condition images) | `Qwen/Qwen-Image-2.1` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen-Image-2.1.md) |
 | `GlmImagePipeline` | GLM-Image | `zai-org/GLM-Image` | ✅︎ | ✅︎ | | | — |
 | `ZImagePipeline` | Z-Image | `Tongyi-MAI/Z-Image-Turbo` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Tongyi-MAI/Z-Image-Turbo.md) |
 | `Krea2Pipeline` | Krea 2 (Raw + Turbo) | `krea/Krea-2-Raw`, `krea/Krea-2-Turbo` | ✅︎ | | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/krea/Krea-2.md) |
@@ -54,12 +56,12 @@ th {
 | `LingBotVideoPipeline` | LingBot-Video dense and MoE T2I, T2V, TI2V | `robbyant/lingbot-video-dense-1.3b`, `robbyant/lingbot-video-moe-30b-a3b` | ✅︎ | | | | — |
 | `SanaVideoPipeline` | SANA-Video-2B T2V (native 480p/720p; Diffusers adapter validated at both resolutions) | `Efficient-Large-Model/SANA-Video_2B_480p_diffusers`, `Efficient-Large-Model/SANA-Video_2B_720p_diffusers` | ✅︎ | | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/NVIDIA/SANA-Video-2B.md) |
 | `SanaImageToVideoPipeline` | SANA-Video-2B I2V (native and Diffusers adapter validated at 480p/720p) | `Efficient-Large-Model/SANA-Video_2B_480p_diffusers`, `Efficient-Large-Model/SANA-Video_2B_720p_diffusers` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/NVIDIA/SANA-Video-2B.md) |
-| `SeedVR2Pipeline` | [SeedVR2 3B](seedvr2.md) video restoration | `ByteDance-Seed/SeedVR2-3B` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/ByteDance/SeedVR2-RTX-5090.md) |
+| `SeedVR2Pipeline` | [SeedVR2 3B](seedvr2.md) video restoration | `ByteDance-Seed/SeedVR2-3B` | ✅︎ | | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/ByteDance/SeedVR2-RTX-5090.md) |
 | `MiniMaxH3Pipeline` | MiniMax H3 T2VA, first/last-keyframe FL2VA, and mixed-reference Ref2VA | `MiniMaxAI/MiniMax-H3` | ✅︎ | ✅︎ | | | [Published](https://recipes.vllm.ai/MiniMaxAI/MiniMax-H3) |
 | `Magi2Pipeline` | MAGI-2 native Preview-stage T2VA and I2VA (272p/540p) | `sand-ai/MAGI-2-preview` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/SandAI/MAGI-2-preview-L20X.md) |
 | `DreamZeroPipeline` | DreamZero-DROID | `GEAR-Dreams/DreamZero-DROID` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `Pi0Pipeline` | π0 (Pi-Zero) VLA | `lerobot/pi0_base` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/lerobot/Pi0.md) |
-| `Pi05Pipeline` | π0.5 VLA | `lerobot/pi05_base` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/lerobot/Pi05.md) |
+| `Pi05Pipeline` | π0.5 VLA | `lerobot/pi05_base` | ✅︎ | | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/lerobot/Pi05.md) |
 | `HeliosPipeline`, `HeliosPyramidPipeline` | Helios | `BestWishYsh/Helios-Base`, `BestWishYsh/Helios-Mid`, `BestWishYsh/Helios-Distilled` | ✅︎ | ✅︎ | ✅︎ | | — |
 | `OvisImagePipeline` | Ovis-Image | `OvisAI/Ovis-Image` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
@@ -70,7 +72,7 @@ th {
 | `StableDiffusionXLPipeline` | Stable-Diffusion-XL | `stabilityai/stable-diffusion-xl-base-1.0` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` | ✅︎ | ✅︎ | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/StabilityAI/Stable-Diffusion-3.5.md) |
 | `CosyVoice3Model` | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `AuKForConditionalGeneration` | AuK / AuK-Flash (instruction-driven speech generation and editing) | `tencent/AuK`, `tencent/AuK-Flash` (with `Qwen/Qwen2.5-Omni-3B`; assemble with `tools/prepare_auk_checkpoint.py`) | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Tencent/AuK-H100.md) |
+| `AuKForConditionalGeneration` | AuK / AuK-Flash (instruction-driven speech generation and editing) | `tencent/AuK`, `tencent/AuK-Flash` (with `Qwen/Qwen2.5-Omni-3B`; assemble with `tools/prepare_auk_checkpoint.py`) | ✅︎ | | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Tencent/AuK-H100.md) |
 | `OmniVoiceModel` | OmniVoice | `k2-fsa/OmniVoice` | ✅︎ | | | ✅︎ | — |
 | `VoxCPM2TalkerForConditionalGeneration` | VoxCPM2 | `openbmb/VoxCPM2` | ✅︎ | | | ✅︎ | — |
 | `BreezeForConditionalGeneration` | Breeze-TTS-2 (voice design, voice clone, voice direction, CFG) | `BreezeBlue/Breeze-TTS-2` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/BreezeBlue/Breeze-TTS-2.md) |
@@ -82,8 +84,8 @@ th {
 | `FluxPipeline` | FLUX.1-schnell | `black-forest-labs/FLUX.1-schnell` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `OmniGen2Pipeline` | OmniGen2 | `OmniGen2/OmniGen2` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `StableAudioPipeline` | Stable-Audio-Open | `stabilityai/stable-audio-open-1.0` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `MiniMaxMusic3ForConditionalGeneration` | MiniMax Music 3 (text-to-music) | `MiniMaxAI/MiniMax-Music3` | ✅︎ | | | | — |
-| `Yue2ForCausalLM` | YuE2-3B (text-to-music with ABC plan) | `m-a-p/YuE2-3B` | ✅︎ | | | | — |
+| `MiniMaxMusic3ForConditionalGeneration` | MiniMax Music 3 (text-to-music) | `MiniMaxAI/MiniMax-Music3` | ✅︎ | | | ✅︎ | — |
+| `Yue2ForCausalLM` | YuE2-3B (text-to-music with ABC plan) | `m-a-p/YuE2-3B` | ✅︎ | | | ✅︎ | [Recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/m-a-p/YuE2-3B.md) / [Audio-to-score](https://github.com/vllm-project/vllm-omni/blob/main/recipes/m-a-p/SheetSage2-H200.md) |
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-CustomVoice | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` | ✅︎ | | | ✅︎ | [Published](https://recipes.vllm.ai/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice) |
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-VoiceDesign | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `Qwen3TTSForConditionalGeneration` | Qwen3-TTS-12Hz-1.7B-Base | `Qwen/Qwen3-TTS-12Hz-1.7B-Base` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
@@ -93,7 +95,7 @@ th {
 | `MossTTSDelayModel` | MOSS-TTS, MOSS-TTSD, MOSS-SoundEffect, MOSS-VoiceGenerator | `OpenMOSS-Team/MOSS-VoiceGenerator` | ✅︎ | | | | — |
 | `MossTTSRealtime` | MOSS-TTS-Realtime | `OpenMOSS-Team/MOSS-TTS-Realtime` | ✅︎ | | | | — |
 | `HiggsAudioV2ForConditionalGeneration` | Higgs-Audio v2 | `bosonai/higgs-audio-v2-generation-3B-base` | ✅︎ | | | | — |
-| `HiggsMultimodalQwen3ForConditionalGeneration` | Higgs-Audio v3 (TTS) | `bosonai/higgs-audio-v3-tts-4b` | ✅︎ | | | | [Published](https://recipes.vllm.ai/bosonai/higgs-audio-v3-tts-4b) |
+| `HiggsMultimodalQwen3ForConditionalGeneration` | Higgs-Audio v3 (TTS) | `bosonai/higgs-audio-v3-tts-4b` | ✅︎ | | | ✅︎ | [Published](https://recipes.vllm.ai/bosonai/higgs-audio-v3-tts-4b) |
 | `IndexTTS2TalkerForConditionalGeneration` | IndexTTS-2 | `IndexTeam/IndexTTS-2` | ✅︎ | | | | — |
 | `IndexTTS25TalkerForConditionalGeneration` | IndexTTS-2.5 | native `checkpoints/` bundle | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/IndexTeam/IndexTTS-2_5.md) |
 | `NextStep11Pipeline` | NextStep-1.1 | `stepfun-ai/NextStep-1.1` | ✅︎ | ✅︎ | | ✅︎ | — |
@@ -106,6 +108,7 @@ th {
 | `SenseNovaU1Pipeline` | SenseNova-U1-A3B (MoE) | `sensenova/SenseNova-U1-A3B-MoT` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/SenseNova/SenseNova-U1.md#a3b-moe-variant) |
 | `LancePipeline` | Lance | `bytedance-research/Lance` | ✅︎ | | | | — |
 | `HunyuanVideo15Pipeline` | HunyuanVideo-1.5-T2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | ✅︎ | ✅︎ | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Tencent/HunyuanVideo-1.5.md) |
+| `Kandinsky6TI2VAPipeline` | Kandinsky 6 TI2VA | `kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Kandinsky/Kandinsky6-TI2VA.md) |
 | `HunyuanVideo15ImageToVideoPipeline` | HunyuanVideo-1.5-I2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v` | ✅︎ | ✅︎ | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Tencent/HunyuanVideo-1.5.md) |
 | `VoxtralTTSForConditionalGeneration` | Voxtral TTS | `mistralai/Voxtral-4B-TTS-2603` | ✅︎ | ✅︎ | | | — |
 | `CovoAudioForConditionalGeneration` | Covo-Audio-Chat | `tencent/Covo-Audio-Chat` | ✅︎ | | | | — |
@@ -115,7 +118,7 @@ th {
 | `GepardTalkerForConditionalGeneration` | Gepard-1.0 | `nineninesix/gepard-1.0` | ✅︎ | | | ✅︎ | — |
 | `HiDreamImagePipeline` | HiDream-I1-Full | `HiDream-ai/HiDream-I1-Full` | ✅︎ | ✅︎ | | | — |
 | `HiDreamO1ImagePipeline` | HiDream-O1-Image | `HiDream-ai/HiDream-O1-Image` | ✅︎ | | | | — |
-| `AnimaPipeline` | Anima | `circlestone-labs/Anima` | ✅︎ | ✅︎ | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/circlestone-labs/Anima.md) |
+| `AnimaPipeline` | Anima | `circlestone-labs/Anima` | ✅︎ | ✅︎ | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/circlestone-labs/Anima.md) |
 
 The `Recipe` column links the corresponding deployment recipe. `Published` is
 preferred when a corresponding `recipes.vllm.ai` page exists; otherwise the

docs/serving/README.md

@@ -154,18 +154,16 @@ All six routes are model- or configuration-dependent. In particular,
 pipeline declares a `duplex_plugin` and its deploy configuration sets
 `session_mode: duplex` (with `session_mode: turn` the same model boots the
 ordinary turn-based serving stack instead). On a duplex server a stock
-Realtime client needs no vendor query parameter; `duplex=0` selects the
-turn-based Realtime handler, which a duplex server does not mount, so that
-connection is refused with `Realtime API is not available`. Such a server
-serves the websocket route plus `POST /v1/chat/completions`, `/v1/models`
-and `/health`, and no other turn-based HTTP route. The chat route is the
-ordinary chat service running on the duplex engine: a request is a
-turn-based generation on the same stages, served alongside the live
-websocket sessions; it opens no duplex session and holds no
-`duplex_session.max_sessions` slot -- see [Full Duplex](full_duplex_api.md).
-On a server that is not duplex, an explicit `?duplex=1` is refused rather
-than answered by the turn-based handler, so a client that requested duplex
-never silently gets the other protocol.
+Realtime client needs no vendor query parameter; `duplex=0` opts out of native
+duplex and selects the turn-based Realtime handler. It runs generation on the
+same stages, alongside live websocket sessions, and opens no duplex session
+or consumes a `duplex_session.max_sessions` slot -- see
+[Full Duplex](full_duplex_api.md).
+Such a server serves the websocket route plus `POST /v1/chat/completions`,
+`/v1/models` and `/health`, and no other turn-based HTTP route. On a server
+that is not duplex, an explicit `?duplex=1` is refused rather than answered by
+the turn-based handler, so a client that requested duplex never silently gets
+the other protocol.
 
 ## Related Endpoints
 

docs/serving/realtime_duplex_api.md

@@ -250,6 +250,40 @@ call; transport failures raise `DuplexConnectionError`; a session that ends
 while you are awaiting it raises `DuplexSessionClosedError`. All three derive
 from `DuplexClientError`.
 
+### Output limits and slow consumers
+
+Deployment settings under `duplex_session` bound each session's pending
+output: `max_pending_output_bytes_per_session` defaults to 2 MiB and
+`max_pending_output_events_per_session` to 512 events. Bytes count compact
+Realtime JSON, including base64 audio, not decoded audio. The limits apply
+to both WebSocket delivery and direct Python handles, not to replay journals
+or client playback queues.
+
+Exceeding a limit fails the active response without committing its partial
+text to history, then closes only that session. If overflow occurs while a
+completion is being queued, its not-yet-queued `response.done` becomes failed
+with the same identity and output, and its response history entry is removed.
+An ending already accepted by the buffer is not replaced or followed by a
+second ending. Open a new session to continue; the closed session cannot be
+resumed. The notifications use these fields:
+
+| Event | Field values |
+| --- | --- |
+| `error` | `error.type: "rate_limit_error"`, `error.code: "output_backpressure"` |
+| `response.done` (active response or not-yet-queued ending) | `response.status: "failed"`, `response.status_details: {"type": "failed", "reason": "output_backpressure"}` |
+| `session.closed` | `reason: "output_backpressure"` |
+
+Queued audio for the affected response is removed. Previously queued non-audio
+events retain their order, but newly emitted ordinary events, including
+audio/text, content-part and output-item completion markers, are suppressed.
+Clients must therefore handle closure without waiting for every completion
+marker. Errors and response endings have an extra 64 KiB / eight-event reserve;
+if that also fills, either notification may be omitted. Closure has its own
+independent slot. A closure notification exceeding 4 KiB is compacted to
+`reason: "close_details_exceed_output_limit"` with empty details, preserving
+the event type and identity. One event held by the consumer is outside the
+queued budget; audio already sent still requires the client to stop playback.
+
 ### Measure latency
 
 `EventCollector` accumulates events for assertions and metrics:
@@ -315,6 +349,8 @@ single-consumer async iterator that ends after `session.closed` /
 `session.expired`; rejected commands come back as `ErrorEvent`s on it.
 Every event renders the wire JSON with `to_realtime()`, so anything written
 against the WebSocket protocol works unchanged on the typed stream.
+If opening fails, the abandoned handle is closed. Late engine events do not
+reopen a locally closed handle.
 
 `vllm_omni.clients.inline_duplex.InlineDuplexClient` wraps a `DuplexOmni`
 behind the `DuplexClient` API, so the same application code runs in-process
@@ -573,7 +609,7 @@ formats (`pcm16`, `pcm_s16le`, `s16le`, `pcm_f32le`, `g711_ulaw`,
 | `session.created` | 2 | Session opened. OpenAI name; adds `attachment_generation`, `resume_token` and vLLM-Omni keys inside `session` (`id` is the server-allocated session id, `epoch`, `turn_id`, `playback`, `capabilities`, ...). |
 | `session.updated` | 2 | Echo of the effective session config after every `session.update` (same extended `session` object). |
 | `session.heartbeat_ack` | 3 | Reply to `session.heartbeat`. |
-| `session.closed` | 3 | Last event on the socket, emitted once the engine released the session; `reason` ∈ `client_close`, `disconnect`, `timeout`, `transport_error`, `shutdown`, ... |
+| `session.closed` | 3 | Last event on the socket, emitted once the engine released the session; `reason` ∈ `client_close`, `disconnect`, `timeout`, `transport_error`, `shutdown`, `output_backpressure`, `close_details_exceed_output_limit`, ... |
 | `session.resumed` | 3 | Resume accepted; carries the new `attachment_generation` and rotated `resume_token`; journaled events are replayed after it. |
 | `session.replaced` | 3 | Sent to the superseded socket when another socket resumes the session. |
 | `session.expired` | 3 | Engine lease reaped (`disconnect_grace_expired`, `idle_ttl_expired`); socket closes after it. |

docs/serving/speech_api.md

@@ -183,6 +183,16 @@ emit these headers.
 **Raw audio stream** (`stream_format="audio"`). Streams raw audio bytes (PCM or
 WAV) as they are decoded.
 
+`pcm` is 16-bit signed little-endian, interleaved, at the model's native sample
+rate and channel count (24 kHz mono for most models; MOSS-TTS-Local v1.5 emits
+48 kHz stereo). Raw streams and non-streaming responses state the format in
+response headers:
+
+| Header | Description |
+| --- | --- |
+| `x-audio-sample-rate` | Sample rate of the returned audio in Hz (after any `sample_rate` resampling). |
+| `x-audio-channels` | Channel count of the returned audio. |
+
 Both streaming modes (`stream_format="audio"` and `"sse"`) require
 `response_format="pcm"` or `"wav"`, and `speed` must be `1.0` (or omitted).
 
@@ -193,9 +203,13 @@ Each event has an `event:` line and a JSON `data:` line:
 - `speech.audio.delta` — a base64 audio chunk:
 
     ```json
-    { "type": "speech.audio.delta", "audio": "<base64>", "response_format": "pcm" }
+    { "type": "speech.audio.delta", "audio": "<base64>", "response_format": "pcm",
+      "sample_rate": 24000, "channels": 1 }
     ```
 
+    `sample_rate` and `channels` extend the OpenAI event and describe the
+    decoded chunk.
+
 - `speech.audio.done` — terminal event, carrying token `usage`:
 
     ```json
@@ -340,7 +354,7 @@ Server -> Client:
 
 | Message | Description |
 | --------- | ------------- |
-| `{"type": "audio.start", "utterance_index": 0, "sentence_index": 0, "sentence_text": "...", "format": "pcm", "sample_rate": 24000}` | Audio generation starting for the buffered input |
+| `{"type": "audio.start", "utterance_index": 0, "sentence_index": 0, "sentence_text": "...", "format": "pcm", "sample_rate": 24000, "channels": 1}` | Audio generation starting for the buffered input. With `stream_audio=true` it is sent with the first audio chunk, and for `pcm` it carries that audio's `sample_rate` and `channels` (omitted when no audio was produced). |
 | Binary frame | Raw audio bytes (one or more PCM chunks when `stream_audio=true`) |
 | `{"type": "audio.done", "utterance_index": 0, "sentence_index": 0, "total_bytes": 96000, "error": false}` | Audio complete for the buffered input |
 | `{"type": "session.done", "utterance_index": 0, "total_sentences": N}` | Flushed utterance complete |

docs/serving/videos_api.md

@@ -237,13 +237,22 @@ At least one mask is required. A nontrivial `video_noise_mask` requires
 `source_audio` or a `source_video` with an audio stream. Mask values are in
 `[0, 1]`: `0` preserves the source, `1` regenerates it, and fractional values
 blend the two behaviors. Exact all-one masks are no-ops and do not require a
-source. Source uploads without a mask are rejected. Masks may be a JSON scalar
-or arrays matching the H3 latent/token grid; pixel-resolution masks must be
-resized or pooled by the client before upload.
+source. Source uploads without a mask are rejected. Masks may be a JSON scalar,
+an array matching the H3 latent/token grid, or (video only) a frame-space
+array that the server resizes to the latent grid.
 
 For an aligned output of `F` frames at `W x H`, the video latent grid is
 `[Tv, H/16, W/16]`, where `Tv = 2 + 5 * ((F - 5) / 17)`. The video mask may be
-a scalar, a flat token vector, `[Tv, H/32, W/32]`, or the full latent grid. For
+a scalar, a flat token vector, `[Tv, H/32, W/32]`, the full latent grid, a
+spatial `[h, w]` mask applied to every frame, or a frame-space `[T, h, w]` mask
+with one slice per 24 fps output frame. A shape that matches the token or full
+latent grid is always read as that grid. Frame-space masks are area-resized to
+`[H/16, W/16]`; along time, a mask shorter than `F` is padded with its last
+slice (as the source video is) and a longer one is truncated, then each latent
+takes the maximum over the frames the causal video VAE folds into it, so any
+regenerated frame regenerates its latent. Because the 8 MiB limit below applies
+to the JSON file, clients should area-downsample frame-space masks by 16
+spatially before upload. For
 the model input, timestep, and velocity, a full-grid mask is max-pooled over
 each 2x2 spatial token and fractional values are rounded upward to 1/256
 levels. The final x0 restore uses the original, unquantized mask, so full-grid

docs/user_guide/diffusion/cpu_offload.md

@@ -42,6 +42,7 @@ higher-priority strategy is selected.
 | --- | --- | --- | :---: | :---: | :---: | --- |
 | Flux2Pipeline | `black-forest-labs/FLUX.2-dev` | `Flux2Transformer2DModel` | yes | yes | — | `transformer_blocks`, `single_transformer_blocks` |
 | LongCatImagePipeline | `meituan-longcat/LongCat-Image` | `LongCatImageTransformer2DModel` | — | yes | — | `transformer_blocks`, `single_transformer_blocks` |
+| JoyImageEditPipeline | `jdopensource/JoyAI-Image-Edit-Diffusers` | `JoyImageEditTransformer3DModel` | yes | yes | — | `double_blocks` |
 | NextStep11Pipeline | `stepfun-ai/NextStep-1.1` | `NextStepModel` | — | yes | — | `layers` |
 | OvisImagePipeline | `AIDC-AI/Ovis-Image-7B` | `OvisImageTransformer2DModel` | — | yes | — | `transformer` |
 | QwenImagePipeline | `Qwen/Qwen-Image` | `QwenImageTransformer2DModel` | yes | yes | — | `transformer_blocks` |

docs/user_guide/diffusion/parallelism/vae_parallelism.md

@@ -1,12 +1,12 @@
 # VAE Parallelism Guide
 
-
 ## Table of Content
 
 - [Overview](#overview)
 - [Quick Start](#quick-start)
 - [Example Script](#example-script)
 - [Configuration Parameters](#configuration-parameters)
+- [Batch Parallel Decode](#batch-parallel-decode)
 - [Best Practices](#best-practices)
 - [Troubleshooting](#troubleshooting)
 - [Summary](#summary)
@@ -15,23 +15,22 @@
 
 ## Overview
 
-VAE parallelism distributes VAE (Variational AutoEncoder) decode/encode work across multiple GPUs. This guide covers VAE patch/tile parallelism, which splits latent space into spatial tiles or patches, and Wan spatial-shard decode, which shards decoder feature maps along height or width.
+VAE parallelism distributes VAE (Variational AutoEncoder) decode/encode work across multiple GPUs. This guide covers patch/tile parallelism, batch parallel decode for image VAEs, and Wan spatial-shard decode. Patch/tile parallelism divides images into spatial regions; batch parallel decode assigns complete images to ranks.
 
 This is particularly useful for:
+
 - **High-resolution image generation** where VAE decode can become a memory bottleneck
 - **Memory-constrained environments** where the VAE decode activation peak exceeds available VRAM
 - **Multi-GPU setups** where you want to leverage distributed resources for the VAE stage
 
 See supported models list in [Supported Models](../../diffusion_features.md#supported-models).
 
-
 VAE patch parallelism uses two strategies based on image size:
 
-| Strategy | Use Case | How It Works | Overlap Handling | Output Quality |
-|----------|----------|--------------|------------------|----------------|
-| **Tiled Decode** | Large images (triggers VAE tiling) | Distributes existing VAE tiling computation across ranks. Each rank decodes a subset of overlapping tiles. | Uses VAE's native `blend_v` and `blend_h` functions to seamlessly merge overlapping regions | Bit-identical (same logic as single-GPU tiling) |
-| **Patch Decode** | Small images (no VAE tiling) | Splits latent into spatial patches with halos. Each rank decodes one patch with boundary context. | Halo regions provide edge context; core regions are directly stitched without blending | Near-identical (diff < 0.5%, visually imperceptible) |
-
+| Strategy         | Use Case                           | How It Works                                                                                               | Overlap Handling                                                                            | Output Quality                                       |
+| ---------------- | ---------------------------------- | ---------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ---------------------------------------------------- |
+| **Tiled Decode** | Large images (triggers VAE tiling) | Distributes existing VAE tiling computation across ranks. Each rank decodes a subset of overlapping tiles. | Uses VAE's native `blend_v` and `blend_h` functions to seamlessly merge overlapping regions | Bit-identical (same logic as single-GPU tiling)      |
+| **Patch Decode** | Small images (no VAE tiling)       | Splits latent into spatial patches with halos. Each rank decodes one patch with boundary context.          | Halo regions provide edge context; core regions are directly stitched without blending      | Near-identical (diff < 0.5%, visually imperceptible) |
 
 VAE Patch Parallelism **reuses the DiT process group** (`dit_group`) and does not initialize a separate ProcessGroup. This means:
 
@@ -110,19 +109,80 @@ vllm serve Tongyi-MAI/Z-Image-Turbo --omni --port 8091 \
 
 In `DiffusionParallelConfig`:
 
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `vae_patch_parallel_size` | int | 1 | Number of GPUs for VAE patch/tile parallelism. Set to 2 or higher to enable. Should typically match `tensor_parallel_size` as they share the same process group. |
-| `vae_parallel_mode` | str | `"tile"` | VAE parallel decode strategy: `"tile"` (default tile/patch parallel decode), `"spatial_shard_height"`, or `"spatial_shard_width"` (spatially-sharded decode, Wan only). See [Spatially-Sharded Decode](#spatially-sharded-decode-wan). |
+| Parameter                 | Type   | Default   | Description                                                                                                                                                       |
+| ------------------------- | ------ | --------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `vae_patch_parallel_size` | int    | 1         | Number of ranks requested for VAE parallelism, including batch mode. Set to 2 or higher to enable. Batch mode caps this degree at the existing worker group size. |
+| `vae_parallel_mode`       | str    | `"tile"`  | `"tile"` for tile/patch decode, `"batch"` for complete-image batch decode, or `"spatial_shard_height"` / `"spatial_shard_width"` for Wan spatial-shard decode.    |
 
 Additional requirements:
 
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| `vae_use_tiling` | bool | False | Must be set to `True` when using VAE patch parallelism. |
+| Parameter        | Type   | Default   | Description                                             |
+| ---------------- | ------ | --------- | ------------------------------------------------------- |
+| `vae_use_tiling` | bool   | False     | Must be set to `True` when using VAE patch parallelism. |
 
 !!! note "Automatic VAE Tiling"
-    When `vae_patch_parallel_size > 1` and the model has a distributed VAE (`DistributedVaeMixin`), the system automatically sets `vae_use_tiling=True` if not already enabled.
+    When `vae_patch_parallel_size > 1` and the model has a distributed VAE (`DistributedVaeMixin`), tile and spatial-shard modes automatically enable `vae_use_tiling`. Batch mode preserves the requested tiling setting.
+
+---
+
+## Batch Parallel Decode
+
+Set `vae_parallel_mode="batch"` to distribute complete images across ranks during
+VAE decode. This mode supports pipelines that load `DistributedAutoencoderKL`
+or `DistributedAutoencoderKLFlux2`, including FLUX.2-dev and FLUX.2 Klein. Klein
+selects the distributed VAE only for batch mode; its default path is unchanged.
+Selecting this mode for an
+unsupported VAE, including Wan, raises an error at model initialization.
+
+For a pipeline already using two tensor-parallel workers, select batch decode
+with these Python settings:
+
+```python
+parallel_config = DiffusionParallelConfig(
+    tensor_parallel_size=2,
+    vae_patch_parallel_size=2,
+    vae_parallel_mode="batch",
+)
+sampling_params = OmniDiffusionSamplingParams(num_outputs_per_prompt=2)
+```
+
+The corresponding serving flags are `--vae-patch-parallel-size 2
+--vae-parallel-mode batch`. Keep the existing DiT parallelism configuration;
+the VAE degree does not allocate extra workers. Send a request for multiple
+images to use the batch split.
+
+Each rank decodes a contiguous part of the image batch through its native VAE
+decoder. Results are reassembled in the original image order and made available
+on every worker. With two images and four workers, two workers decode one image
+each. With five images and four workers, the assigned batch sizes are 2, 1, 1,
+and 1. Workers without images still participate in the required communication.
+
+**Constraints and behavior:**
+
+- Batch mode and distributed tile/patch mode are mutually exclusive. Encoding
+  uses the native local path, including native tiling when enabled, rather than
+  distributing spatial tiles across workers.
+- Batch size one, VAE degree one, or a single worker uses native decode on each
+  worker. Batch mode does not automatically switch to distributed tile/patch
+  decode for a single image, even with `vae_use_tiling=True`. To distribute a
+  single image across workers, select `vae_parallel_mode="tile"` on a pipeline
+  that supports distributed tile/patch decoding.
+- Data parallelism, pipeline parallelism, and CFG parallelism must each have
+  degree one because this implementation communicates over the worker WORLD
+  group. Tensor and sequence parallelism may supply the shared workers.
+- Batch mode does not force tiling. Explicit native VAE tiling and slicing
+  settings remain in effect within each rank's assigned batch and on the
+  single-image fallback. Native tiling splits an image locally on one worker;
+  it does not add cross-worker tile parallelism to batch mode.
+- It does not merge independent serving requests. The VAE must receive a batch
+  containing multiple images, for example through `num_outputs_per_prompt`.
+
+Splitting the batch can reduce per-rank decoder work and activation memory, but
+collecting decoded images adds communication and buffers. Measure complete
+request latency and memory for the intended model, image count, and resolution.
+This feature has no universal speedup or memory-saving guarantee. Changing the
+decoder batch shape can also change floating-point rounding; validate generated
+outputs when selecting a deployment configuration.
 
 ---
 
@@ -194,7 +254,8 @@ python3 benchmarks/diffusion/diffusion_benchmark_serving.py \
 ### Common Issue 1: Model Not Support VAE Patch Parallel
 
 **Symptoms**:
-```
+
+```text
 WARNING: vae_patch_parallel_size=2 is set but VAE patch parallelism is NOT enabled for xxxPipeline; ignoring.
 ```
 
@@ -217,7 +278,6 @@ if vae_pp_size > 1 and not is_distributed_vae:
 
 2. To add support for a new model, implement `DistributedVaeMixin` on its VAE class (contributions are welcome).
 
-
 ### Common Issue 2: `vae_patch_parallel_size` Exceeds DiT Process Group Size
 
 **Symptoms**: Shows warning message, and vae patch parallel size is resized to DiT process group size
@@ -227,6 +287,7 @@ if vae_pp_size > 1 and not is_distributed_vae:
 **Recommendation**: Always set `vae_patch_parallel_size` to be no greater than your DiT process group size.
 
 Note that the size of DiT process group size equals to:
+
 ```text
 dit_parallel_size = data_parallel_size
                   × cfg_parallel_size
@@ -235,7 +296,8 @@ dit_parallel_size = data_parallel_size
                   × tensor_parallel_size
 
 ```
-_sequence_parallel_size = ulysses_degree × ring_degree_
+
+`sequence_parallel_size = ulysses_degree × ring_degree`
 
 ---
 

docs/user_guide/diffusion/vae_fast_path.md

@@ -7,7 +7,8 @@ convolutional network whose reference implementation in diffusers moves every
 activation through memory several times per layer (normalization, activation,
 causal padding, feature-cache bookkeeping, shortcut upsampling). vLLM-Omni
 installs a fast path on every loaded Wan VAE that fuses this data movement into
-a handful of Triton kernels while leaving the convolutions themselves untouched.
+a handful of Triton kernels. The opt-in `channels_last` level also specializes
+first-frame convolutions that have no history.
 
 ## Levels
 
@@ -15,9 +16,9 @@ The fast path is controlled by `--vae-fast-path` (engine argument
 `vae_fast_path`, deploy-config key `vae_fast_path`):
 
 | Level | Default | Output vs. diffusers | What it does |
-|---|---|---|---|
+| --- | --- | --- | --- |
 | `lossless` | yes | bit-identical | Fused RMSNorm epilogue, fused causal-conv input assembly and cache refresh, fused shortcut upsampling and residual adds (with the neighbouring convolution biases folded in), single-pass nearest 2x upsampling, preallocated output assembly. |
-| `channels_last` | no | within tolerance (PSNR typically > 60 dB) | Everything in `lossless`, plus decoder convolution weights converted to channels-last memory format so cuDNN picks its channels-last kernels, and a single-pass channels-last RMSNorm+SiLU kernel that also absorbs the bias of the preceding `conv1`. |
+| `channels_last` | no | within tolerance (PSNR typically > 60 dB) | Everything in `lossless`, plus channels-last convolution weights, a single-pass channels-last RMSNorm+SiLU kernel that also absorbs the preceding `conv1` bias, and Conv2d for supported single-frame causal convolutions without history. |
 | `off` | no | bit-identical | Reference diffusers decoder. |
 
 ```bash
@@ -61,6 +62,12 @@ stages:
 - The `channels_last` level changes the order in which cuDNN accumulates
   convolutions, so outputs differ from the reference in the last bits. Use
   `lossless` when bitwise reproducibility against diffusers matters.
+- For a supported causal convolution with one input frame and no history,
+  the temporal input is `[0, 0, x]`. The `channels_last` level uses Conv2d with
+  the last temporal weight slice, preserving the convolution formula for
+  finite inputs and the existing cache update. Calls with history retain
+  Conv3d. The weight slice is taken on each call, so weight updates and device
+  moves do not leave a stale packed copy.
 - For VAEs kept in fp32, cuDNN's channels-last convolution algorithms run in
   TF32 under PyTorch's default `torch.backends.cudnn.allow_tf32 = True`, which
   dominates the difference to the reference (about 1e-3). Set
@@ -73,10 +80,10 @@ Cosmos3-Nano VAE, 1280x720 x 189 frames, bf16, one GB200 GPU, `bench_wan_vae_dec
 (best of 2 runs after warmup):
 
 | `--vae-fast-path` | Decode time | Speedup | Output vs. `off` |
-|---|-------------|---------|---|
-| `off` | 6.00 s      | 1.00x   | reference |
-| `lossless` | 3.26 s      | 1.84x   | bit-identical |
-| `channels_last` | 2.51 s      | 2.39x   | PSNR 62.6 dB, max abs diff 4.6e-2 |
+| --- | ------------- | --------- | --- |
+| `off` | 6.00 s | 1.00x | reference |
+| `lossless` | 3.26 s | 1.84x | bit-identical |
+| `channels_last` | 2.51 s | 2.39x | PSNR 62.6 dB, max abs diff 4.6e-2 |
 
 Peak decode memory was unchanged (about 13 GiB): it is set by the largest
 activations and the cuDNN workspace, not by the output assembly.
@@ -93,6 +100,22 @@ python benchmarks/diffusion/bench_wan_vae_decode.py --model nvidia/Cosmos3-Nano
     --size 1280x720 --frames 189 --fast-path off,lossless,channels_last --profile
 ```
 
+For a single-GPU comparison that isolates first-frame Conv2d within
+`channels_last`, use `--first-frame-ablation`. It alternates the two paths,
+disables cuDNN TF32 and autotuning, and prints JSON with every timing sample,
+summary statistics, peak allocated memory and output comparisons. Per-call
+weight packing is included in the measured decode time.
+
+```bash
+python benchmarks/diffusion/bench_wan_vae_decode.py \
+    --model Wan-AI/Wan2.1-T2V-1.3B-Diffusers --size 832x480 --frames 1 \
+    --dtype bf16 --first-frame-ablation --warmup 60 --iters 50 --seed 0
+```
+
+The default input is a seeded latent tensor. `--latents /path/to/latents.pt`
+can instead load a saved VAE input after latent mean/std scaling; the benchmark
+selects the temporal prefix requested by `--frames` and checks its shape.
+
 The same script benchmarks multi-GPU VAE decode when launched with `torchrun`;
 the decode is timed across all ranks and rank 0 reports:
 

docs/user_guide/diffusion_features.md

@@ -152,9 +152,10 @@ The following tables show which models support each feature:
 | **HiDream-O1-Image**     |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **HunyuanImage3**        |     ❌     |     ✅      |           ❌           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ✅        |        ✅*        |
 | **Krea 2**               |     ❌     |     ✅      |           ❌           |       ❌        |         ❌         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
+| **JoyAI-Image-Edit**    |     ❌     |     ❌      |           ❌           |       ✅        |         ❌         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **LongCat-Image**        |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **LongCat-Image-Edit**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
-| **MammothModa2(T2I)**    |     ❌     |     ✅      |           ❌           |       ❌        |         ❌         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
+| **MammothModa2(T2I)**    |     ✅     |     ✅      |           ❌           |       ❌        |         ❌         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Nextstep_1(T2I)**      |     ❓     |     ❓      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **OmniGen2**             |     ❌     |     ✅      |           ✅           |       ❌        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **Ovis-Image**           |     ❌     |     ✅      |           ❌           |       ✅        |         ❌         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
@@ -163,6 +164,7 @@ The following tables show which models support each feature:
 | **Qwen-Image-Edit**      |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Qwen-Image-Edit-2509** |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |        ✅ (decode)         |          ✅           |       ❌        |        ❌         |
 | **Qwen-Image-Layered**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
+| **Qwen-Image-2.1**       |     ❌     |     ❌      |           ❓           |       ✅        |         ✅         |          ❌          |   ❓    |             ❓             |      ✅ (decode)      |       ✅        |        ✅         |
 | **SenseNova-U1**         |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **SenseNova-U1.5**       |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ✅        |        ❌         |
 | **SenseNova-U1-A3B**     |     ❌     |     ❌      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
@@ -181,7 +183,8 @@ The following tables show which models support each feature:
 > 5. HunyuanImage3 supports step execution. Multi-request step execution requires `TORCH_SDPA`; see [Diffusion Execution Modes](diffusion/execution_modes.md#step-execution).
 > 6. BAGEL step execution supports image generation with `bagel.yaml`, `bagel_think.yaml`, and `bagel_single_stage.yaml`; two-stage Thinker execution and explicit single-stage text output remain on their existing complete-request paths. Image requests require `num_inference_steps >= 2`. BAGEL step execution cannot currently be combined with sequence parallelism or a diffusion cache backend; see [Diffusion Execution Modes](diffusion/execution_modes.md#step-execution).
 > 7. MammothModa2 runs its DiT stage on the diffusion runner (`StageExecutionType.DIFFUSION`); Cache-DiT is enabled through the standard diffusion-stage knobs on the stage entry of the deploy YAML (`cache_backend: cache_dit`, plus optional `cache_config` / `enable_cache_dit_summary`). The runner installs the backend at startup and the pipeline adopts it per request. Only the repeated main-layer stack is cached; requests with `text_guidance_scale = 1.0` bypass the cache hooks.
-> 8. SenseNova-U1-A3B: `--cfg-parallel-size 2` and TP=2 both complete. CFG-P covers t2i 2-branch (`cond`+`uncond`), img2img 2-branch (`cond`+`img_cond` when `img_cfg_scale=1`), and img2img 3-branch (`cond`+`img_cond`+`uncond` when `img_cfg_scale` is neither 1 nor equal to `cfg_scale`; 3 branches on 2 ranks dispatch as `[[0, 2], [1]]`). Cache-DiT does not: the adapter is declared on `SenseNovaU1Model`, but discovery walks `_dit_modules` (`language_model` / `SenseNovaU1ForCausalLM`) and raises. Layerwise CPU offload is a no-op for the same reason — that class has no `_layerwise_offload_blocks_attrs`, so the 72.2 GiB expert weights stay resident.
+> 8. Qwen-Image-2.1 (`QwenImage21Pipeline`) handles both text-to-image and image-conditioned generation (up to 4 condition images) and supports LoRA through `QwenImageLoraLoaderMixin`. Ulysses sequence parallelism is wired and ring attention is not supported, but multi-GPU padding e2e and a parity number are not in this PR's H200 results, so SP stays ❓. Cache acceleration (TeaCache / Cache-DiT) is unavailable; its built-in KV cache for the text/condition prefix is a separate feature from those cache backends. Online FP8 is documented in the quantization guide with H200 numbers.
+> 9. SenseNova-U1-A3B: `--cfg-parallel-size 2` and TP=2 both complete. CFG-P covers t2i 2-branch (`cond`+`uncond`), img2img 2-branch (`cond`+`img_cond` when `img_cfg_scale=1`), and img2img 3-branch (`cond`+`img_cond`+`uncond` when `img_cfg_scale` is neither 1 nor equal to `cfg_scale`; 3 branches on 2 ranks dispatch as `[[0, 2], [1]]`). Cache-DiT does not: the adapter is declared on `SenseNovaU1Model`, but discovery walks `_dit_modules` (`language_model` / `SenseNovaU1ForCausalLM`) and raises. Layerwise CPU offload is a no-op for the same reason — that class has no `_layerwise_offload_blocks_attrs`, so the 72.2 GiB expert weights stay resident.
 
 ### VideoGen
 
@@ -196,6 +199,7 @@ The following tables show which models support each feature:
 |  **SANA-Video-2B T2V I2V**   |     ❌     |     ❌      |   ✅ (`--usp`, frame-sharded)   |       ✅       |  ✅ (TP=2 only)   |         ❌         |   ❌   |            ❌             |          ❌          |       ❌       |        ❌        |
 | **Helios**                   |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |          ❌           |       ❌        |        ✅*        |
 | **HunyuanVideo-1.5 T2V I2V** |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
+| **Kandinsky 6 TI2VA**        | ✅* (uncalibrated) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ (decode) | ✅ (FP8) | ✅ (1 request) |
 | **Cosmos3**                  |     ❌     |     ✅      |           ✅           |       ✅        |         ✅         |         ❌         |   ✅    |             ✅             |  ✅ (encode/decode)   |       ✅        |        ❌         |
 | **LongCat-Video-Avatar-1.5** |     ❌     |     ❌      |           ❌           |       ❌        |         ❌         |         ❌         |   ❌    |             ❌             |          ❌           |       ❌        |        ❌         |
 | **MiniMax-H3**               | ✅ (FL2VA) |     ✅      |           ✅           |       ❌        |       ✅ (DiT/TE)  |         ❌         |   ✅    |             ✅             |       ✅ (tile)       |      ✅ (DiT)      |        ❌         |

docs/user_guide/examples/offline_inference/image_to_image.md

@@ -3,7 +3,7 @@
 Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inference/image_to_image>.
 
 
-This example edits an input image with `Qwen/Qwen-Image-Edit` using the `image_edit.py` CLI.
+This example edits an input image with `Qwen/Qwen-Image-Edit` and `JoyAI-Image-Edit` using the `image_edit.py` CLI.
 
 ## Local CLI Usage
 
@@ -26,6 +26,27 @@ python image_edit.py \
   --cfg-scale 4.0
 ```
 
+### JoyAI-Image-Edit
+
+JoyAI-Image-Edit uses the same `image_edit.py` CLI and supports one input
+image per request. Requested dimensions are snapped to the nearest supported
+Joy bucket; for square outputs, use `1024x1024`.
+
+```bash
+python image_edit.py \
+  --model jdopensource/JoyAI-Image-Edit-Diffusers \
+  --image input.png \
+  --prompt "Change the background to a clean studio while preserving the subject." \
+  --height 1024 \
+  --width 1024 \
+  --num-inference-steps 50 \
+  --guidance-scale 4.0 \
+  --output output_joyai_edit.png
+```
+
+JoyAI defaults to guidance scale `4.0`; pass `--guidance-scale` explicitly when
+you need a different value.
+
 ### Multiple Image Editing (Qwen-Image-Edit-2509)
 
 For multiple image inputs, use `Qwen/Qwen-Image-Edit-2509` or  `Qwen/Qwen-Image-Edit-2511`:
@@ -43,7 +64,7 @@ python image_edit.py \
 
 Key arguments:
 
-- `--model`: model name or path. Use `Qwen/Qwen-Image-Edit-2509` or later for multiple image support.
+- `--model`: model name or path. Use `Qwen/Qwen-Image-Edit-2509` or later for multiple image support. `jdopensource/JoyAI-Image-Edit-Diffusers` supports one input image.
 - `--image`: path(s) to the source image(s) (PNG/JPG, converted to RGB). Can specify multiple images.
 - `--prompt` / `--negative-prompt`: text description (string).
 - `--cfg-scale`: true classifier-free guidance scale (default: 4.0). Classifier-free guidance is enabled by setting cfg_scale > 1 and providing a negative_prompt. Higher guidance scale encourages images closely linked to the text prompt, usually at the expense of lower image quality.

docs/user_guide/examples/online_serving/image_to_image.md

@@ -3,10 +3,14 @@
 Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/image_to_image>.
 
 
-This example demonstrates how to deploy Qwen-Image-Edit model for online image editing service using vLLM-Omni.
+This example demonstrates how to deploy image-to-image models for online image editing service using vLLM-Omni.
+
+Supported models include Qwen-Image-Edit, BAGEL, JoyAI-Image-Edit, and other image-to-image pipelines.
 
 For **multi-image** input editing, use **Qwen-Image-Edit-2509** (QwenImageEditPlusPipeline) and send multiple images in the user message content.
 
+For **JoyAI-Image-Edit**, use `jdopensource/JoyAI-Image-Edit-Diffusers` and send exactly one input image.
+
 ## Start Server
 
 ### Basic Start
@@ -24,6 +28,12 @@ vllm serve Qwen/Qwen-Image-Edit --omni --port 8092
 vllm serve Qwen/Qwen-Image-Edit-2509 --omni --port 8092
 ```
 
+### JoyAI-Image-Edit
+
+```bash
+vllm serve jdopensource/JoyAI-Image-Edit-Diffusers --omni --port 8092
+```
+
 ### Start with Parameters
 
 
@@ -76,6 +86,11 @@ curl -s http://localhost:8092/v1/chat/completions \
   | cut -d',' -f2 | base64 -d > output.png
 ```
 
+For JoyAI-Image-Edit, set classifier-free guidance with `guidance_scale`
+in `extra_body` (default: `4.0`). `true_cfg_scale` remains supported as an
+alias; if both are provided, their values must match. Values at or below `1.0`
+disable CFG.
+
 ### Method 2: Using OpenAI Python SDK
 
 ```python

docs/user_guide/quantization/fp8.md

@@ -1,4 +1,4 @@
-# FP8 Quantization
+# FP8 Quantization
 
 ## Overview
 
@@ -17,7 +17,7 @@ in deep DiT blocks.
 ## Hardware Support
 
 | Device | Support |
-|--------|---------|
+| -------- | --------- |
 | NVIDIA Blackwell GPU (SM 100+) | ✅ |
 | NVIDIA Ada/Hopper GPU (SM 89+) | ✅ |
 | NVIDIA Ampere GPU (SM 80+) | ✅ |
@@ -93,8 +93,9 @@ warmup_quack_fp8([(14040, 2048, 6144), (14040, 2048, 2048)])
 ### Diffusion Models
 
 | Model | HF models | Online | Pre-calibrated | Recommendation | `ignored_layers` | Text-Encoder quantization |
-|-------|-----------|:-------:|:------:|----------------|------------------|------------------|
+| ------- | ----------- | :-------: | :------: | ---------------- | ------------------ | ------------------ |
 | Qwen-Image | `Qwen/Qwen-Image`, `Qwen/Qwen-Image-2512` | Yes | Yes | Skip sensitive image-stream MLPs when quality regresses | `img_mlp` | |
+| Qwen-Image-2.1 | `Qwen/Qwen-Image-2.1` | Yes | Not validated | Skip image-stream MLPs for best fidelity; all-layer FP8 is loadable. On H200 (1024×1024, teapot prompt) DiT FP8-all is ~30.4 GB / 6.06 s vs BF16 ~36.9 GB / 7.34 s (~33 dB vs BF16). Text encoder FP8 covers the language model only — the vision tower and LM head stay BF16 | `img_mlp` | ✅︎ (language model only) |
 | Wan2.2 | Wan2.2 diffusion pipelines | Not validated | Not validated | Validate against BF16 before documenting as supported | TBD | |
 | LTX-2 | `Lightricks/LTX-2`, `rootonchair/LTX-2-19b-distilled` | Yes | Not validated | Transformer only; use dynamic phase LoRA for ordinary two-stage | None | |
 | LTX-2.3 | `diffusers/LTX-2.3-Diffusers`, `diffusers/LTX-2.3-Distilled-Diffusers` | Yes | Not validated | Transformer only; use dynamic phase LoRA for ordinary two-stage | None | |
@@ -110,20 +111,20 @@ warmup_quack_fp8([(14040, 2048, 6144), (14040, 2048, 2048)])
 
 ### Multi-Stage Omni/TTS Model (Qwen3-Omni, Qwen3-TTS)
 
-| Model | Scope | Format | Status |
-|-------|-------|--------|--------|
+| Model      | Scope                        | Format                                   | Status                              |
+| ---------- | ---------------------------- | ---------------------------------------- | ----------------------------------- |
 | Qwen3-Omni | Thinker language-model stage | [ModelOpt](modelopt.md) `quant_algo=FP8` | Tested for thinker memory reduction |
-| Qwen3-TTS | TTS language-model stage | Checkpoint config | Not validated |
+| Qwen3-TTS  | TTS language-model stage     | Checkpoint config                        | Not validated                       |
 
 Audio encoder, vision encoder, talker, and code2wav stay in BF16 unless a
 model-specific guide says otherwise.
 
 ### Multi-Stage Diffusion Model (BAGEL, GLM-Image)
 
-| Model | Scope | Status | Notes |
-|-------|-------|--------|-------|
-| BAGEL | Stage-specific transformer or DiT module | Not validated | Route FP8 to the intended stage before enabling |
-| GLM-Image | Stage-specific transformer or DiT module | Not validated | Validate quality against BF16 baseline |
+| Model     | Scope                                    | Status        | Notes                                           |
+| --------- | ---------------------------------------- | ------------- | ----------------------------------------------- |
+| BAGEL     | Stage-specific transformer or DiT module | Not validated | Route FP8 to the intended stage before enabling |
+| GLM-Image | Stage-specific transformer or DiT module | Not validated | Validate quality against BF16 baseline          |
 
 ## Configuration
 
@@ -162,7 +163,7 @@ For a pipeline that exposes both a transformer and a quantization-aware text
 encoder, the scope is:
 
 | Configuration | Transformer | Text encoder | Components without supported quantizable layers |
-|---------------|-------------|--------------|-------------------------------------------------|
+| --------------- | ------------- | -------------- | ------------------------------------------------- |
 | `quantization="fp8"` | FP8 | FP8 | checkpoint precision |
 | `{"transformer": {"method": "fp8"}}` | FP8 | checkpoint precision | checkpoint precision |
 | `{"text_encoder": {"method": "fp8"}}` | checkpoint precision | FP8 | checkpoint precision |
@@ -211,7 +212,7 @@ does not fit the workload.
 ## Parameters
 
 | Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
+| ----------- | ------ | --------- | ------------- |
 | `method` | str | - | Quantization method (`"fp8"`) |
 | `ignored_layers` | list[str] | `[]` | Layer name patterns to keep in BF16 |
 | `activation_scheme` | str | `"dynamic"` | `"dynamic"` selects online activation scaling, or `"static"` when scales are available |

examples/offline_inference/image_to_image/image_edit.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 
 """
 Example script for image editing with OmniGen2.
@@ -39,6 +39,20 @@ Usage (multiple images):
         --cfg-scale 4.0 \
         --guidance-scale 1.0
 
+Usage (JoyAI-Image-Edit, single image):
+    python image_edit.py \
+        --model jdopensource/JoyAI-Image-Edit-Diffusers \
+        --image input.png \
+        --prompt "Change the background to a clean studio while preserving the subject." \
+        --height 1024 \
+        --width 1024 \
+        --num-inference-steps 50 \
+        --guidance-scale 4.0 \
+        --output output_joyai_edit.png
+
+    Note: JoyAI-Image-Edit snaps requested dimensions to the nearest supported
+    Joy bucket; for square outputs, use 1024x1024.
+
 Usage (with cache-dit acceleration):
     python image_edit.py \
         --image input.png \
@@ -209,14 +223,14 @@ def parse_profiler_config(value: str) -> dict[str, Any]:
 
 
 def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Edit an image with Qwen-Image-Edit.")
+    parser = argparse.ArgumentParser(description="Edit images with vLLM-Omni.")
     parser.add_argument(
         "--model",
         default="Qwen/Qwen-Image-Edit",
         help=(
             "Diffusion model name or local path. "
-            "For multiple image inputs, use Qwen/Qwen-Image-Edit-2509 or Qwen/Qwen-Image-Edit-2511"
-            "which supports QwenImageEditPlusPipeline."
+            "For multiple image inputs, use Qwen/Qwen-Image-Edit-2509, "
+            "Qwen/Qwen-Image-Edit-2511, or Qwen/Qwen-Image-2.1."
         ),
     )
     parser.add_argument(
@@ -270,9 +284,10 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--cfg-scale",
         type=float,
-        default=4.0,
+        default=None,
         help=(
-            "True classifier-free guidance scale (default: 4.0). Guidance scale as defined in Classifier-Free "
+            "True classifier-free guidance scale. When omitted, the pipeline uses its model-specific default. "
+            "Guidance scale as defined in Classifier-Free "
             "Diffusion Guidance. Classifier-free guidance is enabled by setting cfg_scale > 1 and providing "
             "a negative_prompt. Higher guidance scale encourages images closely linked to the text prompt, "
             "usually at the expense of lower image quality."
@@ -281,9 +296,9 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--guidance-scale",
         type=float,
-        default=1.0,
+        default=None,
         help=(
-            "Guidance scale for guidance-distilled models (default: 1.0, disabled). "
+            "Guidance scale for guidance-distilled models. When omitted, the pipeline uses its model-specific default. "
             "Unlike classifier-free guidance (--cfg-scale), guidance-distilled models take the guidance scale "
             "directly as an input parameter. Enabled when guidance_scale > 1. Ignored when not using guidance-distilled models."
         ),
@@ -368,7 +383,7 @@ def parse_args() -> argparse.Namespace:
         "--color-format",
         type=str,
         default="RGB",
-        help="For Qwen-Image-Layered, set to RGBA.",
+        help="For Qwen-Image-Layered or Qwen-Image-2.1, set to RGBA to preserve transparency.",
     )
 
     # Cache-DiT specific parameters
@@ -523,6 +538,12 @@ def parse_args() -> argparse.Namespace:
             "silently masking with a possibly-wrong prompt."
         ),
     )
+    parser.add_argument(
+        "--init-timeout",
+        type=int,
+        default=600,
+        help="Overall pipeline initialization timeout in seconds.",
+    )
     return parser.parse_args()
 
 
@@ -593,6 +614,7 @@ def main():
         enable_cpu_offload=args.enable_cpu_offload,
         enable_diffusion_pipeline_profiler=args.enable_diffusion_pipeline_profiler,
         profiler_config=args.profiler_config,
+        init_timeout=args.init_timeout,
     )
     if args.enforce_eager is not None:
         omni_kwargs["enforce_eager"] = args.enforce_eager
@@ -647,6 +669,7 @@ def main():
 
     diffusion_params = OmniDiffusionSamplingParams(
         generator=generator,
+        seed=args.seed,
         true_cfg_scale=args.cfg_scale,
         guidance_scale=args.guidance_scale,
         guidance_scale_2=args.guidance_scale_2,

examples/offline_inference/image_to_image/image_to_image.md

@@ -1,6 +1,6 @@
 # Image-To-Image
 
-This example edits an input image with `Qwen/Qwen-Image-Edit` using the `image_edit.py` CLI.
+This example edits an input image with `Qwen/Qwen-Image-Edit` and `JoyAI-Image-Edit` using the `image_edit.py` CLI.
 
 ## Local CLI Usage
 
@@ -23,6 +23,27 @@ python image_edit.py \
   --cfg-scale 4.0
 ```
 
+### JoyAI-Image-Edit
+
+JoyAI-Image-Edit uses the same `image_edit.py` CLI and supports one input
+image per request. Requested dimensions are snapped to the nearest supported Joy
+bucket; for square outputs, use 1024x1024.
+
+```bash
+python image_edit.py \
+  --model jdopensource/JoyAI-Image-Edit-Diffusers \
+  --image input.png \
+  --prompt "Change the background to a clean studio while preserving the subject." \
+  --height 1024 \
+  --width 1024 \
+  --num-inference-steps 50 \
+  --guidance-scale 4.0 \
+  --output output_joyai_edit.png
+```
+
+JoyAI defaults to guidance scale `4.0`; pass `--guidance-scale` explicitly when
+you need a different value.
+
 ### Multiple Image Editing (Qwen-Image-Edit-2509)
 
 For multiple image inputs, use `Qwen/Qwen-Image-Edit-2509` or  `Qwen/Qwen-Image-Edit-2511`:
@@ -40,7 +61,7 @@ python image_edit.py \
 
 Key arguments:
 
-- `--model`: model name or path. Use `Qwen/Qwen-Image-Edit-2509` or later for multiple image support.
+- `--model`: model name or path. Use `Qwen/Qwen-Image-Edit-2509` or later for multiple image support. `jdopensource/JoyAI-Image-Edit-Diffusers` supports one input image.
 - `--image`: path(s) to the source image(s) (PNG/JPG, converted to RGB). Can specify multiple images.
 - `--prompt` / `--negative-prompt`: text description (string).
 - `--cfg-scale`: true classifier-free guidance scale (default: 4.0). Classifier-free guidance is enabled by setting cfg_scale > 1 and providing a negative_prompt. Higher guidance scale encourages images closely linked to the text prompt, usually at the expense of lower image quality.

examples/offline_inference/image_to_image/run_qwen_image_21.sh

@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+set -euo pipefail
+
+python "$(dirname "${BASH_SOURCE[0]}")/image_edit.py" \
+    --model Qwen/Qwen-Image-2.1 \
+    --color-format RGBA \
+    --seed 42 \
+    --image qwen_bear.png \
+    --prompt "Let this mascot dance under the moon, surrounded by floating stars and poetic bubbles such as 'Be Kind'" \
+    --output qwen_image_21_edit.png \
+    --num-inference-steps 50 \
+    --cfg-scale 1.0 \
+    "$@"

examples/offline_inference/image_to_video/README.md

@@ -1,7 +1,7 @@
 # Image-To-Video
 
 This shared example generates videos from images with VACE, Wan2.2, LTX-2,
-HunyuanVideo-1.5, SANA-Video, Cosmos3, MAGI-2, and other compatible pipelines.
+HunyuanVideo-1.5, SANA-Video, Cosmos3, MAGI-2, Kandinsky 6, and other compatible pipelines.
 
 - `image_to_video.py`: command-line script for single video generation with advanced options.
 
@@ -30,6 +30,7 @@ This folder provides a unified CLI script for image-to-video generation using vL
 | `sand-ai/MAGI-2-preview` | 512 x 896 | 125 | 100 | Model-fixed | Native four-GPU TP/SP; resident SP4 default; DLO available |
 | `Efficient-Large-Model/SANA-Video_2B_480p_diffusers` | 480 x 832 | 81 | 50 | 6.0 | Native `SanaImageToVideoPipeline`; Wan VAE |
 | `Efficient-Large-Model/SANA-Video_2B_720p_diffusers` | 704 x 1280 | 81 | 50 | 6.0 | Native `SanaImageToVideoPipeline`; LTX-2 Video VAE |
+| `kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers` | 480 x 864 | 125 | 50 | 5.0 | ~75 GiB reserved (H100, CPU offload); gated Hub repo |
 
 MAGI-2 native Preview setup, four-GPU topology and DLO choices, I2VA commands,
 and eight-GPU validation status are documented in the

examples/offline_inference/text_to_image/text_to_image.py

@@ -14,7 +14,7 @@ from diffusers.utils import numpy_to_pil
 
 from vllm_omni.diffusion.data import logger
 from vllm_omni.diffusion.utils.image_output import extract_images_from_outputs
-from vllm_omni.diffusion.utils.param_utils import apply_declared_extra_args
+from vllm_omni.diffusion.utils.param_utils import apply_declared_extra_args, ar_grid_max_tokens
 from vllm_omni.entrypoints.omni import Omni
 from vllm_omni.entrypoints.openai.stage_params import clone_sampling_params
 from vllm_omni.inputs.data import OmniDiffusionSamplingParams
@@ -72,6 +72,24 @@ def build_text_to_image_prompt(prompt: str, negative_prompt: str | None) -> dict
     return result
 
 
+def build_parallel_knob_kwargs(args: argparse.Namespace) -> dict[str, Any]:
+    """Forward explicitly-set parallel knobs so they can override a deploy YAML.
+
+    The argparse defaults are ``None``; only explicitly supplied values enter
+    the returned dict, letting a deploy YAML's parallel_config stay in effect
+    when the flags are omitted (and letting explicit values equal to the old
+    argparse defaults, e.g. ``--ulysses-degree 1``, still override the YAML).
+    """
+    knob_names = (
+        "ulysses_degree",
+        "ring_degree",
+        "ulysses_mode",
+        "cfg_parallel_size",
+        "vae_patch_parallel_size",
+    )
+    return {name: getattr(args, name) for name in knob_names if getattr(args, name) is not None}
+
+
 def _normalize_images_for_save(images: list[Any]) -> list[Any]:
     """Convert NumPy diffusion outputs to PIL images before saving."""
     normalized = []
@@ -83,13 +101,15 @@ def _normalize_images_for_save(images: list[Any]) -> list[Any]:
     return normalized
 
 
-def parse_args() -> argparse.Namespace:
+def build_parser() -> argparse.ArgumentParser:
+    """Build the example's CLI parser (kept separate for testability)."""
     parser = argparse.ArgumentParser(description="Generate an image with supported diffusion models.")
     parser.add_argument(
         "--model",
         default="Qwen/Qwen-Image",
         help="Diffusion model name or local path. Supported models: "
-        "Qwen/Qwen-Image, Tongyi-MAI/Z-Image-Turbo, Qwen/Qwen-Image-2512, stepfun-ai/NextStep-1.1, "
+        "Qwen/Qwen-Image, Tongyi-MAI/Z-Image-Turbo, Qwen/Qwen-Image-2512, Qwen/Qwen-Image-2.1, "
+        "stepfun-ai/NextStep-1.1, "
         "black-forest-labs/FLUX.1-dev, black-forest-labs/FLUX.2-klein-9B, "
         "black-forest-labs/FLUX.2-dev, tencent/HunyuanImage-3.0-Instruct, "
         "HiDream-ai/HiDream-O1-Image, meituan-longcat/LongCat-Image, OvisAI/Ovis-Image, "
@@ -166,28 +186,31 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--ulysses-degree",
         type=int,
-        default=1,
-        help="Number of GPUs used for ulysses sequence parallelism.",
+        default=None,
+        help="Number of GPUs used for ulysses sequence parallelism. "
+        "Explicitly passing 1 overrides a deploy YAML value.",
     )
     parser.add_argument(
         "--ulysses-mode",
         type=str,
-        default="strict",
+        default=None,
         choices=["strict", "advanced_uaa"],
-        help="Ulysses sequence-parallel mode: 'strict' (divisibility required) or 'advanced_uaa' (UAA).",
+        help="Ulysses sequence-parallel mode: 'strict' (divisibility required) or 'advanced_uaa' (UAA). "
+        "Explicitly passing 'strict' overrides a deploy YAML value.",
     )
     parser.add_argument(
         "--ring-degree",
         type=int,
-        default=1,
-        help="Number of GPUs used for ring sequence parallelism.",
+        default=None,
+        help="Number of GPUs used for ring sequence parallelism. Explicitly passing 1 overrides a deploy YAML value.",
     )
     parser.add_argument(
         "--cfg-parallel-size",
         type=int,
-        default=1,
+        default=None,
         choices=[1, 2],
-        help="Number of GPUs used for classifier free guidance parallel size.",
+        help="Number of GPUs used for classifier free guidance parallel size. "
+        "Explicitly passing 1 overrides a deploy YAML value.",
     )
     parser.add_argument(
         "--enforce-eager",
@@ -296,8 +319,9 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--vae-patch-parallel-size",
         type=int,
-        default=1,
-        help="Number of ranks used for VAE patch/tile parallelism (decode/encode).",
+        default=None,
+        help="Number of ranks used for VAE patch/tile parallelism (decode/encode). "
+        "Explicitly passing 1 overrides a deploy YAML value.",
     )
     # NextStep-1.1 specific arguments
     parser.add_argument(
@@ -413,7 +437,11 @@ def parse_args() -> argparse.Namespace:
         ),
     )
     current_omni_platform.pre_register_and_update(parser)
-    return parser.parse_args()
+    return parser
+
+
+def parse_args() -> argparse.Namespace:
+    return build_parser().parse_args()
 
 
 def _apply_ar_stage_inputs(
@@ -527,14 +555,8 @@ def main():
             "scm_steps_policy": "dynamic",  # SCM steps policy: "dynamic" or "static"
         }
     elif cache_backend == "tea_cache":
-        # TeaCache configuration
-        # All parameters marked with [tea_cache only] in DiffusionCacheConfig
-        cache_config = {
-            # TeaCache parameters [tea_cache only]
-            "rel_l1_thresh": 0.2,  # Threshold for accumulated relative L1 distance
-            # Note: coefficients will use model-specific defaults based on model_type
-            #       (e.g., QwenImagePipeline or FluxPipeline)
-        }
+        # Let TeaCache select the model-specific threshold and coefficients.
+        cache_config = {}
 
     profiler_enabled = args.profiler_config is not None
 
@@ -562,21 +584,11 @@ def main():
 
     omni_kwargs = {
         "model": args.model,
-        "enable_layerwise_offload": args.enable_layerwise_offload,
-        "vae_use_slicing": args.vae_use_slicing,
-        "vae_use_tiling": args.vae_use_tiling,
         "cache_backend": args.cache_backend,
         "cache_config": cache_config,
-        "enable_cache_dit_summary": args.enable_cache_dit_summary,
-        "ulysses_degree": args.ulysses_degree,
-        "ring_degree": args.ring_degree,
-        "ulysses_mode": args.ulysses_mode,
-        "cfg_parallel_size": args.cfg_parallel_size,
-        "vae_patch_parallel_size": args.vae_patch_parallel_size,
         "enable_expert_parallel": args.enable_expert_parallel,
         "enable_cpu_offload": args.enable_cpu_offload,
         "log_stats": args.log_stats,
-        "enable_diffusion_pipeline_profiler": args.enable_diffusion_pipeline_profiler,
         "profiler_config": args.profiler_config,
         "init_timeout": args.init_timeout,
         "stage_init_timeout": args.stage_init_timeout,
@@ -584,6 +596,8 @@ def main():
         **lora_args,
         **quant_kwargs,
     }
+    # Diffusion-only engine args: pass only when explicitly set, so pipelines
+    # without a DIFFUSION stage pass ownership validation.
     if args.tensor_parallel_size is not None:
         omni_kwargs["tensor_parallel_size"] = args.tensor_parallel_size
     if args.enforce_eager is not None:
@@ -603,6 +617,18 @@ def main():
     # gate is an engine-level config (offline analog of the server's --no-guardrails).
     if args.extra_body and "guardrails" in args.extra_body:
         omni_kwargs["model_config"] = {"guardrails": bool(args.extra_body["guardrails"])}
+    if args.enable_layerwise_offload:
+        omni_kwargs["enable_layerwise_offload"] = True
+    if args.vae_use_slicing:
+        omni_kwargs["vae_use_slicing"] = True
+    if args.vae_use_tiling:
+        omni_kwargs["vae_use_tiling"] = True
+    if args.enable_cache_dit_summary:
+        omni_kwargs["enable_cache_dit_summary"] = True
+    if args.enable_diffusion_pipeline_profiler:
+        omni_kwargs["enable_diffusion_pipeline_profiler"] = True
+    omni_kwargs.update(build_parallel_knob_kwargs(args))
+
     omni = Omni(**omni_kwargs)
     model_class_name = get_model_class_name(omni)
     declared_extra_body_params = get_extra_body_params(model_class_name)
@@ -621,11 +647,17 @@ def main():
     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"
+
+    def _knob_display(value: Any, default: Any) -> Any:
+        return value if value is not None else f"deploy/default ({default})"
+
     print(
         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}, "
+        f"ulysses_degree={_knob_display(args.ulysses_degree, 1)}, "
+        f"ulysses_mode={_knob_display(args.ulysses_mode, 'strict')}, "
+        f"ring_degree={_knob_display(args.ring_degree, 1)}, "
+        f"cfg_parallel_size={_knob_display(args.cfg_parallel_size, 1)}, "
+        f"vae_patch_parallel_size={_knob_display(args.vae_patch_parallel_size, 1)}, "
         f"enable_expert_parallel={args.enable_expert_parallel}."
     )
     print(f"  CPU offload: {args.enable_cpu_offload}; CPU Layerwise Offload: {args.enable_layerwise_offload}")
@@ -736,8 +768,9 @@ def main():
             if idx == 0 and prompt_info.get("omni_task") == ["t2i"]:
                 ar_width = int(prompt_info.get("ar_width", [0])[0])
                 ar_height = int(prompt_info.get("ar_height", [0])[0])
-                if ar_width > 0 and ar_height > 0:
-                    params.max_tokens = ar_height * (ar_width + 1) + 1
+                ar_grid_budget = ar_grid_max_tokens(ar_width, ar_height)
+                if ar_grid_budget is not None:
+                    params.max_tokens = ar_grid_budget
 
     if not diffusion_replaced and len(sampling_params_list) == 1:
         sampling_params_list = [diffusion_params]

examples/offline_inference/text_to_video/text_to_video.md

@@ -14,6 +14,7 @@ A unified script for text-to-video generation. Supports multiple models with mod
 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v` | 720x1280 | 121 | 50 | 6.0 | FP8 + VAE tiling required |
 | `nvidia/Cosmos3-Nano` | 720x1280 | 189 | 35 | 6.0 | ~46 GiB (peak, 720p) |
 | `BestWishYsh/Helios-Base` / `Helios-Mid` / `Helios-Distilled` | 384x640 | 99 | 50 | 5.0 / 5.0 / 1.0 | — |
+| `kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers` | 480x864 | 125 | 50 | 5.0 | ~75 GiB reserved (H100, CPU offload, SDPA) |
 | `sand-ai/MAGI-2-preview` | 512x896 | 125 | 100 | Model-fixed | Native four-GPU TP/SP; resident SP4 default; DLO available |
 | `Efficient-Large-Model/SANA-Video_2B_480p_diffusers` | 480x832 | 81 | 50 | 6.0 | BF16 DiT + FP32 Wan VAE wrapper |
 | `Efficient-Large-Model/SANA-Video_2B_720p_diffusers` | 704x1280 | 81 | 50 | 6.0 | BF16 DiT + LTX-2 Video VAE wrapper |
@@ -249,6 +250,24 @@ python text_to_video.py \
 > conditioning tensors that cannot be passed through the JSON `--extra-body`
 > flag; they are out of scope for this text-to-video example.
 
+### Kandinsky 6 (TI2VA)
+
+The Hub checkpoint is gated; export `HF_TOKEN` if download returns 401.
+`--enable-cpu-offload` is required on an 80 GB H100. Audio is on by default.
+
+```bash
+python text_to_video.py \
+  --model kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers \
+  --prompt "A golden retriever runs along a sunny beach, waves crashing, cinematic footage" \
+  --enable-cpu-offload \
+  --output kandinsky6_t2va.mp4
+```
+
+Video only: add `--extra-body '{"sample_audio": false}'`. Image-to-video uses
+the shared [`image_to_video.py`](../image_to_video/README.md) example with
+`--image`. Serving numbers and flags are in the
+[`Kandinsky 6 TI2VA recipe`](../../../recipes/Kandinsky/Kandinsky6-TI2VA.md).
+
 ## Key Arguments
 
 ### Common

examples/offline_inference/text_to_video/text_to_video.py

@@ -235,7 +235,7 @@ def parse_extra_body(value: str) -> dict[str, Any]:
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
         description="Generate a video from a text prompt. "
-        "Supports Wan2.2, HunyuanVideo-1.5, Helios, LingBot-Video, MAGI-2, "
+        "Supports Wan2.2, HunyuanVideo-1.5, Helios, LingBot-Video, MAGI-2, Kandinsky 6, "
         "and other text-to-video models."
     )
     parser.add_argument(

examples/online_serving/image_to_image/README.md

@@ -2,10 +2,12 @@
 
 This example demonstrates how to deploy image-to-image models for online image editing service using vLLM-Omni.
 
-Supported models include Qwen-Image-Edit, BAGEL, and other image-to-image pipelines.
+Supported models include Qwen-Image-Edit, BAGEL, JoyAI-Image-Edit, and other image-to-image pipelines.
 
 For **multi-image** input editing, use **Qwen-Image-Edit-2509** (QwenImageEditPlusPipeline) and send multiple images in the user message content.
 
+For **JoyAI-Image-Edit**, use `jdopensource/JoyAI-Image-Edit-Diffusers` and send exactly one input image.
+
 ## Start Server
 
 ### Basic Start
@@ -29,6 +31,12 @@ vllm serve Qwen/Qwen-Image-Edit-2509 --omni --port 8092
 vllm serve ByteDance-Seed/BAGEL-7B-MoT --omni --port 8091
 ```
 
+### JoyAI-Image-Edit
+
+```bash
+vllm serve jdopensource/JoyAI-Image-Edit-Diffusers --omni --port 8092
+```
+
 ### Start with Parameters
 
 
@@ -77,6 +85,11 @@ EOF
 curl -s http://localhost:8092/v1/chat/completions   -H "Content-Type: application/json"   -d @request.json | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2 | base64 -d > output.png
 ```
 
+For JoyAI-Image-Edit, set classifier-free guidance with `guidance_scale`
+in `extra_body` (default: `4.0`). `true_cfg_scale` remains supported as an
+alias; if both are provided, their values must match. Values at or below `1.0`
+disable CFG.
+
 ### Method 2: Using OpenAI Python SDK
 
 ```python

examples/online_serving/qwen3_omni/README.md

@@ -277,61 +277,6 @@ cd examples/online_serving/qwen3_omni
 python examples/online_serving/openai_chat_completion_client_for_multimodal_generation.py --model Qwen/Qwen3-Omni-30B-A3B-Instruct --query-type use_image --port 8091 --host "localhost"
 ```
 
-#### Realtime WebSocket client (`openai_realtime_client.py`)
-
-[`openai_realtime_client.py`](./openai_realtime_client.py) connects to **`ws://<host>:<port>/v1/realtime`**, streams a local WAV as **PCM16 mono @ 16 kHz** in fixed-size chunks, and receives **`response.output_audio.*`**, **`response.output_text.*`**, and transcript events. By default it uses explicit `input_audio_buffer.commit`; pass **`--server-vad`** to let the server detect speech endpoints and commit the turn automatically. The client concatenates audio deltas and writes **`--output-wav`** (model output is typically **24 kHz**). Optional **`--delta-dump-dir`** saves each delta as `delta_000001.wav`, … for debugging.
-
-Streaming input works well for translation-style use cases; if the Thinker runs while input is still incomplete, consider limiting **`max_tokens`** in your session / server defaults to avoid over-generation.
-
-**Dependencies:**
-
-```bash
-pip install websockets
-```
-
-**From this directory** (`examples/online_serving/qwen3_omni`):
-
-```bash
-python openai_realtime_client.py \
-  --url ws://localhost:8091/v1/realtime \
-  --model Qwen/Qwen3-Omni-30B-A3B-Instruct \
-  --input-wav /path/to/input_16k_mono.wav \
-  --output-wav realtime_output.wav \
-  --server-vad \
-  --delta-dump-dir ./rt_delta_wavs
-```
-
-**Arguments:**
-
-| Flag | Default | Description |
-| ------ | --------- | ------------- |
-| `--url` | `ws://localhost:8091/v1/realtime` | Full WebSocket URL including path |
-| `--model` | `Qwen/Qwen3-Omni-30B-A3B-Instruct` | Must match the served model (sent in `session.update`) |
-| `--input-wav` | *(required)* | Input WAV: mono, 16-bit PCM, **16 kHz** |
-| `--output-wav` | `realtime_output.wav` | Output path for concatenated reply audio |
-| `--output-text` | *(optional)* | If set, write final transcription text to this path |
-| `--chunk-ms` | `200` | Size of each uploaded audio chunk (milliseconds of audio) |
-| `--send-delay-ms` | `0` | Delay between chunk sends (simulate realtime upload) |
-| `--delta-dump-dir` | *(optional)* | Directory to write per-`response.output_audio.delta` WAV files |
-| `--server-vad` | disabled | Enable server-side Silero VAD instead of sending explicit commits |
-| `--num-requests` | `1` | Number of sequential sessions (see `--concurrency`) |
-| `--concurrency` | `1` | Max concurrent WebSocket sessions when `--num-requests` > 1 |
-
-Server VAD uses the pinned Silero v6.2 ONNX artifact from `istupakov/silero-vad-onnx` and never downloads model files while processing a session. Make the artifact available in the Hugging Face cache before startup, or configure a local artifact in the deployment YAML:
-
-```yaml
-base_config: /path/to/vllm-omni/vllm_omni/deploy/qwen3_omni_duplex.yaml
-duplex_session:
-  server_vad_model_path: /models/silero_vad.onnx
-```
-
-For the engine-owned plugin, use an overlay based on
-`vllm_omni/deploy/qwen3_omni_duplex.yaml`, with `duplex_session.server_vad_model_path`
-pointing to your local artifact. See the [complete duplex setup](../realtime_web/README.md#qwen3-with-server-vad-automatic-turns).
-The default `qwen3_omni_moe.yaml` remains a turn deployment. Duplex mode serves
-`/v1/realtime?duplex=1` and `/v1/chat/completions`; the legacy STT handler requires
-a turn deployment.
-
 The Python client supports the following command-line arguments:
 
 - `--query-type` (or `-q`): Query type (default: `use_video`). Options: `text`, `use_audio`, `use_image`, `use_video`
@@ -358,6 +303,13 @@ python examples/online_serving/openai_chat_completion_client_for_multimodal_gene
 bash run_curl_multimodal_generation.sh use_image
 ```
 
+### Realtime WebSocket
+
+The [shared Realtime UI](../realtime_web/README.md#qwen3-explicit-turn-realtime)
+supports explicit turns through `/v1/realtime?duplex=0`. The
+[Server VAD setup](../realtime_web/README.md#qwen3-with-server-vad-automatic-turns)
+uses the duplex endpoint.
+
 ### FAQ
 
 ## Modality control
@@ -582,26 +534,6 @@ The gradio script supports the following arguments:
 
 Run these commands from the repository root with the vLLM-Omni environment activated.
 
-### Without VAD: manually submit each turn
-
-Start the backend:
-
-```bash
-vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
-```
-
-In another terminal, check backend readiness and start the UI:
-
-```bash
-curl --fail http://127.0.0.1:8091/health
-python -m examples.online_serving.qwen3_omni.realtime_web \
-    --backend ws://127.0.0.1:8091 --stt --port 7863
-```
-
-Open `http://localhost:7863`, start a session, speak, and press **Send turn**.
-`--stt` is the default. Each turn uses a separate connection without shared model
-history; the conversation shown in the browser is a local log.
-
 ### With VAD: automatically submit after silence
 
 Start the backend with a Server VAD deployment overlay and a compatible Silero
@@ -610,7 +542,7 @@ ONNX artifact, following the
 After its health check succeeds, start the UI:
 
 ```bash
-python -m examples.online_serving.qwen3_omni.realtime_web \
+python -m examples.online_serving.realtime_web --profile qwen3-turn \
     --backend ws://127.0.0.1:8091 --vad --port 7863
 ```
 
@@ -621,9 +553,7 @@ VAD mode keeps uploading audio during replies and supports speech interruption.
 The engine-owned Qwen duplex plugin maintains conversation history and tracks
 played replies through playback acknowledgements. Each committed utterance starts
 a new generation request; the model does not use native streaming-input KV decoding.
-STT mode pauses microphone upload while the reply is generated and played.
 In VAD mode, click **Camera** to upload sampled frames with your spoken question;
 see [camera setup and limits](../realtime_web/README.md#qwen-vad-camera-input).
-STT remains audio-only. To switch back to manual turns, use the default turn
-deployment, restart the UI with `--stt`, refresh the page, and reconnect. Stop the existing UI before reusing port 7863.
-See the [shared UI guide](../realtime_web/README.md) for HTTPS access and testing.
+For explicit turns, use `--manual` with the shared UI as described in the
+[shared UI guide](../realtime_web/README.md#qwen3-explicit-turn-realtime).

examples/online_serving/realtime_web/README.md

@@ -7,14 +7,14 @@ This implements [RFC #7222](https://github.com/vllm-project/vllm-omni/issues/722
 | Profile | Turn control | Camera | Playback acknowledgement |
 | --- | --- | --- | --- |
 | `minicpm-native` | Model-controlled listen/speak, continuous audio input | Frames accompany audio | Yes |
-| `qwen3-turn --stt` (default) | User presses **Send turn** | No | No |
+| `qwen3-turn --manual` | Press **Send turn**; mic pauses while the reply plays | No | No |
 | `qwen3-turn --vad` | Server detects trailing silence; speech interrupts replies | Sampled frames with each spoken turn | Yes |
 | `aura-ptt` | Hold **Hold to talk**; release commits. Hold also stops local playback | Sticky frames with speech; vision-follow every 2 frames only while unlocked (locked from PTT release until text-final / listen) | Yes |
 
 Qwen3 VAD uses an engine-owned duplex plugin: microphone upload continues
 while replies stream, and speech can interrupt generation and playback. The
 checkpoint still generates committed turns; this is not native streaming-input
-KV decoding. STT remains explicit-turn and audio-only.
+KV decoding.
 
 ## MiniCPM: existing command stays valid
 
@@ -62,45 +62,36 @@ just-committed speech turn on Stage0/1 (`max_num_seqs=1`). Audio still playing
 does not hold the next vision turn once text is done.
 MiniCPM / Qwen profiles do not set `pushToTalk`, so the PTT control stays hidden.
 
-## Qwen3: explicit-turn STT adapter
+## Qwen3: explicit-turn Realtime
 
-Start the backend, then the UI in a second terminal:
+The shared UI sends OpenAI Realtime events to `/v1/realtime?duplex=0` and keeps
+conversation history on the same connection:
+
+Start the turn-based backend:
 
 ```bash
 vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
-
-python -m examples.online_serving.qwen3_omni.realtime_web \
-    --backend ws://127.0.0.1:8091 --stt --port 7863
 ```
 
-Open `http://localhost:7863`, start a session, speak, and press **Send turn**.
-Wait for the answer to finish playing before speaking again. The UI opens a
-fresh STT connection for each turn; the displayed conversation is a local log,
-and **previous turns are not provided as model history**. The STT endpoint
-accepts only model selection, so system-prompt controls are hidden in this mode.
+In another terminal, check readiness and start the UI:
 
-The adapter follows the shipped `qwen3_omni/openai_realtime_client.py` and
-`vllm_omni/entrypoints/openai/realtime_connection.py`:
+```bash
+curl --fail http://127.0.0.1:8091/health
+python -m examples.online_serving.realtime_web --profile qwen3-turn \
+    --backend ws://127.0.0.1:8091 --manual --port 7863
+```
 
-
-1. Explicit `duplex=0` selects the legacy STT handler on a turn deployment.
-   It does not enable STT on a duplex deployment.
-2. Send `{type: "session.update", model: ...}` and `commit(final=false)`.
-3. Stream mono PCM16 at 16 kHz; **Send turn** flushes buffered audio, then sends `commit(final=true)`.
-4. `response.output_audio.delta.audio` contains PCM; `response.output_audio.done`
-   terminates this adapter, including a response with no audio.
-5. The local implementation emits **model output** on `transcription.*`, so the
-   UI displays it as assistant text. This differs from the early RFC's suggested
-   user-transcript mapping. The VAD adapter also supports `response.output_text.*`
-   and audio-transcript events without duplicating the answer.
+Speak, then press **Send turn**. The client commits the audio and requests a
+response; recording pauses while the reply plays.
 
 ## Qwen3 with Server VAD (automatic turns)
 
 Run commands from the repository root with the vLLM-Omni environment activated.
-Prepare the pinned Silero v6.2 ONNX artifact as described in the
-[Qwen3 Server VAD instructions](../qwen3_omni/README.md#realtime-websocket-client-openai_realtime_clientpy).
-The backend requires ONNX Runtime and a compatible local artifact; setting
-`--vad` on the UI alone does not enable VAD on the backend.
+The plugin uses the pinned Silero v6.2 ONNX artifact from
+`istupakov/silero-vad-onnx` and does not download it while serving. Make it
+available in the Hugging Face cache or set `server_vad_model_path` to a local
+copy in the overlay below. The backend requires ONNX Runtime; setting `--vad`
+on the UI alone does not enable VAD.
 
 Create an overlay named `qwen3_vad.yaml`, replacing both paths with absolute paths
 on the backend host:
@@ -124,7 +115,7 @@ In a second terminal, wait until the health check succeeds, then start the UI:
 
 ```bash
 curl --fail http://127.0.0.1:8091/health
-python -m examples.online_serving.qwen3_omni.realtime_web \
+python -m examples.online_serving.realtime_web --profile qwen3-turn \
     --backend ws://127.0.0.1:8091 --vad --port 7863
 ```
 
@@ -148,21 +139,17 @@ The bundled duplex deploy allows four audio inputs and eight images per prompt.
 Use headphones to avoid speaker audio triggering VAD interruption. Reference
 voices and tool calls are not supported by this plugin.
 
-### Switching between VAD and manual turns
+### Using the Qwen3 web UI
 
-| Mode | Backend | UI flag | Submit a turn |
-| --- | --- | --- | --- |
-| Without VAD | Default Qwen turn deployment | `--stt` (default) | Press **Send turn** |
-| With VAD | Server VAD configuration and Silero artifact | `--vad` | Pause after speaking |
+The shared UI supports both explicit turns and Server VAD. Use the manual UI
+command above with the turn-based deployment, or start the VAD deployment above
+and launch the UI with `--vad`.
 
 Stop the existing UI process before starting another on the same port, then
-refresh the browser and reconnect. To use STT, start a backend with the default
-turn deployment and restart the UI with `--stt`. The duplex backend exposes
-Realtime duplex and ChatCompletion; it does not expose the legacy STT handler.
-To enable VAD on a default backend, first restart that backend with the overlay.
-Make `--backend` match the actual backend port; the UI and backend use separate
-ports. `Connection refused` means the target backend is unavailable: check its
-startup log and `/health` before connecting.
+refresh the browser and reconnect. To enable VAD on a default backend, restart
+that backend with the overlay. Make `--backend` match the actual backend port;
+the UI and backend use separate ports. `Connection refused` means the target
+backend is unavailable: check its startup log and `/health` before connecting.
 
 VAD connects with `duplex=1` and sends nested `session.audio.input` configuration
 with `create_response: true` and `interrupt_response: true`. It sends playback
@@ -210,8 +197,7 @@ native camera protocol, where Stage 0 interleaves frames at unit boundaries.
 
 This is a supported subset, not full OpenAI Realtime API compatibility.
 `duplex=1`, `overlap_policy`, `playback.ack`, and `session.close` remain vLLM-Omni
-extensions. The legacy `--stt` mode is unchanged and is not the standard Realtime
-conversation API. This demo does not add WebRTC, semantic VAD, or tool calling.
+extensions. This demo does not add WebRTC, semantic VAD, or tool calling.
 
 ## Shared host options
 
@@ -219,11 +205,14 @@ The shared entry point can select a profile explicitly:
 
 ```bash
 python -m examples.online_serving.realtime_web --profile qwen3-turn \
-    --backend ws://127.0.0.1:8091 --stt --port 7863
+    --backend ws://127.0.0.1:8091 --vad --port 7863
 ```
 
+This Qwen3 UI profile requires the server-VAD deployment above.
+
 - `--backend` / `--ws-backend`: backend WebSocket origin (not the full `/v1/realtime` URL).
 - `--model`: override the wrapper's model name.
+- `--manual` / `--vad`: Qwen turn control; manual turns are the default.
 - `--public-realtime-url`: optional browser-visible WebSocket URL; otherwise the
   static host proxies `/v1/realtime` on the same origin.
 - `--host`, `--port`: UI bind address and port (default port 7862).
@@ -245,7 +234,7 @@ pytest -m cpu tests/model_executor/models/qwen3_omni/test_duplex_plugin.py \
        tests/engine/test_duplex_orchestrator.py
 ```
 
-These CPU tests cover profile messages, two STT turns, VAD terminal/drain ordering,
+These CPU tests cover profile message serialization, VAD terminal/drain ordering,
 backpressure, native capture during playback, server config injection, and shared
 worklets. Backend regressions cover current audio inclusion in history-based
 prompts, delayed playback ACK ordering, and no extra generation during silence

examples/online_serving/realtime_web/app/static/profiles/qwen3_turn.js

@@ -2,24 +2,24 @@
   'use strict';
   const profiles = root.OmniRealtimeProfiles;
   profiles['qwen3-turn'] = (config) => {
-    const vad = config.adapter === 'vad';
+    const vad = config.turnMode === 'vad';
     let interruptOnSpeech = vad;
     let imageSeq = 0;
     let imageItems = [];
     // The engine refuses a ninth image (or 4 MiB of them) for the whole
     // session, so the camera track retires its own oldest frame instead.
     const MAX_IMAGE_ITEMS = 8;
-    const hint = 'This Qwen deploy needs session_mode: duplex and Server VAD support; use --stt or enable Server VAD.';
+    const hint = 'This Qwen deployment needs session_mode: duplex with Server VAD enabled.';
     return {
-      title: 'Qwen3-Omni Voice', eyebrow: vad ? 'Engine-owned duplex call' : 'Turn-based realtime call',
+      title: 'Qwen3-Omni Voice', eyebrow: vad ? 'Engine-owned duplex call' : 'Turn-based Realtime call',
       policy: vad ? 'Server VAD • interruptible responses' : 'Send turn • one response at a time',
       description: vad
         ? 'Pause to send your turn; speak again to interrupt. Microphone upload continues during playback. Requires the Qwen duplex plugin and Silero. Optional camera frames accompany each spoken turn.'
-        : 'Speak, then press Send turn. Microphone upload pauses while the model answers. Each turn uses a fresh connection; conversation history is not carried between turns. No barge-in or camera input.',
+        : 'Speak, then press Send turn. Microphone upload pauses while the model answers. Conversation history stays on the Realtime session. No barge-in or camera input.',
       waiting: 'Waiting for you', camera: vad, cameraMaxDimension: 448, playbackAck: vad, clientCommit: !vad,
-      waitForResponseDone: true, halfDuplex: !vad, deduplicateTranscript: true, closeSession: vad, reconnectEachTurn: !vad,
-      inputSampleRate: vad ? 24000 : 16000,
-      readyEvent: vad ? 'session.updated' : 'session.created', instructions: vad, sendIntervalMs: 200,
+      waitForResponseDone: true, halfDuplex: !vad, deduplicateTranscript: true, closeSession: vad,
+      inputSampleRate: 24000,
+      readyEvent: 'session.updated', instructions: true, sendIntervalMs: 200,
       presets: { assistant: 'You are a helpful assistant. Answer clearly and concisely.' },
       url(config, location) {
         const url = profiles.url(config, location);
@@ -33,22 +33,17 @@
       initialMessages(config, instructions) {
         imageItems = [];
         interruptOnSpeech = vad;
-        if (!vad) return [
-          { type: 'session.update', model: config.model },
-          { type: 'input_audio_buffer.commit', final: false },
-        ];
-        const session = { model: config.model, overlap_policy: 'barge_in_on_speech', audio: { input: {
+        const session = { model: config.model, audio: { input: {
           format: { type: 'audio/pcm', rate: 24000 },
-          turn_detection: { type: 'server_vad', threshold: 0.5, prefix_padding_ms: 300,
-            silence_duration_ms: 500, create_response: true, interrupt_response: true },
+          turn_detection: vad ? { type: 'server_vad', threshold: 0.5, prefix_padding_ms: 300,
+            silence_duration_ms: 500, create_response: true, interrupt_response: true } : null,
         } } };
+        if (vad) session.overlap_policy = 'barge_in_on_speech';
+        else session.type = 'realtime';
         if (instructions) session.instructions = instructions;
         return [{ type: 'session.update', session }];
       },
-      append(audio) {
-        return vad ? { type: 'input_audio_buffer.append', audio }
-          : { type: 'input_audio_buffer.append', audio, format: 'pcm16', sample_rate_hz: 16000 };
-      },
+      append: (audio) => ({ type: 'input_audio_buffer.append', audio }),
       // Qwen is a turn model: a frame is a picture added to the conversation,
       // not a track interleaved into the audio. That is exactly the OpenAI
       // Realtime image interface, so the camera sends conversation items
@@ -68,7 +63,10 @@
         } });
         return messages;
       },
-      commitMessages: () => vad ? [] : [{ type: 'input_audio_buffer.commit', final: true }],
+      commitMessages: () => vad ? [] : [
+        { type: 'input_audio_buffer.commit' },
+        { type: 'response.create' },
+      ],
       ack: (responseId, playedMs) => vad ? { type: 'playback.ack', response_id: responseId,
         item_id: `item_${responseId}`, played_ms: playedMs, committed_ms: playedMs } : null,
       mapEvent(event) {
@@ -87,30 +85,28 @@
           return { kind: 'interrupt' };
         }
         if (event.type === 'playback.acknowledged') return { kind: 'ack', committedMs: (event.event || event).committed_ms || 0 };
-        if (event.type === 'response.output_text.delta' || event.type === 'transcription.delta') {
-          // On the shipped STT route transcription.* is model-generated text,
-          // not a separate ASR transcript of the user. See realtime_connection.py.
+        if (event.type === 'response.output_text.delta') {
           return { kind: 'text', role: 'assistant', channel: 'text', text: event.delta || '' };
         }
-        if (event.type === 'response.output_text.done' || event.type === 'transcription.done') {
+        if (event.type === 'response.output_text.done') {
           return { kind: 'text-final', role: 'assistant', channel: 'text', text: event.text || '' };
         }
         if (vad && (event.type === 'output_audio_buffer.cleared' ||
             (event.type === 'response.done' && event.response?.status === 'cancelled'))) {
           return { kind: 'interrupt', responseId: event.response_id || event.response?.id || null };
         }
-        if (event.type === 'input_audio_buffer.committed') return { kind: 'begin' };
+        if (vad && event.type === 'input_audio_buffer.committed') return { kind: 'begin' };
+        if (!vad && event.type === 'input_audio_buffer.cleared') return { kind: 'ignore' };
         if (event.type === 'input_audio_buffer.cleared') return { kind: 'backpressure', message: 'Input cleared. Model is answering; please wait.' };
-        if (!vad && event.type === 'response.output_audio.done') return { kind: 'done' };
         const action = profiles.event(event);
         if (action.kind === 'error') {
           if (action.code === 'input_backpressure') return { ...action, kind: 'backpressure', message: 'Input buffer is full; please repeat dropped speech after the response.' };
-          if (action.code === 'server_vad_initialization_failed') action.message = 'Server VAD could not load Silero. Configure the server artifact or restart this UI with --stt.';
+          if (action.code === 'server_vad_initialization_failed') action.message = 'Server VAD could not load Silero. Configure the server artifact and restart the UI.';
           else if (vad && action.code === 'unsupported') action.message += ` ${hint}`;
         }
         return action;
       },
-      connectionHint: vad ? hint : 'Check the Qwen /v1/realtime STT endpoint and model name.',
+      connectionHint: vad ? hint : 'Check the Qwen turn-based /v1/realtime deployment and model name.',
     };
   };
 })(globalThis);

examples/online_serving/realtime_web/server.py

@@ -74,12 +74,12 @@ def build_app(
     public_realtime_url: str | None = None,
     ref_audio: str | None = None,
     profile: str = "minicpm-native",
-    adapter: str = "stt",
+    turn_mode: str = "manual",
 ) -> FastAPI:
     if profile not in {"minicpm-native", "qwen3-turn", "aura-ptt"}:
         raise ValueError(f"Unknown profile: {profile}")
-    if adapter not in {"stt", "vad"}:
-        raise ValueError(f"Unknown adapter: {adapter}")
+    if turn_mode not in {"manual", "vad"}:
+        raise ValueError(f"Unknown turn mode: {turn_mode}")
     if profile == "qwen3-turn" and ref_audio:
         raise ValueError("Qwen3 profile does not accept --ref-audio")
     if profile == "aura-ptt" and ref_audio:
@@ -116,7 +116,7 @@ def build_app(
             {
                 "model": model,
                 "profile": profile,
-                "adapter": adapter,
+                "turnMode": turn_mode,
                 "realtimePath": public_realtime_url or "v1/realtime",
                 "refAudio": ref_audio_uri,
                 "appVersion": app_version,
@@ -190,9 +190,9 @@ def main(default_profile: str = "minicpm-native") -> None:
     parser.add_argument("--profile", choices=("minicpm-native", "qwen3-turn", "aura-ptt"), default=default_profile)
     parser.add_argument("--ws-backend", "--backend", dest="ws_backend")
     mode = parser.add_mutually_exclusive_group()
-    mode.add_argument("--stt", dest="adapter", action="store_const", const="stt")
-    mode.add_argument("--vad", dest="adapter", action="store_const", const="vad")
-    parser.set_defaults(adapter="stt")
+    mode.add_argument("--manual", dest="turn_mode", action="store_const", const="manual")
+    mode.add_argument("--vad", dest="turn_mode", action="store_const", const="vad")
+    parser.set_defaults(turn_mode="manual")
     parser.add_argument(
         "--public-realtime-url",
         help="Browser-visible ws:// or wss:// Realtime URL; defaults to the same-origin proxy.",
@@ -225,7 +225,7 @@ def main(default_profile: str = "minicpm-native") -> None:
             public_realtime_url=args.public_realtime_url,
             ref_audio=args.ref_audio,
             profile=args.profile,
-            adapter=args.adapter,
+            turn_mode=args.turn_mode,
         ),
         host=args.host,
         port=args.port,

pyproject.toml

@@ -232,6 +232,11 @@ ignore = [
 # F-correctness rules still apply.
 "vllm_omni/model_executor/models/nemotron_voicechat/nemo_vendored/**" = ["E501", "E721", "E741", "F401", "F841", "I001", "N803", "N814", "UP006", "UP007", "UP008", "UP011", "UP032", "UP035", "W291", "W293"]
 "vllm_omni/experimental/fullduplex/joyvl/decision/prompts.py" = ["E501"]  # System/summarizer prompts are long prose lines
+# Kandinsky 6 keeps native/BigVGAN names (T/H/W, `l`) and NVIDIA vocoder
+# docstring length so the port stays diffable against the source modules.
+"vllm_omni/diffusion/models/kandinsky6/kandinsky6_transformer.py" = ["N803", "E741"]
+"vllm_omni/diffusion/models/kandinsky6/pipeline_kandinsky6.py" = ["N803"]
+"vllm_omni/diffusion/models/kandinsky6/modeling_kandinsky6_audio.py" = ["E501", "E741", "N803", "UP008"]
 
 [tool.ruff.lint.flake8-tidy-imports.banned-api]
 "librosa".msg = "The librosa module is banned, use vllm.multimodal helpers instead"
@@ -277,6 +282,7 @@ markers = [
     "parallel: Parallelism/distributed tests",
     "sp: Sequence parallelism tests",
     "example: Doc example code tests",
+    "nvidia_only: Tests that require NVIDIA CUDA and must not run on ROCm",
     # platform markers
     # Tag with [hardware-platform] for tests.helpers.mark.get_supported_platforms().
     # Add [gpu] on accelerator platforms that should also get pytest.mark.gpu.
@@ -364,6 +370,8 @@ extend-ignore-identifiers-re = [
     # (output feature dim), not "doubt".
     ".*dout.*",
     ".*DOUT.*",
+    # Kandinsky nabla threshold argument (`thr`), not the word "the".
+    "^thr$",
 ]
 extend-ignore-re = [
     "\\.\\.\\.n,d->\\.\\.\\.nd",
@@ -404,3 +412,8 @@ IY = "IY"
 # Speech DAC codec identifiers (convnet_type, transconvnet_type) and is not a
 # typo of "convent".
 convnet = "convnet"
+# Upstream Kandinsky system prompt. "promt" and "scren" are part of the
+# conditioning text and must not be corrected.
+thr = "thr"
+promt = "promt"
+scren = "scren"

recipes/JD/JoyAI-Image-Edit.md

@@ -0,0 +1,221 @@
+# JoyAI-Image-Edit
+
+> Text-guided single-image editing with the native JoyAI-Image-Edit diffusion pipeline
+
+## Summary
+
+- Vendor: JD / JoyImage Team
+- Model: `jdopensource/JoyAI-Image-Edit-Diffusers`
+- Task: Text-guided single-image editing
+- Mode: Offline inference and OpenAI-compatible online serving
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe when you want to run JoyAI-Image-Edit through the shared
+image-to-image examples with a known-good baseline configuration. JoyAI-Image-Edit
+is a large single-image editing model: it requires one reference image per
+request, snaps requested sizes to Joy's supported resolution buckets, and has a
+validated 1024x1024 baseline on an 80 GB NVIDIA GPU.
+
+## References
+
+- Upstream model:
+  [`jdopensource/JoyAI-Image-Edit-Diffusers`](https://huggingface.co/jdopensource/JoyAI-Image-Edit-Diffusers)
+- Upstream project:
+  [`jd-opensource/JoyAI-Image`](https://github.com/jd-opensource/JoyAI-Image)
+- Related offline example:
+  [`examples/offline_inference/image_to_image/image_edit.py`](../../examples/offline_inference/image_to_image/image_edit.py)
+- Related online example:
+  [`examples/online_serving/image_to_image/README.md`](../../examples/online_serving/image_to_image/README.md)
+- User guide:
+  [`docs/user_guide/examples/offline_inference/image_to_image.md`](../../docs/user_guide/examples/offline_inference/image_to_image.md),
+  [`docs/user_guide/examples/online_serving/image_to_image.md`](../../docs/user_guide/examples/online_serving/image_to_image.md)
+
+## Hardware Support
+
+This recipe documents the CUDA configuration used to validate the PR's JoyAI
+integration. Other GPU types should be treated as unvalidated until you rerun
+the smoke and parity checks below.
+
+## GPU
+
+### 1x NVIDIA H800 PCIe 80GB
+
+#### Environment
+
+- OS: Linux
+- Python: `3.12.3`
+- Driver / runtime: NVIDIA driver `580.82.07`, CUDA runtime `13.0`
+- PyTorch: `2.11.0+cu130`
+- Diffusers: `0.39.0.dev0` with `JoyImageEditPipeline` available
+- vLLM: `0.21.0`
+- vLLM-Omni: use the commit you are deploying from
+- GPU: NVIDIA H800 PCIe, 81559 MiB
+
+#### Online Serving
+
+Start the OpenAI-compatible server from the repository root:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 \
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
+vllm serve jdopensource/JoyAI-Image-Edit-Diffusers \
+  --omni \
+  --port 8092 \
+  --init-timeout 1200
+```
+
+The server is ready when the log shows `Application startup complete.`
+
+If you need extra GPU headroom, use one of the memory-saving variants below.
+These variants are useful on tighter 80 GB deployments, but the latency and
+quality numbers in this recipe are from the baseline command above.
+
+```bash
+# Pipeline-level CPU offload for large components.
+CUDA_VISIBLE_DEVICES=0 \
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
+vllm serve jdopensource/JoyAI-Image-Edit-Diffusers \
+  --omni \
+  --port 8092 \
+  --init-timeout 1200 \
+  --enable-cpu-offload
+```
+
+```bash
+# DiT blockwise offload. This trades latency for lower peak GPU memory.
+CUDA_VISIBLE_DEVICES=0 \
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
+vllm serve jdopensource/JoyAI-Image-Edit-Diffusers \
+  --omni \
+  --port 8092 \
+  --init-timeout 1200 \
+  --enable-layerwise-offload
+```
+
+#### Online Verification
+
+Download the public JoyAI plate example:
+
+```bash
+wget -O /tmp/joy_plate.jpg \
+  https://raw.githubusercontent.com/jd-opensource/JoyAI-Image/main/test_images/test_1.jpg
+```
+
+Send one image and one prompt. JoyAI-Image-Edit supports exactly one input image
+per request.
+
+```bash
+IMG_B64=$(base64 -w0 /tmp/joy_plate.jpg)
+
+curl -s http://localhost:8092/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d "$(jq -n --arg img "$IMG_B64" '{
+    messages: [{
+      role: "user",
+      content: [
+        {type: "text", text: "Turn the plate blue"},
+        {type: "image_url", image_url: {url: ("data:image/jpeg;base64," + $img)}}
+      ]
+    }],
+    extra_body: {
+      height: 1024,
+      width: 1024,
+      num_inference_steps: 30,
+      guidance_scale: 4.0,
+      seed: 0
+    }
+  }')" \
+  | jq -r '.choices[0].message.content[0].image_url.url' \
+  | cut -d',' -f2- \
+  | base64 -d > /tmp/joyai_image_edit_plate_online.png
+```
+
+Expected result: `/tmp/joyai_image_edit_plate_online.png` is a 1024x1024 PNG
+where the plate color changes to blue while the surrounding scene is mostly
+preserved. Use a client timeout of at least 600 seconds for first-run or cold
+server checks.
+
+#### Offline Command
+
+Run the same smoke case without starting a server:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 \
+PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
+python examples/offline_inference/image_to_image/image_edit.py \
+  --model jdopensource/JoyAI-Image-Edit-Diffusers \
+  --image /tmp/joy_plate.jpg \
+  --prompt "Turn the plate blue" \
+  --negative-prompt "" \
+  --height 1024 \
+  --width 1024 \
+  --num-inference-steps 30 \
+  --guidance-scale 4.0 \
+  --seed 0 \
+  --enforce-eager \
+  --init-timeout 1200 \
+  --output /tmp/joyai_image_edit_plate_offline.png
+```
+
+For a local checkpoint mirror, replace `--model` with the local model directory.
+The PR validation used the same command shape with
+`/autodl-fs/data/joyai_models/JoyAI-Image-Edit-Diffusers`.
+
+#### Validated Results
+
+The table below is from the 1x H800 PCIe environment above. Resolution requests
+were snapped to the Joy 1024x1024 bucket, batch size was 1, and CFG was 4.0.
+`vLLM peak` is the observed peak GPU memory for the vLLM-Omni run.
+
+| Case | Steps | Seed | vLLM latency | Diffusers latency | vLLM peak | Diffusers peak | SSIM | PSNR |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| `plate_seed123` | 30 | 123 | 50.87 s | 52.74 s | 57.52 GiB | 57.28 GiB | 0.9869 | 34.7051 |
+| `plate_seed0` | 30 | 0 | 51.02 s | 52.01 s | 57.52 GiB | 57.28 GiB | 0.9946 | 41.4351 |
+| `astronaut_seed0` | 40 | 0 | 67.90 s | 69.22 s | 57.52 GiB | 57.28 GiB | 0.9948 | 38.2498 |
+| `crane_removal_seed0` | 40 | 0 | 68.00 s | 69.27 s | 57.52 GiB | 57.28 GiB | 0.9975 | 50.0135 |
+| `object_move_seed0` | 40 | 0 | 68.06 s | 69.32 s | 57.52 GiB | 57.28 GiB | 0.9969 | 46.1138 |
+| `object_rotation_seed0` | 40 | 0 | 67.94 s | 69.28 s | 57.52 GiB | 57.28 GiB | 0.9898 | 35.5365 |
+| `camera_control_seed0` | 40 | 0 | 68.15 s | 69.51 s | 57.52 GiB | 57.28 GiB | 0.9952 | 41.7697 |
+
+#### Output Size Behavior
+
+JoyAI-Image-Edit does not use arbitrary image sizes directly. The pipeline maps
+the input or requested `height` / `width` to the nearest supported Joy bucket,
+then resizes and center-crops the reference image to that bucket before
+denoising.
+
+- Provide both `height` and `width`, or omit both.
+- For square outputs, request `height=1024` and `width=1024`.
+- A request such as `512x512` is also square and is snapped to the 1024x1024 Joy
+  bucket, so the final image size will not match the literal request.
+- Non-square requests are snapped to the nearest aspect-ratio bucket supported
+  by the model.
+
+#### Guidance Parameters
+
+Use `guidance_scale` for JoyAI classifier-free guidance, matching Diffusers.
+The pipeline defaults to `4.0` when the parameter is omitted. `true_cfg_scale`
+remains supported as an alias; if both parameters are supplied, their values
+must match. Values greater than `1.0` enable CFG; an omitted negative prompt is
+treated as an empty string. Values at or below `1.0` disable CFG.
+
+The offline example passes `--guidance-scale 4.0` explicitly for clarity; if it
+is omitted, the JoyAI pipeline uses the same `4.0` default.
+
+#### Known Limitations
+
+- Single image only: JoyAI-Image-Edit rejects requests with zero or multiple
+  input images.
+- Single prompt only: batched prompt requests are rejected.
+- HSDP is not supported yet. Starting with HSDP enabled raises a `ValueError`
+  before the generic HSDP setup reaches the transformer.
+- Tensor Parallelism is not supported yet. Keep `tensor_parallel_size=1`;
+  larger values raise a `ValueError` during pipeline initialization.
+- LoRA is not included for this JoyAI pipeline.
+- Cache-DiT and TeaCache are not included for this JoyAI pipeline. Do not treat
+  the generic image-to-image cache flags as validated JoyAI acceleration knobs.
+- Text encoder quantization is not included.
+- The validation above is CUDA-only; ROCm and NPU configurations are not
+  documented as supported by this recipe.

recipes/Kandinsky/Kandinsky6-TI2VA.md

@@ -0,0 +1,247 @@
+# Kandinsky 6 TI2VA — H100 (v1, single GPU, CPU offload)
+
+> Text/image-to-video-and-audio serving with Kandinsky 6 Pro
+
+## Summary
+
+- Vendor: Kandinsky Lab ([`kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers`](https://huggingface.co/kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers))
+- Model: Kandinsky 6 Pro (TI2VA — text/image-to-video-and-audio)
+- Task: Joint text/image-to-video-and-audio generation
+- Mode: Offline (`Omni(...)`) and online serving with the OpenAI-compatible
+  `/v1/videos` API
+- Hardware: 1x NVIDIA H100 80GB HBM3 with component-level CPU offload
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe to serve Kandinsky 6's TI2VA pipeline — a single DiT that
+jointly denoises video and audio latents, conditioned on a text prompt and,
+optionally, a reference image (image-to-video). Tensor parallel, CFG
+parallel, Ulysses, ring, pipeline parallel, HSDP, layerwise offload,
+MagCache, TeaCache, Cache-DiT, FP8, and tiled VAE patch parallel are wired.
+NaviCache and a registered NABLA backend are not. TeaCache coefficients are
+uncalibrated. See "Known limitations".
+
+## Supported model contract
+
+| Task | Entrypoint | Input | Output |
+|---|---|---|---|
+| Text-to-video-and-audio | `vllm serve kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers --omni` / `Omni(model="kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers")` | text prompt | synchronized MP4 (H.264 video + AAC 44.1 kHz mono audio) |
+| Image-to-video-and-audio | same | text prompt + reference image | synchronized MP4, first frame conditioned on the reference image |
+
+- Audio generation is on by default (`sample_audio=True`); pass
+  `extra_args.sample_audio=false` to generate video only.
+- Batch size is 1 request per generation call (upstream limitation of the
+  ported denoise loop; concurrent requests are still served by vLLM-Omni's
+  own request scheduler, just not batched together within one DiT forward
+  pass yet).
+- Serving defaults follow the Pro production geometry registered in
+  `vllm_omni/model_extras/kandinsky6.py`: **864x480, 125 frames @ 24 fps
+  (5.2 s), 50 steps, CFG 5.0**. Any `4k+1` frame count and 16-divisible
+  resolution can be requested explicitly via `height`/`width`/`num_frames`/
+  `num_inference_steps` — the model is *not* fixed-duration.
+- Audio is always 44.1 kHz; its length is derived from the video duration.
+- Negative prompt defaults to the pipeline's built-in K6 negative prompt
+  when omitted.
+
+## References
+
+- Hub checkpoint: [`kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers`](https://huggingface.co/kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers)
+- Joint video+audio native-model reference: [`recipes/MiniMaxAI/MiniMax-H3.md`](../MiniMaxAI/MiniMax-H3.md)
+- Offline example: [`examples/offline_inference/text_to_video/text_to_video.py`](../../examples/offline_inference/text_to_video/text_to_video.py)
+- Image-to-video example: [`examples/offline_inference/image_to_video/image_to_video.py`](../../examples/offline_inference/image_to_video/image_to_video.py)
+
+## Hardware
+
+- Accelerator model and per-device memory: NVIDIA H100 80GB HBM3
+  (driver 570.133.20)
+- Number of devices: 1 (multi-GPU tensor parallelism is implemented in the
+  transformer's linear layers but not yet validated end-to-end)
+- Device interconnect: N/A (single-device path)
+- Host memory: 1.4 TB installed; the bf16 DiT (~56 GB) plus the text
+  encoders are staged on the host under `--enable-cpu-offload`, so budget
+  at least ~90 GB of free host RAM.
+- Qualification scope: single-GPU T2VA at the Pro default geometry and at a
+  reduced smoke geometry, offline and via `vllm serve`. Numbers below were
+  measured once on the shared box described above; treat them as
+  indicative, not as a benchmark.
+
+## Software environment
+
+- OS: Ubuntu 22.04.5 LTS
+- Python: 3.12.13
+- Driver / runtime: CUDA 12.9 (torch 2.13.0+cu129)
+- vLLM version: 0.29.0
+- vLLM-Omni version or commit: this repository, `vllm_omni/diffusion/models/kandinsky6/`
+- diffusers 0.40.0, transformers 5.14.1
+- Attention: FlashAttention-3 on this box (`flash_attn_interface`). The Pro
+  timings below use it. Without FA3, `attention_engine: "auto"` falls back
+  to PyTorch SDPA; an earlier SDPA Pro run on the same GPU was 26.9 s/it
+  and 1377 s.
+
+## Weights
+
+`Kandinsky6TI2VAPipeline._load_components` loads the public Diffusers layout
+from [`kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers`](https://huggingface.co/kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers)
+(`model_index.json`, `transformer/`, `vae/`, `text_encoder/`, `tokenizer/`,
+`text_encoder_2/`, `tokenizer_2/`, `audio_vae/`, `scheduler/`). Pass that
+repo id (or a local snapshot of it) to `vllm serve` / `Omni(model=...)`.
+The Hub repo is gated; export `HF_TOKEN` if download returns 401.
+
+A raw Kandinsky 6 weight tree is not required. If you already have a local
+Diffusers-layout snapshot, point `--model` at that directory instead of the
+Hub id.
+
+Each weight folder is loaded on its own (`transformer/`, `vae/`, `text_encoder/`, `text_encoder_2/`, `audio_vae/`). The DiT is built with initialization skipped, then the framework loader fills it from `transformer/`.
+
+## Command
+
+```bash
+vllm serve kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers --omni \
+  --host 127.0.0.1 --port 8091 \
+  --num-gpus 1 --enable-cpu-offload
+```
+
+`--enable-cpu-offload` enables model-level offload with mutual exclusion
+between the transformer and the two text encoders (they never co-reside on
+the GPU). Without it the bf16 DiT (~56 GB) plus Qwen2.5-VL-7B (~16 GB) plus
+the VAEs and activations do not fit an 80 GB device at the Pro geometry.
+
+Offline (writes an MP4 with the audio track muxed in):
+
+```bash
+python examples/offline_inference/text_to_video/text_to_video.py \
+  --model kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers \
+  --prompt "A golden retriever runs along a sunny beach, waves crashing, cinematic footage" \
+  --seed 42 --enable-cpu-offload --output kandinsky6_t2va.mp4
+# Video only: add --extra-body '{"sample_audio": false}'
+```
+
+Image-to-video-and-audio uses the shared image-to-video example:
+
+```bash
+python examples/offline_inference/image_to_video/image_to_video.py \
+  --model kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers \
+  --image first_frame.png \
+  --prompt "A golden retriever runs along a sunny beach, waves crashing, cinematic footage" \
+  --seed 42 --enable-cpu-offload --output kandinsky6_i2va.mp4
+```
+
+The generic scripts pick up the Pro defaults from
+`vllm_omni/model_extras/kandinsky6.py`.
+
+## Verification
+
+`/v1/videos` takes multipart form fields (not a JSON body). Submit, poll,
+then download:
+
+```bash
+VID=$(curl -s -X POST http://127.0.0.1:8091/v1/videos \
+  -F prompt="A golden retriever runs along a sunny beach, waves crashing" \
+  -F size=864x480 -F num_frames=125 -F num_inference_steps=50 -F seed=42 \
+  | python3 -c "import sys,json;print(json.load(sys.stdin)['id'])")
+
+# Poll until "status": "completed"
+curl -s http://127.0.0.1:8091/v1/videos/$VID
+
+curl -s -o kandinsky6.mp4 http://127.0.0.1:8091/v1/videos/$VID/content
+# Expect an MP4 with an H.264 video stream (125 frames, 24 fps, 864x480)
+# and an AAC audio stream (44100 Hz, mono) of matching duration.
+```
+
+Smoke-size request for quick validation (~30 s end-to-end on one H100):
+`-F size=512x320 -F num_frames=25 -F num_inference_steps=10`.
+
+## Measurements
+
+Single H100 80GB, seed 42, prompt
+"A golden retriever runs along a sunny beach, waves crashing, cinematic footage",
+negative prompt left at the pipeline default, MagCache off, prompt expansion
+off. Pro geometry: 864x480, 125 frames, 50 steps, CFG 5.0, audio on.
+
+vLLM-Omni: `text_to_video.py --model kandinskylab/Kandinsky-6.0-Pro-5s-Diffusers --enable-cpu-offload --model-class-name Kandinsky6TI2VAPipeline`,
+FlashAttention-3.
+
+| Wall / generation | Step time | Peak GPU | After load |
+|---|---|---|---|
+| 751.7 s generation (12.5 min); engine init 41 s | 14.4 s/it average | 78.39 GiB reserved (78.0 GiB nvidia-smi) | 17.74 GiB |
+
+An earlier SDPA vLLM-Omni Pro run on the same GPU was 26.9 s/it and 1377 s,
+peak reserved 74.97 GiB. A 512x320 / 25-frame / 10-step SDPA smoke was 1.4 s/it
+and 29.5 s at the same reserved peak.
+
+The MP4 is H.264 864x480, 125 frames at 24 fps (5.22 s), plus AAC 44.1 kHz
+mono (230400 samples): a golden retriever on a sunny beach with breaking waves.
+
+vLLM-Omni's VAE decode logged two failed CUDA allocations (7.15 GiB, then
+4.27 GiB) and still wrote a complete file.
+
+Audio is peak-normalized to full scale by default; pass
+`extra_args.audio_normalization="clip"` to keep the raw decoded amplitude
+instead.
+
+## Notes
+
+- Key flags: `extra_args.sample_audio` (bool, default true),
+  `extra_args.audio_normalization` (`"normalize"` default | `"clip"`),
+  `extra_args.visual_cond_scheme` (defaults to `pretrain` for text-only,
+  `tail_cond_first_frame` when an image is supplied).
+- The post-process payload is a flat dict (`video`, `audio` as float32 in
+  `[-1, 1]`, `audio_sample_rate=44100`, `fps`) so the serving muxer picks
+  up the correct sample rate.
+- Acceleration that is wired:
+  - Tensor parallel: the DiT loader narrows full checkpoint tensors onto
+    each rank's `ColumnParallelLinear` / `RowParallelLinear` shard.
+  - CFG parallel (`--cfg-parallel-size 2`): rank 0 runs the conditional
+    DiT, rank 1 the unconditional one, then both all-gather video and
+    audio velocities.
+  - HSDP: `_hsdp_shard_conditions` covers the indexed transformer blocks.
+    Do not combine with tensor parallel.
+  - Sequence parallel: visual tokens (and their RoPE) are sharded;
+    Ulysses all-to-all runs inside visual self-attention, Ring uses the
+    PyTorch ring kernel. Text stays replicated. Audio queries gather the
+    full visual sequence.
+  - Pipeline parallel: `visual_transformer_blocks` are split with
+    `PPMissingLayer`. Embeddings stay on the first stage, output heads on
+    the last. Activations and the final velocity are sent between stages.
+  - VAE patch parallel: Hunyuan decode splits the latent width when
+    `vae_patch_parallel_size > 1` and tiling is on.
+  - MagCache / TeaCache / Cache-DiT are registered. MagCache reuses the
+    Pro `mag_ratios`. TeaCache coefficients are **uncalibrated** (copied
+    from Qwen-Image) so skips are not quality-neutral. Cache-DiT targets
+    `visual_transformer_blocks` only and falls back to the same step cache
+    if the fused `(video, audio)` block return is rejected.
+  - FP8: online quantization runs in the DiT loader via
+    `process_weights_after_loading` after the sharded assign.
+  - `attention_engine: auto` uses FlashAttention-3 when
+    `flash_attn_interface` imports.
+- Known limitations:
+  - NABLA sparse attention is not registered in the framework attention
+    backend. NaviCache is not wired.
+  - Prompt expansion, NF4 Qwen, and the super-resolution cascade are not
+    part of this port.
+  - Distributed layerwise offload still uses one process unless another
+    parallel axis sets `world_size > 1` (for example tensor parallel).
+  - `_load_components` loads the Diffusers checkpoint directly rather than the
+    streamed-prefetch loader.
+
+## Supported features
+
+| Feature | Status | Guide |
+|---|---|---|
+| Text-to-video-and-audio | Supported (validated on H100) | — |
+| Image-to-video-and-audio | Supported (not yet validated on hardware) | — |
+| CPU offload (`--enable-cpu-offload`) | Supported (required on 80 GB) | — |
+| Layerwise offload (`--enable-layerwise-offload`) | Supported (about 36 GiB peak on the smoke geometry) | — |
+| Tensor parallelism | Wired (sharded DiT load) | — |
+| CFG parallelism | Wired (`--cfg-parallel-size 2`) | — |
+| Ulysses / Ring sequence parallel | Wired (visual tokens only) | — |
+| Pipeline parallelism | Wired (visual block split) | — |
+| HSDP | Wired (not with tensor parallel) | — |
+| VAE patch parallel | Wired (width-split decode) | — |
+| MagCache | Wired (Pro ratios, step reuse) | — |
+| TeaCache | Wired, coefficients uncalibrated | — |
+| Cache-DiT | Wired on `visual_transformer_blocks` | — |
+| FP8 | Wired (online quant; smoke peak about 46 GiB vs 75 GiB bf16) | [`FP8 quantization`](../../docs/user_guide/quantization/fp8.md) |
+| NaviCache | Not supported | — |
+| LoRA | Not supported | — |

recipes/MammothModa2/MammothModa2.md

@@ -18,12 +18,24 @@ The generic example formats the AR prompt, drives the AR → DiT stage pipeline,
 and forwards MammothModa2-specific generation parameters through the
 pipeline-declared `extra_body` contract.
 
-MammothModa2's DiT stage runs in the shared diffusion runtime in request mode.
-The first integration intentionally supports one request and one image per
-forward only (`max_num_seqs: 1`, `num_outputs_per_prompt: 1`). Request-level
-batching, step execution, continuous batching, cache acceleration,
-compilation, quantization, parallelism, and offload are not enabled by this
-recipe.
+MammothModa2's DiT stage runs in the shared diffusion runtime. The default
+`mammoth_moda2.yaml` uses request-level batching with up to eight compatible
+requests (`max_num_seqs: 8`) and one image per request
+(`num_outputs_per_prompt: 1`). TeaCache and Cache-DiT are optional alternative
+cache backends in request mode. Enable TeaCache with `--cache-backend tea_cache`
+as shown below, or enable Cache-DiT using the commented settings in that YAML.
+Compilation, DiT quantization, parallelism, and offload are not enabled by
+these presets.
+
+To use step execution and continuous batching, select
+[`mammoth_moda2_step.yaml`](../../vllm_omni/deploy/mammoth_moda2_step.yaml)
+with `--deploy-config vllm_omni/deploy/mammoth_moda2_step.yaml` in the
+text-to-image commands below. This preset inherits the same stage placement
+and capacity, enables step execution for Stage 1, and disables request-batch
+admission waiting and diffusion cache acceleration. Compatible requests can
+join between denoising steps and finish independently, including requests
+with different inference-step counts. Step mode cannot be combined with
+TeaCache, Cache-DiT, or other diffusion cache backends.
 
 Image size, seed, guidance, and denoising steps use the standard diffusion
 request fields. `cfg_range` remains a MammothModa2-specific `extra_body`
@@ -176,7 +188,30 @@ sets the relative step range `[start, end]` over which CFG is applied (default
 `[0.0, 1.0]`). For compatibility, `text_guidance_scale` and
 `num_inference_steps` remain accepted `extra_body` aliases and, when non-null,
 take precedence over the standard request fields. Model extras are filtered
-against the declared `extra_body_params` (see
+against the declared `extra_body_params`.
+
+TeaCache can be enabled for the DiT stage with the same user-facing sampling
+parameters:
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model ./MammothModa2-Preview \
+  --deploy-config vllm_omni/deploy/mammoth_moda2.yaml \
+  --prompt "A stylish woman riding a motorcycle in NYC, movie poster style" \
+  --height 1024 \
+  --width 1024 \
+  --guidance-scale 4.0 \
+  --num-inference-steps 50 \
+  --cache-backend tea_cache \
+  --extra-body '{"cfg_range": [0.0, 1.0]}' \
+  --output mammoth_t2i_teacache.png
+```
+
+The bundled TeaCache coefficients were fitted from MammothModa2 full-compute
+traces. MammothModa2 uses the model-specific default `rel_l1_thresh=0.075`,
+selected for the evaluated 1024x1024, 50-step configuration.
+
+The model-specific keys are declared in
 [`vllm_omni/model_extras/mammothmodal2_preview.py`](../../vllm_omni/model_extras/mammothmodal2_preview.py)),
 so unknown MammothModa2 extras may be dropped.
 
@@ -287,6 +322,44 @@ python3 examples/offline_inference/text_to_image/text_to_image.py \
 
 The first request took 85.224 seconds. The AR stage generated 4,161 visual tokens in 72.996 seconds, and the DiT stage took 12.163 seconds. AR weight loading used 21.4 GiB and took 8.250 seconds. DiT weight loading used 5.49 GiB and took 1.824 seconds. The largest one second whole device memory sample was 106.57 GiB, including the AR KV cache reserved by the 0.5 memory setting.
 
+### 1x A800 80GB, MammothModa2 Preview (serving)
+
+#### Environment
+
+- OS: Linux, x86_64
+- CPU: 18 cores
+- GPU: one NVIDIA A800-SXM4-80GB
+- Deploy config: `vllm_omni/deploy/mammoth_moda2.yaml` (default split: AR `gpu_memory_utilization` 0.5, DiT 0.3)
+- vLLM-Omni version or commit: use the commit you are deploying from
+
+#### Serving Commands
+
+```bash
+vllm-omni serve bytedance-research/MammothModa2-Preview --omni \
+    --port 8091 \
+    --trust-remote-code
+
+python benchmarks/diffusion/diffusion_benchmark_serving.py \
+    --model bytedance-research/MammothModa2-Preview \
+    --endpoint /v1/images/generations \
+    --host 127.0.0.1 --port 8091 \
+    --height 1024 --width 1024 --num-inference-steps 50 \
+    --extra-body '{"text_guidance_scale": 9.0, "cfg_range": [0.0, 1.0]}' \
+    --num-prompts 8 --seed 142 --warmup-requests 0 \
+    --output-file mm2_1024_s50_c1.json
+```
+
+#### Verification
+
+On 1024x1024 / 50 steps, single-concurrency end-to-end mean latency is ~96 s
+(P99 ~101 s) per image and concurrency-4 mean is ~151 s (~0.024 img/s);
+steady-state combined GPU memory is ~50.4 GiB (AR ~39.2 GiB, DiT ~11.1 GiB).
+The AR stage dominates (~77 s of a ~96 s request) because it decodes a fixed
+4,161-token visual grid per image, so latency is insensitive to prompt length
+and scales only partially with DiT step count. See the
+[serving performance dashboard](../../benchmarks/diffusion/performance_dashboard/mammoth_moda2_serving_performance.md)
+for the full sweep, peak-memory, and component-attribution data.
+
 The output was a valid 1024 by 1024 RGB PNG.
 
 ### Startup benchmark

recipes/MiniMaxAI/MiniMax-H3.md

@@ -988,6 +988,42 @@ settings, and both videos with any comparison; the command alone is not quality
 evidence. A matching seed does not imply matching noise over differently sized
 full and windowed latents.
 
+## Request-scoped sampler
+
+H3 supports `euler` (the default) and `res_multistep`. Select the sampler per
+request; it applies to both video and audio, using their separate sigma schedules.
+For the HTTP examples above, set `num_inference_steps=20` and add
+`"sampler":"res_multistep"` to the existing `extra_params` object. For example,
+the T2VA fields become:
+
+```bash
+-F 'num_inference_steps=20' \
+-F 'extra_params={"task":"t2va","duration":8.7,"audio_flow_shift":3.0,"sampler":"res_multistep"}'
+```
+
+For the offline Python API, use `extra_args`:
+
+```python
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+
+sampling_params = OmniDiffusionSamplingParams(
+    num_inference_steps=20,
+    seed=42,
+    extra_args={"task": "t2va", "sampler": "res_multistep"},
+)
+```
+
+Both samplers use one DiT evaluation per step in request mode and step execution.
+`res_multistep` retains the previous denoised prediction independently for each
+request and each stream; its first and final updates use Euler. Omitting `sampler`
+or selecting `euler` preserves the existing Euler update. Reducing the step count
+reduces model evaluations, but quality depends on the prompt and configuration;
+20 RES steps are a candidate to compare against an Euler-50 reference.
+
+Requests using a fixed distilled `base_schedule` from a checkpoint or adapter
+must keep `sampler="euler"`. RES sampling with those schedules has not been
+validated and is rejected with `ValueError`.
+
 ## Request-scoped quality
 
 Add one of these fields to any HTTP request above. No Cache-DiT startup option
@@ -1408,6 +1444,7 @@ four-step adapter keeps its original sampling and fixed-top-k behavior.
 | `duration` | Workload-specific | Decimal seconds in `extra_params`; converted to H3-compatible frame count |
 | `fps` | `24` | H3 output FPS is fixed |
 | `num_inference_steps` | `50` | Matches the reference accuracy workloads |
+| `extra_params.sampler` | `euler` | Per-request `euler` or `res_multistep` for both streams; fixed distilled schedules require Euler |
 | `flow_shift` | `12` | Video sigma shift |
 | `audio_flow_shift` | `3` | Audio sigma shift, passed in `extra_params` |
 | `seed` | Task-specific | Use a fixed value for reproducibility |

recipes/NVIDIA/PersonaPlex.md

@@ -84,8 +84,10 @@ duplex (`is_enabled()` is unconditionally true). Turn-based models
 
 ## Hardware Support
 
-Verified on GPU (Hopper-class). The serving path is plain PyTorch eager plus the
-native modules, so other CUDA GPUs with enough memory are expected to work.
+Verified on GPU (Hopper-class). The shipped deploy config runs the Stage 0
+talker with CUDA graphs and replays the streaming Mimi encode (Stage 0) and
+decode (Stage 1) frame steps from model-local CUDA graphs, bitwise equal to
+eager. Other CUDA GPUs with enough memory are expected to work.
 
 ## GPU
 
@@ -137,9 +139,11 @@ python tests/e2e/online_serving/personaplex_realtime_duplex.py \
 
 #### Notes
 
-- Realtime budget is 80 ms/frame. Verified eager per-tick latency at four
-  concurrent sessions is ~70-74 ms on this hardware class, i.e. all four
-  conversations stay realtime; single-session has comfortable headroom.
+- Realtime budget is 80 ms/frame. The shipped config admits two sessions; on a
+  141 GB card, 64 concurrent 30 s sessions stayed realtime after raising
+  `duplex_session.max_sessions` and both stages' `max_num_seqs` (see the
+  sizing notes in `vllm_omni/deploy/personaplex.yaml`). Longer sessions cost
+  more per step as their context fills.
 - Decoding is greedy only (temperature/top-k knobs are intentionally not
   exposed): greedy is what the parity gates pin against the reference
   implementation, and sampling amplifies sub-bit numeric drift into divergence.

recipes/OpenMOSS/MOSS-TTS.md

@@ -35,9 +35,9 @@ the requested `input` speech. Without a nonblank `ref_text`, Local uses
 audio-reference generation. Reference transcripts must match the reference
 audio; they are not style instructions.
 
-For the optional CUDA MRV2 runner, see the
+For the CUDA MRV2 runner and platform fallbacks, see the
 [Local 1.5 deployment profile](../../docs/configuration/stage_configs.md#moss-tts-local-15-with-model-runner-v2).
-The default Local deployment continues to use V1.
+Local 1.5 defaults to MRV2 on CUDA and retains V1 on other platforms.
 
 ## References
 
@@ -159,8 +159,52 @@ curl -X POST http://localhost:8091/v1/audio/speech \
 
 ## Local 1.5 MRV2 and slot attention
 
-`MOSS-TTS-Local-Transformer-v1.5` supports the native CUDA MRV2 pipeline with
-an explicit deploy profile. The default Local profile continues to use V1.
+`MOSS-TTS-Local-Transformer-v1.5` defaults to the native MRV2 pipeline on
+CUDA, with the original Local projection and sampler. When
+`nvidia-cuda-mps-control` is on `PATH`, it starts private full-quota MPS.
+GPUs with at least 140 GiB total memory use the C128 system profile with
+prefix caching, Triton backbone attention, bounded codec first-chunk decode,
+native Torch sampler compilation and dense codec graph buckets through 128. Its Talker KV budget is 32 GiB; smaller GPUs or a failed memory
+query use C64 with utilization-based memory budgets. When the MPS executable
+is unavailable, the automatic default is C64 MRV2 without MPS. NPU, XPU,
+ROCm and MUSA retain V1. Explicit deploy configs override automatic selection.
+
+```bash
+CUDA_VISIBLE_DEVICES=0 OMP_NUM_THREADS=1 \
+vllm serve OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5 --omni \
+  --stage-init-timeout 1200 --init-timeout 1500
+```
+
+Use `--deploy-config vllm_omni/deploy/moss_tts_local_v1.yaml` to select the
+previous V1 profile explicitly. C128's batch-prefill and direct-token switches
+remain confined to the throughput profile; they are not enabled in C64.
+
+The same `moss_tts_local_mrv2_optimized.yaml` system profile can be selected
+explicitly. It retains the original Local depth projection and sampling algorithm.
+The codec admits fast first chunks below 32 active streams; crowded streams use
+the regular decoder with a dispatch target of 16 and a maximum wait of 6 ms.
+This preserves the low-load first-audio path while coalescing high-load work.
+`local_compile_audio_sampler: true` in the Talker HF overrides compiles the native
+Torch sampler; explicit request generators use the original helper. Set the
+override to `false` in a deployment file to disable that compilation. The setting
+is specific to this C128 system profile.
+Both stages share one GPU, with a 32 GiB Talker KV budget; this profile requires H200-class memory and
+`nvidia-cuda-mps-control` on `PATH`.
+
+```bash
+CUDA_VISIBLE_DEVICES=0 OMP_NUM_THREADS=1 \
+vllm serve OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5 --omni \
+  --deploy-config vllm_omni/deploy/moss_tts_local_mrv2_optimized.yaml \
+  --stage-init-timeout 1200 --init-timeout 1500
+```
+
+Evaluate WER, speaker similarity and streaming latency for your workload;
+throughput gains do not establish quality equivalence. Do not apply partial
+MPS SM quotas: BF16 GEMM outputs were observed incomplete in that configuration
+on the validation environment. MPS uses an owned control socket or an
+explicitly supplied operator socket; only the owned daemon is stopped at shutdown.
+
+The explicit C64 MRV2 and capped-C128 throughput profiles remain available.
 Both profiles below preserve 1-frame initial and 15-frame steady codec chunks
 and the model's sampling defaults.
 

recipes/Qwen/Qwen-Image-2.1.md

@@ -0,0 +1,418 @@
+# Qwen-Image-2.1
+
+> Text-to-image and image-conditioned generation with Qwen-Image 2.1
+
+## Summary
+
+- Vendor: Qwen
+- Model: `Qwen/Qwen-Image-2.1`
+- Task: Text-to-image generation and image-conditioned generation (editing)
+- Mode: Offline inference and online serving with optional step-wise execution
+- Maintainer: Community
+
+> **Model id:** `Qwen/Qwen-Image-2.1` follows the reference diffusers
+> documentation and may change before the official release — the final
+> checkpoint name is subject to the official release.
+
+## When to use this recipe
+
+Use this recipe as a starting point for running `Qwen/Qwen-Image-2.1`. A single
+pipeline class, `QwenImage21Pipeline`, handles both pure text-to-image requests
+(no condition image) and image-conditioned requests with up to 4 condition
+images: prompt and condition images are encoded together by a Qwen3-VL text
+encoder, so editing-style requests go through the same serving path as
+text-to-image.
+
+## References
+
+- Related offline examples:
+  [`examples/offline_inference/text_to_image/text_to_image.py`](../../examples/offline_inference/text_to_image/text_to_image.py),
+  [`examples/offline_inference/image_to_image/image_edit.py`](../../examples/offline_inference/image_to_image/image_edit.py)
+- Supported models table:
+  [`docs/models/supported_models.md`](../../docs/models/supported_models.md)
+- Feature compatibility matrix:
+  [`docs/user_guide/diffusion_features.md`](../../docs/user_guide/diffusion_features.md)
+- Sibling recipes: [`Qwen-Image.md`](./Qwen-Image.md),
+  [`Qwen-Image-Edit.md`](./Qwen-Image-Edit.md)
+
+## Offline Inference
+
+### Text-to-image
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model Qwen/Qwen-Image-2.1 \
+  --prompt "A ceramic teapot on a wooden table" \
+  --output qwen_image_21_t2i.png \
+  --num-inference-steps 50 \
+  --cfg-scale 1.0
+```
+
+### Image-conditioned generation (editing)
+
+```bash
+python examples/offline_inference/image_to_image/image_edit.py \
+  --model Qwen/Qwen-Image-2.1 \
+  --color-format RGBA \
+  --seed 42 \
+  --image qwen_bear.png \
+  --prompt "Let this mascot dance under the moon, surrounded by floating stars" \
+  --output qwen_image_21_edit.png \
+  --num-inference-steps 50 \
+  --cfg-scale 1.0
+```
+
+or via the bundled launcher:
+
+```bash
+bash examples/offline_inference/image_to_image/run_qwen_image_21.sh
+```
+
+Up to 4 condition images can be passed via `--image`:
+
+```bash
+python examples/offline_inference/image_to_image/image_edit.py \
+  --model Qwen/Qwen-Image-2.1 \
+  --color-format RGBA \
+  --seed 42 \
+  --image input1.png input2.png \
+  --prompt "Combine these images into a single scene" \
+  --output qwen_image_21_multi.png \
+  --num-inference-steps 50 \
+  --cfg-scale 1.0
+```
+
+Use `--color-format RGBA` to preserve transparency in condition images. The
+shared example otherwise loads inputs as RGB. Add `--width` and `--height`
+(multiples of 32) to set the output size; when omitted, Qwen-Image 2.1 derives
+it from the last condition image's aspect ratio at approximately 1024×1024.
+Use `--num-outputs-per-prompt` to generate multiple images and `--vae-use-tiling`
+to reduce VAE memory usage. The launcher accepts these options as well.
+
+## Online Serving
+
+Start the server:
+
+```bash
+vllm serve Qwen/Qwen-Image-2.1 --omni --port 8091
+```
+
+To enable the step-wise runtime:
+
+```bash
+vllm serve Qwen/Qwen-Image-2.1 --omni \
+  --port 8091 \
+  --step-execution \
+  --max-num-seqs 8
+```
+
+Step execution batches compatible requests at the same KV-cache phase. The
+pipeline tags every request with a `batch_compatibility_key` (conditioning
+mode plus condition-image layout), so a newly arriving request that would
+otherwise join a batch already past its first denoising step is isolated by
+the scheduler and deferred until the in-flight batch drains — it no longer
+fails the whole batch. `--max-num-seqs` controls batch capacity.
+
+### CUDA Graph decode
+
+Qwen-Image-2.1 automatically uses CUDA Graph for supported fixed-shape denoising
+decode steps when `enable_cuda_graph_decode=True` (the default) and
+`enforce_eager=False`. Prefill remains eager. This works with both request
+execution and `--step-execution`.
+
+To disable graph capture while keeping the rest of the compilation setup:
+
+```bash
+vllm serve Qwen/Qwen-Image-2.1 --omni --no-enable-cuda-graph-decode
+```
+
+The same control is available as `Omni(..., enable_cuda_graph_decode=False)`
+and as `enable_cuda_graph_decode: false` on a stage in the deployment YAML.
+`--enforce-eager` / `enforce_eager: true` remains the broader switch: it
+disables both graph capture and automatic `torch.compile`. The two
+optimizations stack: the DiT blocks are regionally `torch.compile`'d and
+graph capture records the compiled (fused) kernels, while inductor's own
+cudagraphs stay disabled so the two graph layers never nest. Measured on a
+single H200 (1024×1024, 50 steps, seed 42, BF16, `true_cfg_scale=1.0`,
+warmup 1 + median of 3): compile+graph **5.93 s**, compile-only **6.88 s**,
+graph-only **9.52 s**, eager **8.28 s** end-to-end per image; combo and
+compile-only vs eager are **38.10 dB** PSNR (compile fusion drift), and
+graph-only vs eager is bit-identical.
+
+The autoregressive engine's `compilation_config.cudagraph_mode` does not
+control this diffusion path; use `enable_cuda_graph_decode` (or
+`enforce_eager` for everything) to force eager decode execution.
+
+TP/SP/ring parallelism, HSDP, cache hooks, quantized KV caches,
+dynamic LoRA, padded text masks, and a second in-flight request whose cache
+aliases an already-owned graph key fall back to eager decode.
+Model-level CPU offload does not disable decode graphs while
+`enable_cuda_graph_decode` stays on (the default). DiT weights stay on fixed
+device storage so captured pointers remain valid, and that storage is not
+released while the text encoder runs. Pass `--no-enable-cuda-graph-decode`
+(or `enable_cuda_graph_decode: false`) for the eager offload path, which
+moves DiT weights off the device and frees that VRAM. Peak memory for the
+default graph-plus-offload combination was not re-measured in this PR.
+Graphs keep separate entries for different image layouts and copy
+request-owned prefix K/V into static buffers before replay; each graph key
+has a single live owner at a time, so concurrent same-shape requests never
+overwrite each other's prefix. Those buffers require additional memory; graph
+entries are bounded by the transformer's cache limit.
+
+### Verification
+
+For a direct API smoke test:
+
+```bash
+curl http://localhost:8091/v1/images/generations \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Qwen/Qwen-Image-2.1",
+    "prompt": "A ceramic teapot on a wooden table",
+    "size": "1024x1024",
+    "num_inference_steps": 50,
+    "true_cfg_scale": 1.0,
+    "seed": 42
+  }'
+```
+
+For image-conditioned generation, pass condition images through the
+OpenAI-compatible multimodal endpoint (`/v1/chat/completions` with
+`image_url` content parts, or `/v1/images/edits`), same as
+[`Qwen-Image-Edit.md`](./Qwen-Image-Edit.md).
+
+## Parameters
+
+| Parameter | Default | Notes |
+| --- | --- | --- |
+| `true_cfg_scale` | 1.0 | Disables the negative-prompt branch by default. Set above 1 with a `negative_prompt` to enable true CFG. Qwen-Image 2.1 supports true CFG only — there is no `guidance_scale` parameter. |
+| `num_inference_steps` | 50 | Number of denoising steps. |
+| `height` / `width` | 1024×1024 | Must be multiples of 32. For image-conditioned requests, leaving them unset derives the output size from the last condition image's aspect ratio at ~1024×1024. |
+| `negative_prompt` | None | Ignored at the default CFG scale of 1.0; used only when `true_cfg_scale > 1`. |
+| condition images | — | Up to 4 input images per request; more than 4 raises an error. |
+| `seed` | — | Fix for reproducible outputs. |
+
+The default CFG scale of 1.0 runs only the positive-prompt branch at each
+denoising step. To enable negative-prompt guidance, explicitly set
+`--cfg-scale` (offline) or `true_cfg_scale` (API) above 1 and provide a
+negative prompt. This changes the generated output as well as compute cost.
+
+### Prefix KV cache
+
+The transformer caches the text/condition-image prefix across denoising steps
+(a prefill/decode split keyed by CFG branch). The cache is enabled
+automatically when the checkpoint declares `causal_condition: true` and is
+re-created per generation, so no state leaks between requests. It changes the
+compute path — cached prefix vs. full recompute at every step — so exact
+numerical reproduction against a no-cache reference (e.g. the diffusers
+pipeline) requires matching the cache setting on both sides. The prefix KV
+cache is orthogonal to weight quantization and works unchanged under FP8.
+
+### FP8 quantization
+
+Online FP8 quantization of the transformer is supported; the fused
+`to_qkv` projections load correctly, and the prefix KV cache path is
+unaffected. Only block-internal linears are eligible — the boundary
+projections (`img_in`/`txt_in`/`modulation`/`proj_out`/`time_text_embed`) stay
+in BF16 by construction:
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model Qwen/Qwen-Image-2.1 \
+  --prompt "A ceramic teapot on a wooden table" \
+  --output qwen_image_21_fp8.png \
+  --num-inference-steps 50 --cfg-scale 1.0 \
+  --quantization fp8 --ignored-layers "img_mlp"
+```
+
+`ignored_layers` entries are matched as name patterns for this model (e.g.
+`img_mlp` matches every block's `img_mlp.proj`/`img_mlp.gate_layer`/
+`img_mlp.out`); exact full prefixes like
+`transformer.transformer_blocks.3.attn.to_qkv` also work.
+
+A per-module sensitivity sweep (fixed prompt embeds / latent / timestep, 5
+timesteps across the trajectory, relative L2 of the noise prediction vs BF16)
+ranks the groups: `img_mlp` is the most sensitive (3.8% max error on its own,
+driven by `img_mlp.proj` 2.8% and `img_mlp.out` 2.5%), then `attn.to_out`
+(2.7%), then `attn.to_qkv` (1.6%); errors are largest at the late (low-sigma)
+steps. End-to-end on H200, single GPU, same prompt as Quality Comparison
+(`"A ceramic teapot on a wooden table"`, 1024×1024, 50 steps, seed 42,
+eager):
+
+| Config | Quantized block linears | PSNR vs BF16 | Steady-state time/image | Peak memory |
+| --- | --- | --- | --- | --- |
+| BF16 | 0 / 160 | — | **7.34 s** | **36.9 GB** |
+| FP8 all layers | 160 / 160 | **33.0 dB** | **6.06 s** | **30.4 GB** |
+| FP8, `ignored_layers=["img_mlp"]` | 64 / 160 | **34.3 dB** | **6.98 s** | **34.9 GB** |
+
+Keeping `img_mlp` in BF16 is still the higher-fidelity DiT setting vs
+all-layer FP8. On H200, all-layer FP8 is both leaner and faster than BF16
+at this shape.
+
+## Quantization
+
+Online FP8 is supported per component via `quantization_config`:
+
+```python
+from vllm_omni import Omni
+
+omni = Omni(
+    model="Qwen/Qwen-Image-2.1",
+    quantization_config={
+        # DiT: skip the sensitive image-stream MLPs if quality regresses.
+        "transformer": {"method": "fp8", "ignored_layers": ["img_mlp"]},
+        # Qwen3-VL text encoder: only the language-model linear layers are
+        # quantized; the vision tower and the (unused) LM head stay BF16.
+        "text_encoder": {"method": "fp8"},
+    },
+)
+```
+
+Either component can be quantized on its own. Measured on H200 at 1024×1024
+(seed 42, 50 steps, same teapot prompt, eager): text-encoder FP8 lowers peak
+GPU memory from **36.9 GB** (BF16) to **30.4 GB** (−6.5 GB); T2I PSNR vs BF16
+**32.1 dB**. DiT FP8-all is **30.4 GB** / **6.06 s**. Combined DiT+text-encoder
+FP8 was not re-measured on H200. The edit path routes condition images through
+the BF16 vision tower, so it is unaffected by text-encoder FP8. See
+[`docs/user_guide/quantization/fp8.md`](../../docs/user_guide/quantization/fp8.md)
+for the scope rules.
+
+### FP8 prefix KV storage
+
+The cached prefix K/V can be stored in FP8 E4M3 (with per-token-per-head fp32
+scales), halving the prefix-cache memory — relevant for long prompts (up to
+8192 text tokens) and up to 4 condition images (~4096 latent tokens each).
+Prefill quantizes once; decode dequantizes per step, so attention compute is
+unchanged. Enable it through the model-specific `extras` config (e.g. in the
+deploy YAML's stage `extras` or as an `Omni(...)` engine kwarg):
+
+```yaml
+extras:
+  prefix_kv_cache_dtype: "fp8"
+```
+
+Accepted values: `"fp8"` / `"fp8_e4m3"` (K and V quantized), `"fp8_v"` (V
+only — K stays in the native dtype), and `None` / `"auto"` (default, native
+dtype — behavior unchanged). This is independent of
+`diffusion_kv_cache_dtype`, which quantizes attention Q/K/V *compute* per
+forward pass on supported backends.
+
+Measured on one NVIDIA H200, T2I 1024x1024 (seed 42, 50 steps, true CFG 4.0,
+eager, PSNR vs. the bf16 baseline on the same GPU): **`"fp8"` 34.2 dB**,
+**`"fp8_v"` 44.0 dB**. The error is dominated by
+K quantization: post-RoPE keys are the precision-sensitive half of the
+cache, so `"fp8_v"` buys back about 9.8 dB at 75% (instead of 50%) of the original
+cache size. The residual error is inherent e4m3 precision accumulated
+coherently over the denoising trajectory; finer scale granularity
+(per-tensor/per-head/per-token were compared) or Hadamard-rotated V did not
+improve it. The saving scales with prefix length. These sizes are calculated at the
+limit (8192 text tokens + 4 condition images, ~24.6k prefix tokens), not
+measured on the 1024 T2I run above: ~12.9 GB per CFG branch in bf16, ~6.6 GB
+in `"fp8"` and ~9.7 GB in `"fp8_v"`. That is where this option matters. Treat it as an opt-in for
+memory-bound long-prompt / multi-image workloads, not a free lunch;
+`"fp8_v"` is the better default trade-off when quality matters.
+
+Note: a quantized prefix cache is not CUDA-graph capturable — requests using
+this option stay on the eager decode path (a warning is logged once).
+
+### Why not the scheduler-managed paged KV (`DiffusionKVCacheMode.PAGED_SCHEDULER`)?
+
+Evaluated and deliberately not adopted (see the `qwen21-p0-kv` commit message
+for the full analysis): the paged scheduler targets *cross-request* KV
+management — a statically sized page pool, block sharing/dedup, and eviction —
+whereas this prefix cache lives exactly as long as one generation and is
+sized once per request. Migrating would require routing decode attention
+through the worker paged adapter (writing fresh target K/V into pages every
+step), per-request page tables, and re-validating the block-causal
+piecewise-span masking and Ulysses/CFG constraints, for no memory benefit over
+the per-request dense tensors. The FP8 storage above captures the actual
+memory win at ~1% of the integration cost.
+
+## Quality Comparison (head vs diffusers)
+
+The reference is the diffusers Qwen-Image 2.1 pipeline
+([huggingface/diffusers#14804](https://github.com/huggingface/diffusers/pull/14804)).
+Both sides must pin the same checkpoint, inputs, seed, precision (BF16), and
+hardware.
+
+vLLM-Omni (this recipe):
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+  --model Qwen/Qwen-Image-2.1 \
+  --prompt "A ceramic teapot on a wooden table" \
+  --seed 42 \
+  --output qwen_image_21_vllm_omni.png \
+  --num-inference-steps 50 \
+  --cfg-scale 1.0
+```
+
+diffusers reference:
+
+```python
+import torch
+from diffusers import QwenImage21Pipeline  # diffusers PR #14804
+
+pipe = QwenImage21Pipeline.from_pretrained(
+    "Qwen/Qwen-Image-2.1", torch_dtype=torch.bfloat16
+).to("cuda")
+image = pipe(
+    prompt="A ceramic teapot on a wooden table",
+    num_inference_steps=50,
+    true_cfg_scale=1.0,
+    generator=torch.Generator(device="cuda").manual_seed(42),
+    height=1024,
+    width=1024,
+).images[0]
+image.save("qwen_image_21_diffusers.png")
+```
+
+| Metric | vLLM-Omni | diffusers (#14804) | Status |
+| --- | --- | --- | --- |
+| Output image (1024×1024, 50 steps, seed 42, BF16, H200) | generated (eager + default CUDA-graph) | generated | measured |
+| PSNR / LPIPS vs reference | eager vs Diffusers **47.17 dB**; **default compile+graph** vs Diffusers **38.02 dB**; **default compile+graph** vs eager **38.10 dB** | — | measured (H200) |
+| Latency (time/image, same GPU, H200) | **default compile+graph** **6.04 s** (120.8 ms/step); eager **7.31 s** (146.2 ms/step) | **7.74 s** (154.9 ms/step) | measured |
+
+The prefix KV cache changes the compute path (cached prefix vs. full recompute
+at every step), so exact numerical parity is not expected; match the cache
+setting on both sides or compare with PSNR/LPIPS rather than exact pixels.
+Eager stays numerically close to Diffusers (~47 dB). The **default** serving
+path stacks regional `torch.compile` with CUDA-graph decode; on H200 that
+combo lands at **~38.1 dB** vs eager (~1.21× on the Quality Comparison timed
+run). Graph-only vs eager is bit-identical; the ~38 dB drift is the compile
+fusion, not CUDA-graph replay. See "CUDA Graph decode" for the four-mode
+H200 breakdown (warmup 1 + median of 3).
+
+Parity / feature measurements (same prompt/seed/steps/CFG, BF16, eager decode
+unless noted; 4× NVIDIA H200):
+
+- [x] TP=2/4 output parity vs single-card — measured (1024×1024): TP1 vs TP2
+  **39.32 dB**, TP1 vs TP4 **38.37 dB**, TP2 vs TP4 **45.77 dB**. Latency:
+  TP1 **7.30 s**, TP2 **4.89 s**, TP4 **5.12 s** per image.
+- [x] VAE tiling vs non-tiled decode — measured: 1024×1024 nontile vs
+  `--vae-use-tiling` **43.55 dB** (peak mem ~37.7 GB → ~32.7 GB); 2048×2048
+  nontile vs tiled **44.14 dB** (peak mem ~57.9 GB → ~33.3 GB; gen ~36.8 s vs
+  ~37.1 s).
+- [x] CUDA-graph four-mode vs eager — measured on H200 (1024×1024, 50 steps,
+  seed 42, BF16, warmup 1 + median of 3): compile+graph **5.93 s**,
+  compile-only **6.88 s**, graph-only **9.52 s**, eager **8.28 s**. Combo /
+  compile-only vs eager **38.10 dB**; graph-only vs eager bit-identical.
+  Quality Comparison table uses a separate single-run A/B vs Diffusers
+  (compile+graph **6.04 s**, eager **7.31 s**).
+
+## Known Limitations
+
+- Cache acceleration backends (`cache_dit`, `tea_cache`) are not supported;
+  `QwenImage21Pipeline` is registered in `_NO_CACHE_ACCELERATION`.
+- Ulysses sequence parallelism is wired; ring attention is not a supported path.
+  A short H200 smoke (1024×1024, 8 steps, seed 42, true CFG 1.0, eager) finished:
+  Ulysses SP=2 vs SP=1 is **46.5 dB**. That size's target length is divisible by 4,
+  so SP padding was not exercised, and this is not the 50-step parity next to the
+  TP tables. The features matrix therefore stays ❓. Ring SP=2 also ran, at
+  **29.9 dB** vs SP=1, which is why it stays unsupported.
+- Only true CFG is exposed; there is no `guidance_scale` knob.
+- VAE tiling (`--vae-use-tiling`, also implied by `--vae-patch-parallel-size > 1`)
+  decodes in 512px tiles with 384px stride (32 latent pixels per tile at 16x
+  spatial compression). On CUDA OOM the tile size is automatically halved
+  (keeping 25% overlap) and decoding retried, down to 128px tiles. Tile sizes
+  are not yet exposed as engine arguments.

recipes/Qwen/Qwen3-Omni.md

@@ -73,8 +73,20 @@ vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
     "1": {"max_num_seqs": 4},
     "2": {"max_num_seqs": 4}
   }'
+
+# Replay the thinker's image encoder from CUDA graphs (images only)
+vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091 \
+  --stage-overrides '{"0": {"compilation_config": {"cudagraph_mm_encoder": true}}}'
 ```
 
+With the encoder graphs on, the thinker captures one graph per token budget
+(64 to 256 image tokens and up to 4 images per replay by default). A step's
+images replay together when one captured graph holds them all; otherwise they
+run through the eager encoder in one call. Set `encoder_cudagraph_token_budgets`
+and `encoder_cudagraph_max_vision_items_per_batch` in the same
+`compilation_config` to change them; each captured budget adds graph memory on
+the thinker's GPU.
+
 ### Stage-based launch (one stage per process)
 
 Use three terminals (one per stage). Start with the default commands below, then

recipes/README.md

@@ -64,8 +64,12 @@ recipes/
 | [`MiniMaxAI/MiniMax-H3-Spark-GB10.md`](./MiniMaxAI/MiniMax-H3-Spark-GB10.md) | MiniMax H3 T2VA and Ref2VA on DGX Spark | 1x or 2x DGX Spark (GB10) |
 | [`MiniMaxAI/MiniMax-H3-MUSA.md`](./MiniMaxAI/MiniMax-H3-MUSA.md) | MiniMax H3 T2VA, FL2VA, and Ref2VA on MUSA | 4x MTT S5000 (TP4 Ref2VA profile) |
 | [`krea/Krea-2.md`](./krea/Krea-2.md) | Text-to-image (Turbo + Raw), offline + online, with LoRA | 1x H100 80GB / 1x Intel Arc Pro B70 32GB |
+| [`Kandinsky/Kandinsky6-TI2VA.md`](./Kandinsky/Kandinsky6-TI2VA.md) | Text/image-to-video-and-audio, offline and `/v1/videos` | 1x H100 80GB with CPU offload; 2x H100 for parallel modes |
 | [`LTX/LTX-2.md`](./LTX/LTX-2.md) | LTX-2/LTX-2.3 text-to-video and image-to-video with synchronized audio | H200 141GB / 96GB-class GPU / 1x Intel Arc Pro B70 32GB |
 | [`LTX/LTX-2.5.md`](./LTX/LTX-2.5.md) | LTX-2.5-Diffusers: Full/SFT one-stage and distilled two-stage T2V/I2V with synchronized audio | NVIDIA B300; cuDNN-qualified / 1x Intel Arc Pro B70 32GB |
+| [`JD/JoyAI-Image-Edit.md`](./JD/JoyAI-Image-Edit.md) | Text-guided single-image editing | 1x H800 PCIe 80GB |
+| [`m-a-p/SheetSage2-H200.md`](./m-a-p/SheetSage2-H200.md) | Official audio-to-score preprocessing and YuE2 request export | 1x H200 141GB |
+| [`m-a-p/YuE2-3B.md`](./m-a-p/YuE2-3B.md) | Offline and online text-to-music with optional ABC score | 1x RTX 4090 24GB |
 | [`MammothModa2/MammothModa2.md`](./MammothModa2/MammothModa2.md) | Preview and Dev text-to-image (AR → DiT); Dev text/image understanding | Preview: 1x L40S 48GB / 1x ≥40GB GPU; Dev: 1x NVIDIA GPU with sufficient cache headroom |
 | [`NVIDIA/SANA-Video-2B.md`](./NVIDIA/SANA-Video-2B.md) | Native and Diffusers-adapter 480p/720p T2V+I2V | 1x RTX 5090 32GB |
 | [`meituan-longcat/LongCat-Video-Avatar-1.5.md`](./meituan-longcat/LongCat-Video-Avatar-1.5.md) | Audio-driven avatar video generation (AT2V / AI2V, single- and multi-speaker, AVC continuation) | 1x H100 80GB |
@@ -84,6 +88,7 @@ recipes/
 | [`NVIDIA/NemotronLabs-VoiceChat.md`](./NVIDIA/NemotronLabs-VoiceChat.md) | Offline speech-to-speech voice chat (11B, frame-locked 12.5 Hz, 3-stage thinker/talker/code2wav) | 1x H100 80GB |
 | [`NVIDIA/SANA-WM.md`](./NVIDIA/SANA-WM.md) | First-frame image-to-video serving with camera control | 1x 24GB+ CUDA GPU; ~40GB disk |
 | [`Qwen/Qwen-Image.md`](./Qwen/Qwen-Image.md) | Text-to-image serving with step-wise continuous batching replay and ModelOpt mixed FP8/NVFP4; offline text-to-image on XPU | 1x A100 80GB / 2x B200 / 1x Intel Arc Pro B70 32GB |
+| [`Qwen/Qwen-Image-2.1.md`](./Qwen/Qwen-Image-2.1.md) | Text-to-image and image-conditioned generation (editing) with prefix KV cache and CUDA graph decode | 1x GPU with CPU offload / 1x H200 |
 | [`Qwen/Qwen-Image-2512.md`](./Qwen/Qwen-Image-2512.md) | Text-to-image serving with step-wise continuous batching replay and ModelOpt FP8 / mixed FP8/NVFP4 | 1x A800 80GB / 2x B200 |
 | [`Qwen/Qwen-Image-Edit.md`](./Qwen/Qwen-Image-Edit.md) | Text-guided single-image editing | 1x or 2x H200 141GB |
 | [`Qwen/Qwen3-Omni.md`](./Qwen/Qwen3-Omni.md) | Online serving for multimodal chat; offline end-to-end text-to-speech on XPU | 1x A100 80GB / 5x Intel Arc Pro B70 32GB |

recipes/Wan-AI/Wan2.2-I2V.md

@@ -80,9 +80,13 @@ parallelism at `--usp 4` so the two CFG branches cover all 8 GPUs.
 - `--use-hsdp` is recommended for memory efficiency on the 14B DiT backbone.
 - `--vae-patch-parallel-size 8` is recommended for the standard 8-card setup;
   disabling VAE patch parallelism can significantly increase VAE latency.
-- The CUDA Triton fused AdaLayerNorm path is not available in the current
-  release. The `VLLM_OMNI_ENABLE_TRITON_ADALN` toggle is reserved for that
-  future implementation and should not be used until the kernel lands.
+- CUDA eager execution uses the fused Triton AdaLayerNorm path for supported
+  contiguous 3D inputs with shared `(C,)`, `(1, C)`, or `(1, 1, C)` modulation,
+  or per-sample `(B, 1, C)` modulation, including supported strided chunk views.
+  Unsupported inputs use the native path; per-token `(B, L, C)` modulation
+  used by TI2V remains native.
+- During `torch.compile`, AdaLayerNorm uses the native operation chain, which
+  Inductor can fuse. The eager Triton path remains available after compilation.
 - In an 8x NVIDIA H20 test with the official model, `num_inference_steps=4`,
   `flow_shift=12.0`, `--use-hsdp`, `--cfg-parallel-size 2`, `--usp 4`, and
   `--vae-patch-parallel-size 8`, an internal Triton AdaLayerNorm prototype
@@ -181,20 +185,20 @@ for complete client examples and request formats.
 #### Notes
 
 - **Key flags:**
-  - `--omni` — enables vLLM-Omni diffusion serving.
-  - `--use-hsdp` — enables Hybrid Sharded Data Parallelism for the DiT model
+    - `--omni` — enables vLLM-Omni diffusion serving.
+    - `--use-hsdp` — enables Hybrid Sharded Data Parallelism for the DiT model
     weights.
-  - `--usp <N>` — Unified Sequence Parallelism degree.
-  - `--cfg <N>` — Classifier-Free Guidance parallelism; set to 2 for models
+    - `--usp <N>` — Unified Sequence Parallelism degree.
+    - `--cfg <N>` — Classifier-Free Guidance parallelism; set to 2 for models
     that require negative-prompt computation, omit for distilled models.
-  - `--vae-patch-parallel-size 8` — parallelizes VAE decoding across all 8
+    - `--vae-patch-parallel-size 8` — parallelizes VAE decoding across all 8
     cards.
-  - `--vae-use-tiling` — enables tiled VAE decoding to reduce peak memory.
+    - `--vae-use-tiling` — enables tiled VAE decoding to reduce peak memory.
 - **Performance tips:**
-  - Installing mindie-sd and enabling Laser Attention
+    - Installing mindie-sd and enabling Laser Attention
     (`MINDIE_SD_FA_TYPE=ascend_laser_attention`) provides up to ~40%
     performance improvement at 720p resolution due to long-sequence attention
     optimization.
 - **Known limitations:**
-  - `MULTI_STREAM_MEMORY_REUSE=2` is required on NPU when using HSDP/FSDP2
+    - `MULTI_STREAM_MEMORY_REUSE=2` is required on NPU when using HSDP/FSDP2
     due to a multi-stream memory reuse bug. This is not needed on CUDA.

recipes/m-a-p/SheetSage2-H200.md

@@ -0,0 +1,259 @@
+# SheetSage2 audio-to-score preprocessing — H200
+
+## Summary
+
+- Vendor: m-a-p
+- Model: `m-a-p/SheetSage2` with the `m-a-p/MERT-v2-FullSong` parent
+- Task: recording → ABC score, MIDI and timed music annotations
+- Mode: standalone official Transformers inference; optional YuE2 request export
+- Hardware: one NVIDIA H200 (141 GB)
+- Maintainer: [@princepride](https://github.com/princepride)
+
+## When to use this recipe
+
+Use a recording as the musical score for a YuE2 cover, or inspect its melodies,
+chords and structure. The tool calls SheetSage2's official `transcribe()` API.
+It does not register SheetSage2 as a native vLLM model or start an Omni server.
+
+The transcription tool works independently of YuE2. For score-conditioned
+generation, use an Omni checkout containing the merged
+[PR #7886](https://github.com/vllm-project/vllm-omni/pull/7886)
+(`423f34326ed420e5acf0b1fb862a1b5ffb7e0fa7` or later). See the
+[YuE2 recipe](YuE2-3B.md) for the generation model's runtime contract.
+
+## Supported model contract
+
+| Item | Contract |
+| --- | --- |
+| Input | One local recording decodable by FFmpeg; upstream converts it to mono, 24 kHz |
+| Duration | Entire recording by default; `--max-seconds` limits preprocessing to a prefix |
+| Full score | Default: melody voices and chords; exported request uses `cot=full` |
+| Melody score | `--melody-only`: vocal and instrumental melodies, without score/playback chords; request uses `cot=melody` |
+| Outputs | `score.abc`, `transcription.mid`, upstream annotations and `manifest.json` |
+| YuE2 handoff | `--lyrics-file` and `--style` together also write `yue2_request.json` |
+| Output directory | Must be new or empty; failed exports produce no YuE2 request |
+| Deployment profile | One process, one H200, FP32 weight loading and BF16 inference autocast |
+
+`--melody-only` does not isolate or clone a singer. The upstream model still
+decodes its task annotations and retains those raw annotation files. YuE2
+uses the exported score to condition newly generated audio; a transcription
+does not guarantee an exact reproduction of the recording.
+
+## References
+
+- [Official SheetSage2 model card](https://huggingface.co/m-a-p/SheetSage2)
+- [Pinned SheetSage2 source](https://huggingface.co/m-a-p/SheetSage2/tree/cafc0df1021e14f49e928c4b345f5959d414ef64)
+- [Official YuE2 model card and cover documentation](https://huggingface.co/m-a-p/YuE2-3B)
+- Tool: [`tools/sheetsage2_transcribe.py`](../../tools/sheetsage2_transcribe.py)
+- Offline generation: [`examples/offline_inference/yue2/end2end.py`](../../examples/offline_inference/yue2/end2end.py)
+- [Supported generation models](../../docs/models/supported_models.md)
+
+## Hardware
+
+- Accelerator: one NVIDIA H200, 141 GB; no multi-device interconnect required.
+- Qualification: local M4A transcription, melody-only prefix, full-score
+  whole-song export and one offline YuE2 cover. Run transcription and generation
+  sequentially in separate processes. Other devices, CPU inference and
+  performance scaling are not qualified by this recipe.
+
+## Software environment
+
+- Ubuntu 22.04, Python 3.10, NVIDIA driver 590.48.01.
+- PyTorch/torchaudio 2.8.0 (CUDA 12.8 wheels), Transformers 4.45.2.
+- FFmpeg 4.4.2 was used for the local M4A check; upstream recommends FFmpeg 6.1.
+- vLLM: not required in the preprocessing environment.
+- vLLM-Omni: tool developed against main `a038b38179e9c788d3af6a8e73f94652afb247e4`.
+- YuE2 qualification: vLLM 0.30.0, PyTorch 2.13.0+cu130, Transformers 5.14.1;
+  #7886 at `5cc8acc942e76fa6f12f8b2f2558c2663abc2aa5` with the FP32 VAE-loading
+  and scalar `truncated` fixes subsequently included in the merged integration.
+
+## Command
+
+### Set up transcription
+
+Run from this repository's root. Create a **separate environment**: the
+official transcriber's Transformers version differs from Omni's serving
+dependencies. FFmpeg must be on `PATH`.
+
+```bash
+uv venv --python 3.10 .venv-sheetsage2
+uv pip install --python .venv-sheetsage2/bin/python \
+  -r tools/requirements/sheetsage2.txt
+
+.venv-sheetsage2/bin/python tools/sheetsage2_transcribe.py reference.wav \
+  --trust-remote-code --device cuda:0 --melody-only \
+  --output-dir outputs/reference-score
+```
+
+The default Hub checkpoint and its custom Python code are pinned to
+`cafc0df1021e14f49e928c4b345f5959d414ef64`. Its configuration pins the MERT-v2
+parent to `d8ba1c745e733b3908ce6ad16ebeb17ac7600a42`. Review that code before
+passing `--trust-remote-code`. `--revision` can override the default pin;
+custom Hub IDs use their default revision unless one is supplied.
+
+Local snapshots must preserve this pinned source layout: `modeling_sheetsage2.py`
+and its transitive relative-import dependencies are sibling `.py` files in the
+snapshot root. The local module-cache workaround targets that flat layout;
+reorganized snapshots with nested Python packages are not supported by it.
+
+For offline use, download **both** model snapshots, including their Python,
+JSON and safetensors files, before disconnecting:
+
+```bash
+.venv-sheetsage2/bin/hf download m-a-p/SheetSage2 \
+  --revision cafc0df1021e14f49e928c4b345f5959d414ef64 \
+  --local-dir models/SheetSage2
+.venv-sheetsage2/bin/hf download m-a-p/MERT-v2-FullSong \
+  --revision d8ba1c745e733b3908ce6ad16ebeb17ac7600a42 \
+  --local-dir models/MERT-v2-FullSong
+```
+
+### Transcribe a full recording for a cover
+
+Supply target lyrics in `lyrics.txt`, including YuE2 section tags such as
+`[Verse]` and `[Chorus]`. Match the lyric phrasing to the reference melody.
+This example retains the melody and chords of the whole recording:
+
+```bash
+.venv-sheetsage2/bin/python tools/sheetsage2_transcribe.py reference.wav \
+  --model models/SheetSage2 --base-model-path models/MERT-v2-FullSong \
+  --local-files-only --trust-remote-code --device cuda:0 \
+  --lyrics-file lyrics.txt --style 'Chinese folk, guzheng, gentle vocals' \
+  --seed 831001 --output-dir outputs/cover-score
+```
+
+Add `--melody-only` for a new arrangement without the original chords;
+this also changes the exported request from `cot=full` to `cot=melody`.
+For a short transcription smoke test, add `--max-seconds 45` and use lyrics
+appropriate to that excerpt. Use a fresh output directory for each run.
+
+### Generate offline
+
+Switch to your **Omni environment** for generation. Download `m-a-p/YuE2-3B`
+and `m-a-p/YuE2-Vae` to `models/YuE2-3B` and `models/YuE2-Vae`, respectively.
+The offline entrypoint needs a local YuE2 directory containing `qwen.tiktoken`.
+From the repository root, pass the exported ABC to the existing entrypoint:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python examples/offline_inference/yue2/end2end.py \
+  --model models/YuE2-3B --vae models/YuE2-Vae \
+  --lyrics "$(cat lyrics.txt)" \
+  --style 'Chinese folk, guzheng, gentle vocals' \
+  --abc-file outputs/cover-score/score.abc --cot full \
+  --seed 831001 --max-frames 9000 --gpu-memory-utilization 0.25 \
+  --output cover.wav
+```
+
+Use `--cot melody` if the score was transcribed with `--melody-only`.
+Changing `--cot` alone does not remove chords from an existing score.
+The 9,000-frame budget permits up to 360 seconds at 25 frames/s; it does
+not force that duration. The entrypoint's default 200-frame budget caps
+audio at 8 seconds. Check that the final report says `truncated=False`.
+The memory fraction above was used on one H200; it is not a measured
+minimum memory requirement.
+
+### Submit over HTTP (configuration-only)
+
+The exported JSON can also be submitted to YuE2's speech endpoint. These
+commands have not been qualified with an HTTP synthesis run in this recipe.
+Start YuE2 in its **Omni environment**:
+
+```bash
+vllm serve m-a-p/YuE2-3B --omni --port 8091
+```
+
+Submit the exported file:
+
+```bash
+curl --fail-with-body http://localhost:8091/v1/audio/speech \
+  -H 'Content-Type: application/json' \
+  --data-binary @outputs/cover-score/yue2_request.json \
+  --output cover.wav
+```
+
+The JSON contains the score itself, not a server-local file path. It sets
+`stream=false`, requests WAV, and preserves an optional generation seed.
+Use `--yue2-model` to match a custom served model name. The tool does not
+submit the request or set a generation token budget; the server's own
+duration and context limits apply.
+
+## Verification
+
+After a successful transcription, set `output` below to its output directory
+(`outputs/reference-score` for the transcription-only example). The YuE2
+request is checked when exported with `--lyrics-file` and `--style`:
+
+```bash
+.venv-sheetsage2/bin/python - <<'PY'
+import json
+from pathlib import Path
+import mido
+
+output = Path("outputs/cover-score")
+abc = (output / "score.abc").read_text(encoding="utf-8")
+manifest = json.loads((output / "manifest.json").read_text(encoding="utf-8"))
+assert abc.strip()
+request_path = output / "yue2_request.json"
+if request_path.exists():
+    request = json.loads(request_path.read_text(encoding="utf-8"))
+    assert request["extra_params"]["abc"] == abc
+    assert request["extra_params"]["cot"] == ("melody" if manifest["melody_only"] else "full")
+    print("YuE2 request verified")
+midi = mido.MidiFile(output / "transcription.mid")
+assert any(msg.type == "note_on" and msg.velocity for track in midi.tracks for msg in track)
+print("ABC, MIDI and manifest verified")
+PY
+```
+
+Run the CPU CLI contract tests in the repository's normal test
+environment (with pytest and pytest-mock installed):
+
+```bash
+python -m pytest tests/tools/test_sheetsage2_transcribe.py -m 'core_model and cpu' -q
+```
+
+These tests cover request construction, loading options, validation and
+failure handling with a mocked backend. They do not measure transcription
+accuracy or generated cover similarity.
+
+Local H200 checks with the pinned backend:
+
+| Input / mode | Result |
+| --- | --- |
+| 45 s M4A prefix, melody-only | 579 ABC characters; parseable MIDI with 118 note-on events |
+| 242.219 s M4A, full score | 3,033 ABC characters; parseable MIDI with 1,087 note-on events |
+| Default Hub ID, 10 s prefix | Successful ABC and MIDI export |
+| Local snapshots, fresh Transformers module cache | Successful offline full-score export |
+| Both generated JSON requests, #7886 adapter at `5cc8acc942e76fa6f12f8b2f2558c2663abc2aa5` | Validation and prompt construction passed (919 / 2,821 prompt tokens) |
+| Full score + new Mandarin lyrics, offline YuE2, `cot=full`, seed 831001, 9,000-frame budget | 241.319 s, 48 kHz stereo WAV; 6,034 generated tokens; `truncated=False` |
+
+The offline cover used a 108 BPM Chinese traditional pop style and one H200
+with `--gpu-memory-utilization 0.25`. The saved waveform contained finite
+samples, peak amplitude 0.9501, RMS 0.1232 and no full-scale samples. This is
+one functional run, not an accuracy or performance benchmark. It does not
+establish perceptual equivalence to the recording. HTTP synthesis and
+recordings longer than 300 seconds were not exercised. The local recording,
+lyrics, generated scores and cover audio are not included in the repository.
+
+## Notes
+
+- Weights load in FP32 because upstream merges MERT adapters before inference
+  autocast. `--dtype fp32` disables BF16 autocast.
+- For local snapshots the tool prepares transitive Python dependencies in
+  Transformers' module cache. This avoids the pinned Transformers version's
+  direct-import-only copy behavior without editing upstream files.
+- SheetSage2 manages long recordings with its own overlapping windows. This
+  tool leaves windowing and notation generation to the official implementation.
+- Failed runs may retain upstream diagnostic annotations. Choose a new output
+  directory when retrying; no incomplete request is published for synthesis.
+- Model weights have upstream license terms (CC BY-NC 4.0); this repository
+  does not bundle weights or recordings.
+
+## Supported features
+
+| Feature | Status |
+| --- | --- |
+| Audio-to-score preprocessing | Official backend; local file input |
+| YuE2 score-conditioned generation | Offline ABC handoff qualified on H200; HTTP request export validated, HTTP synthesis configuration-only |
+| Native Omni inference, continuous batching, TP/PP/SP | Not implemented for SheetSage2 |
+| Streaming transcription or cover audio | Not supported by this tool |

recipes/m-a-p/YuE2-3B.md

@@ -19,6 +19,11 @@ renders it with the lyrics. `cot=off` skips the score and generates music
 directly. Weights are **CC BY-NC 4.0** (non-commercial); this recipe loads
 user-downloaded weights and bundles none.
 
+To start from a recording, use the [SheetSage2 H200 recipe](SheetSage2-H200.md)
+to transcribe it into ABC in a separate environment, then pass the score to
+YuE2 with `--abc-file`. It includes full-song frame budgets and offline cover
+validation details.
+
 ## References
 
 - Model card: [m-a-p/YuE2-3B](https://huggingface.co/m-a-p/YuE2-3B)

requirements/common.txt

@@ -2,7 +2,7 @@
 # kernels==0.16.1 requires the LayerRepository version=/revision= integration
 # introduced in transformers 5.13. Keep the tested upper bound: 5.15 has
 # a reported model-construction regression; later versions need validation.
-# This range also satisfies vLLM 0.30.0's transformers>=5.10.4 requirement.
+# This range also satisfies vLLM 0.31.0's transformers>=5.10.4,<5.18.0 requirement.
 # MOSS-TTS-Nano's list/set weight-loading compatibility is handled by
 # transformers_keys_to_ignore_compat in model_executor/models/utils.py.
 transformers >= 5.13.0, < 5.15

tests/benchmarks/patch/test_patch.py

@@ -35,6 +35,7 @@ from vllm_omni.benchmarks.patch.patch import (
     _iter_image_reference_inputs,
     _iter_video_reference_inputs,
     _omni_request_timeout_s,
+    async_request_openai_audio_speech,
     async_request_openai_chat_omni_completions,
     async_request_openai_image_edits_omni,
     async_request_openai_image_generations_omni,
@@ -474,6 +475,48 @@ async def test_streaming_error_chunk_marks_request_failed(mocker: MockerFixture)
     assert output.error == "EngineCore encountered an issue"
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    ("headers", "env", "expected_duration_s"),
+    [
+        # Server-stated format wins over the env fallback.
+        ({"X-Audio-Sample-Rate": "48000", "X-Audio-Channels": "2"}, {}, 1.0),
+        ({"X-Audio-Sample-Rate": "48000", "X-Audio-Channels": "2"}, {"rate": "24000", "channels": "1"}, 1.0),
+        # Older servers send no headers: the env (default 24 kHz mono) applies.
+        ({}, {}, 4.0),
+        ({}, {"rate": "48000", "channels": "2"}, 1.0),
+    ],
+)
+async def test_audio_speech_stream_uses_server_pcm_format(
+    mocker: MockerFixture, monkeypatch: pytest.MonkeyPatch, headers, env, expected_duration_s
+):
+    from vllm_omni.metrics import definitions as defs
+
+    for key, var in (("rate", defs.AUDIO_SAMPLE_RATE_ENV), ("channels", defs.AUDIO_CHANNELS_ENV)):
+        if key in env:
+            monkeypatch.setenv(var, env[key])
+        else:
+            monkeypatch.delenv(var, raising=False)
+    request_input = RequestFuncInput(
+        model="test-model",
+        model_name="test-model",
+        prompt="test prompt",
+        api_url="http://test.com/v1/audio/speech",
+        prompt_len=10,
+        output_len=20,
+    )
+    # 1 s of 48 kHz stereo s16le = 192000 bytes.
+    mock_response = MockResponse(200, [b"\x00" * 96000, b"\x00" * 96000])
+    mock_response.headers = headers
+    mock_session = mocker.AsyncMock()
+    mock_session.post = mocker.MagicMock(return_value=mock_response)
+
+    output = await async_request_openai_audio_speech(request_input, mock_session)
+
+    assert output.success is True
+    assert output.audio_duration == pytest.approx(expected_duration_s)
+
+
 @pytest.mark.asyncio
 async def test_positive_client_text_timings_take_precedence_over_stage0(mocker: MockerFixture):
     request_input = RequestFuncInput(

tests/buildkite/test_amd_pipeline.py

@@ -13,6 +13,9 @@ AMD_MERGE_PIPELINE = Path(".buildkite/amd/test-amd-merge.yml")
 AMD_NIGHTLY_PIPELINE = Path(".buildkite/amd/test-amd-nightly.yml")
 AMD_READY_PIPELINE = Path(".buildkite/amd/test-amd-ready.yml")
 AMD_TEMPLATE = Path(".buildkite/amd/test-template-amd-omni.j2")
+AR_PAGED_ATTENTION_LABEL = "ROCm · AR Diffusion Paged Attention GPU Test"
+DIFFUSION_GROUP = ":card_index_dividers: Diffusion Test"
+AR_PAGED_ATTENTION_MARKERS = "core_model and rocm and MI325 and cards_1"
 
 
 def _find_step(label: str, pipeline_path: Path = AMD_MERGE_PIPELINE) -> dict:
@@ -31,6 +34,35 @@ def _find_step(label: str, pipeline_path: Path = AMD_MERGE_PIPELINE) -> dict:
     return step
 
 
+def _find_group(label: str, pipeline_path: Path) -> dict:
+    pipeline = yaml.safe_load(pipeline_path.read_text(encoding="utf-8"))
+    group = next((step for step in pipeline.get("steps", []) if step.get("group") == label), None)
+    assert group is not None, f"missing AMD pipeline group: {label}"
+    return group
+
+
+def test_ar_paged_attention_gpu_lane_is_blocking_and_pinned() -> None:
+    lane_definitions = []
+    for pipeline_path in (AMD_READY_PIPELINE, AMD_MERGE_PIPELINE):
+        group = _find_group(DIFFUSION_GROUP, pipeline_path)
+        step = next((item for item in group["steps"] if item.get("label") == AR_PAGED_ATTENTION_LABEL), None)
+        assert step is not None, f"missing {AR_PAGED_ATTENTION_LABEL} from {DIFFUSION_GROUP} in {pipeline_path}"
+        lane_definitions.append(step)
+
+        assert step["grade"] == "Blocking"
+        assert step["timeout_in_minutes"] == 20
+        assert "export VLLM_OMNI_AR_FA_REQUIRED=1" in step["commands"]
+
+        pytest_command = next(command for command in step["commands"] if "pytest" in command)
+        argv = split(pytest_command)
+        assert argv[:4] == ["timeout", "--signal=TERM", "--kill-after=2m", "15m"]
+        assert "tests/diffusion/ar_diffusion/test_paged_attention.py" in argv
+        assert argv[argv.index("-m") + 1] == AR_PAGED_ATTENTION_MARKERS
+        assert argv[argv.index("--run-level") + 1] == "core_model"
+
+    assert lane_definitions[0] == lane_definitions[1]
+
+
 def test_qwen3_tts_base_preserves_advanced_model_arguments() -> None:
     step = _find_step("Qwen3-TTS Base E2E Test")
     commands = step["commands"]

tests/buildkite/test_amd_ready_r2_01.py

@@ -6,15 +6,17 @@ from shlex import split
 
 import pytest
 import yaml
+from _pytest.mark.expression import Expression
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
 AMD_READY_PIPELINE = Path(".buildkite/amd/test-amd-ready.yml")
-R2_01_LABEL = "ROCm · Engine & Model Executor GPU Coverage (R2-01)"
+R2_01_LABEL = "ROCm · Model Executor GPU Coverage (R2-01)"
+ENGINE_LABEL = "Engine Test"
 SINGLE_GPU_MARKERS = (
-    "core_model and cuda and not (cards_2 or cards_3 or cards_4 or cards_5 or cards_6 or cards_7 or cards_8)"
+    "core_model and cuda and not nvidia_only and not "
+    "(cards_2 or cards_3 or cards_4 or cards_5 or cards_6 or cards_7 or cards_8)"
 )
-EXCLUDED_TEST = "test_code2wav_streaming_batch_matches_ragged_flow_numerics"
 
 
 def _find_step(label: str) -> dict:
@@ -35,14 +37,28 @@ def _find_step(label: str) -> dict:
 
 def _assert_single_gpu_selection(command: str) -> None:
     argv = split(command)
-    assert "tests/engine/" in argv
+    assert "tests/engine/" not in argv
     assert "tests/model_executor/" in argv
     assert "tests/worker/test_batched_omni_output.py" not in argv
     assert argv[argv.index("-m") + 1] == SINGLE_GPU_MARKERS
-    assert argv[argv.index("-k") + 1] == f"not {EXCLUDED_TEST}"
+    assert "-k" not in argv
     assert argv[argv.index("--run-level") + 1] == "core_model"
 
 
+@pytest.mark.parametrize(
+    ("marks", "selected"),
+    [
+        ({"core_model", "cuda", "nvidia_only", "L4", "cards_1"}, False),
+        ({"core_model", "cuda"}, True),
+        ({"core_model", "cuda", "L4", "MI325", "cards_1"}, True),
+        ({"core_model", "cuda", "cards_2"}, False),
+    ],
+)
+def test_r2_01_marker_selection_contract(marks: set[str], selected: bool) -> None:
+    expression = Expression.compile(SINGLE_GPU_MARKERS)
+    assert expression.evaluate(marks.__contains__) is selected
+
+
 def test_r2_01_is_nonblocking_single_gpu_coverage() -> None:
     step = _find_step(R2_01_LABEL)
 
@@ -53,6 +69,11 @@ def test_r2_01_is_nonblocking_single_gpu_coverage() -> None:
     assert step["artifact_paths"] == ["artifacts/rocm-r2-01/**/*"]
 
 
+def test_dedicated_engine_job_preserves_r2_01_engine_coverage() -> None:
+    commands = "\n".join(_find_step(ENGINE_LABEL)["commands"])
+    assert "tests/engine/test_async_omni_engine_abort.py" in commands
+
+
 def test_r2_01_runs_once_fails_closed_and_publishes_results() -> None:
     commands = _find_step(R2_01_LABEL)["commands"]
     pytest_commands = [command for command in commands if command.lstrip().startswith("pytest ")]

tests/buildkite/test_cuda_ar_paged_attention_lane.py

@@ -0,0 +1,31 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+from pathlib import Path
+from shlex import split
+
+import pytest
+import yaml
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+CUDA_MERGE_PIPELINE = Path(".buildkite/cuda/test-merge.yml")
+DIFFUSION_GROUP = ":card_index_dividers: Diffusion Test"
+AR_PAGED_ATTENTION_LABEL = "Diffusion · AR Paged Attention GPU Test"
+
+
+def test_cuda_merge_keeps_ar_paged_attention_gpu_coverage() -> None:
+    pipeline = yaml.safe_load(CUDA_MERGE_PIPELINE.read_text(encoding="utf-8"))
+    group = next(step for step in pipeline["steps"] if step.get("group") == DIFFUSION_GROUP)
+    lane = next(step for step in group["steps"] if step.get("label") == AR_PAGED_ATTENTION_LABEL)
+
+    assert lane["timeout_in_minutes"] == 20
+    assert lane["mirror_hardwares"] == "l4_1"
+    assert "export VLLM_OMNI_AR_FA_REQUIRED=1" in lane["commands"]
+
+    pytest_command = next(command for command in lane["commands"] if "pytest" in command)
+    argv = split(pytest_command)
+    assert argv[:2] == ["timeout", "15m"]
+    assert "tests/diffusion/ar_diffusion/test_paged_attention.py" in argv
+    assert argv[argv.index("-m") + 1] == "core_model and cuda and L4 and cards_1"
+    assert argv[argv.index("--run-level") + 1] == "core_model"

tests/buildkite/test_pi05_ci_routing.py

@@ -0,0 +1,81 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+from pathlib import Path
+from shlex import split
+
+import pytest
+import yaml
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+AMD_MERGE_PIPELINE = Path(".buildkite/amd/test-amd-merge.yml")
+AMD_NIGHTLY_PIPELINE = Path(".buildkite/amd/test-amd-nightly.yml")
+AMD_READY_PIPELINE = Path(".buildkite/amd/test-amd-ready.yml")
+CUDA_MERGE_PIPELINE = Path(".buildkite/cuda/test-merge.yml")
+CUDA_READY_PIPELINE = Path(".buildkite/cuda/test-ready.yml")
+PI05_LABEL = "Simple · Pi0.5 CPU Test"
+PI05_TEST_PATH = "tests/diffusion/models/pi05/test_pi05_units.py"
+
+
+def _pipeline_steps(pipeline_path: Path) -> list[dict]:
+    pipeline = yaml.safe_load(pipeline_path.read_text(encoding="utf-8"))
+    flattened = []
+
+    def walk(steps: list[dict]) -> None:
+        for step in steps:
+            flattened.append(step)
+            walk(step.get("steps", []))
+
+    walk(pipeline.get("steps", []))
+    return flattened
+
+
+def _find_step(label: str, pipeline_path: Path) -> dict:
+    step = next((step for step in _pipeline_steps(pipeline_path) if step.get("label") == label), None)
+    assert step is not None, f"missing pipeline step: {label}"
+    return step
+
+
+@pytest.mark.parametrize(
+    "pipeline_path",
+    [AMD_READY_PIPELINE, AMD_MERGE_PIPELINE],
+    ids=["ready", "merge"],
+)
+def test_pi05_cpu_suite_is_excluded_from_amd_pr_gates(pipeline_path: Path) -> None:
+    assert all(step.get("label") != PI05_LABEL for step in _pipeline_steps(pipeline_path))
+
+    step = _find_step("Simple · Diffusion Test · Shard %N/%t", pipeline_path)
+    pytest_command = next(command for command in step["commands"] if "pytest" in command)
+    assert f"--ignore={PI05_TEST_PATH}" in split(pytest_command)
+
+
+def test_pi05_cpu_suite_runs_nonblocking_in_amd_nightly() -> None:
+    steps = [step for step in _pipeline_steps(AMD_NIGHTLY_PIPELINE) if step.get("label") == PI05_LABEL]
+    assert len(steps) == 1
+
+    step = steps[0]
+    assert step["agent_pool"] == "mi300_1"
+    assert step["grade"] == "NonBlocking"
+    assert step["timeout_in_minutes"] == 60
+
+    pytest_command = next(command for command in step["commands"] if "pytest" in command)
+    argv = split(pytest_command)
+    assert PI05_TEST_PATH in argv
+    assert argv[argv.index("-m") + 1] == "core_model and cpu"
+
+
+@pytest.mark.parametrize(
+    "pipeline_path",
+    [CUDA_READY_PIPELINE, CUDA_MERGE_PIPELINE],
+    ids=["ready", "merge"],
+)
+def test_cuda_pr_gates_retain_pi05_cpu_coverage(pipeline_path: Path) -> None:
+    step = _find_step("Simple · Diffusion Test", pipeline_path)
+    pytest_command = next(command for command in step["commands"] if "pytest" in command)
+    argv = split(pytest_command)
+
+    assert "source_file_dependencies" not in step
+    assert "tests/diffusion" in argv
+    assert argv[argv.index("-m") + 1] == "core_model and cpu"
+    assert f"--ignore={PI05_TEST_PATH}" not in argv

tests/buildkite/test_rocm_dockerfile.py

@@ -10,7 +10,8 @@ pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 REPO_ROOT = Path(__file__).resolve().parents[2]
 AMD_TEMPLATE = REPO_ROOT / ".buildkite/amd/test-template-amd-omni.j2"
 AMD_BUILD_SCRIPT = REPO_ROOT / ".buildkite/amd/scripts/build-ci-image.sh"
-CI_DOCKERFILE = REPO_ROOT / "docker/Dockerfile.ci"
+CUDA_RELEASE_DOCKERFILE = REPO_ROOT / "docker/Dockerfile.cuda"
+CUDA_CI_DOCKERFILE = REPO_ROOT / "docker/Dockerfile.ci"
 ROCM_DOCKERFILE = REPO_ROOT / "docker/Dockerfile.rocm"
 ROCM_DOCKERIGNORE = REPO_ROOT / "docker/Dockerfile.rocm.dockerignore"
 
@@ -30,14 +31,18 @@ def _line_index(lines: list[str], prefix: str) -> int:
     return matches[0]
 
 
-def test_rocm_base_tracks_ci_vllm_release() -> None:
-    ci_release = _docker_arg(CI_DOCKERFILE, "VLLM_BASE_TAG")
+def test_rocm_base_tracks_cuda_vllm_release() -> None:
+    cuda_base = _docker_arg(CUDA_RELEASE_DOCKERFILE, "BASE_IMAGE")
+    cuda_image, _, cuda_release = cuda_base.rpartition(":")
     rocm_base = _docker_arg(ROCM_DOCKERFILE, "BASE_IMAGE")
 
     image_ref, separator, image_tag = rocm_base.rpartition(":")
     assert separator, f"expected a tagged ROCm base image, got {rocm_base}"
-    assert image_ref.rsplit("/", 1)[-1] == "vllm-openai-rocm", image_ref
-    assert image_tag == ci_release
+    assert cuda_image == "vllm/vllm-openai"
+    assert image_ref == "vllm/vllm-openai-rocm"
+    assert image_tag == cuda_release == f"v{_docker_arg(CUDA_CI_DOCKERFILE, 'VLLM_VERSION')}"
+    assert _docker_arg(CUDA_CI_DOCKERFILE, "VLLM_BASE_IMAGE") == cuda_image
+    assert _docker_arg(CUDA_CI_DOCKERFILE, "VLLM_BASE_TAG") == "v${VLLM_VERSION}"
 
 
 def test_rocm_defaults_to_prebuilt_base_image() -> None:
@@ -103,11 +108,11 @@ def test_rocm_build_context_excludes_git_history() -> None:
     assert ".git" in patterns
 
 
-def test_rocm_source_ref_tracks_ci_vllm_release() -> None:
-    ci_release = _docker_arg(CI_DOCKERFILE, "VLLM_BASE_TAG")
+def test_rocm_source_ref_tracks_cuda_vllm_release() -> None:
+    cuda_release = _docker_arg(CUDA_CI_DOCKERFILE, "VLLM_VERSION")
     rocm_source_ref = _docker_arg(ROCM_DOCKERFILE, "VLLM_VERSION_OR_COMMIT_HASH")
 
-    assert rocm_source_ref == ci_release
+    assert rocm_source_ref == f"v{cuda_release}"
 
 
 def test_rocm_dockerfile_contains_vllm_api_canary() -> None:

tests/buildkite/test_upload_pipeline.py

@@ -172,7 +172,11 @@ def test_mirror_hardwares_l4_1_expands_to_agents_and_plugins(monkeypatch: pytest
     env_names = {item["name"] for item in container["env"]}
     assert "VLLM_CI_HF_TOKEN" in env_names
     assert "HF_TOKEN" not in env_names
-    assert step["commands"] == [CUDA_HF_TOKEN_EXPORT, "pytest -sv tests/example"]
+    assert step["commands"] == [
+        CUDA_HF_TOKEN_EXPORT,
+        "python3 .buildkite/cuda/scripts/check_vllm_runtime.py",
+        "pytest -sv tests/example",
+    ]
 
 
 def test_mirror_hardwares_l4_preserves_explicit_retry() -> None:
@@ -239,6 +243,10 @@ def test_all_cuda_mirror_hardwares_restore_hf_token_at_runtime() -> None:
         assert "VLLM_CI_HF_TOKEN" in env_names
         assert "HF_TOKEN" not in env_names
         assert step["commands"][0] == CUDA_HF_TOKEN_EXPORT
+        assert step["commands"][1:] == [
+            "python3 .buildkite/cuda/scripts/check_vllm_runtime.py",
+            "pytest -sv tests/example",
+        ]
 
 
 def _gpu_limit(step: dict) -> int:

tests/config/test_config_factory.py

@@ -2985,6 +2985,9 @@ class TestPlatformOverrides:
             ("moss_tts_local_mrv2.yaml", "moss_tts_local"),
             ("moss_tts_local_mrv2_high_concurrency.yaml", "moss_tts_local"),
             ("moss_tts_local_mrv2_low_latency.yaml", "moss_tts_local"),
+            ("moss_tts_local_mrv2_optimized.yaml", "moss_tts_local"),
+            ("moss_tts_local.yaml", "moss_tts_local"),
+            ("moss_tts_local_mrv2_high_concurrency_mps.yaml", "moss_tts_local"),
         ],
     )
     @pytest.mark.parametrize("platform", ["cuda", "npu", "xpu", "rocm", "musa"])
@@ -3004,9 +3007,142 @@ class TestPlatformOverrides:
             # v2 only engages the native plane on stages declaring support.
             assert all(ps.supports_native_mrv2_data_plane for ps in pipeline.stages)
 
+    @pytest.mark.parametrize("memory_gib", [24, 80, 139, 140, 141, None])
+    @pytest.mark.parametrize("mps_available", [False, True])
+    def test_moss_local_cuda_default_selects_mrv2_capacity_and_mps(self, monkeypatch, memory_gib, mps_available):
+        from vllm.platforms import current_platform
+
+        from vllm_omni.platforms import current_omni_platform
+
+        monkeypatch.setattr(current_omni_platform, "device_name", "cuda")
+
+        def memory():
+            if memory_gib is None:
+                raise RuntimeError("NVML unavailable")
+            return memory_gib * 1024**3
+
+        monkeypatch.setattr(current_platform, "get_device_total_memory", memory)
+        monkeypatch.setattr(
+            "shutil.which", lambda command: "/usr/bin/nvidia-cuda-mps-control" if mps_available else None
+        )
+        pipeline = resolve_pipeline_config("moss_tts_local")
+        high_capacity = mps_available and memory_gib is not None and memory_gib >= 140
+        capacity = 128 if high_capacity else 64
+        config = VllmOmniConfig.from_pipeline_config(pipeline)
+        talker, codec = config.stage_configs
+        assert talker.scheduler_config.max_num_seqs == codec.scheduler_config.max_num_seqs == capacity
+        assert talker.cache_config.kv_cache_memory_bytes == (32 * 1024**3 if high_capacity else None)
+        assert talker.cache_config.gpu_memory_utilization == 0.60
+        assert codec.cache_config.gpu_memory_utilization == 0.30
+        assert talker.cache_config.enable_prefix_caching is high_capacity
+        assert not codec.cache_config.enable_prefix_caching
+        for stage in config.stage_configs:
+            assert stage.model_config.use_v2_model_runner
+            assert stage.runtime_config.cuda_mps is mps_available
+            assert not stage.runtime_config.env
+        overrides = talker.model_config.hf_overrides or {}
+        assert overrides.get("mrv2_batch_prefill", False) is high_capacity
+        assert overrides.get("mrv2_direct_tokens", False) is high_capacity
+        assert overrides.get("local_compile_audio_sampler", False) is high_capacity
+
+        # The serving engine still consumes the legacy representation.
+        stages, _ = StageConfigFactory._create_legacy_from_registry(pipeline, {})
+        for stage in stages:
+            assert stage.yaml_engine_args["max_num_seqs"] == capacity
+            assert stage.yaml_engine_args["use_v2_model_runner"]
+            assert stage.yaml_runtime.get("cuda_mps", False) is mps_available
+        assert stages[0].yaml_engine_args.get("kv_cache_memory_bytes") == (32 * 1024**3 if high_capacity else None)
+        assert stages[0].yaml_engine_args["enable_prefix_caching"] is high_capacity
+
+    @pytest.mark.parametrize("platform", ["cpu", "npu", "xpu", "rocm", "musa"])
+    def test_moss_local_non_cuda_default_preserves_v1(self, monkeypatch, platform):
+        from vllm.platforms import current_platform
+
+        from vllm_omni.platforms import current_omni_platform
+
+        monkeypatch.setattr(current_omni_platform, "device_name", platform)
+
+        def unexpected_probe(*args):
+            pytest.fail("Non-CUDA selection must not probe CUDA memory or MPS")
+
+        monkeypatch.setattr(current_platform, "get_device_total_memory", unexpected_probe)
+        monkeypatch.setattr("shutil.which", unexpected_probe)
+        pipeline = resolve_pipeline_config("moss_tts_local")
+        config = VllmOmniConfig.from_pipeline_config(pipeline)
+        for stage in config.stage_configs:
+            assert not stage.model_config.use_v2_model_runner
+            assert not stage.runtime_config.cuda_mps
+            assert stage.scheduler_config.max_num_seqs == 64
+        assert config.stage_configs[0].cache_config.kv_cache_memory_bytes is None
+        stages, _ = StageConfigFactory._create_legacy_from_registry(pipeline, {})
+        assert all(not stage.yaml_engine_args["use_v2_model_runner"] for stage in stages)
+        assert all(not stage.yaml_runtime.get("cuda_mps", False) for stage in stages)
+
+    @pytest.mark.parametrize(
+        "filename,runner,mps,capacity",
+        [
+            ("moss_tts_local_v1.yaml", False, False, 64),
+            ("moss_tts_local_mrv2.yaml", True, False, 64),
+            ("moss_tts_local_mrv2_high_concurrency.yaml", True, False, 128),
+        ],
+    )
+    def test_moss_local_explicit_profile_overrides_cuda_default(self, monkeypatch, filename, runner, mps, capacity):
+        from vllm.platforms import current_platform
+
+        from vllm_omni.platforms import current_omni_platform
+
+        monkeypatch.setattr(current_omni_platform, "device_name", "cuda")
+        monkeypatch.setattr(current_platform, "get_device_total_memory", lambda: 141 * 1024**3)
+        monkeypatch.setattr("shutil.which", lambda command: "/usr/bin/nvidia-cuda-mps-control")
+        pipeline = resolve_pipeline_config("moss_tts_local")
+        path = get_deploy_config_path(filename)
+        config = VllmOmniConfig.from_pipeline_config(pipeline, deploy_config_path=path)
+        for stage in config.stage_configs:
+            assert stage.model_config.use_v2_model_runner is runner
+            assert stage.runtime_config.cuda_mps is mps
+            assert stage.scheduler_config.max_num_seqs == capacity
+        stages, _ = StageConfigFactory._create_legacy_from_registry(pipeline, {}, deploy_config_path=path)
+        for stage in stages:
+            assert stage.yaml_engine_args["use_v2_model_runner"] is runner
+            assert stage.yaml_runtime.get("cuda_mps", False) is mps
+            assert stage.yaml_engine_args["max_num_seqs"] == capacity
+
+    @pytest.mark.parametrize("platform", ["cuda", "npu", "xpu", "rocm", "musa"])
+    def test_moss_local_system_profile_reaches_native_runner_and_cuda_only_mps(self, platform):
+        pipeline = resolve_pipeline_config("moss_tts_local")
+        path = Path(get_deploy_config_path("moss_tts_local_mrv2_optimized.yaml"))
+        deploy = _apply_platform_overrides(load_deploy_config(path), platform=platform)
+        stages = merge_pipeline_deploy(pipeline, deploy)
+        is_cuda = platform == "cuda"
+        assert deploy.cuda_mps is is_cuda
+        assert all(stage.yaml_runtime.get("cuda_mps", False) is is_cuda for stage in stages)
+        assert all(stage.yaml_engine_args["use_v2_model_runner"] is is_cuda for stage in stages)
+        if is_cuda:
+            args = stages[0].yaml_engine_args
+            assert args["enable_prefix_caching"] is True
+            assert args["hf_overrides"]["mrv2_gpu_slot_state"] is True
+            assert args["hf_overrides"]["mrv2_batch_prefill"] is True
+            assert args["hf_overrides"]["mrv2_direct_tokens"] is True
+            assert deploy.connectors["shm"]["extra"]["codec_first_chunk_fast_path"] == 1
+            assert args["hf_overrides"]["local_compile_audio_sampler"] is True
+            extra = deploy.connectors["shm"]["extra"]
+            assert extra["codec_first_chunk_max_active_streams"] == 32
+            assert extra["generation_min_batch_size"] == 16
+            assert extra["generation_max_wait_ms"] == 6
+            assert not stages[0].yaml_runtime.get("env")
+        else:
+            assert all(not stage.yaml_engine_args["enable_prefix_caching"] for stage in stages)
+            assert all(not stage.yaml_runtime.get("env") for stage in stages)
+
+    def test_platform_mps_rejects_non_boolean(self):
+        deploy = load_deploy_config(get_deploy_config_path("moss_tts_local_mrv2_optimized.yaml"))
+        deploy.platforms["cuda"]["cuda_mps"] = "true"
+        with pytest.raises(ValueError, match="platform cuda_mps must be a boolean"):
+            _apply_platform_overrides(deploy, platform="cuda")
+
     def test_moss_local_mrv2_preserves_base_profile_and_variant_scope(self):
         pipeline = resolve_pipeline_config("moss_tts_local")
-        base_path = Path(get_deploy_config_path(pipeline.default_deploy_config_name))
+        base_path = Path(get_deploy_config_path("moss_tts_local_v1.yaml"))
         candidate_path = Path(get_deploy_config_path("moss_tts_local_mrv2.yaml"))
         base = load_deploy_config(base_path)
         candidate = load_deploy_config(candidate_path)
@@ -3052,7 +3188,7 @@ class TestPlatformOverrides:
         assert compilation["cudagraph_mode"] == "FULL"
         assert compilation["cudagraph_capture_sizes"] == [1, 2, 4, 6, 8, 12, 16, 24, 32, 64, 128]
         assert compilation["inductor_compile_config"] == {"combo_kernels": False, "benchmark_combo_kernel": False}
-        base_path = Path(get_deploy_config_path("moss_tts_local.yaml"))
+        base_path = Path(get_deploy_config_path("moss_tts_local_v1.yaml"))
         for platform in ("npu", "xpu", "rocm", "musa"):
             actual = _apply_platform_overrides(load_deploy_config(path), platform=platform)
             expected = _apply_platform_overrides(load_deploy_config(base_path), platform=platform)

tests/config/test_environment_variables.py

@@ -167,7 +167,7 @@ def test_inventory_matches_reviewed_snapshot_counts():
         EnvironmentVariableCategory.PUBLIC_OMNI: 32,
         EnvironmentVariableCategory.INHERITED_VLLM: 20,
         EnvironmentVariableCategory.PLATFORM_EXTERNAL: 29,
-        EnvironmentVariableCategory.MODEL_SPECIFIC: 95,
+        EnvironmentVariableCategory.MODEL_SPECIFIC: 96,
         EnvironmentVariableCategory.BENCHMARK_TRANSITIONAL: 21,
         EnvironmentVariableCategory.INTERNAL: 7,
     }
@@ -178,7 +178,7 @@ def test_inventory_matches_reviewed_snapshot_counts():
         if item.category is EnvironmentVariableCategory.MODEL_SPECIFIC
     )
     assert {disposition: disposition_counts[disposition] for disposition in ModelEnvironmentVariableDisposition} == {
-        ModelEnvironmentVariableDisposition.PROMOTE: 66,
+        ModelEnvironmentVariableDisposition.PROMOTE: 67,
         ModelEnvironmentVariableDisposition.REQUEST_SCOPE: 5,
         ModelEnvironmentVariableDisposition.EXTERNAL: 0,
         ModelEnvironmentVariableDisposition.INTERNALIZE: 17,

tests/config/test_omni_config.py

@@ -1887,7 +1887,7 @@ def test_from_pipeline_config_rejects_reserved_diffusion_kv_mode(tmp_path):
 
 @pytest.mark.parametrize("source", ["default", "topology", "deploy", "stage-cli"])
 @pytest.mark.parametrize("key_container", [list, tuple])
-def test_diffusion_stage_payload_keys_roundtrip(source, key_container):
+def test_diffusion_stage_payload_keys_roundtrip(source, key_container, tmp_path):
     from vllm_omni.diffusion.data import OmniDiffusionConfig
     from vllm_omni.engine.stage_init_utils import build_engine_args_dict_from_omni_stage_config
 
@@ -1920,7 +1920,7 @@ def test_diffusion_stage_payload_keys_roundtrip(source, key_container):
     legacy_stage = merge_pipeline_deploy(pipeline, deploy)[0]
     legacy_args = {**legacy_stage.yaml_engine_args, **(override_keys if source == "stage-cli" else {})}
     restored_stage = ForkingPickler.loads(ForkingPickler.dumps(stage))
-    engine_args = build_engine_args_dict_from_omni_stage_config(restored_stage, model="test-model")
+    engine_args = build_engine_args_dict_from_omni_stage_config(restored_stage, model=str(tmp_path))
     diffusion_kwargs = omni_config_module.extract_diffusion_stage_config_kwargs(
         engine_args, stage_id=restored_stage.stage_id, include_engine_adapter_metadata=True
     )
@@ -1948,7 +1948,9 @@ def test_diffusion_config_field_classification_covers_current_fields():
 
     assert classified_fields == {f.name for f in fields(omni_config_module._DiffusionConfigProjection)}
     assert {f.name for f in fields(OmniDiffusionConfig)} <= (
-        classified_fields | omni_config_module._DIFFUSION_MOVED_SHARED_FIELDS
+        classified_fields
+        | omni_config_module._DIFFUSION_MOVED_SHARED_FIELDS
+        | omni_config_module._DIFFUSION_INTERNAL_FIELDS
     )
     assert {
         "enable_prompt_embed_cache",

tests/conftest.py

@@ -23,6 +23,7 @@ bootstrap_vllm_layer_custom_op_modules()
 pytest_plugins = (
     "tests.helpers.fixtures.config",
     "tests.helpers.fixtures.clean",
+    "tests.helpers.fixtures.cpu_threads",
     "tests.helpers.fixtures.log",
     "tests.helpers.fixtures.media",
     "tests.helpers.fixtures.pytest_collection",

tests/core/sched/test_generation_batch_coalescing.py

@@ -171,6 +171,7 @@ def test_cancelled_notification_is_filtered_before_coordinator(monkeypatch, mock
     s, _ = scheduler(monkeypatch, [event])
     s._generation_max_wait_s = 0
     s.waiting = []
+    s.kv_holding_waiting = []
     s.input_coordinator._async_chunk = True
     s.input_coordinator.update_request_metadata = mocker.Mock()
     s.input_coordinator.process_pending_chunks = mocker.Mock()
@@ -178,7 +179,11 @@ def test_cancelled_notification_is_filtered_before_coordinator(monkeypatch, mock
     s.input_coordinator.update_request_metadata.assert_called_once_with(
         s.requests, {"0": {"code_predictor_codes": [7]}}, model_mode="generation"
     )
-    s.input_coordinator.process_pending_chunks.assert_called_once_with(s.waiting, s.running, {"0"}, set())
+    s.input_coordinator.process_pending_chunks.assert_called_once()
+    waiting, running, ready, finished = s.input_coordinator.process_pending_chunks.call_args.args
+    assert list(waiting) == []
+    assert running is s.running
+    assert ready == {"0"} and finished == set()
 
 
 @pytest.mark.parametrize("target,wait", [(0, 1), (257, 1), (4, -1), (4, float("nan")), (4, float("inf"))])

tests/core/sched/test_generation_scheduler_init.py

@@ -44,10 +44,13 @@ def _config(*, native=True, stateful=True, tp=1, pp=1, extras=None, capacity=128
     )
 
 
-@pytest.mark.parametrize("profile", ["high_concurrency", "low_latency"])
+@pytest.mark.parametrize(
+    "profile,min_batch,max_wait_ms",
+    [("high_concurrency", 32, 12), ("low_latency", 32, 12), ("optimized", 16, 6)],
+)
 @pytest.mark.parametrize("platform", ["cuda", "npu", "xpu", "rocm", "musa"])
 def test_moss_profile_generation_constructor_after_platform_resolution(
-    construct_scheduler, monkeypatch, mocker, profile, platform
+    construct_scheduler, monkeypatch, mocker, profile, min_batch, max_wait_ms, platform
 ):
     # merge_pipeline_deploy owns platform resolution. Mock the detected device
     # instead of applying an override first and then resolving the worker's
@@ -66,11 +69,12 @@ def test_moss_profile_generation_constructor_after_platform_resolution(
     warning = mocker.patch("vllm_omni.core.sched.omni_generation_scheduler.logger.warning")
     scheduler = construct_scheduler(config)
     # The raw extras persist through fallback; the live constructor must cope.
-    assert extras["generation_min_batch_size"] == 32 and extras["generation_max_wait_ms"] == 12
+    assert extras["generation_min_batch_size"] == min_batch
+    assert extras["generation_max_wait_ms"] == max_wait_ms
     if platform == "cuda":
         assert scheduler._native_data_plane and scheduler.input_coordinator is not None
-        assert scheduler._generation_min_batch_size == 32
-        assert scheduler._generation_max_wait_s == 0.012
+        assert scheduler._generation_min_batch_size == min_batch
+        assert scheduler._generation_max_wait_s == max_wait_ms / 1000
         assert scheduler._generation_max_regular_batch == (16 if profile == "low_latency" else 0)
         warning.assert_not_called()
         if profile == "low_latency":

tests/core/sched/test_generation_scheduler_restore.py

@@ -11,7 +11,9 @@ from types import SimpleNamespace
 import pytest
 import torch
 from vllm import SamplingParams
+from vllm.distributed.aux_output_connector.connector import AuxOutputSchedulerConnector
 from vllm.v1.core.sched.interface import PauseState
+from vllm.v1.core.sched.output import CachedRequestData
 from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
 from vllm.v1.request import Request, RequestStatus
 
@@ -53,12 +55,14 @@ class FakeAdapter:
 def _make_generation_scheduler(waiting_request, *, use_v2_model_runner=False):
     scheduler = OmniGenerationScheduler.__new__(OmniGenerationScheduler)
     scheduler.max_num_scheduled_tokens = 8
+    scheduler.max_num_active_reqs = 1
     scheduler.max_num_running_reqs = 1
     scheduler._pause_state = PauseState.UNPAUSED
     scheduler.running = []
     scheduler.waiting = create_request_queue(SchedulingPolicy.FCFS)
     scheduler.waiting.add_request(waiting_request)
-    scheduler.skipped_waiting = create_request_queue(SchedulingPolicy.FCFS)
+    scheduler.kv_holding_waiting = create_request_queue(SchedulingPolicy.FCFS)
+    scheduler.deferred_waiting = set()
     scheduler.requests = {waiting_request.request_id: waiting_request}
     scheduler.policy = SchedulingPolicy.FCFS
     scheduler.chunk_transfer_adapter = FakeAdapter()
@@ -86,6 +90,7 @@ def _make_generation_scheduler(waiting_request, *, use_v2_model_runner=False):
         get_manager_metadata=lambda: None,
     )
     scheduler.connector = None
+    scheduler.aux_output_connector = None
     scheduler.ec_connector = None
     scheduler.prev_step_scheduled_req_ids = set()
     scheduler._pending_finish_reqs = []
@@ -105,6 +110,45 @@ def _make_generation_scheduler(waiting_request, *, use_v2_model_runner=False):
     return scheduler
 
 
+def test_generation_scheduler_preserves_auxiliary_output_metadata() -> None:
+    request = Request("aux-contract", [1, 2], SamplingParams(max_tokens=2), pooling_params=None)
+    request.block_hashes.append(b"block-hash")
+    scheduler = _make_generation_scheduler(request, use_v2_model_runner=True)
+    scheduler.aux_output_connector = AuxOutputSchedulerConnector()
+
+    output = scheduler._build_generation_scheduler_output(
+        new_reqs_data=[],
+        cached_reqs_data=CachedRequestData.make_empty(),
+        num_scheduled_tokens={request.request_id: 1},
+        scheduled_spec_decode_tokens={},
+        scheduled_encoder_inputs={},
+        num_common_prefix_blocks=[0],
+    )
+
+    metadata = output.aux_output_connector_metadata
+    assert metadata is not None
+    assert metadata.requests == {request.request_id: 0}
+    assert list(metadata.block_hashes[request.request_id]) == request.block_hashes
+
+
+@pytest.mark.parametrize(("runner_slots", "active_limit"), [(1, 2), (2, 1)])
+def test_generation_admits_kv_holder_before_fresh_requests(runner_slots, active_limit) -> None:
+    fresh = Request("fresh", [1, 2], SamplingParams(max_tokens=2), pooling_params=None)
+    holding = Request("holding", [3, 4], SamplingParams(max_tokens=2), pooling_params=None)
+    holding.num_computed_tokens = 1
+    scheduler = _make_generation_scheduler(fresh)
+    scheduler.max_num_running_reqs = runner_slots
+    scheduler.max_num_active_reqs = active_limit
+    scheduler.requests[holding.request_id] = holding
+    scheduler.kv_holding_waiting.add_request(holding)
+
+    output = scheduler.schedule()
+
+    assert set(output.num_scheduled_tokens) == {holding.request_id}
+    assert list(scheduler.waiting) == [fresh]
+    assert list(scheduler.kv_holding_waiting) == []
+
+
 def _chunk_request(request_id, **kwargs):
     defaults = dict(
         request_id=request_id,
@@ -123,7 +167,13 @@ def _chunk_request(request_id, **kwargs):
         record_event=lambda *args, **kwargs: None,
     )
     defaults.update(kwargs)
-    return SimpleNamespace(**defaults)
+    return _HashableChunkRequest(**defaults)
+
+
+class _HashableChunkRequest(SimpleNamespace):
+    # Deferred queues require identity hashing; SimpleNamespace types it as None.
+    __hash__ = object.__hash__  # type: ignore[assignment]
+    __eq__ = object.__eq__
 
 
 def test_chunk_lifecycle_no_resubmit_and_state_survives_requeue(monkeypatch) -> None:
@@ -138,7 +188,8 @@ def test_chunk_lifecycle_no_resubmit_and_state_survives_requeue(monkeypatch) ->
     )
     scheduler.running = []
     scheduler.waiting = create_request_queue(scheduler.policy)
-    scheduler.skipped_waiting = create_request_queue(scheduler.policy)
+    scheduler.kv_holding_waiting = create_request_queue(scheduler.policy)
+    scheduler.deferred_waiting = set()
 
     completed = Request("completed", [1, 2], SamplingParams(max_tokens=4), pooling_params=None)
     completed.status = RequestStatus.RUNNING
@@ -292,6 +343,7 @@ def _express_scheduler():
     scheduler = _make_generation_scheduler(continuation, use_v2_model_runner=True)
     scheduler.waiting.add_request(first)
     scheduler.requests[first.request_id] = first
+    scheduler.max_num_active_reqs = 4
     scheduler.max_num_running_reqs = 4
     scheduler._native_data_plane = True
     scheduler.chunk_transfer_adapter = None

tests/core/sched/test_omni_ar_scheduler_aborted_queue_sweep.py

@@ -9,7 +9,7 @@ handing control to upstream, because upstream ``Scheduler.schedule()`` raises
 admits in a finished state -- which kills the stage's engine core, not just
 the request.
 
-Regression for the sweep missing ``skipped_waiting``, the third queue
+Regression for the sweep missing ``kv_holding_waiting``, the third queue
 upstream admits from. An aborted duplex session parked there was re-selected
 by ``_select_waiting_queue_for_scheduling`` on a later tick and crashed the
 stage.
@@ -49,28 +49,30 @@ class _StubRequest:
         return (self.priority, self.arrival_time) < (other.priority, other.arrival_time)
 
 
-def _make_scheduler(policy: SchedulingPolicy, *, waiting=(), skipped_waiting=(), running=()):
+def _make_scheduler(policy: SchedulingPolicy, *, waiting=(), kv_holding_waiting=(), running=()):
     """Bare scheduler with just the surface the sweep reads."""
     scheduler = OmniARScheduler.__new__(OmniARScheduler)
     scheduler.waiting = create_request_queue(policy)
     for req in waiting:
         scheduler.waiting.add_request(req)
-    scheduler.skipped_waiting = create_request_queue(policy)
-    for req in skipped_waiting:
-        scheduler.skipped_waiting.add_request(req)
+    scheduler.kv_holding_waiting = create_request_queue(policy)
+    scheduler.deferred_waiting = set()
+    for req in kv_holding_waiting:
+        scheduler.kv_holding_waiting.add_request(req)
     scheduler.running = list(running)
     return scheduler
 
 
 @pytest.mark.parametrize("policy", _POLICIES)
-def test_schedule_sweeps_skipped_waiting_before_upstream_selection(
+def test_schedule_sweeps_kv_holding_waiting_before_upstream_selection(
     monkeypatch: pytest.MonkeyPatch,
     policy: SchedulingPolicy,
 ) -> None:
     """The public schedule path must remove aborted skipped requests first."""
     aborted = _StubRequest("req-aborted", RequestStatus.FINISHED_ABORTED)
-    scheduler = _make_scheduler(policy, skipped_waiting=[aborted])
+    scheduler = _make_scheduler(policy, kv_holding_waiting=[aborted])
     scheduler.chunk_transfer_adapter = None
+    scheduler.max_num_active_reqs = 8
     scheduler.max_num_running_reqs = 8
     scheduler.input_coordinator = None
     scheduler.num_waiting_for_streaming_input = 0
@@ -85,7 +87,7 @@ def test_schedule_sweeps_skipped_waiting_before_upstream_selection(
     sentinel = object()
 
     def upstream_schedule(_self, _throttle_prefills: bool = False):
-        assert list(scheduler.skipped_waiting) == []
+        assert list(scheduler.kv_holding_waiting) == []
         return sentinel
 
     monkeypatch.setattr(ar_sched_mod.VLLMScheduler, "schedule", upstream_schedule)