[Bugfix][MiniMax-H3] Apply pre-commit formatting

Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>
[Bugfix][MiniMax-H3] Reconcile temporal decode plans across VAE ranks
2026-09-03 08:44:26 +08:00 · 2026-09-03 08:20:00 +08:00 · 2026-09-02 07:08:40 +08:00 · 2026-09-01 23:11:29 +08:00 · 2026-09-01 23:11:29 +08:00 · 2026-09-01 23:11:28 +08:00
332 changed files with 23282 additions and 1898 deletions
@@ -72,7 +72,22 @@ steps:
        - export VLLM_LOGGING_LEVEL=DEBUG
        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
        - pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
-        - pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
+        - >-
+          pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
+          --deselect='tests/diffusion/offloader/test_diffusion_layerwise_offload.py::test_layerwise_offload_diffusion_model[stabilityai/stable-audio-open-1.0]'
+
+    # TODO: Diagnose unstable Stable Audio layerwise-offload memory savings on ROCm.
+    - label: "Diffusion Stable Audio Layerwise Offload Test (quarantined)"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: NonBlocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - >-
+          pytest -s -v
+          'tests/diffusion/offloader/test_diffusion_layerwise_offload.py::test_layerwise_offload_diffusion_model[stabilityai/stable-audio-open-1.0]'

    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs) · Shard 1/2"
      agent_pool: mi300_4
@@ -175,7 +190,7 @@ steps:
      grade: Blocking
      commands:
        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+        - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py -m 'advanced_model and cuda' --run-level 'advanced_model'

    - label: "Qwen3-TTS CustomVoice E2E Test · Shard %N/%t"
      agent_pool: mi300_1
@@ -223,13 +238,10 @@ steps:
      grade: Blocking
      timeout_in_minutes: 50
      commands:
-        - |
-          timeout 40m bash -c '
-            export VLLM_LOGGING_LEVEL=DEBUG
-            export VLLM_WORKER_MULTIPROC_METHOD=spawn
-            export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-            pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m 'advanced_model and cuda' --run-level 'advanced_model'
-          '
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+        - timeout 40m pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m "advanced_model and cuda" --run-level "advanced_model"

 # TODO: Bagel test on ROCm is very unstable. @tjtanaa
 # Need to debug before reneable numerical changes across large PRs
@@ -85,6 +85,9 @@ steps:
          - vllm_omni/model_executor/models/common/alias_free_activation.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
          - vllm_omni/deploy/qwen3_tts.yaml
+          - vllm_omni/entrypoints/openai/serving_speech.py
+          - vllm_omni/entrypoints/openai/tts_adapters/
+          - vllm_omni/engine/stage_pool.py
        commands:
          - |
            timeout 40m bash -c "
@@ -104,6 +107,9 @@ steps:
          - vllm_omni/model_executor/models/common/alias_free_activation.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
          - vllm_omni/deploy/qwen3_tts.yaml
+          - vllm_omni/entrypoints/openai/serving_speech.py
+          - vllm_omni/entrypoints/openai/tts_adapters/
+          - vllm_omni/engine/stage_pool.py
          - .buildkite/common/scripts/run_cov_split.sh
          - pyproject.toml
        # 40 -> 60: the coverage split runs the model twice.
@@ -122,6 +128,7 @@ steps:
      - label: "Omni · Qwen3-Omni Test"
        source_file_dependencies:
          - tests/e2e/offline_inference/test_qwen3_omni.py
+          - tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py
          - tests/e2e/online_serving/test_qwen3_omni.py
          - tests/helpers/runtime.py
          - vllm_omni/entrypoints/openai/serving_chat.py
@@ -132,9 +139,10 @@ steps:
          - vllm_omni/model_executor/models/qwen3_tts/cuda_graph_decoder_wrapper.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
          - vllm_omni/deploy/qwen3_omni_moe.yaml
+          - vllm_omni/deploy/qwen3_omni_moe_thinking.yaml
        timeout_in_minutes: 50
        commands:
-          - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+          - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/offline_inference/test_qwen3_omni_colocate_async.py -m 'advanced_model and cuda' --run-level 'advanced_model'
        mirror_hardwares: h100_2

      - label: "Omni · MiniCPM-o 4.5 Test"
@@ -229,3 +237,38 @@ steps:
              pytest -s -v tests/e2e/online_serving/test_hunyuan_video_15_expansion.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
            "
        mirror_hardwares: h100_1
+
+      - label: "Diffusion · MiniMax-H3 L3 DLO DP2 and FastH3"
+        source_file_dependencies:
+          - tests/e2e/online_serving/minimax_h3/
+          - tests/helpers/assertions.py
+          - tests/helpers/mark.py
+          - tests/helpers/media.py
+          - tests/helpers/runtime.py
+          - vllm_omni/diffusion/models/minimax_h3/
+          - vllm_omni/diffusion/lora/
+          - vllm_omni/diffusion/offloader/
+          - vllm_omni/diffusion/distributed/
+          - vllm_omni/entrypoints/openai/
+        # Keep four server lifecycles in one Buildkite job, but never in one
+        # pytest process. DLO uses multiprocessing shared-memory queues; the
+        # parent's resource_tracker can retain a stale /psm_* entry during
+        # teardown and strand the next Ref2VA collective.
+        # A per-file process plus a hard timeout also makes a regression fail
+        # instead of leaving the merge queue stuck indefinitely.
+        timeout_in_minutes: 150
+        commands:
+          - |
+            set -eu
+            export VLLM_IMAGE_FETCH_TIMEOUT=60
+            if [ -f vllm_omni/diffusion/models/minimax_h3/lora.py ]; then
+              export VLLM_TEST_MINIMAX_H3_TURBO_LORA=$(python3 -c 'from huggingface_hub import hf_hub_download; print(hf_hub_download(repo_id="lightx2v/Minimax-h3-Turbo", filename="minimax_h3_fl2v_turbo_4step_v1.0_768p_bf16.safetensors"))')
+            fi
+            if [ -f vllm_omni/diffusion/models/minimax_h3/fasth3.py ]; then
+              export VLLM_TEST_MINIMAX_H3_FASTH3_LORA=$(python3 -c 'from huggingface_hub import hf_hub_download; print(hf_hub_download(repo_id="FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA", filename="dense-datafree/adapter_model.safetensors"))')
+            fi
+            timeout 35m pytest -s -v tests/e2e/online_serving/minimax_h3/test_minimax_h3_dlo_dp2_fl2va.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            timeout 35m pytest -s -v tests/e2e/online_serving/minimax_h3/test_minimax_h3_dlo_dp2_ref2va.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            timeout 35m pytest -s -v tests/e2e/online_serving/minimax_h3/test_minimax_h3_turbo_lora.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            timeout 35m pytest -s -v tests/e2e/online_serving/minimax_h3/test_minimax_h3_fasth3_lora.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+        mirror_hardwares: h100_2
@@ -12,16 +12,22 @@ steps:
    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "omni-test"
    steps:

-      - label: ":full_moon: Omni · Function Test with H100"
-        timeout_in_minutes: 120
+      - label: ":full_moon: Omni · Function Test with H100 · Single-GPU"
+        timeout_in_minutes: 90
        commands:
-          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni and cards_1" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Omni · Function Test with H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni and cards_2" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py
        mirror_hardwares: h100_2

      - label: ":full_moon: Omni · MiniCPM-o 4.5 Duplex Test"
        timeout_in_minutes: 50
        commands:
-          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni and cards_1" --run-level "full_model"
        mirror_hardwares: h100_1

      - label: ":full_moon: Omni · Doc Test with L4"
@@ -31,10 +37,10 @@ steps:
          - pytest -s -v tests/examples/ -m "full_model and omni and L4" --run-level "full_model"
        mirror_hardwares: l4_4

-      - label: ":full_moon: Omni · Doc Test with H100"
+      - label: ":full_moon: Omni · Doc Test with H100 · 2-GPU"
        timeout_in_minutes: 90
        commands:
-          - pytest -s -v tests/examples/ -m "full_model and omni and H100" --run-level "full_model"
+          - pytest -s -v tests/examples/ -m "full_model and omni and H100 and cards_2" --run-level "full_model"
        mirror_hardwares: h100_2

      - label: ":full_moon: Omni · Accuracy Test"
@@ -136,15 +142,29 @@ steps:
          - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy
        mirror_hardwares: l4_1

-      - label: ":full_moon: TTS · Perf Test"
-        key: nightly-tts-performance
+      - label: ":full_moon: TTS · Perf Test · Single-GPU"
+        key: nightly-tts-performance-single
        timeout_in_minutes: 180
        commands:
          - export BENCHMARK_DIR=tests/dfx/perf/results
          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
          - |
            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json -m "H100 and cards_1"
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: TTS · Perf Test · 2-GPU"
+        key: nightly-tts-performance-2gpu
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json -m "H100 and cards_2"
            EXIT=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
            exit $$EXIT
@@ -179,7 +199,6 @@ steps:
      build.pull_request.labels includes "nightly-test" ||
      build.pull_request.labels includes "diffusion-x2iat-test"
    steps:
-      # Use explicit file shards so single-card cases do not reserve 2+ GPUs.

      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Single-GPU"
        timeout_in_minutes: 120
@@ -187,24 +206,24 @@ steps:
          - >-
            pytest -sv
            tests/e2e/online_serving/test_qwen_image_expansion.py
-            -m "full_model and diffusion and H100 and not distributed_cuda"
+            -m "full_model and diffusion and H100 and cards_1"
            --run-level "full_model"
        mirror_hardwares: h100_1

-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Qwen-Image"
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · 2-GPU"
        timeout_in_minutes: 120
        commands:
          - >-
            pytest -sv
            tests/e2e/online_serving/test_qwen_image_expansion.py
-            -m "full_model and diffusion and H100 and distributed_cuda"
+            -m "full_model and diffusion and H100 and cards_2"
            --run-level "full_model"
        mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4 · Multi-GPU"
        timeout_in_minutes: 120
        commands:
-          - pytest -sv tests/e2e/ -k "not test_wan and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
+          - pytest -sv tests/e2e/ -k "not test_wan and not hunyuan" -m "full_model and diffusion and L4 and not cards_1" --run-level "full_model" --ignore=tests/e2e/accuracy
        mirror_hardwares: l4_4

      - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
@@ -235,8 +254,8 @@ steps:
          - pytest -s -v tests/e2e/accuracy/test_hidream_o1_image.py -m full_model --run-level full_model
        mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image"
-        key: nightly-diffusion-x2iat-performance-qwen-image
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image · Single-GPU"
+        key: nightly-diffusion-x2iat-performance-qwen-image-single
        timeout_in_minutes: 180
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
@@ -244,7 +263,23 @@ steps:
          - export CACHE_DIT_VERSION=1.5.0
          - |
            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json -m "H100 and cards_1"
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image · 4-GPU"
+        key: nightly-diffusion-x2iat-performance-qwen-image-4gpu
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+          - export CACHE_DIT_VERSION=1.5.0
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json -m "H100 and cards_4"
            EXIT=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
@@ -260,16 +295,28 @@ steps:
      build.pull_request.labels includes "diffusion-x2v-test"
    steps:

-      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · Single-GPU"
        timeout_in_minutes: 90
        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and cards_1" --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda and H100 and cards_2" --run-level "full_model"
        mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test"
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · Single-GPU"
        timeout_in_minutes: 90
        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda" --run-level "full_model"
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and cards_1" --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda and H100 and cards_2" --run-level "full_model"
        mirror_hardwares: h100_2

      - label: ":full_moon: Diffusion X2V · HunyuanVideo-1.5 Function Test"
@@ -310,17 +357,32 @@ steps:
          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m full_model --run-level full_model
        mirror_hardwares: h100_2

-      - label: ":full_moon: Diffusion X2V · Perf Test"
-        key: nightly-diffusion-x2v-performance
+      - label: ":full_moon: Diffusion X2V · Perf Test · Single-GPU"
+        key: nightly-diffusion-x2v-performance-single
        timeout_in_minutes: 180
        commands:
          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
          - |
            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json -m "H100 and cards_1"
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2V · Perf Test · 2-GPU"
+        key: nightly-diffusion-x2v-performance-2gpu
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json -m "H100 and cards_2"
            EXIT1=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json -m "H100 and cards_2"
            EXIT2=$$?
            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
@@ -371,10 +433,16 @@ steps:
          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4' --run-level "full_model"
        mirror_hardwares: l4_4

-      - label: ":full_moon: Diffusion · Distributed Test with H100"
+      - label: ":full_moon: Diffusion · Distributed Test with H100 · 2-GPU"
        timeout_in_minutes: 60
        commands:
-          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100' --run-level "full_model"
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and cards_2' --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion · Distributed Test with H100 · 4-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100 and cards_4' --run-level "full_model"
        mirror_hardwares: h100_4

      - label: ":full_moon: Diffusion · Offloader Test"
@@ -409,19 +477,25 @@ steps:
    depends_on:
      - nightly-omni-performance-no-async-chunk
      - nightly-omni-performance-async-chunk
-      - nightly-tts-performance
-      - nightly-diffusion-x2iat-performance-qwen-image
-      - nightly-diffusion-x2v-performance
+      - nightly-tts-performance-single
+      - nightly-tts-performance-2gpu
+      - nightly-diffusion-x2iat-performance-qwen-image-single
+      - nightly-diffusion-x2iat-performance-qwen-image-4gpu
+      - nightly-diffusion-x2v-performance-single
+      - nightly-diffusion-x2v-performance-2gpu
    if: build.env("NIGHTLY") == "1" && build.env("EMAIL_DISTRIBUTION") == "1"
    commands:
      - pip install openpyxl
      - export DEFAULT_INPUT_DIR=tests/dfx/perf/results
      - export DEFAULT_OUTPUT_DIR=tests/dfx/perf/results
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance-single
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance-2gpu
      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-no-async-chunk
      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-async-chunk
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-single
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image-4gpu
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance-single
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance-2gpu
      - python tools/nightly/generate_nightly_perf_excel.py
      - python tools/nightly/generate_nightly_perf_html.py
      - python tools/nightly/send_nightly_email.py --report-file "tests/dfx/perf/results/*.xlsx, tests/dfx/perf/results/*.html"
@@ -252,6 +252,9 @@ steps:
          - vllm_omni/model_executor/models/common/alias_free_activation.py
          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
          - vllm_omni/deploy/qwen3_tts.yaml
+          - vllm_omni/entrypoints/openai/serving_speech.py
+          - vllm_omni/entrypoints/openai/tts_adapters/
+          - vllm_omni/engine/stage_pool.py
        commands:
          - |
            timeout 20m bash -c "
@@ -260,25 +263,6 @@ steps:
            "
        mirror_hardwares: l4_1

-      - label: "TTS · Qwen3-TTS Base Test"
-        source_file_dependencies:
-          - tests/e2e/online_serving/test_qwen3_tts_base.py
-          - vllm_omni/model_executor/models/qwen3_tts/
-          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
-          - vllm_omni/model_executor/models/common/snake_activation.py
-          - vllm_omni/model_executor/models/common/alias_free_activation.py
-          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
-          - vllm_omni/deploy/qwen3_tts.yaml
-        commands:
-          - |
-            timeout 20m bash -c "
-              export VLLM_WORKER_MULTIPROC_METHOD=spawn
-              export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
-              export VLLM_OMNI_USE_V2_RUNNER=1
-              pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py -m 'core_model' --run-level 'core_model'
-            "
-        mirror_hardwares: l4_1
-
      - label: "Diffusion · Wan22 Test"
        source_file_dependencies:
          - tests/e2e/online_serving/test_wan22_t2v.py
@@ -44,10 +44,16 @@ steps:
          - pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
        mirror_hardwares: h100_4

-      - label: "Reliability Test · Invalid parameters · H100"
-        timeout_in_minutes: 120
+      - label: "Reliability Test · Invalid parameters · H100 · Single-GPU"
+        timeout_in_minutes: 90
        commands:
-          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100"
+          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100 and cards_1"
+        mirror_hardwares: h100_1
+
+      - label: "Reliability Test · Invalid parameters · H100 · 2-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100 and cards_2"
        mirror_hardwares: h100_2

      - label: "Reliability Test · Invalid parameters · L4"
@@ -105,27 +111,37 @@ steps:
    if: build.env("NON_CRITICAL") == "1"
    steps:

-      - label: "Omni · L4"
+      # Qwen2.5 expansion (CUDA 4-card / online 2-card) stays on this l4_4 job.
+      - label: "Omni · L4 · Multi-GPU"
        timeout_in_minutes: 90
        commands:
          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
+          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py -m "slow and L4 and omni and not cards_1" --run-level "core_model"
          - >-
            pytest -s -v tests/e2e/
            --ignore=tests/e2e/online_serving/test_qwen2_5_omni_expansion.py
            --ignore=tests/e2e/offline_inference/test_qwen2_5_omni_expansion.py
-            -m "slow and L4 and omni"
+            -m "slow and L4 and omni and not cards_1"
            --run-level "full_model"
        mirror_hardwares: l4_4

-      - label: "Omni · H100"
+      - label: "Omni · H100 · Single-GPU"
        timeout_in_minutes: 90
        commands:
          - >-
            pytest -sv
-            tests/e2e/ -m "slow and H100 and omni"
+            tests/e2e/ -m "slow and H100 and omni and cards_1"
            --run-level "full_model"
-        mirror_hardwares: h100_2
+        mirror_hardwares: h100_1
+
+      - label: "Omni · H100 · 4-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and H100 and omni and cards_4"
+            --run-level "full_model"
+        mirror_hardwares: h100_4

      - label: "TTS · L4"
        timeout_in_minutes: 120
@@ -169,12 +185,21 @@ steps:
            --run-level "full_model"
        mirror_hardwares: l4_1

-      - label: "Diffusion · L4"
+      - label: "Diffusion · L4 · Single-GPU"
        timeout_in_minutes: 60
        commands:
          - >-
            pytest -sv
-            tests/e2e/ -m "slow and diffusion and L4"
+            tests/e2e/ -m "slow and diffusion and L4 and cards_1"
+            --run-level "full_model"
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · L4 · Multi-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and L4 and not cards_1"
            --run-level "full_model"
        mirror_hardwares: l4_4

@@ -183,15 +208,24 @@ steps:
        commands:
          - >-
            pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and not distributed_cuda"
+            tests/e2e/ -m "slow and diffusion and H100 and cards_1"
            --run-level "full_model"
        mirror_hardwares: h100_1

-      - label: "Diffusion · H100 · Multi-GPU"
+      - label: "Diffusion · H100 · 2-GPU"
        timeout_in_minutes: 90
        commands:
          - >-
            pytest -sv
-            tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
+            tests/e2e/ -m "slow and diffusion and H100 and cards_2"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "Diffusion · H100 · 4-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and H100 and cards_4"
            --run-level "full_model"
        mirror_hardwares: h100_4
@@ -10,6 +10,18 @@ steps:
        -m 'core_model and npu and A3 and omni'
        --run-level 'core_model'

+  - label: "Diffusion NPU layer tests"
+    key: ready-diffusion-npu-layer-tests
+    depends_on: upload-ready-pipeline
+    mirror_hardwares: a3_npu_2
+    commands:
+      - >-
+        pytest -s -v
+        tests/diffusion/layers/test_activation.py
+        tests/diffusion/layers/test_fused_qk_norm_rope_npu.py
+        -m 'core_model and npu and A3'
+        --run-level 'core_model'
+
  - group: ":card_index_dividers: Omni Model Test"
    key: ready-omni-group
    depends_on: upload-ready-pipeline
@@ -338,7 +338,7 @@ Classify the model's **CI priority** first:

 **L2 & L3 online — same file, dual marks on the baseline smoke:** The **first / simplest** case in `test_{slug}.py` (default deploy, minimal steps, single prompt) should carry **both** `@pytest.mark.core_model` and `@pytest.mark.advanced_model` on the **same** function so L2 (`test-ready.yml`) and L3 (`test-merge.yml`) share one smoke test. Heavier deploy variants or API paths in the same file use **`advanced_model` only**. When L3 moves to nightly, migrate those heavier cases into `test_{slug}_expansion.py` with `full_model` and remove the dedicated `test-merge.yml` job (see `test_longcat_image_expansion.py`, `test_qwen_image_expansion.py`).

-**L4 design (high priority):** Combine multiple supported features (Cache-DiT, TP, USP, CFG, HSDP, CPU offload, quantization) into **few parametrized** `OmniServerParams` rows so each feature appears in at least one case without exploding GPU jobs. Shard single-GPU vs multi-GPU cases across the nightly X2I/X2V function steps (1-GPU vs `distributed_cuda`).
+**L4 design (high priority):** Combine multiple supported features (Cache-DiT, TP, USP, CFG, HSDP, CPU offload, quantization) into **few parametrized** `OmniServerParams` rows so each feature appears in at least one case without exploding GPU jobs. Shard single-GPU vs multi-GPU cases across the nightly X2I/X2V function steps (`cards_1` vs `not cards_1`).

 **L4 design (medium / low):** One or two parametrized rows covering the best quality/perf trade-off; skip perf JSON unless the model is high priority.

@@ -34,7 +34,7 @@ but the changes are correct — stage the modified files and re-commit.
 | `check-forbidden-imports` | Stdlib `re`/`base64`, pickle, Hugging Face Hub API, or direct Triton/TileLang | `import regex as re` and `pybase64`; use `vllm.transformers_utils.repo_utils`. Do **not** add the file to `allowed_files` without review |
 | `check-torch-cuda-call` | New `torch.cuda.*` call site | Use `current_omni_platform` / `OmniPlatform`; do not grow `ALLOWED_FILES` without review |
 | `check-tts-adapter-migration` | New `self._tts_model_type` branch in `serving_speech.py` | Put per-model logic in `tts_adapters/`. Lower `MAX_MODEL_TYPE_BRANCHES` when removing branches; do not raise it without review |
-| `check-test-ci-coverage` | New `tests/**/test_*.py` missing level or hardware mark | Add `core_model`/`advanced_model`/… plus `cpu`/`cuda`/`hardware_test(` |
+| `check-mark` | New `tests/**/test_*.py` missing level/hardware mark, or direct `pytest.mark.H100` | Add `core_model`/`advanced_model`/… plus `cpu`/`cuda`/`hardware_test(`; SKU marks only via helpers |
 | `mypy-3.10` | Type error on changed `vllm_omni/` files | Fix the types; model trees are excluded. Extra versions: `pre-commit run --hook-stage manual mypy-3.12` |
 | `markdownlint-cli2` | Docs/README markdown lint | Accept auto-fix under `docs/` / `recipes/` / root README |
 | `check-buildkite` | Invalid `.buildkite/*.yml` | Fix the YAML; do not grow `SKIP_FILES` without review |
@@ -52,8 +52,8 @@ Always attach markers deliberately:
  - `tts` — speech synthesis / TTS-only models (`/v1/audio/speech`, voice clone, etc.)
  - `diffusion` — generative diffusion models (image / audio / text / video from noise)
 - **Cross-cutting area** (when relevant): `parallel`, `cache`, `example`, `benchmark`
- **Hardware**: `cpu`, `gpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `distributed_cuda`, …
- Optional: `slow`, distributed markers when multi-card is required
+- **Hardware**: `cpu`, `gpu`, `cuda`, `rocm`, `npu`, …; SKU (`H100`, `L4`, …) and `cards_{n}` via `@hardware_test(...)` / `hardware_marks(...)` only — never `pytest.mark.H100` (`check-mark` rejects it)
+- Optional: `slow`. Multi-card: `num_cards=N` on the helper (filter with `cards_N` / `not cards_1`)

 **Baseline smoke (L2 + L3):** The simplest e2e case per model — default deploy, minimal request — should usually carry **both** `@pytest.mark.core_model` and `@pytest.mark.advanced_model` on the **same** test function so `test-ready.yml` and `test-merge.yml` share one test. `send_*_request` picks validation depth from `--run-level`. References: `test_voxcpm2_tts.py::test_text_to_audio_001`, `test_qwen3_tts_customvoice.py::test_text_to_audio_001`. Heavier scenarios use **`advanced_model` only**; L4 expansion uses **`full_model`**.

@@ -274,7 +274,7 @@ def test_text_to_video_001(omni_server, online_client) -> None:

 **3. Cross-cutting rules**

- New `.py` files need the Omni SPDX header (`Copyright contributors to the vLLM-Omni project`, not `vLLM project`). `tests/` may keep stdlib `re`/`base64`; pickle is still banned unless the file is already on the pickle allowlist. Every collected `tests/**/test_*.py` needs a CI **level** mark and a **hardware** mark/helper (`check-test-ci-coverage` runs locally; GHA skips it). Do not grow `allowed_files` to land a test. Policy: [docs/contributing/README.md](../../../docs/contributing/README.md#linting).
+- New `.py` files need the Omni SPDX header (`Copyright contributors to the vLLM-Omni project`, not `vLLM project`). `tests/` may keep stdlib `re`/`base64`; pickle is still banned unless the file is already on the pickle allowlist. Every collected `tests/**/test_*.py` needs a CI **level** mark and a **hardware** platform mark/helper (`check-mark` runs locally; GHA skips it). Do not write `pytest.mark.H100` / `pytest.mark.L4`; use the helpers so `cards_{n}` is attached. Do not grow `allowed_files` to land a test. Policy: [docs/contributing/README.md](../../../docs/contributing/README.md#linting).
 - Reuse existing fixtures for the chosen scenario; do not mix “online client” assumptions into offline `OmniRunner` tests without a clear reason.
 - Avoid external network dependency in assertions unless the scenario is explicitly “online serving” or doc examples that require a model hub (then align with CI secrets/cache).
 - Keep **one test function = one intent** (one modality combo, one endpoint contract, or one acceleration combo).
@@ -79,7 +79,7 @@ E2E (L2+) should not rely on mocks unless documenting a rare exception; prefer r
   - `full_model` — L4 nightly (`test-nightly.yml`); some expansion tests still carry both `advanced_model` and `full_model` during migration
 2. **Model type** (pick one): `omni`, `tts`, or `diffusion`
 3. **Cross-cutting** when relevant: `parallel`, `cache`, `example`, `benchmark`
-4. **Hardware**: `cpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `distributed_cuda`, …
+4. **Hardware**: `cpu`, `cuda`, `rocm`, `npu`, `L4`, `H100`, `cards_1`, …
 5. Multi-card: `@hardware_test(...)` in `tests/helpers/mark.py`

 ## Command Templates
@@ -244,7 +244,7 @@ pytest -s -v dfx/reliability/invalid_param_test/test_invalid_image_generation.py

 ```bash
 cd tests
-pytest -s -v -m "core_model and distributed_cuda and L4" --run-level=core_model
+pytest -s -v -m "core_model and L4 and not cards_1" --run-level=core_model
 ```

 ### Concrete e2e paths (common in-tree)
@@ -27,4 +27,4 @@ jobs:
        # Don't run historical-debt hooks against the whole tree in CI yet.
        # They still run locally on changed files.
        env:
-          SKIP: check-test-ci-coverage,markdownlint-cli2,shellcheck,check-spdx-header,mypy-3.10
+          SKIP: check-mark,markdownlint-cli2,shellcheck,check-spdx-header,mypy-3.10
@@ -105,8 +105,8 @@ repos:
      <<: *mypy_common
      stages: [manual]

-    - id: check-test-ci-coverage
-      name: Ensure test files have pytestmarks
+    - id: check-mark
+      name: Ensure test files have CI marks (no direct SKU)
      entry: python tools/pre_commit/check_test_marks.py
      language: python
      types: [python]
@@ -16,6 +16,7 @@ Easy, fast, and cheap omni-modality model serving for everyone

 *Latest News* 🔥

+- [2026/08] We released [0.28.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.28.0), featuring production-ready [MiniMax H3](recipes/MiniMaxAI/MiniMax-H3.md) serving on GPU and NPU, a unified AR/DiT paged KV cache runtime, and enhanced realtime full-duplex serving for the [MiniCPM-o series](recipes/OpenBMB/MiniCPM-o-4_5.md).
 - [2026/08] [VeRL-Omni](https://github.com/verl-project/verl-omni) `v0.2.0` is released: faster diffusion RL powered by vLLM-Omni (request-level/step-wise batching with FA3), rebuilt Qwen3-Omni multimodal training (DPO & GSPO), plus LTX-2.3, Qwen-Image-Edit support and more. See the [release notes](https://github.com/verl-project/verl-omni/releases/tag/v0.2.0).
 - [2026/08] We released [0.26.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.26.0) - aligned with the vLLM 0.26 release line, featuring [MiniMax H3](recipes/MiniMaxAI/MiniMax-H3.md) joint video/audio generation, an experimental full-duplex realtime runtime for [MiniCPM-o 4.5](recipes/OpenBMB/MiniCPM-o-4_5.md), distributed layerwise diffusion offload, and broader model, hardware, streaming, TTS, and quantization support.
 - [2026/07] We released [0.24.0](https://github.com/vllm-project/vllm-omni/releases/tag/v0.24.0) - aligned with the vLLM 0.24 release line, expanding production-ready coverage across TTS, speech, diffusion, image/video generation, and robot-policy serving, with major Omni stage runtime refactoring, diffusion request-level batching, async output materialization, quantization/cache/memory improvements, and broad CUDA/ROCm/XPU/NPU support.
@@ -57,9 +58,9 @@ vLLM-Omni is flexible and easy to use with:
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:

 - **Omni-modality models** (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
- **TTS models** (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
- **Diffusion models** — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
- **Robot-policy and action models** (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
+- **TTS models** (e.g. Qwen3-TTS, IndexTTS 2.5, dots.tts, CosyVoice3)
+- **Diffusion models** — image, video, and audio generation (e.g. MiniMax H3, LTX-2.5, SANA-Video, Wan2.2)
+- **Robot-policy and action models** (e.g. π0, GR00T-N1.7, DreamZero-DROID, InternVLA-A1)

 ## Getting Started

@@ -67,6 +68,7 @@ Visit our [documentation](https://vllm-omni.readthedocs.io/en/latest/) to learn

 - [Installation](https://vllm-omni.readthedocs.io/en/latest/getting_started/installation/)
 - [Quickstart](https://vllm-omni.readthedocs.io/en/latest/getting_started/quickstart/)
+- [Nightly CUDA Docker images](https://hub.docker.com/r/vllm/vllm-omni/tags?name=nightly) built from the latest `main`
 - [List of Supported Models](https://vllm-omni.readthedocs.io/en/latest/models/supported_models/)
 - [Deployment Recipes](https://recipes.vllm.ai) for vLLM-Omni model serving

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0
+ARG VLLM_ASCEND_TAG=v0.28.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 # WORKDIR /vllm-workspace/vllm
@@ -30,6 +30,7 @@ RUN python3 -m pip install -v --no-deps \
      "stepaudio2-minicpmo>=0.1.1" \
      "step-audio2>=1.0.0"

+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

 ENTRYPOINT []
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0-a3
+ARG VLLM_ASCEND_TAG=v0.28.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 # WORKDIR /vllm-workspace/vllm
@@ -30,6 +30,7 @@ RUN python3 -m pip install -v --no-deps \
      "stepaudio2-minicpmo>=0.1.1" \
      "step-audio2>=1.0.0"

+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

 ENTRYPOINT []
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0
+ARG VLLM_ASCEND_TAG=v0.28.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -65,6 +65,7 @@ RUN python3 -m pip install -v --no-deps \
      --trusted-host cache-service.nginx-pypi-cache.svc.cluster.local \
      --default-timeout=60

+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

 ENTRYPOINT []
@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.27.0-a3
+ARG VLLM_ASCEND_TAG=v0.28.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}

 ARG APP_DIR=/vllm-workspace/vllm-omni
@@ -66,6 +66,7 @@ RUN python3 -m pip install -v --no-deps \
      --trusted-host cache-service.nginx-pypi-cache.svc.cluster.local \
      --default-timeout=60

+ENV VLLM_TARGET_DEVICE=npu
 ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

 ENTRYPOINT []
@@ -5,14 +5,22 @@ nav:
    - getting_started/quickstart.md
    - getting_started/installation/*
  - Serving:
-    - OpenAI-Compatible API:
-      - Chat Completions API: serving/chat_completions_api.md
-      - Image Generation: serving/image_generation_api.md
-      - Image Edit: serving/image_edit_api.md
-      - Text to Speech: serving/speech_api.md
-      - Videos: serving/videos_api.md
-      - Audio Generation: serving/audio_generate_api.md
-      - Streaming Video Input: serving/video_stream_api.md
+    - API Server:
+      - Overview: serving/README.md
+      - Core APIs:
+        - Chat Completions: serving/chat_completions_api.md
+        - Text to Speech: serving/speech_api.md
+        - Audio Generation: serving/audio_generate_api.md
+        - Image Generation: serving/image_generation_api.md
+        - Image Edit: serving/image_edit_api.md
+        - Videos: serving/videos_api.md
+      - Streaming and Realtime:
+        - Realtime Audio: serving/realtime_api.md
+        - Full Duplex: serving/full_duplex_api.md
+        - Streaming Video Input: serving/video_stream_api.md
+        - Streaming Video Output: serving/streaming_video_output_api.md
+        - OpenPI Robot Policy: serving/openpi_api.md
+      - Standalone Experimental Servers: serving/standalone_servers.md
  - Examples:
    - examples/README.md
    - Offline Inference:
@@ -29,7 +37,7 @@ nav:
      - Image-To-Video: user_guide/examples/online_serving/image_to_video.md
      - Speech-To-Video: user_guide/examples/online_serving/speech_to_video.md
      - Text-To-Image: user_guide/examples/online_serving/text_to_image.md
-      - Text-To-Speech: user_guide/examples/online_serving/text_to_speech.md
+      - Text-To-Speech (Online Serving): user_guide/examples/online_serving/text_to_speech.md
      - Text-To-Video: user_guide/examples/online_serving/text_to_video.md
  - General:
    - usage/*
@@ -119,6 +127,8 @@ nav:
        - Async Omni Output Materialization: design/feature/omni_async_output_materialization.md
        - Automatic Prefix Caching: design/feature/prefix_caching.md
        - Realtime AR-Diffusion: design/feature/realtime_ar_diffusion.md
+      - Model-Specific Capabilities:
+        - MiniMax H3 Temporal VAE Chunks: design/feature/minimax_h3_temporal_vae_chunks.md
      - Communication:
        - OmniConnector Implementations:
          - Mooncake Store Connector: design/feature/omni_connectors/mooncake_store_connector.md
@@ -57,9 +57,9 @@ vLLM-Omni is flexible and easy to use with:
 vLLM-Omni seamlessly supports most popular open-source models on HuggingFace, including:

 - Omni-modality models (e.g. Qwen3-Omni, MiniCPM-o 4.5, Cosmos3, HunyuanImage, BAGEL)
- TTS models (e.g. Qwen3-TTS, VoxCPM2, Ming-Omni-TTS, CosyVoice3)
- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, Qwen-Image, Wan2.2, FLUX)
- Robot-policy and action models (e.g. GR00T-N1.7, DreamZero-DROID, InternVLA-A1, Cosmos3 action policy)
+- TTS models (e.g. Qwen3-TTS, IndexTTS 2.5, dots.tts, CosyVoice3)
+- Diffusion models — image, video, and audio generation (e.g. MiniMax H3, LTX-2.5, SANA-Video, Wan2.2)
+- Robot-policy and action models (e.g. π0, GR00T-N1.7, DreamZero-DROID, InternVLA-A1)

 For more information, checkout the following:

@@ -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 0.26 configuration guide](https://docs.vllm.ai/en/v0.26.0/configuration/index.html).
+For options within a vLLM Engine, please refer to the [vLLM 0.28 configuration guide](https://docs.vllm.ai/en/v0.28.0/configuration/index.html).

 Each model defines fixed topology in a registered `PipelineConfig` and runtime
 overrides in a deploy YAML.
@@ -151,7 +151,7 @@ their keys only.
 ## Inherited vLLM variables

 vLLM-Omni also reads variables through its aligned vLLM dependency. Refer to
-the [vLLM 0.26 environment-variable reference](https://docs.vllm.ai/en/v0.26.0/configuration/env_vars.html)
+the [vLLM 0.28 environment-variable reference](https://docs.vllm.ai/en/v0.28.0/configuration/env_vars.html)
 for their definitions. This includes vLLM launch, cache, logging, plugin, ROCm,
 XPU, ModelScope, and FlashInfer workspace settings.

@@ -49,7 +49,7 @@ vLLM-Omni's `pre-commit` hooks will now run automatically every time you commit.
    `--all-files` run stays green while historical debt is cleaned up. The
    current `SKIP` list in
    [`.github/workflows/pre-commit.yml`](https://github.com/vllm-project/vllm-omni/blob/main/.github/workflows/pre-commit.yml)
-    is `check-test-ci-coverage`, `markdownlint-cli2`, `shellcheck`,
+    is `check-mark`, `markdownlint-cli2`, `shellcheck`,
    `check-spdx-header`, and `mypy-3.10`. Those hooks still run on **your
    commit** for changed files. A passing GitHub pre-commit check does not mean
    they passed locally. Hooks **not** on that list (forbidden imports,
@@ -66,7 +66,7 @@ already there). `check-pickle-imports` is gone: pickle is now one rule inside
 | `markdownlint-cli2` | Markdown in `docs/`, `recipes/`, `README.md`, `CONTRIBUTING.md` (not `.claude/` / `.cursor/` / `CLAUDE.md`) | skipped |
 | `mypy-3.10` | Type-check changed `vllm_omni/` files (model trees excluded). `tests/` uses `--follow-imports skip` | skipped |
 | `mypy-3.11` / `3.12` / `3.13` | Same checker, extra Python versions | not installed; `pre-commit run --hook-stage manual mypy-3.12` |
-| `check-test-ci-coverage` | Every `tests/**/test_*.py` has a CI level mark and a hardware mark/helper | skipped |
+| `check-mark` | Every `tests/**/test_*.py` has a CI level mark and a hardware platform mark or helper; no direct `pytest.mark.H100` / SKU | skipped |
 | `check-tts-adapter-migration` | `self._tts_model_type` branches in `serving_speech.py` must not increase | runs |
 | `shellcheck` | `*.sh` quoting / undefined vars | skipped |
 | `check-spdx-header` | Omni SPDX header on `.py` / `.pyi` / `.sh` / `.rs` / `.proto` | skipped |
@@ -144,11 +144,13 @@ pre-commit run --hook-stage manual mypy-3.12

 #### Test CI marks

-`check-test-ci-coverage` requires each collected test module under `tests/` to
+`check-mark` requires each collected test module under `tests/` to
 have at least one CI **level** mark (`core_model`, `advanced_model`,
-`full_model`, `local_model`, or `slow`) and a **hardware** mark (`cpu`, `cuda`,
-`H100`, …) or helper (`hardware_test(` / `hardware_marks(`). GitHub Actions
-skips this hook; local commit does not. See the
+`full_model`, `local_model`, or `slow`) and a **hardware** platform mark
+(`cpu`, `cuda`, …) or helper (`hardware_test(` / `hardware_marks(`). SKU marks
+(`H100`, `L4`, …) must come from those helpers so `cards_{n}` is attached;
+direct `pytest.mark.H100` is rejected. GitHub Actions skips this hook; local
+commit does not. See the
 [test writing guide](./ci/test_writing_guide.md).

 #### TTS adapter ratchet
@@ -402,7 +402,7 @@ loads the model once per mode.
 Before splitting, confirm each half actually collects at least one test under the job's forwarded filters — pytest exits with code 5 ("no tests collected") if a half is empty, which the script reports as a failure, red-ing a job that used to pass as one combined invocation.

 Also check the tests' `num_cards` against the job's `mirror_hardwares` preset.
-`cuda_marks` turns `num_cards > 1` into `skipif(device_count() < num_cards)`, so a
+`_cuda_marks` turns `num_cards > 1` into `skipif(device_count() < num_cards)`, so a
 test asking for more GPUs than the preset provides is silently skipped and its
 coverage artifact comes back empty. Splitting such a job produces a file that
 looks fine and measures nothing — "Diffusion · Z Image Test" is the current
@@ -81,8 +81,8 @@ Supported form:

 | Form | Example | Effect |
 | ---- | ------- | ------ |
-| Array (single platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}}, "full_model", "diffusion"]` | Hardware marks from `hardware_marks(...)`; `num_cards` > 1 adds `distributed_*` (+ CUDA `skipif_cuda` when applicable). String entries become extra pytest marks. |
-| Array (multi-platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100", "rocm": "MI325", "npu": "A2"}, "num_cards": {"cuda": 2, "rocm": 2, "npu": 1}}}, "full_model", "omni"]` | Same as above, but declares **multiple platforms** in one case. Filter examples: `-m "full_model and H100 and cuda"`, `-m "full_model and MI325 and rocm"`. |
+| Array (single platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 2}}, "full_model", "diffusion"]` | Hardware marks from `hardware_marks(...)`; `num_cards` adds `cards_{n}` (including `cards_1` when omitted). String entries become extra pytest marks. |
+| Array (multi-platform) | `"mark": [{"hardware_marks": {"res": {"cuda": "H100", "rocm": "MI325"}, "num_cards": 2}}, "full_model", "omni"]` | Same as above, but declares **multiple platforms** on one item. All platforms must share the same `num_cards` (int or identical dict values). Different counts need `@hardware_test` or one `hardware_marks()` per `pytest.param`. Filter examples: `-m "full_model and H100 and cards_2 and cuda"`, `-m "full_model and MI325 and cards_2 and rocm"`. |

 Recommended for L4 perf cases:

@@ -157,7 +157,7 @@ pytest -s -v tests/dfx/perf/scripts/run_benchmark.py \
 ```

 <!-- markdownlint-disable-next-line MD051 -->
-See also [Markers for Tests](#markers-for-tests) for registered hardware markers (`H100`, `L4`, `cuda`, `distributed_cuda`, …).
+See also [Markers for Tests](#markers-for-tests) for registered hardware markers (`H100`, `L4`, `cuda`, `cards_1`, …).

 #### `server_params` Configuration

@@ -88,7 +88,7 @@ Failed jobs: 1/2
    ```bash
    cd tests
    pytest -s -v test_xxxx.py --run-level=core_model
-    pytest -s -v -m "core_model and distributed_cuda and L4" --run-level=core_model
+    pytest -s -v -m "core_model and L4 and not cards_1" --run-level=core_model
    ```

 === "L3 level"
@@ -119,7 +119,7 @@ Failed jobs: 1/2

    ```bash
    pytest -s -v test_xxxx.py --run-level=advanced_model
-    pytest -s -v -m "advanced_model and distributed_cuda and L4" --run-level=advanced_model
+    pytest -s -v -m "advanced_model and L4 and not cards_1" --run-level=advanced_model
    ```

 === "L4 level"
@@ -153,12 +153,12 @@ Failed jobs: 1/2
    ```bash
    cd tests
    pytest -s -v test_xxxx.py --run-level=full_model
-    pytest -s -v -m "full_model and distributed_cuda and L4" --run-level=full_model
+    pytest -s -v -m "full_model and L4 and not cards_1" --run-level=full_model
    pytest -s -v -m "full_model and (omni or tts) and H100" --run-level=full_model
    ```
    If you only want to run specific test cases on a particular platform, you can use:
    ```bash
-    pytest -s -v -m "full_model and distributed_cuda and L4"  --run-level=full_model
+    pytest -s -v -m "full_model and L4 and not cards_1"  --run-level=full_model
    ```
    Note: ``run_benchmark.py`` and ``run_diffusion_benchmark.py`` accept an optional ``--test-config-file``. If omitted, each loads every ``*.json`` under ``tests/dfx/perf/tests/`` (omni/tts vs diffusion split by ``is_diffusion_perf_config``) and pytest ``-m`` filters by each case's JSON ``mark``:
    ```bash
@@ -48,17 +48,12 @@ Defined in `pyproject.toml`:
 | `MI325`            | Tests that require MI325 GPU (AMD/ROCm) *                                                               |
 | `A2`               | Tests that require A2 NPU *                                                                             |
 | `A3`               | Tests that require A3 NPU *                                                                             |
-| `distributed_cuda` | Tests that require multi cards on CUDA platform *                                                       |
-| `distributed_rocm` | Tests that require multi cards on ROCm platform  *                                                      |
-| `distributed_npu`  | Tests that require multi cards on NPU platform  *                                                       |
-| `skipif_cuda`      | Skip if the num of CUDA cards is less than the required *                                               |
-| `skipif_rocm`      | Skip if the num of ROCm cards is less than the required *                                               |
-| `skipif_npu`       | Skip if the num of NPU cards is less than the required *                                                |
+| `cards_{n}`        | Tests that require *n* accelerator cards (`cards_1` … `cards_8`); auto-added from `num_cards` *         |
 | `slow`             | Slow tests (may skip in quick CI)                                                                       |
 | `benchmark`        | Benchmark tests (decorator on runner test functions; perf JSON uses `full_model` + type marker instead) |
 | `local_model`      | Tests requiring local / non-HF-hub model weights                                                        |

-\* Means those markers are auto-added by `@hardware_test` (parametrization decorator) or `hardware_marks` (only returning the list of marks for flexibility).
+\* Means those markers are auto-added by `@hardware_test` (parametrization decorator) or `hardware_marks` (only returning the list of marks for flexibility). Do **not** write `pytest.mark.H100` / `pytest.mark.L4` (or other SKU names) by hand: `check-mark` rejects that so `cards_{n}` cannot be skipped.

 #### Example usage for markers

@@ -81,25 +76,23 @@ def test_video_to_audio()
 This decorator is intended to make hardware-aware, cross-platform test authoring easier and more robust for CI/CD environments. The `hardware_test` decorator in `vllm-omni/tests/helpers/mark.py` performs the following actions:

 1. **Applies platform and resource markers**  
-   Adds the appropriate pytest markers for each specified hardware platform (e.g., `cuda`, `rocm`, `xpu`, `npu`) and resource type (e.g., `L4`, `H100`, `MI325`, `B60`, `A2`, `A3`).
+   Adds the appropriate pytest markers for each specified hardware platform (e.g., `cuda`, `rocm`, `xpu`, `npu`) and resource type (e.g., `L4`, `H100`, `H200`, `B200`, `MI325`, `B60`, `A2`, `A3`).
   ```python
   @pytest.mark.cuda
   @pytest.mark.L4
   ```
-2. **Handles multi-card (distributed) scenarios**  
-   For tests requiring multiple cards, it automatically adds distributed markers such as `distributed_cuda`, `distributed_rocm`, or `distributed_npu`.
-   ```python
-   @pytest.mark.distributed_cuda(num_cards=num_cards)
-   ```
+2. **Handles card-count filtering**  
+   Adds a filterable marker `cards_{n}` (including `cards_1` when `num_cards` is omitted / 1). Use this with the SKU and platform, e.g. `-m "H100 and cards_4 and cuda"` vs `-m "H100 and cards_2 and cuda"`. Any multi-card CUDA test is `-m "cuda and not cards_1"`.
+
+   When `res` lists more than one platform **and** `num_cards` differs by platform, `@hardware_test` collects **one variant per platform** so those `cards_{n}` marks do not stack. `-m "H100 and cards_4 and cuda"` then selects only the CUDA/H100 4-card variant, not a ROCm 2-card sibling. The same count on every platform stays a single item.
 3. **Supports flexible card requirements**  
   Accepts `num_cards` as either a single integer for all platforms or as a dictionary with per-platform values. If not specified, defaults to 1 card per platform.

 4. **Integrates resource validation**  
-   On CUDA, adds a skip marker (`skipif_cuda`) if the system does not have the required number of devices.
-   Support for `skipif_rocm` and `skipif_npu` will be implemented later.
+   On CUDA and XPU, when `num_cards > 1`, attaches pytest's built-in `skipif` if the machine has fewer devices than requested. This is not a named `skipif_*` marker and is not used for `-m` filtering.

 5. **Works with pytest filtering**  
-   Allows tests to be filtered and selected at runtime using standard pytest marker expressions (e.g., `-m "distributed_cuda and L4"`).
+   Allows tests to be filtered and selected at runtime using standard pytest marker expressions (e.g., `-m "H100 and cards_2"`, `-m "L4 and not cards_1"`).

 ##### Example usage for decorator

@@ -117,13 +110,14 @@ This decorator is intended to make hardware-aware, cross-platform test authoring
        num_cards=2,
    )
    ```
- `res` must be a dict; supported resources: CUDA (L4/H100), ROCm (MI325), XPU (B60), MUSA (S5000), NPU (A2/A3)
+- `res` must be a dict. SKU values come from `pyproject.toml` markers tagged `[hardware-resource]` plus the platform key (`[cuda]`, `[rocm]`, `[xpu]`, `[npu]`, `[musa]`).
 - `num_cards` can be int (all platforms) or dict (per platform); defaults to 1 when missing
- Distributed markers (`distributed_cuda`, `distributed_rocm`, `distributed_npu`) are auto-added for multi-card cases
 - Filtering examples:
-    - CUDA only: `pytest -m "distributed_cuda and L4"`
-    - ROCm only: `pytest -m "distributed_rocm and MI325"`
-    - NPU only: `pytest -m "distributed_npu"`
+    - CUDA H100 4-card: `pytest -m "H100 and cards_4 and cuda"`
+    - CUDA H100 2-card: `pytest -m "H100 and cards_2 and cuda"`
+    - CUDA multi-card (any count): `pytest -m "L4 and not cards_1"`
+    - ROCm multi-card: `pytest -m "MI325 and not cards_1"`
+    - NPU multi-card: `pytest -m "npu and not cards_1"`

 #### Function: `hardware_marks`

@@ -179,37 +173,30 @@ If you want to add support for a new platform (e.g., "tpu" for a new accelerator
   [tool.pytest.ini_options]
   markers = [
       # ... existing markers ...
-       "tpu: Tests that require TPU device",
-       "TPU_V3: Tests that require TPU v3 hardware",
-       "distributed_tpu: Tests that require multiple TPU devices",
+       "tpu: [hardware-platform] Tests that require TPU device",
+       # Add [gpu] next to [hardware-platform] if this platform should also get pytest.mark.gpu.
+       "TPU_V3: [hardware-resource] [tpu] Tests that require TPU v3 hardware",
+       "cards_1: [hardware-cards] Tests that require 1 accelerator card",
+       "cards_2: [hardware-cards] Tests that require 2 accelerator cards",
   ]
   ```
+   `check-mark` treats `[hardware-platform]` names (including `cpu`/`gpu`) as allowed `pytest.mark.<name>` values. `hardware_test(res=...)` still rejects `cpu`/`gpu` as `res` keys.
 2. **Implement a marker construction function for your platform** in `vllm-omni/tests/helpers/mark.py`:
   ```python
   # In vllm-omni/tests/helpers/mark.py

-   def tpu_marks(*, res: str, num_cards: int):
-       test_platform = pytest.mark.tpu
-       if res == "TPU_V3":
-           test_resource = pytest.mark.TPU_V3
-       else:
-           raise ValueError(
-               f"Invalid TPU resource type: {res}. Supported: TPU_V3")
-
-       if num_cards == 1:
-           return [test_platform, test_resource]
-       else:
-           test_distributed = pytest.mark.distributed_tpu(num_cards=num_cards)
-           # Optionally: add skipif_tpu when implemented
-           return [test_platform, test_resource, test_distributed]
+   def _tpu_marks(*, res: str, num_cards: int):
+       _require_sku("tpu", res)
+       return [pytest.mark.tpu, getattr(pytest.mark, res), _cards_mark(num_cards)]
   ```
-3. **Update `hardware_marks` to recognize your new platform**:
-    In the relevant place (see the `hardware_marks` implementation), add:
+3. **Register the builder** in `_marks_for_platform`:
    ```python
-    if platform == "tpu":
-        marks = tpu_marks(res=resource, num_cards=cards)
+    builders = {
+        # ... existing platforms ...
+        "tpu": _tpu_marks,
+    }
    ```
-    (`hardware_test` calls `hardware_marks` internally, so both will pick up the change.)
+    (`hardware_test` / `hardware_marks` both go through `_marks_for_platform`.)
 4. **(Recommended) Add a test using your new markers**:
   ```python
   @hardware_test(
@@ -223,11 +210,11 @@ If you want to add support for a new platform (e.g., "tpu" for a new accelerator
 **Summary**:  

 - Add pytest markers for your new platform/resources  
- Implement a marker function (`xxx_marks`)  
- Plug into `hardware_marks`  
- You're done: tests using `@hardware_test` or `hardware_marks` with your platform now automatically get the correct markers, distribution, and isolation!
+- Implement a private marker function (`_xxx_marks`)
+- Register it in `_marks_for_platform`
+- You're done: tests using `@hardware_test` or `hardware_marks` with your platform now automatically get the correct markers and `cards_{n}` filtering.

-See code in `vllm-omni/tests/helpers/mark.py` for existing examples (`cuda_marks`, `rocm_marks`, `npu_marks`).
+See code in `vllm-omni/tests/helpers/mark.py` for existing examples (`_cuda_marks`, `_rocm_marks`, `_npu_marks`).

 ## Test case style

@@ -438,7 +438,7 @@ selects the scheduler and worker family:
 Key topology fields belong to `StagePipelineConfig`:

 | Field | Description |
-|-------|-------------|
+| ------- | ------------- |
 | `model_stage` | Logical stage name (`ar_stage`, `decoder`, etc.). |
 | `execution_type` | Runtime family for this stage. |
 | `input_sources` | Upstream stage IDs that provide input. |
@@ -960,7 +960,7 @@ Common failures and fixes:
 | `check-forbidden-imports` | Stdlib `re`/`base64`, pickle, Hugging Face Hub API, or direct Triton/TileLang | `import regex as re` and `pybase64`; do not grow the allowlist without review |
 | `check-torch-cuda-call` | New `torch.cuda.*` call site | Use `current_omni_platform`; do not grow `ALLOWED_FILES` without review |
 | `check-tts-adapter-migration` | New `_tts_model_type` branch in `serving_speech.py` | Put logic in `tts_adapters/`; lower `MAX_MODEL_TYPE_BRANCHES` when removing branches |
-| `check-test-ci-coverage` | Test file missing level or hardware mark | Add `core_model`/… plus `cpu`/`cuda`/`hardware_test(` |
+| `check-mark` | Test file missing level/hardware mark, or direct `pytest.mark.H100` | Add `core_model`/… plus `cpu`/`cuda`/`hardware_test(`; SKU marks only via helpers |
 | `shellcheck` | Native `shellcheck` missing, or script warning | Install via apt/dnf/brew (or `shellcheck.exe` on Windows). See [Linting](../README.md#linting) |
 | `mypy-3.10` / `markdownlint-cli2` / `check-buildkite` | Types, docs markdown, or Buildkite YAML | See [Linting](../README.md#linting) |

@@ -170,6 +170,10 @@ because the user-visible output is diffusion media rather than a token stream.
 The
 [MiniMax-H3 recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/MiniMaxAI/MiniMax-H3.md)
 contains the hardware-specific profiles and warmup requirements.
+The internal, opt-in
+[temporal VAE chunk callback](feature/minimax_h3_temporal_vae_chunks.md)
+documents the narrower released-source contract for publishing committed video
+frames during decode. It does not change the complete-output serving topology.

 ### Cosmos3: unified MoT reasoner and generator

@@ -0,0 +1,260 @@
+# MiniMax H3 temporal VAE chunk callback
+
+!!! warning "Experimental internal capability"
+
+    This feature is an opt-in model integration seam. The current MiniMax H3
+    pipeline still uses complete-output VAE decode, and no CLI option, serving
+    path, transport, or MP4 sink enables temporal chunks by default.
+
+## Status and scope
+
+MiniMax H3 decodes video latents in temporal windows, but the released VAE
+normally exposes only the complete reconstructed video. The temporal callback
+publishes frames after the released decoder has completed overlap blending and
+padding removal. This gives a future runtime consumer a stable point at which
+it can schedule representation conversion, D2H transfer, and encoding while
+the VAE continues decoding later windows.
+
+The implementation remains model-local:
+
+- `vae.py` owns VAE topology and the thin
+  `decode_latent_with_chunks()` facade;
+- `chunked_decode.py` owns the normalized callback contract, metadata
+  validation, and fixed VAE-group rendezvous points; and
+- `temporal_chunks.py` owns the source-gated compatibility planner and the
+  released temporal assembly control flow.
+
+Crop policy, typed media, transport, backpressure, encoding, and public output
+remain runtime responsibilities. The model code does not schedule requests or
+select a presentation format.
+
+## Execution flow
+
+The default and opt-in paths are intentionally separate:
+
+```text
+Default:
+latent -> MiniMaxH3VideoVAE.decode_latent() -> complete RGB video
+
+Opt-in producer seam:
+latent
+  -> MiniMaxH3VideoVAE.decode_latent_with_chunks()
+  -> group-local preflight and callback-owner validation
+  -> released temporal decode / overlap blending / padding removal
+  -> borrowed decoder-domain chunk
+  -> normalize + clone into owned RGB float32 BCTHW [0, 1]
+  -> synchronous callback
+  -> complete RGB video is still returned
+```
+
+Calling `decode_latent_with_chunks()` is the opt-in. Supplying no callback on a
+VAE peer does not select the default decoder; every rank in a VAE patch group
+must enter the chunked method, and only the output rank receives the callback.
+
+## Producer contract
+
+The normalized callback has this structural signature:
+
+```python
+def callback(
+    frames: torch.Tensor,
+    /,
+    *,
+    chunk_index: int,
+    total_chunks: int,
+    frame_start: int,
+    is_final: bool,
+) -> None:
+    ...
+```
+
+For a successful decode:
+
+- `frames` is owned, contiguous RGB float32 in `BCTHW` layout and `[0, 1]`;
+- callback storage does not alias the returned complete video;
+- the consumer may retain or mutate a chunk;
+- `chunk_index` is zero-based, contiguous, and strictly increasing;
+- `total_chunks` is positive and stable for the decode;
+- `frame_start` equals the end of the previous published range;
+- chunks are nonempty and contain only temporally committed frames;
+- exactly one chunk has `is_final=True`; and
+- concatenating chunks along time exactly reconstructs the returned video.
+
+The callback runs synchronously on the producer's current device stream. A
+consumer that reads the tensor on another stream must establish ordering, for
+example with an event, and retain the allocation for that stream's lifetime.
+A slow callback also blocks VAE production. An overlap-oriented runtime
+consumer should therefore acquire bounded capacity, enqueue side-stream work,
+and return promptly instead of encoding frames inside the callback.
+
+Chunks retain the decoded VAE canvas dimensions. A future runtime integration
+must apply the same requested-height and requested-width crop as the complete
+MiniMax H3 pipeline before declaring the media transport-ready.
+
+The raw compatibility callback is deliberately narrower. It receives borrowed
+decoder-domain tensors and must not retain or mutate them. The coordinator is
+the ownership boundary that normalizes and clones raw chunks before invoking
+the public callback.
+
+## Temporal planning and terminal publication
+
+The released configuration advances five latent tokens per temporal window and
+includes a two-token overlap, so each `_adaptive_decode` input normally contains
+seven tokens. After temporal scaling, pre-padding removal, and five-frame
+overlap blending, the normal committed unit is 17 output frames. The final
+remainder depends on the requested frame plan.
+
+Representative released frame plans include:
+
+| Output frames | Published chunks |
+| ---: | --- |
+| 124 | `7 x 17 + 5` |
+| 209 | `12 x 17 + 5` |
+| 243 | `14 x 17 + 5` |
+| 362 | `21 x 17 + 5` |
+
+The terminal callback is held until the complete frame plan passes its logical
+frame, padding, written-frame, chunk-count, and finality checks. Earlier chunks
+are semantically stable, but they do not imply that the overall request has
+succeeded; a consumer must discard or abort partial output if a later decoder
+failure terminates the request.
+
+## Source and configuration gate
+
+The released VAE has no public temporal-callback API. vLLM-Omni therefore owns
+a compatibility implementation for one exact released code contract. The
+reference revision identifies its provenance, but runtime acceptance is based
+on source content, configuration, and structure rather than repository or
+weight metadata:
+
+- reference Hugging Face revision, not independently checked at runtime:
+  `42ed227ee7df40d41602854ae760620d6eb651fe`;
+- sorted 15-file top-level `video_vae/*.py` manifest (excluding
+  `__init__.py`) SHA-256:
+  `30e64afbaa940696bf8bfe2c86003a4b1666c84b22fa3aa12403a3e6a03f705d`;
+- clip length `17`;
+- token chunk, overlap, and drop `5`, `2`, and `3`;
+- temporal ratio `4`;
+- frame pre-padding and overlap `3` and `5`; and
+- no isolated first or last frame.
+
+The adapter does not verify Hugging Face revision metadata, repository identity,
+or model weights. The manifest pin detects Python compatibility drift; it is
+not a security boundary for remote-code execution. The source manifest, listed
+temporal attributes, structural contract, plan, and dtype are validated before
+the first `_adaptive_decode` call. A mismatch disables only the opt-in chunk path.
+`decode_latent()` remains available and performs no source fingerprint or chunk
+rendezvous.
+
+## Supported models
+
+Support in this table means the internal producer contract above, not public
+serving or end-to-end transport support.
+
+| Model or component | Source contract | Status | Validation |
+| --- | --- | --- | --- |
+| MiniMax H3 FL2VA video VAE | Released manifest/listed temporal attributes above (reference `42ed227e`) | Supported, opt-in | Same-environment full-output parity and L20X/B300 feasibility measured on the implementation through PR commit `60e9bbad` |
+| MiniMax H3 Ref2VA video VAE | Same released manifest/listed temporal attributes | Source-compatible; not separately benchmarked | Ref2VA and FL2VA use byte-identical video-VAE source and configuration in the reference snapshot |
+| MiniMax H3 with a different Python manifest or listed temporal attribute | Different enforced contract | Unsupported for chunk mode | Fails closed before decoder collectives; complete decode remains available |
+| Same manifest/temporal attributes with different weights or other component metadata | Not checked by this gate | Not independently qualified | The adapter does not inspect weights, repository identity, revision metadata, or arbitrary component configuration |
+| Other video models | Any | Unsupported | No generic temporal VAE producer ABI is declared by this feature |
+
+## Feature compatibility
+
+| Dimension or combination | Status | Constraint |
+| --- | --- | --- |
+| Existing complete-output decode | Supported and default | No source check, callback object, or callback rendezvous |
+| Explicit chunk decode | Supported internally | Caller invokes `decode_latent_with_chunks()` directly |
+| VAE patch parallel size 1 | Supported by the model seam | Caller supplies the callback only on the request output owner; no WORLD rendezvous is added |
+| VAE patch parallel size equal to the full DiT group | Supported when data parallel size is 1 | Every VAE-group rank calls; exactly group rank 0 owns the callback |
+| Partial VAE patch subgroup | Unsupported | MiniMax H3 currently accepts size 1 or the full DiT group only |
+| Data parallel size greater than 1 with VAE patch parallel size 1 | Model seam only | Per-replica callback ownership is possible, but no serving/runtime consumer is wired |
+| Data parallel size greater than 1 with VAE patch parallel size greater than 1 | Unsupported | Fails before decoder collectives because no replica-local VAE patch group is defined |
+| Too few spatial tiles for the VAE group | Correctness fallback | Decodes rank-locally and restores native tiling state; slower than patch parallel decode |
+| Device memory and full-output assembly | Not reduced or bounded | The compatibility path still assembles and returns the full decode while allocating owned callback chunks; no peak-HBM reduction is claimed |
+| DiT cache, attention, TP, SP, HSDP, LoRA, or quantization features | No direct callback dependency | This seam runs after denoising and does not independently qualify combinations beyond the MiniMax H3 model's own matrix |
+| Layerwise or distributed component offload | Not separately validated | Component residency must place the video VAE before decode |
+| Step execution or request batching | Not integrated | No pipeline caller binds a runtime chunk consumer |
+| Requested-size crop and device uint8 preparation | Not implemented here | Runtime-owned follow-up |
+| Pinned D2H, SHM/ZMQ, or cross-process transport | Not implemented here | Runtime-owned follow-up |
+| Complete MP4 or fragmented MP4 sink | Not implemented here | Existing serving behavior still waits for complete output |
+| Audio VAE chunks and incremental A/V composition | Not supported | Video chunks do not imply audio or request finality |
+| Cancellation and multi-output lease ownership | Not implemented here | Requires a bounded runtime lifecycle |
+| NVIDIA CUDA | Feasibility evidence | L20X and B300 measurements apply to the implementation through `60e9bbad`; the current coordination refactor was not reprofiled |
+| AMD ROCm | Not validated for this feature | Base MiniMax H3 support is documented separately and does not imply temporal-chunk validation |
+| Ascend, Intel, or other accelerators | No feature support claim | Consult the model support table independently; this design adds no platform qualification |
+
+## Distributed and failure semantics
+
+When VAE patch parallelism is active, all VAE-group ranks run the same
+preflight before entering checkpoint collectives. Source/planner errors and the
+resolved temporal concat dtype are reconciled at fixed group rendezvous points.
+
+Callback ownership is also a group decision. Rank `r` contributes `r + 1` when
+it owns a callback and zero otherwise. A group SUM equals one only when rank 0
+is the sole owner; every invalid owner configuration therefore fails
+identically on all ranks before decode.
+
+Callback exceptions are retained on the output rank. Further publication stops,
+but every rank completes the same remaining native decoder collective schedule.
+The original exception is then raised on the owner and
+`MiniMaxH3ChunkCallbackPeerError` on peers.
+
+Exceptions raised inside the checkpoint's native distributed decode cannot in
+general be reconciled by the adapter: a peer may already be blocked inside a
+checkpoint-owned collective. Resolving such failures requires native collective
+handling or supervisor-level timeout and worker-group teardown. The process
+group must not be assumed reusable after such a failure.
+
+## Validation evidence
+
+The contract suite covers ordinary and padded frame plans, blend boundaries,
+owned storage and mutation isolation, source-manifest/listed-temporal-attribute
+drift, callback failure, and healthy subsequent requests. Real Gloo processes cover
+rank-asymmetric preflight failures, dtype disagreement, two-rank callback
+failure propagation, three-rank wrong-owner rejection, insufficient-tile
+fallback, and group recovery for failures that occur at adapter-owned
+rendezvous points.
+
+Four-device L20X and independent four- and eight-device B300 experiments on the
+implementation through PR commit `60e9bbad` showed exact serial-versus-chunked
+frame/MP4 parity within each environment and lower VAE-to-complete-MP4 latency
+when an experimental consumer was attached. Hashes are not claimed to match
+across different hardware/software environments. The later coordination and
+module-boundary refactors were revalidated with CPU and real Gloo tests but were
+not reprofiled on GPUs. These are feasibility measurements, not a promise for
+default serving, because this change does not contain the transport or encoder
+consumer. See [RFC #6872](https://github.com/vllm-project/vllm-omni/issues/6872)
+and [PR #6885](https://github.com/vllm-project/vllm-omni/pull/6885) for the
+measurement provenance and limitations.
+
+## Generalization boundary and follow-ups
+
+This feature does not define a repository-wide `SupportsVideoChunkDecode`
+interface. H3 temporal blending, Wan causal caches, LTX temporal/spatial halos,
+and pipeline-level generation chunks have different commit and failure
+boundaries. A generic producer contract should be extracted only after a second
+model proves the same semantic event without `Any` arguments or model-name
+dispatch.
+
+The reusable future boundary is the emitted, committed video-media chunk, not a
+universal latent-to-VAE method. It should extend the typed media work in
+[RFC #6541](https://github.com/vllm-project/vllm-omni/issues/6541) and
+[PR #6615](https://github.com/vllm-project/vllm-omni/pull/6615), while keeping
+model-specific halo, cache, padding, blending, distributed, and source-gating
+logic inside each model adapter.
+
+Runtime follow-ups own:
+
+- typed video-plus-audio media and request/output identity;
+- requested-size crop and device preparation;
+- bounded leases, backpressure, cancellation, and cleanup;
+- D2H/IPC transport and observability; and
+- complete-MP4 and fragmented-MP4 consumers.
+
+## Related documentation
+
+- [Diffusion model integration](../module/diffusion/diffusion_model_integration.md)
+- [VAE patch parallelism](vae_parallel.md)
+- [Async diffusion output](async_diffusion_output.md)
+- [MiniMax H3 recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/MiniMaxAI/MiniMax-H3.md)
@@ -26,6 +26,8 @@ Legend: ✅ supported, ⚠️ compatibility path or limited validation, ❌ unsu
 | **TP > 1** | ⚠️ Ordinary TP-aware loader only; no direct checkpoint mmap. | ⚠️ Ordinary TP-aware loader only; no direct checkpoint mmap. |
 | **HSDP** | ❌ Rejected to avoid double-sharding parameters. | ⚠️ Limited end-to-end coverage. |
 | **Per-tensor online FP8 linears** | ✅ Ordinary loader finalizes weights and scales before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
+| **Online INT8 linears** | ✅ Ordinary loader finalizes the int8 weight (contiguous or transposed view) and fp32 scale before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
+| **Online MXFP8 linears** | ✅ Ordinary loader finalizes the fp8 weight (contiguous) and e8m0 block scale (contiguous or transposed view) before DLO sharding. | ✅ Ordinary loader retains complete rank-local tensors. |
 | **Other online quantization methods** | ❌ Rejected until runtime packing and scale layouts are validated. | ⚠️ Allowed through the ordinary loader; validation is method-specific. |
 | **Model-level or standard layerwise CPU offload** | ❌ Disabled because DLO takes priority. | ❌ Disabled because DLO takes priority. |
 | **Resident leading layers** | ❌ Rejected. | ✅ Requires eligible resident paths in the model's `OffloadPlan`. |
@@ -438,11 +440,15 @@ requirements.

 Direct checkpoint mmap can back either transfer path. It is currently limited
 to proven TP1, non-HSDP, non-online-quantized layouts. Other layouts use the
-ordinary loader. Per-tensor online FP8 linears can use DLO AllGather after the
-ordinary loader finalizes their runtime weights and scales; DLO then shards and
-reconstructs those tensors with their recorded layouts. Other online methods
-must use `--dlo-no-use-allgather` or disable online quantization until their
-runtime layouts are validated.
+ordinary loader. Per-tensor online FP8, online INT8, and online MXFP8 linears
+can use DLO AllGather after the ordinary loader finalizes their runtime
+weights and scales; DLO then shards and reconstructs those tensors with their
+recorded layouts (they all keep plain 1-byte dtypes over ordinary strided
+views: online INT8 has an int8 weight plus fp32 scale, online MXFP8 has an
+fp8 weight plus e8m0 block scale, each with a contiguous or transposed-view
+finalized tensor, all covered by the same physical-order packing as online
+FP8). Other online methods must use `--dlo-no-use-allgather` or disable
+online quantization until their runtime layouts are validated.

 The Host Weight Runtime representation, publication, and no-AllGather consumer
 contracts are merged; see
@@ -459,8 +465,8 @@ Current source-level validation includes:
 - HSDP + DLO without AllGather acceptance at configuration level;
 - loader preflight fallback for TP, HSDP, online quantization, unknown custom
  loaders, missing keys, and shape/dtype mismatches;
- ordinary-loader fallback for per-tensor online FP8 linears followed by DLO
-  sharding of finalized weights and scales;
+- ordinary-loader fallback for per-tensor online FP8, online INT8, and online
+  MXFP8 linears followed by DLO sharding of finalized weights and scales;
 - exact loader-to-backend plan transfer and ordinary-loader fallback;
 - ordered publication hashing, overlapped durability, unordered parallel
  checksum fallback, and node-local source-digest reuse/invalidation;
@@ -22,59 +22,60 @@ We use **Qwen-Image** as the reference implementation for decode parallel, and *

 **VAE Patch Parallelism** is an acceleration technique for both **encoding** and **decoding**. Instead of processing the entire tensor at once, the tensor is:

-+ Split into multiple spatial tiles
+- Split into multiple spatial tiles

-+ Distributed across multiple ranks
+- Distributed across multiple ranks

-+ Encoded/Decoded in parallel
+- Encoded/Decoded in parallel

-+ Merged to reconstruct the final output
+- Merged to reconstruct the final output

 This approach:

-+ Distributes computation across multiple devices
+- Distributes computation across multiple devices

-+ Reduces peak memory usage per device
+- Reduces peak memory usage per device

-+ Accelerates encoding/decoding latency
+- Accelerates encoding/decoding latency

 ### When to Use Encode vs Decode Parallel

-| Operation | Use Case | Example |
-|-----------|----------|---------|
-| **Decode Parallel** | Text-to-Image, Text-to-Video | Latent → Image/Video |
-| **Encode Parallel** | Image-to-Video (I2V) | Image → Latent (for conditioning) |
+| Operation           | Use Case                     | Example                           |
+| ------------------- | ---------------------------- | --------------------------------- |
+| **Decode Parallel** | Text-to-Image, Text-to-Video | Latent → Image/Video              |
+| **Encode Parallel** | Image-to-Video (I2V)         | Image → Latent (for conditioning) |

 ### Architecture
+
 We introduce **DistributedVaeExecutor** as the core component responsible for distributed VAE encoding/decoding.

 The executor is model-agnostic and accepts three function parameters:

-+ split – Partition the latent into tiles
+- split – Partition the latent into tiles

-+ exec – Decode a single tile
+- exec – Decode a single tile

-+ merge – Combine decoded tiles into the final output
+- merge – Combine decoded tiles into the final output

 #### Execution Flow

-+ Call split(z) to generate a list of TileTask and a GridSpec
+- Call split(z) to generate a list of TileTask and a GridSpec

-+ Dispatch tasks across ranks using workload-based balancing
+- Dispatch tasks across ranks using workload-based balancing

-+ Each rank executes exec(task) on its assigned tiles
+- Each rank executes exec(task) on its assigned tiles

-+ Gather decoded tile results to rank 0
+- Gather decoded tile results to rank 0

-+ Rank 0 performs merge(...)
+- Rank 0 performs merge(...)

-+ (Optional) Broadcast final result to all ranks
+- (Optional) Broadcast final result to all ranks

 This design separates:

-+ Distributed execution logic
+- Distributed execution logic

-+ Model-specific tiling and merging logic
+- Model-specific tiling and merging logic

 #### Why split / exec / merge is necessary?

@@ -82,23 +83,23 @@ The latent tensor cannot be arbitrarily partitioned.

 During decoding:

-+ Each output pixel may depend on neighboring pixels
+- Each output pixel may depend on neighboring pixels

-+ The receptive field is model-dependent
+- The receptive field is model-dependent

 Therefore:

-+ Tiles must include overlap
+- Tiles must include overlap

-+ Merge must perform blending to avoid seams
+- Merge must perform blending to avoid seams

 ## Step-by-Step Implementation (Decode)

 ### Step 1: Implement DistributedAutoencoderKLQwenImage
+
 `QwenImagePipeline` use `AutoencoderKLQwenImage` for vae, so implement a distributed version:

-
-```
+```python
 class DistributedAutoencoderKLQwenImage(AutoencoderKLQwenImage, DistributedVaeMixin):
    @classmethod
    def from_pretrained(cls, *args: Any, **kwargs: Any):
@@ -106,28 +107,35 @@ class DistributedAutoencoderKLQwenImage(AutoencoderKLQwenImage, DistributedVaeMi
        model.init_distributed()
        return model
 ```
+
 **Key points**:
-+ Inherit both AutoencoderKLQwenImage and DistributedVaeMixin
-+ Call init_distributed() after loading weights
+
+- Inherit both AutoencoderKLQwenImage and DistributedVaeMixin
+- Call init_distributed() after loading weights

 ### Step 2: Implement split/exec/merge
+
 Reuse `AutoencoderKLQwenImage.tiled_decode` logic and divide it into three stages. And we need return tiles with `GridSpec` and `TileTask`:
-```
+
+```python
 class GridSpec:
    split_dims: tuple[int, ...]  # Tensor dimensions being split (e.g., (2, 3) for (B, C, H, W))
    grid_shape: tuple[int, ...]  # Tile grid layout (num_rows, num_cols)
    tile_spec: dict = field(default_factory=dict) # Metadata required for merging
    output_dtype: torch.dtype | None = None # Final output dtype
 ```
-```
+
+```python
 class TileTask:
    tile_id: int # task id
    grid_coord: tuple[int, ...]  # Tile position in grid
    tensor: torch.Tensor | list[torch.Tensor]  # The tile tensor
    workload: int | float = 1 # Used for load balancing (e.g., tile area)
 ```
+
 And tiled base split/exec/merge as follow:
-```
+
+```python
 def tile_split(self, z: torch.Tensor) -> tuple[list[TileTask], GridSpec]:
    # mostly copy from AutoencoderKL
    _, _, num_frames, height, width = z.shape
@@ -210,11 +218,14 @@ def tile_merge(self, coord_tensor_map: dict[tuple[int, ...], torch.Tensor], grid
 ```

 ### Step 3: Override tiled_decode
+
 We need to override tiled_decode, the main logic is:
-+ check distributed is enabled
-+ select split/exec/merge
-+ Invoke self.distributed_executor.execute to decode
-```
+
+- check distributed is enabled
+- select split/exec/merge
+- Invoke self.distributed_executor.execute to decode
+
+```python
 def tiled_decode(self, z: torch.Tensor, return_dict: bool = True):
    if not self.is_distributed_enabled():
        return super().tiled_decode(z, return_dict=return_dict)
@@ -230,11 +241,14 @@ def tiled_decode(self, z: torch.Tensor, return_dict: bool = True):

    return DecoderOutput(sample=result)
 ```
+
 `broadcast_result` is set to True or False depending on the model; when enabled, the result will be used even on ranks other than 0.

 ### Step 4: Modify Pipeline
+
 Change vae model from AutoencoderKLQwenImage to DistributedAutoencoderKLQwenImage
-```
+
+```diff
 class YourModelPipeline(nn.Module):
    def __init__(
        self,
@@ -259,8 +273,8 @@ For models that require VAE encoding (e.g., Image-to-Video), you can also parall

 Similar to decode, split the input tensor into tiles. Key considerations:

-+ **Patchify handling**: If the model uses `patch_size`, scale tile parameters accordingly
-+ **Temporal chunking**: Video VAEs may have temporal compression (e.g., 4x)
+- **Patchify handling**: If the model uses `patch_size`, scale tile parameters accordingly
+- **Temporal chunking**: Video VAEs may have temporal compression (e.g., 4x)

 ```python
 def encode_tile_split(self, x: torch.Tensor) -> tuple[list[TileTask], GridSpec]:
@@ -406,23 +420,28 @@ def tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
 **Key differences from decode parallel:**

 | Aspect | Decode Parallel | Encode Parallel |
-|--------|-----------------|-----------------|
+| -------- | ----------------- | ----------------- |
 | `broadcast_result` | Often `False` (only rank 0 needs output) | `True` (all ranks need latents for diffusion) |
 | Patchify | Applied in merge (unpatchify) | Handled by parent `_encode()` before `tiled_encode()` |
 | Temporal chunking | Frame-by-frame | Chunk-based (e.g., 1 + 4n frames) |

 ## Testing
-Verify numerical consistency between:
-+ vae_patch_parallel_size = 1

-+ vae_patch_parallel_size = N
+Verify numerical consistency between:
+
+- vae_patch_parallel_size = 1
+- vae_patch_parallel_size = N

 Example:
+
+```python
 torch.allclose(output_1, output_n, atol=1e-5)
+```

 Testing requirements:
-+ Fix random seed
-+ Use identical tiling strategy
+
+- Fix random seed
+- Use identical tiling strategy

 ```python
 m = Omni(
@@ -434,6 +453,7 @@ m = Omni(
        ),
    )
 ```
+
 When vae_patch_parallel_size is larger than the DiT world size, it will automatically fall back to using the DiT world size instead.

 ## Reference Implementations
@@ -441,7 +461,7 @@ When vae_patch_parallel_size is larger than the DiT world size, it will automati
 Complete examples in the codebase:

 | Model | Path | Decode Parallel | Encode Parallel |
-|-------|------|-----------------|-----------------|
+| ------- | ------ | ----------------- | ----------------- |
 | **Z-Image** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl.py` | ✅ | ❌ |
 | **Wan2.2** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py` | ✅ | ✅ |
 | **Qwen-Image** | `vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py` | ✅ | ❌ |
@@ -456,7 +476,7 @@ The tile-parallel executor above assigns independent spatial **tiles** to ranks.
 ### How it differs from tile parallel

 | Aspect | Tile parallel (`"tile"`) | Spatially-sharded (`"spatial_shard_height"`/`"spatial_shard_width"`) |
-|--------|--------------------------|-----------------------------------------------|
+| -------- | -------------------------- | ----------------------------------------------- |
 | Unit of work | Independent overlapping tiles | A single global feature map sharded along H or W |
 | Cross-rank communication | Gather tiles to rank 0, stitch + blend | Per-conv **halo exchange** of boundary rows/cols (P2P) |
 | Output assembly | Blend overlapping tiles | All-gather shards on rank 0, trim padding (matches `broadcast_result=False`) |
@@ -481,7 +501,7 @@ serve.py (--vae-parallel-mode) / OmniEngineArgs
 ### Notes

 - The decoder is patched **in place** the first time spatial-shard decode runs and is bound to a single split dimension for the lifetime of the VAE instance.
- Numerical correctness vs. single-GPU decode is covered by `tests/diffusion/distributed/test_wan_spatial_shard.py::test_spatial_shard_decode_matches_reference` (multi-GPU, nightly `full_model` + `distributed_cuda`).
+- Numerical correctness vs. single-GPU decode is covered by `tests/diffusion/distributed/test_wan_spatial_shard.py::test_spatial_shard_decode_matches_reference` (multi-GPU, nightly `full_model` + `cards_2`).

 ---

@@ -25,6 +25,10 @@ implementation contract; it is not, by itself, a general support claim.
 - [Automatic Prefix Caching in Omni Models](feature/prefix_caching.md)
 - [Realtime AR-Diffusion Sessions](feature/realtime_ar_diffusion.md)

+### Model-specific capabilities
+
+- [MiniMax H3 Temporal VAE Chunks](feature/minimax_h3_temporal_vae_chunks.md)
+
 ### Communication

 #### OmniConnector implementations
@@ -57,3 +57,10 @@ cross-stage routing.

 Test registry selection, checkpoint loading, minimal inference, input and output
 contracts, and every declared optional capability.
+
+## Model-specific capability documents
+
+- [MiniMax H3 temporal VAE chunk callback](../../feature/minimax_h3_temporal_vae_chunks.md)
+  defines the source-gated producer seam and its boundary with future runtime
+  media transport. It is an internal opt-in capability, not a public serving
+  feature.
@@ -37,6 +37,7 @@ validation_paths:
  - tests/engine/test_async_omni_engine_input.py
  - tests/engine/test_async_omni_engine_outputs.py
  - tests/engine/test_async_omni_engine_abort.py
+  - tests/e2e/offline_inference/test_qwen3_omni.py
  - tests/engine/test_async_omni_engine_stage_init.py
  - tests/engine/test_orchestrator.py
  - tests/engine/test_orchestrator_error_handling.py
@@ -113,6 +114,15 @@ Numbers become append-only after normative promotion.

 Test routing, output ordering, queue correlation, cancellation, failure
 propagation, shutdown ordering, and representative multi-stage execution.
+AR abort of a final-stage LLM request must deliver a terminal output with
+`finish_reason="abort"` and the generated prefix so collocated training can
+resume; diffusion abort remains whole-sample retry. Aborting a parallel-sampling
+child must drop `parent_requests` once no children remain. EngineCore control
+RPCs (`sleep` / `wake_up` / `pause_scheduler` / `resume_scheduler`) must
+propagate worker exceptions rather than returning `{"supported": False}`.
+When a request has multiple final output stages, only the last abort message
+is request-terminal. Output-processor abort state is committed only after the
+physical EngineCore abort succeeds.
 Changes that cross into stage runtime or public error behavior require review
 from that contract's owners.

@@ -43,6 +43,7 @@ depends_on:
 validation_paths:
  - tests/entrypoints/test_omni_entrypoints.py
  - tests/entrypoints/test_async_omni.py
+  - tests/entrypoints/test_async_omni_pause_sleep_routing.py
  - tests/entrypoints/test_async_omni_duplex.py
  - tests/entrypoints/test_serve.py
  - tests/entrypoints/test_stream_finish_reason.py
@@ -119,7 +120,13 @@ promotion.

 Test request validation, protocol conversion, model-adapter routing,
 streaming, session identity, disconnect/cancellation, and error mapping for
-each affected offline or serving entrypoint.
+each affected offline or serving entrypoint. Sleep must wait for in-flight
+`generate()` admission before EngineCore offload; `wake_up` does not resume
+admission — callers must `resume_generation()`. Sleeping tags are tracked per
+stage so `wake_up(stage_ids=[0])` does not skip a later `wake_up(stage_ids=[1])`.
+Streaming input pumps take an admission slot immediately before each EngineCore
+ADD or update, not while waiting for the next client chunk. Frontend abort
+keeps `request_states` until `generate()` consumes the terminal output.

 ## Promotion gate

@@ -3,4 +3,6 @@
 vLLM-Omni's examples are split into two categories:

 - If you are using vLLM-Omni from within Python code, see the *Offline Inference* section.
- If you are using vLLM-Omni from an HTTP application or client, see the *Online Serving* section.
+- If you are using vLLM-Omni from an HTTP application or client, start with
+  the [API Server guide](../serving/README.md) to choose an endpoint, then use
+  the *Online Serving* section for model-specific examples.
@@ -34,7 +34,7 @@ vLLM-Omni is a Python library that supports the following GPU variants. The libr

 ### Pre-built wheels

-Note: Pre-built wheels are currently available for vLLM-Omni 0.11.0rc1, 0.12.0rc1, 0.14.0rc1, 0.14.0, 0.16.0, 0.18.0, 0.20.0, 0.21.0, 0.22.0, 0.23.0, 0.24.0, 0.25.0, and 0.26.0; the 0.28.0 wheels are published together with the v0.28.0 release this branch produces. If you need a newer unreleased revision, please [build from source](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/gpu/#build-wheel-from-source).
+Note: Pre-built wheels are currently available for vLLM-Omni 0.11.0rc1, 0.12.0rc1, 0.14.0rc1, 0.14.0, 0.16.0, 0.18.0, 0.20.0, 0.21.0, 0.22.0, 0.23.0, 0.24.0, 0.25.0, 0.26.0, and 0.28.0. If you need a newer unreleased revision, please [build from source](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/gpu/#build-wheel-from-source).

 === "NVIDIA CUDA"

@@ -5,7 +5,7 @@
 # --8<-- [end:requirements]
 # --8<-- [start:set-up-using-python]

-vLLM-Omni depends on the matching major/minor release of vLLM. The vLLM-Omni 0.26.x release line uses vLLM 0.26.x.
+vLLM-Omni depends on the matching major/minor release of vLLM. The vLLM-Omni 0.28.x release line uses vLLM 0.28.x.

 !!! 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.
@@ -95,6 +95,13 @@ uv pip install --no-build-isolation --editable .

 vLLM-Omni offers an official docker image for deployment. These images are built on top of vLLM docker images and available on Docker Hub as [vllm/vllm-omni](https://hub.docker.com/r/vllm/vllm-omni/tags). The version of vLLM-Omni indicates which release of vLLM it is based on.

+Nightly CUDA images built from the latest `main` are also available:
+
+- [`vllm/vllm-omni:nightly`](https://hub.docker.com/r/vllm/vllm-omni/tags?name=nightly) is the rolling multi-architecture tag.
+- [`vllm/vllm-omni:nightly-<commit>`](https://hub.docker.com/r/vllm/vllm-omni/tags?name=nightly-) provides commit-pinned multi-architecture tags. The newest 14 are retained.
+
+Use a commit-pinned tag when you need a reproducible nightly environment.
+
 Here's an example deployment command that has been verified on 2 x H100's:
 ```bash
 docker run --runtime nvidia --gpus 2 \
@@ -9,8 +9,8 @@ The recommended way to use vLLM-Omni on NPU is through the vLLM-Ascend pre-built

 ```bash
 # vLLM-Ascend image
-# Atlas A2: quay.io/atlas-ci/vllm-ascend:v0.26.0
-# Atlas A3: quay.io/atlas-ci/vllm-ascend:v0.26.0-a3
+# Atlas A2: quay.io/atlas-ci/vllm-ascend:v0.28.0
+# Atlas A3: quay.io/atlas-ci/vllm-ascend:v0.28.0-a3
 docker run --rm \
    --name vllm-omni-npu \
    --shm-size=1g \
@@ -28,11 +28,11 @@ docker run --rm \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
    -v /root/.cache:/root/.cache \
    -p 8000:8000 \
-    -it quay.io/atlas-ci/vllm-ascend:v0.26.0-a3 bash
+    -it quay.io/atlas-ci/vllm-ascend:v0.28.0-a3 bash

 # Inside the container, install vLLM-Omni from source
 cd /vllm-workspace
-git clone -b v0.26.0 https://github.com/vllm-project/vllm-omni.git
+git clone -b v0.28.0 https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
 pip install -v -e . --no-build-isolation
 # or VLLM_OMNI_TARGET_DEVICE=npu pip install -v -e .
@@ -50,21 +50,11 @@ We are keeping [issue #886](https://github.com/vllm-project/vllm-omni/issues/886

 # --8<-- [start:installation-main]

-You can also build vLLM-Omni from the latest main branch if you want to use the latest features or bug fixes. (But sometimes it will break for a while. You can check [issue #886](https://github.com/vllm-project/vllm-omni/issues/886) for the status of the latest commit of vLLM-Omni main branch on NPU.)
+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 v0.28.0 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.

 ```bash
-# Pin vLLM and vLLM-Ascend to the v0.26 release line
-git clone -b v0.26.0 https://github.com/vllm-project/vllm.git
-cd vllm
-VLLM_TARGET_DEVICE=empty pip install -v -e .
-cd ..
-
-git clone -b releases/v0.26.0rc https://github.com/vllm-project/vllm-ascend.git
-cd vllm-ascend
-pip install -v -e .
-cd ..
-
-# Install vLLM-Omni from the latest main branch
+# Inside the vLLM-Ascend v0.28.0 container, install vLLM-Omni from main
+cd /vllm-workspace
 git clone https://github.com/vllm-project/vllm-omni.git
 cd vllm-omni
 pip install -v -e . --no-build-isolation
@@ -88,8 +78,8 @@ Supported images as following.
 Here's an example deployment command that has been verified on 4 x NPUs:

 ```bash
-# Atlas A2: quay.io/ascend/vllm-omni:v0.26.0
-# Atlas A3: quay.io/ascend/vllm-omni:v0.26.0-a3
+# Atlas A2: quay.io/ascend/vllm-omni:v0.28.0
+# Atlas A3: quay.io/ascend/vllm-omni:v0.28.0-a3
 docker run --rm \
    --name vllm-omni-a3 \
    --shm-size=4g \
@@ -114,7 +104,7 @@ docker run --rm \
    -v /etc/ascend_install.info:/etc/ascend_install.info \
    -v ~/.cache:/root/.cache \
    -p 8091:8091 \
-    -it quay.io/ascend/vllm-omni:v0.26.0-a3 bash
+    -it quay.io/ascend/vllm-omni:v0.28.0-a3 bash
 ```

 !!! tip
@@ -91,7 +91,7 @@ if __name__ == "__main__":

 For more usages, please refer to [offline inference](../user_guide/examples/offline_inference/qwen2_5_omni.md)

-## Online Serving with OpenAI-Completions API
+## Online Serving with the API Server

 Text-to-image generation quickstart with vLLM-Omni:

@@ -100,20 +100,16 @@ vllm serve Tongyi-MAI/Z-Image-Turbo --omni --port 8091
 ```

 ```bash
-curl -s http://localhost:8091/v1/chat/completions \
+curl -s http://localhost:8091/v1/images/generations \
  -H "Content-Type: application/json" \
  -d '{
-    "messages": [
-      {"role": "user", "content": "a cup of coffee on the table"}
-    ],
-    "extra_body": {
-      "height": 1024,
-      "width": 1024,
-      "num_inference_steps": 50,
-      "guidance_scale": 4.0,
-      "seed": 42
-    }
-  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2 | base64 -d > coffee.png
+    "prompt": "a cup of coffee on the table",
+    "size": "1024x1024",
+    "response_format": "b64_json",
+    "seed": 42
+  }' | jq -r '.data[0].b64_json' | base64 -d > coffee.png
 ```

-For more details, please refer to [online serving](../user_guide/examples/online_serving/text_to_image.md).
+See the [API Server guide](../serving/README.md) to choose an endpoint. For
+model-specific details, refer to the [text-to-image online serving
+example](../user_guide/examples/online_serving/text_to_image.md).
@@ -21,7 +21,7 @@ th {
 |--------------|--------|-------------------|------------|---------|-----|-----------|--------|
 | `Qwen3OmniMoeForConditionalGeneration` | Qwen3-Omni | `Qwen/Qwen3-Omni-30B-A3B-Instruct` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Qwen/Qwen3-Omni.md) |
 | `Qwen2_5OmniForConditionalGeneration` | Qwen2.5-Omni | `Qwen/Qwen2.5-Omni-7B`, `Qwen/Qwen2.5-Omni-3B` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
-| `MingFlashOmniForConditionalGeneration` + `MingImagePipeline` | Ming-flash-omni-2.0 (omni-speech + imagegen<sup>1</sup>) | `Jonathan1909/Ming-flash-omni-2.0` | ✅︎ |   |   |   | — |
+| `MingFlashOmniForConditionalGeneration` + `MingImagePipeline` | Ming-flash-omni-2.0 (omni-speech + imagegen<sup>1</sup>) | `Jonathan1909/Ming-flash-omni-2.0` | ✅︎ | | | | — |
 | `BagelForConditionalGeneration` | BAGEL (DiT-only) | `ByteDance-Seed/BAGEL-7B-MoT` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `InternVLAA1Pipeline` | InternVLA-A1 | `InternRobotics/InternVLA-A1-3B` | ✅︎ | ✅︎ | | | — |
 | `Gr00tN1d7Pipeline` | GR00T N1.7 | `nvidia/GR00T-N1.7-3B` | ✅︎ | | | | — |
@@ -62,7 +62,7 @@ th {
 | `LongcatImagePipeline` | LongCat-Image | `meituan-longcat/LongCat-Image` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `LongCatImageEditPipeline` | LongCat-Image-Edit | `meituan-longcat/LongCat-Image-Edit` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `LongCatVideoAvatarPipeline` | LongCat-Video-Avatar-1.5 A2V/AI2V (native single-speaker and multi-speaker AI2V/AVC) | `meituan-longcat/LongCat-Video-Avatar-1.5` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/meituan-longcat/LongCat-Video-Avatar-1.5.md) |
-| `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit` | ✅︎ | | | ✅︎ | — |
+| `BooguImagePipeline` | Boogu-Image | `Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit`, `Boogu/Boogu-Image-0.1-Edit-Turbo` | ✅︎ | | | ✅︎ | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/Boogu/Boogu-Image.md) |
 | `StableDiffusionXLPipeline` | Stable-Diffusion-XL | `stabilityai/stable-diffusion-xl-base-1.0` | ✅︎ | ✅︎ | ✅︎ | ✅︎ | — |
 | `StableDiffusion3Pipeline` | Stable-Diffusion-3 | `stabilityai/stable-diffusion-3.5-medium` | ✅︎ | ✅︎ | | ✅︎ | — |
 | `CosyVoice3Model` | CosyVoice3 | `FunAudioLLM/Fun-CosyVoice3-0.5B-2512` | ✅︎ | ✅︎ | | ✅︎ | — |
@@ -95,7 +95,7 @@ th {
 | `MiMoV2ASRForCausalLM` | MiMo-V2.5-ASR | `XiaomiMiMo/MiMo-V2.5-ASR` | ✅︎ | ✅︎ | | | — |
 | `Flux2Pipeline` | FLUX.2-dev | `black-forest-labs/FLUX.2-dev` | ✅︎ | ✅︎ | | | — |
 | `FishSpeechSlowARForConditionalGeneration` | Fish Speech S2 Pro | `fishaudio/s2-pro` | ✅︎ | ✅︎ | | ✅︎ | — |
-| `SenseNovaU1Pipeline` | SenseNova-U1 (DiT-only) | `SenseNova/SenseNova-U1-8B-MoT` | ✅︎ | | | | — |
+| `SenseNovaU1Pipeline` | SenseNova-U1 (DiT-only), SenseNova-U1.5 | `SenseNova/SenseNova-U1-8B-MoT`, `sensenova/SenseNova-U1.5-8B-MoT` | ✅︎ | | | | [Repository](https://github.com/vllm-project/vllm-omni/blob/main/recipes/SenseNova/SenseNova-U1.5.md) |
 | `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` | ✅︎ | ✅︎ | | | — |
 | `HunyuanVideo15ImageToVideoPipeline` | HunyuanVideo-1.5-I2V | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v`, `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v` | ✅︎ | ✅︎ | | | — |
@@ -0,0 +1,187 @@
+# API Server
+
+vLLM-Omni exposes OpenAI-compatible HTTP APIs plus vLLM-Omni extensions for
+image, audio, video, realtime, and robot-policy workloads. Use this page to
+choose an endpoint; follow the linked reference page for model-specific fields,
+wire protocols, and response details.
+
+Each server instance hosts one model. An endpoint is usable only when the
+loaded model supports that task.
+
+This guide focuses on vLLM-Omni's primary task endpoints. Compatible models
+may also expose standard routes inherited from vLLM, such as
+`POST /v1/completions`; those routes remain model- and task-dependent.
+
+## Start and Verify the Server
+
+Start a model with the shared serving command:
+
+```bash
+vllm serve <model> --omni --port 8091
+```
+
+Some models require additional flags or a deployment configuration. Use the
+model-specific guide when one is provided.
+
+After the server starts, check its health and served model name:
+
+```bash
+export VLLM_OMNI_BASE_URL=http://localhost:8091
+
+curl "$VLLM_OMNI_BASE_URL/health"
+curl "$VLLM_OMNI_BASE_URL/v1/models" | jq .
+```
+
+Use `http://localhost:8091/v1` as the `base_url` for an OpenAI SDK client. If
+the server was started with `--api-key`, include an `Authorization` header with
+`Bearer <api-key>` in requests.
+
+## Choose a Core HTTP Endpoint
+
+Prefer a task-specific endpoint when one matches your workload. Use Chat
+Completions for conversational or heterogeneous omni pipelines, rather than as
+the default wrapper for every generation task.
+
+| Task | Endpoint | Request | Response | Details |
+|------|----------|---------|----------|---------|
+| Conversation or multimodal understanding/generation | `POST /v1/chat/completions` | JSON | OpenAI-style JSON or SSE | [Chat Completions](chat_completions_api.md) |
+| Text-to-speech | `POST /v1/audio/speech` | JSON | Audio bytes or SSE | [Speech](speech_api.md) |
+| Sound, music, or ambient audio generation | `POST /v1/audio/generate` | JSON | Audio bytes | [Audio Generation](audio_generate_api.md) |
+| Text-to-image | `POST /v1/images/generations` | JSON | Base64 JSON or an image file | [Image Generation](image_generation_api.md) |
+| Image editing | `POST /v1/images/edits` | Multipart form | Base64 JSON or SSE | [Image Edit](image_edit_api.md) |
+| Video generation (recommended) | `POST /v1/videos` | Multipart form | Asynchronous job JSON | [Videos](videos_api.md) |
+| Video generation (blocking) | `POST /v1/videos/sync` | Multipart form | Video bytes | [Videos](videos_api.md#synchronous-response) |
+
+`POST /v1/videos/sync` is intended for tests and simple scripts. Use the
+asynchronous job API for production requests so clients can poll, download,
+and delete generated videos independently.
+
+## Minimal Requests
+
+The examples below assume a compatible model is already running. TTS voices,
+media inputs, generation controls, and output capabilities vary by model.
+
+=== "Chat"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/chat/completions" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "messages": [{"role": "user", "content": "Describe vLLM-Omni briefly."}],
+        "modalities": ["text"]
+      }'
+    ```
+
+=== "Speech"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/audio/speech" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "input": "Hello from vLLM-Omni.",
+        "voice": "vivian"
+      }' \
+      --output speech.wav
+    ```
+
+=== "Audio"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/audio/generate" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "input": "Ocean waves on a quiet beach",
+        "audio_length": 5.0
+      }' \
+      --output audio.wav
+    ```
+
+=== "Image"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/images/generations" \
+      -H "Content-Type: application/json" \
+      -d '{
+        "prompt": "A small robot reading beside a window",
+        "size": "1024x1024",
+        "response_format": "file"
+      }' \
+      --output image.png
+    ```
+
+=== "Image edit"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/images/edits" \
+      -F "image=@input.png" \
+      -F "prompt=Turn the background into a snowy mountain" \
+      | jq -r '.data[0].b64_json' \
+      | base64 --decode > edited.png
+    ```
+
+=== "Video"
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/videos" \
+      -F "prompt=A cinematic tracking shot of a mountain lake at sunrise" \
+      -F "size=1280x720" \
+      -F "seconds=5"
+    ```
+
+    The response contains a video job `id`. Poll and download it with:
+
+    ```bash
+    curl "$VLLM_OMNI_BASE_URL/v1/videos/<video_id>"
+    curl -L "$VLLM_OMNI_BASE_URL/v1/videos/<video_id>/content" --output video.mp4
+    ```
+
+## Choose a Streaming or Realtime Endpoint
+
+These WebSocket APIs have different event schemas and cannot be used
+interchangeably.
+
+| Workload | Endpoint | Interaction model | Details |
+|----------|----------|-------------------|---------|
+| Incremental text input for speech synthesis | `WS /v1/audio/speech/stream` | Send text events and receive audio | [Streaming Text to Speech](speech_api.md#streaming-text-input-websocket) |
+| Live video understanding | `WS /v1/video/chat/stream` | Send video frames and receive text/audio | [Streaming Video Input](video_stream_api.md) |
+| Turn-based realtime audio | `WS /v1/realtime` | Stream one audio input and receive transcript/audio events | [Realtime Audio](realtime_api.md) |
+| Continuous speech-to-speech interaction | `WS /v1/realtime?duplex=1` or `WS /v1/duplex` | Listen and speak concurrently with session control | [Full Duplex](full_duplex_api.md) |
+| Generated video chunks | `WS /v1/realtime/video` | Start a diffusion request and receive fragmented MP4 | [Streaming Video Output](streaming_video_output_api.md) |
+| Robot policy inference | `WS /v1/realtime/robot/openpi` | Send MessagePack observations and receive action arrays | [OpenPI Robot Policy](openpi_api.md) |
+
+All six routes are model- or configuration-dependent. In particular,
+`/v1/realtime` is not full duplex unless the client sets `duplex=1` and the
+deployment explicitly enables duplex sessions. Clients should also verify the
+duplex capability payload because the query-parameter form falls back to the
+ordinary realtime handler when duplex is unavailable.
+
+## Related Endpoints
+
+| Purpose | Endpoints | Reference |
+|---------|-----------|-----------|
+| Discovery and readiness | `GET /health`, `GET /v1/models` | This page |
+| Batched conversations | `POST /v1/chat/completions/batch` | [Batch requests](chat_completions_api.md#batch-requests) |
+| Batched speech | `POST /v1/audio/speech/batch` | [Batch speech generation](speech_api.md#batch-speech-generation) |
+| TTS voice management | `GET/POST /v1/audio/voices`, `DELETE /v1/audio/voices/{name}` | [Voices](speech_api.md#voices-endpoint) |
+| Video job lifecycle | `GET /v1/videos`, `GET/DELETE /v1/videos/{video_id}`, `GET /v1/videos/{video_id}/content` | [Video endpoints](videos_api.md#endpoints) |
+| Release and restore stage memory | `POST /v1/omni/sleep`, `POST /v1/omni/wakeup` | [Sleep Mode](../features/sleep_mode.md) |
+
+## Standalone Experimental Servers
+
+PersonaPlex also provides a Moshi-compatible WebSocket server, while JoyVL
+provides a stateful interaction orchestrator in front of another model server.
+These are separate processes with their own routes; they are not additional
+paths on every `vllm serve --omni` instance. See [Standalone Experimental
+Servers](standalone_servers.md) before deploying either one.
+
+## Compatibility Notes
+
+- A registered path does not mean every model supports it. Unsupported tasks
+  return an OpenAI-style error or a WebSocket `error` event.
+- Use JSON for Chat, Speech, Audio Generation, and Image Generation. Use
+  `multipart/form-data` when uploading images, videos, or other files.
+- Generation can take minutes for large diffusion models. Configure client
+  timeouts accordingly; prefer asynchronous video jobs over long blocking
+  requests.
+- Model-specific parameters are vLLM-Omni extensions. Check the endpoint
+  reference before assuming an OpenAI SDK exposes them directly.
@@ -4,14 +4,14 @@ vLLM-Omni provides an API for text-to-audio generation using diffusion-based mod

 Unlike the [Speech API](speech_api.md) which targets text-to-speech synthesis, the Audio Generate API is designed for general-purpose audio generation from text descriptions (sound effects, music, ambient soundscapes, etc.).

-Each server instance runs a single model (specified at startup via `vllm-omni serve <model> --omni`).
+Each server instance runs a single model (specified at startup via `vllm serve <model> --omni`).

 ## Quick Start

 ### Start the Server

 ```bash
-vllm-omni serve stabilityai/stable-audio-open-1.0 \
+vllm serve stabilityai/stable-audio-open-1.0 \
    --host 0.0.0.0 \
    --port 8091 \
    --gpu-memory-utilization 0.9 \
@@ -326,7 +326,7 @@ curl http://localhost:8091/health
 Enable debug logging:

 ```bash
-vllm-omni serve stabilityai/stable-audio-open-1.0 \
+vllm serve stabilityai/stable-audio-open-1.0 \
    --host 0.0.0.0 \
    --port 8091 \
    --gpu-memory-utilization 0.9 \
@@ -1,78 +1,110 @@
 # Chat Completions API

-vLLM-Omni supports generating and editing images via the `/v1/chat/completions`
-endpoint using diffusion models. This page explains how to pass generation
-parameters (such as `num_inference_steps`, `height`, `width`) to diffusion
-models through this endpoint.
+Use `/v1/chat/completions` for conversational and multimodal pipelines that
+follow the OpenAI Chat API schema. Depending on the loaded model, a request can
+contain text, image, audio, or video input and can return text, audio, images,
+or other model-specific output.

-!!! tip
-    For dedicated endpoints that accept generation parameters as top-level
-    fields, see [Image Generation API](image_generation_api.md) and
-    [Image Edit API](image_edit_api.md).
+Some diffusion pipelines also support image generation and editing through
+Chat Completions. Prefer the dedicated [Image Generation
+API](image_generation_api.md) or [Image Edit API](image_edit_api.md) for
+task-specific clients; their request fields and response contracts are more
+direct.

-## Passing Generation Parameters
-
-The `/v1/chat/completions` endpoint follows the OpenAI Chat API schema, which
-does not natively include diffusion-specific fields like `num_inference_steps`
-or `height`. How you pass these extra fields depends on your client.
-
-### curl / Python `requests`
-
-Wrap generation parameters inside an `"extra_body"` key in the JSON body:
+## Basic Request

 ```bash
-curl -s http://localhost:8091/v1/chat/completions \
+curl http://localhost:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
-      {"role": "user", "content": "A beautiful landscape painting"}
+      {"role": "user", "content": "Describe vLLM-Omni briefly."}
    ],
-    "extra_body": {
-      "num_inference_steps": 50,
-      "seed": 42
-    }
+    "modalities": ["text"]
  }'
 ```

-### OpenAI Python SDK
+Use `stream: true` for Server-Sent Events. Input media syntax, supported output
+`modalities`, and response choices depend on the model; see the
+[model-specific online serving examples](../user_guide/examples/online_serving/qwen3_omni.md).

-Use the `extra_body` **keyword argument**. The SDK automatically merges these
-fields into the top-level request body:
+!!! tip
+    Each server hosts one model. Query `GET /v1/models` for its served model
+    name when your client requires the `model` field.

-```python
-response = client.chat.completions.create(
-    model="Qwen/Qwen-Image",
-    messages=[{"role": "user", "content": "A beautiful landscape painting"}],
-    extra_body={
-        "num_inference_steps": 50,
-        "seed": 42,
-    },
-)
+## vLLM Extension Parameters
+
+Send standard and vLLM-specific Chat parameters as top-level fields in a
+direct HTTP request. When using the OpenAI Python SDK, pass vLLM-specific
+fields through the SDK's `extra_body` keyword; the SDK merges them into the
+top-level JSON sent to the server.
+
+=== "curl"
+
+    ```bash
+    curl http://localhost:8091/v1/chat/completions \
+      -H "Content-Type: application/json" \
+      -d '{
+        "messages": [{"role": "user", "content": "Write a short haiku."}],
+        "top_k": 40
+      }'
+    ```
+
+=== "OpenAI Python SDK"
+
+    ```python
+    from openai import OpenAI
+
+    client = OpenAI(base_url="http://localhost:8091/v1", api_key="none")
+
+    response = client.chat.completions.create(
+        model="your-served-model",
+        messages=[{"role": "user", "content": "Write a short haiku."}],
+        extra_body={"top_k": 40},
+    )
+    ```
+
+??? note "Diffusion compatibility"
+
+    Diffusion models exposed through Chat Completions can accept supported
+    fields such as `num_inference_steps`, `seed`, `height`, and `width`. Send
+    them at the top level with direct HTTP, or in the SDK's `extra_body`
+    keyword argument.
+
+    A literal nested `"extra_body"` JSON object is accepted for compatibility,
+    but it is not recommended for new direct HTTP clients. Do not provide the
+    same parameter in multiple locations; the server returns a `400` error for
+    duplicate diffusion parameters. Prefer the dedicated [Image Generation
+    API](image_generation_api.md) or [Image Edit API](image_edit_api.md) when
+    either endpoint matches the task.
+
+## Batch Requests
+
+`POST /v1/chat/completions/batch` accepts the same shared generation fields,
+but `messages` is a list of conversations. The response contains one choice
+per conversation in input order.
+
+```bash
+curl http://localhost:8091/v1/chat/completions/batch \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [
+      [{"role": "user", "content": "Summarize vLLM in one sentence."}],
+      [{"role": "user", "content": "Summarize vLLM-Omni in one sentence."}]
+    ],
+    "max_tokens": 64
+  }'
 ```

-!!! note "SDK `extra_body` vs. JSON `extra_body`"
-    These two `extra_body` usages look similar but work differently under the
-    hood. The SDK flattens the dict into the top-level request JSON, while the
-    curl/requests approach sends it as a nested `"extra_body"` key. Both are
-    handled correctly by the server.
-
-!!! note "About the `ignored fields` warning"
-    You may see a log message like:
-
-    ```
-    WARNING: The following fields were present in the request but ignored: {'height', 'width', ...}
-    ```
-
-    This is **harmless**. It is emitted by vLLM's request validation layer
-    because these fields are not part of the standard OpenAI
-    `ChatCompletionRequest` schema. The fields are still stored internally
-    and correctly forwarded to the diffusion pipeline.
+Batch Chat Completions does not support streaming, tools, beam search, or
+`n > 1`.

 ## Model-Specific Examples

-For complete examples with full request/response details, see the model-specific
-guides:
+For complete examples with model-specific inputs and outputs, see:

+- [Qwen3-Omni](../user_guide/examples/online_serving/qwen3_omni.md)
+- [Qwen2.5-Omni](../user_guide/examples/online_serving/qwen2_5_omni.md)
 - [Text-to-Image (Qwen-Image)](../user_guide/examples/online_serving/text_to_image.md)
 - [Image-to-Image (Qwen-Image-Edit, Qwen-Image-Layered)](../user_guide/examples/online_serving/image_to_image.md)
 - [GLM-Image](../user_guide/examples/online_serving/glm_image.md)
@@ -0,0 +1,116 @@
+# Full-Duplex WebSocket API
+
+vLLM-Omni provides an experimental full-duplex runtime for models that can
+continue receiving speech while producing speech. It adds persistent session
+state, model-specific turn policy, overlap handling, playback acknowledgement,
+and optional session resume.
+
+Full duplex is distinct from the turn-based [Realtime Audio API](realtime_api.md).
+
+## Choose an Endpoint
+
+| Endpoint | Protocol | Recommended use |
+|----------|----------|-----------------|
+| `WS /v1/realtime?duplex=1` | OpenAI Realtime-style event projection | Applications and browser clients |
+| `WS /v1/duplex` | Native vLLM-Omni duplex events | Runtime integration and low-level testing |
+
+Both endpoints use the same duplex engine and require the same model-side
+adapter. Prefer the Realtime projection unless the native lifecycle events are
+specifically required.
+
+## Enable Full Duplex
+
+The route becomes usable only when the deployment configuration explicitly
+sets:
+
+```yaml
+session_mode: duplex
+```
+
+The selected model pipeline must also provide a duplex serving adapter.
+Model-native deployments configure the corresponding engine runtime extension
+and control plane as part of their registered pipeline.
+
+!!! warning
+
+    `WS /v1/duplex` fails with `Duplex API is not available` when duplex is not
+    enabled. By contrast, `/v1/realtime?duplex=1` falls back to the ordinary
+    turn-based realtime handler when no duplex handler exists. Confirm that
+    `session.created.session.capabilities` is present before treating the
+    connection as full duplex.
+
+The current unified-runtime integrations are:
+
+- MiniCPM-o 4.5, using `vllm_omni/deploy/minicpmo_4_5_duplex.yaml`;
+- PersonaPlex, whose default `vllm_omni/deploy/personaplex.yaml` enables duplex.
+
+JoyVL is a separate HTTP interaction orchestrator and does not use these
+WebSocket endpoints. See [Standalone Experimental Servers](standalone_servers.md).
+
+## MiniCPM-o Quick Start
+
+Start the duplex deployment:
+
+```bash
+vllm serve openbmb/MiniCPM-o-4_5 --omni \
+  --deploy-config vllm_omni/deploy/minicpmo_4_5_duplex.yaml \
+  --trust-remote-code \
+  --port 8091
+```
+
+Stream a mono, PCM16, 16 kHz WAV file with the provided client:
+
+```bash
+python examples/online_serving/minicpmo/realtime_duplex_demo.py \
+  --url 'ws://localhost:8091/v1/realtime?duplex=1' \
+  --model openbmb/MiniCPM-o-4_5 \
+  --input-wav input_16k_mono.wav \
+  --ref-audio reference_voice.wav \
+  --output-dir /tmp/minicpmo-duplex
+```
+
+## Realtime Event Lifecycle
+
+A typical `/v1/realtime?duplex=1` session follows this lifecycle:
+
+1. Send `session.update` with the model, modalities, audio formats, and session
+   options.
+2. Wait for `session.created`; inspect `session.capabilities` instead of
+   assuming every model supports the same controls.
+3. Send `input_audio_buffer.append` events while microphone audio arrives.
+4. Send `input_audio_buffer.commit` at a user-turn boundary when required by
+   the model policy.
+5. Consume `response.created`, transcript deltas, `response.audio.delta`, and
+   `response.done` or `response.listen` events.
+6. Send `playback.ack` after audio has been played when the session advertises
+   playback acknowledgement support.
+7. Send `session.close` and wait for `session.closed`.
+
+Unlike the turn-based realtime endpoint, input may continue while a response
+is active. The server can emit `overlap.decision` to describe whether input was
+deferred, treated as a short acknowledgement, or used to interrupt output.
+
+## Capabilities and Model Differences
+
+The `session.created` payload includes capability fields such as
+`supports_barge_in`, `supports_playback_ack`, `supports_multi_session`,
+`supports_session_resume`, and `chunk_period_ms`. Treat this payload as the
+runtime contract.
+
+For example, PersonaPlex supports native overlapping speech but currently
+advertises `supports_barge_in=false`; destructive output interruption and
+model-state rewind have not been validated for that integration. Capacity and
+session-resume behavior also depend on the selected deployment configuration.
+
+## Native Protocol
+
+`WS /v1/duplex` exposes lower-level lifecycle names including
+`session.create`, `input_audio_buffer.append`, `turn.signal`, `playback.ack`,
+and `session.close`. It returns native session, input, response, overlap, and
+error events. This protocol is experimental and may evolve with the runtime;
+applications should use the provided Realtime client where possible.
+
+See the [MiniCPM-o example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/minicpmo),
+[PersonaPlex example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/personaplex),
+and [full-duplex runtime design](https://github.com/vllm-project/vllm-omni/blob/main/vllm_omni/experimental/fullduplex/DESIGN.md)
+for model-specific validation and architecture details.
@@ -0,0 +1,109 @@
+# OpenPI Robot Policy WebSocket API
+
+Use `WS /v1/realtime/robot/openpi` for low-latency robot-policy inference with
+OpenPI-compatible clients. A persistent WebSocket carries observations to the
+loaded policy model and returns action arrays.
+
+Despite the `/realtime` prefix, this endpoint does not use the OpenAI Realtime
+event schema. It uses binary MessagePack frames with NumPy extensions.
+
+## Availability
+
+The endpoint is available only when the loaded diffusion policy configuration
+contains `policy_server_config`. The server sends that model-specific
+configuration to the client as the first binary frame.
+
+For example, DreamZero declares:
+
+```yaml
+policy_server_config:
+  image_resolution: [180, 320]
+  n_external_cameras: 2
+  needs_wrist_camera: true
+  needs_stereo_camera: false
+  needs_session_id: true
+  action_space: joint_position
+```
+
+Without this configuration, the WebSocket returns `Robot policy not available`
+and closes.
+
+## DreamZero Quick Start
+
+Start the bundled DreamZero deployment:
+
+```bash
+vllm serve GEAR-Dreams/DreamZero-DROID --omni --port 8091 \
+  --served-model-name dreamzero-droid \
+  --deploy-config vllm_omni/deploy/dreamzero_tp1_cfg2.yaml \
+  --enforce-eager --disable-log-stats
+```
+
+Install the optional client dependencies, download the sample camera inputs,
+and run the client:
+
+```bash
+pip install openpi-client websockets opencv-python
+
+hf download YangshenDeng/vllm-omni-dreamzero-assets \
+  --repo-type dataset \
+  --local-dir outputs/dreamzero/assets
+
+python examples/online_serving/dreamzero/openpi_client.py \
+  --host 127.0.0.1 \
+  --port 8091 \
+  --video-dir outputs/dreamzero/assets
+```
+
+## Protocol
+
+The connection is request-response after an initial server handshake:
+
+```text
+connect
+  <- msgpack(policy_server_config)
+
+infer
+  -> msgpack({"endpoint": "infer", "session_id": "...", ...observation})
+  <- msgpack(ndarray | dict[str, ndarray])
+
+reset
+  -> msgpack({"endpoint": "reset"})
+  <- msgpack({"status": "reset successful"})
+```
+
+If `endpoint` is omitted, the server treats the message as `infer`. Each
+inference message produces one action response after the engine request
+completes; action tokens or intermediate tensors are not streamed.
+
+NumPy arrays use the marker format implemented by `openpi-client`. The server
+also accepts the legacy vLLM NumPy marker representation on input. JSON text
+frames are not observation messages.
+
+## Sessions and Reset
+
+- The API layer's current-session and first-call counters are scoped to each
+  WebSocket connection.
+- `session_id` identifies model-side state across observations on that
+  connection. If omitted, it defaults to `default`.
+- The first inference for a session is sent to the model with `reset=true`.
+- Changing `session_id`, or sending the `reset` command, causes the next
+  inference to start with `reset=true`.
+- The policy pipeline owns observation transforms and persistent model state,
+  normally keyed by `session_id`; the API layer forwards the raw observation
+  dictionary.
+
+## Limits and Errors
+
+- Maximum inbound payload size is 64 MiB.
+- The server closes an idle connection after 30 seconds.
+- Invalid binary input returns a MessagePack
+  `{"type":"error","message":"Invalid request payload"}` response.
+- Inference failures return a generic `Internal inference error` without
+  exposing an internal traceback.
+- Unsupported NumPy object, structured, and complex dtypes are rejected.
+
+Observation keys, camera layout, state tensors, and action shapes are defined
+by the loaded policy rather than by this transport. Read the handshake before
+constructing observations. See the [DreamZero example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/dreamzero)
+for a complete OpenPI client and DROID simulation loop.
@@ -0,0 +1,82 @@
+# Realtime Audio WebSocket API
+
+Use `WS /v1/realtime` to stream PCM audio into a compatible omni model and
+receive incremental transcription and synthesized response audio. This is a
+turn-based streaming API: it starts one generation over an audio stream and
+finishes after the client closes that input stream.
+
+For a model that listens while it is already speaking, use the experimental
+[Full-Duplex API](full_duplex_api.md) instead.
+
+## Quick Start
+
+Start a compatible model. The currently documented example uses Qwen3-Omni:
+
+```bash
+vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --omni --port 8091
+```
+
+Run the provided client with a mono, 16-bit PCM, 16 kHz WAV file:
+
+```bash
+python examples/online_serving/qwen3_omni/openai_realtime_client.py \
+  --url ws://localhost:8091/v1/realtime \
+  --model Qwen/Qwen3-Omni-30B-A3B-Instruct \
+  --input-wav input_16k_mono.wav \
+  --output-wav response.wav \
+  --output-text response.txt
+```
+
+The client dependency is `websockets`.
+
+## Protocol
+
+Messages in both directions are JSON text frames. Audio carried inside an
+event is base64-encoded raw PCM16.
+
+| Direction | Event | Purpose |
+|-----------|-------|---------|
+| Server to client | `session.created` | Confirms that the WebSocket connection is ready |
+| Client to server | `session.update` | Selects and validates the served model |
+| Client to server | `input_audio_buffer.commit` with `final: false` | Starts generation over the incoming stream |
+| Client to server | `input_audio_buffer.append` | Appends base64 PCM16 audio |
+| Client to server | `input_audio_buffer.commit` with `final: true` | Marks the end of input |
+| Server to client | `transcription.delta` | Carries incremental response text |
+| Server to client | `transcription.done` | Carries final text and token usage |
+| Server to client | `response.audio.delta` | Carries incremental PCM16 response audio |
+| Server to client | `response.audio.done` | Marks the end of response audio |
+| Server to client | `error` | Reports an invalid event, model, or audio payload |
+
+A minimal client sends events in this order:
+
+```json
+{"type":"session.update","model":"Qwen/Qwen3-Omni-30B-A3B-Instruct"}
+{"type":"input_audio_buffer.commit","final":false}
+{"type":"input_audio_buffer.append","audio":"<base64-pcm16>"}
+{"type":"input_audio_buffer.commit","final":true}
+```
+
+The initial non-final commit intentionally starts generation before all audio
+has arrived. Continue sending `input_audio_buffer.append` events while the
+engine is consuming the stream.
+
+## Audio Handling
+
+- Input is mono PCM16 at 16 kHz for the Qwen3-Omni example.
+- `response.audio.delta.audio` contains base64-encoded PCM16 bytes.
+- Read `sample_rate_hz` from each audio event instead of assuming an output
+  rate. Qwen3-Omni output is typically 24 kHz.
+- Concatenate audio deltas in receive order to construct the output waveform.
+- Set `input_audio_buffer.commit.final` to `true` after the last input chunk so
+  the server can terminate the streaming request.
+
+## Availability and Limitations
+
+The path is registered on the unified API server, but it is usable only when
+the loaded pipeline implements realtime audio input and produces compatible
+audio output. Unsupported deployments return an `error` event. The endpoint
+does not provide duplex session resume, playback acknowledgement, overlap
+policy, or barge-in controls.
+
+See the [Qwen3-Omni online serving example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/qwen3_omni)
+for concurrency options, chunk pacing, and per-delta audio debugging.
@@ -2,7 +2,7 @@

 vLLM-Omni provides an OpenAI-compatible API for text-to-speech (TTS) generation. Supported TTS models include:

- **Qwen3-TTS** (`Qwen/Qwen3-TTS-12Hz-*`) -- Qwen3-based TTS with CustomVoice, VoiceDesign, and Base (voice cloning) task types. Output: 24 kHz.
+- **Qwen3-TTS** (`Qwen/Qwen3-TTS-12Hz-*`) -- Qwen3-based TTS with CustomVoice, VoiceDesign, and Base (voice cloning) task types. Native output: 24 kHz; API output can be resampled to 8 kHz.
 - **Fish Speech S2 Pro** (`fishaudio/s2-pro`) -- Dual-AR TTS with DAC codec. Supports text-to-speech and voice cloning via reference audio. Output: 44.1 kHz.
 - **Voxtral TTS** (`mistralai/Voxtral-4B-TTS-2603`) -- AR + FlowMatching TTS with preset voices. Output: 24 kHz.
 - **CosyVoice3** (`FunAudioLLM/Fun-CosyVoice3-0.5B-2512`) -- 2-stage talker + flow-matching code2wav. Voice cloning via `ref_audio` + `ref_text` (no presets). Output: 24 kHz.
@@ -116,6 +116,7 @@ Content-Type: application/json

 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
+| `sample_rate` | integer | model native | Target output sample rate. Qwen3-TTS supports 8000 or 24000 Hz; the model remains native 24 kHz internally and is resampled before encoding. |
 | `task_type` | string | "CustomVoice" | TTS task type: CustomVoice, VoiceDesign, or Base |
 | `language` | string | "Auto" | Language (see supported languages below) |
 | `instructions` | string | "" | Voice style/emotion instructions |
@@ -0,0 +1,109 @@
+# Standalone Experimental Servers
+
+Most vLLM-Omni endpoints run on the unified server started with
+`vllm serve <model> --omni`. The experimental full-duplex package also contains
+two separate user-facing server processes. Their routes are not added to the
+unified API server.
+
+| Server | Transport | Role |
+|--------|-----------|------|
+| PersonaPlex compatibility server | WebSocket | Hosts the official PersonaPlex browser protocol and a raw-PCM alternative |
+| JoyVL interaction server | HTTP | Adds state, memory, proactive decisions, and delegation in front of a separate OpenAI-compatible model backend |
+
+## PersonaPlex Compatibility Server
+
+Use this server when a client requires the official Moshi/PersonaPlex wire
+protocol or the bundled browser UI. For the engine-owned unified duplex path,
+use [`WS /v1/realtime?duplex=1`](full_duplex_api.md) instead.
+
+Start it with:
+
+```bash
+HF_TOKEN=... CUDA_VISIBLE_DEVICES=0 python -m \
+  vllm_omni.experimental.fullduplex.personaplex.serving.server \
+  --port 8091
+```
+
+| Route | Purpose |
+|-------|---------|
+| `GET /` | Official PersonaPlex browser client, when its assets are available |
+| `GET /health` | Readiness check |
+| `WS /api/chat` | Moshi-compatible tagged binary protocol with Opus audio |
+| `WS /v1/audio/duplex` | Simpler float32 raw-PCM protocol |
+
+### `/api/chat` Protocol
+
+The first byte of every binary frame is a message tag:
+
+| Direction | Tag | Payload |
+|-----------|-----|---------|
+| Server to client | `0x00` | Ready handshake |
+| Client to server | `0x01` | Opus microphone audio at 24 kHz |
+| Server to client | `0x01` | Opus response audio |
+| Server to client | `0x02` | UTF-8 inner-monologue text |
+
+The optional `text_prompt` and `voice_prompt` query parameters select a persona
+and voice. The default server admits one conversation; use `--batch-size N` to
+configure multiple lockstep slots. Connections beyond capacity close with
+WebSocket code 1013.
+
+### `/v1/audio/duplex` Protocol
+
+Send `{"type":"open","persona":"...","voice":"..."}` as a JSON text frame,
+then exchange binary float32 PCM frames. The server returns generated float32
+audio as binary frames and text as `{"type":"text","text":"..."}`. Send
+`{"type":"close"}` and wait for `{"type":"done"}` to flush and finish.
+
+See the [PersonaPlex example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/personaplex)
+for the browser and headless clients.
+
+## JoyVL Interaction Server
+
+JoyVL is an orchestration layer, not a model-serving engine. It calls a
+separate OpenAI-compatible backend and adds per-session frame history, memory,
+persona policy, and optional delegation.
+
+Start the model backend and orchestrator separately:
+
+```bash
+vllm serve jdopensource/JoyAI-VL-Interaction-Preview \
+  --served-model-name JoyAI-VL-Interaction-Preview \
+  --port 8092 \
+  --max-model-len 131072 \
+  --enable-prefix-caching \
+  --limit-mm-per-prompt '{"image":256,"video":1}'
+
+python -m vllm_omni.experimental.fullduplex.joyvl.serving.server \
+  --port 8091 \
+  --main-backend-url http://127.0.0.1:8092/v1 \
+  --main-model JoyAI-VL-Interaction-Preview
+```
+
+| Method and route | Purpose |
+|------------------|---------|
+| `GET /health` | Readiness check |
+| `GET /v1/models` | Reports the configured interaction model |
+| `POST /v1/chat/completions` | Processes one frame or interaction tick |
+| `POST /reset` | Resets a session |
+| `POST /v1/streaming/reset` | Alias for `/reset` |
+| `POST /v1/streaming/persona` | Changes the session persona |
+
+Send one multimodal Chat Completions request per video frame, normally around
+one frame per second, and identify the session with `x-session-id`. Responses
+include an `interaction` block whose action is `silence`, `response`, or
+`delegate`.
+
+```bash
+curl http://127.0.0.1:8091/v1/chat/completions \
+  -H 'content-type: application/json' \
+  -H 'x-session-id: demo' \
+  -d '{
+    "messages": [{"role": "user", "content": [
+      {"type": "text", "text": "Alert me if a fire appears"},
+      {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}
+    ]}]
+  }'
+```
+
+See the [JoyVL recipe](https://github.com/vllm-project/vllm-omni/blob/main/recipes/JD/JoyAI-VL-Interaction.md)
+for memory, personas, delegation backends, and the browser UI.
@@ -0,0 +1,92 @@
+# Streaming Video Output WebSocket API
+
+Use `WS /v1/realtime/video` to receive fragmented MP4 chunks while a compatible
+diffusion video model is generating. This endpoint streams generated video
+output; it is unrelated to [`WS /v1/video/chat/stream`](video_stream_api.md),
+which streams live video input into a conversational model.
+
+## Quick Start
+
+Start a model that supports step execution and enable diffusion streaming
+output:
+
+```bash
+vllm serve BestWishYsh/Helios-Distilled --omni \
+  --diffusion-streaming-output \
+  --port 8091
+```
+
+Install the client dependency and run the provided client:
+
+```bash
+pip install av websockets
+
+python examples/online_serving/streaming_video_generation/streaming_video_client.py \
+  --host 127.0.0.1 \
+  --port 8091 \
+  --model BestWishYsh/Helios-Distilled \
+  --prompt "A serene lakeside sunrise with mist over the water" \
+  --output output.mp4
+```
+
+## Protocol
+
+Control events are JSON text frames. Each `video.chunk_metadata` event is
+immediately followed by one binary WebSocket frame containing fragmented MP4
+bytes.
+
+| Direction | Event or frame | Purpose |
+|-----------|----------------|---------|
+| Client to server | `session.start` | Starts generation with a prompt and video parameters |
+| Server to client | `video.start` | Confirms the request ID, format, and accepted configuration |
+| Server to client | `video.chunk_metadata` | Describes the next binary media frame |
+| Server to client | Binary frame | Carries one fragmented MP4 chunk |
+| Client to server | `session.interaction` | Queues an optional prompt update during generation |
+| Server to client | `session.interaction.queued` | Confirms that an interaction was queued |
+| Client to server | `session.ping` | Refreshes the stall clock |
+| Server to client | `session.pong` | Acknowledges a ping |
+| Client to server | `session.stop` | Cancels the active generation |
+| Server to client | `session.done` | Reports completion or cancellation and the chunk count |
+| Server to client | `error` | Reports validation, generation, or timeout failures |
+
+Start a minimal session with:
+
+```json
+{
+  "type": "session.start",
+  "model": "BestWishYsh/Helios-Distilled",
+  "prompt": "A serene lakeside sunrise",
+  "format": "m4s",
+  "width": 640,
+  "height": 384,
+  "fps": 16,
+  "num_frames": 99
+}
+```
+
+Only `m4s` output is currently accepted. Concatenate the binary frames in
+receive order, then remux the fragmented stream if the target player requires
+a progressive MP4 file.
+
+## Mid-Generation Interaction
+
+Models that implement interaction updates can change the active prompt:
+
+```json
+{
+  "type": "session.interaction",
+  "interaction": {
+    "event_id": "sunlight-update",
+    "event": {"prompt": "Sunlight breaks through the mist"},
+    "transition_chunks": 2
+  }
+}
+```
+
+This feature is pipeline-dependent. An enabled endpoint does not imply that
+the loaded model supports midway prompt updates.
+
+The default start timeout is 10 seconds and the server reports a stall after
+about 60 seconds without generation progress or a `session.ping`. See the
+[streaming video generation example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/streaming_video_generation)
+for prompt-update scheduling and browser playback.
@@ -17,6 +17,8 @@ globally.
 | --- | --- | --- |
 | `sparsity` | finite, `[0, 1]` | Nominal dropped-key-block fraction; default `0.8`; `0` disables sparsity |
 | `start_step` | integer, `>= 0` | Number of early denoise steps kept dense |
+| `end_step` | integer, `>= 0` | Number of final denoise steps kept dense (tail fallback) |
+| `precision` | `"bf16"`, `"fp8"`, `"mix"` | Kernel precision mode; default `"bf16"`. Requires MindIE-SD with `sparse_attention(precision=...)` |
 | `skip_layers` | selector such as `"0-3,38"` | DiT blocks kept dense |

 ```bash
@@ -52,6 +54,12 @@ platform raises. It is incompatible with ring sequence parallelism because
 the kernel needs the complete key sequence for ranking; use Ulysses sequence
 parallelism with `ring_degree=1`.

+The `precision` knob requires a MindIE-SD release whose `sparse_attention`
+accepts `precision=`; older releases accept it through `**kwargs` but
+silently ignore it and run the BF16 path. `RAINFUSION_ATTN` raises at runtime
+when a non-`bf16` precision is requested without that support, so pin a
+compatible MindIE-SD release when using `precision="mix"` or `"fp8"`.
+
 ## Geometry handling

 RainFusion handles arbitrary video grids by rearranging video spatially and,
@@ -12,15 +12,17 @@ LoRA adapters are lightweight, model-specific fine-tuning weights that can be ap
 ## LoRA Adapter Format

 ### PEFT (Parameter-Efficient Fine-Tuning) format (default)
+
 A typical PEFT-format LoRA adapter directory structure:

-```
+```text
 lora_adapter/
 ├── adapter_config.json
 └── adapter_model.safetensors
 ```

 The `adapter_config.json` file contains metadata about the LoRA adapter, including:
+
 - `r`: LoRA rank
 - `lora_alpha`: LoRA alpha scaling factor
 - `target_modules`: List of module names to apply LoRA to
@@ -67,7 +69,7 @@ For distilled few-step LoRAs, pass `lora_backend="distill"` together with one or
 For Qwen-Image and Wan pipelines, the distill backend calls `pipeline.load_lora_weights(...)` during worker initialization.

 | Pipeline | Supported distilled LoRA repo | Notes |
-|----------|--------------------------------|-------|
+| ---------- | -------------------------------- | ------- |
 | `QwenImagePipeline` | `lightx2v/Qwen-Image-2512-Lightning` | Used with Qwen-Image-2512 Lightning-style few-step inference. |
 | `Wan22Pipeline` | `lightx2v/Wan2.1-Distill-Loras`, `lightx2v/Wan2.2-Distill-Loras` | Wan2.1 uses one LoRA file. For dual-transformer Wan2.2 MoE, pass high-noise then low-noise LoRA files. |
 | `Wan22I2VPipeline` | `lightx2v/Wan2.2-Distill-Loras` | For dual-transformer Wan2.2 MoE, pass high-noise then low-noise LoRA files. |
@@ -240,6 +242,37 @@ Notes:
 - Output quality and speed depend on the replacement checkpoints and sampling params you choose.
 - If you only need to fuse distilled LoRAs into a Wan2.2 checkpoint at load time (without the full LightX2V convert + assemble pipeline), you can instead pass them directly via `--lora-backend distill --lora-path <high>.safetensors <low>.safetensors`. See the [Distill backend](#distill-backend-fuse-distilled-lora-at-init) section above.

+## MiniMax-H3 adapter-declared schedules
+
+MiniMax-H3 supports two few-step mechanisms that must not be conflated:
+
+- **Checkpoint-pinned schedule**: a merged release writes `base_schedule` into
+  `model_index.json`. Requests must pass `num_inference_steps` as the interval
+  count (for example `4` for `[1.0, 0.7, 0.4, 0.15, 0.0]`).
+- **Runtime Turbo LoRA**: LightX2V Turbo artifacts request five sigma points and
+  enforce `flow_shift=6`, `audio_flow_shift=3`.
+- **Runtime native LoRA**: FlashGen-style artifacts declare
+  `key_format=minimax-h3-native` and embed `base_schedule` in safetensors
+  metadata. When active, the adapter schedule overrides the base checkpoint and
+  requests must use the interval-count contract (`num_inference_steps=4`).
+  Request-mode generation may omit the field and take the count from the adapter
+  schedule; step execution requires it explicitly, because the step scheduler
+  derives the total step count from the request before the adapter schedule is
+  known.
+
+Native artifacts also declare `qkv_layout=grouped`. The H3 loader reorders fused
+`qkv_proj` LoRA rows with the same `_reorder_grouped_qkv_to_qkv` helper used for
+base-weight loading, then binds the packed Q/K/V slices through the legacy PEFT
+manager without modifying `DiffusionLoRAManager`.
+
+Both runtime adapters run with distributed layerwise offload in request-mode
+generation, where the manager keeps the LoRA A/B buffers resident on the compute
+device while DLO streams the base blocks. MiniMax-H3 step execution still
+rejects DLO, so `--step-execution` cannot be combined with
+`--enable-distributed-layerwise-offload`. Model-level CPU offload and standard
+layerwise offload are rejected in every mode, because the dynamic LoRA tensors
+are neither parameters nor registered buffers and therefore do not participate
+in those weight lifecycles.

 ## See Also

@@ -138,7 +138,7 @@ The following tables show which models support each feature:
 | **Qwen-Image-Edit**      |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Qwen-Image-Edit-2509** |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |        ✅ (decode)         |          ✅           |       ❌        |        ❌         |
 | **Qwen-Image-Layered**   |     ✅     |     ✅      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
-| **SenseNova-U1**         |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
+| **SenseNova-U1 / U1.5**  |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |          ❌           |       ❌        |        ❌         |
 | **Stable-Diffusion-XL**  |     ❌     |     ❌      |           ✅           |       ✅        |         ✅         |          ❌          |   ✅    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Stable-Diffusion3.5**  |     ❌     |     ✅      |           ❌           |       ✅        |         ✅         |          ❌          |   ❌    |             ✅             |      ✅ (decode)      |       ❌        |        ❌         |
 | **Z-Image**              |     ✅     |     ✅      |           ✅           |       ❓        |   ✅ (TP=2 only)   |          ❌          |   ✅    |             ❌             |      ✅ (decode)      |       ✅        |        ❌         |
@@ -49,7 +49,7 @@ scenario below for live barge-in validation on the target GPU.
 Default:

 ```bash
-vllm-omni serve openbmb/MiniCPM-o-4_5 \
+vllm serve openbmb/MiniCPM-o-4_5 \
    --omni \
    --deploy-config vllm_omni/deploy/minicpmo_4_5.yaml \
    --trust-remote-code \
@@ -155,6 +155,23 @@ def _response_in_progress(events: list[dict[str, object]]) -> bool:
    )


+async def _commit_input_after_playback_checkpoint(client: RealtimeDuplexClient) -> float:
+    """Commit input only after preserving already-played assistant history.
+
+    Native duplex can finish several responses while the user is still
+    streaming.  Under the ``ack_only`` playback policy, acknowledging those
+    responses after the final input commit is intentionally rejected: it could
+    append an old assistant message after the newly committed user message.
+    Checkpoint playback first so the server commits each assistant item at its
+    original history position.  A second acknowledgement after the commit can
+    then safely advance response-local playback that drained in the meantime.
+    """
+    await client.acknowledge_playback()
+    commit_sent_at_s = time.monotonic()
+    await client.commit()
+    return commit_sent_at_s
+
+
 def _event_count_after(
    events: list[dict[str, object]],
    event_type: str,
@@ -448,8 +465,7 @@ async def run_demo(args: argparse.Namespace) -> dict[str, object]:
            chunk_period_ms=_chunk_period_ms(client.events.events),
        )
        wait_for_post_commit_decision = False
-        commit_sent_at_s = time.monotonic()
-        await client.commit()
+        commit_sent_at_s = await _commit_input_after_playback_checkpoint(client)
        wait_error: str | None = None
        committed_index: int | None = None
        post_commit_decision: str | None = None
@@ -37,7 +37,7 @@ During generation the client normally sends only `session.start` and then receiv
 ## Install Client Dependency

 ```bash
-pip install websockets
+pip install av websockets
 # For the Gradio demo:
 pip install 'vllm-omni[demo]' websockets
 ```
@@ -8,7 +8,7 @@ the received bytes to disk when the session finishes or is interrupted.
 Image/reference input is intentionally not supported in this example yet.

 Requirements:
-    pip install websockets
+    pip install av websockets
 """

 import argparse
@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """OpenAI-compatible client for Qwen3-TTS via /v1/audio/speech endpoint.

 This script demonstrates how to use the OpenAI-compatible speech API
@@ -74,6 +77,8 @@ def run_tts_generation(args) -> None:
        "voice": args.speaker,
        "response_format": args.response_format,
    }
+    if args.sample_rate is not None:
+        payload["sample_rate"] = args.sample_rate

    # Add optional parameters
    if args.instructions:
@@ -246,6 +251,13 @@ def parse_args():
        choices=["wav", "mp3", "flac", "pcm", "aac", "opus"],
        help="Audio output format (default: wav)",
    )
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=None,
+        choices=[8000, 24000],
+        help="Output sample rate in Hz (default: model native)",
+    )
    parser.add_argument(
        "--output",
        "-o",
@@ -248,11 +248,11 @@ addopts = [
    "--dist=loadgroup"
 ]
 markers = [
-    # ci/cd required
-    "core_model: L1&L2 tests (run in each PR)",
-    "advanced_model: L3 level tests (run on each merge)",
-    "full_model: L4 level tests (run nightly)",
-    "local_model: tests (run locally)",
+    # ci/cd required — tag with [ci-level] for tests.helpers.mark.get_level_markers().
+    "core_model: [ci-level] L1&L2 tests (run in each PR)",
+    "advanced_model: [ci-level] L3 level tests (run on each merge)",
+    "full_model: [ci-level] L4 level tests (run nightly)",
+    "local_model: [ci-level] tests (run locally)",
    # function module markers
    "diffusion: Diffusion model tests",
    "omni: Omni model tests",
@@ -262,38 +262,41 @@ markers = [
    "sp: Sequence parallelism tests",
    "example: Doc example code tests",
    # platform markers
-    "cpu: Tests that run on CPU",
-    "gpu: Tests that run on GPU (auto-added)",
-    "cuda: Tests that run on CUDA (auto-added)",
-    "rocm: Tests that run on AMD/ROCm (auto-added)",
-    "xpu: Tests that run on XPU (auto-added)",
-    "npu: Tests that run on NPU/Ascend (auto-added)",
-    "musa: Tests that run on MUSA/Moore Threads (auto-added)",
+    # Tag with [hardware-platform] for tests.helpers.mark.get_supported_platforms().
+    # Add [gpu] on accelerator platforms that should also get pytest.mark.gpu.
+    # cpu/gpu are pytest marks; hardware_test(res=...) rejects them as keys.
+    "cpu: [hardware-platform] Tests that run on CPU",
+    "gpu: [hardware-platform] Tests that run on GPU (auto-added)",
+    "cuda: [hardware-platform] [gpu] Tests that run on CUDA (auto-added)",
+    "rocm: [hardware-platform] [gpu] Tests that run on AMD/ROCm (auto-added)",
+    "xpu: [hardware-platform] [gpu] Tests that run on XPU (auto-added)",
+    "npu: [hardware-platform] Tests that run on NPU/Ascend (auto-added)",
+    "musa: [hardware-platform] [gpu] Tests that run on MUSA/Moore Threads (auto-added)",
    # specified computation resources marks (auto-added)
-    # Tag with [hardware-resource] for tests.helpers.mark.get_hardware_mark_list().
-    "H100: [hardware-resource] Tests that require H100 GPU",
-    "H800: [hardware-resource] Tests that require H800 GPU",
-    "H200: [hardware-resource] Tests that require H200 GPU",
-    "L4: [hardware-resource] Tests that require L4 GPU",
-    "B200: [hardware-resource] Tests that require B200 GPU",
-    "MI325: [hardware-resource] Tests that require MI325 GPU (AMD/ROCm)",
-    "B60: [hardware-resource] Tests that require Intel Arc Pro B60 XPU",
-    "S5000: [hardware-resource] Tests that require S5000 GPU (Moore Threads/MUSA)",
-    "A2: [hardware-resource] Tests that require A2 NPU",
-    "A3: [hardware-resource] Tests that require A3 NPU",
-    "distributed_cuda: Tests that require multi cards on CUDA platform",
-    "distributed_rocm: Tests that require multi cards on ROCm platform",
-    "distributed_xpu: Tests that require multi cards on XPU platform",
-    "distributed_npu: Tests that require multi cards on NPU platform",
-    "distributed_musa: Tests that require multi cards on MUSA platform",
-    "skipif_cuda: Skip if the num of CUDA cards is less than the required",
-    "skipif_rocm: Skip if the num of ROCm cards is less than the required",
-    "skipif_xpu: Skip if the num of XPU cards is less than the required",
-    "skipif_npu: Skip if the num of NPU cards is less than the required",
-    "skipif_musa: Skip if the num of MUSA cards is less than the required",
+    # [hardware-resource] + [cuda]/[rocm]/[xpu]/[npu]/[musa] for hardware_test(res=...).
+    # Do not put [gpu] on SKU lines: that would make gpu a res key.
+    "H100: [hardware-resource] [cuda] Tests that require H100 GPU",
+    "H800: [hardware-resource] [cuda] Tests that require H800 GPU",
+    "H200: [hardware-resource] [cuda] Tests that require H200 GPU",
+    "L4: [hardware-resource] [cuda] Tests that require L4 GPU",
+    "B200: [hardware-resource] [cuda] Tests that require B200 GPU",
+    "MI325: [hardware-resource] [rocm] Tests that require MI325 GPU (AMD/ROCm)",
+    "B60: [hardware-resource] [xpu] Tests that require Intel Arc Pro B60 XPU",
+    "S5000: [hardware-resource] [musa] Tests that require S5000 GPU (Moore Threads/MUSA)",
+    "A2: [hardware-resource] [npu] Tests that require A2 NPU",
+    "A3: [hardware-resource] [npu] Tests that require A3 NPU",
+    # Tag with [hardware-cards] for tests.helpers.mark.get_supported_card_counts().
+    "cards_1: [hardware-cards] Tests that require 1 accelerator card",
+    "cards_2: [hardware-cards] Tests that require 2 accelerator cards",
+    "cards_3: [hardware-cards] Tests that require 3 accelerator cards",
+    "cards_4: [hardware-cards] Tests that require 4 accelerator cards",
+    "cards_5: [hardware-cards] Tests that require 5 accelerator cards",
+    "cards_6: [hardware-cards] Tests that require 6 accelerator cards",
+    "cards_7: [hardware-cards] Tests that require 7 accelerator cards",
+    "cards_8: [hardware-cards] Tests that require 8 accelerator cards",
    # more detailed markers
    "sp: Sequence parallelism tests (multi-GPU)",
-    "slow: Slow tests (may skip in quick CI)",
+    "slow: [ci-level] Slow tests (may skip in quick CI)",
    "benchmark: Benchmark tests",
    "xdist: xdist-parallelizable tests",
 ]
@@ -1,11 +1,14 @@
 # Boogu-Image

-> Text-to-image and image-editing online serving (Boogu-Image-0.1-Base / -Edit)
+> Text-to-image and image-editing online serving
+(Boogu-Image-0.1-Base / -Edit / -Edit-Turbo)

 ## Summary

 - Vendor: Boogu
- Model: `Boogu/Boogu-Image-0.1-Base` (text-to-image), `Boogu/Boogu-Image-0.1-Edit` (image editing)
+- Model: `Boogu/Boogu-Image-0.1-Base` (text-to-image),
+  `Boogu/Boogu-Image-0.1-Edit` (image editing), and
+  `Boogu/Boogu-Image-0.1-Edit-Turbo` (four-step image editing)
 - Task: Text-to-image generation and text-guided image editing (TI2I)
 - Mode: Online serving with the OpenAI-compatible API
 - Maintainer: Community
@@ -13,19 +16,23 @@
 ## When to use this recipe

 Use this recipe when you want a known-good starting point for serving
-`Boogu/Boogu-Image-0.1-Base` with vLLM-Omni's native pipeline (no
+`Boogu/Boogu-Image-0.1-Base`, `Boogu/Boogu-Image-0.1-Edit`, or
+`Boogu/Boogu-Image-0.1-Edit-Turbo` with vLLM-Omni's native pipeline (no
 `--diffusion-load-format diffusers`, and the upstream `boogu` package is not
 required).

 Boogu-Image-0.1 is an Apache-2.0 unified image generation and editing model
-family. This recipe covers the Base text-to-image checkpoint, which pairs a
-Qwen3-VL multimodal encoder with a Diffusion Transformer (DiT) and a flow-match
-Euler scheduler with time-shift. It handles photorealistic generation and
-Chinese/English text rendering.
+family. The Base text-to-image checkpoint pairs a Qwen3-VL multimodal encoder
+with a Diffusion Transformer (DiT) and a flow-match Euler scheduler with
+time-shift. It handles photorealistic generation and Chinese/English text
+rendering. The Edit and Edit-Turbo checkpoints use the same native
+`BooguImagePipeline` for image editing.

 ## References

 - Upstream model card: <https://huggingface.co/Boogu/Boogu-Image-0.1-Base>
+- Edit model card: <https://huggingface.co/Boogu/Boogu-Image-0.1-Edit>
+- Edit-Turbo 1K hotfix: <https://huggingface.co/Boogu/Boogu-Image-0.1-Edit-Turbo/tree/hotfix-1k-20260708>
 - Project page: <https://boogu.org>
 - GitHub: <https://github.com/boogu-project/Boogu-Image>
 - Related example: [`examples/online_serving/text_to_image/`](../../examples/online_serving/text_to_image/README.md)
@@ -93,7 +100,7 @@ curl -s http://localhost:8091/v1/chat/completions \
 - **Memory usage:** ~34.6 GiB on GPU. Use `--vae-use-slicing --vae-use-tiling`
  to trim peak VRAM if needed.
 - **Key flags:**
-  - `--omni` — enables vLLM-Omni diffusion serving.
+    - `--omni` — enables vLLM-Omni diffusion serving.
 - **Guidance:** Boogu-Image uses `guidance_scale` (mapped to the upstream
  `text_guidance_scale`); the default is `4.0`. Classifier-free guidance is
  active whenever `guidance_scale > 1.0`.
@@ -111,13 +118,13 @@ the image-editing (TI2I) path activates automatically when a request carries a
 reference image. The Base text-to-image path is unaffected (no reference image
 is sent).

-#### Command
+### Command

 ```bash
 vllm serve Boogu/Boogu-Image-0.1-Edit --omni --port 8091
 ```

-#### Verification
+### Verification

 Edit an image with `/v1/images/edits` (the model-card example — change a photo
 to a colored-pencil drawing). Diffusion parameters are plain multipart form
@@ -157,14 +164,14 @@ curl -s http://localhost:8091/v1/chat/completions \
  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > edited.png
 ```

-#### Notes
+### Notes

 - **Single reference image:** only one input image is supported for now (the
  upstream "Only support 1 reference image for now" limit).
 - **Guidance semantics:**
-  - `guidance_scale` = text guidance (upstream `text_guidance_scale`, default
+    - `guidance_scale` = text guidance (upstream `text_guidance_scale`, default
    `4.0`); `> 1.0` enables text CFG. Editing typically uses `5.0`.
-  - `guidance_scale_2` = image guidance (upstream `image_guidance_scale`,
+    - `guidance_scale_2` = image guidance (upstream `image_guidance_scale`,
    default `1.0` = off). Setting it `> 1.0` enables the double-guidance path
    (3 model predictions per step), steering more strongly toward the reference
    image.
@@ -178,3 +185,69 @@ curl -s http://localhost:8091/v1/chat/completions \
  supports Boogu-Image-Edit directly
  (`--model Boogu/Boogu-Image-0.1-Edit --guidance-scale 5.0`, optional
  `--guidance-scale-2`).
+
+## Fast image editing (Boogu-Image-0.1-Edit-Turbo)
+
+Edit-Turbo is the distilled image-editing checkpoint. Its `model_index.json`
+declares `BooguImagePipeline`, so it uses the same native TI2I path as Edit.
+The upstream 1K hotfix is recommended for stable results and is pinned below
+to avoid silently loading the older checkpoint from the repository's default
+revision.
+
+### Command
+
+```bash
+vllm serve Boogu/Boogu-Image-0.1-Edit-Turbo \
+  --omni \
+  --revision hotfix-1k-20260708 \
+  --port 8091
+```
+
+### Verification
+
+Edit an image with the distilled four-step settings:
+
+```bash
+curl -s http://localhost:8091/v1/images/edits \
+  -F model="Boogu/Boogu-Image-0.1-Edit-Turbo" \
+  -F image="@input.png" \
+  -F prompt="Change the style to a colored pencil drawing." \
+  -F num_inference_steps=4 \
+  -F guidance_scale=1.0 \
+  -F seed=42 \
+  | jq -r '.data[0].b64_json' | base64 -d > edited-turbo.png
+```
+
+Or via chat completions:
+
+```bash
+curl -s http://localhost:8091/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "messages": [
+      {"role": "user", "content": [
+        {"type": "image_url", "image_url": {"url": "data:image/png;base64,<BASE64>"}},
+        {"type": "text", "text": "Change the style to a colored pencil drawing."}
+      ]}
+    ],
+    "extra_body": {
+      "num_inference_steps": 4,
+      "guidance_scale": 1.0,
+      "seed": 42
+    }
+  }' | jq -r '.choices[0].message.content[0].image_url.url' | cut -d',' -f2- | base64 -d > edited-turbo.png
+```
+
+### Notes
+
+- **Pinned revision:** use `hotfix-1k-20260708`; upstream recommends the 1K
+  hotfix over the 1.5K variant for more stable results.
+- **Recommended settings:** `num_inference_steps=4` and
+  `guidance_scale=1.0`. Edit-Turbo is guidance-distilled, meaning the guidance
+  behavior is already baked into the distilled checkpoint. A scale of `1.0`
+  avoids applying additional classifier-free guidance; unlike the regular Edit
+  checkpoint, it should not be increased to `5.0`.
+- **Reference images:** the same single-reference and `align_res` behavior as
+  the regular Edit checkpoint applies.
+- **Known limitations:** the same single-GPU limitations as Base and Edit apply;
+  CPU offload, Cache-DiT, and multi-GPU parallelism are not yet validated.
@@ -69,11 +69,12 @@ reference-video preparation and MP4 output.

 For CUDA and ROCm deployments, non-streaming MP4 responses are encoded on the
 host CPU through PyAV/libx264 after generation. The response encoder selects the
-path automatically at runtime: inputs with a supported frame shape, common dtype,
-and RGB per-channel contiguous layout use direct planar frames; other inputs
-fall back to the legacy path before the PyAV container is opened. No CLI flag,
-model declaration, or user configuration is required. Streaming fMP4 output is
-unchanged.
+path automatically at runtime. The server-owned parallel converter accepts
+supported frame shapes and dtypes with either per-channel-contiguous or strided
+RGB planes, including interleaved arrays materialized by output transport.
+Standalone callers without a parallel converter retain the legacy fallback for
+strided planes. No CLI flag, model declaration, or user configuration is
+required. Streaming fMP4 output is unchanged.

 A community benchmark on 2x Xeon 8480C reported the following comparison
 between the legacy and direct planar paths
@@ -860,6 +861,129 @@ fixed HBM usage. Model-level and standard layerwise offload remain unsupported.
 The five requested sigma points produce the four denoiser evaluations expected
 by the Turbo artifact.

+### FlashGen native LoRA
+
+The FlashGen 4-step T2VA artifact uses the native MiniMax-H3 module layout and
+declares its distilled sigma schedule in safetensors metadata. It is published on
+[ModelScope](https://modelscope.cn/models/FlashGen/Minimax-H3-4step-lora-flashgen):
+
+```text
+FlashGen/Minimax-H3-4step-lora-flashgen/minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors
+```
+
+Download only that file:
+
+```bash
+python -m pip install modelscope
+export FLASHGEN_DIR=/path/to/minimax-h3-flashgen-lora
+export FLASHGEN_FILE=minimax_h3_t2va_flashgen_4step_v1.0_768p_bf16.safetensors
+modelscope download FlashGen/Minimax-H3-4step-lora-flashgen \
+  --local_dir "${FLASHGEN_DIR}" \
+  --include "${FLASHGEN_FILE}"
+export FLASHGEN_LORA="${FLASHGEN_DIR}/${FLASHGEN_FILE}"
+```
+
+Start from a non-offloaded or DLO FL2VA server command and add
+`--task-type fl2va --lora-backend peft --lora-path "${FLASHGEN_LORA}"`.
+Each request must use T2VA and the distilled interval-count contract:
+
+```bash
+-F 'num_inference_steps=4' \
+-F 'extra_params={"task":"t2va","duration":5.2}' \
+-F "lora={\"name\":\"h3-flashgen-v1.0\",\"path\":\"${FLASHGEN_LORA}\",\"scale\":1.0}"
+```
+
+This path rejects Ref2VA and checkpoints that already pin `base_schedule` in
+`model_index.json`. The adapter metadata carries
+`base_schedule=1.0,0.7,0.4,0.15,0.0`, so `num_inference_steps=4` means four
+denoiser evaluations, not five sigma points. Request-mode generation may omit
+the field and take the count from the adapter schedule; `--step-execution`
+requires it explicitly, because the step scheduler reads the total step count
+off the request at admission, before the adapter schedule is known.
+
+DLO is supported in request-mode generation on the same terms as the Turbo
+adapter: the request-switchable LoRA A/B buffers stay resident on the
+accelerator while DLO streams only the base blocks, so budget for that
+additional fixed HBM usage. The native artifact is rank 64 over 259 target
+modules, and its packed `qkv_proj` and `fc1` layers reuse the full-input A
+tensor per slice while B carries slice-local output rows, so the resident
+footprint exceeds the on-disk payload; measure it for your parallel layout
+rather than assuming the checkpoint size. Pure Ulysses replicates the adapter
+on every rank, while DiT tensor parallelism shards the B buffers. Model-level
+and standard layerwise offload remain unsupported, and `--step-execution`
+cannot be combined with `--enable-distributed-layerwise-offload`.
+
+To validate a deployment, post the same fixed-seed T2VA request twice with the
+adapter and twice without it, then compare the four output digests. The adapter
+is bound and deterministic when each pair matches internally and the two pairs
+differ from each other.
+
+### FastH3 adapter
+
+[FastH3](https://haoailab.com/blogs/fasth3-preview/) is FastVideo's four-step
+DMD2 student of H3-Base. It reuses H3's text encoder, VAEs, tokenizers, and
+schedulers unchanged, replacing 49 denoiser evaluations with four. It is fused
+into the checkpoint at load time rather than switched per request, because it
+carries full-rank deltas that no LoRA layer can express.
+
+The bundle publishes four variants, so download one and point `--lora-path` at
+it; the repository root is refused rather than guessed at:
+
+```bash
+export FASTH3_DIR=/path/to/fasth3
+hf download FastVideo/FastVideo-FastH3-4-step-Preview-v1-LoRA \
+  dense-datafree/adapter_model.safetensors --local-dir "${FASTH3_DIR}"
+export FASTH3_LORA="${FASTH3_DIR}/dense-datafree/adapter_model.safetensors"
+```
+
+Add `--task-type fl2va --lora-path "${FASTH3_LORA}"` to a non-offloaded server
+command. T2VA is served by the FL2VA partition, so `--task-type fl2va` is
+correct even though FastH3 preview v1 distills T2VA only. Because the adapter is
+fused, `--lora-backend` does not apply and a request carrying a `lora=` field is
+rejected rather than served without the adapter it asked for.
+
+```bash
+-F 'num_inference_steps=4' \
+-F 'extra_params={"task":"t2va","duration":4.4}'
+```
+
+Requests must ask for `num_inference_steps=4` and `task=t2va`: the release's five
+sigma points bound four denoiser evaluations, and that count is what the step
+scheduler admits a request on. The server denoises on the release's own ladder,
+keeping H3's per-modality shifts at the checkpoint values, so a request that
+overrides `flow_shift` or `audio_flow_shift` is rejected - it would sample the
+student at noise levels it was never distilled at.
+
+Only a release that identifies itself as FastH3 is fused; any other
+`fastvideo-lora-v2` adapter stays on the dynamic LoRA route. A claimed artifact
+is then held to its own metadata: one that misdeclares its tensor counts or
+leaves a transformer block unedited is refused at startup instead of serving
+mostly base H3 weights on a four-step schedule. Offload is refused for the same
+reason - `--enable-cpu-offload`, `--enable-layerwise-offload` and
+`--enable-distributed-layerwise-offload` all bypass the fusion, so they fail fast.
+
+> [!NOTE]
+> Only the **Dense / Data-Free** variant is supported today. The three VSA
+> variants carry `.set_weight` compression gates for modules that do not exist
+> in vLLM-Omni's dense H3 attention, and the release states dense attention is
+> not a supported substitute for them, so they are refused at startup rather
+> than silently run wrong. Video Sparse Attention for H3's packed
+> `[text | cond | audio | video]` sequence is follow-up work.
+
+Measured on 8x NVIDIA B300 with USP8, VAE patch-parallel 8, `TRTLLM_ATTN`, at
+1344x768, 4.4 s, seed 1101, one warmup excluded and two runs recorded:
+
+| Adapter | Steps | End-to-end | Diffusion engine |
+| --- | ---: | ---: | ---: |
+| none (base H3) | 50 | 25.8 / 26.4 s | 16.22 / 16.28 s |
+| FastH3 Dense | 4 (5 sigma points) | 11.7 / 11.8 s | 2.37 / 2.36 s |
+
+The denoising speedup is 6.9x. End-to-end is 2.2x because text encoding, VAE
+decoding and muxing are a fixed cost that dominates a clip this short; longer
+generations move the end-to-end figure toward the denoising one. Fusing the
+adapter does not measurably change startup: weight loading took 77.3 s with it
+against 85.8 s without.
+
 ## Key parameters

 | Parameter | Recommended value | Notes |
@@ -1018,3 +1142,4 @@ vllm serve "${MODEL_ROOT}/FL2VA" \
 - [Supported models](../../docs/models/supported_models.md)
 - [Video API](../../docs/serving/videos_api.md)
 - [Diffusion parallelism](../../docs/user_guide/diffusion/parallelism/overview.md)
+- [Temporal VAE chunk callback design](../../docs/design/feature/minimax_h3_temporal_vae_chunks.md)
@@ -18,11 +18,11 @@ examples in this repository.

 Qwen3-TTS supports three task types, each backed by a dedicated model checkpoint:

-| Task Type     | Model                                    | Description                                                  |
-| ------------- | ---------------------------------------- | ------------------------------------------------------------ |
-| `CustomVoice` | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice`  | Predefined speaker voices with optional style/emotion control |
-| `VoiceDesign` | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign`  | Generate speech from a natural language voice description     |
-| `Base`        | `Qwen/Qwen3-TTS-12Hz-1.7B-Base`         | Voice cloning from reference audio + transcript              |
+| Task Type     | Model                                    | Description                                                   |
+| ------------- | ---------------------------------------- | ------------------------------------------------------------- |
+| `CustomVoice` | `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice`   | Predefined speaker voices with optional style/emotion control |
+| `VoiceDesign` | `Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign`   | Generate speech from a natural language voice description     |
+| `Base`        | `Qwen/Qwen3-TTS-12Hz-1.7B-Base`          | Voice cloning from reference audio + transcript               |

 Smaller 0.6B variants are also available for `CustomVoice` and `Base`.

@@ -166,6 +166,15 @@ python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py --query-ty
 - Key flags: `--omni` is required. `--deploy-config` points to the bundled two-stage pipeline config.
 - Async chunking: Enabled by default in `qwen3_tts.yaml` for streaming-friendly first-audio latency. Raw audio streaming requires `stream=true`, `stream_format="audio"`, and `response_format="pcm"`.
 - Task/model matching: Each task type requires its matching model checkpoint. Using a CustomVoice model for a Base (voice clone) request will fail.
+- Base codec termination: Base requests without an explicit `max_new_tokens`
+  use a text-scaled safety ceiling (at least 192 codec frames and no more than
+  the configured model limit). If the Talker reaches that ceiling without
+  codec EOS, non-streaming serving discards the incomplete audio and retries
+  once with a fresh seed; an explicit `seed` or `max_new_tokens` disables the
+  retry. SSE and WebSocket clients receive structured errors and must discard
+  previously emitted audio when the error contains `"action":"discard"`;
+  raw PCM streams terminate with a connection error after any partial bytes
+  already sent.
 - Known limitations: The server serves one model variant at a time. To switch task types (e.g., CustomVoice to Base), restart the server with the corresponding model.

 ## Hardware Support
@@ -78,6 +78,7 @@ recipes/
 | [`Qwen/Qwen3-Omni.md`](./Qwen/Qwen3-Omni.md) | Online serving for multimodal chat | 1x A100 80GB |
 | [`Qwen/Qwen3-TTS.md`](./Qwen/Qwen3-TTS.md) | Text-to-speech serving (CustomVoice / VoiceDesign / Base) | 1x H100/A100 80GB / 1x AMD MI300X |
 | [`SenseNova/SenseNova-U1.md`](./SenseNova/SenseNova-U1.md) | Unified image generation and understanding | 1x or 2x H200 / 1x AMD MI300X |
+| [`SenseNova/SenseNova-U1.5.md`](./SenseNova/SenseNova-U1.5.md) | Unified image generation and understanding, with an 8-step distilled LoRA | 1x A800 80GB |
 | [`StabilityAI/Stable-Audio-Open.md`](./StabilityAI/Stable-Audio-Open.md) | Offline + online text-to-audio generation (Stable Audio Open) | 1x RTX 4090 24GB / 1x AMD MI300X |
 | [`Tencent/Covo-Audio-Chat.md`](./Tencent/Covo-Audio-Chat.md) | Online serving for audio chat | 1x A100 80GB |
 | [`Tencent/HunyuanImage-3.0-Instruct.md`](./Tencent/HunyuanImage-3.0-Instruct.md) | DiT-only text-to-image serving and benchmark, including ModelOpt mixed FP8/NVFP4 | 4x H100/H800 80GB / 2x B200 |
@@ -0,0 +1,159 @@
+# SenseNova-U1.5
+
+> Unified image generation and understanding, with an official 8-step distilled LoRA
+
+## Summary
+
+- Vendor: SenseNova
+- Model: `sensenova/SenseNova-U1.5-8B-MoT`
+- LoRA: `sensenova/SenseNova-U1.5-8B-MoT-LoRAs` (`SenseNova-U1.5-8B-MoT-LoRA-8step.safetensors`)
+- Task: text2img, img2img, img2text (visual understanding), text2text (chat)
+- Mode: Offline inference, Online serving (OpenAI-compatible API)
+- Maintainer: Community
+
+## When to use this recipe
+
+U1.5 runs on the same `SenseNovaU1Pipeline` as U1 — the checkpoint keeps
+`model_type: neo_chat`, so it resolves without `--model-class-name`. Relative to U1 the
+config flips two fields, `use_pixel_head` to `true` (the flow-matching head becomes a
+`ConvDecoder`) and `noise_scale_max_value` from 8.0 to 16.0; both are already read by
+`SenseNovaU1Config`. The checkpoint is 13 shards / 50.2 GB on disk, 30.3 GB of that fp32, and
+loads to roughly 34 GB in bf16.
+
+Use this recipe for U1.5 specifically, including its distilled few-step LoRA. For U1 see
+[SenseNova-U1](SenseNova-U1.md).
+
+## Hardware Support
+
+### GPU
+
+#### 1x A800 80GB
+
+- 1x NVIDIA A800 80GB PCIe with an Intel Xeon Gold 6336Y, driver 595.84, Linux
+- Python 3.12.13, vLLM 0.28.0, torch 2.13.0+cu130, CUDA 13.0, diffusers 0.40.0,
+  transformers 5.14.1, flashinfer 0.6.16.post3, BF16, TP=1
+- Peak GPU memory: 34.3 GB at 1024x1024, 36.4 GB at 1536x2720
+
+##### Text-to-Image
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+    --model sensenova/SenseNova-U1.5-8B-MoT \
+    --prompt "Close portrait of an elderly woman by a farmhouse window, warm natural light." \
+    --width 1024 --height 1024 \
+    --seed 42 --num-inference-steps 50 --cfg-scale 4.0 \
+    --extra-body '{"think": false, "cfg_norm": "none", "timestep_shift": 3.0, "t_eps": 0.02}' \
+    --output sensenova_u15_t2i.png
+```
+
+Think mode (`"think": true`) is recommended for higher image quality.
+
+##### Image-to-Image Editing
+
+```bash
+python examples/offline_inference/image_to_image/image_edit.py \
+    --model sensenova/SenseNova-U1.5-8B-MoT \
+    --prompt "Turn this into an oil painting" \
+    --image input.png --resolution 1024 \
+    --seed 42 --num-inference-steps 25 --cfg-scale 4.0 \
+    --extra-args '{"think": true, "img_cfg_scale": 1.0, "cfg_norm": "none", "timestep_shift": 3.0}' \
+    --output sensenova_u15_edit.png
+```
+
+##### 8-step distilled LoRA
+
+The distilled LoRA is fused into the generation tower at load time:
+
+```bash
+python examples/offline_inference/text_to_image/text_to_image.py \
+    --model sensenova/SenseNova-U1.5-8B-MoT \
+    --lora-path SenseNova-U1.5-8B-MoT-LoRA-8step.safetensors --lora-backend distill \
+    --prompt "Close portrait of an elderly woman by a farmhouse window, warm natural light." \
+    --width 1024 --height 1024 \
+    --seed 42 --num-inference-steps 8 --cfg-scale 1.0 \
+    --extra-body '{"think": false, "cfg_norm": "none", "timestep_shift": 3.0, "t_eps": 0.02}' \
+    --output sensenova_u15_lora8.png
+```
+
+**Use `--cfg-scale 1.0` with this LoRA.** It is distilled with DMD and runs without
+classifier-free guidance; the default `4.0` applies guidance twice and produces a
+blown-out, posterised image.
+
+##### Online serving
+
+```bash
+vllm serve sensenova/SenseNova-U1.5-8B-MoT --omni --port 8091
+
+python examples/online_serving/sensenova_u1/openai_chat_client.py \
+    -s http://127.0.0.1:8091 -m img2text -i input.png -p "Describe this image."
+```
+
+`-s` takes the base URL; the client appends `/v1` itself.
+
+#### Measured latency (1x A800 80GB, 25 steps, median of 3 after a warmup)
+
+| Resolution | Step latency | Total |
+| --- | --- | --- |
+| 1024x1024 | 209.5 ms | 5.24 s |
+| 1536x1536 | 458.5 ms | 11.46 s |
+
+At 1536x2720 with 50 steps, end-to-end is 43.0 s.
+
+#### Measured latency (1x NVIDIA H200 139GB, single run, no warmup)
+
+Reported by @hsliuustc0106 against PR head `29c090d`, on one reserved CUDA device.
+
+- Python 3.12.13, vLLM 0.28.0, torch 2.13.0+cu130, CUDA 13.0, diffusers 0.40.0,
+  transformers 5.14.1, flashinfer 0.6.16.post3, BF16, TP=1
+- `sensenova/SenseNova-U1.5-8B-MoT` with `SenseNova-U1.5-8B-MoT-LoRA-8step.safetensors`,
+  seed 42, 1024x1024
+
+| Case | Stage latency | Peak GPU memory |
+| --- | --- | --- |
+| 50 steps, cfg 4.0, think off | 9017 ms | 34384 MiB |
+| 8 steps, cfg 1.0, no LoRA control | 624.00 ms, 77.85 ms/step | 34376 MiB |
+| 8 steps, cfg 1.0, distilled LoRA | 623.85 ms, 77.98 ms/step | 34364 MiB |
+| 50 steps, cfg 4.0, think on, `VLLM_OMNI_SENSENOVA_PAGED_DECODE=1` | 81622 ms | 34594 MiB |
+
+Each row runs the Text-to-Image command above with `--num-inference-steps` and
+`--cfg-scale` as listed and `think` set through `--extra-body`; the LoRA row adds
+`--lora-path` and `--lora-backend distill`.
+
+Single runs without a preceding warmup, so the think-on row carries the first-request
+compile rather than a steady state. The distilled LoRA fused into 168 parameters, and its
+image differs from the same-seed no-LoRA control in 1,048,574 of 1,048,576 pixels, MAE 58.62.
+
+```bash
+pytest -m "cpu and not cuda" tests/diffusion/models/sensenova_u1/ tests/diffusion/lora/test_loader.py -q
+pytest -m "cpu and not cuda" tests/config/test_environment_variables.py tests/diffusion/test_diffusion_worker.py -q
+pytest -m cuda tests/diffusion/models/sensenova_u1/test_sensenova_u1_attention_gqa.py \
+    tests/diffusion/models/sensenova_u1/test_sensenova_u1_paged_decode.py -q --tb=short
+```
+
+71, 13 and 9 passed. Serving reached `/health`; `img2text` returned a 1921-character
+description and `text2text` the expected answer.
+
+#### Verification
+
+```bash
+pytest -q tests/diffusion/models/sensenova_u1/
+```
+
+## Notes
+
+- Both `use_pixel_head` and `noise_scale_max_value` come from `config.json`; no flag is needed.
+- The checkpoint carries no `configuration_neo_chat.py`, so the loader logs a few
+  "does not appear to have a file named configuration_neo_chat.py" errors during startup and
+  then proceeds on the in-tree config. Generation is unaffected.
+- The LoRA targets the generation tower only (`*_mot_gen`); understanding-tower weights are
+  untouched.
+- Autoregressive decode (think, text-to-text and image-to-text) runs on a paged K/V cache under
+  a captured CUDA graph. It falls back to the ordinary cache when the device or the bundled
+  `flash_attn_varlen_func` cannot support it; set `VLLM_OMNI_SENSENOVA_PAGED_DECODE=0` to force
+  that fallback.
+- The first request after startup costs about 0.7 s more than the steady state whether the paged
+  path is on or off. Measured on one A800 with the inductor, triton and vLLM compile caches all
+  cleared, median of three runs: 718 ms above steady with the path on, 679 ms with it off.
+- Each request captures its own graphs, and a think request captures twice because the sequence
+  grows past the 512 bucket, so the capture cost is paid per request rather than once at
+  startup.
@@ -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 legacy and automatic MP4 response encoding on synthetic input."""

 from __future__ import annotations
@@ -11,13 +11,16 @@ import statistics
 import time
 from collections.abc import Callable
 from dataclasses import dataclass
+from functools import partial
 from pathlib import Path
+from typing import cast

 import numpy as np

 from vllm_omni.entrypoints.openai.video_api_utils import (
    _encode_video_bytes,
    _encode_video_bytes_legacy,
+    _PlanarFrameConverter,
 )


@@ -42,12 +45,19 @@ def _build_inputs(
    fps: int,
    audio_sample_rate: int,
    seed: int,
+    input_layout: str = "planar-backed",
 ) -> tuple[np.ndarray, np.ndarray]:
    rng = np.random.default_rng(seed)
    planar = rng.random((3, frames, height, width), dtype=np.float32)
    video = planar.transpose(1, 2, 3, 0)
-    if not all(video[..., channel].flags.c_contiguous for channel in range(3)):
+    if input_layout == "interleaved":
+        video = np.ascontiguousarray(video)
+        if not video.flags.c_contiguous:
+            raise RuntimeError("synthetic video is not C-contiguous")
+    elif input_layout == "planar-backed" and not all(video[..., channel].flags.c_contiguous for channel in range(3)):
        raise RuntimeError("synthetic video does not expose contiguous channel planes")
+    elif input_layout != "planar-backed":
+        raise ValueError(f"unsupported input layout: {input_layout}")

    sample_count = max(1, round(frames / fps * audio_sample_rate))
    timeline = np.arange(sample_count, dtype=np.float32) / audio_sample_rate
@@ -86,8 +96,8 @@ def _measure(

 def _summarize(records: list[dict[str, object]], label: str) -> dict[str, object]:
    selected = [record for record in records if record["label"] == label]
-    wall_values = [float(record["wall_ms"]) for record in selected]
-    cpu_values = [float(record["process_cpu_ms"]) for record in selected]
+    wall_values = [cast(float, record["wall_ms"]) for record in selected]
+    cpu_values = [cast(float, record["process_cpu_ms"]) for record in selected]
    return {
        "runs": len(selected),
        "wall_ms": {
@@ -113,6 +123,16 @@ def _parse_args() -> argparse.Namespace:
    parser.add_argument("--warmup", type=int, default=1)
    parser.add_argument("--rounds", type=_positive_int, default=3)
    parser.add_argument("--seed", type=int, default=20260817)
+    parser.add_argument(
+        "--input-layout",
+        choices=("planar-backed", "interleaved"),
+        default="planar-backed",
+    )
+    parser.add_argument(
+        "--frame-conversion-workers",
+        type=_positive_int,
+        default=8,
+    )
    parser.add_argument("--output-json", type=Path)
    args = parser.parse_args()
    if args.warmup < 0:
@@ -129,33 +149,41 @@ def main() -> None:
        fps=args.fps,
        audio_sample_rate=args.audio_sample_rate,
        seed=args.seed,
+        input_layout=args.input_layout,
    )
    baseline = BenchmarkVariant("legacy", _encode_video_bytes_legacy)
-    candidate = BenchmarkVariant("automatic", _encode_video_bytes)
+    converter = _PlanarFrameConverter(max_workers=args.frame_conversion_workers)
+    candidate = BenchmarkVariant(
+        "automatic",
+        partial(_encode_video_bytes, frame_converter=converter),
+    )

-    for _ in range(args.warmup):
-        for variant in (baseline, candidate):
-            _measure(
-                variant,
-                video=video,
-                audio=audio,
-                fps=args.fps,
-                audio_sample_rate=args.audio_sample_rate,
-            )
+    try:
+        for _ in range(args.warmup):
+            for variant in (baseline, candidate):
+                _measure(
+                    variant,
+                    video=video,
+                    audio=audio,
+                    fps=args.fps,
+                    audio_sample_rate=args.audio_sample_rate,
+                )

-    records: list[dict[str, object]] = []
-    for round_index in range(args.rounds):
-        order = (baseline, candidate) if round_index % 2 == 0 else (candidate, baseline)
-        for variant in order:
-            _, record = _measure(
-                variant,
-                video=video,
-                audio=audio,
-                fps=args.fps,
-                audio_sample_rate=args.audio_sample_rate,
-            )
-            record["round"] = round_index + 1
-            records.append(record)
+        records: list[dict[str, object]] = []
+        for round_index in range(args.rounds):
+            order = (baseline, candidate) if round_index % 2 == 0 else (candidate, baseline)
+            for variant in order:
+                _, record = _measure(
+                    variant,
+                    video=video,
+                    audio=audio,
+                    fps=args.fps,
+                    audio_sample_rate=args.audio_sample_rate,
+                )
+                record["round"] = round_index + 1
+                records.append(record)
+    finally:
+        converter.shutdown()

    output_hashes = {str(record["output_sha256"]) for record in records}
    if len(output_hashes) != 1:
@@ -165,8 +193,8 @@ def main() -> None:
    paired_savings_percent = []
    for round_index in range(args.rounds):
        pair = [record for record in records if record["round"] == round_index + 1]
-        baseline_ms = float(next(record["wall_ms"] for record in pair if record["label"] == baseline.label))
-        candidate_ms = float(next(record["wall_ms"] for record in pair if record["label"] == candidate.label))
+        baseline_ms = cast(float, next(record["wall_ms"] for record in pair if record["label"] == baseline.label))
+        candidate_ms = cast(float, next(record["wall_ms"] for record in pair if record["label"] == candidate.label))
        paired_savings_ms.append(baseline_ms - candidate_ms)
        paired_savings_percent.append((baseline_ms - candidate_ms) / baseline_ms * 100)

@@ -180,6 +208,8 @@ def main() -> None:
            "warmup": args.warmup,
            "rounds": args.rounds,
            "seed": args.seed,
+            "input_layout": args.input_layout,
+            "frame_conversion_workers": args.frame_conversion_workers,
            "video_shape": list(video.shape),
            "video_strides": list(video.strides),
            "codec_options": {"preset": "ultrafast", "threads": "0"},
@@ -279,17 +279,79 @@ def test_unmeasured_tpot_stays_missing_after_tokenizer_fallback():
    assert metrics.request_goodput == 0.0


-def test_measured_zero_itl_is_not_treated_as_missing():
+def test_zero_itl_does_not_create_zero_tpot():
    output = _make_output(100, output_tokens=3)
    output.itl = [0.0, 0.0]
    output.duplex_session_metrics = {"mean_ttft_ms": 100.0}

    metrics = _calculate_test_metrics([output], {"tpot": 1.0})

-    assert (metrics.num_tpot_samples, metrics.num_itl_samples) == (1, 2)
-    assert metrics.mean_tpot_ms == 0.0
+    assert (metrics.num_tpot_samples, metrics.num_itl_samples) == (0, 2)
+    assert math.isnan(metrics.mean_tpot_ms)
    assert metrics.mean_itl_ms == 0.0
-    assert metrics.request_goodput == 1.0
+    assert metrics.request_goodput == 0.0
+
+
+def test_stage_output_tokens_without_client_timing_omit_tpot(capsys):
+    output = _make_output(100, output_tokens=5)
+    output.itl = []
+    output.text_latency = 0.0
+    output.ttft = 0.1
+
+    metrics, _ = calculate_metrics(
+        input_requests=[],
+        outputs=[output],
+        dur_s=1.0,
+        tokenizer=None,
+        selected_percentiles=[50.0, 99.0],
+        goodput_config_dict={},
+        task_type=TaskType.GENERATION,
+        selected_percentile_metrics=["tpot"],
+        max_concurrency=None,
+        request_rate=float("inf"),
+        benchmark_duration=1.0,
+    )
+
+    printed = capsys.readouterr().out
+    assert metrics.num_tpot_samples == 0
+    assert math.isnan(metrics.mean_tpot_ms)
+    assert "Time per Output Token" not in printed
+    assert "Mean TPOT" not in printed
+
+
+def test_consistent_client_latency_can_supply_tpot_fallback():
+    output = _make_output(100, output_tokens=10)
+    output.itl = []
+    output.ttft = 0.1
+    output.text_latency = 1.0
+
+    metrics = _calculate_test_metrics([output])
+
+    assert metrics.num_tpot_samples == 1
+    assert metrics.mean_tpot_ms == pytest.approx(100.0)
+
+
+def test_single_token_responses_do_not_report_zero_tpot(capsys):
+    output = _make_output(100, output_tokens=1)
+
+    metrics, _ = calculate_metrics(
+        input_requests=[],
+        outputs=[output],
+        dur_s=1.0,
+        tokenizer=None,
+        selected_percentiles=[50.0],
+        goodput_config_dict={},
+        task_type=TaskType.GENERATION,
+        selected_percentile_metrics=["tpot"],
+        max_concurrency=None,
+        request_rate=float("inf"),
+        benchmark_duration=1.0,
+    )
+
+    printed = capsys.readouterr().out
+    assert metrics.num_tpot_samples == 0
+    assert math.isnan(metrics.mean_tpot_ms)
+    assert "Time per Output Token" not in printed


 def test_duplex_goodput_does_not_pair_measurements_from_different_requests():
@@ -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

 """
 Unit tests for patch.py
@@ -9,22 +9,98 @@ import asyncio
 import base64
 import json
 import time
+from argparse import Namespace
 from types import SimpleNamespace

 import pytest
 from pytest_mock import MockerFixture
 from vllm.benchmarks.lib.endpoint_request_func import RequestFuncInput

+from vllm_omni.benchmarks.data_modules.seed_tts_dataset import (
+    SeedTTSSampleRequest,
+    SeedTTSTextSampleRequest,
+)
 from vllm_omni.benchmarks.patch.patch import (
    MixRequestFuncOutput,
+    _apply_stage0_token_timings,
+    _attach_seed_tts_to_request_func_input,
    async_request_openai_chat_omni_completions,
    async_request_openai_realtime_duplex,
+    should_request_stage_metrics,
 )
 from vllm_omni.experimental.fullduplex.client import RealtimeEventCollector

 pytestmark = [pytest.mark.core_model, pytest.mark.benchmark, pytest.mark.cpu]


+def _seed_tts_request_func_input() -> RequestFuncInput:
+    return RequestFuncInput(
+        model="test-model",
+        model_name="test-model",
+        prompt="target text",
+        api_url="http://test.com/v1/chat/completions",
+        prompt_len=2,
+        output_len=20,
+        extra_body={"modalities": ["text", "audio"]},
+    )
+
+
+def test_seed_tts_chat_request_carries_reference_audio_once() -> None:
+    reference_audio_url = "data:audio/wav;base64,AAAA"
+    sample = SeedTTSSampleRequest(
+        prompt="target text",
+        prompt_len=2,
+        expected_output_len=20,
+        multi_modal_data=None,
+        seed_tts_system_prompt="Clone the supplied voice.",
+        seed_tts_speech_extra={
+            "ref_audio": reference_audio_url,
+            "ref_text": "reference text",
+            "task_type": "Base",
+        },
+    )
+    request_func_input = _seed_tts_request_func_input()
+
+    _attach_seed_tts_to_request_func_input(sample, request_func_input)
+
+    assert request_func_input.omni_chat_messages == [
+        {
+            "role": "system",
+            "content": [{"type": "text", "text": "Clone the supplied voice."}],
+        },
+        {"role": "user", "content": [{"type": "text", "text": "target text"}]},
+    ]
+    assert request_func_input.extra_body["ref_audio"] == reference_audio_url
+    serialized_request = json.dumps(
+        {"messages": request_func_input.omni_chat_messages, **request_func_input.extra_body},
+    )
+    assert serialized_request.count(reference_audio_url) == 1
+
+
+def test_seed_tts_text_chat_messages_do_not_add_reference_audio() -> None:
+    sample = SeedTTSTextSampleRequest(
+        prompt="target text",
+        prompt_len=2,
+        expected_output_len=20,
+        multi_modal_data=None,
+        seed_tts_system_prompt="Use the configured voice.",
+        seed_tts_speech_extra=None,
+    )
+    request_func_input = _seed_tts_request_func_input()
+
+    _attach_seed_tts_to_request_func_input(sample, request_func_input)
+
+    assert getattr(request_func_input, "seed_tts_row", False) is True
+    assert request_func_input.omni_chat_messages == [
+        {
+            "role": "system",
+            "content": [{"type": "text", "text": "Use the configured voice."}],
+        },
+        {"role": "user", "content": [{"type": "text", "text": "target text"}]},
+    ]
+    assert "ref_audio" not in request_func_input.extra_body
+
+
 class MockResponse:
    """Mock aiohttp response for testing"""

@@ -102,7 +178,24 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
                received_at_s=now + 0.03,
            )
            self.events.add(
-                {"type": "response.done", "response": {"id": response_id}},
+                {
+                    "type": "response.done",
+                    "response": {
+                        "id": response_id,
+                        "metadata": {
+                            "vllm_omni": {
+                                "stage_metrics": {
+                                    "0": {
+                                        "num_tokens_out": 3,
+                                        "vllm_ttft_ms": 20.0,
+                                        "vllm_tpot_ms": 10.0,
+                                        "vllm_itls_ms": [10.0, 10.0],
+                                    }
+                                }
+                            }
+                        },
+                    },
+                },
                received_at_s=now + 0.04,
            )

@@ -143,6 +236,7 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
    assert client.configure_kwargs["native_duplex"] is False
    assert client.configure_kwargs["extra_body"] == {
        "ref_audio": "data:audio/wav;base64,AAAA",
+        "return_stage_metrics": True,
    }
    assert client.sent == [
        event
@@ -167,6 +261,10 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
    assert output.audio_ttfp > output.ttft
    assert output.audio_rtf > 0
    assert output.latency > 0
+    assert output.output_tokens == 12
+    assert output.itl == [0.01, 0.01] * 4
+    assert output.text_latency == pytest.approx(output.ttft + 0.08)
+    assert output.tpot_measured is True
    assert output.tts_turn_pcm_bytes == [b"\x00\x00" * 2400] * 4
    assert output.tts_output_pcm_bytes == b"\x00\x00" * 9600
    session_id = output.duplex_request_metrics[0]["session_id"]
@@ -198,11 +296,160 @@ async def test_seed_tts_realtime_duplex_exports_per_request_metrics(monkeypatch)
    }


+def test_stage0_token_timings_use_weighted_tpot_when_itls_are_incomplete():
+    output = MixRequestFuncOutput(ttft=0.1)
+
+    measured = _apply_stage0_token_timings(
+        output,
+        [
+            {"output_token_count": 3, "itls_ms": [], "tpot_ms": 10.0},
+            {"output_token_count": 2, "itls_ms": [], "tpot_ms": 40.0},
+        ],
+        expected_output_tokens=5,
+    )
+
+    assert measured is True
+    assert output.itl == []
+    assert output.tpot_measured is True
+    assert (output.text_latency - output.ttft) / 4 == pytest.approx(0.02)
+
+
+def test_stage0_zero_itls_fall_back_to_positive_tpot():
+    output = MixRequestFuncOutput(ttft=0.1)
+
+    measured = _apply_stage0_token_timings(
+        output,
+        [{"output_token_count": 3, "itls_ms": [0.0, 0.0], "tpot_ms": 25.0}],
+        expected_output_tokens=3,
+    )
+
+    assert measured is True
+    assert output.itl == []
+    assert output.tpot_measured is True
+    assert (output.text_latency - output.ttft) / 2 == pytest.approx(0.025)
+
+
+def test_stage0_token_count_without_timing_is_not_measured():
+    output = MixRequestFuncOutput(ttft=0.1)
+
+    measured = _apply_stage0_token_timings(
+        output,
+        [{"output_token_count": 3, "itls_ms": [], "tpot_ms": None}],
+        expected_output_tokens=3,
+    )
+
+    assert measured is False
+    assert output.itl == []
+    assert output.text_latency == output.ttft
+    assert output.tpot_measured is False
+
+
 def create_sse_chunk(data_dict):
    """Helper to create SSE formatted chunk"""
    return f"data: {json.dumps(data_dict)}\n\n".encode()


+def test_chat_text_timing_metrics_request_stage_metrics():
+    args = Namespace(
+        backend="openai-chat-omni",
+        percentile_metrics="ttft,tpot,itl,e2el",
+        print_stage=False,
+        extra_body={},
+    )
+
+    assert should_request_stage_metrics(args) is True
+
+
+@pytest.mark.asyncio
+async def test_bundled_first_text_chunk_uses_stage0_token_timings(mocker: MockerFixture):
+    """Engine timings recover TPOT when every text token arrives together."""
+    request_input = RequestFuncInput(
+        model="test-model",
+        model_name="test-model",
+        prompt="test prompt",
+        api_url="http://test.com/v1/chat/completions",
+        prompt_len=10,
+        output_len=20,
+    )
+    chunks = [
+        create_sse_chunk(
+            {
+                "choices": [{"delta": {"content": "ABCD"}}],
+                "modality": "text",
+                "usage": {"prompt_tokens": 10, "completion_tokens": 4, "total_tokens": 14},
+                "metrics": {
+                    "stage_metrics": {
+                        "0": {
+                            "num_tokens_out": 4,
+                            "vllm_itls_ms": [10.0, 11.0, 12.0],
+                            "vllm_tpot_ms": 11.0,
+                        }
+                    }
+                },
+            }
+        ),
+        b"data: [DONE]\n\n",
+    ]
+    mock_response = MockResponse(200, chunks)
+    mock_session = mocker.AsyncMock()
+    mock_session.post = mocker.MagicMock(return_value=mock_response)
+
+    output = await async_request_openai_chat_omni_completions(request_input, mock_session)
+
+    assert output.success is True
+    assert output.output_tokens == 4
+    assert output.itl == pytest.approx([0.010, 0.011, 0.012])
+    assert output.text_latency - output.ttft == pytest.approx(0.033)
+    assert output.tpot_measured is True
+
+
+@pytest.mark.asyncio
+async def test_positive_client_text_timings_take_precedence_over_stage0(mocker: MockerFixture):
+    request_input = RequestFuncInput(
+        model="test-model",
+        model_name="test-model",
+        prompt="test prompt",
+        api_url="http://test.com/v1/chat/completions",
+        prompt_len=10,
+        output_len=20,
+    )
+    chunks = [
+        create_sse_chunk(
+            {
+                "choices": [{"delta": {"content": "A"}}],
+                "modality": "text",
+                "usage": {"prompt_tokens": 10, "completion_tokens": 1, "total_tokens": 11},
+            }
+        ),
+        create_sse_chunk(
+            {
+                "choices": [{"delta": {"content": "B"}}],
+                "modality": "text",
+                "usage": {"prompt_tokens": 10, "completion_tokens": 2, "total_tokens": 12},
+                "metrics": {
+                    "stage_metrics": {
+                        "0": {
+                            "num_tokens_out": 2,
+                            "vllm_itls_ms": [1000.0],
+                            "vllm_tpot_ms": 1000.0,
+                        }
+                    }
+                },
+            }
+        ),
+        b"data: [DONE]\n\n",
+    ]
+    mock_response = MockResponse(200, chunks, delay_between_chunks=0.01)
+    mock_session = mocker.AsyncMock()
+    mock_session.post = mocker.MagicMock(return_value=mock_response)
+
+    output = await async_request_openai_chat_omni_completions(request_input, mock_session)
+
+    assert output.success is True
+    assert len(output.itl) == 1
+    assert 0.0 < output.itl[0] < 0.1
+
+
 # ============================================================================
 # output_tokens Tests
 # ============================================================================
@@ -585,7 +585,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa

@pytest.mark.asyncio
@pytest.mark.parametrize(
-    ("request_metrics", "expected_itl", "expected_text_latency"),
+    ("request_metrics", "expected_itl", "expected_text_latency", "expected_tpot_measured"),
    [
        (
            [
@@ -594,6 +594,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
            ],
            [0.01, 0.02, 0.03],
            0.16,
+            True,
        ),
        (
            [
@@ -602,6 +603,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
            ],
            [],
            0.18,
+            True,
        ),
        (
            [
@@ -610,6 +612,7 @@ async def test_adapter_requires_and_forwards_exact_prepared_payload(tmp_path: Pa
            ],
            [],
            0.1,
+            False,
        ),
    ],
 )
@@ -619,6 +622,7 @@ async def test_adapter_reports_exact_or_weighted_token_timing(
    request_metrics: list[dict[str, object]],
    expected_itl: list[float],
    expected_text_latency: float,
+    expected_tpot_measured: bool,
 ):
    case, options = _case(tmp_path), _session_options(tmp_path)
    request = _request(case, options)
@@ -643,7 +647,7 @@ async def test_adapter_reports_exact_or_weighted_token_timing(
    assert output.success
    assert output.itl == expected_itl
    assert output.text_latency == pytest.approx(expected_text_latency)
-    assert output.tpot_measured is True
+    assert output.tpot_measured is expected_tpot_measured


 def test_batch_finalization_publishes_only_measured_results(tmp_path: Path, monkeypatch: pytest.MonkeyPatch):
@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """Tests for SeedTTSTextDataset, SeedTTSTextSampleRequest, SeedTTSDesignDataset,
 and SeedTTSDesignSampleRequest.

@@ -234,34 +237,6 @@ def test_seed_tts_design_dataset_rejects_missing_description(seed_tts_design_roo
        assert req.seed_tts_utterance_id == "ok"


-def test_attach_sets_seed_tts_row_even_without_extra_body():
-    """seed_tts_row=True must be set for SeedTTSTextSampleRequest (no extra body)."""
-    from vllm_omni.benchmarks.data_modules.seed_tts_dataset import SeedTTSTextSampleRequest
-
-    req = SeedTTSTextSampleRequest(
-        prompt="hello world",
-        prompt_len=2,
-        expected_output_len=100,
-        multi_modal_data=None,
-        request_id="test-0",
-        seed_tts_speech_extra=None,
-        seed_tts_ref_wav_path="",
-    )
-    assert req.seed_tts_speech_extra is None
-    assert req.seed_tts_ref_wav_path == ""
-    # The fix ensures that even with speech_extra=None, the function
-    # sets seed_tts_row=True. We verify the source code has the fix.
-    import inspect
-
-    import vllm_omni.benchmarks.patch.patch as patch_mod
-
-    src = inspect.getsource(patch_mod._attach_seed_tts_to_request_func_input)
-    # seed_tts_row must be set BEFORE the 'if not ex: return' check
-    row_pos = src.index("seed_tts_row")
-    not_ex_pos = src.index("if not ex:")
-    assert row_pos < not_ex_pos, "seed_tts_row must be set before 'if not ex: return'"
-
-
 def test_seed_tts_whisper_transcribe_passes_attention_mask(monkeypatch):
    from vllm_omni.benchmarks.data_modules import seed_tts_eval

@@ -0,0 +1,42 @@
+# 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")
+
+
+def _find_step(label: str) -> dict:
+    pipeline = yaml.safe_load(AMD_MERGE_PIPELINE.read_text(encoding="utf-8"))
+
+    def walk(steps: list[dict]) -> dict | None:
+        for step in steps:
+            if step.get("label") == label:
+                return step
+            if nested := walk(step.get("steps", [])):
+                return nested
+        return None
+
+    step = walk(pipeline.get("steps", []))
+    assert step is not None, f"missing AMD pipeline step: {label}"
+    return step
+
+
+def test_qwen3_tts_base_preserves_advanced_model_arguments() -> None:
+    step = _find_step("Qwen3-TTS Base E2E Test")
+    commands = step["commands"]
+
+    assert all("bash -c" not in command for command in commands)
+    pytest_command = next(command for command in commands if "pytest" in command)
+    argv = split(pytest_command)
+
+    marker_index = argv.index("-m")
+    run_level_index = argv.index("--run-level")
+    assert argv[marker_index + 1] == "advanced_model and cuda"
+    assert argv[run_level_index + 1] == "advanced_model"
@@ -41,6 +41,11 @@ from vllm_omni.engine.arg_utils import SHARED_FIELDS, internal_blacklist_keys

 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]

+SINGLE_STAGE_PIPE_CFG = (StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),)
+# Validation strings to match against in post init
+NO_STAGES_MATCH_STR = "no stages"
+NO_TERMINAL_STAGE_MATCH_STR = "No terminal stage"
+

@pytest.fixture(autouse=True)
 def _stable_test_platform(monkeypatch):
@@ -618,6 +623,7 @@ class TestPipelineDiscovery:
        p = PipelineConfig(
            model_type="custom_collide",
            hf_architectures=("SomeCollidingArch",),
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        assert p.hf_architectures == ("SomeCollidingArch",)

@@ -639,33 +645,63 @@ class TestStagePipelineConfig:


 class TestPipelineConfigNew:
-    def test_frozen(self):
-        p = PipelineConfig(model_type="t", model_arch="A")
-        with pytest.raises(AttributeError):
-            p.model_type = "changed"
+    def test_simple_init(self):
+        # Ensure pipeline config validates in post init.
+        PipelineConfig(
+            model_type="t",
+            model_arch="A",
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )

-    def test_validate_valid(self):
+    def test_frozen(self):
        p = PipelineConfig(
+            model_type="t",
+            model_arch="A",
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        with pytest.raises(AttributeError):
+            p.model_type = "changed"
+
+    def test_validate_no_stages(self):
+        with pytest.raises(ValueError, match=NO_STAGES_MATCH_STR):
+            PipelineConfig(model_type="t", model_arch="A")
+
+    def test_validate_no_terminal_stage(self):
+        """A pipeline with no ``final_output`` stage can never emit a result."""
+        with pytest.raises(ValueError, match=NO_TERMINAL_STAGE_MATCH_STR):
+            PipelineConfig(
+                model_type="t",
+                model_arch="A",
+                stages=(
+                    StagePipelineConfig(stage_id=0, model_stage="a"),
+                    StagePipelineConfig(stage_id=1, model_stage="b", input_sources=(0,)),
+                ),
+            )
+
+    def test_pipeline_can_have_final_output_in_any_stage(self):
+        """Ensure any stage may carry ``final_output``."""
+        PipelineConfig(
            model_type="t",
            model_arch="A",
            stages=(
-                StagePipelineConfig(stage_id=0, model_stage="a"),
+                StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),
                StagePipelineConfig(stage_id=1, model_stage="b", input_sources=(0,)),
            ),
        )
-        assert p.validate() == []
-
-    def test_validate_no_stages(self):
-        p = PipelineConfig(model_type="t", model_arch="A")
-        assert any("no stages" in e.lower() for e in p.validate())


 class TestPipelineRegistration:
    def test_resolve_pipeline_prefers_deploy_pipeline_key(self, clean_pipeline_registry, tmp_path):
        deploy_key = "deploy_selected_pipeline"
        model_type_key = "hf_model_type_pipeline"
-        deploy_pipe = PipelineConfig(model_type=deploy_key)
-        model_type_pipe = PipelineConfig(model_type=model_type_key)
+        deploy_pipe = PipelineConfig(
+            model_type=deploy_key,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        model_type_pipe = PipelineConfig(
+            model_type=model_type_key,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
        register_pipeline(deploy_pipe)
        register_pipeline(model_type_pipe)

@@ -691,7 +727,11 @@ class TestPipelineRegistration:
        tmp_path,
    ):
        model_type_key = "registered_model_type_pipeline"
-        register_pipeline(PipelineConfig(model_type=model_type_key))
+        pipeline_cfg = PipelineConfig(
+            model_type=model_type_key,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        register_pipeline(pipeline_cfg)

        deploy_path = tmp_path / "deploy.yaml"
        deploy_path.write_text("pipeline: missing_pipeline\n", encoding="utf-8")
@@ -726,6 +766,7 @@ class TestPipelineRegistration:
        pipe_cfg = PipelineConfig(
            model_type=pipeline_key,
            hf_architectures=("ArchitectureFallbackForTest",),
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        register_pipeline(pipe_cfg)

@@ -756,15 +797,18 @@ class TestPipelineRegistration:
            model_type="rejected_by_predicate",
            hf_architectures=(shared_arch,),
            hf_config_predicate=rejecting_predicate,
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        raises_cfg = PipelineConfig(
            model_type="raises_in_predicate",
            hf_architectures=(shared_arch,),
            hf_config_predicate=raising_predicate,
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        accept_cfg = PipelineConfig(
            model_type="accepted_by_predicate",
            hf_architectures=(shared_arch,),
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        register_pipeline(reject_cfg)
        register_pipeline(raises_cfg)
@@ -798,6 +842,7 @@ class TestPipelineRegistration:
            model_type="predicate_without_arch_match",
            hf_architectures=("DifferentArchitectureForTest",),
            hf_config_predicate=predicate,
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        register_pipeline(pipe_cfg)

@@ -821,6 +866,7 @@ class TestPipelineRegistration:
        resolved_cfg = PipelineConfig(
            model_type="callable_resolved_pipeline",
            hf_architectures=("CallableArchitectureForTest",),
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        seen_hf_configs = []

@@ -894,7 +940,10 @@ class TestPipelineRegistration:
    def test_pipeline_registration(self, clean_pipeline_registry):
        """Ensure that we can register and create a custom pipeline config."""
        new_model_type = "new_model_type"
-        pipe_cfg = PipelineConfig(model_type=new_model_type)
+        pipe_cfg = PipelineConfig(
+            model_type=new_model_type,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )

        # Register the new PipelineConfig
        assert new_model_type not in OMNI_PIPELINES
@@ -937,7 +986,10 @@ class TestPipelineRegistration:
        def custom_resolver(
            hf_config: FakeConfig,
        ) -> PipelineConfig:
-            return PipelineConfig(model_type=resolved_type)
+            return PipelineConfig(
+                model_type=resolved_type,
+                stages=SINGLE_STAGE_PIPE_CFG,
+            )

        # Register the new PipelineConfig
        assert new_model_type not in OMNI_PIPELINES
@@ -994,7 +1046,7 @@ class TestPipelineRegistration:
        pipe_cfg = PipelineConfig(
            model_type=pipeline_key,
            model_arch="DeployOnlyArch",
-            stages=(StagePipelineConfig(stage_id=0, model_stage="diffusion"),),
+            stages=SINGLE_STAGE_PIPE_CFG,
        )
        register_pipeline(pipe_cfg)

@@ -1027,7 +1079,11 @@ class TestPipelineRegistration:

    def test_structured_path_loads_explicit_deploy_config_once(self, clean_pipeline_registry, tmp_path):
        pipeline_key = "single_load_pipeline"
-        register_pipeline(PipelineConfig(model_type=pipeline_key))
+        pipeline_cfg = PipelineConfig(
+            model_type=pipeline_key,
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        register_pipeline(pipeline_cfg)
        deploy_path = tmp_path / "single_load.yaml"
        deploy_path.write_text(f"pipeline: {pipeline_key}\n", encoding="utf-8")

@@ -1057,6 +1113,7 @@ class TestPipelineRegistration:
        pipeline = PipelineConfig(
            model_type="pipeline_with_default",
            default_deploy_config_name=default_name,
+            stages=SINGLE_STAGE_PIPE_CFG,
        )

        with patch(
@@ -1076,8 +1133,16 @@ class TestPipelineRegistration:
            OmniServingCapability.COMPLETIONS,
            "pipeline_a blocks completions",
        )
-        pipe_a = PipelineConfig(model_type="detect_type", endpoint_restrictions=(restriction,))
-        pipe_b = PipelineConfig(model_type="override_type", endpoint_restrictions=())
+        pipe_a = PipelineConfig(
+            model_type="detect_type",
+            endpoint_restrictions=(restriction,),
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
+        pipe_b = PipelineConfig(
+            model_type="override_type",
+            endpoint_restrictions=(),
+            stages=SINGLE_STAGE_PIPE_CFG,
+        )
        register_pipeline(pipe_a)
        register_pipeline(pipe_b)

@@ -1159,7 +1224,7 @@ class TestDeployConfigLoading:
        stages = merge_pipeline_deploy(pipeline, deploy)

        assert deploy.session_mode == "duplex"
-        assert deploy.active_stream_window == 1
+        assert deploy.active_stream_window == max_sessions
        assert deploy.duplex_session.max_sessions == max_sessions
        assert [stage.session_mode for stage in stages] == ["duplex", "duplex", "duplex"]
        assert [stage.to_omegaconf().session_mode for stage in stages] == ["duplex", "duplex", "duplex"]
@@ -1617,6 +1682,7 @@ stages:
                    model_stage="ar",
                    execution_type=StageExecutionType.LLM_AR,
                    requires_multimodal_data=True,
+                    final_output=True,
                ),
            ),
        )
@@ -1637,6 +1703,7 @@ stages:
                    model_stage="ar",
                    execution_type=StageExecutionType.LLM_AR,
                    scheduler_cls=scheduler_cls,
+                    final_output=True,
                ),
            ),
        )
@@ -1882,7 +1949,6 @@ class TestQwen3OmniPipeline:
        assert isinstance(p, PipelineConfig)
        assert p.model_arch == "Qwen3OmniMoeForConditionalGeneration"
        assert len(p.stages) == 3
-        assert p.validate() == []

    def test_thinker(self):
        p = resolve_pipeline_config(
@@ -1936,7 +2002,6 @@ class TestQwen2_5OmniPipeline:
        assert isinstance(p, PipelineConfig)
        assert p.model_arch == "Qwen2_5OmniForConditionalGeneration"
        assert len(p.stages) == 3
-        assert p.validate() == []

    def test_thinker(self):
        p = resolve_pipeline_config("qwen2_5_omni")
@@ -1985,7 +2050,6 @@ class TestQwen3TTSPipeline:
        assert p is not None
        assert p.model_arch == "Qwen3TTSTalkerForConditionalGeneration"
        assert len(p.stages) == 2
-        assert p.validate() == []

    def test_talker_stage(self):
        p = resolve_pipeline_config("qwen3_tts")
@@ -2065,7 +2129,6 @@ class TestMingFlashOmniPipeline:
        assert isinstance(p, PipelineConfig)
        assert p.model_arch == "MingFlashOmniForConditionalGeneration"
        assert len(p.stages) == 2
-        assert p.validate() == []

    def test_thinker_stage(self):
        p = resolve_pipeline_config("ming_flash_omni")
@@ -2122,7 +2185,6 @@ class TestMingFlashOmniPipeline:
        assert isinstance(p, PipelineConfig)
        assert p.model_arch == "MingFlashOmniTalkerForConditionalGeneration"
        assert len(p.stages) == 1
-        assert p.validate() == []

    def test_tts_stage(self):
        p = resolve_pipeline_config("ming_flash_omni_tts")
@@ -2179,7 +2241,6 @@ class TestMingFlashOmniPipeline:
        assert isinstance(p, PipelineConfig)
        assert p.model_arch == "MingFlashOmniForConditionalGeneration"
        assert len(p.stages) == 1
-        assert p.validate() == []

    def test_thinker_only_stage(self):
        p = resolve_pipeline_config("ming_flash_omni_thinker_only")
@@ -2218,7 +2279,6 @@ class TestMingFlashOmniPipeline:
        assert p is not None
        assert p.model_arch == "MingFlashOmniForConditionalGeneration"
        assert len(p.stages) == 2
-        assert p.validate() == []

    def test_image_thinker_stage(self):
        s = resolve_pipeline_config("ming_flash_omni_image").get_stage(0)
@@ -2339,6 +2399,21 @@ class TestBaseConfigInheritance:
        # CI overrides max_tokens
        assert s0["max_tokens"] == 150

+    def test_qwen3_omni_colocate_async_bounds_only_rocm_kv_cache(self):
+        ci_path = Path(get_deploy_config_path("ci/qwen3_omni_moe_colocate_async.yaml"))
+        pipeline = resolve_pipeline_config("qwen3_omni_moe_thinker_only")
+        assert isinstance(pipeline, PipelineConfig)
+
+        cuda = _apply_platform_overrides(load_deploy_config(ci_path), platform="cuda")
+        cuda_stage = merge_pipeline_deploy(pipeline, cuda)[0]
+        assert cuda_stage.yaml_engine_args["gpu_memory_utilization"] == 0.9
+        assert "kv_cache_memory_bytes" not in cuda_stage.yaml_engine_args
+
+        rocm = _apply_platform_overrides(load_deploy_config(ci_path), platform="rocm")
+        rocm_stage = merge_pipeline_deploy(pipeline, rocm)[0]
+        assert "gpu_memory_utilization" not in rocm_stage.yaml_engine_args
+        assert rocm_stage.yaml_engine_args["kv_cache_memory_bytes"] == 2 * 1024**3
+
    def test_pure_inheritance_overlay(self, tmp_path):
        """An overlay with only ``base_config`` inherits everything."""
        base = Path(get_deploy_config_path("qwen3_omni_moe.yaml"))
@@ -2399,6 +2474,20 @@ class TestPlatformOverrides:
        assert rocm.stages[0].enforce_eager is None
        assert rocm.stages[1].enforce_eager is True

+    def test_higgs_audio_v3_rocm_uses_triton_attention(self):
+        deploy_path = Path(get_deploy_config_path("higgs_multimodal_qwen3.yaml"))
+
+        base = load_deploy_config(deploy_path)
+        assert base.stages[0].engine_extras["attention_backend"] == "FLASHINFER"
+
+        rocm = _apply_platform_overrides(base, platform="rocm")
+        assert rocm.stages[0].engine_extras["attention_backend"] == "TRITON_ATTN"
+
+        pipeline = resolve_pipeline_config("higgs_multimodal_qwen3")
+        assert isinstance(pipeline, PipelineConfig)
+        stages = merge_pipeline_deploy(pipeline, rocm)
+        assert stages[0].yaml_engine_args["attention_backend"] == "TRITON_ATTN"
+
    def test_qwen3_omni_cuda_uses_thinker_rotary_custom_op(self):
        deploy_path = Path(get_deploy_config_path("qwen3_omni_moe.yaml"))
        pipeline = resolve_pipeline_config(
@@ -2786,6 +2875,54 @@ stages:
        assert omega_config.engine_args.max_model_len == 8192
        assert omega_config.engine_args.enforce_eager is True

+    def test_cli_attention_shorthand_replaces_yaml_structured_default(self):
+        stage = StageConfig(
+            stage_id=0,
+            model_stage="dit",
+            stage_type=StageType.DIFFUSION,
+            yaml_engine_args={
+                "diffusion_attention_config": {
+                    "default": {"backend": "FLASH_ATTN"},
+                    "per_role": {"cross": {"backend": "TORCH_SDPA"}},
+                },
+            },
+            runtime_overrides={"diffusion_attention_backend": "SAGE_ATTN"},
+        )
+
+        engine_args = stage.to_omegaconf().engine_args
+
+        assert engine_args.diffusion_attention_backend == "SAGE_ATTN"
+        assert "default" not in engine_args.diffusion_attention_config
+        assert engine_args.diffusion_attention_config.per_role.cross.backend == "TORCH_SDPA"
+
+    def test_cli_attention_shorthand_keeps_yaml_per_role_only_config(self):
+        stage = StageConfig(
+            stage_id=0,
+            model_stage="dit",
+            stage_type=StageType.DIFFUSION,
+            yaml_engine_args={"diffusion_attention_config": {"per_role": {"cross": {"backend": "TORCH_SDPA"}}}},
+            runtime_overrides={"diffusion_attention_backend": "SAGE_ATTN"},
+        )
+
+        engine_args = stage.to_omegaconf().engine_args
+
+        assert engine_args.diffusion_attention_backend == "SAGE_ATTN"
+        assert engine_args.diffusion_attention_config == {"per_role": {"cross": {"backend": "TORCH_SDPA"}}}
+
+    def test_cli_attention_config_replaces_yaml_shorthand(self):
+        stage = StageConfig(
+            stage_id=0,
+            model_stage="dit",
+            stage_type=StageType.DIFFUSION,
+            yaml_engine_args={"diffusion_attention_backend": "TORCH_SDPA"},
+            runtime_overrides={"diffusion_attention_config": {"default": {"backend": "SAGE_ATTN"}}},
+        )
+
+        engine_args = stage.to_omegaconf().engine_args
+
+        assert "diffusion_attention_backend" not in engine_args
+        assert engine_args.diffusion_attention_config.default.backend == "SAGE_ATTN"
+

 class TestSentinelDefaultPrecedence:
    """Caller-typed (non-None) values win over YAML; None values fall through
@@ -167,7 +167,7 @@ def test_inventory_matches_reviewed_snapshot_counts():
        EnvironmentVariableCategory.PUBLIC_OMNI: 24,
        EnvironmentVariableCategory.INHERITED_VLLM: 20,
        EnvironmentVariableCategory.PLATFORM_EXTERNAL: 27,
-        EnvironmentVariableCategory.MODEL_SPECIFIC: 56,
+        EnvironmentVariableCategory.MODEL_SPECIFIC: 57,
        EnvironmentVariableCategory.BENCHMARK_TRANSITIONAL: 20,
        EnvironmentVariableCategory.INTERNAL: 2,
    }
@@ -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: 33,
+        ModelEnvironmentVariableDisposition.PROMOTE: 34,
        ModelEnvironmentVariableDisposition.REQUEST_SCOPE: 6,
        ModelEnvironmentVariableDisposition.EXTERNAL: 0,
        ModelEnvironmentVariableDisposition.INTERNALIZE: 11,
@@ -45,6 +45,7 @@ from vllm_omni.config.stage_config import (
    PipelineConfig,
    StageDeployConfig,
    StageExecutionType,
+    StagePipelineConfig,
    load_deploy_config,
    merge_pipeline_deploy,
 )
@@ -653,6 +654,7 @@ def test_sub_config_fields_match_structured_scopes():
        "ring_degree",
        "allgather_degree",
        "ulysses_mode",
+        "ulysses_a2a_permute",
        "cfg_parallel_size",
        "vae_patch_parallel_size",
        "vae_parallel_mode",
@@ -858,6 +860,23 @@ def test_from_pipeline_config_derives_sequence_parallel_size_from_allgather_degr
    assert stage.parallel_config.world_size == 2


+def test_from_pipeline_config_preserves_deployed_ulysses_a2a_permute() -> None:
+    pipeline = _resolve_pipeline_or_skip("dreamzero")
+    deploy = DeployConfig(
+        stages=[
+            StageDeployConfig(
+                stage_id=0,
+                ulysses_a2a_permute=True,
+            )
+        ]
+    )
+
+    stage = VllmOmniConfig.from_pipeline_config(pipeline, user_deploy_config=deploy).stage_by_id(0)
+
+    assert isinstance(stage, VllmOmniDiffusionStageConfig)
+    assert stage.parallel_config.ulysses_a2a_permute is True
+
+
 def test_diffusion_parallel_config_accepts_four_way_guidance_parallelism():
    cfg = OmniStageDiffusionParallelConfig(cfg_parallel_size=4)

@@ -1021,7 +1040,10 @@ def test_from_pipeline_config_uses_hf_config_for_callable_resolver():


 def test_from_pipeline_config_accepts_pre_resolved_pipeline():
-    resolved_pipeline = PipelineConfig(model_type="callable_resolved_variant")
+    resolved_pipeline = PipelineConfig(
+        model_type="callable_resolved_variant",
+        stages=(StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),),
+    )

    omni_config = VllmOmniConfig.from_pipeline_config(resolved_pipeline)

@@ -1054,6 +1076,7 @@ def test_from_pipeline_config_default_deploy_name_ignores_cwd(monkeypatch, tmp_p
    pipeline = PipelineConfig(
        model_type="pipeline_with_default",
        default_deploy_config_name=default_name,
+        stages=(StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),),
    )
    loaded_paths = []

@@ -1,16 +1,22 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Tests for out of tree registration to OMNI_PIPELINES."""

 import pytest
 from transformers import PretrainedConfig

 from vllm_omni.config.pipeline_registry import OMNI_PIPELINES, register_pipeline
-from vllm_omni.config.stage_config import PipelineConfig, pipeline_cfg_resolver
+from vllm_omni.config.stage_config import PipelineConfig, StagePipelineConfig, pipeline_cfg_resolver

 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]


+def build_fake_pipeline_config(model_type: str) -> PipelineConfig:
+    return PipelineConfig(
+        model_type=model_type, stages=(StagePipelineConfig(stage_id=0, model_stage="a", final_output=True),)
+    )
+
+
@pytest.fixture
 def custom_resolver():
    """Build a reusable custom resolver for PipelineConfigs."""
@@ -22,7 +28,7 @@ def custom_resolver():
    def custom_resolver(
        hf_config: CustomConfigType,
    ) -> PipelineConfig:
-        return PipelineConfig(model_type="resolved_type")
+        return build_fake_pipeline_config("resolved_type")

    return custom_resolver

@@ -30,7 +36,7 @@ def custom_resolver():
 def test_register_pipeline_config(clean_pipeline_registry):
    """Ensure that we can register a custom pipeline config to OMNI_PIPELINES."""
    new_model_type = "new_model_type"
-    pipe_cfg = PipelineConfig(model_type=new_model_type)
+    pipe_cfg = build_fake_pipeline_config(new_model_type)
    assert new_model_type not in OMNI_PIPELINES
    register_pipeline(pipe_cfg)
    assert new_model_type in OMNI_PIPELINES
@@ -41,7 +47,7 @@ def test_register_pipeline_config_with_model_type(clean_pipeline_registry):
    """Ensure that we can register a custom pipeline config with an explicit model_type to OMNI_PIPELINES."""
    new_model_type = "new_model_type"
    unused_model_type = "foo"
-    pipe_cfg = PipelineConfig(model_type=unused_model_type)
+    pipe_cfg = build_fake_pipeline_config(unused_model_type)
    assert new_model_type not in OMNI_PIPELINES
    assert unused_model_type not in OMNI_PIPELINES

@@ -25,9 +25,9 @@ pytest_plugins = (
    "tests.helpers.fixtures.clean",
    "tests.helpers.fixtures.log",
    "tests.helpers.fixtures.media",
-    "tests.helpers.fixtures.run_args",
+    "tests.helpers.fixtures.pytest_collection",
+    "tests.helpers.fixtures.pytest_run_args",
    "tests.helpers.fixtures.runtime",
-    "tests.helpers.fixtures.xdist",
 )


@@ -0,0 +1,89 @@
+"""Tests for ``OmniARScheduler._drop_aborted_queued_requests``.
+
+``schedule()`` sweeps ``FINISHED_ABORTED`` requests out of the queues before
+handing control to upstream, because upstream ``Scheduler.schedule()`` raises
+``RuntimeError: Invalid request status: FINISHED_ABORTED`` for any request it
+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
+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.
+"""
+
+from __future__ import annotations
+
+import pytest
+from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
+from vllm.v1.request import RequestStatus
+
+import vllm_omni.core.sched.omni_ar_scheduler as ar_sched_mod
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+_POLICIES = [
+    pytest.param(SchedulingPolicy.FCFS, id="fcfs"),
+    pytest.param(SchedulingPolicy.PRIORITY, id="priority"),
+]
+
+
+class _StubRequest:
+    """Minimal ``Request`` stub with the surface the sweep exercises.
+
+    Carries the ``(priority, arrival_time)`` ordering ``PriorityRequestQueue``
+    relies on so the same stub works under both scheduling policies.
+    """
+
+    def __init__(self, request_id: str, status: RequestStatus, arrival_time: float = 0.0) -> None:
+        self.request_id = request_id
+        self.status = status
+        self.priority = 0
+        self.arrival_time = arrival_time
+
+    def __lt__(self, other: _StubRequest) -> bool:
+        return (self.priority, self.arrival_time) < (other.priority, other.arrival_time)
+
+
+def _make_scheduler(policy: SchedulingPolicy, *, waiting=(), skipped_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.running = list(running)
+    return scheduler
+
+
+@pytest.mark.parametrize("policy", _POLICIES)
+def test_schedule_sweeps_skipped_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.chunk_transfer_adapter = None
+    scheduler.input_coordinator = None
+    scheduler.num_waiting_for_streaming_input = 0
+    scheduler._process_pending_omni_inputs = lambda model_mode: None
+    scheduler._resync_streaming_input_counter = lambda: None
+    scheduler._should_defer_waiting_admission = lambda: False
+    scheduler._restore_omni_wait_queues = lambda: None
+    scheduler._postprocess_omni_schedule_output = lambda *args, **kwargs: None
+    scheduler.get_finished_requests_needing_kv_transfer = lambda: {}
+    scheduler._wrap_omni_scheduler_output = lambda output, **kwargs: output
+
+    sentinel = object()
+
+    def upstream_schedule(_self, _throttle_prefills: bool = False):
+        assert list(scheduler.skipped_waiting) == []
+        return sentinel
+
+    monkeypatch.setattr(ar_sched_mod.VLLMScheduler, "schedule", upstream_schedule)
+
+    assert OmniARScheduler.schedule(scheduler) is sentinel
@@ -142,13 +142,16 @@ def test_exact_drain_delivers_new_segment_frame() -> None:
    session.num_computed_tokens = 6
    session.num_output_placeholders = 1
    session.num_in_flight_tokens = 1
+    session.async_tokens_to_discard = 1

    _replace_streaming_session(session)
    assert session.num_stale_output_tokens == 1
+    assert session.async_tokens_to_discard == 1

    sched = _make_drain_sched(session)
    assert _run_step(sched, session, num_scheduled=1, token=42) is False  # late frame dropped
    assert session.num_stale_output_tokens == 0
+    assert session.async_tokens_to_discard == 0
    assert _run_step(sched, session, num_scheduled=1, token=43) is True  # new segment survives


@@ -18,7 +18,7 @@ from vllm.sampling_params import SamplingParams
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.outputs import ModelRunnerOutput
 from vllm.v1.request import Request, RequestStatus, StreamingUpdate
-from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARAsyncScheduler, OmniARScheduler

 # isort: on

@@ -28,7 +28,9 @@ pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 def _make_scheduler(*, stage_id: int = 0, session_mode: str = "turn") -> OmniARScheduler:
    sched = OmniARScheduler.__new__(OmniARScheduler)
    sched._new_prompt_len_snapshot = {}
-    sched.vllm_config = SimpleNamespace(model_config=SimpleNamespace(stage_id=stage_id, session_mode=session_mode))
+    sched.vllm_config = SimpleNamespace(
+        model_config=SimpleNamespace(stage_id=stage_id, session_mode=session_mode),
+    )
    sched.num_waiting_for_streaming_input = 0
    sched.log_stats = False
    sched.chunk_transfer_adapter = None
@@ -185,6 +187,7 @@ def test_resumable_segment_boundary_keeps_pre_transition_send_watermark() -> Non

    def replace_with_next_segment(request: Request) -> bool:
        request.num_computed_tokens = 0
+        request._omni_segment_generation = 1
        request.status = RequestStatus.WAITING
        return False

@@ -201,6 +204,7 @@ def test_resumable_segment_boundary_keeps_pre_transition_send_watermark() -> Non
        True,
        new_token_ids=[42],
        confirmed_num_computed_tokens=26,
+        segment_generation=0,
    )


@@ -286,6 +290,140 @@ def test_queued_streaming_update_on_async_stop_fences_in_flight_once() -> None:
    assert session.num_stale_output_tokens == session.num_in_flight_tokens == 1


+def test_stale_async_frame_is_dropped_before_output_processing() -> None:
+    session = _make_request()
+    session.status = RequestStatus.RUNNING
+    session.num_in_flight_tokens = 2
+    session.num_computed_tokens = session.num_tokens + 1
+    session.num_output_placeholders = 1
+    session.async_tokens_to_discard = 1
+    session.sampling_params = SimpleNamespace(num_logprobs=1)
+    num_computed_tokens = session.num_computed_tokens
+    num_output_placeholders = session.num_output_placeholders
+
+    sched = MagicMock()
+    sched.requests = {session.request_id: session}
+    sched.perf_metrics = None
+    sched.structured_output_manager.should_advance.return_value = False
+
+    def discard_stale_output(request: Request, token_ids: list[int]) -> tuple[list[int], bool]:
+        request.async_tokens_to_discard = 0
+        return token_ids, False
+
+    sched._update_request_with_output.side_effect = discard_stale_output
+    sched._process_kv_transfer_trigger.return_value = False
+    sched.chunk_transfer_adapter = MagicMock()
+    sched.running = [session]
+    sched.waiting_for_transfer_free = set()
+    sched.transfer_triggered_requests = set()
+    sched.active_kv_transfers = set()
+    sched.pending_stop_after_extraction = set()
+    sched.connector = None
+    sched.kv_cache_manager.take_events.return_value = None
+    sched.kv_cache_manager.estimate_cached_tokens.return_value = 0
+    sched.finished_req_ids_dict = {}
+    sched.make_stats.return_value = None
+
+    scheduler_output = MagicMock(spec=SchedulerOutput)
+    scheduler_output.num_scheduled_tokens = {session.request_id: 1}
+    scheduler_output.scheduled_spec_decode_tokens = {}
+    scheduler_output.num_invalid_spec_tokens = 0
+
+    model_runner_output = MagicMock(spec=ModelRunnerOutput)
+    model_runner_output.sampled_token_ids = [[42]]
+    model_runner_output.logprobs = None
+    model_runner_output.prompt_logprobs_dict = {}
+    model_runner_output.pooler_output = None
+    model_runner_output.multimodal_outputs = None
+    model_runner_output.inter_stage_outputs = [{"hidden": object()}]
+    model_runner_output.num_nans_in_logits = None
+    model_runner_output.kv_connector_output = None
+    model_runner_output.cudagraph_stats = None
+    model_runner_output.req_id_to_index = {session.request_id: 0}
+    model_runner_output.routed_experts = None
+
+    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
+
+    assert session.async_tokens_to_discard == 0
+    assert session.status == RequestStatus.RUNNING
+    assert session.num_computed_tokens == num_computed_tokens
+    assert session.num_output_placeholders == num_output_placeholders
+    sched.chunk_transfer_adapter.save_async.assert_not_called()
+
+    session.sampling_params = SamplingParams(max_tokens=8)
+    next_payload = {"hidden": object()}
+    sched._update_request_with_output.side_effect = None
+    sched._update_request_with_output.return_value = ([43], False)
+    model_runner_output.sampled_token_ids = [[43]]
+    model_runner_output.inter_stage_outputs = [next_payload]
+
+    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
+
+    sched.chunk_transfer_adapter.save_async.assert_called_once_with(
+        next_payload,
+        session,
+        False,
+        new_token_ids=[43],
+        confirmed_num_computed_tokens=None,
+    )
+
+
+def test_legacy_stale_async_marker_bypasses_real_placeholder_accounting() -> None:
+    request = _make_request()
+    request.status = RequestStatus.RUNNING
+    request.num_in_flight_tokens = 1
+    request.num_output_placeholders = 0
+    request.async_tokens_to_discard = 1
+    request.num_stale_output_tokens = 0
+
+    sched = OmniARAsyncScheduler.__new__(OmniARAsyncScheduler)
+    sched.requests = {request.request_id: request}
+    sched.perf_metrics = None
+    sched.chunk_transfer_adapter = None
+    sched.connector = None
+    sched.ec_connector = None
+    sched.waiting_for_transfer_free = set()
+    sched.transfer_triggered_requests = set()
+    sched.active_kv_transfers = set()
+    sched.pending_stop_after_extraction = set()
+    sched.finished_req_ids_dict = {}
+    sched._new_prompt_len_snapshot = {}
+    sched.kv_cache_manager = MagicMock()
+    sched.kv_cache_manager.take_events.return_value = None
+    sched._remove_stopped_requests_from_queues = MagicMock()
+    sched._handle_failed_kv_load_outputs = MagicMock(return_value=[])
+    sched._cleanup_kv_tracking = MagicMock()
+    sched._aggregate_kv_connector_stats = MagicMock(return_value=None)
+    sched._publish_kv_cache_events = MagicMock()
+    sched._attach_finished_request_sets = MagicMock()
+    sched._attach_scheduler_stats = MagicMock()
+    sched._capture_omni_connector_output = MagicMock()
+
+    scheduler_output = MagicMock(spec=SchedulerOutput)
+    scheduler_output.num_scheduled_tokens = {request.request_id: 1}
+    scheduler_output.scheduled_spec_decode_tokens = {}
+    scheduler_output.num_invalid_spec_tokens = 0
+
+    model_runner_output = MagicMock(spec=ModelRunnerOutput)
+    model_runner_output.sampled_token_ids = [[42]]
+    model_runner_output.logprobs = None
+    model_runner_output.prompt_logprobs_dict = {}
+    model_runner_output.pooler_output = None
+    model_runner_output.multimodal_outputs = None
+    model_runner_output.inter_stage_outputs = None
+    model_runner_output.num_nans_in_logits = None
+    model_runner_output.kv_connector_output = None
+    model_runner_output.cudagraph_stats = None
+    model_runner_output.req_id_to_index = {request.request_id: 0}
+    model_runner_output.routed_experts = None
+
+    OmniARScheduler.update_from_output(sched, scheduler_output, model_runner_output)
+
+    assert request.async_tokens_to_discard == 0
+    assert request.num_output_placeholders == 0
+    assert request._output_token_ids == []
+
+
 def test_stage0_streaming_update_discards_outstanding_async_placeholder_token() -> None:
    sched = _make_scheduler(stage_id=0)
    session = _make_request()
@@ -76,6 +76,9 @@ def _make_scheduler(
        else:
            hf_config.voxcpm2_runtime_config = runtime_config
    sched.vllm_config = SimpleNamespace(model_config=model_config)
+    # Upstream ``Scheduler.__init__`` always creates ``skipped_waiting``; the
+    # abort sweep at the top of ``schedule()`` reads it.
+    sched.skipped_waiting = _MockQueue()
    return sched


@@ -166,3 +166,15 @@ def test_fast_path_schedule_defer_disabled_leaves_fence() -> None:
    OmniGenerationScheduler.schedule(sched)

    assert sched.sched_step_seq == 0
+
+
+def test_generation_schedule_sweeps_and_resyncs_around_pending_inputs() -> None:
+    sched, request = _make_fast_path_sched(defer_block_free=False)
+    calls: list[str] = []
+    sched._drop_aborted_queued_requests.side_effect = lambda: calls.append("sweep")
+    sched._process_pending_omni_inputs.side_effect = lambda model_mode: calls.append(model_mode)
+    sched._resync_streaming_input_counter.side_effect = lambda: calls.append("resync")
+
+    OmniGenerationScheduler.schedule(sched)
+
+    assert calls == ["sweep", "generation", "sweep", "resync"]
@@ -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
 """Unit coverage for the two stage-input timeout hooks.

 ``_process_pending_input_timeouts`` covers the full-payload transport;
@@ -253,6 +253,38 @@ def test_log_failed_chunk_sends_noop_without_adapter():
    scheduler._log_failed_chunk_sends()


+class _FakeReceiveFailureAdapter:
+    def __init__(self, failures):
+        self._failures = dict(failures)
+
+    def collect_failed_receive_request_ids(self):
+        failures, self._failures = self._failures, {}
+        return failures
+
+
+class _FakeReceiveFailureScheduler(OmniSchedulerMixin):
+    def __init__(self, requests, adapter):
+        self.requests = requests
+        self.chunk_transfer_adapter = adapter
+        self.finish_calls = []
+
+    def finish_requests(self, req_ids, status):
+        self.finish_calls.append((set(req_ids), status))
+
+
+def test_process_chunk_receive_failures_delegates_to_finish_requests():
+    request_id = "invalid-chunk"
+    adapter = _FakeReceiveFailureAdapter({request_id: "prompt exceeds limit"})
+    scheduler = _FakeReceiveFailureScheduler({request_id: SimpleNamespace(request_id=request_id)}, adapter)
+
+    scheduler._process_chunk_receive_failures()
+
+    assert len(scheduler.finish_calls) == 1
+    finished_ids, status = scheduler.finish_calls[0]
+    assert finished_ids == {request_id}
+    assert getattr(status, "name", str(status)).endswith("FINISHED_ERROR")
+
+
 # --- R1.4: VLLM_OMNI_INPUT_WAIT_TIMEOUT_S is parsed at import time, so each of
 # these reloads the module under a different environment. One knob now gates the
 # deadline on both transports, which is why a silent 0 matters more than it did
@@ -0,0 +1,168 @@
+"""Tests for the ``num_waiting_for_streaming_input`` resync placed in
+``OmniARScheduler.finish_requests`` / ``OmniGenerationScheduler.finish_requests``
+and in ``OmniARScheduler.schedule``.
+
+Upstream keeps that counter incrementally (``+1`` when a resumable request
+parks in a waiting queue as ``WAITING_FOR_STREAMING_REQ``, ``-1`` when the
+next update arrives or the request is finished) and
+``Scheduler.get_num_unfinished_requests`` *subtracts* it from the waiting
+queues. Omni rewrites ``request.status`` outside both hooks -- the
+chunk-transfer adapter's park/restore and
+``_realign_request_status_to_queues`` -- so a counted request can leave
+``WAITING_FOR_STREAMING_REQ`` without the matching decrement and inflate the
+counter for the lifetime of the process.
+
+An inflated counter does not degrade throughput, it hangs the stage:
+``EngineCore.has_work()`` reads false while a live request sits in
+``waiting``, so the engine blocks in ``input_queue.get()`` and never calls
+``schedule()`` again. Regression for the Qwen3-Omni duplex symptom where the
+server answered the first conversation after a boot and was silent from the
+second on while still reporting healthy.
+"""
+
+from __future__ import annotations
+
+import pytest
+from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
+from vllm.v1.request import RequestStatus
+
+import vllm_omni.core.sched.omni_ar_scheduler as ar_sched_mod
+import vllm_omni.core.sched.omni_generation_scheduler as gen_sched_mod
+from vllm_omni.core.sched.omni_ar_scheduler import OmniARScheduler
+from vllm_omni.core.sched.omni_generation_scheduler import OmniGenerationScheduler
+
+pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
+
+
+class _StubRequest:
+    def __init__(self, request_id: str, status: RequestStatus) -> None:
+        self.request_id = request_id
+        self.status = status
+
+    def is_finished(self) -> bool:
+        return RequestStatus.is_finished(self.status)
+
+
+_SCHEDULER_PARAMS = [
+    pytest.param(OmniARScheduler, ar_sched_mod, id="ar"),
+    pytest.param(OmniGenerationScheduler, gen_sched_mod, id="generation"),
+]
+
+
+def _make_scheduler(scheduler_cls, *, requests, running, waiting, counter, skipped=None):
+    scheduler = scheduler_cls.__new__(scheduler_cls)
+    scheduler.chunk_transfer_adapter = None
+    scheduler.input_coordinator = None
+    scheduler.requests = requests
+    scheduler.running = running
+    scheduler.waiting = waiting
+    scheduler.skipped_waiting = skipped if skipped is not None else []
+    scheduler.num_waiting_for_streaming_input = counter
+    return scheduler
+
+
+@pytest.mark.parametrize(("scheduler_cls", "scheduler_mod"), _SCHEDULER_PARAMS)
+def test_finish_requests_clears_counter_left_by_a_stomped_status(
+    monkeypatch: pytest.MonkeyPatch,
+    scheduler_cls,
+    scheduler_mod,
+) -> None:
+    """The abort case that wedged the talker.
+
+    The request was counted while parked for streaming input, then had its
+    status rewritten to ``RUNNING`` by omni's queue surgery, so upstream's
+    ``finish_requests`` took the running branch and never decremented. With
+    no parked requests left, the counter must land at 0.
+    """
+    aborted = _StubRequest("req-aborted", RequestStatus.RUNNING)
+    scheduler = _make_scheduler(
+        scheduler_cls,
+        requests={},  # upstream already dropped it
+        running=[],
+        waiting=[],
+        counter=1,
+    )
+
+    monkeypatch.setattr(
+        scheduler_mod.VLLMScheduler,
+        "finish_requests",
+        lambda self, request_ids, finished_status: [aborted],
+    )
+
+    scheduler_cls.finish_requests(scheduler, [aborted.request_id], RequestStatus.FINISHED_ABORTED)
+
+    assert scheduler.num_waiting_for_streaming_input == 0
+
+
+def test_resync_counts_parked_requests_in_skipped_waiting() -> None:
+    """``get_num_unfinished_requests`` sums ``waiting`` and ``skipped_waiting``,
+    so the resync has to look at both or it under-counts and reintroduces the
+    spin the counter exists to prevent."""
+    parked = _StubRequest("req-skipped", RequestStatus.WAITING_FOR_STREAMING_REQ)
+    scheduler = _make_scheduler(
+        OmniARScheduler,
+        requests={parked.request_id: parked},
+        running=[],
+        waiting=[],
+        counter=0,
+        skipped=[parked],
+    )
+
+    scheduler._resync_streaming_input_counter()
+
+    assert scheduler.num_waiting_for_streaming_input == 1
+
+
+def test_ar_schedule_resyncs_before_delegating_upstream(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Pins placement in ``schedule``: the chunk-transfer adapter rewrites
+    status during ``process_pending_chunks``, so the resync has to run after
+    that and before upstream schedules, while ``self.waiting`` is still the
+    real queue."""
+    parked = _StubRequest("req-resumed", RequestStatus.RUNNING)
+    scheduler = OmniARScheduler.__new__(OmniARScheduler)
+    scheduler.chunk_transfer_adapter = None
+    scheduler.input_coordinator = None
+    scheduler.requests = {parked.request_id: parked}
+    scheduler.running = [parked]
+    scheduler.waiting = []
+    scheduler.skipped_waiting = []
+    scheduler.num_waiting_for_streaming_input = 1  # leaked by the status rewrite
+    scheduler.policy = "fcfs"
+
+    seen: dict[str, int] = {}
+
+    monkeypatch.setattr(
+        ar_sched_mod.OmniSchedulerMixin, "_consume_pending_connector_output", lambda self, model_mode: None
+    )
+    monkeypatch.setattr(ar_sched_mod.OmniSchedulerMixin, "_process_pending_input_timeouts", lambda self: None)
+    monkeypatch.setattr(OmniARScheduler, "_should_defer_waiting_admission", lambda self: False)
+
+    def fake_schedule(self, throttle_prefills=False):
+        seen["counter"] = self.num_waiting_for_streaming_input
+        raise _StopScheduleError
+
+    class _StopScheduleError(Exception):
+        pass
+
+    monkeypatch.setattr(ar_sched_mod.VLLMScheduler, "schedule", fake_schedule)
+
+    with pytest.raises(_StopScheduleError):
+        OmniARScheduler.schedule(scheduler)
+
+    assert seen["counter"] == 0
+
+
+def test_generation_scheduler_sweep_removes_aborted_requests_from_all_queues() -> None:
+    aborted = _StubRequest("req-generation-aborted", RequestStatus.FINISHED_ABORTED)
+    scheduler = OmniGenerationScheduler.__new__(OmniGenerationScheduler)
+    scheduler.waiting = create_request_queue(SchedulingPolicy.FCFS)
+    scheduler.waiting.add_request(aborted)
+    scheduler.skipped_waiting = create_request_queue(SchedulingPolicy.FCFS)
+    scheduler.skipped_waiting.add_request(aborted)
+    scheduler.running = [aborted]
+
+    scheduler._drop_aborted_queued_requests()
+
+    assert list(scheduler.waiting) == []
+    assert list(scheduler.skipped_waiting) == []
+    assert scheduler.running == []
@@ -1,4 +1,8 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 import json
+import math
 import os
 import threading
 from collections.abc import Callable
@@ -159,6 +163,18 @@ def benchmark_params(request):

 def assert_result(result, params, num_prompt) -> None:
    assert result["completed"] == num_prompt, "Request failures exist"
+    baseline = params.get("baseline")
+    hardware = result.get("Hardware")
+    hardware_baseline = baseline.get(hardware) if isinstance(baseline, dict) and isinstance(hardware, str) else None
+    if isinstance(hardware_baseline, dict) and "mean_tpot_ms" in hardware_baseline:
+        num_tpot_samples = result.get("num_tpot_samples")
+        mean_tpot_ms = result.get("mean_tpot_ms")
+        assert isinstance(num_tpot_samples, int) and not isinstance(num_tpot_samples, bool) and num_tpot_samples > 0, (
+            "TPOT baseline is configured, but no measurable TPOT samples were produced"
+        )
+        assert (
+            isinstance(mean_tpot_ms, int | float) and not isinstance(mean_tpot_ms, bool) and math.isfinite(mean_tpot_ms)
+        ), "TPOT baseline is configured, but mean_tpot_ms is not finite"
    expected_audio_turns = params.get("expected_duplex_audio_turns_per_session")
    if expected_audio_turns is not None:
        session_metrics = result.get("duplex_session_metrics")
@@ -43,7 +43,7 @@
        "extra_body": {
          "save_duplex_request_metrics": true
        },
-        "percentile-metrics": "ttft,e2el,audio_ttfp,audio_rtf,audio_duration",
+        "percentile-metrics": "ttft,tpot,itl,e2el,audio_ttfp,audio_rtf,audio_duration",
        "baseline": {
          "H100": {
            "request_throughput": [
@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """Tests for DFX runner metadata field exclusion."""

 import json
@@ -105,7 +108,7 @@ def test_resolve_pytest_marks_hardware_dict_with_extra():
    assert "H100" in names
    assert "cuda" in names
    assert "gpu" in names
-    assert "distributed_cuda" in names
+    assert "cards_2" in names
    assert "full_model" in names
    assert "diffusion" in names
    assert "local_model" in names
@@ -512,3 +515,41 @@ def test_omni_duplex_expected_audio_turns_rejects_incomplete_session():
            {"expected_duplex_audio_turns_per_session": 4},
            1,
        )
+
+
+def test_omni_tpot_baseline_accepts_measured_finite_sample():
+    from tests.dfx.perf.scripts.run_benchmark import assert_result
+
+    assert_result(
+        {
+            "completed": 1,
+            "Hardware": "H100",
+            "num_tpot_samples": 1,
+            "mean_tpot_ms": 10.0,
+        },
+        {"baseline": {"H100": {"mean_tpot_ms": 20.0}}},
+        1,
+    )
+
+
+@pytest.mark.parametrize(
+    ("num_tpot_samples", "mean_tpot_ms", "match"),
+    [
+        (0, float("nan"), "no measurable TPOT samples"),
+        (1, float("nan"), "mean_tpot_ms is not finite"),
+    ],
+)
+def test_omni_tpot_baseline_rejects_missing_or_nonfinite_sample(num_tpot_samples, mean_tpot_ms, match):
+    from tests.dfx.perf.scripts.run_benchmark import assert_result
+
+    with pytest.raises(AssertionError, match=match):
+        assert_result(
+            {
+                "completed": 1,
+                "Hardware": "H100",
+                "num_tpot_samples": num_tpot_samples,
+                "mean_tpot_ms": mean_tpot_ms,
+            },
+            {"baseline": {"H100": {"mean_tpot_ms": 20.0}}},
+            1,
+        )
@@ -70,10 +70,14 @@ _PARAMS = [
        pytest.param({"guidance_scale": -1.0}, "guidance_scale", id="guidance_scale_negative", marks=_SKIP_ISSUE_3649),
        pytest.param({"guidance_scale": 0}, "guidance_scale", id="guidance_scale_zero", marks=_SKIP_ISSUE_3649),
        pytest.param(
-            {"num_inference_steps": 0}, "num_inference_steps", id="num_inference_steps_zero", marks=_SKIP_ISSUE_3649
+            {"num_inference_steps": 0},
+            ("num_inference_steps", "greater than or equal to 1"),
+            id="num_inference_steps_zero",
        ),
        pytest.param(
-            {"num_inference_steps": -1}, ("number of steps", "non-negative"), id="num_inference_steps_negative"
+            {"num_inference_steps": -1},
+            ("num_inference_steps", "greater than or equal to 1"),
+            id="num_inference_steps_negative",
        ),
        pytest.param(
            {"num_inference_steps": 6000},
@@ -157,7 +157,13 @@ def test_speech_missing_required_fields(omni_server: OmniServer, online_client:
                "ref_audio": None,
                "ref_text": None,
            },
-            ("Invalid voice", "vivian"),
+            # NOTE: This is delegating down the TTS adapter, so the message is both specific
+            # to the Qwen3TTS adapter and dependent on whether or not the model has any speakers
+            # (as opposed to having valida speakers, but getting an invalid speaker). it would
+            # be more ideal to validate that this is landing on `.validate()` on the adapter
+            # and ensuring that the returned message matches what we would get from calling the
+            # adapter validate directly.
+            ("no speakers configured",),
            id="customvoice_invalid_voice",
        ),
        pytest.param(
@@ -11,6 +11,7 @@ Tests cover:
 """

 from types import SimpleNamespace
+from unittest.mock import Mock

 import pytest
 import torch
@@ -101,7 +102,12 @@ class TestAttentionSpec:
    def test_block_sparse_defaults_applied_when_backend_selected(self):
        spec = AttentionSpec(backend="RAINFUSION_ATTN")
        assert spec.block_sparse.sparsity == 0.8
-        assert spec.backend_kwargs() == {"sparsity": 0.8, "start_step": 0}
+        assert spec.backend_kwargs() == {
+            "sparsity": 0.8,
+            "start_step": 0,
+            "end_step": 0,
+            "precision": "bf16",
+        }

    def test_block_sparse_skip_layers_selector_expanded(self):
        spec = AttentionSpec(
@@ -112,6 +118,8 @@ class TestAttentionSpec:
        assert spec.backend_kwargs() == {
            "sparsity": 0.9,
            "start_step": 12,
+            "end_step": 0,
+            "precision": "bf16",
            "skip_layers": [0, 1, 2, 38],
        }

@@ -121,12 +129,44 @@ class TestAttentionSpec:

    @pytest.mark.parametrize(
        "block_sparse",
-        [{"sparsity": 1.5}, {"start_step": -1}],
+        [{"sparsity": 1.5}, {"start_step": -1}, {"end_step": -1}, {"precision": "bogus"}],
    )
    def test_block_sparse_invalid_values(self, block_sparse):
        with pytest.raises(ValueError):
            AttentionSpec(backend="RAINFUSION_ATTN", block_sparse=block_sparse)

+    def test_block_sparse_end_step_precision_forwarded(self):
+        spec = AttentionSpec(
+            backend="RAINFUSION_ATTN",
+            block_sparse={"sparsity": 0.9, "start_step": 5, "end_step": 3, "precision": "fp8"},
+        )
+        assert spec.backend_kwargs() == {
+            "sparsity": 0.9,
+            "start_step": 5,
+            "end_step": 3,
+            "precision": "fp8",
+        }
+
+    def test_block_sparse_precision_enum_input_normalized(self):
+        """RainFusionPrecision enum members must normalize to their string value.
+
+        Regression: the str,Enum members passed the value check but str() later
+        produced "RainFusionPrecision.MIX" instead of "mix" (StrEnum fixes it).
+        """
+        from vllm_omni.diffusion.attention.backends.rainfusion_attn import RainFusionConfig
+        from vllm_omni.diffusion.data import RainFusionPrecision
+
+        spec = AttentionSpec(
+            backend="RAINFUSION_ATTN",
+            block_sparse={"sparsity": 0.8, "precision": RainFusionPrecision.MIX},
+        )
+        kwargs = spec.backend_kwargs()
+        assert kwargs["precision"] == "mix"
+        # The config path is where str(value) used to mangle enum members.
+        cfg = RainFusionConfig.from_backend_kwargs(kwargs)
+        assert cfg.precision == "mix"
+        assert type(cfg.precision) is str
+

 class TestAttentionConfig:
    def test_empty_config(self):
@@ -427,6 +467,120 @@ class TestCurrentDiffusionConfig:


 class TestAttentionInitUsesCurrentDiffusionConfig:
+    @pytest.mark.parametrize("mindiesd_available", [False, True])
+    def test_paged_npu_memory_profile_uses_sdpa_only_without_mindiesd(self, monkeypatch, mindiesd_available):
+        attention = Attention.__new__(Attention)
+        attention._scheduler_paged_kv = True
+        attention.paged_kv_cache_role = "primary"
+        attention.backend_pref = "FLASH_ATTN"
+        attention.attn_backend = SimpleNamespace(supports_piecewise_spans=True, get_name=lambda: "FLASH_ATTN")
+        flash_output = torch.ones(1)
+        sdpa_output = torch.zeros(1)
+        dense_flash = Mock(return_value=flash_output)
+        sdpa = Mock(return_value=sdpa_output)
+        attention.attention = SimpleNamespace(forward=dense_flash)
+        attention.sdpa_fallback = SimpleNamespace(forward=sdpa)
+        monkeypatch.setattr(layer_mod, "is_forward_context_available", lambda: True)
+        monkeypatch.setattr(
+            layer_mod,
+            "get_forward_context",
+            lambda: SimpleNamespace(in_diffusion_kv_memory_profile=True),
+        )
+        monkeypatch.setattr(layer_mod.current_omni_platform, "is_npu", lambda: True)
+        monkeypatch.setattr(
+            layer_mod.current_omni_platform,
+            "supports_diffusion_dense_flash_attention",
+            lambda: mindiesd_available,
+        )
+
+        result = attention._run_local_attention(
+            torch.zeros(1, dtype=torch.bfloat16),
+            torch.zeros(1, dtype=torch.bfloat16),
+            torch.zeros(1, dtype=torch.bfloat16),
+            AttentionMetadata(),
+        )
+
+        if mindiesd_available:
+            assert result is flash_output
+            dense_flash.assert_called_once()
+            sdpa.assert_not_called()
+        else:
+            assert result is sdpa_output
+            dense_flash.assert_not_called()
+            sdpa.assert_called_once()
+
+    def test_dense_flash_import_error_is_not_reclassified(self, monkeypatch):
+        attention = Attention.__new__(Attention)
+        attention._scheduler_paged_kv = False
+        attention.paged_kv_cache_role = "primary"
+        attention.backend_pref = "FLASH_ATTN"
+        attention.attn_backend = SimpleNamespace(supports_piecewise_spans=True, get_name=lambda: "FLASH_ATTN")
+        attention.attention = SimpleNamespace(
+            forward=Mock(side_effect=ModuleNotFoundError("No module named 'mindiesd'", name="mindiesd"))
+        )
+        attention.sdpa_fallback = SimpleNamespace(forward=Mock(side_effect=AssertionError("must not fall back")))
+        monkeypatch.setattr(layer_mod, "is_forward_context_available", lambda: False)
+
+        with pytest.raises(ModuleNotFoundError, match="mindiesd"):
+            attention._run_local_attention(
+                torch.zeros(1, dtype=torch.bfloat16),
+                torch.zeros(1, dtype=torch.bfloat16),
+                torch.zeros(1, dtype=torch.bfloat16),
+                AttentionMetadata(),
+            )
+
+    def test_paged_marker_does_not_change_dense_backend(self, monkeypatch):
+        class _FakeAttentionImpl:
+            def __init__(self, **kwargs):
+                self.kwargs = kwargs
+
+            def forward(self, query, key, value, attn_metadata=None):
+                return query
+
+        class _DenseBackend:
+            supports_paged_kv = False
+
+            @staticmethod
+            def get_name() -> str:
+                return "TORCH_SDPA"
+
+            @staticmethod
+            def get_impl_cls():
+                return _FakeAttentionImpl
+
+        calls = []
+
+        def _fake_get_attn_backend_for_role(*args, **kwargs):
+            calls.append((args, kwargs))
+            return _DenseBackend, None
+
+        monkeypatch.setattr(layer_mod, "get_attn_backend_for_role", _fake_get_attn_backend_for_role)
+        monkeypatch.setattr(layer_mod.SDPABackend, "get_impl_cls", staticmethod(lambda: _FakeAttentionImpl))
+        monkeypatch.setattr(layer_mod, "build_parallel_attention_strategy", lambda **kwargs: object())
+        monkeypatch.setattr(layer_mod, "is_forward_context_available", lambda: False)
+
+        od_config = SimpleNamespace(
+            diffusion_attention_config=AttentionConfig(),
+            diffusion_kv_mode=layer_mod.DiffusionKVCacheMode.DENSE_LEGACY,
+            parallel_config=SimpleNamespace(ring_degree=1),
+            diffusion_kv_cache_dtype=None,
+            diffusion_kv_cache_skip_step_indices=None,
+            diffusion_kv_cache_skip_layer_indices=None,
+        )
+
+        with set_current_diffusion_config(od_config):
+            attention = Attention(
+                num_heads=4,
+                head_size=64,
+                causal=False,
+                softmax_scale=1.0,
+                paged_kv_cache_role="primary",
+            )
+
+        assert len(calls) == 1
+        assert attention.attn_backend is _DenseBackend
+        assert attention.backend_pref is None
+
    def test_attention_init_uses_current_diffusion_config_without_forward_context(self, monkeypatch):
        class _FakeAttentionImpl:
            def __init__(self, **kwargs):
@@ -353,8 +353,15 @@ def test_cross_attn_key_padding_vs_sdpa(k_len):
    print(f"Max absolute difference: {max_diff:.6f}")
    print(f"Mean absolute difference: {mean_diff:.6f}")

-    assert max_diff < 0.01, f"Max difference {max_diff} exceeds threshold 0.01"
-    assert mean_diff < 0.001, f"Mean difference {mean_diff} exceeds threshold 0.001"
+    # Match the platform-specific comparison used by test_fa_vs_sdpa above.
+    if current_omni_platform.is_rocm():
+        # AITER FlashAttention and PyTorch SDPA use different BF16 reduction
+        # implementations on ROCm, so individual values can differ by one BF16
+        # step even though the outputs agree within the expected relative error.
+        torch.testing.assert_close(output_fa, output_sdpa, rtol=1e-2, atol=1.5e-2)
+    else:
+        assert max_diff < 0.01, f"Max difference {max_diff} exceeds threshold 0.01"
+        assert mean_diff < 0.001, f"Mean difference {mean_diff} exceeds threshold 0.001"

    print("✓ Case 3 PASSED: FA and SDPA cross-attention outputs are very close!")

@@ -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
 """End-to-end test for ``piecewise_attn`` (CPU).

 Verify that running attention in segments (causal outside full-attn spans,
@@ -98,9 +98,12 @@ def test_paged_piecewise_runner_uses_packed_indices_and_kv_endpoints():
    query = torch.arange(12 * 2 * 4, dtype=torch.float32).reshape(12, 2, 4)
    key = query + 100
    value = query + 200
-    calls = []
+    calls: list[object] = []

-    def run_segment(segment_query, segment_key, segment_value, segment_batch):
+    output_buffer = torch.empty_like(query)
+
+    def run_segment(segment_query, segment_key, segment_value, segment_batch, segment_output_buffer):
+        assert segment_output_buffer is None
        calls.append(
            (
                [segment.mode for segment in segment_batch.row_segments],
@@ -119,9 +122,13 @@ def test_paged_piecewise_runner_uses_packed_indices_and_kv_endpoints():
        plan,
        plan.segments,
        run_segment,
+        output_buffer=output_buffer,
    )

+    assert output is output_buffer
    torch.testing.assert_close(output, query)
+    assert all(segment.query_range is None for segment in plan.segments)
+    assert not plan.segments_cover_query_contiguously
    assert calls == [
        (["causal", "causal"], [2, 4], [0, 1, 6, 7]),
        (["full", "full"], [5, 7], [2, 3, 4, 8, 9, 10]),
@@ -129,6 +136,161 @@ def test_paged_piecewise_runner_uses_packed_indices_and_kv_endpoints():
    ]


+def test_paged_piecewise_runner_preserves_homogeneous_batch_without_scatter(monkeypatch: pytest.MonkeyPatch):
+    plan = build_paged_piecewise_plan(
+        [[(2, 5)], [(2, 5)]],
+        query_offsets=[0, 0],
+        query_lens=[6, 6],
+        seq_lens=[6, 6],
+        device=DEVICE,
+    )
+    assert plan.homogeneous_batch_shape == (2, 6)
+    query = torch.arange(12 * 2 * 4, dtype=torch.float32).reshape(12, 2, 4)
+    calls: list[torch.Tensor] = []
+
+    def fail_index_copy(*_args, **_kwargs):
+        raise AssertionError("homogeneous piecewise path must not use index_copy_")
+
+    monkeypatch.setattr(torch.Tensor, "index_copy_", fail_index_copy)
+
+    def run_segment(segment_query, segment_key, segment_value, _metadata, segment_output_buffer):
+        assert segment_key is None
+        assert segment_value is None
+        assert segment_output_buffer is None
+        calls.append(segment_query.clone())
+        return segment_query + 1
+
+    output_buffer = torch.empty_like(query)
+    output = run_paged_piecewise_plan(
+        query,
+        None,
+        None,
+        plan,
+        plan.segments,
+        run_segment,
+        output_buffer=output_buffer,
+        use_homogeneous_batch=True,
+    )
+
+    assert output is output_buffer
+    torch.testing.assert_close(output, query + 1)
+    assert [call.shape[0] for call in calls] == [4, 6, 2]
+    torch.testing.assert_close(calls[0], query[[0, 1, 6, 7]])
+    torch.testing.assert_close(calls[1], query[[2, 3, 4, 8, 9, 10]])
+    torch.testing.assert_close(calls[2], query[[5, 11]])
+
+
+def test_paged_piecewise_runner_keeps_indexed_fallback_for_heterogeneous_layout():
+    plan = build_paged_piecewise_plan(
+        [[(2, 5)], [(1, 4)]],
+        query_offsets=[0, 0],
+        query_lens=[6, 6],
+        seq_lens=[6, 6],
+        device=DEVICE,
+    )
+    assert plan.homogeneous_batch_shape is None
+    query = torch.arange(12 * 2 * 4, dtype=torch.float32).reshape(12, 2, 4)
+    output = run_paged_piecewise_plan(
+        query,
+        None,
+        None,
+        plan,
+        plan.segments,
+        lambda segment_query, *_args: segment_query,
+        use_homogeneous_batch=True,
+    )
+    torch.testing.assert_close(output, query)
+
+
+def test_paged_piecewise_runner_slices_contiguous_segments():
+    plan = build_paged_piecewise_plan(
+        [[(2, 5)]],
+        query_offsets=[0],
+        query_lens=[6],
+        seq_lens=[6],
+        device=DEVICE,
+    )
+    query = torch.arange(6 * 2 * 4, dtype=torch.float32).reshape(6, 2, 4)
+    key = query + 100
+    value = query + 200
+    expected_ranges = [(0, 2), (2, 5), (5, 6)]
+    calls: list[tuple[int, int]] = []
+
+    def run_segment(segment_query, segment_key, segment_value, _segment_batch, segment_output_buffer):
+        assert segment_output_buffer is None
+        expected_range = expected_ranges[len(calls)]
+        start, end = expected_range
+        calls.append(expected_range)
+        assert segment_query.untyped_storage().data_ptr() == query.untyped_storage().data_ptr()
+        assert segment_key.untyped_storage().data_ptr() == key.untyped_storage().data_ptr()
+        assert segment_value.untyped_storage().data_ptr() == value.untyped_storage().data_ptr()
+        torch.testing.assert_close(segment_query, query[start:end])
+        return segment_query
+
+    output = run_paged_piecewise_plan(query, key, value, plan, plan.segments, run_segment)
+
+    assert calls == expected_ranges
+    assert [segment.query_range for segment in plan.segments] == expected_ranges
+    assert plan.segments_cover_query_contiguously
+    torch.testing.assert_close(output, query)
+
+
+def test_paged_piecewise_runner_writes_contiguous_segments_into_output_buffer():
+    plan = build_paged_piecewise_plan(
+        [[(2, 5)]],
+        query_offsets=[0],
+        query_lens=[6],
+        seq_lens=[6],
+        device=DEVICE,
+    )
+    query = torch.arange(6 * 2 * 4, dtype=torch.float32).reshape(6, 2, 4)
+    output_buffer = torch.empty(6, 2, 3)
+    targets = []
+
+    def run_segment(segment_query, _key, _value, _metadata, segment_output_buffer):
+        assert segment_output_buffer is not None
+        assert segment_output_buffer.untyped_storage().data_ptr() == output_buffer.untyped_storage().data_ptr()
+        targets.append(segment_output_buffer)
+        return segment_output_buffer.copy_(segment_query[..., :3])
+
+    output = run_paged_piecewise_plan(
+        query,
+        query,
+        query,
+        plan,
+        plan.segments,
+        run_segment,
+        output_buffer=output_buffer,
+    )
+
+    assert output is output_buffer
+    assert [target.shape[0] for target in targets] == [2, 3, 1]
+    torch.testing.assert_close(output, query[..., :3])
+
+
+def test_paged_piecewise_runner_returns_single_full_segment_directly():
+    plan = build_paged_piecewise_plan(
+        [[]],
+        query_offsets=[0],
+        query_lens=[6],
+        seq_lens=[6],
+        device=DEVICE,
+    )
+    query = torch.randn(6, 2, 4)
+    runner_output = query + 1
+
+    output = run_paged_piecewise_plan(
+        query,
+        query,
+        query,
+        plan,
+        plan.segments,
+        lambda *_args: runner_output,
+    )
+
+    assert output is runner_output
+
+
 def test_paged_piecewise_runner_uses_runner_output_shape():
    plan = build_paged_piecewise_plan(
        [[(2, 5)]],
@@ -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

 """Geometry contract RAINFUSION_ATTN enforces before handing a forward to rf_v2.

@@ -13,10 +13,12 @@ to the always-kept prefix segment before generating the block mask.
 import dataclasses
 import sys
 import types
+from unittest import mock

 import pytest
 import torch

+from vllm_omni.diffusion.attention.backends import rainfusion_attn
 from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata, VideoTokenLayout, VideoTokenSpan
 from vllm_omni.diffusion.attention.backends.rainfusion_attn import (
    _BLOCK_SIZE,
@@ -26,6 +28,8 @@ from vllm_omni.diffusion.attention.backends.rainfusion_attn import (
 )
 from vllm_omni.platforms import current_omni_platform

+pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.npu]
+
 PREFIX_ROWS = 710  # 14 text rows + 696 audio rows
 ALIGNED_GRID = (62, 24, 40)  # 1280x768 -> 59520 video rows, 465 blocks
 MISALIGNED_GRID = (62, 24, 42)  # 1344x768 -> 62496 video rows, 488 blocks + 32 rows
@@ -228,7 +232,9 @@ def test_irregular_video_tail_reproduces_dense_attention_with_updated_mindiesd()
    heads, head_dim = 4, 128
    q, k, v = (torch.randn(1, used, heads, head_dim, dtype=torch.bfloat16, device="npu") for _ in range(3))

-    impl = make_impl()
+    # Explicit bf16: the dense comparison exercises the plain sparse kernel,
+    # and older MindIE-SD releases do not accept precision= (gate would raise).
+    impl = make_impl(precision="bf16")
    impl.rainfusion = dataclasses.replace(impl.rainfusion, sparsity=0.0)
    plan = RainFusionPlan(prefix_len=prefix_len, used_len=used, latent_shape=list(grid))
    out = impl._forward_sparse_npu(q, k, v, plan)
@@ -241,3 +247,90 @@ def test_irregular_video_tail_reproduces_dense_attention_with_updated_mindiesd()
    ).transpose(1, 2)
    error = (out.float() - reference.float()).abs().mean() / reference.float().abs().mean()
    assert error < 2e-3, f"mean relative error {error:.4%} against dense attention"
+
+
+# end_step tail fallback: the last ``end_step`` denoise steps must stay dense.
+
+
+def test_end_step_tail_window_stays_dense():
+    """step_idx inside the last end_step window must resolve no sparse plan."""
+    impl = make_impl(end_step=3)
+    fc = mock.Mock()
+    fc.denoise_step_idx = 47
+    fc.total_denoise_steps = 50
+    with (
+        mock.patch.object(rainfusion_attn, "is_forward_context_available", return_value=True),
+        mock.patch.object(rainfusion_attn, "get_forward_context", return_value=fc),
+    ):
+        assert impl._resolve_plan(make_metadata(ALIGNED_GRID)) is None
+
+
+def test_end_step_before_tail_window_still_sparse():
+    """step_idx before the tail window must still resolve a sparse plan."""
+    impl = make_impl(end_step=3)
+    fc = mock.Mock()
+    fc.denoise_step_idx = 46
+    fc.total_denoise_steps = 50
+    with (
+        mock.patch.object(rainfusion_attn, "is_forward_context_available", return_value=True),
+        mock.patch.object(rainfusion_attn, "get_forward_context", return_value=fc),
+    ):
+        assert impl._resolve_plan(make_metadata(ALIGNED_GRID)) is not None
+
+
+def test_end_step_zero_never_triggers_tail_fallback():
+    """end_step=0 must not fall back even on the final denoise step."""
+    impl = make_impl(end_step=0)
+    fc = mock.Mock()
+    fc.denoise_step_idx = 49
+    fc.total_denoise_steps = 50
+    with (
+        mock.patch.object(rainfusion_attn, "is_forward_context_available", return_value=True),
+        mock.patch.object(rainfusion_attn, "get_forward_context", return_value=fc),
+    ):
+        assert impl._resolve_plan(make_metadata(ALIGNED_GRID)) is not None
+
+
+# precision capability gate: a mindiesd without sparse_attention(precision=)
+# must fail fast instead of silently running the BF16 path.
+
+
+def _fake_mindiesd_module():
+    """Install a minimal stand-in so the import inside _forward_sparse_npu succeeds."""
+    import types
+
+    fake = types.ModuleType("mindiesd")
+    fake.sparse_attention = lambda *args, **kwargs: None
+    return fake
+
+
+def test_precision_non_bf16_requires_mindiesd_support():
+    """precision != bf16 against a mindiesd lacking the kwarg must raise RuntimeError."""
+    import sys
+
+    impl = make_impl(precision="mix")
+    sys.modules["mindiesd"] = _fake_mindiesd_module()
+    try:
+        with mock.patch.object(rainfusion_attn, "_mindiesd_supports_precision", return_value=False):
+            with pytest.raises(RuntimeError, match="requires MindIE-SD"):
+                impl._forward_sparse_npu(None, None, None, None)
+    finally:
+        sys.modules.pop("mindiesd", None)
+
+
+def test_precision_non_bf16_passes_gate_when_supported():
+    """precision != bf16 with a capable mindiesd must not raise from the gate."""
+    import sys
+
+    impl = make_impl(precision="mix")
+    sys.modules["mindiesd"] = _fake_mindiesd_module()
+    try:
+        with mock.patch.object(rainfusion_attn, "_mindiesd_supports_precision", return_value=True):
+            # q/k/v shapes: [B, S, N, D]; plan geometry must match S.
+            q = torch.randn(1, 8, 4, 128)
+            plan = RainFusionPlan(prefix_len=0, used_len=8, latent_shape=[2, 2, 2])
+            # The gate must pass; the fake mindiesd returns None so no crash.
+            out = impl._forward_sparse_npu(q, q, q, plan)
+            assert out is None
+    finally:
+        sys.modules.pop("mindiesd", None)
@@ -7,7 +7,7 @@ End-to-end test for diffusion batching via AsyncOmni.
 This test fires multiple concurrent ``AsyncOmni.generate()`` calls for a
 diffusion model and validates that every caller receives its correct
 individual result.  When the underlying diffusion stage is configured with
-``batch_size > 1`` (via stage config or ``StageDiffusionClient``), the
+``max_num_seqs > 1``, the
 requests will be batched internally.

 Even without explicit batching config this test is useful for verifying
@@ -260,7 +260,7 @@ async def compare_single_vs_parallel(
 ) -> None:
    """Run the same prompts sequentially then in parallel and print a comparison."""

-    omni = AsyncOmni(model=model, max_num_seqs=batch_size, diffusion_batch_size=batch_size)
+    omni = AsyncOmni(model=model, max_num_seqs=batch_size)
    try:
        await warmup(omni, WARMUP_PROMPTS)
        single_time = await run_single(omni, prompts)
@@ -288,7 +288,7 @@ async def main(model: str, num_prompts: int, mode: str, batch_size: int = 1) ->
        await compare_single_vs_parallel(model, prompts, batch_size=batch_size)
        return

-    omni = AsyncOmni(model=model, max_num_seqs=batch_size, diffusion_batch_size=batch_size)
+    omni = AsyncOmni(model=model, max_num_seqs=batch_size)
    try:
        await warmup(omni, WARMUP_PROMPTS)

@@ -389,7 +389,7 @@ def test_diffusion_batching_async_concurrent(model_name: str):
    fired via asyncio.gather. Each request_id must appear in the results."""

    async def _inner():
-        omni = AsyncOmni(model=model_name, diffusion_batch_size=1)
+        omni = AsyncOmni(model=model_name, max_num_seqs=1)
        try:
            prompts = TEST_PROMPTS[:4]
            sp = _default_sampling_params()
@@ -430,7 +430,7 @@ def test_diffusion_batching_list_prompt_rejected(model_name: str):
    """

    async def _inner():
-        omni = AsyncOmni(model=model_name, diffusion_batch_size=4)
+        omni = AsyncOmni(model=model_name, max_num_seqs=4)
        try:
            prompts = TEST_PROMPTS[:4]
            sp = _default_sampling_params()
@@ -528,7 +528,7 @@ if __name__ == "__main__":
        "--batch-size",
        type=int,
        default=1,
-        help="Scheduler in-flight width (--max-num-seqs) and client diffusion_batch_size",
+        help="Scheduler in-flight width (--max-num-seqs)",
    )
    parser.add_argument(
        "--mode",
@@ -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

 """
 Unit tests for TeaCache extractor functions.
@@ -18,6 +18,7 @@ Currently implemented:
 - TestMiniMaxH3Extractor: MiniMaxH3DiTModel extractor
 """

+import math
 from abc import ABC, abstractmethod
 from types import SimpleNamespace
 from unittest.mock import MagicMock, Mock, patch
@@ -650,6 +651,29 @@ class TestMiniMaxH3Extractor(BaseExtractorTest):
        torch.testing.assert_close(actual_video, expected_video)
        torch.testing.assert_close(actual_audio, expected_audio)

+    def test_extractor_uses_prepared_rope_table(
+        self,
+        minimax_h3_module,
+        sample_inputs,
+        monkeypatch,
+    ):
+        """TeaCache must consume the denoise-loop RoPE table without rebuilding it."""
+        rope_table = minimax_h3_module.prepare_rope_table(
+            sample_inputs["img_position_ids"],
+            seq_len=sample_inputs["x"].shape[1],
+        )
+
+        def unexpected_rope_build(*args, **kwargs):
+            raise AssertionError("TeaCache extractor rebuilt the prepared RoPE table")
+
+        monkeypatch.setattr(minimax_h3_module, "prepare_rope_table", unexpected_rope_build)
+        monkeypatch.setattr(minimax_h3_module.rope, "forward", unexpected_rope_build)
+        context = extract_minimax_h3_context(
+            minimax_h3_module,
+            **{**sample_inputs, "rope_table": rope_table},
+        )
+        context.run_transformer_blocks()
+
    def test_run_transformer_blocks_forwards_packed_layout(
        self,
        minimax_h3_module,
@@ -705,6 +729,222 @@ class TestMiniMaxH3Extractor(BaseExtractorTest):
        assert video_logits.shape[0] == sample_inputs["img_pos_info"]["position_ids"].shape[0]
        assert audio_logits.shape[0] == sample_inputs["audio_pos_info"]["position_ids"].shape[0]

+    @pytest.mark.sp
+    def test_teacache_strict_sp_keeps_cache_state_rank_local(
+        self,
+        minimax_h3_module,
+        sample_inputs,
+        monkeypatch,
+    ):
+        """Strict SP must not mix local TeaCache state with gathered block rows."""
+        from vllm_omni.diffusion.attention.ops import minimax_h3_modulation
+        from vllm_omni.diffusion.cache.teacache.config import TeaCacheConfig
+        from vllm_omni.diffusion.cache.teacache.hook import TeaCacheHook
+        from vllm_omni.diffusion.distributed import parallel_state
+
+        seq_len = sample_inputs["x"].shape[1]
+        local_len = seq_len // 2
+        hidden_size = minimax_h3_module.hidden_size
+        video_width = minimax_h3_module.arch.latents_dim * math.prod(minimax_h3_module.arch.patch_size)
+        audio_width = minimax_h3_module.arch.audio_latents_dim
+        captured_modulation_indices: list[torch.Tensor] = []
+
+        class _StrictSPPrepare(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.global_indices: list[torch.Tensor] = []
+
+            def forward(self, hidden, rope_table, combined_indices):
+                assert hidden.shape[0] == local_len
+                assert rope_table.shape[0] == local_len
+                self.global_indices.append(combined_indices.clone())
+                return hidden, rope_table, combined_indices.narrow(0, 0, local_len)
+
+        class _StrictSPGather(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.inputs: list[torch.Tensor] = []
+                self.outputs: list[torch.Tensor] = []
+
+            def forward(self, tensor):
+                self.inputs.append(tensor.clone())
+                if tensor.shape[-1] == hidden_size:
+                    raise AssertionError("TeaCache must not gather hidden states in strict SP")
+                assert tensor.shape == (local_len, video_width + audio_width)
+                # Model the all-gather of compact logits from every SP rank.
+                # postprocess() indexes the resulting global layout with
+                # infer_out_pos/audio_pos, including rows owned by another rank.
+                gathered = tensor.repeat(seq_len // local_len, 1)
+                assert gathered.shape == (seq_len, video_width + audio_width)
+                self.outputs.append(gathered)
+                return gathered
+
+        class _StrictSPGroup:
+            def __init__(self):
+                super().__init__()
+                self.local_decisions: list[int] = []
+                self.ops = []
+
+            def all_reduce(self, decision, op):
+                self.local_decisions.append(int(decision.item()))
+                self.ops.append(op)
+                # Simulate another rank requiring the second step to execute
+                # the collective block path even though this rank would cache.
+                if len(self.local_decisions) == 2:
+                    decision.fill_(1)
+                return decision
+
+        class _StrictSPBlock(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.block_indices: list[torch.Tensor] = []
+
+            def adaln_proj(self, t_emb):
+                del t_emb
+                zeros = torch.zeros(3, hidden_size, dtype=torch.float32)
+                return zeros, zeros
+
+            def norm1(self, hidden):
+                return hidden.clone()
+
+            def forward(self, hidden, *, combined_indices, rope_table, **kwargs):
+                del kwargs
+                assert hidden.shape[0] == local_len
+                assert rope_table.shape[0] == local_len
+                self.block_indices.append(combined_indices.clone())
+                return hidden + 1
+
+        class _StrictSPFinalLayer(nn.Module):
+            def forward(self, hidden, *, t_emb, inverse_indices):
+                del t_emb, inverse_indices
+                assert hidden.shape == (local_len, hidden_size)
+                return hidden[:, :video_width].float(), hidden[:, :audio_width].float()
+
+        local_prepare = _StrictSPPrepare()
+        gather = _StrictSPGather()
+        sp_group = _StrictSPGroup()
+        block = _StrictSPBlock()
+        minimax_h3_module.local_sp_prepare = local_prepare
+        minimax_h3_module.sp_gather = gather
+        minimax_h3_module.blocks = nn.ModuleList([block])
+        minimax_h3_module.final_layer = _StrictSPFinalLayer()
+        monkeypatch.setattr(minimax_h3_module, "_rope_local_span", lambda _seq_len: (0, local_len))
+        monkeypatch.setattr(
+            minimax_h3_module,
+            "_embed",
+            lambda **kwargs: (
+                torch.arange(local_len * hidden_size, dtype=torch.float32).reshape(local_len, hidden_size),
+                torch.zeros(3, hidden_size, dtype=torch.float32),
+            ),
+        )
+        monkeypatch.setattr(
+            minimax_h3_module,
+            "prepare_rope_table",
+            lambda *args, **kwargs: torch.zeros(local_len, 6, dtype=torch.float32),
+        )
+        monkeypatch.setattr(parallel_state, "get_sp_group", lambda: sp_group)
+
+        original_indexed_scale_shift = minimax_h3_modulation.indexed_scale_shift_
+
+        def capture_indexed_scale_shift(hidden, shift, scale, indices):
+            captured_modulation_indices.append(indices.clone())
+            return original_indexed_scale_shift(hidden, shift, scale, indices)
+
+        monkeypatch.setattr(minimax_h3_modulation, "indexed_scale_shift_", capture_indexed_scale_shift)
+
+        local_img_pos = torch.tensor([0, 1])
+        local_audio_pos = torch.tensor([2, 3])
+        token_tags = torch.full((seq_len,), -1, dtype=torch.long)
+        token_tags[local_img_pos] = 0
+        token_tags[local_audio_pos] = 2
+        inverse_indices = torch.zeros(seq_len, dtype=torch.long)
+        inputs = {
+            **sample_inputs,
+            "inverse_indices": inverse_indices,
+            "token_tags": token_tags,
+            "update_mask": torch.ones(local_img_pos.numel(), dtype=torch.bool),
+            "prompt_embeds": torch.empty(0, 6),
+            "img_pos_info": {"position_ids": local_img_pos},
+            "audio_pos_info": {"position_ids": local_audio_pos},
+            "text_pos_info": {"position_ids": torch.empty(0, dtype=torch.long)},
+            "img_pos_for_infer_output_info": {"position_ids": local_img_pos},
+            "refiner_packed_seq_params": {
+                "cu_seqlens_q": torch.tensor([0, 0], dtype=torch.int32),
+                "max_seqlen_q": 0,
+            },
+        }
+        expected_local_indices = (inverse_indices * 3 + token_tags.clamp(min=0))[:local_len]
+
+        hook = TeaCacheHook(
+            TeaCacheConfig(
+                transformer_type="MiniMaxH3DiTModel",
+                rel_l1_thresh=0.3,
+            )
+        )
+        hook.initialize_hook(minimax_h3_module)
+
+        hook.new_forward(minimax_h3_module, **inputs)
+        hook.new_forward(minimax_h3_module, **inputs)
+        video_logits, audio_logits = hook.new_forward(minimax_h3_module, **inputs)
+        state = hook.state_manager.get_state()
+
+        assert state.previous_residual is not None
+        assert state.previous_residual.shape == (local_len, hidden_size)
+        assert len(captured_modulation_indices) == 3
+        assert all(torch.equal(indices, expected_local_indices) for indices in captured_modulation_indices)
+        assert sp_group.local_decisions == [1, 0, 0]
+        assert len(sp_group.ops) == 3
+        assert all(op == torch.distributed.ReduceOp.MAX for op in sp_group.ops)
+        assert len(local_prepare.global_indices) == 2
+        assert all(indices.shape == (seq_len,) for indices in local_prepare.global_indices)
+        assert len(block.block_indices) == 2
+        assert all(torch.equal(indices, expected_local_indices) for indices in block.block_indices)
+        assert len(gather.inputs) == 3
+        assert video_logits.shape == (local_img_pos.numel(), video_width)
+        assert audio_logits.shape == (local_audio_pos.numel(), audio_width)
+
+    @pytest.mark.cpu
+    def test_teacache_non_strict_sp_gathers_hidden_once(
+        self,
+        minimax_h3_module,
+        sample_inputs,
+        monkeypatch,
+    ):
+        """The non-strict SP cache path must not gather global hidden rows twice."""
+        from vllm_omni.diffusion.cache.teacache.config import TeaCacheConfig
+        from vllm_omni.diffusion.cache.teacache.hook import TeaCacheHook
+
+        class _Prepare(nn.Module):
+            def forward(self, hidden, rope_table, combined_indices):
+                return hidden, rope_table, combined_indices
+
+        class _Gather(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.inputs: list[torch.Tensor] = []
+
+            def forward(self, tensor):
+                self.inputs.append(tensor.clone())
+                return tensor
+
+        seq_len = sample_inputs["x"].shape[1]
+        gather = _Gather()
+        minimax_h3_module.sp_prepare = _Prepare()
+        minimax_h3_module.sp_gather = gather
+        monkeypatch.setattr(minimax_h3_module, "_rope_local_span", lambda _seq_len: (0, seq_len))
+
+        hook = TeaCacheHook(
+            TeaCacheConfig(
+                transformer_type="MiniMaxH3DiTModel",
+                rel_l1_thresh=0.3,
+            )
+        )
+        hook.initialize_hook(minimax_h3_module)
+        hook.new_forward(minimax_h3_module, **sample_inputs)
+
+        assert len(gather.inputs) == 1
+        assert gather.inputs[0].shape == (seq_len, minimax_h3_module.hidden_size)
+
    def test_invalid_module_raises_error(self):
        """Test that invalid module without blocks raises ValueError."""
        invalid_module = Mock()
@@ -0,0 +1,15 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+
+@pytest.fixture(autouse=True)
+def restore_torch_default_dtype():
+    """Keep process-global dtype changes from leaking between diffusion tests."""
+    default_dtype = torch.get_default_dtype()
+    try:
+        yield
+    finally:
+        torch.set_default_dtype(default_dtype)
@@ -9,6 +9,7 @@ import torch
 from diffusers import DiffusionPipeline  # pyright: ignore[reportPrivateImportUsage]
 from PIL import Image

+import vllm_omni.diffusion.data as diffusion_data
 from tests.helpers.mark import hardware_test
 from tests.helpers.runtime import OmniServer, OmniServerParams, OnlineOmniClient, dummy_messages_from_mix_data
 from vllm_omni.diffusion.data import (
@@ -629,6 +630,46 @@ class TestPipelineArgumentsHandling:
        with pytest.raises(ValueError):
            pipeline.forward(DiffusionRequestBatch(requests=[problematic_request]))

+        interpolation_request = _make_request(
+            sampling_params=OmniDiffusionSamplingParams(
+                enable_frame_interpolation=True,
+            ),
+        )
+        with pytest.raises(ValueError, match="Frame interpolation is not supported with the Diffusers backend"):
+            pipeline.forward(DiffusionRequestBatch(requests=[interpolation_request]))
+
+    def test_diffusers_config_preserves_checkpoint_class_for_metadata(self, mocker):
+        class MockWanImageToVideoPipeline:
+            pass
+
+        od_config = _make_od_config(model_class_name=None)
+        mocker.patch(
+            "vllm_omni.diffusion.utils.hf_utils.get_diffusion_model_index",
+            return_value={"_class_name": "WanImageToVideoPipeline"},
+        )
+        mocker.patch.object(
+            diffusion_data,
+            "diffusers",
+            SimpleNamespace(WanImageToVideoPipeline=MockWanImageToVideoPipeline),
+        )
+        mocker.patch.object(OmniDiffusionConfig, "update_multimodal_support")
+
+        od_config.enrich_config()
+
+        assert od_config.model_class_name == "WanImageToVideoPipeline"
+        assert od_config.diffusers_pipeline_cls is MockWanImageToVideoPipeline
+
+    def test_diffusers_config_falls_back_to_adapter_without_pipeline_index(self, mocker):
+        od_config = _make_od_config(model_class_name=None)
+        mocker.patch(
+            "vllm_omni.diffusion.utils.hf_utils.get_diffusion_model_index",
+            return_value=None,
+        )
+
+        od_config.enrich_config()
+
+        assert od_config.model_class_name == "DiffusersAdapterPipeline"
+

@pytest.mark.advanced_model
@hardware_test(res={"cuda": "L4"}, num_cards=1)
@@ -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

 from __future__ import annotations

@@ -485,8 +485,16 @@ def test_valid_sequence_and_context_install_into_native_rows(monkeypatch: pytest
    assert runner.get_diffusion_kv_row("req-0", 0, "text") == 1
    sequence_binding = runner._resolve_paged_attention_row("req-0", 0, None)
    context_binding = runner._resolve_paged_attention_row("req-0", None, "text")
-    assert (sequence_binding.row_index, sequence_binding.max_seq_len) == (0, 9)
-    assert (context_binding.row_index, context_binding.max_seq_len) == (1, 5)
+    assert (sequence_binding.row_index, sequence_binding.max_seq_len, sequence_binding.block_ids) == (
+        0,
+        9,
+        ((1, 2, 3), (4, 5)),
+    )
+    assert (context_binding.row_index, context_binding.max_seq_len, context_binding.block_ids) == (
+        1,
+        5,
+        ((6, 7), (8,)),
+    )


 def test_block_table_mutations_invalidate_prepared_attention_batches(
@@ -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

 import pytest

@@ -36,7 +36,7 @@ def test_non_mapping_omni_kv_config_is_rejected() -> None:
        OmniDiffusionConfig.from_kwargs(omni_kv_config="paged_scheduler")


-def test_paged_scheduler_rejects_dense_legacy_kv_receive() -> None:
+def test_paged_scheduler_rejects_dense_connector_kv_receive() -> None:
    with pytest.raises(ValueError, match="does not support imported AR KV"):
        OmniDiffusionConfig.from_kwargs(
            diffusion_kv_mode="paged_scheduler",
@@ -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

 from types import SimpleNamespace
 from unittest.mock import Mock
@@ -290,6 +290,7 @@ def test_minus_one_max_model_len_is_auto_fitted_by_native_cache_sizing() -> None
        dtype=torch.bfloat16,
        non_causal=True,
    )
+    model_limit = vllm_config.model_config.max_model_len

    initialization.build_native_kv_cache_configs(
        vllm_config,
@@ -298,4 +299,8 @@ def test_minus_one_max_model_len_is_auto_fitted_by_native_cache_sizing() -> None
    )

    assert vllm_config.model_config.original_max_model_len == -1
-    assert vllm_config.model_config.max_model_len == 256
+    # Sixteen physical pages provide ``16 * block_size`` tokens.  The native
+    # backend chooses the block geometry, so keep this assertion backend
+    # independent instead of assuming the generic 16-token block size.
+    expected_max_model_len = min(model_limit, spec.block_size * 16)
+    assert vllm_config.model_config.max_model_len == expected_max_model_len
@@ -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

 from __future__ import annotations

@@ -20,12 +20,16 @@ from vllm_omni.diffusion.attention.layer import Attention
 from vllm_omni.diffusion.diffusion_kv import paged_attention_adapter as adapter_module
 from vllm_omni.diffusion.diffusion_kv.paged_attention_adapter import (
    DiffusionPagedAttentionAdapter,
+    DiffusionPagedAttentionMetadata,
    DiffusionPagedAttentionRow,
    DiffusionPagedAttentionRowBinding,
+    DiffusionPagedAttentionRuntime,
 )
 from vllm_omni.diffusion.forward_context import (
+    get_forward_context,
    override_paged_kv_adapter,
    set_forward_context,
+    set_forward_context_denoise_step_idx,
 )

 pytestmark = [pytest.mark.core_model, pytest.mark.cpu, pytest.mark.diffusion]
@@ -122,8 +126,8 @@ class _FakeNativeImpl:
    def do_kv_cache_update(
        self,
        _layer,
-        key: torch.Tensor,
-        value: torch.Tensor,
+        key: torch.Tensor | None,
+        value: torch.Tensor | None,
        _kv_cache,
        slot_mapping: torch.Tensor,
    ) -> None:
@@ -155,6 +159,11 @@ def _make_adapter(
    capacity: int = 16,
 ) -> tuple[DiffusionPagedAttentionAdapter, _FakeBlockTables, _FakeLayer, list[tuple]]:
    events: list[tuple] = []
+    monkeypatch.setattr(
+        adapter_module.current_omni_platform,
+        "requires_diffusion_paged_kv_prewrite",
+        lambda: False,
+    )
    block_tables = _FakeBlockTables()
    block_tables.max_num_batched_tokens = capacity
    block_tables.num_blocks.np.fill(math.ceil(capacity / block_tables.block_sizes[0]))
@@ -301,6 +310,47 @@ def test_paged_adapter_accepts_native_gqa_shapes(monkeypatch: pytest.MonkeyPatch
    assert context.value_write.shape == (3, 8, 4)


+def test_paged_adapter_preserves_strided_kv_for_platform_writer(monkeypatch: pytest.MonkeyPatch) -> None:
+    adapter, _, _, _ = _make_adapter(monkeypatch)
+    batch = adapter.prepare_batch(
+        [DiffusionPagedAttentionRow(request_id="req-0", sequence_id=0, query_len=5, seq_len=5)]
+    )
+    packed_qkv = torch.randn(1, 5, 3 * 2 * 4)
+    query, key, value = packed_qkv.split(2 * 4, dim=-1)
+    query = query.reshape(1, 5, 2, 4)
+    key = key.reshape(1, 5, 2, 4)
+    value = value.reshape(1, 5, 2, 4)
+    assert not key.is_contiguous()
+    assert not value.is_contiguous()
+
+    with adapter.activate(batch):
+        context = adapter.prepare_layer_context("layer-0", query, key, value)
+
+    assert context.query.is_contiguous()
+    assert not context.key_write.is_contiguous()
+    assert not context.value_write.is_contiguous()
+    assert context.key_write.data_ptr() == key.data_ptr()
+    assert context.value_write.data_ptr() == value.data_ptr()
+
+
+def test_paged_adapter_packs_noncontiguous_kv_head_layout(monkeypatch: pytest.MonkeyPatch) -> None:
+    adapter, _, _, _ = _make_adapter(monkeypatch)
+    batch = adapter.prepare_batch(
+        [DiffusionPagedAttentionRow(request_id="req-0", sequence_id=0, query_len=5, seq_len=5)]
+    )
+    query = torch.randn(1, 5, 2, 4)
+    key = torch.randn(1, 5, 4, 2).transpose(-1, -2)
+    value = torch.randn(1, 5, 4, 2).transpose(-1, -2)
+    assert key.shape == (1, 5, 2, 4)
+    assert key.stride(-1) != 1
+
+    with adapter.activate(batch):
+        context = adapter.prepare_layer_context("layer-0", query, key, value)
+
+    assert context.key_write.is_contiguous()
+    assert context.value_write.is_contiguous()
+
+
 def test_omni_paged_backend_defers_backend_owned_cache_update(monkeypatch: pytest.MonkeyPatch) -> None:
    adapter, _, layer, _ = _make_adapter(monkeypatch)
    layer.attn_backend.forward_includes_kv_cache_update = True
@@ -350,10 +400,48 @@ def test_omni_piecewise_backend_defers_backend_owned_cache_update(monkeypatch: p
    assert [metadata.query_start_loc_cpu.tolist() for metadata in segment_metadata] == [[0, 1], [0, 2], [0, 1]]
    assert [metadata.positions.tolist() for metadata in segment_metadata] == [[0], [1, 2], [3]]
    assert [metadata.slot_mappings.tolist() for metadata in segment_metadata] == [[[0]], [[1, 2]], [[3]]]
+    for event in events[1:]:
+        build_kwargs = event[1]
+        assert build_kwargs["positions"].untyped_storage().data_ptr() == batch.positions.untyped_storage().data_ptr()
+        assert (
+            build_kwargs["slot_mappings"].untyped_storage().data_ptr()
+            == batch.slot_mappings.untyped_storage().data_ptr()
+        )
+
+
+def test_platform_prewrite_updates_once_then_piecewise_reads_cache(monkeypatch: pytest.MonkeyPatch) -> None:
+    adapter, _, layer, _ = _make_adapter(monkeypatch)
+    layer.attn_backend.forward_includes_kv_cache_update = True
+    monkeypatch.setattr(
+        adapter_module.current_omni_platform,
+        "requires_diffusion_paged_kv_prewrite",
+        lambda: True,
+    )
+    adapter.resolve_row = lambda *_args: DiffusionPagedAttentionRowBinding(
+        row_index=2,
+        max_seq_len=16,
+        block_ids=((3, 7, 9, 11),),
+    )
+    row = DiffusionPagedAttentionRow(request_id="req-0", sequence_id=0, query_len=4, seq_len=4)
+    batch = adapter.prepare_batch([row])
+    qkv = torch.randn(1, 4, 2, 4)
+    metadata = SimpleNamespace(full_attn_spans=[[(1, 3)]], extra={})
+
+    with adapter.activate(batch):
+        context = adapter.prepare_layer_context("layer-0", qkv, qkv, qkv, omni_attn_metadata=metadata)
+        output = _run_omni_paged_backend(context)
+
+    assert torch.equal(output, qkv)
+    assert layer.native_events == ["update", "forward", "forward", "forward"]
+    assert len(layer.updates) == 1
+    assert all(call[1] is None and call[2] is None for call in layer.calls)


 def test_omni_paged_backend_runs_hunyuan_piecewise_segments(monkeypatch: pytest.MonkeyPatch) -> None:
    adapter, _, layer, events = _make_adapter(monkeypatch)
+    # Exercise the Ascend-only homogeneous batch path. CUDA keeps its native
+    # output-buffer contract and is covered by the GPU adapter tests.
+    monkeypatch.setattr(adapter_module.current_omni_platform, "is_npu", lambda: True)
    batch = adapter.prepare_batch(
        [
            DiffusionPagedAttentionRow(
@@ -450,6 +538,48 @@ def test_omni_paged_backend_runs_hunyuan_piecewise_segments(monkeypatch: pytest.
    ]


+def test_piecewise_metadata_snapshots_reused_native_scheduler_buffer(monkeypatch: pytest.MonkeyPatch) -> None:
+    adapter, _, _, _ = _make_adapter(monkeypatch)
+    shared_scheduler_metadata = torch.zeros(1, dtype=torch.int32)
+    build_id = 0
+
+    def build_metadata(**kwargs):
+        nonlocal build_id
+        shared_scheduler_metadata.fill_(build_id)
+        metadata = SimpleNamespace(
+            build_id=build_id,
+            causal=kwargs["causal"],
+            seq_lens=kwargs["seq_lens"].clone(),
+            query_start_loc_cpu=kwargs["query_start_loc_cpu"].clone(),
+            positions=kwargs["positions"].clone(),
+            slot_mappings=kwargs["slot_mappings"].clone(),
+            scheduler_metadata=shared_scheduler_metadata,
+            prefix_scheduler_metadata=None,
+        )
+        build_id += 1
+        return {"layer-0": metadata}
+
+    monkeypatch.setattr(adapter_module.current_omni_platform, "build_diffusion_kv_attn_metadata", build_metadata)
+    batch = adapter.prepare_batch(
+        [DiffusionPagedAttentionRow(request_id="req-0", sequence_id=0, query_len=4, seq_len=4)]
+    )
+    qkv = torch.randn(1, 4, 2, 4)
+    metadata = SimpleNamespace(full_attn_spans=[[(1, 3)]], extra={})
+
+    with adapter.activate(batch):
+        context = adapter.prepare_layer_context(
+            "layer-0",
+            qkv,
+            qkv,
+            qkv,
+            omni_attn_metadata=metadata,
+        )
+
+    segment_schedules = [item.scheduler_metadata for item in context.piecewise_native_metadata]
+    assert [item.item() for item in segment_schedules] == [1, 2, 3]
+    assert len({item.data_ptr() for item in segment_schedules}) == 3
+
+
 def test_omni_paged_backend_treats_empty_full_spans_as_causal(monkeypatch: pytest.MonkeyPatch) -> None:
    adapter, _, layer, events = _make_adapter(monkeypatch)
    batch = adapter.prepare_batch(
@@ -851,6 +981,28 @@ def test_forward_rejects_batched_layout_that_does_not_match_rows(monkeypatch: py
        adapter.prepare_layer_context("layer-0", qkv, qkv, qkv)


+def test_forward_packs_and_restores_right_padded_heterogeneous_rows(monkeypatch: pytest.MonkeyPatch) -> None:
+    adapter, _, _, _ = _make_adapter(monkeypatch)
+    batch = adapter.prepare_batch(
+        [
+            DiffusionPagedAttentionRow(request_id="req-0", sequence_id=0, query_len=1, seq_len=1),
+            DiffusionPagedAttentionRow(request_id="req-1", sequence_id=1, query_len=3, seq_len=3),
+        ]
+    )
+    qkv = torch.arange(2 * 3 * 2 * 4, dtype=torch.float32).reshape(2, 3, 2, 4)
+
+    with adapter.activate(batch):
+        context = adapter.prepare_layer_context("layer-0", qkv, qkv, qkv)
+
+    assert context.output_scatter_indices.tolist() == [0, 3, 4, 5]
+    torch.testing.assert_close(context.query, qkv.reshape(-1, 2, 4)[[0, 3, 4, 5]])
+    restored = context.restore_output(context.query)
+    assert restored.shape == qkv.shape
+    torch.testing.assert_close(restored[0, 0], qkv[0, 0])
+    assert torch.count_nonzero(restored[0, 1:]) == 0
+    torch.testing.assert_close(restored[1], qkv[1])
+
+
 def test_forward_rejects_padded_full_kv(monkeypatch: pytest.MonkeyPatch) -> None:
    adapter, _, _, _ = _make_adapter(monkeypatch)
    batch = adapter.prepare_batch(
@@ -878,6 +1030,41 @@ def test_forward_rejects_padded_full_kv(monkeypatch: pytest.MonkeyPatch) -> None
        adapter.prepare_layer_context("layer-0", query, full_kv, full_kv)


+def test_runner_runtime_switches_from_prefill_to_reusable_denoise_metadata(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    adapter, _, _, _ = _make_adapter(monkeypatch)
+    metadata = DiffusionPagedAttentionMetadata(
+        prefill_rows=(DiffusionPagedAttentionRow(request_id="req-0", sequence_id=0, query_len=6, seq_len=6),),
+        denoise_rows=(
+            DiffusionPagedAttentionRow(
+                request_id="req-0",
+                sequence_id=0,
+                query_len=2,
+                seq_len=5,
+                kv_start_pos=3,
+            ),
+        ),
+    )
+    runtime = DiffusionPagedAttentionRuntime(adapter, metadata)
+
+    with set_forward_context(paged_kv_runtime=runtime), runtime.activate():
+        set_forward_context_denoise_step_idx(0)
+        assert Attention._active_paged_kv_adapter() is adapter
+        assert adapter._active_batch.rows == metadata.prefill_rows
+
+        set_forward_context_denoise_step_idx(1)
+        assert Attention._active_paged_kv_adapter() is adapter
+        denoise_batch = adapter._active_batch
+        assert denoise_batch.rows == metadata.denoise_rows
+
+        set_forward_context_denoise_step_idx(2)
+        assert Attention._active_paged_kv_adapter() is adapter
+        assert adapter._active_batch is denoise_batch
+
+    assert adapter._active_batch is None
+
+
 def test_causal_batch_rejects_non_suffix_write(monkeypatch: pytest.MonkeyPatch) -> None:
    adapter, _, _, _ = _make_adapter(monkeypatch, non_causal=False)
    row = DiffusionPagedAttentionRow(
@@ -992,7 +1179,8 @@ def test_layer_adapter_accepts_platform_native_backend_and_uses_rank_local_heads
    monkeypatch: pytest.MonkeyPatch,
 ) -> None:
    selected_backends = []
-    impl_cls = Mock(return_value=SimpleNamespace(forward=Mock()))
+    specialized_backends = []
+    impl_cls = Mock(return_value=SimpleNamespace(forward=Mock(), do_kv_cache_update=Mock()))
    native_backend = SimpleNamespace(
        get_name=lambda: "ASCEND",
        indexes_kv_by_block_stride=lambda: True,
@@ -1003,16 +1191,28 @@ def test_layer_adapter_accepts_platform_native_backend_and_uses_rank_local_heads
    original_backend_per_kind = {"full": object()}
    config = SimpleNamespace(
        attention_config=SimpleNamespace(backend=None, backend_per_kind=original_backend_per_kind),
+        parallel_config=SimpleNamespace(prefill_context_parallel_size=2),
        model_config=SimpleNamespace(dtype=torch.float16),
        cache_config=SimpleNamespace(cache_dtype="auto"),
    )

    def select_backend(**_kwargs):
-        selected_backends.append((config.attention_config.backend, config.attention_config.backend_per_kind))
+        selected_backends.append(
+            (
+                config.attention_config.backend,
+                config.attention_config.backend_per_kind,
+                config.parallel_config.prefill_context_parallel_size,
+            )
+        )
        return native_backend

    monkeypatch.setattr(adapter_module, "get_attn_backend", select_backend)
    monkeypatch.setattr(adapter_module, "set_current_vllm_config", lambda _config: nullcontext())
+    monkeypatch.setattr(
+        adapter_module.current_omni_platform,
+        "get_diffusion_paged_kv_attn_backend",
+        lambda backend, *, ulysses_degree: specialized_backends.append((backend, ulysses_degree)) or backend,
+    )
    layer = SimpleNamespace(num_heads=8, softmax_scale=0.125)
    spec = FullAttentionSpec(
        block_size=16,
@@ -1031,9 +1231,11 @@ def test_layer_adapter_accepts_platform_native_backend_and_uses_rank_local_heads
        ulysses_degree=2,
    )

-    assert selected_backends == [(adapter_module.AttentionBackendEnum.FLASH_ATTN, {})]
+    assert selected_backends == [(adapter_module.AttentionBackendEnum.FLASH_ATTN, {}, 2)]
+    assert specialized_backends == [(native_backend, 2)]
    assert config.attention_config.backend is None
    assert config.attention_config.backend_per_kind is original_backend_per_kind
+    assert config.parallel_config.prefill_context_parallel_size == 2
    assert native_layer.num_heads == 4
    assert native_layer.num_kv_heads == 2
    assert native_layer.spec.num_kv_heads == 2
@@ -1094,6 +1296,51 @@ def test_omni_attention_wraps_paged_kernel_with_sp_hooks() -> None:
    assert torch.equal(output, torch.full_like(original, 6))


+def test_omni_attention_strips_paged_ulysses_padding_around_kernel() -> None:
+    events: list[tuple[str, tuple[int, ...]]] = []
+
+    class Strategy:
+        name = "ulysses"
+
+        def pre_attention(self, query, key, value, metadata):
+            return query, key, value, metadata, SimpleNamespace(joint_len=0, joint_strategy="front")
+
+        def post_attention(self, output, _ctx):
+            events.append(("post", tuple(output.shape)))
+            return output
+
+    class Adapter:
+        def prepare_layer_context(self, _layer_name, query, key, value, *, omni_attn_metadata):
+            assert omni_attn_metadata is None
+            events.append(("kernel_input", tuple(query.shape)))
+            assert query[:, -1].tolist() == [[[3.0]]]
+            assert torch.equal(query, key)
+            assert torch.equal(query, value)
+            return SimpleNamespace(query=query)
+
+    class Backend:
+        def forward_paged(self, context):
+            return context.query
+
+    layer = Attention.__new__(Attention)
+    nn.Module.__init__(layer)
+    layer.prefix = "layer-0"
+    layer.paged_kv_cache_role = "primary"
+    layer.attn_backend = SimpleNamespace(supports_paged_kv=True, get_name=lambda: "FLASH_ATTN")
+    layer.attention = Backend()
+    layer.use_ring = False
+    layer._no_parallel_strategy = object()
+    layer._get_active_parallel_strategy = lambda: Strategy()
+    tensor = torch.arange(5, dtype=torch.float32).reshape(1, 5, 1, 1)
+
+    with set_forward_context(), override_paged_kv_adapter(Adapter()):
+        get_forward_context().sp_padding_size = 1
+        output = layer._forward_impl(tensor, tensor, tensor)
+
+    assert events == [("kernel_input", (1, 4, 1, 1)), ("post", (1, 5, 1, 1))]
+    assert output.flatten().tolist() == [0.0, 1.0, 2.0, 3.0, 0.0]
+
+
 def test_omni_attention_rejects_paged_request_for_backend_without_paged_support() -> None:
    class Adapter:
        def prepare_layer_context(self, *args, **kwargs):
@@ -1145,3 +1392,15 @@ def test_omni_attention_keeps_dense_kernel_without_active_adapter() -> None:

    assert events == ["pre", "dense", "post"]
    assert output is qkv
+
+
+def test_omni_attention_rejects_scheduler_paged_forward_without_active_adapter() -> None:
+    layer = Attention.__new__(Attention)
+    nn.Module.__init__(layer)
+    layer._scheduler_paged_kv = True
+    layer.paged_kv_cache_role = "primary"
+    layer._get_active_parallel_strategy = lambda: object()
+    qkv = torch.zeros(1, 2, 2, 4)
+
+    with set_forward_context(), pytest.raises(RuntimeError, match="without an active Worker adapter"):
+        layer._forward_impl(qkv, qkv, qkv)
Author	SHA1	Message	Date
hsliuustc0106	81de3f3308	[Bugfix][MiniMax-H3] Apply pre-commit formatting Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-09-03 08:44:26 +08:00
hsliuustc0106	f9c9de8d16	[Bugfix][MiniMax-H3] Reconcile temporal decode plans across VAE ranks Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com> (cherry picked from commit ac4ec9c0fcf3394e8476d3d2d913d7c86f5df8e3)	2026-09-03 08:20:00 +08:00
hsliuustc0106	ff2e33a329	[Docs][MiniMax-H3] Document temporal VAE chunks Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-09-02 07:08:40 +08:00
hsliuustc0106	0b5b0979d8	[Bugfix][MiniMax-H3] Reconcile chunk callback ownership Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-09-01 23:11:29 +08:00
hsliuustc0106	f04ff76c54	[Model][MiniMax-H3] Isolate temporal chunk coordination Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-09-01 23:11:29 +08:00
hsliuustc0106	b220df8397	[Model][MiniMax-H3] Support released VAE chunks directly Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-09-01 23:11:28 +08:00
hsliuustc0106	520e058b95	[Model][MiniMax-H3] Delegate temporal VAE chunks Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-09-01 23:11:27 +08:00
DanaerLeeandHongsheng Liu	0288c3f56e	[Model] Support SenseNova-U1.5-8B-MoT and its distilled 8-step LoRA (#6516 ) Signed-off-by: MrlixiangWE <mrdanaer@gmail.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-09-01 22:53:53 +08:00
andyluo7andHongsheng Liu	477083a103	[CI][ROCm] Fix deterministic AMD L3 blockers (#6884 ) Signed-off-by: andyluo7 <andy.luo@amd.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-09-01 22:53:00 +08:00
Rahul SteigerandRahul Steiger	e1b2f589c1	[Bugfix][Cosmos3] Avoid logging prompts at INFO level (#6913 ) Signed-off-by: Rahul Steiger <rsteiger@nvl72d233-T16.cm.cluster> Co-authored-by: Rahul Steiger <rsteiger@nvl72d233-T16.cm.cluster>	2026-09-01 22:52:15 +08:00
Hongsheng Liuandhsliu_ustc	44c8d1c449	docs: add concise API server endpoint guide (#6114 ) Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: hsliu_ustc <hsliu_ustc@noreply.gitcode.com>	2026-09-01 20:34:09 +08:00
Andy Zhou	2298359f67	[Bugfix][Diffusion] Preserve stage attention backend (#6645 ) Signed-off-by: AndyZhou952 <jzhoubc@connect.ust.hk>	2026-09-01 20:18:44 +08:00
Hongsheng Liu	d1c8beeb18	[Doc] Update README and docs for v0.28.0 (#6858 ) Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>	2026-09-01 19:50:26 +08:00
WeiQing Chen	e2b83db683	[Test] Add MiniMax-H3 DLO DP2 variants and Turbo LoRA L3 test (#6556 ) Signed-off-by: david6666666 <530634352@qq.com>	2026-09-01 10:48:52 +00:00
AuFlowandHongsheng Liu	49c6fc1540	[Frontend] Add configurable output sample rate to Speech API (#6553 ) Signed-off-by: 郑倩倩 <2458983186@qq.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-09-01 17:25:09 +08:00
Alex Brooks	a65e89cbd4	[Core] Validate PipelineConfig in `__post_init__` (#6829 ) Signed-off-by: Alex Brooks <albrooks@redhat.com>	2026-09-01 11:17:57 +08:00
Yuxuan RhoandHongsheng Liu	7992c2213e	[Config] Remove diffusion_batch_size and use max_num_seqs instead. (#6484 ) Signed-off-by: LyxWxj <yuxuanlou621@foxmail.com> Signed-off-by: Yuxuan Rho <104440472+LyxWxj@users.noreply.github.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-09-01 01:02:54 +00:00
XRay2016	8e26dce1f4	[Diffusion] Add Boogu Edit-Turbo support docs and tests (#6701 ) Signed-off-by: xRay2016 <1150722393@qq.com>	2026-09-01 08:41:23 +08:00
Inesh Reddy ChappidiandGao Han	eb11446b7f	[Bugfix][Core] Do not advance the segment watermark past an unmaintained request counter (#6834 ) Signed-off-by: Inesh Reddy Chappidi <ineshreddy249@gmail.com> Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>	2026-08-31 15:27:13 +00:00
Gao Han	705e781b0e	[Bugfix][MiniCPM-o] Checkpoint playback before final input commit (#6821 ) Signed-off-by: Gao Han <hgaoaf@connect.ust.hk>	2026-08-31 15:18:29 +00:00
Gao Han	b81aeb7b86	[CI][MiniCPM-o] Make audio consistency checks deterministic (#6828 ) Signed-off-by: Gao Han <hgaoaf@connect.ust.hk>	2026-08-31 13:08:49 +00:00
wangyu	3d1ad0c0f4	[CI][Bugfix]Drop dummy-weight Qwen3-TTS Base from Ready CI (#6861 ) Signed-off-by: wangyu <410167048@qq.com>	2026-08-31 12:13:26 +00:00
Wang fuyin zwhzzz0821 zwhzzz0821	fe4a2a7477	[Diffusion] Add paged KV cache support for HunyuanImage3 DiT on NPU and GPU (#6563 ) Signed-off-by: Acerak01-fy <wfy2003324@163.com> Signed-off-by: zwhzzz0821 <zwhzzz0821@users.noreply.github.com> Signed-off-by: zwhzzz0821 <2831474076@qq.com> Co-authored-by: zwhzzz0821 <zwhzzz0821@users.noreply.github.com> Co-authored-by: zwhzzz0821 <2831474076@qq.com>	2026-08-31 10:15:46 +00:00
Bo Li	6f16f9546d	[Bugfix][Kernel] Stage strided Ulysses QKV with copy engine (#6814 ) Signed-off-by: Bo Li <22713281+bobboli@users.noreply.github.com>	2026-08-31 17:28:49 +08:00
Alex Brooks	58cb8de68b	[CI] Fix Speech CI Validation Messages (#6827 ) Signed-off-by: Alex Brooks <albrooks@redhat.com>	2026-08-31 08:13:59 +00:00
Rakesh Kariya	b864374089	[Bugfix][MiMo-Audio] Restore make_empty_intermediate_tensors on the talker for vLLM 0.28 (#6803 ) Signed-off-by: rk9595 <rakesh.kariya@somaiya.edu>	2026-08-31 15:26:35 +08:00
WeiQing Chen	2052deef07	[Bugfix][Diffusion] Load plugins in spawned stage process (#6750 ) Signed-off-by: david6666666 <530634352@qq.com>	2026-08-31 14:32:07 +08:00
汪志鹏andHongsheng Liu	86b85c078b	[Feature][MiniMax-H3] Fuse the FastH3 four-step adapter at load time (#6714 ) Signed-off-by: princepride <wangzhipeng628@gmail.com> Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>	2026-08-31 12:04:58 +08:00
Inesh Reddy Chappidi	5e06d2e73c	[CI/Build] Align duplex stage-input deadline with the e2e client timeout (#6831 ) Signed-off-by: Inesh Reddy Chappidi <ineshreddy249@gmail.com>	2026-08-31 02:55:36 +00:00
akshatvishu	f321cdbe16	[Bugfix] Use Seed-TTS reference audio with MiniCPM-o 4.5 (#6628 ) Signed-off-by: akshatvishu <akshatnayak197@gmail.com>	2026-08-31 10:27:34 +08:00
WeiQing Chen	4cca28e9bd	[Doc] Update vLLM-Omni WeChat QR code (#6832 ) Signed-off-by: David Chen <530634352@qq.com>	2026-08-31 09:44:33 +08:00
TJian	ded8934626	[AMD] [CI] Fix tests for v0.28.0 (#6830 ) Signed-off-by: tjtanaa <tunjian.tan@embeddedllm.com>	2026-08-31 09:10:27 +08:00
SYLAR	759aa4ffeb	[Diffusion][Performance] Optimize MiniMax-H3 video output transfer (#6824 ) Signed-off-by: lishunyang12 <lishunyang12@163.com>	2026-08-31 07:07:34 +08:00
mokekeandmokashliu	1a9d598d2b	[Frontend] Encode interleaved video frames in parallel (#6776 ) Signed-off-by: mokashliu <mokashliu@tencent.com> Co-authored-by: mokashliu <mokashliu@tencent.com>	2026-08-31 07:04:27 +08:00
m0g3r	8fe913e461	[Core] Flag pipelines with no terminal output stage in PipelineConfig.validate() (#6291 ) Signed-off-by: m0g3r <87276771+m0g3r@users.noreply.github.com>	2026-08-30 21:33:06 +00:00
Gao Han	71748388f8	[Bugfix] Recover Omni benchmark TPOT from Stage 0 metrics (#6818 ) Signed-off-by: Gao Han <hgaoaf@connect.ust.hk>	2026-08-31 00:43:25 +08:00
Gao Han	f7f48929db	[Bugfix][Diffusion] Restore inline single-stage execution (#6813 ) Signed-off-by: Gao Han <hgaoaf@connect.ust.hk>	2026-08-30 21:40:47 +08:00
Weiming Liao Hongsheng Liu amy-why-3459	18f01befa7	[NPU] upgrade to v0.28.0 (#6674 ) Signed-off-by: Weiming Liao <liaowm5@gmail.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-30 12:28:35 +00:00
Yueqian Lin	1d75e63f7f	[CI/Build] Add dots.tts weekly e2e coverage (#6174 ) Signed-off-by: Yueqian Lin <linyueqian@outlook.com>	2026-08-30 11:37:08 +00:00
akshatvishu	c41925689d	[BugFix][Higgs-Audio-V3] Fix transcript and concurrent sampling tests (#6422 ) Signed-off-by: akshatvishu <akshatnayak197@gmail.com>	2026-08-30 08:52:15 +00:00
Yueqian Lin	a6b559cf54	[Bugfix][MiniMax-Music3] Resolve stage subdirs against a real snapshot (#6640 ) Signed-off-by: Yueqian Lin <linyueqian@outlook.com>	2026-08-30 06:51:24 +00:00
baonudesifeizhai	8be601ed7f	[Diffusion] Add native SymmMem Fast Ulysses transport (#6340 ) Signed-off-by: roG0d <baonudesifeizhai@gmail.com>	2026-08-30 04:14:21 +00:00
y-null	99a2ae1fc8	[Bugfix][MiniCPM-o] Anchor duplex soft-interrupt listen sandwich before commit (#6772 ) Signed-off-by: y_null <y_null@qq.com>	2026-08-30 03:25:43 +00:00
Sy03 Nathan Price Eric	21dd4b33ac	[Bugfix][MiniCPM-o] Stabilize native duplex streaming and Stage-1 handoffs (#6529 ) Signed-off-by: Sy03 <1370724210@qq.com> Co-authored-by: Nathan Price <nathan@abridge.com> Co-authored-by: Eric(Qun) <186020953+0xtoward@users.noreply.github.com>	2026-08-29 13:05:24 -07:00
WeiQing ChenandGao Han	9a88d2d21d	[diffusion][bugfix] Separate Diffusers hooks from model metadata (#6749 ) Signed-off-by: david6666666 <530634352@qq.com> Signed-off-by: Gao Han <hgaoaf@connect.ust.hk> Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>	2026-08-29 17:37:14 +00:00
YancyandAsthenia	73893e73ea	[Bugfix] Fix create_error_response import across vLLM versions (#6773 ) Signed-off-by: Asthenia <asthenia0412@gmail.com> Co-authored-by: Asthenia <asthenia0412@gmail.com>	2026-08-29 17:01:51 +00:00
NATURE	87736a3cc5	[Bugfix][MiniCPM-o] Bound full-duplex Talker context with sliding recompute (#6626 ) Signed-off-by: natureofnature <wzliu@connect.hku.hk>	2026-08-30 00:49:42 +08:00
Sy03	3adee400a0	[Bugfix][Qwen3-TTS] Handle codec generations that exhaust their token budget (#6728 ) Signed-off-by: Sy03 <1370724210@qq.com>	2026-08-29 08:59:05 -07:00
Gao Han	676a35fa8b	Fix MiniCPM-o concurrent audio first-packet latency (#6767 ) Signed-off-by: Gao Han <hgaoaf@connect.ust.hk>	2026-08-29 23:27:47 +08:00
Honghan Zhuandamy-why-3459	72a1ce488b	[Bugfix] Avoid invalid TPOT metrics (#6696 ) Signed-off-by: zhuhh97 <zhuhonghan@huawei.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-29 11:56:37 +00:00
wangyu	e338f0ece3	[CI]Split GPU jobs by cards_* and reject hand-written SKU marks (#6650 ) Signed-off-by: wangyu <410167048@qq.com> Signed-off-by: [Your Name] <your.email@example.com>	2026-08-29 18:42:33 +08:00
HAAZZZEEEE	c50b7de167	[NPU][Diffusion] MiniMax-H3 RainFusion end_step tail fallback (#6037 ) Signed-off-by: HAAZZZEEEE <wang-taicheng@qq.com>	2026-08-29 18:36:05 +08:00
psv666	a5b8c82788	[Bugfix][MiniCPM-o] Fix duplex camera frame fixture (#6757 ) Signed-off-by: psv666 <2693925048@qq.com>	2026-08-29 09:57:53 +00:00
ooooooye	5a5bc55c25	[Feature][Diffusion] Allow online INT8 quantization with DLO AllGather (#6573 ) Signed-off-by: brandneway <gyuan4892@gmail.com>	2026-08-29 09:50:30 +00:00
Wenjia LiandWeiQing Chen	d9980c75e8	Perf/minimax h3 dit swiglu rope cache npu (#6410 ) Signed-off-by: Wenjia Li <wjialish@gmail.com> Co-authored-by: WeiQing Chen <40507679+david6666666@users.noreply.github.com>	2026-08-29 09:14:52 +00:00
Nguyen Kha Nhat Long Cursor Samit	bb5bf8fd0a	[Bugfix] Fix colocate-async sleep admission race and deliver AR abort tokens (#6367 ) Signed-off-by: knlnguyen1802 <knlnguyen1802@gmail.com> Signed-off-by: Cursor <cursoragent@cursor.com> Signed-off-by: Samit <285365963@qq.com> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Samit <285365963@qq.com>	2026-08-29 09:11:39 +00:00
psv666andGao Han	5c47fd0bc8	[Bugfix][Qwen3-Omni] Handle missing packed modules mapping (#6748 ) Signed-off-by: psv666 <2693925048@qq.com> Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>	2026-08-29 15:24:49 +08:00
Zijun HuangandWeiQing Chen	08cdee8dc4	[Feature] Support MiniMax-H3 FlashGen native LoRA with the legacy manager (#6666 ) Signed-off-by: Zijun Huang <zijunhua@usc.edu> Co-authored-by: WeiQing Chen <40507679+david6666666@users.noreply.github.com>	2026-08-29 06:52:19 +00:00
abrahamzewoudie Hongsheng Liu amy-why-3459	143ccc5a2f	Fix Qwen3-Omni audio encoder TP when heads are not divisible by TP size (#4322 ) Signed-off-by: abrahamzewoudie <azewoudi@amd.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>	2026-08-29 14:52:02 +08:00